Formation of parametric images using mixed-effects models: a feasibility study.

PubMed

Huang, Husan-Ming; Shih, Yi-Yu; Lin, Chieh

2016-03-01

Mixed-effects models have been widely used in the analysis of longitudinal data. By presenting the parameters as a combination of fixed effects and random effects, mixed-effects models incorporating both within- and between-subject variations are capable of improving parameter estimation. In this work, we demonstrate the feasibility of using a non-linear mixed-effects (NLME) approach for generating parametric images from medical imaging data of a single study. By assuming that all voxels in the image are independent, we used simulation and animal data to evaluate whether NLME can improve the voxel-wise parameter estimation. For testing purposes, intravoxel incoherent motion (IVIM) diffusion parameters including perfusion fraction, pseudo-diffusion coefficient and true diffusion coefficient were estimated using diffusion-weighted MR images and NLME through fitting the IVIM model. The conventional method of non-linear least squares (NLLS) was used as the standard approach for comparison of the resulted parametric images. In the simulated data, NLME provides more accurate and precise estimates of diffusion parameters compared with NLLS. Similarly, we found that NLME has the ability to improve the signal-to-noise ratio of parametric images obtained from rat brain data. These data have shown that it is feasible to apply NLME in parametric image generation, and the parametric image quality can be accordingly improved with the use of NLME. With the flexibility to be adapted to other models or modalities, NLME may become a useful tool to improve the parametric image quality in the future. Copyright © 2015 John Wiley & Sons, Ltd. Copyright © 2015 John Wiley & Sons, Ltd.

Parametric modelling of cost data in medical studies.

PubMed

Nixon, R M; Thompson, S G

2004-04-30

The cost of medical resources used is often recorded for each patient in clinical studies in order to inform decision-making. Although cost data are generally skewed to the right, interest is in making inferences about the population mean cost. Common methods for non-normal data, such as data transformation, assuming asymptotic normality of the sample mean or non-parametric bootstrapping, are not ideal. This paper describes possible parametric models for analysing cost data. Four example data sets are considered, which have different sample sizes and degrees of skewness. Normal, gamma, log-normal, and log-logistic distributions are fitted, together with three-parameter versions of the latter three distributions. Maximum likelihood estimates of the population mean are found; confidence intervals are derived by a parametric BC(a) bootstrap and checked by MCMC methods. Differences between model fits and inferences are explored.Skewed parametric distributions fit cost data better than the normal distribution, and should in principle be preferred for estimating the population mean cost. However for some data sets, we find that models that fit badly can give similar inferences to those that fit well. Conversely, particularly when sample sizes are not large, different parametric models that fit the data equally well can lead to substantially different inferences. We conclude that inferences are sensitive to choice of statistical model, which itself can remain uncertain unless there is enough data to model the tail of the distribution accurately. Investigating the sensitivity of conclusions to choice of model should thus be an essential component of analysing cost data in practice. Copyright 2004 John Wiley & Sons, Ltd.

Visual Literacy and the Integration of Parametric Modeling in the Problem-Based Curriculum

ERIC Educational Resources Information Center

Assenmacher, Matthew Benedict

2013-01-01

This quasi-experimental study investigated the application of visual literacy skills in the form of parametric modeling software in relation to traditional forms of sketching. The study included two groups of high school technical design students. The control and experimental groups involved in the study consisted of two randomly selected groups…

Parametric study of extended end-plate connection using finite element modeling

NASA Astrophysics Data System (ADS)

Mureşan, Ioana Cristina; Bâlc, Roxana

2017-07-01

End-plate connections with preloaded high strength bolts represent a convenient, fast and accurate solution for beam-to-column joints. The behavior of framework joints build up with this type of connection are sensitive dependent on geometrical and material characteristics of the elements connected. This paper presents results of parametric analyses on the behavior of a bolted extended end-plate connection using finite element modeling program Abaqus. This connection was experimentally tested in the Laboratory of Faculty of Civil Engineering from Cluj-Napoca and the results are briefly reviewed in this paper. The numerical model of the studied connection was described in detail in [1] and provides data for this parametric study.

Parametric Study of a YAV-8B Harrier in Ground Effect Using Time-Dependent Navier-Stokes Computations

NASA Technical Reports Server (NTRS)

Shishir, Pandya; Chaderjian, Neal; Ahmad, Jsaim; Kwak, Dochan (Technical Monitor)

2001-01-01

Flow simulations using the time-dependent Navier-Stokes equations remain a challenge for several reasons. Principal among them are the difficulty to accurately model complex flows, and the time needed to perform the computations. A parametric study of such complex problems is not considered practical due to the large cost associated with computing many time-dependent solutions. The computation time for each solution must be reduced in order to make a parametric study possible. With successful reduction of computation time, the issue of accuracy, and appropriateness of turbulence models will become more tractable.

The impact of parametrized convection on cloud feedback.

PubMed

Webb, Mark J; Lock, Adrian P; Bretherton, Christopher S; Bony, Sandrine; Cole, Jason N S; Idelkadi, Abderrahmane; Kang, Sarah M; Koshiro, Tsuyoshi; Kawai, Hideaki; Ogura, Tomoo; Roehrig, Romain; Shin, Yechul; Mauritsen, Thorsten; Sherwood, Steven C; Vial, Jessica; Watanabe, Masahiro; Woelfle, Matthew D; Zhao, Ming

2015-11-13

We investigate the sensitivity of cloud feedbacks to the use of convective parametrizations by repeating the CMIP5/CFMIP-2 AMIP/AMIP + 4K uniform sea surface temperature perturbation experiments with 10 climate models which have had their convective parametrizations turned off. Previous studies have suggested that differences between parametrized convection schemes are a leading source of inter-model spread in cloud feedbacks. We find however that 'ConvOff' models with convection switched off have a similar overall range of cloud feedbacks compared with the standard configurations. Furthermore, applying a simple bias correction method to allow for differences in present-day global cloud radiative effects substantially reduces the differences between the cloud feedbacks with and without parametrized convection in the individual models. We conclude that, while parametrized convection influences the strength of the cloud feedbacks substantially in some models, other processes must also contribute substantially to the overall inter-model spread. The positive shortwave cloud feedbacks seen in the models in subtropical regimes associated with shallow clouds are still present in the ConvOff experiments. Inter-model spread in shortwave cloud feedback increases slightly in regimes associated with trade cumulus in the ConvOff experiments but is quite similar in the most stable subtropical regimes associated with stratocumulus clouds. Inter-model spread in longwave cloud feedbacks in strongly precipitating regions of the tropics is substantially reduced in the ConvOff experiments however, indicating a considerable local contribution from differences in the details of convective parametrizations. In both standard and ConvOff experiments, models with less mid-level cloud and less moist static energy near the top of the boundary layer tend to have more positive tropical cloud feedbacks. The role of non-convective processes in contributing to inter-model spread in cloud feedback is discussed. © 2015 The Authors.

The impact of parametrized convection on cloud feedback

PubMed Central

Webb, Mark J.; Lock, Adrian P.; Bretherton, Christopher S.; Bony, Sandrine; Cole, Jason N. S.; Idelkadi, Abderrahmane; Kang, Sarah M.; Koshiro, Tsuyoshi; Kawai, Hideaki; Ogura, Tomoo; Roehrig, Romain; Shin, Yechul; Mauritsen, Thorsten; Sherwood, Steven C.; Vial, Jessica; Watanabe, Masahiro; Woelfle, Matthew D.; Zhao, Ming

2015-01-01

We investigate the sensitivity of cloud feedbacks to the use of convective parametrizations by repeating the CMIP5/CFMIP-2 AMIP/AMIP + 4K uniform sea surface temperature perturbation experiments with 10 climate models which have had their convective parametrizations turned off. Previous studies have suggested that differences between parametrized convection schemes are a leading source of inter-model spread in cloud feedbacks. We find however that ‘ConvOff’ models with convection switched off have a similar overall range of cloud feedbacks compared with the standard configurations. Furthermore, applying a simple bias correction method to allow for differences in present-day global cloud radiative effects substantially reduces the differences between the cloud feedbacks with and without parametrized convection in the individual models. We conclude that, while parametrized convection influences the strength of the cloud feedbacks substantially in some models, other processes must also contribute substantially to the overall inter-model spread. The positive shortwave cloud feedbacks seen in the models in subtropical regimes associated with shallow clouds are still present in the ConvOff experiments. Inter-model spread in shortwave cloud feedback increases slightly in regimes associated with trade cumulus in the ConvOff experiments but is quite similar in the most stable subtropical regimes associated with stratocumulus clouds. Inter-model spread in longwave cloud feedbacks in strongly precipitating regions of the tropics is substantially reduced in the ConvOff experiments however, indicating a considerable local contribution from differences in the details of convective parametrizations. In both standard and ConvOff experiments, models with less mid-level cloud and less moist static energy near the top of the boundary layer tend to have more positive tropical cloud feedbacks. The role of non-convective processes in contributing to inter-model spread in cloud feedback is discussed. PMID:26438278

Probing the dynamics of dark energy with divergence-free parametrizations: A global fit study

NASA Astrophysics Data System (ADS)

Li, Hong; Zhang, Xin

2011-09-01

The CPL parametrization is very important for investigating the property of dark energy with observational data. However, the CPL parametrization only respects the past evolution of dark energy but does not care about the future evolution of dark energy, since w ( z ) diverges in the distant future. In a recent paper [J.Z. Ma, X. Zhang, Phys. Lett. B 699 (2011) 233], a robust, novel parametrization for dark energy, w ( z ) = w + w ( l n ( 2 + z ) 1 + z - l n 2 ) , has been proposed, successfully avoiding the future divergence problem in the CPL parametrization. On the other hand, an oscillating parametrization (motivated by an oscillating quintom model) can also avoid the future divergence problem. In this Letter, we use the two divergence-free parametrizations to probe the dynamics of dark energy in the whole evolutionary history. In light of the data from 7-year WMAP temperature and polarization power spectra, matter power spectrum of SDSS DR7, and SN Ia Union2 sample, we perform a full Markov Chain Monte Carlo exploration for the two dynamical dark energy models. We find that the best-fit dark energy model is a quintom model with the EOS across -1 during the evolution. However, though the quintom model is more favored, we find that the cosmological constant still cannot be excluded.

From 2D to 3D: Construction of a 3D Parametric Model for Detection of Dental Roots Shape and Position from a Panoramic Radiograph—A Preliminary Report

PubMed Central

Mazzotta, Laura; Cozzani, Mauro; Mutinelli, Sabrina; Castaldo, Attilio; Silvestrini-Biavati, Armando

2013-01-01

Objectives. To build a 3D parametric model to detect shape and volume of dental roots, from a panoramic radiograph (PAN) of the patient. Materials and Methods. A PAN and a cone beam computed tomography (CBCT) of a patient were acquired. For each tooth, various parameters were considered (coronal and root lengths and widths): these were measured from the CBCT and from the PAN. Measures were compared to evaluate the accuracy level of PAN measurements. By using a CAD software, parametric models of an incisor and of a molar were constructed employing B-spline curves and free-form surfaces. PAN measures of teeth 2.1 and 3.6 were assigned to the parametric models; the same two teeth were segmented from CBCT. The two models were superimposed to assess the accuracy of the parametric model. Results. PAN measures resulted to be accurate and comparable with all other measurements. From model superimposition the maximum error resulted was 1.1 mm on the incisor crown and 2 mm on the molar furcation. Conclusion. This study shows that it is possible to build a 3D parametric model starting from 2D information with a clinically valid accuracy level. This can ultimately lead to a crown-root movement simulation. PMID:23554814

Hyperbolic and semi-parametric models in finance

NASA Astrophysics Data System (ADS)

Bingham, N. H.; Kiesel, Rüdiger

2001-02-01

The benchmark Black-Scholes-Merton model of mathematical finance is parametric, based on the normal/Gaussian distribution. Its principal parametric competitor, the hyperbolic model of Barndorff-Nielsen, Eberlein and others, is briefly discussed. Our main theme is the use of semi-parametric models, incorporating the mean vector and covariance matrix as in the Markowitz approach, plus a non-parametric part, a scalar function incorporating features such as tail-decay. Implementation is also briefly discussed.

Developmental models for estimating ecological responses to environmental variability: structural, parametric, and experimental issues.

PubMed

Moore, Julia L; Remais, Justin V

2014-03-01

Developmental models that account for the metabolic effect of temperature variability on poikilotherms, such as degree-day models, have been widely used to study organism emergence, range and development, particularly in agricultural and vector-borne disease contexts. Though simple and easy to use, structural and parametric issues can influence the outputs of such models, often substantially. Because the underlying assumptions and limitations of these models have rarely been considered, this paper reviews the structural, parametric, and experimental issues that arise when using degree-day models, including the implications of particular structural or parametric choices, as well as assumptions that underlie commonly used models. Linear and non-linear developmental functions are compared, as are common methods used to incorporate temperature thresholds and calculate daily degree-days. Substantial differences in predicted emergence time arose when using linear versus non-linear developmental functions to model the emergence time in a model organism. The optimal method for calculating degree-days depends upon where key temperature threshold parameters fall relative to the daily minimum and maximum temperatures, as well as the shape of the daily temperature curve. No method is shown to be universally superior, though one commonly used method, the daily average method, consistently provides accurate results. The sensitivity of model projections to these methodological issues highlights the need to make structural and parametric selections based on a careful consideration of the specific biological response of the organism under study, and the specific temperature conditions of the geographic regions of interest. When degree-day model limitations are considered and model assumptions met, the models can be a powerful tool for studying temperature-dependent development.

Parametric instabilities of rotor-support systems with application to industrial ventilators

NASA Technical Reports Server (NTRS)

Parszewski, Z.; Krodkiemski, T.; Marynowski, K.

1980-01-01

Rotor support systems interaction with parametric excitation is considered for both unequal principal shaft stiffness (generators) and offset disc rotors (ventilators). Instability regions and types of instability are computed in the first case, and parametric resonances in the second case. Computed and experimental results are compared for laboratory machine models. A field case study of parametric vibrations in industrial ventilators is reported. Computed parametric resonances are confirmed in field measurements, and some industrial failures are explained. Also the dynamic influence and gyroscopic effect of supporting structures are shown and computed.

Brayton Power Conversion System Parametric Design Modelling for Nuclear Electric Propulsion

NASA Technical Reports Server (NTRS)

Ashe, Thomas L.; Otting, William D.

1993-01-01

The parametrically based closed Brayton cycle (CBC) computer design model was developed for inclusion into the NASA LeRC overall Nuclear Electric Propulsion (NEP) end-to-end systems model. The code is intended to provide greater depth to the NEP system modeling which is required to more accurately predict the impact of specific technology on system performance. The CBC model is parametrically based to allow for conducting detailed optimization studies and to provide for easy integration into an overall optimizer driver routine. The power conversion model includes the modeling of the turbines, alternators, compressors, ducting, and heat exchangers (hot-side heat exchanger and recuperator). The code predicts performance to significant detail. The system characteristics determined include estimates of mass, efficiency, and the characteristic dimensions of the major power conversion system components. These characteristics are parametrically modeled as a function of input parameters such as the aerodynamic configuration (axial or radial), turbine inlet temperature, cycle temperature ratio, power level, lifetime, materials, and redundancy.

Comparison Between Linear and Non-parametric Regression Models for Genome-Enabled Prediction in Wheat

PubMed Central

Pérez-Rodríguez, Paulino; Gianola, Daniel; González-Camacho, Juan Manuel; Crossa, José; Manès, Yann; Dreisigacker, Susanne

2012-01-01

In genome-enabled prediction, parametric, semi-parametric, and non-parametric regression models have been used. This study assessed the predictive ability of linear and non-linear models using dense molecular markers. The linear models were linear on marker effects and included the Bayesian LASSO, Bayesian ridge regression, Bayes A, and Bayes B. The non-linear models (this refers to non-linearity on markers) were reproducing kernel Hilbert space (RKHS) regression, Bayesian regularized neural networks (BRNN), and radial basis function neural networks (RBFNN). These statistical models were compared using 306 elite wheat lines from CIMMYT genotyped with 1717 diversity array technology (DArT) markers and two traits, days to heading (DTH) and grain yield (GY), measured in each of 12 environments. It was found that the three non-linear models had better overall prediction accuracy than the linear regression specification. Results showed a consistent superiority of RKHS and RBFNN over the Bayesian LASSO, Bayesian ridge regression, Bayes A, and Bayes B models. PMID:23275882

Comparison between linear and non-parametric regression models for genome-enabled prediction in wheat.

PubMed

Pérez-Rodríguez, Paulino; Gianola, Daniel; González-Camacho, Juan Manuel; Crossa, José; Manès, Yann; Dreisigacker, Susanne

2012-12-01

In genome-enabled prediction, parametric, semi-parametric, and non-parametric regression models have been used. This study assessed the predictive ability of linear and non-linear models using dense molecular markers. The linear models were linear on marker effects and included the Bayesian LASSO, Bayesian ridge regression, Bayes A, and Bayes B. The non-linear models (this refers to non-linearity on markers) were reproducing kernel Hilbert space (RKHS) regression, Bayesian regularized neural networks (BRNN), and radial basis function neural networks (RBFNN). These statistical models were compared using 306 elite wheat lines from CIMMYT genotyped with 1717 diversity array technology (DArT) markers and two traits, days to heading (DTH) and grain yield (GY), measured in each of 12 environments. It was found that the three non-linear models had better overall prediction accuracy than the linear regression specification. Results showed a consistent superiority of RKHS and RBFNN over the Bayesian LASSO, Bayesian ridge regression, Bayes A, and Bayes B models.

Benchmark dose analysis via nonparametric regression modeling

PubMed Central

Piegorsch, Walter W.; Xiong, Hui; Bhattacharya, Rabi N.; Lin, Lizhen

2013-01-01

Estimation of benchmark doses (BMDs) in quantitative risk assessment traditionally is based upon parametric dose-response modeling. It is a well-known concern, however, that if the chosen parametric model is uncertain and/or misspecified, inaccurate and possibly unsafe low-dose inferences can result. We describe a nonparametric approach for estimating BMDs with quantal-response data based on an isotonic regression method, and also study use of corresponding, nonparametric, bootstrap-based confidence limits for the BMD. We explore the confidence limits’ small-sample properties via a simulation study, and illustrate the calculations with an example from cancer risk assessment. It is seen that this nonparametric approach can provide a useful alternative for BMD estimation when faced with the problem of parametric model uncertainty. PMID:23683057

Modeling and Visualization Process of the Curve of Pen Point by GeoGebra

ERIC Educational Resources Information Center

Aktümen, Muharem; Horzum, Tugba; Ceylan, Tuba

2013-01-01

This study describes the mathematical construction of a real-life model by means of parametric equations, as well as the two- and three-dimensional visualization of the model using the software GeoGebra. The model was initially considered as "determining the parametric equation of the curve formed on a plane by the point of a pen, positioned…

The influence of vegetation height heterogeneity on forest and woodland bird species richness across the United States.

PubMed

Huang, Qiongyu; Swatantran, Anu; Dubayah, Ralph; Goetz, Scott J

2014-01-01

Avian diversity is under increasing pressures. It is thus critical to understand the ecological variables that contribute to large scale spatial distribution of avian species diversity. Traditionally, studies have relied primarily on two-dimensional habitat structure to model broad scale species richness. Vegetation vertical structure is increasingly used at local scales. However, the spatial arrangement of vegetation height has never been taken into consideration. Our goal was to examine the efficacies of three-dimensional forest structure, particularly the spatial heterogeneity of vegetation height in improving avian richness models across forested ecoregions in the U.S. We developed novel habitat metrics to characterize the spatial arrangement of vegetation height using the National Biomass and Carbon Dataset for the year 2000 (NBCD). The height-structured metrics were compared with other habitat metrics for statistical association with richness of three forest breeding bird guilds across Breeding Bird Survey (BBS) routes: a broadly grouped woodland guild, and two forest breeding guilds with preferences for forest edge and for interior forest. Parametric and non-parametric models were built to examine the improvement of predictability. Height-structured metrics had the strongest associations with species richness, yielding improved predictive ability for the woodland guild richness models (r(2) = ∼ 0.53 for the parametric models, 0.63 the non-parametric models) and the forest edge guild models (r(2) = ∼ 0.34 for the parametric models, 0.47 the non-parametric models). All but one of the linear models incorporating height-structured metrics showed significantly higher adjusted-r2 values than their counterparts without additional metrics. The interior forest guild richness showed a consistent low association with height-structured metrics. Our results suggest that height heterogeneity, beyond canopy height alone, supplements habitat characterization and richness models of forest bird species. The metrics and models derived in this study demonstrate practical examples of utilizing three-dimensional vegetation data for improved characterization of spatial patterns in species richness.

The Influence of Vegetation Height Heterogeneity on Forest and Woodland Bird Species Richness across the United States

PubMed Central

Huang, Qiongyu; Swatantran, Anu; Dubayah, Ralph; Goetz, Scott J.

2014-01-01

Avian diversity is under increasing pressures. It is thus critical to understand the ecological variables that contribute to large scale spatial distribution of avian species diversity. Traditionally, studies have relied primarily on two-dimensional habitat structure to model broad scale species richness. Vegetation vertical structure is increasingly used at local scales. However, the spatial arrangement of vegetation height has never been taken into consideration. Our goal was to examine the efficacies of three-dimensional forest structure, particularly the spatial heterogeneity of vegetation height in improving avian richness models across forested ecoregions in the U.S. We developed novel habitat metrics to characterize the spatial arrangement of vegetation height using the National Biomass and Carbon Dataset for the year 2000 (NBCD). The height-structured metrics were compared with other habitat metrics for statistical association with richness of three forest breeding bird guilds across Breeding Bird Survey (BBS) routes: a broadly grouped woodland guild, and two forest breeding guilds with preferences for forest edge and for interior forest. Parametric and non-parametric models were built to examine the improvement of predictability. Height-structured metrics had the strongest associations with species richness, yielding improved predictive ability for the woodland guild richness models (r2 = ∼0.53 for the parametric models, 0.63 the non-parametric models) and the forest edge guild models (r2 = ∼0.34 for the parametric models, 0.47 the non-parametric models). All but one of the linear models incorporating height-structured metrics showed significantly higher adjusted-r2 values than their counterparts without additional metrics. The interior forest guild richness showed a consistent low association with height-structured metrics. Our results suggest that height heterogeneity, beyond canopy height alone, supplements habitat characterization and richness models of forest bird species. The metrics and models derived in this study demonstrate practical examples of utilizing three-dimensional vegetation data for improved characterization of spatial patterns in species richness. PMID:25101782

Towards the generation of a parametric foot model using principal component analysis: A pilot study.

PubMed

Scarton, Alessandra; Sawacha, Zimi; Cobelli, Claudio; Li, Xinshan

2016-06-01

There have been many recent developments in patient-specific models with their potential to provide more information on the human pathophysiology and the increase in computational power. However they are not yet successfully applied in a clinical setting. One of the main challenges is the time required for mesh creation, which is difficult to automate. The development of parametric models by means of the Principle Component Analysis (PCA) represents an appealing solution. In this study PCA has been applied to the feet of a small cohort of diabetic and healthy subjects, in order to evaluate the possibility of developing parametric foot models, and to use them to identify variations and similarities between the two populations. Both the skin and the first metatarsal bones have been examined. Besides the reduced sample of subjects considered in the analysis, results demonstrated that the method adopted herein constitutes a first step towards the realization of a parametric foot models for biomechanical analysis. Furthermore the study showed that the methodology can successfully describe features in the foot, and evaluate differences in the shape of healthy and diabetic subjects. Copyright © 2016 IPEM. Published by Elsevier Ltd. All rights reserved.

PRESS-based EFOR algorithm for the dynamic parametrical modeling of nonlinear MDOF systems

NASA Astrophysics Data System (ADS)

Liu, Haopeng; Zhu, Yunpeng; Luo, Zhong; Han, Qingkai

2017-09-01

In response to the identification problem concerning multi-degree of freedom (MDOF) nonlinear systems, this study presents the extended forward orthogonal regression (EFOR) based on predicted residual sums of squares (PRESS) to construct a nonlinear dynamic parametrical model. The proposed parametrical model is based on the non-linear autoregressive with exogenous inputs (NARX) model and aims to explicitly reveal the physical design parameters of the system. The PRESS-based EFOR algorithm is proposed to identify such a model for MDOF systems. By using the algorithm, we built a common-structured model based on the fundamental concept of evaluating its generalization capability through cross-validation. The resulting model aims to prevent over-fitting with poor generalization performance caused by the average error reduction ratio (AERR)-based EFOR algorithm. Then, a functional relationship is established between the coefficients of the terms and the design parameters of the unified model. Moreover, a 5-DOF nonlinear system is taken as a case to illustrate the modeling of the proposed algorithm. Finally, a dynamic parametrical model of a cantilever beam is constructed from experimental data. Results indicate that the dynamic parametrical model of nonlinear systems, which depends on the PRESS-based EFOR, can accurately predict the output response, thus providing a theoretical basis for the optimal design of modeling methods for MDOF nonlinear systems.

Processes controlling surface, bottom and lateral melt of Arctic sea ice in a state of the art sea ice model.

PubMed

Tsamados, Michel; Feltham, Daniel; Petty, Alek; Schroeder, David; Flocco, Daniela

2015-10-13

We present a modelling study of processes controlling the summer melt of the Arctic sea ice cover. We perform a sensitivity study and focus our interest on the thermodynamics at the ice-atmosphere and ice-ocean interfaces. We use the Los Alamos community sea ice model CICE, and additionally implement and test three new parametrization schemes: (i) a prognostic mixed layer; (ii) a three equation boundary condition for the salt and heat flux at the ice-ocean interface; and (iii) a new lateral melt parametrization. Recent additions to the CICE model are also tested, including explicit melt ponds, a form drag parametrization and a halodynamic brine drainage scheme. The various sea ice parametrizations tested in this sensitivity study introduce a wide spread in the simulated sea ice characteristics. For each simulation, the total melt is decomposed into its surface, bottom and lateral melt components to assess the processes driving melt and how this varies regionally and temporally. Because this study quantifies the relative importance of several processes in driving the summer melt of sea ice, this work can serve as a guide for future research priorities. © 2015 The Author(s).

Simulation of parametric model towards the fixed covariate of right censored lung cancer data

NASA Astrophysics Data System (ADS)

Afiqah Muhamad Jamil, Siti; Asrul Affendi Abdullah, M.; Kek, Sie Long; Ridwan Olaniran, Oyebayo; Enera Amran, Syahila

2017-09-01

In this study, simulation procedure was applied to measure the fixed covariate of right censored data by using parametric survival model. The scale and shape parameter were modified to differentiate the analysis of parametric regression survival model. Statistically, the biases, mean biases and the coverage probability were used in this analysis. Consequently, different sample sizes were employed to distinguish the impact of parametric regression model towards right censored data with 50, 100, 150 and 200 number of sample. R-statistical software was utilised to develop the coding simulation with right censored data. Besides, the final model of right censored simulation was compared with the right censored lung cancer data in Malaysia. It was found that different values of shape and scale parameter with different sample size, help to improve the simulation strategy for right censored data and Weibull regression survival model is suitable fit towards the simulation of survival of lung cancer patients data in Malaysia.

Estimating technical efficiency in the hospital sector with panel data: a comparison of parametric and non-parametric techniques.

PubMed

Siciliani, Luigi

2006-01-01

Policy makers are increasingly interested in developing performance indicators that measure hospital efficiency. These indicators may give the purchasers of health services an additional regulatory tool to contain health expenditure. Using panel data, this study compares different parametric (econometric) and non-parametric (linear programming) techniques for the measurement of a hospital's technical efficiency. This comparison was made using a sample of 17 Italian hospitals in the years 1996-9. Highest correlations are found in the efficiency scores between the non-parametric data envelopment analysis under the constant returns to scale assumption (DEA-CRS) and several parametric models. Correlation reduces markedly when using more flexible non-parametric specifications such as data envelopment analysis under the variable returns to scale assumption (DEA-VRS) and the free disposal hull (FDH) model. Correlation also generally reduces when moving from one output to two-output specifications. This analysis suggests that there is scope for developing performance indicators at hospital level using panel data, but it is important that extensive sensitivity analysis is carried out if purchasers wish to make use of these indicators in practice.

A numerical study on piezoelectric energy harvesting by combining transverse galloping and parametric instability phenomena

NASA Astrophysics Data System (ADS)

Franzini, Guilherme Rosa; Santos, Rebeca Caramêz Saraiva; Pesce, Celso Pupo

2017-12-01

This paper aims to numerically investigate the effects of parametric instability on piezoelectric energy harvesting from the transverse galloping of a square prism. A two degrees-of-freedom reduced-order model for this problem is proposed and numerically integrated. A usual quasi-steady galloping model is applied, where the transverse force coefficient is adopted as a cubic polynomial function with respect to the angle of attack. Time-histories of nondimensional prism displacement, electric voltage and power dissipated at both the dashpot and the electrical resistance are obtained as functions of the reduced velocity. Both, oscillation amplitude and electric voltage, increased with the reduced velocity for all parametric excitation conditions tested. For low values of reduced velocity, 2:1 parametric excitation enhances the electric voltage. On the other hand, for higher reduced velocities, a 1:1 parametric excitation (i.e., the same as the natural frequency) enhances both oscillation amplitude and electric voltage. It has been also found that, depending on the parametric excitation frequency, the harvested electrical power can be amplified in 70% when compared to the case under no parametric excitation.

Multilevel mixed effects parametric survival models using adaptive Gauss-Hermite quadrature with application to recurrent events and individual participant data meta-analysis.

PubMed

Crowther, Michael J; Look, Maxime P; Riley, Richard D

2014-09-28

Multilevel mixed effects survival models are used in the analysis of clustered survival data, such as repeated events, multicenter clinical trials, and individual participant data (IPD) meta-analyses, to investigate heterogeneity in baseline risk and covariate effects. In this paper, we extend parametric frailty models including the exponential, Weibull and Gompertz proportional hazards (PH) models and the log logistic, log normal, and generalized gamma accelerated failure time models to allow any number of normally distributed random effects. Furthermore, we extend the flexible parametric survival model of Royston and Parmar, modeled on the log-cumulative hazard scale using restricted cubic splines, to include random effects while also allowing for non-PH (time-dependent effects). Maximum likelihood is used to estimate the models utilizing adaptive or nonadaptive Gauss-Hermite quadrature. The methods are evaluated through simulation studies representing clinically plausible scenarios of a multicenter trial and IPD meta-analysis, showing good performance of the estimation method. The flexible parametric mixed effects model is illustrated using a dataset of patients with kidney disease and repeated times to infection and an IPD meta-analysis of prognostic factor studies in patients with breast cancer. User-friendly Stata software is provided to implement the methods. Copyright © 2014 John Wiley & Sons, Ltd.

Non-parametric wall model and methods of identifying boundary conditions for moments in gas flow equations

NASA Astrophysics Data System (ADS)

Liao, Meng; To, Quy-Dong; Léonard, Céline; Monchiet, Vincent

2018-03-01

In this paper, we use the molecular dynamics simulation method to study gas-wall boundary conditions. Discrete scattering information of gas molecules at the wall surface is obtained from collision simulations. The collision data can be used to identify the accommodation coefficients for parametric wall models such as Maxwell and Cercignani-Lampis scattering kernels. Since these scattering kernels are based on a limited number of accommodation coefficients, we adopt non-parametric statistical methods to construct the kernel to overcome these issues. Different from parametric kernels, the non-parametric kernels require no parameter (i.e. accommodation coefficients) and no predefined distribution. We also propose approaches to derive directly the Navier friction and Kapitza thermal resistance coefficients as well as other interface coefficients associated with moment equations from the non-parametric kernels. The methods are applied successfully to systems composed of CH4 or CO2 and graphite, which are of interest to the petroleum industry.

Parametric Modelling of As-Built Beam Framed Structure in Bim Environment

NASA Astrophysics Data System (ADS)

Yang, X.; Koehl, M.; Grussenmeyer, P.

2017-02-01

A complete documentation and conservation of a historic timber roof requires the integration of geometry modelling, attributional and dynamic information management and results of structural analysis. Recently developed as-built Building Information Modelling (BIM) technique has the potential to provide a uniform platform, which provides possibility to integrate the traditional geometry modelling, parametric elements management and structural analysis together. The main objective of the project presented in this paper is to develop a parametric modelling tool for a timber roof structure whose elements are leaning and crossing beam frame. Since Autodesk Revit, as the typical BIM software, provides the platform for parametric modelling and information management, an API plugin, able to automatically create the parametric beam elements and link them together with strict relationship, was developed. The plugin under development is introduced in the paper, which can obtain the parametric beam model via Autodesk Revit API from total station points and terrestrial laser scanning data. The results show the potential of automatizing the parametric modelling by interactive API development in BIM environment. It also integrates the separate data processing and different platforms into the uniform Revit software.

SPM analysis of parametric (R)-[11C]PK11195 binding images: plasma input versus reference tissue parametric methods.

PubMed

Schuitemaker, Alie; van Berckel, Bart N M; Kropholler, Marc A; Veltman, Dick J; Scheltens, Philip; Jonker, Cees; Lammertsma, Adriaan A; Boellaard, Ronald

2007-05-01

(R)-[11C]PK11195 has been used for quantifying cerebral microglial activation in vivo. In previous studies, both plasma input and reference tissue methods have been used, usually in combination with a region of interest (ROI) approach. Definition of ROIs, however, can be labourious and prone to interobserver variation. In addition, results are only obtained for predefined areas and (unexpected) signals in undefined areas may be missed. On the other hand, standard pharmacokinetic models are too sensitive to noise to calculate (R)-[11C]PK11195 binding on a voxel-by-voxel basis. Linearised versions of both plasma input and reference tissue models have been described, and these are more suitable for parametric imaging. The purpose of this study was to compare the performance of these plasma input and reference tissue parametric methods on the outcome of statistical parametric mapping (SPM) analysis of (R)-[11C]PK11195 binding. Dynamic (R)-[11C]PK11195 PET scans with arterial blood sampling were performed in 7 younger and 11 elderly healthy subjects. Parametric images of volume of distribution (Vd) and binding potential (BP) were generated using linearised versions of plasma input (Logan) and reference tissue (Reference Parametric Mapping) models. Images were compared at the group level using SPM with a two-sample t-test per voxel, both with and without proportional scaling. Parametric BP images without scaling provided the most sensitive framework for determining differences in (R)-[11C]PK11195 binding between younger and elderly subjects. Vd images could only demonstrate differences in (R)-[11C]PK11195 binding when analysed with proportional scaling due to intersubject variation in K1/k2 (blood-brain barrier transport and non-specific binding).

Improvements in Sensible Heat-Flux Parametrization in the High-Resolution Regional Model (HRM) Through the Modified Treatment of the Roughness Length for Heat

NASA Astrophysics Data System (ADS)

Anurose, T. J.; Subrahamanyam, D. Bala

2013-06-01

We discuss the impact of the differential treatment of the roughness lengths for momentum and heat (z_{0m} and z_{0h}) in the flux parametrization scheme of the high-resolution regional model (HRM) for a heterogeneous terrain centred around Thiruvananthapuram, India (8.5°N, 76.9°E). The magnitudes of sensible heat flux ( H) obtained from HRM simulations using the original parametrization scheme differed drastically from the concurrent in situ observations. With a view to improving the performance of this parametrization scheme, two distinct modifications are incorporated: (1) In the first method, a constant value of 100 is assigned to the z_{0m}/z_{0h} ratio; (2) and in the second approach, this ratio is treated as a function of time. Both these modifications in the HRM model showed significant improvements in the H simulations for Thiruvananthapuram and its adjoining regions. Results obtained from the present study provide a first-ever comparison of H simulations using the modified parametrization scheme in the HRM model with in situ observations for the Indian coastal region, and suggest a differential treatment of z_{0m} and z_{0h} in the flux parametrization scheme.

Software Reliability 2002

NASA Technical Reports Server (NTRS)

Wallace, Dolores R.

2003-01-01

In FY01 we learned that hardware reliability models need substantial changes to account for differences in software, thus making software reliability measurements more effective, accurate, and easier to apply. These reliability models are generally based on familiar distributions or parametric methods. An obvious question is 'What new statistical and probability models can be developed using non-parametric and distribution-free methods instead of the traditional parametric method?" Two approaches to software reliability engineering appear somewhat promising. The first study, begin in FY01, is based in hardware reliability, a very well established science that has many aspects that can be applied to software. This research effort has investigated mathematical aspects of hardware reliability and has identified those applicable to software. Currently the research effort is applying and testing these approaches to software reliability measurement, These parametric models require much project data that may be difficult to apply and interpret. Projects at GSFC are often complex in both technology and schedules. Assessing and estimating reliability of the final system is extremely difficult when various subsystems are tested and completed long before others. Parametric and distribution free techniques may offer a new and accurate way of modeling failure time and other project data to provide earlier and more accurate estimates of system reliability.

Geometric Model for a Parametric Study of the Blended-Wing-Body Airplane

NASA Technical Reports Server (NTRS)

Mastin, C. Wayne; Smith, Robert E.; Sadrehaghighi, Ideen; Wiese, Micharl R.

1996-01-01

A parametric model is presented for the blended-wing-body airplane, one concept being proposed for the next generation of large subsonic transports. The model is defined in terms of a small set of parameters which facilitates analysis and optimization during the conceptual design process. The model is generated from a preliminary CAD geometry. From this geometry, airfoil cross sections are cut at selected locations and fitted with analytic curves. The airfoils are then used as boundaries for surfaces defined as the solution of partial differential equations. Both the airfoil curves and the surfaces are generated with free parameters selected to give a good representation of the original geometry. The original surface is compared with the parametric model, and solutions of the Euler equations for compressible flow are computed for both geometries. The parametric model is a good approximation of the CAD model and the computed solutions are qualitatively similar. An optimal NURBS approximation is constructed and can be used by a CAD model for further refinement or modification of the original geometry.

Effect of Monovalent Ion Parameters on Molecular Dynamics Simulations of G-Quadruplexes.

PubMed

Havrila, Marek; Stadlbauer, Petr; Islam, Barira; Otyepka, Michal; Šponer, Jiří

2017-08-08

G-quadruplexes (GQs) are key noncanonical DNA and RNA architectures stabilized by desolvated monovalent cations present in their central channels. We analyze extended atomistic molecular dynamics simulations (∼580 μs in total) of GQs with 11 monovalent cation parametrizations, assessing GQ overall structural stability, dynamics of internal cations, and distortions of the G-tetrad geometries. Majority of simulations were executed with the SPC/E water model; however, test simulations with TIP3P and OPC water models are also reported. The identity and parametrization of ions strongly affect behavior of a tetramolecular d[GGG] 4 GQ, which is unstable with several ion parametrizations. The remaining studied RNA and DNA GQs are structurally stable, though the G-tetrad geometries are always deformed by bifurcated H-bonding in a parametrization-specific manner. Thus, basic 10-μs-scale simulations of fully folded GQs can be safely done with a number of cation parametrizations. However, there are parametrization-specific differences and basic force-field errors affecting the quantitative description of ion-tetrad interactions, which may significantly affect studies of the ion-binding processes and description of the GQ folding landscape. Our d[GGG] 4 simulations indirectly suggest that such studies will also be sensitive to the water models. During exchanges with bulk water, the Na + ions move inside the GQs in a concerted manner, while larger relocations of the K + ions are typically separated. We suggest that the Joung-Cheatham SPC/E K + parameters represent a safe choice in simulation studies of GQs, though variation of ion parameters can be used for specific simulation goals.

Analysis of survival in breast cancer patients by using different parametric models

NASA Astrophysics Data System (ADS)

Enera Amran, Syahila; Asrul Afendi Abdullah, M.; Kek, Sie Long; Afiqah Muhamad Jamil, Siti

2017-09-01

In biomedical applications or clinical trials, right censoring was often arising when studying the time to event data. In this case, some individuals are still alive at the end of the study or lost to follow up at a certain time. It is an important issue to handle the censoring data in order to prevent any bias information in the analysis. Therefore, this study was carried out to analyze the right censoring data with three different parametric models; exponential model, Weibull model and log-logistic models. Data of breast cancer patients from Hospital Sultan Ismail, Johor Bahru from 30 December 2008 until 15 February 2017 was used in this study to illustrate the right censoring data. Besides, the covariates included in this study are the time of breast cancer infection patients survive t, age of each patients X1 and treatment given to the patients X2 . In order to determine the best parametric models in analysing survival of breast cancer patients, the performance of each model was compare based on Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC) and log-likelihood value using statistical software R. When analysing the breast cancer data, all three distributions were shown consistency of data with the line graph of cumulative hazard function resembles a straight line going through the origin. As the result, log-logistic model was the best fitted parametric model compared with exponential and Weibull model since it has the smallest value in AIC and BIC, also the biggest value in log-likelihood.

A Semi-parametric Transformation Frailty Model for Semi-competing Risks Survival Data

PubMed Central

Jiang, Fei; Haneuse, Sebastien

2016-01-01

In the analysis of semi-competing risks data interest lies in estimation and inference with respect to a so-called non-terminal event, the observation of which is subject to a terminal event. Multi-state models are commonly used to analyse such data, with covariate effects on the transition/intensity functions typically specified via the Cox model and dependence between the non-terminal and terminal events specified, in part, by a unit-specific shared frailty term. To ensure identifiability, the frailties are typically assumed to arise from a parametric distribution, specifically a Gamma distribution with mean 1.0 and variance, say, σ2. When the frailty distribution is misspecified, however, the resulting estimator is not guaranteed to be consistent, with the extent of asymptotic bias depending on the discrepancy between the assumed and true frailty distributions. In this paper, we propose a novel class of transformation models for semi-competing risks analysis that permit the non-parametric specification of the frailty distribution. To ensure identifiability, the class restricts to parametric specifications of the transformation and the error distribution; the latter are flexible, however, and cover a broad range of possible specifications. We also derive the semi-parametric efficient score under the complete data setting and propose a non-parametric score imputation method to handle right censoring; consistency and asymptotic normality of the resulting estimators is derived and small-sample operating characteristics evaluated via simulation. Although the proposed semi-parametric transformation model and non-parametric score imputation method are motivated by the analysis of semi-competing risks data, they are broadly applicable to any analysis of multivariate time-to-event outcomes in which a unit-specific shared frailty is used to account for correlation. Finally, the proposed model and estimation procedures are applied to a study of hospital readmission among patients diagnosed with pancreatic cancer. PMID:28439147

Comparison of Survival Models for Analyzing Prognostic Factors in Gastric Cancer Patients

PubMed

Habibi, Danial; Rafiei, Mohammad; Chehrei, Ali; Shayan, Zahra; Tafaqodi, Soheil

2018-03-27

Objective: There are a number of models for determining risk factors for survival of patients with gastric cancer. This study was conducted to select the model showing the best fit with available data. Methods: Cox regression and parametric models (Exponential, Weibull, Gompertz, Log normal, Log logistic and Generalized Gamma) were utilized in unadjusted and adjusted forms to detect factors influencing mortality of patients. Comparisons were made with Akaike Information Criterion (AIC) by using STATA 13 and R 3.1.3 softwares. Results: The results of this study indicated that all parametric models outperform the Cox regression model. The Log normal, Log logistic and Generalized Gamma provided the best performance in terms of AIC values (179.2, 179.4 and 181.1, respectively). On unadjusted analysis, the results of the Cox regression and parametric models indicated stage, grade, largest diameter of metastatic nest, largest diameter of LM, number of involved lymph nodes and the largest ratio of metastatic nests to lymph nodes, to be variables influencing the survival of patients with gastric cancer. On adjusted analysis, according to the best model (log normal), grade was found as the significant variable. Conclusion: The results suggested that all parametric models outperform the Cox model. The log normal model provides the best fit and is a good substitute for Cox regression. Creative Commons Attribution License

Impact of state updating and multi-parametric ensemble for streamflow hindcasting in European river basins

NASA Astrophysics Data System (ADS)

Noh, S. J.; Rakovec, O.; Kumar, R.; Samaniego, L. E.

2015-12-01

Accurate and reliable streamflow prediction is essential to mitigate social and economic damage coming from water-related disasters such as flood and drought. Sequential data assimilation (DA) may facilitate improved streamflow prediction using real-time observations to correct internal model states. In conventional DA methods such as state updating, parametric uncertainty is often ignored mainly due to practical limitations of methodology to specify modeling uncertainty with limited ensemble members. However, if parametric uncertainty related with routing and runoff components is not incorporated properly, predictive uncertainty by model ensemble may be insufficient to capture dynamics of observations, which may deteriorate predictability. Recently, a multi-scale parameter regionalization (MPR) method was proposed to make hydrologic predictions at different scales using a same set of model parameters without losing much of the model performance. The MPR method incorporated within the mesoscale hydrologic model (mHM, http://www.ufz.de/mhm) could effectively represent and control uncertainty of high-dimensional parameters in a distributed model using global parameters. In this study, we evaluate impacts of streamflow data assimilation over European river basins. Especially, a multi-parametric ensemble approach is tested to consider the effects of parametric uncertainty in DA. Because augmentation of parameters is not required within an assimilation window, the approach could be more stable with limited ensemble members and have potential for operational uses. To consider the response times and non-Gaussian characteristics of internal hydrologic processes, lagged particle filtering is utilized. The presentation will be focused on gains and limitations of streamflow data assimilation and multi-parametric ensemble method over large-scale basins.

Why preferring parametric forecasting to nonparametric methods?

PubMed

Jabot, Franck

2015-05-07

A recent series of papers by Charles T. Perretti and collaborators have shown that nonparametric forecasting methods can outperform parametric methods in noisy nonlinear systems. Such a situation can arise because of two main reasons: the instability of parametric inference procedures in chaotic systems which can lead to biased parameter estimates, and the discrepancy between the real system dynamics and the modeled one, a problem that Perretti and collaborators call "the true model myth". Should ecologists go on using the demanding parametric machinery when trying to forecast the dynamics of complex ecosystems? Or should they rely on the elegant nonparametric approach that appears so promising? It will be here argued that ecological forecasting based on parametric models presents two key comparative advantages over nonparametric approaches. First, the likelihood of parametric forecasting failure can be diagnosed thanks to simple Bayesian model checking procedures. Second, when parametric forecasting is diagnosed to be reliable, forecasting uncertainty can be estimated on virtual data generated with the fitted to data parametric model. In contrast, nonparametric techniques provide forecasts with unknown reliability. This argumentation is illustrated with the simple theta-logistic model that was previously used by Perretti and collaborators to make their point. It should convince ecologists to stick to standard parametric approaches, until methods have been developed to assess the reliability of nonparametric forecasting. Copyright © 2015 Elsevier Ltd. All rights reserved.

Joint analysis of input and parametric uncertainties in watershed water quality modeling: A formal Bayesian approach

NASA Astrophysics Data System (ADS)

Han, Feng; Zheng, Yi

2018-06-01

Significant Input uncertainty is a major source of error in watershed water quality (WWQ) modeling. It remains challenging to address the input uncertainty in a rigorous Bayesian framework. This study develops the Bayesian Analysis of Input and Parametric Uncertainties (BAIPU), an approach for the joint analysis of input and parametric uncertainties through a tight coupling of Markov Chain Monte Carlo (MCMC) analysis and Bayesian Model Averaging (BMA). The formal likelihood function for this approach is derived considering a lag-1 autocorrelated, heteroscedastic, and Skew Exponential Power (SEP) distributed error model. A series of numerical experiments were performed based on a synthetic nitrate pollution case and on a real study case in the Newport Bay Watershed, California. The Soil and Water Assessment Tool (SWAT) and Differential Evolution Adaptive Metropolis (DREAM(ZS)) were used as the representative WWQ model and MCMC algorithm, respectively. The major findings include the following: (1) the BAIPU can be implemented and used to appropriately identify the uncertain parameters and characterize the predictive uncertainty; (2) the compensation effect between the input and parametric uncertainties can seriously mislead the modeling based management decisions, if the input uncertainty is not explicitly accounted for; (3) the BAIPU accounts for the interaction between the input and parametric uncertainties and therefore provides more accurate calibration and uncertainty results than a sequential analysis of the uncertainties; and (4) the BAIPU quantifies the credibility of different input assumptions on a statistical basis and can be implemented as an effective inverse modeling approach to the joint inference of parameters and inputs.

A generalized Jaynes-Cummings model: The relativistic parametric amplifier and a single trapped ion

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

Ojeda-Guillén, D., E-mail: dojedag@ipn.mx; Mota, R. D.; Granados, V. D.

2016-06-15

We introduce a generalization of the Jaynes-Cummings model and study some of its properties. We obtain the energy spectrum and eigenfunctions of this model by using the tilting transformation and the squeezed number states of the one-dimensional harmonic oscillator. As physical applications, we connect this new model to two important and novelty problems: the relativistic parametric amplifier and the quantum simulation of a single trapped ion.

The Impact of Three-Dimensional Effects on the Simulation of Turbulence Kinetic Energy in a Major Alpine Valley

NASA Astrophysics Data System (ADS)

Goger, Brigitta; Rotach, Mathias W.; Gohm, Alexander; Fuhrer, Oliver; Stiperski, Ivana; Holtslag, Albert A. M.

2018-02-01

The correct simulation of the atmospheric boundary layer (ABL) is crucial for reliable weather forecasts in truly complex terrain. However, common assumptions for model parametrizations are only valid for horizontally homogeneous and flat terrain. Here, we evaluate the turbulence parametrization of the numerical weather prediction model COSMO with a horizontal grid spacing of Δ x = 1.1 km for the Inn Valley, Austria. The long-term, high-resolution turbulence measurements of the i-Box measurement sites provide a useful data pool of the ABL structure in the valley and on slopes. We focus on days and nights when ABL processes dominate and a thermally-driven circulation is present. Simulations are performed for case studies with both a one-dimensional turbulence parametrization, which only considers the vertical turbulent exchange, and a hybrid turbulence parametrization, also including horizontal shear production and advection in the budget of turbulence kinetic energy (TKE). We find a general underestimation of TKE by the model with the one-dimensional turbulence parametrization. In the simulations with the hybrid turbulence parametrization, the modelled TKE has a more realistic structure, especially in situations when the TKE production is dominated by shear related to the afternoon up-valley flow, and during nights, when a stable ABL is present. The model performance also improves for stations on the slopes. An estimation of the horizontal shear production from the observation network suggests that three-dimensional effects are a relevant part of TKE production in the valley.

The Impact of Three-Dimensional Effects on the Simulation of Turbulence Kinetic Energy in a Major Alpine Valley

NASA Astrophysics Data System (ADS)

Goger, Brigitta; Rotach, Mathias W.; Gohm, Alexander; Fuhrer, Oliver; Stiperski, Ivana; Holtslag, Albert A. M.

2018-07-01

The correct simulation of the atmospheric boundary layer (ABL) is crucial for reliable weather forecasts in truly complex terrain. However, common assumptions for model parametrizations are only valid for horizontally homogeneous and flat terrain. Here, we evaluate the turbulence parametrization of the numerical weather prediction model COSMO with a horizontal grid spacing of Δ x = 1.1 km for the Inn Valley, Austria. The long-term, high-resolution turbulence measurements of the i-Box measurement sites provide a useful data pool of the ABL structure in the valley and on slopes. We focus on days and nights when ABL processes dominate and a thermally-driven circulation is present. Simulations are performed for case studies with both a one-dimensional turbulence parametrization, which only considers the vertical turbulent exchange, and a hybrid turbulence parametrization, also including horizontal shear production and advection in the budget of turbulence kinetic energy (TKE). We find a general underestimation of TKE by the model with the one-dimensional turbulence parametrization. In the simulations with the hybrid turbulence parametrization, the modelled TKE has a more realistic structure, especially in situations when the TKE production is dominated by shear related to the afternoon up-valley flow, and during nights, when a stable ABL is present. The model performance also improves for stations on the slopes. An estimation of the horizontal shear production from the observation network suggests that three-dimensional effects are a relevant part of TKE production in the valley.

Parametric excitation of tire-wheel assemblies by a stiffness non-uniformity

NASA Astrophysics Data System (ADS)

Stutts, D. S.; Krousgrill, C. M.; Soedel, W.

1995-01-01

A simple model of the effect of a concentrated radial stiffness non-uniformity in a passenger car tire is presented. The model treats the tread band of the tire as a rigid ring supported on a viscoelastic foundation. The distributed radial stiffness is lumped into equivalent horizontal (fore-and-aft) and vertical stiffnesses. The concentrated radial stiffness non-uniformity is modeled by treating the tread band as fixed, and the stiffness non-uniformity as rotating around it at the nominal angular velocity of the wheel. Due to loading, the center of mass of the tread band ring model is displaced upward with respect to the wheel spindle and, therefore, the rotating stiffness non-uniformity is alternately compressed and stretched through one complete rotation. This stretching and compressing of the stiffness non-uniformity results in force transmission to the wheel spindle at twice the nominal angular velocity in frequency, and therefore, would excite a given resonance at one-half the nominal angular wheel velocity that a mass unbalance would. The forcing produced by the stiffness non-uniformity is parametric in nature, thus creating the possibility of parametric resonance. The basic theory of the parametric resonance is explained, and a parameter study using derived lumped parameters based on a typical passenger car tire is performed. This study revealed that parametric resonance in passenger car tires, although possible, is unlikely at normal highway speeds as predicted by this model unless the tire is partially deflated.

Selecting a Separable Parametric Spatiotemporal Covariance Structure for Longitudinal Imaging Data

PubMed Central

George, Brandon; Aban, Inmaculada

2014-01-01

Longitudinal imaging studies allow great insight into how the structure and function of a subject’s internal anatomy changes over time. Unfortunately, the analysis of longitudinal imaging data is complicated by inherent spatial and temporal correlation: the temporal from the repeated measures, and the spatial from the outcomes of interest being observed at multiple points in a patients body. We propose the use of a linear model with a separable parametric spatiotemporal error structure for the analysis of repeated imaging data. The model makes use of spatial (exponential, spherical, and Matérn) and temporal (compound symmetric, autoregressive-1, Toeplitz, and unstructured) parametric correlation functions. A simulation study, inspired by a longitudinal cardiac imaging study on mitral regurgitation patients, compared different information criteria for selecting a particular separable parametric spatiotemporal correlation structure as well as the effects on Type I and II error rates for inference on fixed effects when the specified model is incorrect. Information criteria were found to be highly accurate at choosing between separable parametric spatiotemporal correlation structures. Misspecification of the covariance structure was found to have the ability to inflate the Type I error or have an overly conservative test size, which corresponded to decreased power. An example with clinical data is given illustrating how the covariance structure procedure can be done in practice, as well as how covariance structure choice can change inferences about fixed effects. PMID:25293361

Free-form geometric modeling by integrating parametric and implicit PDEs.

PubMed

Du, Haixia; Qin, Hong

2007-01-01

Parametric PDE techniques, which use partial differential equations (PDEs) defined over a 2D or 3D parametric domain to model graphical objects and processes, can unify geometric attributes and functional constraints of the models. PDEs can also model implicit shapes defined by level sets of scalar intensity fields. In this paper, we present an approach that integrates parametric and implicit trivariate PDEs to define geometric solid models containing both geometric information and intensity distribution subject to flexible boundary conditions. The integrated formulation of second-order or fourth-order elliptic PDEs permits designers to manipulate PDE objects of complex geometry and/or arbitrary topology through direct sculpting and free-form modeling. We developed a PDE-based geometric modeling system for shape design and manipulation of PDE objects. The integration of implicit PDEs with parametric geometry offers more general and arbitrary shape blending and free-form modeling for objects with intensity attributes than pure geometric models.

Parametric study of laser photovoltaic energy converters

NASA Technical Reports Server (NTRS)

Walker, G. H.; Heinbockel, J. H.

1987-01-01

Photovoltaic converters are of interest for converting laser power to electrical power in a space-based laser power system. This paper describes a model for photovoltaic laser converters and the application of this model to a neodymium laser silicon photovoltaic converter system. A parametric study which defines the sensitivity of the photovoltaic parameters is described. An optimized silicon photovoltaic converter has an efficiency greater than 50 percent for 1000 W/sq cm of neodymium laser radiation.

Parametric instability of shaft with discs

NASA Astrophysics Data System (ADS)

Wahab, A. M. Abdul; Rasid, Z. A.; Abu, A.; Rudin, N. F. Mohd Noor

2017-12-01

The occurrence of resonance is a major criterion to be considered in the design of shaft. While force resonance occurs merely when the natural frequency of the rotor system equals speed of the shaft, parametric resonance or parametric instability can occur at excitation speed that is integral or sub-multiple of the frequency of the rotor. This makes the study on parametric resonance crucial. Parametric instability of a shaft system consisting of a shaft and disks has been investigated in this study. The finite element formulation of the Mathieu-Hill equation that represents the parametric instability problem of the shaft is developed based on Timoshenko’s beam theory and Nelson’s finite element method (FEM) model that considers the effect of torsional motion on such problem. The Bolotin’s method is used to determine the regions of instability and the Strut-Ince diagram. The validation works show that the results of this study are in close agreement to past results. It is found that a larger radius of disk will cause the shaft to become more unstable compared to smaller radius although both weights are similar. Furthermore, the effect of torsional motion on the parametric instability of the shaft is significant at higher rotating speed.

Direct solar-pumped iodine laser amplifier

NASA Technical Reports Server (NTRS)

Han, K. S.

1985-01-01

This semiannual progress report covers the period from April 1, 1985 to Sept. 30, 1985 under NASA grant NAS1-441 entitled direct solar pumped iodine laser amplifier. During this period the parametric studies of the iodine laser oscillator pumped by a Vortek simulator was carried out before the amplifier studies. The amplifier studies are postponed to the extended period following completion of the parametric studies. In addition, the kinetic modeling of a solar pumped iodine laser amplifier, and the experimental work for a solar pumped dye laser amplifier are in progress. This report contains three parts: (1) the radiation characteristics of solar simulator and the parametric characteristics of photodissociation iodine laser continuously pumped by a Vortek solar simulator; (2) kinetic modeling of a solar pumped iodine laser amplifier; and (3) the study of the dye laser amplifier pumped by a Tamarack solar simulator.

Further Empirical Results on Parametric Versus Non-Parametric IRT Modeling of Likert-Type Personality Data

ERIC Educational Resources Information Center

Maydeu-Olivares, Albert

2005-01-01

Chernyshenko, Stark, Chan, Drasgow, and Williams (2001) investigated the fit of Samejima's logistic graded model and Levine's non-parametric MFS model to the scales of two personality questionnaires and found that the graded model did not fit well. We attribute the poor fit of the graded model to small amounts of multidimensionality present in…

GEE-Smoothing Spline in Semiparametric Model with Correlated Nominal Data

NASA Astrophysics Data System (ADS)

Ibrahim, Noor Akma; Suliadi

2010-11-01

In this paper we propose GEE-Smoothing spline in the estimation of semiparametric models with correlated nominal data. The method can be seen as an extension of parametric generalized estimating equation to semiparametric models. The nonparametric component is estimated using smoothing spline specifically the natural cubic spline. We use profile algorithm in the estimation of both parametric and nonparametric components. The properties of the estimators are evaluated using simulation studies.

On the validation of cloud parametrization schemes in numerical atmospheric models with satellite data from ISCCP

NASA Astrophysics Data System (ADS)

Meinke, I.

2003-04-01

A new method is presented to validate cloud parametrization schemes in numerical atmospheric models with satellite data of scanning radiometers. This method is applied to the regional atmospheric model HRM (High Resolution Regional Model) using satellite data from ISCCP (International Satellite Cloud Climatology Project). Due to the limited reliability of former validations there has been a need for developing a new validation method: Up to now differences between simulated and measured cloud properties are mostly declared as deficiencies of the cloud parametrization scheme without further investigation. Other uncertainties connected with the model or with the measurements have not been taken into account. Therefore changes in the cloud parametrization scheme based on such kind of validations might not be realistic. The new method estimates uncertainties of the model and the measurements. Criteria for comparisons of simulated and measured data are derived to localize deficiencies in the model. For a better specification of these deficiencies simulated clouds are classified regarding their parametrization. With this classification the localized model deficiencies are allocated to a certain parametrization scheme. Applying this method to the regional model HRM the quality of forecasting cloud properties is estimated in detail. The overestimation of simulated clouds in low emissivity heights especially during the night is localized as model deficiency. This is caused by subscale cloudiness. As the simulation of subscale clouds in the regional model HRM is described by a relative humidity parametrization these deficiencies are connected with this parameterization.

A review of parametric approaches specific to aerodynamic design process

NASA Astrophysics Data System (ADS)

Zhang, Tian-tian; Wang, Zhen-guo; Huang, Wei; Yan, Li

2018-04-01

Parametric modeling of aircrafts plays a crucial role in the aerodynamic design process. Effective parametric approaches have large design space with a few variables. Parametric methods that commonly used nowadays are summarized in this paper, and their principles have been introduced briefly. Two-dimensional parametric methods include B-Spline method, Class/Shape function transformation method, Parametric Section method, Hicks-Henne method and Singular Value Decomposition method, and all of them have wide application in the design of the airfoil. This survey made a comparison among them to find out their abilities in the design of the airfoil, and the results show that the Singular Value Decomposition method has the best parametric accuracy. The development of three-dimensional parametric methods is limited, and the most popular one is the Free-form deformation method. Those methods extended from two-dimensional parametric methods have promising prospect in aircraft modeling. Since different parametric methods differ in their characteristics, real design process needs flexible choice among them to adapt to subsequent optimization procedure.

Teaching Mathematics with Intelligent Support in Natural Language. Tertiary Education Students Working with Parametrized Modelling Activities

ERIC Educational Resources Information Center

Rojano, Teresa; García-Campos, Montserrat

2017-01-01

This article reports the outcomes of a study that seeks to investigate the role of feedback, by way of an intelligent support system in natural language, in parametrized modelling activities carried out by a group of tertiary education students. With such a system, it is possible to simultaneously display on a computer screen a dialogue window and…

Prevalence Incidence Mixture Models

Cancer.gov

The R package and webtool fits Prevalence Incidence Mixture models to left-censored and irregularly interval-censored time to event data that is commonly found in screening cohorts assembled from electronic health records. Absolute and relative risk can be estimated for simple random sampling, and stratified sampling (the two approaches of superpopulation and a finite population are supported for target populations). Non-parametric (absolute risks only), semi-parametric, weakly-parametric (using B-splines), and some fully parametric (such as the logistic-Weibull) models are supported.

Genetic Algorithm Based Framework for Automation of Stochastic Modeling of Multi-Season Streamflows

NASA Astrophysics Data System (ADS)

Srivastav, R. K.; Srinivasan, K.; Sudheer, K.

2009-05-01

Synthetic streamflow data generation involves the synthesis of likely streamflow patterns that are statistically indistinguishable from the observed streamflow data. The various kinds of stochastic models adopted for multi-season streamflow generation in hydrology are: i) parametric models which hypothesize the form of the periodic dependence structure and the distributional form a priori (examples are PAR, PARMA); disaggregation models that aim to preserve the correlation structure at the periodic level and the aggregated annual level; ii) Nonparametric models (examples are bootstrap/kernel based methods), which characterize the laws of chance, describing the stream flow process, without recourse to prior assumptions as to the form or structure of these laws; (k-nearest neighbor (k-NN), matched block bootstrap (MABB)); non-parametric disaggregation model. iii) Hybrid models which blend both parametric and non-parametric models advantageously to model the streamflows effectively. Despite many of these developments that have taken place in the field of stochastic modeling of streamflows over the last four decades, accurate prediction of the storage and the critical drought characteristics has been posing a persistent challenge to the stochastic modeler. This is partly because, usually, the stochastic streamflow model parameters are estimated by minimizing a statistically based objective function (such as maximum likelihood (MLE) or least squares (LS) estimation) and subsequently the efficacy of the models is being validated based on the accuracy of prediction of the estimates of the water-use characteristics, which requires large number of trial simulations and inspection of many plots and tables. Still accurate prediction of the storage and the critical drought characteristics may not be ensured. In this study a multi-objective optimization framework is proposed to find the optimal hybrid model (blend of a simple parametric model, PAR(1) model and matched block bootstrap (MABB) ) based on the explicit objective functions of minimizing the relative bias and relative root mean square error in estimating the storage capacity of the reservoir. The optimal parameter set of the hybrid model is obtained based on the search over a multi- dimensional parameter space (involving simultaneous exploration of the parametric (PAR(1)) as well as the non-parametric (MABB) components). This is achieved using the efficient evolutionary search based optimization tool namely, non-dominated sorting genetic algorithm - II (NSGA-II). This approach helps in reducing the drudgery involved in the process of manual selection of the hybrid model, in addition to predicting the basic summary statistics dependence structure, marginal distribution and water-use characteristics accurately. The proposed optimization framework is used to model the multi-season streamflows of River Beaver and River Weber of USA. In case of both the rivers, the proposed GA-based hybrid model yields a much better prediction of the storage capacity (where simultaneous exploration of both parametric and non-parametric components is done) when compared with the MLE-based hybrid models (where the hybrid model selection is done in two stages, thus probably resulting in a sub-optimal model). This framework can be further extended to include different linear/non-linear hybrid stochastic models at other temporal and spatial scales as well.

Bim Automation: Advanced Modeling Generative Process for Complex Structures

NASA Astrophysics Data System (ADS)

Banfi, F.; Fai, S.; Brumana, R.

2017-08-01

The new paradigm of the complexity of modern and historic structures, which are characterised by complex forms, morphological and typological variables, is one of the greatest challenges for building information modelling (BIM). Generation of complex parametric models needs new scientific knowledge concerning new digital technologies. These elements are helpful to store a vast quantity of information during the life cycle of buildings (LCB). The latest developments of parametric applications do not provide advanced tools, resulting in time-consuming work for the generation of models. This paper presents a method capable of processing and creating complex parametric Building Information Models (BIM) with Non-Uniform to NURBS) with multiple levels of details (Mixed and ReverseLoD) based on accurate 3D photogrammetric and laser scanning surveys. Complex 3D elements are converted into parametric BIM software and finite element applications (BIM to FEA) using specific exchange formats and new modelling tools. The proposed approach has been applied to different case studies: the BIM of modern structure for the courtyard of West Block on Parliament Hill in Ottawa (Ontario) and the BIM of Masegra Castel in Sondrio (Italy), encouraging the dissemination and interaction of scientific results without losing information during the generative process.

Convergence optimization of parametric MLEM reconstruction for estimation of Patlak plot parameters.

PubMed

Angelis, Georgios I; Thielemans, Kris; Tziortzi, Andri C; Turkheimer, Federico E; Tsoumpas, Charalampos

2011-07-01

In dynamic positron emission tomography data many researchers have attempted to exploit kinetic models within reconstruction such that parametric images are estimated directly from measurements. This work studies a direct parametric maximum likelihood expectation maximization algorithm applied to [(18)F]DOPA data using reference-tissue input function. We use a modified version for direct reconstruction with a gradually descending scheme of subsets (i.e. 18-6-1) initialized with the FBP parametric image for faster convergence and higher accuracy. The results compared with analytic reconstructions show quantitative robustness (i.e. minimal bias) and clinical reproducibility within six human acquisitions in the region of clinical interest. Bland-Altman plots for all the studies showed sufficient quantitative agreement between the direct reconstructed parametric maps and the indirect FBP (--0.035x+0.48E--5). Copyright © 2011 Elsevier Ltd. All rights reserved.

