Two-Sample Statistics for Testing the Equality of Survival Functions Against Improper Semi-parametric Accelerated Failure Time Alternatives: An Application to the Analysis of a Breast Cancer Clinical Trial

PubMed Central

BROËT, PHILIPPE; TSODIKOV, ALEXANDER; DE RYCKE, YANN; MOREAU, THIERRY

2010-01-01

This paper presents two-sample statistics suited for testing equality of survival functions against improper semi-parametric accelerated failure time alternatives. These tests are designed for comparing either the short- or the long-term effect of a prognostic factor, or both. These statistics are obtained as partial likelihood score statistics from a time-dependent Cox model. As a consequence, the proposed tests can be very easily implemented using widely available software. A breast cancer clinical trial is presented as an example to demonstrate the utility of the proposed tests. PMID:15293627

The linear transformation model with frailties for the analysis of item response times.

PubMed

Wang, Chun; Chang, Hua-Hua; Douglas, Jeffrey A

2013-02-01

The item response times (RTs) collected from computerized testing represent an underutilized source of information about items and examinees. In addition to knowing the examinees' responses to each item, we can investigate the amount of time examinees spend on each item. In this paper, we propose a semi-parametric model for RTs, the linear transformation model with a latent speed covariate, which combines the flexibility of non-parametric modelling and the brevity as well as interpretability of parametric modelling. In this new model, the RTs, after some non-parametric monotone transformation, become a linear model with latent speed as covariate plus an error term. The distribution of the error term implicitly defines the relationship between the RT and examinees' latent speeds; whereas the non-parametric transformation is able to describe various shapes of RT distributions. The linear transformation model represents a rich family of models that includes the Cox proportional hazards model, the Box-Cox normal model, and many other models as special cases. This new model is embedded in a hierarchical framework so that both RTs and responses are modelled simultaneously. A two-stage estimation method is proposed. In the first stage, the Markov chain Monte Carlo method is employed to estimate the parametric part of the model. In the second stage, an estimating equation method with a recursive algorithm is adopted to estimate the non-parametric transformation. Applicability of the new model is demonstrated with a simulation study and a real data application. Finally, methods to evaluate the model fit are suggested. © 2012 The British Psychological Society.

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.

Accelerated failure time models for semi-competing risks data in the presence of complex censoring.

PubMed

Lee, Kyu Ha; Rondeau, Virginie; Haneuse, Sebastien

2017-12-01

Statistical analyses that investigate risk factors for Alzheimer's disease (AD) are often subject to a number of challenges. Some of these challenges arise due to practical considerations regarding data collection such that the observation of AD events is subject to complex censoring including left-truncation and either interval or right-censoring. Additional challenges arise due to the fact that study participants under investigation are often subject to competing forces, most notably death, that may not be independent of AD. Towards resolving the latter, researchers may choose to embed the study of AD within the "semi-competing risks" framework for which the recent statistical literature has seen a number of advances including for the so-called illness-death model. To the best of our knowledge, however, the semi-competing risks literature has not fully considered analyses in contexts with complex censoring, as in studies of AD. This is particularly the case when interest lies with the accelerated failure time (AFT) model, an alternative to the traditional multiplicative Cox model that places emphasis away from the hazard function. In this article, we outline a new Bayesian framework for estimation/inference of an AFT illness-death model for semi-competing risks data subject to complex censoring. An efficient computational algorithm that gives researchers the flexibility to adopt either a fully parametric or a semi-parametric model specification is developed and implemented. The proposed methods are motivated by and illustrated with an analysis of data from the Adult Changes in Thought study, an on-going community-based prospective study of incident AD in western Washington State. © 2017, The International Biometric Society.

A robust semi-parametric warping estimator of the survivor function with an application to two-group comparisons

PubMed Central

Hutson, Alan D

2018-01-01

In this note, we develop a new and novel semi-parametric estimator of the survival curve that is comparable to the product-limit estimator under very relaxed assumptions. The estimator is based on a beta parametrization that warps the empirical distribution of the observed censored and uncensored data. The parameters are obtained using a pseudo-maximum likelihood approach adjusting the survival curve accounting for the censored observations. In the univariate setting, the new estimator tends to better extend the range of the survival estimation given a high degree of censoring. However, the key feature of this paper is that we develop a new two-group semi-parametric exact permutation test for comparing survival curves that is generally superior to the classic log-rank and Wilcoxon tests and provides the best global power across a variety of alternatives. The new test is readily extended to the k group setting. PMID:26988931

Development of Semi-Span Model Test Techniques

NASA Technical Reports Server (NTRS)

Pulnam, L. Elwood (Technical Monitor); Milholen, William E., II; Chokani, Ndaona; McGhee, Robert J.

1996-01-01

A computational investigation was performed to support the development of a semi-span model test capability in the NASA Langley Research Center's National Transonic Facility. This capability is desirable for the testing of advanced subsonic transport aircraft at full-scale Reynolds numbers. A state-of-the-art three-dimensional Navier-Stokes solver was used to examine methods to improve the flow over a semi-span configuration. First, a parametric study is conducted to examine the influence of the stand-off height on the flow over the semi-span model. It is found that decreasing the stand-off height, below the maximum fuselage radius, improves the aerodynamic characteristics of the semi-span model. Next, active sidewall boundary layer control techniques are examined. Juncture region blowing jets, upstream tangential blowing, and sidewall suction are found to improve the flow over the aft portion of the semi-span model. Both upstream blowing and suction are found to reduce the sidewall boundary layer separation. The resulting near surface streamline patterns are improved, and found to be quite similar to the full-span results. Both techniques however adversely affect the pitching moment coefficient.

Empirical Prediction of Aircraft Landing Gear Noise

NASA Technical Reports Server (NTRS)

Golub, Robert A. (Technical Monitor); Guo, Yue-Ping

2005-01-01

This report documents a semi-empirical/semi-analytical method for landing gear noise prediction. The method is based on scaling laws of the theory of aerodynamic noise generation and correlation of these scaling laws with current available test data. The former gives the method a sound theoretical foundation and the latter quantitatively determines the relations between the parameters of the landing gear assembly and the far field noise, enabling practical predictions of aircraft landing gear noise, both for parametric trends and for absolute noise levels. The prediction model is validated by wind tunnel test data for an isolated Boeing 737 landing gear and by flight data for the Boeing 777 airplane. In both cases, the predictions agree well with data, both in parametric trends and in absolute noise levels.

A varying coefficient model to measure the effectiveness of mass media anti-smoking campaigns in generating calls to a Quitline.

PubMed

Bui, Quang M; Huggins, Richard M; Hwang, Wen-Han; White, Victoria; Erbas, Bircan

2010-01-01

Anti-smoking advertisements are an effective population-based smoking reduction strategy. The Quitline telephone service provides a first point of contact for adults considering quitting. Because of data complexity, the relationship between anti-smoking advertising placement, intensity, and time trends in total call volume is poorly understood. In this study we use a recently developed semi-varying coefficient model to elucidate this relationship. Semi-varying coefficient models comprise parametric and nonparametric components. The model is fitted to the daily number of calls to Quitline in Victoria, Australia to estimate a nonparametric long-term trend and parametric terms for day-of-the-week effects and to clarify the relationship with target audience rating points (TARPs) for the Quit and nicotine replacement advertising campaigns. The number of calls to Quitline increased with the TARP value of both the Quit and other smoking cessation advertisement; the TARP values associated with the Quit program were almost twice as effective. The varying coefficient term was statistically significant for peak periods with little or no advertising. Semi-varying coefficient models are useful for modeling public health data when there is little or no information on other factors related to the at-risk population. These models are well suited to modeling call volume to Quitline, because the varying coefficient allowed the underlying time trend to depend on fixed covariates that also vary with time, thereby explaining more of the variation in the call model.

A Varying Coefficient Model to Measure the Effectiveness of Mass Media Anti-Smoking Campaigns in Generating Calls to a Quitline

PubMed Central

Bui, Quang M.; Huggins, Richard M.; Hwang, Wen-Han; White, Victoria; Erbas, Bircan

2010-01-01

Background Anti-smoking advertisements are an effective population-based smoking reduction strategy. The Quitline telephone service provides a first point of contact for adults considering quitting. Because of data complexity, the relationship between anti-smoking advertising placement, intensity, and time trends in total call volume is poorly understood. In this study we use a recently developed semi-varying coefficient model to elucidate this relationship. Methods Semi-varying coefficient models comprise parametric and nonparametric components. The model is fitted to the daily number of calls to Quitline in Victoria, Australia to estimate a nonparametric long-term trend and parametric terms for day-of-the-week effects and to clarify the relationship with target audience rating points (TARPs) for the Quit and nicotine replacement advertising campaigns. Results The number of calls to Quitline increased with the TARP value of both the Quit and other smoking cessation advertisement; the TARP values associated with the Quit program were almost twice as effective. The varying coefficient term was statistically significant for peak periods with little or no advertising. Conclusions Semi-varying coefficient models are useful for modeling public health data when there is little or no information on other factors related to the at-risk population. These models are well suited to modeling call volume to Quitline, because the varying coefficient allowed the underlying time trend to depend on fixed covariates that also vary with time, thereby explaining more of the variation in the call model. PMID:20827036

A Semi-Parametric Bayesian Mixture Modeling Approach for the Analysis of Judge Mediated Data

ERIC Educational Resources Information Center

Muckle, Timothy Joseph

2010-01-01

Existing methods for the analysis of ordinal-level data arising from judge ratings, such as the Multi-Facet Rasch model (MFRM, or the so-called Facets model) have been widely used in assessment in order to render fair examinee ability estimates in situations where the judges vary in their behavior or severity. However, this model makes certain…

Period Estimation for Sparsely-sampled Quasi-periodic Light Curves Applied to Miras

NASA Astrophysics Data System (ADS)

He, Shiyuan; Yuan, Wenlong; Huang, Jianhua Z.; Long, James; Macri, Lucas M.

2016-12-01

We develop a nonlinear semi-parametric Gaussian process model to estimate periods of Miras with sparsely sampled light curves. The model uses a sinusoidal basis for the periodic variation and a Gaussian process for the stochastic changes. We use maximum likelihood to estimate the period and the parameters of the Gaussian process, while integrating out the effects of other nuisance parameters in the model with respect to a suitable prior distribution obtained from earlier studies. Since the likelihood is highly multimodal for period, we implement a hybrid method that applies the quasi-Newton algorithm for Gaussian process parameters and search the period/frequency parameter space over a dense grid. A large-scale, high-fidelity simulation is conducted to mimic the sampling quality of Mira light curves obtained by the M33 Synoptic Stellar Survey. The simulated data set is publicly available and can serve as a testbed for future evaluation of different period estimation methods. The semi-parametric model outperforms an existing algorithm on this simulated test data set as measured by period recovery rate and quality of the resulting period-luminosity relations.

Commercial aspects of semi-reusable launch systems

NASA Astrophysics Data System (ADS)

Obersteiner, M. H.; Müller, H.; Spies, H.

2003-07-01

This paper presents a business planning model for a commercial space launch system. The financing model is based on market analyses and projections combined with market capture models. An operations model is used to derive the annual cash income. Parametric cost modeling, development and production schedules are used for quantifying the annual expenditures, the internal rate of return, break even point of positive cash flow and the respective prices per launch. Alternative consortia structures, cash flow methods, capture rates and launch prices are used to examine the sensitivity of the model. Then the model is applied for a promising semi-reusable launcher concept, showing the general achievability of the commercial approach and the necessary pre-conditions.

A NEW VARIANCE ESTIMATOR FOR PARAMETERS OF SEMI-PARAMETRIC GENERALIZED ADDITIVE MODELS. (R829213)

EPA Science Inventory

The perspectives, information and conclusions conveyed in research project abstracts, progress reports, final reports, journal abstracts and journal publications convey the viewpoints of the principal investigator and may not represent the views and policies of ORD and EPA. Concl...

Semi-Parametric Item Response Functions in the Context of Guessing. CRESST Report 844

ERIC Educational Resources Information Center

Falk, Carl F.; Cai, Li

2015-01-01

We present a logistic function of a monotonic polynomial with a lower asymptote, allowing additional flexibility beyond the three-parameter logistic model. We develop a maximum marginal likelihood based approach to estimate the item parameters. The new item response model is demonstrated on math assessment data from a state, and a computationally…

Hybrid semi-parametric mathematical systems: bridging the gap between systems biology and process engineering.

PubMed

Teixeira, Ana P; Carinhas, Nuno; Dias, João M L; Cruz, Pedro; Alves, Paula M; Carrondo, Manuel J T; Oliveira, Rui

2007-12-01

Systems biology is an integrative science that aims at the global characterization of biological systems. Huge amounts of data regarding gene expression, proteins activity and metabolite concentrations are collected by designing systematic genetic or environmental perturbations. Then the challenge is to integrate such data in a global model in order to provide a global picture of the cell. The analysis of these data is largely dominated by nonparametric modelling tools. In contrast, classical bioprocess engineering has been primarily founded on first principles models, but it has systematically overlooked the details of the embedded biological system. The full complexity of biological systems is currently assumed by systems biology and this knowledge can now be taken by engineers to decide how to optimally design and operate their processes. This paper discusses possible methodologies for the integration of systems biology and bioprocess engineering with emphasis on applications involving animal cell cultures. At the mathematical systems level, the discussion is focused on hybrid semi-parametric systems as a way to bridge systems biology and bioprocess engineering.

Bayesian semi-parametric analysis of Poisson change-point regression models: application to policy making in Cali, Colombia.

PubMed

Park, Taeyoung; Krafty, Robert T; Sánchez, Alvaro I

2012-07-27

A Poisson regression model with an offset assumes a constant baseline rate after accounting for measured covariates, which may lead to biased estimates of coefficients in an inhomogeneous Poisson process. To correctly estimate the effect of time-dependent covariates, we propose a Poisson change-point regression model with an offset that allows a time-varying baseline rate. When the nonconstant pattern of a log baseline rate is modeled with a nonparametric step function, the resulting semi-parametric model involves a model component of varying dimension and thus requires a sophisticated varying-dimensional inference to obtain correct estimates of model parameters of fixed dimension. To fit the proposed varying-dimensional model, we devise a state-of-the-art MCMC-type algorithm based on partial collapse. The proposed model and methods are used to investigate an association between daily homicide rates in Cali, Colombia and policies that restrict the hours during which the legal sale of alcoholic beverages is permitted. While simultaneously identifying the latent changes in the baseline homicide rate which correspond to the incidence of sociopolitical events, we explore the effect of policies governing the sale of alcohol on homicide rates and seek a policy that balances the economic and cultural dependencies on alcohol sales to the health of the public.

Preclinical Biokinetic Modelling of Tc-99m Radiophamaceuticals Obtained from Semi-Automatic Image Processing.

PubMed

Cornejo-Aragón, Luz G; Santos-Cuevas, Clara L; Ocampo-García, Blanca E; Chairez-Oria, Isaac; Diaz-Nieto, Lorenza; García-Quiroz, Janice

2017-01-01

The aim of this study was to develop a semi automatic image processing algorithm (AIPA) based on the simultaneous information provided by X-ray and radioisotopic images to determine the biokinetic models of Tc-99m radiopharmaceuticals from quantification of image radiation activity in murine models. These radioisotopic images were obtained by a CCD (charge couple device) camera coupled to an ultrathin phosphorous screen in a preclinical multimodal imaging system (Xtreme, Bruker). The AIPA consisted of different image processing methods for background, scattering and attenuation correction on the activity quantification. A set of parametric identification algorithms was used to obtain the biokinetic models that characterize the interaction between different tissues and the radiopharmaceuticals considered in the study. The set of biokinetic models corresponded to the Tc-99m biodistribution observed in different ex vivo studies. This fact confirmed the contribution of the semi-automatic image processing technique developed in this study.

Statistical analysis of water-quality data containing multiple detection limits II: S-language software for nonparametric distribution modeling and hypothesis testing

USGS Publications Warehouse

Lee, L.; Helsel, D.

2007-01-01

Analysis of low concentrations of trace contaminants in environmental media often results in left-censored data that are below some limit of analytical precision. Interpretation of values becomes complicated when there are multiple detection limits in the data-perhaps as a result of changing analytical precision over time. Parametric and semi-parametric methods, such as maximum likelihood estimation and robust regression on order statistics, can be employed to model distributions of multiply censored data and provide estimates of summary statistics. However, these methods are based on assumptions about the underlying distribution of data. Nonparametric methods provide an alternative that does not require such assumptions. A standard nonparametric method for estimating summary statistics of multiply-censored data is the Kaplan-Meier (K-M) method. This method has seen widespread usage in the medical sciences within a general framework termed "survival analysis" where it is employed with right-censored time-to-failure data. However, K-M methods are equally valid for the left-censored data common in the geosciences. Our S-language software provides an analytical framework based on K-M methods that is tailored to the needs of the earth and environmental sciences community. This includes routines for the generation of empirical cumulative distribution functions, prediction or exceedance probabilities, and related confidence limits computation. Additionally, our software contains K-M-based routines for nonparametric hypothesis testing among an unlimited number of grouping variables. A primary characteristic of K-M methods is that they do not perform extrapolation and interpolation. Thus, these routines cannot be used to model statistics beyond the observed data range or when linear interpolation is desired. For such applications, the aforementioned parametric and semi-parametric methods must be used.

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

PERIOD ESTIMATION FOR SPARSELY SAMPLED QUASI-PERIODIC LIGHT CURVES APPLIED TO MIRAS

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

He, Shiyuan; Huang, Jianhua Z.; Long, James

2016-12-01

We develop a nonlinear semi-parametric Gaussian process model to estimate periods of Miras with sparsely sampled light curves. The model uses a sinusoidal basis for the periodic variation and a Gaussian process for the stochastic changes. We use maximum likelihood to estimate the period and the parameters of the Gaussian process, while integrating out the effects of other nuisance parameters in the model with respect to a suitable prior distribution obtained from earlier studies. Since the likelihood is highly multimodal for period, we implement a hybrid method that applies the quasi-Newton algorithm for Gaussian process parameters and search the period/frequencymore » parameter space over a dense grid. A large-scale, high-fidelity simulation is conducted to mimic the sampling quality of Mira light curves obtained by the M33 Synoptic Stellar Survey. The simulated data set is publicly available and can serve as a testbed for future evaluation of different period estimation methods. The semi-parametric model outperforms an existing algorithm on this simulated test data set as measured by period recovery rate and quality of the resulting period–luminosity relations.« less

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.

Nonparametric Simulation of Signal Transduction Networks with Semi-Synchronized Update

PubMed Central

Nassiri, Isar; Masoudi-Nejad, Ali; Jalili, Mahdi; Moeini, Ali

2012-01-01

Simulating signal transduction in cellular signaling networks provides predictions of network dynamics by quantifying the changes in concentration and activity-level of the individual proteins. Since numerical values of kinetic parameters might be difficult to obtain, it is imperative to develop non-parametric approaches that combine the connectivity of a network with the response of individual proteins to signals which travel through the network. The activity levels of signaling proteins computed through existing non-parametric modeling tools do not show significant correlations with the observed values in experimental results. In this work we developed a non-parametric computational framework to describe the profile of the evolving process and the time course of the proportion of active form of molecules in the signal transduction networks. The model is also capable of incorporating perturbations. The model was validated on four signaling networks showing that it can effectively uncover the activity levels and trends of response during signal transduction process. PMID:22737250

Effect of a semi-annular thermal acoustic shield on jet exhaust noise

NASA Technical Reports Server (NTRS)

Goodykoontz, J.

1980-01-01

The effect of a semi-annular acoustic shield on jet exhaust noise is investigated with the rationale that such a configuration would reduce or eliminate the multiple reflection mechanism. A limited range of flow conditions for one nozzle/shield configuration were studied at model scale. Noise measurements for a 10 cm conical nozzle with a semi-annular acoustical shield are presented in terms of lossless free field data at various angular locations with respect to the nozzle. Measurements were made on both the shielded and unshielded sides of the nozzle. Model scale overall sound pressure level directivity patterns and comparisons of model scale spectral data are provided. The results show that a semi-annular thermal acoustic shield consisting of a low velocity, high temperature gas stream partially surrounding a central jet exhibits lower noise levels than when the central jet is operated alone. The results are presented parametrically, showing the effects of various shield and central system velocities and temperatures.

Coherent scattering from semi-infinite non-Hermitian potentials

NASA Astrophysics Data System (ADS)

Ahmed, Zafar; Ghosh, Dona; Kumar, Sachin

2018-02-01

When two identical (coherent) beams are injected at a semi-infinite non-Hermitian medium from left and right, we show that both reflection (rL,rR) and transmission (tL,tR) amplitudes are nonreciprocal. In a parametric domain, there exists spectral singularity (SS) at a real energy E =E*=k*2 and the determinant of the time-reversed two port scattering matrix, i.e., |det(S (-k ) ) |=| tL(-k ) tR(-k ) -rL(-k ) rR(-k ) | , vanishes sharply at k =k* , displaying the phenomenon of coherent perfect absorption (CPA). In the complementary parametric domain, the potential becomes either left or right reflectionless at E =Ez . We rule out the existence of invisibility despite rR(Ei) =0 and tR(Ei) =1 but T (Ei)≠1 , in this avenue. We present two simple exactly solvable models where expressions for E*, Ez, Ei, and parametric conditions on the potential have been obtained in explicit and simple forms. Earlier, the phenomena of SS and CPA have been found to occur only in the scattering complex potentials which are spatially localized (vanish asymptotically) and have tL=tR .

Comparison of Two Stochastic Daily Rainfall Models and their Ability to Preserve Multi-year Rainfall Variability

NASA Astrophysics Data System (ADS)

Kamal Chowdhury, AFM; Lockart, Natalie; Willgoose, Garry; Kuczera, George; Kiem, Anthony; Parana Manage, Nadeeka

2016-04-01

Stochastic simulation of rainfall is often required in the simulation of streamflow and reservoir levels for water security assessment. As reservoir water levels generally vary on monthly to multi-year timescales, it is important that these rainfall series accurately simulate the multi-year variability. However, the underestimation of multi-year variability is a well-known issue in daily rainfall simulation. Focusing on this issue, we developed a hierarchical Markov Chain (MC) model in a traditional two-part MC-Gamma Distribution modelling structure, but with a new parameterization technique. We used two parameters of first-order MC process (transition probabilities of wet-to-wet and dry-to-dry days) to simulate the wet and dry days, and two parameters of Gamma distribution (mean and standard deviation of wet day rainfall) to simulate wet day rainfall depths. We found that use of deterministic Gamma parameter values results in underestimation of multi-year variability of rainfall depths. Therefore, we calculated the Gamma parameters for each month of each year from the observed data. Then, for each month, we fitted a multi-variate normal distribution to the calculated Gamma parameter values. In the model, we stochastically sampled these two Gamma parameters from the multi-variate normal distribution for each month of each year and used them to generate rainfall depth in wet days using the Gamma distribution. In another study, Mehrotra and Sharma (2007) proposed a semi-parametric Markov model. They also used a first-order MC process for rainfall occurrence simulation. But, the MC parameters were modified by using an additional factor to incorporate the multi-year variability. Generally, the additional factor is analytically derived from the rainfall over a pre-specified past periods (e.g. last 30, 180, or 360 days). They used a non-parametric kernel density process to simulate the wet day rainfall depths. In this study, we have compared the performance of our hierarchical MC model with the semi-parametric model in preserving rainfall variability in daily, monthly, and multi-year scales. To calibrate the parameters of both models and assess their ability to preserve observed statistics, we have used ground based data from 15 raingauge stations around Australia, which consist a wide range of climate zones including coastal, monsoonal, and arid climate characteristics. In preliminary results, both models show comparative performances in preserving the multi-year variability of rainfall depth and occurrence. However, the semi-parametric model shows a tendency of overestimating the mean rainfall depth, while our model shows a tendency of overestimating the number of wet days. We will discuss further the relative merits of the both models for hydrology simulation in the presentation.

Orthogonal Gaussian process models

DOE PAGES

Plumlee, Matthew; Joseph, V. Roshan

2017-01-01

Gaussian processes models are widely adopted for nonparameteric/semi-parametric modeling. Identifiability issues occur when the mean model contains polynomials with unknown coefficients. Though resulting prediction is unaffected, this leads to poor estimation of the coefficients in the mean model, and thus the estimated mean model loses interpretability. This paper introduces a new Gaussian process model whose stochastic part is orthogonal to the mean part to address this issue. As a result, this paper also discusses applications to multi-fidelity simulations using data examples.

Orthogonal Gaussian process models

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

Plumlee, Matthew; Joseph, V. Roshan

Gaussian processes models are widely adopted for nonparameteric/semi-parametric modeling. Identifiability issues occur when the mean model contains polynomials with unknown coefficients. Though resulting prediction is unaffected, this leads to poor estimation of the coefficients in the mean model, and thus the estimated mean model loses interpretability. This paper introduces a new Gaussian process model whose stochastic part is orthogonal to the mean part to address this issue. As a result, this paper also discusses applications to multi-fidelity simulations using data examples.

Hybrid modeling for quality by design and PAT-benefits and challenges of applications in biopharmaceutical industry.

PubMed

von Stosch, Moritz; Davy, Steven; Francois, Kjell; Galvanauskas, Vytautas; Hamelink, Jan-Martijn; Luebbert, Andreas; Mayer, Martin; Oliveira, Rui; O'Kennedy, Ronan; Rice, Paul; Glassey, Jarka

2014-06-01

This report highlights the drivers, challenges, and enablers of the hybrid modeling applications in biopharmaceutical industry. It is a summary of an expert panel discussion of European academics and industrialists with relevant scientific and engineering backgrounds. Hybrid modeling is viewed in its broader sense, namely as the integration of different knowledge sources in form of parametric and nonparametric models into a hybrid semi-parametric model, for instance the integration of fundamental and data-driven models. A brief description of the current state-of-the-art and industrial uptake of the methodology is provided. The report concludes with a number of recommendations to facilitate further developments and a wider industrial application of this modeling approach. These recommendations are limited to further exploiting the benefits of this methodology within process analytical technology (PAT) applications in biopharmaceutical industry. Copyright © 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

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.

Non-hydrostatic semi-elastic hybrid-coordinate SISL extension of HIRLAM. Part I: numerical scheme

NASA Astrophysics Data System (ADS)

Rõõm, Rein; Männik, Aarne; Luhamaa, Andres

2007-10-01

Two-time-level, semi-implicit, semi-Lagrangian (SISL) scheme is applied to the non-hydrostatic pressure coordinate equations, constituting a modified Miller-Pearce-White model, in hybrid-coordinate framework. Neutral background is subtracted in the initial continuous dynamics, yielding modified equations for geopotential, temperature and logarithmic surface pressure fluctuation. Implicit Lagrangian marching formulae for single time-step are derived. A disclosure scheme is presented, which results in an uncoupled diagnostic system, consisting of 3-D Poisson equation for omega velocity and 2-D Helmholtz equation for logarithmic pressure fluctuation. The model is discretized to create a non-hydrostatic extension to numerical weather prediction model HIRLAM. The discretization schemes, trajectory computation algorithms and interpolation routines, as well as the physical parametrization package are maintained from parent hydrostatic HIRLAM. For stability investigation, the derived SISL model is linearized with respect to the initial, thermally non-equilibrium resting state. Explicit residuals of the linear model prove to be sensitive to the relative departures of temperature and static stability from the reference state. Relayed on the stability study, the semi-implicit term in the vertical momentum equation is replaced to the implicit term, which results in stability increase of the model.

Twenty five years long survival analysis of an individual shortleaf pine trees

Treesearch

Pradip Saud; Thomas B. Lynch; James M. Guldin

2016-01-01

A semi parametric cox proportion hazard model is preferred when censored data and survival time information is available (Kleinbaum and Klein 1996; Alison 2010). Censored data are observations that have incomplete information related to survival time or event time of interest. In repeated forest measurements, usually observations are either right censored or...

Keeping nurses at work: a duration analysis.

PubMed

Holmås, Tor Helge

2002-09-01

A shortage of nurses is currently a problem in several countries, and an important question is therefore how one can increase the supply of nursing labour. In this paper, we focus on the issue of nurses leaving the public health sector by utilising a unique data set containing information on both the supply and demand side of the market. To describe the exit rate from the health sector we apply a semi-parametric hazard rate model. In the estimations, we correct for unobserved heterogeneity by both a parametric (Gamma) and a non-parametric approach. We find that both wages and working conditions have an impact on nurses' decision to quit. Furthermore, failing to correct for the fact that nurses' income partly consists of compensation for inconvenient working hours results in a considerable downward bias of the wage effect. Copyright 2002 John Wiley & Sons, Ltd.

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.

Parametric computational study of sheaths in multicomponent Ar/O2 plasma

NASA Astrophysics Data System (ADS)

Hromadka, J.; Ibehej, T.; Hrach, R.

2018-02-01

Our study is devoted to sheath structures emerging in Ar/O2 plasma. By means of two dimensional PIC/MCC computer model two configurations were investigated - sheath structure in the vicinity of a cylindrical Langmuir probe for two different biases and changes of the sheath structure when a cylindrical probe passes into a semi-planar probe. It was shown that O+ ions play important role in shielding out negative bias of a solid immersed in Ar/O2 plasma and edge effects of a semi-planar probe on its sheath structure were evaluated.

Empirical Bayes Estimation of Semi-parametric Hierarchical Mixture Models for Unbiased Characterization of Polygenic Disease Architectures

PubMed Central

Nishino, Jo; Kochi, Yuta; Shigemizu, Daichi; Kato, Mamoru; Ikari, Katsunori; Ochi, Hidenori; Noma, Hisashi; Matsui, Kota; Morizono, Takashi; Boroevich, Keith A.; Tsunoda, Tatsuhiko; Matsui, Shigeyuki

2018-01-01

Genome-wide association studies (GWAS) suggest that the genetic architecture of complex diseases consists of unexpectedly numerous variants with small effect sizes. However, the polygenic architectures of many diseases have not been well characterized due to lack of simple and fast methods for unbiased estimation of the underlying proportion of disease-associated variants and their effect-size distribution. Applying empirical Bayes estimation of semi-parametric hierarchical mixture models to GWAS summary statistics, we confirmed that schizophrenia was extremely polygenic [~40% of independent genome-wide SNPs are risk variants, most within odds ratio (OR = 1.03)], whereas rheumatoid arthritis was less polygenic (~4 to 8% risk variants, significant portion reaching OR = 1.05 to 1.1). For rheumatoid arthritis, stratified estimations revealed that expression quantitative loci in blood explained large genetic variance, and low- and high-frequency derived alleles were prone to be risk and protective, respectively, suggesting a predominance of deleterious-risk and advantageous-protective mutations. Despite genetic correlation, effect-size distributions for schizophrenia and bipolar disorder differed across allele frequency. These analyses distinguished disease polygenic architectures and provided clues for etiological differences in complex diseases. PMID:29740473

Exploring spatial and temporal variations of cadmium concentrations in pacific oysters from british columbia.

PubMed

Feng, Cindy Xin; Cao, Jiguo; Bendell, Leah

2011-09-01

Oysters from the Pacific Northwest coast of British Columbia, Canada, contain high levels of cadmium, in some cases exceeding some international food safety guidelines. A primary goal of this article is the investigation of the spatial and temporal variation in cadmium concentrations for oysters sampled from coastal British Columbia. Such information is important so that recommendations can be made as to where and when oysters can be cultured such that accumulation of cadmium within these oysters is minimized. Some modern statistical methods are applied to achieve this goal, including monotone spline smoothing, functional principal component analysis, and semi-parametric additive modeling. Oyster growth rates are estimated as the first derivatives of the monotone smoothing growth curves. Some important patterns in cadmium accumulation by oysters are observed. For example, most inland regions tend to have a higher level of cadmium concentration than most coastal regions, so more caution needs to be taken for shellfish aquaculture practices occurring in the inland regions. The semi-parametric additive modeling shows that oyster cadmium concentration decreases with oyster length, and oysters sampled at 7 m have higher average cadmium concentration than those sampled at 1 m. © 2010, The International Biometric Society.

Possible determinants and spatial patterns of anaemia among young children in Nigeria: a Bayesian semi-parametric modelling.

PubMed

Gayawan, Ezra; Arogundade, Ekundayo D; Adebayo, Samson B

2014-03-01

Anaemia is a global public health problem affecting both developing and developed countries with major consequences for human health and socioeconomic development. This paper examines the possible relationship between Hb concentration and severity of anaemia with individual and household characteristics of children aged 6-59 months in Nigeria; and explores possible geographical variations of these outcome variables. Data on Hb concentration and severity of anaemia in children aged 6-59 months that participated in the 2010 Nigeria Malaria Indicator Survey were analysed. A semi-parametric model using a hierarchical Bayesian approach was adopted to examine the putative relationship of covariates of different types and possible spatial variation. Gaussian, binary and ordinal outcome variables were considered in modelling. Spatial analyses reveal a distinct North-South divide in Hb concentration of the children analysed and that states in Northern Nigeria possess a higher risk of anaemia. Other important risk factors include the household wealth index, sex of the child, whether or not the child had fever or malaria in the 2 weeks preceding the survey, and children under 24 months of age. There is a need for state level implementation of specific programmes that target vulnerable children as this can help in reversing the existing patterns.

Estimation and confidence intervals for empirical mixing distributions

USGS Publications Warehouse

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

1995-01-01

Questions regarding collections of parameter estimates can frequently be expressed in terms of an empirical mixing distribution (EMD). This report discusses empirical Bayes estimation of an EMD, with emphasis on the construction of interval estimates. Estimation of the EMD is accomplished by substitution of estimates of prior parameters in the posterior mean of the EMD. This procedure is examined in a parametric model (the normal-normal mixture) and in a semi-parametric model. In both cases, the empirical Bayes bootstrap of Laird and Louis (1987, Journal of the American Statistical Association 82, 739-757) is used to assess the variability of the estimated EMD arising from the estimation of prior parameters. The proposed methods are applied to a meta-analysis of population trend estimates for groups of birds.

Development of Semi-Span Model Test Techniques

NASA Technical Reports Server (NTRS)

Milholen, William E., II; Chokani, Ndaona; McGhee, Robert J.

1996-01-01

A computational investigation was performed to support the development of a semispan model test capability in the NASA Langley Research Center's National Transonic Facility. This capability is desirable for the testing of advanced subsonic transport aircraft at full-scale Reynolds numbers. A state-of-the-art three-dimensional Navier-Stokes solver was used to examine methods to improve the flow over a semi-span configuration. First, a parametric study is conducted to examine the influence of the stand-off height on the flow over the semispan model. It is found that decreasing the stand-off height, below the maximum fuselage radius, improves the aerodynamic characteristics of the semi-span model. Next, active sidewall boundary layer control techniques are examined. Juncture region blowing jets, upstream tangential blowing, and sidewall suction are found to improve the flow over the aft portion of the semispan model. Both upstream blowing and suction are found to reduce the sidewall boundary layer separation. The resulting near surface streamline patterns are improved, and found to be quite similar to the full-span results. Both techniques however adversely affect the pitching moment coefficient.

A tool for the estimation of the distribution of landslide area in R

NASA Astrophysics Data System (ADS)

Rossi, M.; Cardinali, M.; Fiorucci, F.; Marchesini, I.; Mondini, A. C.; Santangelo, M.; Ghosh, S.; Riguer, D. E. L.; Lahousse, T.; Chang, K. T.; Guzzetti, F.

2012-04-01

We have developed a tool in R (the free software environment for statistical computing, http://www.r-project.org/) to estimate the probability density and the frequency density of landslide area. The tool implements parametric and non-parametric approaches to the estimation of the probability density and the frequency density of landslide area, including: (i) Histogram Density Estimation (HDE), (ii) Kernel Density Estimation (KDE), and (iii) Maximum Likelihood Estimation (MLE). The tool is available as a standard Open Geospatial Consortium (OGC) Web Processing Service (WPS), and is accessible through the web using different GIS software clients. We tested the tool to compare Double Pareto and Inverse Gamma models for the probability density of landslide area in different geological, morphological and climatological settings, and to compare landslides shown in inventory maps prepared using different mapping techniques, including (i) field mapping, (ii) visual interpretation of monoscopic and stereoscopic aerial photographs, (iii) visual interpretation of monoscopic and stereoscopic VHR satellite images and (iv) semi-automatic detection and mapping from VHR satellite images. Results show that both models are applicable in different geomorphological settings. In most cases the two models provided very similar results. Non-parametric estimation methods (i.e., HDE and KDE) provided reasonable results for all the tested landslide datasets. For some of the datasets, MLE failed to provide a result, for convergence problems. The two tested models (Double Pareto and Inverse Gamma) resulted in very similar results for large and very large datasets (> 150 samples). Differences in the modeling results were observed for small datasets affected by systematic biases. A distinct rollover was observed in all analyzed landslide datasets, except for a few datasets obtained from landslide inventories prepared through field mapping or by semi-automatic mapping from VHR satellite imagery. The tool can also be used to evaluate the probability density and the frequency density of landslide volume.

