Recommended test methods and pass/fail criteria for a respirator fit capability test of half-mask air-purifying respirators.

PubMed

Zhuang, Ziqing; Bergman, Michael; Lei, Zhipeng; Niezgoda, George; Shaffer, Ronald

2017-06-01

This study assessed key test parameters and pass/fail criteria options for developing a respirator fit capability (RFC) test for half-mask air-purifying particulate respirators. Using a 25-subject test panel, benchmark RFC data were collected for 101 National Institute for Occupational Safety and Health-certified respirator models. These models were further grouped into 61 one-, two-, or three-size families. Fit testing was done using a PortaCount® Plus with N95-Companion accessory and an Occupational Safety and Health Administration-accepted quantitative fit test protocol. Three repeated tests (donnings) per subject/respirator model combination were performed. The panel passing rate (PPR) (number or percentage of the 25-subject panel achieving acceptable fit) was determined for each model using five different alternative criteria for determining acceptable fit. When the 101 models are evaluated individually (i.e., not grouped by families), the percentages of models capable of fitting >75% (19/25 subjects) of the panel were 29% and 32% for subjects achieving a fit factor ≥100 for at least one of the first two donnings and at least one of three donnings, respectively. When the models are evaluated grouped into families and using >75% of panel subjects achieving a fit factor ≥100 for at least one of two donnings as the PPR pass/fail criterion, 48% of all models can pass. When >50% (13/25 subjects) of panel subjects was the PPR criterion, the percentage of passing models increased to 70%. Testing respirators grouped into families and evaluating the first two donnings for each of two respirator sizes provided the best balance between meeting end user expectations and creating a performance bar for manufacturers. Specifying the test criterion for a subject obtaining acceptable fit as achieving a fit factor ≥100 on at least one out of the two donnings is reasonable because a majority of existing respirator families can achieve an PPR of >50% using this criterion. The different test criteria can be considered by standards development organizations when developing standards.

Evaluating the MSCEIT V2.0 via CFA: comment on Mayer et al. (2003).

PubMed

Gignac, Gilles E

2005-06-01

This investigation uncovered several substantial errors in the confirmatory factor analysis results reported by J. D. Mayer, P. Salovey, D. R. Caruso, and G. Sitarenios (see record 2003-02341-015). Specifically, the values associated with the close-fit indices (normed fit index, Tucker-Lewis Index, and root-mean-square error of approximation) are inaccurate. A reanalysis of the Mayer et al. subscale intercorrelation matrix provided accurate values of the close-fit indices, which resulted in different evaluations of the models tested by J. D. Mayer et al. Contrary to J. D. Mayer et al., the 1-factor model and the 2-factor model did not provide good fit. Although the 4-factor model was still considered good fitting, the non-constrained 4-factor model yielded a non-positive definite matrix, which was interpreted to be due to the fact that two of the branch-level factors (Perceiving and Facilitating) were collinear, suggesting that a model with 4 factors was implausible.

Using the Bayes Factors to Evaluate Person Fit in the Item Response Theory

ERIC Educational Resources Information Center

Pan, Tianshu; Yin, Yue

2017-01-01

In this article, we propose using the Bayes factors (BF) to evaluate person fit in item response theory models under the framework of Bayesian evaluation of an informative diagnostic hypothesis. We first discuss the theoretical foundation for this application and how to analyze person fit using BF. To demonstrate the feasibility of this approach,…

Can Policy Alone Stop Decline of Children and Youth Fitness?

PubMed

Zhang, Chunhua; Yang, Yang

2017-03-01

Various models and methods have been proposed to address the worldwide decline in children's and youth's physical fitness, and the social-ecological model has shown some promise. Yet, the impact of the policy intervention, 1 component of that model, has not been evaluated carefully. Using limited data from policy documents, the impact of policy related to children and youth fitness in China was examined, and it was found that the policy alone did not seem to work. Possible reasons are explored, and a call for more policy evaluation research is made.

Model Performance Evaluation and Scenario Analysis (MPESA) Tutorial

EPA Science Inventory

This tool consists of two parts: model performance evaluation and scenario analysis (MPESA). The model performance evaluation consists of two components: model performance evaluation metrics and model diagnostics. These metrics provides modelers with statistical goodness-of-fit m...

Micro-CT evaluation of the marginal fit of CAD/CAM all ceramic crowns

NASA Astrophysics Data System (ADS)

Brenes, Christian

Objectives: Evaluate the marginal fit of CAD/CAM all ceramic crowns made from lithium disilicate and zirconia using two different fabrication protocols (model and model-less). METHODS: Forty anterior all ceramic restorations (20 lithium disilicate, 20 zirconia) were fabricated using a CEREC Bluecam scanner. Two different fabrication methods were used: a full digital approach and a printed model. Completed crowns were cemented and marginal gap was evaluated using Micro-CT. Each specimen was analyzed in sagittal and trans-axial orientations, allowing a 360° evaluation of the vertical and horizontal fit. RESULTS: Vertical measurements in the lingual, distal and mesial views had and estimated marginal gap from 101.9 to 133.9 microns for E-max crowns and 126.4 to 165.4 microns for zirconia. No significant differences were found between model and model-less techniques. CONCLUSION: Lithium disilicate restorations exhibited a more accurate and consistent marginal adaptation when compared to zirconia crowns. No statistically significant differences were observed when comparing model or model-less approaches.

PREdator: a python based GUI for data analysis, evaluation and fitting

PubMed Central

2014-01-01

The analysis of a series of experimental data is an essential procedure in virtually every field of research. The information contained in the data is extracted by fitting the experimental data to a mathematical model. The type of the mathematical model (linear, exponential, logarithmic, etc.) reflects the physical laws that underlie the experimental data. Here, we aim to provide a readily accessible, user-friendly python script for data analysis, evaluation and fitting. PREdator is presented at the example of NMR paramagnetic relaxation enhancement analysis.

Evaluating the Wald Test for Item-Level Comparison of Saturated and Reduced Models in Cognitive Diagnosis

ERIC Educational Resources Information Center

de la Torre, Jimmy; Lee, Young-Sun

2013-01-01

This article used the Wald test to evaluate the item-level fit of a saturated cognitive diagnosis model (CDM) relative to the fits of the reduced models it subsumes. A simulation study was carried out to examine the Type I error and power of the Wald test in the context of the G-DINA model. Results show that when the sample size is small and a…

On Using Surrogates with Genetic Programming.

PubMed

Hildebrandt, Torsten; Branke, Jürgen

2015-01-01

One way to accelerate evolutionary algorithms with expensive fitness evaluations is to combine them with surrogate models. Surrogate models are efficiently computable approximations of the fitness function, derived by means of statistical or machine learning techniques from samples of fully evaluated solutions. But these models usually require a numerical representation, and therefore cannot be used with the tree representation of genetic programming (GP). In this paper, we present a new way to use surrogate models with GP. Rather than using the genotype directly as input to the surrogate model, we propose using a phenotypic characterization. This phenotypic characterization can be computed efficiently and allows us to define approximate measures of equivalence and similarity. Using a stochastic, dynamic job shop scenario as an example of simulation-based GP with an expensive fitness evaluation, we show how these ideas can be used to construct surrogate models and improve the convergence speed and solution quality of GP.

Goodness of fit of probability distributions for sightings as species approach extinction.

PubMed

Vogel, Richard M; Hosking, Jonathan R M; Elphick, Chris S; Roberts, David L; Reed, J Michael

2009-04-01

Estimating the probability that a species is extinct and the timing of extinctions is useful in biological fields ranging from paleoecology to conservation biology. Various statistical methods have been introduced to infer the time of extinction and extinction probability from a series of individual sightings. There is little evidence, however, as to which of these models provide adequate fit to actual sighting records. We use L-moment diagrams and probability plot correlation coefficient (PPCC) hypothesis tests to evaluate the goodness of fit of various probabilistic models to sighting data collected for a set of North American and Hawaiian bird populations that have either gone extinct, or are suspected of having gone extinct, during the past 150 years. For our data, the uniform, truncated exponential, and generalized Pareto models performed moderately well, but the Weibull model performed poorly. Of the acceptable models, the uniform distribution performed best based on PPCC goodness of fit comparisons and sequential Bonferroni-type tests. Further analyses using field significance tests suggest that although the uniform distribution is the best of those considered, additional work remains to evaluate the truncated exponential model more fully. The methods we present here provide a framework for evaluating subsequent models.

Development of Evaluation Indicators for Hospice and Palliative Care Professionals Training Programs in Korea.

PubMed

Kang, Jina; Park, Kyoung-Ok

2017-01-01

The importance of training for Hospice and Palliative Care (HPC) professionals has been increasing with the systemization of HPC in Korea. Hence, the need and importance of training quality for HPC professionals are growing. This study evaluated the construct validity and reliability of the Evaluation Indicators for standard Hospice and Palliative Care Training (EIHPCT) program. As a framework to develop evaluation indicators, an invented theoretical model combining Stufflebeam's CIPP (Context-Input-Process-Product) evaluation model with PRECEDE-PROCEED model was used. To verify the construct validity of the EIHPCT program, a structured survey was performed with 169 professionals who were the HPC training program administrators, trainers, and trainees. To examine the validity of the areas of the EIHPCT program, exploratory factor analysis and confirmatory factor analysis were conducted. First, in the exploratory factor analysis, the indicators with factor loadings above 0.4 were chosen as desirable items, and some cross-loaded items that loaded at 0.4 or higher on two or more factors were adjusted as the higher factor. Second, the model fit of the modified EIHPCT program was quite good in the confirmatory factor analysis (Goodness-of-Fit Index > 0.70, Comparative Fit Index > 0.80, Normed Fit Index > 0.80, Root Mean square of Residuals < 0.05). The modified model of the EIHPCT comprised 4 areas, 13 subdomains, and 61 indicators. The evaluation indicators of the modified model will be valuable references for improving the HPC professional training program.

Accuracy of Digital Impressions and Fitness of Single Crowns Based on Digital Impressions

PubMed Central

Yang, Xin; Lv, Pin; Liu, Yihong; Si, Wenjie; Feng, Hailan

2015-01-01

In this study, the accuracy (precision and trueness) of digital impressions and the fitness of single crowns manufactured based on digital impressions were evaluated. #14-17 epoxy resin dentitions were made, while full-crown preparations of extracted natural teeth were embedded at #16. (1) To assess precision, deviations among repeated scan models made by intraoral scanner TRIOS and MHT and model scanner D700 and inEos were calculated through best-fit algorithm and three-dimensional (3D) comparison. Root mean square (RMS) and color-coded difference images were offered. (2) To assess trueness, micro computed tomography (micro-CT) was used to get the reference model (REF). Deviations between REF and repeated scan models (from (1)) were calculated. (3) To assess fitness, single crowns were manufactured based on TRIOS, MHT, D700 and inEos scan models. The adhesive gaps were evaluated under stereomicroscope after cross-sectioned. Digital impressions showed lower precision and better trueness. Except for MHT, the means of RMS for precision were lower than 10 μm. Digital impressions showed better internal fitness. Fitness of single crowns based on digital impressions was up to clinical standard. Digital impressions could be an alternative method for single crowns manufacturing. PMID:28793417

Robust and fast nonlinear optimization of diffusion MRI microstructure models.

PubMed

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

2017-07-15

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

Linearised and non-linearised isotherm models optimization analysis by error functions and statistical means

PubMed Central

2014-01-01

In adsorption study, to describe sorption process and evaluation of best-fitting isotherm model is a key analysis to investigate the theoretical hypothesis. Hence, numerous statistically analysis have been extensively used to estimate validity of the experimental equilibrium adsorption values with the predicted equilibrium values. Several statistical error analysis were carried out. In the present study, the following statistical analysis were carried out to evaluate the adsorption isotherm model fitness, like the Pearson correlation, the coefficient of determination and the Chi-square test, have been used. The ANOVA test was carried out for evaluating significance of various error functions and also coefficient of dispersion were evaluated for linearised and non-linearised models. The adsorption of phenol onto natural soil (Local name Kalathur soil) was carried out, in batch mode at 30 ± 20 C. For estimating the isotherm parameters, to get a holistic view of the analysis the models were compared between linear and non-linear isotherm models. The result reveled that, among above mentioned error functions and statistical functions were designed to determine the best fitting isotherm. PMID:25018878

The Many Null Distributions of Person Fit Indices.

ERIC Educational Resources Information Center

Molenaar, Ivo W.; Hoijtink, Herbert

1990-01-01

Statistical properties of person fit indices are reviewed as indicators of the extent to which a person's score pattern is in agreement with a measurement model. Distribution of a fit index and ability-free fit evaluation are discussed. The null distribution was simulated for a test of 20 items. (SLD)

Evaluation of weighted regression and sample size in developing a taper model for loblolly pine

Treesearch

Kenneth L. Cormier; Robin M. Reich; Raymond L. Czaplewski; William A. Bechtold

1992-01-01

A stem profile model, fit using pseudo-likelihood weighted regression, was used to estimate merchantable volume of loblolly pine (Pinus taeda L.) in the southeast. The weighted regression increased model fit marginally, but did not substantially increase model performance. In all cases, the unweighted regression models performed as well as the...

PyCorrFit-generic data evaluation for fluorescence correlation spectroscopy.

PubMed

Müller, Paul; Schwille, Petra; Weidemann, Thomas

2014-09-01

We present a graphical user interface (PyCorrFit) for the fitting of theoretical model functions to experimental data obtained by fluorescence correlation spectroscopy (FCS). The program supports many data file formats and features a set of tools specialized in FCS data evaluation. The Python source code is freely available for download from the PyCorrFit web page at http://pycorrfit.craban.de. We offer binaries for Ubuntu Linux, Mac OS X and Microsoft Windows. © The Author 2014. Published by Oxford University Press.

Factor Structure of the Quality of Life Scale for Mental Disorders in Patients With Schizophrenia.

PubMed

Chiu, En-Chi; Lee, Shu-Chun

2018-06-01

The Quality of Life for Mental Disorders (QOLMD) scale was designed to measure health-related quality of life (HRQOL) in patients with mental illness, especially schizophrenia. The QOLMD contains 45 items, which are divided into eight domains. However, the factor structure of the QOLMD has not been evaluated, which restricts the interpretations of the results of this scale. The purpose of this study was to evaluate the factor structures (i.e., unidimensionality, eight-factor structure, and second-order model) of the QOLMD in patients with schizophrenia. Two hundred thirty-eight outpatients with schizophrenia participated. We first conducted confirmatory factor analysis to evaluate the unidimensionality of each domain. After the unidimensionality of the eight individual domains was supported, we examined the eight-factor structure and second-order model. The results of unidimensionality showed sufficient model fit in all of the domains with the exception of the autonomy domain. A good model fit was confirmed for the autonomy domain after deleting two of the original items. The eight-factor structure for the 43-item QOLMD showed an acceptable model fit, although the second-order model showed poor model fit. Our results supported the unidimensionality and eight-factor structure of the 43-item QOLMD. The sum score for each of the domains may be used to reflect its domain-specific function. We recommend using the 43-item QOLMD to capture the multiple domains of HRQOL. However, the second-order model showed an unsatisfactory model fit. Furthermore, caution is advised when interpreting overall HRQOL using the total score for the eight domains.

Tanning Shade Gradations of Models in Mainstream Fitness and Muscle Enthusiast Magazines: Implications for Skin Cancer Prevention in Men.

PubMed

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.

Model Performance Evaluation and Scenario Analysis (MPESA) Tutorial

EPA Pesticide Factsheets

The model performance evaluation consists of metrics and model diagnostics. These metrics provides modelers with statistical goodness-of-fit measures that capture magnitude only, sequence only, and combined magnitude and sequence errors.

On the Complexity of Item Response Theory Models.

PubMed

Bonifay, Wes; Cai, Li

2017-01-01

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

Comparison among cognitive diagnostic models for the TIMSS 2007 fourth grade mathematics assessment.

PubMed

Yamaguchi, Kazuhiro; Okada, Kensuke

2018-01-01

A variety of cognitive diagnostic models (CDMs) have been developed in recent years to help with the diagnostic assessment and evaluation of students. Each model makes different assumptions about the relationship between students' achievement and skills, which makes it important to empirically investigate which CDMs better fit the actual data. In this study, we examined this question by comparatively fitting representative CDMs to the Trends in International Mathematics and Science Study (TIMSS) 2007 assessment data across seven countries. The following two major findings emerged. First, in accordance with former studies, CDMs had a better fit than did the item response theory models. Second, main effects models generally had a better fit than other parsimonious or the saturated models. Related to the second finding, the fit of the traditional parsimonious models such as the DINA and DINO models were not optimal. The empirical educational implications of these findings are discussed.

Comparison among cognitive diagnostic models for the TIMSS 2007 fourth grade mathematics assessment

PubMed Central

Okada, Kensuke

2018-01-01

A variety of cognitive diagnostic models (CDMs) have been developed in recent years to help with the diagnostic assessment and evaluation of students. Each model makes different assumptions about the relationship between students’ achievement and skills, which makes it important to empirically investigate which CDMs better fit the actual data. In this study, we examined this question by comparatively fitting representative CDMs to the Trends in International Mathematics and Science Study (TIMSS) 2007 assessment data across seven countries. The following two major findings emerged. First, in accordance with former studies, CDMs had a better fit than did the item response theory models. Second, main effects models generally had a better fit than other parsimonious or the saturated models. Related to the second finding, the fit of the traditional parsimonious models such as the DINA and DINO models were not optimal. The empirical educational implications of these findings are discussed. PMID:29394257

Developing Best Practices for Detecting Change at Marine Renewable Energy Sites

NASA Astrophysics Data System (ADS)

Linder, H. L.; Horne, J. K.

2016-02-01

In compliance with the National Environmental Policy Act (NEPA), an evaluation of environmental effects is mandatory for obtaining permits for any Marine Renewable Energy (MRE) project in the US. Evaluation includes an assessment of baseline conditions and on-going monitoring during operation to determine if biological conditions change relative to the baseline. Currently, there are no best practices for the analysis of MRE monitoring data. We have developed an approach to evaluate and recommend analytic models used to characterize and detect change in biological monitoring data. The approach includes six steps: review current MRE monitoring practices, identify candidate models to analyze data, fit models to a baseline dataset, develop simulated scenarios of change, evaluate model fit to simulated data, and produce recommendations on the choice of analytic model for monitoring data. An empirical data set from a proposed tidal turbine site at Admiralty Inlet, Puget Sound, Washington was used to conduct the model evaluation. Candidate models that were evaluated included: linear regression, time series, and nonparametric models. Model fit diagnostics Root-Mean-Square-Error and Mean-Absolute-Scaled-Error were used to measure accuracy of predicted values from each model. A power analysis was used to evaluate the ability of each model to measure and detect change from baseline conditions. As many of these models have yet to be applied in MRE monitoring studies, results of this evaluation will generate comprehensive guidelines on choice of model to detect change in environmental monitoring data from MRE sites. The creation of standardized guidelines for model selection enables accurate comparison of change between life stages of a MRE project, within life stages to meet real time regulatory requirements, and comparison of environmental changes among MRE sites.

The Work Role Functioning Questionnaire v2.0 Showed Consistent Factor Structure Across Six Working Samples.

PubMed

Abma, Femke I; Bültmann, Ute; Amick Iii, Benjamin C; Arends, Iris; Dorland, Heleen F; Flach, Peter A; van der Klink, Jac J L; van de Ven, Hardy A; Bjørner, Jakob Bue

2017-09-09

Objective The Work Role Functioning Questionnaire v2.0 (WRFQ) is an outcome measure linking a persons' health to the ability to meet work demands in the twenty-first century. We aimed to examine the construct validity of the WRFQ in a heterogeneous set of working samples in the Netherlands with mixed clinical conditions and job types to evaluate the comparability of the scale structure. Methods Confirmatory factor and multi-group analyses were conducted in six cross-sectional working samples (total N = 2433) to evaluate and compare a five-factor model structure of the WRFQ (work scheduling demands, output demands, physical demands, mental and social demands, and flexibility demands). Model fit indices were calculated based on RMSEA ≤ 0.08 and CFI ≥ 0.95. After fitting the five-factor model, the multidimensional structure of the instrument was evaluated across samples using a second order factor model. Results The factor structure was robust across samples and a multi-group model had adequate fit (RMSEA = 0.63, CFI = 0.972). In sample specific analyses, minor modifications were necessary in three samples (final RMSEA 0.055-0.080, final CFI between 0.955 and 0.989). Applying the previous first order specifications, a second order factor model had adequate fit in all samples. Conclusion A five-factor model of the WRFQ showed consistent structural validity across samples. A second order factor model showed adequate fit, but the second order factor loadings varied across samples. Therefore subscale scores are recommended to compare across different clinical and working samples.

MISFITS: evaluating the goodness of fit between a phylogenetic model and an alignment.

PubMed

Nguyen, Minh Anh Thi; Klaere, Steffen; von Haeseler, Arndt

2011-01-01

As models of sequence evolution become more and more complicated, many criteria for model selection have been proposed, and tools are available to select the best model for an alignment under a particular criterion. However, in many instances the selected model fails to explain the data adequately as reflected by large deviations between observed pattern frequencies and the corresponding expectation. We present MISFITS, an approach to evaluate the goodness of fit (http://www.cibiv.at/software/misfits). MISFITS introduces a minimum number of "extra substitutions" on the inferred tree to provide a biologically motivated explanation why the alignment may deviate from expectation. These extra substitutions plus the evolutionary model then fully explain the alignment. We illustrate the method on several examples and then give a survey about the goodness of fit of the selected models to the alignments in the PANDIT database.

Evaluating the performance of the Lee-Carter method and its variants in modelling and forecasting Malaysian mortality

NASA Astrophysics Data System (ADS)

Zakiyatussariroh, W. H. Wan; Said, Z. Mohammad; Norazan, M. R.

2014-12-01

This study investigated the performance of the Lee-Carter (LC) method and it variants in modeling and forecasting Malaysia mortality. These include the original LC, the Lee-Miller (LM) variant and the Booth-Maindonald-Smith (BMS) variant. These methods were evaluated using Malaysia's mortality data which was measured based on age specific death rates (ASDR) for 1971 to 2009 for overall population while those for 1980-2009 were used in separate models for male and female population. The performance of the variants has been examined in term of the goodness of fit of the models and forecasting accuracy. Comparison was made based on several criteria namely, mean square error (MSE), root mean square error (RMSE), mean absolute deviation (MAD) and mean absolute percentage error (MAPE). The results indicate that BMS method was outperformed in in-sample fitting for overall population and when the models were fitted separately for male and female population. However, in the case of out-sample forecast accuracy, BMS method only best when the data were fitted to overall population. When the data were fitted separately for male and female, LCnone performed better for male population and LM method is good for female population.

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

NASA Astrophysics Data System (ADS)

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

2017-01-01

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

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

PubMed

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

2017-01-04

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

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

PubMed Central

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

2017-01-01

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

Characterizing dissolved organic matter using PARAFAC modeling of fluorescence spectroscopy: a comparison of two models

Treesearch

Jason B. Fellman; Mathew P. Miller; Rose M. Cory; David V. D' Amore; Dan White

2009-01-01

We evaluated whether fitting fluorescence excitation-emission matrices (EEMs) to a previously validated PARAFAC model is an acceptable alternative to building an original model. To do this, we built a l0-component model using 307 EEMscollected from southeast Alaskan soil and streamwater. All 307 EEMs were then fit to the existing model (CM) presented in Cory and...

Model selection for the North American Breeding Bird Survey: A comparison of methods

USGS Publications Warehouse

Link, William; Sauer, John; Niven, Daniel

2017-01-01

The North American Breeding Bird Survey (BBS) provides data for >420 bird species at multiple geographic scales over 5 decades. Modern computational methods have facilitated the fitting of complex hierarchical models to these data. It is easy to propose and fit new models, but little attention has been given to model selection. Here, we discuss and illustrate model selection using leave-one-out cross validation, and the Bayesian Predictive Information Criterion (BPIC). Cross-validation is enormously computationally intensive; we thus evaluate the performance of the Watanabe-Akaike Information Criterion (WAIC) as a computationally efficient approximation to the BPIC. Our evaluation is based on analyses of 4 models as applied to 20 species covered by the BBS. Model selection based on BPIC provided no strong evidence of one model being consistently superior to the others; for 14/20 species, none of the models emerged as superior. For the remaining 6 species, a first-difference model of population trajectory was always among the best fitting. Our results show that WAIC is not reliable as a surrogate for BPIC. Development of appropriate model sets and their evaluation using BPIC is an important innovation for the analysis of BBS data.

An Empirical Investigation of Methods for Assessing Item Fit for Mixed Format Tests

ERIC Educational Resources Information Center

Chon, Kyong Hee; Lee, Won-Chan; Ansley, Timothy N.

2013-01-01

Empirical information regarding performance of model-fit procedures has been a persistent need in measurement practice. Statistical procedures for evaluating item fit were applied to real test examples that consist of both dichotomously and polytomously scored items. The item fit statistics used in this study included the PARSCALE's G[squared],…

Application of zero-inflated poisson mixed models in prognostic factors of hepatitis C.

PubMed

Akbarzadeh Baghban, Alireza; Pourhoseingholi, Asma; Zayeri, Farid; Jafari, Ali Akbar; Alavian, Seyed Moayed

2013-01-01

In recent years, hepatitis C virus (HCV) infection represents a major public health problem. Evaluation of risk factors is one of the solutions which help protect people from the infection. This study aims to employ zero-inflated Poisson mixed models to evaluate prognostic factors of hepatitis C. The data was collected from a longitudinal study during 2005-2010. First, mixed Poisson regression (PR) model was fitted to the data. Then, a mixed zero-inflated Poisson model was fitted with compound Poisson random effects. For evaluating the performance of the proposed mixed model, standard errors of estimators were compared. The results obtained from mixed PR showed that genotype 3 and treatment protocol were statistically significant. Results of zero-inflated Poisson mixed model showed that age, sex, genotypes 2 and 3, the treatment protocol, and having risk factors had significant effects on viral load of HCV patients. Of these two models, the estimators of zero-inflated Poisson mixed model had the minimum standard errors. The results showed that a mixed zero-inflated Poisson model was the almost best fit. The proposed model can capture serial dependence, additional overdispersion, and excess zeros in the longitudinal count data.

Modelling lactation curve for milk fat to protein ratio in Iranian buffaloes (Bubalus bubalis) using non-linear mixed models.

PubMed

Hossein-Zadeh, Navid Ghavi

2016-08-01

The aim of this study was to compare seven non-linear mathematical models (Brody, Wood, Dhanoa, Sikka, Nelder, Rook and Dijkstra) to examine their efficiency in describing the lactation curves for milk fat to protein ratio (FPR) in Iranian buffaloes. Data were 43 818 test-day records for FPR from the first three lactations of Iranian buffaloes which were collected on 523 dairy herds in the period from 1996 to 2012 by the Animal Breeding Center of Iran. Each model was fitted to monthly FPR records of buffaloes using the non-linear mixed model procedure (PROC NLMIXED) in SAS and the parameters were estimated. The models were tested for goodness of fit using Akaike's information criterion (AIC), Bayesian information criterion (BIC) and log maximum likelihood (-2 Log L). The Nelder and Sikka mixed models provided the best fit of lactation curve for FPR in the first and second lactations of Iranian buffaloes, respectively. However, Wood, Dhanoa and Sikka mixed models provided the best fit of lactation curve for FPR in the third parity buffaloes. Evaluation of first, second and third lactation features showed that all models, except for Dijkstra model in the third lactation, under-predicted test time at which daily FPR was minimum. On the other hand, minimum FPR was over-predicted by all equations. Evaluation of the different models used in this study indicated that non-linear mixed models were sufficient for fitting test-day FPR records of Iranian buffaloes.

Tree Height and DBH Growth Model Establishment of Main Tree Species in Wuling Mountain Small Watershed

NASA Astrophysics Data System (ADS)

Luo, Jia; Zhang, Min; Zhou, Xiaoling; Chen, Jianhua; Tian, Yuxin

2018-01-01

Taken 4 main tree species in the Wuling mountain small watershed as research objects, 57 typical sample plots were set up according to the stand type, site conditions and community structure. 311 goal diameter-class sample trees were selected according to diameter-class groups of different tree-height grades, and the optimal fitting models of tree height and DBH growth of main tree species were obtained by stem analysis using Richard, Logistic, Korf, Mitscherlich, Schumacher, Weibull theoretical growth equations, and the correlation coefficient of all optimal fitting models reached above 0.9. Through the evaluation and test, the optimal fitting models possessed rather good fitting precision and forecast dependability.

Detecting Growth Shape Misspecifications in Latent Growth Models: An Evaluation of Fit Indexes

ERIC Educational Resources Information Center

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…

Can Policy Alone Stop Decline of Children and Youth Fitness?

ERIC Educational Resources Information Center

Zhang, Chunhua; Yang, Yang

2017-01-01

Various models and methods have been proposed to address the worldwide decline in children's and youth's physical fitness, and the social-ecological model has shown some promise. Yet, the impact of the policy intervention, one component of that model, has not been evaluated carefully. Using limited data from policy documents, the impact of policy…

Using forecast modelling to evaluate treatment effects in single-group interrupted time series analysis.

PubMed

Linden, Ariel

2018-05-11

Interrupted time series analysis (ITSA) is an evaluation methodology in which a single treatment unit's outcome is studied serially over time and the intervention is expected to "interrupt" the level and/or trend of that outcome. ITSA is commonly evaluated using methods which may produce biased results if model assumptions are violated. In this paper, treatment effects are alternatively assessed by using forecasting methods to closely fit the preintervention observations and then forecast the post-intervention trend. A treatment effect may be inferred if the actual post-intervention observations diverge from the forecasts by some specified amount. The forecasting approach is demonstrated using the effect of California's Proposition 99 for reducing cigarette sales. Three forecast models are fit to the preintervention series-linear regression (REG), Holt-Winters (HW) non-seasonal smoothing, and autoregressive moving average (ARIMA)-and forecasts are generated into the post-intervention period. The actual observations are then compared with the forecasts to assess intervention effects. The preintervention data were fit best by HW, followed closely by ARIMA. REG fit the data poorly. The actual post-intervention observations were above the forecasts in HW and ARIMA, suggesting no intervention effect, but below the forecasts in the REG (suggesting a treatment effect), thereby raising doubts about any definitive conclusion of a treatment effect. In a single-group ITSA, treatment effects are likely to be biased if the model is misspecified. Therefore, evaluators should consider using forecast models to accurately fit the preintervention data and generate plausible counterfactual forecasts, thereby improving causal inference of treatment effects in single-group ITSA studies. © 2018 John Wiley & Sons, Ltd.

Induced subgraph searching for geometric model fitting

NASA Astrophysics Data System (ADS)

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

2017-11-01

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

On the Model-Based Bootstrap with Missing Data: Obtaining a "P"-Value for a Test of Exact Fit

ERIC Educational Resources Information Center

Savalei, Victoria; Yuan, Ke-Hai

2009-01-01

Evaluating the fit of a structural equation model via bootstrap requires a transformation of the data so that the null hypothesis holds exactly in the sample. For complete data, such a transformation was proposed by Beran and Srivastava (1985) for general covariance structure models and applied to structural equation modeling by Bollen and Stine…

Using a second‐order differential model to fit data without baselines in protein isothermal chemical denaturation

PubMed Central

Tang, Chuanning; Lew, Scott

2016-01-01

Abstract In vitro protein stability studies are commonly conducted via thermal or chemical denaturation/renaturation of protein. Conventional data analyses on the protein unfolding/(re)folding require well‐defined pre‐ and post‐transition baselines to evaluate Gibbs free‐energy change associated with the protein unfolding/(re)folding. This evaluation becomes problematic when there is insufficient data for determining the pre‐ or post‐transition baselines. In this study, fitting on such partial data obtained in protein chemical denaturation is established by introducing second‐order differential (SOD) analysis to overcome the limitations that the conventional fitting method has. By reducing numbers of the baseline‐related fitting parameters, the SOD analysis can successfully fit incomplete chemical denaturation data sets with high agreement to the conventional evaluation on the equivalent completed data, where the conventional fitting fails in analyzing them. This SOD fitting for the abbreviated isothermal chemical denaturation further fulfills data analysis methods on the insufficient data sets conducted in the two prevalent protein stability studies. PMID:26757366

Evolution of the Marine Officer Fitness Report: A Multivariate Analysis

DTIC Science & Technology

This thesis explores the evaluation behavior of United States Marine Corps (USMC) Reporting Seniors (RSs) from 2010 to 2017. Using fitness report...RSs evaluate the performance of subordinate active component unrestricted officer MROs over time. I estimate logistic regression models of the...lowest. However, these correlations indicating the effects of race matching on FITREP evaluations narrow in significance when performance-based factors

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

NASA Astrophysics Data System (ADS)

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

2017-03-01

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

Evaluating models of remember-know judgments: complexity, mimicry, and discriminability.

PubMed

Cohen, Andrew L; Rotello, Caren M; Macmillan, Neil A

2008-10-01

Remember-know judgments provide additional information in recognition memory tests, but the nature of this information and the attendant decision process are in dispute. Competing models have proposed that remember judgments reflect a sum of familiarity and recollective information (the one-dimensional model), are based on a difference between these strengths (STREAK), or are purely recollective (the dual-process model). A choice among these accounts is sometimes made by comparing the precision of their fits to data, but this strategy may be muddied by differences in model complexity: Some models that appear to provide good fits may simply be better able to mimic the data produced by other models. To evaluate this possibility, we simulated data with each of the models in each of three popular remember-know paradigms, then fit those data to each of the models. We found that the one-dimensional model is generally less complex than the others, but despite this handicap, it dominates the others as the best-fitting model. For both reasons, the one-dimensional model should be preferred. In addition, we found that some empirical paradigms are ill-suited for distinguishing among models. For example, data collected by soliciting remember/know/new judgments--that is, the trinary task--provide a particularly weak ground for distinguishing models. Additional tables and figures may be downloaded from the Psychonomic Society's Archive of Norms, Stimuli, and Data, at www.psychonomic.org/archive.

The effects of changes in physical fitness on academic performance among New York City youth.

PubMed

Bezold, Carla P; Konty, Kevin J; Day, Sophia E; Berger, Magdalena; Harr, Lindsey; Larkin, Michael; Napier, Melanie D; Nonas, Cathy; Saha, Subir; Harris, Tiffany G; Stark, James H

2014-12-01

To evaluate whether a change in fitness is associated with academic outcomes in New York City (NYC) middle-school students using longitudinal data and to evaluate whether this relationship is modified by student household poverty. This was a longitudinal study of 83,111 New York City middle-school students enrolled between 2006-2007 and 2011-2012. Fitness was measured as a composite percentile based on three fitness tests and categorized based on change from the previous year. The effect of the fitness change level on academic outcomes, measured as a composite percentile based on state standardized mathematics and English Language Arts test scores, was estimated using a multilevel growth model. Models were stratified by sex, and additional models were tested stratified by student household poverty. For both girls and boys, a substantial increase in fitness from the previous year resulted in a greater improvement in academic ranking than was seen in the reference group (girls: .36 greater percentile point improvement, 95% confidence interval: .09-.63; boys: .38 greater percentile point improvement, 95% confidence interval: .09-.66). A substantial decrease in fitness was associated with a decrease in academics in both boys and girls. Effects of fitness on academics were stronger in high-poverty boys and girls than in low-poverty boys and girls. Academic rankings improved for boys and girls who increased their fitness level by >20 percentile points compared to other students. Opportunities for increased physical fitness may be important to support academic performance. Copyright © 2014 Society for Adolescent Health and Medicine. Published by Elsevier Inc. All rights reserved.

An Assessment of the Nonparametric Approach for Evaluating the Fit of Item Response Models

ERIC Educational Resources Information Center

Liang, Tie; Wells, Craig S.; Hambleton, Ronald K.

2014-01-01

As item response theory has been more widely applied, investigating the fit of a parametric model becomes an important part of the measurement process. There is a lack of promising solutions to the detection of model misfit in IRT. Douglas and Cohen introduced a general nonparametric approach, RISE (Root Integrated Squared Error), for detecting…

Feasibility of quasi-random band model in evaluating atmospheric radiance

NASA Technical Reports Server (NTRS)

Tiwari, S. N.; Mirakhur, N.

1980-01-01

The use of the quasi-random band model in evaluating upwelling atmospheric radiation is investigated. The spectral transmittance and total band adsorptance are evaluated for selected molecular bands by using the line by line model, quasi-random band model, exponential sum fit method, and empirical correlations, and these are compared with the available experimental results. The atmospheric transmittance and upwelling radiance were calculated by using the line by line and quasi random band models and were compared with the results of an existing program called LOWTRAN. The results obtained by the exponential sum fit and empirical relations were not in good agreement with experimental results and their use cannot be justified for atmospheric studies. The line by line model was found to be the best model for atmospheric applications, but it is not practical because of high computational costs. The results of the quasi random band model compare well with the line by line and experimental results. The use of the quasi random band model is recommended for evaluation of the atmospheric radiation.

Using Geometry-Based Metrics as Part of Fitness-for-Purpose Evaluations of 3D City Models

NASA Astrophysics Data System (ADS)

Wong, K.; Ellul, C.

2016-10-01

Three-dimensional geospatial information is being increasingly used in a range of tasks beyond visualisation. 3D datasets, however, are often being produced without exact specifications and at mixed levels of geometric complexity. This leads to variations within the models' geometric and semantic complexity as well as the degree of deviation from the corresponding real world objects. Existing descriptors and measures of 3D data such as CityGML's level of detail are perhaps only partially sufficient in communicating data quality and fitness-for-purpose. This study investigates whether alternative, automated, geometry-based metrics describing the variation of complexity within 3D datasets could provide additional relevant information as part of a process of fitness-for-purpose evaluation. The metrics include: mean vertex/edge/face counts per building; vertex/face ratio; minimum 2D footprint area and; minimum feature length. Each metric was tested on six 3D city models from international locations. The results show that geometry-based metrics can provide additional information on 3D city models as part of fitness-for-purpose evaluations. The metrics, while they cannot be used in isolation, may provide a complement to enhance existing data descriptors if backed up with local knowledge, where possible.

Reliability and Model Fit

ERIC Educational Resources Information Center

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…

Promoting Fitness and Safety in Elementary Students: A Randomized Control Study of the Michigan Model for Health

ERIC Educational Resources Information Center

O'Neill, James M.; Clark, Jeffrey K.; Jones, James A.

2016-01-01

Background: In elementary grades, comprehensive health education curricula have demonstrated effectiveness in addressing singular health issues. The Michigan Model for Health (MMH) was implemented and evaluated to determine its impact on nutrition, physical fitness, and safety knowledge and skills. Methods: Schools (N = 52) were randomly assigned…

An astronomer's guide to period searching

NASA Astrophysics Data System (ADS)

Schwarzenberg-Czerny, A.

2003-03-01

We concentrate on analysis of unevenly sampled time series, interrupted by periodic gaps, as often encountered in astronomy. While some of our conclusions may appear surprising, all are based on classical statistical principles of Fisher & successors. Except for discussion of the resolution issues, it is best for the reader to forget temporarily about Fourier transforms and to concentrate on problems of fitting of a time series with a model curve. According to their statistical content we divide the issues into several sections, consisting of: (ii) statistical numerical aspects of model fitting, (iii) evaluation of fitted models as hypotheses testing, (iv) the role of the orthogonal models in signal detection (v) conditions for equivalence of periodograms (vi) rating sensitivity by test power. An experienced observer working with individual objects would benefit little from formalized statistical approach. However, we demonstrate the usefulness of this approach in evaluation of performance of periodograms and in quantitative design of large variability surveys.

A new UK fission yield evaluation UKFY3.7

NASA Astrophysics Data System (ADS)

Mills, Robert William

2017-09-01

The JEFF neutron induced and spontaneous fission product yield evaluation is currently unchanged from JEFF-3.1.1, also known by its UK designation UKFY3.6A. It is based upon experimental data combined with empirically fitted mass, charge and isomeric state models which are then adjusted within the experimental and model uncertainties to conform to the physical constraints of the fission process. A new evaluation has been prepared for JEFF, called UKFY3.7, that incorporates new experimental data and replaces the current empirical models (multi-Gaussian fits of mass distribution and Wahl Zp model for charge distribution combined with parameter extrapolation), with predictions from GEF. The GEF model has the advantage that one set of parameters allows the prediction of many different fissioning nuclides at different excitation energies unlike previous models where each fissioning nuclide at a specific excitation energy had to be fitted individually to the relevant experimental data. The new UKFY3.7 evaluation, submitted for testing as part of JEFF-3.3, is described alongside initial results of testing. In addition, initial ideas for future developments allowing inclusion of new measurements types and changing from any neutron spectrum type to true neutron energy dependence are discussed. Also, a method is proposed to propagate uncertainties of fission product yields based upon the experimental data that underlies the fission yield evaluation. The covariance terms being determined from the evaluated cumulative and independent yields combined with the experimental uncertainties on the cumulative yield measurements.

Predicting responses from Rasch measures.

PubMed

Linacre, John M

2010-01-01

There is a growing family of Rasch models for polytomous observations. Selecting a suitable model for an existing dataset, estimating its parameters and evaluating its fit is now routine. Problems arise when the model parameters are to be estimated from the current data, but used to predict future data. In particular, ambiguities in the nature of the current data, or overfit of the model to the current dataset, may mean that better fit to the current data may lead to worse fit to future data. The predictive power of several Rasch and Rasch-related models are discussed in the context of the Netflix Prize. Rasch-related models are proposed based on Singular Value Decomposition (SVD) and Boltzmann Machines.

A participatory evaluation model for Healthier Communities: developing indicators for New Mexico.

PubMed Central

Wallerstein, N

2000-01-01

Participatory evaluation models that invite community coalitions to take an active role in developing evaluations of their programs are a natural fit with Healthy Communities initiatives. The author describes the development of a participatory evaluation model for New Mexico's Healthier Communities program. She describes evaluation principles, research questions, and baseline findings. The evaluation model shows the links between process, community-level system impacts, and population health changes. PMID:10968754

Dimensionality of the 9-item Utrecht Work Engagement Scale revisited: A Bayesian structural equation modeling approach.

PubMed

Fong, Ted C T; Ho, Rainbow T H

2015-01-01

The aim of this study was to reexamine the dimensionality of the widely used 9-item Utrecht Work Engagement Scale using the maximum likelihood (ML) approach and Bayesian structural equation modeling (BSEM) approach. Three measurement models (1-factor, 3-factor, and bi-factor models) were evaluated in two split samples of 1,112 health-care workers using confirmatory factor analysis and BSEM, which specified small-variance informative priors for cross-loadings and residual covariances. Model fit and comparisons were evaluated by posterior predictive p-value (PPP), deviance information criterion, and Bayesian information criterion (BIC). None of the three ML-based models showed an adequate fit to the data. The use of informative priors for cross-loadings did not improve the PPP for the models. The 1-factor BSEM model with approximately zero residual covariances displayed a good fit (PPP>0.10) to both samples and a substantially lower BIC than its 3-factor and bi-factor counterparts. The BSEM results demonstrate empirical support for the 1-factor model as a parsimonious and reasonable representation of work engagement.

The range of attraction for light traps catching Culicoides biting midges (Diptera: Ceratopogonidae)

PubMed Central

2013-01-01

Background Culicoides are vectors of e.g. bluetongue virus and Schmallenberg virus in northern Europe. Light trapping is an important tool for detecting the presence and quantifying the abundance of vectors in the field. Until now, few studies have investigated the range of attraction of light traps. Methods Here we test a previously described mathematical model (Model I) and two novel models for the attraction of vectors to light traps (Model II and III). In Model I, Culicoides fly to the nearest trap from within a fixed range of attraction. In Model II Culicoides fly towards areas with greater light intensity, and in Model III Culicoides evaluate light sources in the field of view and fly towards the strongest. Model II and III incorporated the directionally dependent light field created around light traps with fluorescent light tubes. All three models were fitted to light trap collections obtained from two novel experimental setups in the field where traps were placed in different configurations. Results Results showed that overlapping ranges of attraction of neighboring traps extended the shared range of attraction. Model I did not fit data from any of the experimental setups. Model II could only fit data from one of the setups, while Model III fitted data from both experimental setups. Conclusions The model with the best fit, Model III, indicates that Culicoides continuously evaluate the light source direction and intensity. The maximum range of attraction of a single 4W CDC light trap was estimated to be approximately 15.25 meters. The attraction towards light traps is different from the attraction to host animals and thus light trap catches may not represent the vector species and numbers attracted to hosts. PMID:23497628

The range of attraction for light traps catching Culicoides biting midges (Diptera: Ceratopogonidae).

PubMed

Kirkeby, Carsten; Græsbøll, Kaare; Stockmarr, Anders; Christiansen, Lasse E; Bødker, René

2013-03-15

Culicoides are vectors of e.g. bluetongue virus and Schmallenberg virus in northern Europe. Light trapping is an important tool for detecting the presence and quantifying the abundance of vectors in the field. Until now, few studies have investigated the range of attraction of light traps. Here we test a previously described mathematical model (Model I) and two novel models for the attraction of vectors to light traps (Model II and III). In Model I, Culicoides fly to the nearest trap from within a fixed range of attraction. In Model II Culicoides fly towards areas with greater light intensity, and in Model III Culicoides evaluate light sources in the field of view and fly towards the strongest. Model II and III incorporated the directionally dependent light field created around light traps with fluorescent light tubes. All three models were fitted to light trap collections obtained from two novel experimental setups in the field where traps were placed in different configurations. Results showed that overlapping ranges of attraction of neighboring traps extended the shared range of attraction. Model I did not fit data from any of the experimental setups. Model II could only fit data from one of the setups, while Model III fitted data from both experimental setups. The model with the best fit, Model III, indicates that Culicoides continuously evaluate the light source direction and intensity. The maximum range of attraction of a single 4W CDC light trap was estimated to be approximately 15.25 meters. The attraction towards light traps is different from the attraction to host animals and thus light trap catches may not represent the vector species and numbers attracted to hosts.

Effective learning among elite football players: the development of a football-specific self-regulated learning questionnaire.

PubMed

Toering, Tynke; Jordet, Geir; Ripegutu, Anders

2013-01-01

The present study aimed to develop a football-specific self-report instrument measuring self-regulated learning in the context of daily practice, which can be used to monitor the extent to which players take responsibility for their own learning. Development of the instrument involved six steps: 1. Literature review based on Zimmerman's (2006) theory of self-regulated learning, 2. Item generation, 3. Item validation, 4. Pilot studies, 5. Exploratory factor analysis (EFA), and 6. Confirmatory factor analysis (CFA). The instrument was tested for reliability and validity among 204 elite youth football players aged 13-16 years (Mage = 14.6; s = 0.60; 123 boys, 81 girls). The EFA indicated that a five-factor model fitted the observed data best (reflection, evaluation, planning, speaking up, and coaching). However, the CFA showed that a three-factor structure including 22 items produced a satisfactory model fit (reflection, evaluation, and planning; non-normed fit index [NNFI] = 0.96, comparative fit index [CFI] = 0.95, root mean square error of approximation [RMSEA] = 0.067). While the self-regulation processes of reflection, evaluation, and planning are strongly related and fit well into one model, other self-regulated learning processes seem to be more individually determined. In conclusion, the questionnaire developed in this study is considered a reliable and valid instrument to measure self-regulated learning among elite football players.

Links between physical fitness and cardiovascular reactivity and recovery to psychological stressors: A meta-analysis.

PubMed

Forcier, Kathleen; Stroud, Laura R; Papandonatos, George D; Hitsman, Brian; Reiches, Meredith; Krishnamoorthy, Jenelle; Niaura, Raymond

2006-11-01

A meta-analysis of published studies with adult human participants was conducted to evaluate whether physical fitness attenuates cardiovascular reactivity and improves recovery from acute psychological stressors. Thirty-three studies met selection criteria; 18 were included in recovery analyses. Effect sizes and moderator influences were calculated by using meta-analysis software. A fixed effects model was fit initially; however, between-studies heterogeneity could not be explained even after inclusion of moderators. Therefore, to account for residual heterogeneity, a random effects model was estimated. Under this model, fit individuals showed significantly attenuated heart rate and systolic blood pressure reactivity and a trend toward attenuated diastolic blood pressure reactivity. Fit individuals also showed faster heart rate recovery, but there were no significant differences in systolic blood pressure or diastolic blood pressure recovery. No significant moderators emerged. Results have important implications for elucidating mechanisms underlying effects of fitness on cardiovascular disease and suggest that fitness may be an important confound in studies of stress reactivity. Copyright 2006 APA, all rights reserved.

Psychometric properties of the pain stages of change questionnaire as evaluated by rasch analysis in patients with chronic musculoskeletal pain

PubMed Central

2014-01-01

Background Our objective was to evaluate the measurement properties of the Pain Stages of Change Questionnaire (PSOCQ) and its four subscales Precontemplation, Contemplation, Action and Maintenance. Methods A total of 231 patients, median age 42 years, with chronic musculoskeletal pain responded to the 30 items in PSOCQ. Thresholds for item scores, and unidimensionality and invariance of the PSOCQ and its four subscales were evaluated by Rasch analysis, partial credit model. Results The items had disordered threshold and needed to be rescored. The 30 items in the PSOCQ did not fit the Rasch model Chi- square item trait statistics. All subscales fitted the Rasch models. The associations to pain (11 point numeric rating scale), emotional distress (Hopkins symptom check list v 25) and self-efficacy (Arthritis Self-Efficacy Scale) were highest for the Precontemplation subscale. Conclusion The present analysis revealed that all four subscales in PSOCQ fitted the Rasch model. No common construct for all subscales were identified, but the Action and Maintenance subscales were closely related. PMID:24646065

History, Epidemic Evolution, and Model Burn-In for a Network of Annual Invasion: Soybean Rust.

PubMed

Sanatkar, M R; Scoglio, C; Natarajan, B; Isard, S A; Garrett, K A

2015-07-01

Ecological history may be an important driver of epidemics and disease emergence. We evaluated the role of history and two related concepts, the evolution of epidemics and the burn-in period required for fitting a model to epidemic observations, for the U.S. soybean rust epidemic (caused by Phakopsora pachyrhizi). This disease allows evaluation of replicate epidemics because the pathogen reinvades the United States each year. We used a new maximum likelihood estimation approach for fitting the network model based on observed U.S. epidemics. We evaluated the model burn-in period by comparing model fit based on each combination of other years of observation. When the miss error rates were weighted by 0.9 and false alarm error rates by 0.1, the mean error rate did decline, for most years, as more years were used to construct models. Models based on observations in years closer in time to the season being estimated gave lower miss error rates for later epidemic years. The weighted mean error rate was lower in backcasting than in forecasting, reflecting how the epidemic had evolved. Ongoing epidemic evolution, and potential model failure, can occur because of changes in climate, host resistance and spatial patterns, or pathogen evolution.

Older driver fitness-to-drive evaluation using naturalistic driving data.

PubMed

Guo, Feng; Fang, Youjia; Antin, Jonathan F

2015-09-01

As our driving population continues to age, it is becoming increasingly important to find a small set of easily administered fitness metrics that can meaningfully and reliably identify at-risk seniors requiring more in-depth evaluation of their driving skills and weaknesses. Sixty driver assessment metrics related to fitness-to-drive were examined for 20 seniors who were followed for a year using the naturalistic driving paradigm. Principal component analysis and negative binomial regression modeling approaches were used to develop parsimonious models relating the most highly predictive of the driver assessment metrics to the safety-related outcomes observed in the naturalistic driving data. This study provides important confirmation using naturalistic driving methods of the relationship between contrast sensitivity and crash-related events. The results of this study provide crucial information on the continuing journey to identify metrics and protocols that could be applied to determine seniors' fitness to drive. Published by Elsevier Ltd.

Amino acids intake and physical fitness among adolescents.

PubMed

Gracia-Marco, Luis; Bel-Serrat, Silvia; Cuenca-Garcia, Magdalena; Gonzalez-Gross, Marcela; Pedrero-Chamizo, Raquel; Manios, Yannis; Marcos, Ascensión; Molnar, Denes; Widhalm, Kurt; Polito, Angela; Vanhelst, Jeremy; Hagströmer, Maria; Sjöström, Michael; Kafatos, Anthony; de Henauw, Stefaan; Gutierrez, Ángel; Castillo, Manuel J; Moreno, Luis A

2017-06-01

The aim was to investigate whether there was an association between amino acid (AA) intake and physical fitness and if so, to assess whether this association was independent of carbohydrates intake. European adolescents (n = 1481, 12.5-17.5 years) were measured. Intake was assessed via two non-consecutive 24-h dietary recalls. Lower and upper limbs muscular fitness was assessed by standing long jump and handgrip strength tests, respectively. Cardiorespiratory fitness was assessed by the 20-m shuttle run test. Physical activity was objectively measured. Socioeconomic status was obtained via questionnaires. Lower limbs muscular fitness seems to be positively associated with tryptophan, histidine and methionine intake in boys, regardless of centre, age, socioeconomic status, physical activity and total energy intake (model 1). However, these associations disappeared once carbohydrates intake was controlled for (model 2). In girls, only proline intake seems to be positively associated with lower limbs muscular fitness (model 2) while cardiorespiratory fitness seems to be positively associated with leucine (model 1) and proline intake (models 1 and 2). None of the observed significant associations remained significant once multiple testing was controlled for. In conclusion, we failed to detect any associations between any of the evaluated AAs and physical fitness after taking into account the effect of multiple testing.

Alternative Loglinear Smoothing Models and Their Effect on Equating Function Accuracy. Research Report. ETS RR-09-48

ERIC Educational Resources Information Center

Moses, Tim; Holland, Paul

2009-01-01

This simulation study evaluated the potential of alternative loglinear smoothing strategies for improving equipercentile equating function accuracy. These alternative strategies use cues from the sample data to make automatable and efficient improvements to model fit, either through the use of indicator functions for fitting large residuals or by…

Exploring the measurement properties of the osteopathy clinical teaching questionnaire using Rasch analysis.

PubMed

Vaughan, Brett

2018-01-01

Clinical teaching evaluations are common in health profession education programs to ensure students are receiving a quality clinical education experience. Questionnaires students use to evaluate their clinical teachers have been developed in professions such as medicine and nursing. The development of a questionnaire that is specifically for the osteopathy on-campus, student-led clinic environment is warranted. Previous work developed the 30-item Osteopathy Clinical Teaching Questionnaire. The current study utilised Rasch analysis to investigate the construct validity of the Osteopathy Clinical Teaching Questionnaire and provide evidence for the validity argument through fit to the Rasch model. Senior osteopathy students at four institutions in Australia, New Zealand and the United Kingdom rated their clinical teachers using the Osteopathy Clinical Teaching Questionnaire. Three hundred and ninety-nine valid responses were received and the data were evaluated for fit to the Rasch model. Reliability estimations (Cronbach's alpha and McDonald's omega) were also evaluated for the final model. The initial analysis demonstrated the data did not fit the Rasch model. Accordingly, modifications to the questionnaire were made including removing items, removing person responses, and rescoring one item. The final model contained 12 items and fit to the Rasch model was adequate. Support for unidimensionality was demonstrated through both the Principal Components Analysis/t-test, and the Cronbach's alpha and McDonald's omega reliability estimates. Analysis of the questionnaire using McDonald's omega hierarchical supported a general factor (quality of clinical teaching in osteopathy). The evidence for unidimensionality and the presence of a general factor support the calculation of a total score for the questionnaire as a sufficient statistic. Further work is now required to investigate the reliability of the 12-item Osteopathy Clinical Teaching Questionnaire to provide evidence for the validity argument.

A goodness-of-fit test for occupancy models with correlated within-season revisits

USGS Publications Warehouse

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 specifically evaluates occupancy model lack of fit related to correlation among detections within a sample unit. Our diagnostic tool is available for practitioners that serially deploy survey equipment as a way to achieve cost savings.

Evaluation of marginal/internal fit of chrome-cobalt crowns: Direct laser metal sintering versus computer-aided design and computer-aided manufacturing.

PubMed

Gunsoy, S; Ulusoy, M

2016-01-01

The purpose of this study was to evaluate the internal and marginal fit of chrome cobalt (Co-Cr) crowns were fabricated with laser sintering, computer-aided design (CAD) and computer-aided manufacturing, and conventional methods. Polyamide master and working models were designed and fabricated. The models were initially designed with a software application for three-dimensional (3D) CAD (Maya, Autodesk Inc.). All models were fabricated models were produced by a 3D printer (EOSINT P380 SLS, EOS). 128 1-unit Co-Cr fixed dental prostheses were fabricated with four different techniques: Conventional lost wax method, milled wax with lost-wax method (MWLW), direct laser metal sintering (DLMS), and milled Co-Cr (MCo-Cr). The cement film thickness of the marginal and internal gaps was measured by an observer using a stereomicroscope after taking digital photos in ×24. Best fit rates according to mean and standard deviations of all measurements was in DLMS both in premolar (65.84) and molar (58.38) models in μm. A significant difference was found DLMS and the rest of fabrication techniques (P < 0.05). No significant difference was found between MCo-CR and MWLW in all fabrication techniques both in premolar and molar models (P > 0.05). DMLS was best fitting fabrication techniques for single crown based on the results.The best fit was found in marginal; the larger gap was found in occlusal.All groups were within the clinically acceptable misfit range.

Particle size distributions by transmission electron microscopy: an interlaboratory comparison case study

PubMed Central

Rice, Stephen B; Chan, Christopher; Brown, Scott C; Eschbach, Peter; Han, Li; Ensor, David S; Stefaniak, Aleksandr B; Bonevich, John; Vladár, András E; Hight Walker, Angela R; Zheng, Jiwen; Starnes, Catherine; Stromberg, Arnold; Ye, Jia; Grulke, Eric A

2015-01-01

This paper reports an interlaboratory comparison that evaluated a protocol for measuring and analysing the particle size distribution of discrete, metallic, spheroidal nanoparticles using transmission electron microscopy (TEM). The study was focused on automated image capture and automated particle analysis. NIST RM8012 gold nanoparticles (30 nm nominal diameter) were measured for area-equivalent diameter distributions by eight laboratories. Statistical analysis was used to (1) assess the data quality without using size distribution reference models, (2) determine reference model parameters for different size distribution reference models and non-linear regression fitting methods and (3) assess the measurement uncertainty of a size distribution parameter by using its coefficient of variation. The interlaboratory area-equivalent diameter mean, 27.6 nm ± 2.4 nm (computed based on a normal distribution), was quite similar to the area-equivalent diameter, 27.6 nm, assigned to NIST RM8012. The lognormal reference model was the preferred choice for these particle size distributions as, for all laboratories, its parameters had lower relative standard errors (RSEs) than the other size distribution reference models tested (normal, Weibull and Rosin–Rammler–Bennett). The RSEs for the fitted standard deviations were two orders of magnitude higher than those for the fitted means, suggesting that most of the parameter estimate errors were associated with estimating the breadth of the distributions. The coefficients of variation for the interlaboratory statistics also confirmed the lognormal reference model as the preferred choice. From quasi-linear plots, the typical range for good fits between the model and cumulative number-based distributions was 1.9 fitted standard deviations less than the mean to 2.3 fitted standard deviations above the mean. Automated image capture, automated particle analysis and statistical evaluation of the data and fitting coefficients provide a framework for assessing nanoparticle size distributions using TEM for image acquisition. PMID:26361398

Identifying best-fitting inputs in health-economic model calibration: a Pareto frontier approach.

PubMed

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.

Comparison of machine learning techniques to predict all-cause mortality using fitness data: the Henry ford exercIse testing (FIT) project.

PubMed

Sakr, Sherif; Elshawi, Radwa; Ahmed, Amjad M; Qureshi, Waqas T; Brawner, Clinton A; Keteyian, Steven J; Blaha, Michael J; Al-Mallah, Mouaz H

2017-12-19

Prior studies have demonstrated that cardiorespiratory fitness (CRF) is a strong marker of cardiovascular health. Machine learning (ML) can enhance the prediction of outcomes through classification techniques that classify the data into predetermined categories. The aim of this study is to present an evaluation and comparison of how machine learning techniques can be applied on medical records of cardiorespiratory fitness and how the various techniques differ in terms of capabilities of predicting medical outcomes (e.g. mortality). We use data of 34,212 patients free of known coronary artery disease or heart failure who underwent clinician-referred exercise treadmill stress testing at Henry Ford Health Systems Between 1991 and 2009 and had a complete 10-year follow-up. Seven machine learning classification techniques were evaluated: Decision Tree (DT), Support Vector Machine (SVM), Artificial Neural Networks (ANN), Naïve Bayesian Classifier (BC), Bayesian Network (BN), K-Nearest Neighbor (KNN) and Random Forest (RF). In order to handle the imbalanced dataset used, the Synthetic Minority Over-Sampling Technique (SMOTE) is used. Two set of experiments have been conducted with and without the SMOTE sampling technique. On average over different evaluation metrics, SVM Classifier has shown the lowest performance while other models like BN, BC and DT performed better. The RF classifier has shown the best performance (AUC = 0.97) among all models trained using the SMOTE sampling. The results show that various ML techniques can significantly vary in terms of its performance for the different evaluation metrics. It is also not necessarily that the more complex the ML model, the more prediction accuracy can be achieved. The prediction performance of all models trained with SMOTE is much better than the performance of models trained without SMOTE. The study shows the potential of machine learning methods for predicting all-cause mortality using cardiorespiratory fitness data.

Sensitivity of Chemical Shift-Encoded Fat Quantification to Calibration of Fat MR Spectrum

PubMed Central

Wang, Xiaoke; Hernando, Diego; Reeder, Scott B.

2015-01-01

Purpose To evaluate the impact of different fat spectral models on proton density fat-fraction (PDFF) quantification using chemical shift-encoded (CSE) MRI. Material and Methods Simulations and in vivo imaging were performed. In a simulation study, spectral models of fat were compared pairwise. Comparison of magnitude fitting and mixed fitting was performed over a range of echo times and fat fractions. In vivo acquisitions from 41 patients were reconstructed using 7 published spectral models of fat. T2-corrected STEAM-MRS was used as reference. Results Simulations demonstrate that imperfectly calibrated spectral models of fat result in biases that depend on echo times and fat fraction. Mixed fitting is more robust against this bias than magnitude fitting. Multi-peak spectral models showed much smaller differences among themselves than when compared to the single-peak spectral model. In vivo studies show all multi-peak models agree better (for mixed fitting, slope ranged from 0.967–1.045 using linear regression) with reference standard than the single-peak model (for mixed fitting, slope=0.76). Conclusion It is essential to use a multi-peak fat model for accurate quantification of fat with CSE-MRI. Further, fat quantification techniques using multi-peak fat models are comparable and no specific choice of spectral model is shown to be superior to the rest. PMID:25845713

Comparing two-zone models of dust exposure.

PubMed

Jones, Rachael M; Simmons, Catherine E; Boelter, Fred W

2011-09-01

The selection and application of mathematical models to work tasks is challenging. Previously, we developed and evaluated a semi-empirical two-zone model that predicts time-weighted average (TWA) concentrations (Ctwa) of dust emitted during the sanding of drywall joint compound. Here, we fit the emission rate and random air speed variables of a mechanistic two-zone model to testing event data and apply and evaluate the model using data from two field studies. We found that the fitted random air speed values and emission rate were sensitive to (i) the size of the near-field and (ii) the objective function used for fitting, but this did not substantially impact predicted dust Ctwa. The mechanistic model predictions were lower than the semi-empirical model predictions and measured respirable dust Ctwa at Site A but were within an acceptable range. At Site B, a 10.5 m3 room, the mechanistic model did not capture the observed difference between PBZ and area Ctwa. The model predicted uniform mixing and predicted dust Ctwa up to an order of magnitude greater than was measured. We suggest that applications of the mechanistic model be limited to contexts where the near-field volume is very small relative to the far-field volume.

An Evaluation Research Model for System-Wide Textbook Selection.

ERIC Educational Resources Information Center

Talmage, Harriet; Walberg, Herbert T.

One component of an evaluation research model for system-wide selection of curriculum materials is reported: implementation of an evaluation design for obtaining data that permits professional and lay persons to base curriculum materials decisions on a "best fit" principle. The design includes teacher characteristics, learning environment…

A Bayesian Approach to Person Fit Analysis in Item Response Theory Models. Research Report.

ERIC Educational Resources Information Center

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…

Accounting for seasonal patterns in syndromic surveillance data for outbreak detection.

PubMed

Burr, Tom; Graves, Todd; Klamann, Richard; Michalak, Sarah; Picard, Richard; Hengartner, Nicolas

2006-12-04

Syndromic surveillance (SS) can potentially contribute to outbreak detection capability by providing timely, novel data sources. One SS challenge is that some syndrome counts vary with season in a manner that is not identical from year to year. Our goal is to evaluate the impact of inconsistent seasonal effects on performance assessments (false and true positive rates) in the context of detecting anomalous counts in data that exhibit seasonal variation. To evaluate the impact of inconsistent seasonal effects, we injected synthetic outbreaks into real data and into data simulated from each of two models fit to the same real data. Using real respiratory syndrome counts collected in an emergency department from 2/1/94-5/31/03, we varied the length of training data from one to eight years, applied a sequential test to the forecast errors arising from each of eight forecasting methods, and evaluated their detection probabilities (DP) on the basis of 1000 injected synthetic outbreaks. We did the same for each of two corresponding simulated data sets. The less realistic, nonhierarchical model's simulated data set assumed that "one season fits all," meaning that each year's seasonal peak has the same onset, duration, and magnitude. The more realistic simulated data set used a hierarchical model to capture violation of the "one season fits all" assumption. This experiment demonstrated optimistic bias in DP estimates for some of the methods when data simulated from the nonhierarchical model was used for DP estimation, thus suggesting that at least for some real data sets and methods, it is not adequate to assume that "one season fits all." For the data we analyze, the "one season fits all " assumption is violated, and DP performance claims based on simulated data that assume "one season fits all," for the forecast methods considered, except for moving average methods, tend to be optimistic. Moving average methods based on relatively short amounts of training data are competitive on all three data sets, but are particularly competitive on the real data and on data from the hierarchical model, which are the two data sets that violate the "one season fits all" assumption.

Linear and Poisson models for genetic evaluation of tick resistance in cross-bred Hereford x Nellore cattle.

PubMed

Ayres, D R; Pereira, R J; Boligon, A A; Silva, F F; Schenkel, F S; Roso, V M; Albuquerque, L G

2013-12-01

Cattle resistance to ticks is measured by the number of ticks infesting the animal. The model used for the genetic analysis of cattle resistance to ticks frequently requires logarithmic transformation of the observations. The objective of this study was to evaluate the predictive ability and goodness of fit of different models for the analysis of this trait in cross-bred Hereford x Nellore cattle. Three models were tested: a linear model using logarithmic transformation of the observations (MLOG); a linear model without transformation of the observations (MLIN); and a generalized linear Poisson model with residual term (MPOI). All models included the classificatory effects of contemporary group and genetic group and the covariates age of animal at the time of recording and individual heterozygosis, as well as additive genetic effects as random effects. Heritability estimates were 0.08 ± 0.02, 0.10 ± 0.02 and 0.14 ± 0.04 for MLIN, MLOG and MPOI models, respectively. The model fit quality, verified by deviance information criterion (DIC) and residual mean square, indicated fit superiority of MPOI model. The predictive ability of the models was compared by validation test in independent sample. The MPOI model was slightly superior in terms of goodness of fit and predictive ability, whereas the correlations between observed and predicted tick counts were practically the same for all models. A higher rank correlation between breeding values was observed between models MLOG and MPOI. Poisson model can be used for the selection of tick-resistant animals. © 2013 Blackwell Verlag GmbH.

Normal tissue complication probability modeling of radiation-induced hypothyroidism after head-and-neck radiation therapy.

PubMed

Bakhshandeh, Mohsen; Hashemi, Bijan; Mahdavi, Seied Rabi Mehdi; Nikoofar, Alireza; Vasheghani, Maryam; Kazemnejad, Anoshirvan

2013-02-01

To determine the dose-response relationship of the thyroid for radiation-induced hypothyroidism in head-and-neck radiation therapy, according to 6 normal tissue complication probability models, and to find the best-fit parameters of the models. Sixty-five patients treated with primary or postoperative radiation therapy for various cancers in the head-and-neck region were prospectively evaluated. Patient serum samples (tri-iodothyronine, thyroxine, thyroid-stimulating hormone [TSH], free tri-iodothyronine, and free thyroxine) were measured before and at regular time intervals until 1 year after the completion of radiation therapy. Dose-volume histograms (DVHs) of the patients' thyroid gland were derived from their computed tomography (CT)-based treatment planning data. Hypothyroidism was defined as increased TSH (subclinical hypothyroidism) or increased TSH in combination with decreased free thyroxine and thyroxine (clinical hypothyroidism). Thyroid DVHs were converted to 2 Gy/fraction equivalent doses using the linear-quadratic formula with α/β = 3 Gy. The evaluated models included the following: Lyman with the DVH reduced to the equivalent uniform dose (EUD), known as LEUD; Logit-EUD; mean dose; relative seriality; individual critical volume; and population critical volume models. The parameters of the models were obtained by fitting the patients' data using a maximum likelihood analysis method. The goodness of fit of the models was determined by the 2-sample Kolmogorov-Smirnov test. Ranking of the models was made according to Akaike's information criterion. Twenty-nine patients (44.6%) experienced hypothyroidism. None of the models was rejected according to the evaluation of the goodness of fit. The mean dose model was ranked as the best model on the basis of its Akaike's information criterion value. The D(50) estimated from the models was approximately 44 Gy. The implemented normal tissue complication probability models showed a parallel architecture for the thyroid. The mean dose model can be used as the best model to describe the dose-response relationship for hypothyroidism complication. Copyright © 2013 Elsevier Inc. All rights reserved.

Discrete time rescaling theorem: determining goodness of fit for discrete time statistical models of neural spiking.