Selecting a separable parametric spatiotemporal covariance structure for longitudinal imaging data.

PubMed

George, Brandon; Aban, Inmaculada

2015-01-15

Longitudinal imaging studies allow great insight into how the structure and function of a subject's internal anatomy changes over time. Unfortunately, the analysis of longitudinal imaging data is complicated by inherent spatial and temporal correlation: the temporal from the repeated measures and the spatial from the outcomes of interest being observed at multiple points in a patient's body. We propose the use of a linear model with a separable parametric spatiotemporal error structure for the analysis of repeated imaging data. The model makes use of spatial (exponential, spherical, and Matérn) and temporal (compound symmetric, autoregressive-1, Toeplitz, and unstructured) parametric correlation functions. A simulation study, inspired by a longitudinal cardiac imaging study on mitral regurgitation patients, compared different information criteria for selecting a particular separable parametric spatiotemporal correlation structure as well as the effects on types I and II error rates for inference on fixed effects when the specified model is incorrect. Information criteria were found to be highly accurate at choosing between separable parametric spatiotemporal correlation structures. Misspecification of the covariance structure was found to have the ability to inflate the type I error or have an overly conservative test size, which corresponded to decreased power. An example with clinical data is given illustrating how the covariance structure procedure can be performed in practice, as well as how covariance structure choice can change inferences about fixed effects. Copyright © 2014 John Wiley & Sons, Ltd.

A discrete epidemic model for bovine Babesiosis disease and tick populations

NASA Astrophysics Data System (ADS)

Aranda, Diego F.; Trejos, Deccy Y.; Valverde, Jose C.

2017-06-01

In this paper, we provide and study a discrete model for the transmission of Babesiosis disease in bovine and tick populations. This model supposes a discretization of the continuous-time model developed by us previously. The results, here obtained by discrete methods as opposed to continuous ones, show that similar conclusions can be obtained for the discrete model subject to the assumption of some parametric constraints which were not necessary in the continuous case. We prove that these parametric constraints are not artificial and, in fact, they can be deduced from the biological significance of the model. Finally, some numerical simulations are given to validate the model and verify our theoretical study.

Recent advances in parametric neuroreceptor mapping with dynamic PET: basic concepts and graphical analyses.

PubMed

Seo, Seongho; Kim, Su Jin; Lee, Dong Soo; Lee, Jae Sung

2014-10-01

Tracer kinetic modeling in dynamic positron emission tomography (PET) has been widely used to investigate the characteristic distribution patterns or dysfunctions of neuroreceptors in brain diseases. Its practical goal has progressed from regional data quantification to parametric mapping that produces images of kinetic-model parameters by fully exploiting the spatiotemporal information in dynamic PET data. Graphical analysis (GA) is a major parametric mapping technique that is independent on any compartmental model configuration, robust to noise, and computationally efficient. In this paper, we provide an overview of recent advances in the parametric mapping of neuroreceptor binding based on GA methods. The associated basic concepts in tracer kinetic modeling are presented, including commonly-used compartment models and major parameters of interest. Technical details of GA approaches for reversible and irreversible radioligands are described, considering both plasma input and reference tissue input models. Their statistical properties are discussed in view of parametric imaging.

Changing space and sound: Parametric design and variable acoustics

NASA Astrophysics Data System (ADS)

Norton, Christopher William

This thesis examines the potential for parametric design software to create performance based design using acoustic metrics as the design criteria. A former soundstage at the University of Southern California used by the Thornton School of Music is used as a case study for a multiuse space for orchestral, percussion, master class and recital use. The criteria used for each programmatic use include reverberation time, bass ratio, and the early energy ratios of the clarity index and objective support. Using a panelized ceiling as a design element to vary the parameters of volume, panel orientation and type of absorptive material, the relationships between these parameters and the design criteria are explored. These relationships and subsequently derived equations are applied to Grasshopper parametric modeling software for Rhino 3D (a NURBS modeling software). Using the target reverberation time and bass ratio for each programmatic use as input for the parametric model, the genomic optimization function of Grasshopper - Galapagos - is run to identify the optimum ceiling geometry and material distribution.

A parametric ribcage geometry model accounting for variations among the adult population.

PubMed

Wang, Yulong; Cao, Libo; Bai, Zhonghao; Reed, Matthew P; Rupp, Jonathan D; Hoff, Carrie N; Hu, Jingwen

2016-09-06

The objective of this study is to develop a parametric ribcage model that can account for morphological variations among the adult population. Ribcage geometries, including 12 pair of ribs, sternum, and thoracic spine, were collected from CT scans of 101 adult subjects through image segmentation, landmark identification (1016 for each subject), symmetry adjustment, and template mesh mapping (26,180 elements for each subject). Generalized procrustes analysis (GPA), principal component analysis (PCA), and regression analysis were used to develop a parametric ribcage model, which can predict nodal locations of the template mesh according to age, sex, height, and body mass index (BMI). Two regression models, a quadratic model for estimating the ribcage size and a linear model for estimating the ribcage shape, were developed. The results showed that the ribcage size was dominated by the height (p=0.000) and age-sex-interaction (p=0.007) and the ribcage shape was significantly affected by the age (p=0.0005), sex (p=0.0002), height (p=0.0064) and BMI (p=0.0000). Along with proper assignment of cortical bone thickness, material properties and failure properties, this parametric ribcage model can directly serve as the mesh of finite element ribcage models for quantifying effects of human characteristics on thoracic injury risks. Copyright © 2016 Elsevier Ltd. All rights reserved.

First-Order Parametric Model of Reflectance Spectra for Dyed Fabrics

DTIC Science & Technology

2016-02-19

Unclassified Unlimited 31 Daniel Aiken (202) 279-5293 Parametric modeling Inverse /direct analysis This report describes a first-order parametric model of...Appendix: Dielectric Response Functions for Dyes Obtained by Inverse Analysis ……………………………...…………………………………………………….19 1 First-Order Parametric...which provides for both their inverse and direct modeling1. The dyes considered contain spectral features that are of interest to the U.S. Navy for

Non-parametric identification of multivariable systems: A local rational modeling approach with application to a vibration isolation benchmark

NASA Astrophysics Data System (ADS)

Voorhoeve, Robbert; van der Maas, Annemiek; Oomen, Tom

2018-05-01

Frequency response function (FRF) identification is often used as a basis for control systems design and as a starting point for subsequent parametric system identification. The aim of this paper is to develop a multiple-input multiple-output (MIMO) local parametric modeling approach for FRF identification of lightly damped mechanical systems with improved speed and accuracy. The proposed method is based on local rational models, which can efficiently handle the lightly-damped resonant dynamics. A key aspect herein is the freedom in the multivariable rational model parametrizations. Several choices for such multivariable rational model parametrizations are proposed and investigated. For systems with many inputs and outputs the required number of model parameters can rapidly increase, adversely affecting the performance of the local modeling approach. Therefore, low-order model structures are investigated. The structure of these low-order parametrizations leads to an undesired directionality in the identification problem. To address this, an iterative local rational modeling algorithm is proposed. As a special case recently developed SISO algorithms are recovered. The proposed approach is successfully demonstrated on simulations and on an active vibration isolation system benchmark, confirming good performance of the method using significantly less parameters compared with alternative approaches.

Numerical parametric studies of spray combustion instability

NASA Technical Reports Server (NTRS)

Pindera, M. Z.

1993-01-01

A coupled numerical algorithm has been developed for studies of combustion instabilities in spray-driven liquid rocket engines. The model couples gas and liquid phase physics using the method of fractional steps. Also introduced is a novel, efficient methodology for accounting for spray formation through direct solution of liquid phase equations. Preliminary parametric studies show marked sensitivity of spray penetration and geometry to droplet diameter, considerations of liquid core, and acoustic interactions. Less sensitivity was shown to the combustion model type although more rigorous (multi-step) formulations may be needed for the differences to become apparent.

Model selection criterion in survival analysis

NASA Astrophysics Data System (ADS)

Karabey, Uǧur; Tutkun, Nihal Ata

2017-07-01

Survival analysis deals with time until occurrence of an event of interest such as death, recurrence of an illness, the failure of an equipment or divorce. There are various survival models with semi-parametric or parametric approaches used in medical, natural or social sciences. The decision on the most appropriate model for the data is an important point of the analysis. In literature Akaike information criteria or Bayesian information criteria are used to select among nested models. In this study,the behavior of these information criterion is discussed for a real data set.

Empirical study of the dependence of the results of multivariable flexible survival analyses on model selection strategy.

PubMed

Binquet, C; Abrahamowicz, M; Mahboubi, A; Jooste, V; Faivre, J; Bonithon-Kopp, C; Quantin, C

2008-12-30

Flexible survival models, which avoid assumptions about hazards proportionality (PH) or linearity of continuous covariates effects, bring the issues of model selection to a new level of complexity. Each 'candidate covariate' requires inter-dependent decisions regarding (i) its inclusion in the model, and representation of its effects on the log hazard as (ii) either constant over time or time-dependent (TD) and, for continuous covariates, (iii) either loglinear or non-loglinear (NL). Moreover, 'optimal' decisions for one covariate depend on the decisions regarding others. Thus, some efficient model-building strategy is necessary.We carried out an empirical study of the impact of the model selection strategy on the estimates obtained in flexible multivariable survival analyses of prognostic factors for mortality in 273 gastric cancer patients. We used 10 different strategies to select alternative multivariable parametric as well as spline-based models, allowing flexible modeling of non-parametric (TD and/or NL) effects. We employed 5-fold cross-validation to compare the predictive ability of alternative models.All flexible models indicated significant non-linearity and changes over time in the effect of age at diagnosis. Conventional 'parametric' models suggested the lack of period effect, whereas more flexible strategies indicated a significant NL effect. Cross-validation confirmed that flexible models predicted better mortality. The resulting differences in the 'final model' selected by various strategies had also impact on the risk prediction for individual subjects.Overall, our analyses underline (a) the importance of accounting for significant non-parametric effects of covariates and (b) the need for developing accurate model selection strategies for flexible survival analyses. Copyright 2008 John Wiley & Sons, Ltd.

Towards an Empirically Based Parametric Explosion Spectral Model

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

Ford, S R; Walter, W R; Ruppert, S

2009-08-31

Small underground nuclear explosions need to be confidently detected, identified, and characterized in regions of the world where they have never before been tested. The focus of our work is on the local and regional distances (< 2000 km) and phases (Pn, Pg, Sn, Lg) necessary to see small explosions. We are developing a parametric model of the nuclear explosion seismic source spectrum that is compatible with the earthquake-based geometrical spreading and attenuation models developed using the Magnitude Distance Amplitude Correction (MDAC) techniques (Walter and Taylor, 2002). The explosion parametric model will be particularly important in regions without any priormore » explosion data for calibration. The model is being developed using the available body of seismic data at local and regional distances for past nuclear explosions at foreign and domestic test sites. Parametric modeling is a simple and practical approach for widespread monitoring applications, prior to the capability to carry out fully deterministic modeling. The achievable goal of our parametric model development is to be able to predict observed local and regional distance seismic amplitudes for event identification and yield determination in regions with incomplete or no prior history of underground nuclear testing. The relationship between the parametric equations and the geologic and containment conditions will assist in our physical understanding of the nuclear explosion source.« less

Combined non-parametric and parametric approach for identification of time-variant systems

NASA Astrophysics Data System (ADS)

Dziedziech, Kajetan; Czop, Piotr; Staszewski, Wieslaw J.; Uhl, Tadeusz

2018-03-01

Identification of systems, structures and machines with variable physical parameters is a challenging task especially when time-varying vibration modes are involved. The paper proposes a new combined, two-step - i.e. non-parametric and parametric - modelling approach in order to determine time-varying vibration modes based on input-output measurements. Single-degree-of-freedom (SDOF) vibration modes from multi-degree-of-freedom (MDOF) non-parametric system representation are extracted in the first step with the use of time-frequency wavelet-based filters. The second step involves time-varying parametric representation of extracted modes with the use of recursive linear autoregressive-moving-average with exogenous inputs (ARMAX) models. The combined approach is demonstrated using system identification analysis based on the experimental mass-varying MDOF frame-like structure subjected to random excitation. The results show that the proposed combined method correctly captures the dynamics of the analysed structure, using minimum a priori information on the model.

Spacelab mission dependent training parametric resource requirements study

NASA Technical Reports Server (NTRS)

Ogden, D. H.; Watters, H.; Steadman, J.; Conrad, L.

1976-01-01

Training flows were developed for typical missions, resource relationships analyzed, and scheduling optimization algorithms defined. Parametric analyses were performed to study the effect of potential changes in mission model, mission complexity and training time required on the resource quantities required to support training of payload or mission specialists. Typical results of these analyses are presented both in graphic and tabular form.

Incorporating parametric uncertainty into population viability analysis models

USGS Publications Warehouse

McGowan, Conor P.; Runge, Michael C.; Larson, Michael A.

2011-01-01

Uncertainty in parameter estimates from sampling variation or expert judgment can introduce substantial uncertainty into ecological predictions based on those estimates. However, in standard population viability analyses, one of the most widely used tools for managing plant, fish and wildlife populations, parametric uncertainty is often ignored in or discarded from model projections. We present a method for explicitly incorporating this source of uncertainty into population models to fully account for risk in management and decision contexts. Our method involves a two-step simulation process where parametric uncertainty is incorporated into the replication loop of the model and temporal variance is incorporated into the loop for time steps in the model. Using the piping plover, a federally threatened shorebird in the USA and Canada, as an example, we compare abundance projections and extinction probabilities from simulations that exclude and include parametric uncertainty. Although final abundance was very low for all sets of simulations, estimated extinction risk was much greater for the simulation that incorporated parametric uncertainty in the replication loop. Decisions about species conservation (e.g., listing, delisting, and jeopardy) might differ greatly depending on the treatment of parametric uncertainty in population models.

Direct Estimation of Kinetic Parametric Images for Dynamic PET

PubMed Central

Wang, Guobao; Qi, Jinyi

2013-01-01

Dynamic positron emission tomography (PET) can monitor spatiotemporal distribution of radiotracer in vivo. The spatiotemporal information can be used to estimate parametric images of radiotracer kinetics that are of physiological and biochemical interests. Direct estimation of parametric images from raw projection data allows accurate noise modeling and has been shown to offer better image quality than conventional indirect methods, which reconstruct a sequence of PET images first and then perform tracer kinetic modeling pixel-by-pixel. Direct reconstruction of parametric images has gained increasing interests with the advances in computing hardware. Many direct reconstruction algorithms have been developed for different kinetic models. In this paper we review the recent progress in the development of direct reconstruction algorithms for parametric image estimation. Algorithms for linear and nonlinear kinetic models are described and their properties are discussed. PMID:24396500

Prepositioning emergency supplies under uncertainty: a parametric optimization method

NASA Astrophysics Data System (ADS)

Bai, Xuejie; Gao, Jinwu; Liu, Yankui

2018-07-01

Prepositioning of emergency supplies is an effective method for increasing preparedness for disasters and has received much attention in recent years. In this article, the prepositioning problem is studied by a robust parametric optimization method. The transportation cost, supply, demand and capacity are unknown prior to the extraordinary event, which are represented as fuzzy parameters with variable possibility distributions. The variable possibility distributions are obtained through the credibility critical value reduction method for type-2 fuzzy variables. The prepositioning problem is formulated as a fuzzy value-at-risk model to achieve a minimum total cost incurred in the whole process. The key difficulty in solving the proposed optimization model is to evaluate the quantile of the fuzzy function in the objective and the credibility in the constraints. The objective function and constraints can be turned into their equivalent parametric forms through chance constrained programming under the different confidence levels. Taking advantage of the structural characteristics of the equivalent optimization model, a parameter-based domain decomposition method is developed to divide the original optimization problem into six mixed-integer parametric submodels, which can be solved by standard optimization solvers. Finally, to explore the viability of the developed model and the solution approach, some computational experiments are performed on realistic scale case problems. The computational results reported in the numerical example show the credibility and superiority of the proposed parametric optimization method.

Bim and Gis: when Parametric Modeling Meets Geospatial Data

NASA Astrophysics Data System (ADS)

Barazzetti, L.; Banfi, F.

2017-12-01

Geospatial data have a crucial role in several projects related to infrastructures and land management. GIS software are able to perform advanced geospatial analyses, but they lack several instruments and tools for parametric modelling typically available in BIM. At the same time, BIM software designed for buildings have limited tools to handle geospatial data. As things stand at the moment, BIM and GIS could appear as complementary solutions, notwithstanding research work is currently under development to ensure a better level of interoperability, especially at the scale of the building. On the other hand, the transition from the local (building) scale to the infrastructure (where geospatial data cannot be neglected) has already demonstrated that parametric modelling integrated with geoinformation is a powerful tool to simplify and speed up some phases of the design workflow. This paper reviews such mixed approaches with both simulated and real examples, demonstrating that integration is already a reality at specific scales, which are not dominated by "pure" GIS or BIM. The paper will also demonstrate that some traditional operations carried out with GIS software are also available in parametric modelling software for BIM, such as transformation between reference systems, DEM generation, feature extraction, and geospatial queries. A real case study is illustrated and discussed to show the advantage of a combined use of both technologies. BIM and GIS integration can generate greater usage of geospatial data in the AECOO (Architecture, Engineering, Construction, Owner and Operator) industry, as well as new solutions for parametric modelling with additional geoinformation.

Analytic modeling of aerosol size distributions

NASA Technical Reports Server (NTRS)

Deepack, A.; Box, G. P.

1979-01-01

Mathematical functions commonly used for representing aerosol size distributions are studied parametrically. Methods for obtaining best fit estimates of the parameters are described. A catalog of graphical plots depicting the parametric behavior of the functions is presented along with procedures for obtaining analytical representations of size distribution data by visual matching of the data with one of the plots. Examples of fitting the same data with equal accuracy by more than one analytic model are also given.

Classification of Company Performance using Weighted Probabilistic Neural Network

NASA Astrophysics Data System (ADS)

Yasin, Hasbi; Waridi Basyiruddin Arifin, Adi; Warsito, Budi

2018-05-01

Classification of company performance can be judged by looking at its financial status, whether good or bad state. Classification of company performance can be achieved by some approach, either parametric or non-parametric. Neural Network is one of non-parametric methods. One of Artificial Neural Network (ANN) models is Probabilistic Neural Network (PNN). PNN consists of four layers, i.e. input layer, pattern layer, addition layer, and output layer. The distance function used is the euclidean distance and each class share the same values as their weights. In this study used PNN that has been modified on the weighting process between the pattern layer and the addition layer by involving the calculation of the mahalanobis distance. This model is called the Weighted Probabilistic Neural Network (WPNN). The results show that the company's performance modeling with the WPNN model has a very high accuracy that reaches 100%.

TOWARD HIGH-PRECISION SEISMIC STUDIES OF WHITE DWARF STARS: PARAMETRIZATION OF THE CORE AND TESTS OF ACCURACY

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

Giammichele, N.; Fontaine, G.; Brassard, P.

We present a prescription for parametrizing the chemical profile in the core of white dwarfs in light of the recent discovery that pulsation modes may sometimes be deeply confined in some cool pulsating white dwarfs. Such modes may be used as unique probes of the complicated chemical stratification that results from several processes that occurred in previous evolutionary phases of intermediate-mass stars. This effort is part of our ongoing quest for more credible and realistic seismic models of white dwarfs using static, parametrized equilibrium structures. Inspired by successful techniques developed in design optimization fields (such as aerodynamics), we exploit Akimamore » splines for the tracing of the chemical profile of oxygen (carbon) in the core of a white dwarf model. A series of tests are then presented to better seize the precision and significance of the results that can be obtained in an asteroseismological context. We also show that the new parametrization passes an essential basic test, as it successfully reproduces the chemical stratification of a full evolutionary model.« less

Toward High-precision Seismic Studies of White Dwarf Stars: Parametrization of the Core and Tests of Accuracy

NASA Astrophysics Data System (ADS)

Giammichele, N.; Charpinet, S.; Fontaine, G.; Brassard, P.

2017-01-01

We present a prescription for parametrizing the chemical profile in the core of white dwarfs in light of the recent discovery that pulsation modes may sometimes be deeply confined in some cool pulsating white dwarfs. Such modes may be used as unique probes of the complicated chemical stratification that results from several processes that occurred in previous evolutionary phases of intermediate-mass stars. This effort is part of our ongoing quest for more credible and realistic seismic models of white dwarfs using static, parametrized equilibrium structures. Inspired by successful techniques developed in design optimization fields (such as aerodynamics), we exploit Akima splines for the tracing of the chemical profile of oxygen (carbon) in the core of a white dwarf model. A series of tests are then presented to better seize the precision and significance of the results that can be obtained in an asteroseismological context. We also show that the new parametrization passes an essential basic test, as it successfully reproduces the chemical stratification of a full evolutionary model.

Stochastic climate dynamics: Stochastic parametrizations and their global effects

NASA Astrophysics Data System (ADS)

Ghil, Michael

2010-05-01

A well-known difficulty in modeling the atmosphere and oceans' general circulation is the limited, albeit increasing resolution possible in the numerical solution of the governing partial differential equations. While the mass, energy and momentum of an individual cloud, in the atmosphere, or convection chimney, in the oceans, is negligible, their combined effects over long times are not. Until recently, small, subgrid-scale processes were represented in general circulation models (GCMs) by deterministic "parametrizations." While A. Arakawa and associates had realized over three decades ago the conceptual need for ensembles of clouds in such parametrizations, it is only very recently that truly stochastic parametrizations have been introduced into GCMs and weather prediction models. These parametrizations essentially transform a deterministic autonomous system into a non-autonomous one, subject to random forcing. To study systematically the long-term effects of such a forcing has to rely on theory of random dynamical systems (RDS). This theory allows one to consider the detailed geometric structure of the random attractors associated with nonlinear, stochastically perturbed systems. These attractors extend the concept of strange attractors from autonomous dynamical systems to non-autonomous systems with random forcing. To illustrate the essence of the theory, its concepts and methods, we carry out a high-resolution numerical study of two "toy" models in their respective phase spaces. This study allows one to obtain a good approximation of their global random attractors, as well as of the time-dependent invariant measures supported by these attractors. The first of the two models studied herein is the Arnol'd family of circle maps in the presence of noise. The maps' fine-grained, resonant landscape --- associated with Arnol'd tongues --- is smoothed by the noise, thus permitting a comparison with the observable aspects of the "Devil's staircase" that arises in modeling the El Nino-Southern Oscillation (ENSO). These results are confirmed by studying a "French garden" that is obtained by smoothing a "Devil's quarry." Such a quarry results from coupling two circle maps, and random forcing leads to a smoothed version thereof. We thus suspect that stochastic parametrizations will stabilize the sensitive dependence on parameters that has been noticed in the development of GCMs. This talk represents joint work with Mickael D. Chekroun, D. Kondrashov, Eric Simonnet and I. Zaliapin. Several other talks and posters complement the results presented here and provide further insights into RDS theory and its application to the geosciences.

Modeling personnel turnover in the parametric organization

NASA Technical Reports Server (NTRS)

Dean, Edwin B.

1991-01-01

A model is developed for simulating the dynamics of a newly formed organization, credible during all phases of organizational development. The model development process is broken down into the activities of determining the tasks required for parametric cost analysis (PCA), determining the skills required for each PCA task, determining the skills available in the applicant marketplace, determining the structure of the model, implementing the model, and testing it. The model, parameterized by the likelihood of job function transition, has demonstrated by the capability to represent the transition of personnel across functional boundaries within a parametric organization using a linear dynamical system, and the ability to predict required staffing profiles to meet functional needs at the desired time. The model can be extended by revisions of the state and transition structure to provide refinements in functional definition for the parametric and extended organization.

A new process sensitivity index to identify important system processes under process model and parametric uncertainty

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

Dai, Heng; Ye, Ming; Walker, Anthony P.

Hydrological models are always composed of multiple components that represent processes key to intended model applications. When a process can be simulated by multiple conceptual-mathematical models (process models), model uncertainty in representing the process arises. While global sensitivity analysis methods have been widely used for identifying important processes in hydrologic modeling, the existing methods consider only parametric uncertainty but ignore the model uncertainty for process representation. To address this problem, this study develops a new method to probe multimodel process sensitivity by integrating the model averaging methods into the framework of variance-based global sensitivity analysis, given that the model averagingmore » methods quantify both parametric and model uncertainty. A new process sensitivity index is derived as a metric of relative process importance, and the index includes variance in model outputs caused by uncertainty in both process models and model parameters. For demonstration, the new index is used to evaluate the processes of recharge and geology in a synthetic study of groundwater reactive transport modeling. The recharge process is simulated by two models that converting precipitation to recharge, and the geology process is also simulated by two models of different parameterizations of hydraulic conductivity; each process model has its own random parameters. The new process sensitivity index is mathematically general, and can be applied to a wide range of problems in hydrology and beyond.« less

Comparison of thawing and freezing dark energy parametrizations

NASA Astrophysics Data System (ADS)

Pantazis, G.; Nesseris, S.; Perivolaropoulos, L.

2016-05-01

Dark energy equation of state w (z ) parametrizations with two parameters and given monotonicity are generically either convex or concave functions. This makes them suitable for fitting either freezing or thawing quintessence models but not both simultaneously. Fitting a data set based on a freezing model with an unsuitable (concave when increasing) w (z ) parametrization [like Chevallier-Polarski-Linder (CPL)] can lead to significant misleading features like crossing of the phantom divide line, incorrect w (z =0 ), incorrect slope, etc., that are not present in the underlying cosmological model. To demonstrate this fact we generate scattered cosmological data at both the level of w (z ) and the luminosity distance DL(z ) based on either thawing or freezing quintessence models and fit them using parametrizations of convex and of concave type. We then compare statistically significant features of the best fit w (z ) with actual features of the underlying model. We thus verify that the use of unsuitable parametrizations can lead to misleading conclusions. In order to avoid these problems it is important to either use both convex and concave parametrizations and select the one with the best χ2 or use principal component analysis thus splitting the redshift range into independent bins. In the latter case, however, significant information about the slope of w (z ) at high redshifts is lost. Finally, we propose a new family of parametrizations w (z )=w0+wa(z/1 +z )n which generalizes the CPL and interpolates between thawing and freezing parametrizations as the parameter n increases to values larger than 1.

Grating lobe elimination in steerable parametric loudspeaker.

PubMed

Shi, Chuang; Gan, Woon-Seng

2011-02-01

In the past two decades, the majority of research on the parametric loudspeaker has concentrated on the nonlinear modeling of acoustic propagation and pre-processing techniques to reduce nonlinear distortion in sound reproduction. There are, however, very few studies on directivity control of the parametric loudspeaker. In this paper, we propose an equivalent circular Gaussian source array that approximates the directivity characteristics of the linear ultrasonic transducer array. By using this approximation, the directivity of the sound beam from the parametric loudspeaker can be predicted by the product directivity principle. New theoretical results, which are verified through measurements, are presented to show the effectiveness of the delay-and-sum beamsteering structure for the parametric loudspeaker. Unlike the conventional loudspeaker array, where the spacing between array elements must be less than half the wavelength to avoid spatial aliasing, the parametric loudspeaker can take advantage of grating lobe elimination to extend the spacing of ultrasonic transducer array to more than 1.5 wavelengths in a typical application.

Generalized Correlation Coefficient for Non-Parametric Analysis of Microarray Time-Course Data.

PubMed

Tan, Qihua; Thomassen, Mads; Burton, Mark; Mose, Kristian Fredløv; Andersen, Klaus Ejner; Hjelmborg, Jacob; Kruse, Torben

2017-06-06

Modeling complex time-course patterns is a challenging issue in microarray study due to complex gene expression patterns in response to the time-course experiment. We introduce the generalized correlation coefficient and propose a combinatory approach for detecting, testing and clustering the heterogeneous time-course gene expression patterns. Application of the method identified nonlinear time-course patterns in high agreement with parametric analysis. We conclude that the non-parametric nature in the generalized correlation analysis could be an useful and efficient tool for analyzing microarray time-course data and for exploring the complex relationships in the omics data for studying their association with disease and health.

The analysis of incontinence episodes and other count data in patients with overactive bladder by Poisson and negative binomial regression.

PubMed

Martina, R; Kay, R; van Maanen, R; Ridder, A

2015-01-01

Clinical studies in overactive bladder have traditionally used analysis of covariance or nonparametric methods to analyse the number of incontinence episodes and other count data. It is known that if the underlying distributional assumptions of a particular parametric method do not hold, an alternative parametric method may be more efficient than a nonparametric one, which makes no assumptions regarding the underlying distribution of the data. Therefore, there are advantages in using methods based on the Poisson distribution or extensions of that method, which incorporate specific features that provide a modelling framework for count data. One challenge with count data is overdispersion, but methods are available that can account for this through the introduction of random effect terms in the modelling, and it is this modelling framework that leads to the negative binomial distribution. These models can also provide clinicians with a clearer and more appropriate interpretation of treatment effects in terms of rate ratios. In this paper, the previously used parametric and non-parametric approaches are contrasted with those based on Poisson regression and various extensions in trials evaluating solifenacin and mirabegron in patients with overactive bladder. In these applications, negative binomial models are seen to fit the data well. Copyright © 2014 John Wiley & Sons, Ltd.

Frustration of resonant preheating by exotic kinetic terms

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

Rahmati, Shohreh; Seahra, Sanjeev S., E-mail: srahmati@unb.ca, E-mail: sseahra@unb.ca

2014-10-01

We study the effects of exotic kinetic terms on parametric resonance during the preheating epoch of the early universe. Specifically, we consider modifications to the action of ordinary matter fields motivated by generalized uncertainty principles, polymer quantization, as well as Dirac-Born-Infeld and k-essence models. To leading order in an ''exotic physics'' scale, the equations of motion derived from each of these models have the same algebraic form involving a nonlinear self-interaction in the matter sector. Neglecting spatial dependence, we show that the nonlinearity effectively shuts down the parametric resonance after a finite time period. We find numeric evidence that themore » frustration of parametric resonance persists to spatially inhomogenous matter in (1+1)-dimensions.« less

Parametric Model of an Aerospike Rocket Engine

NASA Technical Reports Server (NTRS)

Korte, J. J.

2000-01-01

A suite of computer codes was assembled to simulate the performance of an aerospike engine and to generate the engine input for the Program to Optimize Simulated Trajectories. First an engine simulator module was developed that predicts the aerospike engine performance for a given mixture ratio, power level, thrust vectoring level, and altitude. This module was then used to rapidly generate the aerospike engine performance tables for axial thrust, normal thrust, pitching moment, and specific thrust. Parametric engine geometry was defined for use with the engine simulator module. The parametric model was also integrated into the iSIGHTI multidisciplinary framework so that alternate designs could be determined. The computer codes were used to support in-house conceptual studies of reusable launch vehicle designs.

Parametric Model of an Aerospike Rocket Engine

NASA Technical Reports Server (NTRS)

Korte, J. J.

2000-01-01

A suite of computer codes was assembled to simulate the performance of an aerospike engine and to generate the engine input for the Program to Optimize Simulated Trajectories. First an engine simulator module was developed that predicts the aerospike engine performance for a given mixture ratio, power level, thrust vectoring level, and altitude. This module was then used to rapidly generate the aerospike engine performance tables for axial thrust, normal thrust, pitching moment, and specific thrust. Parametric engine geometry was defined for use with the engine simulator module. The parametric model was also integrated into the iSIGHT multidisciplinary framework so that alternate designs could be determined. The computer codes were used to support in-house conceptual studies of reusable launch vehicle designs.

A probabilistic strategy for parametric catastrophe insurance

NASA Astrophysics Data System (ADS)

Figueiredo, Rui; Martina, Mario; Stephenson, David; Youngman, Benjamin

2017-04-01

Economic losses due to natural hazards have shown an upward trend since 1980, which is expected to continue. Recent years have seen a growing worldwide commitment towards the reduction of disaster losses. This requires effective management of disaster risk at all levels, a part of which involves reducing financial vulnerability to disasters ex-ante, ensuring that necessary resources will be available following such events. One way to achieve this is through risk transfer instruments. These can be based on different types of triggers, which determine the conditions under which payouts are made after an event. This study focuses on parametric triggers, where payouts are determined by the occurrence of an event exceeding specified physical parameters at a given location, or at multiple locations, or over a region. This type of product offers a number of important advantages, and its adoption is increasing. The main drawback of parametric triggers is their susceptibility to basis risk, which arises when there is a mismatch between triggered payouts and the occurrence of loss events. This is unavoidable in said programmes, as their calibration is based on models containing a number of different sources of uncertainty. Thus, a deterministic definition of the loss event triggering parameters appears flawed. However, often for simplicity, this is the way in which most parametric models tend to be developed. This study therefore presents an innovative probabilistic strategy for parametric catastrophe insurance. It is advantageous as it recognizes uncertainties and minimizes basis risk while maintaining a simple and transparent procedure. A logistic regression model is constructed here to represent the occurrence of loss events based on certain loss index variables, obtained through the transformation of input environmental variables. Flood-related losses due to rainfall are studied. The resulting model is able, for any given day, to issue probabilities of occurrence of loss events. Due to the nature of parametric programmes, it is still necessary to clearly define when a payout is due or not, and so a decision threshold probability above which a loss event is considered to occur must be set, effectively converting the issued probabilities into deterministic binary outcomes. Model skill and value are evaluated over the range of possible threshold probabilities, with the objective of defining the optimal one. The predictive ability of the model is assessed. In terms of value assessment, a decision model is proposed, allowing users to quantify monetarily their expected expenses when different combinations of model event triggering and actual event occurrence take place, directly tackling the problem of basis risk.

Wave Attenuation and Gas Exchange Velocity in Marginal Sea Ice Zone

NASA Astrophysics Data System (ADS)

Bigdeli, A.; Hara, T.; Loose, B.; Nguyen, A. T.

2018-03-01

The gas transfer velocity in marginal sea ice zones exerts a strong control on the input of anthropogenic gases into the ocean interior. In this study, a sea state-dependent gas exchange parametric model is developed based on the turbulent kinetic energy dissipation rate. The model is tuned to match the conventional gas exchange parametrization in fetch-unlimited, fully developed seas. Next, fetch limitation is introduced in the model and results are compared to fetch limited experiments in lakes, showing that the model captures the effects of finite fetch on gas exchange with good fidelity. Having validated the results in fetch limited waters such as lakes, the model is next applied in sea ice zones using an empirical relation between the sea ice cover and the effective fetch, while accounting for the sea ice motion effect that is unique to sea ice zones. The model results compare favorably with the available field measurements. Applying this parametric model to a regional Arctic numerical model, it is shown that, under the present conditions, gas flux into the Arctic Ocean may be overestimated by 10% if a conventional parameterization is used.

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.

A parametric study of harmonic rotor hub loads

NASA Technical Reports Server (NTRS)

He, Chengjian

1993-01-01

A parametric study of vibratory rotor hub loads in a nonrotating system is presented. The study is based on a CAMRAD/JA model constructed for the GBH (Growth Version of Blackhawk Helicopter) Mach-scaled wind tunnel rotor model with high blade twist (-16 deg). The theoretical hub load predictions are validated by correlation with available measured data. Effects of various blade aeroelastic design changes on the harmonic nonrotating frame hub loads at both low and high forward flight speeds are investigated. The study aims to illustrate some of the physical mechanisms for change in the harmonic rotor hub loads due to blade design variations.

A general framework for parametric survival analysis.

PubMed

Crowther, Michael J; Lambert, Paul C

2014-12-30

Parametric survival models are being increasingly used as an alternative to the Cox model in biomedical research. Through direct modelling of the baseline hazard function, we can gain greater understanding of the risk profile of patients over time, obtaining absolute measures of risk. Commonly used parametric survival models, such as the Weibull, make restrictive assumptions of the baseline hazard function, such as monotonicity, which is often violated in clinical datasets. In this article, we extend the general framework of parametric survival models proposed by Crowther and Lambert (Journal of Statistical Software 53:12, 2013), to incorporate relative survival, and robust and cluster robust standard errors. We describe the general framework through three applications to clinical datasets, in particular, illustrating the use of restricted cubic splines, modelled on the log hazard scale, to provide a highly flexible survival modelling framework. Through the use of restricted cubic splines, we can derive the cumulative hazard function analytically beyond the boundary knots, resulting in a combined analytic/numerical approach, which substantially improves the estimation process compared with only using numerical integration. User-friendly Stata software is provided, which significantly extends parametric survival models available in standard software. Copyright © 2014 John Wiley & Sons, Ltd.

flexsurv: A Platform for Parametric Survival Modeling in R

PubMed Central

Jackson, Christopher H.

2018-01-01

flexsurv is an R package for fully-parametric modeling of survival data. Any parametric time-to-event distribution may be fitted if the user supplies a probability density or hazard function, and ideally also their cumulative versions. Standard survival distributions are built in, including the three and four-parameter generalized gamma and F distributions. Any parameter of any distribution can be modeled as a linear or log-linear function of covariates. The package also includes the spline model of Royston and Parmar (2002), in which both baseline survival and covariate effects can be arbitrarily flexible parametric functions of time. The main model-fitting function, flexsurvreg, uses the familiar syntax of survreg from the standard survival package (Therneau 2016). Censoring or left-truncation are specified in ‘Surv’ objects. The models are fitted by maximizing the full log-likelihood, and estimates and confidence intervals for any function of the model parameters can be printed or plotted. flexsurv also provides functions for fitting and predicting from fully-parametric multi-state models, and connects with the mstate package (de Wreede, Fiocco, and Putter 2011). This article explains the methods and design principles of the package, giving several worked examples of its use. PMID:29593450

Parametric mapping using spectral analysis for 11C-PBR28 PET reveals neuroinflammation in mild cognitive impairment subjects.

PubMed

Fan, Zhen; Dani, Melanie; Femminella, Grazia D; Wood, Melanie; Calsolaro, Valeria; Veronese, Mattia; Turkheimer, Federico; Gentleman, Steve; Brooks, David J; Hinz, Rainer; Edison, Paul

2018-07-01

Neuroinflammation and microglial activation play an important role in amnestic mild cognitive impairment (MCI) and Alzheimer's disease. In this study, we investigated the spatial distribution of neuroinflammation in MCI subjects, using spectral analysis (SA) to generate parametric maps and quantify 11 C-PBR28 PET, and compared these with compartmental and other kinetic models of quantification. Thirteen MCI and nine healthy controls were enrolled in this study. Subjects underwent 11 C-PBR28 PET scans with arterial cannulation. Spectral analysis with an arterial plasma input function was used to generate 11 C-PBR28 parametric maps. These maps were then compared with regional 11 C-PBR28 V T (volume of distribution) using a two-tissue compartment model and Logan graphic analysis. Amyloid load was also assessed with 18 F-Flutemetamol PET. With SA, three component peaks were identified in addition to blood volume. The 11 C-PBR28 impulse response function (IRF) at 90 min produced the lowest coefficient of variation. Single-subject analysis using this IRF demonstrated microglial activation in five out of seven amyloid-positive MCI subjects. IRF parametric maps of 11 C-PBR28 uptake revealed a group-wise significant increase in neuroinflammation in amyloid-positive MCI subjects versus HC in multiple cortical association areas, and particularly in the temporal lobe. Interestingly, compartmental analysis detected group-wise increase in 11 C-PBR28 binding in the thalamus of amyloid-positive MCI subjects, while Logan parametric maps did not perform well. This study demonstrates for the first time that spectral analysis can be used to generate parametric maps of 11 C-PBR28 uptake, and is able to detect microglial activation in amyloid-positive MCI subjects. IRF parametric maps of 11 C-PBR28 uptake allow voxel-wise single-subject analysis and could be used to evaluate microglial activation in individual subjects.

The Use of Metaphors as a Parametric Design Teaching Model: A Case Study

ERIC Educational Resources Information Center

Agirbas, Asli

2018-01-01

Teaching methodologies for parametric design are being researched all over the world, since there is a growing demand for computer programming logic and its fabrication process in architectural education. The computer programming courses in architectural education are usually done in a very short period of time, and so students have no chance to…

A gradient-based model parametrization using Bernstein polynomials in Bayesian inversion of surface wave dispersion

NASA Astrophysics Data System (ADS)

Gosselin, Jeremy M.; Dosso, Stan E.; Cassidy, John F.; Quijano, Jorge E.; Molnar, Sheri; Dettmer, Jan

2017-10-01

This paper develops and applies a Bernstein-polynomial parametrization to efficiently represent general, gradient-based profiles in nonlinear geophysical inversion, with application to ambient-noise Rayleigh-wave dispersion data. Bernstein polynomials provide a stable parametrization in that small perturbations to the model parameters (basis-function coefficients) result in only small perturbations to the geophysical parameter profile. A fully nonlinear Bayesian inversion methodology is applied to estimate shear wave velocity (VS) profiles and uncertainties from surface wave dispersion data extracted from ambient seismic noise. The Bayesian information criterion is used to determine the appropriate polynomial order consistent with the resolving power of the data. Data error correlations are accounted for in the inversion using a parametric autoregressive model. The inversion solution is defined in terms of marginal posterior probability profiles for VS as a function of depth, estimated using Metropolis-Hastings sampling with parallel tempering. This methodology is applied to synthetic dispersion data as well as data processed from passive array recordings collected on the Fraser River Delta in British Columbia, Canada. Results from this work are in good agreement with previous studies, as well as with co-located invasive measurements. The approach considered here is better suited than `layered' modelling approaches in applications where smooth gradients in geophysical parameters are expected, such as soil/sediment profiles. Further, the Bernstein polynomial representation is more general than smooth models based on a fixed choice of gradient type (e.g. power-law gradient) because the form of the gradient is determined objectively by the data, rather than by a subjective parametrization choice.

A global goodness-of-fit test for receiver operating characteristic curve analysis via the bootstrap method.

PubMed

Zou, Kelly H; Resnic, Frederic S; Talos, Ion-Florin; Goldberg-Zimring, Daniel; Bhagwat, Jui G; Haker, Steven J; Kikinis, Ron; Jolesz, Ferenc A; Ohno-Machado, Lucila

2005-10-01

Medical classification accuracy studies often yield continuous data based on predictive models for treatment outcomes. A popular method for evaluating the performance of diagnostic tests is the receiver operating characteristic (ROC) curve analysis. The main objective was to develop a global statistical hypothesis test for assessing the goodness-of-fit (GOF) for parametric ROC curves via the bootstrap. A simple log (or logit) and a more flexible Box-Cox normality transformations were applied to untransformed or transformed data from two clinical studies to predict complications following percutaneous coronary interventions (PCIs) and for image-guided neurosurgical resection results predicted by tumor volume, respectively. We compared a non-parametric with a parametric binormal estimate of the underlying ROC curve. To construct such a GOF test, we used the non-parametric and parametric areas under the curve (AUCs) as the metrics, with a resulting p value reported. In the interventional cardiology example, logit and Box-Cox transformations of the predictive probabilities led to satisfactory AUCs (AUC=0.888; p=0.78, and AUC=0.888; p=0.73, respectively), while in the brain tumor resection example, log and Box-Cox transformations of the tumor size also led to satisfactory AUCs (AUC=0.898; p=0.61, and AUC=0.899; p=0.42, respectively). In contrast, significant departures from GOF were observed without applying any transformation prior to assuming a binormal model (AUC=0.766; p=0.004, and AUC=0.831; p=0.03), respectively. In both studies the p values suggested that transformations were important to consider before applying any binormal model to estimate the AUC. Our analyses also demonstrated and confirmed the predictive values of different classifiers for determining the interventional complications following PCIs and resection outcomes in image-guided neurosurgery.

Parametric correlation functions to model the structure of permanent environmental (co)variances in milk yield random regression models.

PubMed

Bignardi, A B; El Faro, L; Cardoso, V L; Machado, P F; Albuquerque, L G

2009-09-01

The objective of the present study was to estimate milk yield genetic parameters applying random regression models and parametric correlation functions combined with a variance function to model animal permanent environmental effects. A total of 152,145 test-day milk yields from 7,317 first lactations of Holstein cows belonging to herds located in the southeastern region of Brazil were analyzed. Test-day milk yields were divided into 44 weekly classes of days in milk. Contemporary groups were defined by herd-test-day comprising a total of 2,539 classes. The model included direct additive genetic, permanent environmental, and residual random effects. The following fixed effects were considered: contemporary group, age of cow at calving (linear and quadratic regressions), and the population average lactation curve modeled by fourth-order orthogonal Legendre polynomial. Additive genetic effects were modeled by random regression on orthogonal Legendre polynomials of days in milk, whereas permanent environmental effects were estimated using a stationary or nonstationary parametric correlation function combined with a variance function of different orders. The structure of residual variances was modeled using a step function containing 6 variance classes. The genetic parameter estimates obtained with the model using a stationary correlation function associated with a variance function to model permanent environmental effects were similar to those obtained with models employing orthogonal Legendre polynomials for the same effect. A model using a sixth-order polynomial for additive effects and a stationary parametric correlation function associated with a seventh-order variance function to model permanent environmental effects would be sufficient for data fitting.

Model Comparison of Bayesian Semiparametric and Parametric Structural Equation Models

ERIC Educational Resources Information Center

Song, Xin-Yuan; Xia, Ye-Mao; Pan, Jun-Hao; Lee, Sik-Yum

2011-01-01

Structural equation models have wide applications. One of the most important issues in analyzing structural equation models is model comparison. This article proposes a Bayesian model comparison statistic, namely the "L[subscript nu]"-measure for both semiparametric and parametric structural equation models. For illustration purposes, we consider…

Parametric Instability of Static Shafts-Disk System Using Finite Element Method

NASA Astrophysics Data System (ADS)

Wahab, A. M.; Rasid, Z. A.; Abu, A.

2017-10-01

Parametric instability condition is an important consideration in design process as it can cause failure in machine elements. In this study, parametric instability behaviour was studied for a simple shaft and disk system that was subjected to axial load under pinned-pinned boundary condition. The shaft was modelled based on the Nelson’s beam model, which considered translational and rotary inertias, transverse shear deformation and torsional effect. The Floquet’s method was used to estimate the solution for Mathieu equation. Finite element codes were developed using MATLAB to establish the instability chart. The effect of additional disk mass on the stability chart was investigated for pinned-pinned boundary conditions. Numerical results and illustrative examples are given. It is found that the additional disk mass decreases the instability region during static condition. The location of the disk as well has significant effect on the instability region of the shaft.

The use of analysis of variance procedures in biological studies

USGS Publications Warehouse

Williams, B.K.

1987-01-01

The analysis of variance (ANOVA) is widely used in biological studies, yet there remains considerable confusion among researchers about the interpretation of hypotheses being tested. Ambiguities arise when statistical designs are unbalanced, and in particular when not all combinations of design factors are represented in the data. This paper clarifies the relationship among hypothesis testing, statistical modelling and computing procedures in ANOVA for unbalanced data. A simple two-factor fixed effects design is used to illustrate three common parametrizations for ANOVA models, and some associations among these parametrizations are developed. Biologically meaningful hypotheses for main effects and interactions are given in terms of each parametrization, and procedures for testing the hypotheses are described. The standard statistical computing procedures in ANOVA are given along with their corresponding hypotheses. Throughout the development unbalanced designs are assumed and attention is given to problems that arise with missing cells.

Parametric laws to model urban pollutant dispersion with a street network approach

NASA Astrophysics Data System (ADS)

Soulhac, L.; Salizzoni, P.; Mejean, P.; Perkins, R. J.

2013-03-01

This study discusses the reliability of the street network approach for pollutant dispersion modelling in urban areas. This is essentially based on a box model, with parametric relations that explicitly model the main phenomena that contribute to the street canyon ventilation: the mass exchanges between the street and the atmosphere, the pollutant advection along the street axes and the pollutant transfer at street intersections. In the first part of the paper the focus is on the development of a model for the bulk transfer street/atmosphere, which represents the main ventilation mechanisms for wind direction that are almost perpendicular to the axis of the street. We then discuss the role of the advective transfer along the street axis on its ventilation, depending on the length of the street and the direction of the external wind. Finally we evaluate the performances of a box model integrating parametric exchange laws for these transfer phenomena. To that purpose we compare the prediction of the model to wind tunnel experiments of pollutant dispersion within a street canyon placed in an idealised urban district.

Large-scale subject-specific cerebral arterial tree modeling using automated parametric mesh generation for blood flow simulation.

PubMed

Ghaffari, Mahsa; Tangen, Kevin; Alaraj, Ali; Du, Xinjian; Charbel, Fady T; Linninger, Andreas A

2017-12-01

In this paper, we present a novel technique for automatic parametric mesh generation of subject-specific cerebral arterial trees. This technique generates high-quality and anatomically accurate computational meshes for fast blood flow simulations extending the scope of 3D vascular modeling to a large portion of cerebral arterial trees. For this purpose, a parametric meshing procedure was developed to automatically decompose the vascular skeleton, extract geometric features and generate hexahedral meshes using a body-fitted coordinate system that optimally follows the vascular network topology. To validate the anatomical accuracy of the reconstructed vasculature, we performed statistical analysis to quantify the alignment between parametric meshes and raw vascular images using receiver operating characteristic curve. Geometric accuracy evaluation showed an agreement with area under the curves value of 0.87 between the constructed mesh and raw MRA data sets. Parametric meshing yielded on-average, 36.6% and 21.7% orthogonal and equiangular skew quality improvement over the unstructured tetrahedral meshes. The parametric meshing and processing pipeline constitutes an automated technique to reconstruct and simulate blood flow throughout a large portion of the cerebral arterial tree down to the level of pial vessels. This study is the first step towards fast large-scale subject-specific hemodynamic analysis for clinical applications. Copyright © 2017 Elsevier Ltd. All rights reserved.

Control law parameterization for an aeroelastic wind-tunnel model equipped with an active roll control system and comparison with experiment

NASA Technical Reports Server (NTRS)

Perry, Boyd, III; Dunn, H. J.; Sandford, Maynard C.

1988-01-01

Nominal roll control laws were designed, implemented, and tested on an aeroelastically-scaled free-to-roll wind-tunnel model of an advanced fighter configuration. The tests were performed in the NASA Langley Transonic Dynamics Tunnel. A parametric study of the nominal roll control system was conducted. This parametric study determined possible control system gain variations which yielded identical closed-loop stability (roll mode pole location) and identical roll response but different maximum control-surface deflections. Comparison of analytical predictions with wind-tunnel results was generally very good.

Variable selection for distribution-free models for longitudinal zero-inflated count responses.

PubMed

Chen, Tian; Wu, Pan; Tang, Wan; Zhang, Hui; Feng, Changyong; Kowalski, Jeanne; Tu, Xin M

2016-07-20

Zero-inflated count outcomes arise quite often in research and practice. Parametric models such as the zero-inflated Poisson and zero-inflated negative binomial are widely used to model such responses. Like most parametric models, they are quite sensitive to departures from assumed distributions. Recently, new approaches have been proposed to provide distribution-free, or semi-parametric, alternatives. These methods extend the generalized estimating equations to provide robust inference for population mixtures defined by zero-inflated count outcomes. In this paper, we propose methods to extend smoothly clipped absolute deviation (SCAD)-based variable selection methods to these new models. Variable selection has been gaining popularity in modern clinical research studies, as determining differential treatment effects of interventions for different subgroups has become the norm, rather the exception, in the era of patent-centered outcome research. Such moderation analysis in general creates many explanatory variables in regression analysis, and the advantages of SCAD-based methods over their traditional counterparts render them a great choice for addressing this important and timely issues in clinical research. We illustrate the proposed approach with both simulated and real study data. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

Direct solar-pumped iodine laser amplifier

NASA Technical Reports Server (NTRS)

Han, K. S.

1986-01-01

During this period the parametric studies of the iodine laser oscillator pumped by a Vortek simulator were carried out before amplifier studies. The amplifier studies are postponed to the extended period after completing the parametric studies. In addition, the kinetic modeling of a solar-pumped iodine laser amplifier, and the experimental work for a solar pumped dye laser amplifier are in progress. This report contains three parts: (1) a 10 W CW iodine laser pumped by a Vortek solar simulator; (2) kinetic modeling to predict the time to lasing threshold, lasing time, and energy output of solar-pumped iodine laser; and (3) the study of the dye laser amplifier pumped by a Tamarack solar simulator.

Streamflow hindcasting in European river basins via multi-parametric ensemble of the mesoscale hydrologic model (mHM)

NASA Astrophysics Data System (ADS)

Noh, Seong Jin; Rakovec, Oldrich; Kumar, Rohini; Samaniego, Luis

2016-04-01

There have been tremendous improvements in distributed hydrologic modeling (DHM) which made a process-based simulation with a high spatiotemporal resolution applicable on a large spatial scale. Despite of increasing information on heterogeneous property of a catchment, DHM is still subject to uncertainties inherently coming from model structure, parameters and input forcing. Sequential data assimilation (DA) may facilitate improved streamflow prediction via DHM using real-time observations to correct internal model states. In conventional DA methods such as state updating, parametric uncertainty is, however, often ignored mainly due to practical limitations of methodology to specify modeling uncertainty with limited ensemble members. If parametric uncertainty related with routing and runoff components is not incorporated properly, predictive uncertainty by DHM may be insufficient to capture dynamics of observations, which may deteriorate predictability. Recently, a multi-scale parameter regionalization (MPR) method was proposed to make hydrologic predictions at different scales using a same set of model parameters without losing much of the model performance. The MPR method incorporated within the mesoscale hydrologic model (mHM, http://www.ufz.de/mhm) could effectively represent and control uncertainty of high-dimensional parameters in a distributed model using global parameters. In this study, we present a global multi-parametric ensemble approach to incorporate parametric uncertainty of DHM in DA to improve streamflow predictions. To effectively represent and control uncertainty of high-dimensional parameters with limited number of ensemble, MPR method is incorporated with DA. Lagged particle filtering is utilized to consider the response times and non-Gaussian characteristics of internal hydrologic processes. The hindcasting experiments are implemented to evaluate impacts of the proposed DA method on streamflow predictions in multiple European river basins having different climate and catchment characteristics. Because augmentation of parameters is not required within an assimilation window, the approach could be stable with limited ensemble members and viable for practical uses.

Semi-Automatic Modelling of Building FAÇADES with Shape Grammars Using Historic Building Information Modelling

NASA Astrophysics Data System (ADS)

Dore, C.; Murphy, M.

2013-02-01

This paper outlines a new approach for generating digital heritage models from laser scan or photogrammetric data using Historic Building Information Modelling (HBIM). HBIM is a plug-in for Building Information Modelling (BIM) software that uses parametric library objects and procedural modelling techniques to automate the modelling stage. The HBIM process involves a reverse engineering solution whereby parametric interactive objects representing architectural elements are mapped onto laser scan or photogrammetric survey data. A library of parametric architectural objects has been designed from historic manuscripts and architectural pattern books. These parametric objects were built using an embedded programming language within the ArchiCAD BIM software called Geometric Description Language (GDL). Procedural modelling techniques have been implemented with the same language to create a parametric building façade which automatically combines library objects based on architectural rules and proportions. Different configurations of the façade are controlled by user parameter adjustment. The automatically positioned elements of the façade can be subsequently refined using graphical editing while overlaying the model with orthographic imagery. Along with this semi-automatic method for generating façade models, manual plotting of library objects can also be used to generate a BIM model from survey data. After the 3D model has been completed conservation documents such as plans, sections, elevations and 3D views can be automatically generated for conservation projects.

Z/sub n/ Baxter model: symmetries and the Belavin parametrization

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

Richey, M.P.; Tracy, C.A.

1986-02-01

The Z/sub n/ Baxter model is an exactly solvable lattice model in the special case of the Belavin parametrization. For this parametrization the authors calculate the partition function in an antiferromagnetic region and the order parameter in a ferromagnetic region. They find that the order parameter is expressible in terms of a modular function of level n which for n=2 is the Onsager-Yang-Baxter result. In addition they determine the symmetry group of the finite lattice partition function for the general Z/sub n/ Baxter model.

Multi-parametric centrality method for graph network models

NASA Astrophysics Data System (ADS)

Ivanov, Sergei Evgenievich; Gorlushkina, Natalia Nikolaevna; Ivanova, Lubov Nikolaevna

2018-04-01

The graph model networks are investigated to determine centrality, weights and the significance of vertices. For centrality analysis appliesa typical method that includesany one of the properties of graph vertices. In graph theory, methods of analyzing centrality are used: in terms by degree, closeness, betweenness, radiality, eccentricity, page-rank, status, Katz and eigenvector. We have proposed a new method of multi-parametric centrality, which includes a number of basic properties of the network member. The mathematical model of multi-parametric centrality method is developed. Comparison of results for the presented method with the centrality methods is carried out. For evaluate the results for the multi-parametric centrality methodthe graph model with hundreds of vertices is analyzed. The comparative analysis showed the accuracy of presented method, includes simultaneously a number of basic properties of vertices.

Developing Parametric Models for the Assembly of Machine Fixtures for Virtual Multiaxial CNC Machining Centers

NASA Astrophysics Data System (ADS)

Balaykin, A. V.; Bezsonov, K. A.; Nekhoroshev, M. V.; Shulepov, A. P.

2018-01-01

This paper dwells upon a variance parameterization method. Variance or dimensional parameterization is based on sketching, with various parametric links superimposed on the sketch objects and user-imposed constraints in the form of an equation system that determines the parametric dependencies. This method is fully integrated in a top-down design methodology to enable the creation of multi-variant and flexible fixture assembly models, as all the modeling operations are hierarchically linked in the built tree. In this research the authors consider a parameterization method of machine tooling used for manufacturing parts using multiaxial CNC machining centers in the real manufacturing process. The developed method allows to significantly reduce tooling design time when making changes of a part’s geometric parameters. The method can also reduce time for designing and engineering preproduction, in particular, for development of control programs for CNC equipment and control and measuring machines, automate the release of design and engineering documentation. Variance parameterization helps to optimize construction of parts as well as machine tooling using integrated CAE systems. In the framework of this study, the authors demonstrate a comprehensive approach to parametric modeling of machine tooling in the CAD package used in the real manufacturing process of aircraft engines.

Parametric Study Of A Ceramic-Fiber/Metal-Matrix Composite

NASA Technical Reports Server (NTRS)

Murthy, P. L. N.; Hopkins, D. A.; Chamis, C. C.

1992-01-01

Report describes computer-model parametric study of effects of degradation of constituent materials upon mechanical properties of ceramic-fiber/metal-matrix composite material. Contributes to understanding of weakening effects of large changes in temperature and mechanical stresses in fabrication and use. Concerned mainly with influences of in situ fiber and matrix properties upon behavior of composite. Particular attention given to influence of in situ matrix strength and influence of interphase degradation.

Illiquidity premium and expected stock returns in the UK: A new approach

NASA Astrophysics Data System (ADS)

Chen, Jiaqi; Sherif, Mohamed

2016-09-01

This study examines the relative importance of liquidity risk for the time-series and cross-section of stock returns in the UK. We propose a simple way to capture the multidimensionality of illiquidity. Our analysis indicates that existing illiquidity measures have considerable asset specific components, which justifies our new approach. Further, we use an alternative test of the Amihud (2002) measure and parametric and non-parametric methods to investigate whether liquidity risk is priced in the UK. We find that the inclusion of the illiquidity factor in the capital asset pricing model plays a significant role in explaining the cross-sectional variation in stock returns, in particular with the Fama-French three-factor model. Further, using Hansen-Jagannathan non-parametric bounds, we find that the illiquidity-augmented capital asset pricing models yield a small distance error, other non-liquidity based models fail to yield economically plausible distance values. Our findings have important implications for managing the liquidity risk of equity portfolios.

Applying Statistical Models and Parametric Distance Measures for Music Similarity Search

NASA Astrophysics Data System (ADS)

Lukashevich, Hanna; Dittmar, Christian; Bastuck, Christoph

Automatic deriving of similarity relations between music pieces is an inherent field of music information retrieval research. Due to the nearly unrestricted amount of musical data, the real-world similarity search algorithms have to be highly efficient and scalable. The possible solution is to represent each music excerpt with a statistical model (ex. Gaussian mixture model) and thus to reduce the computational costs by applying the parametric distance measures between the models. In this paper we discuss the combinations of applying different parametric modelling techniques and distance measures and weigh the benefits of each one against the others.

Parametric Modeling for Fluid Systems

NASA Technical Reports Server (NTRS)

Pizarro, Yaritzmar Rosario; Martinez, Jonathan

2013-01-01

Fluid Systems involves different projects that require parametric modeling, which is a model that maintains consistent relationships between elements as is manipulated. One of these projects is the Neo Liquid Propellant Testbed, which is part of Rocket U. As part of Rocket U (Rocket University), engineers at NASA's Kennedy Space Center in Florida have the opportunity to develop critical flight skills as they design, build and launch high-powered rockets. To build the Neo testbed; hardware from the Space Shuttle Program was repurposed. Modeling for Neo, included: fittings, valves, frames and tubing, between others. These models help in the review process, to make sure regulations are being followed. Another fluid systems project that required modeling is Plant Habitat's TCUI test project. Plant Habitat is a plan to develop a large growth chamber to learn the effects of long-duration microgravity exposure to plants in space. Work for this project included the design and modeling of a duct vent for flow test. Parametric Modeling for these projects was done using Creo Parametric 2.0.

Housing price prediction: parametric versus semi-parametric spatial hedonic models

NASA Astrophysics Data System (ADS)

Montero, José-María; Mínguez, Román; Fernández-Avilés, Gema

2018-01-01

House price prediction is a hot topic in the economic literature. House price prediction has traditionally been approached using a-spatial linear (or intrinsically linear) hedonic models. It has been shown, however, that spatial effects are inherent in house pricing. This article considers parametric and semi-parametric spatial hedonic model variants that account for spatial autocorrelation, spatial heterogeneity and (smooth and nonparametrically specified) nonlinearities using penalized splines methodology. The models are represented as a mixed model that allow for the estimation of the smoothing parameters along with the other parameters of the model. To assess the out-of-sample performance of the models, the paper uses a database containing the price and characteristics of 10,512 homes in Madrid, Spain (Q1 2010). The results obtained suggest that the nonlinear models accounting for spatial heterogeneity and flexible nonlinear relationships between some of the individual or areal characteristics of the houses and their prices are the best strategies for house price prediction.

Bidirectional reflectance distribution function measurements and analysis of retroreflective materials.

PubMed

Belcour, Laurent; Pacanowski, Romain; Delahaie, Marion; Laville-Geay, Aude; Eupherte, Laure

2014-12-01

We compare the performance of various analytical retroreflecting bidirectional reflectance distribution function (BRDF) models to assess how they reproduce accurately measured data of retroreflecting materials. We introduce a new parametrization, the back vector parametrization, to analyze retroreflecting data, and we show that this parametrization better preserves the isotropy of data. Furthermore, we update existing BRDF models to improve the representation of retroreflective data.

Modelling road accident blackspots data with the discrete generalized Pareto distribution.

PubMed

Prieto, Faustino; Gómez-Déniz, Emilio; Sarabia, José María

2014-10-01

This study shows how road traffic networks events, in particular road accidents on blackspots, can be modelled with simple probabilistic distributions. We considered the number of crashes and the number of fatalities on Spanish blackspots in the period 2003-2007, from Spanish General Directorate of Traffic (DGT). We modelled those datasets, respectively, with the discrete generalized Pareto distribution (a discrete parametric model with three parameters) and with the discrete Lomax distribution (a discrete parametric model with two parameters, and particular case of the previous model). For that, we analyzed the basic properties of both parametric models: cumulative distribution, survival, probability mass, quantile and hazard functions, genesis and rth-order moments; applied two estimation methods of their parameters: the μ and (μ+1) frequency method and the maximum likelihood method; used two goodness-of-fit tests: Chi-square test and discrete Kolmogorov-Smirnov test based on bootstrap resampling; and compared them with the classical negative binomial distribution in terms of absolute probabilities and in models including covariates. We found that those probabilistic models can be useful to describe the road accident blackspots datasets analyzed. Copyright © 2014 Elsevier Ltd. All rights reserved.

Parametric Method Performance for Dynamic 3'-Deoxy-3'-18F-Fluorothymidine PET/CT in Epidermal Growth Factor Receptor-Mutated Non-Small Cell Lung Carcinoma Patients Before and During Therapy.

PubMed

Kramer, Gerbrand Maria; Frings, Virginie; Heijtel, Dennis; Smit, E F; Hoekstra, Otto S; Boellaard, Ronald

2017-06-01

The objective of this study was to validate several parametric methods for quantification of 3'-deoxy-3'- 18 F-fluorothymidine ( 18 F-FLT) PET in advanced-stage non-small cell lung carcinoma (NSCLC) patients with an activating epidermal growth factor receptor mutation who were treated with gefitinib or erlotinib. Furthermore, we evaluated the impact of noise on accuracy and precision of the parametric analyses of dynamic 18 F-FLT PET/CT to assess the robustness of these methods. Methods : Ten NSCLC patients underwent dynamic 18 F-FLT PET/CT at baseline and 7 and 28 d after the start of treatment. Parametric images were generated using plasma input Logan graphic analysis and 2 basis functions-based methods: a 2-tissue-compartment basis function model (BFM) and spectral analysis (SA). Whole-tumor-averaged parametric pharmacokinetic parameters were compared with those obtained by nonlinear regression of the tumor time-activity curve using a reversible 2-tissue-compartment model with blood volume fraction. In addition, 2 statistically equivalent datasets were generated by countwise splitting the original list-mode data, each containing 50% of the total counts. Both new datasets were reconstructed, and parametric pharmacokinetic parameters were compared between the 2 replicates and the original data. Results: After the settings of each parametric method were optimized, distribution volumes (V T ) obtained with Logan graphic analysis, BFM, and SA all correlated well with those derived using nonlinear regression at baseline and during therapy ( R 2 ≥ 0.94; intraclass correlation coefficient > 0.97). SA-based V T images were most robust to increased noise on a voxel-level (repeatability coefficient, 16% vs. >26%). Yet BFM generated the most accurate K 1 values ( R 2 = 0.94; intraclass correlation coefficient, 0.96). Parametric K 1 data showed a larger variability in general; however, no differences were found in robustness between methods (repeatability coefficient, 80%-84%). Conclusion: Both BFM and SA can generate quantitatively accurate parametric 18 F-FLT V T images in NSCLC patients before and during therapy. SA was more robust to noise, yet BFM provided more accurate parametric K 1 data. We therefore recommend BFM as the preferred parametric method for analysis of dynamic 18 F-FLT PET/CT studies; however, SA can also be used. © 2017 by the Society of Nuclear Medicine and Molecular Imaging.

Validation of a Parametric Approach for 3d Fortification Modelling: Application to Scale Models

NASA Astrophysics Data System (ADS)

Jacquot, K.; Chevrier, C.; Halin, G.

