Optimal inference with suboptimal models: Addiction and active Bayesian inference
Schwartenbeck, Philipp; FitzGerald, Thomas H.B.; Mathys, Christoph; Dolan, Ray; Wurst, Friedrich; Kronbichler, Martin; Friston, Karl
2015-01-01
When casting behaviour as active (Bayesian) inference, optimal inference is defined with respect to an agent’s beliefs – based on its generative model of the world. This contrasts with normative accounts of choice behaviour, in which optimal actions are considered in relation to the true structure of the environment – as opposed to the agent’s beliefs about worldly states (or the task). This distinction shifts an understanding of suboptimal or pathological behaviour away from aberrant inference as such, to understanding the prior beliefs of a subject that cause them to behave less ‘optimally’ than our prior beliefs suggest they should behave. Put simply, suboptimal or pathological behaviour does not speak against understanding behaviour in terms of (Bayes optimal) inference, but rather calls for a more refined understanding of the subject’s generative model upon which their (optimal) Bayesian inference is based. Here, we discuss this fundamental distinction and its implications for understanding optimality, bounded rationality and pathological (choice) behaviour. We illustrate our argument using addictive choice behaviour in a recently described ‘limited offer’ task. Our simulations of pathological choices and addictive behaviour also generate some clear hypotheses, which we hope to pursue in ongoing empirical work. PMID:25561321
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…
Suboptimal control of pressurized water reactor power plant using approximate model-following method
International Nuclear Information System (INIS)
Tsuji, Masashi; Ogawa, Yuichi
1987-01-01
We attempted to develop an effective control system that can successfully manage the nuclear steam supply (NSS) system of a PWR power plant in an operational mode requiring relatively small variations of power. A procedure is proposed for synthesizing control system that is a simple, yet practiced, suboptimal control system. The suboptimal control system is designed in two steps; application of the optimal control theory, based on the linear state-feedback control and the use of an approximate model-following method. This procedure can appreciably reduce the complexity of the structure of the controller by accepting a slight deviation from the optimality and by the use of the output-feedback control. This eliminates the engineering difficulty caused by an incompletely state-feedback that is sometimes encountered in practical applications of the optimal state-feedback control theory to complex large-scale dynamical systems. Digital simulations and graphical studies based on the Bode-diagram demonstrate the effectiveness of the suboptimal control, and the applicability of the proposed design method as well. (author)
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.
International Nuclear Information System (INIS)
Martin Llorente, F.
1990-01-01
The models of atmospheric pollutants dispersion are based in mathematic algorithms that describe the transport, diffusion, elimination and chemical reactions of atmospheric contaminants. These models operate with data of contaminants emission and make an estimation of quality air in the area. This model can be applied to several aspects of atmospheric contamination
Correlations in state space can cause sub-optimal adaptation of optimal feedback control models.
Aprasoff, Jonathan; Donchin, Opher
2012-04-01
Control of our movements is apparently facilitated by an adaptive internal model in the cerebellum. It was long thought that this internal model implemented an adaptive inverse model and generated motor commands, but recently many reject that idea in favor of a forward model hypothesis. In theory, the forward model predicts upcoming state during reaching movements so the motor cortex can generate appropriate motor commands. Recent computational models of this process rely on the optimal feedback control (OFC) framework of control theory. OFC is a powerful tool for describing motor control, it does not describe adaptation. Some assume that adaptation of the forward model alone could explain motor adaptation, but this is widely understood to be overly simplistic. However, an adaptive optimal controller is difficult to implement. A reasonable alternative is to allow forward model adaptation to 're-tune' the controller. Our simulations show that, as expected, forward model adaptation alone does not produce optimal trajectories during reaching movements perturbed by force fields. However, they also show that re-optimizing the controller from the forward model can be sub-optimal. This is because, in a system with state correlations or redundancies, accurate prediction requires different information than optimal control. We find that adding noise to the movements that matches noise found in human data is enough to overcome this problem. However, since the state space for control of real movements is far more complex than in our simple simulations, the effects of correlations on re-adaptation of the controller from the forward model cannot be overlooked.
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...
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
Correlations in state space can cause sub-optimal adaptation of optimal feedback control models
Aprasoff, Jonathan; Donchin, Opher
2011-01-01
Control of our movements is apparently facilitated by an adaptive internal model in the cerebellum. It was long thought that this internal model implemented an adaptive inverse model and generated motor commands, but recently many reject that idea in favor of a forward model hypothesis. In theory, the forward model predicts upcoming state during reaching movements so the motor cortex can generate appropriate motor commands. Recent computational models of this process rely on the optimal feedb...
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…
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.
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.
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.
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.
... gov home http://www.girlshealth.gov/ Home Fitness Fitness Want to look and feel your best? Physical ... are? Check out this info: What is physical fitness? top Physical fitness means you can do everyday ...
Suboptimal choice, reward-predictive signals, and temporal information.
Cunningham, Paul J; Shahan, Timothy A
2018-01-01
Suboptimal choice refers to preference for an alternative offering a low probability of food (suboptimal alternative) over an alternative offering a higher probability of food (optimal alternative). Numerous studies have found that stimuli signaling probabilistic food play a critical role in the development and maintenance of suboptimal choice. However, there is still much debate about how to characterize how these stimuli influence suboptimal choice. There is substantial evidence that the temporal information conveyed by a food-predictive signal governs its function as both a Pavlovian conditioned stimulus and as an instrumental conditioned reinforcer. Thus, we explore the possibility that food-predictive signals influence suboptimal choice via the temporal information they convey. Application of this temporal information-theoretic approach to suboptimal choice provides a formal, quantitative framework that describes how food-predictive signals influence suboptimal choice in a manner consistent with related phenomena in Pavlovian conditioning and conditioned reinforcement. Our reanalysis of previous data on suboptimal choice suggests that, generally speaking, preference in the suboptimal choice procedure tracks relative temporal information conveyed by food-predictive signals for the suboptimal and optimal alternatives. The model suggests that suboptimal choice develops when the food-predictive signal for the suboptimal alternative conveys more temporal information than that for the optimal alternative. Finally, incorporating a role for competition between temporal information provided by food-predictive signals and relative primary reinforcement rate provides a reasonable account of existing data on suboptimal choice. (PsycINFO Database Record (c) 2018 APA, all rights reserved).
Ultimate explanations and suboptimal choice.
Vasconcelos, Marco; Machado, Armando; Pandeirada, Josefa N S
2018-07-01
Researchers have unraveled multiple cases in which behavior deviates from rationality principles. We propose that such deviations are valuable tools to understand the adaptive significance of the underpinning mechanisms. To illustrate, we discuss in detail an experimental protocol in which animals systematically incur substantial foraging losses by preferring a lean but informative option over a rich but non-informative one. To understand how adaptive mechanisms may fail to maximize food intake, we review a model inspired by optimal foraging principles that reconciles sub-optimal choice with the view that current behavioral mechanisms were pruned by the optimizing action of natural selection. To move beyond retrospective speculation, we then review critical tests of the model, regarding both its assumptions and its (sometimes counterintuitive) predictions, all of which have been upheld. The overall contention is that (a) known mechanisms can be used to develop better ultimate accounts and that (b) to understand why mechanisms that generate suboptimal behavior evolved, we need to consider their adaptive value in the animal's characteristic ecology. Copyright © 2018 Elsevier B.V. All rights reserved.
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…
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 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.
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).
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
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.
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…
How suboptimal are linear sharing rules?
DEFF Research Database (Denmark)
Jensen, Bjarne Astrup; Nielsen, Jørgen Aase
2016-01-01
that significant losses can arise when investors are diverse in their risk attitude. We also show that an investor with a low degree of risk aversion, like the logarithmic or the square root investor, often applied in portfolio choice models, can either inflict or be subject to severe losses when being forced...... two sources of suboptimal investment decisions as seen from each of the two investors individually. One is the suboptimal choice of portfolio, the other is the forced linear sharing rule. We measure the combined consequence for each investor by their respective loss in wealth equivalent. We show...
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
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…
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
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.
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.
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.
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.
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.
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
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‿
Design considerations for a suboptimal Kalman filter
Difilippo, D. J.
1995-06-01
In designing a suboptimal Kalman filter, the designer must decide how to simplify the system error model without causing the filter estimation errors to increase to unacceptable levels. Deletion of certain error states and decoupling of error state dynamics are the two principal model simplifications that are commonly used in suboptimal filter design. For the most part, the decisions as to which error states can be deleted or decoupled are based on the designer's understanding of the physics of the particular system. Consequently, the details of a suboptimal design are usually unique to the specific application. In this paper, the process of designing a suboptimal Kalman filter is illustrated for the case of an airborne transfer-of-alignment (TOA) system used for synthetic aperture radar (SAR) motion compensation. In this application, the filter must continuously transfer the alignment of an onboard Doppler-damped master inertial navigation system (INS) to a strapdown navigator that processes information from a less accurate inertial measurement unit (IMU) mounted on the radar antenna. The IMU is used to measure spurious antenna motion during the SAR imaging interval, so that compensating phase corrections can be computed and applied to the radar returns, thereby presenting image degradation that would otherwise result from such motions. The principles of SAR are described in many references, for instance. The primary function of the TOA Kalman filter in a SAR motion compensation system is to control strapdown navigator attitude errors, and to a less degree, velocity and heading errors. Unlike a classical navigation application, absolute positional accuracy is not important. The motion compensation requirements for SAR imaging are discussed in some detail. This TOA application is particularly appropriate as a vehicle for discussing suboptimal filter design, because the system contains features that can be exploited to allow both deletion and decoupling of error
Fitting Simpson's neutrino into the standard model
International Nuclear Information System (INIS)
Valle, J.W.F.
1985-01-01
I show how to accomodate the 17 keV state recently by Simpson as one of the neutrinos of the standard model. Experimental constraints can only be satisfied if the μ and tau neutrino combine to a very good approximation to form a Dirac neutrino of 17 keV leaving a light νsub(e). Neutrino oscillations will provide the most stringent test of the model. The cosmological bounds are also satisfied in a natural way in models with Goldstone bosons. Explicit examples are given in the framework of majoron-type models. Constraints on the lepton symmetry breaking scale which follow from astrophysics, cosmology and laboratory experiments are discussed. (orig.)
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)
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
Fitting polytomous Rasch models in SAS
DEFF Research Database (Denmark)
Christensen, Karl Bang
2006-01-01
The item parameters of a polytomous Rasch model can be estimated using marginal and conditional approaches. This paper describes how this can be done in SAS (V8.2) for three item parameter estimation procedures: marginal maximum likelihood estimation, conditional maximum likelihood estimation, an...
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.
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.
A Parametric Model of Shoulder Articulation for Virtual Assessment of Space Suit Fit
Kim, K. Han; Young, Karen S.; Bernal, Yaritza; Boppana, Abhishektha; Vu, Linh Q.; Benson, Elizabeth A.; Jarvis, Sarah; Rajulu, Sudhakar L.
2016-01-01
Suboptimal suit fit is a known risk factor for crewmember shoulder injury. Suit fit assessment is however prohibitively time consuming and cannot be generalized across wide variations of body shapes and poses. In this work, we have developed a new design tool based on the statistical analysis of body shape scans. This tool is aimed at predicting the skin deformation and shape variations for any body size and shoulder pose for a target population. This new process, when incorporated with CAD software, will enable virtual suit fit assessments, predictively quantifying the contact volume, and clearance between the suit and body surface at reduced time and cost.
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
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.
Directory of Open Access Journals (Sweden)
Marion Jaud
2016-06-01
Full Text Available For monitoring purposes and in the context of geomorphological research, Unmanned Aerial Vehicles (UAV appear to be a promising solution to provide multi-temporal Digital Surface Models (DSMs and orthophotographs. There are a variety of photogrammetric software tools available for UAV-based data. The objective of this study is to investigate the level of accuracy that can be achieved using two of these software tools: Agisoft PhotoScan® Pro and an open-source alternative, IGN© MicMac®, in sub-optimal survey conditions (rugged terrain, with a large variety of morphological features covering a range of roughness sizes, poor GPS reception. A set of UAV images has been taken by a hexacopter drone above the Rivière des Remparts, a river on Reunion Island. This site was chosen for its challenging survey conditions: the topography of the study area (i involved constraints on the flight plan; (ii implied errors on some GPS measurements; (iii prevented an optimal distribution of the Ground Control Points (GCPs and; (iv was very complex to reconstruct. Several image processing tests are performed with different scenarios in order to analyze the sensitivity of each software package to different parameters (image quality, numbers of GCPs, etc.. When computing the horizontal and vertical errors within a control region on a set of ground reference targets, both methods provide rather similar results. A precision up to 3–4 cm is achievable with these software packages. The DSM quality is also assessed over the entire study area comparing PhotoScan DSM and MicMac DSM with a Terrestrial Laser Scanner (TLS point cloud. PhotoScan and MicMac DSM are also compared at the scale of particular features. Both software packages provide satisfying results: PhotoScan is more straightforward to use but its source code is not open; MicMac is recommended for experimented users as it is more flexible.
Esscher, Annika; Binder-Finnema, Pauline; Bødker, Birgit; Högberg, Ulf; Mulic-Lutvica, Ajlana; Essén, Birgitta
2014-04-12
Several European countries report differences in risk of maternal mortality between immigrants from low- and middle-income countries and host country women. The present study identified suboptimal factors related to care-seeking, accessibility, and quality of care for maternal deaths that occurred in Sweden from 1988-2010. A subset of maternal death records (n = 75) among foreign-born women from low- and middle-income countries and Swedish-born women were audited using structured implicit review. One case of foreign-born maternal death was matched with two native born Swedish cases of maternal death. An assessment protocol was developed that applied both the 'migration three delays' framework and a modified version of the Confidential Enquiry from the United Kingdom. The main outcomes were major and minor suboptimal factors associated with maternal death in this high-income, low-maternal mortality context. Major and minor suboptimal factors were associated with a majority of maternal deaths and significantly more often to foreign-born women (p = 0.01). The main delays to care-seeking were non-compliance among foreign-born women and communication barriers, such as incongruent language and suboptimal interpreter system or usage. Inadequate care occurred more often among the foreign-born (p = 0.04), whereas delays in consultation/referral and miscommunication between health care providers where equally common between the two groups. Suboptimal care factors, major and minor, were present in more than 2/3 of maternal deaths in this high-income setting. Those related to migration were associated to miscommunication, lack of professional interpreters, and limited knowledge about rare diseases and pregnancy complications. Increased insight into a migration perspective is advocated for maternity clinicians who provide care to foreign-born women.
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....
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.
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....
[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.
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.
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.
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
Suboptimal Criterion Learning in Static and Dynamic Environments.
Directory of Open Access Journals (Sweden)
Elyse H Norton
2017-01-01
Full Text Available Humans often make decisions based on uncertain sensory information. Signal detection theory (SDT describes detection and discrimination decisions as a comparison of stimulus "strength" to a fixed decision criterion. However, recent research suggests that current responses depend on the recent history of stimuli and previous responses, suggesting that the decision criterion is updated trial-by-trial. The mechanisms underpinning criterion setting remain unknown. Here, we examine how observers learn to set a decision criterion in an orientation-discrimination task under both static and dynamic conditions. To investigate mechanisms underlying trial-by-trial criterion placement, we introduce a novel task in which participants explicitly set the criterion, and compare it to a more traditional discrimination task, allowing us to model this explicit indication of criterion dynamics. In each task, stimuli were ellipses with principal orientations drawn from two categories: Gaussian distributions with different means and equal variance. In the covert-criterion task, observers categorized a displayed ellipse. In the overt-criterion task, observers adjusted the orientation of a line that served as the discrimination criterion for a subsequently presented ellipse. We compared performance to the ideal Bayesian learner and several suboptimal models that varied in both computational and memory demands. Under static and dynamic conditions, we found that, in both tasks, observers used suboptimal learning rules. In most conditions, a model in which the recent history of past samples determines a belief about category means fit the data best for most observers and on average. Our results reveal dynamic adjustment of discrimination criterion, even after prolonged training, and indicate how decision criteria are updated over time.
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.
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...
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.
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…
Liang, Ying-Zhi; Chu, Xi; Meng, Shi-Jiao; Zhang, Jie; Wu, Li-Juan; Yan, Yu-Xiang
2018-03-06
The study aimed to develop and validate a model to measure psychosocial factors at work among medical staff in China based on confirmatory factor analysis (CFA). The second aim of the current study was to clarify the association between stress-related psychosocial work factors and suboptimal health status. The cross-sectional study was conducted using clustered sampling method. Xuanwu Hospital, a 3A grade hospital in Beijing. Nine hundred and fourteen medical staff aged over 40 years were sampled. Seven hundred and ninety-seven valid questionnaires were collected and used for further analyses. The sample included 94% of the Han population. The Copenhagen Psychosocial Questionnaire (COPSOQ) and the Suboptimal Health Status Questionnaires-25 were used to assess the psychosocial factors at work and suboptimal health status, respectively. CFA was conducted to establish the evaluating method of COPSOQ. A multivariate logistic regression model was used to estimate the relationship between suboptimal health status and stress-related psychosocial work factors among Chinese medical staff. There was a strong correlation among the five dimensions of COPSOQ based on the first-order factor model. Then, we established two second-order factors including negative and positive psychosocial work stress factors to evaluate psychosocial factors at work, and the second-order factor model fit well. The high score in negative (OR (95% CI)=1.47 (1.34 to 1.62), Pwork factors increased and decreased the risk of suboptimal health, respectively. This relationship remained statistically significant after adjusting for confounders and when using different cut-offs of suboptimal health status. Among medical staff, the second-order factor model was a suitable method to evaluate the COPSOQ. The negative and positive psychosocial work stress factors might be the risk and protective factors of suboptimal health, respectively. Moreover, negative psychosocial work stress was the most associated
Trunet, C; Mtimet, N; Mathot, A-G; Postollec, F; Leguerinel, I; Sohier, D; Couvert, O; Carlin, F; Coroller, L
2015-01-01
The apparent heat resistance of spores of Bacillus weihenstephanensis and Bacillus licheniformis was measured and expressed as the time to first decimal reduction (δ value) at a given recovery temperature and pH. Spores of B. weihenstephanensis were produced at 30°C and 12°C, and spores of B. licheniformis were produced at 45°C and 20°C. B. weihenstephanensis spores were then heat treated at 85°C, 90°C, and 95°C, and B. licheniformis spores were heat treated at 95°C, 100°C, and 105°C. Heat-treated spores were grown on nutrient agar at a range of temperatures (4°C to 40°C for B. weihenstephanensis and 15°C to 60°C for B. licheniformis) or a range of pHs (between pH 4.5 and pH 9.5 for both strains). The recovery temperature had a slight effect on the apparent heat resistance, except very near recovery boundaries. In contrast, a decrease in the recovery pH had a progressive impact on apparent heat resistance. A model describing the heat resistance and the ability to recover according to the sporulation temperature, temperature of treatment, and recovery temperature and pH was proposed. This model derived from secondary mathematical models for growth prediction. Previously published cardinal temperature and pH values were used as input parameters. The fitting of the model with apparent heat resistance data obtained for a wide range of spore treatment and recovery conditions was highly satisfactory. Copyright © 2015, American Society for Microbiology. All Rights Reserved.
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.
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.
On the Origins of Suboptimality in Human Probabilistic Inference
Acerbi, Luigi; Vijayakumar, Sethu; Wolpert, Daniel M.
2014-01-01
Humans have been shown to combine noisy sensory information with previous experience (priors), in qualitative and sometimes quantitative agreement with the statistically-optimal predictions of Bayesian integration. However, when the prior distribution becomes more complex than a simple Gaussian, such as skewed or bimodal, training takes much longer and performance appears suboptimal. It is unclear whether such suboptimality arises from an imprecise internal representation of the complex prior, or from additional constraints in performing probabilistic computations on complex distributions, even when accurately represented. Here we probe the sources of suboptimality in probabilistic inference using a novel estimation task in which subjects are exposed to an explicitly provided distribution, thereby removing the need to remember the prior. Subjects had to estimate the location of a target given a noisy cue and a visual representation of the prior probability density over locations, which changed on each trial. Different classes of priors were examined (Gaussian, unimodal, bimodal). Subjects' performance was in qualitative agreement with the predictions of Bayesian Decision Theory although generally suboptimal. The degree of suboptimality was modulated by statistical features of the priors but was largely independent of the class of the prior and level of noise in the cue, suggesting that suboptimality in dealing with complex statistical features, such as bimodality, may be due to a problem of acquiring the priors rather than computing with them. We performed a factorial model comparison across a large set of Bayesian observer models to identify additional sources of noise and suboptimality. Our analysis rejects several models of stochastic behavior, including probability matching and sample-averaging strategies. Instead we show that subjects' response variability was mainly driven by a combination of a noisy estimation of the parameters of the priors, and by
On the origins of suboptimality in human probabilistic inference.
Directory of Open Access Journals (Sweden)
Luigi Acerbi
2014-06-01
Full Text Available Humans have been shown to combine noisy sensory information with previous experience (priors, in qualitative and sometimes quantitative agreement with the statistically-optimal predictions of Bayesian integration. However, when the prior distribution becomes more complex than a simple Gaussian, such as skewed or bimodal, training takes much longer and performance appears suboptimal. It is unclear whether such suboptimality arises from an imprecise internal representation of the complex prior, or from additional constraints in performing probabilistic computations on complex distributions, even when accurately represented. Here we probe the sources of suboptimality in probabilistic inference using a novel estimation task in which subjects are exposed to an explicitly provided distribution, thereby removing the need to remember the prior. Subjects had to estimate the location of a target given a noisy cue and a visual representation of the prior probability density over locations, which changed on each trial. Different classes of priors were examined (Gaussian, unimodal, bimodal. Subjects' performance was in qualitative agreement with the predictions of Bayesian Decision Theory although generally suboptimal. The degree of suboptimality was modulated by statistical features of the priors but was largely independent of the class of the prior and level of noise in the cue, suggesting that suboptimality in dealing with complex statistical features, such as bimodality, may be due to a problem of acquiring the priors rather than computing with them. We performed a factorial model comparison across a large set of Bayesian observer models to identify additional sources of noise and suboptimality. Our analysis rejects several models of stochastic behavior, including probability matching and sample-averaging strategies. Instead we show that subjects' response variability was mainly driven by a combination of a noisy estimation of the parameters of the
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
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…
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.
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
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.
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
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.
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.
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.)
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.
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.
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…
FITTING OF PARAMETRIC BUILDING MODELS TO OBLIQUE AERIAL IMAGES
Directory of Open Access Journals (Sweden)
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
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
Resolving the paradox of suboptimal choice.
Zentall, Thomas R
2016-01-01
When humans engage in commercial (totally probabilistic) gambling they are making suboptimal choices because the return is generally less than the investment. This review (a) examines the literature on pigeon suboptimal choice, (b) describes the conditions under which it occurs, (c) identifies the mechanisms that appear to be responsible for the effect, and (d) suggests that similar processes may be able to account for analogous suboptimal choice when humans engage in commercial gambling. Pigeons show suboptimal choice when they choose between 1 alternative that 20% of the time provides them with a signal that they will always get fed or 80% of the time with a signal that they will not get fed (overall 20% reinforcement) and a second alternative that 100% of the time provides them with a signal that they will get fed 50% of the time (overall 50% reinforcement). The pigeons' strong preference for the suboptimal choice was investigated in a series of experiments that found the preference for the suboptimal alternative was determined by the value of the signal that predicted reinforcement, rather its frequency and that the frequency of the signal that predicted nonreinforcement had little effect on the suboptimal choice. Paradoxically, this account makes the prediction that pigeons will be indifferent between an alternative that 50% of the time provides a fully predictive stimulus for reinforcement and an alternative that 100% of the time provides a fully predictive stimulus for reinforcement. The similarities and differences of this suboptimal choice task to human gambling are discussed. (c) 2016 APA, all rights reserved).
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
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...
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.
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 ...
Baka, Maria; Verheyen, Davy; Cornette, Nicolas; Vercruyssen, Stijn; Van Impe, Jan F
2017-01-02
The limited knowledge concerning the influence of food (micro)structure on microbial dynamics decreases the accuracy of the developed predictive models, as most studies have mainly been based on experimental data obtained in liquid microbiological media or in/on real foods. The use of model systems has a great potential when studying this complex factor. Apart from the variability in (micro)structural properties, model systems vary in compositional aspects, as a consequence of their (micro)structural variation. In this study, different experimental food model systems, with compositional and physicochemical properties similar to fish patés, are developed to study the influence of food (micro)structure on microbial dynamics. The microbiological safety of fish products is of major importance given the numerous cases of salmonellosis and infections attributed to staphylococcus toxins. The model systems understudy represent food (micro)structures of liquids, aqueous gels, emulsions and gelled emulsions. The growth/inactivation dynamics and a modelling approach of combined growth and inactivation of Salmonella Typhimurium and Staphylococcus aureus, related to fish products, are investigated in/on these model systems at temperatures relevant to fish products' common storage (4°C) and to abuse storage temperatures (8 and 12°C). ComBase (http://www.combase.cc/) predictions compared with the maximum specific growth rate (μ max ) values estimated by the Baranyi and Roberts model in the current study indicated that the (micro)structure influences the microbial dynamics. Overall, ComBase overestimated microbial growth at the same pH, a w and storage temperature. Finally, the storage temperature had also an influence on how much each model system affected the microbial dynamics. Copyright Â© 2016. Published by Elsevier B.V.
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
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
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.
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...
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.
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.…
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
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
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.
International Nuclear Information System (INIS)
Romero, Alberto; Millar, Dean; Carvalho, Monica; Maestre, José M.; Camacho, Eduardo F.
2015-01-01
Mine dewatering can represent up to 5% of the total energy demand of a mine, and is one of the mine systems that aim to guarantee safe operating conditions. As mines go deeper, dewatering pumping heads become bigger, potentially involving several lift stages. Greater depth does not only mean greater dewatering cost, but more complex systems that require more sophisticated control systems, especially if mine operators wish to gain benefits from demand response incentives that are becoming a routine part of electricity tariffs. This work explores a two stage economic optimization procedure of an underground mine dewatering system, comprising two lifting stages, each one including a pump station and a water reservoir. First, the system design is optimized considering hourly characteristic dewatering demands for twelve days, one day representing each month of the year to account for seasonal dewatering demand variations. This design optimization minimizes the annualized cost of the system, and therefore includes the investment costs in underground reservoirs. Reservoir size, as well as an hourly pumping operation plan are calculated for specific operating environments, defined by characteristic hourly electricity prices and water inflows (seepage and water use from production activities), at best known through historical observations for the previous year. There is no guarantee that the system design will remain optimal when it faces the water inflows and market determined electricity prices of the year ahead, or subsequent years ahead, because these remain unknown at design time. Consequently, the dewatering optimized system design is adopted subsequently as part of a Model Predictive Control (MPC) strategy that adaptively maintains optimality during the operations phase. Centralized, distributed and non-centralized MPC strategies are explored. Results show that the system can be reliably controlled using any of these control strategies proposed. Under the operating
Suboptimal processor for anomaly detection for system surveillance and diagnosis
Energy Technology Data Exchange (ETDEWEB)
Ciftcioglu, Oe.; Hoogenboom, J.E.; Dam, H. van
1989-06-01
Anomaly detection for nuclear reactor surveillance and diagnosis is described. The residual noise obtained as a result of autoregressive (AR) modelling is essential to obtain high sensitivity for anomaly detection. By means of the method of hypothesis testing a suboptimal anomaly detection processor is devised for system surveillance and diagnosis. Experiments are carried out to investigate the performance of the processor, which is in particular of interest for on-line and real-time applications.
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…
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.
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.
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.
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.
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
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
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
Fitting and Testing Conditional Multinormal Partial Credit Models
Hessen, David J.
2012-01-01
A multinormal partial credit model for factor analysis of polytomously scored items with ordered response categories is derived using an extension of the Dutch Identity (Holland in "Psychometrika" 55:5-18, 1990). In the model, latent variables are assumed to have a multivariate normal distribution conditional on unweighted sums of item…
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.
Assessment of health surveys: fitting a multidimensional graded response model.
Depaoli, Sarah; Tiemensma, Jitske; Felt, John M
The multidimensional graded response model, an item response theory (IRT) model, can be used to improve the assessment of surveys, even when sample sizes are restricted. Typically, health-based survey development utilizes classical statistical techniques (e.g. reliability and factor analysis). In a review of four prominent journals within the field of Health Psychology, we found that IRT-based models were used in less than 10% of the studies examining scale development or assessment. However, implementing IRT-based methods can provide more details about individual survey items, which is useful when determining the final item content of surveys. An example using a quality of life survey for Cushing's syndrome (CushingQoL) highlights the main components for implementing the multidimensional graded response model. Patients with Cushing's syndrome (n = 397) completed the CushingQoL. Results from the multidimensional graded response model supported a 2-subscale scoring process for the survey. All items were deemed as worthy contributors to the survey. The graded response model can accommodate unidimensional or multidimensional scales, be used with relatively lower sample sizes, and is implemented in free software (example code provided in online Appendix). Use of this model can help to improve the quality of health-based scales being developed within the Health Sciences.
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 $n_s \\simeq 0.96$.
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.
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.
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.
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
Reducing uncertainty based on model fitness: Application to a ...
African Journals Online (AJOL)
A weakness of global sensitivity and uncertainty analysis methodologies is the often subjective definition of prior parameter probability distributions, especially ... The reservoir representing the central part of the wetland, where flood waters separate into several independent distributaries, is a keystone area within the model.
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.
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
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.
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.
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.
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.
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....
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.
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.
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
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.
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)
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…
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
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.
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.
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.
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.
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
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.
Robotic fish tracking method based on suboptimal interval Kalman filter
Tong, Xiaohong; Tang, Chao
2017-11-01
Autonomous Underwater Vehicle (AUV) research focused on tracking and positioning, precise guidance and return to dock and other fields. The robotic fish of AUV has become a hot application in intelligent education, civil and military etc. In nonlinear tracking analysis of robotic fish, which was found that the interval Kalman filter algorithm contains all possible filter results, but the range is wide, relatively conservative, and the interval data vector is uncertain before implementation. This paper proposes a ptimization algorithm of suboptimal interval Kalman filter. Suboptimal interval Kalman filter scheme used the interval inverse matrix with its worst inverse instead, is more approximate nonlinear state equation and measurement equation than the standard interval Kalman filter, increases the accuracy of the nominal dynamic system model, improves the speed and precision of tracking system. Monte-Carlo simulation results show that the optimal trajectory of sub optimal interval Kalman filter algorithm is better than that of the interval Kalman filter method and the standard method of the filter.
