Induced subgraph searching for geometric model fitting
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.
Measured, modeled, and causal conceptions of fitness
Abrams, Marshall
2012-01-01
This paper proposes partial answers to the following questions: in what senses can fitness differences plausibly be considered causes of evolution?What relationships are there between fitness concepts used in empirical research, modeling, and abstract theoretical proposals? How does the relevance of different fitness concepts depend on research questions and methodological constraints? The paper develops a novel taxonomy of fitness concepts, beginning with type fitness (a property of a genotype or phenotype), token fitness (a property of a particular individual), and purely mathematical fitness. Type fitness includes statistical type fitness, which can be measured from population data, and parametric type fitness, which is an underlying property estimated by statistical type fitnesses. Token fitness includes measurable token fitness, which can be measured on an individual, and tendential token fitness, which is assumed to be an underlying property of the individual in its environmental circumstances. Some of the paper's conclusions can be outlined as follows: claims that fitness differences do not cause evolution are reasonable when fitness is treated as statistical type fitness, measurable token fitness, or purely mathematical fitness. Some of the ways in which statistical methods are used in population genetics suggest that what natural selection involves are differences in parametric type fitnesses. Further, it's reasonable to think that differences in parametric type fitness can cause evolution. Tendential token fitnesses, however, are not themselves sufficient for natural selection. Though parametric type fitnesses are typically not directly measurable, they can be modeled with purely mathematical fitnesses and estimated by statistical type fitnesses, which in turn are defined in terms of measurable token fitnesses. The paper clarifies the ways in which fitnesses depend on pragmatic choices made by researchers. PMID:23112804
Stanley, Leanne M.; Edwards, Michael C.
2016-01-01
The purpose of this article is to highlight the distinction between the reliability of test scores and the fit of psychometric measurement models, reminding readers why it is important to consider both when evaluating whether test scores are valid for a proposed interpretation and/or use. It is often the case that an investigator judges both the…
Random-growth urban model with geographical fitness
Kii, Masanobu; Akimoto, Keigo; Doi, Kenji
2012-12-01
This paper formulates a random-growth urban model with a notion of geographical fitness. Using techniques of complex-network theory, we study our system as a type of preferential-attachment model with fitness, and we analyze its macro behavior to clarify the properties of the city-size distributions it predicts. First, restricting the geographical fitness to take positive values and using a continuum approach, we show that the city-size distributions predicted by our model asymptotically approach Pareto distributions with coefficients greater than unity. Then, allowing the geographical fitness to take negative values, we perform local coefficient analysis to show that the predicted city-size distributions can deviate from Pareto distributions, as is often observed in actual city-size distributions. As a result, the model we propose can generate a generic class of city-size distributions, including but not limited to Pareto distributions. For applications to city-population projections, our simple model requires randomness only when new cities are created, not during their subsequent growth. This property leads to smooth trajectories of city population growth, in contrast to other models using Gibrat’s law. In addition, a discrete form of our dynamical equations can be used to estimate past city populations based on present-day data; this fact allows quantitative assessment of the performance of our model. Further study is needed to determine appropriate formulas for the geographical fitness.
Fitting PAC spectra with stochastic models: PolyPacFit
Energy Technology Data Exchange (ETDEWEB)
Zacate, M. O., E-mail: zacatem1@nku.edu [Northern Kentucky University, Department of Physics and Geology (United States); Evenson, W. E. [Utah Valley University, College of Science and Health (United States); Newhouse, R.; Collins, G. S. [Washington State University, Department of Physics and Astronomy (United States)
2010-04-15
PolyPacFit is an advanced fitting program for time-differential perturbed angular correlation (PAC) spectroscopy. It incorporates stochastic models and provides robust options for customization of fits. Notable features of the program include platform independence and support for (1) fits to stochastic models of hyperfine interactions, (2) user-defined constraints among model parameters, (3) fits to multiple spectra simultaneously, and (4) any spin nuclear probe.
The FITS model office ergonomics program: a model for best practice.
Chim, Justine M Y
2014-01-01
An effective office ergonomics program can predict positive results in reducing musculoskeletal injury rates, enhancing productivity, and improving staff well-being and job satisfaction. Its objective is to provide a systematic solution to manage the potential risk of musculoskeletal disorders among computer users in an office setting. A FITS Model office ergonomics program is developed. The FITS Model Office Ergonomics Program has been developed which draws on the legislative requirements for promoting the health and safety of workers using computers for extended periods as well as previous research findings. The Model is developed according to the practical industrial knowledge in ergonomics, occupational health and safety management, and human resources management in Hong Kong and overseas. This paper proposes a comprehensive office ergonomics program, the FITS Model, which considers (1) Furniture Evaluation and Selection; (2) Individual Workstation Assessment; (3) Training and Education; (4) Stretching Exercises and Rest Break as elements of an effective program. An experienced ergonomics practitioner should be included in the program design and implementation. Through the FITS Model Office Ergonomics Program, the risk of musculoskeletal disorders among computer users can be eliminated or minimized, and workplace health and safety and employees' wellness enhanced.
Fitting Equilibrium Search Models to Labour Market Data
DEFF Research Database (Denmark)
Bowlus, Audra J.; Kiefer, Nicholas M.; Neumann, George R.
1996-01-01
Specification and estimation of a Burdett-Mortensen type equilibrium search model is considered. The estimation is nonstandard. An estimation strategy asymptotically equivalent to maximum likelihood is proposed and applied. The results indicate that specifications with a small number of productiv...... of productivity types fit the data well compared to the homogeneous model....
Checking the Adequacy of Fit of Models from Split-Plot Designs
DEFF Research Database (Denmark)
Almini, A. A.; Kulahci, Murat; Montgomery, D. C.
2009-01-01
models. In this article, we propose the computation of two R-2, R-2-adjusted, prediction error sums of squares (PRESS), and R-2-prediction statistics to measure the adequacy of fit for the WP and the SP submodels in a split-plot design. This is complemented with the graphical analysis of the two types......One of the main features that distinguish split-plot experiments from other experiments is that they involve two types of experimental errors: the whole-plot (WP) error and the subplot (SP) error. Taking this into consideration is very important when computing measures of adequacy of fit for split-plot...... of errors to check for any violation of the underlying assumptions and the adequacy of fit of split-plot models. Using examples, we show how computing two measures of model adequacy of fit for each split-plot design model is appropriate and useful as they reveal whether the correct WP and SP effects have...
Local and omnibus goodness-of-fit tests in classical measurement error models
Ma, Yanyuan
2010-09-14
We consider functional measurement error models, i.e. models where covariates are measured with error and yet no distributional assumptions are made about the mismeasured variable. We propose and study a score-type local test and an orthogonal series-based, omnibus goodness-of-fit test in this context, where no likelihood function is available or calculated-i.e. all the tests are proposed in the semiparametric model framework. We demonstrate that our tests have optimality properties and computational advantages that are similar to those of the classical score tests in the parametric model framework. The test procedures are applicable to several semiparametric extensions of measurement error models, including when the measurement error distribution is estimated non-parametrically as well as for generalized partially linear models. The performance of the local score-type and omnibus goodness-of-fit tests is demonstrated through simulation studies and analysis of a nutrition data set.
A Stepwise Fitting Procedure for automated fitting of Ecopath with Ecosim models
Directory of Open Access Journals (Sweden)
Erin Scott
2016-01-01
Full Text Available The Stepwise Fitting Procedure automates testing of alternative hypotheses used for fitting Ecopath with Ecosim (EwE models to observation reference data (Mackinson et al. 2009. The calibration of EwE model predictions to observed data is important to evaluate any model that will be used for ecosystem based management. Thus far, the model fitting procedure in EwE has been carried out manually: a repetitive task involving setting >1000 specific individual searches to find the statistically ‘best fit’ model. The novel fitting procedure automates the manual procedure therefore producing accurate results and lets the modeller concentrate on investigating the ‘best fit’ model for ecological accuracy.
Efficient occupancy model-fitting for extensive citizen-science data
Morgan, Byron J. T.; Freeman, Stephen N.; Ridout, Martin S.; Brereton, Tom M.; Fox, Richard; Powney, Gary D.; Roy, David B.
2017-01-01
Appropriate large-scale citizen-science data present important new opportunities for biodiversity modelling, due in part to the wide spatial coverage of information. Recently proposed occupancy modelling approaches naturally incorporate random effects in order to account for annual variation in the composition of sites surveyed. In turn this leads to Bayesian analysis and model fitting, which are typically extremely time consuming. Motivated by presence-only records of occurrence from the UK Butterflies for the New Millennium data base, we present an alternative approach, in which site variation is described in a standard way through logistic regression on relevant environmental covariates. This allows efficient occupancy model-fitting using classical inference, which is easily achieved using standard computers. This is especially important when models need to be fitted each year, typically for many different species, as with British butterflies for example. Using both real and simulated data we demonstrate that the two approaches, with and without random effects, can result in similar conclusions regarding trends. There are many advantages to classical model-fitting, including the ability to compare a range of alternative models, identify appropriate covariates and assess model fit, using standard tools of maximum likelihood. In addition, modelling in terms of covariates provides opportunities for understanding the ecological processes that are in operation. We show that there is even greater potential; the classical approach allows us to construct regional indices simply, which indicate how changes in occupancy typically vary over a species’ range. In addition we are also able to construct dynamic occupancy maps, which provide a novel, modern tool for examining temporal changes in species distribution. These new developments may be applied to a wide range of taxa, and are valuable at a time of climate change. They also have the potential to motivate citizen
ARA and ARI imperfect repair models: Estimation, goodness-of-fit and reliability prediction
International Nuclear Information System (INIS)
Toledo, Maria Luíza Guerra de; Freitas, Marta A.; Colosimo, Enrico A.; Gilardoni, Gustavo L.
2015-01-01
An appropriate maintenance policy is essential to reduce expenses and risks related to equipment failures. A fundamental aspect to be considered when specifying such policies is to be able to predict the reliability of the systems under study, based on a well fitted model. In this paper, the classes of models Arithmetic Reduction of Age and Arithmetic Reduction of Intensity are explored. Likelihood functions for such models are derived, and a graphical method is proposed for model selection. A real data set involving failures in trucks used by a Brazilian mining is analyzed considering models with different memories. Parameters, namely, shape and scale for Power Law Process, and the efficiency of repair were estimated for the best fitted model. Estimation of model parameters allowed us to derive reliability estimators to predict the behavior of the failure process. These results are a valuable information for the mining company and can be used to support decision making regarding preventive maintenance policy. - Highlights: • Likelihood functions for imperfect repair models are derived. • A goodness-of-fit technique is proposed as a tool for model selection. • Failures in trucks owned by a Brazilian mining are modeled. • Estimation allowed deriving reliability predictors to forecast the future failure process of the trucks
Directory of Open Access Journals (Sweden)
Javier Macias-Guarasa
2012-10-01
Full Text Available This paper presents a novel approach for indoor acoustic source localization using sensor arrays. The proposed solution starts by defining a generative model, designed to explain the acoustic power maps obtained by Steered Response Power (SRP strategies. An optimization approach is then proposed to fit the model to real input SRP data and estimate the position of the acoustic source. Adequately fitting the model to real SRP data, where noise and other unmodelled effects distort the ideal signal, is the core contribution of the paper. Two basic strategies in the optimization are proposed. First, sparse constraints in the parameters of the model are included, enforcing the number of simultaneous active sources to be limited. Second, subspace analysis is used to filter out portions of the input signal that cannot be explained by the model. Experimental results on a realistic speech database show statistically significant localization error reductions of up to 30% when compared with the SRP-PHAT strategies.
McNeish, Daniel; Hancock, Gregory R
2018-03-01
Lance, Beck, Fan, and Carter (2016) recently advanced 6 new fit indices and associated cutoff values for assessing data-model fit in the structural portion of traditional latent variable path models. The authors appropriately argued that, although most researchers' theoretical interest rests with the latent structure, they still rely on indices of global model fit that simultaneously assess both the measurement and structural portions of the model. As such, Lance et al. proposed indices intended to assess the structural portion of the model in isolation of the measurement model. Unfortunately, although these strategies separate the assessment of the structure from the fit of the measurement model, they do not isolate the structure's assessment from the quality of the measurement model. That is, even with a perfectly fitting measurement model, poorer quality (i.e., less reliable) measurements will yield a more favorable verdict regarding structural fit, whereas better quality (i.e., more reliable) measurements will yield a less favorable structural assessment. This phenomenon, referred to by Hancock and Mueller (2011) as the reliability paradox, affects not only traditional global fit indices but also those structural indices proposed by Lance et al. as well. Fortunately, as this comment will clarify, indices proposed by Hancock and Mueller help to mitigate this problem and allow the structural portion of the model to be assessed independently of both the fit of the measurement model as well as the quality of indicator variables contained therein. (PsycINFO Database Record (c) 2018 APA, all rights reserved).
A Proposed Systems Model for Socializing the Graduate Writer
Jones, David R.
2018-01-01
Although researchers chorus the need to support graduate students toward higher levels of writing proficiency, their findings lack a holistic model for doing so. A model emerges upon scrutiny of the factors that have been implicated in supporting writing proficiency. In the proposed model, a socialization theory fits as a proximal process into the…
Fast and exact Newton and Bidirectional fitting of Active Appearance Models.
Kossaifi, Jean; Tzimiropoulos, Yorgos; Pantic, Maja
2016-12-21
Active Appearance Models (AAMs) are generative models of shape and appearance that have proven very attractive for their ability to handle wide changes in illumination, pose and occlusion when trained in the wild, while not requiring large training dataset like regression-based or deep learning methods. The problem of fitting an AAM is usually formulated as a non-linear least squares one and the main way of solving it is a standard Gauss-Newton algorithm. In this paper we extend Active Appearance Models in two ways: we first extend the Gauss-Newton framework by formulating a bidirectional fitting method that deforms both the image and the template to fit a new instance. We then formulate a second order method by deriving an efficient Newton method for AAMs fitting. We derive both methods in a unified framework for two types of Active Appearance Models, holistic and part-based, and additionally show how to exploit the structure in the problem to derive fast yet exact solutions. We perform a thorough evaluation of all algorithms on three challenging and recently annotated inthe- wild datasets, and investigate fitting accuracy, convergence properties and the influence of noise in the initialisation. We compare our proposed methods to other algorithms and show that they yield state-of-the-art results, out-performing other methods while having superior convergence properties.
DEFF Research Database (Denmark)
Ding, Tao; Li, Cheng; Huang, Can
2018-01-01
–slave structure and improves traditional centralized modeling methods by alleviating the big data problem in a control center. Specifically, the transmission-distribution-network coordination issue of the hierarchical modeling method is investigated. First, a curve-fitting approach is developed to provide a cost......In order to solve the reactive power optimization with joint transmission and distribution networks, a hierarchical modeling method is proposed in this paper. It allows the reactive power optimization of transmission and distribution networks to be performed separately, leading to a master...... optimality. Numerical results on two test systems verify the effectiveness of the proposed hierarchical modeling and curve-fitting methods....
A goodness-of-fit test for occupancy models with correlated within-season revisits
Wright, Wilson; Irvine, Kathryn M.; Rodhouse, Thomas J.
2016-01-01
Occupancy modeling is important for exploring species distribution patterns and for conservation monitoring. Within this framework, explicit attention is given to species detection probabilities estimated from replicate surveys to sample units. A central assumption is that replicate surveys are independent Bernoulli trials, but this assumption becomes untenable when ecologists serially deploy remote cameras and acoustic recording devices over days and weeks to survey rare and elusive animals. Proposed solutions involve modifying the detection-level component of the model (e.g., first-order Markov covariate). Evaluating whether a model sufficiently accounts for correlation is imperative, but clear guidance for practitioners is lacking. Currently, an omnibus goodnessof- fit test using a chi-square discrepancy measure on unique detection histories is available for occupancy models (MacKenzie and Bailey, Journal of Agricultural, Biological, and Environmental Statistics, 9, 2004, 300; hereafter, MacKenzie– Bailey test). We propose a join count summary measure adapted from spatial statistics to directly assess correlation after fitting a model. We motivate our work with a dataset of multinight bat call recordings from a pilot study for the North American Bat Monitoring Program. We found in simulations that our join count test was more reliable than the MacKenzie–Bailey test for detecting inadequacy of a model that assumed independence, particularly when serial correlation was low to moderate. A model that included a Markov-structured detection-level covariate produced unbiased occupancy estimates except in the presence of strong serial correlation and a revisit design consisting only of temporal replicates. When applied to two common bat species, our approach illustrates that sophisticated models do not guarantee adequate fit to real data, underscoring the importance of model assessment. Our join count test provides a widely applicable goodness-of-fit test and
Unifying distance-based goodness-of-fit indicators for hydrologic model assessment
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
Directory of Open Access Journals (Sweden)
K S Mwitondi
2013-05-01
Full Text Available Differences in modelling techniques and model performance assessments typically impinge on the quality of knowledge extraction from data. We propose an algorithm for determining optimal patterns in data by separately training and testing three decision tree models in the Pima Indians Diabetes and the Bupa Liver Disorders datasets. Model performance is assessed using ROC curves and the Youden Index. Moving differences between sequential fitted parameters are then extracted, and their respective probability density estimations are used to track their variability using an iterative graphical data visualisation technique developed for this purpose. Our results show that the proposed strategy separates the groups more robustly than the plain ROC/Youden approach, eliminates obscurity, and minimizes over-fitting. Further, the algorithm can easily be understood by non-specialists and demonstrates multi-disciplinary compliance.
Fitting neuron models to spike trains
Directory of Open Access Journals (Sweden)
Cyrille eRossant
2011-02-01
Full Text Available Computational modeling is increasingly used to understand the function of neural circuitsin systems neuroscience.These studies require models of individual neurons with realisticinput-output properties.Recently, it was found that spiking models can accurately predict theprecisely timed spike trains produced by cortical neurons in response tosomatically injected currents,if properly fitted. This requires fitting techniques that are efficientand flexible enough to easily test different candidate models.We present a generic solution, based on the Brian simulator(a neural network simulator in Python, which allowsthe user to define and fit arbitrary neuron models to electrophysiological recordings.It relies on vectorization and parallel computing techniques toachieve efficiency.We demonstrate its use on neural recordings in the barrel cortex andin the auditory brainstem, and confirm that simple adaptive spiking modelscan accurately predict the response of cortical neurons. Finally, we show how a complexmulticompartmental model can be reduced to a simple effective spiking model.
International Nuclear Information System (INIS)
Liang, Zhong Wei; Wang, Yi Jun; Ye, Bang Yan; Brauwer, Richard Kars
2012-01-01
In inspecting the detailed performance results of surface precision modeling in different external parameter conditions, the integrated chip surfaces should be evaluated and assessed during topographic spatial modeling processes. The application of surface fitting algorithms exerts a considerable influence on topographic mathematical features. The influence mechanisms caused by different surface fitting algorithms on the integrated chip surface facilitate the quantitative analysis of different external parameter conditions. By extracting the coordinate information from the selected physical control points and using a set of precise spatial coordinate measuring apparatus, several typical surface fitting algorithms are used for constructing micro topographic models with the obtained point cloud. In computing for the newly proposed mathematical features on surface models, we construct the fuzzy evaluating data sequence and present a new three dimensional fuzzy quantitative evaluating method. Through this method, the value variation tendencies of topographic features can be clearly quantified. The fuzzy influence discipline among different surface fitting algorithms, topography spatial features, and the external science parameter conditions can be analyzed quantitatively and in detail. In addition, quantitative analysis can provide final conclusions on the inherent influence mechanism and internal mathematical relation in the performance results of different surface fitting algorithms, topographic spatial features, and their scientific parameter conditions in the case of surface micro modeling. The performance inspection of surface precision modeling will be facilitated and optimized as a new research idea for micro-surface reconstruction that will be monitored in a modeling process
Energy Technology Data Exchange (ETDEWEB)
Liang, Zhong Wei; Wang, Yi Jun [Guangzhou Univ., Guangzhou (China); Ye, Bang Yan [South China Univ. of Technology, Guangzhou (China); Brauwer, Richard Kars [Indian Institute of Technology, Kanpur (India)
2012-10-15
In inspecting the detailed performance results of surface precision modeling in different external parameter conditions, the integrated chip surfaces should be evaluated and assessed during topographic spatial modeling processes. The application of surface fitting algorithms exerts a considerable influence on topographic mathematical features. The influence mechanisms caused by different surface fitting algorithms on the integrated chip surface facilitate the quantitative analysis of different external parameter conditions. By extracting the coordinate information from the selected physical control points and using a set of precise spatial coordinate measuring apparatus, several typical surface fitting algorithms are used for constructing micro topographic models with the obtained point cloud. In computing for the newly proposed mathematical features on surface models, we construct the fuzzy evaluating data sequence and present a new three dimensional fuzzy quantitative evaluating method. Through this method, the value variation tendencies of topographic features can be clearly quantified. The fuzzy influence discipline among different surface fitting algorithms, topography spatial features, and the external science parameter conditions can be analyzed quantitatively and in detail. In addition, quantitative analysis can provide final conclusions on the inherent influence mechanism and internal mathematical relation in the performance results of different surface fitting algorithms, topographic spatial features, and their scientific parameter conditions in the case of surface micro modeling. The performance inspection of surface precision modeling will be facilitated and optimized as a new research idea for micro-surface reconstruction that will be monitored in a modeling process.
Analytical fitting model for rough-surface BRDF.
Renhorn, Ingmar G E; Boreman, Glenn D
2008-08-18
A physics-based model is developed for rough surface BRDF, taking into account angles of incidence and scattering, effective index, surface autocovariance, and correlation length. Shadowing is introduced on surface correlation length and reflectance. Separate terms are included for surface scatter, bulk scatter and retroreflection. Using the FindFit function in Mathematica, the functional form is fitted to BRDF measurements over a wide range of incident angles. The model has fourteen fitting parameters; once these are fixed, the model accurately describes scattering data over two orders of magnitude in BRDF without further adjustment. The resulting analytical model is convenient for numerical computations.
Efficient Constrained Local Model Fitting for Non-Rigid Face Alignment.
Lucey, Simon; Wang, Yang; Cox, Mark; Sridharan, Sridha; Cohn, Jeffery F
2009-11-01
Active appearance models (AAMs) have demonstrated great utility when being employed for non-rigid face alignment/tracking. The "simultaneous" algorithm for fitting an AAM achieves good non-rigid face registration performance, but has poor real time performance (2-3 fps). The "project-out" algorithm for fitting an AAM achieves faster than real time performance (> 200 fps) but suffers from poor generic alignment performance. In this paper we introduce an extension to a discriminative method for non-rigid face registration/tracking referred to as a constrained local model (CLM). Our proposed method is able to achieve superior performance to the "simultaneous" AAM algorithm along with real time fitting speeds (35 fps). We improve upon the canonical CLM formulation, to gain this performance, in a number of ways by employing: (i) linear SVMs as patch-experts, (ii) a simplified optimization criteria, and (iii) a composite rather than additive warp update step. Most notably, our simplified optimization criteria for fitting the CLM divides the problem of finding a single complex registration/warp displacement into that of finding N simple warp displacements. From these N simple warp displacements, a single complex warp displacement is estimated using a weighted least-squares constraint. Another major advantage of this simplified optimization lends from its ability to be parallelized, a step which we also theoretically explore in this paper. We refer to our approach for fitting the CLM as the "exhaustive local search" (ELS) algorithm. Experiments were conducted on the CMU Multi-PIE database.
Curve fitting methods for solar radiation data modeling
Energy Technology Data Exchange (ETDEWEB)
Karim, Samsul Ariffin Abdul, E-mail: samsul-ariffin@petronas.com.my, E-mail: balbir@petronas.com.my; Singh, Balbir Singh Mahinder, E-mail: samsul-ariffin@petronas.com.my, E-mail: balbir@petronas.com.my [Department of Fundamental and Applied Sciences, Faculty of Sciences and Information Technology, Universiti Teknologi PETRONAS, Bandar Seri Iskandar, 31750 Tronoh, Perak Darul Ridzuan (Malaysia)
2014-10-24
This paper studies the use of several type of curve fitting method to smooth the global solar radiation data. After the data have been fitted by using curve fitting method, the mathematical model of global solar radiation will be developed. The error measurement was calculated by using goodness-fit statistics such as root mean square error (RMSE) and the value of R{sup 2}. The best fitting methods will be used as a starting point for the construction of mathematical modeling of solar radiation received in Universiti Teknologi PETRONAS (UTP) Malaysia. Numerical results indicated that Gaussian fitting and sine fitting (both with two terms) gives better results as compare with the other fitting methods.
Curve fitting methods for solar radiation data modeling
Karim, Samsul Ariffin Abdul; Singh, Balbir Singh Mahinder
2014-10-01
This paper studies the use of several type of curve fitting method to smooth the global solar radiation data. After the data have been fitted by using curve fitting method, the mathematical model of global solar radiation will be developed. The error measurement was calculated by using goodness-fit statistics such as root mean square error (RMSE) and the value of R2. The best fitting methods will be used as a starting point for the construction of mathematical modeling of solar radiation received in Universiti Teknologi PETRONAS (UTP) Malaysia. Numerical results indicated that Gaussian fitting and sine fitting (both with two terms) gives better results as compare with the other fitting methods.
Curve fitting methods for solar radiation data modeling
International Nuclear Information System (INIS)
Karim, Samsul Ariffin Abdul; Singh, Balbir Singh Mahinder
2014-01-01
This paper studies the use of several type of curve fitting method to smooth the global solar radiation data. After the data have been fitted by using curve fitting method, the mathematical model of global solar radiation will be developed. The error measurement was calculated by using goodness-fit statistics such as root mean square error (RMSE) and the value of R 2 . The best fitting methods will be used as a starting point for the construction of mathematical modeling of solar radiation received in Universiti Teknologi PETRONAS (UTP) Malaysia. Numerical results indicated that Gaussian fitting and sine fitting (both with two terms) gives better results as compare with the other fitting methods
Modeling Evolution on Nearly Neutral Network Fitness Landscapes
Yakushkina, Tatiana; Saakian, David B.
2017-08-01
To describe virus evolution, it is necessary to define a fitness landscape. In this article, we consider the microscopic models with the advanced version of neutral network fitness landscapes. In this problem setting, we suppose a fitness difference between one-point mutation neighbors to be small. We construct a modification of the Wright-Fisher model, which is related to ordinary infinite population models with nearly neutral network fitness landscape at the large population limit. From the microscopic models in the realistic sequence space, we derive two versions of nearly neutral network models: with sinks and without sinks. We claim that the suggested model describes the evolutionary dynamics of RNA viruses better than the traditional Wright-Fisher model with few sequences.
Directory of Open Access Journals (Sweden)
Dylan Molenaar
2015-08-01
Full Text Available In the psychometric literature, item response theory models have been proposed that explicitly take the decision process underlying the responses of subjects to psychometric test items into account. Application of these models is however hampered by the absence of general and flexible software to fit these models. In this paper, we present diffIRT, an R package that can be used to fit item response theory models that are based on a diffusion process. We discuss parameter estimation and model fit assessment, show the viability of the package in a simulation study, and illustrate the use of the package with two datasets pertaining to extraversion and mental rotation. In addition, we illustrate how the package can be used to fit the traditional diffusion model (as it has been originally developed in experimental psychology to data.
Fitting Hidden Markov Models to Psychological Data
Directory of Open Access Journals (Sweden)
Ingmar Visser
2002-01-01
Full Text Available Markov models have been used extensively in psychology of learning. Applications of hidden Markov models are rare however. This is partially due to the fact that comprehensive statistics for model selection and model assessment are lacking in the psychological literature. We present model selection and model assessment statistics that are particularly useful in applying hidden Markov models in psychology. These statistics are presented and evaluated by simulation studies for a toy example. We compare AIC, BIC and related criteria and introduce a prediction error measure for assessing goodness-of-fit. In a simulation study, two methods of fitting equality constraints are compared. In two illustrative examples with experimental data we apply selection criteria, fit models with constraints and assess goodness-of-fit. First, data from a concept identification task is analyzed. Hidden Markov models provide a flexible approach to analyzing such data when compared to other modeling methods. Second, a novel application of hidden Markov models in implicit learning is presented. Hidden Markov models are used in this context to quantify knowledge that subjects express in an implicit learning task. This method of analyzing implicit learning data provides a comprehensive approach for addressing important theoretical issues in the field.
Efficient parallel implementation of active appearance model fitting algorithm on GPU.
Wang, Jinwei; Ma, Xirong; Zhu, Yuanping; Sun, Jizhou
2014-01-01
The active appearance model (AAM) is one of the most powerful model-based object detecting and tracking methods which has been widely used in various situations. However, the high-dimensional texture representation causes very time-consuming computations, which makes the AAM difficult to apply to real-time systems. The emergence of modern graphics processing units (GPUs) that feature a many-core, fine-grained parallel architecture provides new and promising solutions to overcome the computational challenge. In this paper, we propose an efficient parallel implementation of the AAM fitting algorithm on GPUs. Our design idea is fine grain parallelism in which we distribute the texture data of the AAM, in pixels, to thousands of parallel GPU threads for processing, which makes the algorithm fit better into the GPU architecture. We implement our algorithm using the compute unified device architecture (CUDA) on the Nvidia's GTX 650 GPU, which has the latest Kepler architecture. To compare the performance of our algorithm with different data sizes, we built sixteen face AAM models of different dimensional textures. The experiment results show that our parallel AAM fitting algorithm can achieve real-time performance for videos even on very high-dimensional textures.
Contrast Gain Control Model Fits Masking Data
Watson, Andrew B.; Solomon, Joshua A.; Null, Cynthia H. (Technical Monitor)
1994-01-01
We studied the fit of a contrast gain control model to data of Foley (JOSA 1994), consisting of thresholds for a Gabor patch masked by gratings of various orientations, or by compounds of two orientations. Our general model includes models of Foley and Teo & Heeger (IEEE 1994). Our specific model used a bank of Gabor filters with octave bandwidths at 8 orientations. Excitatory and inhibitory nonlinearities were power functions with exponents of 2.4 and 2. Inhibitory pooling was broad in orientation, but narrow in spatial frequency and space. Minkowski pooling used an exponent of 4. All of the data for observer KMF were well fit by the model. We have developed a contrast gain control model that fits masking data. Unlike Foley's, our model accepts images as inputs. Unlike Teo & Heeger's, our model did not require multiple channels for different dynamic ranges.
DEFF Research Database (Denmark)
Giardino, P. P.; Kannike, K.; Masina, I.
2014-01-01
We perform a state-of-the-art global fit to all Higgs data. We synthesise them into a 'universal' form, which allows to easily test any desired model. We apply the proposed methodology to extract from data the Higgs branching ratios, production cross sections, couplings and to analyse composite...... Higgs models, models with extra Higgs doublets, supersymmetry, extra particles in the loops, anomalous top couplings, and invisible Higgs decays into Dark Matter. Best fit regions lie around the Standard Model predictions and are well approximated by our 'universal' fit. Latest data exclude the dilaton...... as an alternative to the Higgs, and disfavour fits with negative Yukawa couplings. We derive for the first time the SM Higgs boson mass from the measured rates, rather than from the peak positions, obtaining M-h = 124.4 +/- 1.6 GeV....
International Nuclear Information System (INIS)
Little, M P
2004-01-01
Bystander effects following exposure to α-particles have been observed in many experimental systems, and imply that linearly extrapolating low dose risks from high dose data might materially underestimate risk. Brenner and Sachs (2002 Int. J. Radiat. Biol. 78 593-604; 2003 Health Phys. 85 103-8) have recently proposed a model of the bystander effect which they use to explain the inverse dose rate effect observed for lung cancer in underground miners exposed to radon daughters. In this paper we fit the model of the bystander effect proposed by Brenner and Sachs to 11 cohorts of underground miners, taking account of the covariance structure of the data and the period of latency between the development of the first pre-malignant cell and clinically overt cancer. We also fitted a simple linear relative risk model, with adjustment for age at exposure and attained age. The methods that we use for fitting both models are different from those used by Brenner and Sachs, in particular taking account of the covariance structure, which they did not, and omitting certain unjustifiable adjustments to the miner data. The fit of the original model of Brenner and Sachs (with 0 y period of latency) is generally poor, although it is much improved by assuming a 5 or 6 y period of latency from the first appearance of a pre-malignant cell to cancer. The fit of this latter model is equivalent to that of a linear relative risk model with adjustment for age at exposure and attained age. In particular, both models are capable of describing the observed inverse dose rate effect in this data set
Is panic disorder a disorder of physical fitness? A heuristic proposal
Perna, Giampaolo; Caldirola, Daniela
2018-01-01
Currently, panic disorder (PD) is considered a mental disorder based on the assumptions that panic attacks (PAs) are “false alarms” that arise from abnormally sensitive defense systems in the central nervous system and that PD is treated with therapies specifically acting on anxiety or fear mechanisms. This article aims to propose an alternative perspective based on the results of some experimental studies. Our heuristic proposal suggests not only that PD may be a mental disorder but also that patients with PD have real abnormal body functioning, mainly involving cardiorespiratory and balance systems, leading to a decline in global physical fitness. PAs, as well as physical symptoms or discomfort in some environmental situations, may be “real alarms” signaling that the adaptability resources of an organism are insufficient to respond appropriately to some internal or external changes, thus representing the transient conscious awareness of an imbalance in body functioning. The antipanic properties of several modern treatments for PD may include their beneficial effects on body functions. Although anxiety or fear mechanisms are evidently involved in PD, we hypothesize that a reduction of physical fitness is the “primum movens” of PD, while anxiety or fear is induced and sustained by repeated signals of impaired body functioning. We propose considering panic in a broader perspective that offers a central role to the body and to contemplate the possible role of somatic treatments in PD. PMID:29623195
Strategies for fitting nonlinear ecological models in R, AD Model Builder, and BUGS
DEFF Research Database (Denmark)
Bolker, B.M.; Gardner, B.; Maunder, M.
2013-01-01
Ecologists often use nonlinear fitting techniques to estimate the parameters of complex ecological models, with attendant frustration. This paper compares three open-source model fitting tools and discusses general strategies for defining and fitting models. R is convenient and (relatively) easy...... to learn, AD Model Builder is fast and robust but comes with a steep learning curve, while BUGS provides the greatest flexibility at the price of speed. Our model-fitting suggestions range from general cultural advice (where possible, use the tools and models that are most common in your subfield...
Local fit evaluation of structural equation models using graphical criteria.
Thoemmes, Felix; Rosseel, Yves; Textor, Johannes
2018-03-01
Evaluation of model fit is critically important for every structural equation model (SEM), and sophisticated methods have been developed for this task. Among them are the χ² goodness-of-fit test, decomposition of the χ², derived measures like the popular root mean square error of approximation (RMSEA) or comparative fit index (CFI), or inspection of residuals or modification indices. Many of these methods provide a global approach to model fit evaluation: A single index is computed that quantifies the fit of the entire SEM to the data. In contrast, graphical criteria like d-separation or trek-separation allow derivation of implications that can be used for local fit evaluation, an approach that is hardly ever applied. We provide an overview of local fit evaluation from the viewpoint of SEM practitioners. In the presence of model misfit, local fit evaluation can potentially help in pinpointing where the problem with the model lies. For models that do fit the data, local tests can identify the parts of the model that are corroborated by the data. Local tests can also be conducted before a model is fitted at all, and they can be used even for models that are globally underidentified. We discuss appropriate statistical local tests, and provide applied examples. We also present novel software in R that automates this type of local fit evaluation. (PsycINFO Database Record (c) 2018 APA, all rights reserved).
A bipartite fitness model for online music streaming services
Pongnumkul, Suchit; Motohashi, Kazuyuki
2018-01-01
This paper proposes an evolution model and an analysis of the behavior of music consumers on online music streaming services. While previous studies have observed power-law degree distributions of usage in online music streaming services, the underlying behavior of users has not been well understood. Users and songs can be described using a bipartite network where an edge exists between a user node and a song node when the user has listened that song. The growth mechanism of bipartite networks has been used to understand the evolution of online bipartite networks Zhang et al. (2013). Existing bipartite models are based on a preferential attachment mechanism László Barabási and Albert (1999) in which the probability that a user listens to a song is proportional to its current popularity. This mechanism does not allow for two types of real world phenomena. First, a newly released song with high quality sometimes quickly gains popularity. Second, the popularity of songs normally decreases as time goes by. Therefore, this paper proposes a new model that is more suitable for online music services by adding fitness and aging functions to the song nodes of the bipartite network proposed by Zhang et al. (2013). Theoretical analyses are performed for the degree distribution of songs. Empirical data from an online streaming service, Last.fm, are used to confirm the degree distribution of the object nodes. Simulation results show improvements from a previous model. Finally, to illustrate the application of the proposed model, a simplified royalty cost model for online music services is used to demonstrate how the changes in the proposed parameters can affect the costs for online music streaming providers. Managerial implications are also discussed.
Efficient Parallel Implementation of Active Appearance Model Fitting Algorithm on GPU
Directory of Open Access Journals (Sweden)
Jinwei Wang
2014-01-01
Full Text Available The active appearance model (AAM is one of the most powerful model-based object detecting and tracking methods which has been widely used in various situations. However, the high-dimensional texture representation causes very time-consuming computations, which makes the AAM difficult to apply to real-time systems. The emergence of modern graphics processing units (GPUs that feature a many-core, fine-grained parallel architecture provides new and promising solutions to overcome the computational challenge. In this paper, we propose an efficient parallel implementation of the AAM fitting algorithm on GPUs. Our design idea is fine grain parallelism in which we distribute the texture data of the AAM, in pixels, to thousands of parallel GPU threads for processing, which makes the algorithm fit better into the GPU architecture. We implement our algorithm using the compute unified device architecture (CUDA on the Nvidia’s GTX 650 GPU, which has the latest Kepler architecture. To compare the performance of our algorithm with different data sizes, we built sixteen face AAM models of different dimensional textures. The experiment results show that our parallel AAM fitting algorithm can achieve real-time performance for videos even on very high-dimensional textures.
topicmodels: An R Package for Fitting Topic Models
Directory of Open Access Journals (Sweden)
Bettina Grun
2011-05-01
Full Text Available Topic models allow the probabilistic modeling of term frequency occurrences in documents. The fitted model can be used to estimate the similarity between documents as well as between a set of specified keywords using an additional layer of latent variables which are referred to as topics. The R package topicmodels provides basic infrastructure for fitting topic models based on data structures from the text mining package tm. The package includes interfaces to two algorithms for fitting topic models: the variational expectation-maximization algorithm provided by David M. Blei and co-authors and an algorithm using Gibbs sampling by Xuan-Hieu Phan and co-authors.
Goodness-of-Fit Assessment of Item Response Theory Models
Maydeu-Olivares, Alberto
2013-01-01
The article provides an overview of goodness-of-fit assessment methods for item response theory (IRT) models. It is now possible to obtain accurate "p"-values of the overall fit of the model if bivariate information statistics are used. Several alternative approaches are described. As the validity of inferences drawn on the fitted model…
A fitting LEGACY – modelling Kepler's best stars
Directory of Open Access Journals (Sweden)
Aarslev Magnus J.
2017-01-01
Full Text Available The LEGACY sample represents the best solar-like stars observed in the Kepler mission[5, 8]. The 66 stars in the sample are all on the main sequence or only slightly more evolved. They each have more than one year's observation data in short cadence, allowing for precise extraction of individual frequencies. Here we present model fits using a modified ASTFIT procedure employing two different near-surface-effect corrections, one by Christensen-Dalsgaard[4] and a newer correction proposed by Ball & Gizon[1]. We then compare the results obtained using the different corrections. We find that using the latter correction yields lower masses and significantly lower χ2 values for a large part of the sample.
Directory of Open Access Journals (Sweden)
Yun Wang
2016-01-01
Full Text Available Gamma Gaussian inverse Wishart cardinalized probability hypothesis density (GGIW-CPHD algorithm was always used to track group targets in the presence of cluttered measurements and missing detections. A multiple models GGIW-CPHD algorithm based on best-fitting Gaussian approximation method (BFG and strong tracking filter (STF is proposed aiming at the defect that the tracking error of GGIW-CPHD algorithm will increase when the group targets are maneuvering. The best-fitting Gaussian approximation method is proposed to implement the fusion of multiple models using the strong tracking filter to correct the predicted covariance matrix of the GGIW component. The corresponding likelihood functions are deduced to update the probability of multiple tracking models. From the simulation results we can see that the proposed tracking algorithm MM-GGIW-CPHD can effectively deal with the combination/spawning of groups and the tracking error of group targets in the maneuvering stage is decreased.
Cossio-Bolaños, Marco; Vasquez, Pablo; Luarte-Rocha, Cristian; Sulla-Torres, José; Gómez Campos, Rossana
2016-08-01
Physical fitness may be assessed among children and adolescents in a quantitative and qualitative manner. At present, in Chile, there are no tools available to assess self-perception of physical fitness. Therefore, the purpose of this study was to develop a valid and reliable instrument that would allow to assess selfperception of physical fitness among adolescents and propose standards for age and sex. A survey was administered among adolescent students from six public schools in the Maule Region, Chile, selected in a probabilistic (stratified) fashion. To measure self-perception of physical fitness, a qualitative instrument was developed: the Self-Perception of Physical Fitness Scale (EAPAF, escala de autopercepcion de la aptitud fisica), which is made up of four dimensions and 18 questions. The LMS method (L: Box-Cox coefficient, M: median curve, and S: variation coefficient) was used to establish percentiles and propose references by dimension, age and sex. A total of 3060 adolescents (1702 boys and 1358 girls) aged 11.0 to 18.9 years old were included. The factor analysis evidenced four factors. Saturation values were above 0.40. The percentage of instrument explanation reached 54.24%. In terms of reliability, the 18 questions reflected that Cronbach's alpha was between 0.82 and 0.85. Percentiles (p15, p50 and p85) were developed to classify self-perception of physical fitness by dimension, age and sex. Boys showed higher scores in the self-perception of physical fitness scale when compared to girls (p 〈 0.001). The instrument developed in this study was valid and reliable. In addition, the standards proposed may become a useful tool to classify adolescents in relation to their selfperception of physical fitness. Sociedad Argentina de Pediatría.
Automatic fitting of spiking neuron models to electrophysiological recordings
Directory of Open Access Journals (Sweden)
Cyrille Rossant
2010-03-01
Full Text Available Spiking models can accurately predict the spike trains produced by cortical neurons in response to somatically injected currents. Since the specific characteristics of the model depend on the neuron, a computational method is required to fit models to electrophysiological recordings. The fitting procedure can be very time consuming both in terms of computer simulations and in terms of code writing. We present algorithms to fit spiking models to electrophysiological data (time-varying input and spike trains that can run in parallel on graphics processing units (GPUs. The model fitting library is interfaced with Brian, a neural network simulator in Python. If a GPU is present it uses just-in-time compilation to translate model equations into optimized code. Arbitrary models can then be defined at script level and run on the graphics card. This tool can be used to obtain empirically validated spiking models of neurons in various systems. We demonstrate its use on public data from the INCF Quantitative Single-Neuron Modeling 2009 competition by comparing the performance of a number of neuron spiking models.
Ortega, Francisco B; Cadenas-Sánchez, Cristina; Sánchez-Delgado, Guillermo; Mora-González, José; Martínez-Téllez, Borja; Artero, Enrique G; Castro-Piñero, Jose; Labayen, Idoia; Chillón, Palma; Löf, Marie; Ruiz, Jonatan R
2015-04-01
Physical fitness is a powerful health marker in childhood and adolescence, and it is reasonable to think that it might be just as important in younger children, i.e. preschoolers. At the moment, researchers, clinicians and sport practitioners do not have enough information about which fitness tests are more reliable, valid and informative from the health point of view to be implemented in preschool children. Our aim was to systematically review the studies conducted in preschool children using field-based fitness tests, and examine their (1) reliability, (2) validity, and (3) relationship with health outcomes. Our ultimate goal was to propose a field-based physical fitness-test battery to be used in preschool children. PubMed and Web of Science. Studies conducted in healthy preschool children that included field-based fitness tests. When using PubMed, we included Medical Subject Heading (MeSH) terms to enhance the power of the search. A set of fitness-related terms were combined with 'child, preschool' [MeSH]. The same strategy and terms were used for Web of Science (except for the MeSH option). Since no previous reviews with a similar aim were identified, we searched for all articles published up to 1 April 2014 (no starting date). A total of 2,109 articles were identified, of which 22 articles were finally selected for this review. Most studies focused on reliability of the fitness tests (n = 21, 96%), while very few focused on validity (0 criterion-related validity and 4 (18%) convergent validity) or relationship with health outcomes (0 longitudinal and 1 (5%) cross-sectional study). Motor fitness, particularly balance, was the most studied fitness component, while cardiorespiratory fitness was the least studied. After analyzing the information retrieved in the current systematic review about fitness testing in preschool children, we propose the PREFIT battery, field-based FITness testing in PREschool children. The PREFIT battery is composed of the following
Tacit knowledge in academia: a proposed model and measurement scale.
Leonard, Nancy; Insch, Gary S
2005-11-01
The authors propose a multidimensional model of tacit knowledge and develop a measure of tacit knowledge in academia. They discuss the theory and extant literature on tacit knowledge and propose a 6-factor model. Experiment 1 is a replication of a recent study of academic tacit knowledge using the scale developed and administered at an Israeli university (A. Somech & R. Bogler, 1999). The results of the replication differed from those found in the original study. For Experiment 2, the authors developed a domain-specific measure of academic tacit knowledge, the Academic Tacit Knowledge Scale (ATKS), and used this measure to explore the multidimensionality of tacit knowledge proposed in the model. The results of an exploratory factor analysis (n=142) followed by a confirmatory factor analysis (n=286) are reported. The sample for both experiments was 428 undergraduate students enrolled at a large public university in the eastern United States. Results indicated that a 5-factor model of academic tacit knowledge provided a strong fit for the data.
Strategies for fitting nonlinear ecological models in R, AD Model Builder, and BUGS
Bolker, Benjamin M.; Gardner, Beth; Maunder, Mark; Berg, Casper W.; Brooks, Mollie; Comita, Liza; Crone, Elizabeth; Cubaynes, Sarah; Davies, Trevor; de Valpine, Perry; Ford, Jessica; Gimenez, Olivier; Kéry, Marc; Kim, Eun Jung; Lennert-Cody, Cleridy; Magunsson, Arni; Martell, Steve; Nash, John; Nielson, Anders; Regentz, Jim; Skaug, Hans; Zipkin, Elise
2013-01-01
1. Ecologists often use nonlinear fitting techniques to estimate the parameters of complex ecological models, with attendant frustration. This paper compares three open-source model fitting tools and discusses general strategies for defining and fitting models. 2. R is convenient and (relatively) easy to learn, AD Model Builder is fast and robust but comes with a steep learning curve, while BUGS provides the greatest flexibility at the price of speed. 3. Our model-fitting suggestions range from general cultural advice (where possible, use the tools and models that are most common in your subfield) to specific suggestions about how to change the mathematical description of models to make them more amenable to parameter estimation. 4. A companion web site (https://groups.nceas.ucsb.edu/nonlinear-modeling/projects) presents detailed examples of application of the three tools to a variety of typical ecological estimation problems; each example links both to a detailed project report and to full source code and data.
Are Physical Education Majors Models for Fitness?
Kamla, James; Snyder, Ben; Tanner, Lori; Wash, Pamela
2012-01-01
The National Association of Sport and Physical Education (NASPE) (2002) has taken a firm stance on the importance of adequate fitness levels of physical education teachers stating that they have the responsibility to model an active lifestyle and to promote fitness behaviors. Since the NASPE declaration, national initiatives like Let's Move…
ITEM LEVEL DIAGNOSTICS AND MODEL - DATA FIT IN ITEM ...
African Journals Online (AJOL)
Global Journal
Item response theory (IRT) is a framework for modeling and analyzing item response ... data. Though, there is an argument that the evaluation of fit in IRT modeling has been ... National Council on Measurement in Education ... model data fit should be based on three types of ... prediction should be assessed through the.
Assessing fit in Bayesian models for spatial processes
Jun, M.; Katzfuss, M.; Hu, J.; Johnson, V. E.
2014-01-01
© 2014 John Wiley & Sons, Ltd. Gaussian random fields are frequently used to model spatial and spatial-temporal data, particularly in geostatistical settings. As much of the attention of the statistics community has been focused on defining and estimating the mean and covariance functions of these processes, little effort has been devoted to developing goodness-of-fit tests to allow users to assess the models' adequacy. We describe a general goodness-of-fit test and related graphical diagnostics for assessing the fit of Bayesian Gaussian process models using pivotal discrepancy measures. Our method is applicable for both regularly and irregularly spaced observation locations on planar and spherical domains. The essential idea behind our method is to evaluate pivotal quantities defined for a realization of a Gaussian random field at parameter values drawn from the posterior distribution. Because the nominal distribution of the resulting pivotal discrepancy measures is known, it is possible to quantitatively assess model fit directly from the output of Markov chain Monte Carlo algorithms used to sample from the posterior distribution on the parameter space. We illustrate our method in a simulation study and in two applications.
Assessing fit in Bayesian models for spatial processes
Jun, M.
2014-09-16
© 2014 John Wiley & Sons, Ltd. Gaussian random fields are frequently used to model spatial and spatial-temporal data, particularly in geostatistical settings. As much of the attention of the statistics community has been focused on defining and estimating the mean and covariance functions of these processes, little effort has been devoted to developing goodness-of-fit tests to allow users to assess the models\\' adequacy. We describe a general goodness-of-fit test and related graphical diagnostics for assessing the fit of Bayesian Gaussian process models using pivotal discrepancy measures. Our method is applicable for both regularly and irregularly spaced observation locations on planar and spherical domains. The essential idea behind our method is to evaluate pivotal quantities defined for a realization of a Gaussian random field at parameter values drawn from the posterior distribution. Because the nominal distribution of the resulting pivotal discrepancy measures is known, it is possible to quantitatively assess model fit directly from the output of Markov chain Monte Carlo algorithms used to sample from the posterior distribution on the parameter space. We illustrate our method in a simulation study and in two applications.
W. Hasan, W. Z.
2018-01-01
The power system always has several variations in its profile due to random load changes or environmental effects such as device switching effects when generating further transients. Thus, an accurate mathematical model is important because most system parameters vary with time. Curve modeling of power generation is a significant tool for evaluating system performance, monitoring and forecasting. Several numerical techniques compete to fit the curves of empirical data such as wind, solar, and demand power rates. This paper proposes a new modified methodology presented as a parametric technique to determine the system’s modeling equations based on the Bode plot equations and the vector fitting (VF) algorithm by fitting the experimental data points. The modification is derived from the familiar VF algorithm as a robust numerical method. This development increases the application range of the VF algorithm for modeling not only in the frequency domain but also for all power curves. Four case studies are addressed and compared with several common methods. From the minimal RMSE, the results show clear improvements in data fitting over other methods. The most powerful features of this method is the ability to model irregular or randomly shaped data and to be applied to any algorithms that estimating models using frequency-domain data to provide state-space or transfer function for the model. PMID:29351554
Sabry, A H; W Hasan, W Z; Ab Kadir, M Z A; Radzi, M A M; Shafie, S
2018-01-01
The power system always has several variations in its profile due to random load changes or environmental effects such as device switching effects when generating further transients. Thus, an accurate mathematical model is important because most system parameters vary with time. Curve modeling of power generation is a significant tool for evaluating system performance, monitoring and forecasting. Several numerical techniques compete to fit the curves of empirical data such as wind, solar, and demand power rates. This paper proposes a new modified methodology presented as a parametric technique to determine the system's modeling equations based on the Bode plot equations and the vector fitting (VF) algorithm by fitting the experimental data points. The modification is derived from the familiar VF algorithm as a robust numerical method. This development increases the application range of the VF algorithm for modeling not only in the frequency domain but also for all power curves. Four case studies are addressed and compared with several common methods. From the minimal RMSE, the results show clear improvements in data fitting over other methods. The most powerful features of this method is the ability to model irregular or randomly shaped data and to be applied to any algorithms that estimating models using frequency-domain data to provide state-space or transfer function for the model.
Directory of Open Access Journals (Sweden)
A H Sabry
Full Text Available The power system always has several variations in its profile due to random load changes or environmental effects such as device switching effects when generating further transients. Thus, an accurate mathematical model is important because most system parameters vary with time. Curve modeling of power generation is a significant tool for evaluating system performance, monitoring and forecasting. Several numerical techniques compete to fit the curves of empirical data such as wind, solar, and demand power rates. This paper proposes a new modified methodology presented as a parametric technique to determine the system's modeling equations based on the Bode plot equations and the vector fitting (VF algorithm by fitting the experimental data points. The modification is derived from the familiar VF algorithm as a robust numerical method. This development increases the application range of the VF algorithm for modeling not only in the frequency domain but also for all power curves. Four case studies are addressed and compared with several common methods. From the minimal RMSE, the results show clear improvements in data fitting over other methods. The most powerful features of this method is the ability to model irregular or randomly shaped data and to be applied to any algorithms that estimating models using frequency-domain data to provide state-space or transfer function for the model.
Chow, Sy-Miin; Lu, Zhaohua; Sherwood, Andrew; Zhu, Hongtu
2016-03-01
The past decade has evidenced the increased prevalence of irregularly spaced longitudinal data in social sciences. Clearly lacking, however, are modeling tools that allow researchers to fit dynamic models to irregularly spaced data, particularly data that show nonlinearity and heterogeneity in dynamical structures. We consider the issue of fitting multivariate nonlinear differential equation models with random effects and unknown initial conditions to irregularly spaced data. A stochastic approximation expectation-maximization algorithm is proposed and its performance is evaluated using a benchmark nonlinear dynamical systems model, namely, the Van der Pol oscillator equations. The empirical utility of the proposed technique is illustrated using a set of 24-h ambulatory cardiovascular data from 168 men and women. Pertinent methodological challenges and unresolved issues are discussed.
Automated Model Fit Method for Diesel Engine Control Development
Seykens, X.; Willems, F.P.T.; Kuijpers, B.; Rietjens, C.
2014-01-01
This paper presents an automated fit for a control-oriented physics-based diesel engine combustion model. This method is based on the combination of a dedicated measurement procedure and structured approach to fit the required combustion model parameters. Only a data set is required that is
Automated model fit method for diesel engine control development
Seykens, X.L.J.; Willems, F.P.T.; Kuijpers, B.; Rietjens, C.J.H.
2014-01-01
This paper presents an automated fit for a control-oriented physics-based diesel engine combustion model. This method is based on the combination of a dedicated measurement procedure and structured approach to fit the required combustion model parameters. Only a data set is required that is
Assessing a moderating effect and the global fit of a PLS model on online trading
Directory of Open Access Journals (Sweden)
Juan J. García-Machado
2017-12-01
Full Text Available This paper proposes a PLS Model for the study of Online Trading. Traditional investing has experienced a revolution due to the rise of e-trading services that enable investors to use Internet conduct secure trading. On the hand, model results show that there is a positive, direct and statistically significant relationship between personal outcome expectations, perceived relative advantage, shared vision and economy-based trust with the quality of knowledge. On the other hand, trading frequency and portfolio performance has also this relationship. After including the investor’s income and financial wealth (IFW as moderating effect, the PLS model was enhanced, and we found that the interaction term is negative and statistically significant, so, higher IFW levels entail a weaker relationship between trading frequency and portfolio performance and vice-versa. Finally, with regard to the goodness of overall model fit measures, they showed that the model is fit for SRMR and dG measures, so it is likely that the model is true.
An R package for fitting age, period and cohort models
Directory of Open Access Journals (Sweden)
Adriano Decarli
2014-11-01
Full Text Available In this paper we present the R implementation of a GLIM macro which fits age-period-cohort model following Osmond and Gardner. In addition to the estimates of the corresponding model, owing to the programming capability of R as an object oriented language, methods for printing, plotting and summarizing the results are provided. Furthermore, the researcher has fully access to the output of the main function (apc which returns all the models fitted within the function. It is so possible to critically evaluate the goodness of fit of the resulting model.
FITTING A THREE DIMENSIONAL PEM FUEL CELL MODEL TO MEASUREMENTS BY TUNING THE POROSITY AND
DEFF Research Database (Denmark)
Bang, Mads; Odgaard, Madeleine; Condra, Thomas Joseph
2004-01-01
the distribution of current density and further how thisaffects the polarization curve.The porosity and conductivity of the catalyst layer are some ofthe most difficult parameters to measure, estimate and especiallycontrol. Yet the proposed model shows how these two parameterscan have significant influence...... on the performance of the fuel cell.The two parameters are shown to be key elements in adjusting thethree-dimensional model to fit measured polarization curves.Results from the proposed model are compared to single cellmeasurements on a test MEA from IRD Fuel Cells.......A three-dimensional, computational fluid dynamics (CFD) model of a PEM fuel cell is presented. The model consists ofstraight channels, porous gas diffusion layers, porous catalystlayers and a membrane. In this computational domain, most ofthe transport phenomena which govern the performance of the...
