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Sample records for factor analysis model

  1. Capital Cost Optimization for Prefabrication: A Factor Analysis Evaluation Model

    Directory of Open Access Journals (Sweden)

    Hong Xue

    2018-01-01

    Full Text Available High capital cost is a significant hindrance to the promotion of prefabrication. In order to optimize cost management and reduce capital cost, this study aims to explore the latent factors and factor analysis evaluation model. Semi-structured interviews were conducted to explore potential variables and then questionnaire survey was employed to collect professionals’ views on their effects. After data collection, exploratory factor analysis was adopted to explore the latent factors. Seven latent factors were identified, including “Management Index”, “Construction Dissipation Index”, “Productivity Index”, “Design Efficiency Index”, “Transport Dissipation Index”, “Material increment Index” and “Depreciation amortization Index”. With these latent factors, a factor analysis evaluation model (FAEM, divided into factor analysis model (FAM and comprehensive evaluation model (CEM, was established. The FAM was used to explore the effect of observed variables on the high capital cost of prefabrication, while the CEM was used to evaluate comprehensive cost management level on prefabrication projects. Case studies were conducted to verify the models. The results revealed that collaborative management had a positive effect on capital cost of prefabrication. Material increment costs and labor costs had significant impacts on production cost. This study demonstrated the potential of on-site management and standardization design to reduce capital cost. Hence, collaborative management is necessary for cost management of prefabrication. Innovation and detailed design were needed to improve cost performance. The new form of precast component factories can be explored to reduce transportation cost. Meanwhile, targeted strategies can be adopted for different prefabrication projects. The findings optimized the capital cost and improved the cost performance through providing an evaluation and optimization model, which helps managers to

  2. Bayesian Sensitivity Analysis of a Nonlinear Dynamic Factor Analysis Model with Nonparametric Prior and Possible Nonignorable Missingness.

    Science.gov (United States)

    Tang, Niansheng; Chow, Sy-Miin; Ibrahim, Joseph G; Zhu, Hongtu

    2017-12-01

    Many psychological concepts are unobserved and usually represented as latent factors apprehended through multiple observed indicators. When multiple-subject multivariate time series data are available, dynamic factor analysis models with random effects offer one way of modeling patterns of within- and between-person variations by combining factor analysis and time series analysis at the factor level. Using the Dirichlet process (DP) as a nonparametric prior for individual-specific time series parameters further allows the distributional forms of these parameters to deviate from commonly imposed (e.g., normal or other symmetric) functional forms, arising as a result of these parameters' restricted ranges. Given the complexity of such models, a thorough sensitivity analysis is critical but computationally prohibitive. We propose a Bayesian local influence method that allows for simultaneous sensitivity analysis of multiple modeling components within a single fitting of the model of choice. Five illustrations and an empirical example are provided to demonstrate the utility of the proposed approach in facilitating the detection of outlying cases and common sources of misspecification in dynamic factor analysis models, as well as identification of modeling components that are sensitive to changes in the DP prior specification.

  3. Assessing Heterogeneity for Factor Analysis Model with Continuous and Ordinal Outcomes

    Directory of Open Access Journals (Sweden)

    Ye-Mao Xia

    2016-01-01

    Full Text Available Factor analysis models with continuous and ordinal responses are a useful tool for assessing relations between the latent variables and mixed observed responses. These models have been successfully applied to many different fields, including behavioral, educational, and social-psychological sciences. However, within the Bayesian analysis framework, most developments are constrained within parametric families, of which the particular distributions are specified for the parameters of interest. This leads to difficulty in dealing with outliers and/or distribution deviations. In this paper, we propose a Bayesian semiparametric modeling for factor analysis model with continuous and ordinal variables. A truncated stick-breaking prior is used to model the distributions of the intercept and/or covariance structural parameters. Bayesian posterior analysis is carried out through the simulation-based method. Blocked Gibbs sampler is implemented to draw observations from the complicated posterior. For model selection, the logarithm of pseudomarginal likelihood is developed to compare the competing models. Empirical results are presented to illustrate the application of the methodology.

  4. Critical Factors Analysis for Offshore Software Development Success by Structural Equation Modeling

    Science.gov (United States)

    Wada, Yoshihisa; Tsuji, Hiroshi

    In order to analyze the success/failure factors in offshore software development service by the structural equation modeling, this paper proposes to follow two approaches together; domain knowledge based heuristic analysis and factor analysis based rational analysis. The former works for generating and verifying of hypothesis to find factors and causalities. The latter works for verifying factors introduced by theory to build the model without heuristics. Following the proposed combined approaches for the responses from skilled project managers of the questionnaire, this paper found that the vendor property has high causality for the success compared to software property and project property.

  5. Dimensional Model for Estimating Factors influencing Childhood Obesity: Path Analysis Based Modeling

    Directory of Open Access Journals (Sweden)

    Maryam Kheirollahpour

    2014-01-01

    Full Text Available The main objective of this study is to identify and develop a comprehensive model which estimates and evaluates the overall relations among the factors that lead to weight gain in children by using structural equation modeling. The proposed models in this study explore the connection among the socioeconomic status of the family, parental feeding practice, and physical activity. Six structural models were tested to identify the direct and indirect relationship between the socioeconomic status and parental feeding practice general level of physical activity, and weight status of children. Finally, a comprehensive model was devised to show how these factors relate to each other as well as to the body mass index (BMI of the children simultaneously. Concerning the methodology of the current study, confirmatory factor analysis (CFA was applied to reveal the hidden (secondary effect of socioeconomic factors on feeding practice and ultimately on the weight status of the children and also to determine the degree of model fit. The comprehensive structural model tested in this study suggested that there are significant direct and indirect relationships among variables of interest. Moreover, the results suggest that parental feeding practice and physical activity are mediators in the structural model.

  6. Human Modeling for Ground Processing Human Factors Engineering Analysis

    Science.gov (United States)

    Stambolian, Damon B.; Lawrence, Brad A.; Stelges, Katrine S.; Steady, Marie-Jeanne O.; Ridgwell, Lora C.; Mills, Robert E.; Henderson, Gena; Tran, Donald; Barth, Tim

    2011-01-01

    There have been many advancements and accomplishments over the last few years using human modeling for human factors engineering analysis for design of spacecraft. The key methods used for this are motion capture and computer generated human models. The focus of this paper is to explain the human modeling currently used at Kennedy Space Center (KSC), and to explain the future plans for human modeling for future spacecraft designs

  7. Factor Analysis of Drawings: Application to College Student Models of the Greenhouse Effect

    Science.gov (United States)

    Libarkin, Julie C.; Thomas, Stephen R.; Ording, Gabriel

    2015-01-01

    Exploratory factor analysis was used to identify models underlying drawings of the greenhouse effect made by over 200 entering university freshmen. Initial content analysis allowed deconstruction of drawings into salient features, with grouping of these features via factor analysis. A resulting 4-factor solution explains 62% of the data variance,…

  8. Sparse multivariate factor analysis regression models and its applications to integrative genomics analysis.

    Science.gov (United States)

    Zhou, Yan; Wang, Pei; Wang, Xianlong; Zhu, Ji; Song, Peter X-K

    2017-01-01

    The multivariate regression model is a useful tool to explore complex associations between two kinds of molecular markers, which enables the understanding of the biological pathways underlying disease etiology. For a set of correlated response variables, accounting for such dependency can increase statistical power. Motivated by integrative genomic data analyses, we propose a new methodology-sparse multivariate factor analysis regression model (smFARM), in which correlations of response variables are assumed to follow a factor analysis model with latent factors. This proposed method not only allows us to address the challenge that the number of association parameters is larger than the sample size, but also to adjust for unobserved genetic and/or nongenetic factors that potentially conceal the underlying response-predictor associations. The proposed smFARM is implemented by the EM algorithm and the blockwise coordinate descent algorithm. The proposed methodology is evaluated and compared to the existing methods through extensive simulation studies. Our results show that accounting for latent factors through the proposed smFARM can improve sensitivity of signal detection and accuracy of sparse association map estimation. We illustrate smFARM by two integrative genomics analysis examples, a breast cancer dataset, and an ovarian cancer dataset, to assess the relationship between DNA copy numbers and gene expression arrays to understand genetic regulatory patterns relevant to the disease. We identify two trans-hub regions: one in cytoband 17q12 whose amplification influences the RNA expression levels of important breast cancer genes, and the other in cytoband 9q21.32-33, which is associated with chemoresistance in ovarian cancer. © 2016 WILEY PERIODICALS, INC.

  9. Factor Analysis of Drawings: Application to college student models of the greenhouse effect

    Science.gov (United States)

    Libarkin, Julie C.; Thomas, Stephen R.; Ording, Gabriel

    2015-09-01

    Exploratory factor analysis was used to identify models underlying drawings of the greenhouse effect made by over 200 entering university freshmen. Initial content analysis allowed deconstruction of drawings into salient features, with grouping of these features via factor analysis. A resulting 4-factor solution explains 62% of the data variance, suggesting that 4 archetype models of the greenhouse effect dominate thinking within this population. Factor scores, indicating the extent to which each student's drawing aligned with representative models, were compared to performance on conceptual understanding and attitudes measures, demographics, and non-cognitive features of drawings. Student drawings were also compared to drawings made by scientists to ascertain the extent to which models reflect more sophisticated and accurate models. Results indicate that student and scientist drawings share some similarities, most notably the presence of some features of the most sophisticated non-scientific model held among the study population. Prior knowledge, prior attitudes, gender, and non-cognitive components are also predictive of an individual student's model. This work presents a new technique for analyzing drawings, with general implications for the use of drawings in investigating student conceptions.

