A simplified and powerful image processing methods to separate Thai jasmine rice and sticky rice varieties

NASA Astrophysics Data System (ADS)

Khondok, Piyoros; Sakulkalavek, Aparporn; Suwansukho, Kajpanya

2018-03-01

A simplified and powerful image processing procedures to separate the paddy of KHAW DOK MALI 105 or Thai jasmine rice and the paddy of sticky rice RD6 varieties were proposed. The procedures consist of image thresholding, image chain coding and curve fitting using polynomial function. From the fitting, three parameters of each variety, perimeters, area, and eccentricity, were calculated. Finally, the overall parameters were determined by using principal component analysis. The result shown that these procedures can be significantly separate both varieties.

Geometry modeling of single track cladding deposited by high power diode laser with rectangular beam spot

NASA Astrophysics Data System (ADS)

Liu, Huaming; Qin, Xunpeng; Huang, Song; Hu, Zeqi; Ni, Mao

2018-01-01

This paper presents an investigation on the relationship between the process parameters and geometrical characteristics of the sectional profile for the single track cladding (STC) deposited by High Power Diode Laser (HPDL) with rectangle beam spot (RBS). To obtain the geometry parameters, namely cladding width Wc and height Hc of the sectional profile, a full factorial design (FFD) of experiment was used to conduct the experiments with a total of 27. The pre-placed powder technique has been employed during laser cladding. The influence of the process parameters including laser power, powder thickness and scanning speed on the Wc and Hc was analyzed in detail. A nonlinear fitting model was used to fit the relationship between the process parameters and geometry parameters. And a circular arc was adopted to describe the geometry profile of the cross-section of STC. The above models were confirmed by all the experiments. The results indicated that the geometrical characteristics of the sectional profile of STC can be described as the circular arc, and the other geometry parameters of the sectional profile can be calculated only using Wc and Hc. Meanwhile, the Wc and Hc can be predicted through the process parameters.

Model-based analysis of coupled equilibrium-kinetic processes: indirect kinetic studies of thermodynamic parameters using the dynamic data.

PubMed

Emami, Fereshteh; Maeder, Marcel; Abdollahi, Hamid

2015-05-07

Thermodynamic studies of equilibrium chemical reactions linked with kinetic procedures are mostly impossible by traditional approaches. In this work, the new concept of generalized kinetic study of thermodynamic parameters is introduced for dynamic data. The examples of equilibria intertwined with kinetic chemical mechanisms include molecular charge transfer complex formation reactions, pH-dependent degradation of chemical compounds and tautomerization kinetics in micellar solutions. Model-based global analysis with the possibility of calculating and embedding the equilibrium and kinetic parameters into the fitting algorithm has allowed the complete analysis of the complex reaction mechanisms. After the fitting process, the optimal equilibrium and kinetic parameters together with an estimate of their standard deviations have been obtained. This work opens up a promising new avenue for obtaining equilibrium constants through the kinetic data analysis for the kinetic reactions that involve equilibrium processes.

An indirect method of imaging the Stokes parameters of a submicron particle with sub-diffraction scattering

NASA Astrophysics Data System (ADS)

Ullah, Kaleem; Garcia-Camara, Braulio; Habib, Muhammad; Yadav, N. P.; Liu, Xuefeng

2018-07-01

In this work, we report an indirect way to image the Stokes parameters of a sample under test (SUT) with sub-diffraction scattering information. We apply our previously reported technique called parametric indirect microscopic imaging (PIMI) based on a fitting and filtration process to measure the Stokes parameters of a submicron particle. A comparison with a classical Stokes measurement is also shown. By modulating the incident field in a precise way, fitting and filtration process at each pixel of the detector in PIMI make us enable to resolve and sense the scattering information of SUT and map them in terms of the Stokes parameters. We believe that our finding can be very useful in fields like singular optics, optical nanoantenna, biomedicine and much more. The spatial signature of the Stokes parameters given by our method has been confirmed with finite difference time domain (FDTD) method.

Intelligent methods for the process parameter determination of plastic injection molding

NASA Astrophysics Data System (ADS)

Gao, Huang; Zhang, Yun; Zhou, Xundao; Li, Dequn

2018-03-01

Injection molding is one of the most widely used material processing methods in producing plastic products with complex geometries and high precision. The determination of process parameters is important in obtaining qualified products and maintaining product quality. This article reviews the recent studies and developments of the intelligent methods applied in the process parameter determination of injection molding. These intelligent methods are classified into three categories: Case-based reasoning methods, expert system- based methods, and data fitting and optimization methods. A framework of process parameter determination is proposed after comprehensive discussions. Finally, the conclusions and future research topics are discussed.

Mechanistic equivalent circuit modelling of a commercial polymer electrolyte membrane fuel cell

NASA Astrophysics Data System (ADS)

Giner-Sanz, J. J.; Ortega, E. M.; Pérez-Herranz, V.

2018-03-01

Electrochemical impedance spectroscopy (EIS) has been widely used in the fuel cell field since it allows deconvolving the different physic-chemical processes that affect the fuel cell performance. Typically, EIS spectra are modelled using electric equivalent circuits. In this work, EIS spectra of an individual cell of a commercial PEM fuel cell stack were obtained experimentally. The goal was to obtain a mechanistic electric equivalent circuit in order to model the experimental EIS spectra. A mechanistic electric equivalent circuit is a semiempirical modelling technique which is based on obtaining an equivalent circuit that does not only correctly fit the experimental spectra, but which elements have a mechanistic physical meaning. In order to obtain the aforementioned electric equivalent circuit, 12 different models with defined physical meanings were proposed. These equivalent circuits were fitted to the obtained EIS spectra. A 2 step selection process was performed. In the first step, a group of 4 circuits were preselected out of the initial list of 12, based on general fitting indicators as the determination coefficient and the fitted parameter uncertainty. In the second step, one of the 4 preselected circuits was selected on account of the consistency of the fitted parameter values with the physical meaning of each parameter.

a R-Shiny Based Phenology Analysis System and Case Study Using Digital Camera Dataset

NASA Astrophysics Data System (ADS)

Zhou, Y. K.

2018-05-01

Accurate extracting of the vegetation phenology information play an important role in exploring the effects of climate changes on vegetation. Repeated photos from digital camera is a useful and huge data source in phonological analysis. Data processing and mining on phenological data is still a big challenge. There is no single tool or a universal solution for big data processing and visualization in the field of phenology extraction. In this paper, we proposed a R-shiny based web application for vegetation phenological parameters extraction and analysis. Its main functions include phenological site distribution visualization, ROI (Region of Interest) selection, vegetation index calculation and visualization, data filtering, growth trajectory fitting, phenology parameters extraction, etc. the long-term observation photography data from Freemanwood site in 2013 is processed by this system as an example. The results show that: (1) this system is capable of analyzing large data using a distributed framework; (2) The combination of multiple parameter extraction and growth curve fitting methods could effectively extract the key phenology parameters. Moreover, there are discrepancies between different combination methods in unique study areas. Vegetation with single-growth peak is suitable for using the double logistic module to fit the growth trajectory, while vegetation with multi-growth peaks should better use spline method.

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

NASA Astrophysics Data System (ADS)

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

2017-01-01

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

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

PubMed

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

2017-01-04

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

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

PubMed Central

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

2017-01-01

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

On-Ground Processing of Yaogan-24 Remote Sensing Satellite Attitude Data and Verification Using Geometric Field Calibration

PubMed Central

Wang, Mi; Fan, Chengcheng; Yang, Bo; Jin, Shuying; Pan, Jun

2016-01-01

Satellite attitude accuracy is an important factor affecting the geometric processing accuracy of high-resolution optical satellite imagery. To address the problem whereby the accuracy of the Yaogan-24 remote sensing satellite’s on-board attitude data processing is not high enough and thus cannot meet its image geometry processing requirements, we developed an approach involving on-ground attitude data processing and digital orthophoto (DOM) and the digital elevation model (DEM) verification of a geometric calibration field. The approach focuses on three modules: on-ground processing based on bidirectional filter, overall weighted smoothing and fitting, and evaluation in the geometric calibration field. Our experimental results demonstrate that the proposed on-ground processing method is both robust and feasible, which ensures the reliability of the observation data quality, convergence and stability of the parameter estimation model. In addition, both the Euler angle and quaternion could be used to build a mathematical fitting model, while the orthogonal polynomial fitting model is more suitable for modeling the attitude parameter. Furthermore, compared to the image geometric processing results based on on-board attitude data, the image uncontrolled and relative geometric positioning result accuracy can be increased by about 50%. PMID:27483287

Hybrid PSO-ASVR-based method for data fitting in the calibration of infrared radiometer

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

Yang, Sen; Li, Chengwei, E-mail: heikuanghit@163.com

2016-06-15

The present paper describes a hybrid particle swarm optimization-adaptive support vector regression (PSO-ASVR)-based method for data fitting in the calibration of infrared radiometer. The proposed hybrid PSO-ASVR-based method is based on PSO in combination with Adaptive Processing and Support Vector Regression (SVR). The optimization technique involves setting parameters in the ASVR fitting procedure, which significantly improves the fitting accuracy. However, its use in the calibration of infrared radiometer has not yet been widely explored. Bearing this in mind, the PSO-ASVR-based method, which is based on the statistical learning theory, is successfully used here to get the relationship between the radiationmore » of a standard source and the response of an infrared radiometer. Main advantages of this method are the flexible adjustment mechanism in data processing and the optimization mechanism in a kernel parameter setting of SVR. Numerical examples and applications to the calibration of infrared radiometer are performed to verify the performance of PSO-ASVR-based method compared to conventional data fitting methods.« less

Application of separable parameter space techniques to multi-tracer PET compartment modeling.

PubMed

Zhang, Jeff L; Michael Morey, A; Kadrmas, Dan J

2016-02-07

Multi-tracer positron emission tomography (PET) can image two or more tracers in a single scan, characterizing multiple aspects of biological functions to provide new insights into many diseases. The technique uses dynamic imaging, resulting in time-activity curves that contain contributions from each tracer present. The process of separating and recovering separate images and/or imaging measures for each tracer requires the application of kinetic constraints, which are most commonly applied by fitting parallel compartment models for all tracers. Such multi-tracer compartment modeling presents challenging nonlinear fits in multiple dimensions. This work extends separable parameter space kinetic modeling techniques, previously developed for fitting single-tracer compartment models, to fitting multi-tracer compartment models. The multi-tracer compartment model solution equations were reformulated to maximally separate the linear and nonlinear aspects of the fitting problem, and separable least-squares techniques were applied to effectively reduce the dimensionality of the nonlinear fit. The benefits of the approach are then explored through a number of illustrative examples, including characterization of separable parameter space multi-tracer objective functions and demonstration of exhaustive search fits which guarantee the true global minimum to within arbitrary search precision. Iterative gradient-descent algorithms using Levenberg-Marquardt were also tested, demonstrating improved fitting speed and robustness as compared to corresponding fits using conventional model formulations. The proposed technique overcomes many of the challenges in fitting simultaneous multi-tracer PET compartment models.

Application of separable parameter space techniques to multi-tracer PET compartment modeling

NASA Astrophysics Data System (ADS)

Zhang, Jeff L.; Morey, A. Michael; Kadrmas, Dan J.

2016-02-01

Multi-tracer positron emission tomography (PET) can image two or more tracers in a single scan, characterizing multiple aspects of biological functions to provide new insights into many diseases. The technique uses dynamic imaging, resulting in time-activity curves that contain contributions from each tracer present. The process of separating and recovering separate images and/or imaging measures for each tracer requires the application of kinetic constraints, which are most commonly applied by fitting parallel compartment models for all tracers. Such multi-tracer compartment modeling presents challenging nonlinear fits in multiple dimensions. This work extends separable parameter space kinetic modeling techniques, previously developed for fitting single-tracer compartment models, to fitting multi-tracer compartment models. The multi-tracer compartment model solution equations were reformulated to maximally separate the linear and nonlinear aspects of the fitting problem, and separable least-squares techniques were applied to effectively reduce the dimensionality of the nonlinear fit. The benefits of the approach are then explored through a number of illustrative examples, including characterization of separable parameter space multi-tracer objective functions and demonstration of exhaustive search fits which guarantee the true global minimum to within arbitrary search precision. Iterative gradient-descent algorithms using Levenberg-Marquardt were also tested, demonstrating improved fitting speed and robustness as compared to corresponding fits using conventional model formulations. The proposed technique overcomes many of the challenges in fitting simultaneous multi-tracer PET compartment models.

Radar altimeter waveform modeled parameter recovery. [SEASAT-1 data

NASA Technical Reports Server (NTRS)

1981-01-01

Satellite-borne radar altimeters include waveform sampling gates providing point samples of the transmitted radar pulse after its scattering from the ocean's surface. Averages of the waveform sampler data can be fitted by varying parameters in a model mean return waveform. The theoretical waveform model used is described as well as a general iterative nonlinear least squares procedures used to obtain estimates of parameters characterizing the modeled waveform for SEASAT-1 data. The six waveform parameters recovered by the fitting procedure are: (1) amplitude; (2) time origin, or track point; (3) ocean surface rms roughness; (4) noise baseline; (5) ocean surface skewness; and (6) altitude or off-nadir angle. Additional practical processing considerations are addressed and FORTRAN source listing for subroutines used in the waveform fitting are included. While the description is for the Seasat-1 altimeter waveform data analysis, the work can easily be generalized and extended to other radar altimeter systems.

Parameter estimation techniques based on optimizing goodness-of-fit statistics for structural reliability

NASA Technical Reports Server (NTRS)

Starlinger, Alois; Duffy, Stephen F.; Palko, Joseph L.

1993-01-01

New methods are presented that utilize the optimization of goodness-of-fit statistics in order to estimate Weibull parameters from failure data. It is assumed that the underlying population is characterized by a three-parameter Weibull distribution. Goodness-of-fit tests are based on the empirical distribution function (EDF). The EDF is a step function, calculated using failure data, and represents an approximation of the cumulative distribution function for the underlying population. Statistics (such as the Kolmogorov-Smirnov statistic and the Anderson-Darling statistic) measure the discrepancy between the EDF and the cumulative distribution function (CDF). These statistics are minimized with respect to the three Weibull parameters. Due to nonlinearities encountered in the minimization process, Powell's numerical optimization procedure is applied to obtain the optimum value of the EDF. Numerical examples show the applicability of these new estimation methods. The results are compared to the estimates obtained with Cooper's nonlinear regression algorithm.

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

PubMed Central

Jastrzembski, Tiffany S.; Charness, Neil

2009-01-01

The authors estimate weighted mean values for nine information processing parameters for older adults using the Card, Moran, and Newell (1983) Model Human Processor model. The authors validate a subset of these parameters by modeling two mobile phone tasks using two different phones and comparing model predictions to a sample of younger (N = 20; Mage = 20) and older (N = 20; Mage = 69) adults. Older adult models fit keystroke-level performance at the aggregate grain of analysis extremely well (R = 0.99) and produced equivalent fits to previously validated younger adult models. Critical path analyses highlighted points of poor design as a function of cognitive workload, hardware/software design, and user characteristics. The findings demonstrate that estimated older adult information processing parameters are valid for modeling purposes, can help designers understand age-related performance using existing interfaces, and may support the development of age-sensitive technologies. PMID:18194048

The Model Human Processor and the older adult: parameter estimation and validation within a mobile phone task.

PubMed

Jastrzembski, Tiffany S; Charness, Neil

2007-12-01

The authors estimate weighted mean values for nine information processing parameters for older adults using the Card, Moran, and Newell (1983) Model Human Processor model. The authors validate a subset of these parameters by modeling two mobile phone tasks using two different phones and comparing model predictions to a sample of younger (N = 20; M-sub(age) = 20) and older (N = 20; M-sub(age) = 69) adults. Older adult models fit keystroke-level performance at the aggregate grain of analysis extremely well (R = 0.99) and produced equivalent fits to previously validated younger adult models. Critical path analyses highlighted points of poor design as a function of cognitive workload, hardware/software design, and user characteristics. The findings demonstrate that estimated older adult information processing parameters are valid for modeling purposes, can help designers understand age-related performance using existing interfaces, and may support the development of age-sensitive technologies.

SCOUSE: Semi-automated multi-COmponent Universal Spectral-line fitting Engine

NASA Astrophysics Data System (ADS)

Henshaw, J. D.; Longmore, S. N.; Kruijssen, J. M. D.; Davies, B.; Bally, J.; Barnes, A.; Battersby, C.; Burton, M.; Cunningham, M. R.; Dale, J. E.; Ginsburg, A.; Immer, K.; Jones, P. A.; Kendrew, S.; Mills, E. A. C.; Molinari, S.; Moore, T. J. T.; Ott, J.; Pillai, T.; Rathborne, J.; Schilke, P.; Schmiedeke, A.; Testi, L.; Walker, D.; Walsh, A.; Zhang, Q.

2016-01-01

The Semi-automated multi-COmponent Universal Spectral-line fitting Engine (SCOUSE) is a spectral line fitting algorithm that fits Gaussian files to spectral line emission. It identifies the spatial area over which to fit the data and generates a grid of spectral averaging areas (SAAs). The spatially averaged spectra are fitted according to user-provided tolerance levels, and the best fit is selected using the Akaike Information Criterion, which weights the chisq of a best-fitting solution according to the number of free-parameters. A more detailed inspection of the spectra can be performed to improve the fit through an iterative process, after which SCOUSE integrates the new solutions into the solution file.

Application of separable parameter space techniques to multi-tracer PET compartment modeling

PubMed Central

Zhang, Jeff L; Morey, A Michael; Kadrmas, Dan J

2016-01-01

Multi-tracer positron emission tomography (PET) can image two or more tracers in a single scan, characterizing multiple aspects of biological functions to provide new insights into many diseases. The technique uses dynamic imaging, resulting in time-activity curves that contain contributions from each tracer present. The process of separating and recovering separate images and/or imaging measures for each tracer requires the application of kinetic constraints, which are most commonly applied by fitting parallel compartment models for all tracers. Such multi-tracer compartment modeling presents challenging nonlinear fits in multiple dimensions. This work extends separable parameter space kinetic modeling techniques, previously developed for fitting single-tracer compartment models, to fitting multi-tracer compartment models. The multi-tracer compartment model solution equations were reformulated to maximally separate the linear and nonlinear aspects of the fitting problem, and separable least-squares techniques were applied to effectively reduce the dimensionality of the nonlinear fit. The benefits of the approach are then explored through a number of illustrative examples, including characterization of separable parameter space multi-tracer objective functions and demonstration of exhaustive search fits which guarantee the true global minimum to within arbitrary search precision. Iterative gradient-descent algorithms using Levenberg–Marquardt were also tested, demonstrating improved fitting speed and robustness as compared to corresponding fits using conventional model formulations. The proposed technique overcomes many of the challenges in fitting simultaneous multi-tracer PET compartment models. PMID:26788888

Uncertainty in least-squares fits to the thermal noise spectra of nanomechanical resonators with applications to the atomic force microscope.

PubMed

Sader, John E; Yousefi, Morteza; Friend, James R

2014-02-01

Thermal noise spectra of nanomechanical resonators are used widely to characterize their physical properties. These spectra typically exhibit a Lorentzian response, with additional white noise due to extraneous processes. Least-squares fits of these measurements enable extraction of key parameters of the resonator, including its resonant frequency, quality factor, and stiffness. Here, we present general formulas for the uncertainties in these fit parameters due to sampling noise inherent in all thermal noise spectra. Good agreement with Monte Carlo simulation of synthetic data and measurements of an Atomic Force Microscope (AFM) cantilever is demonstrated. These formulas enable robust interpretation of thermal noise spectra measurements commonly performed in the AFM and adaptive control of fitting procedures with specified tolerances.

Uncertainty in least-squares fits to the thermal noise spectra of nanomechanical resonators with applications to the atomic force microscope

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

Sader, John E., E-mail: jsader@unimelb.edu.au; Yousefi, Morteza; Friend, James R.

2014-02-15

Thermal noise spectra of nanomechanical resonators are used widely to characterize their physical properties. These spectra typically exhibit a Lorentzian response, with additional white noise due to extraneous processes. Least-squares fits of these measurements enable extraction of key parameters of the resonator, including its resonant frequency, quality factor, and stiffness. Here, we present general formulas for the uncertainties in these fit parameters due to sampling noise inherent in all thermal noise spectra. Good agreement with Monte Carlo simulation of synthetic data and measurements of an Atomic Force Microscope (AFM) cantilever is demonstrated. These formulas enable robust interpretation of thermal noisemore » spectra measurements commonly performed in the AFM and adaptive control of fitting procedures with specified tolerances.« less

Pilot Study for OCT Guided Design and Fit of a Prosthetic Device for Treatment of Corneal Disease.

PubMed

Le, Hong-Gam T; Tang, Maolong; Ridges, Ryan; Huang, David; Jacobs, Deborah S

2012-01-01

Purpose. To assess optical coherence tomography (OCT) for guiding design and fit of a prosthetic device for corneal disease. Methods. A prototype time domain OCT scanner was used to image the anterior segment of patients fitted with large diameter (18.5-20 mm) prosthetic devices for corneal disease. OCT images were processed and analyzed to characterize corneal diameter, corneal sagittal height, scleral sagittal height, scleral toricity, and alignment of device. Within-subject variance of OCT-measured parameters was evaluated. OCT-measured parameters were compared with device parameters for each eye fitted. OCT image correspondence with ocular alignment and clinical fit was assessed. Results. Six eyes in 5 patients were studied. OCT measurement of corneal diameter (coefficient of variation, CV = 0.76%), cornea sagittal height (CV = 2.06%), and scleral sagittal height (CV = 3.39%) is highly repeatable within each subject. OCT image-derived measurements reveal strong correlation between corneal sagittal height and device corneal height (r = 0.975) and modest correlation between scleral and on-eye device toricity (r = 0.581). Qualitative assessment of a fitted device on OCT montages reveals correspondence with slit lamp images and clinical assessment of fit. Conclusions. OCT imaging of the anterior segment is suitable for custom design and fit of large diameter (18.5-20 mm) prosthetic devices used in the treatment of corneal disease.

Estimation of kinetics parameters for the adsorption of human serum albumin onto hydroxyapatite-modified silver electrodes by piezoelectric quartz crystal impedance analysis.

PubMed

Tian, Lu; Wei, Wan-Zhi; Mao, You-An

2004-04-01

The adsorption of human serum albumin onto hydroxyapatite-modified silver electrodes has been in situ investigated by utilizing the piezoelectric quartz crystal impedance technique. The changes of equivalent circuit parameters were used to interpret the adsorption process. A kinetic model of two consecutive steps was derived to describe the process and compared with a first-order kinetic model by using residual analysis. The experimental data of frequency shift fitted to the model and kinetics parameters, k1, k2, psi1, psi2 and qr, were obtained. All fitted results were in reasonable agreement with the corresponding experimental results. Two adsorption constants (7.19 kJ mol(-1) and 22.89 kJ mol(-1)) were calculated according to the Arrhenius formula.

Monte Carlo analysis for the determination of the conic constant of an aspheric micro lens based on a scanning white light interferometric measurement

NASA Astrophysics Data System (ADS)

Gugsa, Solomon A.; Davies, Angela

2005-08-01

Characterizing an aspheric micro lens is critical for understanding the performance and providing feedback to the manufacturing. We describe a method to find the best-fit conic of an aspheric micro lens using a least squares minimization and Monte Carlo analysis. Our analysis is based on scanning white light interferometry measurements, and we compare the standard rapid technique where a single measurement is taken of the apex of the lens to the more time-consuming stitching technique where more surface area is measured. Both are corrected for tip/tilt based on a planar fit to the substrate. Four major parameters and their uncertainties are estimated from the measurement and a chi-square minimization is carried out to determine the best-fit conic constant. The four parameters are the base radius of curvature, the aperture of the lens, the lens center, and the sag of the lens. A probability distribution is chosen for each of the four parameters based on the measurement uncertainties and a Monte Carlo process is used to iterate the minimization process. Eleven measurements were taken and data is also chosen randomly from the group during the Monte Carlo simulation to capture the measurement repeatability. A distribution of best-fit conic constants results, where the mean is a good estimate of the best-fit conic and the distribution width represents the combined measurement uncertainty. We also compare the Monte Carlo process for the stitched data and the not stitched data. Our analysis allows us to analyze the residual surface error in terms of Zernike polynomials and determine uncertainty estimates for each coefficient.

A Quadriparametric Model to Describe the Diversity of Waves Applied to Hormonal Data.

PubMed

Abdullah, Saman; Bouchard, Thomas; Klich, Amna; Leiva, Rene; Pyper, Cecilia; Genolini, Christophe; Subtil, Fabien; Iwaz, Jean; Ecochard, René

2018-05-01

Even in normally cycling women, hormone level shapes may widely vary between cycles and between women. Over decades, finding ways to characterize and compare cycle hormone waves was difficult and most solutions, in particular polynomials or splines, do not correspond to physiologically meaningful parameters. We present an original concept to characterize most hormone waves with only two parameters. The modelling attempt considered pregnanediol-3-alpha-glucuronide (PDG) and luteinising hormone (LH) levels in 266 cycles (with ultrasound-identified ovulation day) in 99 normally fertile women aged 18 to 45. The study searched for a convenient wave description process and carried out an extended search for the best fitting density distribution. The highly flexible beta-binomial distribution offered the best fit of most hormone waves and required only two readily available and understandable wave parameters: location and scale. In bell-shaped waves (e.g., PDG curves), early peaks may be fitted with a low location parameter and a low scale parameter; plateau shapes are obtained with higher scale parameters. I-shaped, J-shaped, and U-shaped waves (sometimes the shapes of LH curves) may be fitted with high scale parameter and, respectively, low, high, and medium location parameter. These location and scale parameters will be later correlated with feminine physiological events. Our results demonstrate that, with unimodal waves, complex methods (e.g., functional mixed effects models using smoothing splines, second-order growth mixture models, or functional principal-component- based methods) may be avoided. The use, application, and, especially, result interpretation of four-parameter analyses might be advantageous within the context of feminine physiological events. Schattauer GmbH.

A Model Fit Statistic for Generalized Partial Credit Model

ERIC Educational Resources Information Center

Liang, Tie; Wells, Craig S.

2009-01-01

Investigating the fit of a parametric model is an important part of the measurement process when implementing item response theory (IRT), but research examining it is limited. A general nonparametric approach for detecting model misfit, introduced by J. Douglas and A. S. Cohen (2001), has exhibited promising results for the two-parameter logistic…

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

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

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

2016-02-15

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

The drift diffusion model as the choice rule in reinforcement learning.

PubMed

Pedersen, Mads Lund; Frank, Michael J; Biele, Guido

2017-08-01

Current reinforcement-learning models often assume simplified decision processes that do not fully reflect the dynamic complexities of choice processes. Conversely, sequential-sampling models of decision making account for both choice accuracy and response time, but assume that decisions are based on static decision values. To combine these two computational models of decision making and learning, we implemented reinforcement-learning models in which the drift diffusion model describes the choice process, thereby capturing both within- and across-trial dynamics. To exemplify the utility of this approach, we quantitatively fit data from a common reinforcement-learning paradigm using hierarchical Bayesian parameter estimation, and compared model variants to determine whether they could capture the effects of stimulant medication in adult patients with attention-deficit hyperactivity disorder (ADHD). The model with the best relative fit provided a good description of the learning process, choices, and response times. A parameter recovery experiment showed that the hierarchical Bayesian modeling approach enabled accurate estimation of the model parameters. The model approach described here, using simultaneous estimation of reinforcement-learning and drift diffusion model parameters, shows promise for revealing new insights into the cognitive and neural mechanisms of learning and decision making, as well as the alteration of such processes in clinical groups.

The drift diffusion model as the choice rule in reinforcement learning

PubMed Central

Frank, Michael J.

2017-01-01

Current reinforcement-learning models often assume simplified decision processes that do not fully reflect the dynamic complexities of choice processes. Conversely, sequential-sampling models of decision making account for both choice accuracy and response time, but assume that decisions are based on static decision values. To combine these two computational models of decision making and learning, we implemented reinforcement-learning models in which the drift diffusion model describes the choice process, thereby capturing both within- and across-trial dynamics. To exemplify the utility of this approach, we quantitatively fit data from a common reinforcement-learning paradigm using hierarchical Bayesian parameter estimation, and compared model variants to determine whether they could capture the effects of stimulant medication in adult patients with attention-deficit hyper-activity disorder (ADHD). The model with the best relative fit provided a good description of the learning process, choices, and response times. A parameter recovery experiment showed that the hierarchical Bayesian modeling approach enabled accurate estimation of the model parameters. The model approach described here, using simultaneous estimation of reinforcement-learning and drift diffusion model parameters, shows promise for revealing new insights into the cognitive and neural mechanisms of learning and decision making, as well as the alteration of such processes in clinical groups. PMID:27966103

In situ observations of snow particle size distributions over a cold frontal rainband within an extratropical cyclone

NASA Astrophysics Data System (ADS)

Yang, Jiefan; Lei, Hengchi

2016-02-01

Cloud microphysical properties of a mixed phase cloud generated by a typical extratropical cyclone in the Tongliao area, Inner Mongolia on 3 May 2014, are analyzed primarily using in situ flight observation data. This study is mainly focused on ice crystal concentration, supercooled cloud water content, and vertical distributions of fit parameters of snow particle size distributions (PSDs). The results showed several discrepancies of microphysical properties obtained during two penetrations. During penetration within precipitating cloud, the maximum ice particle concentration, liquid water content, and ice water content were increased by a factor of 2-3 compared with their counterpart obtained during penetration of a nonprecipitating cloud. The heavy rimed and irregular ice crystals obtained by 2D imagery probe as well as vertical distributions of fitting parameters within precipitating cloud show that the ice particles grow during falling via riming and aggregation process, whereas the lightly rimed and pristine ice particles as well as fitting parameters within non-precipitating cloud indicate the domination of sublimation process. During the two cloud penetrations, the PSDs were generally better represented by gamma distributions than the exponential form in terms of the determining coefficient ( R 2). The correlations between parameters of exponential /gamma form within two penetrations showed no obvious differences compared with previous studies.

On the biosorption, by brown seaweed, Lobophora variegata, of Ni(II) from aqueous solutions: equilibrium and thermodynamic studies.

PubMed

Basha, Shaik; Jaiswar, Santlal; Jha, Bhavanath

2010-09-01

The biosorption equilibrium isotherms of Ni(II) onto marine brown algae Lobophora variegata, which was chemically-modified by CaCl(2) were studied and modeled. To predict the biosorption isotherms and to determine the characteristic parameters for process design, twenty-three one-, two-, three-, four- and five-parameter isotherm models were applied to experimental data. The interaction among biosorbed molecules is attractive and biosorption is carried out on energetically different sites and is an endothermic process. The five-parameter Fritz-Schluender model gives the most accurate fit with high regression coefficient, R (2) (0.9911-0.9975) and F-ratio (118.03-179.96), and low standard error, SE (0.0902-0.0.1556) and the residual or sum of square error, SSE (0.0012-0.1789) values to all experimental data in comparison to other models. The biosorption isotherm models fitted the experimental data in the order: Fritz-Schluender (five-parameter) > Freundlich (two-parameter) > Langmuir (two-parameter) > Khan (three-parameter) > Fritz-Schluender (four-parameter). The thermodynamic parameters such as DeltaG (0), DeltaH (0) and DeltaS (0) have been determined, which indicates the sorption of Ni(II) onto L. variegata was spontaneous and endothermic in nature.

A dual-process approach to exploring the role of delay discounting in obesity.

PubMed

Price, Menna; Higgs, Suzanne; Maw, James; Lee, Michelle

2016-08-01

Delay discounting of financial rewards has been related to overeating and obesity. Neuropsychological evidence supports a dual-system account of both discounting and overeating behaviour where the degree of impulsive decision making is determined by the relative strength of reward desire and executive control. A dual-parameter model of discounting behaviour is consistent with this theory. In this study, the fit of the commonly used one-parameter model was compared to a new dual-parameter model for the first time in a sample of adults with wide ranging BMI. Delay discounting data from 79 males and females (males=26) across a wide age (M=28.44years (SD=8.81)) and BMI range (M=25.42 (SD=5.16)) was analysed. A dual-parameter model (saturating-hyperbolic; Doya, [Doya (2008) ]) was applied to the data and compared on model fit indices to the single-parameter model. Discounting was significantly greater in the overweight/obese participants using both models, however, the two parameter model showed a superior fit to data (p<0.0001). The two parameters were shown to be related yet distinct measures consistent with a dual-system account of inter-temporal choice behaviour. The dual-parameter model showed superior fit to data and the two parameters were shown to be related yet distinct indices sensitive to differences between weight groups. Findings are discussed in terms of the impulsive reward and executive control systems that contribute to unhealthy food choice and within the context of obesity related research. Copyright © 2016 Elsevier Inc. All rights reserved.

Fitted hyperelastic parameters for Human brain tissue from reported tension, compression, and shear tests.

PubMed

Moran, Richard; Smith, Joshua H; García, José J

2014-11-28

The mechanical properties of human brain tissue are the subject of interest because of their use in understanding brain trauma and in developing therapeutic treatments and procedures. To represent the behavior of the tissue, we have developed hyperelastic mechanical models whose parameters are fitted in accordance with experimental test results. However, most studies available in the literature have fitted parameters with data of a single type of loading, such as tension, compression, or shear. Recently, Jin et al. (Journal of Biomechanics 46:2795-2801, 2013) reported data from ex vivo tests of human brain tissue under tension, compression, and shear loading using four strain rates and four different brain regions. However, they do not report parameters of energy functions that can be readily used in finite element simulations. To represent the tissue behavior for the quasi-static loading conditions, we aimed to determine the best fit of the hyperelastic parameters of the hyperfoam, Ogden, and polynomial strain energy functions available in ABAQUS for the low strain rate data, while simultaneously considering all three loading modes. We used an optimization process conducted in MATLAB, calling iteratively three finite element models developed in ABAQUS that represent the three loadings. Results showed a relatively good fit to experimental data in all loading modes using two terms in the energy functions. Values for the shear modulus obtained in this analysis (897-1653Pa) are in the range of those presented in other studies. These energy-function parameters can be used in brain tissue simulations using finite element models. Copyright © 2014 Elsevier Ltd. All rights reserved.

Effects of vegetation canopy on the radar backscattering coefficient

NASA Technical Reports Server (NTRS)

Mo, T.; Blanchard, B. J.; Schmugge, T. J.

1983-01-01

Airborne L- and C-band scatterometer data, taken over both vegetation-covered and bare fields, were systematically analyzed and theoretically reproduced, using a recently developed model for calculating radar backscattering coefficients of rough soil surfaces. The results show that the model can reproduce the observed angular variations of radar backscattering coefficient quite well via a least-squares fit method. Best fits to the data provide estimates of the statistical properties of the surface roughness, which is characterized by two parameters: the standard deviation of surface height, and the surface correlation length. In addition, the processes of vegetation attenuation and volume scattering require two canopy parameters, the canopy optical thickness and a volume scattering factor. Canopy parameter values for individual vegetation types, including alfalfa, milo and corn, were also determined from the best-fit results. The uncertainties in the scatterometer data were also explored.

Uncertainty quantification for optical model parameters

DOE PAGES

Lovell, A. E.; Nunes, F. M.; Sarich, J.; ...

2017-02-21

Although uncertainty quantification has been making its way into nuclear theory, these methods have yet to be explored in the context of reaction theory. For example, it is well known that different parameterizations of the optical potential can result in different cross sections, but these differences have not been systematically studied and quantified. The purpose of our work is to investigate the uncertainties in nuclear reactions that result from fitting a given model to elastic-scattering data, as well as to study how these uncertainties propagate to the inelastic and transfer channels. We use statistical methods to determine a best fitmore » and create corresponding 95% confidence bands. A simple model of the process is fit to elastic-scattering data and used to predict either inelastic or transfer cross sections. In this initial work, we assume that our model is correct, and the only uncertainties come from the variation of the fit parameters. Here, we study a number of reactions involving neutron and deuteron projectiles with energies in the range of 5–25 MeV/u, on targets with mass A=12–208. We investigate the correlations between the parameters in the fit. The case of deuterons on 12C is discussed in detail: the elastic-scattering fit and the prediction of 12C(d,p) 13C transfer angular distributions, using both uncorrelated and correlated χ 2 minimization functions. The general features for all cases are compiled in a systematic manner to identify trends. This work shows that, in many cases, the correlated χ 2 functions (in comparison to the uncorrelated χ 2 functions) provide a more natural parameterization of the process. These correlated functions do, however, produce broader confidence bands. Further optimization may require improvement in the models themselves and/or more information included in the fit.« less

PID controller tuning using metaheuristic optimization algorithms for benchmark problems

NASA Astrophysics Data System (ADS)

Gholap, Vishal; Naik Dessai, Chaitali; Bagyaveereswaran, V.

2017-11-01

This paper contributes to find the optimal PID controller parameters using particle swarm optimization (PSO), Genetic Algorithm (GA) and Simulated Annealing (SA) algorithm. The algorithms were developed through simulation of chemical process and electrical system and the PID controller is tuned. Here, two different fitness functions such as Integral Time Absolute Error and Time domain Specifications were chosen and applied on PSO, GA and SA while tuning the controller. The proposed Algorithms are implemented on two benchmark problems of coupled tank system and DC motor. Finally, comparative study has been done with different algorithms based on best cost, number of iterations and different objective functions. The closed loop process response for each set of tuned parameters is plotted for each system with each fitness function.

Parameterizing sorption isotherms using a hybrid global-local fitting procedure.

PubMed

Matott, L Shawn; Singh, Anshuman; Rabideau, Alan J

2017-05-01

Predictive modeling of the transport and remediation of groundwater contaminants requires an accurate description of the sorption process, which is usually provided by fitting an isotherm model to site-specific laboratory data. Commonly used calibration procedures, listed in order of increasing sophistication, include: trial-and-error, linearization, non-linear regression, global search, and hybrid global-local search. Given the considerable variability in fitting procedures applied in published isotherm studies, we investigated the importance of algorithm selection through a series of numerical experiments involving 13 previously published sorption datasets. These datasets, considered representative of state-of-the-art for isotherm experiments, had been previously analyzed using trial-and-error, linearization, or non-linear regression methods. The isotherm expressions were re-fit using a 3-stage hybrid global-local search procedure (i.e. global search using particle swarm optimization followed by Powell's derivative free local search method and Gauss-Marquardt-Levenberg non-linear regression). The re-fitted expressions were then compared to previously published fits in terms of the optimized weighted sum of squared residuals (WSSR) fitness function, the final estimated parameters, and the influence on contaminant transport predictions - where easily computed concentration-dependent contaminant retardation factors served as a surrogate measure of likely transport behavior. Results suggest that many of the previously published calibrated isotherm parameter sets were local minima. In some cases, the updated hybrid global-local search yielded order-of-magnitude reductions in the fitness function. In particular, of the candidate isotherms, the Polanyi-type models were most likely to benefit from the use of the hybrid fitting procedure. In some cases, improvements in fitness function were associated with slight (<10%) changes in parameter values, but in other cases significant (>50%) changes in parameter values were noted. Despite these differences, the influence of isotherm misspecification on contaminant transport predictions was quite variable and difficult to predict from inspection of the isotherms. Copyright © 2017 Elsevier B.V. All rights reserved.

Optimizing Methods of Obtaining Stellar Parameters for the H3 Survey

NASA Astrophysics Data System (ADS)

Ivory, KeShawn; Conroy, Charlie; Cargile, Phillip

2018-01-01

The Stellar Halo at High Resolution with Hectochelle Survey (H3) is in the process of observing and collecting stellar parameters for stars in the Milky Way's halo. With a goal of measuring radial velocities for fainter stars, it is crucial that we have optimal methods of obtaining this and other parameters from the data from these stars.The method currently developed is The Payne, named after Cecilia Payne-Gaposchkin, a code that uses neural networks and Markov Chain Monte Carlo methods to utilize both spectra and photometry to obtain values for stellar parameters. This project was to investigate the benefit of fitting both spectra and spectral energy distributions (SED). Mock spectra using the parameters of the Sun were created and noise was inserted at various signal to noise values. The Payne then fit each mock spectrum with and without a mock SED also generated from solar parameters. The result was that at high signal to noise, the spectrum dominated and the effect of fitting the SED was minimal. But at low signal to noise, the addition of the SED greatly decreased the standard deviation of the data and resulted in more accurate values for temperature and metallicity.

On the cost of approximating and recognizing a noise perturbed straight line or a quadratic curve segment in the plane. [central processing units

NASA Technical Reports Server (NTRS)

Cooper, D. B.; Yalabik, N.

1975-01-01

Approximation of noisy data in the plane by straight lines or elliptic or single-branch hyperbolic curve segments arises in pattern recognition, data compaction, and other problems. The efficient search for and approximation of data by such curves were examined. Recursive least-squares linear curve-fitting was used, and ellipses and hyperbolas are parameterized as quadratic functions in x and y. The error minimized by the algorithm is interpreted, and central processing unit (CPU) times for estimating parameters for fitting straight lines and quadratic curves were determined and compared. CPU time for data search was also determined for the case of straight line fitting. Quadratic curve fitting is shown to require about six times as much CPU time as does straight line fitting, and curves relating CPU time and fitting error were determined for straight line fitting. Results are derived on early sequential determination of whether or not the underlying curve is a straight line.

Attitude determination of a high altitude balloon system. Part 2: Development of the parameter determination process

NASA Technical Reports Server (NTRS)

Nigro, N. J.; Elkouh, A. F.

1975-01-01

The attitude of the balloon system is determined as a function of time if: (a) a method for simulating the motion of the system is available, and (b) the initial state is known. The initial state is obtained by fitting the system motion (as measured by sensors) to the corresponding output predicted by the mathematical model. In the case of the LACATE experiment the sensors consisted of three orthogonally oriented rate gyros and a magnetometer all mounted on the research platform. The initial state was obtained by fitting the angular velocity components measured with the gyros to the corresponding values obtained from the solution of the math model. A block diagram illustrating the attitude determination process employed for the LACATE experiment is shown. The process consists of three essential parts; a process for simulating the balloon system, an instrumentation system for measuring the output, and a parameter estimation process for systematically and efficiently solving the initial state. Results are presented and discussed.

New database for improving virtual system “body-dress”

NASA Astrophysics Data System (ADS)

Yan, J. Q.; Zhang, S. C.; Kuzmichev, V. E.; Adolphe, D. C.

2017-10-01

The aim of this exploration is to develop a new database of solid algorithms and relations between the dress fit and the fabric mechanical properties, the pattern block construction for improving the reality of virtual system “body-dress”. In virtual simulation, the system “body-clothing” sometimes shown distinct results with reality, especially when important changes in pattern block and fabrics were involved. In this research, to enhance the simulation process, diverse fit parameters were proposed: bottom height of dress, angle of front center contours, air volume and its distribution between dress and dummy. Measurements were done and optimized by ruler, camera, 3D body scanner image processing software and 3D modeling software. In the meantime, pattern block indexes were measured and fabric properties were tested by KES. Finally, the correlation and linear regression equations between indexes of fabric properties, pattern blocks and fit parameters were investigated. In this manner, new database could be extended in programming modules of virtual design for more realistic results.

Classical nucleation theory of homogeneous freezing of water: thermodynamic and kinetic parameters.

PubMed

Ickes, Luisa; Welti, André; Hoose, Corinna; Lohmann, Ulrike

2015-02-28

The probability of homogeneous ice nucleation under a set of ambient conditions can be described by nucleation rates using the theoretical framework of Classical Nucleation Theory (CNT). This framework consists of kinetic and thermodynamic parameters, of which three are not well-defined (namely the interfacial tension between ice and water, the activation energy and the prefactor), so that any CNT-based parameterization of homogeneous ice formation is less well-constrained than desired for modeling applications. Different approaches to estimate the thermodynamic and kinetic parameters of CNT are reviewed in this paper and the sensitivity of the calculated nucleation rate to the choice of parameters is investigated. We show that nucleation rates are very sensitive to this choice. The sensitivity is governed by one parameter - the interfacial tension between ice and water, which determines the energetic barrier of the nucleation process. The calculated nucleation rate can differ by more than 25 orders of magnitude depending on the choice of parameterization for this parameter. The second most important parameter is the activation energy of the nucleation process. It can lead to a variation of 16 orders of magnitude. By estimating the nucleation rate from a collection of droplet freezing experiments from the literature, the dependence of these two parameters on temperature is narrowed down. It can be seen that the temperature behavior of these two parameters assumed in the literature does not match with the predicted nucleation rates from the fit in most cases. Moreover a comparison of all possible combinations of theoretical parameterizations of the dominant two free parameters shows that one combination fits the fitted nucleation rates best, which is a description of the interfacial tension coming from a molecular model [Reinhardt and Doye, J. Chem. Phys., 2013, 139, 096102] in combination with the activation energy derived from self-diffusion measurements [Zobrist et al., J. Phys. Chem. C, 2007, 111, 2149]. However, some fundamental understanding of the processes is still missing. Further research in future might help to tackle this problem. The most important questions, which need to be answered to constrain CNT, are raised in this study.

Misspecification in Latent Change Score Models: Consequences for Parameter Estimation, Model Evaluation, and Predicting Change.

PubMed

Clark, D Angus; Nuttall, Amy K; Bowles, Ryan P

2018-01-01

Latent change score models (LCS) are conceptually powerful tools for analyzing longitudinal data (McArdle & Hamagami, 2001). However, applications of these models typically include constraints on key parameters over time. Although practically useful, strict invariance over time in these parameters is unlikely in real data. This study investigates the robustness of LCS when invariance over time is incorrectly imposed on key change-related parameters. Monte Carlo simulation methods were used to explore the impact of misspecification on parameter estimation, predicted trajectories of change, and model fit in the dual change score model, the foundational LCS. When constraints were incorrectly applied, several parameters, most notably the slope (i.e., constant change) factor mean and autoproportion coefficient, were severely and consistently biased, as were regression paths to the slope factor when external predictors of change were included. Standard fit indices indicated that the misspecified models fit well, partly because mean level trajectories over time were accurately captured. Loosening constraint improved the accuracy of parameter estimates, but estimates were more unstable, and models frequently failed to converge. Results suggest that potentially common sources of misspecification in LCS can produce distorted impressions of developmental processes, and that identifying and rectifying the situation is a challenge.

Gamma ray spectroscopy employing divalent europium-doped alkaline earth halides and digital readout for accurate histogramming

DOEpatents

Cherepy, Nerine Jane; Payne, Stephen Anthony; Drury, Owen B.; Sturm, Benjamin W.

2016-02-09

According to one embodiment, a scintillator radiation detector system includes a scintillator, and a processing device for processing pulse traces corresponding to light pulses from the scintillator, where the processing device is configured to: process each pulse trace over at least two temporal windows and to use pulse digitization to improve energy resolution of the system. According to another embodiment, a scintillator radiation detector system includes a processing device configured to: fit digitized scintillation waveforms to an algorithm, perform a direct integration of fit parameters, process multiple integration windows for each digitized scintillation waveform to determine a correction factor, and apply the correction factor to each digitized scintillation waveform.

Health-related morphological characteristics and physiological fitness in connection with nutritional, socio-economic status, occupational workload of tea garden workers.

PubMed

Sengupta, Pallav; Sahoo, Sobhana

2014-09-01

Reports on the cardiorespiratory fitness and body composition of male workers engaged in processing of tea leaves in factories within the tea-estates of West Bengal, under the influence of physiological workload, are quite scanty. This cross-sectional study was conducted to evaluate morphometric characteristics based on physiological status and physical fitness of tea factory laborers who are continuously exposed to tea dust in their work environment for more than two years. Subjects were divided into control and tea garden workers groups. Height and weight were measured and the body mass index (BMI) was computed. Physiological parameters such as resting heart rate, blood pressure, fitness variables like physical fitness index (PFI), energy expenditure (EE), handgrip strength and anthropometric parameters like mid-upper arm (MUAC), thigh circumference (TC), head circumference (HC) and waist-to-hip ratio (WHR) were measured. Body surface area (BSA), BMI, body fat percentage and fitness variables (PFI, EE) showed significant difference (p < 0.05) between the two groups. Anthropometric measures (MUAC, TC, HC, WHR) reflected poor status among laborers. The present study shows that the majority of workers had ectomorph stature, good physical fitness, but had poor nutritional status (BMI and WHR).

ExoSOFT: Exoplanet Simple Orbit Fitting Toolbox

NASA Astrophysics Data System (ADS)

Mede, Kyle; Brandt, Timothy D.

2017-08-01

ExoSOFT provides orbital analysis of exoplanets and binary star systems. It fits any combination of astrometric and radial velocity data, and offers four parameter space exploration techniques, including MCMC. It is packaged with an automated set of post-processing and plotting routines to summarize results, and is suitable for performing orbital analysis during surveys with new radial velocity and direct imaging instruments.

The relation of autonomic function to physical fitness in patients suffering from alcohol dependence.

PubMed

Herbsleb, Marco; Schulz, Steffen; Ostermann, Stephanie; Donath, Lars; Eisenträger, Daniela; Puta, Christian; Voss, Andreas; Gabriel, Holger W; Bär, Karl-Jürgen

2013-10-01

Reduced cardio-vascular health has been found in patients suffering from alcohol dependence. Low cardio-respiratory fitness is an independent predictor of cardio-vascular disease. We investigated physical fitness in 22 alcohol-dependent patients 10 days after acute alcohol withdrawal and compared results with matched controls. The standardized 6-min walk test (6 MWT) was used to analyze the relationship of autonomic dysfunction and physical fitness. Ventilatory indices and gas exchanges were assessed using a portable spiroergometric system while heart rate recordings were obtained separately. We calculated walking distance, indices of heart rate variability and efficiency parameters of heart rate and breathing. In addition, levels of exhaled carbon monoxide were measured in all participants to account for differences in smoking behaviour. Multivariate analyses of variance (MANOVA) were performed to investigate differences between patients and controls with regard to autonomic and efficiency parameters. Patients walked a significantly shorter distance in comparison to healthy subjects during the 6 MWT. Significantly decreased heart rate variability was observed before and after the test in patients when compared to controls, while no such difference was observed during exercise. The efficiency parameters indicated significantly reduced efficiency in physiological regulation when the obtained parameters were normalized to the distance. The 6 MWT is an easily applied instrument to measure physical fitness in alcohol dependent patients. It can also be used during exercise interventions. Reduced physical fitness, as observed in our study, might partly be caused by autonomic dysfunction, leading to less efficient regulation of physiological processes during exercise. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

Correlated parameter fit of arrhenius model for thermal denaturation of proteins and cells.

PubMed

Qin, Zhenpeng; Balasubramanian, Saravana Kumar; Wolkers, Willem F; Pearce, John A; Bischof, John C

2014-12-01

Thermal denaturation of proteins is critical to cell injury, food science and other biomaterial processing. For example protein denaturation correlates strongly with cell death by heating, and is increasingly of interest in focal thermal therapies of cancer and other diseases at temperatures which often exceed 50 °C. The Arrhenius model is a simple yet widely used model for both protein denaturation and cell injury. To establish the utility of the Arrhenius model for protein denaturation at 50 °C and above its sensitivities to the kinetic parameters (activation energy E a and frequency factor A) were carefully examined. We propose a simplified correlated parameter fit to the Arrhenius model by treating E a, as an independent fitting parameter and allowing A to follow dependently. The utility of the correlated parameter fit is demonstrated on thermal denaturation of proteins and cells from the literature as a validation, and new experimental measurements in our lab using FTIR spectroscopy to demonstrate broad applicability of this method. Finally, we demonstrate that the end-temperature within which the denaturation is measured is important and changes the kinetics. Specifically, higher E a and A parameters were found at low end-temperature (50 °C) and reduce as end-temperatures increase to 70 °C. This trend is consistent with Arrhenius parameters for cell injury in the literature that are significantly higher for clonogenics (45-50 °C) vs. membrane dye assays (60-70 °C). Future opportunities to monitor cell injury by spectroscopic measurement of protein denaturation are discussed.

Correlated Parameter Fit of Arrhenius Model for Thermal Denaturation of Proteins and Cells

PubMed Central

Qin, Zhenpeng; Balasubramanian, Saravana Kumar; Wolkers, Willem F.; Pearce, John A.; Bischof, John C.

2014-01-01

Thermal denaturation of proteins is critical to cell injury, food science and other biomaterial processing. For example protein denaturation correlates strongly with cell death by heating, and is increasingly of interest in focal thermal therapies of cancer and other diseases at temperatures which often exceed 50 °C. The Arrhenius model is a simple yet widely used model for both protein denaturation and cell injury. To establish the utility of the Arrhenius model for protein denaturation at 50 °C and above its sensitivities to the kinetic parameters (activation energy Ea and frequency factor A) were carefully examined. We propose a simplified correlated parameter fit to the Arrhenius model by treating Ea, as an independent fitting parameter and allowing A to follow dependently. The utility of the correlated parameter fit is demonstrated on thermal denaturation of proteins and cells from the literature as a validation, and new experimental measurements in our lab using FTIR spectroscopy to demonstrate broad applicability of this method. Finally, we demonstrate that the end-temperature within which the denaturation is measured is important and changes the kinetics. Specifically, higher Ea and A parameters were found at low end-temperature (50°C) and reduce as end-temperatures increase to 70 °C. This trend is consistent with Arrhenius parameters for cell injury in the literature that are significantly higher for clonogenics (45 – 50 °C) vs. membrane dye assays (60 –70 °C). Future opportunities to monitor cell injury by spectroscopic measurement of protein denaturation are discussed. PMID:25205396

Review on the Celestial Sphere Positioning of FITS Format Image Based on WCS and Research on General Visualization

NASA Astrophysics Data System (ADS)

Song, W. M.; Fan, D. W.; Su, L. Y.; Cui, C. Z.

2017-11-01

Calculating the coordinate parameters recorded in the form of key/value pairs in FITS (Flexible Image Transport System) header is the key to determine FITS images' position in the celestial system. As a result, it has great significance in researching the general process of calculating the coordinate parameters. By combining CCD related parameters of astronomical telescope (such as field, focal length, and celestial coordinates in optical axis, etc.), astronomical images recognition algorithm, and WCS (World Coordinate System) theory, the parameters can be calculated effectively. CCD parameters determine the scope of star catalogue, so that they can be used to build a reference star catalogue by the corresponding celestial region of astronomical images; Star pattern recognition completes the matching between the astronomical image and reference star catalogue, and obtains a table with a certain number of stars between CCD plane coordinates and their celestial coordinates for comparison; According to different projection of the sphere to the plane, WCS can build different transfer functions between these two coordinates, and the astronomical position of image pixels can be determined by the table's data we have worked before. FITS images are used to carry out scientific data transmission and analyze as a kind of mainstream data format, but only to be viewed, edited, and analyzed in the professional astronomy software. It decides the limitation of popular science education in astronomy. The realization of a general image visualization method is significant. FITS is converted to PNG or JPEG images firstly. The coordinate parameters in the FITS header are converted to metadata in the form of AVM (Astronomy Visualization Metadata), and then the metadata is added to the PNG or JPEG header. This method can meet amateur astronomers' general needs of viewing and analyzing astronomical images in the non-astronomical software platform. The overall design flow is realized through the java program and tested by SExtractor, WorldWide Telescope, picture viewer, and other software.

Text vectorization based on character recognition and character stroke modeling

NASA Astrophysics Data System (ADS)

Fan, Zhigang; Zhou, Bingfeng; Tse, Francis; Mu, Yadong; He, Tao

2014-03-01

In this paper, a text vectorization method is proposed using OCR (Optical Character Recognition) and character stroke modeling. This is based on the observation that for a particular character, its font glyphs may have different shapes, but often share same stroke structures. Like many other methods, the proposed algorithm contains two procedures, dominant point determination and data fitting. The first one partitions the outlines into segments and second one fits a curve to each segment. In the proposed method, the dominant points are classified as "major" (specifying stroke structures) and "minor" (specifying serif shapes). A set of rules (parameters) are determined offline specifying for each character the number of major and minor dominant points and for each dominant point the detection and fitting parameters (projection directions, boundary conditions and smoothness). For minor points, multiple sets of parameters could be used for different fonts. During operation, OCR is performed and the parameters associated with the recognized character are selected. Both major and minor dominant points are detected as a maximization process as specified by the parameter set. For minor points, an additional step could be performed to test the competing hypothesis and detect degenerated cases.

A program for the fitting of Debye, Cole-Cole, Cole-Davidson, and Havriliak-Negami dispersions to dielectric data.

PubMed

Grosse, Constantino

2014-04-01

The description and interpretation of dielectric spectroscopy data usually require the use of analytical functions, which include unknown parameters that must be determined iteratively by means of a fitting procedure. This is not a trivial task and much effort has been spent to find the best way to accomplish it. While the theoretical approach based on the Levenberg-Marquardt algorithm is well known, no freely available program specifically adapted to the dielectric spectroscopy problem exists to the best of our knowledge. Moreover, even the more general commercial packages usually fail on the following aspects: (1) allow to keep temporarily fixed some of the parameters, (2) allow to freely specify the uncertainty values for each data point, (3) check that parameter values fall within prescribed bounds during the fitting process, and (4) allow to fit either the real, or the imaginary, or simultaneously both parts of the complex permittivity. A program that satisfies all these requirements and allows fitting any superposition of the Debye, Cole-Cole, Cole-Davidson, and Havriliak-Negami dispersions plus a conductivity term to measured dielectric spectroscopy data is presented. It is available on request from the author. Copyright © 2013 Elsevier Inc. All rights reserved.

Fitting model-based psychometric functions to simultaneity and temporal-order judgment data: MATLAB and R routines.

PubMed

Alcalá-Quintana, Rocío; García-Pérez, Miguel A

2013-12-01

Research on temporal-order perception uses temporal-order judgment (TOJ) tasks or synchrony judgment (SJ) tasks in their binary SJ2 or ternary SJ3 variants. In all cases, two stimuli are presented with some temporal delay, and observers judge the order of presentation. Arbitrary psychometric functions are typically fitted to obtain performance measures such as sensitivity or the point of subjective simultaneity, but the parameters of these functions are uninterpretable. We describe routines in MATLAB and R that fit model-based functions whose parameters are interpretable in terms of the processes underlying temporal-order and simultaneity judgments and responses. These functions arise from an independent-channels model assuming arrival latencies with exponential distributions and a trichotomous decision space. Different routines fit data separately for SJ2, SJ3, and TOJ tasks, jointly for any two tasks, or also jointly for the three tasks (for common cases in which two or even the three tasks were used with the same stimuli and participants). Additional routines provide bootstrap p-values and confidence intervals for estimated parameters. A further routine is included that obtains performance measures from the fitted functions. An R package for Windows and source code of the MATLAB and R routines are available as Supplementary Files.

Measuring molecular motions inside single cells with improved analysis of single-particle trajectories

NASA Astrophysics Data System (ADS)

Rowland, David J.; Biteen, Julie S.

2017-04-01

Single-molecule super-resolution imaging and tracking can measure molecular motions inside living cells on the scale of the molecules themselves. Diffusion in biological systems commonly exhibits multiple modes of motion, which can be effectively quantified by fitting the cumulative probability distribution of the squared step sizes in a two-step fitting process. Here we combine this two-step fit into a single least-squares minimization; this new method vastly reduces the total number of fitting parameters and increases the precision with which diffusion may be measured. We demonstrate this Global Fit approach on a simulated two-component system as well as on a mixture of diffusing 80 nm and 200 nm gold spheres to show improvements in fitting robustness and localization precision compared to the traditional Local Fit algorithm.

Hands-on parameter search for neural simulations by a MIDI-controller.

PubMed

Eichner, Hubert; Borst, Alexander

2011-01-01

Computational neuroscientists frequently encounter the challenge of parameter fitting--exploring a usually high dimensional variable space to find a parameter set that reproduces an experimental data set. One common approach is using automated search algorithms such as gradient descent or genetic algorithms. However, these approaches suffer several shortcomings related to their lack of understanding the underlying question, such as defining a suitable error function or getting stuck in local minima. Another widespread approach is manual parameter fitting using a keyboard or a mouse, evaluating different parameter sets following the users intuition. However, this process is often cumbersome and time-intensive. Here, we present a new method for manual parameter fitting. A MIDI controller provides input to the simulation software, where model parameters are then tuned according to the knob and slider positions on the device. The model is immediately updated on every parameter change, continuously plotting the latest results. Given reasonably short simulation times of less than one second, we find this method to be highly efficient in quickly determining good parameter sets. Our approach bears a close resemblance to tuning the sound of an analog synthesizer, giving the user a very good intuition of the problem at hand, such as immediate feedback if and how results are affected by specific parameter changes. In addition to be used in research, our approach should be an ideal teaching tool, allowing students to interactively explore complex models such as Hodgkin-Huxley or dynamical systems.

Hands-On Parameter Search for Neural Simulations by a MIDI-Controller

PubMed Central

Eichner, Hubert; Borst, Alexander

2011-01-01

Computational neuroscientists frequently encounter the challenge of parameter fitting – exploring a usually high dimensional variable space to find a parameter set that reproduces an experimental data set. One common approach is using automated search algorithms such as gradient descent or genetic algorithms. However, these approaches suffer several shortcomings related to their lack of understanding the underlying question, such as defining a suitable error function or getting stuck in local minima. Another widespread approach is manual parameter fitting using a keyboard or a mouse, evaluating different parameter sets following the users intuition. However, this process is often cumbersome and time-intensive. Here, we present a new method for manual parameter fitting. A MIDI controller provides input to the simulation software, where model parameters are then tuned according to the knob and slider positions on the device. The model is immediately updated on every parameter change, continuously plotting the latest results. Given reasonably short simulation times of less than one second, we find this method to be highly efficient in quickly determining good parameter sets. Our approach bears a close resemblance to tuning the sound of an analog synthesizer, giving the user a very good intuition of the problem at hand, such as immediate feedback if and how results are affected by specific parameter changes. In addition to be used in research, our approach should be an ideal teaching tool, allowing students to interactively explore complex models such as Hodgkin-Huxley or dynamical systems. PMID:22066027

Genetic Algorithm Optimizes Q-LAW Control Parameters

NASA Technical Reports Server (NTRS)

Lee, Seungwon; von Allmen, Paul; Petropoulos, Anastassios; Terrile, Richard

2008-01-01

A document discusses a multi-objective, genetic algorithm designed to optimize Lyapunov feedback control law (Q-law) parameters in order to efficiently find Pareto-optimal solutions for low-thrust trajectories for electronic propulsion systems. These would be propellant-optimal solutions for a given flight time, or flight time optimal solutions for a given propellant requirement. The approximate solutions are used as good initial solutions for high-fidelity optimization tools. When the good initial solutions are used, the high-fidelity optimization tools quickly converge to a locally optimal solution near the initial solution. Q-law control parameters are represented as real-valued genes in the genetic algorithm. The performances of the Q-law control parameters are evaluated in the multi-objective space (flight time vs. propellant mass) and sorted by the non-dominated sorting method that assigns a better fitness value to the solutions that are dominated by a fewer number of other solutions. With the ranking result, the genetic algorithm encourages the solutions with higher fitness values to participate in the reproduction process, improving the solutions in the evolution process. The population of solutions converges to the Pareto front that is permitted within the Q-law control parameter space.

An automatic scaling method for obtaining the trace and parameters from oblique ionogram based on hybrid genetic algorithm

NASA Astrophysics Data System (ADS)

Song, Huan; Hu, Yaogai; Jiang, Chunhua; Zhou, Chen; Zhao, Zhengyu; Zou, Xianjian

2016-12-01

Scaling oblique ionogram plays an important role in obtaining ionospheric structure at the midpoint of oblique sounding path. The paper proposed an automatic scaling method to extract the trace and parameters of oblique ionogram based on hybrid genetic algorithm (HGA). The extracted 10 parameters come from F2 layer and Es layer, such as maximum observation frequency, critical frequency, and virtual height. The method adopts quasi-parabolic (QP) model to describe F2 layer's electron density profile that is used to synthesize trace. And it utilizes secant theorem, Martyn's equivalent path theorem, image processing technology, and echoes' characteristics to determine seven parameters' best fit values, and three parameter's initial values in QP model to set up their searching spaces which are the needed input data of HGA. Then HGA searches the three parameters' best fit values from their searching spaces based on the fitness between the synthesized trace and the real trace. In order to verify the performance of the method, 240 oblique ionograms are scaled and their results are compared with manual scaling results and the inversion results of the corresponding vertical ionograms. The comparison results show that the scaling results are accurate or at least adequate 60-90% of the time.

Fitting a defect non-linear model with or without prior, distinguishing nuclear reaction products as an example.

PubMed

Helgesson, P; Sjöstrand, H

2017-11-01

Fitting a parametrized function to data is important for many researchers and scientists. If the model is non-linear and/or defect, it is not trivial to do correctly and to include an adequate uncertainty analysis. This work presents how the Levenberg-Marquardt algorithm for non-linear generalized least squares fitting can be used with a prior distribution for the parameters and how it can be combined with Gaussian processes to treat model defects. An example, where three peaks in a histogram are to be distinguished, is carefully studied. In particular, the probability r 1 for a nuclear reaction to end up in one out of two overlapping peaks is studied. Synthetic data are used to investigate effects of linearizations and other assumptions. For perfect Gaussian peaks, it is seen that the estimated parameters are distributed close to the truth with good covariance estimates. This assumes that the method is applied correctly; for example, prior knowledge should be implemented using a prior distribution and not by assuming that some parameters are perfectly known (if they are not). It is also important to update the data covariance matrix using the fit if the uncertainties depend on the expected value of the data (e.g., for Poisson counting statistics or relative uncertainties). If a model defect is added to the peaks, such that their shape is unknown, a fit which assumes perfect Gaussian peaks becomes unable to reproduce the data, and the results for r 1 become biased. It is, however, seen that it is possible to treat the model defect with a Gaussian process with a covariance function tailored for the situation, with hyper-parameters determined by leave-one-out cross validation. The resulting estimates for r 1 are virtually unbiased, and the uncertainty estimates agree very well with the underlying uncertainty.

Fitting a defect non-linear model with or without prior, distinguishing nuclear reaction products as an example

NASA Astrophysics Data System (ADS)

Helgesson, P.; Sjöstrand, H.

2017-11-01

Fitting a parametrized function to data is important for many researchers and scientists. If the model is non-linear and/or defect, it is not trivial to do correctly and to include an adequate uncertainty analysis. This work presents how the Levenberg-Marquardt algorithm for non-linear generalized least squares fitting can be used with a prior distribution for the parameters and how it can be combined with Gaussian processes to treat model defects. An example, where three peaks in a histogram are to be distinguished, is carefully studied. In particular, the probability r1 for a nuclear reaction to end up in one out of two overlapping peaks is studied. Synthetic data are used to investigate effects of linearizations and other assumptions. For perfect Gaussian peaks, it is seen that the estimated parameters are distributed close to the truth with good covariance estimates. This assumes that the method is applied correctly; for example, prior knowledge should be implemented using a prior distribution and not by assuming that some parameters are perfectly known (if they are not). It is also important to update the data covariance matrix using the fit if the uncertainties depend on the expected value of the data (e.g., for Poisson counting statistics or relative uncertainties). If a model defect is added to the peaks, such that their shape is unknown, a fit which assumes perfect Gaussian peaks becomes unable to reproduce the data, and the results for r1 become biased. It is, however, seen that it is possible to treat the model defect with a Gaussian process with a covariance function tailored for the situation, with hyper-parameters determined by leave-one-out cross validation. The resulting estimates for r1 are virtually unbiased, and the uncertainty estimates agree very well with the underlying uncertainty.

Event generator tunes obtained from underlying event and multiparton scattering measurements.