Suboptimal Choice in Pigeons: Stimulus Value Predicts Choice over Frequencies.
Directory of Open Access Journals (Sweden)
Aaron P Smith
Full Text Available Pigeons have shown suboptimal gambling-like behavior when preferring a stimulus that infrequently signals reliable reinforcement over alternatives that provide greater reinforcement overall. As a mechanism for this behavior, recent research proposed that the stimulus value of alternatives with more reliable signals for reinforcement will be preferred relatively independently of their frequencies. The present study tested this hypothesis using a simplified design of a Discriminative alternative that, 50% of the time, led to either a signal for 100% reinforcement or a blackout period indicative of 0% reinforcement against a Nondiscriminative alternative that always led to a signal that predicted 50% reinforcement. Pigeons showed a strong preference for the Discriminative alternative that remained despite reducing the frequency of the signal for reinforcement in subsequent phases to 25% and then 12.5%. In Experiment 2, using the original design of Experiment 1, the stimulus following choice of the Nondiscriminative alternative was increased to 75% and then to 100%. Results showed that preference for the Discriminative alternative decreased only when the signals for reinforcement for the two alternatives predicted the same probability of reinforcement. The ability of several models to predict this behavior are discussed, but the terminal link stimulus value offers the most parsimonious account of this suboptimal behavior.
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
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
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.
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.
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.
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
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.
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)
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.
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)
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.
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…
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...
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
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…
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.
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.
Segmentation precedes face categorization under suboptimal conditions
Directory of Open Access Journals (Sweden)
Carlijn eVan Den Boomen
2015-05-01
Full Text Available Both categorization and segmentation processes play a crucial role in face perception. However, the functional relation between these subprocesses is currently unclear. The present study investigates the temporal relation between segmentation-related and category-selective responses in the brain, using electroencephalography (EEG. Surface segmentation and category content were both manipulated using texture-defined objects, including faces. This allowed us to study brain activity related to segmentation and to categorization. In the main experiment, participants viewed texture-defined objects for a duration of 800 ms. EEG results revealed that segmentation-related responses precede category-selective responses. Three additional experiments revealed that the presence and timing of categorization depends on stimulus properties and presentation duration. Photographic objects were presented for a long and short (92 ms duration and evoked fast category-selective responses in both cases. On the other hand, presentation of texture-defined objects for a short duration only evoked segmentation-related but no category-selective responses. Category-selective responses were much slower when evoked by texture-defined than by photographic objects. We suggest that in case of categorization of objects under suboptimal conditions, such as when low-level stimulus properties are not sufficient for fast object categorization, segmentation facilitates the slower categorization process.
Segmentation precedes face categorization under suboptimal conditions.
Van Den Boomen, Carlijn; Fahrenfort, Johannes J; Snijders, Tineke M; Kemner, Chantal
2015-01-01
Both categorization and segmentation processes play a crucial role in face perception. However, the functional relation between these subprocesses is currently unclear. The present study investigates the temporal relation between segmentation-related and category-selective responses in the brain, using electroencephalography (EEG). Surface segmentation and category content were both manipulated using texture-defined objects, including faces. This allowed us to study brain activity related to segmentation and to categorization. In the main experiment, participants viewed texture-defined objects for a duration of 800 ms. EEG results revealed that segmentation-related responses precede category-selective responses. Three additional experiments revealed that the presence and timing of categorization depends on stimulus properties and presentation duration. Photographic objects were presented for a long and short (92 ms) duration and evoked fast category-selective responses in both cases. On the other hand, presentation of texture-defined objects for a short duration only evoked segmentation-related but no category-selective responses. Category-selective responses were much slower when evoked by texture-defined than by photographic objects. We suggest that in case of categorization of objects under suboptimal conditions, such as when low-level stimulus properties are not sufficient for fast object categorization, segmentation facilitates the slower categorization process.
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.
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.
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
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.
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.
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...
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...
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
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.
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).
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)
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
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
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.
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
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.
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.
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.
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
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 ...
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...
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.
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.
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/
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.
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.
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.
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.
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...
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.
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...
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.
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
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
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)
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....
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.
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
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).
Adapted strategic plannig model applied to small business: a case study in the fitness area
Directory of Open Access Journals (Sweden)
Eduarda Tirelli Hennig
2012-06-01
Full Text Available The strategic planning is an important management tool in the corporate scenario and shall not be restricted to big Companies. However, this kind of planning process in small business may need special adaptations due to their own characteristics. This paper aims to identify and adapt the existent models of strategic planning to the scenario of a small business in the fitness area. Initially, it is accomplished a comparative study among models of different authors to identify theirs phases and activities. Then, it is defined which of these phases and activities should be present in a model that will be utilized in a small business. That model was applied to a Pilates studio; it involves the establishment of an organizational identity, an environmental analysis as well as the definition of strategic goals, strategies and actions to reach them. Finally, benefits to the organization could be identified, as well as hurdles in the implementation of the tool.
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.
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
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.
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
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.
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.
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.
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.
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
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.
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
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.
Indicators of suboptimal performance embedded in the Wechsler Memory Scale-Fourth Edition (WMS-IV).
Bouman, Zita; Hendriks, Marc P H; Schmand, Ben A; Kessels, Roy P C; Aldenkamp, Albert P
2016-01-01
Recognition and visual working memory tasks from the Wechsler Memory Scale-Fourth Edition (WMS-IV) have previously been documented as useful indicators for suboptimal performance. The present study examined the clinical utility of the Dutch version of the WMS-IV (WMS-IV-NL) for the identification of suboptimal performance using an analogue study design. The patient group consisted of 59 mixed-etiology patients; the experimental malingerers were 50 healthy individuals who were asked to simulate cognitive impairment as a result of a traumatic brain injury; the last group consisted of 50 healthy controls who were instructed to put forth full effort. Experimental malingerers performed significantly lower on all WMS-IV-NL tasks than did the patients and healthy controls. A binary logistic regression analysis was performed on the experimental malingerers and the patients. The first model contained the visual working memory subtests (Spatial Addition and Symbol Span) and the recognition tasks of the following subtests: Logical Memory, Verbal Paired Associates, Designs, Visual Reproduction. The results showed an overall classification rate of 78.4%, and only Spatial Addition explained a significant amount of variation (p < .001). Subsequent logistic regression analysis and receiver operating characteristic (ROC) analysis supported the discriminatory power of the subtest Spatial Addition. A scaled score cutoff of <4 produced 93% specificity and 52% sensitivity for detection of suboptimal performance. The WMS-IV-NL Spatial Addition subtest may provide clinically useful information for the detection of suboptimal performance.
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.
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.
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
Adaptive suboptimal second-order sliding mode control for microgrids
Incremona, Gian Paolo; Cucuzzella, Michele; Ferrara, Antonella
2016-09-01
This paper deals with the design of adaptive suboptimal second-order sliding mode (ASSOSM) control laws for grid-connected microgrids. Due to the presence of the inverter, of unpredicted load changes, of switching among different renewable energy sources, and of electrical parameters variations, the microgrid model is usually affected by uncertain terms which are bounded, but with unknown upper bounds. To theoretically frame the control problem, the class of second-order systems in Brunovsky canonical form, characterised by the presence of matched uncertain terms with unknown bounds, is first considered. Four adaptive strategies are designed, analysed and compared to select the most effective ones to be applied to the microgrid case study. In the first two strategies, the control amplitude is continuously adjusted, so as to arrive at dominating the effect of the uncertainty on the controlled system. When a suitable control amplitude is attained, the origin of the state space of the auxiliary system becomes attractive. In the other two strategies, a suitable blend between two components, one mainly working during the reaching phase, the other being the predominant one in a vicinity of the sliding manifold, is generated, so as to reduce the control amplitude in steady state. The microgrid system in a grid-connected operation mode, controlled via the selected ASSOSM control strategies, exhibits appreciable stability properties, as proved theoretically and shown in simulation.
Levy flights and self-similar exploratory behaviour of termite workers: beyond model fitting.
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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.
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
The paradoxical effect of low reward probabilities in suboptimal choice.
Fortes, Inês; Pinto, Carlos; Machado, Armando; Vasconcelos, Marco
2018-04-01
When offered a choice between 2 alternatives, animals sometimes prefer the option yielding less food. For instance, pigeons and starlings prefer an option that on 20% of the trials presents a stimulus always followed by food, and on the remaining 80% of the trials presents a stimulus never followed by food (the Informative Option), over an option that provides food on 50% of the trials regardless of the stimulus presented (the Noninformative Option). To explain this suboptimal behavior, it has been hypothesized that animals ignore (or do not engage with) the stimulus that is never followed by food in the Informative Option. To assess when pigeons attend to the stimulus usually not followed by food, we increased the probability of reinforcement, p, in the presence of that stimulus. Across 2 experiments, we found that the value of the Informative Option decreased with p. To account for the results, we added to the Reinforcement Rate Model (and also to the Hyperbolic Discounting Model) an engagement function, f(p), that specified the likelihood the animal attends to a stimulus followed by reward with probability p, and then derived the model predictions for 2 forms of f(p), a linear function, and an all-or-none threshold function. Both models predicted the observed findings with a linear engagement function: The higher the probability of reinforcement after a stimulus, the higher the probability of engaging the stimulus, and, surprisingly, the less the value of the option comprising the stimulus. (PsycINFO Database Record (c) 2018 APA, all rights reserved).
Suboptimal palliative sedation in primary care: an exploration.
Pype, Peter; Teuwen, Inge; Mertens, Fien; Sercu, Marij; De Sutter, An
2018-02-01
Palliative sedation is a therapeutic option to control refractory symptoms in terminal palliative patients. This study aims at describing the occurrence and characteristics of suboptimal palliative sedations in primary care and at exploring the way general practitioners (GPs) experience suboptimal palliative sedation in their practice. We conducted a mixed methods study with a quantitative prospective survey in primary care and qualitative semi-structured interviews with GPs. The research team defined suboptimal palliative sedation as a time interval until deep sleep >1.5 h and/ or >2 awakenings after the start of the unconsciousness. Descriptive statistics were calculated on the quantitative data. Thematic analysis was used to analyse interview transcripts. We registered 63 palliative sedations in 1181 home deaths, 27 forms were completed. Eleven palliative sedations were suboptimal: eight due to the long time span until deep sleep; three due the number of unintended awakenings. GPs' interview analysis revealed two major themes: the shifting perception of failure and the burden of responsibility. Suboptimal palliative sedation occurs frequently in primary palliative care. Efficient communication towards family members is needed to prevent them from having unrealistic expectations and to prevent putting pressure on the GP to hasten the procedure. Sharing the burden of decision-making during the procedure with other health care professionals might diminish the heavy responsibility as perceived by GPs.
Fits of the baryon magnetic moments to the quark model and spectrum-generating SU(3)
International Nuclear Information System (INIS)
Bohm, A.; Teese, R.B.
1982-01-01
We show that for theoretical as well as phenomenological reasons the baryon magnetic moments that fulfill simple group transformation properties should be taken in intrinsic rather than nuclear magnetons. A fit of the recent experimental data to the reduced matrix elements of the usual octet electromagnetic current is still not good, and in order to obtain acceptable agreement, one has to add correction terms to the octet current. We have texted two kinds of corrections: U-spin-scalar terms, which are singles out by the model-independent algebraic properties of the hadron electromagnetic current, and octet U-spin vectors, which could come from quark-mass breaking in a nonrelativistic quark model. We find that the U-spin-scalar terms are more important than the U-spin vectors for various levels of demanded theoretical accuracy
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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
Nomogram for suboptimal cytoreduction at primary surgery for advanced stage ovarian cancer.
Gerestein, Cornelis G; Eijkemans, Marinus J; Bakker, Jeanette; Elgersma, Otto E; van der Burg, Maria E L; Kooi, Geertruida S; Burger, Curt W
2011-11-01
Maximal cytoreduction to minimal residual tumor is the most important determinant of prognosis in patients with advanced stage epithelial ovarian cancer (EOC). Preoperative prediction of suboptimal cytoreduction, defined as residual tumor >1 cm, could guide treatment decisions and improve counseling. The objective of this study was to identify predictive computed tomographic (CT) scan and clinical parameters for suboptimal cytoreduction at primary cytoreductive surgery for advanced stage EOC and to generate a nomogram with the identified parameters, which would be easy to use in daily clinical practice. Between October 2005 and December 2008, all patients with primary surgery for suspected advanced stage EOC at six participating teaching hospitals in the South Western part of the Netherlands entered the study protocol. To investigate independent predictors of suboptimal cytoreduction, a Cox proportional hazard model with backward stepwise elimination was utilized. One hundred and fifteen patients with FIGO stage III/IV EOC entered the study protocol. Optimal cytoreduction was achieved in 52 (45%) patients. A suboptimal cytoreduction was predicted by preoperative blood platelet count (p=0.1990; odds ratio (OR)=1.002), diffuse peritoneal thickening (DPT) (p=0.0074; OR=3.021), and presence of ascites on at least two thirds of CT scan slices (p=0.0385; OR=2.294) with a for-optimism corrected c-statistic of 0.67. Suboptimal cytoreduction was predicted by preoperative platelet count, DPT and presence of ascites. The generated nomogram can, after external validation, be used to estimate surgical outcome and to identify those patients, who might benefit from alternative treatment approaches.
Fitting Data to Model: Structural Equation Modeling Diagnosis Using Two Scatter Plots
Yuan, Ke-Hai; Hayashi, Kentaro
2010-01-01
This article introduces two simple scatter plots for model diagnosis in structural equation modeling. One plot contrasts a residual-based M-distance of the structural model with the M-distance for the factor score. It contains information on outliers, good leverage observations, bad leverage observations, and normal cases. The other plot contrasts…
Suboptimal control for drag reduction in turbulent pipe flow
International Nuclear Information System (INIS)
Choi, Jung Il; Sung, Hyung Jin; Xu, Chun Xiao
2001-01-01
A suboptimal control law in turbulent pipe flow is derived and tested. Two sensing variables ∂ρ/∂θ / w and ∂ν θ /∂r / w are applied with two actuations φ θ and φ γ . To test the suboptimal control law, direct numerical simulations of turbulent pipe flow at Re τ =150 are performed. When the control law is applied, a 13∼23% drag reduction is achieved. The most effective drag reduction is made at the pair of ∂υ θ /∂r / w and φ γ . An impenetrable virtual wall concept is useful for analyzing the near-wall suction and blowing. The virtual wall concept is useful for analyzing the near-wall behavior of the controlled flow. Comparison of the present suboptimal control with that of turbulent channel flow reveals that the curvature effect is insignificant
Model Atmosphere Spectrum Fit to the Soft X-Ray Outburst Spectrum of SS Cyg
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V. F. Suleimanov
2015-02-01
Full Text Available The X-ray spectrum of SS Cyg in outburst has a very soft component that can be interpreted as the fast-rotating optically thick boundary layer on the white dwarf surface. This component was carefully investigated by Mauche (2004 using the Chandra LETG spectrum of this object in outburst. The spectrum shows broad ( ≈5 °A spectral features that have been interpreted as a large number of absorption lines on a blackbody continuum with a temperature of ≈250 kK. Because the spectrum resembles the photospheric spectra of super-soft X-ray sources, we tried to fit it with high gravity hot LTE stellar model atmospheres with solar chemical composition, specially computed for this purpose. We obtained a reasonably good fit to the 60–125 °A spectrum with the following parameters: Teff = 190 kK, log g = 6.2, and NH = 8 · 1019 cm−2, although at shorter wavelengths the observed spectrum has a much higher flux. The reasons for this are discussed. The hypothesis of a fast rotating boundary layer is supported by the derived low surface gravity.
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.
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.
FIT ANALYSIS OF INDOSAT DOMPETKU BUSINESS MODEL USING A STRATEGIC DIAGNOSIS APPROACH
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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
Sacral Nerve Stimulation for Constipation: Suboptimal Outcome and Adverse Events
DEFF Research Database (Denmark)
Maeda, Yasuko; Lundby, Lilli; Buntzen, Steen
2010-01-01
Sacral nerve stimulation is an emerging treatment for patients with severe constipation. There has been no substantial report to date on suboptimal outcomes and complications. We report our experience of more than 6 years by focusing on incidents and the management of reportable events.......Sacral nerve stimulation is an emerging treatment for patients with severe constipation. There has been no substantial report to date on suboptimal outcomes and complications. We report our experience of more than 6 years by focusing on incidents and the management of reportable events....
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
Ultra high energy interaction models for Monte Carlo calculations: what model is the best fit
Energy Technology Data Exchange (ETDEWEB)
Stanev, Todor [Bartol Research Institute, University of Delaware, Newark DE 19716 (United States)
2006-01-15
We briefly outline two methods for extension of hadronic interaction models to extremely high energy. Then we compare the main characteristics of representative computer codes that implement the different models and give examples of air shower parameters predicted by those codes.
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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.
International Nuclear Information System (INIS)
Moore, Kevin L.; Schmidt, Rachel; Moiseenko, Vitali; Olsen, Lindsey A.; Tan, Jun; Xiao, Ying; Galvin, James; Pugh, Stephanie; Seider, Michael J.; Dicker, Adam P.; Bosch, Walter; Michalski, Jeff; Mutic, Sasa
2015-01-01
Purpose: The purpose of this study was to quantify the frequency and clinical severity of quality deficiencies in intensity modulated radiation therapy (IMRT) planning in the Radiation Therapy Oncology Group 0126 protocol. Methods and Materials: A total of 219 IMRT patients from the high-dose arm (79.2 Gy) of RTOG 0126 were analyzed. To quantify plan quality, we used established knowledge-based methods for patient-specific dose-volume histogram (DVH) prediction of organs at risk and a Lyman-Kutcher-Burman (LKB) model for grade ≥2 rectal complications to convert DVHs into normal tissue complication probabilities (NTCPs). The LKB model was validated by fitting dose-response parameters relative to observed toxicities. The 90th percentile (22 of 219) of plans with the lowest excess risk (difference between clinical and model-predicted NTCP) were used to create a model for the presumed best practices in the protocol (pDVH 0126,top10% ). Applying the resultant model to the entire sample enabled comparisons between DVHs that patients could have received to DVHs they actually received. Excess risk quantified the clinical impact of suboptimal planning. Accuracy of pDVH predictions was validated by replanning 30 of 219 patients (13.7%), including equal numbers of presumed “high-quality,” “low-quality,” and randomly sampled plans. NTCP-predicted toxicities were compared to adverse events on protocol. Results: Existing models showed that bladder-sparing variations were less prevalent than rectum quality variations and that increased rectal sparing was not correlated with target metrics (dose received by 98% and 2% of the PTV, respectively). Observed toxicities were consistent with current LKB parameters. Converting DVH and pDVH 0126,top10% to rectal NTCPs, we observed 94 of 219 patients (42.9%) with ≥5% excess risk, 20 of 219 patients (9.1%) with ≥10% excess risk, and 2 of 219 patients (0.9%) with ≥15% excess risk. Replanning demonstrated the predicted NTCP
Moore, Kevin L; Schmidt, Rachel; Moiseenko, Vitali; Olsen, Lindsey A; Tan, Jun; Xiao, Ying; Galvin, James; Pugh, Stephanie; Seider, Michael J; Dicker, Adam P; Bosch, Walter; Michalski, Jeff; Mutic, Sasa
2015-06-01
The purpose of this study was to quantify the frequency and clinical severity of quality deficiencies in intensity modulated radiation therapy (IMRT) planning in the Radiation Therapy Oncology Group 0126 protocol. A total of 219 IMRT patients from the high-dose arm (79.2 Gy) of RTOG 0126 were analyzed. To quantify plan quality, we used established knowledge-based methods for patient-specific dose-volume histogram (DVH) prediction of organs at risk and a Lyman-Kutcher-Burman (LKB) model for grade ≥2 rectal complications to convert DVHs into normal tissue complication probabilities (NTCPs). The LKB model was validated by fitting dose-response parameters relative to observed toxicities. The 90th percentile (22 of 219) of plans with the lowest excess risk (difference between clinical and model-predicted NTCP) were used to create a model for the presumed best practices in the protocol (pDVH0126,top10%). Applying the resultant model to the entire sample enabled comparisons between DVHs that patients could have received to DVHs they actually received. Excess risk quantified the clinical impact of suboptimal planning. Accuracy of pDVH predictions was validated by replanning 30 of 219 patients (13.7%), including equal numbers of presumed "high-quality," "low-quality," and randomly sampled plans. NTCP-predicted toxicities were compared to adverse events on protocol. Existing models showed that bladder-sparing variations were less prevalent than rectum quality variations and that increased rectal sparing was not correlated with target metrics (dose received by 98% and 2% of the PTV, respectively). Observed toxicities were consistent with current LKB parameters. Converting DVH and pDVH0126,top10% to rectal NTCPs, we observed 94 of 219 patients (42.9%) with ≥5% excess risk, 20 of 219 patients (9.1%) with ≥10% excess risk, and 2 of 219 patients (0.9%) with ≥15% excess risk. Replanning demonstrated the predicted NTCP reductions while maintaining the volume of the PTV
Effects of Suboptimal Bidding in Combinatorial Auctions
Schneider, Stefan; Shabalin, Pasha; Bichler, Martin
Though the VCG auction assumes a central place in the mechanism design literature, there are a number of reasons for favoring iterative combinatorial auction designs. Several promising ascending auction formats have been developed throughout the past few years based on primal-dual and subgradient algorithms and linear programming theory. Prices are interpreted as a feasible dual solution and the provisional allocation is interpreted as a feasible primal solution. iBundle( 3) (Parkes and Ungar 2000), dVSV (de Vries et al. 2007) and the Ascending Proxy auction (Ausubel and Milgrom 2002) result in VCG payoffs when the coalitional value function satisfies the buyer submodularity condition and bidders bid straightforward, which is an expost Nash equilibrium in that case. iBEA and CreditDebit auctions (Mishra and Parkes 2007) do not even require the buyer submodularity condition and achieve the same properties for general valuations. In many situations, however, one cannot assume bidders to bid straightforward and it is not clear from the theory how these non-linear personalized price auctions (NLPPAs) perform in this case. Robustness of auctions with respect to different bidding behavior is therefore a critical issue for any application. We have conducted a large number of computational experiments to analyze the performance of NLPPA designs with respect to different bidding strategies and different valuation models. We compare the results of NLPPAs to those of the VCG auction and those of iterative combinatorial auctions with approximate linear prices, such as ALPS (Bichler et al. 2009) and the Combinatorial Clock auction (Porter et al. 2003).
Chari, Satyan R; McRae, Prue; Stewart, Matthew J; Webster, Joan; Fenn, Mary; Haines, Terry P
2016-09-01
Objective The aim of the present study was to establish the point prevalence of 'suboptimal' features in footwear reported to have been used by older hospital patients when ambulating, and to explore underpinning factors for their choice of footwear. Method A cross-sectional investigation was undertaken on 95 of 149 eligible in-patients across 22 high fall-risk wards in a large metropolitan hospital in Brisbane, Australia. Results Over 70% of participants experienced an unplanned admission. Although most participants had access to some form of footwear in hospital (92%), nearly all reported ambulating in footwear with 'suboptimal' features (99%). Examples included slippers (27%), backless slippers (16%) or bare feet (27%). For patients who ambulated in bare feet, only one-third reported 'lack of access to footwear' as the primary cause, with others citing foot wounds, pain, oedema and personal choice as the main reason for bare foot ambulation. Conclusions Admitted patients frequently use footwear with 'suboptimal' features for ambulation in hospital. While some footwear options (for example well-fitting slippers) could be suited for limited in-hospital ambulation, others are clearly hazardous and might cause falls. Since footwear choices are influenced by multiple factors in this population, footwear education strategies alone may be insufficient to address the problem of hazardous footwear in at-risk patients. Footwear requirements may be more effectively addressed within a multidisciplinary team approach encompassing foot health, mobility and safety. What is known about the topic? Accidental falls while ambulating are an important health and safety concern for older people. Because certain footwear characteristics have been negatively linked to posture and balance, and specific footwear types linked to falls among seniors, the use of footwear with fewer suboptimal characteristics is generally recommended as a means of reducing the risk of falling. While footwear
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Gurutzeta Guillera-Arroita
Full Text Available In a recent paper, Welsh, Lindenmayer and Donnelly (WLD question the usefulness of models that estimate species occupancy while accounting for detectability. WLD claim that these models are difficult to fit and argue that disregarding detectability can be better than trying to adjust for it. We think that this conclusion and subsequent recommendations are not well founded and may negatively impact the quality of statistical inference in ecology and related management decisions. Here we respond to WLD's claims, evaluating in detail their arguments, using simulations and/or theory to support our points. In particular, WLD argue that both disregarding and accounting for imperfect detection lead to the same estimator performance regardless of sample size when detectability is a function of abundance. We show that this, the key result of their paper, only holds for cases of extreme heterogeneity like the single scenario they considered. Our results illustrate the dangers of disregarding imperfect detection. When ignored, occupancy and detection are confounded: the same naïve occupancy estimates can be obtained for very different true levels of occupancy so the size of the bias is unknowable. Hierarchical occupancy models separate occupancy and detection, and imprecise estimates simply indicate that more data are required for robust inference about the system in question. As for any statistical method, when underlying assumptions of simple hierarchical models are violated, their reliability is reduced. Resorting in those instances where hierarchical occupancy models do no perform well to the naïve occupancy estimator does not provide a satisfactory solution. The aim should instead be to achieve better estimation, by minimizing the effect of these issues during design, data collection and analysis, ensuring that the right amount of data is collected and model assumptions are met, considering model extensions where appropriate.
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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.
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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.
Slater, Graham J; Harmon, Luke J; Wegmann, Daniel; Joyce, Paul; Revell, Liam J; Alfaro, Michael E
2012-03-01
In recent years, a suite of methods has been developed to fit multiple rate models to phylogenetic comparative data. However, most methods have limited utility at broad phylogenetic scales because they typically require complete sampling of both the tree and the associated phenotypic data. Here, we develop and implement a new, tree-based method called MECCA (Modeling Evolution of Continuous Characters using ABC) that uses a hybrid likelihood/approximate Bayesian computation (ABC)-Markov-Chain Monte Carlo approach to simultaneously infer rates of diversification and trait evolution from incompletely sampled phylogenies and trait data. We demonstrate via simulation that MECCA has considerable power to choose among single versus multiple evolutionary rate models, and thus can be used to test hypotheses about changes in the rate of trait evolution across an incomplete tree of life. We finally apply MECCA to an empirical example of body size evolution in carnivores, and show that there is no evidence for an elevated rate of body size evolution in the pinnipeds relative to terrestrial carnivores. ABC approaches can provide a useful alternative set of tools for future macroevolutionary studies where likelihood-dependent approaches are lacking. © 2011 The Author(s). Evolution© 2011 The Society for the Study of Evolution.
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...
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 ∘
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.
Modeling of physical fitness of young karatyst on the pre basic training
Directory of Open Access Journals (Sweden)
V. A. Galimskyi
2014-09-01
Full Text Available Purpose : to develop a program of physical fitness for the correction of the pre basic training on the basis of model performance. Material: 57 young karate sportsmen of 9-11 years old took part in the research. Results : the level of general and special physical preparedness of young karate 9-11 years old was determined. Classes in the control group occurred in the existing program for yous sports school Muay Thai (Thailand boxing. For the experimental group has developed a program of selective development of general and special physical qualities of model-based training sessions. Special program contains 6 direction: 1. Development of static and dynamic balance; 2. Development of vestibular stability (precision movements after rotation; 3. Development rate movements; 4. The development of the capacity for rapid restructuring movements; 5. Development capabilities to differentiate power and spatial parameters of movement; 6. Development of the ability to perform jumping movements of rotation. Development of special physical qualities continued to work to improve engineering complex shock motions on the place and with movement. Conclusions : the use of selective development of special physical qualities based models of training sessions has a significant performance advantage over the control group.
Sub-optimal breastfeeding and its associated factors in rural ...
African Journals Online (AJOL)
Similarly, not attending formal education, low birth order and lack of knowledge about exclusive breastfeeding were also negatively associated with exclusive breastfeeding practice. Conclusion: In this study, sub-optimal breast feeding was found to be high. Delayed initiation and non-exclusive breastfeeding practices were ...
Suboptimality of Sales Promotions and Improvement Through Channel Coordination
B. Wierenga (Berend); H. Soethoudt (Han)
2002-01-01
textabstractThis paper deals with sales promotions in the form of consumer price discounts in fast-moving consumer goods. First, we show analytically that suboptimality is to be expected with respect to the size of the consumer price discount. This is due to the separate decision making of the
Miszta, Przemyslaw; Pasznik, Pawel; Jakowiecki, Jakub; Sztyler, Agnieszka; Latek, Dorota; Filipek, Slawomir
2018-05-21
Due to the involvement of G protein-coupled receptors (GPCRs) in most of the physiological and pathological processes in humans they have been attracting a lot of attention from pharmaceutical industry as well as from scientific community. Therefore, the need for new, high quality structures of GPCRs is enormous. The updated homology modeling service GPCRM (http://gpcrm.biomodellab.eu/) meets those expectations by greatly reducing the execution time of submissions (from days to hours/minutes) with nearly the same average quality of obtained models. Additionally, due to three different scoring functions (Rosetta, Rosetta-MP, BCL::Score) it is possible to select accurate models for the required purposes: the structure of the binding site, the transmembrane domain or the overall shape of the receptor. Currently, no other web service for GPCR modeling provides this possibility. GPCRM is continually upgraded in a semi-automatic way and the number of template structures has increased from 20 in 2013 to over 90 including structures the same receptor with different ligands which can influence the structure not only in the on/off manner. Two types of protein viewers can be used for visual inspection of obtained models. The extended sortable tables with available templates provide links to external databases and display ligand-receptor interactions in visual form.