Is tree loss associated with cardiovascular-disease risk in the Women's Health Initiative? A natural experiment

Treesearch

Geoffrey H. Donovan; Yvonne L. Michael; Demetrios Gatziolis; Jeffrey P. Prestemon; Eric A. Whitsel

2015-01-01

Data from the Women's Health Initiative were used to quantify the relationship between the loss of trees to an invasive forest pest—the emerald ash borer—and cardiovascular disease. We estimated semi- parametric Cox proportional hazards model of time to cardiovascular disease, adjusting for confounders. We defined the incidence of cardiovascular disease as acute...

A semi-analytical study of positive corona discharge in wire-plane electrode configuration

NASA Astrophysics Data System (ADS)

Yanallah, K.; Pontiga, F.; Chen, J. H.

2013-08-01

Wire-to-plane positive corona discharge in air has been studied using an analytical model of two species (electrons and positive ions). The spatial distributions of electric field and charged species are obtained by integrating Gauss's law and the continuity equations of species along the Laplacian field lines. The experimental values of corona current intensity and applied voltage, together with Warburg's law, have been used to formulate the boundary condition for the electron density on the corona wire. To test the accuracy of the model, the approximate electric field distribution has been compared with the exact numerical solution obtained from a finite element analysis. A parametrical study of wire-to-plane corona discharge has then been undertaken using the approximate semi-analytical solutions. Thus, the spatial distributions of electric field and charged particles have been computed for different values of the gas pressure, wire radius and electrode separation. Also, the two dimensional distribution of ozone density has been obtained using a simplified plasma chemistry model. The approximate semi-analytical solutions can be evaluated in a negligible computational time, yet provide precise estimates of corona discharge variables.

Computational wing optimization and comparisons with experiment for a semi-span wing model

NASA Technical Reports Server (NTRS)

Waggoner, E. G.; Haney, H. P.; Ballhaus, W. F.

1978-01-01

A computational wing optimization procedure was developed and verified by an experimental investigation of a semi-span variable camber wing model in the NASA Ames Research Center 14 foot transonic wind tunnel. The Bailey-Ballhaus transonic potential flow analysis and Woodward-Carmichael linear theory codes were linked to Vanderplaats constrained minimization routine to optimize model configurations at several subsonic and transonic design points. The 35 deg swept wing is characterized by multi-segmented leading and trailing edge flaps whose hinge lines are swept relative to the leading and trailing edges of the wing. By varying deflection angles of the flap segments, camber and twist distribution can be optimized for different design conditions. Results indicate that numerical optimization can be both an effective and efficient design tool. The optimized configurations had as good or better lift to drag ratios at the design points as the best designs previously tested during an extensive parametric study.

Vector autoregressive models: A Gini approach

NASA Astrophysics Data System (ADS)

Mussard, Stéphane; Ndiaye, Oumar Hamady

2018-02-01

In this paper, it is proven that the usual VAR models may be performed in the Gini sense, that is, on a ℓ1 metric space. The Gini regression is robust to outliers. As a consequence, when data are contaminated by extreme values, we show that semi-parametric VAR-Gini regressions may be used to obtain robust estimators. The inference about the estimators is made with the ℓ1 norm. Also, impulse response functions and Gini decompositions for prevision errors are introduced. Finally, Granger's causality tests are properly derived based on U-statistics.

A study of the nucleus-nucleus total reaction cross section of stable systems at intermediate energies: An application to 12C

NASA Astrophysics Data System (ADS)

Hu, Liyuan; Song, Yushou; Hou, Yingwei; Liu, Huilan; Li, Hui

2018-07-01

A semi-microscopic analytical expression of the nucleus-nucleus total reaction cross section (σR) was proposed based on the strong absorption model. It is suitable for stable nuclei at intermediate energies. The matter density distributions of nuclei and the nucleon-nucleon total cross section were both considered. Particularly, the Fermi motion effect of the nucleons in a nucleus was also taken into account. The parametrization of σR was applied to the colliding systems including 12C. The experimental data at energies from 30 to 1000 MeV/nucleon were well reproduced, according to which an approach of deriving σR without adjustable parameters was developed. The necessity of considering the Fermi motion effect in the parametrization was discussed.

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.

Modeling gene expression measurement error: a quasi-likelihood approach

PubMed Central

Strimmer, Korbinian

2003-01-01

Background Using suitable error models for gene expression measurements is essential in the statistical analysis of microarray data. However, the true probabilistic model underlying gene expression intensity readings is generally not known. Instead, in currently used approaches some simple parametric model is assumed (usually a transformed normal distribution) or the empirical distribution is estimated. However, both these strategies may not be optimal for gene expression data, as the non-parametric approach ignores known structural information whereas the fully parametric models run the risk of misspecification. A further related problem is the choice of a suitable scale for the model (e.g. observed vs. log-scale). Results Here a simple semi-parametric model for gene expression measurement error is presented. In this approach inference is based an approximate likelihood function (the extended quasi-likelihood). Only partial knowledge about the unknown true distribution is required to construct this function. In case of gene expression this information is available in the form of the postulated (e.g. quadratic) variance structure of the data. As the quasi-likelihood behaves (almost) like a proper likelihood, it allows for the estimation of calibration and variance parameters, and it is also straightforward to obtain corresponding approximate confidence intervals. Unlike most other frameworks, it also allows analysis on any preferred scale, i.e. both on the original linear scale as well as on a transformed scale. It can also be employed in regression approaches to model systematic (e.g. array or dye) effects. Conclusions The quasi-likelihood framework provides a simple and versatile approach to analyze gene expression data that does not make any strong distributional assumptions about the underlying error model. For several simulated as well as real data sets it provides a better fit to the data than competing models. In an example it also improved the power of tests to identify differential expression. PMID:12659637

Non-dimensionalised closed-form parametric analysis of semi-active vehicle suspensions using a quarter-car model

NASA Astrophysics Data System (ADS)

Ahmadian, Mehdi; Blanchard, Emmanuel

2011-02-01

This article provides a non-dimensionalised closed-form analysis of semi-active vehicle suspensions, using a quarter-car model. The derivation of the closed-form solutions for three indices that can be used for ride comfort, vehicle handling, and stability are presented based on non-dimensionalised suspension parameters. The behaviour of semi-active vehicle suspensions is evaluated using skyhook, groundhook, and hybrid control policies, and compared with passive suspensions. The relationship between vibration isolation, suspension deflection, and road holding is studied, using three performance indices based on the mean square of the sprung mass acceleration, rattle space, and tyre deflection, respectively. The results of the study indicate that the hybrid control policy yields significantly better comfort than a passive suspension, without reducing the road-holding quality or increasing the suspension displacement for typical passenger cars. The results also indicate that for typical passenger cars, the hybrid control policy results in a better compromise between comfort, road holding and suspension travel requirements than both the skyhook and groundhook control methods.

Estimating extreme losses for the Florida Public Hurricane Model—part II

NASA Astrophysics Data System (ADS)

Gulati, Sneh; George, Florence; Hamid, Shahid

2018-02-01

Rising global temperatures are leading to an increase in the number of extreme events and losses (http://www.epa.gov/climatechange/science/indicators/). Accurate estimation of these extreme losses with the intention of protecting themselves against them is critical to insurance companies. In a previous paper, Gulati et al. (2014) discussed probable maximum loss (PML) estimation for the Florida Public Hurricane Loss Model (FPHLM) using parametric and nonparametric methods. In this paper, we investigate the use of semi-parametric methods to do the same. Detailed analysis of the data shows that the annual losses from FPHLM do not tend to be very heavy tailed, and therefore, neither the popular Hill's method nor the moment's estimator work well. However, Pickand's estimator with threshold around the 84th percentile provides a good fit for the extreme quantiles for the losses.

Investigation on phase noise of the signal from a singly resonant optical parametric oscillator

NASA Astrophysics Data System (ADS)

Jinxia, Feng; Yuanji, Li; Kuanshou, Zhang

2018-04-01

The phase noise of the signal from a singly resonant optical parametric oscillator (SRO) is investigated theoretically and experimentally. An SRO based on periodically poled lithium niobate is built up that generates the signal with a maximum power of 5.2 W at 1.5 µm. The intensity noise of the signal reaches the shot noise level for frequencies above 5 MHz. The phase noise of the signal oscillates depending on the analysis frequency, and there are phase noise peaks above the shot noise level at the peak frequencies. To explain the phase noise feature of the signal, a semi-classical theoretical model of SROs including the guided acoustic wave Brillouin scattering effect within the nonlinear crystal is developed. The theoretical predictions are in good agreement with the experimental results.

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

Semiparametric Estimation of the Impacts of Longitudinal Interventions on Adolescent Obesity using Targeted Maximum-Likelihood: Accessible Estimation with the ltmle Package

PubMed Central

Decker, Anna L.; Hubbard, Alan; Crespi, Catherine M.; Seto, Edmund Y.W.; Wang, May C.

2015-01-01

While child and adolescent obesity is a serious public health concern, few studies have utilized parameters based on the causal inference literature to examine the potential impacts of early intervention. The purpose of this analysis was to estimate the causal effects of early interventions to improve physical activity and diet during adolescence on body mass index (BMI), a measure of adiposity, using improved techniques. The most widespread statistical method in studies of child and adolescent obesity is multi-variable regression, with the parameter of interest being the coefficient on the variable of interest. This approach does not appropriately adjust for time-dependent confounding, and the modeling assumptions may not always be met. An alternative parameter to estimate is one motivated by the causal inference literature, which can be interpreted as the mean change in the outcome under interventions to set the exposure of interest. The underlying data-generating distribution, upon which the estimator is based, can be estimated via a parametric or semi-parametric approach. Using data from the National Heart, Lung, and Blood Institute Growth and Health Study, a 10-year prospective cohort study of adolescent girls, we estimated the longitudinal impact of physical activity and diet interventions on 10-year BMI z-scores via a parameter motivated by the causal inference literature, using both parametric and semi-parametric estimation approaches. The parameters of interest were estimated with a recently released R package, ltmle, for estimating means based upon general longitudinal treatment regimes. We found that early, sustained intervention on total calories had a greater impact than a physical activity intervention or non-sustained interventions. Multivariable linear regression yielded inflated effect estimates compared to estimates based on targeted maximum-likelihood estimation and data-adaptive super learning. Our analysis demonstrates that sophisticated, optimal semiparametric estimation of longitudinal treatment-specific means via ltmle provides an incredibly powerful, yet easy-to-use tool, removing impediments for putting theory into practice. PMID:26046009

Parametric study on the vortex-induced motions of semi-submersibles: Effect of rounded ratios of the column and pontoon

NASA Astrophysics Data System (ADS)

Liu, Mingyue; Xiao, Longfei; Yang, Jianmin; Tian, Xinliang

2017-05-01

The vortex-induced motions (VIMs) of semi-submersibles have emerged as an important issue in offshore engineering, as they pose a threat to safe and reliable operations and severely affect the fatigue lives of risers and mooring systems. The VIM response depends on the shape of the submerged structure and thus is significantly influenced by the design parameters related to the columns and pontoons. Numerical simulations by the detached Eddy simulation method are validated by experimental data and then used for parametric analysis of the VIM performance of various semi-submersibles with different column rounded ratios (Rc/L ) and pontoon rounded ratios (Rp/Lp ). The results show that the transverse amplitudes of a semi-submersible with circular columns at a 0° current heading are twice as large as those at a 45° current heading. However, the semi-submersible with rounded square columns shows more significant transverse motions at a 45° current heading than at a 0° current heading. Furthermore, at the 45° current heading, the transverse amplitudes of the semi-submersibles show a rapid increase as the column radius increases in the range of Rc/L <0.1 . The peak values remain roughly the same for 0.1 ≤Rc/L ≤0.2 and then decrease as the column radius increases (Rc/L ≥0.3 ). In addition, the effect of the pontoon shape on the transverse response is negligible for semi-submersibles with sharp square columns, while for semi-submersibles with rounded square columns or circular columns, the sharp rectangular pontoons greatly mitigate the VIM response.

Hybrid modeling as a QbD/PAT tool in process development: an industrial E. coli case study.

PubMed

von Stosch, Moritz; Hamelink, Jan-Martijn; Oliveira, Rui

2016-05-01

Process understanding is emphasized in the process analytical technology initiative and the quality by design paradigm to be essential for manufacturing of biopharmaceutical products with consistent high quality. A typical approach to developing a process understanding is applying a combination of design of experiments with statistical data analysis. Hybrid semi-parametric modeling is investigated as an alternative method to pure statistical data analysis. The hybrid model framework provides flexibility to select model complexity based on available data and knowledge. Here, a parametric dynamic bioreactor model is integrated with a nonparametric artificial neural network that describes biomass and product formation rates as function of varied fed-batch fermentation conditions for high cell density heterologous protein production with E. coli. Our model can accurately describe biomass growth and product formation across variations in induction temperature, pH and feed rates. The model indicates that while product expression rate is a function of early induction phase conditions, it is negatively impacted as productivity increases. This could correspond with physiological changes due to cytoplasmic product accumulation. Due to the dynamic nature of the model, rational process timing decisions can be made and the impact of temporal variations in process parameters on product formation and process performance can be assessed, which is central for process understanding.

Assessing noninferiority in a three-arm trial using the Bayesian approach.

PubMed

Ghosh, Pulak; Nathoo, Farouk; Gönen, Mithat; Tiwari, Ram C

2011-07-10

Non-inferiority trials, which aim to demonstrate that a test product is not worse than a competitor by more than a pre-specified small amount, are of great importance to the pharmaceutical community. As a result, methodology for designing and analyzing such trials is required, and developing new methods for such analysis is an important area of statistical research. The three-arm trial consists of a placebo, a reference and an experimental treatment, and simultaneously tests the superiority of the reference over the placebo along with comparing this reference to an experimental treatment. In this paper, we consider the analysis of non-inferiority trials using Bayesian methods which incorporate both parametric as well as semi-parametric models. The resulting testing approach is both flexible and robust. The benefit of the proposed Bayesian methods is assessed via simulation, based on a study examining home-based blood pressure interventions. Copyright © 2011 John Wiley & Sons, Ltd.

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.

Fault Detection for Automotive Shock Absorber

NASA Astrophysics Data System (ADS)

Hernandez-Alcantara, Diana; Morales-Menendez, Ruben; Amezquita-Brooks, Luis

2015-11-01

Fault detection for automotive semi-active shock absorbers is a challenge due to the non-linear dynamics and the strong influence of the disturbances such as the road profile. First obstacle for this task, is the modeling of the fault, which has been shown to be of multiplicative nature. Many of the most widespread fault detection schemes consider additive faults. Two model-based fault algorithms for semiactive shock absorber are compared: an observer-based approach and a parameter identification approach. The performance of these schemes is validated and compared using a commercial vehicle model that was experimentally validated. Early results shows that a parameter identification approach is more accurate, whereas an observer-based approach is less sensible to parametric uncertainty.

The effects of time-varying observation errors on semi-empirical sea-level projections

DOE PAGES

Ruckert, Kelsey L.; Guan, Yawen; Bakker, Alexander M. R.; ...

2016-11-30

Sea-level rise is a key driver of projected flooding risks. The design of strategies to manage these risks often hinges on projections that inform decision-makers about the surrounding uncertainties. Producing semi-empirical sea-level projections is difficult, for example, due to the complexity of the error structure of the observations, such as time-varying (heteroskedastic) observation errors and autocorrelation of the data-model residuals. This raises the question of how neglecting the error structure impacts hindcasts and projections. Here, we quantify this effect on sea-level projections and parameter distributions by using a simple semi-empirical sea-level model. Specifically, we compare three model-fitting methods: a frequentistmore » bootstrap as well as a Bayesian inversion with and without considering heteroskedastic residuals. All methods produce comparable hindcasts, but the parametric distributions and projections differ considerably based on methodological choices. In conclusion, our results show that the differences based on the methodological choices are enhanced in the upper tail projections. For example, the Bayesian inversion accounting for heteroskedasticity increases the sea-level anomaly with a 1% probability of being equaled or exceeded in the year 2050 by about 34% and about 40% in the year 2100 compared to a frequentist bootstrap. These results indicate that neglecting known properties of the observation errors and the data-model residuals can lead to low-biased sea-level projections.« less

The effects of time-varying observation errors on semi-empirical sea-level projections

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

Ruckert, Kelsey L.; Guan, Yawen; Bakker, Alexander M. R.

Sea-level rise is a key driver of projected flooding risks. The design of strategies to manage these risks often hinges on projections that inform decision-makers about the surrounding uncertainties. Producing semi-empirical sea-level projections is difficult, for example, due to the complexity of the error structure of the observations, such as time-varying (heteroskedastic) observation errors and autocorrelation of the data-model residuals. This raises the question of how neglecting the error structure impacts hindcasts and projections. Here, we quantify this effect on sea-level projections and parameter distributions by using a simple semi-empirical sea-level model. Specifically, we compare three model-fitting methods: a frequentistmore » bootstrap as well as a Bayesian inversion with and without considering heteroskedastic residuals. All methods produce comparable hindcasts, but the parametric distributions and projections differ considerably based on methodological choices. In conclusion, our results show that the differences based on the methodological choices are enhanced in the upper tail projections. For example, the Bayesian inversion accounting for heteroskedasticity increases the sea-level anomaly with a 1% probability of being equaled or exceeded in the year 2050 by about 34% and about 40% in the year 2100 compared to a frequentist bootstrap. These results indicate that neglecting known properties of the observation errors and the data-model residuals can lead to low-biased sea-level projections.« less

Parametric study of modern airship productivity

NASA Technical Reports Server (NTRS)

Ardema, M. D.; Flaig, K.

1980-01-01

A method for estimating the specific productivity of both hybrid and fully buoyant airships is developed. Various methods of estimating structural weight of deltoid hybrids are discussed and a derived weight estimating relationship is presented. Specific productivity is used as a figure of merit in a parametric study of fully buoyant ellipsoidal and deltoid hybrid semi-buoyant vehicles. The sensitivity of results as a function of assumptions is also determined. No airship configurations were found to have superior specific productivity to transport airplanes.

Parametric study on the performance of automotive MR shock absorbers

NASA Astrophysics Data System (ADS)

Gołdasz, J.; Dzierżek, S.

2016-09-01

The paper contains the results of a parametric study to explore the influence of various quantities on the performance range of semi-active automotive shock absorbers using the magnetorheological (MR) fluid under steady-state and transient excitations. The analysis was performed with simulated data and using a standard single-tube shock absorber configuration with a single-gap MR valve. Additionally, the impact of material variables and valves geometry was examined as the parameters were varied and its dynamic range studied.

The influence of parametric and external noise in act-and-wait control with delayed feedback.

PubMed

Wang, Jiaxing; Kuske, Rachel

2017-11-01

We apply several novel semi-analytic approaches for characterizing and calculating the effects of noise in a system with act-and-wait control. For concrete illustration, we apply these to a canonical balance model for an inverted pendulum to study the combined effect of delay and noise within the act-and-wait setting. While the act-and-wait control facilitates strong stabilization through deadbeat control, a comparison of different models with continuous vs. discrete updating of the control strategy in the active period illustrates how delays combined with the imprecise application of the control can seriously degrade the performance. We give several novel analyses of a generalized act-and-wait control strategy, allowing flexibility in the updating of the control strategy, in order to understand the sensitivities to delays and random fluctuations. In both the deterministic and stochastic settings, we give analytical and semi-analytical results that characterize and quantify the dynamics of the system. These results include the size and shape of stability regions, densities for the critical eigenvalues that capture the rate of reaching the desired stable equilibrium, and amplification factors for sustained fluctuations in the context of external noise. They also provide the dependence of these quantities on the length of the delay and the active period. In particular, we see that the combined influence of delay, parametric error, or external noise and on-off control can qualitatively change the dynamics, thus reducing the robustness of the control strategy. We also capture the dependence on how frequently the control is updated, allowing an interpolation between continuous and frequent updating. In addition to providing insights for these specific models, the methods we propose are generalizable to other settings with noise, delay, and on-off control, where analytical techniques are otherwise severely scarce.

Stability analysis and backward whirl investigation of cracked rotors with time-varying stiffness

NASA Astrophysics Data System (ADS)

AL-Shudeifat, Mohammad A.

2015-07-01

The dynamic stability of dynamical systems with time-periodic stiffness is addressed here. Cracked rotor systems with time-periodic stiffness are well-known examples of such systems. Time-varying area moments of inertia at the cracked element cross-section of a cracked rotor have been used to formulate the time-periodic finite element stiffness matrix. The semi-infinite coefficient matrix obtained by applying the harmonic balance (HB) solution to the finite element (FE) equations of motion is employed here to study the dynamic stability of the system. Consequently, the sign of the determinant of a scaled version of a sub-matrix of this semi-infinite coefficient matrix at a finite number of harmonics in the HB solution is found to be sufficient for identifying the major unstable zones of the system in the parameter plane. Specifically, it is found that the negative determinant always corresponds to unstable zones in all of the systems considered. This approach is applied to a parametrically excited Mathieu's equation, a two degree-of-freedom linear time-periodic dynamical system, a cracked Jeffcott rotor and a finite element model of the cracked rotor system. Compared to the corresponding results obtained by Floquet's theory, the sign of the determinant of the scaled sub-matrix is found to be an efficient tool for identifying the major unstable zones of the linear time-periodic parametrically excited systems, especially large-scale FE systems. Moreover, it is found that the unstable zones for a FE cracked rotor with an open transverse crack model only appear at the backward whirl. The theoretical and experimental results have been found to agree well for verifying that the open crack model excites the backward whirl amplitudes at the critical backward whirling rotational speeds.

Mesh-To from Segmented Mesh Elements to Bim Model with Limited Parameters

NASA Astrophysics Data System (ADS)

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

2018-05-01

Building Information Modelling (BIM) technique has been widely utilized in heritage documentation and comes to a general term Historical/Heritage BIM (HBIM). The current HBIM project mostly employs the scan-to-BIM process to manually create the geometric model from the point cloud. This paper explains how it is possible to shape from the mesh geometry with reduced human involvement during the modelling process. Aiming at unbuilt heritage, two case studies are handled in this study, including a ruined Roman stone architectural and a severely damaged abbey. The pipeline consists of solid element modelling based on documentation data using Autodesk Revit, a common BIM platform, and the successive modelling from these geometric primitives using Autodesk Dynamo, a visual programming built-in plugin tool in Revit. The BIM-based reconstruction enriches the classic visual model from computer graphics approaches with measurement, semantic and additional information. Dynamo is used to develop a semi-automated function to reduce the manual process, which builds the final BIM model from segmented parametric elements directly. The level of detail (LoD) of the final models is dramatically relevant with the manual involvement in the element creation. The proposed outline also presents two potential issues in the ongoing work: combining the ontology semantics with the parametric BIM model, and introducing the proposed pipeline into the as-built HBIM process.

Sequential causal inference: Application to randomized trials of adaptive treatment strategies

PubMed Central

Dawson, Ree; Lavori, Philip W.

2009-01-01

SUMMARY Clinical trials that randomize subjects to decision algorithms, which adapt treatments over time according to individual response, have gained considerable interest as investigators seek designs that directly inform clinical decision making. We consider designs in which subjects are randomized sequentially at decision points, among adaptive treatment options under evaluation. We present a sequential method to estimate the comparative effects of the randomized adaptive treatments, which are formalized as adaptive treatment strategies. Our causal estimators are derived using Bayesian predictive inference. We use analytical and empirical calculations to compare the predictive estimators to (i) the ‘standard’ approach that allocates the sequentially obtained data to separate strategy-specific groups as would arise from randomizing subjects at baseline; (ii) the semi-parametric approach of marginal mean models that, under appropriate experimental conditions, provides the same sequential estimator of causal differences as the proposed approach. Simulation studies demonstrate that sequential causal inference offers substantial efficiency gains over the standard approach to comparing treatments, because the predictive estimators can take advantage of the monotone structure of shared data among adaptive strategies. We further demonstrate that the semi-parametric asymptotic variances, which are marginal ‘one-step’ estimators, may exhibit significant bias, in contrast to the predictive variances. We show that the conditions under which the sequential method is attractive relative to the other two approaches are those most likely to occur in real studies. PMID:17914714

Advanced semi-active engine and transmission mounts: tools for modelling, analysis, design, and tuning

NASA Astrophysics Data System (ADS)

Farjoud, Alireza; Taylor, Russell; Schumann, Eric; Schlangen, Timothy

2014-02-01

This paper is focused on modelling, design, and testing of semi-active magneto-rheological (MR) engine and transmission mounts used in the automotive industry. The purpose is to develop a complete analysis, synthesis, design, and tuning tool that reduces the need for expensive and time-consuming laboratory and field tests. A detailed mathematical model of such devices is developed using multi-physics modelling techniques for physical systems with various energy domains. The model includes all major features of an MR mount including fluid dynamics, fluid track, elastic components, decoupler, rate-dip, gas-charged chamber, MR fluid rheology, magnetic circuit, electronic driver, and control algorithm. Conventional passive hydraulic mounts can also be studied using the same mathematical model. The model is validated using standard experimental procedures. It is used for design and parametric study of mounts; effects of various geometric and material parameters on dynamic response of mounts can be studied. Additionally, this model can be used to test various control strategies to obtain best vibration isolation performance by tuning control parameters. Another benefit of this work is that nonlinear interactions between sub-components of the mount can be observed and investigated. This is not possible by using simplified linear models currently available.

A Semi-Infinite Programming based algorithm for determining T-optimum designs for model discrimination

PubMed Central

Duarte, Belmiro P.M.; Wong, Weng Kee; Atkinson, Anthony C.

2016-01-01

T-optimum designs for model discrimination are notoriously difficult to find because of the computational difficulty involved in solving an optimization problem that involves two layers of optimization. Only a handful of analytical T-optimal designs are available for the simplest problems; the rest in the literature are found using specialized numerical procedures for a specific problem. We propose a potentially more systematic and general way for finding T-optimal designs using a Semi-Infinite Programming (SIP) approach. The strategy requires that we first reformulate the original minimax or maximin optimization problem into an equivalent semi-infinite program and solve it using an exchange-based method where lower and upper bounds produced by solving the outer and the inner programs, are iterated to convergence. A global Nonlinear Programming (NLP) solver is used to handle the subproblems, thus finding the optimal design and the least favorable parametric configuration that minimizes the residual sum of squares from the alternative or test models. We also use a nonlinear program to check the global optimality of the SIP-generated design and automate the construction of globally optimal designs. The algorithm is successfully used to produce results that coincide with several T-optimal designs reported in the literature for various types of model discrimination problems with normally distributed errors. However, our method is more general, merely requiring that the parameters of the model be estimated by a numerical optimization. PMID:27330230

A Semi-Infinite Programming based algorithm for determining T-optimum designs for model discrimination.

PubMed

Duarte, Belmiro P M; Wong, Weng Kee; Atkinson, Anthony C

2015-03-01

T-optimum designs for model discrimination are notoriously difficult to find because of the computational difficulty involved in solving an optimization problem that involves two layers of optimization. Only a handful of analytical T-optimal designs are available for the simplest problems; the rest in the literature are found using specialized numerical procedures for a specific problem. We propose a potentially more systematic and general way for finding T-optimal designs using a Semi-Infinite Programming (SIP) approach. The strategy requires that we first reformulate the original minimax or maximin optimization problem into an equivalent semi-infinite program and solve it using an exchange-based method where lower and upper bounds produced by solving the outer and the inner programs, are iterated to convergence. A global Nonlinear Programming (NLP) solver is used to handle the subproblems, thus finding the optimal design and the least favorable parametric configuration that minimizes the residual sum of squares from the alternative or test models. We also use a nonlinear program to check the global optimality of the SIP-generated design and automate the construction of globally optimal designs. The algorithm is successfully used to produce results that coincide with several T-optimal designs reported in the literature for various types of model discrimination problems with normally distributed errors. However, our method is more general, merely requiring that the parameters of the model be estimated by a numerical optimization.

A comparison of methods for estimating the random effects distribution of a linear mixed model.

PubMed

Ghidey, Wendimagegn; Lesaffre, Emmanuel; Verbeke, Geert

2010-12-01

This article reviews various recently suggested approaches to estimate the random effects distribution in a linear mixed model, i.e. (1) the smoothing by roughening approach of Shen and Louis,(1) (2) the semi-non-parametric approach of Zhang and Davidian,(2) (3) the heterogeneity model of Verbeke and Lesaffre( 3) and (4) a flexible approach of Ghidey et al. (4) These four approaches are compared via an extensive simulation study. We conclude that for the considered cases, the approach of Ghidey et al. (4) often shows to have the smallest integrated mean squared error for estimating the random effects distribution. An analysis of a longitudinal dental data set illustrates the performance of the methods in a practical example.

Controllable outrigger damping system for high rise building with MR dampers

NASA Astrophysics Data System (ADS)

Wang, Zhihao; Chang, Chia-Ming; Spencer, Billie F., Jr.; Chen, Zhengqing

2010-04-01

A novel energy dissipation system that can achieve the amplified damping ratio for a frame-core tube structures is explored, where vertical dampers are equipped between the outrigger and perimeter columns. The modal characteristics of the structural system with linear viscous dampers are theoretically analyzed from the simplified finite element model by parametric analysis. The result shows that modal damping ratios of the first several modes can increase a lot with this novel damping system. To improve the control performance of system, the semi-active control devices, magnetorheological (MR) dampers, are adopted to develop a controllable outrigger damping system. The clipped optimal control with the linear-quadratic Gaussian (LQG) acceleration feedback is adopted in this paper. The effectiveness of both passive and semi-active control outrigger damping systems is evaluated through the numerical simulation of a representative tall building subjected to two typical earthquake records.

Identifying demand for health resources using waiting times information.

PubMed

Blundell, R; Windmeijer, F

2000-09-01

In this paper the differences in average waiting times are utilized to identify the determinants of demand for health services. The equilibrium waiting time framework is used, but the full equilibrium assumption is relaxed by selecting areas with low waiting times and by estimating a (semi-)parametric selection model. Determinants of supply are used as instruments for the endogeneity of waiting times. A model for the demand for acute services at the ward level in the UK is estimated. The model estimates, and their implications for health service allocations in the UK, are contrasted against more standard allocation models. The present results show that it is critically important to account for rationing by waiting times when identifying needs from care utilization data. Copyright 2000 John Wiley & Sons, Ltd.

a Semi-Automated Point Cloud Processing Methodology for 3d Cultural Heritage Documentation

NASA Astrophysics Data System (ADS)

Kıvılcım, C. Ö.; Duran, Z.

2016-06-01

The preliminary phase in any architectural heritage project is to obtain metric measurements and documentation of the building and its individual elements. On the other hand, conventional measurement techniques require tremendous resources and lengthy project completion times for architectural surveys and 3D model production. Over the past two decades, the widespread use of laser scanning and digital photogrammetry have significantly altered the heritage documentation process. Furthermore, advances in these technologies have enabled robust data collection and reduced user workload for generating various levels of products, from single buildings to expansive cityscapes. More recently, the use of procedural modelling methods and BIM relevant applications for historic building documentation purposes has become an active area of research, however fully automated systems in cultural heritage documentation still remains open. In this paper, we present a semi-automated methodology, for 3D façade modelling of cultural heritage assets based on parametric and procedural modelling techniques and using airborne and terrestrial laser scanning data. We present the contribution of our methodology, which we implemented in an open source software environment using the example project of a 16th century early classical era Ottoman structure, Sinan the Architect's Şehzade Mosque in Istanbul, Turkey.

Extending semi-numeric reionization models to the first stars and galaxies

NASA Astrophysics Data System (ADS)

Koh, Daegene; Wise, John H.

2018-03-01

Semi-numeric methods have made it possible to efficiently model the epoch of reionization (EoR). While most implementations involve a reduction to a simple three-parameter model, we introduce a new mass-dependent ionizing efficiency parameter that folds in physical parameters that are constrained by the latest numerical simulations. This new parametrization enables the effective modelling of a broad range of host halo masses containing ionizing sources, extending from the smallest Population III host haloes with M ˜ 106 M⊙, which are often ignored, to the rarest cosmic peaks with M ˜ 1012 M⊙ during EoR. We compare the resulting ionizing histories with a typical three-parameter model and also compare with the latest constraints from the Planck mission. Our model results in an optical depth due to Thomson scattering, τe = 0.057, that is consistent with Planck. The largest difference in our model is shown in the resulting bubble size distributions that peak at lower characteristic sizes and are broadened. We also consider the uncertainties of the various physical parameters, and comparing the resulting ionizing histories broadly disfavours a small contribution from galaxies. The smallest haloes cease a meaningful contribution to the ionizing photon budget after z = 10, implying that they play a role in determining the start of EoR and little else.

Parametric and non-parametric modeling of short-term synaptic plasticity. Part I: computational study

PubMed Central

Marmarelis, Vasilis Z.; Berger, Theodore W.

2009-01-01

Parametric and non-parametric modeling methods are combined to study the short-term plasticity (STP) of synapses in the central nervous system (CNS). The nonlinear dynamics of STP are modeled by means: (1) previously proposed parametric models based on mechanistic hypotheses and/or specific dynamical processes, and (2) non-parametric models (in the form of Volterra kernels) that transforms the presynaptic signals into postsynaptic signals. In order to synergistically use the two approaches, we estimate the Volterra kernels of the parametric models of STP for four types of synapses using synthetic broadband input–output data. Results show that the non-parametric models accurately and efficiently replicate the input–output transformations of the parametric models. Volterra kernels provide a general and quantitative representation of the STP. PMID:18506609

Maximal near-field radiative heat transfer between two plates

NASA Astrophysics Data System (ADS)

Nefzaoui, Elyes; Ezzahri, Younès; Drévillon, Jérémie; Joulain, Karl

2013-09-01

Near-field radiative transfer is a promising way to significantly and simultaneously enhance both thermo-photovoltaic (TPV) devices power densities and efficiencies. A parametric study of Drude and Lorentz models performances in maximizing near-field radiative heat transfer between two semi-infinite planes separated by nanometric distances at room temperature is presented in this paper. Optimal parameters of these models that provide optical properties maximizing the radiative heat flux are reported and compared to real materials usually considered in similar studies, silicon carbide and heavily doped silicon in this case. Results are obtained by exact and approximate (in the extreme near-field regime and the electrostatic limit hypothesis) calculations. The two methods are compared in terms of accuracy and CPU resources consumption. Their differences are explained according to a mesoscopic description of nearfield radiative heat transfer. Finally, the frequently assumed hypothesis which states a maximal radiative heat transfer when the two semi-infinite planes are of identical materials is numerically confirmed. Its subsequent practical constraints are then discussed. Presented results enlighten relevant paths to follow in order to choose or design materials maximizing nano-TPV devices performances.

Quantifying discrimination of Framingham risk functions with different survival C statistics.

PubMed

Pencina, Michael J; D'Agostino, Ralph B; Song, Linye

2012-07-10

Cardiovascular risk prediction functions offer an important diagnostic tool for clinicians and patients themselves. They are usually constructed with the use of parametric or semi-parametric survival regression models. It is essential to be able to evaluate the performance of these models, preferably with summaries that offer natural and intuitive interpretations. The concept of discrimination, popular in the logistic regression context, has been extended to survival analysis. However, the extension is not unique. In this paper, we define discrimination in survival analysis as the model's ability to separate those with longer event-free survival from those with shorter event-free survival within some time horizon of interest. This definition remains consistent with that used in logistic regression, in the sense that it assesses how well the model-based predictions match the observed data. Practical and conceptual examples and numerical simulations are employed to examine four C statistics proposed in the literature to evaluate the performance of survival models. We observe that they differ in the numerical values and aspects of discrimination that they capture. We conclude that the index proposed by Harrell is the most appropriate to capture discrimination described by the above definition. We suggest researchers report which C statistic they are using, provide a rationale for their selection, and be aware that comparing different indices across studies may not be meaningful. Copyright © 2012 John Wiley & Sons, Ltd.

Mediation Analysis with Survival Outcomes: Accelerated Failure Time vs. Proportional Hazards Models.