PubMed

Khachatryan, V; Sirunyan, A M; Tumasyan, A; Adam, W; Asilar, E; Bergauer, T; Brandstetter, J; Brondolin, E; Dragicevic, M; Erö, J; Friedl, M; Frühwirth, R; Ghete, V M; Hartl, C; Hörmann, N; Hrubec, J; Jeitler, M; Knünz, V; König, A; Krammer, M; Krätschmer, I; Liko, D; Matsushita, T; Mikulec, I; Rabady, D; Rahbaran, B; Rohringer, H; Schieck, J; Schöfbeck, R; Strauss, J; Treberer-Treberspurg, W; Waltenberger, W; Wulz, C-E; Mossolov, V; Shumeiko, N; Suarez Gonzalez, J; Alderweireldt, S; Cornelis, T; De Wolf, E A; Janssen, X; Knutsson, A; Lauwers, J; Luyckx, S; Van De Klundert, M; Van Haevermaet, H; Van Mechelen, P; Van Remortel, N; Van Spilbeeck, A; Abu Zeid, S; Blekman, F; D'Hondt, J; Daci, N; De Bruyn, I; Deroover, K; Heracleous, N; Keaveney, J; Lowette, S; Moreels, L; Olbrechts, A; Python, Q; Strom, D; Tavernier, S; Van Doninck, W; Van Mulders, P; Van Onsem, G P; Van Parijs, I; Barria, P; Brun, H; Caillol, C; Clerbaux, B; De Lentdecker, G; Fasanella, G; Favart, L; Grebenyuk, A; Karapostoli, G; Lenzi, T; Léonard, A; Maerschalk, T; Marinov, A; Perniè, L; Randle-Conde, A; Seva, T; Vander Velde, C; Yonamine, R; Vanlaer, P; Yonamine, R; Zenoni, F; Zhang, F; Adler, V; Beernaert, K; Benucci, L; Cimmino, A; Crucy, S; Dobur, D; Fagot, A; Garcia, G; Gul, M; Mccartin, J; Ocampo Rios, A A; Poyraz, D; Ryckbosch, D; Salva, S; Sigamani, M; Tytgat, M; Van Driessche, W; Yazgan, E; Zaganidis, N; Basegmez, S; Beluffi, C; Bondu, O; Brochet, S; Bruno, G; Caudron, A; Ceard, L; Da Silveira, G G; Delaere, C; Favart, D; Forthomme, L; Giammanco, A; Hollar, J; Jafari, A; Jez, P; Komm, M; Lemaitre, V; Mertens, A; Musich, M; Nuttens, C; Perrini, L; Pin, A; Piotrzkowski, K; Popov, A; Quertenmont, L; Selvaggi, M; Vidal Marono, M; Beliy, N; Hammad, G H; Júnior, W L Aldá; Alves, F L; Alves, G A; Brito, L; Correa Martins Junior, M; Hamer, M; Hensel, C; Moraes, A; Pol, M E; Rebello Teles, P; Belchior Batista Das Chagas, E; Carvalho, W; Chinellato, J; Custódio, A; Da Costa, E M; De Jesus Damiao, D; De Oliveira Martins, C; 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Ortona, G; Paganini, P; Pigard, P; Regnard, S; Salerno, R; Sauvan, J B; Sirois, Y; Strebler, T; Yilmaz, Y; Zabi, A; Agram, J-L; Andrea, J; Aubin, A; Bloch, D; Brom, J-M; Buttignol, M; Chabert, E C; Chanon, N; Collard, C; Conte, E; Coubez, X; Fontaine, J-C; Gelé, D; Goerlach, U; Goetzmann, C; Le Bihan, A-C; Merlin, J A; Skovpen, K; Van Hove, P; Gadrat, S; Beauceron, S; Bernet, C; Boudoul, G; Bouvier, E; Carrillo Montoya, C A; Chierici, R; Contardo, D; Courbon, B; Depasse, P; El Mamouni, H; Fan, J; Fay, J; Gascon, S; Gouzevitch, M; Ille, B; Lagarde, F; Laktineh, I B; Lethuillier, M; Mirabito, L; Pequegnot, A L; Perries, S; Ruiz Alvarez, J D; Sabes, D; Sgandurra, L; Sordini, V; Vander Donckt, M; Verdier, P; Viret, S; Toriashvili, T; Lomidze, D; Autermann, C; Beranek, S; Edelhoff, M; Feld, L; Heister, A; Kiesel, M K; Klein, K; Lipinski, M; Ostapchuk, A; Preuten, M; Raupach, F; Schael, S; Schulte, J F; Verlage, T; Weber, H; Wittmer, B; Zhukov, V; Ata, M; Brodski, M; Dietz-Laursonn, E; Duchardt, D; Endres, M; Erdmann, M; Erdweg, S; Esch, T; Fischer, R; Güth, A; Hebbeker, T; Heidemann, C; Hoepfner, K; Knutzen, S; Kreuzer, P; Merschmeyer, M; Meyer, A; Millet, P; Olschewski, M; Padeken, K; Papacz, P; Pook, T; Radziej, M; Reithler, H; Rieger, M; Scheuch, F; Sonnenschein, L; Teyssier, D; Thüer, S; Cherepanov, V; Erdogan, Y; Flügge, G; Geenen, H; Geisler, M; Hoehle, F; Kargoll, B; Kress, T; Kuessel, Y; Künsken, A; Lingemann, J; Nehrkorn, A; Nowack, A; Nugent, I M; Pistone, C; Pooth, O; Stahl, A; Aldaya Martin, M; Asin, I; Bartosik, N; Behnke, O; Behrens, U; Bell, A J; Borras, K; Burgmeier, A; Campbell, A; Choudhury, S; Costanza, F; Diez Pardos, C; Dolinska, G; Dooling, S; Dorland, T; Eckerlin, G; Eckstein, D; Eichhorn, T; Flucke, G; Gallo, E; Garcia, J Garay; Geiser, A; Gizhko, A; Gunnellini, P; Hauk, J; Hempel, M; Jung, H; Kalogeropoulos, A; Karacheban, O; Kasemann, M; Katsas, P; Kieseler, J; Kleinwort, C; Korol, I; Lange, W; Leonard, J; Lipka, K; Lobanov, A; Lohmann, W; Mankel, R; Marfin, I; Melzer-Pellmann, I-A; Meyer, A B; Mittag, G; Mnich, J; Mussgiller, A; Naumann-Emme, S; Nayak, A; Ntomari, E; Perrey, H; Pitzl, D; Placakyte, R; Raspereza, A; Roland, B; Sahin, M Ö; Saxena, P; Schoerner-Sadenius, T; Schröder, M; Seitz, C; Spannagel, S; Trippkewitz, K D; Walsh, R; Wissing, C; Blobel, V; Centis Vignali, M; Draeger, A R; Erfle, J; Garutti, E; Goebel, K; Gonzalez, D; Görner, M; Haller, J; Hoffmann, M; Höing, R S; Junkes, A; Klanner, R; Kogler, R; Kovalchuk, N; Lapsien, T; Lenz, T; Marchesini, I; Marconi, D; Meyer, M; Nowatschin, D; Ott, J; Pantaleo, F; Peiffer, T; Perieanu, A; Pietsch, N; Poehlsen, J; Rathjens, D; Sander, C; Scharf, C; Schettler, H; Schleper, P; Schlieckau, E; Schmidt, A; Schwandt, J; Sola, V; Stadie, H; Steinbrück, G; Tholen, H; Troendle, D; Usai, E; Vanelderen, L; Vanhoefer, A; Vormwald, B; Barth, C; Baus, C; Berger, J; Böser, C; Butz, E; Chwalek, T; Colombo, F; De Boer, W; Descroix, A; Dierlamm, A; Fink, S; Frensch, F; Friese, R; Giffels, M; Gilbert, A; Haitz, D; Hartmann, F; Heindl, S M; Husemann, U; Katkov, I; Kornmayer, A; Lobelle Pardo, P; Maier, B; Mildner, H; Mozer, M U; Müller, T; Müller, Th; Plagge, M; Quast, G; Rabbertz, K; Röcker, S; Roscher, F; Sieber, G; Simonis, H J; Stober, F M; Ulrich, R; Wagner-Kuhr, J; Wayand, S; Weber, M; Weiler, T; Williamson, S; Wöhrmann, C; Wolf, R; Anagnostou, G; Daskalakis, G; Geralis, T; Giakoumopoulou, V A; Kyriakis, A; Loukas, D; Psallidas, A; Topsis-Giotis, I; Agapitos, A; Kesisoglou, S; Panagiotou, A; Saoulidou, N; Tziaferi, E; Evangelou, I; Flouris, G; Foudas, C; Kokkas, P; Loukas, N; Manthos, N; Papadopoulos, I; Paradas, E; Strologas, J; Bencze, G; Hajdu, C; Hazi, A; Hidas, P; Horvath, D; Sikler, F; Veszpremi, V; Vesztergombi, G; Zsigmond, A J; Beni, N; Czellar, S; Karancsi, J; Molnar, J; Szillasi, Z; Bartók, M; Makovec, A; Raics, P; Trocsanyi, Z L; Ujvari, B; Mal, P; Mandal, K; Sahoo, D K; Sahoo, N; Swain, S K; Bansal, S; Beri, S B; Bhatnagar, V; Chawla, R; Gupta, R; Bhawandeep, U; Kalsi, A K; Kaur, A; Kaur, M; Kumar, R; Mehta, A; Mittal, M; Singh, J B; Walia, G; Kumar, Ashok; Bhardwaj, A; Choudhary, B C; Garg, R B; Kumar, A; Malhotra, S; Naimuddin, M; Nishu, N; Ranjan, K; Sharma, R; Sharma, V; Bhattacharya, S; Chatterjee, K; Dey, S; Dutta, S; Jain, Sa; Majumdar, N; Modak, A; Mondal, K; Mukherjee, S; Mukhopadhyay, S; Roy, A; Roy, D; Roy Chowdhury, S; Sarkar, S; Sharan, M; Abdulsalam, A; Chudasama, R; Dutta, D; Jha, V; Kumar, V; Mohanty, A K; Pant, L M; Shukla, P; Topkar, A; Aziz, T; Banerjee, S; Bhowmik, S; Chatterjee, R M; Dewanjee, R K; Dugad, S; Ganguly, S; Ghosh, S; Guchait, M; Gurtu, A; Kole, G; Kumar, S; Mahakud, B; Maity, M; Majumder, G; Mazumdar, K; Mitra, S; Mohanty, G B; Parida, B; Sarkar, T; Sur, N; Sutar, B; Wickramage, N; Chauhan, S; Dube, S; Kapoor, A; Kothekar, K; Sharma, S; Bakhshiansohi, H; Behnamian, H; Etesami, S M; Fahim, A; Goldouzian, R; Khakzad, M; Mohammadi Najafabadi, M; Naseri, M; Paktinat Mehdiabadi, S; Rezaei Hosseinabadi, F; Safarzadeh, B; Zeinali, M; Felcini, M; Grunewald, M; Abbrescia, M; Calabria, C; Caputo, C; Colaleo, A; Creanza, D; Cristella, L; De Filippis, N; De Palma, M; Fiore, L; Iaselli, G; Maggi, G; Miniello, G; Maggi, M; My, S; Nuzzo, S; Pompili, A; Pugliese, G; Radogna, R; Ranieri, A; Selvaggi, G; Silvestris, L; Venditti, R; Verwilligen, P; Abbiendi, G; Battilana, C; Benvenuti, A C; Bonacorsi, D; Braibant-Giacomelli, S; Brigliadori, L; Campanini, R; Capiluppi, P; Castro, A; Cavallo, F R; Chhibra, S S; Codispoti, G; Cuffiani, M; Dallavalle, G M; Fabbri, F; Fanfani, A; Fasanella, D; Giacomelli, P; Grandi, C; Guiducci, L; Marcellini, S; Masetti, G; Montanari, A; Navarria, F L; Perrotta, A; Rossi, A M; Primavera, F; Rovelli, T; Siroli, G P; Tosi, N; Travaglini, R; Cappello, G; Chiorboli, M; Costa, S; Mattia, A Di; Giordano, F; Potenza, R; Tricomi, A; Tuve, C; Barbagli, G; Ciulli, V; Civinini, C; D'Alessandro, R; Focardi, E; Gonzi, S; Gori, V; Lenzi, P; Meschini, M; Paoletti, S; Sguazzoni, G; Tropiano, A; Viliani, L; Benussi, L; 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Salfeld-Nebgen, J; Stephans, G S F; Sumorok, K; Varma, M; Velicanu, D; Veverka, J; Wang, J; Wang, T W; Wyslouch, B; Yang, M; Zhukova, V; Dahmes, B; Evans, A; Finkel, A; Gude, A; Hansen, P; Kalafut, S; Kao, S C; Klapoetke, K; Kubota, Y; Lesko, Z; Mans, J; Nourbakhsh, S; Ruckstuhl, N; Rusack, R; Tambe, N; Turkewitz, J; Acosta, J G; Oliveros, S; Avdeeva, E; Bloom, K; Bose, S; Claes, D R; Dominguez, A; Fangmeier, C; Gonzalez Suarez, R; Kamalieddin, R; Keller, J; Knowlton, D; Kravchenko, I; Meier, F; Monroy, J; Ratnikov, F; Siado, J E; Snow, G R; Alyari, M; Dolen, J; George, J; Godshalk, A; Harrington, C; Iashvili, I; Kaisen, J; Kharchilava, A; Kumar, A; Rappoccio, S; Roozbahani, B; Alverson, G; Barberis, E; Baumgartel, D; Chasco, M; Hortiangtham, A; Massironi, A; Morse, D M; Nash, D; Orimoto, T; Teixeira De Lima, R; Trocino, D; Wang, R-J; Wood, D; Zhang, J; Hahn, K A; Kubik, A; Mucia, N; Odell, N; Pollack, B; Pozdnyakov, A; Schmitt, M; Stoynev, S; Sung, K; Trovato, M; Velasco, M; Brinkerhoff, A; Dev, N; Hildreth, M; Jessop, C; Karmgard, D J; Kellams, N; Lannon, K; Marinelli, N; Meng, F; Mueller, C; Musienko, Y; Planer, M; Reinsvold, A; Ruchti, R; Smith, G; Taroni, S; Valls, N; Wayne, M; Wolf, M; Woodard, A; Antonelli, L; Brinson, J; Bylsma, B; Durkin, L S; Flowers, S; Hart, A; Hill, C; Hughes, R; Ji, W; Ling, T Y; Liu, B; Luo, W; Puigh, D; Rodenburg, M; Winer, B L; Wulsin, H W; Driga, O; Elmer, P; Hardenbrook, J; Hebda, P; Koay, S A; Lujan, P; Marlow, D; Medvedeva, T; Mooney, M; Olsen, J; Palmer, C; Piroué, P; Saka, H; Stickland, D; Tully, C; Zuranski, A; Malik, S; Barnes, V E; Benedetti, D; Bortoletto, D; Gutay, L; Jha, M K; Jones, M; Jung, K; Miller, D H; Neumeister, N; Primavera, F; Radburn-Smith, B C; Shi, X; Shipsey, I; Silvers, D; Sun, J; Svyatkovskiy, A; Wang, F; Xie, W; Xu, L; Parashar, N; Stupak, J; Adair, A; Akgun, B; Chen, Z; Ecklund, K M; Geurts, F J M; Guilbaud, M; Li, W; Michlin, B; Northup, M; Padley, B P; Redjimi, R; Roberts, J; Rorie, J; Tu, Z; Zabel, J; Betchart, B; Bodek, A; de Barbaro, P; Demina, R; Eshaq, Y; Ferbel, T; Galanti, M; Galanti, M; Garcia-Bellido, A; Han, J; Harel, A; Hindrichs, O; Hindrichs, O; Khukhunaishvili, A; Petrillo, G; Tan, P; Verzetti, M; Arora, S; Barker, A; Chou, J P; Contreras-Campana, C; Contreras-Campana, E; Ferencek, D; Gershtein, Y; Gray, R; Halkiadakis, E; Hidas, D; Hughes, E; Kaplan, S; Kunnawalkam Elayavalli, R; Lath, A; Nash, K; Panwalkar, S; Park, M; Salur, S; Schnetzer, S; Sheffield, D; Somalwar, S; Stone, R; Thomas, S; Thomassen, P; Walker, M; Foerster, M; Riley, G; Rose, K; Spanier, S; York, A; Bouhali, O; Castaneda Hernandez, A; Celik, A; Dalchenko, M; De Mattia, M; Delgado, A; Dildick, S; Dildick, S; Eusebi, R; Gilmore, J; Huang, T; Kamon, T; Krutelyov, V; Krutelyov, V; Mueller, R; Osipenkov, I; Pakhotin, Y; Patel, R; Patel, R; Perloff, A; Rose, A; Safonov, A; Tatarinov, A; Ulmer, K A; Akchurin, N; Cowden, C; Damgov, J; Dragoiu, C; Dudero, P R; Faulkner, J; Kunori, S; Lamichhane, K; Lee, S W; Libeiro, T; Undleeb, S; Volobouev, I; Appelt, E; Delannoy, A G; Greene, S; Gurrola, A; Janjam, R; Johns, W; Maguire, C; Mao, Y; Melo, A; Ni, H; Sheldon, P; Snook, B; Tuo, S; Velkovska, J; Xu, Q; Arenton, M W; Cox, B; Francis, B; Goodell, J; Hirosky, R; Ledovskoy, A; Li, H; Lin, C; Neu, C; Sinthuprasith, T; Sun, X; Wang, Y; Wolfe, E; Wood, J; Xia, F; Clarke, C; Harr, R; Karchin, P E; Kottachchi Kankanamge Don, C; Lamichhane, P; Sturdy, J; Belknap, D A; Carlsmith, D; Cepeda, M; Dasu, S; Dodd, L; Duric, S; Gomber, B; Grothe, M; Hall-Wilton, R; Herndon, M; Hervé, A; Klabbers, P; Lanaro, A; Levine, A; Long, K; Loveless, R; Mohapatra, A; Ojalvo, I; Perry, T; Pierro, G A; Polese, G; Ruggles, T; Sarangi, T; Savin, A; Sharma, A; Smith, N; Smith, W H; Taylor, D; Woods, N

New sets of parameters ("tunes") for the underlying-event (UE) modelling of the pythia8, pythia6 and herwig++ Monte Carlo event generators are constructed using different parton distribution functions. Combined fits to CMS UE proton-proton ([Formula: see text]) data at [Formula: see text] and to UE proton-antiproton ([Formula: see text]) data from the CDF experiment at lower [Formula: see text], are used to study the UE models and constrain their parameters, providing thereby improved predictions for proton-proton collisions at 13[Formula: see text]. In addition, it is investigated whether the values of the parameters obtained from fits to UE observables are consistent with the values determined from fitting observables sensitive to double-parton scattering processes. Finally, comparisons are presented of the UE tunes to "minimum bias" (MB) events, multijet, and Drell-Yan ([Formula: see text] lepton-antilepton+jets) observables at 7 and 8[Formula: see text], as well as predictions for MB and UE observables at 13[Formula: see text].

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

PubMed Central

2014-01-01

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

Anomalous resistivity effect on multiple ion beam emission and hard x-ray generation in a Mather type plasma focus device

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

Behbahani, R. A.; Aghamir, F. M.

Multi ion beam and hard x-ray emissions were detected in a high inductance (more than 100 nH) Mather type plasma focus (PF) device at different filling gas pressures and charging voltages. The signal analysis was performed through the current trace, as it is the fundamental signal from which all of the phenomena in a PF device can be extracted. Two different fitting processes were carried out according to Lee's computational (snow-plow) model. In the first process, only plasma dynamics and classical (Spitzer) resistances were considered as energy consumer parameters for plasma. This led to an unsuccessful fitting and did notmore » answer the energy transfer mechanism into plasma. A second fitting process was considered through the addition of anomalous resistance, which provided the best fit. Anomalous resistance was the source of long decrease in current trace, and multi dips and multi peaks of high voltage probe. Multi-peak features were interpreted considering the second fitting process along with the mechanisms for ion beam production and hard x-ray emission. To show the important role of the anomalous resistance, the duration of the current drop was discussed.« less

Determination of Kinetic Parameters for the Thermal Decomposition of Parthenium hysterophorus

NASA Astrophysics Data System (ADS)

Dhaundiyal, Alok; Singh, Suraj B.; Hanon, Muammel M.; Rawat, Rekha

2018-02-01

A kinetic study of pyrolysis process of Parthenium hysterophorous is carried out by using thermogravimetric analysis (TGA) equipment. The present study investigates the thermal degradation and determination of the kinetic parameters such as activation E and the frequency factor A using model-free methods given by Flynn Wall and Ozawa (FWO), Kissinger-Akahira-Sonuse (KAS) and Kissinger, and model-fitting (Coats Redfern). The results derived from thermal decomposition process demarcate decomposition of Parthenium hysterophorous among the three main stages, such as dehydration, active and passive pyrolysis. It is shown through DTG thermograms that the increase in the heating rate caused temperature peaks at maximum weight loss rate to shift towards higher temperature regime. The results are compared with Coats Redfern (Integral method) and experimental results have shown that values of kinetic parameters obtained from model-free methods are in good agreement. Whereas the results obtained through Coats Redfern model at different heating rates are not promising, however, the diffusion models provided the good fitting with the experimental data.

Group Contribution Methods for Phase Equilibrium Calculations.

PubMed

Gmehling, Jürgen; Constantinescu, Dana; Schmid, Bastian

2015-01-01

The development and design of chemical processes are carried out by solving the balance equations of a mathematical model for sections of or the whole chemical plant with the help of process simulators. For process simulation, besides kinetic data for the chemical reaction, various pure component and mixture properties are required. Because of the great importance of separation processes for a chemical plant in particular, a reliable knowledge of the phase equilibrium behavior is required. The phase equilibrium behavior can be calculated with the help of modern equations of state or g(E)-models using only binary parameters. But unfortunately, only a very small part of the experimental data for fitting the required binary model parameters is available, so very often these models cannot be applied directly. To solve this problem, powerful predictive thermodynamic models have been developed. Group contribution methods allow the prediction of the required phase equilibrium data using only a limited number of group interaction parameters. A prerequisite for fitting the required group interaction parameters is a comprehensive database. That is why for the development of powerful group contribution methods almost all published pure component properties, phase equilibrium data, excess properties, etc., were stored in computerized form in the Dortmund Data Bank. In this review, the present status, weaknesses, advantages and disadvantages, possible applications, and typical results of the different group contribution methods for the calculation of phase equilibria are presented.

DIRT: The Dust InfraRed Toolbox

NASA Astrophysics Data System (ADS)

Pound, M. W.; Wolfire, M. G.; Mundy, L. G.; Teuben, P. J.; Lord, S.

We present DIRT, a Java applet geared toward modeling a variety of processes in envelopes of young and evolved stars. Users can automatically and efficiently search grids of pre-calculated models to fit their data. A large set of physical parameters and dust types are included in the model database, which contains over 500,000 models. The computing cluster for the database is described in the accompanying paper by Teuben et al. (2000). A typical user query will return about 50-100 models, which the user can then interactively filter as a function of 8 model parameters (e.g., extinction, size, flux, luminosity). A flexible, multi-dimensional plotter (Figure 1) allows users to view the models, rotate them, tag specific parameters with color or symbol size, and probe individual model points. For any given model, auxiliary plots such as dust grain properties, radial intensity profiles, and the flux as a function of wavelength and beamsize can be viewed. The user can fit observed data to several models simultaneously and see the results of the fit; the best fit is automatically selected for plotting. The URL for this project is http://dustem.astro.umd.edu.

Primary α and secondary β relaxation dynamics of meta-toluidine in the liquid state investigated by broadband dielectric spectroscopy

NASA Astrophysics Data System (ADS)

Švajdlenková, H.; Ruff, A.; Lunkenheimer, P.; Loidl, A.; Bartoš, J.

2017-08-01

We report a broadband dielectric spectroscopic (BDS) study on the clustering fragile glass-former meta-toluidine (m-TOL) from 187 K up to 289 K over a wide frequency range of 10-3-109 Hz with focus on the primary α relaxation and the secondary β relaxation above the glass temperature Tg. The broadband dielectric spectra were fitted by using the Havriliak-Negami (HN) and Cole-Cole (CC) models. The β process disappearing at Tβ,disap = 1.12Tg exhibits non-Arrhenius dependence fitted by the Vogel-Fulcher-Tamman-Hesse equation with T0βVFTH in accord with the characteristic differential scanning calorimetry (DSC) limiting temperature of the glassy state. The essential feature of the α process consists in the distinct changes of its spectral shape parameter βHN marked by the characteristic BDS temperatures TB1βHN and TB2βHN. The primary α relaxation times were fitted over the entire temperature and frequency range by several current three-parameter up to six-parameter dynamic models. This analysis reveals that the crossover temperatures of the idealized mode coupling theory model (TcMCT), the extended free volume model (T0EFV), and the two-order parameter (TOP) model (Tmc) are close to TB1βHN, which provides a consistent physical rationalization for the first change of the shape parameter. In addition, the other two characteristic TOP temperatures T0TOP and TA are coinciding with the thermodynamic Kauzmann temperature TK and the second change of the shape parameter at around TB2βHN, respectively. These can be related to the onset of the liquid-like domains in the glassy state or the disappearance of the solid-like domains in the normal liquid state.

How well can we measure supermassive black hole spin?

NASA Astrophysics Data System (ADS)

Bonson, K.; Gallo, L. C.

2016-05-01

Being one of only two fundamental properties black holes possess, the spin of supermassive black holes (SMBHs) is of great interest for understanding accretion processes and galaxy evolution. However, in these early days of spin measurements, consistency and reproducibility of spin constraints have been a challenge. Here, we focus on X-ray spectral modelling of active galactic nuclei (AGN), examining how well we can truly return known reflection parameters such as spin under standard conditions. We have created and fit over 4000 simulated Seyfert 1 spectra each with 375±1k counts. We assess the fits with reflection fraction of R = 1 as well as reflection-dominated AGN with R = 5. We also examine the consequence of permitting fits to search for retrograde spin. In general, we discover that most parameters are overestimated when spectroscopy is restricted to the 2.5-10.0 keV regime and that models are insensitive to inner emissivity index and ionization. When the bandpass is extended out to 70 keV, parameters are more accurately estimated. Repeating the process for R = 5 reduces our ability to measure photon index (˜3 to 8 per cent error and overestimated), but increases precision in all other parameters - most notably ionization, which becomes better constrained (±45 erg cm s^{-1}) for low-ionization parameters (ξ < 200 erg cm s^{-1}). In all cases, we find the spin parameter is only well measured for the most rapidly rotating SMBHs (I.e. a > 0.8 to about ±0.10) and that inner emissivity index is never well constrained. Allowing our model to search for retrograde spin did not improve the results.

Kinetics, isothermal and thermodynamics studies of electrocoagulation removal of basic dye rhodamine B from aqueous solution using steel electrodes

NASA Astrophysics Data System (ADS)

Adeogun, Abideen Idowu; Balakrishnan, Ramesh Babu

2017-07-01

Electrocoagulation was used for the removal of basic dye rhodamine B from aqueous solution, and the process was carried out in a batch electrochemical cell with steel electrodes in monopolar connection. The effects of some important parameters such as current density, pH, temperature and initial dye concentration, on the process, were investigated. Equilibrium was attained after 10 min at 30 °C. Pseudo-first-order, pseudo-second-order, Elovich and Avrami kinetic models were used to test the experimental data in order to elucidate the kinetic adsorption process; pseudo-first-order and Avrami models best fitted the data. Experimental data were analysed using six model equations: Langmuir, Freudlinch, Redlich-Peterson, Temkin, Dubinin-Radushkevich and Sips isotherms and it was found that the data fitted well with Sips isotherm model. The study showed that the process depends on current density, temperature, pH and initial dye concentration. The calculated thermodynamics parameters (Δ G°, Δ H° and Δ S°) indicated that the process is spontaneous and endothermic in nature.

Fitting-free algorithm for efficient quantification of collagen fiber alignment in SHG imaging applications.

PubMed

Hall, Gunnsteinn; Liang, Wenxuan; Li, Xingde

2017-10-01

Collagen fiber alignment derived from second harmonic generation (SHG) microscopy images can be important for disease diagnostics. Image processing algorithms are needed to robustly quantify the alignment in images with high sensitivity and reliability. Fourier transform (FT) magnitude, 2D power spectrum, and image autocorrelation have previously been used to extract fiber information from images by assuming a certain mathematical model (e.g. Gaussian distribution of the fiber-related parameters) and fitting. The fitting process is slow and fails to converge when the data is not Gaussian. Herein we present an efficient constant-time deterministic algorithm which characterizes the symmetricity of the FT magnitude image in terms of a single parameter, named the fiber alignment anisotropy R ranging from 0 (randomized fibers) to 1 (perfect alignment). This represents an important improvement of the technology and may bring us one step closer to utilizing the technology for various applications in real time. In addition, we present a digital image phantom-based framework for characterizing and validating the algorithm, as well as assessing the robustness of the algorithm against different perturbations.

Comprehensive data model to characterize long term integrity and process parameter interactions governing the butt fusion process.

DOT National Transportation Integrated Search

2012-12-01

The overall integrity of the plastic piping system is predicated on the long term strength : of its weakest link which often occurs at fitting and joint interfaces, e.g. electrofusion, : mechanical, heat fusion, etc. In order to maximize the overall ...

Universality in the tail of musical note rank distribution

NASA Astrophysics Data System (ADS)

Beltrán del Río, M.; Cocho, G.; Naumis, G. G.

2008-09-01

Although power laws have been used to fit rank distributions in many different contexts, they usually fail at the tails. Languages as sequences of symbols have been a popular subject for ranking distributions, and for this purpose, music can be treated as such. Here we show that more than 1800 musical compositions are very well fitted by the first kind two parameter beta distribution, which arises in the ranking of multiplicative stochastic processes. The parameters a and b are obtained for classical, jazz and rock music, revealing interesting features. Specially, we have obtained a clear trend in the values of the parameters for major and minor tonal modes. Finally, we discuss the distribution of notes for each octave and its connection with the ranking of the notes.

Characterizing and reducing equifinality by constraining a distributed catchment model with regional signatures, local observations, and process understanding

NASA Astrophysics Data System (ADS)

Kelleher, Christa; McGlynn, Brian; Wagener, Thorsten

2017-07-01

Distributed catchment models are widely used tools for predicting hydrologic behavior. While distributed models require many parameters to describe a system, they are expected to simulate behavior that is more consistent with observed processes. However, obtaining a single set of acceptable parameters can be problematic, as parameter equifinality often results in several behavioral sets that fit observations (typically streamflow). In this study, we investigate the extent to which equifinality impacts a typical distributed modeling application. We outline a hierarchical approach to reduce the number of behavioral sets based on regional, observation-driven, and expert-knowledge-based constraints. For our application, we explore how each of these constraint classes reduced the number of behavioral parameter sets and altered distributions of spatiotemporal simulations, simulating a well-studied headwater catchment, Stringer Creek, Montana, using the distributed hydrology-soil-vegetation model (DHSVM). As a demonstrative exercise, we investigated model performance across 10 000 parameter sets. Constraints on regional signatures, the hydrograph, and two internal measurements of snow water equivalent time series reduced the number of behavioral parameter sets but still left a small number with similar goodness of fit. This subset was ultimately further reduced by incorporating pattern expectations of groundwater table depth across the catchment. Our results suggest that utilizing a hierarchical approach based on regional datasets, observations, and expert knowledge to identify behavioral parameter sets can reduce equifinality and bolster more careful application and simulation of spatiotemporal processes via distributed modeling at the catchment scale.

Uncertainty Analysis in 3D Equilibrium Reconstruction

DOE PAGES

Cianciosa, Mark R.; Hanson, James D.; Maurer, David A.

2018-02-21

Reconstruction is an inverse process where a parameter space is searched to locate a set of parameters with the highest probability of describing experimental observations. Due to systematic errors and uncertainty in experimental measurements, this optimal set of parameters will contain some associated uncertainty. This uncertainty in the optimal parameters leads to uncertainty in models derived using those parameters. V3FIT is a three-dimensional (3D) equilibrium reconstruction code that propagates uncertainty from the input signals, to the reconstructed parameters, and to the final model. Here in this paper, we describe the methods used to propagate uncertainty in V3FIT. Using the resultsmore » of whole shot 3D equilibrium reconstruction of the Compact Toroidal Hybrid, this propagated uncertainty is validated against the random variation in the resulting parameters. Two different model parameterizations demonstrate how the uncertainty propagation can indicate the quality of a reconstruction. As a proxy for random sampling, the whole shot reconstruction results in a time interval that will be used to validate the propagated uncertainty from a single time slice.« less

Uncertainty Analysis in 3D Equilibrium Reconstruction

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

Cianciosa, Mark R.; Hanson, James D.; Maurer, David A.

Reconstruction is an inverse process where a parameter space is searched to locate a set of parameters with the highest probability of describing experimental observations. Due to systematic errors and uncertainty in experimental measurements, this optimal set of parameters will contain some associated uncertainty. This uncertainty in the optimal parameters leads to uncertainty in models derived using those parameters. V3FIT is a three-dimensional (3D) equilibrium reconstruction code that propagates uncertainty from the input signals, to the reconstructed parameters, and to the final model. Here in this paper, we describe the methods used to propagate uncertainty in V3FIT. Using the resultsmore » of whole shot 3D equilibrium reconstruction of the Compact Toroidal Hybrid, this propagated uncertainty is validated against the random variation in the resulting parameters. Two different model parameterizations demonstrate how the uncertainty propagation can indicate the quality of a reconstruction. As a proxy for random sampling, the whole shot reconstruction results in a time interval that will be used to validate the propagated uncertainty from a single time slice.« less

Inversion group (IG) fitting: A new T1 mapping method for modified look-locker inversion recovery (MOLLI) that allows arbitrary inversion groupings and rest periods (including no rest period).

PubMed

Sussman, Marshall S; Yang, Issac Y; Fok, Kai-Ho; Wintersperger, Bernd J

2016-06-01

The Modified Look-Locker Inversion Recovery (MOLLI) technique is used for T1 mapping in the heart. However, a drawback of this technique is that it requires lengthy rest periods in between inversion groupings to allow for complete magnetization recovery. In this work, a new MOLLI fitting algorithm (inversion group [IG] fitting) is presented that allows for arbitrary combinations of inversion groupings and rest periods (including no rest period). Conventional MOLLI algorithms use a three parameter fitting model. In IG fitting, the number of parameters is two plus the number of inversion groupings. This increased number of parameters permits any inversion grouping/rest period combination. Validation was performed through simulation, phantom, and in vivo experiments. IG fitting provided T1 values with less than 1% discrepancy across a range of inversion grouping/rest period combinations. By comparison, conventional three parameter fits exhibited up to 30% discrepancy for some combinations. The one drawback with IG fitting was a loss of precision-approximately 30% worse than the three parameter fits. IG fitting permits arbitrary inversion grouping/rest period combinations (including no rest period). The cost of the algorithm is a loss of precision relative to conventional three parameter fits. Magn Reson Med 75:2332-2340, 2016. © 2015 Wiley Periodicals, Inc. © 2015 Wiley Periodicals, Inc.

Advanced approach to the analysis of a series of in-situ nuclear forward scattering experiments

NASA Astrophysics Data System (ADS)

Vrba, Vlastimil; Procházka, Vít; Smrčka, David; Miglierini, Marcel

2017-03-01

This study introduces a sequential fitting procedure as a specific approach to nuclear forward scattering (NFS) data evaluation. Principles and usage of this advanced evaluation method are described in details and its utilization is demonstrated on NFS in-situ investigations of fast processes. Such experiments frequently consist of hundreds of time spectra which need to be evaluated. The introduced procedure allows the analysis of these experiments and significantly decreases the time needed for the data evaluation. The key contributions of the study are the sequential use of the output fitting parameters of a previous data set as the input parameters for the next data set and the model suitability crosscheck option of applying the procedure in ascending and descending directions of the data sets. Described fitting methodology is beneficial for checking of model validity and reliability of obtained results.

Robust and fast nonlinear optimization of diffusion MRI microstructure models.

PubMed

Harms, R L; Fritz, F J; Tobisch, A; Goebel, R; Roebroeck, A

2017-07-15

Advances in biophysical multi-compartment modeling for diffusion MRI (dMRI) have gained popularity because of greater specificity than DTI in relating the dMRI signal to underlying cellular microstructure. A large range of these diffusion microstructure models have been developed and each of the popular models comes with its own, often different, optimization algorithm, noise model and initialization strategy to estimate its parameter maps. Since data fit, accuracy and precision is hard to verify, this creates additional challenges to comparability and generalization of results from diffusion microstructure models. In addition, non-linear optimization is computationally expensive leading to very long run times, which can be prohibitive in large group or population studies. In this technical note we investigate the performance of several optimization algorithms and initialization strategies over a few of the most popular diffusion microstructure models, including NODDI and CHARMED. We evaluate whether a single well performing optimization approach exists that could be applied to many models and would equate both run time and fit aspects. All models, algorithms and strategies were implemented on the Graphics Processing Unit (GPU) to remove run time constraints, with which we achieve whole brain dataset fits in seconds to minutes. We then evaluated fit, accuracy, precision and run time for different models of differing complexity against three common optimization algorithms and three parameter initialization strategies. Variability of the achieved quality of fit in actual data was evaluated on ten subjects of each of two population studies with a different acquisition protocol. We find that optimization algorithms and multi-step optimization approaches have a considerable influence on performance and stability over subjects and over acquisition protocols. The gradient-free Powell conjugate-direction algorithm was found to outperform other common algorithms in terms of run time, fit, accuracy and precision. Parameter initialization approaches were found to be relevant especially for more complex models, such as those involving several fiber orientations per voxel. For these, a fitting cascade initializing or fixing parameter values in a later optimization step from simpler models in an earlier optimization step further improved run time, fit, accuracy and precision compared to a single step fit. This establishes and makes available standards by which robust fit and accuracy can be achieved in shorter run times. This is especially relevant for the use of diffusion microstructure modeling in large group or population studies and in combining microstructure parameter maps with tractography results. Copyright © 2017 The Authors. Published by Elsevier Inc. All rights reserved.

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

Khachatryan, V.; Sirunyan, A. M.; Tumasyan, A.

Here, new sets of parameters (“tunes”) for the underlying-event (UE) modelling of the pythia8, pythia6 and herwig++ Monte Carlo event generators are constructed using different parton distribution functions. Combined fits to CMS UE proton–proton (more » $$\\mathrm {p}\\mathrm {p}$$ ) data at $$\\sqrt{s} = 7\\,\\text {TeV} $$ and to UE proton–antiproton ( $$\\mathrm {p}\\overline{\\mathrm{p}} $$ ) data from the CDF experiment at lower $$\\sqrt{s}$$ , are used to study the UE models and constrain their parameters, providing thereby improved predictions for proton–proton collisions at 13 $$\\,\\text {TeV}$$ . In addition, it is investigated whether the values of the parameters obtained from fits to UE observables are consistent with the values determined from fitting observables sensitive to double-parton scattering processes. Finally, comparisons are presented of the UE tunes to “minimum bias” (MB) events, multijet, and Drell–Yan ( $$ \\mathrm{q} \\overline{\\mathrm{q}} \\rightarrow \\mathrm{Z}/ \\gamma ^* \\rightarrow $$ lepton-antilepton+jets) observables at 7 and 8 $$\\,\\text {TeV}$$ , as well as predictions for MB and UE observables at 13 $$\\,\\text {TeV}$$ .« less

Event generator tunes obtained from underlying event and multiparton scattering measurements

DOE PAGES

Khachatryan, V.; Sirunyan, A. M.; Tumasyan, A.; ...

2016-03-17

Here, new sets of parameters (“tunes”) for the underlying-event (UE) modelling of the pythia8, pythia6 and herwig++ Monte Carlo event generators are constructed using different parton distribution functions. Combined fits to CMS UE proton–proton (more » $$\\mathrm {p}\\mathrm {p}$$ ) data at $$\\sqrt{s} = 7\\,\\text {TeV} $$ and to UE proton–antiproton ( $$\\mathrm {p}\\overline{\\mathrm{p}} $$ ) data from the CDF experiment at lower $$\\sqrt{s}$$ , are used to study the UE models and constrain their parameters, providing thereby improved predictions for proton–proton collisions at 13 $$\\,\\text {TeV}$$ . In addition, it is investigated whether the values of the parameters obtained from fits to UE observables are consistent with the values determined from fitting observables sensitive to double-parton scattering processes. Finally, comparisons are presented of the UE tunes to “minimum bias” (MB) events, multijet, and Drell–Yan ( $$ \\mathrm{q} \\overline{\\mathrm{q}} \\rightarrow \\mathrm{Z}/ \\gamma ^* \\rightarrow $$ lepton-antilepton+jets) observables at 7 and 8 $$\\,\\text {TeV}$$ , as well as predictions for MB and UE observables at 13 $$\\,\\text {TeV}$$ .« less

Dual learning processes underlying human decision-making in reversal learning tasks: functional significance and evidence from the model fit to human behavior

PubMed Central

Bai, Yu; Katahira, Kentaro; Ohira, Hideki

2014-01-01

Humans are capable of correcting their actions based on actions performed in the past, and this ability enables them to adapt to a changing environment. The computational field of reinforcement learning (RL) has provided a powerful explanation for understanding such processes. Recently, the dual learning system, modeled as a hybrid model that incorporates value update based on reward-prediction error and learning rate modulation based on the surprise signal, has gained attention as a model for explaining various neural signals. However, the functional significance of the hybrid model has not been established. In the present study, we used computer simulation in a reversal learning task to address functional significance in a probabilistic reversal learning task. The hybrid model was found to perform better than the standard RL model in a large parameter setting. These results suggest that the hybrid model is more robust against the mistuning of parameters compared with the standard RL model when decision-makers continue to learn stimulus-reward contingencies, which can create abrupt changes. The parameter fitting results also indicated that the hybrid model fit better than the standard RL model for more than 50% of the participants, which suggests that the hybrid model has more explanatory power for the behavioral data than the standard RL model. PMID:25161635

Fitting mechanistic epidemic models to data: A comparison of simple Markov chain Monte Carlo approaches.

PubMed

Li, Michael; Dushoff, Jonathan; Bolker, Benjamin M

2018-07-01

Simple mechanistic epidemic models are widely used for forecasting and parameter estimation of infectious diseases based on noisy case reporting data. Despite the widespread application of models to emerging infectious diseases, we know little about the comparative performance of standard computational-statistical frameworks in these contexts. Here we build a simple stochastic, discrete-time, discrete-state epidemic model with both process and observation error and use it to characterize the effectiveness of different flavours of Bayesian Markov chain Monte Carlo (MCMC) techniques. We use fits to simulated data, where parameters (and future behaviour) are known, to explore the limitations of different platforms and quantify parameter estimation accuracy, forecasting accuracy, and computational efficiency across combinations of modeling decisions (e.g. discrete vs. continuous latent states, levels of stochasticity) and computational platforms (JAGS, NIMBLE, Stan).

Evolution with Stochastic Fitness and Stochastic Migration

PubMed Central

Rice, Sean H.; Papadopoulos, Anthony

2009-01-01

Background Migration between local populations plays an important role in evolution - influencing local adaptation, speciation, extinction, and the maintenance of genetic variation. Like other evolutionary mechanisms, migration is a stochastic process, involving both random and deterministic elements. Many models of evolution have incorporated migration, but these have all been based on simplifying assumptions, such as low migration rate, weak selection, or large population size. We thus have no truly general and exact mathematical description of evolution that incorporates migration. Methodology/Principal Findings We derive an exact equation for directional evolution, essentially a stochastic Price equation with migration, that encompasses all processes, both deterministic and stochastic, contributing to directional change in an open population. Using this result, we show that increasing the variance in migration rates reduces the impact of migration relative to selection. This means that models that treat migration as a single parameter tend to be biassed - overestimating the relative impact of immigration. We further show that selection and migration interact in complex ways, one result being that a strategy for which fitness is negatively correlated with migration rates (high fitness when migration is low) will tend to increase in frequency, even if it has lower mean fitness than do other strategies. Finally, we derive an equation for the effective migration rate, which allows some of the complex stochastic processes that we identify to be incorporated into models with a single migration parameter. Conclusions/Significance As has previously been shown with selection, the role of migration in evolution is determined by the entire distributions of immigration and emigration rates, not just by the mean values. The interactions of stochastic migration with stochastic selection produce evolutionary processes that are invisible to deterministic evolutionary theory. PMID:19816580

Development of a software tool using deterministic logic for the optimization of cochlear implant processor programming.

PubMed

Govaerts, Paul J; Vaerenberg, Bart; De Ceulaer, Geert; Daemers, Kristin; De Beukelaer, Carina; Schauwers, Karen

2010-08-01

An intelligent agent, Fitting to Outcomes eXpert, was developed to optimize and automate Cochlear implant (CI) programming. The current article describes the rationale, development, and features of this tool. Cochlear implant fitting is a time-consuming procedure to define the value of a subset of the available electric parameters based primarily on behavioral responses. It is comfort-driven with high intraindividual and interindividual variability both with respect to the patient and to the clinician. Its validity in terms of process control can be questioned. Good clinical practice would require an outcome-driven approach. An intelligent agent may help solve the complexity of addressing more electric parameters based on a range of outcome measures. A software application was developed that consists of deterministic rules that analyze the map settings in the processor together with psychoacoustic test results (audiogram, A(section sign)E phoneme discrimination, A(section sign)E loudness scaling, speech audiogram) obtained with that map. The rules were based on the daily clinical practice and the expertise of the CI programmers. The data transfer to and from this agent is either manual or through seamless digital communication with the CI fitting database and the psychoacoustic test suite. It recommends and executes modifications to the map settings to improve the outcome. Fitting to Outcomes eXpert is an operational intelligent agent, the principles of which are described. Its development and modes of operation are outlined, and a case example is given. Fitting to Outcomes eXpert is in use for more than a year now and seems to be capable to improve the measured outcome. It is argued that this novel tool allows a systematic approach focusing on outcome, reducing the fitting time, and improving the quality of fitting. It introduces principles of artificial intelligence in the process of CI fitting.

Insight into model mechanisms through automatic parameter fitting: a new methodological framework for model development

PubMed Central

2014-01-01

Background Striking a balance between the degree of model complexity and parameter identifiability, while still producing biologically feasible simulations using modelling is a major challenge in computational biology. While these two elements of model development are closely coupled, parameter fitting from measured data and analysis of model mechanisms have traditionally been performed separately and sequentially. This process produces potential mismatches between model and data complexities that can compromise the ability of computational frameworks to reveal mechanistic insights or predict new behaviour. In this study we address this issue by presenting a generic framework for combined model parameterisation, comparison of model alternatives and analysis of model mechanisms. Results The presented methodology is based on a combination of multivariate metamodelling (statistical approximation of the input–output relationships of deterministic models) and a systematic zooming into biologically feasible regions of the parameter space by iterative generation of new experimental designs and look-up of simulations in the proximity of the measured data. The parameter fitting pipeline includes an implicit sensitivity analysis and analysis of parameter identifiability, making it suitable for testing hypotheses for model reduction. Using this approach, under-constrained model parameters, as well as the coupling between parameters within the model are identified. The methodology is demonstrated by refitting the parameters of a published model of cardiac cellular mechanics using a combination of measured data and synthetic data from an alternative model of the same system. Using this approach, reduced models with simplified expressions for the tropomyosin/crossbridge kinetics were found by identification of model components that can be omitted without affecting the fit to the parameterising data. Our analysis revealed that model parameters could be constrained to a standard deviation of on average 15% of the mean values over the succeeding parameter sets. Conclusions Our results indicate that the presented approach is effective for comparing model alternatives and reducing models to the minimum complexity replicating measured data. We therefore believe that this approach has significant potential for reparameterising existing frameworks, for identification of redundant model components of large biophysical models and to increase their predictive capacity. PMID:24886522

[Rehabilitative measures in hearing-impaired children].

PubMed

von Wedel, H; von Wedel, U C; Zorowka, P

1991-12-01

On the basis of certain fundamental data on the maturation processes of the central auditory pathways in early childhood the importance of early intervention with hearing aids is discussed and emphasized. Pathological hearing, that is acoustical deprivation in early childhood will influence the maturation process. Very often speech development is delayed if diagnosis and therapy or rehabilitation are not early enough. Anamnesis, early diagnosis and clinical differential diagnosis are required before a hearing aid can be fitted. Selection criteria and adjustment parameters are discussed, showing that the hearing aid fitting procedure must be embedded in a complex matrix of requirements related to the development of speech as well as to the cognitive, emotional and social development of the child. As a rule, finding and preparing the "best" hearing aids (binaural fitting is obligatory) for a child is a long and often difficult process, which can only be performed by specialists who are pedo-audiologists. After the binaural fitting of hearing aids an intensive hearing and speech education in close cooperation between parents, pedo-audiologist and teacher must support the whole development of the child.

Survey of Current Practice in the Fitting and Fine-Tuning of Common Signal-Processing Features in Hearing Aids for Adults.

PubMed

Anderson, Melinda C; Arehart, Kathryn H; Souza, Pamela E

2018-02-01

Current guidelines for adult hearing aid fittings recommend the use of a prescriptive fitting rationale with real-ear verification that considers the audiogram for the determination of frequency-specific gain and ratios for wide dynamic range compression. However, the guidelines lack recommendations for how other common signal-processing features (e.g., noise reduction, frequency lowering, directional microphones) should be considered during the provision of hearing aid fittings and fine-tunings for adult patients. The purpose of this survey was to identify how audiologists make clinical decisions regarding common signal-processing features for hearing aid provision in adults. An online survey was sent to audiologists across the United States. The 22 survey questions addressed four primary topics including demographics of the responding audiologists, factors affecting selection of hearing aid devices, the approaches used in the fitting of signal-processing features, and the strategies used in the fine-tuning of these features. A total of 251 audiologists who provide hearing aid fittings to adults completed the electronically distributed survey. The respondents worked in a variety of settings including private practice, physician offices, university clinics, and hospitals/medical centers. Data analysis was based on a qualitative analysis of the question responses. The survey results for each of the four topic areas (demographics, device selection, hearing aid fitting, and hearing aid fine-tuning) are summarized descriptively. Survey responses indicate that audiologists vary in the procedures they use in fitting and fine-tuning based on the specific feature, such that the approaches used for the fitting of frequency-specific gain differ from other types of features (i.e., compression time constants, frequency lowering parameters, noise reduction strength, directional microphones, feedback management). Audiologists commonly rely on prescriptive fitting formulas and probe microphone measures for the fitting of frequency-specific gain and rely on manufacturers' default settings and recommendations for both the initial fitting and the fine-tuning of signal-processing features other than frequency-specific gain. The survey results are consistent with a lack of published protocols and guidelines for fitting and adjusting signal-processing features beyond frequency-specific gain. To streamline current practice, a transparent evidence-based tool that enables clinicians to prescribe the setting of other features from individual patient characteristics would be desirable. American Academy of Audiology

VizieR Online Data Catalog: Vela Junior (RX J0852.0-4622) HESS image (HESS+, 2018)

NASA Astrophysics Data System (ADS)

H. E. S. S. Collaboration; Abdalla, H.; Abramowski, A.; Aharonian, F.; Ait Benkhali, F.; Akhperjanian, A. G.; Andersson, T.; Anguener, E. O.; Arakawa, M.; Arrieta, M.; Aubert, P.; Backes, M.; Balzer, A.; Barnard, M.; Becherini, Y.; Becker Tjus, J.; Berge, D.; Bernhard, S.; Bernloehr, K.; Blackwell, R.; Boettcher, M.; Boisson, C.; Bolmont, J.; Bordas, P.; Bregeon, J.; Brun, F.; Brun, P.; Bryan, M.; Buechele, M.; Bulik, T.; Capasso, M.; Carr, J.; Casanova, S.; Cerruti, M.; Chakraborty, N.; Chalme-Calvet, R.; Chaves, R. C. G.; Chen, A.; Chevalier, J.; Chretien, M.; Coffaro, M.; Colafrancesco, S.; Cologna, G.; Condon, B.; Conrad, J.; Cui, Y.; Davids, I. D.; Decock, J.; Degrange, B.; Deil, C.; Devin, J.; Dewilt, P.; Dirson, L.; Djannati-Atai, A.; Domainko, W.; Donath, A.; Drury, L. O'c.; Dutson, K.; Dyks, J.; Edwards, T.; Egberts, K.; Eger, P.; Ernenwein, J.-P.; Eschbach, S.; Farnier, C.; Fegan, S.; Fernandes, M. V.; Fiasson, A.; Fontaine, G.; Foerster, A.; Funk, S.; Fuessling, M.; Gabici, S.; Gajdus, M.; Gallant, Y. A.; Garrigoux, T.; Giavitto, G.; Giebels, B.; Glicenstein, J. F.; Gottschall, D.; Goyal, A.; Grondin, M.-H.; Hahn, J.; Haupt, M.; Hawkes, J.; Heinzelmann, G.; Henri, G.; Hermann, G.; Hervet, O.; Hinton, J. A.; Hofmann, W.; Hoischen, C.; Holler, M.; Horns, D.; Ivascenko, A.; Iwasaki, H.; Jacholkowska, A.; Jamrozy, M.; Janiak, M.; Jankowsky, D.; Jankowsky, F.; Jingo, M.; Jogler, T.; Jouvin, L.; Jung-Richardt, I.; Kastendieck, M. A.; Katarzynski, K.; Katsuragawa, M.; Katz, U.; Kerszberg, D.; Khangulyan, D.; Khelifi, B.; Kieffer, M.; King, J.; Klepser, S.; Klochkov, D.; Kluzniak, W.; Kolitzus, D.; Komin, Nu.; Kosack, K.; Krakau, S.; Kraus, M.; Krueger, P. P.; Laffon, H.; Lamanna, G.; Lau, J.; Lees, J.-P.; Lefaucheur, J.; Lefranc, V.; Lemiere, A.; Lemoine-Goumard, M.; Lenain, J.-P.; Leser, E.; Lohse, T.; Lorentz, M.; Liu, R.; Lopez-Coto, R.; Lypova, I.; Marandon, V.; Marcowith, A.; Mariaud, C.; Marx, R.; Maurin, G.; Maxted, N.; Mayer, M.; Meintjes, P. J.; Meyer, M.; Mitchell, A. M. W.; Moderski, R.; Mohamed, M.; Mohrmann, L.; Mora, K.; Moulin, E.; Murach, T.; Nakashima, S.; de Naurois, M.; Niederwanger, F.; Niemiec J.; Oakes, L.; O'Brien, P.; Odaka, H.; Oettl, S.; Ohm, S.; Ostrowski, M.; Oya, I.; Padovani, M.; Panter, M.; Parsons, R. D.; Paz Arribas, M.; Pekeur, N. W.; Pelletier, G.; Perennes, C.; Petrucci, P.-O.; Peyaud, B.; Piel, Q.; Pita, S.; Poon, H.; Prokhorov, D.; Prokoph, H.; Puehlhofer, G.; Punch, M.; Quirrenbach, A.; Raab, S.; Reimer, A.; Reimer, O.; Renaud, M.; de Los Reyes, R.; Richter, S.; Rieger, F.; Romoli, C.; Rowell, G.; Rudak, B.; Rulten, C. B.; Sahakian, V.; Saito, S.; Salek, D.; Sanchez, D. A.; Santangelo, A.; Sasaki, M.; Schlickeiser, R.; Schuessler, F.; Schulz, A.; Schwanke, U.; Schwemmer, S.; Seglar-Arroyo, M.; Settimo, M.; Seyffert, A. S.; Shafi, N.; Shilon, I.; Simoni, R.; Sol, H.; Spanier, F.; Spengler, G.; Spies, F.; Stawarz, L.; Steenkamp, R.; Stegmann, C.; Stycz, K.; Sushch, I.; Takahashi, T.; Tavernet, J.-P.; Tavernier, T.; Taylor, A. M.; Terrier, R.; Tibaldo, L.; Tiziani, D.; Tluczykont, M.; Trichard, C.; Tsuji, N.; Tuffs, R.; Uchiyama, Y.; van der, Walt D. J.; van Eldik, C.; van Rensburg, C.; van Soelen, B.; Vasileiadis, G.; Veh, J.; Venter, C.; Viana, A.; Vincent, P.; Vink, J.; Voisin, F.; Voelk, H. J.; Vuillaume, T.; Wadiasingh, Z.; Wagner, S. J.; Wagner, P.; Wagner, R. M.; White, R.; Wierzcholska, A.; Willmann, P.; Woernlein, A.; Wouters, D.; Yang, R.; Zabalza, V.; Zaborov, D.; Zacharias, M.; Zanin, R.; Zdziarski, A. A.; Zech, A.; Zefi, F.; Ziegler, A.; Zywucka, N.

2018-03-01

skymap.fit: H.E.S.S. excess skymap in FITS format of the region comprising Vela Junior and its surroundings. The excess map has been corrected for the gradient of exposure and smoothed with a Gaussian function of width 0.08° to match the analysis point spread function, matching the procedure applied to derive the maps in Fig. 1. sp_stat.txt: H.E.S.S. spectral points and fit parameters for Vela Junior (H.E.S.S. data points in Fig. 3 and Tab. A.2 and H.E.S.S. spectral fit parameters in Tab. 4). The errors in this file represent statistical uncertainties at 1 sigma confidence level. The covariance matrix of the fit is also included in the format: c11 c12 c_13 c21 c22 c_23 c31 c32 c_33 where the subindices represent the following parameters of the power-law with exponential cut-off (ECPL) formula in Tab. 2: 1: flux normalization (Phi0) 2: spectral index (Gamma) 3: inverse of the cutoff energy (lambda=1/Ecut) The units for the covariance matrix are the same as for the fit parameters. Notice that, while the fit parameters section of the file shows E_cut as parameter, the fit was done in lambda=1/Ecut; hence the covariance matrix shows the values for lambda in TeV-1. sp_syst.txt: H.E.S.S. spectral points and fit parameters for Vela Junior (H.E.S.S. data points in Fig. 3 and Tab. A.2 and H.E.S.S. spectral fit parameters in Tab. 4). The errors in this file represent systematic uncertainties at 1 sigma confidence level. The integral fluxes for several energy ranges are also included. (4 data files).

Constraining the inclination of the Low-Mass X-ray Binary Cen X-4

NASA Astrophysics Data System (ADS)

Hammerstein, Erica K.; Cackett, Edward M.; Reynolds, Mark T.; Miller, Jon M.

2018-05-01

We present the results of ellipsoidal light curve modeling of the low mass X-ray binary Cen X-4 in order to constrain the inclination of the system and mass of the neutron star. Near-IR photometric monitoring was performed in May 2008 over a period of three nights at Magellan using PANIC. We obtain J, H and K lightcurves of Cen X-4 using differential photometry. An ellipsoidal modeling code was used to fit the phase folded light curves. The lightcurve fit which makes the least assumptions about the properties of the binary system yields an inclination of 34.9^{+4.9}_{-3.6} degrees (1σ), which is consistent with previous determinations of the system's inclination but with improved statistical uncertainties. When combined with the mass function and mass ratio, this inclination yields a neutron star mass of 1.51^{+0.40}_{-0.55} M⊙. This model allows accretion disk parameters to be free in the fitting process. Fits that do not allow for an accretion disk component in the near-IR flux gives a systematically lower inclination between approximately 33 and 34 degrees, leading to a higher mass neutron star between approximately 1.7 M⊙ and 1.8 M⊙. We discuss the implications of other assumptions made during the modeling process as well as numerous free parameters and their effects on the resulting inclination.

Autonomous Modelling of X-ray Spectra Using Robust Global Optimization Methods

NASA Astrophysics Data System (ADS)

Rogers, Adam; Safi-Harb, Samar; Fiege, Jason

2015-08-01

The standard approach to model fitting in X-ray astronomy is by means of local optimization methods. However, these local optimizers suffer from a number of problems, such as a tendency for the fit parameters to become trapped in local minima, and can require an involved process of detailed user intervention to guide them through the optimization process. In this work we introduce a general GUI-driven global optimization method for fitting models to X-ray data, written in MATLAB, which searches for optimal models with minimal user interaction. We directly interface with the commonly used XSPEC libraries to access the full complement of pre-existing spectral models that describe a wide range of physics appropriate for modelling astrophysical sources, including supernova remnants and compact objects. Our algorithm is powered by the Ferret genetic algorithm and Locust particle swarm optimizer from the Qubist Global Optimization Toolbox, which are robust at finding families of solutions and identifying degeneracies. This technique will be particularly instrumental for multi-parameter models and high-fidelity data. In this presentation, we provide details of the code and use our techniques to analyze X-ray data obtained from a variety of astrophysical sources.

Empirical models for fitting of oral concentration time curves with and without an intravenous reference.

PubMed

Weiss, Michael

2017-06-01

Appropriate model selection is important in fitting oral concentration-time data due to the complex character of the absorption process. When IV reference data are available, the problem is the selection of an empirical input function (absorption model). In the present examples a weighted sum of inverse Gaussian density functions (IG) was found most useful. It is shown that alternative models (gamma and Weibull density) are only valid if the input function is log-concave. Furthermore, it is demonstrated for the first time that the sum of IGs model can be also applied to fit oral data directly (without IV data). In the present examples, a weighted sum of two or three IGs was sufficient. From the parameters of this function, the model-independent measures AUC and mean residence time can be calculated. It turned out that a good fit of the data in the terminal phase is essential to avoid parameter biased estimates. The time course of fractional elimination rate and the concept of log-concavity have proved as useful tools in model selection.

IPMP Global Fit - A one-step direct data analysis tool for predictive microbiology.

PubMed

Huang, Lihan

2017-12-04

The objective of this work is to develop and validate a unified optimization algorithm for performing one-step global regression analysis of isothermal growth and survival curves for determination of kinetic parameters in predictive microbiology. The algorithm is incorporated with user-friendly graphical interfaces (GUIs) to develop a data analysis tool, the USDA IPMP-Global Fit. The GUIs are designed to guide the users to easily navigate through the data analysis process and properly select the initial parameters for different combinations of mathematical models. The software is developed for one-step kinetic analysis to directly construct tertiary models by minimizing the global error between the experimental observations and mathematical models. The current version of the software is specifically designed for constructing tertiary models with time and temperature as the independent model parameters in the package. The software is tested with a total of 9 different combinations of primary and secondary models for growth and survival of various microorganisms. The results of data analysis show that this software provides accurate estimates of kinetic parameters. In addition, it can be used to improve the experimental design and data collection for more accurate estimation of kinetic parameters. IPMP-Global Fit can be used in combination with the regular USDA-IPMP for solving the inverse problems and developing tertiary models in predictive microbiology. Published by Elsevier B.V.

Order-disorder transition in conflicting dynamics leading to rank-frequency generalized beta distributions

NASA Astrophysics Data System (ADS)

Alvarez-Martinez, R.; Martinez-Mekler, G.; Cocho, G.

2011-01-01

The behavior of rank-ordered distributions of phenomena present in a variety of fields such as biology, sociology, linguistics, finance and geophysics has been a matter of intense research. Often power laws have been encountered; however, their validity tends to hold mainly for an intermediate range of rank values. In a recent publication (Martínez-Mekler et al., 2009 [7]), a generalization of the functional form of the beta distribution has been shown to give excellent fits for many systems of very diverse nature, valid for the whole range of rank values, regardless of whether or not a power law behavior has been previously suggested. Here we give some insight on the significance of the two free parameters which appear as exponents in the functional form, by looking into discrete probabilistic branching processes with conflicting dynamics. We analyze a variety of realizations of these so-called expansion-modification models first introduced by Wentian Li (1989) [10]. We focus our attention on an order-disorder transition we encounter as we vary the modification probability p. We characterize this transition by means of the fitting parameters. Our numerical studies show that one of the fitting exponents is related to the presence of long-range correlations exhibited by power spectrum scale invariance, while the other registers the effect of disordering elements leading to a breakdown of these properties. In the absence of long-range correlations, this parameter is sensitive to the occurrence of unlikely events. We also introduce an approximate calculation scheme that relates this dynamics to multinomial multiplicative processes. A better understanding through these models of the meaning of the generalized beta-fitting exponents may contribute to their potential for identifying and characterizing universality classes.

Technical Note: Approximate Bayesian parameterization of a process-based tropical forest model

NASA Astrophysics Data System (ADS)

Hartig, F.; Dislich, C.; Wiegand, T.; Huth, A.

2014-02-01

Inverse parameter estimation of process-based models is a long-standing problem in many scientific disciplines. A key question for inverse parameter estimation is how to define the metric that quantifies how well model predictions fit to the data. This metric can be expressed by general cost or objective functions, but statistical inversion methods require a particular metric, the probability of observing the data given the model parameters, known as the likelihood. For technical and computational reasons, likelihoods for process-based stochastic models are usually based on general assumptions about variability in the observed data, and not on the stochasticity generated by the model. Only in recent years have new methods become available that allow the generation of likelihoods directly from stochastic simulations. Previous applications of these approximate Bayesian methods have concentrated on relatively simple models. Here, we report on the application of a simulation-based likelihood approximation for FORMIND, a parameter-rich individual-based model of tropical forest dynamics. We show that approximate Bayesian inference, based on a parametric likelihood approximation placed in a conventional Markov chain Monte Carlo (MCMC) sampler, performs well in retrieving known parameter values from virtual inventory data generated by the forest model. We analyze the results of the parameter estimation, examine its sensitivity to the choice and aggregation of model outputs and observed data (summary statistics), and demonstrate the application of this method by fitting the FORMIND model to field data from an Ecuadorian tropical forest. Finally, we discuss how this approach differs from approximate Bayesian computation (ABC), another method commonly used to generate simulation-based likelihood approximations. Our results demonstrate that simulation-based inference, which offers considerable conceptual advantages over more traditional methods for inverse parameter estimation, can be successfully applied to process-based models of high complexity. The methodology is particularly suitable for heterogeneous and complex data structures and can easily be adjusted to other model types, including most stochastic population and individual-based models. Our study therefore provides a blueprint for a fairly general approach to parameter estimation of stochastic process-based models.

Hybrid method to estimate two-layered superficial tissue optical properties from simulated data of diffuse reflectance spectroscopy.

PubMed

Hsieh, Hong-Po; Ko, Fan-Hua; Sung, Kung-Bin

2018-04-20

An iterative curve fitting method has been applied in both simulation [J. Biomed. Opt.17, 107003 (2012)JBOPFO1083-366810.1117/1.JBO.17.10.107003] and phantom [J. Biomed. Opt.19, 077002 (2014)JBOPFO1083-366810.1117/1.JBO.19.7.077002] studies to accurately extract optical properties and the top layer thickness of a two-layered superficial tissue model from diffuse reflectance spectroscopy (DRS) data. This paper describes a hybrid two-step parameter estimation procedure to address two main issues of the previous method, including (1) high computational intensity and (2) converging to local minima. The parameter estimation procedure contained a novel initial estimation step to obtain an initial guess, which was used by a subsequent iterative fitting step to optimize the parameter estimation. A lookup table was used in both steps to quickly obtain reflectance spectra and reduce computational intensity. On simulated DRS data, the proposed parameter estimation procedure achieved high estimation accuracy and a 95% reduction of computational time compared to previous studies. Furthermore, the proposed initial estimation step led to better convergence of the following fitting step. Strategies used in the proposed procedure could benefit both the modeling and experimental data processing of not only DRS but also related approaches such as near-infrared spectroscopy.

Primary α and secondary β relaxation dynamics of meta-toluidine in the liquid state investigated by broadband dielectric spectroscopy.

PubMed

Švajdlenková, H; Ruff, A; Lunkenheimer, P; Loidl, A; Bartoš, J

2017-08-28

We report a broadband dielectric spectroscopic (BDS) study on the clustering fragile glass-former meta-toluidine (m-TOL) from 187 K up to 289 K over a wide frequency range of 10 -3 -10 9 Hz with focus on the primary α relaxation and the secondary β relaxation above the glass temperature T g . The broadband dielectric spectra were fitted by using the Havriliak-Negami (HN) and Cole-Cole (CC) models. The β process disappearing at T β,disap = 1.12T g exhibits non-Arrhenius dependence fitted by the Vogel-Fulcher-Tamman-Hesse equation with T 0β VFTH in accord with the characteristic differential scanning calorimetry (DSC) limiting temperature of the glassy state. The essential feature of the α process consists in the distinct changes of its spectral shape parameter β HN marked by the characteristic BDS temperatures T B1 βHN and T B2 βHN . The primary α relaxation times were fitted over the entire temperature and frequency range by several current three-parameter up to six-parameter dynamic models. This analysis reveals that the crossover temperatures of the idealized mode coupling theory model (T c MCT ), the extended free volume model (T 0 EFV ), and the two-order parameter (TOP) model (T m c ) are close to T B1 βHN , which provides a consistent physical rationalization for the first change of the shape parameter. In addition, the other two characteristic TOP temperatures T 0 TOP and T A are coinciding with the thermodynamic Kauzmann temperature T K and the second change of the shape parameter at around T B2 βHN , respectively. These can be related to the onset of the liquid-like domains in the glassy state or the disappearance of the solid-like domains in the normal liquid state.

Signal detection theory and vestibular perception: III. Estimating unbiased fit parameters for psychometric functions.

PubMed

Chaudhuri, Shomesh E; Merfeld, Daniel M

2013-03-01

Psychophysics generally relies on estimating a subject's ability to perform a specific task as a function of an observed stimulus. For threshold studies, the fitted functions are called psychometric functions. While fitting psychometric functions to data acquired using adaptive sampling procedures (e.g., "staircase" procedures), investigators have encountered a bias in the spread ("slope" or "threshold") parameter that has been attributed to the serial dependency of the adaptive data. Using simulations, we confirm this bias for cumulative Gaussian parametric maximum likelihood fits on data collected via adaptive sampling procedures, and then present a bias-reduced maximum likelihood fit that substantially reduces the bias without reducing the precision of the spread parameter estimate and without reducing the accuracy or precision of the other fit parameters. As a separate topic, we explain how to implement this bias reduction technique using generalized linear model fits as well as other numeric maximum likelihood techniques such as the Nelder-Mead simplex. We then provide a comparison of the iterative bootstrap and observed information matrix techniques for estimating parameter fit variance from adaptive sampling procedure data sets. The iterative bootstrap technique is shown to be slightly more accurate; however, the observed information technique executes in a small fraction (0.005 %) of the time required by the iterative bootstrap technique, which is an advantage when a real-time estimate of parameter fit variance is required.

Kinetic study on ferulic acid production from banana stem waste via mechanical extraction

NASA Astrophysics Data System (ADS)

Zainol, Norazwina; Masngut, Nasratun; Khairi Jusup, Muhamad

2018-04-01

Banana is the tropical plants associated with lots of medicinal properties. It has been reported to be a potential source of phenolic compounds such as ferulic acid (FA). FA has excellent antioxidant properties higher than vitamin C and E. FA also have a wide range of biological activities, such as antioxidant activities and anti-microbial activities. This paper presents an experimental and kinetic study on ferulic acid (FA) production from banana stem waste (BSW) via mechanical extraction. The objective of this research is to determine the kinetic parameters in the ferulic acid production. The banana stem waste was randomly collected from the local banana plantation in Felda Lepar Hilir, Pahang. The banana stem juice was mechanically extracted by using sugarcane press machine (KR3176) and further analyzed in high performance liquid chromatography. The differential and integral method was applied to determine the kinetic parameter of the extraction process and the data obtained were fitted into the 0th, 1st and 2nd order of extraction process. Based on the results, the kinetic parameter and R2 value from were 0.05 and 0.93, respectively. It was determined that the 0th kinetic order fitted the reaction processes to best represent the mechanical extraction.

Using the Flipchem Photochemistry Model When Fitting Incoherent Scatter Radar Data

NASA Astrophysics Data System (ADS)

Reimer, A. S.; Varney, R. H.

2017-12-01

The North face Resolute Bay Incoherent Scatter Radar (RISR-N) routinely images the dynamics of the polar ionosphere, providing measurements of the plasma density, electron temperature, ion temperature, and line of sight velocity with seconds to minutes time resolution. RISR-N does not directly measure ionospheric parameters, but backscattered signals, recording them as voltage samples. Using signal processing techniques, radar autocorrelation functions (ACF) are estimated from the voltage samples. A model of the signal ACF is then fitted to the ACF using non-linear least-squares techniques to obtain the best-fit ionospheric parameters. The signal model, and therefore the fitted parameters, depend on the ionospheric ion composition that is used [e.g. Zettergren et. al. (2010), Zou et. al. (2017)].The software used to process RISR-N ACF data includes the "flipchem" model, which is an ion photochemistry model developed by Richards [2011] that was adapted from the Field LineInterhemispheric Plasma (FLIP) model. Flipchem requires neutral densities, neutral temperatures, electron density, ion temperature, electron temperature, solar zenith angle, and F10.7 as inputs to compute ion densities, which are input to the signal model. A description of how the flipchem model is used in RISR-N fitting software will be presented. Additionally, a statistical comparison of the fitted electron density, ion temperature, electron temperature, and velocity obtained using a flipchem ionosphere, a pure O+ ionosphere, and a Chapman O+ ionosphere will be presented. The comparison covers nearly two years of RISR-N data (April 2015 - December 2016). Richards, P. G. (2011), Reexamination of ionospheric photochemistry, J. Geophys. Res., 116, A08307, doi:10.1029/2011JA016613.Zettergren, M., Semeter, J., Burnett, B., Oliver, W., Heinselman, C., Blelly, P.-L., and Diaz, M.: Dynamic variability in F-region ionospheric composition at auroral arc boundaries, Ann. Geophys., 28, 651-664, https://doi.org/10.5194/angeo-28-651-2010, 2010.Zou, S., D. Ozturk, R. Varney, and A. Reimer (2017), Effects of sudden commencement on the ionosphere: PFISR observations and global MHD simulation, Geophys. Res. Lett., 44, 3047-3058, doi:10.1002/2017GL072678.

Mass hierarchy and energy scaling of the Tsallis - Pareto parameters in hadron productions at RHIC and LHC energies

NASA Astrophysics Data System (ADS)

Bíró, Gábor; Barnaföldi, Gergely Gábor; Biró, Tamás Sándor; Shen, Keming

2018-02-01

The latest, high-accuracy identified hadron spectra measurements in highenergy nuclear collisions led us to the investigation of the strongly interacting particles and collective effects in small systems. Since microscopical processes result in a statistical Tsallis - Pareto distribution, the fit parameters q and T are well suited for identifying system size scalings and initial conditions. Moreover, parameter values provide information on the deviation from the extensive, Boltzmann - Gibbs statistics in finite-volumes. We apply here the fit procedure developed in our earlier study for proton-proton collisions [1, 2]. The observed mass and center-of-mass energy trends in the hadron production are compared to RHIC dAu and LHC pPb data in different centrality/multiplicity classes. Here we present new results on mass hierarchy in pp and pA from light to heavy hadrons.

A curve fitting method for extrinsic camera calibration from a single image of a cylindrical object

NASA Astrophysics Data System (ADS)

Winkler, A. W.; Zagar, B. G.

2013-08-01

An important step in the process of optical steel coil quality assurance is to measure the proportions of width and radius of steel coils as well as the relative position and orientation of the camera. This work attempts to estimate these extrinsic parameters from single images by using the cylindrical coil itself as the calibration target. Therefore, an adaptive least-squares algorithm is applied to fit parametrized curves to the detected true coil outline in the acquisition. The employed model allows for strictly separating the intrinsic and the extrinsic parameters. Thus, the intrinsic camera parameters can be calibrated beforehand using available calibration software. Furthermore, a way to segment the true coil outline in the acquired images is motivated. The proposed optimization method yields highly accurate results and can be generalized even to measure other solids which cannot be characterized by the identification of simple geometric primitives.

High-resolution rovibrational study of the Coriolis-coupled v 12 and v 15 modes of [1.1.1]propellane

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

Kirkpatrick, Robynne W; Masiello, Tony; Jariyasopit, Narumol

Infrared spectra of the small strained cage molecule [1.1.1]propellane have been obtained at high resolution (0.0015 cm -1) and the J and K, l rovibrational structure has been resolved for the first time. We recently used combination-differences to obtain ground state parameters for propellane; over 4,100 differences from five fundamental and four combination bands were used in this process. The combination-difference approach eliminated errors due to localized perturbations in the upper state levels of the transitions and gave well-determined ground state parameters. In the current work, these ground state parameters were used in a determination of the upper state parametersmore » for the v 12(e') perpendicular and v 15(a 2") parallel bands. Over 4000 infrared transitions were fitted for each band, with J, K values ranging up to 71, 51 and 92, 90 respectively. While the transition frequencies for both bands can be fit nicely using separate analyses for each band, the strong intensity perturbations observed in the weaker v 12 band indicated that Coriolis coupling between the two modes was significant and should be included. Due to correlations with other parameters, the Coriolis coupling parameter ζ y 15,12a for the v 15 and v 12 interaction is poorly determined by a transition frequency fit alone. However, by combining the frequency fit with a fit of experimental intensities, a value of -0.42 was obtained, quite close to that predicted from the ab initio calculation (-0.44). This intensity fit also yielded a (∂μ z/∂Q 15)/(∂μ x/∂Q 12a) dipole derivative ratio of 36.5, in reasonable agreement with a value of 29.2 predicted by Gaussian ab initio density functional calculations using a cc-pVTZ basis. This ratio is unusually high due to large charge movement as the novel central Caxial-Caxial bond is displaced along the symmetry axis of the molecule for the v 15 mode.« less

A method for cone fitting based on certain sampling strategy in CMM metrology

NASA Astrophysics Data System (ADS)

Zhang, Li; Guo, Chaopeng

2018-04-01

A method of cone fitting in engineering is explored and implemented to overcome shortcomings of current fitting method. In the current method, the calculations of the initial geometric parameters are imprecise which cause poor accuracy in surface fitting. A geometric distance function of cone is constructed firstly, then certain sampling strategy is defined to calculate the initial geometric parameters, afterwards nonlinear least-squares method is used to fit the surface. The experiment is designed to verify accuracy of the method. The experiment data prove that the proposed method can get initial geometric parameters simply and efficiently, also fit the surface precisely, and provide a new accurate way to cone fitting in the coordinate measurement.

An Algorithm and R Program for Fitting and Simulation of Pharmacokinetic and Pharmacodynamic Data.

PubMed

Li, Jijie; Yan, Kewei; Hou, Lisha; Du, Xudong; Zhu, Ping; Zheng, Li; Zhu, Cairong

2017-06-01

Pharmacokinetic/pharmacodynamic link models are widely used in dose-finding studies. By applying such models, the results of initial pharmacokinetic/pharmacodynamic studies can be used to predict the potential therapeutic dose range. This knowledge can improve the design of later comparative large-scale clinical trials by reducing the number of participants and saving time and resources. However, the modeling process can be challenging, time consuming, and costly, even when using cutting-edge, powerful pharmacological software. Here, we provide a freely available R program for expediently analyzing pharmacokinetic/pharmacodynamic data, including data importation, parameter estimation, simulation, and model diagnostics. First, we explain the theory related to the establishment of the pharmacokinetic/pharmacodynamic link model. Subsequently, we present the algorithms used for parameter estimation and potential therapeutic dose computation. The implementation of the R program is illustrated by a clinical example. The software package is then validated by comparing the model parameters and the goodness-of-fit statistics generated by our R package with those generated by the widely used pharmacological software WinNonlin. The pharmacokinetic and pharmacodynamic parameters as well as the potential recommended therapeutic dose can be acquired with the R package. The validation process shows that the parameters estimated using our package are satisfactory. The R program developed and presented here provides pharmacokinetic researchers with a simple and easy-to-access tool for pharmacokinetic/pharmacodynamic analysis on personal computers.

Modeling envelope statistics of blood and myocardium for segmentation of echocardiographic images.

PubMed

Nillesen, Maartje M; Lopata, Richard G P; Gerrits, Inge H; Kapusta, Livia; Thijssen, Johan M; de Korte, Chris L

2008-04-01

The objective of this study was to investigate the use of speckle statistics as a preprocessing step for segmentation of the myocardium in echocardiographic images. Three-dimensional (3D) and biplane image sequences of the left ventricle of two healthy children and one dog (beagle) were acquired. Pixel-based speckle statistics of manually segmented blood and myocardial regions were investigated by fitting various probability density functions (pdf). The statistics of heart muscle and blood could both be optimally modeled by a K-pdf or Gamma-pdf (Kolmogorov-Smirnov goodness-of-fit test). Scale and shape parameters of both distributions could differentiate between blood and myocardium. Local estimation of these parameters was used to obtain parametric images, where window size was related to speckle size (5 x 2 speckles). Moment-based and maximum-likelihood estimators were used. Scale parameters were still able to differentiate blood from myocardium; however, smoothing of edges of anatomical structures occurred. Estimation of the shape parameter required a larger window size, leading to unacceptable blurring. Using these parameters as an input for segmentation resulted in unreliable segmentation. Adaptive mean squares filtering was then introduced using the moment-based scale parameter (sigma(2)/mu) of the Gamma-pdf to automatically steer the two-dimensional (2D) local filtering process. This method adequately preserved sharpness of the edges. In conclusion, a trade-off between preservation of sharpness of edges and goodness-of-fit when estimating local shape and scale parameters is evident for parametric images. For this reason, adaptive filtering outperforms parametric imaging for the segmentation of echocardiographic images.

Research on On-Line Modeling of Fed-Batch Fermentation Process Based on v-SVR

NASA Astrophysics Data System (ADS)

Ma, Yongjun

The fermentation process is very complex and non-linear, many parameters are not easy to measure directly on line, soft sensor modeling is a good solution. This paper introduces v-support vector regression (v-SVR) for soft sensor modeling of fed-batch fermentation process. v-SVR is a novel type of learning machine. It can control the accuracy of fitness and prediction error by adjusting the parameter v. An on-line training algorithm is discussed in detail to reduce the training complexity of v-SVR. The experimental results show that v-SVR has low error rate and better generalization with appropriate v.

Evaluation of Magnetic Diagnostics for MHD Equilibrium Reconstruction of LHD Discharges

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

Sontag, Aaron C; Hanson, James D.; Lazerson, Sam

2011-01-01

Equilibrium reconstruction is the process of determining the set of parameters of an MHD equilibrium that minimize the difference between expected and experimentally observed signals. This is routinely performed in axisymmetric devices, such as tokamaks, and the reconstructed equilibrium solution is then the basis for analysis of stability and transport properties. The V3FIT code [1] has been developed to perform equilibrium reconstruction in cases where axisymmetry cannot be assumed, such as in stellarators. The present work is focused on using V3FIT to analyze plasmas in the Large Helical Device (LHD) [2], a superconducting, heliotron type device with over 25 MWmore » of heating power that is capable of achieving both high-beta ({approx}5%) and high density (>1 x 10{sup 21}/m{sup 3}). This high performance as well as the ability to drive tens of kiloamperes of toroidal plasma current leads to deviations in the equilibrium state from the vacuum flux surfaces. This initial study examines the effectiveness of using magnetic diagnostics as the observed signals in reconstructing experimental plasma parameters for LHD discharges. V3FIT uses the VMEC [3] 3D equilibrium solver to calculate an initial equilibrium solution with closed, nested flux surfaces based on user specified plasma parameters. This equilibrium solution is then used to calculate the expected signals for specified diagnostics. The differences between these expected signal values and the observed values provides a starting {chi}{sup 2} value. V3FIT then varies all of the fit parameters independently, calculating a new equilibrium and corresponding {chi}{sup 2} for each variation. A quasi-Newton algorithm [1] is used to find the path in parameter space that leads to a minimum in {chi}{sup 2}. Effective diagnostic signals must vary in a predictable manner with the variations of the plasma parameters and this signal variation must be of sufficient amplitude to be resolved from the signal noise. Signal effectiveness can be defined for a specific signal and specific reconstruction parameter as the dimensionless fractional reduction in the posterior parameter variance with respect to the signal variance. Here, {sigma}{sub i}{sup sig} is the variance of the ith signal and {sigma}{sub j}{sup param} param is the posterior variance of the jth fit parameter. The sum of all signal effectiveness values for a given reconstruction parameter is normalized to one. This quantity will be used to determine signal effectiveness for various reconstruction cases. The next section will examine the variation of the expected signals with changes in plasma pressure and the following section will show results for reconstructing model plasmas using these signals.« less

Nonholonomic Hamiltonian Method for Molecular Dynamics Simulations of Reacting Shocks

NASA Astrophysics Data System (ADS)

Fahrenthold, Eric; Bass, Joseph

2015-06-01

Conventional molecular dynamics simulations of reacting shocks employ a holonomic Hamiltonian formulation: the breaking and forming of covalent bonds is described by potential functions. In general these potential functions: (a) are algebraically complex, (b) must satisfy strict smoothness requirements, and (c) contain many fitted parameters. In recent research the authors have developed a new noholonomic formulation of reacting molecular dynamics. In this formulation bond orders are determined by rate equations and the bonding-debonding process need not be described by differentiable functions. This simplifies the representation of complex chemistry and reduces the number of fitted model parameters. Example applications of the method show molecular level shock to detonation simulations in nitromethane and RDX. Research supported by the Defense Threat Reduction Agency.