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
Fitting diameter distribution models to data from forest inventories with concentric plot design
Energy Technology Data Exchange (ETDEWEB)
Nanos, N.; Sjöstedt de Luna, S.
2017-11-01
Aim: Several national forest inventories use a complex plot design based on multiple concentric subplots where smaller diameter trees are inventoried when lying in the smaller-radius subplots and ignored otherwise. Data from these plots are truncated with threshold (truncation) diameters varying according to the distance from the plot centre. In this paper we designed a maximum likelihood method to fit the Weibull diameter distribution to data from concentric plots. Material and methods: Our method (M1) was based on multiple truncated probability density functions to build the likelihood. In addition, we used an alternative method (M2) presented recently. We used methods M1 and M2 as well as two other reference methods to estimate the Weibull parameters in 40000 simulated plots. The spatial tree pattern of the simulated plots was generated using four models of spatial point patterns. Two error indices were used to assess the relative performance of M1 and M2 in estimating relevant stand-level variables. In addition, we estimated the Quadratic Mean plot Diameter (QMD) using Expansion Factors (EFs). Main results: Methods M1 and M2 produced comparable estimation errors in random and cluster tree spatial patterns. Method M2 produced biased parameter estimates in plots with inhomogeneous Poisson patterns. Estimation of QMD using EFs produced biased results in plots within inhomogeneous intensity Poisson patterns. Research highlights:We designed a new method to fit the Weibull distribution to forest inventory data from concentric plots that achieves high accuracy and precision in parameter estimates regardless of the within-plot spatial tree pattern.
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.
Patient-centered medical home model: do school-based health centers fit the model?
Larson, Satu A; Chapman, Susan A
2013-01-01
School-based health centers (SBHCs) are an important component of health care reform. The SBHC model of care offers accessible, continuous, comprehensive, family-centered, coordinated, and compassionate care to infants, children, and adolescents. These same elements comprise the patient-centered medical home (PCMH) model of care being promoted by the Affordable Care Act with the hope of lowering health care costs by rewarding clinicians for primary care services. PCMH survey tools have been developed to help payers determine whether a clinician/site serves as a PCMH. Our concern is that current survey tools will be unable to capture how a SBHC may provide a medical home and therefore be denied needed funding. This article describes how SBHCs might meet the requirements of one PCMH tool. SBHC stakeholders need to advocate for the creation or modification of existing survey tools that allow the unique characteristics of SBHCs to qualify as PCMHs.
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
Temperature dependence of bulk respiration of crop stands. Measurement and model fitting
International Nuclear Information System (INIS)
Tani, Takashi; Arai, Ryuji; Tako, Yasuhiro
2007-01-01
The objective of the present study was to examine whether the temperature dependence of respiration at a crop-stand scale could be directly represented by an Arrhenius function that was widely used for representing the temperature dependence of leaf respiration. We determined temperature dependences of bulk respiration of monospecific stands of rice and soybean within a range of the air temperature from 15 to 30degC using large closed chambers. Measured responses of respiration rates of the two stands were well fitted by the Arrhenius function (R 2 =0.99). In the existing model to assess the local radiological impact of the anthropogenic carbon-14, effects of the physical environmental factors on photosynthesis and respiration of crop stands are not taken into account for the calculation of the net amount of carbon per cultivation area in crops at harvest which is the crucial parameter for the estimation of the activity concentration of carbon-14 in crops. Our result indicates that the Arrhenius function is useful for incorporating the effect of the temperature on respiration of crop stands into the model which is expected to contribute to a more realistic estimate of the activity concentration of carbon-14 in crops. (author)
Universal fit to p-p elastic diffraction scattering from the Lorentz contracted geometrical model
International Nuclear Information System (INIS)
Hansen, P.H.; Krisch, A.D.
1976-01-01
The prediction of the Lorentz contracted geometical model for proton-proton elastic scattering at small angles is examined. The model assumes that when two high energy particles collide, each behaves as a geometrical object which has a Gaussian density and is spherically symmetric except for the Lorentz contraction in the incident direction. It is predicted that dsigma/dt should be independent of energy when plotted against the variable β 2 P 2 sub(perpendicular) sigmasub(TOT)(s)/38.3. Thus the energy dependence of the diffraction peak slope (b in an esup(-b mod(t))plot) is given by b(s)=A 2 β 2 sigmasub(TOT)(s)/38.3 where β is the proton's c.m. velocity and A is its radius. Recently measured values of sigmasub(TOT)(s) were used and an excellent fit obtained to the elastic slope in both t regions [-t 2 and 0.1 2 ] at all energies from s=6 to 4000(GeV/c) 2 . (Auth.)
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.
Sub-optimal control of fuzzy linear dynamical systems under granular differentiability concept.
Mazandarani, Mehran; Pariz, Naser
2018-05-01
This paper deals with sub-optimal control of a fuzzy linear dynamical system. The aim is to keep the state variables of the fuzzy linear dynamical system close to zero in an optimal manner. In the fuzzy dynamical system, the fuzzy derivative is considered as the granular derivative; and all the coefficients and initial conditions can be uncertain. The criterion for assessing the optimality is regarded as a granular integral whose integrand is a quadratic function of the state variables and control inputs. Using the relative-distance-measure (RDM) fuzzy interval arithmetic and calculus of variations, the optimal control law is presented as the fuzzy state variables feedback. Since the optimal feedback gains are obtained as fuzzy functions, they need to be defuzzified. This will result in the sub-optimal control law. This paper also sheds light on the restrictions imposed by the approaches which are based on fuzzy standard interval arithmetic (FSIA), and use strongly generalized Hukuhara and generalized Hukuhara differentiability concepts for obtaining the optimal control law. The granular eigenvalues notion is also defined. Using an RLC circuit mathematical model, it is shown that, due to their unnatural behavior in the modeling phenomenon, the FSIA-based approaches may obtain some eigenvalues sets that might be different from the inherent eigenvalues set of the fuzzy dynamical system. This is, however, not the case with the approach proposed in this study. The notions of granular controllability and granular stabilizability of the fuzzy linear dynamical system are also presented in this paper. Moreover, a sub-optimal control for regulating a Boeing 747 in longitudinal direction with uncertain initial conditions and parameters is gained. In addition, an uncertain suspension system of one of the four wheels of a bus is regulated using the sub-optimal control introduced in this paper. Copyright © 2018 ISA. Published by Elsevier Ltd. All rights reserved.
One model to fit all? The pursuit of integrated earth system models in GAIM and AIMES
Uhrqvist, Ola
2015-01-01
Images of Earth from space popularized the view of our planet as a single, fragile entity against the vastness and darkness of space. In the 1980s, the International Geosphere-Biosphere Program (IGBP) was set up to produce a predictive understanding of this fragile entity as the ‘Earth System.’ In order to do so, the program sought to create a common research framework for the different disciplines involved. It suggested that integrated numerical models could provide such a framework. The pap...
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.
Frenken, K.
2001-01-01
The biological evolution of complex organisms, in which the functioning of genes is interdependent, has been analyzed as "hill-climbing" on NK fitness landscapes through random mutation and natural selection. In evolutionary economics, NK fitness landscapes have been used to simulate the evolution
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
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
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
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
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.
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.
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.
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).
Standard Model updates and new physics analysis with the Unitarity Triangle fit
International Nuclear Information System (INIS)
Bevan, A.; Bona, M.; Ciuchini, M.; Derkach, D.; Franco, E.; Silvestrini, L.; Lubicz, V.; Tarantino, C.; Martinelli, G.; Parodi, F.; Schiavi, C.; Pierini, M.; Sordini, V.; Stocchi, A.; Vagnoni, V.
2013-01-01
We present the summer 2012 update of the Unitarity Triangle (UT) analysis performed by the UTfit Collaboration within the Standard Model (SM) and beyond. The increased accuracy on several of the fundamental constraints is now enhancing some of the tensions amongst and within the constraint themselves. In particular, the long standing tension between exclusive and inclusive determinations of the V ub and V cb CKM matrix elements is now playing a major role. Then we present the generalisation the UT analysis to investigate new physics (NP) effects, updating the constraints on NP contributions to ΔF=2 processes. In the NP analysis, both CKM and NP parameters are fitted simultaneously to obtain the possible NP effects in any specific sector. Finally, based on the NP constraints, we derive upper bounds on the coefficients of the most general ΔF=2 effective Hamiltonian. These upper bounds can be translated into lower bounds on the scale of NP that contributes to these low-energy effective interactions
Visually suboptimal bananas: How ripeness affects consumer expectation and perception.
Symmank, Claudia; Zahn, Susann; Rohm, Harald
2018-01-01
One reason for the significant amount of food that is wasted in developed countries is that consumers often expect visually suboptimal food as being less palatable. Using bananas as example, the objective of this study was to determine how appearance affects consumer overall liking, the rating of sensory attributes, purchase intention, and the intended use of bananas. The ripeness degree (RD) of the samples was adjusted to RD 5 (control) and RD 7 (more ripened, visually suboptimal). After preliminary experiments, a total of 233 participants were asked to judge their satisfaction with the intensity of sensory attributes that referred to flavor, taste, and texture using just-about-right scales. Subjects who received peeled samples were asked after tasting, whereas subjects who received unpeeled bananas judged expectation and, after peeling and tasting, perception. Expected overall liking and purchase intention were significantly lower for RD 7 bananas. Purchase intention was still significantly different between RD 5 and RD 7 after tasting, whereas no difference in overall liking was observed. Significant differences between RD 5 and RD 7 were observed when asking participants for their intended use of the bananas. Concerning the sensory attributes, penalty analysis revealed that only the firmness of the RD 7 bananas was still not just-about-right after tasting. The importance that consumers attribute to the shelf-life of food had a pronounced impact on purchase intention of bananas with different ripeness degree. In the case of suboptimal bananas, the results demonstrate a positive relationship between the sensory perception and overall liking and purchase intention. Convincing consumers that visually suboptimal food is still tasty is of high relevance for recommending different ways of communication. Copyright © 2017 Elsevier Ltd. All rights reserved.
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.
Fitting diameter distribution models to data from forest inventories with concentric plot design
Directory of Open Access Journals (Sweden)
Nikos Nanos
2017-10-01
Research highlights:We designed a new method to fit the Weibull distribution to forest inventory data from concentric plots that achieves high accuracy and precision in parameter estimates regardless of the within-plot spatial tree pattern.
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.
Hungry pigeons make suboptimal choices, less hungry pigeons do not.
Laude, Jennifer R; Pattison, Kristina F; Zentall, Thomas R
2012-10-01
Hungry animals will often choose suboptimally by being attracted to reliable signals for food that occur infrequently (they gamble) over less reliable signals for food that occur more often. That is, pigeons prefer an option that 50 % of the time provides them with a reliable signal for the appearance of food but 50 % of the time provides them with a reliable signal for the absence of food (overall 50 % reinforcement) over an alternative that always provides them with a signal for the appearance of food 75 % of the time (overall 75 % reinforcement). The pigeons appear to choose impulsively for the possibility of obtaining the reliable signal for reinforcement. There is evidence that greater hunger is associated with greater impulsivity. We tested the hypothesis that if the pigeons were less hungry, they would be less impulsive and, thus, would choose more optimally (i.e., on the basis of the overall probability of reinforcement). We found that hungry pigeons choose the 50 % reinforcement alternative suboptimally but less hungry pigeons prefer the more optimal 75 % reinforcement. Paradoxically, pigeons that needed the food more received less of it. These findings have implications for how level of motivation may also affect human suboptimal choice (e.g., purchase of lottery tickets and playing slot machines).
Action-Dependent Photobiomodulation on Health, Suboptimal Health, and Disease
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Timon Cheng-Yi Liu
2014-01-01
Full Text Available The global photobiomodulation (PBM on an organism was studied in terms of function-specific homeostasis (FSH and scale-free functional network in this paper. A function can be classified into a normal function in its FSH and a dysfunctional function far from its FSH. An FSH-specific stress (FSS disrupting an FSH can also be classified into a successful stress in its FSS-specific homeostasis (FSSH and a chronic stress far from its FSSH. The internal functions of an organism can be divided into essential, special nonessential, and general nonessential functions. Health may be defined as a state of an organism in which all the essential and special nonessential functions are normal or their stresses are successful. Suboptimal health may be defined as a state of a disease-free organism in which only some special nonessential functions are dysfunctional in comparison with its healthy state. Disease may be defined as a state of an organism which is not in both health and suboptimal health. The global PBM of health, suboptimal health, or disease suggested that the PBM may depend on the organism action.
Treasure, Janet; Leslie, Monica; Chami, Rayane; Fernández-Aranda, Fernando
2018-03-01
Explanatory models for eating disorders have changed over time to account for changing clinical presentations. The transdiagnostic model evolved from the maintenance model, which provided the framework for cognitive behavioural therapy for bulimia nervosa. However, for many individuals (especially those at the extreme ends of the weight spectrum), this account does not fully fit. New evidence generated from research framed within the food addiction hypothesis is synthesized here into a model that can explain recurrent binge eating behaviour. New interventions that target core maintenance elements identified within the model may be useful additions to a complex model of treatment for eating disorders. Copyright © 2018 John Wiley & Sons, Ltd and Eating Disorders Association.
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.)
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…
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…
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
Madsen, Jonas S; Lin, Yu-Cheng; Squyres, Georgia R; Price-Whelan, Alexa; de Santiago Torio, Ana; Song, Angela; Cornell, William C; Sørensen, Søren J; Xavier, Joao B; Dietrich, Lars E P
2015-12-01
As biofilms grow, resident cells inevitably face the challenge of resource limitation. In the opportunistic pathogen Pseudomonas aeruginosa PA14, electron acceptor availability affects matrix production and, as a result, biofilm morphogenesis. The secreted matrix polysaccharide Pel is required for pellicle formation and for colony wrinkling, two activities that promote access to O2. We examined the exploitability and evolvability of Pel production at the air-liquid interface (during pellicle formation) and on solid surfaces (during colony formation). Although Pel contributes to the developmental response to electron acceptor limitation in both biofilm formation regimes, we found variation in the exploitability of its production and necessity for competitive fitness between the two systems. The wild type showed a competitive advantage against a non-Pel-producing mutant in pellicles but no advantage in colonies. Adaptation to the pellicle environment selected for mutants with a competitive advantage against the wild type in pellicles but also caused a severe disadvantage in colonies, even in wrinkled colony centers. Evolution in the colony center produced divergent phenotypes, while adaptation to the colony edge produced mutants with clear competitive advantages against the wild type in this O2-replete niche. In general, the structurally heterogeneous colony environment promoted more diversification than the more homogeneous pellicle. These results suggest that the role of Pel in community structure formation in response to electron acceptor limitation is unique to specific biofilm models and that the facultative control of Pel production is required for PA14 to maintain optimum benefit in different types of communities. Copyright © 2015, American Society for Microbiology. All Rights Reserved.
Worthington, Thomas A.; Zhang, T.; Logue, Daniel R.; Mittelstet, Aaron R.; Brewer, Shannon K.
2016-01-01
Truncated distributions of pelagophilic fishes have been observed across the Great Plains of North America, with water use and landscape fragmentation implicated as contributing factors. Developing conservation strategies for these species is hindered by the existence of multiple competing flow regime hypotheses related to species persistence. Our primary study objective was to compare the predicted distributions of one pelagophil, the Arkansas River Shiner Notropis girardi, constructed using different flow regime metrics. Further, we investigated different approaches for improving temporal transferability of the species distribution model (SDM). We compared four hypotheses: mean annual flow (a baseline), the 75th percentile of daily flow, the number of zero-flow days, and the number of days above 55th percentile flows, to examine the relative importance of flows during the spawning period. Building on an earlier SDM, we added covariates that quantified wells in each catchment, point source discharges, and non-native species presence to a structured variable framework. We assessed the effects on model transferability and fit by reducing multicollinearity using Spearman’s rank correlations, variance inflation factors, and principal component analysis, as well as altering the regularization coefficient (β) within MaxEnt. The 75th percentile of daily flow was the most important flow metric related to structuring the species distribution. The number of wells and point source discharges were also highly ranked. At the default level of β, model transferability was improved using all methods to reduce collinearity; however, at higher levels of β, the correlation method performed best. Using β = 5 provided the best model transferability, while retaining the majority of variables that contributed 95% to the model. This study provides a workflow for improving model transferability and also presents water-management options that may be considered to improve the
Structural robustness with suboptimal responses for linear state space model
Keel, L. H.; Lim, Kyong B.; Juang, Jer-Nan
1989-01-01
A relationship between the closed-loop eigenvalues and the amount of perturbations in the open-loop matrix is addressed in the context of performance robustness. If the allowable perturbation ranges of elements of the open-loop matrix A and the desired tolerance of the closed-loop eigenvalues are given such that max(j) of the absolute value of Delta-lambda(j) (A+BF) should be less than some prescribed value, what is a state feedback controller F which satisfies the closed-loop eigenvalue perturbation-tolerance requirement for a class of given perturbation in A? The paper gives an algorithm to design such a controller. Numerical examples are included for illustration.
van Hout, Moniek S. E.; Schmand, Ben; Wekking, Ellie M.; Hageman, Gerard; Deelman, Betto G.
2003-01-01
Suboptimal performance during neuropsychological testing can seriously complicate assessment in behavioral neurotoxicology. We present data on the prevalence of suboptimal performance in a group of Dutch patients with suspected chronic toxic encephalopathy (CTE) after long-term occupational exposure
van Hout, MSE; Schmand, B; Wekking, EM; Hageman, G; Deelman, BG
Suboptimal performance during neuropsychological testing can seriously complicate assessment in behavioral neurotoxicology. We present data on the prevalence of suboptimal performance in a group of Dutch patients with suspected chronic toxic encephalopathy (CTE) after long-term occupational exposure
Recruit Fitness as a Predictor of Police Academy Graduation.
Shusko, M; Benedetti, L; Korre, M; Eshleman, E J; Farioli, A; Christophi, C A; Kales, S N
2017-10-01
Suboptimal recruit fitness may be a risk factor for poor performance, injury, illness, and lost time during police academy training. To assess the probability of successful completion and graduation from a police academy as a function of recruits' baseline fitness levels at the time of academy entry. Retrospective study where all available records from recruit training courses held (2006-2012) at all Massachusetts municipal police academies were reviewed and analysed. Entry fitness levels were quantified from the following measures, as recorded at the start of each training class: body composition, push-ups, sit-ups, sit-and-reach, and 1.5-mile run-time. The primary outcome of interest was the odds of not successfully graduating from an academy. We used generalized linear mixed models in order to fit logistic regression models with random intercepts for assessing the probability of not graduating, based on entry-level fitness. The primary analyses were restricted to recruits with complete entry-level fitness data. The fitness measures most strongly associated with academy failure were lesser number of push-ups completed (odds ratio [OR] = 5.2, 95% confidence interval [CI] 2.3-11.7, for 20 versus 41-60 push-ups) and slower run times (OR = 3.8, 95% CI 1.8-7.8, [1.5 mile run time of ≥15'20″] versus [12'33″ to 10'37″]). Baseline pushups and 1.5-mile run-time showed the best ability to predict successful academy graduation, especially when considered together. Future research should include prospective validation of entry-level fitness as a predictor of subsequent police academy success. © The Author 2017. Published by Oxford University Press on behalf of the Society of Occupational Medicine.
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.
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
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.
Directory of Open Access Journals (Sweden)
Dayane Rosalyn Izidoro
2009-12-01
Full Text Available In this work, the rheological behaviour of emulsions (mayonnaises stabilized by green banana pulp using the response surface methodology was studied. In addition, the emulsions stability was investigated. Five formulations were developed, according to design for constrained surfaces and mixtures, with the proportion, respectively: water/soy oil/green banana pulp: F1 (0.10/0.20/0.70, F2 (0.20/0.20/0.60, F3 (0.10/0.25/0.65, F4 (0.20/0.25/0.55 and F5 (0.15/0.225/0.625 .Emulsions rheological properties were performed with a rotational Haake Rheostress 600 rheometer and a cone and plate geometry sensor (60-mm diameter, 2º cone angle, using a gap distance of 1mm. The emulsions showed pseudoplastic behaviour and were adequately described by the Power Law model. The rheological responses were influenced by the difference in green banana pulp proportions and also by the temperatures (10 and 25ºC. The formulations with high pulp content (F1 and F3 presented higher shear stress and apparent viscosity. Response surface methodology, described by the quadratic model,showed that the consistency coefficient (K increased with the interaction between green banana pulp and soy oil concentration and the water fraction contributed to the flow behaviour index increase for all emulsions samples. Analysis of variance showed that the second-order model had not significant lack-of-fit and a significant F-value, indicating that quadratic model fitted well into the experimental data. The emulsions that presented better stability were the formulations F4 (0.20/0.25/0.55 and F5 (0.15/0.225/0.625.No presente trabalho, foi estudado o comportamento reológico de emulsões adicionadas de polpa de banana verde utilizando a metodologia de superfície de resposta e também foram investigadas a estabilidade das emulsões. Foram desenvolvidas cinco formulações, de acordo com o delineamento para superfícies limitadas e misturas, com as proporções respectivamente: água/óleo de
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
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
Using Fit Indexes to Select a Covariance Model for Longitudinal Data
Liu, Siwei; Rovine, Michael J.; Molenaar, Peter C. M.
2012-01-01
This study investigated the performance of fit indexes in selecting a covariance structure for longitudinal data. Data were simulated to follow a compound symmetry, first-order autoregressive, first-order moving average, or random-coefficients covariance structure. We examined the ability of the likelihood ratio test (LRT), root mean square error…
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…
Implementation of a Personal Fitness Unit Using the Personalized System of Instruction Model
Prewitt, Steven; Hannon, James C.; Colquitt, Gavin; Brusseau, Timothy A.; Newton, Maria; Shaw, Janet
2015-01-01
Levels of physical activity and health-related fitness (HRF) are decreasing among adolescents in the United States. Several interventions have been implemented to reverse this downtrend. Traditionally, physical educators incorporate a direct instruction (DI) strategy, with teaching potentially leading students to disengage during class. An…
van der Niet, Anneke G.; Hartman, Esther; Smith, Joanne; Visscher, Chris
Objectives: The relationship between physical fitness and academic achievement in children has received much attention, however, whether executive functioning plays a mediating role in this relationship is unclear. The aim of this study therefore was to investigate the relationships between physical
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.
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.
Rybizki, Jan; Just, Andreas; Rix, Hans-Walter
2017-09-01
Elemental abundances of stars are the result of the complex enrichment history of their galaxy. Interpretation of observed abundances requires flexible modeling tools to explore and quantify the information about Galactic chemical evolution (GCE) stored in such data. Here we present Chempy, a newly developed code for GCE modeling, representing a parametrized open one-zone model within a Bayesian framework. A Chempy model is specified by a set of five to ten parameters that describe the effective galaxy evolution along with the stellar and star-formation physics: for example, the star-formation history (SFH), the feedback efficiency, the stellar initial mass function (IMF), and the incidence of supernova of type Ia (SN Ia). Unlike established approaches, Chempy can sample the posterior probability distribution in the full model parameter space and test data-model matches for different nucleosynthetic yield sets. It is essentially a chemical evolution fitting tool. We straightforwardly extend Chempy to a multi-zone scheme. As an illustrative application, we show that interesting parameter constraints result from only the ages and elemental abundances of the Sun, Arcturus, and the present-day interstellar medium (ISM). For the first time, we use such information to infer the IMF parameter via GCE modeling, where we properly marginalize over nuisance parameters and account for different yield sets. We find that 11.6+ 2.1-1.6% of the IMF explodes as core-collapse supernova (CC-SN), compatible with Salpeter (1955, ApJ, 121, 161). We also constrain the incidence of SN Ia per 103M⊙ to 0.5-1.4. At the same time, this Chempy application shows persistent discrepancies between predicted and observed abundances for some elements, irrespective of the chosen yield set. These cannot be remedied by any variations of Chempy's parameters and could be an indication of missing nucleosynthetic channels. Chempy could be a powerful tool to confront predictions from stellar
Yu, Jonas C. P.; Wee, H. M.; Yang, P. C.; Wu, Simon
2016-06-01
One of the supply chain risks for hi-tech products is the result of rapid technological innovation; it results in a significant decline in the selling price and demand after the initial launch period. Hi-tech products include computers and communication consumer's products. From a practical standpoint, a more realistic replenishment policy is needed to consider the impact of risks; especially when some portions of shortages are lost. In this paper, suboptimal and optimal order policies with partial backordering are developed for a buyer when the component cost, the selling price, and the demand rate decline at a continuous rate. Two mathematical models are derived and discussed: one model has the suboptimal solution with the fixed replenishment interval and a simpler computational process; the other one has the optimal solution with the varying replenishment interval and a more complicated computational process. The second model results in more profit. Numerical examples are provided to illustrate the two replenishment models. Sensitivity analysis is carried out to investigate the relationship between the parameters and the net profit.
Risk of suboptimal iodine intake in pregnant Norwegian women.
Brantsæter, Anne Lise; Abel, Marianne Hope; Haugen, Margaretha; Meltzer, Helle Margrete
2013-02-06
Pregnant women and infants are exceptionally vulnerable to iodine deficiency. The aims of the present study were to estimate iodine intake, to investigate sources of iodine, to identify predictors of low or suboptimal iodine intake (defined as intakes below 100 μg/day and 150 μg/day) in a large population of pregnant Norwegian women and to evaluate iodine status in a sub-population. Iodine intake was calculated based on a validated Food Frequency Questionnaire in the Norwegian Mother and Child Cohort. The median iodine intake was 141 μg/day from food and 166 μg/day from food and supplements. Use of iodine-containing supplements was reported by 31.6%. The main source of iodine from food was dairy products, contributing 67% and 43% in non-supplement and iodine-supplement users, respectively. Of 61,904 women, 16.1% had iodine intake below 100 μg/day, 42.0% had iodine intake below 150 μg/day and only 21.7% reached the WHO/UNICEF/ICCIDD recommendation of 250 μg/day. Dietary behaviors associated with increased risk of low and suboptimal iodine intake were: no use of iodine-containing supplements and low intake of milk/yogurt, seafood and eggs. The median urinary iodine concentration measured in 119 participants (69 μg/L) confirmed insufficient iodine intake. Public health strategies are needed to improve and secure the iodine status of pregnant women in Norway.
Implementation of the Iterative Proportion Fitting Algorithm for Geostatistical Facies Modeling
International Nuclear Information System (INIS)
Li Yupeng; Deutsch, Clayton V.
2012-01-01
In geostatistics, most stochastic algorithm for simulation of categorical variables such as facies or rock types require a conditional probability distribution. The multivariate probability distribution of all the grouped locations including the unsampled location permits calculation of the conditional probability directly based on its definition. In this article, the iterative proportion fitting (IPF) algorithm is implemented to infer this multivariate probability. Using the IPF algorithm, the multivariate probability is obtained by iterative modification to an initial estimated multivariate probability using lower order bivariate probabilities as constraints. The imposed bivariate marginal probabilities are inferred from profiles along drill holes or wells. In the IPF process, a sparse matrix is used to calculate the marginal probabilities from the multivariate probability, which makes the iterative fitting more tractable and practical. This algorithm can be extended to higher order marginal probability constraints as used in multiple point statistics. The theoretical framework is developed and illustrated with estimation and simulation example.
Measures of relative fitness of social behaviors in finite structured population models.
Tarnita, Corina E; Taylor, Peter D
2014-10-01
How should we measure the relative selective advantage of different behavioral strategies? The various approaches to this question have fallen into one of the following categories: the fixation probability of a mutant allele in a wild type population, some measures of gene frequency and gene frequency change, and a formulation of the inclusive fitness effect. Countless theoretical studies have examined the relationship between these approaches, and it has generally been thought that, under standard simplifying assumptions, they yield equivalent results. Most of this theoretical work, however, has assumed homogeneity of the population interaction structure--that is, that all individuals are equivalent. We explore the question of selective advantage in a general (heterogeneous) population and show that, although appropriate measures of fixation probability and gene frequency change are equivalent, they are not, in general, equivalent to the inclusive fitness effect. The latter does not reflect effects of selection acting via mutation, which can arise on heterogeneous structures, even for low mutation. Our theoretical framework provides a transparent analysis of the different biological factors at work in the comparison of these fitness measures and suggests that their theoretical and empirical use needs to be revised and carefully grounded in a more general theory.
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...
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.
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.
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.
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
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
Maydeu-Olivares, Alberto; Montano, Rosa
2013-01-01
We investigate the performance of three statistics, R [subscript 1], R [subscript 2] (Glas in "Psychometrika" 53:525-546, 1988), and M [subscript 2] (Maydeu-Olivares & Joe in "J. Am. Stat. Assoc." 100:1009-1020, 2005, "Psychometrika" 71:713-732, 2006) to assess the overall fit of a one-parameter logistic model…
Elghafghuf, Adel; Dufour, Simon; Reyher, Kristen; Dohoo, Ian; Stryhn, Henrik
2014-12-01
Mastitis is a complex disease affecting dairy cows and is considered to be the most costly disease of dairy herds. The hazard of mastitis is a function of many factors, both managerial and environmental, making its control a difficult issue to milk producers. Observational studies of clinical mastitis (CM) often generate datasets with a number of characteristics which influence the analysis of those data: the outcome of interest may be the time to occurrence of a case of mastitis, predictors may change over time (time-dependent predictors), the effects of factors may change over time (time-dependent effects), there are usually multiple hierarchical levels, and datasets may be very large. Analysis of such data often requires expansion of the data into the counting-process format - leading to larger datasets - thus complicating the analysis and requiring excessive computing time. In this study, a nested frailty Cox model with time-dependent predictors and effects was applied to Canadian Bovine Mastitis Research Network data in which 10,831 lactations of 8035 cows from 69 herds were followed through lactation until the first occurrence of CM. The model was fit to the data as a Poisson model with nested normally distributed random effects at the cow and herd levels. Risk factors associated with the hazard of CM during the lactation were identified, such as parity, calving season, herd somatic cell score, pasture access, fore-stripping, and proportion of treated cases of CM in a herd. The analysis showed that most of the predictors had a strong effect early in lactation and also demonstrated substantial variation in the baseline hazard among cows and between herds. A small simulation study for a setting similar to the real data was conducted to evaluate the Poisson maximum likelihood estimation approach with both Gaussian quadrature method and Laplace approximation. Further, the performance of the two methods was compared with the performance of a widely used estimation
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
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.