PubMed

Gelfand, Lois A; MacKinnon, David P; DeRubeis, Robert J; Baraldi, Amanda N

2016-01-01

Survival time is an important type of outcome variable in treatment research. Currently, limited guidance is available regarding performing mediation analyses with survival outcomes, which generally do not have normally distributed errors, and contain unobserved (censored) events. We present considerations for choosing an approach, using a comparison of semi-parametric proportional hazards (PH) and fully parametric accelerated failure time (AFT) approaches for illustration. We compare PH and AFT models and procedures in their integration into mediation models and review their ability to produce coefficients that estimate causal effects. Using simulation studies modeling Weibull-distributed survival times, we compare statistical properties of mediation analyses incorporating PH and AFT approaches (employing SAS procedures PHREG and LIFEREG, respectively) under varied data conditions, some including censoring. A simulated data set illustrates the findings. AFT models integrate more easily than PH models into mediation models. Furthermore, mediation analyses incorporating LIFEREG produce coefficients that can estimate causal effects, and demonstrate superior statistical properties. Censoring introduces bias in the coefficient estimate representing the treatment effect on outcome-underestimation in LIFEREG, and overestimation in PHREG. With LIFEREG, this bias can be addressed using an alternative estimate obtained from combining other coefficients, whereas this is not possible with PHREG. When Weibull assumptions are not violated, there are compelling advantages to using LIFEREG over PHREG for mediation analyses involving survival-time outcomes. Irrespective of the procedures used, the interpretation of coefficients, effects of censoring on coefficient estimates, and statistical properties should be taken into account when reporting results.

Study of microwave drying of wet materials based on one-dimensional two-phase model

NASA Astrophysics Data System (ADS)

Salomatov, Vl V.; Karelin, V. A.

2017-11-01

Currently, microwave is one of the most interesting ways to conduct drying of dielectric materials, in particular coal. In this paper, two processes were considered - heating and drying. The temperature field of the coal semi-mass in the heating mode is found analytically strictly with the use of integral transformations. The drying process is formulated as a nonlinear Stephen problem with a moving boundary of the liquid-vapor phase transformation. The temperature distribution, speed and drying time in this mode are determined approximately analytically. Parametric analysis of the influence of the material and boundary conditions on the dynamics of warming up and drying is revealed.

Effect of quantum nuclear motion on hydrogen bonding

NASA Astrophysics Data System (ADS)

McKenzie, Ross H.; Bekker, Christiaan; Athokpam, Bijyalaxmi; Ramesh, Sai G.

2014-05-01

This work considers how the properties of hydrogen bonded complexes, X-H⋯Y, are modified by the quantum motion of the shared proton. Using a simple two-diabatic state model Hamiltonian, the analysis of the symmetric case, where the donor (X) and acceptor (Y) have the same proton affinity, is carried out. For quantitative comparisons, a parametrization specific to the O-H⋯O complexes is used. The vibrational energy levels of the one-dimensional ground state adiabatic potential of the model are used to make quantitative comparisons with a vast body of condensed phase data, spanning a donor-acceptor separation (R) range of about 2.4 - 3.0 Å, i.e., from strong to weak hydrogen bonds. The position of the proton (which determines the X-H bond length) and its longitudinal vibrational frequency, along with the isotope effects in both are described quantitatively. An analysis of the secondary geometric isotope effect, using a simple extension of the two-state model, yields an improved agreement of the predicted variation with R of frequency isotope effects. The role of bending modes is also considered: their quantum effects compete with those of the stretching mode for weak to moderate H-bond strengths. In spite of the economy in the parametrization of the model used, it offers key insights into the defining features of H-bonds, and semi-quantitatively captures several trends.

Parametric modeling of wideband piezoelectric polymer sensors: Design for optoacoustic applications

NASA Astrophysics Data System (ADS)

Fernández Vidal, A.; Ciocci Brazzano, L.; Matteo, C. L.; Sorichetti, P. A.; González, M. G.

2017-09-01

In this work, we present a three-dimensional model for the design of wideband piezoelectric polymer sensors which includes the geometry and the properties of the transducer materials. The model uses FFT and numerical integration techniques in an explicit, semi-analytical approach. To validate the model, we made electrical and mechanical measurements on homemade sensors for optoacoustic applications. Each device was implemented using a polyvinylidene fluoride thin film piezoelectric polymer with a thickness of 25 μm. The sensors had detection areas in the range between 0.5 mm2 and 35 mm2 and were excited by acoustic pressure pulses of 5 ns (FWHM) from a source with a diameter around 10 μm. The experimental data obtained from the measurements agree well with the model results. We discuss the relative importance of the sensor design parameters for optoacoustic applications and we provide guidelines for the optimization of devices.

Parametric modeling of wideband piezoelectric polymer sensors: Design for optoacoustic applications.

PubMed

Fernández Vidal, A; Ciocci Brazzano, L; Matteo, C L; Sorichetti, P A; González, M G

2017-09-01

In this work, we present a three-dimensional model for the design of wideband piezoelectric polymer sensors which includes the geometry and the properties of the transducer materials. The model uses FFT and numerical integration techniques in an explicit, semi-analytical approach. To validate the model, we made electrical and mechanical measurements on homemade sensors for optoacoustic applications. Each device was implemented using a polyvinylidene fluoride thin film piezoelectric polymer with a thickness of 25 μm. The sensors had detection areas in the range between 0.5 mm 2 and 35 mm 2 and were excited by acoustic pressure pulses of 5 ns (FWHM) from a source with a diameter around 10 μm. The experimental data obtained from the measurements agree well with the model results. We discuss the relative importance of the sensor design parameters for optoacoustic applications and we provide guidelines for the optimization of devices.

Functional Mixed Effects Model for Small Area Estimation.

PubMed

Maiti, Tapabrata; Sinha, Samiran; Zhong, Ping-Shou

2016-09-01

Functional data analysis has become an important area of research due to its ability of handling high dimensional and complex data structures. However, the development is limited in the context of linear mixed effect models, and in particular, for small area estimation. The linear mixed effect models are the backbone of small area estimation. In this article, we consider area level data, and fit a varying coefficient linear mixed effect model where the varying coefficients are semi-parametrically modeled via B-splines. We propose a method of estimating the fixed effect parameters and consider prediction of random effects that can be implemented using a standard software. For measuring prediction uncertainties, we derive an analytical expression for the mean squared errors, and propose a method of estimating the mean squared errors. The procedure is illustrated via a real data example, and operating characteristics of the method are judged using finite sample simulation studies.

Stick balancing with reflex delay in case of parametric forcing

NASA Astrophysics Data System (ADS)

Insperger, Tamas

2011-04-01

The effect of parametric forcing on a PD control of an inverted pendulum is analyzed in the presence of feedback delay. The stability of the time-periodic and time-delayed system is determined numerically using the first-order semi-discretization method in the 5-dimensional parameter space of the pendulum's length, the forcing frequency, the forcing amplitude, the proportional and the differential gains. It is shown that the critical length of the pendulum (that can just be balanced against the time-delay) can significantly be decreased by parametric forcing even if the maximum forcing acceleration is limited. The numerical analysis showed that the critical stick length about 30 cm corresponding to the unforced system with reflex delay 0.1 s can be decreased to 18 cm with keeping maximum acceleration below the gravitational acceleration.

Stability analysis of magnetized neutron stars - a semi-analytic approach

NASA Astrophysics Data System (ADS)

Herbrik, Marlene; Kokkotas, Kostas D.

2017-04-01

We implement a semi-analytic approach for stability analysis, addressing the ongoing uncertainty about stability and structure of neutron star magnetic fields. Applying the energy variational principle, a model system is displaced from its equilibrium state. The related energy density variation is set up analytically, whereas its volume integration is carried out numerically. This facilitates the consideration of more realistic neutron star characteristics within the model compared to analytical treatments. At the same time, our method retains the possibility to yield general information about neutron star magnetic field and composition structures that are likely to be stable. In contrast to numerical studies, classes of parametrized systems can be studied at once, finally constraining realistic configurations for interior neutron star magnetic fields. We apply the stability analysis scheme on polytropic and non-barotropic neutron stars with toroidal, poloidal and mixed fields testing their stability in a Newtonian framework. Furthermore, we provide the analytical scheme for dropping the Cowling approximation in an axisymmetric system and investigate its impact. Our results confirm the instability of simple magnetized neutron star models as well as a stabilization tendency in the case of mixed fields and stratification. These findings agree with analytical studies whose spectrum of model systems we extend by lifting former simplifications.

Stress-intensity factor equations for cracks in three-dimensional finite bodies

NASA Technical Reports Server (NTRS)

Newman, J. C., Jr.; Raju, I. S.

1981-01-01

Empirical stress intensity factor equations are presented for embedded elliptical cracks, semi-elliptical surface cracks, quarter-elliptical corner cracks, semi-elliptical surface cracks at a hole, and quarter-elliptical corner cracks at a hole in finite plates. The plates were subjected to remote tensile loading. Equations give stress intensity factors as a function of parametric angle, crack depth, crack length, plate thickness, and where applicable, hole radius. The stress intensity factors used to develop the equations were obtained from three dimensional finite element analyses of these crack configurations.

Bayesian spatial analysis of childhood diseases in Zimbabwe.

PubMed

Tsiko, Rodney Godfrey

2015-09-02

Many sub-Saharan countries are confronted with persistently high levels of childhood morbidity and mortality because of the impact of a range of demographic, biological and social factors or situational events that directly precipitate ill health. In particular, under-five morbidity and mortality have increased in recent decades due to childhood diarrhoea, cough and fever. Understanding the geographic distribution of such diseases and their relationships to potential risk factors can be invaluable for cost effective intervention. Bayesian semi-parametric regression models were used to quantify the spatial risk of childhood diarrhoea, fever and cough, as well as associations between childhood diseases and a range of factors, after accounting for spatial correlation between neighbouring areas. Such semi-parametric regression models allow joint analysis of non-linear effects of continuous covariates, spatially structured variation, unstructured heterogeneity, and other fixed effects on childhood diseases. Modelling and inference made use of the fully Bayesian approach via Markov Chain Monte Carlo (MCMC) simulation techniques. The analysis was based on data derived from the 1999, 2005/6 and 2010/11 Zimbabwe Demographic and Health Surveys (ZDHS). The results suggest that until recently, sex of child had little or no significant association with childhood diseases. However, a higher proportion of male than female children within a given province had a significant association with childhood cough, fever and diarrhoea. Compared to their counterparts in rural areas, children raised in an urban setting had less exposure to cough, fever and diarrhoea across all the survey years with the exception of diarrhoea in 2010. In addition, the link between sanitation, parental education, antenatal care, vaccination and childhood diseases was found to be both intuitive and counterintuitive. Results also showed marked geographical differences in the prevalence of childhood diarrhoea, fever and cough. Across all the survey years Manicaland province reported the highest cases of childhood diseases. There is also clear evidence of significant high prevalence of childhood diseases in Mashonaland than in Matabeleland provinces.

A Semi-parametric Multivariate Gap-filling Model for Eddy Covariance Latent Heat Flux

NASA Astrophysics Data System (ADS)

Li, M.; Chen, Y.

2010-12-01

Quantitative descriptions of latent heat fluxes are important to study the water and energy exchanges between terrestrial ecosystems and the atmosphere. The eddy covariance approaches have been recognized as the most reliable technique for measuring surface fluxes over time scales ranging from hours to years. However, unfavorable micrometeorological conditions, instrument failures, and applicable measurement limitations may cause inevitable flux gaps in time series data. Development and application of suitable gap-filling techniques are crucial to estimate long term fluxes. In this study, a semi-parametric multivariate gap-filling model was developed to fill latent heat flux gaps for eddy covariance measurements. Our approach combines the advantages of a multivariate statistical analysis (principal component analysis, PCA) and a nonlinear interpolation technique (K-nearest-neighbors, KNN). The PCA method was first used to resolve the multicollinearity relationships among various hydrometeorological factors, such as radiation, soil moisture deficit, LAI, and wind speed. The KNN method was then applied as a nonlinear interpolation tool to estimate the flux gaps as the weighted sum latent heat fluxes with the K-nearest distances in the PCs’ domain. Two years, 2008 and 2009, of eddy covariance and hydrometeorological data from a subtropical mixed evergreen forest (the Lien-Hua-Chih Site) were collected to calibrate and validate the proposed approach with artificial gaps after standard QC/QA procedures. The optimal K values and weighting factors were determined by the maximum likelihood test. The results of gap-filled latent heat fluxes conclude that developed model successful preserving energy balances of daily, monthly, and yearly time scales. Annual amounts of evapotranspiration from this study forest were 747 mm and 708 mm for 2008 and 2009, respectively. Nocturnal evapotranspiration was estimated with filled gaps and results are comparable with other studies. Seasonal and daily variability of latent heat fluxes were also discussed.

Climate change and vector-borne diseases: an economic impact analysis of malaria in Africa.

PubMed

Egbendewe-Mondzozo, Aklesso; Musumba, Mark; McCarl, Bruce A; Wu, Ximing

2011-03-01

A semi-parametric econometric model is used to study the relationship between malaria cases and climatic factors in 25 African countries. Results show that a marginal change in temperature and precipitation levels would lead to a significant change in the number of malaria cases for most countries by the end of the century. Consistent with the existing biophysical malaria model results, the projected effects of climate change are mixed. Our model projects that some countries will see an increase in malaria cases but others will see a decrease. We estimate projected malaria inpatient and outpatient treatment costs as a proportion of annual 2000 health expenditures per 1,000 people. We found that even under minimal climate change scenario, some countries may see their inpatient treatment cost of malaria increase more than 20%.

Towards parameter-free classification of sound effects in movies

NASA Astrophysics Data System (ADS)

Chu, Selina; Narayanan, Shrikanth; Kuo, C.-C. J.

2005-08-01

The problem of identifying intense events via multimedia data mining in films is investigated in this work. Movies are mainly characterized by dialog, music, and sound effects. We begin our investigation with detecting interesting events through sound effects. Sound effects are neither speech nor music, but are closely associated with interesting events such as car chases and gun shots. In this work, we utilize low-level audio features including MFCC and energy to identify sound effects. It was shown in previous work that the Hidden Markov model (HMM) works well for speech/audio signals. However, this technique requires a careful choice in designing the model and choosing correct parameters. In this work, we introduce a framework that will avoid such necessity and works well with semi- and non-parametric learning algorithms.

Heat Transfer in Adhesively Bonded Honeycomb Core Panels

NASA Technical Reports Server (NTRS)

Daryabeigi, Kamran

2001-01-01

The Swann and Pittman semi-empirical relationship has been used as a standard in aerospace industry to predict the effective thermal conductivity of honeycomb core panels. Recent measurements of the effective thermal conductivity of an adhesively bonded titanium honeycomb core panel using three different techniques, two steady-state and one transient radiant step heating method, at four laboratories varied significantly from each other and from the Swann and Pittman predictions. Average differences between the measurements and the predictions varied between 17 and 61% in the temperature range of 300 to 500 K. In order to determine the correct values of the effective thermal conductivity and determine which set of the measurements or predictions were most accurate, the combined radiation and conduction heat transfer in the honeycomb core panel was modeled using a finite volume numerical formulation. The transient radiant step heating measurements provided the best agreement with the numerical results. It was found that a modification of the Swann and Pittman semi-empirical relationship which incorporated the facesheets and adhesive layers in the thermal model provided satisfactory results. Finally, a parametric study was conducted to investigate the influence of adhesive thickness and thermal conductivity on the overall heat transfer through the panel.

Semi-empirical studies of atomic structure. Progress report, 1 July 1982-1 February 1983

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

Curtis, L.J.

1983-01-01

A program of studies of the properties of the heavy and highly ionized atomic systems which often occur as contaminants in controlled fusion devices is continuing. The project combines experimental measurements by fast-ion-beam excitation with semi-empirical data parametrizations to identify and exploit regularities in the properties of these very heavy and very highly ionized systems. The increasing use of spectroscopic line intensities as diagnostics for determining thermonuclear plasma temperatures and densities requires laboratory observation and analysis of such spectra, often to accuracies that exceed the capabilities of ab initio theoretical methods for these highly relativistic many electron systems. Through themore » acquisition and systematization of empirical data, remarkably precise methods for predicting excitation energies, transition wavelengths, transition probabilities, level lifetimes, ionization potentials, core polarizabilities, and core penetrabilities are being developed and applied. Although the data base for heavy, highly ionized atoms is still sparse, parametrized extrapolations and interpolations along isoelectronic, homologous, and Rydberg sequences are providing predictions for large classes of quantities, with a precision that is sharpened by subsequent measurements.« less

Localized Principal Component Analysis based Curve Evolution: A Divide and Conquer Approach

PubMed Central

Appia, Vikram; Ganapathy, Balaji; Yezzi, Anthony; Faber, Tracy

2014-01-01

We propose a novel localized principal component analysis (PCA) based curve evolution approach which evolves the segmenting curve semi-locally within various target regions (divisions) in an image and then combines these locally accurate segmentation curves to obtain a global segmentation. The training data for our approach consists of training shapes and associated auxiliary (target) masks. The masks indicate the various regions of the shape exhibiting highly correlated variations locally which may be rather independent of the variations in the distant parts of the global shape. Thus, in a sense, we are clustering the variations exhibited in the training data set. We then use a parametric model to implicitly represent each localized segmentation curve as a combination of the local shape priors obtained by representing the training shapes and the masks as a collection of signed distance functions. We also propose a parametric model to combine the locally evolved segmentation curves into a single hybrid (global) segmentation. Finally, we combine the evolution of these semilocal and global parameters to minimize an objective energy function. The resulting algorithm thus provides a globally accurate solution, which retains the local variations in shape. We present some results to illustrate how our approach performs better than the traditional approach with fully global PCA. PMID:25520901

Edge Sharpness Assessment by Parametric Modeling: Application to Magnetic Resonance Imaging.

PubMed

Ahmad, R; Ding, Y; Simonetti, O P

2015-05-01

In biomedical imaging, edge sharpness is an important yet often overlooked image quality metric. In this work, a semi-automatic method to quantify edge sharpness in the presence of significant noise is presented with application to magnetic resonance imaging (MRI). The method is based on parametric modeling of image edges. First, an edge map is automatically generated and one or more edges-of-interest (EOI) are manually selected using graphical user interface. Multiple exclusion criteria are then enforced to eliminate edge pixels that are potentially not suitable for sharpness assessment. Second, at each pixel of the EOI, an image intensity profile is read along a small line segment that runs locally normal to the EOI. Third, the profiles corresponding to all EOI pixels are individually fitted with a sigmoid function characterized by four parameters, including one that represents edge sharpness. Last, the distribution of the sharpness parameter is used to quantify edge sharpness. For validation, the method is applied to simulated data as well as MRI data from both phantom imaging and cine imaging experiments. This method allows for fast, quantitative evaluation of edge sharpness even in images with poor signal-to-noise ratio. Although the utility of this method is demonstrated for MRI, it can be adapted for other medical imaging applications.

Robust control of seismically excited cable stayed bridges with MR dampers

NASA Astrophysics Data System (ADS)

YeganehFallah, Arash; Khajeh Ahamd Attari, Nader

2017-03-01

In recent decades active and semi-active structural control are becoming attractive alternatives for enhancing performance of civil infrastructures subjected to seismic and winds loads. However, in order to have reliable active and semi-active control, there is a need to include information of uncertainties in design of the controller. In real world for civil structures, parameters such as loading places, stiffness, mass and damping are time variant and uncertain. These uncertainties in many cases model as parametric uncertainties. The motivation of this research is to design a robust controller for attenuating the vibrational responses of civil infrastructures, regarding their dynamical uncertainties. Uncertainties in structural dynamic’s parameters are modeled as affine uncertainties in state space modeling. These uncertainties are decoupled from the system through Linear Fractional Transformation (LFT) and are assumed to be unknown input to the system but norm bounded. The robust H ∞ controller is designed for the decoupled system to regulate the evaluation outputs and it is robust to effects of uncertainties, disturbance and sensors noise. The cable stayed bridge benchmark which is equipped with MR damper is considered for the numerical simulation. The simulated results show that the proposed robust controller can effectively mitigate undesired uncertainties effects on systems’ responds under seismic loading.

Mediation Analysis with Survival Outcomes: Accelerated Failure Time vs. Proportional Hazards Models

PubMed Central

Gelfand, Lois A.; MacKinnon, David P.; DeRubeis, Robert J.; Baraldi, Amanda N.

2016-01-01

Objective: Survival time is an important type of outcome variable in treatment research. Currently, limited guidance is available regarding performing mediation analyses with survival outcomes, which generally do not have normally distributed errors, and contain unobserved (censored) events. We present considerations for choosing an approach, using a comparison of semi-parametric proportional hazards (PH) and fully parametric accelerated failure time (AFT) approaches for illustration. Method: We compare PH and AFT models and procedures in their integration into mediation models and review their ability to produce coefficients that estimate causal effects. Using simulation studies modeling Weibull-distributed survival times, we compare statistical properties of mediation analyses incorporating PH and AFT approaches (employing SAS procedures PHREG and LIFEREG, respectively) under varied data conditions, some including censoring. A simulated data set illustrates the findings. Results: AFT models integrate more easily than PH models into mediation models. Furthermore, mediation analyses incorporating LIFEREG produce coefficients that can estimate causal effects, and demonstrate superior statistical properties. Censoring introduces bias in the coefficient estimate representing the treatment effect on outcome—underestimation in LIFEREG, and overestimation in PHREG. With LIFEREG, this bias can be addressed using an alternative estimate obtained from combining other coefficients, whereas this is not possible with PHREG. Conclusions: When Weibull assumptions are not violated, there are compelling advantages to using LIFEREG over PHREG for mediation analyses involving survival-time outcomes. Irrespective of the procedures used, the interpretation of coefficients, effects of censoring on coefficient estimates, and statistical properties should be taken into account when reporting results. PMID:27065906

A Parameter Identification Method for Helicopter Noise Source Identification and Physics-Based Semi-Empirical Modeling

NASA Technical Reports Server (NTRS)

Greenwood, Eric, II; Schmitz, Fredric H.

2010-01-01

A new physics-based parameter identification method for rotor harmonic noise sources is developed using an acoustic inverse simulation technique. This new method allows for the identification of individual rotor harmonic noise sources and allows them to be characterized in terms of their individual non-dimensional governing parameters. This new method is applied to both wind tunnel measurements and ground noise measurements of two-bladed rotors. The method is shown to match the parametric trends of main rotor Blade-Vortex Interaction (BVI) noise, allowing accurate estimates of BVI noise to be made for operating conditions based on a small number of measurements taken at different operating conditions.

Controlling for seasonal patterns and time varying confounders in time-series epidemiological models: a simulation study.

PubMed

Perrakis, Konstantinos; Gryparis, Alexandros; Schwartz, Joel; Le Tertre, Alain; Katsouyanni, Klea; Forastiere, Francesco; Stafoggia, Massimo; Samoli, Evangelia

2014-12-10

An important topic when estimating the effect of air pollutants on human health is choosing the best method to control for seasonal patterns and time varying confounders, such as temperature and humidity. Semi-parametric Poisson time-series models include smooth functions of calendar time and weather effects to control for potential confounders. Case-crossover (CC) approaches are considered efficient alternatives that control seasonal confounding by design and allow inclusion of smooth functions of weather confounders through their equivalent Poisson representations. We evaluate both methodological designs with respect to seasonal control and compare spline-based approaches, using natural splines and penalized splines, and two time-stratified CC approaches. For the spline-based methods, we consider fixed degrees of freedom, minimization of the partial autocorrelation function, and general cross-validation as smoothing criteria. Issues of model misspecification with respect to weather confounding are investigated under simulation scenarios, which allow quantifying omitted, misspecified, and irrelevant-variable bias. The simulations are based on fully parametric mechanisms designed to replicate two datasets with different mortality and atmospheric patterns. Overall, minimum partial autocorrelation function approaches provide more stable results for high mortality counts and strong seasonal trends, whereas natural splines with fixed degrees of freedom perform better for low mortality counts and weak seasonal trends followed by the time-season-stratified CC model, which performs equally well in terms of bias but yields higher standard errors. Copyright © 2014 John Wiley & Sons, Ltd.

Clinical and multiple gene expression variables in survival analysis of breast cancer: Analysis with the hypertabastic survival model

PubMed Central

2012-01-01

Background We explore the benefits of applying a new proportional hazard model to analyze survival of breast cancer patients. As a parametric model, the hypertabastic survival model offers a closer fit to experimental data than Cox regression, and furthermore provides explicit survival and hazard functions which can be used as additional tools in the survival analysis. In addition, one of our main concerns is utilization of multiple gene expression variables. Our analysis treats the important issue of interaction of different gene signatures in the survival analysis. Methods The hypertabastic proportional hazards model was applied in survival analysis of breast cancer patients. This model was compared, using statistical measures of goodness of fit, with models based on the semi-parametric Cox proportional hazards model and the parametric log-logistic and Weibull models. The explicit functions for hazard and survival were then used to analyze the dynamic behavior of hazard and survival functions. Results The hypertabastic model provided the best fit among all the models considered. Use of multiple gene expression variables also provided a considerable improvement in the goodness of fit of the model, as compared to use of only one. By utilizing the explicit survival and hazard functions provided by the model, we were able to determine the magnitude of the maximum rate of increase in hazard, and the maximum rate of decrease in survival, as well as the times when these occurred. We explore the influence of each gene expression variable on these extrema. Furthermore, in the cases of continuous gene expression variables, represented by a measure of correlation, we were able to investigate the dynamics with respect to changes in gene expression. Conclusions We observed that use of three different gene signatures in the model provided a greater combined effect and allowed us to assess the relative importance of each in determination of outcome in this data set. These results point to the potential to combine gene signatures to a greater effect in cases where each gene signature represents some distinct aspect of the cancer biology. Furthermore we conclude that the hypertabastic survival models can be an effective survival analysis tool for breast cancer patients. PMID:23241496

Does integration of HIV and sexual and reproductive health services improve technical efficiency in Kenya and Swaziland? An application of a two-stage semi parametric approach incorporating quality measures

PubMed Central

Obure, Carol Dayo; Jacobs, Rowena; Guinness, Lorna; Mayhew, Susannah; Vassall, Anna

2016-01-01

Theoretically, integration of vertically organized services is seen as an important approach to improving the efficiency of health service delivery. However, there is a dearth of evidence on the effect of integration on the technical efficiency of health service delivery. Furthermore, where technical efficiency has been assessed, there have been few attempts to incorporate quality measures within efficiency measurement models particularly in sub-Saharan African settings. This paper investigates the technical efficiency and the determinants of technical efficiency of integrated HIV and sexual and reproductive health (SRH) services using data collected from 40 health facilities in Kenya and Swaziland for 2008/2009 and 2010/2011. Incorporating a measure of quality, we estimate the technical efficiency of health facilities and explore the effect of integration and other environmental factors on technical efficiency using a two-stage semi-parametric double bootstrap approach. The empirical results reveal a high degree of inefficiency in the health facilities studied. The mean bias corrected technical efficiency scores taking quality into consideration varied between 22% and 65% depending on the data envelopment analysis (DEA) model specification. The number of additional HIV services in the maternal and child health unit, public ownership and facility type, have a positive and significant effect on technical efficiency. However, number of additional HIV and STI services provided in the same clinical room, proportion of clinical staff to overall staff, proportion of HIV services provided, and rural location had a negative and significant effect on technical efficiency. The low estimates of technical efficiency and mixed effects of the measures of integration on efficiency challenge the notion that integration of HIV and SRH services may substantially improve the technical efficiency of health facilities. The analysis of quality and efficiency as separate dimensions of performance suggest that efficiency may be achieved without sacrificing quality. PMID:26803655

Does integration of HIV and sexual and reproductive health services improve technical efficiency in Kenya and Swaziland? An application of a two-stage semi parametric approach incorporating quality measures.

PubMed

Obure, Carol Dayo; Jacobs, Rowena; Guinness, Lorna; Mayhew, Susannah; Vassall, Anna

2016-02-01

Theoretically, integration of vertically organized services is seen as an important approach to improving the efficiency of health service delivery. However, there is a dearth of evidence on the effect of integration on the technical efficiency of health service delivery. Furthermore, where technical efficiency has been assessed, there have been few attempts to incorporate quality measures within efficiency measurement models particularly in sub-Saharan African settings. This paper investigates the technical efficiency and the determinants of technical efficiency of integrated HIV and sexual and reproductive health (SRH) services using data collected from 40 health facilities in Kenya and Swaziland for 2008/2009 and 2010/2011. Incorporating a measure of quality, we estimate the technical efficiency of health facilities and explore the effect of integration and other environmental factors on technical efficiency using a two-stage semi-parametric double bootstrap approach. The empirical results reveal a high degree of inefficiency in the health facilities studied. The mean bias corrected technical efficiency scores taking quality into consideration varied between 22% and 65% depending on the data envelopment analysis (DEA) model specification. The number of additional HIV services in the maternal and child health unit, public ownership and facility type, have a positive and significant effect on technical efficiency. However, number of additional HIV and STI services provided in the same clinical room, proportion of clinical staff to overall staff, proportion of HIV services provided, and rural location had a negative and significant effect on technical efficiency. The low estimates of technical efficiency and mixed effects of the measures of integration on efficiency challenge the notion that integration of HIV and SRH services may substantially improve the technical efficiency of health facilities. The analysis of quality and efficiency as separate dimensions of performance suggest that efficiency may be achieved without sacrificing quality. Copyright © 2016 The Authors. Published by Elsevier Ltd.. All rights reserved.

Regression-assisted deconvolution.

PubMed

McIntyre, Julie; Stefanski, Leonard A

2011-06-30

We present a semi-parametric deconvolution estimator for the density function of a random variable biX that is measured with error, a common challenge in many epidemiological studies. Traditional deconvolution estimators rely only on assumptions about the distribution of X and the error in its measurement, and ignore information available in auxiliary variables. Our method assumes the availability of a covariate vector statistically related to X by a mean-variance function regression model, where regression errors are normally distributed and independent of the measurement errors. Simulations suggest that the estimator achieves a much lower integrated squared error than the observed-data kernel density estimator when models are correctly specified and the assumption of normal regression errors is met. We illustrate the method using anthropometric measurements of newborns to estimate the density function of newborn length. Copyright © 2011 John Wiley & Sons, Ltd.

LHC searches for dark sector showers

NASA Astrophysics Data System (ADS)

Cohen, Timothy; Lisanti, Mariangela; Lou, Hou Keong; Mishra-Sharma, Siddharth

2017-11-01

This paper proposes a new search program for dark sector parton showers at the Large Hadron Collider (LHC). These signatures arise in theories characterized by strong dynamics in a hidden sector, such as Hidden Valley models. A dark parton shower can be composed of both invisible dark matter particles as well as dark sector states that decay to Standard Model particles via a portal. The focus here is on the specific case of `semi-visible jets,' jet-like collider objects where the visible states in the shower are Standard Model hadrons. We present a Simplified Model-like parametrization for the LHC observables and propose targeted search strategies for regions of parameter space that are not covered by existing analyses. Following the `mono- X' literature, the portal is modeled using either an effective field theoretic contact operator approach or with one of two ultraviolet completions; sensitivity projections are provided for all three cases. We additionally highlight that the LHC has a unique advantage over direct detection experiments in the search for this class of dark matter theories.

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.

Cross-validation and Peeling Strategies for Survival Bump Hunting using Recursive Peeling Methods

PubMed Central

Dazard, Jean-Eudes; Choe, Michael; LeBlanc, Michael; Rao, J. Sunil

2015-01-01

We introduce a framework to build a survival/risk bump hunting model with a censored time-to-event response. Our Survival Bump Hunting (SBH) method is based on a recursive peeling procedure that uses a specific survival peeling criterion derived from non/semi-parametric statistics such as the hazards-ratio, the log-rank test or the Nelson--Aalen estimator. To optimize the tuning parameter of the model and validate it, we introduce an objective function based on survival or prediction-error statistics, such as the log-rank test and the concordance error rate. We also describe two alternative cross-validation techniques adapted to the joint task of decision-rule making by recursive peeling and survival estimation. Numerical analyses show the importance of replicated cross-validation and the differences between criteria and techniques in both low and high-dimensional settings. Although several non-parametric survival models exist, none addresses the problem of directly identifying local extrema. We show how SBH efficiently estimates extreme survival/risk subgroups unlike other models. This provides an insight into the behavior of commonly used models and suggests alternatives to be adopted in practice. Finally, our SBH framework was applied to a clinical dataset. In it, we identified subsets of patients characterized by clinical and demographic covariates with a distinct extreme survival outcome, for which tailored medical interventions could be made. An R package PRIMsrc (Patient Rule Induction Method in Survival, Regression and Classification settings) is available on CRAN (Comprehensive R Archive Network) and GitHub. PMID:27034730

Semi-nonparametric VaR forecasts for hedge funds during the recent crisis

NASA Astrophysics Data System (ADS)

Del Brio, Esther B.; Mora-Valencia, Andrés; Perote, Javier

2014-05-01

The need to provide accurate value-at-risk (VaR) forecasting measures has triggered an important literature in econophysics. Although these accurate VaR models and methodologies are particularly demanded for hedge fund managers, there exist few articles specifically devoted to implement new techniques in hedge fund returns VaR forecasting. This article advances in these issues by comparing the performance of risk measures based on parametric distributions (the normal, Student’s t and skewed-t), semi-nonparametric (SNP) methodologies based on Gram-Charlier (GC) series and the extreme value theory (EVT) approach. Our results show that normal-, Student’s t- and Skewed t- based methodologies fail to forecast hedge fund VaR, whilst SNP and EVT approaches accurately success on it. We extend these results to the multivariate framework by providing an explicit formula for the GC copula and its density that encompasses the Gaussian copula and accounts for non-linear dependences. We show that the VaR obtained by the meta GC accurately captures portfolio risk and outperforms regulatory VaR estimates obtained through the meta Gaussian and Student’s t distributions.

Assessment of Biomarkers Associated with Joint Injury and Subsequent Post-Traumatic Arthritis

DTIC Science & Technology

2014-10-01

using a modified Mankin score, synovial inflammation using a modified synovitis score with semi-quantitative scales, and osteophyte score6-10...Parametric analyses were performed for bone morphological measures and histological assessment. Subchondral bone thickening was significantly increased in...Soder S, Eger W, Diemtar T, Aigner T. Feb 2003. Osteophyte development-- molecular characterization of differentiation stages. Osteoarthritis

Research on simplified parametric finite element model of automobile frontal crash

NASA Astrophysics Data System (ADS)

Wu, Linan; Zhang, Xin; Yang, Changhai

2018-05-01

The modeling method and key technologies of the automobile frontal crash simplified parametric finite element model is studied in this paper. By establishing the auto body topological structure, extracting and parameterizing the stiffness properties of substructures, choosing appropriate material models for substructures, the simplified parametric FE model of M6 car is built. The comparison of the results indicates that the simplified parametric FE model can accurately calculate the automobile crash responses and the deformation of the key substructures, and the simulation time is reduced from 6 hours to 2 minutes.

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.

Dissipative particle dynamics: Systematic parametrization using water-octanol partition coefficients

NASA Astrophysics Data System (ADS)

Anderson, Richard L.; Bray, David J.; Ferrante, Andrea S.; Noro, Massimo G.; Stott, Ian P.; Warren, Patrick B.

2017-09-01

We present a systematic, top-down, thermodynamic parametrization scheme for dissipative particle dynamics (DPD) using water-octanol partition coefficients, supplemented by water-octanol phase equilibria and pure liquid phase density data. We demonstrate the feasibility of computing the required partition coefficients in DPD using brute-force simulation, within an adaptive semi-automatic staged optimization scheme. We test the methodology by fitting to experimental partition coefficient data for twenty one small molecules in five classes comprising alcohols and poly-alcohols, amines, ethers and simple aromatics, and alkanes (i.e., hexane). Finally, we illustrate the transferability of a subset of the determined parameters by calculating the critical micelle concentrations and mean aggregation numbers of selected alkyl ethoxylate surfactants, in good agreement with reported experimental values.

Linkage analysis of alternative anxiety phenotypes in multiply affected panic disorder families

PubMed Central

Fyer, Abby J.; Costa, Ramiro; Haghighi, Fatemeh; Logue, Mark W.; Knowles, James A.; Weissman, Myrna M.; Hodge, Susan E.; Hamilton, Steven P.

2013-01-01

Background The choice of phenotype definitions for genetic studies of panic and phobic disorders is complicated by family, twin and neurobiological data indicating both distinct and shared risk factors as well as heterogeneity within categories. We previously reported a genome scan in 120 multiplex panic disorder (PD) families using a phenotype that closely adhered to the DSM IV PD definition. Here we extend this work by conducting exploratory linkage analyses in this same pedigree set using ten additional literature- based panic and phobia-related phenotypes that take into account aspects of these hypothesized complexities. Methods Multiply affected families (> 2 individuals with PD) were recruited from clinical and non-clinical sources, evaluated by clinician administered semi-structured interview and subsequent blind consensus best estimate procedure. Each phenotype was analyzed under dominant and recessive models using parametric 2-point (homogeneity and heterogeneity), multipoint, and non-parametric methods. Empirically based permutations were used to estimate model specific and global (across all phenotypes) p-values. Results The highest score was a 2-point lod (4.27, global p < 0.08) on chromosome 13 (D13S793, 76cM) for the phenotype “specific or social phobia” under a recessive model and conditions of homogeneity. There was minimal support for linkage to any of the remaining nine phenotypes. Conclusions Though interpretation of findings is limited by sample size and the large number of phenotypes and models analyzed these data suggest a region on chromosome 13 as a potential site for further exploration in relation to risk for specific and social phobias. PMID:22525237

Acceleration of the direct reconstruction of linear parametric images using nested algorithms.