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

NASA Astrophysics Data System (ADS)

Li, Jiqing; Huang, Jing; Li, Jianchang

2018-06-01

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

Laser surface texturing for high control of interference fit joint load bearing

NASA Astrophysics Data System (ADS)

Obeidi, M. Ahmed; McCarthy, E.; Brabazon, D.

2017-10-01

Laser beams attract the attention of researchers, engineers and manufacturer as they can deliver high energy with finite controlled processing parameters and heat affected zone (HAZ) on almost all kind of materials [1-3]. Laser beams can be generated in the broad range of wavelengths, energies and beam modes in addition to the unique property of propagation in straight lines with less or negligible divergence [3]. These features made lasers preferential for metal treatment and surface modification over the conventional machining and heat treatment methods. Laser material forming and processing is prosperous and competitive because of its flexibility and the creation of new solutions and techniques [3-5]. This study is focused on the laser surface texture of 316L stainless steel pins for the application of interference fit, widely used in automotive and aerospace industry. The main laser processing parameters applied are the power, frequency and the overlapping laser beam scans. The produced samples were characterized by measuring the increase in the insertion diameter, insertion and removal force, surface morphology and cross section alteration and the modified layer chemical composition and residual stresses.

A new methodology based on sensitivity analysis to simplify the recalibration of functional-structural plant models in new conditions.

PubMed

Mathieu, Amélie; Vidal, Tiphaine; Jullien, Alexandra; Wu, QiongLi; Chambon, Camille; Bayol, Benoit; Cournède, Paul-Henry

2018-06-19

Functional-structural plant models (FSPMs) describe explicitly the interactions between plants and their environment at organ to plant scale. However, the high level of description of the structure or model mechanisms makes this type of model very complex and hard to calibrate. A two-step methodology to facilitate the calibration process is proposed here. First, a global sensitivity analysis method was applied to the calibration loss function. It provided first-order and total-order sensitivity indexes that allow parameters to be ranked by importance in order to select the most influential ones. Second, the Akaike information criterion (AIC) was used to quantify the model's quality of fit after calibration with different combinations of selected parameters. The model with the lowest AIC gives the best combination of parameters to select. This methodology was validated by calibrating the model on an independent data set (same cultivar, another year) with the parameters selected in the second step. All the parameters were set to their nominal value; only the most influential ones were re-estimated. Sensitivity analysis applied to the calibration loss function is a relevant method to underline the most significant parameters in the estimation process. For the studied winter oilseed rape model, 11 out of 26 estimated parameters were selected. Then, the model could be recalibrated for a different data set by re-estimating only three parameters selected with the model selection method. Fitting only a small number of parameters dramatically increases the efficiency of recalibration, increases the robustness of the model and helps identify the principal sources of variation in varying environmental conditions. This innovative method still needs to be more widely validated but already gives interesting avenues to improve the calibration of FSPMs.

State-Space Modeling of Dynamic Psychological Processes via the Kalman Smoother Algorithm: Rationale, Finite Sample Properties, and Applications

ERIC Educational Resources Information Center

Song, Hairong; Ferrer, Emilio

2009-01-01

This article presents a state-space modeling (SSM) technique for fitting process factor analysis models directly to raw data. The Kalman smoother via the expectation-maximization algorithm to obtain maximum likelihood parameter estimates is used. To examine the finite sample properties of the estimates in SSM when common factors are involved, a…

Using geometry to improve model fitting and experiment design for glacial isostasy

NASA Astrophysics Data System (ADS)

Kachuck, S. B.; Cathles, L. M.

2017-12-01

As scientists we routinely deal with models, which are geometric objects at their core - the manifestation of a set of parameters as predictions for comparison with observations. When the number of observations exceeds the number of parameters, the model is a hypersurface (the model manifold) in the space of all possible predictions. The object of parameter fitting is to find the parameters corresponding to the point on the model manifold as close to the vector of observations as possible. But the geometry of the model manifold can make this difficult. By curving, ending abruptly (where, for instance, parameters go to zero or infinity), and by stretching and compressing the parameters together in unexpected directions, it can be difficult to design algorithms that efficiently adjust the parameters. Even at the optimal point on the model manifold, parameters might not be individually resolved well enough to be applied to new contexts. In our context of glacial isostatic adjustment, models of sparse surface observations have a broad spread of sensitivity to mixtures of the earth's viscous structure and the surface distribution of ice over the last glacial cycle. This impedes precise statements about crucial geophysical processes, such as the planet's thermal history or the climates that controlled the ice age. We employ geometric methods developed in the field of systems biology to improve the efficiency of fitting (geodesic accelerated Levenberg-Marquardt) and to identify the maximally informative sources of additional data to make better predictions of sea levels and ice configurations (optimal experiment design). We demonstrate this in particular in reconstructions of the Barents Sea Ice Sheet, where we show that only certain kinds of data from the central Barents have the power to distinguish between proposed models.

Visual and Auditory Components in the Perception of Asynchronous Audiovisual Speech

PubMed Central

Alcalá-Quintana, Rocío

2015-01-01

Research on asynchronous audiovisual speech perception manipulates experimental conditions to observe their effects on synchrony judgments. Probabilistic models establish a link between the sensory and decisional processes underlying such judgments and the observed data, via interpretable parameters that allow testing hypotheses and making inferences about how experimental manipulations affect such processes. Two models of this type have recently been proposed, one based on independent channels and the other using a Bayesian approach. Both models are fitted here to a common data set, with a subsequent analysis of the interpretation they provide about how experimental manipulations affected the processes underlying perceived synchrony. The data consist of synchrony judgments as a function of audiovisual offset in a speech stimulus, under four within-subjects manipulations of the quality of the visual component. The Bayesian model could not accommodate asymmetric data, was rejected by goodness-of-fit statistics for 8/16 observers, and was found to be nonidentifiable, which renders uninterpretable parameter estimates. The independent-channels model captured asymmetric data, was rejected for only 1/16 observers, and identified how sensory and decisional processes mediating asynchronous audiovisual speech perception are affected by manipulations that only alter the quality of the visual component of the speech signal. PMID:27551361

Thermodynamic parameters of bonds in glassy materials from viscosity-temperature relationships.

PubMed

Ojovan, Michael I; Travis, Karl P; Hand, Russell J

2007-10-17

Doremus's model of viscosity assumes that viscous flow in amorphous materials is mediated by broken bonds (configurons). The resulting equation contains four coefficients, which are directly related to the entropies and enthalpies of formation and motion of the configurons. Thus by fitting this viscosity equation to experimental viscosity data these enthalpy and entropy terms can be obtained. The non-linear nature of the equation obtained means that the fitting process is non-trivial. A genetic algorithm based approach has been developed to fit the equation to experimental viscosity data for a number of glassy materials, including SiO 2 , GeO 2 , B 2 O 3 , anorthite, diopside, xNa 2 O-(1-x)SiO 2 , xPbO-(1-x)SiO 2 , soda-lime-silica glasses, salol, and α-phenyl-o-cresol. Excellent fits of the equation to the viscosity data were obtained over the entire temperature range. The fitting parameters were used to quantitatively determine the enthalpies and entropies of formation and motion of configurons in the analysed systems and the activation energies for flow at high and low temperatures as well as fragility ratios using the Doremus criterion for fragility. A direct anti-correlation between fragility ratio and configuron percolation threshold, which determines the glass transition temperature in the analysed materials, was found.

A Generalized QMRA Beta-Poisson Dose-Response Model.

PubMed

Xie, Gang; Roiko, Anne; Stratton, Helen; Lemckert, Charles; Dunn, Peter K; Mengersen, Kerrie

2016-10-01

Quantitative microbial risk assessment (QMRA) is widely accepted for characterizing the microbial risks associated with food, water, and wastewater. Single-hit dose-response models are the most commonly used dose-response models in QMRA. Denoting PI(d) as the probability of infection at a given mean dose d, a three-parameter generalized QMRA beta-Poisson dose-response model, PI(d|α,β,r*), is proposed in which the minimum number of organisms required for causing infection, K min , is not fixed, but a random variable following a geometric distribution with parameter 0

Nonlinear Curve-Fitting Program

NASA Technical Reports Server (NTRS)

Everhart, Joel L.; Badavi, Forooz F.

1989-01-01

Nonlinear optimization algorithm helps in finding best-fit curve. Nonlinear Curve Fitting Program, NLINEAR, interactive curve-fitting routine based on description of quadratic expansion of X(sup 2) statistic. Utilizes nonlinear optimization algorithm calculating best statistically weighted values of parameters of fitting function and X(sup 2) minimized. Provides user with such statistical information as goodness of fit and estimated values of parameters producing highest degree of correlation between experimental data and mathematical model. Written in FORTRAN 77.

The ROC Toolbox: A toolbox for analyzing receiver-operating characteristics derived from confidence ratings.

PubMed

Koen, Joshua D; Barrett, Frederick S; Harlow, Iain M; Yonelinas, Andrew P

2017-08-01

Signal-detection theory, and the analysis of receiver-operating characteristics (ROCs), has played a critical role in the development of theories of episodic memory and perception. The purpose of the current paper is to present the ROC Toolbox. This toolbox is a set of functions written in the Matlab programming language that can be used to fit various common signal detection models to ROC data obtained from confidence rating experiments. The goals for developing the ROC Toolbox were to create a tool (1) that is easy to use and easy for researchers to implement with their own data, (2) that can flexibly define models based on varying study parameters, such as the number of response options (e.g., confidence ratings) and experimental conditions, and (3) that provides optimal routines (e.g., Maximum Likelihood estimation) to obtain parameter estimates and numerous goodness-of-fit measures.The ROC toolbox allows for various different confidence scales and currently includes the models commonly used in recognition memory and perception: (1) the unequal variance signal detection (UVSD) model, (2) the dual process signal detection (DPSD) model, and (3) the mixture signal detection (MSD) model. For each model fit to a given data set the ROC toolbox plots summary information about the best fitting model parameters and various goodness-of-fit measures. Here, we present an overview of the ROC Toolbox, illustrate how it can be used to input and analyse real data, and finish with a brief discussion on features that can be added to the toolbox.

Obtaining short-fiber orientation model parameters using non-lubricated squeeze flow

NASA Astrophysics Data System (ADS)

Lambert, Gregory; Wapperom, Peter; Baird, Donald

2017-12-01

Accurate models of fiber orientation dynamics during the processing of polymer-fiber composites are needed for the design work behind important automobile parts. All of the existing models utilize empirical parameters, but a standard method for obtaining them independent of processing does not exist. This study considers non-lubricated squeeze flow through a rectangular channel as a solution. A two-dimensional finite element method simulation of the kinematics and fiber orientation evolution along the centerline of a sample is developed as a first step toward a fully three-dimensional simulation. The model is used to fit to orientation data in a short-fiber-reinforced polymer composite after squeezing. Fiber orientation model parameters obtained in this study do not agree well with those obtained for the same material during startup of simple shear. This is attributed to the vastly different rates at which fibers orient during shearing and extensional flows. A stress model is also used to try to fit to experimental closure force data. Although the model can be tuned to the correct magnitude of the closure force, it does not fully recreate the transient behavior, which is attributed to the lack of any consideration for fiber-fiber interactions.

Non-invasive breast biopsy method using GD-DTPA contrast enhanced MRI series and F-18-FDG PET/CT dynamic image series

NASA Astrophysics Data System (ADS)

Magri, Alphonso William

This study was undertaken to develop a nonsurgical breast biopsy from Gd-DTPA Contrast Enhanced Magnetic Resonance (CE-MR) images and F-18-FDG PET/CT dynamic image series. A five-step process was developed to accomplish this. (1) Dynamic PET series were nonrigidly registered to the initial frame using a finite element method (FEM) based registration that requires fiducial skin markers to sample the displacement field between image frames. A commercial FEM package (ANSYS) was used for meshing and FEM calculations. Dynamic PET image series registrations were evaluated using similarity measurements SAVD and NCC. (2) Dynamic CE-MR series were nonrigidly registered to the initial frame using two registration methods: a multi-resolution free-form deformation (FFD) registration driven by normalized mutual information, and a FEM-based registration method. Dynamic CE-MR image series registrations were evaluated using similarity measurements, localization measurements, and qualitative comparison of motion artifacts. FFD registration was found to be superior to FEM-based registration. (3) Nonlinear curve fitting was performed for each voxel of the PET/CT volume of activity versus time, based on a realistic two-compartmental Patlak model. Three parameters for this model were fitted; two of them describe the activity levels in the blood and in the cellular compartment, while the third characterizes the washout rate of F-18-FDG from the cellular compartment. (4) Nonlinear curve fitting was performed for each voxel of the MR volume of signal intensity versus time, based on a realistic two-compartment Brix model. Three parameters for this model were fitted: rate of Gd exiting the compartment, representing the extracellular space of a lesion; rate of Gd exiting a blood compartment; and a parameter that characterizes the strength of signal intensities. Curve fitting used for PET/CT and MR series was accomplished by application of the Levenburg-Marquardt nonlinear regression algorithm. The best-fit parameters were used to create 3D parametric images. Compartmental modeling evaluation was based on the ability of parameter values to differentiate between tissue types. This evaluation was used on registered and unregistered image series and found that registration improved results. (5) PET and MR parametric images were registered through FEM- and FFD-based registration. Parametric image registration was evaluated using similarity measurements, target registration error, and qualitative comparison. Comparing FFD and FEM-based registration results showed that the FEM method is superior. This five-step process constitutes a novel multifaceted approach to a nonsurgical breast biopsy that successfully executes each step. Comparison of this method to biopsy still needs to be done with a larger set of subject data.

Kinetics and thermodynamics studies of silver ions adsorption onto coconut shell activated carbon.

PubMed

Silva-Medeiros, Flávia V; Consolin-Filho, Nelson; Xavier de Lima, Mateus; Bazzo, Fernando Previato; Barros, Maria Angélica S D; Bergamasco, Rosângela; Tavares, Célia R G

2016-12-01

The presence of silver in the natural water environment has been of great concern because of its toxicity, especially when it is in the free ion form (Ag(+)). This paper aims to study the adsorption kinetics of silver ions from an aqueous solution onto coconut shell activated carbon using batch methods. Batch kinetic data were fitted to the first-order model and the pseudo-second-order model, and this last equation fits correctly the experimental data. Equilibrium experiments were carried out at 30°C, 40°C, and 50°C. The adsorption isotherms were reasonably fit using Langmuir model, and the adsorption process was slightly influenced by changes in temperature. Thermodynamic parameters (ΔH°, ΔG°, and ΔS°) were determined. The adsorption process seems to be non-favorable, exothermic, and have an increase in the orderness.

Parameter Estimation and Model Selection in Computational Biology

PubMed Central

Lillacci, Gabriele; Khammash, Mustafa

2010-01-01

A central challenge in computational modeling of biological systems is the determination of the model parameters. Typically, only a fraction of the parameters (such as kinetic rate constants) are experimentally measured, while the rest are often fitted. The fitting process is usually based on experimental time course measurements of observables, which are used to assign parameter values that minimize some measure of the error between these measurements and the corresponding model prediction. The measurements, which can come from immunoblotting assays, fluorescent markers, etc., tend to be very noisy and taken at a limited number of time points. In this work we present a new approach to the problem of parameter selection of biological models. We show how one can use a dynamic recursive estimator, known as extended Kalman filter, to arrive at estimates of the model parameters. The proposed method follows. First, we use a variation of the Kalman filter that is particularly well suited to biological applications to obtain a first guess for the unknown parameters. Secondly, we employ an a posteriori identifiability test to check the reliability of the estimates. Finally, we solve an optimization problem to refine the first guess in case it should not be accurate enough. The final estimates are guaranteed to be statistically consistent with the measurements. Furthermore, we show how the same tools can be used to discriminate among alternate models of the same biological process. We demonstrate these ideas by applying our methods to two examples, namely a model of the heat shock response in E. coli, and a model of a synthetic gene regulation system. The methods presented are quite general and may be applied to a wide class of biological systems where noisy measurements are used for parameter estimation or model selection. PMID:20221262

Poroviscoelastic cartilage properties in the mouse from indentation.

PubMed

Chiravarambath, Sidharth; Simha, Narendra K; Namani, Ravi; Lewis, Jack L

2009-01-01

A method for fitting parameters in a poroviscoelastic (PVE) model of articular cartilage in the mouse is presented. Indentation is performed using two different sized indenters and then these data are fitted using a PVE finite element program and parameter extraction algorithm. Data from a smaller indenter, a 15 mum diameter flat-ended 60 deg cone, is first used to fit the viscoelastic (VE) parameters, on the basis that for this tip size the gel diffusion time (approximate time constant of the poroelastic (PE) response) is of the order of 0.1 s, so that the PE response is negligible. These parameters are then used to fit the data from a second 170 mum diameter flat-ended 60 deg cone for the PE parameters, using the VE parameters extracted from the data from the 15 mum tip. Data from tests on five different mouse tibial plateaus are presented and fitted. Parameter variation studies for the larger indenter show that for this case the VE and PE time responses overlap in time, necessitating the use of both models.

Model Fit to Experimental Data for Foam-Assisted Deep Vadose Zone Remediation

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

Roostapour, A.; Lee, G.; Zhong, Lirong

2014-01-15

Foam has been regarded as a promising means of remeidal amendment delivery to overcome subsurface heterogeneity in subsurface remediation processes. This study investigates how a foam model, developed by Method of Characteristics and fractional flow analysis in the companion paper of Roostapour and Kam (2012), can be applied to make a fit to a set of existing laboratory flow experiments (Zhong et al., 2009) in an application relevant to deep vadose zone remediation. This study reveals a few important insights regarding foam-assisted deep vadose zone remediation: (i) the mathematical framework established for foam modeling can fit typical flow experiments matchingmore » wave velocities, saturation history , and pressure responses; (ii) the set of input parameters may not be unique for the fit, and therefore conducting experiments to measure basic model parameters related to relative permeability, initial and residual saturations, surfactant adsorption and so on should not be overlooked; and (iii) gas compressibility plays an important role for data analysis, thus should be handled carefully in laboratory flow experiments. Foam kinetics, causing foam texture to reach its steady-state value slowly, may impose additional complications.« less

Fitting Higgs data with nonlinear effective theory.

PubMed

Buchalla, G; Catà, O; Celis, A; Krause, C

2016-01-01

In a recent paper we showed that the electroweak chiral Lagrangian at leading order is equivalent to the conventional [Formula: see text] formalism used by ATLAS and CMS to test Higgs anomalous couplings. Here we apply this fact to fit the latest Higgs data. The new aspect of our analysis is a systematic interpretation of the fit parameters within an EFT. Concentrating on the processes of Higgs production and decay that have been measured so far, six parameters turn out to be relevant: [Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text]. A global Bayesian fit is then performed with the result [Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text]. Additionally, we show how this leading-order parametrization can be generalized to next-to-leading order, thus improving the [Formula: see text] formalism systematically. The differences with a linear EFT analysis including operators of dimension six are also discussed. One of the main conclusions of our analysis is that since the conventional [Formula: see text] formalism can be properly justified within a QFT framework, it should continue to play a central role in analyzing and interpreting Higgs data.

An Applied Framework for Incorporating Multiple Sources of Uncertainty in Fisheries Stock Assessments.

PubMed

Scott, Finlay; Jardim, Ernesto; Millar, Colin P; Cerviño, Santiago

2016-01-01

Estimating fish stock status is very challenging given the many sources and high levels of uncertainty surrounding the biological processes (e.g. natural variability in the demographic rates), model selection (e.g. choosing growth or stock assessment models) and parameter estimation. Incorporating multiple sources of uncertainty in a stock assessment allows advice to better account for the risks associated with proposed management options, promoting decisions that are more robust to such uncertainty. However, a typical assessment only reports the model fit and variance of estimated parameters, thereby underreporting the overall uncertainty. Additionally, although multiple candidate models may be considered, only one is selected as the 'best' result, effectively rejecting the plausible assumptions behind the other models. We present an applied framework to integrate multiple sources of uncertainty in the stock assessment process. The first step is the generation and conditioning of a suite of stock assessment models that contain different assumptions about the stock and the fishery. The second step is the estimation of parameters, including fitting of the stock assessment models. The final step integrates across all of the results to reconcile the multi-model outcome. The framework is flexible enough to be tailored to particular stocks and fisheries and can draw on information from multiple sources to implement a broad variety of assumptions, making it applicable to stocks with varying levels of data availability The Iberian hake stock in International Council for the Exploration of the Sea (ICES) Divisions VIIIc and IXa is used to demonstrate the framework, starting from length-based stock and indices data. Process and model uncertainty are considered through the growth, natural mortality, fishing mortality, survey catchability and stock-recruitment relationship. Estimation uncertainty is included as part of the fitting process. Simple model averaging is used to integrate across the results and produce a single assessment that considers the multiple sources of uncertainty.

A model of the endogenous glucose balance incorporating the characteristics of glucose transporters.

PubMed

Arleth, T; Andreassen, S; Federici, M O; Benedetti, M M

2000-07-01

This paper describes the development and preliminary test of a model of the endogenous glucose balance that incorporates the characteristics of the glucose transporters GLUT1, GLUT3 and GLUT4. In the modeling process the model is parameterized with nine parameters that are subsequently estimated from data in the literature on the hepatic- and endogenous- balances at various combinations of blood glucose and insulin levels. The ability of the resulting endogenous balance to fit blood glucose measured from patients was tested on 20 patients. The fit obtained with this model compared favorably with the fit obtained with the endogenous balance currently incorporated in the DIAS system.

Visualization and curve-parameter estimation strategies for efficient exploration of phenotype microarray kinetics.

PubMed

Vaas, Lea A I; Sikorski, Johannes; Michael, Victoria; Göker, Markus; Klenk, Hans-Peter

2012-01-01

The Phenotype MicroArray (OmniLog® PM) system is able to simultaneously capture a large number of phenotypes by recording an organism's respiration over time on distinct substrates. This technique targets the object of natural selection itself, the phenotype, whereas previously addressed '-omics' techniques merely study components that finally contribute to it. The recording of respiration over time, however, adds a longitudinal dimension to the data. To optimally exploit this information, it must be extracted from the shapes of the recorded curves and displayed in analogy to conventional growth curves. The free software environment R was explored for both visualizing and fitting of PM respiration curves. Approaches using either a model fit (and commonly applied growth models) or a smoothing spline were evaluated. Their reliability in inferring curve parameters and confidence intervals was compared to the native OmniLog® PM analysis software. We consider the post-processing of the estimated parameters, the optimal classification of curve shapes and the detection of significant differences between them, as well as practically relevant questions such as detecting the impact of cultivation times and the minimum required number of experimental repeats. We provide a comprehensive framework for data visualization and parameter estimation according to user choices. A flexible graphical representation strategy for displaying the results is proposed, including 95% confidence intervals for the estimated parameters. The spline approach is less prone to irregular curve shapes than fitting any of the considered models or using the native PM software for calculating both point estimates and confidence intervals. These can serve as a starting point for the automated post-processing of PM data, providing much more information than the strict dichotomization into positive and negative reactions. Our results form the basis for a freely available R package for the analysis of PM data.

Visualization and Curve-Parameter Estimation Strategies for Efficient Exploration of Phenotype Microarray Kinetics

PubMed Central

Vaas, Lea A. I.; Sikorski, Johannes; Michael, Victoria; Göker, Markus; Klenk, Hans-Peter

2012-01-01

Background The Phenotype MicroArray (OmniLog® PM) system is able to simultaneously capture a large number of phenotypes by recording an organism's respiration over time on distinct substrates. This technique targets the object of natural selection itself, the phenotype, whereas previously addressed ‘-omics’ techniques merely study components that finally contribute to it. The recording of respiration over time, however, adds a longitudinal dimension to the data. To optimally exploit this information, it must be extracted from the shapes of the recorded curves and displayed in analogy to conventional growth curves. Methodology The free software environment R was explored for both visualizing and fitting of PM respiration curves. Approaches using either a model fit (and commonly applied growth models) or a smoothing spline were evaluated. Their reliability in inferring curve parameters and confidence intervals was compared to the native OmniLog® PM analysis software. We consider the post-processing of the estimated parameters, the optimal classification of curve shapes and the detection of significant differences between them, as well as practically relevant questions such as detecting the impact of cultivation times and the minimum required number of experimental repeats. Conclusions We provide a comprehensive framework for data visualization and parameter estimation according to user choices. A flexible graphical representation strategy for displaying the results is proposed, including 95% confidence intervals for the estimated parameters. The spline approach is less prone to irregular curve shapes than fitting any of the considered models or using the native PM software for calculating both point estimates and confidence intervals. These can serve as a starting point for the automated post-processing of PM data, providing much more information than the strict dichotomization into positive and negative reactions. Our results form the basis for a freely available R package for the analysis of PM data. PMID:22536335

Comments on Different techniques for finding best-fit parameters

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

Fenimore, Edward E.; Triplett, Laurie A.

2014-07-01

A common data analysis problem is to find best-fit parameters through chi-square minimization. Levenberg-Marquardt is an often used system that depends on gradients and converges when successive iterations do not change chi-square more than a specified amount. We point out in cases where the sought-after parameter weakly affects the fit and cases where the overall scale factor is a parameter, that a Golden Search technique can often do better. The Golden Search converges when the best-fit point is within a specified range and that range can be made arbitrarily small. It does not depend on the value of chi-square.

BAIAP2 is related to emotional modulation of human memory strength.

PubMed

Luksys, Gediminas; Ackermann, Sandra; Coynel, David; Fastenrath, Matthias; Gschwind, Leo; Heck, Angela; Rasch, Bjoern; Spalek, Klara; Vogler, Christian; Papassotiropoulos, Andreas; de Quervain, Dominique

2014-01-01

Memory performance is the result of many distinct mental processes, such as memory encoding, forgetting, and modulation of memory strength by emotional arousal. These processes, which are subserved by partly distinct molecular profiles, are not always amenable to direct observation. Therefore, computational models can be used to make inferences about specific mental processes and to study their genetic underpinnings. Here we combined a computational model-based analysis of memory-related processes with high density genetic information derived from a genome-wide study in healthy young adults. After identifying the best-fitting model for a verbal memory task and estimating the best-fitting individual cognitive parameters, we found a common variant in the gene encoding the brain-specific angiogenesis inhibitor 1-associated protein 2 (BAIAP2) that was related to the model parameter reflecting modulation of verbal memory strength by negative valence. We also observed an association between the same genetic variant and a similar emotional modulation phenotype in a different population performing a picture memory task. Furthermore, using functional neuroimaging we found robust genotype-dependent differences in activity of the parahippocampal cortex that were specifically related to successful memory encoding of negative versus neutral information. Finally, we analyzed cortical gene expression data of 193 deceased subjects and detected significant BAIAP2 genotype-dependent differences in BAIAP2 mRNA levels. Our findings suggest that model-based dissociation of specific cognitive parameters can improve the understanding of genetic underpinnings of human learning and memory.

ZASPE: A Code to Measure Stellar Atmospheric Parameters and their Covariance from Spectra

NASA Astrophysics Data System (ADS)

Brahm, Rafael; Jordán, Andrés; Hartman, Joel; Bakos, Gáspár

2017-05-01

We describe the Zonal Atmospheric Stellar Parameters Estimator (zaspe), a new algorithm, and its associated code, for determining precise stellar atmospheric parameters and their uncertainties from high-resolution echelle spectra of FGK-type stars. zaspe estimates stellar atmospheric parameters by comparing the observed spectrum against a grid of synthetic spectra only in the most sensitive spectral zones to changes in the atmospheric parameters. Realistic uncertainties in the parameters are computed from the data itself, by taking into account the systematic mismatches between the observed spectrum and the best-fitting synthetic one. The covariances between the parameters are also estimated in the process. zaspe can in principle use any pre-calculated grid of synthetic spectra, but unbiased grids are required to obtain accurate parameters. We tested the performance of two existing libraries, and we concluded that neither is suitable for computing precise atmospheric parameters. We describe a process to synthesize a new library of synthetic spectra that was found to generate consistent results when compared with parameters obtained with different methods (interferometry, asteroseismology, equivalent widths).

Microscopic Evaluation of Friction Plug Welds- Correlation to a Processing Analysis

NASA Technical Reports Server (NTRS)

Rabenberg, Ellen M.; Chen, Poshou; Gorti, Sridhar

2017-01-01

Recently an analysis of dynamic forge load data from the friction plug weld (FPW) process and the corresponding tensile test results showed that good plug welds fit well within an analytically determined processing parameter box. There were, however, some outliers that compromised the predictions. Here the microstructure of the plug weld material is presented in view of the load analysis with the intent of further understanding the FPW process and how it is affected by the grain structure and subsequent mechanical properties.

Robust parameter design for automatically controlled systems and nanostructure synthesis

NASA Astrophysics Data System (ADS)

Dasgupta, Tirthankar

2007-12-01

This research focuses on developing comprehensive frameworks for developing robust parameter design methodology for dynamic systems with automatic control and for synthesis of nanostructures. In many automatically controlled dynamic processes, the optimal feedback control law depends on the parameter design solution and vice versa and therefore an integrated approach is necessary. A parameter design methodology in the presence of feedback control is developed for processes of long duration under the assumption that experimental noise factors are uncorrelated over time. Systems that follow a pure-gain dynamic model are considered and the best proportional-integral and minimum mean squared error control strategies are developed by using robust parameter design. The proposed method is illustrated using a simulated example and a case study in a urea packing plant. This idea is also extended to cases with on-line noise factors. The possibility of integrating feedforward control with a minimum mean squared error feedback control scheme is explored. To meet the needs of large scale synthesis of nanostructures, it is critical to systematically find experimental conditions under which the desired nanostructures are synthesized reproducibly, at large quantity and with controlled morphology. The first part of the research in this area focuses on modeling and optimization of existing experimental data. Through a rigorous statistical analysis of experimental data, models linking the probabilities of obtaining specific morphologies to the process variables are developed. A new iterative algorithm for fitting a Multinomial GLM is proposed and used. The optimum process conditions, which maximize the above probabilities and make the synthesis process less sensitive to variations of process variables around set values, are derived from the fitted models using Monte-Carlo simulations. The second part of the research deals with development of an experimental design methodology, tailor-made to address the unique phenomena associated with nanostructure synthesis. A sequential space filling design called Sequential Minimum Energy Design (SMED) for exploring best process conditions for synthesis of nanowires. The SMED is a novel approach to generate sequential designs that are model independent, can quickly "carve out" regions with no observable nanostructure morphology, and allow for the exploration of complex response surfaces.

Evaluation of the geomorphometric results and residual values of a robust plane fitting method applied to different DTMs of various scales and accuracy

NASA Astrophysics Data System (ADS)

Koma, Zsófia; Székely, Balázs; Dorninger, Peter; Kovács, Gábor

2013-04-01

Due to the need for quantitative analysis of various geomorphological landforms, the importance of fast and effective automatic processing of the different kind of digital terrain models (DTMs) is increasing. The robust plane fitting (segmentation) method, developed at the Institute of Photogrammetry and Remote Sensing at Vienna University of Technology, allows the processing of large 3D point clouds (containing millions of points), performs automatic detection of the planar elements of the surface via parameter estimation, and provides a considerable data reduction for the modeled area. Its geoscientific application allows the modeling of different landforms with the fitted planes as planar facets. In our study we aim to analyze the accuracy of the resulting set of fitted planes in terms of accuracy, model reliability and dependence on the input parameters. To this end we used DTMs of different scales and accuracy: (1) artificially generated 3D point cloud model with different magnitudes of error; (2) LiDAR data with 0.1 m error; (3) SRTM (Shuttle Radar Topography Mission) DTM database with 5 m accuracy; (4) DTM data from HRSC (High Resolution Stereo Camera) of the planet Mars with 10 m error. The analysis of the simulated 3D point cloud with normally distributed errors comprised different kinds of statistical tests (for example Chi-square and Kolmogorov-Smirnov tests) applied on the residual values and evaluation of dependence of the residual values on the input parameters. These tests have been repeated on the real data supplemented with the categorization of the segmentation result depending on the input parameters, model reliability and the geomorphological meaning of the fitted planes. The simulation results show that for the artificially generated data with normally distributed errors the null hypothesis can be accepted based on the residual value distribution being also normal, but in case of the test on the real data the residual value distribution is often mixed or unknown. The residual values are found to be dependent on two input parameters (standard deviation and maximum point-plane distance both defining distance thresholds for assigning points to a segment) mainly and the curvature of the surface affected mostly the distributions. The results of the analysis helped to decide which parameter set is the best for further modelling and provides the highest accuracy. With these results in mind the success of quasi-automatic modelling of the planar (for example plateau-like) features became more successful and often provided more accuracy. These studies were carried out partly in the framework of TMIS.ascrea project (Nr. 2001978) financed by the Austrian Research Promotion Agency (FFG); the contribution of ZsK was partly funded by Campus Hungary Internship TÁMOP-424B1.

A comparative modeling study of a dual tracer experiment in a large lysimeter under atmospheric conditions

NASA Astrophysics Data System (ADS)

Stumpp, C.; Nützmann, G.; Maciejewski, S.; Maloszewski, P.

2009-09-01

SummaryIn this paper, five model approaches with different physical and mathematical concepts varying in their model complexity and requirements were applied to identify the transport processes in the unsaturated zone. The applicability of these model approaches were compared and evaluated investigating two tracer breakthrough curves (bromide, deuterium) in a cropped, free-draining lysimeter experiment under natural atmospheric boundary conditions. The data set consisted of time series of water balance, depth resolved water contents, pressure heads and resident concentrations measured during 800 days. The tracer transport parameters were determined using a simple stochastic (stream tube model), three lumped parameter (constant water content model, multi-flow dispersion model, variable flow dispersion model) and a transient model approach. All of them were able to fit the tracer breakthrough curves. The identified transport parameters of each model approach were compared. Despite the differing physical and mathematical concepts the resulting parameters (mean water contents, mean water flux, dispersivities) of the five model approaches were all in the same range. The results indicate that the flow processes are also describable assuming steady state conditions. Homogeneous matrix flow is dominant and a small pore volume with enhanced flow velocities near saturation was identified with variable saturation flow and transport approach. The multi-flow dispersion model also identified preferential flow and additionally suggested a third less mobile flow component. Due to high fitting accuracy and parameter similarity all model approaches indicated reliable results.

Accelerated signal encoding and reconstruction using pixon method

DOEpatents

Puetter, Richard; Yahil, Amos; Pina, Robert

2005-05-17

The method identifies a Pixon element, which is a fundamental and indivisible unit of information, and a Pixon basis, which is the set of possible functions from which the Pixon elements are selected. The actual Pixon elements selected from this basis during the reconstruction process represents the smallest number of such units required to fit the data and representing the minimum number of parameters necessary to specify the image. The Pixon kernels can have arbitrary properties (e.g., shape, size, and/or position) as needed to best fit the data.

Estimation and Identifiability of Model Parameters in Human Nociceptive Processing Using Yes-No Detection Responses to Electrocutaneous Stimulation.

PubMed

Yang, Huan; Meijer, Hil G E; Buitenweg, Jan R; van Gils, Stephan A

2016-01-01

Healthy or pathological states of nociceptive subsystems determine different stimulus-response relations measured from quantitative sensory testing. In turn, stimulus-response measurements may be used to assess these states. In a recently developed computational model, six model parameters characterize activation of nerve endings and spinal neurons. However, both model nonlinearity and limited information in yes-no detection responses to electrocutaneous stimuli challenge to estimate model parameters. Here, we address the question whether and how one can overcome these difficulties for reliable parameter estimation. First, we fit the computational model to experimental stimulus-response pairs by maximizing the likelihood. To evaluate the balance between model fit and complexity, i.e., the number of model parameters, we evaluate the Bayesian Information Criterion. We find that the computational model is better than a conventional logistic model regarding the balance. Second, our theoretical analysis suggests to vary the pulse width among applied stimuli as a necessary condition to prevent structural non-identifiability. In addition, the numerically implemented profile likelihood approach reveals structural and practical non-identifiability. Our model-based approach with integration of psychophysical measurements can be useful for a reliable assessment of states of the nociceptive system.

Intelligent tuning method of PID parameters based on iterative learning control for atomic force microscopy.

PubMed

Liu, Hui; Li, Yingzi; Zhang, Yingxu; Chen, Yifu; Song, Zihang; Wang, Zhenyu; Zhang, Suoxin; Qian, Jianqiang

2018-01-01

Proportional-integral-derivative (PID) parameters play a vital role in the imaging process of an atomic force microscope (AFM). Traditional parameter tuning methods require a lot of manpower and it is difficult to set PID parameters in unattended working environments. In this manuscript, an intelligent tuning method of PID parameters based on iterative learning control is proposed to self-adjust PID parameters of the AFM according to the sample topography. This method gets enough information about the output signals of PID controller and tracking error, which will be used to calculate the proper PID parameters, by repeated line scanning until convergence before normal scanning to learn the topography. Subsequently, the appropriate PID parameters are obtained by fitting method and then applied to the normal scanning process. The feasibility of the method is demonstrated by the convergence analysis. Simulations and experimental results indicate that the proposed method can intelligently tune PID parameters of the AFM for imaging different topographies and thus achieve good tracking performance. Copyright © 2017 Elsevier Ltd. All rights reserved.

Hearing aid fine-tuning based on Dutch descriptions.

PubMed

Thielemans, Thijs; Pans, Donné; Chenault, Michelene; Anteunis, Lucien

2017-07-01

The aim of this study was to derive an independent fitting assistant based on expert consensus. Two questions were asked: (1) what (Dutch) terms do hearing impaired listeners use nowadays to describe their specific hearing aid fitting problems? (2) What is the expert consensus on how to resolve these complaints by adjusting hearing aid parameters? Hearing aid dispensers provided descriptors that impaired listeners use to describe their reactions to specific hearing aid fitting problems. Hearing aid fitting experts were asked "How would you adjust the hearing aid if its user reports that the aid sounds…?" with the blank filled with each of the 40 most frequently mentioned descriptors. 112 hearing aid dispensers and 15 hearing aid experts. The expert solution with the highest weight value was considered the best solution for that descriptor. Principal component analysis (PCA) was performed to identify a factor structure in fitting problems. Nine fitting problems could be identified resulting in an expert-based, hearing aid manufacturer independent, fine-tuning fitting assistant for clinical use. The construction of an expert-based, hearing aid manufacturer independent, fine-tuning fitting assistant to be used as an additional tool in the iterative fitting process is feasible.

Quantifying the Role of Population Subdivision in Evolution on Rugged Fitness Landscapes

PubMed Central

Bitbol, Anne-Florence; Schwab, David J.

2014-01-01

Natural selection drives populations towards higher fitness, but crossing fitness valleys or plateaus may facilitate progress up a rugged fitness landscape involving epistasis. We investigate quantitatively the effect of subdividing an asexual population on the time it takes to cross a fitness valley or plateau. We focus on a generic and minimal model that includes only population subdivision into equivalent demes connected by global migration, and does not require significant size changes of the demes, environmental heterogeneity or specific geographic structure. We determine the optimal speedup of valley or plateau crossing that can be gained by subdivision, if the process is driven by the deme that crosses fastest. We show that isolated demes have to be in the sequential fixation regime for subdivision to significantly accelerate crossing. Using Markov chain theory, we obtain analytical expressions for the conditions under which optimal speedup is achieved: valley or plateau crossing by the subdivided population is then as fast as that of its fastest deme. We verify our analytical predictions through stochastic simulations. We demonstrate that subdivision can substantially accelerate the crossing of fitness valleys and plateaus in a wide range of parameters extending beyond the optimal window. We study the effect of varying the degree of subdivision of a population, and investigate the trade-off between the magnitude of the optimal speedup and the width of the parameter range over which it occurs. Our results, obtained for fitness valleys and plateaus, also hold for weakly beneficial intermediate mutations. Finally, we extend our work to the case of a population connected by migration to one or several smaller islands. Our results demonstrate that subdivision with migration alone can significantly accelerate the crossing of fitness valleys and plateaus, and shed light onto the quantitative conditions necessary for this to occur. PMID:25122220

Comparison of adsorption equilibrium models for the study of CL-, NO3- and SO4(2-) removal from aqueous solutions by an anion exchange resin.

PubMed

Dron, Julien; Dodi, Alain

2011-06-15

The removal of chloride, nitrate and sulfate ions from aqueous solutions by a macroporous resin is studied through the ion exchange systems OH(-)/Cl(-), OH(-)/NO(3)(-), OH(-)/SO(4)(2-), and HCO(3)(-)/Cl(-), Cl(-)/NO(3)(-), Cl(-)/SO(4)(2-). They are investigated by means of Langmuir, Freundlich, Dubinin-Radushkevitch (D-R) and Dubinin-Astakhov (D-A) single-component adsorption isotherms. The sorption parameters and the fitting of the models are determined by nonlinear regression and discussed. The Langmuir model provides a fair estimation of the sorption capacity whatever the system under study, on the contrary to Freundlich and D-R models. The adsorption energies deduced from Dubinin and Langmuir isotherms are in good agreement, and the surface parameter of the D-A isotherm appears consistent. All models agree on the order of affinity OH(-)

Computational modeling of the human auditory periphery: Auditory-nerve responses, evoked potentials and hearing loss.

PubMed

Verhulst, Sarah; Altoè, Alessandro; Vasilkov, Viacheslav

2018-03-01

Models of the human auditory periphery range from very basic functional descriptions of auditory filtering to detailed computational models of cochlear mechanics, inner-hair cell (IHC), auditory-nerve (AN) and brainstem signal processing. It is challenging to include detailed physiological descriptions of cellular components into human auditory models because single-cell data stems from invasive animal recordings while human reference data only exists in the form of population responses (e.g., otoacoustic emissions, auditory evoked potentials). To embed physiological models within a comprehensive human auditory periphery framework, it is important to capitalize on the success of basic functional models of hearing and render their descriptions more biophysical where possible. At the same time, comprehensive models should capture a variety of key auditory features, rather than fitting their parameters to a single reference dataset. In this study, we review and improve existing models of the IHC-AN complex by updating their equations and expressing their fitting parameters into biophysical quantities. The quality of the model framework for human auditory processing is evaluated using recorded auditory brainstem response (ABR) and envelope-following response (EFR) reference data from normal and hearing-impaired listeners. We present a model with 12 fitting parameters from the cochlea to the brainstem that can be rendered hearing impaired to simulate how cochlear gain loss and synaptopathy affect human population responses. The model description forms a compromise between capturing well-described single-unit IHC and AN properties and human population response features. Copyright © 2018 The Authors. Published by Elsevier B.V. All rights reserved.

Extracting the QCD ΛMS¯ parameter in Drell-Yan process using Collins-Soper-Sterman approach

NASA Astrophysics Data System (ADS)

Taghavi, R.; Mirjalili, A.

2017-03-01

In this work, we directly fit the QCD dimensional transmutation parameter, ΛMS¯, to experimental data of Drell-Yan (DY) observables. For this purpose, we first obtain the evolution of transverse momentum dependent parton distribution functions (TMDPDFs) up to the next-to-next-to-leading logarithm (NNLL) approximation based on Collins-Soper-Sterman (CSS) formalism. As is expecting the TMDPDFs are appearing at larger values of transverse momentum by increasing the energy scales and also the order of approximation. Then we calculate the cross-section related to the TMDPDFs in the DY process. As a consequence of global fitting to the five sets of experimental data at different low center-of-mass energies and one set at high center-of-mass energy, using CETQ06 parametrizations as our boundary condition, we obtain ΛMS¯ = 221 ± 7(stat) ± 54(theory) MeV corresponding to the renormalized coupling constant αs(Mz2) = 0.117 ± 0.001(stat) ± 0.004(theory) which is within the acceptable range for this quantity. The goodness of χ2/d.o.f = 1.34 shows the results for DY cross-section are in good agreement with different experimental sets, containing E288, E605 and R209 at low center-of-mass energies and D0, CDF data at high center-of-mass energy. The repeated calculations, using HERAPDFs parametrizations is yielding us numerical values for fitted parameters very close to what we obtain using CETQ06 PDFs set. This indicates that the obtained results have enough stability by variations in the boundary conditions.

Comparative research on activation technique for GaAs photocathodes

NASA Astrophysics Data System (ADS)

Chen, Liang; Qian, Yunsheng; Chang, Benkang; Chen, Xinlong; Yang, Rui

2012-03-01

The properties of GaAs photocathodes mainly depend on the material design and activation technique. In early researches, high-low temperature two-step activation has been proved to get more quantum efficiency than high-temperature single-step activation. But the variations of surface barriers for two activation techniques have not been well studied, thus the best activation temperature, best Cs-O ratio and best activation time for two-step activation technique have not been well found. Because the surface photovoltage spectroscopy (SPS) before activation is only in connection with the body parameters for GaAs photocathode such as electron diffusion length and the spectral response current (SRC) after activation is in connection with not only body parameters but also surface barriers, thus the surface escape probability (SEP) can be well fitted through the comparative research between SPS before activation and SEP after activation. Through deduction for the tunneling process of surface barriers by Schrödinger equation, the width and height for surface barrier I and II can be well fitted through the curves of SEP. The fitting results were well proved and analyzed by quantitative analysis of angle-dependent X-ray photoelectron spectroscopy (ADXPS) which can also study the surface chemical compositions, atomic concentration percentage and layer thickness for GaAs photocathodes. This comparative research method for fitting parameters of surface barriers through SPS before activation and SRC after activation shows a better real-time in system method for the researches of activation techniques.

Association of Angiotensin Converting Enzyme I/D and α-actinin-3 R577X Genotypes with Growth Factors and Physical Fitness in Korean Children

PubMed Central

Ahn, Nayoung; Cheun, Wookwang; Byun, Jayoung; Joo, Youngsik

2015-01-01

This study analyzed the differences in aerobic and anaerobic exercise ability and growth-related indicators, depending on the polymorphism of the ACE and the ACTN3 genes, to understand the genetic influence of exercise ability in the growth process of children. The subjects of the study consisted of elementary school students (n=856, age 10.32±0.07 yr). The anthropometric parameters, physical fitness and growth factors were compared among groups of the ACE I/D or the ACTN3 R577X polymorphisms. There were no significant differences between the anthropometric parameters, physical fitness and growth factors for the ACE gene ID or the ACTN3 gene R577X polymorphism. However, the DD type of ACE gene was highest in the side step test (p<0.05), and the DD type was significantly higher than the II+ID type (p<0.05) in the early bone age. The combined group of the ACE gene II+ID and the ACTN3 gene XX type significantly showed lower early bone age (p< 0.05). This study did not find any individual or compounding effects of the polymorphism in the ACE I/D or the ACTN3 R577X polymorphisms on the anthropometric parameters, physical fitness and growth factors of Korean children. However, the exercise experience and the DD type of the ACE gene may affect the early maturity of the bones. PMID:25729275

The 6300 A O/1-D/ airglow and dissociative recombination

NASA Technical Reports Server (NTRS)

Wickwar, V. B.; Cogger, L. L.; Carlson, H. C.

1974-01-01

Measurements of night-time 6300 A airglow intensities at the Arecibo Observatory have been compared with dissociative recombination calculations based on electron densities derived from simultaneous incoherent backscatter measurements. The agreement indicates that the nightglow can be fully accounted for by dissociative recombination. The comparisons are examined to determine the importance of quenching, heavy ions, ionization above the F-layer peak, and the temperature parameter of the model atmosphere. Comparable fits between the observed and calculated intensities are found for several available model atmospheres. The least-squares fitting process, used to make the comparisons, produces comparable fits over a wide range of combinations of neutral densities and of reaction constants. Yet, the fitting places constraints upon the possible combinations; these constraints indicate that the latest laboratory chemical constants and densities extrapolated to a base altitude are mutually consistent.

Model misspecification detection by means of multiple generator errors, using the observed potential map.

PubMed

Zhang, Z; Jewett, D L

1994-01-01

Due to model misspecification, currently-used Dipole Source Localization (DSL) methods may contain Multiple-Generator Errors (MulGenErrs) when fitting simultaneously-active dipoles. The size of the MulGenErr is a function of both the model used, and the dipole parameters, including the dipoles' waveforms (time-varying magnitudes). For a given fitting model, by examining the variation of the MulGenErrs (or the fit parameters) under different waveforms for the same generating-dipoles, the accuracy of the fitting model for this set of dipoles can be determined. This method of testing model misspecification can be applied to evoked potential maps even when the parameters of the generating-dipoles are unknown. The dipole parameters fitted in a model should only be accepted if the model can be shown to be sufficiently accurate.

Technical Note: Approximate Bayesian parameterization of a complex tropical forest model

NASA Astrophysics Data System (ADS)

Hartig, F.; Dislich, C.; Wiegand, T.; Huth, A.

2013-08-01

Inverse parameter estimation of process-based models is a long-standing problem in ecology and evolution. A key problem of inverse parameter estimation is to define a metric that quantifies how well model predictions fit to the data. Such a metric can be expressed by general cost or objective functions, but statistical inversion approaches are based on a particular metric, the probability of observing the data given the model, known as the likelihood. Deriving likelihoods for dynamic models requires making assumptions about the probability for observations to deviate from mean model predictions. For technical reasons, these assumptions are usually derived without explicit consideration of the processes in the simulation. Only in recent years have new methods become available that allow generating likelihoods directly from stochastic simulations. Previous applications of these approximate Bayesian methods have concentrated on relatively simple models. Here, we report on the application of a simulation-based likelihood approximation for FORMIND, a parameter-rich individual-based model of tropical forest dynamics. We show that approximate Bayesian inference, based on a parametric likelihood approximation placed in a conventional MCMC, performs well in retrieving known parameter values from virtual field data generated by the forest model. We analyze the results of the parameter estimation, examine the sensitivity towards the choice and aggregation of model outputs and observed data (summary statistics), and show results from using this method to fit the FORMIND model to field data from an Ecuadorian tropical forest. Finally, we discuss differences of this approach to Approximate Bayesian Computing (ABC), another commonly used method to generate simulation-based likelihood approximations. Our results demonstrate that simulation-based inference, which offers considerable conceptual advantages over more traditional methods for inverse parameter estimation, can successfully be applied to process-based models of high complexity. The methodology is particularly suited to heterogeneous and complex data structures and can easily be adjusted to other model types, including most stochastic population and individual-based models. Our study therefore provides a blueprint for a fairly general approach to parameter estimation of stochastic process-based models in ecology and evolution.

Kepler Uniform Modeling of KOIs: MCMC Notes for Data Release 25

NASA Technical Reports Server (NTRS)

Hoffman, Kelsey L.; Rowe, Jason F.

2017-01-01

This document describes data products related to the reported planetary parameters and uncertainties for the Kepler Objects of Interest (KOIs) based on a Markov-Chain-Monte-Carlo (MCMC) analysis. Reported parameters, uncertainties and data products can be found at the NASA Exoplanet Archive . The codes used for this data analysis are available on the Github website (Rowe 2016). The relevant paper for details of the calculations is Rowe et al. (2015). The main differences between the model fits discussed here and those in the DR24 catalogue are that the DR25 light curves were used in the analysis, our processing of the MAST light curves took into account different data flags, the number of chains calculated was doubled to 200 000, and the parameters which are reported are based on a damped least-squares fit, instead of the median value from the Markov chain or the chain with the lowest 2 as reported in the past.

Comprehensive analysis of NMR data using advanced line shape fitting.

PubMed

Niklasson, Markus; Otten, Renee; Ahlner, Alexandra; Andresen, Cecilia; Schlagnitweit, Judith; Petzold, Katja; Lundström, Patrik

2017-10-01

NMR spectroscopy is uniquely suited for atomic resolution studies of biomolecules such as proteins, nucleic acids and metabolites, since detailed information on structure and dynamics are encoded in positions and line shapes of peaks in NMR spectra. Unfortunately, accurate determination of these parameters is often complicated and time consuming, in part due to the need for different software at the various analysis steps and for validating the results. Here, we present an integrated, cross-platform and open-source software that is significantly more versatile than the typical line shape fitting application. The software is a completely redesigned version of PINT ( https://pint-nmr.github.io/PINT/ ). It features a graphical user interface and includes functionality for peak picking, editing of peak lists and line shape fitting. In addition, the obtained peak intensities can be used directly to extract, for instance, relaxation rates, heteronuclear NOE values and exchange parameters. In contrast to most available software the entire process from spectral visualization to preparation of publication-ready figures is done solely using PINT and often within minutes, thereby, increasing productivity for users of all experience levels. Unique to the software are also the outstanding tools for evaluating the quality of the fitting results and extensive, but easy-to-use, customization of the fitting protocol and graphical output. In this communication, we describe the features of the new version of PINT and benchmark its performance.

Reduced arterial stiffness in very fit boys and girls.

PubMed

Weberruß, Heidi; Pirzer, Raphael; Schulz, Thorsten; Böhm, Birgit; Dalla Pozza, Robert; Netz, Heinrich; Oberhoffer, Renate

2017-01-01

Low cardiorespiratory fitness is associated with higher cardiovascular risk, whereas high levels of cardiorespiratory fitness protect the cardiovascular system. Carotid intima-media thickness and arterial distensibility are well-established parameters to identify subclinical cardiovascular disease. Therefore, this study investigated the influence of cardiorespiratory fitness and muscular strength on carotid intima-media thickness and arterial distensibility in 697 children and adolescents (376 girls), aged 7-17 years. Cardiorespiratory fitness and strength were measured with the test battery FITNESSGRAM; carotid intima-media thickness, arterial compliance, elastic modulus, stiffness index β, and pulse wave velocity β were assessed by B- and M-mode ultrasound at the common carotid artery. In bivariate correlation, cardiorespiratory fitness was significantly associated with all cardiovascular parameters and was an independent predictor in multivariate regression analysis. No significant associations were obtained for muscular strength. In a one-way variance analysis, very fit boys and girls (58 boys and 74 girls>80th percentile for cardiorespiratory fitness) had significantly decreased stiffness parameters (expressed in standard deviation scores) compared with low fit subjects (71 boys and 77 girls<20th percentile for cardiorespiratory fitness): elastic modulus -0.16±1.02 versus 0.19±1.17, p=0.009; stiffness index β -0.15±1.08 versus 0.16±1.1, p=0.03; and pulse wave velocity β -0.19±1.02 versus 0.19±1.14, p=0.005. Cardiorespiratory fitness was associated with healthier arteries in children and adolescents. Comparison of very fit with unfit subjects revealed better distensibility parameters in very fit boys and girls.

Fast and accurate fitting and filtering of noisy exponentials in Legendre space.

PubMed

Bao, Guobin; Schild, Detlev

2014-01-01

The parameters of experimentally obtained exponentials are usually found by least-squares fitting methods. Essentially, this is done by minimizing the mean squares sum of the differences between the data, most often a function of time, and a parameter-defined model function. Here we delineate a novel method where the noisy data are represented and analyzed in the space of Legendre polynomials. This is advantageous in several respects. First, parameter retrieval in the Legendre domain is typically two orders of magnitude faster than direct fitting in the time domain. Second, data fitting in a low-dimensional Legendre space yields estimates for amplitudes and time constants which are, on the average, more precise compared to least-squares-fitting with equal weights in the time domain. Third, the Legendre analysis of two exponentials gives satisfactory estimates in parameter ranges where least-squares-fitting in the time domain typically fails. Finally, filtering exponentials in the domain of Legendre polynomials leads to marked noise removal without the phase shift characteristic for conventional lowpass filters.

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

USGS Publications Warehouse

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

2015-01-01

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

A Simple Model for Fine Structure Transitions in Alkali-Metal Noble-Gas Collisions

DTIC Science & Technology

2015-03-01

63 33 Effect of Scaling the VRG(R) Radial Coupling Fit Parameter, V0, for KHe, KNe, and KAr...64 ix Figure Page 34 Effect of Scaling the VRG(R) Radial Coupling Fit Parameter, V0, for RbHe, RbNe, and...RbAr . . . . . . . . . . . . . . . . . . . . . . . . . 64 35 Effect of Scaling the VRG(R) Radial Coupling Fit Parameter, V0, for CsHe, CsNe, and CsAr

Simple and Reliable Determination of Intravoxel Incoherent Motion Parameters for the Differential Diagnosis of Head and Neck Tumors

PubMed Central

Sasaki, Miho; Sumi, Misa; Eida, Sato; Katayama, Ikuo; Hotokezaka, Yuka; Nakamura, Takashi

2014-01-01

Intravoxel incoherent motion (IVIM) imaging can characterize diffusion and perfusion of normal and diseased tissues, and IVIM parameters are authentically determined by using cumbersome least-squares method. We evaluated a simple technique for the determination of IVIM parameters using geometric analysis of the multiexponential signal decay curve as an alternative to the least-squares method for the diagnosis of head and neck tumors. Pure diffusion coefficients (D), microvascular volume fraction (f), perfusion-related incoherent microcirculation (D*), and perfusion parameter that is heavily weighted towards extravascular space (P) were determined geometrically (Geo D, Geo f, and Geo P) or by least-squares method (Fit D, Fit f, and Fit D*) in normal structures and 105 head and neck tumors. The IVIM parameters were compared for their levels and diagnostic abilities between the 2 techniques. The IVIM parameters were not able to determine in 14 tumors with the least-squares method alone and in 4 tumors with the geometric and least-squares methods. The geometric IVIM values were significantly different (p<0.001) from Fit values (+2±4% and −7±24% for D and f values, respectively). Geo D and Fit D differentiated between lymphomas and SCCs with similar efficacy (78% and 80% accuracy, respectively). Stepwise approaches using combinations of Geo D and Geo P, Geo D and Geo f, or Fit D and Fit D* differentiated between pleomorphic adenomas, Warthin tumors, and malignant salivary gland tumors with the same efficacy (91% accuracy = 21/23). However, a stepwise differentiation using Fit D and Fit f was less effective (83% accuracy = 19/23). Considering cumbersome procedures with the least squares method compared with the geometric method, we concluded that the geometric determination of IVIM parameters can be an alternative to least-squares method in the diagnosis of head and neck tumors. PMID:25402436

Optimum surface roughness prediction for titanium alloy by adopting response surface methodology

NASA Astrophysics Data System (ADS)

Yang, Aimin; Han, Yang; Pan, Yuhang; Xing, Hongwei; Li, Jinze

Titanium alloy has been widely applied in industrial engineering products due to its advantages of great corrosion resistance and high specific strength. This paper investigated the processing parameters for finish turning of titanium alloy TC11. Firstly, a three-factor central composite design of experiment, considering the cutting speed, feed rate and depth of cut, are conducted in titanium alloy TC11 and the corresponding surface roughness are obtained. Then a mathematic model is constructed by the response surface methodology to fit the relationship between the process parameters and the surface roughness. The prediction accuracy was verified by the one-way ANOVA. Finally, the contour line of the surface roughness under different combination of process parameters are obtained and used for the optimum surface roughness prediction. Verification experimental results demonstrated that material removal rate (MRR) at the obtained optimum can be significantly improved without sacrificing the surface roughness.

Modelling of influential parameters on a continuous evaporation process by Doehlert shells

PubMed Central

Porte, Catherine; Havet, Jean-Louis; Daguet, David

2003-01-01

The modelling of the parameters that influence the continuous evaporation of an alcoholic extract was considered using Doehlert matrices. The work was performed with a wiped falling film evaporator that allowed us to study the influence of the pressure, temperature, feed flow and dry matter of the feed solution on the dry matter contents of the resulting concentrate, and the productivity of the process. The Doehlert shells were used to model the influential parameters. The pattern obtained from the experimental results was checked allowing for some dysfunction in the unit. The evaporator was modified and a new model applied; the experimental results were then in agreement with the equations. The model was finally determined and successfully checked in order to obtain an 8% dry matter concentrate with the best productivity; the results fit in with the industrial constraints of subsequent processes. PMID:18924887

Fundamental Analysis of the Linear Multiple Regression Technique for Quantification of Water Quality Parameters from Remote Sensing Data. Ph.D. Thesis - Old Dominion Univ.

NASA Technical Reports Server (NTRS)

Whitlock, C. H., III

1977-01-01

Constituents with linear radiance gradients with concentration may be quantified from signals which contain nonlinear atmospheric and surface reflection effects for both homogeneous and non-homogeneous water bodies provided accurate data can be obtained and nonlinearities are constant with wavelength. Statistical parameters must be used which give an indication of bias as well as total squared error to insure that an equation with an optimum combination of bands is selected. It is concluded that the effect of error in upwelled radiance measurements is to reduce the accuracy of the least square fitting process and to increase the number of points required to obtain a satisfactory fit. The problem of obtaining a multiple regression equation that is extremely sensitive to error is discussed.

Approaches to automatic parameter fitting in a microscopy image segmentation pipeline: An exploratory parameter space analysis.

PubMed

Held, Christian; Nattkemper, Tim; Palmisano, Ralf; Wittenberg, Thomas

2013-01-01

Research and diagnosis in medicine and biology often require the assessment of a large amount of microscopy image data. Although on the one hand, digital pathology and new bioimaging technologies find their way into clinical practice and pharmaceutical research, some general methodological issues in automated image analysis are still open. In this study, we address the problem of fitting the parameters in a microscopy image segmentation pipeline. We propose to fit the parameters of the pipeline's modules with optimization algorithms, such as, genetic algorithms or coordinate descents, and show how visual exploration of the parameter space can help to identify sub-optimal parameter settings that need to be avoided. This is of significant help in the design of our automatic parameter fitting framework, which enables us to tune the pipeline for large sets of micrographs. The underlying parameter spaces pose a challenge for manual as well as automated parameter optimization, as the parameter spaces can show several local performance maxima. Hence, optimization strategies that are not able to jump out of local performance maxima, like the hill climbing algorithm, often result in a local maximum.

Approaches to automatic parameter fitting in a microscopy image segmentation pipeline: An exploratory parameter space analysis

PubMed Central

Held, Christian; Nattkemper, Tim; Palmisano, Ralf; Wittenberg, Thomas

2013-01-01

Introduction: Research and diagnosis in medicine and biology often require the assessment of a large amount of microscopy image data. Although on the one hand, digital pathology and new bioimaging technologies find their way into clinical practice and pharmaceutical research, some general methodological issues in automated image analysis are still open. Methods: In this study, we address the problem of fitting the parameters in a microscopy image segmentation pipeline. We propose to fit the parameters of the pipeline's modules with optimization algorithms, such as, genetic algorithms or coordinate descents, and show how visual exploration of the parameter space can help to identify sub-optimal parameter settings that need to be avoided. Results: This is of significant help in the design of our automatic parameter fitting framework, which enables us to tune the pipeline for large sets of micrographs. Conclusion: The underlying parameter spaces pose a challenge for manual as well as automated parameter optimization, as the parameter spaces can show several local performance maxima. Hence, optimization strategies that are not able to jump out of local performance maxima, like the hill climbing algorithm, often result in a local maximum. PMID:23766941

Nine-Year Wilkinson Microwave Anisotropy Probe (WMAP) Observations: Final Maps and Results

NASA Technical Reports Server (NTRS)

Bennett, C. L.; Larson, D.; Weiland, J. L.; Jaorsik, N.; Hinshaw, G.; Odegard, N.; Smith, K. M.; Hill, R. S.; Gold, B.; Halpern, M;

A Survey of Xenon Ion Sputter Yield Data and Fits Relevant to Electric Propulsion Spacecraft Integration

NASA Technical Reports Server (NTRS)

Yim, John T.

2017-01-01

A survey of low energy xenon ion impact sputter yields was conducted to provide a more coherent baseline set of sputter yield data and accompanying fits for electric propulsion integration. Data uncertainties are discussed and different available curve fit formulas are assessed for their general suitability. A Bayesian parameter fitting approach is used with a Markov chain Monte Carlo method to provide estimates for the fitting parameters while characterizing the uncertainties for the resulting yield curves.

NLINEAR - NONLINEAR CURVE FITTING PROGRAM

NASA Technical Reports Server (NTRS)

Everhart, J. L.

1994-01-01

A common method for fitting data is a least-squares fit. In the least-squares method, a user-specified fitting function is utilized in such a way as to minimize the sum of the squares of distances between the data points and the fitting curve. The Nonlinear Curve Fitting Program, NLINEAR, is an interactive curve fitting routine based on a description of the quadratic expansion of the chi-squared statistic. NLINEAR utilizes a nonlinear optimization algorithm that calculates the best statistically weighted values of the parameters of the fitting function and the chi-square that is to be minimized. The inputs to the program are the mathematical form of the fitting function and the initial values of the parameters to be estimated. This approach provides the user with statistical information such as goodness of fit and estimated values of parameters that produce the highest degree of correlation between the experimental data and the mathematical model. In the mathematical formulation of the algorithm, the Taylor expansion of chi-square is first introduced, and justification for retaining only the first term are presented. From the expansion, a set of n simultaneous linear equations are derived, which are solved by matrix algebra. To achieve convergence, the algorithm requires meaningful initial estimates for the parameters of the fitting function. NLINEAR is written in Fortran 77 for execution on a CDC Cyber 750 under NOS 2.3. It has a central memory requirement of 5K 60 bit words. Optionally, graphical output of the fitting function can be plotted. Tektronix PLOT-10 routines are required for graphics. NLINEAR was developed in 1987.

Association of physical fitness and fatness with cognitive function in women with fibromyalgia.

PubMed

Soriano-Maldonado, Alberto; Artero, Enrique G; Segura-Jiménez, Víctor; Aparicio, Virgina A; Estévez-López, Fernando; Álvarez-Gallardo, Inmaculada C; Munguía-Izquierdo, Diego; Casimiro-Andújar, Antonio J; Delgado-Fernández, Manuel; Ortega, Francisco B

2016-09-01

This study assessed the association of fitness and fatness with cognitive function in women with fibromyalgia, and the independent influence of their single components on cognitive tasks. A total of 468 women with fibromyalgia were included. Speed of information processing and working memory (Paced Auditory Serial Addition Task), as well as immediate and delayed recall, verbal learning and delayed recognition (Rey Auditory Verbal Learning Test) were assessed. Aerobic fitness, muscle strength, flexibility and motor agility were assessed with the Senior Fitness Test battery. Body mass index, percent body fat, fat-mass index and waist circumference were measured. Aerobic fitness was associated with attention and working memory (all, p < 0.05). All fitness components were generally associated with delayed recall, verbal learning and delayed recognition (all, p < 0.05). Aerobic fitness showed the most powerful association with attention, working memory, delayed recall and verbal learning, while motor agility was the most powerful indicator of delayed recognition. None of the fatness parameters were associated with any of the outcomes (all, p > 0.05). Our results suggest that fitness, but not fatness, is associated with cognitive function in women with fibromyalgia. Aerobic fitness appears to be the most powerful fitness component regarding the cognitive tasks evaluated.

Inverse Monte Carlo method in a multilayered tissue model for diffuse reflectance spectroscopy

NASA Astrophysics Data System (ADS)

Fredriksson, Ingemar; Larsson, Marcus; Strömberg, Tomas

2012-04-01

Model based data analysis of diffuse reflectance spectroscopy data enables the estimation of optical and structural tissue parameters. The aim of this study was to present an inverse Monte Carlo method based on spectra from two source-detector distances (0.4 and 1.2 mm), using a multilayered tissue model. The tissue model variables include geometrical properties, light scattering properties, tissue chromophores such as melanin and hemoglobin, oxygen saturation and average vessel diameter. The method utilizes a small set of presimulated Monte Carlo data for combinations of different levels of epidermal thickness and tissue scattering. The path length distributions in the different layers are stored and the effect of the other parameters is added in the post-processing. The accuracy of the method was evaluated using Monte Carlo simulations of tissue-like models containing discrete blood vessels, evaluating blood tissue fraction and oxygenation. It was also compared to a homogeneous model. The multilayer model performed better than the homogeneous model and all tissue parameters significantly improved spectral fitting. Recorded in vivo spectra were fitted well at both distances, which we previously found was not possible with a homogeneous model. No absolute intensity calibration is needed and the algorithm is fast enough for real-time processing.

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

NASA Astrophysics Data System (ADS)

Lee, Kyu Sang; Gill, Wonpyong

2017-11-01

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

A new simple local muscle recovery model and its theoretical and experimental validation.

PubMed

Ma, Liang; Zhang, Wei; Wu, Su; Zhang, Zhanwu

2015-01-01

This study was conducted to provide theoretical and experimental validation of a local muscle recovery model. Muscle recovery has been modeled in different empirical and theoretical approaches to determine work-rest allowance for musculoskeletal disorder (MSD) prevention. However, time-related parameters and individual attributes have not been sufficiently considered in conventional approaches. A new muscle recovery model was proposed by integrating time-related task parameters and individual attributes. Theoretically, this muscle recovery model was compared to other theoretical models mathematically. Experimentally, a total of 20 subjects participated in the experimental validation. Hand grip force recovery and shoulder joint strength recovery were measured after a fatiguing operation. The recovery profile was fitted by using the recovery model, and individual recovery rates were calculated as well after fitting. Good fitting values (r(2) > .8) were found for all the subjects. Significant differences in recovery rates were found among different muscle groups (p < .05). The theoretical muscle recovery model was primarily validated by characterization of the recovery process after fatiguing operation. The determined recovery rate may be useful to represent individual recovery attribute.

Tap density equations of granular powders based on the rate process theory and the free volume concept.

PubMed

Hao, Tian

2015-02-28

The tap density of a granular powder is often linked to the flowability via the Carr index that measures how tight a powder can be packed, under an assumption that more easily packed powders usually flow poorly. Understanding how particles are packed is important for revealing why a powder flows better than others. There are two types of empirical equations that were proposed to fit the experimental data of packing fractions vs. numbers of taps in the literature: the inverse logarithmic and the stretched exponential. Using the rate process theory and the free volume concept under the assumption that particles will obey similar thermodynamic laws during the tapping process if the "granular temperature" is defined in a different way, we obtain the tap density equations, and they are reducible to the two empirical equations currently widely used in literature. Our equations could potentially fit experimental data better with an additional adjustable parameter. The tapping amplitude and frequency, the weight of the granular materials, and the environmental temperature are grouped into this parameter that weighs the pace of the packing process. The current results, in conjunction with our previous findings, may imply that both "dry" (granular) and "wet" (colloidal and polymeric) particle systems are governed by the same physical mechanisms in term of the role of the free volume and how particles behave (a rate controlled process).

Event-scale power law recession analysis: quantifying methodological uncertainty

NASA Astrophysics Data System (ADS)

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

2017-01-01

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

xspec_emcee: XSPEC-friendly interface for the emcee package

NASA Astrophysics Data System (ADS)

Sanders, Jeremy

2018-05-01

XSPEC_EMCEE is an XSPEC-friendly interface for emcee (ascl:1303.002). It carries out MCMC analyses of X-ray spectra in the X-ray spectral fitting program XSPEC (ascl:9910.005). It can run multiple xspec processes simultaneously, speeding up the analysis, and can switch to parameterizing norm parameters in log space.

Fitting the Mixed Rasch Model to a Reading Comprehension Test: Identifying Reader Types

ERIC Educational Resources Information Center

Baghaei, Purya; Carstensen, Claus H.

2013-01-01

Standard unidimensional Rasch models assume that persons with the same ability parameters are comparable. That is, the same interpretation applies to persons with identical ability estimates as regards the underlying mental processes triggered by the test. However, research in cognitive psychology shows that persons at the same trait level may…

SBSI: an extensible distributed software infrastructure for parameter estimation in systems biology.

PubMed

Adams, Richard; Clark, Allan; Yamaguchi, Azusa; Hanlon, Neil; Tsorman, Nikos; Ali, Shakir; Lebedeva, Galina; Goltsov, Alexey; Sorokin, Anatoly; Akman, Ozgur E; Troein, Carl; Millar, Andrew J; Goryanin, Igor; Gilmore, Stephen

2013-03-01

Complex computational experiments in Systems Biology, such as fitting model parameters to experimental data, can be challenging to perform. Not only do they frequently require a high level of computational power, but the software needed to run the experiment needs to be usable by scientists with varying levels of computational expertise, and modellers need to be able to obtain up-to-date experimental data resources easily. We have developed a software suite, the Systems Biology Software Infrastructure (SBSI), to facilitate the parameter-fitting process. SBSI is a modular software suite composed of three major components: SBSINumerics, a high-performance library containing parallelized algorithms for performing parameter fitting; SBSIDispatcher, a middleware application to track experiments and submit jobs to back-end servers; and SBSIVisual, an extensible client application used to configure optimization experiments and view results. Furthermore, we have created a plugin infrastructure to enable project-specific modules to be easily installed. Plugin developers can take advantage of the existing user-interface and application framework to customize SBSI for their own uses, facilitated by SBSI's use of standard data formats. All SBSI binaries and source-code are freely available from http://sourceforge.net/projects/sbsi under an Apache 2 open-source license. The server-side SBSINumerics runs on any Unix-based operating system; both SBSIVisual and SBSIDispatcher are written in Java and are platform independent, allowing use on Windows, Linux and Mac OS X. The SBSI project website at http://www.sbsi.ed.ac.uk provides documentation and tutorials.

Statistical inference for noisy nonlinear ecological dynamic systems.

PubMed

Wood, Simon N

2010-08-26

Chaotic ecological dynamic systems defy conventional statistical analysis. Systems with near-chaotic dynamics are little better. Such systems are almost invariably driven by endogenous dynamic processes plus demographic and environmental process noise, and are only observable with error. Their sensitivity to history means that minute changes in the driving noise realization, or the system parameters, will cause drastic changes in the system trajectory. This sensitivity is inherited and amplified by the joint probability density of the observable data and the process noise, rendering it useless as the basis for obtaining measures of statistical fit. Because the joint density is the basis for the fit measures used by all conventional statistical methods, this is a major theoretical shortcoming. The inability to make well-founded statistical inferences about biological dynamic models in the chaotic and near-chaotic regimes, other than on an ad hoc basis, leaves dynamic theory without the methods of quantitative validation that are essential tools in the rest of biological science. Here I show that this impasse can be resolved in a simple and general manner, using a method that requires only the ability to simulate the observed data on a system from the dynamic model about which inferences are required. The raw data series are reduced to phase-insensitive summary statistics, quantifying local dynamic structure and the distribution of observations. Simulation is used to obtain the mean and the covariance matrix of the statistics, given model parameters, allowing the construction of a 'synthetic likelihood' that assesses model fit. This likelihood can be explored using a straightforward Markov chain Monte Carlo sampler, but one further post-processing step returns pure likelihood-based inference. I apply the method to establish the dynamic nature of the fluctuations in Nicholson's classic blowfly experiments.

VizieR Online Data Catalog: Activity cycles in 3203 Kepler stars (Reinhold+, 2017)

NASA Astrophysics Data System (ADS)

Reinhold, T.; Cameron, R. H.; Gizon, L.

2017-05-01

Rvar time series, sine fit parameters, mean rotation periods, and false alarm probabilities of all 3203 Kepler stars are presented. For simplicity, the KIC number and the fit parameters of a certain star are repeated in each line. The fit function to the Rvar(t) time series equals y_fit=Acyc*sin(2*pi/(Pcyc*365)*(t-t0))+Offset. (2 data files).

Investigation of the leading and subleading high-energy behavior of hadron-hadron total cross sections using a best-fit analysis of hadronic scattering data

NASA Astrophysics Data System (ADS)

Giordano, M.; Meggiolaro, E.; Silva, P. V. R. G.