PubMed

Haslinger, Robert; Pipa, Gordon; Brown, Emery

2010-10-01

One approach for understanding the encoding of information by spike trains is to fit statistical models and then test their goodness of fit. The time-rescaling theorem provides a goodness-of-fit test consistent with the point process nature of spike trains. The interspike intervals (ISIs) are rescaled (as a function of the model's spike probability) to be independent and exponentially distributed if the model is accurate. A Kolmogorov-Smirnov (KS) test between the rescaled ISIs and the exponential distribution is then used to check goodness of fit. This rescaling relies on assumptions of continuously defined time and instantaneous events. However, spikes have finite width, and statistical models of spike trains almost always discretize time into bins. Here we demonstrate that finite temporal resolution of discrete time models prevents their rescaled ISIs from being exponentially distributed. Poor goodness of fit may be erroneously indicated even if the model is exactly correct. We present two adaptations of the time-rescaling theorem to discrete time models. In the first we propose that instead of assuming the rescaled times to be exponential, the reference distribution be estimated through direct simulation by the fitted model. In the second, we prove a discrete time version of the time-rescaling theorem that analytically corrects for the effects of finite resolution. This allows us to define a rescaled time that is exponentially distributed, even at arbitrary temporal discretizations. We demonstrate the efficacy of both techniques by fitting generalized linear models to both simulated spike trains and spike trains recorded experimentally in monkey V1 cortex. Both techniques give nearly identical results, reducing the false-positive rate of the KS test and greatly increasing the reliability of model evaluation based on the time-rescaling theorem.

Estimation of retinal vessel caliber using model fitting and random forests

NASA Astrophysics Data System (ADS)

Araújo, Teresa; Mendonça, Ana Maria; Campilho, Aurélio

2017-03-01

Retinal vessel caliber changes are associated with several major diseases, such as diabetes and hypertension. These caliber changes can be evaluated using eye fundus images. However, the clinical assessment is tiresome and prone to errors, motivating the development of automatic methods. An automatic method based on vessel crosssection intensity profile model fitting for the estimation of vessel caliber in retinal images is herein proposed. First, vessels are segmented from the image, vessel centerlines are detected and individual segments are extracted and smoothed. Intensity profiles are extracted perpendicularly to the vessel, and the profile lengths are determined. Then, model fitting is applied to the smoothed profiles. A novel parametric model (DoG-L7) is used, consisting on a Difference-of-Gaussians multiplied by a line which is able to describe profile asymmetry. Finally, the parameters of the best-fit model are used for determining the vessel width through regression using ensembles of bagged regression trees with random sampling of the predictors (random forests). The method is evaluated on the REVIEW public dataset. A precision close to the observers is achieved, outperforming other state-of-the-art methods. The method is robust and reliable for width estimation in images with pathologies and artifacts, with performance independent of the range of diameters.

Correlation of Respirator Fit Measured on Human Subjects and a Static Advanced Headform

PubMed Central

Bergman, Michael S.; He, Xinjian; Joseph, Michael E.; Zhuang, Ziqing; Heimbuch, Brian K.; Shaffer, Ronald E.; Choe, Melanie; Wander, Joseph D.

2015-01-01

This study assessed the correlation of N95 filtering face-piece respirator (FFR) fit between a Static Advanced Headform (StAH) and 10 human test subjects. Quantitative fit evaluations were performed on test subjects who made three visits to the laboratory. On each visit, one fit evaluation was performed on eight different FFRs of various model/size variations. Additionally, subject breathing patterns were recorded. Each fit evaluation comprised three two-minute exercises: “Normal Breathing,” “Deep Breathing,” and again “Normal Breathing.” The overall test fit factors (FF) for human tests were recorded. The same respirator samples were later mounted on the StAH and the overall test manikin fit factors (MFF) were assessed utilizing the recorded human breathing patterns. Linear regression was performed on the mean log10-transformed FF and MFF values to assess the relationship between the values obtained from humans and the StAH. This is the first study to report a positive correlation of respirator fit between a headform and test subjects. The linear regression by respirator resulted in R2 = 0.95, indicating a strong linear correlation between FF and MFF. For all respirators the geometric mean (GM) FF values were consistently higher than those of the GM MFF. For 50% of respirators, GM FF and GM MFF values were significantly different between humans and the StAH. For data grouped by subject/respirator combinations, the linear regression resulted in R2 = 0.49. A weaker correlation (R2 = 0.11) was found using only data paired by subject/respirator combination where both the test subject and StAH had passed a real-time leak check before performing the fit evaluation. For six respirators, the difference in passing rates between the StAH and humans was < 20%, while two respirators showed a difference of 29% and 43%. For data by test subject, GM FF and GM MFF values were significantly different for 40% of the subjects. Overall, the advanced headform system has potential for assessing fit for some N95 FFR model/sizes. PMID:25265037

Identifying best-fitting inputs in health-economic model calibration: a Pareto frontier approach

PubMed Central

Enns, Eva A.; Cipriano, Lauren E.; Simons, Cyrena T.; Kong, Chung Yin

2014-01-01

Background 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. Methods We demonstrate the Pareto frontier approach in the calibration of two 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 two possible weighted-sum GOF scoring systems, and compare the health economic conclusions arising from these different definitions of best-fitting. Results For the simple model, outcomes evaluated over the best-fitting input sets according to the two weighted-sum GOF schemes were virtually non-overlapping on the cost-effectiveness plane and resulted in very different incremental cost-effectiveness ratios ($79,300 [95%CI: 72,500 – 87,600] vs. $139,700 [95%CI: 79,900 - 182,800] per 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. Conclusions 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. PMID:24799456

Forecasting plant phenology: evaluating the phenological models for Betula pendula and Padus racemosa spring phases, Latvia.

PubMed

Kalvāns, Andis; Bitāne, Māra; Kalvāne, Gunta

2015-02-01

A historical phenological record and meteorological data of the period 1960-2009 are used to analyse the ability of seven phenological models to predict leaf unfolding and beginning of flowering for two tree species-silver birch Betula pendula and bird cherry Padus racemosa-in Latvia. Model stability is estimated performing multiple model fitting runs using half of the data for model training and the other half for evaluation. Correlation coefficient, mean absolute error and mean squared error are used to evaluate model performance. UniChill (a model using sigmoidal development rate and temperature relationship and taking into account the necessity for dormancy release) and DDcos (a simple degree-day model considering the diurnal temperature fluctuations) are found to be the best models for describing the considered spring phases. A strong collinearity between base temperature and required heat sum is found for several model fitting runs of the simple degree-day based models. Large variation of the model parameters between different model fitting runs in case of more complex models indicates similar collinearity and over-parameterization of these models. It is suggested that model performance can be improved by incorporating the resolved daily temperature fluctuations of the DDcos model into the framework of the more complex models (e.g. UniChill). The average base temperature, as found by DDcos model, for B. pendula leaf unfolding is 5.6 °C and for the start of the flowering 6.7 °C; for P. racemosa, the respective base temperatures are 3.2 °C and 3.4 °C.

Assessing Local Model Adequacy in Bayesian Hierarchical Models Using the Partitioned Deviance Information Criterion

PubMed Central

Wheeler, David C.; Hickson, DeMarc A.; Waller, Lance A.

2010-01-01

Many diagnostic tools and goodness-of-fit measures, such as the Akaike information criterion (AIC) and the Bayesian deviance information criterion (DIC), are available to evaluate the overall adequacy of linear regression models. In addition, visually assessing adequacy in models has become an essential part of any regression analysis. In this paper, we focus on a spatial consideration of the local DIC measure for model selection and goodness-of-fit evaluation. We use a partitioning of the DIC into the local DIC, leverage, and deviance residuals to assess local model fit and influence for both individual observations and groups of observations in a Bayesian framework. We use visualization of the local DIC and differences in local DIC between models to assist in model selection and to visualize the global and local impacts of adding covariates or model parameters. We demonstrate the utility of the local DIC in assessing model adequacy using HIV prevalence data from pregnant women in the Butare province of Rwanda during 1989-1993 using a range of linear model specifications, from global effects only to spatially varying coefficient models, and a set of covariates related to sexual behavior. Results of applying the diagnostic visualization approach include more refined model selection and greater understanding of the models as applied to the data. PMID:21243121

Body mass index and physical fitness in Brazilian adolescents.

PubMed

Lopes, Vitor P; Malina, Robert M; Gomez-Campos, Rossana; Cossio-Bolaños, Marco; Arruda, Miguel de; Hobold, Edilson

2018-05-05

Evaluate the relationship between body mass index and physical fitness in a cross-sectional sample of Brazilian youth. Participants were 3849 adolescents (2027 girls) aged 10-17 years. Weight and height were measured; body mass index was calculated. Physical fitness was evaluated with a multistage 20m shuttle run (cardiovascular endurance), standing long jump (power), and push-ups (upper body strength). Participants were grouped by sex into four age groups: 10-11, 12-13, 14-15, and 16-17 years. Sex-specific ANOVA was used to evaluate differences in each physical fitness item among weight status categories by age group. Relationships between body mass index and each physical fitness item were evaluated with quadratic regression models by age group within each sex. The physical fitness of thin and normal youth was, with few exceptions, significantly better than the physical fitness of overweight and obese youth in each age group by sex. On the other hand, physical fitness performances did not consistently differ, on average, between thin and normal weight and between overweight and obese youths. Results of the quadratic regressions indicated a curvilinear (parabolic) relationship between body mass index and each physical fitness item in most age groups. Better performances were attained by adolescents in the mid-range of the body mass index distribution, while performances of youth at the low and high ends of the body mass index distribution were lower. Relationships between the body mass index and physical fitness were generally nonlinear (parabolic) in youth 10-17 years. Copyright © 2018 Sociedade Brasileira de Pediatria. Published by Elsevier Editora Ltda. All rights reserved.

Child t-shirt size data set from 3D body scanner anthropometric measurements and a questionnaire.

PubMed

Pierola, A; Epifanio, I; Alemany, S

2017-04-01

A dataset of a fit assessment study in children is presented. Anthropometric measurements of 113 children were obtained using a 3D body scanner. Children tested a t-shirt of different sizes and a different model for boys and girls, and their fit was assessed by an expert. This expert labeled the fit as 0 (correct), -1 (if the garment was small for that child), or 1 (if the garment was large for that child) in an ordered factor called Size-fit. Moreover, the fit was numerically assessed from 1 (very poor fit) to 10 (perfect fit) in a variable called Expert evaluation. This data set contains the differences between the reference mannequin of the evaluated size and the child׳s anthropometric measurements for 27 variables. Besides these variables, in the data set, we can also find the gender, the size evaluated, and the size recommended by the expert, including if an intermediate, but nonexistent size between two consecutive sizes would have been the right size. In total, there are 232 observations. The analysis of these data can be found in Pierola et al. (2016) [2].

Development and evaluation of social cognitive measures related to adolescent physical activity.

PubMed

Dewar, Deborah L; Lubans, David Revalds; Morgan, Philip James; Plotnikoff, Ronald C

2013-05-01

This study aimed to develop and evaluate the construct validity and reliability of modernized social cognitive measures relating to physical activity behaviors in adolescents. An instrument was developed based on constructs from Bandura's Social Cognitive Theory and included the following scales: self-efficacy, situation (perceived physical environment), social support, behavioral strategies, and outcome expectations and expectancies. The questionnaire was administered in a sample of 171 adolescents (age = 13.6 ± 1.2 years, females = 61%). Confirmatory factor analysis was employed to examine model-fit for each scale using multiple indices, including chi-square index, comparative-fit index (CFI), goodness-of-fit index (GFI), and the root mean square error of approximation (RMSEA). Reliability properties were also examined (ICC and Cronbach's alpha). Each scale represented a statistically sound measure: fit indices indicated each model to be an adequate-to-exact fit to the data; internal consistency was acceptable to good (α = 0.63-0.79); rank order repeatability was strong (ICC = 0.82-0.91). Results support the validity and reliability of social cognitive scales relating to physical activity among adolescents. As such, the developed scales have utility for the identification of potential social cognitive correlates of youth physical activity, mediators of physical activity behavior changes and the testing of theoretical models based on Social Cognitive Theory.

Discrete Time Rescaling Theorem: Determining Goodness of Fit for Discrete Time Statistical Models of Neural Spiking

PubMed Central

Haslinger, Robert; Pipa, Gordon; Brown, Emery

2010-01-01

One approach for understanding the encoding of information by spike trains is to fit statistical models and then test their goodness of fit. The time rescaling theorem provides a goodness of fit test consistent with the point process nature of spike trains. The interspike intervals (ISIs) are rescaled (as a function of the model’s spike probability) to be independent and exponentially distributed if the model is accurate. A Kolmogorov Smirnov (KS) test between the rescaled ISIs and the exponential distribution is then used to check goodness of fit. This rescaling relies upon assumptions of continuously defined time and instantaneous events. However spikes have finite width and statistical models of spike trains almost always discretize time into bins. Here we demonstrate that finite temporal resolution of discrete time models prevents their rescaled ISIs from being exponentially distributed. Poor goodness of fit may be erroneously indicated even if the model is exactly correct. We present two adaptations of the time rescaling theorem to discrete time models. In the first we propose that instead of assuming the rescaled times to be exponential, the reference distribution be estimated through direct simulation by the fitted model. In the second, we prove a discrete time version of the time rescaling theorem which analytically corrects for the effects of finite resolution. This allows us to define a rescaled time which is exponentially distributed, even at arbitrary temporal discretizations. We demonstrate the efficacy of both techniques by fitting Generalized Linear Models (GLMs) to both simulated spike trains and spike trains recorded experimentally in monkey V1 cortex. Both techniques give nearly identical results, reducing the false positive rate of the KS test and greatly increasing the reliability of model evaluation based upon the time rescaling theorem. PMID:20608868

Four- and five-factor models of the WAIS-IV in a clinical sample: Variations in indicator configuration and factor correlational structure.

PubMed

Staffaroni, Adam M; Eng, Megan E; Moses, James A; Zeiner, Harriet Katz; Wickham, Robert E

2018-05-01

A growing body of research supports the validity of 5-factor models for interpreting the Wechsler Adult Intelligence Scale-Fourth Edition (WAIS-IV). The majority of these studies have utilized the WAIS-IV normative or clinical sample, the latter of which differs in its diagnostic composition from the referrals seen at outpatient neuropsychology clinics. To address this concern, 2 related studies were conducted on a sample of 322 American military Veterans who were referred for outpatient neuropsychological assessment. In Study 1, 4 hierarchical models with varying indicator configurations were evaluated: 3 extant 5-factor models from the literature and the traditional 4-factor model. In Study 2, we evaluated 3 variations in correlation structure in the models from Study 1: indirect hierarchical (i.e., higher-order g), bifactor (direct hierarchical), and oblique models. The results from Study 1 suggested that both 4- and 5-factor models showed acceptable fit. The results from Study 2 showed that bifactor and oblique models offer improved fit over the typically specified indirect hierarchical model, and the oblique models outperformed the orthogonal bifactor models. An exploratory analysis found improved fit when bifactor models were specified with oblique rather than orthogonal latent factors. (PsycINFO Database Record (c) 2018 APA, all rights reserved).

Time series modeling and forecasting using memetic algorithms for regime-switching models.

PubMed

Bergmeir, Christoph; Triguero, Isaac; Molina, Daniel; Aznarte, José Luis; Benitez, José Manuel

2012-11-01

In this brief, we present a novel model fitting procedure for the neuro-coefficient smooth transition autoregressive model (NCSTAR), as presented by Medeiros and Veiga. The model is endowed with a statistically founded iterative building procedure and can be interpreted in terms of fuzzy rule-based systems. The interpretability of the generated models and a mathematically sound building procedure are two very important properties of forecasting models. The model fitting procedure employed by the original NCSTAR is a combination of initial parameter estimation by a grid search procedure with a traditional local search algorithm. We propose a different fitting procedure, using a memetic algorithm, in order to obtain more accurate models. An empirical evaluation of the method is performed, applying it to various real-world time series originating from three forecasting competitions. The results indicate that we can significantly enhance the accuracy of the models, making them competitive to models commonly used in the field.

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

PubMed

Lutchen, K R

1990-08-01

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

Experimental study of water desorption isotherms and thin-layer convective drying kinetics of bay laurel leaves

NASA Astrophysics Data System (ADS)

Ghnimi, Thouraya; Hassini, Lamine; Bagane, Mohamed

2016-12-01

The aim of this work is to determine the desorption isotherms and the drying kinetics of bay laurel leaves ( Laurus Nobilis L.). The desorption isotherms were performed at three temperature levels: 50, 60 and 70 °C and at water activity ranging from 0.057 to 0.88 using the statistic gravimetric method. Five sorption models were used to fit desorption experimental isotherm data. It was found that Kuhn model offers the best fitting of experimental moisture isotherms in the mentioned investigated ranges of temperature and water activity. The Net isosteric heat of water desorption was evaluated using The Clausius-Clapeyron equation and was then best correlated to equilibrium moisture content by the empirical Tsami's equation. Thin layer convective drying curves of bay laurel leaves were obtained for temperatures of 45, 50, 60 and 70 °C, relative humidity of 5, 15, 30 and 45 % and air velocities of 1, 1.5 and 2 m/s. A non linear regression procedure of Levenberg-Marquardt was used to fit drying curves with five semi empirical mathematical models available in the literature, The R2 and χ2 were used to evaluate the goodness of fit of models to data. Based on the experimental drying curves the drying characteristic curve (DCC) has been established and fitted with a third degree polynomial function. It was found that the Midilli Kucuk model was the best semi-empirical model describing thin layer drying kinetics of bay laurel leaves. The bay laurel leaves effective moisture diffusivity and activation energy were also identified.

Brief Lags in Interrupted Sequential Performance: Evaluating a Model and Model Evaluation Method

DTIC Science & Technology

2015-01-05

rehearsal mechanism in the model. To evaluate the model we developed a simple new goodness-of-fit test based on analysis of variance that offers an...repeated step). Sequen- tial constraints are common in medicine, equipment maintenance, computer programming and technical support, data analysis ...legal analysis , accounting, and many other home and workplace environ- ments. Sequential constraints also play a role in such basic cognitive processes

The role of social capital and community belongingness for exercise adherence: An exploratory study of the CrossFit gym model.

PubMed

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.

Comparative evaluation of human heat stress indices on selected hospital admissions in Sydney, Australia.

PubMed

Goldie, James; Alexander, Lisa; Lewis, Sophie C; Sherwood, Steven

2017-08-01

To find appropriate regression model specifications for counts of the daily hospital admissions of a Sydney cohort and determine which human heat stress indices best improve the models' fit. We built parent models of eight daily counts of admission records using weather station observations, census population estimates and public holiday data. We added heat stress indices; models with lower Akaike Information Criterion scores were judged a better fit. Five of the eight parent models demonstrated adequate fit. Daily maximum Simplified Wet Bulb Globe Temperature (sWBGT) consistently improved fit more than most other indices; temperature and heatwave indices also modelled some health outcomes well. Humidity and heat-humidity indices better fit counts of patients who died following admission. Maximum sWBGT is an ideal measure of heat stress for these types of Sydney hospital admissions. Simple temperature indices are a good fallback where a narrower range of conditions is investigated. Implications for public health: This study confirms the importance of selecting appropriate heat stress indices for modelling. Epidemiologists projecting Sydney hospital admissions should use maximum sWBGT as a common measure of heat stress. Health organisations interested in short-range forecasting may prefer simple temperature indices. © 2017 The Authors.

The FITS model office ergonomics program: a model for best practice.

PubMed

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.

Development of a modified independent parallel reactions kinetic model and comparison with the distributed activation energy model for the pyrolysis of a wide variety of biomass fuels.

PubMed

Sfakiotakis, Stelios; Vamvuka, Despina

2015-12-01

The pyrolysis of six waste biomass samples was studied and the fuels were kinetically evaluated. A modified independent parallel reactions scheme (IPR) and a distributed activation energy model (DAEM) were developed and their validity was assessed and compared by checking their accuracy of fitting the experimental results, as well as their prediction capability in different experimental conditions. The pyrolysis experiments were carried out in a thermogravimetric analyzer and a fitting procedure, based on least squares minimization, was performed simultaneously at different experimental conditions. A modification of the IPR model, considering dependence of the pre-exponential factor on heating rate, was proved to give better fit results for the same number of tuned kinetic parameters, comparing to the known IPR model and very good prediction results for stepwise experiments. Fit of calculated data to the experimental ones using the developed DAEM model was also proved to be very good. Copyright © 2015 Elsevier Ltd. All rights reserved.

A CAD System for Evaluating Footwear Fit

NASA Astrophysics Data System (ADS)

Savadkoohi, Bita Ture; de Amicis, Raffaele

With the great growth in footwear demand, the footwear manufacturing industry, for achieving commercial success, must be able to provide the footwear that fulfills consumer's requirement better than it's competitors. Accurate fitting for shoes is an important factor in comfort and functionality. Footwear fitter measurement have been using manual measurement for a long time, but the development of 3D acquisition devices and the advent of powerful 3D visualization and modeling techniques, automatically analyzing, searching and interpretation of the models have now made automatic determination of different foot dimensions feasible. In this paper, we proposed an approach for finding footwear fit within the shoe last data base. We first properly aligned the 3D models using "Weighted" Principle Component Analysis (WPCA). After solving the alignment problem we used an efficient algorithm for cutting the 3D model in order to find the footwear fit from shoe last data base.

Performance of the score systems Acute Physiology and Chronic Health Evaluation II and III at an interdisciplinary intensive care unit, after customization

PubMed Central

Markgraf, Rainer; Deutschinoff, Gerd; Pientka, Ludger; Scholten, Theo; Lorenz, Cristoph

2001-01-01

Background: Mortality predictions calculated using scoring scales are often not accurate in populations other than those in which the scales were developed because of differences in case-mix. The present study investigates the effect of first-level customization, using a logistic regression technique, on discrimination and calibration of the Acute Physiology and Chronic Health Evaluation (APACHE) II and III scales. Method: Probabilities of hospital death for patients were estimated by applying APACHE II and III and comparing these with observed outcomes. Using the split sample technique, a customized model to predict outcome was developed by logistic regression. The overall goodness-of-fit of the original and the customized models was assessed. Results: Of 3383 consecutive intensive care unit (ICU) admissions over 3 years, 2795 patients could be analyzed, and were split randomly into development and validation samples. The discriminative powers of APACHE II and III were unchanged by customization (areas under the receiver operating characteristic [ROC] curve 0.82 and 0.85, respectively). Hosmer-Lemeshow goodness-of-fit tests showed good calibration for APACHE II, but insufficient calibration for APACHE III. Customization improved calibration for both models, with a good fit for APACHE III as well. However, fit was different for various subgroups. Conclusions: The overall goodness-of-fit of APACHE III mortality prediction was improved significantly by customization, but uniformity of fit in different subgroups was not achieved. Therefore, application of the customized model provides no advantage, because differences in case-mix still limit comparisons of quality of care. PMID:11178223

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

PubMed

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

2014-03-01

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

Evaluation of the internal construct validity of the Personal Care Participation Assessment and Resource Tool (PC-PART) using Rasch analysis.

PubMed

Darzins, Susan; Imms, Christine; Di Stefano, Marilyn; Taylor, Nicholas F; Pallant, Julie F

2014-11-05

The Personal Care Participation Assessment and Resource Tool (PC-PART) is a 43-item, clinician-administered assessment, designed to identify patients' unmet needs (participation restrictions) in activities of daily living (ADL) required for community life. This information is important for identifying problems that need addressing to enable, for example, discharge from inpatient settings to community living. The objective of this study was to evaluate internal construct validity of the PC-PART using Rasch methods. Fit to the Rasch model was evaluated for 41 PC-PART items, assessing threshold ordering, overall model fit, individual item fit, person fit, internal consistency, Differential Item Functioning (DIF), targeting of items and dimensionality. Data used in this research were taken from admission data from a randomised controlled trial conducted at two publically funded inpatient rehabilitation units in Melbourne, Australia, with 996 participants (63% women; mean age 74 years) and with various impairment types. PC-PART items assessed as one scale, and original PC-PART domains evaluated as separate scales, demonstrated poor fit to the Rasch model. Adequate fit to the Rasch model was achieved in two newly formed PC-PART scales: Self-Care (16 items) and Domestic Life (14 items). Both scales were unidimensional, had acceptable internal consistency (PSI =0.85, 0.76, respectively) and well-targeted items. Rasch analysis did not support conventional summation of all PC-PART item scores to create a total score. However, internal construct validity of the newly formed PC-PART scales, Self-Care and Domestic Life, was supported. Their Rasch-derived scores provided interval-level measurement enabling summation of scores to form a total score on each scale. These scales may assist clinicians, managers and researchers in rehabilitation settings to assess and measure changes in ADL participation restrictions relevant to community living. Data used in this research were gathered during a registered randomised controlled trial: Australian and New Zealand Clinical Trials Registry ACTRN12609000973213. Ethics committee approval was gained for secondary analysis of data for this study.

Rasch analysis of the patient-rated wrist evaluation questionnaire.

PubMed

Esakki, Saravanan; MacDermid, Joy C; Vincent, Joshua I; Packham, Tara L; Walton, David; Grewal, Ruby

2018-01-01

The Patient-Rated Wrist Evaluation (PRWE) was developed as a wrist joint specific measure of pain and disability and evidence of sound validity has been accumulated through classical psychometric methods. Rasch analysis (RA) has been endorsed as a newer method for analyzing the clinical measurement properties of self-report outcome measures. The purpose of this study was to evaluate the PRWE using Rasch modeling. We employed the Rasch model to assess overall fit, response scaling, individual item fit, differential item functioning (DIF), local dependency, unidimensionality and person separation index (PSI). A convenience sample of 382 patients with distal radius fracture was recruited from the hand and upper limb clinic at large academic healthcare organization, London, Ontario, Canada, 6-month post-injury scores of the PRWE was used. RA was conducted on the 3 subscales (pain, specific activities, and usual activities) of the PRWE separately. The pain subscale adequately fit the Rasch model when item 4 "Pain - When it is at its worst" was deleted to eliminate non-uniform DIF by age group, and item 5 "How often do you have pain" was rescored by collapsing into 8 intervals to eliminate disordered thresholds. Uniform DIF for "Use my affected hand to push up from the chair" (by work status) and "Use bathroom tissue with my affected hand" (by injured hand) was addressed by splitting the items for analysis. After background rescoring of 2 items in pain subscale, 2 items in specific activities and 3 items in usual activities, all three subscales of the PRWE were well targeted and had high reliability (PSI = 0.86). These changes provided a unidimensional, interval-level scaled measure. Like a previous analysis of the Patient-Rated Wrist and Hand Evaluation, this study found the PRWE could be fit to the Rasch model with rescoring of multiple items. However, the modifications required to achieve fit were not the same across studies, our fit statistics also suggested one of the pain items should be deleted. This study adds to the pool of evidence supporting the PRWE, but cannot confidently provide a Rasch-based scoring algorithm.

On the accuracy of models for predicting sound propagation in fitted rooms.

PubMed

Hodgson, M

1990-08-01

The objective of this article is to make a contribution to the evaluation of the accuracy and applicability of models for predicting the sound propagation in fitted rooms such as factories, classrooms, and offices. The models studied are 1:50 scale models; the method-of-image models of Jovicic, Lindqvist, Hodgson, Kurze, and of Lemire and Nicolas; the emprical formula of Friberg; and Ondet and Barbry's ray-tracing model. Sound propagation predictions by the analytic models are compared with the results of sound propagation measurements in a 1:50 scale model and in a warehouse, both containing various densities of approximately isotropically distributed, rectangular-parallelepipedic fittings. The results indicate that the models of Friberg and of Lemire and Nicolas are fundamentally incorrect. While more generally applicable versions exist, the versions of the models of Jovicic and Kurze studied here are found to be of limited applicability since they ignore vertical-wall reflections. The Hodgson and Lindqvist models appear to be accurate in certain limited cases. This preliminary study found the ray-tracing model of Ondet and Barbry to be the most accurate of all the cases studied. Furthermore, it has the necessary flexibility with respect to room geometry, surface-absorption distribution, and fitting distribution. It appears to be the model with the greatest applicability to fitted-room sound propagation prediction.

FracFit: A Robust Parameter Estimation Tool for Anomalous Transport Problems

NASA Astrophysics Data System (ADS)

Kelly, J. F.; Bolster, D.; Meerschaert, M. M.; Drummond, J. D.; Packman, A. I.

2016-12-01

Anomalous transport cannot be adequately described with classical Fickian advection-dispersion equations (ADE). Rather, fractional calculus models may be used, which capture non-Fickian behavior (e.g. skewness and power-law tails). FracFit is a robust parameter estimation tool based on space- and time-fractional models used to model anomalous transport. Currently, four fractional models are supported: 1) space fractional advection-dispersion equation (sFADE), 2) time-fractional dispersion equation with drift (TFDE), 3) fractional mobile-immobile equation (FMIE), and 4) tempered fractional mobile-immobile equation (TFMIE); additional models may be added in the future. Model solutions using pulse initial conditions and continuous injections are evaluated using stable distribution PDFs and CDFs or subordination integrals. Parameter estimates are extracted from measured breakthrough curves (BTCs) using a weighted nonlinear least squares (WNLS) algorithm. Optimal weights for BTCs for pulse initial conditions and continuous injections are presented, facilitating the estimation of power-law tails. Two sample applications are analyzed: 1) continuous injection laboratory experiments using natural organic matter and 2) pulse injection BTCs in the Selke river. Model parameters are compared across models and goodness-of-fit metrics are presented, assisting model evaluation. The sFADE and time-fractional models are compared using space-time duality (Baeumer et. al., 2009), which links the two paradigms.

In Search of Golden Rules: Comment on Hypothesis-Testing Approaches to Setting Cutoff Values for Fit Indexes and Dangers in Overgeneralizing Hu and Bentler's (1999) Findings

ERIC Educational Resources Information Center

Marsh, Herbert W.; Hau, Kit-Tai; Wen, Zhonglin

2004-01-01

Goodness-of-fit (GOF) indexes provide "rules of thumb"?recommended cutoff values for assessing fit in structural equation modeling. Hu and Bentler (1999) proposed a more rigorous approach to evaluating decision rules based on GOF indexes and, on this basis, proposed new and more stringent cutoff values for many indexes. This article discusses…

Statistical evaluation of the metallurgical test data in the ORR-PSF-PVS irradiation experiment. [PWR; BWR

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

Stallmann, F.W.

1984-08-01

A statistical analysis of Charpy test results of the two-year Pressure Vessel Simulation metallurgical irradiation experiment was performed. Determination of transition temperature and upper shelf energy derived from computer fits compare well with eyeball fits. Uncertainties for all results can be obtained with computer fits. The results were compared with predictions in Regulatory Guide 1.99 and other irradiation damage models.

Convex Regression with Interpretable Sharp Partitions

PubMed Central

Petersen, Ashley; Simon, Noah; Witten, Daniela

2016-01-01

We consider the problem of predicting an outcome variable on the basis of a small number of covariates, using an interpretable yet non-additive model. We propose convex regression with interpretable sharp partitions (CRISP) for this task. CRISP partitions the covariate space into blocks in a data-adaptive way, and fits a mean model within each block. Unlike other partitioning methods, CRISP is fit using a non-greedy approach by solving a convex optimization problem, resulting in low-variance fits. We explore the properties of CRISP, and evaluate its performance in a simulation study and on a housing price data set. PMID:27635120

Testing the multidimensionality of the inventory of school motivation in a Dutch student sample.

PubMed

Korpershoek, Hanke; Xu, Kun; Mok, Magdalena Mo Ching; McInerney, Dennis M; van der Werf, Greetje

2015-01-01

A factor analytic and a Rasch measurement approach were applied to evaluate the multidimensional nature of the school motivation construct among more than 7,000 Dutch secondary school students. The Inventory of School Motivation (McInerney and Ali, 2006) was used, which intends to measure four motivation dimensions (mastery, performance, social, and extrinsic motivation), each comprising of two first-order factors. One unidimensional model and three multidimensional models (4-factor, 8-factor, higher order) were fit to the data. Results of both approaches showed that the multidimensional models validly represented the school motivation among Dutch secondary school pupils, whereas model fit of the unidimensional model was poor. The differences in model fit between the three multidimensional models were small, although a different model was favoured by the two approaches. The need for improvement of some of the items and the need to increase measurement precision of several first-order factors are discussed.

Quantitative evaluation of sputtering induced surface roughness and its influence on AES depth profiles of polycrystalline Ni/Cu multilayer thin films

NASA Astrophysics Data System (ADS)

Yan, X. L.; Coetsee, E.; Wang, J. Y.; Swart, H. C.; Terblans, J. J.

2017-07-01

The polycrystalline Ni/Cu multilayer thin films consisting of 8 alternating layers of Ni and Cu were deposited on a SiO2 substrate by means of electron beam evaporation in a high vacuum. Concentration-depth profiles of the as-deposited multilayered Ni/Cu thin films were determined with Auger electron spectroscopy (AES) in combination with Ar+ ion sputtering, under various bombardment conditions with the samples been stationary as well as rotating in some cases. The Mixing-Roughness-Information depth (MRI) model used for the fittings of the concentration-depth profiles accounts for the interface broadening of the experimental depth profiling. The interface broadening incorporates the effects of atomic mixing, surface roughness and information depth of the Auger electrons. The roughness values extracted from the MRI model fitting of the depth profiling data agrees well with those measured by atomic force microscopy (AFM). The ion sputtering induced surface roughness during the depth profiling was accordingly quantitatively evaluated from the fitted MRI parameters with sample rotation and stationary conditions. The depth resolutions of the AES depth profiles were derived directly from the values determined by the fitting parameters in the MRI model.

Blueprint XAS: a Matlab-based toolbox for the fitting and analysis of XAS spectra.

PubMed

Delgado-Jaime, Mario Ulises; Mewis, Craig Philip; Kennepohl, Pierre

2010-01-01

Blueprint XAS is a new Matlab-based program developed to fit and analyse X-ray absorption spectroscopy (XAS) data, most specifically in the near-edge region of the spectrum. The program is based on a methodology that introduces a novel background model into the complete fit model and that is capable of generating any number of independent fits with minimal introduction of user bias [Delgado-Jaime & Kennepohl (2010), J. Synchrotron Rad. 17, 119-128]. The functions and settings on the five panels of its graphical user interface are designed to suit the needs of near-edge XAS data analyzers. A batch function allows for the setting of multiple jobs to be run with Matlab in the background. A unique statistics panel allows the user to analyse a family of independent fits, to evaluate fit models and to draw statistically supported conclusions. The version introduced here (v0.2) is currently a toolbox for Matlab. Future stand-alone versions of the program will also incorporate several other new features to create a full package of tools for XAS data processing.

Factors associated with parasite dominance in fishes from Brazil.

PubMed

Amarante, Cristina Fernandes do; Tassinari, Wagner de Souza; Luque, Jose Luis; Pereira, Maria Julia Salim

2016-06-14

The present study used regression models to evaluate the existence of factors that may influence the numerical parasite dominance with an epidemiological approximation. A database including 3,746 fish specimens and their respective parasites were used to evaluate the relationship between parasite dominance and biotic characteristics inherent to the studied hosts and the parasite taxa. Multivariate, classical, and mixed effects linear regression models were fitted. The calculations were performed using R software (95% CI). In the fitting of the classical multiple linear regression model, freshwater and planktivorous fish species and body length, as well as the species of the taxa Trematoda, Monogenea, and Hirudinea, were associated with parasite dominance. However, the fitting of the mixed effects model showed that the body length of the host and the species of the taxa Nematoda, Trematoda, Monogenea, Hirudinea, and Crustacea were significantly associated with parasite dominance. Studies that consider specific biological aspects of the hosts and parasites should expand the knowledge regarding factors that influence the numerical dominance of fish in Brazil. The use of a mixed model shows, once again, the importance of the appropriate use of a model correlated with the characteristics of the data to obtain consistent results.

Leadership perceptions as a function of race-occupation fit: the case of Asian Americans.

PubMed

Sy, Thomas; Shore, Lynn M; Strauss, Judy; Shore, Ted H; Tram, Susanna; Whiteley, Paul; Ikeda-Muromachi, Kristine

2010-09-01

On the basis of the connectionist model of leadership, we examined perceptions of leadership as a function of the contextual factors of race (Asian American, Caucasian American) and occupation (engineering, sales) in 3 experiments (1 student sample and 2 industry samples). Race and occupation exhibited differential effects for within- and between-race comparisons. With regard to within-race comparisons, leadership perceptions of Asian Americans were higher when race-occupation was a good fit (engineer position) than when race-occupation was a poor fit (sales position) for the two industry samples. With regard to between-race comparisons, leadership perceptions of Asian Americans were low relative to those of Caucasian Americans. Additionally, when race-occupation was a good fit for Asian Americans, such individuals were evaluated higher on perceptions of technical competence than were Caucasian Americans, whereas they were evaluated lower when race-occupation was a poor fit. Furthermore, our results demonstrated that race affects leadership perceptions through the activation of prototypic leadership attributes (i.e., implicit leadership theories). Implications for the findings are discussed in terms of the connectionist model of leadership and leadership opportunities for Asian Americans. Copyright 2010 APA, all rights reserved

Rasch measurement of self-regulated learning in an information and communication technology (ICT)-rich environment.

PubMed

Njiru, Joseph N; Waugh, Russell F

2007-01-01

This report describes how a linear scale of self-regulated learning in an ICT-rich environment was created by analysing student data using the Rasch measurement model. A person convenience sample of (N = 409) university students in Western Australia was used. The stem-item sample was initially 41, answered in two perspectives ("I aim for this" and "I actually do this"), and reduced to 16 that fitted the measurement model to form a unidimensional scale. Items for motivation (extrinsic rewards, intrinsic rewards, and social rewards), academic goals (fear of performing poorly) (but not standards), self-learning beliefs (ability and interest), task management (strategies and time management) (but not cooperative learning), Volition (action control (but not environmental control), and self-evaluation (cognitive self-evaluation and metacognition) fitted the measurement model. The proportion of observed variance considered true was 0.90. A new instrument is proposed to handle the conceptually valid but non-fitting items. Characteristics of high self-regulated learners are measured.

Effect of heating rate and kinetic model selection on activation energy of nonisothermal crystallization of amorphous felodipine.

PubMed

Chattoraj, Sayantan; Bhugra, Chandan; Li, Zheng Jane; Sun, Changquan Calvin

2014-12-01

The nonisothermal crystallization kinetics of amorphous materials is routinely analyzed by statistically fitting the crystallization data to kinetic models. In this work, we systematically evaluate how the model-dependent crystallization kinetics is impacted by variations in the heating rate and the selection of the kinetic model, two key factors that can lead to significant differences in the crystallization activation energy (Ea ) of an amorphous material. Using amorphous felodipine, we show that the Ea decreases with increase in the heating rate, irrespective of the kinetic model evaluated in this work. The model that best describes the crystallization phenomenon cannot be identified readily through the statistical fitting approach because several kinetic models yield comparable R(2) . Here, we propose an alternate paired model-fitting model-free (PMFMF) approach for identifying the most suitable kinetic model, where Ea obtained from model-dependent kinetics is compared with those obtained from model-free kinetics. The most suitable kinetic model is identified as the one that yields Ea values comparable with the model-free kinetics. Through this PMFMF approach, nucleation and growth is identified as the main mechanism that controls the crystallization kinetics of felodipine. Using this PMFMF approach, we further demonstrate that crystallization mechanism from amorphous phase varies with heating rate. © 2014 Wiley Periodicals, Inc. and the American Pharmacists Association.

Statistical modelling for recurrent events: an application to sports injuries

PubMed Central

Ullah, Shahid; Gabbett, Tim J; Finch, Caroline F

2014-01-01

Background Injuries are often recurrent, with subsequent injuries influenced by previous occurrences and hence correlation between events needs to be taken into account when analysing such data. Objective This paper compares five different survival models (Cox proportional hazards (CoxPH) model and the following generalisations to recurrent event data: Andersen-Gill (A-G), frailty, Wei-Lin-Weissfeld total time (WLW-TT) marginal, Prentice-Williams-Peterson gap time (PWP-GT) conditional models) for the analysis of recurrent injury data. Methods Empirical evaluation and comparison of different models were performed using model selection criteria and goodness-of-fit statistics. Simulation studies assessed the size and power of each model fit. Results The modelling approach is demonstrated through direct application to Australian National Rugby League recurrent injury data collected over the 2008 playing season. Of the 35 players analysed, 14 (40%) players had more than 1 injury and 47 contact injuries were sustained over 29 matches. The CoxPH model provided the poorest fit to the recurrent sports injury data. The fit was improved with the A-G and frailty models, compared to WLW-TT and PWP-GT models. Conclusions Despite little difference in model fit between the A-G and frailty models, in the interest of fewer statistical assumptions it is recommended that, where relevant, future studies involving modelling of recurrent sports injury data use the frailty model in preference to the CoxPH model or its other generalisations. The paper provides a rationale for future statistical modelling approaches for recurrent sports injury. PMID:22872683

Data Analysis & Statistical Methods for Command File Errors

NASA Technical Reports Server (NTRS)

Meshkat, Leila; Waggoner, Bruce; Bryant, Larry

2014-01-01

This paper explains current work on modeling for managing the risk of command file errors. It is focused on analyzing actual data from a JPL spaceflight mission to build models for evaluating and predicting error rates as a function of several key variables. We constructed a rich dataset by considering the number of errors, the number of files radiated, including the number commands and blocks in each file, as well as subjective estimates of workload and operational novelty. We have assessed these data using different curve fitting and distribution fitting techniques, such as multiple regression analysis, and maximum likelihood estimation to see how much of the variability in the error rates can be explained with these. We have also used goodness of fit testing strategies and principal component analysis to further assess our data. Finally, we constructed a model of expected error rates based on the what these statistics bore out as critical drivers to the error rate. This model allows project management to evaluate the error rate against a theoretically expected rate as well as anticipate future error rates.

Evaluation of a large-scale quantitative respirator-fit testing program for healthcare workers: survey results.

PubMed

Wilkinson, Irene J; Pisaniello, Dino; Ahmad, Junaid; Edwards, Suzanne

2010-09-01

To present the evaluation of a large-scale quantitative respirator-fit testing program. Concurrent questionnaire survey of fit testers and test subjects. Ambulatory care, home nursing care, and acute care hospitals across South Australia. Quantitative facial-fit testing was performed with TSI PortaCount instruments for healthcare workers (HCWs) who wore 5 different models of a disposable P2 (N95-equivalent) respirator. The questionnaire included questions about the HCW's age, sex, race, occupational category, main area of work, smoking status, facial characteristics, prior training and experience in use of respiratory masks, and number of attempts to obtain a respirator fit. A total of 6,160 HCWs were successfully fitted during the period from January through July 2007. Of the 4,472 HCWs who responded to the questionnaire and were successfully fitted, 3,707 (82.9%) were successfully fitted with the first tested respirator, 551 (12.3%) required testing with a second model, and 214 (4.8%) required 3 or more tests. We noted an increased pass rate on the first attempt over time. Asians (excluding those from South and Central Asia) had the highest failure rate (16.3% [45 of 276 Asian HCWs were unsuccessfully fitted]), and whites had the lowest (9.8% [426 of 4,338 white HCWs]). Race was highly correlated with facial shape. Among occupational groups, doctors had the highest failure rate (13.4% [81 of 604 doctors]), but they also had the highest proportion of Asians. Prior education and/or training in respirator use were not associated with a higher pass rate. Certain facial characteristics were associated with higher or lower pass rates with regard to fit testing, and fit testers were able to select a suitable respirator on the basis of a visual assessment in the majority of cases. For the fit tester, training and experience were important factors; however, for the HCW being fitted, prior experience in respirator use was not an important factor.

Changes in Collegiate Ice Hockey Player Anthropometrics and Aerobic Fitness Over Three Decades.

PubMed

Triplett, Ashley N; Ebbing, Amy C; Green, Matthew R; Connolly, Christopher P; Carrier, David P; Pivarnik, James M

2018-04-09

Over the past several decades, an increased emphasis on fitness training has emerged among collegiate ice hockey teams, with the objective to improve on-ice performance. However, it is unknown if this increase in training has translated over time to changes in anthropometric and fitness profiles of collegiate ice hockey players. The purposes of this study were to describe anthropometric (height, weight, BMI, %fat) and aerobic fitness (VO_2peak) characteristics of collegiate ice hockey players over 36 years, and to evaluate whether these characteristics differ between player positions. Anthropometric and physiologic data were obtained through preseason fitness testing of players (N=279) from a NCAA Division I men's ice hockey team from the years of 1980 through 2015. Changes over time in the anthropometric and physiologic variables were evaluated via regression analysis using linear and polynomial models and differences between player position were compared via ANOVA (p<0.05). Regression analysis revealed a cubic model best predicted changes in mean height (R²=0.65), weight (R²=0.77), and BMI (R²=0.57), while a quadratic model best fit change in %fat by year (R²=0.30). Little change was observed over time in the anthropometric characteristics. Defensemen were significantly taller than forwards (184.7±12.1 vs. 181.3±5.9cm)(p=0.007) and forwards had a higher relative VO_2peak compared to defensemen (58.7±4.7 vs. 57.2±4.4ml/kg/min)(p=0.032). No significant differences were observed in %fat or weight by position. While average player heights and weights fluctuated over time, increased emphasis on fitness training did not affect athletes' relative aerobic fitness. Differences in height and aerobic fitness levels were observed between player position.

Structure of Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition Criteria for Obsessive–Compulsive Personality Disorder in Patients With Binge Eating Disorder

PubMed Central

Ansell, Emily B; Pinto, Anthony; Edelen, Maria Orlando; Grilo, Carlos M

2013-01-01

Objective To examine 1-, 2-, and 3-factor model structures through confirmatory analytic procedures for Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition (DSM-IV) obsessive–compulsive personality disorder (OCPD) criteria in patients with binge eating disorder (BED). Method Participants were consecutive outpatients (n = 263) with binge eating disorder and were assessed with semi-structured interviews. The 8 OCPD criteria were submitted to confirmatory factor analyses in Mplus Version 4.2 (Los Angeles, CA) in which previously identified factor models of OCPD were compared for fit, theoretical relevance, and parsimony. Nested models were compared for significant improvements in model fit. Results Evaluation of indices of fit in combination with theoretical considerations suggest a multifactorial model is a significant improvement in fit over the current DSM-IV single-factor model of OCPD. Though the data support both 2- and 3-factor models, the 3-factor model is hindered by an underspecified third factor. Conclusion A multifactorial model of OCPD incorporating the factors perfectionism and rigidity represents the best compromise of fit and theory in modelling the structure of OCPD in patients with BED. A third factor representing miserliness may be relevant in BED populations but needs further development. The perfectionism and rigidity factors may represent distinct intrapersonal and interpersonal attempts at control and may have implications for the assessment of OCPD. PMID:19087485

Structure of diagnostic and statistical manual of mental disorders, fourth edition criteria for obsessive-compulsive personality disorder in patients with binge eating disorder.

PubMed

Ansell, Emily B; Pinto, Anthony; Edelen, Maria Orlando; Grilo, Carlos M

2008-12-01

To examine 1-, 2-, and 3-factor model structures through confirmatory analytic procedures for Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition (DSM-IV) obsessive-compulsive personality disorder (OCPD) criteria in patients with binge eating disorder (BED). Participants were consecutive outpatients (n = 263) with binge eating disorder and were assessed with semi-structured interviews. The 8 OCPD criteria were submitted to confirmatory factor analyses in Mplus Version 4.2 (Los Angeles, CA) in which previously identified factor models of OCPD were compared for fit, theoretical relevance, and parsimony. Nested models were compared for significant improvements in model fit. Evaluation of indices of fit in combination with theoretical considerations suggest a multifactorial model is a significant improvement in fit over the current DSM-IV single- factor model of OCPD. Though the data support both 2- and 3-factor models, the 3-factor model is hindered by an underspecified third factor. A multifactorial model of OCPD incorporating the factors perfectionism and rigidity represents the best compromise of fit and theory in modelling the structure of OCPD in patients with BED. A third factor representing miserliness may be relevant in BED populations but needs further development. The perfectionism and rigidity factors may represent distinct intrapersonal and interpersonal attempts at control and may have implications for the assessment of OCPD.

Evaluation of EGM2008 Earth Gravitational Model in Algeria using gravity and GPS/levelling data

NASA Astrophysics Data System (ADS)

Benahmed Daho, S. A.

2009-04-01

The present work focuses on the evaluation of the EGM2008 geopotential model that was recently released by the NGA (National Geospatial-Intelligence Agency, U.S)/EGM-development team, in Algeria using the free air gravity anomalies supplied by BGI and GETECH, some of the precise GPS data collected from the international TYRGEONET (TYRhenian GEOdynamical NETwork) and ALGEONET (ALGerian GEOdynamical NETwork) projects and the last Algerian local gravimetric geoid model. Additional comparisons of the terrestrial point data with the corresponding values obtained from other geopotential models were made. Five global geopotential models were used in this comparison: the Preliminary Earth Gravitational Model PGM2007A, the combined CHAMP and GRACE model EIGEN-CG01C, the combined GRACE and LAGEOS model EIGEN-GL04C, OSU91A and EGM96. The study shows that all tested models are an improvement over OSU91A geopotential model used in all previous Algerian geoid computations and that new released combined model (EGM2008) is relatively superior to other tested models in the Algerian region. According to our numerical results, the new EGM2008 model fits better the observed values used in this investigation. Its standard deviations fit with GPS/levelling data are 21.4cm and 18.7cm before and after fitting using four-parameters transformation model. We strongly recommend the use of this new model in the remove-restore technique for the computation of the improved geoid for Algeria. In addition to these more general investigations, special GPS campaign has been performed for altimetric auscultation of a storage tank in which we wanted to test the possibilities to replace levelling by GPS measurements. The evaluation revealed promising results but also that much attention has to be paid on the GPS evaluation method. Key words: Geopotential model, TYRGEONET and ALGEONET projects, GPS/levelling data.

Decomposition of mineral absorption bands using nonlinear least squares curve fitting: Application to Martian meteorites and CRISM data

NASA Astrophysics Data System (ADS)

Parente, Mario; Makarewicz, Heather D.; Bishop, Janice L.

2011-04-01

This study advances curve-fitting modeling of absorption bands of reflectance spectra and applies this new model to spectra of Martian meteorites ALH 84001 and EETA 79001 and data from the Compact Reconnaissance Imaging Spectrometer for Mars (CRISM). This study also details a recently introduced automated parameter initialization technique. We assess the performance of this automated procedure by comparing it to the currently available initialization method and perform a sensitivity analysis of the fit results to variation in initial guesses. We explore the issues related to the removal of the continuum, offer guidelines for continuum removal when modeling the absorptions and explore different continuum-removal techniques. We further evaluate the suitability of curve fitting techniques using Gaussians/Modified Gaussians to decompose spectra into individual end-member bands. We show that nonlinear least squares techniques such as the Levenberg-Marquardt algorithm achieve comparable results to the MGM model ( Sunshine and Pieters, 1993; Sunshine et al., 1990) for meteorite spectra. Finally we use Gaussian modeling to fit CRISM spectra of pyroxene and olivine-rich terrains on Mars. Analysis of CRISM spectra of two regions show that the pyroxene-dominated rock spectra measured at Juventae Chasma were modeled well with low Ca pyroxene, while the pyroxene-rich spectra acquired at Libya Montes required both low-Ca and high-Ca pyroxene for a good fit.

Using evolutionary algorithms for fitting high-dimensional models to neuronal data.

PubMed

Svensson, Carl-Magnus; Coombes, Stephen; Peirce, Jonathan Westley

2012-04-01

In the study of neurosciences, and of complex biological systems in general, there is frequently a need to fit mathematical models with large numbers of parameters to highly complex datasets. Here we consider algorithms of two different classes, gradient following (GF) methods and evolutionary algorithms (EA) and examine their performance in fitting a 9-parameter model of a filter-based visual neuron to real data recorded from a sample of 107 neurons in macaque primary visual cortex (V1). Although the GF method converged very rapidly on a solution, it was highly susceptible to the effects of local minima in the error surface and produced relatively poor fits unless the initial estimates of the parameters were already very good. Conversely, although the EA required many more iterations of evaluating the model neuron's response to a series of stimuli, it ultimately found better solutions in nearly all cases and its performance was independent of the starting parameters of the model. Thus, although the fitting process was lengthy in terms of processing time, the relative lack of human intervention in the evolutionary algorithm, and its ability ultimately to generate model fits that could be trusted as being close to optimal, made it far superior in this particular application than the gradient following methods. This is likely to be the case in many further complex systems, as are often found in neuroscience.

Assessing Chinese coach drivers' fitness to drive: The development of a toolkit based on cognition measurements.

PubMed

Wang, Huarong; Mo, Xian; Wang, Ying; Liu, Ruixue; Qiu, Peiyu; Dai, Jiajun

2016-10-01

Road traffic accidents resulting in group deaths and injuries are often related to coach drivers' inappropriate operations and behaviors. Thus, the evaluation of coach drivers' fitness to drive is an important measure for improving the safety of public transportation. Previous related research focused on drivers' age and health condition. Comprehensive studies about commercial drivers' cognitive capacities are limited. This study developed a toolkit consisting of nine cognition measurements across driver perception/sensation, attention, and reaction. A total of 1413 licensed coach drivers in Jiangsu Province, China were investigated and tested. Results indicated that drivers with accident history within three years performed overwhelmingly worse (p<0.001) on dark adaptation, dynamic visual acuity, depth perception, attention concentration, attention span, and significantly worse (p<0.05) on reaction to complex tasks compared with drivers with clear accident records. These findings supported that in the assessment of fitness to drive, cognitive capacities are sensitive to the detection of drivers with accident proneness. We first developed a simple evaluation model based on the percentile distribution of all single measurements, which defined the normal range of "fit-to-drive" by eliminating a 5% tail of each measurement. A comprehensive evaluation model was later constructed based on the kernel principal component analysis, in which the eliminated 5% tail was calculated from on integrated index. Methods to categorizing qualified, good, and excellent coach drivers and criteria for evaluating and training Chinese coach drivers' fitness to drive were also proposed. Copyright © 2015 Elsevier Ltd. All rights reserved.

Postural effects on intracranial pressure: modeling and clinical evaluation.

PubMed

Qvarlander, Sara; Sundström, Nina; Malm, Jan; Eklund, Anders

2013-11-01

The physiological effect of posture on intracranial pressure (ICP) is not well described. This study defined and evaluated three mathematical models describing the postural effects on ICP, designed to predict ICP at different head-up tilt angles from the supine ICP value. Model I was based on a hydrostatic indifference point for the cerebrospinal fluid (CSF) system, i.e., the existence of a point in the system where pressure is independent of body position. Models II and III were based on Davson's equation for CSF absorption, which relates ICP to venous pressure, and postulated that gravitational effects within the venous system are transferred to the CSF system. Model II assumed a fully communicating venous system, and model III assumed that collapse of the jugular veins at higher tilt angles creates two separate hydrostatic compartments. Evaluation of the models was based on ICP measurements at seven tilt angles (0-71°) in 27 normal pressure hydrocephalus patients. ICP decreased with tilt angle (ANOVA: P < 0.01). The reduction was well predicted by model III (ANOVA lack-of-fit: P = 0.65), which showed excellent fit against measured ICP. Neither model I nor II adequately described the reduction in ICP (ANOVA lack-of-fit: P < 0.01). Postural changes in ICP could not be predicted based on the currently accepted theory of a hydrostatic indifference point for the CSF system, but a new model combining Davson's equation for CSF absorption and hydrostatic gradients in a collapsible venous system performed well and can be useful in future research on gravity and CSF physiology.

Fitting Nonlinear Ordinary Differential Equation Models with Random Effects and Unknown Initial Conditions Using the Stochastic Approximation Expectation-Maximization (SAEM) Algorithm.

PubMed

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.

FITTING NONLINEAR ORDINARY DIFFERENTIAL EQUATION MODELS WITH RANDOM EFFECTS AND UNKNOWN INITIAL CONDITIONS USING THE STOCHASTIC APPROXIMATION EXPECTATION–MAXIMIZATION (SAEM) ALGORITHM

PubMed Central

Chow, Sy- Miin; Lu, Zhaohua; Zhu, Hongtu; Sherwood, Andrew

2014-01-01

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. PMID:25416456

Hepatic function imaging using dynamic Gd-EOB-DTPA enhanced MRI and pharmacokinetic modeling.

PubMed

Ning, Jia; Yang, Zhiying; Xie, Sheng; Sun, Yongliang; Yuan, Chun; Chen, Huijun

2017-10-01

To determine whether pharmacokinetic modeling parameters with different output assumptions of dynamic contrast-enhanced MRI (DCE-MRI) using Gd-EOB-DTPA correlate with serum-based liver function tests, and compare the goodness of fit of the different output assumptions. A 6-min DCE-MRI protocol was performed in 38 patients. Four dual-input two-compartment models with different output assumptions and a published one-compartment model were used to calculate hepatic function parameters. The Akaike information criterion fitting error was used to evaluate the goodness of fit. Imaging-based hepatic function parameters were compared with blood chemistry using correlation with multiple comparison correction. The dual-input two-compartment model assuming venous flow equals arterial flow plus portal venous flow and no bile duct output better described the liver tissue enhancement with low fitting error and high correlation with blood chemistry. The relative uptake rate Kir derived from this model was found to be significantly correlated with direct bilirubin (r = -0.52, P = 0.015), prealbumin concentration (r = 0.58, P = 0.015), and prothrombin time (r = -0.51, P = 0.026). It is feasible to evaluate hepatic function by proper output assumptions. The relative uptake rate has the potential to serve as a biomarker of function. Magn Reson Med 78:1488-1495, 2017. © 2016 International Society for Magnetic Resonance in Medicine. © 2016 International Society for Magnetic Resonance in Medicine.

Insights into the equilibrium, kinetic and thermodynamics of nickel removal by environmental friendly Lansium domesticum peel biosorbent.

PubMed

Lam, Yun Fung; Lee, Lai Yee; Chua, Song Jun; Lim, Siew Shee; Gan, Suyin

2016-05-01

Lansium domesticum peel (LDP), a waste material generated from the fruit consumption, was evaluated as a biosorbent for nickel removal from aqueous media. The effects of dosage, contact time, initial pH, initial concentration and temperature on the biosorption process were investigated in batch experiments. Equilibrium data were fitted by the Langmuir, Freundlich, Temkin and Dubinin-Radushkevich models using nonlinear regression method with the best-fit model evaluated based on coefficient of determination (R(2)) and Chi-square (χ(2)). The best-fit isotherm was found to be the Langmuir model exhibiting R(2) very close to unity (0.997-0.999), smallest χ(2) (0.0138-0.0562) and largest biosorption capacity (10.1mg/g) at 30°C. Kinetic studies showed that the initial nickel removal was rapid with the equilibrium state established within 30min. Pseudo-second-order model was the best-fit kinetic model indicating the chemisorption nature of the biosorption process. Further data analysis by the intraparticle diffusion model revealed the involvement of several rate-controlling steps such as boundary layer and intraparticle diffusion. Thermodynamically, the process was exothermic, spontaneous and feasible. Regeneration studies indicated that LDP biosorbent could be regenerated using hydrochloric acid solution with up to 85% efficiency. The present investigation proved that LDP having no economic value can be used as an alternative eco-friendly biosorbent for remediation of nickel contaminated water. Copyright © 2016 Elsevier Inc. All rights reserved.

GROWTH AND INEQUALITY: MODEL EVALUATION BASED ON AN ESTIMATION-CALIBRATION STRATEGY

PubMed Central

Jeong, Hyeok; Townsend, Robert

2010-01-01

This paper evaluates two well-known models of growth with inequality that have explicit micro underpinnings related to household choice. With incomplete markets or transactions costs, wealth can constrain investment in business and the choice of occupation and also constrain the timing of entry into the formal financial sector. Using the Thai Socio-Economic Survey (SES), we estimate the distribution of wealth and the key parameters that best fit cross-sectional data on household choices and wealth. We then simulate the model economies for two decades at the estimated initial wealth distribution and analyze whether the model economies at those micro-fit parameter estimates can explain the observed macro and sectoral aspects of income growth and inequality change. Both models capture important features of Thai reality. Anomalies and comparisons across the two distinct models yield specific suggestions for improved research on the micro foundations of growth and inequality. PMID:20448833

Engelmann Spruce Site Index Models: A Comparison of Model Functions and Parameterizations

PubMed Central

Nigh, Gordon

2015-01-01

Engelmann spruce (Picea engelmannii Parry ex Engelm.) is a high-elevation species found in western Canada and western USA. As this species becomes increasingly targeted for harvesting, better height growth information is required for good management of this species. This project was initiated to fill this need. The objective of the project was threefold: develop a site index model for Engelmann spruce; compare the fits and modelling and application issues between three model formulations and four parameterizations; and more closely examine the grounded-Generalized Algebraic Difference Approach (g-GADA) model parameterization. The model fitting data consisted of 84 stem analyzed Engelmann spruce site trees sampled across the Engelmann Spruce – Subalpine Fir biogeoclimatic zone. The fitted models were based on the Chapman-Richards function, a modified Hossfeld IV function, and the Schumacher function. The model parameterizations that were tested are indicator variables, mixed-effects, GADA, and g-GADA. Model evaluation was based on the finite-sample corrected version of Akaike’s Information Criteria and the estimated variance. Model parameterization had more of an influence on the fit than did model formulation, with the indicator variable method providing the best fit, followed by the mixed-effects modelling (9% increase in the variance for the Chapman-Richards and Schumacher formulations over the indicator variable parameterization), g-GADA (optimal approach) (335% increase in the variance), and the GADA/g-GADA (with the GADA parameterization) (346% increase in the variance). Factors related to the application of the model must be considered when selecting the model for use as the best fitting methods have the most barriers in their application in terms of data and software requirements. PMID:25853472

Nonbleeding adenomas: Evidence of systematic false-negative fecal immunochemical test results and their implications for screening effectiveness-A modeling study.

PubMed

van der Meulen, Miriam P; Lansdorp-Vogelaar, Iris; van Heijningen, Else-Mariëtte B; Kuipers, Ernst J; van Ballegooijen, Marjolein

2016-06-01

If some adenomas do not bleed over several years, they will cause systematic false-negative fecal immunochemical test (FIT) results. The long-term effectiveness of FIT screening has been estimated without accounting for such systematic false-negativity. There are now data with which to evaluate this issue. The authors developed one microsimulation model (MISCAN [MIcrosimulation SCreening ANalysis]-Colon) without systematic false-negative FIT results and one model that allowed a percentage of adenomas to be systematically missed in successive FIT screening rounds. Both variants were adjusted to reproduce the first-round findings of the Dutch CORERO FIT screening trial. The authors then compared simulated detection rates in the second screening round with those observed, and adjusted the simulated percentage of systematically missed adenomas to those data. Finally, the authors calculated the impact of systematic false-negative FIT results on the effectiveness of repeated FIT screening. The model without systematic false-negativity simulated higher detection rates in the second screening round than observed. These observed rates could be reproduced when assuming that FIT systematically missed 26% of advanced and 73% of nonadvanced adenomas. To reduce the false-positive rate in the second round to the observed level, the authors also had to assume that 30% of false-positive findings were systematically false-positive. Systematic false-negative FIT testing limits the long-term reduction of biennial FIT screening in the incidence of colorectal cancer (35.6% vs 40.9%) and its mortality (55.2% vs 59.0%) in participants. The results of the current study provide convincing evidence based on the combination of real-life and modeling data that a percentage of adenomas are systematically missed by repeat FIT screening. This impairs the efficacy of FIT screening. Cancer 2016;122:1680-8. © 2016 American Cancer Society. © 2016 American Cancer Society.

Normal Tissue Complication Probability Modeling of Radiation-Induced Hypothyroidism After Head-and-Neck Radiation Therapy

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

Bakhshandeh, Mohsen; Hashemi, Bijan, E-mail: bhashemi@modares.ac.ir; Mahdavi, Seied Rabi Mehdi

Purpose: To determine the dose-response relationship of the thyroid for radiation-induced hypothyroidism in head-and-neck radiation therapy, according to 6 normal tissue complication probability models, and to find the best-fit parameters of the models. Methods and Materials: Sixty-five patients treated with primary or postoperative radiation therapy for various cancers in the head-and-neck region were prospectively evaluated. Patient serum samples (tri-iodothyronine, thyroxine, thyroid-stimulating hormone [TSH], free tri-iodothyronine, and free thyroxine) were measured before and at regular time intervals until 1 year after the completion of radiation therapy. Dose-volume histograms (DVHs) of the patients' thyroid gland were derived from their computed tomography (CT)-basedmore » treatment planning data. Hypothyroidism was defined as increased TSH (subclinical hypothyroidism) or increased TSH in combination with decreased free thyroxine and thyroxine (clinical hypothyroidism). Thyroid DVHs were converted to 2 Gy/fraction equivalent doses using the linear-quadratic formula with {alpha}/{beta} = 3 Gy. The evaluated models included the following: Lyman with the DVH reduced to the equivalent uniform dose (EUD), known as LEUD; Logit-EUD; mean dose; relative seriality; individual critical volume; and population critical volume models. The parameters of the models were obtained by fitting the patients' data using a maximum likelihood analysis method. The goodness of fit of the models was determined by the 2-sample Kolmogorov-Smirnov test. Ranking of the models was made according to Akaike's information criterion. Results: Twenty-nine patients (44.6%) experienced hypothyroidism. None of the models was rejected according to the evaluation of the goodness of fit. The mean dose model was ranked as the best model on the basis of its Akaike's information criterion value. The D{sub 50} estimated from the models was approximately 44 Gy. Conclusions: The implemented normal tissue complication probability models showed a parallel architecture for the thyroid. The mean dose model can be used as the best model to describe the dose-response relationship for hypothyroidism complication.« less

Quantitative reactive modeling and verification.

PubMed

Henzinger, Thomas A

Formal verification aims to improve the quality of software by detecting errors before they do harm. At the basis of formal verification is the logical notion of correctness , which purports to capture whether or not a program behaves as desired. We suggest that the boolean partition of software into correct and incorrect programs falls short of the practical need to assess the behavior of software in a more nuanced fashion against multiple criteria. We therefore propose to introduce quantitative fitness measures for programs, specifically for measuring the function, performance, and robustness of reactive programs such as concurrent processes. This article describes the goals of the ERC Advanced Investigator Project QUAREM. The project aims to build and evaluate a theory of quantitative fitness measures for reactive models. Such a theory must strive to obtain quantitative generalizations of the paradigms that have been success stories in qualitative reactive modeling, such as compositionality, property-preserving abstraction and abstraction refinement, model checking, and synthesis. The theory will be evaluated not only in the context of software and hardware engineering, but also in the context of systems biology. In particular, we will use the quantitative reactive models and fitness measures developed in this project for testing hypotheses about the mechanisms behind data from biological experiments.

Relationship between long working hours and depression: a 3-year longitudinal study of clerical workers.

PubMed

Amagasa, Takashi; Nakayama, Takeo

2013-08-01

To clarify how long working hours affect the likelihood of current and future depression. Using data from four repeated measurements collected from 218 clerical workers, four models associating work-related factors to the depressive mood scale were established. The final model was constructed after comparing and testing the goodness-of-fit index using structural equation modeling. Multiple logistic regression analysis was also performed. The final model showed the best fit (normed fit index = 0.908; goodness-of-fit index = 0.936; root-mean-square error of approximation = 0.018). Its standardized total effect indicated that long working hours affected depression at the time of evaluation and 1 to 3 years later. The odds ratio for depression risk was 14.7 in employees who were not long-hours overworked according to the initial survey but who were long-hours overworked according to the second survey. Long working hours increase current and future risks of depression.

Carbon dioxide stripping in aquaculture -- part III: model verification

USGS Publications Warehouse

Colt, John; Watten, Barnaby; Pfeiffer, Tim

2012-01-01

Based on conventional mass transfer models developed for oxygen, the use of the non-linear ASCE method, 2-point method, and one parameter linear-regression method were evaluated for carbon dioxide stripping data. For values of KLaCO2 < approximately 1.5/h, the 2-point or ASCE method are a good fit to experimental data, but the fit breaks down at higher values of KLaCO2. How to correct KLaCO2 for gas phase enrichment remains to be determined. The one-parameter linear regression model was used to vary the C*CO2 over the test, but it did not result in a better fit to the experimental data when compared to the ASCE or fixed C*CO2 assumptions.

Performance of DIMTEST-and NOHARM-Based Statistics for Testing Unidimensionality

ERIC Educational Resources Information Center

Finch, Holmes; Habing, Brian

2007-01-01

This Monte Carlo study compares the ability of the parametric bootstrap version of DIMTEST with three goodness-of-fit tests calculated from a fitted NOHARM model to detect violations of the assumption of unidimensionality in testing data. The effectiveness of the procedures was evaluated for different numbers of items, numbers of examinees,…

EVALUATING EFFECTS OF LOW QUALITY HABITATS ON REGIONAL GROWTH IN PEOMYCUS LEUCOPUS: INSIGHTS FROM FIELD-PARAMETERIZED SPATIAL MATRIX MODELS.

EPA Science Inventory

Due to complex population dynamics and source-sink metapopulation processes, animal fitness sometimes varies across landscapes in ways that cannot be deduced from simple density patterns. In this study, we examine spatial patterns in fitness using a combination of intensive fiel...

Bootstrap evaluation of a young Douglas-fir height growth model for the Pacific Northwest

Treesearch

Nicholas R. Vaughn; Eric C. Turnblom; Martin W. Ritchie

2010-01-01

We evaluated the stability of a complex regression model developed to predict the annual height growth of young Douglas-fir. This model is highly nonlinear and is fit in an iterative manner for annual growth coefficients from data with multiple periodic remeasurement intervals. The traditional methods for such a sensitivity analysis either involve laborious math or...

Validation of the LOD score compared with APACHE II score in prediction of the hospital outcome in critically ill patients.

PubMed

Khwannimit, Bodin

2008-01-01

The Logistic Organ Dysfunction score (LOD) is an organ dysfunction score that can predict hospital mortality. The aim of this study was to validate the performance of the LOD score compared with the Acute Physiology and Chronic Health Evaluation II (APACHE II) score in a mixed intensive care unit (ICU) at a tertiary referral university hospital in Thailand. The data were collected prospectively on consecutive ICU admissions over a 24 month period from July1, 2004 until June 30, 2006. Discrimination was evaluated by the area under the receiver operating characteristic curve (AUROC). The calibration was assessed by the Hosmer-Lemeshow goodness-of-fit H statistic. The overall fit of the model was evaluated by the Brier's score. Overall, 1,429 patients were enrolled during the study period. The mortality in the ICU was 20.9% and in the hospital was 27.9%. The median ICU and hospital lengths of stay were 3 and 18 days, respectively, for all patients. Both models showed excellent discrimination. The AUROC for the LOD and APACHE II were 0.860 [95% confidence interval (CI) = 0.838-0.882] and 0.898 (95% Cl = 0.879-0.917), respectively. The LOD score had perfect calibration with the Hosmer-Lemeshow goodness-of-fit H chi-2 = 10 (p = 0.44). However, the APACHE II had poor calibration with the Hosmer-Lemeshow goodness-of-fit H chi-2 = 75.69 (p < 0.001). Brier's score showed the overall fit for both models were 0.123 (95%Cl = 0.107-0.141) and 0.114 (0.098-0.132) for the LOD and APACHE II, respectively. Thus, the LOD score was found to be accurate for predicting hospital mortality for general critically ill patients in Thailand.