2013-02-01

Parametric modelling approach applied to cultural heritage virtual representation is a field of research explored for years since it can address many limitations of digitising tools. For example, essential historical sources for fortification virtual reconstructions like plans-reliefs have several shortcomings when they are scanned. To overcome those problems, knowledge based-modelling can be used: knowledge models based on the analysis of theoretical literature of a specific domain such as bastioned fortification treatises can be the cornerstone of the creation of a parametric library of fortification components. Implemented in Grasshopper, these components are manually adjusted on the data available (i.e. 3D surveys of plans-reliefs or scanned maps). Most of the fortification area is now modelled and the question of accuracy assessment is raised. A specific method is used to evaluate the accuracy of the parametric components. The results of the assessment process will allow us to validate the parametric approach. The automation of the adjustment process can finally be planned. The virtual model of fortification is part of a larger project aimed at valorising and diffusing a very unique cultural heritage item: the collection of plans-reliefs. As such, knowledge models are precious assets when automation and semantic enhancements will be considered.

A Parametric Approach to Numerical Modeling of TKR Contact Forces

PubMed Central

Lundberg, Hannah J.; Foucher, Kharma C.; Wimmer, Markus A.

2009-01-01

In vivo knee contact forces are difficult to determine using numerical methods because there are more unknown forces than equilibrium equations available. We developed parametric methods for computing contact forces across the knee joint during the stance phase of level walking. Three-dimensional contact forces were calculated at two points of contact between the tibia and the femur, one on the lateral aspect of the tibial plateau, and one on the medial side. Muscle activations were parametrically varied over their physiologic range resulting in a solution space of contact forces. The obtained solution space was reasonably small and the resulting force pattern compared well to a previous model from the literature for kinematics and external kinetics from the same patient. Peak forces of the parametric model and the previous model were similar for the first half of the stance phase, but differed for the second half. The previous model did not take into account the transverse external moment about the knee and could not calculate muscle activation levels. Ultimately, the parametric model will result in more accurate contact force inputs for total knee simulators, as current inputs are not generally based on kinematics and kinetics inputs from TKR patients. PMID:19155015

Development and Validation of a Statistical Shape Modeling-Based Finite Element Model of the Cervical Spine Under Low-Level Multiple Direction Loading Conditions

PubMed Central

Bredbenner, Todd L.; Eliason, Travis D.; Francis, W. Loren; McFarland, John M.; Merkle, Andrew C.; Nicolella, Daniel P.

2014-01-01

Cervical spinal injuries are a significant concern in all trauma injuries. Recent military conflicts have demonstrated the substantial risk of spinal injury for the modern warfighter. Finite element models used to investigate injury mechanisms often fail to examine the effects of variation in geometry or material properties on mechanical behavior. The goals of this study were to model geometric variation for a set of cervical spines, to extend this model to a parametric finite element model, and, as a first step, to validate the parametric model against experimental data for low-loading conditions. Individual finite element models were created using cervical spine (C3–T1) computed tomography data for five male cadavers. Statistical shape modeling (SSM) was used to generate a parametric finite element model incorporating variability of spine geometry, and soft-tissue material property variation was also included. The probabilistic loading response of the parametric model was determined under flexion-extension, axial rotation, and lateral bending and validated by comparison to experimental data. Based on qualitative and quantitative comparison of the experimental loading response and model simulations, we suggest that the model performs adequately under relatively low-level loading conditions in multiple loading directions. In conclusion, SSM methods coupled with finite element analyses within a probabilistic framework, along with the ability to statistically validate the overall model performance, provide innovative and important steps toward describing the differences in vertebral morphology, spinal curvature, and variation in material properties. We suggest that these methods, with additional investigation and validation under injurious loading conditions, will lead to understanding and mitigating the risks of injury in the spine and other musculoskeletal structures. PMID:25506051

A Strategy for a Parametric Flood Insurance Using Proxies

NASA Astrophysics Data System (ADS)

Haraguchi, M.; Lall, U.

2017-12-01

Traditionally, the design of flood control infrastructure and flood plain zoning require the estimation of return periods, which have been calculated by river hydraulic models with rainfall-runoff models. However, this multi-step modeling process leads to significant uncertainty to assess inundation. In addition, land use change and changing climate alter the potential losses, as well as make the modeling results obsolete. For these reasons, there is a strong need to create parametric indexes for the financial risk transfer for large flood events, to enable rapid response and recovery. Hence, this study examines the possibility of developing a parametric flood index at the national or regional level in Asia, which can be quickly mobilized after catastrophic floods. Specifically, we compare a single trigger based on rainfall index with multiple triggers using rainfall and streamflow indices by conducting case studies in Bangladesh and Thailand. The proposed methodology is 1) selecting suitable indices of rainfall and streamflow (if available), 2) identifying trigger levels for specified return periods for losses using stepwise and logistic regressions, 3) measuring the performance of indices, and 4) deriving return periods of selected windows and trigger levels. Based on the methodology, actual trigger levels were identified for Bangladesh and Thailand. Models based on multiple triggers reduced basis risks, an inherent problem in an index insurance. The proposed parametric flood index can be applied to countries with similar geographic and meteorological characteristics, and serve as a promising method for ex-ante risk financing for developing countries. This work is intended to be a preliminary work supporting future work on pricing risk transfer mechanisms in ex-ante risk finance.

Coupled oscillators in identification of nonlinear damping of a real parametric pendulum

NASA Astrophysics Data System (ADS)

Olejnik, Paweł; Awrejcewicz, Jan

2018-01-01

A damped parametric pendulum with friction is identified twice by means of its precise and imprecise mathematical model. A laboratory test stand designed for experimental investigations of nonlinear effects determined by a viscous resistance and the stick-slip phenomenon serves as the model mechanical system. An influence of accurateness of mathematical modeling on the time variability of the nonlinear damping coefficient of the oscillator is proved. A free decay response of a precisely and imprecisely modeled physical pendulum is dependent on two different time-varying coefficients of damping. The coefficients of the analyzed parametric oscillator are identified with the use of a new semi-empirical method based on a coupled oscillators approach, utilizing the fractional order derivative of the discrete measurement series treated as an input to the numerical model. Results of application of the proposed method of identification of the nonlinear coefficients of the damped parametric oscillator have been illustrated and extensively discussed.

Modeling the directivity of parametric loudspeaker

NASA Astrophysics Data System (ADS)

Shi, Chuang; Gan, Woon-Seng

2012-09-01

The emerging applications of the parametric loudspeaker, such as 3D audio, demands accurate directivity control at the audible frequency (i.e. the difference frequency). Though the delay-and-sum beamforming has been proven adequate to adjust the steering angles of the parametric loudspeaker, accurate prediction of the mainlobe and sidelobes remains a challenging problem. It is mainly because of the approximations that are used to derive the directivity of the difference frequency from the directivity of the primary frequency, and the mismatches between the theoretical directivity and the measured directivity caused by system errors incurred at different stages of the implementation. In this paper, we propose a directivity model of the parametric loudspeaker. The directivity model consists of two tuning vectors corresponding to the spacing error and the weight error for the primary frequency. The directivity model adopts a modified form of the product directivity principle for the difference frequency to further improve the modeling accuracy.

Parametric Cost Models for Space Telescopes

NASA Technical Reports Server (NTRS)

Stahl, H. Philip; Henrichs, Todd; Dollinger, Courtney

2010-01-01

Multivariable parametric cost models for space telescopes provide several benefits to designers and space system project managers. They identify major architectural cost drivers and allow high-level design trades. They enable cost-benefit analysis for technology development investment. And, they provide a basis for estimating total project cost. A survey of historical models found that there is no definitive space telescope cost model. In fact, published models vary greatly [1]. Thus, there is a need for parametric space telescopes cost models. An effort is underway to develop single variable [2] and multi-variable [3] parametric space telescope cost models based on the latest available data and applying rigorous analytical techniques. Specific cost estimating relationships (CERs) have been developed which show that aperture diameter is the primary cost driver for large space telescopes; technology development as a function of time reduces cost at the rate of 50% per 17 years; it costs less per square meter of collecting aperture to build a large telescope than a small telescope; and increasing mass reduces cost.

Injury risk functions based on population-based finite element model responses: Application to femurs under dynamic three-point bending.

PubMed

Park, Gwansik; Forman, Jason; Kim, Taewung; Panzer, Matthew B; Crandall, Jeff R

2018-02-28

The goal of this study was to explore a framework for developing injury risk functions (IRFs) in a bottom-up approach based on responses of parametrically variable finite element (FE) models representing exemplar populations. First, a parametric femur modeling tool was developed and validated using a subject-specific (SS)-FE modeling approach. Second, principal component analysis and regression were used to identify parametric geometric descriptors of the human femur and the distribution of those factors for 3 target occupant sizes (5th, 50th, and 95th percentile males). Third, distributions of material parameters of cortical bone were obtained from the literature for 3 target occupant ages (25, 50, and 75 years) using regression analysis. A Monte Carlo method was then implemented to generate populations of FE models of the femur for target occupants, using a parametric femur modeling tool. Simulations were conducted with each of these models under 3-point dynamic bending. Finally, model-based IRFs were developed using logistic regression analysis, based on the moment at fracture observed in the FE simulation. In total, 100 femur FE models incorporating the variation in the population of interest were generated, and 500,000 moments at fracture were observed (applying 5,000 ultimate strains for each synthesized 100 femur FE models) for each target occupant characteristics. Using the proposed framework on this study, the model-based IRFs for 3 target male occupant sizes (5th, 50th, and 95th percentiles) and ages (25, 50, and 75 years) were developed. The model-based IRF was located in the 95% confidence interval of the test-based IRF for the range of 15 to 70% injury risks. The 95% confidence interval of the developed IRF was almost in line with the mean curve due to a large number of data points. The framework proposed in this study would be beneficial for developing the IRFs in a bottom-up manner, whose range of variabilities is informed by the population-based FE model responses. Specifically, this method mitigates the uncertainties in applying empirical scaling and may improve IRF fidelity when a limited number of experimental specimens are available.

Advanced transportation system studies. Alternate propulsion subsystem concepts: Propulsion database

NASA Technical Reports Server (NTRS)

Levack, Daniel

1993-01-01

The Advanced Transportation System Studies alternate propulsion subsystem concepts propulsion database interim report is presented. The objective of the database development task is to produce a propulsion database which is easy to use and modify while also being comprehensive in the level of detail available. The database is to be available on the Macintosh computer system. The task is to extend across all three years of the contract. Consequently, a significant fraction of the effort in this first year of the task was devoted to the development of the database structure to ensure a robust base for the following years' efforts. Nonetheless, significant point design propulsion system descriptions and parametric models were also produced. Each of the two propulsion databases, parametric propulsion database and propulsion system database, are described. The descriptions include a user's guide to each code, write-ups for models used, and sample output. The parametric database has models for LOX/H2 and LOX/RP liquid engines, solid rocket boosters using three different propellants, a hybrid rocket booster, and a NERVA derived nuclear thermal rocket engine.

Multiresponse semiparametric regression for modelling the effect of regional socio-economic variables on the use of information technology

NASA Astrophysics Data System (ADS)

Wibowo, Wahyu; Wene, Chatrien; Budiantara, I. Nyoman; Permatasari, Erma Oktania

2017-03-01

Multiresponse semiparametric regression is simultaneous equation regression model and fusion of parametric and nonparametric model. The regression model comprise several models and each model has two components, parametric and nonparametric. The used model has linear function as parametric and polynomial truncated spline as nonparametric component. The model can handle both linearity and nonlinearity relationship between response and the sets of predictor variables. The aim of this paper is to demonstrate the application of the regression model for modeling of effect of regional socio-economic on use of information technology. More specific, the response variables are percentage of households has access to internet and percentage of households has personal computer. Then, predictor variables are percentage of literacy people, percentage of electrification and percentage of economic growth. Based on identification of the relationship between response and predictor variable, economic growth is treated as nonparametric predictor and the others are parametric predictors. The result shows that the multiresponse semiparametric regression can be applied well as indicate by the high coefficient determination, 90 percent.

Non-parametric directionality analysis - Extension for removal of a single common predictor and application to time series.

PubMed

Halliday, David M; Senik, Mohd Harizal; Stevenson, Carl W; Mason, Rob

2016-08-01

The ability to infer network structure from multivariate neuronal signals is central to computational neuroscience. Directed network analyses typically use parametric approaches based on auto-regressive (AR) models, where networks are constructed from estimates of AR model parameters. However, the validity of using low order AR models for neurophysiological signals has been questioned. A recent article introduced a non-parametric approach to estimate directionality in bivariate data, non-parametric approaches are free from concerns over model validity. We extend the non-parametric framework to include measures of directed conditional independence, using scalar measures that decompose the overall partial correlation coefficient summatively by direction, and a set of functions that decompose the partial coherence summatively by direction. A time domain partial correlation function allows both time and frequency views of the data to be constructed. The conditional independence estimates are conditioned on a single predictor. The framework is applied to simulated cortical neuron networks and mixtures of Gaussian time series data with known interactions. It is applied to experimental data consisting of local field potential recordings from bilateral hippocampus in anaesthetised rats. The framework offers a non-parametric approach to estimation of directed interactions in multivariate neuronal recordings, and increased flexibility in dealing with both spike train and time series data. The framework offers a novel alternative non-parametric approach to estimate directed interactions in multivariate neuronal recordings, and is applicable to spike train and time series data. Copyright © 2016 Elsevier B.V. All rights reserved.

A Backward-Lagrangian-Stochastic Footprint Model for the Urban Environment

NASA Astrophysics Data System (ADS)

Wang, Chenghao; Wang, Zhi-Hua; Yang, Jiachuan; Li, Qi

2018-02-01

Built terrains, with their complexity in morphology, high heterogeneity, and anthropogenic impact, impose substantial challenges in Earth-system modelling. In particular, estimation of the source areas and footprints of atmospheric measurements in cities requires realistic representation of the landscape characteristics and flow physics in urban areas, but has hitherto been heavily reliant on large-eddy simulations. In this study, we developed physical parametrization schemes for estimating urban footprints based on the backward-Lagrangian-stochastic algorithm, with the built environment represented by street canyons. The vertical profile of mean streamwise velocity is parametrized for the urban canopy and boundary layer. Flux footprints estimated by the proposed model show reasonable agreement with analytical predictions over flat surfaces without roughness elements, and with experimental observations over sparse plant canopies. Furthermore, comparisons of canyon flow and turbulence profiles and the subsequent footprints were made between the proposed model and large-eddy simulation data. The results suggest that the parametrized canyon wind and turbulence statistics, based on the simple similarity theory used, need to be further improved to yield more realistic urban footprint modelling.

A comparison of confidence/credible interval methods for the area under the ROC curve for continuous diagnostic tests with small sample size.

PubMed

Feng, Dai; Cortese, Giuliana; Baumgartner, Richard

2017-12-01

The receiver operating characteristic (ROC) curve is frequently used as a measure of accuracy of continuous markers in diagnostic tests. The area under the ROC curve (AUC) is arguably the most widely used summary index for the ROC curve. Although the small sample size scenario is common in medical tests, a comprehensive study of small sample size properties of various methods for the construction of the confidence/credible interval (CI) for the AUC has been by and large missing in the literature. In this paper, we describe and compare 29 non-parametric and parametric methods for the construction of the CI for the AUC when the number of available observations is small. The methods considered include not only those that have been widely adopted, but also those that have been less frequently mentioned or, to our knowledge, never applied to the AUC context. To compare different methods, we carried out a simulation study with data generated from binormal models with equal and unequal variances and from exponential models with various parameters and with equal and unequal small sample sizes. We found that the larger the true AUC value and the smaller the sample size, the larger the discrepancy among the results of different approaches. When the model is correctly specified, the parametric approaches tend to outperform the non-parametric ones. Moreover, in the non-parametric domain, we found that a method based on the Mann-Whitney statistic is in general superior to the others. We further elucidate potential issues and provide possible solutions to along with general guidance on the CI construction for the AUC when the sample size is small. Finally, we illustrate the utility of different methods through real life examples.

Establishment of Biological Reference Intervals and Reference Curve for Urea by Exploratory Parametric and Non-Parametric Quantile Regression Models.

PubMed

Sarkar, Rajarshi

2013-07-01

The validity of the entire renal function tests as a diagnostic tool depends substantially on the Biological Reference Interval (BRI) of urea. Establishment of BRI of urea is difficult partly because exclusion criteria for selection of reference data are quite rigid and partly due to the compartmentalization considerations regarding age and sex of the reference individuals. Moreover, construction of Biological Reference Curve (BRC) of urea is imperative to highlight the partitioning requirements. This a priori study examines the data collected by measuring serum urea of 3202 age and sex matched individuals, aged between 1 and 80 years, by a kinetic UV Urease/GLDH method on a Roche Cobas 6000 auto-analyzer. Mann-Whitney U test of the reference data confirmed the partitioning requirement by both age and sex. Further statistical analysis revealed the incompatibility of the data for a proposed parametric model. Hence the data was non-parametrically analysed. BRI was found to be identical for both sexes till the 2(nd) decade, and the BRI for males increased progressively 6(th) decade onwards. Four non-parametric models were postulated for construction of BRC: Gaussian kernel, double kernel, local mean and local constant, of which the last one generated the best-fitting curves. Clinical decision making should become easier and diagnostic implications of renal function tests should become more meaningful if this BRI is followed and the BRC is used as a desktop tool in conjunction with similar data for serum creatinine.

Latent component-based gear tooth fault detection filter using advanced parametric modeling

NASA Astrophysics Data System (ADS)

Ettefagh, M. M.; Sadeghi, M. H.; Rezaee, M.; Chitsaz, S.

2009-10-01

In this paper, a new parametric model-based filter is proposed for gear tooth fault detection. The designing of the filter consists of identifying the most proper latent component (LC) of the undamaged gearbox signal by analyzing the instant modules (IMs) and instant frequencies (IFs) and then using the component with lowest IM as the proposed filter output for detecting fault of the gearbox. The filter parameters are estimated by using the LC theory in which an advanced parametric modeling method has been implemented. The proposed method is applied on the signals, extracted from simulated gearbox for detection of the simulated gear faults. In addition, the method is used for quality inspection of the produced Nissan-Junior vehicle gearbox by gear profile error detection in an industrial test bed. For evaluation purpose, the proposed method is compared with the previous parametric TAR/AR-based filters in which the parametric model residual is considered as the filter output and also Yule-Walker and Kalman filter are implemented for estimating the parameters. The results confirm the high performance of the new proposed fault detection method.

Single-arm phase II trial design under parametric cure models.

PubMed

Wu, Jianrong

2015-01-01

The current practice of designing single-arm phase II survival trials is limited under the exponential model. Trial design under the exponential model may not be appropriate when a portion of patients are cured. There is no literature available for designing single-arm phase II trials under the parametric cure model. In this paper, a test statistic is proposed, and a sample size formula is derived for designing single-arm phase II trials under a class of parametric cure models. Extensive simulations showed that the proposed test and sample size formula perform very well under different scenarios. Copyright © 2015 John Wiley & Sons, Ltd.

A model-averaging method for assessing groundwater conceptual model uncertainty.

PubMed

Ye, Ming; Pohlmann, Karl F; Chapman, Jenny B; Pohll, Greg M; Reeves, Donald M

2010-01-01

This study evaluates alternative groundwater models with different recharge and geologic components at the northern Yucca Flat area of the Death Valley Regional Flow System (DVRFS), USA. Recharge over the DVRFS has been estimated using five methods, and five geological interpretations are available at the northern Yucca Flat area. Combining the recharge and geological components together with additional modeling components that represent other hydrogeological conditions yields a total of 25 groundwater flow models. As all the models are plausible given available data and information, evaluating model uncertainty becomes inevitable. On the other hand, hydraulic parameters (e.g., hydraulic conductivity) are uncertain in each model, giving rise to parametric uncertainty. Propagation of the uncertainty in the models and model parameters through groundwater modeling causes predictive uncertainty in model predictions (e.g., hydraulic head and flow). Parametric uncertainty within each model is assessed using Monte Carlo simulation, and model uncertainty is evaluated using the model averaging method. Two model-averaging techniques (on the basis of information criteria and GLUE) are discussed. This study shows that contribution of model uncertainty to predictive uncertainty is significantly larger than that of parametric uncertainty. For the recharge and geological components, uncertainty in the geological interpretations has more significant effect on model predictions than uncertainty in the recharge estimates. In addition, weighted residuals vary more for the different geological models than for different recharge models. Most of the calibrated observations are not important for discriminating between the alternative models, because their weighted residuals vary only slightly from one model to another.

A nonparametric spatial scan statistic for continuous data.

PubMed

Jung, Inkyung; Cho, Ho Jin

2015-10-20

Spatial scan statistics are widely used for spatial cluster detection, and several parametric models exist. For continuous data, a normal-based scan statistic can be used. However, the performance of the model has not been fully evaluated for non-normal data. We propose a nonparametric spatial scan statistic based on the Wilcoxon rank-sum test statistic and compared the performance of the method with parametric models via a simulation study under various scenarios. The nonparametric method outperforms the normal-based scan statistic in terms of power and accuracy in almost all cases under consideration in the simulation study. The proposed nonparametric spatial scan statistic is therefore an excellent alternative to the normal model for continuous data and is especially useful for data following skewed or heavy-tailed distributions.

Stochastic sensitivity analysis of the variability of dynamics and transition to chaos in the business cycles model

NASA Astrophysics Data System (ADS)

Bashkirtseva, Irina; Ryashko, Lev; Ryazanova, Tatyana

2018-01-01

A problem of mathematical modeling of complex stochastic processes in macroeconomics is discussed. For the description of dynamics of income and capital stock, the well-known Kaldor model of business cycles is used as a basic example. The aim of the paper is to give an overview of the variety of stochastic phenomena which occur in Kaldor model forced by additive and parametric random noise. We study a generation of small- and large-amplitude stochastic oscillations, and their mixed-mode intermittency. To analyze these phenomena, we suggest a constructive approach combining the study of the peculiarities of deterministic phase portrait, and stochastic sensitivity of attractors. We show how parametric noise can stabilize the unstable equilibrium and transform dynamics of Kaldor system from order to chaos.

Elastic full-waveform inversion and parametrization analysis applied to walk-away vertical seismic profile data for unconventional (heavy oil) reservoir characterization

NASA Astrophysics Data System (ADS)

Pan, Wenyong; Innanen, Kristopher A.; Geng, Yu

2018-06-01

Seismic full-waveform inversion (FWI) methods hold strong potential to recover multiple subsurface elastic properties for hydrocarbon reservoir characterization. Simultaneously updating multiple physical parameters introduces the problem of interparameter trade-off, arising from the simultaneous variations of different physical parameters, which increase the nonlinearity and uncertainty of multiparameter FWI. The coupling effects of different physical parameters are significantly influenced by model parametrization and acquisition arrangement. An appropriate choice of model parametrization is important to successful field data applications of multiparameter FWI. The objective of this paper is to examine the performance of various model parametrizations in isotropic-elastic FWI with walk-away vertical seismic profile (W-VSP) data for unconventional heavy oil reservoir characterization. Six model parametrizations are considered: velocity-density (α, β and ρ΄), modulus-density (κ, μ and ρ), Lamé-density (λ, μ΄ and ρ‴), impedance-density (IP, IS and ρ″), velocity-impedance-I (α΄, β΄ and I_P^' }) and velocity-impedance-II (α″, β″ and I_S^' }). We begin analysing the interparameter trade-off by making use of scattering radiation patterns, which is a common strategy for qualitative parameter resolution analysis. We discuss the advantages and limitations of the scattering radiation patterns and recommend that interparameter trade-offs be evaluated using interparameter contamination kernels, which provide quantitative, second-order measurements of the interparameter contaminations and can be constructed efficiently with an adjoint-state approach. Synthetic W-VSP isotropic-elastic FWI experiments in the time domain verify our conclusions about interparameter trade-offs for various model parametrizations. Density profiles are most strongly influenced by the interparameter contaminations; depending on model parametrization, the inverted density profile can be overestimated, underestimated or spatially distorted. Among the six cases, only the velocity-density parametrization provides stable and informative density features not included in the starting model. Field data applications of multicomponent W-VSP isotropic-elastic FWI in the time domain were also carried out. The heavy oil reservoir target zone, characterized by low α-to-β ratios and low Poisson's ratios, can be identified clearly with the inverted isotropic-elastic parameters.

Modelling and multi-parametric control for delivery of anaesthetic agents.

PubMed

Dua, Pinky; Dua, Vivek; Pistikopoulos, Efstratios N

2010-06-01

This article presents model predictive controllers (MPCs) and multi-parametric model-based controllers for delivery of anaesthetic agents. The MPC can take into account constraints on drug delivery rates and state of the patient but requires solving an optimization problem at regular time intervals. The multi-parametric controller has all the advantages of the MPC and does not require repetitive solution of optimization problem for its implementation. This is achieved by obtaining the optimal drug delivery rates as a set of explicit functions of the state of the patient. The derivation of the controllers relies on using detailed models of the system. A compartmental model for the delivery of three drugs for anaesthesia is developed. The key feature of this model is that mean arterial pressure, cardiac output and unconsciousness of the patient can be simultaneously regulated. This is achieved by using three drugs: dopamine (DP), sodium nitroprusside (SNP) and isoflurane. A number of dynamic simulation experiments are carried out for the validation of the model. The model is then used for the design of model predictive and multi-parametric controllers, and the performance of the controllers is analyzed.

Nonlinear effects in a plain journal bearing. I - Analytical study. II - Results

NASA Technical Reports Server (NTRS)

Choy, F. K.; Braun, M. J.; Hu, Y.

1991-01-01

In the first part of this work, a numerical model is presented which couples the variable-property Reynolds equation with a rotor-dynamics model for the calculation of a plain journal bearing's nonlinear characteristics when working with a cryogenic fluid, LOX. The effects of load on the linear/nonlinear plain journal bearing characteristics are analyzed and presented in a parametric form. The second part of this work presents numerical results obtained for specific parametric-study input variables (lubricant inlet temperature, external load, angular rotational speed, and axial misalignment). Attention is given to the interrelations between pressure profiles and bearing linear and nonlinear characteristics.

Hbim to VR. Semantic Awareness and Data Enrichment Interoperability for Parametric Libraries of Historical Architecture

NASA Astrophysics Data System (ADS)

Quattrini, R.; Battini, C.; Mammoli, R.

2018-05-01

Recently we assist to an increasing availability of HBIM models rich in geometric and informative terms. Instead, there is still a lack of researches implementing dedicated libraries, based on parametric intelligence and semantically aware, related to the architectural heritage. Additional challenges became from their portability in non-desktop environment (such as VR). The research article demonstrates the validity of a workflow applied to the architectural heritage, which starting from the semantic modeling reaches the visualization in a virtual reality environment, passing through the necessary phases of export, data migration and management. The three-dimensional modeling of the classical Doric order takes place in the BIM work environment and is configured as a necessary starting point for the implementation of data, parametric intelligences and definition of ontologies that exclusively qualify the model. The study also enables an effective method for data migration from the BIM model to databases integrated into VR technologies for AH. Furthermore, the process intends to propose a methodology, applicable in a return path, suited to the achievement of an appropriate data enrichment of each model and to the possibility of interaction in VR environment with the model.

Determination of Acreage Thermal Protection Foam Loss From Ice and Foam Impacts

NASA Technical Reports Server (NTRS)

Carney, Kelly S.; Lawrence, Charles

2015-01-01

A parametric study was conducted to establish Thermal Protection System (TPS) loss from foam and ice impact conditions similar to what might occur on the Space Launch System. This study was based upon the large amount of testing and analysis that was conducted with both ice and foam debris impacts on TPS acreage foam for the Space Shuttle Project External Tank. Test verified material models and modeling techniques that resulted from Space Shuttle related testing were utilized for this parametric study. Parameters varied include projectile mass, impact velocity and impact angle (5 degree and 10 degree impacts). The amount of TPS acreage foam loss as a result of the various impact conditions is presented.

Modeling and stochastic analysis of dynamic mechanisms of the perception

NASA Astrophysics Data System (ADS)

Pisarchik, A.; Bashkirtseva, I.; Ryashko, L.

2017-10-01

Modern studies in physiology and cognitive neuroscience consider a noise as an important constructive factor of the brain functionality. Under the adequate noise, the brain can rapidly access different ordered states, and provide decision-making by preventing deadlocks. Bistable dynamic models are often used for the study of the underlying mechanisms of the visual perception. In the present paper, we consider a bistable energy model subject to both additive and parametric noise. Using the catastrophe theory formalism and stochastic sensitivity functions technique, we analyze a response of the equilibria to noise, and study noise-induced transitions between equilibria. We demonstrate and analyse the effect of hysteresis squeezing when the intensity of noise is increased. Stochastic bifurcations connected with the suppression of oscillations by parametric noises are discussed.

Quantifying parametric uncertainty in the Rothermel model

Treesearch

S. Goodrick

2008-01-01

The purpose of the present work is to quantify parametric uncertainty in the Rothermel wildland fire spreadmodel (implemented in software such as fire spread models in the United States. This model consists of a non-linear system of equations that relates environmentalvariables (input parameter groups...

Thermal-structural modeling of polymer Bragg grating waveguides illuminated by a light emitting diode.

PubMed

Joon Kim, Kyoung; Bar-Cohen, Avram; Han, Bongtae

2012-02-20

This study reports both analytical and numerical thermal-structural models of polymer Bragg grating (PBG) waveguides illuminated by a light emitting diode (LED). A polymethyl methacrylate (PMMA) Bragg grating (BG) waveguide is chosen as an analysis vehicle to explore parametric effects of incident optical powers and substrate materials on the thermal-structural behavior of the BG. Analytical models are verified by comparing analytically predicted average excess temperatures, and thermally induced axial strains and stresses with numerical predictions. A parametric study demonstrates that the PMMA substrate induces more adverse effects, such as higher excess temperatures, complex axial temperature profiles, and greater and more complicated thermally induced strains in the BG compared with the Si substrate. © 2012 Optical Society of America

Assessing statistical differences between parameters estimates in Partial Least Squares path modeling.

PubMed

Rodríguez-Entrena, Macario; Schuberth, Florian; Gelhard, Carsten

2018-01-01

Structural equation modeling using partial least squares (PLS-SEM) has become a main-stream modeling approach in various disciplines. Nevertheless, prior literature still lacks a practical guidance on how to properly test for differences between parameter estimates. Whereas existing techniques such as parametric and non-parametric approaches in PLS multi-group analysis solely allow to assess differences between parameters that are estimated for different subpopulations, the study at hand introduces a technique that allows to also assess whether two parameter estimates that are derived from the same sample are statistically different. To illustrate this advancement to PLS-SEM, we particularly refer to a reduced version of the well-established technology acceptance model.

Sparkle model for AM1 calculation of lanthanide complexes: improved parameters for europium.

PubMed

Rocha, Gerd B; Freire, Ricardo O; Da Costa, Nivan B; De Sá, Gilberto F; Simas, Alfredo M

2004-04-05

In the present work, we sought to improve our sparkle model for the calculation of lanthanide complexes, SMLC,in various ways: (i) inclusion of the europium atomic mass, (ii) reparametrization of the model within AM1 from a new response function including all distances of the coordination polyhedron for tris(acetylacetonate)(1,10-phenanthroline) europium(III), (iii) implementation of the model in the software package MOPAC93r2, and (iv) inclusion of spherical Gaussian functions in the expression which computes the core-core repulsion energy. The parametrization results indicate that SMLC II is superior to the previous version of the model because Gaussian functions proved essential if one requires a better description of the geometries of the complexes. In order to validate our parametrization, we carried out calculations on 96 europium(III) complexes, selected from Cambridge Structural Database 2003, and compared our predicted ground state geometries with the experimental ones. Our results show that this new parametrization of the SMLC model, with the inclusion of spherical Gaussian functions in the core-core repulsion energy, is better capable of predicting the Eu-ligand distances than the previous version. The unsigned mean error for all interatomic distances Eu-L, in all 96 complexes, which, for the original SMLC is 0.3564 A, is lowered to 0.1993 A when the model was parametrized with the inclusion of two Gaussian functions. Our results also indicate that this model is more applicable to europium complexes with beta-diketone ligands. As such, we conclude that this improved model can be considered a powerful tool for the study of lanthanide complexes and their applications, such as the modeling of light conversion molecular devices.

Regional vertical total electron content (VTEC) modeling together with satellite and receiver differential code biases (DCBs) using semi-parametric multivariate adaptive regression B-splines (SP-BMARS)

NASA Astrophysics Data System (ADS)

Durmaz, Murat; Karslioglu, Mahmut Onur

2015-04-01

There are various global and regional methods that have been proposed for the modeling of ionospheric vertical total electron content (VTEC). Global distribution of VTEC is usually modeled by spherical harmonic expansions, while tensor products of compactly supported univariate B-splines can be used for regional modeling. In these empirical parametric models, the coefficients of the basis functions as well as differential code biases (DCBs) of satellites and receivers can be treated as unknown parameters which can be estimated from geometry-free linear combinations of global positioning system observables. In this work we propose a new semi-parametric multivariate adaptive regression B-splines (SP-BMARS) method for the regional modeling of VTEC together with satellite and receiver DCBs, where the parametric part of the model is related to the DCBs as fixed parameters and the non-parametric part adaptively models the spatio-temporal distribution of VTEC. The latter is based on multivariate adaptive regression B-splines which is a non-parametric modeling technique making use of compactly supported B-spline basis functions that are generated from the observations automatically. This algorithm takes advantage of an adaptive scale-by-scale model building strategy that searches for best-fitting B-splines to the data at each scale. The VTEC maps generated from the proposed method are compared numerically and visually with the global ionosphere maps (GIMs) which are provided by the Center for Orbit Determination in Europe (CODE). The VTEC values from SP-BMARS and CODE GIMs are also compared with VTEC values obtained through calibration using local ionospheric model. The estimated satellite and receiver DCBs from the SP-BMARS model are compared with the CODE distributed DCBs. The results show that the SP-BMARS algorithm can be used to estimate satellite and receiver DCBs while adaptively and flexibly modeling the daily regional VTEC.

Simulation Analysis and Performance Study of CoCrMo Porous Structure Manufactured by Selective Laser Melting

NASA Astrophysics Data System (ADS)

Guoqing, Zhang; Junxin, Li; Jin, Li; Chengguang, Zhang; Zefeng, Xiao

2018-04-01

To fabricate porous implants with improved biocompatibility and mechanical properties that are matched to their application using selective laser melting (SLM), flow within the mold and compressive properties and performance of the porous structures must be comprehensively studied. Parametric modeling was used to build 3D models of octahedron and hexahedron structures. Finite element analysis was used to evaluate the mold flow and compressive properties of the parametric porous structures. A DiMetal-100 SLM molding apparatus was used to manufacture the porous structures and the results evaluated by light microscopy. The results showed that parametric modeling can produce robust models. Square structures caused higher blood cell adhesion than cylindrical structures. "Vortex" flow in square structures resulted in chaotic distribution of blood elements, whereas they were mostly distributed around the connecting parts in the cylindrical structures. No significant difference in elastic moduli or compressive strength was observed in square and cylindrical porous structures of identical characteristics. Hexahedron, square and cylindrical porous structures had the same stress-strain properties. For octahedron porous structures, cylindrical structures had higher stress-strain properties. Using these modeling and molding results, an important basis for designing and the direct manufacture of fixed biological implants is provided.

Simulation Analysis and Performance Study of CoCrMo Porous Structure Manufactured by Selective Laser Melting

NASA Astrophysics Data System (ADS)

Guoqing, Zhang; Junxin, Li; Jin, Li; Chengguang, Zhang; Zefeng, Xiao

2018-05-01

To fabricate porous implants with improved biocompatibility and mechanical properties that are matched to their application using selective laser melting (SLM), flow within the mold and compressive properties and performance of the porous structures must be comprehensively studied. Parametric modeling was used to build 3D models of octahedron and hexahedron structures. Finite element analysis was used to evaluate the mold flow and compressive properties of the parametric porous structures. A DiMetal-100 SLM molding apparatus was used to manufacture the porous structures and the results evaluated by light microscopy. The results showed that parametric modeling can produce robust models. Square structures caused higher blood cell adhesion than cylindrical structures. "Vortex" flow in square structures resulted in chaotic distribution of blood elements, whereas they were mostly distributed around the connecting parts in the cylindrical structures. No significant difference in elastic moduli or compressive strength was observed in square and cylindrical porous structures of identical characteristics. Hexahedron, square and cylindrical porous structures had the same stress-strain properties. For octahedron porous structures, cylindrical structures had higher stress-strain properties. Using these modeling and molding results, an important basis for designing and the direct manufacture of fixed biological implants is provided.

Robust simulation of buckled structures using reduced order modeling

NASA Astrophysics Data System (ADS)

Wiebe, R.; Perez, R. A.; Spottswood, S. M.

2016-09-01

Lightweight metallic structures are a mainstay in aerospace engineering. For these structures, stability, rather than strength, is often the critical limit state in design. For example, buckling of panels and stiffeners may occur during emergency high-g maneuvers, while in supersonic and hypersonic aircraft, it may be induced by thermal stresses. The longstanding solution to such challenges was to increase the sizing of the structural members, which is counter to the ever present need to minimize weight for reasons of efficiency and performance. In this work we present some recent results in the area of reduced order modeling of post- buckled thin beams. A thorough parametric study of the response of a beam to changing harmonic loading parameters, which is useful in exposing complex phenomena and exercising numerical models, is presented. Two error metrics that use but require no time stepping of a (computationally expensive) truth model are also introduced. The error metrics are applied to several interesting forcing parameter cases identified from the parametric study and are shown to yield useful information about the quality of a candidate reduced order model. Parametric studies, especially when considering forcing and structural geometry parameters, coupled environments, and uncertainties would be computationally intractable with finite element models. The goal is to make rapid simulation of complex nonlinear dynamic behavior possible for distributed systems via fast and accurate reduced order models. This ability is crucial in allowing designers to rigorously probe the robustness of their designs to account for variations in loading, structural imperfections, and other uncertainties.

Comparison of Cox's Regression Model and Parametric Models in Evaluating the Prognostic Factors for Survival after Liver Transplantation in Shiraz during 2000-2012.

PubMed

Adelian, R; Jamali, J; Zare, N; Ayatollahi, S M T; Pooladfar, G R; Roustaei, N

2015-01-01

Identification of the prognostic factors for survival in patients with liver transplantation is challengeable. Various methods of survival analysis have provided different, sometimes contradictory, results from the same data. To compare Cox's regression model with parametric models for determining the independent factors for predicting adults' and pediatrics' survival after liver transplantation. This study was conducted on 183 pediatric patients and 346 adults underwent liver transplantation in Namazi Hospital, Shiraz, southern Iran. The study population included all patients undergoing liver transplantation from 2000 to 2012. The prognostic factors sex, age, Child class, initial diagnosis of the liver disease, PELD/MELD score, and pre-operative laboratory markers were selected for survival analysis. Among 529 patients, 346 (64.5%) were adult and 183 (34.6%) were pediatric cases. Overall, the lognormal distribution was the best-fitting model for adult and pediatric patients. Age in adults (HR=1.16, p<0.05) and weight (HR=2.68, p<0.01) and Child class B (HR=2.12, p<0.05) in pediatric patients were the most important factors for prediction of survival after liver transplantation. Adult patients younger than the mean age and pediatric patients weighing above the mean and Child class A (compared to those with classes B or C) had better survival. Parametric regression model is a good alternative for the Cox's regression model.

Maximum Marginal Likelihood Estimation of a Monotonic Polynomial Generalized Partial Credit Model with Applications to Multiple Group Analysis.

PubMed

Falk, Carl F; Cai, Li

2016-06-01

We present a semi-parametric approach to estimating item response functions (IRF) useful when the true IRF does not strictly follow commonly used functions. Our approach replaces the linear predictor of the generalized partial credit model with a monotonic polynomial. The model includes the regular generalized partial credit model at the lowest order polynomial. Our approach extends Liang's (A semi-parametric approach to estimate IRFs, Unpublished doctoral dissertation, 2007) method for dichotomous item responses to the case of polytomous data. Furthermore, item parameter estimation is implemented with maximum marginal likelihood using the Bock-Aitkin EM algorithm, thereby facilitating multiple group analyses useful in operational settings. Our approach is demonstrated on both educational and psychological data. We present simulation results comparing our approach to more standard IRF estimation approaches and other non-parametric and semi-parametric alternatives.

The influence of a time-varying least squares parametric model when estimating SFOAEs evoked with swept-frequency tones

NASA Astrophysics Data System (ADS)

Hajicek, Joshua J.; Selesnick, Ivan W.; Henin, Simon; Talmadge, Carrick L.; Long, Glenis R.

2018-05-01

Stimulus frequency otoacoustic emissions (SFOAEs) were evoked and estimated using swept-frequency tones with and without the use of swept suppressor tones. SFOAEs were estimated using a least-squares fitting procedure. The estimated SFOAEs for the two paradigms (with- and without-suppression) were similar in amplitude and phase. The fitting procedure minimizes the square error between a parametric model of total ear-canal pressure (with unknown amplitudes and phases) and ear-canal pressure acquired during each paradigm. Modifying the parametric model to allow SFOAE amplitude and phase to vary over time revealed additional amplitude and phase fine structure in the without-suppressor, but not the with-suppressor paradigm. The use of a time-varying parametric model to estimate SFOAEs without-suppression may provide additional information about cochlear mechanics not available when using a with-suppressor paradigm.

Parametrization of Stillinger-Weber potential based on valence force field model: application to single-layer MoS2 and black phosphorus

NASA Astrophysics Data System (ADS)

Jiang, Jin-Wu

2015-08-01

We propose parametrizing the Stillinger-Weber potential for covalent materials starting from the valence force-field model. All geometrical parameters in the Stillinger-Weber potential are determined analytically according to the equilibrium condition for each individual potential term, while the energy parameters are derived from the valence force-field model. This parametrization approach transfers the accuracy of the valence force field model to the Stillinger-Weber potential. Furthermore, the resulting Stilliinger-Weber potential supports stable molecular dynamics simulations, as each potential term is at an energy-minimum state separately at the equilibrium configuration. We employ this procedure to parametrize Stillinger-Weber potentials for single-layer MoS2 and black phosphorous. The obtained Stillinger-Weber potentials predict an accurate phonon spectrum and mechanical behaviors. We also provide input scripts of these Stillinger-Weber potentials used by publicly available simulation packages including GULP and LAMMPS.

Parametrization of Stillinger-Weber potential based on valence force field model: application to single-layer MoS2 and black phosphorus.

PubMed

Jiang, Jin-Wu

2015-08-07

We propose parametrizing the Stillinger-Weber potential for covalent materials starting from the valence force-field model. All geometrical parameters in the Stillinger-Weber potential are determined analytically according to the equilibrium condition for each individual potential term, while the energy parameters are derived from the valence force-field model. This parametrization approach transfers the accuracy of the valence force field model to the Stillinger-Weber potential. Furthermore, the resulting Stilliinger-Weber potential supports stable molecular dynamics simulations, as each potential term is at an energy-minimum state separately at the equilibrium configuration. We employ this procedure to parametrize Stillinger-Weber potentials for single-layer MoS2 and black phosphorous. The obtained Stillinger-Weber potentials predict an accurate phonon spectrum and mechanical behaviors. We also provide input scripts of these Stillinger-Weber potentials used by publicly available simulation packages including GULP and LAMMPS.

Trial-dependent psychometric functions accounting for perceptual learning in 2-AFC discrimination tasks.

PubMed

Kattner, Florian; Cochrane, Aaron; Green, C Shawn

2017-09-01

The majority of theoretical models of learning consider learning to be a continuous function of experience. However, most perceptual learning studies use thresholds estimated by fitting psychometric functions to independent blocks, sometimes then fitting a parametric function to these block-wise estimated thresholds. Critically, such approaches tend to violate the basic principle that learning is continuous through time (e.g., by aggregating trials into large "blocks" for analysis that each assume stationarity, then fitting learning functions to these aggregated blocks). To address this discrepancy between base theory and analysis practice, here we instead propose fitting a parametric function to thresholds from each individual trial. In particular, we implemented a dynamic psychometric function whose parameters were allowed to change continuously with each trial, thus parameterizing nonstationarity. We fit the resulting continuous time parametric model to data from two different perceptual learning tasks. In nearly every case, the quality of the fits derived from the continuous time parametric model outperformed the fits derived from a nonparametric approach wherein separate psychometric functions were fit to blocks of trials. Because such a continuous trial-dependent model of perceptual learning also offers a number of additional advantages (e.g., the ability to extrapolate beyond the observed data; the ability to estimate performance on individual critical trials), we suggest that this technique would be a useful addition to each psychophysicist's analysis toolkit.

Parametric robust control and system identification: Unified approach

NASA Technical Reports Server (NTRS)

Keel, Leehyun

1994-01-01

Despite significant advancement in the area of robust parametric control, the problem of synthesizing such a controller is still a wide open problem. Thus, we attempt to give a solution to this important problem. Our approach captures the parametric uncertainty as an H(sub infinity) unstructured uncertainty so that H(sub infinity) synthesis techniques are applicable. Although the techniques cannot cope with the exact parametric uncertainty, they give a reasonable guideline to model the unstructured uncertainty that contains the parametric uncertainty. An additional loop shaping technique is also introduced to relax its conservatism.

Cardiac-gated parametric images from 82 Rb PET from dynamic frames and direct 4D reconstruction.

PubMed

Germino, Mary; Carson, Richard E

2018-02-01

Cardiac perfusion PET data can be reconstructed as a dynamic sequence and kinetic modeling performed to quantify myocardial blood flow, or reconstructed as static gated images to quantify function. Parametric images from dynamic PET are conventionally not gated, to allow use of all events with lower noise. An alternative method for dynamic PET is to incorporate the kinetic model into the reconstruction algorithm itself, bypassing the generation of a time series of emission images and directly producing parametric images. So-called "direct reconstruction" can produce parametric images with lower noise than the conventional method because the noise distribution is more easily modeled in projection space than in image space. In this work, we develop direct reconstruction of cardiac-gated parametric images for 82 Rb PET with an extension of the Parametric Motion compensation OSEM List mode Algorithm for Resolution-recovery reconstruction for the one tissue model (PMOLAR-1T). PMOLAR-1T was extended to accommodate model terms to account for spillover from the left and right ventricles into the myocardium. The algorithm was evaluated on a 4D simulated 82 Rb dataset, including a perfusion defect, as well as a human 82 Rb list mode acquisition. The simulated list mode was subsampled into replicates, each with counts comparable to one gate of a gated acquisition. Parametric images were produced by the indirect (separate reconstructions and modeling) and direct methods for each of eight low-count and eight normal-count replicates of the simulated data, and each of eight cardiac gates for the human data. For the direct method, two initialization schemes were tested: uniform initialization, and initialization with the filtered iteration 1 result of the indirect method. For the human dataset, event-by-event respiratory motion compensation was included. The indirect and direct methods were compared for the simulated dataset in terms of bias and coefficient of variation as a function of iteration. Convergence of direct reconstruction was slow with uniform initialization; lower bias was achieved in fewer iterations by initializing with the filtered indirect iteration 1 images. For most parameters and regions evaluated, the direct method achieved the same or lower absolute bias at matched iteration as the indirect method, with 23%-65% lower noise. Additionally, the direct method gave better contrast between the perfusion defect and surrounding normal tissue than the indirect method. Gated parametric images from the human dataset had comparable relative performance of indirect and direct, in terms of mean parameter values per iteration. Changes in myocardial wall thickness and blood pool size across gates were readily visible in the gated parametric images, with higher contrast between myocardium and left ventricle blood pool in parametric images than gated SUV images. Direct reconstruction can produce parametric images with less noise than the indirect method, opening the potential utility of gated parametric imaging for perfusion PET. © 2017 American Association of Physicists in Medicine.

Design and Parametric Study of the Magnetic Sensor for Position Detection in Linear Motor Based on Nonlinear Parametric Model Order Reduction

PubMed Central

Paul, Sarbajit; Chang, Junghwan

2017-01-01

This paper presents a design approach for a magnetic sensor module to detect mover position using the proper orthogonal decomposition-dynamic mode decomposition (POD-DMD)-based nonlinear parametric model order reduction (PMOR). The parameterization of the sensor module is achieved by using the multipolar moment matching method. Several geometric variables of the sensor module are considered while developing the parametric study. The operation of the sensor module is based on the principle of the airgap flux density distribution detection by the Hall Effect IC. Therefore, the design objective is to achieve a peak flux density (PFD) greater than 0.1 T and total harmonic distortion (THD) less than 3%. To fulfill the constraint conditions, the specifications for the sensor module is achieved by using POD-DMD based reduced model. The POD-DMD based reduced model provides a platform to analyze the high number of design models very fast, with less computational burden. Finally, with the final specifications, the experimental prototype is designed and tested. Two different modes, 90° and 120° modes respectively are used to obtain the position information of the linear motor mover. The position information thus obtained are compared with that of the linear scale data, used as a reference signal. The position information obtained using the 120° mode has a standard deviation of 0.10 mm from the reference linear scale signal, whereas the 90° mode position signal shows a deviation of 0.23 mm from the reference. The deviation in the output arises due to the mechanical tolerances introduced into the specification during the manufacturing process. This provides a scope for coupling the reliability based design optimization in the design process as a future extension. PMID:28671580

Design and Parametric Study of the Magnetic Sensor for Position Detection in Linear Motor Based on Nonlinear Parametric model order reduction.

PubMed

Paul, Sarbajit; Chang, Junghwan

2017-07-01

This paper presents a design approach for a magnetic sensor module to detect mover position using the proper orthogonal decomposition-dynamic mode decomposition (POD-DMD)-based nonlinear parametric model order reduction (PMOR). The parameterization of the sensor module is achieved by using the multipolar moment matching method. Several geometric variables of the sensor module are considered while developing the parametric study. The operation of the sensor module is based on the principle of the airgap flux density distribution detection by the Hall Effect IC. Therefore, the design objective is to achieve a peak flux density (PFD) greater than 0.1 T and total harmonic distortion (THD) less than 3%. To fulfill the constraint conditions, the specifications for the sensor module is achieved by using POD-DMD based reduced model. The POD-DMD based reduced model provides a platform to analyze the high number of design models very fast, with less computational burden. Finally, with the final specifications, the experimental prototype is designed and tested. Two different modes, 90° and 120° modes respectively are used to obtain the position information of the linear motor mover. The position information thus obtained are compared with that of the linear scale data, used as a reference signal. The position information obtained using the 120° mode has a standard deviation of 0.10 mm from the reference linear scale signal, whereas the 90° mode position signal shows a deviation of 0.23 mm from the reference. The deviation in the output arises due to the mechanical tolerances introduced into the specification during the manufacturing process. This provides a scope for coupling the reliability based design optimization in the design process as a future extension.

Parameterization of Highly Charged Metal Ions Using the 12-6-4 LJ-Type Nonbonded Model in Explicit Water

PubMed Central

2015-01-01

Highly charged metal ions act as catalytic centers and structural elements in a broad range of chemical complexes. The nonbonded model for metal ions is extensively used in molecular simulations due to its simple form, computational speed, and transferability. We have proposed and parametrized a 12-6-4 LJ (Lennard-Jones)-type nonbonded model for divalent metal ions in previous work, which showed a marked improvement over the 12-6 LJ nonbonded model. In the present study, by treating the experimental hydration free energies and ion–oxygen distances of the first solvation shell as targets for our parametrization, we evaluated 12-6 LJ parameters for 18 M(III) and 6 M(IV) metal ions for three widely used water models (TIP3P, SPC/E, and TIP4PEW). As expected, the interaction energy underestimation of the 12-6 LJ nonbonded model increases dramatically for the highly charged metal ions. We then parametrized the 12-6-4 LJ-type nonbonded model for these metal ions with the three water models. The final parameters reproduced the target values with good accuracy, which is consistent with our previous experience using this potential. Finally, tests were performed on a protein system, and the obtained results validate the transferability of these nonbonded model parameters. PMID:25145273

Evaluation of model-based versus non-parametric monaural noise-reduction approaches for hearing aids.

PubMed

Harlander, Niklas; Rosenkranz, Tobias; Hohmann, Volker

2012-08-01

Single channel noise reduction has been well investigated and seems to have reached its limits in terms of speech intelligibility improvement, however, the quality of such schemes can still be advanced. This study tests to what extent novel model-based processing schemes might improve performance in particular for non-stationary noise conditions. Two prototype model-based algorithms, a speech-model-based, and a auditory-model-based algorithm were compared to a state-of-the-art non-parametric minimum statistics algorithm. A speech intelligibility test, preference rating, and listening effort scaling were performed. Additionally, three objective quality measures for the signal, background, and overall distortions were applied. For a better comparison of all algorithms, particular attention was given to the usage of the similar Wiener-based gain rule. The perceptual investigation was performed with fourteen hearing-impaired subjects. The results revealed that the non-parametric algorithm and the auditory model-based algorithm did not affect speech intelligibility, whereas the speech-model-based algorithm slightly decreased intelligibility. In terms of subjective quality, both model-based algorithms perform better than the unprocessed condition and the reference in particular for highly non-stationary noise environments. Data support the hypothesis that model-based algorithms are promising for improving performance in non-stationary noise conditions.

Parametric Study of Synthetic-Jet-Based Flow Control on a Vertical Tail Model

NASA Astrophysics Data System (ADS)

Monastero, Marianne; Lindstrom, Annika; Beyar, Michael; Amitay, Michael

2015-11-01

Separation control over the rudder of the vertical tail of a commercial airplane using synthetic-jet-based flow control can lead to a reduction in tail size, with an associated decrease in drag and increase in fuel savings. A parametric, experimental study was undertaken using an array of finite span synthetic jets to investigate the sensitivity of the enhanced vertical tail side force to jet parameters, such as jet spanwise spacing and jet momentum coefficient. A generic wind tunnel model was designed and fabricated to fundamentally study the effects of the jet parameters at varying rudder deflection and model sideslip angles. Wind tunnel results obtained from pressure measurements and tuft flow visualization in the Rensselaer Polytechnic Subsonic Wind Tunnel show a decrease in separation severity and increase in model performance in comparison to the baseline, non-actuated case. The sensitivity to various parameters will be presented.

Parametric studies with an atmospheric diffusion model that assesses toxic fuel hazards due to the ground clouds generated by rocket launches

NASA Technical Reports Server (NTRS)

Stewart, R. B.; Grose, W. L.

1975-01-01

Parametric studies were made with a multilayer atmospheric diffusion model to place quantitative limits on the uncertainty of predicting ground-level toxic rocket-fuel concentrations. Exhaust distributions in the ground cloud, cloud stabilized geometry, atmospheric coefficients, the effects of exhaust plume afterburning of carbon monoxide CO, assumed surface mixing-layer division in the model, and model sensitivity to different meteorological regimes were studied. Large-scale differences in ground-level predictions are quantitatively described. Cloud alongwind growth for several meteorological conditions is shown to be in error because of incorrect application of previous diffusion theory. In addition, rocket-plume calculations indicate that almost all of the rocket-motor carbon monoxide is afterburned to carbon dioxide CO2, thus reducing toxic hazards due to CO. The afterburning is also shown to have a significant effect on cloud stabilization height and on ground-level concentrations of exhaust products.

Model Robust Calibration: Method and Application to Electronically-Scanned Pressure Transducers

NASA Technical Reports Server (NTRS)

Walker, Eric L.; Starnes, B. Alden; Birch, Jeffery B.; Mays, James E.

2010-01-01

This article presents the application of a recently developed statistical regression method to the controlled instrument calibration problem. The statistical method of Model Robust Regression (MRR), developed by Mays, Birch, and Starnes, is shown to improve instrument calibration by reducing the reliance of the calibration on a predetermined parametric (e.g. polynomial, exponential, logarithmic) model. This is accomplished by allowing fits from the predetermined parametric model to be augmented by a certain portion of a fit to the residuals from the initial regression using a nonparametric (locally parametric) regression technique. The method is demonstrated for the absolute scale calibration of silicon-based pressure transducers.

Choice Inconsistencies among the Elderly: Evidence from Plan Choice in the Medicare Part D Program: Reply

PubMed Central

ABALUCK, JASON

2017-01-01

We explore the in- and out- of sample robustness of tests for choice inconsistencies based on parameter restrictions in parametric models, focusing on tests proposed by Ketcham, Kuminoff and Powers (KKP). We argue that their non-parametric alternatives are inherently conservative with respect to detecting mistakes. We then show that our parametric model is robust to KKP’s suggested specification checks, and that comprehensive goodness of fit measures perform better with our model than the expected utility model. Finally, we explore the robustness of our 2011 results to alternative normative assumptions highlighting the role of brand fixed effects and unobservable characteristics. PMID:29170561

Nonrelativistic approaches derived from point-coupling relativistic models

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

Lourenco, O.; Dutra, M.; Delfino, A.

2010-03-15

We construct nonrelativistic versions of relativistic nonlinear hadronic point-coupling models, based on new normalized spinor wave functions after small component reduction. These expansions give us energy density functionals that can be compared to their relativistic counterparts. We show that the agreement between the nonrelativistic limit approach and the Skyrme parametrizations becomes strongly dependent on the incompressibility of each model. We also show that the particular case A=B=0 (Walecka model) leads to the same energy density functional of the Skyrme parametrizations SV and ZR2, while the truncation scheme, up to order {rho}{sup 3}, leads to parametrizations for which {sigma}=1.

Nonlinear dynamic analysis of cantilevered piezoelectric energy harvesters under simultaneous parametric and external excitations

NASA Astrophysics Data System (ADS)

Fang, Fei; Xia, Guanghui; Wang, Jianguo

2018-02-01

The nonlinear dynamics of cantilevered piezoelectric beams is investigated under simultaneous parametric and external excitations. The beam is composed of a substrate and two piezoelectric layers and assumed as an Euler-Bernoulli model with inextensible deformation. A nonlinear distributed parameter model of cantilevered piezoelectric energy harvesters is proposed using the generalized Hamilton's principle. The proposed model includes geometric and inertia nonlinearity, but neglects the material nonlinearity. Using the Galerkin decomposition method and harmonic balance method, analytical expressions of the frequency-response curves are presented when the first bending mode of the beam plays a dominant role. Using these expressions, we investigate the effects of the damping, load resistance, electromechanical coupling, and excitation amplitude on the frequency-response curves. We also study the difference between the nonlinear lumped-parameter and distributed-parameter model for predicting the performance of the energy harvesting system. Only in the case of parametric excitation, we demonstrate that the energy harvesting system has an initiation excitation threshold below which no energy can be harvested. We also illustrate that the damping and load resistance affect the initiation excitation threshold.

MEASURING DARK MATTER PROFILES NON-PARAMETRICALLY IN DWARF SPHEROIDALS: AN APPLICATION TO DRACO

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

Jardel, John R.; Gebhardt, Karl; Fabricius, Maximilian H.

2013-02-15

We introduce a novel implementation of orbit-based (or Schwarzschild) modeling that allows dark matter density profiles to be calculated non-parametrically in nearby galaxies. Our models require no assumptions to be made about velocity anisotropy or the dark matter profile. The technique can be applied to any dispersion-supported stellar system, and we demonstrate its use by studying the Local Group dwarf spheroidal galaxy (dSph) Draco. We use existing kinematic data at larger radii and also present 12 new radial velocities within the central 13 pc obtained with the VIRUS-W integral field spectrograph on the 2.7 m telescope at McDonald Observatory. Ourmore » non-parametric Schwarzschild models find strong evidence that the dark matter profile in Draco is cuspy for 20 {<=} r {<=} 700 pc. The profile for r {>=} 20 pc is well fit by a power law with slope {alpha} = -1.0 {+-} 0.2, consistent with predictions from cold dark matter simulations. Our models confirm that, despite its low baryon content relative to other dSphs, Draco lives in a massive halo.« less

Nonlinear dynamic analysis of cantilevered piezoelectric energy harvesters under simultaneous parametric and external excitations

NASA Astrophysics Data System (ADS)

Fang, Fei; Xia, Guanghui; Wang, Jianguo

2018-06-01

The nonlinear dynamics of cantilevered piezoelectric beams is investigated under simultaneous parametric and external excitations. The beam is composed of a substrate and two piezoelectric layers and assumed as an Euler-Bernoulli model with inextensible deformation. A nonlinear distributed parameter model of cantilevered piezoelectric energy harvesters is proposed using the generalized Hamilton's principle. The proposed model includes geometric and inertia nonlinearity, but neglects the material nonlinearity. Using the Galerkin decomposition method and harmonic balance method, analytical expressions of the frequency-response curves are presented when the first bending mode of the beam plays a dominant role. Using these expressions, we investigate the effects of the damping, load resistance, electromechanical coupling, and excitation amplitude on the frequency-response curves. We also study the difference between the nonlinear lumped-parameter and distributed-parameter model for predicting the performance of the energy harvesting system. Only in the case of parametric excitation, we demonstrate that the energy harvesting system has an initiation excitation threshold below which no energy can be harvested. We also illustrate that the damping and load resistance affect the initiation excitation threshold.

CuBe: parametric modeling of 3D foveal shape using cubic Bézier

PubMed Central

Yadav, Sunil Kumar; Motamedi, Seyedamirhosein; Oberwahrenbrock, Timm; Oertel, Frederike Cosima; Polthier, Konrad; Paul, Friedemann; Kadas, Ella Maria; Brandt, Alexander U.

2017-01-01

Optical coherence tomography (OCT) allows three-dimensional (3D) imaging of the retina, and is commonly used for assessing pathological changes of fovea and macula in many diseases. Many neuroinflammatory conditions are known to cause modifications to the fovea shape. In this paper, we propose a method for parametric modeling of the foveal shape. Our method exploits invariant features of the macula from OCT data and applies a cubic Bézier polynomial along with a least square optimization to produce a best fit parametric model of the fovea. Additionally, we provide several parameters of the foveal shape based on the proposed 3D parametric modeling. Our quantitative and visual results show that the proposed model is not only able to reconstruct important features from the foveal shape, but also produces less error compared to the state-of-the-art methods. Finally, we apply the model in a comparison of healthy control eyes and eyes from patients with neuroinflammatory central nervous system disorders and optic neuritis, and show that several derived model parameters show significant differences between the two groups. PMID:28966857

Model Adaptation in Parametric Space for POD-Galerkin Models

NASA Astrophysics Data System (ADS)

Gao, Haotian; Wei, Mingjun

2017-11-01

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

Parametric, nonparametric and parametric modelling of a chaotic circuit time series

NASA Astrophysics Data System (ADS)

Timmer, J.; Rust, H.; Horbelt, W.; Voss, H. U.

2000-09-01

The determination of a differential equation underlying a measured time series is a frequently arising task in nonlinear time series analysis. In the validation of a proposed model one often faces the dilemma that it is hard to decide whether possible discrepancies between the time series and model output are caused by an inappropriate model or by bad estimates of parameters in a correct type of model, or both. We propose a combination of parametric modelling based on Bock's multiple shooting algorithm and nonparametric modelling based on optimal transformations as a strategy to test proposed models and if rejected suggest and test new ones. We exemplify this strategy on an experimental time series from a chaotic circuit where we obtain an extremely accurate reconstruction of the observed attractor.

Testing in semiparametric models with interaction, with applications to gene-environment interactions.

PubMed

Maity, Arnab; Carroll, Raymond J; Mammen, Enno; Chatterjee, Nilanjan

2009-01-01

Motivated from the problem of testing for genetic effects on complex traits in the presence of gene-environment interaction, we develop score tests in general semiparametric regression problems that involves Tukey style 1 degree-of-freedom form of interaction between parametrically and non-parametrically modelled covariates. We find that the score test in this type of model, as recently developed by Chatterjee and co-workers in the fully parametric setting, is biased and requires undersmoothing to be valid in the presence of non-parametric components. Moreover, in the presence of repeated outcomes, the asymptotic distribution of the score test depends on the estimation of functions which are defined as solutions of integral equations, making implementation difficult and computationally taxing. We develop profiled score statistics which are unbiased and asymptotically efficient and can be performed by using standard bandwidth selection methods. In addition, to overcome the difficulty of solving functional equations, we give easy interpretations of the target functions, which in turn allow us to develop estimation procedures that can be easily implemented by using standard computational methods. We present simulation studies to evaluate type I error and power of the method proposed compared with a naive test that does not consider interaction. Finally, we illustrate our methodology by analysing data from a case-control study of colorectal adenoma that was designed to investigate the association between colorectal adenoma and the candidate gene NAT2 in relation to smoking history.

Analysis of Parametric Adaptive Signal Detection with Applications to Radars and Hyperspectral Imaging

DTIC Science & Technology

2010-02-01

98 8.4.5 Training Screening ............................. .................................................................99 8.5 Experimental...associated with the proposed parametric model. Several im- portant issues are discussed, including model order selection, training screening , and time...parameters associated with the NS-AR model. In addition, we develop model order selection, training screening , and time-series based whitening and

Estimating solar ultraviolet irradiance (290-385 nm) by means of the spectral parametric models: SPCTRAL2 and SMARTS2

NASA Astrophysics Data System (ADS)

Foyo-Moreno, I.; Vida, J.; Olmo, F. J.; Alados-Arboledas, L.

2000-11-01

Since the discovery of the ozone depletion in Antarctic and the globally declining trend of stratospheric ozone concentration, public and scientific concern has been raised in the last decades. A very important consequence of this fact is the increased broadband and spectral UV radiation in the environment and the biological effects and heath risks that may take place in the near future. The absence of widespread measurements of this radiometric flux has lead to the development and use of alternative estimation procedures such as the parametric approaches. Parametric models compute the radiant energy using available atmospheric parameters. Some parametric models compute the global solar irradiance at surface level by addition of its direct beam and diffuse components. In the present work, we have developed a comparison between two cloudless sky parametrization schemes. Both methods provide an estimation of the solar spectral irradiance that can be integrated spectrally within the limits of interest. For this test we have used data recorded in a radiometric station located at Granada (37.180°N, 3.580°W, 660 m a.m.s.l.), an inland location. The database includes hourly values of the relevant variables covering the years 1994-95. The performance of the models has been tested in relation to their predictive capability of global solar irradiance in the UV range (290-385 nm). After our study, it appears that information concerning the aerosol radiative effects is fundamental in order to obtain a good estimation. The original version of SPCTRAL2 provides estimates of the experimental values with negligible mean bias deviation. This suggests not only the appropriateness of the model but also the convenience of the aerosol features fixed in it to Granada conditions. SMARTS2 model offers increased flexibility concerning the selection of different aerosol models included in the code and provides the best results when the selected models are those considered as urban. Although SMARTS2 provide slightly worse results, both models give estimates of solar ultraviolet irradiance with mean bias deviation below 5%, and root mean square deviation close to experimental errors.

Estimating and modeling the cure fraction in population-based cancer survival analysis.

PubMed

Lambert, Paul C; Thompson, John R; Weston, Claire L; Dickman, Paul W

2007-07-01

In population-based cancer studies, cure is said to occur when the mortality (hazard) rate in the diseased group of individuals returns to the same level as that expected in the general population. The cure fraction (the proportion of patients cured of disease) is of interest to patients and is a useful measure to monitor trends in survival of curable disease. There are 2 main types of cure fraction model, the mixture cure fraction model and the non-mixture cure fraction model, with most previous work concentrating on the mixture cure fraction model. In this paper, we extend the parametric non-mixture cure fraction model to incorporate background mortality, thus providing estimates of the cure fraction in population-based cancer studies. We compare the estimates of relative survival and the cure fraction between the 2 types of model and also investigate the importance of modeling the ancillary parameters in the selected parametric distribution for both types of model.