2017-08-01

In the present investigation we study the leading and subleading high-energy behavior of hadron-hadron total cross sections using a best-fit analysis of hadronic scattering data. The parametrization used for the hadron-hadron total cross sections at high energy is inspired by recent results obtained by Giordano and Meggiolaro [J. High Energy Phys. 03 (2014) 002, 10.1007/JHEP03(2014)002] using a nonperturbative approach in the framework of QCD, and it reads σtot˜B ln2s +C ln s ln ln s . We critically investigate if B and C can be obtained by means of best-fits to data for proton-proton and antiproton-proton scattering, including recent data obtained at the LHC, and also to data for other meson-baryon and baryon-baryon scattering processes. In particular, following the above-mentioned nonperturbative QCD approach, we also consider fits where the parameters B and C are set to B =κ Bth and C =κ Cth, where Bth and Cth are universal quantities related to the QCD stable spectrum, while κ (treated as an extra free parameter) is related to the asymptotic value of the ratio σel/σtot. Different possible scenarios are then considered and compared.

Induced subgraph searching for geometric model fitting

NASA Astrophysics Data System (ADS)

Xiao, Fan; Xiao, Guobao; Yan, Yan; Wang, Xing; Wang, Hanzi

2017-11-01

In this paper, we propose a novel model fitting method based on graphs to fit and segment multiple-structure data. In the graph constructed on data, each model instance is represented as an induced subgraph. Following the idea of pursuing the maximum consensus, the multiple geometric model fitting problem is formulated as searching for a set of induced subgraphs including the maximum union set of vertices. After the generation and refinement of the induced subgraphs that represent the model hypotheses, the searching process is conducted on the "qualified" subgraphs. Multiple model instances can be simultaneously estimated by solving a converted problem. Then, we introduce the energy evaluation function to determine the number of model instances in data. The proposed method is able to effectively estimate the number and the parameters of model instances in data severely corrupted by outliers and noises. Experimental results on synthetic data and real images validate the favorable performance of the proposed method compared with several state-of-the-art fitting methods.

Fundamental Parameters Line Profile Fitting in Laboratory Diffractometers

PubMed Central

Cheary, R. W.; Coelho, A. A.; Cline, J. P.

2004-01-01

The fundamental parameters approach to line profile fitting uses physically based models to generate the line profile shapes. Fundamental parameters profile fitting (FPPF) has been used to synthesize and fit data from both parallel beam and divergent beam diffractometers. The refined parameters are determined by the diffractometer configuration. In a divergent beam diffractometer these include the angular aperture of the divergence slit, the width and axial length of the receiving slit, the angular apertures of the axial Soller slits, the length and projected width of the x-ray source, the absorption coefficient and axial length of the sample. In a parallel beam system the principal parameters are the angular aperture of the equatorial analyser/Soller slits and the angular apertures of the axial Soller slits. The presence of a monochromator in the beam path is normally accommodated by modifying the wavelength spectrum and/or by changing one or more of the axial divergence parameters. Flat analyzer crystals have been incorporated into FPPF as a Lorentzian shaped angular acceptance function. One of the intrinsic benefits of the fundamental parameters approach is its adaptability any laboratory diffractometer. Good fits can normally be obtained over the whole 20 range without refinement using the known properties of the diffractometer, such as the slit sizes and diffractometer radius, and emission profile. PMID:27366594

On the Complexity of Item Response Theory Models.

PubMed

Bonifay, Wes; Cai, Li

2017-01-01

Complexity in item response theory (IRT) has traditionally been quantified by simply counting the number of freely estimated parameters in the model. However, complexity is also contingent upon the functional form of the model. We examined four popular IRT models-exploratory factor analytic, bifactor, DINA, and DINO-with different functional forms but the same number of free parameters. In comparison, a simpler (unidimensional 3PL) model was specified such that it had 1 more parameter than the previous models. All models were then evaluated according to the minimum description length principle. Specifically, each model was fit to 1,000 data sets that were randomly and uniformly sampled from the complete data space and then assessed using global and item-level fit and diagnostic measures. The findings revealed that the factor analytic and bifactor models possess a strong tendency to fit any possible data. The unidimensional 3PL model displayed minimal fitting propensity, despite the fact that it included an additional free parameter. The DINA and DINO models did not demonstrate a proclivity to fit any possible data, but they did fit well to distinct data patterns. Applied researchers and psychometricians should therefore consider functional form-and not goodness-of-fit alone-when selecting an IRT model.

Modeling two strains of disease via aggregate-level infectivity curves.

PubMed

Romanescu, Razvan; Deardon, Rob

2016-04-01

Well formulated models of disease spread, and efficient methods to fit them to observed data, are powerful tools for aiding the surveillance and control of infectious diseases. Our project considers the problem of the simultaneous spread of two related strains of disease in a context where spatial location is the key driver of disease spread. We start our modeling work with the individual level models (ILMs) of disease transmission, and extend these models to accommodate the competing spread of the pathogens in a two-tier hierarchical population (whose levels we refer to as 'farm' and 'animal'). The postulated interference mechanism between the two strains is a period of cross-immunity following infection. We also present a framework for speeding up the computationally intensive process of fitting the ILM to data, typically done using Markov chain Monte Carlo (MCMC) in a Bayesian framework, by turning the inference into a two-stage process. First, we approximate the number of animals infected on a farm over time by infectivity curves. These curves are fit to data sampled from farms, using maximum likelihood estimation, then, conditional on the fitted curves, Bayesian MCMC inference proceeds for the remaining parameters. Finally, we use posterior predictive distributions of salient epidemic summary statistics, in order to assess the model fitted.

Eye and Head Movement Characteristics in Free Visual Search of Flight-Simulator Imagery

DTIC Science & Technology

2010-03-01

conspicuity. However, only gaze amplitude varied significantly with IFOV. A two- parameter (scale and exponent) power function was fitted to the...main-sequence amplitude-duration data. Both parameters varied significantly with target conspicuity, but in opposite directions. Neither parameter ...IFOV. A two- parameter (scale and exponent) power function was fitted to the main-sequence amplitude-duration data. Both parameters varied

More Sophisticated Fits of the Oribts of Haumea's Interacting Moons

NASA Astrophysics Data System (ADS)

Oldroyd, William Jared; Ragozzine, Darin; Porter, Simon

2018-04-01

Since the discovery of Haumea's moons, it has been a challenge to model the orbits of its moons, Hi’iaka and Namaka. With many precision HST observations, Ragozzine & Brown 2009 succeeded in calculating a three-point mass model which was essential because Keplerian orbits were not a statistically acceptable fit. New data obtained in 2010 could be fit by adding a J2 and spin pole to Haumea, but new data from 2015 was far from the predicted locations, even after an extensive exploration using Bayesian Markov Chain Monte Carlo methods (using emcee). Here we report on continued investigations as to why our model cannot fit the full 10-year baseline of data. We note that by ignoring Haumea and instead examining the relative motion of the two moons in the Hi’iaka centered frame leads to adequate fits for the data. This suggests there are additional parameters connected to Haumea that will be required in a full model. These parameters are potentially related to photocenter-barycenter shifts which could be significant enough to affect the fitting process; these are unlikely to be caused by the newly discovered ring (Ortiz et al. 2017) or by unknown satellites (Burkhart et al. 2016). Additionally, we have developed a new SPIN+N-bodY integrator called SPINNY that self-consistently calculates the interactions between n-quadrupoles and is designed to test the importance of other possible effects (Haumea C22, satellite torques on the spin-pole, Sun, etc.) on our astrometric fits. By correctly determining the orbit of Haumea’s satellites we develop a better understanding of the physical properties of each of the objects with implications for the formation of Haumea, its moons, and its collisional family.

Rain-rate data base development and rain-rate climate analysis

NASA Technical Reports Server (NTRS)

Crane, Robert K.

1993-01-01

The single-year rain-rate distribution data available within the archives of Consultative Committee for International Radio (CCIR) Study Group 5 were compiled into a data base for use in rain-rate climate modeling and for the preparation of predictions of attenuation statistics. The four year set of tip-time sequences provided by J. Goldhirsh for locations near Wallops Island were processed to compile monthly and annual distributions of rain rate and of event durations for intervals above and below preset thresholds. A four-year data set of tropical rain-rate tip-time sequences were acquired from the NASA TRMM program for 30 gauges near Darwin, Australia. They were also processed for inclusion in the CCIR data base and the expanded data base for monthly observations at the University of Oklahoma. The empirical rain-rate distributions (edfs) accepted for inclusion in the CCIR data base were used to estimate parameters for several rain-rate distribution models: the lognormal model, the Crane two-component model, and the three parameter model proposed by Moupfuma. The intent of this segment of the study is to obtain a limited set of parameters that can be mapped globally for use in rain attenuation predictions. If the form of the distribution can be established, then perhaps available climatological data can be used to estimate the parameters rather than requiring years of rain-rate observations to set the parameters. The two-component model provided the best fit to the Wallops Island data but the Moupfuma model provided the best fit to the Darwin data.

Fitting a Point Cloud to a 3d Polyhedral Surface

NASA Astrophysics Data System (ADS)

Popov, E. V.; Rotkov, S. I.

2017-05-01

The ability to measure parameters of large-scale objects in a contactless fashion has a tremendous potential in a number of industrial applications. However, this problem is usually associated with an ambiguous task to compare two data sets specified in two different co-ordinate systems. This paper deals with the study of fitting a set of unorganized points to a polyhedral surface. The developed approach uses Principal Component Analysis (PCA) and Stretched grid method (SGM) to substitute a non-linear problem solution with several linear steps. The squared distance (SD) is a general criterion to control the process of convergence of a set of points to a target surface. The described numerical experiment concerns the remote measurement of a large-scale aerial in the form of a frame with a parabolic shape. The experiment shows that the fitting process of a point cloud to a target surface converges in several linear steps. The method is applicable to the geometry remote measurement of large-scale objects in a contactless fashion.

Parametrization of free ion levels of four isoelectronic 4f2 systems: Insights into configuration interaction parameters

NASA Astrophysics Data System (ADS)

Yeung, Yau Yuen; Tanner, Peter A.

2013-12-01

The experimental free ion 4f2 energy level data sets comprising 12 or 13 J-multiplets of La+, Ce2+, Pr3+ and Nd4+ have been fitted by a semiempirical atomic Hamiltonian comprising 8, 10, or 12 freely-varying parameters. The root mean square errors were 16.1, 1.3, 0.3 and 0.3 cm-1, respectively for fits with 10 parameters. The fitted inter-electronic repulsion and magnetic parameters vary linearly with ionic charge, i, but better linear fits are obtained with (4-i)2, although the reason is unclear at present. The two-body configuration interaction parameters α and β exhibit a linear relation with [ΔE(bc)]-1, where ΔE(bc) is the energy difference between the 4f2 barycentre and that of the interacting configuration, namely 4f6p for La+, Ce2+, and Pr3+, and 5p54f3 for Nd4+. The linear fit provides the rationale for the negative value of α for the case of La+, where the interacting configuration is located below 4f2.

The challenge associated with the robust computation of meteor velocities from video and photographic records

NASA Astrophysics Data System (ADS)

Egal, A.; Gural, P. S.; Vaubaillon, J.; Colas, F.; Thuillot, W.

2017-09-01

The CABERNET project was designed to push the limits for obtaining accurate measurements of meteoroids orbits from photographic and video meteor camera recordings. The discrepancy between the measured and theoretic orbits of these objects heavily depends on the semi-major axis determination, and thus on the reliability of the pre-atmospheric velocity computation. With a spatial resolution of 0.01° per pixel and a temporal resolution of up to 10 ms, CABERNET should be able to provide accurate measurements of velocities and trajectories of meteors. To achieve this, it is necessary to improve the precision of the data reduction processes, and especially the determination of the meteor's velocity. In this work, most of the steps of the velocity computation are thoroughly investigated in order to reduce the uncertainties and error contributions at each stage of the reduction process. The accuracy of the measurement of meteor centroids is established and results in a precision of 0.09 pixels for CABERNET, which corresponds to 3.24‧‧. Several methods to compute the velocity were investigated based on the trajectory determination algorithms described in Ceplecha (1987) and Borovicka (1990), as well as the multi-parameter fitting (MPF) method proposed by Gural (2012). In the case of the MPF, many optimization methods were implemented in order to find the most efficient and robust technique to solve the minimization problem. The entire data reduction process is assessed using simulated meteors, with different geometrical configurations and deceleration behaviors. It is shown that the multi-parameter fitting method proposed by Gural(2012)is the most accurate method to compute the pre-atmospheric velocity in all circumstances. Many techniques that assume constant velocity at the beginning of the path as derived from the trajectory determination using Ceplecha (1987) or Borovicka (1990) can lead to large errors for decelerating meteors. The MPF technique also allows one to reliably compute the velocity for very low convergence angles (∼ 1°). Despite the better accuracy of this method, the poor conditioning of the velocity propagation models used in the meteor community and currently employed by the multi-parameter fitting method prevent us from optimally computing the pre-atmospheric velocity. Specifically, the deceleration parameters are particularly difficult to determine. The quality of the data provided by the CABERNET network limits the error induced by this effect to achieve an accuracy of about 1% on the velocity computation. Such a precision would not be achievable with lower resolution camera networks and today's commonly used trajectory reduction algorithms. To improve the performance of the multi-parameter fitting method, a linearly independent deceleration formulation needs to be developed.

Nonlinear method for including the mass uncertainty of standards and the system measurement errors in the fitting of calibration curves

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

Pickles, W.L.; McClure, J.W.; Howell, R.H.

1978-01-01

A sophisticated non-linear multiparameter fitting program has been used to produce a best fit calibration curve for the response of an x-ray fluorescence analyzer to uranium nitrate, freeze dried, 0.2% accurate, gravimetric standards. The program is based on unconstrained minimization subroutine, VA02A. The program considers the mass values of the gravimetric standards as parameters to be fit along with the normal calibration curve parameters. The fitting procedure weights with the system errors and the mass errors in a consistent way. The resulting best fit calibration curve parameters reflect the fact that the masses of the standard samples are measured quantitiesmore » with a known error. Error estimates for the calibration curve parameters can be obtined from the curvature of the Chi-Squared Matrix or from error relaxation techniques. It has been shown that non-dispersive x-ray fluorescence analysis of 0.1 to 1 mg freeze-dried UNO/sub 3/ can have an accuracy of 0.2% in 1000 sec.« less

Fast and Accurate Fitting and Filtering of Noisy Exponentials in Legendre Space

PubMed Central

Bao, Guobin; Schild, Detlev

2014-01-01

The parameters of experimentally obtained exponentials are usually found by least-squares fitting methods. Essentially, this is done by minimizing the mean squares sum of the differences between the data, most often a function of time, and a parameter-defined model function. Here we delineate a novel method where the noisy data are represented and analyzed in the space of Legendre polynomials. This is advantageous in several respects. First, parameter retrieval in the Legendre domain is typically two orders of magnitude faster than direct fitting in the time domain. Second, data fitting in a low-dimensional Legendre space yields estimates for amplitudes and time constants which are, on the average, more precise compared to least-squares-fitting with equal weights in the time domain. Third, the Legendre analysis of two exponentials gives satisfactory estimates in parameter ranges where least-squares-fitting in the time domain typically fails. Finally, filtering exponentials in the domain of Legendre polynomials leads to marked noise removal without the phase shift characteristic for conventional lowpass filters. PMID:24603904

Use of a non-linear method for including the mass uncertainty of gravimetric standards and system measurement errors in the fitting of calibration curves for XRFA freeze-dried UNO/sub 3/ standards

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

Pickles, W.L.; McClure, J.W.; Howell, R.H.

1978-05-01

A sophisticated nonlinear multiparameter fitting program was used to produce a best fit calibration curve for the response of an x-ray fluorescence analyzer to uranium nitrate, freeze dried, 0.2% accurate, gravimetric standards. The program is based on unconstrained minimization subroutine, VA02A. The program considers the mass values of the gravimetric standards as parameters to be fit along with the normal calibration curve parameters. The fitting procedure weights with the system errors and the mass errors in a consistent way. The resulting best fit calibration curve parameters reflect the fact that the masses of the standard samples are measured quantities withmore » a known error. Error estimates for the calibration curve parameters can be obtained from the curvature of the ''Chi-Squared Matrix'' or from error relaxation techniques. It was shown that nondispersive XRFA of 0.1 to 1 mg freeze-dried UNO/sub 3/ can have an accuracy of 0.2% in 1000 s.« less

Volume effects of late term normal tissue toxicity in prostate cancer radiotherapy

NASA Astrophysics Data System (ADS)

Bonta, Dacian Viorel

Modeling of volume effects for treatment toxicity is paramount for optimization of radiation therapy. This thesis proposes a new model for calculating volume effects in gastro-intestinal and genito-urinary normal tissue complication probability (NTCP) following radiation therapy for prostate carcinoma. The radiobiological and the pathological basis for this model and its relationship to other models are detailed. A review of the radiobiological experiments and published clinical data identified salient features and specific properties a biologically adequate model has to conform to. The new model was fit to a set of actual clinical data. In order to verify the goodness of fit, two established NTCP models and a non-NTCP measure for complication risk were fitted to the same clinical data. The method of fit for the model parameters was maximum likelihood estimation. Within the framework of the maximum likelihood approach I estimated the parameter uncertainties for each complication prediction model. The quality-of-fit was determined using the Aikaike Information Criterion. Based on the model that provided the best fit, I identified the volume effects for both types of toxicities. Computer-based bootstrap resampling of the original dataset was used to estimate the bias and variance for the fitted parameter values. Computer simulation was also used to estimate the population size that generates a specific uncertainty level (3%) in the value of predicted complication probability. The same method was used to estimate the size of the patient population needed for accurate choice of the model underlying the NTCP. The results indicate that, depending on the number of parameters of a specific NTCP model, 100 (for two parameter models) and 500 patients (for three parameter models) are needed for accurate parameter fit. Correlation of complication occurrence in patients was also investigated. The results suggest that complication outcomes are correlated in a patient, although the correlation coefficient is rather small.

Simultaneous fits in ISIS on the example of GRO J1008-57

NASA Astrophysics Data System (ADS)

Kühnel, Matthias; Müller, Sebastian; Kreykenbohm, Ingo; Schwarm, Fritz-Walter; Grossberger, Christoph; Dauser, Thomas; Pottschmidt, Katja; Ferrigno, Carlo; Rothschild, Richard E.; Klochkov, Dmitry; Staubert, Rüdiger; Wilms, Joern

2015-04-01

Parallel computing and steadily increasing computation speed have led to a new tool for analyzing multiple datasets and datatypes: fitting several datasets simultaneously. With this technique, physically connected parameters of individual data can be treated as a single parameter by implementing this connection into the fit directly. We discuss the terminology, implementation, and possible issues of simultaneous fits based on the X-ray data analysis tool Interactive Spectral Interpretation System (ISIS). While all data modeling tools in X-ray astronomy allow in principle fitting data from multiple data sets individually, the syntax used in these tools is not often well suited for this task. Applying simultaneous fits to the transient X-ray binary GRO J1008-57, we find that the spectral shape is only dependent on X-ray flux. We determine time independent parameters such as, e.g., the folding energy E_fold, with unprecedented precision.

Real-time computation of parameter fitting and image reconstruction using graphical processing units

NASA Astrophysics Data System (ADS)

Locans, Uldis; Adelmann, Andreas; Suter, Andreas; Fischer, Jannis; Lustermann, Werner; Dissertori, Günther; Wang, Qiulin

2017-06-01

In recent years graphical processing units (GPUs) have become a powerful tool in scientific computing. Their potential to speed up highly parallel applications brings the power of high performance computing to a wider range of users. However, programming these devices and integrating their use in existing applications is still a challenging task. In this paper we examined the potential of GPUs for two different applications. The first application, created at Paul Scherrer Institut (PSI), is used for parameter fitting during data analysis of μSR (muon spin rotation, relaxation and resonance) experiments. The second application, developed at ETH, is used for PET (Positron Emission Tomography) image reconstruction and analysis. Applications currently in use were examined to identify parts of the algorithms in need of optimization. Efficient GPU kernels were created in order to allow applications to use a GPU, to speed up the previously identified parts. Benchmarking tests were performed in order to measure the achieved speedup. During this work, we focused on single GPU systems to show that real time data analysis of these problems can be achieved without the need for large computing clusters. The results show that the currently used application for parameter fitting, which uses OpenMP to parallelize calculations over multiple CPU cores, can be accelerated around 40 times through the use of a GPU. The speedup may vary depending on the size and complexity of the problem. For PET image analysis, the obtained speedups of the GPU version were more than × 40 larger compared to a single core CPU implementation. The achieved results show that it is possible to improve the execution time by orders of magnitude.

Parallel algorithm for solving Kepler’s equation on Graphics Processing Units: Application to analysis of Doppler exoplanet searches

NASA Astrophysics Data System (ADS)

Ford, Eric B.

2009-05-01

We present the results of a highly parallel Kepler equation solver using the Graphics Processing Unit (GPU) on a commercial nVidia GeForce 280GTX and the "Compute Unified Device Architecture" (CUDA) programming environment. We apply this to evaluate a goodness-of-fit statistic (e.g., χ2) for Doppler observations of stars potentially harboring multiple planetary companions (assuming negligible planet-planet interactions). Given the high-dimensionality of the model parameter space (at least five dimensions per planet), a global search is extremely computationally demanding. We expect that the underlying Kepler solver and model evaluator will be combined with a wide variety of more sophisticated algorithms to provide efficient global search, parameter estimation, model comparison, and adaptive experimental design for radial velocity and/or astrometric planet searches. We tested multiple implementations using single precision, double precision, pairs of single precision, and mixed precision arithmetic. We find that the vast majority of computations can be performed using single precision arithmetic, with selective use of compensated summation for increased precision. However, standard single precision is not adequate for calculating the mean anomaly from the time of observation and orbital period when evaluating the goodness-of-fit for real planetary systems and observational data sets. Using all double precision, our GPU code outperforms a similar code using a modern CPU by a factor of over 60. Using mixed precision, our GPU code provides a speed-up factor of over 600, when evaluating nsys > 1024 models planetary systems each containing npl = 4 planets and assuming nobs = 256 observations of each system. We conclude that modern GPUs also offer a powerful tool for repeatedly evaluating Kepler's equation and a goodness-of-fit statistic for orbital models when presented with a large parameter space.

Stimulus dependence of local field potential spectra: experiment versus theory.

PubMed

Barbieri, Francesca; Mazzoni, Alberto; Logothetis, Nikos K; Panzeri, Stefano; Brunel, Nicolas

2014-10-29

The local field potential (LFP) captures different neural processes, including integrative synaptic dynamics that cannot be observed by measuring only the spiking activity of small populations. Therefore, investigating how LFP power is modulated by external stimuli can offer important insights into sensory neural representations. However, gaining such insight requires developing data-driven computational models that can identify and disambiguate the neural contributions to the LFP. Here, we investigated how networks of excitatory and inhibitory integrate-and-fire neurons responding to time-dependent inputs can be used to interpret sensory modulations of LFP spectra. We computed analytically from such models the LFP spectra and the information that they convey about input and used these analytical expressions to fit the model to LFPs recorded in V1 of anesthetized macaques (Macaca mulatta) during the presentation of color movies. Our expressions explain 60%-98% of the variance of the LFP spectrum shape and its dependency upon movie scenes and we achieved this with realistic values for the best-fit parameters. In particular, synaptic best-fit parameters were compatible with experimental measurements and the predictions of firing rates, based only on the fit of LFP data, correlated with the multiunit spike rate recorded from the same location. Moreover, the parameters characterizing the input to the network across different movie scenes correlated with cross-scene changes of several image features. Our findings suggest that analytical descriptions of spiking neuron networks may become a crucial tool for the interpretation of field recordings. Copyright © 2014 the authors 0270-6474/14/3414589-17$15.00/0.

Discovery and characterization of 3000+ main-sequence binaries from APOGEE spectra

NASA Astrophysics Data System (ADS)

El-Badry, Kareem; Ting, Yuan-Sen; Rix, Hans-Walter; Quataert, Eliot; Weisz, Daniel R.; Cargile, Phillip; Conroy, Charlie; Hogg, David W.; Bergemann, Maria; Liu, Chao

2018-05-01

We develop a data-driven spectral model for identifying and characterizing spatially unresolved multiple-star systems and apply it to APOGEE DR13 spectra of main-sequence stars. Binaries and triples are identified as targets whose spectra can be significantly better fit by a superposition of two or three model spectra, drawn from the same isochrone, than any single-star model. From an initial sample of ˜20 000 main-sequence targets, we identify ˜2500 binaries in which both the primary and secondary stars contribute detectably to the spectrum, simultaneously fitting for the velocities and stellar parameters of both components. We additionally identify and fit ˜200 triple systems, as well as ˜700 velocity-variable systems in which the secondary does not contribute detectably to the spectrum. Our model simplifies the process of simultaneously fitting single- or multi-epoch spectra with composite models and does not depend on a velocity offset between the two components of a binary, making it sensitive to traditionally undetectable systems with periods of hundreds or thousands of years. In agreement with conventional expectations, almost all the spectrally identified binaries with measured parallaxes fall above the main sequence in the colour-magnitude diagram. We find excellent agreement between spectrally and dynamically inferred mass ratios for the ˜600 binaries in which a dynamical mass ratio can be measured from multi-epoch radial velocities. We obtain full orbital solutions for 64 systems, including 14 close binaries within hierarchical triples. We make available catalogues of stellar parameters, abundances, mass ratios, and orbital parameters.

Identification of Arbitrary Zonation in Groundwater Parameters using the Level Set Method and a Parallel Genetic Algorithm

NASA Astrophysics Data System (ADS)

Lei, H.; Lu, Z.; Vesselinov, V. V.; Ye, M.

2017-12-01

Simultaneous identification of both the zonation structure of aquifer heterogeneity and the hydrogeological parameters associated with these zones is challenging, especially for complex subsurface heterogeneity fields. In this study, a new approach, based on the combination of the level set method and a parallel genetic algorithm is proposed. Starting with an initial guess for the zonation field (including both zonation structure and the hydraulic properties of each zone), the level set method ensures that material interfaces are evolved through the inverse process such that the total residual between the simulated and observed state variables (hydraulic head) always decreases, which means that the inversion result depends on the initial guess field and the minimization process might fail if it encounters a local minimum. To find the global minimum, the genetic algorithm (GA) is utilized to explore the parameters that define initial guess fields, and the minimal total residual corresponding to each initial guess field is considered as the fitness function value in the GA. Due to the expensive evaluation of the fitness function, a parallel GA is adapted in combination with a simulated annealing algorithm. The new approach has been applied to several synthetic cases in both steady-state and transient flow fields, including a case with real flow conditions at the chromium contaminant site at the Los Alamos National Laboratory. The results show that this approach is capable of identifying the arbitrary zonation structures of aquifer heterogeneity and the hydrogeological parameters associated with these zones effectively.

Motor-cognitive dual-task performance: effects of a concurrent motor task on distinct components of visual processing capacity.

PubMed

Künstler, E C S; Finke, K; Günther, A; Klingner, C; Witte, O; Bublak, P

2018-01-01

Dual tasking, or the simultaneous execution of two continuous tasks, is frequently associated with a performance decline that can be explained within a capacity sharing framework. In this study, we assessed the effects of a concurrent motor task on the efficiency of visual information uptake based on the 'theory of visual attention' (TVA). TVA provides parameter estimates reflecting distinct components of visual processing capacity: perceptual threshold, visual processing speed, and visual short-term memory (VSTM) storage capacity. Moreover, goodness-of-fit values and bootstrapping estimates were derived to test whether the TVA-model is validly applicable also under dual task conditions, and whether the robustness of parameter estimates is comparable in single- and dual-task conditions. 24 subjects of middle to higher age performed a continuous tapping task, and a visual processing task (whole report of briefly presented letter arrays) under both single- and dual-task conditions. Results suggest a decline of both visual processing capacity and VSTM storage capacity under dual-task conditions, while the perceptual threshold remained unaffected by a concurrent motor task. In addition, goodness-of-fit values and bootstrapping estimates support the notion that participants processed the visual task in a qualitatively comparable, although quantitatively less efficient way under dual-task conditions. The results support a capacity sharing account of motor-cognitive dual tasking and suggest that even performing a relatively simple motor task relies on central attentional capacity that is necessary for efficient visual information uptake.

FAST: Fitting and Assessment of Synthetic Templates

NASA Astrophysics Data System (ADS)

Kriek, Mariska; van Dokkum, Pieter G.; Labbé, Ivo; Franx, Marijn; Illingworth, Garth D.; Marchesini, Danilo; Quadri, Ryan F.; Aird, James; Coil, Alison L.; Georgakakis, Antonis

2018-03-01

FAST (Fitting and Assessment of Synthetic Templates) fits stellar population synthesis templates to broadband photometry and/or spectra. FAST is compatible with the photometric redshift code EAzY (ascl:1010.052) when fitting broadband photometry; it uses the photometric redshifts derived by EAzY, and the input files (for examply, photometric catalog and master filter file) are the same. FAST fits spectra in combination with broadband photometric data points or simultaneously fits two components, allowing for an AGN contribution in addition to the host galaxy light. Depending on the input parameters, FAST outputs the best-fit redshift, age, dust content, star formation timescale, metallicity, stellar mass, star formation rate (SFR), and their confidence intervals. Though some of FAST's functions overlap with those of HYPERZ (ascl:1108.010), it differs by fitting fluxes instead of magnitudes, allows the user to completely define the grid of input stellar population parameters and easily input photometric redshifts and their confidence intervals, and calculates calibrated confidence intervals for all parameters. Note that FAST is not a photometric redshift code, though it can be used as one.

Modelling and analysis of creep deformation and fracture in a 1 Cr 1/2 Mo ferritic steel

NASA Astrophysics Data System (ADS)

Dyson, B. F.; Osgerby, D.

A quantitative model, based upon a proposed new mechanism of creep deformation in particle-hardened alloys, has been validated by analysis of creep data from a 13CrMo 4 4 (1Cr 1/2 Mo) material tested under a range of stresses and temperatures. The methodology that has been used to extract the model parameters quantifies, as a first approximation, only the main degradation (damage) processes - in the case of the 1CR 1/2 Mo steel, these are considered to be the parallel operation of particle-coarsening and a progressively increasing stress due to a constant-load boundary condition. These 'global' model parameters can then be modified (only slightly) as required to obtain a detailed description and 'fit' to the rupture lifetime and strain/time trajectory of any individual test. The global model parameter approach may be thought of as predicting average behavior and the detailed fits as taking account of uncertainties (scatter) due to variability in the material. Using the global parameter dataset, predictions have also been made of behavior under biaxial stressing; constant straining rate; constant total strain (stress relaxation) and the likely success or otherwise of metallographic and mechanical remanent lifetime procedures.

Model of head-neck joint fast movements in the frontal plane.

PubMed

Pedrocchi, A; Ferrigno, G

2004-06-01

The objective of this work is to develop a model representing the physiological systems driving fast head movements in frontal plane. All the contributions occurring mechanically in the head movement are considered: damping, stiffness, physiological limit of range of motion, gravitational field, and muscular torques due to voluntary activation as well as to stretch reflex depending on fusal afferences. Model parameters are partly derived from the literature, when possible, whereas undetermined block parameters are determined by optimising the model output, fitting to real kinematics data acquired by a motion capture system in specific experimental set-ups. The optimisation for parameter identification is performed by genetic algorithms. Results show that the model represents very well fast head movements in the whole range of inclination in the frontal plane. Such a model could be proposed as a tool for transforming kinematics data on head movements in 'neural equivalent data', especially for assessing head control disease and properly planning the rehabilitation process. In addition, the use of genetic algorithms seems to fit well the problem of parameter identification, allowing for the use of a very simple experimental set-up and granting model robustness.

Three-dimensional whole-brain perfusion quantification using pseudo-continuous arterial spin labeling MRI at multiple post-labeling delays: accounting for both arterial transit time and impulse response function.

PubMed

Qin, Qin; Huang, Alan J; Hua, Jun; Desmond, John E; Stevens, Robert D; van Zijl, Peter C M

2014-02-01

Measurement of the cerebral blood flow (CBF) with whole-brain coverage is challenging in terms of both acquisition and quantitative analysis. In order to fit arterial spin labeling-based perfusion kinetic curves, an empirical three-parameter model which characterizes the effective impulse response function (IRF) is introduced, which allows the determination of CBF, the arterial transit time (ATT) and T(1,eff). The accuracy and precision of the proposed model were compared with those of more complicated models with four or five parameters through Monte Carlo simulations. Pseudo-continuous arterial spin labeling images were acquired on a clinical 3-T scanner in 10 normal volunteers using a three-dimensional multi-shot gradient and spin echo scheme at multiple post-labeling delays to sample the kinetic curves. Voxel-wise fitting was performed using the three-parameter model and other models that contain two, four or five unknown parameters. For the two-parameter model, T(1,eff) values close to tissue and blood were assumed separately. Standard statistical analysis was conducted to compare these fitting models in various brain regions. The fitted results indicated that: (i) the estimated CBF values using the two-parameter model show appreciable dependence on the assumed T(1,eff) values; (ii) the proposed three-parameter model achieves the optimal balance between the goodness of fit and model complexity when compared among the models with explicit IRF fitting; (iii) both the two-parameter model using fixed blood T1 values for T(1,eff) and the three-parameter model provide reasonable fitting results. Using the proposed three-parameter model, the estimated CBF (46 ± 14 mL/100 g/min) and ATT (1.4 ± 0.3 s) values averaged from different brain regions are close to the literature reports; the estimated T(1,eff) values (1.9 ± 0.4 s) are higher than the tissue T1 values, possibly reflecting a contribution from the microvascular arterial blood compartment. Copyright © 2013 John Wiley & Sons, Ltd.

Process air quality data

NASA Technical Reports Server (NTRS)

Butler, C. M.; Hogge, J. E.

1978-01-01

Air quality sampling was conducted. Data for air quality parameters, recorded on written forms, punched cards or magnetic tape, are available for 1972 through 1975. Computer software was developed to (1) calculate several daily statistical measures of location, (2) plot time histories of data or the calculated daily statistics, (3) calculate simple correlation coefficients, and (4) plot scatter diagrams. Computer software was developed for processing air quality data to include time series analysis and goodness of fit tests. Computer software was developed to (1) calculate a larger number of daily statistical measures of location, and a number of daily monthly and yearly measures of location, dispersion, skewness and kurtosis, (2) decompose the extended time series model and (3) perform some goodness of fit tests. The computer program is described, documented and illustrated by examples. Recommendations are made for continuation of the development of research on processing air quality data.

Electron-impact Multiple-ionization Cross Sections for Atoms and Ions of Helium through Zinc

NASA Astrophysics Data System (ADS)

Hahn, M.; Müller, A.; Savin, D. W.

2017-12-01

We compiled a set of electron-impact multiple-ionization (EIMI) cross section for astrophysically relevant ions. EIMIs can have a significant effect on the ionization balance of non-equilibrium plasmas. For example, it can be important if there is a rapid change in the electron temperature or if there is a non-thermal electron energy distribution, such as a kappa distribution. Cross section for EIMI are needed in order to account for these processes in plasma modeling and for spectroscopic interpretation. Here, we describe our comparison of proposed semiempirical formulae to available experimental EIMI cross-section data. Based on this comparison, we interpolated and extrapolated fitting parameters to systems that have not yet been measured. A tabulation of the fit parameters is provided for 3466 EIMI cross sections and the associated Maxwellian plasma rate coefficients. We also highlight some outstanding issues that remain to be resolved.

Equilibrium expert: an add-in to Microsoft Excel for multiple binding equilibrium simulations and parameter estimations.

PubMed

Raguin, Olivier; Gruaz-Guyon, Anne; Barbet, Jacques

2002-11-01

An add-in to Microsoft Excel was developed to simulate multiple binding equilibriums. A partition function, readily written even when the equilibrium is complex, describes the experimental system. It involves the concentrations of the different free molecular species and of the different complexes present in the experiment. As a result, the software is not restricted to a series of predefined experimental setups but can handle a large variety of problems involving up to nine independent molecular species. Binding parameters are estimated by nonlinear least-square fitting of experimental measurements as supplied by the user. The fitting process allows user-defined weighting of the experimental data. The flexibility of the software and the way it may be used to describe common experimental situations and to deal with usual problems such as tracer reactivity or nonspecific binding is demonstrated by a few examples. The software is available free of charge upon request.

Effect of pH and pulsed electric field process parameters on the aflatoxin reduction in model system using response surface methodology: Effect of pH and PEF on Aflatoxin Reduction.

PubMed

Vijayalakshmi, Subramanian; Nadanasabhapathi, Shanmugam; Kumar, Ranganathan; Sunny Kumar, S

2018-03-01

The presence of aflatoxin, a carcinogenic and toxigenic secondary metabolite produced by Aspergillus species, in food matrix has been a major worldwide problem for years now. Food processing methods such as roasting, extrusion, etc. have been employed for effective destruction of aflatoxins, which are known for their thermo-stable nature. The high temperature treatment, adversely affects the nutritive and other quality attributes of the food, leading to the necessity of application of non-thermal processing techniques such as ultrasonication, gamma irradiation, high pressure processing, pulsed electric field (PEF), etc. The present study was focused on analysing the efficacy of the PEF process in the reduction of the toxin content, which was subsequently quantified using HPLC. The process parameters of different pH model system (potato dextrose agar) artificially spiked with aflatoxin mix standard was optimized using the response surface methodology. The optimization of PEF process effects on the responses aflatoxin B1 and total aflatoxin reduction (%) by pH (4-10), pulse width (10-26 µs) and output voltage (20-65%), fitted 2FI model and quadratic model respectively. The response surface plots obtained for the processes were of saddle point type, with the absence of minimum or maximum response at the centre point. The implemented numerical optimization showed that the predicted and actual values were similar, proving the adequacy of the fitted models and also proved the possible application of PEF in toxin reduction.

Video segmentation and camera motion characterization using compressed data

NASA Astrophysics Data System (ADS)

Milanese, Ruggero; Deguillaume, Frederic; Jacot-Descombes, Alain

1997-10-01

We address the problem of automatically extracting visual indexes from videos, in order to provide sophisticated access methods to the contents of a video server. We focus on tow tasks, namely the decomposition of a video clip into uniform segments, and the characterization of each shot by camera motion parameters. For the first task we use a Bayesian classification approach to detecting scene cuts by analyzing motion vectors. For the second task a least- squares fitting procedure determines the pan/tilt/zoom camera parameters. In order to guarantee the highest processing speed, all techniques process and analyze directly MPEG-1 motion vectors, without need for video decompression. Experimental results are reported for a database of news video clips.

Numerically Integrated Orbits of the Major Saturnian Satellites fit to Earthbased Observations

NASA Technical Reports Server (NTRS)

Jacobson, R. A.; Vaughan, R. M.

1993-01-01

We have fit numerically integrated orbits of the eight major satellites of Saturn to all available astrometric and meridian circle observations for the period of 1971 to 1992. The integration was carried out in cartesian coordinates in the J2000 system. The force model included the gravitational effects of the oblate primary, the mutual perturbations of the satellites, and perturbations due to Jupiter and the Sun. Values of the gravitational parameters of the Saturnian system, e.g. planet and satellite masses, were taken from Campbell, et. al., 1989, only the epoch state vectors of the satellites were adjusted to obtain orbits which fit the observations. All astrometric data was processed in the form of satellite relative positions which were weighted according to observer and opposition to reflect the varying data quality...

FitSKIRT: genetic algorithms to automatically fit dusty galaxies with a Monte Carlo radiative transfer code

NASA Astrophysics Data System (ADS)

De Geyter, G.; Baes, M.; Fritz, J.; Camps, P.

2013-02-01

We present FitSKIRT, a method to efficiently fit radiative transfer models to UV/optical images of dusty galaxies. These images have the advantage that they have better spatial resolution compared to FIR/submm data. FitSKIRT uses the GAlib genetic algorithm library to optimize the output of the SKIRT Monte Carlo radiative transfer code. Genetic algorithms prove to be a valuable tool in handling the multi- dimensional search space as well as the noise induced by the random nature of the Monte Carlo radiative transfer code. FitSKIRT is tested on artificial images of a simulated edge-on spiral galaxy, where we gradually increase the number of fitted parameters. We find that we can recover all model parameters, even if all 11 model parameters are left unconstrained. Finally, we apply the FitSKIRT code to a V-band image of the edge-on spiral galaxy NGC 4013. This galaxy has been modeled previously by other authors using different combinations of radiative transfer codes and optimization methods. Given the different models and techniques and the complexity and degeneracies in the parameter space, we find reasonable agreement between the different models. We conclude that the FitSKIRT method allows comparison between different models and geometries in a quantitative manner and minimizes the need of human intervention and biasing. The high level of automation makes it an ideal tool to use on larger sets of observed data.

Linking aquifer spatial properties and non-Fickian transport in mobile-immobile like alluvial settings

USGS Publications Warehouse

Zhang, Yong; Green, Christopher T.; Baeumer, Boris

2014-01-01

Time-nonlocal transport models can describe non-Fickian diffusion observed in geological media, but the physical meaning of parameters can be ambiguous, and most applications are limited to curve-fitting. This study explores methods for predicting the parameters of a temporally tempered Lévy motion (TTLM) model for transient sub-diffusion in mobile–immobile like alluvial settings represented by high-resolution hydrofacies models. The TTLM model is a concise multi-rate mass transfer (MRMT) model that describes a linear mass transfer process where the transfer kinetics and late-time transport behavior are controlled by properties of the host medium, especially the immobile domain. The intrinsic connection between the MRMT and TTLM models helps to estimate the main time-nonlocal parameters in the TTLM model (which are the time scale index, the capacity coefficient, and the truncation parameter) either semi-analytically or empirically from the measurable aquifer properties. Further applications show that the TTLM model captures the observed solute snapshots, the breakthrough curves, and the spatial moments of plumes up to the fourth order. Most importantly, the a priori estimation of the time-nonlocal parameters outside of any breakthrough fitting procedure provides a reliable “blind” prediction of the late-time dynamics of subdiffusion observed in a spectrum of alluvial settings. Predictability of the time-nonlocal parameters may be due to the fact that the late-time subdiffusion is not affected by the exact location of each immobile zone, but rather is controlled by the time spent in immobile blocks surrounding the pathway of solute particles. Results also show that the effective dispersion coefficient has to be fitted due to the scale effect of transport, and the mean velocity can differ from local measurements or volume averages. The link between medium heterogeneity and time-nonlocal parameters will help to improve model predictability for non-Fickian transport in alluvial settings.

FTOOLS: A FITS Data Processing and Analysis Software Package

NASA Astrophysics Data System (ADS)

Blackburn, J. K.

FTOOLS, a highly modular collection of over 110 utilities for processing and analyzing data in the FITS (Flexible Image Transport System) format, has been developed in support of the HEASARC (High Energy Astrophysics Science Archive Research Center) at NASA's Goddard Space Flight Center. Each utility performs a single simple task such as presentation of file contents, extraction of specific rows or columns, appending or merging tables, binning values in a column or selecting subsets of rows based on a boolean expression. Individual utilities can easily be chained together in scripts to achieve more complex operations such as the generation and displaying of spectra or light curves. The collection of utilities provides both generic processing and analysis utilities and utilities specific to high energy astrophysics data sets used for the ASCA, ROSAT, GRO, and XTE missions. A core set of FTOOLS providing support for generic FITS data processing, FITS image analysis and timing analysis can easily be split out of the full software package for users not needing the high energy astrophysics mission utilities. The FTOOLS software package is designed to be both compatible with IRAF and completely stand alone in a UNIX or VMS environment. The user interface is controlled by standard IRAF parameter files. The package is self documenting through the IRAF help facility and a stand alone help task. Software is written in ANSI C and \\fortran to provide portability across most computer systems. The data format dependencies between hardware platforms are isolated through the FITSIO library package.

Stochastic approach to data analysis in fluorescence correlation spectroscopy.

PubMed

Rao, Ramachandra; Langoju, Rajesh; Gösch, Michael; Rigler, Per; Serov, Alexandre; Lasser, Theo

2006-09-21

Fluorescence correlation spectroscopy (FCS) has emerged as a powerful technique for measuring low concentrations of fluorescent molecules and their diffusion constants. In FCS, the experimental data is conventionally fit using standard local search techniques, for example, the Marquardt-Levenberg (ML) algorithm. A prerequisite for these categories of algorithms is the sound knowledge of the behavior of fit parameters and in most cases good initial guesses for accurate fitting, otherwise leading to fitting artifacts. For known fit models and with user experience about the behavior of fit parameters, these local search algorithms work extremely well. However, for heterogeneous systems or where automated data analysis is a prerequisite, there is a need to apply a procedure, which treats FCS data fitting as a black box and generates reliable fit parameters with accuracy for the chosen model in hand. We present a computational approach to analyze FCS data by means of a stochastic algorithm for global search called PGSL, an acronym for Probabilistic Global Search Lausanne. This algorithm does not require any initial guesses and does the fitting in terms of searching for solutions by global sampling. It is flexible as well as computationally faster at the same time for multiparameter evaluations. We present the performance study of PGSL for two-component with triplet fits. The statistical study and the goodness of fit criterion for PGSL are also presented. The robustness of PGSL on noisy experimental data for parameter estimation is also verified. We further extend the scope of PGSL by a hybrid analysis wherein the output of PGSL is fed as initial guesses to ML. Reliability studies show that PGSL and the hybrid combination of both perform better than ML for various thresholds of the mean-squared error (MSE).

Effect of insecticide resistance on development, longevity and reproduction of field or laboratory selected Aedes aegypti populations.

PubMed

Martins, Ademir Jesus; Ribeiro, Camila Dutra e Mello; Bellinato, Diogo Fernandes; Peixoto, Alexandre Afranio; Valle, Denise; Lima, José Bento Pereira

2012-01-01

Aedes aegypti dispersion is the major reason for the increase in dengue transmission in South America. In Brazil, control of this mosquito strongly relies on the use of pyrethroids and organophosphates against adults and larvae, respectively. In consequence, many Ae. aegypti field populations are resistant to these compounds. Resistance has a significant adaptive value in the presence of insecticide treatment. However some selected mechanisms can influence important biological processes, leading to a high fitness cost in the absence of insecticide pressure. We investigated the dynamics of insecticide resistance and its potential fitness cost in five field populations and in a lineage selected for deltamethrin resistance in the laboratory, for nine generations. For all populations the life-trait parameters investigated were larval development, sex ratio, adult longevity, relative amount of ingested blood, rate of ovipositing females, size of egglaying and eggs viability. In the five natural populations, the effects on the life-trait parameters were discrete but directly proportional to resistance level. In addition, several viability parameters were strongly affected in the laboratory selected population compared to its unselected control. Our results suggest that mechanisms selected for organophosphate and pyrethroid resistance caused the accumulation of alleles with negative effects on different life-traits and corroborate the hypothesis that insecticide resistance is associated with a high fitness cost.

Criticality triggers the emergence of collective intelligence in groups.

PubMed

De Vincenzo, Ilario; Giannoccaro, Ilaria; Carbone, Giuseppe; Grigolini, Paolo

2017-08-01

A spinlike model mimicking human behavior in groups is employed to investigate the dynamics of the decision-making process. Within the model, the temporal evolution of the state of systems is governed by a time-continuous Markov chain. The transition rates of the resulting master equation are defined in terms of the change of interaction energy between the neighboring agents (change of the level of conflict) and the change of a locally defined agent fitness. Three control parameters can be identified: (i) the social interaction strength βJ measured in units of social temperature, (ii) the level of confidence β^{'} that each individual has on his own expertise, and (iii) the level of knowledge p that identifies the expertise of each member. Based on these three parameters, the phase diagrams of the system show that a critical transition front exists where a sharp and concurrent change in fitness and consensus takes place. We show that at the critical front, the information leakage from the fitness landscape to the agents is maximized. This event triggers the emergence of the collective intelligence of the group, and in the end it leads to a dramatic improvement in the decision-making performance of the group. The effect of size M of the system is also investigated, showing that, depending on the value of the control parameters, increasing M may be either beneficial or detrimental.

Criticality triggers the emergence of collective intelligence in groups

NASA Astrophysics Data System (ADS)

De Vincenzo, Ilario; Giannoccaro, Ilaria; Carbone, Giuseppe; Grigolini, Paolo

2017-08-01

A spinlike model mimicking human behavior in groups is employed to investigate the dynamics of the decision-making process. Within the model, the temporal evolution of the state of systems is governed by a time-continuous Markov chain. The transition rates of the resulting master equation are defined in terms of the change of interaction energy between the neighboring agents (change of the level of conflict) and the change of a locally defined agent fitness. Three control parameters can be identified: (i) the social interaction strength β J measured in units of social temperature, (ii) the level of confidence β' that each individual has on his own expertise, and (iii) the level of knowledge p that identifies the expertise of each member. Based on these three parameters, the phase diagrams of the system show that a critical transition front exists where a sharp and concurrent change in fitness and consensus takes place. We show that at the critical front, the information leakage from the fitness landscape to the agents is maximized. This event triggers the emergence of the collective intelligence of the group, and in the end it leads to a dramatic improvement in the decision-making performance of the group. The effect of size M of the system is also investigated, showing that, depending on the value of the control parameters, increasing M may be either beneficial or detrimental.

Effect of Insecticide Resistance on Development, Longevity and Reproduction of Field or Laboratory Selected Aedes aegypti Populations

PubMed Central

Bellinato, Diogo Fernandes; Peixoto, Alexandre Afranio; Valle, Denise; Lima, José Bento Pereira

2012-01-01

Aedes aegypti dispersion is the major reason for the increase in dengue transmission in South America. In Brazil, control of this mosquito strongly relies on the use of pyrethroids and organophosphates against adults and larvae, respectively. In consequence, many Ae. aegypti field populations are resistant to these compounds. Resistance has a significant adaptive value in the presence of insecticide treatment. However some selected mechanisms can influence important biological processes, leading to a high fitness cost in the absence of insecticide pressure. We investigated the dynamics of insecticide resistance and its potential fitness cost in five field populations and in a lineage selected for deltamethrin resistance in the laboratory, for nine generations. For all populations the life-trait parameters investigated were larval development, sex ratio, adult longevity, relative amount of ingested blood, rate of ovipositing females, size of egglaying and eggs viability. In the five natural populations, the effects on the life-trait parameters were discrete but directly proportional to resistance level. In addition, several viability parameters were strongly affected in the laboratory selected population compared to its unselected control. Our results suggest that mechanisms selected for organophosphate and pyrethroid resistance caused the accumulation of alleles with negative effects on different life-traits and corroborate the hypothesis that insecticide resistance is associated with a high fitness cost. PMID:22431967

Power law versus exponential state transition dynamics: application to sleep-wake architecture.

PubMed

Chu-Shore, Jesse; Westover, M Brandon; Bianchi, Matt T

2010-12-02

Despite the common experience that interrupted sleep has a negative impact on waking function, the features of human sleep-wake architecture that best distinguish sleep continuity versus fragmentation remain elusive. In this regard, there is growing interest in characterizing sleep architecture using models of the temporal dynamics of sleep-wake stage transitions. In humans and other mammals, the state transitions defining sleep and wake bout durations have been described with exponential and power law models, respectively. However, sleep-wake stage distributions are often complex, and distinguishing between exponential and power law processes is not always straightforward. Although mono-exponential distributions are distinct from power law distributions, multi-exponential distributions may in fact resemble power laws by appearing linear on a log-log plot. To characterize the parameters that may allow these distributions to mimic one another, we systematically fitted multi-exponential-generated distributions with a power law model, and power law-generated distributions with multi-exponential models. We used the Kolmogorov-Smirnov method to investigate goodness of fit for the "incorrect" model over a range of parameters. The "zone of mimicry" of parameters that increased the risk of mistakenly accepting power law fitting resembled empiric time constants obtained in human sleep and wake bout distributions. Recognizing this uncertainty in model distinction impacts interpretation of transition dynamics (self-organizing versus probabilistic), and the generation of predictive models for clinical classification of normal and pathological sleep architecture.

The effect of dimethylsulfoxide on the water transport response of rat hepatocytes during freezing.

PubMed

Smith, D J; Schulte, M; Bischof, J C

1998-10-01

Successful improvement of cryopreservation protocols for cells in suspension requires knowledge of how such cells respond to the biophysical stresses of freezing (intracellular ice formation, water transport) while in the presence of a cryoprotective agent (CPA). This work investigates the biophysical water transport response in a clinically important cell type--isolated hepatocytes--during freezing in the presence of dimethylsulfoxide (DMSO). Sprague-Dawley rat liver hepatocytes were frozen in Williams E media supplemented with 0, 1, and 2 M DMSO, at rates of 5, 10, and 50 degrees C/min. The water transport was measured by cell volumetric changes as assessed by cryomicroscopy and image analysis. Assuming that water is the only species transported under these conditions, a water transport model of the form dV/dT = f(Lpg([CPA]), ELp([CPA]), T(t)) was curve-fit to the experimental data to obtain the biophysical parameters of water transport--the reference hydraulic permeability (Lpg) and activation energy of water transport (ELp)--for each DMSO concentration. These parameters were estimated two ways: (1) by curve-fitting the model to the average volume of the pooled cell data, and (2) by curve-fitting individual cell volume data and averaging the resulting parameters. The experimental data showed that less dehydration occurs during freezing at a given rate in the presence of DMSO at temperatures between 0 and -10 degrees C. However, dehydration was able to continue at lower temperatures (< -10 degrees C) in the presence of DMSO. The values of Lpg and ELp obtained using the individual cell volume data both decreased from their non-CPA values--4.33 x 10(-13) m3/N-s (2.69 microns/min-atm) and 317 kJ/mol (75.9 kcal/mol), respectively--to 0.873 x 10(-13) m3/N-s (0.542 micron/min-atm) and 137 kJ/mol (32.8 kcal/mol), respectively, in 1 M DMSO and 0.715 x 10(-13) m3/N-s (0.444 micron/min-atm) and 107 kJ/mol (25.7 kcal/mol), respectively, in 2 M DMSO. The trends in the pooled volume values for Lpg and ELp were very similar, but the overall fit was considered worse than for the individual volume parameters. A unique way of presenting the curve-fitting results supports a clear trend of reduction of both biophysical parameters in the presence of DMSO, and no clear trend in cooling rate dependence of the biophysical parameters. In addition, these results suggest that close proximity of the experimental cell volume data to the equilibrium volume curve may significantly reduce the efficiency of the curve-fitting process.

Sensitivity analysis of respiratory parameter uncertainties: impact of criterion function form and constraints.

PubMed

Lutchen, K R

1990-08-01

A sensitivity analysis based on weighted least-squares regression is presented to evaluate alternative methods for fitting lumped-parameter models to respiratory impedance data. The goal is to maintain parameter accuracy simultaneously with practical experiment design. The analysis focuses on predicting parameter uncertainties using a linearized approximation for joint confidence regions. Applications are with four-element parallel and viscoelastic models for 0.125- to 4-Hz data and a six-element model with separate tissue and airway properties for input and transfer impedance data from 2-64 Hz. The criterion function form was evaluated by comparing parameter uncertainties when data are fit as magnitude and phase, dynamic resistance and compliance, or real and imaginary parts of input impedance. The proper choice of weighting can make all three criterion variables comparable. For the six-element model, parameter uncertainties were predicted when both input impedance and transfer impedance are acquired and fit simultaneously. A fit to both data sets from 4 to 64 Hz could reduce parameter estimate uncertainties considerably from those achievable by fitting either alone. For the four-element models, use of an independent, but noisy, measure of static compliance was assessed as a constraint on model parameters. This may allow acceptable parameter uncertainties for a minimum frequency of 0.275-0.375 Hz rather than 0.125 Hz. This reduces data acquisition requirements from a 16- to a 5.33- to 8-s breath holding period. These results are approximations, and the impact of using the linearized approximation for the confidence regions is discussed.

Universally Sloppy Parameter Sensitivities in Systems Biology Models

PubMed Central

Gutenkunst, Ryan N; Waterfall, Joshua J; Casey, Fergal P; Brown, Kevin S; Myers, Christopher R; Sethna, James P

2007-01-01

Quantitative computational models play an increasingly important role in modern biology. Such models typically involve many free parameters, and assigning their values is often a substantial obstacle to model development. Directly measuring in vivo biochemical parameters is difficult, and collectively fitting them to other experimental data often yields large parameter uncertainties. Nevertheless, in earlier work we showed in a growth-factor-signaling model that collective fitting could yield well-constrained predictions, even when it left individual parameters very poorly constrained. We also showed that the model had a “sloppy” spectrum of parameter sensitivities, with eigenvalues roughly evenly distributed over many decades. Here we use a collection of models from the literature to test whether such sloppy spectra are common in systems biology. Strikingly, we find that every model we examine has a sloppy spectrum of sensitivities. We also test several consequences of this sloppiness for building predictive models. In particular, sloppiness suggests that collective fits to even large amounts of ideal time-series data will often leave many parameters poorly constrained. Tests over our model collection are consistent with this suggestion. This difficulty with collective fits may seem to argue for direct parameter measurements, but sloppiness also implies that such measurements must be formidably precise and complete to usefully constrain many model predictions. We confirm this implication in our growth-factor-signaling model. Our results suggest that sloppy sensitivity spectra are universal in systems biology models. The prevalence of sloppiness highlights the power of collective fits and suggests that modelers should focus on predictions rather than on parameters. PMID:17922568

Universally sloppy parameter sensitivities in systems biology models.

PubMed

Gutenkunst, Ryan N; Waterfall, Joshua J; Casey, Fergal P; Brown, Kevin S; Myers, Christopher R; Sethna, James P

2007-10-01

Quantitative computational models play an increasingly important role in modern biology. Such models typically involve many free parameters, and assigning their values is often a substantial obstacle to model development. Directly measuring in vivo biochemical parameters is difficult, and collectively fitting them to other experimental data often yields large parameter uncertainties. Nevertheless, in earlier work we showed in a growth-factor-signaling model that collective fitting could yield well-constrained predictions, even when it left individual parameters very poorly constrained. We also showed that the model had a "sloppy" spectrum of parameter sensitivities, with eigenvalues roughly evenly distributed over many decades. Here we use a collection of models from the literature to test whether such sloppy spectra are common in systems biology. Strikingly, we find that every model we examine has a sloppy spectrum of sensitivities. We also test several consequences of this sloppiness for building predictive models. In particular, sloppiness suggests that collective fits to even large amounts of ideal time-series data will often leave many parameters poorly constrained. Tests over our model collection are consistent with this suggestion. This difficulty with collective fits may seem to argue for direct parameter measurements, but sloppiness also implies that such measurements must be formidably precise and complete to usefully constrain many model predictions. We confirm this implication in our growth-factor-signaling model. Our results suggest that sloppy sensitivity spectra are universal in systems biology models. The prevalence of sloppiness highlights the power of collective fits and suggests that modelers should focus on predictions rather than on parameters.

Outdoor ground impedance models.

PubMed

Attenborough, Keith; Bashir, Imran; Taherzadeh, Shahram

2011-05-01

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

Testing models of thorium and particle cycling in the ocean using data from station GT11-22 of the U.S. GEOTRACES North Atlantic section

NASA Astrophysics Data System (ADS)

Lerner, Paul; Marchal, Olivier; Lam, Phoebe J.; Anderson, Robert F.; Buesseler, Ken; Charette, Matthew A.; Edwards, R. Lawrence; Hayes, Christopher T.; Huang, Kuo-Fang; Lu, Yanbin; Robinson, Laura F.; Solow, Andrew

2016-07-01

Thorium is a highly particle-reactive element that possesses different measurable radio-isotopes in seawater, with well-constrained production rates and very distinct half-lives. As a result, Th has emerged as a key tracer for the cycling of marine particles and of their chemical constituents, including particulate organic carbon. Here two different versions of a model of Th and particle cycling in the ocean are tested using an unprecedented data set from station GT11-22 of the U.S. GEOTRACES North Atlantic Section: (i) 228,230,234Th activities of dissolved and particulate fractions, (ii) 228Ra activities, (iii) 234,238U activities estimated from salinity data and an assumed 234U/238U ratio, and (iv) particle concentrations, below a depth of 125 m. The two model versions assume a single class of particles but rely on different assumptions about the rate parameters for sorption reactions and particle processes: a first version (V1) assumes vertically uniform parameters (a popular description), whereas the second (V2) does not. Both versions are tested by fitting to the GT11-22 data using generalized nonlinear least squares and by analyzing residuals normalized to the data errors. We find that model V2 displays a significantly better fit to the data than model V1. Thus, the mere allowance of vertical variations in the rate parameters can lead to a significantly better fit to the data, without the need to modify the structure or add any new processes to the model. To understand how the better fit is achieved we consider two parameters, K =k1 /(k-1 +β-1) and K/P, where k1 is the adsorption rate constant, k-1 the desorption rate constant, β-1 the remineralization rate constant, and P the particle concentration. We find that the rate constant ratio K is large (⩾ 0.2) in the upper 1000 m and decreases to a nearly uniform value of ca. 0.12 below 2000 m, implying that the specific rate at which Th attaches to particles relative to that at which it is released from particles is higher in the upper ocean than in the deep ocean. In contrast, K/P increases with depth below 500 m. The parameters K and K/P display significant positive and negative monotonic relationship with P, respectively, which is collectively consistent with a particle concentration effect.

Analysis of convergence of an evolutionary algorithm with self-adaptation using a stochastic Lyapunov function.

PubMed

Semenov, Mikhail A; Terkel, Dmitri A

2003-01-01

This paper analyses the convergence of evolutionary algorithms using a technique which is based on a stochastic Lyapunov function and developed within the martingale theory. This technique is used to investigate the convergence of a simple evolutionary algorithm with self-adaptation, which contains two types of parameters: fitness parameters, belonging to the domain of the objective function; and control parameters, responsible for the variation of fitness parameters. Although both parameters mutate randomly and independently, they converge to the "optimum" due to the direct (for fitness parameters) and indirect (for control parameters) selection. We show that the convergence velocity of the evolutionary algorithm with self-adaptation is asymptotically exponential, similar to the velocity of the optimal deterministic algorithm on the class of unimodal functions. Although some martingale inequalities have not be proved analytically, they have been numerically validated with 0.999 confidence using Monte-Carlo simulations.

Enabling Computational Nanotechnology through JavaGenes in a Cycle Scavenging Environment

NASA Technical Reports Server (NTRS)

Globus, Al; Menon, Madhu; Srivastava, Deepak; Biegel, Bryan A. (Technical Monitor)

2002-01-01

A genetic algorithm procedure is developed and implemented for fitting parameters for many-body inter-atomic force field functions for simulating nanotechnology atomistic applications using portable Java on cycle-scavenged heterogeneous workstations. Given a physics based analytic functional form for the force field, correlated parameters in a multi-dimensional environment are typically chosen to fit properties given either by experiments and/or by higher accuracy quantum mechanical simulations. The implementation automates this tedious procedure using an evolutionary computing algorithm operating on hundreds of cycle-scavenged computers. As a proof of concept, we demonstrate the procedure for evaluating the Stillinger-Weber (S-W) potential by (a) reproducing the published parameters for Si using S-W energies in the fitness function, and (b) evolving a "new" set of parameters using semi-empirical tightbinding energies in the fitness function. The "new" parameters are significantly better suited for Si cluster energies and forces as compared to even the published S-W potential.

Single-level resonance parameters fit nuclear cross-sections

NASA Technical Reports Server (NTRS)

Drawbaugh, D. W.; Gibson, G.; Miller, M.; Page, S. L.

1970-01-01

Least squares analyses of experimental differential cross-section data for the U-235 nucleus have yielded single level Breit-Wigner resonance parameters that fit, simultaneously, three nuclear cross sections of capture, fission, and total.

The principle of sufficiency and the evolution of control: using control analysis to understand the design principles of biological systems.

PubMed

Brown, Guy C

2010-10-01

Control analysis can be used to try to understand why (quantitatively) systems are the way that they are, from rate constants within proteins to the relative amount of different tissues in organisms. Many biological parameters appear to be optimized to maximize rates under the constraint of minimizing space utilization. For any biological process with multiple steps that compete for control in series, evolution by natural selection will tend to even out the control exerted by each step. This is for two reasons: (i) shared control maximizes the flux for minimum protein concentration, and (ii) the selection pressure on any step is proportional to its control, and selection will, by increasing the rate of a step (relative to other steps), decrease its control over a pathway. The control coefficient of a parameter P over fitness can be defined as (∂N/N)/(∂P/P), where N is the number of individuals in the population, and ∂N is the change in that number as a result of the change in P. This control coefficient is equal to the selection pressure on P. I argue that biological systems optimized by natural selection will conform to a principle of sufficiency, such that the control coefficient of all parameters over fitness is 0. Thus in an optimized system small changes in parameters will have a negligible effect on fitness. This principle naturally leads to (and is supported by) the dominance of wild-type alleles over null mutants.

Clustered Single Cellulosic Fiber Dissolution Kinetics and Mechanisms through Optical Microscopy under Limited Dissolving Conditions.

PubMed

Mäkelä, Valtteri; Wahlström, Ronny; Holopainen-Mantila, Ulla; Kilpeläinen, Ilkka; King, Alistair W T

2018-05-14

Herein, we describe a new method of assessing the kinetics of dissolution of single fibers by dissolution under limited dissolving conditions. The dissolution is followed by optical microscopy under limited dissolving conditions. Videos of the dissolution were processed in ImageJ to yield kinetics for dissolution, based on the disappearance of pixels associated with intact fibers. Data processing was performed using the Python language, utilizing available scientific libraries. The methods of processing the data include clustering of the single fiber data, identifying clusters associated with different fiber types, producing average dissolution traces and also extraction of practical parameters, such as, time taken to dissolve 25, 50, 75, 95, and 99.5% of the clustered fibers. In addition to these simple parameters, exponential fitting was also performed yielding rate constants for fiber dissolution. Fits for sample and cluster averages were variable, although demonstrating first-order kinetics for dissolution overall. To illustrate this process, two reference pulps (a bleached softwood kraft pulp and a bleached hardwood pre-hydrolysis kraft pulp) and their cellulase-treated versions were analyzed. As expected, differences in the kinetics and dissolution mechanisms between these samples were observed. Our initial interpretations are presented, based on the combined mechanistic observations and single fiber dissolution kinetics for these different samples. While the dissolution mechanisms observed were similar to those published previously, the more direct link of mechanistic information with the kinetics improve our understanding of cell wall structure and pre-treatments, toward improved processability.

Aromaticity Parameters in Asphalt Binders Calculated From Profile Fitting X-ray Line Spectra Using Pearson VII and Pseudo-Voigt Functions

NASA Astrophysics Data System (ADS)

Shirokoff, J.; Lewis, J. Courtenay

2010-10-01

The aromaticity and crystallite parameters in asphalt binders are calculated from data obtained after profile fitting x-ray line spectra using Pearson VII and pseudo-Voigt functions. The results are presented and discussed in terms of the peak profile fit parameters used, peak deconvolution procedure, and differences in calculated values that can arise owing to peak shape and additional peaks present in the pattern. These results have implications concerning the evaluation and performance of asphalt binders used in highways and road applications.

A modified temporal criterion to meta-optimize the extended Kalman filter for land cover classification of remotely sensed time series

NASA Astrophysics Data System (ADS)

Salmon, B. P.; Kleynhans, W.; Olivier, J. C.; van den Bergh, F.; Wessels, K. J.

2018-05-01

Humans are transforming land cover at an ever-increasing rate. Accurate geographical maps on land cover, especially rural and urban settlements are essential to planning sustainable development. Time series extracted from MODerate resolution Imaging Spectroradiometer (MODIS) land surface reflectance products have been used to differentiate land cover classes by analyzing the seasonal patterns in reflectance values. The proper fitting of a parametric model to these time series usually requires several adjustments to the regression method. To reduce the workload, a global setting of parameters is done to the regression method for a geographical area. In this work we have modified a meta-optimization approach to setting a regression method to extract the parameters on a per time series basis. The standard deviation of the model parameters and magnitude of residuals are used as scoring function. We successfully fitted a triply modulated model to the seasonal patterns of our study area using a non-linear extended Kalman filter (EKF). The approach uses temporal information which significantly reduces the processing time and storage requirements to process each time series. It also derives reliability metrics for each time series individually. The features extracted using the proposed method are classified with a support vector machine and the performance of the method is compared to the original approach on our ground truth data.

Predictive process simulation of cryogenic implants for leading edge transistor design

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

Gossmann, Hans-Joachim; Zographos, Nikolas; Park, Hugh

2012-11-06

Two cryogenic implant TCAD-modules have been developed: (i) A continuum-based compact model targeted towards a TCAD production environment calibrated against an extensive data-set for all common dopants. Ion-specific calibration parameters related to damage generation and dynamic annealing were used and resulted in excellent fits to the calibration data-set. (ii) A Kinetic Monte Carlo (kMC) model including the full time dependence of ion-exposure that a particular spot on the wafer experiences, as well as the resulting temperature vs. time profile of this spot. It was calibrated by adjusting damage generation and dynamic annealing parameters. The kMC simulations clearly demonstrate the importancemore » of the time-structure of the beam for the amorphization process: Assuming an average dose-rate does not capture all of the physics and may lead to incorrect conclusions. The model enables optimization of the amorphization process through tool parameters such as scan speed or beam height.« less

Closed-form solution of the Ogden-Hill's compressible hyperelastic model for ramp loading

NASA Astrophysics Data System (ADS)

Berezvai, Szabolcs; Kossa, Attila

2017-05-01

This article deals with the visco-hyperelastic modelling approach for compressible polymer foam materials. Polymer foams can exhibit large elastic strains and displacements in case of volumetric compression. In addition, they often show significant rate-dependent properties. This material behaviour can be accurately modelled using the visco-hyperelastic approach, in which the large strain viscoelastic description is combined with the rate-independent hyperelastic material model. In case of polymer foams, the most widely used compressible hyperelastic material model, the so-called Ogden-Hill's model, was applied, which is implemented in the commercial finite element (FE) software Abaqus. The visco-hyperelastic model is defined in hereditary integral form, therefore, obtaining a closed-form solution for the stress is not a trivial task. However, the parameter-fitting procedure could be much faster and accurate if closed-form solution exists. In this contribution, exact stress solutions are derived in case of uniaxial, biaxial and volumetric compression loading cases using ramp-loading history. The analytical stress solutions are compared with the stress results in Abaqus using FE analysis. In order to highlight the benefits of the analytical closed-form solution during the parameter-fitting process experimental work has been carried out on a particular open-cell memory foam material. The results of the material identification process shows significant accuracy improvement in the fitting procedure by applying the derived analytical solutions compared to the so-called separated approach applied in the engineering practice.

Interactive model evaluation tool based on IPython notebook

NASA Astrophysics Data System (ADS)

Balemans, Sophie; Van Hoey, Stijn; Nopens, Ingmar; Seuntjes, Piet

2015-04-01

In hydrological modelling, some kind of parameter optimization is mostly performed. This can be the selection of a single best parameter set, a split in behavioural and non-behavioural parameter sets based on a selected threshold or a posterior parameter distribution derived with a formal Bayesian approach. The selection of the criterion to measure the goodness of fit (likelihood or any objective function) is an essential step in all of these methodologies and will affect the final selected parameter subset. Moreover, the discriminative power of the objective function is also dependent from the time period used. In practice, the optimization process is an iterative procedure. As such, in the course of the modelling process, an increasing amount of simulations is performed. However, the information carried by these simulation outputs is not always fully exploited. In this respect, we developed and present an interactive environment that enables the user to intuitively evaluate the model performance. The aim is to explore the parameter space graphically and to visualize the impact of the selected objective function on model behaviour. First, a set of model simulation results is loaded along with the corresponding parameter sets and a data set of the same variable as the model outcome (mostly discharge). The ranges of the loaded parameter sets define the parameter space. A selection of the two parameters visualised can be made by the user. Furthermore, an objective function and a time period of interest need to be selected. Based on this information, a two-dimensional parameter response surface is created, which actually just shows a scatter plot of the parameter combinations and assigns a color scale corresponding with the goodness of fit of each parameter combination. Finally, a slider is available to change the color mapping of the points. Actually, the slider provides a threshold to exclude non behaviour parameter sets and the color scale is only attributed to the remaining parameter sets. As such, by interactively changing the settings and interpreting the graph, the user gains insight in the model structural behaviour. Moreover, a more deliberate choice of objective function and periods of high information content can be identified. The environment is written in an IPython notebook and uses the available interactive functions provided by the IPython community. As such, the power of the IPython notebook as a development environment for scientific computing is illustrated (Shen, 2014).

Gene flow from domesticated species to wild relatives: migration load in a model of multivariate selection.

PubMed

Tufto, Jarle

2010-01-01

Domesticated species frequently spread their genes into populations of wild relatives through interbreeding. The domestication process often involves artificial selection for economically desirable traits. This can lead to an indirect response in unknown correlated traits and a reduction in fitness of domesticated individuals in the wild. Previous models for the effect of gene flow from domesticated species to wild relatives have assumed that evolution occurs in one dimension. Here, I develop a quantitative genetic model for the balance between migration and multivariate stabilizing selection. Different forms of correlational selection consistent with a given observed ratio between average fitness of domesticated and wild individuals offsets the phenotypic means at migration-selection balance away from predictions based on simpler one-dimensional models. For almost all parameter values, correlational selection leads to a reduction in the migration load. For ridge selection, this reduction arises because the distance the immigrants deviates from the local optimum in effect is reduced. For realistic parameter values, however, the effect of correlational selection on the load is small, suggesting that simpler one-dimensional models may still be adequate in terms of predicting mean population fitness and viability.

Temperature dependence of nuclear fission time in heavy-ion fusion-fission reactions

NASA Astrophysics Data System (ADS)

Eccles, Chris; Roy, Sanil; Gray, Thomas H.; Zaccone, Alessio

2017-11-01

Accounting for viscous damping within Fokker-Planck equations led to various improvements in the understanding and analysis of nuclear fission of heavy nuclei. Analytical expressions for the fission time are typically provided by Kramers' theory, which improves on the Bohr-Wheeler estimate by including the time scale related to many-particle dissipative processes along the deformation coordinate. However, Kramers' formula breaks down for sufficiently high excitation energies where Kramers' assumption of a large barrier no longer holds. Focusing on the overdamped regime for energies T >1 MeV, Kramers' theory should be replaced by a new analytical theory derived from the Ornstein-Uhlenbeck first-passage time method that is proposed here. The theory is applied to fission time data from fusion-fission experiments on 16O+208Pb→224Th . The proposed model provides an internally consistent one-parameter fitting of fission data with a constant nuclear friction as the fitting parameter, whereas Kramers' fitting requires a value of friction which falls out of the allowed range. The theory provides also an analytical formula that in future work can be easily implemented in numerical codes such as cascade or joanne4.

Surface Fitting Filtering of LIDAR Point Cloud with Waveform Information

NASA Astrophysics Data System (ADS)

Xing, S.; Li, P.; Xu, Q.; Wang, D.; Li, P.

2017-09-01

Full-waveform LiDAR is an active technology of photogrammetry and remote sensing. It provides more detailed information about objects along the path of a laser pulse than discrete-return topographic LiDAR. The point cloud and waveform information with high quality can be obtained by waveform decomposition, which could make contributions to accurate filtering. The surface fitting filtering method with waveform information is proposed to present such advantage. Firstly, discrete point cloud and waveform parameters are resolved by global convergent Levenberg Marquardt decomposition. Secondly, the ground seed points are selected, of which the abnormal ones are detected by waveform parameters and robust estimation. Thirdly, the terrain surface is fitted and the height difference threshold is determined in consideration of window size and mean square error. Finally, the points are classified gradually with the rising of window size. The filtering process is finished until window size is larger than threshold. The waveform data in urban, farmland and mountain areas from "WATER (Watershed Allied Telemetry Experimental Research)" are selected for experiments. Results prove that compared with traditional method, the accuracy of point cloud filtering is further improved and the proposed method has highly practical value.