Application Mail Tracking Using RSA Algorithm As Security Data and HOT-Fit a Model for Evaluation System

NASA Astrophysics Data System (ADS)

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.

Effects of Inventory Bias on Landslide Susceptibility Calculations

NASA Technical Reports Server (NTRS)

Stanley, T. A.; Kirschbaum, D. B.

2017-01-01

Many landslide inventories are known to be biased, especially inventories for large regions such as Oregon's SLIDO or NASA's Global Landslide Catalog. These biases must affect the results of empirically derived susceptibility models to some degree. We evaluated the strength of the susceptibility model distortion from postulated biases by truncating an unbiased inventory. We generated a synthetic inventory from an existing landslide susceptibility map of Oregon, then removed landslides from this inventory to simulate the effects of reporting biases likely to affect inventories in this region, namely population and infrastructure effects. Logistic regression models were fitted to the modified inventories. Then the process of biasing a susceptibility model was repeated with SLIDO data. We evaluated each susceptibility model with qualitative and quantitative methods. Results suggest that the effects of landslide inventory bias on empirical models should not be ignored, even if those models are, in some cases, useful. We suggest fitting models in well-documented areas and extrapolating across the study region as a possible approach to modeling landslide susceptibility with heavily biased inventories.

Effects of Inventory Bias on Landslide Susceptibility Calculations

NASA Technical Reports Server (NTRS)

Stanley, Thomas; Kirschbaum, Dalia B.

2017-01-01

Many landslide inventories are known to be biased, especially inventories for large regions such as Oregons SLIDO or NASAs Global Landslide Catalog. These biases must affect the results of empirically derived susceptibility models to some degree. We evaluated the strength of the susceptibility model distortion from postulated biases by truncating an unbiased inventory. We generated a synthetic inventory from an existing landslide susceptibility map of Oregon, then removed landslides from this inventory to simulate the effects of reporting biases likely to affect inventories in this region, namely population and infrastructure effects. Logistic regression models were fitted to the modified inventories. Then the process of biasing a susceptibility model was repeated with SLIDO data. We evaluated each susceptibility model with qualitative and quantitative methods. Results suggest that the effects of landslide inventory bias on empirical models should not be ignored, even if those models are, in some cases, useful. We suggest fitting models in well-documented areas and extrapolating across the study region as a possible approach to modelling landslide susceptibility with heavily biased inventories.

Validation of a Best-Fit Pharmacokinetic Model for Scopolamine Disposition after Intranasal Administration

NASA Technical Reports Server (NTRS)

Wu, L.; Chow, D. S-L.; Tam, V.; Putcha, L.

2015-01-01

An intranasal gel formulation of scopolamine (INSCOP) was developed for the treatment of Motion Sickness. Bioavailability and pharmacokinetics (PK) were determined per Investigative New Drug (IND) evaluation guidance by the Food and Drug Administration. Earlier, we reported the development of a PK model that can predict the relationship between plasma, saliva and urinary scopolamine (SCOP) concentrations using data collected from an IND clinical trial with INSCOP. This data analysis project is designed to validate the reported best fit PK model for SCOP by comparing observed and model predicted SCOP concentration-time profiles after administration of INSCOP.

Field data-based mathematical modeling by Bode equations and vector fitting algorithm for renewable energy applications.

PubMed

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.

Field data-based mathematical modeling by Bode equations and vector fitting algorithm for renewable energy applications

PubMed Central

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

Evaluation of an Interdisciplinary, Physically Active Lifestyle Course Model

ERIC Educational Resources Information Center

Fede, Marybeth H.

2009-01-01

The purpose of this study was to evaluate a fit for life program at a university and to use the findings from an extensive literature review, consultations with formative and summative committees, and data collection to develop an interdisciplinary, physically active lifestyle (IPAL) course model. To address the 5 research questions examined in…

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

NASA Astrophysics Data System (ADS)

Ford, Eric B.

2009-05-01

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

Fitting and Calibrating a Multilevel Mixed-Effects Stem Taper Model for Maritime Pine in NW Spain

PubMed Central

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

Models for forecasting hospital bed requirements in the acute sector.

PubMed Central

Farmer, R D; Emami, J

1990-01-01

STUDY OBJECTIVE--The aim was to evaluate the current approach to forecasting hospital bed requirements. DESIGN--The study was a time series and regression analysis. The time series for mean duration of stay for general surgery in the age group 15-44 years (1969-1982) was used in the evaluation of different methods of forecasting future values of mean duration of stay and its subsequent use in the formation of hospital bed requirements. RESULTS--It has been suggested that the simple trend fitting approach suffers from model specification error and imposes unjustified restrictions on the data. Time series approach (Box-Jenkins method) was shown to be a more appropriate way of modelling the data. CONCLUSION--The simple trend fitting approach is inferior to the time series approach in modelling hospital bed requirements. PMID:2277253

The Dutch-Flemish PROMIS Physical Function item bank exhibited strong psychometric properties in patients with chronic pain.

PubMed

Crins, Martine H P; Terwee, Caroline B; Klausch, Thomas; Smits, Niels; de Vet, Henrica C W; Westhovens, Rene; Cella, David; Cook, Karon F; Revicki, Dennis A; van Leeuwen, Jaap; Boers, Maarten; Dekker, Joost; Roorda, Leo D

2017-07-01

The objective of this study was to assess the psychometric properties of the Dutch-Flemish Patient-Reported Outcomes Measurement Information System (PROMIS) Physical Function item bank in Dutch patients with chronic pain. A bank of 121 items was administered to 1,247 Dutch patients with chronic pain. Unidimensionality was assessed by fitting a one-factor confirmatory factor analysis and evaluating resulting fit statistics. Items were calibrated with the graded response model and its fit was evaluated. Cross-cultural validity was assessed by testing items for differential item functioning (DIF) based on language (Dutch vs. English). Construct validity was evaluated by calculation correlations between scores on the Dutch-Flemish PROMIS Physical Function measure and scores on generic and disease-specific measures. Results supported the Dutch-Flemish PROMIS Physical Function item bank's unidimensionality (Comparative Fit Index = 0.976, Tucker Lewis Index = 0.976) and model fit. Item thresholds targeted a wide range of physical function construct (threshold-parameters range: -4.2 to 5.6). Cross-cultural validity was good as four items only showed DIF for language and their impact on item scores was minimal. Physical Function scores were strongly associated with scores on all other measures (all correlations ≤ -0.60 as expected). The Dutch-Flemish PROMIS Physical Function item bank exhibited good psychometric properties. Development of a computer adaptive test based on the large bank is warranted. Copyright © 2017 Elsevier Inc. All rights reserved.

Test of a habitat suitability index for black bears in the southern Appalachians

USGS Publications Warehouse

Mitchell, M.S.; Zimmerman, J.W.; Powell, R.A.

2002-01-01

We present a habitat suitability index (HSI) model for black bears (Ursus americanus) living in the southern Appalachians that was developed a priori from the literature, then tested using location and home range data collected in the Pisgah Bear Sanctuary, North Carolina, over a 12-year period. The HSI was developed and initially tested using habitat and bear data collected over 2 years in the sanctuary. We increased number of habitat sampling sites, included data collected in areas affected by timber harvest, used more recent Geographic Information System (GIS) technology to create a more accurate depiction of the HSI for the sanctuary, evaluated effects of input variability on HSI values, and duplicated the original tests using more data. We found that the HSI predicted habitat selection by bears on population and individual levels and the distribution of collared bears were positively correlated with HSI values. We found a stronger relationship between habitat selection by bears and a second-generation HSI. We evaluated our model with criteria suggested by Roloff and Kernohan (1999) for evaluating HSI model reliability and concluded that our model was reliable and robust. The model's strength is that it was developed as an a priori hypothesis directly modeling the relationship between critical resources and fitness of bears and tested with independent data. We present the HSI spatially as a continuous fitness surface where potential contribution of habitat to the fitness of a bear is depicted at each point in space.

Psychometric Evaluation of the HIV Disclosure Belief Scale: A Rasch Model Approach.

PubMed

Hu, Jinxiang; Serovich, Julianne M; Chen, Yi-Hsin; Brown, Monique J; Kimberly, Judy A

2017-01-01

This study provides psychometric assessment of an HIV disclosure belief scale (DBS) among men who have sex with men (MSM). This study used baseline data from a clinical trial evaluating the effectiveness of an HIV serostatus disclosure intervention of 338 HIV-positive MSM. The Rasch model was used after unidimensionality and local independence assumptions were tested for application of the model. Results suggest that there was only one item that did not fit the model well. After removing the item, the DBS showed good model-data fit and high item and person reliabilities. This instrument showed measurement invariance across two different age groups, but some items showed differential item functioning between Caucasian and other minority groups. The findings suggest that the DBS is suitable for measuring the HIV disclosure beliefs, but it should be cautioned when the DBS is used to compare the disclosure beliefs between different racial/ethnic groups.

Fast and exact Newton and Bidirectional fitting of Active Appearance Models.

PubMed

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.

Comparison of marginal and internal fit of 3-unit ceramic fixed dental prostheses made with either a conventional or digital impression.

PubMed

Su, Ting-Shu; Sun, Jian

2016-09-01

For 20 years, the intraoral digital impression technique has been applied to the fabrication of computer aided design and computer aided manufacturing (CAD-CAM) fixed dental prostheses (FDPs). Clinical fit is one of the main determinants of the success of an FDP. Studies of the clinical fit of 3-unit ceramic FDPs made by means of a conventional impression versus a digital impression technology are limited. The purpose of this in vitro study was to evaluate and compare the internal fit and marginal fit of CAD-CAM, 3-unit ceramic FDP frameworks fabricated from an intraoral digital impression and a conventional impression. A standard model was designed for a prepared maxillary left canine and second premolar and missing first premolar. The model was scanned with an intraoral digital scanner, exporting stereolithography (STL) files as the experimental group (digital group). The model was used to fabricate 10 stone casts that were scanned with an extraoral scanner, exporting STL files to a computer connected to the scanner as the control group (conventional group). The STL files were used to produce zirconia FDP frameworks with CAD-CAM. These frameworks were seated on the standard model and evaluated for marginal and internal fit. Each framework was segmented into 4 sections per abutment teeth, resulting in 8 sections per framework, and was observed using optical microscopy with ×50 magnification. Four measurement points were selected on each section as marginal discrepancy (P1), mid-axial wall (P2), axio-occusal edge (P3), and central-occlusal point (P4). Mean marginal fit values of the digital group (64 ±16 μm) were significantly smaller than those of the conventional group (76 ±18 μm) (P<.05). The mean internal fit values of the digital group (111 ±34 μm) were significantly smaller than those of the conventional group (132 ±44 μm) (P<.05). CAD-CAM 3-unit zirconia FDP frameworks fabricated from intraoral digital and conventional impressions showed clinically acceptable marginal and internal fit. The marginal and internal fit of frameworks fabricated from the intraoral digital impression system were better than those fabricated from conventional impressions. Copyright © 2016 Editorial Council for the Journal of Prosthetic Dentistry. Published by Elsevier Inc. All rights reserved.

A three-dimensional finite element analysis of a passive and friction fit implant abutment interface and the influence of occlusal table dimension on the stress distribution pattern on the implant and surrounding bone

PubMed Central

Sarfaraz, Hasan; Paulose, Anoopa; Shenoy, K. Kamalakanth; Hussain, Akhter

2015-01-01

Aims: The aim of the study was to evaluate the stress distribution pattern in the implant and the surrounding bone for a passive and a friction fit implant abutment interface and to analyze the influence of occlusal table dimension on the stress generated. Materials and Methods: CAD models of two different types of implant abutment connections, the passive fit or the slip-fit represented by the Nobel Replace Tri-lobe connection and the friction fit or active fit represented by the Nobel active conical connection were made. The stress distribution pattern was studied at different occlusal dimension. Six models were constructed in PRO-ENGINEER 05 of the two implant abutment connection for three different occlusal dimensions each. The implant and abutment complex was placed in cortical and cancellous bone modeled using a computed tomography scan. This complex was subjected to a force of 100 N in the axial and oblique direction. The amount of stress and the pattern of stress generated were recorded on a color scale using ANSYS 13 software. Results: The results showed that overall maximum Von Misses stress on the bone is significantly less for friction fit than the passive fit in any loading conditions stresses on the implant were significantly higher for the friction fit than the passive fit. The narrow occlusal table models generated the least amount of stress on the implant abutment interface. Conclusion: It can thus be concluded that the conical connection distributes more stress to the implant body and dissipates less stress to the surrounding bone. A narrow occlusal table considerably reduces the occlusal overload. PMID:26929518

Exploring the measurement structure of the Gambling Related Cognitions Scale (GRCS) in treatment-seekers: A Bayesian structural equation modelling approach.

PubMed

Smith, David; Woodman, Richard; Drummond, Aaron; Battersby, Malcolm

2016-03-30

Knowledge of a problem gambler's underlying gambling related cognitions plays an important role in treatment planning. The Gambling Related Cognitions Scale (GRCS) is therefore frequently used in clinical settings for screening and evaluation of treatment outcomes. However, GRCS validation studies have generated conflicting results regarding its latent structure using traditional confirmatory factor analyses (CFA). This may partly be due to the rigid constraints imposed on cross-factor loadings with traditional CFA. The aim of this investigation was to determine whether a Bayesian structural equation modelling (BSEM) approach to examination of the GRCS factor structure would better replicate substantive theory and also inform model re-specifications. Participants were 454 treatment-seekers at first presentation to a gambling treatment centre between January 2012 and December 2014. Model fit indices were well below acceptable standards for CFA. In contrast, the BSEM model which included small informative priors for the residual covariance matrix in addition to cross-loadings produced excellent model fit for the original hypothesised factor structure. The results also informed re-specification of the CFA model which provided more reasonable model fit. These conclusions have implications that should be useful to both clinicians and researchers evaluating measurement models relating to gambling related cognitions in treatment-seekers. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Associations of physical fitness and academic performance among schoolchildren.

PubMed

Van Dusen, Duncan P; Kelder, Steven H; Kohl, Harold W; Ranjit, Nalini; Perry, Cheryl L

2011-12-01

Public schools provide opportunities for physical activity and fitness surveillance, but are evaluated and funded based on students' academic performance, not their physical fitness. Empirical research evaluating the connections between fitness and academic performance is needed to justify curriculum allocations to physical activity programs. Analyses were based on a convenience sample of 254,743 individually matched standardized academic (TAKS™) and fitness (FITNESSGRAM(®) ) test records of students, grades 3-11, collected by 13 Texas school districts. We categorized fitness results in quintiles by age and gender and used mixed effects regression models to compare the academic performance of the top and bottom fitness groups for each test. All fitness variables except body mass index (BMI) showed significant, positive associations with academic performance after adjustment for socio-demographic covariates, with standardized mean difference effect sizes ranging from .07 to .34. Cardiovascular fitness showed the largest interquintile difference in TAKS score (32-75 points), followed by curl-ups. Additional adjustment for BMI and curl-ups showed dose-response associations between cardiovascular fitness and academic scores (p < .001 for both genders and outcomes). Analysis of BMI demonstrated limited, nonlinear association with academic performance after socio-demographic and fitness adjustments. Fitness was strongly and significantly related to academic performance. Cardiovascular fitness showed a dose-response association with academic performance independent of other socio-demographic and fitness variables. The association appears to peak in late middle to early high school. We recommend that policymakers consider physical education (PE) mandates in middle high school, school administrators consider increasing PE time, and PE practitioners emphasize cardiovascular fitness. © 2011, American School Health Association.

An application of model-fitting procedures for marginal structural models.

PubMed

Mortimer, Kathleen M; Neugebauer, Romain; van der Laan, Mark; Tager, Ira B

2005-08-15

Marginal structural models (MSMs) are being used more frequently to obtain causal effect estimates in observational studies. Although the principal estimator of MSM coefficients has been the inverse probability of treatment weight (IPTW) estimator, there are few published examples that illustrate how to apply IPTW or discuss the impact of model selection on effect estimates. The authors applied IPTW estimation of an MSM to observational data from the Fresno Asthmatic Children's Environment Study (2000-2002) to evaluate the effect of asthma rescue medication use on pulmonary function and compared their results with those obtained through traditional regression methods. Akaike's Information Criterion and cross-validation methods were used to fit the MSM. In this paper, the influence of model selection and evaluation of key assumptions such as the experimental treatment assignment assumption are discussed in detail. Traditional analyses suggested that medication use was not associated with an improvement in pulmonary function--a finding that is counterintuitive and probably due to confounding by symptoms and asthma severity. The final MSM estimated that medication use was causally related to a 7% improvement in pulmonary function. The authors present examples that should encourage investigators who use IPTW estimation to undertake and discuss the impact of model-fitting procedures to justify the choice of the final weights.

Accounting for non-independent detection when estimating abundance of organisms with a Bayesian approach

USGS Publications Warehouse

Martin, Julien; Royle, J. Andrew; MacKenzie, Darryl I.; Edwards, Holly H.; Kery, Marc; Gardner, Beth

2011-01-01

Summary 1. Binomial mixture models use repeated count data to estimate abundance. They are becoming increasingly popular because they provide a simple and cost-effective way to account for imperfect detection. However, these models assume that individuals are detected independently of each other. This assumption may often be violated in the field. For instance, manatees (Trichechus manatus latirostris) may surface in turbid water (i.e. become available for detection during aerial surveys) in a correlated manner (i.e. in groups). However, correlated behaviour, affecting the non-independence of individual detections, may also be relevant in other systems (e.g. correlated patterns of singing in birds and amphibians). 2. We extend binomial mixture models to account for correlated behaviour and therefore to account for non-independent detection of individuals. We simulated correlated behaviour using beta-binomial random variables. Our approach can be used to simultaneously estimate abundance, detection probability and a correlation parameter. 3. Fitting binomial mixture models to data that followed a beta-binomial distribution resulted in an overestimation of abundance even for moderate levels of correlation. In contrast, the beta-binomial mixture model performed considerably better in our simulation scenarios. We also present a goodness-of-fit procedure to evaluate the fit of beta-binomial mixture models. 4. We illustrate our approach by fitting both binomial and beta-binomial mixture models to aerial survey data of manatees in Florida. We found that the binomial mixture model did not fit the data, whereas there was no evidence of lack of fit for the beta-binomial mixture model. This example helps illustrate the importance of using simulations and assessing goodness-of-fit when analysing ecological data with N-mixture models. Indeed, both the simulations and the goodness-of-fit procedure highlighted the limitations of the standard binomial mixture model for aerial manatee surveys. 5. Overestimation of abundance by binomial mixture models owing to non-independent detections is problematic for ecological studies, but also for conservation. For example, in the case of endangered species, it could lead to inappropriate management decisions, such as downlisting. These issues will be increasingly relevant as more ecologists apply flexible N-mixture models to ecological data.

Fit-for-purpose: species distribution model performance depends on evaluation criteria - Dutch Hoverflies as a case study.

PubMed

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 characteristics (i.e. sample size, spatial distribution).

Fitness consequences of sex-specific selection.

PubMed

Connallon, Tim; Cox, Robert M; Calsbeek, Ryan

2010-06-01

Theory suggests that sex-specific selection can facilitate adaptation in sexually reproducing populations. However, sexual conflict theory and recent experiments indicate that sex-specific selection is potentially costly due to sexual antagonism: alleles harmful to one sex can accumulate within a population because they are favored in the other sex. Whether sex-specific selection provides a net fitness benefit or cost depends, in part, on the relative frequency and strength of sexually concordant versus sexually antagonistic selection throughout a species' genome. Here, we model the net fitness consequences of sex-specific selection while explicitly considering both sexually concordant and sexually antagonistic selection. The model shows that, even when sexual antagonism is rare, the fitness costs that it imposes will generally overwhelm fitness benefits of sexually concordant selection. Furthermore, the cost of sexual antagonism is, at best, only partially resolved by the evolution of sex-limited gene expression. To evaluate the key parameters of the model, we analyze an extensive dataset of sex-specific selection gradients from wild populations, along with data from the experimental evolution literature. The model and data imply that sex-specific selection may likely impose a net cost on sexually reproducing species, although additional research will be required to confirm this conclusion.

Star clusters: age, metallicity and extinction from integrated spectra

NASA Astrophysics Data System (ADS)

González Delgado, Rosa M.; Cid Fernandes, Roberto

2010-01-01

Integrated optical spectra of star clusters in the Magellanic Clouds and a few Galactic globular clusters are fitted using high-resolution spectral models for single stellar populations. The goal is to estimate the age, metallicity and extinction of the clusters, and evaluate the degeneracies among these parameters. Several sets of evolutionary models that were computed with recent high-spectral-resolution stellar libraries (MILES, GRANADA, STELIB), are used as inputs to the starlight code to perform the fits. The comparison of the results derived from this method and previous estimates available in the literature allow us to evaluate the pros and cons of each set of models to determine star cluster properties. In addition, we quantify the uncertainties associated with the age, metallicity and extinction determinations resulting from variance in the ingredients for the analysis.

Concept and evaluation of food craving: unidimensional scales based on the Trait and the State Food Craving Questionnaire.

PubMed

Maranhão, Mara Fernandes; Estella, Nara Mendes; Cogo-Moreira, Hugo; Schmidt, Ulrike; Campbell, Iain C; Claudino, Angélica Medeiros

2018-01-01

"Craving" is a motivational state that promotes an intense desire related to consummatory behaviors. Despite growing interest in the concept of food craving, there is a lack of available instruments to assess it in Brazilian Portuguese. The objectives were to translate and adapt the Trait and the State Food Craving Questionnaire (FCQ-T and FCQ-S) to Brazilian Portuguese and to evaluate the psychometric properties of these versions.The FCQ-T and FCQ-S were translated and adapted to Brazilian Portuguese and administered to students at the Federal University of São Paulo. Both questionnaires in their original models were examined considering different estimators (frequentist and bayesian). The goodness of fit underlying the items from both scales was assessed through the following fit indices: χ2, WRMR residual, comparative fit index, Tucker-Lewis index and RMSEA. Data from 314 participants were included in the analyses. Poor fit indices were obtained for both of the original questionnaires regardless of the estimator used and original structural model. Thus, three eating disorder experts reviewed the content of the instruments and selected the items which were considered to assess the core aspects of the craving construct. The new and reduced models (questionnaires) generated good fit indices. Our abbreviated versions of FCQ-S and FCQ-T considerably diverge from the conceptual framework of the original questionnaires. Based on the results of this study, we propose a possible alternative, i.e., to assess craving for food as a unidimensional construct.

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

NASA Astrophysics Data System (ADS)

Magri, Alphonso William

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

Evaluation of marginal fit of two all-ceramic copings with two finish lines

PubMed Central

Subasi, Gulce; Ozturk, Nilgun; Inan, Ozgur; Bozogullari, Nalan

2012-01-01

Objectives: This in-vitro study investigated the marginal fit of two all-ceramic copings with 2 finish line designs. Methods: Forty machined stainless steel molar die models with two different margin designs (chamfer and rounded shoulder) were prepared. A total of 40 standardized copings were fabricated and divided into 4 groups (n=10 for each finish line-coping material). Coping materials tested were IPS e.max Press and Zirkonzahn; luting agent was Variolink II. Marginal fit was evaluated after cementation with a stereomicroscope (Leica MZ16). Two-way analysis of variance and Tukey-HSD test were performed to assess the influence of each finish line design and ceramic type on the marginal fit of 2 all-ceramic copings (α =.05). Results: Two-way analysis of variance revealed no statistically significant differences for marginal fit relative to finish lines (P=.362) and ceramic types (P=.065). Conclusion: Within the limitations of this study, both types of all-ceramic copings demonstrated that the mean marginal fit was considered acceptable for clinical application (⩽120 μm). PMID:22509119

Executive Control Mediates the Association Between Aerobic Fitness and Emotion Regulation in Preadolescent Children.

PubMed

Lott, Mark A; Jensen, Chad D

2017-03-01

This study evaluated direct and indirect associations between aerobic fitness, executive control, and emotion regulation among a community sample of preadolescent children. Two-hundred and seventy-eight children aged 8-12 years completed measures of aerobic fitness (Progressive Aerobic Cardiovascular Endurance Run) and executive control (Stroop Test). Parents completed questionnaires assessing child emotion regulation and executive control (Emotion Regulation Checklist; Early Adolescent Temperament Questionnaire). We evaluated associations between these constructs using structural equation modeling. Study findings supported a moderate direct association between childhood aerobic fitness and executive control, a strong direct negative association between executive control and emotion regulation, and a moderate indirect association between aerobic fitness and emotion regulation through executive control. These findings provide preliminary evidence that executive control functions as a mediator between aerobic fitness and emotion regulation and may help explain the mechanism by which aerobic exercise influences emotional well-being among preadolescent children. © The Author 2016. Published by Oxford University Press on behalf of the Society of Pediatric Psychology. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com

Survival modeling for the estimation of transition probabilities in model-based economic evaluations in the absence of individual patient data: a tutorial.

PubMed

Diaby, Vakaramoko; Adunlin, Georges; Montero, Alberto J

2014-02-01

Survival modeling techniques are increasingly being used as part of decision modeling for health economic evaluations. As many models are available, it is imperative for interested readers to know about the steps in selecting and using the most suitable ones. The objective of this paper is to propose a tutorial for the application of appropriate survival modeling techniques to estimate transition probabilities, for use in model-based economic evaluations, in the absence of individual patient data (IPD). An illustration of the use of the tutorial is provided based on the final progression-free survival (PFS) analysis of the BOLERO-2 trial in metastatic breast cancer (mBC). An algorithm was adopted from Guyot and colleagues, and was then run in the statistical package R to reconstruct IPD, based on the final PFS analysis of the BOLERO-2 trial. It should be emphasized that the reconstructed IPD represent an approximation of the original data. Afterwards, we fitted parametric models to the reconstructed IPD in the statistical package Stata. Both statistical and graphical tests were conducted to verify the relative and absolute validity of the findings. Finally, the equations for transition probabilities were derived using the general equation for transition probabilities used in model-based economic evaluations, and the parameters were estimated from fitted distributions. The results of the application of the tutorial suggest that the log-logistic model best fits the reconstructed data from the latest published Kaplan-Meier (KM) curves of the BOLERO-2 trial. Results from the regression analyses were confirmed graphically. An equation for transition probabilities was obtained for each arm of the BOLERO-2 trial. In this paper, a tutorial was proposed and used to estimate the transition probabilities for model-based economic evaluation, based on the results of the final PFS analysis of the BOLERO-2 trial in mBC. The results of our study can serve as a basis for any model (Markov) that needs the parameterization of transition probabilities, and only has summary KM plots available.

Forecasting the probability of future groundwater levels declining below specified low thresholds in the conterminous U.S.

USGS Publications Warehouse

Dudley, Robert W.; Hodgkins, Glenn A.; Dickinson, Jesse

2017-01-01

We present a logistic regression approach for forecasting the probability of future groundwater levels declining or maintaining below specific groundwater-level thresholds. We tested our approach on 102 groundwater wells in different climatic regions and aquifers of the United States that are part of the U.S. Geological Survey Groundwater Climate Response Network. We evaluated the importance of current groundwater levels, precipitation, streamflow, seasonal variability, Palmer Drought Severity Index, and atmosphere/ocean indices for developing the logistic regression equations. Several diagnostics of model fit were used to evaluate the regression equations, including testing of autocorrelation of residuals, goodness-of-fit metrics, and bootstrap validation testing. The probabilistic predictions were most successful at wells with high persistence (low month-to-month variability) in their groundwater records and at wells where the groundwater level remained below the defined low threshold for sustained periods (generally three months or longer). The model fit was weakest at wells with strong seasonal variability in levels and with shorter duration low-threshold events. We identified challenges in deriving probabilistic-forecasting models and possible approaches for addressing those challenges.

TransFit: Finite element analysis data fitting software

NASA Technical Reports Server (NTRS)

Freeman, Mark

1993-01-01

The Advanced X-Ray Astrophysics Facility (AXAF) mission support team has made extensive use of geometric ray tracing to analyze the performance of AXAF developmental and flight optics. One important aspect of this performance modeling is the incorporation of finite element analysis (FEA) data into the surface deformations of the optical elements. TransFit is software designed for the fitting of FEA data of Wolter I optical surface distortions with a continuous surface description which can then be used by SAO's analytic ray tracing software, currently OSAC (Optical Surface Analysis Code). The improved capabilities of Transfit over previous methods include bicubic spline fitting of FEA data to accommodate higher spatial frequency distortions, fitted data visualization for assessing the quality of fit, the ability to accommodate input data from three FEA codes plus other standard formats, and options for alignment of the model coordinate system with the ray trace coordinate system. TransFit uses the AnswerGarden graphical user interface (GUI) to edit input parameters and then access routines written in PV-WAVE, C, and FORTRAN to allow the user to interactively create, evaluate, and modify the fit. The topics covered include an introduction to TransFit: requirements, designs philosophy, and implementation; design specifics: modules, parameters, fitting algorithms, and data displays; a procedural example; verification of performance; future work; and appendices on online help and ray trace results of the verification section.

Dynamical Cognitive Models of Social Issues in Russia

NASA Astrophysics Data System (ADS)

Mitina, Olga; Abraham, Fred; Petrenko, Victor

We examine and model dynamics in three areas of social cognition: (1) political transformations within Russia, (2) evaluation of political trends in other countries by Russians, and (3) evaluation of Russian stereotypes concerning women. We try to represent consciousness as vectorfields and trajectories in a cognitive state space. We use psychosemantic techniques that allow definition of the state space and the systematic construction of these vectorfields and trajectories and their portrait from research data. Then we construct models to fit them, using multiple regression methods to obtain linear differential equations. These dynamical models of social cognition fit the data quite well. (1) The political transformations were modeled by a spiral repellor in a two-dimensional space of a democratic-totalitarian factor and social depression-optimism factor. (2) The evaluation of alien political trends included a flow away from a saddle toward more stable and moderate political regimes in a 2D space, of democratic-totalitarian and unstable-stable cognitive dimensions. (3) The gender study showed expectations (attractors) for more liberated, emancipated roles for women in the future.

Evaluation of viral load thresholds for predicting new WHO Stage 3 and 4 events in HIV-infected children receiving highly active antiretroviral therapy

PubMed Central

Siberry, George K; Harris, D. Robert; Oliveira, Ricardo Hugo; Krauss, Margot R.; Hofer, Cristina B.; Tiraboschi, Adriana Aparecida; Marques, Heloisa; Succi, Regina C.; Abreu, Thalita; Negra, Marinella Della; Mofenson, Lynne M.; Hazra, Rohan

2012-01-01

Background This study evaluated a wide range of viral load (VL) thresholds to identify a cut-point that best predicts new clinical events in children on stable highly-active antiretroviral therapy (HAART). Methods Cox proportional hazards modeling was used to assess the adjusted risk of World Health Organization stage 3 or 4 clinical events (WHO events) as a function of time-varying CD4, VL, and hemoglobin values in a cohort study of Latin American children on HAART ≥ 6 months. Models were fit using different VL cut-points between 400 and 50,000 copies/mL, with model fit evaluated on the basis of the minimum Akaike Information Criterion (AIC) value, a standard model fit statistic. Results Models were based on 67 subjects with WHO events out of 550 subjects on study. The VL cutpoints of > 2600 copies/mL and > 32,000 copies/mL corresponded to the lowest AIC values and were associated with the highest hazard ratios [2.0 (p = 0.015) and 2.1 (p = 0.0058), respectively] for WHO events. Conclusions In HIV-infected Latin American children on stable HAART, two distinct VL thresholds (> 2,600 copies/mL and > 32,000 copies/mL) were identified for predicting children at significantly increased risk of HIV-related clinical illness, after accounting for CD4 level, hemoglobin level, and other significant factors. PMID:22343177

Do Men and Women Need to Be Screened Differently with Fecal Immunochemical Testing? A Cost-Effectiveness Analysis.

PubMed

Meulen, Miriam P van der; Kapidzic, Atija; Leerdam, Monique E van; van der Steen, Alex; Kuipers, Ernst J; Spaander, Manon C W; de Koning, Harry J; Hol, Lieke; Lansdorp-Vogelaar, Iris

2017-08-01

Background: Several studies suggest that test characteristics for the fecal immunochemical test (FIT) differ by gender, triggering a debate on whether men and women should be screened differently. We used the microsimulation model MISCAN-Colon to evaluate whether screening stratified by gender is cost-effective. Methods: We estimated gender-specific FIT characteristics based on first-round positivity and detection rates observed in a FIT screening pilot (CORERO-1). Subsequently, we used the model to estimate harms, benefits, and costs of 480 gender-specific FIT screening strategies and compared them with uniform screening. Results: Biennial FIT screening from ages 50 to 75 was less effective in women than men [35.7 vs. 49.0 quality-adjusted life years (QALY) gained, respectively] at higher costs (€42,161 vs. -€5,471, respectively). However, the incremental QALYs gained and costs of annual screening compared with biennial screening were more similar for both genders (8.7 QALYs gained and €26,394 for women vs. 6.7 QALYs gained and €20,863 for men). Considering all evaluated screening strategies, optimal gender-based screening yielded at most 7% more QALYs gained than optimal uniform screening and even resulted in equal costs and QALYs gained from a willingness-to-pay threshold of €1,300. Conclusions: FIT screening is less effective in women, but the incremental cost-effectiveness is similar in men and women. Consequently, screening stratified by gender is not more cost-effective than uniform FIT screening. Impact: Our conclusions support the current policy of uniform FIT screening. Cancer Epidemiol Biomarkers Prev; 26(8); 1328-36. ©2017 AACR . ©2017 American Association for Cancer Research.

A Generalized Form of Context-Dependent Psychophysiological Interactions (gPPI): A Comparison to Standard Approaches

PubMed Central

McLaren, Donald G.; Ries, Michele L.; Xu, Guofan; Johnson, Sterling C.

2012-01-01

Functional MRI (fMRI) allows one to study task-related regional responses and task-dependent connectivity analysis using psychophysiological interaction (PPI) methods. The latter affords the additional opportunity to understand how brain regions interact in a task-dependent manner. The current implementation of PPI in Statistical Parametric Mapping (SPM8) is configured primarily to assess connectivity differences between two task conditions, when in practice fMRI tasks frequently employ more than two conditions. Here we evaluate how a generalized form of context-dependent PPI (gPPI; http://www.nitrc.org/projects/gppi), which is configured to automatically accommodate more than two task conditions in the same PPI model by spanning the entire experimental space, compares to the standard implementation in SPM8. These comparisons are made using both simulations and an empirical dataset. In the simulated dataset, we compare the interaction beta estimates to their expected values and model fit using the Akaike Information Criterion (AIC). We found that interaction beta estimates in gPPI were robust to different simulated data models, were not different from the expected beta value, and had better model fits than when using standard PPI (sPPI) methods. In the empirical dataset, we compare the model fit of the gPPI approach to sPPI. We found that the gPPI approach improved model fit compared to sPPI. There were several regions that became non-significant with gPPI. These regions all showed significantly better model fits with gPPI. Also, there were several regions where task-dependent connectivity was only detected using gPPI methods, also with improved model fit. Regions that were detected with all methods had more similar model fits. These results suggest that gPPI may have greater sensitivity and specificity than standard implementation in SPM. This notion is tempered slightly as there is no gold standard; however, data simulations with a known outcome support our conclusions about gPPI. In sum, the generalized form of context-dependent PPI approach has increased flexibility of statistical modeling, and potentially improves model fit, specificity to true negative findings, and sensitivity to true positive findings. PMID:22484411

Soft X-ray spectral fits of Geminga with model neutron star atmospheres

NASA Technical Reports Server (NTRS)

Meyer, R. D.; Pavlov, G. G.; Meszaros, P.

1994-01-01

The spectrum of the soft X-ray pulsar Geminga consists of two components, a softer one which can be interpreted as thermal-like radiation from the surface of the neutron star, and a harder one interpreted as radiation from a polar cap heated by relativistic particles. We have fitted the soft spectrum using a detailed magnetized hydrogen atmosphere model. The fitting parameters are the hydrogen column density, the effective temperature T(sub eff), the gravitational redshift z, and the distance to radius ratio, for different values of the magnetic field B. The best fits for this model are obtained when B less than or approximately 1 x 10(exp 12) G and z lies on the upper boundary of the explored range (z = 0.45). The values of T(sub eff) approximately = (2-3) x 10(exp 5) K are a factor of 2-3 times lower than the value of T(sub eff) obtained for blackbody fits with the same z. The lower T(sub eff) increases the compatibility with some proposed schemes for fast neutrino cooling of neutron stars (NSs) by the direct Urca process or by exotic matter, but conventional cooling cannot be excluded. The hydrogen atmosphere fits also imply a smaller distance to Geminga than that inferred from a blackbody fit. An accurate evaluation of the distance would require a better knowledge of the ROSAT Position Sensitive Proportional Counter (PSPC) response to the low-energy region of the incident spectrum. Our modeling of the soft component with a cooler magnetized atmosphere also implies that the hard-component fit requires a characteristic temperature which is higher (by a factor of approximately 2-3) and a surface area which is smaller (by a factor of 10(exp 3), compared to previous blackbody fits.

Experimental congruence of interval scale production from paired comparisons and ranking for image evaluation

NASA Astrophysics Data System (ADS)

Handley, John C.; Babcock, Jason S.; Pelz, Jeff B.

2003-12-01

Image evaluation tasks are often conducted using paired comparisons or ranking. To elicit interval scales, both methods rely on Thurstone's Law of Comparative Judgment in which objects closer in psychological space are more often confused in preference comparisons by a putative discriminal random process. It is often debated whether paired comparisons and ranking yield the same interval scales. An experiment was conducted to assess scale production using paired comparisons and ranking. For this experiment a Pioneer Plasma Display and Apple Cinema Display were used for stimulus presentation. Observers performed rank order and paired comparisons tasks on both displays. For each of five scenes, six images were created by manipulating attributes such as lightness, chroma, and hue using six different settings. The intention was to simulate the variability from a set of digital cameras or scanners. Nineteen subjects, (5 females, 14 males) ranging from 19-51 years of age participated in this experiment. Using a paired comparison model and a ranking model, scales were estimated for each display and image combination yielding ten scale pairs, ostensibly measuring the same psychological scale. The Bradley-Terry model was used for the paired comparisons data and the Bradley-Terry-Mallows model was used for the ranking data. Each model was fit using maximum likelihood estimation and assessed using likelihood ratio tests. Approximate 95% confidence intervals were also constructed using likelihood ratios. Model fits for paired comparisons were satisfactory for all scales except those from two image/display pairs; the ranking model fit uniformly well on all data sets. Arguing from overlapping confidence intervals, we conclude that paired comparisons and ranking produce no conflicting decisions regarding ultimate ordering of treatment preferences, but paired comparisons yield greater precision at the expense of lack-of-fit.

A Rigorous Test of the Fit of the Circumplex Model to Big Five Personality Data: Theoretical and Methodological Issues and Two Large Sample Empirical Tests.

PubMed

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.

[A preliminary study on the forming quality of titanium alloy removable partial denture frameworks fabricated by selective laser melting].

PubMed

Liu, Y F; Yu, H; Wang, W N; Gao, B

2017-06-09

Objective: To evaluate the processing accuracy, internal quality and suitability of the titanium alloy frameworks of removable partial denture (RPD) fabricated by selective laser melting (SLM) technique, and to provide reference for clinical application. Methods: The plaster model of one clinical patient was used as the working model, and was scanned and reconstructed into a digital working model. A RPD framework was designed on it. Then, eight corresponding RPD frameworks were fabricated using SLM technique. Three-dimensional (3D) optical scanner was used to scan and obtain the 3D data of the frameworks and the data was compared with the original computer aided design (CAD) model to evaluate their processing precision. The traditional casting pure titanium frameworks was used as the control group, and the internal quality was analyzed by X-ray examination. Finally, the fitness of the frameworks was examined on the plaster model. Results: The overall average deviation of the titanium alloy RPD framework fabricated by SLM technology was (0.089±0.076) mm, the root mean square error was 0.103 mm. No visible pores, cracks and other internal defects was detected in the frameworks. The framework fits on the plaster model completely, and its tissue surface fitted on the plaster model well. There was no obvious movement. Conclusions: The titanium alloy RPD framework fabricated by SLM technology is of good quality.

Using R and WinBUGS to fit a Generalized Partial Credit Model for developing and evaluating patient-reported outcomes assessments

PubMed Central

Li, Yuelin; Baser, Ray

2013-01-01

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 in IRT (GPCM). 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. PMID:22362655

Using R and WinBUGS to fit a generalized partial credit model for developing and evaluating patient-reported outcomes assessments.

PubMed

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.

Statistical distributions of extreme dry spell in Peninsular Malaysia

NASA Astrophysics Data System (ADS)

Zin, Wan Zawiah Wan; Jemain, Abdul Aziz

2010-11-01

Statistical distributions of annual extreme (AE) series and partial duration (PD) series for dry-spell event are analyzed for a database of daily rainfall records of 50 rain-gauge stations in Peninsular Malaysia, with recording period extending from 1975 to 2004. The three-parameter generalized extreme value (GEV) and generalized Pareto (GP) distributions are considered to model both series. In both cases, the parameters of these two distributions are fitted by means of the L-moments method, which provides a robust estimation of them. The goodness-of-fit (GOF) between empirical data and theoretical distributions are then evaluated by means of the L-moment ratio diagram and several goodness-of-fit tests for each of the 50 stations. It is found that for the majority of stations, the AE and PD series are well fitted by the GEV and GP models, respectively. Based on the models that have been identified, we can reasonably predict the risks associated with extreme dry spells for various return periods.

Customer Satisfaction with Training Programs.

ERIC Educational Resources Information Center

Mulder, Martin

2001-01-01

A model for evaluating customer satisfaction with training programs was tested with training purchasers. The model confirmed two types of projects: training aimed at achieving learning results and at changing job performance. The model did not fit for training intended to support organizational change. (Contains 31 references.) (SK)

Breeding site selection by coho salmon (Oncorhynchus kisutch) in relation to large wood additions and factors that influence reproductive success

USGS Publications Warehouse

Clark, Steven M.; Dunham, Jason B.; McEnroe, Jeffery R.; Lightcap, Scott W.

2014-01-01

The fitness of female Pacific salmon (Oncorhynchus spp.) with respect to breeding behavior can be partitioned into at least four fitness components: survival to reproduction, competition for breeding sites, success of egg incubation, and suitability of the local environment near breeding sites for early rearing of juveniles. We evaluated the relative influences of habitat features linked to these fitness components with respect to selection of breeding sites by coho salmon (Oncorhynchus kisutch). We also evaluated associations between breeding site selection and additions of large wood, as the latter were introduced into the study system as a means of restoring habitat conditions to benefit coho salmon. We used a model selection approach to organize specific habitat features into groupings reflecting fitness components and influences of large wood. Results of this work suggest that female coho salmon likely select breeding sites based on a wide range of habitat features linked to all four hypothesized fitness components. More specifically, model parameter estimates indicated that breeding site selection was most strongly influenced by proximity to pool-tail crests and deeper water (mean and maximum depths). Linkages between large wood and breeding site selection were less clear. Overall, our findings suggest that breeding site selection by coho salmon is influenced by a suite of fitness components in addition to the egg incubation environment, which has been the emphasis of much work in the past.

The “Dry-Run” Analysis: A Method for Evaluating Risk Scores for Confounding Control

PubMed Central

Wyss, Richard; Hansen, Ben B.; Ellis, Alan R.; Gagne, Joshua J.; Desai, Rishi J.; Glynn, Robert J.; Stürmer, Til

2017-01-01

Abstract A propensity score (PS) model's ability to control confounding can be assessed by evaluating covariate balance across exposure groups after PS adjustment. The optimal strategy for evaluating a disease risk score (DRS) model's ability to control confounding is less clear. DRS models cannot be evaluated through balance checks within the full population, and they are usually assessed through prediction diagnostics and goodness-of-fit tests. A proposed alternative is the “dry-run” analysis, which divides the unexposed population into “pseudo-exposed” and “pseudo-unexposed” groups so that differences on observed covariates resemble differences between the actual exposed and unexposed populations. With no exposure effect separating the pseudo-exposed and pseudo-unexposed groups, a DRS model is evaluated by its ability to retrieve an unconfounded null estimate after adjustment in this pseudo-population. We used simulations and an empirical example to compare traditional DRS performance metrics with the dry-run validation. In simulations, the dry run often improved assessment of confounding control, compared with the C statistic and goodness-of-fit tests. In the empirical example, PS and DRS matching gave similar results and showed good performance in terms of covariate balance (PS matching) and controlling confounding in the dry-run analysis (DRS matching). The dry-run analysis may prove useful in evaluating confounding control through DRS models. PMID:28338910

Regression Models for Identifying Noise Sources in Magnetic Resonance Images

PubMed Central

Zhu, Hongtu; Li, Yimei; Ibrahim, Joseph G.; Shi, Xiaoyan; An, Hongyu; Chen, Yashen; Gao, Wei; Lin, Weili; Rowe, Daniel B.; Peterson, Bradley S.

2009-01-01

Stochastic noise, susceptibility artifacts, magnetic field and radiofrequency inhomogeneities, and other noise components in magnetic resonance images (MRIs) can introduce serious bias into any measurements made with those images. We formally introduce three regression models including a Rician regression model and two associated normal models to characterize stochastic noise in various magnetic resonance imaging modalities, including diffusion-weighted imaging (DWI) and functional MRI (fMRI). Estimation algorithms are introduced to maximize the likelihood function of the three regression models. We also develop a diagnostic procedure for systematically exploring MR images to identify noise components other than simple stochastic noise, and to detect discrepancies between the fitted regression models and MRI data. The diagnostic procedure includes goodness-of-fit statistics, measures of influence, and tools for graphical display. The goodness-of-fit statistics can assess the key assumptions of the three regression models, whereas measures of influence can isolate outliers caused by certain noise components, including motion artifacts. The tools for graphical display permit graphical visualization of the values for the goodness-of-fit statistic and influence measures. Finally, we conduct simulation studies to evaluate performance of these methods, and we analyze a real dataset to illustrate how our diagnostic procedure localizes subtle image artifacts by detecting intravoxel variability that is not captured by the regression models. PMID:19890478

Using the Mixed Rasch Model to analyze data from the beliefs and attitudes about memory survey.

PubMed

Smith, Everett V; Ying, Yuping; Brown, Scott W

2012-01-01

In this study, we used the Mixed Rasch Model (MRM) to analyze data from the Beliefs and Attitudes About Memory Survey (BAMS; Brown, Garry, Silver, and Loftus, 1997). We used the original 5-point BAMS data to investigate the functioning of the "Neutral" category via threshold analysis under a 2-class MRM solution. The "Neutral" category was identified as not eliciting the model expected responses and observations in the "Neutral" category were subsequently treated as missing data. For the BAMS data without the "Neutral" category, exploratory MRM analyses specifying up to 5 latent classes were conducted to evaluate data-model fit using the consistent Akaike information criterion (CAIC). For each of three BAMS subscales, a two latent class solution was identified as fitting the mixed Rasch rating scale model the best. Results regarding threshold analysis, person parameters, and item fit based on the final models are presented and discussed as well as the implications of this study.

Measurement and Modeling of Respiration Rate of Tomato (Cultivar Roma) for Modified Atmosphere Storage.

PubMed

Kandasamy, Palani; Moitra, Ranabir; Mukherjee, Souti

2015-01-01

Experiments were conducted to determine the respiration rate of tomato at 10, 20 and 30 °C using closed respiration system. Oxygen depletion and carbon dioxide accumulation in the system containing tomato was monitored. Respiration rate was found to decrease with increasing CO2 and decreasing O2 concentration. Michaelis-Menten type model based on enzyme kinetics was evaluated using experimental data generated for predicting the respiration rate. The model parameters that obtained from the respiration rate at different O2 and CO2 concentration levels were used to fit the model against the storage temperatures. The fitting was fair (R2 = 0.923 to 0.970) when the respiration rate was expressed as O2 concentation. Since inhibition constant for CO2 concentration tended towards negetive, the model was modified as a function of O2 concentration only. The modified model was fitted to the experimental data and showed good agreement (R2 = 0.998) with experimentally estimated respiration rate.

Workplace support, discrimination, and person-organization fit: tests of the theory of work adjustment with LGB individuals.

PubMed

Velez, Brandon L; Moradi, Bonnie

2012-07-01

The present study explored the links of 2 workplace contextual variables--perceptions of workplace heterosexist discrimination and lesbian, gay, and bisexual (LGB)-supportive climates--with job satisfaction and turnover intentions in a sample of LGB employees. An extension of the theory of work adjustment (TWA) was used as the conceptual framework for the study; as such, perceived person-organization (P-O) fit was tested as a mediator of the relations between the workplace contextual variables and job outcomes. Data were analyzed from 326 LGB employees. Zero-order correlations indicated that perceptions of workplace heterosexist discrimination and LGB-supportive climates were correlated in expected directions with P-O fit, job satisfaction, and turnover intentions. Structural equation modeling (SEM) was used to compare multiple alternative measurement models evaluating the discriminant validity of the 2 workplace contextual variables relative to one another, and the 3 TWA job variables relative to one another; SEM was also used to test the hypothesized mediation model. Comparisons of multiple alternative measurement models supported the construct distinctiveness of the variables of interest. The test of the hypothesized structural model revealed that only LGB-supportive climates (and not workplace heterosexist discrimination) had a unique direct positive link with P-O fit and, through the mediating role of P-O fit, had significant indirect positive and negative relations with job satisfaction and turnover intentions, respectively. Moreover, P-O fit had a significant indirect negative link with turnover intentions through job satisfaction.

Genetic evaluation and selection response for growth in meat-type quail through random regression models using B-spline functions and Legendre polynomials.

PubMed

Mota, L F M; Martins, P G M A; Littiere, T O; Abreu, L R A; Silva, M A; Bonafé, C M

2018-04-01

The objective was to estimate (co)variance functions using random regression models (RRM) with Legendre polynomials, B-spline function and multi-trait models aimed at evaluating genetic parameters of growth traits in meat-type quail. A database containing the complete pedigree information of 7000 meat-type quail was utilized. The models included the fixed effects of contemporary group and generation. Direct additive genetic and permanent environmental effects, considered as random, were modeled using B-spline functions considering quadratic and cubic polynomials for each individual segment, and Legendre polynomials for age. Residual variances were grouped in four age classes. Direct additive genetic and permanent environmental effects were modeled using 2 to 4 segments and were modeled by Legendre polynomial with orders of fit ranging from 2 to 4. The model with quadratic B-spline adjustment, using four segments for direct additive genetic and permanent environmental effects, was the most appropriate and parsimonious to describe the covariance structure of the data. The RRM using Legendre polynomials presented an underestimation of the residual variance. Lesser heritability estimates were observed for multi-trait models in comparison with RRM for the evaluated ages. In general, the genetic correlations between measures of BW from hatching to 35 days of age decreased as the range between the evaluated ages increased. Genetic trend for BW was positive and significant along the selection generations. The genetic response to selection for BW in the evaluated ages presented greater values for RRM compared with multi-trait models. In summary, RRM using B-spline functions with four residual variance classes and segments were the best fit for genetic evaluation of growth traits in meat-type quail. In conclusion, RRM should be considered in genetic evaluation of breeding programs.

The relationship between offspring size and fitness: integrating theory and empiricism.

PubMed

Rollinson, Njal; Hutchings, Jeffrey A

2013-02-01

How parents divide the energy available for reproduction between size and number of offspring has a profound effect on parental reproductive success. Theory indicates that the relationship between offspring size and offspring fitness is of fundamental importance to the evolution of parental reproductive strategies: this relationship predicts the optimal division of resources between size and number of offspring, it describes the fitness consequences for parents that deviate from optimality, and its shape can predict the most viable type of investment strategy in a given environment (e.g., conservative vs. diversified bet-hedging). Many previous attempts to estimate this relationship and the corresponding value of optimal offspring size have been frustrated by a lack of integration between theory and empiricism. In the present study, we draw from C. Smith and S. Fretwell's classic model to explain how a sound estimate of the offspring size--fitness relationship can be derived with empirical data. We evaluate what measures of fitness can be used to model the offspring size--fitness curve and optimal size, as well as which statistical models should and should not be used to estimate offspring size--fitness relationships. To construct the fitness curve, we recommend that offspring fitness be measured as survival up to the age at which the instantaneous rate of offspring mortality becomes random with respect to initial investment. Parental fitness is then expressed in ecologically meaningful, theoretically defensible, and broadly comparable units: the number of offspring surviving to independence. Although logistic and asymptotic regression have been widely used to estimate offspring size-fitness relationships, the former provides relatively unreliable estimates of optimal size when offspring survival and sample sizes are low, and the latter is unreliable under all conditions. We recommend that the Weibull-1 model be used to estimate this curve because it provides modest improvements in prediction accuracy under experimentally relevant conditions.

Connecting clinical and actuarial prediction with rule-based methods.

PubMed

Fokkema, Marjolein; Smits, Niels; Kelderman, Henk; Penninx, Brenda W J H

2015-06-01

Meta-analyses comparing the accuracy of clinical versus actuarial prediction have shown actuarial methods to outperform clinical methods, on average. However, actuarial methods are still not widely used in clinical practice, and there has been a call for the development of actuarial prediction methods for clinical practice. We argue that rule-based methods may be more useful than the linear main effect models usually employed in prediction studies, from a data and decision analytic as well as a practical perspective. In addition, decision rules derived with rule-based methods can be represented as fast and frugal trees, which, unlike main effects models, can be used in a sequential fashion, reducing the number of cues that have to be evaluated before making a prediction. We illustrate the usability of rule-based methods by applying RuleFit, an algorithm for deriving decision rules for classification and regression problems, to a dataset on prediction of the course of depressive and anxiety disorders from Penninx et al. (2011). The RuleFit algorithm provided a model consisting of 2 simple decision rules, requiring evaluation of only 2 to 4 cues. Predictive accuracy of the 2-rule model was very similar to that of a logistic regression model incorporating 20 predictor variables, originally applied to the dataset. In addition, the 2-rule model required, on average, evaluation of only 3 cues. Therefore, the RuleFit algorithm appears to be a promising method for creating decision tools that are less time consuming and easier to apply in psychological practice, and with accuracy comparable to traditional actuarial methods. (c) 2015 APA, all rights reserved).

A Model-Free Diagnostic for Single-Peakedness of Item Responses Using Ordered Conditional Means.

PubMed

Polak, Marike; de Rooij, Mark; Heiser, Willem J

2012-09-01

In this article we propose a model-free diagnostic for single-peakedness (unimodality) of item responses. Presuming a unidimensional unfolding scale and a given item ordering, we approximate item response functions of all items based on ordered conditional means (OCM). The proposed OCM methodology is based on Thurstone & Chave's (1929) criterion of irrelevance, which is a graphical, exploratory method for evaluating the "relevance" of dichotomous attitude items. We generalized this criterion to graded response items and quantified the relevance by fitting a unimodal smoother. The resulting goodness-of-fit was used to determine item fit and aggregated scale fit. Based on a simulation procedure, cutoff values were proposed for the measures of item fit. These cutoff values showed high power rates and acceptable Type I error rates. We present 2 applications of the OCM method. First, we apply the OCM method to personality data from the Developmental Profile; second, we analyze attitude data collected by Roberts and Laughlin (1996) concerning opinions of capital punishment.

Predicting motor vehicle collisions using Bayesian neural network models: an empirical analysis.

PubMed

Xie, Yuanchang; Lord, Dominique; Zhang, Yunlong

2007-09-01

Statistical models have frequently been used in highway safety studies. They can be utilized for various purposes, including establishing relationships between variables, screening covariates and predicting values. Generalized linear models (GLM) and hierarchical Bayes models (HBM) have been the most common types of model favored by transportation safety analysts. Over the last few years, researchers have proposed the back-propagation neural network (BPNN) model for modeling the phenomenon under study. Compared to GLMs and HBMs, BPNNs have received much less attention in highway safety modeling. The reasons are attributed to the complexity for estimating this kind of model as well as the problem related to "over-fitting" the data. To circumvent the latter problem, some statisticians have proposed the use of Bayesian neural network (BNN) models. These models have been shown to perform better than BPNN models while at the same time reducing the difficulty associated with over-fitting the data. The objective of this study is to evaluate the application of BNN models for predicting motor vehicle crashes. To accomplish this objective, a series of models was estimated using data collected on rural frontage roads in Texas. Three types of models were compared: BPNN, BNN and the negative binomial (NB) regression models. The results of this study show that in general both types of neural network models perform better than the NB regression model in terms of data prediction. Although the BPNN model can occasionally provide better or approximately equivalent prediction performance compared to the BNN model, in most cases its prediction performance is worse than the BNN model. In addition, the data fitting performance of the BPNN model is consistently worse than the BNN model, which suggests that the BNN model has better generalization abilities than the BPNN model and can effectively alleviate the over-fitting problem without significantly compromising the nonlinear approximation ability. The results also show that BNNs could be used for other useful analyses in highway safety, including the development of accident modification factors and for improving the prediction capabilities for evaluating different highway design alternatives.

Item Response Theory Modeling of the Philadelphia Naming Test.

PubMed

Fergadiotis, Gerasimos; Kellough, Stacey; Hula, William D

2015-06-01

In this study, we investigated the fit of the Philadelphia Naming Test (PNT; Roach, Schwartz, Martin, Grewal, & Brecher, 1996) to an item-response-theory measurement model, estimated the precision of the resulting scores and item parameters, and provided a theoretical rationale for the interpretation of PNT overall scores by relating explanatory variables to item difficulty. This article describes the statistical model underlying the computer adaptive PNT presented in a companion article (Hula, Kellough, & Fergadiotis, 2015). Using archival data, we evaluated the fit of the PNT to 1- and 2-parameter logistic models and examined the precision of the resulting parameter estimates. We regressed the item difficulty estimates on three predictor variables: word length, age of acquisition, and contextual diversity. The 2-parameter logistic model demonstrated marginally better fit, but the fit of the 1-parameter logistic model was adequate. Precision was excellent for both person ability and item difficulty estimates. Word length, age of acquisition, and contextual diversity all independently contributed to variance in item difficulty. Item-response-theory methods can be productively used to analyze and quantify anomia severity in aphasia. Regression of item difficulty on lexical variables supported the validity of the PNT and interpretation of anomia severity scores in the context of current word-finding models.

Fitness components and ecological risk of transgenic release: a model using Japanese medaka (Oryzias latipes).

PubMed

Muir, W M; Howard, R D

2001-07-01

Any release of transgenic organisms into nature is a concern because ecological relationships between genetically engineered organisms and other organisms (including their wild-type conspecifics) are unknown. To address this concern, we developed a method to evaluate risk in which we input estimates of fitness parameters from a founder population into a recurrence model to predict changes in transgene frequency after a simulated transgenic release. With this method, we grouped various aspects of an organism's life cycle into six net fitness components: juvenile viability, adult viability, age at sexual maturity, female fecundity, male fertility, and mating advantage. We estimated these components for wild-type and transgenic individuals using the fish, Japanese medaka (Oryzias latipes). We generalized our model's predictions using various combinations of fitness component values in addition to our experimentally derived estimates. Our model predicted that, for a wide range of parameter values, transgenes could spread in populations despite high juvenile viability costs if transgenes also have sufficiently high positive effects on other fitness components. Sensitivity analyses indicated that transgene effects on age at sexual maturity should have the greatest impact on transgene frequency, followed by juvenile viability, mating advantage, female fecundity, and male fertility, with changes in adult viability, resulting in the least impact.

The structure of vulnerabilities for social anxiety disorder.

PubMed

Rodebaugh, Thomas L; Levinson, Cheri A; Langer, Julia K; Weeks, Justin W; Heimberg, Richard G; Brown, Patrick J; Menatti, Andrew R; Schneier, Franklin R; Blanco, Carlos; Liebowitz, Michael R

2017-04-01

Social anxiety disorder symptoms are generally proposed to be related to broad temperamental vulnerabilities (e.g., a low level of approach and high level of avoidance temperament), specific psychological vulnerabilities (e.g., fears of negative and positive evaluation), and additional disorders (e.g., major depressive disorder). However, existing tests of such a model have either not considered depressive symptoms or relied on samples of undergraduates. We examined these and related questions via a latent variable model in a large dataset (N=2253) that combined participants across a variety of studies. The model had adequate fit in the whole sample, and good fit in a subsample in which more participants completed the depression measure. The model indicated that low level of approach and high level of avoidance temperament contributed to fears of evaluation and social anxiety symptoms, and that fears of evaluation additionally contributed independently to social anxiety symptoms. The relationship between social anxiety and depressive symptoms was entirely accounted for by these vulnerabilities: Depressive symptoms were only predicted by avoidance temperament. Copyright © 2017 Elsevier Ireland Ltd. All rights reserved.

Forecasting mortality of road traffic injuries in China using seasonal autoregressive integrated moving average model.

PubMed

Zhang, Xujun; Pang, Yuanyuan; Cui, Mengjing; Stallones, Lorann; Xiang, Huiyun

2015-02-01

Road traffic injuries have become a major public health problem in China. This study aimed to develop statistical models for predicting road traffic deaths and to analyze seasonality of deaths in China. A seasonal autoregressive integrated moving average (SARIMA) model was used to fit the data from 2000 to 2011. Akaike Information Criterion, Bayesian Information Criterion, and mean absolute percentage error were used to evaluate the constructed models. Autocorrelation function and partial autocorrelation function of residuals and Ljung-Box test were used to compare the goodness-of-fit between the different models. The SARIMA model was used to forecast monthly road traffic deaths in 2012. The seasonal pattern of road traffic mortality data was statistically significant in China. SARIMA (1, 1, 1) (0, 1, 1)12 model was the best fitting model among various candidate models; the Akaike Information Criterion, Bayesian Information Criterion, and mean absolute percentage error were -483.679, -475.053, and 4.937, respectively. Goodness-of-fit testing showed nonautocorrelations in the residuals of the model (Ljung-Box test, Q = 4.86, P = .993). The fitted deaths using the SARIMA (1, 1, 1) (0, 1, 1)12 model for years 2000 to 2011 closely followed the observed number of road traffic deaths for the same years. The predicted and observed deaths were also very close for 2012. This study suggests that accurate forecasting of road traffic death incidence is possible using SARIMA model. The SARIMA model applied to historical road traffic deaths data could provide important evidence of burden of road traffic injuries in China. Copyright © 2015 Elsevier Inc. All rights reserved.

Confirmatory factor analysis of the female sexual function index.

PubMed

Opperman, Emily A; Benson, Lindsay E; Milhausen, Robin R

2013-01-01

The Female Sexual Functioning Index (Rosen et al., 2000 ) was designed to assess the key dimensions of female sexual functioning using six domains: desire, arousal, lubrication, orgasm, satisfaction, and pain. A full-scale score was proposed to represent women's overall sexual function. The fifth revision to the Diagnostic and Statistical Manual (DSM) is currently underway and includes a proposal to combine desire and arousal problems. The objective of this article was to evaluate and compare four models of the Female Sexual Functioning Index: (a) single-factor model, (b) six-factor model, (c) second-order factor model, and (4) five-factor model combining the desire and arousal subscales. Cross-sectional and observational data from 85 women were used to conduct a confirmatory factor analysis on the Female Sexual Functioning Index. Local and global goodness-of-fit measures, the chi-square test of differences, squared multiple correlations, and regression weights were used. The single-factor model fit was not acceptable. The original six-factor model was confirmed, and good model fit was found for the second-order and five-factor models. Delta chi-square tests of differences supported best fit for the six-factor model validating usage of the six domains. However, when revisions are made to the DSM-5, the Female Sexual Functioning Index can adapt to reflect these changes and remain a valid assessment tool for women's sexual functioning, as the five-factor structure was also supported.

Kernel Method Based Human Model for Enhancing Interactive Evolutionary Optimization

PubMed Central

Zhao, Qiangfu; Liu, Yong

2015-01-01

A fitness landscape presents the relationship between individual and its reproductive success in evolutionary computation (EC). However, discrete and approximate landscape in an original search space may not support enough and accurate information for EC search, especially in interactive EC (IEC). The fitness landscape of human subjective evaluation in IEC is very difficult and impossible to model, even with a hypothesis of what its definition might be. In this paper, we propose a method to establish a human model in projected high dimensional search space by kernel classification for enhancing IEC search. Because bivalent logic is a simplest perceptual paradigm, the human model is established by considering this paradigm principle. In feature space, we design a linear classifier as a human model to obtain user preference knowledge, which cannot be supported linearly in original discrete search space. The human model is established by this method for predicting potential perceptual knowledge of human. With the human model, we design an evolution control method to enhance IEC search. From experimental evaluation results with a pseudo-IEC user, our proposed model and method can enhance IEC search significantly. PMID:25879050

[Validity and factorial invariance of the social ecological model in explaining fruit intake in Mexican schoolchildren].

PubMed

Ochoa-Meza, Gerardo; Sierra, Juan Carlos; Pérez-Rodrigo, Carmen; Aranceta Bartrina, Javier; Esparza-Del Villar, Óscar A

2014-11-24

To test the goodness of fit of a Motivation-Ability-Opportunity model (MAO-model) to evaluate the observed variance in Mexican schoolchildren's preferences to eat fruit and daily fruit intake; also to evaluate the factorial invariance across the gender and type of population (urban and semi-urban) in which children reside. A model with seven constructs was designed from a validated questionnaire to assess preferences, cognitive abilities, attitude, modelling, perceived barriers, accessibility at school, accessibility at home, and fruit intake frequency. The instrument was administered in a representative sample of 1434 schoolchildren of 5th and 6th grade of primary school in a cross-sectional and ex post fact study conducted in 2013 in six cities of the State of Chihuahua, Mexico. The goodness of fit indexes was adequate for the MAO-model and explained 39% of the variance in preference to eat fruit. The structure of the model showed very good factor structure stability and the dimensions of the scale were equivalent in the different samples analyzed. The model analyzed with structural equation modeling showed a parsimonious model that can be used to explain the variation in fruit intake of 10 to 12 year old Mexican schoolchildren. The structure of the model was strictly invariant in the different samples analyzed and showed evidence of cross validation. Finally, implications about the modification model to fit data from scholar settings and guidelines for future research are discussed. Copyright AULA MEDICA EDICIONES 2014. Published by AULA MEDICA. All rights reserved.

Bayesian Evaluation of Dynamical Soil Carbon Models Using Soil Carbon Flux Data

NASA Astrophysics Data System (ADS)

Xie, H. W.; Romero-Olivares, A.; Guindani, M.; Allison, S. D.

2017-12-01

2016 was Earth's hottest year in the modern temperature record and the third consecutive record-breaking year. As the planet continues to warm, temperature-induced changes in respiration rates of soil microbes could reduce the amount of carbon sequestered in the soil organic carbon (SOC) pool, one of the largest terrestrial stores of carbon. This would accelerate temperature increases. In order to predict the future size of the SOC pool, mathematical soil carbon models (SCMs) describing interactions between the biosphere and atmosphere are needed. SCMs must be validated before they can be chosen for predictive use. In this study, we check two SCMs called CON and AWB for consistency with observed data using Bayesian goodness of fit testing that can be used in the future to compare other models. We compare the fit of the models to longitudinal soil respiration data from a meta-analysis of soil heating experiments using a family of Bayesian goodness of fit metrics called information criteria (IC), including the Widely Applicable Information Criterion (WAIC), the Leave-One-Out Information Criterion (LOOIC), and the Log Pseudo Marginal Likelihood (LPML). These IC's take the entire posterior distribution into account, rather than just one outputted model fit line. A lower WAIC and LOOIC and larger LPML indicate a better fit. We compare AWB and CON with fixed steady state model pool sizes. At equivalent SOC, dissolved organic carbon, and microbial pool sizes, CON always outperforms AWB quantitatively by all three IC's used. AWB monotonically improves in fit as we reduce the SOC steady state pool size while fixing all other pool sizes, and the same is almost true for CON. The AWB model with the lowest SOC is the best performing AWB model, while the CON model with the second lowest SOC is the best performing model. We observe that AWB displays more changes in slope sign and qualitatively displays more adaptive dynamics, which prevents AWB from being fully ruled out for predictive use, but based on IC's, CON is clearly the superior model for fitting the data. Hence, we demonstrate that Bayesian goodness of fit testing with information criteria helps us rigorously determine the consistency of models with data. Models that demonstrate their consistency to multiple data sets with our approach can then be selected for further refinement.

Investigating different approaches to develop informative priors in hierarchical Bayesian safety performance functions.

PubMed

Yu, Rongjie; Abdel-Aty, Mohamed

2013-07-01

The Bayesian inference method has been frequently adopted to develop safety performance functions. One advantage of the Bayesian inference is that prior information for the independent variables can be included in the inference procedures. However, there are few studies that discussed how to formulate informative priors for the independent variables and evaluated the effects of incorporating informative priors in developing safety performance functions. This paper addresses this deficiency by introducing four approaches of developing informative priors for the independent variables based on historical data and expert experience. Merits of these informative priors have been tested along with two types of Bayesian hierarchical models (Poisson-gamma and Poisson-lognormal models). Deviance information criterion (DIC), R-square values, and coefficients of variance for the estimations were utilized as evaluation measures to select the best model(s). Comparison across the models indicated that the Poisson-gamma model is superior with a better model fit and it is much more robust with the informative priors. Moreover, the two-stage Bayesian updating informative priors provided the best goodness-of-fit and coefficient estimation accuracies. Furthermore, informative priors for the inverse dispersion parameter have also been introduced and tested. Different types of informative priors' effects on the model estimations and goodness-of-fit have been compared and concluded. Finally, based on the results, recommendations for future research topics and study applications have been made. Copyright © 2013 Elsevier Ltd. All rights reserved.

Technical Analysis of Teacher Responses to the Self-Evaluation Scale-Teacher (SES-T) Version

ERIC Educational Resources Information Center

Erford, Bradley T.; Lowe, Samantha; Chang, Catherine Y.

2011-01-01

The Self-Evaluation Scale--Teacher version, used to assess teacher perceived self-esteem of students, was analyzed. A unidimensional model emerged from exploratory factor analysis, with cautious acceptance of data fit. Reliability and external aspects of validity were supported by the Self-Evaluation Scale--Teacher data.

[Does the GHQ-12 scoring system affect its factor structure? An exploratory study of Ibero American students].