Directory of Open Access Journals (Sweden)
Grant B. Morgan
2015-02-01
Full Text Available Bi-factor confirmatory factor models have been influential in research on cognitive abilities because they often better fit the data than correlated factors and higher-order models. They also instantiate a perspective that differs from that offered by other models. Motivated by previous work that hypothesized an inherent statistical bias of fit indices favoring the bi-factor model, we compared the fit of correlated factors, higher-order, and bi-factor models via Monte Carlo methods. When data were sampled from a true bi-factor structure, each of the approximate fit indices was more likely than not to identify the bi-factor solution as the best fitting. When samples were selected from a true multiple correlated factors structure, approximate fit indices were more likely overall to identify the correlated factors solution as the best fitting. In contrast, when samples were generated from a true higher-order structure, approximate fit indices tended to identify the bi-factor solution as best fitting. There was extensive overlap of fit values across the models regardless of true structure. Although one model may fit a given dataset best relative to the other models, each of the models tended to fit the data well in absolute terms. Given this variability, models must also be judged on substantive and conceptual grounds.
Brain MRI Tumor Detection using Active Contour Model and Local Image Fitting Energy
Nabizadeh, Nooshin; John, Nigel
2014-03-01
Automatic abnormality detection in Magnetic Resonance Imaging (MRI) is an important issue in many diagnostic and therapeutic applications. Here an automatic brain tumor detection method is introduced that uses T1-weighted images and K. Zhang et. al.'s active contour model driven by local image fitting (LIF) energy. Local image fitting energy obtains the local image information, which enables the algorithm to segment images with intensity inhomogeneities. Advantage of this method is that the LIF energy functional has less computational complexity than the local binary fitting (LBF) energy functional; moreover, it maintains the sub-pixel accuracy and boundary regularization properties. In Zhang's algorithm, a new level set method based on Gaussian filtering is used to implement the variational formulation, which is not only vigorous to prevent the energy functional from being trapped into local minimum, but also effective in keeping the level set function regular. Experiments show that the proposed method achieves high accuracy brain tumor segmentation results.
International Nuclear Information System (INIS)
Pronyaev, V.G.
2003-01-01
The information entropy is taken as a measure of knowledge about the object and the reduced univariante variance as a common measure of uncertainty. Covariances in the model versus non-model least square fits are discussed
CUSUM-based person-fit statistics for adaptive testing
van Krimpen-Stoop, Edith; Meijer, R.R.
2001-01-01
Item scores that do not fit an assumed item response theory model may cause the latent trait value to be inaccurately estimated. Several person-fit statistics for detecting nonfitting score patterns for paper-and-pencil tests have been proposed. In the context of computerized adaptive tests (CAT),
CUSUM-based person-fit statistics for adaptive testing
van Krimpen-Stoop, Edith; Meijer, R.R.
1999-01-01
Item scores that do not fit an assumed item response theory model may cause the latent trait value to be estimated inaccurately. Several person-fit statistics for detecting nonfitting score patterns for paper-and-pencil tests have been proposed. In the context of computerized adaptive tests (CAT),
A Model Fit Statistic for Generalized Partial Credit Model
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…
Pedata, Paola; Corvino, Anna Rita; Napolitano, Raffaele Carmine; Garzillo, Elpidio Maria; Furfaro, Ciro; Lamberti, Monica
2016-01-20
From many years now, thanks to the development of modern diving techniques, there has been a rapid spread of diving activities everywhere. In fact, divers are ever more numerous both among the Armed Forces and civilians who dive for work, like fishing, biological research and archeology. The aim of the study was to propose a health protocol for work fitness of professional divers keeping in mind the peculiar work activity, existing Italian legislation that is almost out of date and the technical and scientific evolution in this occupational field. We performed an analysis of the most frequently occurring diseases among professional divers and of the clinical investigation and imaging techniques used for work fitness assessment of professional divers. From analysis of the health protocol recommended by D.M. 13 January 1979 (Ministerial Decree), that is most used by occupational health physician, several critical issues emerged. Very often the clinical investigation and imaging techniques still used are almost obsolete, ignoring the execution of simple and inexpensive investigations that are more useful for work fitness assessment. Considering the out-dated legislation concerning diving disciplines, it is necessary to draw up a common health protocol that takes into account clinical and scientific knowledge and skills acquired in this area. This protocol's aim is to propose a useful tool for occupational health physicians who work in this sector.
Directory of Open Access Journals (Sweden)
K. Rahmani
2018-05-01
Full Text Available In this paper we present a pipeline for high quality semantic segmentation of building facades using Structured Random Forest (SRF, Region Proposal Network (RPN based on a Convolutional Neural Network (CNN as well as rectangular fitting optimization. Our main contribution is that we employ features created by the RPN as channels in the SRF.We empirically show that this is very effective especially for doors and windows. Our pipeline is evaluated on two datasets where we outperform current state-of-the-art methods. Additionally, we quantify the contribution of the RPN and the rectangular fitting optimization on the accuracy of the result.
A Comparison of Item Fit Statistics for Mixed IRT Models
Chon, Kyong Hee; Lee, Won-Chan; Dunbar, Stephen B.
2010-01-01
In this study we examined procedures for assessing model-data fit of item response theory (IRT) models for mixed format data. The model fit indices used in this study include PARSCALE's G[superscript 2], Orlando and Thissen's S-X[superscript 2] and S-G[superscript 2], and Stone's chi[superscript 2*] and G[superscript 2*]. To investigate the…
Sensitivity of Fit Indices to Misspecification in Growth Curve Models
Wu, Wei; West, Stephen G.
2010-01-01
This study investigated the sensitivity of fit indices to model misspecification in within-individual covariance structure, between-individual covariance structure, and marginal mean structure in growth curve models. Five commonly used fit indices were examined, including the likelihood ratio test statistic, root mean square error of…
Standard error propagation in R-matrix model fitting for light elements
International Nuclear Information System (INIS)
Chen Zhenpeng; Zhang Rui; Sun Yeying; Liu Tingjin
2003-01-01
The error propagation features with R-matrix model fitting 7 Li, 11 B and 17 O systems were researched systematically. Some laws of error propagation were revealed, an empirical formula P j = U j c / U j d = K j · S-bar · √m / √N for describing standard error propagation was established, the most likely error ranges for standard cross sections of 6 Li(n,t), 10 B(n,α0) and 10 B(n,α1) were estimated. The problem that the standard error of light nuclei standard cross sections may be too small results mainly from the R-matrix model fitting, which is not perfect. Yet R-matrix model fitting is the most reliable evaluation method for such data. The error propagation features of R-matrix model fitting for compound nucleus system of 7 Li, 11 B and 17 O has been studied systematically, some laws of error propagation are revealed, and these findings are important in solving the problem mentioned above. Furthermore, these conclusions are suitable for similar model fitting in other scientific fields. (author)
Model-fitting approach to kinetic analysis of non-isothermal oxidation of molybdenite
International Nuclear Information System (INIS)
Ebrahimi Kahrizsangi, R.; Abbasi, M. H.; Saidi, A.
2007-01-01
The kinetics of molybdenite oxidation was studied by non-isothermal TGA-DTA with heating rate 5 d eg C .min -1 . The model-fitting kinetic approach applied to TGA data. The Coats-Redfern method used of model fitting. The popular model-fitting gives excellent fit non-isothermal data in chemically controlled regime. The apparent activation energy was determined to be about 34.2 kcalmol -1 With pre-exponential factor about 10 8 sec -1 for extent of reaction less than 0.5
The fitting parameters extraction of conversion model of the low dose rate effect in bipolar devices
International Nuclear Information System (INIS)
Bakerenkov, Alexander
2011-01-01
The Enhanced Low Dose Rate Sensitivity (ELDRS) in bipolar devices consists of in base current degradation of NPN and PNP transistors increase as the dose rate is decreased. As a result of almost 20-year studying, the some physical models of effect are developed, being described in detail. Accelerated test methods, based on these models use in standards. The conversion model of the effect, that allows to describe the inverse S-shaped excess base current dependence versus dose rate, was proposed. This paper presents the problem of conversion model fitting parameters extraction.
Bosone, Lucia; Martinez, Frédéric; Kalampalikis, Nikos
2015-04-01
In health-promotional campaigns, positive and negative role models can be deployed to illustrate the benefits or costs of certain behaviors. The main purpose of this article is to investigate why, how, and when exposure to role models strengthens the persuasiveness of a message, according to regulatory fit theory. We argue that exposure to a positive versus a negative model activates individuals' goals toward promotion rather than prevention. By means of two experiments, we demonstrate that high levels of persuasion occur when a message advertising healthy dietary habits offers a regulatory fit between its framing and the described role model. Our data also establish that the effects of such internal regulatory fit by vicarious experience depend on individuals' perceptions of response-efficacy and self-efficacy. Our findings constitute a significant theoretical complement to previous research on regulatory fit and contain valuable practical implications for health-promotional campaigns. © 2015 by the Society for Personality and Social Psychology, Inc.
Flexible competing risks regression modeling and goodness-of-fit
DEFF Research Database (Denmark)
Scheike, Thomas; Zhang, Mei-Jie
2008-01-01
In this paper we consider different approaches for estimation and assessment of covariate effects for the cumulative incidence curve in the competing risks model. The classic approach is to model all cause-specific hazards and then estimate the cumulative incidence curve based on these cause...... models that is easy to fit and contains the Fine-Gray model as a special case. One advantage of this approach is that our regression modeling allows for non-proportional hazards. This leads to a new simple goodness-of-fit procedure for the proportional subdistribution hazards assumption that is very easy...... of the flexible regression models to analyze competing risks data when non-proportionality is present in the data....
Sueiro, Manuel J.; Abad, Francisco J.
2011-01-01
The distance between nonparametric and parametric item characteristic curves has been proposed as an index of goodness of fit in item response theory in the form of a root integrated squared error index. This article proposes to use the posterior distribution of the latent trait as the nonparametric model and compares the performance of an index…
An NCME Instructional Module on Item-Fit Statistics for Item Response Theory Models
Ames, Allison J.; Penfield, Randall D.
2015-01-01
Drawing valid inferences from item response theory (IRT) models is contingent upon a good fit of the data to the model. Violations of model-data fit have numerous consequences, limiting the usefulness and applicability of the model. This instructional module provides an overview of methods used for evaluating the fit of IRT models. Upon completing…
Critical elements on fitting the Bayesian multivariate Poisson Lognormal model
Zamzuri, Zamira Hasanah binti
2015-10-01
Motivated by a problem on fitting multivariate models to traffic accident data, a detailed discussion of the Multivariate Poisson Lognormal (MPL) model is presented. This paper reveals three critical elements on fitting the MPL model: the setting of initial estimates, hyperparameters and tuning parameters. These issues have not been highlighted in the literature. Based on simulation studies conducted, we have shown that to use the Univariate Poisson Model (UPM) estimates as starting values, at least 20,000 iterations are needed to obtain reliable final estimates. We also illustrated the sensitivity of the specific hyperparameter, which if it is not given extra attention, may affect the final estimates. The last issue is regarding the tuning parameters where they depend on the acceptance rate. Finally, a heuristic algorithm to fit the MPL model is presented. This acts as a guide to ensure that the model works satisfactorily given any data set.
Item level diagnostics and model - data fit in item response theory ...
African Journals Online (AJOL)
Item response theory (IRT) is a framework for modeling and analyzing item response data. Item-level modeling gives IRT advantages over classical test theory. The fit of an item score pattern to an item response theory (IRT) models is a necessary condition that must be assessed for further use of item and models that best fit ...
Fitting ARMA Time Series by Structural Equation Models.
van Buuren, Stef
1997-01-01
This paper outlines how the stationary ARMA (p,q) model (G. Box and G. Jenkins, 1976) can be specified as a structural equation model. Maximum likelihood estimates for the parameters in the ARMA model can be obtained by software for fitting structural equation models. The method is applied to three problem types. (SLD)
Parametric fitting of data obtained from detectors with finite resolution and limited acceptance
International Nuclear Information System (INIS)
Gagunashvili, N.D.
2011-01-01
A goodness-of-fit test for fitting of a parametric model to data obtained from a detector with finite resolution and limited acceptance is proposed. The parameters of the model are found by minimization of a statistic that is used for comparing experimental data and simulated reconstructed data. Numerical examples are presented to illustrate and validate the fitting procedure.
Identifying best-fitting inputs in health-economic model calibration: a Pareto frontier approach.
Enns, Eva A; Cipriano, Lauren E; Simons, Cyrena T; Kong, Chung Yin
2015-02-01
To identify best-fitting input sets using model calibration, individual calibration target fits are often combined into a single goodness-of-fit (GOF) measure using a set of weights. Decisions in the calibration process, such as which weights to use, influence which sets of model inputs are identified as best-fitting, potentially leading to different health economic conclusions. We present an alternative approach to identifying best-fitting input sets based on the concept of Pareto-optimality. A set of model inputs is on the Pareto frontier if no other input set simultaneously fits all calibration targets as well or better. We demonstrate the Pareto frontier approach in the calibration of 2 models: a simple, illustrative Markov model and a previously published cost-effectiveness model of transcatheter aortic valve replacement (TAVR). For each model, we compare the input sets on the Pareto frontier to an equal number of best-fitting input sets according to 2 possible weighted-sum GOF scoring systems, and we compare the health economic conclusions arising from these different definitions of best-fitting. For the simple model, outcomes evaluated over the best-fitting input sets according to the 2 weighted-sum GOF schemes were virtually nonoverlapping on the cost-effectiveness plane and resulted in very different incremental cost-effectiveness ratios ($79,300 [95% CI 72,500-87,600] v. $139,700 [95% CI 79,900-182,800] per quality-adjusted life-year [QALY] gained). Input sets on the Pareto frontier spanned both regions ($79,000 [95% CI 64,900-156,200] per QALY gained). The TAVR model yielded similar results. Choices in generating a summary GOF score may result in different health economic conclusions. The Pareto frontier approach eliminates the need to make these choices by using an intuitive and transparent notion of optimality as the basis for identifying best-fitting input sets. © The Author(s) 2014.
Gfitter - Revisiting the global electroweak fit of the Standard Model and beyond
Energy Technology Data Exchange (ETDEWEB)
Flaecher, H.; Hoecker, A. [European Organization for Nuclear Research (CERN), Geneva (Switzerland); Goebel, M. [Deutsches Elektronen-Synchrotron (DESY), Hamburg (Germany)]|[Deutsches Elektronen-Synchrotron (DESY), Zeuthen (Germany)]|[Hamburg Univ. (Germany). Inst. fuer Experimentalphysik; Haller, J. [Hamburg Univ. (Germany). Inst. fuer Experimentalphysik; Moenig, K.; Stelzer, J. [Deutsches Elektronen-Synchrotron (DESY), Hamburg (Germany)]|[Deutsches Elektronen-Synchrotron (DESY), Zeuthen (Germany)
2008-11-15
The global fit of the Standard Model to electroweak precision data, routinely performed by the LEP electroweak working group and others, demonstrated impressively the predictive power of electroweak unification and quantum loop corrections. We have revisited this fit in view of (i) the development of the new generic fitting package, Gfitter, allowing flexible and efficient model testing in high-energy physics, (ii) the insertion of constraints from direct Higgs searches at LEP and the Tevatron, and (iii) a more thorough statistical interpretation of the results. Gfitter is a modular fitting toolkit, which features predictive theoretical models as independent plugins, and a statistical analysis of the fit results using toy Monte Carlo techniques. The state-of-the-art electroweak Standard Model is fully implemented, as well as generic extensions to it. Theoretical uncertainties are explicitly included in the fit through scale parameters varying within given error ranges. This paper introduces the Gfitter project, and presents state-of-the-art results for the global electroweak fit in the Standard Model, and for a model with an extended Higgs sector (2HDM). Numerical and graphical results for fits with and without including the constraints from the direct Higgs searches at LEP and Tevatron are given. Perspectives for future colliders are analysed and discussed. Including the direct Higgs searches, we find M{sub H}=116.4{sup +18.3}{sub -1.3} GeV, and the 2{sigma} and 3{sigma} allowed regions [114,145] GeV and [[113,168] and [180,225
SPSS macros to compare any two fitted values from a regression model.
Weaver, Bruce; Dubois, Sacha
2012-12-01
In regression models with first-order terms only, the coefficient for a given variable is typically interpreted as the change in the fitted value of Y for a one-unit increase in that variable, with all other variables held constant. Therefore, each regression coefficient represents the difference between two fitted values of Y. But the coefficients represent only a fraction of the possible fitted value comparisons that might be of interest to researchers. For many fitted value comparisons that are not captured by any of the regression coefficients, common statistical software packages do not provide the standard errors needed to compute confidence intervals or carry out statistical tests-particularly in more complex models that include interactions, polynomial terms, or regression splines. We describe two SPSS macros that implement a matrix algebra method for comparing any two fitted values from a regression model. The !OLScomp and !MLEcomp macros are for use with models fitted via ordinary least squares and maximum likelihood estimation, respectively. The output from the macros includes the standard error of the difference between the two fitted values, a 95% confidence interval for the difference, and a corresponding statistical test with its p-value.
LEP asymmetries and fits of the standard model
International Nuclear Information System (INIS)
Pietrzyk, B.
1994-01-01
The lepton and quark asymmetries measured at LEP are presented. The results of the Standard Model fits to the electroweak data presented at this conference are given. The top mass obtained from the fit to the LEP data is 172 -14-20 +13+18 GeV; it is 177 -11-19 +11+18 when also the collider, ν and A LR data are included. (author). 10 refs., 3 figs., 2 tabs
Goodness-of-fit tests and model diagnostics for negative binomial regression of RNA sequencing data.
Mi, Gu; Di, Yanming; Schafer, Daniel W
2015-01-01
This work is about assessing model adequacy for negative binomial (NB) regression, particularly (1) assessing the adequacy of the NB assumption, and (2) assessing the appropriateness of models for NB dispersion parameters. Tools for the first are appropriate for NB regression generally; those for the second are primarily intended for RNA sequencing (RNA-Seq) data analysis. The typically small number of biological samples and large number of genes in RNA-Seq analysis motivate us to address the trade-offs between robustness and statistical power using NB regression models. One widely-used power-saving strategy, for example, is to assume some commonalities of NB dispersion parameters across genes via simple models relating them to mean expression rates, and many such models have been proposed. As RNA-Seq analysis is becoming ever more popular, it is appropriate to make more thorough investigations into power and robustness of the resulting methods, and into practical tools for model assessment. In this article, we propose simulation-based statistical tests and diagnostic graphics to address model adequacy. We provide simulated and real data examples to illustrate that our proposed methods are effective for detecting the misspecification of the NB mean-variance relationship as well as judging the adequacy of fit of several NB dispersion models.
Development and Analysis of Volume Multi-Sphere Method Model Generation using Electric Field Fitting
Ingram, G. J.
Electrostatic modeling of spacecraft has wide-reaching applications such as detumbling space debris in the Geosynchronous Earth Orbit regime before docking, servicing and tugging space debris to graveyard orbits, and Lorentz augmented orbits. The viability of electrostatic actuation control applications relies on faster-than-realtime characterization of the electrostatic interaction. The Volume Multi-Sphere Method (VMSM) seeks the optimal placement and radii of a small number of equipotential spheres to accurately model the electrostatic force and torque on a conducting space object. Current VMSM models tuned using force and torque comparisons with commercially available finite element software are subject to the modeled probe size and numerical errors of the software. This work first investigates fitting of VMSM models to Surface-MSM (SMSM) generated electrical field data, removing modeling dependence on probe geometry while significantly increasing performance and speed. A proposed electric field matching cost function is compared to a force and torque cost function, the inclusion of a self-capacitance constraint is explored and 4 degree-of-freedom VMSM models generated using electric field matching are investigated. The resulting E-field based VMSM development framework is illustrated on a box-shaped hub with a single solar panel, and convergence properties of select models are qualitatively analyzed. Despite the complex non-symmetric spacecraft geometry, elegantly simple 2-sphere VMSM solutions provide force and torque fits within a few percent.
A CAD System for Evaluating Footwear Fit
Savadkoohi, Bita Ture; de Amicis, Raffaele
With the great growth in footwear demand, the footwear manufacturing industry, for achieving commercial success, must be able to provide the footwear that fulfills consumer's requirement better than it's competitors. Accurate fitting for shoes is an important factor in comfort and functionality. Footwear fitter measurement have been using manual measurement for a long time, but the development of 3D acquisition devices and the advent of powerful 3D visualization and modeling techniques, automatically analyzing, searching and interpretation of the models have now made automatic determination of different foot dimensions feasible. In this paper, we proposed an approach for finding footwear fit within the shoe last data base. We first properly aligned the 3D models using "Weighted" Principle Component Analysis (WPCA). After solving the alignment problem we used an efficient algorithm for cutting the 3D model in order to find the footwear fit from shoe last data base.
Repair models of cell survival and corresponding computer program for survival curve fitting
International Nuclear Information System (INIS)
Shen Xun; Hu Yiwei
1992-01-01
Some basic concepts and formulations of two repair models of survival, the incomplete repair (IR) model and the lethal-potentially lethal (LPL) model, are introduced. An IBM-PC computer program for survival curve fitting with these models was developed and applied to fit the survivals of human melanoma cells HX118 irradiated at different dose rates. Comparison was made between the repair models and two non-repair models, the multitar get-single hit model and the linear-quadratic model, in the fitting and analysis of the survival-dose curves. It was shown that either IR model or LPL model can fit a set of survival curves of different dose rates with same parameters and provide information on the repair capacity of cells. These two mathematical models could be very useful in quantitative study on the radiosensitivity and repair capacity of cells
HDFITS: Porting the FITS data model to HDF5
Price, D. C.; Barsdell, B. R.; Greenhill, L. J.
2015-09-01
The FITS (Flexible Image Transport System) data format has been the de facto data format for astronomy-related data products since its inception in the late 1970s. While the FITS file format is widely supported, it lacks many of the features of more modern data serialization, such as the Hierarchical Data Format (HDF5). The HDF5 file format offers considerable advantages over FITS, such as improved I/O speed and compression, but has yet to gain widespread adoption within astronomy. One of the major holdbacks is that HDF5 is not well supported by data reduction software packages and image viewers. Here, we present a comparison of FITS and HDF5 as a format for storage of astronomy datasets. We show that the underlying data model of FITS can be ported to HDF5 in a straightforward manner, and that by doing so the advantages of the HDF5 file format can be leveraged immediately. In addition, we present a software tool, fits2hdf, for converting between FITS and a new 'HDFITS' format, where data are stored in HDF5 in a FITS-like manner. We show that HDFITS allows faster reading of data (up to 100x of FITS in some use cases), and improved compression (higher compression ratios and higher throughput). Finally, we show that by only changing the import lines in Python-based FITS utilities, HDFITS formatted data can be presented transparently as an in-memory FITS equivalent.
GRace: a MATLAB-based application for fitting the discrimination-association model.
Stefanutti, Luca; Vianello, Michelangelo; Anselmi, Pasquale; Robusto, Egidio
2014-10-28
The Implicit Association Test (IAT) is a computerized two-choice discrimination task in which stimuli have to be categorized as belonging to target categories or attribute categories by pressing, as quickly and accurately as possible, one of two response keys. The discrimination association model has been recently proposed for the analysis of reaction time and accuracy of an individual respondent to the IAT. The model disentangles the influences of three qualitatively different components on the responses to the IAT: stimuli discrimination, automatic association, and termination criterion. The article presents General Race (GRace), a MATLAB-based application for fitting the discrimination association model to IAT data. GRace has been developed for Windows as a standalone application. It is user-friendly and does not require any programming experience. The use of GRace is illustrated on the data of a Coca Cola-Pepsi Cola IAT, and the results of the analysis are interpreted and discussed.
Permutation tests for goodness-of-fit testing of mathematical models to experimental data.
Fişek, M Hamit; Barlas, Zeynep
2013-03-01
This paper presents statistical procedures for improving the goodness-of-fit testing of theoretical models to data obtained from laboratory experiments. We use an experimental study in the expectation states research tradition which has been carried out in the "standardized experimental situation" associated with the program to illustrate the application of our procedures. We briefly review the expectation states research program and the fundamentals of resampling statistics as we develop our procedures in the resampling context. The first procedure we develop is a modification of the chi-square test which has been the primary statistical tool for assessing goodness of fit in the EST research program, but has problems associated with its use. We discuss these problems and suggest a procedure to overcome them. The second procedure we present, the "Average Absolute Deviation" test, is a new test and is proposed as an alternative to the chi square test, as being simpler and more informative. The third and fourth procedures are permutation versions of Jonckheere's test for ordered alternatives, and Kendall's tau(b), a rank order correlation coefficient. The fifth procedure is a new rank order goodness-of-fit test, which we call the "Deviation from Ideal Ranking" index, which we believe may be more useful than other rank order tests for assessing goodness-of-fit of models to experimental data. The application of these procedures to the sample data is illustrated in detail. We then present another laboratory study from an experimental paradigm different from the expectation states paradigm - the "network exchange" paradigm, and describe how our procedures may be applied to this data set. Copyright © 2012 Elsevier Inc. All rights reserved.
Proposal for the ion optics and for the kinematical fitting at the magnetic spectrograph BIG KARL
International Nuclear Information System (INIS)
Hinterberger, F.
1986-01-01
For the magnetic spectrograph BIG KARL the installation of an additional quadrupole lens is purposed. From this the possibility of a telescopic ion optic results. For future experiments a standard focusing with a spatial dispersion of 6.6 m and vanishing angular dispersion is proposed. The D/M ratio (dispersion/magnification) extends to 14.0 m, the maximal spatial angle lies at 3 msr. The energy range extends at a focal plane length of 0.66 m to 20%. For the kinematical fitting of the spectrograph the focal plane is shifted. This shift can be simply and rapidly realized for different K values by means of a software correction, if generally two spatial spectra in the focal plane are taken up. By this additionally for each event the actual scattering angle can be determined with relatively good resolution. The dispersion fit is completely decoupled from the kinematical fitting of the magnetic spectrograph. (orig.) [de
Proposal of custom made wrist orthoses based on 3D modelling and 3D printing.
Abreu de Souza, Mauren; Schmitz, Cristiane; Marega Pinhel, Marcelo; Palma Setti, Joao A; Nohama, Percy
2017-07-01
Accessibility to three-dimensional (3D) technologies, such as 3D scanning systems and additive manufacturing (like 3D printers), allows a variety of 3D applications. For medical applications in particular, these modalities are gaining a lot of attention enabling several opportunities for healthcare applications. The literature brings several cases applying both technologies, but none of them focus on the spreading of how this technology could benefit the health segment. This paper proposes a new methodology, which employs both 3D modelling and 3D printing for building orthoses, which could better fit the demands of different patients. Additionally, there is an opportunity for sharing expertise, as it represents a trendy in terms of the maker-movement. Therefore, as a result of the proposed approach, we present a case study based on a volunteer who needs an immobilization orthosis, which was built for exemplification of the whole process. This proposal also employs freely available 3D models and software, having a strong social impact. As a result, it enables the implementation and effective usability for a variety of built to fit solutions, hitching useful and smarter technologies for the healthcare sector.
Duarte, Adam; Adams, Michael J.; Peterson, James T.
2018-01-01
Monitoring animal populations is central to wildlife and fisheries management, and the use of N-mixture models toward these efforts has markedly increased in recent years. Nevertheless, relatively little work has evaluated estimator performance when basic assumptions are violated. Moreover, diagnostics to identify when bias in parameter estimates from N-mixture models is likely is largely unexplored. We simulated count data sets using 837 combinations of detection probability, number of sample units, number of survey occasions, and type and extent of heterogeneity in abundance or detectability. We fit Poisson N-mixture models to these data, quantified the bias associated with each combination, and evaluated if the parametric bootstrap goodness-of-fit (GOF) test can be used to indicate bias in parameter estimates. We also explored if assumption violations can be diagnosed prior to fitting N-mixture models. In doing so, we propose a new model diagnostic, which we term the quasi-coefficient of variation (QCV). N-mixture models performed well when assumptions were met and detection probabilities were moderate (i.e., ≥0.3), and the performance of the estimator improved with increasing survey occasions and sample units. However, the magnitude of bias in estimated mean abundance with even slight amounts of unmodeled heterogeneity was substantial. The parametric bootstrap GOF test did not perform well as a diagnostic for bias in parameter estimates when detectability and sample sizes were low. The results indicate the QCV is useful to diagnose potential bias and that potential bias associated with unidirectional trends in abundance or detectability can be diagnosed using Poisson regression. This study represents the most thorough assessment to date of assumption violations and diagnostics when fitting N-mixture models using the most commonly implemented error distribution. Unbiased estimates of population state variables are needed to properly inform management decision
Clark, D Angus; Bowles, Ryan P
2018-04-23
In exploratory item factor analysis (IFA), researchers may use model fit statistics and commonly invoked fit thresholds to help determine the dimensionality of an assessment. However, these indices and thresholds may mislead as they were developed in a confirmatory framework for models with continuous, not categorical, indicators. The present study used Monte Carlo simulation methods to investigate the ability of popular model fit statistics (chi-square, root mean square error of approximation, the comparative fit index, and the Tucker-Lewis index) and their standard cutoff values to detect the optimal number of latent dimensions underlying sets of dichotomous items. Models were fit to data generated from three-factor population structures that varied in factor loading magnitude, factor intercorrelation magnitude, number of indicators, and whether cross loadings or minor factors were included. The effectiveness of the thresholds varied across fit statistics, and was conditional on many features of the underlying model. Together, results suggest that conventional fit thresholds offer questionable utility in the context of IFA.
Modelling population dynamics model formulation, fitting and assessment using state-space methods
Newman, K B; Morgan, B J T; King, R; Borchers, D L; Cole, D J; Besbeas, P; Gimenez, O; Thomas, L
2014-01-01
This book gives a unifying framework for estimating the abundance of open populations: populations subject to births, deaths and movement, given imperfect measurements or samples of the populations. The focus is primarily on populations of vertebrates for which dynamics are typically modelled within the framework of an annual cycle, and for which stochastic variability in the demographic processes is usually modest. Discrete-time models are developed in which animals can be assigned to discrete states such as age class, gender, maturity, population (within a metapopulation), or species (for multi-species models). The book goes well beyond estimation of abundance, allowing inference on underlying population processes such as birth or recruitment, survival and movement. This requires the formulation and fitting of population dynamics models. The resulting fitted models yield both estimates of abundance and estimates of parameters characterizing the underlying processes.
Revisiting the Global Electroweak Fit of the Standard Model and Beyond with Gfitter
Flächer, Henning; Haller, J; Höcker, A; Mönig, K; Stelzer, J
2009-01-01
The global fit of the Standard Model to electroweak precision data, routinely performed by the LEP electroweak working group and others, demonstrated impressively the predictive power of electroweak unification and quantum loop corrections. We have revisited this fit in view of (i) the development of the new generic fitting package, Gfitter, allowing flexible and efficient model testing in high-energy physics, (ii) the insertion of constraints from direct Higgs searches at LEP and the Tevatron, and (iii) a more thorough statistical interpretation of the results. Gfitter is a modular fitting toolkit, which features predictive theoretical models as independent plugins, and a statistical analysis of the fit results using toy Monte Carlo techniques. The state-of-the-art electroweak Standard Model is fully implemented, as well as generic extensions to it. Theoretical uncertainties are explicitly included in the fit through scale parameters varying within given error ranges. This paper introduces the Gfitter projec...
Tests of fit of historically-informed models of African American Admixture.
Gross, Jessica M
2018-02-01
African American populations in the U.S. formed primarily by mating between Africans and Europeans over the last 500 years. To date, studies of admixture have focused on either a one-time admixture event or continuous input into the African American population from Europeans only. Our goal is to gain a better understanding of the admixture process by examining models that take into account (a) assortative mating by ancestry in the African American population, (b) continuous input from both Europeans and Africans, and (c) historically informed variation in the rate of African migration over time. We used a model-based clustering method to generate distributions of African ancestry in three samples comprised of 147 African Americans from two published sources. We used a log-likelihood method to examine the fit of four models to these distributions and used a log-likelihood ratio test to compare the relative fit of each model. The mean ancestry estimates for our datasets of 77% African/23% European to 83% African/17% European ancestry are consistent with previous studies. We find admixture models that incorporate continuous gene flow from Europeans fit significantly better than one-time event models, and that a model involving continuous gene flow from Africans and Europeans fits better than one with continuous gene flow from Europeans only for two samples. Importantly, models that involve continuous input from Africans necessitate a higher level of gene flow from Europeans than previously reported. We demonstrate that models that take into account information about the rate of African migration over the past 500 years fit observed patterns of African ancestry better than alternative models. Our approach will enrich our understanding of the admixture process in extant and past populations. © 2017 Wiley Periodicals, Inc.
[How to fit and interpret multilevel models using SPSS].
Pardo, Antonio; Ruiz, Miguel A; San Martín, Rafael
2007-05-01
Hierarchic or multilevel models are used to analyse data when cases belong to known groups and sample units are selected both from the individual level and from the group level. In this work, the multilevel models most commonly discussed in the statistic literature are described, explaining how to fit these models using the SPSS program (any version as of the 11 th ) and how to interpret the outcomes of the analysis. Five particular models are described, fitted, and interpreted: (1) one-way analysis of variance with random effects, (2) regression analysis with means-as-outcomes, (3) one-way analysis of covariance with random effects, (4) regression analysis with random coefficients, and (5) regression analysis with means- and slopes-as-outcomes. All models are explained, trying to make them understandable to researchers in health and behaviour sciences.
A method for fitting regression splines with varying polynomial order in the linear mixed model.
Edwards, Lloyd J; Stewart, Paul W; MacDougall, James E; Helms, Ronald W
2006-02-15
The linear mixed model has become a widely used tool for longitudinal analysis of continuous variables. The use of regression splines in these models offers the analyst additional flexibility in the formulation of descriptive analyses, exploratory analyses and hypothesis-driven confirmatory analyses. We propose a method for fitting piecewise polynomial regression splines with varying polynomial order in the fixed effects and/or random effects of the linear mixed model. The polynomial segments are explicitly constrained by side conditions for continuity and some smoothness at the points where they join. By using a reparameterization of this explicitly constrained linear mixed model, an implicitly constrained linear mixed model is constructed that simplifies implementation of fixed-knot regression splines. The proposed approach is relatively simple, handles splines in one variable or multiple variables, and can be easily programmed using existing commercial software such as SAS or S-plus. The method is illustrated using two examples: an analysis of longitudinal viral load data from a study of subjects with acute HIV-1 infection and an analysis of 24-hour ambulatory blood pressure profiles.
Armour, Cherie; Raudzah Ghazali, Siti; Elklit, Ask
2013-03-30
The underlying latent structure of Posttraumatic Stress Disorder (PTSD) is widely researched. However, despite a plethora of factor analytic studies, no single model has consistently been shown as superior to alternative models. The two most often supported models are the Emotional Numbing and the Dysphoria models. However, a recently proposed five-factor Dysphoric Arousal model has been gathering support over and above existing models. Data for the current study were gathered from Malaysian Tsunami survivors (N=250). Three competing models (Emotional Numbing/Dysphoria/Dysphoric Arousal) were specified and estimated using Confirmatory Factor Analysis (CFA). The Dysphoria model provided superior fit to the data compared to the Emotional Numbing model. However, using chi-square difference tests, the Dysphoric Arousal model showed a superior fit compared to both the Emotional Numbing and Dysphoria models. In conclusion, the current results suggest that the Dysphoric Arousal model better represents PTSD's latent structure and that items measuring sleeping difficulties, irritability/anger and concentration difficulties form a separate, unique PTSD factor. These results are discussed in relation to the role of Hyperarousal in PTSD's on-going symptom maintenance and in relation to the DSM-5. Copyright © 2012 Elsevier Ireland Ltd. All rights reserved.
Using geometry to improve model fitting and experiment design for glacial isostasy
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.
A versatile curve-fit model for linear to deeply concave rank abundance curves
Neuteboom, J.H.; Struik, P.C.
2005-01-01
A new, flexible curve-fit model for linear to concave rank abundance curves was conceptualized and validated using observational data. The model links the geometric-series model and log-series model and can also fit deeply concave rank abundance curves. The model is based ¿ in an unconventional way
Describing the Process of Adopting Nutrition and Fitness Apps: Behavior Stage Model Approach.
König, Laura M; Sproesser, Gudrun; Schupp, Harald T; Renner, Britta
2018-03-13
Although mobile technologies such as smartphone apps are promising means for motivating people to adopt a healthier lifestyle (mHealth apps), previous studies have shown low adoption and continued use rates. Developing the means to address this issue requires further understanding of mHealth app nonusers and adoption processes. This study utilized a stage model approach based on the Precaution Adoption Process Model (PAPM), which proposes that people pass through qualitatively different motivational stages when adopting a behavior. To establish a better understanding of between-stage transitions during app adoption, this study aimed to investigate the adoption process of nutrition and fitness app usage, and the sociodemographic and behavioral characteristics and decision-making style preferences of people at different adoption stages. Participants (N=1236) were recruited onsite within the cohort study Konstanz Life Study. Use of mobile devices and nutrition and fitness apps, 5 behavior adoption stages of using nutrition and fitness apps, preference for intuition and deliberation in eating decision-making (E-PID), healthy eating style, sociodemographic variables, and body mass index (BMI) were assessed. Analysis of the 5 behavior adoption stages showed that stage 1 ("unengaged") was the most prevalent motivational stage for both nutrition and fitness app use, with half of the participants stating that they had never thought about using a nutrition app (52.41%, 533/1017), whereas less than one-third stated they had never thought about using a fitness app (29.25%, 301/1029). "Unengaged" nonusers (stage 1) showed a higher preference for an intuitive decision-making style when making eating decisions, whereas those who were already "acting" (stage 4) showed a greater preference for a deliberative decision-making style (F 4,1012 =21.83, Pdigital interventions. This study highlights that new user groups might be better reached by apps designed to address a more intuitive
Fast Algorithms for Fitting Active Appearance Models to Unconstrained Images
Tzimiropoulos, Georgios; Pantic, Maja
2016-01-01
Fitting algorithms for Active Appearance Models (AAMs) are usually considered to be robust but slow or fast but less able to generalize well to unseen variations. In this paper, we look into AAM fitting algorithms and make the following orthogonal contributions: We present a simple “project-out‿
Thissen, David
2013-01-01
In this commentary, David Thissen states that "Goodness-of-fit assessment for IRT models is maturing; it has come a long way from zero." Thissen then references prior works on "goodness of fit" in the index of Lord and Novick's (1968) classic text; Yen (1984); Drasgow, Levine, Tsien, Williams, and Mead (1995); Chen and…
The l z ( p ) * Person-Fit Statistic in an Unfolding Model Context.
Tendeiro, Jorge N
2017-01-01
Although person-fit analysis has a long-standing tradition within item response theory, it has been applied in combination with dominance response models almost exclusively. In this article, a popular log likelihood-based parametric person-fit statistic under the framework of the generalized graded unfolding model is used. Results from a simulation study indicate that the person-fit statistic performed relatively well in detecting midpoint response style patterns and not so well in detecting extreme response style patterns.
Fit Gap Analysis – The Role of Business Process Reference Models
Directory of Open Access Journals (Sweden)
Dejan Pajk
2013-12-01
Full Text Available Enterprise resource planning (ERP systems support solutions for standard business processes such as financial, sales, procurement and warehouse. In order to improve the understandability and efficiency of their implementation, ERP vendors have introduced reference models that describe the processes and underlying structure of an ERP system. To select and successfully implement an ERP system, the capabilities of that system have to be compared with a company’s business needs. Based on a comparison, all of the fits and gaps must be identified and further analysed. This step usually forms part of ERP implementation methodologies and is called fit gap analysis. The paper theoretically overviews methods for applying reference models and describes fit gap analysis processes in detail. The paper’s first contribution is its presentation of a fit gap analysis using standard business process modelling notation. The second contribution is the demonstration of a process-based comparison approach between a supply chain process and an ERP system process reference model. In addition to its theoretical contributions, the results can also be practically applied to projects involving the selection and implementation of ERP systems.
International Nuclear Information System (INIS)
Gao, Xiankun; Cui, Yan; Hu, Jianjun; Xu, Guangyin; Yu, Yongchang
2016-01-01
Highlights: • Lambert W-function based exact representation (LBER) is presented for double diode model (DDM). • Fitness difference between LBER and DDM is verified by reported parameter values. • The proposed LBER can better represent the I–V and P–V characteristics of solar cells. • Parameter extraction difference between LBER and DDM is validated by two algorithms. • The parameter values extracted from LBER are more accurate than those from DDM. - Abstract: Accurate modeling and parameter extraction of solar cells play an important role in the simulation and optimization of PV systems. This paper presents a Lambert W-function based exact representation (LBER) for traditional double diode model (DDM) of solar cells, and then compares their fitness and parameter extraction performance. Unlike existing works, the proposed LBER is rigorously derived from DDM, and in LBER the coefficients of Lambert W-function are not extra parameters to be extracted or arbitrary scalars but the vectors of terminal voltage and current of solar cells. The fitness difference between LBER and DDM is objectively validated by the reported parameter values and experimental I–V data of a solar cell and four solar modules from different technologies. The comparison results indicate that under the same parameter values, the proposed LBER can better represent the I–V and P–V characteristics of solar cells and provide a closer representation to actual maximum power points of all module types. Two different algorithms are used to compare the parameter extraction performance of LBER and DDM. One is our restart-based bound constrained Nelder-Mead (rbcNM) algorithm implemented in Matlab, and the other is the reported R_c_r-IJADE algorithm executed in Visual Studio. The comparison results reveal that, the parameter values extracted from LBER using two algorithms are always more accurate and robust than those from DDM despite more time consuming. As an improved version of DDM, the
Tay, Louis; Drasgow, Fritz
2012-01-01
Two Monte Carlo simulation studies investigated the effectiveness of the mean adjusted X[superscript 2]/df statistic proposed by Drasgow and colleagues and, because of problems with the method, a new approach for assessing the goodness of fit of an item response theory model was developed. It has been previously recommended that mean adjusted…
Directory of Open Access Journals (Sweden)
Yu-Bo Jiao
2015-01-01
Full Text Available The paper presents an effective approach for damage identification of bridge based on Chebyshev polynomial fitting and fuzzy logic systems without considering baseline model data. The modal curvature of damaged bridge can be obtained through central difference approximation based on displacement modal shape. Depending on the modal curvature of damaged structure, Chebyshev polynomial fitting is applied to acquire the curvature of undamaged one without considering baseline parameters. Therefore, modal curvature difference can be derived and used for damage localizing. Subsequently, the normalized modal curvature difference is treated as input variable of fuzzy logic systems for damage condition assessment. Numerical simulation on a simply supported bridge was carried out to demonstrate the feasibility of the proposed method.
A hands-on approach for fitting long-term survival models under the GAMLSS framework.
de Castro, Mário; Cancho, Vicente G; Rodrigues, Josemar
2010-02-01
In many data sets from clinical studies there are patients insusceptible to the occurrence of the event of interest. Survival models which ignore this fact are generally inadequate. The main goal of this paper is to describe an application of the generalized additive models for location, scale, and shape (GAMLSS) framework to the fitting of long-term survival models. In this work the number of competing causes of the event of interest follows the negative binomial distribution. In this way, some well known models found in the literature are characterized as particular cases of our proposal. The model is conveniently parameterized in terms of the cured fraction, which is then linked to covariates. We explore the use of the gamlss package in R as a powerful tool for inference in long-term survival models. The procedure is illustrated with a numerical example. Copyright 2009 Elsevier Ireland Ltd. All rights reserved.
International Nuclear Information System (INIS)
Mbagwu, J.S.C.
1994-05-01
Among the many models developed for monitoring the infiltration process those of Philip and Kostiakov have been studied in detail because of their simplicity and the ease of estimating their fitting parameters. The important soil physical factors influencing the fitting parameters in these infiltration models are reported in this study. The results of the study show that the single most important soil property affecting the fitting parameters in these models is the effective porosity. 36 refs, 2 figs, 5 tabs
International Nuclear Information System (INIS)
Mbagwu, J.S.C.
1993-10-01
Six infiltration models, some obtained by reformulating the fitting parameters of the classical Kostiakov (1932) and Philip (1957) equations, were investigated for their ability to describe water infiltration into highly permeable sandy soils from the Nsukka plains of SE Nigeria. The models were Kostiakov, Modified Kostiakov (A), Modified Kostiakov (B), Philip, Modified Philip (A) and Modified Philip (B). Infiltration data were obtained from double ring infiltrometers on field plots established on a Knadic Paleustult (Nkpologu series) to investigate the effects of land use on soil properties and maize yield. The treatments were; (i) tilled-mulched (TM), (ii) tilled-unmulched (TU), (iii) untilled-mulched (UM), (iv) untilled-unmulched (UU) and (v) continuous pasture (CP). Cumulative infiltration was highest on the TM and lowest on the CP plots. All estimated model parameters obtained by the best fit of measured data differed significantly among the treatments. Based on the magnitude of R 2 values, the Kostiakov, Modified Kostiakov (A), Philip and Modified Philip (A) models provided best predictions of cumulative infiltration as a function of time. Comparing experimental with model-predicted cumulative infiltration showed, however, that on all treatments the values predicted by the classical Kostiakov, Philip and Modified Philip (A) models deviated most from experimental data. The other models produced values that agreed very well with measured data. Considering the eases of determining the fitting parameters it is proposed that on soils with high infiltration rates, either Modified Kostiakov model (I = Kt a + Ict) or Modified Philip model (I St 1/2 + Ict), (where I is cumulative infiltration, K, the time coefficient, t, time elapsed, 'a' the time exponent, Ic the equilibrium infiltration rate and S, the soil water sorptivity), be used for routine characterization of the infiltration process. (author). 33 refs, 3 figs 6 tabs
Directory of Open Access Journals (Sweden)
Jaclyn K Mann
2014-08-01
Full Text Available Viral immune evasion by sequence variation is a major hindrance to HIV-1 vaccine design. To address this challenge, our group has developed a computational model, rooted in physics, that aims to predict the fitness landscape of HIV-1 proteins in order to design vaccine immunogens that lead to impaired viral fitness, thus blocking viable escape routes. Here, we advance the computational models to address previous limitations, and directly test model predictions against in vitro fitness measurements of HIV-1 strains containing multiple Gag mutations. We incorporated regularization into the model fitting procedure to address finite sampling. Further, we developed a model that accounts for the specific identity of mutant amino acids (Potts model, generalizing our previous approach (Ising model that is unable to distinguish between different mutant amino acids. Gag mutation combinations (17 pairs, 1 triple and 25 single mutations within these predicted to be either harmful to HIV-1 viability or fitness-neutral were introduced into HIV-1 NL4-3 by site-directed mutagenesis and replication capacities of these mutants were assayed in vitro. The predicted and measured fitness of the corresponding mutants for the original Ising model (r = -0.74, p = 3.6×10-6 are strongly correlated, and this was further strengthened in the regularized Ising model (r = -0.83, p = 3.7×10-12. Performance of the Potts model (r = -0.73, p = 9.7×10-9 was similar to that of the Ising model, indicating that the binary approximation is sufficient for capturing fitness effects of common mutants at sites of low amino acid diversity. However, we show that the Potts model is expected to improve predictive power for more variable proteins. Overall, our results support the ability of the computational models to robustly predict the relative fitness of mutant viral strains, and indicate the potential value of this approach for understanding viral immune evasion
Correcting Model Fit Criteria for Small Sample Latent Growth Models with Incomplete Data
McNeish, Daniel; Harring, Jeffrey R.
2017-01-01
To date, small sample problems with latent growth models (LGMs) have not received the amount of attention in the literature as related mixed-effect models (MEMs). Although many models can be interchangeably framed as a LGM or a MEM, LGMs uniquely provide criteria to assess global data-model fit. However, previous studies have demonstrated poor…
The Fitbit Fault Line: Two Proposals to Protect Health and Fitness Data at Work.
Brown, Elizabeth A
2016-01-01
Employers are collecting and using their employees' health data, mined from wearable fitness devices and health apps, in new, profitable, and barely regulated ways. The importance of protecting employee health and fitness data will grow exponentially in the future. This is the moment for a robust discussion of how law can better protect employees from the potential misuse of their health data. While scholars have just begun to examine the problem of health data privacy, this Article contributes to the academic literature in three important ways. First, it analyzes the convergence of three trends resulting in an unprecedented growth of health-related data: the Internet of Things, the Quantified Self movement, and the Rise of Health Platforms. Second, it describes the insufficiencies of specific data privacy laws and federal agency actions in the context of protecting employee health data from employer misuse. Finally, it provides two detailed and workable solutions for remedying the current lack of protection of employee health data that will realign employer use with reasonable expectations of health and fitness privacy. The Article proceeds in four Parts. Part I describes the growth of self-monitoring apps, devices, and other sensor-enabled technology that can monitor a wide range of data related to an employee's health and fitness and the relationship of this growth to both the Quantified Self movement and the Internet of Things. Part II explains the increasing use of employee monitoring through a wide range of sensors, including wearable devices, and the potential uses of that health and fitness data. Part III explores the various regulations and agency actions that might protect employees from the potential misuse of their health and fitness data and the shortcomings of each. Part IV proposes two specific measures that would help ameliorate the ineffective legal protections that currently exist in this context. In order to improve employee notice of and control
Supersymmetry with prejudice: Fitting the wrong model to LHC data
Allanach, B. C.; Dolan, Matthew J.
2012-09-01
We critically examine interpretations of hypothetical supersymmetric LHC signals, fitting to alternative wrong models of supersymmetry breaking. The signals we consider are some of the most constraining on the sparticle spectrum: invariant mass distributions with edges and endpoints from the golden decay chain q˜→qχ20(→l˜±l∓q)→χ10l+l-q. We assume a constrained minimal supersymmetric standard model (CMSSM) point to be the ‘correct’ one, but fit the signals instead with minimal gauge mediated supersymmetry breaking models (mGMSB) with a neutralino quasistable lightest supersymmetric particle, minimal anomaly mediation and large volume string compactification models. Minimal anomaly mediation and large volume scenario can be unambiguously discriminated against the CMSSM for the assumed signal and 1fb-1 of LHC data at s=14TeV. However, mGMSB would not be discriminated on the basis of the kinematic endpoints alone. The best-fit point spectra of mGMSB and CMSSM look remarkably similar, making experimental discrimination at the LHC based on the edges or Higgs properties difficult. However, using rate information for the golden chain should provide the additional separation required.
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.
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.
A person fit test for IRT models for polytomous items
Glas, Cornelis A.W.; Dagohoy, A.V.
2007-01-01
A person fit test based on the Lagrange multiplier test is presented for three item response theory models for polytomous items: the generalized partial credit model, the sequential model, and the graded response model. The test can also be used in the framework of multidimensional ability
ROBUST CYLINDER FITTING IN THREE-DIMENSIONAL POINT CLOUD DATA
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A. Nurunnabi
2017-05-01
Full Text Available This paper investigates the problems of cylinder fitting in laser scanning three-dimensional Point Cloud Data (PCD. Most existing methods require full cylinder data, do not study the presence of outliers, and are not statistically robust. But especially mobile laser scanning often has incomplete data, as street poles for example are only scanned from the road. Moreover, existence of outliers is common. Outliers may occur as random or systematic errors, and may be scattered and/or clustered. In this paper, we present a statistically robust cylinder fitting algorithm for PCD that combines Robust Principal Component Analysis (RPCA with robust regression. Robust principal components as obtained by RPCA allow estimating cylinder directions more accurately, and an existing efficient circle fitting algorithm following robust regression principles, properly fit cylinder. We demonstrate the performance of the proposed method on artificial and real PCD. Results show that the proposed method provides more accurate and robust results: (i in the presence of noise and high percentage of outliers, (ii for incomplete as well as complete data, (iii for small and large number of points, and (iv for different sizes of radius. On 1000 simulated quarter cylinders of 1m radius with 10% outliers a PCA based method fit cylinders with a radius of on average 3.63 meter (m; the proposed method on the other hand fit cylinders of on average 1.02 m radius. The algorithm has potential in applications such as fitting cylindrical (e.g., light and traffic poles, diameter at breast height estimation for trees, and building and bridge information modelling.
The lz(p)* Person-Fit Statistic in an Unfolding Model Context
Tendeiro, Jorge N.
2017-01-01
Although person-fit analysis has a long-standing tradition within item response theory, it has been applied in combination with dominance response models almost exclusively. In this article, a popular log likelihood-based parametric person-fit statistic under the framework of the generalized graded
Pre-processing by data augmentation for improved ellipse fitting.
Kumar, Pankaj; Belchamber, Erika R; Miklavcic, Stanley J
2018-01-01
Ellipse fitting is a highly researched and mature topic. Surprisingly, however, no existing method has thus far considered the data point eccentricity in its ellipse fitting procedure. Here, we introduce the concept of eccentricity of a data point, in analogy with the idea of ellipse eccentricity. We then show empirically that, irrespective of ellipse fitting method used, the root mean square error (RMSE) of a fit increases with the eccentricity of the data point set. The main contribution of the paper is based on the hypothesis that if the data point set were pre-processed to strategically add additional data points in regions of high eccentricity, then the quality of a fit could be improved. Conditional validity of this hypothesis is demonstrated mathematically using a model scenario. Based on this confirmation we propose an algorithm that pre-processes the data so that data points with high eccentricity are replicated. The improvement of ellipse fitting is then demonstrated empirically in real-world application of 3D reconstruction of a plant root system for phenotypic analysis. The degree of improvement for different underlying ellipse fitting methods as a function of data noise level is also analysed. We show that almost every method tested, irrespective of whether it minimizes algebraic error or geometric error, shows improvement in the fit following data augmentation using the proposed pre-processing algorithm.
Hipson, Will E.; Séguin, Daniel G.
2016-01-01
The Goodness-of-Fit model [Thomas, A., & Chess, S. (1977). Temperament and development. New York: Brunner/Mazel] proposes that a child's temperament interacts with the environment to influence child outcomes. In the past, researchers have shown how the association between the quality of the teacher-child relationship in daycare and child…
Fitting a Bivariate Measurement Error Model for Episodically Consumed Dietary Components
Zhang, Saijuan; Krebs-Smith, Susan M.; Midthune, Douglas; Perez, Adriana; Buckman, Dennis W.; Kipnis, Victor; Freedman, Laurence S.; Dodd, Kevin W.; Carroll, Raymond J
2011-01-01
There has been great public health interest in estimating usual, i.e., long-term average, intake of episodically consumed dietary components that are not consumed daily by everyone, e.g., fish, red meat and whole grains. Short-term measurements of episodically consumed dietary components have zero-inflated skewed distributions. So-called two-part models have been developed for such data in order to correct for measurement error due to within-person variation and to estimate the distribution of usual intake of the dietary component in the univariate case. However, there is arguably much greater public health interest in the usual intake of an episodically consumed dietary component adjusted for energy (caloric) intake, e.g., ounces of whole grains per 1000 kilo-calories, which reflects usual dietary composition and adjusts for different total amounts of caloric intake. Because of this public health interest, it is important to have models to fit such data, and it is important that the model-fitting methods can be applied to all episodically consumed dietary components.We have recently developed a nonlinear mixed effects model (Kipnis, et al., 2010), and have fit it by maximum likelihood using nonlinear mixed effects programs and methodology (the SAS NLMIXED procedure). Maximum likelihood fitting of such a nonlinear mixed model is generally slow because of 3-dimensional adaptive Gaussian quadrature, and there are times when the programs either fail to converge or converge to models with a singular covariance matrix. For these reasons, we develop a Monte-Carlo (MCMC) computation of fitting this model, which allows for both frequentist and Bayesian inference. There are technical challenges to developing this solution because one of the covariance matrices in the model is patterned. Our main application is to the National Institutes of Health (NIH)-AARP Diet and Health Study, where we illustrate our methods for modeling the energy-adjusted usual intake of fish and whole
Fitting a Bivariate Measurement Error Model for Episodically Consumed Dietary Components
Zhang, Saijuan
2011-01-06
There has been great public health interest in estimating usual, i.e., long-term average, intake of episodically consumed dietary components that are not consumed daily by everyone, e.g., fish, red meat and whole grains. Short-term measurements of episodically consumed dietary components have zero-inflated skewed distributions. So-called two-part models have been developed for such data in order to correct for measurement error due to within-person variation and to estimate the distribution of usual intake of the dietary component in the univariate case. However, there is arguably much greater public health interest in the usual intake of an episodically consumed dietary component adjusted for energy (caloric) intake, e.g., ounces of whole grains per 1000 kilo-calories, which reflects usual dietary composition and adjusts for different total amounts of caloric intake. Because of this public health interest, it is important to have models to fit such data, and it is important that the model-fitting methods can be applied to all episodically consumed dietary components.We have recently developed a nonlinear mixed effects model (Kipnis, et al., 2010), and have fit it by maximum likelihood using nonlinear mixed effects programs and methodology (the SAS NLMIXED procedure). Maximum likelihood fitting of such a nonlinear mixed model is generally slow because of 3-dimensional adaptive Gaussian quadrature, and there are times when the programs either fail to converge or converge to models with a singular covariance matrix. For these reasons, we develop a Monte-Carlo (MCMC) computation of fitting this model, which allows for both frequentist and Bayesian inference. There are technical challenges to developing this solution because one of the covariance matrices in the model is patterned. Our main application is to the National Institutes of Health (NIH)-AARP Diet and Health Study, where we illustrate our methods for modeling the energy-adjusted usual intake of fish and whole
Nonlinear models for fitting growth curves of Nellore cows reared in the Amazon Biome
Directory of Open Access Journals (Sweden)
Kedma Nayra da Silva Marinho
2013-09-01
Full Text Available Growth curves of Nellore cows were estimated by comparing six nonlinear models: Brody, Logistic, two alternatives by Gompertz, Richards and Von Bertalanffy. The models were fitted to weight-age data, from birth to 750 days of age of 29,221 cows, born between 1976 and 2006 in the Brazilian states of Acre, Amapá, Amazonas, Pará, Rondônia, Roraima and Tocantins. The models were fitted by the Gauss-Newton method. The goodness of fit of the models was evaluated by using mean square error, adjusted coefficient of determination, prediction error and mean absolute error. Biological interpretation of parameters was accomplished by plotting estimated weights versus the observed weight means, instantaneous growth rate, absolute maturity rate, relative instantaneous growth rate, inflection point and magnitude of the parameters A (asymptotic weight and K (maturing rate. The Brody and Von Bertalanffy models fitted the weight-age data but the other models did not. The average weight (A and growth rate (K were: 384.6±1.63 kg and 0.0022±0.00002 (Brody and 313.40±0.70 kg and 0.0045±0.00002 (Von Bertalanffy. The Brody model provides better goodness of fit than the Von Bertalanffy model.