Evaluation of transverse dispersion effects in tank experiments by numerical modeling: parameter estimation, sensitivity analysis and revision of experimental design.

PubMed

Ballarini, E; Bauer, S; Eberhardt, C; Beyer, C

2012-06-01

Transverse dispersion represents an important mixing process for transport of contaminants in groundwater and constitutes an essential prerequisite for geochemical and biodegradation reactions. Within this context, this work describes the detailed numerical simulation of highly controlled laboratory experiments using uranine, bromide and oxygen depleted water as conservative tracers for the quantification of transverse mixing in porous media. Synthetic numerical experiments reproducing an existing laboratory experimental set-up of quasi two-dimensional flow through tank were performed to assess the applicability of an analytical solution of the 2D advection-dispersion equation for the estimation of transverse dispersivity as fitting parameter. The fitted dispersivities were compared to the "true" values introduced in the numerical simulations and the associated error could be precisely estimated. A sensitivity analysis was performed on the experimental set-up in order to evaluate the sensitivities of the measurements taken at the tank experiment on the individual hydraulic and transport parameters. From the results, an improved experimental set-up as well as a numerical evaluation procedure could be developed, which allow for a precise and reliable determination of dispersivities. The improved tank set-up was used for new laboratory experiments, performed at advective velocities of 4.9 m d(-1) and 10.5 m d(-1). Numerical evaluation of these experiments yielded a unique and reliable parameter set, which closely fits the measured tracer concentration data. For the porous medium with a grain size of 0.25-0.30 mm, the fitted longitudinal and transverse dispersivities were 3.49×10(-4) m and 1.48×10(-5) m, respectively. The procedures developed in this paper for the synthetic and rigorous design and evaluation of the experiments can be generalized and transferred to comparable applications. Copyright © 2012 Elsevier B.V. All rights reserved.

Statistically Self-Consistent and Accurate Errors for SuperDARN Data

NASA Astrophysics Data System (ADS)

Reimer, A. S.; Hussey, G. C.; McWilliams, K. A.

2018-01-01

The Super Dual Auroral Radar Network (SuperDARN)-fitted data products (e.g., spectral width and velocity) are produced using weighted least squares fitting. We present a new First-Principles Fitting Methodology (FPFM) that utilizes the first-principles approach of Reimer et al. (2016) to estimate the variance of the real and imaginary components of the mean autocorrelation functions (ACFs) lags. SuperDARN ACFs fitted by the FPFM do not use ad hoc or empirical criteria. Currently, the weighting used to fit the ACF lags is derived from ad hoc estimates of the ACF lag variance. Additionally, an overcautious lag filtering criterion is used that sometimes discards data that contains useful information. In low signal-to-noise (SNR) and/or low signal-to-clutter regimes the ad hoc variance and empirical criterion lead to underestimated errors for the fitted parameter because the relative contributions of signal, noise, and clutter to the ACF variance is not taken into consideration. The FPFM variance expressions include contributions of signal, noise, and clutter. The clutter is estimated using the maximal power-based self-clutter estimator derived by Reimer and Hussey (2015). The FPFM was successfully implemented and tested using synthetic ACFs generated with the radar data simulator of Ribeiro, Ponomarenko, et al. (2013). The fitted parameters and the fitted-parameter errors produced by the FPFM are compared with the current SuperDARN fitting software, FITACF. Using self-consistent statistical analysis, the FPFM produces reliable or trustworthy quantitative measures of the errors of the fitted parameters. For an SNR in excess of 3 dB and velocity error below 100 m/s, the FPFM produces 52% more data points than FITACF.

Extension of the PC version of VEPFIT with input and output routines running under Windows

NASA Astrophysics Data System (ADS)

Schut, H.; van Veen, A.

1995-01-01

The fitting program VEPFIT has been extended with applications running under the Microsoft-Windows environment facilitating the input and output of the VEPFIT fitting module. We have exploited the Microsoft-Windows graphical users interface by making use of dialog windows, scrollbars, command buttons, etc. The user communicates with the program simply by clicking and dragging with the mouse pointing device. Keyboard actions are limited to a minimum. Upon changing one or more input parameters the results of the modeling of the S-parameter and Ps fractions versus positron implantation energy are updated and displayed. This action can be considered as the first step in the fitting procedure upon which the user can decide to further adapt the input parameters or to forward these parameters as initial values to the fitting routine. The modeling step has proven to be helpful for designing positron beam experiments.

Estimation of parameters of dose volume models and their confidence limits

NASA Astrophysics Data System (ADS)

van Luijk, P.; Delvigne, T. C.; Schilstra, C.; Schippers, J. M.

2003-07-01

Predictions of the normal-tissue complication probability (NTCP) for the ranking of treatment plans are based on fits of dose-volume models to clinical and/or experimental data. In the literature several different fit methods are used. In this work frequently used methods and techniques to fit NTCP models to dose response data for establishing dose-volume effects, are discussed. The techniques are tested for their usability with dose-volume data and NTCP models. Different methods to estimate the confidence intervals of the model parameters are part of this study. From a critical-volume (CV) model with biologically realistic parameters a primary dataset was generated, serving as the reference for this study and describable by the NTCP model. The CV model was fitted to this dataset. From the resulting parameters and the CV model, 1000 secondary datasets were generated by Monte Carlo simulation. All secondary datasets were fitted to obtain 1000 parameter sets of the CV model. Thus the 'real' spread in fit results due to statistical spreading in the data is obtained and has been compared with estimates of the confidence intervals obtained by different methods applied to the primary dataset. The confidence limits of the parameters of one dataset were estimated using the methods, employing the covariance matrix, the jackknife method and directly from the likelihood landscape. These results were compared with the spread of the parameters, obtained from the secondary parameter sets. For the estimation of confidence intervals on NTCP predictions, three methods were tested. Firstly, propagation of errors using the covariance matrix was used. Secondly, the meaning of the width of a bundle of curves that resulted from parameters that were within the one standard deviation region in the likelihood space was investigated. Thirdly, many parameter sets and their likelihood were used to create a likelihood-weighted probability distribution of the NTCP. It is concluded that for the type of dose response data used here, only a full likelihood analysis will produce reliable results. The often-used approximations, such as the usage of the covariance matrix, produce inconsistent confidence limits on both the parameter sets and the resulting NTCP values.

An improved hybrid of particle swarm optimization and the gravitational search algorithm to produce a kinetic parameter estimation of aspartate biochemical pathways.

PubMed

Ismail, Ahmad Muhaimin; Mohamad, Mohd Saberi; Abdul Majid, Hairudin; Abas, Khairul Hamimah; Deris, Safaai; Zaki, Nazar; Mohd Hashim, Siti Zaiton; Ibrahim, Zuwairie; Remli, Muhammad Akmal

2017-12-01

Mathematical modelling is fundamental to understand the dynamic behavior and regulation of the biochemical metabolisms and pathways that are found in biological systems. Pathways are used to describe complex processes that involve many parameters. It is important to have an accurate and complete set of parameters that describe the characteristics of a given model. However, measuring these parameters is typically difficult and even impossible in some cases. Furthermore, the experimental data are often incomplete and also suffer from experimental noise. These shortcomings make it challenging to identify the best-fit parameters that can represent the actual biological processes involved in biological systems. Computational approaches are required to estimate these parameters. The estimation is converted into multimodal optimization problems that require a global optimization algorithm that can avoid local solutions. These local solutions can lead to a bad fit when calibrating with a model. Although the model itself can potentially match a set of experimental data, a high-performance estimation algorithm is required to improve the quality of the solutions. This paper describes an improved hybrid of particle swarm optimization and the gravitational search algorithm (IPSOGSA) to improve the efficiency of a global optimum (the best set of kinetic parameter values) search. The findings suggest that the proposed algorithm is capable of narrowing down the search space by exploiting the feasible solution areas. Hence, the proposed algorithm is able to achieve a near-optimal set of parameters at a fast convergence speed. The proposed algorithm was tested and evaluated based on two aspartate pathways that were obtained from the BioModels Database. The results show that the proposed algorithm outperformed other standard optimization algorithms in terms of accuracy and near-optimal kinetic parameter estimation. Nevertheless, the proposed algorithm is only expected to work well in small scale systems. In addition, the results of this study can be used to estimate kinetic parameter values in the stage of model selection for different experimental conditions. Copyright © 2017 Elsevier B.V. All rights reserved.

dPotFit: A computer program to fit diatomic molecule spectral data to potential energy functions

NASA Astrophysics Data System (ADS)

Le Roy, Robert J.

2017-01-01

This paper describes program dPotFit, which performs least-squares fits of diatomic molecule spectroscopic data consisting of any combination of microwave, infrared or electronic vibrational bands, fluorescence series, and tunneling predissociation level widths, involving one or more electronic states and one or more isotopologs, and for appropriate systems, second virial coefficient data, to determine analytic potential energy functions defining the observed levels and other properties of each state. Four families of analytical potential functions are available for fitting in the current version of dPotFit: the Expanded Morse Oscillator (EMO) function, the Morse/Long-Range (MLR) function, the Double-Exponential/Long-Range (DELR) function, and the 'Generalized Potential Energy Function' (GPEF) of Šurkus, which incorporates a variety of polynomial functional forms. In addition, dPotFit allows sets of experimental data to be tested against predictions generated from three other families of analytic functions, namely, the 'Hannover Polynomial' (or "X-expansion") function, and the 'Tang-Toennies' and Scoles-Aziz 'HFD', exponential-plus-van der Waals functions, and from interpolation-smoothed pointwise potential energies, such as those obtained from ab initio or RKR calculations. dPotFit also allows the fits to determine atomic-mass-dependent Born-Oppenheimer breakdown functions, and singlet-state Λ-doubling, or 2Σ splitting radial strength functions for one or more electronic states. dPotFit always reports both the 95% confidence limit uncertainty and the "sensitivity" of each fitted parameter; the latter indicates the number of significant digits that must be retained when rounding fitted parameters, in order to ensure that predictions remain in full agreement with experiment. It will also, if requested, apply a "sequential rounding and refitting" procedure to yield a final parameter set defined by a minimum number of significant digits, while ensuring no significant loss of accuracy in the predictions yielded by those parameters.

A flexible, interactive software tool for fitting the parameters of neuronal models.

PubMed

Friedrich, Péter; Vella, Michael; Gulyás, Attila I; Freund, Tamás F; Káli, Szabolcs

2014-01-01

The construction of biologically relevant neuronal models as well as model-based analysis of experimental data often requires the simultaneous fitting of multiple model parameters, so that the behavior of the model in a certain paradigm matches (as closely as possible) the corresponding output of a real neuron according to some predefined criterion. Although the task of model optimization is often computationally hard, and the quality of the results depends heavily on technical issues such as the appropriate choice (and implementation) of cost functions and optimization algorithms, no existing program provides access to the best available methods while also guiding the user through the process effectively. Our software, called Optimizer, implements a modular and extensible framework for the optimization of neuronal models, and also features a graphical interface which makes it easy for even non-expert users to handle many commonly occurring scenarios. Meanwhile, educated users can extend the capabilities of the program and customize it according to their needs with relatively little effort. Optimizer has been developed in Python, takes advantage of open-source Python modules for nonlinear optimization, and interfaces directly with the NEURON simulator to run the models. Other simulators are supported through an external interface. We have tested the program on several different types of problems of varying complexity, using different model classes. As targets, we used simulated traces from the same or a more complex model class, as well as experimental data. We successfully used Optimizer to determine passive parameters and conductance densities in compartmental models, and to fit simple (adaptive exponential integrate-and-fire) neuronal models to complex biological data. Our detailed comparisons show that Optimizer can handle a wider range of problems, and delivers equally good or better performance than any other existing neuronal model fitting tool.

Data Processing Algorithm for Diagnostics of Combustion Using Diode Laser Absorption Spectrometry.

PubMed

Mironenko, Vladimir R; Kuritsyn, Yuril A; Liger, Vladimir V; Bolshov, Mikhail A

2018-02-01

A new algorithm for the evaluation of the integral line intensity for inferring the correct value for the temperature of a hot zone in the diagnostic of combustion by absorption spectroscopy with diode lasers is proposed. The algorithm is based not on the fitting of the baseline (BL) but on the expansion of the experimental and simulated spectra in a series of orthogonal polynomials, subtracting of the first three components of the expansion from both the experimental and simulated spectra, and fitting the spectra thus modified. The algorithm is tested in the numerical experiment by the simulation of the absorption spectra using a spectroscopic database, the addition of white noise, and the parabolic BL. Such constructed absorption spectra are treated as experimental in further calculations. The theoretical absorption spectra were simulated with the parameters (temperature, total pressure, concentration of water vapor) close to the parameters used for simulation of the experimental data. Then, spectra were expanded in the series of orthogonal polynomials and first components were subtracted from both spectra. The value of the correct integral line intensities and hence the correct temperature evaluation were obtained by fitting of the thus modified experimental and simulated spectra. The dependence of the mean and standard deviation of the evaluation of the integral line intensity on the linewidth and the number of subtracted components (first two or three) were examined. The proposed algorithm provides a correct estimation of temperature with standard deviation better than 60 K (for T = 1000 K) for the line half-width up to 0.6 cm -1 . The proposed algorithm allows for obtaining the parameters of a hot zone without the fitting of usually unknown BL.

A flexible, interactive software tool for fitting the parameters of neuronal models

PubMed Central

Friedrich, Péter; Vella, Michael; Gulyás, Attila I.; Freund, Tamás F.; Káli, Szabolcs

2014-01-01

The construction of biologically relevant neuronal models as well as model-based analysis of experimental data often requires the simultaneous fitting of multiple model parameters, so that the behavior of the model in a certain paradigm matches (as closely as possible) the corresponding output of a real neuron according to some predefined criterion. Although the task of model optimization is often computationally hard, and the quality of the results depends heavily on technical issues such as the appropriate choice (and implementation) of cost functions and optimization algorithms, no existing program provides access to the best available methods while also guiding the user through the process effectively. Our software, called Optimizer, implements a modular and extensible framework for the optimization of neuronal models, and also features a graphical interface which makes it easy for even non-expert users to handle many commonly occurring scenarios. Meanwhile, educated users can extend the capabilities of the program and customize it according to their needs with relatively little effort. Optimizer has been developed in Python, takes advantage of open-source Python modules for nonlinear optimization, and interfaces directly with the NEURON simulator to run the models. Other simulators are supported through an external interface. We have tested the program on several different types of problems of varying complexity, using different model classes. As targets, we used simulated traces from the same or a more complex model class, as well as experimental data. We successfully used Optimizer to determine passive parameters and conductance densities in compartmental models, and to fit simple (adaptive exponential integrate-and-fire) neuronal models to complex biological data. Our detailed comparisons show that Optimizer can handle a wider range of problems, and delivers equally good or better performance than any other existing neuronal model fitting tool. PMID:25071540

Gaussian quadrature exponential sum modeling of near infrared methane laboratory spectra obtained at temperatures from 106 to 297 K

NASA Technical Reports Server (NTRS)

Giver, Lawrence P.; Benner, D. C.; Tomasko, M. G.; Fink, U.; Kerola, D.

1990-01-01

Transmission measurements made on near-infrared laboratory methane spectra have previously been fit using a Malkmus band model. The laboratory spectra were obtained in three groups at temperatures averaging 112, 188, and 295 K; band model fitting was done separately for each temperature group. These band model parameters cannot be used directly in scattering atmosphere model computations, so an exponential sum model is being developed which includes pressure and temperature fitting parameters. The goal is to obtain model parameters by least square fits at 10/cm intervals from 3800 to 9100/cm. These results will be useful in the interpretation of current planetary spectra and also NIMS spectra of Jupiter anticipated from the Galileo mission.

Division of labour and the evolution of multicellularity

PubMed Central

Ispolatov, Iaroslav; Ackermann, Martin; Doebeli, Michael

2012-01-01

Understanding the emergence and evolution of multicellularity and cellular differentiation is a core problem in biology. We develop a quantitative model that shows that a multicellular form emerges from genetically identical unicellular ancestors when the compartmentalization of poorly compatible physiological processes into component cells of an aggregate produces a fitness advantage. This division of labour between the cells in the aggregate occurs spontaneously at the regulatory level owing to mechanisms present in unicellular ancestors and does not require any genetic predisposition for a particular role in the aggregate or any orchestrated cooperative behaviour of aggregate cells. Mathematically, aggregation implies an increase in the dimensionality of phenotype space that generates a fitness landscape with new fitness maxima, in which the unicellular states of optimized metabolism become fitness saddle points. Evolution of multicellularity is modelled as evolution of a hereditary parameter: the propensity of cells to stick together, which determines the fraction of time a cell spends in the aggregate form. Stickiness can increase evolutionarily owing to the fitness advantage generated by the division of labour between cells in an aggregate. PMID:22158952

Synthetic control of a fitness tradeoff in yeast nitrogen metabolism

PubMed Central

Bayer, Travis S; Hoff, Kevin G; Beisel, Chase L; Lee, Jack J; Smolke, Christina D

2009-01-01

Background Microbial communities are involved in many processes relevant to industrial and medical biotechnology, such as the formation of biofilms, lignocellulosic degradation, and hydrogen production. The manipulation of synthetic and natural microbial communities and their underlying ecological parameters, such as fitness, evolvability, and variation, is an increasingly important area of research for synthetic biology. Results Here, we explored how synthetic control of an endogenous circuit can be used to regulate a tradeoff between fitness in resource abundant and resource limited environments in a population of Saccharomyces cerevisiae. We found that noise in the expression of a key enzyme in ammonia assimilation, Gdh1p, mediated a tradeoff between growth in low nitrogen environments and stress resistance in high ammonia environments. We implemented synthetic control of an endogenous Gdh1p regulatory network to construct an engineered strain in which the fitness of the population was tunable in response to an exogenously-added small molecule across a range of ammonia environments. Conclusion The ability to tune fitness and biological tradeoffs will be important components of future efforts to engineer microbial communities. PMID:19118500

Clarifications regarding the use of model-fitting methods of kinetic analysis for determining the activation energy from a single non-isothermal curve.

PubMed

Sánchez-Jiménez, Pedro E; Pérez-Maqueda, Luis A; Perejón, Antonio; Criado, José M

2013-02-05

This paper provides some clarifications regarding the use of model-fitting methods of kinetic analysis for estimating the activation energy of a process, in response to some results recently published in Chemistry Central journal. The model fitting methods of Arrhenius and Savata are used to determine the activation energy of a single simulated curve. It is shown that most kinetic models correctly fit the data, each providing a different value for the activation energy. Therefore it is not really possible to determine the correct activation energy from a single non-isothermal curve. On the other hand, when a set of curves are recorded under different heating schedules are used, the correct kinetic parameters can be clearly discerned. Here, it is shown that the activation energy and the kinetic model cannot be unambiguously determined from a single experimental curve recorded under non isothermal conditions. Thus, the use of a set of curves recorded under different heating schedules is mandatory if model-fitting methods are employed.

Bayesian parameter estimation for chiral effective field theory

NASA Astrophysics Data System (ADS)

Wesolowski, Sarah; Furnstahl, Richard; Phillips, Daniel; Klco, Natalie

2016-09-01

The low-energy constants (LECs) of a chiral effective field theory (EFT) interaction in the two-body sector are fit to observable data using a Bayesian parameter estimation framework. By using Bayesian prior probability distributions (pdfs), we quantify relevant physical expectations such as LEC naturalness and include them in the parameter estimation procedure. The final result is a posterior pdf for the LECs, which can be used to propagate uncertainty resulting from the fit to data to the final observable predictions. The posterior pdf also allows an empirical test of operator redundancy and other features of the potential. We compare results of our framework with other fitting procedures, interpreting the underlying assumptions in Bayesian probabilistic language. We also compare results from fitting all partial waves of the interaction simultaneously to cross section data compared to fitting to extracted phase shifts, appropriately accounting for correlations in the data. Supported in part by the NSF and DOE.

TaiWan Ionospheric Model (TWIM) prediction based on time series autoregressive analysis

NASA Astrophysics Data System (ADS)

Tsai, L. C.; Macalalad, Ernest P.; Liu, C. H.

2014-10-01

As described in a previous paper, a three-dimensional ionospheric electron density (Ne) model has been constructed from vertical Ne profiles retrieved from the FormoSat3/Constellation Observing System for Meteorology, Ionosphere, and Climate GPS radio occultation measurements and worldwide ionosonde foF2 and foE data and named the TaiWan Ionospheric Model (TWIM). The TWIM exhibits vertically fitted α-Chapman-type layers with distinct F2, F1, E, and D layers, and surface spherical harmonic approaches for the fitted layer parameters including peak density, peak density height, and scale height. To improve the TWIM into a real-time model, we have developed a time series autoregressive model to forecast short-term TWIM coefficients. The time series of TWIM coefficients are considered as realizations of stationary stochastic processes within a processing window of 30 days. These autocorrelation coefficients are used to derive the autoregressive parameters and then forecast the TWIM coefficients, based on the least squares method and Lagrange multiplier technique. The forecast root-mean-square relative TWIM coefficient errors are generally <30% for 1 day predictions. The forecast TWIM values of foE and foF2 values are also compared and evaluated using worldwide ionosonde data.

A New Model Based on Adaptation of the External Loop to Compensate the Hysteresis of Tactile Sensors

PubMed Central

Sánchez-Durán, José A.; Vidal-Verdú, Fernando; Oballe-Peinado, Óscar; Castellanos-Ramos, Julián; Hidalgo-López, José A.

2015-01-01

This paper presents a novel method to compensate for hysteresis nonlinearities observed in the response of a tactile sensor. The External Loop Adaptation Method (ELAM) performs a piecewise linear mapping of the experimentally measured external curves of the hysteresis loop to obtain all possible internal cycles. The optimal division of the input interval where the curve is approximated is provided by the error minimization algorithm. This process is carried out off line and provides parameters to compute the split point in real time. A different linear transformation is then performed at the left and right of this point and a more precise fitting is achieved. The models obtained with the ELAM method are compared with those obtained from three other approaches. The results show that the ELAM method achieves a more accurate fitting. Moreover, the involved mathematical operations are simpler and therefore easier to implement in devices such as Field Programmable Gate Array (FPGAs) for real time applications. Furthermore, the method needs to identify fewer parameters and requires no previous selection process of operators or functions. Finally, the method can be applied to other sensors or actuators with complex hysteresis loop shapes. PMID:26501279

Dual-process models of associative recognition in young and older adults: evidence from receiver operating characteristics.

PubMed

Healy, Michael R; Light, Leah L; Chung, Christie

2005-07-01

In 3 experiments, young and older adults studied lists of unrelated word pairs and were given confidence-rated item and associative recognition tests. Several different models of recognition were fit to the confidence-rating data using techniques described by S. Macho (2002, 2004). Concordant with previous findings, item recognition data were best fit by an unequal-variance signal detection theory model for both young and older adults. For both age groups, associative recognition performance was best explained by models incorporating both recollection and familiarity components. Examination of parameter estimates supported the conclusion that recollection is reduced in old age, but inferences about age differences in familiarity were highly model dependent. Implications for dual-process models of memory in old age are discussed. ((c) 2005 APA, all rights reserved).

Relaxation Time Distribution (RTD) of Spectral Induced Polarization (SIP) data from environmental studies

NASA Astrophysics Data System (ADS)

Ntarlagiannis, D.; Ustra, A.; Slater, L. D.; Zhang, C.; Mendonça, C. A.

2015-12-01

In this work we present an alternative formulation of the Debye Decomposition (DD) of complex conductivity spectra, with a new set of parameters that are directly related to the continuous Debye relaxation model. The procedure determines the relaxation time distribution (RTD) and two frequency-independent parameters that modulate the induced polarization spectra. The distribution of relaxation times quantifies the contribution of each distinct relaxation process, which can in turn be associated with specific polarization processes and characterized in terms of electrochemical and interfacial parameters as derived from mechanistic models. Synthetic tests show that the procedure can successfully fit spectral induced polarization (SIP) data and accurately recover the RTD. The procedure was applied to different data sets, focusing on environmental applications. We focus on data of sand-clay mixtures artificially contaminated with toluene, and crude oil-contaminated sands experiencing biodegradation. The results identify characteristic relaxation times that can be associated with distinct polarization processes resulting from either the contaminant itself or transformations associated with biodegradation. The inversion results provide information regarding the relative strength and dominant relaxation time of these polarization processes.

A strategy to complete databases with parameters of refined line shapes and its test for CO in He, Ar and Kr

NASA Astrophysics Data System (ADS)

Ngo, N. H.; Hartmann, J.-M.

2017-12-01

We propose a strategy to generate parameters of the Hartmann-Tran profile (HTp) by simultaneously using first principle calculations and broadening coefficients deduced from Voigt/Lorentz fits of experimental spectra. We start from reference absorptions simulated, at pressures between 10 and 950 Torr, using the HTp with parameters recently obtained from high quality experiments for the P(1) and P(17) lines of the 3-0 band of CO in He, Ar and Kr. Using requantized Classical Molecular Dynamics Simulations (rCMDS), we calculate spectra under the same conditions. We then correct them using a single parameter deduced from Lorentzian fits of both reference and calculated absorptions at a single pressure. The corrected rCMDS spectra are then simultaneously fitted using the HTp, yielding the parameters of this model and associated spectra. Comparisons between the retrieved and input (reference) HTp parameters show a quite satisfactory agreement. Furthermore, differences between the reference spectra and those computed with the HT model fitted to the corrected-rCMDS predictions are much smaller than those obtained with a Voigt line shape. Their full amplitudes are in most cases smaller than 1%, and often below 0.5%, of the peak absorption. This opens the route to completing spectroscopic databases using calculations and the very numerous broadening coefficients available from Voigt fits of laboratory spectra.

Kinetic compensation effect in logistic distributed activation energy model for lignocellulosic biomass pyrolysis.

PubMed

Xu, Di; Chai, Meiyun; Dong, Zhujun; Rahman, Md Maksudur; Yu, Xi; Cai, Junmeng

2018-06-04

The kinetic compensation effect in the logistic distributed activation energy model (DAEM) for lignocellulosic biomass pyrolysis was investigated. The sum of square error (SSE) surface tool was used to analyze two theoretically simulated logistic DAEM processes for cellulose and xylan pyrolysis. The logistic DAEM coupled with the pattern search method for parameter estimation was used to analyze the experimental data of cellulose pyrolysis. The results showed that many parameter sets of the logistic DAEM could fit the data at different heating rates very well for both simulated and experimental processes, and a perfect linear relationship between the logarithm of the frequency factor and the mean value of the activation energy distribution was found. The parameters of the logistic DAEM can be estimated by coupling the optimization method and isoconversional kinetic methods. The results would be helpful for chemical kinetic analysis using DAEM. Copyright © 2018 Elsevier Ltd. All rights reserved.

Quantifying the sensitivity of feedstock properties and process conditions on hydrochar yield, carbon content, and energy content.

PubMed

Li, Liang; Wang, Yiying; Xu, Jiting; Flora, Joseph R V; Hoque, Shamia; Berge, Nicole D

2018-08-01

Hydrothermal carbonization (HTC) is a wet, low temperature thermal conversion process that continues to gain attention for the generation of hydrochar. The importance of specific process conditions and feedstock properties on hydrochar characteristics is not well understood. To evaluate this, linear and non-linear models were developed to describe hydrochar characteristics based on data collected from HTC-related literature. A Sobol analysis was subsequently conducted to identify parameters that most influence hydrochar characteristics. Results from this analysis indicate that for each investigated hydrochar property, the model fit and predictive capability associated with the random forest models is superior to both the linear and regression tree models. Based on results from the Sobol analysis, the feedstock properties and process conditions most influential on hydrochar yield, carbon content, and energy content were identified. In addition, a variational process parameter sensitivity analysis was conducted to determine how feedstock property importance changes with process conditions. Copyright © 2018 Elsevier Ltd. All rights reserved.

Photofragment image analysis using the Onion-Peeling Algorithm

NASA Astrophysics Data System (ADS)

Manzhos, Sergei; Loock, Hans-Peter

2003-07-01

With the growing popularity of the velocity map imaging technique, a need for the analysis of photoion and photoelectron images arose. Here, a computer program is presented that allows for the analysis of cylindrically symmetric images. It permits the inversion of the projection of the 3D charged particle distribution using the Onion Peeling Algorithm. Further analysis includes the determination of radial and angular distributions, from which velocity distributions and spatial anisotropy parameters are obtained. Identification and quantification of the different photolysis channels is therefore straightforward. In addition, the program features geometry correction, centering, and multi-Gaussian fitting routines, as well as a user-friendly graphical interface and the possibility of generating synthetic images using either the fitted or user-defined parameters. Program summaryTitle of program: Glass Onion Catalogue identifier: ADRY Program Summary URL:http://cpc.cs.qub.ac.uk/summaries/ADRY Program obtainable from: CPC Program Library, Queen's University of Belfast, N. Ireland Licensing provisions: none Computer: IBM PC Operating system under which the program has been tested: Windows 98, Windows 2000, Windows NT Programming language used: Delphi 4.0 Memory required to execute with typical data: 18 Mwords No. of bits in a word: 32 No. of bytes in distributed program, including test data, etc.: 9 911 434 Distribution format: zip file Keywords: Photofragment image, onion peeling, anisotropy parameters Nature of physical problem: Information about velocity and angular distributions of photofragments is the basis on which the analysis of the photolysis process resides. Reconstructing the three-dimensional distribution from the photofragment image is the first step, further processing involving angular and radial integration of the inverted image to obtain velocity and angular distributions. Provisions have to be made to correct for slight distortions of the image, and to verify the accuracy of the analysis process. Method of solution: The "Onion Peeling" algorithm described by Helm [Rev. Sci. Instrum. 67 (6) (1996)] is used to perform the image reconstruction. Angular integration with a subsequent multi-Gaussian fit supplies information about the velocity distribution of the photofragments, whereas radial integration with subsequent expansion of the angular distributions over Legendre Polynomials gives the spatial anisotropy parameters. Fitting algorithms have been developed to centre the image and to correct for image distortion. Restrictions on the complexity of the problem: The maximum image size (1280×1280) and resolution (16 bit) are restricted by available memory and can be changed in the source code. Initial centre coordinates within 5 pixels may be required for the correction and the centering algorithm to converge. Peaks on the velocity profile separated by less then the peak width may not be deconvolved. In the charged particle image reconstruction, it is assumed that the kinetic energy released in the dissociation process is small compared to the energy acquired in the electric field. For the fitting parameters to be physically meaningful, cylindrical symmetry of the image has to be assumed but the actual inversion algorithm is stable to distortions of such symmetry in experimental images. Typical running time: The analysis procedure can be divided into three parts: inversion, fitting, and geometry correction. The inversion time grows approx. as R3, where R is the radius of the region of interest: for R=200 pixels it is less than a minute, for R=400 pixels less then 6 min on a 400 MHz IBM personal computer. The time for the velocity fitting procedure to converge depends strongly on the number of peaks in the velocity profile and the convergence criterion. It ranges between less then a second for simple curves and a few minutes for profiles with up to twenty peaks. The time taken for the image correction scales as R2 and depends on the curve profile. It is on the order of a few minutes for images with R=500 pixels. Unusual features of the program: Our centering and image correction algorithm is based on Fourier analysis of the radial distribution to insure the sharpest velocity profile and is insensitive to an uneven intensity distribution. There exists an angular averaging option to stabilize the inversion algorithm and not to loose the resolution at the same time.

Sorption testing and generalized composite surface complexation models for determining uranium sorption parameters at a proposed in-situ recovery site

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

Johnson, Raymond H.; Truax, Ryan A.; Lankford, David A.

Solid-phase iron concentrations and generalized composite surface complexation models were used to evaluate procedures in determining uranium sorption on oxidized aquifer material at a proposed U in situ recovery (ISR) site. At the proposed Dewey Burdock ISR site in South Dakota, USA, oxidized aquifer material occurs downgradient of the U ore zones. Solid-phase Fe concentrations did not explain our batch sorption test results,though total extracted Fe appeared to be positively correlated with overall measured U sorption. Batch sorption test results were used to develop generalized composite surface complexation models that incorporated the full genericsorption potential of each sample, without detailedmore » mineralogiccharacterization. The resultant models provide U sorption parameters (site densities and equilibrium constants) for reactive transport modeling. The generalized composite surface complexation sorption models were calibrated to batch sorption data from three oxidized core samples using inverse modeling, and gave larger sorption parameters than just U sorption on the measured solidphase Fe. These larger sorption parameters can significantly influence reactive transport modeling, potentially increasing U attenuation. Because of the limited number of calibration points, inverse modeling required the reduction of estimated parameters by fixing two parameters. The best-fit models used fixed values for equilibrium constants, with the sorption site densities being estimated by the inversion process. While these inverse routines did provide best-fit sorption parameters, local minima and correlated parameters might require further evaluation. Despite our limited number of proxy samples, the procedures presented provide a valuable methodology to consider for sites where metal sorption parameters are required. Furthermore, these sorption parameters can be used in reactive transport modeling to assess downgradient metal attenuation, especially when no other calibration data are available, such as at proposed U ISR sites.« less

Sorption testing and generalized composite surface complexation models for determining uranium sorption parameters at a proposed in-situ recovery site

DOE PAGES

Johnson, Raymond H.; Truax, Ryan A.; Lankford, David A.; ...

2016-02-03

Solid-phase iron concentrations and generalized composite surface complexation models were used to evaluate procedures in determining uranium sorption on oxidized aquifer material at a proposed U in situ recovery (ISR) site. At the proposed Dewey Burdock ISR site in South Dakota, USA, oxidized aquifer material occurs downgradient of the U ore zones. Solid-phase Fe concentrations did not explain our batch sorption test results,though total extracted Fe appeared to be positively correlated with overall measured U sorption. Batch sorption test results were used to develop generalized composite surface complexation models that incorporated the full genericsorption potential of each sample, without detailedmore » mineralogiccharacterization. The resultant models provide U sorption parameters (site densities and equilibrium constants) for reactive transport modeling. The generalized composite surface complexation sorption models were calibrated to batch sorption data from three oxidized core samples using inverse modeling, and gave larger sorption parameters than just U sorption on the measured solidphase Fe. These larger sorption parameters can significantly influence reactive transport modeling, potentially increasing U attenuation. Because of the limited number of calibration points, inverse modeling required the reduction of estimated parameters by fixing two parameters. The best-fit models used fixed values for equilibrium constants, with the sorption site densities being estimated by the inversion process. While these inverse routines did provide best-fit sorption parameters, local minima and correlated parameters might require further evaluation. Despite our limited number of proxy samples, the procedures presented provide a valuable methodology to consider for sites where metal sorption parameters are required. Furthermore, these sorption parameters can be used in reactive transport modeling to assess downgradient metal attenuation, especially when no other calibration data are available, such as at proposed U ISR sites.« less

The Elementary Mass Action Rate Constants of P-gp Transport for a Confluent Monolayer of MDCKII-hMDR1 Cells

PubMed Central

Tran, Thuy Thanh; Mittal, Aditya; Aldinger, Tanya; Polli, Joseph W.; Ayrton, Andrew; Ellens, Harma; Bentz, Joe

2005-01-01

The human multi-drug resistance membrane transporter, P-glycoprotein, or P-gp, has been extensively studied due to its importance to human health and disease. Thus far, the kinetic analysis of P-gp transport has been limited to steady-state Michaelis-Menten approaches or to compartmental models, neither of which can prove molecular mechanisms. Determination of the elementary kinetic rate constants of transport will be essential to understanding how P-gp works. The experimental system we use is a confluent monolayer of MDCKII-hMDR1 cells that overexpress P-gp. It is a physiologically relevant model system, and transport is measured without biochemical manipulations of P-gp. The Michaelis-Menten mass action reaction is used to model P-gp transport. Without imposing the steady-state assumptions, this reaction depends upon several parameters that must be simultaneously fitted. An exhaustive fitting of transport data to find all possible parameter vectors that best fit the data was accomplished with a reasonable computation time using a hierarchical algorithm. For three P-gp substrates (amprenavir, loperamide, and quinidine), we have successfully fitted the elementary rate constants, i.e., drug association to P-gp from the apical membrane inner monolayer, drug dissociation back into the apical membrane inner monolayer, and drug efflux from P-gp into the apical chamber, as well as the density of efflux active P-gp. All three drugs had overlapping ranges for the efflux active P-gp, which was a benchmark for the validity of the fitting process. One novel finding was that the association to P-gp appears to be rate-limited solely by drug lateral diffusion within the inner monolayer of the plasma membrane for all three drugs. This would be expected if P-gp structure were open to the lipids of the apical membrane inner monolayer, as has been suggested by recent structural studies. The fitted kinetic parameters show how P-gp efflux of a wide range of xenobiotics has been maximized. PMID:15501934

PSO-tuned PID controller for coupled tank system via priority-based fitness scheme

NASA Astrophysics Data System (ADS)

Jaafar, Hazriq Izzuan; Hussien, Sharifah Yuslinda Syed; Selamat, Nur Asmiza; Abidin, Amar Faiz Zainal; Aras, Mohd Shahrieel Mohd; Nasir, Mohamad Na'im Mohd; Bohari, Zul Hasrizal

2015-05-01

The industrial applications of Coupled Tank System (CTS) are widely used especially in chemical process industries. The overall process is require liquids to be pumped, stored in the tank and pumped again to another tank. Nevertheless, the level of liquid in tank need to be controlled and flow between two tanks must be regulated. This paper presents development of an optimal PID controller for controlling the desired liquid level of the CTS. Two method of Particle Swarm Optimization (PSO) algorithm will be tested in optimizing the PID controller parameters. These two methods of PSO are standard Particle Swarm Optimization (PSO) and Priority-based Fitness Scheme in Particle Swarm Optimization (PFPSO). Simulation is conducted within Matlab environment to verify the performance of the system in terms of settling time (Ts), steady state error (SSE) and overshoot (OS). It has been demonstrated that implementation of PSO via Priority-based Fitness Scheme (PFPSO) for this system is potential technique to control the desired liquid level and improve the system performances compared with standard PSO.

Physical mechanism responsible for the stretched exponential decay behavior of aging organic light-emitting diodes

NASA Astrophysics Data System (ADS)

Féry, C.; Racine, B.; Vaufrey, D.; Doyeux, H.; Cinà, S.

2005-11-01

The main process responsible for the luminance degradation in organic light-emitting diodes (OLEDs) driven under constant current has not yet been identified. In this paper, we propose an approach to describe the intrinsic mechanisms involved in the OLED aging. We first show that a stretched exponential decay can be used to fit almost all the luminance versus time curves obtained under different driving conditions. In this way, we are able to prove that they can all be described by employing a single free parameter model. By using an approach based on local relaxation events, we will demonstrate that a single mechanism is responsible for the dominant aging process. Furthermore, we will demonstrate that the main relaxation event is the annihilation of one emissive center. We then use our model to fit all the experimental data measured under different driving condition, and show that by carefully fitting the accelerated luminance lifetime-curves, we can extrapolate the low-luminance lifetime needed for real display applications, with a high degree of accuracy.

An evolutionary firefly algorithm for the estimation of nonlinear biological model parameters.

PubMed

Abdullah, Afnizanfaizal; Deris, Safaai; Anwar, Sohail; Arjunan, Satya N V

2013-01-01

The development of accurate computational models of biological processes is fundamental to computational systems biology. These models are usually represented by mathematical expressions that rely heavily on the system parameters. The measurement of these parameters is often difficult. Therefore, they are commonly estimated by fitting the predicted model to the experimental data using optimization methods. The complexity and nonlinearity of the biological processes pose a significant challenge, however, to the development of accurate and fast optimization methods. We introduce a new hybrid optimization method incorporating the Firefly Algorithm and the evolutionary operation of the Differential Evolution method. The proposed method improves solutions by neighbourhood search using evolutionary procedures. Testing our method on models for the arginine catabolism and the negative feedback loop of the p53 signalling pathway, we found that it estimated the parameters with high accuracy and within a reasonable computation time compared to well-known approaches, including Particle Swarm Optimization, Nelder-Mead, and Firefly Algorithm. We have also verified the reliability of the parameters estimated by the method using an a posteriori practical identifiability test.

An Evolutionary Firefly Algorithm for the Estimation of Nonlinear Biological Model Parameters

PubMed Central

Abdullah, Afnizanfaizal; Deris, Safaai; Anwar, Sohail; Arjunan, Satya N. V.

2013-01-01

The development of accurate computational models of biological processes is fundamental to computational systems biology. These models are usually represented by mathematical expressions that rely heavily on the system parameters. The measurement of these parameters is often difficult. Therefore, they are commonly estimated by fitting the predicted model to the experimental data using optimization methods. The complexity and nonlinearity of the biological processes pose a significant challenge, however, to the development of accurate and fast optimization methods. We introduce a new hybrid optimization method incorporating the Firefly Algorithm and the evolutionary operation of the Differential Evolution method. The proposed method improves solutions by neighbourhood search using evolutionary procedures. Testing our method on models for the arginine catabolism and the negative feedback loop of the p53 signalling pathway, we found that it estimated the parameters with high accuracy and within a reasonable computation time compared to well-known approaches, including Particle Swarm Optimization, Nelder-Mead, and Firefly Algorithm. We have also verified the reliability of the parameters estimated by the method using an a posteriori practical identifiability test. PMID:23469172

Evolution of haploid-diploid life cycles when haploid and diploid fitnesses are not equal.

PubMed

Scott, Michael F; Rescan, Marie

2017-02-01

Many organisms spend a significant portion of their life cycle as haploids and as diploids (a haploid-diploid life cycle). However, the evolutionary processes that could maintain this sort of life cycle are unclear. Most previous models of ploidy evolution have assumed that the fitness effects of new mutations are equal in haploids and homozygous diploids, however, this equivalency is not supported by empirical data. With different mutational effects, the overall (intrinsic) fitness of a haploid would not be equal to that of a diploid after a series of substitution events. Intrinsic fitness differences between haploids and diploids can also arise directly, for example because diploids tend to have larger cell sizes than haploids. Here, we incorporate intrinsic fitness differences into genetic models for the evolution of time spent in the haploid versus diploid phases, in which ploidy affects whether new mutations are masked. Life-cycle evolution can be affected by intrinsic fitness differences between phases, the masking of mutations, or a combination of both. We find parameter ranges where these two selective forces act and show that the balance between them can favor convergence on a haploid-diploid life cycle, which is not observed in the absence of intrinsic fitness differences. © 2016 The Author(s). Evolution © 2016 The Society for the Study of Evolution.

Standard and goodness-of-fit parameter estimation methods for the three-parameter lognormal distribution

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

Kane, V.E.

1982-01-01

A class of goodness-of-fit estimators is found to provide a useful alternative in certain situations to the standard maximum likelihood method which has some undesirable estimation characteristics for estimation from the three-parameter lognormal distribution. The class of goodness-of-fit tests considered include the Shapiro-Wilk and Filliben tests which reduce to a weighted linear combination of the order statistics that can be maximized in estimation problems. The weighted order statistic estimators are compared to the standard procedures in Monte Carlo simulations. Robustness of the procedures are examined and example data sets analyzed.

Polynomials to model the growth of young bulls in performance tests.

PubMed

Scalez, D C B; Fragomeni, B O; Passafaro, T L; Pereira, I G; Toral, F L B

2014-03-01

The use of polynomial functions to describe the average growth trajectory and covariance functions of Nellore and MA (21/32 Charolais+11/32 Nellore) young bulls in performance tests was studied. The average growth trajectories and additive genetic and permanent environmental covariance functions were fit with Legendre (linear through quintic) and quadratic B-spline (with two to four intervals) polynomials. In general, the Legendre and quadratic B-spline models that included more covariance parameters provided a better fit with the data. When comparing models with the same number of parameters, the quadratic B-spline provided a better fit than the Legendre polynomials. The quadratic B-spline with four intervals provided the best fit for the Nellore and MA groups. The fitting of random regression models with different types of polynomials (Legendre polynomials or B-spline) affected neither the genetic parameters estimates nor the ranking of the Nellore young bulls. However, fitting different type of polynomials affected the genetic parameters estimates and the ranking of the MA young bulls. Parsimonious Legendre or quadratic B-spline models could be used for genetic evaluation of body weight of Nellore young bulls in performance tests, whereas these parsimonious models were less efficient for animals of the MA genetic group owing to limited data at the extreme ages.

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

USGS Publications Warehouse

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

1994-01-01

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

Exploratory Analyses To Improve Model Fit: Errors Due to Misspecification and a Strategy To Reduce Their Occurrence.

ERIC Educational Resources Information Center

Green, Samuel B.; Thompson, Marilyn S.; Poirier, Jennifer

1999-01-01

The use of Lagrange multiplier (LM) tests in specification searches and the efforts that involve the addition of extraneous parameters to models are discussed. Presented are a rationale and strategy for conducting specification searches in two stages that involve adding parameters to LM tests to maximize fit and then deleting parameters not needed…

A partition function-based weighting scheme in force field parameter development using ab initio calculation results in global configurational space.

PubMed

Wu, Yao; Dai, Xiaodong; Huang, Niu; Zhao, Lifeng

2013-06-05

In force field parameter development using ab initio potential energy surfaces (PES) as target data, an important but often neglected matter is the lack of a weighting scheme with optimal discrimination power to fit the target data. Here, we developed a novel partition function-based weighting scheme, which not only fits the target potential energies exponentially like the general Boltzmann weighting method, but also reduces the effect of fitting errors leading to overfitting. The van der Waals (vdW) parameters of benzene and propane were reparameterized by using the new weighting scheme to fit the high-level ab initio PESs probed by a water molecule in global configurational space. The molecular simulation results indicate that the newly derived parameters are capable of reproducing experimental properties in a broader range of temperatures, which supports the partition function-based weighting scheme. Our simulation results also suggest that structural properties are more sensitive to vdW parameters than partial atomic charge parameters in these systems although the electrostatic interactions are still important in energetic properties. As no prerequisite conditions are required, the partition function-based weighting method may be applied in developing any types of force field parameters. Copyright © 2013 Wiley Periodicals, Inc.

Prediction of the use of conventional hearing aids in Korean adults with unilateral hearing impairment.

PubMed

Lee, Dong-Hee; Noh, Heil

2015-01-01

To determine the use of a hearing aid at six months post-fitting and to evaluate the predictors of its ongoing use in Korean adults with unilateral hearing impairment (HI). Retrospective study at a secondary referral hospital over a 15-year period. This study analysed 119 adults with unilateral HI who had been recommended for hearing amplification (55 men and 64 women, mean age, 58.0 ± 11.7 years). Six months after the fitting, all of the participants were surveyed regarding subsequent decisions and actions about obtaining hearing aids. General uptake rate for a hearing aid was 68.1% (58.0% of participants surveyed were successful users, and 10.1% were intermittent users). The most significant parameter associated with hearing-aid use was social and/or work activities (R(2) = 0.457), and the significant predictors for successful hearing-aid use were social and/or work activities and method of signal processing (discriminatory power = 56.3%). Six months post-fitting, 68.1% of Korean adults with unilateral HI who had agreed to try a hearing aid continued to use it regularly. The predictors for hearing-aid use six months post-fitting included social and/or work activities and digital signal processing.

Multi-scale comparison of source parameter estimation using empirical Green's function approach

NASA Astrophysics Data System (ADS)

Chen, X.; Cheng, Y.

2015-12-01

Analysis of earthquake source parameters requires correction of path effect, site response, and instrument responses. Empirical Green's function (EGF) method is one of the most effective methods in removing path effects and station responses by taking the spectral ratio between a larger and smaller event. Traditional EGF method requires identifying suitable event pairs, and analyze each event individually. This allows high quality estimations for strictly selected events, however, the quantity of resolvable source parameters is limited, which challenges the interpretation of spatial-temporal coherency. On the other hand, methods that exploit the redundancy of event-station pairs are proposed, which utilize the stacking technique to obtain systematic source parameter estimations for a large quantity of events at the same time. This allows us to examine large quantity of events systematically, facilitating analysis of spatial-temporal patterns, and scaling relationship. However, it is unclear how much resolution is scarified during this process. In addition to the empirical Green's function calculation, choice of model parameters and fitting methods also lead to biases. Here, using two regional focused arrays, the OBS array in the Mendocino region, and the borehole array in the Salton Sea geothermal field, I compare the results from the large scale stacking analysis, small-scale cluster analysis, and single event-pair analysis with different fitting methods to systematically compare the results within completely different tectonic environment, in order to quantify the consistency and inconsistency in source parameter estimations, and the associated problems.

Competition Between Two Large-Amplitude Motion Models: New Hybrid Hamiltonian Versus Old Pure-Tunneling Hamiltonian

NASA Astrophysics Data System (ADS)

Kleiner, Isabelle; Hougen, Jon T.

2017-06-01

In this talk we report on our progress in trying to make the hybrid Hamiltonian competitive with the pure-tunneling Hamiltonian for treating large-amplitude motions in methylamine. A treatment using the pure-tunneling model has the advantages of: (i) requiring relatively little computer time, (ii) working with relatively uncorrelated fitting parameters, and (iii) yielding in the vast majority of cases fits to experimental measurement accuracy. These advantages are all illustrated in the work published this past year on a gigantic v_{t} = 1 data set for the torsional fundamental band in methyl amine. A treatment using the hybrid model has the advantages of: (i) being able to carry out a global fit involving both v_{t} = 0 and v_{t} = 1 energy levels and (ii) working with fitting parameters that have a clearer physical interpretation. Unfortunately, a treatment using the hybrid model has the great disadvantage of requiring a highly correlated set of fitting parameters to achieve reasonable fitting accuracy, which complicates the search for a good set of molecular fitting parameters and a fit to experimental accuracy. At the time of writing this abstract, we have been able to carry out a fit with J up to 15 that includes all available infrared data in the v_{t} = 1-0 torsional fundamental band, all ground-state microwave data with K up to 10 and J up to 15, and about a hundred microwave lines within the v_{t} = 1 torsional state, achieving weighted root-mean-square (rms) deviations of about 1.4, 2.8, and 4.2 for these three categories of data. We will give an update of this situation at the meeting. I. Gulaczyk, M. Kreglewski, V.-M. Horneman, J. Mol. Spectrosc., in Press (2017).

Implications for Fitness Programming---The Geriatric Population.

ERIC Educational Resources Information Center

Brown, Stanley P.; And Others

1989-01-01

This article discusses the relevance of fitness programing for an aging population and provides parameters for a geriatric fitness program. Emphasized are physical activity as a preventive measure against age-related illness and management of a geriatric fitness program. (IAH)

Model for the alpha and beta shear-mechanical properties of supercooled liquids and its comparison to squalane data

NASA Astrophysics Data System (ADS)

Hecksher, Tina; Olsen, Niels Boye; Dyre, Jeppe C.

2017-04-01

This paper presents data for supercooled squalane's frequency-dependent shear modulus covering frequencies from 10 mHz to 30 kHz and temperatures from 168 K to 190 K; measurements are also reported for the glass phase down to 146 K. The data reveal a strong mechanical beta process. A model is proposed for the shear response of the metastable equilibrium liquid phase of supercooled liquids. The model is an electrical equivalent-circuit characterized by additivity of the dynamic shear compliances of the alpha and beta processes. The nontrivial parts of the alpha and beta processes are each represented by a "Cole-Cole retardation element" defined as a series connection of a capacitor and a constant-phase element, resulting in the Cole-Cole compliance function well-known from dielectrics. The model, which assumes that the high-frequency decay of the alpha shear compliance loss varies with the angular frequency as ω-1 /2, has seven parameters. Assuming time-temperature superposition for the alpha and beta processes separately, the number of parameters varying with temperature is reduced to four. The model provides a better fit to the data than an equally parametrized Havriliak-Negami type model. From the temperature dependence of the best-fit model parameters, the following conclusions are drawn: (1) the alpha relaxation time conforms to the shoving model; (2) the beta relaxation loss-peak frequency is almost temperature independent; (3) the alpha compliance magnitude, which in the model equals the inverse of the instantaneous shear modulus, is only weakly temperature dependent; (4) the beta compliance magnitude decreases by a factor of three upon cooling in the temperature range studied. The final part of the paper briefly presents measurements of the dynamic adiabatic bulk modulus covering frequencies from 10 mHz to 10 kHz in the temperature range from 172 K to 200 K. The data are qualitatively similar to the shear modulus data by having a significant beta process. A single-order-parameter framework is suggested to rationalize these similarities.

Model for the alpha and beta shear-mechanical properties of supercooled liquids and its comparison to squalane data.

PubMed

Hecksher, Tina; Olsen, Niels Boye; Dyre, Jeppe C

2017-04-21

This paper presents data for supercooled squalane's frequency-dependent shear modulus covering frequencies from 10 mHz to 30 kHz and temperatures from 168 K to 190 K; measurements are also reported for the glass phase down to 146 K. The data reveal a strong mechanical beta process. A model is proposed for the shear response of the metastable equilibrium liquid phase of supercooled liquids. The model is an electrical equivalent-circuit characterized by additivity of the dynamic shear compliances of the alpha and beta processes. The nontrivial parts of the alpha and beta processes are each represented by a "Cole-Cole retardation element" defined as a series connection of a capacitor and a constant-phase element, resulting in the Cole-Cole compliance function well-known from dielectrics. The model, which assumes that the high-frequency decay of the alpha shear compliance loss varies with the angular frequency as ω -1/2 , has seven parameters. Assuming time-temperature superposition for the alpha and beta processes separately, the number of parameters varying with temperature is reduced to four. The model provides a better fit to the data than an equally parametrized Havriliak-Negami type model. From the temperature dependence of the best-fit model parameters, the following conclusions are drawn: (1) the alpha relaxation time conforms to the shoving model; (2) the beta relaxation loss-peak frequency is almost temperature independent; (3) the alpha compliance magnitude, which in the model equals the inverse of the instantaneous shear modulus, is only weakly temperature dependent; (4) the beta compliance magnitude decreases by a factor of three upon cooling in the temperature range studied. The final part of the paper briefly presents measurements of the dynamic adiabatic bulk modulus covering frequencies from 10 mHz to 10 kHz in the temperature range from 172 K to 200 K. The data are qualitatively similar to the shear modulus data by having a significant beta process. A single-order-parameter framework is suggested to rationalize these similarities.

Type Ia Supernova Intrinsic Magnitude Dispersion and the Fitting of Cosmological Parameters

NASA Astrophysics Data System (ADS)

Kim, A. G.

2011-02-01

I present an analysis for fitting cosmological parameters from a Hubble diagram of a standard candle with unknown intrinsic magnitude dispersion. The dispersion is determined from the data, simultaneously with the cosmological parameters. This contrasts with the strategies used to date. The advantages of the presented analysis are that it is done in a single fit (it is not iterative), it provides a statistically founded and unbiased estimate of the intrinsic dispersion, and its cosmological-parameter uncertainties account for the intrinsic-dispersion uncertainty. Applied to Type Ia supernovae, my strategy provides a statistical measure to test for subtypes and assess the significance of any magnitude corrections applied to the calibrated candle. Parameter bias and differences between likelihood distributions produced by the presented and currently used fitters are negligibly small for existing and projected supernova data sets.

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

NASA Astrophysics Data System (ADS)

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

2018-05-01

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

Computational Software for Fitting Seismic Data to Epidemic-Type Aftershock Sequence Models

NASA Astrophysics Data System (ADS)

Chu, A.

2014-12-01

Modern earthquake catalogs are often analyzed using spatial-temporal point process models such as the epidemic-type aftershock sequence (ETAS) models of Ogata (1998). My work introduces software to implement two of ETAS models described in Ogata (1998). To find the Maximum-Likelihood Estimates (MLEs), my software provides estimates of the homogeneous background rate parameter and the temporal and spatial parameters that govern triggering effects by applying the Expectation-Maximization (EM) algorithm introduced in Veen and Schoenberg (2008). Despite other computer programs exist for similar data modeling purpose, using EM-algorithm has the benefits of stability and robustness (Veen and Schoenberg, 2008). Spatial shapes that are very long and narrow cause difficulties in optimization convergence and problems with flat or multi-modal log-likelihood functions encounter similar issues. My program uses a robust method to preset a parameter to overcome the non-convergence computational issue. In addition to model fitting, the software is equipped with useful tools for examining modeling fitting results, for example, visualization of estimated conditional intensity, and estimation of expected number of triggered aftershocks. A simulation generator is also given with flexible spatial shapes that may be defined by the user. This open-source software has a very simple user interface. The user may execute it on a local computer, and the program also has potential to be hosted online. Java language is used for the software's core computing part and an optional interface to the statistical package R is provided.

Optimization Methods in Sherpa

NASA Astrophysics Data System (ADS)

Siemiginowska, Aneta; Nguyen, Dan T.; Doe, Stephen M.; Refsdal, Brian L.

2009-09-01

Forward fitting is a standard technique used to model X-ray data. A statistic, usually assumed weighted chi^2 or Poisson likelihood (e.g. Cash), is minimized in the fitting process to obtain a set of the best model parameters. Astronomical models often have complex forms with many parameters that can be correlated (e.g. an absorbed power law). Minimization is not trivial in such setting, as the statistical parameter space becomes multimodal and finding the global minimum is hard. Standard minimization algorithms can be found in many libraries of scientific functions, but they are usually focused on specific functions. However, Sherpa designed as general fitting and modeling application requires very robust optimization methods that can be applied to variety of astronomical data (X-ray spectra, images, timing, optical data etc.). We developed several optimization algorithms in Sherpa targeting a wide range of minimization problems. Two local minimization methods were built: Levenberg-Marquardt algorithm was obtained from MINPACK subroutine LMDIF and modified to achieve the required robustness; and Nelder-Mead simplex method has been implemented in-house based on variations of the algorithm described in the literature. A global search Monte-Carlo method has been implemented following a differential evolution algorithm presented by Storn and Price (1997). We will present the methods in Sherpa and discuss their usage cases. We will focus on the application to Chandra data showing both 1D and 2D examples. This work is supported by NASA contract NAS8-03060 (CXC).

Simultaneous calibration of end-member thermodynamic data and solution properties with correlated uncertainties

NASA Astrophysics Data System (ADS)

Antoshechkina, P. M.; Wolf, A. S.; Hamecher, E. A.; Asimow, P. D.; Ghiorso, M. S.

2013-12-01

Community databases such as EarthChem, LEPR, and AMCSD both increase demand for quantitative petrological tools, including thermodynamic models like the MELTS family of algorithms, and are invaluable in development of such tools. The need to extend existing solid solution models to include minor components such as Cr and Na has been evident for years but as the number of components increases it becomes impossible to completely separate derivation of end-member thermodynamic data from calibration of solution properties. In Hamecher et al. (2012; 2013) we developed a calibration scheme that directly interfaces with a MySQL database based on LEPR, with volume data from AMCSD and elsewhere. Here we combine that scheme with a Bayesian approach, where independent constraints on parameter values (e.g. existence of miscibility gaps) are combined with uncertainty propagation to give a more reliable best-fit along with associated model uncertainties. We illustrate the scheme with a new model of molar volume for (Ca,Fe,Mg,Mn,Na)3(Al,Cr,Fe3+,Fe2+,Mg,Mn,Si,Ti)2Si3O12 cubic garnets. For a garnet in this chemical system, the model molar volume is obtained by adding excess volume terms to a linear combination of nine independent end-member volumes. The model calibration is broken into three main stages: (1) estimation of individual end-member thermodynamic properties; (2) calibration of standard state volumes for all available independent and dependent end members; (3) fitting of binary and mixed composition data. For each calibration step, the goodness-of-fit includes weighted residuals as well as χ2-like penalty terms representing the (not necessarily Gaussian) prior constraints on parameter values. Using the Bayesian approach, uncertainties are correctly propagated forward to subsequent steps, allowing determination of final parameter values and correlated uncertainties that account for the entire calibration process. For the aluminosilicate garnets, optimal values of the bulk modulus and its pressure derivative are obtained by fitting published compression data using the Vinet equation of state, with the Mie-Grüneisen-Debye thermal pressure formalism to model thermal expansion. End-member thermal parameters are obtained by fitting volume data while ensuring that the heat capacity is consistent with the thermodynamic database of Berman and co-workers. For other end members, data for related compositions are used where such data exist; otherwise ultrasonic data or density functional theory results are taken or, for thermal parameters, systematics in cation radii are used. In stages (2) and (3) the remaining data at ambient conditions are fit. Using this step-wise calibration scheme, most parameters are modified little by subsequent calibration steps but some, such as the standard state volume of the Ti-bearing end member, can vary within calculated uncertainties. The final model satisfies desired criteria and fits almost all the data (more than 1000 points); only excess parameters that are justified by the data are activated. The scheme can be easily extended to calibration of end-member and solution properties from experimental phase equilibria. As a first step we obtain the internally consistent standard state entropy and enthalpy of formation for knorringite and discuss differences between our results and those of Klemme and co-workers.

Quantitative analysis of Ni2+/Ni3+ in Li[NixMnyCoz]O2 cathode materials: Non-linear least-squares fitting of XPS spectra

NASA Astrophysics Data System (ADS)

Fu, Zewei; Hu, Juntao; Hu, Wenlong; Yang, Shiyu; Luo, Yunfeng

2018-05-01

Quantitative analysis of Ni2+/Ni3+ using X-ray photoelectron spectroscopy (XPS) is important for evaluating the crystal structure and electrochemical performance of Lithium-nickel-cobalt-manganese oxide (Li[NixMnyCoz]O2, NMC). However, quantitative analysis based on Gaussian/Lorentzian (G/L) peak fitting suffers from the challenges of reproducibility and effectiveness. In this study, the Ni2+ and Ni3+ standard samples and a series of NMC samples with different Ni doping levels were synthesized. The Ni2+/Ni3+ ratios in NMC were quantitatively analyzed by non-linear least-squares fitting (NLLSF). Two Ni 2p overall spectra of synthesized Li [Ni0.33Mn0.33Co0.33]O2(NMC111) and bulk LiNiO2 were used as the Ni2+ and Ni3+ reference standards. Compared to G/L peak fitting, the fitting parameters required no adjustment, meaning that the spectral fitting process was free from operator dependence and the reproducibility was improved. Comparison of residual standard deviation (STD) showed that the fitting quality of NLLSF was superior to that of G/L peaks fitting. Overall, these findings confirmed the reproducibility and effectiveness of the NLLSF method in XPS quantitative analysis of Ni2+/Ni3+ ratio in Li[NixMnyCoz]O2 cathode materials.

Biosorption of Congo Red from aqueous solution onto burned root of Eichhornia crassipes biomass

NASA Astrophysics Data System (ADS)

Roy, Tapas Kumar; Mondal, Naba Kumar

2017-07-01

Biosorption is becoming a promising alternative to replace or supplement the present dye removal processes from dye containing waste water. In this work, adsorption of Congo Red (CR) from aqueous solution on burned root of Eichhornia crassipes ( BREC) biomass was investigated. A series of batch experiments were performed utilizing BREC biomass to remove CR dye from aqueous systems. Under optimized batch conditions, the BREC could remove up to 94.35 % of CR from waste water. The effects of operating parameters such as initial concentration, pH, adsorbent dose and contact time on the adsorption of CR were analyzed using response surface methodology. The proposed quadratic model for central composite design fitted very well to the experimental data. Response surface plots were used to determine the interaction effects of main factors and optimum conditions of the process. The optimum adsorption conditions were found to be initial CR concentration = 5 mg/L-1, pH = 7, adsorbent dose = 0.125 g and contact time = 45 min. The experimental isotherms data were analyzed using Langmuir, Freundlich, Temkin and Dubinin-Radushkevich (D-R) isotherm equations and the results indicated that the Freundlich isotherm showed a better fit for CR adsorption. Thermodynamic parameters were calculated from Van't Hoff plot, confirming that the adsorption process was spontaneous and exothermic. The high CR adsorptive removal ability and regeneration efficiency of this adsorbent suggest its applicability in industrial/household systems and data generated would help in further upscaling of the adsorption process.

Testing the Performance and Accuracy of the RELXILL Model for the Relativistic X-Ray Reflection from Accretion Disks

NASA Astrophysics Data System (ADS)

Choudhury, Kishalay; García, Javier A.; Steiner, James F.; Bambi, Cosimo

2017-12-01

The reflection spectroscopic model RELXILL is commonly implemented in studying relativistic X-ray reflection from accretion disks around black holes. We present a systematic study of the model’s capability to constrain the dimensionless spin and ionization parameters from ∼6000 Nuclear Spectroscopic Telescope Array (NuSTAR) simulations of a bright X-ray source employing the lamp-post geometry. We employ high-count spectra to show the limitations in the model without being confused with limitations in signal-to-noise. We find that both parameters are well-recovered at 90% confidence with improving constraints at higher reflection fraction, high spin, and low source height. We test spectra across a broad range—first at 106–107 and then ∼105 total source counts across the effective 3–79 keV band of NuSTAR, and discover a strong dependence of the results on how fits are performed around the starting parameters, owing to the complexity of the model itself. A blind fit chosen over an approach that carries some estimates of the actual parameter values can lead to significantly worse recovery of model parameters. We further stress the importance to span the space of nonlinear-behaving parameters like {log} ξ carefully and thoroughly for the model to avoid misleading results. In light of selecting fitting procedures, we recall the necessity to pay attention to the choice of data binning and fit statistics used to test the goodness of fit by demonstrating the effect on the photon index Γ. We re-emphasize and implore the need to account for the detector resolution while binning X-ray data and using Poisson fit statistics instead while analyzing Poissonian data.

Real-time monitoring and massive inversion of source parameters of very long period seismic signals: An application to Stromboli Volcano, Italy

USGS Publications Warehouse

Auger, E.; D'Auria, L.; Martini, M.; Chouet, B.; Dawson, P.

2006-01-01

We present a comprehensive processing tool for the real-time analysis of the source mechanism of very long period (VLP) seismic data based on waveform inversions performed in the frequency domain for a point source. A search for the source providing the best-fitting solution is conducted over a three-dimensional grid of assumed source locations, in which the Green's functions associated with each point source are calculated by finite differences using the reciprocal relation between source and receiver. Tests performed on 62 nodes of a Linux cluster indicate that the waveform inversion and search for the best-fitting signal over 100,000 point sources require roughly 30 s of processing time for a 2-min-long record. The procedure is applied to post-processing of a data archive and to continuous automatic inversion of real-time data at Stromboli, providing insights into different modes of degassing at this volcano. Copyright 2006 by the American Geophysical Union.

Modelling wastewater treatment in a submerged anaerobic membrane bioreactor.

PubMed

Spagni, Alessandro; Ferraris, Marco; Casu, Stefania

2015-01-01

Mathematical modelling has been widely applied to membrane bioreactor (MBRs) processes. However, to date, very few studies have reported on the application of the anaerobic digestion model N.1 (ADM1) to anaerobic membrane processes. The aim of this study was to evaluate the applicability of the ADM1 to a submerged anaerobic MBR (SAMBR) treating simulated industrial wastewater composed of cheese whey and sucrose. This study demonstrated that the biological processes involved in SAMBRs can be modelled by using the ADM1. Moreover, the results showed that very few modifications of the parameters describing the ADM1 were required to reasonably fit the experimental data. In particular, adaptation to the specific conditions of the coefficients describing the wastewater characterisation and the reduction of the hydrolysis rate of particulate carbohydrate (khyd,ch) from 0.25 d(-1) (as suggested by the ADM1 for high-rate mesophilic reactors) to 0.13 d(-1) were required to fit the experimental data.

Framework based on stochastic L-Systems for modeling IP traffic with multifractal behavior

NASA Astrophysics Data System (ADS)

Salvador, Paulo S.; Nogueira, Antonio; Valadas, Rui

2003-08-01

In a previous work we have introduced a multifractal traffic model based on so-called stochastic L-Systems, which were introduced by biologist A. Lindenmayer as a method to model plant growth. L-Systems are string rewriting techniques, characterized by an alphabet, an axiom (initial string) and a set of production rules. In this paper, we propose a novel traffic model, and an associated parameter fitting procedure, which describes jointly the packet arrival and the packet size processes. The packet arrival process is modeled through a L-System, where the alphabet elements are packet arrival rates. The packet size process is modeled through a set of discrete distributions (of packet sizes), one for each arrival rate. In this way the model is able to capture correlations between arrivals and sizes. We applied the model to measured traffic data: the well-known pOct Bellcore, a trace of aggregate WAN traffic and two traces of specific applications (Kazaa and Operation Flashing Point). We assess the multifractality of these traces using Linear Multiscale Diagrams. The suitability of the traffic model is evaluated by comparing the empirical and fitted probability mass and autocovariance functions; we also compare the packet loss ratio and average packet delay obtained with the measured traces and with traces generated from the fitted model. Our results show that our L-System based traffic model can achieve very good fitting performance in terms of first and second order statistics and queuing behavior.

Explanation of temporal clustering of tsunami sources using the epidemic-type aftershock sequence model

USGS Publications Warehouse

Geist, Eric L.

2014-01-01

Temporal clustering of tsunami sources is examined in terms of a branching process model. It previously was observed that there are more short interevent times between consecutive tsunami sources than expected from a stationary Poisson process. The epidemic‐type aftershock sequence (ETAS) branching process model is fitted to tsunami catalog events, using the earthquake magnitude of the causative event from the Centennial and Global Centroid Moment Tensor (CMT) catalogs and tsunami sizes above a completeness level as a mark to indicate that a tsunami was generated. The ETAS parameters are estimated using the maximum‐likelihood method. The interevent distribution associated with the ETAS model provides a better fit to the data than the Poisson model or other temporal clustering models. When tsunamigenic conditions (magnitude threshold, submarine location, dip‐slip mechanism) are applied to the Global CMT catalog, ETAS parameters are obtained that are consistent with those estimated from the tsunami catalog. In particular, the dip‐slip condition appears to result in a near zero magnitude effect for triggered tsunami sources. The overall consistency between results from the tsunami catalog and that from the earthquake catalog under tsunamigenic conditions indicates that ETAS models based on seismicity can provide the structure for understanding patterns of tsunami source occurrence. The fractional rate of triggered tsunami sources on a global basis is approximately 14%.

Is it growing exponentially fast? -- Impact of assuming exponential growth for characterizing and forecasting epidemics with initial near-exponential growth dynamics.

PubMed

Chowell, Gerardo; Viboud, Cécile

2016-10-01

The increasing use of mathematical models for epidemic forecasting has highlighted the importance of designing models that capture the baseline transmission characteristics in order to generate reliable epidemic forecasts. Improved models for epidemic forecasting could be achieved by identifying signature features of epidemic growth, which could inform the design of models of disease spread and reveal important characteristics of the transmission process. In particular, it is often taken for granted that the early growth phase of different growth processes in nature follow early exponential growth dynamics. In the context of infectious disease spread, this assumption is often convenient to describe a transmission process with mass action kinetics using differential equations and generate analytic expressions and estimates of the reproduction number. In this article, we carry out a simulation study to illustrate the impact of incorrectly assuming an exponential-growth model to characterize the early phase (e.g., 3-5 disease generation intervals) of an infectious disease outbreak that follows near-exponential growth dynamics. Specifically, we assess the impact on: 1) goodness of fit, 2) bias on the growth parameter, and 3) the impact on short-term epidemic forecasts. Designing transmission models and statistical approaches that more flexibly capture the profile of epidemic growth could lead to enhanced model fit, improved estimates of key transmission parameters, and more realistic epidemic forecasts.

SBSI: an extensible distributed software infrastructure for parameter estimation in systems biology

PubMed Central

Adams, Richard; Clark, Allan; Yamaguchi, Azusa; Hanlon, Neil; Tsorman, Nikos; Ali, Shakir; Lebedeva, Galina; Goltsov, Alexey; Sorokin, Anatoly; Akman, Ozgur E.; Troein, Carl; Millar, Andrew J.; Goryanin, Igor; Gilmore, Stephen

2013-01-01

Summary: Complex computational experiments in Systems Biology, such as fitting model parameters to experimental data, can be challenging to perform. Not only do they frequently require a high level of computational power, but the software needed to run the experiment needs to be usable by scientists with varying levels of computational expertise, and modellers need to be able to obtain up-to-date experimental data resources easily. We have developed a software suite, the Systems Biology Software Infrastructure (SBSI), to facilitate the parameter-fitting process. SBSI is a modular software suite composed of three major components: SBSINumerics, a high-performance library containing parallelized algorithms for performing parameter fitting; SBSIDispatcher, a middleware application to track experiments and submit jobs to back-end servers; and SBSIVisual, an extensible client application used to configure optimization experiments and view results. Furthermore, we have created a plugin infrastructure to enable project-specific modules to be easily installed. Plugin developers can take advantage of the existing user-interface and application framework to customize SBSI for their own uses, facilitated by SBSI’s use of standard data formats. Availability and implementation: All SBSI binaries and source-code are freely available from http://sourceforge.net/projects/sbsi under an Apache 2 open-source license. The server-side SBSINumerics runs on any Unix-based operating system; both SBSIVisual and SBSIDispatcher are written in Java and are platform independent, allowing use on Windows, Linux and Mac OS X. The SBSI project website at http://www.sbsi.ed.ac.uk provides documentation and tutorials. Contact: stg@inf.ed.ac.uk Supplementary information: Supplementary data are available at Bioinformatics online. PMID:23329415

Effects of climatological parameters in modeling and forecasting seasonal influenza transmission in Abidjan, Cote d'Ivoire.

PubMed

N'gattia, A K; Coulibaly, D; Nzussouo, N Talla; Kadjo, H A; Chérif, D; Traoré, Y; Kouakou, B K; Kouassi, P D; Ekra, K D; Dagnan, N S; Williams, T; Tiembré, I

2016-09-13

In temperate regions, influenza epidemics occur in the winter and correlate with certain climatological parameters. In African tropical regions, the effects of climatological parameters on influenza epidemics are not well defined. This study aims to identify and model the effects of climatological parameters on seasonal influenza activity in Abidjan, Cote d'Ivoire. We studied the effects of weekly rainfall, humidity, and temperature on laboratory-confirmed influenza cases in Abidjan from 2007 to 2010. We used the Box-Jenkins method with the autoregressive integrated moving average (ARIMA) process to create models using data from 2007-2010 and to assess the predictive value of best model on data from 2011 to 2012. The weekly number of influenza cases showed significant cross-correlation with certain prior weeks for both rainfall, and relative humidity. The best fitting multivariate model (ARIMAX (2,0,0) _RF) included the number of influenza cases during 1-week and 2-weeks prior, and the rainfall during the current week and 5-weeks prior. The performance of this model showed an increase of >3 % for Akaike Information Criterion (AIC) and 2.5 % for Bayesian Information Criterion (BIC) compared to the reference univariate ARIMA (2,0,0). The prediction of the weekly number of influenza cases during 2011-2012 with the best fitting multivariate model (ARIMAX (2,0,0) _RF), showed that the observed values were within the 95 % confidence interval of the predicted values during 97 of 104 weeks. Including rainfall increases the performances of fitted and predicted models. The timing of influenza in Abidjan can be partially explained by rainfall influence, in a setting with little change in temperature throughout the year. These findings can help clinicians to anticipate influenza cases during the rainy season by implementing preventive measures.

HDDM: Hierarchical Bayesian estimation of the Drift-Diffusion Model in Python.