  10. Foundations of factor analysis

    CERN Document Server

    Mulaik, Stanley A

    2009-01-01

    Introduction Factor Analysis and Structural Theories Brief History of Factor Analysis as a Linear Model Example of Factor AnalysisMathematical Foundations for Factor Analysis Introduction Scalar AlgebraVectorsMatrix AlgebraDeterminants Treatment of Variables as Vectors Maxima and Minima of FunctionsComposite Variables and Linear Transformations Introduction Composite Variables Unweighted Composite VariablesDifferentially Weighted Composites Matrix EquationsMulti

  11. Container Throughput Forecasting Using Dynamic Factor Analysis and ARIMAX Model

    Directory of Open Access Journals (Sweden)

    Marko Intihar

    2017-11-01

    Full Text Available The paper examines the impact of integration of macroeconomic indicators on the accuracy of container throughput time series forecasting model. For this purpose, a Dynamic factor analysis and AutoRegressive Integrated Moving-Average model with eXogenous inputs (ARIMAX are used. Both methodologies are integrated into a novel four-stage heuristic procedure. Firstly, dynamic factors are extracted from external macroeconomic indicators influencing the observed throughput. Secondly, the family of ARIMAX models of different orders is generated based on the derived factors. In the third stage, the diagnostic and goodness-of-fit testing is applied, which includes statistical criteria such as fit performance, information criteria, and parsimony. Finally, the best model is heuristically selected and tested on the real data of the Port of Koper. The results show that by applying macroeconomic indicators into the forecasting model, more accurate future throughput forecasts can be achieved. The model is also used to produce future forecasts for the next four years indicating a more oscillatory behaviour in (2018-2020. Hence, care must be taken concerning any bigger investment decisions initiated from the management side. It is believed that the proposed model might be a useful reinforcement of the existing forecasting module in the observed port.

  12. A receptor model for urban aerosols based on oblique factor analysis

    DEFF Research Database (Denmark)

    Keiding, Kristian; Sørensen, Morten S.; Pind, Niels

    1987-01-01

    A procedure is outlined for the construction of receptor models of urban aerosols, based on factor analysis. The advantage of the procedure is that the covariation of source impacts is included in the construction of the models. The results are compared with results obtained by other receptor......-modelling procedures. It was found that procedures based on correlating sources were physically sound as well as in mutual agreement. Procedures based on non-correlating sources were found to generate physically obscure models....

  13. Analysis of operational events by ATHEANA framework for human factor modelling

    International Nuclear Information System (INIS)

    Bedreaga, Luminita; Constntinescu, Cristina; Doca, Cezar; Guzun, Basarab

    2007-01-01

    In the area of human reliability assessment, the experts recognise the fact that the current methods have not represented correctly the role of human in prevention, initiating and mitigating the accidents in nuclear power plants. The nature of this deficiency appears because the current methods used in modelling of human factor have not taken into account the human performance and reliability such as it has been observed in the operational events. ATHEANA - A Technique for Human Error ANAlysis - is a new methodology for human analysis that has included the specific data of operational events and also psychological models for human behaviour. This method has included new elements such as the unsafe action and error mechanisms. In this paper we present the application of ATHEANA framework in the analysis of operational events that appeared in different nuclear power plants during 1979-2002. The analysis of operational events has consisted of: - identification of the unsafe actions; - including the unsafe actions into a category, omission ar commission; - establishing the type of error corresponding to the unsafe action: slip, lapse, mistake and circumvention; - establishing the influence of performance by shaping the factors and some corrective actions. (authors)

  14. Rotordynamic analysis for stepped-labyrinth gas seals using moody's friction-factor model

    International Nuclear Information System (INIS)

    Ha, Tae Woong

    2001-01-01

    The governing equations are derived for the analysis of a stepped labyrinth gas seal generally used in high performance compressors, gas turbines, and steam turbines. The bulk-flow is assumed for a single cavity control volume set up in a stepped labyrinth cavity and the flow is assumed to be completely turbulent in the circumferential direction. The Moody's wall-friction-factor model is used for the calculation of wall shear stresses in the single cavity control volume. For the reaction force developed by the stepped labyrinth gas seal, linearized zeroth-order and first-order perturbation equations are developed for small motion about a centered position. Integration of the resultant first-order pressure distribution along and around the seal defines the rotordynamic coefficients of the stepped labyrinth gas seal. The resulting leakage and rotordynamic characteristics of the stepped labyrinth gas seal are presented and compared with Scharrer's theoretical analysis using Blasius' wall-friction-factor model. The present analysis shows a good qualitative agreement of leakage characteristics with Scharrer's analysis, but underpredicts by about 20 %. For the rotordynamic coefficients, the present analysis generally yields smaller predicted values compared with Scharrer's analysis

  15. Structural equation modeling analysis of factors influencing architects' trust in project design teams

    Institute of Scientific and Technical Information of China (English)

    DING Zhi-kun; NG Fung-fai; WANG Jia-yuan

    2009-01-01

    This paper describes a structural equation modeling (SEM) analysis of factors influencing architects' trust in project design teams. We undertook a survey of architects, during which we distributed 193 questionnaires in 29 A-level architectural We used Amos 6.0 for SEM to identify significant personal construct based factors affecting interpersonal trust. The results show that only social interaction between architects significantly affects their interpersonal trust. The explained variance of trust is not very high in the model. Therefore, future research should add more factors into the current model. The practical implication is that team managers should promote the social interactions between team members such that the interpersonal trust level between team members can be improved.

  16. An integrated factor analysis model for product eco-design based on full life cycle assessment

    Energy Technology Data Exchange (ETDEWEB)

    Zhou, Z.; Xiao, T.; Li, D.

    2016-07-01

    Among the methods of comprehensive analysis for a product or an enterprise, there exist defects and deficiencies in traditional standard cost analyses and life cycle assessment methods. For example, some methods only emphasize one dimension (such as economic or environmental factors) while neglecting other relevant dimensions. This paper builds a factor analysis model of resource value flow, based on full life cycle assessment and eco-design theory, in order to expose the relevant internal logic between these two factors. The model considers the efficient multiplication of resources, economic efficiency, and environmental efficiency as its core objectives. The model studies the status of resource value flow during the entire life cycle of a product, and gives an in-depth analysis on the mutual logical relationship of product performance, value, resource consumption, and environmental load to reveal the symptoms and potentials in different dimensions. This provides comprehensive, accurate and timely decision-making information for enterprise managers regarding product eco-design, as well as production and management activities. To conclude, it verifies the availability of this evaluation and analysis model using a Chinese SUV manufacturer as an example. (Author)

  17. Model Fit and Item Factor Analysis: Overfactoring, Underfactoring, and a Program to Guide Interpretation.

    Science.gov (United States)

    Clark, D Angus; Bowles, Ryan P

    2018-04-23

    In exploratory item factor analysis (IFA), researchers may use model fit statistics and commonly invoked fit thresholds to help determine the dimensionality of an assessment. However, these indices and thresholds may mislead as they were developed in a confirmatory framework for models with continuous, not categorical, indicators. The present study used Monte Carlo simulation methods to investigate the ability of popular model fit statistics (chi-square, root mean square error of approximation, the comparative fit index, and the Tucker-Lewis index) and their standard cutoff values to detect the optimal number of latent dimensions underlying sets of dichotomous items. Models were fit to data generated from three-factor population structures that varied in factor loading magnitude, factor intercorrelation magnitude, number of indicators, and whether cross loadings or minor factors were included. The effectiveness of the thresholds varied across fit statistics, and was conditional on many features of the underlying model. Together, results suggest that conventional fit thresholds offer questionable utility in the context of IFA.

  18. Dynamic factor analysis in the frequency domain: causal modeling of multivariate psychophysiological time series

    NARCIS (Netherlands)

    Molenaar, P.C.M.

    1987-01-01

    Outlines a frequency domain analysis of the dynamic factor model and proposes a solution to the problem of constructing a causal filter of lagged factor loadings. The method is illustrated with applications to simulated and real multivariate time series. The latter applications involve topographic

  19. Risk factors of chronic periodontitis on healing response: a multilevel modelling analysis.

    Science.gov (United States)

    Song, J; Zhao, H; Pan, C; Li, C; Liu, J; Pan, Y

    2017-09-15

    Chronic periodontitis is a multifactorial polygenetic disease with an increasing number of associated factors that have been identified over recent decades. Longitudinal epidemiologic studies have demonstrated that the risk factors were related to the progression of the disease. A traditional multivariate regression model was used to find risk factors associated with chronic periodontitis. However, the approach requirement of standard statistical procedures demands individual independence. Multilevel modelling (MLM) data analysis has widely been used in recent years, regarding thorough hierarchical structuring of the data, decomposing the error terms into different levels, and providing a new analytic method and framework for solving this problem. The purpose of our study is to investigate the relationship of clinical periodontal index and the risk factors in chronic periodontitis through MLM analysis and to identify high-risk individuals in the clinical setting. Fifty-four patients with moderate to severe periodontitis were included. They were treated by means of non-surgical periodontal therapy, and then made follow-up visits regularly at 3, 6, and 12 months after therapy. Each patient answered a questionnaire survey and underwent measurement of clinical periodontal parameters. Compared with baseline, probing depth (PD) and clinical attachment loss (CAL) improved significantly after non-surgical periodontal therapy with regular follow-up visits at 3, 6, and 12 months after therapy. The null model and variance component models with no independent variables included were initially obtained to investigate the variance of the PD and CAL reductions across all three levels, and they showed a statistically significant difference (P periodontal therapy with regular follow-up visits had a remarkable curative effect. All three levels had a substantial influence on the reduction of PD and CAL. Site-level had the largest effect on PD and CAL reductions.

  20. Exploratory Bi-factor Analysis: The Oblique Case

    OpenAIRE

    Jennrich, Robert L.; Bentler, Peter M.

    2011-01-01

    Bi-factor analysis is a form of confirmatory factor analysis originally introduced by Holzinger and Swineford (1937). The bi-factor model has a general factor, a number of group factors, and an explicit bi-factor structure. Jennrich and Bentler (2011) introduced an exploratory form of bi-factor analysis that does not require one to provide an explicit bi-factor structure a priori. They use exploratory factor analysis and a bi-factor rotation criterion designed to produce a rotated loading mat...

  1. Replica Analysis for Portfolio Optimization with Single-Factor Model

    Science.gov (United States)

    Shinzato, Takashi

    2017-06-01

    In this paper, we use replica analysis to investigate the influence of correlation among the return rates of assets on the solution of the portfolio optimization problem. We consider the behavior of an optimal solution for the case where the return rate is described with a single-factor model and compare the findings obtained from our proposed methods with correlated return rates with those obtained with independent return rates. We then analytically assess the increase in the investment risk when correlation is included. Furthermore, we also compare our approach with analytical procedures for minimizing the investment risk from operations research.