PubMed

Urzúa, Alfonso; Caqueo-Urízar, Alejandra; Bargsted, Mariana; Irarrázaval, Matías

2015-06-01

This study aimed to evaluate whether the scoring system of the General Health Questionnaire (GHQ-12) alters the instrument's factor structure. The method considered 1,972 university students from nine Ibero American countries. Modeling was performed with structural equations for 1, 2, and 3 latent factors. The mechanism for scoring the questions was analyzed within each type of structure. The results indicate that models with 2 and 3 factors show better goodness-of-fit. In relation to scoring mechanisms, procedure 0-1-1-1 for models with 2 and 3 factors showed the best fit. In conclusion, there appears to be a relationship between the response format and the number of factors identified in the instrument's structure. The model with the best fit was 3-factor 0-1-1-1-formatted, but 0-1-2-3 has acceptable and more stable indicators and provides a better format for two- and three-dimensional models.

Fitting N-mixture models to count data with unmodeled heterogeneity: Bias, diagnostics, and alternative approaches

USGS Publications Warehouse

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 making. Therefore, we also discuss alternative approaches to yield unbiased estimates of population state variables using similar data types, and we stress that there is no substitute for an effective sample design that is grounded upon well-defined management objectives.

Forecasting incidence of hemorrhagic fever with renal syndrome in China using ARIMA model

PubMed Central

2011-01-01

Background China is a country that is most seriously affected by hemorrhagic fever with renal syndrome (HFRS) with 90% of HFRS cases reported globally. At present, HFRS is getting worse with increasing cases and natural foci in China. Therefore, there is an urgent need for monitoring and predicting HFRS incidence to make the control of HFRS more effective. In this study, we applied a stochastic autoregressive integrated moving average (ARIMA) model with the objective of monitoring and short-term forecasting HFRS incidence in China. Methods Chinese HFRS data from 1975 to 2008 were used to fit ARIMA model. Akaike Information Criterion (AIC) and Ljung-Box test were used to evaluate the constructed models. Subsequently, the fitted ARIMA model was applied to obtain the fitted HFRS incidence from 1978 to 2008 and contrast with corresponding observed values. To assess the validity of the proposed model, the mean absolute percentage error (MAPE) between the observed and fitted HFRS incidence (1978-2008) was calculated. Finally, the fitted ARIMA model was used to forecast the incidence of HFRS of the years 2009 to 2011. All analyses were performed using SAS9.1 with a significant level of p < 0.05. Results The goodness-of-fit test of the optimum ARIMA (0,3,1) model showed non-significant autocorrelations in the residuals of the model (Ljung-Box Q statistic = 5.95,P = 0.3113). The fitted values made by ARIMA (0,3,1) model for years 1978-2008 closely followed the observed values for the same years, with a mean absolute percentage error (MAPE) of 12.20%. The forecast values from 2009 to 2011 were 0.69, 0.86, and 1.21per 100,000 population, respectively. Conclusion ARIMA models applied to historical HFRS incidence data are an important tool for HFRS surveillance in China. This study shows that accurate forecasting of the HFRS incidence is possible using an ARIMA model. If predicted values from this study are accurate, China can expect a rise in HFRS incidence. PMID:21838933

Parallel approaches to composite production: interfaces that behave contrary to expectation.

PubMed

Frowd, Charlie D; Bruce, Vicki; Ness, Hayley; Bowie, Leslie; Paterson, Jenny; Thomson-Bogner, Claire; McIntyre, Alexander; Hancock, Peter J B

2007-04-01

This paper examines two facial composite systems that present multiple faces during construction to more closely resemble natural face processing. A 'parallel' version of PRO-fit was evaluated, which presents facial features in sets of six or twelve, and EvoFIT, a system in development, which contains a holistic face model and an evolutionary interface. The PRO-fit parallel interface turned out not to be quite as good as the 'serial' version as it appeared to interfere with holistic face processing. Composites from EvoFIT were named almost three times better than PRO-fit, but a benefit emerged under feature encoding, suggesting that recall has a greater role for EvoFIT than was previously thought. In general, an advantage was found for feature encoding, replicating a previous finding in this area, and also for a novel 'holistic' interview.

Repeated holdout Cross-Validation of Model to Estimate Risk of Lyme Disease by Landscape Attributes

EPA Science Inventory

We previously modeled Lyme disease (LD) risk at the landscape scale; here we evaluate the model's overall goodness-of-fit using holdout validation. Landscapes were characterized within road-bounded analysis units (AU). Observed LD cases (obsLD) were ascertained per AU. Data were ...

Small-Sample Adjustments for Tests of Moderators and Model Fit in Robust Variance Estimation in Meta-Regression

ERIC Educational Resources Information Center

Tipton, Elizabeth; Pustejovsky, James E.

2015-01-01

Randomized experiments are commonly used to evaluate the effectiveness of educational interventions. The goal of the present investigation is to develop small-sample corrections for multiple contrast hypothesis tests (i.e., F-tests) such as the omnibus test of meta-regression fit or a test for equality of three or more levels of a categorical…

Evaluation of an activated carbon packed bed for the adsorption of phenols from petroleum refinery wastewater.

PubMed

El-Naas, Muftah H; Alhaija, Manal A; Al-Zuhair, Sulaiman

2017-03-01

The performance of an adsorption column packed with granular activated carbon was evaluated for the removal of phenols from refinery wastewater. The effects of phenol feed concentration (80-182 mg/l), feed flow rate (5-20 ml/min), and activated carbon packing mass (5-15 g) on the breakthrough characteristics of the adsorption system were determined. The continuous adsorption process was simulated using batch data and the parameters for a new empirical model were determined. Different dynamic models such as Adams-Bohart, Wolborsko, Thomas, and Yoon-Nelson models were also fitted to the experimental data for the sake of comparison. The empirical, Yoon-Nelson and Thomas models showed a high degree of fitting at different operation conditions, with the empirical model giving the best fit based on the Akaike information criterion (AIC). At an initial phenol concentration of 175 mg/l, packing mass of 10 g, a flow rate of 10 ml/min and a temperature of 25 °C, the SSE of the new empirical and Thomas models were identical (248.35) and very close to that of the Yoon-Nelson model (259.49). The values were significantly lower than that of the Adams-Bohart model, which was determined to be 19,358.48. The superiority of the new empirical model and the Thomas model was also confirmed from the values of the R 2 and AIC, which were 0.99 and 38.3, respectively, compared to 0.92 and 86.2 for Adams-Bohart model.

Assessing fitness to stand trial: the utility of the Fitness Interview Test (revised edition).

PubMed

Zapf, P A; Roesch, R; Viljoen, J L

2001-06-01

In Canada most evaluations of fitness to stand trial are conducted on an inpatient basis. This costs time and money, and deprives those defendants remanded for evaluation of liberty. This research assessed the predictive efficiency of the Fitness Interview Test, revised edition (FIT) as a screening instrument for fitness to stand trial. We compared decisions about fitness to stand trial, based on the FIT, with the results of institution-based evaluations for 2 samples of men remanded for inpatient fitness assessments. The FIT demonstrates excellent utility as a screening instrument. The FIT shows good sensitivity and negative predictive power, which suggests that it can reliably screen those individuals who are clearly fit to stand trial, before they are remanded to an inpatient facility for a fitness assessment. We discuss the implications for evaluating fitness to stand trial, particularly in terms of the need for community-based alternatives to traditional forensic assessments.

Work Engagement among Rescue Workers: Psychometric Properties of the Portuguese UWES

PubMed Central

Sinval, Jorge; Marques-Pinto, Alexandra; Queirós, Cristina; Marôco, João

2018-01-01

Rescue workers have a stressful and risky occupation where being engaged is crucial to face physical and emotional risks in order to help other persons. This study aims to estimate work engagement levels of rescue workers (namely comparing nurses, firefighters, and police officers) and to assess the validity evidence related to the internal structure of the Portuguese versions of the UWES-17 and UWES-9, namely, dimensionality, measurement invariance between occupational groups, and reliability of the scores. To evaluate the dimensionality, we compared the fit of the three-factor model with the fit of a second-order model. A Portuguese version of the instrument was applied to a convenience sample of 3,887 rescue workers (50% nurses, 39% firefighters, and 11% police officers). Work engagement levels were moderate to high, with firefighters being the highest and nurses being the lowest engaged. Psychometric properties were evaluated in the three-factor original structure revealing acceptable fit to the data in the UWES-17, although the UWES-9 had better psychometric properties. Given the observed statistically significant correlations between the three original factors, we proposed a 2nd hierarchal structure that we named work engagement. The UWES-9 first-order model obtained full uniqueness measurement invariance, and the second-order model obtained partial (metric) second-order invariance. PMID:29403403

Fitting mathematical models to describe the rheological behaviour of chocolate pastes

NASA Astrophysics Data System (ADS)

Barbosa, Carla; Diogo, Filipa; Alves, M. Rui

2016-06-01

The flow behavior is of utmost importance for the chocolate industry. The objective of this work was to study two mathematical models, Casson and Windhab models that can be used to fit chocolate rheological data and evaluate which better infers or previews the rheological behaviour of different chocolate pastes. Rheological properties (viscosity, shear stress and shear rates) were obtained with a rotational viscometer equipped with a concentric cylinder. The chocolate samples were white chocolate and chocolate with varying percentages in cacao (55%, 70% and 83%). The results showed that the Windhab model was the best to describe the flow behaviour of all the studied samples with higher determination coefficients (r2 > 0.9).

Evaluation of the NMC community specialist practitioner award.

PubMed

Chambers, Claire; Goodman-Brown, Jane; Horn, Sue; Ryder, Elaine

2007-10-01

This paper discusses the evaluation of the NMC community specialist practitioner (CSP) programme over a period of four years. The purpose of the evaluation was to assess if the programme produced practitioners who were fit for purpose and fit for practice as well as assessing whether they were supported in their new roles. The evaluation took place eight months after qualification at a workshop where the practitioners discussed their experiences in focus groups. The evaluation is presented using Kirkpatrick's model and the results indicate the importance of collaboration between HEI's and their sponsors in meeting students' needs. Issues about support and work life balance are also highlighted as areas that were addressed as a result of the evaluation. Continued development of the programme through collaboration is desirable to produce effective practitioners who are able to function in the changing primary care arena.

Automation of reliability evaluation procedures through CARE - The computer-aided reliability estimation program.

NASA Technical Reports Server (NTRS)

Mathur, F. P.

1972-01-01

Description of an on-line interactive computer program called CARE (Computer-Aided Reliability Estimation) which can model self-repair and fault-tolerant organizations and perform certain other functions. Essentially CARE consists of a repository of mathematical equations defining the various basic redundancy schemes. These equations, under program control, are then interrelated to generate the desired mathematical model to fit the architecture of the system under evaluation. The mathematical model is then supplied with ground instances of its variables and is then evaluated to generate values for the reliability-theoretic functions applied to the model.

Improved parameter inference in catchment models: 1. Evaluating parameter uncertainty

NASA Astrophysics Data System (ADS)

Kuczera, George

1983-10-01

A Bayesian methodology is developed to evaluate parameter uncertainty in catchment models fitted to a hydrologic response such as runoff, the goal being to improve the chance of successful regionalization. The catchment model is posed as a nonlinear regression model with stochastic errors possibly being both autocorrelated and heteroscedastic. The end result of this methodology, which may use Box-Cox power transformations and ARMA error models, is the posterior distribution, which summarizes what is known about the catchment model parameters. This can be simplified to a multivariate normal provided a linearization in parameter space is acceptable; means of checking and improving this assumption are discussed. The posterior standard deviations give a direct measure of parameter uncertainty, and study of the posterior correlation matrix can indicate what kinds of data are required to improve the precision of poorly determined parameters. Finally, a case study involving a nine-parameter catchment model fitted to monthly runoff and soil moisture data is presented. It is shown that use of ordinary least squares when its underlying error assumptions are violated gives an erroneous description of parameter uncertainty.

An analytical model for regular respiratory signals derived from the probability density function of Rayleigh distribution.

PubMed

Li, Xin; Li, Ye

2015-01-01

Regular respiratory signals (RRSs) acquired with physiological sensing systems (e.g., the life-detection radar system) can be used to locate survivors trapped in debris in disaster rescue, or predict the breathing motion to allow beam delivery under free breathing conditions in external beam radiotherapy. Among the existing analytical models for RRSs, the harmonic-based random model (HRM) is shown to be the most accurate, which, however, is found to be subject to considerable error if the RRS has a slowly descending end-of-exhale (EOE) phase. The defect of the HRM motivates us to construct a more accurate analytical model for the RRS. In this paper, we derive a new analytical RRS model from the probability density function of Rayleigh distribution. We evaluate the derived RRS model by using it to fit a real-life RRS in the sense of least squares, and the evaluation result shows that, our presented model exhibits lower error and fits the slowly descending EOE phases of the real-life RRS better than the HRM.

Modelling job support, job fit, job role and job satisfaction for school of nursing sessional academic staff.

PubMed

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.

Quantified Choice of Root-Mean-Square Errors of Approximation for Evaluation and Power Analysis of Small Differences between Structural Equation Models

ERIC Educational Resources Information Center

Li, Libo; Bentler, Peter M.

2011-01-01

MacCallum, Browne, and Cai (2006) proposed a new framework for evaluation and power analysis of small differences between nested structural equation models (SEMs). In their framework, the null and alternative hypotheses for testing a small difference in fit and its related power analyses were defined by some chosen root-mean-square error of…

Vegetable parenting practices scale: Item response modeling analyses

USDA-ARS?s Scientific Manuscript database

Our objective was to evaluate the psychometric properties of a vegetable parenting practices scale using multidimensional polytomous item response modeling which enables assessing item fit to latent variables and the distributional characteristics of the items in comparison to the respondents. We al...

Hyperopic photorefractive keratectomy and central islands

NASA Astrophysics Data System (ADS)

Gobbi, Pier Giorgio; Carones, Francesco; Morico, Alessandro; Vigo, Luca; Brancato, Rosario

1998-06-01

We have evaluated the refractive evolution in patients treated with yhyperopic PRK to assess the extent of the initial overcorrection and the time constant of regression. To this end, the time history of the refractive error (i.e. the difference between achieved and intended refractive correction) has been fitted by means of an exponential statistical model, giving information characterizing the surgical procedure with a direct clinical meaning. Both hyperopic and myopic PRk procedures have been analyzed by this method. The analysis of the fitting model parameters shows that hyperopic PRK patients exhibit a definitely higher initial overcorrection than myopic ones, and a regression time constant which is much longer. A common mechanism is proposed to be responsible for the refractive outcomes in hyperopic treatments and in myopic patients exhibiting significant central islands. The interpretation is in terms of superhydration of the central cornea, and is based on a simple physical model evaluating the amount of centripetal compression in the apical cornea.

An approximation of herd effect due to vaccinating children against seasonal influenza - a potential solution to the incorporation of indirect effects into static models.

PubMed

Van Vlaenderen, Ilse; Van Bellinghen, Laure-Anne; Meier, Genevieve; Nautrup, Barbara Poulsen

2013-01-22

Indirect herd effect from vaccination of children offers potential for improving the effectiveness of influenza prevention in the remaining unvaccinated population. Static models used in cost-effectiveness analyses cannot dynamically capture herd effects. The objective of this study was to develop a methodology to allow herd effect associated with vaccinating children against seasonal influenza to be incorporated into static models evaluating the cost-effectiveness of influenza vaccination. Two previously published linear equations for approximation of herd effects in general were compared with the results of a structured literature review undertaken using PubMed searches to identify data on herd effects specific to influenza vaccination. A linear function was fitted to point estimates from the literature using the sum of squared residuals. The literature review identified 21 publications on 20 studies for inclusion. Six studies provided data on a mathematical relationship between effective vaccine coverage in subgroups and reduction of influenza infection in a larger unvaccinated population. These supported a linear relationship when effective vaccine coverage in a subgroup population was between 20% and 80%. Three studies evaluating herd effect at a community level, specifically induced by vaccinating children, provided point estimates for fitting linear equations. The fitted linear equation for herd protection in the target population for vaccination (children) was slightly less conservative than a previously published equation for herd effects in general. The fitted linear equation for herd protection in the non-target population was considerably less conservative than the previously published equation. This method of approximating herd effect requires simple adjustments to the annual baseline risk of influenza in static models: (1) for the age group targeted by the childhood vaccination strategy (i.e. children); and (2) for other age groups not targeted (e.g. adults and/or elderly). Two approximations provide a linear relationship between effective coverage and reduction in the risk of infection. The first is a conservative approximation, recommended as a base-case for cost-effectiveness evaluations. The second, fitted to data extracted from a structured literature review, provides a less conservative estimate of herd effect, recommended for sensitivity analyses.

A Comparison of Two Mathematical Modeling Frameworks for Evaluating Sexually Transmitted Infection Epidemiology.

PubMed

Johnson, Leigh F; Geffen, Nathan

2016-03-01

Different models of sexually transmitted infections (STIs) can yield substantially different conclusions about STI epidemiology, and it is important to understand how and why models differ. Frequency-dependent models make the simplifying assumption that STI incidence is proportional to STI prevalence in the population, whereas network models calculate STI incidence more realistically by classifying individuals according to their partners' STI status. We assessed a deterministic frequency-dependent model approximation to a microsimulation network model of STIs in South Africa. Sexual behavior and demographic parameters were identical in the 2 models. Six STIs were simulated using each model: HIV, herpes, syphilis, gonorrhea, chlamydia, and trichomoniasis. For all 6 STIs, the frequency-dependent model estimated a higher STI prevalence than the network model, with the difference between the 2 models being relatively large for the curable STIs. When the 2 models were fitted to the same STI prevalence data, the best-fitting parameters differed substantially between models, with the frequency-dependent model suggesting more immunity and lower transmission probabilities. The fitted frequency-dependent model estimated that the effects of a hypothetical elimination of concurrent partnerships and a reduction in commercial sex were both smaller than estimated by the fitted network model, whereas the latter model estimated a smaller impact of a reduction in unprotected sex in spousal relationships. The frequency-dependent assumption is problematic when modeling short-term STIs. Frequency-dependent models tend to underestimate the importance of high-risk groups in sustaining STI epidemics, while overestimating the importance of long-term partnerships and low-risk groups.

Revisiting Gaussian Process Regression Modeling for Localization in Wireless Sensor Networks

PubMed Central

Richter, Philipp; Toledano-Ayala, Manuel

2015-01-01

Signal strength-based positioning in wireless sensor networks is a key technology for seamless, ubiquitous localization, especially in areas where Global Navigation Satellite System (GNSS) signals propagate poorly. To enable wireless local area network (WLAN) location fingerprinting in larger areas while maintaining accuracy, methods to reduce the effort of radio map creation must be consolidated and automatized. Gaussian process regression has been applied to overcome this issue, also with auspicious results, but the fit of the model was never thoroughly assessed. Instead, most studies trained a readily available model, relying on the zero mean and squared exponential covariance function, without further scrutinization. This paper studies the Gaussian process regression model selection for WLAN fingerprinting in indoor and outdoor environments. We train several models for indoor/outdoor- and combined areas; we evaluate them quantitatively and compare them by means of adequate model measures, hence assessing the fit of these models directly. To illuminate the quality of the model fit, the residuals of the proposed model are investigated, as well. Comparative experiments on the positioning performance verify and conclude the model selection. In this way, we show that the standard model is not the most appropriate, discuss alternatives and present our best candidate. PMID:26370996

A new mobile ubiquitous computing application to control obesity: SapoFit.

PubMed

Rodrigues, Joel J P C; Lopes, Ivo M C; Silva, Bruno M C; Torre, Isabel de La

2013-01-01

The objective of this work was the proposal, design, construction and validation of a mobile health system for dietetic monitoring and assessment, called SapoFit. This application may be personalized to keep a daily personal health record of an individual's food intake and daily exercise and to share this with a social network. The initiative is a partnership with SAPO - Portugal Telecom. SapoFit uses Web services architecture, a relatively new model for distributed computing and application integration. SapoFit runs on a range of mobile platforms, and it has been implemented successfully in a range of mobile devices and has been evaluated by over 100 users. Most users strongly agree that SapoFit has an attractive design, the environment is user-friendly and intuitive, and the navigation options are clear.

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

PubMed

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

2016-01-01

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

Rasch Analysis of the Edmonton Symptom Assessment System.

PubMed

Sprague, Emma; Siegert, Richard J; Medvedev, Oleg; Roberts, Margaret H

2018-05-01

The Edmonton Symptom Assessment System (ESAS) is a widely used multisymptom assessment tool in cancer and palliative care settings, but its psychometric properties have not been widely tested using modern psychometric methods such as Rasch analysis. To apply Rasch analysis to the ESAS in a community palliative care setting and determine its suitability for assessing symptom burden in this group. ESAS data collected from 229 patients enrolled in a community hospice service were evaluated using a partial credit Rasch model with RUMM2030 software (RUMM Laboratory Pty, Ltd., Duncraig, WA). Where disordered thresholds were discovered, item rescoring was undertaken. Rasch model fit and differential item functioning were evaluated after each iterative phase. Uniform rescoring was necessary for all 12 items to display ordered thresholds. The best model fit was achieved after item rescoring and combining three pairs of locally dependent items into three superitems (χ 2  = 29.56 [27]; P = 0.33) that permitted ordinal-to-interval conversion. The ESAS satisfied unidimensional Rasch model expectations in a 12-item format after minor modifications. This included uniform rescoring of the disordered response categories and creating superitems to improve model fit and clinical utility. The accuracy of the ESAS scores can be improved by using ordinal-to-interval conversion tables published in the article. Copyright © 2018 The Authors. Published by Elsevier Inc. All rights reserved.

In vitro differential diagnosis of clavus and verruca by a predictive model generated from electrical impedance.

PubMed

Hung, Chien-Ya; Sun, Pei-Lun; Chiang, Shu-Jen; Jaw, Fu-Shan

2014-01-01

Similar clinical appearances prevent accurate diagnosis of two common skin diseases, clavus and verruca. In this study, electrical impedance is employed as a novel tool to generate a predictive model for differentiating these two diseases. We used 29 clavus and 28 verruca lesions. To obtain impedance parameters, a LCR-meter system was applied to measure capacitance (C), resistance (Re), impedance magnitude (Z), and phase angle (θ). These values were combined with lesion thickness (d) to characterize the tissue specimens. The results from clavus and verruca were then fitted to a univariate logistic regression model with the generalized estimating equations (GEE) method. In model generation, log ZSD and θSD were formulated as predictors by fitting a multiple logistic regression model with the same GEE method. The potential nonlinear effects of covariates were detected by fitting generalized additive models (GAM). Moreover, the model was validated by the goodness-of-fit (GOF) assessments. Significant mean differences of the index d, Re, Z, and θ are found between clavus and verruca (p<0.001). A final predictive model is established with Z and θ indices. The model fits the observed data quite well. In GOF evaluation, the area under the receiver operating characteristics (ROC) curve is 0.875 (>0.7), the adjusted generalized R2 is 0.512 (>0.3), and the p value of the Hosmer-Lemeshow GOF test is 0.350 (>0.05). This technique promises to provide an approved model for differential diagnosis of clavus and verruca. It could provide a rapid, relatively low-cost, safe and non-invasive screening tool in clinic use.

Evaluating Measurement of Dynamic Constructs: Defining a Measurement Model of Derivatives

PubMed Central

Estabrook, Ryne

2015-01-01

While measurement evaluation has been embraced as an important step in psychological research, evaluating measurement structures with longitudinal data is fraught with limitations. This paper defines and tests a measurement model of derivatives (MMOD), which is designed to assess the measurement structure of latent constructs both for analyses of between-person differences and for the analysis of change. Simulation results indicate that MMOD outperforms existing models for multivariate analysis and provides equivalent fit to data generation models. Additional simulations show MMOD capable of detecting differences in between-person and within-person factor structures. Model features, applications and future directions are discussed. PMID:24364383

The Biasing Effects of Unmodeled ARMA Time Series Processes on Latent Growth Curve Model Estimates

ERIC Educational Resources Information Center

Sivo, Stephen; Fan, Xitao; Witta, Lea

2005-01-01

The purpose of this study was to evaluate the robustness of estimated growth curve models when there is stationary autocorrelation among manifest variable errors. The results suggest that when, in practice, growth curve models are fitted to longitudinal data, alternative rival hypotheses to consider would include growth models that also specify…

Direct and Indirect Effects of Parental Influence upon Adolescent Alcohol Use: A Structural Equation Modeling Analysis

ERIC Educational Resources Information Center

Kim, Young-Mi; Neff, James Alan

2010-01-01

A model incorporating the direct and indirect effects of parental monitoring on adolescent alcohol use was evaluated by applying structural equation modeling (SEM) techniques to data on 4,765 tenth-graders in the 2001 Monitoring the Future Study. Analyses indicated good fit of hypothesized measurement and structural models. Analyses supported both…

Use of Selected Goodness-of-Fit Statistics to Assess the Accuracy of a Model of Henry Hagg Lake, Oregon

NASA Astrophysics Data System (ADS)

Rounds, S. A.; Sullivan, A. B.

2004-12-01

Assessing a model's ability to reproduce field data is a critical step in the modeling process. For any model, some method of determining goodness-of-fit to measured data is needed to aid in calibration and to evaluate model performance. Visualizations and graphical comparisons of model output are an excellent way to begin that assessment. At some point, however, model performance must be quantified. Goodness-of-fit statistics, including the mean error (ME), mean absolute error (MAE), root mean square error, and coefficient of determination, typically are used to measure model accuracy. Statistical tools such as the sign test or Wilcoxon test can be used to test for model bias. The runs test can detect phase errors in simulated time series. Each statistic is useful, but each has its limitations. None provides a complete quantification of model accuracy. In this study, a suite of goodness-of-fit statistics was applied to a model of Henry Hagg Lake in northwest Oregon. Hagg Lake is a man-made reservoir on Scoggins Creek, a tributary to the Tualatin River. Located on the west side of the Portland metropolitan area, the Tualatin Basin is home to more than 450,000 people. Stored water in Hagg Lake helps to meet the agricultural and municipal water needs of that population. Future water demands have caused water managers to plan for a potential expansion of Hagg Lake, doubling its storage to roughly 115,000 acre-feet. A model of the lake was constructed to evaluate the lake's water quality and estimate how that quality might change after raising the dam. The laterally averaged, two-dimensional, U.S. Army Corps of Engineers model CE-QUAL-W2 was used to construct the Hagg Lake model. Calibrated for the years 2000 and 2001 and confirmed with data from 2002 and 2003, modeled parameters included water temperature, ammonia, nitrate, phosphorus, algae, zooplankton, and dissolved oxygen. Several goodness-of-fit statistics were used to quantify model accuracy and bias. Model performance was judged to be excellent for water temperature (annual ME: -0.22 to 0.05 ° C; annual MAE: 0.62 to 0.68 ° C) and dissolved oxygen (annual ME: -0.28 to 0.18 mg/L; annual MAE: 0.43 to 0.92 mg/L), showing that the model is sufficiently accurate for future water resources planning and management.

Random regression models using Legendre polynomials or linear splines for test-day milk yield of dairy Gyr (Bos indicus) cattle.

PubMed

Pereira, R J; Bignardi, A B; El Faro, L; Verneque, R S; Vercesi Filho, A E; Albuquerque, L G

2013-01-01

Studies investigating the use of random regression models for genetic evaluation of milk production in Zebu cattle are scarce. In this study, 59,744 test-day milk yield records from 7,810 first lactations of purebred dairy Gyr (Bos indicus) and crossbred (dairy Gyr × Holstein) cows were used to compare random regression models in which additive genetic and permanent environmental effects were modeled using orthogonal Legendre polynomials or linear spline functions. Residual variances were modeled considering 1, 5, or 10 classes of days in milk. Five classes fitted the changes in residual variances over the lactation adequately and were used for model comparison. The model that fitted linear spline functions with 6 knots provided the lowest sum of residual variances across lactation. On the other hand, according to the deviance information criterion (DIC) and bayesian information criterion (BIC), a model using third-order and fourth-order Legendre polynomials for additive genetic and permanent environmental effects, respectively, provided the best fit. However, the high rank correlation (0.998) between this model and that applying third-order Legendre polynomials for additive genetic and permanent environmental effects, indicates that, in practice, the same bulls would be selected by both models. The last model, which is less parameterized, is a parsimonious option for fitting dairy Gyr breed test-day milk yield records. Copyright © 2013 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.

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

NASA Astrophysics Data System (ADS)

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

2003-08-01

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

Nutrition, dieting, and fitness messages in a magazine for adolescent women, 1970-1990.

PubMed

Guillen, E O; Barr, S I

1994-09-01

This study was designed to characterize nutrition and fitness messages presented between 1970-1990 in a magazine for adolescent women; to evaluate whether these messages changed over time; and to assess the body shape portrayed as desirable, and whether this changed over time. A data collection form was developed to code nutrition and fitness-related written items, advertisements and recipes, and the page coverage allocated to these items. Body shape was assessed by measuring bust:waist and hip:waist ratios of photographs of models wearing bathing suits or underwear. Magazines from even years between 1970-1990 (n = 132) were coded. Both nutrition-related and fitness-related coverage emphasized weight loss and physical appearance. Half the major nutrition-related articles presented a weight-loss plan, and weight loss was frequently addressed in other nutrition articles. The primary reasons presented for following a nutrition of fitness plan were to lose weight and become more attractive. Statements that the product or service would promote weight loss were found in 47% of nutrition-related advertisements. Nutrition coverage did not exhibit a net change over time, whereas fitness coverage increased (F = 6.6, p < .005), and the ratio of nutrition: fitness coverage changed from 10:1 in 1970 to 0.75:1 in 1990. Models' body shapes were less curvaceous than those in magazines for adult women, and the hip:waist ratio decreased over time (F = 7.3, p < .01). The nutrition and fitness messages in this magazine for adolescent women emphasize body shape and appearance, similar to findings from adult women's magazines, and contribute to the cultural milieu in which thinness is an expectation for women. Between 1970-1990, the emphasis on fitness increased, and the body shape of models tended to become more linear.

Evaluating multi-level models to test occupancy state responses of Plethodontid salamanders

USGS Publications Warehouse

Kroll, Andrew J.; Garcia, Tiffany S.; Jones, Jay E.; Dugger, Catherine; Murden, Blake; Johnson, Josh; Peerman, Summer; Brintz, Ben; Rochelle, Michael

2015-01-01

Plethodontid salamanders are diverse and widely distributed taxa and play critical roles in ecosystem processes. Due to salamander use of structurally complex habitats, and because only a portion of a population is available for sampling, evaluation of sampling designs and estimators is critical to provide strong inference about Plethodontid ecology and responses to conservation and management activities. We conducted a simulation study to evaluate the effectiveness of multi-scale and hierarchical single-scale occupancy models in the context of a Before-After Control-Impact (BACI) experimental design with multiple levels of sampling. Also, we fit the hierarchical single-scale model to empirical data collected for Oregon slender and Ensatina salamanders across two years on 66 forest stands in the Cascade Range, Oregon, USA. All models were fit within a Bayesian framework. Estimator precision in both models improved with increasing numbers of primary and secondary sampling units, underscoring the potential gains accrued when adding secondary sampling units. Both models showed evidence of estimator bias at low detection probabilities and low sample sizes; this problem was particularly acute for the multi-scale model. Our results suggested that sufficient sample sizes at both the primary and secondary sampling levels could ameliorate this issue. Empirical data indicated Oregon slender salamander occupancy was associated strongly with the amount of coarse woody debris (posterior mean = 0.74; SD = 0.24); Ensatina occupancy was not associated with amount of coarse woody debris (posterior mean = -0.01; SD = 0.29). Our simulation results indicate that either model is suitable for use in an experimental study of Plethodontid salamanders provided that sample sizes are sufficiently large. However, hierarchical single-scale and multi-scale models describe different processes and estimate different parameters. As a result, we recommend careful consideration of study questions and objectives prior to sampling data and fitting models.

Intravoxel incoherent motion modeling in the kidneys: Comparison of mono-, bi-, and triexponential fit.

PubMed

van Baalen, Sophie; Leemans, Alexander; Dik, Pieter; Lilien, Marc R; Ten Haken, Bennie; Froeling, Martijn

2017-07-01

To evaluate if a three-component model correctly describes the diffusion signal in the kidney and whether it can provide complementary anatomical or physiological information about the underlying tissue. Ten healthy volunteers were examined at 3T, with T 2 -weighted imaging, diffusion tensor imaging (DTI), and intravoxel incoherent motion (IVIM). Diffusion tensor parameters (mean diffusivity [MD] and fractional anisotropy [FA]) were obtained by iterative weighted linear least squares fitting of the DTI data and mono-, bi-, and triexponential fit parameters (D 1 , D 2 , D 3 , f fast2 , f fast3 , and f interm ) using a nonlinear fit of the IVIM data. Average parameters were calculated for three regions of interest (ROIs) (cortex, medulla, and rest) and from fiber tractography. Goodness of fit was assessed with adjusted R 2 ( Radj2) and the Shapiro-Wilk test was used to test residuals for normality. Maps of diffusion parameters were also visually compared. Fitting the diffusion signal was feasible for all models. The three-component model was best able to describe fast signal decay at low b values (b < 50), which was most apparent in Radj2 of the ROI containing high diffusion signals (ROI rest ), which was 0.42 ± 0.14, 0.61 ± 0.11, 0.77 ± 0.09, and 0.81 ± 0.08 for DTI, one-, two-, and three-component models, respectively, and in visual comparison of the fitted and measured S 0 . None of the models showed significant differences (P > 0.05) between the diffusion constant of the medulla and cortex, whereas the f fast component of the two and three-component models were significantly different (P < 0.001). Triexponential fitting is feasible for the diffusion signal in the kidney, and provides additional information. 2 Technical Efficacy: Stage 1 J. MAGN. RESON. IMAGING 2017;46:228-239. © 2016 The Authors Journal of Magnetic Resonance Imaging published by Wiley Periodicals, Inc. on behalf of International Society for Magnetic Resonance in Medicine.

Evaluating a health behaviour model for persons with and without an intellectual disability.

PubMed

Brehmer-Rinderer, B; Zigrovic, L; Weber, G

2014-06-01

Based on the idea of the Common Sense Model of Illness Representations by Leventhal as well as Lohaus's concepts of health and illness, a health behaviour model was designed to explain health behaviours applied by persons with intellectual disabilities (ID). The key proposal of this model is that the way someone understands the concepts of health, illness and disability influences the way they perceive themselves and what behavioural approaches to them they take. To test this model and explain health differences between the general population and person with ID, 230 people with ID and a comparative sample of 533 persons without ID were included in this Austrian study. Data were collected on general socio-demographics, personal perceptions of illness and disability, perceptions of oneself and health-related behaviours. Psychometric analysis of the instruments used showed that they were valid and reliable and hence can provide a valuable tool for studying health-related issues in persons with and without ID. With respect to the testing of the suggested health model, two latent variables were defined in accordance to the theory. The general model fit was evaluated by calculating different absolute and descriptive fit indices. Most indices indicated an acceptable model fit for all samples. This study presents the first attempt to explore the systematic differences in health behaviour between people with and without ID based on a suggested health model. Limitations of the study as well as implications for practice and future research are discussed. © 2013 MENCAP and International Association of the Scientific Study of Intellectual and Developmental Disabilities and John Wiley & Sons Ltd.

An empirical evaluation of the structure of DSM-IV personality disorders in a nationally representative sample: results of confirmatory factor analysis in the National Epidemiologic Survey on Alcohol and Related Conditions Waves 1 and 2.

PubMed

Cox, Brian J; Clara, Ian P; Worobec, Lydia M; Grant, Bridget F

2012-12-01

Individual personality disorders (PD) are grouped into three clusters in the DSM-IV (A, B, and C). There is very little empirical evidence available concerning the validity of this model in the general population. The current study included all 10 of the DSM-IV PD assessed in Wave 1 and Wave 2 of the National Epidemiologic Survey on Alcohol and Related Conditions (NESARC). Confirmatory factor analysis was used to evaluate three plausible models of the structure of Axis II personality disorders (the current hierarchical DSM-IV three-factor model in which individual PD are believed to load on their assigned clusters, which in turn load onto a single Axis II factor; a general single-factor model; and three independent factors). Each of these models was tested in both the total and also separately for gender. The higher order DSM-IV model demonstrated good fit to the data on a number of goodness-of-fit indices. The results for this model were very similar across genders. A model of PD based on the current DSM-IV hierarchical conceptualization of a higher order classification scheme received strong empirical support through confirmatory factor analysis using a number of goodness-of-fit indices in a nationally representative sample. Other models involving broad, higher order personality domains such as neuroticism in relation to personality disorders have yet to be tested in epidemiologic surveys and represent an important avenue for future research.

Feed-in tariff structure development for photovoltaic electricity and the associated benefits for the Kingdom of Bahrain

NASA Astrophysics Data System (ADS)

Haji, Shaker; Durazi, Amal; Al-Alawi, Yaser

2018-05-01

In this study, the feed-in tariff (FIT) scheme was considered to facilitate an effective introduction of renewable energy in the Kingdom of Bahrain. An economic model was developed for the estimation of feasible FIT rates for photovoltaic (PV) electricity on a residential scale. The calculations of FIT rates were based mainly on the local solar radiation, the cost of a grid-connected PV system, the operation and maintenance cost, and the provided financial support. The net present value and internal rate of return methods were selected for model evaluation with the guide of simple payback period to determine the cost of energy and feasible FIT rates under several scenarios involving different capital rebate percentages, loan down payment percentages, and PV system costs. Moreover, to capitalise on the FIT benefits, its impact on the stakeholders beyond the households was investigated in terms of natural gas savings, emissions cutback, job creation, and PV-electricity contribution towards the energy demand growth. The study recommended the introduction of the FIT scheme in the Kingdom of Bahrain due to its considerable benefits through a setup where each household would purchase the PV system through a loan, with the government and the electricity customers sharing the FIT cost.

Project FIT: A School, Community and Social Marketing Intervention Improves Healthy Eating Among Low-Income Elementary School Children.

PubMed

Alaimo, Katherine; Carlson, Joseph J; Pfeiffer, Karin A; Eisenmann, Joey C; Paek, Hye-Jin; Betz, Heather H; Thompson, Tracy; Wen, Yalu; Norman, Gregory J

2015-08-01

Project FIT was a two-year multi-component nutrition and physical activity intervention delivered in ethnically-diverse low-income elementary schools in Grand Rapids, MI. This paper reports effects on children's nutrition outcomes and process evaluation of the school component. A quasi-experimental design was utilized. 3rd, 4th and 5th-grade students (Yr 1 baseline: N = 410; Yr 2 baseline: N = 405; age range: 7.5-12.6 years) were measured in the fall and spring over the two-year intervention. Ordinal logistic, mixed effect models and generalized estimating equations were fitted, and the robust standard errors were utilized. Primary outcomes favoring the intervention students were found regarding consumption of fruits, vegetables and whole grain bread during year 2. Process evaluation revealed that implementation of most intervention components increased during year 2. Project FIT resulted in small but beneficial effects on consumption of fruits, vegetables, and whole grain bread in ethnically diverse low-income elementary school children.

Evaluation of an Approximate Method for Synthesizing Covariance Matrices for Use in Meta-Analytic SEM

ERIC Educational Resources Information Center

Beretvas, S. Natasha; Furlow, Carolyn F.

2006-01-01

Meta-analytic structural equation modeling (MA-SEM) is increasingly being used to assess model-fit for variables' interrelations synthesized across studies. MA-SEM researchers have analyzed synthesized correlation matrices using structural equation modeling (SEM) estimation that is designed for covariance matrices. This can produce incorrect…

Modeling Face Identification Processing in Children and Adults.

ERIC Educational Resources Information Center

Schwarzer, Gudrun; Massaro, Dominic W.

2001-01-01

Two experiments studied whether and how 5-year-olds integrate single facial features to identify faces. Results indicated that children could evaluate and integrate information from eye and mouth features to identify a face when salience of features was varied. A weighted Fuzzy Logical Model of Perception fit better than a Single Channel Model,…

Fit of interim crowns fabricated using photopolymer-jetting 3D printing.

PubMed

Mai, Hang-Nga; Lee, Kyu-Bok; Lee, Du-Hyeong

2017-08-01

The fit of interim crowns fabricated using 3-dimensional (3D) printing is unknown. The purpose of this in vitro study was to evaluate the fit of interim crowns fabricated using photopolymer-jetting 3D printing and to compare it with that of milling and compression molding methods. Twelve study models were fabricated by making an impression of a metal master model of the mandibular first molar. On each study model, interim crowns (N=36) were fabricated using compression molding (molding group, n=12), milling (milling group, n=12), and 3D polymer-jetting methods. The crowns were prepared as follows: molding group, overimpression technique; milling group, a 5-axis dental milling machine; and polymer-jetting group using a 3D printer. The fit of interim crowns was evaluated in the proximal, marginal, internal axial, and internal occlusal regions by using the image-superimposition and silicone-replica techniques. The Mann-Whitney U test and Kruskal-Wallis tests were used to compare the results among groups (α=.05). Compared with the molding group, the milling and polymer-jetting groups showed more accurate results in the proximal and marginal regions (P<.001). In the axial regions, even though the mean discrepancy was smallest in the molding group, the data showed large deviations. In the occlusal region, the polymer-jetting group was the most accurate, and compared with the other groups, the milling group showed larger internal discrepancies (P<.001). Polymer-jet 3D printing significantly enhanced the fit of interim crowns, particularly in the occlusal region. Copyright © 2016 Editorial Council for the Journal of Prosthetic Dentistry. Published by Elsevier Inc. All rights reserved.

Normalization of time-series satellite reflectance data to a standard sun-target-sensor geometry using a semi-empirical model

NASA Astrophysics Data System (ADS)

Zhao, Yongguang; Li, Chuanrong; Ma, Lingling; Tang, Lingli; Wang, Ning; Zhou, Chuncheng; Qian, Yonggang

2017-10-01

Time series of satellite reflectance data have been widely used to characterize environmental phenomena, describe trends in vegetation dynamics and study climate change. However, several sensors with wide spatial coverage and high observation frequency are usually designed to have large field of view (FOV), which cause variations in the sun-targetsensor geometry in time-series reflectance data. In this study, on the basis of semiempirical kernel-driven BRDF model, a new semi-empirical model was proposed to normalize the sun-target-sensor geometry of remote sensing image. To evaluate the proposed model, bidirectional reflectance under different canopy growth conditions simulated by Discrete Anisotropic Radiative Transfer (DART) model were used. The semi-empirical model was first fitted by using all simulated bidirectional reflectance. Experimental result showed a good fit between the bidirectional reflectance estimated by the proposed model and the simulated value. Then, MODIS time-series reflectance data was normalized to a common sun-target-sensor geometry by the proposed model. The experimental results showed the proposed model yielded good fits between the observed and estimated values. The noise-like fluctuations in time-series reflectance data was also reduced after the sun-target-sensor normalization process.

A TCP model for external beam treatment of intermediate-risk prostate cancer

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

Walsh, Sean; Putten, Wil van der

2013-03-15

Purpose: Biological models offer the ability to predict clinical outcomes. The authors describe a model to predict the clinical response of intermediate-risk prostate cancer to external beam radiotherapy for a variety of fractionation regimes. Methods: A fully heterogeneous population averaged tumor control probability model was fit to clinical outcome data for hyper, standard, and hypofractionated treatments. The tumor control probability model was then employed to predict the clinical outcome of extreme hypofractionation regimes, as utilized in stereotactic body radiotherapy. Results: The tumor control probability model achieves an excellent level of fit, R{sup 2} value of 0.93 and a root meanmore » squared error of 1.31%, to the clinical outcome data for hyper, standard, and hypofractionated treatments using realistic values for biological input parameters. Residuals Less-Than-Or-Slanted-Equal-To 1.0% are produced by the tumor control probability model when compared to clinical outcome data for stereotactic body radiotherapy. Conclusions: The authors conclude that this tumor control probability model, used with the optimized radiosensitivity values obtained from the fit, is an appropriate mechanistic model for the analysis and evaluation of external beam RT plans with regard to tumor control for these clinical conditions.« less

Longitudinal factorial invariance of the PedsQL 4.0 Generic Core Scales child self-report Version: one year prospective evidence from the California State Children's Health Insurance Program (SCHIP).

PubMed

Varni, James W; Limbers, Christine A; Newman, Daniel A; Seid, Michael

2008-11-01

The measurement of health-related quality of life (HRQOL) in pediatric medicine and health services research has grown significantly over the past decade. The paradigm shift toward patient-reported outcomes (PROs) has provided the opportunity to emphasize the value and critical need for pediatric patient self-report. In order for changes in HRQOL/PRO outcomes to be meaningful over time, it is essential to demonstrate longitudinal factorial invariance. This study examined the longitudinal factor structure of the PedsQL 4.0 Generic Core Scales over a one-year period for child self-report ages 5-17 in 2,887 children from a statewide evaluation of the California State Children's Health Insurance Program (SCHIP) utilizing a structural equation modeling framework. Specifying four- and five-factor measurement models, longitudinal structural equation modeling was used to compare factor structures over a one-year interval on the PedsQL 4.0 Generic Core Scales. While the four-factor conceptually-derived measurement model for the PedsQL 4.0 Generic Core Scales produced an acceptable fit, the five-factor empirically-derived measurement model from the initial field test of the PedsQL 4.0 Generic Core Scales produced a marginally superior fit in comparison to the four-factor model. For the five-factor measurement model, the best fitting model, strict factorial invariance of the PedsQL 4.0 Generic Core Scales across the two measurement occasions was supported by the stability of the comparative fit index between the unconstrained and constrained models, and several additional indices of practical fit including the root mean squared error of approximation, the non-normed fit index, and the parsimony normed fit index. The findings support an equivalent factor structure on the PedsQL 4.0 Generic Core Scales over time. Based on these data, it can be concluded that over a one-year period children in our study interpreted items on the PedsQL 4.0 Generic Core Scales in a similar manner.

Rasch analysis of the UK Functional Assessment Measure in patients with complex disability after stroke.

PubMed

Medvedev, Oleg N; Turner-Stokes, Lynne; Ashford, Stephen; Siegert, Richard J

2018-02-28

To determine whether the UK Functional Assessment Measure (UK FIM+FAM) fits the Rasch model in stroke patients with complex disability and, if so, to derive a conversion table of Rasch-transformed interval level scores. The sample included a UK multicentre cohort of 1,318 patients admitted for specialist rehabilitation following a stroke. Rasch analysis was conducted for the 30-item scale including 3 domains of items measuring physical, communication and psychosocial functions. The fit of items to the Rasch model was examined using 3 different analytical approaches referred to as "pathways". The best fit was achieved in the pathway where responses from motor, communication and psychosocial domains were summarized into 3 super-items and where some items were split because of differential item functioning (DIF) relative to left and right hemisphere location (χ2 (10) = 14.48, p = 0.15). Re-scoring of items showing disordered thresholds did not significantly improve the overall model fit. The UK FIM+FAM with domain super-items satisfies expectations of the unidimensional Rasch model without the need for re-scoring. A conversion table was produced to convert the total scale scores into interval-level data based on person estimates of the Rasch model. The clinical benefits of interval-transformed scores require further evaluation.

Spillover in the Academy: Marriage Stability and Faculty Evaluations.

ERIC Educational Resources Information Center

Ludlow, Larry H.; Alvarez-Salvat, Rose M.

2001-01-01

Studied the spillover between family and work by examining the link between marital status and work performance across marriage, divorce, and remarriage. A polynomial regression model was fit to the data from 78 evaluations of an individual professor, and a cubic curve through the 3 periods was statistically significant. (SLD)

Evaluation of Internal Construct Validity and Unidimensionality of the Brachial Assessment Tool, A Patient-Reported Outcome Measure for Brachial Plexus Injury.

PubMed

Hill, Bridget; Pallant, Julie; Williams, Gavin; Olver, John; Ferris, Scott; Bialocerkowski, Andrea

2016-12-01

To evaluate the internal construct validity and dimensionality of a new patient-reported outcome measure for people with traumatic brachial plexus injury (BPI) based on the International Classification of Functioning, Disability and Health definition of activity. Cross-sectional study. Outpatient clinics. Adults (age range, 18-82y) with a traumatic BPI (N=106). There were 106 people with BPI who completed a 51-item 5-response questionnaire. Responses were analyzed in 4 phases (missing responses, item correlations, exploratory factor analysis, and Rasch analysis) to evaluate the properties of fit to the Rasch model, threshold response, local dependency, dimensionality, differential item functioning, and targeting. Not applicable, as this study addresses the development of an outcome measure. Six items were deleted for missing responses, and 10 were deleted for high interitem correlations >.81. The remaining 35 items, while demonstrating fit to the Rasch model, showed evidence of local dependency and multidimensionality. Items were divided into 3 subscales: dressing and grooming (8 items), arm and hand (17 items), and no hand (6 items). All 3 subscales demonstrated fit to the model with no local dependency, minimal disordered thresholds, no unidimensionality or differential item functioning for age, time postinjury, or self-selected dominance. Subscales were combined into 3 subtests and demonstrated fit to the model, no misfit, and unidimensionality, allowing calculation of a summary score. This preliminary analysis supports the internal construct validity of the Brachial Assessment Tool, a unidimensional targeted 4-response patient-reported outcome measure designed to solely assess activity after traumatic BPI regardless of level of injury, age at recruitment, premorbid limb dominance, and time postinjury. Further examination is required to determine test-retest reliability and responsiveness. Copyright © 2016 American Congress of Rehabilitation Medicine. Published by Elsevier Inc. All rights reserved.

Comparing masked target transform volume (MTTV) clutter metric to human observer evaluation of visual clutter

NASA Astrophysics Data System (ADS)

Camp, H. A.; Moyer, Steven; Moore, Richard K.

2010-04-01

The Night Vision and Electronic Sensors Directorate's current time-limited search (TLS) model, which makes use of the targeting task performance (TTP) metric to describe image quality, does not explicitly account for the effects of visual clutter on observer performance. The TLS model is currently based on empirical fits to describe human performance for a time of day, spectrum and environment. Incorporating a clutter metric into the TLS model may reduce the number of these empirical fits needed. The masked target transform volume (MTTV) clutter metric has been previously presented and compared to other clutter metrics. Using real infrared imagery of rural images with varying levels of clutter, NVESD is currently evaluating the appropriateness of the MTTV metric. NVESD had twenty subject matter experts (SME) rank the amount of clutter in each scene in a series of pair-wise comparisons. MTTV metric values were calculated and then compared to the SME observers rankings. The MTTV metric ranked the clutter in a similar manner to the SME evaluation, suggesting that the MTTV metric may emulate SME response. This paper is a first step in quantifying clutter and measuring the agreement to subjective human evaluation.

Diffusion weighted imaging in patients with rectal cancer: Comparison between Gaussian and non-Gaussian models

PubMed Central

Marias, Kostas; Lambregts, Doenja M. J.; Nikiforaki, Katerina; van Heeswijk, Miriam M.; Bakers, Frans C. H.; Beets-Tan, Regina G. H.

2017-01-01

Purpose The purpose of this study was to compare the performance of four diffusion models, including mono and bi-exponential both Gaussian and non-Gaussian models, in diffusion weighted imaging of rectal cancer. Material and methods Nineteen patients with rectal adenocarcinoma underwent MRI examination of the rectum before chemoradiation therapy including a 7 b-value diffusion sequence (0, 25, 50, 100, 500, 1000 and 2000 s/mm2) at a 1.5T scanner. Four different diffusion models including mono- and bi-exponential Gaussian (MG and BG) and non-Gaussian (MNG and BNG) were applied on whole tumor volumes of interest. Two different statistical criteria were recruited to assess their fitting performance, including the adjusted-R2 and Root Mean Square Error (RMSE). To decide which model better characterizes rectal cancer, model selection was relied on Akaike Information Criteria (AIC) and F-ratio. Results All candidate models achieved a good fitting performance with the two most complex models, the BG and the BNG, exhibiting the best fitting performance. However, both criteria for model selection indicated that the MG model performed better than any other model. In particular, using AIC Weights and F-ratio, the pixel-based analysis demonstrated that tumor areas better described by the simplest MG model in an average area of 53% and 33%, respectively. Non-Gaussian behavior was illustrated in an average area of 37% according to the F-ratio, and 7% using AIC Weights. However, the distributions of the pixels best fitted by each of the four models suggest that MG failed to perform better than any other model in all patients, and the overall tumor area. Conclusion No single diffusion model evaluated herein could accurately describe rectal tumours. These findings probably can be explained on the basis of increased tumour heterogeneity, where areas with high vascularity could be fitted better with bi-exponential models, and areas with necrosis would mostly follow mono-exponential behavior. PMID:28863161

Diffusion weighted imaging in patients with rectal cancer: Comparison between Gaussian and non-Gaussian models.

PubMed

Manikis, Georgios C; Marias, Kostas; Lambregts, Doenja M J; Nikiforaki, Katerina; van Heeswijk, Miriam M; Bakers, Frans C H; Beets-Tan, Regina G H; Papanikolaou, Nikolaos

2017-01-01

The purpose of this study was to compare the performance of four diffusion models, including mono and bi-exponential both Gaussian and non-Gaussian models, in diffusion weighted imaging of rectal cancer. Nineteen patients with rectal adenocarcinoma underwent MRI examination of the rectum before chemoradiation therapy including a 7 b-value diffusion sequence (0, 25, 50, 100, 500, 1000 and 2000 s/mm2) at a 1.5T scanner. Four different diffusion models including mono- and bi-exponential Gaussian (MG and BG) and non-Gaussian (MNG and BNG) were applied on whole tumor volumes of interest. Two different statistical criteria were recruited to assess their fitting performance, including the adjusted-R2 and Root Mean Square Error (RMSE). To decide which model better characterizes rectal cancer, model selection was relied on Akaike Information Criteria (AIC) and F-ratio. All candidate models achieved a good fitting performance with the two most complex models, the BG and the BNG, exhibiting the best fitting performance. However, both criteria for model selection indicated that the MG model performed better than any other model. In particular, using AIC Weights and F-ratio, the pixel-based analysis demonstrated that tumor areas better described by the simplest MG model in an average area of 53% and 33%, respectively. Non-Gaussian behavior was illustrated in an average area of 37% according to the F-ratio, and 7% using AIC Weights. However, the distributions of the pixels best fitted by each of the four models suggest that MG failed to perform better than any other model in all patients, and the overall tumor area. No single diffusion model evaluated herein could accurately describe rectal tumours. These findings probably can be explained on the basis of increased tumour heterogeneity, where areas with high vascularity could be fitted better with bi-exponential models, and areas with necrosis would mostly follow mono-exponential behavior.

Conditional statistical inference with multistage testing designs.

PubMed

Zwitser, Robert J; Maris, Gunter

2015-03-01

In this paper it is demonstrated how statistical inference from multistage test designs can be made based on the conditional likelihood. Special attention is given to parameter estimation, as well as the evaluation of model fit. Two reasons are provided why the fit of simple measurement models is expected to be better in adaptive designs, compared to linear designs: more parameters are available for the same number of observations; and undesirable response behavior, like slipping and guessing, might be avoided owing to a better match between item difficulty and examinee proficiency. The results are illustrated with simulated data, as well as with real data.

A Weibull model to describe antimicrobial kinetics of oregano and lemongrass essential oils against Salmonella Enteritidis in ground beef during refrigerated storage.

PubMed

de Oliveira, Thales Leandro Coutinho; Soares, Rodrigo de Araújo; Piccoli, Roberta Hilsdorf

2013-03-01

The antimicrobial effect of oregano (Origanum vulgare L.) and lemongrass (Cymbopogon citratus (DC.) Stapf.) essential oils (EOs) against Salmonella enterica serotype Enteritidis in in vitro experiments, and inoculated in ground bovine meat during refrigerated storage (4±2 °C) for 6 days was evaluated. The Weibull model was tested to fit survival/inactivation bacterial curves (estimating of p and δ parameters). The minimum inhibitory concentration (MIC) value for both EOs on S. Enteritidis was 3.90 μl/ml. The EO concentrations applied in the ground beef were 3.90, 7.80 and 15.60 μl/g, based on MIC levels and possible activity reduction by food constituents. Both evaluated EOs in all tested levels, showed antimicrobial effects, with microbial populations reducing (p≤0.05) along time storage. Evaluating fit-quality parameters (RSS and RSE) Weibull models are able to describe the inactivation curves of EOs against S. Enteritidis. The application of EOs in processed meats can be used to control pathogens during refrigerated shelf-life. Copyright © 2012 Elsevier Ltd. All rights reserved.

Modeling growth from weaning to maturity in beef cattle breeds

USDA-ARS?s Scientific Manuscript database

To better understand growth trajectory and maturity differences between beef breeds, three models – Brody, spline, and quadratic – were fit to cow growth data, and resulting parameter estimates were evaluated for 3 breed categories – British, continental, and Brahman-influenced. The data were weight...

Cultural Artifact Detection in Long Wave Infrared Imagery.

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

Anderson, Dylan Zachary; Craven, Julia M.; Ramon, Eric

2017-01-01

Detection of cultural artifacts from airborne remotely sensed data is an important task in the context of on-site inspections. Airborne artifact detection can reduce the size of the search area the ground based inspection team must visit, thereby improving the efficiency of the inspection process. This report details two algorithms for detection of cultural artifacts in aerial long wave infrared imagery. The first algorithm creates an explicit model for cultural artifacts, and finds data that fits the model. The second algorithm creates a model of the background and finds data that does not fit the model. Both algorithms are appliedmore » to orthomosaic imagery generated as part of the MSFE13 data collection campaign under the spectral technology evaluation project.« less

Killing the cMSSM softly

DOE PAGES

Bechtle, Philip; Camargo-Molina, José Eliel; Desch, Klaus; ...

2016-02-24

We investigate the constrained Minimal Supersymmetric Standard Model (cMSSM) in the light of constraining experimental and observational data from precision measurements, astrophysics, direct supersymmetry searches at the LHC and measurements of the properties of the Higgs boson, by means of a global fit using the program Fittino. As in previous studies, we find rather poor agreement of the best fit point with the global data. We also investigate the stability of the electro-weak vacuum in the preferred region of parameter space around the best fit point.We find that the vacuum is metastable, with a lifetime significantly longer than the agemore » of the Universe. For the first time in a global fit of supersymmetry, we employ a consistent methodology to evaluate the goodness-of-fit of the cMSSM in a frequentist approach by deriving p values from large sets of toy experiments. We analyse analytically and quantitatively the impact of the choice of the observable set on the p value, and in particular its dilution when confronting the model with a large number of barely constraining measurements. Lastly, for the preferred sets of observables, we obtain p values for the cMSSM below 10 %, i.e. we exclude the cMSSM as a model at the 90 % confidence level.« less

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

Walsh, Seán, E-mail: walshsharp@gmail.com; Department of Oncology, Gray Institute for Radiation Oncology and Biology, University of Oxford, Oxford OX3 7DQ; Roelofs, Erik

Purpose: A fully heterogeneous population averaged mechanistic tumor control probability (TCP) model is appropriate for the analysis of external beam radiotherapy (EBRT). This has been accomplished for EBRT photon treatment of intermediate-risk prostate cancer. Extending the TCP model for low and high-risk patients would be beneficial in terms of overall decision making. Furthermore, different radiation treatment modalities such as protons and carbon-ions are becoming increasingly available. Consequently, there is a need for a complete TCP model. Methods: A TCP model was fitted and validated to a primary endpoint of 5-year biological no evidence of disease clinical outcome data obtained frommore » a review of the literature for low, intermediate, and high-risk prostate cancer patients (5218 patients fitted, 1088 patients validated), treated by photons, protons, or carbon-ions. The review followed the preferred reporting item for systematic reviews and meta-analyses statement. Treatment regimens include standard fractionation and hypofractionation treatments. Residual analysis and goodness of fit statistics were applied. Results: The TCP model achieves a good level of fit overall, linear regression results in a p-value of <0.000 01 with an adjusted-weighted-R{sup 2} value of 0.77 and a weighted root mean squared error (wRMSE) of 1.2%, to the fitted clinical outcome data. Validation of the model utilizing three independent datasets obtained from the literature resulted in an adjusted-weighted-R{sup 2} value of 0.78 and a wRMSE of less than 1.8%, to the validation clinical outcome data. The weighted mean absolute residual across the entire dataset is found to be 5.4%. Conclusions: This TCP model fitted and validated to clinical outcome data, appears to be an appropriate model for the inclusion of all clinical prostate cancer risk categories, and allows evaluation of current EBRT modalities with regard to tumor control prediction.« less

Predicting Quantitative Traits With Regression Models for Dense Molecular Markers and Pedigree

PubMed Central

de los Campos, Gustavo; Naya, Hugo; Gianola, Daniel; Crossa, José; Legarra, Andrés; Manfredi, Eduardo; Weigel, Kent; Cotes, José Miguel

2009-01-01

The availability of genomewide dense markers brings opportunities and challenges to breeding programs. An important question concerns the ways in which dense markers and pedigrees, together with phenotypic records, should be used to arrive at predictions of genetic values for complex traits. If a large number of markers are included in a regression model, marker-specific shrinkage of regression coefficients may be needed. For this reason, the Bayesian least absolute shrinkage and selection operator (LASSO) (BL) appears to be an interesting approach for fitting marker effects in a regression model. This article adapts the BL to arrive at a regression model where markers, pedigrees, and covariates other than markers are considered jointly. Connections between BL and other marker-based regression models are discussed, and the sensitivity of BL with respect to the choice of prior distributions assigned to key parameters is evaluated using simulation. The proposed model was fitted to two data sets from wheat and mouse populations, and evaluated using cross-validation methods. Results indicate that inclusion of markers in the regression further improved the predictive ability of models. An R program that implements the proposed model is freely available. PMID:19293140

A random walk model for evaluating clinical trials involving serial observations.

PubMed

Hopper, J L; Young, G P

1988-05-01

For clinical trials where the variable of interest is ordered and categorical (for example, disease severity, symptom scale), and where measurements are taken at intervals, it might be possible to achieve a greater discrimination between the efficacy of treatments by modelling each patient's progress as a stochastic process. The random walk is a simple, easily interpreted model that can be fitted by maximum likelihood using a maximization routine with inference based on standard likelihood theory. In general the model can allow for randomly censored data, incorporates measured prognostic factors, and inference is conditional on the (possibly non-random) allocation of patients. Tests of fit and of model assumptions are proposed, and application to two therapeutic trials of gastroenterological disorders are presented. The model gave measures of the rate of, and variability in, improvement for patients under different treatments. A small simulation study suggested that the model is more powerful than considering the difference between initial and final scores, even when applied to data generated by a mechanism other than the random walk model assumed in the analysis. It thus provides a useful additional statistical method for evaluating clinical trials.

Spatially explicit habitat models for 28 fishes from the Upper Mississippi River System (AHAG 2.0)

USGS Publications Warehouse

Ickes, Brian S.; Sauer, J.S.; Richards, N.; Bowler, M.; Schlifer, B.

2014-01-01

Environmental management actions in the Upper Mississippi River System (UMRS) typically require pre-project assessments of predicted benefits under a range of project scenarios. The U.S. Army Corps of Engineers (USACE) now requires certified and peer-reviewed models to conduct these assessments. Previously, habitat benefits were estimated for fish communities in the UMRS using the Aquatic Habitat Appraisal Guide (AHAG v.1.0; AHAG from hereon). This spreadsheet-based model used a habitat suitability index (HSI) approach that drew heavily upon Habitat Evaluation Procedures (HEP; U.S. Fish and Wildlife Service, 1980) by the U.S. Fish and Wildlife Service (USFWS). The HSI approach requires developing species response curves for different environmental variables that seek to broadly represent habitat. The AHAG model uses species-specific response curves assembled from literature values, data from other ecosystems, or best professional judgment. A recent scientific review of the AHAG indicated that the model’s effectiveness is reduced by its dated approach to large river ecosystems, uncertainty regarding its data inputs and rationale for habitat-species response relationships, and lack of field validation (Abt Associates Inc., 2011). The reviewers made two major recommendations: (1) incorporate empirical data from the UMRS into defining the empirical response curves, and (2) conduct post-project biological evaluations to test pre-project benefits estimated by AHAG. Our objective was to address the first recommendation and generate updated response curves for AHAG using data from the Upper Mississippi River Restoration-Environmental Management Program (UMRR-EMP) Long Term Resource Monitoring Program (LTRMP) element. Fish community data have been collected by LTRMP (Gutreuter and others, 1995; Ratcliff and others, in press) for 20 years from 6 study reaches representing 1,930 kilometers of river and >140 species of fish. We modeled a subset of these data (28 different species; occurrences at sampling sites as observed in day electrofishing samples) using multiple logistic regression with presence/absence responses. Each species’ probability of occurrence, at each sample site, was modeled as a function of 17 environmental variables observed at each sample site by LTRMP standardized protocols. The modeling methods used (1) a forward-selection process to identify the most important predictors and their relative contributions to predictions; (2) partial methods on the predictor set to control variance inflation; and (3) diagnostics for LTRMP design elements that may influence model fits. Models were fit for 28 species, representing 3 habitat guilds (Lentic, Lotic, and Generalist). We intended to develop “systemic models” using data from all six LTRMP study reaches simultaneously; however, this proved impossible. Thus, we “regionalized” the models, creating two models for each species: “Upper Reach” models, using data from Pools 4, 8, and 13; and “Lower Reach” models, using data from Pool 26, the Open River Reach of the Mississippi River, and the La Grange reach of the Illinois River. A total of 56 models were attempted. For any given site-scale prediction, each model used data from the three LTRMP study reaches comprising the regional model to make predictions. For example, a site-scale prediction in Pool 8 was made using data from Pools 4, 8, and 13. This is the fundamental nature and trade-off of regionalizing these models for broad management application. Model fits were deemed “certifiably good” using the Hosmer and Lemeshow Goodness-of-Fit statistic (Hosmer and Lemeshow, 2000). This test post-partitions model predictions into 10 groups and conducts inferential tests on correspondences between observed and expected probability of occurrence across all partitions, under Chi-square distributional assumptions. This permits an inferential test of how well the models fit and a tool for reporting when they did not (and perhaps why). Our goal was to develop regionalized models, and to assess and describe circumstances when a good fit was not possible. Seven fish species composed the Lentic guild. Good fits were achieved for six Upper Reach models. In the Lower Reach, no model produced good fits for the Lentic guild. This was due to (1) lentic species being much less prominent in the Lower Reach study areas, and (2) those that do express greater prominence principally do so only in the La Grange reach of the Illinois River. Thus, developing Lower Reach models for Lentic species will require parsing La Grange from the other two Lower Reach study areas and fitting separate models. We did not do that as part of this study, but it could be done at a later time. Nine species comprised the Lotic guild. Good fits were achieved for seven Upper Reach models and six Lower Reach models. Four species had good fits for both regions (flathead catfish, blue sucker, sauger, and shorthead redhorse). Three species showed zoogeographic zonation, with a good model fit in one of the regions, but not in the region in which they were absent or rarely occurred (blue catfish, rock bass, and skipjack herring). Twelve species comprised the Generalist guild. Good fits were achieved for five Upper Reach models and eight Lower Reach models. Six species had good fits for both regions (brook silverside, emerald shiner, freshwater drum, logperch, longnose gar, and white bass). Two species showed zoogeographic zonation, with a good model fit in one of the regions, but not in the region in which they were absent or rarely occurred (red shiner and blackstripe topminnow). Poorly fit models were almost always due to the diagnostic variable “field station,” a surrogate for river mile. In these circumstances, the residuals for “field station” were non-randomly distributed and often strongly ordered. This indicates either fitting “pool scale” models for these species and regions, or explicitly model covariances between “field station” and the other predictors within the existing modeling framework. Further efforts on these models should seek to resolve these issues using one of these two approaches. In total, nine species, representing two of the three guilds (Lotic and Generalist), produced well-fit models for both regions. These nine species should comprise the basis for AHAG 2.0. Additional work, likely requiring downscaling of the regional models to pool-scale models, will be needed to incorporate additional species. Alternately, a regionalized AHAG could be comprised of those species, per region, that achieved well-fit models. The number of species and the composition of the regional species pools will differ among regions as a consequence. Each of these alternatives has both pros and cons, and managers are encouraged to consider them fully before further advancing this approach to modeling multi-species habitat suitability.

Nuclear Matter Properties with the Re-evaluated Coefficients of Liquid Drop Model

NASA Astrophysics Data System (ADS)

Chowdhury, P. Roy; Basu, D. N.

2006-06-01

The coefficients of the volume, surface, Coulomb, asymmetry and pairing energy terms of the semiempirical liquid drop model mass formula have been determined by furnishing best fit to the observed mass excesses. Slightly different sets of the weighting parameters for liquid drop model mass formula have been obtained from minimizations of \\chi 2 and mean square deviation. The most recent experimental and estimated mass excesses from Audi-Wapstra-Thibault atomic mass table have been used for the least square fitting procedure. Equation of state, nuclear incompressibility, nuclear mean free path and the most stable nuclei for corresponding atomic numbers, all are in good agreement with the experimental results.

An update on modeling dose-response relationships: Accounting for correlated data structure and heterogeneous error variance in linear and nonlinear mixed models.

PubMed

Gonçalves, M A D; Bello, N M; Dritz, S S; Tokach, M D; DeRouchey, J M; Woodworth, J C; Goodband, R D

2016-05-01

Advanced methods for dose-response assessments are used to estimate the minimum concentrations of a nutrient that maximizes a given outcome of interest, thereby determining nutritional requirements for optimal performance. Contrary to standard modeling assumptions, experimental data often present a design structure that includes correlations between observations (i.e., blocking, nesting, etc.) as well as heterogeneity of error variances; either can mislead inference if disregarded. Our objective is to demonstrate practical implementation of linear and nonlinear mixed models for dose-response relationships accounting for correlated data structure and heterogeneous error variances. To illustrate, we modeled data from a randomized complete block design study to evaluate the standardized ileal digestible (SID) Trp:Lys ratio dose-response on G:F of nursery pigs. A base linear mixed model was fitted to explore the functional form of G:F relative to Trp:Lys ratios and assess model assumptions. Next, we fitted 3 competing dose-response mixed models to G:F, namely a quadratic polynomial (QP) model, a broken-line linear (BLL) ascending model, and a broken-line quadratic (BLQ) ascending model, all of which included heteroskedastic specifications, as dictated by the base model. The GLIMMIX procedure of SAS (version 9.4) was used to fit the base and QP models and the NLMIXED procedure was used to fit the BLL and BLQ models. We further illustrated the use of a grid search of initial parameter values to facilitate convergence and parameter estimation in nonlinear mixed models. Fit between competing dose-response models was compared using a maximum likelihood-based Bayesian information criterion (BIC). The QP, BLL, and BLQ models fitted on G:F of nursery pigs yielded BIC values of 353.7, 343.4, and 345.2, respectively, thus indicating a better fit of the BLL model. The BLL breakpoint estimate of the SID Trp:Lys ratio was 16.5% (95% confidence interval [16.1, 17.0]). Problems with the estimation process rendered results from the BLQ model questionable. Importantly, accounting for heterogeneous variance enhanced inferential precision as the breadth of the confidence interval for the mean breakpoint decreased by approximately 44%. In summary, the article illustrates the use of linear and nonlinear mixed models for dose-response relationships accounting for heterogeneous residual variances, discusses important diagnostics and their implications for inference, and provides practical recommendations for computational troubleshooting.