DEFF Research Database (Denmark)
Bennike, Søren
Samfundet forandrer sig og ligeså gør danskernes idrætsmønstre. Fodbold Fitness, der er afhandlingens omdrejningspunkt, kan iagttages som en reaktion på disse forandringer. Afhandlingen ser nærmere på Fodbold Fitness og implementeringen af dette, der ingenlunde er nogen let opgave. Bennike bidrager...
Fu, You; Gao, Zan; Hannon, James; Shultz, Barry; Newton, Maria; Sibthorp, Jim
2013-12-01
This study was designed to explore the effects of a health-related physical fitness physical education model on students' physical activity, perceived competence, and enjoyment. 61 students (25 boys, 36 girls; M age = 12.6 yr., SD = 0.6) were assigned to two groups (health-related physical fitness physical education group, and traditional physical education group), and participated in one 50-min. weekly basketball class for 6 wk. Students' in-class physical activity was assessed using NL-1000 pedometers. The physical subscale of the Perceived Competence Scale for Children was employed to assess perceived competence, and children's enjoyment was measured using the Sport Enjoyment Scale. The findings suggest that students in the intervention group increased their perceived competence, enjoyment, and physical activity over a 6-wk. intervention, while the comparison group simply increased physical activity over time. Children in the intervention group had significantly greater enjoyment.
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.
Next-to-leading order unitarity fits in Two-Higgs-Doublet models with soft ℤ{sub 2} breaking
Energy Technology Data Exchange (ETDEWEB)
Cacchio, Vincenzo; Chowdhury, Debtosh; Eberhardt, Otto [Istituto Nazionale di Fisica Nucleare, Sezione di Roma,Piazzale Aldo Moro 2, I-00185 Roma (Italy); Murphy, Christopher W. [Scuola Normale Superiore,Piazza dei Cavalieri 7, I-56126 Pisa (Italy)
2016-11-07
We fit the next-to-leading order unitarity conditions to the Two-Higgs-Doublet model with a softly broken ℤ{sub 2} symmetry. In doing so, we alleviate the existing uncertainty on how to treat higher order corrections to quartic couplings of its Higgs potential. A simplified approach to implementing the next-to-leading order unitarity conditions is presented. These new bounds are then combined with all other relevant constraints, including the complete set of LHC Run I data. The upper 95% bounds we find are 4.2 on the absolute values of the quartic couplings, and 235 GeV (100 GeV) for the mass degeneracies between the heavy Higgs particles in the type I (type II) scenario. In type II, we exclude an unbroken ℤ{sub 2} symmetry with a probability of 95%. All fits are performed using the open-source code HEPfit.
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].
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
Brodie, E.; King, E.; Molins, S.; Karaoz, U.; Steefel, C. I.; Banfield, J. F.; Beller, H. R.; Anantharaman, K.; Ligocki, T. J.; Trebotich, D.
2015-12-01
Pore-scale processes mediated by microorganisms underlie a range of critical ecosystem services, regulating carbon stability, nutrient flux, and the purification of water. Advances in cultivation-independent approaches now provide us with the ability to reconstruct thousands of genomes from microbial populations from which functional roles may be assigned. With this capability to reveal microbial metabolic potential, the next step is to put these microbes back where they belong to interact with their natural environment, i.e. the pore scale. At this scale, microorganisms communicate, cooperate and compete across their fitness landscapes with communities emerging that feedback on the physical and chemical properties of their environment, ultimately altering the fitness landscape and selecting for new microbial communities with new properties and so on. We have developed a trait-based model of microbial activity that simulates coupled functional guilds that are parameterized with unique combinations of traits that govern fitness under dynamic conditions. Using a reactive transport framework, we simulate the thermodynamics of coupled electron donor-acceptor reactions to predict energy available for cellular maintenance, respiration, biomass development, and enzyme production. From metagenomics, we directly estimate some trait values related to growth and identify the linkage of key traits associated with respiration and fermentation, macromolecule depolymerizing enzymes, and other key functions such as nitrogen fixation. Our simulations were carried out to explore abiotic controls on community emergence such as seasonally fluctuating water table regimes across floodplain organic matter hotspots. Simulations and metagenomic/metatranscriptomic observations highlighted the many dependencies connecting the relative fitness of functional guilds and the importance of chemolithoautotrophic lifestyles. Using an X-Ray microCT-derived soil microaggregate physical model combined
Reoperation for suboptimal outcomes after deep brain stimulation surgery.
Ellis, Tina-Marie; Foote, Kelly D; Fernandez, Hubert H; Sudhyadhom, Atchar; Rodriguez, Ramon L; Zeilman, Pamela; Jacobson, Charles E; Okun, Michael S
2008-10-01
To examine a case series of reoperations for deep brain stimulation (DBS) leads in which clinical scenarios revealed suboptimal outcome from a previous operation. Suboptimally placed DBS leads are one potential reason for unsatisfactory results after surgery for Parkinson's disease (PD), essential tremor (ET), or dystonia. In a previous study of patients who experienced suboptimal results, 19 of 41 patients had misplaced leads. Similarly, another report commented that lead placement beyond a 2- to 3-mm window resulted in inadequate clinical benefit, and, in 1 patient, revision improved outcome. The goal of the current study was to perform an unblinded retrospective chart review of DBS patients with unsatisfactory outcomes who presented for reoperation. Patients who had DBS lead replacements after reoperation were assessed with the use of a retrospective review of an institutional review board-approved movement disorders database. Cases of reoperation for suboptimal clinical benefit were included, and cases of replacement of DBS leads caused by infection or hardware malfunction were excluded. Data points studied included age, disease duration, diagnosis, motor outcomes (the Unified Parkinson Disease Rating Scale III in PD, the Tremor Rating Scale in ET, and the Unified Dystonia Rating Scale in dystonia), quality of life (Parkinson's Disease Questionnaire-39 in PD), and the Clinician Global Impression scale. The data from before and after reoperation were examined to determine the estimated impact of repeat surgery. There were 11 patients with PD, 7 with ET, and 4 with dystonia. The average age of the PD group was 52 years, the disease duration was 10 years, and the average vector distance of the location of the active DBS contact was adjusted 5.5 mm. Six patients (54%) with PD had preoperative off medication on DBS Unified Parkinson Disease Rating Scale scores that could be compared with postoperative off medication on DBS scores. The average improvement across this
Fitting non-gaussian Models to Financial data: An Empirical Study
Directory of Open Access Journals (Sweden)
Pablo Olivares
2011-04-01
Full Text Available In this paper are presented some experiences about the modeling of financial data by three classes of models as alternative to Gaussian Linear models. Dynamic Volatility, Stable L'evy and Diffusion with Jumps models are considered. The techniques are illustrated with some examples of financial series on currency, futures and indexes.
3D Product Development for Loose-Fitting Garments Based on Parametric Human Models
Krzywinski, S.; Siegmund, J.
2017-10-01
Researchers and commercial suppliers worldwide pursue the objective of achieving a more transparent garment construction process that is computationally linked to a virtual body, in order to save development costs over the long term. The current aim is not to transfer the complete pattern making step to a 3D design environment but to work out basic constructions in 3D that provide excellent fit due to their accurate construction and morphological pattern grading (automatic change of sizes in 3D) in respect of sizes and body types. After a computer-aided derivation of 2D pattern parts, these can be made available to the industry as a basis on which to create more fashionable variations.
DEFF Research Database (Denmark)
Madsen, Jonas Stenløkke; Lin, Yu Cheng; Squyres, Georgia R.
2015-01-01
response to electron acceptor limitation in both biofilm formation regimes, we found variation in the exploitability of its production and necessity for competitive fitness between the two systems. The wild type showed a competitive advantage against a non-Pel-producing mutant in pellicles but no advantage...... in colonies. Adaptation to the pellicle environment selected for mutants with a competitive advantage against the wild type in pellicles but also caused a severe disadvantage in colonies, even in wrinkled colony centers. Evolution in the colony center produced divergent phenotypes, while adaptation...... to the colony edge produced mutants with clear competitive advantages against the wild type in this O2-replete niche. In general, the structurally heterogeneous colony environment promoted more diversification than the more homogeneous pellicle. These results suggest that the role of Pel in community structure...
Yu, Tai-Kuei; Yu, Tai-Yi
2010-01-01
Understanding learners' behaviour, perceptions and influence in terms of learner performance is crucial to predict the use of electronic learning systems. By integrating the task-technology fit (TTF) model and the theory of planned behaviour (TPB), this paper investigates the online learning utilisation of Taiwanese students. This paper provides a…
The Electroweak Fit of the Standard Model after the Discovery of a New Boson at the LHC
Baak, M.
2012-11-03
In view of the discovery of a new boson by the ATLAS and CMS Collaborations at the LHC, we present an update of the global Standard Model (SM) fit to electroweak precision data. Assuming the new particle to be the SM Higgs boson, all fundamental parameters of the SM are known allowing, for the first time, to overconstrain the SM at the electroweak scale and assert its validity. Including the effects of radiative corrections and the experimental and theoretical uncertainties, the global fit exhibits a p-value of 0.07. The mass measurements by ATLAS and CMS agree within 1.3sigma with the indirect determination M_H=(94 +25 -22) GeV. Within the SM the W boson mass and the effective weak mixing angle can be accurately predicted to be M_W=(80.359 +- 0.011) GeV and sin^2(theta_eff^ell)=(0.23150 +- 0.00010) from the global fit. These results are compatible with, and exceed in precision, the direct measurements. For the indirect determination of the top quark mass we find m_t=(175.8 +2.7 -2.4) GeV, in agreement with t...
The electroweak fit of the standard model after the discovery of a new boson at the LHC
International Nuclear Information System (INIS)
Baak, M.; Hoecker, A.; Schott, M.; Goebel, M.; Kennedy, D.; Moenig, K.; Haller, J.; Kogler, R.; Stelzer, J.
2012-09-01
In view of the discovery of a new boson by the ATLAS and CMS Collaborations at the LHC, we present an update of the global Standard Model (SM) fit to electroweak precision data. Assuming the new particle to be the SM Higgs boson, all fundamental parameters of the SM are known allowing, for the first time, to overconstrain the SM at the electroweak scale and assert its validity. Including the effects of radiative corrections and the experimental and theoretical uncertainties, the global fit exhibits a p-value of 0.07. The mass measurements by ATLAS and CMS agree within 1.3σ with the indirect determination M H =94 +25 -22 GeV. Within the SM the W boson mass and the effective weak mixing angle can be accurately predicted to be M W =80.359±0.011 GeV and sin 2 θ l eff =0.23150±0.00010 from the global fit. These results are compatible with, and exceed in precision, the direct measurements. For the indirect determination of the top quark mass we find m t =175.8 +2.7 -2.4 GeV, in agreement with the kinematic and cross-section based measurements.
Tahir, M Ramzan; Tran, Quang X; Nikulin, Mikhail S
2017-05-30
We studied the problem of testing a hypothesized distribution in survival regression models when the data is right censored and survival times are influenced by covariates. A modified chi-squared type test, known as Nikulin-Rao-Robson statistic, is applied for the comparison of accelerated failure time models. This statistic is used to test the goodness-of-fit for hypertabastic survival model and four other unimodal hazard rate functions. The results of simulation study showed that the hypertabastic distribution can be used as an alternative to log-logistic and log-normal distribution. In statistical modeling, because of its flexible shape of hazard functions, this distribution can also be used as a competitor of Birnbaum-Saunders and inverse Gaussian distributions. The results for the real data application are shown. Copyright © 2017 John Wiley & Sons, Ltd. Copyright © 2017 John Wiley & Sons, Ltd.
Increasing Cone-beam projection usage by temporal fitting
DEFF Research Database (Denmark)
Lyksborg, Mark; Hansen, Mads Fogtmann; Larsen, Rasmus
2010-01-01
A Cone-beam CT system can be used to image the lung region. The system records 2D projections which will allow 3D reconstruction however a reconstruction based on all projections will lead to a blurred reconstruction in regions were respiratory motion occur. To avoid this the projections are typi......A Cone-beam CT system can be used to image the lung region. The system records 2D projections which will allow 3D reconstruction however a reconstruction based on all projections will lead to a blurred reconstruction in regions were respiratory motion occur. To avoid this the projections...... in [6] prior knowledge of the lung deformation estimated from the planning CT could be used to include all projections into the reconstruction. It has also been attempted to estimate both the motion and 3D volume simultaneously in [4]. Problems with motion estimation are ill-posed leading to suboptimal...... motion which in return affects the reconstruction. By directly including time into the image representation the effect of suboptimal motion fields are avoided and we are still capable of using phase neighbour projections. The 4D image model is fitted by solving a statistical cost function based...
Ge, Junqiang; Yan, Renbin; Cappellari, Michele; Mao, Shude; Li, Hongyu; Lu, Youjun
2018-05-01
Using mock spectra based on Vazdekis/MILES library fitted within the wavelength region 3600-7350Å, we analyze the bias and scatter on the resulting physical parameters induced by the choice of fitting algorithms and observational uncertainties, but avoid effects of those model uncertainties. We consider two full-spectrum fitting codes: pPXF and STARLIGHT, in fitting for stellar population age, metallicity, mass-to-light ratio, and dust extinction. With pPXF we find that both the bias μ in the population parameters and the scatter σ in the recovered logarithmic values follows the expected trend μ ∝ σ ∝ 1/(S/N). The bias increases for younger ages and systematically makes recovered ages older, M*/Lr larger and metallicities lower than the true values. For reference, at S/N=30, and for the worst case (t = 108yr), the bias is 0.06 dex in M/Lr, 0.03 dex in both age and [M/H]. There is no significant dependence on either E(B-V) or the shape of the error spectrum. Moreover, the results are consistent for both our 1-SSP and 2-SSP tests. With the STARLIGHT algorithm, we find trends similar to pPXF, when the input E(B-V)values, with significantly underestimated dust extinction and [M/H], and larger ages and M*/Lr. Results degrade when moving from our 1-SSP to the 2-SSP tests. The STARLIGHT convergence to the true values can be improved by increasing Markov Chains and annealing loops to the "slow mode". For the same input spectrum, pPXF is about two order of magnitudes faster than STARLIGHT's "default mode" and about three order of magnitude faster than STARLIGHT's "slow mode".
Fitting Social Network Models Using Varying Truncation Stochastic Approximation MCMC Algorithm
Jin, Ick Hoon; Liang, Faming
2013-01-01
The exponential random graph model (ERGM) plays a major role in social network analysis. However, parameter estimation for the ERGM is a hard problem due to the intractability of its normalizing constant and the model degeneracy. The existing
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
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...
Culture and Parenting: Family Models Are Not One-Size-Fits-All. FPG Snapshot #67
FPG Child Development Institute, 2012
2012-01-01
Family process models guide theories and research about family functioning and child development outcomes. Theory and research, in turn, inform policies and services aimed at families. But are widely accepted models valid across cultural groups? To address these gaps, FPG researchers examined the utility of two family process models for families…
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.
UK energy policy ambition and UK energy modelling-fit for purpose?
International Nuclear Information System (INIS)
Strachan, Neil
2011-01-01
Aiming to lead amongst other G20 countries, the UK government has classified the twin energy policy priorities of decarbonisation and security of supply as a 'centennial challenge'. This viewpoint discusses the UK's capacity for energy modelling and scenario building as a critical underpinning of iterative decision making to meet these policy ambitions. From a nadir, over the last decade UK modelling expertise has been steadily built up. However extreme challenges remain in the level and consistency of funding of core model teams - critical to ensure a full scope of energy model types and hence insights, and in developing new state-of-the-art models to address evolving uncertainties. Meeting this challenge will facilitate a broad scope of types and geographical scale of UK's analytical tools to responsively deliver the evidence base for a range of public and private sector decision makers, and ensure that the UK contributes to global efforts to advance the field of energy-economic modelling. - Research highlights: → Energy modelling capacity is a critical underpinning for iterative energy policy making. → Full scope of energy models and analytical approaches is required. → Extreme challenges remain in consistent and sustainable funding of energy modelling teams. → National governments that lead in global energy policy also need to invest in modelling capacity.
Energy expenditures & physical activity in rats with chronic suboptimal nutrition.
Rising, Russell; Lifshitz, Fima
2006-01-31
Sub-optimally nourished rats show reduced growth, biochemical and physiological changes. However, no one has assessed metabolic rate adaptations in rats subjected to chronic suboptimal nutrition (CSN). In this study energy expenditure (EE; kcal/100 g body weight) and physical activity (PA; oscillations in weight/min/kg body weight) were assessed in rats subjected to three levels of CSN. Body weight gain was diminished (76.7 +/- 12.0 and 61.6 +/- 11.0 g) in rats fed 70 and 60% of the ad-libitum fed controls which gained more weight (148.5 +/- 32.3 g). The rats fed 80% gained weight similarly to controls (136.3 +/- 10.5 g). Percent Fat-free body mass was reduced (143.8 +/- 8.7 and 142.0 +/- 7.6 g) in rats fed 70 and 60% of ad-libitum, but not in those fed 80% (200.8 +/- 17.5 g) as compared with controls (201.6 +/- 33.4 g). Body fat (g) decreased in rats fed 80% (19.7 +/- 5.3), 70% (15.3 +/- 3.5) and 60% (9.6 +/- 2.7) of ad-libitum in comparison to controls (26.0 +/- 6.7). EE and PA were also altered by CSN. The control rats increased their EE and PA during the dark periods by 1.4 +/- 0.8 and 1.7 +/- 1.1 respectively, as compared with light the period; whereas CSN rats fed 80 and 70% of ad-libitum energy intake had reduced EE and PA during the dark periods as compared with the light period EE(7.5 +/- 1.4 and 7.8 +/- 0.6 vs. 9.0 +/- 1.2 and 9.7 +/- 0.8; p vs. 4.1 +/- 0.9 and 2.4 +/- 0.4; p vs. 0.95 +/- 0.03 and 0.91 +/- 0.05 p light (7.1 +/- 1.4) and dark period (6.2 +/- 1.0) EE and PA (3.4 +/- 0.9 and 2.5 +/- 0.5 respectively) were reduced in rats fed 60% of ad-libitum energy intake. CSN rats adapt to mild energy restriction by reducing body fat, EE and PA mainly during the dark period while growth proceeds and lean body mass is preserved. At higher levels of energy restrictions there is decreased growth, body fat and lean mass. Moreover EE and PA are also reduced during both light and dark periods.
Energy expenditures & physical activity in rats with chronic suboptimal nutrition
Directory of Open Access Journals (Sweden)
Lifshitz Fima
2006-01-01
Full Text Available Abstract Background Sub-optimally nourished rats show reduced growth, biochemical and physiological changes. However, no one has assessed metabolic rate adaptations in rats subjected to chronic suboptimal nutrition (CSN. In this study energy expenditure (EE; kcal/100 g body weight and physical activity (PA; oscillations in weight/min/kg body weight were assessed in rats subjected to three levels of CSN. Results Body weight gain was diminished (76.7 ± 12.0 and 61.6 ± 11.0 g in rats fed 70 and 60% of the ad-libitum fed controls which gained more weight (148.5 ± 32.3 g. The rats fed 80% gained weight similarly to controls (136.3 ± 10.5 g. Percent Fat-free body mass was reduced (143.8 ± 8.7 and 142.0 ± 7.6 g in rats fed 70 and 60% of ad-libitum, but not in those fed 80% (200.8 ± 17.5 g as compared with controls (201.6 ± 33.4 g. Body fat (g decreased in rats fed 80% (19.7 ± 5.3, 70% (15.3 ± 3.5 and 60% (9.6 ± 2.7 of ad-libitum in comparison to controls (26.0 ± 6.7. EE and PA were also altered by CSN. The control rats increased their EE and PA during the dark periods by 1.4 ± 0.8 and 1.7 ± 1.1 respectively, as compared with light the period; whereas CSN rats fed 80 and 70% of ad-libitum energy intake had reduced EE and PA during the dark periods as compared with the light period EE(7.5 ± 1.4 and 7.8 ± 0.6 vs. 9.0 ± 1.2 and 9.7 ± 0.8; p Conclusion CSN rats adapt to mild energy restriction by reducing body fat, EE and PA mainly during the dark period while growth proceeds and lean body mass is preserved. At higher levels of energy restrictions there is decreased growth, body fat and lean mass. Moreover EE and PA are also reduced during both light and dark periods.
The inert doublet model in the light of Fermi-LAT gamma-ray data: a global fit analysis
Eiteneuer, Benedikt; Goudelis, Andreas; Heisig, Jan
2017-09-01
We perform a global fit within the inert doublet model taking into account experimental observables from colliders, direct and indirect dark matter searches and theoretical constraints. In particular, we consider recent results from searches for dark matter annihilation-induced gamma-rays in dwarf spheroidal galaxies and relax the assumption that the inert doublet model should account for the entire dark matter in the Universe. We, moreover, study in how far the model is compatible with a possible dark matter explanation of the so-called Galactic center excess. We find two distinct parameter space regions that are consistent with existing constraints and can simultaneously explain the excess: One with dark matter masses near the Higgs resonance and one around 72 GeV where dark matter annihilates predominantly into pairs of virtual electroweak gauge bosons via the four-vertex arising from the inert doublet's kinetic term. We briefly discuss future prospects to probe these scenarios.
The inert doublet model in the light of Fermi-LAT gamma-ray data: a global fit analysis
Energy Technology Data Exchange (ETDEWEB)
Eiteneuer, Benedikt; Heisig, Jan [RWTH Aachen University, Institute for Theoretical Particle Physics and Cosmology, Aachen (Germany); Goudelis, Andreas [UMR 7589 CNRS and UPMC, Laboratoire de Physique Theorique et Hautes Energies (LPTHE), Paris (France)
2017-09-15
We perform a global fit within the inert doublet model taking into account experimental observables from colliders, direct and indirect dark matter searches and theoretical constraints. In particular, we consider recent results from searches for dark matter annihilation-induced gamma-rays in dwarf spheroidal galaxies and relax the assumption that the inert doublet model should account for the entire dark matter in the Universe. We, moreover, study in how far the model is compatible with a possible dark matter explanation of the so-called Galactic center excess. We find two distinct parameter space regions that are consistent with existing constraints and can simultaneously explain the excess: One with dark matter masses near the Higgs resonance and one around 72 GeV where dark matter annihilates predominantly into pairs of virtual electroweak gauge bosons via the four-vertex arising from the inert doublet's kinetic term. We briefly discuss future prospects to probe these scenarios. (orig.)
A Suboptimal Power-Saving Transmission Scheme in Multiple Component Carrier Networks
Chung, Yao-Liang; Tsai, Zsehong
Power consumption due to transmissions in base stations (BSs) has been a major contributor to communication-related CO2 emissions. A power optimization model is developed in this study with respect to radio resource allocation and activation in a multiple Component Carrier (CC) environment. We formulate and solve the power-minimization problem of the BS transceivers for multiple-CC networks with carrier aggregation, while maintaining the overall system and respective users' utilities above minimum levels. The optimized power consumption based on this model can be viewed as a lower bound of that of other algorithms employed in practice. A suboptimal scheme with low computation complexity is proposed. Numerical results show that the power consumption of our scheme is much better than that of the conventional one in which all CCs are always active, if both schemes maintain the same required utilities.
Fast and exact Newton and Bidirectional fitting of Active Appearance Models
Kossaifi, Jean; Tzimiropoulos, Georgios; Pantic, Maja
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
de Vries, S O; Fidler, Vaclav; Kuipers, Wietze D; Hunink, Maria G M
1998-01-01
The purpose of this study was to develop a model that predicts the outcome of supervised exercise for intermittent claudication. The authors present an example of the use of autoregressive logistic regression for modeling observed longitudinal data. Data were collected from 329 participants in a
Dijkstra, T.K.; Henseler, J.
2011-01-01
The recent advent of nonlinear structural equation models with indices poses a new challenge to the measurement of scientific constructs. We discuss, exemplify and add to a family of statistical methods aimed at creating linear indices, and compare their suitability in a complex path model with
Inferring Fitness Effects from Time-Resolved Sequence Data with a Delay-Deterministic Model.
Nené, Nuno R; Dunham, Alistair S; Illingworth, Christopher J R
2018-05-01
A common challenge arising from the observation of an evolutionary system over time is to infer the magnitude of selection acting upon a specific genetic variant, or variants, within the population. The inference of selection may be confounded by the effects of genetic drift in a system, leading to the development of inference procedures to account for these effects. However, recent work has suggested that deterministic models of evolution may be effective in capturing the effects of selection even under complex models of demography, suggesting the more general application of deterministic approaches to inference. Responding to this literature, we here note a case in which a deterministic model of evolution may give highly misleading inferences, resulting from the nondeterministic properties of mutation in a finite population. We propose an alternative approach that acts to correct for this error, and which we denote the delay-deterministic model. Applying our model to a simple evolutionary system, we demonstrate its performance in quantifying the extent of selection acting within that system. We further consider the application of our model to sequence data from an evolutionary experiment. We outline scenarios in which our model may produce improved results for the inference of selection, noting that such situations can be easily identified via the use of a regular deterministic model. Copyright © 2018 Nené et al.
Robust Adaptive LCMV Beamformer Based On An Iterative Suboptimal Solution
Directory of Open Access Journals (Sweden)
Xiansheng Guo
2015-06-01
Full Text Available The main drawback of closed-form solution of linearly constrained minimum variance (CF-LCMV beamformer is the dilemma of acquiring long observation time for stable covariance matrix estimates and short observation time to track dynamic behavior of targets, leading to poor performance including low signal-noise-ratio (SNR, low jammer-to-noise ratios (JNRs and small number of snapshots. Additionally, CF-LCMV suffers from heavy computational burden which mainly comes from two matrix inverse operations for computing the optimal weight vector. In this paper, we derive a low-complexity Robust Adaptive LCMV beamformer based on an Iterative Suboptimal solution (RAIS-LCMV using conjugate gradient (CG optimization method. The merit of our proposed method is threefold. Firstly, RAIS-LCMV beamformer can reduce the complexity of CF-LCMV remarkably. Secondly, RAIS-LCMV beamformer can adjust output adaptively based on measurement and its convergence speed is comparable. Finally, RAIS-LCMV algorithm has robust performance against low SNR, JNRs, and small number of snapshots. Simulation results demonstrate the superiority of our proposed algorithms.
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....
Czech Academy of Sciences Publication Activity Database
Suda, Jan; Herben, Tomáš
2013-01-01
Roč. 280, č. 1751 (2013), no.20122387 ISSN 0962-8452 Institutional support: RVO:67985939 Keywords : cytometry * statiscical modelling * polyploidy Subject RIV: EF - Botanics Impact factor: 5.292, year: 2013
Occupant behavior in building energy simulation: towards a fit-for-purpose modeling strategy
Gaetani, I.; Hoes, P.; Hensen, J.L.M.
2016-01-01
Occupant behavior is nowadays acknowledged as a main source of discrepancy between predicted and actual building performance; therefore, researchers attempt to model occupants' presence and adaptive actions more realistically. Literature shows a proliferation of increasingly complex, data-based
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.
SDSS-II: Determination of shape and color parameter coefficients for SALT-II fit model
Energy Technology Data Exchange (ETDEWEB)
Dojcsak, L.; Marriner, J.; /Fermilab
2010-08-01
In this study we look at the SALT-II model of Type IA supernova analysis, which determines the distance moduli based on the known absolute standard candle magnitude of the Type IA supernovae. We take a look at the determination of the shape and color parameter coefficients, {alpha} and {beta} respectively, in the SALT-II model with the intrinsic error that is determined from the data. Using the SNANA software package provided for the analysis of Type IA supernovae, we use a standard Monte Carlo simulation to generate data with known parameters to use as a tool for analyzing the trends in the model based on certain assumptions about the intrinsic error. In order to find the best standard candle model, we try to minimize the residuals on the Hubble diagram by calculating the correct shape and color parameter coefficients. We can estimate the magnitude of the intrinsic errors required to obtain results with {chi}{sup 2}/degree of freedom = 1. We can use the simulation to estimate the amount of color smearing as indicated by the data for our model. We find that the color smearing model works as a general estimate of the color smearing, and that we are able to use the RMS distribution in the variables as one method of estimating the correct intrinsic errors needed by the data to obtain the correct results for {alpha} and {beta}. We then apply the resultant intrinsic error matrix to the real data and show our results.
Fitting identity in the reasoned action framework: A meta-analysis and model comparison.
Paquin, Ryan S; Keating, David M
2017-01-01
Several competing models have been put forth regarding the role of identity in the reasoned action framework. The standard model proposes that identity is a background variable. Under a typical augmented model, identity is treated as an additional direct predictor of intention and behavior. Alternatively, it has been proposed that identity measures are inadvertent indicators of an underlying intention factor (e.g., a manifest-intention model). In order to test these competing hypotheses, we used data from 73 independent studies (total N = 23,917) to conduct a series of meta-analytic structural equation models. We also tested for moderation effects based on whether there was a match between identity constructs and the target behaviors examined (e.g., if the study examined a "smoker identity" and "smoking behavior," there would be a match; if the study examined a "health conscious identity" and "smoking behavior," there would not be a match). Average effects among primary reasoned action variables were all substantial, rs = .37-.69. Results gave evidence for the manifest-intention model over the other explanations, and a moderation effect by identity-behavior matching.