Fitting boxes to Manhattan scenes using linear integer programming
Li, Minglei; Nan, Liangliang; Liu, Shaochuang
2016-01-01
We propose an approach for automatic generation of building models by assembling a set of boxes using a Manhattan-world assumption. The method first aligns the point cloud with a per-building local coordinate system, and then fits axis
Curve fitting for RHB Islamic Bank annual net profit
Nadarajan, Dineswary; Noor, Noor Fadiya Mohd
2015-05-01
The RHB Islamic Bank net profit data are obtained from 2004 to 2012. Curve fitting is done by assuming the data are exact or experimental due to smoothing process. Higher order Lagrange polynomial and cubic spline with curve fitting procedure are constructed using Maple software. Normality test is performed to check the data adequacy. Regression analysis with curve estimation is conducted in SPSS environment. All the eleven models are found to be acceptable at 10% significant level of ANOVA. Residual error and absolute relative true error are calculated and compared. The optimal model based on the minimum average error is proposed.
Multi-binding site model-based curve-fitting program for the computation of RIA data
International Nuclear Information System (INIS)
Malan, P.G.; Ekins, R.P.; Cox, M.G.; Long, E.M.R.
1977-01-01
In this paper, a comparison will be made of model-based and empirical curve-fitting procedures. The implementation of a multiple binding-site curve-fitting model which will successfully fit a wide range of assay data, and which can be run on a mini-computer is described. The latter sophisticated model also provides estimates of binding site concentrations and the values of the respective equilibrium constants present: the latter have been used for refining assay conditions using computer optimisation techniques. (orig./AJ) [de
Fitness for duty: A tried-and-true model for decision making
International Nuclear Information System (INIS)
Horn, G.L.
1989-01-01
The US Nuclear Regulatory Commission (NRC) rules and regulations pertaining to fitness for duty specify development of programs designed to ensure that nuclear power plant personnel are not under the influence of legal or illegal substances that cause mental or physical impairment of work performance such that public safety is compromised. These regulations specify the type of decision loop to employ in determining the employee's movement through the process of initial restriction of access to the point at which his access authorization is restores. Suggestions are also offered to determine the roles that various components of the organization should take in the decision loop. This paper discusses some implications and labor concerns arising from the suggested role of employee assistance programs (EAPs) in the decision loop for clinical assessment and return-to-work evaluation of chemical testing failures. A model for a decision loop addressing some of the issues raised is presented. The proposed model has been implemented in one nuclear facility and has withstood the scrutiny of an NRC audit
Using the Flipchem Photochemistry Model When Fitting Incoherent Scatter Radar Data
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
Fitting Latent Cluster Models for Networks with latentnet
Directory of Open Access Journals (Sweden)
Pavel N. Krivitsky
2007-12-01
Full Text Available latentnet is a package to fit and evaluate statistical latent position and cluster models for networks. Hoﬀ, Raftery, and Handcock (2002 suggested an approach to modeling networks based on positing the existence of an latent space of characteristics of the actors. Relationships form as a function of distances between these characteristics as well as functions of observed dyadic level covariates. In latentnet social distances are represented in a Euclidean space. It also includes a variant of the extension of the latent position model to allow for clustering of the positions developed in Handcock, Raftery, and Tantrum (2007.The package implements Bayesian inference for the models based on an Markov chain Monte Carlo algorithm. It can also compute maximum likelihood estimates for the latent position model and a two-stage maximum likelihood method for the latent position cluster model. For latent position cluster models, the package provides a Bayesian way of assessing how many groups there are, and thus whether or not there is any clustering (since if the preferred number of groups is 1, there is little evidence for clustering. It also estimates which cluster each actor belongs to. These estimates are probabilistic, and provide the probability of each actor belonging to each cluster. It computes four types of point estimates for the coefficients and positions: maximum likelihood estimate, posterior mean, posterior mode and the estimator which minimizes Kullback-Leibler divergence from the posterior. You can assess the goodness-of-fit of the model via posterior predictive checks. It has a function to simulate networks from a latent position or latent position cluster model.
Twitter classification model: the ABC of two million fitness tweets.
Vickey, Theodore A; Ginis, Kathleen Martin; Dabrowski, Maciej
2013-09-01
The purpose of this project was to design and test data collection and management tools that can be used to study the use of mobile fitness applications and social networking within the context of physical activity. This project was conducted over a 6-month period and involved collecting publically shared Twitter data from five mobile fitness apps (Nike+, RunKeeper, MyFitnessPal, Endomondo, and dailymile). During that time, over 2.8 million tweets were collected, processed, and categorized using an online tweet collection application and a customized JavaScript. Using the grounded theory, a classification model was developed to categorize and understand the types of information being shared by application users. Our data show that by tracking mobile fitness app hashtags, a wealth of information can be gathered to include but not limited to daily use patterns, exercise frequency, location-based workouts, and overall workout sentiment.
von Cramon-Taubadel, Noreen; Lycett, Stephen J
2008-05-01
Recent studies comparing craniometric and neutral genetic affinity matrices have concluded that, on average, human cranial variation fits a model of neutral expectation. While human craniometric and genetic data fit a model of isolation by geographic distance, it is not yet clear whether this is due to geographically mediated gene flow or human dispersal events. Recently, human genetic data have been shown to fit an iterative founder effect model of dispersal with an African origin, in line with the out-of-Africa replacement model for modern human origins, and Manica et al. (Nature 448 (2007) 346-349) have demonstrated that human craniometric data also fit this model. However, in contrast with the neutral model of cranial evolution suggested by previous studies, Manica et al. (2007) made the a priori assumption that cranial form has been subject to climatically driven natural selection and therefore correct for climate prior to conducting their analyses. Here we employ a modified theoretical and methodological approach to test whether human cranial variability fits the iterative founder effect model. In contrast with Manica et al. (2007) we employ size-adjusted craniometric variables, since climatic factors such as temperature have been shown to correlate with aspects of cranial size. Despite these differences, we obtain similar results to those of Manica et al. (2007), with up to 26% of global within-population craniometric variation being explained by geographic distance from sub-Saharan Africa. Comparative analyses using non-African origins do not yield significant results. The implications of these results are discussed in the light of the modern human origins debate. (c) 2007 Wiley-Liss, Inc.
Rapid world modeling: Fitting range data to geometric primitives
International Nuclear Information System (INIS)
Feddema, J.; Little, C.
1996-01-01
For the past seven years, Sandia National Laboratories has been active in the development of robotic systems to help remediate DOE's waste sites and decommissioned facilities. Some of these facilities have high levels of radioactivity which prevent manual clean-up. Tele-operated and autonomous robotic systems have been envisioned as the only suitable means of removing the radioactive elements. World modeling is defined as the process of creating a numerical geometric model of a real world environment or workspace. This model is often used in robotics to plan robot motions which perform a task while avoiding obstacles. In many applications where the world model does not exist ahead of time, structured lighting, laser range finders, and even acoustical sensors have been used to create three dimensional maps of the environment. These maps consist of thousands of range points which are difficult to handle and interpret. This paper presents a least squares technique for fitting range data to planar and quadric surfaces, including cylinders and ellipsoids. Once fit to these primitive surfaces, the amount of data associated with a surface is greatly reduced up to three orders of magnitude, thus allowing for more rapid handling and analysis of world data
Fractal Image Coding Based on a Fitting Surface
Directory of Open Access Journals (Sweden)
Sheng Bi
2014-01-01
Full Text Available A no-search fractal image coding method based on a fitting surface is proposed. In our research, an improved gray-level transform with a fitting surface is introduced. One advantage of this method is that the fitting surface is used for both the range and domain blocks and one set of parameters can be saved. Another advantage is that the fitting surface can approximate the range and domain blocks better than the previous fitting planes; this can result in smaller block matching errors and better decoded image quality. Since the no-search and quadtree techniques are adopted, smaller matching errors also imply less number of blocks matching which results in a faster encoding process. Moreover, by combining all the fitting surfaces, a fitting surface image (FSI is also proposed to speed up the fractal decoding. Experiments show that our proposed method can yield superior performance over the other three methods. Relative to range-averaged image, FSI can provide faster fractal decoding process. Finally, by combining the proposed fractal coding method with JPEG, a hybrid coding method is designed which can provide higher PSNR than JPEG while maintaining the same Bpp.
Irvine, Michael A; Hollingsworth, T Déirdre
2018-05-26
Fitting complex models to epidemiological data is a challenging problem: methodologies can be inaccessible to all but specialists, there may be challenges in adequately describing uncertainty in model fitting, the complex models may take a long time to run, and it can be difficult to fully capture the heterogeneity in the data. We develop an adaptive approximate Bayesian computation scheme to fit a variety of epidemiologically relevant data with minimal hyper-parameter tuning by using an adaptive tolerance scheme. We implement a novel kernel density estimation scheme to capture both dispersed and multi-dimensional data, and directly compare this technique to standard Bayesian approaches. We then apply the procedure to a complex individual-based simulation of lymphatic filariasis, a human parasitic disease. The procedure and examples are released alongside this article as an open access library, with examples to aid researchers to rapidly fit models to data. This demonstrates that an adaptive ABC scheme with a general summary and distance metric is capable of performing model fitting for a variety of epidemiological data. It also does not require significant theoretical background to use and can be made accessible to the diverse epidemiological research community. Copyright © 2018 The Authors. Published by Elsevier B.V. All rights reserved.
2011-01-19
... Extension of Existing Information Collection; Hoist Operators' Physical Fitness AGENCY: Mine Safety and... fitness. DATES: All comments must be received by midnight Eastern Standard Time on March 21, 2011... 56.19057 and 57.19057 require the annual examination and certification of hoist operators' fitness by...
Model-independent partial wave analysis using a massively-parallel fitting framework
Sun, L.; Aoude, R.; dos Reis, A. C.; Sokoloff, M.
2017-10-01
The functionality of GooFit, a GPU-friendly framework for doing maximum-likelihood fits, has been extended to extract model-independent {\\mathscr{S}}-wave amplitudes in three-body decays such as D + → h + h + h -. A full amplitude analysis is done where the magnitudes and phases of the {\\mathscr{S}}-wave amplitudes are anchored at a finite number of m 2(h + h -) control points, and a cubic spline is used to interpolate between these points. The amplitudes for {\\mathscr{P}}-wave and {\\mathscr{D}}-wave intermediate states are modeled as spin-dependent Breit-Wigner resonances. GooFit uses the Thrust library, with a CUDA backend for NVIDIA GPUs and an OpenMP backend for threads with conventional CPUs. Performance on a variety of platforms is compared. Executing on systems with GPUs is typically a few hundred times faster than executing the same algorithm on a single CPU.
Using the Bayes Factors to Evaluate Person Fit in the Item Response Theory
Pan, Tianshu; Yin, Yue
2017-01-01
In this article, we propose using the Bayes factors (BF) to evaluate person fit in item response theory models under the framework of Bayesian evaluation of an informative diagnostic hypothesis. We first discuss the theoretical foundation for this application and how to analyze person fit using BF. To demonstrate the feasibility of this approach,…
The issue of statistical power for overall model fit in evaluating structural equation models
Directory of Open Access Journals (Sweden)
Richard HERMIDA
2015-06-01
Full Text Available Statistical power is an important concept for psychological research. However, examining the power of a structural equation model (SEM is rare in practice. This article provides an accessible review of the concept of statistical power for the Root Mean Square Error of Approximation (RMSEA index of overall model fit in structural equation modeling. By way of example, we examine the current state of power in the literature by reviewing studies in top Industrial-Organizational (I/O Psychology journals using SEMs. Results indicate that in many studies, power is very low, which implies acceptance of invalid models. Additionally, we examined methodological situations which may have an influence on statistical power of SEMs. Results showed that power varies significantly as a function of model type and whether or not the model is the main model for the study. Finally, results indicated that power is significantly related to model fit statistics used in evaluating SEMs. The results from this quantitative review imply that researchers should be more vigilant with respect to power in structural equation modeling. We therefore conclude by offering methodological best practices to increase confidence in the interpretation of structural equation modeling results with respect to statistical power issues.
Directory of Open Access Journals (Sweden)
Thomas J Matthews
2014-06-01
Full Text Available A species abundance distribution (SAD characterises patterns in the commonness and rarity of all species within an ecological community. As such, the SAD provides the theoretical foundation for a number of other biogeographical and macroecological patterns, such as the species–area relationship, as well as being an interesting pattern in its own right. While there has been resurgence in the study of SADs in the last decade, less focus has been placed on methodology in SAD research, and few attempts have been made to synthesise the vast array of methods which have been employed in SAD model evaluation. As such, our review has two aims. First, we provide a general overview of SADs, including descriptions of the commonly used distributions, plotting methods and issues with evaluating SAD models. Second, we review a number of recent advances in SAD model fitting and comparison. We conclude by providing a list of recommendations for fitting and evaluating SAD models. We argue that it is time for SAD studies to move away from many of the traditional methods available for fitting and evaluating models, such as sole reliance on the visual examination of plots, and embrace statistically rigorous techniques. In particular, we recommend the use of both goodness-of-fit tests and model-comparison analyses because each provides unique information which one can use to draw inferences.
Fitting direct covariance structures by the MSTRUCT modeling language of the CALIS procedure.
Yung, Yiu-Fai; Browne, Michael W; Zhang, Wei
2015-02-01
This paper demonstrates the usefulness and flexibility of the general structural equation modelling (SEM) approach to fitting direct covariance patterns or structures (as opposed to fitting implied covariance structures from functional relationships among variables). In particular, the MSTRUCT modelling language (or syntax) of the CALIS procedure (SAS/STAT version 9.22 or later: SAS Institute, 2010) is used to illustrate the SEM approach. The MSTRUCT modelling language supports a direct covariance pattern specification of each covariance element. It also supports the input of additional independent and dependent parameters. Model tests, fit statistics, estimates, and their standard errors are then produced under the general SEM framework. By using numerical and computational examples, the following tests of basic covariance patterns are illustrated: sphericity, compound symmetry, and multiple-group covariance patterns. Specification and testing of two complex correlation structures, the circumplex pattern and the composite direct product models with or without composite errors and scales, are also illustrated by the MSTRUCT syntax. It is concluded that the SEM approach offers a general and flexible modelling of direct covariance and correlation patterns. In conjunction with the use of SAS macros, the MSTRUCT syntax provides an easy-to-use interface for specifying and fitting complex covariance and correlation structures, even when the number of variables or parameters becomes large. © 2014 The British Psychological Society.
Matthew P. Adams; Catherine J. Collier; Sven Uthicke; Yan X. Ow; Lucas Langlois; Katherine R. O’Brien
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 evaluat...
Tøndel, Kristin; Niederer, Steven A; Land, Sander; Smith, Nicolas P
2014-05-20
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. 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
A GPS Satellite Clock Offset Prediction Method Based on Fitting Clock Offset Rates Data
Directory of Open Access Journals (Sweden)
WANG Fuhong
2016-12-01
Full Text Available It is proposed that a satellite atomic clock offset prediction method based on fitting and modeling clock offset rates data. This method builds quadratic model or linear model combined with periodic terms to fit the time series of clock offset rates, and computes the model coefficients of trend with the best estimation. The clock offset precisely estimated at the initial prediction epoch is directly adopted to calculate the model coefficient of constant. The clock offsets in the rapid ephemeris (IGR provided by IGS are used as modeling data sets to perform certain experiments for different types of GPS satellite clocks. The results show that the clock prediction accuracies of the proposed method for 3, 6, 12 and 24 h achieve 0.43, 0.58, 0.90 and 1.47 ns respectively, which outperform the traditional prediction method based on fitting original clock offsets by 69.3%, 61.8%, 50.5% and 37.2%. Compared with the IGU real-time clock products provided by IGS, the prediction accuracies of the new method have improved about 15.7%, 23.7%, 27.4% and 34.4% respectively.
Person-fit to the Five Factor Model of personality
Czech Academy of Sciences Publication Activity Database
Allik, J.; Realo, A.; Mõttus, R.; Borkenau, P.; Kuppens, P.; Hřebíčková, Martina
2012-01-01
Roč. 71, č. 1 (2012), s. 35-45 ISSN 1421-0185 R&D Projects: GA ČR GAP407/10/2394 Institutional research plan: CEZ:AV0Z70250504 Keywords : Five Factor Model * cross - cultural comparison * person-fit Subject RIV: AN - Psychology Impact factor: 0.638, year: 2012
International Nuclear Information System (INIS)
Ji Zhilong; Ma Yuanwei; Wang Dezhong
2014-01-01
Background: In radioactive nuclides atmospheric diffusion models, the empirical dispersion coefficients were deduced under certain experiment conditions, whose difference with nuclear accident conditions is a source of deviation. A better estimation of the radioactive nuclide's actual dispersion process could be done by correcting dispersion coefficients with observation data, and Genetic Algorithm (GA) is an appropriate method for this correction procedure. Purpose: This study is to analyze the fitness functions' influence on the correction procedure and the forecast ability of diffusion model. Methods: GA, coupled with Lagrange dispersion model, was used in a numerical simulation to compare 4 fitness functions' impact on the correction result. Results: In the numerical simulation, the fitness function with observation deviation taken into consideration stands out when significant deviation exists in the observed data. After performing the correction procedure on the Kincaid experiment data, a significant boost was observed in the diffusion model's forecast ability. Conclusion: As the result shows, in order to improve dispersion models' forecast ability using GA, observation data should be given different weight in the fitness function corresponding to their error. (authors)
Model Fitting for Predicted Precipitation in Darwin: Some Issues with Model Choice
Farmer, Jim
2010-01-01
In Volume 23(2) of the "Australian Senior Mathematics Journal," Boncek and Harden present an exercise in fitting a Markov chain model to rainfall data for Darwin Airport (Boncek & Harden, 2009). Days are subdivided into those with precipitation and precipitation-free days. The author abbreviates these labels to wet days and dry days.…
Klijn, Sven L; Weijenberg, Matty P; Lemmens, Paul; van den Brandt, Piet A; Lima Passos, Valéria
2017-10-01
Background and objective Group-based trajectory modelling is a model-based clustering technique applied for the identification of latent patterns of temporal changes. Despite its manifold applications in clinical and health sciences, potential problems of the model selection procedure are often overlooked. The choice of the number of latent trajectories (class-enumeration), for instance, is to a large degree based on statistical criteria that are not fail-safe. Moreover, the process as a whole is not transparent. To facilitate class enumeration, we introduce a graphical summary display of several fit and model adequacy criteria, the fit-criteria assessment plot. Methods An R-code that accepts universal data input is presented. The programme condenses relevant group-based trajectory modelling output information of model fit indices in automated graphical displays. Examples based on real and simulated data are provided to illustrate, assess and validate fit-criteria assessment plot's utility. Results Fit-criteria assessment plot provides an overview of fit criteria on a single page, placing users in an informed position to make a decision. Fit-criteria assessment plot does not automatically select the most appropriate model but eases the model assessment procedure. Conclusions Fit-criteria assessment plot is an exploratory, visualisation tool that can be employed to assist decisions in the initial and decisive phase of group-based trajectory modelling analysis. Considering group-based trajectory modelling's widespread resonance in medical and epidemiological sciences, a more comprehensive, easily interpretable and transparent display of the iterative process of class enumeration may foster group-based trajectory modelling's adequate use.
Radial artery pulse waveform analysis based on curve fitting using discrete Fourier series.
Jiang, Zhixing; Zhang, David; Lu, Guangming
2018-04-19
Radial artery pulse diagnosis has been playing an important role in traditional Chinese medicine (TCM). For its non-invasion and convenience, the pulse diagnosis has great significance in diseases analysis of modern medicine. The practitioners sense the pulse waveforms in patients' wrist to make diagnoses based on their non-objective personal experience. With the researches of pulse acquisition platforms and computerized analysis methods, the objective study on pulse diagnosis can help the TCM to keep up with the development of modern medicine. In this paper, we propose a new method to extract feature from pulse waveform based on discrete Fourier series (DFS). It regards the waveform as one kind of signal that consists of a series of sub-components represented by sine and cosine (SC) signals with different frequencies and amplitudes. After the pulse signals are collected and preprocessed, we fit the average waveform for each sample using discrete Fourier series by least squares. The feature vector is comprised by the coefficients of discrete Fourier series function. Compared with the fitting method using Gaussian mixture function, the fitting errors of proposed method are smaller, which indicate that our method can represent the original signal better. The classification performance of proposed feature is superior to the other features extracted from waveform, liking auto-regression model and Gaussian mixture model. The coefficients of optimized DFS function, who is used to fit the arterial pressure waveforms, can obtain better performance in modeling the waveforms and holds more potential information for distinguishing different psychological states. Copyright © 2018 Elsevier B.V. All rights reserved.
2017-08-01
k2 – k1) 3.3 Universal Kinetic Rate Platform Development Kinetic rate models range from pure chemical reactions to mass transfer...14 8. The rate model that best fits the experimental data is a first-order or homogeneous catalytic reaction ...Avrami (7), and intraparticle diffusion (6) rate equations to name a few. A single fitting algorithm (kinetic rate model ) for a reaction does not
The global electroweak Standard Model fit after the Higgs discovery
Baak, Max
2013-01-01
We present an update of the global Standard Model (SM) fit to electroweak precision data under the assumption that the new particle discovered at the LHC is the SM Higgs boson. In this scenario all parameters entering the calculations of electroweak precision observalbes are known, allowing, for the first time, to over-constrain the SM at the electroweak scale and assert its validity. Within the SM the W boson mass and the effective weak mixing angle can be accurately predicted from the global fit. The results are compatible with, and exceed in precision, the direct measurements. An updated determination of the S, T and U parameters, which parametrize the oblique vacuum corrections, is given. The obtained values show good consistency with the SM expectation and no direct signs of new physics are seen. We conclude with an outlook to the global electroweak fit for a future e+e- collider.
Directory of Open Access Journals (Sweden)
Gitte Keidser
2016-04-01
Full Text Available A self-contained, self-fitting hearing aid (SFHA is a device that enables the user to perform both threshold measurements leading to a prescribed hearing aid setting and fine-tuning, without the need for audiological support or access to other equipment. The SFHA has been proposed as a potential solution to address unmet hearing health care in developing countries and remote locations in the developed world and is considered a means to lower cost and increase uptake of hearing aids in developed countries. This article reviews the status of the SFHA and the evidence for its feasibility and challenges and predicts where it is heading. Devices that can be considered partly or fully self-fitting without audiological support were identified in the direct-to-consumer market. None of these devices are considered self-contained as they require access to other hardware such as a proprietary interface, computer, smartphone, or tablet for manipulation. While there is evidence that self-administered fitting processes can provide valid and reliable results, their success relies on user-friendly device designs and interfaces and easy-to-interpret instructions. Until these issues have been sufficiently addressed, optional assistance with the self-fitting process and on-going use of SFHAs is recommended. Affordability and a sustainable delivery system remain additional challenges for the SFHA in developing countries. Future predictions include a growth in self-fitting products, with most future SFHAs consisting of earpieces that connect wirelessly with a smartphone and providers offering assistance through a telehealth infrastructure, and the integration of SFHAs into the traditional hearing health-care model.
Residuals and the Residual-Based Statistic for Testing Goodness of Fit of Structural Equation Models
Foldnes, Njal; Foss, Tron; Olsson, Ulf Henning
2012-01-01
The residuals obtained from fitting a structural equation model are crucial ingredients in obtaining chi-square goodness-of-fit statistics for the model. The authors present a didactic discussion of the residuals, obtaining a geometrical interpretation by recognizing the residuals as the result of oblique projections. This sheds light on the…
Physician behavioral adaptability: A model to outstrip a "one size fits all" approach.
Carrard, Valérie; Schmid Mast, Marianne
2015-10-01
Based on a literature review, we propose a model of physician behavioral adaptability (PBA) with the goal of inspiring new research. PBA means that the physician adapts his or her behavior according to patients' different preferences. The PBA model shows how physicians infer patients' preferences and adapt their interaction behavior from one patient to the other. We claim that patients will benefit from better outcomes if their physicians show behavioral adaptability rather than a "one size fits all" approach. This literature review is based on a literature search of the PsycINFO(®) and MEDLINE(®) databases. The literature review and first results stemming from the authors' research support the validity and viability of parts of the PBA model. There is evidence suggesting that physicians are able to show behavioral flexibility when interacting with their different patients, that a match between patients' preferences and physician behavior is related to better consultation outcomes, and that physician behavioral adaptability is related to better consultation outcomes. Training of physicians' behavioral flexibility and their ability to infer patients' preferences can facilitate physician behavioral adaptability and positive patient outcomes. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.
Using the PLUM procedure of SPSS to fit unequal variance and generalized signal detection models.
DeCarlo, Lawrence T
2003-02-01
The recent addition of aprocedure in SPSS for the analysis of ordinal regression models offers a simple means for researchers to fit the unequal variance normal signal detection model and other extended signal detection models. The present article shows how to implement the analysis and how to interpret the SPSS output. Examples of fitting the unequal variance normal model and other generalized signal detection models are given. The approach offers a convenient means for applying signal detection theory to a variety of research.
Limitations of inclusive fitness.
Allen, Benjamin; Nowak, Martin A; Wilson, Edward O
2013-12-10
Until recently, inclusive fitness has been widely accepted as a general method to explain the evolution of social behavior. Affirming and expanding earlier criticism, we demonstrate that inclusive fitness is instead a limited concept, which exists only for a small subset of evolutionary processes. Inclusive fitness assumes that personal fitness is the sum of additive components caused by individual actions. This assumption does not hold for the majority of evolutionary processes or scenarios. To sidestep this limitation, inclusive fitness theorists have proposed a method using linear regression. On the basis of this method, it is claimed that inclusive fitness theory (i) predicts the direction of allele frequency changes, (ii) reveals the reasons for these changes, (iii) is as general as natural selection, and (iv) provides a universal design principle for evolution. In this paper we evaluate these claims, and show that all of them are unfounded. If the objective is to analyze whether mutations that modify social behavior are favored or opposed by natural selection, then no aspect of inclusive fitness theory is needed.
Lenski, Richard E.; Wiser, Michael J.; Ribeck, Noah; Blount, Zachary D.; Nahum, Joshua R.; Morris, J. Jeffrey; Zaman, Luis; Turner, Caroline B.; Wade, Brian D.; Maddamsetti, Rohan; Burmeister, Alita R.; Baird, Elizabeth J.; Bundy, Jay; Grant, Nkrumah A.; Card, Kyle J.; Rowles, Maia; Weatherspoon, Kiyana; Papoulis, Spiridon E.; Sullivan, Rachel; Clark, Colleen; Mulka, Joseph S.; Hajela, Neerja
2015-01-01
Many populations live in environments subject to frequent biotic and abiotic changes. Nonetheless, it is interesting to ask whether an evolving population's mean fitness can increase indefinitely, and potentially without any limit, even in a constant environment. A recent study showed that fitness trajectories of Escherichia coli populations over 50 000 generations were better described by a power-law model than by a hyperbolic model. According to the power-law model, the rate of fitness gain declines over time but fitness has no upper limit, whereas the hyperbolic model implies a hard limit. Here, we examine whether the previously estimated power-law model predicts the fitness trajectory for an additional 10 000 generations. To that end, we conducted more than 1100 new competitive fitness assays. Consistent with the previous study, the power-law model fits the new data better than the hyperbolic model. We also analysed the variability in fitness among populations, finding subtle, but significant, heterogeneity in mean fitness. Some, but not all, of this variation reflects differences in mutation rate that evolved over time. Taken together, our results imply that both adaptation and divergence can continue indefinitely—or at least for a long time—even in a constant environment. PMID:26674951
A flexible, interactive software tool for fitting the parameters of neuronal models.
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.
A flexible, interactive software tool for fitting the parameters of neuronal models
Directory of Open Access Journals (Sweden)
Péter eFriedrich
2014-07-01
Full Text Available 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 problem 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
Experimental model for non-Newtonian fluid viscosity estimation: Fit to mathematical expressions
Directory of Open Access Journals (Sweden)
Guillem Masoliver i Marcos
2017-01-01
Full Text Available The construction process of a viscometer, developed in collaboration with a final project student, is here presented. It is intended to be used by first year's students to know the viscosity as a fluid property, for both Newtonian and non-Newtonian flows. Viscosity determination is crucial for the fluids behaviour knowledge related to their reologic and physical properties. These have great implications in engineering aspects such as friction or lubrication. With the present experimental model device three different fluids are analyzed (water, kétchup and a mixture with cornstarch and water. Tangential stress is measured versus velocity in order to characterize all the fluids in different thermal conditions. A mathematical fit process is proposed to be done in order to adjust the results to expected analytical expressions, obtaining good results for these fittings, with R2 greater than 0.88 in any case.
Ranger, Jochen; Kuhn, Jörg-Tobias; Szardenings, Carsten
2017-05-01
Cognitive psychometric models embed cognitive process models into a latent trait framework in order to allow for individual differences. Due to their close relationship to the response process the models allow for profound conclusions about the test takers. However, before such a model can be used its fit has to be checked carefully. In this manuscript we give an overview over existing tests of model fit and show their relation to the generalized moment test of Newey (Econometrica, 53, 1985, 1047) and Tauchen (J. Econometrics, 30, 1985, 415). We also present a new test, the Hausman test of misspecification (Hausman, Econometrica, 46, 1978, 1251). The Hausman test consists of a comparison of two estimates of the same item parameters which should be similar if the model holds. The performance of the Hausman test is evaluated in a simulation study. In this study we illustrate its application to two popular models in cognitive psychometrics, the Q-diffusion model and the D-diffusion model (van der Maas, Molenaar, Maris, Kievit, & Boorsboom, Psychol Rev., 118, 2011, 339; Molenaar, Tuerlinckx, & van der Maas, J. Stat. Softw., 66, 2015, 1). We also compare the performance of the test to four alternative tests of model fit, namely the M 2 test (Molenaar et al., J. Stat. Softw., 66, 2015, 1), the moment test (Ranger et al., Br. J. Math. Stat. Psychol., 2016) and the test for binned time (Ranger & Kuhn, Psychol. Test. Asess. , 56, 2014b, 370). The simulation study indicates that the Hausman test is superior to the latter tests. The test closely adheres to the nominal Type I error rate and has higher power in most simulation conditions. © 2017 The British Psychological Society.
Peng, Jui-Chen; Lee, Yin-Ling; Tseng, Mei-Man
2014-03-01
Person-organization (P-O) fit is an important influencing factor on the intentions and attitudes of hospital nurses. The authors used a motivation-mechanism approach to conceptualize work engagement as a mediator and demand-ability (D-A) fit as a moderator to elicit the role of P-O fit in the turnover intention of nurses. This article explores whether the work engagement of nurses mediates the relationship between P-O fit and turnover intention and examines whether D-A fit moderates this relationship. The sample comprised 349 nurses working for two regional hospitals in Yilan County, Taiwan. Linear regression modeling analysis was conducted to test the proposed hypotheses. Results indicate that P-O fit has a negative effect on participant turnover intention. In addition, the work engagement of participants was found to mediate the impact of P-O fit on turnover intention. A new significant interactive relationship was discovered such that high D-A fit strengthened the negative relationship between P-O fit and turnover intention. The work engagement of professional nurses has attracted increasing attention in the literature on fit, particularly with regard to the linkage between P-O and fit-turnover intention. This study enhances the understanding of the function of P-O fit by considering perceived D-A fit. Nurse turnover is the main reason for the current shortage of nurses in Taiwan. Therefore, if the cognitive values of nurses and the organizational culture fit with hospital value systems, common values may facilitate a higher degree of nurse work engagement and, in turn, decrease turnover intention. In addition, recruiting employees with high D-A fit may help hospitals enhance the negative relationship between P-O fit and nurse turnover intention.
Fitness Landscapes of Functional RNAs
Directory of Open Access Journals (Sweden)
Ádám Kun
2015-08-01
Full Text Available The notion of fitness landscapes, a map between genotype and fitness, was proposed more than 80 years ago. For most of this time data was only available for a few alleles, and thus we had only a restricted view of the whole fitness landscape. Recently, advances in genetics and molecular biology allow a more detailed view of them. Here we review experimental and theoretical studies of fitness landscapes of functional RNAs, especially aptamers and ribozymes. We find that RNA structures can be divided into critical structures, connecting structures, neutral structures and forbidden structures. Such characterisation, coupled with theoretical sequence-to-structure predictions, allows us to construct the whole fitness landscape. Fitness landscapes then can be used to study evolution, and in our case the development of the RNA world.
A Bayesian Approach to Multistage Fitting of the Variation of the Skeletal Age Features
Directory of Open Access Journals (Sweden)
Dong Hua
2009-01-01
Full Text Available Accurate assessment of skeletal maturity is important clinically. Skeletal age assessment is usually based on features encoded in ossification centers. Therefore, it is critical to design a mechanism to capture as much as possible characteristics of features. We have observed that given a feature, there exist stages of the skeletal age such that the variation pattern of the feature differs in these stages. Based on this observation, we propose a Bayesian cut fitting to describe features in response to the skeletal age. With our approach, appropriate positions for stage separation are determined automatically by a Bayesian approach, and a model is used to fit the variation of a feature within each stage. Our experimental results show that the proposed method surpasses the traditional fitting using only one line or one curve not only in the efficiency and accuracy of fitting but also in global and local feature characterization.
A Monte Carlo-adjusted goodness-of-fit test for parametric models describing spatial point patterns
Dao, Ngocanh
2014-04-03
Assessing the goodness-of-fit (GOF) for intricate parametric spatial point process models is important for many application fields. When the probability density of the statistic of the GOF test is intractable, a commonly used procedure is the Monte Carlo GOF test. Additionally, if the data comprise a single dataset, a popular version of the test plugs a parameter estimate in the hypothesized parametric model to generate data for theMonte Carlo GOF test. In this case, the test is invalid because the resulting empirical level does not reach the nominal level. In this article, we propose a method consisting of nested Monte Carlo simulations which has the following advantages: the bias of the resulting empirical level of the test is eliminated, hence the empirical levels can always reach the nominal level, and information about inhomogeneity of the data can be provided.We theoretically justify our testing procedure using Taylor expansions and demonstrate that it is correctly sized through various simulation studies. In our first data application, we discover, in agreement with Illian et al., that Phlebocarya filifolia plants near Perth, Australia, can follow a homogeneous Poisson clustered process that provides insight into the propagation mechanism of these plants. In our second data application, we find, in contrast to Diggle, that a pairwise interaction model provides a good fit to the micro-anatomy data of amacrine cells designed for analyzing the developmental growth of immature retina cells in rabbits. This article has supplementary material online. © 2013 American Statistical Association, Institute of Mathematical Statistics, and Interface Foundation of North America.
Kinetic modeling and fitting software for interconnected reaction schemes: VisKin.
Zhang, Xuan; Andrews, Jared N; Pedersen, Steen E
2007-02-15
Reaction kinetics for complex, highly interconnected kinetic schemes are modeled using analytical solutions to a system of ordinary differential equations. The algorithm employs standard linear algebra methods that are implemented using MatLab functions in a Visual Basic interface. A graphical user interface for simple entry of reaction schemes facilitates comparison of a variety of reaction schemes. To ensure microscopic balance, graph theory algorithms are used to determine violations of thermodynamic cycle constraints. Analytical solutions based on linear differential equations result in fast comparisons of first order kinetic rates and amplitudes as a function of changing ligand concentrations. For analysis of higher order kinetics, we also implemented a solution using numerical integration. To determine rate constants from experimental data, fitting algorithms that adjust rate constants to fit the model to imported data were implemented using the Levenberg-Marquardt algorithm or using Broyden-Fletcher-Goldfarb-Shanno methods. We have included the ability to carry out global fitting of data sets obtained at varying ligand concentrations. These tools are combined in a single package, which we have dubbed VisKin, to guide and analyze kinetic experiments. The software is available online for use on PCs.
Adam, A. M. A.; Bashier, E. B. M.; Hashim, M. H. A.; Patidar, K. C.
2017-07-01
In this work, we design and analyze a fitted numerical method to solve a reaction-diffusion model with time delay, namely, a delayed version of a population model which is an extension of the logistic growth (LG) equation for a food-limited population proposed by Smith [F.E. Smith, Population dynamics in Daphnia magna and a new model for population growth, Ecology 44 (1963) 651-663]. Seeing that the analytical solution (in closed form) is hard to obtain, we seek for a robust numerical method. The method consists of a Fourier-pseudospectral semi-discretization in space and a fitted operator implicit-explicit scheme in temporal direction. The proposed method is analyzed for convergence and we found that it is unconditionally stable. Illustrative numerical results will be presented at the conference.
On the Usefulness of a Multilevel Logistic Regression Approach to Person-Fit Analysis
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…
Feature extraction through least squares fit to a simple model
International Nuclear Information System (INIS)
Demuth, H.B.
1976-01-01
The Oak Ridge National Laboratory (ORNL) presented the Los Alamos Scientific Laboratory (LASL) with 18 radiographs of fuel rod test bundles. The problem is to estimate the thickness of the gap between some cylindrical rods and a flat wall surface. The edges of the gaps are poorly defined due to finite source size, x-ray scatter, parallax, film grain noise, and other degrading effects. The radiographs were scanned and the scan-line data were averaged to reduce noise and to convert the problem to one dimension. A model of the ideal gap, convolved with an appropriate point-spread function, was fit to the averaged data with a least squares program; and the gap width was determined from the final fitted-model parameters. The least squares routine did converge and the gaps obtained are of reasonable size. The method is remarkably insensitive to noise. This report describes the problem, the techniques used to solve it, and the results and conclusions. Suggestions for future work are also given
Application of the health and fitness service quality scale (HAFSQ) in ...
African Journals Online (AJOL)
Service quality in health and recreational services represents a growing, yet currently understudied area in the service industry. Consequently, this study proposes an integrated model of health and fitness service quality and examines the relationship among service quality, satisfaction and loyalty. A thirty-nine item health ...
A new mobile ubiquitous computing application to control obesity: SapoFit.
Rodrigues, Joel J P C; Lopes, Ivo M C; Silva, Bruno M C; Torre, Isabel de La
2013-01-01
The objective of this work was the proposal, design, construction and validation of a mobile health system for dietetic monitoring and assessment, called SapoFit. This application may be personalized to keep a daily personal health record of an individual's food intake and daily exercise and to share this with a social network. The initiative is a partnership with SAPO - Portugal Telecom. SapoFit uses Web services architecture, a relatively new model for distributed computing and application integration. SapoFit runs on a range of mobile platforms, and it has been implemented successfully in a range of mobile devices and has been evaluated by over 100 users. Most users strongly agree that SapoFit has an attractive design, the environment is user-friendly and intuitive, and the navigation options are clear.
Haberman, Shelby J; Sinharay, Sandip; Chon, Kyong Hee
2013-07-01
Residual analysis (e.g. Hambleton & Swaminathan, Item response theory: principles and applications, Kluwer Academic, Boston, 1985; Hambleton, Swaminathan, & Rogers, Fundamentals of item response theory, Sage, Newbury Park, 1991) is a popular method to assess fit of item response theory (IRT) models. We suggest a form of residual analysis that may be applied to assess item fit for unidimensional IRT models. The residual analysis consists of a comparison of the maximum-likelihood estimate of the item characteristic curve with an alternative ratio estimate of the item characteristic curve. The large sample distribution of the residual is proved to be standardized normal when the IRT model fits the data. We compare the performance of our suggested residual to the standardized residual of Hambleton et al. (Fundamentals of item response theory, Sage, Newbury Park, 1991) in a detailed simulation study. We then calculate our suggested residuals using data from an operational test. The residuals appear to be useful in assessing the item fit for unidimensional IRT models.
Toribo, S.G.; Gray, B.R.; Liang, S.
2011-01-01
The N-mixture model proposed by Royle in 2004 may be used to approximate the abundance and detection probability of animal species in a given region. In 2006, Royle and Dorazio discussed the advantages of using a Bayesian approach in modelling animal abundance and occurrence using a hierarchical N-mixture model. N-mixture models assume replication on sampling sites, an assumption that may be violated when the site is not closed to changes in abundance during the survey period or when nominal replicates are defined spatially. In this paper, we studied the robustness of a Bayesian approach to fitting the N-mixture model for pseudo-replicated count data. Our simulation results showed that the Bayesian estimates for abundance and detection probability are slightly biased when the actual detection probability is small and are sensitive to the presence of extra variability within local sites.
DEFF Research Database (Denmark)
Nielsen, Karen L.; Pedersen, Thomas M.; Udekwu, Klas I.
2012-01-01
phage types, predominantly only penicillin resistant. We investigated whether isolates of this epidemic were associated with a fitness cost, and we employed a mathematical model to ask whether these fitness costs could have led to the observed reduction in frequency. Bacteraemia isolates of S. aureus...... from Denmark have been stored since 1957. We chose 40 S. aureus isolates belonging to phage complex 83A, clonal complex 8 based on spa type, ranging in time of isolation from 1957 to 1980 and with varyous antibiograms, including both methicillin-resistant and -susceptible isolates. The relative fitness...... of each isolate was determined in a growth competition assay with a reference isolate. Significant fitness costs of 215 were determined for the MRSA isolates studied. There was a significant negative correlation between number of antibiotic resistances and relative fitness. Multiple regression analysis...
GOODNESS-OF-FIT TEST FOR THE ACCELERATED FAILURE TIME MODEL BASED ON MARTINGALE RESIDUALS
Czech Academy of Sciences Publication Activity Database
Novák, Petr
2013-01-01
Roč. 49, č. 1 (2013), s. 40-59 ISSN 0023-5954 R&D Projects: GA MŠk(CZ) 1M06047 Grant - others:GA MŠk(CZ) SVV 261315/2011 Keywords : accelerated failure time model * survival analysis * goodness-of-fit Subject RIV: BB - Applied Statistics, Operational Research Impact factor: 0.563, year: 2013 http://library.utia.cas.cz/separaty/2013/SI/novak-goodness-of-fit test for the aft model based on martingale residuals.pdf
The person response function as a tool in person-fit research
Sijtsma, Klaas; Meijer, R.R.
2001-01-01
Item responses that do not fit an item response theory (IRT) model may cause the latent trait value to be inaccurately estimated. In the past two decades several statistics have been proposed that can be used to identify nonfitting item score patterns. These statistics all yieldscalar values. Here,
Proposal for an ecoradiological centre model
International Nuclear Information System (INIS)
Perovic, S.M.; Zunic, Z.; Demajo, M.; Konjevic, N.
1998-01-01
The problem of establishing an optimal Ecoradiological Centre Model is studied in some detail for the town of Kotor which is under the protection of the World Cultural and Natural Heritage. The proposed structure of the Centre is analyzed from the view of Engineering, Education and Scientific parameters. This Model is suitable for implementation as a network Centre Model for the state of Montenegro. Further, the modelling strategy of the ecoradiological condition control of natural, construction, bio and technological systems is elaborated. The proposal includes the ecoradiological monitoring, radioactive and electromagnetic radiation processing and protection for different natural zones as well as their different geostructures, aerial and hydrogeological conditions. The programme also includes all housing objects (hotels, flats, houses, office premises etc.). Here will also be presented the radiation protection and recommendations for the implementation of Title VII of the European Basic Safety Standards Directive (BSS), concerning significant increase in exposure due to natural radiation sources. Also, the proposal of Local Radiation Protection for the town of Kotor is presented. Our proposal for an Ecoradiological Centre Model presented here is in a form of a pilot programme, applicable also for other towns and states. (author)
Wavelet transform approach for fitting financial time series data
Ahmed, Amel Abdoullah; Ismail, Mohd Tahir
2015-10-01
This study investigates a newly developed technique; a combined wavelet filtering and VEC model, to study the dynamic relationship among financial time series. Wavelet filter has been used to annihilate noise data in daily data set of NASDAQ stock market of US, and three stock markets of Middle East and North Africa (MENA) region, namely, Egypt, Jordan, and Istanbul. The data covered is from 6/29/2001 to 5/5/2009. After that, the returns of generated series by wavelet filter and original series are analyzed by cointegration test and VEC model. The results show that the cointegration test affirms the existence of cointegration between the studied series, and there is a long-term relationship between the US, stock markets and MENA stock markets. A comparison between the proposed model and traditional model demonstrates that, the proposed model (DWT with VEC model) outperforms traditional model (VEC model) to fit the financial stock markets series well, and shows real information about these relationships among the stock markets.
A new fit-for-purpose model testing framework: Decision Crash Tests
Tolson, Bryan; Craig, James
2016-04-01
Decision-makers in water resources are often burdened with selecting appropriate multi-million dollar strategies to mitigate the impacts of climate or land use change. Unfortunately, the suitability of existing hydrologic simulation models to accurately inform decision-making is in doubt because the testing procedures used to evaluate model utility (i.e., model validation) are insufficient. For example, many authors have identified that a good standard framework for model testing called the Klemes Crash Tests (KCTs), which are the classic model validation procedures from Klemeš (1986) that Andréassian et al. (2009) rename as KCTs, have yet to become common practice in hydrology. Furthermore, Andréassian et al. (2009) claim that the progression of hydrological science requires widespread use of KCT and the development of new crash tests. Existing simulation (not forecasting) model testing procedures such as KCTs look backwards (checking for consistency between simulations and past observations) rather than forwards (explicitly assessing if the model is likely to support future decisions). We propose a fundamentally different, forward-looking, decision-oriented hydrologic model testing framework based upon the concept of fit-for-purpose model testing that we call Decision Crash Tests or DCTs. Key DCT elements are i) the model purpose (i.e., decision the model is meant to support) must be identified so that model outputs can be mapped to management decisions ii) the framework evaluates not just the selected hydrologic model but the entire suite of model-building decisions associated with model discretization, calibration etc. The framework is constructed to directly and quantitatively evaluate model suitability. The DCT framework is applied to a model building case study on the Grand River in Ontario, Canada. A hypothetical binary decision scenario is analysed (upgrade or not upgrade the existing flood control structure) under two different sets of model building
Assessing model fit in latent class analysis when asymptotics do not hold
van Kollenburg, Geert H.; Mulder, Joris; Vermunt, Jeroen K.
2015-01-01
The application of latent class (LC) analysis involves evaluating the LC model using goodness-of-fit statistics. To assess the misfit of a specified model, say with the Pearson chi-squared statistic, a p-value can be obtained using an asymptotic reference distribution. However, asymptotic p-values
Directory of Open Access Journals (Sweden)
Misztal Ignacy
2009-01-01
Full Text Available Abstract A semi-parametric non-linear longitudinal hierarchical model is presented. The model assumes that individual variation exists both in the degree of the linear change of performance (slope beyond a particular threshold of the independent variable scale and in the magnitude of the threshold itself; these individual variations are attributed to genetic and environmental components. During implementation via a Bayesian MCMC approach, threshold levels were sampled using a Metropolis step because their fully conditional posterior distributions do not have a closed form. The model was tested by simulation following designs similar to previous studies on genetics of heat stress. Posterior means of parameters of interest, under all simulation scenarios, were close to their true values with the latter always being included in the uncertain regions, indicating an absence of bias. The proposed models provide flexible tools for studying genotype by environmental interaction as well as for fitting other longitudinal traits subject to abrupt changes in the performance at particular points on the independent variable scale.
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.
A scaled Lagrangian method for performing a least squares fit of a model to plant data
International Nuclear Information System (INIS)
Crisp, K.E.
1988-01-01
Due to measurement errors, even a perfect mathematical model will not be able to match all the corresponding plant measurements simultaneously. A further discrepancy may be introduced if an un-modelled change in conditions occurs within the plant which should have required a corresponding change in model parameters - e.g. a gradual deterioration in the performance of some component(s). Taking both these factors into account, what is required is that the overall discrepancy between the model predictions and the plant data is kept to a minimum. This process is known as 'model fitting', A method is presented for minimising any function which consists of the sum of squared terms, subject to any constraints. Its most obvious application is in the process of model fitting, where a weighted sum of squares of the differences between model predictions and plant data is the function to be minimised. When implemented within existing Central Electricity Generating Board computer models, it will perform a least squares fit of a model to plant data within a single job submission. (author)
Anshel, Mark H; Brinthaupt, Thomas M; Kang, Minsoo
2010-01-01
This study examined the effect of a 10-week wellness program on changes in physical fitness and mental well-being. The conceptual framework for this study was the Disconnected Values Model (DVM). According to the DVM, detecting the inconsistencies between negative habits and values (e.g., health, family, faith, character) and concluding that these "disconnects" are unacceptable promotes the need for health behavior change. Participants were 164 full-time employees at a university in the southeastern U.S. The program included fitness coaching and a 90-minute orientation based on the DVM. Multivariate Mixed Model analyses indicated significantly improved scores from pre- to post-intervention on selected measures of physical fitness and mental well-being. The results suggest that the Disconnected Values Model provides an effective cognitive-behavioral approach to generating health behavior change in a 10-week workplace wellness program.
Goodness-of-fit tests in mixed models
Claeskens, Gerda
2009-05-12
Mixed models, with both random and fixed effects, are most often estimated on the assumption that the random effects are normally distributed. In this paper we propose several formal tests of the hypothesis that the random effects and/or errors are normally distributed. Most of the proposed methods can be extended to generalized linear models where tests for non-normal distributions are of interest. Our tests are nonparametric in the sense that they are designed to detect virtually any alternative to normality. In case of rejection of the null hypothesis, the nonparametric estimation method that is used to construct a test provides an estimator of the alternative distribution. © 2009 Sociedad de Estadística e Investigación Operativa.
Franzetti, Paolo; Scodeggio, Marco
2012-10-01
GOSSIP fits the electro-magnetic emission of an object (the SED, Spectral Energy Distribution) against synthetic models to find the simulated one that best reproduces the observed data. It builds-up the observed SED of an object (or a large sample of objects) combining magnitudes in different bands and eventually a spectrum; then it performs a chi-square minimization fitting procedure versus a set of synthetic models. The fitting results are used to estimate a number of physical parameters like the Star Formation History, absolute magnitudes, stellar mass and their Probability Distribution Functions.
Directory of Open Access Journals (Sweden)
Cristina García Magro
2015-06-01
Full Text Available Purpose: The aims of the paper is offers a model of analysis which allows to measure the impact on the performance of fairs, as well as the knowledge or not of the motives of participation of the visitors on the part of the exhibitors. Design/methodology: A review of the literature is established concerning two of the principal interested agents, exhibitors and visitors, focusing. The study is focused on the line of investigation referred to the motives of participation or not in a trade show. According to the information thrown by each perspectives of study, a comparative analysis is carried out in order to determine the degree of existing understanding between both. Findings: The trade shows allow to be studied from an integrated strategic marketing approach. The fit model between the reasons for participation of exhibitors and visitors offer information on the lack of an understanding between exhibitors and visitors, leading to dissatisfaction with the participation, a fact that is reflected in the fair success. The model identified shows that a strategic plan must be designed in which the reason for participation of visitor was incorporated as moderating variable of the reason for participation of exhibitors. The article concludes with the contribution of a series of proposals for the improvement of fairground results. Social implications: The fit model that improve the performance of trade shows, implicitly leads to successful achievement of targets for multiple stakeholders beyond the consideration of visitors and exhibitors. Originality/value: The integrated perspective of stakeholders allows the study of the existing relationships between the principal groups of interest, in such a way that, having knowledge on the condition of the question of the trade shows facilitates the task of the investigator in future academic works and allows that the interested groups obtain a better performance to the participation in fairs, as visitor or as
Proposed Reliability/Cost Model
Delionback, L. M.
1982-01-01
New technique estimates cost of improvement in reliability for complex system. Model format/approach is dependent upon use of subsystem cost-estimating relationships (CER's) in devising cost-effective policy. Proposed methodology should have application in broad range of engineering management decisions.
A Global Moving Hotspot Reference Frame: How well it fits?
Doubrovine, P. V.; Steinberger, B.; Torsvik, T. H.
2010-12-01
Since the early 1970s, when Jason Morgan proposed that hotspot tracks record motion of lithosphere over deep-seated mantle plumes, the concept of fixed hotspots has dominated the way we think about absolute plate reconstructions. In the last decade, with compelling evidence for southward drift of the Hawaiian hotspot from paleomagnetic studies, and for the relative motion between the Pacific and Indo-Atlantic hotspots from refined plate circuit reconstructions, the perception changed and a global moving hotspot reference frame (GMHRF) was introduced, in which numerical models of mantle convection and advection of plume conduits in the mantle flow were used to estimate hotspot motion. This reference frame showed qualitatively better performance in fitting hotspot tracks globally, but the error analysis and formal estimates of the goodness of fitted rotations were lacking in this model. Here we present a new generation of the GMHRF, in which updated plate circuit reconstructions and radiometric age data from the hotspot tracks were combined with numerical models of plume motion, and uncertainties of absolute plate rotations were estimated through spherical regression analysis. The overall quality of fit was evaluated using a formal statistical test, by comparing misfits produced by the model with uncertainties assigned to the data. Alternative plate circuit models linking the Pacific plate to the plates of Indo-Atlantic hemisphere were tested and compared to the fixed hotspot models with identical error budgets. Our results show that, with an appropriate choice of the Pacific plate circuit, it is possible to reconcile relative plate motions and modeled motions of mantle plumes globally back to Late Cretaceous time (80 Ma). In contrast, all fixed hotspot models failed to produce acceptable fits for Paleogene to Late Cretaceous time (30-80 Ma), highlighting significance of relative motion between the Pacific and Indo-Atlantic hotspots during this interval. The
Mr-Moose: An advanced SED-fitting tool for heterogeneous multi-wavelength datasets
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.
Quantitative analyses of empirical fitness landscapes
International Nuclear Information System (INIS)
Szendro, Ivan G; Franke, Jasper; Krug, Joachim; Schenk, Martijn F; De Visser, J Arjan G M
2013-01-01
The concept of a fitness landscape is a powerful metaphor that offers insight into various aspects of evolutionary processes and guidance for the study of evolution. Until recently, empirical evidence on the ruggedness of these landscapes was lacking, but since it became feasible to construct all possible genotypes containing combinations of a limited set of mutations, the number of studies has grown to a point where a classification of landscapes becomes possible. The aim of this review is to identify measures of epistasis that allow a meaningful comparison of fitness landscapes and then apply them to the empirical landscapes in order to discern factors that affect ruggedness. The various measures of epistasis that have been proposed in the literature appear to be equivalent. Our comparison shows that the ruggedness of the empirical landscape is affected by whether the included mutations are beneficial or deleterious and by whether intragenic or intergenic epistasis is involved. Finally, the empirical landscapes are compared to landscapes generated with the rough Mt Fuji model. Despite the simplicity of this model, it captures the features of the experimental landscapes remarkably well. (paper)
Bui, Hong; Zeng, Yolanda; Higgs, Malcolm
2017-01-01
Purpose - This paper explores the relationship between transformational leadership and employees’ work engagement based on fit theory. The paper reports an investigation into the way in which employees’ perceptions of transformational leadership and person-job fit affect their work engagement. Design/methodology/approach – To test our hypotheses, we performed structure equation modelling with maximum likelihood estimation on Mplus with bootstrapping proposed by Hayes (2009) with data from 691...
Hagell, Peter; Westergren, Albert
Sample size is a major factor in statistical null hypothesis testing, which is the basis for many approaches to testing Rasch model fit. Few sample size recommendations for testing fit to the Rasch model concern the Rasch Unidimensional Measurement Models (RUMM) software, which features chi-square and ANOVA/F-ratio based fit statistics, including Bonferroni and algebraic sample size adjustments. This paper explores the occurrence of Type I errors with RUMM fit statistics, and the effects of algebraic sample size adjustments. Data with simulated Rasch model fitting 25-item dichotomous scales and sample sizes ranging from N = 50 to N = 2500 were analysed with and without algebraically adjusted sample sizes. Results suggest the occurrence of Type I errors with N less then or equal to 500, and that Bonferroni correction as well as downward algebraic sample size adjustment are useful to avoid such errors, whereas upward adjustment of smaller samples falsely signal misfit. Our observations suggest that sample sizes around N = 250 to N = 500 may provide a good balance for the statistical interpretation of the RUMM fit statistics studied here with respect to Type I errors and under the assumption of Rasch model fit within the examined frame of reference (i.e., about 25 item parameters well targeted to the sample).
Fluctuating fitness shapes the clone-size distribution of immune repertoires.