PubMed

Wiecki, Thomas V; Sofer, Imri; Frank, Michael J

2013-01-01

The diffusion model is a commonly used tool to infer latent psychological processes underlying decision-making, and to link them to neural mechanisms based on response times. Although efficient open source software has been made available to quantitatively fit the model to data, current estimation methods require an abundance of response time measurements to recover meaningful parameters, and only provide point estimates of each parameter. In contrast, hierarchical Bayesian parameter estimation methods are useful for enhancing statistical power, allowing for simultaneous estimation of individual subject parameters and the group distribution that they are drawn from, while also providing measures of uncertainty in these parameters in the posterior distribution. Here, we present a novel Python-based toolbox called HDDM (hierarchical drift diffusion model), which allows fast and flexible estimation of the the drift-diffusion model and the related linear ballistic accumulator model. HDDM requires fewer data per subject/condition than non-hierarchical methods, allows for full Bayesian data analysis, and can handle outliers in the data. Finally, HDDM supports the estimation of how trial-by-trial measurements (e.g., fMRI) influence decision-making parameters. This paper will first describe the theoretical background of the drift diffusion model and Bayesian inference. We then illustrate usage of the toolbox on a real-world data set from our lab. Finally, parameter recovery studies show that HDDM beats alternative fitting methods like the χ(2)-quantile method as well as maximum likelihood estimation. The software and documentation can be downloaded at: http://ski.clps.brown.edu/hddm_docs/

exocartographer: Constraining surface maps orbital parameters of exoplanets

NASA Astrophysics Data System (ADS)

Farr, Ben; Farr, Will M.; Cowan, Nicolas B.; Haggard, Hal M.; Robinson, Tyler

2018-05-01

exocartographer solves the exo-cartography inverse problem. This flexible forward-modeling framework, written in Python, retrieves the albedo map and spin geometry of a planet based on time-resolved photometry; it uses a Markov chain Monte Carlo method to extract albedo maps and planet spin and their uncertainties. Gaussian Processes use the data to fit for the characteristic length scale of the map and enforce smooth maps.

Laboratory Experiments on Bentonite Samples: FY16 Progress

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

Ruth M. Tinnacher; Tournassat, Christophe; James A. Davis

2016-08-22

The primary goal of this study is to improve the understanding of U(VI) sorption and diffusion behavior in sodium-montmorillonite in order to support the development of realistic conceptual models describing these processes in performance assessment models while (1) accounting for potential changes in system conditions over time and space, (2) avoiding overly conservative transport predictions, and (3) using a minimum number of fitting parameters.

Comparing basal area growth models, consistency of parameters, and accuracy of prediction

Treesearch

J.J. Colbert; Michael Schuckers; Desta Fekedulegn

2002-01-01

We fit alternative sigmoid growth models to sample tree basal area historical data derived from increment cores and disks taken at breast height. We examine and compare the estimated parameters for these models across a range of sample sites. Models are rated on consistency of parameters and on their ability to fit growth data from four sites that are located across a...

New Approaches For Asteroid Spin State and Shape Modeling From Delay-Doppler Radar Images

NASA Astrophysics Data System (ADS)

Raissi, Chedy; Lamee, Mehdi; Mosiane, Olorato; Vassallo, Corinne; Busch, Michael W.; Greenberg, Adam; Benner, Lance A. M.; Naidu, Shantanu P.; Duong, Nicholas

2016-10-01

Delay-Doppler radar imaging is a powerful technique to characterize the trajectories, shapes, and spin states of near-Earth asteroids; and has yielded detailed models of dozens of objects. Reconstructing objects' shapes and spins from delay-Doppler data is a computationally intensive inversion problem. Since the 1990s, delay-Doppler data has been analyzed using the SHAPE software. SHAPE performs sequential single-parameter fitting, and requires considerable computer runtime and human intervention (Hudson 1993, Magri et al. 2007). Recently, multiple-parameter fitting algorithms have been shown to more efficiently invert delay-Doppler datasets (Greenberg & Margot 2015) - decreasing runtime while improving accuracy. However, extensive human oversight of the shape modeling process is still required. We have explored two new techniques to better automate delay-Doppler shape modeling: Bayesian optimization and a machine-learning neural network.One of the most time-intensive steps of the shape modeling process is to perform a grid search to constrain the target's spin state. We have implemented a Bayesian optimization routine that uses SHAPE to autonomously search the space of spin-state parameters. To test the efficacy of this technique, we compared it to results with human-guided SHAPE for asteroids 1992 UY4, 2000 RS11, and 2008 EV5. Bayesian optimization yielded similar spin state constraints within a factor of 3 less computer runtime.The shape modeling process could be further accelerated using a deep neural network to replace iterative fitting. We have implemented a neural network with a variational autoencoder (VAE), using a subset of known asteroid shapes and a large set of synthetic radar images as inputs to train the network. Conditioning the VAE in this manner allows the user to give the network a set of radar images and get a 3D shape model as an output. Additional development will be required to train a network to reliably render shapes from delay-Doppler images.This work was supported by NASA Ames, NVIDIA, Autodesk and the SETI Institute as part of the NASA Frontier Development Lab program.

Using data to inform soil microbial carbon model structure and parameters

NASA Astrophysics Data System (ADS)

Hagerty, S. B.; Schimel, J.

2016-12-01

There is increasing consensus that explicitly representing microbial mechanisms in soil carbon models can improve model predictions of future soil carbon stocks. However, which microbial mechanisms must be represented in these new models and how remains under debate. One of the major challenges in developing microbially explicit soil carbon models is that there is little data available to validate model structure. Empirical studies of microbial mechanisms often fail to capture the full range of microbial processes; from the cellular processes that occur within minutes to hours of substrate consumption to community turnover which may occur over weeks or longer. We added isotopically labeled 14C-glucose to soil incubated in the lab and traced its movement into the microbial biomass, carbon dioxide, and K2SO4 extractable carbon pool. We measured the concentration of 14C in each of these pools at 1, 3, 6, 24, and 72 hours and at 7, 14, and 21 days. We used this data to compare data fits among models that match our conceptual understanding of microbial carbon transformations and to estimate microbial parameters that control the fate of soil carbon. Over 90% of the added glucose was consumed within the first hour after it was added and concentration of the label was highest in biomass at this time. After the first hour, the label in biomass declined, with the rate that the label moved from the biomass slowing after 24hours, because of this models representing the microbial biomass as two pools fit best. Recovery of the label decreased with incubation time, from nearly 80% in the first hour to 67% after three weeks, indicating that carbon is moving into unextractable pools in the soil likely as microbial products and necromass sorb to soil particles and that these mechanisms must be represented in microbial models. This data fitting exercise demonstrates how isotopic data can be useful in validating model structure and estimating microbial model parameters. Future studies can apply this inverse modeling approach to compare the response of microbial parameters to changes in environmental conditions.

Barkhausen noise in FeCoB amorphous alloys (abstract)

NASA Astrophysics Data System (ADS)

Durin, G.; Bertotti, G.

1996-04-01

In recent years, the Barkhausen effect has been indicated as a promising tool to investigate and verify the ideas about the self-organization of physical complex systems displaying power law distributions and 1/f noise. When measured at low magnetization rates, the Barkhausen signal displays 1/fα-type spectra (with α=1.5÷2) and power law distributions of duration and size of the Barkhausen jumps. These experimental data are quite well described by the model of Alessandro et al. which is based on a stochastic description of the domain wall dynamics over a pinning field with brownian properties. Yet, this model always predicts a 1/f 2 spectrum, and, at the moment, it is not clear if it can take into account possible effects of self-organization of the magnetization process. In order to improve the power of the model and clarify this problem, we have performed a thorough investigation of the noise spectra and the amplitude distributions of a wide set of FeCoB amorphous alloys. The stationary amplitude distribution of the signal is very well fitted by the gamma distribution P(ν)=νc-1 exp(-ν)/Γ(c), where ν is proportional to the domain wall velocity, and c is a dimensionless parameter. As predicted in Ref. , this parameter is found to have a parabolic dependence on the magnetization rate. In particular, the linear coefficient is related to the amplitude of the fluctuations of the pinning field, a parameter which can be measured directly from the power spectra. In all measured cases, the power spectra show α exponents less than 2, and thus poorly fitted by the model. Actually, the absolute value of the high frequency spectral density is not consistent with the c parameter determined from the amplitude distribution data. This discrepancy requires to introduce effects not taken into account in the model, as the propagation of the jumps along the domain wall. This highly enhances the fit of the data and indicates effects of propagation on the scale of a few millimeters. These results are analyzed in terms of new descriptions of the statistical properties of the pinning field based on fractional brownian processes.

Electron Impact Multiple Ionization Cross Sections for Solar Physics

NASA Astrophysics Data System (ADS)

Hahn, M.; Savin, D. W.; Mueller, A.

2017-12-01

We have compiled a set of electron-impact multiple ionization (EIMI) cross sections for astrophysically relevant ions. EIMI can have a significant effect on the ionization balance of non-equilibrium plasmas. For example, it can be important if there is a rapid change in the electron temperature, as in solar flares or in nanoflare coronal heating. EIMI is also likely to be significant when the electron energy distribution is non-thermal, such as if the electrons follow a kappa distribution. Cross sections for EIMI are needed in order to account for these processes in plasma modeling and for spectroscopic interpretation. Here, we describe our comparison of proposed semiempirical formulae to the available experimental EIMI cross section data. Based on this comparison, we have interpolated and extrapolated fitting parameters to systems that have not yet been measured. A tabulation of the fit parameters is provided for thousands of EIMI cross sections. We also highlight some outstanding issues that remain to be resolved.

Estimating age at a specified length from the von Bertalanffy growth function

USGS Publications Warehouse

Ogle, Derek H.; Isermann, Daniel A.

2017-01-01

Estimating the time required (i.e., age) for fish in a population to reach a specific length (e.g., legal harvest length) is useful for understanding population dynamics and simulating the potential effects of length-based harvest regulations. The age at which a population reaches a specific mean length is typically estimated by fitting a von Bertalanffy growth function to length-at-age data and then rearranging the best-fit equation to solve for age at the specified length. This process precludes the use of standard frequentist methods to compute confidence intervals and compare estimates of age at the specified length among populations. We provide a parameterization of the von Bertalanffy growth function that has age at a specified length as a parameter. With this parameterization, age at a specified length is directly estimated, and standard methods can be used to construct confidence intervals and make among-group comparisons for this parameter. We demonstrate use of the new parameterization with two data sets.

Post-processing of seismic parameter data based on valid seismic event determination

DOEpatents

McEvilly, Thomas V.

1985-01-01

An automated seismic processing system and method are disclosed, including an array of CMOS microprocessors for unattended battery-powered processing of a multi-station network. According to a characterizing feature of the invention, each channel of the network is independently operable to automatically detect, measure times and amplitudes, and compute and fit Fast Fourier transforms (FFT's) for both P- and S- waves on analog seismic data after it has been sampled at a given rate. The measured parameter data from each channel are then reviewed for event validity by a central controlling microprocessor and if determined by preset criteria to constitute a valid event, the parameter data are passed to an analysis computer for calculation of hypocenter location, running b-values, source parameters, event count, P- wave polarities, moment-tensor inversion, and Vp/Vs ratios. The in-field real-time analysis of data maximizes the efficiency of microearthquake surveys allowing flexibility in experimental procedures, with a minimum of traditional labor-intensive postprocessing. A unique consequence of the system is that none of the original data (i.e., the sensor analog output signals) are necessarily saved after computation, but rather, the numerical parameters generated by the automatic analysis are the sole output of the automated seismic processor.

Applicability of Different Hydraulic Parameters to Describe Soil Detachment in Eroding Rills

PubMed Central

Wirtz, Stefan; Seeger, Manuel; Zell, Andreas; Wagner, Christian; Wagner, Jean-Frank; Ries, Johannes B.

2013-01-01

This study presents the comparison of experimental results with assumptions used in numerical models. The aim of the field experiments is to test the linear relationship between different hydraulic parameters and soil detachment. For example correlations between shear stress, unit length shear force, stream power, unit stream power and effective stream power and the detachment rate does not reveal a single parameter which consistently displays the best correlation. More importantly, the best fit does not only vary from one experiment to another, but even between distinct measurement points. Different processes in rill erosion are responsible for the changing correlations. However, not all these procedures are considered in soil erosion models. Hence, hydraulic parameters alone are not sufficient to predict detachment rates. They predict the fluvial incising in the rill's bottom, but the main sediment sources are not considered sufficiently in its equations. The results of this study show that there is still a lack of understanding of the physical processes underlying soil erosion. Exerted forces, soil stability and its expression, the abstraction of the detachment and transport processes in shallow flowing water remain still subject of unclear description and dependence. PMID:23717669

Least Squares Best Fit Method for the Three Parameter Weibull Distribution: Analysis of Tensile and Bend Specimens with Volume or Surface Flaw Failure

NASA Technical Reports Server (NTRS)

Gross, Bernard

1996-01-01

Material characterization parameters obtained from naturally flawed specimens are necessary for reliability evaluation of non-deterministic advanced ceramic structural components. The least squares best fit method is applied to the three parameter uniaxial Weibull model to obtain the material parameters from experimental tests on volume or surface flawed specimens subjected to pure tension, pure bending, four point or three point loading. Several illustrative example problems are provided.

Charmless B_{(s)}→ VV decays in factorization-assisted topological-amplitude approach

NASA Astrophysics Data System (ADS)

Wang, Chao; Zhang, Qi-An; Li, Ying; Lü, Cai-Dian

2017-05-01

Within the factorization-assisted topological-amplitude approach, we studied the 33 charmless B_{(s)} → VV decays, where V stands for a light vector meson. According to the flavor flows, the amplitude of each process can be decomposed into eight different topologies. In contrast to the conventional flavor diagrammatic approach, we further factorize each topological amplitude into decay constant, form factors and unknown universal parameters. By χ ^2 fitting 46 experimental observables, we extracted 10 theoretical parameters with χ ^2 per degree of freedom around 2. Using the fitted parameters, we calculated the branching fractions, polarization fractions, CP asymmetries and relative phases between polarization amplitudes of each decay mode. The decay channels dominated by tree diagram have large branching fractions and large longitudinal polarization fraction. The branching fractions and longitudinal polarization fractions of color-suppressed decays become smaller. Current experimental data of large transverse polarization fractions in the penguin dominant decay channels can be explained by only one transverse amplitude of penguin annihilation diagram. Our predictions of the not yet measured channels can be tested in the ongoing LHCb experiment and the Belle-II experiment in the future.

Large ensemble modeling of the last deglacial retreat of the West Antarctic Ice Sheet: comparison of simple and advanced statistical techniques

NASA Astrophysics Data System (ADS)

Pollard, David; Chang, Won; Haran, Murali; Applegate, Patrick; DeConto, Robert

2016-05-01

A 3-D hybrid ice-sheet model is applied to the last deglacial retreat of the West Antarctic Ice Sheet over the last ˜ 20 000 yr. A large ensemble of 625 model runs is used to calibrate the model to modern and geologic data, including reconstructed grounding lines, relative sea-level records, elevation-age data and uplift rates, with an aggregate score computed for each run that measures overall model-data misfit. Two types of statistical methods are used to analyze the large-ensemble results: simple averaging weighted by the aggregate score, and more advanced Bayesian techniques involving Gaussian process-based emulation and calibration, and Markov chain Monte Carlo. The analyses provide sea-level-rise envelopes with well-defined parametric uncertainty bounds, but the simple averaging method only provides robust results with full-factorial parameter sampling in the large ensemble. Results for best-fit parameter ranges and envelopes of equivalent sea-level rise with the simple averaging method agree well with the more advanced techniques. Best-fit parameter ranges confirm earlier values expected from prior model tuning, including large basal sliding coefficients on modern ocean beds.

Time series behaviour of the number of Air Asia passengers: A distributional approach

NASA Astrophysics Data System (ADS)

Asrah, Norhaidah Mohd; Djauhari, Maman Abdurachman

2013-09-01

The common practice to time series analysis is by fitting a model and then further analysis is conducted on the residuals. However, if we know the distributional behavior of time series, the analyses in model identification, parameter estimation, and model checking are more straightforward. In this paper, we show that the number of Air Asia passengers can be represented as a geometric Brownian motion process. Therefore, instead of using the standard approach in model fitting, we use an appropriate transformation to come up with a stationary, normally distributed and even independent time series. An example in forecasting the number of Air Asia passengers will be given to illustrate the advantages of the method.

Local Minima Free Parameterized Appearance Models

PubMed Central

Nguyen, Minh Hoai; De la Torre, Fernando

2010-01-01

Parameterized Appearance Models (PAMs) (e.g. Eigentracking, Active Appearance Models, Morphable Models) are commonly used to model the appearance and shape variation of objects in images. While PAMs have numerous advantages relative to alternate approaches, they have at least two drawbacks. First, they are especially prone to local minima in the fitting process. Second, often few if any of the local minima of the cost function correspond to acceptable solutions. To solve these problems, this paper proposes a method to learn a cost function by explicitly optimizing that the local minima occur at and only at the places corresponding to the correct fitting parameters. To the best of our knowledge, this is the first paper to address the problem of learning a cost function to explicitly model local properties of the error surface to fit PAMs. Synthetic and real examples show improvement in alignment performance in comparison with traditional approaches. PMID:21804750

A fitting empirical potential for NiTi alloy and its application

NASA Astrophysics Data System (ADS)

Ren, Guowu; Tang, Tiegang; Sehitoglu, Huseyin

Due to its superelastic behavior, NiTi shape memory alloy receives considerable attentions over a wide range of industrial and commercial applications. Limited to its complex structural transformation and multiple variants, semiempirical potentials for performing large-scale molecular dynamics simulations to investigate the atomistic mechanical process, are very few. In this work, we construct a new interatomic potential for the NiTi alloy by fitting to experimental or ab initio data. The fitting potential correctly predicts the lattice parameter, structural stability, equation of state for cubic B2(austenite) and monoclinic B19'(martensite) phases. In particular the elastic properties(three elastic constants for B2 and thirteen ones for B19') are in satisfactory agreement with the experiments or ab initio calculations. Furthermore, we apply this potential to conduct the molecular dynamics simulations of the mechanical behavior for NiTi alloy and the results capture its reversible transformation.

Analysis of Mining-Induced Subsidence Prediction by Exponent Knothe Model Combined with Insar and Leveling

NASA Astrophysics Data System (ADS)

Chen, Lei; Zhang, Liguo; Tang, Yixian; Zhang, Hong

2018-04-01

The principle of exponent Knothe model was introduced in detail and the variation process of mining subsidence with time was analysed based on the formulas of subsidence, subsidence velocity and subsidence acceleration in the paper. Five scenes of radar images and six levelling measurements were collected to extract ground deformation characteristics in one coal mining area in this study. Then the unknown parameters of exponent Knothe model were estimated by combined levelling data with deformation information along the line of sight obtained by InSAR technique. By compared the fitting and prediction results obtained by InSAR and levelling with that obtained only by levelling, it was shown that the accuracy of fitting and prediction combined with InSAR and levelling was obviously better than the other that. Therefore, the InSAR measurements can significantly improve the fitting and prediction accuracy of exponent Knothe model.

Anomalous Hall effect scaling in ferromagnetic thin films

NASA Astrophysics Data System (ADS)

Grigoryan, Vahram L.; Xiao, Jiang; Wang, Xuhui; Xia, Ke

2017-10-01

We propose a scaling law for anomalous Hall effect in ferromagnetic thin films. Our approach distinguishes multiple scattering sources, namely, bulk impurity, phonon for Hall resistivity, and most importantly the rough surface contribution to longitudinal resistivity. In stark contrast to earlier laws that rely on temperature- and thickness-dependent fitting coefficients, this scaling law fits the recent experimental data excellently with constant parameters that are independent of temperature and film thickness, strongly indicating that this law captures the underlying physical processes. Based on a few data points, this scaling law can even fit all experimental data in full temperature and thickness range. We apply this law to interpret the experimental data for Fe, Co, and Ni and conclude that (i) the phonon-induced skew scattering is unimportant as expected; (ii) contribution from the impurity-induced skew scattering is negative; (iii) the intrinsic (extrinsic) mechanism dominates in Fe (Co), and both the extrinsic and intrinsic contributions are important in Ni.

Emergence of cooperation with self-organized criticality

NASA Astrophysics Data System (ADS)

Park, Sangmin; Jeong, Hyeong-Chai

2012-02-01

Cooperation and self-organized criticality are two main keywords in current studies of evolution. We propose a generalized Bak-Sneppen model and provide a natural mechanism which accounts for both phenomena simultaneously. We use the prisoner's dilemma games to mimic the interactions among the members in the population. Each member is identified by its cooperation probability, and its fitness is given by the payoffs from neighbors. The least fit member with the minimum payoff is replaced by a new member with a random cooperation probability. When the neighbors of the least fit one are also replaced with a non-zero probability, a strong cooperation emerges. The Bak-Sneppen process builds a self-organized structure so that the cooperation can emerge even in the parameter region where a uniform or random population decreases the number of cooperators. The emergence of cooperation is due to the same dynamical correlation that leads to self-organized criticality in replacement activities.

Equilibrium, kinetics and process design of acid yellow 132 adsorption onto red pine sawdust.

PubMed

Can, Mustafa

2015-01-01

Linear and non-linear regression procedures have been applied to the Langmuir, Freundlich, Tempkin, Dubinin-Radushkevich, and Redlich-Peterson isotherms for adsorption of acid yellow 132 (AY132) dye onto red pine (Pinus resinosa) sawdust. The effects of parameters such as particle size, stirring rate, contact time, dye concentration, adsorption dose, pH, and temperature were investigated, and interaction was characterized by Fourier transform infrared spectroscopy and field emission scanning electron microscope. The non-linear method of the Langmuir isotherm equation was found to be the best fitting model to the equilibrium data. The maximum monolayer adsorption capacity was found as 79.5 mg/g. The calculated thermodynamic results suggested that AY132 adsorption onto red pine sawdust was an exothermic, physisorption, and spontaneous process. Kinetics was analyzed by four different kinetic equations using non-linear regression analysis. The pseudo-second-order equation provides the best fit with experimental data.

Impact of Pathogen Population Heterogeneity and Stress-Resistant Variants on Food Safety.

PubMed

Abee, T; Koomen, J; Metselaar, K I; Zwietering, M H; den Besten, H M W

2016-01-01

This review elucidates the state-of-the-art knowledge about pathogen population heterogeneity and describes the genotypic and phenotypic analyses of persister subpopulations and stress-resistant variants. The molecular mechanisms underlying the generation of persister phenotypes and genetic variants are identified. Zooming in on Listeria monocytogenes, a comparative whole-genome sequence analysis of wild types and variants that enabled the identification of mutations in variants obtained after a single exposure to lethal food-relevant stresses is described. Genotypic and phenotypic features are compared to those for persistent strains isolated from food processing environments. Inactivation kinetics, models used for fitting, and the concept of kinetic modeling-based schemes for detection of variants are presented. Furthermore, robustness and fitness parameters of L. monocytogenes wild type and variants are used to model their performance in food chains. Finally, the impact of stress-resistant variants and persistence in food processing environments on food safety is discussed.

Dynamic stiffness of chemically and physically ageing rubber vibration isolators in the audible frequency range. Part 1: constitutive equations

NASA Astrophysics Data System (ADS)

Kari, Leif

2017-09-01

The constitutive equations of chemically and physically ageing rubber in the audible frequency range are modelled as a function of ageing temperature, ageing time, actual temperature, time and frequency. The constitutive equations are derived by assuming nearly incompressible material with elastic spherical response and viscoelastic deviatoric response, using Mittag-Leffler relaxation function of fractional derivative type, the main advantage being the minimum material parameters needed to successfully fit experimental data over a broad frequency range. The material is furthermore assumed essentially entropic and thermo-mechanically simple while using a modified William-Landel-Ferry shift function to take into account temperature dependence and physical ageing, with fractional free volume evolution modelled by a nonlinear, fractional differential equation with relaxation time identical to that of the stress response and related to the fractional free volume by Doolittle equation. Physical ageing is a reversible ageing process, including trapping and freeing of polymer chain ends, polymer chain reorganizations and free volume changes. In contrast, chemical ageing is an irreversible process, mainly attributed to oxygen reaction with polymer network either damaging the network by scission or reformation of new polymer links. The chemical ageing is modelled by inner variables that are determined by inner fractional evolution equations. Finally, the model parameters are fitted to measurements results of natural rubber over a broad audible frequency range, and various parameter studies are performed including comparison with results obtained by ordinary, non-fractional ageing evolution differential equations.

Development and characterization of a new encapsulating agent from orange juice by-products.

PubMed

Kaderides, Kyriakos; Goula, Athanasia M

2017-10-01

The replacement of maltodextrins as carriers for the spray drying of sticky and sugar based bioactives is an important development for the food industry. In this work, orange juice industry by-product was used to obtain a high dietary fiber powder to be used as carrier material. This powder was characterized with respect to its physical and chemical properties related to the process of encapsulation by spray drying. Adsorption isotherms of orange waste powder were determined at 30, 45, and 60°C. The data were fitted to several models including two-parameter (BET, Halsey, Smith, and Oswin), three-parameter (GAB), and four-parameter (Peleg) relationships. The GAB model best fitted the experimental data. The isosteric heat of sorption was determined from the equilibrium sorption data using the Clausius-Clapeyron equation. Isosteric heats of sorption were found to decrease exponentially with increasing moisture content. The enthalpy-entropy compensation theory was applied to the sorption isotherms and indicated an enthalpy controlled sorption process. Glass transition temperatures (T g ) of orange waste powder conditioned at various water activities were determined and a strong plasticizing effect of water on T g was found. These data were satisfactory correlated by the Gordon and Taylor model. The critical water activity and moisture content for the orange waste powder were 0.82 and 0.18g water/g solids, respectively, at a storage temperature of 25°C. Copyright © 2017 Elsevier Ltd. All rights reserved.

Removal of three nitrophenols from aqueous solutions by adsorption onto char ash: equilibrium and kinetic modeling

NASA Astrophysics Data System (ADS)

Magdy, Yehia M.; Altaher, Hossam; ElQada, E.

2018-03-01

In this research, the removal of 2,4 dinitrophenol, 2 nitrophenol and 4 nitrophenol from aqueous solution using char ash from animal bones was investigated using batch technique. Three 2-parameter isotherms (Freundlich, Langmuir, and Temkin) were applied to analyze the experimental data. Both linear and nonlinear regression analyses were performed for these models to estimate the isotherm parameters. Three 3-parameter isotherms (Redlich-Peterson, Sips, Toth) were also tested. Moreover, the kinetic data were tested using pseudo-first order, pseudo-second order, Elovich, Intraparticle diffusion and Boyd methods. Langmuir adsorption isotherm provided the best fit for the experimental data indicating monolayer adsorption. The maximum adsorption capacity was 8.624, 7.55, 7.384 mg/g for 2 nitrophenol, 2,4 dinitrophenol, and 4 nitrophenol, respectively. The experimental data fitted well to pseudo-second order model suggested a chemical nature of the adsorption process. The R 2 values for this model were 0.973 up to 0.999. This result with supported by the Temkin model indicating heat of adsorption to be greater than 10 kJ/mol. The rate controlling step was intraparticle diffusion for 2 nitrophenol, and a combination of intraparticle diffusion and film diffusion for the other two phenols. The pH and temperature of solution were found to have a considerable effect, and the temperature indicated the exothermic nature of the adsorption process. The highest adsorption capacity was obtained at pH 9 and 25 °C.

Removing Visual Bias in Filament Identification: A New Goodness-of-fit Measure

NASA Astrophysics Data System (ADS)

Green, C.-E.; Cunningham, M. R.; Dawson, J. R.; Jones, P. A.; Novak, G.; Fissel, L. M.

2017-05-01

Different combinations of input parameters to filament identification algorithms, such as disperse and filfinder, produce numerous different output skeletons. The skeletons are a one-pixel-wide representation of the filamentary structure in the original input image. However, these output skeletons may not necessarily be a good representation of that structure. Furthermore, a given skeleton may not be as good of a representation as another. Previously, there has been no mathematical “goodness-of-fit” measure to compare output skeletons to the input image. Thus far this has been assessed visually, introducing visual bias. We propose the application of the mean structural similarity index (MSSIM) as a mathematical goodness-of-fit measure. We describe the use of the MSSIM to find the output skeletons that are the most mathematically similar to the original input image (the optimum, or “best,” skeletons) for a given algorithm, and independently of the algorithm. This measure makes possible systematic parameter studies, aimed at finding the subset of input parameter values returning optimum skeletons. It can also be applied to the output of non-skeleton-based filament identification algorithms, such as the Hessian matrix method. The MSSIM removes the need to visually examine thousands of output skeletons, and eliminates the visual bias, subjectivity, and limited reproducibility inherent in that process, representing a major improvement upon existing techniques. Importantly, it also allows further automation in the post-processing of output skeletons, which is crucial in this era of “big data.”

A reduced successive quadratic programming strategy for errors-in-variables estimation.

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

Tjoa, I.-B.; Biegler, L. T.; Carnegie-Mellon Univ.

Parameter estimation problems in process engineering represent a special class of nonlinear optimization problems, because the maximum likelihood structure of the objective function can be exploited. Within this class, the errors in variables method (EVM) is particularly interesting. Here we seek a weighted least-squares fit to the measurements with an underdetermined process model. Thus, both the number of variables and degrees of freedom available for optimization increase linearly with the number of data sets. Large optimization problems of this type can be particularly challenging and expensive to solve because, for general-purpose nonlinear programming (NLP) algorithms, the computational effort increases atmore » least quadratically with problem size. In this study we develop a tailored NLP strategy for EVM problems. The method is based on a reduced Hessian approach to successive quadratic programming (SQP), but with the decomposition performed separately for each data set. This leads to the elimination of all variables but the model parameters, which are determined by a QP coordination step. In this way the computational effort remains linear in the number of data sets. Moreover, unlike previous approaches to the EVM problem, global and superlinear properties of the SQP algorithm apply naturally. Also, the method directly incorporates inequality constraints on the model parameters (although not on the fitted variables). This approach is demonstrated on five example problems with up to 102 degrees of freedom. Compared to general-purpose NLP algorithms, large improvements in computational performance are observed.« less

Multivariable Time Series Prediction for the Icing Process on Overhead Power Transmission Line

PubMed Central

Li, Peng; Zhao, Na; Zhou, Donghua; Cao, Min; Li, Jingjie; Shi, Xinling

2014-01-01

The design of monitoring and predictive alarm systems is necessary for successful overhead power transmission line icing. Given the characteristics of complexity, nonlinearity, and fitfulness in the line icing process, a model based on a multivariable time series is presented here to predict the icing load of a transmission line. In this model, the time effects of micrometeorology parameters for the icing process have been analyzed. The phase-space reconstruction theory and machine learning method were then applied to establish the prediction model, which fully utilized the history of multivariable time series data in local monitoring systems to represent the mapping relationship between icing load and micrometeorology factors. Relevant to the characteristic of fitfulness in line icing, the simulations were carried out during the same icing process or different process to test the model's prediction precision and robustness. According to the simulation results for the Tao-Luo-Xiong Transmission Line, this model demonstrates a good accuracy of prediction in different process, if the prediction length is less than two hours, and would be helpful for power grid departments when deciding to take action in advance to address potential icing disasters. PMID:25136653

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

USGS Publications Warehouse

Liu, S.; Anderson, P.; Zhou, G.; Kauffman, B.; Hughes, F.; Schimel, D.; Watson, Vicente; Tosi, Joseph

2008-01-01

Objectively assessing the performance of a model and deriving model parameter values from observations are critical and challenging in landscape to regional modeling. In this paper, we applied a nonlinear inversion technique to calibrate the ecosystem model CENTURY against carbon (C) and nitrogen (N) stock measurements collected from 39 mature tropical forest sites in seven life zones in Costa Rica. Net primary productivity from the Moderate-Resolution Imaging Spectroradiometer (MODIS), C and N stocks in aboveground live biomass, litter, coarse woody debris (CWD), and in soils were used to calibrate the model. To investigate the resolution of available observations on the number of adjustable parameters, inversion was performed using nine setups of adjustable parameters. Statistics including observation sensitivity, parameter correlation coefficient, parameter sensitivity, and parameter confidence limits were used to evaluate the information content of observations, resolution of model parameters, and overall model performance. Results indicated that soil organic carbon content, soil nitrogen content, and total aboveground biomass carbon had the highest information contents, while measurements of carbon in litter and nitrogen in CWD contributed little to the parameter estimation processes. The available information could resolve the values of 2-4 parameters. Adjusting just one parameter resulted in under-fitting and unacceptable model performance, while adjusting five parameters simultaneously led to over-fitting. Results further indicated that the MODIS NPP values were compressed as compared with the spatial variability of net primary production (NPP) values inferred from inverse modeling. Using inverse modeling to infer NPP and other sensitive model parameters from C and N stock observations provides an opportunity to utilize data collected by national to regional forest inventory systems to reduce the uncertainties in the carbon cycle and generate valuable databases to validate and improve MODIS NPP algorithms.

Measurement and computations of line shape parameters for the 12201 ← 03301, 11101 ← 10002 and 12201 ← 11102 self-broadened CO2 Q-branches

NASA Astrophysics Data System (ADS)

Al Mashwood, Abdullah; Predoi-Cross, Adriana; Devi, V. Malathy; Rozario, Hoimonti; Billinghurst, Brant

2018-06-01

Pure CO2 spectra recorded at room temperature and different pressures (0.2-140 Torr) have been analyzed with the help of a fitting routine that takes into account asymmetries arising in the spectral lines due to pressure induced effects such as line mixing. The fitting procedure used in this study allows one to adjust the ro-vibrational constants for the band rather than fitting for individual line parameters. These constrained parameters greatly reduce the measurement uncertainties and allow us to observe the behavior of the weak lines corresponding to high J quantum numbers. We have also calculated line mixing parameters using approximations based on exponential nature of the energy difference between ground and upper vibrational states involved in the ro-vibrational band transitions. The calculated results show good agreement when compared with the experimentally determined parameters.

Immediate list recall as a measure of short-term episodic memory: insights from the serial position effect and item response theory.

PubMed

Gavett, Brandon E; Horwitz, Julie E

2012-03-01

The serial position effect shows that two interrelated cognitive processes underlie immediate recall of a supraspan word list. The current study used item response theory (IRT) methods to determine whether the serial position effect poses a threat to the construct validity of immediate list recall as a measure of verbal episodic memory. Archival data were obtained from a national sample of 4,212 volunteers aged 28-84 in the Midlife Development in the United States study. Telephone assessment yielded item-level data for a single immediate recall trial of the Rey Auditory Verbal Learning Test (RAVLT). Two parameter logistic IRT procedures were used to estimate item parameters and the Q(1) statistic was used to evaluate item fit. A two-dimensional model better fit the data than a unidimensional model, supporting the notion that list recall is influenced by two underlying cognitive processes. IRT analyses revealed that 4 of the 15 RAVLT items (1, 12, 14, and 15) were misfit (p < .05). Item characteristic curves for items 14 and 15 decreased monotonically, implying an inverse relationship between the ability level and the probability of recall. Elimination of the four misfit items provided better fit to the data and met necessary IRT assumptions. Performance on a supraspan list learning test is influenced by multiple cognitive abilities; failure to account for the serial position of words decreases the construct validity of the test as a measure of episodic memory and may provide misleading results. IRT methods can ameliorate these problems and improve construct validity.

Continuous Odour Measurement with Chemosensor Systems

NASA Astrophysics Data System (ADS)

Boeker, Peter; Haas, T.; Diekmann, B.; Lammer, P. Schulze

2009-05-01

The continuous odour measurement is a challenging task for chemosensor systems. Firstly, a long term and stable measurement mode must be guaranteed in order to preserve the validity of the time consuming and expensive olfactometric calibration data. Secondly, a method is needed to deal with the incoming sensor data. The continuous online detection of signal patterns, the correlated gas emission and the assigned odour data is essential for the continuous odour measurement. Thirdly, a severe danger of over-fitting in the process of the odour calibration is present, because of the high measurement uncertainty of the olfactometry. In this contribution we present a technical solution for continuous measurements comprising of a hybrid QMB-sensor array and electrochemical cells. A set of software tools enables the efficient data processing and calibration and computes the calibration parameters. The internal software of the measurement systems microcontroller processes the calibration parameters online for the output of the desired odour information.

Modeling a Material's Instantaneous Velocity during Acceleration Driven by a Detonation's Gas-Push Process

NASA Astrophysics Data System (ADS)

Backofen, Joseph E.

2005-07-01

This paper will describe both the scientific findings and the model developed in order to quantfy a material's instantaneous velocity versus position, time, or the expansion ratio of an explosive's gaseous products while its gas pressure is accelerating the material. The formula derived to represent this gas-push process for the 2nd stage of the BRIGS Two-Step Detonation Propulsion Model was found to fit very well the published experimental data available for twenty explosives. When the formula's two key parameters (the ratio Vinitial / Vfinal and ExpansionRatioFinal) were adjusted slightly from the average values describing closely many explosives to values representing measured data for a particular explosive, the formula's representation of that explosive's gas-push process was improved. The time derivative of the velocity formula representing acceleration and/or pressure compares favorably to Jones-Wilkins-Lee equation-of-state model calculations performed using published JWL parameters.

The H,G_1,G_2 photometric system with scarce observational data

NASA Astrophysics Data System (ADS)

Penttilä, A.; Granvik, M.; Muinonen, K.; Wilkman, O.

2014-07-01

The H,G_1,G_2 photometric system was officially adopted at the IAU General Assembly in Beijing, 2012. The system replaced the H,G system from 1985. The 'photometric system' is a parametrized model V(α; params) for the magnitude-phase relation of small Solar System bodies, and the main purpose is to predict the magnitude at backscattering, H := V(0°), i.e., the (absolute) magnitude of the object. The original H,G system was designed using the best available data in 1985, but since then new observations have been made showing certain features, especially near backscattering, to which the H,G function has troubles adjusting to. The H,G_1,G_2 system was developed especially to address these issues [1]. With a sufficient number of high-accuracy observations and with a wide phase-angle coverage, the H,G_1,G_2 system performs well. However, with scarce low-accuracy data the system has troubles producing a reliable fit, as would any other three-parameter nonlinear function. Therefore, simultaneously with the H,G_1,G_2 system, a two-parameter version of the model, the H,G_{12} system, was introduced [1]. The two-parameter version ties the parameters G_1,G_2 into a single parameter G_{12} by a linear relation, and still uses the H,G_1,G_2 system in the background. This version dramatically improves the possibility to receive a reliable phase-curve fit to scarce data. The amount of observed small bodies is increasing all the time, and so is the need to produce estimates for the absolute magnitude/diameter/albedo and other size/composition related parameters. The lack of small-phase-angle observations is especially topical for near-Earth objects (NEOs). With these, even the two- parameter version faces problems. The previous procedure with the H,G system in such circumstances has been that the G-parameter has been fixed to some constant value, thus only fitting a single-parameter function. In conclusion, there is a definitive need for a reliable procedure to produce photometric fits to very scarce and low-accuracy data. There are a few details that should be considered with the H,G_1,G_2 or H,G_{12} systems with scarce data. The first point is the distribution of errors in the fit. The original H,G system allowed linear regression in the flux space, thus making the estimation computationally easier. The same principle was repeated with the H,G_1,G_2 system. There is, however, a major hidden assumption in the transformation. With regression modeling, the residuals should be distributed symmetrically around zero. If they are normally distributed, even better. We have noticed that, at least with some NEO observations, the residuals in the flux space are far from symmetric, and seem to be much more symmetric in the magnitude space. The result is that the nonlinear fit in magnitude space is far more reliable than the linear fit in the flux space. Since the computers and nonlinear regression algorithms are efficient enough, we conclude that, in many cases, with low-accuracy data the nonlinear fit should be favored. In fact, there are statistical procedures that should be employed with the photometric fit. At the moment, the choice between the three-parameter and two-parameter versions is simply based on subjective decision-making. By checking parameter error and model comparison statistics, the choice could be done objectively. Similarly, the choice between the linear fit in flux space and the nonlinear fit in magnitude space should be based on a statistical test of unbiased residuals. Furthermore, the so-called Box-Cox transform could be employed to find an optimal transformation somewhere between the magnitude and flux spaces. The H,G_1,G_2 system is based on cubic splines, and is therefore a bit more complicated to implement than a system with simpler basis functions. The same applies to a complete program that would automatically choose the best transforms to data, test if two- or three-parameter version of the model should be fitted, and produce the fitted parameters with their error estimates. Our group has already made implementations of the H,G_1,G_2 system publicly available [2]. We plan to implement the abovementioned improvements to the system and make also these tools public.

Physicochemical and thermodynamic investigation of hydrogen absorption and desorption in LaNi3.8Al1.0Mn0.2 using the statistical physics modeling

NASA Astrophysics Data System (ADS)

Bouaziz, Nadia; Ben Manaa, Marwa; Ben Lamine, Abdelmottaleb

2018-06-01

In the present work, experimental absorption and desorption isotherms of hydrogen in LaNi3.8Al1.0Mn0.2 metal at two temperatures (T = 433 K, 453 K) have been fitted using a monolayer model with two energies treated by statistical physics formalism by means of the grand canonical ensemble. Six parameters of the model are adjusted, namely the numbers of hydrogen atoms per site nα and nβ, the receptor site densities Nmα and Nmβ, and the energetic parameters Pα and Pβ. The behaviors of these parameters are discussed in relationship with temperature of absorption/desorption process. Then, a dynamic investigation of the simultaneous evolution with pressure of the two α and β phases in the absorption and desorption phenomena using the adjustment parameters. Thanks to the energetic parameters, we calculated the sorption energies which are typically ranged between 276.107 and 310.711 kJ/mol for absorption process and between 277.01 and 310.9 kJ/mol for desorption process comparable to usual chemical bond energies. The calculated thermodynamic parameters such as entropy, Gibbs free energy and internal energy from experimental data showed that the absorption/desorption of hydrogen in LaNi3.8Al1.0Mn0.2 alloy was feasible, spontaneous and exothermic in nature.

The numerical simulation and experiment on extrusion roller embossing of light diffusion plate with micro-structure

NASA Astrophysics Data System (ADS)

Zang, Gongzheng; Fu, Zhihong; Zhang, Lei; Wan, Yue

2018-01-01

Extrusion roller embossing process has demonstrated the ability to produce polymer film with micro-structure. However the influence of various parameters on the forming quality has not been understood clearly. In this paper, a light diffusion plate with semi cylindrical micro-structure array as the research object, the influence of the main processing parameters such as roller speed, pressuring distance and polymer film temperature to the rolling quality was investigated in detail by simulation and experimental methods. The results show that the thickness of the light diffusion plate and the micro-structure fitting diameter increases with the increasing of the roll speed and the polymer film temperature, and decreases with the increasing of the pressing distance. Besides, the simulation results conformed well to the experimental results.

An empirical model for dissolution profile and its application to floating dosage forms.

PubMed

Weiss, Michael; Kriangkrai, Worawut; Sungthongjeen, Srisagul

2014-06-02

A sum of two inverse Gaussian functions is proposed as a highly flexible empirical model for fitting of in vitro dissolution profiles. The model was applied to quantitatively describe theophylline release from effervescent multi-layer coated floating tablets containing different amounts of the anti-tacking agents talc or glyceryl monostearate. Model parameters were estimated by nonlinear regression (mixed-effects modeling). The estimated parameters were used to determine the mean dissolution time, as well as to reconstruct the time course of release rate for each formulation, whereby the fractional release rate can serve as a diagnostic tool for classification of dissolution processes. The approach allows quantification of dissolution behavior and could provide additional insights into the underlying processes. Copyright © 2014 Elsevier B.V. All rights reserved.

Capturing Revolute Motion and Revolute Joint Parameters with Optical Tracking

NASA Astrophysics Data System (ADS)

Antonya, C.

2017-12-01

Optical tracking of users and various technical systems are becoming more and more popular. It consists of analysing sequence of recorded images using video capturing devices and image processing algorithms. The returned data contains mainly point-clouds, coordinates of markers or coordinates of point of interest. These data can be used for retrieving information related to the geometry of the objects, but also to extract parameters for the analytical model of the system useful in a variety of computer aided engineering simulations. The parameter identification of joints deals with extraction of physical parameters (mainly geometric parameters) for the purpose of constructing accurate kinematic and dynamic models. The input data are the time-series of the marker’s position. The least square method was used for fitting the data into different geometrical shapes (ellipse, circle, plane) and for obtaining the position and orientation of revolute joins.

Reverse engineering of aircraft wing data using a partial differential equation surface model

NASA Astrophysics Data System (ADS)

Huband, Jacalyn Mann

Reverse engineering is a multi-step process used in industry to determine a production representation of an existing physical object. This representation is in the form of mathematical equations that are compatible with computer-aided design and computer-aided manufacturing (CAD/CAM) equipment. The four basic steps to the reverse engineering process are data acquisition, data separation, surface or curve fitting, and CAD/CAM production. The surface fitting step determines the design representation of the object, and thus is critical to the success or failure of the reverse engineering process. Although surface fitting methods described in the literature are used to model a variety of surfaces, they are not suitable for reversing aircraft wings. In this dissertation, we develop and demonstrate a new strategy for reversing a mathematical representation of an aircraft wing. The basis of our strategy is to take an aircraft design model and determine if an inverse model can be derived. A candidate design model for this research is the partial differential equation (PDE) surface model, proposed by Bloor and Wilson and used in the Rapid Airplane Parameter Input Design (RAPID) tool at the NASA-LaRC Geolab. There are several basic mathematical problems involved in reversing the PDE surface model: (i) deriving a computational approximation of the surface function; (ii) determining a radial parametrization of the wing; (iii) choosing mathematical models or classes of functions for representation of the boundary functions; (iv) fitting the boundary data points by the chosen boundary functions; and (v) simultaneously solving for the axial parameterization and the derivative boundary functions. The study of the techniques to solve the above mathematical problems has culminated in a reverse PDE surface model and two reverse PDE surface algorithms. One reverse PDE surface algorithm recovers engineering design parameters for the RAPID tool from aircraft wing data and the other generates a PDE surface model with spline boundary functions from an arbitrary set of grid points. Our numerical tests show that the reverse PDE surface model and the reverse PDE surface algorithms can be used for the reverse engineering of aircraft wing data.

CEval: All-in-one software for data processing and statistical evaluations in affinity capillary electrophoresis.

PubMed

Dubský, Pavel; Ördögová, Magda; Malý, Michal; Riesová, Martina

2016-05-06

We introduce CEval software (downloadable for free at echmet.natur.cuni.cz) that was developed for quicker and easier electrophoregram evaluation and further data processing in (affinity) capillary electrophoresis. This software allows for automatic peak detection and evaluation of common peak parameters, such as its migration time, area, width etc. Additionally, the software includes a nonlinear regression engine that performs peak fitting with the Haarhoff-van der Linde (HVL) function, including automated initial guess of the HVL function parameters. HVL is a fundamental peak-shape function in electrophoresis, based on which the correct effective mobility of the analyte represented by the peak is evaluated. Effective mobilities of an analyte at various concentrations of a selector can be further stored and plotted in an affinity CE mode. Consequently, the mobility of the free analyte, μA, mobility of the analyte-selector complex, μAS, and the apparent complexation constant, K('), are first guessed automatically from the linearized data plots and subsequently estimated by the means of nonlinear regression. An option that allows two complexation dependencies to be fitted at once is especially convenient for enantioseparations. Statistical processing of these data is also included, which allowed us to: i) express the 95% confidence intervals for the μA, μAS and K(') least-squares estimates, ii) do hypothesis testing on the estimated parameters for the first time. We demonstrate the benefits of the CEval software by inspecting complexation of tryptophan methyl ester with two cyclodextrins, neutral heptakis(2,6-di-O-methyl)-β-CD and charged heptakis(6-O-sulfo)-β-CD. Copyright © 2016 Elsevier B.V. All rights reserved.

Isotherm Modelling, Kinetic Study and Optimization of Batch Parameters Using Response Surface Methodology for Effective Removal of Cr(VI) Using Fungal Biomass

PubMed Central

Chidambaram, Ramalingam

2015-01-01

Biosorption is a promising alternative method to replace the existing conventional technique for Cr(VI) removal from the industrial effluent. In the present experimental design, the removal of Cr(VI) from the aqueous solution was studied by Aspergillus niger MSR4 under different environmental conditions in the batch systems. The optimum conditions of biosorption were determined by investigating pH (2.0) and temperature (27°C). The effects of parameters such as biomass dosage (g/L), initial Cr(VI) concentration (mg/L) and contact time (min) on Cr(VI) biosorption were analyzed using a three parameter Box–Behnken design (BBD). The experimental data well fitted to the Langmuir isotherm, in comparison to the other isotherm models tested. The results of the D-R isotherm model suggested that a chemical ion-exchange mechanism was involved in the biosorption process. The biosorption process followed the pseudo-second-order kinetic model, which indicates that the rate limiting step is chemisorption process. Fourier transform infrared (FT-IR) spectroscopic studies revealed the possible involvement of functional groups, such as hydroxyl, carboxyl, amino and carbonyl group in the biosorption process. The thermodynamic parameters for Cr(VI) biosorption were also calculated, and the negative ∆Gº values indicated the spontaneous nature of biosorption process. PMID:25786227

SU-F-BRD-16: Relative Biological Effectiveness of Double-Strand Break Induction for Modeling Cell Survival in Pristine Proton Beams of Different Dose-Averaged Linear Energy Transfers

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

Peeler, C; Bronk, L; UT Graduate School of Biomedical Sciences at Houston, Houston, TX

2015-06-15

Purpose: High throughput in vitro experiments assessing cell survival following proton radiation indicate that both the alpha and the beta parameters of the linear quadratic model increase with increasing proton linear energy transfer (LET). We investigated the relative biological effectiveness (RBE) of double-strand break (DSB) induction as a means of explaining the experimental results. Methods: Experiments were performed with two lung cancer cell lines and a range of proton LET values (0.94 – 19.4 keV/µm) using an experimental apparatus designed to irradiate cells in a 96 well plate such that each column encounters protons of different dose-averaged LET (LETd). Traditionalmore » linear quadratic survival curve fitting was performed, and alpha, beta, and RBE values obtained. Survival curves were also fit with a model incorporating RBE of DSB induction as the sole fit parameter. Fitted values of the RBE of DSB induction were then compared to values obtained using Monte Carlo Damage Simulation (MCDS) software and energy spectra calculated with Geant4. Other parameters including alpha, beta, and number of DSBs were compared to those obtained from traditional fitting. Results: Survival curve fitting with RBE of DSB induction yielded alpha and beta parameters that increase with proton LETd, which follows from the standard method of fitting; however, relying on a single fit parameter provided more consistent trends. The fitted values of RBE of DSB induction increased beyond what is predicted from MCDS data above proton LETd of approximately 10 keV/µm. Conclusion: In order to accurately model in vitro proton irradiation experiments performed with high throughput methods, the RBE of DSB induction must increase more rapidly than predicted by MCDS above LETd of 10 keV/µm. This can be explained by considering the increased complexity of DSBs or the nature of intra-track pairwise DSB interactions in this range of LETd values. NIH Grant 2U19CA021239-35.« less

Predicting bioactive glass properties from the molecular chemical composition: glass transition temperature.

PubMed

O'Donnell, Matthew D

2011-05-01

The glass transition temperature (T(g)) of inorganic glasses is an important parameter than can be used to correlate with other glass properties, such as dissolution rate, which governs in vitro and in vivo bioactivity. Seven bioactive glass compositional series reported in the literature (77 in total) were analysed here with T(g) values obtained by a number of different methods: differential thermal analysis, differential scanning calorimetry and dilatometry. An iterative least-squares fitting method was used to correlate T(g) from thermal analysis of these compositions with the levels of individual oxide and fluoride components in the glasses. When all seven series were fitted a reasonable correlation was found between calculated and experimental values (R(2)=0.89). When the two compositional series that were designed in weight percentages (the remaining five were designed in molar percentage) were removed from the model an improved fit was achieved (R(2)=0.97). This study shows that T(g) for a wide range in compositions (e.g. SiO(2) content of 37.3-68.4 mol.%) can be predicted to reasonable accuracy enabling processing parameters to be predicted such as annealing, fibre-drawing and sintering temperatures. Copyright © 2011 Acta Materialia Inc. Published by Elsevier Ltd. All rights reserved.

Uncertainty Quantification in High Throughput Screening ...

EPA Pesticide Factsheets

Using uncertainty quantification, we aim to improve the quality of modeling data from high throughput screening assays for use in risk assessment. ToxCast is a large-scale screening program that analyzes thousands of chemicals using over 800 assays representing hundreds of biochemical and cellular processes, including endocrine disruption, cytotoxicity, and zebrafish development. Over 2.6 million concentration response curves are fit to models to extract parameters related to potency and efficacy. Models built on ToxCast results are being used to rank and prioritize the toxicological risk of tested chemicals and to predict the toxicity of tens of thousands of chemicals not yet tested in vivo. However, the data size also presents challenges. When fitting the data, the choice of models, model selection strategy, and hit call criteria must reflect the need for computational efficiency and robustness, requiring hard and somewhat arbitrary cutoffs. When coupled with unavoidable noise in the experimental concentration response data, these hard cutoffs cause uncertainty in model parameters and the hit call itself. The uncertainty will then propagate through all of the models built on the data. Left unquantified, this uncertainty makes it difficult to fully interpret the data for risk assessment. We used bootstrap resampling methods to quantify the uncertainty in fitting models to the concentration response data. Bootstrap resampling determines confidence intervals for

Time-ordered product expansions for computational stochastic system biology.

PubMed

Mjolsness, Eric

2013-06-01

The time-ordered product framework of quantum field theory can also be used to understand salient phenomena in stochastic biochemical networks. It is used here to derive Gillespie's stochastic simulation algorithm (SSA) for chemical reaction networks; consequently, the SSA can be interpreted in terms of Feynman diagrams. It is also used here to derive other, more general simulation and parameter-learning algorithms including simulation algorithms for networks of stochastic reaction-like processes operating on parameterized objects, and also hybrid stochastic reaction/differential equation models in which systems of ordinary differential equations evolve the parameters of objects that can also undergo stochastic reactions. Thus, the time-ordered product expansion can be used systematically to derive simulation and parameter-fitting algorithms for stochastic systems.

Design and optimization of the micro-engine turbine rotor manufacturing using the rapid prototyping technology

NASA Astrophysics Data System (ADS)

Vdovin, R. A.; Smelov, V. G.

2017-02-01

This work describes the experience in manufacturing the turbine rotor for the micro-engine. It demonstrates the design principles for the complex investment casting process combining the use of the ProCast software and the rapid prototyping techniques. At the virtual modelling stage, in addition to optimized process parameters, the casting structure was improved to obtain the defect-free section. The real production stage allowed demonstrating the performance and fitness of rapid prototyping techniques for the manufacture of geometrically-complex engine-building parts.

Studies on Thorium Adsorption Characteristics upon Activated Titanium Hydroxide Prepared from Rosetta Ilmenite Concentrate

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

Gado, M, E-mail: parq28@yahoo.com; Zaki, S

2016-01-01

The titanium hydroxide prepared from Rosetta ilmenite concentrate has been applied for Th (IV) adsorption from its acid aqueous solutions. The prepared hydroxide is first characterized by both Fourier transform infrared (FT-IR) spectrum and thermogravimetric analysis. The relevant factors affecting the adsorption process have been studied. The obtained equilibrium data fits well with the Langmuir isotherm rather than Freundlich isotherm, while the adsorption kinetic data follow the pseudo-second order model. The different thermodynamic parameters have also been calculated and indicate that the adsorption process is spontaneous.

Bayesian and Frequentist Methods for Estimating Joint Uncertainty of Freundlich Adsorption Isotherm Fitting Parameters

EPA Science Inventory

In this paper, we present methods for estimating Freundlich isotherm fitting parameters (K and N) and their joint uncertainty, which have been implemented into the freeware software platforms R and WinBUGS. These estimates were determined by both Frequentist and Bayesian analyse...

Toward a Micro-Scale Acoustic Direction-Finding Sensor with Integrated Electronic Readout

DTIC Science & Technology

2013-06-01

measurements with curve fits . . . . . . . . . . . . . . . 20 Figure 2.10 Failure testing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22...2.1 Sensor parameters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 Table 2.2 Curve fit parameters...elastic, the quantity of interest is the elastic stiffness. In a typical nanoindentation test, the loading curve is nonlinear due to combined plastic

Classification of high-resolution multi-swath hyperspectral data using Landsat 8 surface reflectance data as a calibration target and a novel histogram based unsupervised classification technique to determine natural classes from biophysically relevant fit parameters

NASA Astrophysics Data System (ADS)

McCann, C.; Repasky, K. S.; Morin, M.; Lawrence, R. L.; Powell, S. L.

2016-12-01

Compact, cost-effective, flight-based hyperspectral imaging systems can provide scientifically relevant data over large areas for a variety of applications such as ecosystem studies, precision agriculture, and land management. To fully realize this capability, unsupervised classification techniques based on radiometrically-calibrated data that cluster based on biophysical similarity rather than simply spectral similarity are needed. An automated technique to produce high-resolution, large-area, radiometrically-calibrated hyperspectral data sets based on the Landsat surface reflectance data product as a calibration target was developed and applied to three subsequent years of data covering approximately 1850 hectares. The radiometrically-calibrated data allows inter-comparison of the temporal series. Advantages of the radiometric calibration technique include the need for minimal site access, no ancillary instrumentation, and automated processing. Fitting the reflectance spectra of each pixel using a set of biophysically relevant basis functions reduces the data from 80 spectral bands to 9 parameters providing noise reduction and data compression. Examination of histograms of these parameters allows for determination of natural splitting into biophysical similar clusters. This method creates clusters that are similar in terms of biophysical parameters, not simply spectral proximity. Furthermore, this method can be applied to other data sets, such as urban scenes, by developing other physically meaningful basis functions. The ability to use hyperspectral imaging for a variety of important applications requires the development of data processing techniques that can be automated. The radiometric-calibration combined with the histogram based unsupervised classification technique presented here provide one potential avenue for managing big-data associated with hyperspectral imaging.

Characterization of the ultrasonic attenuation coefficient and its frequency dependence in a polymer gel dosimeter.

PubMed

Crescenti, Remo A; Bamber, Jeffrey C; Partridge, Mike; Bush, Nigel L; Webb, Steve

2007-11-21

Research on polymer-gel dosimetry has been driven by the need for three-dimensional dosimetry, and because alternative dosimeters are unsatisfactory or too slow for that task. Magnetic resonance tomography is currently the most well-developed technique for determining radiation-induced changes in polymer structure, but quick low-cost alternatives remain of significant interest. In previous work, ultrasound attenuation and speed of sound were found to change as a function of absorbed radiation dose in polymer-gel dosimeters, although the investigations were restricted to one ultrasound frequency. Here, the ultrasound attenuation coefficient mu in one polymer gel (MAGIC) was investigated as a function of radiation dose D and as a function of ultrasonic frequency f in a frequency range relevant for imaging dose distributions. The nonlinearity of the frequency dependence was characterized, fitting a power-law model mu = af(b); the fitting parameters were examined for potential use as additional dose readout parameters. In the observed relationship between the attenuation coefficient and dose, the slopes in a quasi-linear dose range from 0 to 30 Gy were found to vary with the gel batch but lie between 0.0222 and 0.0348 dB cm(-1) Gy(-1) at 2.3 MHz, between 0.0447 and 0.0608 dB cm(-1) Gy(-1) at 4.1 MHz and between 0.0663 and 0.0880 dB cm(-1) Gy(-1) at 6.0 MHz. The mean standard deviation of the slope for all samples and frequencies was 15.8%. The slope was greater at higher frequencies, but so were the intra-batch fluctuations and intra-sample standard deviations. Further investigations are required to overcome the observed variability, which was largely associated with the sample preparation technique, before it can be determined whether any frequency is superior to others in terms of accuracy and precision in dose determination. Nevertheless, lower frequencies will allow measurements through larger samples. The fit parameter a of the frequency dependence, describing the attenuation coefficient at 1 MHz, was found to be dose dependent, which is consistent with our expectations, as polymerization is known to be associated with increased absorption of ultrasound. No significant dose dependence was found for the fit parameter b, which describes the nonlinearity with frequency. This is consistent with the increased absorption being due to the introduction of new relaxation processes with characteristic frequencies similar to those of existing processes. The data presented here will help with optimizing the design of future 3D dose-imaging systems using ultrasound methods.

Genome-wide heterogeneity of nucleotide substitution model fit.

PubMed

Arbiza, Leonardo; Patricio, Mateus; Dopazo, Hernán; Posada, David

2011-01-01

At a genomic scale, the patterns that have shaped molecular evolution are believed to be largely heterogeneous. Consequently, comparative analyses should use appropriate probabilistic substitution models that capture the main features under which different genomic regions have evolved. While efforts have concentrated in the development and understanding of model selection techniques, no descriptions of overall relative substitution model fit at the genome level have been reported. Here, we provide a characterization of best-fit substitution models across three genomic data sets including coding regions from mammals, vertebrates, and Drosophila (24,000 alignments). According to the Akaike Information Criterion (AIC), 82 of 88 models considered were selected as best-fit models at least in one occasion, although with very different frequencies. Most parameter estimates also varied broadly among genes. Patterns found for vertebrates and Drosophila were quite similar and often more complex than those found in mammals. Phylogenetic trees derived from models in the 95% confidence interval set showed much less variance and were significantly closer to the tree estimated under the best-fit model than trees derived from models outside this interval. Although alternative criteria selected simpler models than the AIC, they suggested similar patterns. All together our results show that at a genomic scale, different gene alignments for the same set of taxa are best explained by a large variety of different substitution models and that model choice has implications on different parameter estimates including the inferred phylogenetic trees. After taking into account the differences related to sample size, our results suggest a noticeable diversity in the underlying evolutionary process. All together, we conclude that the use of model selection techniques is important to obtain consistent phylogenetic estimates from real data at a genomic scale.

The Exoplanet Simple Orbit Fitting Toolbox (ExoSOFT): An Open-source Tool for Efficient Fitting of Astrometric and Radial Velocity Data

NASA Astrophysics Data System (ADS)

Mede, Kyle; Brandt, Timothy D.

2017-03-01

We present the Exoplanet Simple Orbit Fitting Toolbox (ExoSOFT), a new, open-source suite to fit the orbital elements of planetary or stellar-mass companions to any combination of radial velocity and astrometric data. To explore the parameter space of Keplerian models, ExoSOFT may be operated with its own multistage sampling approach or interfaced with third-party tools such as emcee. In addition, ExoSOFT is packaged with a collection of post-processing tools to analyze and summarize the results. Although only a few systems have been observed with both radial velocity and direct imaging techniques, this number will increase, thanks to upcoming spacecraft and ground-based surveys. Providing both forms of data enables simultaneous fitting that can help break degeneracies in the orbital elements that arise when only one data type is available. The dynamical mass estimates this approach can produce are important when investigating the formation mechanisms and subsequent evolution of substellar companions. ExoSOFT was verified through fitting to artificial data and was implemented using the Python and Cython programming languages; it is available for public download at https://github.com/kylemede/ExoSOFT under GNU General Public License v3.

Effect of 6-month community-based exercise interventions on gait and functional fitness of an older population: a quasi-experimental study.

PubMed

Ramalho, Fátima; Santos-Rocha, Rita; Branco, Marco; Moniz-Pereira, Vera; André, Helô-Isa; Veloso, António P; Carnide, Filomena

2018-01-01

Gait ability in older adults has been associated with independent living, increased survival rates, fall prevention, and quality of life. There are inconsistent findings regarding the effects of exercise interventions in the maintenance of gait parameters. The aim of the study was to analyze the effects of a community-based periodized exercise intervention on the improvement of gait parameters and functional fitness in an older adult group compared with a non-periodized program. A quasi-experimental study with follow-up was performed in a periodized exercise group (N=15) and in a non-periodized exercise group (N=13). The primary outcomes were plantar pressure gait parameters, and the secondary outcomes were physical activity, aerobic endurance, lower limb strength, agility, and balance. These variables were recorded at baseline and after 6 months of intervention. Both programs were tailored to older adults' functional fitness level and proved to be effective in reducing the age-related decline regarding functional fitness and gait parameters. Gait parameters were sensitive to both the exercise interventions. These exercise protocols can be used by exercise professionals in prescribing community exercise programs, as well as by health professionals in promoting active aging.

Evaluation of anthropometric parameters and physical fitness in elderly Japanese.

PubMed

Miyatake, Nobuyuki; Miyachi, Motohiko; Tabata, Izumi; Numata, Takeyuki

2012-01-01

We evaluated anthropometric parameters and physical fitness in elderly Japanese. A total of 2,106 elderly Japanese (749 men and 1,357 women), aged 60-79 years, were enrolled in a cross-sectional investigation study. Anthropometric parameters and physical fitness, i.e., muscle strength and flexibility, were measured. Of the 2,106 subjects, 569 subjects (302 men and 267 women) were further evaluated for aerobic exercise level, using the ventilatory threshold (VT). Muscle strength in subjects in their 70s was significantly lower than that in subjects in their 60s in both sexes. Two hundred and twenty-nine men (30.6%) and 540 women (39.8%) were taking no medications. In men, anthropometric parameters were significantly lower and muscle strength, flexibility, and work rate at VT were significantly higher in subjects without medications than these values in subjects with medications. In women, body weight, body mass index (BMI), and abdominal circumference were significantly lower, and muscle strength was significantly higher in subjects without medications than these values in subjects with medications. This mean value may provide a useful database for evaluating anthropometric parameters and physical fitness in elderly Japanese subjects.

Tight-binding analysis of Si and GaAs ultrathin bodies with subatomic wave-function resolution

NASA Astrophysics Data System (ADS)

Tan, Yaohua P.; Povolotskyi, Michael; Kubis, Tillmann; Boykin, Timothy B.; Klimeck, Gerhard

2015-08-01

Empirical tight-binding (ETB) methods are widely used in atomistic device simulations. Traditional ways of generating the ETB parameters rely on direct fitting to bulk experiments or theoretical electronic bands. However, ETB calculations based on existing parameters lead to unphysical results in ultrasmall structures like the As-terminated GaAs ultrathin bodies (UTBs). In this work, it is shown that more transferable ETB parameters with a short interaction range can be obtained by a process of mapping ab initio bands and wave functions to ETB models. This process enables the calibration of not only the ETB energy bands but also the ETB wave functions with corresponding ab initio calculations. Based on the mapping process, ETB models of Si and GaAs are parameterized with respect to hybrid functional calculations. Highly localized ETB basis functions are obtained. Both the ETB energy bands and wave functions with subatomic resolution of UTBs show good agreement with the corresponding hybrid functional calculations. The ETB methods can then be used to explain realistically extended devices in nonequilibrium that cannot be tackled with ab initio methods.

Simulation of the detonation process of an ammonium nitrate based emulsion explosive using the lee-tarver reactive flow model

NASA Astrophysics Data System (ADS)

Ribeiro, José B.; Silva, Cristóvão; Mendes, Ricardo; Plaksin, I.; Campos, Jose

2012-03-01

The use of emulsion explosives [EEx] for processing materials (compaction, welding and forming) requires the ability to perform detailed simulations of its detonation process [DP]. Detailed numerical simulations of the DP of this kind of explosives, characterized by having a finite reaction zone thickness, are thought to be suitably performed using the Lee-Tarver reactive flow model. In this work a real coded genetic algorithm methodology was used to estimate the 15 parameters of the reaction rate equation [RRE] of that model for a particular EEx. This methodology allows, in a single optimization procedure, using only one experimental result and without the need of any starting solution, to seek for the 15 parameters of the RRE that fit the numerical to the experimental results. Mass averaging and the Plate-Gap Model have been used for the determination of the shock data used in the unreacted explosive JWL EoS assessment, and the thermochemical code THOR retrieved the data used in the detonation products JWL EoS assessment. The obtained parameters allow a reasonable description of the experimental data.

A data processing method based on tracking light spot for the laser differential confocal component parameters measurement system

NASA Astrophysics Data System (ADS)

Shao, Rongjun; Qiu, Lirong; Yang, Jiamiao; Zhao, Weiqian; Zhang, Xin

2013-12-01

We have proposed the component parameters measuring method based on the differential confocal focusing theory. In order to improve the positioning precision of the laser differential confocal component parameters measurement system (LDDCPMS), the paper provides a data processing method based on tracking light spot. To reduce the error caused by the light point moving in collecting the axial intensity signal, the image centroiding algorithm is used to find and track the center of Airy disk of the images collected by the laser differential confocal system. For weakening the influence of higher harmonic noises during the measurement, Gaussian filter is used to process the axial intensity signal. Ultimately the zero point corresponding to the focus of the objective in a differential confocal system is achieved by linear fitting for the differential confocal axial intensity data. Preliminary experiments indicate that the method based on tracking light spot can accurately collect the axial intensity response signal of the virtual pinhole, and improve the anti-interference ability of system. Thus it improves the system positioning accuracy.

Weighted spline based integration for reconstruction of freeform wavefront.

PubMed

Pant, Kamal K; Burada, Dali R; Bichra, Mohamed; Ghosh, Amitava; Khan, Gufran S; Sinzinger, Stefan; Shakher, Chandra

2018-02-10

In the present work, a spline-based integration technique for the reconstruction of a freeform wavefront from the slope data has been implemented. The slope data of a freeform surface contain noise due to their machining process and that introduces reconstruction error. We have proposed a weighted cubic spline based least square integration method (WCSLI) for the faithful reconstruction of a wavefront from noisy slope data. In the proposed method, the measured slope data are fitted into a piecewise polynomial. The fitted coefficients are determined by using a smoothing cubic spline fitting method. The smoothing parameter locally assigns relative weight to the fitted slope data. The fitted slope data are then integrated using the standard least squares technique to reconstruct the freeform wavefront. Simulation studies show the improved result using the proposed technique as compared to the existing cubic spline-based integration (CSLI) and the Southwell methods. The proposed reconstruction method has been experimentally implemented to a subaperture stitching-based measurement of a freeform wavefront using a scanning Shack-Hartmann sensor. The boundary artifacts are minimal in WCSLI which improves the subaperture stitching accuracy and demonstrates an improved Shack-Hartmann sensor for freeform metrology application.

Physical fitness profile of professional Italian firefighters: differences among age groups.

PubMed

Perroni, Fabrizio; Cignitti, Lamberto; Cortis, Cristina; Capranica, Laura

2014-05-01

Firefighters perform many tasks which require a high level of fitness and their personal safety may be compromised by the physiological aging process. The aim of the study was to evaluate strength (bench-press), power (countermovement jump), sprint (20 m) and endurance (with and without Self Contained Breathing Apparatus - S.C.B.A.) of 161 Italian firefighters recruits in relation to age groups (<25 yr; 26-30 yr; 31-35 yr; 36-40 yr; 41-42 yr). Descriptive statistics and an ANOVA were calculated to provide the physical fitness profile for each parameter and to assess differences (p < 0.05) among age groups. Anthropometric values showed an age-effect for height and BMI, while performances values showed statistical differences for strength, power, sprint tests and endurance test with S.C.B.A. Wearing the S.C.B.A., 14% of all recruits failed to complete the endurance test. We propose that the firefighters should participate in an assessment of work capacity and specific fitness programs aimed to maintain an optimal fitness level for all ages. Copyright © 2013 Elsevier Ltd and The Ergonomics Society. All rights reserved.

An improved model to estimate trapping parameters in polymeric materials and its application on normal and aged low-density polyethylenes

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

Liu, Ning, E-mail: nl4g12@soton.ac.uk; He, Miao; Alghamdi, Hisham

2015-08-14

Trapping parameters can be considered as one of the important attributes to describe polymeric materials. In the present paper, a more accurate charge dynamics model has been developed, which takes account of charge dynamics in both volts-on and off stage into simulation. By fitting with measured charge data with the highest R-square value, trapping parameters together with injection barrier of both normal and aged low-density polyethylene samples were estimated using the improved model. The results show that, after long-term ageing process, the injection barriers of both electrons and holes is lowered, overall trap depth is shallower, and trap density becomesmore » much greater. Additionally, the changes in parameters for electrons are more sensitive than those of holes after ageing.« less

The curvature of sensitometric curves for Kodak XV-2 film irradiated with photon and electron beams.

PubMed

van Battum, L J; Huizenga, H

2006-07-01

Sensitometric curves of Kodak XV-2 film, obtained in a time period of ten years with various types of equipment, have been analyzed both for photon and electron beams. The sensitometric slope in the dataset varies more than a factor of 2, which is attributed mainly to variations in developer conditions. In the literature, the single hit equation has been proposed as a model for the sensitometric curve, as with the parameters of the sensitivity and maximum optical density. In this work, the single hit equation has been translated into a polynomial like function as with the parameters of the sensitometric slope and curvature. The model has been applied to fit the sensitometric data. If the dataset is fitted for each single sensitometric curve separately, a large variation is observed for both fit parameters. When sensitometric curves are fitted simultaneously it appears that all curves can be fitted adequately with a sensitometric curvature that is related to the sensitometric slope. When fitting each curve separately, apparently measurement uncertainty hides this relation. This relation appears to be dependent only on the type of densitometer used. No significant differences between beam energies or beam modalities are observed. Using the intrinsic relation between slope and curvature in fitting sensitometric data, e.g., for pretreatment verification of intensity-modulated radiotherapy, will increase the accuracy of the sensitometric curve. A calibration at a single dose point, together with a predetermined densitometer-dependent parameter ODmax will be adequate to find the actual relation between optical density and dose.

Product development using process monitoring and NDE data fusion

NASA Astrophysics Data System (ADS)

Peterson, Todd; Bossi, Richard H.

1998-03-01

Composite process/product development relies on both process monitoring information and nondestructive evaluation measurements for determining application suitability. In the past these activities have been performed and analyzed independently. Our present approach is to present the process monitoring and NDE data together in a data fusion workstation. This methodology leads to final product acceptance based on a combined process monitoring and NDE criteria. The data fusion work station combines process parameter and NDE data in a single workspace enabling all the data to be used in the acceptance/rejection decision process. An example application is the induction welding process, a unique joining method for assembling primary composite structure, that offers significant cost and weight advantages over traditional fasted structure. The determination of the required time, temperature and pressure conditions used in the process to achieve a complete weld is being aided by the use of ultrasonic inspection techniques. Full waveform ultrasonic inspection data is employed to evaluate the quality of spar cap to skin fit, an essential element of the welding process, and is processed to find a parameter that can be used for weld acceptance. Certification of the completed weld incorporates the data fusion methodology.

Adiposity as a full mediator of the influence of cardiorespiratory fitness and inflammation in schoolchildren: The FUPRECOL Study.

PubMed

Garcia-Hermoso, A; Agostinis-Sobrinho, C; Mota, J; Santos, R M; Correa-Bautista, J E; Ramírez-Vélez, R

2017-06-01

Studies in the paediatric population have shown inconsistent associations between cardiorespiratory fitness and inflammation independently of adiposity. The purpose of this study was (i) to analyse the combined association of cardiorespiratory fitness and adiposity with high-sensitivity C-reactive protein (hs-CRP), and (ii) to determine whether adiposity acts as a mediator on the association between cardiorespiratory fitness and hs-CRP in children and adolescents. This cross-sectional study included 935 (54.7% girls) healthy children and adolescents from Bogotá, Colombia. The 20 m shuttle run test was used to estimate cardiorespiratory fitness. We assessed the following adiposity parameters: body mass index, waist circumference, and fat mass index and the sum of subscapular and triceps skinfold thickness. High sensitivity assays were used to obtain hs-CRP. Linear regression models were fitted for mediation analyses examined whether the association between cardiorespiratory fitness and hs-CRP was mediated by each of adiposity parameters according to Baron and Kenny procedures. Lower levels of hs-CRP were associated with the best schoolchildren profiles (high cardiorespiratory fitness + low adiposity) (p for trend <0.001 in the four adiposity parameters), compared with unfit and overweight (low cardiorespiratory fitness + high adiposity) counterparts. Linear regression models suggest a full mediation of adiposity on the association between cardiorespiratory fitness and hs-CRP levels. Our findings seem to emphasize the importance of obesity prevention in childhood, suggesting that having high levels of cardiorespiratory fitness may not counteract the negative consequences ascribed to adiposity on hs-CRP. Copyright © 2017 The Italian Society of Diabetology, the Italian Society for the Study of Atherosclerosis, the Italian Society of Human Nutrition, and the Department of Clinical Medicine and Surgery, Federico II University. Published by Elsevier B.V. All rights reserved.

The effect of pelleting on in situ rumen degradability of compound feed containing brown rice for dairy cows.