  2. Spatial Bayesian latent factor regression modeling of coordinate-based meta-analysis data.

    Science.gov (United States)

    Montagna, Silvia; Wager, Tor; Barrett, Lisa Feldman; Johnson, Timothy D; Nichols, Thomas E

    2018-03-01

    Now over 20 years old, functional MRI (fMRI) has a large and growing literature that is best synthesised with meta-analytic tools. As most authors do not share image data, only the peak activation coordinates (foci) reported in the article are available for Coordinate-Based Meta-Analysis (CBMA). Neuroimaging meta-analysis is used to (i) identify areas of consistent activation; and (ii) build a predictive model of task type or cognitive process for new studies (reverse inference). To simultaneously address these aims, we propose a Bayesian point process hierarchical model for CBMA. We model the foci from each study as a doubly stochastic Poisson process, where the study-specific log intensity function is characterized as a linear combination of a high-dimensional basis set. A sparse representation of the intensities is guaranteed through latent factor modeling of the basis coefficients. Within our framework, it is also possible to account for the effect of study-level covariates (meta-regression), significantly expanding the capabilities of the current neuroimaging meta-analysis methods available. We apply our methodology to synthetic data and neuroimaging meta-analysis datasets. © 2017, The International Biometric Society.

  3. Spatial Bayesian Latent Factor Regression Modeling of Coordinate-based Meta-analysis Data

    Science.gov (United States)

    Montagna, Silvia; Wager, Tor; Barrett, Lisa Feldman; Johnson, Timothy D.; Nichols, Thomas E.

    2017-01-01

    Summary Now over 20 years old, functional MRI (fMRI) has a large and growing literature that is best synthesised with meta-analytic tools. As most authors do not share image data, only the peak activation coordinates (foci) reported in the paper are available for Coordinate-Based Meta-Analysis (CBMA). Neuroimaging meta-analysis is used to 1) identify areas of consistent activation; and 2) build a predictive model of task type or cognitive process for new studies (reverse inference). To simultaneously address these aims, we propose a Bayesian point process hierarchical model for CBMA. We model the foci from each study as a doubly stochastic Poisson process, where the study-specific log intensity function is characterised as a linear combination of a high-dimensional basis set. A sparse representation of the intensities is guaranteed through latent factor modeling of the basis coefficients. Within our framework, it is also possible to account for the effect of study-level covariates (meta-regression), significantly expanding the capabilities of the current neuroimaging meta-analysis methods available. We apply our methodology to synthetic data and neuroimaging meta-analysis datasets. PMID:28498564

  4. Multivariate sensitivity analysis to measure global contribution of input factors in dynamic models

    International Nuclear Information System (INIS)

    Lamboni, Matieyendou; Monod, Herve; Makowski, David

    2011-01-01

    Many dynamic models are used for risk assessment and decision support in ecology and crop science. Such models generate time-dependent model predictions, with time either discretised or continuous. Their global sensitivity analysis is usually applied separately on each time output, but Campbell et al. (2006 ) advocated global sensitivity analyses on the expansion of the dynamics in a well-chosen functional basis. This paper focuses on the particular case when principal components analysis is combined with analysis of variance. In addition to the indices associated with the principal components, generalised sensitivity indices are proposed to synthesize the influence of each parameter on the whole time series output. Index definitions are given when the uncertainty on the input factors is either discrete or continuous and when the dynamic model is either discrete or functional. A general estimation algorithm is proposed, based on classical methods of global sensitivity analysis. The method is applied to a dynamic wheat crop model with 13 uncertain parameters. Three methods of global sensitivity analysis are compared: the Sobol'-Saltelli method, the extended FAST method, and the fractional factorial design of resolution 6.

  5. Multivariate sensitivity analysis to measure global contribution of input factors in dynamic models

    Energy Technology Data Exchange (ETDEWEB)

    Lamboni, Matieyendou [INRA, Unite MIA (UR341), F78352 Jouy en Josas Cedex (France); Monod, Herve, E-mail: herve.monod@jouy.inra.f [INRA, Unite MIA (UR341), F78352 Jouy en Josas Cedex (France); Makowski, David [INRA, UMR Agronomie INRA/AgroParisTech (UMR 211), BP 01, F78850 Thiverval-Grignon (France)

    2011-04-15

    Many dynamic models are used for risk assessment and decision support in ecology and crop science. Such models generate time-dependent model predictions, with time either discretised or continuous. Their global sensitivity analysis is usually applied separately on each time output, but Campbell et al. (2006) advocated global sensitivity analyses on the expansion of the dynamics in a well-chosen functional basis. This paper focuses on the particular case when principal components analysis is combined with analysis of variance. In addition to the indices associated with the principal components, generalised sensitivity indices are proposed to synthesize the influence of each parameter on the whole time series output. Index definitions are given when the uncertainty on the input factors is either discrete or continuous and when the dynamic model is either discrete or functional. A general estimation algorithm is proposed, based on classical methods of global sensitivity analysis. The method is applied to a dynamic wheat crop model with 13 uncertain parameters. Three methods of global sensitivity analysis are compared: the Sobol'-Saltelli method, the extended FAST method, and the fractional factorial design of resolution 6.

  6. Derivation and application of mathematical model for well test analysis with variable skin factor in hydrocarbon reservoirs

    Directory of Open Access Journals (Sweden)

    Pengcheng Liu

    2016-06-01

    Full Text Available Skin factor is often regarded as a constant in most of the mathematical model for well test analysis in oilfields, but this is only a kind of simplified treatment with the actual skin factor changeable. This paper defined the average permeability of a damaged area as a function of time by using the definition of skin factor. Therefore a relationship between a variable skin factor and time was established. The variable skin factor derived was introduced into existing traditional models rather than using a constant skin factor, then, this newly derived mathematical model for well test analysis considering variable skin factor was solved by Laplace transform. The dimensionless wellbore pressure and its derivative changed with dimensionless time were plotted with double logarithm and these plots can be used for type curve fitting. The effects of all the parameters in the expression of variable skin factor were analyzed based on the dimensionless wellbore pressure and its derivative. Finally, actual well testing data were used to fit the type curves developed which validates the applicability of the mathematical model from Sheng-2 Block, Shengli Oilfield, China.

  7. Decision making model design for antivirus software selection using Factor Analysis and Analytical Hierarchy Process

    Directory of Open Access Journals (Sweden)

    Nurhayati Ai

    2018-01-01

    Full Text Available Virus spread increase significantly through the internet in 2017. One of the protection method is using antivirus software. The wide variety of antivirus software in the market tends to creating confusion among consumer. Selecting the right antivirus according to their needs has become difficult. This is the reason we conduct our research. We formulate a decision making model for antivirus software consumer. The model is constructed by using factor analysis and AHP method. First we spread questionnaires to consumer, then from those questionnaires we identified 16 variables that needs to be considered on selecting antivirus software. This 16 variables then divided into 5 factors by using factor analysis method in SPSS software. These five factors are security, performance, internal, time and capacity. To rank those factors we spread questionnaires to 6 IT expert then the data is analyzed using AHP method. The result is that performance factors gained the highest rank from all of the other factors. Thus, consumer can select antivirus software by judging the variables in the performance factors. Those variables are software loading speed, user friendly, no excessive memory use, thorough scanning, and scanning virus fast and accurately.

  8. Analysis of automotive rolling lobe air spring under alternative factors with finite element model

    International Nuclear Information System (INIS)

    Wong, Pak Kin; Xie, Zhengchao; Zhao, Jing; Xu, Tao; He, Feng

    2014-01-01

    Air springs are widely used in automotive suspensions for their superior performance in terms of low friction motion, adjustable load carrying capacity and user-friendly ride height control. However, it has posed great difficulties in constructing an accurate model as well as the analysis of the influence of alternative factors, such as cord angle, cord diameter and initial pressure. In this paper, a numerical model of the rolling lobe air spring (RLAS) is built by using finite element method and compared with an existing analytical model. An experiment with respect to the vertical stiffness of the RLAS is carried out to validate the accuracy of the proposed model. Evaluation result reveals that the existing analytical model cannot represent the performance of the RLAS very well, whereas the accuracy of the numerical model is very good. With the verified numerical model, the impacts of many alternative factors on the characteristics of the RLAS are analyzed. Numerical results show that the newly proposed model is reliable to determine the vertical characteristic and physical dimensions of the RLAS under the alternative factors.

  9. Modeling Indicator Systems for Evaluating Environmental Sustainable Development Based on Factor Analysis

    Institute of Scientific and Technical Information of China (English)

    WU Hao; CHEN Xiaoling; HE Ying; HE Xiaorong; CAI Xiaobin; XU Keyan

    2006-01-01

    Indicator systems of environmental sustainable development in the Poyang Lake Basin are established from 51 elementary indexes by factor analysis, which is composed of four steps such as the factor model, the parameter estimation, the factor rotation and the factor score. Under the condition that the cumulative proportion is greater than 85%, 5 explicit factors of environmental sustainable development as well as its factor score by region are carried out. The result indicates some impact factors to the basin environmental in descending sort order are volume of water, volume of waste gas discharge, volume of solid wastes, the degree to comprehensive utilization of waste gas, waste water and solid wastes, the emission volume of waste gas, waste water and solid wastes. It is helpful and important to provide decision support for constituting sustainable development strategies and evaluate the sustainable development status of each city.

  10. Factor Analysis and Modelling for Rapid Quality Assessment of Croatian Wheat Cultivars with Different Gluten Characteristics

    Directory of Open Access Journals (Sweden)

    Želimir Kurtanjek

    2008-01-01

    Full Text Available Factor analysis and multivariate chemometric modelling for rapid assessment of baking quality of wheat cultivars from Slavonia region, Croatia, have been applied. The cultivars Žitarka, Kata, Monika, Ana, Demetra, Divana and Sana were grown under controlled conditions at the experimental field of Agricultural Institute Osijek during three years (2000–2002. Their quality properties were evaluated by 45 different chemical, physical and biochemical variables. The measured variables were grouped as: indirect quality parameters (6, farinographic parameters (7, extensographic parameters (5, baking test parameters (2 and reversed phase-high performance liquid chromatography (RP-HPLC of gluten proteins (25. The aim of this study is to establish minimal number (three, i.e. principal factors, among the 45 variables and to derive multivariate linear regression models for their use in simple and fast prediction of wheat properties. Selection of the principal factors based on the principal component analysis (PCA has been applied. The first three main factors of the analysis include: total glutenins (TGT, total ω-gliadins (Tω- and the ratio of dough resistance/extensibility (R/Ext. These factors account for 76.45 % of the total variance. Linear regression models gave average regression coefficients (R evaluated for the parameter groups: indirect quality R=0.91, baking test R=0.63, farinographic R=0.78, extensographic R=0.95 and RP-HPLC of gluten data R=0.90. Errors in the model predictions were evaluated by the 95 % significance intervals of the calibration lines. Practical applications of the models for rapid quality assessment and laboratory experiment planning were emphasized.