Directory of Open Access Journals (Sweden)
Cheol-Eung Lee
2017-02-01
Full Text Available Several natural disasters occur because of torrential rainfalls. The change in global climate most likely increases the occurrences of such downpours. Hence, it is necessary to investigate the characteristics of the torrential rainfall events in order to introduce effective measures for mitigating disasters such as urban floods and landslides. However, one of the major problems is evaluating the number of torrential rainfall events from a statistical viewpoint. If the number of torrential rainfall occurrences during a month is considered as count data, their frequency distribution could be identified using a probability distribution. Generally, the number of torrential rainfall occurrences has been analyzed using the Poisson distribution (POI or the Generalized Poisson Distribution (GPD. However, it was reported that POI and GPD often overestimated or underestimated the observed count data when additional or fewer zeros were included. Hence, in this study, a zero-inflated model concept was applied to solve this problem existing in the conventional models. Zero-Inflated Poisson (ZIP model, Zero-Inflated Generalized Poisson (ZIGP model, and the Bayesian ZIGP model have often been applied to fit the count data having additional or fewer zeros. However, the applications of these models in water resource management have been very limited despite their efficiency and accuracy. The five models, namely, POI, GPD, ZIP, ZIGP, and Bayesian ZIGP, were applied to the torrential rainfall data having additional zeros obtained from two rain gauges in South Korea, and their applicability was examined in this study. In particular, the informative prior distributions evaluated via the empirical Bayes method using ten rain gauges were developed in the Bayesian ZIGP model. Finally, it was suggested to avoid using the POI and GPD models to fit the frequency of torrential rainfall data. In addition, it was concluded that the Bayesian ZIGP model used in this study
Middelkamp, P.J.C.; Wolfhagen, P.; Steenbergen, B.
2015-01-01
Introduction: The transtheoretical model of behaviour change (TTM) is often used to understand and predict changes in health related behaviour, for example exercise behaviour and eating behaviour. Fitness professionals like personal trainers typically service and support clients in improving
Improving the Fit of a Land-Surface Model to Data Using its Adjoint
Raoult, N.; Jupp, T. E.; Cox, P. M.; Luke, C.
2015-12-01
Land-surface models (LSMs) are of growing importance in the world of climate prediction. They are crucial components of larger Earth system models that are aimed at understanding the effects of land surface processes on the global carbon cycle. The Joint UK Land Environment Simulator (JULES) is the land-surface model used by the UK Met Office. It has been automatically differentiated using commercial software from FastOpt, resulting in an analytical gradient, or 'adjoint', of the model. Using this adjoint, the adJULES parameter estimation system has been developed, to search for locally optimum parameter sets by calibrating against observations. adJULES presents an opportunity to confront JULES with many different observations, and make improvements to the model parameterisation. In the newest version of adJULES, multiple sites can be used in the calibration, to giving a generic set of parameters that can be generalised over plant functional types. We present an introduction to the adJULES system and its applications to data from a variety of flux tower sites. We show that calculation of the 2nd derivative of JULES allows us to produce posterior probability density functions of the parameters and how knowledge of parameter values is constrained by observations.
Suttinger, Matthew; Go, Rowel; Figueiredo, Pedro; Todi, Ankesh; Shu, Hong; Leshin, Jason; Lyakh, Arkadiy
2018-01-01
Experimental and model results for 15-stage broad area quantum cascade lasers (QCLs) are presented. Continuous wave (CW) power scaling from 1.62 to 2.34 W has been experimentally demonstrated for 3.15-mm long, high reflection-coated QCLs for an active region width increased from 10 to 20 μm. A semiempirical model for broad area devices operating in CW mode is presented. The model uses measured pulsed transparency current, injection efficiency, waveguide losses, and differential gain as input parameters. It also takes into account active region self-heating and sublinearity of pulsed power versus current laser characteristic. The model predicts that an 11% improvement in maximum CW power and increased wall-plug efficiency can be achieved from 3.15 mm×25 μm devices with 21 stages of the same design, but half doping in the active region. For a 16-stage design with a reduced stage thickness of 300 Å, pulsed rollover current density of 6 kA/cm2, and InGaAs waveguide layers, an optical power increase of 41% is projected. Finally, the model projects that power level can be increased to ˜4.5 W from 3.15 mm×31 μm devices with the baseline configuration with T0 increased from 140 K for the present design to 250 K.
Do telemonitoring projects of heart failure fit the Chronic Care Model?
Willemse, Evi; Adriaenssens, Jef; Dilles, Tinne; Remmen, Roy
2014-07-01
This study describes the characteristics of extramural and transmural telemonitoring projects on chronic heart failure in Belgium. It describes to what extent these telemonitoring projects coincide with the Chronic Care Model of Wagner. The Chronic Care Model describes essential components for high-quality health care. Telemonitoring can be used to optimise home care for chronic heart failure. It provides a potential prospective to change the current care organisation. This qualitative study describes seven non-invasive home-care telemonitoring projects in patients with heart failure in Belgium. A qualitative design, including interviews and literature review, was used to describe the correspondence of these home-care telemonitoring projects with the dimensions of the Chronic Care Model. The projects were situated in primary and secondary health care. Their primary goal was to reduce the number of readmissions for chronic heart failure. None of these projects succeeded in a final implementation of telemonitoring in home care after the pilot phase. Not all the projects were initiated to accomplish all of the dimensions of the Chronic Care Model. A central role for the patient was sparse. Limited financial resources hampered continuation after the pilot phase. Cooperation and coordination in telemonitoring appears to be major barriers but are, within primary care as well as between the lines of care, important links in follow-up. This discrepancy can be prohibitive for deployment of good chronic care. Chronic Care Model is recommended as basis for future.
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).
Fitting and interpreting continuous-time latent Markov models for panel data.
Lange, Jane M; Minin, Vladimir N
2013-11-20
Multistate models characterize disease processes within an individual. Clinical studies often observe the disease status of individuals at discrete time points, making exact times of transitions between disease states unknown. Such panel data pose considerable modeling challenges. Assuming the disease process progresses accordingly, a standard continuous-time Markov chain (CTMC) yields tractable likelihoods, but the assumption of exponential sojourn time distributions is typically unrealistic. More flexible semi-Markov models permit generic sojourn distributions yet yield intractable likelihoods for panel data in the presence of reversible transitions. One attractive alternative is to assume that the disease process is characterized by an underlying latent CTMC, with multiple latent states mapping to each disease state. These models retain analytic tractability due to the CTMC framework but allow for flexible, duration-dependent disease state sojourn distributions. We have developed a robust and efficient expectation-maximization algorithm in this context. Our complete data state space consists of the observed data and the underlying latent trajectory, yielding computationally efficient expectation and maximization steps. Our algorithm outperforms alternative methods measured in terms of time to convergence and robustness. We also examine the frequentist performance of latent CTMC point and interval estimates of disease process functionals based on simulated data. The performance of estimates depends on time, functional, and data-generating scenario. Finally, we illustrate the interpretive power of latent CTMC models for describing disease processes on a dataset of lung transplant patients. We hope our work will encourage wider use of these models in the biomedical setting. Copyright © 2013 John Wiley & Sons, Ltd.
Fitness club
2013-01-01
Nordic Walking Classes Come join the Nordic walking classes and outings offered by the CERN Fitness Club starting September 2013. Our licensed instructor Christine offers classes for people who’ve never tried Nordic Walking and who would like to learn the technique, and outings for people who have completed the classes and enjoy going out as a group. Course 1: Tuesdays 12:30 - 13:30 24 September, 1 October, 8 October, 15 October Course 2: Tuesdays 12:30 - 13:30 5 November, 12 November, 19 November, 26 November Outings will take place on Thursdays (12:30 to 13:30) from 12 September 2013. We meet at the CERN Club Barracks car park (close to Entrance A) 10 minutes before departure. Prices: 50 CHF for 4 classes, including the 10 CHF Club membership. Payments made directly to instructor. Renting Poles: Poles can be rented from Christine at 5 CHF / hour. Subscription: Please subscribe at: http://cern.ch/club-fitness Looking forward to seeing you among us! Fitness Club FitnessClub@c...
Fitness Club
2012-01-01
Get in Shape for Summer with the CERN Fitness Club Saturday 23 June 2012 from 14:30 to 16.30 (doors open at 14.00) Germana’s Fitness Workshop. Build strength and stamina, sculpt and tone your body and get your heart pumping with Germana’s workout mixture of Cardio Attack, Power Pump, Power Step, Cardio Combat and Cross-Training. Where: 216 (Pump room – equipped with changing rooms and showers). What to wear: comfortable clothes and indoor sports shoes + bring a drink! How much: 15 chf Sign up here: https://espace.cern.ch/club-fitness/Lists/Test_Subscription/NewForm.aspx? Join the Party and dance yourself into shape at Marco + Marials Zumba Masterclass. Saturday 30 June 2012 from 15:00 to 16:30 Marco + Mariel’s Zumba Masterclass Where: 216 (Pump room – equipped with changing rooms and showers). What to wear: comfortable clothes and indoor sports shoes + bring a drink! How much: 25 chf Sign up here: https://espace.cern.ch/club-fitness/Lists/Zumba%20...
Fitness Club
2010-01-01
Nordic Walking Please note that the subscriptions for the general fitness classes from July to December are open: Subscriptions general fitness classes Jul-Dec 2010 Sign-up to the Fitness Club mailing list here Nordic Walking: Sign-up to the Nordic Walking mailing list here Beginners Nordic walking lessons Monday Lunchtimes (rdv 12:20 for 12:30 departure) 13.09/20.09/27.09/04.10 11.10/18.10/08.11/15.11 22.11/29.11/06.12/20.12 Nordic walking lessons Tuesday evenings (rdv 17:50 for 18:00 departure) 07.09/14.09/21.09/28.09 05.10/12.10/19.10/26.10 Intermediate/Advanced Nordic walking outings (follow the nordic walking lessons before signing up for the outings) every Thursday from 16.09 - 16.12, excluding 28.10 and 09.12 Subscriptions and info: fitness.club@cern.ch
Fitness Club
2012-01-01
The CERN Fitness Club is pleased to announce its new early morning class which will be taking place on: Tuesdays from 24th April 07:30 to 08:15 216 (Pump Hall, close to entrance C) – Facilities include changing rooms and showers. The Classes: The early morning classes will focus on workouts which will help you build not only strength and stamina, but will also improve your balance, and coordination. Our qualified instructor Germana will accompany you throughout the workout to ensure you stay motivated so you achieve the best results. Sign up and discover the best way to start your working day full of energy! How to subscribe? We invite you along to a FREE trial session, if you enjoy the activity, please sign up via our website: https://espace.cern.ch/club-fitness/Activities/SUBSCRIBE.aspx. * * * * * * * * Saturday 28th April Get in shape for the summer at our fitness workshop and zumba dance party: Fitness workshop with Germana 13:00 to 14:30 - 216 (Pump Hall) Price...
Fitting Social Network Models Using Varying Truncation Stochastic Approximation MCMC Algorithm
Jin, Ick Hoon
2013-10-01
The exponential random graph model (ERGM) plays a major role in social network analysis. However, parameter estimation for the ERGM is a hard problem due to the intractability of its normalizing constant and the model degeneracy. The existing algorithms, such as Monte Carlo maximum likelihood estimation (MCMLE) and stochastic approximation, often fail for this problem in the presence of model degeneracy. In this article, we introduce the varying truncation stochastic approximation Markov chain Monte Carlo (SAMCMC) algorithm to tackle this problem. The varying truncation mechanism enables the algorithm to choose an appropriate starting point and an appropriate gain factor sequence, and thus to produce a reasonable parameter estimate for the ERGM even in the presence of model degeneracy. The numerical results indicate that the varying truncation SAMCMC algorithm can significantly outperform the MCMLE and stochastic approximation algorithms: for degenerate ERGMs, MCMLE and stochastic approximation often fail to produce any reasonable parameter estimates, while SAMCMC can do; for nondegenerate ERGMs, SAMCMC can work as well as or better than MCMLE and stochastic approximation. The data and source codes used for this article are available online as supplementary materials. © 2013 American Statistical Association, Institute of Mathematical Statistics, and Interface Foundation of North America.
Where Does Creativity Fit into a Productivist Industrial Model of Knowledge Production?
Ghassib, Hisham B.
2010-01-01
The basic premise of this paper is the fact that science has become a major industry: the knowledge industry. The paper throws some light on the reasons for the transformation of science from a limited, constrained and marginal craft into a major industry. It, then, presents a productivist industrial model of knowledge production, which shows its…
Understanding the Listening Process: Rethinking the "One Size Fits All" Model
Wolvin, Andrew
2013-01-01
Robert Bostrom's seminal contributions to listening theory and research represent an impressive legacy and provide listening scholars with important perspectives on the complexities of listening cognition and behavior. Bostrom's work provides a solid foundation on which to build models that more realistically explain how listeners function…
A turbulent mixing Reynolds stress model fitted to match linear interaction analysis predictions
International Nuclear Information System (INIS)
Griffond, J; Soulard, O; Souffland, D
2010-01-01
To predict the evolution of turbulent mixing zones developing in shock tube experiments with different gases, a turbulence model must be able to reliably evaluate the production due to the shock-turbulence interaction. In the limit of homogeneous weak turbulence, 'linear interaction analysis' (LIA) can be applied. This theory relies on Kovasznay's decomposition and allows the computation of waves transmitted or produced at the shock front. With assumptions about the composition of the upstream turbulent mixture, one can connect the second-order moments downstream from the shock front to those upstream through a transfer matrix, depending on shock strength. The purpose of this work is to provide a turbulence model that matches LIA results for the shock-turbulent mixture interaction. Reynolds stress models (RSMs) with additional equations for the density-velocity correlation and the density variance are considered here. The turbulent states upstream and downstream from the shock front calculated with these models can also be related through a transfer matrix, provided that the numerical implementation is based on a pseudo-pressure formulation. Then, the RSM should be modified in such a way that its transfer matrix matches the LIA one. Using the pseudo-pressure to introduce ad hoc production terms, we are able to obtain a close agreement between LIA and RSM matrices for any shock strength and thus improve the capabilities of the RSM.
Fitness effects of beneficial mutations: the mutational landscape model in experimental evolution
DEFF Research Database (Denmark)
Betancourt, Andrea J.; Bollback, Jonathan Paul
2006-01-01
of beneficial mutations should be roughly exponentially distributed. The prediction appears to be borne out by most of these studies, at least qualitatively. Another study showed that a modified version of the model was able to predict, with reasonable accuracy, which of a ranked set of beneficial alleles...
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
A rat model system to study complex disease risks, fitness, aging, and longevity.
Koch, Lauren Gerard; Britton, Steven L; Wisløff, Ulrik
2012-02-01
The association between low exercise capacity and all-cause morbidity and mortality is statistically strong yet mechanistically unresolved. By connecting clinical observation with a theoretical base, we developed a working hypothesis that variation in capacity for oxygen metabolism is the central mechanistic determinant between disease and health (aerobic hypothesis). As an unbiased test, we show that two-way artificial selective breeding of rats for low and high intrinsic endurance exercise capacity also produces rats that differ for numerous disease risks, including the metabolic syndrome, cardiovascular complications, premature aging, and reduced longevity. This contrasting animal model system may prove to be translationally superior relative to more widely used simplistic models for understanding geriatric biology and medicine. Copyright © 2012 Elsevier Inc. All rights reserved.
Phenomenological model to fit complex permittivity data of water from radio to optical frequencies.
Shubitidze, Fridon; Osterberg, Ulf
2007-04-01
A general factorized form of the dielectric function together with a fractional model-based parameter estimation method is used to provide an accurate analytical formula for the complex refractive index in water for the frequency range 10(8)-10(16)Hz . The analytical formula is derived using a combination of a microscopic frequency-dependent rational function for adjusting zeros and poles of the dielectric dispersion together with the macroscopic statistical Fermi-Dirac distribution to provide a description of both the real and imaginary parts of the complex permittivity for water. The Fermi-Dirac distribution allows us to model the dramatic reduction in the imaginary part of the permittivity in the visible window of the water spectrum.
The fitness of drug-resistant malaria parasites in a rodent model: multiplicity of infection
Huijben, Silvie; Sim, Derek G.; Nelson, William, A.; Read, Andrew F.
2011-01-01
Malaria infections normally consist of more than one clonally-replicating lineage. Within-host interactions between sensitive and resistant parasites can have profound effects on the evolution of drug resistance. Here, using the Plasmodium chabaudi mouse malaria model, we ask whether the costs and benefits of resistance are affected by the number of co-infecting strains competing with a resistant clone. We found strong competitive suppression of resistant parasites in untreated infections and...
Exact Solution of Mutator Model with Linear Fitness and Finite Genome Length
Saakian, David B.
2017-08-01
We considered the infinite population version of the mutator phenomenon in evolutionary dynamics, looking at the uni-directional mutations in the mutator-specific genes and linear selection. We solved exactly the model for the finite genome length case, looking at the quasispecies version of the phenomenon. We calculated the mutator probability both in the statics and dynamics. The exact solution is important for us because the mutator probability depends on the genome length in a highly non-trivial way.
International Nuclear Information System (INIS)
Mancusi, D; Charity, R J; Cugnon, J
2013-01-01
The de-excitation of compound nuclei has been successfully described for several decades by means of statistical models. However, accurate predictions require some fine-tuning of the model parameters. This task can be simplified by studying several entrance channels, which populate different regions of the parameter space of the compound nucleus. Fusion reactions play an important role in this strategy because they minimise the uncertainty on the entrance channel by fixing mass, charge and excitation energy of the compound nucleus. If incomplete fusion is negligible, the only uncertainty on the compound nucleus comes from the spin distribution. However, some de-excitation channels, such as fission, are quite sensitive to spin. Other entrance channels can then be used to discriminate between equivalent parameter sets. The focus of this work is on fission and intermediate-mass-fragment emission cross sections of compound nuclei with 70 70 ≲ A ≲ 240. 240. The statistical de-excitation model is GEMINI++. The choice of the observables is natural in the framework of GEMINI++, which describes fragment emission using a fissionlike formalism. Equivalent parameter sets for fusion reactions can be resolved using the spallation entrance channel. This promising strategy can lead to the identification of a minimal set of physical ingredients necessary for a unified quantitative description of nuclear de-excitation.
McPherson, Andrew W; Chan, Fong Chun; Shah, Sohrab P
2018-02-01
The ability to accurately model evolutionary dynamics in cancer would allow for prediction of progression and response to therapy. As a prelude to quantitative understanding of evolutionary dynamics, researchers must gather observations of in vivo tumor evolution. High-throughput genome sequencing now provides the means to profile the mutational content of evolving tumor clones from patient biopsies. Together with the development of models of tumor evolution, reconstructing evolutionary histories of individual tumors generates hypotheses about the dynamics of evolution that produced the observed clones. In this review, we provide a brief overview of the concepts involved in predicting evolutionary histories, and provide a workflow based on bulk and targeted-genome sequencing. We then describe the application of this workflow to time series data obtained for transformed and progressed follicular lymphomas (FL), and contrast the observed evolutionary dynamics between these two subtypes. We next describe results from a spatial sampling study of high-grade serous (HGS) ovarian cancer, propose mechanisms of disease spread based on the observed clonal mixtures, and provide examples of diversification through subclonal acquisition of driver mutations and convergent evolution. Finally, we state implications of the techniques discussed in this review as a necessary but insufficient step on the path to predictive modelling of disease dynamics. Copyright © 2018 Cold Spring Harbor Laboratory Press; all rights reserved.
Does Vocational Education Model fit to Fulfil Prisoners’ Needs Based on Gender?
Hayzaki, S. H.; Nurhaeni, I. D. A.
2018-02-01
Men and women have different needs, based on their gender or the socio-cultural construction. The government has issued a policy about accelerating the equivalence of gender since 2012 through responsive planning and budgeting. With the policy, every institution (including the institutions under the ministry of law and human rights) must integrate its gender perspective on planning and budgeting, then it can fulfill the different needs between men and women. One of the programs developed in prisons for prisoners is vocational education and technology for preparing the prisoners’ life after being released from the prison cells. This article was made for evaluating the vocational education and training given to the prisoners. Gender perspective is employed as the analyzing tool. The result was then used as the basis of formulating vocational education model integrating gender perspective. The research was conducted at the Prison of Demak Regency, Indonesia. The method used in the research is qualitative descriptive with data collection techniques using by in-depth interviews, observation and documentation. The data analysis uses statistic description of Harvard’s checklist category model and combined with Moser category model. The result shows that vocational education and training given have not considered the differences between men and women. As a result, the prisoners were still not able to understand their different needs which can cause gender injustice when they come into job market. It is suggested that gender perspective must be included as a teaching material in the vocational education and training.
International Nuclear Information System (INIS)
Lim, Chang Seon; Cho, A Ra; Hur, Yera; Choi, Seong Youl
2017-01-01
Radiological Technologists deals with the life of a person which means professional competency is essential for the job. Nevertheless, there have been no studies in Korea that identified the job competence of radiologists. In order to define the core job competencies of Korean radiologists and to present the factor models, 147 questionnaires on job competency of radiology were analyzed using 'PASW Statistics Version 18.0' and 'AMOS Version 18.0'. The valid model consisted of five core job competencies ('Patient management', 'Health and safety', 'Operation of equipment', 'Procedures and management') and 17 sub – competencies. As a result of the factor analysis, the RMSEA value was 0.1 and the CFI, and TLI values were close to 0.9 in the measurement model of the five core job competencies. The validity analysis showed that the mean variance extraction was 0.5 or more and the conceptual reliability value was 0.7 or more , And there was a high correlation between subordinate competencies included in each subordinate competencies. The results of this study are expected to provide specific information necessary for the training and management of human resources centered on competence by clearly showing the job competence required for radiologists in Korea's health environment
Bonfini, P.; González-Martín, O.; Fritz, J.; Bitsakis, T.; Bruzual, G.; Sodi, B. Cervantes
2018-05-01
A large fraction of early-type galaxies (ETGs) host prominent dust features, and central dust rings are arguably the most interesting among them. We present here `Lord Of The Rings' (LOTR), a new methodology which allows to integrate the extinction by dust rings in a 2D fitting modelling of the surface brightness distribution. Our pipeline acts in two steps, first using the surface fitting software GALFIT to determine the unabsorbed stellar emission, and then adopting the radiative transfer code SKIRT to apply dust extinction. We apply our technique to NGC 4552 and NGC 4494, two nearby ETGs. We show that the extinction by a dust ring can mimic, in a surface brightness profile, a central point source (e.g. an unresolved nuclear stellar cluster or an active galactic nucleus; AGN) superimposed to a `core' (i.e. a central flattening of the stellar light commonly observed in massive ETGs). We discuss how properly accounting for dust features is of paramount importance to derive correct fluxes especially for low luminosity AGNs (LLAGNs). We suggest that the geometries of dust features are strictly connected with how relaxed is the gravitational potential, i.e. with the evolutionary stage of the host galaxy. Additionally, we find hints that the dust mass contained in the ring relates to the AGN activity.
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.
Lepping, R. P.; Wu, C.-C.; Berdichevsky, D. B.; Szabo, A.
2018-04-01
We give the results of parameter fitting of the magnetic clouds (MCs) observed by the Wind spacecraft for the three-year period 2013 to the end of 2015 (called the "Present" period) using the MC model of Lepping, Jones, and Burlaga ( J. Geophys. Res. 95, 11957, 1990). The Present period is almost coincident with the solar maximum of the sunspot number, which has a broad peak starting in about 2012 and extending to almost 2015. There were 49 MCs identified in the Present period. The modeling gives MC quantities such as size, axial attitude, field handedness, axial magnetic-field strength, center time, and closest-approach vector. Derived quantities are also estimated, such as axial magnetic flux, axial current density, and total axial current. Quality estimates are assigned representing excellent, fair/good, and poor. We provide error estimates on the specific fit parameters for the individual MCs, where the poor cases are excluded. Model-fitting results that are based on the Present period are compared to the results of the full Wind mission from 1995 to the end of 2015 (Long-term period), and compared to the results of two other recent studies that encompassed the periods 2007 - 2009 and 2010 - 2012, inclusive. We see that during the Present period, the MCs are, on average, slightly slower, slightly weaker in axial magnetic field (by 8.7%), and larger in diameter (by 6.5%) than those in the Long-term period. However, in most respects, the MCs in the Present period are significantly closer in characteristics to those of the Long-term period than to those of the two recent three-year periods. However, the rate of occurrence of MCs for the Long-term period is 10.3 year^{-1}, whereas this rate for the Present period is 16.3 year^{-1}, similar to that of the period 2010 - 2012. Hence, the MC occurrence rate has increased appreciably in the last six years. MC Type (N-S, S-N, All N, All S, etc.) is assigned to each MC; there is an inordinately large percentage of All S
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
International Nuclear Information System (INIS)
Masseran, N.; Razali, A.M.; Ibrahim, K.; Latif, M.T.
2013-01-01
Highlights: • We suggest a simple way for wind direction modeling using the mixture of von Mises distribution. • We determine the most suitable probability model for wind direction regime in Malaysia. • We provide the circular density plots to show the most prominent directions of wind blows. - Abstract: A statistical distribution for describing wind direction provides information about the wind regime at a particular location. In addition, this information complements knowledge of wind speed, which allows researchers to draw some conclusions about the energy potential of wind and aids the development of efficient wind energy generation. This study focuses on modeling the frequency distribution of wind direction, including some characteristics of wind regime that cannot be represented by a unimodal distribution. To identify the most suitable model, a finite mixture of von Mises distributions were fitted to the average hourly wind direction data for nine wind stations located in Peninsular Malaysia. The data used were from the years 2000 to 2009. The suitability of each mixture distribution was judged based on the R 2 coefficient and the histogram plot with a density line. The results showed that the finite mixture of the von Mises distribution with H number of components was the best distribution to describe the wind direction distributions in Malaysia. In addition, the circular density plots of the suitable model clearly showed the most prominent directions of wind blows than the other directions
A fully Bayesian method for jointly fitting instrumental calibration and X-ray spectral models
International Nuclear Information System (INIS)
Xu, Jin; Yu, Yaming; Van Dyk, David A.; Kashyap, Vinay L.; Siemiginowska, Aneta; Drake, Jeremy; Ratzlaff, Pete; Connors, Alanna; Meng, Xiao-Li
2014-01-01
Owing to a lack of robust principled methods, systematic instrumental uncertainties have generally been ignored in astrophysical data analysis despite wide recognition of the importance of including them. Ignoring calibration uncertainty can cause bias in the estimation of source model parameters and can lead to underestimation of the variance of these estimates. We previously introduced a pragmatic Bayesian method to address this problem. The method is 'pragmatic' in that it introduced an ad hoc technique that simplified computation by neglecting the potential information in the data for narrowing the uncertainty for the calibration product. Following that work, we use a principal component analysis to efficiently represent the uncertainty of the effective area of an X-ray (or γ-ray) telescope. Here, however, we leverage this representation to enable a principled, fully Bayesian method that coherently accounts for the calibration uncertainty in high-energy spectral analysis. In this setting, the method is compared with standard analysis techniques and the pragmatic Bayesian method. The advantage of the fully Bayesian method is that it allows the data to provide information not only for estimation of the source parameters but also for the calibration product—here the effective area, conditional on the adopted spectral model. In this way, it can yield more accurate and efficient estimates of the source parameters along with valid estimates of their uncertainty. Provided that the source spectrum can be accurately described by a parameterized model, this method allows rigorous inference about the effective area by quantifying which possible curves are most consistent with the data.
Optimization of parameters for fitting linear accelerator photon beams using a modified CBEAM model
International Nuclear Information System (INIS)
Ayyangar, K.; Daftari, I.; Palta, J.; Suntharalingam, N.
1989-01-01
Measured beam profiles and central-axis depth-dose data for 6- and 25-MV photon beams are used to generate a dose matrix which represents the full beam. A corresponding dose matrix is also calculated using the modified CBEAM model. The calculational model uses the usual set of three parameters to define the intensity at beam edges and the parameter that accounts for collimator transmission. An additional set of three parameters is used for the primary profile factor, expressed as a function of distance from the central axis. An optimization program has been adapted to automatically adjust these parameters to minimize the χ 2 between the measured and calculated data. The average values of the parameters for small (6x6 cm 2 ), medium (10x10 cm 2 ), and large (20x20 cm 2 ) field sizes are found to represent the beam adequately for all field sizes. The calculated and the measured doses at any point agree to within 2% for any field size in the range 4x4 to 40x40 cm 2
Czech Academy of Sciences Publication Activity Database
Vinš, Václav; Jäger, A.; Hrubý, Jan; Span, R.
2017-01-01
Roč. 435, March (2017), s. 104-117 ISSN 0378-3812 R&D Projects: GA MŠk(CZ) 7F14466; GA ČR(CZ) GJ15-07129Y Institutional support: RVO:61388998 Keywords : carbon capture and storage * clathrate * parameter fitting Subject RIV: BJ - Thermodynamics Impact factor: 2.473, year: 2016 http://ac.els-cdn.com/S0378381216306069/1-s2.0-S0378381216306069-main.pdf?_tid=7b6bf82c-2f22-11e7-8661-00000aab0f02&acdnat=1493721260_17561db239dd867f17c2ad3bda9a5540
Hierarchical Winner-Take-All Particle Swarm Optimization Social Network for Neural Model Fitting
Coventry, Brandon S.; Parthasarathy, Aravindakshan; Sommer, Alexandra L.; Bartlett, Edward L.
2016-01-01
Particle swarm optimization (PSO) has gained widespread use as a general mathematical programming paradigm and seen use in a wide variety of optimization and machine learning problems. In this work, we introduce a new variant on the PSO social network and apply this method to the inverse problem of input parameter selection from recorded auditory neuron tuning curves. The topology of a PSO social network is a major contributor to optimization success. Here we propose a new social network which draws influence from winner-take-all coding found in visual cortical neurons. We show that the winner-take-all network performs exceptionally well on optimization problems with greater than 5 dimensions and runs at a lower iteration count as compared to other PSO topologies. Finally we show that this variant of PSO is able to recreate auditory frequency tuning curves and modulation transfer functions, making it a potentially useful tool for computational neuroscience models. PMID:27726048
Hierarchical winner-take-all particle swarm optimization social network for neural model fitting.