Intratumor distribution and test-retest comparisons of physiological parameters quantified by dynamic contrast-enhanced MRI in rat U251 glioma.

PubMed

Aryal, Madhava P; Nagaraja, Tavarekere N; Brown, Stephen L; Lu, Mei; Bagher-Ebadian, Hassan; Ding, Guangliang; Panda, Swayamprava; Keenan, Kelly; Cabral, Glauber; Mikkelsen, Tom; Ewing, James R

2014-10-01

The distribution of dynamic contrast-enhanced MRI (DCE-MRI) parametric estimates in a rat U251 glioma model was analyzed. Using Magnevist as contrast agent (CA), 17 nude rats implanted with U251 cerebral glioma were studied by DCE-MRI twice in a 24 h interval. A data-driven analysis selected one of three models to estimate either (1) plasma volume (vp), (2) vp and forward volume transfer constant (K(trans)) or (3) vp, K(trans) and interstitial volume fraction (ve), constituting Models 1, 2 and 3, respectively. CA distribution volume (VD) was estimated in Model 3 regions by Logan plots. Regions of interest (ROIs) were selected by model. In the Model 3 ROI, descriptors of parameter distributions--mean, median, variance and skewness--were calculated and compared between the two time points for repeatability. All distributions of parametric estimates in Model 3 ROIs were positively skewed. Test-retest differences between population summaries for any parameter were not significant (p ≥ 0.10; Wilcoxon signed-rank and paired t tests). These and similar measures of parametric distribution and test-retest variance from other tumor models can be used to inform the choice of biomarkers that best summarize tumor status and treatment effects. Copyright © 2014 John Wiley & Sons, Ltd.

Registration of parametric dynamic F-18-FDG PET/CT breast images with parametric dynamic Gd-DTPA breast images

NASA Astrophysics Data System (ADS)

Magri, Alphonso; Krol, Andrzej; Lipson, Edward; Mandel, James; McGraw, Wendy; Lee, Wei; Tillapaugh-Fay, Gwen; Feiglin, David

2009-02-01

This study was undertaken to register 3D parametric breast images derived from Gd-DTPA MR and F-18-FDG PET/CT dynamic image series. Nonlinear curve fitting (Levenburg-Marquardt algorithm) based on realistic two-compartment models was performed voxel-by-voxel separately for MR (Brix) and PET (Patlak). PET dynamic series consists of 50 frames of 1-minute duration. Each consecutive PET image was nonrigidly registered to the first frame using a finite element method and fiducial skin markers. The 12 post-contrast MR images were nonrigidly registered to the precontrast frame using a free-form deformation (FFD) method. Parametric MR images were registered to parametric PET images via CT using FFD because the first PET time frame was acquired immediately after the CT image on a PET/CT scanner and is considered registered to the CT image. We conclude that nonrigid registration of PET and MR parametric images using CT data acquired during PET/CT scan and the FFD method resulted in their improved spatial coregistration. The success of this procedure was limited due to relatively large target registration error, TRE = 15.1+/-7.7 mm, as compared to spatial resolution of PET (6-7 mm), and swirling image artifacts created in MR parametric images by the FFD. Further refinement of nonrigid registration of PET and MR parametric images is necessary to enhance visualization and integration of complex diagnostic information provided by both modalities that will lead to improved diagnostic performance.

SOCR Analyses - an Instructional Java Web-based Statistical Analysis Toolkit.

PubMed

Chu, Annie; Cui, Jenny; Dinov, Ivo D

2009-03-01

The Statistical Online Computational Resource (SOCR) designs web-based tools for educational use in a variety of undergraduate courses (Dinov 2006). Several studies have demonstrated that these resources significantly improve students' motivation and learning experiences (Dinov et al. 2008). SOCR Analyses is a new component that concentrates on data modeling and analysis using parametric and non-parametric techniques supported with graphical model diagnostics. Currently implemented analyses include commonly used models in undergraduate statistics courses like linear models (Simple Linear Regression, Multiple Linear Regression, One-Way and Two-Way ANOVA). In addition, we implemented tests for sample comparisons, such as t-test in the parametric category; and Wilcoxon rank sum test, Kruskal-Wallis test, Friedman's test, in the non-parametric category. SOCR Analyses also include several hypothesis test models, such as Contingency tables, Friedman's test and Fisher's exact test.The code itself is open source (http://socr.googlecode.com/), hoping to contribute to the efforts of the statistical computing community. The code includes functionality for each specific analysis model and it has general utilities that can be applied in various statistical computing tasks. For example, concrete methods with API (Application Programming Interface) have been implemented in statistical summary, least square solutions of general linear models, rank calculations, etc. HTML interfaces, tutorials, source code, activities, and data are freely available via the web (www.SOCR.ucla.edu). Code examples for developers and demos for educators are provided on the SOCR Wiki website.In this article, the pedagogical utilization of the SOCR Analyses is discussed, as well as the underlying design framework. As the SOCR project is on-going and more functions and tools are being added to it, these resources are constantly improved. The reader is strongly encouraged to check the SOCR site for most updated information and newly added models.

Income inequality in Romania: The exponential-Pareto distribution

NASA Astrophysics Data System (ADS)

Oancea, Bogdan; Andrei, Tudorel; Pirjol, Dan

2017-03-01

We present a study of the distribution of the gross personal income and income inequality in Romania, using individual tax income data, and both non-parametric and parametric methods. Comparing with official results based on household budget surveys (the Family Budgets Survey and the EU-SILC data), we find that the latter underestimate the income share of the high income region, and the overall income inequality. A parametric study shows that the income distribution is well described by an exponential distribution in the low and middle incomes region, and by a Pareto distribution in the high income region with Pareto coefficient α = 2.53. We note an anomaly in the distribution in the low incomes region (∼9,250 RON), and present a model which explains it in terms of partial income reporting.

Comprehensive Analysis Modeling of Small-Scale UAS Rotors

NASA Technical Reports Server (NTRS)

Russell, Carl R.; Sekula, Martin K.

2017-01-01

Multicopter unmanned aircraft systems (UAS), or drones, have continued their explosive growth in recent years. With this growth comes demand for increased performance as the limits of existing technologies are reached. In order to better design multicopter UAS aircraft, better performance prediction tools are needed. This paper presents the results of a study aimed at using the rotorcraft comprehensive analysis code CAMRAD II to model a multicopter UAS rotor in hover. Parametric studies were performed to determine the level of fidelity needed in the analysis code inputs to achieve results that match test data. Overall, the results show that CAMRAD II is well suited to model small-scale UAS rotors in hover. This paper presents the results of the parametric studies as well as recommendations for the application of comprehensive analysis codes to multicopter UAS rotors.

Influence of spatial beam inhomogeneities on the parameters of a petawatt laser system based on multi-stage parametric amplification

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

Frolov, S A; Trunov, V I; Pestryakov, Efim V

2013-05-31

We have developed a technique for investigating the evolution of spatial inhomogeneities in high-power laser systems based on multi-stage parametric amplification. A linearised model of the inhomogeneity development is first devised for parametric amplification with the small-scale self-focusing taken into account. It is shown that the application of this model gives the results consistent (with high accuracy and in a wide range of inhomogeneity parameters) with the calculation without approximations. Using the linearised model, we have analysed the development of spatial inhomogeneities in a petawatt laser system based on multi-stage parametric amplification, developed at the Institute of Laser Physics, Siberianmore » Branch of the Russian Academy of Sciences (ILP SB RAS). (control of laser radiation parameters)« less

Integrated modeling for parametric evaluation of smart x-ray optics

NASA Astrophysics Data System (ADS)

Dell'Agostino, S.; Riva, M.; Spiga, D.; Basso, S.; Civitani, Marta

2014-08-01

This work is developed in the framework of AXYOM project, which proposes to study the application of a system of piezoelectric actuators to grazing-incidence X-ray telescope optic prototypes: thin glass or plastic foils, in order to increase their angular resolution. An integrated optomechanical model has been set up to evaluate the performances of X-ray optics under deformation induced by Piezo Actuators. Parametric evaluation has been done looking at different number and position of actuators to optimize the outcome. Different evaluations have also been done over the actuator types, considering Flexible Piezoceramic, Multi Fiber Composites piezo actuators, and PVDF.

Modeling of enhanced spontaneous parametric down-conversion in plasmonic and dielectric structures with realistic waves

NASA Astrophysics Data System (ADS)

Loot, A.; Hizhnyakov, V.

2018-05-01

A numerical study of the enhancement of the spontaneous parametric down-conversion in plasmonic and dielectric structures is considered. The modeling is done using a nonlinear transfer-matrix method which is extended to include vacuum fluctuations and realistic waves (e.g. Gaussian beam). The results indicate that in the case of short-range surface plasmon polaritons, the main limiting factor of the enhancement is the short length of the coherent buildup. In the case of long-range surface plasmon polaritons or dielectric guided waves, the very narrow resonances are the main limiting factor instead.

Parametric System Model for a Stirling Radioisotope Generator

NASA Technical Reports Server (NTRS)

Schmitz, Paul C.

2015-01-01

A Parametric System Model (PSM) was created in order to explore conceptual designs, the impact of component changes and power level on the performance of the Stirling Radioisotope Generator (SRG). Using the General Purpose Heat Source (GPHS approximately 250 Wth) modules as the thermal building block from which a SRG is conceptualized, trade studies are performed to understand the importance of individual component scaling on isotope usage. Mathematical relationships based on heat and power throughput, temperature, mass, and volume were developed for each of the required subsystems. The PSM uses these relationships to perform component- and system-level trades.

Parametric System Model for a Stirling Radioisotope Generator

NASA Technical Reports Server (NTRS)

Schmitz, Paul C.

2014-01-01

A Parametric System Model (PSM) was created in order to explore conceptual designs, the impact of component changes and power level on the performance of Stirling Radioisotope Generator (SRG). Using the General Purpose Heat Source (GPHS approximately 250 watt thermal) modules as the thermal building block around which a SRG is conceptualized, trade studies are performed to understand the importance of individual component scaling on isotope usage. Mathematical relationships based on heat and power throughput, temperature, mass and volume were developed for each of the required subsystems. The PSM uses these relationships to perform component and system level trades.

A model for straight and helical solar jets: II. Parametric study of the plasma beta.

PubMed

Pariat, E; Dalmasse, K; DeVore, C R; Antiochos, S K; Karpen, J T

2016-12-01

Jets are dynamic, impulsive, well-collimated plasma events that develop at many different scales and in different layers of the solar atmosphere. Jets are believed to be induced by magnetic reconnection, a process central to many astrophysical phenomena. Within the solar atmosphere, jet-like events develop in many different environments, e.g., in the vicinity of active regions as well as in coronal holes, and at various scales, from small photospheric spicules to large coronal jets. In all these events, signatures of helical structure and/or twisting/rotating motions are regularly observed. The present study aims to establish that a single model can generally reproduce the observed properties of these jet-like events. In this study, using our state-of-the-art numerical solver ARMS, we present a parametric study of a numerical tridimensional magnetohydrodynamic (MHD) model of solar jet-like events. Within the MHD paradigm, we study the impact of varying the atmospheric plasma β on the generation and properties of solar-like jets. The parametric study validates our model of jets for plasma β ranging from 10 -3 to 1, typical of the different layers and magnetic environments of the solar atmosphere. Our model of jets can robustly explain the generation of helical solar jet-like events at various β ≤ 1. This study introduces the new original result that the plasma β modifies the morphology of the helical jet, explaining the different observed shapes of jets at different scales and in different layers of the solar atmosphere. Our results allow us to understand the energisation, triggering, and driving processes of jet-like events. Our model allows us to make predictions of the impulsiveness and energetics of jets as determined by the surrounding environment, as well as the morphological properties of the resulting jets.

Three-Phased Wake Vortex Decay

NASA Technical Reports Server (NTRS)

Proctor, Fred H.; Ahmad, Nashat N.; Switzer, George S.; LimonDuparcmeur, Fanny M.

2010-01-01

A detailed parametric study is conducted that examines vortex decay within turbulent and stratified atmospheres. The study uses a large eddy simulation model to simulate the out-of-ground effect behavior of wake vortices due to their interaction with atmospheric turbulence and thermal stratification. This paper presents results from a parametric investigation and suggests improvements for existing fast-time wake prediction models. This paper also describes a three-phased decay for wake vortices. The third phase is characterized by a relatively slow rate of circulation decay, and is associated with the ringvortex stage that occurs following vortex linking. The three-phased decay is most prevalent for wakes imbedded within environments having low-turbulence and near-neutral stratification.

Computation of the intensities of parametric holographic scattering patterns in photorefractive crystals.

PubMed

Schwalenberg, Simon

2005-06-01

The present work represents a first attempt to perform computations of output intensity distributions for different parametric holographic scattering patterns. Based on the model for parametric four-wave mixing processes in photorefractive crystals and taking into account realistic material properties, we present computed images of selected scattering patterns. We compare these calculated light distributions to the corresponding experimental observations. Our analysis is especially devoted to dark scattering patterns as they make high demands on the underlying model.

A convolution model for computing the far-field directivity of a parametric loudspeaker array.

PubMed

Shi, Chuang; Kajikawa, Yoshinobu

2015-02-01

This paper describes a method to compute the far-field directivity of a parametric loudspeaker array (PLA), whereby the steerable parametric loudspeaker can be implemented when phased array techniques are applied. The convolution of the product directivity and the Westervelt's directivity is suggested, substituting for the past practice of using the product directivity only. Computed directivity of a PLA using the proposed convolution model achieves significant improvement in agreement to measured directivity at a negligible computational cost.

Full-direct method for imaging pharmacokinetic parameters in dynamic fluorescence molecular tomography

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

Zhang, Guanglei, E-mail: guangleizhang@bjtu.edu.cn; Department of Biomedical Engineering, School of Computer and Information Technology, Beijing Jiaotong University, Beijing 100044; Pu, Huangsheng

2015-02-23

Images of pharmacokinetic parameters (also known as parametric images) in dynamic fluorescence molecular tomography (FMT) can provide three-dimensional metabolic information for biological studies and drug development. However, the ill-posed nature of FMT and the high temporal variation of fluorophore concentration together make it difficult to obtain accurate parametric images in small animals in vivo. In this letter, we present a method to directly reconstruct the parametric images from the boundary measurements based on hybrid FMT/X-ray computed tomography (XCT) system. This method can not only utilize structural priors obtained from the XCT system to mitigate the ill-posedness of FMT but alsomore » make full use of the temporal correlations of boundary measurements to model the high temporal variation of fluorophore concentration. The results of numerical simulation and mouse experiment demonstrate that the proposed method leads to significant improvements in the reconstruction quality of parametric images.« less

Reliability and Maintainability model (RAM) user and maintenance manual. Part 2

NASA Technical Reports Server (NTRS)

Ebeling, Charles E.

1995-01-01

This report documents the procedures for utilizing and maintaining the Reliability and Maintainability Model (RAM) developed by the University of Dayton for the NASA Langley Research Center (LaRC). The RAM model predicts reliability and maintainability (R&M) parameters for conceptual space vehicles using parametric relationships between vehicle design and performance characteristics and subsystem mean time between maintenance actions (MTBM) and manhours per maintenance action (MH/MA). These parametric relationships were developed using aircraft R&M data from over thirty different military aircraft of all types. This report describes the general methodology used within the model, the execution and computational sequence, the input screens and data, the output displays and reports, and study analyses and procedures. A source listing is provided.

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.

Revisiting dark energy models using differential ages of galaxies

NASA Astrophysics Data System (ADS)

Rani, Nisha; Jain, Deepak; Mahajan, Shobhit; Mukherjee, Amitabha; Biesiada, Marek

2017-03-01

In this work, we use a test based on the differential ages of galaxies for distinguishing the dark energy models. As proposed by Jimenez and Loeb in [1], relative ages of galaxies can be used to put constraints on various cosmological parameters. In the same vein, we reconstruct H0dt/dz and its derivative (H0d2t/dz2) using a model independent technique called non-parametric smoothing. Basically, dt/dz is the change in the age of the object as a function of redshift which is directly linked with the Hubble parameter. Hence for reconstruction of this quantity, we use the most recent H(z) data. Further, we calculate H0dt/dz and its derivative for several models like Phantom, Einstein de Sitter (EdS), ΛCDM, Chevallier-Polarski-Linder (CPL) parametrization, Jassal-Bagla-Padmanabhan (JBP) parametrization and Feng-Shen-Li-Li (FSLL) parametrization. We check the consistency of these models with the results of reconstruction obtained in a model independent way from the data. It is observed that H0dt/dz as a tool is not able to distinguish between the ΛCDM, CPL, JBP and FSLL parametrizations but, as expected, EdS and Phantom models show noticeable deviation from the reconstructed results. Further, the derivative of H0dt/dz for various dark energy models is more sensitive at low redshift. It is found that the FSLL model is not consistent with the reconstructed results, however, the ΛCDM model is in concordance with the 3σ region of the reconstruction at redshift z>= 0.3.

40 CFR Appendix C to Part 75 - Missing Data Estimation Procedures

Code of Federal Regulations, 2010 CFR

2010-07-01

... certification of a parametric, empirical, or process simulation method or model for calculating substitute data... available process simulation methods and models. 1.2Petition Requirements Continuously monitor, determine... desulfurization, a corresponding empirical correlation or process simulation parametric method using appropriate...

Impact Response Comparison Between Parametric Human Models and Postmortem Human Subjects with a Wide Range of Obesity Levels.

PubMed

Zhang, Kai; Cao, Libo; Wang, Yulong; Hwang, Eunjoo; Reed, Matthew P; Forman, Jason; Hu, Jingwen

2017-10-01

Field data analyses have shown that obesity significantly increases the occupant injury risks in motor vehicle crashes, but the injury assessment tools for people with obesity are largely lacking. The objectives of this study were to use a mesh morphing method to rapidly generate parametric finite element models with a wide range of obesity levels and to evaluate their biofidelity against impact tests using postmortem human subjects (PMHS). Frontal crash tests using three PMHS seated in a vehicle rear seat compartment with body mass index (BMI) from 24 to 40 kg/m 2 were selected. To develop the human models matching the PMHS geometry, statistical models of external body shape, rib cage, pelvis, and femur were applied to predict the target geometry using age, sex, stature, and BMI. A mesh morphing method based on radial basis functions was used to rapidly morph a baseline human model into the target geometry. The model-predicted body excursions and injury measures were compared to the PMHS tests. Comparisons of occupant kinematics and injury measures between the tests and simulations showed reasonable correlations across the wide range of BMI levels. The parametric human models have the capability to account for the obesity effects on the occupant impact responses and injury risks. © 2017 The Obesity Society.

Constraints on a generalized deceleration parameter from cosmic chronometers

NASA Astrophysics Data System (ADS)

Mamon, Abdulla Al

2018-04-01

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

Marginally specified priors for non-parametric Bayesian estimation

PubMed Central

Kessler, David C.; Hoff, Peter D.; Dunson, David B.

2014-01-01

Summary Prior specification for non-parametric Bayesian inference involves the difficult task of quantifying prior knowledge about a parameter of high, often infinite, dimension. A statistician is unlikely to have informed opinions about all aspects of such a parameter but will have real information about functionals of the parameter, such as the population mean or variance. The paper proposes a new framework for non-parametric Bayes inference in which the prior distribution for a possibly infinite dimensional parameter is decomposed into two parts: an informative prior on a finite set of functionals, and a non-parametric conditional prior for the parameter given the functionals. Such priors can be easily constructed from standard non-parametric prior distributions in common use and inherit the large support of the standard priors on which they are based. Additionally, posterior approximations under these informative priors can generally be made via minor adjustments to existing Markov chain approximation algorithms for standard non-parametric prior distributions. We illustrate the use of such priors in the context of multivariate density estimation using Dirichlet process mixture models, and in the modelling of high dimensional sparse contingency tables. PMID:25663813

A micro-machined source transducer for a parametric array in air.

PubMed

Lee, Haksue; Kang, Daesil; Moon, Wonkyu

2009-04-01

Parametric array applications in air, such as highly directional parametric loudspeaker systems, usually rely on large radiators to generate the high-intensity primary beams required for nonlinear interactions. However, a conventional transducer, as a primary wave projector, requires a great deal of electrical power because its electroacoustic efficiency is very low due to the large characteristic mechanical impedance in air. The feasibility of a micro-machined ultrasonic transducer as an efficient finite-amplitude wave projector was studied. A piezoelectric micro-machined ultrasonic transducer array consisting of lead zirconate titanate uni-morph elements was designed and fabricated for this purpose. Theoretical and experimental evaluations showed that a micro-machined ultrasonic transducer array can be used as an efficient source transducer for a parametric array in air. The beam patterns and propagation curves of the difference frequency wave and the primary wave generated by the micro-machined ultrasonic transducer array were measured. Although the theoretical results were based on ideal parametric array models, the theoretical data explained the experimental results reasonably well. These experiments demonstrated the potential of micro-machined primary wave projector.

Mixture models for undiagnosed prevalent disease and interval-censored incident disease: applications to a cohort assembled from electronic health records.

PubMed

Cheung, Li C; Pan, Qing; Hyun, Noorie; Schiffman, Mark; Fetterman, Barbara; Castle, Philip E; Lorey, Thomas; Katki, Hormuzd A

2017-09-30

For cost-effectiveness and efficiency, many large-scale general-purpose cohort studies are being assembled within large health-care providers who use electronic health records. Two key features of such data are that incident disease is interval-censored between irregular visits and there can be pre-existing (prevalent) disease. Because prevalent disease is not always immediately diagnosed, some disease diagnosed at later visits are actually undiagnosed prevalent disease. We consider prevalent disease as a point mass at time zero for clinical applications where there is no interest in time of prevalent disease onset. We demonstrate that the naive Kaplan-Meier cumulative risk estimator underestimates risks at early time points and overestimates later risks. We propose a general family of mixture models for undiagnosed prevalent disease and interval-censored incident disease that we call prevalence-incidence models. Parameters for parametric prevalence-incidence models, such as the logistic regression and Weibull survival (logistic-Weibull) model, are estimated by direct likelihood maximization or by EM algorithm. Non-parametric methods are proposed to calculate cumulative risks for cases without covariates. We compare naive Kaplan-Meier, logistic-Weibull, and non-parametric estimates of cumulative risk in the cervical cancer screening program at Kaiser Permanente Northern California. Kaplan-Meier provided poor estimates while the logistic-Weibull model was a close fit to the non-parametric. Our findings support our use of logistic-Weibull models to develop the risk estimates that underlie current US risk-based cervical cancer screening guidelines. Published 2017. This article has been contributed to by US Government employees and their work is in the public domain in the USA. Published 2017. This article has been contributed to by US Government employees and their work is in the public domain in the USA.

Evaluation of Stochastic Rainfall Models in Capturing Climate Variability for Future Drought and Flood Risk Assessment

NASA Astrophysics Data System (ADS)

Chowdhury, A. F. M. K.; Lockart, N.; Willgoose, G. R.; Kuczera, G. A.; Kiem, A.; Nadeeka, P. M.

2016-12-01

One of the key objectives of stochastic rainfall modelling is to capture the full variability of climate system for future drought and flood risk assessment. However, it is not clear how well these models can capture the future climate variability when they are calibrated to Global/Regional Climate Model data (GCM/RCM) as these datasets are usually available for very short future period/s (e.g. 20 years). This study has assessed the ability of two stochastic daily rainfall models to capture climate variability by calibrating them to a dynamically downscaled RCM dataset in an east Australian catchment for 1990-2010, 2020-2040, and 2060-2080 epochs. The two stochastic models are: (1) a hierarchical Markov Chain (MC) model, which we developed in a previous study and (2) a semi-parametric MC model developed by Mehrotra and Sharma (2007). Our hierarchical model uses stochastic parameters of MC and Gamma distribution, while the semi-parametric model uses a modified MC process with memory of past periods and kernel density estimation. This study has generated multiple realizations of rainfall series by using parameters of each model calibrated to the RCM dataset for each epoch. The generated rainfall series are used to generate synthetic streamflow by using a SimHyd hydrology model. Assessing the synthetic rainfall and streamflow series, this study has found that both stochastic models can incorporate a range of variability in rainfall as well as streamflow generation for both current and future periods. However, the hierarchical model tends to overestimate the multiyear variability of wet spell lengths (therefore, is less likely to simulate long periods of drought and flood), while the semi-parametric model tends to overestimate the mean annual rainfall depths and streamflow volumes (hence, simulated droughts are likely to be less severe). Sensitivity of these limitations of both stochastic models in terms of future drought and flood risk assessment will be discussed.

Robust biological parametric mapping: an improved technique for multimodal brain image analysis

NASA Astrophysics Data System (ADS)

Yang, Xue; Beason-Held, Lori; Resnick, Susan M.; Landman, Bennett A.

2011-03-01

Mapping the quantitative relationship between structure and function in the human brain is an important and challenging problem. Numerous volumetric, surface, region of interest and voxelwise image processing techniques have been developed to statistically assess potential correlations between imaging and non-imaging metrics. Recently, biological parametric mapping has extended the widely popular statistical parametric approach to enable application of the general linear model to multiple image modalities (both for regressors and regressands) along with scalar valued observations. This approach offers great promise for direct, voxelwise assessment of structural and functional relationships with multiple imaging modalities. However, as presented, the biological parametric mapping approach is not robust to outliers and may lead to invalid inferences (e.g., artifactual low p-values) due to slight mis-registration or variation in anatomy between subjects. To enable widespread application of this approach, we introduce robust regression and robust inference in the neuroimaging context of application of the general linear model. Through simulation and empirical studies, we demonstrate that our robust approach reduces sensitivity to outliers without substantial degradation in power. The robust approach and associated software package provides a reliable way to quantitatively assess voxelwise correlations between structural and functional neuroimaging modalities.

Design of a completely model free adaptive control in the presence of parametric, non-parametric uncertainties and random control signal delay.

PubMed

Tutsoy, Onder; Barkana, Duygun Erol; Tugal, Harun

2018-05-01

In this paper, an adaptive controller is developed for discrete time linear systems that takes into account parametric uncertainty, internal-external non-parametric random uncertainties, and time varying control signal delay. Additionally, the proposed adaptive control is designed in such a way that it is utterly model free. Even though these properties are studied separately in the literature, they are not taken into account all together in adaptive control literature. The Q-function is used to estimate long-term performance of the proposed adaptive controller. Control policy is generated based on the long-term predicted value, and this policy searches an optimal stabilizing control signal for uncertain and unstable systems. The derived control law does not require an initial stabilizing control assumption as in the ones in the recent literature. Learning error, control signal convergence, minimized Q-function, and instantaneous reward are analyzed to demonstrate the stability and effectiveness of the proposed adaptive controller in a simulation environment. Finally, key insights on parameters convergence of the learning and control signals are provided. Copyright © 2018 ISA. Published by Elsevier Ltd. All rights reserved.

Development of cubic Bezier curve and curve-plane intersection method for parametric submarine hull form design to optimize hull resistance using CFD

NASA Astrophysics Data System (ADS)

Chrismianto, Deddy; Zakki, Ahmad Fauzan; Arswendo, Berlian; Kim, Dong Joon

2015-12-01

Optimization analysis and computational fluid dynamics (CFDs) have been applied simultaneously, in which a parametric model plays an important role in finding the optimal solution. However, it is difficult to create a parametric model for a complex shape with irregular curves, such as a submarine hull form. In this study, the cubic Bezier curve and curve-plane intersection method are used to generate a solid model of a parametric submarine hull form taking three input parameters into account: nose radius, tail radius, and length-height hull ratio ( L/ H). Application program interface (API) scripting is also used to write code in the ANSYS design modeler. The results show that the submarine shape can be generated with some variation of the input parameters. An example is given that shows how the proposed method can be applied successfully to a hull resistance optimization case. The parametric design of the middle submarine type was chosen to be modified. First, the original submarine model was analyzed, in advance, using CFD. Then, using the response surface graph, some candidate optimal designs with a minimum hull resistance coefficient were obtained. Further, the optimization method in goal-driven optimization (GDO) was implemented to find the submarine hull form with the minimum hull resistance coefficient ( C t ). The minimum C t was obtained. The calculated difference in C t values between the initial submarine and the optimum submarine is around 0.26%, with the C t of the initial submarine and the optimum submarine being 0.001 508 26 and 0.001 504 29, respectively. The results show that the optimum submarine hull form shows a higher nose radius ( r n ) and higher L/ H than those of the initial submarine shape, while the radius of the tail ( r t ) is smaller than that of the initial shape.

Gallium arsenide (GaAs) solar cell modeling studies

NASA Technical Reports Server (NTRS)

Heinbockel, J. H.

1980-01-01

Various models were constructed which will allow for the variation of system components. Computer studies were then performed using the models constructed in order to study the effects of various system changes. In particular, GaAs and Si flat plate solar power arrays were studied and compared. Series and shunt resistance models were constructed. Models for the chemical kinetics of the annealing process were prepared. For all models constructed, various parametric studies were performed.

Comparison of Cox’s Regression Model and Parametric Models in Evaluating the Prognostic Factors for Survival after Liver Transplantation in Shiraz during 2000–2012

PubMed Central

Adelian, R.; Jamali, J.; Zare, N.; Ayatollahi, S. M. T.; Pooladfar, G. R.; Roustaei, N.

2015-01-01

Background: Identification of the prognostic factors for survival in patients with liver transplantation is challengeable. Various methods of survival analysis have provided different, sometimes contradictory, results from the same data. Objective: To compare Cox’s regression model with parametric models for determining the independent factors for predicting adults’ and pediatrics’ survival after liver transplantation. Method: This study was conducted on 183 pediatric patients and 346 adults underwent liver transplantation in Namazi Hospital, Shiraz, southern Iran. The study population included all patients undergoing liver transplantation from 2000 to 2012. The prognostic factors sex, age, Child class, initial diagnosis of the liver disease, PELD/MELD score, and pre-operative laboratory markers were selected for survival analysis. Result: Among 529 patients, 346 (64.5%) were adult and 183 (34.6%) were pediatric cases. Overall, the lognormal distribution was the best-fitting model for adult and pediatric patients. Age in adults (HR=1.16, p<0.05) and weight (HR=2.68, p<0.01) and Child class B (HR=2.12, p<0.05) in pediatric patients were the most important factors for prediction of survival after liver transplantation. Adult patients younger than the mean age and pediatric patients weighing above the mean and Child class A (compared to those with classes B or C) had better survival. Conclusion: Parametric regression model is a good alternative for the Cox’s regression model. PMID:26306158

Parametric Study of Sealant Nozzle

NASA Astrophysics Data System (ADS)

Yamamoto, Yoshimi

It has become apparent in recent years the advancement of manufacturing processes in the aerospace industry. Sealant nozzles are a critical device in the use of fuel tank applications for optimal bonds and for ground service support and repair. Sealants has always been a challenging area for optimizing and understanding the flow patterns. A parametric study was conducted to better understand geometric effects of sealant flow and to determine whether the sealant rheology can be numerically modeled. The Star-CCM+ software was used to successfully develop the parametric model, material model, physics continua, and simulate the fluid flow for the sealant nozzle. The simulation results of Semco sealant nozzles showed the geometric effects of fluid flow patterns and the influences from conical area reduction, tip length, inlet diameter, and tip angle parameters. A smaller outlet diameter induced maximum outlet velocity at the exit, and contributed to a high pressure drop. The conical area reduction, tip angle and inlet diameter contributed most to viscosity variation phenomenon. Developing and simulating 2 different flow models (Segregated Flow and Viscous Flow) proved that both can be used to obtain comparable velocity and pressure drop results, however; differences are seen visually in the non-uniformity of the velocity and viscosity fields for the Viscous Flow Model (VFM). A comprehensive simulation setup for sealant nozzles was developed so other analysts can utilize the data.

Variable selection in a flexible parametric mixture cure model with interval-censored data.

PubMed

Scolas, Sylvie; El Ghouch, Anouar; Legrand, Catherine; Oulhaj, Abderrahim

2016-03-30

In standard survival analysis, it is generally assumed that every individual will experience someday the event of interest. However, this is not always the case, as some individuals may not be susceptible to this event. Also, in medical studies, it is frequent that patients come to scheduled interviews and that the time to the event is only known to occur between two visits. That is, the data are interval-censored with a cure fraction. Variable selection in such a setting is of outstanding interest. Covariates impacting the survival are not necessarily the same as those impacting the probability to experience the event. The objective of this paper is to develop a parametric but flexible statistical model to analyze data that are interval-censored and include a fraction of cured individuals when the number of potential covariates may be large. We use the parametric mixture cure model with an accelerated failure time regression model for the survival, along with the extended generalized gamma for the error term. To overcome the issue of non-stable and non-continuous variable selection procedures, we extend the adaptive LASSO to our model. By means of simulation studies, we show good performance of our method and discuss the behavior of estimates with varying cure and censoring proportion. Lastly, our proposed method is illustrated with a real dataset studying the time until conversion to mild cognitive impairment, a possible precursor of Alzheimer's disease. © 2015 The Authors. Statistics in Medicine Published by John Wiley & Sons Ltd.

Hybrid Model of Inhomogeneous Solar Wind Plasma Heating by Alfven Wave Spectrum: Parametric Studies

NASA Technical Reports Server (NTRS)

Ofman, L.

2010-01-01

Observations of the solar wind plasma at 0.3 AU and beyond show that a turbulent spectrum of magnetic fluctuations is present. Remote sensing observations of the corona indicate that heavy ions are hotter than protons and their temperature is anisotropic (T(sub perpindicular / T(sub parallel) >> 1). We study the heating and the acceleration of multi-ion plasma in the solar wind by a turbulent spectrum of Alfvenic fluctuations using a 2-D hybrid numerical model. In the hybrid model the protons and heavy ions are treated kinetically as particles, while the electrons are included as neutralizing background fluid. This is the first two-dimensional hybrid parametric study of the solar wind plasma that includes an input turbulent wave spectrum guided by observation with inhomogeneous background density. We also investigate the effects of He++ ion beams in the inhomogeneous background plasma density on the heating of the solar wind plasma. The 2-D hybrid model treats parallel and oblique waves, together with cross-field inhomogeneity, self-consistently. We investigate the parametric dependence of the perpendicular heating, and the temperature anisotropy in the H+-He++ solar wind plasma. It was found that the scaling of the magnetic fluctuations power spectrum steepens in the higher-density regions, and the heating is channeled to these regions from the surrounding lower-density plasma due to wave refraction. The model parameters are applicable to the expected solar wind conditions at about 10 solar radii.

Experimental parametric study of servers cooling management in data centers buildings

NASA Astrophysics Data System (ADS)

Nada, S. A.; Elfeky, K. E.; Attia, Ali M. A.; Alshaer, W. G.

2017-06-01

A parametric study of air flow and cooling management of data centers servers is experimentally conducted for different design conditions. A physical scale model of data center accommodating one rack of four servers was designed and constructed for testing purposes. Front and rear rack and server's temperatures distributions and supply/return heat indices (SHI/RHI) are used to evaluate data center thermal performance. Experiments were conducted to parametrically study the effects of perforated tiles opening ratio, servers power load variation and rack power density. The results showed that (1) perforated tile of 25% opening ratio provides the best results among the other opening ratios, (2) optimum benefit of cold air in servers cooling is obtained at uniformly power loading of servers (3) increasing power density decrease air re-circulation but increase air bypass and servers temperature. The present results are compared with previous experimental and CFD results and fair agreement was found.

Parametric analysis of ATM solar array.

NASA Technical Reports Server (NTRS)

Singh, B. K.; Adkisson, W. B.

1973-01-01

The paper discusses the methods used for the calculation of ATM solar array performance characteristics and provides the parametric analysis of solar panels used in SKYLAB. To predict the solar array performance under conditions other than test conditions, a mathematical model has been developed. Four computer programs have been used to convert the solar simulator test data to the parametric curves. The first performs module summations, the second determines average solar cell characteristics which will cause a mathematical model to generate a curve matching the test data, the third is a polynomial fit program which determines the polynomial equations for the solar cell characteristics versus temperature, and the fourth program uses the polynomial coefficients generated by the polynomial curve fit program to generate the parametric data.

Sgr A* Emission Parametrizations from GRMHD Simulations

NASA Astrophysics Data System (ADS)

Anantua, Richard; Ressler, Sean; Quataert, Eliot

2018-06-01

Galactic Center emission near the vicinity of the central black hole, Sagittarius (Sgr) A*, is modeled using parametrizations involving the electron temperature, which is found from general relativistic magnetohydrodynamic (GRMHD) simulations to be highest in the disk-outflow corona. Jet-motivated prescriptions generalizing equipartition of particle and magnetic energies, e.g., by scaling relativistic electron energy density to powers of the magnetic field strength, are also introduced. GRMHD jet (or outflow)/accretion disk/black hole (JAB) simulation postprocessing codes IBOTHROS and GRMONTY are employed in the calculation of images and spectra. Various parametric models reproduce spectral and morphological features, such as the sub-mm spectral bump in electron temperature models and asymmetric photon rings in equipartition-based models. The Event Horizon Telescope (EHT) will provide unprecedentedly high-resolution 230+ GHz observations of the "shadow" around Sgr A*'s supermassive black hole, which the synthetic models presented here will reverse-engineer. Both electron temperature and equipartition-based models can be constructed to be compatible with EHT size constraints for the emitting region of Sgr A*. This program sets the groundwork for devising a unified emission parametrization flexible enough to model disk, corona and outflow/jet regions with a small set of parameters including electron heating fraction and plasma beta.

Comparison of four approaches to a rock facies classification problem

USGS Publications Warehouse

Dubois, M.K.; Bohling, Geoffrey C.; Chakrabarti, S.

2007-01-01

In this study, seven classifiers based on four different approaches were tested in a rock facies classification problem: classical parametric methods using Bayes' rule, and non-parametric methods using fuzzy logic, k-nearest neighbor, and feed forward-back propagating artificial neural network. Determining the most effective classifier for geologic facies prediction in wells without cores in the Panoma gas field, in Southwest Kansas, was the objective. Study data include 3600 samples with known rock facies class (from core) with each sample having either four or five measured properties (wire-line log curves), and two derived geologic properties (geologic constraining variables). The sample set was divided into two subsets, one for training and one for testing the ability of the trained classifier to correctly assign classes. Artificial neural networks clearly outperformed all other classifiers and are effective tools for this particular classification problem. Classical parametric models were inadequate due to the nature of the predictor variables (high dimensional and not linearly correlated), and feature space of the classes (overlapping). The other non-parametric methods tested, k-nearest neighbor and fuzzy logic, would need considerable improvement to match the neural network effectiveness, but further work, possibly combining certain aspects of the three non-parametric methods, may be justified. ?? 2006 Elsevier Ltd. All rights reserved.

Preclinical evaluation of parametric image reconstruction of [18F]FMISO PET: correlation with ex vivo immunohistochemistry

NASA Astrophysics Data System (ADS)

Cheng, Xiaoyin; Bayer, Christine; Maftei, Constantin-Alin; Astner, Sabrina T.; Vaupel, Peter; Ziegler, Sibylle I.; Shi, Kuangyu

2014-01-01

Compared to indirect methods, direct parametric image reconstruction (PIR) has the advantage of high quality and low statistical errors. However, it is not yet clear if this improvement in quality is beneficial for physiological quantification. This study aimed to evaluate direct PIR for the quantification of tumor hypoxia using the hypoxic fraction (HF) assessed from immunohistological data as a physiological reference. Sixteen mice with xenografted human squamous cell carcinomas were scanned with dynamic [18F]FMISO PET. Afterward, tumors were sliced and stained with H&E and the hypoxia marker pimonidazole. The hypoxic signal was segmented using k-means clustering and HF was specified as the ratio of the hypoxic area over the viable tumor area. The parametric Patlak slope images were obtained by indirect voxel-wise modeling on reconstructed images using filtered back projection and ordered-subset expectation maximization (OSEM) and by direct PIR (e.g., parametric-OSEM, POSEM). The mean and maximum Patlak slopes of the tumor area were investigated and compared with HF. POSEM resulted in generally higher correlations between slope and HF among the investigated methods. A strategy for the delineation of the hypoxic tumor volume based on thresholding parametric images at half maximum of the slope is recommended based on the results of this study.

A Parametric Computational Model of the Action Potential of Pacemaker Cells.

PubMed

Ai, Weiwei; Patel, Nitish D; Roop, Partha S; Malik, Avinash; Andalam, Sidharta; Yip, Eugene; Allen, Nathan; Trew, Mark L

2018-01-01

A flexible, efficient, and verifiable pacemaker cell model is essential to the design of real-time virtual hearts that can be used for closed-loop validation of cardiac devices. A new parametric model of pacemaker action potential is developed to address this need. The action potential phases are modeled using hybrid automaton with one piecewise-linear continuous variable. The model can capture rate-dependent dynamics, such as action potential duration restitution, conduction velocity restitution, and overdrive suppression by incorporating nonlinear update functions. Simulated dynamics of the model compared well with previous models and clinical data. The results show that the parametric model can reproduce the electrophysiological dynamics of a variety of pacemaker cells, such as sinoatrial node, atrioventricular node, and the His-Purkinje system, under varying cardiac conditions. This is an important contribution toward closed-loop validation of cardiac devices using real-time heart models.

Fitting C 2 Continuous Parametric Surfaces to Frontiers Delimiting Physiologic Structures

PubMed Central

Bayer, Jason D.

2014-01-01

We present a technique to fit C 2 continuous parametric surfaces to scattered geometric data points forming frontiers delimiting physiologic structures in segmented images. Such mathematical representation is interesting because it facilitates a large number of operations in modeling. While the fitting of C 2 continuous parametric curves to scattered geometric data points is quite trivial, the fitting of C 2 continuous parametric surfaces is not. The difficulty comes from the fact that each scattered data point should be assigned a unique parametric coordinate, and the fit is quite sensitive to their distribution on the parametric plane. We present a new approach where a polygonal (quadrilateral or triangular) surface is extracted from the segmented image. This surface is subsequently projected onto a parametric plane in a manner to ensure a one-to-one mapping. The resulting polygonal mesh is then regularized for area and edge length. Finally, from this point, surface fitting is relatively trivial. The novelty of our approach lies in the regularization of the polygonal mesh. Process performance is assessed with the reconstruction of a geometric model of mouse heart ventricles from a computerized tomography scan. Our results show an excellent reproduction of the geometric data with surfaces that are C 2 continuous. PMID:24782911

Current estimates of the cure fraction: a feasibility study of statistical cure for breast and colorectal cancer.

PubMed

Stedman, Margaret R; Feuer, Eric J; Mariotto, Angela B

2014-11-01

The probability of cure is a long-term prognostic measure of cancer survival. Estimates of the cure fraction, the proportion of patients "cured" of the disease, are based on extrapolating survival models beyond the range of data. The objective of this work is to evaluate the sensitivity of cure fraction estimates to model choice and study design. Data were obtained from the Surveillance, Epidemiology, and End Results (SEER)-9 registries to construct a cohort of breast and colorectal cancer patients diagnosed from 1975 to 1985. In a sensitivity analysis, cure fraction estimates are compared from different study designs with short- and long-term follow-up. Methods tested include: cause-specific and relative survival, parametric mixture, and flexible models. In a separate analysis, estimates are projected for 2008 diagnoses using study designs including the full cohort (1975-2008 diagnoses) and restricted to recent diagnoses (1998-2008) with follow-up to 2009. We show that flexible models often provide higher estimates of the cure fraction compared to parametric mixture models. Log normal models generate lower estimates than Weibull parametric models. In general, 12 years is enough follow-up time to estimate the cure fraction for regional and distant stage colorectal cancer but not for breast cancer. 2008 colorectal cure projections show a 15% increase in the cure fraction since 1985. Estimates of the cure fraction are model and study design dependent. It is best to compare results from multiple models and examine model fit to determine the reliability of the estimate. Early-stage cancers are sensitive to survival type and follow-up time because of their longer survival. More flexible models are susceptible to slight fluctuations in the shape of the survival curve which can influence the stability of the estimate; however, stability may be improved by lengthening follow-up and restricting the cohort to reduce heterogeneity in the data. Published by Oxford University Press 2014.

Conditional parametric models for storm sewer runoff

NASA Astrophysics Data System (ADS)

Jonsdottir, H.; Nielsen, H. Aa; Madsen, H.; Eliasson, J.; Palsson, O. P.; Nielsen, M. K.

2007-05-01

The method of conditional parametric modeling is introduced for flow prediction in a sewage system. It is a well-known fact that in hydrological modeling the response (runoff) to input (precipitation) varies depending on soil moisture and several other factors. Consequently, nonlinear input-output models are needed. The model formulation described in this paper is similar to the traditional linear models like final impulse response (FIR) and autoregressive exogenous (ARX) except that the parameters vary as a function of some external variables. The parameter variation is modeled by local lines, using kernels for local linear regression. As such, the method might be referred to as a nearest neighbor method. The results achieved in this study were compared to results from the conventional linear methods, FIR and ARX. The increase in the coefficient of determination is substantial. Furthermore, the new approach conserves the mass balance better. Hence this new approach looks promising for various hydrological models and analysis.

Estimating piecewise exponential frailty model with changing prior for baseline hazard function

NASA Astrophysics Data System (ADS)

Thamrin, Sri Astuti; Lawi, Armin

2016-02-01

Piecewise exponential models provide a very flexible framework for modelling univariate survival data. It can be used to estimate the effects of different covariates which are influenced by the survival data. Although in a strict sense it is a parametric model, a piecewise exponential hazard can approximate any shape of a parametric baseline hazard. In the parametric baseline hazard, the hazard function for each individual may depend on a set of risk factors or explanatory variables. However, it usually does not explain all such variables which are known or measurable, and these variables become interesting to be considered. This unknown and unobservable risk factor of the hazard function is often termed as the individual's heterogeneity or frailty. This paper analyses the effects of unobserved population heterogeneity in patients' survival times. The issue of model choice through variable selection is also considered. A sensitivity analysis is conducted to assess the influence of the prior for each parameter. We used the Markov Chain Monte Carlo method in computing the Bayesian estimator on kidney infection data. The results obtained show that the sex and frailty are substantially associated with survival in this study and the models are relatively quite sensitive to the choice of two different priors.

Toward uniform implementation of parametric map Digital Imaging and Communication in Medicine standard in multisite quantitative diffusion imaging studies.

PubMed

Malyarenko, Dariya; Fedorov, Andriy; Bell, Laura; Prah, Melissa; Hectors, Stefanie; Arlinghaus, Lori; Muzi, Mark; Solaiyappan, Meiyappan; Jacobs, Michael; Fung, Maggie; Shukla-Dave, Amita; McManus, Kevin; Boss, Michael; Taouli, Bachir; Yankeelov, Thomas E; Quarles, Christopher Chad; Schmainda, Kathleen; Chenevert, Thomas L; Newitt, David C

2018-01-01

This paper reports on results of a multisite collaborative project launched by the MRI subgroup of Quantitative Imaging Network to assess current capability and provide future guidelines for generating a standard parametric diffusion map Digital Imaging and Communication in Medicine (DICOM) in clinical trials that utilize quantitative diffusion-weighted imaging (DWI). Participating sites used a multivendor DWI DICOM dataset of a single phantom to generate parametric maps (PMs) of the apparent diffusion coefficient (ADC) based on two models. The results were evaluated for numerical consistency among models and true phantom ADC values, as well as for consistency of metadata with attributes required by the DICOM standards. This analysis identified missing metadata descriptive of the sources for detected numerical discrepancies among ADC models. Instead of the DICOM PM object, all sites stored ADC maps as DICOM MR objects, generally lacking designated attributes and coded terms for quantitative DWI modeling. Source-image reference, model parameters, ADC units and scale, deemed important for numerical consistency, were either missing or stored using nonstandard conventions. Guided by the identified limitations, the DICOM PM standard has been amended to include coded terms for the relevant diffusion models. Open-source software has been developed to support conversion of site-specific formats into the standard representation.

Multi-parametric variational data assimilation for hydrological forecasting

NASA Astrophysics Data System (ADS)

Alvarado-Montero, R.; Schwanenberg, D.; Krahe, P.; Helmke, P.; Klein, B.

2017-12-01

Ensemble forecasting is increasingly applied in flow forecasting systems to provide users with a better understanding of forecast uncertainty and consequently to take better-informed decisions. A common practice in probabilistic streamflow forecasting is to force deterministic hydrological model with an ensemble of numerical weather predictions. This approach aims at the representation of meteorological uncertainty but neglects uncertainty of the hydrological model as well as its initial conditions. Complementary approaches use probabilistic data assimilation techniques to receive a variety of initial states or represent model uncertainty by model pools instead of single deterministic models. This paper introduces a novel approach that extends a variational data assimilation based on Moving Horizon Estimation to enable the assimilation of observations into multi-parametric model pools. It results in a probabilistic estimate of initial model states that takes into account the parametric model uncertainty in the data assimilation. The assimilation technique is applied to the uppermost area of River Main in Germany. We use different parametric pools, each of them with five parameter sets, to assimilate streamflow data, as well as remotely sensed data from the H-SAF project. We assess the impact of the assimilation in the lead time performance of perfect forecasts (i.e. observed data as forcing variables) as well as deterministic and probabilistic forecasts from ECMWF. The multi-parametric assimilation shows an improvement of up to 23% for CRPS performance and approximately 20% in Brier Skill Scores with respect to the deterministic approach. It also improves the skill of the forecast in terms of rank histogram and produces a narrower ensemble spread.

Self-tuning bistable parametric feedback oscillator: Near-optimal amplitude maximization without model information

NASA Astrophysics Data System (ADS)

Braun, David J.; Sutas, Andrius; Vijayakumar, Sethu

2017-01-01

Theory predicts that parametrically excited oscillators, tuned to operate under resonant condition, are capable of large-amplitude oscillation useful in diverse applications, such as signal amplification, communication, and analog computation. However, due to amplitude saturation caused by nonlinearity, lack of robustness to model uncertainty, and limited sensitivity to parameter modulation, these oscillators require fine-tuning and strong modulation to generate robust large-amplitude oscillation. Here we present a principle of self-tuning parametric feedback excitation that alleviates the above-mentioned limitations. This is achieved using a minimalistic control implementation that performs (i) self-tuning (slow parameter adaptation) and (ii) feedback pumping (fast parameter modulation), without sophisticated signal processing past observations. The proposed approach provides near-optimal amplitude maximization without requiring model-based control computation, previously perceived inevitable to implement optimal control principles in practical application. Experimental implementation of the theory shows that the oscillator self-tunes itself near to the onset of dynamic bifurcation to achieve extreme sensitivity to small resonant parametric perturbations. As a result, it achieves large-amplitude oscillations by capitalizing on the effect of nonlinearity, despite substantial model uncertainties and strong unforeseen external perturbations. We envision the present finding to provide an effective and robust approach to parametric excitation when it comes to real-world application.

Influence of stochastic sea ice parametrization on climate and the role of atmosphere–sea ice–ocean interaction

PubMed Central

Juricke, Stephan; Jung, Thomas

2014-01-01

The influence of a stochastic sea ice strength parametrization on the mean climate is investigated in a coupled atmosphere–sea ice–ocean model. The results are compared with an uncoupled simulation with a prescribed atmosphere. It is found that the stochastic sea ice parametrization causes an effective weakening of the sea ice. In the uncoupled model this leads to an Arctic sea ice volume increase of about 10–20% after an accumulation period of approximately 20–30 years. In the coupled model, no such increase is found. Rather, the stochastic perturbations lead to a spatial redistribution of the Arctic sea ice thickness field. A mechanism involving a slightly negative atmospheric feedback is proposed that can explain the different responses in the coupled and uncoupled system. Changes in integrated Antarctic sea ice quantities caused by the stochastic parametrization are generally small, as memory is lost during the melting season because of an almost complete loss of sea ice. However, stochastic sea ice perturbations affect regional sea ice characteristics in the Southern Hemisphere, both in the uncoupled and coupled model. Remote impacts of the stochastic sea ice parametrization on the mean climate of non-polar regions were found to be small. PMID:24842027

Implicit Priors in Galaxy Cluster Mass and Scaling Relation Determinations

NASA Technical Reports Server (NTRS)

Mantz, A.; Allen, S. W.

2011-01-01

Deriving the total masses of galaxy clusters from observations of the intracluster medium (ICM) generally requires some prior information, in addition to the assumptions of hydrostatic equilibrium and spherical symmetry. Often, this information takes the form of particular parametrized functions used to describe the cluster gas density and temperature profiles. In this paper, we investigate the implicit priors on hydrostatic masses that result from this fully parametric approach, and the implications of such priors for scaling relations formed from those masses. We show that the application of such fully parametric models of the ICM naturally imposes a prior on the slopes of the derived scaling relations, favoring the self-similar model, and argue that this prior may be influential in practice. In contrast, this bias does not exist for techniques which adopt an explicit prior on the form of the mass profile but describe the ICM non-parametrically. Constraints on the slope of the cluster mass-temperature relation in the literature show a separation based the approach employed, with the results from fully parametric ICM modeling clustering nearer the self-similar value. Given that a primary goal of scaling relation analyses is to test the self-similar model, the application of methods subject to strong, implicit priors should be avoided. Alternative methods and best practices are discussed.

Bounded Parametric Model Checking for Elementary Net Systems

NASA Astrophysics Data System (ADS)

Knapik, Michał; Szreter, Maciej; Penczek, Wojciech

Bounded Model Checking (BMC) is an efficient verification method for reactive systems. BMC has been applied so far to verification of properties expressed in (timed) modal logics, but never to their parametric extensions. In this paper we show, for the first time that BMC can be extended to PRTECTL - a parametric extension of the existential version of CTL. To this aim we define a bounded semantics and a translation from PRTECTL to SAT. The implementation of the algorithm for Elementary Net Systems is presented, together with some experimental results.

Parametric nanomechanical amplification at very high frequency.

PubMed

Karabalin, R B; Feng, X L; Roukes, M L

2009-09-01

Parametric resonance and amplification are important in both fundamental physics and technological applications. Here we report very high frequency (VHF) parametric resonators and mechanical-domain amplifiers based on nanoelectromechanical systems (NEMS). Compound mechanical nanostructures patterned by multilayer, top-down nanofabrication are read out by a novel scheme that parametrically modulates longitudinal stress in doubly clamped beam NEMS resonators. Parametric pumping and signal amplification are demonstrated for VHF resonators up to approximately 130 MHz and provide useful enhancement of both resonance signal amplitude and quality factor. We find that Joule heating and reduced thermal conductance in these nanostructures ultimately impose an upper limit to device performance. We develop a theoretical model to account for both the parametric response and nonequilibrium thermal transport in these composite nanostructures. The results closely conform to our experimental observations, elucidate the frequency and threshold-voltage scaling in parametric VHF NEMS resonators and sensors, and establish the ultimate sensitivity limits of this approach.

Parametric Study and Optimization of a Piezoelectric Energy Harvester from Flow Induced Vibration

NASA Astrophysics Data System (ADS)

Ashok, P.; Jawahar Chandra, C.; Neeraj, P.; Santhosh, B.

2018-02-01

Self-powered systems have become the need of the hour and several devices and techniques were proposed in favour of this crisis. Among the various sources, vibrations, being the most practical scenario, is chosen in the present study to investigate for the possibility of harvesting energy. Various methods were devised to trap the energy generated by vibrating bodies, which would otherwise be wasted. One such concept is termed as flow-induced vibration which involves the flow of a fluid across a bluff body that oscillates due to a phenomenon known as vortex shedding. These oscillations can be converted into electrical energy by the use of piezoelectric patches. A two degree of freedom system containing a cylinder as the primary mass and a cantilever beam as the secondary mass attached with a piezoelectric circuit, was considered to model the problem. Three wake oscillator models were studied in order to determine the one which can generate results with high accuracy. It was found that Facchinetti model produced better results than the other two and hence a parametric study was performed to determine the favourable range of the controllable variables of the system. A fitness function was formulated and optimization of the selected parameters was done using genetic algorithm. The parametric optimization led to a considerable improvement in the harvested voltage from the system owing to the high displacement of secondary mass.

Developing integrated parametric planning models for budgeting and managing complex projects

NASA Technical Reports Server (NTRS)

Etnyre, Vance A.; Black, Ken U.

1988-01-01

The applicability of integrated parametric models for the budgeting and management of complex projects is investigated. Methods for building a very flexible, interactive prototype for a project planning system, and software resources available for this purpose, are discussed and evaluated. The prototype is required to be sensitive to changing objectives, changing target dates, changing costs relationships, and changing budget constraints. To achieve the integration of costs and project and task durations, parametric cost functions are defined by a process of trapezoidal segmentation, where the total cost for the project is the sum of the various project cost segments, and each project cost segment is the integral of a linearly segmented cost loading function over a specific interval. The cost can thus be expressed algebraically. The prototype was designed using Lotus-123 as the primary software tool. This prototype implements a methodology for interactive project scheduling that provides a model of a system that meets most of the goals for the first phase of the study and some of the goals for the second phase.

Revisiting dark energy models using differential ages of galaxies

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

Rani, Nisha; Mahajan, Shobhit; Mukherjee, Amitabha

In this work, we use a test based on the differential ages of galaxies for distinguishing the dark energy models. As proposed by Jimenez and Loeb in [1], relative ages of galaxies can be used to put constraints on various cosmological parameters. In the same vein, we reconstruct H {sub 0} {sub dt} / dz and its derivative ( H {sub 0} {sub d} {sup 2} {sup t} / dz {sup 2}) using a model independent technique called non-parametric smoothing . Basically, dt / dz is the change in the age of the object as a function of redshift whichmore » is directly linked with the Hubble parameter. Hence for reconstruction of this quantity, we use the most recent H ( z ) data. Further, we calculate H {sub 0} {sub dt} / dz and its derivative for several models like Phantom, Einstein de Sitter (EdS), ΛCDM, Chevallier-Polarski-Linder (CPL) parametrization, Jassal-Bagla-Padmanabhan (JBP) parametrization and Feng-Shen-Li-Li (FSLL) parametrization. We check the consistency of these models with the results of reconstruction obtained in a model independent way from the data. It is observed that H {sub 0} {sub dt} / dz as a tool is not able to distinguish between the ΛCDM, CPL, JBP and FSLL parametrizations but, as expected, EdS and Phantom models show noticeable deviation from the reconstructed results. Further, the derivative of H {sub 0} {sub dt} / dz for various dark energy models is more sensitive at low redshift. It is found that the FSLL model is not consistent with the reconstructed results, however, the ΛCDM model is in concordance with the 3σ region of the reconstruction at redshift z ≥ 0.3.« less

Gravity-Driven Thin Film Flow of an Ellis Fluid.

PubMed

Kheyfets, Vitaly O; Kieweg, Sarah L

2013-12-01

The thin film lubrication approximation has been studied extensively for moving contact lines of Newtonian fluids. However, many industrial and biological applications of the thin film equation involve shear-thinning fluids, which often also exhibit a Newtonian plateau at low shear. This study presents new numerical simulations of the three-dimensional (i.e. two-dimensional spreading), constant-volume, gravity-driven, free surface flow of an Ellis fluid. The numerical solution was validated with a new similarity solution, compared to previous experiments, and then used in a parametric study. The parametric study centered around rheological data for an example biological application of thin film flow: topical drug delivery of anti-HIV microbicide formulations, e.g. hydroxyethylcellulose (HEC) polymer solutions. The parametric study evaluated how spreading length and front velocity saturation depend on Ellis parameters. A lower concentration polymer solution with smaller zero shear viscosity ( η 0 ), τ 1/2 , and λ values spread further. However, when comparing any two fluids with any possible combinations of Ellis parameters, the impact of changing one parameter on spreading length depends on the direction and magnitude of changes in the other two parameters. In addition, the isolated effect of the shear-thinning parameter, λ , on the front velocity saturation depended on τ 1/2 . This study highlighted the relative effects of the individual Ellis parameters, and showed that the shear rates in this flow were in both the shear-thinning and plateau regions of rheological behavior, emphasizing the importance of characterizing the full range of shear-rates in rheological measurements. The validated numerical model and parametric study provides a useful tool for future steps to optimize flow of a fluid with rheological behavior well-described by the Ellis constitutive model, in a range of industrial and biological applications.

Parametric reduced models for the nonlinear Schrödinger equation

NASA Astrophysics Data System (ADS)

Harlim, John; Li, Xiantao

2015-05-01

Reduced models for the (defocusing) nonlinear Schrödinger equation are developed. In particular, we develop reduced models that only involve the low-frequency modes given noisy observations of these modes. The ansatz of the reduced parametric models are obtained by employing a rational approximation and a colored-noise approximation, respectively, on the memory terms and the random noise of a generalized Langevin equation that is derived from the standard Mori-Zwanzig formalism. The parameters in the resulting reduced models are inferred from noisy observations with a recently developed ensemble Kalman filter-based parametrization method. The forecasting skill across different temperature regimes are verified by comparing the moments up to order four, a two-time correlation function statistics, and marginal densities of the coarse-grained variables.

Parametric reduced models for the nonlinear Schrödinger equation.

PubMed

Harlim, John; Li, Xiantao

2015-05-01

Reduced models for the (defocusing) nonlinear Schrödinger equation are developed. In particular, we develop reduced models that only involve the low-frequency modes given noisy observations of these modes. The ansatz of the reduced parametric models are obtained by employing a rational approximation and a colored-noise approximation, respectively, on the memory terms and the random noise of a generalized Langevin equation that is derived from the standard Mori-Zwanzig formalism. The parameters in the resulting reduced models are inferred from noisy observations with a recently developed ensemble Kalman filter-based parametrization method. The forecasting skill across different temperature regimes are verified by comparing the moments up to order four, a two-time correlation function statistics, and marginal densities of the coarse-grained variables.

Update on Parametric Cost Models for Space Telescopes

NASA Technical Reports Server (NTRS)

Stahl. H. Philip; Henrichs, Todd; Luedtke, Alexander; West, Miranda

2011-01-01

Since the June 2010 Astronomy Conference, an independent review of our cost data base discovered some inaccuracies and inconsistencies which can modify our previously reported results. This paper will review changes to the data base, our confidence in those changes and their effect on various parametric cost models

The non-linear response of a muscle in transverse compression: assessment of geometry influence using a finite element model.

PubMed

Gras, Laure-Lise; Mitton, David; Crevier-Denoix, Nathalie; Laporte, Sébastien

2012-01-01

Most recent finite element models that represent muscles are generic or subject-specific models that use complex, constitutive laws. Identification of the parameters of such complex, constitutive laws could be an important limit for subject-specific approaches. The aim of this study was to assess the possibility of modelling muscle behaviour in compression with a parametric model and a simple, constitutive law. A quasi-static compression test was performed on the muscles of dogs. A parametric finite element model was designed using a linear, elastic, constitutive law. A multi-variate analysis was performed to assess the effects of geometry on muscle response. An inverse method was used to define Young's modulus. The non-linear response of the muscles was obtained using a subject-specific geometry and a linear elastic law. Thus, a simple muscle model can be used to have a bio-faithful, biomechanical response.

Enceladus' Plumes Reflectance. Particle-in-Cell Model Parametric Study

NASA Astrophysics Data System (ADS)

Kotlarz, J. P.; Zalewska, N. E.

2018-06-01

In our work we are using kinetic numerical model to describe plumes curtain forms as a result of kinetic and thermodynamic processes: a) in the ocean, b) inside Tiger Stripes icy forms and c) over moon's surface.

Democratizing science with the aid of parametric design and additive manufacturing: Design and fabrication of a versatile and low-cost optical instrument for scattering measurement.

PubMed

Nadal-Serrano, Jose M; Nadal-Serrano, Adolfo; Lopez-Vallejo, Marisa

2017-01-01

This paper focuses on the application of rapid prototyping techniques using additive manufacturing in combination with parametric design to create low-cost, yet accurate and reliable instruments. The methodology followed makes it possible to make instruments with a degree of customization until now available only to a narrow audience, helping democratize science. The proposal discusses a holistic design-for-manufacturing approach that comprises advanced modeling techniques, open-source design strategies, and an optimization algorithm using free parametric software for both professional and educational purposes. The design and fabrication of an instrument for scattering measurement is used as a case of study to present the previous concepts.

Democratizing science with the aid of parametric design and additive manufacturing: Design and fabrication of a versatile and low-cost optical instrument for scattering measurement

PubMed Central

Lopez-Vallejo, Marisa

2017-01-01

This paper focuses on the application of rapid prototyping techniques using additive manufacturing in combination with parametric design to create low-cost, yet accurate and reliable instruments. The methodology followed makes it possible to make instruments with a degree of customization until now available only to a narrow audience, helping democratize science. The proposal discusses a holistic design-for-manufacturing approach that comprises advanced modeling techniques, open-source design strategies, and an optimization algorithm using free parametric software for both professional and educational purposes. The design and fabrication of an instrument for scattering measurement is used as a case of study to present the previous concepts. PMID:29112987

A parametric shell analysis of the shuttle 51-L SRB AFT field joint

NASA Technical Reports Server (NTRS)

Davis, Randall C.; Bowman, Lynn M.; Hughes, Robert M., IV; Jackson, Brian J.

1990-01-01

Following the Shuttle 51-L accident, an investigation was conducted to determine the cause of the failure. Investigators at the Langley Research Center focused attention on the structural behavior of the field joints with O-ring seals in the steel solid rocket booster (SRB) cases. The shell-of-revolution computer program BOSOR4 was used to model the aft field joint of the solid rocket booster case. The shell model consisted of the SRB wall and joint geometry present during the Shuttle 51-L flight. A parametric study of the joint was performed on the geometry, including joint clearances, contact between the joint components, and on the loads, induced and applied. In addition combinations of geometry and loads were evaluated. The analytical results from the parametric study showed that contact between the joint components was a primary contributor to allowing hot gases to blow by the O-rings. Based upon understanding the original joint behavior, various proposed joint modifications are shown and analyzed in order to provide additional insight and information. Finally, experimental results from a hydro-static pressurization of a test rocket booster case to study joint motion are presented and verified analytically.

Antarctic boundary layer parametrization in a general circulation model: 1-D simulations facing summer observations at Dome C

NASA Astrophysics Data System (ADS)

Vignon, Etienne; Hourdin, Frédéric; Genthon, Christophe; Gallée, Hubert; Bazile, Eric; Lefebvre, Marie-Pierre; Madeleine, Jean-Baptiste; Van de Wiel, Bas J. H.

2017-07-01

The parametrization of the atmospheric boundary layer (ABL) is critical over the Antarctic Plateau for climate modelling since it affects the climatological temperature inversion and the negatively buoyant near-surface flow over the ice-sheet. This study challenges state-of-the-art parametrizations used in general circulation models to represent the clear-sky summertime diurnal cycle of the ABL at Dome C, Antarctic Plateau. The Laboratoire de Météorologie Dynamique-Zoom model is run in a 1-D configuration on the fourth Global Energy and Water Cycle Exchanges Project Atmospheric Boundary Layers Study case. Simulations are analyzed and compared to observations, giving insights into the sensitivity of one model that participates to the intercomparison exercise. Snow albedo and thermal inertia are calibrated leading to better surface temperatures. Using the so-called "thermal plume model" improves the momentum mixing in the diurnal ABL. In stable conditions, four turbulence schemes are tested. Best simulations are those in which the turbulence cuts off above 35 m in the middle of the night, highlighting the contribution of the longwave radiation in the ABL heat budget. However, the nocturnal surface layer is not stable enough to distinguish between surface fluxes computed with different stability functions. The absence of subsidence in the forcings and an underestimation of downward longwave radiation are identified to be likely responsible for a cold bias in the nocturnal ABL. Apart from model-specific improvements, the paper clarifies on which are the critical aspects to improve in general circulation models to correctly represent the summertime ABL over the Antarctic Plateau.