Desponds, Jonathan; Mora, Thierry; Walczak, Aleksandra M
2016-01-12
The adaptive immune system relies on the diversity of receptors expressed on the surface of B- and T cells to protect the organism from a vast amount of pathogenic threats. The proliferation and degradation dynamics of different cell types (B cells, T cells, naive, memory) is governed by a variety of antigenic and environmental signals, yet the observed clone sizes follow a universal power-law distribution. Guided by this reproducibility we propose effective models of somatic evolution where cell fate depends on an effective fitness. This fitness is determined by growth factors acting either on clones of cells with the same receptor responding to specific antigens, or directly on single cells with no regard for clones. We identify fluctuations in the fitness acting specifically on clones as the essential ingredient leading to the observed distributions. Combining our models with experiments, we characterize the scale of fluctuations in antigenic environments and we provide tools to identify the relevant growth signals in different tissues and organisms. Our results generalize to any evolving population in a fluctuating environment.
Fitting measurement models to vocational interest data: are dominance models ideal?
Tay, Louis; Drasgow, Fritz; Rounds, James; Williams, Bruce A
2009-09-01
In this study, the authors examined the item response process underlying 3 vocational interest inventories: the Occupational Preference Inventory (C.-P. Deng, P. I. Armstrong, & J. Rounds, 2007), the Interest Profiler (J. Rounds, T. Smith, L. Hubert, P. Lewis, & D. Rivkin, 1999; J. Rounds, C. M. Walker, et al., 1999), and the Interest Finder (J. E. Wall & H. E. Baker, 1997; J. E. Wall, L. L. Wise, & H. E. Baker, 1996). Item response theory (IRT) dominance models, such as the 2-parameter and 3-parameter logistic models, assume that item response functions (IRFs) are monotonically increasing as the latent trait increases. In contrast, IRT ideal point models, such as the generalized graded unfolding model, have IRFs that peak where the latent trait matches the item. Ideal point models are expected to fit better because vocational interest inventories ask about typical behavior, as opposed to requiring maximal performance. Results show that across all 3 interest inventories, the ideal point model provided better descriptions of the response process. The importance of specifying the correct item response model for precise measurement is discussed. In particular, scores computed by a dominance model were shown to be sometimes illogical: individuals endorsing mostly realistic or mostly social items were given similar scores, whereas scores based on an ideal point model were sensitive to which type of items respondents endorsed.
Fitted HBT radii versus space-time variances in flow-dominated models
International Nuclear Information System (INIS)
Lisa, Mike; Frodermann, Evan; Heinz, Ulrich
2007-01-01
The inability of otherwise successful dynamical models to reproduce the 'HBT radii' extracted from two-particle correlations measured at the Relativistic Heavy Ion Collider (RHIC) is known as the 'RHIC HBT Puzzle'. Most comparisons between models and experiment exploit the fact that for Gaussian sources the HBT radii agree with certain combinations of the space-time widths of the source which can be directly computed from the emission function, without having to evaluate, at significant expense, the two-particle correlation function. We here study the validity of this approach for realistic emission function models some of which exhibit significant deviations from simple Gaussian behaviour. By Fourier transforming the emission function we compute the 2-particle correlation function and fit it with a Gaussian to partially mimic the procedure used for measured correlation functions. We describe a novel algorithm to perform this Gaussian fit analytically. We find that for realistic hydrodynamic models the HBT radii extracted from this procedure agree better with the data than the values previously extracted from the space-time widths of the emission function. Although serious discrepancies between the calculated and measured HBT radii remain, we show that a more 'apples-to-apples' comparison of models with data can play an important role in any eventually successful theoretical description of RHIC HBT data. (author)
An evolutionary fitness enhancement conferred by the circadian system in cyanobacteria
International Nuclear Information System (INIS)
Ma, Peijun; Woelfle, Mark A.; Johnson, Carl Hirschie
2013-01-01
Circadian clocks are found in a wide variety of organisms from cyanobacteria to mammals. Many believe that the circadian clock system evolved as an adaption to the daily cycles in light and temperature driven by the rotation of the earth. Studies on the cyanobacterium, Synechococcus elongatus PCC 7942, have confirmed that the circadian clock in resonance with environmental cycles confers an adaptive advantage to cyanobacterial strains with different clock properties when grown in competition under light–dark cycles. The results thus far suggest that in a cyclic environment, the cyanobacterial strains whose free running periods are closest to the environmental period are the most fit and the strains lacking a functional circadian clock are at a competitive disadvantage relative to strains with a functional clock. In contrast, the circadian system provides little or no advantage to cyanobacteria grown in competition in constant light. To explain the potential mechanism of this clock-mediated enhancement in fitness in cyanobacteria, several models have been proposed; these include the limiting resource model, the diffusible inhibitor model and the cell-to-cell communication model. None of these models have been excluded by the currently available experimental data and the mechanistic basis of clock-mediated fitness enhancement remains elusive
Selb, Juliette; Ogden, Tyler M; Dubb, Jay; Fang, Qianqian; Boas, David A
2014-01-01
Near-infrared spectroscopy (NIRS) estimations of the adult brain baseline optical properties based on a homogeneous model of the head are known to introduce significant contamination from extracerebral layers. More complex models have been proposed and occasionally applied to in vivo data, but their performances have never been characterized on realistic head structures. Here we implement a flexible fitting routine of time-domain NIRS data using graphics processing unit based Monte Carlo simulations. We compare the results for two different geometries: a two-layer slab with variable thickness of the first layer and a template atlas head registered to the subject's head surface. We characterize the performance of the Monte Carlo approaches for fitting the optical properties from simulated time-resolved data of the adult head. We show that both geometries provide better results than the commonly used homogeneous model, and we quantify the improvement in terms of accuracy, linearity, and cross-talk from extracerebral layers.
Directory of Open Access Journals (Sweden)
Xuchu Wang
2014-01-01
that uses region-scalable discriminant and fitting energy functional for handling the intensity inhomogeneity and weak boundary problems in medical image segmentation. The region-scalable discriminant and fitting energy functional is defined to capture the image intensity characteristics in local and global regions for driving the evolution of active contour. The discriminant term in the model aims at separating background and foreground in scalable regions while the fitting term tends to fit the intensity in these regions. This model is then transformed into a variational level set formulation with a level set regularization term for accurate computation. The new model utilizes intensity information in the local and global regions as much as possible; so it not only handles better intensity inhomogeneity, but also allows more robustness to noise and more flexible initialization in comparison to the original global region and regional-scalable based models. Experimental results for synthetic and real medical image segmentation show the advantages of the proposed method in terms of accuracy and robustness.
Baczyńska, Anna K; Rowiński, Tomasz; Cybis, Natalia
2016-01-01
Competency models provide insight into key skills which are common to many positions in an organization. Moreover, there is a range of competencies that is used by many companies. Researchers have developed core competency terminology to underline their cross-organizational value. The article presents a theoretical model of core competencies consisting of two main higher-order competencies called performance and entrepreneurship. Each of them consists of three elements: the performance competency includes cooperation, organization of work and goal orientation, while entrepreneurship includes innovativeness, calculated risk-taking and pro-activeness. However, there is lack of empirical validation of competency concepts in organizations and this would seem crucial for obtaining reliable results from organizational research. We propose a two-step empirical validation procedure: (1) confirmation factor analysis, and (2) classification of employees. The sample consisted of 636 respondents (M = 44.5; SD = 15.1). Participants were administered a questionnaire developed for the study purpose. The reliability, measured by Cronbach's alpha, ranged from 0.60 to 0.83 for six scales. Next, we tested the model using a confirmatory factor analysis. The two separate, single models of performance and entrepreneurial orientations fit quite well to the data, while a complex model based on the two single concepts needs further research. In the classification of employees based on the two higher order competencies we obtained four main groups of employees. Their profiles relate to those found in the literature, including so-called niche finders and top performers. Some proposal for organizations is discussed.
A cautionary note on the use of information fit indexes in covariance structure modeling with means
Wicherts, J.M.; Dolan, C.V.
2004-01-01
Information fit indexes such as Akaike Information Criterion, Consistent Akaike Information Criterion, Bayesian Information Criterion, and the expected cross validation index can be valuable in assessing the relative fit of structural equation models that differ regarding restrictiveness. In cases
Directory of Open Access Journals (Sweden)
Mónica A Silva
Full Text Available Argos recently implemented a new algorithm to calculate locations of satellite-tracked animals that uses a Kalman filter (KF. The KF algorithm is reported to increase the number and accuracy of estimated positions over the traditional Least Squares (LS algorithm, with potential advantages to the application of state-space methods to model animal movement data. We tested the performance of two Bayesian state-space models (SSMs fitted to satellite tracking data processed with KF algorithm. Tracks from 7 harbour seals (Phoca vitulina tagged with ARGOS satellite transmitters equipped with Fastloc GPS loggers were used to calculate the error of locations estimated from SSMs fitted to KF and LS data, by comparing those to "true" GPS locations. Data on 6 fin whales (Balaenoptera physalus were used to investigate consistency in movement parameters, location and behavioural states estimated by switching state-space models (SSSM fitted to data derived from KF and LS methods. The model fit to KF locations improved the accuracy of seal trips by 27% over the LS model. 82% of locations predicted from the KF model and 73% of locations from the LS model were <5 km from the corresponding interpolated GPS position. Uncertainty in KF model estimates (5.6 ± 5.6 km was nearly half that of LS estimates (11.6 ± 8.4 km. Accuracy of KF and LS modelled locations was sensitive to precision but not to observation frequency or temporal resolution of raw Argos data. On average, 88% of whale locations estimated by KF models fell within the 95% probability ellipse of paired locations from LS models. Precision of KF locations for whales was generally higher. Whales' behavioural mode inferred by KF models matched the classification from LS models in 94% of the cases. State-space models fit to KF data can improve spatial accuracy of location estimates over LS models and produce equally reliable behavioural estimates.
A Proposed Conceptual Model of Military Medical Readiness
National Research Council Canada - National Science Library
Van Hall, Brian M
2007-01-01
.... The basis for the proposed conceptual model builds on common and accepted latent variable and theoretical modeling techniques proposed by healthcare scholars, organizational theorists, mathematical...
Evolutionary Graphs with Frequency Dependent Fitness
Nie, Pu-Yan; Zhang, Pei-Ai
Evolutionary graph theory was recently proposed by Lieberman et al. in 2005. In the previous papers about evolutionary graphs (EGs), the fitness of the residents in the EGs is in general assumed to be unity, and the fitness of a mutant is assumed to be a constant r. We aim to extend EG to general cases in this paper, namely, the fitness of a mutant is heavily dependent upon frequency. The corresponding properties for these new EGs are analyzed, and the fixation probability is obtained for large population.
Goodness-of-fit tests in mixed models
Claeskens, Gerda; Hart, Jeffrey D.
2009-01-01
Mixed models, with both random and fixed effects, are most often estimated on the assumption that the random effects are normally distributed. In this paper we propose several formal tests of the hypothesis that the random effects and/or errors
DEFF Research Database (Denmark)
Hansen, Maj; Hyland, Philip; Karstoft, Karen-Inge
2017-01-01
words, does the size of PTSD really matter? Methods: The aim was investigated by examining differences in diagnostic rates between the two diagnostic systems and independently examining the model fit of the competing DSM-5 and ICD-11 models of PTSD across three trauma samples: university students (N......Background: Researchers and clinicians within the field of trauma have to choose between different diagnostic descriptions of posttraumatic stress disorder (PTSD) in the DSM-5 and the proposed ICD-11. Several studies support different competing models of the PTSD structure according to both....... The proposed ICD-11 three-factor model provided the best fit of the tested ICD-11 models across all samples, whereas the DSM-5 seven-factor Hybrid model provided the best fit in the university and pain samples, and the DSM-5 six-factor Anhedonia model provided the best fit in the military sample of the tested...
The FIT Model - Fuel-cycle Integration and Tradeoffs
International Nuclear Information System (INIS)
Piet, Steven J.; Soelberg, Nick R.; Bays, Samuel E.; Pereira, Candido; Pincock, Layne F.; Shaber, Eric L.; Teague, Melissa C.; Teske, Gregory M.; Vedros, Kurt G.
2010-01-01
All mass streams from fuel separation and fabrication are products that must meet some set of product criteria - fuel feedstock impurity limits, waste acceptance criteria (WAC), material storage (if any), or recycle material purity requirements such as zirconium for cladding or lanthanides for industrial use. These must be considered in a systematic and comprehensive way. The FIT model and the 'system losses study' team that developed it (Shropshire2009, Piet2010) are an initial step by the FCR and D program toward a global analysis that accounts for the requirements and capabilities of each component, as well as major material flows within an integrated fuel cycle. This will help the program identify near-term R and D needs and set longer-term goals. The question originally posed to the 'system losses study' was the cost of separation, fuel fabrication, waste management, etc. versus the separation efficiency. In other words, are the costs associated with marginal reductions in separations losses (or improvements in product recovery) justified by the gains in the performance of other systems? We have learned that that is the wrong question. The right question is: how does one adjust the compositions and quantities of all mass streams, given uncertain product criteria, to balance competing objectives including cost? FIT is a method to analyze different fuel cycles using common bases to determine how chemical performance changes in one part of a fuel cycle (say used fuel cooling times or separation efficiencies) affect other parts of the fuel cycle. FIT estimates impurities in fuel and waste via a rough estimate of physics and mass balance for a set of technologies. If feasibility is an issue for a set, as it is for 'minimum fuel treatment' approaches such as melt refining and AIROX, it can help to make an estimate of how performances would have to change to achieve feasibility.
Directory of Open Access Journals (Sweden)
Junyi eDai
2015-03-01
Full Text Available The Iowa Gambling Task (IGT and the Soochow Gambling Task (SGT are two experience-based risky decision-making tasks for examining decision-making deficits in clinical populations. Several cognitive models, including the expectancy-valence learning model (EVL and the prospect valence learning model (PVL, have been developed to disentangle the motivational, cognitive, and response processes underlying the explicit choices in these tasks. The purpose of the current study was to develop an improved model that can fit empirical data better than the EVL and PVL models and, in addition, produce more consistent parameter estimates across the IGT and SGT. Twenty-six opiate users (mean age 34.23; SD 8.79 and 27 control participants (mean age 35; SD 10.44 completed both tasks. Eighteen cognitive models varying in evaluation, updating, and choice rules were fit to individual data and their performances were compared to that of a statistical baseline model to find a best fitting model. The results showed that the model combining the prospect utility function treating gains and losses separately, the decay-reinforcement updating rule, and the trial-independent choice rule performed the best in both tasks. Furthermore, the winning model produced more consistent individual parameter estimates across the two tasks than any of the other models.
Detecting Growth Shape Misspecifications in Latent Growth Models: An Evaluation of Fit Indexes
Leite, Walter L.; Stapleton, Laura M.
2011-01-01
In this study, the authors compared the likelihood ratio test and fit indexes for detection of misspecifications of growth shape in latent growth models through a simulation study and a graphical analysis. They found that the likelihood ratio test, MFI, and root mean square error of approximation performed best for detecting model misspecification…
Fitting the Fractional Polynomial Model to Non-Gaussian Longitudinal Data
Directory of Open Access Journals (Sweden)
Ji Hoon Ryoo
2017-08-01
Full Text Available As in cross sectional studies, longitudinal studies involve non-Gaussian data such as binomial, Poisson, gamma, and inverse-Gaussian distributions, and multivariate exponential families. A number of statistical tools have thus been developed to deal with non-Gaussian longitudinal data, including analytic techniques to estimate parameters in both fixed and random effects models. However, as yet growth modeling with non-Gaussian data is somewhat limited when considering the transformed expectation of the response via a linear predictor as a functional form of explanatory variables. In this study, we introduce a fractional polynomial model (FPM that can be applied to model non-linear growth with non-Gaussian longitudinal data and demonstrate its use by fitting two empirical binary and count data models. The results clearly show the efficiency and flexibility of the FPM for such applications.
DEFF Research Database (Denmark)
Hansen, Maj; Hyland, Philip; Karstoft, Karen-Inge
2017-01-01
words, does the size of PTSD really matter? Methods: The aim was investigated by examining differences in diagnostic rates between the two diagnostic systems and independently examining the model fit of the competing DSM-5 and ICD-11 models of PTSD across three trauma samples: university students (N...... = 4213), chronic pain patients (N = 573), and military personnel (N = 118). Results: Diagnostic rates of PTSD were significantly lower according to the proposed ICD-11 criteria in the university sample, but no significant differences were found for chronic pain patients and military personnel....... The proposed ICD-11 three-factor model provided the best fit of the tested ICD-11 models across all samples, whereas the DSM-5 seven-factor Hybrid model provided the best fit in the university and pain samples, and the DSM-5 six-factor Anhedonia model provided the best fit in the military sample of the tested...
DEFF Research Database (Denmark)
Farhi, E.; Y., Debab,; Willendrup, Peter Kjær
2014-01-01
and noisy problems. These optimizers can then be used to fit models onto data objects, and optimize McStas instrument simulations. As an application, we propose a methodology to analyse neutron scattering measurements in a pure Monte Carlo optimization procedure using McStas and iFit. As opposed...
Basch, Corey H; Hillyer, Grace Clarke; Ethan, Danna; Berdnik, Alyssa; Basch, Charles E
2015-07-01
Tanned skin has been associated with perceptions of fitness and social desirability. Portrayal of models in magazines may reflect and perpetuate these perceptions. Limited research has investigated tanning shade gradations of models in men's versus women's fitness and muscle enthusiast magazines. Such findings are relevant in light of increased incidence and prevalence of melanoma in the United States. This study evaluated and compared tanning shade gradations of adult Caucasian male and female model images in mainstream fitness and muscle enthusiast magazines. Sixty-nine U.S. magazine issues (spring and summer, 2013) were utilized. Two independent reviewers rated tanning shade gradations of adult Caucasian male and female model images on magazines' covers, advertisements, and feature articles. Shade gradations were assessed using stock photographs of Caucasian models with varying levels of tanned skin on an 8-shade scale. A total of 4,683 images were evaluated. Darkest tanning shades were found among males in muscle enthusiast magazines and lightest among females in women's mainstream fitness magazines. By gender, male model images were 54% more likely to portray a darker tanning shade. In this study, images in men's (vs. women's) fitness and muscle enthusiast magazines portrayed Caucasian models with darker skin shades. Despite these magazines' fitness-related messages, pro-tanning images may promote attitudes and behaviors associated with higher skin cancer risk. To date, this is the first study to explore tanning shades in men's magazines of these genres. Further research is necessary to identify effects of exposure to these images among male readers. © The Author(s) 2014.
Directory of Open Access Journals (Sweden)
Loreen eHertäg
2012-09-01
Full Text Available For large-scale network simulations, it is often desirable to have computationally tractable, yet in a defined sense still physiologically valid neuron models. In particular, these models should be able to reproduce physiological measurements, ideally in a predictive sense, and under different input regimes in which neurons may operate in vivo. Here we present an approach to parameter estimation for a simple spiking neuron model mainly based on standard f-I curves obtained from in vitro recordings. Such recordings are routinely obtained in standard protocols and assess a neuron's response under a wide range of mean input currents. Our fitting procedure makes use of closed-form expressions for the firing rate derived from an approximation to the adaptive exponential integrate-and-fire (AdEx model. The resulting fitting process is simple and about two orders of magnitude faster compared to methods based on numerical integration of the differential equations. We probe this method on different cell types recorded from rodent prefrontal cortex. After fitting to the f-I current-clamp data, the model cells are tested on completely different sets of recordings obtained by fluctuating ('in-vivo-like' input currents. For a wide range of different input regimes, cell types, and cortical layers, the model could predict spike times on these test traces quite accurately within the bounds of physiological reliability, although no information from these distinct test sets was used for model fitting. Further analyses delineated some of the empirical factors constraining model fitting and the model's generalization performance. An even simpler adaptive LIF neuron was also examined in this context. Hence, we have developed a 'high-throughput' model fitting procedure which is simple and fast, with good prediction performance, and which relies only on firing rate information and standard physiological data widely and easily available.
Badillo-Olvera, A.; Begovich, O.; Peréz-González, A.
2017-01-01
The present paper is motivated by the purpose of detection and isolation of a single leak considering the Fault Model Approach (FMA) focused on pipelines with changes in their geometry. These changes generate a different pressure drop that those produced by the friction, this phenomenon is a common scenario in real pipeline systems. The problem arises, since the dynamical model of the fluid in a pipeline only considers straight geometries without fittings. In order to address this situation, several papers work with a virtual model of a pipeline that generates a equivalent straight length, thus, friction produced by the fittings is taking into account. However, when this method is applied, the leak is isolated in a virtual length, which for practical reasons does not represent a complete solution. This research proposes as a solution to the problem of leak isolation in a virtual length, the use of a polynomial interpolation function in order to approximate the conversion of the virtual position to a real-coordinates value. Experimental results in a real prototype are shown, concluding that the proposed methodology has a good performance.
Fast fitting of non-Gaussian state-space models to animal movement data via Template Model Builder
DEFF Research Database (Denmark)
Albertsen, Christoffer Moesgaard; Whoriskey, Kim; Yurkowski, David
2015-01-01
recommend using the Laplace approximation combined with automatic differentiation (as implemented in the novel R package Template Model Builder; TMB) for the fast fitting of continuous-time multivariate non-Gaussian SSMs. Through Argos satellite tracking data, we demonstrate that the use of continuous...... are able to estimate additional parameters compared to previous methods, all without requiring a substantial increase in computational time. The model implementation is made available through the R package argosTrack....
Directory of Open Access Journals (Sweden)
André Ricardo de Pinho Ronzani
2017-12-01
Full Text Available In this work, a Capital Asset Pricing Model (CAPM with time-varying betas is considered. These betas evolve over time, conditional on financial and non-financial variables. Indeed, the model proposed by Adrian and Franzoni (2009 is adapted to assess the behavior of some selected Brazilian equities. For each equity, several models are fitted, and the best model is chosen based on goodness-of-fit tests and parameters significance. Finally, using the selected dynamic models, VaR (Value-at-Risk measures are calculated. We can conclude that CAPM with time-varying betas provide less conservative VaR measures than those based on CAPM with static betas or historical VaR.
Log-normal frailty models fitted as Poisson generalized linear mixed models.
Hirsch, Katharina; Wienke, Andreas; Kuss, Oliver
2016-12-01
The equivalence of a survival model with a piecewise constant baseline hazard function and a Poisson regression model has been known since decades. As shown in recent studies, this equivalence carries over to clustered survival data: A frailty model with a log-normal frailty term can be interpreted and estimated as a generalized linear mixed model with a binary response, a Poisson likelihood, and a specific offset. Proceeding this way, statistical theory and software for generalized linear mixed models are readily available for fitting frailty models. This gain in flexibility comes at the small price of (1) having to fix the number of pieces for the baseline hazard in advance and (2) having to "explode" the data set by the number of pieces. In this paper we extend the simulations of former studies by using a more realistic baseline hazard (Gompertz) and by comparing the model under consideration with competing models. Furthermore, the SAS macro %PCFrailty is introduced to apply the Poisson generalized linear mixed approach to frailty models. The simulations show good results for the shared frailty model. Our new %PCFrailty macro provides proper estimates, especially in case of 4 events per piece. The suggested Poisson generalized linear mixed approach for log-normal frailty models based on the %PCFrailty macro provides several advantages in the analysis of clustered survival data with respect to more flexible modelling of fixed and random effects, exact (in the sense of non-approximate) maximum likelihood estimation, and standard errors and different types of confidence intervals for all variance parameters. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.
Multiple organ definition in CT using a Bayesian approach for 3D model fitting
Boes, Jennifer L.; Weymouth, Terry E.; Meyer, Charles R.
1995-08-01
Organ definition in computed tomography (CT) is of interest for treatment planning and response monitoring. We present a method for organ definition using a priori information about shape encoded in a set of biometric organ models--specifically for the liver and kidney-- that accurately represents patient population shape information. Each model is generated by averaging surfaces from a learning set of organ shapes previously registered into a standard space defined by a small set of landmarks. The model is placed in a specific patient's data set by identifying these landmarks and using them as the basis for model deformation; this preliminary representation is then iteratively fit to the patient's data based on a Bayesian formulation of the model's priors and CT edge information, yielding a complete organ surface. We demonstrate this technique using a set of fifteen abdominal CT data sets for liver surface definition both before and after the addition of a kidney model to the fitting; we demonstrate the effectiveness of this tool for organ surface definition in this low-contrast domain.
Fitting boxes to Manhattan scenes using linear integer programming
Li, Minglei
2016-02-19
We propose an approach for automatic generation of building models by assembling a set of boxes using a Manhattan-world assumption. The method first aligns the point cloud with a per-building local coordinate system, and then fits axis-aligned planes to the point cloud through an iterative regularization process. The refined planes partition the space of the data into a series of compact cubic cells (candidate boxes) spanning the entire 3D space of the input data. We then choose to approximate the target building by the assembly of a subset of these candidate boxes using a binary linear programming formulation. The objective function is designed to maximize the point cloud coverage and the compactness of the final model. Finally, all selected boxes are merged into a lightweight polygonal mesh model, which is suitable for interactive visualization of large scale urban scenes. Experimental results and a comparison with state-of-the-art methods demonstrate the effectiveness of the proposed framework.
The 'fitting problem' in cosmology
International Nuclear Information System (INIS)
Ellis, G.F.R.; Stoeger, W.
1987-01-01
The paper considers the best way to fit an idealised exactly homogeneous and isotropic universe model to a realistic ('lumpy') universe; whether made explicit or not, some such approach of necessity underlies the use of the standard Robertson-Walker models as models of the real universe. Approaches based on averaging, normal coordinates and null data are presented, the latter offering the best opportunity to relate the fitting procedure to data obtainable by astronomical observations. (author)
Pulmonary lobe segmentation based on ridge surface sampling and shape model fitting
Energy Technology Data Exchange (ETDEWEB)
Ross, James C., E-mail: jross@bwh.harvard.edu [Channing Laboratory, Brigham and Women' s Hospital, Boston, Massachusetts 02215 (United States); Surgical Planning Lab, Brigham and Women' s Hospital, Boston, Massachusetts 02215 (United States); Laboratory of Mathematics in Imaging, Brigham and Women' s Hospital, Boston, Massachusetts 02126 (United States); Kindlmann, Gordon L. [Computer Science Department and Computation Institute, University of Chicago, Chicago, Illinois 60637 (United States); Okajima, Yuka; Hatabu, Hiroto [Department of Radiology, Brigham and Women' s Hospital, Boston, Massachusetts 02215 (United States); Díaz, Alejandro A. [Pulmonary and Critical Care Division, Brigham and Women' s Hospital and Harvard Medical School, Boston, Massachusetts 02215 and Department of Pulmonary Diseases, Pontificia Universidad Católica de Chile, Santiago (Chile); Silverman, Edwin K. [Channing Laboratory, Brigham and Women' s Hospital, Boston, Massachusetts 02215 and Pulmonary and Critical Care Division, Brigham and Women' s Hospital and Harvard Medical School, Boston, Massachusetts 02215 (United States); Washko, George R. [Pulmonary and Critical Care Division, Brigham and Women' s Hospital and Harvard Medical School, Boston, Massachusetts 02215 (United States); Dy, Jennifer [ECE Department, Northeastern University, Boston, Massachusetts 02115 (United States); Estépar, Raúl San José [Department of Radiology, Brigham and Women' s Hospital, Boston, Massachusetts 02215 (United States); Surgical Planning Lab, Brigham and Women' s Hospital, Boston, Massachusetts 02215 (United States); Laboratory of Mathematics in Imaging, Brigham and Women' s Hospital, Boston, Massachusetts 02126 (United States)
2013-12-15
Purpose: Performing lobe-based quantitative analysis of the lung in computed tomography (CT) scans can assist in efforts to better characterize complex diseases such as chronic obstructive pulmonary disease (COPD). While airways and vessels can help to indicate the location of lobe boundaries, segmentations of these structures are not always available, so methods to define the lobes in the absence of these structures are desirable. Methods: The authors present a fully automatic lung lobe segmentation algorithm that is effective in volumetric inspiratory and expiratory computed tomography (CT) datasets. The authors rely on ridge surface image features indicating fissure locations and a novel approach to modeling shape variation in the surfaces defining the lobe boundaries. The authors employ a particle system that efficiently samples ridge surfaces in the image domain and provides a set of candidate fissure locations based on the Hessian matrix. Following this, lobe boundary shape models generated from principal component analysis (PCA) are fit to the particles data to discriminate between fissure and nonfissure candidates. The resulting set of particle points are used to fit thin plate spline (TPS) interpolating surfaces to form the final boundaries between the lung lobes. Results: The authors tested algorithm performance on 50 inspiratory and 50 expiratory CT scans taken from the COPDGene study. Results indicate that the authors' algorithm performs comparably to pulmonologist-generated lung lobe segmentations and can produce good results in cases with accessory fissures, incomplete fissures, advanced emphysema, and low dose acquisition protocols. Dice scores indicate that only 29 out of 500 (5.85%) lobes showed Dice scores lower than 0.9. Two different approaches for evaluating lobe boundary surface discrepancies were applied and indicate that algorithm boundary identification is most accurate in the vicinity of fissures detectable on CT. Conclusions: The
Pulmonary lobe segmentation based on ridge surface sampling and shape model fitting
International Nuclear Information System (INIS)
Ross, James C.; Kindlmann, Gordon L.; Okajima, Yuka; Hatabu, Hiroto; Díaz, Alejandro A.; Silverman, Edwin K.; Washko, George R.; Dy, Jennifer; Estépar, Raúl San José
2013-01-01
Purpose: Performing lobe-based quantitative analysis of the lung in computed tomography (CT) scans can assist in efforts to better characterize complex diseases such as chronic obstructive pulmonary disease (COPD). While airways and vessels can help to indicate the location of lobe boundaries, segmentations of these structures are not always available, so methods to define the lobes in the absence of these structures are desirable. Methods: The authors present a fully automatic lung lobe segmentation algorithm that is effective in volumetric inspiratory and expiratory computed tomography (CT) datasets. The authors rely on ridge surface image features indicating fissure locations and a novel approach to modeling shape variation in the surfaces defining the lobe boundaries. The authors employ a particle system that efficiently samples ridge surfaces in the image domain and provides a set of candidate fissure locations based on the Hessian matrix. Following this, lobe boundary shape models generated from principal component analysis (PCA) are fit to the particles data to discriminate between fissure and nonfissure candidates. The resulting set of particle points are used to fit thin plate spline (TPS) interpolating surfaces to form the final boundaries between the lung lobes. Results: The authors tested algorithm performance on 50 inspiratory and 50 expiratory CT scans taken from the COPDGene study. Results indicate that the authors' algorithm performs comparably to pulmonologist-generated lung lobe segmentations and can produce good results in cases with accessory fissures, incomplete fissures, advanced emphysema, and low dose acquisition protocols. Dice scores indicate that only 29 out of 500 (5.85%) lobes showed Dice scores lower than 0.9. Two different approaches for evaluating lobe boundary surface discrepancies were applied and indicate that algorithm boundary identification is most accurate in the vicinity of fissures detectable on CT. Conclusions: The proposed
Fitting the CDO correlation skew: a tractable structural jump-diffusion model
DEFF Research Database (Denmark)
Willemann, Søren
2007-01-01
We extend a well-known structural jump-diffusion model for credit risk to handle both correlations through diffusion of asset values and common jumps in asset value. Through a simplifying assumption on the default timing and efficient numerical techniques, we develop a semi-analytic framework...... allowing for instantaneous calibration to heterogeneous CDS curves and fast computation of CDO tranche spreads. We calibrate the model to CDX and iTraxx data from February 2007 and achieve a satisfactory fit. To price the senior tranches for both indices, we require a risk-neutral probability of a market...
Modified Distribution-Free Goodness-of-Fit Test Statistic.
Chun, So Yeon; Browne, Michael W; Shapiro, Alexander
2018-03-01
Covariance structure analysis and its structural equation modeling extensions have become one of the most widely used methodologies in social sciences such as psychology, education, and economics. An important issue in such analysis is to assess the goodness of fit of a model under analysis. One of the most popular test statistics used in covariance structure analysis is the asymptotically distribution-free (ADF) test statistic introduced by Browne (Br J Math Stat Psychol 37:62-83, 1984). The ADF statistic can be used to test models without any specific distribution assumption (e.g., multivariate normal distribution) of the observed data. Despite its advantage, it has been shown in various empirical studies that unless sample sizes are extremely large, this ADF statistic could perform very poorly in practice. In this paper, we provide a theoretical explanation for this phenomenon and further propose a modified test statistic that improves the performance in samples of realistic size. The proposed statistic deals with the possible ill-conditioning of the involved large-scale covariance matrices.
Development and design of a late-model fitness test instrument based on LabView
Xie, Ying; Wu, Feiqing
2010-12-01
Undergraduates are pioneers of China's modernization program and undertake the historic mission of rejuvenating our nation in the 21st century, whose physical fitness is vital. A smart fitness test system can well help them understand their fitness and health conditions, thus they can choose more suitable approaches and make practical plans for exercising according to their own situation. following the future trends, a Late-model fitness test Instrument based on LabView has been designed to remedy defects of today's instruments. The system hardware consists of fives types of sensors with their peripheral circuits, an acquisition card of NI USB-6251 and a computer, while the system software, on the basis of LabView, includes modules of user register, data acquisition, data process and display, and data storage. The system, featured by modularization and an open structure, is able to be revised according to actual needs. Tests results have verified the system's stability and reliability.
Molitor, John
2012-03-01
Bayesian methods have seen an increase in popularity in a wide variety of scientific fields, including epidemiology. One of the main reasons for their widespread application is the power of the Markov chain Monte Carlo (MCMC) techniques generally used to fit these models. As a result, researchers often implicitly associate Bayesian models with MCMC estimation procedures. However, Bayesian models do not always require Markov-chain-based methods for parameter estimation. This is important, as MCMC estimation methods, while generally quite powerful, are complex and computationally expensive and suffer from convergence problems related to the manner in which they generate correlated samples used to estimate probability distributions for parameters of interest. In this issue of the Journal, Cole et al. (Am J Epidemiol. 2012;175(5):368-375) present an interesting paper that discusses non-Markov-chain-based approaches to fitting Bayesian models. These methods, though limited, can overcome some of the problems associated with MCMC techniques and promise to provide simpler approaches to fitting Bayesian models. Applied researchers will find these estimation approaches intuitively appealing and will gain a deeper understanding of Bayesian models through their use. However, readers should be aware that other non-Markov-chain-based methods are currently in active development and have been widely published in other fields.
Decision making on fitness landscapes
Arthur, R.; Sibani, P.
2017-04-01
We discuss fitness landscapes and how they can be modified to account for co-evolution. We are interested in using the landscape as a way to model rational decision making in a toy economic system. We develop a model very similar to the Tangled Nature Model of Christensen et al. that we call the Tangled Decision Model. This is a natural setting for our discussion of co-evolutionary fitness landscapes. We use a Monte Carlo step to simulate decision making and investigate two different decision making procedures.
Decision Making on Fitness Landscapes
DEFF Research Database (Denmark)
Arthur, Rudy; Sibani, Paolo
2017-01-01
We discuss fitness landscapes and how they can be modified to account for co-evolution. We are interested in using the landscape as a way to model rational decision making in a toy economic system. We develop a model very similar to the Tangled Nature Model of Christensen et. al. that we call...... the Tangled Decision Model. This is a natural setting for our discussion of co-evolutionary fitness landscapes. We use a Monte Carlo step to simulate decision making and investigate two different decision making procedures....
Yuan, Shupei; Ma, Wenjuan; Kanthawala, Shaheen; Peng, Wei
2015-09-01
Health and fitness applications (apps) are one of the major app categories in the current mobile app market. Few studies have examined this area from the users' perspective. This study adopted the Extended Unified Theory of Acceptance and Use of Technology (UTAUT2) Model to examine the predictors of the users' intention to adopt health and fitness apps. A survey (n=317) was conducted with college-aged smartphone users at a Midwestern university in the United States. Performance expectancy, hedonic motivations, price value, and habit were significant predictors of users' intention of continued usage of health and fitness apps. However, effort expectancy, social influence, and facilitating conditions were not found to predict users' intention of continued usage of health and fitness apps. This study extends the UTATU2 Model to the mobile apps domain and provides health professions, app designers, and marketers with the insights of user experience in terms of continuously using health and fitness apps.
THE EFFECT OF PERSON-JOB FIT ON THE PERFORMANCE APPRAISAL: A RESEARCH IN KONYA TEXTILE ENTERPRISES
Directory of Open Access Journals (Sweden)
Ali Şükrü ÇETİNKAYA
2017-12-01
Full Text Available Lack of person-job fit may be one of the possible reasons for the symptoms of absence at work, increase in social loafing, employee turnover, burnout, stress etc. among employees in an organization. The compatibility of knowledge level, skills and personal traits with job characteristics determines the level of employee job satisfaction, effectiveness, productivity and performance. Performance appraisal practices, which assess employee performance level, enable organizations get aware of the importance of person-job fit. This quantitative research aimed to determine the effect of person-job fit on performance appraisal in organizations. Exploratory factor analysis and structural equation modeling techniques were employed to test the proposed hypotheses based on 223 valid responses gathered through questionnaire from textile enterprises operating in Konya province. Research findings revealed that both person fit and job fit components of the person-job fit structure have positive effect on performance appraisal. Results revealed that when person-job fit was satisfied, employees have positive attitude against performance appraisal practices.
Shavit Grievink, Liat; Penny, David; Hendy, Michael D; Holland, Barbara R
2010-05-01
Commonly used phylogenetic models assume a homogeneous process through time in all parts of the tree. However, it is known that these models can be too simplistic as they do not account for nonhomogeneous lineage-specific properties. In particular, it is now widely recognized that as constraints on sequences evolve, the proportion and positions of variable sites can vary between lineages causing heterotachy. The extent to which this model misspecification affects tree reconstruction is still unknown. Here, we evaluate the effect of changes in the proportions and positions of variable sites on model fit and tree estimation. We consider 5 current models of nucleotide sequence evolution in a Bayesian Markov chain Monte Carlo framework as well as maximum parsimony (MP). We show that for a tree with 4 lineages where 2 nonsister taxa undergo a change in the proportion of variable sites tree reconstruction under the best-fitting model, which is chosen using a relative test, often results in the wrong tree. In this case, we found that an absolute test of model fit is a better predictor of tree estimation accuracy. We also found further evidence that MP is not immune to heterotachy. In addition, we show that increased sampling of taxa that have undergone a change in proportion and positions of variable sites is critical for accurate tree reconstruction.
Measuring fit of sequence data to phylogenetic model: gain of power using marginal tests.
Waddell, Peter J; Ota, Rissa; Penny, David
2009-10-01
Testing fit of data to model is fundamentally important to any science, but publications in the field of phylogenetics rarely do this. Such analyses discard fundamental aspects of science as prescribed by Karl Popper. Indeed, not without cause, Popper (Unended quest: an intellectual autobiography. Fontana, London, 1976) once argued that evolutionary biology was unscientific as its hypotheses were untestable. Here we trace developments in assessing fit from Penny et al. (Nature 297:197-200, 1982) to the present. We compare the general log-likelihood ratio (the G or G (2) statistic) statistic between the evolutionary tree model and the multinomial model with that of marginalized tests applied to an alignment (using placental mammal coding sequence data). It is seen that the most general test does not reject the fit of data to model (P approximately 0.5), but the marginalized tests do. Tests on pairwise frequency (F) matrices, strongly (P < 0.001) reject the most general phylogenetic (GTR) models commonly in use. It is also clear (P < 0.01) that the sequences are not stationary in their nucleotide composition. Deviations from stationarity and homogeneity seem to be unevenly distributed amongst taxa; not necessarily those expected from examining other regions of the genome. By marginalizing the 4( t ) patterns of the i.i.d. model to observed and expected parsimony counts, that is, from constant sites, to singletons, to parsimony informative characters of a minimum possible length, then the likelihood ratio test regains power, and it too rejects the evolutionary model with P < 0.001. Given such behavior over relatively recent evolutionary time, readers in general should maintain a healthy skepticism of results, as the scale of the systematic errors in published trees may really be far larger than the analytical methods (e.g., bootstrap) report.
UROX 2.0: an interactive tool for fitting atomic models into electron-microscopy reconstructions
International Nuclear Information System (INIS)
Siebert, Xavier; Navaza, Jorge
2009-01-01
UROX is software designed for the interactive fitting of atomic models into electron-microscopy reconstructions. The main features of the software are presented, along with a few examples. Electron microscopy of a macromolecular structure can lead to three-dimensional reconstructions with resolutions that are typically in the 30–10 Å range and sometimes even beyond 10 Å. Fitting atomic models of the individual components of the macromolecular structure (e.g. those obtained by X-ray crystallography or nuclear magnetic resonance) into an electron-microscopy map allows the interpretation of the latter at near-atomic resolution, providing insight into the interactions between the components. Graphical software is presented that was designed for the interactive fitting and refinement of atomic models into electron-microscopy reconstructions. Several characteristics enable it to be applied over a wide range of cases and resolutions. Firstly, calculations are performed in reciprocal space, which results in fast algorithms. This allows the entire reconstruction (or at least a sizeable portion of it) to be used by taking into account the symmetry of the reconstruction both in the calculations and in the graphical display. Secondly, atomic models can be placed graphically in the map while the correlation between the model-based electron density and the electron-microscopy reconstruction is computed and displayed in real time. The positions and orientations of the models are refined by a least-squares minimization. Thirdly, normal-mode calculations can be used to simulate conformational changes between the atomic model of an individual component and its corresponding density within a macromolecular complex determined by electron microscopy. These features are illustrated using three practical cases with different symmetries and resolutions. The software, together with examples and user instructions, is available free of charge at http://mem.ibs.fr/UROX/
Stojek, Monika M K; Montoya, Amanda K; Drescher, Christopher F; Newberry, Andrew; Sultan, Zain; Williams, Celestine F; Pollock, Norman K; Davis, Catherine L
We used mediation models to examine the mechanisms underlying the relationships among physical fitness, sleep-disordered breathing (SDB), symptoms of depression, and cognitive functioning. We conducted a cross-sectional secondary analysis of the cohorts involved in the 2003-2006 project PLAY (a trial of the effects of aerobic exercise on health and cognition) and the 2008-2011 SMART study (a trial of the effects of exercise on cognition). A total of 397 inactive overweight children aged 7-11 received a fitness test, standardized cognitive test (Cognitive Assessment System, yielding Planning, Attention, Simultaneous, Successive, and Full Scale scores), and depression questionnaire. Parents completed a Pediatric Sleep Questionnaire. We used bootstrapped mediation analyses to test whether SDB mediated the relationship between fitness and depression and whether SDB and depression mediated the relationship between fitness and cognition. Fitness was negatively associated with depression ( B = -0.041; 95% CI, -0.06 to -0.02) and SDB ( B = -0.005; 95% CI, -0.01 to -0.001). SDB was positively associated with depression ( B = 0.99; 95% CI, 0.32 to 1.67) after controlling for fitness. The relationship between fitness and depression was mediated by SDB (indirect effect = -0.005; 95% CI, -0.01 to -0.0004). The relationship between fitness and the attention component of cognition was independently mediated by SDB (indirect effect = 0.058; 95% CI, 0.004 to 0.13) and depression (indirect effect = -0.071; 95% CI, -0.01 to -0.17). SDB mediates the relationship between fitness and depression, and SDB and depression separately mediate the relationship between fitness and the attention component of cognition.
The Predicting Model of E-commerce Site Based on the Ideas of Curve Fitting
Tao, Zhang; Li, Zhang; Dingjun, Chen
On the basis of the idea of the second multiplication curve fitting, the number and scale of Chinese E-commerce site is analyzed. A preventing increase model is introduced in this paper, and the model parameters are solved by the software of Matlab. The validity of the preventing increase model is confirmed though the numerical experiment. The experimental results show that the precision of preventing increase model is ideal.
Roberts, James S.
Stone and colleagues (C. Stone, R. Ankenman, S. Lane, and M. Liu, 1993; C. Stone, R. Mislevy and J. Mazzeo, 1994; C. Stone, 2000) have proposed a fit index that explicitly accounts for the measurement error inherent in an estimated theta value, here called chi squared superscript 2, subscript i*. The elements of this statistic are natural…
Directory of Open Access Journals (Sweden)
Xiaosheng Yu
2013-01-01
Full Text Available We propose a novel active contour model in a variational level set formulation for image segmentation and target localization. We combine a local image fitting term and a global image fitting term to drive the contour evolution. Our model can efficiently segment the images with intensity inhomogeneity with the contour starting anywhere in the image. In its numerical implementation, an efficient numerical schema is used to ensure sufficient numerical accuracy. We validated its effectiveness in numerous synthetic images and real images, and the promising experimental results show its advantages in terms of accuracy, efficiency, and robustness.
Milani, G.; Milani, F.
2016-01-01
A GUI software (GURU) for experimental data fitting of rheometer curves in Natural Rubber (NR) vulcanized with sulphur at different curing temperatures is presented. Experimental data are automatically loaded in GURU from an Excel spreadsheet coming from the output of the experimental machine (moving die rheometer). To fit the experimental data, the general reaction scheme proposed by Han and co-workers for NR vulcanized with sulphur is considered. From the simplified kinetic scheme adopted, ...
Testing the validity of stock-recruitment curve fits
International Nuclear Information System (INIS)
Christensen, S.W.; Goodyear, C.P.
1988-01-01
The utilities relied heavily on the Ricker stock-recruitment model as the basis for quantifying biological compensation in the Hudson River power case. They presented many fits of the Ricker model to data derived from striped bass catch and effort records compiled by the National Marine Fisheries Service. Based on this curve-fitting exercise, a value of 4 was chosen for the parameter alpha in the Ricker model, and this value was used to derive the utilities' estimates of the long-term impact of power plants on striped bass populations. A technique was developed and applied to address a single fundamental question: if the Ricker model were applicable to the Hudson River striped bass population, could the estimates of alpha from the curve-fitting exercise be considered reliable. The technique involved constructing a simulation model that incorporated the essential biological features of the population and simulated the characteristics of the available actual catch-per-unit-effort data through time. The ability or failure to retrieve the known parameter values underlying the simulation model via the curve-fitting exercise was a direct test of the reliability of the results of fitting stock-recruitment curves to the real data. The results demonstrated that estimates of alpha from the curve-fitting exercise were not reliable. The simulation-modeling technique provides an effective way to identify whether or not particular data are appropriate for use in fitting such models. 39 refs., 2 figs., 3 tabs
Cowin, Leanne S; Moroney, Robyn
2018-01-01
Sessional academic staff are an important part of nursing education. Increases in casualisation of the academic workforce continue and satisfaction with the job role is an important bench mark for quality curricula delivery and influences recruitment and retention. This study examined relations between four job constructs - organisation fit, organisation support, staff role and job satisfaction for Sessional Academic Staff at a School of Nursing by creating two path analysis models. A cross-sectional correlational survey design was utilised. Participants who were currently working as sessional or casual teaching staff members were invited to complete an online anonymous survey. The data represents a convenience sample of Sessional Academic Staff in 2016 at a large school of Nursing and Midwifery in Australia. After psychometric evaluation of each of the job construct measures in this study we utilised Structural Equation Modelling to better understand the relations of the variables. The measures used in this study were found to be both valid and reliable for this sample. Job support and job fit are positively linked to job satisfaction. Although the hypothesised model did not meet model fit standards, a new 'nested' model made substantive sense. This small study explored a new scale for measuring academic job role, and demonstrated how it promotes the constructs of job fit and job supports. All four job constructs are important in providing job satisfaction - an outcome that in turn supports staffing stability, retention, and motivation.
A CONTRASTIVE ANALYSIS OF THE FACTORIAL STRUCTURE OF THE PCL-R: WHICH MODEL FITS BEST THE DATA?
Directory of Open Access Journals (Sweden)
Beatriz Pérez
2015-01-01
Full Text Available The aim of this study was to determine which of the factorial solutions proposed for the Hare Psychopathy Checklist-Revised (PCL-R of two, three, four factors, and unidimensional fitted best the data. Two trained and experienced independent raters scored 197 prisoners from the Villabona Penitentiary (Asturias, Spain, age range 21 to 73 years (M = 36.0, SD = 9.7, of whom 60.12% were reoffenders and 73% had committed violent crimes. The results revealed that the two-factor correlational, three-factor hierarchical without testlets, four-factor correlational and hierarchical, and unidimensional models were a poor fit for the data (CFI ≤ .86, and the three-factor model with testlets was a reasonable fit for the data (CFI = .93. The scale resulting from the three-factor hierarchical model with testlets (13 items classified psychopathy significantly higher than the original 20-item scale. The results are discussed in terms of their implications for theoretical models of psychopathy, decision-making, prison classification and intervention, and prevention. Se diseñó un estudio con el objetivo de conocer cuál de las soluciones factoriales propuestas para la Hare Psychopathy Checklist-Revised (PCL-R de dos, tres y cuatro factores y unidimensional era la que presentaba mejor ajuste a los datos. Para ello, dos evaluadores entrenados y con experiencia evaluaron de forma independiente a 197 internos en la prisión Villabona (Asturias, España, con edades comprendidas entre los 21 y los 73 años (M = 36.0, DT = 9.7, de los cuales el 60.12% eran reincidentes y el 73% había cometido delitos violentos. Los resultados mostraron que los modelos unidimensional, correlacional de 2 factores, jerárquico de 3 factores sin testlest y correlacional y jerárquico de 4 factores, presentaban un pobre ajuste con los datos (CFI ≤ .86 y un ajuste razonable del modelo jerárquico de tres factores con testlets (CFI = .93. La escala resultante del modelo de tres factores
Schlemm, Eckhard
2015-09-01
The Bak-Sneppen model is an abstract representation of a biological system that evolves according to the Darwinian principles of random mutation and selection. The species in the system are characterized by a numerical fitness value between zero and one. We show that in the case of five species the steady-state fitness distribution can be obtained as a solution to a linear differential equation of order five with hypergeometric coefficients. Similar representations for the asymptotic fitness distribution in larger systems may help pave the way towards a resolution of the question of whether or not, in the limit of infinitely many species, the fitness is asymptotically uniformly distributed on the interval [fc, 1] with fc ≳ 2/3. Copyright © 2015 Elsevier Inc. All rights reserved.
Energy Technology Data Exchange (ETDEWEB)
Furlan, E. [Infrared Processing and Analysis Center, California Institute of Technology, 770 S. Wilson Ave., Pasadena, CA 91125 (United States); Fischer, W. J. [Goddard Space Flight Center, 8800 Greenbelt Road, Greenbelt, MD 20771 (United States); Ali, B. [Space Science Institute, 4750 Walnut Street, Boulder, CO 80301 (United States); Stutz, A. M. [Max-Planck-Institut für Astronomie, Königstuhl 17, D-69117 Heidelberg (Germany); Stanke, T. [ESO, Karl-Schwarzschild-Strasse 2, D-85748 Garching bei München (Germany); Tobin, J. J. [National Radio Astronomy Observatory, Charlottesville, VA 22903 (United States); Megeath, S. T.; Booker, J. [Ritter Astrophysical Research Center, Department of Physics and Astronomy, University of Toledo, 2801 W. Bancroft Street, Toledo, OH 43606 (United States); Osorio, M. [Instituto de Astrofísica de Andalucía, CSIC, Camino Bajo de Huétor 50, E-18008 Granada (Spain); Hartmann, L.; Calvet, N. [Department of Astronomy, University of Michigan, 500 Church Street, Ann Arbor, MI 48109 (United States); Poteet, C. A. [New York Center for Astrobiology, Rensselaer Polytechnic Institute, 110 Eighth Street, Troy, NY 12180 (United States); Manoj, P. [Department of Astronomy and Astrophysics, Tata Institute of Fundamental Research, Homi Bhabha Road, Colaba, Mumbai 400005 (India); Watson, D. M. [Department of Physics and Astronomy, University of Rochester, Rochester, NY 14627 (United States); Allen, L., E-mail: furlan@ipac.caltech.edu [National Optical Astronomy Observatory, 950 N. Cherry Avenue, Tucson, AZ 85719 (United States)
2016-05-01
We present key results from the Herschel Orion Protostar Survey: spectral energy distributions (SEDs) and model fits of 330 young stellar objects, predominantly protostars, in the Orion molecular clouds. This is the largest sample of protostars studied in a single, nearby star formation complex. With near-infrared photometry from 2MASS, mid- and far-infrared data from Spitzer and Herschel , and submillimeter photometry from APEX, our SEDs cover 1.2–870 μ m and sample the peak of the protostellar envelope emission at ∼100 μ m. Using mid-IR spectral indices and bolometric temperatures, we classify our sample into 92 Class 0 protostars, 125 Class I protostars, 102 flat-spectrum sources, and 11 Class II pre-main-sequence stars. We implement a simple protostellar model (including a disk in an infalling envelope with outflow cavities) to generate a grid of 30,400 model SEDs and use it to determine the best-fit model parameters for each protostar. We argue that far-IR data are essential for accurate constraints on protostellar envelope properties. We find that most protostars, and in particular the flat-spectrum sources, are well fit. The median envelope density and median inclination angle decrease from Class 0 to Class I to flat-spectrum protostars, despite the broad range in best-fit parameters in each of the three categories. We also discuss degeneracies in our model parameters. Our results confirm that the different protostellar classes generally correspond to an evolutionary sequence with a decreasing envelope infall rate, but the inclination angle also plays a role in the appearance, and thus interpretation, of the SEDs.
Surface Fitting for Quasi Scattered Data from Coordinate Measuring Systems.
Mao, Qing; Liu, Shugui; Wang, Sen; Ma, Xinhui
2018-01-13
Non-uniform rational B-spline (NURBS) surface fitting from data points is wildly used in the fields of computer aided design (CAD), medical imaging, cultural relic representation and object-shape detection. Usually, the measured data acquired from coordinate measuring systems is neither gridded nor completely scattered. The distribution of this kind of data is scattered in physical space, but the data points are stored in a way consistent with the order of measurement, so it is named quasi scattered data in this paper. Therefore they can be organized into rows easily but the number of points in each row is random. In order to overcome the difficulty of surface fitting from this kind of data, a new method based on resampling is proposed. It consists of three major steps: (1) NURBS curve fitting for each row, (2) resampling on the fitted curve and (3) surface fitting from the resampled data. Iterative projection optimization scheme is applied in the first and third step to yield advisable parameterization and reduce the time cost of projection. A resampling approach based on parameters, local peaks and contour curvature is proposed to overcome the problems of nodes redundancy and high time consumption in the fitting of this kind of scattered data. Numerical experiments are conducted with both simulation and practical data, and the results show that the proposed method is fast, effective and robust. What's more, by analyzing the fitting results acquired form data with different degrees of scatterness it can be demonstrated that the error introduced by resampling is negligible and therefore it is feasible.
Sarkar, Uttam Kumar; Naskar, Malay; Roy, Koushik; Sudeeshan, Deepa; Srivastava, Pankaj; Gupta, Sandipan; Bose, Arun Kumar; Verma, Vinod Kumar; Sarkar, Soma Das; Karnatak, Gunjan; Nandy, Saurav Kumar
2017-09-07
The concept of threshold condition factor (Fulton), beyond which more than 50% of the female fish population may attain readiness for spawning coined as pre-spawning fitness (K spawn50 ), has been proposed in the present article and has been estimated by applying the non-parametric Kaplan-Meier method for fitting survival function. A binary coding strategy of gonadal maturity stages was used to classify whether a female fish is "ready to spawn" or not. The proposed K spawn50 has been generated for female Mystus tengara (1.13-1.21 units), M. cavasius (0.846-0.945 units), and Eutropiichthys vacha (0.716-0.799 units). Information on the range of egg parameters (fecundity, egg weight, egg diameter) expected at the pre-spawning stage was also generated. Additional information on species-specific thermal and precipitation window (climate preferendum) within which K spawn50 is attained was also generated through the LOESS smoothing technique. Water temperatures between 31 and 36 °C (M. tengara), 30 and 32 °C (M. cavasius), and 29.5 and 31 °C (E. vacha) and monthly rainfall between 200 and 325 mm (M. tengara), > 250 mm (M. cavasius), and around 50 mm and between 350 and 850 mm (E. vacha) were found to be optimum for attainment of K spawn50 . The importance of parameterization and benchmarking of K spawn50 in addition to other conventional reproductive biology parameters has been discussed in the present article. The purposes of the present study were fulfilled by generating baseline information and similar information may be generated for other species replicating the innovative methodology used in this study.
Anticipating mismatches of HIT investments: Developing a viability-fit model for e-health services.
Mettler, Tobias
2016-01-01
Albeit massive investments in the recent years, the impact of health information technology (HIT) has been controversial and strongly disputed by both research and practice. While many studies are concerned with the development of new or the refinement of existing measurement models for assessing the impact of HIT adoption (ex post), this study presents an initial attempt to better understand the factors affecting viability and fit of HIT and thereby underscores the importance of also having instruments for managing expectations (ex ante). We extend prior research by undertaking a more granular investigation into the theoretical assumptions of viability and fit constructs. In doing so, we use a mixed-methods approach, conducting qualitative focus group discussions and a quantitative field study to improve and validate a viability-fit measurement instrument. Our findings suggest two issues for research and practice. First, the results indicate that different stakeholders perceive HIT viability and fit of the same e-health services very unequally. Second, the analysis also demonstrates that there can be a great discrepancy between the organizational viability and individual fit of a particular e-health service. The findings of this study have a number of important implications such as for health policy making, HIT portfolios, and stakeholder communication. Copyright © 2015. Published by Elsevier Ireland Ltd.
Amino acids intake and physical fitness among adolescents.