Coventry, Brandon S; Parthasarathy, Aravindakshan; Sommer, Alexandra L; Bartlett, Edward L
2017-02-01
Particle swarm optimization (PSO) has gained widespread use as a general mathematical programming paradigm and seen use in a wide variety of optimization and machine learning problems. In this work, we introduce a new variant on the PSO social network and apply this method to the inverse problem of input parameter selection from recorded auditory neuron tuning curves. The topology of a PSO social network is a major contributor to optimization success. Here we propose a new social network which draws influence from winner-take-all coding found in visual cortical neurons. We show that the winner-take-all network performs exceptionally well on optimization problems with greater than 5 dimensions and runs at a lower iteration count as compared to other PSO topologies. Finally we show that this variant of PSO is able to recreate auditory frequency tuning curves and modulation transfer functions, making it a potentially useful tool for computational neuroscience models.
Fitness Club
2012-01-01
Nordic Walking Classes Sessions of four classes of one hour each are held on Tuesdays. RDV barracks parking at Entrance A, 10 minutes before class time. Session 1 = 11.09 / 18.09 / 25.09 / 02.10, 18:15 - 19:15 Session 2 = 25.09 / 02.10 / 09.10 / 16.10, 12:30 - 13:30 Session 3 = 23.10 / 30.10 / 06.11 / 13.11, 12:30 - 13:30 Session 4 = 20.11 / 27.11 / 04.12 / 11.12, 12:30 - 13:30 Prices 40 CHF per session + 10 CHF club membership 5 CHF/hour pole rental Check out our schedule and enroll at http://cern.ch/club-fitness Hope to see you among us! fitness.club@cern.ch In spring 2012 there was a long-awaited progress in CERN Fitness club. We have officially opened a Powerlifting @ CERN, and the number of members of the new section has been increasing since then reaching 70+ people in less than 4 months. Powerlifting is a strength sport, which is simple as 1-2-3 and efficient. The "1-2-3" are the three basic lifts (bench press...
Sun, Xiangqing; Elston, Robert C; Barnholtz-Sloan, Jill S; Falk, Gary W; Grady, William M; Faulx, Ashley; Mittal, Sumeet K; Canto, Marcia; Shaheen, Nicholas J; Wang, Jean S; Iyer, Prasad G; Abrams, Julian A; Tian, Ye D; Willis, Joseph E; Guda, Kishore; Markowitz, Sanford D; Chandar, Apoorva; Warfe, James M; Brock, Wendy; Chak, Amitabh
2016-05-01
Barrett's esophagus is often asymptomatic and only a small portion of Barrett's esophagus patients are currently diagnosed and under surveillance. Therefore, it is important to develop risk prediction models to identify high-risk individuals with Barrett's esophagus. Familial aggregation of Barrett's esophagus and esophageal adenocarcinoma, and the increased risk of esophageal adenocarcinoma for individuals with a family history, raise the necessity of including genetic factors in the prediction model. Methods to determine risk prediction models using both risk covariates and ascertained family data are not well developed. We developed a Barrett's Esophagus Translational Research Network (BETRNet) risk prediction model from 787 singly ascertained Barrett's esophagus pedigrees and 92 multiplex Barrett's esophagus pedigrees, fitting a multivariate logistic model that incorporates family history and clinical risk factors. The eight risk factors, age, sex, education level, parental status, smoking, heartburn frequency, regurgitation frequency, and use of acid suppressant, were included in the model. The prediction accuracy was evaluated on the training dataset and an independent validation dataset of 643 multiplex Barrett's esophagus pedigrees. Our results indicate family information helps to predict Barrett's esophagus risk, and predicting in families improves both prediction calibration and discrimination accuracy. Our model can predict Barrett's esophagus risk for anyone with family members known to have, or not have, had Barrett's esophagus. It can predict risk for unrelated individuals without knowing any relatives' information. Our prediction model will shed light on effectively identifying high-risk individuals for Barrett's esophagus screening and surveillance, consequently allowing intervention at an early stage, and reducing mortality from esophageal adenocarcinoma. Cancer Epidemiol Biomarkers Prev; 25(5); 727-35. ©2016 AACR. ©2016 American Association for
The conceptual basis of mathematics in cardiology IV: statistics and model fitting.
Bates, Jason H T; Sobel, Burton E
2003-06-01
This is the fourth in a series of four articles developed for the readers of Coronary Artery Disease. Without language ideas cannot be articulated. What may not be so immediately obvious is that they cannot be formulated either. One of the essential languages of cardiology is mathematics. Unfortunately, medical education does not emphasize, and in fact, often neglects empowering physicians to think mathematically. Reference to statistics, conditional probability, multicompartmental modeling, algebra, calculus and transforms is common but often without provision of genuine conceptual understanding. At the University of Vermont College of Medicine, Professor Bates developed a course designed to address these deficiencies. The course covered mathematical principles pertinent to clinical cardiovascular and pulmonary medicine and research. It focused on fundamental concepts to facilitate formulation and grasp of ideas. This series of four articles was developed to make the material available for a wider audience. The articles will be published sequentially in Coronary Artery Disease. Beginning with fundamental axioms and basic algebraic manipulations they address algebra, function and graph theory, real and complex numbers, calculus and differential equations, mathematical modeling, linear system theory and integral transforms and statistical theory. The principles and concepts they address provide the foundation needed for in-depth study of any of these topics. Perhaps of even more importance, they should empower cardiologists and cardiovascular researchers to utilize the language of mathematics in assessing the phenomena of immediate pertinence to diagnosis, pathophysiology and therapeutics. The presentations are interposed with queries (by Coronary Artery Disease abbreviated as CAD) simulating the nature of interactions that occurred during the course itself. Each article concludes with one or more examples illustrating application of the concepts covered to
Garner, Annie A.; Peugh, James; Becker, Stephen P.; Kingery, Kathleen M.; Tamm, Leanne; Vaughn, Aaron J.; Ciesielski, Heather; Simon, John O.; Loren, Richard E. A.; Epstein, Jeffery N.
2014-01-01
Objective Studies demonstrate sluggish cognitive tempo (SCT) symptoms to be distinct from inattentive and hyperactive-impulsive dimensions of Attention-Deficit/Hyperactivity Disorder (ADHD). No study has examined SCT within a bi-factor model of ADHD whereby SCT may form a specific factor distinct from inattention and hyperactivity/impulsivity while still fitting within a general ADHD factor, which was the purpose of the current study. Method 168 children were recruited from an ADHD clinic. Most (92%) met diagnostic criteria for ADHD. Parents and teachers completed measures of ADHD and SCT. Results Although SCT symptoms were strongly associated with inattention they loaded onto a factor independent of ADHD ‘g’. Results were consistent across parent and teacher ratings. Conclusions SCT is structurally distinct from inattention as well as from the general ADHD latent symptom structure. Findings support a growing body of research suggesting SCT to be distinct and separate from ADHD. PMID:25005039
Directory of Open Access Journals (Sweden)
Tsair-Fwu Lee
2015-01-01
Full Text Available To develop the logistic and the probit models to analyse electromyographic (EMG equivalent uniform voltage- (EUV- response for the tenderness of tennis elbow. In total, 78 hands from 39 subjects were enrolled. In this study, surface EMG (sEMG signal is obtained by an innovative device with electrodes over forearm region. The analytical endpoint was defined as Visual Analog Score (VAS 3+ tenderness of tennis elbow. The logistic and the probit diseased probability (DP models were established for the VAS score and EMG absolute voltage-time histograms (AVTH. TV50 is the threshold equivalent uniform voltage predicting a 50% risk of disease. Twenty-one out of 78 samples (27% developed VAS 3+ tenderness of tennis elbow reported by the subject and confirmed by the physician. The fitted DP parameters were TV50 = 153.0 mV (CI: 136.3–169.7 mV, γ50 = 0.84 (CI: 0.78–0.90 and TV50 = 155.6 mV (CI: 138.9–172.4 mV, m = 0.54 (CI: 0.49–0.59 for logistic and probit models, respectively. When the EUV ≥ 153 mV, the DP of the patient is greater than 50% and vice versa. The logistic and the probit models are valuable tools to predict the DP of VAS 3+ tenderness of tennis elbow.
Lin, Wei-Chun; Lin, Shu-Yuan; Wu, Li-Fu; Guo, Shih-Sian; Huang, Hsiang-Jui; Chao, Pei-Ju
2015-01-01
To develop the logistic and the probit models to analyse electromyographic (EMG) equivalent uniform voltage- (EUV-) response for the tenderness of tennis elbow. In total, 78 hands from 39 subjects were enrolled. In this study, surface EMG (sEMG) signal is obtained by an innovative device with electrodes over forearm region. The analytical endpoint was defined as Visual Analog Score (VAS) 3+ tenderness of tennis elbow. The logistic and the probit diseased probability (DP) models were established for the VAS score and EMG absolute voltage-time histograms (AVTH). TV50 is the threshold equivalent uniform voltage predicting a 50% risk of disease. Twenty-one out of 78 samples (27%) developed VAS 3+ tenderness of tennis elbow reported by the subject and confirmed by the physician. The fitted DP parameters were TV50 = 153.0 mV (CI: 136.3–169.7 mV), γ 50 = 0.84 (CI: 0.78–0.90) and TV50 = 155.6 mV (CI: 138.9–172.4 mV), m = 0.54 (CI: 0.49–0.59) for logistic and probit models, respectively. When the EUV ≥ 153 mV, the DP of the patient is greater than 50% and vice versa. The logistic and the probit models are valuable tools to predict the DP of VAS 3+ tenderness of tennis elbow. PMID:26380281
Chow, Sy-Miin; Bendezú, Jason J; Cole, Pamela M; Ram, Nilam
2016-01-01
Several approaches exist for estimating the derivatives of observed data for model exploration purposes, including functional data analysis (FDA; Ramsay & Silverman, 2005 ), generalized local linear approximation (GLLA; Boker, Deboeck, Edler, & Peel, 2010 ), and generalized orthogonal local derivative approximation (GOLD; Deboeck, 2010 ). These derivative estimation procedures can be used in a two-stage process to fit mixed effects ordinary differential equation (ODE) models. While the performance and utility of these routines for estimating linear ODEs have been established, they have not yet been evaluated in the context of nonlinear ODEs with mixed effects. We compared properties of the GLLA and GOLD to an FDA-based two-stage approach denoted herein as functional ordinary differential equation with mixed effects (FODEmixed) in a Monte Carlo (MC) study using a nonlinear coupled oscillators model with mixed effects. Simulation results showed that overall, the FODEmixed outperformed both the GLLA and GOLD across all the embedding dimensions considered, but a novel use of a fourth-order GLLA approach combined with very high embedding dimensions yielded estimation results that almost paralleled those from the FODEmixed. We discuss the strengths and limitations of each approach and demonstrate how output from each stage of FODEmixed may be used to inform empirical modeling of young children's self-regulation.
Screening Applications to Test Cellular Fitness in Transwell® Models After Nanoparticle Treatment.
Christ, Bastian; Fey, Christina; Cubukova, Alevtina; Walles, Heike; Dembski, Sofia; Metzger, Marco
2017-01-01
Nanoparticles (NPs) in biotechnology hold great promise for revolutionizing medical treatments and therapies. In order to bring NPs into clinical application there is a number of preclinical in vitro and in vivo tests, which have to be applied before. The initial in vitro evaluation includes a detailed physicochemical characterization as well as biocompatibility tests, among others. For determination of biocompatibility at the cellular level, the correct choice of the in vitro assay as well as NP pretreatment is absolutely essential. There are a variety of assay technologies available that use standard plate readers to measure metabolic markers to estimate the number of viable cells in culture. Each cell viability assay has its own set of advantages and disadvantages. Regardless of the assay method chosen, the major factors critical for reproducibility and success include: (1) choosing the right assay after comparing optical NP properties with the read-out method of the assay, (2) verifying colloidal stability of NPs in cell culture media, (3) preparing a sterile and stable NP dispersion in cell culture media used in the assay, (4) using a tightly controlled and consistent cell model allowing appropriate characterization of NPs. This chapter will briefly summarize these different critical points, which can occur during biocompatibility screening applications of NPs.
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)
Kondratenko, I.; Jablonska, B. [ECN , Petten (Netherlands)
2009-11-15
There is growing justification and political commitment in Europe to tackle energy efficiency in new and existing buildings, both residential and non-residential. It requires shifting the construction sector towards large scale successful implementation of innovative energy efficient and sustainable building concepts. Nowadays, a number of innovative building concepts exist, to name a few: Very Low Energy, Passive House, Zero-Energy, Zero-Emission, Energy Producing, etc. However, the experience in the Netherlands has shown inflexibility in the behaviour within the traditional building process to allow the necessary changes for large scale introduction of innovative integral building concepts. This has led to examining the question: 'How well does the building process organisation model in the Netherlands allow the implementation of innovative housing building concepts?' Answering this question requires specific knowledge of the behaviour of the traditional building process, its characteristics, advantages and disadvantages, phases and actors involved. It also demanded to focus on the nature of barriers within, and on possibilities to overcome identified bottlenecks. Also, examined was the suitability of several selected novel concepts in their opportunities for adapting the building process organisation model and the behaviour of actors within, such as: The Life Cycle Analysis, (including the Whole Building Life Cycle Costing and the Least Life Cycle Costing), The Cradle to Cradle (C2C) Concept, The Living Building Concept and Private Initiatives. This also included recent experiences with building projects implementing these concepts in the Netherlands. In summary, there are a number of challenges. The traditional building process in the Netherlands is fragmented in nature into successive phases with a strict way of cooperation and division of tasks and responsibilities between actors. There is also a cost / risk approach preventing enabling long
Factors associated with suboptimal adherence to antiretroviral therapy in Asia
Jiamsakul, Awachana; Kumarasamy, Nagalingeswaran; Ditangco, Rossana; Li, Patrick CK; Phanuphak, Praphan; Sirisanthana, Thira; Sungkanuparph, Somnuek; Kantipong, Pacharee; Lee, Christopher KC; Mustafa, Mahiran; Merati, Tuti; Kamarulzaman, Adeeba; Singtoroj, Thida; Law, Matthew
2014-01-01
Introduction Adherence to antiretroviral therapy (ART) plays an important role in treatment outcomes. It is crucial to identify factors influencing adherence in order to optimize treatment responses. The aim of this study was to assess the rates of, and factors associated with, suboptimal adherence (SubAdh) in the first 24 months of ART in an Asian HIV cohort. Methods As part of a prospective resistance monitoring study, the TREAT Asia Studies to Evaluate Resistance Monitoring Study (TASER-M) collected patients’ adherence based on the World Health Organization-validated Adherence Visual Analogue Scale. SubAdh was defined in two ways: (i) 14 days. Time was divided into four intervals: 0–6, 6–12, 12–18 and 18–24 months. Factors associated with SubAdh were analysed using generalized estimating equations. Results Out of 1316 patients, 32% ever reported 2 assessments per patient per year had an odds ratio (OR)=0.7 (95% confidence interval (CI) (0.55 to 0.90), p=0.006), compared to sites with ≤2 assessments per patient per year. Compared to heterosexual exposure, SubAdh was higher in injecting drug users (IDUs) (OR=1.92, 95% CI (1.23 to 3.00), p=0.004) and lower in homosexual exposure (OR=0.52, 95% CI (0.38 to 0.71), p<0.001). Patients taking a nucleoside transcriptase inhibitor and protease inhibitor (NRTI+PI) combination were less likely to report adherence <100% (OR=0.36, 95% CI (0.20 to 0.67), p=0.001) compared to patients taking an NRTI and non-nucleoside transcriptase inhibitor (NRTI+NNRTI) combination. SubAdh decreased with increasing time on ART (all p<0.001). Similar associations were found with adherence <95% as the outcome. Conclusions We found that SubAdh, defined as either <100% and <95%, was associated with mode of HIV exposure, ART regimen, time on ART and frequency of adherence measurement. The more frequently sites assessed patients, the lower the SubAdh, possibly reflecting site resourcing for patient counselling. Although social
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
Global fits of the two-loop renormalized Two-Higgs-Doublet model with soft Z 2 breaking
Chowdhury, Debtosh; Eberhardt, Otto
2015-11-01
We determine the next-to-leading order renormalization group equations for the Two-Higgs-Doublet model with a softly broken Z 2 symmetry and CP conservation in the scalar potential. We use them to identify the parameter regions which are stable up to the Planck scale and find that in this case the quartic couplings of the Higgs potential cannot be larger than 1 in magnitude and that the absolute values of the S-matrix eigenvalues cannot exceed 2 .5 at the electroweak symmetry breaking scale. Interpreting the 125 GeV resonance as the light CP -even Higgs eigenstate, we combine stability constraints, electroweak precision and flavour observables with the latest ATLAS and CMS data on Higgs signal strengths and heavy Higgs searches in global parameter fits to all four types of Z 2 symmetry. We quantify the maximal deviations from the alignment limit and find that in type II and Y the mass of the heavy CP -even ( CP -odd) scalar cannot be smaller than 340 GeV (360 GeV). Also, we pinpoint the physical parameter regions compatible with a stable scalar potential up to the Planck scale. Motivated by the question how natural a Higgs mass of 125 GeV can be in the context of a Two-Higgs-Doublet model, we also address the hierarchy problem and find that the Two-Higgs-Doublet model does not offer a perturbative solution to it beyond 5 TeV.
A global fit of the γ-ray galactic center excess within the scalar singlet Higgs portal model
International Nuclear Information System (INIS)
Cuoco, Alessandro; Eiteneuer, Benedikt; Heisig, Jan; Krämer, Michael
2016-01-01
We analyse the excess in the γ-ray emission from the center of our galaxy observed by Fermi-LAT in terms of dark matter annihilation within the scalar Higgs portal model. In particular, we include the astrophysical uncertainties from the dark matter distribution and allow for unspecified additional dark matter components. We demonstrate through a detailed numerical fit that the strength and shape of the γ-ray spectrum can indeed be described by the model in various regions of dark matter masses and couplings. Constraints from invisible Higgs decays, direct dark matter searches, indirect searches in dwarf galaxies and for γ-ray lines, and constraints from the dark matter relic density reduce the parameter space to dark matter masses near the Higgs resonance. We find two viable regions: one where the Higgs-dark matter coupling is of O(10"−"2), and an additional dark matter component beyond the scalar WIMP of our model is preferred, and one region where the Higgs-dark matter coupling may be significantly smaller, but where the scalar WIMP constitutes a significant fraction or even all of dark matter. Both viable regions are hard to probe in future direct detection and collider experiments.
Fitness club
2013-01-01
Nordic Walking Classes New session of 4 classes of 1 hour each will be held on Tuesdays in May 2013. Meet at the CERN barracks parking at Entrance A, 10 minutes before class time. Dates and time: 07.05, 14.05, 21.05 and 28.05, fom 12 h 30 to 13 h 30 Prices: 40 CHF per session + 10 CHF club membership – 5 CHF / hour pole rental Check out our schedule and enroll at http://cern.ch/club-fitness Hope to see you among us!
Reike, Dennis; Schwarz, Wolf
2016-01-01
The time required to determine the larger of 2 digits decreases with their numerical distance, and, for a given distance, increases with their magnitude (Moyer & Landauer, 1967). One detailed quantitative framework to account for these effects is provided by random walk models. These chronometric models describe how number-related noisy…
Directory of Open Access Journals (Sweden)
Colebunders Robert
2011-02-01
Full Text Available Abstract Background Antiretroviral therapy (ART partially corrects immune dysfunction associated with HIV infection. The levels of T-cell immune activation and exhaustion after long-term, suppressive ART and their correlation with CD4 T-cell count reconstitution among ART-treated patients in African cohorts have not been extensively evaluated. Methods T-cell activation (CD38+HLA-DR+ and immune exhaustion (PD-1+ were measured in a prospective cohort of patients initiated on ART; 128 patient samples were evaluated and subcategorized by CD4 reconstitution after long-term suppressive treatment: Suboptimal [median CD4 count increase 129 (-43-199 cells/μl], N = 34 ], optimal [282 (200-415 cells/μl, N = 64] and super-optimal [528 (416-878 cells/μl, N = 30]. Results Both CD4+ and CD8 T-cell activation was significantly higher among suboptimal CD4 T-cell responders compared to super-optimal responders. In a multivariate model, CD4+CD38+HLADR+ T-cells were associated with suboptimal CD4 reconstitution [AOR, 5.7 (95% CI, 1.4-23, P = 0.014]. T-cell exhaustion (CD4+PD1+ and CD8+PD1+ was higher among suboptimal relative to optimal (P P = 0.022]. Conclusion T-cell activation and exhaustion persist among HIV-infected patients despite long-term, sustained HIV-RNA viral suppression. These immune abnormalities were associated with suboptimal CD4 reconstitution and their regulation may modify immune recovery among suboptimal responders to ART.
Directory of Open Access Journals (Sweden)
Lazar Andreas
2008-02-01
Full Text Available Abstract Background In real-time PCR, it is necessary to consider the efficiency of amplification (EA of amplicons in order to determine initial target levels properly. EAs can be deduced from standard curves, but these involve extra effort and cost and may yield invalid EAs. Alternatively, EA can be extracted from individual fluorescence curves. Unfortunately, this is not reliable enough. Results Here we introduce simultaneous non-linear fitting to determine – without standard curves – an optimal common EA for all samples of a group. In order to adjust EA as a function of target fluorescence, and still to describe fluorescence as a function of cycle number, we use an iterative algorithm that increases fluorescence cycle by cycle and thus simulates the PCR process. A Gauss peak function is used to model the decrease of EA with increasing amplicon accumulation. Our approach was validated experimentally with hydrolysis probe or SYBR green detection with dilution series of 5 different targets. It performed distinctly better in terms of accuracy than standard curve, DART-PCR, and LinRegPCR approaches. Based on reliable EAs, it was possible to detect that for some amplicons, extraordinary fluorescence (EA > 2.00 was generated with locked nucleic acid hydrolysis probes, but not with SYBR green. Conclusion In comparison to previously reported approaches that are based on the separate analysis of each curve and on modelling EA as a function of cycle number, our approach yields more accurate and precise estimates of relative initial target levels.
Directory of Open Access Journals (Sweden)
James Tsai
2017-06-01
In conclusion, among adult ever users of marijuana, current tobacco use is high and strongly associated with suboptimal SRH; regular marijuana smoking with or without current tobacco use is significantly associated with suboptimal SRH.
Voelpel, Sven C.; Leibold, Marius; Tekie, Eden B.
2003-01-01
In today's rapidly changing business landscapes, new sources of sustainable competitive advantage can often only be attained from business model reinvention, based on disruptive innovation and not incremental change or continuous improvement. Extant literature indicates that business models and their reinvention have recently been the focus of scholarly investigations in the field of strategic management, especially focusing on the search for new bases of building strategic competitive advant...
Kortlandt, W.; Endeman, H.J.; Hoeke, J.O.O.
In a simplified approach to the reaction kinetics of enzyme-linked immunoassays, a Langmuir-type equation y = [ax/(b + x)] + c was derived. This model proved to be superior to logit-log and semilog models in the curve-fitting of standard curves. An assay for α-fetoprotein developed in our laboratory
DEFF Research Database (Denmark)
Armour, C.; Carragher, N.; Elhai, J. D.
2013-01-01
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...
Chu, A.
2016-12-01
Modern earthquake catalogs are often analyzed using spatial-temporal point process models such as the epidemic-type aftershock sequence (ETAS) models of Ogata (1998). My work implements three of the homogeneous ETAS models described in Ogata (1998). With a model's log-likelihood function, my software finds the Maximum-Likelihood Estimates (MLEs) of the model's parameters to estimate the homogeneous background rate and the temporal and spatial parameters that govern triggering effects. EM-algorithm is employed for its advantages of stability and robustness (Veen and Schoenberg, 2008). My work also presents comparisons among the three models in robustness, convergence speed, and implementations from theory to computing practice. Up-to-date regional seismic data of seismic active areas such as Southern California and Japan are used to demonstrate the comparisons. Data analysis has been done using computer languages Java and R. Java has the advantages of being strong-typed and easiness of controlling memory resources, while R has the advantages of having numerous available functions in statistical computing. Comparisons are also made between the two programming languages in convergence and stability, computational speed, and easiness of implementation. Issues that may affect convergence such as spatial shapes are discussed.
Veinot, Tiffany C; Senteio, Charles R; Hanauer, David; Lowery, Julie C
2018-06-01
To describe a new, comprehensive process model of clinical information interaction in primary care (Clinical Information Interaction Model, or CIIM) based on a systematic synthesis of published research. We used the "best fit" framework synthesis approach. Searches were performed in PubMed, Embase, the Cumulative Index to Nursing and Allied Health Literature (CINAHL), PsycINFO, Library and Information Science Abstracts, Library, Information Science and Technology Abstracts, and Engineering Village. Two authors reviewed articles according to inclusion and exclusion criteria. Data abstraction and content analysis of 443 published papers were used to create a model in which every element was supported by empirical research. The CIIM documents how primary care clinicians interact with information as they make point-of-care clinical decisions. The model highlights 3 major process components: (1) context, (2) activity (usual and contingent), and (3) influence. Usual activities include information processing, source-user interaction, information evaluation, selection of information, information use, clinical reasoning, and clinical decisions. Clinician characteristics, patient behaviors, and other professionals influence the process. The CIIM depicts the complete process of information interaction, enabling a grasp of relationships previously difficult to discern. The CIIM suggests potentially helpful functionality for clinical decision support systems (CDSSs) to support primary care, including a greater focus on information processing and use. The CIIM also documents the role of influence in clinical information interaction; influencers may affect the success of CDSS implementations. The CIIM offers a new framework for achieving CDSS workflow integration and new directions for CDSS design that can support the work of diverse primary care clinicians.
Design Of Real-Time Implementable Distributed Suboptimal Control: An LQR Perspective
Jaleel, Hassan; Shamma, Jeff S.
2017-01-01
of establishing a communication link between two base stations with minimum energy consumption. We show through simulations that the performance under the proposed framework is close to the optimal performance and the suboptimal policy can be efficiently
Directory of Open Access Journals (Sweden)
Masahiko Umei, MD
2017-10-01
Conclusions: With good adherence, the clinical course associated with DOACs is comparatively good. In the future, suboptimal low-dose DOAC therapy may serve as an appropriate choice for some patients with a high risk of stroke and bleeding.
Prevalence and Determinants of Suboptimal Vitamin D Levels in a Multiethnic Asian Population.
Man, Ryan Eyn Kidd; Li, Ling-Jun; Cheng, Ching-Yu; Wong, Tien Yin; Lamoureux, Ecosse; Sabanayagam, Charumathi
2017-03-22
This population-based cross-sectional study examined the prevalence and risk factors of suboptimal vitamin D levels (assessed using circulating 25-hydroxycholecalciferol (25(OH)D)) in a multi-ethnic sample of Asian adults. Plasma 25(OH)D concentration of 1139 Chinese, Malay and Indians (40-80 years) were stratified into normal (≥30 ng/mL), and suboptimal (including insufficiency and deficiency, 65 years), Malay and Indian ethnicities (compared to Chinese ethnicity), and higher body mass index, HbA1c, education and income levels were associated with suboptimal 25(OH)D concentration ( p < 0.05). In a population-based sample of Asian adults, approximately 75% had suboptimal 25(OH)D concentration. Targeted interventions and stricter reinforcements of existing guidelines for vitamin D supplementation are needed for groups at risk of vitamin D insufficiency/deficiency.
Sub-Optimal Management of Type 2 Diabetes Mellitus – A Local Audit
African Journals Online (AJOL)
Original Research: Sub-Optimal Management of Type 2 Diabetes Mellitus – A Local Audit ... despite clinical trial data documenting improved outcomes associated not ... were used to define the Metabolic Syndrome.9 Central obesity was.
Schipper, L. A.; Robinson, J.; O'Neill, T.; Ryburn, J.; Arcus, V. L.
2015-12-01
Developing robust models of the temperature response and sensitivity of soil respiration is critical for determining changes carbon cycling in response to climate change and at daily to annual time scales. Currently, approaches for measuring temperature dependence of soil respiration generally use long incubation times (days to weeks and months) at a limited number of incubation temperatures. Long incubation times likely allow thermal adaptation by the microbial population so that results are poorly representative of in situ soil responses. Additionally, too few incubation temperatures allows for the fit and justification of many different predictive equations, which can lead to inaccuracies when used for carbon budgeting purposes. We have developed a method to rapidly determine the response of soil respiration rate to wide range of temperatures. An aluminium block with 44 sample slots is heated at one end and cooled at the other to give a temperature gradient from 0 to 55°C at about one degree increments. Soil respiration is measured within 5 hours to minimise the possibility of thermal adaptation. We have used this method to demonstrate the similarity of temperature sensitivity of respiration for different soils from the same location across seasons. We are currently testing whether long-term (weeks to months) incubation alter temperature response and sensitivity that occurs in situ responses. This method is also well suited for determining the most appropriate models of temperature dependence and sensitivity of soil respiration (including macromolecular rate theory MMRT). With additional testing, this method is expected to be a more reliable method of measuring soil respiration rate for soil quality and modelling of soil carbon processes.
Indicators of suboptimal performance embedded in the Wechsler Memory Scale-Fourth Edition (WMS-IV)
Bouman, Zita; Hendriks, Marc PH; Schmand, Ben A; Kessels, Roy PC; Aldenkamp, Albert
2016-01-01
Introduction. Recognition and visual working memory tasks from the Wechsler Memory Scale-Fourth Edition (WMS-IV) have previously been documented as useful indicators for suboptimal performance. The present study examined the clinical utility of the Dutch version of the WMS-IV (WMS-IV-NL) for the identification of suboptimal performance using an analogue study design. Method. The patient group consisted of 59 mixed-etiology patients; the experimental malingerers were 50 healthy individuals...
Indicators of suboptimal performance embedded in the Wechsler Memory Scale-Fourth Edition (WMS-IV)
Bouman, Zita; Hendriks, Marc P.H.; Schmand, Ben A.; Kessels, Roy P.C.; Aldenkamp, Albert P.
2016-01-01
Introduction. Recognition and visual working memory tasks from the Wechsler Memory Scale-Fourth Edition (WMS-IV) have previously been documented as useful indicators for suboptimal performance. The present study examined the clinical utility of the Dutch version of the WMS-IV (WMS-IV-NL) for the identification of suboptimal performance using an analogue study design.Method. The patient group consisted of 59 mixed-etiology patients; the experimental malingerers were 50 healthy individuals who ...