  11. Model endophenotype for bipolar disorder: Qualitative Analysis, etiological factors, and research areas

    Directory of Open Access Journals (Sweden)

    Naraiana de Oliveira Tavares

    2014-12-01

    Full Text Available The aim of this study is to present an updated view of the writings on the endophenotype model for bipolar disorder using analytical methodologies. A review and analysis of networks was performed through descriptors and keywords that characterize the composition of the endophenotype model as a model of health. Information was collected from between 1992 and 2014, and the main thematic areas covered in the articles were identified. We discuss the results and question their cohesion, emphasizing the need to strengthen and identify the points of connection between etiological factors and characteristics that make up the model of endophenotypes for bipolar disorder.

  12. Confirmatory Factor Analysis of the Structure of Statistics Anxiety Measure: An Examination of Four Alternative Models

    Directory of Open Access Journals (Sweden)

    Hossein Bevrani, PhD

    2011-09-01

    Full Text Available Objective: The aim of this study is to explore the confirmatory factor analysis results of the Persian adaptation of Statistics Anxiety Measure (SAM, proposed by Earp.Method: The validity and reliability assessments of the scale were performed on 298 college students chosen randomly from Tabriz University in Iran. Confirmatory factor analysis (CFA was carried out to determine the factor structures of the Persian adaptation of SAM.Results: As expected, the second order model provided a better fit to the data than the three alternative models. Conclusions: Hence, SAM provides an equally valid measure for use among college students. The study both expands and adds support to the existing body of math anxiety literature.

  13. Exploratory Bi-Factor Analysis: The Oblique Case

    Science.gov (United States)

    Jennrich, Robert I.; Bentler, Peter M.

    2012-01-01

    Bi-factor analysis is a form of confirmatory factor analysis originally introduced by Holzinger and Swineford ("Psychometrika" 47:41-54, 1937). The bi-factor model has a general factor, a number of group factors, and an explicit bi-factor structure. Jennrich and Bentler ("Psychometrika" 76:537-549, 2011) introduced an exploratory form of bi-factor…

  14. Confirmatory factory analysis of the Neck Disability Index in a general problematic neck population indicates a one-factor model.

    Science.gov (United States)

    Gabel, Charles Philip; Cuesta-Vargas, Antonio I; Osborne, Jason W; Burkett, Brendan; Melloh, Markus

    2014-08-01

    The Neck Disability Index frequently is used to measure outcomes of the neck. The statistical rigor of the Neck Disability Index has been assessed with conflicting outcomes. To date, Confirmatory Factor Analysis of the Neck Disability Index has not been reported for a suitably large population study. Because the Neck Disability Index is not a condition-specific measure of neck function, initial Confirmatory Factor Analysis should consider problematic neck patients as a homogenous group. We sought to analyze the factor structure of the Neck Disability Index through Confirmatory Factor Analysis in a symptomatic, homogeneous, neck population, with respect to pooled populations and gender subgroups. This was a secondary analysis of pooled data. A total of 1,278 symptomatic neck patients (67.5% female, median age 41 years), 803 nonspecific and 475 with whiplash-associated disorder. The Neck Disability Index was used to measure outcomes. We analyzed pooled baseline data from six independent studies of patients with neck problems who completed Neck Disability Index questionnaires at baseline. The Confirmatory Factor Analysis was considered in three scenarios: the full sample and separate sexes. Models were compared empirically for best fit. Two-factor models have good psychometric properties across both the pooled and sex subgroups. However, according to these analyses, the one-factor solution is preferable from both a statistical perspective and parsimony. The two-factor model was close to significant for the male subgroup (pfactor structure when analyzed by Confirmatory Factor Analysis in a pooled, homogenous sample of neck problem patients. However, a two-factor model did approach significance for male subjects where questions separated into constructs of mental and physical function. Further investigations in different conditions, subgroup and sex-specific populations are warranted. Copyright © 2014 Elsevier Inc. All rights reserved.

  15. Nominal Performance Biosphere Dose Conversion Factor Analysis

    International Nuclear Information System (INIS)

    M.A. Wasiolek

    2005-01-01

    This analysis report is one of the technical reports containing documentation of the Environmental Radiation Model for Yucca Mountain, Nevada (ERMYN), a biosphere model supporting the Total System Performance Assessment (TSPA) for the license application (LA) for the Yucca Mountain repository. This analysis report describes the development of biosphere dose conversion factors (BDCFs) for the groundwater exposure scenario, and the development of conversion factors for assessing compliance with the groundwater protection standards. A graphical representation of the documentation hierarchy for the ERMYN is presented in Figure 1-1. This figure shows the interrelationships among the products (i.e., analysis and model reports) developed for biosphere modeling and provides an understanding of how this analysis report contributes to biosphere modeling. This report is one of two reports that develop BDCFs, which are input parameters for the TSPA-LA model. The ''Biosphere Model Report'' (BSC 2004 [DIRS 169460]) describes in detail the ERMYN conceptual model and mathematical model. The input parameter reports, shown to the right of the ''Biosphere Model Report'' in Figure 1-1, contain detailed description of the model input parameters, their development, and the relationship between the parameters and specific features events and processes (FEPs). This report describes biosphere model calculations and their output, the BDCFs, for the groundwater exposure scenario. This analysis receives direct input from the outputs of the ''Biosphere Model Report'' (BSC 2004 [DIRS 169460]) and the five analyses that develop parameter values for the biosphere model (BSC 2005 [DIRS 172827]; BSC 2004 [DIRS 169672]; BSC 2004 [DIRS 169673]; BSC 2004 [DIRS 169458]; BSC 2004 [DIRS 169459]). The results of this report are further analyzed in the ''Biosphere Dose Conversion Factor Importance and Sensitivity Analysis'' (Figure 1-1). The objectives of this analysis are to develop BDCFs for the

  16. Analysis on trust influencing factors and trust model from multiple perspectives of online Auction

    Science.gov (United States)

    Yu, Wang

    2017-10-01

    Current reputation models lack the research on online auction trading completely so they cannot entirely reflect the reputation status of users and may cause problems on operability. To evaluate the user trust in online auction correctly, a trust computing model based on multiple influencing factors is established. It aims at overcoming the efficiency of current trust computing methods and the limitations of traditional theoretical trust models. The improved model comprehensively considers the trust degree evaluation factors of three types of participants according to different participation modes of online auctioneers, to improve the accuracy, effectiveness and robustness of the trust degree. The experiments test the efficiency and the performance of our model under different scale of malicious user, under environment like eBay and Sporas model. The experimental results analysis show the model proposed in this paper makes up the deficiency of existing model and it also has better feasibility.

  17. Energy-Water Modeling and Analysis | Energy Analysis | NREL

    Science.gov (United States)

    Generation (ReEDS Model Analysis) U.S. Energy Sector Vulnerabilities to Climate Change and Extreme Weather Modeling and Analysis Energy-Water Modeling and Analysis NREL's energy-water modeling and analysis vulnerabilities from various factors, including water. Example Projects Renewable Electricity Futures Study

  18. Nominal Performance Biosphere Dose Conversion Factor Analysis

    Energy Technology Data Exchange (ETDEWEB)

    M.A. Wasiolek

    2003-07-25

    This analysis report is one of the technical reports containing documentation of the Environmental Radiation Model for Yucca Mountain, Nevada (ERMYN), a biosphere model supporting the Total System Performance Assessment (TSPA) for the license application (LA) for the Yucca Mountain repository. This analysis report describes the development of biosphere dose conversion factors (BDCFs) for the groundwater exposure scenario, and the development of conversion factors for assessing compliance with the groundwater protection standard. A graphical representation of the documentation hierarchy for the ERMYN is presented in Figure 1-1. This figure shows the interrelationships among the products (i.e., analysis and model reports) developed for biosphere modeling and provides an understanding of how this analysis report contributes to biosphere modeling. This report is one of two reports that develop biosphere BDCFs, which are input parameters for the TSPA model. The ''Biosphere Model Report'' (BSC 2003 [DIRS 164186]) describes in detail the ERMYN conceptual model and mathematical model. The input parameter reports (BSC 2003 [DIRS 160964]; BSC 2003 [DIRS 160965]; BSC 2003 [DIRS 160976]; BSC 2003 [DIRS 161239]; BSC 2003 [DIRS 161241]) contain detailed description of the model input parameters. This report describes biosphere model calculations and their output, the BDCFs, for the groundwater exposure scenario. The objectives of this analysis are to develop BDCFs and conversion factors for the TSPA. The BDCFs will be used in performance assessment for calculating annual doses for a given concentration of radionuclides in groundwater. The conversion factors will be used for calculating gross alpha particle activity in groundwater and the annual dose from beta- and photon-emitting radionuclides.

  19. Nominal Performance Biosphere Dose Conversion Factor Analysis

    Energy Technology Data Exchange (ETDEWEB)

    M.A. Wasiolek

    2005-04-28

    This analysis report is one of the technical reports containing documentation of the Environmental Radiation Model for Yucca Mountain, Nevada (ERMYN), a biosphere model supporting the Total System Performance Assessment (TSPA) for the license application (LA) for the Yucca Mountain repository. This analysis report describes the development of biosphere dose conversion factors (BDCFs) for the groundwater exposure scenario, and the development of conversion factors for assessing compliance with the groundwater protection standards. A graphical representation of the documentation hierarchy for the ERMYN is presented in Figure 1-1. This figure shows the interrelationships among the products (i.e., analysis and model reports) developed for biosphere modeling and provides an understanding of how this analysis report contributes to biosphere modeling. This report is one of two reports that develop BDCFs, which are input parameters for the TSPA-LA model. The ''Biosphere Model Report'' (BSC 2004 [DIRS 169460]) describes in detail the ERMYN conceptual model and mathematical model. The input parameter reports, shown to the right of the ''Biosphere Model Report'' in Figure 1-1, contain detailed description of the model input parameters, their development, and the relationship between the parameters and specific features events and processes (FEPs). This report describes biosphere model calculations and their output, the BDCFs, for the groundwater exposure scenario. This analysis receives direct input from the outputs of the ''Biosphere Model Report'' (BSC 2004 [DIRS 169460]) and the five analyses that develop parameter values for the biosphere model (BSC 2005 [DIRS 172827]; BSC 2004 [DIRS 169672]; BSC 2004 [DIRS 169673]; BSC 2004 [DIRS 169458]; BSC 2004 [DIRS 169459]). The results of this report are further analyzed in the ''Biosphere Dose Conversion Factor Importance and Sensitivity Analysis