Determination of in vivo mechanical properties of long bones from their impedance response curves

NASA Technical Reports Server (NTRS)

Borders, S. G.

1981-01-01

A mathematical model consisting of a uniform, linear, visco-elastic, Euler-Bernoulli beam to represent the ulna or tibia of the vibrating forearm or leg system is developed. The skin and tissue compressed between the probe and bone is represented by a spring in series with the beam. The remaining skin and tissue surrounding the bone is represented by a visco-elastic foundation with mass. An extensive parametric study is carried out to determine the effect of each parameter of the mathematical model on its impedance response. A system identification algorithm is developed and programmed on a digital computer to determine the parametric values of the model which best simulate the data obtained from an impedance test.

General solution of a cosmological model induced from higher dimensions using a kinematical constraint

NASA Astrophysics Data System (ADS)

Akarsu, Özgür; Dereli, Tekin; Katırcı, Nihan; Sheftel, Mikhail B.

2015-05-01

In a recent study Akarsu and Dereli (Gen. Relativ. Gravit. 45:1211, 2013) discussed the dynamical reduction of a higher dimensional cosmological model which is augmented by a kinematical constraint characterized by a single real parameter, correlating and controlling the expansion of both the external (physical) and internal spaces. In that paper explicit solutions were found only for the case of three dimensional internal space (). Here we derive a general solution of the system using Lie group symmetry properties, in parametric form for arbitrary number of internal dimensions. We also investigate the dynamical reduction of the model as a function of cosmic time for various values of and generate parametric plots to discuss cosmologically relevant results.

Nonparametric autocovariance estimation from censored time series by Gaussian imputation.

PubMed

Park, Jung Wook; Genton, Marc G; Ghosh, Sujit K

2009-02-01

One of the most frequently used methods to model the autocovariance function of a second-order stationary time series is to use the parametric framework of autoregressive and moving average models developed by Box and Jenkins. However, such parametric models, though very flexible, may not always be adequate to model autocovariance functions with sharp changes. Furthermore, if the data do not follow the parametric model and are censored at a certain value, the estimation results may not be reliable. We develop a Gaussian imputation method to estimate an autocovariance structure via nonparametric estimation of the autocovariance function in order to address both censoring and incorrect model specification. We demonstrate the effectiveness of the technique in terms of bias and efficiency with simulations under various rates of censoring and underlying models. We describe its application to a time series of silicon concentrations in the Arctic.

Efficient model reduction of parametrized systems by matrix discrete empirical interpolation

NASA Astrophysics Data System (ADS)

Negri, Federico; Manzoni, Andrea; Amsallem, David

2015-12-01

In this work, we apply a Matrix version of the so-called Discrete Empirical Interpolation (MDEIM) for the efficient reduction of nonaffine parametrized systems arising from the discretization of linear partial differential equations. Dealing with affinely parametrized operators is crucial in order to enhance the online solution of reduced-order models (ROMs). However, in many cases such an affine decomposition is not readily available, and must be recovered through (often) intrusive procedures, such as the empirical interpolation method (EIM) and its discrete variant DEIM. In this paper we show that MDEIM represents a very efficient approach to deal with complex physical and geometrical parametrizations in a non-intrusive, efficient and purely algebraic way. We propose different strategies to combine MDEIM with a state approximation resulting either from a reduced basis greedy approach or Proper Orthogonal Decomposition. A posteriori error estimates accounting for the MDEIM error are also developed in the case of parametrized elliptic and parabolic equations. Finally, the capability of MDEIM to generate accurate and efficient ROMs is demonstrated on the solution of two computationally-intensive classes of problems occurring in engineering contexts, namely PDE-constrained shape optimization and parametrized coupled problems.

Estimating survival of radio-tagged birds

USGS Publications Warehouse

Bunck, C.M.; Pollock, K.H.; Lebreton, J.-D.; North, P.M.

1993-01-01

Parametric and nonparametric methods for estimating survival of radio-tagged birds are described. The general assumptions of these methods are reviewed. An estimate based on the assumption of constant survival throughout the period is emphasized in the overview of parametric methods. Two nonparametric methods, the Kaplan-Meier estimate of the survival funcrion and the log rank test, are explained in detail The link between these nonparametric methods and traditional capture-recapture models is discussed aloag with considerations in designing studies that use telemetry techniques to estimate survival.

Design for disassembly and sustainability assessment to support aircraft end-of-life treatment

NASA Astrophysics Data System (ADS)

Savaria, Christian

Gas turbine engine design is a multidisciplinary and iterative process. Many design iterations are necessary to address the challenges among the disciplines. In the creation of a new engine architecture, the design time is crucial in capturing new business opportunities. At the detail design phase, it was proven very difficult to correct an unsatisfactory design. To overcome this difficulty, the concept of Multi-Disciplinary Optimization (MDO) at the preliminary design phase (Preliminary MDO or PMDO) is used allowing more freedom to perform changes in the design. PMDO also reduces the design time at the preliminary design phase. The concept of PMDO was used was used to create parametric models, and new correlations for high pressure gas turbine housing and shroud segments towards a new design process. First, dedicated parametric models were created because of their reusability and versatility. Their ease of use compared to non-parameterized models allows more design iterations thus reduces set up and design time. Second, geometry correlations were created to minimize the number of parameters used in turbine housing and shroud segment design. Since the turbine housing and the shroud segment geometries are required in tip clearance analyses, care was taken as to not oversimplify the parametric formulation. In addition, a user interface was developed to interact with the parametric models and improve the design time. Third, the cooling flow predictions require many engine parameters (i.e. geometric and performance parameters and air properties) and a reference shroud segments. A second correlation study was conducted to minimize the number of engine parameters required in the cooling flow predictions and to facilitate the selection of a reference shroud segment. Finally, the parametric models, the geometry correlations, and the user interface resulted in a time saving of 50% and an increase in accuracy of 56% in the new design system compared to the existing design system. Also, regarding the cooling flow correlations, the number of engine parameters was reduced by a factor of 6 to create a simplified prediction model and hence a faster shroud segment selection process. None

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.

Bayesian non-parametric inference for stochastic epidemic models using Gaussian Processes.

PubMed

Xu, Xiaoguang; Kypraios, Theodore; O'Neill, Philip D

2016-10-01

This paper considers novel Bayesian non-parametric methods for stochastic epidemic models. Many standard modeling and data analysis methods use underlying assumptions (e.g. concerning the rate at which new cases of disease will occur) which are rarely challenged or tested in practice. To relax these assumptions, we develop a Bayesian non-parametric approach using Gaussian Processes, specifically to estimate the infection process. The methods are illustrated with both simulated and real data sets, the former illustrating that the methods can recover the true infection process quite well in practice, and the latter illustrating that the methods can be successfully applied in different settings. © The Author 2016. Published by Oxford University Press.

Non-Parametric Collision Probability for Low-Velocity Encounters

NASA Technical Reports Server (NTRS)

Carpenter, J. Russell

2007-01-01

An implicit, but not necessarily obvious, assumption in all of the current techniques for assessing satellite collision probability is that the relative position uncertainty is perfectly correlated in time. If there is any mis-modeling of the dynamics in the propagation of the relative position error covariance matrix, time-wise de-correlation of the uncertainty will increase the probability of collision over a given time interval. The paper gives some examples that illustrate this point. This paper argues that, for the present, Monte Carlo analysis is the best available tool for handling low-velocity encounters, and suggests some techniques for addressing the issues just described. One proposal is for the use of a non-parametric technique that is widely used in actuarial and medical studies. The other suggestion is that accurate process noise models be used in the Monte Carlo trials to which the non-parametric estimate is applied. A further contribution of this paper is a description of how the time-wise decorrelation of uncertainty increases the probability of collision.

Theoretical studies of the solar atmosphere and interstellar pickup ions

NASA Technical Reports Server (NTRS)

1994-01-01

Solar atmosphere research activities are summarized. Specific topics addressed include: (1) coronal mass ejections and related phenomena; (2) parametric instabilities of Alfven waves; (3) pickup ions in the solar wind; and (4) cosmic rays in the outer heliosphere. Also included is a list of publications covering the following topics: catastrophic evolution of a force-free flux rope; maximum energy release in flux-rope models of eruptive flares; sheet approximations in models of eruptive flares; material ejection, motions of loops and ribbons of two-ribbon flares; dispersion relations for parametric instabilities of parallel-propagating; parametric instabilities of parallel-propagating Alfven waves; beat, modulation, and decay instabilities of a circularly-polarized Alfven wave; effects of time-dependent photoionization on interstellar pickup helium; observation of waves generated by the solar wind pickup of interstellar hydrogen ions; ion thermalization and wave excitation downstream of the quasi-perpendicular bowshock; ion cyclotron instability and the inverse correlation between proton anisotrophy and proton beta; and effects of cosmic rays and interstellar gas on the dynamics of a wind.

Linkage analysis of high myopia susceptibility locus in 26 families.

PubMed

Paget, Sandrine; Julia, Sophie; Vitezica, Zulma G; Soler, Vincent; Malecaze, François; Calvas, Patrick

2008-01-01

We conducted a linkage analysis in high myopia families to replicate suggestive results from chromosome 7q36 using a model of autosomal dominant inheritance and genetic heterogeneity. We also performed a genome-wide scan to identify novel loci. Twenty-six families, with at least two high-myopic subjects (ie. refractive value in the less affected eye of -5 diopters) in each family, were included. Phenotypic examination included standard autorefractometry, ultrasonographic eye length measurement, and clinical confirmation of the non-syndromic character of the refractive disorder. Nine families were collected de novo including 136 available members of whom 34 were highly myopic subjects. Twenty new subjects were added in 5 of the 17 remaining families. A total of 233 subjects were submitted to a genome scan using ABI linkage mapping set LMSv2-MD-10, additional markers in all regions where preliminary LOD scores were greater than 1.5 were used. Multipoint parametric and non-parametric analyses were conducted with the software packages Genehunter 2.0 and Merlin 1.0.1. Two autosomal recessive, two autosomal dominant, and four autosomal additive models were used in the parametric linkage analyses. No linkage was found using the subset of nine newly collected families. Study of the entire population of 26 families with a parametric model did not yield a significant LOD score (>3), even for the previously suggestive locus on 7q36. A non-parametric model demonstrated significant linkage to chromosome 7p15 in the entire population (Z-NPL=4.07, p=0.00002). The interval is 7.81 centiMorgans (cM) between markers D7S2458 and D7S2515. The significant interval reported here needs confirmation in other cohorts. Among possible susceptibility genes in the interval, certain candidates are likely to be involved in eye growth and development.

A robust multi-kernel change detection framework for detecting leaf beetle defoliation using Landsat 7 ETM+ data

NASA Astrophysics Data System (ADS)

Anees, Asim; Aryal, Jagannath; O'Reilly, Małgorzata M.; Gale, Timothy J.; Wardlaw, Tim

2016-12-01

A robust non-parametric framework, based on multiple Radial Basic Function (RBF) kernels, is proposed in this study, for detecting land/forest cover changes using Landsat 7 ETM+ images. One of the widely used frameworks is to find change vectors (difference image) and use a supervised classifier to differentiate between change and no-change. The Bayesian Classifiers e.g. Maximum Likelihood Classifier (MLC), Naive Bayes (NB), are widely used probabilistic classifiers which assume parametric models, e.g. Gaussian function, for the class conditional distributions. However, their performance can be limited if the data set deviates from the assumed model. The proposed framework exploits the useful properties of Least Squares Probabilistic Classifier (LSPC) formulation i.e. non-parametric and probabilistic nature, to model class posterior probabilities of the difference image using a linear combination of a large number of Gaussian kernels. To this end, a simple technique, based on 10-fold cross-validation is also proposed for tuning model parameters automatically instead of selecting a (possibly) suboptimal combination from pre-specified lists of values. The proposed framework has been tested and compared with Support Vector Machine (SVM) and NB for detection of defoliation, caused by leaf beetles (Paropsisterna spp.) in Eucalyptus nitens and Eucalyptus globulus plantations of two test areas, in Tasmania, Australia, using raw bands and band combination indices of Landsat 7 ETM+. It was observed that due to multi-kernel non-parametric formulation and probabilistic nature, the LSPC outperforms parametric NB with Gaussian assumption in change detection framework, with Overall Accuracy (OA) ranging from 93.6% (κ = 0.87) to 97.4% (κ = 0.94) against 85.3% (κ = 0.69) to 93.4% (κ = 0.85), and is more robust to changing data distributions. Its performance was comparable to SVM, with added advantages of being probabilistic and capable of handling multi-class problems naturally with its original formulation.

Rank-preserving regression: a more robust rank regression model against outliers.

PubMed

Chen, Tian; Kowalski, Jeanne; Chen, Rui; Wu, Pan; Zhang, Hui; Feng, Changyong; Tu, Xin M

2016-08-30

Mean-based semi-parametric regression models such as the popular generalized estimating equations are widely used to improve robustness of inference over parametric models. Unfortunately, such models are quite sensitive to outlying observations. The Wilcoxon-score-based rank regression (RR) provides more robust estimates over generalized estimating equations against outliers. However, the RR and its extensions do not sufficiently address missing data arising in longitudinal studies. In this paper, we propose a new approach to address outliers under a different framework based on the functional response models. This functional-response-model-based alternative not only addresses limitations of the RR and its extensions for longitudinal data, but, with its rank-preserving property, even provides more robust estimates than these alternatives. The proposed approach is illustrated with both real and simulated data. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

Multi-Innovation Gradient Iterative Locally Weighted Learning Identification for A Nonlinear Ship Maneuvering System

NASA Astrophysics Data System (ADS)

Bai, Wei-wei; Ren, Jun-sheng; Li, Tie-shan

2018-06-01

This paper explores a highly accurate identification modeling approach for the ship maneuvering motion with fullscale trial. A multi-innovation gradient iterative (MIGI) approach is proposed to optimize the distance metric of locally weighted learning (LWL), and a novel non-parametric modeling technique is developed for a nonlinear ship maneuvering system. This proposed method's advantages are as follows: first, it can avoid the unmodeled dynamics and multicollinearity inherent to the conventional parametric model; second, it eliminates the over-learning or underlearning and obtains the optimal distance metric; and third, the MIGI is not sensitive to the initial parameter value and requires less time during the training phase. These advantages result in a highly accurate mathematical modeling technique that can be conveniently implemented in applications. To verify the characteristics of this mathematical model, two examples are used as the model platforms to study the ship maneuvering.

Development of Parametric Mass and Volume Models for an Aerospace SOFC/Gas Turbine Hybrid System

NASA Technical Reports Server (NTRS)

Tornabene, Robert; Wang, Xiao-yen; Steffen, Christopher J., Jr.; Freeh, Joshua E.

2005-01-01

In aerospace power systems, mass and volume are key considerations to produce a viable design. The utilization of fuel cells is being studied for a commercial aircraft electrical power unit. Based on preliminary analyses, a SOFC/gas turbine system may be a potential solution. This paper describes the parametric mass and volume models that are used to assess an aerospace hybrid system design. The design tool utilizes input from the thermodynamic system model and produces component sizing, performance, and mass estimates. The software is designed such that the thermodynamic model is linked to the mass and volume model to provide immediate feedback during the design process. It allows for automating an optimization process that accounts for mass and volume in its figure of merit. Each component in the system is modeled with a combination of theoretical and empirical approaches. A description of the assumptions and design analyses is presented.

Modeling the World Health Organization Disability Assessment Schedule II using non-parametric item response models.

PubMed

Galindo-Garre, Francisca; Hidalgo, María Dolores; Guilera, Georgina; Pino, Oscar; Rojo, J Emilio; Gómez-Benito, Juana

2015-03-01

The World Health Organization Disability Assessment Schedule II (WHO-DAS II) is a multidimensional instrument developed for measuring disability. It comprises six domains (getting around, self-care, getting along with others, life activities and participation in society). The main purpose of this paper is the evaluation of the psychometric properties for each domain of the WHO-DAS II with parametric and non-parametric Item Response Theory (IRT) models. A secondary objective is to assess whether the WHO-DAS II items within each domain form a hierarchy of invariantly ordered severity indicators of disability. A sample of 352 patients with a schizophrenia spectrum disorder is used in this study. The 36 items WHO-DAS II was administered during the consultation. Partial Credit and Mokken scale models are used to study the psychometric properties of the questionnaire. The psychometric properties of the WHO-DAS II scale are satisfactory for all the domains. However, we identify a few items that do not discriminate satisfactorily between different levels of disability and cannot be invariantly ordered in the scale. In conclusion the WHO-DAS II can be used to assess overall disability in patients with schizophrenia, but some domains are too general to assess functionality in these patients because they contain items that are not applicable to this pathology. Copyright © 2014 John Wiley & Sons, Ltd.

Multiple Imputation of a Randomly Censored Covariate Improves Logistic Regression Analysis.

PubMed

Atem, Folefac D; Qian, Jing; Maye, Jacqueline E; Johnson, Keith A; Betensky, Rebecca A

2016-01-01

Randomly censored covariates arise frequently in epidemiologic studies. The most commonly used methods, including complete case and single imputation or substitution, suffer from inefficiency and bias. They make strong parametric assumptions or they consider limit of detection censoring only. We employ multiple imputation, in conjunction with semi-parametric modeling of the censored covariate, to overcome these shortcomings and to facilitate robust estimation. We develop a multiple imputation approach for randomly censored covariates within the framework of a logistic regression model. We use the non-parametric estimate of the covariate distribution or the semiparametric Cox model estimate in the presence of additional covariates in the model. We evaluate this procedure in simulations, and compare its operating characteristics to those from the complete case analysis and a survival regression approach. We apply the procedures to an Alzheimer's study of the association between amyloid positivity and maternal age of onset of dementia. Multiple imputation achieves lower standard errors and higher power than the complete case approach under heavy and moderate censoring and is comparable under light censoring. The survival regression approach achieves the highest power among all procedures, but does not produce interpretable estimates of association. Multiple imputation offers a favorable alternative to complete case analysis and ad hoc substitution methods in the presence of randomly censored covariates within the framework of logistic regression.

Mathematical algorithm development and parametric studies with the GEOFRAC three-dimensional stochastic model of natural rock fracture systems

NASA Astrophysics Data System (ADS)

Ivanova, Violeta M.; Sousa, Rita; Murrihy, Brian; Einstein, Herbert H.

2014-06-01

This paper presents results from research conducted at MIT during 2010-2012 on modeling of natural rock fracture systems with the GEOFRAC three-dimensional stochastic model. Following a background summary of discrete fracture network models and a brief introduction of GEOFRAC, the paper provides a thorough description of the newly developed mathematical and computer algorithms for fracture intensity, aperture, and intersection representation, which have been implemented in MATLAB. The new methods optimize, in particular, the representation of fracture intensity in terms of cumulative fracture area per unit volume, P32, via the Poisson-Voronoi Tessellation of planes into polygonal fracture shapes. In addition, fracture apertures now can be represented probabilistically or deterministically whereas the newly implemented intersection algorithms allow for computing discrete pathways of interconnected fractures. In conclusion, results from a statistical parametric study, which was conducted with the enhanced GEOFRAC model and the new MATLAB-based Monte Carlo simulation program FRACSIM, demonstrate how fracture intensity, size, and orientations influence fracture connectivity.

Solid state SPS microwave generation and transmission study. Volume 1: Phase 2

NASA Technical Reports Server (NTRS)

Maynard, O. E.

1980-01-01

The solid state sandwich concept for Solar Power Station (SPS) was investigated. The design effort concentrated on the spacetenna, but did include some system analysis for parametric comparison reasons. The study specifically included definition and math modeling of basic solid state microwave devices, an initial conceptual subsystems and system design, sidelobe control and system selection, an assessment of selected system concept and parametric solid state microwave power transmission system data relevant to the SPS concept. Although device efficiency was not a goal, the sensitivities to design of this efficiency were parametrically treated. Sidelobe control consisted of various single step tapers, multistep tapers, and Gaussian tapers. A preliminary assessment of a hybrid concept using tubes and solid state is also included. There is a considerable amount of thermal analysis provided with emphasis on sensitivities to waste heat radiator form factor, emissivity, absorptivity, amplifier efficiency, material and junction temperature.

Parametrization of Drag and Turbulence for Urban Neighbourhoods with Trees

NASA Astrophysics Data System (ADS)

Krayenhoff, E. S.; Santiago, J.-L.; Martilli, A.; Christen, A.; Oke, T. R.

2015-08-01

Urban canopy parametrizations designed to be coupled with mesoscale models must predict the integrated effect of urban obstacles on the flow at each height in the canopy. To assess these neighbourhood-scale effects, results of microscale simulations may be horizontally-averaged. Obstacle-resolving computational fluid dynamics (CFD) simulations of neutrally-stratified flow through canopies of blocks (buildings) with varying distributions and densities of porous media (tree foliage) are conducted, and the spatially-averaged impacts on the flow of these building-tree combinations are assessed. The accuracy with which a one-dimensional (column) model with a one-equation (-) turbulence scheme represents spatially-averaged CFD results is evaluated. Individual physical mechanisms by which trees and buildings affect flow in the column model are evaluated in terms of relative importance. For the treed urban configurations considered, effects of buildings and trees may be considered independently. Building drag coefficients and length scale effects need not be altered due to the presence of tree foliage; therefore, parametrization of spatially-averaged flow through urban neighbourhoods with trees is greatly simplified. The new parametrization includes only source and sink terms significant for the prediction of spatially-averaged flow profiles: momentum drag due to buildings and trees (and the associated wake production of turbulent kinetic energy), modification of length scales by buildings, and enhanced dissipation of turbulent kinetic energy due to the small scale of tree foliage elements. Coefficients for the Santiago and Martilli (Boundary-Layer Meteorol 137: 417-439, 2010) parametrization of building drag coefficients and length scales are revised. Inclusion of foliage terms from the new parametrization in addition to the Santiago and Martilli building terms reduces root-mean-square difference (RMSD) of the column model streamwise velocity component and turbulent kinetic energy relative to the CFD model by 89 % in the canopy and 71 % above the canopy on average for the highest leaf area density scenarios tested: . RMSD values with the new parametrization are less than 20 % of mean layer magnitude for the streamwise velocity component within and above the canopy, and for above-canopy turbulent kinetic energy; RMSD values for within-canopy turbulent kinetic energy are negligible for most scenarios. The foliage-related portion of the new parametrization is required for scenarios with tree foliage of equal or greater height than the buildings, and for scenarios with foliage below roof height for building plan area densities less than approximately 0.25.

Parametric Studies of Square Solar Sails Using Finite Element Analysis

NASA Technical Reports Server (NTRS)

Sleight, David W.; Muheim, Danniella M.

2004-01-01

Parametric studies are performed on two generic square solar sail designs to identify parameters of interest. The studies are performed on systems-level models of full-scale solar sails, and include geometric nonlinearity and inertia relief, and use a Newton-Raphson scheme to apply sail pre-tensioning and solar pressure. Computational strategies and difficulties encountered during the analyses are also addressed. The purpose of this paper is not to compare the benefits of one sail design over the other. Instead, the results of the parametric studies may be used to identify general response trends, and areas of potential nonlinear structural interactions for future studies. The effects of sail size, sail membrane pre-stress, sail membrane thickness, and boom stiffness on the sail membrane and boom deformations, boom loads, and vibration frequencies are studied. Over the range of parameters studied, the maximum sail deflection and boom deformations are a nonlinear function of the sail properties. In general, the vibration frequencies and modes are closely spaced. For some vibration mode shapes, local deformation patterns that dominate the response are identified. These localized patterns are attributed to the presence of negative stresses in the sail membrane that are artifacts of the assumption of ignoring the effects of wrinkling in the modeling process, and are not believed to be physically meaningful. Over the range of parameters studied, several regions of potential nonlinear modal interaction are identified.

Parametrically excited oscillation of stay cable and its control in cable-stayed bridges.

PubMed

Sun, Bing-nan; Wang, Zhi-gang; Ko, J M; Ni, Y Q

2003-01-01

This paper presents a nonlinear dynamic model for simulation and analysis of a kind of parametrically excited vibration of stay cable caused by support motion in cable-stayed bridges. The sag, inclination angle of the stay cable are considered in the model, based on which, the oscillation mechanism and dynamic response characteristics of this kind of vibration are analyzed through numerical calculation. It is noted that parametrically excited oscillation of a stay cable with certain sag, inclination angle and initial static tension force may occur in cable-stayed bridges due to deck vibration under the condition that the natural frequency of a cable approaches to about half of the first model frequency of the bridge deck system. A new vibration control system installed on the cable anchorage is proposed as a possible damping system to suppress the cable parametric oscillation. The numerical calculation results showed that with the use of this damping system, the cable oscillation due to the vibration of the deck and/or towers will be considerably reduced.

Definition of NASTRAN sets by use of parametric geometry

NASA Technical Reports Server (NTRS)

Baughn, Terry V.; Tiv, Mehran

1989-01-01

Many finite element preprocessors describe finite element model geometry with points, lines, surfaces and volumes. One method for describing these basic geometric entities is by use of parametric cubics which are useful for representing complex shapes. The lines, surfaces and volumes may be discretized for follow on finite element analysis. The ability to limit or selectively recover results from the finite element model is extremely important to the analyst. Equally important is the ability to easily apply boundary conditions. Although graphical preprocessors have made these tasks easier, model complexity may not lend itself to easily identify a group of grid points desired for data recovery or application of constraints. A methodology is presented which makes use of the assignment of grid point locations in parametric coordinates. The parametric coordinates provide a convenient ordering of the grid point locations and a method for retrieving the grid point ID's from the parent geometry. The selected grid points may then be used for the generation of the appropriate set and constraint cards.

Modelling present-day basal melt rates for Antarctic ice shelves using a parametrization of buoyant meltwater plumes

NASA Astrophysics Data System (ADS)

Lazeroms, Werner M. J.; Jenkins, Adrian; Hilmar Gudmundsson, G.; van de Wal, Roderik S. W.

2018-01-01

Basal melting below ice shelves is a major factor in mass loss from the Antarctic Ice Sheet, which can contribute significantly to possible future sea-level rise. Therefore, it is important to have an adequate description of the basal melt rates for use in ice-dynamical models. Most current ice models use rather simple parametrizations based on the local balance of heat between ice and ocean. In this work, however, we use a recently derived parametrization of the melt rates based on a buoyant meltwater plume travelling upward beneath an ice shelf. This plume parametrization combines a non-linear ocean temperature sensitivity with an inherent geometry dependence, which is mainly described by the grounding-line depth and the local slope of the ice-shelf base. For the first time, this type of parametrization is evaluated on a two-dimensional grid covering the entire Antarctic continent. In order to apply the essentially one-dimensional parametrization to realistic ice-shelf geometries, we present an algorithm that determines effective values for the grounding-line depth and basal slope in any point beneath an ice shelf. Furthermore, since detailed knowledge of temperatures and circulation patterns in the ice-shelf cavities is sparse or absent, we construct an effective ocean temperature field from observational data with the purpose of matching (area-averaged) melt rates from the model with observed present-day melt rates. Our results qualitatively replicate large-scale observed features in basal melt rates around Antarctica, not only in terms of average values, but also in terms of the spatial pattern, with high melt rates typically occurring near the grounding line. The plume parametrization and the effective temperature field presented here are therefore promising tools for future simulations of the Antarctic Ice Sheet requiring a more realistic oceanic forcing.

An Efficient Non-iterative Bulk Parametrization of Surface Fluxes for Stable Atmospheric Conditions Over Polar Sea-Ice

NASA Astrophysics Data System (ADS)

Gryanik, Vladimir M.; Lüpkes, Christof

2018-02-01

In climate and weather prediction models the near-surface turbulent fluxes of heat and momentum and related transfer coefficients are usually parametrized on the basis of Monin-Obukhov similarity theory (MOST). To avoid iteration, required for the numerical solution of the MOST equations, many models apply parametrizations of the transfer coefficients based on an approach relating these coefficients to the bulk Richardson number Rib. However, the parametrizations that are presently used in most climate models are valid only for weaker stability and larger surface roughnesses than those documented during the Surface Heat Budget of the Arctic Ocean campaign (SHEBA). The latter delivered a well-accepted set of turbulence data in the stable surface layer over polar sea-ice. Using stability functions based on the SHEBA data, we solve the MOST equations applying a new semi-analytic approach that results in transfer coefficients as a function of Rib and roughness lengths for momentum and heat. It is shown that the new coefficients reproduce the coefficients obtained by the numerical iterative method with a good accuracy in the most relevant range of stability and roughness lengths. For small Rib, the new bulk transfer coefficients are similar to the traditional coefficients, but for large Rib they are much smaller than currently used coefficients. Finally, a possible adjustment of the latter and the implementation of the new proposed parametrizations in models are discussed.

ACCELERATING MR PARAMETER MAPPING USING SPARSITY-PROMOTING REGULARIZATION IN PARAMETRIC DIMENSION

PubMed Central

Velikina, Julia V.; Alexander, Andrew L.; Samsonov, Alexey

2013-01-01

MR parameter mapping requires sampling along additional (parametric) dimension, which often limits its clinical appeal due to a several-fold increase in scan times compared to conventional anatomic imaging. Data undersampling combined with parallel imaging is an attractive way to reduce scan time in such applications. However, inherent SNR penalties of parallel MRI due to noise amplification often limit its utility even at moderate acceleration factors, requiring regularization by prior knowledge. In this work, we propose a novel regularization strategy, which utilizes smoothness of signal evolution in the parametric dimension within compressed sensing framework (p-CS) to provide accurate and precise estimation of parametric maps from undersampled data. The performance of the method was demonstrated with variable flip angle T1 mapping and compared favorably to two representative reconstruction approaches, image space-based total variation regularization and an analytical model-based reconstruction. The proposed p-CS regularization was found to provide efficient suppression of noise amplification and preservation of parameter mapping accuracy without explicit utilization of analytical signal models. The developed method may facilitate acceleration of quantitative MRI techniques that are not suitable to model-based reconstruction because of complex signal models or when signal deviations from the expected analytical model exist. PMID:23213053

A physiology-based parametric imaging method for FDG-PET data

NASA Astrophysics Data System (ADS)

Scussolini, Mara; Garbarino, Sara; Sambuceti, Gianmario; Caviglia, Giacomo; Piana, Michele

2017-12-01

Parametric imaging is a compartmental approach that processes nuclear imaging data to estimate the spatial distribution of the kinetic parameters governing tracer flow. The present paper proposes a novel and efficient computational method for parametric imaging which is potentially applicable to several compartmental models of diverse complexity and which is effective in the determination of the parametric maps of all kinetic coefficients. We consider applications to [18 F]-fluorodeoxyglucose positron emission tomography (FDG-PET) data and analyze the two-compartment catenary model describing the standard FDG metabolization by an homogeneous tissue and the three-compartment non-catenary model representing the renal physiology. We show uniqueness theorems for both models. The proposed imaging method starts from the reconstructed FDG-PET images of tracer concentration and preliminarily applies image processing algorithms for noise reduction and image segmentation. The optimization procedure solves pixel-wise the non-linear inverse problem of determining the kinetic parameters from dynamic concentration data through a regularized Gauss-Newton iterative algorithm. The reliability of the method is validated against synthetic data, for the two-compartment system, and experimental real data of murine models, for the renal three-compartment system.

A parametric model order reduction technique for poroelastic finite element models.

PubMed

Lappano, Ettore; Polanz, Markus; Desmet, Wim; Mundo, Domenico

2017-10-01

This research presents a parametric model order reduction approach for vibro-acoustic problems in the frequency domain of systems containing poroelastic materials (PEM). The method is applied to the Finite Element (FE) discretization of the weak u-p integral formulation based on the Biot-Allard theory and makes use of reduced basis (RB) methods typically employed for parametric problems. The parametric reduction is obtained rewriting the Biot-Allard FE equations for poroelastic materials using an affine representation of the frequency (therefore allowing for RB methods) and projecting the frequency-dependent PEM system on a global reduced order basis generated with the proper orthogonal decomposition instead of standard modal approaches. This has proven to be better suited to describe the nonlinear frequency dependence and the strong coupling introduced by damping. The methodology presented is tested on two three-dimensional systems: in the first experiment, the surface impedance of a PEM layer sample is calculated and compared with results of the literature; in the second, the reduced order model of a multilayer system coupled to an air cavity is assessed and the results are compared to those of the reference FE model.

Modeling and experimental parametric study of a tri-leg compliant orthoplanar spring based multi-mode piezoelectric energy harvester

NASA Astrophysics Data System (ADS)

Dhote, Sharvari; Yang, Zhengbao; Zu, Jean

2018-01-01

This paper presents the modeling and experimental parametric study of a nonlinear multi-frequency broad bandwidth piezoelectric vibration-based energy harvester. The proposed harvester consists of a tri-leg compliant orthoplanar spring (COPS) and multiple masses with piezoelectric plates attached at three different locations. The vibration modes, resonant frequencies, and strain distributions are studied using the finite element analysis. The prototype is manufactured and experimentally investigated to study the effect of single as well as multiple light-weight masses on the bandwidth. The dynamic behavior of the harvester with a mass at the center is modeled numerically and characterized experimentally. The simulation and experimental results are in good agreement. A wide bandwidth with three close nonlinear vibration modes is observed during the experiments when four masses are added to the proposed harvester. The current generator with four masses shows a significant performance improvement with multiple nonlinear peaks under both forward and reverse frequency sweeps.

Theoretical Analysis of Penalized Maximum-Likelihood Patlak Parametric Image Reconstruction in Dynamic PET for Lesion Detection.

PubMed

Yang, Li; Wang, Guobao; Qi, Jinyi

2016-04-01

Detecting cancerous lesions is a major clinical application of emission tomography. In a previous work, we studied penalized maximum-likelihood (PML) image reconstruction for lesion detection in static PET. Here we extend our theoretical analysis of static PET reconstruction to dynamic PET. We study both the conventional indirect reconstruction and direct reconstruction for Patlak parametric image estimation. In indirect reconstruction, Patlak parametric images are generated by first reconstructing a sequence of dynamic PET images, and then performing Patlak analysis on the time activity curves (TACs) pixel-by-pixel. In direct reconstruction, Patlak parametric images are estimated directly from raw sinogram data by incorporating the Patlak model into the image reconstruction procedure. PML reconstruction is used in both the indirect and direct reconstruction methods. We use a channelized Hotelling observer (CHO) to assess lesion detectability in Patlak parametric images. Simplified expressions for evaluating the lesion detectability have been derived and applied to the selection of the regularization parameter value to maximize detection performance. The proposed method is validated using computer-based Monte Carlo simulations. Good agreements between the theoretical predictions and the Monte Carlo results are observed. Both theoretical predictions and Monte Carlo simulation results show the benefit of the indirect and direct methods under optimized regularization parameters in dynamic PET reconstruction for lesion detection, when compared with the conventional static PET reconstruction.

A Hybrid Wind-Farm Parametrization for Mesoscale and Climate Models

NASA Astrophysics Data System (ADS)

Pan, Yang; Archer, Cristina L.

2018-04-01

To better understand the potential impact of wind farms on weather and climate at the regional to global scales, a new hybrid wind-farm parametrization is proposed for mesoscale and climate models. The proposed parametrization is a hybrid model because it is not based on physical processes or conservation laws, but on the multiple linear regression of the results of large-eddy simulations (LES) with the geometric properties of the wind-farm layout (e.g., the blockage ratio and blockage distance). The innovative aspect is that each wind turbine is treated individually based on its position in the farm and on the wind direction by predicting the velocity upstream of each turbine. The turbine-induced forces and added turbulence kinetic energy (TKE) are first derived analytically and then implemented in the Weather Research and Forecasting model. Idealized simulations of the offshore Lillgrund wind farm are conducted. The wind-speed deficit and TKE predicted with the hybrid model are in excellent agreement with those from the LES results, while the wind-power production estimated with the hybrid model is within 10% of that observed. Three additional wind farms with larger inter-turbine spacing than at Lillgrund are also considered, and a similar agreement with LES results is found, proving that the hybrid parametrization works well with any wind farm regardless of the spacing between turbines. These results indicate the wind-turbine position, wind direction, and added TKE are essential in accounting for the wind-farm effects on the surroundings, for which the hybrid wind-farm parametrization is a promising tool.

SOCR Analyses – an Instructional Java Web-based Statistical Analysis Toolkit

PubMed Central

Chu, Annie; Cui, Jenny; Dinov, Ivo D.

2011-01-01

The Statistical Online Computational Resource (SOCR) designs web-based tools for educational use in a variety of undergraduate courses (Dinov 2006). Several studies have demonstrated that these resources significantly improve students' motivation and learning experiences (Dinov et al. 2008). SOCR Analyses is a new component that concentrates on data modeling and analysis using parametric and non-parametric techniques supported with graphical model diagnostics. Currently implemented analyses include commonly used models in undergraduate statistics courses like linear models (Simple Linear Regression, Multiple Linear Regression, One-Way and Two-Way ANOVA). In addition, we implemented tests for sample comparisons, such as t-test in the parametric category; and Wilcoxon rank sum test, Kruskal-Wallis test, Friedman's test, in the non-parametric category. SOCR Analyses also include several hypothesis test models, such as Contingency tables, Friedman's test and Fisher's exact test. The code itself is open source (http://socr.googlecode.com/), hoping to contribute to the efforts of the statistical computing community. The code includes functionality for each specific analysis model and it has general utilities that can be applied in various statistical computing tasks. For example, concrete methods with API (Application Programming Interface) have been implemented in statistical summary, least square solutions of general linear models, rank calculations, etc. HTML interfaces, tutorials, source code, activities, and data are freely available via the web (www.SOCR.ucla.edu). Code examples for developers and demos for educators are provided on the SOCR Wiki website. In this article, the pedagogical utilization of the SOCR Analyses is discussed, as well as the underlying design framework. As the SOCR project is on-going and more functions and tools are being added to it, these resources are constantly improved. The reader is strongly encouraged to check the SOCR site for most updated information and newly added models. PMID:21546994

Robust outer synchronization between two nonlinear complex networks with parametric disturbances and mixed time-varying delays

NASA Astrophysics Data System (ADS)

Zhang, Chuan; Wang, Xingyuan; Luo, Chao; Li, Junqiu; Wang, Chunpeng

2018-03-01

In this paper, we focus on the robust outer synchronization problem between two nonlinear complex networks with parametric disturbances and mixed time-varying delays. Firstly, a general complex network model is proposed. Besides the nonlinear couplings, the network model in this paper can possess parametric disturbances, internal time-varying delay, discrete time-varying delay and distributed time-varying delay. Then, according to the robust control strategy, linear matrix inequality and Lyapunov stability theory, several outer synchronization protocols are strictly derived. Simple linear matrix controllers are designed to driver the response network synchronize to the drive network. Additionally, our results can be applied on the complex networks without parametric disturbances. Finally, by utilizing the delayed Lorenz chaotic system as the dynamics of all nodes, simulation examples are given to demonstrate the effectiveness of our theoretical results.

Application of characteristic time concepts for hydraulic fracture configuration design, control, and optimization

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

Advani, S.H.; Lee, T.S.; Moon, H.

1992-10-01

The analysis of pertinent energy components or affiliated characteristic times for hydraulic stimulation processes serves as an effective tool for fracture configuration designs optimization, and control. This evaluation, in conjunction with parametric sensitivity studies, provides a rational base for quantifying dominant process mechanisms and the roles of specified reservoir properties relative to controllable hydraulic fracture variables for a wide spectrum of treatment scenarios. Results are detailed for the following multi-task effort: (a) Application of characteristic time concept and parametric sensitivity studies for specialized fracture geometries (rectangular, penny-shaped, elliptical) and three-layered elliptic crack models (in situ stress, elastic moduli, and fracturemore » toughness contrasts). (b) Incorporation of leak-off effects for models investigated in (a). (c) Simulation of generalized hydraulic fracture models and investigation of the role of controllable vaxiables and uncontrollable system properties. (d) Development of guidelines for hydraulic fracture design and optimization.« less

Application of characteristic time concepts for hydraulic fracture configuration design, control, and optimization. Final report

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

Advani, S.H.; Lee, T.S.; Moon, H.

1992-10-01

The analysis of pertinent energy components or affiliated characteristic times for hydraulic stimulation processes serves as an effective tool for fracture configuration designs optimization, and control. This evaluation, in conjunction with parametric sensitivity studies, provides a rational base for quantifying dominant process mechanisms and the roles of specified reservoir properties relative to controllable hydraulic fracture variables for a wide spectrum of treatment scenarios. Results are detailed for the following multi-task effort: (a) Application of characteristic time concept and parametric sensitivity studies for specialized fracture geometries (rectangular, penny-shaped, elliptical) and three-layered elliptic crack models (in situ stress, elastic moduli, and fracturemore » toughness contrasts). (b) Incorporation of leak-off effects for models investigated in (a). (c) Simulation of generalized hydraulic fracture models and investigation of the role of controllable vaxiables and uncontrollable system properties. (d) Development of guidelines for hydraulic fracture design and optimization.« less

Sensitivity to imputation models and assumptions in receiver operating characteristic analysis with incomplete data

PubMed Central

Karakaya, Jale; Karabulut, Erdem; Yucel, Recai M.

2015-01-01

Modern statistical methods using incomplete data have been increasingly applied in a wide variety of substantive problems. Similarly, receiver operating characteristic (ROC) analysis, a method used in evaluating diagnostic tests or biomarkers in medical research, has also been increasingly popular problem in both its development and application. While missing-data methods have been applied in ROC analysis, the impact of model mis-specification and/or assumptions (e.g. missing at random) underlying the missing data has not been thoroughly studied. In this work, we study the performance of multiple imputation (MI) inference in ROC analysis. Particularly, we investigate parametric and non-parametric techniques for MI inference under common missingness mechanisms. Depending on the coherency of the imputation model with the underlying data generation mechanism, our results show that MI generally leads to well-calibrated inferences under ignorable missingness mechanisms. PMID:26379316

How to Make Data a Blessing to Parametric Uncertainty Quantification and Reduction?

NASA Astrophysics Data System (ADS)

Ye, M.; Shi, X.; Curtis, G. P.; Kohler, M.; Wu, J.

2013-12-01

In a Bayesian point of view, probability of model parameters and predictions are conditioned on data used for parameter inference and prediction analysis. It is critical to use appropriate data for quantifying parametric uncertainty and its propagation to model predictions. However, data are always limited and imperfect. When a dataset cannot properly constrain model parameters, it may lead to inaccurate uncertainty quantification. While in this case data appears to be a curse to uncertainty quantification, a comprehensive modeling analysis may help understand the cause and characteristics of parametric uncertainty and thus turns data into a blessing. In this study, we illustrate impacts of data on uncertainty quantification and reduction using an example of surface complexation model (SCM) developed to simulate uranyl (U(VI)) adsorption. The model includes two adsorption sites, referred to as strong and weak sites. The amount of uranium adsorption on these sites determines both the mean arrival time and the long tail of the breakthrough curves. There is one reaction on the weak site but two reactions on the strong site. The unknown parameters include fractions of the total surface site density of the two sites and surface complex formation constants of the three reactions. A total of seven experiments were conducted with different geochemical conditions to estimate these parameters. The experiments with low initial concentration of U(VI) result in a large amount of parametric uncertainty. A modeling analysis shows that it is because the experiments cannot distinguish the relative adsorption affinity of the strong and weak sites on uranium adsorption. Therefore, the experiments with high initial concentration of U(VI) are needed, because in the experiments the strong site is nearly saturated and the weak site can be determined. The experiments with high initial concentration of U(VI) are a blessing to uncertainty quantification, and the experiments with low initial concentration help modelers turn a curse into a blessing. The data impacts on uncertainty quantification and reduction are quantified using probability density functions of model parameters obtained from Markov Chain Monte Carlo simulation using the DREAM algorithm. This study provides insights to model calibration, uncertainty quantification, experiment design, and data collection in groundwater reactive transport modeling and other environmental modeling.

Bayesian quantile regression-based partially linear mixed-effects joint models for longitudinal data with multiple features.

PubMed

Zhang, Hanze; Huang, Yangxin; Wang, Wei; Chen, Henian; Langland-Orban, Barbara

2017-01-01

In longitudinal AIDS studies, it is of interest to investigate the relationship between HIV viral load and CD4 cell counts, as well as the complicated time effect. Most of common models to analyze such complex longitudinal data are based on mean-regression, which fails to provide efficient estimates due to outliers and/or heavy tails. Quantile regression-based partially linear mixed-effects models, a special case of semiparametric models enjoying benefits of both parametric and nonparametric models, have the flexibility to monitor the viral dynamics nonparametrically and detect the varying CD4 effects parametrically at different quantiles of viral load. Meanwhile, it is critical to consider various data features of repeated measurements, including left-censoring due to a limit of detection, covariate measurement error, and asymmetric distribution. In this research, we first establish a Bayesian joint models that accounts for all these data features simultaneously in the framework of quantile regression-based partially linear mixed-effects models. The proposed models are applied to analyze the Multicenter AIDS Cohort Study (MACS) data. Simulation studies are also conducted to assess the performance of the proposed methods under different scenarios.

Bayesian inversion of marine CSEM data from the Scarborough gas field using a transdimensional 2-D parametrization

NASA Astrophysics Data System (ADS)

Ray, Anandaroop; Key, Kerry; Bodin, Thomas; Myer, David; Constable, Steven

2014-12-01

We apply a reversible-jump Markov chain Monte Carlo method to sample the Bayesian posterior model probability density function of 2-D seafloor resistivity as constrained by marine controlled source electromagnetic data. This density function of earth models conveys information on which parts of the model space are illuminated by the data. Whereas conventional gradient-based inversion approaches require subjective regularization choices to stabilize this highly non-linear and non-unique inverse problem and provide only a single solution with no model uncertainty information, the method we use entirely avoids model regularization. The result of our approach is an ensemble of models that can be visualized and queried to provide meaningful information about the sensitivity of the data to the subsurface, and the level of resolution of model parameters. We represent models in 2-D using a Voronoi cell parametrization. To make the 2-D problem practical, we use a source-receiver common midpoint approximation with 1-D forward modelling. Our algorithm is transdimensional and self-parametrizing where the number of resistivity cells within a 2-D depth section is variable, as are their positions and geometries. Two synthetic studies demonstrate the algorithm's use in the appraisal of a thin, segmented, resistive reservoir which makes for a challenging exploration target. As a demonstration example, we apply our method to survey data collected over the Scarborough gas field on the Northwest Australian shelf.

Interstitial ultrasound ablation of vertebral and paraspinal tumours: Parametric and patient-specific simulations

PubMed Central

Scott, Serena J.; Salgaonkar, Vasant; Prakash, Punit; Burdette, E. Clif; Diederich, Chris J.

2015-01-01

Purpose Theoretical parametric and patient-specific models are applied to assess the feasibility of interstitial ultrasound ablation of tumours in and near the spine and to identify potential treatment delivery strategies. Methods 3D patient-specific finite element models (n=11) of interstitial ultrasound ablation of tumours associated with spine were generated. Gaseous nerve insulation and various applicator configurations, frequencies (3 and 7 MHz), placement trajectories, and tumour locations were simulated. Parametric studies with multilayered models investigated the impacts of tumour attenuation, tumour dimension, and the thickness of bone insulating critical structures. Temperature and thermal dose were calculated to define ablation (>240 equivalent minutes at 43°C (EM43°C)) and safety margins (<45°C & <6 EM43°C), and to determine performance and required delivery parameters. Results Osteolytic tumours (≤44 mm) encapsulated by bone could be successfully ablated with 7 MHz interstitial ultrasound (8.1-16.6 W/cm2, 120-5900 J, 0.4-15 min). Ablation of tumours (94.6-100% volumetric) 0-14.5 mm from the spinal canal was achieved within 3-15 min without damaging critical nerves. 3 MHz devices provided faster ablation (390 versus 930 s) of an 18 mm diameter osteoblastic (high bone content) volume than 7 MHz devices. Critical anatomy in proximity to the tumour could be protected by selection of appropriate applicator configurations, active sectors, and applied power schemas, and through gaseous insulation. Preferential ultrasound absorption at bone surfaces facilitated faster, more effective ablations in osteolytic tumours and provided isolation of ablative energies and temperatures. Conclusions Parametric and patient-specific studies demonstrated the feasibility and potential advantages of interstitial ultrasound ablation treatment of paraspinal and osteolytic vertebral tumours. PMID:25017322

Experimental realization of a feedback optical parametric amplifier with four-wave mixing

NASA Astrophysics Data System (ADS)

Pan, Xiaozhou; Chen, Hui; Wei, Tianxiang; Zhang, Jun; Marino, Alberto M.; Treps, Nicolas; Glasser, Ryan T.; Jing, Jietai

2018-04-01

Optical parametric amplifiers (OPAs) play a fundamental role in the generation of quantum correlation for quantum information processing and quantum metrology. In order to increase the communication fidelity of the quantum information protocol and the measurement precision of quantum metrology, it requires a high degree of quantum correlation. In this Rapid Communication we report a feedback optical parametric amplifier that employs a four-wave mixing (FWM) process as the underlying OPA and a beam splitter as the feedback controller. We first construct a theoretical model for this feedback-based FWM process and experimentally study the effect of the feedback control on the quantum properties of the system. Specifically, we find that the quantum correlation between the output fields can be enhanced by tuning the strength of the feedback.

Space transfer vehicle concepts and requirements study. Volume 3, book 1: Program cost estimates

NASA Technical Reports Server (NTRS)

Peffley, Al F.

1991-01-01

The Space Transfer Vehicle (STV) Concepts and Requirements Study cost estimate and program planning analysis is presented. The cost estimating technique used to support STV system, subsystem, and component cost analysis is a mixture of parametric cost estimating and selective cost analogy approaches. The parametric cost analysis is aimed at developing cost-effective aerobrake, crew module, tank module, and lander designs with the parametric cost estimates data. This is accomplished using cost as a design parameter in an iterative process with conceptual design input information. The parametric estimating approach segregates costs by major program life cycle phase (development, production, integration, and launch support). These phases are further broken out into major hardware subsystems, software functions, and tasks according to the STV preliminary program work breakdown structure (WBS). The WBS is defined to a low enough level of detail by the study team to highlight STV system cost drivers. This level of cost visibility provided the basis for cost sensitivity analysis against various design approaches aimed at achieving a cost-effective design. The cost approach, methodology, and rationale are described. A chronological record of the interim review material relating to cost analysis is included along with a brief summary of the study contract tasks accomplished during that period of review and the key conclusions or observations identified that relate to STV program cost estimates. The STV life cycle costs are estimated on the proprietary parametric cost model (PCM) with inputs organized by a project WBS. Preliminary life cycle schedules are also included.

Accounting for parameter uncertainty in the definition of parametric distributions used to describe individual patient variation in health economic models.

PubMed

Degeling, Koen; IJzerman, Maarten J; Koopman, Miriam; Koffijberg, Hendrik

2017-12-15

Parametric distributions based on individual patient data can be used to represent both stochastic and parameter uncertainty. Although general guidance is available on how parameter uncertainty should be accounted for in probabilistic sensitivity analysis, there is no comprehensive guidance on reflecting parameter uncertainty in the (correlated) parameters of distributions used to represent stochastic uncertainty in patient-level models. This study aims to provide this guidance by proposing appropriate methods and illustrating the impact of this uncertainty on modeling outcomes. Two approaches, 1) using non-parametric bootstrapping and 2) using multivariate Normal distributions, were applied in a simulation and case study. The approaches were compared based on point-estimates and distributions of time-to-event and health economic outcomes. To assess sample size impact on the uncertainty in these outcomes, sample size was varied in the simulation study and subgroup analyses were performed for the case-study. Accounting for parameter uncertainty in distributions that reflect stochastic uncertainty substantially increased the uncertainty surrounding health economic outcomes, illustrated by larger confidence ellipses surrounding the cost-effectiveness point-estimates and different cost-effectiveness acceptability curves. Although both approaches performed similar for larger sample sizes (i.e. n = 500), the second approach was more sensitive to extreme values for small sample sizes (i.e. n = 25), yielding infeasible modeling outcomes. Modelers should be aware that parameter uncertainty in distributions used to describe stochastic uncertainty needs to be reflected in probabilistic sensitivity analysis, as it could substantially impact the total amount of uncertainty surrounding health economic outcomes. If feasible, the bootstrap approach is recommended to account for this uncertainty.

Parametric Modeling as a Technology of Rapid Prototyping in Light Industry

NASA Astrophysics Data System (ADS)

Tomilov, I. N.; Grudinin, S. N.; Frolovsky, V. D.; Alexandrov, A. A.

2016-04-01

The paper deals with the parametric modeling method of virtual mannequins for the purposes of design automation in clothing industry. The described approach includes the steps of generation of the basic model on the ground of the initial one (obtained in 3D-scanning process), its parameterization and deformation. The complex surfaces are presented by the wireframe model. The modeling results are evaluated with the set of similarity factors. Deformed models are compared with their virtual prototypes. The results of modeling are estimated by the standard deviation factor.

Using multiple decrement models to estimate risk and morbidity from specific AIDS illnesses. Multicenter AIDS Cohort Study (MACS).

PubMed

Hoover, D R; Peng, Y; Saah, A J; Detels, R R; Day, R S; Phair, J P

A simple non-parametric approach is developed to simultaneously estimate net incidence and morbidity time from specific AIDS illnesses in populations at high risk for death from these illnesses and other causes. The disease-death process has four-stages that can be recast as two sandwiching three-state multiple decrement processes. Non-parametric estimation of net incidence and morbidity time with error bounds are achieved from these sandwiching models through modification of methods from Aalen and Greenwood, and bootstrapping. An application to immunosuppressed HIV-1 infected homosexual men reveals that cytomegalovirus disease, Kaposi's sarcoma and Pneumocystis pneumonia are likely to occur and cause significant morbidity time.

Multivariable Parametric Cost Model for Ground Optical Telescope Assembly

NASA Technical Reports Server (NTRS)

Stahl, H. Philip; Rowell, Ginger Holmes; Reese, Gayle; Byberg, Alicia

2005-01-01

A parametric cost model for ground-based telescopes is developed using multivariable statistical analysis of both engineering and performance parameters. While diameter continues to be the dominant cost driver, diffraction-limited wavelength is found to be a secondary driver. Other parameters such as radius of curvature are examined. The model includes an explicit factor for primary mirror segmentation and/or duplication (i.e., multi-telescope phased-array systems). Additionally, single variable models Based on aperture diameter are derived.

Shape-driven 3D segmentation using spherical wavelets.

PubMed

Nain, Delphine; Haker, Steven; Bobick, Aaron; Tannenbaum, Allen

2006-01-01

This paper presents a novel active surface segmentation algorithm using a multiscale shape representation and prior. We define a parametric model of a surface using spherical wavelet functions and learn a prior probability distribution over the wavelet coefficients to model shape variations at different scales and spatial locations in a training set. Based on this representation, we derive a parametric active surface evolution using the multiscale prior coefficients as parameters for our optimization procedure to naturally include the prior in the segmentation framework. Additionally, the optimization method can be applied in a coarse-to-fine manner. We apply our algorithm to the segmentation of brain caudate nucleus, of interest in the study of schizophrenia. Our validation shows our algorithm is computationally efficient and outperforms the Active Shape Model algorithm by capturing finer shape details.

Nuclear ``pasta'' phase within density dependent hadronic models

NASA Astrophysics Data System (ADS)

Avancini, S. S.; Brito, L.; Marinelli, J. R.; Menezes, D. P.; de Moraes, M. M. W.; Providência, C.; Santos, A. M.

2009-03-01

In the present paper, we investigate the onset of the “pasta” phase with different parametrizations of the density dependent hadronic model and compare the results with one of the usual parametrizations of the nonlinear Walecka model. The influence of the scalar-isovector virtual δ meson is shown. At zero temperature, two different methods are used, one based on coexistent phases and the other on the Thomas-Fermi approximation. At finite temperature, only the coexistence phases method is used. npe matter with fixed proton fractions and in β equilibrium are studied. We compare our results with restrictions imposed on the values of the density and pressure at the inner edge of the crust, obtained from observations of the Vela pulsar and recent isospin diffusion data from heavy-ion reactions, and with predictions from spinodal calculations.

Slanted snaking of localized Faraday waves

NASA Astrophysics Data System (ADS)

Pradenas, Bastián; Araya, Isidora; Clerc, Marcel G.; Falcón, Claudio; Gandhi, Punit; Knobloch, Edgar

2017-06-01

We report on an experimental, theoretical, and numerical study of slanted snaking of spatially localized parametrically excited waves on the surface of a water-surfactant mixture in a Hele-Shaw cell. We demonstrate experimentally the presence of a hysteretic transition to spatially extended parametrically excited surface waves when the acceleration amplitude is varied, as well as the presence of spatially localized waves exhibiting slanted snaking. The latter extend outside the hysteresis loop. We attribute this behavior to the presence of a conserved quantity, the liquid volume trapped within the meniscus, and introduce a universal model based on symmetry arguments, which couples the wave amplitude with such a conserved quantity. The model captures both the observed slanted snaking and the presence of localized waves outside the hysteresis loop, as demonstrated by numerical integration of the model equations.

Parametric analysis of electromechanical and fatigue performance of total knee replacement bearing with embedded piezoelectric transducers.

PubMed

Safaei, Mohsen; Meneghini, R Michael; Anton, Steven R

2017-09-01

Total knee arthroplasty (TKA) is a common procedure in the United States; it has been estimated that about 4 million people are currently living with primary knee replacement in this country. Despite huge improvements in material properties, implant design, and surgical techniques, some implants fail a few years after surgery. A lack of information about in vivo kinetics of the knee prevents the establishment of a correlated intra- and postoperative loading pattern in knee implants. In this study, a conceptual design of an ultra high molecular weight (UHMW) knee bearing with embedded piezoelectric transducers is proposed, which is able to measure the reaction forces from knee motion as well as harvest energy to power embedded electronics. A simplified geometry consisting of a disk of UHMW with a single embedded piezoelectric ceramic is used in this work to study the general parametric trends of an instrumented knee bearing. A combined finite element and electromechanical modeling framework is employed to investigate the fatigue behavior of the instrumented bearing and the electromechanical performance of the embedded piezoelectric. The model is validated through experimental testing and utilized for further parametric studies. Parametric studies consist of the investigation of the effects of several dimensional and piezoelectric material parameters on the durability of the bearing and electrical output of the transducers. Among all the parameters, it is shown that adding large fillet radii results in noticeable improvement in the fatigue life of the bearing. Additionally, the design is highly sensitive to the depth of piezoelectric pocket. Finally, using PZT-5H piezoceramics, higher voltage and slightly enhanced fatigue life is achieved.

Parametric analysis of electromechanical and fatigue performance of total knee replacement bearing with embedded piezoelectric transducers

NASA Astrophysics Data System (ADS)

Safaei, Mohsen; Meneghini, R. Michael; Anton, Steven R.

2017-09-01

Total knee arthroplasty is a common procedure in the United States; it has been estimated that about 4 million people are currently living with primary knee replacement in this country. Despite huge improvements in material properties, implant design, and surgical techniques, some implants fail a few years after surgery. A lack of information about in vivo kinetics of the knee prevents the establishment of a correlated intra- and postoperative loading pattern in knee implants. In this study, a conceptual design of an ultra high molecular weight (UHMW) knee bearing with embedded piezoelectric transducers is proposed, which is able to measure the reaction forces from knee motion as well as harvest energy to power embedded electronics. A simplified geometry consisting of a disk of UHMW with a single embedded piezoelectric ceramic is used in this work to study the general parametric trends of an instrumented knee bearing. A combined finite element and electromechanical modeling framework is employed to investigate the fatigue behavior of the instrumented bearing and the electromechanical performance of the embedded piezoelectric. The model is validated through experimental testing and utilized for further parametric studies. Parametric studies consist of the investigation of the effects of several dimensional and piezoelectric material parameters on the durability of the bearing and electrical output of the transducers. Among all the parameters, it is shown that adding large fillet radii results in noticeable improvement in the fatigue life of the bearing. Additionally, the design is highly sensitive to the depth of piezoelectric pocket. Finally, using PZT-5H piezoceramics, higher voltage and slightly enhanced fatigue life is achieved.

Stochastic Modelling, Analysis, and Simulations of the Solar Cycle Dynamic Process

NASA Astrophysics Data System (ADS)

Turner, Douglas C.; Ladde, Gangaram S.

2018-03-01

Analytical solutions, discretization schemes and simulation results are presented for the time delay deterministic differential equation model of the solar dynamo presented by Wilmot-Smith et al. In addition, this model is extended under stochastic Gaussian white noise parametric fluctuations. The introduction of stochastic fluctuations incorporates variables affecting the dynamo process in the solar interior, estimation error of parameters, and uncertainty of the α-effect mechanism. Simulation results are presented and analyzed to exhibit the effects of stochastic parametric volatility-dependent perturbations. The results generalize and extend the work of Hazra et al. In fact, some of these results exhibit the oscillatory dynamic behavior generated by the stochastic parametric additative perturbations in the absence of time delay. In addition, the simulation results of the modified stochastic models influence the change in behavior of the very recently developed stochastic model of Hazra et al.

Comparison of radiation parametrizations within the HARMONIE-AROME NWP model

NASA Astrophysics Data System (ADS)

Rontu, Laura; Lindfors, Anders V.

2018-05-01

Downwelling shortwave radiation at the surface (SWDS, global solar radiation flux), given by three different parametrization schemes, was compared to observations in the HARMONIE-AROME numerical weather prediction (NWP) model experiments over Finland in spring 2017. Simulated fluxes agreed well with each other and with the observations in the clear-sky cases. In the cloudy-sky conditions, all schemes tended to underestimate SWDS at the daily level, as compared to the measurements. Large local and temporal differences between the model results and observations were seen, related to the variations and uncertainty of the predicted cloud properties. The results suggest a possibility to benefit from the use of different radiative transfer parametrizations in a NWP model to obtain perturbations for the fine-resolution ensemble prediction systems. In addition, we recommend usage of the global radiation observations for the standard validation of the NWP models.

Latest astronomical constraints on some non-linear parametric dark energy models

NASA Astrophysics Data System (ADS)

Yang, Weiqiang; Pan, Supriya; Paliathanasis, Andronikos

2018-04-01

We consider non-linear redshift-dependent equation of state parameters as dark energy models in a spatially flat Friedmann-Lemaître-Robertson-Walker universe. To depict the expansion history of the universe in such cosmological scenarios, we take into account the large-scale behaviour of such parametric models and fit them using a set of latest observational data with distinct origin that includes cosmic microwave background radiation, Supernove Type Ia, baryon acoustic oscillations, redshift space distortion, weak gravitational lensing, Hubble parameter measurements from cosmic chronometers, and finally the local Hubble constant from Hubble space telescope. The fitting technique avails the publicly available code Cosmological Monte Carlo (COSMOMC), to extract the cosmological information out of these parametric dark energy models. From our analysis, it follows that those models could describe the late time accelerating phase of the universe, while they are distinguished from the Λ-cosmology.

Extended parametric representation of compressor fans and turbines. Volume 2: Part user's manual (parametric turbine)

NASA Technical Reports Server (NTRS)

Coverse, G. L.

1984-01-01

A turbine modeling technique has been developed which will enable the user to obtain consistent and rapid off-design performance from design point input. This technique is applicable to both axial and radial flow turbine with flow sizes ranging from about one pound per second to several hundred pounds per second. The axial flow turbines may or may not include variable geometry in the first stage nozzle. A user-specified option will also permit the calculation of design point cooling flow levels and corresponding changes in efficiency for the axial flow turbines. The modeling technique has been incorporated into a time-sharing program in order to facilitate its use. Because this report contains a description of the input output data, values of typical inputs, and example cases, it is suitable as a user's manual. This report is the second of a three volume set. The titles of the three volumes are as follows: (1) Volume 1 CMGEN USER's Manual (Parametric Compressor Generator); (2) Volume 2 PART USER's Manual (Parametric Turbine); (3) Volume 3 MODFAN USER's Manual (Parametric Modulation Flow Fan).

An Investigation of Two Finite Element Modeling Solutions for Biomechanical Simulation Using a Case Study of a Mandibular Bone.

PubMed

Liu, Yun-Feng; Fan, Ying-Ying; Dong, Hui-Yue; Zhang, Jian-Xing

2017-12-01

The method used in biomechanical modeling for finite element method (FEM) analysis needs to deliver accurate results. There are currently two solutions used in FEM modeling for biomedical model of human bone from computerized tomography (CT) images: one is based on a triangular mesh and the other is based on the parametric surface model and is more popular in practice. The outline and modeling procedures for the two solutions are compared and analyzed. Using a mandibular bone as an example, several key modeling steps are then discussed in detail, and the FEM calculation was conducted. Numerical calculation results based on the models derived from the two methods, including stress, strain, and displacement, are compared and evaluated in relation to accuracy and validity. Moreover, a comprehensive comparison of the two solutions is listed. The parametric surface based method is more helpful when using powerful design tools in computer-aided design (CAD) software, but the triangular mesh based method is more robust and efficient.

REGENERATION/REACTIVATION OF CARBON ADSORBENTS BY RADIO FREQUENCY (RF) INDUCTION HEATING

EPA Science Inventory

We will use the experimental results to verify the numerical models and then use the models in parametric studies to determine the relative importance of each of the governing phenomena: electrical properties, heat transfer, RF applicator and adsorbent bed geometry...

A Comparison of Uniform DIF Effect Size Estimators under the MIMIC and Rasch Models

ERIC Educational Resources Information Center

Jin, Ying; Myers, Nicholas D.; Ahn, Soyeon; Penfield, Randall D.

2013-01-01

The Rasch model, a member of a larger group of models within item response theory, is widely used in empirical studies. Detection of uniform differential item functioning (DIF) within the Rasch model typically employs null hypothesis testing with a concomitant consideration of effect size (e.g., signed area [SA]). Parametric equivalence between…

Non-intrusive reduced order modeling of nonlinear problems using neural networks

NASA Astrophysics Data System (ADS)

Hesthaven, J. S.; Ubbiali, S.

2018-06-01

We develop a non-intrusive reduced basis (RB) method for parametrized steady-state partial differential equations (PDEs). The method extracts a reduced basis from a collection of high-fidelity solutions via a proper orthogonal decomposition (POD) and employs artificial neural networks (ANNs), particularly multi-layer perceptrons (MLPs), to accurately approximate the coefficients of the reduced model. The search for the optimal number of neurons and the minimum amount of training samples to avoid overfitting is carried out in the offline phase through an automatic routine, relying upon a joint use of the Latin hypercube sampling (LHS) and the Levenberg-Marquardt (LM) training algorithm. This guarantees a complete offline-online decoupling, leading to an efficient RB method - referred to as POD-NN - suitable also for general nonlinear problems with a non-affine parametric dependence. Numerical studies are presented for the nonlinear Poisson equation and for driven cavity viscous flows, modeled through the steady incompressible Navier-Stokes equations. Both physical and geometrical parametrizations are considered. Several results confirm the accuracy of the POD-NN method and show the substantial speed-up enabled at the online stage as compared to a traditional RB strategy.