Gracia-Marco, Luis; Bel-Serrat, Silvia; Cuenca-Garcia, Magdalena; Gonzalez-Gross, Marcela; Pedrero-Chamizo, Raquel; Manios, Yannis; Marcos, Ascensión; Molnar, Denes; Widhalm, Kurt; Polito, Angela; Vanhelst, Jeremy; Hagströmer, Maria; Sjöström, Michael; Kafatos, Anthony; de Henauw, Stefaan; Gutierrez, Ángel; Castillo, Manuel J; Moreno, Luis A
2017-06-01
The aim was to investigate whether there was an association between amino acid (AA) intake and physical fitness and if so, to assess whether this association was independent of carbohydrates intake. European adolescents (n = 1481, 12.5-17.5 years) were measured. Intake was assessed via two non-consecutive 24-h dietary recalls. Lower and upper limbs muscular fitness was assessed by standing long jump and handgrip strength tests, respectively. Cardiorespiratory fitness was assessed by the 20-m shuttle run test. Physical activity was objectively measured. Socioeconomic status was obtained via questionnaires. Lower limbs muscular fitness seems to be positively associated with tryptophan, histidine and methionine intake in boys, regardless of centre, age, socioeconomic status, physical activity and total energy intake (model 1). However, these associations disappeared once carbohydrates intake was controlled for (model 2). In girls, only proline intake seems to be positively associated with lower limbs muscular fitness (model 2) while cardiorespiratory fitness seems to be positively associated with leucine (model 1) and proline intake (models 1 and 2). None of the observed significant associations remained significant once multiple testing was controlled for. In conclusion, we failed to detect any associations between any of the evaluated AAs and physical fitness after taking into account the effect of multiple testing.
Fitting the two-compartment model in DCE-MRI by linear inversion.
Flouri, Dimitra; Lesnic, Daniel; Sourbron, Steven P
2016-09-01
Model fitting of dynamic contrast-enhanced-magnetic resonance imaging-MRI data with nonlinear least squares (NLLS) methods is slow and may be biased by the choice of initial values. The aim of this study was to develop and evaluate a linear least squares (LLS) method to fit the two-compartment exchange and -filtration models. A second-order linear differential equation for the measured concentrations was derived where model parameters act as coefficients. Simulations of normal and pathological data were performed to determine calculation time, accuracy and precision under different noise levels and temporal resolutions. Performance of the LLS was evaluated by comparison against the NLLS. The LLS method is about 200 times faster, which reduces the calculation times for a 256 × 256 MR slice from 9 min to 3 s. For ideal data with low noise and high temporal resolution the LLS and NLLS were equally accurate and precise. The LLS was more accurate and precise than the NLLS at low temporal resolution, but less accurate at high noise levels. The data show that the LLS leads to a significant reduction in calculation times, and more reliable results at low noise levels. At higher noise levels the LLS becomes exceedingly inaccurate compared to the NLLS, but this may be improved using a suitable weighting strategy. Magn Reson Med 76:998-1006, 2016. © 2015 Wiley Periodicals, Inc. © 2015 Wiley Periodicals, Inc.
James W. Hardin; Henrik Schmeidiche; Raymond J. Carroll
2003-01-01
This paper discusses and illustrates the method of regression calibration. This is a straightforward technique for fitting models with additive measurement error. We present this discussion in terms of generalized linear models (GLMs) following the notation defined in Hardin and Carroll (2003). Discussion will include specified measurement error, measurement error estimated by replicate error-prone proxies, and measurement error estimated by instrumental variables. The discussion focuses on s...
Directory of Open Access Journals (Sweden)
Rita Yi Man Li
2012-03-01
Full Text Available Entrepreneurs have always born the risk of running their business. They reap a profit in return for their risk taking and work. Housing developers are no different. In many countries, such as Australia, the United Kingdom and the United States, they interpret the tastes of the buyers and provide the dwellings they develop with basic fittings such as floor and wall coverings, bathroom fittings and kitchen cupboards. In mainland China, however, in most of the developments, units or houses are sold without floor or wall coverings, kitchen or bathroom fittings. What is the motive behind this choice? This paper analyses the factors affecting housing developers’ decisions to provide fittings based on 1701 housing developments in Hangzhou, Chongqing and Hangzhou using a Probit model. The results show that developers build a higher proportion of bare units in mainland China when: 1 there is shortage of housing; 2 land costs are high so that the comparative costs of providing fittings become relatively low.
Organizational intellectual capital and the role of the nurse manager: A proposed conceptual model.
Gilbert, Jason H; Von Ah, Diane; Broome, Marion E
Nurse managers must leverage both the human capital and social capital of the teams they lead in order to produce quality outcomes. Little is known about the relationship between human capital and social capital and how these concepts may work together to produce organizational outcomes through leadership of nurses. The purpose of this article was to explore the concepts of human capital and social capital as they relate to nursing leadership in health care organizations. Specific aims included (a) to synthesize the literature related to human capital and social capital in leadership, (b) to refine the conceptual definitions of human capital and social capital with associated conceptual antecedents and consequences, and (c) to propose a synthesized conceptual model guiding further empirical research of social capital and human capital in nursing leadership. A systematic integrative review of leadership literature using criteria informed by Whittemore and Knafl (2005) was completed. CINAHL Plus with Full Text, Academic Search Premier, Business Source Premier, Health Business FullTEXT, MEDLINE, and PsychINFO databases were searched for the years 1995 to 2016 using terms "human capital," "social capital," and "management." Analysis of conceptual definitions, theoretical and conceptual models, antecedents and consequences, propositions or hypotheses, and empirical support for 37 articles fitting review criteria resulted in the synthesis of the proposed Gilbert Conceptual Model of Organizational Intellectual Capital. The Gilbert Conceptual Model of Organizational Intellectual Capital advances the propositions of human capital theory and social capital theory and is the first model to conceptualize the direct and moderating effects that nurse leaders have on the human capital and social capital of the teams they lead. This model provides a framework for further empirical study and may have implications for practice, organizational policy, and education related to nursing
FIREFLY (Fitting IteRativEly For Likelihood analYsis): a full spectral fitting code
Wilkinson, David M.; Maraston, Claudia; Goddard, Daniel; Thomas, Daniel; Parikh, Taniya
2017-12-01
We present a new spectral fitting code, FIREFLY, for deriving the stellar population properties of stellar systems. FIREFLY is a chi-squared minimization fitting code that fits combinations of single-burst stellar population models to spectroscopic data, following an iterative best-fitting process controlled by the Bayesian information criterion. No priors are applied, rather all solutions within a statistical cut are retained with their weight. Moreover, no additive or multiplicative polynomials are employed to adjust the spectral shape. This fitting freedom is envisaged in order to map out the effect of intrinsic spectral energy distribution degeneracies, such as age, metallicity, dust reddening on galaxy properties, and to quantify the effect of varying input model components on such properties. Dust attenuation is included using a new procedure, which was tested on Integral Field Spectroscopic data in a previous paper. The fitting method is extensively tested with a comprehensive suite of mock galaxies, real galaxies from the Sloan Digital Sky Survey and Milky Way globular clusters. We also assess the robustness of the derived properties as a function of signal-to-noise ratio (S/N) and adopted wavelength range. We show that FIREFLY is able to recover age, metallicity, stellar mass, and even the star formation history remarkably well down to an S/N ∼ 5, for moderately dusty systems. Code and results are publicly available.1
Design of spatial experiments: Model fitting and prediction
Energy Technology Data Exchange (ETDEWEB)
Fedorov, V.V.
1996-03-01
The main objective of the paper is to describe and develop model oriented methods and algorithms for the design of spatial experiments. Unlike many other publications in this area, the approach proposed here is essentially based on the ideas of convex design theory.
FITTING OF PARAMETRIC BUILDING MODELS TO OBLIQUE AERIAL IMAGES
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U. S. Panday
2012-09-01
Full Text Available In literature and in photogrammetric workstations many approaches and systems to automatically reconstruct buildings from remote sensing data are described and available. Those building models are being used for instance in city modeling or in cadastre context. If a roof overhang is present, the building walls cannot be estimated correctly from nadir-view aerial images or airborne laser scanning (ALS data. This leads to inconsistent building outlines, which has a negative inﬂuence on visual impression, but more seriously also represents a wrong legal boundary in the cadaster. Oblique aerial images as opposed to nadir-view images reveal greater detail, enabling to see different views of an object taken from different directions. Building walls are visible from oblique images directly and those images are used for automated roof overhang estimation in this research. A fitting algorithm is employed to find roof parameters of simple buildings. It uses a least squares algorithm to fit projected wire frames to their corresponding edge lines extracted from the images. Self-occlusion is detected based on intersection result of viewing ray and the planes formed by the building whereas occlusion from other objects is detected using an ALS point cloud. Overhang and ground height are obtained by sweeping vertical and horizontal planes respectively. Experimental results are verified with high resolution ortho-images, field survey, and ALS data. Planimetric accuracy of 1cm mean and 5cm standard deviation was obtained, while buildings' orientation were accurate to mean of 0.23° and standard deviation of 0.96° with ortho-image. Overhang parameters were aligned to approximately 10cm with field survey. The ground and roof heights were accurate to mean of – 9cm and 8cm with standard deviations of 16cm and 8cm with ALS respectively. The developed approach reconstructs 3D building models well in cases of sufficient texture. More images should be acquired for
Attracting applicants on the web: PO fit, industry culture stereotypes, and website design
de Goede, M.E.E.; van Vianen, A.E.M.; Klehe, U.-C.
2011-01-01
This study tested the extent to which industry culture stereotypes influence job-seekers' Person-Organization fit when job-seekers inspect organizations' websites. We proposed that PO fit would relate to Person-Industry (PI) fit, which is the fit between personal values and industry culture
Active appearance pyramids for object parametrisation and fitting.
Zhang, Qiang; Bhalerao, Abhir; Dickenson, Edward; Hutchinson, Charles
2016-08-01
Object class representation is one of the key problems in various medical image analysis tasks. We propose a part-based parametric appearance model we refer to as an Active Appearance Pyramid (AAP). The parts are delineated by multi-scale Local Feature Pyramids (LFPs) for superior spatial specificity and distinctiveness. An AAP models the variability within a population with local translations of multi-scale parts and linear appearance variations of the assembly of the parts. It can fit and represent new instances by adjusting the shape and appearance parameters. The fitting process uses a two-step iterative strategy: local landmark searching followed by shape regularisation. We present a simultaneous local feature searching and appearance fitting algorithm based on the weighted Lucas and Kanade method. A shape regulariser is derived to calculate the maximum likelihood shape with respect to the prior and multiple landmark candidates from multi-scale LFPs, with a compact closed-form solution. We apply the 2D AAP on the modelling of variability in patients with lumbar spinal stenosis (LSS) and validate its performance on 200 studies consisting of routine axial and sagittal MRI scans. Intervertebral sagittal and parasagittal cross-sections are typically used for the diagnosis of LSS, we therefore build three AAPs on L3/4, L4/5 and L5/S1 axial cross-sections and three on parasagittal slices. Experiments show significant improvement in convergence range, robustness to local minima and segmentation precision compared with Constrained Local Models (CLMs), Active Shape Models (ASMs) and Active Appearance Models (AAMs), as well as superior performance in appearance reconstruction compared with AAMs. We also validate the performance on 3D CT volumes of hip joints from 38 studies. Compared to AAMs, AAPs achieve a higher segmentation and reconstruction precision. Moreover, AAPs have a significant improvement in efficiency, consuming about half the memory and less than 10% of
Fitting the Probability Distribution Functions to Model Particulate Matter Concentrations
International Nuclear Information System (INIS)
El-Shanshoury, Gh.I.
2017-01-01
The main objective of this study is to identify the best probability distribution and the plotting position formula for modeling the concentrations of Total Suspended Particles (TSP) as well as the Particulate Matter with an aerodynamic diameter<10 μm (PM 10 ). The best distribution provides the estimated probabilities that exceed the threshold limit given by the Egyptian Air Quality Limit value (EAQLV) as well the number of exceedance days is estimated. The standard limits of the EAQLV for TSP and PM 10 concentrations are 24-h average of 230 μg/m 3 and 70 μg/m 3 , respectively. Five frequency distribution functions with seven formula of plotting positions (empirical cumulative distribution functions) are compared to fit the average of daily TSP and PM 10 concentrations in year 2014 for Ain Sokhna city. The Quantile-Quantile plot (Q-Q plot) is used as a method for assessing how closely a data set fits a particular distribution. A proper probability distribution that represents the TSP and PM 10 has been chosen based on the statistical performance indicator values. The results show that Hosking and Wallis plotting position combined with Frechet distribution gave the highest fit for TSP and PM 10 concentrations. Burr distribution with the same plotting position follows Frechet distribution. The exceedance probability and days over the EAQLV are predicted using Frechet distribution. In 2014, the exceedance probability and days for TSP concentrations are 0.052 and 19 days, respectively. Furthermore, the PM 10 concentration is found to exceed the threshold limit by 174 days
Wang, Ziyin; Liu, Mandan; Cheng, Yicheng; Wang, Rubin
2017-06-01
In this paper, a dynamical recurrent artificial neural network (ANN) is proposed and studied. Inspired from a recent research in neuroscience, we introduced nonsynaptic coupling to form a dynamical component of the network. We mathematically proved that, with adequate neurons provided, this dynamical ANN model is capable of approximating any continuous dynamic system with an arbitrarily small error in a limited time interval. Its extreme concise Jacobian matrix makes the local stability easy to control. We designed this ANN for fitting and forecasting dynamic data and obtained satisfied results in simulation. The fitting performance is also compared with those of both the classic dynamic ANN and the state-of-the-art models. Sufficient trials and the statistical results indicated that our model is superior to those have been compared. Moreover, we proposed a robust approximation problem, which asking the ANN to approximate a cluster of input-output data pairs in large ranges and to forecast the output of the system under previously unseen input. Our model and learning scheme proposed in this paper have successfully solved this problem, and through this, the approximation becomes much more robust and adaptive to noise, perturbation, and low-order harmonic wave. This approach is actually an efficient method for compressing massive external data of a dynamic system into the weight of the ANN.
A simple non-parametric goodness-of-fit test for elliptical copulas
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Jaser Miriam
2017-12-01
Full Text Available In this paper, we propose a simple non-parametric goodness-of-fit test for elliptical copulas of any dimension. It is based on the equality of Kendall’s tau and Blomqvist’s beta for all bivariate margins. Nominal level and power of the proposed test are investigated in a Monte Carlo study. An empirical application illustrates our goodness-of-fit test at work.
vFitness: a web-based computing tool for improving estimation of in vitro HIV-1 fitness experiments
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Demeter Lisa
2010-05-01
Full Text Available Abstract Background The replication rate (or fitness between viral variants has been investigated in vivo and in vitro for human immunodeficiency virus (HIV. HIV fitness plays an important role in the development and persistence of drug resistance. The accurate estimation of viral fitness relies on complicated computations based on statistical methods. This calls for tools that are easy to access and intuitive to use for various experiments of viral fitness. Results Based on a mathematical model and several statistical methods (least-squares approach and measurement error models, a Web-based computing tool has been developed for improving estimation of virus fitness in growth competition assays of human immunodeficiency virus type 1 (HIV-1. Conclusions Unlike the two-point calculation used in previous studies, the estimation here uses linear regression methods with all observed data in the competition experiment to more accurately estimate relative viral fitness parameters. The dilution factor is introduced for making the computational tool more flexible to accommodate various experimental conditions. This Web-based tool is implemented in C# language with Microsoft ASP.NET, and is publicly available on the Web at http://bis.urmc.rochester.edu/vFitness/.
Fitted Hanbury-Brown Twiss radii versus space-time variances in flow-dominated models
Frodermann, Evan; Heinz, Ulrich; Lisa, Michael Annan
2006-04-01
The inability of otherwise successful dynamical models to reproduce the Hanbury-Brown Twiss (HBT) radii extracted from two-particle correlations measured at the Relativistic Heavy Ion Collider (RHIC) is known as the RHIC HBT Puzzle. Most comparisons between models and experiment exploit the fact that for Gaussian sources the HBT radii agree with certain combinations of the space-time widths of the source that can be directly computed from the emission function without having to evaluate, at significant expense, the two-particle correlation function. We here study the validity of this approach for realistic emission function models, some of which exhibit significant deviations from simple Gaussian behavior. By Fourier transforming the emission function, we compute the two-particle correlation function, and fit it with a Gaussian to partially mimic the procedure used for measured correlation functions. We describe a novel algorithm to perform this Gaussian fit analytically. We find that for realistic hydrodynamic models the HBT radii extracted from this procedure agree better with the data than the values previously extracted from the space-time widths of the emission function. Although serious discrepancies between the calculated and the measured HBT radii remain, we show that a more apples-to-apples comparison of models with data can play an important role in any eventually successful theoretical description of RHIC HBT data.
Fitted Hanbury-Brown-Twiss radii versus space-time variances in flow-dominated models
International Nuclear Information System (INIS)
Frodermann, Evan; Heinz, Ulrich; Lisa, Michael Annan
2006-01-01
The inability of otherwise successful dynamical models to reproduce the Hanbury-Brown-Twiss (HBT) radii extracted from two-particle correlations measured at the Relativistic Heavy Ion Collider (RHIC) is known as the RHIC HBT Puzzle. Most comparisons between models and experiment exploit the fact that for Gaussian sources the HBT radii agree with certain combinations of the space-time widths of the source that can be directly computed from the emission function without having to evaluate, at significant expense, the two-particle correlation function. We here study the validity of this approach for realistic emission function models, some of which exhibit significant deviations from simple Gaussian behavior. By Fourier transforming the emission function, we compute the two-particle correlation function, and fit it with a Gaussian to partially mimic the procedure used for measured correlation functions. We describe a novel algorithm to perform this Gaussian fit analytically. We find that for realistic hydrodynamic models the HBT radii extracted from this procedure agree better with the data than the values previously extracted from the space-time widths of the emission function. Although serious discrepancies between the calculated and the measured HBT radii remain, we show that a more apples-to-apples comparison of models with data can play an important role in any eventually successful theoretical description of RHIC HBT data
Worm plot to diagnose fit in quantile regression
Buuren, S. van
2007-01-01
The worm plot is a series of detrended Q-Q plots, split by covariate levels. The worm plot is a diagnostic tool for visualizing how well a statistical model fits the data, for finding locations at which the fit can be improved, and for comparing the fit of different models. This paper shows how the
Worm plot to diagnose fit in quantile regression
Buuren, S. van
2007-01-01
The worm plot is a series of detrended Q-Q plots, split by covariate levels. The worm plot is a diagnostic tool for visualizing how well a statistical model fits the data, for finding locations at which the fit can be improved, and for comparing the fit of different models. This paper shows how
Kinetic Behaviors of a Competitive Population and Fitness System in Exchange-Driven Growth
International Nuclear Information System (INIS)
Lu Ke; Lin Zhenquan; Sun Yunfei
2008-01-01
We proposed an aggregation model of two species aggregates of fitness and population to study the interaction between the two species in their exchange-driven processes of the same species by introducing the monomer birth of fitness catalyzed by the population, where the fitness aggregates perform self-death process and the population aggregates perform self-birth process. The kinetic behaviors of the aggregate size distributions of the fitness and population were analyzed by the rate equation approach with their exchange rate kernel K 1 (k,j) = K 1 kj and K 2 (k,j) = K 2 kj, the fitness aggregate's self-death rate kernel J 1 (k) = J 1 k, population aggregate's self-birth rate kernel J 2 (k) = J 2 k and population-catalyzed fitness birth rate kernel I(k,j) = Ikj v . The kinetic behavior of the fitness was found depending crucially on the parameter v, which reflects the dependence of the population-catalyzed fitness birth rate on the size of the catalyst (population) aggregate. (i) In the v ≤ 0 case, the effect of catalyzed-birth of fitness is rather weak and the exchange-driven aggregation and self-death of the fitness dominate the process, and the fitness aggregate size distribution a k (t) does not have scale form. (ii) When v > 0, the effect of the population-catalyzed birth of fitness gets strong enough, and the catalyzed-birth and self-death of the fitness aggregates, together with the self-birth of the population aggregates dominate the evolution process of the fitness aggregates. The aggregate size distribution a k (t) approaches a generalized scaling form
Directory of Open Access Journals (Sweden)
Bo You
2015-01-01
Full Text Available In order to predict pressing quality of precision press-fit assembly, press-fit curves and maximum press-mounting force of press-fit assemblies were investigated by finite element analysis (FEA. The analysis was based on a 3D Solidworks model using the real dimensions of the microparts and the subsequent FEA model that was built using ANSYS Workbench. The press-fit process could thus be simulated on the basis of static structure analysis. To verify the FEA results, experiments were carried out using a press-mounting apparatus. The results show that the press-fit curves obtained by FEA agree closely with the curves obtained using the experimental method. In addition, the maximum press-mounting force calculated by FEA agrees with that obtained by the experimental method, with the maximum deviation being 4.6%, a value that can be tolerated. The comparison shows that the press-fit curve and max press-mounting force calculated by FEA can be used for predicting the pressing quality during precision press-fit assembly.
Direct fit of a theoretical model of phase transition in oscillatory finger motions.
Newell, K.M.; Molenaar, P.C.M.
2003-01-01
This paper presents a general method to fit the Schoner-Haken-Kelso (SHK) model of human movement phase transitions directly to time series data. A robust variant of the extended Kalman filter technique is applied to the data of a single subject. The options of covariance resetting and iteration
Complex growing networks with intrinsic vertex fitness
International Nuclear Information System (INIS)
Bedogne, C.; Rodgers, G. J.
2006-01-01
One of the major questions in complex network research is to identify the range of mechanisms by which a complex network can self organize into a scale-free state. In this paper we investigate the interplay between a fitness linking mechanism and both random and preferential attachment. In our models, each vertex is assigned a fitness x, drawn from a probability distribution ρ(x). In Model A, at each time step a vertex is added and joined to an existing vertex, selected at random, with probability p and an edge is introduced between vertices with fitnesses x and y, with a rate f(x,y), with probability 1-p. Model B differs from Model A in that, with probability p, edges are added with preferential attachment rather than randomly. The analysis of Model A shows that, for every fixed fitness x, the network's degree distribution decays exponentially. In Model B we recover instead a power-law degree distribution whose exponent depends only on p, and we show how this result can be generalized. The properties of a number of particular networks are examined
Proposed reliability cost model
Delionback, L. M.
1973-01-01
The research investigations which were involved in the study include: cost analysis/allocation, reliability and product assurance, forecasting methodology, systems analysis, and model-building. This is a classic example of an interdisciplinary problem, since the model-building requirements include the need for understanding and communication between technical disciplines on one hand, and the financial/accounting skill categories on the other. The systems approach is utilized within this context to establish a clearer and more objective relationship between reliability assurance and the subcategories (or subelements) that provide, or reenforce, the reliability assurance for a system. Subcategories are further subdivided as illustrated by a tree diagram. The reliability assurance elements can be seen to be potential alternative strategies, or approaches, depending on the specific goals/objectives of the trade studies. The scope was limited to the establishment of a proposed reliability cost-model format. The model format/approach is dependent upon the use of a series of subsystem-oriented CER's and sometimes possible CTR's, in devising a suitable cost-effective policy.
Large-region acoustic source mapping using a movable array and sparse covariance fitting.
Zhao, Shengkui; Tuna, Cagdas; Nguyen, Thi Ngoc Tho; Jones, Douglas L
2017-01-01
Large-region acoustic source mapping is important for city-scale noise monitoring. Approaches using a single-position measurement scheme to scan large regions using small arrays cannot provide clean acoustic source maps, while deploying large arrays spanning the entire region of interest is prohibitively expensive. A multiple-position measurement scheme is applied to scan large regions at multiple spatial positions using a movable array of small size. Based on the multiple-position measurement scheme, a sparse-constrained multiple-position vectorized covariance matrix fitting approach is presented. In the proposed approach, the overall sample covariance matrix of the incoherent virtual array is first estimated using the multiple-position array data and then vectorized using the Khatri-Rao (KR) product. A linear model is then constructed for fitting the vectorized covariance matrix and a sparse-constrained reconstruction algorithm is proposed for recovering source powers from the model. The user parameter settings are discussed. The proposed approach is tested on a 30 m × 40 m region and a 60 m × 40 m region using simulated and measured data. Much cleaner acoustic source maps and lower sound pressure level errors are obtained compared to the beamforming approaches and the previous sparse approach [Zhao, Tuna, Nguyen, and Jones, Proc. IEEE Intl. Conf. on Acoustics, Speech and Signal Processing (ICASSP) (2016)].
Information Theoretic Tools for Parameter Fitting in Coarse Grained Models
Kalligiannaki, Evangelia; Harmandaris, Vagelis; Katsoulakis, Markos A.; Plechac, Petr
2015-01-01
We study the application of information theoretic tools for model reduction in the case of systems driven by stochastic dynamics out of equilibrium. The model/dimension reduction is considered by proposing parametrized coarse grained dynamics
Mapping the perceptual structure of rectangles through goodness-of-fit ratings.
Palmer, Stephen E; Guidi, Stefano
2011-01-01
Three experiments were carried out to investigate the internal structure of a rectangular frame to test Arnheim's (1974 Art and Visual Perception, 1988 The Power of the Center) proposals about its 'structural skeleton'. Observers made subjective ratings of how well a small probe circle fit within a rectangle at different interior positions. In experiment 1, ratings of 77 locations were highest in the center, decreased with distance from the center, greatly elevated along vertical and horizontal symmetry axes, and somewhat elevated along the local symmetry axes. A linear regression model with six symmetry-related factors accounted for 95% of the variance. In experiment 2 we measured perceived fit along local symmetry axes versus global diagonals near the corners to determine which factor was relevant. 2AFC probabilities were elevated only along the local symmetry axes and were higher when the probe was closer to the vertex. In experiment 3 we examined the effect of dividing a rectangular frame into two rectangular 'subframes' using an additional line. The results show that the primary determinant of good fit is the position of the target circle within the local subframes. In general, the results are consistent with Arnheim's proposals about the internal structure of a rectangular frame, but an alternative interpretation is offered in terms of the Gestalt concept of figural goodness.
A Spline-Based Lack-Of-Fit Test for Independent Variable Effect in Poisson Regression.
Li, Chin-Shang; Tu, Wanzhu
2007-05-01
In regression analysis of count data, independent variables are often modeled by their linear effects under the assumption of log-linearity. In reality, the validity of such an assumption is rarely tested, and its use is at times unjustifiable. A lack-of-fit test is proposed for the adequacy of a postulated functional form of an independent variable within the framework of semiparametric Poisson regression models based on penalized splines. It offers added flexibility in accommodating the potentially non-loglinear effect of the independent variable. A likelihood ratio test is constructed for the adequacy of the postulated parametric form, for example log-linearity, of the independent variable effect. Simulations indicate that the proposed model performs well, and misspecified parametric model has much reduced power. An example is given.
Identifying person-fit latent classes, and explanation of categorical and continuous person misfit
Conijn, J.M.; Sijtsma, K.; Emons, W.H.M.
2016-01-01
Latent class (LC) cluster analysis of a set of subscale lz person-fit statistics was proposed to explain person misfit on multiscale measures. The proposed explanatory LC person-fit analysis was used to analyze data of students (N = 91,648) on the nine-subscale School Attitude Questionnaire Internet
Canary, Jana D; Blizzard, Leigh; Barry, Ronald P; Hosmer, David W; Quinn, Stephen J
2016-05-01
Generalized linear models (GLM) with a canonical logit link function are the primary modeling technique used to relate a binary outcome to predictor variables. However, noncanonical links can offer more flexibility, producing convenient analytical quantities (e.g., probit GLMs in toxicology) and desired measures of effect (e.g., relative risk from log GLMs). Many summary goodness-of-fit (GOF) statistics exist for logistic GLM. Their properties make the development of GOF statistics relatively straightforward, but it can be more difficult under noncanonical links. Although GOF tests for logistic GLM with continuous covariates (GLMCC) have been applied to GLMCCs with log links, we know of no GOF tests in the literature specifically developed for GLMCCs that can be applied regardless of link function chosen. We generalize the Tsiatis GOF statistic originally developed for logistic GLMCCs, (TG), so that it can be applied under any link function. Further, we show that the algebraically related Hosmer-Lemeshow (HL) and Pigeon-Heyse (J(2) ) statistics can be applied directly. In a simulation study, TG, HL, and J(2) were used to evaluate the fit of probit, log-log, complementary log-log, and log models, all calculated with a common grouping method. The TG statistic consistently maintained Type I error rates, while those of HL and J(2) were often lower than expected if terms with little influence were included. Generally, the statistics had similar power to detect an incorrect model. An exception occurred when a log GLMCC was incorrectly fit to data generated from a logistic GLMCC. In this case, TG had more power than HL or J(2) . © 2015 John Wiley & Sons Ltd/London School of Economics.
Measuring Quasar Spin via X-ray Continuum Fitting
Jenkins, Matthew; Pooley, David; Rappaport, Saul; Steiner, Jack
2018-01-01
We have identified several quasars whose X-ray spectra appear very soft. When fit with power-law models, the best-fit indices are greater than 3. This is very suggestive of thermal disk emission, indicating that the X-ray spectrum is dominated by the disk component. Galactic black hole binaries in such states have been successfully fit with disk-blackbody models to constrain the inner radius, which also constrains the spin of the black hole. We have fit those models to XMM-Newton spectra of several of our identified soft X-ray quasars to place constraints on the spins of the supermassive black holes.
RATES OF FITNESS DECLINE AND REBOUND SUGGEST PERVASIVE EPISTASIS
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
Analytical fits to the synchrotron functions
Fouka, Mourad; Ouichaoui, Saad
2013-06-01
Accurate fitting formulae to the synchrotron function, F(x), and its complementary function, G(x), are performed and presented. The corresponding relative errors are less than 0.26% and 0.035% for F(x) and G(x), respectively. To this end we have, first, fitted the modified Bessel functions, K5/3(x) and K2/3(x). For all the fitted functions, the general fit expression is the same, and is based on the well known asymptotic forms for low and large values of x for each function. It consists of multiplying each asymptotic form by a function that tends to unity or zero for low and large values of x. Simple formulae are suggested in this paper, depending on adjustable parameters. The latter have been determined by adopting the Levenberg-Marquardt algorithm. The proposed formulae should be of great utility and simplicity for computing spectral powers and the degree of polarization for synchrotron radiation, both for laboratory and astrophysical applications.
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.
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Electrostatic point charge fitting as an inverse problem: Revealing the underlying ill-conditioning
International Nuclear Information System (INIS)
Ivanov, Maxim V.; Talipov, Marat R.; Timerghazin, Qadir K.
2015-01-01
Atom-centered point charge (PC) model of the molecular electrostatics—a major workhorse of the atomistic biomolecular simulations—is usually parameterized by least-squares (LS) fitting of the point charge values to a reference electrostatic potential, a procedure that suffers from numerical instabilities due to the ill-conditioned nature of the LS problem. To reveal the origins of this ill-conditioning, we start with a general treatment of the point charge fitting problem as an inverse problem and construct an analytical model with the point charges spherically arranged according to Lebedev quadrature which is naturally suited for the inverse electrostatic problem. This analytical model is contrasted to the atom-centered point-charge model that can be viewed as an irregular quadrature poorly suited for the problem. This analysis shows that the numerical problems of the point charge fitting are due to the decay of the curvatures corresponding to the eigenvectors of LS sum Hessian matrix. In part, this ill-conditioning is intrinsic to the problem and is related to decreasing electrostatic contribution of the higher multipole moments, that are, in the case of Lebedev grid model, directly associated with the Hessian eigenvectors. For the atom-centered model, this association breaks down beyond the first few eigenvectors related to the high-curvature monopole and dipole terms; this leads to even wider spread-out of the Hessian curvature values. Using these insights, it is possible to alleviate the ill-conditioning of the LS point-charge fitting without introducing external restraints and/or constraints. Also, as the analytical Lebedev grid PC model proposed here can reproduce multipole moments up to a given rank, it may provide a promising alternative to including explicit multipole terms in a force field
Hamiltonian inclusive fitness: a fitter fitness concept.
Costa, James T
2013-01-01
In 1963-1964 W. D. Hamilton introduced the concept of inclusive fitness, the only significant elaboration of Darwinian fitness since the nineteenth century. I discuss the origin of the modern fitness concept, providing context for Hamilton's discovery of inclusive fitness in relation to the puzzle of altruism. While fitness conceptually originates with Darwin, the term itself stems from Spencer and crystallized quantitatively in the early twentieth century. Hamiltonian inclusive fitness, with Price's reformulation, provided the solution to Darwin's 'special difficulty'-the evolution of caste polymorphism and sterility in social insects. Hamilton further explored the roles of inclusive fitness and reciprocation to tackle Darwin's other difficulty, the evolution of human altruism. The heuristically powerful inclusive fitness concept ramified over the past 50 years: the number and diversity of 'offspring ideas' that it has engendered render it a fitter fitness concept, one that Darwin would have appreciated.
Fitting the Phenomenological MSSM
AbdusSalam, S S; Quevedo, F; Feroz, F; Hobson, M
2010-01-01
We perform a global Bayesian fit of the phenomenological minimal supersymmetric standard model (pMSSM) to current indirect collider and dark matter data. The pMSSM contains the most relevant 25 weak-scale MSSM parameters, which are simultaneously fit using `nested sampling' Monte Carlo techniques in more than 15 years of CPU time. We calculate the Bayesian evidence for the pMSSM and constrain its parameters and observables in the context of two widely different, but reasonable, priors to determine which inferences are robust. We make inferences about sparticle masses, the sign of the $\\mu$ parameter, the amount of fine tuning, dark matter properties and the prospects for direct dark matter detection without assuming a restrictive high-scale supersymmetry breaking model. We find the inferred lightest CP-even Higgs boson mass as an example of an approximately prior independent observable. This analysis constitutes the first statistically convergent pMSSM global fit to all current data.
CRAPONE, Optical Model Potential Fit of Neutron Scattering Data
International Nuclear Information System (INIS)
Fabbri, F.; Fratamico, G.; Reffo, G.
2004-01-01
1 - Description of problem or function: Automatic search for local and non-local optical potential parameters for neutrons. Total, elastic, differential elastic cross sections, l=0 and l=1 strength functions and scattering length can be considered. 2 - Method of solution: A fitting procedure is applied to different sets of experimental data depending on the local or non-local approximation chosen. In the non-local approximation the fitting procedure can be simultaneously performed over the whole energy range. The best fit is obtained when a set of parameters is found where CHI 2 is at its minimum. The solution of the system equations is obtained by diagonalization of the matrix according to the Jacobi method
Lee, Yu; Yu, Chanki; Lee, Sang Wook
2018-01-10
We present a sequential fitting-and-separating algorithm for surface reflectance components that separates individual dominant reflectance components and simultaneously estimates the corresponding bidirectional reflectance distribution function (BRDF) parameters from the separated reflectance values. We tackle the estimation of a Lafortune BRDF model, which combines a nonLambertian diffuse reflection and multiple specular reflectance components with a different specular lobe. Our proposed method infers the appropriate number of BRDF lobes and their parameters by separating and estimating each of the reflectance components using an interval analysis-based branch-and-bound method in conjunction with iterative K-ordered scale estimation. The focus of this paper is the estimation of the Lafortune BRDF model. Nevertheless, our proposed method can be applied to other analytical BRDF models such as the Cook-Torrance and Ward models. Experiments were carried out to validate the proposed method using isotropic materials from the Mitsubishi Electric Research Laboratories-Massachusetts Institute of Technology (MERL-MIT) BRDF database, and the results show that our method is superior to a conventional minimization algorithm.
Schmader, Toni; Sedikides, Constantine
2017-10-01
People seek out situations that "fit," but the concept of fit is not well understood. We introduce State Authenticity as Fit to the Environment (SAFE), a conceptual framework for understanding how social identities motivate the situations that people approach or avoid. Drawing from but expanding the authenticity literature, we first outline three types of person-environment fit: self-concept fit, goal fit, and social fit. Each type of fit, we argue, facilitates cognitive fluency, motivational fluency, and social fluency that promote state authenticity and drive approach or avoidance behaviors. Using this model, we assert that contexts subtly signal social identities in ways that implicate each type of fit, eliciting state authenticity for advantaged groups but state inauthenticity for disadvantaged groups. Given that people strive to be authentic, these processes cascade down to self-segregation among social groups, reinforcing social inequalities. We conclude by mapping out directions for research on relevant mechanisms and boundary conditions.
Aguirre-Gutiérrez, Jesús; Carvalheiro, Luísa G; Polce, Chiara; van Loon, E Emiel; Raes, Niels; Reemer, Menno; Biesmeijer, Jacobus C
2013-01-01
Understanding species distributions and the factors limiting them is an important topic in ecology and conservation, including in nature reserve selection and predicting climate change impacts. While Species Distribution Models (SDM) are the main tool used for these purposes, choosing the best SDM algorithm is not straightforward as these are plentiful and can be applied in many different ways. SDM are used mainly to gain insight in 1) overall species distributions, 2) their past-present-future probability of occurrence and/or 3) to understand their ecological niche limits (also referred to as ecological niche modelling). The fact that these three aims may require different models and outputs is, however, rarely considered and has not been evaluated consistently. Here we use data from a systematically sampled set of species occurrences to specifically test the performance of Species Distribution Models across several commonly used algorithms. Species range in distribution patterns from rare to common and from local to widespread. We compare overall model fit (representing species distribution), the accuracy of the predictions at multiple spatial scales, and the consistency in selection of environmental correlations all across multiple modelling runs. As expected, the choice of modelling algorithm determines model outcome. However, model quality depends not only on the algorithm, but also on the measure of model fit used and the scale at which it is used. Although model fit was higher for the consensus approach and Maxent, Maxent and GAM models were more consistent in estimating local occurrence, while RF and GBM showed higher consistency in environmental variables selection. Model outcomes diverged more for narrowly distributed species than for widespread species. We suggest that matching study aims with modelling approach is essential in Species Distribution Models, and provide suggestions how to do this for different modelling aims and species' data
Mandal, S.; Choudhury, B. U.
2015-07-01
Sagar Island, setting on the continental shelf of Bay of Bengal, is one of the most vulnerable deltas to the occurrence of extreme rainfall-driven climatic hazards. Information on probability of occurrence of maximum daily rainfall will be useful in devising risk management for sustaining rainfed agrarian economy vis-a-vis food and livelihood security. Using six probability distribution models and long-term (1982-2010) daily rainfall data, we studied the probability of occurrence of annual, seasonal and monthly maximum daily rainfall (MDR) in the island. To select the best fit distribution models for annual, seasonal and monthly time series based on maximum rank with minimum value of test statistics, three statistical goodness of fit tests, viz. Kolmogorove-Smirnov test (K-S), Anderson Darling test ( A 2 ) and Chi-Square test ( X 2) were employed. The fourth probability distribution was identified from the highest overall score obtained from the three goodness of fit tests. Results revealed that normal probability distribution was best fitted for annual, post-monsoon and summer seasons MDR, while Lognormal, Weibull and Pearson 5 were best fitted for pre-monsoon, monsoon and winter seasons, respectively. The estimated annual MDR were 50, 69, 86, 106 and 114 mm for return periods of 2, 5, 10, 20 and 25 years, respectively. The probability of getting an annual MDR of >50, >100, >150, >200 and >250 mm were estimated as 99, 85, 40, 12 and 03 % level of exceedance, respectively. The monsoon, summer and winter seasons exhibited comparatively higher probabilities (78 to 85 %) for MDR of >100 mm and moderate probabilities (37 to 46 %) for >150 mm. For different recurrence intervals, the percent probability of MDR varied widely across intra- and inter-annual periods. In the island, rainfall anomaly can pose a climatic threat to the sustainability of agricultural production and thus needs adequate adaptation and mitigation measures.
Zhai, Xuetong; Chakraborty, Dev P
2017-06-01
The objective was to design and implement a bivariate extension to the contaminated binormal model (CBM) to fit paired receiver operating characteristic (ROC) datasets-possibly degenerate-with proper ROC curves. Paired datasets yield two correlated ratings per case. Degenerate datasets have no interior operating points and proper ROC curves do not inappropriately cross the chance diagonal. The existing method, developed more than three decades ago utilizes a bivariate extension to the binormal model, implemented in CORROC2 software, which yields improper ROC curves and cannot fit degenerate datasets. CBM can fit proper ROC curves to unpaired (i.e., yielding one rating per case) and degenerate datasets, and there is a clear scientific need to extend it to handle paired datasets. In CBM, nondiseased cases are modeled by a probability density function (pdf) consisting of a unit variance peak centered at zero. Diseased cases are modeled with a mixture distribution whose pdf consists of two unit variance peaks, one centered at positive μ with integrated probability α, the mixing fraction parameter, corresponding to the fraction of diseased cases where the disease was visible to the radiologist, and one centered at zero, with integrated probability (1-α), corresponding to disease that was not visible. It is shown that: (a) for nondiseased cases the bivariate extension is a unit variances bivariate normal distribution centered at (0,0) with a specified correlation ρ 1 ; (b) for diseased cases the bivariate extension is a mixture distribution with four peaks, corresponding to disease not visible in either condition, disease visible in only one condition, contributing two peaks, and disease visible in both conditions. An expression for the likelihood function is derived. A maximum likelihood estimation (MLE) algorithm, CORCBM, was implemented in the R programming language that yields parameter estimates and the covariance matrix of the parameters, and other statistics
Building Customer Churn Prediction Models in Fitness Industry with Machine Learning Methods
Shan, Min
2017-01-01
With the rapid growth of digital systems, churn management has become a major focus within customer relationship management in many industries. Ample research has been conducted for churn prediction in different industries with various machine learning methods. This thesis aims to combine feature selection and supervised machine learning methods for defining models of churn prediction and apply them on fitness industry. Forward selection is chosen as feature selection methods. Support Vector ...
Greguras, Gary J; Diefendorff, James M
2009-03-01
Integrating and expanding upon the person-environment fit (PE fit) and the self-determination theory literatures, the authors hypothesized and tested a model in which the satisfaction of the psychological needs for autonomy, relatedness, and competence partially mediated the relations between different types of perceived PE fit (i.e., person-organization fit, person-group fit, and job demands-abilities fit) with employee affective organizational commitment and overall job performance. Data from 163 full-time working employees and their supervisors were collected across 3 time periods. Results indicate that different types of PE fit predicted different types of psychological need satisfaction and that psychological need satisfaction predicted affective commitment and performance. Further, person-organization fit and demands-abilities fit also evidenced direct effects on employee affective commitment. These results begin to explicate the processes through which different types of PE fit relate to employee attitudes and behaviors. (c) 2009 APA, all rights reserved.
A Proposal for a Flexible Trend Specification in DSGE Models
Directory of Open Access Journals (Sweden)
Slanicay Martin
2016-06-01
Full Text Available In this paper I propose a flexible trend specification for estimating DSGE models on log differences. I demonstrate this flexible trend specification on a New Keynesian DSGE model of two economies, which I consequently estimate on data from the Czech economy and the euro area, using Bayesian techniques. The advantage of the trend specification proposed is that the trend component and the cyclical component are modelled jointly in a single model. The proposed trend specification is flexible in the sense that smoothness of the trend can be easily modified by different calibration of some of the trend parameters. The results suggest that this method is capable of finding a very reasonable trend in the data. Moreover, comparison of forecast performance reveals that the proposed specification offers more reliable forecasts than the original variant of the model.
Fit reduced GUTS models online: From theory to practice.
Baudrot, Virgile; Veber, Philippe; Gence, Guillaume; Charles, Sandrine
2018-05-20
Mechanistic modeling approaches, such as the toxicokinetic-toxicodynamic (TKTD) framework, are promoted by international institutions such as the European Food Safety Authority and the Organization for Economic Cooperation and Development to assess the environmental risk of chemical products generated by human activities. TKTD models can encompass a large set of mechanisms describing the kinetics of compounds inside organisms (e.g., uptake and elimination) and their effect at the level of individuals (e.g., damage accrual, recovery, and death mechanism). Compared to classical dose-response models, TKTD approaches have many advantages, including accounting for temporal aspects of exposure and toxicity, considering data points all along the experiment and not only at the end, and making predictions for untested situations as realistic exposure scenarios. Among TKTD models, the general unified threshold model of survival (GUTS) is within the most recent and innovative framework but is still underused in practice, especially by risk assessors, because specialist programming and statistical skills are necessary to run it. Making GUTS models easier to use through a new module freely available from the web platform MOSAIC (standing for MOdeling and StAtistical tools for ecotoxIClogy) should promote GUTS operability in support of the daily work of environmental risk assessors. This paper presents the main features of MOSAIC_GUTS: uploading of the experimental data, GUTS fitting analysis, and LCx estimates with their uncertainty. These features will be exemplified from literature data. Integr Environ Assess Manag 2018;00:000-000. © 2018 SETAC. © 2018 SETAC.
Proposal for the award of an industrial support contract for indoor fitting work at CERN
2006-01-01
This document concerns the award of a contract for indoor fitting work (carpentry, glazing, venetian and roller blinds) at CERN. The Finance Committee is invited to agree to the negotiation of a contract with the firm ROLAND COSTA MENUISERIE (CH), the lowest bidder, for the provision of indoor fitting work at CERN, for three years for a total amount not exceeding 1 213 403 Swiss francs, not subject to revision for two years. The contract will include options for two one-year extensions beyond the initial three-year period.
14 CFR 302.211 - Procedures in certificate cases involving initial or continuing fitness.
2010-01-01
... initial or continuing fitness. 302.211 Section 302.211 Aeronautics and Space OFFICE OF THE SECRETARY... Disposition of Applications § 302.211 Procedures in certificate cases involving initial or continuing fitness... applicant's fitness to operate. Where such applications propose the operation of scheduled service in...
International Nuclear Information System (INIS)
Li, Ruiqin; Zhang, Chi; Zhang, Liwen; Cui, Yan; Shen, Wenfei
2017-01-01
Highlights: •A thermal-mechanical coupled finite element model was developed to simulate the whole process. •Heat capacity added layer was used to extend the limited time for the process. •Shrink-fitted experiments were performed to verify the simulation results. -- Abstract: The rotor-can of reactor coolant pump (RCP) is generally assembled on the rotor using shrink fitting technique. The rotor-can is characterized by large height and ultrathin-walled cylinder, thus, its rigidity is weak and heat capacity is quite limited. The shrink fitting process has to be completed within a short limited-time, which makes it difficult for rotor to insert in the rotor-can completely. In order to solve this problem, a new method was proposed to extend the limited time by using a heat capacity added layer (HCAL) during the shrink fitting process. A thermal-mechanical coupled finite element (FE) model was developed to simulate the whole process. The transient heat exchange with a narrow gap between rotor and rotor-can during the shrink fitting process was taken into consideration. The limited time was predicted by calculating and analyzing the evolutions of temperature field and radial displacement field of the rotor-can. The simulation results indicate that the limited time of the shrink fitting process can be significantly extended with the increase of HCAL in thickness. Then, shrink fitting experiments were performed to confirm the extending effect of the HCAL. The experimental results of limited time show good agreement with the predicted values. The current results will certainly help the designer to improve the shrink fitting technique.
Fitness for duty in the nuclear power industry: Responses to public comments
International Nuclear Information System (INIS)
Bush, L.L.; Grimes, B.K.
1989-05-01
The Nuclear Regulatory Commission published for public comment a proposed rule concerning the fitness for duty of commercial nuclear power plant workers (53 FR 36795). The proposed rule focused on methods for controlling the use of substances that may affect the trustworthiness and performance of workers. It provides for chemical testing, behavioral observation, employee awareness and education, and employee assistance programs as means for assuring fitness for duty. This report summarizes the comments received on the proposed rule and provides the staff resolutions of the issues raised by the comments. 3 refs
Balázs, Csaba; Li, Tong
2016-05-01
In this work we perform a comprehensive statistical analysis of the AMS-02 electron, positron fluxes and the antiproton-to-proton ratio in the context of a simplified dark matter model. We include known, standard astrophysical sources and a dark matter component in the cosmic ray injection spectra. To predict the AMS-02 observables we use propagation parameters extracted from observed fluxes of heavier nuclei and the low energy part of the AMS-02 data. We assume that the dark matter particle is a Majorana fermion coupling to third generation fermions via a spin-0 mediator, and annihilating to multiple channels at once. The simultaneous presence of various annihilation channels provides the dark matter model with additional flexibility, and this enables us to simultaneously fit all cosmic ray spectra using a simple particle physics model and coherent astrophysical assumptions. Our results indicate that AMS-02 observations are not only consistent with the dark matter hypothesis within the uncertainties, but adding a dark matter contribution improves the fit to the data. Assuming, however, that dark matter is solely responsible for this improvement of the fit, it is difficult to evade the latest CMB limits in this model.
Energy Technology Data Exchange (ETDEWEB)
Balázs, Csaba; Li, Tong [ARC Centre of Excellence for Particle Physics at the Tera-scale,School of Physics and Astronomy, Monash University, Melbourne, Victoria 3800 (Australia)
2016-05-05
In this work we perform a comprehensive statistical analysis of the AMS-02 electron, positron fluxes and the antiproton-to-proton ratio in the context of a simplified dark matter model. We include known, standard astrophysical sources and a dark matter component in the cosmic ray injection spectra. To predict the AMS-02 observables we use propagation parameters extracted from observed fluxes of heavier nuclei and the low energy part of the AMS-02 data. We assume that the dark matter particle is a Majorana fermion coupling to third generation fermions via a spin-0 mediator, and annihilating to multiple channels at once. The simultaneous presence of various annihilation channels provides the dark matter model with additional flexibility, and this enables us to simultaneously fit all cosmic ray spectra using a simple particle physics model and coherent astrophysical assumptions. Our results indicate that AMS-02 observations are not only consistent with the dark matter hypothesis within the uncertainties, but adding a dark matter contribution improves the fit to the data. Assuming, however, that dark matter is solely responsible for this improvement of the fit, it is difficult to evade the latest CMB limits in this model.
A History of Regression and Related Model-Fitting in the Earth Sciences (1636?-2000)
International Nuclear Information System (INIS)
Howarth, Richard J.
2001-01-01
The (statistical) modeling of the behavior of a dependent variate as a function of one or more predictors provides examples of model-fitting which span the development of the earth sciences from the 17th Century to the present. The historical development of these methods and their subsequent application is reviewed. Bond's predictions (c. 1636 and 1668) of change in the magnetic declination at London may be the earliest attempt to fit such models to geophysical data. Following publication of Newton's theory of gravitation in 1726, analysis of data on the length of a 1 o meridian arc, and the length of a pendulum beating seconds, as a function of sin 2 (latitude), was used to determine the ellipticity of the oblate spheroid defining the Figure of the Earth. The pioneering computational methods of Mayer in 1750, Boscovich in 1755, and Lambert in 1765, and the subsequent independent discoveries of the principle of least squares by Gauss in 1799, Legendre in 1805, and Adrain in 1808, and its later substantiation on the basis of probability theory by Gauss in 1809 were all applied to the analysis of such geodetic and geophysical data. Notable later applications include: the geomagnetic survey of Ireland by Lloyd, Sabine, and Ross in 1836, Gauss's model of the terrestrial magnetic field in 1838, and Airy's 1845 analysis of the residuals from a fit to pendulum lengths, from which he recognized the anomalous character of measurements of gravitational force which had been made on islands. In the early 20th Century applications to geological topics proliferated, but the computational burden effectively held back applications of multivariate analysis. Following World War II, the arrival of digital computers in universities in the 1950s facilitated computation, and fitting linear or polynomial models as a function of geographic coordinates, trend surface analysis, became popular during the 1950-60s. The inception of geostatistics in France at this time by Matheron had its
Ward, Jeffery Kurt; Hastie, Peter A; Wadsworth, Danielle D; Foote, Shelby; Brock, Sheri J; Hollett, Nikki
2017-09-01
The purpose of this study was to determine the extent to which a sport education season of fitness could provide students with recommended levels of in-class moderate-to-vigorous physical activity (MVPA) while also increasing students' fitness knowledge and fitness achievement. One hundred and sixty-six 5th-grade students (76 boys, 90 girls) participated in a 20-lesson season called "CrossFit Challenge" during a 4-week period. The Progressive Aerobic Cardiovascular Endurance Run, push-ups, and curl-ups tests of the FITNESSGRAM® were used to assess fitness at pretest and posttest, while fitness knowledge was assessed through a validated, grade-appropriate test of health-related fitness knowledge (HRF). Physical activity was measured with Actigraph GT3X triaxial accelerometers. Results indicated a significant time effect for all fitness tests and the knowledge test. Across the entire season, the students spent an average of 54.5% of lesson time engaged in MVPA, irrespective of the type of lesson (instruction, free practice, or competition). The results suggest that configuring the key principles of sport education within a unit of fitness is an efficient model for providing students with the opportunity to improve fitness skill and HRF knowledge while attaining recommended levels of MVPA.
Implementace marketingové komunikace firmy Adidas v partnerských fitness centrech
Bártová, Monika
2014-01-01
Title: The proposal of improving the adidas company promotion in partner fitness centers Objective: Aim of the diploma thesis is implementation in marketing communication for partner fitness centers of adidas company. The thema is based on situational analysis of the company and marketing custommer behavior research. These phases are the starting point for proposals of implementation of existing communicational channels which is focused on utilization of particular comumnicational channels an...
International Nuclear Information System (INIS)
Jaafar, Hazriq Izzuan; Ali, Nursabillilah Mohd; Selamat, Nur Asmiza; Kassim, Anuar Mohamed; Mohamed, Z; Abidin, Amar Faiz Zainal; Jamian, J J
2013-01-01
This paper presents development of an optimal PID and PD controllers for controlling the nonlinear gantry crane system. The proposed Binary Particle Swarm Optimization (BPSO) algorithm that uses Priority-based Fitness Scheme is adopted in obtaining five optimal controller gains. The optimal gains are tested on a control structure that combines PID and PD controllers to examine system responses including trolley displacement and payload oscillation. The dynamic model of gantry crane system is derived using Lagrange equation. Simulation is conducted within Matlab environment to verify the performance of system in terms of settling time (Ts), steady state error (SSE) and overshoot (OS). This proposed technique demonstrates that implementation of Priority-based Fitness Scheme in BPSO is effective and able to move the trolley as fast as possible to the various desired position
Directory of Open Access Journals (Sweden)
Zhe Wang
2018-06-01
Full Text Available This study investigates the influence of employee fitness programs on organizational sustainability performance from the perspective of organizational support as perceived by employees. Organizational sustainability performance was specified as a second-order factor, which was affected by three first-order factors: financial performance, social performance, and environmental performance. A snowball sampling method was employed to conduct an online survey of working adults in Shanghai to test the proposed hypotheses. Results show that perceived organizational support toward employee fitness has a positive and significant effect on organizational sustainability performance, and the positive effect is mediated by job satisfaction and organizational commitment. This study also provides theoretical and managerial implications.
Stiglbauer, Barbara; Kovacs, Carrie
2017-12-28
In organizational psychology research, autonomy is generally seen as a job resource with a monotone positive relationship with desired occupational outcomes such as well-being. However, both Warr's vitamin model and person-environment (PE) fit theory suggest that negative outcomes may result from excesses of some job resources, including autonomy. Thus, the current studies used survey methodology to explore cross-sectional relationships between environmental autonomy, person-environment autonomy (mis)fit, and well-being. We found that autonomy and autonomy (mis)fit explained between 6% and 22% of variance in well-being, depending on type of autonomy (scheduling, method, or decision-making) and type of (mis)fit operationalization (atomistic operationalization through the separate assessment of actual and ideal autonomy levels vs. molecular operationalization through the direct assessment of perceived autonomy (mis)fit). Autonomy (mis)fit (PE-fit perspective) explained more unique variance in well-being than environmental autonomy itself (vitamin model perspective). Detrimental effects of autonomy excess on well-being were most evident for method autonomy and least consistent for decision-making autonomy. We argue that too-much-of-a-good-thing effects of job autonomy on well-being exist, but suggest that these may be dependent upon sample characteristics (range of autonomy levels), type of operationalization (molecular vs. atomistic fit), autonomy facet (method, scheduling, or decision-making), as well as individual and organizational moderators. (PsycINFO Database Record (c) 2017 APA, all rights reserved).
Global fits of GUT-scale SUSY models with GAMBIT
Athron, Peter; Balázs, Csaba; Bringmann, Torsten; Buckley, Andy; Chrząszcz, Marcin; Conrad, Jan; Cornell, Jonathan M.; Dal, Lars A.; Edsjö, Joakim; Farmer, Ben; Jackson, Paul; Krislock, Abram; Kvellestad, Anders; Mahmoudi, Farvah; Martinez, Gregory D.; Putze, Antje; Raklev, Are; Rogan, Christopher; de Austri, Roberto Ruiz; Saavedra, Aldo; Savage, Christopher; Scott, Pat; Serra, Nicola; Weniger, Christoph; White, Martin
2017-12-01
We present the most comprehensive global fits to date of three supersymmetric models motivated by grand unification: the constrained minimal supersymmetric standard model (CMSSM), and its Non-Universal Higgs Mass generalisations NUHM1 and NUHM2. We include likelihoods from a number of direct and indirect dark matter searches, a large collection of electroweak precision and flavour observables, direct searches for supersymmetry at LEP and Runs I and II of the LHC, and constraints from Higgs observables. Our analysis improves on existing results not only in terms of the number of included observables, but also in the level of detail with which we treat them, our sampling techniques for scanning the parameter space, and our treatment of nuisance parameters. We show that stau co-annihilation is now ruled out in the CMSSM at more than 95% confidence. Stop co-annihilation turns out to be one of the most promising mechanisms for achieving an appropriate relic density of dark matter in all three models, whilst avoiding all other constraints. We find high-likelihood regions of parameter space featuring light stops and charginos, making them potentially detectable in the near future at the LHC. We also show that tonne-scale direct detection will play a largely complementary role, probing large parts of the remaining viable parameter space, including essentially all models with multi-TeV neutralinos.