PubMed

Wang, Guobao; Qi, Jinyi

2010-03-07

Parametric imaging using dynamic positron emission tomography (PET) provides important information for biological research and clinical diagnosis. Indirect and direct methods have been developed for reconstructing linear parametric images from dynamic PET data. Indirect methods are relatively simple and easy to implement because the image reconstruction and kinetic modeling are performed in two separate steps. Direct methods estimate parametric images directly from raw PET data and are statistically more efficient. However, the convergence rate of direct algorithms can be slow due to the coupling between the reconstruction and kinetic modeling. Here we present two fast gradient-type algorithms for direct reconstruction of linear parametric images. The new algorithms decouple the reconstruction and linear parametric modeling at each iteration by employing the principle of optimization transfer. Convergence speed is accelerated by running more sub-iterations of linear parametric estimation because the computation cost of the linear parametric modeling is much less than that of the image reconstruction. Computer simulation studies demonstrated that the new algorithms converge much faster than the traditional expectation maximization (EM) and the preconditioned conjugate gradient algorithms for dynamic PET.

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.

Trajectories of Diurnal Cortisol in Mothers of Children with Autism and Other Developmental Disabilities: Relations to Health and Mental Health

ERIC Educational Resources Information Center

Dykens, Elisabeth M.; Lambert, Warren

2013-01-01

This study used a stress biomarker, diurnal cortisol, to identify how elevated stress in mothers of children and adults with autism and other disabilities relates to their health and mental health. Based on semi-parametric, group-based trajectory analysis of 91 mothers, two distinctive cortisol trajectories emerged: blunted (63%) or steep (37%).…

Effect of a semi-annular thermal acoustic shield on jet exhaust noise

NASA Technical Reports Server (NTRS)

Goodykoontz, J.

1980-01-01

Reductions in jet exhaust noise obtained by the use of an annular thermal acoustic shield consisting of a high temperature, low velocity gas stream surrounding a high velocity central jet exhaust appear to be limited by multiple reflections. The effect of a semi-annular shield on jet exhaust noise was investigted with the rationale that such a configuration would eliminate or reduce the multiple reflection mechanism. Noise measurements for a 10 cm conical nozzle with a semi-annular acoustic shield are presented in terms of lossless free field data at various angular locations with respect to the nozzle. Measurements were made on both the shielded and unshielded sides of the nozzle. The results are presented parametrically, showing the effects of various shield and central system velocities and temperatures. Selected results are scaled up to a typical full scale engine size to determine the perceived noise level reductions.

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…

A model of icebergs and sea ice in a joint continuum framework

NASA Astrophysics Data System (ADS)

Vaňková, Irena; Holland, David M.

2017-04-01

The ice mélange, a mixture of sea ice and icebergs, often present in front of tidewater glaciers in Greenland or ice shelves in Antarctica, can have a profound effect on the dynamics of the ice-ocean system. The current inability to numerically model the ice mélange motivates a new modeling approach proposed here. A continuum sea-ice model is taken as a starting point and icebergs are represented as thick and compact pieces of sea ice held together by large tensile and shear strength selectively introduced into the sea ice rheology. In order to modify the rheology correctly, a semi-Lagrangian time stepping scheme is introduced and at each time step a Lagrangian grid is constructed such that iceberg shape is preserved exactly. With the proposed treatment, sea ice and icebergs are considered a single fluid with spatially varying rheological properties, mutual interactions are thus automatically included without the need of further parametrization. An important advantage of the presented framework for an ice mélange model is its potential to be easily included in existing climate models.

A Model of Icebergs and Sea Ice in a Joint Continuum Framework

NASA Astrophysics Data System (ADS)

VaÅková, Irena; Holland, David M.

2017-11-01

The ice mélange, a mixture of sea ice and icebergs, often present in front of outlet glaciers in Greenland or ice shelves in Antarctica, can have a profound effect on the dynamics of the ice-ocean system. The current inability to numerically model the ice mélange motivates a new modeling approach proposed here. A continuum sea-ice model is taken as a starting point and icebergs are represented as thick and compact pieces of sea ice held together by large tensile and shear strength, selectively introduced into the sea-ice rheology. In order to modify the rheology correctly, an iceberg tracking procedure is implemented within a semi-Lagrangian time-stepping scheme, designed to exactly preserve iceberg shape through time. With the proposed treatment, sea ice and icebergs are considered a single fluid with spatially varying rheological properties. Mutual interactions are thus automatically included without the need for further parametrization. An important advantage of the presented framework for an ice mélange model is its potential to be easily included within sea-ice components of existing climate models.

Potential risks of nitrate pollution in aquifers from agricultural practices in the Nurra region, northwestern Sardinia, Italy

NASA Astrophysics Data System (ADS)

Ghiglieri, Giorgio; Barbieri, Giulio; Vernier, Antonio; Carletti, Alberto; Demurtas, Nicola; Pinna, Rosanna; Pittalis, Daniele

2009-12-01

SummaryThe paper describes the methodological and innovative approach, which aims to evaluate the potential risk of nitrate pollution in aquifers from agricultural practices by combining intrinsic aquifer vulnerability to contamination, according to the SINTACS R5 method, with agricultural nitrates hazard assessment, according to the IPNOA index. The proposed parametric model adopts a geographically based integrated evaluation system, comprising qualitative and semi-quantitative indicators. In some cases, the authors have modified this model, revising and adjusting scores and weights of the parameter to account for the different environmental conditions, and calibrating accordingly. The method has been successfully implemented and validated in the pilot area of the Alghero coastal plain (northwestern Sardinia, Italy) where aquifers with high productivity are present. The classes with a major score (high potential risk) are in the central part of the plain, in correspondence with the most productive aquifers, where most actual or potential pollution sources are concentrated. These are mainly represented by intensive agricultural activities, by industrial agglomerate and diffused urbanisation. For calibrating the model and optimizing and/or weighting the examined factors, the modelling results were validated by comparison with groundwater quality data, in particular nitrate content, and with the potential pollution sources census data. The parametric method is a popular approach to groundwater vulnerability assessment, in contrast to groundwater flow model and statistical method ones: it is, indeed, relatively inexpensive and straightforward, and use data commonly available or that can be estimated. The zoning of nitrate vulnerable areas provides regional authorities with a useful decision support tool for planning land-use properly managing groundwater and combating and/or mitigating desertification processes. However, a careful validation of the results is indispensable for reliable application.

A flexible, interpretable framework for assessing sensitivity to unmeasured confounding.

PubMed

Dorie, Vincent; Harada, Masataka; Carnegie, Nicole Bohme; Hill, Jennifer

2016-09-10

When estimating causal effects, unmeasured confounding and model misspecification are both potential sources of bias. We propose a method to simultaneously address both issues in the form of a semi-parametric sensitivity analysis. In particular, our approach incorporates Bayesian Additive Regression Trees into a two-parameter sensitivity analysis strategy that assesses sensitivity of posterior distributions of treatment effects to choices of sensitivity parameters. This results in an easily interpretable framework for testing for the impact of an unmeasured confounder that also limits the number of modeling assumptions. We evaluate our approach in a large-scale simulation setting and with high blood pressure data taken from the Third National Health and Nutrition Examination Survey. The model is implemented as open-source software, integrated into the treatSens package for the R statistical programming language. © 2016 The Authors. Statistics in Medicine Published by John Wiley & Sons Ltd. © 2016 The Authors. Statistics in Medicine Published by John Wiley & Sons Ltd.

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.

Fair Inference on Outcomes

PubMed Central

Nabi, Razieh; Shpitser, Ilya

2017-01-01

In this paper, we consider the problem of fair statistical inference involving outcome variables. Examples include classification and regression problems, and estimating treatment effects in randomized trials or observational data. The issue of fairness arises in such problems where some covariates or treatments are “sensitive,” in the sense of having potential of creating discrimination. In this paper, we argue that the presence of discrimination can be formalized in a sensible way as the presence of an effect of a sensitive covariate on the outcome along certain causal pathways, a view which generalizes (Pearl 2009). A fair outcome model can then be learned by solving a constrained optimization problem. We discuss a number of complications that arise in classical statistical inference due to this view and provide workarounds based on recent work in causal and semi-parametric inference.

Estimating and modelling cure in population-based cancer studies within the framework of flexible parametric survival models.

PubMed

Andersson, Therese M L; Dickman, Paul W; Eloranta, Sandra; Lambert, Paul C

2011-06-22

When the mortality among a cancer patient group returns to the same level as in the general population, that is, the patients no longer experience excess mortality, the patients still alive are considered "statistically cured". Cure models can be used to estimate the cure proportion as well as the survival function of the "uncured". One limitation of parametric cure models is that the functional form of the survival of the "uncured" has to be specified. It can sometimes be hard to find a survival function flexible enough to fit the observed data, for example, when there is high excess hazard within a few months from diagnosis, which is common among older age groups. This has led to the exclusion of older age groups in population-based cancer studies using cure models. Here we have extended the flexible parametric survival model to incorporate cure as a special case to estimate the cure proportion and the survival of the "uncured". Flexible parametric survival models use splines to model the underlying hazard function, and therefore no parametric distribution has to be specified. We have compared the fit from standard cure models to our flexible cure model, using data on colon cancer patients in Finland. This new method gives similar results to a standard cure model, when it is reliable, and better fit when the standard cure model gives biased estimates. Cure models within the framework of flexible parametric models enables cure modelling when standard models give biased estimates. These flexible cure models enable inclusion of older age groups and can give stage-specific estimates, which is not always possible from parametric cure models. © 2011 Andersson et al; licensee BioMed Central Ltd.

Estimating and modelling cure in population-based cancer studies within the framework of flexible parametric survival models

PubMed Central

2011-01-01

Background When the mortality among a cancer patient group returns to the same level as in the general population, that is, the patients no longer experience excess mortality, the patients still alive are considered "statistically cured". Cure models can be used to estimate the cure proportion as well as the survival function of the "uncured". One limitation of parametric cure models is that the functional form of the survival of the "uncured" has to be specified. It can sometimes be hard to find a survival function flexible enough to fit the observed data, for example, when there is high excess hazard within a few months from diagnosis, which is common among older age groups. This has led to the exclusion of older age groups in population-based cancer studies using cure models. Methods Here we have extended the flexible parametric survival model to incorporate cure as a special case to estimate the cure proportion and the survival of the "uncured". Flexible parametric survival models use splines to model the underlying hazard function, and therefore no parametric distribution has to be specified. Results We have compared the fit from standard cure models to our flexible cure model, using data on colon cancer patients in Finland. This new method gives similar results to a standard cure model, when it is reliable, and better fit when the standard cure model gives biased estimates. Conclusions Cure models within the framework of flexible parametric models enables cure modelling when standard models give biased estimates. These flexible cure models enable inclusion of older age groups and can give stage-specific estimates, which is not always possible from parametric cure models. PMID:21696598

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.

Semi-automated brain tumor segmentation on multi-parametric MRI using regularized non-negative matrix factorization.

PubMed

Sauwen, Nicolas; Acou, Marjan; Sima, Diana M; Veraart, Jelle; Maes, Frederik; Himmelreich, Uwe; Achten, Eric; Huffel, Sabine Van

2017-05-04

Segmentation of gliomas in multi-parametric (MP-)MR images is challenging due to their heterogeneous nature in terms of size, appearance and location. Manual tumor segmentation is a time-consuming task and clinical practice would benefit from (semi-) automated segmentation of the different tumor compartments. We present a semi-automated framework for brain tumor segmentation based on non-negative matrix factorization (NMF) that does not require prior training of the method. L1-regularization is incorporated into the NMF objective function to promote spatial consistency and sparseness of the tissue abundance maps. The pathological sources are initialized through user-defined voxel selection. Knowledge about the spatial location of the selected voxels is combined with tissue adjacency constraints in a post-processing step to enhance segmentation quality. The method is applied to an MP-MRI dataset of 21 high-grade glioma patients, including conventional, perfusion-weighted and diffusion-weighted MRI. To assess the effect of using MP-MRI data and the L1-regularization term, analyses are also run using only conventional MRI and without L1-regularization. Robustness against user input variability is verified by considering the statistical distribution of the segmentation results when repeatedly analyzing each patient's dataset with a different set of random seeding points. Using L1-regularized semi-automated NMF segmentation, mean Dice-scores of 65%, 74 and 80% are found for active tumor, the tumor core and the whole tumor region. Mean Hausdorff distances of 6.1 mm, 7.4 mm and 8.2 mm are found for active tumor, the tumor core and the whole tumor region. Lower Dice-scores and higher Hausdorff distances are found without L1-regularization and when only considering conventional MRI data. Based on the mean Dice-scores and Hausdorff distances, segmentation results are competitive with state-of-the-art in literature. Robust results were found for most patients, although careful voxel selection is mandatory to avoid sub-optimal segmentation.

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

Sivers effect in single spin asymmetry based on the covariant parton model

NASA Astrophysics Data System (ADS)

Saffar, H. Mahdizadeh; Mirjalili, A.; Tehrani, S. Atashbar; Yazdanpanah, M. M.

2017-10-01

Sivers effect is describing the correlation between the transverse polarization of nucleon and the transverse momentum, k⊥, of its unpolarized constituent partons. This effect is an outstanding subject and in this regard, a great deal in recent years has been considered from experimental and phenomenological points of view. It also plays an essential role to extend our understanding from nucleon structure. Semi-inclusive DIS (SIDIS) process provides us an opportunity to access to Sivers function which is dependent on transverse momentum of partons. In this paper, for the first time the covariant parton model is used to deliver us the k⊥ and x dependence part of Sivers function. Based on this model, this combinatory dependence is arising out from the HERAPDF parametrization group. In this paper the other required parametrized functions in Sivers function is also changed with respect to Ref. 1. The unknown parameters which exist in Sivers function can be extracted, doing a global fit over the recent available experimental data, including HERMES, COMPASS and JLAB collaborations for the single spin asymmetry (SSA) in π- and π+ meson production as well as kaon production to constrain the evolved strange quark. This is done, considering advanced mathematical manipulations to overcome the difficulties which exist to compute the required multiple integrals and finally employing the CERN MINIUTE program to do a global fit. Our results for SSA are in good agreement with the available experimental data. For more confirmation a comparison between our results and the ones from Ref. 2 is also done.

Future discharge of the French tributaries of the Rhine: a semi-distributed multi-model approach using CMIP5 projections

NASA Astrophysics Data System (ADS)

Thirel, Guillaume; de Lavenne, Alban; Wagner, Jean-Pierre; Perrin, Charles; Gerlinger, Kai; Drogue, Gilles; Renard, Benjamin

2016-04-01

Several projects studied the impact of climate change on the Rhine basin during the past years, using the CMIP3 projections (see Explore2070, FLOW MS, RheinBlick2050 or VULNAR), either on the French or German sides. These studies showed the likely decrease of low flows and a high uncertainty regarding the evolution of high flows. This may have tremendous impacts on several aspects related to discharge, including pollution, flood protection, irrigation, rivers ecosystems and drinking water. While focusing on the same basin (or part of it), many differences including the climate scenarios and models, the hydrological models and the study periods used for these projects make the outcomes of these projects difficult to compare rigorously. Therefore the MOSARH21 (stands for MOselle-SArre-RHine discharge in the 21st century) was built to update and homogenise discharge projections for the French tributaries of the Rhine basin. Two types of models were used: the physically-oriented LARSIM model, which is widely used in Germany and was used in one of the previous projects (FLOW MS), and the semi-distributed conceptual GRSD model tested on French catchments for various objectives. Through the use of these two hydrological models and multiple sets of parameters obtained by various calibrations runs, the structural and parametric uncertainties in the hydrological projections were quantified, as they tend to be neglected in climate change impact studies. The focus of the impact analysis is put on low flows, high flows and regime. Although this study considers only French tributaries of the Rhine, it will foster further cooperation on transboundary basins across Europe, and should contribute to propose better bases for the future definition of adaptation strategies between riverine countries.

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.

Evaluation and Comparison of the Processing Methods of Airborne Gravimetry Concerning the Errors Effects on Downward Continuation Results: Case Studies in Louisiana (USA) and the Tibetan Plateau (China).

PubMed

Zhao, Qilong; Strykowski, Gabriel; Li, Jiancheng; Pan, Xiong; Xu, Xinyu

2017-05-25

Gravity data gaps in mountainous areas are nowadays often filled in with the data from airborne gravity surveys. Because of the errors caused by the airborne gravimeter sensors, and because of rough flight conditions, such errors cannot be completely eliminated. The precision of the gravity disturbances generated by the airborne gravimetry is around 3-5 mgal. A major obstacle in using airborne gravimetry are the errors caused by the downward continuation. In order to improve the results the external high-accuracy gravity information e.g., from the surface data can be used for high frequency correction, while satellite information can be applying for low frequency correction. Surface data may be used to reduce the systematic errors, while regularization methods can reduce the random errors in downward continuation. Airborne gravity surveys are sometimes conducted in mountainous areas and the most extreme area of the world for this type of survey is the Tibetan Plateau. Since there are no high-accuracy surface gravity data available for this area, the above error minimization method involving the external gravity data cannot be used. We propose a semi-parametric downward continuation method in combination with regularization to suppress the systematic error effect and the random error effect in the Tibetan Plateau; i.e., without the use of the external high-accuracy gravity data. We use a Louisiana airborne gravity dataset from the USA National Oceanic and Atmospheric Administration (NOAA) to demonstrate that the new method works effectively. Furthermore, and for the Tibetan Plateau we show that the numerical experiment is also successfully conducted using the synthetic Earth Gravitational Model 2008 (EGM08)-derived gravity data contaminated with the synthetic errors. The estimated systematic errors generated by the method are close to the simulated values. In addition, we study the relationship between the downward continuation altitudes and the error effect. The analysis results show that the proposed semi-parametric method combined with regularization is efficient to address such modelling problems.

Evaluation and Comparison of the Processing Methods of Airborne Gravimetry Concerning the Errors Effects on Downward Continuation Results: Case Studies in Louisiana (USA) and the Tibetan Plateau (China)

PubMed Central

Zhao, Qilong; Strykowski, Gabriel; Li, Jiancheng; Pan, Xiong; Xu, Xinyu

2017-01-01

Gravity data gaps in mountainous areas are nowadays often filled in with the data from airborne gravity surveys. Because of the errors caused by the airborne gravimeter sensors, and because of rough flight conditions, such errors cannot be completely eliminated. The precision of the gravity disturbances generated by the airborne gravimetry is around 3–5 mgal. A major obstacle in using airborne gravimetry are the errors caused by the downward continuation. In order to improve the results the external high-accuracy gravity information e.g., from the surface data can be used for high frequency correction, while satellite information can be applying for low frequency correction. Surface data may be used to reduce the systematic errors, while regularization methods can reduce the random errors in downward continuation. Airborne gravity surveys are sometimes conducted in mountainous areas and the most extreme area of the world for this type of survey is the Tibetan Plateau. Since there are no high-accuracy surface gravity data available for this area, the above error minimization method involving the external gravity data cannot be used. We propose a semi-parametric downward continuation method in combination with regularization to suppress the systematic error effect and the random error effect in the Tibetan Plateau; i.e., without the use of the external high-accuracy gravity data. We use a Louisiana airborne gravity dataset from the USA National Oceanic and Atmospheric Administration (NOAA) to demonstrate that the new method works effectively. Furthermore, and for the Tibetan Plateau we show that the numerical experiment is also successfully conducted using the synthetic Earth Gravitational Model 2008 (EGM08)-derived gravity data contaminated with the synthetic errors. The estimated systematic errors generated by the method are close to the simulated values. In addition, we study the relationship between the downward continuation altitudes and the error effect. The analysis results show that the proposed semi-parametric method combined with regularization is efficient to address such modelling problems. PMID:28587086

Evaluation and Comparison of the Processing Methods of Airborne Gravimetry Concerning the Errors Effects on Downward Continuation Results: Case Studies in Louisiana (USA) and the Tibetan Plateau (China)

NASA Astrophysics Data System (ADS)

Zhao, Q.

2017-12-01

Gravity data gaps in mountainous areas are nowadays often filled in with the data from airborne gravity surveys. Because of the errors caused by the airborne gravimeter sensors, and because of rough flight conditions, such errors cannot be completely eliminated. The precision of the gravity disturbances generated by the airborne gravimetry is around 3-5 mgal. A major obstacle in using airborne gravimetry are the errors caused by the downward continuation. In order to improve the results the external high-accuracy gravity information e.g., from the surface data can be used for high frequency correction, while satellite information can be applying for low frequency correction. Surface data may be used to reduce the systematic errors, while regularization methods can reduce the random errors in downward continuation. Airborne gravity surveys are sometimes conducted in mountainous areas and the most extreme area of the world for this type of survey is the Tibetan Plateau. Since there are no high-accuracy surface gravity data available for this area, the above error minimization method involving the external gravity data cannot be used. We propose a semi-parametric downward continuation method in combination with regularization to suppress the systematic error effect and the random error effect in the Tibetan Plateau; i.e., without the use of the external high-accuracy gravity data. We use a Louisiana airborne gravity dataset from the USA National Oceanic and Atmospheric Administration (NOAA) to demonstrate that the new method works effectively. Furthermore, and for the Tibetan Plateau we show that the numerical experiment is also successfully conducted using the synthetic Earth Gravitational Model 2008 (EGM08)-derived gravity data contaminated with the synthetic errors. The estimated systematic errors generated by the method are close to the simulated values. In addition, we study the relationship between the downward continuation altitudes and the error effect. The analysis results show that the proposed semi-parametric method combined with regularization is efficient to address such modelling problems.

Parametric versus Cox's model: an illustrative analysis of divorce in Canada.

PubMed

Balakrishnan, T R; Rao, K V; Krotki, K J; Lapierre-adamcyk, E

1988-06-01

Recent demographic literature clearly recognizes the importance of survival modes in the analysis of cross-sectional event histories. Of the various survival models, Cox's (1972) partial parametric model has been very popular due to its simplicity, and readily available computer software for estimation, sometimes at the cost of precision and parsimony of the model. This paper focuses on parametric failure time models for event history analysis such as Weibell, lognormal, loglogistic, and exponential models. The authors also test the goodness of fit of these parametric models versus the Cox's proportional hazards model taking Kaplan-Meier estimate as base. As an illustration, the authors reanalyze the Canadian Fertility Survey data on 1st marriage dissolution with parametric models. Though these parametric model estimates were not very different from each other, there seemed to be a slightly better fit with loglogistic. When 8 covariates were used in the analysis, it was found that the coefficients were similar in the models, and the overall conclusions about the relative risks would not have been different. The findings reveal that in marriage dissolution, the differences according to demographic and socioeconomic characteristics may be far more important than is generally found in many studies. Therefore, one should not treat the population as homogeneous in analyzing survival probabilities of marriages, other than for cursory analysis of overall trends.

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.

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

Using Multivariate Adaptive Regression Spline and Artificial Neural Network to Simulate Urbanization in Mumbai, India

NASA Astrophysics Data System (ADS)

Ahmadlou, M.; Delavar, M. R.; Tayyebi, A.; Shafizadeh-Moghadam, H.

2015-12-01

Land use change (LUC) models used for modelling urban growth are different in structure and performance. Local models divide the data into separate subsets and fit distinct models on each of the subsets. Non-parametric models are data driven and usually do not have a fixed model structure or model structure is unknown before the modelling process. On the other hand, global models perform modelling using all the available data. In addition, parametric models have a fixed structure before the modelling process and they are model driven. Since few studies have compared local non-parametric models with global parametric models, this study compares a local non-parametric model called multivariate adaptive regression spline (MARS), and a global parametric model called artificial neural network (ANN) to simulate urbanization in Mumbai, India. Both models determine the relationship between a dependent variable and multiple independent variables. We used receiver operating characteristic (ROC) to compare the power of the both models for simulating urbanization. Landsat images of 1991 (TM) and 2010 (ETM+) were used for modelling the urbanization process. The drivers considered for urbanization in this area were distance to urban areas, urban density, distance to roads, distance to water, distance to forest, distance to railway, distance to central business district, number of agricultural cells in a 7 by 7 neighbourhoods, and slope in 1991. The results showed that the area under the ROC curve for MARS and ANN was 94.77% and 95.36%, respectively. Thus, ANN performed slightly better than MARS to simulate urban areas in Mumbai, India.

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

B97-3c: A revised low-cost variant of the B97-D density functional method

NASA Astrophysics Data System (ADS)

Brandenburg, Jan Gerit; Bannwarth, Christoph; Hansen, Andreas; Grimme, Stefan

2018-02-01

A revised version of the well-established B97-D density functional approximation with general applicability for chemical properties of large systems is proposed. Like B97-D, it is based on Becke's power-series ansatz from 1997 and is explicitly parametrized by including the standard D3 semi-classical dispersion correction. The orbitals are expanded in a modified valence triple-zeta Gaussian basis set, which is available for all elements up to Rn. Remaining basis set errors are mostly absorbed in the modified B97 parametrization, while an established atom-pairwise short-range potential is applied to correct for the systematically too long bonds of main group elements which are typical for most semi-local density functionals. The new composite scheme (termed B97-3c) completes the hierarchy of "low-cost" electronic structure methods, which are all mainly free of basis set superposition error and account for most interactions in a physically sound and asymptotically correct manner. B97-3c yields excellent molecular and condensed phase geometries, similar to most hybrid functionals evaluated in a larger basis set expansion. Results on the comprehensive GMTKN55 energy database demonstrate its good performance for main group thermochemistry, kinetics, and non-covalent interactions, when compared to functionals of the same class. This also transfers to metal-organic reactions, which is a major area of applicability for semi-local functionals. B97-3c can be routinely applied to hundreds of atoms on a single processor and we suggest it as a robust computational tool, in particular, for more strongly correlated systems where our previously published "3c" schemes might be problematic.

SEMIPARAMETRIC ZERO-INFLATED MODELING IN MULTI-ETHNIC STUDY OF ATHEROSCLEROSIS (MESA)

PubMed Central

Liu, Hai; Ma, Shuangge; Kronmal, Richard; Chan, Kung-Sik

2013-01-01

We analyze the Agatston score of coronary artery calcium (CAC) from the Multi-Ethnic Study of Atherosclerosis (MESA) using semi-parametric zero-inflated modeling approach, where the observed CAC scores from this cohort consist of high frequency of zeroes and continuously distributed positive values. Both partially constrained and unconstrained models are considered to investigate the underlying biological processes of CAC development from zero to positive, and from small amount to large amount. Different from existing studies, a model selection procedure based on likelihood cross-validation is adopted to identify the optimal model, which is justified by comparative Monte Carlo studies. A shrinkaged version of cubic regression spline is used for model estimation and variable selection simultaneously. When applying the proposed methods to the MESA data analysis, we show that the two biological mechanisms influencing the initiation of CAC and the magnitude of CAC when it is positive are better characterized by an unconstrained zero-inflated normal model. Our results are significantly different from those in published studies, and may provide further insights into the biological mechanisms underlying CAC development in human. This highly flexible statistical framework can be applied to zero-inflated data analyses in other areas. PMID:23805172

Comprehensive Modeling of Temperature-Dependent Degradation Mechanisms in Lithium Iron Phosphate Batteries

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

Schimpe, Michael; von Kuepach, M. E.; Naumann, M.

For reliable lifetime predictions of lithium-ion batteries, models for cell degradation are required. A comprehensive semi-empirical model based on a reduced set of internal cell parameters and physically justified degradation functions for the capacity loss is developed and presented for a commercial lithium iron phosphate/graphite cell. One calendar and several cycle aging effects are modeled separately. Emphasis is placed on the varying degradation at different temperatures. Degradation mechanisms for cycle aging at high and low temperatures as well as the increased cycling degradation at high state of charge are calculated separately. For parameterization, a lifetime test study is conducted includingmore » storage and cycle tests. Additionally, the model is validated through a dynamic current profile based on real-world application in a stationary energy storage system revealing the accuracy. Tests for validation are continued for up to 114 days after the longest parametrization tests. In conclusion, the model error for the cell capacity loss in the application-based tests is at the end of testing below 1% of the original cell capacity and the maximum relative model error is below 21%.« less

Comprehensive Modeling of Temperature-Dependent Degradation Mechanisms in Lithium Iron Phosphate Batteries

DOE PAGES

Schimpe, Michael; von Kuepach, M. E.; Naumann, M.; ...

2018-01-12

For reliable lifetime predictions of lithium-ion batteries, models for cell degradation are required. A comprehensive semi-empirical model based on a reduced set of internal cell parameters and physically justified degradation functions for the capacity loss is developed and presented for a commercial lithium iron phosphate/graphite cell. One calendar and several cycle aging effects are modeled separately. Emphasis is placed on the varying degradation at different temperatures. Degradation mechanisms for cycle aging at high and low temperatures as well as the increased cycling degradation at high state of charge are calculated separately. For parameterization, a lifetime test study is conducted includingmore » storage and cycle tests. Additionally, the model is validated through a dynamic current profile based on real-world application in a stationary energy storage system revealing the accuracy. Tests for validation are continued for up to 114 days after the longest parametrization tests. In conclusion, the model error for the cell capacity loss in the application-based tests is at the end of testing below 1% of the original cell capacity and the maximum relative model error is below 21%.« less

Prediction of the interaction between a simple moving vehicle and an infinite periodically supported rail - Green's functions approach

NASA Astrophysics Data System (ADS)

Mazilu, Traian

2010-09-01

This paper herein describes the interaction between a simple moving vehicle and an infinite periodically supported rail, in order to signalise the basic features of the vehicle/track vibration behaviour in general, and wheel/rail vibration, in particular. The rail is modelled as an infinite Timoshenko beam resting on semi-sleepers via three-directional rail pads and ballast. The time-domain analysis was performed applying Green's matrix of the track method. This method allows taking into account the nonlinearities of the wheel/rail contact and the Doppler effect. The numerical analysis is dedicated to the wheel/rail response due to two types of excitation: the steady-state interaction and rail irregularities. The study points out to certain aspects regarding the parametric resonance, the amplitude-modulated vibration due to corrugation and the Doppler effect.

Degree of coupling and efficiency of energy converters far-from-equilibrium

NASA Astrophysics Data System (ADS)

Vroylandt, Hadrien; Lacoste, David; Verley, Gatien

2018-02-01

In this paper, we introduce a real symmetric and positive semi-definite matrix, which we call the non-equilibrium conductance matrix, and which generalizes the Onsager response matrix for a system in a non-equilibrium stationary state. We then express the thermodynamic efficiency in terms of the coefficients of this matrix using a parametrization similar to the one used near equilibrium. This framework, then valid arbitrarily far from equilibrium allows to set bounds on the thermodynamic efficiency by a universal function depending only on the degree of coupling between input and output currents. It also leads to new general power-efficiency trade-offs valid for macroscopic machines that are compared to trade-offs previously obtained from uncertainty relations. We illustrate our results on an unicycle heat to heat converter and on a discrete model of a molecular motor.

Discrete element modeling of shock-induced particle jetting

NASA Astrophysics Data System (ADS)

Xue, Kun; Cui, Haoran

2018-05-01

The dispersal of particle shell or ring by divergent impulsive loads takes the form of coherent particle jets with the dimensions several orders larger than that of constituent grain. Particle-scale simulations based on the discrete element method have been carried out to reveal the evolution of jets in semi-two-dimensional rings before they burst out of the external surface. We identify two key events which substantially change the resulted jetting pattern, specifically, the annihilation of incipient jets and the tip-slipping of jets, which become active in different phases of jet evolution. Parametric investigations have been done to assess the correlations between the jetting pattern and a variety of structural parameters. Overpressure, the internal and outer diameters of ring as well as the packing density are found to have effects on the jet evolution with different relative importance.

A robust approach for analysing dispersion of elastic waves in an orthotropic cylindrical shell

NASA Astrophysics Data System (ADS)

Kaplunov, J.; Nobili, A.

2017-08-01

Dispersion of elastic waves in a thin orthotropic cylindrical shell is considered, within the framework of classical 2D Kirchhoff-Love theory. In contrast to direct multi-parametric analysis of the lowest propagating modes, an alternative robust approach is proposed that simply requires evaluation of the evanescent modes (quasi-static edge effect), which, at leading order, do not depend on vibration frequency. A shortened dispersion relation for the propagating modes is then derived by polynomial division and its accuracy is numerically tested against the full Kirchhoff-Love dispersion relation. It is shown that the same shortened relation may be also obtained from a refined dynamic version of the semi-membrane theory for cylindrical shells. The presented results may be relevant for modelling various types of nanotubes which, according to the latest experimental findings, possess strong material anisotropy.

Ultra-fast computation of electronic spectra for large systems by tight-binding based simplified Tamm-Dancoff approximation (sTDA-xTB)

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

Grimme, Stefan, E-mail: grimme@thch.uni-bonn.de; Bannwarth, Christoph

2016-08-07

The computational bottleneck of the extremely fast simplified Tamm-Dancoff approximated (sTDA) time-dependent density functional theory procedure [S. Grimme, J. Chem. Phys. 138, 244104 (2013)] for the computation of electronic spectra for large systems is the determination of the ground state Kohn-Sham orbitals and eigenvalues. This limits such treatments to single structures with a few hundred atoms and hence, e.g., sampling along molecular dynamics trajectories for flexible systems or the calculation of chromophore aggregates is often not possible. The aim of this work is to solve this problem by a specifically designed semi-empirical tight binding (TB) procedure similar to the wellmore » established self-consistent-charge density functional TB scheme. The new special purpose method provides orbitals and orbital energies of hybrid density functional character for a subsequent and basically unmodified sTDA procedure. Compared to many previous semi-empirical excited state methods, an advantage of the ansatz is that a general eigenvalue problem in a non-orthogonal, extended atomic orbital basis is solved and therefore correct occupied/virtual orbital energy splittings as well as Rydberg levels are obtained. A key idea for the success of the new model is that the determination of atomic charges (describing an effective electron-electron interaction) and the one-particle spectrum is decoupled and treated by two differently parametrized Hamiltonians/basis sets. The three-diagonalization-step composite procedure can routinely compute broad range electronic spectra (0-8 eV) within minutes of computation time for systems composed of 500-1000 atoms with an accuracy typical of standard time-dependent density functional theory (0.3-0.5 eV average error). An easily extendable parametrization based on coupled-cluster and density functional computed reference data for the elements H–Zn including transition metals is described. The accuracy of the method termed sTDA-xTB is first benchmarked for vertical excitation energies of open- and closed-shell systems in comparison to other semi-empirical methods and applied to exemplary problems in electronic spectroscopy. As side products of the development, a robust and efficient valence electron TB method for the accurate determination of atomic charges as well as a more accurate calculation scheme of dipole rotatory strengths within the Tamm-Dancoff approximation is proposed.« less

Parametric Mass Modeling for Mars Entry, Descent and Landing System Analysis Study

NASA Technical Reports Server (NTRS)

Samareh, Jamshid A.; Komar, D. R.

2011-01-01

This paper provides an overview of the parametric mass models used for the Entry, Descent, and Landing Systems Analysis study conducted by NASA in FY2009-2010. The study examined eight unique exploration class architectures that included elements such as a rigid mid-L/D aeroshell, a lifting hypersonic inflatable decelerator, a drag supersonic inflatable decelerator, a lifting supersonic inflatable decelerator implemented with a skirt, and subsonic/supersonic retro-propulsion. Parametric models used in this study relate the component mass to vehicle dimensions and mission key environmental parameters such as maximum deceleration and total heat load. The use of a parametric mass model allows the simultaneous optimization of trajectory and mass sizing parameters.

Parametric Cost Models for Space Telescopes

NASA Technical Reports Server (NTRS)

Stahl, H. Philip

2010-01-01

A study is in-process to develop a multivariable parametric cost model for space telescopes. Cost and engineering parametric data has been collected on 30 different space telescopes. Statistical correlations have been developed between 19 variables of 59 variables sampled. Single Variable and Multi-Variable Cost Estimating Relationships have been developed. Results are being published.

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.

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.

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

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.

On the unification of nuclear-structure theory: A response to Bortignon and Broglia

NASA Astrophysics Data System (ADS)

Cook, Norman D.