Limited-information goodness-of-fit testing of diagnostic classification item response models.

PubMed

Hansen, Mark; Cai, Li; Monroe, Scott; Li, Zhen

2016-11-01

Despite the growing popularity of diagnostic classification models (e.g., Rupp et al., 2010, Diagnostic measurement: theory, methods, and applications, Guilford Press, New York, NY) in educational and psychological measurement, methods for testing their absolute goodness of fit to real data remain relatively underdeveloped. For tests of reasonable length and for realistic sample size, full-information test statistics such as Pearson's X 2 and the likelihood ratio statistic G 2 suffer from sparseness in the underlying contingency table from which they are computed. Recently, limited-information fit statistics such as Maydeu-Olivares and Joe's (2006, Psychometrika, 71, 713) M 2 have been found to be quite useful in testing the overall goodness of fit of item response theory models. In this study, we applied Maydeu-Olivares and Joe's (2006, Psychometrika, 71, 713) M 2 statistic to diagnostic classification models. Through a series of simulation studies, we found that M 2 is well calibrated across a wide range of diagnostic model structures and was sensitive to certain misspecifications of the item model (e.g., fitting disjunctive models to data generated according to a conjunctive model), errors in the Q-matrix (adding or omitting paths, omitting a latent variable), and violations of local item independence due to unmodelled testlet effects. On the other hand, M 2 was largely insensitive to misspecifications in the distribution of higher-order latent dimensions and to the specification of an extraneous attribute. To complement the analyses of the overall model goodness of fit using M 2 , we investigated the utility of the Chen and Thissen (1997, J. Educ. Behav. Stat., 22, 265) local dependence statistic XLD2 for characterizing sources of misfit, an important aspect of model appraisal often overlooked in favour of overall statements. The XLD2 statistic was found to be slightly conservative (with Type I error rates consistently below the nominal level) but still useful in pinpointing the sources of misfit. Patterns of local dependence arising due to specific model misspecifications are illustrated. Finally, we used the M 2 and XLD2 statistics to evaluate a diagnostic model fit to data from the Trends in Mathematics and Science Study, drawing upon analyses previously conducted by Lee et al., (2011, IJT, 11, 144). © 2016 The British Psychological Society.

A Smoluchowski model of crystallization dynamics of small colloidal clusters

NASA Astrophysics Data System (ADS)

Beltran-Villegas, Daniel J.; Sehgal, Ray M.; Maroudas, Dimitrios; Ford, David M.; Bevan, Michael A.

2011-10-01

We investigate the dynamics of colloidal crystallization in a 32-particle system at a fixed value of interparticle depletion attraction that produces coexisting fluid and solid phases. Free energy landscapes (FELs) and diffusivity landscapes (DLs) are obtained as coefficients of 1D Smoluchowski equations using as order parameters either the radius of gyration or the average crystallinity. FELs and DLs are estimated by fitting the Smoluchowski equations to Brownian dynamics (BD) simulations using either linear fits to locally initiated trajectories or global fits to unbiased trajectories using Bayesian inference. The resulting FELs are compared to Monte Carlo Umbrella Sampling results. The accuracy of the FELs and DLs for modeling colloidal crystallization dynamics is evaluated by comparing mean first-passage times from BD simulations with analytical predictions using the FEL and DL models. While the 1D models accurately capture dynamics near the free energy minimum fluid and crystal configurations, predictions near the transition region are not quantitatively accurate. A preliminary investigation of ensemble averaged 2D order parameter trajectories suggests that 2D models are required to capture crystallization dynamics in the transition region.

The ACTIVE conceptual framework as a structural equation model.

PubMed

Gross, Alden L; Payne, Brennan R; Casanova, Ramon; Davoudzadeh, Pega; Dzierzewski, Joseph M; Farias, Sarah; Giovannetti, Tania; Ip, Edward H; Marsiske, Michael; Rebok, George W; Schaie, K Warner; Thomas, Kelsey; Willis, Sherry; Jones, Richard N

2018-01-01

Background/Study Context: Conceptual frameworks are analytic models at a high level of abstraction. Their operationalization can inform randomized trial design and sample size considerations. The Advanced Cognitive Training for Independent and Vital Elderly (ACTIVE) conceptual framework was empirically tested using structural equation modeling (N=2,802). ACTIVE was guided by a conceptual framework for cognitive training in which proximal cognitive abilities (memory, inductive reasoning, speed of processing) mediate treatment-related improvement in primary outcomes (everyday problem-solving, difficulty with activities of daily living, everyday speed, driving difficulty), which in turn lead to improved secondary outcomes (health-related quality of life, health service utilization, mobility). Measurement models for each proximal, primary, and secondary outcome were developed and tested using baseline data. Each construct was then combined in one model to evaluate fit (RMSEA, CFI, normalized residuals of each indicator). To expand the conceptual model and potentially inform future trials, evidence of modification of structural model parameters was evaluated by age, years of education, sex, race, and self-rated health status. Preconceived measurement models for memory, reasoning, speed of processing, everyday problem-solving, instrumental activities of daily living (IADL) difficulty, everyday speed, driving difficulty, and health-related quality of life each fit well to the data (all RMSEA < .05; all CFI > .95). Fit of the full model was excellent (RMSEA = .038; CFI = .924). In contrast with previous findings from ACTIVE regarding who benefits from training, interaction testing revealed associations between proximal abilities and primary outcomes are stronger on average by nonwhite race, worse health, older age, and less education (p < .005). Empirical data confirm the hypothesized ACTIVE conceptual model. Findings suggest that the types of people who show intervention effects on cognitive performance potentially may be different from those with the greatest chance of transfer to real-world activities.

Rasch measurement: the Arm Activity measure (ArmA) passive function sub-scale.

PubMed

Ashford, Stephen; Siegert, Richard J; Alexandrescu, Roxana

2016-01-01

To evaluate the conformity of the Arm Activity measure (ArmA) passive function sub-scale to the Rasch model. A consecutive cohort of patients (n = 92) undergoing rehabilitation, including upper limb rehabilitation and spasticity management, at two specialist rehabilitation units were included. Rasch analysis was used to examine scaling and conformity to the model. Responses were analysed using Rasch unidimensional measurement models (RUMM 2030). The following aspects were considered: overall model and individual item fit statistics and fit residuals, internal reliability, item response threshold ordering, item bias, local dependency and unidimensionality. ArmA contains both active and passive function sub-scales, but in this analysis only the passive function sub-scale was considered. Four of the seven items in the ArmA passive function sub-scale initially had disordered thresholds. These items were rescored to four response options, which resulted in ordered thresholds for all items. Once the items with disordered thresholds had been rescored, item bias was not identified for age, global disability level or diagnosis, but with a small difference in difficulty between males and females for one item of the scale. Local dependency was not observed and the unidimensionality of the sub-scale was supported and good fit to the Rasch model was identified. The person separation index (PSI) was 0.95 indicating that the scale is able to reliably differentiate at least two groups of patients. The ArmA passive function sub-scale was shown in this evaluation to conform to the Rasch model once disordered thresholds had been addressed. Using the logit scores produced by the Rasch model it was possible to convert this back to the original scale range. Implications for Rehabilitation The ArmA passive function sub-scale was shown, in this evaluation, to conform to the Rasch model once disordered thresholds had been addressed and therefore to be a clinically applicable and potentially useful hierarchical measure. Using Rasch logit scores it has be possible to convert back to the original ordinal scale range and provide an indication of real change to enable evaluation of clinical outcome of importance to patients and clinicians.

Uncertainty in eddy covariance measurements and its application to physiological models

Treesearch

D.Y. Hollinger; A.D. Richardson; A.D. Richardson

2005-01-01

Flux data are noisy, and this uncertainty is largely due to random measurement error. Knowledge of uncertainty is essential for the statistical evaluation of modeled andmeasured fluxes, for comparison of parameters derived by fitting models to measured fluxes and in formal data-assimilation efforts. We used the difference between simultaneous measurements from two...

The Local Minima Problem in Hierarchical Classes Analysis: An Evaluation of a Simulated Annealing Algorithm and Various Multistart Procedures

ERIC Educational Resources Information Center

Ceulemans, Eva; Van Mechelen, Iven; Leenen, Iwin

2007-01-01

Hierarchical classes models are quasi-order retaining Boolean decomposition models for N-way N-mode binary data. To fit these models to data, rationally started alternating least squares (or, equivalently, alternating least absolute deviations) algorithms have been proposed. Extensive simulation studies showed that these algorithms succeed quite…

Predicting nonstationary flood frequencies: Evidence supports an updated stationarity thesis in the United States

NASA Astrophysics Data System (ADS)

Luke, Adam; Vrugt, Jasper A.; AghaKouchak, Amir; Matthew, Richard; Sanders, Brett F.

2017-07-01

Nonstationary extreme value analysis (NEVA) can improve the statistical representation of observed flood peak distributions compared to stationary (ST) analysis, but management of flood risk relies on predictions of out-of-sample distributions for which NEVA has not been comprehensively evaluated. In this study, we apply split-sample testing to 1250 annual maximum discharge records in the United States and compare the predictive capabilities of NEVA relative to ST extreme value analysis using a log-Pearson Type III (LPIII) distribution. The parameters of the LPIII distribution in the ST and nonstationary (NS) models are estimated from the first half of each record using Bayesian inference. The second half of each record is reserved to evaluate the predictions under the ST and NS models. The NS model is applied for prediction by (1) extrapolating the trend of the NS model parameters throughout the evaluation period and (2) using the NS model parameter values at the end of the fitting period to predict with an updated ST model (uST). Our analysis shows that the ST predictions are preferred, overall. NS model parameter extrapolation is rarely preferred. However, if fitting period discharges are influenced by physical changes in the watershed, for example from anthropogenic activity, the uST model is strongly preferred relative to ST and NS predictions. The uST model is therefore recommended for evaluation of current flood risk in watersheds that have undergone physical changes. Supporting information includes a MATLAB® program that estimates the (ST/NS/uST) LPIII parameters from annual peak discharge data through Bayesian inference.

Assessing the construct validity and reliability of the Parental Perception on Antibiotics (PAPA) scales.

PubMed

Alumran, Arwa; Hou, Xiang-Yu; Sun, Jiandong; Yousef, Abdullah A; Hurst, Cameron

2014-01-23

The overuse of antibiotics is becoming an increasing concern. Antibiotic resistance, which increases both the burden of disease, and the cost of health services, is perhaps the most profound impact of antibiotics overuse. Attempts have been made to develop instruments to measure the psychosocial constructs underlying antibiotics use, however, none of these instruments have undergone thorough psychometric validation. This study evaluates the psychometric properties of the Parental Perceptions on Antibiotics (PAPA) scales. The PAPA scales attempt to measure the factors influencing parental use of antibiotics in children. 1111 parents of children younger than 12 years old were recruited from primary schools' parental meetings in the Eastern Province of Saudi Arabia from September 2012 to January 2013. The structure of the PAPA instrument was validated using Confirmatory Factor Analysis (CFA) with measurement model fit evaluated using the raw and scaled χ2, Goodness of Fit Index, and Root Mean Square Error of Approximation. A five-factor model was confirmed with the model showing good fit. Constructs in the model include: Knowledge and Beliefs, Behaviors, Sources of information, Adherence, and Awareness about antibiotics resistance. The instrument was shown to have good internal consistency, and good discriminant and convergent validity. The availability of an instrument able to measure the psychosocial factors underlying antibiotics usage allows the risk factors underlying antibiotic use and overuse to now be investigated.

Peak fitting and integration uncertainties for the Aerodyne Aerosol Mass Spectrometer

NASA Astrophysics Data System (ADS)

Corbin, J. C.; Othman, A.; Haskins, J. D.; Allan, J. D.; Sierau, B.; Worsnop, D. R.; Lohmann, U.; Mensah, A. A.

2015-04-01

The errors inherent in the fitting and integration of the pseudo-Gaussian ion peaks in Aerodyne High-Resolution Aerosol Mass Spectrometers (HR-AMS's) have not been previously addressed as a source of imprecision for these instruments. This manuscript evaluates the significance of these uncertainties and proposes a method for their estimation in routine data analysis. Peak-fitting uncertainties, the most complex source of integration uncertainties, are found to be dominated by errors in m/z calibration. These calibration errors comprise significant amounts of both imprecision and bias, and vary in magnitude from ion to ion. The magnitude of these m/z calibration errors is estimated for an exemplary data set, and used to construct a Monte Carlo model which reproduced well the observed trends in fits to the real data. The empirically-constrained model is used to show that the imprecision in the fitted height of isolated peaks scales linearly with the peak height (i.e., as n1), thus contributing a constant-relative-imprecision term to the overall uncertainty. This constant relative imprecision term dominates the Poisson counting imprecision term (which scales as n0.5) at high signals. The previous HR-AMS uncertainty model therefore underestimates the overall fitting imprecision. The constant relative imprecision in fitted peak height for isolated peaks in the exemplary data set was estimated as ~4% and the overall peak-integration imprecision was approximately 5%. We illustrate the importance of this constant relative imprecision term by performing Positive Matrix Factorization (PMF) on a~synthetic HR-AMS data set with and without its inclusion. Finally, the ability of an empirically-constrained Monte Carlo approach to estimate the fitting imprecision for an arbitrary number of known overlapping peaks is demonstrated. Software is available upon request to estimate these error terms in new data sets.

Differentiating Categories and Dimensions: Evaluating the Robustness of Taxometric Analyses

ERIC Educational Resources Information Center

Ruscio, John; Kaczetow, Walter

2009-01-01

Interest in modeling the structure of latent variables is gaining momentum, and many simulation studies suggest that taxometric analysis can validly assess the relative fit of categorical and dimensional models. The generation and parallel analysis of categorical and dimensional comparison data sets reduces the subjectivity required to interpret…

Applying Bayesian Modeling and Receiver Operating Characteristic Methodologies for Test Utility Analysis

ERIC Educational Resources Information Center

Wang, Qiu; Diemer, Matthew A.; Maier, Kimberly S.

2013-01-01

This study integrated Bayesian hierarchical modeling and receiver operating characteristic analysis (BROCA) to evaluate how interest strength (IS) and interest differentiation (ID) predicted low–socioeconomic status (SES) youth's interest-major congruence (IMC). Using large-scale Kuder Career Search online-assessment data, this study fit three…

Nested Structural Equation Models: Noncentrality and Power of Restriction Test.

ERIC Educational Resources Information Center

Raykov, Tenko; Penev, Spiridon

1998-01-01

Discusses the difference in noncentrality parameters of nested structural equation models and their utility in evaluating statistical power associated with the pertinent restriction test. Asymptotic confidence intervals for that difference are presented. These intervals represent a useful adjunct to goodness-of-fit indexes in assessing constraints…

Diagnostic Procedures for Detecting Nonlinear Relationships between Latent Variables

ERIC Educational Resources Information Center

Bauer, Daniel J.; Baldasaro, Ruth E.; Gottfredson, Nisha C.

2012-01-01

Structural equation models are commonly used to estimate relationships between latent variables. Almost universally, the fitted models specify that these relationships are linear in form. This assumption is rarely checked empirically, largely for lack of appropriate diagnostic techniques. This article presents and evaluates two procedures that can…

An External Independent Validation of APACHE IV in a Malaysian Intensive Care Unit.

PubMed

Wong, Rowena S Y; Ismail, Noor Azina; Tan, Cheng Cheng

2015-04-01

Intensive care unit (ICU) prognostic models are predominantly used in more developed nations such as the United States, Europe and Australia. These are not that popular in Southeast Asian countries due to costs and technology considerations. The purpose of this study is to evaluate the suitability of the acute physiology and chronic health evaluation (APACHE) IV model in a single centre Malaysian ICU. A prospective study was conducted at the single centre ICU in Hospital Sultanah Aminah (HSA) Malaysia. External validation of APACHE IV involved a cohort of 916 patients who were admitted in 2009. Model performance was assessed through its calibration and discrimination abilities. A first-level customisation using logistic regression approach was also applied to improve model calibration. APACHE IV exhibited good discrimination, with an area under receiver operating characteristic (ROC) curve of 0.78. However, the model's overall fit was observed to be poor, as indicated by the Hosmer-Lemeshow goodness-of-fit test (Ĉ = 113, P <0.001). Predicted in-ICU mortality rate (28.1%) was significantly higher than the actual in-ICU mortality rate (18.8%). Model calibration was improved after applying first-level customisation (Ĉ = 6.39, P = 0.78) although discrimination was not affected. APACHE IV is not suitable for application in HSA ICU, without further customisation. The model's lack of fit in the Malaysian study is attributed to differences in the baseline characteristics between HSA ICU and APACHE IV datasets. Other possible factors could be due to differences in clinical practice, quality and services of health care systems between Malaysia and the United States.

On Multi-Objective Based Constitutive Modelling Methodology and Numerical Validation in Small-Hole Drilling of Al6063/SiCp Composites

PubMed Central

Xiang, Junfeng; Xie, Lijing; Gao, Feinong; Zhang, Yu; Yi, Jie; Wang, Tao; Pang, Siqin; Wang, Xibin

2018-01-01

Discrepancies in capturing material behavior of some materials, such as Particulate Reinforced Metal Matrix Composites, by using conventional ad hoc strategy make the applicability of Johnson-Cook constitutive model challenged. Despites applicable efforts, its extended formalism with more fitting parameters would increase the difficulty in identifying constitutive parameters. A weighted multi-objective strategy for identifying any constitutive formalism is developed to predict mechanical behavior in static and dynamic loading conditions equally well. These varying weighting is based on the Gaussian-distributed noise evaluation of experimentally obtained stress-strain data in quasi-static or dynamic mode. This universal method can be used to determine fast and directly whether the constitutive formalism is suitable to describe the material constitutive behavior by measuring goodness-of-fit. A quantitative comparison of different fitting strategies on identifying Al6063/SiCp’s material parameters is made in terms of performance evaluation including noise elimination, correlation, and reliability. Eventually, a three-dimensional (3D) FE model in small-hole drilling of Al6063/SiCp composites, using multi-objective identified constitutive formalism, is developed. Comparison with the experimental observations in thrust force, torque, and chip morphology provides valid evidence on the applicability of the developed multi-objective identification strategy in identifying constitutive parameters. PMID:29324688

Right-Sizing Statistical Models for Longitudinal Data

PubMed Central

Wood, Phillip K.; Steinley, Douglas; Jackson, Kristina M.

2015-01-01

Arguments are proposed that researchers using longitudinal data should consider more and less complex statistical model alternatives to their initially chosen techniques in an effort to “right-size” the model to the data at hand. Such model comparisons may alert researchers who use poorly fitting overly parsimonious models to more complex better fitting alternatives, and, alternatively, may identify more parsimonious alternatives to overly complex (and perhaps empirically under-identified and/or less powerful) statistical models. A general framework is proposed for considering (often nested) relationships between a variety of psychometric and growth curve models. A three-step approach is proposed in which models are evaluated based on the number and patterning of variance components prior to selection of better-fitting growth models that explain both mean and variation/covariation patterns. The orthogonal, free-curve slope-intercept (FCSI) growth model is considered as a general model which includes, as special cases, many models including the Factor Mean model (FM, McArdle & Epstein, 1987), McDonald's (1967) linearly constrained factor model, Hierarchical Linear Models (HLM), Repeated Measures MANOVA, and the Linear Slope Intercept (LinearSI) Growth Model. The FCSI model, in turn, is nested within the Tuckerized factor model. The approach is illustrated by comparing alternative models in a longitudinal study of children's vocabulary and by comparison of several candidate parametric growth and chronometric models in a Monte Carlo study. PMID:26237507

Right-sizing statistical models for longitudinal data.

PubMed

Wood, Phillip K; Steinley, Douglas; Jackson, Kristina M

2015-12-01

Arguments are proposed that researchers using longitudinal data should consider more and less complex statistical model alternatives to their initially chosen techniques in an effort to "right-size" the model to the data at hand. Such model comparisons may alert researchers who use poorly fitting, overly parsimonious models to more complex, better-fitting alternatives and, alternatively, may identify more parsimonious alternatives to overly complex (and perhaps empirically underidentified and/or less powerful) statistical models. A general framework is proposed for considering (often nested) relationships between a variety of psychometric and growth curve models. A 3-step approach is proposed in which models are evaluated based on the number and patterning of variance components prior to selection of better-fitting growth models that explain both mean and variation-covariation patterns. The orthogonal free curve slope intercept (FCSI) growth model is considered a general model that includes, as special cases, many models, including the factor mean (FM) model (McArdle & Epstein, 1987), McDonald's (1967) linearly constrained factor model, hierarchical linear models (HLMs), repeated-measures multivariate analysis of variance (MANOVA), and the linear slope intercept (linearSI) growth model. The FCSI model, in turn, is nested within the Tuckerized factor model. The approach is illustrated by comparing alternative models in a longitudinal study of children's vocabulary and by comparing several candidate parametric growth and chronometric models in a Monte Carlo study. (c) 2015 APA, all rights reserved).

Species area relationships in mediterranean-climate plant communities

USGS Publications Warehouse

Keeley, Jon E.; Fotheringham, C.J.

2003-01-01

Aim To determine the best-fit model of species–area relationships for Mediterranean-type plant communities and evaluate how community structure affects these species–area models.Location Data were collected from California shrublands and woodlands and compared with literature reports for other Mediterranean-climate regions.Methods The number of species was recorded from 1, 100 and 1000 m2 nested plots. Best fit to the power model or exponential model was determined by comparing adjusted r2 values from the least squares regression, pattern of residuals, homoscedasticity across scales, and semi-log slopes at 1–100 m2 and 100–1000 m2. Dominance–diversity curves were tested for fit to the lognormal model, MacArthur's broken stick model, and the geometric and harmonic series.Results Early successional Western Australia and California shrublands represented the extremes and provide an interesting contrast as the exponential model was the best fit for the former, and the power model for the latter, despite similar total species richness. We hypothesize that structural differences in these communities account for the different species–area curves and are tied to patterns of dominance, equitability and life form distribution. Dominance–diversity relationships for Western Australian heathlands exhibited a close fit to MacArthur's broken stick model, indicating more equitable distribution of species. In contrast, Californian shrublands, both postfire and mature stands, were best fit by the geometric model indicating strong dominance and many minor subordinate species. These regions differ in life form distribution, with annuals being a major component of diversity in early successional Californian shrublands although they are largely lacking in mature stands. Both young and old Australian heathlands are dominated by perennials, and annuals are largely absent. Inherent in all of these ecosystems is cyclical disequilibrium caused by periodic fires. The potential for community reassembly is greater in Californian shrublands where only a quarter of the flora resprout, whereas three quarters resprout in Australian heathlands.Other Californian vegetation types sampled include coniferous forests, oak savannas and desert scrub, and demonstrate that different community structures may lead to a similar species–area relationship. Dominance–diversity relationships for coniferous forests closely follow a geometric model whereas associated oak savannas show a close fit to the lognormal model. However, for both communities, species–area curves fit a power model. The primary driver appears to be the presence of annuals. Desert scrub communities illustrate dramatic changes in both species diversity and dominance–diversity relationships in high and low rainfall years, because of the disappearance of annuals in drought years.Main conclusions Species–area curves for immature shrublands in California and the majority of Mediterranean plant communities fit a power function model. Exceptions that fit the exponential model are not because of sampling error or scaling effects, rather structural differences in these communities provide plausible explanations. The exponential species–area model may arise in more than one way. In the highly diverse Australian heathlands it results from a rapid increase in species richness at small scales. In mature California shrublands it results from very depauperate richness at the community scale. In both instances the exponential model is tied to a preponderance of perennials and paucity of annuals. For communities fit by a power model, coefficients z and log c exhibit a number of significant correlations with other diversity parameters, suggesting that they have some predictive value in ecological communities.

Stochastic approach to data analysis in fluorescence correlation spectroscopy.

PubMed

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

2006-09-21

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

[Health coaching as a part of integration into the job market--evaluation of a qualification concept].

PubMed

Heinmüller, M; Liel, K; Angerer, P; Gündel, H; Geldermann, B; Gottwald, M; Kimil, A; Limm, H

2014-03-01

The aim of this study is to develop, implement and evaluate an education programme enabling the pedagogic staff of employment promotion agencies to integrate health promotion approaches und activities in vocational training programmes. The evaluation of the education programme is based on Kirkpatrick's 4 levels training evaluation model. Besides the participants' verbal end of session feedback, a standardised questionnaire was used at the end of the education programme and after 3 months practical experience. Process evaluation included the implementation level of the methods learned. From a total of 71 participants, 56 completed the first and 31 the second questionnaire (return rate 79% and 44%, respectively). The participants' mean age was 42 years, 80% were female. Only 22% of them integrated health topics systematically into their daily work. A 3-day basic training followed by case conferences during practical work was developed to transfer knowledge and practical competence in person-to-person talks and group activities (so called FIT-counselling and FIT-group). For 96% of participants, their expectations regarding the education programme were met completely or predominantly. 91% indicated a rise in motivation to work as health coach. When rating the training material, 96% judged it helpful for implementation/transfer. Many participants marked the education programme as being too short and wished more time for the topic of "mental health" and exchange of experiences. The follow-up after 3 months on-the-job training revealed that 84 and 97%, respectively, found FIT-counselling and FIT-groups helpful for their daily work. In all employment promotion agencies FIT-counselling and FIT-groups were implemented. Our results affirm the need for and prove the acceptance of education programmes enabling the pedagogic staff of job-training programmes to deliver health coaching. Periodic case conferences take into account the participants' request for more exchange of experiences, facilitate implementation and contribute to quality and sustainability. Further development of the education programme is ongoing. © Georg Thieme Verlag KG Stuttgart · New York.

A minimum stochastic model evaluating the interplay between population density and drift for species coexistence

NASA Astrophysics Data System (ADS)

Guariento, Rafael Dettogni; Caliman, Adriano

2017-02-01

Despite the general acknowledgment of the role of niche and stochastic process in community dynamics, the role of species relative abundances according to both perspectives may have different effects regarding coexistence patterns. In this study, we explore a minimum probabilistic stochastic model to determine the relationship of populations relative and total abundances with species chances to outcompete each other and their persistence in time (i.e., unstable coexistence). Our model is focused on the effects drift (i.e., random sampling of recruitment) under different scenarios of selection (i.e., fitness differences between species). Our results show that taking into account the stochasticity in demographic properties and conservation of individuals in closed communities (zero-sum assumption), initial population abundance can strongly influence species chances to outcompete each other, despite fitness inequalities between populations, and also, influence the period of coexistence of these species in a particular time interval. Systems carrying capacity can have an important role in species coexistence by exacerbating fitness inequalities and affecting the size of the period of coexistence. Overall, the simple stochastic formulation used in this study demonstrated that populations initial abundances could act as an equalizing mechanism, reducing fitness inequalities, which can favor species coexistence and even make less fitted species to be more likely to outcompete better-fitted species, and thus to dominate ecological communities in the absence of niche mechanisms. Although our model is restricted to a pair of interacting species, and overall conclusions are already predicted by the Neutral Theory of Biodiversity, our main objective was to derive a model that can explicitly show the functional relationship between population densities and community mono-dominance odds. Overall, our study provides a straightforward understanding of how a stochastic process (i.e., drift) may affect the expected outcome based on species selection (i.e., fitness inequalities among species) and the resulting outcome regarding unstable coexistence among species.

Event-scale power law recession analysis: quantifying methodological uncertainty

NASA Astrophysics Data System (ADS)

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

2017-01-01

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

Modeling Group Differences in OLS and Orthogonal Regression: Implications for Differential Validity Studies

ERIC Educational Resources Information Center

Kane, Michael T.; Mroch, Andrew A.

2010-01-01

In evaluating the relationship between two measures across different groups (i.e., in evaluating "differential validity") it is necessary to examine differences in correlation coefficients and in regression lines. Ordinary least squares (OLS) regression is the standard method for fitting lines to data, but its criterion for optimal fit…

Comparison of statistical models to estimate parasite growth rate in the induced blood stage malaria model.

PubMed

Wockner, Leesa F; Hoffmann, Isabell; O'Rourke, Peter; McCarthy, James S; Marquart, Louise

2017-08-25

The efficacy of vaccines aimed at inhibiting the growth of malaria parasites in the blood can be assessed by comparing the growth rate of parasitaemia in the blood of subjects treated with a test vaccine compared to controls. In studies using induced blood stage malaria (IBSM), a type of controlled human malaria infection, parasite growth rate has been measured using models with the intercept on the y-axis fixed to the inoculum size. A set of statistical models was evaluated to determine an optimal methodology to estimate parasite growth rate in IBSM studies. Parasite growth rates were estimated using data from 40 subjects published in three IBSM studies. Data was fitted using 12 statistical models: log-linear, sine-wave with the period either fixed to 48 h or not fixed; these models were fitted with the intercept either fixed to the inoculum size or not fixed. All models were fitted by individual, and overall by study using a mixed effects model with a random effect for the individual. Log-linear models and sine-wave models, with the period fixed or not fixed, resulted in similar parasite growth rate estimates (within 0.05 log 10 parasites per mL/day). Average parasite growth rate estimates for models fitted by individual with the intercept fixed to the inoculum size were substantially lower by an average of 0.17 log 10 parasites per mL/day (range 0.06-0.24) compared with non-fixed intercept models. Variability of parasite growth rate estimates across the three studies analysed was substantially higher (3.5 times) for fixed-intercept models compared with non-fixed intercept models. The same tendency was observed in models fitted overall by study. Modelling data by individual or overall by study had minimal effect on parasite growth estimates. The analyses presented in this report confirm that fixing the intercept to the inoculum size influences parasite growth estimates. The most appropriate statistical model to estimate the growth rate of blood-stage parasites in IBSM studies appears to be a log-linear model fitted by individual and with the intercept estimated in the log-linear regression. Future studies should use this model to estimate parasite growth rates.

Detection of mastitis in dairy cattle by use of mixture models for repeated somatic cell scores: a Bayesian approach via Gibbs sampling.

PubMed

Odegård, J; Jensen, J; Madsen, P; Gianola, D; Klemetsdal, G; Heringstad, B

2003-11-01

The distribution of somatic cell scores could be regarded as a mixture of at least two components depending on a cow's udder health status. A heteroscedastic two-component Bayesian normal mixture model with random effects was developed and implemented via Gibbs sampling. The model was evaluated using datasets consisting of simulated somatic cell score records. Somatic cell score was simulated as a mixture representing two alternative udder health statuses ("healthy" or "diseased"). Animals were assigned randomly to the two components according to the probability of group membership (Pm). Random effects (additive genetic and permanent environment), when included, had identical distributions across mixture components. Posterior probabilities of putative mastitis were estimated for all observations, and model adequacy was evaluated using measures of sensitivity, specificity, and posterior probability of misclassification. Fitting different residual variances in the two mixture components caused some bias in estimation of parameters. When the components were difficult to disentangle, so were their residual variances, causing bias in estimation of Pm and of location parameters of the two underlying distributions. When all variance components were identical across mixture components, the mixture model analyses returned parameter estimates essentially without bias and with a high degree of precision. Including random effects in the model increased the probability of correct classification substantially. No sizable differences in probability of correct classification were found between models in which a single cow effect (ignoring relationships) was fitted and models where this effect was split into genetic and permanent environmental components, utilizing relationship information. When genetic and permanent environmental effects were fitted, the between-replicate variance of estimates of posterior means was smaller because the model accounted for random genetic drift.

Binocular contrast discrimination needs monocular multiplicative noise

PubMed Central

Ding, Jian; Levi, Dennis M.

2016-01-01

The effects of signal and noise on contrast discrimination are difficult to separate because of a singularity in the signal-detection-theory model of two-alternative forced-choice contrast discrimination (Katkov, Tsodyks, & Sagi, 2006). In this article, we show that it is possible to eliminate the singularity by combining that model with a binocular combination model to fit monocular, dichoptic, and binocular contrast discrimination. We performed three experiments using identical stimuli to measure the perceived phase, perceived contrast, and contrast discrimination of a cyclopean sine wave. In the absence of a fixation point, we found a binocular advantage in contrast discrimination both at low contrasts (<4%), consistent with previous studies, and at high contrasts (≥34%), which has not been previously reported. However, control experiments showed no binocular advantage at high contrasts in the presence of a fixation point or for observers without accommodation. We evaluated two putative contrast-discrimination mechanisms: a nonlinear contrast transducer and multiplicative noise (MN). A binocular combination model (the DSKL model; Ding, Klein, & Levi, 2013b) was first fitted to both the perceived-phase and the perceived-contrast data sets, then combined with either the nonlinear contrast transducer or the MN mechanism to fit the contrast-discrimination data. We found that the best model combined the DSKL model with early MN. Model simulations showed that, after going through interocular suppression, the uncorrelated noise in the two eyes became anticorrelated, resulting in less binocular noise and therefore a binocular advantage in the discrimination task. Combining a nonlinear contrast transducer or MN with a binocular combination model (DSKL) provides a powerful method for evaluating the two putative contrast-discrimination mechanisms. PMID:26982370

Binocular contrast discrimination needs monocular multiplicative noise.

PubMed

Ding, Jian; Levi, Dennis M

2016-01-01

The effects of signal and noise on contrast discrimination are difficult to separate because of a singularity in the signal-detection-theory model of two-alternative forced-choice contrast discrimination (Katkov, Tsodyks, & Sagi, 2006). In this article, we show that it is possible to eliminate the singularity by combining that model with a binocular combination model to fit monocular, dichoptic, and binocular contrast discrimination. We performed three experiments using identical stimuli to measure the perceived phase, perceived contrast, and contrast discrimination of a cyclopean sine wave. In the absence of a fixation point, we found a binocular advantage in contrast discrimination both at low contrasts (<4%), consistent with previous studies, and at high contrasts (≥34%), which has not been previously reported. However, control experiments showed no binocular advantage at high contrasts in the presence of a fixation point or for observers without accommodation. We evaluated two putative contrast-discrimination mechanisms: a nonlinear contrast transducer and multiplicative noise (MN). A binocular combination model (the DSKL model; Ding, Klein, & Levi, 2013b) was first fitted to both the perceived-phase and the perceived-contrast data sets, then combined with either the nonlinear contrast transducer or the MN mechanism to fit the contrast-discrimination data. We found that the best model combined the DSKL model with early MN. Model simulations showed that, after going through interocular suppression, the uncorrelated noise in the two eyes became anticorrelated, resulting in less binocular noise and therefore a binocular advantage in the discrimination task. Combining a nonlinear contrast transducer or MN with a binocular combination model (DSKL) provides a powerful method for evaluating the two putative contrast-discrimination mechanisms.

An integrated analysis of phenotypic selection on insect body size and development time.

PubMed

Eck, Daniel J; Shaw, Ruth G; Geyer, Charles J; Kingsolver, Joel G

2015-09-01

Most studies of phenotypic selection do not estimate selection or fitness surfaces for multiple components of fitness within a unified statistical framework. This makes it difficult or impossible to assess how selection operates on traits through variation in multiple components of fitness. We describe a new generation of aster models that can evaluate phenotypic selection by accounting for timing of life-history transitions and their effect on population growth rate, in addition to survival and reproductive output. We use this approach to estimate selection on body size and development time for a field population of the herbivorous insect, Manduca sexta (Lepidoptera: Sphingidae). Estimated fitness surfaces revealed strong and significant directional selection favoring both larger adult size (via effects on egg counts) and more rapid rates of early larval development (via effects on larval survival). Incorporating the timing of reproduction and its influence on population growth rate into the analysis resulted in larger values for size in early larval development at which fitness is maximized, and weaker selection on size in early larval development. These results illustrate how the interplay of different components of fitness can influence selection on size and development time. This integrated modeling framework can be readily applied to studies of phenotypic selection via multiple fitness components in other systems. © 2015 The Author(s). Evolution © 2015 The Society for the Study of Evolution.

Evaluation properties of the French version of the OUT-PATSAT35 satisfaction with care questionnaire according to classical and item response theory analyses.

PubMed

Panouillères, M; Anota, A; Nguyen, T V; Brédart, A; Bosset, J F; Monnier, A; Mercier, M; Hardouin, J B

2014-09-01

The present study investigates the properties of the French version of the OUT-PATSAT35 questionnaire, which evaluates the outpatients' satisfaction with care in oncology using classical analysis (CTT) and item response theory (IRT). This cross-sectional multicenter study includes 692 patients who completed the questionnaire at the end of their ambulatory treatment. CTT analyses tested the main psychometric properties (convergent and divergent validity, and internal consistency). IRT analyses were conducted separately for each OUT-PATSAT35 domain (the doctors, the nurses or the radiation therapists and the services/organization) by models from the Rasch family. We examined the fit of the data to the model expectations and tested whether the model assumptions of unidimensionality, monotonicity and local independence were respected. A total of 605 (87.4%) respondents were analyzed with a mean age of 64 years (range 29-88). Internal consistency for all scales separately and for the three main domains was good (Cronbach's α 0.74-0.98). IRT analyses were performed with the partial credit model. No disordered thresholds of polytomous items were found. Each domain showed high reliability but fitted poorly to the Rasch models. Three items in particular, the item about "promptness" in the doctors' domain and the items about "accessibility" and "environment" in the services/organization domain, presented the highest default of fit. A correct fit of the Rasch model can be obtained by dropping these items. Most of the local dependence concerned items about "information provided" in each domain. A major deviation of unidimensionality was found in the nurses' domain. CTT showed good psychometric properties of the OUT-PATSAT35. However, the Rasch analysis revealed some misfitting and redundant items. Taking the above problems into consideration, it could be interesting to refine the questionnaire in a future study.

The early maximum likelihood estimation model of audiovisual integration in speech perception.

PubMed

Andersen, Tobias S

2015-05-01

Speech perception is facilitated by seeing the articulatory mouth movements of the talker. This is due to perceptual audiovisual integration, which also causes the McGurk-MacDonald illusion, and for which a comprehensive computational account is still lacking. Decades of research have largely focused on the fuzzy logical model of perception (FLMP), which provides excellent fits to experimental observations but also has been criticized for being too flexible, post hoc and difficult to interpret. The current study introduces the early maximum likelihood estimation (MLE) model of audiovisual integration to speech perception along with three model variations. In early MLE, integration is based on a continuous internal representation before categorization, which can make the model more parsimonious by imposing constraints that reflect experimental designs. The study also shows that cross-validation can evaluate models of audiovisual integration based on typical data sets taking both goodness-of-fit and model flexibility into account. All models were tested on a published data set previously used for testing the FLMP. Cross-validation favored the early MLE while more conventional error measures favored more complex models. This difference between conventional error measures and cross-validation was found to be indicative of over-fitting in more complex models such as the FLMP.

Mathematical modelling of temperature effect on growth kinetics of Pseudomonas spp. on sliced mushroom (Agaricus bisporus).

PubMed

Tarlak, Fatih; Ozdemir, Murat; Melikoglu, Mehmet

2018-02-02

The growth data of Pseudomonas spp. on sliced mushrooms (Agaricus bisporus) stored between 4 and 28°C were obtained and fitted to three different primary models, known as the modified Gompertz, logistic and Baranyi models. The goodness of fit of these models was compared by considering the mean squared error (MSE) and the coefficient of determination for nonlinear regression (pseudo-R 2 ). The Baranyi model yielded the lowest MSE and highest pseudo-R 2 values. Therefore, the Baranyi model was selected as the best primary model. Maximum specific growth rate (r max ) and lag phase duration (λ) obtained from the Baranyi model were fitted to secondary models namely, the Ratkowsky and Arrhenius models. High pseudo-R 2 and low MSE values indicated that the Arrhenius model has a high goodness of fit to determine the effect of temperature on r max . Observed number of Pseudomonas spp. on sliced mushrooms from independent experiments was compared with the predicted number of Pseudomonas spp. with the models used by considering the B f and A f values. The B f and A f values were found to be 0.974 and 1.036, respectively. The correlation between the observed and predicted number of Pseudomonas spp. was high. Mushroom spoilage was simulated as a function of temperature with the models used. The models used for Pseudomonas spp. growth can provide a fast and cost-effective alternative to traditional microbiological techniques to determine the effect of storage temperature on product shelf-life. The models can be used to evaluate the growth behaviour of Pseudomonas spp. on sliced mushroom, set limits for the quantitative detection of the microbial spoilage and assess product shelf-life. Copyright © 2017 Elsevier B.V. All rights reserved.

Predicting the incidence of hand, foot and mouth disease in Sichuan province, China using the ARIMA model.

PubMed

Liu, L; Luan, R S; Yin, F; Zhu, X P; Lü, Q

2016-01-01

Hand, foot and mouth disease (HFMD) is an infectious disease caused by enteroviruses, which usually occurs in children aged <5 years. In China, the HFMD situation is worsening, with increasing number of cases nationwide. Therefore, monitoring and predicting HFMD incidence are urgently needed to make control measures more effective. In this study, we applied an autoregressive integrated moving average (ARIMA) model to forecast HFMD incidence in Sichuan province, China. HFMD infection data from January 2010 to June 2014 were used to fit the ARIMA model. The coefficient of determination (R 2), normalized Bayesian Information Criterion (BIC) and mean absolute percentage of error (MAPE) were used to evaluate the goodness-of-fit of the constructed models. The fitted ARIMA model was applied to forecast the incidence of HMFD from April to June 2014. The goodness-of-fit test generated the optimum general multiplicative seasonal ARIMA (1,0,1) × (0,1,0)12 model (R 2 = 0·692, MAPE = 15·982, BIC = 5·265), which also showed non-significant autocorrelations in the residuals of the model (P = 0·893). The forecast incidence values of the ARIMA (1,0,1) × (0,1,0)12 model from July to December 2014 were 4103-9987, which were proximate forecasts. The ARIMA model could be applied to forecast HMFD incidence trend and provide support for HMFD prevention and control. Further observations should be carried out continually into the time sequence, and the parameters of the models could be adjusted because HMFD incidence will not be absolutely stationary in the future.

10 CFR 26.419 - Suitability and fitness evaluations.

Code of Federal Regulations, 2014 CFR

2014-01-01

... 10 Energy 1 2014-01-01 2014-01-01 false Suitability and fitness evaluations. 26.419 Section 26.419 Energy NUCLEAR REGULATORY COMMISSION FITNESS FOR DUTY PROGRAMS FFD Program for Construction § 26.419 Suitability and fitness evaluations. Licensees and other entities who implement FFD programs under this...

10 CFR 26.419 - Suitability and fitness evaluations.

Code of Federal Regulations, 2013 CFR

2013-01-01

... 10 Energy 1 2013-01-01 2013-01-01 false Suitability and fitness evaluations. 26.419 Section 26.419 Energy NUCLEAR REGULATORY COMMISSION FITNESS FOR DUTY PROGRAMS FFD Program for Construction § 26.419 Suitability and fitness evaluations. Licensees and other entities who implement FFD programs under this...

10 CFR 26.419 - Suitability and fitness evaluations.

Code of Federal Regulations, 2012 CFR

2012-01-01

... 10 Energy 1 2012-01-01 2012-01-01 false Suitability and fitness evaluations. 26.419 Section 26.419 Energy NUCLEAR REGULATORY COMMISSION FITNESS FOR DUTY PROGRAMS FFD Program for Construction § 26.419 Suitability and fitness evaluations. Licensees and other entities who implement FFD programs under this...

10 CFR 26.419 - Suitability and fitness evaluations.

Code of Federal Regulations, 2011 CFR

2011-01-01

... 10 Energy 1 2011-01-01 2011-01-01 false Suitability and fitness evaluations. 26.419 Section 26.419 Energy NUCLEAR REGULATORY COMMISSION FITNESS FOR DUTY PROGRAMS FFD Program for Construction § 26.419 Suitability and fitness evaluations. Licensees and other entities who implement FFD programs under this...

10 CFR 26.419 - Suitability and fitness evaluations.

Code of Federal Regulations, 2010 CFR

2010-01-01

... 10 Energy 1 2010-01-01 2010-01-01 false Suitability and fitness evaluations. 26.419 Section 26.419 Energy NUCLEAR REGULATORY COMMISSION FITNESS FOR DUTY PROGRAMS FFD Program for Construction § 26.419 Suitability and fitness evaluations. Licensees and other entities who implement FFD programs under this...

Practical Findings from Applying the PSD Model for Evaluating Software Design Specifications

NASA Astrophysics Data System (ADS)

Räisänen, Teppo; Lehto, Tuomas; Oinas-Kukkonen, Harri

This paper presents practical findings from applying the PSD model to evaluating the support for persuasive features in software design specifications for a mobile Internet device. On the one hand, our experiences suggest that the PSD model fits relatively well for evaluating design specifications. On the other hand, the model would benefit from more specific heuristics for evaluating each technique to avoid unnecessary subjectivity. Better distinction between the design principles in the social support category would also make the model easier to use. Practitioners who have no theoretical background can apply the PSD model to increase the persuasiveness of the systems they design. The greatest benefit of the PSD model for researchers designing new systems may be achieved when it is applied together with a sound theory, such as the Elaboration Likelihood Model. Using the ELM together with the PSD model, one may increase the chances for attitude change.

Comparison of random regression models with Legendre polynomials and linear splines for production traits and somatic cell score of Canadian Holstein cows.

PubMed

Bohmanova, J; Miglior, F; Jamrozik, J; Misztal, I; Sullivan, P G

2008-09-01

A random regression model with both random and fixed regressions fitted by Legendre polynomials of order 4 was compared with 3 alternative models fitting linear splines with 4, 5, or 6 knots. The effects common for all models were a herd-test-date effect, fixed regressions on days in milk (DIM) nested within region-age-season of calving class, and random regressions for additive genetic and permanent environmental effects. Data were test-day milk, fat and protein yields, and SCS recorded from 5 to 365 DIM during the first 3 lactations of Canadian Holstein cows. A random sample of 50 herds consisting of 96,756 test-day records was generated to estimate variance components within a Bayesian framework via Gibbs sampling. Two sets of genetic evaluations were subsequently carried out to investigate performance of the 4 models. Models were compared by graphical inspection of variance functions, goodness of fit, error of prediction of breeding values, and stability of estimated breeding values. Models with splines gave lower estimates of variances at extremes of lactations than the model with Legendre polynomials. Differences among models in goodness of fit measured by percentages of squared bias, correlations between predicted and observed records, and residual variances were small. The deviance information criterion favored the spline model with 6 knots. Smaller error of prediction and higher stability of estimated breeding values were achieved by using spline models with 5 and 6 knots compared with the model with Legendre polynomials. In general, the spline model with 6 knots had the best overall performance based upon the considered model comparison criteria.

A New Stellar Atmosphere Grid and Comparisons with HST /STIS CALSPEC Flux Distributions

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

Bohlin, Ralph C.; Fleming, Scott W.; Gordon, Karl D.

The Space Telescope Imaging Spectrograph has measured the spectral energy distributions for several stars of types O, B, A, F, and G. These absolute fluxes from the CALSPEC database are fit with a new spectral grid computed from the ATLAS-APOGEE ATLAS9 model atmosphere database using a chi-square minimization technique in four parameters. The quality of the fits are compared for complete LTE grids by Castelli and Kurucz (CK04) and our new comprehensive LTE grid (BOSZ). For the cooler stars, the fits with the MARCS LTE grid are also evaluated, while the hottest stars are also fit with the NLTE Lanzmore » and Hubeny OB star grids. Unfortunately, these NLTE models do not transition smoothly in the infrared to agree with our new BOSZ LTE grid at the NLTE lower limit of T {sub eff} = 15,000 K. The new BOSZ grid is available via the Space Telescope Institute MAST archive and has a much finer sampled IR wavelength scale than CK04, which will facilitate the modeling of stars observed by the James Webb Space Telescope . Our result for the angular diameter of Sirius agrees with the ground-based interferometric value.« less

A New Stellar Atmosphere Grid and Comparisons with HST/STIS CALSPEC Flux Distributions

NASA Astrophysics Data System (ADS)

Bohlin, Ralph C.; Mészáros, Szabolcs; Fleming, Scott W.; Gordon, Karl D.; Koekemoer, Anton M.; Kovács, József

2017-05-01

The Space Telescope Imaging Spectrograph has measured the spectral energy distributions for several stars of types O, B, A, F, and G. These absolute fluxes from the CALSPEC database are fit with a new spectral grid computed from the ATLAS-APOGEE ATLAS9 model atmosphere database using a chi-square minimization technique in four parameters. The quality of the fits are compared for complete LTE grids by Castelli & Kurucz (CK04) and our new comprehensive LTE grid (BOSZ). For the cooler stars, the fits with the MARCS LTE grid are also evaluated, while the hottest stars are also fit with the NLTE Lanz & Hubeny OB star grids. Unfortunately, these NLTE models do not transition smoothly in the infrared to agree with our new BOSZ LTE grid at the NLTE lower limit of T eff = 15,000 K. The new BOSZ grid is available via the Space Telescope Institute MAST archive and has a much finer sampled IR wavelength scale than CK04, which will facilitate the modeling of stars observed by the James Webb Space Telescope. Our result for the angular diameter of Sirius agrees with the ground-based interferometric value.

Formulation development of physiological environment responsive periodontal drug delivery system for local delviery of metronidazole benzoate.

PubMed

Dabhi, Mahesh R; Sheth, Navin R

2013-03-01

The objective of the present investigation was to develop and evaluate physiological environment responsive periodontal drug delivery system (PERPDDS) for local delivery of metronidazole benzoate. Poly-ϵ-caprolactone an in situ precipitating polymer was used in combination with, carbopol 934P, a pH simulative polymer to develop PERPDDS. The prepared PERPDDS was evaluated for various parameters such as in vitro gelling capacity, viscosity, rheology, compatibility study, and in vitro diffusion study. A 3(2) full factorial design was used to investigate the influence of formulation variables. Drug release data from all formulations were fitted to different kinetic models and the korsemeyer-peppas model was found the best fit model. The value of diffusional exponent (n) was in between 0.3283 and 0.3979 indicating purely fickian diffusion release mechanism. Increasing the concentration of each polymeric component increases viscosity, and time for 50% and 90% drug release was observed and graphically represented by the surface response and contour plots.

Precipitation-runoff modeling system; user's manual

USGS Publications Warehouse

Leavesley, G.H.; Lichty, R.W.; Troutman, B.M.; Saindon, L.G.

1983-01-01

The concepts, structure, theoretical development, and data requirements of the precipitation-runoff modeling system (PRMS) are described. The precipitation-runoff modeling system is a modular-design, deterministic, distributed-parameter modeling system developed to evaluate the impacts of various combinations of precipitation, climate, and land use on streamflow, sediment yields, and general basin hydrology. Basin response to normal and extreme rainfall and snowmelt can be simulated to evaluate changes in water balance relationships, flow regimes, flood peaks and volumes, soil-water relationships, sediment yields, and groundwater recharge. Parameter-optimization and sensitivity analysis capabilites are provided to fit selected model parameters and evaluate their individual and joint effects on model output. The modular design provides a flexible framework for continued model system enhancement and hydrologic modeling research and development. (Author 's abstract)

Could the differences in the biochemistry of prostate carcinoma compared to benign prostate tissue biopsy fragments be evaluated through Raman spectroscopy?

NASA Astrophysics Data System (ADS)

Silveira, Landulfo; Leite, Kátia Ramos M.; Srougi, Miguel; Silveira, Fabrício L.; Pacheco, Marcos Tadeu T.; Zângaro, Renato A.; Pasqualucci, Carlos A.

2013-03-01

It has been proposed a spectral model to evaluate the biochemical differences between prostate carcinoma and benign fragments using dispersive Raman spectroscopy. We have examined 51 prostate fragments from surgically removed PrCa; each fragment was snap-frozen and stored (-80°C) prior spectral analysis. Raman spectrum was measured using a Raman spectrometer (830 nm excitation) coupled to a fiber-optic probe. Integration time and laser power were set to 50 s and 300 mW, respectively. It has been collected triplicate spectra from each fragment (total 153 spectra). Some samples exhibited a strong fluorescence, which was removed by a 7th order polynomial fitting. It has been developed a spectral model based on the least-squares fitting of the spectra of pure biochemicals (actin, collagen, elastin, carotene, glycogen, phosphatidylcholine, hemoglobin, and water) with the spectra of tissues, where the fitting parameters are the relative contribution of the compounds to the tissue spectrum. The spectra (600-1800 cm-1 range) are dominated by bands of proteins; it has been found a small difference in the mean spectra of PrCa compared to the benign tissue, mainly in the 1000-1400 cm-1 region, indicating similar biochemical constitution. The spectral fitting model revealed that elastin and phosphatidylcholine were increased in PrCa, whereas blood and water were reduced in malignant lesions (p < 0.05). A discrimination of PrCa from benign tissue using Mahalanobis distance applied to the contribution of elastin, hemoglobin and phosphatidylcholine resulted in sensitivity of 72% and specificity of 70%.

Determination of excitation profile and dielectric function spatial nonuniformity in porous silicon by using WKB approach.

PubMed

He, Wei; Yurkevich, Igor V; Canham, Leigh T; Loni, Armando; Kaplan, Andrey

2014-11-03

We develop an analytical model based on the WKB approach to evaluate the experimental results of the femtosecond pump-probe measurements of the transmittance and reflectance obtained on thin membranes of porous silicon. The model allows us to retrieve a pump-induced nonuniform complex dielectric function change along the membrane depth. We show that the model fitting to the experimental data requires a minimal number of fitting parameters while still complying with the restriction imposed by the Kramers-Kronig relation. The developed model has a broad range of applications for experimental data analysis and practical implementation in the design of devices involving a spatially nonuniform dielectric function, such as in biosensing, wave-guiding, solar energy harvesting, photonics and electro-optical devices.

Deriving sulfamethoxazole dissipation endpoints in pasture soils using first order and biphasic kinetic models.

PubMed

Srinivasan, Prakash; Sarmah, Ajit K; Rohan, Maheswaran

2014-08-01

Single first-order (SFO) kinetic model is often used to derive the dissipation endpoints of an organic chemical in soil. This model is used due to its simplicity and requirement by regulatory agencies. However, using the SFO model for all types of decay pattern could lead to under- or overestimation of dissipation endpoints when the deviation from first-order is significant. In this study the performance of three biphasic kinetic models - bi-exponential decay (BEXP), first-order double exponential decay (FODED), and first-order two-compartment (FOTC) models was evaluated using dissipation datasets of sulfamethoxazole (SMO) antibiotic in three different soils under varying concentration, depth, temperature, and sterile conditions. Corresponding 50% (DT50) and 90% (DT90) dissipation times for the antibiotics were numerically obtained and compared against those obtained using the SFO model. The fit of each model to the measured values was evaluated based on an array of statistical measures such as coefficient of determination (R(2)adj), root mean square error (RMSE), chi-square (χ(2)) test at 1% significance, Bayesian Information Criteria (BIC) and % model error. Box-whisker residual plots were also used to compare the performance of each model to the measured datasets. The antibiotic dissipation was successfully predicted by all four models. However, the nonlinear biphasic models improved the goodness-of-fit parameters for all datasets. Deviations from datasets were also often less evident with the biphasic models. The fits of FOTC and FODED models for SMO dissipation datasets were identical in most cases, and were found to be superior to the BEXP model. Among the biphasic models, the FOTC model was found to be the most suitable for obtaining the endpoints and could provide a mechanistic explanation for SMO dissipation in the soils. Copyright © 2014 Elsevier B.V. All rights reserved.

Evaluating flow cytometer performance with weighted quadratic least squares analysis of LED and multi-level bead data

PubMed Central

Parks, David R.; Khettabi, Faysal El; Chase, Eric; Hoffman, Robert A.; Perfetto, Stephen P.; Spidlen, Josef; Wood, James C.S.; Moore, Wayne A.; Brinkman, Ryan R.

2017-01-01

We developed a fully automated procedure for analyzing data from LED pulses and multi-level bead sets to evaluate backgrounds and photoelectron scales of cytometer fluorescence channels. The method improves on previous formulations by fitting a full quadratic model with appropriate weighting and by providing standard errors and peak residuals as well as the fitted parameters themselves. Here we describe the details of the methods and procedures involved and present a set of illustrations and test cases that demonstrate the consistency and reliability of the results. The automated analysis and fitting procedure is generally quite successful in providing good estimates of the Spe (statistical photoelectron) scales and backgrounds for all of the fluorescence channels on instruments with good linearity. The precision of the results obtained from LED data is almost always better than for multi-level bead data, but the bead procedure is easy to carry out and provides results good enough for most purposes. Including standard errors on the fitted parameters is important for understanding the uncertainty in the values of interest. The weighted residuals give information about how well the data fits the model, and particularly high residuals indicate bad data points. Known photoelectron scales and measurement channel backgrounds make it possible to estimate the precision of measurements at different signal levels and the effects of compensated spectral overlap on measurement quality. Combining this information with measurements of standard samples carrying dyes of biological interest, we can make accurate comparisons of dye sensitivity among different instruments. Our method is freely available through the R/Bioconductor package flowQB. PMID:28160404

Growth characterisation of intra-thoracic organs of children on CT scans.

PubMed

Coulongeat, François; Jarrar, Mohamed-Salah; Thollon, Lionel; Serre, Thierry

2013-01-01

This paper analyses the geometry of intra-thoracic organs from computed tomography (CT) scans performed on 20 children aged from 4 months to 16 years. The aim is to find the most reliable measurements to characterise the growth of heart and lungs from CT data. Standard measurements available on chest radiographies are compared with original measurements only available on CT scans. These measurements should characterise the growth of organs as well as the changes in their position relative to the thorax. Measurements were considered as functions of age. Quadratic regression models were fitted to the data. Goodness of fit of the models was then evaluated. Positions of organs relative to the thorax have a high variability compared with their changes with age. The length and volume of the heart and lungs as well as the diameter of the thorax fit well to the models of growth. It could be interesting to study these measurements with a larger sample size in order to define growth standards.

Kinematic modelling of disc galaxies using graphics processing units

NASA Astrophysics Data System (ADS)

Bekiaris, G.; Glazebrook, K.; Fluke, C. J.; Abraham, R.

2016-01-01

With large-scale integral field spectroscopy (IFS) surveys of thousands of galaxies currently under-way or planned, the astronomical community is in need of methods, techniques and tools that will allow the analysis of huge amounts of data. We focus on the kinematic modelling of disc galaxies and investigate the potential use of massively parallel architectures, such as the graphics processing unit (GPU), as an accelerator for the computationally expensive model-fitting procedure. We review the algorithms involved in model-fitting and evaluate their suitability for GPU implementation. We employ different optimization techniques, including the Levenberg-Marquardt and nested sampling algorithms, but also a naive brute-force approach based on nested grids. We find that the GPU can accelerate the model-fitting procedure up to a factor of ˜100 when compared to a single-threaded CPU, and up to a factor of ˜10 when compared to a multithreaded dual CPU configuration. Our method's accuracy, precision and robustness are assessed by successfully recovering the kinematic properties of simulated data, and also by verifying the kinematic modelling results of galaxies from the GHASP and DYNAMO surveys as found in the literature. The resulting GBKFIT code is available for download from: http://supercomputing.swin.edu.au/gbkfit.

An approximation of herd effect due to vaccinating children against seasonal influenza – a potential solution to the incorporation of indirect effects into static models

PubMed Central

2013-01-01

Background Indirect herd effect from vaccination of children offers potential for improving the effectiveness of influenza prevention in the remaining unvaccinated population. Static models used in cost-effectiveness analyses cannot dynamically capture herd effects. The objective of this study was to develop a methodology to allow herd effect associated with vaccinating children against seasonal influenza to be incorporated into static models evaluating the cost-effectiveness of influenza vaccination. Methods Two previously published linear equations for approximation of herd effects in general were compared with the results of a structured literature review undertaken using PubMed searches to identify data on herd effects specific to influenza vaccination. A linear function was fitted to point estimates from the literature using the sum of squared residuals. Results The literature review identified 21 publications on 20 studies for inclusion. Six studies provided data on a mathematical relationship between effective vaccine coverage in subgroups and reduction of influenza infection in a larger unvaccinated population. These supported a linear relationship when effective vaccine coverage in a subgroup population was between 20% and 80%. Three studies evaluating herd effect at a community level, specifically induced by vaccinating children, provided point estimates for fitting linear equations. The fitted linear equation for herd protection in the target population for vaccination (children) was slightly less conservative than a previously published equation for herd effects in general. The fitted linear equation for herd protection in the non-target population was considerably less conservative than the previously published equation. Conclusions This method of approximating herd effect requires simple adjustments to the annual baseline risk of influenza in static models: (1) for the age group targeted by the childhood vaccination strategy (i.e. children); and (2) for other age groups not targeted (e.g. adults and/or elderly). Two approximations provide a linear relationship between effective coverage and reduction in the risk of infection. The first is a conservative approximation, recommended as a base-case for cost-effectiveness evaluations. The second, fitted to data extracted from a structured literature review, provides a less conservative estimate of herd effect, recommended for sensitivity analyses. PMID:23339290

Cs{sup +} ion exchange kinetics in complex electrolyte solutions using hydrous crystalline silicotitanates

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

Gu, D.; Nguyen, L.; Philip, C.V.

1997-12-01

TAM-5 is a hydrous crystalline sodium silicotitanate inorganic ion exchanger with a high selectivity for Cs{sup +}. The kinetics of Cs{sup +}-Na{sup +} ion exchange using TAM-5 in multicomponent electrolyte solutions were determined using batch experiments. For the powder, which is composed of crystals, a single-phase, homogeneous model fit the data best. For the granules, which were prepared from the powder, a two-phase, heterogeneous model resulted in an excellent fit of the data. Macropore and crystal diffusivities were determined by fitting the model to experimental data collected on the powder and the granules. Intracrystalline diffusivities were concentration dependent and weremore » on the order of 10{sup {minus}19} m{sup 2}/s. Macropore diffusivities were on the order of 10{sup {minus}10} m{sup 2}/s. Resistance to diffusion in the macropores was not significant for granules with diameters less than 15 {micro}m. A two-phase, homogeneous model, where liquid within the pores is in equilibrium with the solid, was also evaluated for the granules. Surprisingly, for the granules, an excellent fit of the data was obtained; however, the effective macropore diffusivity was 1.1 {times} 10{sup {minus}11} m{sup 2}/s, an order of magnitude smaller than the macropore diffusivity found using the two-phase, heterogeneous model.« less

Accounting for tagging-to-harvest mortality in a Brownie tag-recovery model by incorporating radio-telemetry data.

PubMed

Buderman, Frances E; Diefenbach, Duane R; Casalena, Mary Jo; Rosenberry, Christopher S; Wallingford, Bret D

2014-04-01

The Brownie tag-recovery model is useful for estimating harvest rates but assumes all tagged individuals survive to the first hunting season; otherwise, mortality between time of tagging and the hunting season will cause the Brownie estimator to be negatively biased. Alternatively, fitting animals with radio transmitters can be used to accurately estimate harvest rate but may be more costly. We developed a joint model to estimate harvest and annual survival rates that combines known-fate data from animals fitted with transmitters to estimate the probability of surviving the period from capture to the first hunting season, and data from reward-tagged animals in a Brownie tag-recovery model. We evaluated bias and precision of the joint estimator, and how to optimally allocate effort between animals fitted with radio transmitters and inexpensive ear tags or leg bands. Tagging-to-harvest survival rates from >20 individuals with radio transmitters combined with 50-100 reward tags resulted in an unbiased and precise estimator of harvest rates. In addition, the joint model can test whether transmitters affect an individual's probability of being harvested. We illustrate application of the model using data from wild turkey, Meleagris gallapavo, to estimate harvest rates, and data from white-tailed deer, Odocoileus virginianus, to evaluate whether the presence of a visible radio transmitter is related to the probability of a deer being harvested. The joint known-fate tag-recovery model eliminates the requirement to capture and mark animals immediately prior to the hunting season to obtain accurate and precise estimates of harvest rate. In addition, the joint model can assess whether marking animals with radio transmitters affects the individual's probability of being harvested, caused by hunter selectivity or changes in a marked animal's behavior.

Accounting for tagging-to-harvest mortality in a Brownie tag-recovery model by incorporating radio-telemetry data

USGS Publications Warehouse

Buderman, Frances E.; Diefenbach, Duane R.; Casalena, Mary Jo; Rosenberry, Christopher S.; Wallingford, Bret D.

2014-01-01

The Brownie tag-recovery model is useful for estimating harvest rates but assumes all tagged individuals survive to the first hunting season; otherwise, mortality between time of tagging and the hunting season will cause the Brownie estimator to be negatively biased. Alternatively, fitting animals with radio transmitters can be used to accurately estimate harvest rate but may be more costly. We developed a joint model to estimate harvest and annual survival rates that combines known-fate data from animals fitted with transmitters to estimate the probability of surviving the period from capture to the first hunting season, and data from reward-tagged animals in a Brownie tag-recovery model. We evaluated bias and precision of the joint estimator, and how to optimally allocate effort between animals fitted with radio transmitters and inexpensive ear tags or leg bands. Tagging-to-harvest survival rates from >20 individuals with radio transmitters combined with 50–100 reward tags resulted in an unbiased and precise estimator of harvest rates. In addition, the joint model can test whether transmitters affect an individual's probability of being harvested. We illustrate application of the model using data from wild turkey, Meleagris gallapavo,to estimate harvest rates, and data from white-tailed deer, Odocoileus virginianus, to evaluate whether the presence of a visible radio transmitter is related to the probability of a deer being harvested. The joint known-fate tag-recovery model eliminates the requirement to capture and mark animals immediately prior to the hunting season to obtain accurate and precise estimates of harvest rate. In addition, the joint model can assess whether marking animals with radio transmitters affects the individual's probability of being harvested, caused by hunter selectivity or changes in a marked animal's behavior.

Fuzzy Performance between Surface Fitting and Energy Distribution in Turbulence Runner

PubMed Central

Liang, Zhongwei; Liu, Xiaochu; Ye, Bangyan; Brauwer, Richard Kars

2012-01-01

Because the application of surface fitting algorithms exerts a considerable fuzzy influence on the mathematical features of kinetic energy distribution, their relation mechanism in different external conditional parameters must be quantitatively analyzed. Through determining the kinetic energy value of each selected representative position coordinate point by calculating kinetic energy parameters, several typical algorithms of complicated surface fitting are applied for constructing microkinetic energy distribution surface models in the objective turbulence runner with those obtained kinetic energy values. On the base of calculating the newly proposed mathematical features, we construct fuzzy evaluation data sequence and present a new three-dimensional fuzzy quantitative evaluation method; then the value change tendencies of kinetic energy distribution surface features can be clearly quantified, and the fuzzy performance mechanism discipline between the performance results of surface fitting algorithms, the spatial features of turbulence kinetic energy distribution surface, and their respective environmental parameter conditions can be quantitatively analyzed in detail, which results in the acquirement of final conclusions concerning the inherent turbulence kinetic energy distribution performance mechanism and its mathematical relation. A further turbulence energy quantitative study can be ensured. PMID:23213287

Revision of laser-induced damage threshold evaluation from damage probability data

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

Bataviciute, Gintare; Grigas, Povilas; Smalakys, Linas

2013-04-15

In this study, the applicability of commonly used Damage Frequency Method (DFM) is addressed in the context of Laser-Induced Damage Threshold (LIDT) testing with pulsed lasers. A simplified computer model representing the statistical interaction between laser irradiation and randomly distributed damage precursors is applied for Monte Carlo experiments. The reproducibility of LIDT predicted from DFM is examined under both idealized and realistic laser irradiation conditions by performing numerical 1-on-1 tests. A widely accepted linear fitting resulted in systematic errors when estimating LIDT and its error bars. For the same purpose, a Bayesian approach was proposed. A novel concept of parametricmore » regression based on varying kernel and maximum likelihood fitting technique is introduced and studied. Such approach exhibited clear advantages over conventional linear fitting and led to more reproducible LIDT evaluation. Furthermore, LIDT error bars are obtained as a natural outcome of parametric fitting which exhibit realistic values. The proposed technique has been validated on two conventionally polished fused silica samples (355 nm, 5.7 ns).« less

COMPARING THE IMPAIRMENT PROFILES OF OLDER DRIVERS AND NON-DRIVERS: TOWARD THE DEVELOPMENT OF A FITNESS-TO-DRIVE MODEL

PubMed Central

Antin, Jonathan F.; Stanley, Laura M.; Guo, Feng

2011-01-01

The purpose of this research effort was to compare older driver and non-driver functional impairment profiles across some 60 assessment metrics in an initial effort to contribute to the development of fitness-to-drive assessment models. Of the metrics evaluated, 21 showed statistically significant differences, almost all favoring the drivers. Also, it was shown that a logistic regression model comprised of five of the assessment scores could completely and accurately separate the two groups. The results of this study imply that older drivers are far less functionally impaired than non-drivers of similar ages, and that a parsimonious model can accurately assign individuals to either group. With such models, any driver classified or diagnosed as a non-driver would be a strong candidate for further investigation and intervention. PMID:22058607

Analysing model fit of psychometric process models: An overview, a new test and an application to the diffusion model.

PubMed

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.

Developmental Relations between Vocabulary Knowledge and Reading Comprehension: A Latent Change Score Modeling Study

ERIC Educational Resources Information Center

Quinn, Jamie M.; Wagner, Richard K.; Petscher, Yaacov; Lopez, Danielle

2015-01-01

The present study followed a sample of first-grade (N = 316, M[subscript age] = 7.05 at first test) through fourth-grade students to evaluate dynamic developmental relations between vocabulary knowledge and reading comprehension. Using latent change score modeling, competing models were fit to the repeated measurements of vocabulary knowledge and…

On the fit of models to covariances and methodology to the Bulletin.

PubMed

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.

Distributed Evaluation of Local Sensitivity Analysis (DELSA), with application to hydrologic models

USGS Publications Warehouse

Rakovec, O.; Hill, Mary C.; Clark, M.P.; Weerts, A. H.; Teuling, A. J.; Uijlenhoet, R.