Global fits of GUT-scale SUSY models with GAMBIT
Energy Technology Data Exchange (ETDEWEB)
Athron, Peter [Monash University, School of Physics and Astronomy, Melbourne, VIC (Australia); Australian Research Council Centre of Excellence for Particle Physics at the Tera-scale (Australia); Balazs, Csaba [Monash University, School of Physics and Astronomy, Melbourne, VIC (Australia); Australian Research Council Centre of Excellence for Particle Physics at the Tera-scale (Australia); Bringmann, Torsten; Dal, Lars A.; Krislock, Abram; Raklev, Are [University of Oslo, Department of Physics, Oslo (Norway); Buckley, Andy [University of Glasgow, SUPA, School of Physics and Astronomy, Glasgow (United Kingdom); Chrzaszcz, Marcin [Universitaet Zuerich, Physik-Institut, Zurich (Switzerland); H. Niewodniczanski Institute of Nuclear Physics, Polish Academy of Sciences, Krakow (Poland); Conrad, Jan; Edsjoe, Joakim; Farmer, Ben [AlbaNova University Centre, Oskar Klein Centre for Cosmoparticle Physics, Stockholm (Sweden); Stockholm University, Department of Physics, Stockholm (Sweden); Cornell, Jonathan M. [McGill University, Department of Physics, Montreal, QC (Canada); Jackson, Paul; White, Martin [Australian Research Council Centre of Excellence for Particle Physics at the Tera-scale (Australia); University of Adelaide, Department of Physics, Adelaide, SA (Australia); Kvellestad, Anders; Savage, Christopher [NORDITA, Stockholm (Sweden); Mahmoudi, Farvah [Univ Lyon, Univ Lyon 1, CNRS, ENS de Lyon, Centre de Recherche Astrophysique de Lyon UMR5574, Saint-Genis-Laval (France); Theoretical Physics Department, CERN, Geneva (Switzerland); Martinez, Gregory D. [University of California, Physics and Astronomy Department, Los Angeles, CA (United States); Putze, Antje [LAPTh, Universite de Savoie, CNRS, Annecy-le-Vieux (France); Rogan, Christopher [Harvard University, Department of Physics, Cambridge, MA (United States); Ruiz de Austri, Roberto [IFIC-UV/CSIC, Instituto de Fisica Corpuscular, Valencia (Spain); Saavedra, Aldo [Australian Research Council Centre of Excellence for Particle Physics at the Tera-scale (Australia); The University of Sydney, Faculty of Engineering and Information Technologies, Centre for Translational Data Science, School of Physics, Camperdown, NSW (Australia); Scott, Pat [Imperial College London, Department of Physics, Blackett Laboratory, London (United Kingdom); Serra, Nicola [Universitaet Zuerich, Physik-Institut, Zurich (Switzerland); Weniger, Christoph [University of Amsterdam, GRAPPA, Institute of Physics, Amsterdam (Netherlands); Collaboration: The GAMBIT Collaboration
2017-12-15
We present the most comprehensive global fits to date of three supersymmetric models motivated by grand unification: the constrained minimal supersymmetric standard model (CMSSM), and its Non-Universal Higgs Mass generalisations NUHM1 and NUHM2. We include likelihoods from a number of direct and indirect dark matter searches, a large collection of electroweak precision and flavour observables, direct searches for supersymmetry at LEP and Runs I and II of the LHC, and constraints from Higgs observables. Our analysis improves on existing results not only in terms of the number of included observables, but also in the level of detail with which we treat them, our sampling techniques for scanning the parameter space, and our treatment of nuisance parameters. We show that stau co-annihilation is now ruled out in the CMSSM at more than 95% confidence. Stop co-annihilation turns out to be one of the most promising mechanisms for achieving an appropriate relic density of dark matter in all three models, whilst avoiding all other constraints. We find high-likelihood regions of parameter space featuring light stops and charginos, making them potentially detectable in the near future at the LHC. We also show that tonne-scale direct detection will play a largely complementary role, probing large parts of the remaining viable parameter space, including essentially all models with multi-TeV neutralinos. (orig.)
Bereczkei, Tamas; Mesko, Norbert
2007-01-01
Multiple Fitness Model states that attractiveness varies across multiple dimensions, with each feature representing a different aspect of mate value. In the present study, male raters judged the attractiveness of young females with neotenous and mature facial features, with various hair lengths. Results revealed that the physical appearance of long-haired women was rated high, regardless of their facial attractiveness being valued high or low. Women rated as most attractive were those whose f...
Saunders, Christina T; Blume, Jeffrey D
2017-10-26
Mediation analysis explores the degree to which an exposure's effect on an outcome is diverted through a mediating variable. We describe a classical regression framework for conducting mediation analyses in which estimates of causal mediation effects and their variance are obtained from the fit of a single regression model. The vector of changes in exposure pathway coefficients, which we named the essential mediation components (EMCs), is used to estimate standard causal mediation effects. Because these effects are often simple functions of the EMCs, an analytical expression for their model-based variance follows directly. Given this formula, it is instructive to revisit the performance of routinely used variance approximations (e.g., delta method and resampling methods). Requiring the fit of only one model reduces the computation time required for complex mediation analyses and permits the use of a rich suite of regression tools that are not easily implemented on a system of three equations, as would be required in the Baron-Kenny framework. Using data from the BRAIN-ICU study, we provide examples to illustrate the advantages of this framework and compare it with the existing approaches. © The Author 2017. Published by Oxford University Press.
Wasylkiw, L; Emms, A A; Meuse, R; Poirier, K F
2009-03-01
The current study is a content analysis of women appearing in advertisements in two types of magazines: fitness/health versus fashion/beauty chosen because of their large and predominantly female readerships. Women appearing in advertisements of the June 2007 issue of five fitness/health magazines were compared to women appearing in advertisements of the June 2007 issue of five beauty/fashion magazines. Female models appearing in advertisements of both types of magazines were primarily young, thin Caucasians; however, images of models were more likely to emphasize appearance over performance when they appeared in fashion magazines. This difference in emphasis has implications for future research.
Fitness networks for real world systems via modified preferential attachment
Shang, Ke-ke; Small, Michael; Yan, Wei-sheng
2017-05-01
Complex networks are virtually ubiquitous, and the Barabási and Albert model (BA model) has became an acknowledged standard for the modelling of these systems. The so-called BA model is a kind of preferential attachment growth model based on the intuitive premise that popularity is attractive. However, preferential attachment alone is insufficient to describe the diversity of complex networks observed in the real world. In this paper we first use the accuracy of a link prediction method, as a metric for network fitness. The link prediction method predicts the occurrence of links consistent with preferential attachment, the performance of this link prediction scheme is then a natural measure of the ;preferential-attachment-likeness; of a given network. We then propose several modification methods and modified BA models to construct networks which more accurately describe the fitness properties of real networks. We find that all features assortativity, degree distribution and rich-club formation can play significant roles for the network construction and eventual structure. Moreover, link sparsity and the size of a network are key factors for network reconstruction. In addition, we find that the structure of the network which is limited by geographic location (nodes are embedded in a Euclidean space and connectivity is correlated with distances) differs from other typical networks. In social networks, we observe that the high school contact network has similar structure as the friends network and so we speculate that the contact behaviours can reflect real friendships.
A Monte Carlo-adjusted goodness-of-fit test for parametric models describing spatial point patterns
Dao, Ngocanh; Genton, Marc G.
2014-01-01
Assessing the goodness-of-fit (GOF) for intricate parametric spatial point process models is important for many application fields. When the probability density of the statistic of the GOF test is intractable, a commonly used procedure is the Monte
International Nuclear Information System (INIS)
Tiilikainen, J; Bosund, V; Mattila, M; Hakkarainen, T; Sormunen, J; Lipsanen, H
2007-01-01
Nonunique solutions of the x-ray reflectivity (XRR) curve fitting problem were studied by modelling layer structures with neural networks and designing a fitness function to handle the nonidealities of measurements. Modelled atomic-layer-deposited aluminium oxide film structures were used in the simulations to calculate XRR curves based on Parratt's formalism. This approach reduced the dimensionality of the parameter space and allowed the use of fitness landscapes in the study of nonunique solutions. Fitness landscapes, where the height in a map represents the fitness value as a function of the process parameters, revealed tracks where the local fitness optima lie. The tracks were projected on the physical parameter space thus allowing the construction of the crosserror equation between weakly determined parameters, i.e. between the mass density and the surface roughness of a layer. The equation gives the minimum error for the other parameters which is a consequence of the nonuniqueness of the solution if noise is present. Furthermore, the existence of a possible unique solution in a certain parameter range was found to be dependent on the layer thickness and the signal-to-noise ratio
Parametric resonance in the early Universe—a fitting analysis
Energy Technology Data Exchange (ETDEWEB)
Figueroa, Daniel G. [Theoretical Physics Department, CERN, Geneva (Switzerland); Torrentí, Francisco, E-mail: daniel.figueroa@cern.ch, E-mail: f.torrenti@csic.es [Instituto de Física Teórica IFT-UAM/CSIC, Universidad Autónoma de Madrid, Cantoblanco 28049, Madrid (Spain)
2017-02-01
Particle production via parametric resonance in the early Universe, is a non-perturbative, non-linear and out-of-equilibrium phenomenon. Although it is a well studied topic, whenever a new scenario exhibits parametric resonance, a full re-analysis is normally required. To avoid this tedious task, many works present often only a simplified linear treatment of the problem. In order to surpass this circumstance in the future, we provide a fitting analysis of parametric resonance through all its relevant stages: initial linear growth, non-linear evolution, and relaxation towards equilibrium. Using lattice simulations in an expanding grid in 3+1 dimensions, we parametrize the dynamics' outcome scanning over the relevant ingredients: role of the oscillatory field, particle coupling strength, initial conditions, and background expansion rate. We emphasize the inaccuracy of the linear calculation of the decay time of the oscillatory field, and propose a more appropriate definition of this scale based on the subsequent non-linear dynamics. We provide simple fits to the relevant time scales and particle energy fractions at each stage. Our fits can be applied to post-inflationary preheating scenarios, where the oscillatory field is the inflaton, or to spectator-field scenarios, where the oscillatory field can be e.g. a curvaton, or the Standard Model Higgs.
Parametric resonance in the early Universe—a fitting analysis
International Nuclear Information System (INIS)
Figueroa, Daniel G.; Torrentí, Francisco
2017-01-01
Particle production via parametric resonance in the early Universe, is a non-perturbative, non-linear and out-of-equilibrium phenomenon. Although it is a well studied topic, whenever a new scenario exhibits parametric resonance, a full re-analysis is normally required. To avoid this tedious task, many works present often only a simplified linear treatment of the problem. In order to surpass this circumstance in the future, we provide a fitting analysis of parametric resonance through all its relevant stages: initial linear growth, non-linear evolution, and relaxation towards equilibrium. Using lattice simulations in an expanding grid in 3+1 dimensions, we parametrize the dynamics' outcome scanning over the relevant ingredients: role of the oscillatory field, particle coupling strength, initial conditions, and background expansion rate. We emphasize the inaccuracy of the linear calculation of the decay time of the oscillatory field, and propose a more appropriate definition of this scale based on the subsequent non-linear dynamics. We provide simple fits to the relevant time scales and particle energy fractions at each stage. Our fits can be applied to post-inflationary preheating scenarios, where the oscillatory field is the inflaton, or to spectator-field scenarios, where the oscillatory field can be e.g. a curvaton, or the Standard Model Higgs.
Armour, Cherie; Carragher, Natacha; Elhai, Jon D
2013-01-01
Since the initial inclusion of PTSD in the DSM nomenclature, PTSD symptomatology has been distributed across three symptom clusters. However, a wealth of empirical research has concluded that PTSD's latent structure is best represented by one of two four-factor models: Numbing or Dysphoria. Recently, a newly proposed five-factor Dysphoric Arousal model, which separates the DSM-IV's Arousal cluster into two factors of Anxious Arousal and Dysphoric Arousal, has gathered support across a variety of trauma samples. To date, the Dysphoric Arousal model has not been assessed using nationally representative epidemiological data. We employed confirmatory factor analysis to examine PTSD's latent structure in two independent population based surveys from American (NESARC) and Australia (NSWHWB). We specified and estimated the Numbing model, the Dysphoria model, and the Dysphoric Arousal model in both samples. Results revealed that the Dysphoric Arousal model provided superior fit to the data compared to the alternative models. In conclusion, these findings suggest that items D1-D3 (sleeping difficulties; irritability; concentration difficulties) represent a separate, fifth factor within PTSD's latent structure using nationally representative epidemiological data in addition to single trauma specific samples. Copyright © 2012 Elsevier Ltd. All rights reserved.
A Data Forward Stepwise Fitting Algorithm Based on Orthogonal Function System
Directory of Open Access Journals (Sweden)
Li Han-Ju
2017-01-01
Full Text Available Data fitting is the main method of functional data analysis, and it is widely used in the fields of economy, social science, engineering technology and so on. Least square method is the main method of data fitting, but the least square method is not convergent, no memory property, big fitting error and it is easy to over fitting. Based on the orthogonal trigonometric function system, this paper presents a data forward stepwise fitting algorithm. This algorithm takes forward stepwise fitting strategy, each time using the nearest base function to fit the residual error generated by the previous base function fitting, which makes the residual mean square error minimum. In this paper, we theoretically prove the convergence, the memory property and the fitting error diminishing character for the algorithm. Experimental results show that the proposed algorithm is effective, and the fitting performance is better than that of the least square method and the forward stepwise fitting algorithm based on the non-orthogonal function system.
Self-Fitting Hearing Aids: Status Quo and Future Predictions.
Keidser, Gitte; Convery, Elizabeth
2016-04-12
A self-contained, self-fitting hearing aid (SFHA) is a device that enables the user to perform both threshold measurements leading to a prescribed hearing aid setting and fine-tuning, without the need for audiological support or access to other equipment. The SFHA has been proposed as a potential solution to address unmet hearing health care in developing countries and remote locations in the developed world and is considered a means to lower cost and increase uptake of hearing aids in developed countries. This article reviews the status of the SFHA and the evidence for its feasibility and challenges and predicts where it is heading. Devices that can be considered partly or fully self-fitting without audiological support were identified in the direct-to-consumer market. None of these devices are considered self-contained as they require access to other hardware such as a proprietary interface, computer, smartphone, or tablet for manipulation. While there is evidence that self-administered fitting processes can provide valid and reliable results, their success relies on user-friendly device designs and interfaces and easy-to-interpret instructions. Until these issues have been sufficiently addressed, optional assistance with the self-fitting process and on-going use of SFHAs is recommended. Affordability and a sustainable delivery system remain additional challenges for the SFHA in developing countries. Future predictions include a growth in self-fitting products, with most future SFHAs consisting of earpieces that connect wirelessly with a smartphone and providers offering assistance through a telehealth infrastructure, and the integration of SFHAs into the traditional hearing health-care model. © The Author(s) 2016.
A proposed best practice model validation framework for banks
Directory of Open Access Journals (Sweden)
Pieter J. (Riaan de Jongh
2017-06-01
Full Text Available Background: With the increasing use of complex quantitative models in applications throughout the financial world, model risk has become a major concern. The credit crisis of 2008–2009 provoked added concern about the use of models in finance. Measuring and managing model risk has subsequently come under scrutiny from regulators, supervisors, banks and other financial institutions. Regulatory guidance indicates that meticulous monitoring of all phases of model development and implementation is required to mitigate this risk. Considerable resources must be mobilised for this purpose. The exercise must embrace model development, assembly, implementation, validation and effective governance. Setting: Model validation practices are generally patchy, disparate and sometimes contradictory, and although the Basel Accord and some regulatory authorities have attempted to establish guiding principles, no definite set of global standards exists. Aim: Assessing the available literature for the best validation practices. Methods: This comprehensive literature study provided a background to the complexities of effective model management and focussed on model validation as a component of model risk management. Results: We propose a coherent ‘best practice’ framework for model validation. Scorecard tools are also presented to evaluate if the proposed best practice model validation framework has been adequately assembled and implemented. Conclusion: The proposed best practice model validation framework is designed to assist firms in the construction of an effective, robust and fully compliant model validation programme and comprises three principal elements: model validation governance, policy and process.
Statistical alignment: computational properties, homology testing and goodness-of-fit
DEFF Research Database (Denmark)
Hein, J; Wiuf, Carsten; Møller, Martin
2000-01-01
The model of insertions and deletions in biological sequences, first formulated by Thorne, Kishino, and Felsenstein in 1991 (the TKF91 model), provides a basis for performing alignment within a statistical framework. Here we investigate this model.Firstly, we show how to accelerate the statistical...... alignment algorithms several orders of magnitude. The main innovations are to confine likelihood calculations to a band close to the similarity based alignment, to get good initial guesses of the evolutionary parameters and to apply an efficient numerical optimisation algorithm for finding the maximum...... analysis.Secondly, we propose a new homology test based on this model, where homology means that an ancestor to a sequence pair can be found finitely far back in time. This test has statistical advantages relative to the traditional shuffle test for proteins.Finally, we describe a goodness-of-fit test...
Energy Technology Data Exchange (ETDEWEB)
Abdeldayem, H.M.; Ruiz, P.; Delmon, B. [Unite de Catalyse et Chimie des Materiaux Divises, Universite Catholique de Louvain, Louvain-La-Neuve (Belgium); Thyrion, F.C. [Unite des Procedes Faculte des Sciences Appliquees, Universite Catholique de Louvain, Louvain-La-Neuve (Belgium)
1998-12-31
A new kinetic model for a more accurate and detailed fitting of the experimental data is proposed. The model is based on the remote control mechanism (RCM). The RCM assumes that some oxides (called `donors`) are able to activate molecular oxygen transforming it to very active mobile species (spillover oxygen (O{sub OS})). O{sub OS} migrates onto the surface of the other oxide (called `acceptor`) where it creates and/or regenerates the active sites during the reaction. The model contains tow terms, one considering the creation of selective sites and the other the catalytic reaction at each site. The model has been tested in the selective oxidation of propene into acrolein (T=380, 400, 420 C; oxygen and propene partial pressures between 38 and 152 Torr). Catalysts were prepared as pure MoO{sub 3} (acceptor) and their mechanical mixtures with {alpha}-Sb{sub 2}O{sub 4} (donor) in different proportions. The presence of {alpha}-Sb{sub 2}O{sub 4} changes the reaction order, the activation energy of the reaction and the number of active sites of MoO{sub 3} produced by oxygen spillover. These changes are consistent with a modification in the degree of irrigation of the surface by oxygen spillover. The fitting of the model to experimental results shows that the number of sites created by O{sub SO} increases with the amount of {alpha}-Sb{sub 2}O{sub 4}. (orig.)
The methodology of composing the exercises system with fit balls.
Directory of Open Access Journals (Sweden)
Voronov N.P.
2011-02-01
Full Text Available The original methodology of composing the exercises system with fit balls was considered. More than 10 publications were analysed. On the lesson with fit balls the problem was revealed. In the experiment took part 30 students at the age from 18 till 21. All the famous exercises were systematized. As a result, a big attractiveness and assimilability of the proposed complex was revealed. The effectiveness of the complex of physical exercises with fit balls for students was proved.
Fitting and Calibrating a Multilevel Mixed-Effects Stem Taper Model for Maritime Pine in NW Spain
Arias-Rodil, Manuel; Castedo-Dorado, Fernando; Cámara-Obregón, Asunción; Diéguez-Aranda, Ulises
2015-01-01
Stem taper data are usually hierarchical (several measurements per tree, and several trees per plot), making application of a multilevel mixed-effects modelling approach essential. However, correlation between trees in the same plot/stand has often been ignored in previous studies. Fitting and calibration of a variable-exponent stem taper function were conducted using data from 420 trees felled in even-aged maritime pine (Pinus pinaster Ait.) stands in NW Spain. In the fitting step, the tree level explained much more variability than the plot level, and therefore calibration at plot level was omitted. Several stem heights were evaluated for measurement of the additional diameter needed for calibration at tree level. Calibration with an additional diameter measured at between 40 and 60% of total tree height showed the greatest improvement in volume and diameter predictions. If additional diameter measurement is not available, the fixed-effects model fitted by the ordinary least squares technique should be used. Finally, we also evaluated how the expansion of parameters with random effects affects the stem taper prediction, as we consider this a key question when applying the mixed-effects modelling approach to taper equations. The results showed that correlation between random effects should be taken into account when assessing the influence of random effects in stem taper prediction. PMID:26630156
Abrahart, R. J.; Dawson, C. W.; Heppenstall, A. J.; See, L. M.
2009-04-01
The most critical issue in developing a neural network model is generalisation: how well will the preferred solution perform when it is applied to unseen datasets? The reported experiments used far-reaching sequences of model architectures and training periods to investigate the potential damage that could result from the impact of several interrelated items: (i) over-fitting - a machine learning concept related to exceeding some optimal architectural size; (ii) over-training - a machine learning concept related to the amount of adjustment that is applied to a specific model - based on the understanding that too much fine-tuning might result in a model that had accommodated random aspects of its training dataset - items that had no causal relationship to the target function; and (iii) over-parameterisation - a statistical modelling concept that is used to restrict the number of parameters in a model so as to match the information content of its calibration dataset. The last item in this triplet stems from an understanding that excessive computational complexities might permit an absurd and false solution to be fitted to the available material. Numerous feedforward multilayered perceptrons were trialled and tested. Two different methods of model construction were also compared and contrasted: (i) traditional Backpropagation of Error; and (ii) state-of-the-art Symbiotic Adaptive Neuro-Evolution. Modelling solutions were developed using the reported experimental set ups of Gaume & Gosset (2003). The models were applied to a near-linear hydrological modelling scenario in which past upstream and past downstream discharge records were used to forecast current discharge at the downstream gauging station [CS1: River Marne]; and a non-linear hydrological modelling scenario in which past river discharge measurements and past local meteorological records (precipitation and evaporation) were used to forecast current discharge at the river gauging station [CS2: Le Sauzay].
Universality Classes of Interaction Structures for NK Fitness Landscapes
Hwang, Sungmin; Schmiegelt, Benjamin; Ferretti, Luca; Krug, Joachim
2018-02-01
Kauffman's NK-model is a paradigmatic example of a class of stochastic models of genotypic fitness landscapes that aim to capture generic features of epistatic interactions in multilocus systems. Genotypes are represented as sequences of L binary loci. The fitness assigned to a genotype is a sum of contributions, each of which is a random function defined on a subset of k ≤ L loci. These subsets or neighborhoods determine the genetic interactions of the model. Whereas earlier work on the NK model suggested that most of its properties are robust with regard to the choice of neighborhoods, recent work has revealed an important and sometimes counter-intuitive influence of the interaction structure on the properties of NK fitness landscapes. Here we review these developments and present new results concerning the number of local fitness maxima and the statistics of selectively accessible (that is, fitness-monotonic) mutational pathways. In particular, we develop a unified framework for computing the exponential growth rate of the expected number of local fitness maxima as a function of L, and identify two different universality classes of interaction structures that display different asymptotics of this quantity for large k. Moreover, we show that the probability that the fitness landscape can be traversed along an accessible path decreases exponentially in L for a large class of interaction structures that we characterize as locally bounded. Finally, we discuss the impact of the NK interaction structures on the dynamics of evolution using adaptive walk models.
International Nuclear Information System (INIS)
Sosa, M.; Manjón, G.; Mantero, J.; García-Tenorio, R.
2014-01-01
The objective of this work is to propose an exponential fit for the low alpha-counting efficiency as a function of a sample quenching parameter using a Quantulus liquid scintillation counter. The sample quenching parameter in a Quantulus is the Spectral Quench Parameter of the External Standard (SQP(E)), which is defined as the number of channel under which lies the 99% of Compton spectrum generated by a gamma emitter ( 152 Eu). Although in the literature one usually finds a polynomial fitting of the alpha counting efficiency, it is shown here that an exponential function is a better description. - Highlights: • We have studied the quenching in alpha measurement by liquid scintillation counting. • We have reviewed typical fitting of alpha counting efficiency versus quenching parameter. • Exponential fitting of data is proposed as better fitting. • We consider exponential fitting has a physical basis
Energy Technology Data Exchange (ETDEWEB)
Sosa, M. [Departamento de Ingeniería Física, Campus León, Universidad de Guanajuato, 37150 León, Guanajuato (Mexico); Universidad de Sevilla, Departamento de Física Aplicada II, E.T.S. Arquitectura, Av. Reina Mercedes, 2, 41012 Sevilla (Spain); Manjón, G., E-mail: manjon@us.es [Universidad de Sevilla, Departamento de Física Aplicada II, E.T.S. Arquitectura, Av. Reina Mercedes, 2, 41012 Sevilla (Spain); Mantero, J.; García-Tenorio, R. [Universidad de Sevilla, Departamento de Física Aplicada II, E.T.S. Arquitectura, Av. Reina Mercedes, 2, 41012 Sevilla (Spain)
2014-05-01
The objective of this work is to propose an exponential fit for the low alpha-counting efficiency as a function of a sample quenching parameter using a Quantulus liquid scintillation counter. The sample quenching parameter in a Quantulus is the Spectral Quench Parameter of the External Standard (SQP(E)), which is defined as the number of channel under which lies the 99% of Compton spectrum generated by a gamma emitter ({sup 152}Eu). Although in the literature one usually finds a polynomial fitting of the alpha counting efficiency, it is shown here that an exponential function is a better description. - Highlights: • We have studied the quenching in alpha measurement by liquid scintillation counting. • We have reviewed typical fitting of alpha counting efficiency versus quenching parameter. • Exponential fitting of data is proposed as better fitting. • We consider exponential fitting has a physical basis.
Statistical topography of fitness landscapes
Franke, Jasper
2011-01-01
Fitness landscapes are generalized energy landscapes that play an important conceptual role in evolutionary biology. These landscapes provide a relation between the genetic configuration of an organism and that organism’s adaptive properties. In this work, global topographical features of these fitness landscapes are investigated using theoretical models. The resulting predictions are compared to empirical landscapes. It is shown that these landscapes allow, at least with respe...
Proposing a Compartmental Model for Leprosy and Parameterizing Using Regional Incidence in Brazil.
Smith, Rebecca Lee
2016-08-01
Hansen's disease (HD), or leprosy, is still considered a public health risk in much of Brazil. Understanding the dynamics of the infection at a regional level can aid in identification of targets to improve control. A compartmental continuous-time model for leprosy dynamics was designed based on understanding of the biology of the infection. The transmission coefficients for the model and the rate of detection were fit for each region using Approximate Bayesian Computation applied to paucibacillary and multibacillary incidence data over the period of 2000 to 2010, and model fit was validated on incidence data from 2011 to 2012. Regional variation was noted in detection rate, with cases in the Midwest estimated to be infectious for 10 years prior to detection compared to 5 years for most other regions. Posterior predictions for the model estimated that elimination of leprosy as a public health risk would require, on average, 44-45 years in the three regions with the highest prevalence. The model is easily adaptable to other settings, and can be studied to determine the efficacy of improved case finding on leprosy control.
Directory of Open Access Journals (Sweden)
Victor S. Coswig
2018-05-01
Full Text Available Background Among combat sports, Judo and Brazilian Jiu-Jitsu (BJJ present elevated physical fitness demands from the high-intensity intermittent efforts. However, information regarding how metabolic and neuromuscular physical fitness is associated with technical-tactical performance in Judo and BJJ fights is not available. This study aimed to relate indicators of physical fitness with combat performance variables in Judo and BJJ. Methods The sample consisted of Judo (n = 16 and BJJ (n = 24 male athletes. At the first meeting, the physical tests were applied and, in the second, simulated fights were performed for later notational analysis. Results The main findings indicate: (i high reproducibility of the proposed instrument and protocol used for notational analysis in a mobile device; (ii differences in the technical-tactical and time-motion patterns between modalities; (iii performance-related variables are different in Judo and BJJ; and (iv regression models based on metabolic fitness variables may account for up to 53% of the variances in technical-tactical and/or time-motion variables in Judo and up to 31% in BJJ, whereas neuromuscular fitness models can reach values up to 44 and 73% of prediction in Judo and BJJ, respectively. When all components are combined, they can explain up to 90% of high intensity actions in Judo. Discussion In conclusion, performance prediction models in simulated combat indicate that anaerobic, aerobic and neuromuscular fitness variables contribute to explain time-motion variables associated with high intensity and technical-tactical variables in Judo and BJJ fights.
Simulation model for centrifugal pump in flow networks based on internal characteristics
International Nuclear Information System (INIS)
Sun, Ji-Lin; Xue, Ruo-Jun; Peng, Min-Jun
2018-01-01
For the simulation of centrifugal pump in flow network system, in general three approaches can be used, the fitting model, the numerical method and the internal characteristics model. The fitting model is simple and rapid thus widely used. The numerical method can provide more detailed information in comparison with the fitting model, but increases implementation complexity and computational cost. In real-time simulations of flow networks, to simulate the condition out of the rated condition, especially for the volume flow rate, which the accuracy of fitting model is incredible, a new method for simulating centrifugal pumps was proposed in this research. The method based on the theory head and hydraulic loss in centrifugal pumps, and cavitation is also to be considered. The simulation results are verified with experimental benchmark data from an actual pump. The comparison confirms that the proposed method could fit the flow-head curves well, and the responses of main parameters in dynamic-state operations are consistent with theoretical analyses.
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.
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.
A No-Scale Inflationary Model to Fit Them All
Ellis, John; Nanopoulos, Dimitri; Olive, Keith
2014-01-01
The magnitude of B-mode polarization in the cosmic microwave background as measured by BICEP2 favours models of chaotic inflation with a quadratic $m^2 \\phi^2/2$ potential, whereas data from the Planck satellite favour a small value of the tensor-to-scalar perturbation ratio $r$ that is highly consistent with the Starobinsky $R + R^2$ model. Reality may lie somewhere between these two scenarios. In this paper we propose a minimal two-field no-scale supergravity model that interpolates between quadratic and Starobinsky-like inflation as limiting cases, while retaining the successful prediction $n_s \\simeq 0.96$.
Maximum likelihood fitting of FROC curves under an initial-detection-and-candidate-analysis model
International Nuclear Information System (INIS)
Edwards, Darrin C.; Kupinski, Matthew A.; Metz, Charles E.; Nishikawa, Robert M.
2002-01-01
We have developed a model for FROC curve fitting that relates the observer's FROC performance not to the ROC performance that would be obtained if the observer's responses were scored on a per image basis, but rather to a hypothesized ROC performance that the observer would obtain in the task of classifying a set of 'candidate detections' as positive or negative. We adopt the assumptions of the Bunch FROC model, namely that the observer's detections are all mutually independent, as well as assumptions qualitatively similar to, but different in nature from, those made by Chakraborty in his AFROC scoring methodology. Under the assumptions of our model, we show that the observer's FROC performance is a linearly scaled version of the candidate analysis ROC curve, where the scaling factors are just given by the FROC operating point coordinates for detecting initial candidates. Further, we show that the likelihood function of the model parameters given observational data takes on a simple form, and we develop a maximum likelihood method for fitting a FROC curve to this data. FROC and AFROC curves are produced for computer vision observer datasets and compared with the results of the AFROC scoring method. Although developed primarily with computer vision schemes in mind, we hope that the methodology presented here will prove worthy of further study in other applications as well
Quantifying and Reducing Curve-Fitting Uncertainty in Isc
Energy Technology Data Exchange (ETDEWEB)
Campanelli, Mark; Duck, Benjamin; Emery, Keith
2015-06-14
Current-voltage (I-V) curve measurements of photovoltaic (PV) devices are used to determine performance parameters and to establish traceable calibration chains. Measurement standards specify localized curve fitting methods, e.g., straight-line interpolation/extrapolation of the I-V curve points near short-circuit current, Isc. By considering such fits as statistical linear regressions, uncertainties in the performance parameters are readily quantified. However, the legitimacy of such a computed uncertainty requires that the model be a valid (local) representation of the I-V curve and that the noise be sufficiently well characterized. Using more data points often has the advantage of lowering the uncertainty. However, more data points can make the uncertainty in the fit arbitrarily small, and this fit uncertainty misses the dominant residual uncertainty due to so-called model discrepancy. Using objective Bayesian linear regression for straight-line fits for Isc, we investigate an evidence-based method to automatically choose data windows of I-V points with reduced model discrepancy. We also investigate noise effects. Uncertainties, aligned with the Guide to the Expression of Uncertainty in Measurement (GUM), are quantified throughout.
Fitting monthly Peninsula Malaysian rainfall using Tweedie distribution
Yunus, R. M.; Hasan, M. M.; Zubairi, Y. Z.
2017-09-01
In this study, the Tweedie distribution was used to fit the monthly rainfall data from 24 monitoring stations of Peninsula Malaysia for the period from January, 2008 to April, 2015. The aim of the study is to determine whether the distributions within the Tweedie family fit well the monthly Malaysian rainfall data. Within the Tweedie family, the gamma distribution is generally used for fitting the rainfall totals, however the Poisson-gamma distribution is more useful to describe two important features of rainfall pattern, which are the occurrences (dry months) and the amount (wet months). First, the appropriate distribution of the monthly rainfall was identified within the Tweedie family for each station. Then, the Tweedie Generalised Linear Model (GLM) with no explanatory variable was used to model the monthly rainfall data. Graphical representation was used to assess model appropriateness. The QQ plots of quantile residuals show that the Tweedie models fit the monthly rainfall data better for majority of the stations in the west coast and mid land than those in the east coast of Peninsula. This significant finding suggests that the best fitted distribution depends on the geographical location of the monitoring station. In this paper, a simple model is developed for generating synthetic rainfall data for use in various areas, including agriculture and irrigation. We have showed that the data that were simulated using the Tweedie distribution have fairly similar frequency histogram to that of the actual data. Both the mean number of rainfall events and mean amount of rain for a month were estimated simultaneously for the case that the Poisson gamma distribution fits the data reasonably well. Thus, this work complements previous studies that fit the rainfall amount and the occurrence of rainfall events separately, each to a different distribution.
A proposed general model of information behaviour.
Directory of Open Access Journals (Sweden)
2003-01-01
Full Text Available Presents a critical description of Wilson's (1996 global model of information behaviour and proposes major modification on the basis of research into information behaviour of managers, conducted in Poland. The theoretical analysis and research results suggest that Wilson's model has certain imperfections, both in its conceptual content, and in graphical presentation. The model, for example, cannot be used to describe managers' information behaviour, since managers basically are not the end users of external from organization or computerized information services, and they acquire information mainly through various intermediaries. Therefore, the model cannot be considered as a general model, applicable to every category of information users. The proposed new model encompasses the main concepts of Wilson's model, such as: person-in-context, three categories of intervening variables (individual, social and environmental, activating mechanisms, cyclic character of information behaviours, and the adoption of a multidisciplinary approach to explain them. However, the new model introduces several changes. They include: 1. identification of 'context' with the intervening variables; 2. immersion of the chain of information behaviour in the 'context', to indicate that the context variables influence behaviour at all stages of the process (identification of needs, looking for information, processing and using it; 3. stress is put on the fact that the activating mechanisms also can occur at all stages of the information acquisition process; 4. introduction of two basic strategies of looking for information: personally and/or using various intermediaries.
A Bayesian Approach to Person Fit Analysis in Item Response Theory Models. Research Report.
Glas, Cees A. W.; Meijer, Rob R.
A Bayesian approach to the evaluation of person fit in item response theory (IRT) models is presented. In a posterior predictive check, the observed value on a discrepancy variable is positioned in its posterior distribution. In a Bayesian framework, a Markov Chain Monte Carlo procedure can be used to generate samples of the posterior distribution…
Fitness Tracker for Weight Lifting Style Workouts
Energy Technology Data Exchange (ETDEWEB)
Wihl, B. M. [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)
2017-02-01
This document proposes an early, high level design for a fitness tracking system which can automatically log weight lifting style workouts. The system will provide an easy to use interface both physically through the use of several wireless wristband style motion trackers worn on the limbs, and graphically through a smartphone application. Exercise classification will be accomplished by calibration of the user’s specific motions. The system will accurately track a user’s workout, miscounting no more than one repetition in every 20, have sufficient battery life to last several hours, work with existing smartphones and have a cost similar to those of current fitness tracking devices. This document presents the mission background, current state-of-theart, stakeholders and their expectations, the proposed system’s context and concepts, implementation concepts, system requirements, first sublevel function decomposition, possible risks for the system, and a reflection on the design process.
The Many Null Distributions of Person Fit Indices.
Molenaar, Ivo W.; Hoijtink, Herbert
1990-01-01
Statistical properties of person fit indices are reviewed as indicators of the extent to which a person's score pattern is in agreement with a measurement model. Distribution of a fit index and ability-free fit evaluation are discussed. The null distribution was simulated for a test of 20 items. (SLD)
Mathematical formalization of theories of motivation proposed by Maslow and Herzberg
Kotliarov,Ivan
2008-01-01
Maslow's theory is by far the most known theory of motivation, and the most common in the business and management practice. Herzberg's theory fits the observations and explains some aspects of human motivation left unexplained by Maslow. However, these theories have never been formalized on a strictly mathematical basis. The present article gives an outline of a mathematical model of theories of motivation proposed by Abraham Maslow and Frederick Herzberg. This model is built on a basis of sp...
Inverse problem theory methods for data fitting and model parameter estimation
Tarantola, A
2002-01-01
Inverse Problem Theory is written for physicists, geophysicists and all scientists facing the problem of quantitative interpretation of experimental data. Although it contains a lot of mathematics, it is not intended as a mathematical book, but rather tries to explain how a method of acquisition of information can be applied to the actual world.The book provides a comprehensive, up-to-date description of the methods to be used for fitting experimental data, or to estimate model parameters, and to unify these methods into the Inverse Problem Theory. The first part of the book deals wi
2016-09-01
PROPERTIES OF PHYSICAL FITNESS UNIFORMS AND MODELED HEAT STRAIN AND THERMAL COMFORT DISCLAIMER The opinions or assertions contained herein are the...SHIRTS: COMPARISON OF SPECTROPHOTOMETRIC AND OTHER BIOPHYSICAL PROPERTIES OF PHYSICAL FITNESS UNIFORMS AND MODELED HEAT STRAIN AND THERMAL COMFORT ...the impact of the environment on the wearer. To model these impacts on human thermal sensation (e.g., thermal comfort ) and thermoregulatory
Fitness to drive after traumatic brain injury
Brouwer, WH; Withaar, FK
This paper deals with the issue of fitness to drive in patients suffering from traumatic brain injury (TBI). Guidelines for assessment are proposed and three types of studies are reviewed: studies about impairments of attention and information processing, studies of driving competence, and driver
Virtual Suit Fit Assessment Using Body Shape Model
National Aeronautics and Space Administration — Shoulder injury is one of the most serious risks for crewmembers in long-duration spaceflight. While suboptimal suit fit and contact pressures between the shoulder...
Custom Hardware Processor to Compute a Figure of Merit for the Fit of X-Ray Diffraction
International Nuclear Information System (INIS)
Gomez-Pulido, P.J.A.; Vega-Rodriguez, M.A.; Sanchez-Perez, J.M.; Sanchez-Bajo, F.; Santos, S.P.D.
2008-01-01
A custom processor based on re configurable hardware technology is proposed in order to compute the figure of merit used to measure the quality of the fit of X-ray diffraction peaks. As the experimental X-ray profiles can present many peaks severely overlapped, it is necessary to select the best model among a large set of reasonably good solutions. Determining the best solution is computationally intensive, because this is a hard combinatorial optimization problem. The proposed processors, working in parallel, increase the performance relative to a software implementation.
Directory of Open Access Journals (Sweden)
Liyun Su
2012-01-01
Full Text Available We introduce the extension of local polynomial fitting to the linear heteroscedastic regression model. Firstly, the local polynomial fitting is applied to estimate heteroscedastic function, then the coefficients of regression model are obtained by using generalized least squares method. One noteworthy feature of our approach is that we avoid the testing for heteroscedasticity by improving the traditional two-stage method. Due to nonparametric technique of local polynomial estimation, we do not need to know the heteroscedastic function. Therefore, we can improve the estimation precision, when the heteroscedastic function is unknown. Furthermore, we focus on comparison of parameters and reach an optimal fitting. Besides, we verify the asymptotic normality of parameters based on numerical simulations. Finally, this approach is applied to a case of economics, and it indicates that our method is surely effective in finite-sample situations.
Convolution based profile fitting
International Nuclear Information System (INIS)
Kern, A.; Coelho, A.A.; Cheary, R.W.
2002-01-01
Full text: In convolution based profile fitting, profiles are generated by convoluting functions together to form the observed profile shape. For a convolution of 'n' functions this process can be written as, Y(2θ)=F 1 (2θ)x F 2 (2θ)x... x F i (2θ)x....xF n (2θ). In powder diffractometry the functions F i (2θ) can be interpreted as the aberration functions of the diffractometer, but in general any combination of appropriate functions for F i (2θ) may be used in this context. Most direct convolution fitting methods are restricted to combinations of F i (2θ) that can be convoluted analytically (e.g. GSAS) such as Lorentzians, Gaussians, the hat (impulse) function and the exponential function. However, software such as TOPAS is now available that can accurately convolute and refine a wide variety of profile shapes numerically, including user defined profiles, without the need to convolute analytically. Some of the most important advantages of modern convolution based profile fitting are: 1) virtually any peak shape and angle dependence can normally be described using minimal profile parameters in laboratory and synchrotron X-ray data as well as in CW and TOF neutron data. This is possible because numerical convolution and numerical differentiation is used within the refinement procedure so that a wide range of functions can easily be incorporated into the convolution equation; 2) it can use physically based diffractometer models by convoluting the instrument aberration functions. This can be done for most laboratory based X-ray powder diffractometer configurations including conventional divergent beam instruments, parallel beam instruments, and diffractometers used for asymmetric diffraction. It can also accommodate various optical elements (e.g. multilayers and monochromators) and detector systems (e.g. point and position sensitive detectors) and has already been applied to neutron powder diffraction systems (e.g. ANSTO) as well as synchrotron based
Geometric Modelling of Octagonal Lamp Poles
Chan, T. O.; Lichti, D. D.
2014-06-01
Lamp poles are one of the most abundant highway and community components in modern cities. Their supporting parts are primarily tapered octagonal cones specifically designed for wind resistance. The geometry and the positions of the lamp poles are important information for various applications. For example, they are important to monitoring deformation of aged lamp poles, maintaining an efficient highway GIS system, and also facilitating possible feature-based calibration of mobile LiDAR systems. In this paper, we present a novel geometric model for octagonal lamp poles. The model consists of seven parameters in which a rotation about the z-axis is included, and points are constrained by the trigonometric property of 2D octagons after applying the rotations. For the geometric fitting of the lamp pole point cloud captured by a terrestrial LiDAR, accurate initial parameter values are essential. They can be estimated by first fitting the points to a circular cone model and this is followed by some basic point cloud processing techniques. The model was verified by fitting both simulated and real data. The real data includes several lamp pole point clouds captured by: (1) Faro Focus 3D and (2) Velodyne HDL-32E. The fitting results using the proposed model are promising, and up to 2.9 mm improvement in fitting accuracy was realized for the real lamp pole point clouds compared to using the conventional circular cone model. The overall result suggests that the proposed model is appropriate and rigorous.
Fitting outbreak models to data from many small norovirus outbreaks
Directory of Open Access Journals (Sweden)
Eamon B. O’Dea
2014-03-01
Full Text Available Infectious disease often occurs in small, independent outbreaks in populations with varying characteristics. Each outbreak by itself may provide too little information for accurate estimation of epidemic model parameters. Here we show that using standard stochastic epidemic models for each outbreak and allowing parameters to vary between outbreaks according to a linear predictor leads to a generalized linear model that accurately estimates parameters from many small and diverse outbreaks. By estimating initial growth rates in addition to transmission rates, we are able to characterize variation in numbers of initially susceptible individuals or contact patterns between outbreaks. With simulation, we find that the estimates are fairly robust to the data being collected at discrete intervals and imputation of about half of all infectious periods. We apply the method by fitting data from 75 norovirus outbreaks in health-care settings. Our baseline regression estimates are 0.0037 transmissions per infective-susceptible day, an initial growth rate of 0.27 transmissions per infective day, and a symptomatic period of 3.35 days. Outbreaks in long-term-care facilities had significantly higher transmission and initial growth rates than outbreaks in hospitals.
Minimal see-saw model predicting best fit lepton mixing angles
International Nuclear Information System (INIS)
King, Stephen F.
2013-01-01
We discuss a minimal predictive see-saw model in which the right-handed neutrino mainly responsible for the atmospheric neutrino mass has couplings to (ν e ,ν μ ,ν τ ) proportional to (0,1,1) and the right-handed neutrino mainly responsible for the solar neutrino mass has couplings to (ν e ,ν μ ,ν τ ) proportional to (1,4,2), with a relative phase η=−2π/5. We show how these patterns of couplings could arise from an A 4 family symmetry model of leptons, together with Z 3 and Z 5 symmetries which fix η=−2π/5 up to a discrete phase choice. The PMNS matrix is then completely determined by one remaining parameter which is used to fix the neutrino mass ratio m 2 /m 3 . The model predicts the lepton mixing angles θ 12 ≈34 ∘ ,θ 23 ≈41 ∘ ,θ 13 ≈9.5 ∘ , which exactly coincide with the current best fit values for a normal neutrino mass hierarchy, together with the distinctive prediction for the CP violating oscillation phase δ≈106 ∘
A proposed wind shift model for the German reactor safety study
International Nuclear Information System (INIS)
Aldrich, D.C.; Bayer, A.; Schueckler, M.
1979-04-01
To account for hourly wind direction changes, a wind shift model has been proposed. Using hourly recorded wind speed and direction data, the model modifies the angular distribution of radionuclide concentrations calculated by a straightline model, and is intended to better represent the concentrations in areas close to the reactor where potential doses might exceed the threshold level for early fatalities. 115 weather sequences were used, both with and without the proposed wind shift model, to calculate probability distributions for early fatalities. The use of the proposed model results in a reduction of the mean and peak values of that distribution by 36% and 29%, respectively. (orig./HP) [de
GENFIT - a generic track-fitting toolkit
Energy Technology Data Exchange (ETDEWEB)
Rauch, Johannes [Technische Universitaet Muenchen (Germany); Schlueter, Tobias [Ludwig-Maximilians-Universitaet Muenchen (Germany)
2014-07-01
GENFIT is an experiment-independent track-fitting toolkit, which combines fitting algorithms, track representations, and measurement geometries into a modular framework. We report on a significantly improved version of GENFIT, based on experience gained in the Belle II, PANDA, and FOPI experiments. Improvements concern the implementation of additional track-fitting algorithms, enhanced implementations of Kalman fitters, enhanced visualization capabilities, and additional implementations of measurement types suited for various kinds of tracking detectors. The data model has been revised, allowing for efficient track merging, smoothing, residual calculation and alignment.
New ROOT Graphical User Interfaces for fitting
International Nuclear Information System (INIS)
Maline, D Gonzalez; Moneta, L; Antcheva, I
2010-01-01
ROOT, as a scientific data analysis framework, provides extensive capabilities via Graphical User Interfaces (GUI) for performing interactive analysis and visualizing data objects like histograms and graphs. A new interface for fitting has been developed for performing, exploring and comparing fits on data point sets such as histograms, multi-dimensional graphs or trees. With this new interface, users can build interactively the fit model function, set parameter values and constraints and select fit and minimization methods with their options. Functionality for visualizing the fit results is as well provided, with the possibility of drawing residuals or confidence intervals. Furthermore, the new fit panel reacts as a standalone application and it does not prevent users from interacting with other windows. We will describe in great detail the functionality of this user interface, covering as well new capabilities provided by the new fitting and minimization tools introduced recently in the ROOT framework.
Levy flights and self-similar exploratory behaviour of termite workers: beyond model fitting.
Directory of Open Access Journals (Sweden)
Octavio Miramontes
Full Text Available Animal movements have been related to optimal foraging strategies where self-similar trajectories are central. Most of the experimental studies done so far have focused mainly on fitting statistical models to data in order to test for movement patterns described by power-laws. Here we show by analyzing over half a million movement displacements that isolated termite workers actually exhibit a range of very interesting dynamical properties--including Lévy flights--in their exploratory behaviour. Going beyond the current trend of statistical model fitting alone, our study analyses anomalous diffusion and structure functions to estimate values of the scaling exponents describing displacement statistics. We evince the fractal nature of the movement patterns and show how the scaling exponents describing termite space exploration intriguingly comply with mathematical relations found in the physics of transport phenomena. By doing this, we rescue a rich variety of physical and biological phenomenology that can be potentially important and meaningful for the study of complex animal behavior and, in particular, for the study of how patterns of exploratory behaviour of individual social insects may impact not only their feeding demands but also nestmate encounter patterns and, hence, their dynamics at the social scale.
Campobello, Daniela; Hare, James F; Sarà, Maurizio
2015-04-01
In social species, fitness consequences are associated with both individual and social phenotypes. Social selection analysis has quantified the contribution of conspecific social traits to individual fitness. There has been no attempt, however, to apply a social selection approach to quantify the fitness implications of heterospecific social phenotypes. Here, we propose a novel social selection based approach integrating the role of all social interactions at the community level. We extended multilevel selection analysis by including a term accounting for the group phenotype of heterospecifics. We analyzed nest activity as a model social trait common to two species, the lesser kestrel (Falco naumanni) and jackdaw (Corvus monedula), nesting in either single- or mixed-species colonies. By recording reproductive outcome as a measure of relative fitness, our results reveal an asymmetric system wherein only jackdaw breeding performance was affected by the activity phenotypes of both conspecific and heterospecific neighbors. Our model incorporating heterospecific social phenotypes is applicable to animal communities where interacting species share a common social trait, thus allowing an assessment of the selection pressure imposed by interspecific interactions in nature. Finally, we discuss the potential role of ecological limitations accounting for random or preferential assortments among interspecific social phenotypes, and the implications of such processes to community evolution. © 2015 The Author(s).
Tikhonov, Mikhail; Monasson, Remi
2018-01-01
Much of our understanding of ecological and evolutionary mechanisms derives from analysis of low-dimensional models: with few interacting species, or few axes defining "fitness". It is not always clear to what extent the intuition derived from low-dimensional models applies to the complex, high-dimensional reality. For instance, most naturally occurring microbial communities are strikingly diverse, harboring a large number of coexisting species, each of which contributes to shaping the environment of others. Understanding the eco-evolutionary interplay in these systems is an important challenge, and an exciting new domain for statistical physics. Recent work identified a promising new platform for investigating highly diverse ecosystems, based on the classic resource competition model of MacArthur. Here, we describe how the same analytical framework can be used to study evolutionary questions. Our analysis illustrates how, at high dimension, the intuition promoted by a one-dimensional (scalar) notion of fitness can become misleading. Specifically, while the low-dimensional picture emphasizes organism cost or efficiency, we exhibit a regime where cost becomes irrelevant for survival, and link this observation to generic properties of high-dimensional geometry.
Gamification in online education: proposal for a participatory learning model
Directory of Open Access Journals (Sweden)
Fabiana Bigão Silva
2017-09-01
Full Text Available Empirical studies have suggested limitations on the form of application of gamification mechanics in the context of online education. These mechanics have been applied without reference to a theoretical model dedicated to this type of education. The objective of the paper is to propose a model for a gamified platform for online education that contributes to a more participatory learning, taking into account the different student profiles. Based on literature review about approaches to gamification systems design, a set of steps was followed in order to develop a generic model for a framework dedicated to online education. The model proposed is based on the Educational Gamification Design Principles proposed by Dicheva et al. (2015. The model may contribute to the promotion of participatory learning, taking into account the different student profiles. The results of such evaluation will be published in the future.
Right-sizing statistical models for longitudinal data.
Wood, Phillip K; Steinley, Douglas; Jackson, Kristina M
2015-12-01
Arguments are proposed that researchers using longitudinal data should consider more and less complex statistical model alternatives to their initially chosen techniques in an effort to "right-size" the model to the data at hand. Such model comparisons may alert researchers who use poorly fitting, overly parsimonious models to more complex, better-fitting alternatives and, alternatively, may identify more parsimonious alternatives to overly complex (and perhaps empirically underidentified and/or less powerful) statistical models. A general framework is proposed for considering (often nested) relationships between a variety of psychometric and growth curve models. A 3-step approach is proposed in which models are evaluated based on the number and patterning of variance components prior to selection of better-fitting growth models that explain both mean and variation-covariation patterns. The orthogonal free curve slope intercept (FCSI) growth model is considered a general model that includes, as special cases, many models, including the factor mean (FM) model (McArdle & Epstein, 1987), McDonald's (1967) linearly constrained factor model, hierarchical linear models (HLMs), repeated-measures multivariate analysis of variance (MANOVA), and the linear slope intercept (linearSI) growth model. The FCSI model, in turn, is nested within the Tuckerized factor model. The approach is illustrated by comparing alternative models in a longitudinal study of children's vocabulary and by comparing several candidate parametric growth and chronometric models in a Monte Carlo study. (c) 2015 APA, all rights reserved).
A proposed residual stress model for oblique turning
International Nuclear Information System (INIS)
Elkhabeery, M. M.
2001-01-01
A proposed mathematical model is presented for predicting the residual stresses caused by turning. Effects of change in tool free length, cutting speed, feed rate, and the tensile strength of work piece material on the maximum residual stress are investigated. The residual stress distribution in the surface region due to turning under unlubricated condition is determined using a deflection etching technique. To reduce the number of experiments required and build the mathematical model for these variables, Response Surface Methodology (RSM) is used. In addition, variance analysis and an experimental check are conducted to determine the prominent parameters and the adequacy of the model. The results show that the tensile stress of the work piece material, cutting speed, and feed rate have significant effects on the maximum residual stresses. The proposed model, that offering good correlation between the experimental and predicted results, is useful in selecting suitable cutting parameters for the machining of different materials. (author)
A new non-iterative method for fitting Lorentzian to Moessbauer spectra
International Nuclear Information System (INIS)
Mukoyama, T.; Vegh, J.
1980-01-01
A new method for fitting a Lorentzian function without an iterative procedure is presented. The method is quicker and simpler than the previously proposed method of non-iterative fitting. Comparison with the previous method and with the conventional iterative method has been made. It is shown that the present method gives satisfactory results. (orig.)
Li, Jia; Liu, Xuan; Ma, Ling; Zhang, Weiqiang
2018-03-05
The key step in changing health behavior is understanding why users continue to use fitness apps. Therefore, we intend to investigate the users' intention to continue using social fitness-tracking apps. We identify two major forces driving continuous behavior. Expectation confirmation is the internal driving force and social comparison is the external driving force. A survey was conducted to test this proposed research model. We obtained 211 valid questionnaires. Our results indicate that activity amount ranking (p fitness-tracking apps. In addition, the impact of activity amount ranking and activity frequency ranking on continuous intention is moderated by expectation confirmation. Meanwhile, as the upward comparison tendency increases, the positive effect of confirmation on continuous intention decreases (p Social rank expectation and confirmation are the primary driving forces of continuous intention in individuals using fitness-tracking apps. Social rank is a meaningful and straightforward measurement individuals can use to evaluate their activity performance. An upward comparison tendency weakens the effect of confirmation on continuous intention.
ACCELERATED FITTING OF STELLAR SPECTRA
Energy Technology Data Exchange (ETDEWEB)
Ting, Yuan-Sen; Conroy, Charlie [Harvard–Smithsonian Center for Astrophysics, 60 Garden Street, Cambridge, MA 02138 (United States); Rix, Hans-Walter [Max Planck Institute for Astronomy, Königstuhl 17, D-69117 Heidelberg (Germany)
2016-07-20
Stellar spectra are often modeled and fitted by interpolating within a rectilinear grid of synthetic spectra to derive the stars’ labels: stellar parameters and elemental abundances. However, the number of synthetic spectra needed for a rectilinear grid grows exponentially with the label space dimensions, precluding the simultaneous and self-consistent fitting of more than a few elemental abundances. Shortcuts such as fitting subsets of labels separately can introduce unknown systematics and do not produce correct error covariances in the derived labels. In this paper we present a new approach—Convex Hull Adaptive Tessellation (chat)—which includes several new ideas for inexpensively generating a sufficient stellar synthetic library, using linear algebra and the concept of an adaptive, data-driven grid. A convex hull approximates the region where the data lie in the label space. A variety of tests with mock data sets demonstrate that chat can reduce the number of required synthetic model calculations by three orders of magnitude in an eight-dimensional label space. The reduction will be even larger for higher dimensional label spaces. In chat the computational effort increases only linearly with the number of labels that are fit simultaneously. Around each of these grid points in the label space an approximate synthetic spectrum can be generated through linear expansion using a set of “gradient spectra” that represent flux derivatives at every wavelength point with respect to all labels. These techniques provide new opportunities to fit the full stellar spectra from large surveys with 15–30 labels simultaneously.