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
International Nuclear Information System (INIS)
Hameeteman, K; Niessen, W J; Klein, S; Van 't Klooster, R; Selwaness, M; Van der Lugt, A; Witteman, J C M
2013-01-01
We present a method for carotid vessel wall volume quantification from magnetic resonance imaging (MRI). The method combines lumen and outer wall segmentation based on deformable model fitting with a learning-based segmentation correction step. After selecting two initialization points, the vessel wall volume in a region around the bifurcation is automatically determined. The method was trained on eight datasets (16 carotids) from a population-based study in the elderly for which one observer manually annotated both the lumen and outer wall. An evaluation was carried out on a separate set of 19 datasets (38 carotids) from the same study for which two observers made annotations. Wall volume and normalized wall index measurements resulting from the manual annotations were compared to the automatic measurements. Our experiments show that the automatic method performs comparably to the manual measurements. All image data and annotations used in this study together with the measurements are made available through the website http://ergocar.bigr.nl. (paper)
Directory of Open Access Journals (Sweden)
Lemieux Sébastien
2006-08-01
Full Text Available Abstract Background The identification of differentially expressed genes (DEGs from Affymetrix GeneChips arrays is currently done by first computing expression levels from the low-level probe intensities, then deriving significance by comparing these expression levels between conditions. The proposed PL-LM (Probe-Level Linear Model method implements a linear model applied on the probe-level data to directly estimate the treatment effect. A finite mixture of Gaussian components is then used to identify DEGs using the coefficients estimated by the linear model. This approach can readily be applied to experimental design with or without replication. Results On a wholly defined dataset, the PL-LM method was able to identify 75% of the differentially expressed genes within 10% of false positives. This accuracy was achieved both using the three replicates per conditions available in the dataset and using only one replicate per condition. Conclusion The method achieves, on this dataset, a higher accuracy than the best set of tools identified by the authors of the dataset, and does so using only one replicate per condition.
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.
Beldad, Ardion Daroca; Hegner, Sabrina
2017-01-01
According to one market research, fitness or running apps are hugely popular in Germany. Such a trend prompts the question concerning the factors influencing German users’ intention to continue using a specific fitness app. To address the research question, the expanded Technology Acceptance Model
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
Cai, Li
2015-06-01
Lord and Wingersky's (Appl Psychol Meas 8:453-461, 1984) recursive algorithm for creating summed score based likelihoods and posteriors has a proven track record in unidimensional item response theory (IRT) applications. Extending the recursive algorithm to handle multidimensionality is relatively simple, especially with fixed quadrature because the recursions can be defined on a grid formed by direct products of quadrature points. However, the increase in computational burden remains exponential in the number of dimensions, making the implementation of the recursive algorithm cumbersome for truly high-dimensional models. In this paper, a dimension reduction method that is specific to the Lord-Wingersky recursions is developed. This method can take advantage of the restrictions implied by hierarchical item factor models, e.g., the bifactor model, the testlet model, or the two-tier model, such that a version of the Lord-Wingersky recursive algorithm can operate on a dramatically reduced set of quadrature points. For instance, in a bifactor model, the dimension of integration is always equal to 2, regardless of the number of factors. The new algorithm not only provides an effective mechanism to produce summed score to IRT scaled score translation tables properly adjusted for residual dependence, but leads to new applications in test scoring, linking, and model fit checking as well. Simulated and empirical examples are used to illustrate the new applications.
Directory of Open Access Journals (Sweden)
Eric eBoyd
2012-06-01
Full Text Available The extent to which geochemical variation constrains the distribution of phototrophic metabolisms was modeled based on 439 observations in geothermal springs in Yellowstone National Park (YNP, Wyoming. Generalized additive models (GAMs were developed to predict the distribution of photosynthesis as a function of spring temperature, pH, and total sulfide. GAMs comprised of temperature explained 42.7% of the variation in the distribution of phototrophic metabolisms whereas GAMs comprised of sulfide and pH explained 20.7% and 11.7% of the variation, respectively. These results suggest that of the measured variables, temperature is the primary constraint on the distribution of phototrophic metabolism in YNP. GAMs comprised of multiple variables explained a larger percentage of the variation in the distribution of phototrophic metabolism, indicating additive interactions among variables. A GAM that combined temperature and sulfide explained the greatest variation in the dataset (54.8% while minimizing the introduction of degrees of freedom. In an effort to verify the extent to which phototroph distribution reflects constraints on activity, we examined the influence of sulfide and temperature on dissolved inorganic carbon (DIC uptake rates under both light and dark conditions. Light-driven DIC uptake decreased systematically with increasing concentrations of sulfide in acidic, algal-dominated systems, but was unaffected in alkaline, bacterial-dominated systems. In both alkaline and acidic systems, light-driven DIC uptake was suppressed in cultures incubated at temperatures 10°C greater than their in situ temperature. Collectively, these results suggest that the habitat range of phototrophs in YNP springs, specifically that of cyanobacteria and algae, largely results from constraints imposed by temperature and sulfide on the activity and fitness of these populations, a finding that is consistent with the predictions from GAMs.
International Nuclear Information System (INIS)
Syrmalenios, Panayotis
1973-01-01
This research thesis addresses the issue of safety of fast neutron reactors, and more particularly is a contribution of the study of mechanisms of interaction between molten fuel and sodium. It aims at developing tools of prediction of consequences of three main types of accidents: local fusion of a fuel rod and contact of the fuel with the surrounding sodium, failure of an assembly due to the fusion of several rods and fuel-coolant interaction within the assembly, and fuel-coolant interaction at the level of the reactor core. The author first proposes a bibliographical analysis of experimental and theoretical studies related to this issue of interaction between a hot body and a cold liquid, and of its consequences. Then, he introduces a mathematical model and its resolution method, and reports the use of the associated code (Corfou) for the interpretation of experimental results: expulsion of cold sodium column by expansion of an overheated sodium mass, fusion of a rod by Joule effect, interaction between UO_2 molten by high frequency with liquid sodium. Finally, the author discusses a comparison between the Corfou code and other models which are being currently developed [fr
Cogoni, Marco; Busonera, Giovanni; Anedda, Paolo; Zanetti, Gianluigi
2015-01-01
We generalize previous studies on critical phenomena in communication networks [1,2] by adding computational capabilities to the nodes. In our model, a set of tasks with random origin, destination and computational structure is distributed on a computational network, modeled as a graph. By varying the temperature of a Metropolis Montecarlo, we explore the global latency for an optimal to suboptimal resource assignment at a given time instant. By computing the two-point correlation function for the local overload, we study the behavior of the correlation distance (both for links and nodes) while approaching the congested phase: a transition from peaked to spread g(r) is seen above a critical (Montecarlo) temperature Tc. The average latency trend of the system is predicted by averaging over several network traffic realizations while maintaining a spatially detailed information for each node: a sharp decrease of performance is found over Tc independently of the workload. The globally optimized computational resource allocation and network routing defines a baseline for a future comparison of the transition behavior with respect to existing routing strategies [3,4] for different network topologies.
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.
DEFF Research Database (Denmark)
Andersen, Andreas; Rieckmann, Andreas
2016-01-01
In this article, we illustrate how to use mi impute chained with intreg to fit an analysis of covariance analysis of censored and nondetectable immunological concentrations measured in a randomized pretest–posttest design.......In this article, we illustrate how to use mi impute chained with intreg to fit an analysis of covariance analysis of censored and nondetectable immunological concentrations measured in a randomized pretest–posttest design....
Anshel, Mark H.; Kang, Minsoo
2007-01-01
The authors' purpose in this action study was to examine the effect of a 10-week intervention, using the Disconnected Values Model (DVM), on changes in selected measures of fitness, blood lipids, and exercise adherence among 51 university faculty (10 men and 41 women) from a school in the southeastern United States. The DVM is an intervention…
Middelkamp, J.; Rooijen, M. Van; Wolfhagen, P.; Steenbergen, B.
2017-01-01
The transtheoretical model of behavior change (TTM) is often used to understand changes in health-related behavior, like exercise. Exercise behavior in fitness clubs is an understudied topic, but preliminary studies showed low frequencies and large numbers of drop-out. An initial 12-week
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…
Ishihara, Toru; Morita, Noriteru; Nakajima, Toshihiro; Okita, Koichi; Sagawa, Masato; Yamatsu, Koji
2018-04-25
The aim of this study was to determine, using structural equation modeling, the comprehensive relationships of achievement motivation (self-fulfillment achievement motivation [SFAM] and competitive achievement motivation [CAM]), daily behaviors (exercise habits, screen time, and learning duration), body mass index [BMI], and cardiorespiratory fitness [CRF]) with academic performance among schoolchildren. Three hundred twenty-five schoolchildren (172 males and 153 females; 12-13 years old) were recruited. Academic performance was assessed using the total grade points in 8 academic subjects (GP8); CRF using the 20-m shuttle run; and achievement motivation, daily behaviors, and socioeconomic status using questionnaires. Socioeconomic status was included as a control variable. In males, two cascade associations of achievement motivation to GP8 were detected: (1) SFAM → screen time/learning duration → GP8, and (2) CAM → exercise habit → CRF → GP8 (χ 2 = 8.72, p = .19, AGFI = .92). In females, two cascade associations were also detected: (1) SFAM → screen time/learning habit → GP8, and (2) exercise habit → BMI 2 → GP8 (χ 2 = 6.17, p = .41, AGFI = .93). Our results suggest that greater achievement motivation is associated with academic success via various physiological/behavioral factors, and that these associations differ by gender. Copyright © 2018. Published by Elsevier Inc.
Directory of Open Access Journals (Sweden)
Di Pierro F
2012-07-01
Full Text Available Francesco Di Pierro,1 Nicola Villanova,2 Federica Agostini,2 Rebecca Marzocchi,2 Valentina Soverini,2 Giulio Marchesini21Scientific Department, Velleja Research, Milano, 2Diseases of Metabolism, S Orsola Malpighi Hospital, Bologna, ItalyBackground: Suboptimal glycemic control is a common situation in diabetes, regardless of the wide range of drugs available to reach glycemic targets. Basic research in diabetes is endeavoring to identify new actives working as insulin savers, use of which could delay the introduction of injectable insulin or reduce the insulin dose needed. Commonly available as a nutraceutical, berberine is a potential candidate.Methods and results: Because its low oral bioavailability can be overcome by P-glycoprotein inhibitors like herbal polyphenols, we have tested the nutraceutical combination of Berberis aristata extract and Silybum marianum extract (Berberol® in type 2 diabetes in terms of its additive effect when combined with a conventional oral regimen for patients with suboptimal glycemic control. After 90 days of treatment, the nutraceutical association had a positive effect on glycemic and lipid parameters, significantly reducing glycosylated hemoglobin, basal insulin, homeostatic model assessment of insulin resistance, total and low-density lipoprotein cholesterol, and triglycerides. A relevant effect was also observed in terms of liver function by measuring aspartate transaminase and alanine transaminase. The product had a good safety profile, with distinctive gastrointestinal side effects likely due to its acarbose-like action.Conclusion: Although further studies should be carried out to confirm our data, Berberol could be considered a good candidate as an adjunctive treatment option in diabetes, especially in patients with suboptimal glycemic control.Keywords: berberine, silymarin, glycosylated hemoglobin, diabetes
Lansoprazole induces sensitivity to suboptimal doses of paclitaxel in human melanoma.
Azzarito, Tommaso; Venturi, Giulietta; Cesolini, Albino; Fais, Stefano
2015-01-28
Tumor acidity is now considered an important determinant of drug-resistance and tumor progression, and anti-acidic approaches, such as Proton Pump inhibitors (PPIs), have demonstrated promising antitumor and chemo-sensitizing efficacy. The main purpose of the present study was to evaluate the possible PPI-induced sensitization of human melanoma cells to Paclitaxel (PTX). Our results show that PTX and the PPI Lansoprazole (LAN) combination was extremely efficient against metastatic melanoma cells, as compared to the single treatments, both in vitro and in vivo. We also showed that acidity plays an important role on the anti-tumor activity of these drugs, being detrimental for PTX activity, while crucial for the synergistic effect of PTX following pretreatment with LAN, due to its nature of pro-drug needing protonation for a full activation. We obtained straightforward results in a human melanoma xenograft model combining well tolerated LAN doses with suboptimal and poorly toxic doses of PTX. With this study we provide a clear evidence that the PPI LAN may be included in new combined therapy of human melanoma together with low doses of PTX. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.
Unar-Munguía, Mishel; Meza, Rafael; Colchero, M Arantxa; Torres-Mejía, Gabriela; de Cosío, Teresita Gonzalez
2017-12-01
Exclusive breastfeeding and longer breastfeeding reduce women's breast cancer risk but Mexico has one of the lowest breastfeeding rates worldwide. We estimated the lifetime economic and disease burden of breast cancer in Mexico if 95% of parous women breastfeed each child exclusively for 6 months and continue breastfeeding for over a year. We used a static microsimulation model with a cost-of-illness approach to simulate a cohort of Mexican women. We estimated breast cancer incidence, premature mortality, disability-adjusted life years (DALYs), medical costs, and income losses due to breast cancer and extrapolated the results to 1.116 million Mexican women of age 15 in 2012. Costs were expressed in 2015 US dollars and discounted at a 3% annual rate. We estimated that 2,186 premature deaths (95% CI 2,123-2,248), 9,936 breast cancer cases (95% CI 9,651-10,220), 45,109 DALYs (95% CI 43,000-47,217), and $245 million USD (95% CI 234-256) in medical costs and income losses owing to breast cancer could be saved over a cohort's lifetime. Medical costs account for 80% of the economic burden; income losses and opportunity costs for caregivers account for 15 and 5%, respectively. In Mexico, the burden of breast cancer due to suboptimal breastfeeding in women is high in terms of morbidity, premature mortality, and the economic costs for the health sector and society.
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.
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.
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.
Suboptimal maternal and paternal mental health are associated with child bullying perpetration.
Shetgiri, Rashmi; Lin, Hua; Flores, Glenn
2015-06-01
This study examines associations between maternal and paternal mental health and child bullying perpetration among school-age children, and whether having one or both parents with suboptimal mental health is associated with bullying. The 2007 National Survey of Children's Health, a nationally-representative, random-digit-dial survey, was analyzed, using a parent-reported bullying measure. Suboptimal mental health was defined as fair/poor (vs. good/very good/excellent) parental self-reported mental and emotional health. Of the 61,613 parents surveyed, more than half were parents of boys and were white, 20% were Latino, 15% African American, and 7% other race/ethnicity. Suboptimal maternal (OR 1.4; 95% CI 1.1-1.8) and paternal (OR 1.5; 95% CI 1.1-2.2) mental health are associated with bullying. Compared with children with no parents with suboptimal mental health, children with only one or both parents with suboptimal mental health have higher bullying odds. Addressing the mental health of both parents may prove beneficial in preventing bullying.
Directory of Open Access Journals (Sweden)
Jurjen van der Schans
Full Text Available Attention-Deficit/Hyperactivity Disorder (ADHD is a common psychiatric disorder in children and adolescents. Immediate-release methylphenidate (IR-MPH is the medical treatment of first choice. The necessity to use several IR-MPH tablets per day and associated potential social stigma at school often leads to reduced compliance, sub-optimal treatment, and therefore economic loss. Replacement of IR-MPH with a single-dose extended release (ER-MPH formulation may improve drug response and economic efficiency.To evaluate the cost-effectiveness from a societal perspective of a switch from IR-MPH to ER-MPH in patients who are sub-optimally treated.A daily Markov-cycle model covering a time-span of 10 years was developed including four different health states: (1 optimal response, (2 sub-optimal response, (3 discontinued treatment, and (4 natural remission. ER-MPH options included methylphenidate osmotic release oral system (MPH-OROS and Equasym XL/Medikinet CR. Both direct costs and indirect costs were included in the analysis, and effects were expressed as quality-adjusted life years (QALYs. Univariate, multivariate as well as probabilistic sensitivity analysis were conducted and the main outcomes were incremental cost-effectiveness ratios.Switching sub-optimally treated patients from IR-MPH to MPH-OROS or Equasym XL/Medikinet CR led to per-patient cost-savings of €4200 and €5400, respectively, over a 10-year treatment span. Sensitivity analysis with plausible variations of input parameters resulted in cost-savings in the vast majority of estimations.This study lends economic support to switching patients with ADHD with suboptimal response to short-acting IR-MPH to long-acting ER-MPH regimens.
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.
Exogenous recombinant human growth hormone effects during suboptimal energy and zinc intake
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Duro Debora
2005-04-01
Full Text Available Abstract Background Energy and Zinc (Zn deficiencies have been associated with nutritional related growth retardation as well as growth hormone (GH resistance. In this study, the relationship between suboptimal energy and/or Zn intake and growth in rats and their response to immunoreactive exogenous recombinant human GH (GHi, was determined. Results Rats treated with GHi and fed ad-libitum energy and Zn (100/100 had increased IGFBP-3 (p Conclusion These results suggest that GHi enhances weight gain in rats with suboptimal energy and Zn intake but does not modify energy expenditure or physical activity index. Suboptimal Zn intake did not exacerbate the reduced growth or decrease in energy expenditure observed with energy restriction.
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.
de Villiers, Marelize; Kriticos, Darren J; Veldtman, Ruan
2017-01-01
The European wasp, Vespula germanica (Fabricius) (Hymenoptera: Vespidae), is of Palaearctic origin, being native to Europe, northern Africa and Asia, and introduced into North America, Chile, Argentina, Iceland, Ascension Island, South Africa, Australia and New Zealand. Due to its polyphagous nature and scavenging behaviour, V. germanica threatens agriculture and silviculture, and negatively affects biodiversity, while its aggressive nature and venomous sting pose a health risk to humans. In areas with warmer winters and longer summers, queens and workers can survive the winter months, leading to the build-up of large nests during the following season; thereby increasing the risk posed by this species. To prevent or prepare for such unwanted impacts it is important to know where the wasp may be able to establish, either through natural spread or through introduction as a result of human transport. Distribution data from Argentina and Australia, and seasonal phenology data from Argentina were used to determine the potential distribution of V. germanica using CLIMEX modelling. In contrast to previous models, the influence of irrigation on its distribution was also investigated. Under a natural rainfall scenario, the model showed similarities to previous models. When irrigation is applied, dry stress is alleviated, leading to larger areas modelled climatically suitable compared with previous models, which provided a better fit with the actual distribution of the species. The main areas at risk of invasion by V. germanica include western USA, Mexico, small areas in Central America and in the north-western region of South America, eastern Brazil, western Russia, north-western China, Japan, the Mediterranean coastal regions of North Africa, and parts of southern and eastern Africa.
Directory of Open Access Journals (Sweden)
Marelize de Villiers
Full Text Available The European wasp, Vespula germanica (Fabricius (Hymenoptera: Vespidae, is of Palaearctic origin, being native to Europe, northern Africa and Asia, and introduced into North America, Chile, Argentina, Iceland, Ascension Island, South Africa, Australia and New Zealand. Due to its polyphagous nature and scavenging behaviour, V. germanica threatens agriculture and silviculture, and negatively affects biodiversity, while its aggressive nature and venomous sting pose a health risk to humans. In areas with warmer winters and longer summers, queens and workers can survive the winter months, leading to the build-up of large nests during the following season; thereby increasing the risk posed by this species. To prevent or prepare for such unwanted impacts it is important to know where the wasp may be able to establish, either through natural spread or through introduction as a result of human transport. Distribution data from Argentina and Australia, and seasonal phenology data from Argentina were used to determine the potential distribution of V. germanica using CLIMEX modelling. In contrast to previous models, the influence of irrigation on its distribution was also investigated. Under a natural rainfall scenario, the model showed similarities to previous models. When irrigation is applied, dry stress is alleviated, leading to larger areas modelled climatically suitable compared with previous models, which provided a better fit with the actual distribution of the species. The main areas at risk of invasion by V. germanica include western USA, Mexico, small areas in Central America and in the north-western region of South America, eastern Brazil, western Russia, north-western China, Japan, the Mediterranean coastal regions of North Africa, and parts of southern and eastern Africa.
Reducing suboptimal employee decisions can build the business case for employee benefits.
Goldsmith, Christopher; Cyboran, Steven F
2013-01-01
Suboptimal employee decisions are prevalent in employee benefit plans. Poor decisions have significant consequences for employees and employers. Improving participant decisions produces beneficial outcomes such as lower labor costs, higher productivity and better workforce management. The business case for employee benefits can be strengthened by applying lessons learned from the field of behavioral economics to employee benefit plan design and to workforce communication. This article explains the types of behavioral biases that influence suboptimal decisions and explores how enlightened employee benefit plan choice architecture and vivid behavioral messaging contribute to human and better organizational outcomes.
Suboptimal care and maternal mortality among foreign-born women in Sweden
DEFF Research Database (Denmark)
Esscher, Annika; Binder-Finnema, Pauline; Bødker, Birgit
2014-01-01
that occurred in Sweden from 1988-2010. METHODS: A subset of maternal death records (n = 75) among foreign-born women from low- and middle-income countries and Swedish-born women were audited using structured implicit review. One case of foreign-born maternal death was matched with two native born Swedish cases...... language and suboptimal interpreter system or usage. Inadequate care occurred more often among the foreign-born (p = 0.04), whereas delays in consultation/referral and miscommunication between health care providers where equally common between the two groups. CONCLUSIONS: Suboptimal care factors, major...
Detection of suboptimal effort with symbol span: development of a new embedded index.
Young, J Christopher; Caron, Joshua E; Baughman, Brandon C; Sawyer, R John
2012-03-01
Developing embedded indicators of suboptimal effort on objective neurocognitive testing is essential for detecting increasingly sophisticated forms of symptom feigning. The current study explored whether Symbol Span, a novel Wechsler Memory Scale-fourth edition measure of supraspan visual attention, could be used to discriminate adequate effort from suboptimal effort. Archival data were collected from 136 veterans classified into Poor Effort (n = 42) and Good Effort (n = 94) groups based on symptom validity test (SVT) performance. The Poor Effort group had significantly lower raw scores (p Span test. A raw score cutoff of Span can effectively differentiate veterans with multiple failures on established free-standing and embedded SVTs.
Frequency and predictors of suboptimal glycemic control in an African diabetic population
Directory of Open Access Journals (Sweden)
Kibirige D
2017-02-01
Full Text Available Davis Kibirige,1 George Patrick Akabwai,2 Leaticia Kampiire,3 Daniel Ssekikubo Kiggundu,4 William Lumu5 1Department of Medicine/Diabetic and Hypertension Clinics, Our Lady of Consolota Hospital, Kisubi, 2Baylor College of Medicine, Children’s Foundation, 3Infectious Diseases Research Collaboration, Kampala, 4Nephrology Unit, Mulago National Referral and Teaching Hospital, Kampala, 5Department of Medicine and Diabetes/Endocrine Unit, Mengo Hospital, Mengo, Uganda Background: Persistent suboptimal glycemic control is invariably associated with onset and progression of acute and chronic diabetic complications in diabetic patients. In Uganda, studies documenting the magnitude and predictors of suboptimal glycemic control in adult ambulatory diabetic patients are limited. This study aimed at determining the frequency and predictors of suboptimal glycemic control in adult diabetic patients attending three urban outpatient diabetic clinics in Uganda. Methods: In this hospital-based cross-sectional study, eligible ambulatory adult diabetic patients attending outpatient diabetic clinics of three urban hospitals were consecutively enrolled over 11 months. Suboptimal glycemic control was defined as glycated hemoglobin (HbA1c level ≥7%. Multivariable analysis was applied to determine the predictors. Results: The mean age of the study participants was 52.2±14.4 years, and the majority of them were females (283, 66.9%. The median (interquartile range HbA1c level was 9% (6.8%–12.4%. Suboptimal glycemic control was noted in 311 study participants, accounting for 73.52% of the participants. HbA1c levels of 7%–8%, 8.1%–9.9%, and ≥10% were noted in 56 (13.24%, 76 (17.97%, and 179 (42.32% study participants, respectively. The documented predictors of suboptimal glycemic control were metformin monotherapy (odds ratio: 0.36, 95% confidence interval: 0.21–0.63, p<0.005 and insulin therapy (odds ratio: 2.41, 95% confidence interval: 1.41–4.12, p=0
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)
Directory of Open Access Journals (Sweden)
Alejandra Rumi
2007-06-01
Full Text Available The genus Drepanotrema includes six species in Argentina. The life cycle in natural systems of Drepanotrema depressissimum, and D. lucidum has been little studied, except for some casual observations. The aim of this study is to analyze main population trends (age structures, recruitment periods, life span and curves of individual growth in Paiva pond, Argentina. We explored growth model fitting and comparison methodologies between species and environments in Paiva pond and Isla Martín García (IMG, to determine interspecific patterns. Theoretical curves of von Bertalanffy’s model for each population were contrasted with samplings using the χ2 test. Expected sizes were transformed into a percentage of maximum size and cohorts started from zero time, which allowed them to be independent of the real or estimated starting date and a comparison was possible. A similar time scale was used, because the k values proved to be sensitive to time scale. Maximum size reached by D. lucidum was 6.9 mm and by D. depressissimum 9.38 mm. Growth rates (k fluctuated from 1.302 to 1.368 in the first and 1.339 to 1.509 in the second species. No statistically significant differences were found in growth curves among species inhabiting the Paiva pond and in the different IMG water bodies independent of the beginning of each cohort and maximum size. In general, no winter cohorts were observed, except in one population of D. kermatoides (IMG. Comparing circannual and biannual growth rhythms most of the species reached 60 % of their development during their first year, and 85 % or more during their second year. Rev. Biol. Trop. 55 (2: 559-567. Epub 2007 June, 29.En Argentina se encuentran seis especies del género Drepanotrema. El ciclo de vida en sistemas naturales de Drepanotrema depressissimu y D. lucidum ha sido muy poco estudiado, excepto por algunas observaciones aisladas. El objetivo de este estudio es analizar las principales tendencias de la población en la
Permadi, Ginanjar Setyo; Adi, Kusworo; Gernowo, Rahmad
2018-02-01
RSA algorithm give security in the process of the sending of messages or data by using 2 key, namely private key and public key .In this research to ensure and assess directly systems are made have meet goals or desire using a comprehensive evaluation methods HOT-Fit system .The purpose of this research is to build a information system sending mail by applying methods of security RSA algorithm and to evaluate in uses the method HOT-Fit to produce a system corresponding in the faculty physics. Security RSA algorithm located at the difficulty of factoring number of large coiled factors prima, the results of the prime factors has to be done to obtain private key. HOT-Fit has three aspects assessment, in the aspect of technology judging from the system status, the quality of system and quality of service. In the aspect of human judging from the use of systems and satisfaction users while in the aspect of organization judging from the structure and environment. The results of give a tracking system sending message based on the evaluation acquired.
Milani, G.; Milani, F.
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, a closed form solution can be found for the crosslink density, with the only limitation that the induction period is excluded from computations. Three kinetic constants must be determined in such a way to minimize the absolute error between normalized experimental data and numerical prediction. Usually, this result is achieved by means of standard least-squares data fitting. On the contrary, GURU works interactively by means of a Graphical User Interface (GUI) to minimize the error and allows an interactive calibration of the kinetic constants by means of sliders. A simple mouse click on the sliders allows the assignment of a value for each kinetic constant and a visual comparison between numerical and experimental curves. Users will thus find optimal values of the constants by means of a classic trial and error strategy. An experimental case of technical relevance is shown as benchmark.
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Setyo Permadi Ginanjar
2018-01-01
Full Text Available RSA algorithm give security in the process of the sending of messages or data by using 2 key, namely private key and public key .In this research to ensure and assess directly systems are made have meet goals or desire using a comprehensive evaluation methods HOT-Fit system .The purpose of this research is to build a information system sending mail by applying methods of security RSA algorithm and to evaluate in uses the method HOT-Fit to produce a system corresponding in the faculty physics. Security RSA algorithm located at the difficulty of factoring number of large coiled factors prima, the results of the prime factors has to be done to obtain private key. HOT-Fit has three aspects assessment, in the aspect of technology judging from the system status, the quality of system and quality of service. In the aspect of human judging from the use of systems and satisfaction users while in the aspect of organization judging from the structure and environment. The results of give a tracking system sending message based on the evaluation acquired.
Indicators of suboptimal performance embedded in the Wechsler Memory Scale-Fourth Edition (WMS-IV)
Bouman, Z.; Hendriks, M.P.H.; Schmand, B.A.; Kessels, R.P.C.; Aldenkamp, A.P.
2016-01-01
INTRODUCTION: Recognition and visual working memory tasks from the Wechsler Memory Scale-Fourth Edition (WMS-IV) have previously been documented as useful indicators for suboptimal performance. The present study examined the clinical utility of the Dutch version of the WMS-IV (WMS-IV-NL) for the
Indicators of suboptimal performance embedded in the Wechsler Memory Scale-Fourth Edition (WMS-IV)
Bouman, Zita; Hendriks, Marc P. H.; Schmand, Ben A.; Kessels, Roy P. C.; Aldenkamp, Albert P.
2016-01-01
Recognition and visual working memory tasks from the Wechsler Memory Scale-Fourth Edition (WMS-IV) have previously been documented as useful indicators for suboptimal performance. The present study examined the clinical utility of the Dutch version of the WMS-IV (WMS-IV-NL) for the identification of
Indicators of suboptimal performance embedded in the Wechsler Memory Scale : Fourth Edition (WMS-IV)
Bouman, Z.; Hendriks, M.P.H.; Schmand, B.A.; Kessels, R.P.C.; Aldenkamp, A.P.
2016-01-01
INTRODUCTION: Recognition and visual working memory tasks from the Wechsler Memory Scale-Fourth Edition (WMS-IV) have previously been documented as useful indicators for suboptimal performance. The present study examined the clinical utility of the Dutch version of the WMS-IV (WMS-IV-NL) for the
Indicators of suboptimal performance embedded in the Wechsler Memory Scale-Fourth Edition (WMS-IV)
Bouman, Zita; Hendriks, Marc P.H.; Schmand, Ben A.; Kessels, Roy P.C.; Aldenkamp, Albert P.