A Model for Straight and Helical Solar Jets: II. Parametric Study of the Plasma Beta

NASA Technical Reports Server (NTRS)

Pariat, E.; Dalmasse, K.; DeVore, C. R.; Antiochos, S. K.; Karpen, J. T.

2016-01-01

Context. Jets are dynamic, impulsive, well-collimated plasma events that develop at many different scales and in different layers of the solar atmosphere. Aims. Jets are believed to be induced by magnetic reconnection, a process central to many astrophysical phenomena. Within the solar atmosphere, jet-like events develop in many different environments, e.g. in the vicinity of active regions as well as in coronal holes, and at various scales, from small photospheric spicules to large coronal jets. In all these events, signatures of helical structure and/or twisting/rotating motions are regularly observed. The present study aims to establish that a single model can generally reproduce the observed properties of these jet-like events. Methods. In this study, using our state-of-the-art numerical solver ARMS, we present a parametric study of a numerical tridimensional magnetohydrodynamic (MHD) model of solar jet-like events. Within the MHD paradigm, we study the impact of varying the atmospheric plasma beta on the generation and properties of solar-like jets. Results. The parametric study validates our model of jets for plasma beta ranging from 10(sup 3) to 1, typical of the different layers and magnetic environments of the solar atmosphere. Our model of jets can robustly explain the generation of helical solar jet-like events at various beta less than or equal to 1. We show that the plasma beta modifies the morphology of the helical jet, explaining the different observed shapes of jets at different scales and in different layers of the solar atmosphere. Conclusions. Our results allow us to understand the energisation, triggering, and driving processes of jet-like events. Our model allows us to make predictions of the impulsiveness and energetics of jets as determined by the surrounding environment, as well as the morphological properties of the resulting jets.

Parametric Sensitivity Analysis of Oscillatory Delay Systems with an Application to Gene Regulation.

PubMed

Ingalls, Brian; Mincheva, Maya; Roussel, Marc R

2017-07-01

A parametric sensitivity analysis for periodic solutions of delay-differential equations is developed. Because phase shifts cause the sensitivity coefficients of a periodic orbit to diverge, we focus on sensitivities of the extrema, from which amplitude sensitivities are computed, and of the period. Delay-differential equations are often used to model gene expression networks. In these models, the parametric sensitivities of a particular genotype define the local geometry of the evolutionary landscape. Thus, sensitivities can be used to investigate directions of gradual evolutionary change. An oscillatory protein synthesis model whose properties are modulated by RNA interference is used as an example. This model consists of a set of coupled delay-differential equations involving three delays. Sensitivity analyses are carried out at several operating points. Comments on the evolutionary implications of the results are offered.

Survival analysis of patients with esophageal cancer using parametric cure model.

PubMed

Rasouli, Mahboube; Ghadimi, Mahmood Reza; Mahmoodi, Mahmood; Mohammad, Kazem; Zeraati, Hojjat; Hosseini, Mostafa

2011-01-01

Esophageal cancer is a major cause of mortality and morbidity in the Caspian littoral north-eastern part of Iran. The aim of this study was to calculate cure function as well as to identify the factors that are related to this function among patients with esophageal cancer in this geographical area. Three hundred fifty nine cases of esophageal cancer registered in the Babol cancer registry during the period of 1990 to 1991 (inclusive) were followed up for 15 years up to 2006. Parametric cure model was used to calculate cure fraction and investigate the factors responsible for probability of cure among patients. Sample of subjects encompassed 62.7% men and 37.3% women, with mean ages of diagnosis was 60.0 and 55.3 years, respectively. The median survival time reached about 9 months and estimated survival rates in 1, 3, and 5 years following diagnosis were 23%, 15% and 13%, respectively. Results show the family history affects the cured fraction independently of its effect on early outcome and has a significant effect on the probability of uncured. The average cure fraction was estimated to be 0.10. As the proportionality assumption of Cox model does not meet in certain circumstances, a parametric cure model can provide a better fit and a better description of survival related outcome.

Evaluation of Second-Level Inference in fMRI Analysis

PubMed Central

Roels, Sanne P.; Loeys, Tom; Moerkerke, Beatrijs

2016-01-01

We investigate the impact of decisions in the second-level (i.e., over subjects) inferential process in functional magnetic resonance imaging on (1) the balance between false positives and false negatives and on (2) the data-analytical stability, both proxies for the reproducibility of results. Second-level analysis based on a mass univariate approach typically consists of 3 phases. First, one proceeds via a general linear model for a test image that consists of pooled information from different subjects. We evaluate models that take into account first-level (within-subjects) variability and models that do not take into account this variability. Second, one proceeds via inference based on parametrical assumptions or via permutation-based inference. Third, we evaluate 3 commonly used procedures to address the multiple testing problem: familywise error rate correction, False Discovery Rate (FDR) correction, and a two-step procedure with minimal cluster size. Based on a simulation study and real data we find that the two-step procedure with minimal cluster size results in most stable results, followed by the familywise error rate correction. The FDR results in most variable results, for both permutation-based inference and parametrical inference. Modeling the subject-specific variability yields a better balance between false positives and false negatives when using parametric inference. PMID:26819578

A strategy for improved computational efficiency of the method of anchored distributions

NASA Astrophysics Data System (ADS)

Over, Matthew William; Yang, Yarong; Chen, Xingyuan; Rubin, Yoram

2013-06-01

This paper proposes a strategy for improving the computational efficiency of model inversion using the method of anchored distributions (MAD) by "bundling" similar model parametrizations in the likelihood function. Inferring the likelihood function typically requires a large number of forward model (FM) simulations for each possible model parametrization; as a result, the process is quite expensive. To ease this prohibitive cost, we present an approximation for the likelihood function called bundling that relaxes the requirement for high quantities of FM simulations. This approximation redefines the conditional statement of the likelihood function as the probability of a set of similar model parametrizations "bundle" replicating field measurements, which we show is neither a model reduction nor a sampling approach to improving the computational efficiency of model inversion. To evaluate the effectiveness of these modifications, we compare the quality of predictions and computational cost of bundling relative to a baseline MAD inversion of 3-D flow and transport model parameters. Additionally, to aid understanding of the implementation we provide a tutorial for bundling in the form of a sample data set and script for the R statistical computing language. For our synthetic experiment, bundling achieved a 35% reduction in overall computational cost and had a limited negative impact on predicted probability distributions of the model parameters. Strategies for minimizing error in the bundling approximation, for enforcing similarity among the sets of model parametrizations, and for identifying convergence of the likelihood function are also presented.

Force Project Technology Presentation to the NRCC

DTIC Science & Technology

2014-02-04

Functional Bridge components Smart Odometer Adv Pretreatment Smart Bridge Multi-functional Gap Crossing Fuel Automated Tracking System Adv...comprehensive matrix of candidate composite material systems and textile reinforcement architectures via modeling/analyses and testing. Product(s...Validated Dynamic Modeling tool based on parametric study using material models to reliably predict the textile mechanics of the hose

Rapid calculation of accurate atomic charges for proteins via the electronegativity equalization method.

PubMed

Ionescu, Crina-Maria; Geidl, Stanislav; Svobodová Vařeková, Radka; Koča, Jaroslav

2013-10-28

We focused on the parametrization and evaluation of empirical models for fast and accurate calculation of conformationally dependent atomic charges in proteins. The models were based on the electronegativity equalization method (EEM), and the parametrization procedure was tailored to proteins. We used large protein fragments as reference structures and fitted the EEM model parameters using atomic charges computed by three population analyses (Mulliken, Natural, iterative Hirshfeld), at the Hartree-Fock level with two basis sets (6-31G*, 6-31G**) and in two environments (gas phase, implicit solvation). We parametrized and successfully validated 24 EEM models. When tested on insulin and ubiquitin, all models reproduced quantum mechanics level charges well and were consistent with respect to population analysis and basis set. Specifically, the models showed on average a correlation of 0.961, RMSD 0.097 e, and average absolute error per atom 0.072 e. The EEM models can be used with the freely available EEM implementation EEM_SOLVER.

Probing the matter and dark energy sources in a viable Big Rip model of the Universe

NASA Astrophysics Data System (ADS)

Kumar, Suresh

2014-08-01

Chevallier-Polarski-Linder (CPL) parametrization for the equation of state (EoS) of dark energy in terms of cosmic redshift or scale factor have been frequently studied in the literature. In this study, we consider cosmic time-based CPL parametrization for the EoS parameter of the effective cosmic fluid that fills the fabric of spatially flat and homogeneous Robertson-Walker (RW) spacetime in General Relativity. The model exhibits two worthy features: (i) It fits the observational data from the latest H(z) and Union 2.1 SN Ia compilations matching the success of ΛCDM model. (ii) It describes the evolution of the Universe from the matter-dominated phase to the recent accelerating phase similar to the ΛCDM model but leads to Big Rip end of the Universe contrary to the everlasting de Sitter expansion in the ΛCDM model. We investigate the matter and dark energy sources in the model, in particular, behavior of the dynamical dark energy responsible for the Big Rip end of Universe.

A derivation of the Cramer-Rao lower bound of euclidean parameters under equality constraints via score function

NASA Astrophysics Data System (ADS)

Susyanto, Nanang

2017-12-01

We propose a simple derivation of the Cramer-Rao Lower Bound (CRLB) of parameters under equality constraints from the CRLB without constraints in regular parametric models. When a regular parametric model and an equality constraint of the parameter are given, a parametric submodel can be defined by restricting the parameter under that constraint. The tangent space of this submodel is then computed with the help of the implicit function theorem. Finally, the score function of the restricted parameter is obtained by projecting the efficient influence function of the unrestricted parameter on the appropriate inner product spaces.

Parametric model of the scala tympani for haptic-rendered cochlear implantation.

PubMed

Todd, Catherine; Naghdy, Fazel

2005-01-01

A parametric model of the human scala tympani has been designed for use in a haptic-rendered computer simulation of cochlear implant surgery. It will be the first surgical simulator of this kind. A geometric model of the Scala Tympani has been derived from measured data for this purpose. The model is compared with two existing descriptions of the cochlear spiral. A first approximation of the basilar membrane is also produced. The structures are imported into a force-rendering software application for system development.

Approximation Model Building for Reliability & Maintainability Characteristics of Reusable Launch Vehicles

NASA Technical Reports Server (NTRS)

Unal, Resit; Morris, W. Douglas; White, Nancy H.; Lepsch, Roger A.; Brown, Richard W.

2000-01-01

This paper describes the development of parametric models for estimating operational reliability and maintainability (R&M) characteristics for reusable vehicle concepts, based on vehicle size and technology support level. A R&M analysis tool (RMAT) and response surface methods are utilized to build parametric approximation models for rapidly estimating operational R&M characteristics such as mission completion reliability. These models that approximate RMAT, can then be utilized for fast analysis of operational requirements, for lifecycle cost estimating and for multidisciplinary sign optimization.

Multivariable Parametric Cost Model for Ground Optical: Telescope Assembly

NASA Technical Reports Server (NTRS)

Stahl, H. Philip; Rowell, Ginger Holmes; Reese, Gayle; Byberg, Alicia

2004-01-01

A parametric cost model for ground-based telescopes is developed using multi-variable statistical analysis of both engineering and performance parameters. While diameter continues to be the dominant cost driver, diffraction limited wavelength is found to be a secondary driver. Other parameters such as radius of curvature were examined. The model includes an explicit factor for primary mirror segmentation and/or duplication (i.e. multi-telescope phased-array systems). Additionally, single variable models based on aperture diameter were derived.

Parametric Modeling in the CAE Process: Creating a Family of Models

NASA Technical Reports Server (NTRS)

Brown, Christopher J.

2011-01-01

This Presentation meant as an example - Give ideas of approaches to use - The significant benefit of PARAMETRIC geometry based modeling The importance of planning before you build Showcase some NX capabilities - Mesh Controls - Associativity - Divide Face - Offset Surface Reminder - This only had to be done once! - Can be used for any cabinet in that "family" Saves a lot of time if pre-planned Allows re-use in the future

Development, Evaluation, and Sensitivity Analysis of Parametric Finite Element Whole-Body Human Models in Side Impacts.

PubMed

Hwang, Eunjoo; Hu, Jingwen; Chen, Cong; Klein, Katelyn F; Miller, Carl S; Reed, Matthew P; Rupp, Jonathan D; Hallman, Jason J

2016-11-01

Occupant stature and body shape may have significant effects on injury risks in motor vehicle crashes, but the current finite element (FE) human body models (HBMs) only represent occupants with a few sizes and shapes. Our recent studies have demonstrated that, by using a mesh morphing method, parametric FE HBMs can be rapidly developed for representing a diverse population. However, the biofidelity of those models across a wide range of human attributes has not been established. Therefore, the objectives of this study are 1) to evaluate the accuracy of HBMs considering subject-specific geometry information, and 2) to apply the parametric HBMs in a sensitivity analysis for identifying the specific parameters affecting body responses in side impact conditions. Four side-impact tests with two male post-mortem human subjects (PMHSs) were selected to evaluate the accuracy of the geometry and impact responses of the morphed HBMs. For each PMHS test, three HBMs were simulated to compare with the test results: the original Total Human Model for Safety (THUMS) v4.01 (O-THUMS), a parametric THUMS (P-THUMS), and a subject-specific THUMS (S-THUMS). The P-THUMS geometry was predicted from only age, sex, stature, and BMI using our statistical geometry models of skeleton and body shape, while the S-THUMS geometry was based on each PMHS's CT data. The simulation results showed a preliminary trend that the correlations between the PTHUMS- predicted impact responses and the four PMHS tests (mean-CORA: 0.84, 0.78, 0.69, 0.70) were better than those between the O-THUMS and the normalized PMHS responses (mean-CORA: 0.74, 0.72, 0.55, 0.63), while they are similar to the correlations between S-THUMS and the PMHS tests (mean-CORA: 0.85, 0.85, 0.67, 0.72). The sensitivity analysis using the PTHUMS showed that, in side impact conditions, the HBM skeleton and body shape geometries as well as the body posture were more important in modeling the occupant impact responses than the bone and soft tissue material properties and the padding stiffness with the given parameter ranges. More investigations are needed to further support these findings.

Correlation between a Student's Performance on the Mental Cutting Test and Their 3D Parametric Modeling Ability

ERIC Educational Resources Information Center

Steinhauer, H. M.

2012-01-01

Engineering graphics has historically been viewed as a challenging course to teach as students struggle to grasp and understand the fundamental concepts and then to master their proper application. The emergence of stable, fast, affordable 3D parametric modeling platforms such as CATIA, Pro-E, and AutoCAD while providing several pedagogical…

Quantum Treatment of Two Coupled Oscillators in Interaction with a Two-Level Atom:

NASA Astrophysics Data System (ADS)

Khalil, E. M.; Abdalla, M. Sebawe; Obada, A. S.-F.

In this communication we handle a modified model representing the interaction between a two-level atom and two modes of the electromagnetic field in a cavity. The interaction between the modes is assumed to be of a parametric amplifier type. The model consists of two different systems, one represents the Jaynes-Cummings model (atom-field interaction) and the other represents the two mode parametric amplifier model (field-field interaction). After some canonical transformations the constants of the motion have been obtained and used to derive the time evolution operator. The wave function in the Schrödinger picture is constructed and employed to discuss some statistical properties related to the system. Further discussion related to the statistical properties of some physical quantities is given where we have taken into account an initial correlated pair-coherent state for the modes. We concentrate in our examination on the system behavior that occurred as a result of the variation of the parametric amplifier coupling parameter as well as the detuning parameter. It has been shown that the interaction of the parametric amplifier term increases the revival period and consequently longer period of strong interaction between the atom and the fields.

Numerical modeling of nonlinear modulation of coda wave interferometry in a multiple scattering medium with the presence of a localized micro-cracked zone

NASA Astrophysics Data System (ADS)

Chen, Guangzhi; Pageot, Damien; Legland, Jean-Baptiste; Abraham, Odile; Chekroun, Mathieu; Tournat, Vincent

2018-04-01

The spectral element method is used to perform a parametric sensitivity study of the nonlinear coda wave interferometry (NCWI) method in a homogeneous sample with localized damage [1]. The influence of a strong pump wave on a localized nonlinear damage zone is modeled as modifications to the elastic properties of an effective damage zone (EDZ), depending on the pump wave amplitude. The local change of the elastic modulus and the attenuation coefficient have been shown to vary linearly with respect to the excitation amplitude of the pump wave as in previous experimental studies of Zhang et al. [2]. In this study, the boundary conditions of the cracks, i.e. clapping effects is taken into account in the modeling of the damaged zone. The EDZ is then modeled with random cracks of random orientations, new parametric studies are established to model the pump wave influence with two new parameters: the change of the crack length and the crack density. The numerical results reported constitute another step towards quantification and forecasting of the nonlinear acoustic response of a cracked material, which proves to be necessary for quantitative non-destructive evaluation.

GRACILE: a comprehensive climatology of atmospheric gravity wave parameters based on satellite limb soundings

NASA Astrophysics Data System (ADS)

Ern, Manfred; Trinh, Quang Thai; Preusse, Peter; Gille, John C.; Mlynczak, Martin G.; Russell, James M., III; Riese, Martin

2018-04-01

Gravity waves are one of the main drivers of atmospheric dynamics. The spatial resolution of most global atmospheric models, however, is too coarse to properly resolve the small scales of gravity waves, which range from tens to a few thousand kilometers horizontally, and from below 1 km to tens of kilometers vertically. Gravity wave source processes involve even smaller scales. Therefore, general circulation models (GCMs) and chemistry climate models (CCMs) usually parametrize the effect of gravity waves on the global circulation. These parametrizations are very simplified. For this reason, comparisons with global observations of gravity waves are needed for an improvement of parametrizations and an alleviation of model biases. We present a gravity wave climatology based on atmospheric infrared limb emissions observed by satellite (GRACILE). GRACILE is a global data set of gravity wave distributions observed in the stratosphere and the mesosphere by the infrared limb sounding satellite instruments High Resolution Dynamics Limb Sounder (HIRDLS) and Sounding of the Atmosphere using Broadband Emission Radiometry (SABER). Typical distributions (zonal averages and global maps) of gravity wave vertical wavelengths and along-track horizontal wavenumbers are provided, as well as gravity wave temperature variances, potential energies and absolute momentum fluxes. This global data set captures the typical seasonal variations of these parameters, as well as their spatial variations. The GRACILE data set is suitable for scientific studies, and it can serve for comparison with other instruments (ground-based, airborne, or other satellite instruments) and for comparison with gravity wave distributions, both resolved and parametrized, in GCMs and CCMs. The GRACILE data set is available as supplementary data at https://doi.org/10.1594/PANGAEA.879658.

Spontaneous symmetry breaking in coupled parametrically driven waveguides.

PubMed

Dror, Nir; Malomed, Boris A

2009-01-01

We introduce a system of linearly coupled parametrically driven damped nonlinear Schrödinger equations, which models a laser based on a nonlinear dual-core waveguide with parametric amplification symmetrically applied to both cores. The model may also be realized in terms of parallel ferromagnetic films, in which the parametric gain is provided by an external field. We analyze spontaneous symmetry breaking (SSB) of fundamental and multiple solitons in this system, which was not studied systematically before in linearly coupled dissipative systems with intrinsic nonlinearity. For fundamental solitons, the analysis reveals three distinct SSB scenarios. Unlike the standard dual-core-fiber model, the present system gives rise to a vast bistability region, which may be relevant to applications. Other noteworthy findings are restabilization of the symmetric soliton after it was destabilized by the SSB bifurcation, and the existence of a generic situation with all solitons unstable in the single-component (decoupled) model, while both symmetric and asymmetric solitons may be stable in the coupled system. The stability of the asymmetric solitons is identified via direct simulations, while for symmetric and antisymmetric ones the stability is verified too through the computation of stability eigenvalues, families of antisymmetric solitons being entirely unstable. In this way, full stability maps for the symmetric solitons are produced. We also investigate the SSB bifurcation of two-soliton bound states (it breaks the symmetry between the two components, while the two peaks in the shape of the soliton remain mutually symmetric). The family of the asymmetric double-peak states may decouple from its symmetric counterpart, being no longer connected to it by the bifurcation, with a large portion of the asymmetric family remaining stable.

A climatology of gravity wave parameters based on satellite limb soundings

NASA Astrophysics Data System (ADS)

Ern, Manfred; Trinh, Quang Thai; Preusse, Peter; Riese, Martin

2017-04-01

Gravity waves are one of the main drivers of atmospheric dynamics. The resolution of most global circulation models (GCMs) and chemistry climate models (CCMs), however, is too coarse to properly resolve the small scales of gravity waves. Horizontal scales of gravity waves are in the range of tens to a few thousand kilometers. Gravity wave source processes involve even smaller scales. Therefore GCMs/CCMs usually parametrize the effect of gravity waves on the global circulation. These parametrizations are very simplified, and comparisons with global observations of gravity waves are needed for an improvement of parametrizations and an alleviation of model biases. In our study, we present a global data set of gravity wave distributions observed in the stratosphere and the mesosphere by the infrared limb sounding satellite instruments High Resolution Dynamics Limb Sounder (HIRDLS) and Sounding of the Atmosphere using Broadband Emission Radiometry (SABER). We provide various gravity wave parameters (for example, gravity variances, potential energies and absolute momentum fluxes). This comprehensive climatological data set can serve for comparison with other instruments (ground based, airborne, or other satellite instruments), as well as for comparison with gravity wave distributions, both resolved and parametrized, in GCMs and CCMs. The purpose of providing various different parameters is to make our data set useful for a large number of potential users and to overcome limitations of other observation techniques, or of models, that may be able to provide only one of those parameters. We present a climatology of typical average global distributions and of zonal averages, as well as their natural range of variations. In addition, we discuss seasonal variations of the global distribution of gravity waves, as well as limitations of our method of deriving gravity wave parameters from satellite data.

On performance of parametric and distribution-free models for zero-inflated and over-dispersed count responses.

PubMed

Tang, Wan; Lu, Naiji; Chen, Tian; Wang, Wenjuan; Gunzler, Douglas David; Han, Yu; Tu, Xin M

2015-10-30

Zero-inflated Poisson (ZIP) and negative binomial (ZINB) models are widely used to model zero-inflated count responses. These models extend the Poisson and negative binomial (NB) to address excessive zeros in the count response. By adding a degenerate distribution centered at 0 and interpreting it as describing a non-risk group in the population, the ZIP (ZINB) models a two-component population mixture. As in applications of Poisson and NB, the key difference between ZIP and ZINB is the allowance for overdispersion by the ZINB in its NB component in modeling the count response for the at-risk group. Overdispersion arising in practice too often does not follow the NB, and applications of ZINB to such data yield invalid inference. If sources of overdispersion are known, other parametric models may be used to directly model the overdispersion. Such models too are subject to assumed distributions. Further, this approach may not be applicable if information about the sources of overdispersion is unavailable. In this paper, we propose a distribution-free alternative and compare its performance with these popular parametric models as well as a moment-based approach proposed by Yu et al. [Statistics in Medicine 2013; 32: 2390-2405]. Like the generalized estimating equations, the proposed approach requires no elaborate distribution assumptions. Compared with the approach of Yu et al., it is more robust to overdispersed zero-inflated responses. We illustrate our approach with both simulated and real study data. Copyright © 2015 John Wiley & Sons, Ltd.

A Novel Computer-Aided Approach for Parametric Investigation of Custom Design of Fracture Fixation Plates.

PubMed

Chen, Xiaozhong; He, Kunjin; Chen, Zhengming

2017-01-01

The present study proposes an integrated computer-aided approach combining femur surface modeling, fracture evidence recover plate creation, and plate modification in order to conduct a parametric investigation of the design of custom plate for a specific patient. The study allows for improving the design efficiency of specific plates on the patients' femur parameters and the fracture information. Furthermore, the present approach will lead to exploration of plate modification and optimization. The three-dimensional (3D) surface model of a detailed femur and the corresponding fixation plate were represented with high-level feature parameters, and the shape of the specific plate was recursively modified in order to obtain the optimal plate for a specific patient. The proposed approach was tested and verified on a case study, and it could be helpful for orthopedic surgeons to design and modify the plate in order to fit the specific femur anatomy and the fracture information.

Quantitative estimation of source complexity in tsunami-source inversion

NASA Astrophysics Data System (ADS)

Dettmer, Jan; Cummins, Phil R.; Hawkins, Rhys; Jakir Hossen, M.

2016-04-01

This work analyses tsunami waveforms to infer the spatiotemporal evolution of sea-surface displacement (the tsunami source) caused by earthquakes or other sources. Since the method considers sea-surface displacement directly, no assumptions about the fault or seafloor deformation are required. While this approach has no ability to study seismic aspects of rupture, it greatly simplifies the tsunami source estimation, making it much less dependent on subjective fault and deformation assumptions. This results in a more accurate sea-surface displacement evolution in the source region. The spatial discretization is by wavelet decomposition represented by a trans-D Bayesian tree structure. Wavelet coefficients are sampled by a reversible jump algorithm and additional coefficients are only included when required by the data. Therefore, source complexity is consistent with data information (parsimonious) and the method can adapt locally in both time and space. Since the source complexity is unknown and locally adapts, no regularization is required, resulting in more meaningful displacement magnitudes. By estimating displacement uncertainties in a Bayesian framework we can study the effect of parametrization choice on the source estimate. Uncertainty arises from observation errors and limitations in the parametrization to fully explain the observations. As a result, parametrization choice is closely related to uncertainty estimation and profoundly affects inversion results. Therefore, parametrization selection should be included in the inference process. Our inversion method is based on Bayesian model selection, a process which includes the choice of parametrization in the inference process and makes it data driven. A trans-dimensional (trans-D) model for the spatio-temporal discretization is applied here to include model selection naturally and efficiently in the inference by sampling probabilistically over parameterizations. The trans-D process results in better uncertainty estimates since the parametrization adapts parsimoniously (in both time and space) according to the local data resolving power and the uncertainty about the parametrization choice is included in the uncertainty estimates. We apply the method to the tsunami waveforms recorded for the great 2011 Japan tsunami. All data are recorded on high-quality sensors (ocean-bottom pressure sensors, GPS gauges, and DART buoys). The sea-surface Green's functions are computed by JAGURS and include linear dispersion effects. By treating the noise level at each gauge as unknown, individual gauge contributions to the source estimate are appropriately and objectively weighted. The results show previously unreported detail of the source, quantify uncertainty spatially, and produce excellent data fits. The source estimate shows an elongated peak trench-ward from the hypo centre that closely follows the trench, indicating significant sea-floor deformation near the trench. Also notable is a bi-modal (negative to positive) displacement feature in the northern part of the source near the trench. The feature has ~2 m amplitude and is clearly resolved by the data with low uncertainties.

Parametric regression model for survival data: Weibull regression model as an example

PubMed Central

2016-01-01

Weibull regression model is one of the most popular forms of parametric regression model that it provides estimate of baseline hazard function, as well as coefficients for covariates. Because of technical difficulties, Weibull regression model is seldom used in medical literature as compared to the semi-parametric proportional hazard model. To make clinical investigators familiar with Weibull regression model, this article introduces some basic knowledge on Weibull regression model and then illustrates how to fit the model with R software. The SurvRegCensCov package is useful in converting estimated coefficients to clinical relevant statistics such as hazard ratio (HR) and event time ratio (ETR). Model adequacy can be assessed by inspecting Kaplan-Meier curves stratified by categorical variable. The eha package provides an alternative method to model Weibull regression model. The check.dist() function helps to assess goodness-of-fit of the model. Variable selection is based on the importance of a covariate, which can be tested using anova() function. Alternatively, backward elimination starting from a full model is an efficient way for model development. Visualization of Weibull regression model after model development is interesting that it provides another way to report your findings. PMID:28149846

Shuttle cryogenic supply system. Optimization study. Volume 5 B-1: Programmers manual for math models

NASA Technical Reports Server (NTRS)

1973-01-01

A computer program for rapid parametric evaluation of various types of cryogenics spacecraft systems is presented. The mathematical techniques of the program provide the capability for in-depth analysis combined with rapid problem solution for the production of a large quantity of soundly based trade-study data. The program requires a large data bank capable of providing characteristics performance data for a wide variety of component assemblies used in cryogenic systems. The program data requirements are divided into: (1) the semipermanent data tables and source data for performance characteristics and (2) the variable input data which contains input parameters which may be perturbated for parametric system studies.

Volterra model of the parametric array loudspeaker operating at ultrasonic frequencies.

PubMed

Shi, Chuang; Kajikawa, Yoshinobu

2016-11-01

The parametric array loudspeaker (PAL) is an application of the parametric acoustic array in air, which can be applied to transmit a narrow audio beam from an ultrasonic emitter. However, nonlinear distortion is very perceptible in the audio beam. Modulation methods to reduce the nonlinear distortion are available for on-axis far-field applications. For other applications, preprocessing techniques are wanting. In order to develop a preprocessing technique with general applicability to a wide range of operating conditions, the Volterra filter is investigated as a nonlinear model of the PAL in this paper. Limitations of the standard audio-to-audio Volterra filter are elaborated. An improved ultrasound-to-ultrasound Volterra filter is proposed and empirically demonstrated to be a more generic Volterra model of the PAL.

Parametric resonance in the early Universe—a fitting analysis

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

Figueroa, Daniel G.; Torrentí, Francisco, E-mail: daniel.figueroa@cern.ch, E-mail: f.torrenti@csic.es

Particle production via parametric resonance in the early Universe, is a non-perturbative, non-linear and out-of-equilibrium phenomenon. Although it is a well studied topic, whenever a new scenario exhibits parametric resonance, a full re-analysis is normally required. To avoid this tedious task, many works present often only a simplified linear treatment of the problem. In order to surpass this circumstance in the future, we provide a fitting analysis of parametric resonance through all its relevant stages: initial linear growth, non-linear evolution, and relaxation towards equilibrium. Using lattice simulations in an expanding grid in 3+1 dimensions, we parametrize the dynamics' outcome scanningmore » over the relevant ingredients: role of the oscillatory field, particle coupling strength, initial conditions, and background expansion rate. We emphasize the inaccuracy of the linear calculation of the decay time of the oscillatory field, and propose a more appropriate definition of this scale based on the subsequent non-linear dynamics. We provide simple fits to the relevant time scales and particle energy fractions at each stage. Our fits can be applied to post-inflationary preheating scenarios, where the oscillatory field is the inflaton, or to spectator-field scenarios, where the oscillatory field can be e.g. a curvaton, or the Standard Model Higgs.« less

Ground-Based Telescope Parametric Cost Model

NASA Technical Reports Server (NTRS)

Stahl, H. Philip; Rowell, Ginger Holmes

2004-01-01

A parametric cost model for ground-based telescopes is developed using multi-variable statistical analysis, The model includes both engineering and performance parameters. While diameter continues to be the dominant cost driver, other significant factors include primary mirror radius of curvature and diffraction limited wavelength. The model includes an explicit factor for primary mirror segmentation and/or duplication (i.e.. multi-telescope phased-array systems). Additionally, single variable models based on aperture diameter are derived. This analysis indicates that recent mirror technology advances have indeed reduced the historical telescope cost curve.

Incorporating external evidence in trial-based cost-effectiveness analyses: the use of resampling methods

PubMed Central

2014-01-01

Background Cost-effectiveness analyses (CEAs) that use patient-specific data from a randomized controlled trial (RCT) are popular, yet such CEAs are criticized because they neglect to incorporate evidence external to the trial. A popular method for quantifying uncertainty in a RCT-based CEA is the bootstrap. The objective of the present study was to further expand the bootstrap method of RCT-based CEA for the incorporation of external evidence. Methods We utilize the Bayesian interpretation of the bootstrap and derive the distribution for the cost and effectiveness outcomes after observing the current RCT data and the external evidence. We propose simple modifications of the bootstrap for sampling from such posterior distributions. Results In a proof-of-concept case study, we use data from a clinical trial and incorporate external evidence on the effect size of treatments to illustrate the method in action. Compared to the parametric models of evidence synthesis, the proposed approach requires fewer distributional assumptions, does not require explicit modeling of the relation between external evidence and outcomes of interest, and is generally easier to implement. A drawback of this approach is potential computational inefficiency compared to the parametric Bayesian methods. Conclusions The bootstrap method of RCT-based CEA can be extended to incorporate external evidence, while preserving its appealing features such as no requirement for parametric modeling of cost and effectiveness outcomes. PMID:24888356

Incorporating external evidence in trial-based cost-effectiveness analyses: the use of resampling methods.

PubMed

Sadatsafavi, Mohsen; Marra, Carlo; Aaron, Shawn; Bryan, Stirling

2014-06-03

Cost-effectiveness analyses (CEAs) that use patient-specific data from a randomized controlled trial (RCT) are popular, yet such CEAs are criticized because they neglect to incorporate evidence external to the trial. A popular method for quantifying uncertainty in a RCT-based CEA is the bootstrap. The objective of the present study was to further expand the bootstrap method of RCT-based CEA for the incorporation of external evidence. We utilize the Bayesian interpretation of the bootstrap and derive the distribution for the cost and effectiveness outcomes after observing the current RCT data and the external evidence. We propose simple modifications of the bootstrap for sampling from such posterior distributions. In a proof-of-concept case study, we use data from a clinical trial and incorporate external evidence on the effect size of treatments to illustrate the method in action. Compared to the parametric models of evidence synthesis, the proposed approach requires fewer distributional assumptions, does not require explicit modeling of the relation between external evidence and outcomes of interest, and is generally easier to implement. A drawback of this approach is potential computational inefficiency compared to the parametric Bayesian methods. The bootstrap method of RCT-based CEA can be extended to incorporate external evidence, while preserving its appealing features such as no requirement for parametric modeling of cost and effectiveness outcomes.

Real-time solution of linear computational problems using databases of parametric reduced-order models with arbitrary underlying meshes

NASA Astrophysics Data System (ADS)

Amsallem, David; Tezaur, Radek; Farhat, Charbel

2016-12-01

A comprehensive approach for real-time computations using a database of parametric, linear, projection-based reduced-order models (ROMs) based on arbitrary underlying meshes is proposed. In the offline phase of this approach, the parameter space is sampled and linear ROMs defined by linear reduced operators are pre-computed at the sampled parameter points and stored. Then, these operators and associated ROMs are transformed into counterparts that satisfy a certain notion of consistency. In the online phase of this approach, a linear ROM is constructed in real-time at a queried but unsampled parameter point by interpolating the pre-computed linear reduced operators on matrix manifolds and therefore computing an interpolated linear ROM. The proposed overall model reduction framework is illustrated with two applications: a parametric inverse acoustic scattering problem associated with a mockup submarine, and a parametric flutter prediction problem associated with a wing-tank system. The second application is implemented on a mobile device, illustrating the capability of the proposed computational framework to operate in real-time.

Solid state SPS microwave generation and transmission study. Volume 2, phase 2: Appendices

NASA Technical Reports Server (NTRS)

Maynard, O. E.

1980-01-01

The solid state sandwich concept for SPS was further defined. The design effort concentrated on the spacetenna, but did include some system analysis for parametric comparison reasons. Basic solid state microwave devices were defined and modeled. An initial conceptual subsystems and system design was performed as well as sidelobe control and system selection. The selected system concept and parametric solid state microwave power transmission system data were assessed relevant to the SPS concept. Although device efficiency was not a goal, the sensitivities to design of this efficiency were parametrically treated. Sidelobe control consisted of various single step tapers, multistep tapers and Gaussian tapers. A hybrid concept using tubes and solid state was evaluated. Thermal analyses are included with emphasis on sensitivities to waste heat radiator form factor, emissivity, absorptivity, amplifier efficiency, material and junction temperature.

Warm and cold pasta phase in relativistic mean field theory

NASA Astrophysics Data System (ADS)

Avancini, S. S.; Menezes, D. P.; Alloy, M. D.; Marinelli, J. R.; Moraes, M. M. W.; Providência, C.

2008-07-01

In the present article we investigate the onset of the pasta phase with different parametrizations of the nonlinear Walecka model. At zero temperature two different methods are used, one based on coexistent phases and the other on the Thomas-Fermi approximation. At finite temperature only the coexistence phases method is used. npe matter with fixed proton fractions and in β equilibrium is studied. The pasta phase decreases with the increase of temperature. The internal pasta structure and the beginning of the homogeneous phase vary depending on the proton fraction (or the imposition of β equilibrium), on the method used, and on the chosen parametrization. It is shown that a good parametrization of the surface tension with dependence on the temperature, proton fraction, and geometry is essential to describe correctly large isospin asymmetries and the transition from pasta to homogeneous matter.

Fitting Item Response Theory Models to Two Personality Inventories: Issues and Insights.

PubMed

Chernyshenko, O S; Stark, S; Chan, K Y; Drasgow, F; Williams, B

2001-10-01

The present study compared the fit of several IRT models to two personality assessment instruments. Data from 13,059 individuals responding to the US-English version of the Fifth Edition of the Sixteen Personality Factor Questionnaire (16PF) and 1,770 individuals responding to Goldberg's 50 item Big Five Personality measure were analyzed. Various issues pertaining to the fit of the IRT models to personality data were considered. We examined two of the most popular parametric models designed for dichotomously scored items (i.e., the two- and three-parameter logistic models) and a parametric model for polytomous items (Samejima's graded response model). Also examined were Levine's nonparametric maximum likelihood formula scoring models for dichotomous and polytomous data, which were previously found to provide good fits to several cognitive ability tests (Drasgow, Levine, Tsien, Williams, & Mead, 1995). The two- and three-parameter logistic models fit some scales reasonably well but not others; the graded response model generally did not fit well. The nonparametric formula scoring models provided the best fit of the models considered. Several implications of these findings for personality measurement and personnel selection were described.

Stability analysis of a time-periodic 2-dof MEMS structure

NASA Astrophysics Data System (ADS)

Kniffka, Till Jochen; Welte, Johannes; Ecker, Horst

2012-11-01

Microelectromechanical systems (MEMS) are becoming important for all kinds of industrial applications. Among them are filters in communication devices, due to the growing demand for efficient and accurate filtering of signals. In recent developments single degree of freedom (1-dof) oscillators, that are operated at a parametric resonances, are employed for such tasks. Typically vibration damping is low in such MEM systems. While parametric excitation (PE) is used so far to take advantage of a parametric resonance, this contribution suggests to also exploit parametric anti-resonances in order to improve the damping behavior of such systems. Modeling aspects of a 2-dof MEM system and first results of the analysis of the non-linear and the linearized system are the focus of this paper. In principle the investigated system is an oscillating mechanical system with two degrees of freedom x = [x1x2]T that can be described by Mx+Cx+K1x+K3(x2)x+Fes(x,V(t)) = 0. The system is inherently non-linear because of the cubic mechanical stiffness K3 of the structure, but also because of electrostatic forces (1+cos(ωt))Fes(x) that act on the system. Electrostatic forces are generated by comb drives and are proportional to the applied time-periodic voltage V(t). These drives also provide the means to introduce time-periodic coefficients, i.e. parametric excitation (1+cos(ωt)) with frequency ω. For a realistic MEM system the coefficients of the non-linear set of differential equations need to be scaled for efficient numerical treatment. The final mathematical model is a set of four non-linear time-periodic homogeneous differential equations of first order. Numerical results are obtained from two different methods. The linearized time-periodic (LTP) system is studied by calculating the Monodromy matrix of the system. The eigenvalues of this matrix decide on the stability of the LTP-system. To study the unabridged non-linear system, the bifurcation software ManLab is employed. Continuation analysis including stability evaluations are executed and show the frequency ranges for which the 2-dof system becomes unstable due to parametric resonances. Moreover, the existence of frequency intervals are shown where enhanced damping for the system is observed for this MEMS. The results from the stability studies are confirmed by simulation results.

An Application of Semi-parametric Estimator with Weighted Matrix of Data Depth in Variance Component Estimation

NASA Astrophysics Data System (ADS)

Pan, X. G.; Wang, J. Q.; Zhou, H. Y.

2013-05-01

The variance component estimation (VCE) based on semi-parametric estimator with weighted matrix of data depth has been proposed, because the coupling system model error and gross error exist in the multi-source heterogeneous measurement data of space and ground combined TT&C (Telemetry, Tracking and Command) technology. The uncertain model error has been estimated with the semi-parametric estimator model, and the outlier has been restrained with the weighted matrix of data depth. On the basis of the restriction of the model error and outlier, the VCE can be improved and used to estimate weighted matrix for the observation data with uncertain model error or outlier. Simulation experiment has been carried out under the circumstance of space and ground combined TT&C. The results show that the new VCE based on the model error compensation can determine the rational weight of the multi-source heterogeneous data, and restrain the outlier data.

Hybrid pathwise sensitivity methods for discrete stochastic models of chemical reaction systems.

PubMed

Wolf, Elizabeth Skubak; Anderson, David F

2015-01-21

Stochastic models are often used to help understand the behavior of intracellular biochemical processes. The most common such models are continuous time Markov chains (CTMCs). Parametric sensitivities, which are derivatives of expectations of model output quantities with respect to model parameters, are useful in this setting for a variety of applications. In this paper, we introduce a class of hybrid pathwise differentiation methods for the numerical estimation of parametric sensitivities. The new hybrid methods combine elements from the three main classes of procedures for sensitivity estimation and have a number of desirable qualities. First, the new methods are unbiased for a broad class of problems. Second, the methods are applicable to nearly any physically relevant biochemical CTMC model. Third, and as we demonstrate on several numerical examples, the new methods are quite efficient, particularly if one wishes to estimate the full gradient of parametric sensitivities. The methods are rather intuitive and utilize the multilevel Monte Carlo philosophy of splitting an expectation into separate parts and handling each in an efficient manner.

Cosmological implications of quantum mechanics parametrization of dark energy

NASA Astrophysics Data System (ADS)

Szydłowski, Marek; Stachowski, Aleksander; Urbanowski, Krzysztof

2017-08-01

We consider the cosmology with the running dark energy. The parametrization of dark energy is derived from the quantum process of transition from the false vacuum state to the true vacuum state. This model is the generalized interacting CDM model. We consider the energy density of dark energy parametrization, which is given by the Breit-Wigner energy distribution function. The idea of the process of the quantum mechanical decay of unstable states was formulated by Krauss and Dent. We used this idea in our considerations. In this model is an energy transfer in the dark sector. In this evolutional scenario the universe starts from the false vacuum state and goes to the true vacuum state of the present day universe. The intermediate regime during the passage from false to true vacuum states takes place. In this way the cosmological constant problem can be tried to solve. We estimate the cosmological parameters for this model. This model is in a good agreement with the astronomical data and is practically indistinguishable from CDM model.

Numerical analysis of the hot-gas-side and coolant-side heat transfer in liquid rocket engine combustors

NASA Technical Reports Server (NTRS)

Wang, Ten-See; Van, Luong

1992-01-01

The objective of this paper are to develop a multidisciplinary computational methodology to predict the hot-gas-side and coolant-side heat transfer and to use it in parametric studies to recommend optimized design of the coolant channels for a regeneratively cooled liquid rocket engine combustor. An integrated numerical model which incorporates CFD for the hot-gas thermal environment, and thermal analysis for the liner and coolant channels, was developed. This integrated CFD/thermal model was validated by comparing predicted heat fluxes with those of hot-firing test and industrial design methods for a 40 k calorimeter thrust chamber and the Space Shuttle Main Engine Main Combustion Chamber. Parametric studies were performed for the Advanced Main Combustion Chamber to find a strategy for a proposed combustion chamber coolant channel design.

Axially grooved heat pipe study

NASA Technical Reports Server (NTRS)

1977-01-01

A technology evaluation study on axially grooved heat pipes is presented. The state-of-the-art is reviewed and present and future requirements are identified. Analytical models, the Groove Analysis Program (GAP) and a closed form solution, were developed to facilitate parametric performance evaluations. GAP provides a numerical solution of the differential equations which govern the hydrodynamic flow. The model accounts for liquid recession, liquid/vapor shear interaction, puddle flow as well as laminar and turbulent vapor flow conditions. The closed form solution was developed to reduce computation time and complexity in parametric evaluations. It is applicable to laminar and ideal charge conditions, liquid/vapor shear interaction, and an empirical liquid flow factor which accounts for groove geometry and liquid recession effects. The validity of the closed form solution is verified by comparison with GAP predictions and measured data.

Pluripotency gene network dynamics: System views from parametric analysis.

PubMed

Akberdin, Ilya R; Omelyanchuk, Nadezda A; Fadeev, Stanislav I; Leskova, Natalya E; Oschepkova, Evgeniya A; Kazantsev, Fedor V; Matushkin, Yury G; Afonnikov, Dmitry A; Kolchanov, Nikolay A

2018-01-01

Multiple experimental data demonstrated that the core gene network orchestrating self-renewal and differentiation of mouse embryonic stem cells involves activity of Oct4, Sox2 and Nanog genes by means of a number of positive feedback loops among them. However, recent studies indicated that the architecture of the core gene network should also incorporate negative Nanog autoregulation and might not include positive feedbacks from Nanog to Oct4 and Sox2. Thorough parametric analysis of the mathematical model based on this revisited core regulatory circuit identified that there are substantial changes in model dynamics occurred depending on the strength of Oct4 and Sox2 activation and molecular complexity of Nanog autorepression. The analysis showed the existence of four dynamical domains with different numbers of stable and unstable steady states. We hypothesize that these domains can constitute the checkpoints in a developmental progression from naïve to primed pluripotency and vice versa. During this transition, parametric conditions exist, which generate an oscillatory behavior of the system explaining heterogeneity in expression of pluripotent and differentiation factors in serum ESC cultures. Eventually, simulations showed that addition of positive feedbacks from Nanog to Oct4 and Sox2 leads mainly to increase of the parametric space for the naïve ESC state, in which pluripotency factors are strongly expressed while differentiation ones are repressed.

Free response approach in a parametric system

NASA Astrophysics Data System (ADS)

Huang, Dishan; Zhang, Yueyue; Shao, Hexi

2017-07-01

In this study, a new approach to predict the free response in a parametric system is investigated. It is proposed in the special form of a trigonometric series with an exponentially decaying function of time, based on the concept of frequency splitting. By applying harmonic balance, the parametric vibration equation is transformed into an infinite set of homogeneous linear equations, from which the principal oscillation frequency can be computed, and all coefficients of harmonic components can be obtained. With initial conditions, arbitrary constants in a general solution can be determined. To analyze the computational accuracy and consistency, an approach error function is defined, which is used to assess the computational error in the proposed approach and in the standard numerical approach based on the Runge-Kutta algorithm. Furthermore, an example of a dynamic model of airplane wing flutter on a turbine engine is given to illustrate the applicability of the proposed approach. Numerical solutions show that the proposed approach exhibits high accuracy in mathematical expression, and it is valuable for theoretical research and engineering applications of parametric systems.

Investigating the possibility of a turning point in the dark energy equation of state

NASA Astrophysics Data System (ADS)

Hu, YaZhou; Li, Miao; Li, XiaoDong; Zhang, ZhenHui

2014-08-01

We investigate a second order parabolic parametrization, w( a) = w t + w a ( a t - a)2, which is a direct characterization of a possible turning in w. The cosmological consequence of this parametrization is explored by using the observational data of the SNLS3 type Ia supernovae sample, the CMB measurements from WMAP9 and Planck, the Hubble parameter measurement from HST, and the baryon acoustic oscillation (BAO) measurements from 6dFGS, BOSS DR11 and improved WiggleZ. We found the existence of a turning point in w at a ˜ 0.7 is favored at 1 σ CL. In the epoch 0.55 < a < 0.9, w < -1 is favored at 1 σ CL, and this significance increases near a = 0.8, reaching a 2 σ CL. The parabolic parametrization achieve equivalent performance to the ΛCDM and Chevallier-Polarski-Linder (CPL) models when the Akaike information criterion was used to assess them. Our analysis shows the value of considering high order parametrizations when studying the cosmological constraints on w.

Parameterization models for pesticide exposure via crop consumption.

PubMed

Fantke, Peter; Wieland, Peter; Juraske, Ronnie; Shaddick, Gavin; Itoiz, Eva Sevigné; Friedrich, Rainer; Jolliet, Olivier

2012-12-04

An approach for estimating human exposure to pesticides via consumption of six important food crops is presented that can be used to extend multimedia models applied in health risk and life cycle impact assessment. We first assessed the variation of model output (pesticide residues per kg applied) as a function of model input variables (substance, crop, and environmental properties) including their possible correlations using matrix algebra. We identified five key parameters responsible for between 80% and 93% of the variation in pesticide residues, namely time between substance application and crop harvest, degradation half-lives in crops and on crop surfaces, overall residence times in soil, and substance molecular weight. Partition coefficients also play an important role for fruit trees and tomato (Kow), potato (Koc), and lettuce (Kaw, Kow). Focusing on these parameters, we develop crop-specific models by parametrizing a complex fate and exposure assessment framework. The parametric models thereby reflect the framework's physical and chemical mechanisms and predict pesticide residues in harvest using linear combinations of crop, crop surface, and soil compartments. Parametric model results correspond well with results from the complex framework for 1540 substance-crop combinations with total deviations between a factor 4 (potato) and a factor 66 (lettuce). Predicted residues also correspond well with experimental data previously used to evaluate the complex framework. Pesticide mass in harvest can finally be combined with reduction factors accounting for food processing to estimate human exposure from crop consumption. All parametric models can be easily implemented into existing assessment frameworks.

Observations and simulations of microplastic marine debris in the ocean surface boundary layer

NASA Astrophysics Data System (ADS)

Kukulka, T.; Brunner, K.; Proskurowski, G. K.; Lavender Law, K. L.

2016-02-01

Motivated by observations of buoyant microplastic marine debris (MPMD) in the ocean surface boundary layer (OSBL), this study applies a large eddy simulation model and a parametric one-dimensional column model to examine vertical distributions of MPMD. MPMD is widely distributed in vast regions of the subtropical gyres and has emerged as a major open ocean pollutant whose distribution is subject to upper ocean turbulence. The models capture wind-driven turbulence, Langmuir turbulence (LT), and enhanced turbulent kinetic energy input due to breaking waves (BW). Model results are only consistent with MPMD observations if LT effects are included. Neither BW nor shear-driven turbulence is capable of deeply submerging MPMD, suggesting that the observed vertical MPMD distributions are a characteristic signature of wave-driven LT. Thus, this study demonstrates that LT substantially increases turbulent transport in the OSBL, resulting in deep submergence of buoyant tracers. The parametric model is applied to eleven years of observations in the North Atlantic and North Pacific subtropical gyres to show that surface measurements substantially underestimate MPMD concentrations by a factor of three to thirteen.

Circuit theory and model-based inference for landscape connectivity

USGS Publications Warehouse

Hanks, Ephraim M.; Hooten, Mevin B.

2013-01-01

Circuit theory has seen extensive recent use in the field of ecology, where it is often applied to study functional connectivity. The landscape is typically represented by a network of nodes and resistors, with the resistance between nodes a function of landscape characteristics. The effective distance between two locations on a landscape is represented by the resistance distance between the nodes in the network. Circuit theory has been applied to many other scientific fields for exploratory analyses, but parametric models for circuits are not common in the scientific literature. To model circuits explicitly, we demonstrate a link between Gaussian Markov random fields and contemporary circuit theory using a covariance structure that induces the necessary resistance distance. This provides a parametric model for second-order observations from such a system. In the landscape ecology setting, the proposed model provides a simple framework where inference can be obtained for effects that landscape features have on functional connectivity. We illustrate the approach through a landscape genetics study linking gene flow in alpine chamois (Rupicapra rupicapra) to the underlying landscape.

Two parametric voice source models and their asymptotic analysis

NASA Astrophysics Data System (ADS)

Leonov, A. S.; Sorokin, V. N.

2014-05-01

The paper studies the asymptotic behavior of the function for the area of the glottis near moments of its opening and closing for two mathematical voice source models. It is shown that in the first model, the asymptotics of the area function obeys a power law with an exponent of no less that 1. Detailed analysis makes it possible to refine these limits depending on the relative sizes of the intervals of a closed and open glottis. This work also studies another parametric model of the area of the glottis, which is based on a simplified physical-geometrical representation of vocal-fold vibration processes. This is a special variant of the well-known two-mass model and contains five parameters: the period of the main tone, equivalent masses on the lower and upper edge of vocal folds, the coefficient of elastic resistance of the lower vocal fold, and the delay time between openings of the upper and lower folds. It is established that the asymptotics of the obtained function for the area of the glottis obey a power law with an exponent of 1 both for opening and closing.

Efficient Parallel Levenberg-Marquardt Model Fitting towards Real-Time Automated Parametric Imaging Microscopy

PubMed Central

Zhu, Xiang; Zhang, Dianwen

2013-01-01

We present a fast, accurate and robust parallel Levenberg-Marquardt minimization optimizer, GPU-LMFit, which is implemented on graphics processing unit for high performance scalable parallel model fitting processing. GPU-LMFit can provide a dramatic speed-up in massive model fitting analyses to enable real-time automated pixel-wise parametric imaging microscopy. We demonstrate the performance of GPU-LMFit for the applications in superresolution localization microscopy and fluorescence lifetime imaging microscopy. PMID:24130785

A parametric model of muscle moment arm as a function of joint angle: application to the dorsiflexor muscle group in mice.

PubMed

Miller, S W; Dennis, R G

1996-12-01

A parametric model was developed to describe the relationship between muscle moment arm and joint angle. The model was applied to the dorsiflexor muscle group in mice, for which the moment arm was determined as a function of ankle angle. The moment arm was calculated from the torque measured about the ankle upon application of a known force along the line of action of the dorsiflexor muscle group. The dependence of the dorsiflexor moment arm on ankle angle was modeled as r = R sin(a + delta), where r is the moment arm calculated from the measured torque and a is the joint angle. A least-squares curve fit yielded values for R, the maximum moment arm, and delta, the angle at which the maximum moment arm occurs as offset from 90 degrees. Parametric models were developed for two strains of mice, and no differences were found between the moment arms determined for each strain. Values for the maximum moment arm, R, for the two different strains were 0.99 and 1.14 mm, in agreement with the limited data available from the literature. While in some cases moment arm data may be better fitted by a polynomial, use of the parametric model provides a moment arm relationship with meaningful anatomical constants, allowing for the direct comparison of moment arm characteristics between different strains and species.

Light Absorption Enhancement of Black Carbon Aerosol Constrained by Particle Morphology.

PubMed

Wu, Yu; Cheng, Tianhai; Liu, Dantong; Allan, James D; Zheng, Lijuan; Chen, Hao

2018-06-19

The radiative forcing of black carbon aerosol (BC) is one of the largest sources of uncertainty in climate change assessments. Contrasting results of BC absorption enhancement ( E abs ) after aging are estimated by field measurements and modeling studies, causing ambiguous parametrizations of BC solar absorption in climate models. Here we quantify E abs using a theoretical model parametrized by the complex particle morphology of BC in different aging scales. We show that E abs continuously increases with aging and stabilizes with a maximum of ∼3.5, suggesting that previous seemingly contrast results of E abs can be explicitly described by BC aging with corresponding particle morphology. We also report that current climate models using Mie Core-Shell model may overestimate E abs at a certain aging stage with a rapid rise of E abs , which is commonly observed in the ambient. A correction coefficient for this overestimation is suggested to improve model predictions of BC climate impact.

Wind Plant Power Optimization through Yaw Control using a Parametric Model for Wake Effects -- A CFD Simulation Study

DOE PAGES

Gebraad, P. M. O.; Teeuwisse, F. W.; van Wingerden, J. W.; ...

2016-01-01

This article presents a wind plant control strategy that optimizes the yaw settings of wind turbines for improved energy production of the whole wind plant by taking into account wake effects. The optimization controller is based on a novel internal parametric model for wake effects, called the FLOw Redirection and Induction in Steady-state (FLORIS) model. The FLORIS model predicts the steady-state wake locations and the effective flow velocities at each turbine, and the resulting turbine electrical energy production levels, as a function of the axial induction and the yaw angle of the different rotors. The FLORIS model has a limitedmore » number of parameters that are estimated based on turbine electrical power production data. In high-fidelity computational fluid dynamics simulations of a small wind plant, we demonstrate that the optimization control based on the FLORIS model increases the energy production of the wind plant, with a reduction of loads on the turbines as an additional effect.« less

Shape-Driven 3D Segmentation Using Spherical Wavelets

PubMed Central

Nain, Delphine; Haker, Steven; Bobick, Aaron; Tannenbaum, Allen

2013-01-01

This paper presents a novel active surface segmentation algorithm using a multiscale shape representation and prior. We define a parametric model of a surface using spherical wavelet functions and learn a prior probability distribution over the wavelet coefficients to model shape variations at different scales and spatial locations in a training set. Based on this representation, we derive a parametric active surface evolution using the multiscale prior coefficients as parameters for our optimization procedure to naturally include the prior in the segmentation framework. Additionally, the optimization method can be applied in a coarse-to-fine manner. We apply our algorithm to the segmentation of brain caudate nucleus, of interest in the study of schizophrenia. Our validation shows our algorithm is computationally efficient and outperforms the Active Shape Model algorithm by capturing finer shape details. PMID:17354875

Simulation and parametric study of a film-coated controlled-release pharmaceutical.

PubMed

Borgquist, Per; Zackrisson, Gunnar; Nilsson, Bernt; Axelsson, Anders

2002-04-23

Pharmaceutical formulations can be designed as Multiple Unit Systems, such as Roxiam CR, studied in this work. The dose is administrated as a capsule, which contains about 100 individual pellets, which in turn contain the active drug remoxipride. Experimental data for a large number of single pellets can be obtained by studying the release using microtitre plates. This makes it possible to study the release of the individual subunits making up the total dose. A mathematical model for simulating the release of remoxipride from single film-coated pellets is presented including internal and external mass transfer hindrance apart from the most important film resistance. The model can successfully simulate the release of remoxipride from single film-coated pellets if the lag phase of the experimental data is ignored. This was shown to have a minor influence on the release rate. The use of the present model is demonstrated by a parametric study showing that the release process is film-controlled, i.e. is limited by the mass transport through the polymer coating. The model was used to fit the film thickness and the drug loading to the experimental release data. The variation in the fitted values was similar to that obtained in the experiments.

Dynamic PET simulator via tomographic emission projection for kinetic modeling and parametric image studies.

PubMed

Häggström, Ida; Beattie, Bradley J; Schmidtlein, C Ross

2016-06-01

To develop and evaluate a fast and simple tool called dpetstep (Dynamic PET Simulator of Tracers via Emission Projection), for dynamic PET simulations as an alternative to Monte Carlo (MC), useful for educational purposes and evaluation of the effects of the clinical environment, postprocessing choices, etc., on dynamic and parametric images. The tool was developed in matlab using both new and previously reported modules of petstep (PET Simulator of Tracers via Emission Projection). Time activity curves are generated for each voxel of the input parametric image, whereby effects of imaging system blurring, counting noise, scatters, randoms, and attenuation are simulated for each frame. Each frame is then reconstructed into images according to the user specified method, settings, and corrections. Reconstructed images were compared to MC data, and simple Gaussian noised time activity curves (GAUSS). dpetstep was 8000 times faster than MC. Dynamic images from dpetstep had a root mean square error that was within 4% on average of that of MC images, whereas the GAUSS images were within 11%. The average bias in dpetstep and MC images was the same, while GAUSS differed by 3% points. Noise profiles in dpetstep images conformed well to MC images, confirmed visually by scatter plot histograms, and statistically by tumor region of interest histogram comparisons that showed no significant differences (p < 0.01). Compared to GAUSS, dpetstep images and noise properties agreed better with MC. The authors have developed a fast and easy one-stop solution for simulations of dynamic PET and parametric images, and demonstrated that it generates both images and subsequent parametric images with very similar noise properties to those of MC images, in a fraction of the time. They believe dpetstep to be very useful for generating fast, simple, and realistic results, however since it uses simple scatter and random models it may not be suitable for studies investigating these phenomena. dpetstep can be downloaded free of cost from https://github.com/CRossSchmidtlein/dPETSTEP.

Age-dependent biochemical quantities: an approach for calculating reference intervals.

PubMed

Bjerner, J

2007-01-01

A parametric method is often preferred when calculating reference intervals for biochemical quantities, as non-parametric methods are less efficient and require more observations/study subjects. Parametric methods are complicated, however, because of three commonly encountered features. First, biochemical quantities seldom display a Gaussian distribution, and there must either be a transformation procedure to obtain such a distribution or a more complex distribution has to be used. Second, biochemical quantities are often dependent on a continuous covariate, exemplified by rising serum concentrations of MUC1 (episialin, CA15.3) with increasing age. Third, outliers often exert substantial influence on parametric estimations and therefore need to be excluded before calculations are made. The International Federation of Clinical Chemistry (IFCC) currently recommends that confidence intervals be calculated for the reference centiles obtained. However, common statistical packages allowing for the adjustment of a continuous covariate do not make this calculation. In the method described in the current study, Tukey's fence is used to eliminate outliers and two-stage transformations (modulus-exponential-normal) in order to render Gaussian distributions. Fractional polynomials are employed to model functions for mean and standard deviations dependent on a covariate, and the model is selected by maximum likelihood. Confidence intervals are calculated for the fitted centiles by combining parameter estimation and sampling uncertainties. Finally, the elimination of outliers was made dependent on covariates by reiteration. Though a good knowledge of statistical theory is needed when performing the analysis, the current method is rewarding because the results are of practical use in patient care.

A comparative study between nonlinear regression and nonparametric approaches for modelling Phalaris paradoxa seedling emergence

USDA-ARS?s Scientific Manuscript database

Parametric non-linear regression (PNR) techniques commonly are used to develop weed seedling emergence models. Such techniques, however, require statistical assumptions that are difficult to meet. To examine and overcome these limitations, we compared PNR with a nonparametric estimation technique. F...

A Nonparametric Approach for Assessing Goodness-of-Fit of IRT Models in a Mixed Format Test

ERIC Educational Resources Information Center

Liang, Tie; Wells, Craig S.

2015-01-01

Investigating the fit of a parametric model plays a vital role in validating an item response theory (IRT) model. An area that has received little attention is the assessment of multiple IRT models used in a mixed-format test. The present study extends the nonparametric approach, proposed by Douglas and Cohen (2001), to assess model fit of three…

Parametric FEM for geometric biomembranes

NASA Astrophysics Data System (ADS)

Bonito, Andrea; Nochetto, Ricardo H.; Sebastian Pauletti, M.

2010-05-01

We consider geometric biomembranes governed by an L2-gradient flow for bending energy subject to area and volume constraints (Helfrich model). We give a concise derivation of a novel vector formulation, based on shape differential calculus, and corresponding discretization via parametric FEM using quadratic isoparametric elements and a semi-implicit Euler method. We document the performance of the new parametric FEM with a number of simulations leading to dumbbell, red blood cell and toroidal equilibrium shapes while exhibiting large deformations.

Failure Time Distributions: Estimates and Asymptotic Results.

DTIC Science & Technology

1980-01-01

of the models. A parametric family of distributions is proposed for approximating life distri- butions whose hazard rate is bath-tub shaped, this...of the limiting dirtributions of the models. A parametric family of distributions is proposed for approximating life distribution~s whose hazard rate...12. always justified. But, because of this gener- ality, the possible limit laws for the maximum form a very large family . The

Application of Group-Level Item Response Models in the Evaluation of Consumer Reports about Health Plan Quality

ERIC Educational Resources Information Center

Reise, Steven P.; Meijer, Rob R.; Ainsworth, Andrew T.; Morales, Leo S.; Hays, Ron D.

2006-01-01

Group-level parametric and non-parametric item response theory models were applied to the Consumer Assessment of Healthcare Providers and Systems (CAHPS[R]) 2.0 core items in a sample of 35,572 Medicaid recipients nested within 131 health plans. Results indicated that CAHPS responses are dominated by within health plan variation, and only weakly…

Z/sub n/ Baxter model: Critical behavior

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

Tracy, C.A.

1986-07-01

The Z/sub n/ Baxter Model is an exactly solvable lattice model in the special case of the Belavin parametrization. We calculate the critical behavior of Prob/sub n/ (q = w/sup k/) using techniques developed in number theory in the study of the congruence properties of p(m), the number of unrestricted partitions of an integer m.

A Methodology for the Parametric Reconstruction of Non-Steady and Noisy Meteorological Time Series

NASA Astrophysics Data System (ADS)

Rovira, F.; Palau, J. L.; Millán, M.

2009-09-01

Climatic and meteorological time series often show some persistence (in time) in the variability of certain features. One could regard annual, seasonal and diurnal time variability as trivial persistence in the variability of some meteorological magnitudes (as, e.g., global radiation, air temperature above surface, etc.). In these cases, the traditional Fourier transform into frequency space will show the principal harmonics as the components with the largest amplitude. Nevertheless, meteorological measurements often show other non-steady (in time) variability. Some fluctuations in measurements (at different time scales) are driven by processes that prevail on some days (or months) of the year but disappear on others. By decomposing a time series into time-frequency space through the continuous wavelet transformation, one is able to determine both the dominant modes of variability and how those modes vary in time. This study is based on a numerical methodology to analyse non-steady principal harmonics in noisy meteorological time series. This methodology combines both the continuous wavelet transform and the development of a parametric model that includes the time evolution of the principal and the most statistically significant harmonics of the original time series. The parameterisation scheme proposed in this study consists of reproducing the original time series by means of a statistically significant finite sum of sinusoidal signals (waves), each defined by using the three usual parameters: amplitude, frequency and phase. To ensure the statistical significance of the parametric reconstruction of the original signal, we propose a standard statistical t-student analysis of the confidence level of the amplitude in the parametric spectrum for the different wave components. Once we have assured the level of significance of the different waves composing the parametric model, we can obtain the statistically significant principal harmonics (in time) of the original time series by using the Fourier transform of the modelled signal. Acknowledgements The CEAM Foundation is supported by the Generalitat Valenciana and BANCAIXA (València, Spain). This study has been partially funded by the European Commission (FP VI, Integrated Project CIRCE - No. 036961) and by the Ministerio de Ciencia e Innovación, research projects "TRANSREG” (CGL2007-65359/CLI) and "GRACCIE” (CSD2007-00067, Program CONSOLIDER-INGENIO 2010).

Orbit Transfer Rocket Engine Technology Program, Advanced Engine Study Task D.6

DTIC Science & Technology

1992-02-28

l!J~iliiJl 1. Report No. 2. Government Accession No. 3 . Recipient’s Catalog No. NASA 187215 4. Title and Subtitle 5. Report Date ORBIT TRANSFER ROCKET...Engine Study, three primary subtasks were accomplished: 1) Design and Parametric Data, 2) Engine Requirement Variation Studies, and 3 ) Vehicle Study...Mixture Ratio Parametrics 18 3 . Thrust Parametrics Off-Design Mixture Ratio Scans 22 4. Expansion Area Ratio Parametrics 24 5. OTV 20 klbf Engine Off

Parametric modeling of the intervertebral disc space in 3D: application to CT images of the lumbar spine.