PubMed

Tagawa, Shin-Ichi; Yoshida, Norio; Iino, Yukihiro; Horiguchi, Ken-Ichi; Takahashi, Toshiyoshi; Watanabe, Maria; Takemura, Kei; Ito, Syuhei; Mikami, Toyoji

2017-01-01

This study was conducted to determine the effect of pelleting on in situ dry matter degradability of pelleted compound feed containing brown rice for dairy cows. Mash feed of the same composition was used as a control and the in situ study was conducted using three non-lactating Holstein steers fitted with a rumen cannula. The feeds contained 32.3% brown rice, 19.4% rapeseed meal, 11.4% wheat bran and 10.6% soybean meal (fresh weight basis). Except for moisture content, the chemical composition of the feed was not affected by pelleting. In situ dry matter disappearance of the feed increased from 0 to 2 h and after 72 h of incubation with pellet processing. Integration of the dry matter disappearance values over time revealed that degradability parameter a (soluble fraction) increased with pellet processing, whereas parameter b (potentially degradable fraction) decreased. Parameter c (fractional rate of degradation) and effective degradability (5% passage rate) were not affected by pellet processing. We concluded that pellet processing promotes rumen degradability at early incubation hours when the pelleted feed contains brown rice. © 2016 Japanese Society of Animal Science.

Exponential Sum-Fitting of Dwell-Time Distributions without Specifying Starting Parameters

PubMed Central

Landowne, David; Yuan, Bin; Magleby, Karl L.

2013-01-01

Fitting dwell-time distributions with sums of exponentials is widely used to characterize histograms of open- and closed-interval durations recorded from single ion channels, as well as for other physical phenomena. However, it can be difficult to identify the contributing exponential components. Here we extend previous methods of exponential sum-fitting to present a maximum-likelihood approach that consistently detects all significant exponentials without the need for user-specified starting parameters. Instead of searching for exponentials, the fitting starts with a very large number of initial exponentials with logarithmically spaced time constants, so that none are missed. Maximum-likelihood fitting then determines the areas of all the initial exponentials keeping the time constants fixed. In an iterative manner, with refitting after each step, the analysis then removes exponentials with negligible area and combines closely spaced adjacent exponentials, until only those exponentials that make significant contributions to the dwell-time distribution remain. There is no limit on the number of significant exponentials and no starting parameters need be specified. We demonstrate fully automated detection for both experimental and simulated data, as well as for classical exponential-sum-fitting problems. PMID:23746510

Adsorption kinetics, isotherm, and thermodynamics studies of acetyl-11-keto-β-boswellic acids (AKBA) from Boswellia serrata extract using macroporous resin.

PubMed

Niphadkar, Sonali S; Rathod, Virendra K

2017-09-14

An acetyl-11-keto-β-boswellic acid (AKBA) is potent anti-inflammatory agent found in Boswellia serrata oleogum resin. Adsorption characteristics of AKBA from B. serrata were studied using macroporous adsorbent resin to understand separation and adsorption mechanism of targeted molecules. Different macroporous resins were screened for adsorption and desorption of AKBA and Indion 830 was screened as it showed higher adsorption capacity. The kinetic equations were studied and results showed that the adsorption of AKBA on Indion 830 was well fitted to the pseudo first-order kinetic model. The influence of two parameters such as temperature (298, 303, and 308 K) and pH (5-8) on the adsorption process was also studied. The experimental data was further investigated using Langmuir, Freundlich, and Temkin isotherm models. It was observed that Langmuir isotherm model was found to be the best fit for AKBA adsorption by Indion 830 and highest adsorption capacity (50.34 mg/g) was obtained at temperature of 303 K. The values of thermodynamic parameters such as the change of Gibbs free energy (ΔG*), entropy (ΔS*), and enthalpy (ΔH*), indicated that the process of adsorption was spontaneous, favourable, and exothermic.

An investigation on the modelling of kinetics of thermal decomposition of hazardous mercury wastes.

PubMed

Busto, Yailen; M G Tack, Filip; Peralta, Luis M; Cabrera, Xiomara; Arteaga-Pérez, Luis E

2013-09-15

The kinetics of mercury removal from solid wastes generated by chlor-alkali plants were studied. The reaction order and model-free method with an isoconversional approach were used to estimate the kinetic parameters and reaction mechanism that apply to the thermal decomposition of hazardous mercury wastes. As a first approach to the understanding of thermal decomposition for this type of systems (poly-disperse and multi-component), a novel scheme of six reactions was proposed to represent the behaviour of mercury compounds in the solid matrix during the treatment. An integration-optimization algorithm was used in the screening of nine mechanistic models to develop kinetic expressions that best describe the process. The kinetic parameters were calculated by fitting each of these models to the experimental data. It was demonstrated that the D₁-diffusion mechanism appeared to govern the process at 250°C and high residence times, whereas at 450°C a combination of the diffusion mechanism (D₁) and the third order reaction mechanism (F3) fitted the kinetics of the conversions. The developed models can be applied in engineering calculations to dimension the installations and determine the optimal conditions to treat a mercury containing sludge. Copyright © 2013 Elsevier B.V. All rights reserved.

Utilization of unconventional lignocellulosic waste biomass for the biosorption of toxic triphenylmethane dye malachite green from aqueous solution.

PubMed

Selvasembian, Rangabhashiyam; P, Balasubramanian

2018-05-12

Biosorption potential of novel lignocellulosic biosorbents Musa sp. peel (MSP) and Aegle marmelos shell (AMS) was investigated for the removal of toxic triphenylmethane dye malachite green (MG), from aqueous solution. Batch experiments were performed to study the biosorption characteristics of malachite green onto lignocellulosic biosorbents as a function of initial solution pH, initial malachite green concentration, biosorbents dosage, and temperature. Biosorption equilibrium data were fitted to two and three parameters isotherm models. Three-parameter isotherm models better described the equilibrium data. The maximum monolayer biosorption capacities obtained using the Langmuir model for MG removal using MSP and AMS was 47.61 and 18.86 mg/g, respectively. The biosorption kinetic data were analyzed using pseudo-first-order, pseudo-second-order, Elovich and intraparticle diffusion models. The pseudo-second-order kinetic model best fitted the experimental data, indicated the MG biosorption using MSP and AMS as chemisorption process. The removal of MG using AMS was found as highly dependent on the process temperature. The removal efficiency of MG showed declined effect at the higher concentrations of NaCl and CaCl 2 . The regeneration test of the biosorbents toward MG removal was successful up to three cycles.

Virtual plate pre-bending for the long bone fracture based on axis pre-alignment.

PubMed

Liu, Bin; Luo, Xinjian; Huang, Rui; Wan, Chao; Zhang, Bingbing; Hu, Weihua; Yue, Zongge

2014-06-01

In this paper, a modeling and visualizing system for assisting surgeons in correctly registering for the closed fracture reduction surgery is presented. By using this system, the geometric parameters of the target fixation plate before the long bone fracture operation can be obtained. The main processing scheme consists of following steps: firstly (image data process), utilize the Curvelet transform to denoise the CT images of fracture part and then reconstruct the 3D models of the broken bones. Secondly (pre-alignment), extract the axial lines of the broken bones and spatially align them. Then drive the broken bone models to be pre-aligned. Thirdly (mesh segmentation), a method based on vertex normal feature is utilized to obtain the broken bone cross-sections mesh models. Fourthly (fine registration), the ICP (Iterative Closest Point) algorithm is used to register the cross-sections and the broken bone models are driven to achieve the fine registration posture. Lastly (plate fitting), an accurate NURBS surface fitting method is used to construct the virtual plate. The experiment proved that the obtained models of the pre-bended plates were closely bonded to the surface of the registered long bone models. Finally, the lengths, angles and other interested geometric parameters can be measured on the plate models. Copyright © 2014 Elsevier Ltd. All rights reserved.

Characterisation of group behaviour surface texturing with multi-layers fitting method

NASA Astrophysics Data System (ADS)

Kang, Zhengyang; Fu, Yonghong; Ji, Jinghu; Wang, Hao

2016-07-01

Surface texturing was widely applied in improving the tribological properties of mechanical components, but study of measurement of this technology was still insufficient. This study proposed the multi-layers fitting (MLF) method to characterise the dimples array texture surface. Based on the synergistic effect among the dimples, the 3D morphology of texture surface was rebuilt by 2D stylus profiler in the MLF method. The feasible regions of texture patterns and sensitive parameters were confirmed by non-linear programming, and the processing software of MLF method was developed based on the Matlab®. The characterisation parameters system of dimples was defined mathematically, and the accuracy of MLF method was investigated by comparison experiment. The surface texture specimens were made by laser surface texturing technology, in which high consistency of dimples' size and distribution was achieved. Then, 2D profiles of different dimples were captured by employing Hommel-T1000 stylus profiler, and the data were further processed by MLF software to rebuild 3D morphology of single dimple. The experiment results indicated that the MLF characterisation results were similar to those of Wyko T1100, the white light interference microscope. It was also found that the stability of MLF characterisation results highly depended on the number of captured cross-sections.

Optimization of electrocoagulation process for the treatment of landfill leachate

NASA Astrophysics Data System (ADS)

Huda, N.; Raman, A. A.; Ramesh, S.

2017-06-01

The main problem of landfill leachate is its diverse composition comprising of persistent organic pollutants (POPs) which must be removed before being discharge into the environment. In this study, the treatment of leachate using electrocoagulation (EC) was investigated. Iron was used as both the anode and cathode. Response surface methodology was used for experimental design and to study the effects of operational parameters. Central Composite Design was used to study the effects of initial pH, inter-electrode distance, and electrolyte concentration on color, and COD removals. The process could remove up to 84 % color and 49.5 % COD. The experimental data was fitted onto second order polynomial equations. All three factors were found to be significantly affect the color removal. On the other hand, electrolyte concentration was the most significant parameter affecting the COD removal. Numerical optimization was conducted to obtain the optimum process performance. Further work will be conducted towards integrating EC with other wastewater treatment processes such as electro-Fenton.

Global parameter estimation for thermodynamic models of transcriptional regulation.

PubMed

Suleimenov, Yerzhan; Ay, Ahmet; Samee, Md Abul Hassan; Dresch, Jacqueline M; Sinha, Saurabh; Arnosti, David N

2013-07-15

Deciphering the mechanisms involved in gene regulation holds the key to understanding the control of central biological processes, including human disease, population variation, and the evolution of morphological innovations. New experimental techniques including whole genome sequencing and transcriptome analysis have enabled comprehensive modeling approaches to study gene regulation. In many cases, it is useful to be able to assign biological significance to the inferred model parameters, but such interpretation should take into account features that affect these parameters, including model construction and sensitivity, the type of fitness calculation, and the effectiveness of parameter estimation. This last point is often neglected, as estimation methods are often selected for historical reasons or for computational ease. Here, we compare the performance of two parameter estimation techniques broadly representative of local and global approaches, namely, a quasi-Newton/Nelder-Mead simplex (QN/NMS) method and a covariance matrix adaptation-evolutionary strategy (CMA-ES) method. The estimation methods were applied to a set of thermodynamic models of gene transcription applied to regulatory elements active in the Drosophila embryo. Measuring overall fit, the global CMA-ES method performed significantly better than the local QN/NMS method on high quality data sets, but this difference was negligible on lower quality data sets with increased noise or on data sets simplified by stringent thresholding. Our results suggest that the choice of parameter estimation technique for evaluation of gene expression models depends both on quality of data, the nature of the models [again, remains to be established] and the aims of the modeling effort. Copyright © 2013 Elsevier Inc. All rights reserved.

A combined approach of generalized additive model and bootstrap with small sample sets for fault diagnosis in fermentation process of glutamate.

PubMed

Liu, Chunbo; Pan, Feng; Li, Yun

2016-07-29

Glutamate is of great importance in food and pharmaceutical industries. There is still lack of effective statistical approaches for fault diagnosis in the fermentation process of glutamate. To date, the statistical approach based on generalized additive model (GAM) and bootstrap has not been used for fault diagnosis in fermentation processes, much less the fermentation process of glutamate with small samples sets. A combined approach of GAM and bootstrap was developed for the online fault diagnosis in the fermentation process of glutamate with small sample sets. GAM was first used to model the relationship between glutamate production and different fermentation parameters using online data from four normal fermentation experiments of glutamate. The fitted GAM with fermentation time, dissolved oxygen, oxygen uptake rate and carbon dioxide evolution rate captured 99.6 % variance of glutamate production during fermentation process. Bootstrap was then used to quantify the uncertainty of the estimated production of glutamate from the fitted GAM using 95 % confidence interval. The proposed approach was then used for the online fault diagnosis in the abnormal fermentation processes of glutamate, and a fault was defined as the estimated production of glutamate fell outside the 95 % confidence interval. The online fault diagnosis based on the proposed approach identified not only the start of the fault in the fermentation process, but also the end of the fault when the fermentation conditions were back to normal. The proposed approach only used a small sample sets from normal fermentations excitements to establish the approach, and then only required online recorded data on fermentation parameters for fault diagnosis in the fermentation process of glutamate. The proposed approach based on GAM and bootstrap provides a new and effective way for the fault diagnosis in the fermentation process of glutamate with small sample sets.

On the Use of Machine Learning Techniques for the Mechanical Characterization of Soft Biological Tissues.

PubMed

Cilla, M; Pérez-Rey, I; Martínez, M A; Peña, Estefania; Martínez, Javier

2018-06-23

Motivated by the search for new strategies for fitting a material model, a new approach is explored in the present work. The use of numerical and complex algorithms based on machine learning techniques such as support vector machines for regression, bagged decision trees and artificial neural networks is proposed for solving the parameter identification of constitutive laws for soft biological tissues. First, the mathematical tools were trained with analytical uniaxial data (circumferential and longitudinal directions) as inputs, and their corresponding material parameters of the Gasser, Ogden and Holzapfel strain energy function as outputs. The train and test errors show great efficiency during the training process in finding correlations between inputs and outputs; besides, the correlation coefficients were very close to 1. Second, the tool was validated with unseen observations of analytical circumferential and longitudinal uniaxial data. The results show an excellent agreement between the prediction of the material parameters of the SEF and the analytical curves. Finally, data from real circumferential and longitudinal uniaxial tests on different cardiovascular tissues were fitted, thus the material model of these tissues was predicted. We found that the method was able to consistently identify model parameters, and we believe that the use of these numerical tools could lead to an improvement in the characterization of soft biological tissues. This article is protected by copyright. All rights reserved.

Towards Personalized Cardiology: Multi-Scale Modeling of the Failing Heart

PubMed Central

Amr, Ali; Neumann, Dominik; Georgescu, Bogdan; Seegerer, Philipp; Kamen, Ali; Haas, Jan; Frese, Karen S.; Irawati, Maria; Wirsz, Emil; King, Vanessa; Buss, Sebastian; Mereles, Derliz; Zitron, Edgar; Keller, Andreas; Katus, Hugo A.; Comaniciu, Dorin; Meder, Benjamin

2015-01-01

Background Despite modern pharmacotherapy and advanced implantable cardiac devices, overall prognosis and quality of life of HF patients remain poor. This is in part due to insufficient patient stratification and lack of individualized therapy planning, resulting in less effective treatments and a significant number of non-responders. Methods and Results State-of-the-art clinical phenotyping was acquired, including magnetic resonance imaging (MRI) and biomarker assessment. An individualized, multi-scale model of heart function covering cardiac anatomy, electrophysiology, biomechanics and hemodynamics was estimated using a robust framework. The model was computed on n=46 HF patients, showing for the first time that advanced multi-scale models can be fitted consistently on large cohorts. Novel multi-scale parameters derived from the model of all cases were analyzed and compared against clinical parameters, cardiac imaging, lab tests and survival scores to evaluate the explicative power of the model and its potential for better patient stratification. Model validation was pursued by comparing clinical parameters that were not used in the fitting process against model parameters. Conclusion This paper illustrates how advanced multi-scale models can complement cardiovascular imaging and how they could be applied in patient care. Based on obtained results, it becomes conceivable that, after thorough validation, such heart failure models could be applied for patient management and therapy planning in the future, as we illustrate in one patient of our cohort who received CRT-D implantation. PMID:26230546

An optimized knife-edge method for on-orbit MTF estimation of optical sensors using powell parameter fitting

NASA Astrophysics Data System (ADS)

Han, Lu; Gao, Kun; Gong, Chen; Zhu, Zhenyu; Guo, Yue

2017-08-01

On-orbit Modulation Transfer Function (MTF) is an important indicator to evaluate the performance of the optical remote sensors in a satellite. There are many methods to estimate MTF, such as pinhole method, slit method and so on. Among them, knife-edge method is quite efficient, easy-to-use and recommended in ISO12233 standard for the wholefrequency MTF curve acquisition. However, the accuracy of the algorithm is affected by Edge Spread Function (ESF) fitting accuracy significantly, which limits the range of application. So in this paper, an optimized knife-edge method using Powell algorithm is proposed to improve the ESF fitting precision. Fermi function model is the most popular ESF fitting model, yet it is vulnerable to the initial values of the parameters. Considering the characteristics of simple and fast convergence, Powell algorithm is applied to fit the accurate parameters adaptively with the insensitivity to the initial parameters. Numerical simulation results reveal the accuracy and robustness of the optimized algorithm under different SNR, edge direction and leaning angles conditions. Experimental results using images of the camera in ZY-3 satellite show that this method is more accurate than the standard knife-edge method of ISO12233 in MTF estimation.

Evidence of opposing fitness effects of parental heterozygosity and relatedness in a critically endangered marine turtle?

PubMed

Phillips, K P; Jorgensen, T H; Jolliffe, K G; Richardson, D S

2017-11-01

How individual genetic variability relates to fitness is important in understanding evolution and the processes affecting populations of conservation concern. Heterozygosity-fitness correlations (HFCs) have been widely used to study this link in wild populations, where key parameters that affect both variability and fitness, such as inbreeding, can be difficult to measure. We used estimates of parental heterozygosity and genetic similarity ('relatedness') derived from 32 microsatellite markers to explore the relationship between genetic variability and fitness in a population of the critically endangered hawksbill turtle, Eretmochelys imbricata. We found no effect of maternal MLH (multilocus heterozygosity) on clutch size or egg success rate, and no single-locus effects. However, we found effects of paternal MLH and parental relatedness on egg success rate that interacted in a way that may result in both positive and negative effects of genetic variability. Multicollinearity in these tests was within safe limits, and null simulations suggested that the effect was not an artefact of using paternal genotypes reconstructed from large samples of offspring. Our results could imply a tension between inbreeding and outbreeding depression in this system, which is biologically feasible in turtles: female-biased natal philopatry may elevate inbreeding risk and local adaptation, and both processes may be disrupted by male-biased dispersal. Although this conclusion should be treated with caution due to a lack of significant identity disequilibrium, our study shows the importance of considering both positive and negative effects when assessing how variation in genetic variability affects fitness in wild systems. © 2017 European Society For Evolutionary Biology. Journal of Evolutionary Biology © 2017 European Society For Evolutionary Biology.

Ion distribution in dry polyelectrolyte multilayers: a neutron reflectometry study.

PubMed

Ghoussoub, Yara E; Zerball, Maximilian; Fares, Hadi M; Ankner, John F; von Klitzing, Regine; Schlenoff, Joseph B

2018-02-28

Ultrathin films of complexed polycation poly(diallyldimethylammonium), PDADMA, and polyanion poly(styrenesulfonate), PSS, were prepared on silicon wafers using the layer-by-layer adsorption technique. When terminated with PDADMA, all films had excess PDADMA, which was balanced by counterions. Neutron reflectivity of these as-made multilayers was compared with measurements on multilayers which had been further processed to ensure 1 : 1 stoichiometry of PDADMA and PSS. The compositions of all films, including polymers and counterions, were determined experimentally rather than by fitting, reducing the number of fit parameters required to model the reflectivity. For each sample, acetate, either protiated, CH 3 COO - , or deuterated, CD 3 COO - , served as the counterion. All films were maintained dry under vacuum. Scattering length density profiles were constrained to fit reflectivity data from samples having either counterion. The best fits were obtained with uniform counterion concentrations, even for stoichiometric samples that had been exposed to PDADMA for ca. 5 minutes, showing that surprisingly fast and complete transport of excess cationic charge occurs throughout the multilayer during its construction.

Modeling and forecasting foreign exchange daily closing prices with normal inverse Gaussian

NASA Astrophysics Data System (ADS)

Teneng, Dean

2013-09-01

We fit the normal inverse Gaussian(NIG) distribution to foreign exchange closing prices using the open software package R and select best models by Käärik and Umbleja (2011) proposed strategy. We observe that daily closing prices (12/04/2008 - 07/08/2012) of CHF/JPY, AUD/JPY, GBP/JPY, NZD/USD, QAR/CHF, QAR/EUR, SAR/CHF, SAR/EUR, TND/CHF and TND/EUR are excellent fits while EGP/EUR and EUR/GBP are good fits with a Kolmogorov-Smirnov test p-value of 0.062 and 0.08 respectively. It was impossible to estimate normal inverse Gaussian parameters (by maximum likelihood; computational problem) for JPY/CHF but CHF/JPY was an excellent fit. Thus, while the stochastic properties of an exchange rate can be completely modeled with a probability distribution in one direction, it may be impossible the other way around. We also demonstrate that foreign exchange closing prices can be forecasted with the normal inverse Gaussian (NIG) Lévy process, both in cases where the daily closing prices can and cannot be modeled by NIG distribution.

Charge Transport in Nonaqueous Liquid Electrolytes: A Paradigm Shift

DTIC Science & Technology

2015-05-18

that provide inadequate descriptions of experimental data, often using empirical equations whose fitting parameters have no physical significance...provide inadequate descriptions of experimental data, often using empirical equations whose fitting parameters have no physical significance...Ea The hydrodynamic model, utilizing the Stokes equation describes isothermal conductivity, self-diffusion coefficient, and the dielectric

Predicting Performance on a Firefighter's Ability Test from Fitness Parameters

ERIC Educational Resources Information Center

Michaelides, Marcos A.; Parpa, Koulla M.; Thompson, Jerald; Brown, Barry

2008-01-01

The purpose of this project was to identify the relationships between various fitness parameters such as upper body muscular endurance, upper and lower body strength, flexibility, body composition and performance on an ability test (AT) that included simulated firefighting tasks. A second intent was to create a regression model that would predict…

New analysis strategies for micro aspheric lens metrology

NASA Astrophysics Data System (ADS)

Gugsa, Solomon Abebe

Effective characterization of an aspheric micro lens is critical for understanding and improving processing in micro-optic manufacturing. Since most microlenses are plano-convex, where the convex geometry is a conic surface, current practice is often limited to obtaining an estimate of the lens conic constant, which average out the surface geometry that departs from an exact conic surface and any addition surface irregularities. We have developed a comprehensive approach of estimating the best fit conic and its uncertainty, and in addition propose an alternative analysis that focuses on surface errors rather than best-fit conic constant. We describe our new analysis strategy based on the two most dominant micro lens metrology methods in use today, namely, scanning white light interferometry (SWLI) and phase shifting interferometry (PSI). We estimate several parameters from the measurement. The major uncertainty contributors for SWLI are the estimates of base radius of curvature, the aperture of the lens, the sag of the lens, noise in the measurement, and the center of the lens. In the case of PSI the dominant uncertainty contributors are noise in the measurement, the radius of curvature, and the aperture. Our best-fit conic procedure uses least squares minimization to extract a best-fit conic value, which is then subjected to a Monte Carlo analysis to capture combined uncertainty. In our surface errors analysis procedure, we consider the surface errors as the difference between the measured geometry and the best-fit conic surface or as the difference between the measured geometry and the design specification for the lens. We focus on a Zernike polynomial description of the surface error, and again a Monte Carlo analysis is used to estimate a combined uncertainty, which in this case is an uncertainty for each Zernike coefficient. Our approach also allows us to investigate the effect of individual uncertainty parameters and measurement noise on both the best-fit conic constant analysis and the surface errors analysis, and compare the individual contributions to the overall uncertainty.

Combined Effects of Lignosus rhinocerotis Supplementation and Resistance Training on Isokinetic Muscular Strength and Power, Anaerobic and Aerobic Fitness Level, and Immune Parameters in Young Males.

PubMed

Chen, Chee Keong; Hamdan, Nor Faeiza; Ooi, Foong Kiew; Wan Abd Hamid, Wan Zuraida

2016-01-01

This study investigated the effects of Lignosus rhinocerotis (LRS) supplementation and resistance training (RT) on isokinetic muscular strength and power, anaerobic and aerobic fitness, and immune parameters in young males. Participants were randomly assigned to four groups: Control (C), LRS, RT, and combined RT-LRS (RT-LRS). Participants in the LRS and RT-LRS groups consumed 500 mg of LRS daily for 8 weeks. RT was conducted 3 times/week for 8 weeks for participants in the RT and RT-LRS groups. The following parameters were measured before and after the intervention period: Anthropometric data, isokinetic muscular strength and power, and anaerobic and aerobic fitness. Blood samples were also collected to determine immune parameters. Isokinetic muscular strength and power were increased ( P < 0.05) in participants of both RT and RT-LRS groups. RT-LRS group had shown increases ( P < 0.05) in shoulder extension peak torque, shoulder flexion and extension average power, knee flexion peak torque, and knee flexion and extension average power. There were also increases ( P < 0.05) in anaerobic power and capacity and aerobic fitness in this group. Similarly, RT group had increases ( P < 0.05) in shoulder flexion average power, knee flexion and extension peak torque, and knee flexion and extension average power. In addition, increases ( P < 0.05) in anaerobic power and capacity, aerobic fitness, T lymphocytes (CD3 and CD4), and B lymphocytes (CD19) counts were observed in the RT group. RT elicited increased isokinetic muscular strength and power, anaerobic and aerobic fitness, and immune parameters among young males. However, supplementation with LRS during RT did not provide additive benefits.

Combined Effects of Lignosus rhinocerotis Supplementation and Resistance Training on Isokinetic Muscular Strength and Power, Anaerobic and Aerobic Fitness Level, and Immune Parameters in Young Males

PubMed Central

Chen, Chee Keong; Hamdan, Nor Faeiza; Ooi, Foong Kiew; Wan Abd Hamid, Wan Zuraida

2016-01-01

Background: This study investigated the effects of Lignosus rhinocerotis (LRS) supplementation and resistance training (RT) on isokinetic muscular strength and power, anaerobic and aerobic fitness, and immune parameters in young males. Methods: Participants were randomly assigned to four groups: Control (C), LRS, RT, and combined RT-LRS (RT-LRS). Participants in the LRS and RT-LRS groups consumed 500 mg of LRS daily for 8 weeks. RT was conducted 3 times/week for 8 weeks for participants in the RT and RT-LRS groups. The following parameters were measured before and after the intervention period: Anthropometric data, isokinetic muscular strength and power, and anaerobic and aerobic fitness. Blood samples were also collected to determine immune parameters. Results: Isokinetic muscular strength and power were increased (P < 0.05) in participants of both RT and RT-LRS groups. RT-LRS group had shown increases (P < 0.05) in shoulder extension peak torque, shoulder flexion and extension average power, knee flexion peak torque, and knee flexion and extension average power. There were also increases (P < 0.05) in anaerobic power and capacity and aerobic fitness in this group. Similarly, RT group had increases (P < 0.05) in shoulder flexion average power, knee flexion and extension peak torque, and knee flexion and extension average power. In addition, increases (P < 0.05) in anaerobic power and capacity, aerobic fitness, T lymphocytes (CD3 and CD4), and B lymphocytes (CD19) counts were observed in the RT group. Conclusions: RT elicited increased isokinetic muscular strength and power, anaerobic and aerobic fitness, and immune parameters among young males. However, supplementation with LRS during RT did not provide additive benefits. PMID:27833721

Erratum to. Energy calibration of gamma spectra in plastic scintillators using Compton kinematics [Nucl. Instr. and Meth. A 594 (2008) 232–243

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

Siciliano, Edward R.; Ely, James H.; Kouzes, Richard T.

2009-11-01

In recent work at our laboratory, we were re-examining our data and found an inconsistency between the values listed for 137Cs in Table 2 (Siciliano et al. 2008) and results plotted for that source in Figures 11 and 12. In the course of fitting the parabolic function (Equation 4) to the Compton maxima, two ranges of channels were used when determining the parameters for 137Cs. The parabolic fit curve shown in Figure 11 resulted from fitting channels 50 to 70. The parameters for that fit are: are: A = 0.972(12), B = 1.42(24) x 10 -3, and C NO =more » 60.2(5). The parameters for 137Cs listed in Table 2 (and also used to determine the calibration relations in Figure 12—the main result of this paper) came from fitting the 137Cs data in channels 40 to 80. Although the curves plotted from these two different sets of parameters would be visually distinguishable in Figure 11, when incorporated with the other isotope values shown in Figure 12 to obtain the linear energy-channel fit, the 50-70 channel parameter set plus the correction from the Compton maximum to the Compton edge gives a negligible change in the slope [6.470(41) as opposed to the reported 6.454(15) keV/channel] and a small change in the intercept [41(8) as opposed to 47(3) keV] for the dashed line. The conclusions of the article therefore do not change as a result of this inconsistency.« less

Cosmological parameter extraction and biases from type Ia supernova magnitude evolution

NASA Astrophysics Data System (ADS)

Linden, S.; Virey, J.-M.; Tilquin, A.

2009-11-01

We study different one-parametric models of type Ia supernova magnitude evolution on cosmic time scales. Constraints on cosmological and supernova evolution parameters are obtained by combined fits on the actual data coming from supernovae, the cosmic microwave background, and baryonic acoustic oscillations. We find that the best-fit values imply supernova magnitude evolution such that high-redshift supernovae appear some percent brighter than would be expected in a standard cosmos with a dark energy component. However, the errors on the evolution parameters are of the same order, and data are consistent with nonevolving magnitudes at the 1σ level, except for special cases. We simulate a future data scenario where SN magnitude evolution is allowed for, and neglect the possibility of such an evolution in the fit. We find the fiducial models for which the wrong model assumption of nonevolving SN magnitude is not detectable, and for which biases on the fitted cosmological parameters are introduced at the same time. Of the cosmological parameters, the overall mass density ΩM has the strongest chances to be biased due to the wrong model assumption. Whereas early-epoch models with a magnitude offset Δ m˜ z2 show up to be not too dangerous when neglected in the fitting procedure, late epoch models with Δ m˜√{z} have high chances of undetectably biasing the fit results. Centre de Physique Théorique is UMR 6207 - “Unité Mixte de Recherche” of CNRS and of the Universities “de Provence”, “de la Mediterranée”, and “du Sud Toulon-Var” - Laboratory affiliated with FRUMAM (FR2291).

Experimental rugged fitness landscape in protein sequence space.

PubMed

Hayashi, Yuuki; Aita, Takuyo; Toyota, Hitoshi; Husimi, Yuzuru; Urabe, Itaru; Yomo, Tetsuya

2006-12-20

The fitness landscape in sequence space determines the process of biomolecular evolution. To plot the fitness landscape of protein function, we carried out in vitro molecular evolution beginning with a defective fd phage carrying a random polypeptide of 139 amino acids in place of the g3p minor coat protein D2 domain, which is essential for phage infection. After 20 cycles of random substitution at sites 12-130 of the initial random polypeptide and selection for infectivity, the selected phage showed a 1.7x10(4)-fold increase in infectivity, defined as the number of infected cells per ml of phage suspension. Fitness was defined as the logarithm of infectivity, and we analyzed (1) the dependence of stationary fitness on library size, which increased gradually, and (2) the time course of changes in fitness in transitional phases, based on an original theory regarding the evolutionary dynamics in Kauffman's n-k fitness landscape model. In the landscape model, single mutations at single sites among n sites affect the contribution of k other sites to fitness. Based on the results of these analyses, k was estimated to be 18-24. According to the estimated parameters, the landscape was plotted as a smooth surface up to a relative fitness of 0.4 of the global peak, whereas the landscape had a highly rugged surface with many local peaks above this relative fitness value. Based on the landscapes of these two different surfaces, it appears possible for adaptive walks with only random substitutions to climb with relative ease up to the middle region of the fitness landscape from any primordial or random sequence, whereas an enormous range of sequence diversity is required to climb further up the rugged surface above the middle region.

Experimental Rugged Fitness Landscape in Protein Sequence Space

PubMed Central

Hayashi, Yuuki; Aita, Takuyo; Toyota, Hitoshi; Husimi, Yuzuru; Urabe, Itaru; Yomo, Tetsuya

2006-01-01

The fitness landscape in sequence space determines the process of biomolecular evolution. To plot the fitness landscape of protein function, we carried out in vitro molecular evolution beginning with a defective fd phage carrying a random polypeptide of 139 amino acids in place of the g3p minor coat protein D2 domain, which is essential for phage infection. After 20 cycles of random substitution at sites 12–130 of the initial random polypeptide and selection for infectivity, the selected phage showed a 1.7×104-fold increase in infectivity, defined as the number of infected cells per ml of phage suspension. Fitness was defined as the logarithm of infectivity, and we analyzed (1) the dependence of stationary fitness on library size, which increased gradually, and (2) the time course of changes in fitness in transitional phases, based on an original theory regarding the evolutionary dynamics in Kauffman's n-k fitness landscape model. In the landscape model, single mutations at single sites among n sites affect the contribution of k other sites to fitness. Based on the results of these analyses, k was estimated to be 18–24. According to the estimated parameters, the landscape was plotted as a smooth surface up to a relative fitness of 0.4 of the global peak, whereas the landscape had a highly rugged surface with many local peaks above this relative fitness value. Based on the landscapes of these two different surfaces, it appears possible for adaptive walks with only random substitutions to climb with relative ease up to the middle region of the fitness landscape from any primordial or random sequence, whereas an enormous range of sequence diversity is required to climb further up the rugged surface above the middle region. PMID:17183728

Quantifying the effects of higher order coupling terms on fits using a second order Jahn-Teller Hamiltonian

NASA Astrophysics Data System (ADS)

Tran, Henry K.; Stanton, John F.; Miller, Terry A.

2018-01-01

The limitations associated with the common practice of fitting a quadratic Hamiltonian to vibronic levels of a Jahn-Teller system have been explored quantitatively. Satisfactory results for the prototypical X∼2E‧ state of Li3 are obtained from fits to both experimental spectral data and to an "artificial" spectrum calculated by a quartic Hamiltonian which accurately reproduces the adiabatic potential obtained from state-of-the-art quantum chemistry calculations. However the values of the Jahn-Teller parameters, stabilization energy, and pseudo-rotation barrier obtained from the quadratic fit differ markedly from those associated with the ab initio potential. Nonetheless the RMS deviations of the fits are not strikingly different. Guidelines are suggested for comparing parameters obtained from fits to experiment to those obtained by direct calculation, but a principal conclusion of this work is that such comparisons must be done with a high degree of caution.

Exploration and extension of an improved Riemann track fitting algorithm

NASA Astrophysics Data System (ADS)

Strandlie, A.; Frühwirth, R.

2017-09-01

Recently, a new Riemann track fit which operates on translated and scaled measurements has been proposed. This study shows that the new Riemann fit is virtually as precise as popular approaches such as the Kalman filter or an iterative non-linear track fitting procedure, and significantly more precise than other, non-iterative circular track fitting approaches over a large range of measurement uncertainties. The fit is then extended in two directions: first, the measurements are allowed to lie on plane sensors of arbitrary orientation; second, the full error propagation from the measurements to the estimated circle parameters is computed. The covariance matrix of the estimated track parameters can therefore be computed without recourse to asymptotic properties, and is consequently valid for any number of observation. It does, however, assume normally distributed measurement errors. The calculations are validated on a simulated track sample and show excellent agreement with the theoretical expectations.

Bayesian Modal Estimation of the Four-Parameter Item Response Model in Real, Realistic, and Idealized Data Sets.

PubMed

Waller, Niels G; Feuerstahler, Leah

2017-01-01

In this study, we explored item and person parameter recovery of the four-parameter model (4PM) in over 24,000 real, realistic, and idealized data sets. In the first analyses, we fit the 4PM and three alternative models to data from three Minnesota Multiphasic Personality Inventory-Adolescent form factor scales using Bayesian modal estimation (BME). Our results indicated that the 4PM fits these scales better than simpler item Response Theory (IRT) models. Next, using the parameter estimates from these real data analyses, we estimated 4PM item parameters in 6,000 realistic data sets to establish minimum sample size requirements for accurate item and person parameter recovery. Using a factorial design that crossed discrete levels of item parameters, sample size, and test length, we also fit the 4PM to an additional 18,000 idealized data sets to extend our parameter recovery findings. Our combined results demonstrated that 4PM item parameters and parameter functions (e.g., item response functions) can be accurately estimated using BME in moderate to large samples (N ⩾ 5, 000) and person parameters can be accurately estimated in smaller samples (N ⩾ 1, 000). In the supplemental files, we report annotated [Formula: see text] code that shows how to estimate 4PM item and person parameters in [Formula: see text] (Chalmers, 2012 ).

Occurrence analysis of daily rainfalls through non-homogeneous Poissonian processes

NASA Astrophysics Data System (ADS)

Sirangelo, B.; Ferrari, E.; de Luca, D. L.

2011-06-01

A stochastic model based on a non-homogeneous Poisson process, characterised by a time-dependent intensity of rainfall occurrence, is employed to explain seasonal effects of daily rainfalls exceeding prefixed threshold values. The data modelling has been performed with a partition of observed daily rainfall data into a calibration period for parameter estimation and a validation period for checking on occurrence process changes. The model has been applied to a set of rain gauges located in different geographical areas of Southern Italy. The results show a good fit for time-varying intensity of rainfall occurrence process by 2-harmonic Fourier law and no statistically significant evidence of changes in the validation period for different threshold values.

Count distribution for mixture of two exponentials as renewal process duration with applications

NASA Astrophysics Data System (ADS)

Low, Yeh Ching; Ong, Seng Huat

2016-06-01

A count distribution is presented by considering a renewal process where the distribution of the duration is a finite mixture of exponential distributions. This distribution is able to model over dispersion, a feature often found in observed count data. The computation of the probabilities and renewal function (expected number of renewals) are examined. Parameter estimation by the method of maximum likelihood is considered with applications of the count distribution to real frequency count data exhibiting over dispersion. It is shown that the mixture of exponentials count distribution fits over dispersed data better than the Poisson process and serves as an alternative to the gamma count distribution.

Experimental Investigation – Magnetic Assisted Electro Discharge Machining

NASA Astrophysics Data System (ADS)

Kesava Reddy, Chirra; Manzoor Hussain, M.; Satyanarayana, S.; Krishna, M. V. S. Murali

2018-04-01

Emerging technology needs advanced machined parts with high strength and temperature resistance, high fatigue life at low production cost with good surface quality to fit into various industrial applications. Electro discharge machine is one of the extensively used machines to manufacture advanced machined parts which cannot be machined by other traditional machine with high precision and accuracy. Machining of DIN 17350-1.2080 (High Carbon High Chromium steel), using electro discharge machining has been discussed in this paper. In the present investigation an effort is made to use permanent magnet at various positions near the spark zone to improve surface quality of the machined surface. Taguchi methodology is used to obtain optimal choice for each machining parameter such as peak current, pulse duration, gap voltage and Servo reference voltage etc. Process parameters have significant influence on machining characteristics and surface finish. Improvement in surface finish is observed when process parameters are set at optimum condition under the influence of magnetic field at various positions.

Method for extracting relevant electrical parameters from graphene field-effect transistors using a physical model

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

Boscá, A., E-mail: alberto.bosca@upm.es; Dpto. de Ingeniería Electrónica, E.T.S.I. de Telecomunicación, Universidad Politécnica de Madrid, Madrid 28040; Pedrós, J.

2015-01-28

Due to its intrinsic high mobility, graphene has proved to be a suitable material for high-speed electronics, where graphene field-effect transistor (GFET) has shown excellent properties. In this work, we present a method for extracting relevant electrical parameters from GFET devices using a simple electrical characterization and a model fitting. With experimental data from the device output characteristics, the method allows to calculate parameters such as the mobility, the contact resistance, and the fixed charge. Differentiated electron and hole mobilities and direct connection with intrinsic material properties are some of the key aspects of this method. Moreover, the method outputmore » values can be correlated with several issues during key fabrication steps such as the graphene growth and transfer, the lithographic steps, or the metalization processes, providing a flexible tool for quality control in GFET fabrication, as well as a valuable feedback for improving the material-growth process.« less

Bayesian parameter estimation for stochastic models of biological cell migration

NASA Astrophysics Data System (ADS)

Dieterich, Peter; Preuss, Roland

2013-08-01

Cell migration plays an essential role under many physiological and patho-physiological conditions. It is of major importance during embryonic development and wound healing. In contrast, it also generates negative effects during inflammation processes, the transmigration of tumors or the formation of metastases. Thus, a reliable quantification and characterization of cell paths could give insight into the dynamics of these processes. Typically stochastic models are applied where parameters are extracted by fitting models to the so-called mean square displacement of the observed cell group. We show that this approach has several disadvantages and problems. Therefore, we propose a simple procedure directly relying on the positions of the cell's trajectory and the covariance matrix of the positions. It is shown that the covariance is identical with the spatial aging correlation function for the supposed linear Gaussian models of Brownian motion with drift and fractional Brownian motion. The technique is applied and illustrated with simulated data showing a reliable parameter estimation from single cell paths.

Sulfate passivation in the lead-acid system as a capacity limiting process

NASA Astrophysics Data System (ADS)

Kappus, W.; Winsel, A.

1982-10-01

Calculations of the discharge capacity of Pb and PbO 2 electrodes as a function of various parameters are presented. They are based on the solution-precipitation mechanism for the discharge reaction and its formulation by Winsel et al. A logarithmic pore size distribution is used to fit experimental porosigrams of Pb and PbO 2 electrodes. Based on this pore size distribution the capacity is calculated as a function of current, BET surface, and porosity of the PbSO 4 diaphragm. The PbSO 4 supersaturation as the driving force of the diffusive transport is chosen as a free parameter.

Spectral Analysis of B Stars: An Application of Bayesian Statistics

NASA Astrophysics Data System (ADS)

Mugnes, J.-M.; Robert, C.

2012-12-01

To better understand the processes involved in stellar physics, it is necessary to obtain accurate stellar parameters (effective temperature, surface gravity, abundances…). Spectral analysis is a powerful tool for investigating stars, but it is also vital to reduce uncertainties at a decent computational cost. Here we present a spectral analysis method based on a combination of Bayesian statistics and grids of synthetic spectra obtained with TLUSTY. This method simultaneously constrains the stellar parameters by using all the lines accessible in observed spectra and thus greatly reduces uncertainties and improves the overall spectrum fitting. Preliminary results are shown using spectra from the Observatoire du Mont-Mégantic.

Atmospheric Properties Of T Dwarfs Inferred From Model Fits At Low Spectral Resolution

NASA Astrophysics Data System (ADS)

Giorla Godfrey, Paige A.; Rice, Emily L.; Filippazzo, Joseph C.; Douglas, Stephanie E.

2016-09-01

Brown dwarf spectral types (M, L, T, Y) correlate with spectral morphology, and generally appear to correspond with decreasing mass and effective temperature (Teff). Model fits to observed spectra suggest, however, that spectral subclasses do not share this monotonic temperature correlation, indicating that secondary parameters (gravity, metallicity, dust) significantly influence spectral morphology. We seekto disentangle the fundamental parameters that underlie the spectral type sequence of the coolest fully populated spectral class of brown dwarfs using atmosphere models. We investigate the relationship between spectral type and best fit model parameters for a sample of over 150 T dwarfs with low resolution (R 75-100) near-infrared ( 0.8-2.5 micron) SpeX Prism spectra. We use synthetic spectra from four model grids (Saumon & Marley 2008, Morley+ 2012, Saumon+ 2012, BT Settl 2013) and a Markov-Chain Monte Carlo (MCMC) analysis to determine robust best fit parameters and their uncertainties. We compare the consistency of each model grid by performing our analysis on the full spectrum and also on individual wavelength bands (Y,J,H,K). We find more consistent results between the J band and full spectrum fits and that our best fit spectral type-Teff results agree with the polynomial relationships of Stephens+2009 and Filippazzo+ 2015 using bolometric luminosities. Our analysis consists of the most extensive low resolution T dwarf model comparison to date, and lays the foundation for interpretation of cool brown dwarf and exoplanet spectra.

Impact of fitting algorithms on errors of parameter estimates in dynamic contrast-enhanced MRI

NASA Astrophysics Data System (ADS)

Debus, C.; Floca, R.; Nörenberg, D.; Abdollahi, A.; Ingrisch, M.

2017-12-01

Parameter estimation in dynamic contrast-enhanced MRI (DCE MRI) is usually performed by non-linear least square (NLLS) fitting of a pharmacokinetic model to a measured concentration-time curve. The two-compartment exchange model (2CXM) describes the compartments ‘plasma’ and ‘interstitial volume’ and their exchange in terms of plasma flow and capillary permeability. The model function can be defined by either a system of two coupled differential equations or a closed-form analytical solution. The aim of this study was to compare these two representations in terms of accuracy, robustness and computation speed, depending on parameter combination and temporal sampling. The impact on parameter estimation errors was investigated by fitting the 2CXM to simulated concentration-time curves. Parameter combinations representing five tissue types were used, together with two arterial input functions, a measured and a theoretical population based one, to generate 4D concentration images at three different temporal resolutions. Images were fitted by NLLS techniques, where the sum of squared residuals was calculated by either numeric integration with the Runge-Kutta method or convolution. Furthermore two example cases, a prostate carcinoma and a glioblastoma multiforme patient, were analyzed in order to investigate the validity of our findings in real patient data. The convolution approach yields improved results in precision and robustness of determined parameters. Precision and stability are limited in curves with low blood flow. The model parameter ve shows great instability and little reliability in all cases. Decreased temporal resolution results in significant errors for the differential equation approach in several curve types. The convolution excelled in computational speed by three orders of magnitude. Uncertainties in parameter estimation at low temporal resolution cannot be compensated by usage of the differential equations. Fitting with the convolution approach is superior in computational time, with better stability and accuracy at the same time.

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

PubMed

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

2017-11-01

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

Unleashing Empirical Equations with "Nonlinear Fitting" and "GUM Tree Calculator"

NASA Astrophysics Data System (ADS)

Lovell-Smith, J. W.; Saunders, P.; Feistel, R.

2017-10-01

Empirical equations having large numbers of fitted parameters, such as the international standard reference equations published by the International Association for the Properties of Water and Steam (IAPWS), which form the basis of the "Thermodynamic Equation of Seawater—2010" (TEOS-10), provide the means to calculate many quantities very accurately. The parameters of these equations are found by least-squares fitting to large bodies of measurement data. However, the usefulness of these equations is limited since uncertainties are not readily available for most of the quantities able to be calculated, the covariance of the measurement data is not considered, and further propagation of the uncertainty in the calculated result is restricted since the covariance of calculated quantities is unknown. In this paper, we present two tools developed at MSL that are particularly useful in unleashing the full power of such empirical equations. "Nonlinear Fitting" enables propagation of the covariance of the measurement data into the parameters using generalized least-squares methods. The parameter covariance then may be published along with the equations. Then, when using these large, complex equations, "GUM Tree Calculator" enables the simultaneous calculation of any derived quantity and its uncertainty, by automatic propagation of the parameter covariance into the calculated quantity. We demonstrate these tools in exploratory work to determine and propagate uncertainties associated with the IAPWS-95 parameters.

Modal analysis using a Fourier analyzer, curve-fitting, and modal tuning

NASA Technical Reports Server (NTRS)

Craig, R. R., Jr.; Chung, Y. T.

1981-01-01

The proposed modal test program differs from single-input methods in that preliminary data may be acquired using multiple inputs, and modal tuning procedures may be employed to define closely spaced frquency modes more accurately or to make use of frequency response functions (FRF's) which are based on several input locations. In some respects the proposed modal test proram resembles earlier sine-sweep and sine-dwell testing in that broadband FRF's are acquired using several input locations, and tuning is employed to refine the modal parameter estimates. The major tasks performed in the proposed modal test program are outlined. Data acquisition and FFT processing, curve fitting, and modal tuning phases are described and examples are given to illustrate and evaluate them.

Mössbauer and X-ray study of biodegradation of 57Fe3 O 4 magnetic nanoparticles in rat brain

NASA Astrophysics Data System (ADS)

Gabbasov, R. R.; Cherepanov, V. M.; Chuev, M. A.; Lomov, A. A.; Mischenko, I. N.; Nikitin, M. P.; Polikarpov, M. A.; Panchenko, V. Y.

2016-12-01

Biodegradation of a 57Fe3 O 4 - based dextran - stabilized ferrofluid in the ventricular cavities of the rat brain was studied by X-ray diffraction and Mössbauer spectroscopy. A two-step process of biodegradation, consisting of fast disintegration of the initial composite magnetic beads into separate superparamagnetic nanoparticles and subsequent slow dissolution of the nanoparticles has been found. Joint fitting of the couples of Mössbauer spectra measured at different temperatures in the formalism of multi-level relaxation model with one set of fitting parameters, allowed us to measure concentration of exogenous iron in the rat brain as a function of time after the injection of nanoparticles.

Bayesian inference in an item response theory model with a generalized student t link function

NASA Astrophysics Data System (ADS)

Azevedo, Caio L. N.; Migon, Helio S.

2012-10-01

In this paper we introduce a new item response theory (IRT) model with a generalized Student t-link function with unknown degrees of freedom (df), named generalized t-link (GtL) IRT model. In this model we consider only the difficulty parameter in the item response function. GtL is an alternative to the two parameter logit and probit models, since the degrees of freedom (df) play a similar role to the discrimination parameter. However, the behavior of the curves of the GtL is different from those of the two parameter models and the usual Student t link, since in GtL the curve obtained from different df's can cross the probit curves in more than one latent trait level. The GtL model has similar proprieties to the generalized linear mixed models, such as the existence of sufficient statistics and easy parameter interpretation. Also, many techniques of parameter estimation, model fit assessment and residual analysis developed for that models can be used for the GtL model. We develop fully Bayesian estimation and model fit assessment tools through a Metropolis-Hastings step within Gibbs sampling algorithm. We consider a prior sensitivity choice concerning the degrees of freedom. The simulation study indicates that the algorithm recovers all parameters properly. In addition, some Bayesian model fit assessment tools are considered. Finally, a real data set is analyzed using our approach and other usual models. The results indicate that our model fits the data better than the two parameter models.

A self-taught artificial agent for multi-physics computational model personalization.

PubMed

Neumann, Dominik; Mansi, Tommaso; Itu, Lucian; Georgescu, Bogdan; Kayvanpour, Elham; Sedaghat-Hamedani, Farbod; Amr, Ali; Haas, Jan; Katus, Hugo; Meder, Benjamin; Steidl, Stefan; Hornegger, Joachim; Comaniciu, Dorin

2016-12-01

Personalization is the process of fitting a model to patient data, a critical step towards application of multi-physics computational models in clinical practice. Designing robust personalization algorithms is often a tedious, time-consuming, model- and data-specific process. We propose to use artificial intelligence concepts to learn this task, inspired by how human experts manually perform it. The problem is reformulated in terms of reinforcement learning. In an off-line phase, Vito, our self-taught artificial agent, learns a representative decision process model through exploration of the computational model: it learns how the model behaves under change of parameters. The agent then automatically learns an optimal strategy for on-line personalization. The algorithm is model-independent; applying it to a new model requires only adjusting few hyper-parameters of the agent and defining the observations to match. The full knowledge of the model itself is not required. Vito was tested in a synthetic scenario, showing that it could learn how to optimize cost functions generically. Then Vito was applied to the inverse problem of cardiac electrophysiology and the personalization of a whole-body circulation model. The obtained results suggested that Vito could achieve equivalent, if not better goodness of fit than standard methods, while being more robust (up to 11% higher success rates) and with faster (up to seven times) convergence rate. Our artificial intelligence approach could thus make personalization algorithms generalizable and self-adaptable to any patient and any model. Copyright © 2016. Published by Elsevier B.V.

Evolutionary Computing Methods for Spectral Retrieval

NASA Technical Reports Server (NTRS)

Terrile, Richard; Fink, Wolfgang; Huntsberger, Terrance; Lee, Seugwon; Tisdale, Edwin; VonAllmen, Paul; Tinetti, Geivanna

2009-01-01

A methodology for processing spectral images to retrieve information on underlying physical, chemical, and/or biological phenomena is based on evolutionary and related computational methods implemented in software. In a typical case, the solution (the information that one seeks to retrieve) consists of parameters of a mathematical model that represents one or more of the phenomena of interest. The methodology was developed for the initial purpose of retrieving the desired information from spectral image data acquired by remote-sensing instruments aimed at planets (including the Earth). Examples of information desired in such applications include trace gas concentrations, temperature profiles, surface types, day/night fractions, cloud/aerosol fractions, seasons, and viewing angles. The methodology is also potentially useful for retrieving information on chemical and/or biological hazards in terrestrial settings. In this methodology, one utilizes an iterative process that minimizes a fitness function indicative of the degree of dissimilarity between observed and synthetic spectral and angular data. The evolutionary computing methods that lie at the heart of this process yield a population of solutions (sets of the desired parameters) within an accuracy represented by a fitness-function value specified by the user. The evolutionary computing methods (ECM) used in this methodology are Genetic Algorithms and Simulated Annealing, both of which are well-established optimization techniques and have also been described in previous NASA Tech Briefs articles. These are embedded in a conceptual framework, represented in the architecture of the implementing software, that enables automatic retrieval of spectral and angular data and analysis of the retrieved solutions for uniqueness.

Periodic orbits of hybrid systems and parameter estimation via AD.

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

Guckenheimer, John.; Phipps, Eric Todd; Casey, Richard

Rhythmic, periodic processes are ubiquitous in biological systems; for example, the heart beat, walking, circadian rhythms and the menstrual cycle. Modeling these processes with high fidelity as periodic orbits of dynamical systems is challenging because: (1) (most) nonlinear differential equations can only be solved numerically; (2) accurate computation requires solving boundary value problems; (3) many problems and solutions are only piecewise smooth; (4) many problems require solving differential-algebraic equations; (5) sensitivity information for parameter dependence of solutions requires solving variational equations; and (6) truncation errors in numerical integration degrade performance of optimization methods for parameter estimation. In addition, mathematical modelsmore » of biological processes frequently contain many poorly-known parameters, and the problems associated with this impedes the construction of detailed, high-fidelity models. Modelers are often faced with the difficult problem of using simulations of a nonlinear model, with complex dynamics and many parameters, to match experimental data. Improved computational tools for exploring parameter space and fitting models to data are clearly needed. This paper describes techniques for computing periodic orbits in systems of hybrid differential-algebraic equations and parameter estimation methods for fitting these orbits to data. These techniques make extensive use of automatic differentiation to accurately and efficiently evaluate derivatives for time integration, parameter sensitivities, root finding and optimization. The boundary value problem representing a periodic orbit in a hybrid system of differential algebraic equations is discretized via multiple-shooting using a high-degree Taylor series integration method [GM00, Phi03]. Numerical solutions to the shooting equations are then estimated by a Newton process yielding an approximate periodic orbit. A metric is defined for computing the distance between two given periodic orbits which is then minimized using a trust-region minimization algorithm [DS83] to find optimal fits of the model to a reference orbit [Cas04]. There are two different yet related goals that motivate the algorithmic choices listed above. The first is to provide a simple yet powerful framework for studying periodic motions in mechanical systems. Formulating mechanically correct equations of motion for systems of interconnected rigid bodies, while straightforward, is a time-consuming error prone process. Much of this difficulty stems from computing the acceleration of each rigid body in an inertial reference frame. The acceleration is computed most easily in a redundant set of coordinates giving the spatial positions of each body: since the acceleration is just the second derivative of these positions. Rather than providing explicit formulas for these derivatives, automatic differentiation can be employed to compute these quantities efficiently during the course of a simulation. The feasibility of these ideas was investigated by applying these techniques to the problem of locating stable walking motions for a disc-foot passive walking machine [CGMR01, Gar99, McG91]. The second goal for this work was to investigate the application of smooth optimization methods to periodic orbit parameter estimation problems in neural oscillations. Others [BB93, FUS93, VB99] have favored non-continuous optimization methods such as genetic algorithms, stochastic search methods, simulated annealing and brute-force random searches because of their perceived suitability to the landscape of typical objective functions in parameter space, particularly for multi-compartmental neural models. Here we argue that a carefully formulated optimization problem is amenable to Newton-like methods and has a sufficiently smooth landscape in parameter space that these methods can be an efficient and effective alternative. The plan of this paper is as follows. In Section 1 we provide a definition of hybrid systems that is the basis for modeling systems with discontinuities or discrete transitions. Sections 2, 3, and 4 briefly describe the Taylor series integration, periodic orbit tracking, and parameter estimation algorithms. For full treatments of these algorithms, we refer the reader to [Phi03, Cas04, CPG04]. The software implementation of these algorithms is briefly described in Section 5 with particular emphasis on the automatic differentiation software ADMC++. Finally, these algorithms are applied to the bipedal walking and Hodgkin-Huxley based neural oscillation problems discussed above in Section 6.« less

Hierarchical data-driven approach to fitting numerical relativity data for nonprecessing binary black holes with an application to final spin and radiated energy

NASA Astrophysics Data System (ADS)

Jiménez-Forteza, Xisco; Keitel, David; Husa, Sascha; Hannam, Mark; Khan, Sebastian; Pürrer, Michael

2017-03-01

Numerical relativity is an essential tool in studying the coalescence of binary black holes (BBHs). It is still computationally prohibitive to cover the BBH parameter space exhaustively, making phenomenological fitting formulas for BBH waveforms and final-state properties important for practical applications. We describe a general hierarchical bottom-up fitting methodology to design and calibrate fits to numerical relativity simulations for the three-dimensional parameter space of quasicircular nonprecessing merging BBHs, spanned by mass ratio and by the individual spin components orthogonal to the orbital plane. Particular attention is paid to incorporating the extreme-mass-ratio limit and to the subdominant unequal-spin effects. As an illustration of the method, we provide two applications, to the final spin and final mass (or equivalently: radiated energy) of the remnant black hole. Fitting to 427 numerical relativity simulations, we obtain results broadly consistent with previously published fits, but improving in overall accuracy and particularly in the approach to extremal limits and for unequal-spin configurations. We also discuss the importance of data quality studies when combining simulations from diverse sources, how detailed error budgets will be necessary for further improvements of these already highly accurate fits, and how this first detailed study of unequal-spin effects helps in choosing the most informative parameters for future numerical relativity runs.

Model selection using cosmic chronometers with Gaussian Processes

NASA Astrophysics Data System (ADS)

Melia, Fulvio; Yennapureddy, Manoj K.

2018-02-01

The use of Gaussian Processes with a measurement of the cosmic expansion rate based solely on the observation of cosmic chronometers provides a completely cosmology-independent reconstruction of the Hubble constant H(z) suitable for testing different models. The corresponding dispersion σH is smaller than ~ 9% over the entire redshift range (lesssim zlesssim 20) of the observations, rivaling many kinds of cosmological measurements available today. We use the reconstructed H(z) function to test six different cosmologies, and show that it favours the Rh=ct universe, which has only one free parameter (i.e., H0) over other models, including Planck ΛCDM . The parameters of the standard model may be re-optimized to improve the fits to the reconstructed H(z) function, but the results have smaller p-values than one finds with Rh=ct.

Thermodynamic parameters for adsorption equilibrium of heavy metals and dyes from wastewaters: Research updated.

PubMed

Chang, Yingju; Lai, Juin-Yih; Lee, Duu-Jong

2016-12-01

The standard Gibbs free energy, enthalpy and entropy change data for adsorption equilibrium reported in biosorption literature during January 2013-May2016 were listed. Since the studied biosorption systems are all near-equilibrium processes, the enthalpy and entropy change data evaluated by fitting temperature-dependent free energy data using van Hoff's equation reveal a compensation artifact. Additional confusion is introduced with arbitrarily chosen adsorbate concentration unit in bulk solution that added free energy change of mixing into the reported free energy and enthalpy change data. Different standard states may be chosen for properly describing biosorption processes; however, this makes the general comparison between data from different systems inappropriate. No conclusion should be drawn based on unjustified thermodynamic parameters reported in biosorption studies. Copyright © 2016 Elsevier Ltd. All rights reserved.

Improving RNA nearest neighbor parameters for helices by going beyond the two-state model.

PubMed

Spasic, Aleksandar; Berger, Kyle D; Chen, Jonathan L; Seetin, Matthew G; Turner, Douglas H; Mathews, David H

2018-06-01

RNA folding free energy change nearest neighbor parameters are widely used to predict folding stabilities of secondary structures. They were determined by linear regression to datasets of optical melting experiments on small model systems. Traditionally, the optical melting experiments are analyzed assuming a two-state model, i.e. a structure is either complete or denatured. Experimental evidence, however, shows that structures exist in an ensemble of conformations. Partition functions calculated with existing nearest neighbor parameters predict that secondary structures can be partially denatured, which also directly conflicts with the two-state model. Here, a new approach for determining RNA nearest neighbor parameters is presented. Available optical melting data for 34 Watson-Crick helices were fit directly to a partition function model that allows an ensemble of conformations. Fitting parameters were the enthalpy and entropy changes for helix initiation, terminal AU pairs, stacks of Watson-Crick pairs and disordered internal loops. The resulting set of nearest neighbor parameters shows a 38.5% improvement in the sum of residuals in fitting the experimental melting curves compared to the current literature set.

Temporal binning of time-correlated single photon counting data improves exponential decay fits and imaging speed

PubMed Central

Walsh, Alex J.; Sharick, Joe T.; Skala, Melissa C.; Beier, Hope T.

2016-01-01

Time-correlated single photon counting (TCSPC) enables acquisition of fluorescence lifetime decays with high temporal resolution within the fluorescence decay. However, many thousands of photons per pixel are required for accurate lifetime decay curve representation, instrument response deconvolution, and lifetime estimation, particularly for two-component lifetimes. TCSPC imaging speed is inherently limited due to the single photon per laser pulse nature and low fluorescence event efficiencies (<10%) required to reduce bias towards short lifetimes. Here, simulated fluorescence lifetime decays are analyzed by SPCImage and SLIM Curve software to determine the limiting lifetime parameters and photon requirements of fluorescence lifetime decays that can be accurately fit. Data analysis techniques to improve fitting accuracy for low photon count data were evaluated. Temporal binning of the decays from 256 time bins to 42 time bins significantly (p<0.0001) improved fit accuracy in SPCImage and enabled accurate fits with low photon counts (as low as 700 photons/decay), a 6-fold reduction in required photons and therefore improvement in imaging speed. Additionally, reducing the number of free parameters in the fitting algorithm by fixing the lifetimes to known values significantly reduced the lifetime component error from 27.3% to 3.2% in SPCImage (p<0.0001) and from 50.6% to 4.2% in SLIM Curve (p<0.0001). Analysis of nicotinamide adenine dinucleotide–lactate dehydrogenase (NADH-LDH) solutions confirmed temporal binning of TCSPC data and a reduced number of free parameters improves exponential decay fit accuracy in SPCImage. Altogether, temporal binning (in SPCImage) and reduced free parameters are data analysis techniques that enable accurate lifetime estimation from low photon count data and enable TCSPC imaging speeds up to 6x and 300x faster, respectively, than traditional TCSPC analysis. PMID:27446663

Quantifying the predictive consequences of model error with linear subspace analysis

USGS Publications Warehouse

White, Jeremy T.; Doherty, John E.; Hughes, Joseph D.

2014-01-01

All computer models are simplified and imperfect simulators of complex natural systems. The discrepancy arising from simplification induces bias in model predictions, which may be amplified by the process of model calibration. This paper presents a new method to identify and quantify the predictive consequences of calibrating a simplified computer model. The method is based on linear theory, and it scales efficiently to the large numbers of parameters and observations characteristic of groundwater and petroleum reservoir models. The method is applied to a range of predictions made with a synthetic integrated surface-water/groundwater model with thousands of parameters. Several different observation processing strategies and parameterization/regularization approaches are examined in detail, including use of the Karhunen-Loève parameter transformation. Predictive bias arising from model error is shown to be prediction specific and often invisible to the modeler. The amount of calibration-induced bias is influenced by several factors, including how expert knowledge is applied in the design of parameterization schemes, the number of parameters adjusted during calibration, how observations and model-generated counterparts are processed, and the level of fit with observations achieved through calibration. Failure to properly implement any of these factors in a prediction-specific manner may increase the potential for predictive bias in ways that are not visible to the calibration and uncertainty analysis process.

Intensity response function of the photopic negative response (PhNR): effect of age and test-retest reliability.

PubMed

Joshi, Nabin R; Ly, Emma; Viswanathan, Suresh

2017-08-01

To assess the effect of age and test-retest reliability of the intensity response function of the full-field photopic negative response (PhNR) in normal healthy human subjects. Full-field electroretinograms (ERGs) were recorded from one eye of 45 subjects, and 39 of these subjects were tested on two separate days with a Diagnosys Espion System (Lowell, MA, USA). The visual stimuli consisted of brief (<5 ms) red flashes ranging from 0.00625 to 6.4 phot cd.s/m 2 , delivered on a constant 7 cd/m 2 blue background. PhNR amplitudes were measured at its trough from baseline (BT) and from the preceding b-wave peak (PT), and b-wave amplitude was measured at its peak from the preceding a-wave trough or baseline if the a-wave was not present. The intensity response data of all three ERG measures were fitted with a generalized Naka-Rushton function to derive the saturated amplitude (V max ), semisaturation constant (K) and slope (n) parameters. Effect of age on the fit parameters was assessed with linear regression, and test-retest reliability was assessed with the Wilcoxon signed-rank test and Bland-Altman analysis. Holm's correction was applied to account for multiple comparisons. V max of BT was significantly smaller than that of PT and b-wave, and the V max of PT and b-wave was not significantly different from each other. The slope parameter n was smallest for BT and the largest for b-wave and the difference between the slopes of all three measures were statistically significant. Small differences observed in the mean values of K for the different measures did not reach statistical significance. The Wilcoxon signed-rank test indicated no significant differences between the two test visits for any of the Naka-Rushton parameters for the three ERG measures, and the Bland-Altman plots indicated that the mean difference between test and retest measurements of the different fit parameters was close to zero and within 6% of the average of the test and retest values of the respective parameters for all three ERG measurements, indicating minimal bias. While the coefficient of reliability (COR, defined as 1.96 times the standard deviation of the test and retest difference) of each fit parameter was more or less comparable across the three ERG measurements, the %COR (COR normalized to the mean test and retest measures) was generally larger for BT compared to both PT and b-wave for each fit parameter. The Naka-Rushton fit parameters did not show statistically significant changes with age for any of the ERG measures when corrections were applied for multiple comparisons. However, the V max of BT demonstrated a weak correlation with age prior to correction for multiple comparisons, and the effect of age on this parameter showed greater significance when the measure was expressed as a ratio of the V max of b-wave from the same subject. V max of the BT amplitude measure of PhNR at the best was weakly correlated with age. None of the other parameters of the Naka-Rushton fit to the intensity response data of either the PhNR or the b-wave showed any systematic changes with age. The test-retest reliability of the fit parameters for PhNR BT amplitude measurements appears to be lower than those of the PhNR PT and b-wave amplitude measurements.

Item Response Theory Modeling of the Philadelphia Naming Test.

PubMed

Fergadiotis, Gerasimos; Kellough, Stacey; Hula, William D

2015-06-01

In this study, we investigated the fit of the Philadelphia Naming Test (PNT; Roach, Schwartz, Martin, Grewal, & Brecher, 1996) to an item-response-theory measurement model, estimated the precision of the resulting scores and item parameters, and provided a theoretical rationale for the interpretation of PNT overall scores by relating explanatory variables to item difficulty. This article describes the statistical model underlying the computer adaptive PNT presented in a companion article (Hula, Kellough, & Fergadiotis, 2015). Using archival data, we evaluated the fit of the PNT to 1- and 2-parameter logistic models and examined the precision of the resulting parameter estimates. We regressed the item difficulty estimates on three predictor variables: word length, age of acquisition, and contextual diversity. The 2-parameter logistic model demonstrated marginally better fit, but the fit of the 1-parameter logistic model was adequate. Precision was excellent for both person ability and item difficulty estimates. Word length, age of acquisition, and contextual diversity all independently contributed to variance in item difficulty. Item-response-theory methods can be productively used to analyze and quantify anomia severity in aphasia. Regression of item difficulty on lexical variables supported the validity of the PNT and interpretation of anomia severity scores in the context of current word-finding models.

Biomolecular Interaction Analysis Using an Optical Surface Plasmon Resonance Biosensor: The Marquardt Algorithm vs Newton Iteration Algorithm

PubMed Central

Hu, Jiandong; Ma, Liuzheng; Wang, Shun; Yang, Jianming; Chang, Keke; Hu, Xinran; Sun, Xiaohui; Chen, Ruipeng; Jiang, Min; Zhu, Juanhua; Zhao, Yuanyuan

2015-01-01

Kinetic analysis of biomolecular interactions are powerfully used to quantify the binding kinetic constants for the determination of a complex formed or dissociated within a given time span. Surface plasmon resonance biosensors provide an essential approach in the analysis of the biomolecular interactions including the interaction process of antigen-antibody and receptors-ligand. The binding affinity of the antibody to the antigen (or the receptor to the ligand) reflects the biological activities of the control antibodies (or receptors) and the corresponding immune signal responses in the pathologic process. Moreover, both the association rate and dissociation rate of the receptor to ligand are the substantial parameters for the study of signal transmission between cells. A number of experimental data may lead to complicated real-time curves that do not fit well to the kinetic model. This paper presented an analysis approach of biomolecular interactions established by utilizing the Marquardt algorithm. This algorithm was intensively considered to implement in the homemade bioanalyzer to perform the nonlinear curve-fitting of the association and disassociation process of the receptor to ligand. Compared with the results from the Newton iteration algorithm, it shows that the Marquardt algorithm does not only reduce the dependence of the initial value to avoid the divergence but also can greatly reduce the iterative regression times. The association and dissociation rate constants, ka, kd and the affinity parameters for the biomolecular interaction, KA, KD, were experimentally obtained 6.969×105 mL·g-1·s-1, 0.00073 s-1, 9.5466×108 mL·g-1 and 1.0475×10-9 g·mL-1, respectively from the injection of the HBsAg solution with the concentration of 16ng·mL-1. The kinetic constants were evaluated distinctly by using the obtained data from the curve-fitting results. PMID:26147997

Investigating the effect of invasion characteristics on onion thrips (Thysanoptera: Thripidae) populations in onions with a temperature-driven process model.

PubMed

Mo, Jianhua; Stevens, Mark; Liu, De Li; Herron, Grant

2009-12-01

A temperature-driven process model was developed to describe the seasonal patterns of populations of onion thrips, Thrips tabaci Lindeman, in onions. The model used daily cohorts (individuals of the same developmental stage and daily age) as the population unit. Stage transitions were modeled as a logistic function of accumulated degree-days to account for variability in development rate among individuals. Daily survival was modeled as a logistic function of daily mean temperature. Parameters for development, survival, and fecundity were estimated from published data. A single invasion event was used to initiate the population process, starting at 1-100 d after onion emergence (DAE) for 10-100 d at the daily rate of 0.001-0.9 adults/plant/d. The model was validated against five observed seasonal patterns of onion thrips populations from two unsprayed sites in the Riverina, New South Wales, Australia, during 2003-2006. Performance of the model was measured by a fit index based on the proportion of variations in observed data explained by the model (R (2)) and the differences in total thrips-days between observed and predicted populations. Satisfactory matching between simulated and observed seasonal patterns was obtained within the ranges of invasion parameters tested. Model best-fit was obtained at invasion starting dates of 6-98 DAE with a daily invasion rate of 0.002-0.2 adults/plant/d and an invasion duration of 30-100 d. Under the best-fit invasion scenarios, the model closely reproduced the observed seasonal patterns, explaining 73-95% of variability in adult and larval densities during population increase periods. The results showed that small invasions of adult thrips followed by a gradual population build-up of thrips within onion crops were sufficient to bring about the observed seasonal patterns of onion thrips populations in onion. Implications of the model on timing of chemical controls are discussed.

An MLE method for finding LKB NTCP model parameters using Monte Carlo uncertainty estimates

NASA Astrophysics Data System (ADS)

Carolan, Martin; Oborn, Brad; Foo, Kerwyn; Haworth, Annette; Gulliford, Sarah; Ebert, Martin

2014-03-01

The aims of this work were to establish a program to fit NTCP models to clinical data with multiple toxicity endpoints, to test the method using a realistic test dataset, to compare three methods for estimating confidence intervals for the fitted parameters and to characterise the speed and performance of the program.

Thermodynamic assessment of Ag–Cu–In

DOE PAGES

Muzzillo, Christopher P.; Anderson, Tim

2018-01-16

The Ag-Cu-In thermodynamic material system is of interest for brazing alloys and chalcopyrite thin-film photovoltaics. To advance these applications, Ag-Cu-In was assessed and a Calphad model was developed. Binary Ag-Cu and Cu-In parameters were taken from previous assessments, while Ag-In was re-assessed. Structure-based models were employed for ..beta..-bcc(A2)-Ag 3In, ..gamma..-Ag 9In 4, and AgIn 2 to obtain good fit to enthalpy, phase boundary, and invariant reaction data for Ag-In. Ternary Ag-Cu-In parameters were optimized to achieve excellent fit to activity, enthalpy, and extensive phase equilibrium data. Relative to the previous Ag-Cu-In assessment, fit was improved while fewer parameters were used.

Effect of X-ray Line Spectra Profile Fitting with Pearson VII, Pseudo-Voigt and Generalized Fermi Functions on Asphalt Binder Aromaticity and Crystallite Parameters

NASA Astrophysics Data System (ADS)

Gebresellasie, K.; Shirokoff, J.; Lewis, J. C.

2012-12-01

X-ray line spectra profile fitting using Pearson VII, pseudo-Voigt and generalized Fermi functions was performed on asphalt binders prior to the calculation of aromaticity and crystallite size parameters. The effects of these functions on the results are presented and discussed in terms of the peak profile fit parameters, the uncertainties in calculated values that can arise owing to peak shape, peak features in the pattern and crystallite size according to the asphalt models (Yen, modified Yen or Yen-Mullins) and theories. Interpretation of these results is important in terms of evaluating the performance of asphalt binders widely used in the application of transportation systems (roads, highways, airports).

Thermodynamic assessment of Ag–Cu–In

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

Muzzillo, Christopher P.; Anderson, Tim

The Ag-Cu-In thermodynamic material system is of interest for brazing alloys and chalcopyrite thin-film photovoltaics. To advance these applications, Ag-Cu-In was assessed and a Calphad model was developed. Binary Ag-Cu and Cu-In parameters were taken from previous assessments, while Ag-In was re-assessed. Structure-based models were employed for ..beta..-bcc(A2)-Ag 3In, ..gamma..-Ag 9In 4, and AgIn 2 to obtain good fit to enthalpy, phase boundary, and invariant reaction data for Ag-In. Ternary Ag-Cu-In parameters were optimized to achieve excellent fit to activity, enthalpy, and extensive phase equilibrium data. Relative to the previous Ag-Cu-In assessment, fit was improved while fewer parameters were used.

Groundwater flow and transport modeling

USGS Publications Warehouse

Konikow, Leonard F.; Mercer, J.W.

1988-01-01

Deterministic, distributed-parameter, numerical simulation models for analyzing groundwater flow and transport problems have come to be used almost routinely during the past decade. A review of the theoretical basis and practical use of groundwater flow and solute transport models is used to illustrate the state-of-the-art. Because of errors and uncertainty in defining model parameters, models must be calibrated to obtain a best estimate of the parameters. For flow modeling, data generally are sufficient to allow calibration. For solute-transport modeling, lack of data not only limits calibration, but also causes uncertainty in process description. Where data are available, model reliability should be assessed on the basis of sensitivity tests and measures of goodness-of-fit. Some of these concepts are demonstrated by using two case histories. ?? 1988.

Estimation of time- and state-dependent delays and other parameters in functional differential equations

NASA Technical Reports Server (NTRS)

Murphy, K. A.