2016-01-01
Introduction. Recognition and visual working memory tasks from the Wechsler Memory Scale-Fourth Edition (WMS-IV) have previously been documented as useful indicators for suboptimal performance. The present study examined the clinical utility of the Dutch version of the WMS-IV (WMS-IV-NL) for the
Sub-optimal birth weight in newborns of a high socioeconomic status population
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Conceição Aparecida de Mattos Segre
2008-09-01
Full Text Available Objective: To compare sub-optimal birth weight (2,500 to 2,999 g term newborns to appropriate for gestational age (birth weight ≥ 3,000 g term newborns, regarding maternal data and newborn morbidity and mortality. Methods: Single term newborns, appropriate for gestational age from a high socioeconomic population (n = 1,242 with birth weight ranging from 2,500 to 2,999 g (Group I were compared to 4,907 newborns with birth weight ≥ than 3,000 g (Group II. Maternal and newborn characteristics were compared between the groups. The Mann-Whitney test, χ2 test and multivariate analysis were used. The significance level adopted was p < 0.05. Rresults: The frequency of sub-optimal birth weight newborns in the population studied was 20.2%. There was a significant association between sub-optimal birth weight and maternal weight before pregnancy and body mass index, maternal weight gain, height, smoking habit and hypertension. Newborns’ 1-minute Apgar score, neonatal hypoglycemia, jaundice, transient tachypnea, congenital pneumonia and hospital stay were significantly different between the groups (p < 0.05. A significant relationship could not be established with the 5-minute Apgar score and pulmonary hypertension in both groups. Neonatal mortality did not differ between the groups. Cconclusions: Socioeconomic status was not a risk factor for sub-optimal birth weight in the studied population. Genetic and environmental factors were associated to sub-optimal weight and neonatal diseases. According to these data, this group of newborns should receive special attention from the health team.
Suboptimal treatment adherence in bipolar disorder: impact on clinical outcomes and functioning
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Montes JM
2013-01-01
Full Text Available Jose Manuel Montes1, Jorge Maurino2, Consuelo de Dios3, Esteban Medina21Department of Psychiatry, Hospital Universitario del Sureste, 2AstraZeneca Medical Department, 3Department of Psychiatry, Hospital Universitario La Paz, Madrid, SpainBackground: The primary aim of this study was to assess drug treatment adherence in patients with bipolar disorder and to identify factors associated with adherence. The secondary aim was to analyze the impact of suboptimal adherence on clinical and functional outcomes.Methods: A cross-sectional study was conducted in a sample of outpatients receiving an oral antipsychotic drug. Medication adherence was assessed combining the 10-item Drug Attitude Inventory, the Morisky Green Adherence Questionnaire, and the Compliance Rating Scale. Logistic regression was used to determine significant variables associated with suboptimal adherence to medication.Results: Three hundred and three patients were enrolled into the study. The mean age was 45.9 ± 12.8 years, and 59.7% were females. Sixty-nine percent of patients showed suboptimal adherence. Disease severity and functioning were significantly worse in the suboptimal group than in the adherent group. Multivariate analysis showed depressive polarity of the last acute episode, presence of subsyndromal symptoms, and substance abuse/dependence to be significantly associated with suboptimal treatment adherence (odds ratios 3.41, 2.13, and 1.95, respectively.Conclusion: A high prevalence of nonadherence was found in an outpatient sample with bipolar disorder. Identification of factors related to treatment adherence would give clinicians the opportunity to select more adequately patients who are eligible for potential adherence-focused interventions.Keywords: bipolar disorder, treatment adherence, functioning, polarity, subsyndromal symptoms
HistFactory: A tool for creating statistical models for use with RooFit and RooStats
Cranmer, Kyle; Moneta, Lorenzo; Shibata, Akira; Verkerke, Wouter
2012-01-01
The HistFactory is a tool to build parametrized probability density functions (pdfs) in the RooFit/RooStats framework based based on simple ROOT histograms organized in an XML file. The pdf has a restricted form, but it is sufficiently flexible to describe many analyses based on template histograms. The tool takes a modular approach to build complex pdfs from more primative conceptual building blocks. The resulting PDF is stored in a RooWorkspace which can be saved to and read from a ROOT file. This document describes the defaults and interface in HistFactory 5.32.
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
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Wakhid Slamet Ciptono
2011-02-01
Full Text Available This study purposively is to conduct an empirical analysis of the structural relations among critical factors of quality management practices (QMPs, world-class company practice (WCC, operational excellence practice (OE, and company performance (company non-financial performance or CNFP and company financial performance or CFP in the oil and gas companies operating in Indonesia. The current study additionally examines the relationships between QMPs and CFP through WCC, OE, and CNFP (as partial mediators simultaneously. The study uses data from a survey of 140 strategic business units (SBUs within 49 oil and gas contractor companies in Indonesia. The findings suggest that all six QMPs have positive and significant indirect relationships on CFP through WCC and CNFP. Only four of six QMPs have positive and significant indirect relationships on CFP through OE and CNFP. Hence, WCC, OE, and CNFP play as partial mediators between QMPs and CFP. CNFP has a significant influence on CFP. A major implication of this study is that oil and gas managers need to recognize the structural relations model fit by developing all of the research constructs simultaneously associated with a comprehensive TQM practice. Furthermore, the findings will assist oil and gas companies by improving CNFP, which is very critical to TQM, thereby contributing to a better achievement of CFP. The current study uses the Deming’s principles, Hayes and Wheelwright dimensions of world-class company practice, Chevron Texaco’s operational excellence practice, and the dimensions of company financial and non-financial performances. The paper also provides an insight into the sustainability of TQM implementation model and their effect on company financial performance in oil and gas companies in Indonesia.
Hong, Eun-Hye; Song, Jae-Hyoung; Shim, Aeri; Lee, Bo-Ra; Kwon, Bo-Eun; Song, Hyuk-Hwan; Kim, Yeon-Jeong; Chang, Sun-Young; Jeong, Hyeon Gun; Kim, Jong Geal; Seo, Sang-Uk; Kim, HyunPyo; Kwon, YongSoo; Ko, Hyun-Jeong
2015-01-01
Several anti-influenza drugs that reduce disease manifestation exist, and although these drugs provide clinical benefits in infected patients, their efficacy is limited by the emergence of drug-resistant influenza viruses. In the current study, we assessed the therapeutic strategy of enhancing the antiviral efficacy of an existing neuraminidase inhibitor, oseltamivir, by coadministering with the leaf extract from Hedera helix L, commonly known as ivy. Ivy extract has anti-inflammatory, antibacterial, antifungal, and antihelminthic properties. In the present study, we investigated its potential antiviral properties against influenza A/PR/8 (PR8) virus in a mouse model with suboptimal oseltamivir that mimics a poor clinical response to antiviral drug treatment. Suboptimal oseltamivir resulted in insufficient protection against PR8 infection. Oral administration of ivy extract with suboptimal oseltamivir increased the antiviral activity of oseltamivir. Ivy extract and its compounds, particularly hedrasaponin F, significantly reduced the cytopathic effect in PR8-infected A549 cells in the presence of oseltamivir. Compared with oseltamivir treatment alone, coadministration of the fraction of ivy extract that contained the highest proportion of hedrasaponin F with oseltamivir decreased pulmonary inflammation in PR8-infected mice. Inflammatory cytokines and chemokines, including tumor necrosis factor-alpha and chemokine (C-C motif) ligand 2, were reduced by treatment with oseltamivir and the fraction of ivy extract. Analysis of inflammatory cell infiltration in the bronchial alveolar of PR8-infected mice revealed that CD11b+Ly6G+ and CD11b+Ly6Cint cells were recruited after virus infection; coadministration of the ivy extract fraction with oseltamivir reduced infiltration of these inflammatory cells. In a model of suboptimal oseltamivir treatment, coadministration of ivy extract fraction that includes hedrasaponin F increased protection against PR8 infection that could be
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Eun-Hye Hong
Full Text Available Several anti-influenza drugs that reduce disease manifestation exist, and although these drugs provide clinical benefits in infected patients, their efficacy is limited by the emergence of drug-resistant influenza viruses. In the current study, we assessed the therapeutic strategy of enhancing the antiviral efficacy of an existing neuraminidase inhibitor, oseltamivir, by coadministering with the leaf extract from Hedera helix L, commonly known as ivy. Ivy extract has anti-inflammatory, antibacterial, antifungal, and antihelminthic properties. In the present study, we investigated its potential antiviral properties against influenza A/PR/8 (PR8 virus in a mouse model with suboptimal oseltamivir that mimics a poor clinical response to antiviral drug treatment. Suboptimal oseltamivir resulted in insufficient protection against PR8 infection. Oral administration of ivy extract with suboptimal oseltamivir increased the antiviral activity of oseltamivir. Ivy extract and its compounds, particularly hedrasaponin F, significantly reduced the cytopathic effect in PR8-infected A549 cells in the presence of oseltamivir. Compared with oseltamivir treatment alone, coadministration of the fraction of ivy extract that contained the highest proportion of hedrasaponin F with oseltamivir decreased pulmonary inflammation in PR8-infected mice. Inflammatory cytokines and chemokines, including tumor necrosis factor-alpha and chemokine (C-C motif ligand 2, were reduced by treatment with oseltamivir and the fraction of ivy extract. Analysis of inflammatory cell infiltration in the bronchial alveolar of PR8-infected mice revealed that CD11b+Ly6G+ and CD11b+Ly6Cint cells were recruited after virus infection; coadministration of the ivy extract fraction with oseltamivir reduced infiltration of these inflammatory cells. In a model of suboptimal oseltamivir treatment, coadministration of ivy extract fraction that includes hedrasaponin F increased protection against PR8
Anan, Mohammad Tarek M.; Al-Saadi, Mohannad H.
2015-01-01
Objective The aim of this study was to compare the fit accuracies of metal partial removable dental prosthesis (PRDP) frameworks fabricated by the traditional technique (TT) or the light-curing modeling material technique (LCMT). Materials and methods A metal model of a Kennedy class III modification 1 mandibular dental arch with two edentulous spaces of different spans, short and long, was used for the study. Thirty identical working casts were used to produce 15 PRDP frameworks each by TT and by LCMT. Every framework was transferred to a metal master cast to measure the gap between the metal base of the framework and the crest of the alveolar ridge of the cast. Gaps were measured at three points on each side by a USB digital intraoral camera at ×16.5 magnification. Images were transferred to a graphics editing program. A single examiner performed all measurements. The two-tailed t-test was performed at the 5% significance level. Results The mean gap value was significantly smaller in the LCMT group compared to the TT group. The mean value of the short edentulous span was significantly smaller than that of the long edentulous span in the LCMT group, whereas the opposite result was obtained in the TT group. Conclusion Within the limitations of this study, it can be concluded that the fit of the LCMT-fabricated frameworks was better than the fit of the TT-fabricated frameworks. The framework fit can differ according to the span of the edentate ridge and the fabrication technique for the metal framework. PMID:26236129
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.
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.)
Sakamoto, Toshihiro
2018-04-01
Crop phenological information is a critical variable in evaluating the influence of environmental stress on the final crop yield in spatio-temporal dimensions. Although the MODIS (Moderate Resolution Imaging Spectroradiometer) Land Cover Dynamics product (MCD12Q2) is widely used in place of crop phenological information, the definitions of MCD12Q2-derived phenological events (e.g. green-up date, dormancy date) were not completely consistent with those of crop development stages used in statistical surveys (e.g. emerged date, harvested date). It has been necessary to devise an alternative method focused on detecting continental-scale crop developmental stages using a different approach. Therefore, this study aimed to refine the Shape Model Fitting (SMF) method to improve its applicability to multiple major U.S. crops. The newly-refined SMF methods could estimate the timing of 36 crop-development stages of major U.S. crops, including corn, soybeans, winter wheat, spring wheat, barley, sorghum, rice, and cotton. The newly-developed calibration process did not require any long-term field observation data, and could calibrate crop-specific phenological parameters, which were used as coefficients in estimated equation, by using only freely accessible public data. The calibration of phenological parameters was conducted in two steps. In the first step, the national common phenological parameters, referred to as X0[base], were calibrated by using the statistical data of 2008. The SMF method coupled using X0[base] was named the rSMF[base] method. The second step was a further calibration to gain regionally-adjusted phenological parameters for each state, referred to as X0[local], by using additional statistical data of 2015 and 2016. The rSMF method using the X0[local] was named the rSMF[local] method. This second calibration process improved the estimation accuracy for all tested crops. When applying the rSMF[base] method to the validation data set (2009-2014), the root
Drijfhout, E.; Oeveren, J.C. van; Jansen, R.C.
1991-01-01
A non-destructive method has been developed to select common bean (Phaseolus vulgaris L.) plants whose growth is less effected at a suboptimal temperature. Shoot weight was determined at a suboptimal (14°C) and optimal temperature (20°C), 38 days after sowing and accessions identified with a
Kommers, D.R.; Oei, S.G.; Chen, W.; Feijs, L.M.G.; Bambang Oetomo, S.
This review aimed to raise awareness of the consequences of suboptimal bonding caused by prematurity. In addition to hypoxia–ischaemia, infection and malnutrition, suboptimal bonding is one of the many unnatural stimuli that preterm infants are exposed to, compromising their physiological
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Lumu W
2017-02-01
Full Text Available William Lumu,1 Leaticia Kampiire,2 George Patrick Akabwai,3 Daniel Ssekikubo Kiggundu,4 Davis Kibirige5 1Department of Medicine and Diabetes/Endocrine Unit, Mengo Hospital, 2Infectious Disease Research Collaboration, 3Baylor College of Medicine Children’s Foundation, 4Nephrology Unit, Mulago National Referral and Teaching Hospital, 5Department of Medicine, Uganda Martyrs Hospital Lubaga, Kampala, Uganda Background: Hypertension is one of the recognized risk factors of cardiovascular diseases in adult diabetic patients. High prevalence of suboptimal blood pressure (BP control has been well documented in the majority of studies assessing BP control in diabetic patients in sub-Saharan Africa. In Uganda, there is a dearth of similar studies. This study evaluated the prevalence and correlates of suboptimal BP control in an adult diabetic population in Uganda.Patients and methods: This was a cross-sectional study that enrolled 425 eligible ambulatory adult diabetic patients attending three urban diabetic outpatient clinics over 11 months. Data about their sociodemographic characteristics and clinical history were collected using pre-tested questionnaires. Suboptimal BP control was defined according to the 2015 American Diabetes Association standards of diabetes care guideline as BP levels ≥140/90 mmHg.Results: The mean age of the study participants was 52.2±14.4 years, with the majority being females (283, 66.9%. Suboptimal BP control was documented in 192 (45.3% study participants and was independently associated with the study site (private hospitals; odds ratio 2.01, 95% confidence interval 1.18–3.43, P=0.01 and use of statin therapy (odds ratio 0.5, 95% confidence interval 0.26–0.96, P=0.037.Conclusion: Suboptimal BP control was highly prevalent in this study population. Strategies to improve optimal BP control, especially in the private hospitals, and the use of statin therapy should be encouraged in adult diabetic patients
ONE SIZE DOES NOT FIT ALL — UNDERSTANDING THE FRONT-END AND BACK-END OF BUSINESS MODEL INNOVATION
FRANZISKA GÜNZEL; ANNA B. HOLM
2013-01-01
Business model innovation is becoming a central research topic in management. However, a lack of a common understanding of the nature of the business model leads to disregarding its multifaceted structure when analyzing the business model innovation process. This article proposes a more detailed understanding of the business model innovation process by drawing on existing knowledge from new product development literature and examining the front-end and the back-end of business model innovatio...
One size does not fit all - understanding the front-end and back-ens of business model innovation
Günzel, Franziska; Holm, Anna B.
2013-01-01
Business model innovation is becoming a central research topic in management. However, a lack of a common understanding of the nature of the business model leads to disregarding its multifaceted structure when analyzing the business model innovation process. This article proposes a more detailed understanding of the business model innovation process by drawing on existing knowledge from new product development literature and examining the front-end and the back-end of business model innovatio...
Energy Technology Data Exchange (ETDEWEB)
Mavroidis, P; Price, A; Kostich, M; Green, R; Das, S; Marks, L; Chera, B [University North Carolina, Chapel Hill, NC (United States); Amdur, R; Mendenhall, W [University of Florida, Gainesville, FL (United States); Sheets, N [University of North Carolina, Raleigh, NC (United States)
2016-06-15
Purpose: To estimate the radiobiological parameters of four popular NTCP models that describe the dose-response relations of salivary glands to the severity of patient reported dry mouth 6 months post chemo-radiotherapy. To identify the glands, which best correlate with the manifestation of those clinical endpoints. Finally, to evaluate the goodness-of-fit of the NTCP models. Methods: Forty-three patients were treated on a prospective multiinstitutional phase II study for oropharyngeal squamous cell carcinoma. All the patients received 60 Gy IMRT and they reported symptoms using the novel patient reported outcome version of the CTCAE. We derived the individual patient dosimetric data of the parotid and submandibular glands (SMG) as separate structures as well as combinations. The Lyman-Kutcher-Burman (LKB), Relative Seriality (RS), Logit and Relative Logit (RL) NTCP models were used to fit the patients data. The fitting of the different models was assessed through the area under the receiver operating characteristic curve (AUC) and the Odds Ratio methods. Results: The AUC values were highest for the contralateral parotid for Grade ≥ 2 (0.762 for the LKB, RS, Logit and 0.753 for the RL). For the salivary glands the AUC values were: 0.725 for the LKB, RS, Logit and 0.721 for the RL. For the contralateral SMG the AUC values were: 0.721 for LKB, 0.714 for Logit and 0.712 for RS and RL. The Odds Ratio for the contralateral parotid was 5.8 (1.3–25.5) for all the four NTCP models for the radiobiological dose threshold of 21Gy. Conclusion: It was shown that all the examined NTCP models could fit the clinical data well with very similar accuracy. The contralateral parotid gland appears to correlated best with the clinical endpoints of severe/very severe dry mouth. An EQD2Gy dose of 21Gy appears to be a safe threshold to be used as a constraint in treatment planning.
Burnham, Andrew J; Armstrong, Jianling; Lowen, Anice C; Webster, Robert G; Govorkova, Elena A
2015-04-01
of seasonal influenza virus infections worldwide. The development of resistance to a single class of available antivirals, the neuraminidase (NA) inhibitors (NAIs), is a public health concern. Amino acid substitutions in the NA glycoprotein of influenza B virus not only can confer antiviral resistance but also can alter viral fitness. Here we used normal human bronchial epithelial (NHBE) cells, a model of the human upper respiratory tract, to examine the replicative capacities and fitness of NAI-resistant influenza B viruses. We show that virus with an E119A NA substitution can replicate efficiently in NHBE cells in the presence of oseltamivir or zanamivir and that virus with the H274Y NA substitution has a relative fitness greater than that of the wild-type NAI-susceptible virus. This study is the first to use NHBE cells to determine the fitness of NAI-resistant influenza B viruses. Copyright © 2015, American Society for Microbiology. All Rights Reserved.
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…
DEFF Research Database (Denmark)
Jensen, Jens-Ole
2003-01-01
Artiklen redegør for udbredelsen af fitness blandt unge og diskuterer, hvor det er blevet så populært at dyrke fitness.......Artiklen redegør for udbredelsen af fitness blandt unge og diskuterer, hvor det er blevet så populært at dyrke fitness....
Zhao, Z G; Rong, E H; Li, S C; Zhang, L J; Zhang, Z W; Guo, Y Q; Ma, R Y
2016-08-01
Monitoring of oriental fruit moths (Grapholita molesta Busck) is a prerequisite for its control. This study introduced a digital image-processing method and logistic model for the control of oriental fruit moths. First, five triangular sex pheromone traps were installed separately within each area of 667 m2 in a peach orchard to monitor oriental fruit moths consecutively for 3 years. Next, full view images of oriental fruit moths were collected via a digital camera and then subjected to graying, separation and morphological analysis for automatic counting using MATLAB software. Afterwards, the results of automatic counting were used for fitting a logistic model to forecast the control threshold and key control period. There was a high consistency between automatic counting and manual counting (0.99, P model, oriental fruit moths had four occurrence peaks during a year, with a time-lag of 15-18 days between adult occurrence peak and the larval damage peak. Additionally, the key control period was from 28 June to 3 July each year, when the wormy fruit rate reached up to 5% and the trapping volume was approximately 10.2 per day per trap. Additionally, the key control period for the overwintering generation was 25 April. This study provides an automatic counting method and fitted logistic model with a great potential for application to the control of oriental fruit moths.
Li, Yuelin; Baser, Ray
2012-08-15
The US Food and Drug Administration recently announced the final guidelines on the development and validation of patient-reported outcomes (PROs) assessments in drug labeling and clinical trials. This guidance paper may boost the demand for new PRO survey questionnaires. Henceforth, biostatisticians may encounter psychometric methods more frequently, particularly item response theory (IRT) models to guide the shortening of a PRO assessment instrument. This article aims to provide an introduction on the theory and practical analytic skills in fitting a generalized partial credit model (GPCM) in IRT. GPCM theory is explained first, with special attention to a clearer exposition of the formal mathematics than what is typically available in the psychometric literature. Then, a worked example is presented, using self-reported responses taken from the international personality item pool. The worked example contains step-by-step guides on using the statistical languages r and WinBUGS in fitting the GPCM. Finally, the Fisher information function of the GPCM model is derived and used to evaluate, as an illustrative example, the usefulness of assessment items by their information contents. This article aims to encourage biostatisticians to apply IRT models in the re-analysis of existing data and in future research. Copyright © 2012 John Wiley & Sons, Ltd.
Paul, Fabian; Noé, Frank; Weikl, Thomas R
2018-03-27
Unstructured proteins and peptides typically fold during binding to ligand proteins. A challenging problem is to identify the mechanism and kinetics of these binding-induced folding processes in experiments and atomistic simulations. In this Article, we present a detailed picture for the folding of the inhibitor peptide PMI into a helix during binding to the oncoprotein fragment 25-109 Mdm2 obtained from atomistic, explicit-water simulations and Markov state modeling. We find that binding-induced folding of PMI is highly parallel and can occur along a multitude of pathways. Some pathways are induced-fit-like with binding occurring prior to PMI helix formation, while other pathways are conformational-selection-like with binding after helix formation. On the majority of pathways, however, binding is intricately coupled to folding, without clear temporal ordering. A central feature of these pathways is PMI motion on the Mdm2 surface, along the binding groove of Mdm2 or over the rim of this groove. The native binding groove of Mdm2 thus appears as an asymmetric funnel for PMI binding. Overall, binding-induced folding of PMI does not fit into the classical picture of induced fit or conformational selection that implies a clear temporal ordering of binding and folding events. We argue that this holds in general for binding-induced folding processes because binding and folding events in these processes likely occur on similar time scales and do exhibit the time-scale separation required for temporal ordering.
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....
Winkielman, Piotr; Gogolushko, Yekaterina
2018-01-01
Affective stimuli can influence immediate reactions as well as spontaneous behaviors. Much evidence for such influence comes from studies of facial expressions. However, it is unclear whether these effects hold for other affective stimuli, and how the amount of stimulus processing changes the nature of the influence. This paper addresses these issues by comparing the influence on consumption behaviors of emotional pictures and valence-matched words presented at suboptimal and supraliminal durations. In Experiment 1, both suboptimal and supraliminal emotional facial expressions influenced consumption in an affect-congruent, assimilative way. In Experiment 2, pictures of both high- and low-frequency emotional objects congruently influenced consumption. In comparison, words tended to produce incongruent effects. We discuss these findings in light of privileged access theories, which hold that pictures better convey affective meaning than words, and embodiment theories, which hold that pictures better elicit somatosensory and motor responses. PMID:29434556
Suboptimal investments and M&A deals in emerging capital markets
Directory of Open Access Journals (Sweden)
Cherkasova Victoria
2016-01-01
Full Text Available This paper focuses on the efficiency of target-company investment decisions before and after Merger & Acquisition deals. We study whether M&A deals help to solve the problem of suboptimal investment after the acquisition. Using a sample of 145 target companies from BRICS countries that were acquired during the period 2004-2014, we outline those that had over- or underinvested before the deal and show that more than half the companies managed to optimize the investment level after the deal. We determine the key factors that improve the inefficiency of investment decisions and demonstrate that the industry and country have an impact on the degree of suboptimal investment.
Directory of Open Access Journals (Sweden)
Tarasak Poramate
2010-01-01
Full Text Available Active interference cancellation (AIC is an effective technique to provide interference avoidance feature for an ultrawideband (UWB OFDM transmitter. Partial transmit sequence-AIC (PTS-AIC, which was recently proposed as an improvement of AIC, requires high computational complexity by doing the exhaustive search of all possible weighting factors whose number grows exponentially with the number of subblocks used. To reduce the complexity of PTS-AIC, this paper proposes a suboptimal way, called particle swarm optimization (PSO, to choose the weighting factors suboptimally without much performance degradation. Both continuous and discrete versions of PSO have been evaluated, and it has been shown that the discrete PSO is able to reduce the complexity significantly without sacrificing the performance of PTS-AIC in many cases.
André, Marcel J
2013-08-01
Photosynthetic assimilation of CO2 in plants results in the balance between the photochemical energy developed by light in chloroplasts, and the consumption of that energy by the oxygenation processes, mainly the photorespiration in C3 plants. The analysis of classical biological models shows the difficulties to bring to fore the oxygenation rate due to the photorespiration pathway. As for other parameters, the most important key point is the estimation of the electron transport rate (ETR or J), i.e. the flux of biochemical energy, which is shared between the reductive and oxidative cycles of carbon. The only reliable method to quantify the linear electron flux responsible for the production of reductive energy is to directly measure the O2 evolution by (18)O2 labelling and mass spectrometry. The hypothesis that the respective rates of reductive and oxidative cycles of carbon are only determined by the kinetic parameters of Rubisco, the respective concentrations of CO2 and O2 at the Rubisco site and the available electron transport rate, ultimately leads to propose new expressions of biochemical model equations. The modelling of (18)O2 and (16)O2 unidirectional fluxes in plants shows that a simple model can fit the photosynthetic and photorespiration exchanges for a wide range of environmental conditions. Its originality is to express the carboxylation and the oxygenation as a function of external gas concentrations, by the definition of a plant specificity factor Sp that mimics the internal reactions of Rubisco in plants. The difference between the specificity factors of plant (Sp) and of Rubisco (Sr) is directly related to the conductance values to CO2 transfer between the atmosphere and the Rubisco site. This clearly illustrates that the values and the variation of conductance are much more important, in higher C3 plants, than the small variations of the Rubisco specificity factor. The simple model systematically expresses the reciprocal variations of
SU-F-I-63: Relaxation Times of Lipid Resonances in NAFLD Animal Model Using Enhanced Curve Fitting
Energy Technology Data Exchange (ETDEWEB)
Song, K-H; Yoo, C-H; Lim, S-I; Choe, B-Y [Department of Biomedical Engineering, and Research Institute of Biomedical Engineering, The Catholic University of Korea College of Medicine, Seoul (Korea, Republic of)
2016-06-15
Purpose: The objective of this study is to evaluate the relaxation time of methylene resonance in comparison with other lipid resonances. Methods: The examinations were performed on a 3.0T MRI scanner using a four-channel animal coil. Eight more Sprague-Dawley rats in the same baseline weight range were housed with ad libitum access to water and a high-fat (HF) diet (60% fat, 20% protein, and 20% carbohydrate). In order to avoid large blood vessels, a voxel (0.8×0.8×0.8 cm{sup 3}) was placed in a homogeneous area of the liver parenchyma during free breathing. Lipid relaxations in NC and HF diet rats were estimated at a fixed repetition time (TR) of 6000 msec, and multi echo time (TEs) of 40–220 msec. All spectra for data measurement were processed using the Advanced Method for Accurate, Robust, and Efficient Spectral (AMARES) fitting algorithm of the Java-based Magnetic Resonance User Interface (jMRUI) package. Results: The mean T2 relaxation time of the methylene resonance in normal-chow diet was 37.1 msec (M{sub 0}, 2.9±0.5), with a standard deviation of 4.3 msec. The mean T2 relaxation time of the methylene resonance was 31.4 msec (M{sub 0}, 3.7±0.3), with a standard deviation of 1.8 msec. The T2 relaxation times of methylene protons were higher in normal-chow diet rats than in HF rats (p<0.05), and the extrapolated M{sub 0} values were higher in HF rats than in NC rats (p<0.005). The excellent linear fit with R{sup 2}>0.9971 and R{sup 2}>0.9987 indicates T2 relaxation decay curves with mono-exponential function. Conclusion: In in vivo, a sufficient spectral resolution and a sufficiently high signal-to-noise ratio (SNR) can be achieved, so that the data measured over short TE values can be extrapolated back to TE = 0 to produce better estimates of the relative weights of the spectral components. In the short term, treating the effective decay rate as exponential is an adequate approximation.
One size does not fit all - understanding the front-end and back-ens of business model innovation
DEFF Research Database (Denmark)
Günzel, Franziska; Holm, Anna B.
2013-01-01
Business model innovation is becoming a central research topic in management. However, a lack of a common understanding of the nature of the business model leads to disregarding its multifaceted structure when analyzing the business model innovation process. This article proposes a more detailed...... understanding of the business model innovation process by drawing on existing knowledge from new product development literature and examining the front-end and the back-end of business model innovation of three leading Danish newspapers. We studied how changes introduced during the development of digital news...... production and delivery have affected key components of these business models, namely value creation, proposition, delivery and capture in the period 2002–2011. Our findings suggest the need to distinguish between front-end and back-end business model innovation processes, and to recognize the importance...
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...
International Nuclear Information System (INIS)
Halpern, J.; Whittemore, A.S.
1987-01-01
Two methods were used to examine how lung cancer death rates vary with cumulative exposures to radiation and tobacco among uranium miners. The two methods produced similar results when death rate ratios were taken to be the product of radiation and tobacco effects. The estimates were discrepant when death rate ratios were taken to be the sum of radiation and tobacco effects. Both methods indicated better fit for the multiplicative model. It may be that cumulative exposures are inappropriate measures of the effects of radiation and tobacco on lung cancer death rates, as well as for other pollutants where the assumption of cumulative dose is the basis for risk assessments