PubMed

Korez, Robert; Likar, Boštjan; Pernuš, Franjo; Vrtovec, Tomaž

2014-10-01

Gradual degeneration of intervertebral discs of the lumbar spine is one of the most common causes of low back pain. Although conservative treatment for low back pain may provide relief to most individuals, surgical intervention may be required for individuals with significant continuing symptoms, which is usually performed by replacing the degenerated intervertebral disc with an artificial implant. For designing implants with good bone contact and continuous force distribution, the morphology of the intervertebral disc space and vertebral body endplates is of considerable importance. In this study, we propose a method for parametric modeling of the intervertebral disc space in three dimensions (3D) and show its application to computed tomography (CT) images of the lumbar spine. The initial 3D model of the intervertebral disc space is generated according to the superquadric approach and therefore represented by a truncated elliptical cone, which is initialized by parameters obtained from 3D models of adjacent vertebral bodies. In an optimization procedure, the 3D model of the intervertebral disc space is incrementally deformed by adding parameters that provide a more detailed morphometric description of the observed shape, and aligned to the observed intervertebral disc space in the 3D image. By applying the proposed method to CT images of 20 lumbar spines, the shape and pose of each of the 100 intervertebral disc spaces were represented by a 3D parametric model. The resulting mean (±standard deviation) accuracy of modeling was 1.06±0.98mm in terms of radial Euclidean distance against manually defined ground truth points, with the corresponding success rate of 93% (i.e. 93 out of 100 intervertebral disc spaces were modeled successfully). As the resulting 3D models provide a description of the shape of intervertebral disc spaces in a complete parametric form, morphometric analysis was straightforwardly enabled and allowed the computation of the corresponding heights, widths and volumes, as well as of other geometric features that in detail describe the shape of intervertebral disc spaces. Copyright © 2014 Elsevier Ltd. All rights reserved.

Stability analysis of a controlled mechanical system with parametric uncertainties in LuGre friction model

NASA Astrophysics Data System (ADS)

Sun, Yun-Hsiang; Sun, Yuming; Wu, Christine Qiong; Sepehri, Nariman

2018-04-01

Parameters of friction model identified for a specific control system development are not constants. They vary over time and have a significant effect on the control system stability. Although much research has been devoted to the stability analysis under parametric uncertainty, less attention has been paid to incorporating a realistic friction model into their analysis. After reviewing the common friction models for controller design, a modified LuGre friction model is selected to carry out the stability analysis in this study. Two parameters of the LuGre model, namely σ0 and σ1, are critical to the demonstration of dynamic friction features, yet the identification of which is difficult to carry out, resulting in a high level of uncertainties in their values. Aiming at uncovering the effect of the σ0 and σ1 variations on the control system stability, a servomechanism with modified LuGre friction model is investigated. Two set-point position controllers are synthesised based on the servomechanism model to form two case studies. Through Lyapunov exponents, it is clear that the variation of σ0 and σ1 has an obvious effect on the stabiltiy of the studied systems and should not be overlooked in the design phase.

Long-range parametric amplification of THz wave with absorption loss exceeding parametric gain.

PubMed

Wang, Tsong-Dong; Huang, Yen-Chieh; Chuang, Ming-Yun; Lin, Yen-Hou; Lee, Ching-Han; Lin, Yen-Yin; Lin, Fan-Yi; Kitaeva, Galiya Kh

2013-01-28

Optical parametric mixing is a popular scheme to generate an idler wave at THz frequencies, although the THz wave is often absorbing in the nonlinear optical material. It is widely suggested that the useful material length for co-directional parametric mixing with strong THz-wave absorption is comparable to the THz-wave absorption length in the material. Here we show that, even in the limit of the absorption loss exceeding parametric gain, the THz idler wave can grows monotonically from optical parametric amplification over a much longer distance in a nonlinear optical material until pump depletion. The coherent production of the non-absorbing signal wave can assist the growth of the highly absorbing idler wave. We also show that, for the case of an equal input pump and signal in difference frequency generation, the quick saturation of the THz idler wave predicted from a much simplified and yet popular plane-wave model fails when fast diffraction of the THz wave from the co-propagating optical mixing waves is considered.

A new approach to correct the QT interval for changes in heart rate using a nonparametric regression model in beagle dogs.

PubMed

Watanabe, Hiroyuki; Miyazaki, Hiroyasu

2006-01-01

Over- and/or under-correction of QT intervals for changes in heart rate may lead to misleading conclusions and/or masking the potential of a drug to prolong the QT interval. This study examines a nonparametric regression model (Loess Smoother) to adjust the QT interval for differences in heart rate, with an improved fitness over a wide range of heart rates. 240 sets of (QT, RR) observations collected from each of 8 conscious and non-treated beagle dogs were used as the materials for investigation. The fitness of the nonparametric regression model to the QT-RR relationship was compared with four models (individual linear regression, common linear regression, and Bazett's and Fridericia's correlation models) with reference to Akaike's Information Criterion (AIC). Residuals were visually assessed. The bias-corrected AIC of the nonparametric regression model was the best of the models examined in this study. Although the parametric models did not fit, the nonparametric regression model improved the fitting at both fast and slow heart rates. The nonparametric regression model is the more flexible method compared with the parametric method. The mathematical fit for linear regression models was unsatisfactory at both fast and slow heart rates, while the nonparametric regression model showed significant improvement at all heart rates in beagle dogs.

Parameterized Finite Element Modeling and Buckling Analysis of Six Typical Composite Grid Cylindrical Shells

NASA Astrophysics Data System (ADS)

Lai, Changliang; Wang, Junbiao; Liu, Chuang

2014-10-01

Six typical composite grid cylindrical shells are constructed by superimposing three basic types of ribs. Then buckling behavior and structural efficiency of these shells are analyzed under axial compression, pure bending, torsion and transverse bending by finite element (FE) models. The FE models are created by a parametrical FE modeling approach that defines FE models with original natural twisted geometry and orients cross-sections of beam elements exactly. And the approach is parameterized and coded by Patran Command Language (PCL). The demonstrations of FE modeling indicate the program enables efficient generation of FE models and facilitates parametric studies and design of grid shells. Using the program, the effects of helical angles on the buckling behavior of six typical grid cylindrical shells are determined. The results of these studies indicate that the triangle grid and rotated triangle grid cylindrical shell are more efficient than others under axial compression and pure bending, whereas under torsion and transverse bending, the hexagon grid cylindrical shell is most efficient. Additionally, buckling mode shapes are compared and provide an understanding of composite grid cylindrical shells that is useful in preliminary design of such structures.

Carbon accounting and economic model uncertainty of emissions from biofuels-induced land use change.

PubMed

Plevin, Richard J; Beckman, Jayson; Golub, Alla A; Witcover, Julie; O'Hare, Michael

2015-03-03

Few of the numerous published studies of the emissions from biofuels-induced "indirect" land use change (ILUC) attempt to propagate and quantify uncertainty, and those that have done so have restricted their analysis to a portion of the modeling systems used. In this study, we pair a global, computable general equilibrium model with a model of greenhouse gas emissions from land-use change to quantify the parametric uncertainty in the paired modeling system's estimates of greenhouse gas emissions from ILUC induced by expanded production of three biofuels. We find that for the three fuel systems examined--US corn ethanol, Brazilian sugar cane ethanol, and US soybean biodiesel--95% of the results occurred within ±20 g CO2e MJ(-1) of the mean (coefficient of variation of 20-45%), with economic model parameters related to crop yield and the productivity of newly converted cropland (from forestry and pasture) contributing most of the variance in estimated ILUC emissions intensity. Although the experiments performed here allow us to characterize parametric uncertainty, changes to the model structure have the potential to shift the mean by tens of grams of CO2e per megajoule and further broaden distributions for ILUC emission intensities.

Multi-state modelling of repeated hospitalisation and death in patients with heart failure: The use of large administrative databases in clinical epidemiology.

PubMed

Ieva, Francesca; Jackson, Christopher H; Sharples, Linda D

2017-06-01

In chronic diseases like heart failure (HF), the disease course and associated clinical event histories for the patient population vary widely. To improve understanding of the prognosis of patients and enable health care providers to assess and manage resources, we wish to jointly model disease progression, mortality and their relation with patient characteristics. We show how episodes of hospitalisation for disease-related events, obtained from administrative data, can be used as a surrogate for disease status. We propose flexible multi-state models for serial hospital admissions and death in HF patients, that are able to accommodate important features of disease progression, such as multiple ordered events and competing risks. Fully parametric and semi-parametric semi-Markov models are implemented using freely available software in R. The models were applied to a dataset from the administrative data bank of the Lombardia region in Northern Italy, which included 15,298 patients who had a first hospitalisation ending in 2006 and 4 years of follow-up thereafter. This provided estimates of the associations of age and gender with rates of hospital admission and length of stay in hospital, and estimates of the expected total time spent in hospital over five years. For example, older patients and men were readmitted more frequently, though the total time in hospital was roughly constant with age. We also discuss the relative merits of parametric and semi-parametric multi-state models, and model assessment and comparison.

Scalability of the muscular action in a parametric 3D model of the index finger.

PubMed

Sancho-Bru, Joaquín L; Vergara, Margarita; Rodríguez-Cervantes, Pablo-Jesús; Giurintano, David J; Pérez-González, Antonio

2008-01-01

A method for scaling the muscle action is proposed and used to achieve a 3D inverse dynamic model of the human finger with all its components scalable. This method is based on scaling the physiological cross-sectional area (PCSA) in a Hill muscle model. Different anthropometric parameters and maximal grip force data have been measured and their correlations have been analyzed and used for scaling the PCSA of each muscle. A linear relationship between the normalized PCSA and the product of the length and breadth of the hand has been finally used for scaling, with a slope of 0.01315 cm(-2), with the length and breadth of the hand expressed in centimeters. The parametric muscle model has been included in a parametric finger model previously developed by the authors, and it has been validated reproducing the results of an experiment in which subjects from different population groups exerted maximal voluntary forces with their index finger in a controlled posture.

Modeling absolute differences in life expectancy with a censored skew-normal regression approach

PubMed Central

Clough-Gorr, Kerri; Zwahlen, Marcel

2015-01-01

Parameter estimates from commonly used multivariable parametric survival regression models do not directly quantify differences in years of life expectancy. Gaussian linear regression models give results in terms of absolute mean differences, but are not appropriate in modeling life expectancy, because in many situations time to death has a negative skewed distribution. A regression approach using a skew-normal distribution would be an alternative to parametric survival models in the modeling of life expectancy, because parameter estimates can be interpreted in terms of survival time differences while allowing for skewness of the distribution. In this paper we show how to use the skew-normal regression so that censored and left-truncated observations are accounted for. With this we model differences in life expectancy using data from the Swiss National Cohort Study and from official life expectancy estimates and compare the results with those derived from commonly used survival regression models. We conclude that a censored skew-normal survival regression approach for left-truncated observations can be used to model differences in life expectancy across covariates of interest. PMID:26339544

A statistical approach to bioclimatic trend detection in the airborne pollen records of Catalonia (NE Spain)

NASA Astrophysics Data System (ADS)

Fernández-Llamazares, Álvaro; Belmonte, Jordina; Delgado, Rosario; De Linares, Concepción

2014-04-01

Airborne pollen records are a suitable indicator for the study of climate change. The present work focuses on the role of annual pollen indices for the detection of bioclimatic trends through the analysis of the aerobiological spectra of 11 taxa of great biogeographical relevance in Catalonia over an 18-year period (1994-2011), by means of different parametric and non-parametric statistical methods. Among others, two non-parametric rank-based statistical tests were performed for detecting monotonic trends in time series data of the selected airborne pollen types and we have observed that they have similar power in detecting trends. Except for those cases in which the pollen data can be well-modeled by a normal distribution, it is better to apply non-parametric statistical methods to aerobiological studies. Our results provide a reliable representation of the pollen trends in the region and suggest that greater pollen quantities are being liberated to the atmosphere in the last years, specially by Mediterranean taxa such as Pinus, Total Quercus and Evergreen Quercus, although the trends may differ geographically. Longer aerobiological monitoring periods are required to corroborate these results and survey the increasing levels of certain pollen types that could exert an impact in terms of public health.

Laboratory modeling of edge wave generation over a plane beach by breaking waves

NASA Astrophysics Data System (ADS)

Abcha, Nizar; Ezersky, Alexander; Pelinovsky, Efim

2015-04-01

Edge waves play an important role in coastal hydrodynamics: in sediment transport, in formation of coastline structure and coastal bottom topography. Investigation of physical mechanisms leading to the edge waves generation allows us to determine their effect on the characteristics of spatially periodic patterns like crescent submarine bars and cusps observed in the coastal zone. In the present paper we investigate parametric excitation of edge wave with frequency two times less than the frequency of surface wave propagating perpendicular to the beach. Such mechanism of edge wave generation has been studied previously in a large number of papers using the assumption of non-breaking waves. This assumption was used in theoretical calculations and such conditions were created in laboratory experiments. In the natural conditions, the wave breaking is typical when edge waves are generated at sea beach. We study features of such processes in laboratory experiments. Experiments were performed in the wave flume of the Laboratory of Continental and Coast Morphodynamics (M2C), Caen. The flume is equipment with a wave maker controlled by computer. To model a plane beach, a PVC plate is placed at small angle to the horizontal bottom. Several resistive probes were used to measure characteristics of waves: one of them was used to measure free surface displacement near the wave maker and two probes were glued on the inclined plate. These probes allowed us to measure run-up due to parametrically excited edge waves. Run-up height is determined by processing a movie shot by high-speed camera. Sub-harmonic generation of standing edge waves is observed for definite control parameters: edge waves represent themselves a spatial mode with wavelength equal to double width of the flume; the frequency of edge wave is equal to half of surface wave frequency. Appearance of sub-harmonic mode instability is studied using probes and movie processing. The dependence of edge wave exponential growth rate index on the amplitude of surface wave is found. On the plane of parameters (amplitude - frequency) of surface wave we have found a region corresponding parametric instability leading to excitation of edge waves. It is shown that for small super criticalities, the amplitude of edge wave grows with amplitude of surface wave. For large amplitude of surface wave, wave breaking appears and parametric instability is suppressed. Such suppression of instability is caused by increasing of turbulent viscosity in near shore zone. It was shown that parametric excitation of edge wave can increase significantly (up to two times) the maximal run-up. Theoretical model is developed to explain suppression of instability due to turbulent viscosity. This theoretical model is based on nonlinear mode amplitude equation including terms responsible for parametric forcing, frequency detuning, nonlinear detuning, linear and nonlinear edge wave damping. Dependence of coefficients on turbulent viscosity is discussed.

Model for nucleon valence structure functions at all x, all p ⊥ and all Q 2 from the correspondence between QCD and DTU

NASA Astrophysics Data System (ADS)

Cohen-Tannoudji, G.; El Hassouni, A.; Mantrach, A.; Oudrhiri-Safiani, E. G.

1982-09-01

We propose a simple parametrization of the nucleon valence structure functions at all x, all p ⊥ and all Q 2. We use the DTU parton model to fix the parametrization at a reference point ( Q {0/2}=3 GeV2) and we mimic the QCD evolution by replacing the dimensioned parameters of the DTU parton model by functions depending on Q 2. Excellent agreement is obtained with existing data.

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

El-Atwani, O.; Norris, S. A.; Ludwig, K.

In this study, several proposed mechanisms and theoretical models exist concerning nanostructure evolution on III-V semiconductors (particularly GaSb) via ion beam irradiation. However, making quantitative contact between experiment on the one hand and model-parameter dependent predictions from different theories on the other is usually difficult. In this study, we take a different approach and provide an experimental investigation with a range of targets (GaSb, GaAs, GaP) and ion species (Ne, Ar, Kr, Xe) to determine new parametric trends regarding nanostructure evolution. Concurrently, atomistic simulations using binary collision approximation over the same ion/target combinations were performed to determine parametric trends onmore » several quantities related to existing model. A comparison of experimental and numerical trends reveals that the two are broadly consistent under the assumption that instabilities are driven by chemical instability based on phase separation. Furthermore, the atomistic simulations and a survey of material thermodynamic properties suggest that a plausible microscopic mechanism for this process is an ion-enhanced mobility associated with energy deposition by collision cascades.« less

Experimental Parametric Model for Indirect Adhesion Wear Measurement in the Dry Turning of UNS A97075 (Al-Zn) Alloy

PubMed Central

Trujillo, Francisco Javier; Sevilla, Lorenzo; Marcos, Mariano

2017-01-01

In this work, the study of the influence of cutting parameters (cutting speed, feed, and depth of cut) on the tool wear used in in the dry turning of cylindrical bars of the UNS A97075 (Al-Zn) alloy, has been analyzed. In addition, a study of the physicochemical mechanisms of the secondary adhesion wear has been carried out. The behavior of this alloy, from the point of view of tool wear, has been compared to similar aeronautical aluminum alloys, such as the UNS A92024 (Al-Cu) alloy and UNS A97050 (Al-Zn) alloy. Furthermore, a first approach to the measurement of the 2D surface of the adhered material on the rake face of the tool has been conducted. Finally, a parametric model has been developed from the experimental results. This model allows predicting the intensity of the secondary adhesion wear as a function of the cutting parameters applied. PMID:28772510

Spectral decompositions of multiple time series: a Bayesian non-parametric approach.

PubMed

Macaro, Christian; Prado, Raquel

2014-01-01

We consider spectral decompositions of multiple time series that arise in studies where the interest lies in assessing the influence of two or more factors. We write the spectral density of each time series as a sum of the spectral densities associated to the different levels of the factors. We then use Whittle's approximation to the likelihood function and follow a Bayesian non-parametric approach to obtain posterior inference on the spectral densities based on Bernstein-Dirichlet prior distributions. The prior is strategically important as it carries identifiability conditions for the models and allows us to quantify our degree of confidence in such conditions. A Markov chain Monte Carlo (MCMC) algorithm for posterior inference within this class of frequency-domain models is presented.We illustrate the approach by analyzing simulated and real data via spectral one-way and two-way models. In particular, we present an analysis of functional magnetic resonance imaging (fMRI) brain responses measured in individuals who participated in a designed experiment to study pain perception in humans.

A novel approach to study effects of asymmetric stiffness on parametric instabilities of multi-rotor-system

NASA Astrophysics Data System (ADS)

Jain, Anuj Kumar; Rastogi, Vikas; Agrawal, Atul Kumar

2018-01-01

The main focus of this paper is to study effects of asymmetric stiffness on parametric instabilities of multi-rotor-system through extended Lagrangian formalism, where symmetries are broken in terms of the rotor stiffness. The complete insight of dynamic behaviour of multi-rotor-system with asymmetries is evaluated through extension of Lagrangian equation with a case study. In this work, a dynamic mathematical model of a multi-rotor-system through a novel approach of extension of Lagrangian mechanics is developed, where the system is having asymmetries due to varying stiffness. The amplitude and the natural frequency of the rotor are obtained analytically through the proposed methodology. The bond graph modeling technique is used for modeling the asymmetric rotor. Symbol-shakti® software is used for the simulation of the model. The effects of the stiffness of multi-rotor-system on amplitude and frequencies are studied using numerical simulation. Simulation results show a considerable agreement with the theoretical results obtained through extended Lagrangian formalism. It is further shown that amplitude of the rotor increases inversely the stiffness of the rotor up to a certain limit, which is also affirmed theoretically.

Model-free estimation of the psychometric function

PubMed Central

Żychaluk, Kamila; Foster, David H.

2009-01-01

A subject's response to the strength of a stimulus is described by the psychometric function, from which summary measures, such as a threshold or slope, may be derived. Traditionally, this function is estimated by fitting a parametric model to the experimental data, usually the proportion of successful trials at each stimulus level. Common models include the Gaussian and Weibull cumulative distribution functions. This approach works well if the model is correct, but it can mislead if not. In practice, the correct model is rarely known. Here, a nonparametric approach based on local linear fitting is advocated. No assumption is made about the true model underlying the data, except that the function is smooth. The critical role of the bandwidth is identified, and its optimum value estimated by a cross-validation procedure. As a demonstration, seven vision and hearing data sets were fitted by the local linear method and by several parametric models. The local linear method frequently performed better and never worse than the parametric ones. Supplemental materials for this article can be downloaded from app.psychonomic-journals.org/content/supplemental. PMID:19633355

Parametric Model Based On Imputations Techniques for Partly Interval Censored Data

NASA Astrophysics Data System (ADS)

Zyoud, Abdallah; Elfaki, F. A. M.; Hrairi, Meftah

2017-12-01

The term ‘survival analysis’ has been used in a broad sense to describe collection of statistical procedures for data analysis. In this case, outcome variable of interest is time until an event occurs where the time to failure of a specific experimental unit might be censored which can be right, left, interval, and Partly Interval Censored data (PIC). In this paper, analysis of this model was conducted based on parametric Cox model via PIC data. Moreover, several imputation techniques were used, which are: midpoint, left & right point, random, mean, and median. Maximum likelihood estimate was considered to obtain the estimated survival function. These estimations were then compared with the existing model, such as: Turnbull and Cox model based on clinical trial data (breast cancer data), for which it showed the validity of the proposed model. Result of data set indicated that the parametric of Cox model proved to be more superior in terms of estimation of survival functions, likelihood ratio tests, and their P-values. Moreover, based on imputation techniques; the midpoint, random, mean, and median showed better results with respect to the estimation of survival function.

A Robust Adaptive Autonomous Approach to Optimal Experimental Design

NASA Astrophysics Data System (ADS)

Gu, Hairong

Experimentation is the fundamental tool of scientific inquiries to understand the laws governing the nature and human behaviors. Many complex real-world experimental scenarios, particularly in quest of prediction accuracy, often encounter difficulties to conduct experiments using an existing experimental procedure for the following two reasons. First, the existing experimental procedures require a parametric model to serve as the proxy of the latent data structure or data-generating mechanism at the beginning of an experiment. However, for those experimental scenarios of concern, a sound model is often unavailable before an experiment. Second, those experimental scenarios usually contain a large number of design variables, which potentially leads to a lengthy and costly data collection cycle. Incompetently, the existing experimental procedures are unable to optimize large-scale experiments so as to minimize the experimental length and cost. Facing the two challenges in those experimental scenarios, the aim of the present study is to develop a new experimental procedure that allows an experiment to be conducted without the assumption of a parametric model while still achieving satisfactory prediction, and performs optimization of experimental designs to improve the efficiency of an experiment. The new experimental procedure developed in the present study is named robust adaptive autonomous system (RAAS). RAAS is a procedure for sequential experiments composed of multiple experimental trials, which performs function estimation, variable selection, reverse prediction and design optimization on each trial. Directly addressing the challenges in those experimental scenarios of concern, function estimation and variable selection are performed by data-driven modeling methods to generate a predictive model from data collected during the course of an experiment, thus exempting the requirement of a parametric model at the beginning of an experiment; design optimization is performed to select experimental designs on the fly of an experiment based on their usefulness so that fewest designs are needed to reach useful inferential conclusions. Technically, function estimation is realized by Bayesian P-splines, variable selection is realized by Bayesian spike-and-slab prior, reverse prediction is realized by grid-search and design optimization is realized by the concepts of active learning. The present study demonstrated that RAAS achieves statistical robustness by making accurate predictions without the assumption of a parametric model serving as the proxy of latent data structure while the existing procedures can draw poor statistical inferences if a misspecified model is assumed; RAAS also achieves inferential efficiency by taking fewer designs to acquire useful statistical inferences than non-optimal procedures. Thus, RAAS is expected to be a principled solution to real-world experimental scenarios pursuing robust prediction and efficient experimentation.

Resolution of a Rank-Deficient Adjustment Model Via an Isomorphic Geometrical Setup with Tensor Structure.

DTIC Science & Technology

1987-03-01

would be transcribed as L =AX - V where L, X, and V are the vectors of constant terms, parametric corrections , and b_o bresiduals, respectively. The...tensor. a Just as du’ represents the parametric corrections in tensor notations, the necessary associated metric tensor a’ corresponds to the variance...observations, n residuals, and 0 n- parametric corrections to X (an initial set of parameters), respectively. b 0 b The vctor L is formed as 1. L where

Cluster-level statistical inference in fMRI datasets: The unexpected behavior of random fields in high dimensions.

PubMed

Bansal, Ravi; Peterson, Bradley S

2018-06-01

Identifying regional effects of interest in MRI datasets usually entails testing a priori hypotheses across many thousands of brain voxels, requiring control for false positive findings in these multiple hypotheses testing. Recent studies have suggested that parametric statistical methods may have incorrectly modeled functional MRI data, thereby leading to higher false positive rates than their nominal rates. Nonparametric methods for statistical inference when conducting multiple statistical tests, in contrast, are thought to produce false positives at the nominal rate, which has thus led to the suggestion that previously reported studies should reanalyze their fMRI data using nonparametric tools. To understand better why parametric methods may yield excessive false positives, we assessed their performance when applied both to simulated datasets of 1D, 2D, and 3D Gaussian Random Fields (GRFs) and to 710 real-world, resting-state fMRI datasets. We showed that both the simulated 2D and 3D GRFs and the real-world data contain a small percentage (<6%) of very large clusters (on average 60 times larger than the average cluster size), which were not present in 1D GRFs. These unexpectedly large clusters were deemed statistically significant using parametric methods, leading to empirical familywise error rates (FWERs) as high as 65%: the high empirical FWERs were not a consequence of parametric methods failing to model spatial smoothness accurately, but rather of these very large clusters that are inherently present in smooth, high-dimensional random fields. In fact, when discounting these very large clusters, the empirical FWER for parametric methods was 3.24%. Furthermore, even an empirical FWER of 65% would yield on average less than one of those very large clusters in each brain-wide analysis. Nonparametric methods, in contrast, estimated distributions from those large clusters, and therefore, by construct rejected the large clusters as false positives at the nominal FWERs. Those rejected clusters were outlying values in the distribution of cluster size but cannot be distinguished from true positive findings without further analyses, including assessing whether fMRI signal in those regions correlates with other clinical, behavioral, or cognitive measures. Rejecting the large clusters, however, significantly reduced the statistical power of nonparametric methods in detecting true findings compared with parametric methods, which would have detected most true findings that are essential for making valid biological inferences in MRI data. Parametric analyses, in contrast, detected most true findings while generating relatively few false positives: on average, less than one of those very large clusters would be deemed a true finding in each brain-wide analysis. We therefore recommend the continued use of parametric methods that model nonstationary smoothness for cluster-level, familywise control of false positives, particularly when using a Cluster Defining Threshold of 2.5 or higher, and subsequently assessing rigorously the biological plausibility of the findings, even for large clusters. Finally, because nonparametric methods yielded a large reduction in statistical power to detect true positive findings, we conclude that the modest reduction in false positive findings that nonparametric analyses afford does not warrant a re-analysis of previously published fMRI studies using nonparametric techniques. Copyright © 2018 Elsevier Inc. All rights reserved.

Predicting ecosystem dynamics at regional scales: an evaluation of a terrestrial biosphere model for the forests of northeastern North America.

PubMed

Medvigy, David; Moorcroft, Paul R

2012-01-19

Terrestrial biosphere models are important tools for diagnosing both the current state of the terrestrial carbon cycle and forecasting terrestrial ecosystem responses to global change. While there are a number of ongoing assessments of the short-term predictive capabilities of terrestrial biosphere models using flux-tower measurements, to date there have been relatively few assessments of their ability to predict longer term, decadal-scale biomass dynamics. Here, we present the results of a regional-scale evaluation of the Ecosystem Demography version 2 (ED2)-structured terrestrial biosphere model, evaluating the model's predictions against forest inventory measurements for the northeast USA and Quebec from 1985 to 1995. Simulations were conducted using a default parametrization, which used parameter values from the literature, and a constrained model parametrization, which had been developed by constraining the model's predictions against 2 years of measurements from a single site, Harvard Forest (42.5° N, 72.1° W). The analysis shows that the constrained model parametrization offered marked improvements over the default model formulation, capturing large-scale variation in patterns of biomass dynamics despite marked differences in climate forcing, land-use history and species-composition across the region. These results imply that data-constrained parametrizations of structured biosphere models such as ED2 can be successfully used for regional-scale ecosystem prediction and forecasting. We also assess the model's ability to capture sub-grid scale heterogeneity in the dynamics of biomass growth and mortality of different sizes and types of trees, and then discuss the implications of these analyses for further reducing the remaining biases in the model's predictions.

Zero- vs. one-dimensional, parametric vs. non-parametric, and confidence interval vs. hypothesis testing procedures in one-dimensional biomechanical trajectory analysis.

PubMed

Pataky, Todd C; Vanrenterghem, Jos; Robinson, Mark A

2015-05-01

Biomechanical processes are often manifested as one-dimensional (1D) trajectories. It has been shown that 1D confidence intervals (CIs) are biased when based on 0D statistical procedures, and the non-parametric 1D bootstrap CI has emerged in the Biomechanics literature as a viable solution. The primary purpose of this paper was to clarify that, for 1D biomechanics datasets, the distinction between 0D and 1D methods is much more important than the distinction between parametric and non-parametric procedures. A secondary purpose was to demonstrate that a parametric equivalent to the 1D bootstrap exists in the form of a random field theory (RFT) correction for multiple comparisons. To emphasize these points we analyzed six datasets consisting of force and kinematic trajectories in one-sample, paired, two-sample and regression designs. Results showed, first, that the 1D bootstrap and other 1D non-parametric CIs were qualitatively identical to RFT CIs, and all were very different from 0D CIs. Second, 1D parametric and 1D non-parametric hypothesis testing results were qualitatively identical for all six datasets. Last, we highlight the limitations of 1D CIs by demonstrating that they are complex, design-dependent, and thus non-generalizable. These results suggest that (i) analyses of 1D data based on 0D models of randomness are generally biased unless one explicitly identifies 0D variables before the experiment, and (ii) parametric and non-parametric 1D hypothesis testing provide an unambiguous framework for analysis when one׳s hypothesis explicitly or implicitly pertains to whole 1D trajectories. Copyright © 2015 Elsevier Ltd. All rights reserved.

Estimation Model of Spacecraft Parameters and Cost Based on a Statistical Analysis of COMPASS Designs

NASA Technical Reports Server (NTRS)

Gerberich, Matthew W.; Oleson, Steven R.

2013-01-01

The Collaborative Modeling for Parametric Assessment of Space Systems (COMPASS) team at Glenn Research Center has performed integrated system analysis of conceptual spacecraft mission designs since 2006 using a multidisciplinary concurrent engineering process. The set of completed designs was archived in a database, to allow for the study of relationships between design parameters. Although COMPASS uses a parametric spacecraft costing model, this research investigated the possibility of using a top-down approach to rapidly estimate the overall vehicle costs. This paper presents the relationships between significant design variables, including breakdowns of dry mass, wet mass, and cost. It also develops a model for a broad estimate of these parameters through basic mission characteristics, including the target location distance, the payload mass, the duration, the delta-v requirement, and the type of mission, propulsion, and electrical power. Finally, this paper examines the accuracy of this model in regards to past COMPASS designs, with an assessment of outlying spacecraft, and compares the results to historical data of completed NASA missions.

Adjusting Expected Mortality Rates Using Information From a Control Population: An Example Using Socioeconomic Status.

PubMed

Bower, Hannah; Andersson, Therese M-L; Crowther, Michael J; Dickman, Paul W; Lambe, Mats; Lambert, Paul C

2018-04-01

Expected or reference mortality rates are commonly used in the calculation of measures such as relative survival in population-based cancer survival studies and standardized mortality ratios. These expected rates are usually presented according to age, sex, and calendar year. In certain situations, stratification of expected rates by other factors is required to avoid potential bias if interest lies in quantifying measures according to such factors as, for example, socioeconomic status. If data are not available on a population level, information from a control population could be used to adjust expected rates. We have presented two approaches for adjusting expected mortality rates using information from a control population: a Poisson generalized linear model and a flexible parametric survival model. We used a control group from BCBaSe-a register-based, matched breast cancer cohort in Sweden with diagnoses between 1992 and 2012-to illustrate the two methods using socioeconomic status as a risk factor of interest. Results showed that Poisson and flexible parametric survival approaches estimate similar adjusted mortality rates according to socioeconomic status. Additional uncertainty involved in the methods to estimate stratified, expected mortality rates described in this study can be accounted for using a parametric bootstrap, but this might make little difference if using a large control population.

Super learning to hedge against incorrect inference from arbitrary parametric assumptions in marginal structural modeling.

PubMed

Neugebauer, Romain; Fireman, Bruce; Roy, Jason A; Raebel, Marsha A; Nichols, Gregory A; O'Connor, Patrick J

2013-08-01

Clinical trials are unlikely to ever be launched for many comparative effectiveness research (CER) questions. Inferences from hypothetical randomized trials may however be emulated with marginal structural modeling (MSM) using observational data, but success in adjusting for time-dependent confounding and selection bias typically relies on parametric modeling assumptions. If these assumptions are violated, inferences from MSM may be inaccurate. In this article, we motivate the application of a data-adaptive estimation approach called super learning (SL) to avoid reliance on arbitrary parametric assumptions in CER. Using the electronic health records data from adults with new-onset type 2 diabetes, we implemented MSM with inverse probability weighting (IPW) estimation to evaluate the effect of three oral antidiabetic therapies on the worsening of glomerular filtration rate. Inferences from IPW estimation were noticeably sensitive to the parametric assumptions about the associations between both the exposure and censoring processes and the main suspected source of confounding, that is, time-dependent measurements of hemoglobin A1c. SL was successfully implemented to harness flexible confounding and selection bias adjustment from existing machine learning algorithms. Erroneous IPW inference about clinical effectiveness because of arbitrary and incorrect modeling decisions may be avoided with SL. Copyright © 2013 Elsevier Inc. All rights reserved.

Parametric Analysis of Cyclic Phase Change and Energy Storage in Solar Heat Receivers

NASA Technical Reports Server (NTRS)

Hall, Carsie A., III; Glakpe, Emmanuel K.; Cannon, Joseph N.; Kerslake, Thomas W.

1997-01-01

A parametric study on cyclic melting and freezing of an encapsulated phase change material (PCM), integrated into a solar heat receiver, has been performed. The cyclic nature of the present melt/freeze problem is relevant to latent heat thermal energy storage (LHTES) systems used to power solar Brayton engines in microgravity environments. Specifically, a physical and numerical model of the solar heat receiver component of NASA Lewis Research Center's Ground Test Demonstration (GTD) project was developed. Multi-conjugate effects such as the convective fluid flow of a low-Prandtl-number fluid, coupled with thermal conduction in the phase change material, containment tube and working fluid conduit were accounted for in the model. A single-band thermal radiation model was also included to quantify reradiative energy exchange inside the receiver and losses through the aperture. The eutectic LiF-CaF2 was used as the phase change material (PCM) and a mixture of He/Xe was used as the working fluid coolant. A modified version of the computer code HOTTube was used to generate results in the two-phase regime. Results indicate that parametric changes in receiver gas inlet temperature and receiver heat input effects higher sensitivity to changes in receiver gas exit temperatures.

Analysis of a Rocket Based Combined Cycle Engine during Rocket Only Operation

NASA Technical Reports Server (NTRS)

Smith, T. D.; Steffen, C. J., Jr.; Yungster, S.; Keller, D. J.

1998-01-01

The all rocket mode of operation is a critical factor in the overall performance of a rocket based combined cycle (RBCC) vehicle. However, outside of performing experiments or a full three dimensional analysis, there are no first order parametric models to estimate performance. As a result, an axisymmetric RBCC engine was used to analytically determine specific impulse efficiency values based upon both full flow and gas generator configurations. Design of experiments methodology was used to construct a test matrix and statistical regression analysis was used to build parametric models. The main parameters investigated in this study were: rocket chamber pressure, rocket exit area ratio, percent of injected secondary flow, mixer-ejector inlet area, mixer-ejector area ratio, and mixer-ejector length-to-inject diameter ratio. A perfect gas computational fluid dynamics analysis was performed to obtain values of vacuum specific impulse. Statistical regression analysis was performed based on both full flow and gas generator engine cycles. Results were also found to be dependent upon the entire cycle assumptions. The statistical regression analysis determined that there were five significant linear effects, six interactions, and one second-order effect. Two parametric models were created to provide performance assessments of an RBCC engine in the all rocket mode of operation.

PARAMETRIC DISTANCE WEIGHTING OF LANDSCAPE INFLUENCE ON STREAMS

EPA Science Inventory

We present a parametric model for estimating the areas within watersheds whose land use best predicts indicators of stream ecological condition. We regress a stream response variable on the distance-weighted proportion of watershed area that has a specific land use, such as agric...

More than the sum of its parts: Coarse-grained peptide-lipid interactions from a simple cross-parametrization

NASA Astrophysics Data System (ADS)

Bereau, Tristan; Wang, Zun-Jing; Deserno, Markus

2014-03-01

Interfacial systems are at the core of fascinating phenomena in many disciplines, such as biochemistry, soft-matter physics, and food science. However, the parametrization of accurate, reliable, and consistent coarse-grained (CG) models for systems at interfaces remains a challenging endeavor. In the present work, we explore to what extent two independently developed solvent-free CG models of peptides and lipids—of different mapping schemes, parametrization methods, target functions, and validation criteria—can be combined by only tuning the cross-interactions. Our results show that the cross-parametrization can reproduce a number of structural properties of membrane peptides (for example, tilt and hydrophobic mismatch), in agreement with existing peptide-lipid CG force fields. We find encouraging results for two challenging biophysical problems: (i) membrane pore formation mediated by the cooperative action of several antimicrobial peptides, and (ii) the insertion and folding of the helix-forming peptide WALP23 in the membrane.

Parametric and non-parametric approach for sensory RATA (Rate-All-That-Apply) method of ledre profile attributes

NASA Astrophysics Data System (ADS)

Hastuti, S.; Harijono; Murtini, E. S.; Fibrianto, K.

2018-03-01

This current study is aimed to investigate the use of parametric and non-parametric approach for sensory RATA (Rate-All-That-Apply) method. Ledre as Bojonegoro unique local food product was used as point of interest, in which 319 panelists were involved in the study. The result showed that ledre is characterized as easy-crushed texture, sticky in mouth, stingy sensation and easy to swallow. It has also strong banana flavour with brown in colour. Compared to eggroll and semprong, ledre has more variances in terms of taste as well the roll length. As RATA questionnaire is designed to collect categorical data, non-parametric approach is the common statistical procedure. However, similar results were also obtained as parametric approach, regardless the fact of non-normal distributed data. Thus, it suggests that parametric approach can be applicable for consumer study with large number of respondents, even though it may not satisfy the assumption of ANOVA (Analysis of Variances).

Entangled Parametric Hierarchies: Problems for an Overspecified Universal Grammar

PubMed Central

Boeckx, Cedric; Leivada, Evelina

2013-01-01

This study addresses the feasibility of the classical notion of parameter in linguistic theory from the perspective of parametric hierarchies. A novel program-based analysis is implemented in order to show certain empirical problems related to these hierarchies. The program was developed on the basis of an enriched data base spanning 23 contemporary and 5 ancient languages. The empirical issues uncovered cast doubt on classical parametric models of language acquisition as well as on the conceptualization of an overspecified Universal Grammar that has parameters among its primitives. Pinpointing these issues leads to the proposal that (i) the (bio)logical problem of language acquisition does not amount to a process of triggering innately pre-wired values of parameters and (ii) it paves the way for viewing language, epigenetic (‘parametric’) variation as an externalization-related epiphenomenon, whose learning component may be more important than what sometimes is assumed. PMID:24019867

Tuning a climate model using nudging to reanalysis.

NASA Astrophysics Data System (ADS)

Cheedela, S. K.; Mapes, B. E.

2014-12-01

Tuning a atmospheric general circulation model involves a daunting task of adjusting non-observable parameters to adjust the mean climate. These parameters arise from necessity to describe unresolved flow through parametrizations. Tuning a climate model is often done with certain set of priorities, such as global mean temperature, net top of the atmosphere radiation. These priorities are hard enough to reach let alone reducing systematic biases in the models. The goal of currently study is to explore alternate ways to tune a climate model to reduce some systematic biases that can be used in synergy with existing efforts. Nudging a climate model to a known state is a poor man's inverse of tuning process described above. Our approach involves nudging the atmospheric model to state of art reanalysis fields thereby providing a balanced state with respect to the global mean temperature and winds. The tendencies derived from nudging are negative of errors from physical parametrizations as the errors from dynamical core would be small. Patterns of nudging are compared to the patterns of different physical parametrizations to decipher the cause for certain biases in relation to tuning parameters. This approach might also help in understanding certain compensating errors that arise from tuning process. ECHAM6 is a comprehensive general model, also used in recent Coupled Model Intercomparision Project(CMIP5). The approach used to tune it and effect of certain parameters that effect its mean climate are reported clearly, hence it serves as a benchmark for our approach. Our planned experiments include nudging ECHAM6 atmospheric model to European Center Reanalysis (ERA-Interim) and reanalysis from National Center for Environmental Prediction (NCEP) and decipher choice of certain parameters that lead to systematic biases in its simulations. Of particular interest are reducing long standing biases related to simulation of Asian summer monsoon.

Direct adaptive robust tracking control for 6 DOF industrial robot with enhanced accuracy.

PubMed

Yin, Xiuxing; Pan, Li

2018-01-01

A direct adaptive robust tracking control is proposed for trajectory tracking of 6 DOF industrial robot in the presence of parametric uncertainties, external disturbances and uncertain nonlinearities. The controller is designed based on the dynamic characteristics in the working space of the end-effector of the 6 DOF robot. The controller includes robust control term and model compensation term that is developed directly based on the input reference or desired motion trajectory. A projection-type parametric adaptation law is also designed to compensate for parametric estimation errors for the adaptive robust control. The feasibility and effectiveness of the proposed direct adaptive robust control law and the associated projection-type parametric adaptation law have been comparatively evaluated based on two 6 DOF industrial robots. The test results demonstrate that the proposed control can be employed to better maintain the desired trajectory tracking even in the presence of large parametric uncertainties and external disturbances as compared with PD controller and nonlinear controller. The parametric estimates also eventually converge to the real values along with the convergence of tracking errors, which further validate the effectiveness of the proposed parametric adaption law. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

Analysis of Lateral Rail Restraint.

DOT National Transportation Integrated Search

1983-09-01

This report deals with the analysis of lateral rail strength using the results of experimental investigations and a nonlinear rail response model. Part of the analysis involves the parametric study of the influence of track parameters on lateral rail...

Estimating restricted mean treatment effects with stacked survival models

PubMed Central

Wey, Andrew; Vock, David M.; Connett, John; Rudser, Kyle

2016-01-01

The difference in restricted mean survival times between two groups is a clinically relevant summary measure. With observational data, there may be imbalances in confounding variables between the two groups. One approach to account for such imbalances is estimating a covariate-adjusted restricted mean difference by modeling the covariate-adjusted survival distribution, and then marginalizing over the covariate distribution. Since the estimator for the restricted mean difference is defined by the estimator for the covariate-adjusted survival distribution, it is natural to expect that a better estimator of the covariate-adjusted survival distribution is associated with a better estimator of the restricted mean difference. We therefore propose estimating restricted mean differences with stacked survival models. Stacked survival models estimate a weighted average of several survival models by minimizing predicted error. By including a range of parametric, semi-parametric, and non-parametric models, stacked survival models can robustly estimate a covariate-adjusted survival distribution and, therefore, the restricted mean treatment effect in a wide range of scenarios. We demonstrate through a simulation study that better performance of the covariate-adjusted survival distribution often leads to better mean-squared error of the restricted mean difference although there are notable exceptions. In addition, we demonstrate that the proposed estimator can perform nearly as well as Cox regression when the proportional hazards assumption is satisfied and significantly better when proportional hazards is violated. Finally, the proposed estimator is illustrated with data from the United Network for Organ Sharing to evaluate post-lung transplant survival between large and small-volume centers. PMID:26934835

Aerodynamic parameter studies and sensitivity analysis for rotor blades in axial flight

NASA Technical Reports Server (NTRS)

Chiu, Y. Danny; Peters, David A.

1991-01-01

The analytical capability is offered for aerodynamic parametric studies and sensitivity analyses of rotary wings in axial flight by using a 3-D undistorted wake model in curved lifting line theory. The governing equations are solved by both the Multhopp Interpolation technique and the Vortex Lattice method. The singularity from the bound vortices is eliminated through the Hadamard's finite part concept. Good numerical agreement between both analytical methods and finite differences methods are found. Parametric studies were made to assess the effects of several shape variables on aerodynamic loads. It is found, e.g., that a rotor blade with out-of-plane and inplane curvature can theoretically increase lift in the inboard and outboard regions respectively without introducing an additional induced drag.

SFM-FDTD analysis of triangular-lattice AAA structure: Parametric study of the TEM mode

NASA Astrophysics Data System (ADS)

Hamidi, M.; Chemrouk, C.; Belkhir, A.; Kebci, Z.; Ndao, A.; Lamrous, O.; Baida, F. I.

2014-05-01

This theoretical work reports a parametric study of enhanced transmission through annular aperture array (AAA) structure arranged in a triangular lattice. The effect of the incidence angle in addition to the inner and outer radii values on the evolution of the transmission spectra is carried out. To this end, a 3D Finite-Difference Time-Domain code based on the Split Field Method (SFM) is used to calculate the spectral response of the structure for any angle of incidence. In order to work through an orthogonal unit cell which presents the advantage to reduce time and space of computation, special periodic boundary conditions are implemented. This study provides a new modeling of AAA structures useful for producing tunable ultra-compact devices.

Numerical analysis of the dynamic interaction between wheel set and turnout crossing using the explicit finite element method

NASA Astrophysics Data System (ADS)

Xin, L.; Markine, V. L.; Shevtsov, I. Y.

2016-03-01

A three-dimensional (3-D) explicit dynamic finite element (FE) model is developed to simulate the impact of the wheel on the crossing nose. The model consists of a wheel set moving over the turnout crossing. Realistic wheel, wing rail and crossing geometries have been used in the model. Using this model the dynamic responses of the system such as the contact forces between the wheel and the crossing, crossing nose displacements and accelerations, stresses in rail material as well as in sleepers and ballast can be obtained. Detailed analysis of the wheel set and crossing interaction using the local contact stress state in the rail is possible as well, which provides a good basis for prediction of the long-term behaviour of the crossing (fatigue analysis). In order to tune and validate the FE model field measurements conducted on several turnouts in the railway network in the Netherlands are used here. The parametric study including variations of the crossing nose geometries performed here demonstrates the capabilities of the developed model. The results of the validation and parametric study are presented and discussed.

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

Murphy, L.T.; Hickey, M.

This paper summarizes the progress to date by CH2M HILL and the UKAEA in development of a parametric modelling capability for estimating the costs of large nuclear decommissioning projects in the United Kingdom (UK) and Europe. The ability to successfully apply parametric cost estimating techniques will be a key factor to commercial success in the UK and European multi-billion dollar waste management, decommissioning and environmental restoration markets. The most useful parametric models will be those that incorporate individual components representing major elements of work: reactor decommissioning, fuel cycle facility decommissioning, waste management facility decommissioning and environmental restoration. Models must bemore » sufficiently robust to estimate indirect costs and overheads, permit pricing analysis and adjustment, and accommodate the intricacies of international monetary exchange, currency fluctuations and contingency. The development of a parametric cost estimating capability is also a key component in building a forward estimating strategy. The forward estimating strategy will enable the preparation of accurate and cost-effective out-year estimates, even when work scope is poorly defined or as yet indeterminate. Preparation of cost estimates for work outside the organizations current sites, for which detailed measurement is not possible and historical cost data does not exist, will also be facilitated. (authors)« less

Fission properties of Po isotopes in different macroscopic-microscopic models

NASA Astrophysics Data System (ADS)

Bartel, J.; Pomorski, K.; Nerlo-Pomorska, B.; Schmitt, Ch

2015-11-01

Fission-barrier heights of nuclei in the Po isotopic chain are investigated in several macroscopic-microscopic models. Using the Yukawa-folded single-particle potential, the Lublin-Strasbourg drop (LSD) model, the Strutinsky shell-correction method to yield the shell corrections and the BCS theory for the pairing contributions, fission-barrier heights are calculated and found in quite good agreement with the experimental data. This turns out, however, to be only the case when the underlying macroscopic, liquid-drop (LD) type, theory is well chosen. Together with the LSD approach, different LD parametrizations proposed by Moretto et al are tested. Four deformation parameters describing respectively elongation, neck-formation, reflectional-asymmetric, and non-axiality of the nuclear shape thus defining the so called modified Funny Hills shape parametrization are used in the calculation. The present study clearly demonstrates that nuclear fission-barrier heights constitute a challenging and selective tool to discern between such different macroscopic approaches.

Characterizing rainfall of hot arid region by using time-series modeling and sustainability approaches: a case study from Gujarat, India

NASA Astrophysics Data System (ADS)

Machiwal, Deepesh; Kumar, Sanjay; Dayal, Devi

2016-05-01

This study aimed at characterization of rainfall dynamics in a hot arid region of Gujarat, India by employing time-series modeling techniques and sustainability approach. Five characteristics, i.e., normality, stationarity, homogeneity, presence/absence of trend, and persistence of 34-year (1980-2013) period annual rainfall time series of ten stations were identified/detected by applying multiple parametric and non-parametric statistical tests. Furthermore, the study involves novelty of proposing sustainability concept for evaluating rainfall time series and demonstrated the concept, for the first time, by identifying the most sustainable rainfall series following reliability ( R y), resilience ( R e), and vulnerability ( V y) approach. Box-whisker plots, normal probability plots, and histograms indicated that the annual rainfall of Mandvi and Dayapar stations is relatively more positively skewed and non-normal compared with that of other stations, which is due to the presence of severe outlier and extreme. Results of Shapiro-Wilk test and Lilliefors test revealed that annual rainfall series of all stations significantly deviated from normal distribution. Two parametric t tests and the non-parametric Mann-Whitney test indicated significant non-stationarity in annual rainfall of Rapar station, where the rainfall was also found to be non-homogeneous based on the results of four parametric homogeneity tests. Four trend tests indicated significantly increasing rainfall trends at Rapar and Gandhidham stations. The autocorrelation analysis suggested the presence of persistence of statistically significant nature in rainfall series of Bhachau (3-year time lag), Mundra (1- and 9-year time lag), Nakhatrana (9-year time lag), and Rapar (3- and 4-year time lag). Results of sustainability approach indicated that annual rainfall of Mundra and Naliya stations ( R y = 0.50 and 0.44; R e = 0.47 and 0.47; V y = 0.49 and 0.46, respectively) are the most sustainable and dependable compared with that of other stations. The highest values of sustainability index at Mundra (0.120) and Naliya (0.112) stations confirmed the earlier findings of R y- R e- V y approach. In general, annual rainfall of the study area is less reliable, less resilient, and moderately vulnerable, which emphasizes the need of developing suitable strategies for managing water resources of the area on sustainable basis. Finally, it is recommended that multiple statistical tests (at least two) should be used in time-series modeling for making reliable decisions. Moreover, methodology and findings of the sustainability concept in rainfall time series can easily be adopted in other arid regions of the world.

Predictive power of theoretical modelling of the nuclear mean field: examples of improving predictive capacities

NASA Astrophysics Data System (ADS)

Dedes, I.; Dudek, J.

2018-03-01

We examine the effects of the parametric correlations on the predictive capacities of the theoretical modelling keeping in mind the nuclear structure applications. The main purpose of this work is to illustrate the method of establishing the presence and determining the form of parametric correlations within a model as well as an algorithm of elimination by substitution (see text) of parametric correlations. We examine the effects of the elimination of the parametric correlations on the stabilisation of the model predictions further and further away from the fitting zone. It follows that the choice of the physics case and the selection of the associated model are of secondary importance in this case. Under these circumstances we give priority to the relative simplicity of the underlying mathematical algorithm, provided the model is realistic. Following such criteria, we focus specifically on an important but relatively simple case of doubly magic spherical nuclei. To profit from the algorithmic simplicity we chose working with the phenomenological spherically symmetric Woods–Saxon mean-field. We employ two variants of the underlying Hamiltonian, the traditional one involving both the central and the spin orbit potential in the Woods–Saxon form and the more advanced version with the self-consistent density-dependent spin–orbit interaction. We compare the effects of eliminating of various types of correlations and discuss the improvement of the quality of predictions (‘predictive power’) under realistic parameter adjustment conditions.

Hybrid-Wing-Body Vehicle Composite Fuselage Analysis and Case Study

NASA Technical Reports Server (NTRS)

Mukhopadhyay, Vivek

2014-01-01

Recent progress in the structural analysis of a Hybrid Wing-Body (HWB) fuselage concept is presented with the objective of structural weight reduction under a set of critical design loads. This pressurized efficient HWB fuselage design is presently being investigated by the NASA Environmentally Responsible Aviation (ERA) project in collaboration with the Boeing Company, Huntington Beach. The Pultruded Rod-Stiffened Efficient Unitized Structure (PRSEUS) composite concept, developed at the Boeing Company, is approximately modeled for an analytical study and finite element analysis. Stiffened plate linear theories are employed for a parametric case study. Maximum deflection and stress levels are obtained with appropriate assumptions for a set of feasible stiffened panel configurations. An analytical parametric case study is presented to examine the effects of discrete stiffener spacing and skin thickness on structural weight, deflection and stress. A finite-element model (FEM) of an integrated fuselage section with bulkhead is developed for an independent assessment. Stress analysis and scenario based case studies are conducted for design improvement. The FEM model specific weight of the improved fuselage concept is computed and compared to previous studies, in order to assess the relative weight/strength advantages of this advanced composite airframe technology

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.

Hybrid pathwise sensitivity methods for discrete stochastic models of chemical reaction systems

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

Wolf, Elizabeth Skubak, E-mail: ewolf@saintmarys.edu; Anderson, David F., E-mail: anderson@math.wisc.edu

2015-01-21

Stochastic models are often used to help understand the behavior of intracellular biochemical processes. The most common such models are continuous time Markov chains (CTMCs). Parametric sensitivities, which are derivatives of expectations of model output quantities with respect to model parameters, are useful in this setting for a variety of applications. In this paper, we introduce a class of hybrid pathwise differentiation methods for the numerical estimation of parametric sensitivities. The new hybrid methods combine elements from the three main classes of procedures for sensitivity estimation and have a number of desirable qualities. First, the new methods are unbiased formore » a broad class of problems. Second, the methods are applicable to nearly any physically relevant biochemical CTMC model. Third, and as we demonstrate on several numerical examples, the new methods are quite efficient, particularly if one wishes to estimate the full gradient of parametric sensitivities. The methods are rather intuitive and utilize the multilevel Monte Carlo philosophy of splitting an expectation into separate parts and handling each in an efficient manner.« less

Broadband and tunable optical parametric generator for remote detection of gas molecules in the short and mid-infrared.

PubMed

Lambert-Girard, Simon; Allard, Martin; Piché, Michel; Babin, François

2015-04-01

The development of a novel broadband and tunable optical parametric generator (OPG) is presented. The OPG properties are studied numerically and experimentally in order to optimize the generator's use in a broadband spectroscopic LIDAR operating in the short and mid-infrared. This paper discusses trade-offs to be made on the properties of the pump, crystal, and seeding signal in order to optimize the pulse spectral density and divergence while enabling energy scaling. A seed with a large spectral bandwidth is shown to enhance the pulse-to-pulse stability and optimize the pulse spectral density. A numerical model shows excellent agreement with output power measurements; the model predicts that a pump having a large number of longitudinal modes improves conversion efficiency and pulse stability.

An Empirical Polarizable Force Field Based on the Classical Drude Oscillator Model: Development History and Recent Applications

PubMed Central

2016-01-01

Molecular mechanics force fields that explicitly account for induced polarization represent the next generation of physical models for molecular dynamics simulations. Several methods exist for modeling induced polarization, and here we review the classical Drude oscillator model, in which electronic degrees of freedom are modeled by charged particles attached to the nuclei of their core atoms by harmonic springs. We describe the latest developments in Drude force field parametrization and application, primarily in the last 15 years. Emphasis is placed on the Drude-2013 polarizable force field for proteins, DNA, lipids, and carbohydrates. We discuss its parametrization protocol, development history, and recent simulations of biologically interesting systems, highlighting specific studies in which induced polarization plays a critical role in reproducing experimental observables and understanding physical behavior. As the Drude oscillator model is computationally tractable and available in a wide range of simulation packages, it is anticipated that use of these more complex physical models will lead to new and important discoveries of the physical forces driving a range of chemical and biological phenomena. PMID:26815602

Parametric Adjustments to the Rankine Vortex Wind Model for Gulf of Mexico Hurricanes

DTIC Science & Technology

2012-11-01

2012 4. TITLE AND SUBTITLE Parametric Adjustments to the Rankine Vortex Wind Model for Gulf of Mexico Hurricanes 5a. CONTRACT NUMBER 5b. GRANT ...may be used to construct spatially varying wind fields for the GOM region (e.g., Thompson and Cardone [12]), but this requires using a complicated...Storm Damage Reduc- tion, and Dredging Operations and Environmental Research (DOER). The USACE Headquarters granted permission to publish this paper

Four photon parametric amplification. [in unbiased Josephson junction

NASA Technical Reports Server (NTRS)

Parrish, P. T.; Feldman, M. J.; Ohta, H.; Chiao, R. Y.

1974-01-01

An analysis is presented describing four-photon parametric amplification in an unbiased Josephson junction. Central to the theory is the model of the Josephson effect as a nonlinear inductance. Linear, small signal analysis is applied to the two-fluid model of the Josephson junction. The gain, gain-bandwidth product, high frequency limit, and effective noise temperature are calculated for a cavity reflection amplifier. The analysis is extended to multiple (series-connected) junctions and subharmonic pumping.

Scalable Learning for Geostatistics and Speaker Recognition

DTIC Science & Technology

2011-01-01

of prior knowledge of the model or due to improved robustness requirements). Both these methods have their own advantages and disadvantages. The use...application. If the data is well-correlated and low-dimensional, any prior knowledge available on the data can be used to build a parametric model. In the...absence of prior knowledge , non-parametric methods can be used. If the data is high-dimensional, PCA based dimensionality reduction is often the first

Model independent constraints on transition redshift

NASA Astrophysics Data System (ADS)

Jesus, J. F.; Holanda, R. F. L.; Pereira, S. H.

2018-05-01

This paper aims to put constraints on the transition redshift zt, which determines the onset of cosmic acceleration, in cosmological-model independent frameworks. In order to perform our analyses, we consider a flat universe and assume a parametrization for the comoving distance DC(z) up to third degree on z, a second degree parametrization for the Hubble parameter H(z) and a linear parametrization for the deceleration parameter q(z). For each case, we show that type Ia supernovae and H(z) data complement each other on the parameter space and tighter constrains for the transition redshift are obtained. By combining the type Ia supernovae observations and Hubble parameter measurements it is possible to constrain the values of zt, for each approach, as 0.806± 0.094, 0.870± 0.063 and 0.973± 0.058 at 1σ c.l., respectively. Then, such approaches provide cosmological-model independent estimates for this parameter.

Component isolation for multi-component signal analysis using a non-parametric gaussian latent feature model

NASA Astrophysics Data System (ADS)

Yang, Yang; Peng, Zhike; Dong, Xingjian; Zhang, Wenming; Clifton, David A.

2018-03-01

A challenge in analysing non-stationary multi-component signals is to isolate nonlinearly time-varying signals especially when they are overlapped in time and frequency plane. In this paper, a framework integrating time-frequency analysis-based demodulation and a non-parametric Gaussian latent feature model is proposed to isolate and recover components of such signals. The former aims to remove high-order frequency modulation (FM) such that the latter is able to infer demodulated components while simultaneously discovering the number of the target components. The proposed method is effective in isolating multiple components that have the same FM behavior. In addition, the results show that the proposed method is superior to generalised demodulation with singular-value decomposition-based method, parametric time-frequency analysis with filter-based method and empirical model decomposition base method, in recovering the amplitude and phase of superimposed components.

Holographic Dark Energy Density

NASA Astrophysics Data System (ADS)

Saadat, Hassan

2011-06-01

In this article we consider the cosmological model based on the holographic dark energy. We study dark energy density in Universe with arbitrary spatially curvature described by the Friedmann-Robertson-Walker metric. We use Chevallier-Polarski-Linder parametrization to specify dark energy density.

Parametric Cooling of Ultracold Atoms

NASA Astrophysics Data System (ADS)

Boguslawski, Matthew; Bharath, H. M.; Barrios, Maryrose; Chapman, Michael

2017-04-01

An oscillator is characterized by a restoring force which determines the natural frequency at which oscillations occur. The amplitude and phase-noise of these oscillations can be amplified or squeezed by modulating the magnitude of this force (e.g. the stiffness of the spring) at twice the natural frequency. This is parametric excitation; a long-studied phenomena in both the classical and quantum regimes. Parametric cooling, or the parametric squeezing of thermo-mechanical noise in oscillators has been studied in micro-mechanical oscillators and trapped ions. We study parametric cooling in ultracold atoms. This method shows a modest reduction of the variance of atomic momenta, and can be easily employed with pre-existing controls in many experiments. Parametric cooling is comparable to delta-kicked cooling, sharing similar limitations. We expect this cooling to find utility in microgravity experiments where the experiment duration is limited by atomic free expansion.

Solar tower power plant using a particle-heated steam generator: Modeling and parametric study

NASA Astrophysics Data System (ADS)

Krüger, Michael; Bartsch, Philipp; Pointner, Harald; Zunft, Stefan

2016-05-01

Within the framework of the project HiTExStor II, a system model for the entire power plant consisting of volumetric air receiver, air-sand heat exchanger, sand storage system, steam generator and water-steam cycle was implemented in software "Ebsilon Professional". As a steam generator, the two technologies fluidized bed cooler and moving bed heat exchangers were considered. Physical models for the non-conventional power plant components as air- sand heat exchanger, fluidized bed coolers and moving bed heat exchanger had to be created and implemented in the simulation environment. Using the simulation model for the power plant, the individual components and subassemblies have been designed and the operating parameters were optimized in extensive parametric studies in terms of the essential degrees of freedom. The annual net electricity output for different systems was determined in annual performance calculations at a selected location (Huelva, Spain) using the optimized values for the studied parameters. The solution with moderate regenerative feed water heating has been found the most advantageous. Furthermore, the system with moving bed heat exchanger prevails over the system with fluidized bed cooler due to a 6 % higher net electricity yield.

Fluidic Energy Harvester Optimization in Grid Turbulence

NASA Astrophysics Data System (ADS)

Danesh-Yazdi, Amir; Elvin, Niell; Andreopoulos, Yiannis

2017-11-01

Even though it is omnipresent in nature, there has not been a great deal of research in the literature involving turbulence as an energy source for piezoelectric fluidic harvesters. In the present work, a grid-generated turbulence forcing function model which we derived previously is employed in the single degree-of-freedom electromechanical equations to find the power output and tip displacement of piezoelectric cantilever beams. Additionally, we utilize simplified, deterministic models of the turbulence forcing function to obtain closed-form expressions for the power output. These theoretical models are studied using experiments that involve separately placing a hot-wire anemometer probe and a short PVDF beam in flows where turbulence is generated by means of passive and semi-passive grids. From a parametric study on the deterministic models, we show that the white noise forcing function best mimics the experimental data. Furthermore, our parametric study of the response spectrum of a generic fluidic harvester in grid-generated turbulent flow shows that optimum power output is attained for beams placed closer to the grid with a low natural frequency and damping ratio and a large electromechanical coupling coefficient. NSF Grant No. CBET 1033117.

Automated, Parametric Geometry Modeling and Grid Generation for Turbomachinery Applications

NASA Technical Reports Server (NTRS)

Harrand, Vincent J.; Uchitel, Vadim G.; Whitmire, John B.

2000-01-01

The objective of this Phase I project is to develop a highly automated software system for rapid geometry modeling and grid generation for turbomachinery applications. The proposed system features a graphical user interface for interactive control, a direct interface to commercial CAD/PDM systems, support for IGES geometry output, and a scripting capability for obtaining a high level of automation and end-user customization of the tool. The developed system is fully parametric and highly automated, and, therefore, significantly reduces the turnaround time for 3D geometry modeling, grid generation and model setup. This facilitates design environments in which a large number of cases need to be generated, such as for parametric analysis and design optimization of turbomachinery equipment. In Phase I we have successfully demonstrated the feasibility of the approach. The system has been tested on a wide variety of turbomachinery geometries, including several impellers and a multi stage rotor-stator combination. In Phase II, we plan to integrate the developed system with turbomachinery design software and with commercial CAD/PDM software.

A semi-parametric within-subject mixture approach to the analyses of responses and response times.

PubMed

Molenaar, Dylan; Bolsinova, Maria; Vermunt, Jeroen K

2018-05-01

In item response theory, modelling the item response times in addition to the item responses may improve the detection of possible between- and within-subject differences in the process that resulted in the responses. For instance, if respondents rely on rapid guessing on some items but not on all, the joint distribution of the responses and response times will be a multivariate within-subject mixture distribution. Suitable parametric methods to detect these within-subject differences have been proposed. In these approaches, a distribution needs to be assumed for the within-class response times. In this paper, it is demonstrated that these parametric within-subject approaches may produce false positives and biased parameter estimates if the assumption concerning the response time distribution is violated. A semi-parametric approach is proposed which resorts to categorized response times. This approach is shown to hardly produce false positives and parameter bias. In addition, the semi-parametric approach results in approximately the same power as the parametric approach. © 2017 The British Psychological Society.

Direct parametric reconstruction in dynamic PET myocardial perfusion imaging: in vivo studies.