  20. Uncertainty Evaluation of the SFR Subchannel Thermal-Hydraulic Modeling Using a Hot Channel Factors Analysis

    International Nuclear Information System (INIS)

    Choi, Sun Rock; Cho, Chung Ho; Kim, Sang Ji

    2011-01-01

    In an SFR core analysis, a hot channel factors (HCF) method is most commonly used to evaluate uncertainty. It was employed to the early design such as the CRBRP and IFR. In other ways, the improved thermal design procedure (ITDP) is able to calculate the overall uncertainty based on the Root Sum Square technique and sensitivity analyses of each design parameters. The Monte Carlo method (MCM) is also employed to estimate the uncertainties. In this method, all the input uncertainties are randomly sampled according to their probability density functions and the resulting distribution for the output quantity is analyzed. Since an uncertainty analysis is basically calculated from the temperature distribution in a subassembly, the core thermal-hydraulic modeling greatly affects the resulting uncertainty. At KAERI, the SLTHEN and MATRA-LMR codes have been utilized to analyze the SFR core thermal-hydraulics. The SLTHEN (steady-state LMR core thermal hydraulics analysis code based on the ENERGY model) code is a modified version of the SUPERENERGY2 code, which conducts a multi-assembly, steady state calculation based on a simplified ENERGY model. The detailed subchannel analysis code MATRA-LMR (Multichannel Analyzer for Steady-State and Transients in Rod Arrays for Liquid Metal Reactors), an LMR version of MATRA, was also developed specifically for the SFR core thermal-hydraulic analysis. This paper describes comparative studies for core thermal-hydraulic models. The subchannel analysis and a hot channel factors based uncertainty evaluation system is established to estimate the core thermofluidic uncertainties using the MATRA-LMR code and the results are compared to those of the SLTHEN code

  1. Analysis on influence factors of China's CO2 emissions based on Path-STIRPAT model

    International Nuclear Information System (INIS)

    Li Huanan; Mu Hailin; Zhang Ming; Li Nan

    2011-01-01

    With the intensification of global warming and continued growth in energy consumption, China is facing increasing pressure to cut its CO 2 (carbon dioxide) emissions down. This paper discusses the driving forces influencing China's CO 2 emissions based on Path-STIRPAT model-a method combining Path analysis with STIRPAT (stochastic impacts by regression on population, affluence and technology) model. The analysis shows that GDP per capita (A), industrial structure (IS), population (P), urbanization level (R) and technology level (T) are the main factors influencing China's CO 2 emissions, which exert an influence interactively and collaboratively. The sequence of the size of factors' direct influence on China's CO 2 emission is A>T>P>R>IS, while that of factors' total influence is A>R>P>T>IS. One percent increase in A, IS, P, R and T leads to 0.44, 1.58, 1.31, 1.12 and -1.09 percentage change in CO 2 emission totally, where their direct contribution is 0.45, 0.07, 0.63, 0.08, 0.92, respectively. Improving T is the most important way for CO 2 reduction in China. - Highlights: → We analyze the driving forces influencing China's CO 2 emissions. → Five macro factors like per capita GDP are the main influencing factors. → These factors exert an influence interactively and collaboratively. → Different factors' direct and total influence on China's CO 2 emission is different. → Improving technology level is the most important way for CO 2 reduction in China.

  2. Nominal Performance Biosphere Dose Conversion Factor Analysis

    Energy Technology Data Exchange (ETDEWEB)

    M. Wasiolek

    2004-09-08

    This analysis report is one of the technical reports containing documentation of the Environmental Radiation Model for Yucca Mountain, Nevada (ERMYN), a biosphere model supporting the Total System Performance Assessment (TSPA) for the license application (LA) for the Yucca Mountain repository. This analysis report describes the development of biosphere dose conversion factors (BDCFs) for the groundwater exposure scenario, and the development of conversion factors for assessing compliance with the groundwater protection standard. A graphical representation of the documentation hierarchy for the ERMYN is presented in Figure 1-1. This figure shows the interrelationships among the products (i.e., analysis and model reports) developed for biosphere modeling and provides an understanding of how this analysis report contributes to biosphere modeling. This report is one of two reports that develop biosphere BDCFs, which are input parameters for the TSPA-LA model. The ''Biosphere Model Report'' (BSC 2004 [DIRS 169460]) describes in detail the ERMYN conceptual model and mathematical model. The input parameter reports, shown to the right of the ''Biosphere Model Report'' in Figure 1-1, contain detailed description of the model input parameters, their development, and the relationship between the parameters and specific features events and processes (FEPs). This report describes biosphere model calculations and their output, the BDCFs, for the groundwater exposure scenario. The objectives of this analysis are to develop BDCFs for the groundwater exposure scenario for the three climate states considered in the TSPA-LA as well as conversion factors for evaluating compliance with the groundwater protection standard. The BDCFs will be used in performance assessment for calculating all-pathway annual doses for a given concentration of radionuclides in groundwater. The conversion factors will be used for calculating gross alpha particle

  3. Nominal Performance Biosphere Dose Conversion Factor Analysis

    International Nuclear Information System (INIS)

    M. Wasiolek

    2004-01-01

    This analysis report is one of the technical reports containing documentation of the Environmental Radiation Model for Yucca Mountain, Nevada (ERMYN), a biosphere model supporting the Total System Performance Assessment (TSPA) for the license application (LA) for the Yucca Mountain repository. This analysis report describes the development of biosphere dose conversion factors (BDCFs) for the groundwater exposure scenario, and the development of conversion factors for assessing compliance with the groundwater protection standard. A graphical representation of the documentation hierarchy for the ERMYN is presented in Figure 1-1. This figure shows the interrelationships among the products (i.e., analysis and model reports) developed for biosphere modeling and provides an understanding of how this analysis report contributes to biosphere modeling. This report is one of two reports that develop biosphere BDCFs, which are input parameters for the TSPA-LA model. The ''Biosphere Model Report'' (BSC 2004 [DIRS 169460]) describes in detail the ERMYN conceptual model and mathematical model. The input parameter reports, shown to the right of the ''Biosphere Model Report'' in Figure 1-1, contain detailed description of the model input parameters, their development, and the relationship between the parameters and specific features events and processes (FEPs). This report describes biosphere model calculations and their output, the BDCFs, for the groundwater exposure scenario. The objectives of this analysis are to develop BDCFs for the groundwater exposure scenario for the three climate states considered in the TSPA-LA as well as conversion factors for evaluating compliance with the groundwater protection standard. The BDCFs will be used in performance assessment for calculating all-pathway annual doses for a given concentration of radionuclides in groundwater. The conversion factors will be used for calculating gross alpha particle activity in groundwater and the annual dose

  4. [Lake eutrophication modeling in considering climatic factors change: a review].

    Science.gov (United States)

    Su, Jie-Qiong; Wang, Xuan; Yang, Zhi-Feng

    2012-11-01

    Climatic factors are considered as the key factors affecting the trophic status and its process in most lakes. Under the background of global climate change, to incorporate the variations of climatic factors into lake eutrophication models could provide solid technical support for the analysis of the trophic evolution trend of lake and the decision-making of lake environment management. This paper analyzed the effects of climatic factors such as air temperature, precipitation, sunlight, and atmosphere on lake eutrophication, and summarized the research results about the lake eutrophication modeling in considering in considering climatic factors change, including the modeling based on statistical analysis, ecological dynamic analysis, system analysis, and intelligent algorithm. The prospective approaches to improve the accuracy of lake eutrophication modeling with the consideration of climatic factors change were put forward, including 1) to strengthen the analysis of the mechanisms related to the effects of climatic factors change on lake trophic status, 2) to identify the appropriate simulation models to generate several scenarios under proper temporal and spatial scales and resolutions, and 3) to integrate the climatic factors change simulation, hydrodynamic model, ecological simulation, and intelligent algorithm into a general modeling system to achieve an accurate prediction of lake eutrophication under climatic change.

  5. DETERMINANTS OF SOVEREIGN RATING: FACTOR BASED ORDERED PROBIT MODELS FOR PANEL DATA ANALYSIS MODELING FRAMEWORK

    Directory of Open Access Journals (Sweden)

    Dilek Teker

    2013-01-01

    Full Text Available The aim of this research is to compose a new rating methodology and provide credit notches to 23 countries which of 13 are developed and 10 are emerging. There are various literature that explains the determinants of credit ratings. Following the literature, we select 11 variables for our model which of 5 are eliminated by the factor analysis. We use specific dummies to investigate the structural breaks in time and cross section such as pre crises, post crises, BRIC membership, EU membership, OPEC membership, shipbuilder country and platinum reserved country. Then we run an ordered probit model and give credit notches to the countries. We use FITCH ratings as benchmark. Thus, at the end we compare the notches of FITCH with the ones we derive out of our estimated model.

  6. Disruptive Event Biosphere Dose Conversion Factor Analysis

    Energy Technology Data Exchange (ETDEWEB)

    M. A. Wasiolek

    2003-07-21

    This analysis report, ''Disruptive Event Biosphere Dose Conversion Factor Analysis'', is one of the technical reports containing documentation of the ERMYN (Environmental Radiation Model for Yucca Mountain Nevada) biosphere model for the geologic repository at Yucca Mountain, its input parameters, and the application of the model to perform the dose assessment for the repository. The biosphere model is one of a series of process models supporting the Total System Performance Assessment (TSPA) for the Yucca Mountain repository. A graphical representation of the documentation hierarchy for the ERMYN is presented in Figure 1-1. This figure shows the interrelationships among the products (i.e., analysis and model reports) developed for biosphere modeling and provides an understanding of how this analysis report contributes to biosphere modeling. This report is one of the two reports that develop biosphere dose conversion factors (BDCFs), which are input parameters for the TSPA model. The ''Biosphere Model Report'' (BSC 2003 [DIRS 164186]) describes in detail the conceptual model as well as the mathematical model and lists its input parameters. Model input parameters are developed and described in detail in five analysis report (BSC 2003 [DIRS 160964], BSC 2003 [DIRS 160965], BSC 2003 [DIRS 160976], BSC 2003 [DIRS 161239], and BSC 2003 [DIRS 161241]). The objective of this analysis was to develop the BDCFs for the volcanic ash exposure scenario and the dose factors (DFs) for calculating inhalation doses during volcanic eruption (eruption phase of the volcanic event). The volcanic ash exposure scenario is hereafter referred to as the volcanic ash scenario. For the volcanic ash scenario, the mode of radionuclide release into the biosphere is a volcanic eruption through the repository with the resulting entrainment of contaminated waste in the tephra and the subsequent atmospheric transport and dispersion of contaminated material in