2014-01-01

This paper presents a hybrid local-global sensitivity analysis method termed the Distributed Evaluation of Local Sensitivity Analysis (DELSA), which is used here to identify important and unimportant parameters and evaluate how model parameter importance changes as parameter values change. DELSA uses derivative-based “local” methods to obtain the distribution of parameter sensitivity across the parameter space, which promotes consideration of sensitivity analysis results in the context of simulated dynamics. This work presents DELSA, discusses how it relates to existing methods, and uses two hydrologic test cases to compare its performance with the popular global, variance-based Sobol' method. The first test case is a simple nonlinear reservoir model with two parameters. The second test case involves five alternative “bucket-style” hydrologic models with up to 14 parameters applied to a medium-sized catchment (200 km2) in the Belgian Ardennes. Results show that in both examples, Sobol' and DELSA identify similar important and unimportant parameters, with DELSA enabling more detailed insight at much lower computational cost. For example, in the real-world problem the time delay in runoff is the most important parameter in all models, but DELSA shows that for about 20% of parameter sets it is not important at all and alternative mechanisms and parameters dominate. Moreover, the time delay was identified as important in regions producing poor model fits, whereas other parameters were identified as more important in regions of the parameter space producing better model fits. The ability to understand how parameter importance varies through parameter space is critical to inform decisions about, for example, additional data collection and model development. The ability to perform such analyses with modest computational requirements provides exciting opportunities to evaluate complicated models as well as many alternative models.

An efficient parallel sampling technique for Multivariate Poisson-Lognormal model: Analysis with two crash count datasets

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

Zhan, Xianyuan; Aziz, H. M. Abdul; Ukkusuri, Satish V.

Our study investigates the Multivariate Poisson-lognormal (MVPLN) model that jointly models crash frequency and severity accounting for correlations. The ordinary univariate count models analyze crashes of different severity level separately ignoring the correlations among severity levels. The MVPLN model is capable to incorporate the general correlation structure and takes account of the over dispersion in the data that leads to a superior data fitting. But, the traditional estimation approach for MVPLN model is computationally expensive, which often limits the use of MVPLN model in practice. In this work, a parallel sampling scheme is introduced to improve the original Markov Chainmore » Monte Carlo (MCMC) estimation approach of the MVPLN model, which significantly reduces the model estimation time. Two MVPLN models are developed using the pedestrian vehicle crash data collected in New York City from 2002 to 2006, and the highway-injury data from Washington State (5-year data from 1990 to 1994) The Deviance Information Criteria (DIC) is used to evaluate the model fitting. The estimation results show that the MVPLN models provide a superior fit over univariate Poisson-lognormal (PLN), univariate Poisson, and Negative Binomial models. Moreover, the correlations among the latent effects of different severity levels are found significant in both datasets that justifies the importance of jointly modeling crash frequency and severity accounting for correlations.« less

An efficient parallel sampling technique for Multivariate Poisson-Lognormal model: Analysis with two crash count datasets

DOE PAGES

Zhan, Xianyuan; Aziz, H. M. Abdul; Ukkusuri, Satish V.

2015-11-19

Our study investigates the Multivariate Poisson-lognormal (MVPLN) model that jointly models crash frequency and severity accounting for correlations. The ordinary univariate count models analyze crashes of different severity level separately ignoring the correlations among severity levels. The MVPLN model is capable to incorporate the general correlation structure and takes account of the over dispersion in the data that leads to a superior data fitting. But, the traditional estimation approach for MVPLN model is computationally expensive, which often limits the use of MVPLN model in practice. In this work, a parallel sampling scheme is introduced to improve the original Markov Chainmore » Monte Carlo (MCMC) estimation approach of the MVPLN model, which significantly reduces the model estimation time. Two MVPLN models are developed using the pedestrian vehicle crash data collected in New York City from 2002 to 2006, and the highway-injury data from Washington State (5-year data from 1990 to 1994) The Deviance Information Criteria (DIC) is used to evaluate the model fitting. The estimation results show that the MVPLN models provide a superior fit over univariate Poisson-lognormal (PLN), univariate Poisson, and Negative Binomial models. Moreover, the correlations among the latent effects of different severity levels are found significant in both datasets that justifies the importance of jointly modeling crash frequency and severity accounting for correlations.« less

Fuzzy Analytic Hierarchy Process-based Chinese Resident Best Fitness Behavior Method Research.

PubMed

Wang, Dapeng; Zhang, Lan

2015-01-01

With explosive development in Chinese economy and science and technology, people's pursuit of health becomes more and more intense, therefore Chinese resident sports fitness activities have been rapidly developed. However, different fitness events popularity degrees and effects on body energy consumption are different, so bases on this, the paper researches on fitness behaviors and gets Chinese residents sports fitness behaviors exercise guide, which provides guidance for propelling to national fitness plan's implementation and improving Chinese resident fitness scientization. The paper starts from the perspective of energy consumption, it mainly adopts experience method, determines Chinese resident favorite sports fitness event energy consumption through observing all kinds of fitness behaviors energy consumption, and applies fuzzy analytic hierarchy process to make evaluation on bicycle riding, shadowboxing practicing, swimming, rope skipping, jogging, running, aerobics these seven fitness events. By calculating fuzzy rate model's membership and comparing their sizes, it gets fitness behaviors that are more helpful for resident health, more effective and popular. Finally, it gets conclusions that swimming is a best exercise mode and its membership is the highest. Besides, the memberships of running, rope skipping and shadowboxing practicing are also relative higher. It should go in for bodybuilding by synthesizing above several kinds of fitness events according to different physical conditions; different living conditions so that can better achieve the purpose of fitness exercises.

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

NASA Astrophysics Data System (ADS)

Fredriksson, Ingemar; Larsson, Marcus; Strömberg, Tomas

2012-04-01

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

Linking normative models of natural tasks to descriptive models of neural response.

PubMed

Jaini, Priyank; Burge, Johannes

2017-10-01

Understanding how nervous systems exploit task-relevant properties of sensory stimuli to perform natural tasks is fundamental to the study of perceptual systems. However, there are few formal methods for determining which stimulus properties are most useful for a given natural task. As a consequence, it is difficult to develop principled models for how to compute task-relevant latent variables from natural signals, and it is difficult to evaluate descriptive models fit to neural response. Accuracy maximization analysis (AMA) is a recently developed Bayesian method for finding the optimal task-specific filters (receptive fields). Here, we introduce AMA-Gauss, a new faster form of AMA that incorporates the assumption that the class-conditional filter responses are Gaussian distributed. Then, we use AMA-Gauss to show that its assumptions are justified for two fundamental visual tasks: retinal speed estimation and binocular disparity estimation. Next, we show that AMA-Gauss has striking formal similarities to popular quadratic models of neural response: the energy model and the generalized quadratic model (GQM). Together, these developments deepen our understanding of why the energy model of neural response have proven useful, improve our ability to evaluate results from subunit model fits to neural data, and should help accelerate psychophysics and neuroscience research with natural stimuli.

Evaluation of the Use of Zero-Augmented Regression Techniques to Model Incidence of Campylobacter Infections in FoodNet.

PubMed

Tremblay, Marlène; Crim, Stacy M; Cole, Dana J; Hoekstra, Robert M; Henao, Olga L; Döpfer, Dörte

2017-10-01

The Foodborne Diseases Active Surveillance Network (FoodNet) is currently using a negative binomial (NB) regression model to estimate temporal changes in the incidence of Campylobacter infection. FoodNet active surveillance in 483 counties collected data on 40,212 Campylobacter cases between years 2004 and 2011. We explored models that disaggregated these data to allow us to account for demographic, geographic, and seasonal factors when examining changes in incidence of Campylobacter infection. We hypothesized that modeling structural zeros and including demographic variables would increase the fit of FoodNet's Campylobacter incidence regression models. Five different models were compared: NB without demographic covariates, NB with demographic covariates, hurdle NB with covariates in the count component only, hurdle NB with covariates in both zero and count components, and zero-inflated NB with covariates in the count component only. Of the models evaluated, the nonzero-augmented NB model with demographic variables provided the best fit. Results suggest that even though zero inflation was not present at this level, individualizing the level of aggregation and using different model structures and predictors per site might be required to correctly distinguish between structural and observational zeros and account for risk factors that vary geographically.

Using web search query data to monitor dengue epidemics: a new model for neglected tropical disease surveillance.

PubMed

Chan, Emily H; Sahai, Vikram; Conrad, Corrie; Brownstein, John S

2011-05-01

A variety of obstacles including bureaucracy and lack of resources have interfered with timely detection and reporting of dengue cases in many endemic countries. Surveillance efforts have turned to modern data sources, such as Internet search queries, which have been shown to be effective for monitoring influenza-like illnesses. However, few have evaluated the utility of web search query data for other diseases, especially those of high morbidity and mortality or where a vaccine may not exist. In this study, we aimed to assess whether web search queries are a viable data source for the early detection and monitoring of dengue epidemics. Bolivia, Brazil, India, Indonesia and Singapore were chosen for analysis based on available data and adequate search volume. For each country, a univariate linear model was then built by fitting a time series of the fraction of Google search query volume for specific dengue-related queries from that country against a time series of official dengue case counts for a time-frame within 2003-2010. The specific combination of queries used was chosen to maximize model fit. Spurious spikes in the data were also removed prior to model fitting. The final models, fit using a training subset of the data, were cross-validated against both the overall dataset and a holdout subset of the data. All models were found to fit the data quite well, with validation correlations ranging from 0.82 to 0.99. Web search query data were found to be capable of tracking dengue activity in Bolivia, Brazil, India, Indonesia and Singapore. Whereas traditional dengue data from official sources are often not available until after some substantial delay, web search query data are available in near real-time. These data represent valuable complement to assist with traditional dengue surveillance.

A Comparison of Latent Growth Models for Constructs Measured by Multiple Items

ERIC Educational Resources Information Center

Leite, Walter L.

2007-01-01

Univariate latent growth modeling (LGM) of composites of multiple items (e.g., item means or sums) has been frequently used to analyze the growth of latent constructs. This study evaluated whether LGM of composites yields unbiased parameter estimates, standard errors, chi-square statistics, and adequate fit indexes. Furthermore, LGM was compared…

Evaluating the Factor Validity of the Children's Organizational Skills Scale in Youth with ADHD.

PubMed

Molitor, Stephen J; Langberg, Joshua M; Evans, Steven W; Dvorsky, Melissa R; Bourchtein, Elizaveta; Eddy, Laura D; Smith, Zoe R; Oddo, Lauren E

2017-06-01

Children and adolescents with ADHD often have difficulties with organization, time management, and planning (OTMP) skills, and these skills are a common target of intervention. A limited array of tools for measuring these abilities in youth is available, and one of the most prominent measures is the Children's Organizational Skills Scale (COSS). Although the COSS fills an important need, a replication of the COSS factor structure outside of initial measure development has not been conducted in any population. Given that the COSS is frequently used in ADHD research, the current study evaluated the factor structure of the parent-rated COSS in a sample ( N = 619) of adolescents with ADHD. Results indicated that the original factor structure could be replicated, although the use of item parcels appeared to affect model fit statistics. An alternative bi-factor model was also tested that did not require the use of parcels, with results suggesting similar model fit in comparison to the original factor structure. Exploratory validity tests indicated that the domain-general factor of the bi-factor model appears related to broad executive functioning abilities.

A contact-area model for rail-pads connections in 2-D simulations: sensitivity analysis of train-induced vibrations

NASA Astrophysics Data System (ADS)

Ferrara, R.; Leonardi, G.; Jourdan, F.

2013-09-01

A numerical model to predict train-induced vibrations is presented. The dynamic computation considers mutual interactions in vehicle/track coupled systems by means of a finite and discrete elements method. The rail defects and the case of out-of-round wheels are considered. The dynamic interaction between the wheel-sets and the rail is accomplished by using the non-linear Hertzian model with hysteresis damping. A sensitivity analysis is done to evaluate the variables affecting more the maintenance costs. The rail-sleeper contact is assumed extended to an area-defined contact zone, rather than a single-point assumption which fits better real case studies. Experimental validations show how prediction fits well experimental data.

Understanding the early dynamics of the 2014 porcine epidemic diarrhea virus (PEDV) outbreak in Ontario using the incidence decay and exponential adjustment (IDEA) model.

PubMed

Greer, Amy L; Spence, Kelsey; Gardner, Emma

2017-01-05

The United States swine industry was first confronted with porcine epidemic diarrhea virus (PEDV) in 2013. In young pigs, the virus is highly pathogenic and the associated morbidity and mortality has a significant negative impact on the swine industry. We have applied the IDEA model to better understand the 2014 PEDV outbreak in Ontario, Canada. Using our simple, 2-parameter IDEA model, we have evaluated the early epidemic dynamics of PEDV on Ontario swine farms. We estimated the best-fit R 0 and control parameter (d) for the between farm transmission component of the outbreak by fitting the model to publically available cumulative incidence data. We used maximum likelihood to compare model fit estimates for different combinations of the R 0 and d parameters. Using our initial findings from the iterative fitting procedure, we projected the time course of the epidemic using only a subset of the early epidemic data. The IDEA model projections showed excellent agreement with the observed data based on a 7-day generation time estimate. The best-fit estimate for R 0 was 1.87 (95% CI: 1.52 - 2.34) and for the control parameter (d) was 0.059 (95% CI: 0.022 - 0.117). Using data from the first three generations of the outbreak, our iterative fitting procedure suggests that R 0 and d had stabilized sufficiently to project the time course of the outbreak with reasonable accuracy. The emergence and spread of PEDV represents an important agricultural emergency. The virus presents a significant ongoing threat to the Canadian swine industry. Developing an understanding of the important epidemiological characteristics and disease transmission dynamics of a novel pathogen such as PEDV is critical for helping to guide the implementation of effective, efficient, and economically feasible disease control and prevention strategies that are able to help decrease the impact of an outbreak.

Psychometric Evaluation of a Cultural Competency Assessment Instrument for Health Professionals

PubMed Central

Haywood, Sonja H.; Goode, Tawara; Gao, Yong; Smith, Kristyn; Bronheim, Suzanne; Flocke, Susan A; Zyzanski, Steve

2012-01-01

Background Few valid and reliable measures exist for health care professionals interested in determining their levels of cultural and linguistic competence. Objective To evaluate the measurement properties of the Cultural Competence Health Practitioner Assessment (CCHPA-129). Methods The CCHPA-129 is a 129-item web-based instrument, developed by the National Center for Cultural Competence (NCCC). Responses on the CCHPA -129 were examined using factor analysis; Rasch modeling; and Differential Item Functioning (DIF) across race, ethnicity, gender, and profession. Subjects 2504 practitioners, including 1864 nurses (RN/LPN,/BSN); 341 clinicians (PA/NP); and 299 physicians (MD/DO), who completed the CCHPA-129 online between 2005 and 2008. Results Three factors representing domains of knowledge, adapting practice, and promoting health for culturally and linguistically diverse populations accounted for 46% of the variance. Among Knowledge factor items, 53% (23/43) fit the Rasch model, item difficulties ranged from −1.01 logits (least difficult) to +1.11 logits (most difficult), separation index (SI) 13.82, and Cronbach’s α 0.92. Forty-seven percent (21/44) Adapting Practice factor items fit the model, item difficulties −0.07 to +1.11 logits, SI 11.59, Cronbach’s α 0.88; and 58% (23/39). Promoting Health factor items fit the model, item difficulties −1.01 to +1.38 logits, SI 22.64, Cronbach’s α 0.92. Early evidence of validity was established by known groups having statistically different scores. Conclusion The 67-item CCHPA-67 is psychometrically sound. This shorted instrument can be used to establish associations between practitioners’ cultural and linguistic competence and health outcomes as well as to evaluate interventions to increase practitioners’ cultural and linguistic competence. PMID:22437625

Practical Consequences of Item Response Theory Model Misfit in the Context of Test Equating with Mixed-Format Test Data

PubMed Central

Zhao, Yue; Hambleton, Ronald K.

2017-01-01

In item response theory (IRT) models, assessing model-data fit is an essential step in IRT calibration. While no general agreement has ever been reached on the best methods or approaches to use for detecting misfit, perhaps the more important comment based upon the research findings is that rarely does the research evaluate IRT misfit by focusing on the practical consequences of misfit. The study investigated the practical consequences of IRT model misfit in examining the equating performance and the classification of examinees into performance categories in a simulation study that mimics a typical large-scale statewide assessment program with mixed-format test data. The simulation study was implemented by varying three factors, including choice of IRT model, amount of growth/change of examinees’ abilities between two adjacent administration years, and choice of IRT scaling methods. Findings indicated that the extent of significant consequences of model misfit varied over the choice of model and IRT scaling methods. In comparison with mean/sigma (MS) and Stocking and Lord characteristic curve (SL) methods, separate calibration with linking and fixed common item parameter (FCIP) procedure was more sensitive to model misfit and more robust against various amounts of ability shifts between two adjacent administrations regardless of model fit. SL was generally the least sensitive to model misfit in recovering equating conversion and MS was the least robust against ability shifts in recovering the equating conversion when a substantial degree of misfit was present. The key messages from the study are that practical ways are available to study model fit, and, model fit or misfit can have consequences that should be considered when choosing an IRT model. Not only does the study address the consequences of IRT model misfit, but also it is our hope to help researchers and practitioners find practical ways to study model fit and to investigate the validity of particular IRT models for achieving a specified purpose, to assure that the successful use of the IRT models are realized, and to improve the applications of IRT models with educational and psychological test data. PMID:28421011

A novel method to assess primary stability of press-fit acetabular cups.

PubMed

Crosnier, Emilie A; Keogh, Patrick S; Miles, Anthony W

2014-11-01

Initial stability is an essential prerequisite to achieve osseointegration of press-fit acetabular cups in total hip replacements. Most in vitro methods that assess cup stability do not reproduce physiological loading conditions and use simplified acetabular models with a spherical cavity. The aim of this study was to investigate the effect of bone density and acetabular geometry on cup stability using a novel method for measuring acetabular cup micromotion. A press-fit cup was inserted into Sawbones(®) foam blocks having different densities to simulate normal and osteoporotic bone variations and different acetabular geometries. The stability of the cup was assessed in two ways: (a) measurement of micromotion of the cup in 6 degrees of freedom under physiological loading and (b) uniaxial push-out tests. The results indicate that changes in bone substrate density and acetabular geometry affect the stability of press-fit acetabular cups. They also suggest that cups implanted into weaker, for example, osteoporotic, bone are subjected to higher levels of micromotion and are therefore more prone to loosening. The decrease in stability of the cup in the physiological model suggests that using simplified spherical cavities to model the acetabulum over-estimates the initial stability of press-fit cups. This novel testing method should provide the basis for a more representative protocol for future pre-clinical evaluation of new acetabular cup designs. © IMechE 2014.

Elevation data fitting and precision analysis of Google Earth in road survey

NASA Astrophysics Data System (ADS)

Wei, Haibin; Luan, Xiaohan; Li, Hanchao; Jia, Jiangkun; Chen, Zhao; Han, Leilei

2018-05-01

Objective: In order to improve efficiency of road survey and save manpower and material resources, this paper intends to apply Google Earth to the feasibility study stage of road survey and design. Limited by the problem that Google Earth elevation data lacks precision, this paper is focused on finding several different fitting or difference methods to improve the data precision, in order to make every effort to meet the accuracy requirements of road survey and design specifications. Method: On the basis of elevation difference of limited public points, any elevation difference of the other points can be fitted or interpolated. Thus, the precise elevation can be obtained by subtracting elevation difference from the Google Earth data. Quadratic polynomial surface fitting method, cubic polynomial surface fitting method, V4 interpolation method in MATLAB and neural network method are used in this paper to process elevation data of Google Earth. And internal conformity, external conformity and cross correlation coefficient are used as evaluation indexes to evaluate the data processing effect. Results: There is no fitting difference at the fitting point while using V4 interpolation method. Its external conformity is the largest and the effect of accuracy improvement is the worst, so V4 interpolation method is ruled out. The internal and external conformity of the cubic polynomial surface fitting method both are better than those of the quadratic polynomial surface fitting method. The neural network method has a similar fitting effect with the cubic polynomial surface fitting method, but its fitting effect is better in the case of a higher elevation difference. Because the neural network method is an unmanageable fitting model, the cubic polynomial surface fitting method should be mainly used and the neural network method can be used as the auxiliary method in the case of higher elevation difference. Conclusions: Cubic polynomial surface fitting method can obviously improve data precision of Google Earth. The error of data in hilly terrain areas meets the requirement of specifications after precision improvement and it can be used in feasibility study stage of road survey and design.

An evaluation of the Bayesian approach to fitting the N-mixture model for use with pseudo-replicated count data

USGS Publications Warehouse

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.

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

PubMed

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

2018-01-01

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

The Very Essentials of Fitness for Trial Assessment in Canada

ERIC Educational Resources Information Center

Newby, Diana; Faltin, Robert

2008-01-01

Fitness for trial constitutes the most frequent referral to forensic assessment services. Several approaches to this evaluation exist in Canada, including the Fitness Interview Test and Basic Fitness for Trial Test. The following article presents a review of the issues and a method for basic fitness for trial evaluation.

Mixed-Methods Evaluation of a Healthy Exercise, Eating, and Lifestyle Program for Primary Schools

ERIC Educational Resources Information Center

Cochrane, Thomas; Davey, Rachel C.

2017-01-01

BAckground: Reversing decline in physical fitness and increase in excess body weight in school children are considered major public health challenges. We evaluated a proposed model to integrate a screening and healthy exercise, eating, and lifestyle program (HEELP) into primary schools in Canberra, Australia. Objectives were: (1) to establish body…

Predicting recycling behaviour: Comparison of a linear regression model and a fuzzy logic model.

PubMed

Vesely, Stepan; Klöckner, Christian A; Dohnal, Mirko

2016-03-01

In this paper we demonstrate that fuzzy logic can provide a better tool for predicting recycling behaviour than the customarily used linear regression. To show this, we take a set of empirical data on recycling behaviour (N=664), which we randomly divide into two halves. The first half is used to estimate a linear regression model of recycling behaviour, and to develop a fuzzy logic model of recycling behaviour. As the first comparison, the fit of both models to the data included in estimation of the models (N=332) is evaluated. As the second comparison, predictive accuracy of both models for "new" cases (hold-out data not included in building the models, N=332) is assessed. In both cases, the fuzzy logic model significantly outperforms the regression model in terms of fit. To conclude, when accurate predictions of recycling and possibly other environmental behaviours are needed, fuzzy logic modelling seems to be a promising technique. Copyright © 2015 Elsevier Ltd. All rights reserved.

Evaluating a federated medical search engine: tailoring the methodology and reporting the evaluation outcomes.

PubMed

Saparova, D; Belden, J; Williams, J; Richardson, B; Schuster, K

2014-01-01

Federated medical search engines are health information systems that provide a single access point to different types of information. Their efficiency as clinical decision support tools has been demonstrated through numerous evaluations. Despite their rigor, very few of these studies report holistic evaluations of medical search engines and even fewer base their evaluations on existing evaluation frameworks. To evaluate a federated medical search engine, MedSocket, for its potential net benefits in an established clinical setting. This study applied the Human, Organization, and Technology (HOT-fit) evaluation framework in order to evaluate MedSocket. The hierarchical structure of the HOT-factors allowed for identification of a combination of efficiency metrics. Human fit was evaluated through user satisfaction and patterns of system use; technology fit was evaluated through the measurements of time-on-task and the accuracy of the found answers; and organization fit was evaluated from the perspective of system fit to the existing organizational structure. Evaluations produced mixed results and suggested several opportunities for system improvement. On average, participants were satisfied with MedSocket searches and confident in the accuracy of retrieved answers. However, MedSocket did not meet participants' expectations in terms of download speed, access to information, and relevance of the search results. These mixed results made it necessary to conclude that in the case of MedSocket, technology fit had a significant influence on the human and organization fit. Hence, improving technological capabilities of the system is critical before its net benefits can become noticeable. The HOT-fit evaluation framework was instrumental in tailoring the methodology for conducting a comprehensive evaluation of the search engine. Such multidimensional evaluation of the search engine resulted in recommendations for system improvement.

Improved accuracy and precision of tracer kinetic parameters by joint fitting to variable flip angle and dynamic contrast enhanced MRI data.

PubMed

Dickie, Ben R; Banerji, Anita; Kershaw, Lucy E; McPartlin, Andrew; Choudhury, Ananya; West, Catharine M; Rose, Chris J

2016-10-01

To improve the accuracy and precision of tracer kinetic model parameter estimates for use in dynamic contrast enhanced (DCE) MRI studies of solid tumors. Quantitative DCE-MRI requires an estimate of precontrast T1 , which is obtained prior to fitting a tracer kinetic model. As T1 mapping and tracer kinetic signal models are both a function of precontrast T1 it was hypothesized that its joint estimation would improve the accuracy and precision of both precontrast T1 and tracer kinetic model parameters. Accuracy and/or precision of two-compartment exchange model (2CXM) parameters were evaluated for standard and joint fitting methods in well-controlled synthetic data and for 36 bladder cancer patients. Methods were compared under a number of experimental conditions. In synthetic data, joint estimation led to statistically significant improvements in the accuracy of estimated parameters in 30 of 42 conditions (improvements between 1.8% and 49%). Reduced accuracy was observed in 7 of the remaining 12 conditions. Significant improvements in precision were observed in 35 of 42 conditions (between 4.7% and 50%). In clinical data, significant improvements in precision were observed in 18 of 21 conditions (between 4.6% and 38%). Accuracy and precision of DCE-MRI parameter estimates are improved when signal models are fit jointly rather than sequentially. Magn Reson Med 76:1270-1281, 2016. © 2015 Wiley Periodicals, Inc. © 2015 Wiley Periodicals, Inc.

The effect of different screw-tightening techniques on the strain generated on an internal-connection implant superstructure. Part 2: Models created with a splinted impression technique.

PubMed

Choi, Jung-Han

2011-01-01

This study aimed to evaluate the effect of different screw-tightening sequences, torques, and methods on the strains generated on an internal-connection implant (Astra Tech) superstructure with good fit. An edentulous mandibular master model and a metal framework directly connected to four parallel implants with a passive fit to each other were fabricated. Six stone casts were made from a dental stone master model by a splinted impression technique to represent a well-fitting situation with the metal framework. Strains generated by four screw-tightening sequences (1-2-3-4, 4-3-2-1, 2-4-3-1, and 2-3-1-4), two torques (10 and 20 Ncm), and two methods (one-step and two-step) were evaluated. In the two-step method, screws were tightened to the initial torque (10 Ncm) in a predetermined screw-tightening sequence and then to the final torque (20 Ncm) in the same sequence. Strains were recorded twice by three strain gauges attached to the framework (superior face midway between abutments). Deformation data were analyzed using multiple analysis of variance at a .05 level of statistical significance. In all stone casts, strains were produced by connection of the superstructure, regardless of screw-tightening sequence, torque, and method. No statistically significant differences in superstructure strains were found based on screw-tightening sequences (range, -409.8 to -413.8 μm/m), torques (-409.7 and -399.1 μm/m), or methods (-399.1 and -410.3 μm/m). Within the limitations of this in vitro study, screw-tightening sequence, torque, and method were not critical factors for the strain generated on a well-fitting internal-connection implant superstructure by the splinted impression technique. Further studies are needed to evaluate the effect of screw-tightening techniques on the preload stress in various different clinical situations.

Quantitative evaluation of dual-flip-angle T1 mapping on DCE-MRI kinetic parameter estimation in head and neck

PubMed Central

Chow, Steven Kwok Keung; Yeung, David Ka Wai; Ahuja, Anil T; King, Ann D

2012-01-01

Purpose To quantitatively evaluate the kinetic parameter estimation for head and neck (HN) dynamic contrast-enhanced (DCE) MRI with dual-flip-angle (DFA) T1 mapping. Materials and methods Clinical DCE-MRI datasets of 23 patients with HN tumors were included in this study. T1 maps were generated based on multiple-flip-angle (MFA) method and different DFA combinations. Tofts model parameter maps of kep, Ktrans and vp based on MFA and DFAs were calculated and compared. Fitted parameter by MFA and DFAs were quantitatively evaluated in primary tumor, salivary gland and muscle. Results T1 mapping deviations by DFAs produced remarkable kinetic parameter estimation deviations in head and neck tissues. In particular, the DFA of [2º, 7º] overestimated, while [7º, 12º] and [7º, 15º] underestimated Ktrans and vp, significantly (P<0.01). [2º, 15º] achieved the smallest but still statistically significant overestimation for Ktrans and vp in primary tumors, 32.1% and 16.2% respectively. kep fitting results by DFAs were relatively close to the MFA reference compared to Ktrans and vp. Conclusions T1 deviations induced by DFA could result in significant errors in kinetic parameter estimation, particularly Ktrans and vp, through Tofts model fitting. MFA method should be more reliable and robust for accurate quantitative pharmacokinetic analysis in head and neck. PMID:23289084

Evaluating the relationship between job stress and job satisfaction among female hospital nurses in Babol: An application of structural equation modeling.

PubMed

Bagheri Hosseinabadi, Majid; Etemadinezhad, Siavash; Khanjani, Narges; Ahmadi, Omran; Gholinia, Hemat; Galeshi, Mina; Samaei, Seyed Ehsan

2018-01-01

Background: This study was designed to investigate job satisfaction and its relation to perceived job stress among hospital nurses in Babol County, Iran. Methods: This cross-sectional study was conducted on 406 female nurses in 6 Babol hospitals. Respondents completed the Minnesota Satisfaction Questionnaire (MSQ), the health and safety executive (HSE) indicator tool and a demographic questionnaire. Descriptive, analytical and structural equation modeling (SEM) analyses were carried out applying SPSS v. 22 and AMOS v. 22. Results: The Normed Fit Index (NFI), Non-normed Fit Index (NNFI), Incremental Fit Index (IFI)and Comparative Fit Index (CFI) were greater than 0.9. Also, goodness of fit index (GFI=0.99)and adjusted goodness of fit index (AGFI) were greater than 0.8, and root mean square error of approximation (RMSEA) were 0.04, The model was found to be with an appropriate fit. The R-squared was 0.42 for job satisfaction, and all its dimensions were related to job stress. The dimensions of job stress explained 42% of changes in the variance of job satisfaction. There was a significant relationship between the dimensions of job stress such as demand (β =0.173,CI =0.095 - 0.365, P≤0.001), control (β =0.135, CI =0.062 - 0.404, P =0.008), relationships(β =-0.208, CI =-0.637- -0.209; P≤0.001) and changes (β =0.247, CI =0.360 - 1.026, P≤0.001)with job satisfaction. Conclusion: One of the important interventions to increase job satisfaction among nurses maybe improvement in the workplace. Reducing the level of workload in order to improve job demand and minimizing role conflict through reducing conflicting demands are recommended.

Evaluating the relationship between job stress and job satisfaction among female hospital nurses in Babol: An application of structural equation modeling

PubMed Central

Bagheri Hosseinabadi, Majid; Etemadinezhad, Siavash; khanjani, Narges; Ahmadi, Omran; Gholinia, Hemat; Galeshi, Mina; Samaei, Seyed Ehsan

2018-01-01

Background: This study was designed to investigate job satisfaction and its relation to perceived job stress among hospital nurses in Babol County, Iran. Methods: This cross-sectional study was conducted on 406 female nurses in 6 Babol hospitals. Respondents completed the Minnesota Satisfaction Questionnaire (MSQ), the health and safety executive (HSE) indicator tool and a demographic questionnaire. Descriptive, analytical and structural equation modeling (SEM) analyses were carried out applying SPSS v. 22 and AMOS v. 22. Results: The Normed Fit Index (NFI), Non-normed Fit Index (NNFI), Incremental Fit Index (IFI)and Comparative Fit Index (CFI) were greater than 0.9. Also, goodness of fit index (GFI=0.99)and adjusted goodness of fit index (AGFI) were greater than 0.8, and root mean square error of approximation (RMSEA) were 0.04, The model was found to be with an appropriate fit. The R-squared was 0.42 for job satisfaction, and all its dimensions were related to job stress. The dimensions of job stress explained 42% of changes in the variance of job satisfaction. There was a significant relationship between the dimensions of job stress such as demand (β =0.173,CI =0.095 - 0.365, P≤0.001), control (β =0.135, CI =0.062 - 0.404, P =0.008), relationships(β =-0.208, CI =-0.637– -0.209; P≤0.001) and changes (β =0.247, CI =0.360 - 1.026, P≤0.001)with job satisfaction. Conclusion: One of the important interventions to increase job satisfaction among nurses maybe improvement in the workplace. Reducing the level of workload in order to improve job demand and minimizing role conflict through reducing conflicting demands are recommended. PMID:29744305

Rates and risk factors of injury in CrossFitTM: a prospective cohort study.

PubMed

Moran, Sebastian; Booker, Harry; Staines, Jacob; Williams, Sean

2017-09-01

CrossFitTM is a strength and conditioning program that has gained widespread popularity since its inception approximately 15 years ago. However, at present little is known about the level of injury risk associated with this form of training. Movement competency, assessed using the Functional Movement ScreenTM (FMS), has been identified as a risk factor for injury in numerous athletic populations, but its role in CrossFit participants is currently unclear. The aim of this study was to evaluate the level of injury risk associated with CrossFit training, and examine the influence of a number of potential risk factors (including movement competency). A cohort of 117 CrossFit participants were followed prospectively for 12 weeks. Participants' characteristics, previous injury history and training experience were recorded at baseline, and an FMS assessment was conducted. The overall injury incidence rate was 2.10 per 1000 training hours (90% confidence limits: 1.32-3.33). A multivariate Poisson regression model identified males (rate ratio [RR]: 4.44 ×/÷ 3.30, very likely harmful) and those with previous injuries (RR: 2.35 ×/÷ 2.37, likely harmful) as having a higher injury risk. Inferences relating to FMS variables were unclear in the multivariate model, although number of asymmetries was a clear risk factor in a univariate model (RR per two additional asymmetries: 2.62 ×/÷ 1.53, likely harmful). The injury incidence rate associated with CrossFit training was low, and comparable to other forms of recreational fitness activities. Previous injury and gender were identified as risk factors for injury, whilst the role of movement competency in this setting warrants further investigation.

The classification of body dysmorphic disorder symptoms in male and female adolescents.

PubMed

Schneider, Sophie C; Baillie, Andrew J; Mond, Jonathan; Turner, Cynthia M; Hudson, Jennifer L

2018-01-01

Body dysmorphic disorder (BDD) was categorised in DSM-5 within the newly created 'obsessive-compulsive and related disorders' chapter, however this classification remains subject to debate. Confirmatory factor analysis was used to test competing models of the co-occurrence of symptoms of BDD, obsessive-compulsive disorder, unipolar depression, anxiety, and eating disorders in a community sample of adolescents, and to explore potential sex differences in these models. Self-report questionnaires assessing disorder symptoms were completed by 3149 Australian adolescents. The fit of correlated factor models was calculated separately in males and females, and measurement invariance testing compared parameters of the best-fitting model between males and females. All theoretical models of the classification of BDD had poor fit to the data. Good fit was found for a novel model where BDD symptoms formed a distinct latent factor, correlated with affective disorder and eating disorder latent factors. Metric non-invariance was found between males and females, and the majority of factor loadings differed between males and females. Correlations between some latent factors also differed by sex. Only cross-sectional data were collected, and the study did not assess a broad range of DSM-5 defined eating disorder symptoms or other disorders in the DSM-5 obsessive-compulsive and related disorders chapter. This study is the first to statistically evaluate competing models of BDD classification. The findings highlight the unique features of BDD and its associations with affective and eating disorders. Future studies examining the classification of BDD should consider developmental and sex differences in their models. Copyright © 2017. Published by Elsevier B.V.

Density dependence and risk of extinction in a small population of sea otters

USGS Publications Warehouse

Gerber, L.R.; Buenau, K.E.; VanBlaricom, G.

2004-01-01

Sea otters (Enhydra lutris (L.)) were hunted to extinction off the coast of Washington State early in the 20th century. A new population was established by translocations from Alaska in 1969 and 1970. The population, currently numbering at least 550 animals, A major threat to the population is the ongoing risk of majour oil spills in sea otter habitat. We apply population models to census and demographic data in order to evaluate the status of the population. We fit several density dependent models to test for density dependence and determine plausible values for the carrying capacity (K) by comparing model goodness of fit to an exponential model. Model fits were compared using Akaike Information Criterion (AIC). A significant negative relationship was found between the population growth rate and population size (r2=0.27, F=5.57, df=16, p<0.05), suggesting density dependence in Washington state sea otters. Information criterion statistics suggest that the model is the most parsimonious, followed closely by the logistic Beverton-Holt model. Values of K ranged from 612 to 759 with best-fit parameter estimates for the Beverton-Holt model including 0.26 for r and 612 for K. The latest (2001) population index count (555) puts the population at 87-92% of the estimated carrying capacity, above the suggested range for optimum sustainable population (OSP). Elasticity analysis was conducted to examine the effects of proportional changes in vital rates on the population growth rate (??). The elasticity values indicate the population is most sensitive to changes in survival rates (particularly adult survival).

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

NASA Technical Reports Server (NTRS)

Gross, Bernard

1996-01-01

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

A Bayesian model averaging method for the derivation of reservoir operating rules

NASA Astrophysics Data System (ADS)

Zhang, Jingwen; Liu, Pan; Wang, Hao; Lei, Xiaohui; Zhou, Yanlai

2015-09-01

Because the intrinsic dynamics among optimal decision making, inflow processes and reservoir characteristics are complex, functional forms of reservoir operating rules are always determined subjectively. As a result, the uncertainty of selecting form and/or model involved in reservoir operating rules must be analyzed and evaluated. In this study, we analyze the uncertainty of reservoir operating rules using the Bayesian model averaging (BMA) model. Three popular operating rules, namely piecewise linear regression, surface fitting and a least-squares support vector machine, are established based on the optimal deterministic reservoir operation. These individual models provide three-member decisions for the BMA combination, enabling the 90% release interval to be estimated by the Markov Chain Monte Carlo simulation. A case study of China's the Baise reservoir shows that: (1) the optimal deterministic reservoir operation, superior to any reservoir operating rules, is used as the samples to derive the rules; (2) the least-squares support vector machine model is more effective than both piecewise linear regression and surface fitting; (3) BMA outperforms any individual model of operating rules based on the optimal trajectories. It is revealed that the proposed model can reduce the uncertainty of operating rules, which is of great potential benefit in evaluating the confidence interval of decisions.

Calibration and Validation of the Dutch-Flemish PROMIS Pain Interference Item Bank in Patients with Chronic Pain

PubMed Central

Crins, Martine H. P.; Roorda, Leo D.; Smits, Niels; de Vet, Henrica C. W.; Westhovens, Rene; Cella, David; Cook, Karon F.; Revicki, Dennis; van Leeuwen, Jaap; Boers, Maarten; Dekker, Joost; Terwee, Caroline B.

2015-01-01

The Dutch-Flemish PROMIS Group translated the adult PROMIS Pain Interference item bank into Dutch-Flemish. The aims of the current study were to calibrate the parameters of these items using an item response theory (IRT) model, to evaluate the cross-cultural validity of the Dutch-Flemish translations compared to the original English items, and to evaluate their reliability and construct validity. The 40 items in the bank were completed by 1085 Dutch chronic pain patients. Before calibrating the items, IRT model assumptions were evaluated using confirmatory factor analysis (CFA). Items were calibrated using the graded response model (GRM), an IRT model appropriate for items with more than two response options. To evaluate cross-cultural validity, differential item functioning (DIF) for language (Dutch vs. English) was examined. Reliability was evaluated based on standard errors and Cronbach’s alpha. To evaluate construct validity correlations with scores on legacy instruments (e.g., the Disabilities of the Arm, Shoulder and Hand Questionnaire) were calculated. Unidimensionality of the Dutch-Flemish PROMIS Pain Interference item bank was supported by CFA tests of model fit (CFI = 0.986, TLI = 0.986). Furthermore, the data fit the GRM and showed good coverage across the pain interference continuum (threshold-parameters range: -3.04 to 3.44). The Dutch-Flemish PROMIS Pain Interference item bank has good cross-cultural validity (only two out of 40 items showing DIF), good reliability (Cronbach’s alpha = 0.98), and good construct validity (Pearson correlations between 0.62 and 0.75). A computer adaptive test (CAT) and Dutch-Flemish PROMIS short forms of the Dutch-Flemish PROMIS Pain Interference item bank can now be developed. PMID:26214178

Calibration and Validation of the Dutch-Flemish PROMIS Pain Interference Item Bank in Patients with Chronic Pain.

PubMed

Crins, Martine H P; Roorda, Leo D; Smits, Niels; de Vet, Henrica C W; Westhovens, Rene; Cella, David; Cook, Karon F; Revicki, Dennis; van Leeuwen, Jaap; Boers, Maarten; Dekker, Joost; Terwee, Caroline B

2015-01-01

The Dutch-Flemish PROMIS Group translated the adult PROMIS Pain Interference item bank into Dutch-Flemish. The aims of the current study were to calibrate the parameters of these items using an item response theory (IRT) model, to evaluate the cross-cultural validity of the Dutch-Flemish translations compared to the original English items, and to evaluate their reliability and construct validity. The 40 items in the bank were completed by 1085 Dutch chronic pain patients. Before calibrating the items, IRT model assumptions were evaluated using confirmatory factor analysis (CFA). Items were calibrated using the graded response model (GRM), an IRT model appropriate for items with more than two response options. To evaluate cross-cultural validity, differential item functioning (DIF) for language (Dutch vs. English) was examined. Reliability was evaluated based on standard errors and Cronbach's alpha. To evaluate construct validity correlations with scores on legacy instruments (e.g., the Disabilities of the Arm, Shoulder and Hand Questionnaire) were calculated. Unidimensionality of the Dutch-Flemish PROMIS Pain Interference item bank was supported by CFA tests of model fit (CFI = 0.986, TLI = 0.986). Furthermore, the data fit the GRM and showed good coverage across the pain interference continuum (threshold-parameters range: -3.04 to 3.44). The Dutch-Flemish PROMIS Pain Interference item bank has good cross-cultural validity (only two out of 40 items showing DIF), good reliability (Cronbach's alpha = 0.98), and good construct validity (Pearson correlations between 0.62 and 0.75). A computer adaptive test (CAT) and Dutch-Flemish PROMIS short forms of the Dutch-Flemish PROMIS Pain Interference item bank can now be developed.

Evaluating a Federated Medical Search Engine

PubMed Central

Belden, J.; Williams, J.; Richardson, B.; Schuster, K.

2014-01-01

Summary Background Federated medical search engines are health information systems that provide a single access point to different types of information. Their efficiency as clinical decision support tools has been demonstrated through numerous evaluations. Despite their rigor, very few of these studies report holistic evaluations of medical search engines and even fewer base their evaluations on existing evaluation frameworks. Objectives To evaluate a federated medical search engine, MedSocket, for its potential net benefits in an established clinical setting. Methods This study applied the Human, Organization, and Technology (HOT-fit) evaluation framework in order to evaluate MedSocket. The hierarchical structure of the HOT-factors allowed for identification of a combination of efficiency metrics. Human fit was evaluated through user satisfaction and patterns of system use; technology fit was evaluated through the measurements of time-on-task and the accuracy of the found answers; and organization fit was evaluated from the perspective of system fit to the existing organizational structure. Results Evaluations produced mixed results and suggested several opportunities for system improvement. On average, participants were satisfied with MedSocket searches and confident in the accuracy of retrieved answers. However, MedSocket did not meet participants’ expectations in terms of download speed, access to information, and relevance of the search results. These mixed results made it necessary to conclude that in the case of MedSocket, technology fit had a significant influence on the human and organization fit. Hence, improving technological capabilities of the system is critical before its net benefits can become noticeable. Conclusions The HOT-fit evaluation framework was instrumental in tailoring the methodology for conducting a comprehensive evaluation of the search engine. Such multidimensional evaluation of the search engine resulted in recommendations for system improvement. PMID:25298813

Two-Drug Antimicrobial Chemotherapy: A Mathematical Model and Experiments with Mycobacterium marinum

PubMed Central

Ankomah, Peter; Levin, Bruce R.

2012-01-01

Multi-drug therapy is the standard-of-care treatment for tuberculosis. Despite this, virtually all studies of the pharmacodynamics (PD) of mycobacterial drugs employed for the design of treatment protocols are restricted to single agents. In this report, mathematical models and in vitro experiments with Mycobacterium marinum and five antimycobacterial drugs are used to quantitatively evaluate the pharmaco-, population and evolutionary dynamics of two-drug antimicrobial chemotherapy regimes. Time kill experiments with single and pairs of antibiotics are used to estimate the parameters and evaluate the fit of Hill-function-based PD models. While Hill functions provide excellent fits for the PD of each single antibiotic studied, rifampin, amikacin, clarithromycin, streptomycin and moxifloxacin, two-drug Hill functions with a unique interaction parameter cannot account for the PD of any of the 10 pairs of these drugs. If we assume two antibiotic-concentration dependent functions for the interaction parameter, one for sub-MIC and one for supra-MIC drug concentrations, the modified biphasic Hill function provides a reasonably good fit for the PD of all 10 pairs of antibiotics studied. Monte Carlo simulations of antibiotic treatment based on the experimentally-determined PD functions are used to evaluate the potential microbiological efficacy (rate of clearance) and evolutionary consequences (likelihood of generating multi-drug resistance) of these different drug combinations as well as their sensitivity to different forms of non-adherence to therapy. These two-drug treatment simulations predict varying outcomes for the different pairs of antibiotics with respect to the aforementioned measures of efficacy. In summary, Hill functions with biphasic drug-drug interaction terms provide accurate analogs for the PD of pairs of antibiotics and M. marinum. The models, experimental protocols and computer simulations used in this study can be applied to evaluate the potential microbiological and evolutionary efficacy of two-drug therapy for any bactericidal antibiotics and bacteria that can be cultured in vitro. PMID:22253599

Statistical Power of Alternative Structural Models for Comparative Effectiveness Research: Advantages of Modeling Unreliability.

PubMed

Coman, Emil N; Iordache, Eugen; Dierker, Lisa; Fifield, Judith; Schensul, Jean J; Suggs, Suzanne; Barbour, Russell

2014-05-01

The advantages of modeling the unreliability of outcomes when evaluating the comparative effectiveness of health interventions is illustrated. Adding an action-research intervention component to a regular summer job program for youth was expected to help in preventing risk behaviors. A series of simple two-group alternative structural equation models are compared to test the effect of the intervention on one key attitudinal outcome in terms of model fit and statistical power with Monte Carlo simulations. Some models presuming parameters equal across the intervention and comparison groups were underpowered to detect the intervention effect, yet modeling the unreliability of the outcome measure increased their statistical power and helped in the detection of the hypothesized effect. Comparative Effectiveness Research (CER) could benefit from flexible multi-group alternative structural models organized in decision trees, and modeling unreliability of measures can be of tremendous help for both the fit of statistical models to the data and their statistical power.

Validity and reliability of the Multidimensional Body Image Scale in Malaysian university students.

PubMed

Gan, W Y; Mohd, Nasir M T; Siti, Aishah H; Zalilah, M S

2012-12-01

This study aimed to evaluate the validity and reliability of the Multidimensional Body Image Scale (MBIS), a seven-factor, 62-item scale developed for Malaysian female adolescents. This scale was evaluated among male and female Malaysian university students. A total of 671 university students (52.2% women and 47.8% men) completed a self-administered questionnaire on MBIS, Eating Attitude Test-26, and Rosenberg Self-Esteem Scale. Their height and weight were measured. Results in confirmatory factor analysis showed that the 62-item MBIS reported poor fit to the data, xhi2/df = 4.126, p < 0.001, CFI = 0.808, SRMR = 0.070, RMSEA = 0.068 (90% CI = 0.067, 0.070). After re-specification of the model, the model fit was improved with 46 items remaining, chi2/df = 3.346, p < 0.001, CFI = 0.903, SRMR = 0.053, RMSEA = 0.059 (90% CI = 0.057, 0.061), and the model showed good fit to the data for men and women separately. This 46-item MBIS had good internal consistency in both men (Cronbach's alpha = 0.88) and women (Cronbach's alpha = 0.92). In terms of construct validity, it showed positive correlations with disordered eating and body weight status, but negative correlation with self-esteem. Also, this scale discriminated well between participants with and without disordered eating. The MBIS-46 demonstrated good reliability and validity for the evaluation of body image among university students. Further studies need to be conducted to confirm the validation results of the 46-item MBIS.

Clinical Fit of Partial Removable Dental Prostheses Based on Alginate or Polyvinyl Siloxane Impressions.

PubMed

Fokkinga, Wietske A; Witter, Dick J; Bronkhorst, Ewald M; Creugers, Nico H

The aim of this study was to analyze the clinical fit of metal-frame partial removable dental prostheses (PRDPs) based on custom trays used with alginate or polyvinyl siloxane impression material. Fifth-year students of the Nijmegen Dental School made 25 correct impressions for 23 PRDPs for 21 patients using alginate, and 31 correct impressions for 30 PRDPs for 28 patients using polyvinyl siloxane. Clinical fit of the framework as a whole and of each retainer separately were evaluated by calibrated supervisors during framework try-in before (first evaluation) and after (second evaluation) possible adjustments (score 0 = poor fit, up to score 3 = good fit). Framework fit and fit of the denture base were evaluated at delivery (third evaluation). Finally, postinsertion sessions were evaluated and total number of sessions needed, sore spots, adjustments to the denture base, and reported food-impaction were recorded. No significant differences in clinical fit (of the framework as a whole, for the retainers, or for the denture base) were found between the groups in the three evaluation sessions. Differences were not found for postinsertion sessions with one exception: in the alginate group, four subjects reported food impaction, versus none in the polyvinyl siloxane group. Clinical fit of metal-frame PRDPs based on impressions with custom trays combined with alginate or polyvinyl siloxane was similar.

Emotional intelligence is a second-stratum factor of intelligence: evidence from hierarchical and bifactor models.

PubMed

MacCann, Carolyn; Joseph, Dana L; Newman, Daniel A; Roberts, Richard D

2014-04-01

This article examines the status of emotional intelligence (EI) within the structure of human cognitive abilities. To evaluate whether EI is a 2nd-stratum factor of intelligence, data were fit to a series of structural models involving 3 indicators each for fluid intelligence, crystallized intelligence, quantitative reasoning, visual processing, and broad retrieval ability, as well as 2 indicators each for emotion perception, emotion understanding, and emotion management. Unidimensional, multidimensional, hierarchical, and bifactor solutions were estimated in a sample of 688 college and community college students. Results suggest adequate fit for 2 models: (a) an oblique 8-factor model (with 5 traditional cognitive ability factors and 3 EI factors) and (b) a hierarchical solution (with cognitive g at the highest level and EI representing a 2nd-stratum factor that loads onto g at λ = .80). The acceptable relative fit of the hierarchical model confirms the notion that EI is a group factor of cognitive ability, marking the expression of intelligence in the emotion domain. The discussion proposes a possible expansion of Cattell-Horn-Carroll theory to include EI as a 2nd-stratum factor of similar standing to factors such as fluid intelligence and visual processing.

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

PubMed Central

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

2016-01-01

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

Satisfaction with hearing aids: a consumer research perspective.

PubMed

Wong, Lena L N; Hickson, Louise; McPherson, Bradley

2009-01-01

This research aimed at describing satisfaction with hearing aids from the perspective of the client as a consumer. A disconfirmation-expectancy model, derived from consumer research, was evaluated. This model posits that pre-fitting expectations, post-fitting performance, and the experience of how performance compares to expectations (disconfirmation), contribute to satisfaction. Positive disconfirmation occurs when performance is better than original expectations and is associated with higher satisfaction. Negative disconfirmation is when performance is poorer than expectations and is associated with dissatisfaction. New hearing aid users in Hong Kong (N=42) were tested with a newly developed self-report measure (PHACS: profile of hearing aid consumer satisfaction) that included items focused on hearing ability, problems, cost, and service. Pre-fitting expectations and post-fitting performance, disconfirmation, and satisfaction were measured. Results showed that expectations were generally not related to satisfaction, that disconfirmation was correlated with many aspects of satisfaction, and that performance was most strongly related to satisfaction. The implications of the findings are that hearing aid performance is the most important element for determining satisfaction; however disconfirmation should not be overlooked.

An Occupational Performance Test Validation Program for Fire Fighters at the Kennedy Space Center

NASA Technical Reports Server (NTRS)

Schonfeld, Brian R.; Doerr, Donald F.; Convertino, Victor A.

1990-01-01

We evaluated performance of a modified Combat Task Test (CTT) and of standard fitness tests in 20 male subjects to assess the prediction of occupational performance standards for Kennedy Space Center fire fighters. The CTT consisted of stair-climbing, a chopping simulation, and a victim rescue simulation. Average CTT performance time was 3.61 +/- 0.25 min (SEM) and all CTT tasks required 93% to 97% maximal heart rate. By using scores from the standard fitness tests, a multiple linear regression model was fitted to each parameter: the stairclimb (r(exp 2) = .905, P less than .05), the chopping performance time (r(exp 2) = .582, P less than .05), the victim rescue time (r(exp 2) = .218, P = not significant), and the total performance time (r(exp 2) = .769, P less than .05). Treadmill time was the predominant variable, being the major predictor in two of four models. These results indicated that standardized fitness tests can predict performance on some CTT tasks and that test predictors were amenable to exercise training.

Development and exploration of a new methodology for the fitting and analysis of XAS data.

PubMed

Delgado-Jaime, Mario Ulises; Kennepohl, Pierre

2010-01-01

A new data analysis methodology for X-ray absorption near-edge spectroscopy (XANES) is introduced and tested using several examples. The methodology has been implemented within the context of a new Matlab-based program discussed in a companion related article [Delgado-Jaime et al. (2010), J. Synchrotron Rad. 17, 132-137]. The approach makes use of a Monte Carlo search method to seek appropriate starting points for a fit model, allowing for the generation of a large number of independent fits with minimal user-induced bias. The applicability of this methodology is tested using various data sets on the Cl K-edge XAS data for tetragonal CuCl(4)(2-), a common reference compound used for calibration and covalency estimation in M-Cl bonds. A new background model function that effectively blends together background profiles with spectral features is an important component of the discussed methodology. The development of a robust evaluation function to fit multiple-edge data is discussed and the implications regarding standard approaches to data analysis are discussed and explored within these examples.

Development and exploration of a new methodology for the fitting and analysis of XAS data

PubMed Central

Delgado-Jaime, Mario Ulises; Kennepohl, Pierre

2010-01-01

A new data analysis methodology for X-ray absorption near-edge spectroscopy (XANES) is introduced and tested using several examples. The methodology has been implemented within the context of a new Matlab-based program discussed in a companion related article [Delgado-Jaime et al. (2010 ▶), J. Synchrotron Rad. 17, 132–137]. The approach makes use of a Monte Carlo search method to seek appropriate starting points for a fit model, allowing for the generation of a large number of independent fits with minimal user-induced bias. The applicability of this methodology is tested using various data sets on the Cl K-edge XAS data for tetragonal CuCl4 2−, a common reference compound used for calibration and covalency estimation in M—Cl bonds. A new background model function that effectively blends together background profiles with spectral features is an important component of the discussed methodology. The development of a robust evaluation function to fit multiple-edge data is discussed and the implications regarding standard approaches to data analysis are discussed and explored within these examples. PMID:20029120

Three-dimensional deformable-model-based localization and recognition of road vehicles.

PubMed

Zhang, Zhaoxiang; Tan, Tieniu; Huang, Kaiqi; Wang, Yunhong

2012-01-01

We address the problem of model-based object recognition. Our aim is to localize and recognize road vehicles from monocular images or videos in calibrated traffic scenes. A 3-D deformable vehicle model with 12 shape parameters is set up as prior information, and its pose is determined by three parameters, which are its position on the ground plane and its orientation about the vertical axis under ground-plane constraints. An efficient local gradient-based method is proposed to evaluate the fitness between the projection of the vehicle model and image data, which is combined into a novel evolutionary computing framework to estimate the 12 shape parameters and three pose parameters by iterative evolution. The recovery of pose parameters achieves vehicle localization, whereas the shape parameters are used for vehicle recognition. Numerous experiments are conducted in this paper to demonstrate the performance of our approach. It is shown that the local gradient-based method can evaluate accurately and efficiently the fitness between the projection of the vehicle model and the image data. The evolutionary computing framework is effective for vehicles of different types and poses is robust to all kinds of occlusion.

Fitness, obesity and risk of heat illness among army trainees.

PubMed

Bedno, S A; Urban, N; Boivin, M R; Cowan, D N

2014-09-01

Exertional heat illness (EHI) affects military personnel, athletes and occupational groups such as agricultural workers, despite knowledge of preventive measures. To evaluate EHI diagnoses during US Army basic training and its associations with fitness and body fat on entering military service. From February 2005 to September 2006, US Army recruits at six different military entrance stations took a pre-accession fitness test, including a 5-min step test scored as pass or fail. Subsequent EHI incidence and incidence rate ratios were analysed with reference to subjects' fitness (step test performance) and whether they met (weight qualified [WQ]) or exceeded body fat (EBF) standards. Among the 8621 WQ and 834 EBF male subjects, there were 67 incidents of EHI within 180 days of entering military service. Among WQ subjects, step test failure was significantly associated with EHI (odds ratio [OR] 2.00, 95% confidence interval [CI] 1.13, 3.53). For those passing the step test, the risk of EHI was significantly higher in EBF than in WQ subjects (OR 3.98, 95% CI 2.17, 7.29). Expected ORs for the joint effects of step test failure and EBF classification under additive and multiplicative models were 4.98 and 7.96, respectively. There were too few women to evaluate their data in detail. This study demonstrated that fitness and body fat are independently associated with incident EHI, and the effect of both was substantially higher. Those with low fitness levels and/or obesity should be evaluated further before engaging in intense physical activity, especially in warmer months. Published by Oxford University Press on behalf of the Society of Occupational Medicine 2014.This work is written by (a) US Government employee(s) and is in the public domain in the US.

DSM-5 and ICD-11 as competing models of PTSD in preadolescent children exposed to a natural disaster: assessing validity and co-occurring symptomatology

PubMed Central

La Greca, Annette M.; Danzi, BreAnne A.; Chan, Sherilynn F.

2017-01-01

ABSTRACT Background: Major revisions have been made to the DSM and ICD models of post-traumatic stress disorder (PTSD). However, it is not known whether these models fit children’s post-trauma responses, even though children are a vulnerable population following disasters. Objective: Using data from Hurricane Ike, we examined how well trauma-exposed children’s symptoms fit the DSM-IV, DSM-5 and ICD-11 models, and whether the models varied by gender. We also evaluated whether elevated symptoms of depression and anxiety characterized children meeting PTSD criteria based on DSM-5 and ICD-11. Method: Eight-months post-disaster, children (N = 327, 7–11 years) affected by Hurricane Ike completed measures of PTSD, anxiety and depression. Algorithms approximated a PTSD diagnosis based on DSM-5 and ICD-11 models. Results: Using confirmatory factor analysis, ICD-11 had the best-fitting model, followed by DSM-IV and DSM-5. The ICD-11 model also demonstrated strong measurement invariance across gender. Analyses revealed poor overlap between DSM-5 and ICD-11, although children meeting either set of criteria reported severe PTSD symptoms. Further, children who met PTSD criteria for DSM-5, but not for ICD-11, reported significantly higher levels of depression and general anxiety than children not meeting DSM-5 criteria. Conclusions: Findings support the parsimonious ICD-11 model of PTSD for trauma-exposed children, although adequate fit also was obtained for DSM-5. Use of only one model of PTSD, be it DSM-5 or ICD-11, will likely miss children with significant post-traumatic stress. DSM-5 may identify children with high levels of comorbid symptomatology, which may require additional clinical intervention. PMID:28451076

DSM-5 and ICD-11 as competing models of PTSD in preadolescent children exposed to a natural disaster: assessing validity and co-occurring symptomatology.

PubMed

La Greca, Annette M; Danzi, BreAnne A; Chan, Sherilynn F

2017-01-01

Background : Major revisions have been made to the DSM and ICD models of post-traumatic stress disorder (PTSD). However, it is not known whether these models fit children's post-trauma responses, even though children are a vulnerable population following disasters. Objective : Using data from Hurricane Ike, we examined how well trauma-exposed children's symptoms fit the DSM-IV, DSM-5 and ICD-11 models, and whether the models varied by gender. We also evaluated whether elevated symptoms of depression and anxiety characterized children meeting PTSD criteria based on DSM-5 and ICD-11. Method : Eight-months post-disaster, children ( N  = 327, 7-11 years) affected by Hurricane Ike completed measures of PTSD, anxiety and depression. Algorithms approximated a PTSD diagnosis based on DSM-5 and ICD-11 models. Results : Using confirmatory factor analysis, ICD-11 had the best-fitting model, followed by DSM-IV and DSM-5. The ICD-11 model also demonstrated strong measurement invariance across gender. Analyses revealed poor overlap between DSM-5 and ICD-11, although children meeting either set of criteria reported severe PTSD symptoms. Further, children who met PTSD criteria for DSM-5, but not for ICD-11, reported significantly higher levels of depression and general anxiety than children not meeting DSM-5 criteria. Conclusions : Findings support the parsimonious ICD-11 model of PTSD for trauma-exposed children, although adequate fit also was obtained for DSM-5. Use of only one model of PTSD, be it DSM-5 or ICD-11, will likely miss children with significant post-traumatic stress. DSM-5 may identify children with high levels of comorbid symptomatology, which may require additional clinical intervention.

Modeling the structure of the attitudes and belief scale 2 using CFA and bifactor approaches: Toward the development of an abbreviated version.

PubMed

Hyland, Philip; Shevlin, Mark; Adamson, Gary; Boduszek, Daniel

2014-01-01

The Attitudes and Belief Scale-2 (ABS-2: DiGiuseppe, Leaf, Exner, & Robin, 1988. The development of a measure of rational/irrational thinking. Paper presented at the World Congress of Behavior Therapy, Edinburg, Scotland.) is a 72-item self-report measure of evaluative rational and irrational beliefs widely used in Rational Emotive Behavior Therapy research contexts. However, little psychometric evidence exists regarding the measure's underlying factor structure. Furthermore, given the length of the ABS-2 there is a need for an abbreviated version that can be administered when there are time demands on the researcher, such as in clinical settings. This study sought to examine a series of theoretical models hypothesized to represent the latent structure of the ABS-2 within an alternative models framework using traditional confirmatory factor analysis as well as utilizing a bifactor modeling approach. Furthermore, this study also sought to develop a psychometrically sound abbreviated version of the ABS-2. Three hundred and thirteen (N = 313) active emergency service personnel completed the ABS-2. Results indicated that for each model, the application of bifactor modeling procedures improved model fit statistics, and a novel eight-factor intercorrelated solution was identified as the best fitting model of the ABS-2. However, the observed fit indices failed to satisfy commonly accepted standards. A 24-item abbreviated version was thus constructed and an intercorrelated eight-factor solution yielded satisfactory model fit statistics. Current results support the use of a bifactor modeling approach to determining the factor structure of the ABS-2. Furthermore, results provide empirical support for the psychometric properties of the newly developed abbreviated version.

Why item parcels are (almost) never appropriate: two wrongs do not make a right--camouflaging misspecification with item parcels in CFA models.

PubMed

Marsh, Herbert W; Lüdtke, Oliver; Nagengast, Benjamin; Morin, Alexandre J S; Von Davier, Matthias

2013-09-01

The present investigation has a dual focus: to evaluate problematic practice in the use of item parcels and to suggest exploratory structural equation models (ESEMs) as a viable alternative to the traditional independent clusters confirmatory factor analysis (ICM-CFA) model (with no cross-loadings, subsidiary factors, or correlated uniquenesses). Typically, it is ill-advised to (a) use item parcels when ICM-CFA models do not fit the data, and (b) retain ICM-CFA models when items cross-load on multiple factors. However, the combined use of (a) and (b) is widespread and often provides such misleadingly good fit indexes that applied researchers might believe that misspecification problems are resolved--that 2 wrongs really do make a right. Taking a pragmatist perspective, in 4 studies we demonstrate with responses to the Rosenberg Self-Esteem Inventory (Rosenberg, 1965), Big Five personality factors, and simulated data that even small cross-loadings seriously distort relations among ICM-CFA constructs or even decisions on the number of factors; although obvious in item-level analyses, this is camouflaged by the use of parcels. ESEMs provide a viable alternative to ICM-CFAs and a test for the appropriateness of parcels. The use of parcels with an ICM-CFA model is most justifiable when the fit of both ICM-CFA and ESEM models is acceptable and equally good, and when substantively important interpretations are similar. However, if the ESEM model fits the data better than the ICM-CFA model, then the use of parcels with an ICM-CFA model typically is ill-advised--particularly in studies that are also interested in scale development, latent means, and measurement invariance.

A cross-validation study of the TGMD-2: The case of an adolescent population.

PubMed

Issartel, Johann; McGrane, Bronagh; Fletcher, Richard; O'Brien, Wesley; Powell, Danielle; Belton, Sarahjane

2017-05-01

This study proposes an extension of a widely used test evaluating fundamental movement skills proficiency to an adolescent population, with a specific emphasis on validity and reliability for this older age group. Cross-sectional observational study. A total of 844 participants (n=456 male, 12.03±0.49) participated in this study. The 12 fundamental movement skills of the TGMD-2 were assessed. Inter-rater reliability was examined to ensure a minimum of 95% consistency between coders. Confirmatory factor analysis was undertaken with a one-factor model (all 12 skills) and two-factor model (6 locomotor skills and 6 object-control skills) as proposed by Ulrich et al. (2000). The model fit was examined using χ 2 , TLI, CFI and RMSEA. Test-retest reliability was carried out with a subsample of 35 participants. The test-retest reliability reached Intraclass Correlation Coefficient of 0.78 (locomotor), 0.76 (object related) and 0.91 (gross motor skill proficiency). The confirmatory factor analysis did not display a good fit for either the one-factor or two-factor model due to a really low contribution of several skills. A reduction in the number of skills to just seven (run, gallop, hop, horizontal jump, bounce, kick and roll) revealed an overall good fit by TLI, CFI and RMSEA measures. The proposed new model offers the possibility of longitudinal studies to track the maturation of fundamental movement skills across the child and adolescent spectrum, while also giving researchers a valid assessment to tool to evaluate adolescent fundamental movement skills proficiency level. Copyright © 2016 Sports Medicine Australia. All rights reserved.

Applying the transtheoretical model to promote functional fitness of community older adults participating in elastic band exercises.

PubMed

Yang, Hui-Ju; Chen, Kuei-Min; Chen, Ming-De; Wu, Hui-Chuan; Chang, Wen-Jane; Wang, Yueh-Chin; Huang, Hsin-Ting

2015-10-01

The transtheoretical model was applied to promote behavioural change and test the effects of a group senior elastic band exercise programme on the functional fitness of community older adults in the contemplation and preparation stages of behavioural change. Forming regular exercise habits is challenging for older adults. The transtheoretical model emphasizes using different strategies in various stages to facilitate behavioural changes. Quasi-experimental design with pre-test and post-tests on two groups. Six senior activity centres were randomly assigned to either the experimental or control group. The data were collected during 2011. A total of 199 participants were recruited and 169 participants completed the study (experimental group n = 84, control group n = 85). The elastic band exercises were performed for 40 minutes, three times per week for 6 months. The functional fitness of the participants was evaluated at baseline and at the third and sixth month of the intervention. Statistical analyses included a two-way mixed design analysis of variance, one-way repeated measures analysis of variance and an analysis of covariance. All of the functional fitness indicators had significant changes at post-tests from pre-test in the experimental group. The experimental group had better performances than the control group in all of the functional fitness indicators after three months and 6 months of the senior elastic band exercises. The exercise programme provided older adults with appropriate strategies for maintaining functional fitness, which improved significantly after the participants exercising regularly for 6 months. © 2015 John Wiley & Sons Ltd.

Bayesian spatiotemporal crash frequency models with mixture components for space-time interactions.

PubMed

Cheng, Wen; Gill, Gurdiljot Singh; Zhang, Yongping; Cao, Zhong

2018-03-01

The traffic safety research has developed spatiotemporal models to explore the variations in the spatial pattern of crash risk over time. Many studies observed notable benefits associated with the inclusion of spatial and temporal correlation and their interactions. However, the safety literature lacks sufficient research for the comparison of different temporal treatments and their interaction with spatial component. This study developed four spatiotemporal models with varying complexity due to the different temporal treatments such as (I) linear time trend; (II) quadratic time trend; (III) Autoregressive-1 (AR-1); and (IV) time adjacency. Moreover, the study introduced a flexible two-component mixture for the space-time interaction which allows greater flexibility compared to the traditional linear space-time interaction. The mixture component allows the accommodation of global space-time interaction as well as the departures from the overall spatial and temporal risk patterns. This study performed a comprehensive assessment of mixture models based on the diverse criteria pertaining to goodness-of-fit, cross-validation and evaluation based on in-sample data for predictive accuracy of crash estimates. The assessment of model performance in terms of goodness-of-fit clearly established the superiority of the time-adjacency specification which was evidently more complex due to the addition of information borrowed from neighboring years, but this addition of parameters allowed significant advantage at posterior deviance which subsequently benefited overall fit to crash data. The Base models were also developed to study the comparison between the proposed mixture and traditional space-time components for each temporal model. The mixture models consistently outperformed the corresponding Base models due to the advantages of much lower deviance. For cross-validation comparison of predictive accuracy, linear time trend model was adjudged the best as it recorded the highest value of log pseudo marginal likelihood (LPML). Four other evaluation criteria were considered for typical validation using the same data for model development. Under each criterion, observed crash counts were compared with three types of data containing Bayesian estimated, normal predicted, and model replicated ones. The linear model again performed the best in most scenarios except one case of using model replicated data and two cases involving prediction without including random effects. These phenomena indicated the mediocre performance of linear trend when random effects were excluded for evaluation. This might be due to the flexible mixture space-time interaction which can efficiently absorb the residual variability escaping from the predictable part of the model. The comparison of Base and mixture models in terms of prediction accuracy further bolstered the superiority of the mixture models as the mixture ones generated more precise estimated crash counts across all four models, suggesting that the advantages associated with mixture component at model fit were transferable to prediction accuracy. Finally, the residual analysis demonstrated the consistently superior performance of random effect models which validates the importance of incorporating the correlation structures to account for unobserved heterogeneity. Copyright © 2017 Elsevier Ltd. All rights reserved.