A simulation-based goodness-of-fit test for random effects in generalized linear mixed models
DEFF Research Database (Denmark)
Waagepetersen, Rasmus
2006-01-01
The goodness-of-fit of the distribution of random effects in a generalized linear mixed model is assessed using a conditional simulation of the random effects conditional on the observations. Provided that the specified joint model for random effects and observations is correct, the marginal...... distribution of the simulated random effects coincides with the assumed random effects distribution. In practice, the specified model depends on some unknown parameter which is replaced by an estimate. We obtain a correction for this by deriving the asymptotic distribution of the empirical distribution...
A simulation-based goodness-of-fit test for random effects in generalized linear mixed models
DEFF Research Database (Denmark)
Waagepetersen, Rasmus Plenge
The goodness-of-fit of the distribution of random effects in a generalized linear mixed model is assessed using a conditional simulation of the random effects conditional on the observations. Provided that the specified joint model for random effects and observations is correct, the marginal...... distribution of the simulated random effects coincides with the assumed random effects distribution. In practice the specified model depends on some unknown parameter which is replaced by an estimate. We obtain a correction for this by deriving the asymptotic distribution of the empirical distribution function...
Introduction: Occam’s Razor (SOT - Fit for Purpose workshop introduction)
Mathematical models provide important, reproducible, and transparent information for risk-based decision making. However, these models must be constructed to fit the needs of the problem to be solved. A “fit for purpose” model is an abstraction of a complicated problem that allow...
A proposed experiment on ball lightning model
International Nuclear Information System (INIS)
Ignatovich, Vladimir K.; Ignatovich, Filipp V.
2011-01-01
Highlights: → We propose to put a glass sphere inside an excited gas. → Then to put a light ray inside the glass in a whispering gallery mode. → If the light is resonant to gas excitation, it will be amplified at every reflection. → In ms time the light in the glass will be amplified, and will melt the glass. → A liquid shell kept integer by electrostriction forces is the ball lightning model. -- Abstract: We propose an experiment for strong light amplification at multiple total reflections from active gaseous media.
VizieR Online Data Catalog: GRB prompt emission fitted with the DREAM model (Ahlgren+, 2015)
Ahlgren, B.; Larsson, J.; Nymark, T.; Ryde, F.; Pe'Er, A.
2018-01-01
We illustrate the application of the DREAM model by fitting it to two different, bright Fermi GRBs; GRB 090618 and GRB 100724B. While GRB 090618 is well fitted by a Band function, GRB 100724B was the first example of a burst with a significant additional BB component (Guiriec et al. 2011ApJ...727L..33G). GRB 090618 is analysed using Gamma-ray Burst Monitor (GBM) data (Meegan et al. 2009ApJ...702..791M) from the NaI and BGO detectors. For GRB 100724B, we used GBM data from the NaI and BGO detectors as well as Large Area Telescope Low Energy (LAT-LLE) data. For both bursts we selected NaI detectors seeing the GRB at an off-axis angle lower than 60° and the BGO detector as being the best aligned of the two BGO detectors. The spectra were fitted in the energy ranges 8-1000 keV (NaI), 200-40000 keV (BGO) and 30-1000 MeV (LAT-LLE). (2 data files).
Covariance fitting of highly-correlated data in lattice QCD
Yoon, Boram; Jang, Yong-Chull; Jung, Chulwoo; Lee, Weonjong
2013-07-01
We address a frequently-asked question on the covariance fitting of highly-correlated data such as our B K data based on the SU(2) staggered chiral perturbation theory. Basically, the essence of the problem is that we do not have a fitting function accurate enough to fit extremely precise data. When eigenvalues of the covariance matrix are small, even a tiny error in the fitting function yields a large chi-square value and spoils the fitting procedure. We have applied a number of prescriptions available in the market, such as the cut-off method, modified covariance matrix method, and Bayesian method. We also propose a brand new method, the eigenmode shift (ES) method, which allows a full covariance fitting without modifying the covariance matrix at all. We provide a pedagogical example of data analysis in which the cut-off method manifestly fails in fitting, but the rest work well. In our case of the B K fitting, the diagonal approximation, the cut-off method, the ES method, and the Bayesian method work reasonably well in an engineering sense. However, interpreting the meaning of χ 2 is easier in the case of the ES method and the Bayesian method in a theoretical sense aesthetically. Hence, the ES method can be a useful alternative optional tool to check the systematic error caused by the covariance fitting procedure.
Stochastic process corrosion growth models for pipeline reliability
International Nuclear Information System (INIS)
Bazán, Felipe Alexander Vargas; Beck, André Teófilo
2013-01-01
Highlights: •Novel non-linear stochastic process corrosion growth model is proposed. •Corrosion rate modeled as random Poisson pulses. •Time to corrosion initiation and inherent time-variability properly represented. •Continuous corrosion growth histories obtained. •Model is shown to precisely fit actual corrosion data at two time points. -- Abstract: Linear random variable corrosion models are extensively employed in reliability analysis of pipelines. However, linear models grossly neglect well-known characteristics of the corrosion process. Herein, a non-linear model is proposed, where corrosion rate is represented as a Poisson square wave process. The resulting model represents inherent time-variability of corrosion growth, produces continuous growth and leads to mean growth at less-than-one power of time. Different corrosion models are adjusted to the same set of actual corrosion data for two inspections. The proposed non-linear random process corrosion growth model leads to the best fit to the data, while better representing problem physics
Directory of Open Access Journals (Sweden)
Mohd Tahir Ismail
2016-06-01
Full Text Available The daily returns of four African countries' stock market indices for the period January 2, 2000, to December 31, 2014, were employed to compare the GARCH(1,1 model and a newly proposed Maximal Overlap Discreet Wavelet Transform (MODWT-GARCH(1,1 model. The results showed that although both models fit the returns data well, the forecast produced by the GARCH(1,1 model underestimates the observed returns whereas the newly proposed MODWT-GARCH(1,1 model generates an accurate forecast value of the observed returns. The results generally showed that the newly proposed MODWT-GARCH(1,1 model best fits returns series for these African countries. Hence the proposed MODWT-GARCH should be applied on other context to further verify its validity.
Renault, Nisa K E; Pritchett, Sonja M; Howell, Robin E; Greer, Wenda L; Sapienza, Carmen; Ørstavik, Karen Helene; Hamilton, David C
2013-01-01
In eutherian mammals, one X-chromosome in every XX somatic cell is transcriptionally silenced through the process of X-chromosome inactivation (XCI). Females are thus functional mosaics, where some cells express genes from the paternal X, and the others from the maternal X. The relative abundance of the two cell populations (X-inactivation pattern, XIP) can have significant medical implications for some females. In mice, the ‘choice' of which X to inactivate, maternal or paternal, in each cell of the early embryo is genetically influenced. In humans, the timing of XCI choice and whether choice occurs completely randomly or under a genetic influence is debated. Here, we explore these questions by analysing the distribution of XIPs in large populations of normal females. Models were generated to predict XIP distributions resulting from completely random or genetically influenced choice. Each model describes the discrete primary distribution at the onset of XCI, and the continuous secondary distribution accounting for changes to the XIP as a result of development and ageing. Statistical methods are used to compare models with empirical data from Danish and Utah populations. A rigorous data treatment strategy maximises information content and allows for unbiased use of unphased XIP data. The Anderson–Darling goodness-of-fit statistics and likelihood ratio tests indicate that a model of genetically influenced XCI choice better fits the empirical data than models of completely random choice. PMID:23652377
McCluskey, Ken W.
2010-01-01
This article presents the author's comments on Hisham B. Ghassib's "Where Does Creativity Fit into a Productivist Industrial Model of Knowledge Production?" Ghassib's article focuses on the transformation of science from pre-modern times to the present. Ghassib (2010) notes that, unlike in an earlier era when the economy depended on static…
Supersymmetric Fits after the Higgs Discovery and Implications for Model Building
Ellis, John
2014-01-01
The data from the first run of the LHC at 7 and 8 TeV, together with the information provided by other experiments such as precision electroweak measurements, flavour measurements, the cosmological density of cold dark matter and the direct search for the scattering of dark matter particles in the LUX experiment, provide important constraints on supersymmetric models. Important information is provided by the ATLAS and CMS measurements of the mass of the Higgs boson, as well as the negative results of searches at the LHC for events with missing transverse energy accompanied by jets, and the LHCb and CMS measurements off BR($B_s \\to \\mu^+ \\mu^-$). Results are presented from frequentist analyses of the parameter spaces of the CMSSM and NUHM1. The global $\\chi^2$ functions for the supersymmetric models vary slowly over most of the parameter spaces allowed by the Higgs mass and the missing transverse energy search, with best-fit values that are comparable to the $\\chi^2$ for the Standard Model. The $95\\%$ CL lower...
International Nuclear Information System (INIS)
Wilkins, T.A.; Chadney, D.C.; Bryant, J.; Palmstroem, S.H.; Winder, R.L.
1977-01-01
Using the simple univalent antigen univalent-antibody equilibrium model the dose-response curve of a radioimmunoassay (RIA) may be expressed as a function of Y, X and the four physical parameters of the idealised system. A compact but powerful mini-computer program has been written in BASIC for rapid iterative non-linear least squares curve fitting and dose interpolation with this function. In its simplest form the program can be operated in an 8K byte mini-computer. The program has been extensively tested with data from 10 different assay systems (RIA and CPBA) for measurement of drugs and hormones ranging in molecular size from thyroxine to insulin. For each assay system the results have been analysed in terms of (a) curve fitting biases and (b) direct comparison with manual fitting. In all cases the quality of fitting was remarkably good in spite of the fact that the chemistry of each system departed significantly from one or more of the assumptions implicit in the model used. A mathematical analysis of departures from the model's principal assumption has provided an explanation for this somewhat unexpected observation. The essential features of this analysis are presented in this paper together with the statistical analyses of the performance of the program. From these and the results obtained to date in the routine quality control of these 10 assays, it is concluded that the method of curve fitting and dose interpolation presented in this paper is likely to be of general applicability. (orig.) [de
Fits combining hyperon semileptonic decays and magnetic moments and CVC
International Nuclear Information System (INIS)
Bohm, A.; Kielanowski, P.
1982-10-01
We have performed a test of CVC by determining the baryon charges and magnetic moments from the hyperon semileptonic data. Then CVC was applied in order to make a joint fit of all baryon semileptonic decay data and baryon magnetic moments for the spectrum generating group (SG) model as well as for the conventional (cabibbo and magnetic moments in nuclear magnetons) model. The SG model gives a very good fit with chi 2 /n/sub D/ = 25/20 approximately equals 21% C.L. whereas the conventional model gives a fit with chi 2 /n/sub D/ = 244/20
Mandys, Frantisek; Dolan, Conor V.; Molenaar, Peter C. M.
1994-01-01
Studied the conditions under which the quasi-Markov simplex model fits a linear growth curve covariance structure and determined when the model is rejected. Presents a quasi-Markov simplex model with structured means and gives an example. (SLD)
Time series modeling and forecasting using memetic algorithms for regime-switching models.
Bergmeir, Christoph; Triguero, Isaac; Molina, Daniel; Aznarte, José Luis; Benitez, José Manuel
2012-11-01
In this brief, we present a novel model fitting procedure for the neuro-coefficient smooth transition autoregressive model (NCSTAR), as presented by Medeiros and Veiga. The model is endowed with a statistically founded iterative building procedure and can be interpreted in terms of fuzzy rule-based systems. The interpretability of the generated models and a mathematically sound building procedure are two very important properties of forecasting models. The model fitting procedure employed by the original NCSTAR is a combination of initial parameter estimation by a grid search procedure with a traditional local search algorithm. We propose a different fitting procedure, using a memetic algorithm, in order to obtain more accurate models. An empirical evaluation of the method is performed, applying it to various real-world time series originating from three forecasting competitions. The results indicate that we can significantly enhance the accuracy of the models, making them competitive to models commonly used in the field.
Determining Mission Statement Effectiveness from a Fit Perspective
Directory of Open Access Journals (Sweden)
Toh Seong-Yuen
2017-08-01
Full Text Available The purpose of this paper is to study the relationship between the organization's mission statement and its outcomes from a fit perspective in the alignment of the organization's structural and cultural elements. Based on an extension of Campbell's (1991 mission model by combination of ideas from two schools of thought in mission statement studies (structural and cultural, the authors introduce the concept of “fit” to show how it contributes towards a new mission statement model. The results show that both alignments are important to create a fit situation in order to positively impact organization outcomes. Based on Cohen (1988, the detected effect size of .322 is considered large. The managerial implication is that there should be more focus on managing organisational alignment to support a fit situation as this is instrumental to mission statement effectiveness. The originality of this study stems from the idea that while past studies develop model based on ideas from within the confine of a particular school of thought, this study is one of the first to combine ideas from both the structural and cultural schools of thought by extending Campbell's (1991 mission model using the fit perspective.
Quantifying and Reducing Curve-Fitting Uncertainty in Isc: Preprint
Energy Technology Data Exchange (ETDEWEB)
Campanelli, Mark; Duck, Benjamin; Emery, Keith
2015-09-28
Current-voltage (I-V) curve measurements of photovoltaic (PV) devices are used to determine performance parameters and to establish traceable calibration chains. Measurement standards specify localized curve fitting methods, e.g., straight-line interpolation/extrapolation of the I-V curve points near short-circuit current, Isc. By considering such fits as statistical linear regressions, uncertainties in the performance parameters are readily quantified. However, the legitimacy of such a computed uncertainty requires that the model be a valid (local) representation of the I-V curve and that the noise be sufficiently well characterized. Using more data points often has the advantage of lowering the uncertainty. However, more data points can make the uncertainty in the fit arbitrarily small, and this fit uncertainty misses the dominant residual uncertainty due to so-called model discrepancy. Using objective Bayesian linear regression for straight-line fits for Isc, we investigate an evidence-based method to automatically choose data windows of I-V points with reduced model discrepancy. We also investigate noise effects. Uncertainties, aligned with the Guide to the Expression of Uncertainty in Measurement (GUM), are quantified throughout.
MOPET: a context-aware and user-adaptive wearable system for fitness training.
Buttussi, Fabio; Chittaro, Luca
2008-02-01
Cardiovascular disease, obesity, and lack of physical fitness are increasingly common and negatively affect people's health, requiring medical assistance and decreasing people's wellness and productivity. In the last years, researchers as well as companies have been increasingly investigating wearable devices for fitness applications with the aim of improving user's health, in terms of cardiovascular benefits, loss of weight or muscle strength. Dedicated GPS devices, accelerometers, step counters and heart rate monitors are already commercially available, but they are usually very limited in terms of user interaction and artificial intelligence capabilities. This significantly limits the training and motivation support provided by current systems, making them poorly suited for untrained people who are more interested in fitness for health rather than competitive purposes. To better train and motivate users, we propose the mobile personal trainer (MOPET) system. MOPET is a wearable system that supervises a physical fitness activity based on alternating jogging and fitness exercises in outdoor environments. By exploiting real-time data coming from sensors, knowledge elicited from a sport physiologist and a professional trainer, and a user model that is built and periodically updated through a guided autotest, MOPET can provide motivation as well as safety and health advice, adapted to the user and the context. To better interact with the user, MOPET also displays a 3D embodied agent that speaks, suggests stretching or strengthening exercises according to user's current condition, and demonstrates how to correctly perform exercises with interactive 3D animations. By describing MOPET, we show how context-aware and user-adaptive techniques can be applied to the fitness domain. In particular, we describe how such techniques can be exploited to train, motivate, and supervise users in a wearable personal training system for outdoor fitness activity.
Converting point-wise nuclear cross sections to pole representation using regularized vector fitting
Peng, Xingjie; Ducru, Pablo; Liu, Shichang; Forget, Benoit; Liang, Jingang; Smith, Kord
2018-03-01
Direct Doppler broadening of nuclear cross sections in Monte Carlo codes has been widely sought for coupled reactor simulations. One recent approach proposed analytical broadening using a pole representation of the commonly used resonance models and the introduction of a local windowing scheme to improve performance (Hwang, 1987; Forget et al., 2014; Josey et al., 2015, 2016). This pole representation has been achieved in the past by converting resonance parameters in the evaluation nuclear data library into poles and residues. However, cross sections of some isotopes are only provided as point-wise data in ENDF/B-VII.1 library. To convert these isotopes to pole representation, a recent approach has been proposed using the relaxed vector fitting (RVF) algorithm (Gustavsen and Semlyen, 1999; Gustavsen, 2006; Liu et al., 2018). This approach however needs to specify ahead of time the number of poles. This article addresses this issue by adding a poles and residues filtering step to the RVF procedure. This regularized VF (ReV-Fit) algorithm is shown to efficiently converge the poles close to the physical ones, eliminating most of the superfluous poles, and thus enabling the conversion of point-wise nuclear cross sections.
Theoretically unprejudiced fits to proton scattering
International Nuclear Information System (INIS)
Kobos, A.M.; Mackintosh, R.S.
1979-01-01
By using a spline interpolation method applied to all components of the proton optical potential we have fitted elastic scattering from 40 Ca and from 16 O at a range of energies. The potentials are highly oscillatory and we have shown that similar oscillations are found when the spline fitting procedure is applied to pseudo-data generated from potentials of known l-dependence. Moreover, we show how to find an l-independent potential equivalent to one that is l-dependent and we find that it is oscillatory and that various characteristic features of empirical spline fit potentials can be explained. Thus, by fitting the data with model indenpendt l-independent potentials we have found support for the contention that the nucleon optical potential should be viewed as being l-dependent. This work may be regarded as an example of the kind of physical information that can be gained by pursuing exact fits to proton elastic scattering data
Emerging Multinational Companies and Strategic Fit
DEFF Research Database (Denmark)
Gammeltoft, Peter; Filatotchev, Igor; Hobdari, Bersant
2012-01-01
There is an increasing awareness in international business that institutional factors need to be better incorporated into the understanding of international investments decisions of multinational companies. This applies equally to outward foreign direct investment by emerging economy firms...... has been suggested in terms of integrating various theoretical frameworks however and developing a more holistic understanding of these new investment flows. In this Editorial we propose that outward FDI from emerging economies can be better understood by analyzing them within a broad institutional...... which considers flows of outward investment from emerging economies as framed by institutional pressures at the firm level towards achieving fit between the environment, strategies, structures, resources and practices of the firm. For the multinational firm this fit must be attained along multiple...
A comparison of approaches in fitting continuum SEDs
International Nuclear Information System (INIS)
Liu Yao; Wang Hong-Chi; Madlener David; Wolf Sebastian
2013-01-01
We present a detailed comparison of two approaches, the use of a pre-calculated database and simulated annealing (SA), for fitting the continuum spectral energy distribution (SED) of astrophysical objects whose appearance is dominated by surrounding dust. While pre-calculated databases are commonly used to model SED data, only a few studies to date employed SA due to its unclear accuracy and convergence time for this specific problem. From a methodological point of view, different approaches lead to different fitting quality, demand on computational resources and calculation time. We compare the fitting quality and computational costs of these two approaches for the task of SED fitting to provide a guide to the practitioner to find a compromise between desired accuracy and available resources. To reduce uncertainties inherent to real datasets, we introduce a reference model resembling a typical circumstellar system with 10 free parameters. We derive the SED of the reference model with our code MC3 D at 78 logarithmically distributed wavelengths in the range [0.3 μm, 1.3 mm] and use this setup to simulate SEDs for the database and SA. Our result directly demonstrates the applicability of SA in the field of SED modeling, since the algorithm regularly finds better solutions to the optimization problem than a pre-calculated database. As both methods have advantages and shortcomings, a hybrid approach is preferable. While the database provides an approximate fit and overall probability distributions for all parameters deduced using Bayesian analysis, SA can be used to improve upon the results returned by the model grid.
Fitness club
2011-01-01
General fitness Classes Enrolments are open for general fitness classes at CERN taking place on Monday, Wednesday, and Friday lunchtimes in the Pump Hall (building 216). There are shower facilities for both men and women. It is possible to pay for 1, 2 or 3 classes per week for a minimum of 1 month and up to 6 months. Check out our rates and enrol at: http://cern.ch/club-fitness Hope to see you among us! CERN Fitness Club fitness.club@cern.ch
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.
Directory of Open Access Journals (Sweden)
T. Diana L. Van Aduard de Macedo-Soares
2011-02-01
Full Text Available In order to sustain their competitive advantage in the current increasingly globalized and turbulent context, more and more firms are competing globally in alliances and networks that oblige them to adopt new managerial paradigms and tools. However, their strategic analyses rarely take into account the strategic implications of these alliances and networks, considering their global relational characteristics, admittedly because of a lack of adequate tools to do so. This paper contributes to research that seeks to fill this gap by proposing the Global Strategic Network Analysis - SNA - framework. Its purpose is to help firms that compete globally in alliances and networks to carry out their strategic assessments and decision-making with a view to ensuring dynamic strategic fit from both a global and relational perspective.
van Koulil, S; van Lankveld, W; Kraaimaat, F W; van Helmond, T; Vedder, A; van Hoorn, H; Donders, A R T; Wirken, L; Cats, H; van Riel, P L C M; Evers, A W M
2011-12-01
Patients with fibromyalgia have diminished levels of physical fitness, which may lead to functional disability and exacerbating complaints. Multidisciplinary treatment comprising cognitive-behavioural therapy (CBT) and exercise training has been shown to be effective in improving physical fitness. However, due to the high drop-out rates and large variability in patients' functioning, it was proposed that a tailored treatment approach might yield more promising treatment outcomes. High-risk fibromyalgia patients were randomly assigned to a waiting list control group (WLC) or a treatment condition (TC), with the treatment consisting of 16 twice-weekly sessions of CBT and exercise training tailored to the patient's cognitive-behavioural pattern. Physical fitness was assessed with two physical tests before and 3 months after treatment and at corresponding intervals in the WLC. Treatment effects were evaluated using linear mixed models. The level of physical fitness had improved significantly in the TC compared with the WLC. Attrition rates were low, effect sizes large and reliable change indices indicated a clinically relevant improvement among the TC. A tailored multidisciplinary treatment approach for fibromyalgia consisting of CBT and exercise training is well tolerated, yields clinically relevant changes, and appears a promising approach to improve patients' physical fitness. ClinicalTrials.gov ID NCT00268606.
LOCO with Constraints and Improved Fitting Technique
International Nuclear Information System (INIS)
Not Available
2007-01-01
LOCO has been a powerful beam-based diagnostics and optics control method for storage rings and synchrotrons worldwide ever since it was established at NSLS by J. Safranek. This method measures the orbit response matrix and optionally the dispersion function of the machine. The data are then fitted to a lattice model by adjusting parameters such as quadrupole and skew quadrupole strengths in the model, BPM gains and rolls, corrector gains and rolls of the measurement system. Any abnormality of the machine that affects the machine optics can then be identified. The resulting lattice model is equivalent to the real machine lattice as seen by the BPMs. Since there are usually two or more BPMs per betatron period in modern circular accelerators, the model is often a very accurate representation of the real machine. According to the fitting result, one can correct the machine lattice to the design lattice by changing the quadrupole and skew quadrupole strengths. LOCO is so important that it is routinely performed at many electron storage rings to guarantee machine performance, especially after the Matlab-based LOCO code became available. However, for some machines, LOCO is not easy to carry out. In some cases, LOCO fitting converges to an unrealistic solution with large changes to the quadrupole strengths ΔK. The quadrupole gradient changes can be so large that the resulting lattice model fails to find a closed orbit and subsequent iterations become impossible. In cases when LOCO converges, the solution can have ΔK that is larger than realistic and often along with a spurious zigzag pattern between adjacent quadrupoles. This degeneracy behavior of LOCO is due to the correlation between the fitting parameters - usually between neighboring quadrupoles. The fitting scheme is therefore less restrictive over certain patterns of changes to these quadrupoles with which the correlated quadrupoles fight each other and the net effect is very inefficient χ 2 reduction, i
Exploration and extension of an improved Riemann track fitting algorithm
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.
Fitness voter model: Damped oscillations and anomalous consensus.
Woolcock, Anthony; Connaughton, Colm; Merali, Yasmin; Vazquez, Federico
2017-09-01
We study the dynamics of opinion formation in a heterogeneous voter model on a complete graph, in which each agent is endowed with an integer fitness parameter k≥0, in addition to its + or - opinion state. The evolution of the distribution of k-values and the opinion dynamics are coupled together, so as to allow the system to dynamically develop heterogeneity and memory in a simple way. When two agents with different opinions interact, their k-values are compared, and with probability p the agent with the lower value adopts the opinion of the one with the higher value, while with probability 1-p the opposite happens. The agent that keeps its opinion (winning agent) increments its k-value by one. We study the dynamics of the system in the entire 0≤p≤1 range and compare with the case p=1/2, in which opinions are decoupled from the k-values and the dynamics is equivalent to that of the standard voter model. When 0≤psystem approaches exponentially fast to the consensus state of the initial majority opinion. The mean consensus time τ appears to grow logarithmically with the number of agents N, and it is greatly decreased relative to the linear behavior τ∼N found in the standard voter model. When 1/2system initially relaxes to a state with an even coexistence of opinions, but eventually reaches consensus by finite-size fluctuations. The approach to the coexistence state is monotonic for 1/2oscillations around the coexistence value. The final approach to coexistence is approximately a power law t^{-b(p)} in both regimes, where the exponent b increases with p. Also, τ increases respect to the standard voter model, although it still scales linearly with N. The p=1 case is special, with a relaxation to coexistence that scales as t^{-2.73} and a consensus time that scales as τ∼N^{β}, with β≃1.45.
Local and omnibus goodness-of-fit tests in classical measurement error models
Ma, Yanyuan; Hart, Jeffrey D.; Janicki, Ryan; Carroll, Raymond J.
2010-01-01
We consider functional measurement error models, i.e. models where covariates are measured with error and yet no distributional assumptions are made about the mismeasured variable. We propose and study a score-type local test and an orthogonal
A Proposed Conceptual Model of Military Medical Readiness
National Research Council Canada - National Science Library
Van Hall, Brian M
2007-01-01
.... The purpose of this research is to consolidate existing literature on the latent variable of medical readiness, and to propose a composite theoretical model of medical readiness that may provide...
Directory of Open Access Journals (Sweden)
Ednilson Sergio Ramalho de Souza
2016-07-01
Full Text Available The pedagogical work with mathematical modeling assumes investigate situations of reality. However, mental models formed from the contact with the experiential world are generally incompatible with the conceptual models. So David Hestenes supports the view that one of the biggest challenges of teaching and learning in science and mathematics is to coordinate conceptual models with mental models, which led to the elaboration of a didactic in mathematical modeling: Modeling Instruction. Our goal is to present a proposal for thematic modeling cycle drawn up in hestenesianos assumptions and discuss possibilities for scientific literacy. The main question was to know how to emerge indicators for scientific literacy for the proposed cycle. This is a bibliographic research in order to identify the available literature contributions on the subject and raise the possibility and challenges for the brazilian teaching science and mathematics. Preliminary results indicate that the proposed modeling cycle can develop indicators for scientific literacy of different natures.
Zhang, Jin-Yu; Meng, Xiang-Bing; Xu, Wei; Zhang, Wei; Zhang, Yong
2014-01-01
This paper has proposed a new thermal wave image sequence compression algorithm by combining double exponential decay fitting model and differential evolution algorithm. This study benchmarked fitting compression results and precision of the proposed method was benchmarked to that of the traditional methods via experiment; it investigated the fitting compression performance under the long time series and improved model and validated the algorithm by practical thermal image sequence compression and reconstruction. The results show that the proposed algorithm is a fast and highly precise infrared image data processing method. PMID:24696649
Directory of Open Access Journals (Sweden)
Jin-Yu Zhang
2014-01-01
Full Text Available This paper has proposed a new thermal wave image sequence compression algorithm by combining double exponential decay fitting model and differential evolution algorithm. This study benchmarked fitting compression results and precision of the proposed method was benchmarked to that of the traditional methods via experiment; it investigated the fitting compression performance under the long time series and improved model and validated the algorithm by practical thermal image sequence compression and reconstruction. The results show that the proposed algorithm is a fast and highly precise infrared image data processing method.
A New Proposed Cost Model for List Accessing Problem using Buffering
Mohanty, Rakesh; Bhoi, Seetaya; Tripathy, Sasmita
2011-01-01
There are many existing well known cost models for the list accessing problem. The standard cost model developed by Sleator and Tarjan is most widely used. In this paper, we have made a comprehensive study of the existing cost models and proposed a new cost model for the list accessing problem. In our proposed cost model, for calculating the processing cost of request sequence using a singly linked list, we consider the access cost, matching cost and replacement cost. The cost of processing a...
International Nuclear Information System (INIS)
Hu, Menglong; Wang, Juan; Kong, Lingcong; An, Kang; Bi, Tao; Guo, Baohong; Dong, Enzeng
2015-01-01
Highlights: •A novel fitness evaluation method integrating environmental information is presented. •The introduction of neighbors’ payoff favors the promotion of cooperation in the PDG. •The role of direct neighbors becomes much more prominent. •In the SDG, the cooperative behavior is also improved by this new mechanism. -- Abstract: We propose an improved fitness evaluation method to investigate the evolution of cooperation in the spatial social dilemmas. In our model, a focal player’s fitness is calculated as the linear combination of his own payoff, the average payoffs of direct and indirect neighbors in which two independent selection parameters (α and β) are used to control the proportion of various payoff contribution to the current fitness. Then, the fitness-based strategy update rule is still Fermi-like, and asynchronous update is adopted here. A large plethora of numerical simulations are performed to validate the behaviors of the current model, and the results unambiguously demonstrate that the cooperation level is greatly enhanced by introducing the payoffs from the surrounding players. In particular, the influence of direct neighbors become more evident when compared with indirect neighbors since the correlation between focal players and their direct neighbors is much closer. Current outcomes are significant for us to further illustrate the origin and emergence of cooperation within a wide variety of natural and man-made systems
Predictive Models for Photovoltaic Electricity Production in Hot Weather Conditions
Directory of Open Access Journals (Sweden)
Jabar H. Yousif
2017-07-01
Full Text Available The process of finding a correct forecast equation for photovoltaic electricity production from renewable sources is an important matter, since knowing the factors affecting the increase in the proportion of renewable energy production and reducing the cost of the product has economic and scientific benefits. This paper proposes a mathematical model for forecasting energy production in photovoltaic (PV panels based on a self-organizing feature map (SOFM model. The proposed model is compared with other models, including the multi-layer perceptron (MLP and support vector machine (SVM models. Moreover, a mathematical model based on a polynomial function for fitting the desired output is proposed. Different practical measurement methods are used to validate the findings of the proposed neural and mathematical models such as mean square error (MSE, mean absolute error (MAE, correlation (R, and coefficient of determination (R2. The proposed SOFM model achieved a final MSE of 0.0007 in the training phase and 0.0005 in the cross-validation phase. In contrast, the SVM model resulted in a small MSE value equal to 0.0058, while the MLP model achieved a final MSE of 0.026 with a correlation coefficient of 0.9989, which indicates a strong relationship between input and output variables. The proposed SOFM model closely fits the desired results based on the R2 value, which is equal to 0.9555. Finally, the comparison results of MAE for the three models show that the SOFM model achieved a best result of 0.36156, whereas the SVM and MLP models yielded 4.53761 and 3.63927, respectively. A small MAE value indicates that the output of the SOFM model closely fits the actual results and predicts the desired output.
Ning, Jia; Schubert, Tilman; Johnson, Kevin M; Roldán-Alzate, Alejandro; Chen, Huijun; Yuan, Chun; Reeder, Scott B
2018-06-01
To propose a simple method to correct vascular input function (VIF) due to inflow effects and to test whether the proposed method can provide more accurate VIFs for improved pharmacokinetic modeling. A spoiled gradient echo sequence-based inflow quantification and contrast agent concentration correction method was proposed. Simulations were conducted to illustrate improvement in the accuracy of VIF estimation and pharmacokinetic fitting. Animal studies with dynamic contrast-enhanced MR scans were conducted before, 1 week after, and 2 weeks after portal vein embolization (PVE) was performed in the left portal circulation of pigs. The proposed method was applied to correct the VIFs for model fitting. Pharmacokinetic parameters fitted using corrected and uncorrected VIFs were compared between different lobes and visits. Simulation results demonstrated that the proposed method can improve accuracy of VIF estimation and pharmacokinetic fitting. In animal study results, pharmacokinetic fitting using corrected VIFs demonstrated changes in perfusion consistent with changes expected after PVE, whereas the perfusion estimates derived by uncorrected VIFs showed no significant changes. The proposed correction method improves accuracy of VIFs and therefore provides more precise pharmacokinetic fitting. This method may be promising in improving the reliability of perfusion quantification. Magn Reson Med 79:3093-3102, 2018. © 2017 International Society for Magnetic Resonance in Medicine. © 2017 International Society for Magnetic Resonance in Medicine.
Physical Work Demands and Fitness
DEFF Research Database (Denmark)
Larsen, Mette Korshøj
. The effects were evaluated with objective physiological or diurnal data in an intention-to-treat analysis using multi-adjusted mixed models. The results indicated that the intervention led to several improvements in risk factors for cardiovascular disease, e.g. enhanced cardiorespiratory fitness, reduced...... exposed to high relative aerobic workloads obtained more pronounced increases of resting and 24-hour ambulatory blood pressure, an unaltered cardiorespiratory fitness and a reduced sleeping heart rate. The enhanced resting and 24-hour ambulatory blood pressure may be explained as a potential...
Whiteman-Sandland, Jessica; Hawkins, Jemma; Clayton, Debbie
2016-08-01
This is the first study to measure the 'sense of community' reportedly offered by the CrossFit gym model. A cross-sectional study adapted Social Capital and General Belongingness scales to compare perceptions of a CrossFit gym and a traditional gym. CrossFit gym members reported significantly higher levels of social capital (both bridging and bonding) and community belongingness compared with traditional gym members. However, regression analysis showed neither social capital, community belongingness, nor gym type was an independent predictor of gym attendance. Exercise and health professionals may benefit from evaluating further the 'sense of community' offered by gym-based exercise programmes.
Energy Technology Data Exchange (ETDEWEB)
Smith, D.L.; Guenther, P.T.
1983-11-01
We suggest a procedure for estimating uncertainties in neutron cross sections calculated with a nuclear model descriptive of a specific mass region. It applies standard error propagation techniques, using a model-parameter covariance matrix. Generally, available codes do not generate covariance information in conjunction with their fitting algorithms. Therefore, we resort to estimating a relative covariance matrix a posteriori from a statistical examination of the scatter of elemental parameter values about the regional representation. We numerically demonstrate our method by considering an optical-statistical model analysis of a body of total and elastic scattering data for the light fission-fragment mass region. In this example, strong uncertainty correlations emerge and they conspire to reduce estimated errors to some 50% of those obtained from a naive uncorrelated summation in quadrature. 37 references.
International Nuclear Information System (INIS)
Smith, D.L.; Guenther, P.T.
1983-11-01
We suggest a procedure for estimating uncertainties in neutron cross sections calculated with a nuclear model descriptive of a specific mass region. It applies standard error propagation techniques, using a model-parameter covariance matrix. Generally, available codes do not generate covariance information in conjunction with their fitting algorithms. Therefore, we resort to estimating a relative covariance matrix a posteriori from a statistical examination of the scatter of elemental parameter values about the regional representation. We numerically demonstrate our method by considering an optical-statistical model analysis of a body of total and elastic scattering data for the light fission-fragment mass region. In this example, strong uncertainty correlations emerge and they conspire to reduce estimated errors to some 50% of those obtained from a naive uncorrelated summation in quadrature. 37 references
Nevill, Alan M; Duncan, Michael J; Lahart, Ian; Sandercock, Gavin
2016-11-01
The association between being overweight/obese and deprivation is a serious concern in English schoolchildren. To model this association incorporating known confounders and to discover whether physical fitness and physical activity may reduce or eliminate this association. Cross-sectional data were collected between 2007-2009, from 8053 10-16 year old children from the East-of-England Healthy Heart Study. Weight status was assessed using waist circumference (cm) and body mass (kg). Deprivation was measured using the Index of Multiple Deprivation (IMD). Confounding variables used in the proportional, allometric models were hip circumference, stature, age and sex. Children's fitness levels were assessed using predicted VO 2 max (20-metre shuttle-run test) and physical activity was estimated using the Physical Activity Questionnaire for Adolescents or Children. A strong association was found between both waist circumference and body mass and the IMD. These associations persisted after controlling for all confounding variables. When the children's physical activity and fitness levels were added to the models, the association was either greatly reduced or, in the case of body mass, absent. To reduce deprivation inequalities in children's weight-status, health practitioners should focus on increasing physical fitness via physical activity in areas of greater deprivation.
Software reliability growth models with normal failure time distributions
International Nuclear Information System (INIS)
Okamura, Hiroyuki; Dohi, Tadashi; Osaki, Shunji
2013-01-01
This paper proposes software reliability growth models (SRGM) where the software failure time follows a normal distribution. The proposed model is mathematically tractable and has sufficient ability of fitting to the software failure data. In particular, we consider the parameter estimation algorithm for the SRGM with normal distribution. The developed algorithm is based on an EM (expectation-maximization) algorithm and is quite simple for implementation as software application. Numerical experiment is devoted to investigating the fitting ability of the SRGMs with normal distribution through 16 types of failure time data collected in real software projects
Variational and robust density fitting of four-center two-electron integrals in local metrics
Reine, Simen; Tellgren, Erik; Krapp, Andreas; Kjærgaard, Thomas; Helgaker, Trygve; Jansik, Branislav; Høst, Stinne; Salek, Paweł
2008-09-01
Density fitting is an important method for speeding up quantum-chemical calculations. Linear-scaling developments in Hartree-Fock and density-functional theories have highlighted the need for linear-scaling density-fitting schemes. In this paper, we present a robust variational density-fitting scheme that allows for solving the fitting equations in local metrics instead of the traditional Coulomb metric, as required for linear scaling. Results of fitting four-center two-electron integrals in the overlap and the attenuated Gaussian damped Coulomb metric are presented, and we conclude that density fitting can be performed in local metrics at little loss of chemical accuracy. We further propose to use this theory in linear-scaling density-fitting developments.
On the fit of models to covariances and methodology to the Bulletin.
Bentler, P M
1992-11-01
It is noted that 7 of the 10 top-cited articles in the Psychological Bulletin deal with methodological topics. One of these is the Bentler-Bonett (1980) article on the assessment of fit in covariance structure models. Some context is provided on the popularity of this article. In addition, a citation study of methodology articles appearing in the Bulletin since 1978 was carried out. It verified that publications in design, evaluation, measurement, and statistics continue to be important to psychological research. Some thoughts are offered on the role of the journal in making developments in these areas more accessible to psychologists.
Experimental Rugged Fitness Landscape in Protein Sequence Space
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
Experimental rugged fitness landscape in protein sequence space.
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.
Directory of Open Access Journals (Sweden)
Yuuki Hayashi
Full Text Available 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.
Robust mislabel logistic regression without modeling mislabel probabilities.
Hung, Hung; Jou, Zhi-Yu; Huang, Su-Yun
2018-03-01
Logistic regression is among the most widely used statistical methods for linear discriminant analysis. In many applications, we only observe possibly mislabeled responses. Fitting a conventional logistic regression can then lead to biased estimation. One common resolution is to fit a mislabel logistic regression model, which takes into consideration of mislabeled responses. Another common method is to adopt a robust M-estimation by down-weighting suspected instances. In this work, we propose a new robust mislabel logistic regression based on γ-divergence. Our proposal possesses two advantageous features: (1) It does not need to model the mislabel probabilities. (2) The minimum γ-divergence estimation leads to a weighted estimating equation without the need to include any bias correction term, that is, it is automatically bias-corrected. These features make the proposed γ-logistic regression more robust in model fitting and more intuitive for model interpretation through a simple weighting scheme. Our method is also easy to implement, and two types of algorithms are included. Simulation studies and the Pima data application are presented to demonstrate the performance of γ-logistic regression. © 2017, The International Biometric Society.
International Nuclear Information System (INIS)
Wang, Shaoqing; Jin, Zhi; Muhammad, Asif; Peng, Songang; Huang, Xinnan; Zhang, Dayong; Shi, Jingyuan
2016-01-01
The most common method of mobility extraction for graphene field-effect transistors is proposed by Kim. Kim’s method assumes a constant mobility independent of carrier density and gets the mobility by fitting the transfer curves. However, carrier mobility changes with the carrier density, leading to the inaccuracy of Kim’s method. In our paper, a new and more accurate method is proposed to extract mobility by fitting the output curves at a constant gate voltage. The output curves are fitted using several kinds of current–voltage models. Besides the models in the literature, we present a modified model, which takes into account not only the quantum capacitance, contact resistance, but also a modified drift velocity-field relationship. Comparing with the other models, this new model can fit better with our experimental data. The dependence of carrier intrinsic mobility on carrier density is obtained based on this model. (paper)
Whole Protein Native Fitness Potentials
Faraggi, Eshel; Kloczkowski, Andrzej
2013-03-01
Protein structure prediction can be separated into two tasks: sample the configuration space of the protein chain, and assign a fitness between these hypothetical models and the native structure of the protein. One of the more promising developments in this area is that of knowledge based energy functions. However, standard approaches using pair-wise interactions have shown shortcomings demonstrated by the superiority of multi-body-potentials. These shortcomings are due to residue pair-wise interaction being dependent on other residues along the chain. We developed a method that uses whole protein information filtered through machine learners to score protein models based on their likeness to native structures. For all models we calculated parameters associated with the distance to the solvent and with distances between residues. These parameters, in addition to energy estimates obtained by using a four-body-potential, DFIRE, and RWPlus were used as training for machine learners to predict the fitness of the models. Testing on CASP 9 targets showed that our method is superior to DFIRE, RWPlus, and the four-body potential, which are considered standards in the field.
Liou, Jing-Yang; Ting, Chien-Kun; Mandell, M Susan; Chang, Kuang-Yi; Teng, Wei-Nung; Huang, Yu-Yin; Tsou, Mei-Yung
2016-08-01
Selecting an effective dose of sedative drugs in combined upper and lower gastrointestinal endoscopy is complicated by varying degrees of pain stimulation. We tested the ability of 5 response surface models to predict depth of sedation after administration of midazolam and alfentanil in this complex model. The procedure was divided into 3 phases: esophagogastroduodenoscopy (EGD), colonoscopy, and the time interval between the 2 (intersession). The depth of sedation in 33 adult patients was monitored by Observer Assessment of Alertness/Scores. A total of 218 combinations of midazolam and alfentanil effect-site concentrations derived from pharmacokinetic models were used to test 5 response surface models in each of the 3 phases of endoscopy. Model fit was evaluated with objective function value, corrected Akaike Information Criterion (AICc), and Spearman ranked correlation. A model was arbitrarily defined as accurate if the predicted probability is effect-site concentrations tested ranged from 1 to 76 ng/mL and from 5 to 80 ng/mL for midazolam and alfentanil, respectively. Midazolam and alfentanil had synergistic effects in colonoscopy and EGD, but additivity was observed in the intersession group. Adequate prediction rates were 84% to 85% in the intersession group, 84% to 88% during colonoscopy, and 82% to 87% during EGD. The reduced Greco and Fixed alfentanil concentration required for 50% of the patients to achieve targeted response Hierarchy models performed better with comparable predictive strength. The reduced Greco model had the lowest AICc with strong correlation in all 3 phases of endoscopy. Dynamic, rather than fixed, γ and γalf in the Hierarchy model improved model fit. The reduced Greco model had the lowest objective function value and AICc and thus the best fit. This model was reliable with acceptable predictive ability based on adequate clinical correlation. We suggest that this model has practical clinical value for patients undergoing procedures
International Nuclear Information System (INIS)
Fruehwirth, R.
1993-01-01
We present an estimation procedure of the error components in a linear regression model with multiple independent stochastic error contributions. After solving the general problem we apply the results to the estimation of the actual trajectory in track fitting with multiple scattering. (orig.)
Spreadsheets, Graphing Calculators and the Line of Best Fit
Directory of Open Access Journals (Sweden)
Bernie O'Sullivan
2003-07-01
One technique that can now be done, almost mindlessly, is the line of best fit. Both the graphing calculator and the Excel spreadsheet produce models for collected data that appear to be very good fits, but upon closer scrutiny, are revealed to be quite poor. This article will examine one such case. I will couch the paper within the framework of a very good classroom investigation that will help generate students’ understanding of the basic principles of curve fitting and will enable them to produce a very accurate model of collected data by combining the technology of the graphing calculator and the spreadsheet.
Information Theoretic Tools for Parameter Fitting in Coarse Grained Models
Kalligiannaki, Evangelia
2015-01-07
We study the application of information theoretic tools for model reduction in the case of systems driven by stochastic dynamics out of equilibrium. The model/dimension reduction is considered by proposing parametrized coarse grained dynamics and finding the optimal parameter set for which the relative entropy rate with respect to the atomistic dynamics is minimized. The minimization problem leads to a generalization of the force matching methods to non equilibrium systems. A multiplicative noise example reveals the importance of the diffusion coefficient in the optimization problem.
Carlotti, Massimo; Brizzi, Gabriele; Papandrea, Enzo; Prevedelli, Marco; Ridolfi, Marco; Dinelli, Bianca Maria; Magnani, Luca
2006-02-01
We present a new retrieval model designed to analyze the observations of the Michelson Interferometer for Passive Atmospheric Sounding (MIPAS), which is on board the ENVironmental SATellite (ENVISAT). The new geo-fit multitarget retrieval model (GMTR) implements the geo-fit two-dimensional inversion for the simultaneous retrieval of several targets including a set of atmospheric constituents that are not considered by the ground processor of the MIPAS experiment. We describe the innovative solutions adopted in the inversion algorithm and the main functionalities of the corresponding computer code. The performance of GMTR is compared with that of the MIPAS ground processor in terms of accuracy of the retrieval products. Furthermore, we show the capability of GMTR to resolve the horizontal structures of the atmosphere. The new retrieval model is implemented in an optimized computer code that is distributed by the European Space Agency as "open source" in a package that includes a full set of auxiliary data for the retrieval of 28 atmospheric targets.
The structure of PTSD symptoms according to DSM-5 and IDC-11 proposal: A multi-sample analysis.
Cyniak-Cieciura, M; Staniaszek, K; Popiel, A; Pragłowska, E; Zawadzki, B
2017-07-01
Posttraumatic stress disorder (PTSD) symptoms structure is a subject of ongoing debate since its inclusion in DSM-III classification in 1980. Different research on PTSD symptoms structure proved the better fit of four-factor and five-factor models comparing to the one proposed by DSM-IV. With the publication of DSM-5 classification, which introduced significant changes to PTSD diagnosis, the question arises about the adequacy of the proposed criteria to the real structure of disorder symptoms. Recent analyses suggest that seven-factor hybrid model is the best reflection of symptoms structure proposed to date. At the same time, some researchers and ICD-11 classification postulate a simplification of PTSD diagnosis restricting it to only three core criteria and adding additional diagnostic unit of complex-PTSD. This research aimed at checking symptoms' structure according to well-known and supported four-, five-, six- and seven-factor models based on DSM-5 symptoms and the conceptualization proposed by the ICD-11 as well as examining the relation between PTSD symptoms categories with borderline personality disorder. Four different trauma populations were examined with self-reported Posttraumatic Diagnostic Scale for DSM-5 (PDS-5) measure. The results suggest that six- and seven-factor hybrid model as well as three-factor ICD-11 concept fits the data better than other models. The core PTSD symptoms were less related to borderline personality disorder than other, broader, symptoms categories only in one sample. Combination of ICD-11 simplified PTSD diagnosis with the more complex approach (e.g. basing on a seven-factor model) may be an attractive proposal for both scientists and practitioners, however does not necessarily lower its comorbidity with borderline personality disorder. Copyright © 2017 Elsevier Masson SAS. All rights reserved.
Combining playware exergaming with a mobile fitness app
DEFF Research Database (Denmark)
Giannisakis, Emmanouil; Lund, Henrik Hautop
2015-01-01
We propose a novel playware as a merge between exergames and mobile fitness apps to engage the users in physical exercises, not only as competitive play, but also in the form of cooperative play. The concept connects modular interactive tiles with radio communication to Android tablets and smart...
Muon g-2 Estimates. Can One Trust Effective Lagrangians and Global Fits?
Energy Technology Data Exchange (ETDEWEB)
Benayoun, M.; DelBuono, L. [Paris VI et Paris VII Univ. (France). LPNHE; David, P. [Paris VI et Paris VII Univ. (France). LPNHE; Paris-Diderot Univ./CNRS UMR 8236 (France). LIED; Jegerlehner, F. [Humboldt-Universitaet, Berlin (Germany). Inst. fuer Physik; Deutsches Elektronen-Synchrotron (DESY), Zeuthen (Germany)
2015-07-15
Previous studies have shown that the Hidden Local Symmetry (HLS) Model, supplied with appropriate symmetry breaking mechanisms, provides an Effective Lagrangian (BHLS) which encompasses a large number of processes within a unified framework; a global fit procedure allows for a simultaneous description of the e{sup +}e{sup -} annihilation into the 6 final states - π{sup +}π{sup -}, π{sup 0}γ, ηγ, π{sup +}π{sup -}π{sup 0}, K{sup +}K{sup -}, K{sub L}K{sub S} - and includes the dipion spectrum in the τ decay and some more light meson decay partial widths. The contribution to the muon anomalous magnetic moment a{sup th}{sub μ} of these annihilation channels over the range of validity of the HLS model (up to 1.05 GeV) is found much improved compared to its partner derived from integrating the measured spectra directly. However, most spectra for the process e{sup +}e{sup -} → π{sup +}π{sup -} undergo overall scale uncertainties which dominate the other sources, and one may suspect some bias in the dipion contribution to a{sup th}{sub μ}. However, an iterated fit algorithm, shown to lead to unbiased results by a Monte Carlo study, is defined and applied succesfully to the e{sup +}e{sup -} → π{sup +}π{sup -} data samples from CMD2, SND, KLOE (including the latest sample) and BaBar. The iterated fit solution is shown to be further improved and leads to a value for a{sub μ} different from aexp above the 4σ level. The contribution of the π{sup +}π{sup -} intermediate state up to 1.05 GeV to a{sub μ} derived from the iterated fit benefits from an uncertainty about 3 times smaller than the corresponding usual estimate. Therefore, global fit techniques are shown to work and lead to improved unbiased results. The main issue raised in this study and the kind of solution proposed may be of concern for other data driven methods when the data samples are dominated by global normalization uncertainties.
Muon g-2 Estimates. Can One Trust Effective Lagrangians and Global Fits?
International Nuclear Information System (INIS)
Benayoun, M.; DelBuono, L.
2015-07-01
Previous studies have shown that the Hidden Local Symmetry (HLS) Model, supplied with appropriate symmetry breaking mechanisms, provides an Effective Lagrangian (BHLS) which encompasses a large number of processes within a unified framework; a global fit procedure allows for a simultaneous description of the e + e - annihilation into the 6 final states - π + π - , π 0 γ, ηγ, π + π - π 0 , K + K - , K L K S - and includes the dipion spectrum in the τ decay and some more light meson decay partial widths. The contribution to the muon anomalous magnetic moment a th μ of these annihilation channels over the range of validity of the HLS model (up to 1.05 GeV) is found much improved compared to its partner derived from integrating the measured spectra directly. However, most spectra for the process e + e - → π + π - undergo overall scale uncertainties which dominate the other sources, and one may suspect some bias in the dipion contribution to a th μ . However, an iterated fit algorithm, shown to lead to unbiased results by a Monte Carlo study, is defined and applied succesfully to the e + e - → π + π - data samples from CMD2, SND, KLOE (including the latest sample) and BaBar. The iterated fit solution is shown to be further improved and leads to a value for a μ different from aexp above the 4σ level. The contribution of the π + π - intermediate state up to 1.05 GeV to a μ derived from the iterated fit benefits from an uncertainty about 3 times smaller than the corresponding usual estimate. Therefore, global fit techniques are shown to work and lead to improved unbiased results. The main issue raised in this study and the kind of solution proposed may be of concern for other data driven methods when the data samples are dominated by global normalization uncertainties.
Optimum track fitting in the presence of multiple scattering
International Nuclear Information System (INIS)
Lutz, G.
1987-06-01
A method for track fitting is proposed which attempts to be as close as possible to the real track along the full path length. This is done by the introduction of scattering planes in which the particle is allowed to change its direction. A fit over the full track length includes the probability of direction change by scattering. Using matrix notation a fairly simple formalism for error estimation has been developed. Results of this method are compared to those of more widely used procedures for 'typical' examples of High Energy Spectrometers. (orig.)
Isochrone Fitting of Hubble Photometry in UV–VIS–IR Bands
Barker, Hallie; Paust, Nathaniel E. Q.
2018-03-01
We present new isochrone fits to color–magnitude diagrams from Hubble Space Telescope Wide Field Camera 3 and Advanced Camera for Surveys photometry of the globular clusters M13 and M80 in five bands from the ultraviolet to near-infrared. Isochrone fits to the photometry using the Dartmouth Stellar Evolution Program (DSEP), the PAdova and TRieste Stellar Evolution Code (PARSEC), and MESA Isochrones and Stellar Tracks (MIST) are examined to study the isochrone morphology. Additionally, cluster ages, extinctions, and distances are found from the visible-infrared color–magnitude diagrams. We conduct careful qualitative analysis on the inconsistencies of the fits across twelve color combinations of the five observed bands, and find that the (F606W‑F814W) color generally produces very good fits, but that there are large discrepancies when the data is fit using colors including UV bands for all three models. We also find that the best fits in the UV are achieved using MIST isochrones, but that they require metallicities that are lower than the other two models, as well published spectroscopic values. Finally, we directly compare DSEP and PARSEC by performing isochrone-isochrone fitting, and find that, for globular cluster aged populations, similar appearing PARSEC isochrones are on average 1.5 Gyr younger than DSEP isochrones. We find that the two models become less discrepant at lower metallicities.
Hansen, Maj; Hyland, Philip; Karstoft, Karen-Inge; Vaegter, Henrik B.; Bramsen, Rikke H.; Nielsen, Anni B. S.; Armour, Cherie; Andersen, Søren B.; Høybye, Mette Terp; Larsen, Simone Kongshøj; Andersen, Tonny E.
2017-01-01
ABSTRACT Background: Researchers and clinicians within the field of trauma have to choose between different diagnostic descriptions of posttraumatic stress disorder (PTSD) in the DSM-5 and the proposed ICD-11. Several studies support different competing models of the PTSD structure according to both diagnostic systems; however, findings show that the choice of diagnostic systems can affect the estimated prevalence rates. Objectives: The present study aimed to investigate the potential impact of using a large (i.e. the DSM-5) compared to a small (i.e. the ICD-11) diagnostic description of PTSD. In other words, does the size of PTSD really matter? Methods: The aim was investigated by examining differences in diagnostic rates between the two diagnostic systems and independently examining the model fit of the competing DSM-5 and ICD-11 models of PTSD across three trauma samples: university students (N = 4213), chronic pain patients (N = 573), and military personnel (N = 118). Results: Diagnostic rates of PTSD were significantly lower according to the proposed ICD-11 criteria in the university sample, but no significant differences were found for chronic pain patients and military personnel. The proposed ICD-11 three-factor model provided the best fit of the tested ICD-11 models across all samples, whereas the DSM-5 seven-factor Hybrid model provided the best fit in the university and pain samples, and the DSM-5 six-factor Anhedonia model provided the best fit in the military sample of the tested DSM-5 models. Conclusions: The advantages and disadvantages of using a broad or narrow set of symptoms for PTSD can be debated, however, this study demonstrated that choice of diagnostic system may influence the estimated PTSD rates both qualitatively and quantitatively. In the current described diagnostic criteria only the ICD-11 model can reflect the configuration of symptoms satisfactorily. Thus, size does matter when assessing PTSD. PMID:29201287
Hansen, Maj; Hyland, Philip; Karstoft, Karen-Inge; Vaegter, Henrik B; Bramsen, Rikke H; Nielsen, Anni B S; Armour, Cherie; Andersen, Søren B; Høybye, Mette Terp; Larsen, Simone Kongshøj; Andersen, Tonny E
2017-01-01
Background : Researchers and clinicians within the field of trauma have to choose between different diagnostic descriptions of posttraumatic stress disorder (PTSD) in the DSM-5 and the proposed ICD-11. Several studies support different competing models of the PTSD structure according to both diagnostic systems; however, findings show that the choice of diagnostic systems can affect the estimated prevalence rates. Objectives : The present study aimed to investigate the potential impact of using a large (i.e. the DSM-5) compared to a small (i.e. the ICD-11) diagnostic description of PTSD. In other words, does the size of PTSD really matter? Methods: The aim was investigated by examining differences in diagnostic rates between the two diagnostic systems and independently examining the model fit of the competing DSM-5 and ICD-11 models of PTSD across three trauma samples: university students ( N = 4213), chronic pain patients ( N = 573), and military personnel ( N = 118). Results : Diagnostic rates of PTSD were significantly lower according to the proposed ICD-11 criteria in the university sample, but no significant differences were found for chronic pain patients and military personnel. The proposed ICD-11 three-factor model provided the best fit of the tested ICD-11 models across all samples, whereas the DSM-5 seven-factor Hybrid model provided the best fit in the university and pain samples, and the DSM-5 six-factor Anhedonia model provided the best fit in the military sample of the tested DSM-5 models. Conclusions : The advantages and disadvantages of using a broad or narrow set of symptoms for PTSD can be debated, however, this study demonstrated that choice of diagnostic system may influence the estimated PTSD rates both qualitatively and quantitatively. In the current described diagnostic criteria only the ICD-11 model can reflect the configuration of symptoms satisfactorily. Thus, size does matter when assessing PTSD.