2016-09-01

Nuclear-structure theory is unusual among the diverse fields of quantum physics. Although it provides a coherent description of all known isotopes on the basis of a quantum-mechanical understanding of nucleon states, nevertheless, in the absence of a fundamental theory of the nuclear force acting between nucleons, the prediction of all ground-state and excited-state nuclear binding energies is inherently semi-empirical. I suggest that progress can be made by returning to the foundational work of Eugene Wigner from 1937, where the mathematical symmetries of nucleon states were first defined. Those symmetries were later successfully exploited in the development of the independent-particle model ( IPM ˜ shell model , but the geometrical implications noted by Wigner were neglected. Here I review how the quantum-mechanical, but remarkably easy-to-understand geometrical interpretation of the IPM provides constraints on the parametrization of the nuclear force. The proposed "geometrical IPM" indicates a way forward toward the unification of nuclear-structure theory that Bortignon and Broglia have called for.

Methods of Stochastic Analysis of Complex Regimes in the 3D Hindmarsh-Rose Neuron Model

NASA Astrophysics Data System (ADS)

Bashkirtseva, Irina; Ryashko, Lev; Slepukhina, Evdokia

A problem of the stochastic nonlinear analysis of neuronal activity is studied by the example of the Hindmarsh-Rose (HR) model. For the parametric region of tonic spiking oscillations, it is shown that random noise transforms the spiking dynamic regime into the bursting one. This stochastic phenomenon is specified by qualitative changes in distributions of random trajectories and interspike intervals (ISIs). For a quantitative analysis of the noise-induced bursting, we suggest a constructive semi-analytical approach based on the stochastic sensitivity function (SSF) technique and the method of confidence domains that allows us to describe geometrically a distribution of random states around the deterministic attractors. Using this approach, we develop a new algorithm for estimation of critical values for the noise intensity corresponding to the qualitative changes in stochastic dynamics. We show that the obtained estimations are in good agreement with the numerical results. An interplay between noise-induced bursting and transitions from order to chaos is discussed.

Parametric models of reflectance spectra for dyed fabrics

NASA Astrophysics Data System (ADS)

Aiken, Daniel C.; Ramsey, Scott; Mayo, Troy; Lambrakos, Samuel G.; Peak, Joseph

2016-05-01

This study examines parametric modeling of NIR reflectivity spectra for dyed fabrics, which provides for both their inverse and direct modeling. The dye considered for prototype analysis is triarylamine dye. The fabrics considered are camouflage textiles characterized by color variations. The results of this study provide validation of the constructed parametric models, within reasonable error tolerances for practical applications, including NIR spectral characteristics in camouflage textiles, for purposes of simulating NIR spectra corresponding to various dye concentrations in host fabrics, and potentially to mixtures of dyes.

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 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

Assessment of Biomarkers Associated with Joint Injury and Subsequent Post-Traumatic Arthritis

DTIC Science & Technology

2014-10-01

synovitis score with semi-quantitative scales, and osteophyte score6-10. Parametric analyses were performed for bone morphological measures and...histological assessment. Subchondral bone thickening was significantly increased in the C57BL/6 mice compared to the MRL/MpJ mice in the medial femur (p...biochemical and metabolic data. J Bone Joint Surg Am. 53:523-537. 10. Gelse K, Soder S, Eger W, Diemtar T, Aigner T. Feb 2003. Osteophyte development

Developmental Trajectories of Adaptive Behaviors from Early Childhood to Adolescence in a Cohort of 152 Children with Autism Spectrum Disorders

ERIC Educational Resources Information Center

Baghdadli, Amaria; Assouline, Brigitte; Sonie, Sandrine; Pernon, Eric; Darrou, Celine; Michelon, Cecile; Picot, Marie-Christine; Aussilloux, Charles; Pry, Rene

2012-01-01

This study examines change in 152 children over an almost 10-year period (T1: 4.9 (plus or minus 1.3) years; T2: 8.1 (plus or minus 1.3) years; T3: 15(plus or minus 1.6) years) using a group-based, semi-parametric method in order to identify distinct developmental trajectories. Important deficits remain at adolescence in the adaptive abilities of…

Novel Semi-Parametric Algorithm for Interference-Immune Tunable Absorption Spectroscopy Gas Sensing

PubMed Central

Michelucci, Umberto; Venturini, Francesca

2017-01-01

One of the most common limits to gas sensor performance is the presence of unwanted interference fringes arising, for example, from multiple reflections between surfaces in the optical path. Additionally, since the amplitude and the frequency of these interferences depend on the distance and alignment of the optical elements, they are affected by temperature changes and mechanical disturbances, giving rise to a drift of the signal. In this work, we present a novel semi-parametric algorithm that allows the extraction of a signal, like the spectroscopic absorption line of a gas molecule, from a background containing arbitrary disturbances, without having to make any assumption on the functional form of these disturbances. The algorithm is applied first to simulated data and then to oxygen absorption measurements in the presence of strong fringes.To the best of the authors’ knowledge, the algorithm enables an unprecedented accuracy particularly if the fringes have a free spectral range and amplitude comparable to those of the signal to be detected. The described method presents the advantage of being based purely on post processing, and to be of extremely straightforward implementation if the functional form of the Fourier transform of the signal is known. Therefore, it has the potential to enable interference-immune absorption spectroscopy. Finally, its relevance goes beyond absorption spectroscopy for gas sensing, since it can be applied to any kind of spectroscopic data. PMID:28991161

A Robust Semi-Parametric Test for Detecting Trait-Dependent Diversification.

PubMed

Rabosky, Daniel L; Huang, Huateng

2016-03-01

Rates of species diversification vary widely across the tree of life and there is considerable interest in identifying organismal traits that correlate with rates of speciation and extinction. However, it has been challenging to develop methodological frameworks for testing hypotheses about trait-dependent diversification that are robust to phylogenetic pseudoreplication and to directionally biased rates of character change. We describe a semi-parametric test for trait-dependent diversification that explicitly requires replicated associations between character states and diversification rates to detect effects. To use the method, diversification rates are reconstructed across a phylogenetic tree with no consideration of character states. A test statistic is then computed to measure the association between species-level traits and the corresponding diversification rate estimates at the tips of the tree. The empirical value of the test statistic is compared to a null distribution that is generated by structured permutations of evolutionary rates across the phylogeny. The test is applicable to binary discrete characters as well as continuous-valued traits and can accommodate extremely sparse sampling of character states at the tips of the tree. We apply the test to several empirical data sets and demonstrate that the method has acceptable Type I error rates. © The Author(s) 2015. Published by Oxford University Press, on behalf of the Society of Systematic Biologists. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Model risk for European-style stock index options.

PubMed

Gençay, Ramazan; Gibson, Rajna

2007-01-01

In empirical modeling, there have been two strands for pricing in the options literature, namely the parametric and nonparametric models. Often, the support for the nonparametric methods is based on a benchmark such as the Black-Scholes (BS) model with constant volatility. In this paper, we study the stochastic volatility (SV) and stochastic volatility random jump (SVJ) models as parametric benchmarks against feedforward neural network (FNN) models, a class of neural network models. Our choice for FNN models is due to their well-studied universal approximation properties of an unknown function and its partial derivatives. Since the partial derivatives of an option pricing formula are risk pricing tools, an accurate estimation of the unknown option pricing function is essential for pricing and hedging. Our findings indicate that FNN models offer themselves as robust option pricing tools, over their sophisticated parametric counterparts in predictive settings. There are two routes to explain the superiority of FNN models over the parametric models in forecast settings. These are nonnormality of return distributions and adaptive learning.

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…

Comparing of Cox model and parametric models in analysis of effective factors on event time of neuropathy in patients with type 2 diabetes.

PubMed

Kargarian-Marvasti, Sadegh; Rimaz, Shahnaz; Abolghasemi, Jamileh; Heydari, Iraj

2017-01-01

Cox proportional hazard model is the most common method for analyzing the effects of several variables on survival time. However, under certain circumstances, parametric models give more precise estimates to analyze survival data than Cox. The purpose of this study was to investigate the comparative performance of Cox and parametric models in a survival analysis of factors affecting the event time of neuropathy in patients with type 2 diabetes. This study included 371 patients with type 2 diabetes without neuropathy who were registered at Fereydunshahr diabetes clinic. Subjects were followed up for the development of neuropathy between 2006 to March 2016. To investigate the factors influencing the event time of neuropathy, significant variables in univariate model ( P < 0.20) were entered into the multivariate Cox and parametric models ( P < 0.05). In addition, Akaike information criterion (AIC) and area under ROC curves were used to evaluate the relative goodness of fitted model and the efficiency of each procedure, respectively. Statistical computing was performed using R software version 3.2.3 (UNIX platforms, Windows and MacOS). Using Kaplan-Meier, survival time of neuropathy was computed 76.6 ± 5 months after initial diagnosis of diabetes. After multivariate analysis of Cox and parametric models, ethnicity, high-density lipoprotein and family history of diabetes were identified as predictors of event time of neuropathy ( P < 0.05). According to AIC, "log-normal" model with the lowest Akaike's was the best-fitted model among Cox and parametric models. According to the results of comparison of survival receiver operating characteristics curves, log-normal model was considered as the most efficient and fitted model.

Semi-analytical modelling of positive corona discharge in air

NASA Astrophysics Data System (ADS)

Pontiga, Francisco; Yanallah, Khelifa; Chen, Junhong

2013-09-01

Semianalytical approximate solutions of the spatial distribution of electric field and electron and ion densities have been obtained by solving Poisson's equations and the continuity equations for the charged species along the Laplacian field lines. The need to iterate for the correct value of space charge on the corona electrode has been eliminated by using the corona current distribution over the grounded plane derived by Deutsch, which predicts a cos m θ law similar to Warburg's law. Based on the results of the approximated model, a parametric study of the influence of gas pressure, the corona wire radius, and the inter-electrode wire-plate separation has been carried out. Also, the approximate solutions of the electron number density has been combined with a simplified plasma chemistry model in order to compute the ozone density generated by the corona discharge in the presence of a gas flow. This work was supported by the Consejeria de Innovacion, Ciencia y Empresa (Junta de Andalucia) and by the Ministerio de Ciencia e Innovacion, Spain, within the European Regional Development Fund contracts FQM-4983 and FIS2011-25161.

A Bayesian approach for estimating under-reported dengue incidence with a focus on non-linear associations between climate and dengue in Dhaka, Bangladesh.

PubMed

Sharmin, Sifat; Glass, Kathryn; Viennet, Elvina; Harley, David

2018-04-01

Determining the relation between climate and dengue incidence is challenging due to under-reporting of disease and consequent biased incidence estimates. Non-linear associations between climate and incidence compound this. Here, we introduce a modelling framework to estimate dengue incidence from passive surveillance data while incorporating non-linear climate effects. We estimated the true number of cases per month using a Bayesian generalised linear model, developed in stages to adjust for under-reporting. A semi-parametric thin-plate spline approach was used to quantify non-linear climate effects. The approach was applied to data collected from the national dengue surveillance system of Bangladesh. The model estimated that only 2.8% (95% credible interval 2.7-2.8) of all cases in the capital Dhaka were reported through passive case reporting. The optimal mean monthly temperature for dengue transmission is 29℃ and average monthly rainfall above 15 mm decreases transmission. Our approach provides an estimate of true incidence and an understanding of the effects of temperature and rainfall on dengue transmission in Dhaka, Bangladesh.

Parametrically Guided Generalized Additive Models with Application to Mergers and Acquisitions Data

PubMed Central

Fan, Jianqing; Maity, Arnab; Wang, Yihui; Wu, Yichao

2012-01-01

Generalized nonparametric additive models present a flexible way to evaluate the effects of several covariates on a general outcome of interest via a link function. In this modeling framework, one assumes that the effect of each of the covariates is nonparametric and additive. However, in practice, often there is prior information available about the shape of the regression functions, possibly from pilot studies or exploratory analysis. In this paper, we consider such situations and propose an estimation procedure where the prior information is used as a parametric guide to fit the additive model. Specifically, we first posit a parametric family for each of the regression functions using the prior information (parametric guides). After removing these parametric trends, we then estimate the remainder of the nonparametric functions using a nonparametric generalized additive model, and form the final estimates by adding back the parametric trend. We investigate the asymptotic properties of the estimates and show that when a good guide is chosen, the asymptotic variance of the estimates can be reduced significantly while keeping the asymptotic variance same as the unguided estimator. We observe the performance of our method via a simulation study and demonstrate our method by applying to a real data set on mergers and acquisitions. PMID:23645976

Parametrically Guided Generalized Additive Models with Application to Mergers and Acquisitions Data.

PubMed

Fan, Jianqing; Maity, Arnab; Wang, Yihui; Wu, Yichao

2013-01-01

Generalized nonparametric additive models present a flexible way to evaluate the effects of several covariates on a general outcome of interest via a link function. In this modeling framework, one assumes that the effect of each of the covariates is nonparametric and additive. However, in practice, often there is prior information available about the shape of the regression functions, possibly from pilot studies or exploratory analysis. In this paper, we consider such situations and propose an estimation procedure where the prior information is used as a parametric guide to fit the additive model. Specifically, we first posit a parametric family for each of the regression functions using the prior information (parametric guides). After removing these parametric trends, we then estimate the remainder of the nonparametric functions using a nonparametric generalized additive model, and form the final estimates by adding back the parametric trend. We investigate the asymptotic properties of the estimates and show that when a good guide is chosen, the asymptotic variance of the estimates can be reduced significantly while keeping the asymptotic variance same as the unguided estimator. We observe the performance of our method via a simulation study and demonstrate our method by applying to a real data set on mergers and acquisitions.

Examining deterrence of adult sex crimes: A semi-parametric intervention time series approach.

PubMed

Park, Jin-Hong; Bandyopadhyay, Dipankar; Letourneau, Elizabeth

2014-01-01

Motivated by recent developments on dimension reduction (DR) techniques for time series data, the association of a general deterrent effect towards South Carolina (SC)'s registration and notification (SORN) policy for preventing sex crimes was examined. Using adult sex crime arrestee data from 1990 to 2005, the the idea of Central Mean Subspace (CMS) is extended to intervention time series analysis (CMS-ITS) to model the sequential intervention effects of 1995 (the year SC's SORN policy was initially implemented) and 1999 (the year the policy was revised to include online notification) on the time series spectrum. The CMS-ITS model estimation was achieved via kernel smoothing techniques, and compared to interrupted auto-regressive integrated time series (ARIMA) models. Simulation studies and application to the real data underscores our model's ability towards achieving parsimony, and to detect intervention effects not earlier determined via traditional ARIMA models. From a public health perspective, findings from this study draw attention to the potential general deterrent effects of SC's SORN policy. These findings are considered in light of the overall body of research on sex crime arrestee registration and notification policies, which remain controversial.

Testing in Microbiome-Profiling Studies with MiRKAT, the Microbiome Regression-Based Kernel Association Test

PubMed Central

Zhao, Ni; Chen, Jun; Carroll, Ian M.; Ringel-Kulka, Tamar; Epstein, Michael P.; Zhou, Hua; Zhou, Jin J.; Ringel, Yehuda; Li, Hongzhe; Wu, Michael C.

2015-01-01

High-throughput sequencing technology has enabled population-based studies of the role of the human microbiome in disease etiology and exposure response. Distance-based analysis is a popular strategy for evaluating the overall association between microbiome diversity and outcome, wherein the phylogenetic distance between individuals’ microbiome profiles is computed and tested for association via permutation. Despite their practical popularity, distance-based approaches suffer from important challenges, especially in selecting the best distance and extending the methods to alternative outcomes, such as survival outcomes. We propose the microbiome regression-based kernel association test (MiRKAT), which directly regresses the outcome on the microbiome profiles via the semi-parametric kernel machine regression framework. MiRKAT allows for easy covariate adjustment and extension to alternative outcomes while non-parametrically modeling the microbiome through a kernel that incorporates phylogenetic distance. It uses a variance-component score statistic to test for the association with analytical p value calculation. The model also allows simultaneous examination of multiple distances, alleviating the problem of choosing the best distance. Our simulations demonstrated that MiRKAT provides correctly controlled type I error and adequate power in detecting overall association. “Optimal” MiRKAT, which considers multiple candidate distances, is robust in that it suffers from little power loss in comparison to when the best distance is used and can achieve tremendous power gain in comparison to when a poor distance is chosen. Finally, we applied MiRKAT to real microbiome datasets to show that microbial communities are associated with smoking and with fecal protease levels after confounders are controlled for. PMID:25957468

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.

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.

Modeling Nonstationary Emotion Dynamics in Dyads using a Time-Varying Vector-Autoregressive Model.

PubMed

Bringmann, Laura F; Ferrer, Emilio; Hamaker, Ellen L; Borsboom, Denny; Tuerlinckx, Francis

2018-01-01

Emotion dynamics are likely to arise in an interpersonal context. Standard methods to study emotions in interpersonal interaction are limited because stationarity is assumed. This means that the dynamics, for example, time-lagged relations, are invariant across time periods. However, this is generally an unrealistic assumption. Whether caused by an external (e.g., divorce) or an internal (e.g., rumination) event, emotion dynamics are prone to change. The semi-parametric time-varying vector-autoregressive (TV-VAR) model is based on well-studied generalized additive models, implemented in the software R. The TV-VAR can explicitly model changes in temporal dependency without pre-existing knowledge about the nature of change. A simulation study is presented, showing that the TV-VAR model is superior to the standard time-invariant VAR model when the dynamics change over time. The TV-VAR model is applied to empirical data on daily feelings of positive affect (PA) from a single couple. Our analyses indicate reliable changes in the male's emotion dynamics over time, but not in the female's-which were not predicted by her own affect or that of her partner. This application illustrates the usefulness of using a TV-VAR model to detect changes in the dynamics in a system.

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.

Application of Archimedean copulas to the impact assessment of hydro-climatic variables in semi-arid aquifers of western India

NASA Astrophysics Data System (ADS)

Wable, Pawan S.; Jha, Madan K.

2018-02-01

The effects of rainfall and the El Niño Southern Oscillation (ENSO) on groundwater in a semi-arid basin of India were analyzed using Archimedean copulas considering 17 years of data for monsoon rainfall, post-monsoon groundwater level (PMGL) and ENSO Index. The evaluated dependence among these hydro-climatic variables revealed that PMGL-Rainfall and PMGL-ENSO Index pairs have significant dependence. Hence, these pairs were used for modeling dependence by employing four types of Archimedean copulas: Ali-Mikhail-Haq, Clayton, Gumbel-Hougaard, and Frank. For the copula modeling, the results of probability distributions fitting to these hydro-climatic variables indicated that the PMGL and rainfall time series are best represented by Weibull and lognormal distributions, respectively, while the non-parametric kernel-based normal distribution is the most suitable for the ENSO Index. Further, the PMGL-Rainfall pair is best modeled by the Clayton copula, and the PMGL-ENSO Index pair is best modeled by the Frank copula. The Clayton copula-based conditional probability of PMGL being less than or equal to its average value at a given mean rainfall is above 70% for 33% of the study area. In contrast, the spatial variation of the Frank copula-based probability of PMGL being less than or equal to its average value is 35-40% in 23% of the study area during El Niño phase, while it is below 15% in 35% of the area during the La Niña phase. This copula-based methodology can be applied under data-scarce conditions for exploring the impacts of rainfall and ENSO on groundwater at basin scales.

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.

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.

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.

Resolving the inner disk of UX Orionis

NASA Astrophysics Data System (ADS)

Kreplin, A.; Madlener, D.; Chen, L.; Weigelt, G.; Kraus, S.; Grinin, V.; Tambovtseva, L.; Kishimoto, M.

2016-05-01

Aims: The cause of the UX Ori variability in some Herbig Ae/Be stars is still a matter of debate. Detailed studies of the circumstellar environment of UX Ori objects (UXORs) are required to test the hypothesis that the observed drop in photometry might be related to obscuration events. Methods: Using near- and mid-infrared interferometric AMBER and MIDI observations, we resolved the inner circumstellar disk region around UX Ori. Results: We fitted the K-, H-, and N-band visibilities and the spectral energy distribution (SED) of UX Ori with geometric and parametric disk models. The best-fit K-band geometric model consists of an inclined ring and a halo component. We obtained a ring-fit radius of 0.45 ± 0.07 AU (at a distance of 460 pc), an inclination of 55.6 ± 2.4°, a position angle of the system axis of 127.5 ± 24.5°, and a flux contribution of the over-resolved halo component to the total near-infrared excess of 16.8 ± 4.1%. The best-fit N-band model consists of an elongated Gaussian with a HWHM ~ 5 AU of the semi-major axis and an axis ration of a/b ~ 3.4 (corresponding to an inclination of ~72°). With a parametric disk model, we fitted all near- and mid-infrared visibilities and the SED simultaneously. The model disk starts at an inner radius of 0.46 ± 0.06 AU with an inner rim temperature of 1498 ± 70 K. The disk is seen under an nearly edge-on inclination of 70 ± 5°. This supports any theories that require high-inclination angles to explain obscuration events in the line of sight to the observer, for example, in UX Ori objects where orbiting dust clouds in the disk or disk atmosphere can obscure the central star. Based on observations made with ESO telescopes at Paranal Observatory under program IDs: 090.C-0769, 074.C-0552.

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

Simplified numerical approach for estimation of effective segregation coefficient at the melt/crystal interface

NASA Astrophysics Data System (ADS)

Prostomolotov, A. I.; Verezub, N. A.; Voloshin, A. E.

2014-09-01

A thermo-gravitational convection and impurity transfer in the melt were investigated using a simplified numerical model for Bridgman GaSb(Te) crystal growth in microgravity conditions. Simplifications were as follows: flat melt/crystal interface, fixed melt sizes and only lateral ampoule heating. Calculations were carried out by Ansys®Fluent® code employing a two-dimensional Navier-Stokes-Boussinesq and heat and mass transfer equations in a coordinate system moving with the melt/crystal interface. The parametric dependence of the effective segregation coefficient Keff at the melt/crystal interface was studied for various ampoule sizes and for microgravity conditions. For the uprising one-vortex flow, the resulting dependences were presented as Keff vs. Vmax-the maximum velocity value. These dependences were compared with the formulas by Burton-Prim-Slichter's, Ostrogorsky-Muller's, as well as with the semi-analytical solutions.

Band gap narrowing in n-type and p-type 3C-, 2H-, 4H-, 6H-SiC, and Si

NASA Astrophysics Data System (ADS)

Persson, C.; Lindefelt, U.; Sernelius, B. E.

1999-10-01

Doping-induced energy shifts of the conduction band minimum and the valence band maximum have been calculated for n-type and p-type 3C-, 2H-, 4H-, 6H-SiC, and Si. The narrowing of the fundamental band gap and of the optical band gap are presented as functions of ionized impurity concentration. The calculations go beyond the common parabolic treatments of the ground state energy dispersion by using energy dispersion and overlap integrals from band structure calculations. The nonparabolic valence band curvatures influence strongly the energy shifts especially in p-type materials. The utilized method is based on a zero-temperature Green's function formalism within the random phase approximation with local field correction according to Hubbard. We have parametrized the shifts of the conduction and the valence bands and made comparisons with recently published results from a semi-empirical model.

Evaluation d'un ecosysteme pastoral sahelien: Apport de la geomatique (Oursi, Burkina Faso)

NASA Astrophysics Data System (ADS)

Kabore, Seraphine Sawadogo

L'objectif principal de cette recherche est la mise au point d'une architecture d'integration de donnees socio-bio-geographiques et de donnees satellitales dans un Systeme d'Information Geographique (SIG) en vue d'une aide a la prise de decisions dans un environnement semi-aride au nord du Burkina Faso. Elle repond a la question fondamentale de l'interpretation des effets des facteurs climatiques et socioeconomiques sur le milieu pastoral. La recherche s'est appuyee sur plusieurs hypotheses de travail: possibilite d'utilisation de modele de simulation, d'approche multicritere et de donnees de teledetection dans un cadre de systeme d'information geographique. L'evolution spatiotemporelle des parametres de productivite du milieu a ete evaluee par approche dynamique selon le modele de Wu et al. (1996) qui modelise les interactions entre le climat, le milieu physique, le vegetal et l'animal pour mieux quantifier la biomasse primaire. A ce modele, quatre parametres ont ete integres par approche floue et multicritere afin de prendre en compte la dimension socioeconomique de la productivite pastorale (apport majeur de la recherche): la sante, l'education, l'agriculture et l'eau. La teledetection (imagerie SPOT) a permis de definir la production primaire a partir de laquelle les simulations ont ete realisees sur 10 annees. Les resultats obtenus montrent une bonne correlation entre biomasse primaire in situ et celle calculee pour les deux modeles, avec toutefois une meilleure efficacite du modele modifie (4 fois plus) dans les zones de forte productivite ou l'on note un taux de surexploitation agricole eleve. A cause de la variabilite spatiale de la production primaire in situ, les erreurs des resultats de simulation (8 a 11%) sont acceptables et montrent la pertinence de l'approche grace a l'utilisation des SIG pour la spatialisation et l'integration des differents parametres des modeles. Les types de production secondaire preconises (production de lait pendant 7 mois ou de viande pendant 6 mois) sont bases sur les besoins de l'UBT et le disponible fourrager qui est de qualite mediocre en saison seche. Dans les deux cas de figure, un deficit fourrager est observe. Deux types de transhumance sont proposes afin d'assurer une production durable selon deux scenarios: exploitation rationnelle des unites pastorales selon un plan de rotation annuelle et mise en defens a moyen terme des zones degradees pour une regeneration. Les zones potentielles pour la transhumance ont ete determinees selon les limites acceptables des criteres d'exploitation durable des milieux saheliens definis par Kessler (1994) soit 0,2 UBT.ha-1.

Morphing wing structure with controllable twist based on adaptive bending-twist coupling

NASA Astrophysics Data System (ADS)

Raither, Wolfram; Heymanns, Matthias; Bergamini, Andrea; Ermanni, Paolo

2013-06-01

A novel semi-passive morphing airfoil concept based on variable bending-twist coupling induced by adaptive shear center location and torsional stiffness is presented. Numerical parametric studies and upscaling show that the concept relying on smart materials permits effective twist control while offering the potential of being lightweight and energy efficient. By means of an experimental characterization of an adaptive beam and a scaled adaptive wing structure, effectiveness and producibility of the structural concept are demonstrated.

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.

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.

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.

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.

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.

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.

Probing kinematics and fate of the Universe with linearly time-varying deceleration parameter

NASA Astrophysics Data System (ADS)

Akarsu, Özgür; Dereli, Tekin; Kumar, Suresh; Xu, Lixin

2014-02-01

The parametrizations q = q 0+ q 1 z and q = q 0+ q 1(1 - a/ a 0) (Chevallier-Polarski-Linder parametrization) of the deceleration parameter, which are linear in cosmic redshift z and scale factor a , have been frequently utilized in the literature to study the kinematics of the Universe. In this paper, we follow a strategy that leads to these two well-known parametrizations of the deceleration parameter as well as an additional new parametrization, q = q 0+ q 1(1 - t/ t 0), which is linear in cosmic time t. We study the features of this linearly time-varying deceleration parameter in contrast with the other two linear parametrizations. We investigate in detail the kinematics of the Universe by confronting the three models with the latest observational data. We further study the dynamics of the Universe by considering the linearly time-varying deceleration parameter model in comparison with the standard ΛCDM model. We also discuss the future of the Universe in the context of the models under consideration.

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.

Tensor models, Kronecker coefficients and permutation centralizer algebras

NASA Astrophysics Data System (ADS)

Geloun, Joseph Ben; Ramgoolam, Sanjaye

2017-11-01

We show that the counting of observables and correlators for a 3-index tensor model are organized by the structure of a family of permutation centralizer algebras. These algebras are shown to be semi-simple and their Wedderburn-Artin decompositions into matrix blocks are given in terms of Clebsch-Gordan coefficients of symmetric groups. The matrix basis for the algebras also gives an orthogonal basis for the tensor observables which diagonalizes the Gaussian two-point functions. The centres of the algebras are associated with correlators which are expressible in terms of Kronecker coefficients (Clebsch-Gordan multiplicities of symmetric groups). The color-exchange symmetry present in the Gaussian model, as well as a large class of interacting models, is used to refine the description of the permutation centralizer algebras. This discussion is extended to a general number of colors d: it is used to prove the integrality of an infinite family of number sequences related to color-symmetrizations of colored graphs, and expressible in terms of symmetric group representation theory data. Generalizing a connection between matrix models and Belyi maps, correlators in Gaussian tensor models are interpreted in terms of covers of singular 2-complexes. There is an intriguing difference, between matrix and higher rank tensor models, in the computational complexity of superficially comparable correlators of observables parametrized by Young diagrams.

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.

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.

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.

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.

A modular approach to large-scale design optimization of aerospace systems

NASA Astrophysics Data System (ADS)

Hwang, John T.

Gradient-based optimization and the adjoint method form a synergistic combination that enables the efficient solution of large-scale optimization problems. Though the gradient-based approach struggles with non-smooth or multi-modal problems, the capability to efficiently optimize up to tens of thousands of design variables provides a valuable design tool for exploring complex tradeoffs and finding unintuitive designs. However, the widespread adoption of gradient-based optimization is limited by the implementation challenges for computing derivatives efficiently and accurately, particularly in multidisciplinary and shape design problems. This thesis addresses these difficulties in two ways. First, to deal with the heterogeneity and integration challenges of multidisciplinary problems, this thesis presents a computational modeling framework that solves multidisciplinary systems and computes their derivatives in a semi-automated fashion. This framework is built upon a new mathematical formulation developed in this thesis that expresses any computational model as a system of algebraic equations and unifies all methods for computing derivatives using a single equation. The framework is applied to two engineering problems: the optimization of a nanosatellite with 7 disciplines and over 25,000 design variables; and simultaneous allocation and mission optimization for commercial aircraft involving 330 design variables, 12 of which are integer variables handled using the branch-and-bound method. In both cases, the framework makes large-scale optimization possible by reducing the implementation effort and code complexity. The second half of this thesis presents a differentiable parametrization of aircraft geometries and structures for high-fidelity shape optimization. Existing geometry parametrizations are not differentiable, or they are limited in the types of shape changes they allow. This is addressed by a novel parametrization that smoothly interpolates aircraft components, providing differentiability. An unstructured quadrilateral mesh generation algorithm is also developed to automate the creation of detailed meshes for aircraft structures, and a mesh convergence study is performed to verify that the quality of the mesh is maintained as it is refined. As a demonstration, high-fidelity aerostructural analysis is performed for two unconventional configurations with detailed structures included, and aerodynamic shape optimization is applied to the truss-braced wing, which finds and eliminates a shock in the region bounded by the struts and the wing.

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...

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.

Parametric modeling studies of turbulent non-premixed jet flames with thin reaction zones

NASA Astrophysics Data System (ADS)

Wang, Haifeng

2013-11-01

The Sydney piloted jet flame series (Flames L, B, and M) feature thinner reaction zones and hence impose greater challenges to modeling than the Sanida Piloted jet flames (Flames D, E, and F). Recently, the Sydney flames received renewed interest due to these challenges. Several new modeling efforts have emerged. However, no systematic parametric modeling studies have been reported for the Sydney flames. A large set of modeling computations of the Sydney flames is presented here by using the coupled large eddy simulation (LES)/probability density function (PDF) method. Parametric studies are performed to gain insight into the model performance, its sensitivity and the effect of numerics.

Parametric estimation for reinforced concrete relief shelter for Aceh cases

NASA Astrophysics Data System (ADS)

Atthaillah; Saputra, Eri; Iqbal, Muhammad

2018-05-01

This paper was a work in progress (WIP) to discover a rapid parametric framework for post-disaster permanent shelter’s materials estimation. The intended shelters were reinforced concrete construction with bricks as its wall. Inevitably, in post-disaster cases, design variations were needed to help suited victims condition. It seemed impossible to satisfy a beneficiary with a satisfactory design utilizing the conventional method. This study offered a parametric framework to overcome slow construction-materials estimation issue against design variations. Further, this work integrated parametric tool, which was Grasshopper to establish algorithms that simultaneously model, visualize, calculate and write the calculated data to a spreadsheet in a real-time. Some customized Grasshopper components were created using GHPython scripting for a more optimized algorithm. The result from this study was a partial framework that successfully performed modeling, visualization, calculation and writing the calculated data simultaneously. It meant design alterations did not escalate time needed for modeling, visualization, and material estimation. Further, the future development of the parametric framework will be made open source.

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.

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.

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.

Implications of heterogeneous impacts of protected areas on deforestation and poverty

PubMed Central

Hanauer, Merlin M.; Canavire-Bacarreza, Gustavo

2015-01-01

Protected areas are a popular policy instrument in the global fight against loss of biodiversity and ecosystem services. However, the effectiveness of protected areas in preventing deforestation, and their impacts on poverty, are not well understood. Recent studies have found that Bolivia's protected-area system, on average, reduced deforestation and poverty. We implement several non-parametric and semi-parametric econometric estimators to characterize the heterogeneity in Bolivia's protected-area impacts on joint deforestation and poverty outcomes across a number of socioeconomic and biophysical moderators. Like previous studies from Costa Rica and Thailand, we find that Bolivia's protected areas are not associated with poverty traps. Our results also indicate that protection did not have a differential impact on indigenous populations. However, results from new multidimensional non-parametric estimators provide evidence that the biophysical characteristics associated with the greatest avoided deforestation are the characteristics associated with the potential for poverty exacerbation from protection. We demonstrate that these results would not be identified using the methods implemented in previous studies. Thus, this study provides valuable practical information on the impacts of Bolivia's protected areas for conservation practitioners and demonstrates methods that are likely to be valuable to researchers interested in better understanding the heterogeneity in conservation impacts. PMID:26460125

Implications of heterogeneous impacts of protected areas on deforestation and poverty.

PubMed

Hanauer, Merlin M; Canavire-Bacarreza, Gustavo

2015-11-05

Protected areas are a popular policy instrument in the global fight against loss of biodiversity and ecosystem services. However, the effectiveness of protected areas in preventing deforestation, and their impacts on poverty, are not well understood. Recent studies have found that Bolivia's protected-area system, on average, reduced deforestation and poverty. We implement several non-parametric and semi-parametric econometric estimators to characterize the heterogeneity in Bolivia's protected-area impacts on joint deforestation and poverty outcomes across a number of socioeconomic and biophysical moderators. Like previous studies from Costa Rica and Thailand, we find that Bolivia's protected areas are not associated with poverty traps. Our results also indicate that protection did not have a differential impact on indigenous populations. However, results from new multidimensional non-parametric estimators provide evidence that the biophysical characteristics associated with the greatest avoided deforestation are the characteristics associated with the potential for poverty exacerbation from protection. We demonstrate that these results would not be identified using the methods implemented in previous studies. Thus, this study provides valuable practical information on the impacts of Bolivia's protected areas for conservation practitioners and demonstrates methods that are likely to be valuable to researchers interested in better understanding the heterogeneity in conservation impacts. © 2015 The Author(s).

Secondary Circulations between an isolated semi-arid Pine Forest and the surrounding Shrub Land: A LES Study

NASA Astrophysics Data System (ADS)

Kroeniger, K.; De Roo, F.; Huq, S.; Brugger, P.; Mauder, M.

2017-12-01

Afforestation in semi-arid regions is a topic of growing interest in times of changing climate. However, due to the extreme environmental conditions in these regions, survival of artificial eco-systems is not guaranteed a priori and therefore needs to be investigated. As an example of an artificially planted semi-arid forest, we study the Israeli pine forest Yatir, which is located at the northern edge of the Negev desert. The coincidence of large global radiation with the low forest albedo results in net radiation intensities of more than 700 W m-2, requiring an efficient mechanism of heat removal, indispensable for tree survival. In the distinct dry summer periods evaporative cooling becomes negligible, while a substantially enhanced sensible heat flux removes the largest part of the incoming energy load. This enhanced sensible heat flux above the forest is believed to be amplified by the albedo difference between the forest and the surrounding shrub land, generating secondary circulations between these two eco-systems. The focus of our work is studying the interconnection of enhanced sensible heat flux and secondary circulations in varying conditions. We perform large eddy simulations (LES) for a region that is sufficiently large to model the forest and parts of the surrounding shrub land (20 km x 20 km) with a grid resolution of 2.5 m. This fine resolution allows us to explicitly resolve the forest canopy, to obtain more realistic results than by applying roughness length parametrizations. The LES are initialized by measured data from several field campaigns, eddy covariance towers and satellite images. By varying the background wind and the underlying topography in several LES, we study the influence of those parameters on the canopy-atmosphere exchange. Additional, we compare the LES output to LIDAR measurements taken above and upstream of the forest for model validation.

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.

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.