  7. Disruptive Event Biosphere Dose Conversion Factor Analysis

    International Nuclear Information System (INIS)

    M. A. Wasiolek

    2003-01-01

    This analysis report, ''Disruptive Event Biosphere Dose Conversion Factor Analysis'', is one of the technical reports containing documentation of the ERMYN (Environmental Radiation Model for Yucca Mountain Nevada) biosphere model for the geologic repository at Yucca Mountain, its input parameters, and the application of the model to perform the dose assessment for the repository. The biosphere model is one of a series of process models supporting the Total System Performance Assessment (TSPA) for the Yucca Mountain repository. A graphical representation of the documentation hierarchy for the ERMYN is presented in Figure 1-1. This figure shows the interrelationships among the products (i.e., analysis and model reports) developed for biosphere modeling and provides an understanding of how this analysis report contributes to biosphere modeling. This report is one of the two reports that develop biosphere dose conversion factors (BDCFs), which are input parameters for the TSPA model. The ''Biosphere Model Report'' (BSC 2003 [DIRS 164186]) describes in detail the conceptual model as well as the mathematical model and lists its input parameters. Model input parameters are developed and described in detail in five analysis report (BSC 2003 [DIRS 160964], BSC 2003 [DIRS 160965], BSC 2003 [DIRS 160976], BSC 2003 [DIRS 161239], and BSC 2003 [DIRS 161241]). The objective of this analysis was to develop the BDCFs for the volcanic ash exposure scenario and the dose factors (DFs) for calculating inhalation doses during volcanic eruption (eruption phase of the volcanic event). The volcanic ash exposure scenario is hereafter referred to as the volcanic ash scenario. For the volcanic ash scenario, the mode of radionuclide release into the biosphere is a volcanic eruption through the repository with the resulting entrainment of contaminated waste in the tephra and the subsequent atmospheric transport and dispersion of contaminated material in the biosphere. The biosphere process

  8. The Five-Factor Model personality traits in schizophrenia: A meta-analysis.

    Science.gov (United States)

    Ohi, Kazutaka; Shimada, Takamitsu; Nitta, Yusuke; Kihara, Hiroaki; Okubo, Hiroaki; Uehara, Takashi; Kawasaki, Yasuhiro

    2016-06-30

    Personality is one of important factors in the pathogenesis of schizophrenia because it affects patients' symptoms, cognition and social functioning. Several studies have reported specific personality traits in patients with schizophrenia compared with healthy subjects. However, the results were inconsistent among studies. The NEO Five-Factor Inventory (NEO-FFI) measures five personality traits: Neuroticism (N), Extraversion (E), Openness (O), Agreeableness (A) and Conscientiousness (C). Here, we performed a meta-analysis of these personality traits assessed by the NEO-FFI in 460 patients with schizophrenia and 486 healthy subjects from the published literature and investigated possible associations between schizophrenia and these traits. There was no publication bias for any traits. Because we found evidence of significant heterogeneity in all traits among the studies, we applied a random-effect model to perform the meta-analysis. Patients with schizophrenia showed a higher score for N and lower scores for E, O, A and C compared with healthy subjects. The effect sizes of these personality traits ranged from moderate to large. These differences were not affected by possible moderator factors, such as gender distribution and mean age in each study, expect for gender effect for A. These findings suggest that patients with schizophrenia have a different personality profile compared with healthy subjects. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

  9. Verification of the model of predisposition in triathlon – structural model of confirmative factor analysis

    Directory of Open Access Journals (Sweden)

    Lenka Kovářová

    2012-09-01

    Full Text Available BACKGROUND: The triathlon is a combination of three different types of sport – swimming, cycling, and running. Each of these requires different top level predispositions and complex approach to talent selection is a rather difficult process. Attempts to identify assumptions in the triathlon have so far been specific and focused only on some groups of predispositions (physiology, motor tests, and psychology. The latest studies missed the structural approach and were based on determinants of sport performance, theory of sports training and expert assessment. OBJECTIVE: The aim of our study was to verify the model of predisposition in the short triathlon for talent assessment of young male athletes age 17–20 years. METHODS: The research sample consisted of 55 top level triathletes – men, who were included in the Government supported sports talent programme in the Czech Republic at the age of 17–20 years. We used a confirmative factor analysis (FA and Path diagram to verify the model, which allow us to explain mutual relationships among observed variables. For statistical data processing we used a structure equating modeling (SEM by software Lisrel L88. RESULTS: The study confirms best structural model for talent selection in triathlon at the age of 17–20 years old men, which composed seventeen indicators (tests and explained 91% of all cross-correlations (Goodness of Fit Index /GFI/ 0.91, Root Mean Square Residual /RMSR/ 0.13. Tests for predispositions in triathlons were grouped into five items, three motor predispositions (swimming, cycling and running skills, aerobic and psychological predispositions. Aerobic predispositions showed the highest importance to the assumptions to the general factor (1.00; 0. Running predispositions were measured as a very significant factor (–0.85; 0.28 which confirms importance of this critical stage of the race. Lower factor weight showed clusters of swimming (–0.61; 0.63 and cycling (0.53; 0

  10. Factor Analysis for Clustered Observations.

    Science.gov (United States)

    Longford, N. T.; Muthen, B. O.

    1992-01-01

    A two-level model for factor analysis is defined, and formulas for a scoring algorithm for this model are derived. A simple noniterative method based on decomposition of total sums of the squares and cross-products is discussed and illustrated with simulated data and data from the Second International Mathematics Study. (SLD)

  11. Disruptive Event Biosphere Dose Conversion Factor Analysis

    Energy Technology Data Exchange (ETDEWEB)

    M. Wasiolek

    2004-09-08

    This analysis report is one of the technical reports containing documentation of the Environmental Radiation Model for Yucca Mountain, Nevada (ERMYN), a biosphere model supporting the total system performance assessment (TSPA) for the license application (LA) for the Yucca Mountain repository. This analysis report describes the development of biosphere dose conversion factors (BDCFs) for the volcanic ash exposure scenario, and the development of dose factors for calculating inhalation dose during volcanic eruption. A graphical representation of the documentation hierarchy for the ERMYN is presented in Figure 1-1. This figure shows the interrelationships among the products (i.e., analysis and model reports) developed for biosphere modeling and provides an understanding of how this analysis report contributes to biosphere modeling. This report is one of two reports that develop biosphere BDCFs, which are input parameters for the TSPA model. The ''Biosphere Model Report'' (BSC 2004 [DIRS 169460]) describes in detail the ERMYN conceptual model and mathematical model. The input parameter reports, shown to the right of the Biosphere Model Report in Figure 1-1, contain detailed descriptions of the model input parameters, their development and the relationship between the parameters and specific features, events and processes (FEPs). This report describes biosphere model calculations and their output, the BDCFs, for the volcanic ash exposure scenario. This analysis receives direct input from the outputs of the ''Biosphere Model Report'' (BSC 2004 [DIRS 169460]) and from the five analyses that develop parameter values for the biosphere model (BSC 2004 [DIRS 169671]; BSC 2004 [DIRS 169672]; BSC 2004 [DIRS 169673]; BSC 2004 [DIRS 169458]; and BSC 2004 [DIRS 169459]). The results of this report are further analyzed in the ''Biosphere Dose Conversion Factor Importance and Sensitivity Analysis''. The objective of this

  12. Disruptive Event Biosphere Dose Conversion Factor Analysis

    International Nuclear Information System (INIS)

    M. Wasiolek

    2004-01-01

    This analysis report is one of the technical reports containing documentation of the Environmental Radiation Model for Yucca Mountain, Nevada (ERMYN), a biosphere model supporting the total system performance assessment (TSPA) for the license application (LA) for the Yucca Mountain repository. This analysis report describes the development of biosphere dose conversion factors (BDCFs) for the volcanic ash exposure scenario, and the development of dose factors for calculating inhalation dose during volcanic eruption. A graphical representation of the documentation hierarchy for the ERMYN is presented in Figure 1-1. This figure shows the interrelationships among the products (i.e., analysis and model reports) developed for biosphere modeling and provides an understanding of how this analysis report contributes to biosphere modeling. This report is one of two reports that develop biosphere BDCFs, which are input parameters for the TSPA model. The ''Biosphere Model Report'' (BSC 2004 [DIRS 169460]) describes in detail the ERMYN conceptual model and mathematical model. The input parameter reports, shown to the right of the Biosphere Model Report in Figure 1-1, contain detailed descriptions of the model input parameters, their development and the relationship between the parameters and specific features, events and processes (FEPs). This report describes biosphere model calculations and their output, the BDCFs, for the volcanic ash exposure scenario. This analysis receives direct input from the outputs of the ''Biosphere Model Report'' (BSC 2004 [DIRS 169460]) and from the five analyses that develop parameter values for the biosphere model (BSC 2004 [DIRS 169671]; BSC 2004 [DIRS 169672]; BSC 2004 [DIRS 169673]; BSC 2004 [DIRS 169458]; and BSC 2004 [DIRS 169459]). The results of this report are further analyzed in the ''Biosphere Dose Conversion Factor Importance and Sensitivity Analysis''. The objective of this analysis was to develop the BDCFs for the volcanic ash

  13. Text mining factor analysis (TFA) in green tea patent data

    Science.gov (United States)

    Rahmawati, Sela; Suprijadi, Jadi; Zulhanif

    2017-03-01

    Factor analysis has become one of the most widely used multivariate statistical procedures in applied research endeavors across a multitude of domains. There are two main types of analyses based on factor analysis: Exploratory Factor Analysis (EFA) and Confirmatory Factor Analysis (CFA). Both EFA and CFA aim to observed relationships among a group of indicators with a latent variable, but they differ fundamentally, a priori and restrictions made to the factor model. This method will be applied to patent data technology sector green tea to determine the development technology of green tea in the world. Patent analysis is useful in identifying the future technological trends in a specific field of technology. Database patent are obtained from agency European Patent Organization (EPO). In this paper, CFA model will be applied to the nominal data, which obtain from the presence absence matrix. While doing processing, analysis CFA for nominal data analysis was based on Tetrachoric matrix. Meanwhile, EFA model will be applied on a title from sector technology dominant. Title will be pre-processing first using text mining analysis.