Performance analysis of 60-min to 1-min integration time rain rate conversion models in Malaysia

NASA Astrophysics Data System (ADS)

Ng, Yun-Yann; Singh, Mandeep Singh Jit; Thiruchelvam, Vinesh

2018-01-01

Utilizing the frequency band above 10 GHz is in focus nowadays as a result of the fast expansion of radio communication systems in Malaysia. However, rain fade is the critical factor in attenuation of signal propagation for frequencies above 10 GHz. Malaysia is located in a tropical and equatorial region with high rain intensity throughout the year, and this study will review rain distribution and evaluate the performance of 60-min to 1-min integration time rain rate conversion methods for Malaysia. Several conversion methods such as Segal, Chebil & Rahman, Burgeono, Emiliani, Lavergnat and Gole (LG), Simplified Moupfouma, Joo et al., fourth order polynomial fit and logarithmic model have been chosen to evaluate the performance to predict 1-min rain rate for 10 sites in Malaysia. After the completion of this research, the results show that Chebil & Rahman model, Lavergnat & Gole model, Fourth order polynomial fit and Logarithmic model have shown the best performances in 60-min to 1-min rain rate conversion over 10 sites. In conclusion, it is proven that there is no single model which can claim to perform the best across 10 sites. By averaging RMSE and SC-RMSE over 10 sites, Chebil and Rahman model is the best method.

Novel Virtual User Models of Mild Cognitive Impairment for Simulating Dementia

PubMed Central

Segkouli, Sofia; Tzovaras, Dimitrios; Tsakiris, Thanos; Tsolaki, Magda; Karagiannidis, Charalampos

2015-01-01

Virtual user modeling research has attempted to address critical issues of human-computer interaction (HCI) such as usability and utility through a large number of analytic, usability-oriented approaches as cognitive models in order to provide users with experiences fitting to their specific needs. However, there is demand for more specific modules embodied in cognitive architecture that will detect abnormal cognitive decline across new synthetic task environments. Also, accessibility evaluation of graphical user interfaces (GUIs) requires considerable effort for enhancing ICT products accessibility for older adults. The main aim of this study is to develop and test virtual user models (VUM) simulating mild cognitive impairment (MCI) through novel specific modules, embodied at cognitive models and defined by estimations of cognitive parameters. Well-established MCI detection tests assessed users' cognition, elaborated their ability to perform multitasks, and monitored the performance of infotainment related tasks to provide more accurate simulation results on existing conceptual frameworks and enhanced predictive validity in interfaces' design supported by increased tasks' complexity to capture a more detailed profile of users' capabilities and limitations. The final outcome is a more robust cognitive prediction model, accurately fitted to human data to be used for more reliable interfaces' evaluation through simulation on the basis of virtual models of MCI users. PMID:26339282

Seasonal Influenza Forecasting in Real Time Using the Incidence Decay With Exponential Adjustment Model.

PubMed

Nasserie, Tahmina; Tuite, Ashleigh R; Whitmore, Lindsay; Hatchette, Todd; Drews, Steven J; Peci, Adriana; Kwong, Jeffrey C; Friedman, Dara; Garber, Gary; Gubbay, Jonathan; Fisman, David N

2017-01-01

Seasonal influenza epidemics occur frequently. Rapid characterization of seasonal dynamics and forecasting of epidemic peaks and final sizes could help support real-time decision-making related to vaccination and other control measures. Real-time forecasting remains challenging. We used the previously described "incidence decay with exponential adjustment" (IDEA) model, a 2-parameter phenomenological model, to evaluate the characteristics of the 2015-2016 influenza season in 4 Canadian jurisdictions: the Provinces of Alberta, Nova Scotia and Ontario, and the City of Ottawa. Model fits were updated weekly with receipt of incident virologically confirmed case counts. Best-fit models were used to project seasonal influenza peaks and epidemic final sizes. The 2015-2016 influenza season was mild and late-peaking. Parameter estimates generated through fitting were consistent in the 2 largest jurisdictions (Ontario and Alberta) and with pooled data including Nova Scotia counts (R 0 approximately 1.4 for all fits). Lower R 0 estimates were generated in Nova Scotia and Ottawa. Final size projections that made use of complete time series were accurate to within 6% of true final sizes, but final size was using pre-peak data. Projections of epidemic peaks stabilized before the true epidemic peak, but these were persistently early (~2 weeks) relative to the true peak. A simple, 2-parameter influenza model provided reasonably accurate real-time projections of influenza seasonal dynamics in an atypically late, mild influenza season. Challenges are similar to those seen with more complex forecasting methodologies. Future work includes identification of seasonal characteristics associated with variability in model performance.

Health benefits and cost-effectiveness of a hybrid screening strategy for colorectal cancer.

PubMed

Dinh, Tuan; Ladabaum, Uri; Alperin, Peter; Caldwell, Cindy; Smith, Robert; Levin, Theodore R

2013-09-01

Colorectal cancer (CRC) screening guidelines recommend screening schedules for each single type of test except for concurrent sigmoidoscopy and fecal occult blood test (FOBT). We investigated the cost-effectiveness of a hybrid screening strategy that was based on a fecal immunological test (FIT) and colonoscopy. We conducted a cost-effectiveness analysis by using the Archimedes Model to evaluate the effects of different CRC screening strategies on health outcomes and costs related to CRC in a population that represents members of Kaiser Permanente Northern California. The Archimedes Model is a large-scale simulation of human physiology, diseases, interventions, and health care systems. The CRC submodel in the Archimedes Model was derived from public databases, published epidemiologic studies, and clinical trials. A hybrid screening strategy led to substantial reductions in CRC incidence and mortality, gains in quality-adjusted life years (QALYs), and reductions in costs, comparable with those of the best single-test strategies. Screening by annual FIT of patients 50-65 years old and then a single colonoscopy when they were 66 years old (FIT/COLOx1) reduced CRC incidence by 72% and gained 110 QALYs for every 1000 people during a period of 30 years, compared with no screening. Compared with annual FIT, FIT/COLOx1 gained 1400 QALYs/100,000 persons at an incremental cost of $9700/QALY gained and required 55% fewer FITs. Compared with FIT/COLOx1, colonoscopy at 10-year intervals gained 500 QALYs/100,000 at an incremental cost of $35,100/QALY gained but required 37% more colonoscopies. Over the ranges of parameters examined, the cost-effectiveness of hybrid screening strategies was slightly more sensitive to the adherence rate with colonoscopy than the adherence rate with yearly FIT. Uncertainties associated with estimates of FIT performance within a program setting and sensitivities for flat and right-sided lesions are expected to have significant impacts on the cost-effectiveness results. In our simulation model, a strategy of annual or biennial FIT, beginning when patients are 50 years old, with a single colonoscopy when they are 66 years old, delivers clinical and economic outcomes similar to those of CRC screening by single-modality strategies, with a favorable impact on resources demand. Copyright © 2013 AGA Institute. Published by Elsevier Inc. All rights reserved.

Dissemination Strategies and Devices, Part Four, Final Report for Phase I, Rural Shared Services.

ERIC Educational Resources Information Center

Northern Montana Coll., Havre.

Part Four of a four-part report, designed to identify, synthesize, and evaluate shared services research and development efforts throughout the nation, presents a model to disseminate information concerning shared services information to rural educators. The discussion does not prescribe a "best-fit" model but presents several with an expanded…

Body Image as a Mediator of Non-Suicidal Self-Injury in Adolescents

ERIC Educational Resources Information Center

Muehlenkamp, Jennifer J.; Brausch, Amy M.

2012-01-01

Attitudes towards the body have been largely overlooked as a potential risk factor for adolescent non-suicidal self-injury (NSSI) despite theorizing that a negative body image may play a critical role in the development of this behavior. The current study used structural equation modeling to evaluate the fit of a theoretical model specifying body…

A new risk prediction model for critical care: the Intensive Care National Audit & Research Centre (ICNARC) model.

PubMed

Harrison, David A; Parry, Gareth J; Carpenter, James R; Short, Alasdair; Rowan, Kathy

2007-04-01

To develop a new model to improve risk prediction for admissions to adult critical care units in the UK. Prospective cohort study. The setting was 163 adult, general critical care units in England, Wales, and Northern Ireland, December 1995 to August 2003. Patients were 216,626 critical care admissions. None. The performance of different approaches to modeling physiologic measurements was evaluated, and the best methods were selected to produce a new physiology score. This physiology score was combined with other information relating to the critical care admission-age, diagnostic category, source of admission, and cardiopulmonary resuscitation before admission-to develop a risk prediction model. Modeling interactions between diagnostic category and physiology score enabled the inclusion of groups of admissions that are frequently excluded from risk prediction models. The new model showed good discrimination (mean c index 0.870) and fit (mean Shapiro's R 0.665, mean Brier's score 0.132) in 200 repeated validation samples and performed well when compared with recalibrated versions of existing published risk prediction models in the cohort of patients eligible for all models. The hypothesis of perfect fit was rejected for all models, including the Intensive Care National Audit & Research Centre (ICNARC) model, as is to be expected in such a large cohort. The ICNARC model demonstrated better discrimination and overall fit than existing risk prediction models, even following recalibration of these models. We recommend it be used to replace previously published models for risk adjustment in the UK.

A Simulated Annealing based Optimization Algorithm for Automatic Variogram Model Fitting

NASA Astrophysics Data System (ADS)

Soltani-Mohammadi, Saeed; Safa, Mohammad

2016-09-01

Fitting a theoretical model to an experimental variogram is an important issue in geostatistical studies because if the variogram model parameters are tainted with uncertainty, the latter will spread in the results of estimations and simulations. Although the most popular fitting method is fitting by eye, in some cases use is made of the automatic fitting method on the basis of putting together the geostatistical principles and optimization techniques to: 1) provide a basic model to improve fitting by eye, 2) fit a model to a large number of experimental variograms in a short time, and 3) incorporate the variogram related uncertainty in the model fitting. Effort has been made in this paper to improve the quality of the fitted model by improving the popular objective function (weighted least squares) in the automatic fitting. Also, since the variogram model function (£) and number of structures (m) too affect the model quality, a program has been provided in the MATLAB software that can present optimum nested variogram models using the simulated annealing method. Finally, to select the most desirable model from among the single/multi-structured fitted models, use has been made of the cross-validation method, and the best model has been introduced to the user as the output. In order to check the capability of the proposed objective function and the procedure, 3 case studies have been presented.

Evaluation of the National Research Council (2001) dairy model and derivation of new prediction equations. 1. Digestibility of fiber, fat, protein, and nonfiber carbohydrate.

PubMed

White, R R; Roman-Garcia, Y; Firkins, J L; VandeHaar, M J; Armentano, L E; Weiss, W P; McGill, T; Garnett, R; Hanigan, M D

2017-05-01

Evaluation of ration balancing systems such as the National Research Council (NRC) Nutrient Requirements series is important for improving predictions of animal nutrient requirements and advancing feeding strategies. This work used a literature data set (n = 550) to evaluate predictions of total-tract digested neutral detergent fiber (NDF), fatty acid (FA), crude protein (CP), and nonfiber carbohydrate (NFC) estimated by the NRC (2001) dairy model. Mean biases suggested that the NRC (2001) lactating cow model overestimated true FA and CP digestibility by 26 and 7%, respectively, and under-predicted NDF digestibility by 16%. All NRC (2001) estimates had notable mean and slope biases and large root mean squared prediction error (RMSPE), and concordance (CCC) ranged from poor to good. Predicting NDF digestibility with independent equations for legumes, corn silage, other forages, and nonforage feeds improved CCC (0.85 vs. 0.76) compared with the re-derived NRC (2001) equation form (NRC equation with parameter estimates re-derived against this data set). Separate FA digestion coefficients were derived for different fat supplements (animal fats, oils, and other fat types) and for the basal diet. This equation returned improved (from 0.76 to 0.94) CCC compared with the re-derived NRC (2001) equation form. Unique CP digestibility equations were derived for forages, animal protein feeds, plant protein feeds, and other feeds, which improved CCC compared with the re-derived NRC (2001) equation form (0.74 to 0.85). New NFC digestibility coefficients were derived for grain-specific starch digestibilities, with residual organic matter assumed to be 98% digestible. A Monte Carlo cross-validation was performed to evaluate repeatability of model fit. In this procedure, data were randomly subsetted 500 times into derivation (60%) and evaluation (40%) data sets, and equations were derived using the derivation data and then evaluated against the independent evaluation data. Models derived with random study effects demonstrated poor repeatability of fit in independent evaluation. Similar equations derived without random study effects showed improved fit against independent data and little evidence of biased parameter estimates associated with failure to include study effects. The equations derived in this analysis provide interesting insight into how NDF, starch, FA, and CP digestibilities are affected by intake, feed type, and diet composition. The Authors. Published by the Federation of Animal Science Societies and Elsevier Inc. on behalf of the American Dairy Science Association®. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/3.0/).

KNGEOID14: A national hybrid geoid model in Korea

NASA Astrophysics Data System (ADS)

Kang, S.; Sung, Y. M.; KIM, H.; Kim, Y. S.

2016-12-01

This study describes in brief the construction of a national hybrid geoid model in Korea, KNGEOID14, which can be used as an accurate vertical datum in/around Korea. The hybrid geoid model should be determined by fitting the gravimetric geoid to the geometric geoid undulations from GNSS/Leveling data which were presented the local vertical level. For developing the gravimetric geoid model, we determined all frequency parts (long, middle and short-frequency) of gravimetric geoid using all available data with optimal remove-restore technique based on EGM2008 reference surface. In remove-restore technique, the EGM2008 model to degree 360, RTM reduction method were used for calculating the long, middle and short-frequency part of gravimetric geoid, respectively. A number of gravity data compiled for modeling the middle-frequency part, residual geoid, containing 8,866 points gravity data on land and ocean areas. And, the DEM data gridded by 100m×100m were used for short-frequency part, is the topographic effect on the geoid generated by RTM method. The accuracy of gravimetric geoid model were evaluated by comparison with GNSS/Leveling data was about -0.362m ± 0.055m. Finally, we developed the national hybrid geoid model in Korea, KNGEOID14, corrected to gravimetric geoid with the correction term by fitting the about 1,200 GNSS/Leveling data on Korean bench marks. The correction term is modeled using the difference between GNSS/Leveling derived geoidal heights and gravimetric geoidal heights. The stochastic model used in the calculation of correction term is the LSC technique based on second-order Markov covariance function. The post-fit error (mean and std. dev.) of the KNGEOID14 model was evaluated as 0.001m ± 0.033m. Concerning the result of this study, the accurate orthometric height at any points in Korea will be easily and precisely calculated by combining the geoidal height from KNGEOID14 and ellipsoidal height from GPS observation technique.

Bayesian model evidence as a model evaluation metric

NASA Astrophysics Data System (ADS)

Guthke, Anneli; Höge, Marvin; Nowak, Wolfgang

2017-04-01

When building environmental systems models, we are typically confronted with the questions of how to choose an appropriate model (i.e., which processes to include or neglect) and how to measure its quality. Various metrics have been proposed that shall guide the modeller towards a most robust and realistic representation of the system under study. Criteria for evaluation often address aspects of accuracy (absence of bias) or of precision (absence of unnecessary variance) and need to be combined in a meaningful way in order to address the inherent bias-variance dilemma. We suggest using Bayesian model evidence (BME) as a model evaluation metric that implicitly performs a tradeoff between bias and variance. BME is typically associated with model weights in the context of Bayesian model averaging (BMA). However, it can also be seen as a model evaluation metric in a single-model context or in model comparison. It combines a measure for goodness of fit with a penalty for unjustifiable complexity. Unjustifiable refers to the fact that the appropriate level of model complexity is limited by the amount of information available for calibration. Derived in a Bayesian context, BME naturally accounts for measurement errors in the calibration data as well as for input and parameter uncertainty. BME is therefore perfectly suitable to assess model quality under uncertainty. We will explain in detail and with schematic illustrations what BME measures, i.e. how complexity is defined in the Bayesian setting and how this complexity is balanced with goodness of fit. We will further discuss how BME compares to other model evaluation metrics that address accuracy and precision such as the predictive logscore or other model selection criteria such as the AIC, BIC or KIC. Although computationally more expensive than other metrics or criteria, BME represents an appealing alternative because it provides a global measure of model quality. Even if not applicable to each and every case, we aim at stimulating discussion about how to judge the quality of hydrological models in the presence of uncertainty in general by dissecting the mechanism behind BME.

Evaluating flow cytometer performance with weighted quadratic least squares analysis of LED and multi-level bead data.

PubMed

Parks, David R; El Khettabi, Faysal; Chase, Eric; Hoffman, Robert A; Perfetto, Stephen P; Spidlen, Josef; Wood, James C S; Moore, Wayne A; Brinkman, Ryan R

2017-03-01

We developed a fully automated procedure for analyzing data from LED pulses and multilevel bead sets to evaluate backgrounds and photoelectron scales of cytometer fluorescence channels. The method improves on previous formulations by fitting a full quadratic model with appropriate weighting and by providing standard errors and peak residuals as well as the fitted parameters themselves. Here we describe the details of the methods and procedures involved and present a set of illustrations and test cases that demonstrate the consistency and reliability of the results. The automated analysis and fitting procedure is generally quite successful in providing good estimates of the Spe (statistical photoelectron) scales and backgrounds for all the fluorescence channels on instruments with good linearity. The precision of the results obtained from LED data is almost always better than that from multilevel bead data, but the bead procedure is easy to carry out and provides results good enough for most purposes. Including standard errors on the fitted parameters is important for understanding the uncertainty in the values of interest. The weighted residuals give information about how well the data fits the model, and particularly high residuals indicate bad data points. Known photoelectron scales and measurement channel backgrounds make it possible to estimate the precision of measurements at different signal levels and the effects of compensated spectral overlap on measurement quality. Combining this information with measurements of standard samples carrying dyes of biological interest, we can make accurate comparisons of dye sensitivity among different instruments. Our method is freely available through the R/Bioconductor package flowQB. © 2017 International Society for Advancement of Cytometry. © 2017 International Society for Advancement of Cytometry.

Evaluating the influence of perceived organizational learning capability on user acceptance of information technology among operating room nurse staff.

PubMed

Lee, Chien-Ching; Lin, Shih-Pin; Yang, Shu-Ling; Tsou, Mei-Yung; Chang, Kuang-Yi

2013-03-01

Medical institutions are eager to introduce new information technology to improve patient safety and clinical efficiency. However, the acceptance of new information technology by medical personnel plays a key role in its adoption and application. This study aims to investigate whether perceived organizational learning capability (OLC) is associated with user acceptance of information technology among operating room nurse staff. Nurse anesthetists and operating room nurses were recruited in this questionnaire survey. A pilot study was performed to ensure the reliability and validity of the translated questionnaire, which consisted of 14 items from the four dimensions of OLC, and 16 items from the four constructs of user acceptance of information technology, including performance expectancy, effort expectancy, social influence, and behavioral intention. Confirmatory factor analysis was applied in the main survey to evaluate the construct validity of the questionnaire. Structural equation modeling was used to test the hypothetical relationships between the four dimensions of user acceptance of information technology and the second-ordered OLC. Goodness of fit of the hypothetic model was also assessed. Performance expectancy, effort expectancy, and social influence positively influenced behavioral intention of users of the clinical information system (all p < 0.001) and accounted for 75% of its variation. The second-ordered OLC was positively associated with performance expectancy, effort expectancy, and social influence (all p < 0.001). However, the hypothetic relationship between perceived OLC and behavioral intention was not significant (p = 0.87). The fit statistical analysis indicated reasonable model fit to data (root mean square error of approximation = 0.07 and comparative fit index = 0.91). Perceived OLC indirectly affects user behavioral intention through the mediation of performance expectancy, effort expectancy, and social influence in the operating room setting. Copyright © 2013. Published by Elsevier B.V.

The good, the bad and the dubious: VHELIBS, a validation helper for ligands and binding sites

PubMed Central

2013-01-01

Background Many Protein Data Bank (PDB) users assume that the deposited structural models are of high quality but forget that these models are derived from the interpretation of experimental data. The accuracy of atom coordinates is not homogeneous between models or throughout the same model. To avoid basing a research project on a flawed model, we present a tool for assessing the quality of ligands and binding sites in crystallographic models from the PDB. Results The Validation HElper for LIgands and Binding Sites (VHELIBS) is software that aims to ease the validation of binding site and ligand coordinates for non-crystallographers (i.e., users with little or no crystallography knowledge). Using a convenient graphical user interface, it allows one to check how ligand and binding site coordinates fit to the electron density map. VHELIBS can use models from either the PDB or the PDB_REDO databank of re-refined and re-built crystallographic models. The user can specify threshold values for a series of properties related to the fit of coordinates to electron density (Real Space R, Real Space Correlation Coefficient and average occupancy are used by default). VHELIBS will automatically classify residues and ligands as Good, Dubious or Bad based on the specified limits. The user is also able to visually check the quality of the fit of residues and ligands to the electron density map and reclassify them if needed. Conclusions VHELIBS allows inexperienced users to examine the binding site and the ligand coordinates in relation to the experimental data. This is an important step to evaluate models for their fitness for drug discovery purposes such as structure-based pharmacophore development and protein-ligand docking experiments. PMID:23895374

Evaluation of different machine learning models for predicting and mapping the susceptibility of gully erosion

NASA Astrophysics Data System (ADS)

Rahmati, Omid; Tahmasebipour, Nasser; Haghizadeh, Ali; Pourghasemi, Hamid Reza; Feizizadeh, Bakhtiar

2017-12-01

Gully erosion constitutes a serious problem for land degradation in a wide range of environments. The main objective of this research was to compare the performance of seven state-of-the-art machine learning models (SVM with four kernel types, BP-ANN, RF, and BRT) to model the occurrence of gully erosion in the Kashkan-Poldokhtar Watershed, Iran. In the first step, a gully inventory map consisting of 65 gully polygons was prepared through field surveys. Three different sample data sets (S1, S2, and S3), including both positive and negative cells (70% for training and 30% for validation), were randomly prepared to evaluate the robustness of the models. To model the gully erosion susceptibility, 12 geo-environmental factors were selected as predictors. Finally, the goodness-of-fit and prediction skill of the models were evaluated by different criteria, including efficiency percent, kappa coefficient, and the area under the ROC curves (AUC). In terms of accuracy, the RF, RBF-SVM, BRT, and P-SVM models performed excellently both in the degree of fitting and in predictive performance (AUC values well above 0.9), which resulted in accurate predictions. Therefore, these models can be used in other gully erosion studies, as they are capable of rapidly producing accurate and robust gully erosion susceptibility maps (GESMs) for decision-making and soil and water management practices. Furthermore, it was found that performance of RF and RBF-SVM for modelling gully erosion occurrence is quite stable when the learning and validation samples are changed.

Adding thin-ideal internalization and impulsiveness to the cognitive-behavioral model of bulimic symptoms.

PubMed

Schnitzler, Caroline E; von Ranson, Kristin M; Wallace, Laurel M

2012-08-01

This study evaluated the cognitive-behavioral (CB) model of bulimia nervosa and an extension that included two additional maintaining factors - thin-ideal internalization and impulsiveness - in 327 undergraduate women. Participants completed measures of demographics, self-esteem, concern about shape and weight, dieting, bulimic symptoms, thin-ideal internalization, and impulsiveness. Both the original CB model and the extended model provided good fits to the data. Although structural equation modeling analyses suggested that the original CB model was most parsimonious, hierarchical regression analyses indicated that the additional variables accounted for significantly more variance. Additional analyses showed that the model fit could be improved by adding a path from concern about shape and weight, and deleting the path from dieting, to bulimic symptoms. Expanding upon the factors considered in the model may better capture the scope of variables maintaining bulimic symptoms in young women with a range of severity of bulimic symptoms. Copyright © 2012 Elsevier Ltd. All rights reserved.

Construct validity test of evaluation tool for professional behaviors of entry-level occupational therapy students in the United States.

PubMed

Yuen, Hon K; Azuero, Andres; Lackey, Kaitlin W; Brown, Nicole S; Shrestha, Sangita

2016-01-01

This study aimed to test the construct validity of an instrument to measure student professional behaviors in entry-level occupational therapy (OT) students in the academic setting. A total of 718 students from 37 OT programs across the United States answered a self-assessment survey of professional behavior that we developed. The survey consisted of ranking 28 attributes, each on a 5-point Likert scale. A split-sample approach was used for exploratory and then confirmatory factor analysis. A three-factor solution with nine items was extracted using exploratory factor analysis [EFA] (n=430, 60%). The factors were 'Commitment to Learning' (2 items), 'Skills for Learning' (4 items), and 'Cultural Competence' (3 items). Confirmatory factor analysis (CFA) on the validation split (n=288, 40%) indicated fair fit for this three-factor model (fit indices: CFI=0.96, RMSEA=0.06, and SRMR=0.05). Internal consistency reliability estimates of each factor and the instrument ranged from 0.63 to 0.79. Results of the CFA in a separate validation dataset provided robust measures of goodness-of-fit for the three-factor solution developed in the EFA, and indicated that the three-factor model fitted the data well enough. Therefore, we can conclude that this student professional behavior evaluation instrument is a structurally validated tool to measure professional behaviors reported by entry-level OT students. The internal consistency reliability of each individual factor and the whole instrument was considered to be adequate to good.

Impact of IRT item misfit on score estimates and severity classifications: an examination of PROMIS depression and pain interference item banks.

PubMed

Zhao, Yue

2017-03-01

In patient-reported outcome research that utilizes item response theory (IRT), using statistical significance tests to detect misfit is usually the focus of IRT model-data fit evaluations. However, such evaluations rarely address the impact/consequence of using misfitting items on the intended clinical applications. This study was designed to evaluate the impact of IRT item misfit on score estimates and severity classifications and to demonstrate a recommended process of model-fit evaluation. Using secondary data sources collected from the Patient-Reported Outcome Measurement Information System (PROMIS) wave 1 testing phase, analyses were conducted based on PROMIS depression (28 items; 782 cases) and pain interference (41 items; 845 cases) item banks. The identification of misfitting items was assessed using Orlando and Thissen's summed-score item-fit statistics and graphical displays. The impact of misfit was evaluated according to the agreement of both IRT-derived T-scores and severity classifications between inclusion and exclusion of misfitting items. The examination of the presence and impact of misfit suggested that item misfit had a negligible impact on the T-score estimates and severity classifications with the general population sample in the PROMIS depression and pain interference item banks, implying that the impact of item misfit was insignificant. Findings support the T-score estimates in the two item banks as robust against item misfit at both the group and individual levels and add confidence to the use of T-scores for severity diagnosis in the studied sample. Recommendations on approaches for identifying item misfit (statistical significance) and assessing the misfit impact (practical significance) are given.

A generic framework to simulate realistic lung, liver and renal pathologies in CT imaging

NASA Astrophysics Data System (ADS)

Solomon, Justin; Samei, Ehsan

2014-11-01

Realistic three-dimensional (3D) mathematical models of subtle lesions are essential for many computed tomography (CT) studies focused on performance evaluation and optimization. In this paper, we develop a generic mathematical framework that describes the 3D size, shape, contrast, and contrast-profile characteristics of a lesion, as well as a method to create lesion models based on CT data of real lesions. Further, we implemented a technique to insert the lesion models into CT images in order to create hybrid CT datasets. This framework was used to create a library of realistic lesion models and corresponding hybrid CT images. The goodness of fit of the models was assessed using the coefficient of determination (R2) and the visual appearance of the hybrid images was assessed with an observer study using images of both real and simulated lesions and receiver operator characteristic (ROC) analysis. The average R2 of the lesion models was 0.80, implying that the models provide a good fit to real lesion data. The area under the ROC curve was 0.55, implying that the observers could not readily distinguish between real and simulated lesions. Therefore, we conclude that the lesion-modeling framework presented in this paper can be used to create realistic lesion models and hybrid CT images. These models could be instrumental in performance evaluation and optimization of novel CT systems.

An experimental comparison of the Marcus-Hush and Butler-Volmer descriptions of electrode kinetics applied to cyclic voltammetry. The one electron reductions of europium (III) and 2-methyl-2-nitropropane studied at a mercury microhemisphere electrode

NASA Astrophysics Data System (ADS)

Henstridge, Martin C.; Wang, Yijun; Limon-Petersen, Juan G.; Laborda, Eduardo; Compton, Richard G.

2011-11-01

We present a comparative experimental evaluation of the Butler-Volmer and Marcus-Hush models using cyclic voltammetry at a microelectrode. Numerical simulations are used to fit experimental voltammetry of the one electron reductions of europium (III) and 2-methyl-2-nitropropane, in water and acetonitrile, respectively, at a mercury microhemisphere electrode. For Eu (III) very accurate fits to experiment were obtained over a wide range of scan rates using Butler-Volmer kinetics, whereas the Marcus-Hush model was less accurate. The reduction of 2-methyl-2-nitropropane was well simulated by both models, however Marcus-Hush required a reorganisation energy lower than expected.

An interactive program for pharmacokinetic modeling.

PubMed

Lu, D R; Mao, F

1993-05-01

A computer program, PharmK, was developed for pharmacokinetic modeling of experimental data. The program was written in C computer language based on the high-level user-interface Macintosh operating system. The intention was to provide a user-friendly tool for users of Macintosh computers. An interactive algorithm based on the exponential stripping method is used for the initial parameter estimation. Nonlinear pharmacokinetic model fitting is based on the maximum likelihood estimation method and is performed by the Levenberg-Marquardt method based on chi 2 criterion. Several methods are available to aid the evaluation of the fitting results. Pharmacokinetic data sets have been examined with the PharmK program, and the results are comparable with those obtained with other programs that are currently available for IBM PC-compatible and other types of computers.

The FIT Game III: Reducing the Operating Expenses of a Game-Based Approach to Increasing Healthy Eating in Elementary Schools.

PubMed

Joyner, Damon; Wengreen, Heidi J; Aguilar, Sheryl S; Spruance, Lori Andersen; Morrill, Brooke A; Madden, Gregory J

2017-04-01

Previously published versions of the healthy eating "FIT Game" were administered by teachers in all grades at elementary schools. The present study evaluated whether the game would retain its efficacy if teachers were relieved of this task; presenting instead all game materials on visual displays in the school cafeteria. Participants were 572 children attending two Title 1 elementary schools (grades K-5). Following a no-intervention baseline period in which fruit and vegetable consumption were measured from food waste, the schools played the FIT Game. In the game, the children's vegetable consumption influenced events in a good versus evil narrative presented in comic book-formatted episodes in the school cafeteria. When daily vegetable-consumption goals were met, new FIT Game episodes were displayed. Game elements included a game narrative, competition, virtual currency, and limited player autonomy. The two intervention phases were separated by a second baseline phase (within-school reversal design). Simulation Modeling Analysis (a bootstrapping technique appropriate to within-group time-series designs) was used to evaluate whether vegetable consumption increased significantly above baseline levels in the FIT Game phases (P < 0.05). Vegetable consumption increased significantly from 21.3 g during the two baseline phases to 42.5 g during the FIT Game phases; a 99.9% increase. The Game did not significantly increase fruit consumption (which was not targeted for change), nor was there a decrease in fruit consumption. Labor-reductions in the FIT Game did not reduce its positive impact on healthy eating.

The Health Action Process Approach as a motivational model for physical activity self-management for people with multiple sclerosis: a path analysis.

PubMed

Chiu, Chung-Yi; Lynch, Ruth T; Chan, Fong; Berven, Norman L

2011-08-01

To evaluate the Health Action Process Approach (HAPA) as a motivational model for physical activity self-management for people with multiple sclerosis (MS). Quantitative descriptive research design using path analysis. One hundred ninety-five individuals with MS were recruited from the National Multiple Sclerosis Society and a neurology clinic at a university teaching hospital in the Midwest. Outcome was measured by the Physical Activity Stages of Change Instrument, along with measures for nine predictors (severity, action self-efficacy, outcome expectancy, risk perception, perceived barriers, intention, maintenance self-efficacy, action and coping planning, and recovery self-efficacy). The respecified HAPA physical activity model fit the data relatively well (goodness-of-fit index = .92, normed fit index = .91, and comparative fit index = .93) explaining 38% of the variance in physical activity. Recovery self-efficacy, action and coping planning, and perceived barriers directly contributed to the prediction of physical activity. Outcome expectancy significantly influenced intention and the relationship between intention and physical activity is mediated by action and coping planning. Action self-efficacy, maintenance self-efficacy, and recovery self-efficacy directly or indirectly affected physical activity. Severity of MS and action self-efficacy had an inverse relationship with perceived barriers and perceived barriers influenced physical activity. Empirical support was found for the proposed HAPA model of physical activity for people with MS. The HAPA model appears to provide useful information for clinical rehabilitation and health promotion interventions.

The relationship between cost estimates reliability and BIM adoption: SEM analysis

NASA Astrophysics Data System (ADS)

Ismail, N. A. A.; Idris, N. H.; Ramli, H.; Rooshdi, R. R. Raja Muhammad; Sahamir, S. R.

2018-02-01

This paper presents the usage of Structural Equation Modelling (SEM) approach in analysing the effects of Building Information Modelling (BIM) technology adoption in improving the reliability of cost estimates. Based on the questionnaire survey results, SEM analysis using SPSS-AMOS application examined the relationships between BIM-improved information and cost estimates reliability factors, leading to BIM technology adoption. Six hypotheses were established prior to SEM analysis employing two types of SEM models, namely the Confirmatory Factor Analysis (CFA) model and full structural model. The SEM models were then validated through the assessment on their uni-dimensionality, validity, reliability, and fitness index, in line with the hypotheses tested. The final SEM model fit measures are: P-value=0.000, RMSEA=0.079<0.08, GFI=0.824, CFI=0.962>0.90, TLI=0.956>0.90, NFI=0.935>0.90 and ChiSq/df=2.259; indicating that the overall index values achieved the required level of model fitness. The model supports all the hypotheses evaluated, confirming that all relationship exists amongst the constructs are positive and significant. Ultimately, the analysis verified that most of the respondents foresee better understanding of project input information through BIM visualization, its reliable database and coordinated data, in developing more reliable cost estimates. They also perceive to accelerate their cost estimating task through BIM adoption.

A generalized multivariate regression model for modelling ocean wave heights

NASA Astrophysics Data System (ADS)

Wang, X. L.; Feng, Y.; Swail, V. R.

2012-04-01

In this study, a generalized multivariate linear regression model is developed to represent the relationship between 6-hourly ocean significant wave heights (Hs) and the corresponding 6-hourly mean sea level pressure (MSLP) fields. The model is calibrated using the ERA-Interim reanalysis of Hs and MSLP fields for 1981-2000, and is validated using the ERA-Interim reanalysis for 2001-2010 and ERA40 reanalysis of Hs and MSLP for 1958-2001. The performance of the fitted model is evaluated in terms of Pierce skill score, frequency bias index, and correlation skill score. Being not normally distributed, wave heights are subjected to a data adaptive Box-Cox transformation before being used in the model fitting. Also, since 6-hourly data are being modelled, lag-1 autocorrelation must be and is accounted for. The models with and without Box-Cox transformation, and with and without accounting for autocorrelation, are inter-compared in terms of their prediction skills. The fitted MSLP-Hs relationship is then used to reconstruct historical wave height climate from the 6-hourly MSLP fields taken from the Twentieth Century Reanalysis (20CR, Compo et al. 2011), and to project possible future wave height climates using CMIP5 model simulations of MSLP fields. The reconstructed and projected wave heights, both seasonal means and maxima, are subject to a trend analysis that allows for non-linear (polynomial) trends.

Associations of Physical Fitness and Academic Performance among Schoolchildren

ERIC Educational Resources Information Center

Van Dusen, Duncan P.; Kelder, Steven H.; Kohl, Harold W., III; Ranjit, Nalini; Perry, Cheryl L.

2011-01-01

Background: Public schools provide opportunities for physical activity and fitness surveillance, but are evaluated and funded based on students' academic performance, not their physical fitness. Empirical research evaluating the connections between fitness and academic performance is needed to justify curriculum allocations to physical activity…

Extending Structural Analyses of the Rosenberg Self-Esteem Scale to Consider Criterion-Related Validity: Can Composite Self-Esteem Scores Be Good Enough?

PubMed

Donnellan, M Brent; Ackerman, Robert A; Brecheen, Courtney

2016-01-01

Although the Rosenberg Self-Esteem Scale (RSES) is the most widely used measure of global self-esteem in the literature, there are ongoing disagreements about its factor structure. This methodological debate informs how the measure should be used in substantive research. Using a sample of 1,127 college students, we test the overall fit of previously specified models for the RSES, including a newly proposed bifactor solution (McKay, Boduszek, & Harvey, 2014 ). We extend previous work by evaluating how various latent factors from these structural models are related to a set of criterion variables frequently studied in the self-esteem literature. A strict unidimensional model poorly fit the data, whereas models that accounted for correlations between negatively and positively keyed items tended to fit better. However, global factors from viable structural models had similar levels of association with criterion variables and with the pattern of results obtained with a composite global self-esteem variable calculated from observed scores. Thus, we did not find compelling evidence that different structural models had substantive implications, thereby reducing (but not eliminating) concerns about the integrity of the self-esteem literature based on overall composite scores for the RSES.

An optimized knife-edge method for on-orbit MTF estimation of optical sensors using powell parameter fitting

NASA Astrophysics Data System (ADS)

Han, Lu; Gao, Kun; Gong, Chen; Zhu, Zhenyu; Guo, Yue

2017-08-01

On-orbit Modulation Transfer Function (MTF) is an important indicator to evaluate the performance of the optical remote sensors in a satellite. There are many methods to estimate MTF, such as pinhole method, slit method and so on. Among them, knife-edge method is quite efficient, easy-to-use and recommended in ISO12233 standard for the wholefrequency MTF curve acquisition. However, the accuracy of the algorithm is affected by Edge Spread Function (ESF) fitting accuracy significantly, which limits the range of application. So in this paper, an optimized knife-edge method using Powell algorithm is proposed to improve the ESF fitting precision. Fermi function model is the most popular ESF fitting model, yet it is vulnerable to the initial values of the parameters. Considering the characteristics of simple and fast convergence, Powell algorithm is applied to fit the accurate parameters adaptively with the insensitivity to the initial parameters. Numerical simulation results reveal the accuracy and robustness of the optimized algorithm under different SNR, edge direction and leaning angles conditions. Experimental results using images of the camera in ZY-3 satellite show that this method is more accurate than the standard knife-edge method of ISO12233 in MTF estimation.

Fecal immunochemical test for predicting mucosal healing in ulcerative colitis patients: A systematic review and meta-analysis.

PubMed

Dai, Cong; Jiang, Min; Sun, Ming-Jun; Cao, Qin

2018-05-01

Fecal immunochemical test (FIT) is a promising marker for assessment of inflammatory bowel disease activity. However, the utility of FIT for predicting mucosal healing (MH) of ulcerative colitis (UC) patients has yet to be clearly demonstrated. The objective of our study was to perform a diagnostic test accuracy test meta-analysis evaluating the diagnostic accuracy of FIT in predicting MH of UC patients. We systematically searched the databases from inception to November 2017 that evaluated MH in UC. The methodological quality of each study was assessed according to the Quality Assessment of Diagnostic Accuracy Studies checklist. The extracted data were pooled using a summary receiver operating characteristic curve model. Random-effects model was used to summarize the diagnostic odds ratio, sensitivity, specificity, positive likelihood ratio, and negative likelihood ratio. Six studies comprising 625 UC patients were included in the meta-analysis. The pooled sensitivity and specificity values for predicting MH in UC were 0.77 (95% confidence interval [CI], 0.72-0.81) and 0.81 (95% CI, 0.76-0.85), respectively. The FIT level had a high rule-in value (positive likelihood ratio, 3.79; 95% CI, 2.85-5.03) and a moderate rule-out value (negative likelihood ratio, 0.26; 95% CI, 0.16-0.43) for predicting MH in UC. The results of the receiver operating characteristic curve analysis (area under the curve, 0.88; standard error of the mean, 0.02) and diagnostic odds ratio (18.08; 95% CI, 9.57-34.13) also revealed improved discrimination for identifying MH in UC with FIT concentration. Our meta-analysis has found that FIT is a simple, reliable non-invasive marker for predicting MH in UC patients. © 2018 Journal of Gastroenterology and Hepatology Foundation and John Wiley & Sons Australia, Ltd.

The ACTIVE conceptual framework as a structural equation model

PubMed Central

Gross, Alden L.; Payne, Brennan R.; Casanova, Ramon; Davoudzadeh, Pega; Dzierzewski, Joseph M.; Farias, Sarah; Giovannetti, Tania; Ip, Edward H.; Marsiske, Michael; Rebok, George W.; Schaie, K. Warner; Thomas, Kelsey; Willis, Sherry; Jones, Richard N.

2018-01-01

Background/Study Context Conceptual frameworks are analytic models at a high level of abstraction. Their operationalization can inform randomized trial design and sample size considerations. Methods The Advanced Cognitive Training for Independent and Vital Elderly (ACTIVE) conceptual framework was empirically tested using structural equation modeling (N=2,802). ACTIVE was guided by a conceptual framework for cognitive training in which proximal cognitive abilities (memory, inductive reasoning, speed of processing) mediate treatment-related improvement in primary outcomes (everyday problem-solving, difficulty with activities of daily living, everyday speed, driving difficulty), which in turn lead to improved secondary outcomes (health-related quality of life, health service utilization, mobility). Measurement models for each proximal, primary, and secondary outcome were developed and tested using baseline data. Each construct was then combined in one model to evaluate fit (RMSEA, CFI, normalized residuals of each indicator). To expand the conceptual model and potentially inform future trials, evidence of modification of structural model parameters was evaluated by age, years of education, sex, race, and self-rated health status. Results Preconceived measurement models for memory, reasoning, speed of processing, everyday problem-solving, instrumental activities of daily living (IADL) difficulty, everyday speed, driving difficulty, and health-related quality of life each fit well to the data (all RMSEA < .05; all CFI > .95). Fit of the full model was excellent (RMSEA = .038; CFI = .924). In contrast with previous findings from ACTIVE regarding who benefits from training, interaction testing revealed associations between proximal abilities and primary outcomes are stronger on average by nonwhite race, worse health, older age, and less education (p < .005). Conclusions Empirical data confirm the hypothesized ACTIVE conceptual model. Findings suggest that the types of people who show intervention effects on cognitive performance potentially may be different from those with the greatest chance of transfer to real-world activities. PMID:29303475

Microporous pure beta-tricalcium phosphate implants for press-fit fixation of anterior cruciate ligament grafts: strength and healing in a sheep model.

PubMed

Mayr, Hermann O; Dietrich, Markwart; Fraedrich, Franz; Hube, Robert; Nerlich, Andreas; von Eisenhart-Rothe, Rüdiger; Hein, Werner; Bernstein, Anke

2009-09-01

A sheep study was conducted to test a press-fit technique using microporous pure beta-tricalcium phosphate (beta-TCP) dowels for fixation of the anterior cruciate ligament (ACL) graft. Microporous (5 mum) cylindrical plugs of beta-TCP (diameter, 7 mm; length, 25 mm) with interconnecting pores were used. The material featured a novel configuration of structure and surface geometry. Implants were tested by use of press-fit fixation of ACL grafts with and without bone blocks in 42 sheep over a period of 24 weeks. Biomechanical, radiologic, histologic, and immunohistochemical evaluations were performed. In load-to-failure tests at 6, 12, and 24 weeks after surgery, the intra-articular graft always failed, not the fixation. Grafts showed bony fixation in the tunnel at 6 weeks and primary healing at the junction of the tunnel and joint after 24 weeks. Tricalcium phosphate was resorbed and simultaneously replaced by bone. Remodeling was still incomplete at 24 weeks. In the sheep model microporous beta-TCP implants used with press-fit fixation of ACL grafts permit early functional rehabilitation. After 6 weeks, the graft is fixed by woven bone or bony integration. Implanted microporous tricalcium phosphate is resorbed and replaced by bone. In a sheep model we showed that primary healing of ACL grafts with resorption and bony replacement of the fixating implant can be achieved by means of press-fit fixation with pure beta-TCP.

Testing the metacognitive model against the benchmark CBT model of social anxiety disorder: Is it time to move beyond cognition?

PubMed Central

2017-01-01

The recommended treatment for Social Phobia is individual Cognitive-Behavioural Therapy (CBT). CBT-treatments emphasize social self-beliefs (schemas) as the core underlying factor for maladaptive self-processing and social anxiety symptoms. However, the need for such beliefs in models of psychopathology has recently been questioned. Specifically, the metacognitive model of psychological disorders asserts that particular beliefs about thinking (metacognitive beliefs) are involved in most disorders, including social anxiety, and are a more important factor underlying pathology. Comparing the relative importance of these disparate underlying belief systems has the potential to advance conceptualization and treatment for SAD. In the cognitive model, unhelpful self-regulatory processes (self-attention and safety behaviours) arise from (e.g. correlate with) cognitive beliefs (schemas) whilst the metacognitive model proposes that such processes arise from metacognitive beliefs. In the present study we therefore set out to evaluate the absolute and relative fit of the cognitive and metacognitive models in a longitudinal data-set, using structural equation modelling. Five-hundred and five (505) participants completed a battery of self-report questionnaires at two time points approximately 8 weeks apart. We found that both models fitted the data, but that the metacognitive model was a better fit to the data than the cognitive model. Further, a specified metacognitive model, emphasising negative metacognitive beliefs about the uncontrollability and danger of thoughts and cognitive confidence improved the model fit further and was significantly better than the cognitive model. It would seem that advances in understanding and treating social anxiety could benefit from moving to a full metacognitive theory that includes negative metacognitive beliefs about the uncontrollability and danger of thoughts, and judgements of cognitive confidence. These findings challenge a core assumption of the cognitive model and treatment of social phobia and offer further support to the metacognitive model. PMID:28472176

Testing the metacognitive model against the benchmark CBT model of social anxiety disorder: Is it time to move beyond cognition?

PubMed

Nordahl, Henrik; Wells, Adrian

2017-01-01

The recommended treatment for Social Phobia is individual Cognitive-Behavioural Therapy (CBT). CBT-treatments emphasize social self-beliefs (schemas) as the core underlying factor for maladaptive self-processing and social anxiety symptoms. However, the need for such beliefs in models of psychopathology has recently been questioned. Specifically, the metacognitive model of psychological disorders asserts that particular beliefs about thinking (metacognitive beliefs) are involved in most disorders, including social anxiety, and are a more important factor underlying pathology. Comparing the relative importance of these disparate underlying belief systems has the potential to advance conceptualization and treatment for SAD. In the cognitive model, unhelpful self-regulatory processes (self-attention and safety behaviours) arise from (e.g. correlate with) cognitive beliefs (schemas) whilst the metacognitive model proposes that such processes arise from metacognitive beliefs. In the present study we therefore set out to evaluate the absolute and relative fit of the cognitive and metacognitive models in a longitudinal data-set, using structural equation modelling. Five-hundred and five (505) participants completed a battery of self-report questionnaires at two time points approximately 8 weeks apart. We found that both models fitted the data, but that the metacognitive model was a better fit to the data than the cognitive model. Further, a specified metacognitive model, emphasising negative metacognitive beliefs about the uncontrollability and danger of thoughts and cognitive confidence improved the model fit further and was significantly better than the cognitive model. It would seem that advances in understanding and treating social anxiety could benefit from moving to a full metacognitive theory that includes negative metacognitive beliefs about the uncontrollability and danger of thoughts, and judgements of cognitive confidence. These findings challenge a core assumption of the cognitive model and treatment of social phobia and offer further support to the metacognitive model.

Protofit: A program for determining surface protonation constants from titration data

NASA Astrophysics Data System (ADS)

Turner, Benjamin F.; Fein, Jeremy B.

2006-11-01

Determining the surface protonation behavior of natural adsorbents is essential to understand how they interact with their environments. ProtoFit is a tool for analysis of acid-base titration data and optimization of surface protonation models. The program offers a number of useful features including: (1) enables visualization of adsorbent buffering behavior; (2) uses an optimization approach independent of starting titration conditions or initial surface charge; (3) does not require an initial surface charge to be defined or to be treated as an optimizable parameter; (4) includes an error analysis intrinsically as part of the computational methods; and (5) generates simulated titration curves for comparison with observation. ProtoFit will typically be run through ProtoFit-GUI, a graphical user interface providing user-friendly control of model optimization, simulation, and data visualization. ProtoFit calculates an adsorbent proton buffering value as a function of pH from raw titration data (including pH and volume of acid or base added). The data is reduced to a form where the protons required to change the pH of the solution are subtracted out, leaving protons exchanged between solution and surface per unit mass of adsorbent as a function of pH. The buffering intensity function Qads* is calculated as the instantaneous slope of this reduced titration curve. Parameters for a surface complexation model are obtained by minimizing the sum of squares between the modeled (i.e. simulated) buffering intensity curve and the experimental data. The variance in the slope estimate, intrinsically produced as part of the Qads* calculation, can be used to weight the sum of squares calculation between the measured buffering intensity and a simulated curve. Effects of analytical error on data visualization and model optimization are discussed. Examples are provided of using ProtoFit for data visualization, model optimization, and model evaluation.

Linear time algorithms to construct populations fitting multiple constraint distributions at genomic scales.

PubMed

Siragusa, Enrico; Haiminen, Niina; Utro, Filippo; Parida, Laxmi

2017-10-09

Computer simulations can be used to study population genetic methods, models and parameters, as well as to predict potential outcomes. For example, in plant populations, predicting the outcome of breeding operations can be studied using simulations. In-silico construction of populations with pre-specified characteristics is an important task in breeding optimization and other population genetic studies. We present two linear time Simulation using Best-fit Algorithms (SimBA) for two classes of problems where each co-fits two distributions: SimBA-LD fits linkage disequilibrium and minimum allele frequency distributions, while SimBA-hap fits founder-haplotype and polyploid allele dosage distributions. An incremental gap-filling version of previously introduced SimBA-LD is here demonstrated to accurately fit the target distributions, allowing efficient large scale simulations. SimBA-hap accuracy and efficiency is demonstrated by simulating tetraploid populations with varying numbers of founder haplotypes, we evaluate both a linear time greedy algoritm and an optimal solution based on mixed-integer programming. SimBA is available on http://researcher.watson.ibm.com/project/5669.

Mathematical analysis of compressive/tensile molecular and nuclear structures

NASA Astrophysics Data System (ADS)

Wang, Dayu

Mathematical analysis in chemistry is a fascinating and critical tool to explain experimental observations. In this dissertation, mathematical methods to present chemical bonding and other structures for many-particle systems are discussed at different levels (molecular, atomic, and nuclear). First, the tetrahedral geometry of single, double, or triple carbon-carbon bonds gives an unsatisfying demonstration of bond lengths, compared to experimental trends. To correct this, Platonic solids and Archimedean solids were evaluated as atoms in covalent carbon or nitrogen bond systems in order to find the best solids for geometric fitting. Pentagonal solids, e.g. the dodecahedron and icosidodecahedron, give the best fit with experimental bond lengths; an ideal pyramidal solid which models covalent bonds was also generated. Second, the macroscopic compression/tension architectural approach was applied to forces at the molecular level, considering atomic interactions as compressive (repulsive) and tensile (attractive) forces. Two particle interactions were considered, followed by a model of the dihydrogen molecule (H2; two protons and two electrons). Dihydrogen was evaluated as two different types of compression/tension structures: a coaxial spring model and a ring model. Using similar methods, covalent diatomic molecules (made up of C, N, O, or F) were evaluated. Finally, the compression/tension model was extended to the nuclear level, based on the observation that nuclei with certain numbers of protons/neutrons (magic numbers) have extra stability compared to other nucleon ratios. A hollow spherical model was developed that combines elements of the classic nuclear shell model and liquid drop model. Nuclear structure and the trend of the "island of stability" for the current and extended periodic table were studied.

Application of an OCT data-based mathematical model of the foveal pit in Parkinson disease.

PubMed

Ding, Yin; Spund, Brian; Glazman, Sofya; Shrier, Eric M; Miri, Shahnaz; Selesnick, Ivan; Bodis-Wollner, Ivan

2014-11-01

Spectral-domain Optical coherence tomography (OCT) has shown remarkable utility in the study of retinal disease and has helped to characterize the fovea in Parkinson disease (PD) patients. We developed a detailed mathematical model based on raw OCT data to allow differentiation of foveae of PD patients from healthy controls. Of the various models we tested, a difference of a Gaussian and a polynomial was found to have "the best fit". Decision was based on mathematical evaluation of the fit of the model to the data of 45 control eyes versus 50 PD eyes. We compared the model parameters in the two groups using receiver-operating characteristics (ROC). A single parameter discriminated 70 % of PD eyes from controls, while using seven of the eight parameters of the model allowed 76 % to be discriminated. The future clinical utility of mathematical modeling in study of diffuse neurodegenerative conditions that also affect the fovea is discussed.

Variation across mitochondrial gene trees provides evidence for systematic error: How much gene tree variation is biological?

PubMed

Richards, Emilie J; Brown, Jeremy M; Barley, Anthony J; Chong, Rebecca A; Thomson, Robert C

2018-02-19

The use of large genomic datasets in phylogenetics has highlighted extensive topological variation across genes. Much of this discordance is assumed to result from biological processes. However, variation among gene trees can also be a consequence of systematic error driven by poor model fit, and the relative importance of biological versus methodological factors in explaining gene tree variation is a major unresolved question. Using mitochondrial genomes to control for biological causes of gene tree variation, we estimate the extent of gene tree discordance driven by systematic error and employ posterior prediction to highlight the role of model fit in producing this discordance. We find that the amount of discordance among mitochondrial gene trees is similar to the amount of discordance found in other studies that assume only biological causes of variation. This similarity suggests that the role of systematic error in generating gene tree variation is underappreciated and critical evaluation of fit between assumed models and the data used for inference is important for the resolution of unresolved phylogenetic questions.

Lunar gravitational field estimation and the effects of mismodeling upon lunar satellite orbit prediction. M.S. Thesis

NASA Technical Reports Server (NTRS)

Davis, John H.

1993-01-01

Lunar spherical harmonic gravity coefficients are estimated from simulated observations of a near-circular low altitude polar orbiter disturbed by lunar mascons. Lunar gravity sensing missions using earth-based nearside observations with and without satellite-based far-side observations are simulated and least squares maximum likelihood estimates are developed for spherical harmonic expansion fit models. Simulations and parameter estimations are performed by a modified version of the Smithsonian Astrophysical Observatory's Planetary Ephemeris Program. Two different lunar spacecraft mission phases are simulated to evaluate the estimated fit models. Results for predicting state covariances one orbit ahead are presented along with the state errors resulting from the mismodeled gravity field. The position errors from planning a lunar landing maneuver with a mismodeled gravity field are also presented. These simulations clearly demonstrate the need to include observations of satellite motion over the far side in estimating the lunar gravity field. The simulations also illustrate that the eighth degree and order expansions used in the simulated fits were unable to adequately model lunar mascons.

Many-level multilevel structural equation modeling: An efficient evaluation strategy.

PubMed

Pritikin, Joshua N; Hunter, Michael D; von Oertzen, Timo; Brick, Timothy R; Boker, Steven M

2017-01-01

Structural equation models are increasingly used for clustered or multilevel data in cases where mixed regression is too inflexible. However, when there are many levels of nesting, these models can become difficult to estimate. We introduce a novel evaluation strategy, Rampart, that applies an orthogonal rotation to the parts of a model that conform to commonly met requirements. This rotation dramatically simplifies fit evaluation in a way that becomes more potent as the size of the data set increases. We validate and evaluate the implementation using a 3-level latent regression simulation study. Then we analyze data from a state-wide child behavioral health measure administered by the Oklahoma Department of Human Services. We demonstrate the efficiency of Rampart compared to other similar software using a latent factor model with a 5-level decomposition of latent variance. Rampart is implemented in OpenMx, a free and open source software.

Simultaneous quantification of soil phosphorus labile pool and desorption kinetics using DGTs and 3D-DIFS

NASA Astrophysics Data System (ADS)

Menezes-Blackburn, Daniel; Sun, Jiahui; Lehto, Niklas; Zhang, Hao; Stutter, Marc; Giles, Courtney D.; Darch, Tegan; George, Timothy S.; Shand, Charles; Lumsdon, David; Blackwell, Martin; Wearing, Catherine; Cooper, Patricia; Wendler, Renate; Brown, Lawrie; Haygarth, Philip M.

2017-04-01

The phosphorus (P) labile pool and desorption kinetics were simultaneously evaluated in ten representative UK soils using the technique of Diffusive gradients in thin films (DGT). The DGT-induced fluxes in soil and sediments model (DIFS) was fitted to the time series of DGT deployment (1h to 240h). The desorbable P concentration (labile P) was obtained by multiplying the fitted Kd by the soil solution P concentration obtained using Diffusive Equilibration in Thin Films (DET) devices. The labile P was then compared to several soil P extracts including Olsen P, Resin P, FeO-P and water extractable P, in order to assess if these analytical procedures can be used to represent the labile P across different soils. The Olsen P, commonly used as a representation of the soil labile P pool, overestimated the desorbable P concentration by a seven fold factor. The use of this approach for the quantification of soil P desorption kinetics parameters was somewhat unprecise, showing a wide range of equally valid solutions for the response of the system P equilibration time (Tc). Additionally, the performance of different DIFS model versions (1D, 2D and 3D) was compared. Although these models had a good fit to experimental DGT time series data, the fitted parameters showed a poor agreement between different model versions. The limitations of the DIFS model family are associated with the assumptions taken in the modelling approach and the 3D version is here considered to be the most precise among them.

Physical fitness is associated with anxiety levels in women with fibromyalgia: the al-Ándalus project.

PubMed

Córdoba-Torrecilla, S; Aparicio, V A; Soriano-Maldonado, A; Estévez-López, F; Segura-Jiménez, V; Álvarez-Gallardo, I; Femia, P; Delgado-Fernández, M

2016-04-01

To assess the independent associations of individual physical fitness components with anxiety in women with fibromyalgia and to test which physical fitness component shows the greatest association. This population-based cross-sectional study included 439 women with fibromyalgia (age 52.2 ± 8.0 years). Anxiety symptoms were measured with the State Trait Anxiety Inventory (STAI) and the anxiety item of the Revised Fibromyalgia Impact Questionnaire (FIQR). Physical fitness was assessed through the Senior Fitness Test battery and handgrip strength test. Overall, lower physical fitness was associated with higher anxiety levels (all, p < 0.05). The coefficients of the optimal regression model (stepwise selection method) between anxiety symptoms and physical fitness components adjusted for age, body fat percentage and anxiolytics intake showed that the back scratch test (b = -0.18), the chair sit-and-reach test (b = -0.12; p = 0.027) and the 6-min walk test (b = -0.02; p = 0.024) were independently and inversely associated with STAI. The back scratch test and the arm- curl test were associated with FIQR-anxiety (b = -0.05; p < 0.001 and b = -0.07; p = 0.021, respectively). Physical fitness was inversely and consistently associated with anxiety in women with fibromyalgia, regardless of the fitness component evaluated. In particular, upper-body flexibility was an independent indicator of anxiety levels, followed by cardiorespiratory fitness and muscular strength.

FITNESSGRAM[R] Administration: Tips for Educators

ERIC Educational Resources Information Center

Mosier, Brian

2012-01-01

The first national youth fitness evaluation conducted in the United States was in 1958. Since that time, schools have continued to administer fitness evaluations using a variety of tests with no national-level assessment of youth fitness [Institute of Medicine (IOM), 2012]. However, in September 2012, the Presidential Youth Fitness Program (PYFP)…

Best Statistical Distribution of flood variables for Johor River in Malaysia

NASA Astrophysics Data System (ADS)

Salarpour Goodarzi, M.; Yusop, Z.; Yusof, F.

2012-12-01

A complex flood event is always characterized by a few characteristics such as flood peak, flood volume, and flood duration, which might be mutually correlated. This study explored the statistical distribution of peakflow, flood duration and flood volume at Rantau Panjang gauging station on the Johor River in Malaysia. Hourly data were recorded for 45 years. The data were analysed based on water year (July - June). Five distributions namely, Log Normal, Generalize Pareto, Log Pearson, Normal and Generalize Extreme Value (GEV) were used to model the distribution of all the three variables. Anderson-Darling and Kolmogorov-Smirnov goodness-of-fit tests were used to evaluate the best fit. Goodness-of-fit tests at 5% level of significance indicate that all the models can be used to model the distribution of peakflow, flood duration and flood volume. However, Generalize Pareto distribution is found to be the most suitable model when tested with the Anderson-Darling test and the, Kolmogorov-Smirnov suggested that GEV is the best for peakflow. The result of this research can be used to improve flood frequency analysis. Comparison between Generalized Extreme Value, Generalized Pareto and Log Pearson distributions in the Cumulative Distribution Function of peakflow

Testing the psychometric properties of the Environmental Attitudes Inventory on undergraduate students in the Arab context: A test-retest approach.

PubMed

AlMenhali, Entesar Ali; Khalid, Khalizani; Iyanna, Shilpa

2018-01-01

The Environmental Attitudes Inventory (EAI) was developed to evaluate the multidimensional nature of environmental attitudes; however, it is based on a dataset from outside the Arab context. This study reinvestigated the construct validity of the EAI with a new dataset and confirmed the feasibility of applying it in the Arab context. One hundred and forty-eight subjects in Study 1 and 130 in Study 2 provided valid responses. An exploratory factor analysis (EFA) was used to extract a new factor structure in Study 1, and confirmatory factor analysis (CFA) was performed in Study 2. Both studies generated a seven-factor model, and the model fit was discussed for both the studies. Study 2 exhibited satisfactory model fit indices compared to Study 1. Factor loading values of a few items in Study 1 affected the reliability values and average variance extracted values, which demonstrated low discriminant validity. Based on the results of the EFA and CFA, this study showed sufficient model fit and suggested the feasibility of applying the EAI in the Arab context with a good construct validity and internal consistency.

Testing the psychometric properties of the Environmental Attitudes Inventory on undergraduate students in the Arab context: A test-retest approach

PubMed Central

2018-01-01

The Environmental Attitudes Inventory (EAI) was developed to evaluate the multidimensional nature of environmental attitudes; however, it is based on a dataset from outside the Arab context. This study reinvestigated the construct validity of the EAI with a new dataset and confirmed the feasibility of applying it in the Arab context. One hundred and forty-eight subjects in Study 1 and 130 in Study 2 provided valid responses. An exploratory factor analysis (EFA) was used to extract a new factor structure in Study 1, and confirmatory factor analysis (CFA) was performed in Study 2. Both studies generated a seven-factor model, and the model fit was discussed for both the studies. Study 2 exhibited satisfactory model fit indices compared to Study 1. Factor loading values of a few items in Study 1 affected the reliability values and average variance extracted values, which demonstrated low discriminant validity. Based on the results of the EFA and CFA, this study showed sufficient model fit and suggested the feasibility of applying the EAI in the Arab context with a good construct validity and internal consistency. PMID:29758021

Evaluation of leaf litter leaching kinetics through commonly-used mathematical models

NASA Astrophysics Data System (ADS)

Montoya, J. V.; Bastianoni, A.; Mendez, C.; Paolini, J.

2012-04-01

Leaching is defined as the abiotic process by which soluble compounds of the litter are released into the water. Most studies dealing with leaf litter breakdown and leaching kinetics apply the single exponential decay model since it corresponds well with the understanding of the biology of decomposition. However, during leaching important mass losses occur and mathematical models often fail in describing this process adequately. During the initial hours of leaching leaf litter experience high decay rates which are not properly modelled. Adjusting leaching losses to mathematical models has not been investigated thoroughly and the use of models assuming constant decay rates leads to inappropriate assessments of leaching kinetics. We aim to describe, assess, and compare different leaching kinetics models fitted to leaf litter mass losses from six Neotropical riparian forest species. Leaf litter from each species was collected in the lower reaches of San Miguel stream in Northern Venezuela. Air-dried leaves from each species were incubated in 250 ml of water in the dark at room temperature. At 1h, 6h, 1d, 2d, 4d, 8d and 15d, three jars were removed from the assay in a no-replacement experimental design. At each time leaves from each jar were removed and oven-dried. Afterwards, dried up leaves were weighed and remaining dry mass was determined and expressed as ash-free dry mass. Mass losses of leaf litter showed steep declines for the first two days followed by a steady decrease in mass loss. Data was fitted to three different models: single-exponential, power and rational. Our results showed that the mass loss predicted with the single-exponential model did not reflect the real data at any stage of the leaching process. The power model showed a better adjustment, but fails predicting successfully the behavior during leaching's early stages. To evaluate the performance of our models we used three criteria: Adj-R2, Akaike's Information Criteria (AIC), and residual distribution. Higher Adj-R2 were obtained for the power and the rational-type models. However, when AIC and residuals distribution were used, the only model that could satisfactory predict the behavior of our dataset was the rational-type. Even if the Adj-R2 was higher for some species when using the power model compared to the rational-type; our results showed that this criterion alone cannot demonstrate the predicting performance of any model. Usually Adj-R2 is used when assessing the goodness of fit for any mathematical model disregarding the fact that a good Adj-R2 could be obtained even when statistical assumptions required for the validity of the model are not satisfied. Our results showed that sampling at the initial stages of leaching is necessary to adequately describe this process. We also provided evidence that using traditional mathematical models is not the best option to evaluate leaching kinetics because of its mathematical inability to properly describe the abrupt changes that occur during the early stages of leaching. We also found useful applying different criteria to evaluate the goodness-of-fit and performance of any model considered taking into account both statistical and biological meaning of the results.

Plurality of Type A evaluations of uncertainty

NASA Astrophysics Data System (ADS)

Possolo, Antonio; Pintar, Adam L.

2017-10-01

The evaluations of measurement uncertainty involving the application of statistical methods to measurement data (Type A evaluations as specified in the Guide to the Expression of Uncertainty in Measurement, GUM) comprise the following three main steps: (i) developing a statistical model that captures the pattern of dispersion or variability in the experimental data, and that relates the data either to the measurand directly or to some intermediate quantity (input quantity) that the measurand depends on; (ii) selecting a procedure for data reduction that is consistent with this model and that is fit for the purpose that the results are intended to serve; (iii) producing estimates of the model parameters, or predictions based on the fitted model, and evaluations of uncertainty that qualify either those estimates or these predictions, and that are suitable for use in subsequent uncertainty propagation exercises. We illustrate these steps in uncertainty evaluations related to the measurement of the mass fraction of vanadium in a bituminous coal reference material, including the assessment of the homogeneity of the material, and to the calibration and measurement of the amount-of-substance fraction of a hydrochlorofluorocarbon in air, and of the age of a meteorite. Our goal is to expose the plurality of choices that can reasonably be made when taking each of the three steps outlined above, and to show that different choices typically lead to different estimates of the quantities of interest, and to different evaluations of the associated uncertainty. In all the examples, the several alternatives considered represent choices that comparably competent statisticians might make, but who differ in the assumptions that they are prepared to rely on, and in their selection of approach to statistical inference. They represent also alternative treatments that the same statistician might give to the same data when the results are intended for different purposes.

Predictive Validation of an Influenza Spread Model

PubMed Central

Hyder, Ayaz; Buckeridge, David L.; Leung, Brian

2013-01-01

Background Modeling plays a critical role in mitigating impacts of seasonal influenza epidemics. Complex simulation models are currently at the forefront of evaluating optimal mitigation strategies at multiple scales and levels of organization. Given their evaluative role, these models remain limited in their ability to predict and forecast future epidemics leading some researchers and public-health practitioners to question their usefulness. The objective of this study is to evaluate the predictive ability of an existing complex simulation model of influenza spread. Methods and Findings We used extensive data on past epidemics to demonstrate the process of predictive validation. This involved generalizing an individual-based model for influenza spread and fitting it to laboratory-confirmed influenza infection data from a single observed epidemic (1998–1999). Next, we used the fitted model and modified two of its parameters based on data on real-world perturbations (vaccination coverage by age group and strain type). Simulating epidemics under these changes allowed us to estimate the deviation/error between the expected epidemic curve under perturbation and observed epidemics taking place from 1999 to 2006. Our model was able to forecast absolute intensity and epidemic peak week several weeks earlier with reasonable reliability and depended on the method of forecasting-static or dynamic. Conclusions Good predictive ability of influenza epidemics is critical for implementing mitigation strategies in an effective and timely manner. Through the process of predictive validation applied to a current complex simulation model of influenza spread, we provided users of the model (e.g. public-health officials and policy-makers) with quantitative metrics and practical recommendations on mitigating impacts of seasonal influenza epidemics. This methodology may be applied to other models of communicable infectious diseases to test and potentially improve their predictive ability. PMID:23755236

Estimation of renal allograft half-life: fact or fiction?

PubMed

Azancot, M Antonieta; Cantarell, Carme; Perelló, Manel; Torres, Irina B; Serón, Daniel; Seron, Daniel; Moreso, Francesc; Arias, Manuel; Campistol, Josep M; Curto, Jordi; Hernandez, Domingo; Morales, José M; Sanchez-Fructuoso, Ana; Abraira, Victor

2011-09-01

Renal allograft half-life time (t½) is the most straightforward representation of long-term graft survival. Since some statistical models overestimate this parameter, we compare different approaches to evaluate t½. Patients with a 1-year functioning graft transplanted in Spain during 1990, 1994, 1998 and 2002 were included. Exponential, Weibull, gamma, lognormal and log-logistic models censoring the last year of follow-up were evaluated. The goodness of fit of these models was evaluated according to the Cox-Snell residuals and the Akaike's information criterion (AIC) was employed to compare these models. We included 4842 patients. Real t½ in 1990 was 14.2 years. Median t½ (95% confidence interval) in 1990 and 2002 was 15.8 (14.2-17.5) versus 52.6 (35.6-69.5) according to the exponential model (P < 0.001). No differences between 1990 and 2002 were observed when t½ was estimated with the other models. In 1990 and 2002, t½ was 14.0 (13.1-15.0) versus 18.0 (13.7-22.4) according to Weibull, 15.5 (13.9-17.1) versus 19.1 (15.6-22.6) according to gamma, 14.4 (13.3-15.6) versus 18.3 (14.2-22.3) according to the log-logistic and 15.2 (13.8-16.6) versus 18.8 (15.3-22.3) according to the lognormal models. The AIC confirmed that the exponential model had the lowest goodness of fit, while the other models yielded a similar result. The exponential model overestimates t½, especially in cohorts of patients with a short follow-up, while any of the other studied models allow a better estimation even in cohorts with short follow-up.