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

DOE PAGES

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

2017-12-14

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

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

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

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

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

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

NASA Astrophysics Data System (ADS)

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

2017-12-01

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

SURE Estimates for a Heteroscedastic Hierarchical Model

PubMed Central

Xie, Xianchao; Kou, S. C.; Brown, Lawrence D.

2014-01-01

Hierarchical models are extensively studied and widely used in statistics and many other scientific areas. They provide an effective tool for combining information from similar resources and achieving partial pooling of inference. Since the seminal work by James and Stein (1961) and Stein (1962), shrinkage estimation has become one major focus for hierarchical models. For the homoscedastic normal model, it is well known that shrinkage estimators, especially the James-Stein estimator, have good risk properties. The heteroscedastic model, though more appropriate for practical applications, is less well studied, and it is unclear what types of shrinkage estimators are superior in terms of the risk. We propose in this paper a class of shrinkage estimators based on Stein’s unbiased estimate of risk (SURE). We study asymptotic properties of various common estimators as the number of means to be estimated grows (p → ∞). We establish the asymptotic optimality property for the SURE estimators. We then extend our construction to create a class of semi-parametric shrinkage estimators and establish corresponding asymptotic optimality results. We emphasize that though the form of our SURE estimators is partially obtained through a normal model at the sampling level, their optimality properties do not heavily depend on such distributional assumptions. We apply the methods to two real data sets and obtain encouraging results. PMID:25301976

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.

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

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.

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.

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.

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

Parametric mediational g-formula approach to mediation analysis with time-varying exposures, mediators, and confounders

PubMed Central

Lin, Sheng-Hsuan; Young, Jessica; Logan, Roger; Tchetgen Tchetgen, Eric J.; VanderWeele, Tyler J.

2016-01-01

The assessment of direct and indirect effects with time-varying mediators and confounders is a common but challenging problem, and standard mediation analysis approaches are generally not applicable in this context. The mediational g-formula was recently proposed to address this problem, paired with a semi-parametric estimation approach to evaluate longitudinal mediation effects empirically. In this paper, we develop a parametric estimation approach to the mediational g-formula, including a feasible algorithm implemented in a freely available SAS macro. In the Framingham Heart Study data, we apply this method to estimate the interventional analogues of natural direct and indirect effects of smoking behaviors sustained over a 10-year period on blood pressure when considering weight change as a time-varying mediator. Compared with not smoking, smoking 20 cigarettes per day for 10 years was estimated to increase blood pressure by 1.2 (95 % CI: −0.7, 2.7) mm-Hg. The direct effect was estimated to increase blood pressure by 1.5 (95 % CI: −0.3, 2.9) mm-Hg, and the indirect effect was −0.3 (95% CI: −0.5, −0.1) mm-Hg, which is negative because smoking which is associated with lower weight is associated in turn with lower blood pressure. These results provide evidence that weight change in fact partially conceals the detrimental effects of cigarette smoking on blood pressure. Our work represents, to our knowledge, the first application of the parametric mediational g-formula in an epidemiologic cohort study. PMID:27984420

A non-ideal portal frame energy harvester controlled using a pendulum

NASA Astrophysics Data System (ADS)

Iliuk, I.; Balthazar, J. M.; Tusset, A. M.; Piqueira, J. R. C.; Rodrigues de Pontes, B.; Felix, J. L. P.; Bueno, Á. M.

2013-09-01

A model of energy harvester based on a simple portal frame structure is presented. The system is considered to be non-ideal system (NIS) due to interaction with the energy source, a DC motor with limited power supply and the system structure. The nonlinearities present in the piezoelectric material are considered in the piezoelectric coupling mathematical model. The system is a bi-stable Duffing oscillator presenting a chaotic behavior. Analyzing the average power variation, and bifurcation diagrams, the value of the control variable that optimizes power or average value that stabilizes the chaotic system in the periodic orbit is determined. The control sensitivity is determined to parametric errors in the damping and stiffness parameters of the portal frame. The proposed passive control technique uses a simple pendulum to tuned to the vibration of the structure to improve the energy harvesting. The results show that with the implementation of the control strategy it is possible to eliminate the need for active or semi active control, usually more complex. The control also provides a way to regulate the energy captured to a desired operating frequency.

Interference of Locally Forced Internal Waves in Non-Uniform Stratifications

NASA Astrophysics Data System (ADS)

Supekar, Rohit; Peacock, Thomas

2017-11-01

Several studies have investigated the effect of constructive or destructive interference on the transmission of internal waves propagating through non-uniform stratifications. Such studies have been performed for internal waves that are spatiotemporally harmonic. To understand the effect of localization, we perform a theoretical and experimental study of the transmission of two-dimensional internal waves that are generated by a spatiotemporally localized boundary forcing. This is done by considering an idealized problem and applying a weakly viscous semi-analytic linear model. Parametric studies using this model show that localization leads to the disappearance of transmission peaks and troughs that would otherwise be present for a harmonic forcing. Laboratory experiments that we perform provide a clear indication of this physical effect. Based on the group velocity and angle of propagation of the internal waves, a practical criteria that assesses when the transmission peaks or troughs are evident, is obtained. It is found that there is a significant difference in the predicted energy transfer due to a harmonic and non-harmonic forcing which has direct implications to various physical forcings such as a storm over the ocean.

Comparing and combining biomarkers as principle surrogates for time-to-event clinical endpoints.

PubMed

Gabriel, Erin E; Sachs, Michael C; Gilbert, Peter B

2015-02-10

Principal surrogate endpoints are useful as targets for phase I and II trials. In many recent trials, multiple post-randomization biomarkers are measured. However, few statistical methods exist for comparison of or combination of biomarkers as principal surrogates, and none of these methods to our knowledge utilize time-to-event clinical endpoint information. We propose a Weibull model extension of the semi-parametric estimated maximum likelihood method that allows for the inclusion of multiple biomarkers in the same risk model as multivariate candidate principal surrogates. We propose several methods for comparing candidate principal surrogates and evaluating multivariate principal surrogates. These include the time-dependent and surrogate-dependent true and false positive fraction, the time-dependent and the integrated standardized total gain, and the cumulative distribution function of the risk difference. We illustrate the operating characteristics of our proposed methods in simulations and outline how these statistics can be used to evaluate and compare candidate principal surrogates. We use these methods to investigate candidate surrogates in the Diabetes Control and Complications Trial. Copyright © 2014 John Wiley & Sons, Ltd.

Improving gross count gamma-ray logging in uranium mining with the NGRS probe

NASA Astrophysics Data System (ADS)

Carasco, C.; Pérot, B.; Ma, J.-L.; Toubon, H.; Dubille-Auchère, A.

2018-01-01

AREVA Mines and the Nuclear Measurement Laboratory of CEA Cadarache are collaborating to improve the sensitivity and precision of uranium concentration measurement by means of gamma ray logging. The determination of uranium concentration in boreholes is performed with the Natural Gamma Ray Sonde (NGRS) based on a NaI(Tl) scintillation detector. The total gamma count rate is converted into uranium concentration using a calibration coefficient measured in concrete blocks with known uranium concentration in the AREVA Mines calibration facility located in Bessines, France. Until now, to take into account gamma attenuation in a variety of boreholes diameters, tubing materials, diameters and thicknesses, filling fluid densities and compositions, a semi-empirical formula was used to correct the calibration coefficient measured in Bessines facility. In this work, we propose to use Monte Carlo simulations to improve gamma attenuation corrections. To this purpose, the NGRS probe and the calibration measurements in the standard concrete blocks have been modeled with MCNP computer code. The calibration coefficient determined by simulation, 5.3 s-1.ppmU-1 ± 10%, is in good agreement with the one measured in Bessines, 5.2 s-1.ppmU-1. Based on the validated MCNP model, several parametric studies have been performed. For instance, the rock density and chemical composition proved to have a limited impact on the calibration coefficient. However, gamma self-absorption in uranium leads to a nonlinear relationship between count rate and uranium concentration beyond approximately 1% of uranium weight fraction, the underestimation of the uranium content reaching more than a factor 2.5 for a 50 % uranium weight fraction. Next steps will concern parametric studies with different tubing materials, diameters and thicknesses, as well as different borehole filling fluids representative of real measurement conditions.

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

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.

Tuned grid generation with ICEM CFD

NASA Technical Reports Server (NTRS)

Wulf, Armin; Akdag, Vedat

1995-01-01

ICEM CFD is a CAD based grid generation package that supports multiblock structured, unstructured tetrahedral and unstructured hexahedral grids. Major development efforts have been spent to extend ICEM CFD's multiblock structured and hexahedral unstructured grid generation capabilities. The modules added are: a parametric grid generation module and a semi-automatic hexahedral grid generation module. A fully automatic version of the hexahedral grid generation module for around a set of predefined objects in rectilinear enclosures has been developed. These modules will be presented and the procedures used will be described, and examples will be discussed.

A biological approach to characterizing exposure to metalworking fluids and risk of prostate cancer (United States).

PubMed

Agalliu, Ilir; Eisen, Ellen A; Kriebel, David; Quinn, Margaret M; Wegman, David H

2005-05-01

Prostate cancer is hormone-related and chemicals that interfere with hormones may contribute to carcinogenesis. In a cohort of autoworkers we characterized exposure to metalworking fluids (MWF) into age windows with homogenous biological risk for prostate cancer, and examined exposure-response relationships using semi-parametric modeling. Incident cases (n=872) were identified via Michigan cancer registry from 1985 through 2000. Controls were selected using incidence-density sampling, 5:1 ratio. Using a hormonal-based model, exposure was accumulated in three windows: (1) late puberty, (2) adulthood, and (3) middle age. We used penalized splines to model risk as a smooth function of exposure, and controlled for race and calendar year of diagnosis in a Cox model. Risk of prostate cancer linearly increased with exposure to straight MWF in the first window, with a relative risk of 2.4 per 10 mg/m(3)-years. Autoworkers exposed to MWF at a young age also had an increased risk associated with MWF exposure incurred later in life. For soluble MWF there was a slightly increased risk in the third window. Exposure characterization based on a hormonal model identified heightened risk with early age of exposure to straight MWF. Results also support a long latency period for exposure related prostate cancer.

Patterns of healthcare service utilisation following severe traumatic brain injury: an idiographic analysis of injury compensation claims data.

PubMed

Collie, A; Prang, K-H

2013-11-01

The rate and extent of recovery after severe traumatic brain injury (TBI) is heterogeneous making prediction of likely healthcare service utilisation (HSU) difficult. Patterns of HSU derived from nomothetic samples do not represent the diverse range of outcomes possible within this patient group. Group-based trajectory model is a semi-parametric statistical technique that seeks to identify clusters of individuals whose outcome (however measured) follows a similar pattern of change over time. To identify and characterise patterns of HSU in the 5-year period following severe TBI. Detailed healthcare treatment payments data in 316 adults with severe TBI (Glasgow Coma Scale score 3-8) from the transport accident compensation system in the state of Victoria, Australia was accessed for this analysis. A semi-parametric group-based trajectory analytical technique for longitudinal data was applied to monthly observation counts of HSU data to identify distinct clusters of participants' trajectories. Comparison between trajectory groups on demographic, injury, disability and compensation relevant outcomes was undertaken. Four distinct patterns (trajectories) of HSU were identified in the sample. The first trajectory group comprised 27% of participants and displayed a rapid decrease in HSU in the first year post-injury. The second group comprised 24% of participants and showed a sharp peak in HSU during the first 12 months post-injury followed by a decline over time. The third group comprised 32% of participants and showed a slight peak in HSU in the first few months post-injury and then a slow decline over time. The fourth group comprised 17% of participants and displayed a steady rise in HSU up to 30 months post-injury, followed by a gradual decline to a level consistent with that received in the first months post-injury. Significant differences were observed between groups on factors such as age, injury severity, and use of disability services. There is substantial variation in patterns of HSU following severe TBI. Idiographic analysis can provide rich information for describing and understanding the resources required to help people with TBI. Copyright © 2013 Elsevier Ltd. All rights reserved.

Parameterizing the interstellar dust temperature

NASA Astrophysics Data System (ADS)

Hocuk, S.; Szűcs, L.; Caselli, P.; Cazaux, S.; Spaans, M.; Esplugues, G. B.

2017-08-01

The temperature of interstellar dust particles is of great importance to astronomers. It plays a crucial role in the thermodynamics of interstellar clouds, because of the gas-dust collisional coupling. It is also a key parameter in astrochemical studies that governs the rate at which molecules form on dust. In 3D (magneto)hydrodynamic simulations often a simple expression for the dust temperature is adopted, because of computational constraints, while astrochemical modelers tend to keep the dust temperature constant over a large range of parameter space. Our aim is to provide an easy-to-use parametric expression for the dust temperature as a function of visual extinction (AV) and to shed light on the critical dependencies of the dust temperature on the grain composition. We obtain an expression for the dust temperature by semi-analytically solving the dust thermal balance for different types of grains and compare to a collection of recent observational measurements. We also explore the effect of ices on the dust temperature. Our results show that a mixed carbonaceous-silicate type dust with a high carbon volume fraction matches the observations best. We find that ice formation allows the dust to be warmer by up to 15% at high optical depths (AV> 20 mag) in the interstellar medium. Our parametric expression for the dust temperature is presented as Td = [ 11 + 5.7 × tanh(0.61 - log 10(AV) ]χuv1/5.9, where χuv is in units of the Draine (1978, ApJS, 36, 595) UV field.

The nonlinear oil-water two-phase flow behavior for a horizontal well in triple media carbonate reservoir

NASA Astrophysics Data System (ADS)

Wang, Yong; Tao, Zhengwu; Chen, Liang; Ma, Xin

2017-10-01

Carbonate reservoir is one of the important reservoirs in the world. Because of the characteristics of carbonate reservoir, horizontal well has become a key technology for efficiently developing carbonate reservoir. Establishing corresponding mathematical models and analyzing transient pressure behaviors of this type of well-reservoir configuration can provide a better understanding of fluid flow patterns in formation as well as estimations of important parameters. A mathematical model for a oil-water two-phase flow horizontal well in triple media carbonate reservoir by conceptualizing vugs as spherical shapes are presented in this article. A semi-analytical solution is obtained in the Laplace domain using source function theory, Laplace transformation, and superposition principle. Analysis of transient pressure responses indicates that seven characteristic flow periods of horizontal well in triple media carbonate reservoir can be identified. Parametric analysis shows that water saturation of matrix, vug and fracture system, horizontal section length, and horizontal well position can significantly influence the transient pressure responses of horizontal well in triple media carbonate reservoir. The model presented in this article can be applied to obtain important parameters pertinent to reservoir by type curve matching.

A Parametric Study of Unsteady Rotor-Stator Interaction in a Simplified Francis Turbine

NASA Astrophysics Data System (ADS)

Wouden, Alex; Cimbala, John; Lewis, Bryan

2011-11-01

CFD analysis is becoming a critical stage in the design of hydroturbines. However, its capability to represent unsteady flow interactions between the rotor and stator (which requires a 360-degree, mesh-refined model of the turbine passage) is hindered. For CFD to become a more effective tool in predicting the performance of a hydroturbine, the key interactions between the rotor and stator need to be understood using current numerical methods. As a first step towards evaluating this unsteady behavior without the burden of a computationally expensive domain, the stator and Francis-type rotor blades are reduced to flat plates. Local and global variables are compared using periodic, semi-periodic, and 360-degree geometric models and various turbulence models (k-omega, k-epsilon, and Spalart-Allmaras). The computations take place within the OpenFOAM® environment and utilize a general grid interface (GGI) between the rotor and stator computational domains. The rotor computational domain is capable of dynamic rotation. The results demonstrate some of the strengths and limitations of utilizing CFD for hydroturbine analysis. These case studies will also serve as tutorials to help others learn how to use CFD for turbomachinery. This research is funded by a grant from the DOE.

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...

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…

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.

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).

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

Determining the optimal model for role-substitution in NHS dental services in the United Kingdom.

PubMed

Brocklehurst, Paul; Birch, Stephen; McDonald, Ruth; Tickle, Martin

2013-09-24

Role-substitution describes a model of dental care where Dental Care Professionals (DCPs) provide some of the clinical activity previously undertaken by General Dental Practitioners. This has the potential to increase technical efficiency, the capacity to care and reduce costs. Technical efficiency is defined as the production of the maximum amount of output from a given amount of input so that the service operates at the production frontier i.e. optimal level of productivity. Academic research into technical efficiency is becoming increasingly utilised in health care, although no studies have investigated the efficiency of NHS dentistry or role-substitution in high-street dental practices. The aim of this study is to examine the barriers and enablers that exist for role-substitution in general dental practices in the NHS and to determine the most technically efficient model for role-substitution. A screening questionnaire will be sent to DCPs to determine the type and location of role-substitutive models employed in NHS dental practices in the United Kingdom (UK). Semi-structured interviews will then be conducted with practice owners, DCPs and patients at selected sites identified by the questionnaire. Detail will be recorded about the organisational structure of the dental team, the number of NHS hours worked and the clinical activity undertaken. The interviews will continue until saturation and will record the views and attitudes of the members of the dental team. Final numbers of interviews will be determined by saturation.The second work-stream will examine the technical efficiency of the selected practices using Data Envelopment Analysis and Stochastic Frontier Modeling. The former is a non-parametric technique and is considered to be a highly flexible approach for applied health applications. The latter is parametric and is based on frontier regression models that estimate a conventional cost function. Maximising health for a given level and mix of resources is an ethical imperative for health service planners. This study will determine the technical efficiency of role-substitution and so address one of the key recommendations of the Independent Review of NHS dentistry in England.

Determining the optimal model for role-substitution in NHS dental services in the United Kingdom

PubMed Central

2013-01-01

Background Role-substitution describes a model of dental care where Dental Care Professionals (DCPs) provide some of the clinical activity previously undertaken by General Dental Practitioners. This has the potential to increase technical efficiency, the capacity to care and reduce costs. Technical efficiency is defined as the production of the maximum amount of output from a given amount of input so that the service operates at the production frontier i.e. optimal level of productivity. Academic research into technical efficiency is becoming increasingly utilised in health care, although no studies have investigated the efficiency of NHS dentistry or role-substitution in high-street dental practices. The aim of this study is to examine the barriers and enablers that exist for role-substitution in general dental practices in the NHS and to determine the most technically efficient model for role-substitution. Methods/design A screening questionnaire will be sent to DCPs to determine the type and location of role-substitutive models employed in NHS dental practices in the United Kingdom (UK). Semi-structured interviews will then be conducted with practice owners, DCPs and patients at selected sites identified by the questionnaire. Detail will be recorded about the organisational structure of the dental team, the number of NHS hours worked and the clinical activity undertaken. The interviews will continue until saturation and will record the views and attitudes of the members of the dental team. Final numbers of interviews will be determined by saturation. The second work-stream will examine the technical efficiency of the selected practices using Data Envelopment Analysis and Stochastic Frontier Modeling. The former is a non-parametric technique and is considered to be a highly flexible approach for applied health applications. The latter is parametric and is based on frontier regression models that estimate a conventional cost function. Discussion Maximising health for a given level and mix of resources is an ethical imperative for health service planners. This study will determine the technical efficiency of role-substitution and so address one of the key recommendations of the Independent Review of NHS dentistry in England. PMID:24063247

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.

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

Global scaling for semi-quantitative analysis in FP-CIT SPECT.

PubMed

Kupitz, D; Apostolova, I; Lange, C; Ulrich, G; Amthauer, H; Brenner, W; Buchert, R

2014-01-01

Semi-quantitative characterization of dopamine transporter availability from single photon emission computed tomography (SPECT) with 123I-ioflupane (FP-CIT) is based on uptake ratios relative to a reference region. The aim of this study was to evaluate the whole brain as reference region for semi-quantitative analysis of FP-CIT SPECT. The rationale was that this might reduce statistical noise associated with the estimation of non-displaceable FP-CIT uptake. 150 FP-CIT SPECTs were categorized as neurodegenerative or non-neurodegenerative by an expert. Semi-quantitative analysis of specific binding ratios (SBR) was performed with a custom-made tool based on the Statistical Parametric Mapping software package using predefined regions of interest (ROIs) in the anatomical space of the Montreal Neurological Institute. The following reference regions were compared: predefined ROIs for frontal and occipital lobe and whole brain (without striata, thalamus and brainstem). Tracer uptake in the reference region was characterized by the mean, median or 75th percentile of its voxel intensities. The area (AUC) under the receiver operating characteristic curve was used as performance measure. The highest AUC of 0.973 was achieved by the SBR of the putamen with the 75th percentile in the whole brain as reference. The lowest AUC for the putamen SBR of 0.937 was obtained with the mean in the frontal lobe as reference. We recommend the 75th percentile in the whole brain as reference for semi-quantitative analysis in FP-CIT SPECT. This combination provided the best agreement of the semi-quantitative analysis with visual evaluation of the SPECT images by an expert and, therefore, is appropriate to support less experienced physicians.

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.

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.

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

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.

Ensemble survival tree models to reveal pairwise interactions of variables with time-to-events outcomes in low-dimensional setting

PubMed Central

Dazard, Jean-Eudes; Ishwaran, Hemant; Mehlotra, Rajeev; Weinberg, Aaron; Zimmerman, Peter

2018-01-01

Unraveling interactions among variables such as genetic, clinical, demographic and environmental factors is essential to understand the development of common and complex diseases. To increase the power to detect such variables interactions associated with clinical time-to-events outcomes, we borrowed established concepts from random survival forest (RSF) models. We introduce a novel RSF-based pairwise interaction estimator and derive a randomization method with bootstrap confidence intervals for inferring interaction significance. Using various linear and nonlinear time-to-events survival models in simulation studies, we first show the efficiency of our approach: true pairwise interaction-effects between variables are uncovered, while they may not be accompanied with their corresponding main-effects, and may not be detected by standard semi-parametric regression modeling and test statistics used in survival analysis. Moreover, using a RSF-based cross-validation scheme for generating prediction estimators, we show that informative predictors may be inferred. We applied our approach to an HIV cohort study recording key host gene polymorphisms and their association with HIV change of tropism or AIDS progression. Altogether, this shows how linear or nonlinear pairwise statistical interactions of variables may be efficiently detected with a predictive value in observational studies with time-to-event outcomes. PMID:29453930

SEMIPARAMETRIC EFFICIENT ESTIMATION FOR SHARED-FRAILTY MODELS WITH DOUBLY-CENSORED CLUSTERED DATA

PubMed Central

Wang, Jane-Ling

2018-01-01

In this paper, we investigate frailty models for clustered survival data that are subject to both left- and right-censoring, termed “doubly-censored data”. This model extends current survival literature by broadening the application of frailty models from right-censoring to a more complicated situation with additional left censoring. Our approach is motivated by a recent Hepatitis B study where the sample consists of families. We adopt a likelihood approach that aims at the nonparametric maximum likelihood estimators (NPMLE). A new algorithm is proposed, which not only works well for clustered data but also improve over existing algorithm for independent and doubly-censored data, a special case when the frailty variable is a constant equal to one. This special case is well known to be a computational challenge due to the left censoring feature of the data. The new algorithm not only resolves this challenge but also accommodate the additional frailty variable effectively. Asymptotic properties of the NPMLE are established along with semi-parametric efficiency of the NPMLE for the finite-dimensional parameters. The consistency of Bootstrap estimators for the standard errors of the NPMLE is also discussed. We conducted some simulations to illustrate the numerical performance and robustness of the proposed algorithm, which is also applied to the Hepatitis B data. PMID:29527068

Ensemble survival tree models to reveal pairwise interactions of variables with time-to-events outcomes in low-dimensional setting.

PubMed

Dazard, Jean-Eudes; Ishwaran, Hemant; Mehlotra, Rajeev; Weinberg, Aaron; Zimmerman, Peter

2018-02-17

Unraveling interactions among variables such as genetic, clinical, demographic and environmental factors is essential to understand the development of common and complex diseases. To increase the power to detect such variables interactions associated with clinical time-to-events outcomes, we borrowed established concepts from random survival forest (RSF) models. We introduce a novel RSF-based pairwise interaction estimator and derive a randomization method with bootstrap confidence intervals for inferring interaction significance. Using various linear and nonlinear time-to-events survival models in simulation studies, we first show the efficiency of our approach: true pairwise interaction-effects between variables are uncovered, while they may not be accompanied with their corresponding main-effects, and may not be detected by standard semi-parametric regression modeling and test statistics used in survival analysis. Moreover, using a RSF-based cross-validation scheme for generating prediction estimators, we show that informative predictors may be inferred. We applied our approach to an HIV cohort study recording key host gene polymorphisms and their association with HIV change of tropism or AIDS progression. Altogether, this shows how linear or nonlinear pairwise statistical interactions of variables may be efficiently detected with a predictive value in observational studies with time-to-event outcomes.

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

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

New approaches to the analysis of population trends in land birds: Comment

USGS Publications Warehouse

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

1997-01-01

James et al. (1996, Ecology 77:13-27) used data from the North American Breeding Bird Survey (BBS) to examine geographic variability in patterns of population change for 26 species of wood warblers. They emphasized the importance of evaluating nonlinear patterns of change in bird populations, proposed LOESS-based non-parametric and semi-parametric analyses of BBS data, and contrasted their results with other analyses, including those of Robbins et al. (1989, Proceedings of the National Academy of Sciences 86: 7658-7662) and Peterjohn et al. (1995, Pages 3-39 in T. E. Martin and D. M. Finch, eds. Ecology and management of Neotropical migratory birds: a synthesis and review of critical issues. Oxford University Press, New York.). In this note, we briefly comment on some of the issues that arose from their analysis of BBS data, suggest a few aspects of the survey that should inspire caution in analysts, and review the differences between the LOESS-based procedures and other procedures (e.g., Link and Sauer 1994). We strongly discourage the use of James et al.'s completely non-parametric procedure, which fails to account for observer effects. Our comparisons of estimators adds to the evidence already present in the literature of the bias associated with omitting observer information in analyses of BBS data. Bias resulting from change in observer abilities should be a consideration in any analysis of BBS data.

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.

Portfolio management under sudden changes in volatility and heterogeneous investment horizons

NASA Astrophysics Data System (ADS)

Fernandez, Viviana; Lucey, Brian M.

2007-03-01

We analyze the implications for portfolio management of accounting for conditional heteroskedasticity and sudden changes in volatility, based on a sample of weekly data of the Dow Jones Country Titans, the CBT-municipal bond, spot and futures prices of commodities for the period 1992-2005. To that end, we first proceed to utilize the ICSS algorithm to detect long-term volatility shifts, and incorporate that information into PGARCH models fitted to the returns series. At the next stage, we simulate returns series and compute a wavelet-based value at risk, which takes into consideration the investor's time horizon. We repeat the same procedure for artificial data generated from semi-parametric estimates of the distribution functions of returns, which account for fat tails. Our estimation results show that neglecting GARCH effects and volatility shifts may lead to an overestimation of financial risk at different time horizons. In addition, we conclude that investors benefit from holding commodities as their low or even negative correlation with stock and bond indices contribute to portfolio diversification.

Distributed intelligent control and management (DICAM) applications and support for semi-automated development

NASA Technical Reports Server (NTRS)

Hayes-Roth, Frederick; Erman, Lee D.; Terry, Allan; Hayes-Roth, Barbara

1992-01-01

We have recently begun a 4-year effort to develop a new technology foundation and associated methodology for the rapid development of high-performance intelligent controllers. Our objective in this work is to enable system developers to create effective real-time systems for control of multiple, coordinated entities in much less time than is currently required. Our technical strategy for achieving this objective is like that in other domain-specific software efforts: analyze the domain and task underlying effective performance, construct parametric or model-based generic components and overall solutions to the task, and provide excellent means for specifying, selecting, tailoring or automatically generating the solution elements particularly appropriate for the problem at hand. In this paper, we first present our specific domain focus, briefly describe the methodology and environment we are developing to provide a more regular approach to software development, and then later describe the issues this raises for the research community and this specific workshop.

Accounting for dropout reason in longitudinal studies with nonignorable dropout.

PubMed

Moore, Camille M; MaWhinney, Samantha; Forster, Jeri E; Carlson, Nichole E; Allshouse, Amanda; Wang, Xinshuo; Routy, Jean-Pierre; Conway, Brian; Connick, Elizabeth

2017-08-01

Dropout is a common problem in longitudinal cohort studies and clinical trials, often raising concerns of nonignorable dropout. Selection, frailty, and mixture models have been proposed to account for potentially nonignorable missingness by relating the longitudinal outcome to time of dropout. In addition, many longitudinal studies encounter multiple types of missing data or reasons for dropout, such as loss to follow-up, disease progression, treatment modifications and death. When clinically distinct dropout reasons are present, it may be preferable to control for both dropout reason and time to gain additional clinical insights. This may be especially interesting when the dropout reason and dropout times differ by the primary exposure variable. We extend a semi-parametric varying-coefficient method for nonignorable dropout to accommodate dropout reason. We apply our method to untreated HIV-infected subjects recruited to the Acute Infection and Early Disease Research Program HIV cohort and compare longitudinal CD4 + T cell count in injection drug users to nonusers with two dropout reasons: anti-retroviral treatment initiation and loss to follow-up.

Efficient logistic regression designs under an imperfect population identifier.

PubMed

Albert, Paul S; Liu, Aiyi; Nansel, Tonja

2014-03-01

Motivated by actual study designs, this article considers efficient logistic regression designs where the population is identified with a binary test that is subject to diagnostic error. We consider the case where the imperfect test is obtained on all participants, while the gold standard test is measured on a small chosen subsample. Under maximum-likelihood estimation, we evaluate the optimal design in terms of sample selection as well as verification. We show that there may be substantial efficiency gains by choosing a small percentage of individuals who test negative on the imperfect test for inclusion in the sample (e.g., verifying 90% test-positive cases). We also show that a two-stage design may be a good practical alternative to a fixed design in some situations. Under optimal and nearly optimal designs, we compare maximum-likelihood and semi-parametric efficient estimators under correct and misspecified models with simulations. The methodology is illustrated with an analysis from a diabetes behavioral intervention trial. © 2013, The International Biometric Society.

Non-Linear Steady State Vibrations of Beams Excited by Vortex Shedding

NASA Astrophysics Data System (ADS)

LEWANDOWSKI, R.

2002-05-01

In this paper the non-linear vibrations of beams excited by vortex-shedding are considered. In particular, the steady state responses of beams near the synchronization region are taken into account. The main aerodynamic properties of wind are described by using the semi-empirical model proposed by Hartlen and Currie. The finite element method and the strip method are used to formulate the equation of motion of the system treated. The harmonic balance method is adopted to derive the amplitude equations. These equations are solved with the help of the continuation method which is very convenient to perform the parametric studies of the problem and to determine the response curve in the synchronization region. Moreover, the equations of motion are also integrated using the Newmark method. The results of calculations of several example problems are also shown to confirm the efficiency and accuracy of the presented method. The results obtained by the harmonic balance method and by the Newmark methods are in good agreement with each other.

Comparative study on the performance of textural image features for active contour segmentation.

PubMed

Moraru, Luminita; Moldovanu, Simona

2012-07-01

We present a computerized method for the semi-automatic detection of contours in ultrasound images. The novelty of our study is the introduction of a fast and efficient image function relating to parametric active contour models. This new function is a combination of the gray-level information and first-order statistical features, called standard deviation parameters. In a comprehensive study, the developed algorithm and the efficiency of segmentation were first tested for synthetic images. Tests were also performed on breast and liver ultrasound images. The proposed method was compared with the watershed approach to show its efficiency. The performance of the segmentation was estimated using the area error rate. Using the standard deviation textural feature and a 5×5 kernel, our curve evolution was able to produce results close to the minimal area error rate (namely 8.88% for breast images and 10.82% for liver images). The image resolution was evaluated using the contrast-to-gradient method. The experiments showed promising segmentation results.

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.

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.

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.

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

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.

Risk Factors for Mortality among Adult HIV/AIDS Patients Following Antiretroviral Therapy in Southwestern Ethiopia: An Assessment through Survival Models

PubMed Central

Seyoum, Dinberu; Degryse, Jean-Marie; Kifle, Yehenew Getachew; Taye, Ayele; Tadesse, Mulualem; Birlie, Belay; Banbeta, Akalu; Rosas-Aguirre, Angel; Duchateau, Luc; Speybroeck, Niko

2017-01-01

Introduction: Efforts have been made to reduce HIV/AIDS-related mortality by delivering antiretroviral therapy (ART) treatment. However, HIV patients in resource-poor settings are still dying, even if they are on ART treatment. This study aimed to explore the factors associated with HIV/AIDS-related mortality in Southwestern Ethiopia. Method: A non-concurrent retrospective cohort study which collected data from the clinical records of adult HIV/AIDS patients, who initiated ART treatment and were followed between January 2006 and December 2010, was conducted, to explore the factors associated with HIV/AIDS-related mortality at Jimma University Specialized Hospital (JUSH). Survival times (i.e., the time from the onset of ART treatment to the death or censoring) and different characteristics of patients were retrospectively examined. A best-fit model was chosen for the survival data, after the comparison between native semi-parametric Cox regression and parametric survival models (i.e., exponential, Weibull, and log-logistic). Result: A total of 456 HIV patients were included in the study, mostly females (312, 68.4%), with a median age of 30 years (inter-quartile range (IQR): 23–37 years). Estimated follow-up until December 2010 accounted for 1245 person-years at risk (PYAR) and resulted in 66 (14.5%) deaths and 390 censored individuals, representing a median survival time of 34.0 months ( IQR: 22.8–42.0 months). The overall mortality rate was 5.3/100 PYAR: 6.5/100 PYAR for males and 4.8/100 PYAR for females. The Weibull survival model was the best model for fitting the data (lowest AIC). The main factors associated with mortality were: baseline age (>35 years old, AHR = 3.8, 95% CI: 1.6–9.1), baseline weight (AHR = 0.93, 95% CI: 0.90–0.97), baseline WHO stage IV (AHR = 6.2, 95% CI: 2.2–14.2), and low adherence to ART treatment (AHR = 4.2, 95% CI: 2.5–7.1). Conclusion: An effective reduction in HIV/AIDS mortality could be achieved through timely ART treatment onset and maintaining high levels of treatment adherence. PMID:28287498

A new approach to global seismic tomography based on regularization by sparsity in a novel 3D spherical wavelet basis

NASA Astrophysics Data System (ADS)

Loris, Ignace; Simons, Frederik J.; Daubechies, Ingrid; Nolet, Guust; Fornasier, Massimo; Vetter, Philip; Judd, Stephen; Voronin, Sergey; Vonesch, Cédric; Charléty, Jean

2010-05-01

Global seismic wavespeed models are routinely parameterized in terms of spherical harmonics, networks of tetrahedral nodes, rectangular voxels, or spherical splines. Up to now, Earth model parametrizations by wavelets on the three-dimensional ball remain uncommon. Here we propose such a procedure with the following three goals in mind: (1) The multiresolution character of a wavelet basis allows for the models to be represented with an effective spatial resolution that varies as a function of position within the Earth. (2) This property can be used to great advantage in the regularization of seismic inversion schemes by seeking the most sparse solution vector, in wavelet space, through iterative minimization of a combination of the ℓ2 (to fit the data) and ℓ1 norms (to promote sparsity in wavelet space). (3) With the continuing increase in high-quality seismic data, our focus is also on numerical efficiency and the ability to use parallel computing in reconstructing the model. In this presentation we propose a new wavelet basis to take advantage of these three properties. To form the numerical grid we begin with a surface tesselation known as the 'cubed sphere', a construction popular in fluid dynamics and computational seismology, coupled with an semi-regular radial subdivison that honors the major seismic discontinuities between the core-mantle boundary and the surface. This mapping first divides the volume of the mantle into six portions. In each 'chunk' two angular and one radial variable are used for parametrization. In the new variables standard 'cartesian' algorithms can more easily be used to perform the wavelet transform (or other common transforms). Edges between chunks are handled by special boundary filters. We highlight the benefits of this construction and use it to analyze the information present in several published seismic compressional-wavespeed models of the mantle, paying special attention to the statistics of wavelet and scaling coefficients across scales. We also focus on the likely gains of future inversions of finite-frequency seismic data using a sparsity promoting penalty in combination with our new wavelet approach.

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.

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.