  14. The multi-factor energy input–output model

    International Nuclear Information System (INIS)

    Guevara, Zeus; Domingos, Tiago

    2017-01-01

    Energy input–output analysis (EIO analysis) is a noteworthy tool for the analysis of the role of energy in the economy. However, it has relied on models that provide a limited description of energy flows in the economic system and do not allow an adequate analysis of energy efficiency. This paper introduces a novel energy input–output model, the multi-factor energy input–output model (MF-EIO model), which is obtained from a partitioning of a hybrid-unit input–output system of the economy. This model improves on current models by describing the energy flows according to the processes of energy conversion and the levels of energy use in the economy. It characterizes the vector of total energy output as a function of seven factors: two energy efficiency indicators; two characteristics of end-use energy consumption; and three economic features of the rest of the economy. Moreover, it is consistent with the standard model for EIO analysis, i.e., the hybrid-unit model. This paper also introduces an approximate version of the MF-EIO model, which is equivalent to the former under equal energy prices for industries and final consumers, but requires less data processing. The latter is composed by two linked models: a model of the energy sector in physical units, and a model of the rest of the economy in monetary units. In conclusion, the proposed modelling framework improves EIO analysis and extends EIO applications to the accounting for energy efficiency of the economy. - Highlights: • A novel energy input–output model is introduced. • It allows a more adequate analysis of energy flows than current models. • It describes energy flows according to processes of energy conversion and use. • It can be used for other environmental applications (material use and emissions). • An approximate version of the model is introduced, simpler and less data intensive.

  15. First course in factor analysis

    CERN Document Server

    Comrey, Andrew L

    2013-01-01

    The goal of this book is to foster a basic understanding of factor analytic techniques so that readers can use them in their own research and critically evaluate their use by other researchers. Both the underlying theory and correct application are emphasized. The theory is presented through the mathematical basis of the most common factor analytic models and several methods used in factor analysis. On the application side, considerable attention is given to the extraction problem, the rotation problem, and the interpretation of factor analytic results. Hence, readers are given a background of

  16. Confirmatory Factor Analysis of the Combined Social Phobia Scale and Social Interaction Anxiety Scale: Support for a Bifactor Model

    OpenAIRE

    Gomez, Rapson; Watson, Shaun D.

    2017-01-01

    For the Social Phobia Scale (SPS) and the Social Interaction Anxiety Scale (SIAS) together, this study examined support for a bifactor model, and also the internal consistency reliability and external validity of the factors in this model. Participants (N = 526) were adults from the general community who completed the SPS and SIAS. Confirmatory factor analysis (CFA) of their ratings indicated good support for the bifactor model. For this model, the loadings for all but six items were higher o...

  17. DISRUPTIVE EVENT BIOSPHERE DOSE CONVERSION FACTOR ANALYSIS

    International Nuclear Information System (INIS)

    M.A. Wasiolek

    2005-01-01

    This analysis report is one of the technical reports containing documentation of the Environmental Radiation Model for Yucca Mountain, Nevada (ERMYN), a biosphere model supporting the total system performance assessment (TSPA) for the license application (LA) for the Yucca Mountain repository. This analysis report describes the development of biosphere dose conversion factors (BDCFs) for the volcanic ash exposure scenario, and the development of dose factors for calculating inhalation dose during volcanic eruption. A graphical representation of the documentation hierarchy for the ERMYN is presented in Figure 1-1. This figure shows the interrelationships among the products (i.e., analysis and model reports) developed for biosphere modeling and provides an understanding of how this analysis report contributes to biosphere modeling. This report is one of two reports that develop biosphere BDCFs, which are input parameters for the TSPA model. The Biosphere Model Report (BSC 2004 [DIRS 169460]) describes in detail the ERMYN conceptual model and mathematical model. The input parameter reports, shown to the right of the Biosphere Model Report in Figure 1-1, contain detailed descriptions of the model input parameters, their development and the relationship between the parameters and specific features, events and processes (FEPs). This report describes biosphere model calculations and their output, the BDCFs, for the volcanic ash exposure scenario. This analysis receives direct input from the outputs of the ''Biosphere Model Report'' (BSC 2004 [DIRS 169460]) and from the five analyses that develop parameter values for the biosphere model (BSC 2005 [DIRS 172827]; BSC 2004 [DIRS 169672]; BSC 2004 [DIRS 169673]; BSC 2004 [DIRS 169458]; and BSC 2004 [DIRS 169459]). The results of this report are further analyzed in the ''Biosphere Dose Conversion Factor Importance and Sensitivity Analysis'' (Figure 1-1). The objective of this analysis was to develop the BDCFs for the volcanic

  18. Testing of technology readiness index model based on exploratory factor analysis approach

    Science.gov (United States)

    Ariani, AF; Napitupulu, D.; Jati, RK; Kadar, JA; Syafrullah, M.

    2018-04-01

    SMEs readiness in using ICT will determine the adoption of ICT in the future. This study aims to evaluate the model of technology readiness in order to apply the technology on SMEs. The model is tested to find if TRI model is relevant to measure ICT adoption, especially for SMEs in Indonesia. The research method used in this paper is survey to a group of SMEs in South Tangerang. The survey measures the readiness to adopt ICT based on four variables which is Optimism, Innovativeness, Discomfort, and Insecurity. Each variable contains several indicators to make sure the variable is measured thoroughly. The data collected through survey is analysed using factor analysis methodwith the help of SPSS software. The result of this study shows that TRI model gives more descendants on some indicators and variables. This result can be caused by SMEs owners’ knowledge is not homogeneous about either the technology that they are used, knowledge or the type of their business.

  19. Quantification of source impact to PM using three-dimensional weighted factor model analysis on multi-site data

    Science.gov (United States)

    Shi, Guoliang; Peng, Xing; Huangfu, Yanqi; Wang, Wei; Xu, Jiao; Tian, Yingze; Feng, Yinchang; Ivey, Cesunica E.; Russell, Armistead G.

    2017-07-01

    Source apportionment technologies are used to understand the impacts of important sources of particulate matter (PM) air quality, and are widely used for both scientific studies and air quality management. Generally, receptor models apportion speciated PM data from a single sampling site. With the development of large scale monitoring networks, PM speciation are observed at multiple sites in an urban area. For these situations, the models should account for three factors, or dimensions, of the PM, including the chemical species concentrations, sampling periods and sampling site information, suggesting the potential power of a three-dimensional source apportionment approach. However, the principle of three-dimensional Parallel Factor Analysis (Ordinary PARAFAC) model does not always work well in real environmental situations for multi-site receptor datasets. In this work, a new three-way receptor model, called "multi-site three way factor analysis" model is proposed to deal with the multi-site receptor datasets. Synthetic datasets were developed and introduced into the new model to test its performance. Average absolute error (AAE, between estimated and true contributions) for extracted sources were all less than 50%. Additionally, three-dimensional ambient datasets from a Chinese mega-city, Chengdu, were analyzed using this new model to assess the application. Four factors are extracted by the multi-site WFA3 model: secondary source have the highest contributions (64.73 and 56.24 μg/m3), followed by vehicular exhaust (30.13 and 33.60 μg/m3), crustal dust (26.12 and 29.99 μg/m3) and coal combustion (10.73 and 14.83 μg/m3). The model was also compared to PMF, with general agreement, though PMF suggested a lower crustal contribution.

  20. The Recoverability of P-Technique Factor Analysis

    Science.gov (United States)

    Molenaar, Peter C. M.; Nesselroade, John R.

    2009-01-01

    It seems that just when we are about to lay P-technique factor analysis finally to rest as obsolete because of newer, more sophisticated multivariate time-series models using latent variables--dynamic factor models--it rears its head to inform us that an obituary may be premature. We present the results of some simulations demonstrating that even…

  1. Sensitivity Analysis of b-factor in Microwave Emission Model for Soil Moisture Retrieval: A Case Study for SMAP Mission

    Directory of Open Access Journals (Sweden)

    Dugwon Seo

    2010-05-01

    Full Text Available Sensitivity analysis is critically needed to better understand the microwave emission model for soil moisture retrieval using passive microwave remote sensing data. The vegetation b-factor along with vegetation water content and surface characteristics has significant impact in model prediction. This study evaluates the sensitivity of the b-factor, which is function of vegetation type. The analysis is carried out using Passive and Active L and S-band airborne sensor (PALS and measured field soil moisture from Southern Great Plains experiment (SGP99. The results show that the relative sensitivity of the b-factor is 86% in wet soil condition and 88% in high vegetated condition compared to the sensitivity of the soil moisture. Apparently, the b-factor is found to be more sensitive than the vegetation water content, surface roughness and surface temperature; therefore, the effect of the b-factor is fairly large to the microwave emission in certain conditions. Understanding the dependence of the b-factor on the soil and vegetation is important in studying the soil moisture retrieval algorithm, which can lead to potential improvements in model development for the Soil Moisture Active-Passive (SMAP mission.

  2. Confirmatory Factor Analysis of the Combined Social Phobia Scale and Social Interaction Anxiety Scale: Support for a Bifactor Model

    Science.gov (United States)

    Gomez, Rapson; Watson, Shaun D.

    2017-01-01

    For the Social Phobia Scale (SPS) and the Social Interaction Anxiety Scale (SIAS) together, this study examined support for a bifactor model, and also the internal consistency reliability and external validity of the factors in this model. Participants (N = 526) were adults from the general community who completed the SPS and SIAS. Confirmatory factor analysis (CFA) of their ratings indicated good support for the bifactor model. For this model, the loadings for all but six items were higher on the general factor than the specific factors. The three positively worded items had negligible loadings on the general factor. The general factor explained most of the common variance in the SPS and SIAS, and demonstrated good model-based internal consistency reliability (omega hierarchical) and a strong association with fear of negative evaluation and extraversion. The practical implications of the findings for the utilization of the SPS and SIAS, and the theoretical and clinical implications for social anxiety are discussed. PMID:28210232

  3. Confirmatory Factor Analysis of the Combined Social Phobia Scale and Social Interaction Anxiety Scale: Support for a Bifactor Model.

    Science.gov (United States)

    Gomez, Rapson; Watson, Shaun D

    2017-01-01

    For the Social Phobia Scale (SPS) and the Social Interaction Anxiety Scale (SIAS) together, this study examined support for a bifactor model, and also the internal consistency reliability and external validity of the factors in this model. Participants ( N = 526) were adults from the general community who completed the SPS and SIAS. Confirmatory factor analysis (CFA) of their ratings indicated good support for the bifactor model. For this model, the loadings for all but six items were higher on the general factor than the specific factors. The three positively worded items had negligible loadings on the general factor. The general factor explained most of the common variance in the SPS and SIAS, and demonstrated good model-based internal consistency reliability (omega hierarchical) and a strong association with fear of negative evaluation and extraversion. The practical implications of the findings for the utilization of the SPS and SIAS, and the theoretical and clinical implications for social anxiety are discussed.