1988-01-01

A parameter estimation algorithm is developed which can be used to estimate unknown time- or state-dependent delays and other parameters (e.g., initial condition) appearing within a nonlinear nonautonomous functional differential equation. The original infinite dimensional differential equation is approximated using linear splines, which are allowed to move with the variable delay. The variable delays are approximated using linear splines as well. The approximation scheme produces a system of ordinary differential equations with nice computational properties. The unknown parameters are estimated within the approximating systems by minimizing a least-squares fit-to-data criterion. Convergence theorems are proved for time-dependent delays and state-dependent delays within two classes, which say essentially that fitting the data by using approximations will, in the limit, provide a fit to the data using the original system. Numerical test examples are presented which illustrate the method for all types of delay.

Estimation of time- and state-dependent delays and other parameters in functional differential equations

NASA Technical Reports Server (NTRS)

Murphy, K. A.

1990-01-01

A parameter estimation algorithm is developed which can be used to estimate unknown time- or state-dependent delays and other parameters (e.g., initial condition) appearing within a nonlinear nonautonomous functional differential equation. The original infinite dimensional differential equation is approximated using linear splines, which are allowed to move with the variable delay. The variable delays are approximated using linear splines as well. The approximation scheme produces a system of ordinary differential equations with nice computational properties. The unknown parameters are estimated within the approximating systems by minimizing a least-squares fit-to-data criterion. Convergence theorems are proved for time-dependent delays and state-dependent delays within two classes, which say essentially that fitting the data by using approximations will, in the limit, provide a fit to the data using the original system. Numerical test examples are presented which illustrate the method for all types of delay.

The Kinetics of Dissolution Revisited

NASA Astrophysics Data System (ADS)

Antonel, Paula S.; Hoijemberg, Pablo A.; Maiante, Leandro M.; Lagorio, M. Gabriela

2003-09-01

An experiment analyzing the kinetics of dissolution of a solid with cylindrical geometry in water is presented. The dissolution process is followed by measuring the solid mass and its size parameters (thickness and diameter) as a function of time. It is verified that the dissolution rate follows the Nernst model. Data treatment is compared with the dissolution of a spherical solid previously described. Kinetics, diffusion concepts, and polynomial fitting of experimental data are combined in this simple experiment.

Optimal allocation in annual plants and its implications for drought response

NASA Astrophysics Data System (ADS)

Caldararu, Silvia; Smith, Matthew; Purves, Drew

2015-04-01

The concept of plant optimality refers to the plastic behaviour of plants that results in lifetime and offspring fitness. Optimality concepts have been used in vegetation models for a variety of processes, including stomatal conductance, leaf phenology and biomass allocation. Including optimality in vegetation models has the advantages of creating process based models with a relatively low complexity in terms of parameter numbers but which are capable of reproducing complex plant behaviour. We present a general model of plant growth for annual plants based on the hypothesis that plants allocate biomass to aboveground and belowground vegetative organs in order to maintain an optimal C:N ratio. The model also represents reproductive growth through a second optimality criteria, which states that plants flower when they reach peak nitrogen uptake. We apply this model to wheat and maize crops at 15 locations corresponding to FLUXNET cropland sites. The model parameters are data constrained using a Bayesian fitting algorithm to eddy covariance data, satellite derived vegetation indices, specifically the MODIS fAPAR product and field level crop yield data. We use the model to simulate the plant drought response under the assumption of plant optimality and show that the plants maintain unstressed total biomass levels under drought for a reduction in precipitation of up to 40%. Beyond that level plant response stops being plastic and growth decreases sharply. This behaviour results simply from the optimal allocation criteria as the model includes no explicit drought sensitivity component. Models that use plant optimality concepts are a useful tool for simulation plant response to stress without the addition of artificial thresholds and parameters.

Typecasting catchments: Classification, directionality, and the pursuit of universality

NASA Astrophysics Data System (ADS)

Smith, Tyler; Marshall, Lucy; McGlynn, Brian

2018-02-01

Catchment classification poses a significant challenge to hydrology and hydrologic modeling, restricting widespread transfer of knowledge from well-studied sites. The identification of important physical, climatological, or hydrologic attributes (to varying degrees depending on application/data availability) has traditionally been the focus for catchment classification. Classification approaches are regularly assessed with regard to their ability to provide suitable hydrologic predictions - commonly by transferring fitted hydrologic parameters at a data-rich catchment to a data-poor catchment deemed similar by the classification. While such approaches to hydrology's grand challenges are intuitive, they often ignore the most uncertain aspect of the process - the model itself. We explore catchment classification and parameter transferability and the concept of universal donor/acceptor catchments. We identify the implications of the assumption that the transfer of parameters between "similar" catchments is reciprocal (i.e., non-directional). These concepts are considered through three case studies situated across multiple gradients that include model complexity, process description, and site characteristics. Case study results highlight that some catchments are more successfully used as donor catchments and others are better suited as acceptor catchments. These results were observed for both black-box and process consistent hydrologic models, as well as for differing levels of catchment similarity. Therefore, we suggest that similarity does not adequately satisfy the underlying assumptions being made in parameter regionalization approaches regardless of model appropriateness. Furthermore, we suggest that the directionality of parameter transfer is an important factor in determining the success of parameter regionalization approaches.

Testing feedback-modified dark matter haloes with galaxy rotation curves: estimation of halo parameters and consistency with ΛCDM scaling relations

NASA Astrophysics Data System (ADS)

Katz, Harley; Lelli, Federico; McGaugh, Stacy S.; Di Cintio, Arianna; Brook, Chris B.; Schombert, James M.

2017-04-01

Cosmological N-body simulations predict dark matter (DM) haloes with steep central cusps (e.g. NFW). This contradicts observations of gas kinematics in low-mass galaxies that imply the existence of shallow DM cores. Baryonic processes such as adiabatic contraction and gas outflows can, in principle, alter the initial DM density profile, yet their relative contributions to the halo transformation remain uncertain. Recent high-resolution, cosmological hydrodynamic simulations by Di Cintio et al. (DC14) predict that inner density profiles depend systematically on the ratio of stellar-to-DM mass (M*/Mhalo). Using a Markov Chain Monte Carlo approach, we test the NFW and the M*/Mhalo-dependent DC14 halo models against a sample of 147 galaxy rotation curves from the new Spitzer Photometry and Accurate Rotation Curves data set. These galaxies all have extended H I rotation curves from radio interferometry as well as accurate stellar-mass-density profiles from near-infrared photometry. The DC14 halo profile provides markedly better fits to the data compared to the NFW profile. Unlike NFW, the DC14 halo parameters found in our rotation-curve fits naturally fall within two standard deviations of the mass-concentration relation predicted by Λ cold dark matter (ΛCDM) and the stellar mass-halo mass relation inferred from abundance matching with few outliers. Halo profiles modified by baryonic processes are therefore more consistent with expectations from ΛCDM cosmology and provide better fits to galaxy rotation curves across a wide range of galaxy properties than do halo models that neglect baryonic physics. Our results offer a solution to the decade long cusp-core discrepancy.

X-ray microtomography study of the compaction process of rods under tapping.

PubMed

Fu, Yang; Xi, Yan; Cao, Yixin; Wang, Yujie

2012-05-01

We present an x-ray microtomography study of the compaction process of cylindrical rods under tapping. The process is monitored by measuring the evolution of the orientational order parameter, local, and overall packing densities as a function of the tapping number for different tapping intensities. The slow relaxation dynamics of the orientational order parameter can be well fitted with a stretched-exponential law with stretching exponents ranging from 0.9 to 1.6. The corresponding relaxation time versus tapping intensity follows an Arrhenius behavior which is reminiscent of the slow dynamics in thermal glassy systems. We also investigated the boundary effect on the ordering process and found that boundary rods order faster than interior ones. In searching for the underlying mechanism of the slow dynamics, we estimated the initial random velocities of the rods under tapping and found that the ordering process is compatible with a diffusion mechanism. The average coordination number as a function of the tapping number at different tapping intensities has also been measured, which spans a range from 6 to 8.

Convergence in parameters and predictions using computational experimental design.

PubMed

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

2013-08-06

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

The impacts of data constraints on the predictive performance of a general process-based crop model (PeakN-crop v1.0)

NASA Astrophysics Data System (ADS)

Caldararu, Silvia; Purves, Drew W.; Smith, Matthew J.

2017-04-01

Improving international food security under a changing climate and increasing human population will be greatly aided by improving our ability to modify, understand and predict crop growth. What we predominantly have at our disposal are either process-based models of crop physiology or statistical analyses of yield datasets, both of which suffer from various sources of error. In this paper, we present a generic process-based crop model (PeakN-crop v1.0) which we parametrise using a Bayesian model-fitting algorithm to three different sources: data-space-based vegetation indices, eddy covariance productivity measurements and regional crop yields. We show that the model parametrised without data, based on prior knowledge of the parameters, can largely capture the observed behaviour but the data-constrained model greatly improves both the model fit and reduces prediction uncertainty. We investigate the extent to which each dataset contributes to the model performance and show that while all data improve on the prior model fit, the satellite-based data and crop yield estimates are particularly important for reducing model error and uncertainty. Despite these improvements, we conclude that there are still significant knowledge gaps, in terms of available data for model parametrisation, but our study can help indicate the necessary data collection to improve our predictions of crop yields and crop responses to environmental changes.

A simple computational algorithm of model-based choice preference.

PubMed

Toyama, Asako; Katahira, Kentaro; Ohira, Hideki

2017-08-01

A broadly used computational framework posits that two learning systems operate in parallel during the learning of choice preferences-namely, the model-free and model-based reinforcement-learning systems. In this study, we examined another possibility, through which model-free learning is the basic system and model-based information is its modulator. Accordingly, we proposed several modified versions of a temporal-difference learning model to explain the choice-learning process. Using the two-stage decision task developed by Daw, Gershman, Seymour, Dayan, and Dolan (2011), we compared their original computational model, which assumes a parallel learning process, and our proposed models, which assume a sequential learning process. Choice data from 23 participants showed a better fit with the proposed models. More specifically, the proposed eligibility adjustment model, which assumes that the environmental model can weight the degree of the eligibility trace, can explain choices better under both model-free and model-based controls and has a simpler computational algorithm than the original model. In addition, the forgetting learning model and its variation, which assume changes in the values of unchosen actions, substantially improved the fits to the data. Overall, we show that a hybrid computational model best fits the data. The parameters used in this model succeed in capturing individual tendencies with respect to both model use in learning and exploration behavior. This computational model provides novel insights into learning with interacting model-free and model-based components.

Model Construction and Analysis of Respiration in Halobacterium salinarum.

PubMed

Talaue, Cherryl O; del Rosario, Ricardo C H; Pfeiffer, Friedhelm; Mendoza, Eduardo R; Oesterhelt, Dieter

2016-01-01

The archaeon Halobacterium salinarum can produce energy using three different processes, namely photosynthesis, oxidative phosphorylation and fermentation of arginine, and is thus a model organism in bioenergetics. Compared to its bacteriorhodopsin-driven photosynthesis, less attention has been devoted to modeling its respiratory pathway. We created a system of ordinary differential equations that models its oxidative phosphorylation. The model consists of the electron transport chain, the ATP synthase, the potassium uniport and the sodium-proton antiport. By fitting the model parameters to experimental data, we show that the model can explain data on proton motive force generation, ATP production, and the charge balancing of ions between the sodium-proton antiporter and the potassium uniport. We performed sensitivity analysis of the model parameters to determine how the model will respond to perturbations in parameter values. The model and the parameters we derived provide a resource that can be used for analytical studies of the bioenergetics of H. salinarum.

Adsorption of crystal violet with diatomite earth&carbon by a modification of hydrothermal carbonization process.

PubMed

Zhang, Yanzhuo; Li, Jun; Chen, Guanghui; Bian, Wei; Lu, Yun; Li, Wenjing; Zheng, Zhaoming; Cheng, Xiaojie

2016-01-01

The high colority and difficulty of decolorization are the most important tasks on printing and dyeing wastewater. This study investigates the ability of diatomite earth&carbon (DE&C) as an adsorbent to removal crystal violet (CV) from aqueous solutions. Fourier transform infrared spectroscopy results indicate the importance of functional groups during the adsorption of CV. The obtained N2 adsorption-desorption isotherm values accord with well IUPAC type II. Our calculations determined a surface area of 73.15 m(2) g(-1) for DE&C and an average pore diameter of 10.56 nm. Equilibrium data of the adsorption process fitted very well to the Langmuir model (R(2) > 0.99). The results of kinetics study showed that the pseudo-second-order model fitted to the experimental data well. The thermodynamic parameters were also evaluated. ΔH° <0, ΔS° > 0 and ΔG° < 0 demonstrated that the adsorption process was spontaneous and exothermic for dye. Furthermore the positive value of ΔS° reflected good affinity of the CV dye.

Stepwise sensitivity analysis from qualitative to quantitative: Application to the terrestrial hydrological modeling of a Conjunctive Surface-Subsurface Process (CSSP) land surface model

NASA Astrophysics Data System (ADS)

Gan, Yanjun; Liang, Xin-Zhong; Duan, Qingyun; Choi, Hyun Il; Dai, Yongjiu; Wu, Huan

2015-06-01

An uncertainty quantification framework was employed to examine the sensitivities of 24 model parameters from a newly developed Conjunctive Surface-Subsurface Process (CSSP) land surface model (LSM). The sensitivity analysis (SA) was performed over 18 representative watersheds in the contiguous United States to examine the influence of model parameters in the simulation of terrestrial hydrological processes. Two normalized metrics, relative bias (RB) and Nash-Sutcliffe efficiency (NSE), were adopted to assess the fit between simulated and observed streamflow discharge (SD) and evapotranspiration (ET) for a 14 year period. SA was conducted using a multiobjective two-stage approach, in which the first stage was a qualitative SA using the Latin Hypercube-based One-At-a-Time (LH-OAT) screening, and the second stage was a quantitative SA using the Multivariate Adaptive Regression Splines (MARS)-based Sobol' sensitivity indices. This approach combines the merits of qualitative and quantitative global SA methods, and is effective and efficient for understanding and simplifying large, complex system models. Ten of the 24 parameters were identified as important across different watersheds. The contribution of each parameter to the total response variance was then quantified by Sobol' sensitivity indices. Generally, parameter interactions contribute the most to the response variance of the CSSP, and only 5 out of 24 parameters dominate model behavior. Four photosynthetic and respiratory parameters are shown to be influential to ET, whereas reference depth for saturated hydraulic conductivity is the most influential parameter for SD in most watersheds. Parameter sensitivity patterns mainly depend on hydroclimatic regime, as well as vegetation type and soil texture. This article was corrected on 26 JUN 2015. See the end of the full text for details.

Unifying distance-based goodness-of-fit indicators for hydrologic model assessment

NASA Astrophysics Data System (ADS)

Cheng, Qinbo; Reinhardt-Imjela, Christian; Chen, Xi; Schulte, Achim

2014-05-01

The goodness-of-fit indicator, i.e. efficiency criterion, is very important for model calibration. However, recently the knowledge about the goodness-of-fit indicators is all empirical and lacks a theoretical support. Based on the likelihood theory, a unified distance-based goodness-of-fit indicator termed BC-GED model is proposed, which uses the Box-Cox (BC) transformation to remove the heteroscedasticity of model errors and the generalized error distribution (GED) with zero-mean to fit the distribution of model errors after BC. The BC-GED model can unify all recent distance-based goodness-of-fit indicators, and reveals the mean square error (MSE) and the mean absolute error (MAE) that are widely used goodness-of-fit indicators imply statistic assumptions that the model errors follow the Gaussian distribution and the Laplace distribution with zero-mean, respectively. The empirical knowledge about goodness-of-fit indicators can be also easily interpreted by BC-GED model, e.g. the sensitivity to high flow of the goodness-of-fit indicators with large power of model errors results from the low probability of large model error in the assumed distribution of these indicators. In order to assess the effect of the parameters (i.e. the BC transformation parameter λ and the GED kurtosis coefficient β also termed the power of model errors) of BC-GED model on hydrologic model calibration, six cases of BC-GED model were applied in Baocun watershed (East China) with SWAT-WB-VSA model. Comparison of the inferred model parameters and model simulation results among the six indicators demonstrates these indicators can be clearly separated two classes by the GED kurtosis β: β >1 and β ≤ 1. SWAT-WB-VSA calibrated by the class β >1 of distance-based goodness-of-fit indicators captures high flow very well and mimics the baseflow very badly, but it calibrated by the class β ≤ 1 mimics the baseflow very well, because first the larger value of β, the greater emphasis is put on high flow and second the derivative of GED probability density function at zero is zero as β >1, but discontinuous as β ≤ 1, and even infinity as β < 1 with which the maximum likelihood estimation can guarantee the model errors approach zero as well as possible. The BC-GED that estimates the parameters (i.e. λ and β) of BC-GED model as well as hydrologic model parameters is the best distance-based goodness-of-fit indicator because not only the model validation using groundwater levels is very good, but also the model errors fulfill the statistic assumption best. However, in some cases of model calibration with a few observations e.g. calibration of single-event model, for avoiding estimation of the parameters of BC-GED model the MAE i.e. the boundary indicator (β = 1) of the two classes, can replace the BC-GED, because the model validation of MAE is best.

On the Least-Squares Fitting of Correlated Data: a Priorivs a PosterioriWeighting

NASA Astrophysics Data System (ADS)

Tellinghuisen, Joel

1996-10-01

One of the methods in common use for analyzing large data sets is a two-step procedure, in which subsets of the full data are first least-squares fitted to a preliminary set of parameters, and the latter are subsequently merged to yield the final parameters. The second step of this procedure is properly a correlated least-squares fit and requires the variance-covariance matrices from the first step to construct the weight matrix for the merge. There is, however, an ambiguity concerning the manner in which the first-step variance-covariance matrices are assessed, which leads to different statistical properties for the quantities determined in the merge. The issue is one ofa priorivsa posterioriassessment of weights, which is an application of what was originally calledinternalvsexternal consistencyby Birge [Phys. Rev.40,207-227 (1932)] and Deming ("Statistical Adjustment of Data." Dover, New York, 1964). In the present work the simplest case of a merge fit-that of an average as obtained from a global fit vs a two-step fit of partitioned data-is used to illustrate that only in the case of a priori weighting do the results have the usually expected and desired statistical properties: normal distributions for residuals,tdistributions for parameters assessed a posteriori, and χ2distributions for variances.

Realistic Subsurface Anomaly Discrimination Using Electromagnetic Induction and an SVM Classifier

DTIC Science & Technology

2010-01-01

proposed by Pasion and Oldenburg [25]: Q(t) = kt−βe−γt. (10) Various combinations of these fitting parameters can be used as inputs to classifier... Pasion -Oldenburg parameters k, β, and γ for each anomaly by a direct nonlinear least-squares fit of (10) and by linear (pseudo)inversion of its...combinations of the Pasion -Oldenburg parameters. Com- bining k and γ yields results similar to those of k and R, as Figure 7 and Table 2 show. Figure 8 and

Investigation of skin structures based on infrared wave parameter indirect microscopic imaging

NASA Astrophysics Data System (ADS)

Zhao, Jun; Liu, Xuefeng; Xiong, Jichuan; Zhou, Lijuan

2017-02-01

Detailed imaging and analysis of skin structures are becoming increasingly important in modern healthcare and clinic diagnosis. Nanometer resolution imaging techniques such as SEM and AFM can cause harmful damage to the sample and cannot measure the whole skin structure from the very surface through epidermis, dermis to subcutaneous. Conventional optical microscopy has the highest imaging efficiency, flexibility in onsite applications and lowest cost in manufacturing and usage, but its image resolution is too low to be accepted for biomedical analysis. Infrared parameter indirect microscopic imaging (PIMI) uses an infrared laser as the light source due to its high transmission in skins. The polarization of optical wave through the skin sample was modulated while the variation of the optical field was observed at the imaging plane. The intensity variation curve of each pixel was fitted to extract the near field polarization parameters to form indirect images. During the through-skin light modulation and image retrieving process, the curve fitting removes the blurring scattering from neighboring pixels and keeps only the field variations related to local skin structures. By using the infrared PIMI, we can break the diffraction limit, bring the wide field optical image resolution to sub-200nm, in the meantime of taking advantage of high transmission of infrared waves in skin structures.

A new model for simulating microbial cyanide production and optimizing the medium parameters for recovering precious metals from waste printed circuit boards.

PubMed

Yuan, Zhihui; Ruan, Jujun; Li, Yaying; Qiu, Rongliang

2018-04-10

Bioleaching is a green recycling technology for recovering precious metals from waste printed circuit boards (WPCBs). However, this technology requires increasing cyanide production to obtain desirable recovery efficiency. Luria-Bertani medium (LB medium, containing tryptone 10 g/L, yeast extract 5 g/L, NaCl 10 g/L) was commonly used in bioleaching of precious metal. In this study, results showed that LB medium did not produce highest yield of cyanide. Under optimal culture conditions (25 °C, pH 7.5), the maximum cyanide yield of the optimized medium (containing tryptone 6 g/L and yeast extract 5 g/L) was 1.5 times as high as that of LB medium. In addition, kinetics and relationship of cell growth and cyanide production was studied. Data of cell growth fitted logistics model well. Allometric model was demonstrated effective in describing relationship between cell growth and cyanide production. By inserting logistics equation into allometric equation, we got a novel hybrid equation containing five parameters. Kinetic data for cyanide production were well fitted to the new model. Model parameters reflected both cell growth and cyanide production process. Copyright © 2018 Elsevier B.V. All rights reserved.

A Statistical Approach to Identify Superluminous Supernovae and Probe Their Diversity

NASA Astrophysics Data System (ADS)

Inserra, C.; Prajs, S.; Gutierrez, C. P.; Angus, C.; Smith, M.; Sullivan, M.

2018-02-01

We investigate the identification of hydrogen-poor superluminous supernovae (SLSNe I) using a photometric analysis, without including an arbitrary magnitude threshold. We assemble a homogeneous sample of previously classified SLSNe I from the literature, and fit their light curves using Gaussian processes. From the fits, we identify four photometric parameters that have a high statistical significance when correlated, and combine them in a parameter space that conveys information on their luminosity and color evolution. This parameter space presents a new definition for SLSNe I, which can be used to analyze existing and future transient data sets. We find that 90% of previously classified SLSNe I meet our new definition. We also examine the evidence for two subclasses of SLSNe I, combining their photometric evolution with spectroscopic information, namely the photospheric velocity and its gradient. A cluster analysis reveals the presence of two distinct groups. “Fast” SLSNe show fast light curves and color evolution, large velocities, and a large velocity gradient. “Slow” SLSNe show slow light curve and color evolution, small expansion velocities, and an almost non-existent velocity gradient. Finally, we discuss the impact of our analyses in the understanding of the powering engine of SLSNe, and their implementation as cosmological probes in current and future surveys.

Fitting Photometry of Blended Microlensing Events

NASA Astrophysics Data System (ADS)

Thomas, Christian L.; Griest, Kim

2006-03-01

We reexamine the usefulness of fitting blended light-curve models to microlensing photometric data. We find agreement with previous workers (e.g., Woźniak & Paczyński) that this is a difficult proposition because of the degeneracy of blend fraction with other fit parameters. We show that follow-up observations at specific point along the light curve (peak region and wings) of high-magnification events are the most helpful in removing degeneracies. We also show that very small errors in the baseline magnitude can result in problems in measuring the blend fraction and study the importance of non-Gaussian errors in the fit results. The biases and skewness in the distribution of the recovered blend fraction is discussed. We also find a new approximation formula relating the blend fraction and the unblended fit parameters to the underlying event duration needed to estimate microlensing optical depth.

Eight weeks of a combination of high intensity interval training and conventional training reduce visceral adiposity and improve physical fitness: a group-based intervention.

PubMed

Giannaki, Christoforos D; Aphamis, George; Sakkis, Panikos; Hadjicharalambous, Marios

2016-04-01

High intensity interval training (HIIT) has been recently promoted as an effective, low volume and time-efficient training method for improving fitness and health related parameters. The aim of the current study was to examine the effect of a combination of a group-based HIIT and conventional gym training on physical fitness and body composition parameters in healthy adults. Thirty nine healthy adults volunteered to participate in this eight-week intervention study. Twenty three participants performed regular gym training 4 days a week (C group), whereas the remaining 16 participants engaged twice a week in HIIT and twice in regular gym training (HIIT-C group) as the other group. Total body fat and visceral adiposity levels were calculated using bioelectrical impedance analysis. Physical fitness parameters such as cardiorespiratory fitness, speed, lower limb explosiveness, flexibility and isometric arm strength were assessed through a battery of field tests. Both exercise programs were effective in reducing total body fat and visceral adiposity (P<0.05) and improving handgrip strength, sprint time, jumping ability and flexibility (P<0.05) whilst only the combination of HIIT and conventional training improved cardiorespiratory fitness levels (P<0.05). A between of group changes analysis revealed that HIIT-C resulted in significantly greater reduction in both abdominal girth and visceral adiposity compared with conventional training (P<0.05). Eight weeks of combined group-based HIIT and conventional training improve various physical fitness parameters and reduce both total and visceral fat levels. This type of training was also found to be superior compared with conventional exercise training alone in terms of reducing more visceral adiposity levels. Group-based HIIT may consider as a good methods for individuals who exercise in gyms and craving to acquire significant fitness benefits in relatively short period of time.

Selective laser sintering: A qualitative and objective approach

NASA Astrophysics Data System (ADS)

Kumar, Sanjay

2003-10-01

This article presents an overview of selective laser sintering (SLS) work as reported in various journals and proceedings. Selective laser sintering was first done mainly on polymers and nylon to create prototypes for audio-visual help and fit-to-form tests. Gradually it was expanded to include metals and alloys to manufacture functional prototypes and develop rapid tooling. The growth gained momentum with the entry of commercial entities such as DTM Corporation and EOS GmbH Electro Optical Systems. Computational modeling has been used to understand the SLS process, optimize the process parameters, and enhance the efficiency of the sintering machine.

Design, fabrication and characterization of a poly-silicon PN junction

NASA Astrophysics Data System (ADS)

Tower, Jason D.

This thesis details the design, fabrication, and characterization of a PN junction formed from p-type mono-crystalline silicon and n-type poly-crystalline silicon. The primary product of this project was a library of standard operating procedures (SOPs) for the fabrication of such devices, laying the foundations for future work and the development of a class in fabrication processes. The fabricated PN junction was characterized; in particular its current-voltage relationship was measured and fit to models. This characterization was to determine whether or not the fabrication process could produce working PN junctions with acceptable operational parameters.

Electronic processes in TTF-derived complexes studied by IR spectroscopy

NASA Astrophysics Data System (ADS)

Graja, Andrzej

2001-09-01

We focus our attention on the plasma-edge-like dispersion of the reflectance spectra of the selected bis(ethylenodithio)tetrathiafulvalene (BEDT-TTF)-derived organic conductors. The standard procedure to determine the electron transport parameters in low-dimensional organic conductors consists of fitting the appropriate theoretical models with the experimental reflectance data. This procedure provides us with basic information like plasma frequency, the optical effective mass of charge carriers, their number, mean free path and damping constant. Therefore, it is concluded that the spectroscopy is a powerful tool to study the electronic processes in conducting organic solids.

Inverse Gaussian gamma distribution model for turbulence-induced fading in free-space optical communication.

PubMed

Cheng, Mingjian; Guo, Ya; Li, Jiangting; Zheng, Xiaotong; Guo, Lixin

2018-04-20

We introduce an alternative distribution to the gamma-gamma (GG) distribution, called inverse Gaussian gamma (IGG) distribution, which can efficiently describe moderate-to-strong irradiance fluctuations. The proposed stochastic model is based on a modulation process between small- and large-scale irradiance fluctuations, which are modeled by gamma and inverse Gaussian distributions, respectively. The model parameters of the IGG distribution are directly related to atmospheric parameters. The accuracy of the fit among the IGG, log-normal, and GG distributions with the experimental probability density functions in moderate-to-strong turbulence are compared, and results indicate that the newly proposed IGG model provides an excellent fit to the experimental data. As the receiving diameter is comparable with the atmospheric coherence radius, the proposed IGG model can reproduce the shape of the experimental data, whereas the GG and LN models fail to match the experimental data. The fundamental channel statistics of a free-space optical communication system are also investigated in an IGG-distributed turbulent atmosphere, and a closed-form expression for the outage probability of the system is derived with Meijer's G-function.

On the chaotic diffusion in multidimensional Hamiltonian systems

NASA Astrophysics Data System (ADS)

Cincotta, P. M.; Giordano, C. M.; Martí, J. G.; Beaugé, C.

2018-01-01

We present numerical evidence that diffusion in the herein studied multidimensional near-integrable Hamiltonian systems departs from a normal process, at least for realistic timescales. Therefore, the derivation of a diffusion coefficient from a linear fit on the variance evolution of the unperturbed integrals fails. We review some topics on diffusion in the Arnold Hamiltonian and yield numerical and theoretical arguments to show that in the examples we considered, a standard coefficient would not provide a good estimation of the speed of diffusion. However, numerical experiments concerning diffusion would provide reliable information about the stability of the motion within chaotic regions of the phase space. In this direction, we present an extension of previous results concerning the dynamical structure of the Laplace resonance in Gliese-876 planetary system considering variations of the orbital parameters accordingly to the error introduced by the radial velocity determination. We found that a slight variation of the eccentricity of planet c would destabilize the inner region of the resonance that, though chaotic, shows stable when adopting the best fit values for the parameters.

Parametric study and global sensitivity analysis for co-pyrolysis of rape straw and waste tire via variance-based decomposition.

PubMed

Xu, Li; Jiang, Yong; Qiu, Rong

2018-01-01

In present study, co-pyrolysis behavior of rape straw, waste tire and their various blends were investigated. TG-FTIR indicated that co-pyrolysis was characterized by a four-step reaction, and H 2 O, CH, OH, CO 2 and CO groups were the main products evolved during the process. Additionally, using BBD-based experimental results, best-fit multiple regression models with high R 2 -pred values (94.10% for mass loss and 95.37% for reaction heat), which correlated explanatory variables with the responses, were presented. The derived models were analyzed by ANOVA at 95% confidence interval, F-test, lack-of-fit test and residues normal probability plots implied the models described well the experimental data. Finally, the model uncertainties as well as the interactive effect of these parameters were studied, the total-, first- and second-order sensitivity indices of operating factors were proposed using Sobol' variance decomposition. To the authors' knowledge, this is the first time global parameter sensitivity analysis has been performed in (co-)pyrolysis literature. Copyright © 2017 Elsevier Ltd. All rights reserved.

A Weibull model to describe antimicrobial kinetics of oregano and lemongrass essential oils against Salmonella Enteritidis in ground beef during refrigerated storage.

PubMed

de Oliveira, Thales Leandro Coutinho; Soares, Rodrigo de Araújo; Piccoli, Roberta Hilsdorf

2013-03-01

The antimicrobial effect of oregano (Origanum vulgare L.) and lemongrass (Cymbopogon citratus (DC.) Stapf.) essential oils (EOs) against Salmonella enterica serotype Enteritidis in in vitro experiments, and inoculated in ground bovine meat during refrigerated storage (4±2 °C) for 6 days was evaluated. The Weibull model was tested to fit survival/inactivation bacterial curves (estimating of p and δ parameters). The minimum inhibitory concentration (MIC) value for both EOs on S. Enteritidis was 3.90 μl/ml. The EO concentrations applied in the ground beef were 3.90, 7.80 and 15.60 μl/g, based on MIC levels and possible activity reduction by food constituents. Both evaluated EOs in all tested levels, showed antimicrobial effects, with microbial populations reducing (p≤0.05) along time storage. Evaluating fit-quality parameters (RSS and RSE) Weibull models are able to describe the inactivation curves of EOs against S. Enteritidis. The application of EOs in processed meats can be used to control pathogens during refrigerated shelf-life. Copyright © 2012 Elsevier Ltd. All rights reserved.

Monte Carlo analysis of neutron diffuse scattering data

NASA Astrophysics Data System (ADS)

Goossens, D. J.; Heerdegen, A. P.; Welberry, T. R.; Gutmann, M. J.

2006-11-01

This paper presents a discussion of a technique developed for the analysis of neutron diffuse scattering data. The technique involves processing the data into reciprocal space sections and modelling the diffuse scattering in these sections. A Monte Carlo modelling approach is used in which the crystal energy is a function of interatomic distances between molecules and torsional rotations within molecules. The parameters of the model are the spring constants governing the interactions, as they determine the correlations which evolve when the model crystal structure is relaxed at finite temperature. When the model crystal has reached equilibrium its diffraction pattern is calculated and a χ2 goodness-of-fit test between observed and calculated data slices is performed. This allows a least-squares refinement of the fit parameters and so automated refinement can proceed. The first application of this methodology to neutron, rather than X-ray, data is outlined. The sample studied was deuterated benzil, d-benzil, C14D10O2, for which data was collected using time-of-flight Laue diffraction on SXD at ISIS.

Properties of Martian Hematite at Meridiani Planum by Simultaneous Fitting of Mars Mossbauer Spectra

NASA Technical Reports Server (NTRS)

Agresti, D. G.; Fleischer, I.; Klingelhoefer, G.; Morris, R. V.

2010-01-01

Mossbauer spectrometers [1] on the two Mars Exploration Rovers (MERs) have been making measurements of surface rocks and soils since January 2004, recording spectra in 10-K-wide temperature bins ranging from 180 K to 290 K. Initial analyses focused on modeling individual spectra directly as acquired or, to increase statistical quality, as sums of single-rock or soil spectra over temperature or as sums over similar rock or soil type [2, 3]. Recently, we have begun to apply simultaneous fitting procedures [4] to Mars Mossbauer data [5-7]. During simultaneous fitting (simfitting), many spectra are modeled similarly and fit together to a single convergence criterion. A satisfactory simfit with parameter values consistent among all spectra is more likely than many single-spectrum fits of the same data because fitting parameters are shared among multiple spectra in the simfit. Consequently, the number of variable parameters, as well as the correlations among them, is greatly reduced. Here we focus on applications of simfitting to interpret the hematite signature in Moessbauer spectra acquired at Meridiani Planum, results of which were reported in [7]. The Spectra. We simfit two sets of spectra with large hematite content [7]: 1) 60 rock outcrop spectra from Eagle Crater; and 2) 46 spectra of spherule-rich lag deposits (Table 1). Spectra of 10 different targets acquired at several distinct temperatures are included in each simfit set. In the table, each Sol (martian day) represents a different target, NS is the number of spectra for a given sol, and NT is the number of spectra for a given temperature. The spectra are indexed to facilitate definition of parameter relations and constraints. An example spectrum is shown in Figure 1, together with a typical fitting model. Results. We have shown that simultaneous fitting is effective in analyzing a large set of related MER Mossbauer spectra. By using appropriate constraints, we derive target-specific quantities and the temperature dependence of certain parameters. By examining different fitting models, we demonstrate an improved fit for martian hematite modeled with two sextets rather than as a single sextet, and show that outcrop and spherule hematite are distinct. For outcrop, the weaker sextet indicates a Morin transition typical of well-crystallized and chemically pure hematite, while most of the outcrop hematite remains in a weakly ferromagnetic state at all temperatures. For spherule spectra, both sextets are consistent with weakly ferromagnetic hematite with no Morin transition. For both hematites, there is evidence for a range of particle sizes.

Mr-Moose: An advanced SED-fitting tool for heterogeneous multi-wavelength datasets

NASA Astrophysics Data System (ADS)

Drouart, G.; Falkendal, T.

2018-04-01

We present the public release of Mr-Moose, a fitting procedure that is able to perform multi-wavelength and multi-object spectral energy distribution (SED) fitting in a Bayesian framework. This procedure is able to handle a large variety of cases, from an isolated source to blended multi-component sources from an heterogeneous dataset (i.e. a range of observation sensitivities and spectral/spatial resolutions). Furthermore, Mr-Moose handles upper-limits during the fitting process in a continuous way allowing models to be gradually less probable as upper limits are approached. The aim is to propose a simple-to-use, yet highly-versatile fitting tool fro handling increasing source complexity when combining multi-wavelength datasets with fully customisable filter/model databases. The complete control of the user is one advantage, which avoids the traditional problems related to the "black box" effect, where parameter or model tunings are impossible and can lead to overfitting and/or over-interpretation of the results. Also, while a basic knowledge of Python and statistics is required, the code aims to be sufficiently user-friendly for non-experts. We demonstrate the procedure on three cases: two artificially-generated datasets and a previous result from the literature. In particular, the most complex case (inspired by a real source, combining Herschel, ALMA and VLA data) in the context of extragalactic SED fitting, makes Mr-Moose a particularly-attractive SED fitting tool when dealing with partially blended sources, without the need for data deconvolution.

MR-MOOSE: an advanced SED-fitting tool for heterogeneous multi-wavelength data sets

NASA Astrophysics Data System (ADS)

Drouart, G.; Falkendal, T.

2018-07-01

We present the public release of MR-MOOSE, a fitting procedure that is able to perform multi-wavelength and multi-object spectral energy distribution (SED) fitting in a Bayesian framework. This procedure is able to handle a large variety of cases, from an isolated source to blended multi-component sources from a heterogeneous data set (i.e. a range of observation sensitivities and spectral/spatial resolutions). Furthermore, MR-MOOSE handles upper limits during the fitting process in a continuous way allowing models to be gradually less probable as upper limits are approached. The aim is to propose a simple-to-use, yet highly versatile fitting tool for handling increasing source complexity when combining multi-wavelength data sets with fully customisable filter/model data bases. The complete control of the user is one advantage, which avoids the traditional problems related to the `black box' effect, where parameter or model tunings are impossible and can lead to overfitting and/or over-interpretation of the results. Also, while a basic knowledge of PYTHON and statistics is required, the code aims to be sufficiently user-friendly for non-experts. We demonstrate the procedure on three cases: two artificially generated data sets and a previous result from the literature. In particular, the most complex case (inspired by a real source, combining Herschel, ALMA, and VLA data) in the context of extragalactic SED fitting makes MR-MOOSE a particularly attractive SED fitting tool when dealing with partially blended sources, without the need for data deconvolution.

ISOFIT - A PROGRAM FOR FITTING SORPTION ISOTHERMS TO EXPERIMENTAL DATA

EPA Science Inventory

Isotherm expressions are important for describing the partitioning of contaminants in environmental systems. ISOFIT (ISOtherm FItting Tool) is a software program that fits isotherm parameters to experimental data via the minimization of a weighted sum of squared error (WSSE) obje...

RATES OF FITNESS DECLINE AND REBOUND SUGGEST PERVASIVE EPISTASIS

PubMed Central

Perfeito, L; Sousa, A; Bataillon, T; Gordo, I

2014-01-01

Unraveling the factors that determine the rate of adaptation is a major question in evolutionary biology. One key parameter is the effect of a new mutation on fitness, which invariably depends on the environment and genetic background. The fate of a mutation also depends on population size, which determines the amount of drift it will experience. Here, we manipulate both population size and genotype composition and follow adaptation of 23 distinct Escherichia coli genotypes. These have previously accumulated mutations under intense genetic drift and encompass a substantial fitness variation. A simple rule is uncovered: the net fitness change is negatively correlated with the fitness of the genotype in which new mutations appear—a signature of epistasis. We find that Fisher's geometrical model can account for the observed patterns of fitness change and infer the parameters of this model that best fit the data, using Approximate Bayesian Computation. We estimate a genomic mutation rate of 0.01 per generation for fitness altering mutations, albeit with a large confidence interval, a mean fitness effect of mutations of −0.01, and an effective number of traits nine in mutS− E. coli. This framework can be extended to confront a broader range of models with data and test different classes of fitness landscape models. PMID:24372601

The NANOGrav 11-year Data Set: High-precision Timing of 45 Millisecond Pulsars

NASA Astrophysics Data System (ADS)

Arzoumanian, Zaven; Brazier, Adam; Burke-Spolaor, Sarah; Chamberlin, Sydney; Chatterjee, Shami; Christy, Brian; Cordes, James M.; Cornish, Neil J.; Crawford, Fronefield; Thankful Cromartie, H.; Crowter, Kathryn; DeCesar, Megan E.; Demorest, Paul B.; Dolch, Timothy; Ellis, Justin A.; Ferdman, Robert D.; Ferrara, Elizabeth C.; Fonseca, Emmanuel; Garver-Daniels, Nathan; Gentile, Peter A.; Halmrast, Daniel; Huerta, E. A.; Jenet, Fredrick A.; Jessup, Cody; Jones, Glenn; Jones, Megan L.; Kaplan, David L.; Lam, Michael T.; Lazio, T. Joseph W.; Levin, Lina; Lommen, Andrea; Lorimer, Duncan R.; Luo, Jing; Lynch, Ryan S.; Madison, Dustin; Matthews, Allison M.; McLaughlin, Maura A.; McWilliams, Sean T.; Mingarelli, Chiara; Ng, Cherry; Nice, David J.; Pennucci, Timothy T.; Ransom, Scott M.; Ray, Paul S.; Siemens, Xavier; Simon, Joseph; Spiewak, Renée; Stairs, Ingrid H.; Stinebring, Daniel R.; Stovall, Kevin; Swiggum, Joseph K.; Taylor, Stephen R.; Vallisneri, Michele; van Haasteren, Rutger; Vigeland, Sarah J.; Zhu, Weiwei; The NANOGrav Collaboration

2018-04-01

We present high-precision timing data over time spans of up to 11 years for 45 millisecond pulsars observed as part of the North American Nanohertz Observatory for Gravitational Waves (NANOGrav) project, aimed at detecting and characterizing low-frequency gravitational waves. The pulsars were observed with the Arecibo Observatory and/or the Green Bank Telescope at frequencies ranging from 327 MHz to 2.3 GHz. Most pulsars were observed with approximately monthly cadence, and six high-timing-precision pulsars were observed weekly. All were observed at widely separated frequencies at each observing epoch in order to fit for time-variable dispersion delays. We describe our methods for data processing, time-of-arrival (TOA) calculation, and the implementation of a new, automated method for removing outlier TOAs. We fit a timing model for each pulsar that includes spin, astrometric, and (for binary pulsars) orbital parameters; time-variable dispersion delays; and parameters that quantify pulse-profile evolution with frequency. The timing solutions provide three new parallax measurements, two new Shapiro delay measurements, and two new measurements of significant orbital-period variations. We fit models that characterize sources of noise for each pulsar. We find that 11 pulsars show significant red noise, with generally smaller spectral indices than typically measured for non-recycled pulsars, possibly suggesting a different origin. A companion paper uses these data to constrain the strength of the gravitational-wave background.

Detection of quasi-periodic processes in repeated measurements: New approach for the fitting and clusterization of different data

NASA Astrophysics Data System (ADS)

Nigmatullin, R.; Rakhmatullin, R.

2014-12-01

Many experimentalists were accustomed to think that any independent measurement forms a non-correlated measurement that depends weakly from others. We are trying to reconsider this conventional point of view and prove that similar measurements form a strongly-correlated sequence of random functions with memory. In other words, successive measurements "remember" each other at least their nearest neighbors. This observation and justification on real data help to fit the wide set of data based on the Prony's function. The Prony's decomposition follows from the quasi-periodic (QP) properties of the measured functions and includes the Fourier transform as a partial case. New type of decomposition helps to obtain a specific amplitude-frequency response (AFR) of the measured (random) functions analyzed and each random function contains less number of the fitting parameters in comparison with its number of initial data points. Actually, the calculated AFR can be considered as the generalized Prony's spectrum (GPS), which will be extremely useful in cases where the simple model pretending on description of the measured data is absent but vital necessity of their quantitative description is remained. These possibilities open a new way for clusterization of the initial data and new information that is contained in these data gives a chance for their detailed analysis. The electron paramagnetic resonance (EPR) measurements realized for empty resonator (pure noise data) and resonator containing a sample (CeO2 in our case) confirmed the existence of the QP processes in reality. But we think that the detection of the QP processes is a common feature of many repeated measurements and this new property of successive measurements can attract an attention of many experimentalists. To formulate some general conditions that help to identify and then detect the presence of some QP process in the repeated experimental measurements. To find a functional equation and its solution that yields the description of the identified QP process. To suggest some computing algorithm for fitting of the QP data to the analytical function that follows from the solution of the corresponding functional equation. The content of this paper is organized as follows. In the Section 2 we will try to find the answers on the problem posed in this introductory section. It contains also the mathematical description of the QP process and interpretation of the meaning of the generalized Prony's spectrum (GPS). The GPS includes the conventional Fourier decomposition as a partial case. Section 3 contains the experimental details associated with receiving of the desired data. Section 4 includes some important details explaining specific features of application of general algorithm to concrete data. In Section 5 we summarize the results and outline the perspectives of this approach for quantitative description of time-dependent random data that are registered in different complex systems and experimental devices. Here we should notice that under the complex system we imply a system when a conventional model is absent[6]. Under simplicity of the acceptable model we imply the proper hypothesis ("best fit" model) containing minimal number of the fitting parameters that describes the behavior of the system considered quantitatively. The different approaches that exist in nowadays for description of these systems are collected in the recent review [7].

Precision PEP-II optics measurement with an SVD-enhanced Least-Square fitting

NASA Astrophysics Data System (ADS)

Yan, Y. T.; Cai, Y.

2006-03-01

A singular value decomposition (SVD)-enhanced Least-Square fitting technique is discussed. By automatic identifying, ordering, and selecting dominant SVD modes of the derivative matrix that responds to the variations of the variables, the converging process of the Least-Square fitting is significantly enhanced. Thus the fitting speed can be fast enough for a fairly large system. This technique has been successfully applied to precision PEP-II optics measurement in which we determine all quadrupole strengths (both normal and skew components) and sextupole feed-downs as well as all BPM gains and BPM cross-plane couplings through Least-Square fitting of the phase advances and the Local Green's functions as well as the coupling ellipses among BPMs. The local Green's functions are specified by 4 local transfer matrix components R12, R34, R32, R14. These measurable quantities (the Green's functions, the phase advances and the coupling ellipse tilt angles and axis ratios) are obtained by analyzing turn-by-turn Beam Position Monitor (BPM) data with a high-resolution model-independent analysis (MIA). Once all of the quadrupoles and sextupole feed-downs are determined, we obtain a computer virtual accelerator which matches the real accelerator in linear optics. Thus, beta functions, linear coupling parameters, and interaction point (IP) optics characteristics can be measured and displayed.

On the Usefulness of a Multilevel Logistic Regression Approach to Person-Fit Analysis

ERIC Educational Resources Information Center

Conijn, Judith M.; Emons, Wilco H. M.; van Assen, Marcel A. L. M.; Sijtsma, Klaas

2011-01-01

The logistic person response function (PRF) models the probability of a correct response as a function of the item locations. Reise (2000) proposed to use the slope parameter of the logistic PRF as a person-fit measure. He reformulated the logistic PRF model as a multilevel logistic regression model and estimated the PRF parameters from this…

VizieR Online Data Catalog: Parameters and IR excesses of Gaia DR1 stars (McDonald+, 2017)

NASA Astrophysics Data System (ADS)

McDonald, I.; Zijlstra, A. A.; Watson, R. A.

2017-08-01

Spectral energy distribution fits are presented for stars from the Tycho-Gaia Astrometric Solution (TGAS) from Gaia Data Release 1. Hipparcos-Gaia stars are presented in a separate table. Effective temperatures, bolometric luminosities, and infrared excesses are presented (alongside other parameters pertinent to the model fits), plus the source photometry used. (3 data files).

The Impact of Model Misspecification on Parameter Estimation and Item-Fit Assessment in Log-Linear Diagnostic Classification Models

ERIC Educational Resources Information Center

Kunina-Habenicht, Olga; Rupp, Andre A.; Wilhelm, Oliver

2012-01-01

Using a complex simulation study we investigated parameter recovery, classification accuracy, and performance of two item-fit statistics for correct and misspecified diagnostic classification models within a log-linear modeling framework. The basic manipulated test design factors included the number of respondents (1,000 vs. 10,000), attributes (3…

Optimization of process parameters for pilot-scale liquid-state bioconversion of sewage sludge by mixed fungal inoculation.

PubMed

Rahman, Roshanida A; Molla, Abul Hossain; Barghash, Hind F A; Fakhru'l-Razi, Ahmadun

2016-01-01

Liquid-state bioconversion (LSB) technique has great potential for application in bioremediation of sewage sludge. The purpose of this study is to determine the optimum level of LSB process of sewage sludge treatment by mixed fungal (Aspergillus niger and Penicillium corylophilum) inoculation in a pilot-scale bioreactor. The optimization of process factors was investigated using response surface methodology based on Box-Behnken design considering hydraulic retention time (HRT) and substrate influent concentration (S0) on nine responses for optimizing and fitted to the regression model. The optimum region was successfully depicted by optimized conditions, which was identified as the best fit for convenient multiple responses. The results from process verification were in close agreement with those obtained through predictions. Considering five runs of different conditions of HRT (low, medium and high 3.62, 6.13 and 8.27 days, respectively) with the range of S0 value (the highest 12.56 and the lowest 7.85 g L(-1)), it was monitored as the lower HRT was considered as the best option because it required minimum days of treatment than the others with influent concentration around 10 g L(-1). Therefore, optimum process factors of 3.62 days for HRT and 10.12 g L(-1) for S0 were identified as the best fit for LSB process and its performance was deviated by less than 5% in most of the cases compared to the predicted values. The recorded optimized results address a dynamic development in commercial-scale biological treatment of wastewater for safe and environment-friendly disposal in near future.

Quantitative Rheological Model Selection

NASA Astrophysics Data System (ADS)

Freund, Jonathan; Ewoldt, Randy

2014-11-01

The more parameters in a rheological the better it will reproduce available data, though this does not mean that it is necessarily a better justified model. Good fits are only part of model selection. We employ a Bayesian inference approach that quantifies model suitability by balancing closeness to data against both the number of model parameters and their a priori uncertainty. The penalty depends upon prior-to-calibration expectation of the viable range of values that model parameters might take, which we discuss as an essential aspect of the selection criterion. Models that are physically grounded are usually accompanied by tighter physical constraints on their respective parameters. The analysis reflects a basic principle: models grounded in physics can be expected to enjoy greater generality and perform better away from where they are calibrated. In contrast, purely empirical models can provide comparable fits, but the model selection framework penalizes their a priori uncertainty. We demonstrate the approach by selecting the best-justified number of modes in a Multi-mode Maxwell description of PVA-Borax. We also quantify relative merits of the Maxwell model relative to powerlaw fits and purely empirical fits for PVA-Borax, a viscoelastic liquid, and gluten.

Nonlinear Viscoelastic Characterization of the Porcine Spinal Cord

PubMed Central

Shetye, Snehal; Troyer, Kevin; Streijger, Femke; Lee, Jae H. T.; Kwon, Brian K.; Cripton, Peter; Puttlitz, Christian M.

2014-01-01

Although quasi-static and quasi-linear viscoelastic properties of the spinal cord have been reported previously, there are no published studies that have investigated the fully (strain-dependent) nonlinear viscoelastic properties of the spinal cord. In this study, stress relaxation experiments and dynamic cycling were performed on six fresh porcine lumbar cord specimens to examine their viscoelastic mechanical properties. The stress relaxation data were fitted to a modified superposition formulation and a novel finite ramp time correction technique was applied. The parameters obtained from this fitting methodology were used to predict the average dynamic cyclic viscoelastic behavior of the porcine cord. The data indicate that the porcine spinal cord exhibited fully nonlinear viscoelastic behavior. The average weighted RMSE for a Heaviside ramp fit was 2.8kPa, which was significantly greater (p < 0.001) than that of the nonlinear (comprehensive viscoelastic characterization (CVC) method) fit (0.365kPa). Further, the nonlinear mechanical parameters obtained were able to accurately predict the dynamic behavior, thus exemplifying the reliability of the obtained nonlinear parameters. These parameters will be important for future studies investigating various damage mechanisms of the spinal cord and studies developing high resolution finite elements models of the spine. PMID:24211612

Games among relatives revisited.

PubMed

Allen, Benjamin; Nowak, Martin A

2015-08-07

We present a simple model for the evolution of social behavior in family-structured, finite sized populations. Interactions are represented as evolutionary games describing frequency-dependent selection. Individuals interact more frequently with siblings than with members of the general population, as quantified by an assortment parameter r, which can be interpreted as "relatedness". Other models, mostly of spatially structured populations, have shown that assortment can promote the evolution of cooperation by facilitating interaction between cooperators, but this effect depends on the details of the evolutionary process. For our model, we find that sibling assortment promotes cooperation in stringent social dilemmas such as the Prisoner's Dilemma, but not necessarily in other situations. These results are obtained through straightforward calculations of changes in gene frequency. We also analyze our model using inclusive fitness. We find that the quantity of inclusive fitness does not exist for general games. For special games, where inclusive fitness exists, it provides less information than the straightforward analysis. Copyright © 2015 Elsevier Ltd. All rights reserved.

Iterative fitting method for the evaluation and quantification of PAES spectra

NASA Astrophysics Data System (ADS)

Zimnik, Samantha; Hackenberg, Mathias; Hugenschmidt, Christoph

2017-01-01

The elemental composition of surfaces is of great importance for the understanding of many surface processes such as catalysis. For a reliable analysis and a comparison of results, the quantification of the measured data is indispensable. Positron annihilation induced Auger Electron Spectroscopy (PAES) is a spectroscopic technique that measures the elemental composition with outstanding surface sensitivity, but up to now, no standardized evaluation procedure for PAES spectra is available. In this paper we present a new approach for the evaluation of PAES spectra of compounds, using the spectra obtained for the pure elements as reference. The measured spectrum is then fitted by a linear combination of the reference spectra by varying their intensities. The comparison of the results of the fitting routine with a calculation of the full parameter range shows an excellent agreement. We present the results of the new analysis method to evaluate the PAES spectra of sub-monolayers of Ni on a Pd substrate.

Adjoint Methods for Adjusting Three-Dimensional Atmosphere and Surface Properties to Fit Multi-Angle Multi-Pixel Polarimetric Measurements

NASA Technical Reports Server (NTRS)

Martin, William G.; Cairns, Brian; Bal, Guillaume

2014-01-01

This paper derives an efficient procedure for using the three-dimensional (3D) vector radiative transfer equation (VRTE) to adjust atmosphere and surface properties and improve their fit with multi-angle/multi-pixel radiometric and polarimetric measurements of scattered sunlight. The proposed adjoint method uses the 3D VRTE to compute the measurement misfit function and the adjoint 3D VRTE to compute its gradient with respect to all unknown parameters. In the remote sensing problems of interest, the scalar-valued misfit function quantifies agreement with data as a function of atmosphere and surface properties, and its gradient guides the search through this parameter space. Remote sensing of the atmosphere and surface in a three-dimensional region may require thousands of unknown parameters and millions of data points. Many approaches would require calls to the 3D VRTE solver in proportion to the number of unknown parameters or measurements. To avoid this issue of scale, we focus on computing the gradient of the misfit function as an alternative to the Jacobian of the measurement operator. The resulting adjoint method provides a way to adjust 3D atmosphere and surface properties with only two calls to the 3D VRTE solver for each spectral channel, regardless of the number of retrieval parameters, measurement view angles or pixels. This gives a procedure for adjusting atmosphere and surface parameters that will scale to the large problems of 3D remote sensing. For certain types of multi-angle/multi-pixel polarimetric measurements, this encourages the development of a new class of three-dimensional retrieval algorithms with more flexible parametrizations of spatial heterogeneity, less reliance on data screening procedures, and improved coverage in terms of the resolved physical processes in the Earth?s atmosphere.

Process model comparison and transferability across bioreactor scales and modes of operation for a mammalian cell bioprocess.

PubMed

Craven, Stephen; Shirsat, Nishikant; Whelan, Jessica; Glennon, Brian

2013-01-01

A Monod kinetic model, logistic equation model, and statistical regression model were developed for a Chinese hamster ovary cell bioprocess operated under three different modes of operation (batch, bolus fed-batch, and continuous fed-batch) and grown on two different bioreactor scales (3 L bench-top and 15 L pilot-scale). The Monod kinetic model was developed for all modes of operation under study and predicted cell density, glucose glutamine, lactate, and ammonia concentrations well for the bioprocess. However, it was computationally demanding due to the large number of parameters necessary to produce a good model fit. The transferability of the Monod kinetic model structure and parameter set across bioreactor scales and modes of operation was investigated and a parameter sensitivity analysis performed. The experimentally determined parameters had the greatest influence on model performance. They changed with scale and mode of operation, but were easily calculated. The remaining parameters, which were fitted using a differential evolutionary algorithm, were not as crucial. Logistic equation and statistical regression models were investigated as alternatives to the Monod kinetic model. They were less computationally intensive to develop due to the absence of a large parameter set. However, modeling of the nutrient and metabolite concentrations proved to be troublesome due to the logistic equation model structure and the inability of both models to incorporate a feed. The complexity, computational load, and effort required for model development has to be balanced with the necessary level of model sophistication when choosing which model type to develop for a particular application. Copyright © 2012 American Institute of Chemical Engineers (AIChE).

Optimization of parameters for enhanced oil recovery from enzyme treated wild apricot kernels.

PubMed

Rajaram, Mahatre R; Kumbhar, Baburao K; Singh, Anupama; Lohani, Umesh Chandra; Shahi, Navin C

2012-08-01

Present investigation was undertaken with the overall objective of optimizing the enzymatic parameters i.e. moisture content during hydrolysis, enzyme concentration, enzyme ratio and incubation period on wild apricot kernel processing for better oil extractability and increased oil recovery. Response surface methodology was adopted in the experimental design. A central composite rotatable design of four variables at five levels was chosen. The parameters and their range for the experiments were moisture content during hydrolysis (20-32%, w.b.), enzyme concentration (12-16% v/w of sample), combination of pectolytic and cellulolytic enzyme i.e. enzyme ratio (30:70-70:30) and incubation period (12-16 h). Aspergillus foetidus and Trichoderma viride was used for production of crude enzyme i.e. pectolytic and cellulolytic enzyme respectively. A complete second order model for increased oil recovery as the function of enzymatic parameters fitted the data well. The best fit model for oil recovery was also developed. The effect of various parameters on increased oil recovery was determined at linear, quadric and interaction level. The increased oil recovery ranged from 0.14 to 2.53%. The corresponding conditions for maximum oil recovery were 23% (w.b.), 15 v/w of the sample, 60:40 (pectolytic:cellulolytic), 13 h. Results of the study indicated that incubation period during enzymatic hydrolysis is the most important factor affecting oil yield followed by enzyme ratio, moisture content and enzyme concentration in the decreasing order. Enzyme ratio, incubation period and moisture content had insignificant effect on oil recovery. Second order model for increased oil recovery as a function of enzymatic hydrolysis parameters predicted the data adequately.

Kernel learning at the first level of inference.

PubMed

Cawley, Gavin C; Talbot, Nicola L C

2014-05-01

Kernel learning methods, whether Bayesian or frequentist, typically involve multiple levels of inference, with the coefficients of the kernel expansion being determined at the first level and the kernel and regularisation parameters carefully tuned at the second level, a process known as model selection. Model selection for kernel machines is commonly performed via optimisation of a suitable model selection criterion, often based on cross-validation or theoretical performance bounds. However, if there are a large number of kernel parameters, as for instance in the case of automatic relevance determination (ARD), there is a substantial risk of over-fitting the model selection criterion, resulting in poor generalisation performance. In this paper we investigate the possibility of learning the kernel, for the Least-Squares Support Vector Machine (LS-SVM) classifier, at the first level of inference, i.e. parameter optimisation. The kernel parameters and the coefficients of the kernel expansion are jointly optimised at the first level of inference, minimising a training criterion with an additional regularisation term acting on the kernel parameters. The key advantage of this approach is that the values of only two regularisation parameters need be determined in model selection, substantially alleviating the problem of over-fitting the model selection criterion. The benefits of this approach are demonstrated using a suite of synthetic and real-world binary classification benchmark problems, where kernel learning at the first level of inference is shown to be statistically superior to the conventional approach, improves on our previous work (Cawley and Talbot, 2007) and is competitive with Multiple Kernel Learning approaches, but with reduced computational expense. Copyright © 2014 Elsevier Ltd. All rights reserved.

An application of the Krylov-FSP-SSA method to parameter fitting with maximum likelihood

NASA Astrophysics Data System (ADS)

Dinh, Khanh N.; Sidje, Roger B.

2017-12-01

Monte Carlo methods such as the stochastic simulation algorithm (SSA) have traditionally been employed in gene regulation problems. However, there has been increasing interest to directly obtain the probability distribution of the molecules involved by solving the chemical master equation (CME). This requires addressing the curse of dimensionality that is inherent in most gene regulation problems. The finite state projection (FSP) seeks to address the challenge and there have been variants that further reduce the size of the projection or that accelerate the resulting matrix exponential. The Krylov-FSP-SSA variant has proved numerically efficient by combining, on one hand, the SSA to adaptively drive the FSP, and on the other hand, adaptive Krylov techniques to evaluate the matrix exponential. Here we apply this Krylov-FSP-SSA to a mutual inhibitory gene network synthetically engineered in Saccharomyces cerevisiae, in which bimodality arises. We show numerically that the approach can efficiently approximate the transient probability distribution, and this has important implications for parameter fitting, where the CME has to be solved for many different parameter sets. The fitting scheme amounts to an optimization problem of finding the parameter set so that the transient probability distributions fit the observations with maximum likelihood. We compare five optimization schemes for this difficult problem, thereby providing further insights into this approach of parameter estimation that is often applied to models in systems biology where there is a need to calibrate free parameters. Work supported by NSF grant DMS-1320849.

Improved accuracy and precision of tracer kinetic parameters by joint fitting to variable flip angle and dynamic contrast enhanced MRI data.

PubMed

Dickie, Ben R; Banerji, Anita; Kershaw, Lucy E; McPartlin, Andrew; Choudhury, Ananya; West, Catharine M; Rose, Chris J

2016-10-01

To improve the accuracy and precision of tracer kinetic model parameter estimates for use in dynamic contrast enhanced (DCE) MRI studies of solid tumors. Quantitative DCE-MRI requires an estimate of precontrast T1 , which is obtained prior to fitting a tracer kinetic model. As T1 mapping and tracer kinetic signal models are both a function of precontrast T1 it was hypothesized that its joint estimation would improve the accuracy and precision of both precontrast T1 and tracer kinetic model parameters. Accuracy and/or precision of two-compartment exchange model (2CXM) parameters were evaluated for standard and joint fitting methods in well-controlled synthetic data and for 36 bladder cancer patients. Methods were compared under a number of experimental conditions. In synthetic data, joint estimation led to statistically significant improvements in the accuracy of estimated parameters in 30 of 42 conditions (improvements between 1.8% and 49%). Reduced accuracy was observed in 7 of the remaining 12 conditions. Significant improvements in precision were observed in 35 of 42 conditions (between 4.7% and 50%). In clinical data, significant improvements in precision were observed in 18 of 21 conditions (between 4.6% and 38%). Accuracy and precision of DCE-MRI parameter estimates are improved when signal models are fit jointly rather than sequentially. Magn Reson Med 76:1270-1281, 2016. © 2015 Wiley Periodicals, Inc. © 2015 Wiley Periodicals, Inc.

Theoretical Investigation of Kinetic Processes in Small Radicals of Importance in Combustion

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

Alexander, Millard; Dagdigian, Paul J.

Our group studies inelastic and reactive collisions of small molecules, focusing on radicals important in combustion environments. The goal is the better understanding of kinetic processes that may be difficult to access experimentally. An essential component is the accurate determination and fitting of potential energy surfaces (PESs). After fitting the ab initio points to obtain global PESs, we treat the dynamics using time-independent (close-coupling) methods. Cross sections and rate constants for collisions of are determined with our Hibridon program suite . We have studied energy transfer (rotationally, vibrationally, and/or electronically inelastic) in small hydrocarbon radicals (CH 2 and CH 3)more » and the CN radical. We have made a comparison with experimental measurements of relevant rate constants for collisions of these radicals. Also, we have calculated accurate transport properties using state-of-the-art PESs and to investigate the sensitivity to these parameters in 1-dimensional flame simulations. Of particular interest are collision pairs involving the light H atom.« less

Molecular versus squared Woods-Saxon α-nucleus potentials in the 27Al(α, t)28Si reaction

NASA Astrophysics Data System (ADS)

Abdullah, M. N. A.; Das, S. K.; Tariq, A. S. B.; Mahbub, M. S.; Mondal, A. S.; Uddin, M. A.; Basak, A. K.; Gupta, H. M. Sen; Malik, F. B.

2003-06-01

The differential cross-section of the 27Al(alpha, t)28Si reaction for 64.5 MeV incident energy has been reanalysed in DWBA with full finite range using a squared Woods-Saxon (Michel) alpha-nucleus potential with the modified value of the depth parameter alpha = 2.0 as reported in a comment article by Michel and Reidemeister. This new value produces significant improvement in fitting the data of the reaction with its overall performance, in some cases, close to that previously observed for the molecular potential. Although the non-monotonic shallow molecular potential with a soft repulsive core and the Michel potentials produce the same quality fits to the elastic scattering and non-elastic processes, they are not phase equivalent. The two types of potential produce altogether different cross-sections, particularly at large reaction angles. The importance of the experimental cross-sections at large angles for both elastic scattering and non-elastic processes is elucidated.

Clay-cement suspensions - rheological and functional properties

NASA Astrophysics Data System (ADS)

Wojcik, L.; Izak, P.; Mastalska-Poplawska, J.; Gajek, M.

2017-01-01

The piping erosion in soil is highly unexpected in civil engineering. Elimination of such damages is difficult, expensive and time-consuming. One of the possibility is the grouting method. This method is still developed into direction of process automation as well as other useful properties of suspensions. Main way of modernization of the grouting method is connected it with rheology of injection and eventuality of fitting them to specific problems conditions. Very popular and useful became binders based on modified clays (clay-cement suspensions). Important principle of efficiency of the grouting method is using of time-dependent pseudothixotropic properties of the clay-cement suspensions. The pseudo-rheounstability aspect of the suspensions properties should be dedicated and fitted to dynamic changes of soil conditions destructions. Whole process of the modification of the suspension rheology is stimulated by the specific agents. This article contains a description of practical aspects of the rheological parameters managing of the clay-cement suspensions, dedicated to the building damages, hydrotechnic constructions etc.

Emergent neutrality drives phytoplankton species coexistence

PubMed Central

Segura, Angel M.; Calliari, Danilo; Kruk, Carla; Conde, Daniel; Bonilla, Sylvia; Fort, Hugo

2011-01-01

The mechanisms that drive species coexistence and community dynamics have long puzzled ecologists. Here, we explain species coexistence, size structure and diversity patterns in a phytoplankton community using a combination of four fundamental factors: organism traits, size-based constraints, hydrology and species competition. Using a ‘microscopic’ Lotka–Volterra competition (MLVC) model (i.e. with explicit recipes to compute its parameters), we provide a mechanistic explanation of species coexistence along a niche axis (i.e. organismic volume). We based our model on empirically measured quantities, minimal ecological assumptions and stochastic processes. In nature, we found aggregated patterns of species biovolume (i.e. clumps) along the volume axis and a peak in species richness. Both patterns were reproduced by the MLVC model. Observed clumps corresponded to niche zones (volumes) where species fitness was highest, or where fitness was equal among competing species. The latter implies the action of equalizing processes, which would suggest emergent neutrality as a plausible mechanism to explain community patterns. PMID:21177680

Adaptive Memory: Evaluating Alternative Forms of Fitness-Relevant Processing in the Survival Processing Paradigm

PubMed Central

Sandry, Joshua; Trafimow, David; Marks, Michael J.; Rice, Stephen

2013-01-01

Memory may have evolved to preserve information processed in terms of its fitness-relevance. Based on the assumption that the human mind comprises different fitness-relevant adaptive mechanisms contributing to survival and reproductive success, we compared alternative fitness-relevant processing scenarios with survival processing. Participants rated words for relevancy to fitness-relevant and control conditions followed by a delay and surprise recall test (Experiment 1a). Participants recalled more words processed for their relevance to a survival situation. We replicated these findings in an online study (Experiment 2) and a study using revised fitness-relevant scenarios (Experiment 3). Across all experiments, we did not find a mnemonic benefit for alternative fitness-relevant processing scenarios, questioning assumptions associated with an evolutionary account of remembering. Based on these results, fitness-relevance seems to be too wide-ranging of a construct to account for the memory findings associated with survival processing. We propose that memory may be hierarchically sensitive to fitness-relevant processing instructions. We encourage future researchers to investigate the underlying mechanisms responsible for survival processing effects and work toward developing a taxonomy of adaptive memory. PMID:23585858

BioNetFit: a fitting tool compatible with BioNetGen, NFsim and distributed computing environments

DOE PAGES

Thomas, Brandon R.; Chylek, Lily A.; Colvin, Joshua; ...

2015-11-09

Rule-based models are analyzed with specialized simulators, such as those provided by the BioNetGen and NFsim open-source software packages. Here in this paper, we present BioNetFit, a general-purpose fitting tool that is compatible with BioNetGen and NFsim. BioNetFit is designed to take advantage of distributed computing resources. This feature facilitates fitting (i.e. optimization of parameter values for consistency with data) when simulations are computationally expensive.

Design of Backpack to Aid Elderly for the Mazu Touring Procession in Taiwan

NASA Astrophysics Data System (ADS)

Chao, F. L.; Huang, Y. C.; Su, J. Y.; Sun, C. L.; Chen, C. C.

2017-09-01

The Dajia Mazu Touring Procession is a 9-day long religious event held annually. However, for the elderly participants, it is a big burden especially in regards to physical strength. The goal of designing backpack is to reduce the physiological stress of elderly during the procession. Firstly, physical parameters were measured to explore the dimension parameters by testing. The height of the chair is different from that of the kneeling pad; a smooth curve was chosen to coordinate the two as the main outline of the backpack. Secondly, material selections based on following limits were considered: (1) acceptable weight and size, (2) intermediate price and (3) a design that is fitting to the Dajia event. The material and structural strength were evaluated for wood, bamboo, stainless steel. Two design concept were proposed, wood is selected for construction and testing by users. The texture of the backpack is Rush grass, it was built successfully to cover the backpack’s external surface to meet local culture features.

Delta-Isobar Production in the Hard Photodisintegration of a Deuteron

NASA Astrophysics Data System (ADS)

Granados, Carlos; Sargsian, Misak

2010-02-01

Hard photodisintegration of the deuteron in delta-isobar production channels is proposed as a useful process in identifying the quark structure of hadrons and of hadronic interactions at large momentum and energy transfer. The reactions are modeled using the hard re scattering model, HRM, following previous works on hard breakup of a nucleon nucleon (NN) system in light nuclei. Here,quantitative predictions through the HRM require the numerical input of fits of experimental NN hard elastic scattering cross sections. Because of the lack of data in hard NN scattering into δ-isobar channels, the cross section of the corresponding photodisintegration processes cannot be predicted in the same way. Instead, the corresponding NN scattering process is modeled through the quark interchange mechanism, QIM, leaving an unknown normalization parameter. The observables of interest are ratios of differential cross sections of δ-isobar production channels to NN breakup in deuteron photodisintegration. Both entries in these ratios are derived through the HRM and QIM so that normalization parameters cancel out and numerical predictions can be obtained. )

Laser Metal Deposition as Repair Technology for a Gas Turbine Burner Made of Inconel 718

NASA Astrophysics Data System (ADS)

Petrat, Torsten; Graf, Benjamin; Gumenyuk, Andrey; Rethmeier, Michael

Maintenance, repair and overhaul of components are of increasing interest for parts of high complexity and expensive manufacturing costs. In this paper a production process for laser metal deposition is presented, and used to repair a gas turbine burner of Inconel 718. Different parameters for defined track geometries were determined to attain a near net shape deposition with consistent build-up rate for changing wall thicknesses over the manufacturing process. Spot diameter, powder feed rate, welding velocity and laser power were changed as main parameters for a different track size. An optimal overlap rate for a constant layer height was used to calculate the best track size for a fitting layer width similar to the part dimension. Deviations in width and height over the whole build-up process were detected and customized build-up strategies for the 3D sequences were designed. The results show the possibility of a near net shape repair by using different track geometries with laser metal deposition.

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

PubMed

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

2001-10-01

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

Maximum-likelihood fitting of data dominated by Poisson statistical uncertainties

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

Stoneking, M.R.; Den Hartog, D.J.

1996-06-01

The fitting of data by {chi}{sup 2}-minimization is valid only when the uncertainties in the data are normally distributed. When analyzing spectroscopic or particle counting data at very low signal level (e.g., a Thomson scattering diagnostic), the uncertainties are distributed with a Poisson distribution. The authors have developed a maximum-likelihood method for fitting data that correctly treats the Poisson statistical character of the uncertainties. This method maximizes the total probability that the observed data are drawn from the assumed fit function using the Poisson probability function to determine the probability for each data point. The algorithm also returns uncertainty estimatesmore » for the fit parameters. They compare this method with a {chi}{sup 2}-minimization routine applied to both simulated and real data. Differences in the returned fits are greater at low signal level (less than {approximately}20 counts per measurement). the maximum-likelihood method is found to be more accurate and robust, returning a narrower distribution of values for the fit parameters with fewer outliers.« less

Fitting a three-parameter lognormal distribution with applications to hydrogeochemical data from the National Uranium Resource Evaluation Program

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

Kane, V.E.

1979-10-01

The standard maximum likelihood and moment estimation procedures are shown to have some undesirable characteristics for estimating the parameters in a three-parameter lognormal distribution. A class of goodness-of-fit estimators is found which provides a useful alternative to the standard methods. The class of goodness-of-fit tests considered include the Shapiro-Wilk and Shapiro-Francia tests which reduce to a weighted linear combination of the order statistics that can be maximized in estimation problems. The weighted-order statistic estimators are compared to the standard procedures in Monte Carlo simulations. Bias and robustness of the procedures are examined and example data sets analyzed including geochemical datamore » from the National Uranium Resource Evaluation Program.« less

Syndromes of collateral-reported psychopathology for ages 18-59 in 18 Societies

PubMed Central

Ivanova, Masha Y.; Achenbach, Thomas M.; Rescorla, Leslie A.; Turner, Lori V.; Árnadóttir, Hervör Alma; Au, Alma; Caldas, J. Carlos; Chaalal, Nebia; Chen, Yi Chuen; da Rocha, Marina M.; Decoster, Jeroen; Fontaine, Johnny R.J.; Funabiki, Yasuko; Guðmundsson, Halldór S.; Kim, Young Ah; Leung, Patrick; Liu, Jianghong; Malykh, Sergey; Marković, Jasminka; Oh, Kyung Ja; Petot, Jean-Michel; Samaniego, Virginia C.; Silvares, Edwiges Ferreira de Mattos; Šimulionienė, Roma; Šobot, Valentina; Sokoli, Elvisa; Sun, Guiju; Talcott, Joel B.; Vázquez, Natalia; Zasępa, Ewa

2017-01-01

The purpose was to advance research and clinical methodology for assessing psychopathology by testing the international generalizability of an 8-syndrome model derived from collateral ratings of adult behavioral, emotional, social, and thought problems. Collateral informants rated 8,582 18–59-year-old residents of 18 societies on the Adult Behavior Checklist (ABCL). Confirmatory factor analyses tested the fit of the 8-syndrome model to ratings from each society. The primary model fit index (Root Mean Square Error of Approximation) showed good model fit for all societies, while secondary indices (Tucker Lewis Index, Comparative Fit Index) showed acceptable to good fit for 17 societies. Factor loadings were robust across societies and items. Of the 5,007 estimated parameters, 4 (0.08%) were outside the admissible parameter space, but 95% confidence intervals included the admissible space, indicating that the 4 deviant parameters could be due to sampling fluctuations. The findings are consistent with previous evidence for the generalizability of the 8-syndrome model in self-ratings from 29 societies, and support the 8-syndrome model for operationalizing phenotypes of adult psychopathology from multi-informant ratings in diverse societies. PMID:29399019