The clustering of baryonic matter. I: a halo-model approach

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

Fedeli, C., E-mail: cosimo.fedeli@oabo.inaf.it

2014-04-01

In this paper I generalize the halo model for the clustering of dark matter in order to produce the power spectra of the two main baryonic matter components in the Universe: stars and hot gas. As a natural extension, this can be also used to describe the clustering of all mass. According to the design of the halo model, the large-scale power spectra of the various matter components are physically connected with the distribution of each component within bound structures and thus, ultimately, with the complete set of physical processes that drive the formation of galaxies and galaxy clusters. Besidesmore » being practical for cosmological and parametric studies, the semi-analytic model presented here has also other advantages. Most importantly, it allows one to understand on physical ground what is the relative contribution of each matter component to the total clustering of mass as a function of scale, and thus it opens an interesting new window to infer the distribution of baryons through high precision cosmic shear measurements. This is particularly relevant for future wide-field photometric surveys such as Euclid. In this work the concept of the model and its uncertainties are illustrated in detail, while in a companion paper we use a set of numerical hydrodynamic simulations to show a practical application and to investigate where the model itself needs to be improved.« less

Comparison of Weibull and Lognormal Cure Models with Cox in the Survival Analysis Of Breast Cancer Patients in Rafsanjan.

PubMed

Hoseini, Mina; Bahrampour, Abbas; Mirzaee, Moghaddameh

2017-02-16

Breast cancer is the most common cancer after lung cancer and the second cause of death. In this study we compared Weibull and Lognormal Cure Models with Cox regression on the survival of breast cancer. A cohort study. The current study retrospective cohort study was conducted on 140 patients referred to Ali Ibn Abitaleb Hospital, Rafsanjan southeastern Iran from 2001 to 2015 suffering from breast cancer. We determined and analyzed the effective survival causes by different models using STATA14. According to AIC, log-normal model was more consistent than Weibull. In the multivariable Lognormal model, the effective factors like smoking, second -hand smoking, drinking herbal tea and the last breast-feeding period were included. In addition, using Cox regression factors of significant were the disease grade, size of tumor and its metastasis (p-value<0.05). As Rafsanjan is surrounded by pistachio orchards and pesticides applied by farmers, people of this city are exposed to agricultural pesticides and its harmful consequences. The effect of the pesticide on breast cancer was studied and the results showed that the effect of pesticides on breast cancer was not in agreement with the models used in this study. Based on different methods for survival analysis, researchers can decide how they can reach a better conclusion. This comparison indicates the result of semi-parametric Cox method is closer to clinical experiences evidences.

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.

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.

Sports and Child Development

PubMed Central

2016-01-01

The role of curricular activities for the formation of education, health and behavioural outcomes has been widely studied. Yet, the role of extra-curricular activities has received little attention. This study analyzes the effect of participation in sports clubs—one of the most popular extra-curricular activities among children. We use alternative datasets and flexible semi-parametric estimation methods with a specific way to use the panel dimension of the data to address selection into sports. We find positive and robust effects on children’s school performance and peer relations. Crowding out of passive leisure activities can partially explain the effects. PMID:27144474

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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…

Precipitation Processes Developed During ARM (1997), TOGA COARE (1992) GATE (1974), SCSMEX (1998), and KWAJEX (1999): Consistent 3D, Semi-3D and 3D Cloud Resolving Model Simulations

NASA Technical Reports Server (NTRS)

Tao, W.-K.; Hou, A.; Atlas, R.; Starr, D.; Sud, Y.

2003-01-01

Real clouds and cloud systems are inherently three-dimensional (3D). Because of the limitations in computer resources, however, most cloud-resolving models (CRMs) today are still two-dimensional (2D) have been used to study the response of clouds to large-scale forcing. IN these 3D simulators, the model domain was small, and the integration time was 6 hours. Only recently have 3D experiments been performed for multi-day periods for tropical clouds systems with large horizontal domains at the National Center of Atmospheric Research (NCAR) and at NASA Goddard Space Center. At Goddard, a 3D cumulus Ensemble (GCE) model was used to simulate periods during TOGA COARE, GATE, SCSMEX, ARM, and KWAJEX using a 512 by 512 km domain (with 2-km resolution). The result indicate that surface precipitation and latent heating profiles are very similar between the 2D and 3D GCE model simulation. The major objective of this paper are: (1) to assess the performance of the super-parametrization technique, (2) calculate and examine the surface energy (especially radiation) and water budget, and (3) identify the differences and similarities in the organization and entrainment rates of convection between simulated 2D and 3D cloud systems.

A gap-filling model for eddy covariance latent heat flux: Estimating evapotranspiration of a subtropical seasonal evergreen broad-leaved forest as an example

NASA Astrophysics Data System (ADS)

Chen, Yi-Ying; Chu, Chia-Ren; Li, Ming-Hsu

2012-10-01

SummaryIn this paper we present a semi-parametric multivariate gap-filling model for tower-based measurement of latent heat flux (LE). Two statistical techniques, the principal component analysis (PCA) and a nonlinear interpolation approach were integrated into this LE gap-filling model. The PCA was first used to resolve the multicollinearity relationships among various environmental variables, including radiation, soil moisture deficit, leaf area index, wind speed, etc. Two nonlinear interpolation methods, multiple regressions (MRS) and the K-nearest neighbors (KNNs) were examined with random selected flux gaps for both clear sky and nighttime/cloudy data to incorporate into this LE gap-filling model. Experimental results indicated that the KNN interpolation approach is able to provide consistent LE estimations while MRS presents over estimations during nighttime/cloudy. Rather than using empirical regression parameters, the KNN approach resolves the nonlinear relationship between the gap-filled LE flux and principal components with adaptive K values under different atmospheric states. The developed LE gap-filling model (PCA with KNN) works with a RMSE of 2.4 W m-2 (˜0.09 mm day-1) at a weekly time scale by adding 40% artificial flux gaps into original dataset. Annual evapotranspiration at this study site were estimated at 736 mm (1803 MJ) and 728 mm (1785 MJ) for year 2008 and 2009, respectively.

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.

Entangled photons from single atoms and molecules

NASA Astrophysics Data System (ADS)

Nordén, Bengt

2018-05-01

The first two-photon entanglement experiment performed 50 years ago by Kocher and Commins (KC) provided isolated pairs of entangled photons from an atomic three-state fluorescence cascade. In view of questioning of Bell's theorem, data from these experiments are re-analyzed and shown sufficiently precise to confirm quantum mechanical and dismiss semi-classical theory without need for Bell's inequalities. Polarization photon correlation anisotropy (A) is useful: A is near unity as predicted quantum mechanically and well above the semi-classic range, 0 ⩽ A ⩽ 1 / 2 . Although yet to be found, one may envisage a three-state molecule emitting entangled photon pairs, in analogy with the KC atomic system. Antibunching in fluorescence from single molecules in matrix and entangled photons from quantum dots promise it be possible. Molecules can have advantages to parametric down-conversion as the latter photon distribution is Poissonian and unsuitable for producing isolated pairs of entangled photons. Analytical molecular applications of entangled light are also envisaged.

Estimation of optimal biomass fraction measuring cycle formunicipal solid waste incineration facilities in Korea.

PubMed

Kang, Seongmin; Cha, Jae Hyung; Hong, Yoon-Jung; Lee, Daekyeom; Kim, Ki-Hyun; Jeon, Eui-Chan

2018-01-01

This study estimates the optimum sampling cycle using a statistical method for biomass fraction. More than ten samples were collected from each of the three municipal solid waste (MSW) facilities between June 2013 and March 2015 and the biomass fraction was analyzed. The analysis data were grouped into monthly, quarterly, semi-annual, and annual intervals and the optimum sampling cycle for the detection of the biomass fraction was estimated. Biomass fraction data did not show a normal distribution. Therefore, the non-parametric Kruskal-Wallis test was applied to compare the average values for each sample group. The Kruskal-Wallis test results showed that the average monthly, quarterly, semi-annual, and annual values for all three MSW incineration facilities were equal. Therefore, the biomass fraction at the MSW incineration facilities should be calculated on a yearly cycle which is the longest period of the temporal cycles tested. Copyright © 2017 Elsevier Ltd. All rights reserved.

Parallel pumping of a ferromagnetic nanostripe: Confinement quantization and off-resonant driving

NASA Astrophysics Data System (ADS)

Yarbrough, P. M.; Livesey, K. L.

2018-01-01

The parametric excitation of spin waves in a rectangular, ferromagnetic nanowire in the parallel pump configuration and with an applied field along the long axis of the wire is studied theoretically, using a semi-classical and semi-analytic Hamiltonian approach. We find that as a function of static applied field strength, there are jumps in the pump power needed to excite thermal spin waves. At these jumps, there is the possibility to non-resonantly excite spin waves near kz = 0. Spin waves with negative or positive group velocity and with different standing wave structures across the wire width can be excited by tuning the applied field. By using a magnetostatic Green's function that depends on both the nanowire's width and thickness—rather than just its aspect ratio—we also find that the threshold field strength varies considerably for nanowires with the same aspect ratio but of different sizes. Comparisons between different methods of calculations are made and the advantages and disadvantages of each are discussed.

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.

"Role magnets"? An empirical investigation of popularity trajectories for life-course persistent individuals during adolescence.

PubMed

Young, Jacob T N

2014-01-01

Recent scholarship has focused on the role of social status in peer groups to explain the fact that delinquency is disproportionately committed during adolescence. Yet, the precise mechanism linking adolescence, social status, and antisocial behavior is not well understood. Dual-taxonomy postulates a testable mechanism that links the sudden increase in risky behavior among adolescents to the social magnetism of a small group of persistently antisocial individuals, referred to here as the "role magnet" hypothesis. Using semi-parametric group-based trajectory modeling and growth-curve modeling, this study provides the first test of this hypothesis by examining physical violence and popularity trajectories for 1,845 male respondents age 11-32 from a nationally representative sample (54 % non-Hispanic White; 21 % non-Hispanic African American; 17 % Hispanic; 8 % Asian). Individuals assigned to a "chronic violence" trajectory group showed consistently lower average levels of popularity from 11 to 19. But, these same individuals experienced increases in popularity during early adolescence and subsequent declines in late adolescence. These findings are linked to current research examining social status as a mechanism generating antisocial behavior during adolescence and the consequences of delayed entry into adult roles.

Study of Solid Particle Behavior in High Temperature Gas Flows

NASA Astrophysics Data System (ADS)

Majid, A.; Bauder, U.; Stindl, T.; Fertig, M.; Herdrich, G.; Röser, H.-P.

2009-01-01

The Euler-Lagrangian approach is used for the simulation of solid particles in hypersonic entry flows. For flow field simulation, the program SINA (Sequential Iterative Non-equilibrium Algorithm) developed at the Institut für Raumfahrtsysteme is used. The model for the effect of the carrier gas on a particle includes drag force and particle heating only. Other parameters like lift Magnus force or damping torque are not taken into account so far. The reverse effect of the particle phase on the gaseous phase is currently neglected. Parametric analysis is done regarding the impact of variation in the physical input conditions like position, velocity, size and material of the particle. Convective heat fluxes onto the surface of the particle and its radiative cooling are discussed. The variation of particle temperature under different conditions is presented. The influence of various input conditions on the trajectory is explained. A semi empirical model for the particle wall interaction is also discussed and the influence of the wall on the particle trajectory with different particle conditions is presented. The heat fluxes onto the wall due to impingement of particles are also computed and compared with the heat fluxes from the gas.

Maximum Likelihood Estimations and EM Algorithms with Length-biased Data

PubMed Central

Qin, Jing; Ning, Jing; Liu, Hao; Shen, Yu

2012-01-01

SUMMARY Length-biased sampling has been well recognized in economics, industrial reliability, etiology applications, epidemiological, genetic and cancer screening studies. Length-biased right-censored data have a unique data structure different from traditional survival data. The nonparametric and semiparametric estimations and inference methods for traditional survival data are not directly applicable for length-biased right-censored data. We propose new expectation-maximization algorithms for estimations based on full likelihoods involving infinite dimensional parameters under three settings for length-biased data: estimating nonparametric distribution function, estimating nonparametric hazard function under an increasing failure rate constraint, and jointly estimating baseline hazards function and the covariate coefficients under the Cox proportional hazards model. Extensive empirical simulation studies show that the maximum likelihood estimators perform well with moderate sample sizes and lead to more efficient estimators compared to the estimating equation approaches. The proposed estimates are also more robust to various right-censoring mechanisms. We prove the strong consistency properties of the estimators, and establish the asymptotic normality of the semi-parametric maximum likelihood estimators under the Cox model using modern empirical processes theory. We apply the proposed methods to a prevalent cohort medical study. Supplemental materials are available online. PMID:22323840

Finite time convergent learning law for continuous neural networks.

PubMed

Chairez, Isaac

2014-02-01

This paper addresses the design of a discontinuous finite time convergent learning law for neural networks with continuous dynamics. The neural network was used here to obtain a non-parametric model for uncertain systems described by a set of ordinary differential equations. The source of uncertainties was the presence of some external perturbations and poor knowledge of the nonlinear function describing the system dynamics. A new adaptive algorithm based on discontinuous algorithms was used to adjust the weights of the neural network. The adaptive algorithm was derived by means of a non-standard Lyapunov function that is lower semi-continuous and differentiable in almost the whole space. A compensator term was included in the identifier to reject some specific perturbations using a nonlinear robust algorithm. Two numerical examples demonstrated the improvements achieved by the learning algorithm introduced in this paper compared to classical schemes with continuous learning methods. The first one dealt with a benchmark problem used in the paper to explain how the discontinuous learning law works. The second one used the methane production model to show the benefits in engineering applications of the learning law proposed in this paper. Copyright © 2013 Elsevier Ltd. All rights reserved.

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).

Symmetric Positive 4th Order Tensors & Their Estimation from Diffusion Weighted MRI⋆

PubMed Central

Barmpoutis, Angelos; Jian, Bing; Vemuri, Baba C.; Shepherd, Timothy M.

2009-01-01

In Diffusion Weighted Magnetic Resonance Image (DW-MRI) processing a 2nd order tensor has been commonly used to approximate the diffusivity function at each lattice point of the DW-MRI data. It is now well known that this 2nd-order approximation fails to approximate complex local tissue structures, such as fibers crossings. In this paper we employ a 4th order symmetric positive semi-definite (PSD) tensor approximation to represent the diffusivity function and present a novel technique to estimate these tensors from the DW-MRI data guaranteeing the PSD property. There have been several published articles in literature on higher order tensor approximations of the diffusivity function but none of them guarantee the positive semi-definite constraint, which is a fundamental constraint since negative values of the diffusivity coefficients are not meaningful. In our methods, we parameterize the 4th order tensors as a sum of squares of quadratic forms by using the so called Gram matrix method from linear algebra and its relation to the Hilbert’s theorem on ternary quartics. This parametric representation is then used in a nonlinear-least squares formulation to estimate the PSD tensors of order 4 from the data. We define a metric for the higher-order tensors and employ it for regularization across the lattice. Finally, performance of this model is depicted on synthetic data as well as real DW-MRI from an isolated rat hippocampus. PMID:17633709

Bayesian structured additive regression modeling of epidemic data: application to cholera

PubMed Central

2012-01-01

Background A significant interest in spatial epidemiology lies in identifying associated risk factors which enhances the risk of infection. Most studies, however, make no, or limited use of the spatial structure of the data, as well as possible nonlinear effects of the risk factors. Methods We develop a Bayesian Structured Additive Regression model for cholera epidemic data. Model estimation and inference is based on fully Bayesian approach via Markov Chain Monte Carlo (MCMC) simulations. The model is applied to cholera epidemic data in the Kumasi Metropolis, Ghana. Proximity to refuse dumps, density of refuse dumps, and proximity to potential cholera reservoirs were modeled as continuous functions; presence of slum settlers and population density were modeled as fixed effects, whereas spatial references to the communities were modeled as structured and unstructured spatial effects. Results We observe that the risk of cholera is associated with slum settlements and high population density. The risk of cholera is equal and lower for communities with fewer refuse dumps, but variable and higher for communities with more refuse dumps. The risk is also lower for communities distant from refuse dumps and potential cholera reservoirs. The results also indicate distinct spatial variation in the risk of cholera infection. Conclusion The study highlights the usefulness of Bayesian semi-parametric regression model analyzing public health data. These findings could serve as novel information to help health planners and policy makers in making effective decisions to control or prevent cholera epidemics. PMID:22866662

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.

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.

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.

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.

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.

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

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.

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.

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.

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).

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.

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.

Low velocity impact analysis of composite laminated plates

NASA Astrophysics Data System (ADS)

Zheng, Daihua

2007-12-01

In the past few decades polymer composites have been utilized more in structures where high strength and light weight are major concerns, e.g., aircraft, high-speed boats and sports supplies. It is well known that they are susceptible to damage resulting from lateral impact by foreign objects, such as dropped tools, hail and debris thrown up from the runway. The impact response of the structures depends not only on the material properties but also on the dynamic behavior of the impacted structure. Although commercial software is capable of analyzing such impact processes, it often requires extensive expertise and rigorous training for design and analysis. Analytical models are useful as they allow parametric studies and provide a foundation for validating the numerical results from large-scale commercial software. Therefore, it is necessary to develop analytical or semi-analytical models to better understand the behaviors of composite structures under impact and their associated failure process. In this study, several analytical models are proposed in order to analyze the impact response of composite laminated plates. Based on Meyer's Power Law, a semi-analytical model is obtained for small mass impact response of infinite composite laminates by the method of asymptotic expansion. The original nonlinear second-order ordinary differential equation is transformed into two linear ordinary differential equations. This is achieved by neglecting high-order terms in the asymptotic expansion. As a result, the semi-analytical solution of the overall impact response can be applied to contact laws with varying coefficients. Then an analytical model accounting for permanent deformation based on an elasto-plastic contact law is proposed to obtain the closed-form solutions of the wave-controlled impact responses of composite laminates. The analytical model is also used to predict the threshold velocity for delamination onset by combining with an existing quasi-static delamination criterion. The predictions are compared with experimental data and explicit finite element LS-DYNA simulation. The comparisons show reasonable agreement. Furthermore, an analytical model is developed to evaluate the combined effects of prestresses and permanent deformation based on the linearized elasto-plastic contact law and the Laplace Transform technique. It is demonstrated that prestresses do not have noticeable effects on the time history of contact force and strains, but they have significant consequences on the plate central displacement. For a impacted composite laminate with the presence of prestresses, the contact force increases with the increasing of the mass of impactor, thickness and interlaminar shear strength of the laminate. The combined analytical and numerical investigations provide validated models for elastic and elasto-plastic impact analysis of composite structures and shed light on the design of impact-resistant composite systems.

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.

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.

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.

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

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.

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.

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.

New earth system model for optical performance evaluation of space instruments.

PubMed

Ryu, Dongok; Kim, Sug-Whan; Breault, Robert P

2017-03-06

In this study, a new global earth system model is introduced for evaluating the optical performance of space instruments. Simultaneous imaging and spectroscopic results are provided using this global earth system model with fully resolved spatial, spectral, and temporal coverage of sub-models of the Earth. The sun sub-model is a Lambertian scattering sphere with a 6-h scale and 295 lines of solar spectral irradiance. The atmospheric sub-model has a 15-layer three-dimensional (3D) ellipsoid structure. The land sub-model uses spectral bidirectional reflectance distribution functions (BRDF) defined by a semi-empirical parametric kernel model. The ocean is modeled with the ocean spectral albedo after subtracting the total integrated scattering of the sun-glint scatter model. A hypothetical two-mirror Cassegrain telescope with a 300-mm-diameter aperture and 21.504 mm × 21.504-mm focal plane imaging instrument is designed. The simulated image results are compared with observational data from HRI-VIS measurements during the EPOXI mission for approximately 24 h from UTC Mar. 18, 2008. Next, the defocus mapping result and edge spread function (ESF) measuring result show that the distance between the primary and secondary mirror increases by 55.498 μm from the diffraction-limited condition. The shift of the focal plane is determined to be 5.813 mm shorter than that of the defocused focal plane, and this result is confirmed through the estimation of point spread function (PSF) measurements. This study shows that the earth system model combined with an instrument model is a powerful tool that can greatly help the development phase of instrument missions.

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%.

Semi-span model testing in the National Transonic Facility

NASA Technical Reports Server (NTRS)

Chokani, Ndaona; Milholen, William E., II

1993-01-01

A semi-span testing technique has been proposed for the NASA Langley Research Center's National Transonic Facility (NTF). Semi-span testing has several advantages including (1) larger model size, giving increased Reynolds number capability; (2) improved model fidelity, allowing ease of flap and slat positioning which ultimately improves data quality; and (3) reduced construction costs compared with a full-span model. In addition, the increased model size inherently allows for increased model strength, reducing aeroelastic effects at the high dynamic pressure levels necessary to simulate flight Reynolds numbers. The Energy Efficient Transport (EET) full-span model has been modified to become the EET semi-span model. The full-span EET model was tested extensively at both NASA LRC and NASA Ames Research Center. The available full-span data will be useful in validating the semi-span test strategy in the NTF. In spite of the advantages discussed above, the use of a semi-span model does introduce additional challenges which must be addressed in the testing procedure. To minimize the influence of the sidewall boundary layer on the flow over the semi-span model, the model must be off-set from the sidewall. The objective is to remove the semi-span model from the sidewall boundary layer by use of a stand-off geometry. When this is done however, the symmetry along the centerline of the full-span model is lost when the semi-span model is mounted on the wind tunnel sidewall. In addition, the large semi-span model will impose a significant pressure loading on the sidewall boundary layer, which may cause separation. Even under flow conditions where the sidewall boundary layer remains attached, the sidewall boundary layer may adversely effect the flow over the semi-span model. Also, the increased model size and sidewall mounting requires a modified wall correction strategy. With these issues in mind, the semi-span model has been well instrumented with surface pressure taps to obtain data on the expected complex flow field in the near wall region. This status report summarizes the progress to date on developing the semi-span geometry definition suitable for generating structured grids for the computational research. In addition, the progress on evaluating three state-of-the-art Navier-Stokes codes is presented.

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.

On comparison of net survival curves.

PubMed

Pavlič, Klemen; Perme, Maja Pohar

2017-05-02

Relative survival analysis is a subfield of survival analysis where competing risks data are observed, but the causes of death are unknown. A first step in the analysis of such data is usually the estimation of a net survival curve, possibly followed by regression modelling. Recently, a log-rank type test for comparison of net survival curves has been introduced and the goal of this paper is to explore its properties and put this methodological advance into the context of the field. We build on the association between the log-rank test and the univariate or stratified Cox model and show the analogy in the relative survival setting. We study the properties of the methods using both the theoretical arguments as well as simulations. We provide an R function to enable practical usage of the log-rank type test. Both the log-rank type test and its model alternatives perform satisfactory under the null, even if the correlation between their p-values is rather low, implying that both approaches cannot be used simultaneously. The stratified version has a higher power in case of non-homogeneous hazards, but also carries a different interpretation. The log-rank type test and its stratified version can be interpreted in the same way as the results of an analogous semi-parametric additive regression model despite the fact that no direct theoretical link can be established between the test statistics.

Modification of the nuclear landscape in the inverse problem framework using the generalized Bethe-Weizsäcker mass formula

NASA Astrophysics Data System (ADS)

Mavrodiev, S. Cht.; Deliyergiyev, M. A.

We formalized the nuclear mass problem in the inverse problem framework. This approach allows us to infer the underlying model parameters from experimental observation, rather than to predict the observations from the model parameters. The inverse problem was formulated for the numerically generalized semi-empirical mass formula of Bethe and von Weizsäcker. It was solved in a step-by-step way based on the AME2012 nuclear database. The established parametrization describes the measured nuclear masses of 2564 isotopes with a maximum deviation less than 2.6MeV, starting from the number of protons and number of neutrons equal to 1. The explicit form of unknown functions in the generalized mass formula was discovered in a step-by-step way using the modified least χ2 procedure, that realized in the algorithms which were developed by Lubomir Aleksandrov to solve the nonlinear systems of equations via the Gauss-Newton method, lets us to choose the better one between two functions with same χ2. In the obtained generalized model, the corrections to the binding energy depend on nine proton (2, 8, 14, 20, 28, 50, 82, 108, 124) and ten neutron (2, 8, 14, 20, 28, 50, 82, 124, 152, 202) magic numbers as well on the asymptotic boundaries of their influence. The obtained results were compared with the predictions of other models.

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.

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

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…

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…

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.

Assessment of energy harvesting and vibration mitigation of a pendulum dynamic absorber

NASA Astrophysics Data System (ADS)

Kecik, Krzysztof

2018-06-01

The paper presents a novel system for simultaneous energy harvesting and vibration mitigation. The system consists of two main parts: an autoparametric pendulum vibration absorber and an energy harvester device. The recovered energy is from oscillation of a levitating magnet in a coil. The energy harvesting system is mounted in a pendulum structure. The system allows energy recovery from a semi-trivial solution (pendulum in rest) or/and swinging of a pendulum. The influence of harvester parameters on the system response and energy harvesting in a parametric resonance is studied in detail. The harvester device does not decrease vibration reduction effectiveness.

Lattice QCD Studies of Transverse Momentum-Dependent Parton Distribution Functions

NASA Astrophysics Data System (ADS)

Engelhardt, M.; Musch, B.; Hägler, P.; Negele, J.; Schäfer, A.

2015-09-01

Transverse momentum-dependent parton distributions (TMDs) relevant for semi-inclusive deep inelastic scattering and the Drell-Yan process can be defined in terms of matrix elements of a quark bilocal operator containing a staple-shaped gauge link. Such a definition opens the possibility of evaluating TMDs within lattice QCD. By parametrizing the aforementioned matrix elements in terms of invariant amplitudes, the problem can be cast in a Lorentz frame suited for the lattice calculation. Results for selected TMD observables are presented, including a particular focus on their dependence on a Collins-Soper-type evolution parameter, which quantifies proximity of the staple-shaped gauge links to the light cone.

Transverse Momentum-Dependent Parton Distributions from Lattice QCD

NASA Astrophysics Data System (ADS)

Engelhardt, M.; Musch, B.; Hägler, P.; Negele, J.; Schäfer, A.

Starting from a definition of transverse momentum-dependent parton distributions for semi-inclusive deep inelastic scattering and the Drell-Yan process, given in terms of matrix elements of a quark bilocal operator containing a staple-shaped Wilson connection, a scheme to determine such observables in lattice QCD is developed and explored. Parametrizing the aforementioned matrix elements in terms of invariant amplitudes permits a simple transformation of the problem to a Lorentz frame suited for the lattice calculation. Results for the Sivers and Boer-Mulders transverse momentum shifts are presented, focusing in particular on their dependence on the staple extent and the Collins-Soper evolution parameter.

Transverse Momentum-Dependent Parton Distributions From Lattice QCD

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

Michael Engelhardt, Bernhard Musch, Philipp Haegler, Andreas Schaefer

Starting from a definition of transverse momentum-dependent parton distributions for semi-inclusive deep inelastic scattering and the Drell-Yan process, given in terms of matrix elements of a quark bilocal operator containing a staple-shaped Wilson connection, a scheme to determine such observables in lattice QCD is developed and explored. Parametrizing the aforementioned matrix elements in terms of invariant amplitudes permits a simple transformation of the problem to a Lorentz frame suited for the lattice calculation. Results for the Sivers and Boer-Mulders transverse momentum shifts are presented, focusing in particular on their dependence on the staple extent and the Collins-Soper evolution parameter.

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.

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.

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

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.

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.

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.

Kinetic particle simulation of discharge and wall erosion of a Hall thruster

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

Cho, Shinatora; Komurasaki, Kimiya; Arakawa, Yoshihiro

2013-06-15

The primary lifetime limiting factor of Hall thrusters is the wall erosion caused by the ion induced sputtering, which is predominated by dielectric wall sheath and pre-sheath. However, so far only fluid or hybrid simulation models were applied to wall erosion and lifetime studies in which this non-quasi-neutral and non-equilibrium area cannot be treated directly. Thus, in this study, a 2D fully kinetic particle-in-cell model was presented for Hall thruster discharge and lifetime simulation. Because the fully kinetic lifetime simulation was yet to be achieved so far due to the high computational cost, the semi-implicit field solver and the techniquemore » of mass ratio manipulation was employed to accelerate the computation. However, other artificial manipulations like permittivity or geometry scaling were not used in order to avoid unrecoverable change of physics. Additionally, a new physics recovering model for the mass ratio was presented for better preservation of electron mobility at the weakly magnetically confined plasma region. The validity of the presented model was examined by various parametric studies, and the thrust performance and wall erosion rate of a laboratory model magnetic layer type Hall thruster was modeled for different operation conditions. The simulation results successfully reproduced the measurement results with typically less than 10% discrepancy without tuning any numerical parameters. It is also shown that the computational cost was reduced to the level that the Hall thruster fully kinetic lifetime simulation is feasible.« less

Economic outcomes of maintenance gefitinib for locally advanced/metastatic non-small-cell lung cancer with unknown EGFR mutations: a semi-Markov model analysis.

PubMed

Zeng, Xiaohui; Li, Jianhe; Peng, Liubao; Wang, Yunhua; Tan, Chongqing; Chen, Gannong; Wan, Xiaomin; Lu, Qiong; Yi, Lidan

2014-01-01

Maintenance gefitinib significantly prolonged progression-free survival (PFS) compared with placebo in patients from eastern Asian with locally advanced/metastatic non-small-cell lung cancer (NSCLC) after four chemotherapeutic cycles (21 days per cycle) of first-line platinum-based combination chemotherapy without disease progression. The objective of the current study was to evaluate the cost-effectiveness of maintenance gefitinib therapy after four chemotherapeutic cycle's stand first-line platinum-based chemotherapy for patients with locally advanced or metastatic NSCLC with unknown EGFR mutations, from a Chinese health care system perspective. A semi-Markov model was designed to evaluate cost-effectiveness of the maintenance gefitinib treatment. Two-parametric Weibull and Log-logistic distribution were fitted to PFS and overall survival curves independently. One-way and probabilistic sensitivity analyses were conducted to assess the stability of the model designed. The model base-case analysis suggested that maintenance gefitinib would increase benefits in a 1, 3, 6 or 10-year time horizon, with incremental $184,829, $19,214, $19,328, and $21,308 per quality-adjusted life-year (QALY) gained, respectively. The most sensitive influential variable in the cost-effectiveness analysis was utility of PFS plus rash, followed by utility of PFS plus diarrhoea, utility of progressed disease, price of gefitinib, cost of follow-up treatment in progressed survival state, and utility of PFS on oral therapy. The price of gefitinib is the most significant parameter that could reduce the incremental cost per QALY. Probabilistic sensitivity analysis indicated that the cost-effective probability of maintenance gefitinib was zero under the willingness-to-pay (WTP) threshold of $16,349 (3 × per-capita gross domestic product of China). The sensitivity analyses all suggested that the model was robust. Maintenance gefitinib following first-line platinum-based chemotherapy for patients with locally advanced/metastatic NSCLC with unknown EGFR mutations is not cost-effective. Decreasing the price of gefitinib may be a preferential choice for meeting widely treatment demands in China.

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.

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.

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 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.

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.

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

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.

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.

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.

Soil Moisture Estimate under Forest using a Semi-empirical Model at P-Band

NASA Astrophysics Data System (ADS)

Truong-Loi, M.; Saatchi, S.; Jaruwatanadilok, S.

2013-12-01

In this paper we show the potential of a semi-empirical algorithm to retrieve soil moisture under forests using P-band polarimetric SAR data. In past decades, several remote sensing techniques have been developed to estimate the surface soil moisture. In most studies associated with radar sensing of soil moisture, the proposed algorithms are focused on bare or sparsely vegetated surfaces where the effect of vegetation can be ignored. At long wavelengths such as L-band, empirical or physical models such as the Small Perturbation Model (SPM) provide reasonable estimates of surface soil moisture at depths of 0-5cm. However for densely covered vegetated surfaces such as forests, the problem becomes more challenging because the vegetation canopy is a complex scattering environment. For this reason there have been only few studies focusing on retrieving soil moisture under vegetation canopy in the literature. Moghaddam et al. developed an algorithm to estimate soil moisture under a boreal forest using L- and P-band SAR data. For their studied area, double-bounce between trunks and ground appear to be the most important scattering mechanism. Thereby, they implemented parametric models of radar backscatter for double-bounce using simulations of a numerical forest scattering model. Hajnsek et al. showed the potential of estimating the soil moisture under agricultural vegetation using L-band polarimetric SAR data and using polarimetric-decomposition techniques to remove the vegetation layer. Here we use an approach based on physical formulation of dominant scattering mechanisms and three parameters that integrates the vegetation and soil effects at long wavelengths. The algorithm is a simplification of a 3-D coherent model of forest canopy based on the Distorted Born Approximation (DBA). The simplified model has three equations and three unknowns, preserving the three dominant scattering mechanisms of volume, double-bounce and surface for three polarized backscattering coefficients: σHH, σVV and σHV. The inversion process, which is not an ill-posed problem, uses the non-linear optimization method of Levenberg-Marquardt and estimates the three model parameters: vegetation aboveground biomass, average soil moisture and surface roughness. The model analytical formulation will be first recalled and sensitivity analyses will be shown. Then some results obtained with real SAR data will be presented and compared to ground estimates.

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 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 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.

Offense Trajectories, the Unfolding of Sexual and Non-Sexual Criminal Activity, and Sex Offense Characteristics of Adolescent Sex Offenders.

PubMed

Cale, Jesse; Smallbone, Stephen; Rayment-McHugh, Sue; Dowling, Chris

2016-12-01

The current study examines offending trajectories of adolescent sexual offenders (ASOs). Until recently, classification frameworks have not been designed to account for the heterogeneity of offending patterns in adolescence, how these are associated with the unfolding of sexual and non-sexual criminal activity, and whether and to what extent they are related to the characteristics of sex offenses in adolescence. The current study takes a longitudinal view of offending in adolescence by examining retrospective longitudinal data of 217 ASOs referred for treatment to a clinical service between 2001 and 2009 in Australia. General offending trajectories in adolescence were examined using semi-parametric group-based modeling, and compared according to non-violent non-sexual, violent-non-sexual, and sex offending criminal activity parameters (e.g., participation, onset, frequency, specialization/versatility) and the characteristics of the referral sexual offense. The results show distinct differences in the unfolding of sexual and non-sexual criminal activity along different offending trajectories of ASOs, and further, that these trajectories were differentially associated with the characteristics of the sexual offenses they committed. © The Author(s) 2015.

Transit timing variations for planets co-orbiting in the horseshoe regime

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

Vokrouhlický, David; Nesvorný, David, E-mail: vokrouhl@cesnet.cz, E-mail: davidn@boulder.swri.edu

2014-08-10

Although not yet detected, pairs of exoplanets in 1:1 mean motion resonance probably exist. Low eccentricity, near-planar orbits, which in the comoving frame follow horseshoe trajectories, are one of the possible stable configurations. Here we study transit timing variations (TTVs) produced by mutual gravitational interaction of planets in this orbital architecture, with the goal to develop methods that can be used to recognize this case in observational data. In particular, we use a semi-analytic model to derive parametric constraints that should facilitate data analysis. We show that characteristic traits of the TTVs can directly constrain the (1) ratio of planetarymore » masses and (2) their total mass (divided by that of the central star) as a function of the minimum angular separation as seen from the star. In an ideal case, when transits of both planets are observed and well characterized, the minimum angular separation can also be inferred from the data. As a result, parameters derived from the observed transit timing series alone can directly provide both planetary masses scaled to the central star mass.« less

Exact nonstationary responses of rectangular thin plate on Pasternak foundation excited by stochastic moving loads

NASA Astrophysics Data System (ADS)

Chen, Guohai; Meng, Zeng; Yang, Dixiong

2018-01-01

This paper develops an efficient method termed as PE-PIM to address the exact nonstationary responses of pavement structure, which is modeled as a rectangular thin plate resting on bi-parametric Pasternak elastic foundation subjected to stochastic moving loads with constant acceleration. Firstly, analytical power spectral density (PSD) functions of random responses for thin plate are derived by integrating pseudo excitation method (PEM) with Duhamel's integral. Based on PEM, the new equivalent von Mises stress (NEVMS) is proposed, whose PSD function contains all cross-PSD functions between stress components. Then, the PE-PIM that combines the PEM with precise integration method (PIM) is presented to achieve efficiently stochastic responses of the plate by replacing Duhamel's integral with the PIM. Moreover, the semi-analytical Monte Carlo simulation is employed to verify the computational results of the developed PE-PIM. Finally, numerical examples demonstrate the high accuracy and efficiency of PE-PIM for nonstationary random vibration analysis. The effects of velocity and acceleration of moving load, boundary conditions of the plate and foundation stiffness on the deflection and NEVMS responses are scrutinized.

QCD evolution of the Sivers function

NASA Astrophysics Data System (ADS)

Aybat, S. M.; Collins, J. C.; Qiu, J. W.; Rogers, T. C.