Characterization of small-to-medium head-and-face dimensions for developing respirator fit test panels and evaluating fit of filtering facepiece respirators with different faceseal design

PubMed Central

Lin, Yi-Chun

2017-01-01

A respirator fit test panel (RFTP) with facial size distribution representative of intended users is essential to the evaluation of respirator fit for new models of respirators. In this study an anthropometric survey was conducted among youths representing respirator users in mid-Taiwan to characterize head-and-face dimensions key to RFTPs for application to small-to-medium facial features. The participants were fit-tested for three N95 masks of different facepiece design and the results compared to facial size distribution specified in the RFTPs of bivariate and principal component analysis design developed in this study to realize the influence of facial characteristics to respirator fit in relation to facepiece design. Nineteen dimensions were measured for 206 participants. In fit testing the qualitative fit test (QLFT) procedures prescribed by the U.S. Occupational Safety and Health Administration were adopted. As the results show, the bizygomatic breadth of the male and female participants were 90.1 and 90.8% of their counterparts reported for the U.S. youths (P < 0.001), respectively. Compared to the bivariate distribution, the PCA design better accommodated variation in facial contours among different respirator user groups or populations, with the RFTPs reported in this study and from literature consistently covering over 92% of the participants. Overall, the facial fit of filtering facepieces increased with increasing facial dimensions. The total percentages of the tests wherein the final maneuver being completed was “Moving head up-and-down”, “Talking” or “Bending over” in bivariate and PCA RFTPs were 13.3–61.9% and 22.9–52.8%, respectively. The respirators with a three-panel flat fold structured in the facepiece provided greater fit, particularly when the users moved heads. When the facial size distribution in a bivariate RFTP did not sufficiently represent petite facial size, the fit testing was inclined to overestimate the general fit, thus for small-to-medium facial dimensions a distinct RFTP should be considered. PMID:29176833

Characterization of small-to-medium head-and-face dimensions for developing respirator fit test panels and evaluating fit of filtering facepiece respirators with different faceseal design.

PubMed

Lin, Yi-Chun; Chen, Chen-Peng

2017-01-01

A respirator fit test panel (RFTP) with facial size distribution representative of intended users is essential to the evaluation of respirator fit for new models of respirators. In this study an anthropometric survey was conducted among youths representing respirator users in mid-Taiwan to characterize head-and-face dimensions key to RFTPs for application to small-to-medium facial features. The participants were fit-tested for three N95 masks of different facepiece design and the results compared to facial size distribution specified in the RFTPs of bivariate and principal component analysis design developed in this study to realize the influence of facial characteristics to respirator fit in relation to facepiece design. Nineteen dimensions were measured for 206 participants. In fit testing the qualitative fit test (QLFT) procedures prescribed by the U.S. Occupational Safety and Health Administration were adopted. As the results show, the bizygomatic breadth of the male and female participants were 90.1 and 90.8% of their counterparts reported for the U.S. youths (P < 0.001), respectively. Compared to the bivariate distribution, the PCA design better accommodated variation in facial contours among different respirator user groups or populations, with the RFTPs reported in this study and from literature consistently covering over 92% of the participants. Overall, the facial fit of filtering facepieces increased with increasing facial dimensions. The total percentages of the tests wherein the final maneuver being completed was "Moving head up-and-down", "Talking" or "Bending over" in bivariate and PCA RFTPs were 13.3-61.9% and 22.9-52.8%, respectively. The respirators with a three-panel flat fold structured in the facepiece provided greater fit, particularly when the users moved heads. When the facial size distribution in a bivariate RFTP did not sufficiently represent petite facial size, the fit testing was inclined to overestimate the general fit, thus for small-to-medium facial dimensions a distinct RFTP should be considered.

Fit Assessment of N95 Filtering-Facepiece Respirators in the U.S. Centers for Disease Control and Prevention Strategic National Stockpile.

PubMed

Bergman, Michael; Zhuang, Ziqing; Brochu, Elizabeth; Palmiero, Andrew

National Institute for Occupational Safety and Health (NIOSH)-approved N95 filtering-facepiece respirators (FFR) are currently stockpiled by the U.S. Centers for Disease Control and Prevention (CDC) for emergency deployment to healthcare facilities in the event of a widespread emergency such as an influenza pandemic. This study assessed the fit of N95 FFRs purchased for the CDC Strategic National Stockpile. The study addresses the question of whether the fit achieved by specific respirator sizes relates to facial size categories as defined by two NIOSH fit test panels. Fit test data were analyzed from 229 test subjects who performed a nine-donning fit test on seven N95 FFR models using a quantitative fit test protocol. An initial respirator model selection process was used to determine if the subject could achieve an adequate fit on a particular model; subjects then tested the adequately fitting model for the nine-donning fit test. Only data for models which provided an adequate initial fit (through the model selection process) for a subject were analyzed for this study. For the nine-donning fit test, six of the seven respirator models accommodated the fit of subjects (as indicated by geometric mean fit factor > 100) for not only the intended NIOSH bivariate and PCA panel sizes corresponding to the respirator size, but also for other panel sizes which were tested for each model. The model which showed poor performance may not be accurately represented because only two subjects passed the initial selection criteria to use this model. Findings are supportive of the current selection of facial dimensions for the new NIOSH panels. The various FFR models selected for the CDC Strategic National Stockpile provide a range of sizing options to fit a variety of facial sizes.

Fit Assessment of N95 Filtering-Facepiece Respirators in the U.S. Centers for Disease Control and Prevention Strategic National Stockpile

PubMed Central

Bergman, Michael; Zhuang, Ziqing; Brochu, Elizabeth; Palmiero, Andrew

2016-01-01

National Institute for Occupational Safety and Health (NIOSH)-approved N95 filtering-facepiece respirators (FFR) are currently stockpiled by the U.S. Centers for Disease Control and Prevention (CDC) for emergency deployment to healthcare facilities in the event of a widespread emergency such as an influenza pandemic. This study assessed the fit of N95 FFRs purchased for the CDC Strategic National Stockpile. The study addresses the question of whether the fit achieved by specific respirator sizes relates to facial size categories as defined by two NIOSH fit test panels. Fit test data were analyzed from 229 test subjects who performed a nine-donning fit test on seven N95 FFR models using a quantitative fit test protocol. An initial respirator model selection process was used to determine if the subject could achieve an adequate fit on a particular model; subjects then tested the adequately fitting model for the nine-donning fit test. Only data for models which provided an adequate initial fit (through the model selection process) for a subject were analyzed for this study. For the nine-donning fit test, six of the seven respirator models accommodated the fit of subjects (as indicated by geometric mean fit factor > 100) for not only the intended NIOSH bivariate and PCA panel sizes corresponding to the respirator size, but also for other panel sizes which were tested for each model. The model which showed poor performance may not be accurately represented because only two subjects passed the initial selection criteria to use this model. Findings are supportive of the current selection of facial dimensions for the new NIOSH panels. The various FFR models selected for the CDC Strategic National Stockpile provide a range of sizing options to fit a variety of facial sizes. PMID:26877587

Kinetic modeling and long-term test-retest reproducibility of the mGluR5 PET tracer 18F-FPEB in human brain.

PubMed

Leurquin-Sterk, Gil; Postnov, Andrey; de Laat, Bart; Casteels, Cindy; Celen, Sofie; Crunelle, Cleo L; Bormans, Guy; Koole, Michel; Van Laere, Koen

2016-04-01

(18)F-FPEB is a promising PET tracer for studying the metabotropic glutamate subtype 5 receptor (mGluR5) expression in neuropsychiatric disorders. To assess the potential of (18)F-FPEB for longitudinal mGluR5 evaluation in patient studies, we evaluated the long-term test-retest reproducibility using various kinetic models in the human brain. Nine healthy volunteers underwent consecutive scans separated by a 6-month period. Dynamic PET was combined with arterial sampling and radiometabolite analysis. Total distribution volume (V(T)) and nondisplaceable binding potential (BP(ND)) were derived from a two-tissue compartment model without constraints (2TCM) and with constraining the K(1)/k(2) ratio to the value of either cerebellum (2TCM-CBL) or pons (2TCM-PONS). The effect of fitting different functions to the tracer parent fractions and reducing scan duration were assessed. Regional absolute test-retest variability (aTRV), coefficient of repeatability (CR) and intraclass correlation coefficient (ICC) were computed. The 2TCM-CBL showed best fits. The mean 6-month aTRV of V(T) ranged from 8 to 13% (CR < 25%) with ICC > 0.6 for all kinetic models. BPND from 2TCM-CBL with a sigmoid fit for the parent fractions showed the best reproducibility, with aTRV ≤ 7% (CR < 16%) and ICC > 0.9 in most regions. Reducing the scan duration from 90 to 60 min did not affect reproducibility. These results demonstrate for the first time that (18)F-FPEB brain PET has good long-term reproducibility, therefore validating its use to monitor mGluR5 expression in longitudinal clinical studies. We suggest a 2TCM-CBL with fitting a sigmoid function to the parent fractions to be optimal for this tracer. © 2016 Wiley Periodicals, Inc.

Using a Model of Analysts' Judgments to Augment an Item Calibration Process

ERIC Educational Resources Information Center

Hauser, Carl; Thum, Yeow Meng; He, Wei; Ma, Lingling

2015-01-01

When conducting item reviews, analysts evaluate an array of statistical and graphical information to assess the fit of a field test (FT) item to an item response theory model. The process can be tedious, particularly when the number of human reviews (HR) to be completed is large. Furthermore, such a process leads to decisions that are susceptible…

A New Statistic for Evaluating Item Response Theory Models for Ordinal Data. CRESST Report 839

ERIC Educational Resources Information Center

Cai, Li; Monroe, Scott

2014-01-01

We propose a new limited-information goodness of fit test statistic C[subscript 2] for ordinal IRT models. The construction of the new statistic lies formally between the M[subscript 2] statistic of Maydeu-Olivares and Joe (2006), which utilizes first and second order marginal probabilities, and the M*[subscript 2] statistic of Cai and Hansen…

Structural Examination of RIASEC Scales in High School Students: Variation across Ethnicity and Method

ERIC Educational Resources Information Center

Gupta, Saurabh; Tracey, Terence J. G.; Gore, Paul A., Jr.

2008-01-01

The structural validity of Holland's model of vocational interests across racial/ethnic groups was examined in the population of high school juniors in two states. The fit of the circumplex model to Holland's RIASEC types as assessed by the UNIACT-R was evaluated for the general sample and five subgroups: Caucasian/Euro-Americans, African…

Predicting the Best Fit: A Comparison of Response Surface Models for Midazolam and Alfentanil Sedation in Procedures With Varying Stimulation.

PubMed

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 <0.5 from the observed response. The 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 with varying degrees of stimulation.

Evaluation of marginal and internal gaps of metal ceramic crowns obtained from conventional impressions and casting techniques with those obtained from digital techniques.

PubMed

Rai, Rathika; Kumar, S Arun; Prabhu, R; Govindan, Ranjani Thillai; Tanveer, Faiz Mohamed

2017-01-01

Accuracy in fit of cast metal restoration has always remained as one of the primary factors in determining the success of the restoration. A well-fitting restoration needs to be accurate both along its margin and with regard to its internal surface. The aim of the study is to evaluate the marginal fit of metal ceramic crowns obtained by conventional inlay casting wax pattern using conventional impression with the metal ceramic crowns obtained by computer-aided design and computer-aided manufacturing (CAD/CAM) technique using direct and indirect optical scanning. This in vitro study on preformed custom-made stainless steel models with former assembly that resembles prepared tooth surfaces of standardized dimensions comprised three groups: the first group included ten samples of metal ceramic crowns fabricated with conventional technique, the second group included CAD/CAM-milled direct metal laser sintering (DMLS) crowns using indirect scanning, and the third group included DMLS crowns fabricated by direct scanning of the stainless steel model. The vertical marginal gap and the internal gap were evaluated with the stereomicroscope (Zoomstar 4); post hoc Turkey's test was used for statistical analysis. One-way analysis of variance method was used to compare the mean values. Metal ceramic crowns obtained from direct optical scanning showed the least marginal and internal gap when compared to the castings obtained from inlay casting wax and indirect optical scanning. Indirect and direct optical scanning had yielded results within clinically acceptable range.

Structural differences within negative and depressive syndrome dimensions in schizophrenia, organic brain disease, and major depression: A confirmatory factor analysis of the positive and negative syndrome scale.

PubMed

Eisenberg, Daniel P; Aniskin, Dmitry B; White, Leonard; Stein, Judith A; Harvey, Philip D; Galynker, Igor I

2009-01-01

The emerging dimensional approach to classification and treatment of psychiatric disorders calls for better understanding of diagnosis-related variations in psychiatric syndromes and for proper validation of psychometric scales used for the evaluation of those syndromes. This study tested the hypothesis that negative and depressive syndromes as measured by the Positive and Negative Syndrome Scale (PANSS) are consistent across different diagnoses. We administered the PANSS to subjects with schizophrenia (n = 305), organic brain disease (OBD, n = 66) and major depressive disorder (MDD, n = 75). Confirmatory factor analysis (CFA) was used to establish if the PANSS items for negative symptoms and for depression fit the hypothesized factor structure and if the item factor loadings were similar among the diagnostic groups. The negative and depressive symptom subscales fit well according to a variety of fit indexes for all groups individually after some modest model modification. However, multisample modeling procedures indicated that the pattern of factor loadings was significantly different among the groups in most cases. The results of this study indicate diagnosis-related variations in the negative and depressive syndrome dimensions in schizophrenia, OBD and MDD. These results also validate limited use of the PANSS for evaluation of negative and depressive syndromes in disorders other than schizophrenia. Larger studies are warranted to further evaluate clinical and nosologic significance of diagnostic categories, dimensions and structures of psychiatric syndromes. 2009 S. Karger AG, Basel.

76 FR 454 - Hazardous Materials Transportation: Revisions of Special Permits Procedures

Federal Register 2010, 2011, 2012, 2013, 2014

2011-01-05

... the agency to evaluate the applicant's fitness and the safety impact of operations that would be... applicant granted a special permit undergoes a safety fitness evaluation, further assuring the safety of... operations to enable the agency to evaluate the applicant's fitness and the safety impact of operations that...

78 FR 40757 - Merchant Marine Personnel Advisory Committee: Intercessional Meeting

Federal Register 2010, 2011, 2012, 2013, 2014

2013-07-08

... CG-719K/E, Merchant Mariner Evaluation of Fitness for Entry Level Ratings.'' This meeting will be..., qualifications, certification, documentation, and fitness standards. The Committee will advise, consult with, and..., Merchant Mariner Credential Medical Evaluation Report and CG-719K/E, Merchant Mariner Evaluation of Fitness...

Calibration and accuracy analysis of a focused plenoptic camera

NASA Astrophysics Data System (ADS)

Zeller, N.; Quint, F.; Stilla, U.

2014-08-01

In this article we introduce new methods for the calibration of depth images from focused plenoptic cameras and validate the results. We start with a brief description of the concept of a focused plenoptic camera and how from the recorded raw image a depth map can be estimated. For this camera, an analytical expression of the depth accuracy is derived for the first time. In the main part of the paper, methods to calibrate a focused plenoptic camera are developed and evaluated. The optical imaging process is calibrated by using a method which is already known from the calibration of traditional cameras. For the calibration of the depth map two new model based methods, which make use of the projection concept of the camera are developed. These new methods are compared to a common curve fitting approach, which is based on Taylor-series-approximation. Both model based methods show significant advantages compared to the curve fitting method. They need less reference points for calibration than the curve fitting method and moreover, supply a function which is valid in excess of the range of calibration. In addition the depth map accuracy of the plenoptic camera was experimentally investigated for different focal lengths of the main lens and is compared to the analytical evaluation.

The Nursing Performance Instrument: Exploratory and Confirmatory Factor Analyses in Registered Nurses.

PubMed

Sagherian, Knar; Steege, Linsey M; Geiger-Brown, Jeanne; Harrington, Donna

2018-04-01

The optimal performance of nurses in healthcare settings plays a critical role in care quality and patient safety. Despite this importance, few measures are provided in the literature that evaluate nursing performance as an independent construct from competencies. The nine-item Nursing Performance Instrument (NPI) was developed to fill this gap. The aim of this study was to examine and confirm the underlying factor structure of the NPI in registered nurses. The design was cross-sectional, using secondary data collected between February 2008 and April 2009 for the "Fatigue in Nursing Survey" (N = 797). The sample was predominantly dayshift female nurses working in acute care settings. Using Mplus software, exploratory and confirmatory factor analyses were applied to the NPI data, which were divided into two equal subsamples. Multiple fit indices were used to evaluate the fit of the alternative models. The three-factor model was determined to fit the data adequately. The factors that were labeled as "physical/mental decrements," "consistent practice," and "behavioral change" were moderately to strongly intercorrelated, indicating good convergent validity. The reliability coefficients for the subscales were acceptable. The NPI consists of three latent constructs. This instrument has the potentialto be used as a self-monitoring instrument that addressesnurses' perceptions of performance while providing patient care.

Human Fitting Studies of Cleveland Clinic Continuous-Flow Total Artificial Heart

PubMed Central

Karimov, Jamshid H.; Steffen, Robert J.; Byram, Nicole; Sunagawa, Gengo; Horvath, David; Cruz, Vincent; Golding, Leonard A.R.; Fukamachi, Kiyotaka; Moazami, Nader

2015-01-01

Implantation of mechanical circulatory support devices is challenging, especially in patients with a small chest cavity. We evaluated how well the Cleveland Clinic continuous-flow total artificial heart (CFTAH) fit the anatomy of patients about to receive a heart transplant. A mock pump model of the CFTAH was rapid-prototyped using biocompatible materials. The model was brought to the operative table, and the direction, length, and angulation of the inflow/outflow ports and outflow conduits were evaluated after the recipient's ventricles had been resected. Thoracic cavity measurements were based on preoperative computed tomographic data. The CFTAH fit well in all five patients (height, 170 ± 9 cm; weight, 75 ± 24 kg). Body surface area was 1.9 ± 0.3 m2 (range, 1.6-2.1 m2). The required inflow and outflow port orientation of both the left and right housings appeared consistent with the current version of the CFTAH implanted in calves. The left outflow conduit remained straight, but the right outflow direction necessitated a 73 ± 22 degree angulation to prevent potential kinking when crossing over the connected left outflow. These data support the fact that our design achieves the proper anatomical relationship of the CFTAH to a patient's native vessels. PMID:25806613

Data error and highly parameterized groundwater models

USGS Publications Warehouse

Hill, M.C.

2008-01-01

Strengths and weaknesses of highly parameterized models, in which the number of parameters exceeds the number of observations, are demonstrated using a synthetic test case. Results suggest that the approach can yield close matches to observations but also serious errors in system representation. It is proposed that avoiding the difficulties of highly parameterized models requires close evaluation of: (1) model fit, (2) performance of the regression, and (3) estimated parameter distributions. Comparisons to hydrogeologic information are expected to be critical to obtaining credible models. Copyright ?? 2008 IAHS Press.

Zirconium Evaluations for ENDF/B-VII.2 for the Fast Region

NASA Astrophysics Data System (ADS)

Brown, D. A.; Arcilla, R.; Capote, R.; Mughabghab, S. F.; Herman, M. W.; Trkov, A.; Kim, H. I.

2014-04-01

We have performed a new combined set of evaluations for 90-96Zr, including new resolved resonance parameterizations from Said Mughabghab for 90,91,92,94,96Zr and fast region calculations made with EMPIRE-3.1. Because 90Zr is a magic nucleus, stable Zr isotopes are nearly spherical. A new soft-rotor optical model potential is used allowing calculations of the inelastic scattering on low-lying coupled levels of vibrational nature. A soft rotor model describes dynamical deformations of the nucleus around the spherical shape and is implemented in EMPIRE/OPTMAN code. The same potential is used with rigid rotor couplings for odd-A nuclei. This then led to improved elastic angular distributions, helping to resolve improper leakage in the older ENDF/B-VII.1β evaluation in KAPL proprietary, ZPR and TRIGA benchmarks. Another consequence of 90Zr being a magic nucleus is that the level densities in both 90Zr and 91Zr are unusually low causing the (n,el) and (n,tot) cross sections to exhibit large fluctuations above the resolved resonance region. To accommodate these fluctuations, we performed a simultaneous constrained generalized least-square fit to (n,tot) for all isotopic and elemental Zr data in EXFOR, using EMPIRE's TOTRED scaling factor. TOTRED rescales total cross sections so that the optical model calculations are unaltered by the rescaling and the correct competition between channels is maintained. In this fit, all (n,tot) data in EXFOR was used for Ein>100 keV, provided the target isotopic makeup could be correctly understood, including spectrum averaged data and data with broad energy resolution. As a result of our fitting procedure, we will have full cross material and cross reaction covariance for all Zr isotopes and reactions.

Confirmatory factor analysis of 2 versions of the Brief Pain Inventory in an ambulatory population indicates that sleep interference should be interpreted separately.

PubMed

Walton, David M; Putos, Joseph; Beattie, Tyler; MacDermid, Joy C

2016-07-01

The Brief Pain Inventory (BPI-SF) is a widely-used generic pain interference scale, however its factor structure remains unclear. An expanded 10-item version of the Interference subscale has been proposed, but the additional value of the 3 extra items has not been rigorously evaluated. The purpose of this study was to evaluate and contrast the factorial and concurrent validity of the original 7-item and 10-item versions of the BPI-SF in a large heterogeneous sample of patients with chronic pain. Exploratory and confirmatory factor analyses were conducted on independent subsets of the sample, and concurrent correlations with scales capturing similar constructs were evaluated. Two independent exploratory factor analyses (n=500 each) supported a single interference factor in both the 7- and 10-item versions, while confirmatory factor analysis (N=1000) suggested that a 2-factor structure (Physical and Affective) provided better fit. A 3-factor model, where sleep interference was the third factor, improved in model fit further. There was no significant difference in model fit between the 7- and 10-item versions. Concurrent associations with measures of general health, pain intensity and pain-related cognitions were all in the anticipated direction and magnitude and were not different by version of the BPI-SF. The addition of 3 extra items to the original 7-item Interference subscale of the BPI-SF did not improve psychometric properties. The combined results lead us to endorse a 3-factor structure (Physical, Affective, and Sleep Interference) as the more statistically and conceptually sound option. Copyright © 2016 Scandinavian Association for the Study of Pain. Published by Elsevier B.V. All rights reserved.

Cost-effectiveness of one versus two sample faecal immunochemical testing for colorectal cancer screening.

PubMed

Goede, S Lucas; van Roon, Aafke H C; Reijerink, Jacqueline C I Y; van Vuuren, Anneke J; Lansdorp-Vogelaar, Iris; Habbema, J Dik F; Kuipers, Ernst J; van Leerdam, Monique E; van Ballegooijen, Marjolein

2013-05-01

The sensitivity and specificity of a single faecal immunochemical test (FIT) are limited. The performance of FIT screening can be improved by increasing the screening frequency or by providing more than one sample in each screening round. This study aimed to evaluate if two-sample FIT screening is cost-effective compared with one-sample FIT. The MISCAN-colon microsimulation model was used to estimate costs and benefits of strategies with either one or two-sample FIT screening. The FIT cut-off level varied between 50 and 200 ng haemoglobin/ml, and the screening schedule was varied with respect to age range and interval. In addition, different definitions for positivity of the two-sample FIT were considered: at least one positive sample, two positive samples, or the mean of both samples being positive. Within an exemplary screening strategy, biennial FIT from the age of 55-75 years, one-sample FIT provided 76.0-97.0 life-years gained (LYG) per 1000 individuals, at a cost of € 259,000-264,000 (range reflects different FIT cut-off levels). Two-sample FIT screening with at least one sample being positive provided 7.3-12.4 additional LYG compared with one-sample FIT at an extra cost of € 50,000-59,000. However, when all screening intervals and age ranges were considered, intensifying screening with one-sample FIT provided equal or more LYG at lower costs compared with two-sample FIT. If attendance to screening does not differ between strategies it is recommended to increase the number of screening rounds with one-sample FIT screening, before considering increasing the number of FIT samples provided per screening round.

Optical coherence tomography assessment of vessel wall degradation in aneurysmatic thoracic aortas

NASA Astrophysics Data System (ADS)

Real, Eusebio; Eguizabal, Alma; Pontón, Alejandro; Val-Bernal, J. Fernando; Mayorga, Marta; Revuelta, José M.; López-Higuera, José; Conde, Olga M.

2013-06-01

Optical coherence tomographic images of ascending thoracic human aortas from aneurysms exhibit disorders on the smooth muscle cell structure of the media layer of the aortic vessel as well as elastin degradation. Ex-vivo measurements of human samples provide results that correlate with pathologist diagnosis in aneurysmatic and control aortas. The observed disorders are studied as possible hallmarks for aneurysm diagnosis. To this end, the backscattering profile along the vessel thickness has been evaluated by fitting its decay against two different models, a third order polynomial fitting and an exponential fitting. The discontinuities present on the vessel wall on aneurysmatic aortas are slightly better identified with the exponential approach. Aneurysmatic aortic walls present uneven reflectivity decay when compared with healthy vessels. The fitting error has revealed as the most favorable indicator for aneurysm diagnosis as it provides a measure of how uniform is the decay along the vessel thickness.

Simulation Study on Fit Indexes in CFA Based on Data with Slightly Distorted Simple Structure

ERIC Educational Resources Information Center

Beauducel, Andre; Wittmann, Werner W.

2005-01-01

Fit indexes were compared with respect to a specific type of model misspecification. Simple structure was violated with some secondary loadings that were present in the true models that were not specified in the estimated models. The c2 test, Comparative Fit Index, Goodness-of-Fit Index, Incremental Fit Index, Nonnormed Fit Index, root mean…

Individual differences in long-range time representation.

PubMed

Agostino, Camila S; Caetano, Marcelo S; Balci, Fuat; Claessens, Peter M E; Zana, Yossi

2017-04-01

On the basis of experimental data, long-range time representation has been proposed to follow a highly compressed power function, which has been hypothesized to explain the time inconsistency found in financial discount rate preferences. The aim of this study was to evaluate how well linear and power function models explain empirical data from individual participants tested in different procedural settings. The line paradigm was used in five different procedural variations with 35 adult participants. Data aggregated over the participants showed that fitted linear functions explained more than 98% of the variance in all procedures. A linear regression fit also outperformed a power model fit for the aggregated data. An individual-participant-based analysis showed better fits of a linear model to the data of 14 participants; better fits of a power function with an exponent β > 1 to the data of 12 participants; and better fits of a power function with β < 1 to the data of the remaining nine participants. Of the 35 volunteers, the null hypothesis β = 1 was rejected for 20. The dispersion of the individual β values was approximated well by a normal distribution. These results suggest that, on average, humans perceive long-range time intervals not in a highly compressed, biased manner, but rather in a linear pattern. However, individuals differ considerably in their subjective time scales. This contribution sheds new light on the average and individual psychophysical functions of long-range time representation, and suggests that any attribution of deviation from exponential discount rates in intertemporal choice to the compressed nature of subjective time must entail the characterization of subjective time on an individual-participant basis.

Computer codes for the evaluation of thermodynamic and transport properties for equilibrium air to 30000 K

NASA Technical Reports Server (NTRS)

Thompson, Richard A.; Lee, Kam-Pui; Gupta, Roop N.

1991-01-01

The computer codes developed here provide self-consistent thermodynamic and transport properties for equilibrium air for temperatures from 500 to 30000 K over a temperature range of 10 (exp -4) to 10 (exp -2) atm. These properties are computed through the use of temperature dependent curve fits for discrete values of pressure. Interpolation is employed for intermediate values of pressure. The curve fits are based on mixture values calculated from an 11-species air model. Individual species properties used in the mixture relations are obtained from a recent study by the present authors. A review and discussion of the sources and accuracy of the curve fitted data used herein are given in NASA RP 1260.

Effect of X-ray Line Spectra Profile Fitting with Pearson VII, Pseudo-Voigt and Generalized Fermi Functions on Asphalt Binder Aromaticity and Crystallite Parameters

NASA Astrophysics Data System (ADS)

Gebresellasie, K.; Shirokoff, J.; Lewis, J. C.

2012-12-01

X-ray line spectra profile fitting using Pearson VII, pseudo-Voigt and generalized Fermi functions was performed on asphalt binders prior to the calculation of aromaticity and crystallite size parameters. The effects of these functions on the results are presented and discussed in terms of the peak profile fit parameters, the uncertainties in calculated values that can arise owing to peak shape, peak features in the pattern and crystallite size according to the asphalt models (Yen, modified Yen or Yen-Mullins) and theories. Interpretation of these results is important in terms of evaluating the performance of asphalt binders widely used in the application of transportation systems (roads, highways, airports).

Evaluating impacts of fire management strategies on native and invasive plants using an individual-based model

NASA Astrophysics Data System (ADS)

Gangur, Alexander N.; Fill, Jennifer M.; Northfield, Tobin D.; van de Wiel, Marco

2017-04-01

The capacity for species to coexist and potentially exclude one another can broadly be attributed to drivers that influence fitness differences (such as competitive ability) and niche differences (such as environmental change). These drivers, and thus the determinants of coexistence they influence, can interact and fluctuate both spatially and temporally. Understanding the spatiotemporal variation in niche and fitness differences in systems prone to fluctuating drivers, such as fire, can help to inform the management of invasive species. In the Cape floristic region of South Africa, invasive Pinus pinaster seedlings are strong competitors in the post-burn environment of the fire-driven Fynbos vegetation. In this, system native Protea spp. are especially vulnerable to unseasonal burns, but seasonal prescribed (Summer) burns are thought to present a high safety risk. Together, these issues have limited the appeal of prescribed burn management as an alternative to costly manual eradication of P. pinaster. Using a spatially-explicit field-of-neighbourhood individual-based model, we represent the drivers of spatiotemporal variation in niche differences (driven by fire regimes) and fitness differences (driven by competitive ability). In doing so, we evaluate optimal fire management strategies to a) control invasive P. pinaster in the Cape floristic region of South Africa, while b) minimizing deleterious effects of management on native Protea spp. The scarcity of appropriate data for model calibration has been problematic for models in invasion biology, but we use recent advances in Approximate Bayesian Computing techniques to overcome this limitation. We present early conclusions on the viability of prescribed burn management to control P. pinaster in South Africa.

Genetic Parameters for Milk Yield and Lactation Persistency Using Random Regression Models in Girolando Cattle

PubMed Central

Canaza-Cayo, Ali William; Lopes, Paulo Sávio; da Silva, Marcos Vinicius Gualberto Barbosa; de Almeida Torres, Robledo; Martins, Marta Fonseca; Arbex, Wagner Antonio; Cobuci, Jaime Araujo

2015-01-01

A total of 32,817 test-day milk yield (TDMY) records of the first lactation of 4,056 Girolando cows daughters of 276 sires, collected from 118 herds between 2000 and 2011 were utilized to estimate the genetic parameters for TDMY via random regression models (RRM) using Legendre’s polynomial functions whose orders varied from 3 to 5. In addition, nine measures of persistency in milk yield (PSi) and the genetic trend of 305-day milk yield (305MY) were evaluated. The fit quality criteria used indicated RRM employing the Legendre’s polynomial of orders 3 and 5 for fitting the genetic additive and permanent environment effects, respectively, as the best model. The heritability and genetic correlation for TDMY throughout the lactation, obtained with the best model, varied from 0.18 to 0.23 and from −0.03 to 1.00, respectively. The heritability and genetic correlation for persistency and 305MY varied from 0.10 to 0.33 and from −0.98 to 1.00, respectively. The use of PS7 would be the most suitable option for the evaluation of Girolando cattle. The estimated breeding values for 305MY of sires and cows showed significant and positive genetic trends. Thus, the use of selection indices would be indicated in the genetic evaluation of Girolando cattle for both traits. PMID:26323397

Determinants of moderate to vigorous physical activity and obesity in children: a structural equation modeling analysis.

PubMed

Yeung, Daniel Chi-Shing; Yuan, Xin; Hui, Stanley Sai-Chuen; Feresu, Shingairai Aliifina

2016-05-01

The determinants of physical activity (PA) and body fatness in Chinese adolescents are rarely examined. This study aimed to investigate the effect of attitude toward PA, screen time, parents' socioeconomic status (SES), and exercise habit on PA and body fatness among Chinese children by using structural equation modeling (SEM) analysis. Data obtained from the second Community Fitness Survey in Hong Kong were utilized, in which students from one secondary school of each of the 18 districts of Hong Kong were recruited. A total of 2517 questionnaires with physical fitness items were successfully distributed to students aged 13-19 years in these districts. Families' SES, parents' exercise habit, children's intention to participate in PA, amount of moderate to vigorous PA (MVPA), screen time, children's attitude toward PA, and children's body fat percentage were measured and analyzed with SEM. The structural equation model was composed of a measurement model and a structural model. The model was tested with Mplus 6. The Chi-square test, root mean square error of approximation, comparative fit index, and Tucker-Lewis index were calculated to evaluate model fit. The model was then modified based on the model fit indices. Children's intention to participate in PA was a strong predictor of their engagement in MVPA. Parents' exercise habit had both direct and indirect (via attitude) effects on their children's intention to participate in PA. Screen time was not a predictor of body composition. Children's intention to participate in PA directly affected their body composition. Children's attitude toward PA, parents' exercise habit, and SES had significant effects on the children's intention to participate in PA. Furthermore, obesity had a negative effect on the children's attitude toward PA. To promote MVPA and prevent obesity in Chinese children of Hong Kong, it is important to design intervention that enhances children's intention and attitude in PA, as well as parent's exercise habits. Tailormade programs that take SES into consideration are also essential. Further studies are necessary to extend the results and test the model in other metropolitan areas in China.

Investigating mental fitness and school connectedness in Prince Edward Island and New Brunswick, Canada.

PubMed

Murnaghan, Donna; Morrison, William; Laurence, Courtney; Bell, Brandi

2014-07-01

As youth struggle with anxiety and depression, promoting positive mental fitness is a primary concern. Canadian school-based mental health programs that focus on positive psychology and positive mental health initiatives emphasize safe and supportive environments, student engagement, resilience, and self-determination. This study examined predictors of mental fitness and its 3 components (autonomy, competence, and relatedness). School Health Action Planning and Evaluation System-Prince Edward Island (SHAPES-PEI) and the New Brunswick Student Wellness Survey (NB SWS) are data collection and feedback systems that survey youth about 4 health behaviors. Grade 7-12 students in Prince Edward Island (N = 3318) and New Brunswick (N = 7314) completed a mental fitness questionnaire in 2008-2009 (PEI) and 2006-2007 (NB). Four linear regression models were conducted to examine student characteristics associated with mental fitness, autonomy, competence, and relatedness. Positive associations were found between school connectedness (p < .0001) and mental fitness, as well as autonomy, competence, and relatedness. There were also significant relationships between affect, pro-social and antisocial behaviors, tried smoking, and mental fitness. A better understanding of adolescent health and its predictors is needed. By identifying core parameters for mental fitness, we can inform how to address students' needs through appropriate programs and policies supporting healthy school environments. © 2014, American School Health Association.

Direct and indirect relationships of physical fitness, weight status, and learning duration to academic performance in Japanese schoolchildren.

PubMed

Ishihara, Toru; Morita, Noriteru; Nakajima, Toshihiro; Okita, Koichi; Yamatsu, Koji; Sagawa, Masato

2018-03-01

The purpose of this study was to determine, using structural equation modelling (SEM), the direct and indirect influence of daily behaviours (i.e. exercise/learning durations), weight status, and physical fitness on academic performance among seventh-grade schoolchildren, after controlling for socioeconomic status. We analysed cross-sectional data from 274 schoolchildren (159 males and 115 females; 12-13 years old). Academic performance was assessed using the total grade points in eight academic subjects. Physical fitness was evaluated using the total score of eight physical fitness tests and weight status using body mass index. The daily behaviours and socioeconomic status were assessed by the questionnaire. The SEM showed an adequate fit to the data (χ 2  = 0.684, p = .710, RMSEA = .000). Physical fitness and learning durations had direct effects on academic performance (β = .301, p < .001; β = .132, p = .037, respectively) after controlling for confounders. Healthy weight status and exercise habits positively indirectly influenced academic performance via physical fitness. These findings suggest that, independent of socioeconomic status and learning durations, exercise habits and maintaining healthy weight status may indirectly contribute to academic success via better physical fitness in children.

The influence of inherited plumage colour morph on morphometric traits and breeding investment in zebra finches (Taeniopygia guttata).

PubMed

Krause, E Tobias; Krüger, Oliver; Hoffman, Joseph I

2017-01-01

Melanin-based plumage polymorphism occurs in many wild bird populations and has been linked to fitness variation in several species. These fitness differences often arise as a consequence of variation in traits such as behaviour, immune responsiveness, body size and reproductive investment. However, few studies have controlled for genetic differences between colour morphs that could potentially generate artefactual associations between plumage colouration and trait variation. Here, we used zebra finches (Taeniopygia guttata) as a model system in order to evaluate whether life-history traits such as adult body condition and reproductive investment could be influenced by plumage morph. To maximise any potential differences, we selected wild-type and white plumage morphs, which differ maximally in their extent of melanisation, while using a controlled three-generation breeding design to homogenise the genetic background. We found that F2 adults with white plumage colouration were on average lighter and had poorer body condition than wild-type F2 birds. However, they appeared to compensate for this by reproducing earlier and producing heavier eggs relative to their own body mass. Our study thus reveals differences in morphological and life history traits that could be relevant to fitness variation, although further studies will be required to evaluate fitness effects under natural conditions as well as to characterise any potential fitness costs of compensatory strategies in white zebra finches.

[Acute responses on lipid profile by practicing cycling].

PubMed

Díaz-Ríos, Lillian Karina; Rivera-Cisneros, Antonio Eugenio; Macías-Cervantes, Maciste Habacuc; Sánchez-González, Jorge Manuel; Guerrero-Martínez, Francisco Javier

2008-01-01

it has been demonstrated an association between the increase in physical activity and improvements in the lipid profile. to evaluate changes in the serum lipids caused by spinning practice. nine men and twelve women were studied, they underwent to an initial evaluation that included a treadmill effort test, in order to establish the physical fitness level. With the purpose of determine the lipids change, a blood sample was obtained before and after a typical spinning session. The design was prospective, experimental, longitudinal and comparative study. Student's t-test and regression model were used to determine the changes in the lipids concentrations, and its relation with the physical fitness level. A p value < or = 0.05 was required for statistical significance. lipids increase concentrations were observed (p < 0.05), except at triglycerides in men, in which it had a decrease. It was statistically significant relation between the physical fitness level and the percentage of high-density lipoproteins variation (r = 0.44, p = 0.046). the percentage of high-density lipoproteins variation was greater when the values of VO(2)max were higher. At greater level of medical fitness greater positive answer in this lipoproteins. In the case of the rest serum lipids, it was not observed relation between the level of medical fitness and the percentage of variation due to the execution of the spinning session.

The influence of inherited plumage colour morph on morphometric traits and breeding investment in zebra finches (Taeniopygia guttata)

PubMed Central

Krüger, Oliver

2017-01-01

Melanin-based plumage polymorphism occurs in many wild bird populations and has been linked to fitness variation in several species. These fitness differences often arise as a consequence of variation in traits such as behaviour, immune responsiveness, body size and reproductive investment. However, few studies have controlled for genetic differences between colour morphs that could potentially generate artefactual associations between plumage colouration and trait variation. Here, we used zebra finches (Taeniopygia guttata) as a model system in order to evaluate whether life-history traits such as adult body condition and reproductive investment could be influenced by plumage morph. To maximise any potential differences, we selected wild-type and white plumage morphs, which differ maximally in their extent of melanisation, while using a controlled three-generation breeding design to homogenise the genetic background. We found that F2 adults with white plumage colouration were on average lighter and had poorer body condition than wild-type F2 birds. However, they appeared to compensate for this by reproducing earlier and producing heavier eggs relative to their own body mass. Our study thus reveals differences in morphological and life history traits that could be relevant to fitness variation, although further studies will be required to evaluate fitness effects under natural conditions as well as to characterise any potential fitness costs of compensatory strategies in white zebra finches. PMID:29190647

Path analysis and multi-criteria decision making: an approach for multivariate model selection and analysis in health.

PubMed

Vasconcelos, A G; Almeida, R M; Nobre, F F

2001-08-01

This paper introduces an approach that includes non-quantitative factors for the selection and assessment of multivariate complex models in health. A goodness-of-fit based methodology combined with fuzzy multi-criteria decision-making approach is proposed for model selection. Models were obtained using the Path Analysis (PA) methodology in order to explain the interrelationship between health determinants and the post-neonatal component of infant mortality in 59 municipalities of Brazil in the year 1991. Socioeconomic and demographic factors were used as exogenous variables, and environmental, health service and agglomeration as endogenous variables. Five PA models were developed and accepted by statistical criteria of goodness-of fit. These models were then submitted to a group of experts, seeking to characterize their preferences, according to predefined criteria that tried to evaluate model relevance and plausibility. Fuzzy set techniques were used to rank the alternative models according to the number of times a model was superior to ("dominated") the others. The best-ranked model explained above 90% of the endogenous variables variation, and showed the favorable influences of income and education levels on post-neonatal mortality. It also showed the unfavorable effect on mortality of fast population growth, through precarious dwelling conditions and decreased access to sanitation. It was possible to aggregate expert opinions in model evaluation. The proposed procedure for model selection allowed the inclusion of subjective information in a clear and systematic manner.

SU-E-T-664: Radiobiological Modeling of Prophylactic Cranial Irradiation in Mice

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

Smith, D; Debeb, B; Woodward, W

Purpose: Prophylactic cranial irradiation (PCI) is a clinical technique used to reduce the incidence of brain metastasis and improve overall survival in select patients with ALL and SCLC, and we have shown the potential of PCI in select breast cancer patients through a mouse model (manuscript in preparation). We developed a computational model using our experimental results to demonstrate the advantage of treating brain micro-metastases early. Methods: MATLAB was used to develop the computational model of brain metastasis and PCI in mice. The number of metastases per mouse and the volume of metastases from four- and eight-week endpoints were fitmore » to normal and log-normal distributions, respectively. Model input parameters were optimized so that model output would match the experimental number of metastases per mouse. A limiting dilution assay was performed to validate the model. The effect of radiation at different time points was computationally evaluated through the endpoints of incidence, number of metastases, and tumor burden. Results: The correlation between experimental number of metastases per mouse and the Gaussian fit was 87% and 66% at the two endpoints. The experimental volumes and the log-normal fit had correlations of 99% and 97%. In the optimized model, the correlation between number of metastases per mouse and the Gaussian fit was 96% and 98%. The log-normal volume fit and the model agree 100%. The model was validated by a limiting dilution assay, where the correlation was 100%. The model demonstrates that cells are very sensitive to radiation at early time points, and delaying treatment introduces a threshold dose at which point the incidence and number of metastases decline. Conclusion: We have developed a computational model of brain metastasis and PCI in mice that is highly correlated to our experimental data. The model shows that early treatment of subclinical disease is highly advantageous.« less

Environmentally friendly biosorbents (husks, pods and seeds) from Moringa oleifera for Pb(II) removal from contaminated water.

PubMed

Tavares, Fernanda Oliveira; Pinto, Laura Adriane de Moraes; Bassetti, Fátima de Jesus; Vieira, Marcelo Fernandes; Bergamasco, Rosângela; Vieira, Angélica Marquetotti Salcedo

2017-12-01

Lead is a heavy metal considered highly toxic, responsible for causing several health problems as well as being extremely harmful to fauna and flora. Given this fact, several techniques have been studied for the removal of this metal from contaminated water, in which stands out adsorption. In this sense, the objective of this study was to evaluate the potential of lead(II) biosorption from contaminated water by seed husks, seeds and pods of Moringa oleifera Lam (moringa). Biomass was characterized by energy-dispersive X-ray spectroscopy, Scanning Electron Microscopy and Fourier transform infrared spectroscopy analyses. From the studied parameters, the optimal conditions obtained for the three analyzed biosorbents are: 30 min to equilibrium, pH 6 and 25°C temperature. The pseudo-second-order kinetic model was the best fitted to the experimental data for the three evaluated biosorbents. Regarding the adsorption isotherms, the model that best fitted to the experimental data for seed and seed husk was that proposed by Freundlich, and for the pod the Langmuir model. The analysis of the obtained thermodynamic data showed that the adsorption process is favorable and of exothermic nature. Through the results it was concluded that the evaluated biosorbents are efficient in lead(II) biosorption.

Random genetic drift, natural selection, and noise in human cranial evolution.

PubMed

Roseman, Charles C

2016-08-01

This study assesses the extent to which relationships among groups complicate comparative studies of adaptation in recent human cranial variation and the extent to which departures from neutral additive models of evolution hinder the reconstruction of population relationships among groups using cranial morphology. Using a maximum likelihood evolutionary model fitting approach and a mixed population genomic and cranial data set, I evaluate the relative fits of several widely used models of human cranial evolution. Moreover, I compare the goodness of fit of models of cranial evolution constrained by genomic variation to test hypotheses about population specific departures from neutrality. Models from population genomics are much better fits to cranial variation than are traditional models from comparative human biology. There is not enough evolutionary information in the cranium to reconstruct much of recent human evolution but the influence of population history on cranial variation is strong enough to cause comparative studies of adaptation serious difficulties. Deviations from a model of random genetic drift along a tree-like population history show the importance of environmental effects, gene flow, and/or natural selection on human cranial variation. Moreover, there is a strong signal of the effect of natural selection or an environmental factor on a group of humans from Siberia. The evolution of the human cranium is complex and no one evolutionary process has prevailed at the expense of all others. A holistic unification of phenome, genome, and environmental context, gives us a strong point of purchase on these problems, which is unavailable to any one traditional approach alone. Am J Phys Anthropol 160:582-592, 2016. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

Theoretical Systematics of Future Baryon Acoustic Oscillation Surveys

NASA Astrophysics Data System (ADS)

Ding, Zhejie; Seo, Hee-Jong; Vlah, Zvonimir; Feng, Yu; Schmittfull, Marcel; Beutler, Florian

2018-05-01

Future Baryon Acoustic Oscillation surveys aim at observing galaxy clustering over a wide range of redshift and galaxy populations at great precision, reaching tenths of a percent, in order to detect any deviation of dark energy from the ΛCDM model. We utilize a set of paired quasi-N-body FastPM simulations that were designed to mitigate the sample variance effect on the BAO feature and evaluated the BAO systematics as precisely as ˜0.01%. We report anisotropic BAO scale shifts before and after density field reconstruction in the presence of redshift-space distortions over a wide range of redshift, galaxy/halo biases, and shot noise levels. We test different reconstruction schemes and different smoothing filter scales, and introduce physically-motivated BAO fitting models. For the first time, we derive a Galilean-invariant infrared resummed model for halos in real and redshift space. We test these models from the perspective of robust BAO measurements and non-BAO information such as growth rate and nonlinear bias. We find that pre-reconstruction BAO scale has moderate fitting-model dependence at the level of 0.1% - 0.2% for matter while the dependence is substantially reduced to less than 0.07% for halos. We find that post-reconstruction BAO shifts are generally reduced to below 0.1% in the presence of galaxy/halo bias and show much smaller fitting model dependence. Different reconstruction conventions can potentially make a much larger difference on the line-of-sight BAO scale, upto 0.3%. Meanwhile, the precision (error) of the BAO measurements is quite consistent regardless of the choice of the fitting model or reconstruction convention.

Seasonal Influenza Forecasting in Real Time Using the Incidence Decay With Exponential Adjustment Model

PubMed Central

Nasserie, Tahmina; Tuite, Ashleigh R; Whitmore, Lindsay; Hatchette, Todd; Drews, Steven J; Peci, Adriana; Kwong, Jeffrey C; Friedman, Dara; Garber, Gary; Gubbay, Jonathan

2017-01-01

Abstract Background Seasonal influenza epidemics occur frequently. Rapid characterization of seasonal dynamics and forecasting of epidemic peaks and final sizes could help support real-time decision-making related to vaccination and other control measures. Real-time forecasting remains challenging. Methods We used the previously described “incidence decay with exponential adjustment” (IDEA) model, a 2-parameter phenomenological model, to evaluate the characteristics of the 2015–2016 influenza season in 4 Canadian jurisdictions: the Provinces of Alberta, Nova Scotia and Ontario, and the City of Ottawa. Model fits were updated weekly with receipt of incident virologically confirmed case counts. Best-fit models were used to project seasonal influenza peaks and epidemic final sizes. Results The 2015–2016 influenza season was mild and late-peaking. Parameter estimates generated through fitting were consistent in the 2 largest jurisdictions (Ontario and Alberta) and with pooled data including Nova Scotia counts (R0 approximately 1.4 for all fits). Lower R0 estimates were generated in Nova Scotia and Ottawa. Final size projections that made use of complete time series were accurate to within 6% of true final sizes, but final size was using pre-peak data. Projections of epidemic peaks stabilized before the true epidemic peak, but these were persistently early (~2 weeks) relative to the true peak. Conclusions A simple, 2-parameter influenza model provided reasonably accurate real-time projections of influenza seasonal dynamics in an atypically late, mild influenza season. Challenges are similar to those seen with more complex forecasting methodologies. Future work includes identification of seasonal characteristics associated with variability in model performance. PMID:29497629

Research on a Community-based Platform for Promoting Health and Physical Fitness in the Elderly Community

PubMed Central

Tsai, Tsai-Hsuan; Wong, Alice May-Kuen; Hsu, Chien-Lung; Tseng, Kevin C.

2013-01-01

This study aims to assess the acceptability of a fitness testing platform (iFit) for installation in an assisted living community with the aim of promoting fitness and slowing the onset of frailty. The iFit platform develops a means of testing Bureau of Health Promotion mandated health assessment items for the elderly (including flexibility tests, grip strength tests, balance tests, and reaction time tests) and integrates wireless remote sensors in a game-like environment to capture and store subject response data, thus providing individuals in elderly care contexts with a greater awareness of their own physical condition. In this study, we specifically evaluated the users’ intention of using the iFit using a technology acceptance model (TAM). A total of 101 elderly subjects (27 males and 74 females) were recruited. A survey was conducted to measure technology acceptance, to verify that the platform could be used as intended to promote fitness among the elderly. Results indicate that perceived usefulness, perceived ease of use and usage attitude positively impact behavioral intention to use the platform. The iFit platform can offer user-friendly solutions for a community-based fitness care and monitoring of elderly subjects. In summary, iFit was determined by three key drivers and discussed as follows: risk factors among the frail elderly, mechanism for slowing the advance frailty, and technology acceptance and support for promoting physical fitness. PMID:23460859

LIFESTYLE INDICATORS AND CARDIORESPIRATORY FITNESS IN ADOLESCENTS

PubMed Central

de Victo, Eduardo Rossato; Ferrari, Gerson Luis de Moraes; da Silva, João Pedro; Araújo, Timóteo Leandro; Matsudo, Victor Keihan Rodrigues

2017-01-01

ABSTRACT Objective: To evaluate the lifestyle indicators associated with cardiorespiratory fitness in adolescents from Ilhabela, São Paulo, Brazil. Methods: The sample consisted of 181 adolescents (53% male) from the Mixed Longitudinal Project on Growth, Development, and Physical Fitness of Ilhabela. Body composition (weight, height, and body mass index, or BMI), school transportation, time spent sitting, physical activity, sports, television time (TV), having a TV in the bedroom, sleep, health perception, diet, and economic status (ES) were analyzed. Cardiorespiratory fitness was estimated by the submaximal progressive protocol performed on a cycle ergometer. Linear regression models were used with the stepwise method. Results: The sample average age was 14.8 years, and the average cardiorespiratory fitness was 42.2 mL.kg-1.min-1 (42.9 for boys and 41.4 for girls; p=0.341). In the total sample, BMI (unstandardized regression coefficient [B]=-0.03), height (B=-0.01), ES (B=0.10), gender (B=0.12), and age (B=0.03) were significantly associated with cardiorespiratory fitness. In boys, BMI, height, not playing any sports, and age were significantly associated with cardiorespiratory fitness. In girls, BMI, ES, and having a TV in the bedroom were significantly associated with cardiorespiratory fitness. Conclusions: Lifestyle indicators influenced the cardiorespiratory fitness; BMI, ES, and age influenced both sexes. Not playing any sports, for boys, and having a TV in the bedroom, for girls, also influenced cardiorespiratory fitness. Public health measures to improve lifestyle indicators can help to increase cardiorespiratory fitness levels. PMID:28977318

Changes in Clavicle Length and Maturation in Americans: 1840-1980.

PubMed

Langley, Natalie R; Cridlin, Sandra

2016-01-01

Secular changes refer to short-term biological changes ostensibly due to environmental factors. Two well-documented secular trends in many populations are earlier age of menarche and increasing stature. This study synthesizes data on maximum clavicle length and fusion of the medial epiphysis in 1840-1980 American birth cohorts to provide a comprehensive assessment of developmental and morphological change in the clavicle. Clavicles from the Hamann-Todd Human Osteological Collection (n = 354), McKern and Stewart Korean War males (n = 341), Forensic Anthropology Data Bank (n = 1,239), and the McCormick Clavicle Collection (n = 1,137) were used in the analysis. Transition analysis was used to evaluate fusion of the medial epiphysis (scored as unfused, fusing, or fused). Several statistical treatments were used to assess fluctuations in maximum clavicle length. First, Durbin-Watson tests were used to evaluate autocorrelation, and a local regression (LOESS) was used to identify visual shifts in the regression slope. Next, piecewise regression was used to fit linear regression models before and after the estimated breakpoints. Multiple starting parameters were tested in the range determined to contain the breakpoint, and the model with the smallest mean squared error was chosen as the best fit. The parameters from the best-fit models were then used to derive the piecewise models, which were compared with the initial simple linear regression models to determine which model provided the best fit for the secular change data. The epiphyseal union data indicate a decline in the age at onset of fusion since the early twentieth century. Fusion commences approximately four years earlier in mid- to late twentieth-century birth cohorts than in late nineteenth- and early twentieth-century birth cohorts. However, fusion is completed at roughly the same age across cohorts. The most significant decline in age at onset of epiphyseal union appears to have occurred since the mid-twentieth century. LOESS plots show a breakpoint in the clavicle length data around the mid-twentieth century in both sexes, and piecewise regression models indicate a significant decrease in clavicle length in the American population after 1940. The piecewise model provides a slightly better fit than the simple linear model. Since the model standard error is not substantially different from the piecewise model, an argument could be made to select the less complex linear model. However, we chose the piecewise model to detect changes in clavicle length that are overfitted with a linear model. The decrease in maximum clavicle length is in line with a documented narrowing of the American skeletal form, as shown by analyses of cranial and facial breadth and bi-iliac breadth of the pelvis. Environmental influences on skeletal form include increases in body mass index, health improvements, improved socioeconomic status, and elimination of infectious diseases. Secular changes in bony dimensions and skeletal maturation stipulate that medical and forensic standards used to deduce information about growth, health, and biological traits must be derived from modern populations.

Effect of feldspathic porcelain layering on the marginal fit of zirconia and titanium complete-arch fixed implant-supported frameworks.

PubMed

Yilmaz, Burak; Alshahrani, Faris A; Kale, Ediz; Johnston, William M

2018-02-06

Veneering with porcelain may adversely affect the marginal fit of long-span computer-aided design and computer-aided manufacturing (CAD-CAM) implant-supported fixed prostheses. Moreover, data regarding the precision of fit of CAD-CAM-fabricated implant-supported complete zirconia fixed dental prostheses (FDPs) before and after porcelain layering are limited. The purpose of this in vitro study was to evaluate the effect of porcelain layering on the marginal fit of CAD-CAM-fabricated complete-arch implant-supported, screw-retained FDPs with presintered zirconia frameworks compared with titanium. An autopolymerizing acrylic resin-fixed complete denture framework prototype was fabricated on an edentulous typodont master model (all-on-4 concept; Nobel Biocare) with 2 straight in the anterior and 2 distally tilted internal-hexagon dental implants in the posterior with multiunit abutments bilaterally in canine and first molar locations. A 3-dimensional (3D) laser scanner (S600 ARTI; Zirkonzahn) was used to digitize the prototype and the master model by using scan bodies to generate a virtual 3D CAD framework. Five presintered zirconia (ICE Zirkon Translucent - 95H16; Zirkonzahn) and 5 titanium (Titan 5 - 95H14; Zirkonzahn) frameworks were fabricated using the CAM milling unit (M1 Wet Heavy Metal Milling Unit; Zirkonzahn).The 1-screw test was applied by fixing the frameworks at the location of the maxillary left first molar abutment, and an industrial computed tomography (CT) scanner (XT H 225 - Basic Configuration; Nikon) was used to scan the framework-model complex to evaluate the passive fit of the frameworks on the master model. The scanned data were transported in standard tessellation language (STL) from Volume Graphics analysis software to PolyWorks analysis software by using the maximum-fit algorithm to fit scanned planes in order to mimic the mating surfaces in the best way. 3D virtual assessment of the marginal fit was performed at the abutment-framework interface at the maxillary right canine (gap 3) and right first molar (gap 4) abutments without prosthetic screws. The facial or buccal aspects of the teeth on frameworks were layered with corresponding porcelain (Initial Dental Ceramic System; GC) and CT-scanned again using the same protocol. Marginal fit measurements were made for 4 groups: titanium (Ti) (control), porcelain-layered titanium (Ti-P) (control), zirconia (Zr), and porcelain-layered zirconia (Zr-P). 3D discrepancy mean values were computed and calculated, and the results were analyzed with a repeated measures 3-way ANOVA using the maximum likelihood estimation method and Bonferroni adjustments for selected pairwise comparison t-tests (α=.05). The 3D fit was measured at gap 3 and gap 4. Statistically significant differences in mean 3D discrepancies were observed between Zr-P (175 μm) and Zr (89 μm) and between Zr-P and Ti-P (71 μm) (P<.001). Porcelain layering had a significant effect on the marginal fit of CAD-CAM-fabricated complete-arch implant-supported, screw-retained FDPs with partially sintered zirconia frameworks. 3D marginal discrepancy mean values for all groups were within clinically acceptable limits (<120 μm), except for the layered zirconia framework. Copyright © 2017 Editorial Council for the Journal of Prosthetic Dentistry. Published by Elsevier Inc. All rights reserved.

Energy Savings Analysis for Energy Monitoring and Control Systems

DTIC Science & Technology

1995-01-01

for evaluating design and construction a:-0 quality, and for studying the effectiveness of air - tightening AC retrofits. No simple relationship...Energy These models of residential infiltration are based on statistical "Resource Center (1983) include information on air tightening in fits of

Developmental Inter-Relationships Among Concrete Operational Tasks: An Investigation of Piaget's Stage Concept

ERIC Educational Resources Information Center

Jamison, Wesley

1977-01-01

Two models of intertask relations, Wohlwill's divergent-decalage and reciprocal-interaction patterns, were evaluated for their fit to cross-classification tables which showed the joint classification of 101 children's performance on all possible pairs of eight concrete operational tasks. (SB)

Rationale and study protocol for the 'eCoFit' randomized controlled trial: Integrating smartphone technology, social support and the outdoor physical environment to improve health-related fitness among adults at risk of, or diagnosed with, Type 2 Diabetes.

PubMed

Wilczynska, Magdalena; Lubans, David R; Cohen, Kristen E; Smith, Jordan J; Robards, Sara L; Plotnikoff, Ronald C

2016-07-01

The prevalence and risk of Type 2 Diabetes (T2D) has dramatically increased over the past decade. Physical activity (PA) has significant benefits for the treatment and prevention of T2D. The aim of this study is to develop, implement and evaluate a community-based PA intervention to improve aerobic and muscular fitness among adults at risk of, or diagnosed with T2D. The eCoFit pilot intervention will be evaluated using a randomized controlled trial (RCT) design. The 20-week (Phases 1 and 2) multi-component intervention was guided by Social Cognitive Theory, Health Action Process Approach Model, and Cognitive Behavior Therapy strategies. Phase 1 (Weeks 1-10) includes: i) 5 group face-to-face sessions consisting of outdoor training and cognitive mentoring; and ii) the use of the eCoFit smartphone application with a description of where and how to use the outdoor environment to be more physically active. Phase 2 (Weeks 11-20) includes the use of the eCoFit smartphone application only. Assessments are to be conducted at baseline, 10-weeks (primary end-point) and 20-weeks (secondary end-point) post-baseline. Primary outcomes are cardio-respiratory fitness and muscular fitness (lower body). Secondary outcomes include physical, behavioral, mental health and quality of life, and social-cognitive outcomes. eCoFit is an innovative, multi-component intervention, which integrates smartphone technology, social support and the outdoor physical environment to promote aerobic and resistance training PA among adults at risk of, or diagnosed with T2D. The findings will be used to guide future interventions and to develop and implement effective community-based prevention programs. Australian New Zealand Clinical Trials Registry No: ACTRN12615000990527. Copyright © 2016 Elsevier Inc. All rights reserved.

Comparison of beam position calculation methods for application in digital acquisition systems

NASA Astrophysics Data System (ADS)

Reiter, A.; Singh, R.

2018-05-01

Different approaches to the data analysis of beam position monitors in hadron accelerators are compared adopting the perspective of an analog-to-digital converter in a sampling acquisition system. Special emphasis is given to position uncertainty and robustness against bias and interference that may be encountered in an accelerator environment. In a time-domain analysis of data in the presence of statistical noise, the position calculation based on the difference-over-sum method with algorithms like signal integral or power can be interpreted as a least-squares analysis of a corresponding fit function. This link to the least-squares method is exploited in the evaluation of analysis properties and in the calculation of position uncertainty. In an analytical model and experimental evaluations the positions derived from a straight line fit or equivalently the standard deviation are found to be the most robust and to offer the least variance. The measured position uncertainty is consistent with the model prediction in our experiment, and the results of tune measurements improve significantly.

Reliability and responsiveness of measures of pain in people with osteoarthritis of the knee: a psychometric evaluation

PubMed Central

Turner, Katie V.; Moreton, Bryan M.; Walsh, David A.; Lincoln, Nadina B.

2017-01-01

Abstract Purpose: To examine the fit between data from the Short Form McGill Pain Questionnaire (SF-MPQ-2) and the Rasch model, and to explore the reliability and internal responsiveness of measures of pain in people with knee osteoarthritis. Methods: Participants with knee osteoarthritis completed the SF-MPQ-2, Intermittent and Constant Osteoarthritis Pain questionnaire (ICOAP) and painDETECT. Participants were sent the same questionnaires 3 and 6 months later. Results: Fit to the Rasch model was not achieved for the SF-MPQ-2 Total scale. The Continuous subscale yielded adequate fit statistics after splitting item 10 on uniform DIF for gender, and removing item 9. The Intermittent subscale fit the Rasch model after rescoring items. The Neuropathic subscale had relatively good fit to the model. Test–retest reliability was satisfactory for most scales using both original and Rasch scoring ranging from fair to substantial. Effect sizes ranged from 0.13 to 1.79 indicating good internal responsiveness for most scales. Conclusions: These findings support the use of ICOAP subscales as reliable and responsive measure of pain in people with knee osteoarthritis. The MPQ-SF-2 subscales found to be acceptable alternatives. Implications for RehabilitationThe McGill Pain Questionnaire short version 2 is not a unidimensional scale in people with knee osteoarthritis, whereas three of the subscales are unidimensional.The McGill Pain Questionnaire short version 2 Affective subscale does not have good measurement properties for people with knee osteoarthritis.The McGill Pain Questionnaire short version 2 and the Intermittent and Constant Osteoarthritis Pain scales can be used to assess change over time.The painDETECT performs better as a screening measure than as an outcome measure. PMID:27027698

Psychometric evaluation of the revised Illness Perception Questionnaire (IPQ-R) in cancer patients: confirmatory factor analysis and Rasch analysis.

PubMed

Ashley, Laura; Smith, Adam B; Keding, Ada; Jones, Helen; Velikova, Galina; Wright, Penny

2013-12-01

To provide new insights into the psychometrics of the revised Illness Perception Questionnaire (IPQ-R) in cancer patients. To undertake, for the first time using data from breast, colorectal and prostate cancer patients, a confirmatory factor analysis (CFA) to assess the validity of the IPQ-R's core seven-factor structure. Also, for the first time in any illness group, to undertake Rasch analysis to explore the extent to which the IPQ-R factors form unidimensional scales, with linear measurement properties and no Differential Item Functioning (DIF). Patients with potentially curable breast, colorectal or prostate cancer, within 6months post-diagnosis, completed the IPQ-R online (N=531). CFA was conducted, including multi-sample analysis, and for each IPQ-R factor fit to the Rasch model was assessed by examining, amongst other things, item fit, DIF and unidimensionality. The CFA showed a moderate fit of the data to the IPQ-R model, and stability across diagnosis, although fit was significantly improved following the removal of selected items. All seven factors achieved fit to the Rasch model, and exhibited unidimensionality and minimal DIF, although in most cases this was after some item rescoring and/or deletion. In both analyses, IPQ-R items 12, 18 and 24 were indicated as misfitting and removed. Given the rigorous standard of Rasch measurement, and the generic nature of the IPQ-R, it stood up well to the demands of the Rasch model in this study. Importantly, the results show that with some relatively minor, pragmatic modifications the IPQ-R could possess Rasch-standard measurement in cancer patients. © 2013.

A parametric and non-parametric metamodeling approach for the bias-correction of Satellite Rainfall Estimates using rain gauge measurements. Cases of study: Magdalena Basin (Colombia), Imperial Basin (Chile) and Paraiba do Sul (Brazil).

NASA Astrophysics Data System (ADS)

Rebolledo Coy, M. A.; Villanueva, O. M. B.; Bartz-Beielstein, T.; Ribbe, L.

2017-12-01

Rainfall measurement plays an important role on the understanding and modeling of the water cycle. However, the assessment of scarce data regions using common rain gauge information, cannot be done using a straightforward approach. Some of the main problems concerning rainfall assessment are; the lack of a sufficiently dense grid of ground stations in extensive areas and the unstable spatial accuracy of the Satellite Rainfall Estimates (SREs). Following previous works on SREs analysis and bias-correction, we generate an ensemble model that corrects the bias error on a seasonal and yearly basis using six different state-of-the-art SREs (TRMM 3B42RT, TRMM 3B42v7, PERSIANN-CDR, CHIRPSv2, CMORPH and MSWEPv1.2) in a point-to-pixel approach for the studied period (2003-2015). Three different basins; Magdalena in Colombia, Imperial in Chile and Paraiba do Sul in Brazil are evaluated. Using Gaussian process regression and Bayesian robust regression we model the behavior of the ground stations and evaluate its goodness-of-fit by using the modified Kling-Gupta efficiency (KGE'). Following this evaluation, the models are re-fitted by taking into account the error distribution in each point and the corresponding KGE' is evaluated again. Both models were specified using the probabilistic language STAN. To improve the efficiency of the Gaussian model a clustering of the data was implemented. We also compared the performance of both models in term of uncertainty and stability against the raw input concluding that both models represent better the study areas. The results show that the error displays an exponential behavior for days where precipitation was present, this allows the models to be corrected according to the observed rainfall values. The seasonal evaluations also show improved performance in relation to the yearly evaluations. The use of bias-corrected SREs for hydrologic purposes in scarce data regions is highly recommended in order to merge the punctual values from the ground measurements and the spatial distribution of rainfall from the satellite estimates.

Choosing colors for map display icons using models of visual search.

PubMed

Shive, Joshua; Francis, Gregory

2013-04-01

We show how to choose colors for icons on maps to minimize search time using predictions of a model of visual search. The model analyzes digital images of a search target (an icon on a map) and a search display (the map containing the icon) and predicts search time as a function of target-distractor color distinctiveness and target eccentricity. We parameterized the model using data from a visual search task and performed a series of optimization tasks to test the model's ability to choose colors for icons to minimize search time across icons. Map display designs made by this procedure were tested experimentally. In a follow-up experiment, we examined the model's flexibility to assign colors in novel search situations. The model fits human performance, performs well on the optimization tasks, and can choose colors for icons on maps with novel stimuli to minimize search time without requiring additional model parameter fitting. Models of visual search can suggest color choices that produce search time reductions for display icons. Designers should consider constructing visual search models as a low-cost method of evaluating color assignments.

A comparison of methods of fitting several models to nutritional response data.

PubMed

Vedenov, D; Pesti, G M

2008-02-01

A variety of models have been proposed to fit nutritional input-output response data. The models are typically nonlinear; therefore, fitting the models usually requires sophisticated statistical software and training to use it. An alternative tool for fitting nutritional response models was developed by using widely available and easier-to-use Microsoft Excel software. The tool, implemented as an Excel workbook (NRM.xls), allows simultaneous fitting and side-by-side comparisons of several popular models. This study compared the results produced by the tool we developed and PROC NLIN of SAS. The models compared were the broken line (ascending linear and quadratic segments), saturation kinetics, 4-parameter logistics, sigmoidal, and exponential models. The NRM.xls workbook provided results nearly identical to those of PROC NLIN. Furthermore, the workbook successfully fit several models that failed to converge in PROC NLIN. Two data sets were used as examples to compare fits by the different models. The results suggest that no particular nonlinear model is necessarily best for all nutritional response data.

Using Web Search Query Data to Monitor Dengue Epidemics: A New Model for Neglected Tropical Disease Surveillance

PubMed Central

Chan, Emily H.; Sahai, Vikram; Conrad, Corrie; Brownstein, John S.

2011-01-01

Background A variety of obstacles including bureaucracy and lack of resources have interfered with timely detection and reporting of dengue cases in many endemic countries. Surveillance efforts have turned to modern data sources, such as Internet search queries, which have been shown to be effective for monitoring influenza-like illnesses. However, few have evaluated the utility of web search query data for other diseases, especially those of high morbidity and mortality or where a vaccine may not exist. In this study, we aimed to assess whether web search queries are a viable data source for the early detection and monitoring of dengue epidemics. Methodology/Principal Findings Bolivia, Brazil, India, Indonesia and Singapore were chosen for analysis based on available data and adequate search volume. For each country, a univariate linear model was then built by fitting a time series of the fraction of Google search query volume for specific dengue-related queries from that country against a time series of official dengue case counts for a time-frame within 2003–2010. The specific combination of queries used was chosen to maximize model fit. Spurious spikes in the data were also removed prior to model fitting. The final models, fit using a training subset of the data, were cross-validated against both the overall dataset and a holdout subset of the data. All models were found to fit the data quite well, with validation correlations ranging from 0.82 to 0.99. Conclusions/Significance Web search query data were found to be capable of tracking dengue activity in Bolivia, Brazil, India, Indonesia and Singapore. Whereas traditional dengue data from official sources are often not available until after some substantial delay, web search query data are available in near real-time. These data represent valuable complement to assist with traditional dengue surveillance. PMID:21647308