The validity and value of inclusive fitness theory.
Bourke, Andrew F G
2011-11-22
Social evolution is a central topic in evolutionary biology, with the evolution of eusociality (societies with altruistic, non-reproductive helpers) representing a long-standing evolutionary conundrum. Recent critiques have questioned the validity of the leading theory for explaining social evolution and eusociality, namely inclusive fitness (kin selection) theory. I review recent and past literature to argue that these critiques do not succeed. Inclusive fitness theory has added fundamental insights to natural selection theory. These are the realization that selection on a gene for social behaviour depends on its effects on co-bearers, the explanation of social behaviours as unalike as altruism and selfishness using the same underlying parameters, and the explanation of within-group conflict in terms of non-coinciding inclusive fitness optima. A proposed alternative theory for eusocial evolution assumes mistakenly that workers' interests are subordinate to the queen's, contains no new elements and fails to make novel predictions. The haplodiploidy hypothesis has yet to be rigorously tested and positive relatedness within diploid eusocial societies supports inclusive fitness theory. The theory has made unique, falsifiable predictions that have been confirmed, and its evidence base is extensive and robust. Hence, inclusive fitness theory deserves to keep its position as the leading theory for social evolution.
Energy Technology Data Exchange (ETDEWEB)
Torello, David [GW Woodruff School of Mechanical Engineering, Georgia Tech (United States); Kim, Jin-Yeon [School of Civil and Environmental Engineering, Georgia Tech (United States); Qu, Jianmin [Department of Civil and Environmental Engineering, Northwestern University (United States); Jacobs, Laurence J. [School of Civil and Environmental Engineering, Georgia Tech and GW Woodruff School of Mechanical Engineering, Georgia Tech (United States)
2015-03-31
This research considers the effects of diffraction, attenuation, and the nonlinearity of generating sources on measurements of nonlinear ultrasonic Rayleigh wave propagation. A new theoretical framework for correcting measurements made with air-coupled and contact piezoelectric receivers for the aforementioned effects is provided based on analytical models and experimental considerations. A method for extracting the nonlinearity parameter β{sub 11} is proposed based on a nonlinear least squares curve-fitting algorithm that is tailored for Rayleigh wave measurements. Quantitative experiments are conducted to confirm the predictions for the nonlinearity of the piezoelectric source and to demonstrate the effectiveness of the curve-fitting procedure. These experiments are conducted on aluminum 2024 and 7075 specimens and a β{sub 11}{sup 7075}/β{sub 11}{sup 2024} measure of 1.363 agrees well with previous literature and earlier work.
Fitness Club
2012-01-01
Open to All: http://cern.ch/club-fitness fitness.club@cern.ch Boxing Your supervisor makes your life too tough ! You really need to release the pressure you've been building up ! Come and join the fit-boxers. We train three times a week in Bd 216, classes for beginners and advanced available. Visit our website cern.ch/Boxing General Fitness Escape from your desk with our general fitness classes, to strengthen your heart, muscles and bones, improve you stamina, balance and flexibility, achieve new goals, be more productive and experience a sense of well-being, every Monday, Wednesday and Friday lunchtime, Tuesday mornings before work and Thursday evenings after work – join us for one of our monthly fitness workshops. Nordic Walking Enjoy the great outdoors; Nordic Walking is a great way to get your whole body moving and to significantly improve the condition of your muscles, heart and lungs. It will boost your energy levels no end. Pilates A body-conditioning technique de...
Links between Natural Variation in the Microbiome and Host Fitness in Wild Mammals.
Suzuki, Taichi A
2017-10-01
Recent studies in model organisms have shown that compositional variation in the microbiome can affect a variety of host phenotypes including those related to digestion, development, immunity, and behavior. Natural variation in the microbiome within and between natural populations and species may also affect host phenotypes and thus fitness in the wild. Here, I review recent evidence that compositional variation in the microbiome may affect host phenotypes and fitness in wild mammals. Studies over the last decade indicate that natural variation in the mammalian microbiome may be important in the assistance of energy uptake from different diet types, detoxification of plant secondary compounds, protection from pathogens, chemical communication, and behavior. I discuss the importance of combining both field observations and manipulative experiments in a single system to fully characterize the functions and fitness effects of the microbiome. Finally, I discuss the evolutionary consequences of mammal-microbiome associations by proposing a framework to test how natural selection on hosts is mediated by the microbiome. © The Author 2017. Published by Oxford University Press on behalf of the Society for Integrative and Comparative Biology. All rights reserved. For permissions please email: journals.permissions@oup.com.
Fitting the e+e- → e+e- lineshape
International Nuclear Information System (INIS)
Martinez, M.; Miquel, R.
1992-01-01
The implications of different treatments of the e + e - →e + e - cross sections in the context of Z parameter fitting are discussed. We show that fitting with a complete description of the process might become important for an accurate determination of the Z parameters. A fitting formula describing the integrated cross section in terms of the Z parameters is presented. This formula agrees with the most accurate calculations in the Standard Model to within 1 per mil. (orig.)
Goodness-of-fit tests with dependent observations
International Nuclear Information System (INIS)
Chicheportiche, Rémy; Bouchaud, Jean-Philippe
2011-01-01
We revisit the Kolmogorov–Smirnov and Cramér–von Mises goodness-of-fit (GoF) tests and propose a generalization to identically distributed, but dependent univariate random variables. We show that the dependence leads to a reduction of the 'effective' number of independent observations. The generalized GoF tests are not distribution-free but rather depend on all the lagged bivariate copulas. These objects, that we call 'self-copulas', encode all the non-linear temporal dependences. We introduce a specific, log-normal model for these self-copulas, for which a number of analytical results are derived. An application to financial time series is provided. As is well known, the dependence is to be long-ranged in this case, a finding that we confirm using self-copulas. As a consequence, the acceptance rates for GoF tests are substantially higher than if the returns were iid random variables
Zhao, Yongguang; Li, Chuanrong; Ma, Lingling; Tang, Lingli; Wang, Ning; Zhou, Chuncheng; Qian, Yonggang
2017-10-01
Time series of satellite reflectance data have been widely used to characterize environmental phenomena, describe trends in vegetation dynamics and study climate change. However, several sensors with wide spatial coverage and high observation frequency are usually designed to have large field of view (FOV), which cause variations in the sun-targetsensor geometry in time-series reflectance data. In this study, on the basis of semiempirical kernel-driven BRDF model, a new semi-empirical model was proposed to normalize the sun-target-sensor geometry of remote sensing image. To evaluate the proposed model, bidirectional reflectance under different canopy growth conditions simulated by Discrete Anisotropic Radiative Transfer (DART) model were used. The semi-empirical model was first fitted by using all simulated bidirectional reflectance. Experimental result showed a good fit between the bidirectional reflectance estimated by the proposed model and the simulated value. Then, MODIS time-series reflectance data was normalized to a common sun-target-sensor geometry by the proposed model. The experimental results showed the proposed model yielded good fits between the observed and estimated values. The noise-like fluctuations in time-series reflectance data was also reduced after the sun-target-sensor normalization process.
Main features of the proposed NCRP respiratory tract model
International Nuclear Information System (INIS)
Phalen, R.F.; Fisher, G.L.; Moss, O.R.; Schlesinger, R.B.; Swift, D.L.
1991-01-01
The proposed NCRP respiratory tract dosimetry model regions include the naso-oro-pharyngo-laryngeal (NOPL), the tracheobronchial (TB), the pulmonary (P), and the lymph nodes (LN). Input aerosol concentrations are derived from a consideration of particle-size-dependent inspirability. Particle deposition in the respiratory tract is modelled using the mechanisms of inertial impaction, sedimentation and diffusion. The rates of absorption of particles, and transport to the blood, have been derived from clearance data from people and laboratory animals. The effect of body growth on particle deposition is considered. Particle clearance rates are assumed to be independent of age. The proposed respiratory tract model differs significantly from the 1966 Task Group Model in that (1) inspirability is considered; (2) new sub-regions of the respiratory tract are considered; (3) absorption of materials by the blood is treated in a more sophisticated fashion; and (4) body size (and thus age) is taken into account. (author)
FITS: a function-fitting program
Energy Technology Data Exchange (ETDEWEB)
Balestrini, S.J.; Chezem, C.G.
1982-01-01
FITS is an iterating computer program that adjusts the parameters of a function to fit a set of data points according to the least squares criterion and then lists and plots the results. The function can be programmed or chosen from a library that is provided. The library can be expanded to include up to 99 functions. A general plotting routine, contained in the program but useful in its own right, is described separately in an Appendix.
International Nuclear Information System (INIS)
Tashiro, Tohru
2014-01-01
We propose a new model about diffusion of a product which includes a memory of how many adopters or advertisements a non-adopter met, where (non-)adopters mean people (not) possessing the product. This effect is lacking in the Bass model. As an application, we utilize the model to fit the iPod sales data, and so the better agreement is obtained than the Bass model
Tashiro, Tohru
2014-03-01
We propose a new model about diffusion of a product which includes a memory of how many adopters or advertisements a non-adopter met, where (non-)adopters mean people (not) possessing the product. This effect is lacking in the Bass model. As an application, we utilize the model to fit the iPod sales data, and so the better agreement is obtained than the Bass model.
Automatic fitting of Gaussian peaks using abductive machine learning
Abdel-Aal, R. E.
1998-02-01
Analytical techniques have been used for many years for fitting Gaussian peaks in nuclear spectroscopy. However, the complexity of the approach warrants looking for machine-learning alternatives where intensive computations are required only once (during training), while actual analysis on individual spectra is greatly simplified and quickened. This should allow the use of simple portable systems for fast and automated analysis of large numbers of spectra, particularly in situations where accuracy may be traded for speed and simplicity. This paper proposes the use of abductive networks machine learning for this purpose. The Abductory Induction Mechanism (AIM) tool was used to build models for analyzing both single and double Gaussian peaks in the presence of noise depicting statistical uncertainties in collected spectra. AIM networks were synthesized by training on 1000 representative simulated spectra and evaluated on 500 new spectra. A classifier network determines the multiplicity of single/double peaks with an accuracy of 5.8%. With statistical uncertainties corresponding to a peak count of 100, average percentage absolute errors for the height, position, and width of single peaks are 4.9, 2.9, and 4.2%, respectively. For double peaks, these average errors are within 7.0, 3.1, and 5.9%, respectively. Models have been developed which account for the effect of a linear background on a single peak. Performance is compared with a neural network application and with an analytical curve-fitting routine, and the new technique is applied to actual data of an alpha spectrum.
Automatic fitting of Gaussian peaks using abductive machine learning
International Nuclear Information System (INIS)
Abdel-Aal, R.E.
1998-01-01
Analytical techniques have been used for many years for fitting Gaussian peaks in nuclear spectroscopy. However, the complexity of the approach warrants looking for machine-learning alternatives where intensive computations are required only once (during training), while actual analysis on individual spectra is greatly simplified and quickened. This should allow the use of simple portable systems for fast and automated analysis of large numbers of spectra, particularly in situations where accuracy may be traded for speed and simplicity. This paper proposes the use of abductive networks machine learning for this purpose. The Abductory Induction Mechanism (AIM) tool was used to build models for analyzing both single and double Gaussian peaks in the presence of noise depicting statistical uncertainties in collected spectra. AIM networks were synthesized by training on 1,000 representative simulated spectra and evaluated on 500 new spectra. A classifier network determines the multiplicity of single/double peaks with an accuracy of 98%. With statistical uncertainties corresponding to a peak count of 100, average percentage absolute errors for the height, position, and width of single peaks are 4.9, 2.9, and 4.2%, respectively. For double peaks, these average errors are within 7.0, 3.1, and 5.9%, respectively. Models have been developed which account for the effect of a linear background on a single peak. Performance is compared with a neural network application and with an analytical curve-fitting routine, and the new technique is applied to actual data of an alpha spectrum
Efficient scheme for parametric fitting of data in arbitrary dimensions.
Pang, Ning-Ning; Tzeng, Wen-Jer; Kao, Hisen-Ching
2008-07-01
We propose an efficient scheme for parametric fitting expressed in terms of the Legendre polynomials. For continuous systems, our scheme is exact and the derived explicit expression is very helpful for further analytical studies. For discrete systems, our scheme is almost as accurate as the method of singular value decomposition. Through a few numerical examples, we show that our algorithm costs much less CPU time and memory space than the method of singular value decomposition. Thus, our algorithm is very suitable for a large amount of data fitting. In addition, the proposed scheme can also be used to extract the global structure of fluctuating systems. We then derive the exact relation between the correlation function and the detrended variance function of fluctuating systems in arbitrary dimensions and give a general scaling analysis.
Structural Equation Models in a Redundancy Analysis Framework With Covariates.
Lovaglio, Pietro Giorgio; Vittadini, Giorgio
2014-01-01
A recent method to specify and fit structural equation modeling in the Redundancy Analysis framework based on so-called Extended Redundancy Analysis (ERA) has been proposed in the literature. In this approach, the relationships between the observed exogenous variables and the observed endogenous variables are moderated by the presence of unobservable composites, estimated as linear combinations of exogenous variables. However, in the presence of direct effects linking exogenous and endogenous variables, or concomitant indicators, the composite scores are estimated by ignoring the presence of the specified direct effects. To fit structural equation models, we propose a new specification and estimation method, called Generalized Redundancy Analysis (GRA), allowing us to specify and fit a variety of relationships among composites, endogenous variables, and external covariates. The proposed methodology extends the ERA method, using a more suitable specification and estimation algorithm, by allowing for covariates that affect endogenous indicators indirectly through the composites and/or directly. To illustrate the advantages of GRA over ERA we propose a simulation study of small samples. Moreover, we propose an application aimed at estimating the impact of formal human capital on the initial earnings of graduates of an Italian university, utilizing a structural model consistent with well-established economic theory.
Fitness Club
2011-01-01
The CERN Fitness Club is organising Zumba Classes on the first Wednesday of each month, starting 7 September (19.00 – 20.00). What is Zumba®? It’s an exhilarating, effective, easy-to-follow, Latin-inspired, calorie-burning dance fitness-party™ that’s moving millions of people toward joy and health. Above all it’s great fun and an excellent work out. Price: 22 CHF/person Sign-up via the following form: https://espace.cern.ch/club-fitness/Lists/Zumba%20Subscription/NewForm.aspx For more info: fitness.club@cern.ch
Sensitivity of goodness-of-fit statistics to rainfall data rounding off
Deidda, Roberto; Puliga, Michelangelo
An analysis based on the L-moments theory suggests of adopting the generalized Pareto distribution to interpret daily rainfall depths recorded by the rain-gauge network of the Hydrological Survey of the Sardinia Region. Nevertheless, a big problem, not yet completely resolved, arises in the estimation of a left-censoring threshold able to assure a good fitting of rainfall data with the generalized Pareto distribution. In order to detect an optimal threshold, keeping the largest possible number of data, we chose to apply a “failure-to-reject” method based on goodness-of-fit tests, as it was proposed by Choulakian and Stephens [Choulakian, V., Stephens, M.A., 2001. Goodness-of-fit tests for the generalized Pareto distribution. Technometrics 43, 478-484]. Unfortunately, the application of the test, using percentage points provided by Choulakian and Stephens (2001), did not succeed in detecting a useful threshold value in most analyzed time series. A deeper analysis revealed that these failures are mainly due to the presence of large quantities of rounding off values among sample data, affecting the distribution of goodness-of-fit statistics and leading to significant departures from percentage points expected for continuous random variables. A procedure based on Monte Carlo simulations is thus proposed to overcome these problems.
Wang, Lu; Xu, Lisheng; Feng, Shuting; Meng, Max Q-H; Wang, Kuanquan
2013-11-01
Analysis of pulse waveform is a low cost, non-invasive method for obtaining vital information related to the conditions of the cardiovascular system. In recent years, different Pulse Decomposition Analysis (PDA) methods have been applied to disclose the pathological mechanisms of the pulse waveform. All these methods decompose single-period pulse waveform into a constant number (such as 3, 4 or 5) of individual waves. Furthermore, those methods do not pay much attention to the estimation error of the key points in the pulse waveform. The estimation of human vascular conditions depends on the key points' positions of pulse wave. In this paper, we propose a Multi-Gaussian (MG) model to fit real pulse waveforms using an adaptive number (4 or 5 in our study) of Gaussian waves. The unknown parameters in the MG model are estimated by the Weighted Least Squares (WLS) method and the optimized weight values corresponding to different sampling points are selected by using the Multi-Criteria Decision Making (MCDM) method. Performance of the MG model and the WLS method has been evaluated by fitting 150 real pulse waveforms of five different types. The resulting Normalized Root Mean Square Error (NRMSE) was less than 2.0% and the estimation accuracy for the key points was satisfactory, demonstrating that our proposed method is effective in compressing, synthesizing and analyzing pulse waveforms. Copyright © 2013 Elsevier Ltd. All rights reserved.
van Krimpen-Stoop, Edith M. L. A.; Meijer, Rob R.
Person-fit research in the context of paper-and-pencil tests is reviewed, and some specific problems regarding person fit in the context of computerized adaptive testing (CAT) are discussed. Some new methods are proposed to investigate person fit in a CAT environment. These statistics are based on Statistical Process Control (SPC) theory. A…
Linear approximation model network and its formation via ...
Indian Academy of Sciences (India)
To overcome the deficiency of `local model network' (LMN) techniques, an alternative `linear approximation model' (LAM) network approach is proposed. Such a network models a nonlinear or practical system with multiple linear models fitted along operating trajectories, where individual models are simply networked ...
Kuhlman, J. M.
1979-01-01
The aerodynamic design of a wind-tunnel model of a wing representative of that of a subsonic jet transport aircraft, fitted with winglets, was performed using two recently developed optimal wing-design computer programs. Both potential flow codes use a vortex lattice representation of the near-field of the aerodynamic surfaces for determination of the required mean camber surfaces for minimum induced drag, and both codes use far-field induced drag minimization procedures to obtain the required spanloads. One code uses a discrete vortex wake model for this far-field drag computation, while the second uses a 2-D advanced panel wake model. Wing camber shapes for the two codes are very similar, but the resulting winglet camber shapes differ widely. Design techniques and considerations for these two wind-tunnel models are detailed, including a description of the necessary modifications of the design geometry to format it for use by a numerically controlled machine for the actual model construction.
Exploring the fitness landscape of poliovirus
Bianco, Simone; Acevedo, Ashely; Andino, Raul; Tang, Chao
2012-02-01
RNA viruses are known to display extraordinary adaptation capabilities to different environments, due to high mutation rates. Their very dynamical evolution is captured by the quasispecies concept, according to which the viral population forms a swarm of genetic variants linked through mutation, which cooperatively interact at a functional level and collectively contribute to the characteristics of the population. The description of the viral fitness landscape becomes paramount towards a more thorough understanding of the virus evolution and spread. The high mutation rate, together with the cooperative nature of the quasispecies, makes it particularly challenging to explore its fitness landscape. I will present an investigation of the dynamical properties of poliovirus fitness landscape, through both the adoption of new experimental techniques and theoretical models.
Energy Technology Data Exchange (ETDEWEB)
Hurtado, S. [Servicio de Radioisotopos, Centro de Investigacion, Tecnologia e Innovacion (CITIUS), Universidad de Sevilla, Avda. Reina Mercedes s/n, 41012 Sevilla (Spain)], E-mail: shurtado@us.es; Garcia-Leon, M. [Departamento de Fisica Atomica, Molecular y Nuclear, Facultad de Fisica, Universidad de Sevilla, Aptd. 1065, 41080 Sevilla (Spain); Garcia-Tenorio, R. [Departamento de Fisica Aplicada II, E.T.S.A. Universidad de Sevilla, Avda, Reina Mercedes 2, 41012 Sevilla (Spain)
2008-09-11
In this work several mathematical functions are compared in order to perform the full-energy peak efficiency calibration of HPGe detectors using a 126cm{sup 3} HPGe coaxial detector and gamma-ray energies ranging from 36 to 1460 keV. Statistical tests and Monte Carlo simulations were used to study the performance of the fitting curve equations. Furthermore the fitting procedure of these complex functional forms to experimental data is a non-linear multi-parameter minimization problem. In gamma-ray spectrometry usually non-linear least-squares fitting algorithms (Levenberg-Marquardt method) provide a fast convergence while minimizing {chi}{sub R}{sup 2}, however, sometimes reaching only local minima. In order to overcome that shortcoming a hybrid algorithm based on simulated annealing (HSA) techniques is proposed. Additionally a new function is suggested that models the efficiency curve of germanium detectors in gamma-ray spectrometry.
International Nuclear Information System (INIS)
Dorado Delgado, Jennifer; Burbano Criollo, Juan Carlos; Molina Tabares, Jose Manuel; Carvajal Escobar, Yesid; Aristizabal, Hector Fabio
2006-01-01
In this study, space and time variability of monthly and annual rainfall was analyzed for the downstream influence zone of a Colombian supply-regulation reservoir, Sara-Brut, located on the Cauca valley department. Monthly precipitation data from 18 gauge stations and for a 29-year record (1975-2003) were used. These data were processed by means of time series completion, consistency analyses and sample statistics computations. Theoretical probabilistic distribution models such as Gumbel, normal, lognormal and wake by, and other empirical distributions such as Weibull and Landwehr were applied in order to fit the historical precipitation data set. The fit standard error (FSE) was used to test the goodness of fit of the theoretical distribution models and to choose the best of this probabilistic function. The wake by approach showed the best goodness of fit in 89% of the total gauges taken into account. Time variability was analyzed by means of wake by estimated values of monthly and annual precipitation associated with return periods of 1,052, 1,25, 2, 10, 20 and 50 years. Precipitation space variability is presents by means of ArcGis v8.3 and using krigging as interpolation method. In general terms the results obtained from this study show significant distribution variability in precipitation over the whole area, and particularity, the formation of dry and humid nucleus over the northeastern strip and microclimates at the southwestern and central zone of the study area were observed, depending on the season of year. The mentioned distribution pattern is likely caused by the influence of pacific wind streams, which come from the Andean western mountain range. It is expected that the results from this work be helpful for future planning and hydrologic project design
Directory of Open Access Journals (Sweden)
Lilith K Whittles
2017-10-01
Full Text Available Gonorrhoea is one of the most common bacterial sexually transmitted infections in England. Over 41,000 cases were recorded in 2015, more than half of which occurred in men who have sex with men (MSM. As the bacterium has developed resistance to each first-line antibiotic in turn, we need an improved understanding of fitness benefits and costs of antibiotic resistance to inform control policy and planning. Cefixime was recommended as a single-dose treatment for gonorrhoea from 2005 to 2010, during which time resistance increased, and subsequently declined.We developed a stochastic compartmental model representing the natural history and transmission of cefixime-sensitive and cefixime-resistant strains of Neisseria gonorrhoeae in MSM in England, which was applied to data on diagnoses and prescriptions between 2008 and 2015. We estimated that asymptomatic carriers play a crucial role in overall transmission dynamics, with 37% (95% credible interval CrI 24%-52% of infections remaining asymptomatic and untreated, accounting for 89% (95% CrI 82%-93% of onward transmission. The fitness cost of cefixime resistance in the absence of cefixime usage was estimated to be such that the number of secondary infections caused by resistant strains is only about half as much as for the susceptible strains, which is insufficient to maintain persistence. However, we estimated that treatment of cefixime-resistant strains with cefixime was unsuccessful in 83% (95% CrI 53%-99% of cases, representing a fitness benefit of resistance. This benefit was large enough to counterbalance the fitness cost when 31% (95% CrI 26%-36% of cases were treated with cefixime, and when more than 55% (95% CrI 44%-66% of cases were treated with cefixime, the resistant strain had a net fitness advantage over the susceptible strain. Limitations include sparse data leading to large intervals on key model parameters and necessary assumptions in the modelling of a complex epidemiological process
Study of the nonlinear imperfect software debugging model
International Nuclear Information System (INIS)
Wang, Jinyong; Wu, Zhibo
2016-01-01
In recent years there has been a dramatic proliferation of research on imperfect software debugging phenomena. Software debugging is a complex process and is affected by a variety of factors, including the environment, resources, personnel skills, and personnel psychologies. Therefore, the simple assumption that debugging is perfect is inconsistent with the actual software debugging process, wherein a new fault can be introduced when removing a fault. Furthermore, the fault introduction process is nonlinear, and the cumulative number of nonlinearly introduced faults increases over time. Thus, this paper proposes a nonlinear, NHPP imperfect software debugging model in consideration of the fact that fault introduction is a nonlinear process. The fitting and predictive power of the NHPP-based proposed model are validated through related experiments. Experimental results show that this model displays better fitting and predicting performance than the traditional NHPP-based perfect and imperfect software debugging models. S-confidence bounds are set to analyze the performance of the proposed model. This study also examines and discusses optimal software release-time policy comprehensively. In addition, this research on the nonlinear process of fault introduction is significant given the recent surge of studies on software-intensive products, such as cloud computing and big data. - Highlights: • Fault introduction is a nonlinear changing process during the debugging phase. • The assumption that the process of fault introduction is nonlinear is credible. • Our proposed model can better fit and accurately predict software failure behavior. • Research on fault introduction case is significant to software-intensive products.
Suryani, Woro Dwi; Sumiyana, Sumiyana
2015-01-01
This study raises the issue that information system success could be enhanced by complementingother factors. This study investigates the success of information systems by inducing2the task-technology fit (TTF) and person-job fit (PJF) into the DeLone and McLean model. Thisstudy aims to examine, among the two induced factors, which one is able to explain andimprove the success of the information systems implementation.The results of this study indicate that the TTF explains the models’ goodnes...
Piecewise Polynomial Fitting with Trend Item Removal and Its Application in a Cab Vibration Test
Directory of Open Access Journals (Sweden)
Wu Ren
2018-01-01
Full Text Available The trend item of a long-term vibration signal is difficult to remove. This paper proposes a piecewise integration method to remove trend items. Examples of direct integration without trend item removal, global integration after piecewise polynomial fitting with trend item removal, and direct integration after piecewise polynomial fitting with trend item removal were simulated. The results showed that direct integration of the fitted piecewise polynomial provided greater acceleration and displacement precision than the other two integration methods. A vibration test was then performed on a special equipment cab. The results indicated that direct integration by piecewise polynomial fitting with trend item removal was highly consistent with the measured signal data. However, the direct integration method without trend item removal resulted in signal distortion. The proposed method can help with frequency domain analysis of vibration signals and modal parameter identification for such equipment.
Mathematical formalization of theories of motivation proposed by Maslow and Herzberg
Directory of Open Access Journals (Sweden)
Ivan Kotliarov
2008-12-01
Full Text Available Maslow's theory is by far the most known theory of motivation, and the most common in the business and management practice. Herzberg's theory fits the observations and explains some aspects of human motivation left unexplained by Maslow. However, these theories have never been formalized on a strictly mathematical basis. The present article gives an outline of a mathematical model of theories of motivation proposed by Abraham Maslow and Frederick Herzberg. This model is built on a basis of special non-continuous functions. This description may be a good basis for HR software and may be useful for business and management.
An Analytical Analysis of Hydraulic Jump in Triangular Channel: A Proposed Model
Khan, S. A.
2013-05-01
The paper presents the theoretical study of hydraulic jump in triangular channel section. Presuming the jump as one dimensional free shear layer with hydrostatic pressure distribution across it and using momentum equation, specific force equation is obtained. Using the specific force equation and eddy viscosity equation, analytical models for sequent depth, dimensionless profile, turbulent shear stress distribution and energy loss for various initial Froude numbers have been obtained. The proposed models for sequent depth and energy loss are also compared with the other developed models. The proposed energy loss model also provides the energy loss at any point along the jump, while this provision is not available in the models of other investigators. Newton-Raphson and Runge-Kutta methods are used for the solution of the proposed model. The outcome of this study can be used in the design of stilling basin floor and side walls on permeable foundations.
A new three-dimensional track fit with multiple scattering
International Nuclear Information System (INIS)
Berger, Niklaus; Kozlinskiy, Alexandr; Kiehn, Moritz; Schöning, André
2017-01-01
Modern semiconductor detectors allow for charged particle tracking with ever increasing position resolution. Due to the reduction of the spatial hit uncertainties, multiple Coulomb scattering in the detector layers becomes the dominant source for tracking uncertainties. In this case long distance effects can be ignored for the momentum measurement, and the track fit can consequently be formulated as a sum of independent fits to hit triplets. In this paper we present an analytical solution for a three-dimensional triplet(s) fit in a homogeneous magnetic field based on a multiple scattering model. Track fitting of hit triplets is performed using a linearization ansatz. The momentum resolution is discussed for a typical spectrometer setup. Furthermore the track fit is compared with other track fits for two different pixel detector geometries, namely the Mu3e experiment at PSI and a typical high-energy collider experiment. For a large momentum range the triplets fit provides a significantly better performance than a single helix fit. The triplets fit is fast and can easily be parallelized, which makes it ideal for the implementation on parallel computing architectures.
A new three-dimensional track fit with multiple scattering
Energy Technology Data Exchange (ETDEWEB)
Berger, Niklaus; Kozlinskiy, Alexandr [Physikalisches Institut, Heidelberg University, Heidelberg (Germany); Institut für Kernphysik and PRISMA cluster of excellence, Mainz University, Mainz (Germany); Kiehn, Moritz; Schöning, André [Physikalisches Institut, Heidelberg University, Heidelberg (Germany)
2017-02-01
Modern semiconductor detectors allow for charged particle tracking with ever increasing position resolution. Due to the reduction of the spatial hit uncertainties, multiple Coulomb scattering in the detector layers becomes the dominant source for tracking uncertainties. In this case long distance effects can be ignored for the momentum measurement, and the track fit can consequently be formulated as a sum of independent fits to hit triplets. In this paper we present an analytical solution for a three-dimensional triplet(s) fit in a homogeneous magnetic field based on a multiple scattering model. Track fitting of hit triplets is performed using a linearization ansatz. The momentum resolution is discussed for a typical spectrometer setup. Furthermore the track fit is compared with other track fits for two different pixel detector geometries, namely the Mu3e experiment at PSI and a typical high-energy collider experiment. For a large momentum range the triplets fit provides a significantly better performance than a single helix fit. The triplets fit is fast and can easily be parallelized, which makes it ideal for the implementation on parallel computing architectures.
A Proposed Model for Protein Crystal Nucleation and Growth
Pusey, Marc; Curreri, Peter A. (Technical Monitor)
2002-01-01
How does one take a molecule, strongly asymmetric in both shape and charge distribution, and assemble it into a crystal? We propose a model for the nucleation and crystal growth process for tetragonal lysozyme, based upon fluorescence, light, neutron, and X-ray scattering data, size exclusion chromatography experiments, dialysis kinetics, AFM, and modeling of growth rate data, from this and other laboratories. The first species formed is postulated to be a 'head to side' dimer. Through repeating associations involving the same intermolecular interactions this grows to a 4(sub 3) helix structure, that in turn serves as the basic unit for nucleation and subsequent crystal growth. High salt attenuates surface charges while promoting hydrophobic interactions. Symmetry facilitates subsequent helix-helix self-association. Assembly stability is enhanced when a four helix structure is obtained, with each bound to two neighbors. Only two unique interactions are required. The first are those for helix formation, where the dominant interaction is the intermolecular bridging anion. The second is the anti-parallel side-by-side helix-helix interaction, guided by alternating pairs of symmetry related salt bridges along each side. At this stage all eight unique positions of the P4(sub3)2(sub 1),2(sub 1) unit cell are filled. The process is one of a) attenuating the most strongly interacting groups, such that b) the molecules begin to self-associate in defined patterns, so that c) symmetry is obtained, which d) propagates as a growing crystal. Simple and conceptually obvious in hindsight, this tells much about what we are empirically doing when we crystallize macromolecules. By adjusting the growth parameters we are empirically balancing the intermolecular interactions, preferentially attenuating the dominant strong (for lysozyme the charged groups) while strengthening the lesser strong (hydrophobic) interactions. In the general case for proteins the lack of a singularly defined
Adaptive memory: young children show enhanced retention of fitness-related information.
Aslan, Alp; Bäuml, Karl-Heinz T
2012-01-01
Evolutionary psychologists propose that human cognition evolved through natural selection to solve adaptive problems related to survival and reproduction, with its ultimate function being the enhancement of reproductive fitness. Following this proposal and the evolutionary-developmental view that ancestral selection pressures operated not only on reproductive adults, but also on pre-reproductive children, the present study examined whether young children show superior memory for information that is processed in terms of its survival value. In two experiments, we found such survival processing to enhance retention in 4- to 10-year-old children, relative to various control conditions that also required deep, meaningful processing but were not related to survival. These results suggest that, already in very young children, survival processing is a special and extraordinarily effective form of memory encoding. The results support the functional-evolutionary proposal that young children's memory is "tuned" to process and retain fitness-related information. Copyright © 2011 Elsevier B.V. All rights reserved.
Boundary fitting based segmentation of fluorescence microscopy images
Lee, Soonam; Salama, Paul; Dunn, Kenneth W.; Delp, Edward J.
2015-03-01
Segmentation is a fundamental step in quantifying characteristics, such as volume, shape, and orientation of cells and/or tissue. However, quantification of these characteristics still poses a challenge due to the unique properties of microscopy volumes. This paper proposes a 2D segmentation method that utilizes a combination of adaptive and global thresholding, potentials, z direction refinement, branch pruning, end point matching, and boundary fitting methods to delineate tubular objects in microscopy volumes. Experimental results demonstrate that the proposed method achieves better performance than an active contours based scheme.
The FitTrack Index as fitness indicator: A pilot study | van Rensburg ...
African Journals Online (AJOL)
Conclusions: These results suggest that the web-based FitTrack Index may be considered an appropriate tool to evaluate exercise capacity and cardiovascular fitness in healthy individuals following an aerobic training programme. Keywords: Aerobic fitness, Exercise ability, Recreational fitness, Cardiovascular fitness, ...
Validation of kinetic modeling of progesterone release from polymeric membranes
Directory of Open Access Journals (Sweden)
Analia Irma Romero
2018-01-01
Full Text Available Mathematical modeling in drug release systems is fundamental in development and optimization of these systems, since it allows to predict drug release rates and to elucidate the physical transport mechanisms involved. In this paper we validate a novel mathematical model that describes progesterone (Prg controlled release from poly-3-hydroxybutyric acid (PHB membranes. A statistical analysis was conducted to compare the fitting of our model with six different models and the Akaike information criterion (AIC was used to find the equation with best-fit. A simple relation between mass and drug released rate was found, which allows predicting the effect of Prg loads on the release behavior. Our proposed model was the one with minimum AIC value, and therefore it was the one that statistically fitted better the experimental data obtained for all the Prg loads tested. Furthermore, the initial release rate was calculated and therefore, the interface mass transfer coefficient estimated and the equilibrium distribution constant of Prg between the PHB and the release medium was also determined. The results lead us to conclude that our proposed model is the one which best fits the experimental data and can be successfully used to describe Prg drug release in PHB membranes.
FITS: a function-fitting program
Energy Technology Data Exchange (ETDEWEB)
Balestrini, S.J.; Chezem, C.G.
1982-08-01
FITS is an iterating computer program that adjusts the parameters of a function to fit a set of data points according to the least squares criterion and then lists and plots the results. The function can be programmed or chosen from a library that is provided. The library can be expanded to include up to 99 functions. A general plotting routine, contained in the program but useful in its own right, is described separately in Appendix A. An example problem file and its solution is given in Appendix B.
Proposal of a new biokinetic model for niobium
International Nuclear Information System (INIS)
Oliveira, Roges
2006-01-01
There are two niobium isotopes generated in nuclear power plants: 95 Nb and 94 Nb. Workers and members of the public are subjects to intake these radionuclides in accident situation. For dose calculation purpose, it is very important to develop a model that describes in a more realistic way the kinetics of niobium inside of the human body. Presently the model adopted by ICRP (ICRP, 1989) is based on animal studies and describes the behavior of niobium in human being in a simple manner. The new model proposal describes the kinetics of the niobium from the intake into the blood until the excretion, doing this in a more realistic form and considering not only data from animals but data from human beings as well. For this objective, a workers group of a niobium extraction and processing industry exposed to stable niobium (93 Nb) in oxide insoluble form with associated uranium, was monitored for uranium and niobium determination in urinary and fecal excretion, by mass spectrometry. Based in the ratios of the niobium concentration in urinary and faecal excretion of this workers and animal data study, a new biokinetic model for niobium was proposed, with the followings modifications relative to ICRP model: a new compartment that represents muscular tissue; the fractions which are deposited into the compartment are modified; a third component in the retention equation of the bone tissue; introduction of recirculation between organs and blood. The new model was applied for a case of accidental intake and described adequately the experimental data
Quarkonium level fitting with two-power potentials
International Nuclear Information System (INIS)
Joshi, G.C.; Wignall, J.W.G.
1981-01-01
An attempt has been made to fit psi and UPSILON energy levels and leptonic decay width ratios with a non-relativistic potential model using a potential of the form V(r) = Arsup(p) + Brsup(q) + C. It is found that reasonable fits to states below hadronic decay threshold can be obtained for values of the powers p and q anywhere along a family of curves in the (p,q) plane that smoothly join the Martin potential (p = 0, q = 0.1) to the potential forms with p approximately -1 suggested by QCD; for the latter case the best fit is obtained with q approximately 0.4 - 0.5
Transfer function fitting using a continuous Ant Colony Optimization (ACO algorithm
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A. Reineix
2015-03-01
Full Text Available An original approach is proposed in order to achieve the fitting of ultra-wideband complex frequency functions, such as the complex impedances, by using the so-called ACO (Ant Colony Optimization methods. First, we present the optimization principle of ACO, which originally was dedicated to the combinatorial problems. Further on, the extension to the continuous and mixed problems is explained in more details. The interest in this approach is proved by its ability to define practical constraints and objectives, such as minimizing the number of filters used in the model with respect to a fixed relative error. Finally, the establishment of the model for the first and second order filter types illustrates the power of the method and its interest for the time-domain electromagnetic computation.
Sex-specific determinants of fitness in a social mammal.
Lardy, Sophie; Allainé, Dominique; Bonenfant, Christophe; Cohas, Aurélie
2015-11-01
Sociality should evolve when the fitness benefits of group living outweigh the costs. Theoretical models predict an optimal group size maximizing individual fitness. However, beyond the number of individuals present in a group, the characteristics of these individuals, like their sex, are likely to affect the fitness payoffs of group living. Using 20 years of individually based data on a social mammal, the Alpine marmot (Marmota marmota), we tested for the occurrence of an optimal group size and composition, and for sex-specific effects of group characteristics on fitness. Based on lifetime data of 52 males and 39 females, our findings support the existence of an optimal group size maximizing male fitness and an optimal group composition maximizing fitness of males and females. Additionally, although group characteristics (i.e., size, composition and instability) affecting male and female fitness differed, fitness depended strongly on the number of same-sex subordinates within the social group in the two sexes. By comparing multiple measures of social group characteristics and of fitness in both sexes, we highlighted the sex-specific determinants of fitness in the two sexes and revealed the crucial role of intrasexual competition in shaping social group composition.
Crossing fitness canyons by a finite population
Saakian, David B.; Bratus, Alexander S.; Hu, Chin-Kun
2017-06-01
We consider the Wright-Fisher model of the finite population evolution on a fitness landscape defined in the sequence space by a path of nearly neutral mutations. We study a specific structure of the fitness landscape: One of the intermediate mutations on the mutation path results in either a large fitness value (climbing up a fitness hill) or a low fitness value (crossing a fitness canyon), the rest of the mutations besides the last one are neutral, and the last sequence has much higher fitness than any intermediate sequence. We derive analytical formulas for the first arrival time of the mutant with two point mutations. For the first arrival problem for the further mutants in the case of canyon crossing, we analytically deduce how the mean first arrival time scales with the population size and fitness difference. The location of the canyon on the path of sequences has a crucial role. If the canyon is at the beginning of the path, then it significantly prolongs the first arrival time; otherwise it just slightly changes it. Furthermore, the fitness hill at the beginning of the path strongly prolongs the arrival time period; however, the hill located near the end of the path shortens it. We optimize the first arrival time by applying a nonzero selection to the intermediate sequences. We extend our results and provide a scaling for the valley crossing time via the depth of the canyon and population size in the case of a fitness canyon at the first position. Our approach is useful for understanding some complex evolution systems, e.g., the evolution of cancer.
On 4-degree-of-freedom biodynamic models of seated occupants: Lumped-parameter modeling
Bai, Xian-Xu; Xu, Shi-Xu; Cheng, Wei; Qian, Li-Jun
2017-08-01
It is useful to develop an effective biodynamic model of seated human occupants to help understand the human vibration exposure to transportation vehicle vibrations and to help design and improve the anti-vibration devices and/or test dummies. This study proposed and demonstrated a methodology for systematically identifying the best configuration or structure of a 4-degree-of-freedom (4DOF) human vibration model and for its parameter identification. First, an equivalent simplification expression for the models was made. Second, all of the possible 23 structural configurations of the models were identified. Third, each of them was calibrated using the frequency response functions recommended in a biodynamic standard. An improved version of non-dominated sorting genetic algorithm (NSGA-II) based on Pareto optimization principle was used to determine the model parameters. Finally, a model evaluation criterion proposed in this study was used to assess the models and to identify the best one, which was based on both the goodness of curve fits and comprehensive goodness of the fits. The identified top configurations were better than those reported in the literature. This methodology may also be extended and used to develop the models with other DOFs.
Valdes, Alice
This document presents baseline data on physical fitness that provides an outline for assessing the physical fitness of students. It consists of 4 tasks and a 13-item questionnaire on fitness-related behaviors. The fitness test evaluates cardiorespiratory endurance by a steady state jog; muscular strength and endurance with a two-minute bent-knee…
A Modified Microfinance Model Proposed for the United States
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Eldon H Bernstein
2014-07-01
While the goal in the traditional model in developing markets is the elimination of poverty, we show how those critical conditions help to explain the lack of success in the United States. We propose a modified model whose goal is the creation of an entrepreneurial venture or improving the performance of an existing small enterprise.
DeGeest, David Scott; Schmidt, Frank
2015-01-01
Our objective was to apply the rigorous test developed by Browne (1992) to determine whether the circumplex model fits Big Five personality data. This test has yet to be applied to personality data. Another objective was to determine whether blended items explained correlations among the Big Five traits. We used two working adult samples, the Eugene-Springfield Community Sample and the Professional Worker Career Experience Survey. Fit to the circumplex was tested via Browne's (1992) procedure. Circumplexes were graphed to identify items with loadings on multiple traits (blended items), and to determine whether removing these items changed five-factor model (FFM) trait intercorrelations. In both samples, the circumplex structure fit the FFM traits well. Each sample had items with dual-factor loadings (8 items in the first sample, 21 in the second). Removing blended items had little effect on construct-level intercorrelations among FFM traits. We conclude that rigorous tests show that the fit of personality data to the circumplex model is good. This finding means the circumplex model is competitive with the factor model in understanding the organization of personality traits. The circumplex structure also provides a theoretically and empirically sound rationale for evaluating intercorrelations among FFM traits. Even after eliminating blended items, FFM personality traits remained correlated.
Push-off tests and strength evaluation of joints combining shrink fitting with bonding
Yoneno, Masahiro; Sawa, Toshiyuki; Shimotakahara, Ken; Motegi, Yoichi
1997-03-01
Shrink fitted joints have been used in mechanical structures. Recently, joints combining shrink fitting with anaerobic adhesives bonded between the shrink fitted surfaces have been appeared in order to increase the joint strength. In this paper, push-off test was carried out on strength of joints combining shrink fitting with bonding by material testing machine. In addition, the push-off strength of shrink fitting joints without an anaerobic adhesive was also measured. In the experiments, the effects of the shrinking allowance and the outer diameter of the rings on the joint strength are examined. The interface stress distribution in bonded shrink fitted joints subjected to a push-off load is analyzed using axisymmetrical theory of elasticity as a four-body contact problem. Using the interface stress distribution, a method for estimating joint strength is proposed. The experimental results are in a fairly good agreement with the numerical results. It is found that the strength of combination joints is greater than that of shrink fitted joints.
Fitting oscillating string gas cosmology to supernova data
International Nuclear Information System (INIS)
Ferrer, Francesc; Multamaeki, Tuomas; Raesaenen, Syksy
2009-01-01
In string gas cosmology, extra dimensions are stabilised by a gas of strings. In the matter-dominated era, competition between matter pushing the extra dimensions to expand and the string gas pulling them back can lead to oscillations of the extra dimensions and acceleration in the visible dimensions. We fit this model to supernova data, taking into account the Big Bang Nucleosynthesis constraint on the energy density of the string gas. The fit to the Union set of supernova data is acceptable, but the fit to the ESSENCE data is poor.
Directory of Open Access Journals (Sweden)
Ahmad Heru Mujianto
2017-01-01
Abstract Private Higher Education (PHE in Jombang apply online admission of new students selection process, so applicants simply register through admission of new students online website owned by their respective private universities, without needing to the university. But in the implementation there are still prospective students who apply directly to the office of admission of new students PHE, it makes the need to measure the success rate of admission of new students website online application in PHE. Also, so far admission of new students online website PHE in Jombang has never been evaluated to determine the success rate. HOT (Human Organization Technology Fit model is a model of success that can be used as a model for evaluating information systems. There are seven variables used by HOT Fit, i.e., system quality, information quality, service quality, system use, user satisfaction, net beneFits, organizational structure (organization structure. The result of the research shows that there are three assessment indicators with satisfaction value below 85%, the response time is 76,1%; the availability of 71.6% of aid facilities; and 64.2% display satisfaction. So that three indicators need to be increased again to get better results and can optimize the implementation of admission of new students website online PHE in Jombang. Keywords: Admission of new students; HOT Fit; Human Organization Technology; Private Higher Education; PHE.
Hysteresis and creep: Comparison between a power-law model and Kuhnen's model
Energy Technology Data Exchange (ETDEWEB)
Oliveri, Alberto; Stellino, Flavio; Parodi, Mauro; Storace, Marco, E-mail: marco.storace@unige.it
2016-04-01
In this paper we analyze some properties of a recently proposed model of hysteresis and creep (related to a circuit model, whose only nonlinear element is based on a power law) and compare it with the well-known Kuhnen's model. A first qualitative comparison relies on the analysis of the behavior of the elementary cell of each model. Their responses to step inputs (which allow to better evidence the creep effect) are analyzed and compared. Then, a quantitative comparison is proposed, based on the fitting performances of the two models on experimental data measured from a commercial piezoelectric actuator.
Identification and modelling of Lithium ion battery
International Nuclear Information System (INIS)
Tsang, K.M.; Sun, L.; Chan, W.L.
2010-01-01
A universal battery model for the charging process has been identified for Lithium ion battery working at constant temperature. Mathematical models are fitted to different collected charging profiles using the least squares algorithm. With the removal of the component which is related to the DC resistance of the battery, a universal model can be fitted to predict profiles of different charging rates after time scaling. Experimental results are included to demonstrate the goodness of fit of the model at different charging rates and for batteries of different capacities. Comparison with standard electrical-circuit model is also presented. With the proposed model, it is possible to derive more effective way to monitor the status of Lithium ion batteries, and to develop a universal quick charger for different capacities of batteries to result with a more effective usage of Lithium ion batteries.
Curve fitting and modeling with splines using statistical variable selection techniques
Smith, P. L.
1982-01-01
The successful application of statistical variable selection techniques to fit splines is demonstrated. Major emphasis is given to knot selection, but order determination is also discussed. Two FORTRAN backward elimination programs, using the B-spline basis, were developed. The program for knot elimination is compared in detail with two other spline-fitting methods and several statistical software packages. An example is also given for the two-variable case using a tensor product basis, with a theoretical discussion of the difficulties of their use.
Parametric fitting of corneal height data to a biconic surface.
Janunts, Edgar; Kannengießer, Marc; Langenbucher, Achim
2015-03-01
As the average corneal shape can effectively be approximated by a conic section, a determination of the corneal shape by biconic parameters is desired. The purpose of the paper is to introduce a straightforward mathematical approach for extracting clinically relevant parameters of corneal surface, such as radii of curvature and conic constants for principle meridians and astigmatism. A general description for modeling the ocular surfaces in a biconic form is given, based on which an implicit parametric surface fitting algorithm is introduced. The solution of the biconic fitting is obtained by a two sequential least squares optimization approach with constrains. The data input can be raw information from any corneal topographer with not necessarily a uniform data distribution. Various simulated and clinical data are studied including surfaces with rotationally symmetric and non-symmetric geometries. The clinical data was obtained from the Pentacam (Oculus) for the patient having undergone a refractive surgery. A sub-micrometer fitting accuracy was obtained for all simulated surfaces: 0,08 μm RMS fitting error at max for rotationally symmetric and 0,125 μm for non-symmetric surfaces. The astigmatism was recovered in a sub-minutes resolution. The equality in rotational symmetric and the superiority in non-symmetric surfaces of the presented model over the widely used quadric fitting model is shown. The introduced biconic surface fitting algorithm is able to recover the apical radii of curvature and conic constants in principle meridians. This methodology could be a platform for advanced IOL calculations and enhanced contact lens fitting. Copyright © 2014. Published by Elsevier GmbH.
Freyth, Katharina; Janowitz, Tim; Nunes, Frank; Voss, Melanie; Heinick, Alexander; Bertaux, Joanne; Scheu, Stefan; Paul, Rüdiger J
2010-10-01
Laboratory breeding conditions of the model organism C. elegans do not correspond with the conditions in its natural soil habitat. To assess the consequences of the differences in environmental conditions, the effects of air composition, medium and bacterial food on reproductive fitness and/or dietary-choice behavior of C. elegans were investigated. The reproductive fitness of C. elegans was maximal under oxygen deficiency and not influenced by a high fractional share of carbon dioxide. In media approximating natural soil structure, reproductive fitness was much lower than in standard laboratory media. In seminatural media, the reproductive fitness of C. elegans was low with the standard laboratory food bacterium E. coli (γ-Proteobacteria), but significantly higher with C. arvensicola (Bacteroidetes) and B. tropica (β-Proteobacteria) as food. Dietary-choice experiments in semi-natural media revealed a low preference of C. elegans for E. coli but significantly higher preferences for C. arvensicola and B. tropica (among other bacteria). Dietary-choice experiments under quasi-natural conditions, which were feasible by fluorescence in situ hybridization (FISH) of bacteria, showed a high preference of C. elegans for Cytophaga-Flexibacter-Bacteroides, Firmicutes, and β-Proteobacteria, but a low preference for γ-Proteobacteria. The results show that data on C. elegans under standard laboratory conditions have to be carefully interpreted with respect to their biological significance.
A Modified Active Appearance Model Based on an Adaptive Artificial Bee Colony
Othman, Zulaiha Ali
2014-01-01
Active appearance model (AAM) is one of the most popular model-based approaches that have been extensively used to extract features by highly accurate modeling of human faces under various physical and environmental circumstances. However, in such active appearance model, fitting the model with original image is a challenging task. State of the art shows that optimization method is applicable to resolve this problem. However, another common problem is applying optimization. Hence, in this paper we propose an AAM based face recognition technique, which is capable of resolving the fitting problem of AAM by introducing a new adaptive ABC algorithm. The adaptation increases the efficiency of fitting as against the conventional ABC algorithm. We have used three datasets: CASIA dataset, property 2.5D face dataset, and UBIRIS v1 images dataset in our experiments. The results have revealed that the proposed face recognition technique has performed effectively, in terms of accuracy of face recognition. PMID:25165748
Wenseleers, Tom; Helanterä, Heikki; Alves, Denise A.; Dueñez-Guzmán, Edgar; Pamilo, Pekka
2013-01-01
The conflicts over sex allocation and male production in insect societies have long served as an important test bed for Hamilton's theory of inclusive fitness, but have for the most part been considered separately. Here, we develop new coevolutionary models to examine the interaction between these two conflicts and demonstrate that sex ratio and colony productivity costs of worker reproduction can lead to vastly different outcomes even in species that show no variation in their relatedness structure. Empirical data on worker-produced males in eight species of Melipona bees support the predictions from a model that takes into account the demographic details of colony growth and reproduction. Overall, these models contribute significantly to explaining behavioural variation that previous theories could not account for. PMID:24132088
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…
FIT ANALYSIS OF INDOSAT DOMPETKU BUSINESS MODEL USING A STRATEGIC DIAGNOSIS APPROACH
Directory of Open Access Journals (Sweden)
Fauzi Ridwansyah
2015-09-01
Full Text Available Mobile payment is an industry's response to global and regional technological-driven, as well as national social-economical driven in less cash society development. The purposes of this study were 1 identifying positioning of PT. Indosat in providing a response to Indonesian mobile payment market, 2 analyzing Indosat’s internal capabilities and business model fit with environment turbulence, and 3 formulating the optimum mobile payment business model development design for Indosat. The method used in this study was a combination of qualitative and quantitative analysis through in-depth interviews with purposive judgment sampling. The analysis tools used in this study were Business Model Canvas (MBC and Ansoff’s Strategic Diagnosis. The interviewees were the representatives of PT. Indosat internal management and mobile payment business value chain stakeholders. Based on BMC mapping which is then analyzed by strategic diagnosis model, a considerable gap (>1 between the current market environment and Indosat strategy of aggressiveness with the expected future of environment turbulence level was obtained. Therefore, changes in the competitive strategy that need to be conducted include 1 developing a new customer segment, 2 shifting the value proposition that leads to the extensification of mobile payment, 3 monetizing effective value proposition, and 4 integrating effective collaboration for harmonizing company’s objective with the government's vision. Keywords: business model canvas, Indosat, mobile payment, less cash society, strategic diagnosis
A generalized right truncated bivariate Poisson regression model with applications to health data.
Islam, M Ataharul; Chowdhury, Rafiqul I
2017-01-01
A generalized right truncated bivariate Poisson regression model is proposed in this paper. Estimation and tests for goodness of fit and over or under dispersion are illustrated for both untruncated and right truncated bivariate Poisson regression models using marginal-conditional approach. Estimation and test procedures are illustrated for bivariate Poisson regression models with applications to Health and Retirement Study data on number of health conditions and the number of health care services utilized. The proposed test statistics are easy to compute and it is evident from the results that the models fit the data very well. A comparison between the right truncated and untruncated bivariate Poisson regression models using the test for nonnested models clearly shows that the truncated model performs significantly better than the untruncated model.
Fast and sensitive rigid-body fitting into cryo-EM density maps with PowerFit
C.p.van Zundert, Gydo; M.j.j. Bonvin, Alexandre
2015-01-01
Cryo-EM is a rapidly developing method to investigate the three dimensional structure of large macromolecular complexes. In spite of all the advances in the field, the resolution of most cryo-EM density maps is too low for de novo model building. Therefore, the data are often complemented by fitting
Particle precipitation: How the spectrum fit impacts atmospheric chemistry
Wissing, J. M.; Nieder, H.; Yakovchouk, O. S.; Sinnhuber, M.
2016-11-01
Particle precipitation causes atmospheric ionization. Modeled ionization rates are widely used in atmospheric chemistry/climate simulations of the upper atmosphere. As ionization rates are based on particle measurements some assumptions concerning the energy spectrum are required. While detectors measure particles binned into certain energy ranges only, the calculation of a ionization profile needs a fit for the whole energy spectrum. Therefore the following assumptions are needed: (a) fit function (e.g. power-law or Maxwellian), (b) energy range, (c) amount of segments in the spectral fit, (d) fixed or variable positions of intersections between these segments. The aim of this paper is to quantify the impact of different assumptions on ionization rates as well as their consequences for atmospheric chemistry modeling. As the assumptions about the particle spectrum are independent from the ionization model itself the results of this paper are not restricted to a single ionization model, even though the Atmospheric Ionization Module OSnabrück (AIMOS, Wissing and Kallenrode, 2009) is used here. We include protons only as this allows us to trace changes in the chemistry model directly back to the different assumptions without the need to interpret superposed ionization profiles. However, since every particle species requires a particle spectrum fit with the mentioned assumptions the results are generally applicable to all precipitating particles. The reader may argue that the selection of assumptions of the particle fit is of minor interest, but we would like to emphasize on this topic as it is a major, if not the main, source of discrepancies between different ionization models (and reality). Depending on the assumptions single ionization profiles may vary by a factor of 5, long-term calculations may show systematic over- or underestimation in specific altitudes and even for ideal setups the definition of the energy-range involves an intrinsic 25% uncertainty for the
Modeling the behavioral substrates of associate learning and memory - Adaptive neural models
Lee, Chuen-Chien
1991-01-01
Three adaptive single-neuron models based on neural analogies of behavior modification episodes are proposed, which attempt to bridge the gap between psychology and neurophysiology. The proposed models capture the predictive nature of Pavlovian conditioning, which is essential to the theory of adaptive/learning systems. The models learn to anticipate the occurrence of a conditioned response before the presence of a reinforcing stimulus when training is complete. Furthermore, each model can find the most nonredundant and earliest predictor of reinforcement. The behavior of the models accounts for several aspects of basic animal learning phenomena in Pavlovian conditioning beyond previous related models. Computer simulations show how well the models fit empirical data from various animal learning paradigms.