2012-02-01

We extend the Collins-Soper-Sterman (CSS) formalism to apply it to the spin dependence governed by the Sivers function. We use it to give a correct numerical QCD evolution of existing fixed-scale fits of the Sivers function. With the aid of approximations useful for the nonperturbative region, we present the results as parametrizations of a Gaussian form in transverse-momentum space, rather than in the Fourier conjugate transverse coordinate space normally used in the CSS formalism. They are specifically valid at small transverse momentum. Since evolution has been applied, our results can be used to make predictions for Drell-Yan and semi-inclusive deep inelastic scattering at energies different from those where the original fits were made. Our evolved functions are of a form that they can be used in the same parton-model factorization formulas as used in the original fits, but now with a predicted scale dependence in the fit parameters. We also present a method by which our evolved functions can be corrected to allow for twist-3 contributions at large parton transverse momentum.

Field Research and Parametric Analysis in a Medical-Surgical Unit.

PubMed

Nanda, Upali; Pati, Sipra; Nejati, Adeleh

2015-01-01

To study the workplace in a medical-surgical (med-surg) unit and to identify suboptimal environmental conditions that can be improved in the current unit and avoided in future design, through rapidly deployed field research and timely simulation. Literature emphasizes the importance of the healthcare workplace and the effect on patient outcomes. What is lacking are studies conducted on-site and used for immediate application in design to assess and improve workplace conditions. A rapidly deployed field research and simulation study was conducted in a 40-bed med-surg unit of a large healthcare system as part of the process of designing a new medical tower. Online surveys, systematic behavioral observations, semi-structured interviews, sound studies, and advanced spatial analysis through parametric modeling were conducted. The following created challenges for patient monitoring, care coordination, and management: (1) waste and variability in walking, (2) limited point-of-use access to supplies, (3) large distances traveled for minor tasks, and (4) low visibility and connectivity. The corridor is used as a workspace/communication hub. There is a distinct difference in beginning of day and night shift patterns and between walking "distance" and walking "sequence." There is a tendency for nurses to multitask, but a simulation exercise shows that for key tasks like medication delivery, multitasking may not always reduce walking distances. Co-location of medications, supplies, and nourishment; accommodation for work on wheels; and spatial and technological connectivity between care team and patients should be considered while designing a med-surg unit. Understanding the key activity sequences helps determine the proximity of spaces in relationship to patient rooms and each other. © The Author(s) 2015.

The use of experimental data in an MTR-type nuclear reactor safety analysis

NASA Astrophysics Data System (ADS)

Day, Simon E.

Reactivity initiated accidents (RIAs) are a category of events required for research reactor safety analysis. A subset of this is unprotected RIAs in which mechanical systems or human intervention are not credited in the response of the system. Light-water cooled and moderated MTR-type ( i.e., aluminum-clad uranium plate fuel) reactors are self-limiting up to some reactivity insertion limit beyond which fuel damage occurs. This characteristic was studied in the Borax and Spert reactor tests of the 1950s and 1960s in the USA. This thesis considers the use of this experimental data in generic MTR-type reactor safety analysis. The approach presented herein is based on fundamental phenomenological understanding and uses correlations in the reactor test data with suitable account taken for differences in important system parameters. Specifically, a semi-empirical approach is used to quantify the relationship between the power, energy and temperature rise response of the system as well as parametric dependencies on void coefficient and the degree of subcooling. Secondary effects including the dependence on coolant flow are also examined. A rigorous curve fitting approach and error assessment is used to quantify the trends in the experimental data. In addition to the initial power burst stage of an unprotected transient, the longer term stability of the system is considered with a stylized treatment of characteristic power/temperature oscillations (chugging). A bridge from the HEU-based experimental data to the LEU fuel cycle is assessed and outlined based on existing simulation results presented in the literature. A cell-model based parametric study is included. The results are used to construct a practical safety analysis methodology for determining reactivity insertion safety limits for a light-water moderated and cooled MTR-type core.

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.

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

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.

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.

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.

Improved Horvitz-Thompson Estimation of Model Parameters from Two-phase Stratified Samples: Applications in Epidemiology

PubMed Central

Breslow, Norman E.; Lumley, Thomas; Ballantyne, Christie M; Chambless, Lloyd E.; Kulich, Michal

2009-01-01

The case-cohort study involves two-phase sampling: simple random sampling from an infinite super-population at phase one and stratified random sampling from a finite cohort at phase two. Standard analyses of case-cohort data involve solution of inverse probability weighted (IPW) estimating equations, with weights determined by the known phase two sampling fractions. The variance of parameter estimates in (semi)parametric models, including the Cox model, is the sum of two terms: (i) the model based variance of the usual estimates that would be calculated if full data were available for the entire cohort; and (ii) the design based variance from IPW estimation of the unknown cohort total of the efficient influence function (IF) contributions. This second variance component may be reduced by adjusting the sampling weights, either by calibration to known cohort totals of auxiliary variables correlated with the IF contributions or by their estimation using these same auxiliary variables. Both adjustment methods are implemented in the R survey package. We derive the limit laws of coefficients estimated using adjusted weights. The asymptotic results suggest practical methods for construction of auxiliary variables that are evaluated by simulation of case-cohort samples from the National Wilms Tumor Study and by log-linear modeling of case-cohort data from the Atherosclerosis Risk in Communities Study. Although not semiparametric efficient, estimators based on adjusted weights may come close to achieving full efficiency within the class of augmented IPW estimators. PMID:20174455

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

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.

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.

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.

Event-based hydrological modeling for detecting dominant hydrological process and suitable model strategy for semi-arid catchments

NASA Astrophysics Data System (ADS)

Huang, Pengnian; Li, Zhijia; Chen, Ji; Li, Qiaoling; Yao, Cheng

2016-11-01

To simulate the hydrological processes in semi-arid areas properly is still challenging. This study assesses the impact of different modeling strategies on simulating flood processes in semi-arid catchments. Four classic hydrological models, TOPMODEL, XINANJIANG (XAJ), SAC-SMA and TANK, were selected and applied to three semi-arid catchments in North China. Based on analysis and comparison of the simulation results of these classic models, four new flexible models were constructed and used to further investigate the suitability of various modeling strategies for semi-arid environments. Numerical experiments were also designed to examine the performances of the models. The results show that in semi-arid catchments a suitable model needs to include at least one nonlinear component to simulate the main process of surface runoff generation. If there are more than two nonlinear components in the hydrological model, they should be arranged in parallel, rather than in series. In addition, the results show that the parallel nonlinear components should be combined by multiplication rather than addition. Moreover, this study reveals that the key hydrological process over semi-arid catchments is the infiltration excess surface runoff, a non-linear component.

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.

An innovative computationally efficient hydromechanical coupling approach for fault reactivation in geological subsurface utilization

NASA Astrophysics Data System (ADS)

Adams, M.; Kempka, T.; Chabab, E.; Ziegler, M.

2018-02-01

Estimating the efficiency and sustainability of geological subsurface utilization, i.e., Carbon Capture and Storage (CCS) requires an integrated risk assessment approach, considering the occurring coupled processes, beside others, the potential reactivation of existing faults. In this context, hydraulic and mechanical parameter uncertainties as well as different injection rates have to be considered and quantified to elaborate reliable environmental impact assessments. Consequently, the required sensitivity analyses consume significant computational time due to the high number of realizations that have to be carried out. Due to the high computational costs of two-way coupled simulations in large-scale 3D multiphase fluid flow systems, these are not applicable for the purpose of uncertainty and risk assessments. Hence, an innovative semi-analytical hydromechanical coupling approach for hydraulic fault reactivation will be introduced. This approach determines the void ratio evolution in representative fault elements using one preliminary base simulation, considering one model geometry and one set of hydromechanical parameters. The void ratio development is then approximated and related to one reference pressure at the base of the fault. The parametrization of the resulting functions is then directly implemented into a multiphase fluid flow simulator to carry out the semi-analytical coupling for the simulation of hydromechanical processes. Hereby, the iterative parameter exchange between the multiphase and mechanical simulators is omitted, since the update of porosity and permeability is controlled by one reference pore pressure at the fault base. The suggested procedure is capable to reduce the computational time required by coupled hydromechanical simulations of a multitude of injection rates by a factor of up to 15.

Assessing the Three-North Shelter Forest Program in China by a novel framework for characterizing vegetation changes

NASA Astrophysics Data System (ADS)

Qiu, Bingwen; Chen, Gong; Tang, Zhenghong; Lu, Difei; Wang, Zhuangzhuang; Chen, Chongchen

2017-11-01

The Three-North Shelter Forest Program (TNSFP) in China has been intensely invested for approximately 40 years. However, the efficacy of the TNSFP has been debatable due to the spatiotemporal complexity of vegetation changes. A novel framework was proposed for characterizing vegetation changes in the TNSFP region through Combining Trend and Temporal Similarity trajectory (COTTS). This framework could automatically and continuously address the fundamental questions on where, what, how and when vegetation changes have occurred. Vegetation trend was measured by a non-parametric method. The temporal similarity trajectory was tracked by the Jeffries-Matusita (JM) distance of the inter-annual vegetation indices temporal profiles and modeled using the logistic function. The COTTS approach was applied to examine the afforestation efforts of the TNSFP using 500 m 8-day composites MODIS datasets from 2001 to 2015. Accuracy assessment from the 1109 reference sites reveals that the COTTS is capable of automatically determining vegetation dynamic patterns, with an overall accuracy of 90.08% and a kappa coefficient of 0.8688. The efficacy of the TNSFP was evaluated through comprehensive considerations of vegetation, soil and wetness. Around 45.78% areas obtained increasing vegetation trend, 2.96% areas achieved bare soil decline and 4.50% areas exhibited increasing surface wetness. There were 4.49% areas under vegetation degradation & desertification. Spatiotemporal heterogeneity of efficacy of the TNSFP was revealed: great vegetation gain through the abrupt dynamic pattern in the semi-humid and humid regions, bare soil decline & potential efficacy in the semi-arid region and remarkable efficacy in functional region of Eastern Ordos.

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.

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

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.

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.

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.

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.

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.

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.

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.

A review on data mining and continuous optimization applications in computational biology and medicine.

PubMed

Weber, Gerhard-Wilhelm; Ozöğür-Akyüz, Süreyya; Kropat, Erik

2009-06-01

An emerging research area in computational biology and biotechnology is devoted to mathematical modeling and prediction of gene-expression patterns; it nowadays requests mathematics to deeply understand its foundations. This article surveys data mining and machine learning methods for an analysis of complex systems in computational biology. It mathematically deepens recent advances in modeling and prediction by rigorously introducing the environment and aspects of errors and uncertainty into the genetic context within the framework of matrix and interval arithmetics. Given the data from DNA microarray experiments and environmental measurements, we extract nonlinear ordinary differential equations which contain parameters that are to be determined. This is done by a generalized Chebychev approximation and generalized semi-infinite optimization. Then, time-discretized dynamical systems are studied. By a combinatorial algorithm which constructs and follows polyhedra sequences, the region of parametric stability is detected. In addition, we analyze the topological landscape of gene-environment networks in terms of structural stability. As a second strategy, we will review recent model selection and kernel learning methods for binary classification which can be used to classify microarray data for cancerous cells or for discrimination of other kind of diseases. This review is practically motivated and theoretically elaborated; it is devoted to a contribution to better health care, progress in medicine, a better education, and more healthy living conditions.

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.

An investigation of the joint longitudinal trajectories of low body weight, binge eating, and purging in women with anorexia nervosa and bulimia nervosa

PubMed Central

Lavender, Jason M.; De Young, Kyle P.; Franko, Debra L.; Eddy, Kamryn T.; Kass, Andrea E.; Sears, Meredith S.; Herzog, David B.

2015-01-01

Objectives To describe the longitudinal course of three core eating disorder symptoms – low body weight, binge eating, and purging – in women with anorexia nervosa (AN) and bulimia nervosa (BN) using a novel statistical approach. Method Treatment-seeking women with AN (n=136) or BN (n=110) completed the Eating Disorders Longitudinal Interval Follow-Up Evaluation interview every six months, yielding weekly eating disorder symptom data for a five-year period. Semi-parametric mixture modeling was used to identify longitudinal trajectories for the three core symptoms. Results Four individual trajectories were identified for each eating disorder symptom. The number and general shape of the individual trajectories was similar across symptoms, with each model including trajectories depicting stable absence and stable presence of symptoms as well as one or more trajectories depicting the declining presence of symptoms. Unique trajectories were found for low body weight (fluctuating presence) and purging (increasing presence). Conjunction analyses yielded the following joint trajectories: low body weight and binge eating, low body weight and purging, and binge eating and purging. Conclusions The course of individual eating disorder symptoms among patients with AN and BN is highly variable. Future research identifying clinical predictors of trajectory membership may inform treatment and nosological research. PMID:22072404

Trend assessment: applications for hydrology and climate research

NASA Astrophysics Data System (ADS)

Kallache, M.; Rust, H. W.; Kropp, J.

2005-02-01

The assessment of trends in climatology and hydrology still is a matter of debate. Capturing typical properties of time series, like trends, is highly relevant for the discussion of potential impacts of global warming or flood occurrences. It provides indicators for the separation of anthropogenic signals and natural forcing factors by distinguishing between deterministic trends and stochastic variability. In this contribution river run-off data from gauges in Southern Germany are analysed regarding their trend behaviour by combining a deterministic trend component and a stochastic model part in a semi-parametric approach. In this way the trade-off between trend and autocorrelation structure can be considered explicitly. A test for a significant trend is introduced via three steps: First, a stochastic fractional ARIMA model, which is able to reproduce short-term as well as long-term correlations, is fitted to the empirical data. In a second step, wavelet analysis is used to separate the variability of small and large time-scales assuming that the trend component is part of the latter. Finally, a comparison of the overall variability to that restricted to small scales results in a test for a trend. The extraction of the large-scale behaviour by wavelet analysis provides a clue concerning the shape of the trend.

RTD-based Material Tracking in a Fully-Continuous Dry Granulation Tableting Line.

PubMed

Martinetz, M C; Karttunen, A-P; Sacher, S; Wahl, P; Ketolainen, J; Khinast, J G; Korhonen, O

2018-06-06

Continuous manufacturing (CM) offers quality and cost-effectiveness benefits over currently dominating batch processing. One challenge that needs to be addressed when implementing CM is traceability of materials through the process, which is needed for the batch/lot definition and control strategy. In this work the residence time distributions (RTD) of single unit operations (blender, roller compactor and tablet press) of a continuous dry granulation tableting line were captured with NIR based methods at selected mass flow rates to create training data. RTD models for continuous operated unit operations and the entire line were developed based on transfer functions. For semi-continuously operated bucket conveyor and pneumatic transport an assumption based the operation frequency was used. For validation of the parametrized process model, a pre-defined API step change and its propagation through the manufacturing line was computed and compared to multi-scale experimental runs conducted with the fully assembled continuous operated manufacturing line. This novel approach showed a very good prediction power at the selected mass flow rates for a complete continuous dry granulation line. Furthermore, it shows and proves the capabilities of process simulation as a tool to support development and control of pharmaceutical manufacturing processes. Copyright © 2018. Published by Elsevier B.V.

Automatic assessment of dynamic contrast-enhanced MRI in an ischemic rat hindlimb model: an exploratory study of transplanted multipotent progenitor cells.

PubMed

Hsu, Li-Yueh; Wragg, Andrew; Anderson, Stasia A; Balaban, Robert S; Boehm, Manfred; Arai, Andrew E

2008-02-01

This study presents computerized automatic image analysis for quantitatively evaluating dynamic contrast-enhanced MRI in an ischemic rat hindlimb model. MRI at 7 T was performed on animals in a blinded placebo-controlled experiment comparing multipotent adult progenitor cell-derived progenitor cell (MDPC)-treated, phosphate buffered saline (PBS)-injected, and sham-operated rats. Ischemic and non-ischemic limb regions of interest were automatically segmented from time-series images for detecting changes in perfusion and late enhancement. In correlation analysis of the time-signal intensity histograms, the MDPC-treated limbs correlated well with their corresponding non-ischemic limbs. However, the correlation coefficient of the PBS control group was significantly lower than that of the MDPC-treated and sham-operated groups. In semi-quantitative parametric maps of contrast enhancement, there was no significant difference in hypo-enhanced area between the MDPC and PBS groups at early perfusion-dependent time frames. However, the late-enhancement area was significantly larger in the PBS than the MDPC group. The results of this exploratory study show that MDPC-treated rats could be objectively distinguished from PBS controls. The differences were primarily determined by late contrast enhancement of PBS-treated limbs. These computerized methods appear promising for assessing perfusion and late enhancement in dynamic contrast-enhanced MRI.

Identifying depressive symptom trajectory groups among Korean adults and psychosocial factors as group determinants.

PubMed

Kwon, Tae Yeon

2015-06-01

Longitudinal research is needed to examine the depressive symptom trajectories of different groups during adulthood and their antecedents and consequences, because depressive symptoms may be changeable and heterogeneous over time. This study examined the number of trajectory groups describing the depressive symptoms of Korean adults, as well as the shape of the trajectories and the association between trajectory group membership and psychosocial factors identified based on the ecosystem model. This study used Nagin's semi-parametric group-based modeling to analyze Year 1 to Year 7 data from Korea Welfare Panel Survey (N = 13,735), a nationally representative sample of community-dwelling adults. Three distinct trajectory groups were identified: a low stable depressive symptoms group, a moderate depressive symptoms group and a high depressive symptoms group. Result from multinominal logit analysis showed that all psychosocial factors except family relationships affected the likelihood of membership in the three depressive symptoms groups. Especially, self-esteem was the psychosocial factor with the largest impact on depressive symptom trajectory group membership. When screening for depressive symptoms, individuals with a low socioeconomic status should be a primary concern and intervention should be made available to them. Prevention or intervention with members of the identified trajectory groups would likely require integrative approaches targeting psychosocial factors across multiple contexts. © The Author(s) 2015.

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.

MODEL DEVELOPMENT AND TESTING FOR SEMI-VOLATILES (ATRAZINE)

EPA Science Inventory

The Community Multi-Scale Air Quality (CMAQ) model, air quality model within EPA's Models-3 system, can be adapted to simulate the fate of semi-volatile compounds that are emitted into the atmosphere. "Semi-volatile" refers to compounds that partition their mass between two ph...

Short-term forecasting of meteorological time series using Nonparametric Functional Data Analysis (NPFDA)

NASA Astrophysics Data System (ADS)

Curceac, S.; Ternynck, C.; Ouarda, T.

2015-12-01

Over the past decades, a substantial amount of research has been conducted to model and forecast climatic variables. In this study, Nonparametric Functional Data Analysis (NPFDA) methods are applied to forecast air temperature and wind speed time series in Abu Dhabi, UAE. The dataset consists of hourly measurements recorded for a period of 29 years, 1982-2010. The novelty of the Functional Data Analysis approach is in expressing the data as curves. In the present work, the focus is on daily forecasting and the functional observations (curves) express the daily measurements of the above mentioned variables. We apply a non-linear regression model with a functional non-parametric kernel estimator. The computation of the estimator is performed using an asymmetrical quadratic kernel function for local weighting based on the bandwidth obtained by a cross validation procedure. The proximities between functional objects are calculated by families of semi-metrics based on derivatives and Functional Principal Component Analysis (FPCA). Additionally, functional conditional mode and functional conditional median estimators are applied and the advantages of combining their results are analysed. A different approach employs a SARIMA model selected according to the minimum Akaike (AIC) and Bayessian (BIC) Information Criteria and based on the residuals of the model. The performance of the models is assessed by calculating error indices such as the root mean square error (RMSE), relative RMSE, BIAS and relative BIAS. The results indicate that the NPFDA models provide more accurate forecasts than the SARIMA models. Key words: Nonparametric functional data analysis, SARIMA, time series forecast, air temperature, wind speed

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

Lewis, John R.; Brooks, Dusty Marie

In pressurized water reactors, the prevention, detection, and repair of cracks within dissimilar metal welds is essential to ensure proper plant functionality and safety. Weld residual stresses, which are difficult to model and cannot be directly measured, contribute to the formation and growth of cracks due to primary water stress corrosion cracking. Additionally, the uncertainty in weld residual stress measurements and modeling predictions is not well understood, further complicating the prediction of crack evolution. The purpose of this document is to develop methodology to quantify the uncertainty associated with weld residual stress that can be applied to modeling predictions andmore » experimental measurements. Ultimately, the results can be used to assess the current state of uncertainty and to build confidence in both modeling and experimental procedures. The methodology consists of statistically modeling the variation in the weld residual stress profiles using functional data analysis techniques. Uncertainty is quantified using statistical bounds (e.g. confidence and tolerance bounds) constructed with a semi-parametric bootstrap procedure. Such bounds describe the range in which quantities of interest, such as means, are expected to lie as evidenced by the data. The methodology is extended to provide direct comparisons between experimental measurements and modeling predictions by constructing statistical confidence bounds for the average difference between the two quantities. The statistical bounds on the average difference can be used to assess the level of agreement between measurements and predictions. The methodology is applied to experimental measurements of residual stress obtained using two strain relief measurement methods and predictions from seven finite element models developed by different organizations during a round robin study.« less

The impact of galaxy formation on satellite kinematics and redshift-space distortions

NASA Astrophysics Data System (ADS)

Orsi, Álvaro A.; Angulo, Raúl E.

2018-04-01

Galaxy surveys aim to map the large-scale structure of the Universe and use redshift-space distortions to constrain deviations from general relativity and probe the existence of massive neutrinos. However, the amount of information that can be extracted is limited by the accuracy of theoretical models used to analyse the data. Here, by using the L-Galaxies semi-analytical model run over the Millennium-XXL N-body simulation, we assess the impact of galaxy formation on satellite kinematics and the theoretical modelling of redshift-space distortions. We show that different galaxy selection criteria lead to noticeable differences in the radial distributions and velocity structure of satellite galaxies. Specifically, whereas samples of stellar mass selected galaxies feature satellites that roughly follow the dark matter, emission line satellite galaxies are located preferentially in the outskirts of haloes and display net infall velocities. We demonstrate that capturing these differences is crucial for modelling the multipoles of the correlation function in redshift space, even on large scales. In particular, we show how modelling small-scale velocities with a single Gaussian distribution leads to a poor description of the measured clustering. In contrast, we propose a parametrization that is flexible enough to model the satellite kinematics and that leads to an accurate description of the correlation function down to sub-Mpc scales. We anticipate that our model will be a necessary ingredient in improved theoretical descriptions of redshift-space distortions, which together could result in significantly tighter cosmological constraints and a more optimal exploitation of future large data sets.

Integrated ray tracing simulation of annual variation of spectral bio-signatures from cloud free 3D optical Earth model

NASA Astrophysics Data System (ADS)

Ryu, Dongok; Kim, Sug-Whan; Kim, Dae Wook; Lee, Jae-Min; Lee, Hanshin; Park, Won Hyun; Seong, Sehyun; Ham, Sun-Jeong

2010-09-01

Understanding the Earth spectral bio-signatures provides an important reference datum for accurate de-convolution of collapsed spectral signals from potential earth-like planets of other star systems. This study presents a new ray tracing computation method including an improved 3D optical earth model constructed with the coastal line and vegetation distribution data from the Global Ecological Zone (GEZ) map. Using non-Lambertian bidirectional scattering distribution function (BSDF) models, the input earth surface model is characterized with three different scattering properties and their annual variations depending on monthly changes in vegetation distribution, sea ice coverage and illumination angle. The input atmosphere model consists of one layer with Rayleigh scattering model from the sea level to 100 km in altitude and its radiative transfer characteristics is computed for four seasons using the SMART codes. The ocean scattering model is a combination of sun-glint scattering and Lambertian scattering models. The land surface scattering is defined with the semi empirical parametric kernel method used for MODIS and POLDER missions. These three component models were integrated into the final Earth model that was then incorporated into the in-house built integrated ray tracing (IRT) model capable of computing both spectral imaging and radiative transfer performance of a hypothetical space instrument as it observes the Earth from its designated orbit. The IRT model simulation inputs include variation in earth orientation, illuminated phases, and seasonal sea ice and vegetation distribution. The trial simulation runs result in the annual variations in phase dependent disk averaged spectra (DAS) and its associated bio-signatures such as NDVI. The full computational details are presented together with the resulting annual variation in DAS and its associated bio-signatures.

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.

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

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.

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.

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.

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.

Lyapounov Functions of Closed Cone Fields: From Conley Theory to Time Functions

NASA Astrophysics Data System (ADS)

Bernard, Patrick; Suhr, Stefan

2018-03-01

We propose a theory "à la Conley" for cone fields using a notion of relaxed orbits based on cone enlargements, in the spirit of space time geometry. We work in the setting of closed (or equivalently semi-continuous) cone fields with singularities. This setting contains (for questions which are parametrization independent such as the existence of Lyapounov functions) the case of continuous vector-fields on manifolds, of differential inclusions, of Lorentzian metrics, and of continuous cone fields. We generalize to this setting the equivalence between stable causality and the existence of temporal functions. We also generalize the equivalence between global hyperbolicity and the existence of a steep temporal function.

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.

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.

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.

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.

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.

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…

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.

Instrumental variables estimation of exposure effects on a time-to-event endpoint using structural cumulative survival models.

PubMed

Martinussen, Torben; Vansteelandt, Stijn; Tchetgen Tchetgen, Eric J; Zucker, David M

2017-12-01

The use of instrumental variables for estimating the effect of an exposure on an outcome is popular in econometrics, and increasingly so in epidemiology. This increasing popularity may be attributed to the natural occurrence of instrumental variables in observational studies that incorporate elements of randomization, either by design or by nature (e.g., random inheritance of genes). Instrumental variables estimation of exposure effects is well established for continuous outcomes and to some extent for binary outcomes. It is, however, largely lacking for time-to-event outcomes because of complications due to censoring and survivorship bias. In this article, we make a novel proposal under a class of structural cumulative survival models which parameterize time-varying effects of a point exposure directly on the scale of the survival function; these models are essentially equivalent with a semi-parametric variant of the instrumental variables additive hazards model. We propose a class of recursive instrumental variable estimators for these exposure effects, and derive their large sample properties along with inferential tools. We examine the performance of the proposed method in simulation studies and illustrate it in a Mendelian randomization study to evaluate the effect of diabetes on mortality using data from the Health and Retirement Study. We further use the proposed method to investigate potential benefit from breast cancer screening on subsequent breast cancer mortality based on the HIP-study. © 2017, The International Biometric Society.

Numerical developments for short-pulsed Near Infra-Red laser spectroscopy. Part I: direct treatment

NASA Astrophysics Data System (ADS)

Boulanger, Joan; Charette, André

2005-03-01

This two part study is devoted to the numerical treatment of short-pulsed laser near infra-red spectroscopy. The overall goal is to address the possibility of numerical inverse treatment based on a recently developed direct model to solve the transient radiative transfer equation. This model has been constructed in order to incorporate the last improvements in short-pulsed laser interaction with semi-transparent media and combine a discrete ordinates computing of the implicit source term appearing in the radiative transfer equation with an explicit treatment of the transport of the light intensity using advection schemes, a method encountered in reactive flow dynamics. The incident collimated beam is analytically solved through Bouger Beer Lambert extinction law. In this first part, the direct model is extended to fully non-homogeneous materials and tested with two different spatial schemes in order to be adapted to the inversion methods presented in the following second part. As a first point, fundamental methods and schemes used in the direct model are presented. Then, tests are conducted by comparison with numerical simulations given as references. In a third and last part, multi-dimensional extensions of the code are provided. This allows presentation of numerical results of short pulses propagation in 1, 2 and 3D homogeneous and non-homogeneous materials given some parametrical studies on medium properties and pulse shape. For comparison, an integral method adapted to non-homogeneous media irradiated by a pulsed laser beam is also developed for the 3D case.

Sufficient Forecasting Using Factor Models

PubMed Central

Fan, Jianqing; Xue, Lingzhou; Yao, Jiawei

2017-01-01

We consider forecasting a single time series when there is a large number of predictors and a possible nonlinear effect. The dimensionality was first reduced via a high-dimensional (approximate) factor model implemented by the principal component analysis. Using the extracted factors, we develop a novel forecasting method called the sufficient forecasting, which provides a set of sufficient predictive indices, inferred from high-dimensional predictors, to deliver additional predictive power. The projected principal component analysis will be employed to enhance the accuracy of inferred factors when a semi-parametric (approximate) factor model is assumed. Our method is also applicable to cross-sectional sufficient regression using extracted factors. The connection between the sufficient forecasting and the deep learning architecture is explicitly stated. The sufficient forecasting correctly estimates projection indices of the underlying factors even in the presence of a nonparametric forecasting function. The proposed method extends the sufficient dimension reduction to high-dimensional regimes by condensing the cross-sectional information through factor models. We derive asymptotic properties for the estimate of the central subspace spanned by these projection directions as well as the estimates of the sufficient predictive indices. We further show that the natural method of running multiple regression of target on estimated factors yields a linear estimate that actually falls into this central subspace. Our method and theory allow the number of predictors to be larger than the number of observations. We finally demonstrate that the sufficient forecasting improves upon the linear forecasting in both simulation studies and an empirical study of forecasting macroeconomic variables. PMID:29731537

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.

Supercritical nonlinear parametric dynamics of Timoshenko microbeams

NASA Astrophysics Data System (ADS)

Farokhi, Hamed; Ghayesh, Mergen H.

2018-06-01

The nonlinear supercritical parametric dynamics of a Timoshenko microbeam subject to an axial harmonic excitation force is examined theoretically, by means of different numerical techniques, and employing a high-dimensional analysis. The time-variant axial load is assumed to consist of a mean value along with harmonic fluctuations. In terms of modelling, a continuous expression for the elastic potential energy of the system is developed based on the modified couple stress theory, taking into account small-size effects; the kinetic energy of the system is also modelled as a continuous function of the displacement field. Hamilton's principle is employed to balance the energies and to obtain the continuous model of the system. Employing the Galerkin scheme along with an assumed-mode technique, the energy terms are reduced, yielding a second-order reduced-order model with finite number of degrees of freedom. A transformation is carried out to convert the second-order reduced-order model into a double-dimensional first order one. A bifurcation analysis is performed for the system in the absence of the axial load fluctuations. Moreover, a mean value for the axial load is selected in the supercritical range, and the principal parametric resonant response, due to the time-variant component of the axial load, is obtained - as opposed to transversely excited systems, for parametrically excited system (such as our problem here), the nonlinear resonance occurs in the vicinity of twice any natural frequency of the linear system; this is accomplished via use of the pseudo-arclength continuation technique, a direct time integration, an eigenvalue analysis, and the Floquet theory for stability. The natural frequencies of the system prior to and beyond buckling are also determined. Moreover, the effect of different system parameters on the nonlinear supercritical parametric dynamics of the system is analysed, with special consideration to the effect of the length-scale parameter.

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.

Parametric second Stokes Raman laser output pulse shortening to 300 ps due to depletion of pumping of intracavity Raman conversion

NASA Astrophysics Data System (ADS)

Smetanin, S. N.; Jelínek, M.; Kubeček, V.; Jelínková, H.; Ivleva, L. I.

2016-10-01

A new effect of the pulse shortening of the parametrically generated radiation down to hundreds of picosecond via depletion of pumping of intracavity Raman conversion in the miniature passively Q-switched Nd: SrMoO4 parametric self-Raman laser with the increasing energy of the shortened pulse under pulsed pumping by a high-power laser diode bar is demonstrated. The theoretical estimation of the depletion stage duration of the convertible fundamental laser radiation via intracavity Raman conversion is in agreement with the experimentally demonstrated duration of the parametrically generated pulse. Using the mathematical modeling of the pulse shortening quality and quantity deterioration is disclosed, and the solution ways are found by the optimization of the laser parameters.

Parametric pendulum based wave energy converter

NASA Astrophysics Data System (ADS)

Yurchenko, Daniil; Alevras, Panagiotis

2018-01-01

The paper investigates the dynamics of a novel wave energy converter based on the parametrically excited pendulum. The herein developed concept of the parametric pendulum allows reducing the influence of the gravity force thereby significantly improving the device performance at a regular sea state, which could not be achieved in the earlier proposed original point-absorber design. The suggested design of a wave energy converter achieves a dominant rotational motion without any additional mechanisms, like a gearbox, or any active control involvement. Presented numerical results of deterministic and stochastic modeling clearly reflect the advantage of the proposed design. A set of experimental results confirms the numerical findings and validates the new design of a parametric pendulum based wave energy converter. Power harvesting potential of the novel device is also presented.

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.

Non-linear auto-regressive models for cross-frequency coupling in neural time series

PubMed Central

Tallot, Lucille; Grabot, Laetitia; Doyère, Valérie; Grenier, Yves; Gramfort, Alexandre

2017-01-01

We address the issue of reliably detecting and quantifying cross-frequency coupling (CFC) in neural time series. Based on non-linear auto-regressive models, the proposed method provides a generative and parametric model of the time-varying spectral content of the signals. As this method models the entire spectrum simultaneously, it avoids the pitfalls related to incorrect filtering or the use of the Hilbert transform on wide-band signals. As the model is probabilistic, it also provides a score of the model “goodness of fit” via the likelihood, enabling easy and legitimate model selection and parameter comparison; this data-driven feature is unique to our model-based approach. Using three datasets obtained with invasive neurophysiological recordings in humans and rodents, we demonstrate that these models are able to replicate previous results obtained with other metrics, but also reveal new insights such as the influence of the amplitude of the slow oscillation. Using simulations, we demonstrate that our parametric method can reveal neural couplings with shorter signals than non-parametric methods. We also show how the likelihood can be used to find optimal filtering parameters, suggesting new properties on the spectrum of the driving signal, but also to estimate the optimal delay between the coupled signals, enabling a directionality estimation in the coupling. PMID:29227989

Development of an acoustic filter for parametric loudspeaker using phononic crystals.

PubMed

Ji, Peifeng; Hu, Wenlin; Yang, Jun

2016-04-01

The spurious signal generated as a result of nonlinearity at the receiving system affects the measurement of the difference-frequency sound in the parametric loudspeaker, especially in the nearfield or near the beam axis. In this paper, an acoustic filter is designed using phononic crystals and its theoretical simulations are carried out by quasi-one- and two-dimensional models with Comsol Multiphysics. According to the simulated transmission loss (TL), an acoustic filter is prototyped consisting of 5×7 aluminum alloy cylinders and its performance is verified experimentally. There is good agreement with the simulation result for TL. After applying our proposed filter in the axial measurement of the parametric loudspeaker, a clear frequency dependence from parametric array effect is detected, which exhibits a good match with the well-known theory described by the Gaussian-beam expansion technique. During the directivity measurement for the parametric loudspeaker, the proposed filter has also proved to be effective and is only needed for small angles. Copyright © 2016 Elsevier B.V. All rights reserved.

Contaminant transport in soil with depth-dependent reaction coefficients and time-dependent boundary conditions.

PubMed

Gao, Guangyao; Fu, Bojie; Zhan, Hongbin; Ma, Ying

2013-05-01

Predicting the fate and movement of contaminant in soils and groundwater is essential to assess and reduce the risk of soil contamination and groundwater pollution. Reaction processes of contaminant often decreased monotonously with depth. Time-dependent input sources usually occurred at the inlet of natural or human-made system such as radioactive waste disposal site. This study presented a one-dimensional convection-dispersion equation (CDE) for contaminant transport in soils with depth-dependent reaction coefficients and time-dependent inlet boundary conditions, and derived its analytical solution. The adsorption coefficient and degradation rate were represented as sigmoidal functions of soil depth. Solute breakthrough curves (BTCs) and concentration profiles obtained from CDE with depth-dependent and constant reaction coefficients were compared, and a constant effective reaction coefficient, which was calculated by arithmetically averaging the depth-dependent reaction coefficient, was proposed to reflect the lumped depth-dependent reaction effect. With the effective adsorption coefficient and degradation rate, CDE could produce similar BTCs and concentration profiles as those from CDE with depth-dependent reactions in soils with moderate chemical heterogeneity. In contrast, the predicted concentrations of CDE with fitted reaction coefficients at a certain depth departed significantly from those of CDE with depth-dependent reactions. Parametric analysis was performed to illustrate the effects of sinusoidally and exponentially decaying input functions on solute BTCs. The BTCs and concentration profiles obtained from the solutions for finite and semi-infinite domain were compared to investigate the effects of effluent boundary condition. The finite solution produced higher concentrations at the increasing limb of the BTCs and possessed a higher peak concentration than the semi-infinite solution which had a slightly long tail. Furthermore, the finite solution gave a higher concentration in the immediate vicinity of the exit boundary than the semi-infinite solution. The applicability of the proposed model was tested with a field herbicide and tracer leaching experiment in an agricultural area of northeastern Greece. The simulation results indicated that the proposed CDE with depth-dependent reaction coefficients was able to capture the evolution of metolachlor concentration at the upper soil depths. However, the simulation results at deep depths were not satisfactory as the proposed model did not account for preferential flow observed in the field. Copyright © 2013 Elsevier Ltd. All rights reserved.