PubMed

Petibon, Yoann; Rakvongthai, Yothin; El Fakhri, Georges; Ouyang, Jinsong

2017-05-07

Dynamic PET myocardial perfusion imaging (MPI) used in conjunction with tracer kinetic modeling enables the quantification of absolute myocardial blood flow (MBF). However, MBF maps computed using the traditional indirect method (i.e. post-reconstruction voxel-wise fitting of kinetic model to PET time-activity-curves-TACs) suffer from poor signal-to-noise ratio (SNR). Direct reconstruction of kinetic parameters from raw PET projection data has been shown to offer parametric images with higher SNR compared to the indirect method. The aim of this study was to extend and evaluate the performance of a direct parametric reconstruction method using in vivo dynamic PET MPI data for the purpose of quantifying MBF. Dynamic PET MPI studies were performed on two healthy pigs using a Siemens Biograph mMR scanner. List-mode PET data for each animal were acquired following a bolus injection of ~7-8 mCi of 18 F-flurpiridaz, a myocardial perfusion agent. Fully-3D dynamic PET sinograms were obtained by sorting the coincidence events into 16 temporal frames covering ~5 min after radiotracer administration. Additionally, eight independent noise realizations of both scans-each containing 1/8th of the total number of events-were generated from the original list-mode data. Dynamic sinograms were then used to compute parametric maps using the conventional indirect method and the proposed direct method. For both methods, a one-tissue compartment model accounting for spillover from the left and right ventricle blood-pools was used to describe the kinetics of 18 F-flurpiridaz. An image-derived arterial input function obtained from a TAC taken in the left ventricle cavity was used for tracer kinetic analysis. For the indirect method, frame-by-frame images were estimated using two fully-3D reconstruction techniques: the standard ordered subset expectation maximization (OSEM) reconstruction algorithm on one side, and the one-step late maximum a posteriori (OSL-MAP) algorithm on the other side, which incorporates a quadratic penalty function. The parametric images were then calculated using voxel-wise weighted least-square fitting of the reconstructed myocardial PET TACs. For the direct method, parametric images were estimated directly from the dynamic PET sinograms using a maximum a posteriori (MAP) parametric reconstruction algorithm which optimizes an objective function comprised of the Poisson log-likelihood term, the kinetic model and a quadratic penalty function. Maximization of the objective function with respect to each set of parameters was achieved using a preconditioned conjugate gradient algorithm with a specifically developed pre-conditioner. The performance of the direct method was evaluated by comparing voxel- and segment-wise estimates of [Formula: see text], the tracer transport rate (ml · min -1 · ml -1 ), to those obtained using the indirect method applied to both OSEM and OSL-MAP dynamic reconstructions. The proposed direct reconstruction method produced [Formula: see text] maps with visibly lower noise than the indirect method based on OSEM and OSL-MAP reconstructions. At normal count levels, the direct method was shown to outperform the indirect method based on OSL-MAP in the sense that at matched level of bias, reduced regional noise levels were obtained. At lower count levels, the direct method produced [Formula: see text] estimates with significantly lower standard deviation across noise realizations than the indirect method based on OSL-MAP at matched bias level. In all cases, the direct method yielded lower noise and standard deviation than the indirect method based on OSEM. Overall, the proposed direct reconstruction offered a better bias-variance tradeoff than the indirect method applied to either OSEM and OSL-MAP. Direct parametric reconstruction as applied to in vivo dynamic PET MPI data is therefore a promising method for producing MBF maps with lower variance.

Direct parametric reconstruction in dynamic PET myocardial perfusion imaging: in-vivo studies

PubMed Central

Petibon, Yoann; Rakvongthai, Yothin; Fakhri, Georges El; Ouyang, Jinsong

2017-01-01

Dynamic PET myocardial perfusion imaging (MPI) used in conjunction with tracer kinetic modeling enables the quantification of absolute myocardial blood flow (MBF). However, MBF maps computed using the traditional indirect method (i.e. post-reconstruction voxel-wise fitting of kinetic model to PET time-activity-curves -TACs) suffer from poor signal-to-noise ratio (SNR). Direct reconstruction of kinetic parameters from raw PET projection data has been shown to offer parametric images with higher SNR compared to the indirect method. The aim of this study was to extend and evaluate the performance of a direct parametric reconstruction method using in-vivo dynamic PET MPI data for the purpose of quantifying MBF. Dynamic PET MPI studies were performed on two healthy pigs using a Siemens Biograph mMR scanner. List-mode PET data for each animal were acquired following a bolus injection of ~7-8 mCi of 18F-flurpiridaz, a myocardial perfusion agent. Fully-3D dynamic PET sinograms were obtained by sorting the coincidence events into 16 temporal frames covering ~5 min after radiotracer administration. Additionally, eight independent noise realizations of both scans - each containing 1/8th of the total number of events - were generated from the original list-mode data. Dynamic sinograms were then used to compute parametric maps using the conventional indirect method and the proposed direct method. For both methods, a one-tissue compartment model accounting for spillover from the left and right ventricle blood-pools was used to describe the kinetics of 18F-flurpiridaz. An image-derived arterial input function obtained from a TAC taken in the left ventricle cavity was used for tracer kinetic analysis. For the indirect method, frame-by-frame images were estimated using two fully-3D reconstruction techniques: the standard Ordered Subset Expectation Maximization (OSEM) reconstruction algorithm on one side, and the One-Step Late Maximum a Posteriori (OSL-MAP) algorithm on the other side, which incorporates a quadratic penalty function. The parametric images were then calculated using voxel-wise weighted least-square fitting of the reconstructed myocardial PET TACs. For the direct method, parametric images were estimated directly from the dynamic PET sinograms using a maximum a posteriori (MAP) parametric reconstruction algorithm which optimizes an objective function comprised of the Poisson log-likelihood term, the kinetic model and a quadratic penalty function. Maximization of the objective function with respect to each set of parameters was achieved using a preconditioned conjugate gradient algorithm with a specifically developed pre-conditioner. The performance of the direct method was evaluated by comparing voxel- and segment-wise estimates of K1, the tracer transport rate (mL.min−1.mL−1), to those obtained using the indirect method applied to both OSEM and OSL-MAP dynamic reconstructions. The proposed direct reconstruction method produced K1 maps with visibly lower noise than the indirect method based on OSEM and OSL-MAP reconstructions. At normal count levels, the direct method was shown to outperform the indirect method based on OSL-MAP in the sense that at matched level of bias, reduced regional noise levels were obtained. At lower count levels, the direct method produced K1 estimates with significantly lower standard deviation across noise realizations than the indirect method based on OSL-MAP at matched bias level. In all cases, the direct method yielded lower noise and standard deviation than the indirect method based on OSEM. Overall, the proposed direct reconstruction offered a better bias-variance tradeoff than the indirect method applied to either OSEM and OSL-MAP. Direct parametric reconstruction as applied to in-vivo dynamic PET MPI data is therefore a promising method for producing MBF maps with lower variance. PMID:28379843

Direct parametric reconstruction in dynamic PET myocardial perfusion imaging: in vivo studies

NASA Astrophysics Data System (ADS)

Petibon, Yoann; Rakvongthai, Yothin; El Fakhri, Georges; Ouyang, Jinsong

2017-05-01

Dynamic PET myocardial perfusion imaging (MPI) used in conjunction with tracer kinetic modeling enables the quantification of absolute myocardial blood flow (MBF). However, MBF maps computed using the traditional indirect method (i.e. post-reconstruction voxel-wise fitting of kinetic model to PET time-activity-curves-TACs) suffer from poor signal-to-noise ratio (SNR). Direct reconstruction of kinetic parameters from raw PET projection data has been shown to offer parametric images with higher SNR compared to the indirect method. The aim of this study was to extend and evaluate the performance of a direct parametric reconstruction method using in vivo dynamic PET MPI data for the purpose of quantifying MBF. Dynamic PET MPI studies were performed on two healthy pigs using a Siemens Biograph mMR scanner. List-mode PET data for each animal were acquired following a bolus injection of ~7-8 mCi of 18F-flurpiridaz, a myocardial perfusion agent. Fully-3D dynamic PET sinograms were obtained by sorting the coincidence events into 16 temporal frames covering ~5 min after radiotracer administration. Additionally, eight independent noise realizations of both scans—each containing 1/8th of the total number of events—were generated from the original list-mode data. Dynamic sinograms were then used to compute parametric maps using the conventional indirect method and the proposed direct method. For both methods, a one-tissue compartment model accounting for spillover from the left and right ventricle blood-pools was used to describe the kinetics of 18F-flurpiridaz. An image-derived arterial input function obtained from a TAC taken in the left ventricle cavity was used for tracer kinetic analysis. For the indirect method, frame-by-frame images were estimated using two fully-3D reconstruction techniques: the standard ordered subset expectation maximization (OSEM) reconstruction algorithm on one side, and the one-step late maximum a posteriori (OSL-MAP) algorithm on the other side, which incorporates a quadratic penalty function. The parametric images were then calculated using voxel-wise weighted least-square fitting of the reconstructed myocardial PET TACs. For the direct method, parametric images were estimated directly from the dynamic PET sinograms using a maximum a posteriori (MAP) parametric reconstruction algorithm which optimizes an objective function comprised of the Poisson log-likelihood term, the kinetic model and a quadratic penalty function. Maximization of the objective function with respect to each set of parameters was achieved using a preconditioned conjugate gradient algorithm with a specifically developed pre-conditioner. The performance of the direct method was evaluated by comparing voxel- and segment-wise estimates of {{K}1} , the tracer transport rate (ml · min-1 · ml-1), to those obtained using the indirect method applied to both OSEM and OSL-MAP dynamic reconstructions. The proposed direct reconstruction method produced {{K}1} maps with visibly lower noise than the indirect method based on OSEM and OSL-MAP reconstructions. At normal count levels, the direct method was shown to outperform the indirect method based on OSL-MAP in the sense that at matched level of bias, reduced regional noise levels were obtained. At lower count levels, the direct method produced {{K}1} estimates with significantly lower standard deviation across noise realizations than the indirect method based on OSL-MAP at matched bias level. In all cases, the direct method yielded lower noise and standard deviation than the indirect method based on OSEM. Overall, the proposed direct reconstruction offered a better bias-variance tradeoff than the indirect method applied to either OSEM and OSL-MAP. Direct parametric reconstruction as applied to in vivo dynamic PET MPI data is therefore a promising method for producing MBF maps with lower variance.

A new process sensitivity index to identify important system processes under process model and parametric uncertainty

DOE PAGES

Dai, Heng; Ye, Ming; Walker, Anthony P.; ...

2017-03-28

A hydrological model consists of multiple process level submodels, and each submodel represents a process key to the operation of the simulated system. Global sensitivity analysis methods have been widely used to identify important processes for system model development and improvement. The existing methods of global sensitivity analysis only consider parametric uncertainty, and are not capable of handling model uncertainty caused by multiple process models that arise from competing hypotheses about one or more processes. To address this problem, this study develops a new method to probe model output sensitivity to competing process models by integrating model averaging methods withmore » variance-based global sensitivity analysis. A process sensitivity index is derived as a single summary measure of relative process importance, and the index includes variance in model outputs caused by uncertainty in both process models and their parameters. Here, for demonstration, the new index is used to assign importance to the processes of recharge and geology in a synthetic study of groundwater reactive transport modeling. The recharge process is simulated by two models that convert precipitation to recharge, and the geology process is simulated by two models of hydraulic conductivity. Each process model has its own random parameters. Finally, the new process sensitivity index is mathematically general, and can be applied to a wide range of problems in hydrology and beyond.« less

A new process sensitivity index to identify important system processes under process model and parametric uncertainty

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

Dai, Heng; Ye, Ming; Walker, Anthony P.

A hydrological model consists of multiple process level submodels, and each submodel represents a process key to the operation of the simulated system. Global sensitivity analysis methods have been widely used to identify important processes for system model development and improvement. The existing methods of global sensitivity analysis only consider parametric uncertainty, and are not capable of handling model uncertainty caused by multiple process models that arise from competing hypotheses about one or more processes. To address this problem, this study develops a new method to probe model output sensitivity to competing process models by integrating model averaging methods withmore » variance-based global sensitivity analysis. A process sensitivity index is derived as a single summary measure of relative process importance, and the index includes variance in model outputs caused by uncertainty in both process models and their parameters. Here, for demonstration, the new index is used to assign importance to the processes of recharge and geology in a synthetic study of groundwater reactive transport modeling. The recharge process is simulated by two models that convert precipitation to recharge, and the geology process is simulated by two models of hydraulic conductivity. Each process model has its own random parameters. Finally, the new process sensitivity index is mathematically general, and can be applied to a wide range of problems in hydrology and beyond.« less

Development of suspended core soft glass fibers for far-detuned parametric conversion

NASA Astrophysics Data System (ADS)

Rampur, Anupamaa; Ciąćka, Piotr; Cimek, Jarosław; Kasztelanic, Rafał; Buczyński, Ryszard; Klimczak, Mariusz

2018-04-01

Light sources utilizing χ (2) parametric conversion combine high brightness with attractive operation wavelengths in the near and mid-infrared. In optical fibers, it is possible to use χ (3) degenerate four-wave mixing in order to obtain signal-to-idler frequency detuning of over 100 THz. We report on a test series of nonlinear soft glass suspended core fibers intended for parametric conversion of 1000-1100 nm signal wavelengths available from an array of mature lasers into the near-to-mid-infrared range of 2700-3500 nm under pumping with an erbium sub-picosecond laser system. The presented discussion includes modelling of the fiber properties, details of their physical development and characterization, and experimental tests of parametric conversion.

A parametric study of golf car and personal transport vehicle braking stability and their deficiencies.

PubMed

Seluga, Kristopher J; Baker, Lowell L; Ojalvo, Irving U

2009-07-01

This paper describes research and parametric analyses of braking effectiveness and directional stability for golf cars, personal transport vehicles (PTVs) and low speed vehicles (LSVs). It is shown that current designs, which employ brakes on only the rear wheels, can lead to rollovers if the brakes are applied while traveling downhill. After summarizing the current state of existing safety standards and brake system designs, both of which appear deficient from a safety perspective, a previously developed dynamic simulation model is used to identify which parameters have the greatest influence on the vehicles' yaw stability. The simulation results are then used to parametrically quantify which combination of these factors can lead to yaw induced rollover during hard braking. Vehicle velocity, steering input, path slope and tire friction are all identified as important parameters in determining braking stability, the effects of which on rollover propensity are presented graphically. The results further show that when vehicles are equipped with front brakes or four-wheel brakes, the probability of a yaw induced rollover is almost entirely eliminated. Furthermore, the parametric charts provided may be used as an aid in developing guidelines for golf car and PTV path design if rear brake vehicles are used.

Model developments in TERRA_URB, the upcoming standard urban parametrization of the atmospheric numerical model COSMO(-CLM)

NASA Astrophysics Data System (ADS)

Wouters, Hendrik; Blahak, Ulrich; Helmert, Jürgen; Raschendorfer, Matthias; Demuzere, Matthias; Fay, Barbara; Trusilova, Kristina; Mironov, Dmitrii; Reinert, Daniel; Lüthi, Daniel; Machulskaya, Ekaterina

2015-04-01

In order to address urban climate at the regional scales, a new efficient urban land-surface parametrization TERRA_URB has been developed and coupled to the atmospheric numerical model COSMO-CLM. Hereby, several new advancements for urban land-surface models are introduced which are crucial for capturing the urban surface-energy balance and its seasonal dependency in the mid-latitudes. This includes a new PDF-based water-storage parametrization for impervious land, the representation of radiative absorption and emission by greenhouse gases in the infra-red spectrum in the urban canopy layer, and the inclusion of heat emission from human activity. TERRA_URB has been applied in offline urban-climate studies during European observation campaigns at Basel (BUBBLE), Toulouse (CAPITOUL), and Singapore, and currently applied in online studies for urban areas in Belgium, Germany, Switzerland, Helsinki, Singapore, and Melbourne. Because of its computational efficiency, high accuracy and its to-the-point conceptual easiness, TERRA_URB has been selected to become the standard urban parametrization of the atmospheric numerical model COSMO(-CLM). This allows for better weather forecasts for temperature and precipitation in cities with COSMO, and an improved assessment of urban outdoor hazards in the context of global climate change and urban expansion with COSMO-CLM. We propose additional extensions to TERRA_URB towards a more robust representation of cities over the world including their structural design. In a first step, COSMO's standard EXTernal PARarameter (EXTPAR) tool is updated for representing the cities into the land cover over the entire globe. Hereby, global datasets in the standard EXTPAR tool are used to retrieve the 'Paved' or 'sealed' surface Fraction (PF) referring to the presence of buildings and streets. Furthermore, new global data sets are incorporated in EXTPAR for describing the Anthropogenic Heat Flux (AHF) due to human activity, and optionally the Surface Area Index (SAI) derived from the Floor Space Index (FSI). In a second step, it is focussed on the urban/rural contrast in terms of turbulent transport in the surface layer by means of model sensivity experiments: On the theoretical basis of the TKE-based surface-layer transfer scheme of COSMO, we investigate the consistency between empirical functions for thermal roughness lengths and the urban/rural canopy morphology.

Evaluating sufficient similarity for drinking-water disinfection by-product (DBP) mixtures with bootstrap hypothesis test procedures.

PubMed

Feder, Paul I; Ma, Zhenxu J; Bull, Richard J; Teuschler, Linda K; Rice, Glenn

2009-01-01

In chemical mixtures risk assessment, the use of dose-response data developed for one mixture to estimate risk posed by a second mixture depends on whether the two mixtures are sufficiently similar. While evaluations of similarity may be made using qualitative judgments, this article uses nonparametric statistical methods based on the "bootstrap" resampling technique to address the question of similarity among mixtures of chemical disinfectant by-products (DBP) in drinking water. The bootstrap resampling technique is a general-purpose, computer-intensive approach to statistical inference that substitutes empirical sampling for theoretically based parametric mathematical modeling. Nonparametric, bootstrap-based inference involves fewer assumptions than parametric normal theory based inference. The bootstrap procedure is appropriate, at least in an asymptotic sense, whether or not the parametric, distributional assumptions hold, even approximately. The statistical analysis procedures in this article are initially illustrated with data from 5 water treatment plants (Schenck et al., 2009), and then extended using data developed from a study of 35 drinking-water utilities (U.S. EPA/AMWA, 1989), which permits inclusion of a greater number of water constituents and increased structure in the statistical models.

Automated a complex computer aided design concept generated using macros programming

NASA Astrophysics Data System (ADS)

Rizal Ramly, Mohammad; Asrokin, Azharrudin; Abd Rahman, Safura; Zulkifly, Nurul Ain Md

2013-12-01

Changing a complex Computer Aided design profile such as car and aircraft surfaces has always been difficult and challenging. The capability of CAD software such as AutoCAD and CATIA show that a simple configuration of a CAD design can be easily modified without hassle, but it is not the case with complex design configuration. Design changes help users to test and explore various configurations of the design concept before the production of a model. The purpose of this study is to look into macros programming as parametric method of the commercial aircraft design. Macros programming is a method where the configurations of the design are done by recording a script of commands, editing the data value and adding a certain new command line to create an element of parametric design. The steps and the procedure to create a macro programming are discussed, besides looking into some difficulties during the process of creation and advantage of its usage. Generally, the advantages of macros programming as a method of parametric design are; allowing flexibility for design exploration, increasing the usability of the design solution, allowing proper contained by the model while restricting others and real time feedback changes.

Intensity and temporal noise characteristics in femtosecond optical parametric amplifiers.

PubMed

Chen, Wei; Fan, Jintao; Ge, Aichen; Song, Huanyu; Song, Youjian; Liu, Bowen; Chai, Lu; Wang, Chingyue; Hu, Minglie

2017-12-11

We characterize the relative intensity noise (RIN) and relative timing jitter (RTJ) between the signal and pump pulses of optical parametric amplifiers (OPAs) seeded by three different seed sources. Compared to a white-light continuum (WLC) seeded- and an optical parametric generator (OPG) seeded OPA, the narrowband CW seeded OPA exhibits the lowest root-mean-square (RMS) RIN and RTJ of 0.79% and 0.32 fs, respectively, integrated from 1 kHz to the Nyquist frequency of 1.25 MHz. An improved numerical model based on a forward Maxwell equation (FME) is built to investigate the transfers of the pump and seed's noise to the resulting OPAs' intensity and temporal fluctuation. Both the experimental and numerical study indicate that the low level of noise from the narrowband CW seeded OPA is attributed to the elimination of the RIN and RTJ coupled from the noise of seed source, being one of the important contributions to RIN and timing jitter in the other two OPAs. The approach to achieve lower level of noise from this CW seeded OPA by driving it close to saturation is also discussed with the same numerical model.

New cancer-treatment model of photodynamic therapy combined with a type I topoisomerase inhibitor, CPT-11, against HeLa cell tumors in nude mice used by OPO parametric tunable laser

NASA Astrophysics Data System (ADS)

Yoshida, Takato O.; Matsuzawa, Eiji; Matsuo, Tetsumichi; Koide, Yukio; Terakawa, Susumu; Yokokura, Teruo; Hirano, Toru

1995-03-01

A new cancer-treatment model, photodynamic therapy (PDT) combined with a type I topoisomerase inhibitor, camptothecin derivative (CPT-11), against HeLa cell tumors in BALB/c nude mice has been developed using a wide-band tunable coherent light source operated on optical parametric oscillation (OPO parametric tunable laser). The Photosan-3 PDT and CPT-11 combined therapy was remarkably effective, that is the inhibition rate (I.R.) 40 - 80%, as compared to PDT only in vivo. The analysis of HpD (Photosan-3) and CPT-11 effects on cultured HeLa cells in vitro has been studied by a video-enhanced contrast differential interference contrast microscope (VEC-DIC). Photosan-3 with 600 nm light killed cells by mitochondrial damage within 50 min, but not with 700 nm light. CPT-11 with 700 - 400 nm light killed cells within 50 min after nucleolus damage appeared after around 30 min. The localization of CPT-11 in cells was observed as fluorescence images in the nucleus, particularly the nucleoral area produced clear images using an Argus 100.

Modelling Pollutant Dispersion in a Street Network

NASA Astrophysics Data System (ADS)

Salem, N. Ben; Garbero, V.; Salizzoni, P.; Lamaison, G.; Soulhac, L.

2015-04-01

This study constitutes a further step in the analysis of the performances of a street network model to simulate atmospheric pollutant dispersion in urban areas. The model, named SIRANE, is based on the decomposition of the urban atmosphere into two sub-domains: the urban boundary layer, whose dynamics is assumed to be well established, and the urban canopy, represented as a series of interconnected boxes. Parametric laws govern the mass exchanges between the boxes under the assumption that the pollutant dispersion within the canopy can be fully simulated by modelling three main bulk transfer phenomena: channelling along street axes, transfers at street intersections, and vertical exchange between street canyons and the overlying atmosphere. Here, we aim to evaluate the reliability of the parametrizations adopted to simulate these phenomena, by focusing on their possible dependence on the external wind direction. To this end, we test the model against concentration measurements within an idealized urban district whose geometrical layout closely matches the street network represented in SIRANE. The analysis is performed for an urban array with a fixed geometry and a varying wind incidence angle. The results show that the model provides generally good results with the reference parametrizations adopted in SIRANE and that its performances are quite robust for a wide range of the model parameters. This proves the reliability of the street network approach in simulating pollutant dispersion in densely built city districts. The results also show that the model performances may be improved by considering a dependence of the wind fluctuations at street intersections and of the vertical exchange velocity on the direction of the incident wind. This opens the way for further investigations to clarify the dependence of these parameters on wind direction and street aspect ratios.

X-1 to X-Wings: Developing a Parametric Cost Model

NASA Technical Reports Server (NTRS)

Sterk, Steve; McAtee, Aaron

2015-01-01

In todays cost-constrained environment, NASA needs an X-Plane database and parametric cost model that can quickly provide rough order of magnitude predictions of cost from initial concept to first fight of potential X-Plane aircraft. This paper takes a look at the steps taken in developing such a model and reports the results. The challenges encountered in the collection of historical data and recommendations for future database management are discussed. A step-by-step discussion of the development of Cost Estimating Relationships (CERs) is then covered.

Parametric studies and orbital analysis for an electric orbit transfer vehicle space flight demonstration

NASA Astrophysics Data System (ADS)

Avila, Edward R.

The Electric Insertion Transfer Experiment (ELITE) is an Air Force Advanced Technology Transition Demonstration which is being executed as a cooperative Research and Development Agreement between the Phillips Lab and TRW. The objective is to build, test, and fly a solar-electric orbit transfer and orbit maneuvering vehicle, as a precursor to an operational electric orbit transfer vehicle (EOTV). This paper surveys some of the analysis tools used to do parametric studies and discusses the study results. The primary analysis tool was the Electric Vehicle Analyzer (EVA) developed by the Phillips Lab and modified by The Aerospace Corporation. It uses a simple orbit averaging approach to model low-thrust transfer performance, and runs in a PC environment. The assumptions used in deriving the EVA math model are presented. This tool and others surveyed were used to size the solar array power required for the spacecraft, and develop a baseline mission profile that meets the requirements of the ELITE mission.

Thermophoresis on boundary layer heat and mass transfer flow of Walters-B fluid past a radiate plate with heat sink/source

NASA Astrophysics Data System (ADS)

Vasu, B.; Gorla, Rama Subba Reddy; Murthy, P. V. S. N.

2017-05-01

The Walters-B liquid model is employed to simulate medical creams and other rheological liquids encountered in biotechnology and chemical engineering. This rheological model introduces supplementary terms into the momentum conservation equation. The combined effects of thermal radiation and heat sink/source on transient free convective, laminar flow and mass transfer in a viscoelastic fluid past a vertical plate are presented by taking thermophoresis effect into account. The transformed conservation equations are solved using a stable, robust finite difference method. A parametric study illustrating the influence of viscoelasticity parameter ( Γ), thermophoretic parameter ( τ), thermal radiation parameter ( F), heat sink/source ( ϕ), Prandtl number ( Pr), Schmidt number ( Sc), thermal Grashof number ( Gr), solutal Grashof number ( Gm), temperature and concentration profiles as well as local skin-friction, Nusselt and Sherwood number is conducted. The results of this parametric study are shown graphically and inform of table. The study has applications in polymer materials processing.

FLRW cosmological models with quark and strange quark matters in f(R,T) gravity

NASA Astrophysics Data System (ADS)

Nagpal, Ritika; Singh, J. K.; Aygün, S.

2018-06-01

In this paper, we have studied the magnetized quark matter (QM) and strange quark matter (SQM) distributions in the presence of f(R,T) gravity in the background of Friedmann-Lemaître-Robertson-Walker (FLRW) metric. To get exact solutions of modified field equations we have used f(R,T ) = R + 2 f(T) model given by Harko et al. with two different parametrization of geometrical parameters i.e. the parametrization of the deceleration parameter q , and the scale factor a in hybrid expansion form. Also, we have obtained Einstein Static Universe (ESU) solutions for QM and SQM distributions in f(R,T) gravity and General Relativity (GR). All models in f(R,T) gravity and GR for FRW and ESU Universes with QM also SQM distributions, we get zero magnetic field. These results agree with the solutions of Aktaş and Aygün in f(R,T) gravity. However, we have also discussed the physical consequences of our obtained models.

Signature of charge migration in modulations of double ionization

NASA Astrophysics Data System (ADS)

Mauger, François; Abanador, Paul M.; Bruner, Adam; Sissay, Adonay; Gaarde, Mette B.; Lopata, Kenneth; Schafer, Kenneth J.

2018-04-01

We present a theoretical investigation of charge migration following strong-field ionization in a multielectron system. We study a model homonuclear molecule with two electrons, each restricted to one dimension (1 +1 D ), interacting with a strong, static electric field. We show that in this system charge migration results from the interplay between multiple ionization channels that overlap in space, creating a coherent electron-hole wave packet in the cation. We also find that, in our case, charge migration following the first ionization manifests as a modulation of the subsequent double-ionization signal. We derive a parametrized semiclassical model from the full multielectron system and we discuss the importance of the choice of cation electronic-structure basis for the efficacy of the semiclassical representation. We use the ab initio solution of the full 1 +1 D system as a reference for the qualitative and quantitative results of the parametrized semiclassical model. We discuss the extension of our model to long-wavelength time-dependent fields with full-dimension, many-electron targets.

A parametric simulation of solar chimney power plant

NASA Astrophysics Data System (ADS)

Beng Hooi, Lim; Kannan Thangavelu, Saravana

2018-01-01

The strong solar radiation, continuous supplies of sunlight and environmental friendly factors have made the solar chimney power plant becoming highly feasible to build in Malaysia. Solar chimney power plant produces upward buoyancy force through the greenhouse effect. Numerical simulation was performed on the model of a solar chimney power plant using the ANSYS Fluent software by applying standard k-epsilon turbulence model and discrete ordinates (DO) radiation model to solve the relevant equations. A parametric study was carried out to evaluate the performance of solar chimney power plant, which focused on the temperature rise in the collector, air velocity at the chimney base, and pressure drop inside the chimney were based on the results of temperature, velocity, and static pressure distributions. The results demonstrate reliability by comparing a model with the experimental data of Manzanares Spanish prototype. Based on the numerical results, power capacity and efficiency were analysed theoretically. Results indicate that a stronger solar radiation and larger prototype will improve the performance of solar chimney power plant.

Semiparametric time varying coefficient model for matched case-crossover studies.

PubMed

Ortega-Villa, Ana Maria; Kim, Inyoung; Kim, H

2017-03-15

In matched case-crossover studies, it is generally accepted that the covariates on which a case and associated controls are matched cannot exert a confounding effect on independent predictors included in the conditional logistic regression model. This is because any stratum effect is removed by the conditioning on the fixed number of sets of the case and controls in the stratum. Hence, the conditional logistic regression model is not able to detect any effects associated with the matching covariates by stratum. However, some matching covariates such as time often play an important role as an effect modification leading to incorrect statistical estimation and prediction. Therefore, we propose three approaches to evaluate effect modification by time. The first is a parametric approach, the second is a semiparametric penalized approach, and the third is a semiparametric Bayesian approach. Our parametric approach is a two-stage method, which uses conditional logistic regression in the first stage and then estimates polynomial regression in the second stage. Our semiparametric penalized and Bayesian approaches are one-stage approaches developed by using regression splines. Our semiparametric one stage approach allows us to not only detect the parametric relationship between the predictor and binary outcomes, but also evaluate nonparametric relationships between the predictor and time. We demonstrate the advantage of our semiparametric one-stage approaches using both a simulation study and an epidemiological example of a 1-4 bi-directional case-crossover study of childhood aseptic meningitis with drinking water turbidity. We also provide statistical inference for the semiparametric Bayesian approach using Bayes Factors. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

Machine-learning in grading of gliomas based on multi-parametric magnetic resonance imaging at 3T.

PubMed

Citak-Er, Fusun; Firat, Zeynep; Kovanlikaya, Ilhami; Ture, Ugur; Ozturk-Isik, Esin

2018-06-15

The objective of this study was to assess the contribution of multi-parametric (mp) magnetic resonance imaging (MRI) quantitative features in the machine learning-based grading of gliomas with a multi-region-of-interests approach. Forty-three patients who were newly diagnosed as having a glioma were included in this study. The patients were scanned prior to any therapy using a standard brain tumor magnetic resonance (MR) imaging protocol that included T1 and T2-weighted, diffusion-weighted, diffusion tensor, MR perfusion and MR spectroscopic imaging. Three different regions-of-interest were drawn for each subject to encompass tumor, immediate tumor periphery, and distant peritumoral edema/normal. The normalized mp-MRI features were used to build machine-learning models for differentiating low-grade gliomas (WHO grades I and II) from high grades (WHO grades III and IV). In order to assess the contribution of regional mp-MRI quantitative features to the classification models, a support vector machine-based recursive feature elimination method was applied prior to classification. A machine-learning model based on support vector machine algorithm with linear kernel achieved an accuracy of 93.0%, a specificity of 86.7%, and a sensitivity of 96.4% for the grading of gliomas using ten-fold cross validation based on the proposed subset of the mp-MRI features. In this study, machine-learning based on multiregional and multi-parametric MRI data has proven to be an important tool in grading glial tumors accurately even in this limited patient population. Future studies are needed to investigate the use of machine learning algorithms for brain tumor classification in a larger patient cohort. Copyright © 2018. Published by Elsevier Ltd.

Technical Topic 3.2.2.d Bayesian and Non-Parametric Statistics: Integration of Neural Networks with Bayesian Networks for Data Fusion and Predictive Modeling

DTIC Science & Technology

2016-05-31

and included explosives such as TATP, HMTD, RDX, RDX, ammonium nitrate , potassium perchlorate, potassium nitrate , sugar, and TNT. The approach...Distribution Unlimited UU UU UU UU 31-05-2016 15-Apr-2014 14-Jan-2015 Final Report: Technical Topic 3.2.2. d Bayesian and Non- parametric Statistics...of Papers published in non peer-reviewed journals: Final Report: Technical Topic 3.2.2. d Bayesian and Non-parametric Statistics: Integration of Neural

Parametrized modified gravity and the CMB bispectrum

NASA Astrophysics Data System (ADS)

Di Valentino, Eleonora; Melchiorri, Alessandro; Salvatelli, Valentina; Silvestri, Alessandra

2012-09-01

We forecast the constraints on modified theories of gravity from the cosmic microwave background (CMB) anisotropies bispectrum that arises from correlations between lensing and the Integrated Sachs-Wolfe effect. In models of modified gravity the evolution of the metric potentials is generally altered and the contribution to the CMB bispectrum signal can differ significantly from the one expected in the standard cosmological model. We adopt a parametrized approach and focus on three different classes of models: Linder’s growth index, Chameleon-type models, and f(R) theories. We show that the constraints on the parameters of the models will significantly improve with future CMB bispectrum measurements.

Parametric Net Influx Rate Images of 68Ga-DOTATOC and 68Ga-DOTATATE: Quantitative Accuracy and Improved Image Contrast.

PubMed

Ilan, Ezgi; Sandström, Mattias; Velikyan, Irina; Sundin, Anders; Eriksson, Barbro; Lubberink, Mark

2017-05-01

68 Ga-DOTATOC and 68 Ga-DOTATATE are radiolabeled somatostatin analogs used for the diagnosis of somatostatin receptor-expressing neuroendocrine tumors (NETs), and SUV measurements are suggested for treatment monitoring. However, changes in net influx rate ( K i ) may better reflect treatment effects than those of the SUV, and accordingly there is a need to compute parametric images showing K i at the voxel level. The aim of this study was to evaluate parametric methods for computation of parametric K i images by comparison to volume of interest (VOI)-based methods and to assess image contrast in terms of tumor-to-liver ratio. Methods: Ten patients with metastatic NETs underwent a 45-min dynamic PET examination followed by whole-body PET/CT at 1 h after injection of 68 Ga-DOTATOC and 68 Ga-DOTATATE on consecutive days. Parametric K i images were computed using a basis function method (BFM) implementation of the 2-tissue-irreversible-compartment model and the Patlak method using a descending aorta image-derived input function, and mean tumor K i values were determined for 50% isocontour VOIs and compared with K i values based on nonlinear regression (NLR) of the whole-VOI time-activity curve. A subsample of healthy liver was delineated in the whole-body and K i images, and tumor-to-liver ratios were calculated to evaluate image contrast. Correlation ( R 2 ) and agreement between VOI-based and parametric K i values were assessed using regression and Bland-Altman analysis. Results: The R 2 between NLR-based and parametric image-based (BFM) tumor K i values was 0.98 (slope, 0.81) and 0.97 (slope, 0.88) for 68 Ga-DOTATOC and 68 Ga-DOTATATE, respectively. For Patlak analysis, the R 2 between NLR-based and parametric-based (Patlak) tumor K i was 0.95 (slope, 0.71) and 0.92 (slope, 0.74) for 68 Ga-DOTATOC and 68 Ga-DOTATATE, respectively. There was no bias between NLR and parametric-based K i values. Tumor-to-liver contrast was 1.6 and 2.0 times higher in the parametric BFM K i images and 2.3 and 3.0 times in the Patlak images than in the whole-body images for 68 Ga-DOTATOC and 68 Ga-DOTATATE, respectively. Conclusion: A high R 2 and agreement between NLR- and parametric-based K i values was found, showing that K i images are quantitatively accurate. In addition, tumor-to-liver contrast was superior in the parametric K i images compared with whole-body images for both 68 Ga-DOTATOC and 68 Ga DOTATATE. © 2017 by the Society of Nuclear Medicine and Molecular Imaging.

Parametric Methods for Dynamic 11C-Phenytoin PET Studies.

PubMed

Mansor, Syahir; Yaqub, Maqsood; Boellaard, Ronald; Froklage, Femke E; de Vries, Anke; Bakker, Esther D M; Voskuyl, Rob A; Eriksson, Jonas; Schwarte, Lothar A; Verbeek, Joost; Windhorst, Albert D; Lammertsma, Adriaan A

2017-03-01

In this study, the performance of various methods for generating quantitative parametric images of dynamic 11 C-phenytoin PET studies was evaluated. Methods: Double-baseline 60-min dynamic 11 C-phenytoin PET studies, including online arterial sampling, were acquired for 6 healthy subjects. Parametric images were generated using Logan plot analysis, a basis function method, and spectral analysis. Parametric distribution volume (V T ) and influx rate ( K 1 ) were compared with those obtained from nonlinear regression analysis of time-activity curves. In addition, global and regional test-retest (TRT) variability was determined for parametric K 1 and V T values. Results: Biases in V T observed with all parametric methods were less than 5%. For K 1 , spectral analysis showed a negative bias of 16%. The mean TRT variabilities of V T and K 1 were less than 10% for all methods. Shortening the scan duration to 45 min provided similar V T and K 1 with comparable TRT performance compared with 60-min data. Conclusion: Among the various parametric methods tested, the basis function method provided parametric V T and K 1 values with the least bias compared with nonlinear regression data and showed TRT variabilities lower than 5%, also for smaller volume-of-interest sizes (i.e., higher noise levels) and shorter scan duration. © 2017 by the Society of Nuclear Medicine and Molecular Imaging.

Possible stretched exponential parametrization for humidity absorption in polymers.

PubMed

Hacinliyan, A; Skarlatos, Y; Sahin, G; Atak, K; Aybar, O O

2009-04-01

Polymer thin films have irregular transient current characteristics under constant voltage. In hydrophilic and hydrophobic polymers, the irregularity is also known to depend on the humidity absorbed by the polymer sample. Different stretched exponential models are studied and it is shown that the absorption of humidity as a function of time can be adequately modelled by a class of these stretched exponential absorption models.

Parametrics on 2D Navier-Stokes analysis of a Mach 2.68 bifurcated rectangular mixed-compression inlet

NASA Technical Reports Server (NTRS)

Mizukami, M.; Saunders, J. D.

1995-01-01

The supersonic diffuser of a Mach 2.68 bifurcated, rectangular, mixed-compression inlet was analyzed using a two-dimensional (2D) Navier-Stokes flow solver. Parametric studies were performed on turbulence models, computational grids and bleed models. The computer flowfield was substantially different from the original inviscid design, due to interactions of shocks, boundary layers, and bleed. Good agreement with experimental data was obtained in many aspects. Many of the discrepancies were thought to originate primarily from 3D effects. Therefore, a balance should be struck between expending resources on a high fidelity 2D simulation, and the inherent limitations of 2D analysis. The solutions were fairly insensitive to turbulence models, grids and bleed models. Overall, the k-e turbulence model, and the bleed models based on unchoked bleed hole discharge coefficients or uniform velocity are recommended. The 2D Navier-Stokes methods appear to be a useful tool for the design and analysis of supersonic inlets, by providing a higher fidelity simulation of the inlet flowfield than inviscid methods, in a reasonable turnaround time.

Thermofluid Analysis of Magnetocaloric Refrigeration

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

Abdelaziz, Omar; Gluesenkamp, Kyle R; Vineyard, Edward Allan

While there have been extensive studies on thermofluid characteristics of different magnetocaloric refrigeration systems, a conclusive optimization study using non-dimensional parameters which can be applied to a generic system has not been reported yet. In this study, a numerical model has been developed for optimization of active magnetic refrigerator (AMR). This model is computationally efficient and robust, making it appropriate for running the thousands of simulations required for parametric study and optimization. The governing equations have been non-dimensionalized and numerically solved using finite difference method. A parametric study on a wide range of non-dimensional numbers has been performed. While themore » goal of AMR systems is to improve the performance of competitive parameters including COP, cooling capacity and temperature span, new parameters called AMR performance index-1 have been introduced in order to perform multi objective optimization and simultaneously exploit all these parameters. The multi-objective optimization is carried out for a wide range of the non-dimensional parameters. The results of this study will provide general guidelines for designing high performance AMR systems.« less

Simulating the hydrological impacts of inter-annual and seasonal variability in land use land cover change on streamflow

NASA Astrophysics Data System (ADS)

Taxak, A. K.; Ojha, C. S. P.

2017-12-01

Land use and land cover (LULC) changes within a watershed are recognised as an important factor affecting hydrological processes and water resources. LULC changes continuously not only in long term but also on the inter-annual and season level. Changes in LULC affects the interception, storage and moisture. A widely used approach in rainfall-runoff modelling through Land surface models (LSM)/ hydrological models is to keep LULC same throughout the model running period. In long term simulations where land use change take place during the run period, using a single LULC does not represent a true picture of ground conditions could result in stationarity of model responses. The present work presents a case study in which changes in LULC are incorporated by using multiple LULC layers. LULC for the study period were created using imageries from Landsat series, Sentinal, EO-1 ALI. Distributed, physically based Variable Infiltration Capacity (VIC) model was modified to allow inclusion of LULC as a time varying variable just like climate. The Narayani basin was simulated with LULC, leaf area index (LAI), albedo and climate data for 1992-2015. The results showed that the model simulation with varied parametrization approach has a large improvement over the conventional fixed parametrization approach in terms of long-term water balance. The proposed modelling approach could improve hydrological modelling for applications like land cover change studies, water budget studies etc.

Model-free aftershock forecasts constructed from similar sequences in the past

NASA Astrophysics Data System (ADS)

van der Elst, N.; Page, M. T.

2017-12-01

The basic premise behind aftershock forecasting is that sequences in the future will be similar to those in the past. Forecast models typically use empirically tuned parametric distributions to approximate past sequences, and project those distributions into the future to make a forecast. While parametric models do a good job of describing average outcomes, they are not explicitly designed to capture the full range of variability between sequences, and can suffer from over-tuning of the parameters. In particular, parametric forecasts may produce a high rate of "surprises" - sequences that land outside the forecast range. Here we present a non-parametric forecast method that cuts out the parametric "middleman" between training data and forecast. The method is based on finding past sequences that are similar to the target sequence, and evaluating their outcomes. We quantify similarity as the Poisson probability that the observed event count in a past sequence reflects the same underlying intensity as the observed event count in the target sequence. Event counts are defined in terms of differential magnitude relative to the mainshock. The forecast is then constructed from the distribution of past sequences outcomes, weighted by their similarity. We compare the similarity forecast with the Reasenberg and Jones (RJ95) method, for a set of 2807 global aftershock sequences of M≥6 mainshocks. We implement a sequence-specific RJ95 forecast using a global average prior and Bayesian updating, but do not propagate epistemic uncertainty. The RJ95 forecast is somewhat more precise than the similarity forecast: 90% of observed sequences fall within a factor of two of the median RJ95 forecast value, whereas the fraction is 85% for the similarity forecast. However, the surprise rate is much higher for the RJ95 forecast; 10% of observed sequences fall in the upper 2.5% of the (Poissonian) forecast range. The surprise rate is less than 3% for the similarity forecast. The similarity forecast may be useful to emergency managers and non-specialists when confidence or expertise in parametric forecasting may be lacking. The method makes over-tuning impossible, and minimizes the rate of surprises. At the least, this forecast constitutes a useful benchmark for more precisely tuned parametric forecasts.

Self Diagnostic Adhesive for Bonded Joints in Aircraft Structures

DTIC Science & Technology

2016-10-04

validated under the fatigue/dynamic loading condition. 3) Both SEM (Spectral Element Modeling) and FEM ( Finite Element Modeling) simulation of the...Sensors ..................................................................... 22 Parametric Study of Sensor Performance via Finite Element Simulation...The frequency range that we are interested is around 800 kHz. Conventional linear finite element method (FEM) requires a very fine spatial

A parametric study of the drift-tearing mode using an extended-magnetohydrodynamic model

DOE PAGES

King, Jacob R.; Kruger, S. E.

2014-10-24

The linear, collisional, constant-ψ drift-tearing mode is analyzed for different regimes of the plasma-β, ion-skin-depth parameter space with an unreduced, extended-magnetohydrodynamic model. Here, new dispersion relations are found at moderate plasma β and previous drift-tearing results are classified as applicable at small plasma β.

Uncertainty importance analysis using parametric moment ratio functions.

PubMed

Wei, Pengfei; Lu, Zhenzhou; Song, Jingwen

2014-02-01

This article presents a new importance analysis framework, called parametric moment ratio function, for measuring the reduction of model output uncertainty when the distribution parameters of inputs are changed, and the emphasis is put on the mean and variance ratio functions with respect to the variances of model inputs. The proposed concepts efficiently guide the analyst to achieve a targeted reduction on the model output mean and variance by operating on the variances of model inputs. The unbiased and progressive unbiased Monte Carlo estimators are also derived for the parametric mean and variance ratio functions, respectively. Only a set of samples is needed for implementing the proposed importance analysis by the proposed estimators, thus the computational cost is free of input dimensionality. An analytical test example with highly nonlinear behavior is introduced for illustrating the engineering significance of the proposed importance analysis technique and verifying the efficiency and convergence of the derived Monte Carlo estimators. Finally, the moment ratio function is applied to a planar 10-bar structure for achieving a targeted 50% reduction of the model output variance. © 2013 Society for Risk Analysis.

Synthesis and Analysis of Custom Bi-directional Reflectivity Distribution Functions in DIRSIG

NASA Astrophysics Data System (ADS)

Dank, J.; Allen, D.

2016-09-01

The bi-directional reflectivity distribution (BRDF) function is a fundamental optical property of materials, characterizing important properties of light scattered by a surface. For accurate radiance calculations using synthetic targets and numerical simulations such as the Digital Imaging and Remote Sensing Image Generation (DIRSIG) model, fidelity of the target BRDFs is critical. While fits to measured BRDF data can be used in DIRSIG, obtaining high-quality data over a large spectral continuum can be time-consuming and expensive, requiring significant investment in illumination sources, sensors, and other specialized hardware. As a consequence, numerous parametric BRDF models are available to approximate actual behavior; but these all have shortcomings. Further, DIRSIG doesn't allow direct visualization of BRDFs, making it difficult for the user to understand the numerical impact of various models. Here, we discuss the innovative use of "mixture maps" to synthesize custom BRDFs as linear combinations of parametric models and measured data. We also show how DIRSIG's interactive mode can be used to visualize and analyze both available parametric models currently used in DIRSIG and custom BRDFs developed using our methods.

Developing Software for Pharmacodynamics and Bioassay Studies

DTIC Science & Technology

The objective of the project is to develop a software system to process general pharmacologic, toxicological, or other biomedical research data that...exhibit a non-monotonic dose-response relationship - for which the current parametric models fail. The software will analyze dose-response

Test of the Chevallier-Polarski-Linder parametrization for rapid dark energy equation of state transitions

NASA Astrophysics Data System (ADS)

Linden, Sebastian; Virey, Jean-Marc

2008-07-01

We test the robustness and flexibility of the Chevallier-Polarski-Linder (CPL) parametrization of the dark energy equation of state w(z)=w0+wa(z)/(1+z) in recovering a four-parameter steplike fiducial model. We constrain the parameter space region of the underlying fiducial model where the CPL parametrization offers a reliable reconstruction. It turns out that non-negligible biases leak into the results for recent (z<2.5) rapid transitions, but that CPL yields a good reconstruction in all other cases. The presented analysis is performed with supernova Ia data as forecasted for a space mission like SNAP/JDEM, combined with future expectations for the cosmic microwave background shift parameter R and the baryonic acoustic oscillation parameter A.

Parametric spatiotemporal oscillation in reaction-diffusion systems.

PubMed

Ghosh, Shyamolina; Ray, Deb Shankar

2016-03-01

We consider a reaction-diffusion system in a homogeneous stable steady state. On perturbation by a time-dependent sinusoidal forcing of a suitable scaling parameter the system exhibits parametric spatiotemporal instability beyond a critical threshold frequency. We have formulated a general scheme to calculate the threshold condition for oscillation and the range of unstable spatial modes lying within a V-shaped region reminiscent of Arnold's tongue. Full numerical simulations show that depending on the specificity of nonlinearity of the models, the instability may result in time-periodic stationary patterns in the form of standing clusters or spatially localized breathing patterns with characteristic wavelengths. Our theoretical analysis of the parametric oscillation in reaction-diffusion system is corroborated by full numerical simulation of two well-known chemical dynamical models: chlorite-iodine-malonic acid and Briggs-Rauscher reactions.

Parametric spatiotemporal oscillation in reaction-diffusion systems

NASA Astrophysics Data System (ADS)

Ghosh, Shyamolina; Ray, Deb Shankar

2016-03-01

We consider a reaction-diffusion system in a homogeneous stable steady state. On perturbation by a time-dependent sinusoidal forcing of a suitable scaling parameter the system exhibits parametric spatiotemporal instability beyond a critical threshold frequency. We have formulated a general scheme to calculate the threshold condition for oscillation and the range of unstable spatial modes lying within a V-shaped region reminiscent of Arnold's tongue. Full numerical simulations show that depending on the specificity of nonlinearity of the models, the instability may result in time-periodic stationary patterns in the form of standing clusters or spatially localized breathing patterns with characteristic wavelengths. Our theoretical analysis of the parametric oscillation in reaction-diffusion system is corroborated by full numerical simulation of two well-known chemical dynamical models: chlorite-iodine-malonic acid and Briggs-Rauscher reactions.

A parametric study of Enceladus plumes based on DSMC calculations for retrieving the outgassing parameters as measured by Cassini instruments

NASA Astrophysics Data System (ADS)

Mahieux, Arnaud; Goldstein, David B.; Varghese, Philip; Trafton, Laurence M.

2017-10-01

The vapor and particulate plumes arising from the southern polar regions of Enceladus are a key signature of what lies below the surface. Multiple Cassini instruments (INMS, CDA, CAPS, MAG, UVIS, VIMS, ISS) measured the gas-particle plume over the warm Tiger Stripe region and there have been several close flybys. Numerous observations also exist of the near-vent regions in the visible and the IR. The most likely source for these extensive geysers is a subsurface liquid reservoir of somewhat saline water and other volatiles boiling off through crevasse-like conduits into the vacuum of space.In this work, we use a DSMC code to simulate the plume as it exits a vent, considering axisymmetric conditions, in a vertical domain extending up to 10 km. Above 10 km altitude, the flow is collisionless and well modeled in a separate free molecular code. We perform a DSMC parametric and sensitivity study of the following vent parameters: vent diameter, outgassed flow density, water gas/water ice mass flow ratio, gas and ice speed, and ice grain diameter. We build parametric expressions of the plume characteristics at the 10 km upper boundary (number density, temperature, velocity) that will be used in a Bayesian inversion algorithm in order to constrain source conditions from fits to plume observations by various instruments on board the Cassini spacecraft and assess the parametric sensitivity study.

Gravitational wave production from preheating: parameter dependence

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

Figueroa, Daniel G.; Torrentí, Francisco, E-mail: daniel.figueroa@cern.ch, E-mail: f.torrenti@csic.es

Parametric resonance is among the most efficient phenomena generating gravitational waves (GWs) in the early Universe. The dynamics of parametric resonance, and hence of the GWs, depend exclusively on the resonance parameter q . The latter is determined by the properties of each scenario: the initial amplitude and potential curvature of the oscillating field, and its coupling to other species. Previous works have only studied the GW production for fixed value(s) of q . We present an analytical derivation of the GW amplitude dependence on q , valid for any scenario, which we confront against numerical results. By running latticemore » simulations in an expanding grid, we study for a wide range of q values, the production of GWs in post-inflationary preheating scenarios driven by parametric resonance. We present simple fits for the final amplitude and position of the local maxima in the GW spectrum. Our parametrization allows to predict the location and amplitude of the GW background today, for an arbitrary q . The GW signal can be rather large, as h {sup 2Ω}{sub GW}( f {sub p} ) ∼< 10{sup −11}, but it is always peaked at high frequencies f {sub p} ∼> 10{sup 7} Hz. We also discuss the case of spectator-field scenarios, where the oscillatory field can be e.g. a curvaton, or the Standard Model Higgs.« less

Parametric investigations of target normal sheath acceleration experiments

NASA Astrophysics Data System (ADS)

Zani, Alessandro; Sgattoni, Andrea; Passoni, Matteo

2011-10-01

One of the most important challenges related to laser-driven ion acceleration research is to actively control some important ion beam features. This is a peculiar topic in the light of future possible technological applications. In the present work we make use of one theoretical model for target normal sheath acceleration in order to reproduce recent experimental parametric studies about maximum ion energy dependencies on laser parameters. The key role played by pulse energy and intensity is enlightened. Finally the effective dependence of maximum ion energy on intensity is evaluated using a combined theoretical approach, obtained by means of an analytical and a particle-in-cell numerical investigation.

Numerical modeling of ultrasonic cavitation in ionic liquids

NASA Astrophysics Data System (ADS)

Calvisi, Michael L.; Elder, Ross M.

2017-11-01

Ionic liquids have favorable properties for sonochemistry applications in which the high temperatures and pressures achieved by cavitation bubbles are important drivers of chemical processes. Two different numerical models are presented to simulate ultrasonic cavitation in ionic liquids, each with different capabilities and physical assumptions. A model based on a compressible form of the Rayleigh-Plesset equation (RPE) simulates ultrasonic cavitation of a spherical bubble with a homogeneous interior, incorporating evaporation and condensation at the bubble surface, and temperature-varying thermodynamic properties in the interior. A second, more computationally intensive model of a spherical bubble uses the finite element method (FEM) and accounts for spatial variations in pressure and temperature throughout the flow domain. This model provides insight into heat transfer across the bubble surface and throughout the bubble interior and exterior. Parametric studies are presented for sonochemistry applications involving ionic liquids as a solvent, examining a range of realistic ionic liquid properties and initial conditions to determine their effect on temperature and pressure. Results from the two models are presented for parametric variations including viscosity, thermal conductivity, water content of the ionic liquid solvent, acoustic frequency, and initial bubble pressure. An additional study performed with the FEM model examines thermal penetration into the surrounding ionic liquid during bubble oscillation. The results suggest the prospect of tuning ionic liquid properties for specific applications.

Three-Dimensional Modeling of Aircraft High-Lift Components with Vehicle Sketch Pad

NASA Technical Reports Server (NTRS)

Olson, Erik D.

2016-01-01

Vehicle Sketch Pad (OpenVSP) is a parametric geometry modeler that has been used extensively for conceptual design studies of aircraft, including studies using higher-order analysis. OpenVSP can model flap and slat surfaces using simple shearing of the airfoil coordinates, which is an appropriate level of complexity for lower-order aerodynamic analysis methods. For three-dimensional analysis, however, there is not a built-in method for defining the high-lift components in OpenVSP in a realistic manner, or for controlling their complex motions in a parametric manner that is intuitive to the designer. This paper seeks instead to utilize OpenVSP's existing capabilities, and establish a set of best practices for modeling high-lift components at a level of complexity suitable for higher-order analysis methods. Techniques are described for modeling the flap and slat components as separate three-dimensional surfaces, and for controlling their motion using simple parameters defined in the local hinge-axis frame of reference. To demonstrate the methodology, an OpenVSP model for the Energy-Efficient Transport (EET) AR12 wind-tunnel model has been created, taking advantage of OpenVSP's Advanced Parameter Linking capability to translate the motions of the high-lift components from the hinge-axis coordinate system to a set of transformations in OpenVSP's frame of reference.

Algorithm for parametric community detection in networks.

PubMed

Bettinelli, Andrea; Hansen, Pierre; Liberti, Leo

2012-07-01

Modularity maximization is extensively used to detect communities in complex networks. It has been shown, however, that this method suffers from a resolution limit: Small communities may be undetectable in the presence of larger ones even if they are very dense. To alleviate this defect, various modifications of the modularity function have been proposed as well as multiresolution methods. In this paper we systematically study a simple model (proposed by Pons and Latapy [Theor. Comput. Sci. 412, 892 (2011)] and similar to the parametric model of Reichardt and Bornholdt [Phys. Rev. E 74, 016110 (2006)]) with a single parameter α that balances the fraction of within community edges and the expected fraction of edges according to the configuration model. An exact algorithm is proposed to find optimal solutions for all values of α as well as the corresponding successive intervals of α values for which they are optimal. This algorithm relies upon a routine for exact modularity maximization and is limited to moderate size instances. An agglomerative hierarchical heuristic is therefore proposed to address parametric modularity detection in large networks. At each iteration the smallest value of α for which it is worthwhile to merge two communities of the current partition is found. Then merging is performed and the data are updated accordingly. An implementation is proposed with the same time and space complexity as the well-known Clauset-Newman-Moore (CNM) heuristic [Phys. Rev. E 70, 066111 (2004)]. Experimental results on artificial and real world problems show that (i) communities are detected by both exact and heuristic methods for all values of the parameter α; (ii) the dendrogram summarizing the results of the heuristic method provides a useful tool for substantive analysis, as illustrated particularly on a Les Misérables data set; (iii) the difference between the parametric modularity values given by the exact method and those given by the heuristic is moderate; (iv) the heuristic version of the proposed parametric method, viewed as a modularity maximization tool, gives better results than the CNM heuristic for large instances.

Parametric Study of a YAV-8B Harrier in Ground Effect using Time-Dependent Navier-Stokes Computations

NASA Technical Reports Server (NTRS)

Pandya, Shishir; Chaderjian, Neal; Ahmad, Jasim; Kwak, Dochan (Technical Monitor)

2002-01-01

A process is described which enables the generation of 35 time-dependent viscous solutions for a YAV-8B Harrier in ground effect in one week. Overset grids are used to model the complex geometry of the Harrier aircraft and the interaction of its jets with the ground plane and low-speed ambient flow. The time required to complete this parametric study is drastically reduced through the use of process automation, modern computational platforms, and parallel computing. Moreover, a dual-time-stepping algorithm is described which improves solution robustness. Unsteady flow visualization and a frequency domain analysis are also used to identify and correlated key flow structures with the time variation of lift.

Assessment and Selection of Competing Models for Zero-Inflated Microbiome Data

PubMed Central

Xu, Lizhen; Paterson, Andrew D.; Turpin, Williams; Xu, Wei

2015-01-01

Typical data in a microbiome study consist of the operational taxonomic unit (OTU) counts that have the characteristic of excess zeros, which are often ignored by investigators. In this paper, we compare the performance of different competing methods to model data with zero inflated features through extensive simulations and application to a microbiome study. These methods include standard parametric and non-parametric models, hurdle models, and zero inflated models. We examine varying degrees of zero inflation, with or without dispersion in the count component, as well as different magnitude and direction of the covariate effect on structural zeros and the count components. We focus on the assessment of type I error, power to detect the overall covariate effect, measures of model fit, and bias and effectiveness of parameter estimations. We also evaluate the abilities of model selection strategies using Akaike information criterion (AIC) or Vuong test to identify the correct model. The simulation studies show that hurdle and zero inflated models have well controlled type I errors, higher power, better goodness of fit measures, and are more accurate and efficient in the parameter estimation. Besides that, the hurdle models have similar goodness of fit and parameter estimation for the count component as their corresponding zero inflated models. However, the estimation and interpretation of the parameters for the zero components differs, and hurdle models are more stable when structural zeros are absent. We then discuss the model selection strategy for zero inflated data and implement it in a gut microbiome study of > 400 independent subjects. PMID:26148172

Assessment and Selection of Competing Models for Zero-Inflated Microbiome Data.

PubMed

Xu, Lizhen; Paterson, Andrew D; Turpin, Williams; Xu, Wei

2015-01-01

Typical data in a microbiome study consist of the operational taxonomic unit (OTU) counts that have the characteristic of excess zeros, which are often ignored by investigators. In this paper, we compare the performance of different competing methods to model data with zero inflated features through extensive simulations and application to a microbiome study. These methods include standard parametric and non-parametric models, hurdle models, and zero inflated models. We examine varying degrees of zero inflation, with or without dispersion in the count component, as well as different magnitude and direction of the covariate effect on structural zeros and the count components. We focus on the assessment of type I error, power to detect the overall covariate effect, measures of model fit, and bias and effectiveness of parameter estimations. We also evaluate the abilities of model selection strategies using Akaike information criterion (AIC) or Vuong test to identify the correct model. The simulation studies show that hurdle and zero inflated models have well controlled type I errors, higher power, better goodness of fit measures, and are more accurate and efficient in the parameter estimation. Besides that, the hurdle models have similar goodness of fit and parameter estimation for the count component as their corresponding zero inflated models. However, the estimation and interpretation of the parameters for the zero components differs, and hurdle models are more stable when structural zeros are absent. We then discuss the model selection strategy for zero inflated data and implement it in a gut microbiome study of > 400 independent subjects.

Parametric inference for biological sequence analysis.

PubMed

Pachter, Lior; Sturmfels, Bernd

2004-11-16

One of the major successes in computational biology has been the unification, by using the graphical model formalism, of a multitude of algorithms for annotating and comparing biological sequences. Graphical models that have been applied to these problems include hidden Markov models for annotation, tree models for phylogenetics, and pair hidden Markov models for alignment. A single algorithm, the sum-product algorithm, solves many of the inference problems that are associated with different statistical models. This article introduces the polytope propagation algorithm for computing the Newton polytope of an observation from a graphical model. This algorithm is a geometric version of the sum-product algorithm and is used to analyze the parametric behavior of maximum a posteriori inference calculations for graphical models.

Constellation Program Life-cycle Cost Analysis Model (LCAM)

NASA Technical Reports Server (NTRS)

Prince, Andy; Rose, Heidi; Wood, James

2008-01-01

The Constellation Program (CxP) is NASA's effort to replace the Space Shuttle, return humans to the moon, and prepare for a human mission to Mars. The major elements of the Constellation Lunar sortie design reference mission architecture are shown. Unlike the Apollo Program of the 1960's, affordability is a major concern of United States policy makers and NASA management. To measure Constellation affordability, a total ownership cost life-cycle parametric cost estimating capability is required. This capability is being developed by the Constellation Systems Engineering and Integration (SE&I) Directorate, and is called the Lifecycle Cost Analysis Model (LCAM). The requirements for LCAM are based on the need to have a parametric estimating capability in order to do top-level program analysis, evaluate design alternatives, and explore options for future systems. By estimating the total cost of ownership within the context of the planned Constellation budget, LCAM can provide Program and NASA management with the cost data necessary to identify the most affordable alternatives. LCAM is also a key component of the Integrated Program Model (IPM), an SE&I developed capability that combines parametric sizing tools with cost, schedule, and risk models to perform program analysis. LCAM is used in the generation of cost estimates for system level trades and analyses. It draws upon the legacy of previous architecture level cost models, such as the Exploration Systems Mission Directorate (ESMD) Architecture Cost Model (ARCOM) developed for Simulation Based Acquisition (SBA), and ATLAS. LCAM is used to support requirements and design trade studies by calculating changes in cost relative to a baseline option cost. Estimated costs are generally low fidelity to accommodate available input data and available cost estimating relationships (CERs). LCAM is capable of interfacing with the Integrated Program Model to provide the cost estimating capability for that suite of tools.

Oxidative response of human monocytes and macrophages cultured under low oxygen culture conditions to ion parametric resonance magnetic fields.

EPA Science Inventory

INTRODUCTION One proposed mechanism of action of electromagnetic fields (EMFs) on biological systems is the Ion Parametric Resonance (IPR) model, which has been experimentally validated in neuronal PC-12 cells [1, 2]. It proposes that when applied EMFs are tuned to resonate with...

A Nonparametric Approach to Estimate Classification Accuracy and Consistency

ERIC Educational Resources Information Center

Lathrop, Quinn N.; Cheng, Ying

2014-01-01

When cut scores for classifications occur on the total score scale, popular methods for estimating classification accuracy (CA) and classification consistency (CC) require assumptions about a parametric form of the test scores or about a parametric response model, such as item response theory (IRT). This article develops an approach to estimate CA…

[Representation and mathematical analysis of human crystalline lens].

PubMed

Tălu, Stefan; Giovanzana, Stefano; Tălu, Mihai

2011-01-01

The surface of human crystalline lens can be described and analyzed using mathematical models based on parametric representations, used in biomechanical studies and 3D solid modeling of the lens. The mathematical models used in lens biomechanics allow the study and the behavior of crystalline lens on variables and complex dynamic loads. Also, the lens biomechanics has the potential to improve the results in the development of intraocular lenses and cataract surgery. The paper presents the most representative mathematical models currently used for the modeling of human crystalline lens, both optically and biomechanically.

Trans-dimensional inversion of microtremor array dispersion data with hierarchical autoregressive error models

NASA Astrophysics Data System (ADS)

Dettmer, Jan; Molnar, Sheri; Steininger, Gavin; Dosso, Stan E.; Cassidy, John F.

2012-02-01

This paper applies a general trans-dimensional Bayesian inference methodology and hierarchical autoregressive data-error models to the inversion of microtremor array dispersion data for shear wave velocity (vs) structure. This approach accounts for the limited knowledge of the optimal earth model parametrization (e.g. the number of layers in the vs profile) and of the data-error statistics in the resulting vs parameter uncertainty estimates. The assumed earth model parametrization influences estimates of parameter values and uncertainties due to different parametrizations leading to different ranges of data predictions. The support of the data for a particular model is often non-unique and several parametrizations may be supported. A trans-dimensional formulation accounts for this non-uniqueness by including a model-indexing parameter as an unknown so that groups of models (identified by the indexing parameter) are considered in the results. The earth model is parametrized in terms of a partition model with interfaces given over a depth-range of interest. In this work, the number of interfaces (layers) in the partition model represents the trans-dimensional model indexing. In addition, serial data-error correlations are addressed by augmenting the geophysical forward model with a hierarchical autoregressive error model that can account for a wide range of error processes with a small number of parameters. Hence, the limited knowledge about the true statistical distribution of data errors is also accounted for in the earth model parameter estimates, resulting in more realistic uncertainties and parameter values. Hierarchical autoregressive error models do not rely on point estimates of the model vector to estimate data-error statistics, and have no requirement for computing the inverse or determinant of a data-error covariance matrix. This approach is particularly useful for trans-dimensional inverse problems, as point estimates may not be representative of the state space that spans multiple subspaces of different dimensionalities. The order of the autoregressive process required to fit the data is determined here by posterior residual-sample examination and statistical tests. Inference for earth model parameters is carried out on the trans-dimensional posterior probability distribution by considering ensembles of parameter vectors. In particular, vs uncertainty estimates are obtained by marginalizing the trans-dimensional posterior distribution in terms of vs-profile marginal distributions. The methodology is applied to microtremor array dispersion data collected at two sites with significantly different geology in British Columbia, Canada. At both sites, results show excellent agreement with estimates from invasive measurements.

Estimation of a partially linear additive model for data from an outcome-dependent sampling design with a continuous outcome

PubMed Central

Tan, Ziwen; Qin, Guoyou; Zhou, Haibo

2016-01-01

Outcome-dependent sampling (ODS) designs have been well recognized as a cost-effective way to enhance study efficiency in both statistical literature and biomedical and epidemiologic studies. A partially linear additive model (PLAM) is widely applied in real problems because it allows for a flexible specification of the dependence of the response on some covariates in a linear fashion and other covariates in a nonlinear non-parametric fashion. Motivated by an epidemiological study investigating the effect of prenatal polychlorinated biphenyls exposure on children's intelligence quotient (IQ) at age 7 years, we propose a PLAM in this article to investigate a more flexible non-parametric inference on the relationships among the response and covariates under the ODS scheme. We propose the estimation method and establish the asymptotic properties of the proposed estimator. Simulation studies are conducted to show the improved efficiency of the proposed ODS estimator for PLAM compared with that from a traditional simple random sampling design with the same sample size. The data of the above-mentioned study is analyzed to illustrate the proposed method. PMID:27006375

Correlation tracking study for meter-class solar telescope on space shuttle. [solar granulation

NASA Technical Reports Server (NTRS)

Smithson, R. C.; Tarbell, T. D.

1977-01-01

The theory and expected performance level of correlation trackers used to control the pointing of a solar telescope in space using white light granulation as a target were studied. Three specific trackers were modeled and their performance levels predicted for telescopes of various apertures. The performance of the computer model trackers on computer enhanced granulation photographs was evaluated. Parametric equations for predicting tracker performance are presented.