  4. Time Series Factor Analysis with an Application to Measuring Money

    NARCIS (Netherlands)

    Gilbert, Paul D.; Meijer, Erik

    2005-01-01

    Time series factor analysis (TSFA) and its associated statistical theory is developed. Unlike dynamic factor analysis (DFA), TSFA obviates the need for explicitly modeling the process dynamics of the underlying phenomena. It also differs from standard factor analysis (FA) in important respects: the

  5. Decision making model design for antivirus software selection using Factor Analysis and Analytical Hierarchy Process

    OpenAIRE

    Nurhayati Ai; Gautama Aditya; Naseer Muchammad

    2018-01-01

    Virus spread increase significantly through the internet in 2017. One of the protection method is using antivirus software. The wide variety of antivirus software in the market tends to creating confusion among consumer. Selecting the right antivirus according to their needs has become difficult. This is the reason we conduct our research. We formulate a decision making model for antivirus software consumer. The model is constructed by using factor analysis and AHP method. First we spread que...

  6. Multiple Statistical Models Based Analysis of Causative Factors and Loess Landslides in Tianshui City, China

    Science.gov (United States)

    Su, Xing; Meng, Xingmin; Ye, Weilin; Wu, Weijiang; Liu, Xingrong; Wei, Wanhong

    2018-03-01

    Tianshui City is one of the mountainous cities that are threatened by severe geo-hazards in Gansu Province, China. Statistical probability models have been widely used in analyzing and evaluating geo-hazards such as landslide. In this research, three approaches (Certainty Factor Method, Weight of Evidence Method and Information Quantity Method) were adopted to quantitively analyze the relationship between the causative factors and the landslides, respectively. The source data used in this study are including the SRTM DEM and local geological maps in the scale of 1:200,000. 12 causative factors (i.e., altitude, slope, aspect, curvature, plan curvature, profile curvature, roughness, relief amplitude, and distance to rivers, distance to faults, distance to roads, and the stratum lithology) were selected to do correlation analysis after thorough investigation of geological conditions and historical landslides. The results indicate that the outcomes of the three models are fairly consistent.

  7. Delineation of geochemical anomalies based on stream sediment data utilizing fractal modeling and staged factor analysis

    Science.gov (United States)

    Afzal, Peyman; Mirzaei, Misagh; Yousefi, Mahyar; Adib, Ahmad; Khalajmasoumi, Masoumeh; Zarifi, Afshar Zia; Foster, Patrick; Yasrebi, Amir Bijan

    2016-07-01

    Recognition of significant geochemical signatures and separation of geochemical anomalies from background are critical issues in interpretation of stream sediment data to define exploration targets. In this paper, we used staged factor analysis in conjunction with the concentration-number (C-N) fractal model to generate exploration targets for prospecting Cr and Fe mineralization in Balvard area, SE Iran. The results show coexistence of derived multi-element geochemical signatures of the deposit-type sought and ultramafic-mafic rocks in the NE and northern parts of the study area indicating significant chromite and iron ore prospects. In this regard, application of staged factor analysis and fractal modeling resulted in recognition of significant multi-element signatures that have a high spatial association with host lithological units of the deposit-type sought, and therefore, the generated targets are reliable for further prospecting of the deposit in the study area.

  8. A discriminant analysis prediction model of non-syndromic cleft lip with or without cleft palate based on risk factors.

    Science.gov (United States)

    Li, Huixia; Luo, Miyang; Luo, Jiayou; Zheng, Jianfei; Zeng, Rong; Du, Qiyun; Fang, Junqun; Ouyang, Na

    2016-11-23

    A risk prediction model of non-syndromic cleft lip with or without cleft palate (NSCL/P) was established by a discriminant analysis to predict the individual risk of NSCL/P in pregnant women. A hospital-based case-control study was conducted with 113 cases of NSCL/P and 226 controls without NSCL/P. The cases and the controls were obtained from 52 birth defects' surveillance hospitals in Hunan Province, China. A questionnaire was administered in person to collect the variables relevant to NSCL/P by face to face interviews. Logistic regression models were used to analyze the influencing factors of NSCL/P, and a stepwise Fisher discriminant analysis was subsequently used to construct the prediction model. In the univariate analysis, 13 influencing factors were related to NSCL/P, of which the following 8 influencing factors as predictors determined the discriminant prediction model: family income, maternal occupational hazards exposure, premarital medical examination, housing renovation, milk/soymilk intake in the first trimester of pregnancy, paternal occupational hazards exposure, paternal strong tea drinking, and family history of NSCL/P. The model had statistical significance (lambda = 0.772, chi-square = 86.044, df = 8, P Self-verification showed that 83.8 % of the participants were correctly predicted to be NSCL/P cases or controls with a sensitivity of 74.3 % and a specificity of 88.5 %. The area under the receiver operating characteristic curve (AUC) was 0.846. The prediction model that was established using the risk factors of NSCL/P can be useful for predicting the risk of NSCL/P. Further research is needed to improve the model, and confirm the validity and reliability of the model.

  9. Confirmatory factor analysis applied to the Force Concept Inventory

    Science.gov (United States)

    Eaton, Philip; Willoughby, Shannon D.

    2018-06-01

    In 1995, Huffman and Heller used exploratory factor analysis to draw into question the factors of the Force Concept Inventory (FCI). Since then several papers have been published examining the factors of the FCI on larger sets of student responses and understandable factors were extracted as a result. However, none of these proposed factor models have been verified to not be unique to their original sample through the use of independent sets of data. This paper seeks to confirm the factor models proposed by Scott et al. in 2012, and Hestenes et al. in 1992, as well as another expert model proposed within this study through the use of confirmatory factor analysis (CFA) and a sample of 20 822 postinstruction student responses to the FCI. Upon application of CFA using the full sample, all three models were found to fit the data with acceptable global fit statistics. However, when CFA was performed using these models on smaller sample sizes the models proposed by Scott et al. and Eaton and Willoughby were found to be far more stable than the model proposed by Hestenes et al. The goodness of fit of these models to the data suggests that the FCI can be scored on factors that are not unique to a single class. These scores could then be used to comment on how instruction methods effect the performance of students along a single factor and more in-depth analyses of curriculum changes may be possible as a result.

  10. Analysis of Factors that Influence Infiltration Rates using the HELP Model

    International Nuclear Information System (INIS)

    Dyer, J.; Shipmon, J.

    2017-01-01

    The Hydrologic Evaluation of Landfill Performance (HELP) model is used by Savannah River National Laboratory (SRNL) in conjunction with PORFLOW groundwater flow simulation software to make longterm predictions of the fate and transport of radionuclides in the environment at radiological waste sites. The work summarized in this report supports preparation of the planned 2018 Performance Assessment for the E-Area Low-Level Waste Facility (LLWF) at the Savannah River Site (SRS). More specifically, this project focused on conducting a sensitivity analysis of infiltration (i.e., the rate at which water travels vertically in soil) through the proposed E-Area LLWF closure cap. A sensitivity analysis was completed using HELP v3.95D to identify the cap design and material property parameters that most impact infiltration rates through the proposed closure cap for a 10,000-year simulation period. The results of the sensitivity analysis indicate that saturated hydraulic conductivity (Ksat) for select cap layers, precipitation rate, surface vegetation type, and geomembrane layer defect density are dominant factors limiting infiltration rate. Interestingly, calculated infiltration rates were substantially influenced by changes in the saturated hydraulic conductivity of the Upper Foundation and Lateral Drainage layers. For example, an order-of-magnitude decrease in Ksat for the Upper Foundation layer lowered the maximum infiltration rate from a base-case 11 inches per year to only two inches per year. Conversely, an order-of-magnitude increase in Ksat led to an increase in infiltration rate from 11 to 15 inches per year. This work and its results provide a framework for quantifying uncertainty in the radionuclide transport and dose models for the planned 2018 E-Area Performance Assessment. Future work will focus on the development of a nonlinear regression model for infiltration rate using Minitab 17® to facilitate execution of probabilistic simulations in the GoldSim® overall

  11. Analysis of Factors that Influence Infiltration Rates using the HELP Model

    Energy Technology Data Exchange (ETDEWEB)

    Dyer, J. [Savannah River Site (SRS), Aiken, SC (United States). Savannah River National Lab. (SRNL); Shipmon, J. [Savannah River Site (SRS), Aiken, SC (United States). Savannah River National Lab. (SRNL)

    2017-09-28

    The Hydrologic Evaluation of Landfill Performance (HELP) model is used by Savannah River National Laboratory (SRNL) in conjunction with PORFLOW groundwater flow simulation software to make longterm predictions of the fate and transport of radionuclides in the environment at radiological waste sites. The work summarized in this report supports preparation of the planned 2018 Performance Assessment for the E-Area Low-Level Waste Facility (LLWF) at the Savannah River Site (SRS). More specifically, this project focused on conducting a sensitivity analysis of infiltration (i.e., the rate at which water travels vertically in soil) through the proposed E-Area LLWF closure cap. A sensitivity analysis was completed using HELP v3.95D to identify the cap design and material property parameters that most impact infiltration rates through the proposed closure cap for a 10,000-year simulation period. The results of the sensitivity analysis indicate that saturated hydraulic conductivity (Ksat) for select cap layers, precipitation rate, surface vegetation type, and geomembrane layer defect density are dominant factors limiting infiltration rate. Interestingly, calculated infiltration rates were substantially influenced by changes in the saturated hydraulic conductivity of the Upper Foundation and Lateral Drainage layers. For example, an order-of-magnitude decrease in Ksat for the Upper Foundation layer lowered the maximum infiltration rate from a base-case 11 inches per year to only two inches per year. Conversely, an order-of-magnitude increase in Ksat led to an increase in infiltration rate from 11 to 15 inches per year. This work and its results provide a framework for quantifying uncertainty in the radionuclide transport and dose models for the planned 2018 E-Area Performance Assessment. Future work will focus on the development of a nonlinear regression model for infiltration rate using Minitab 17® to facilitate execution of probabilistic simulations in the GoldSim® overall

  12. Cross-Cultural Validation of the Modified Practice Attitudes Scale: Initial Factor Analysis and a New Factor Model.

    Science.gov (United States)

    Park, Heehoon; Ebesutani, Chad K; Chung, Kyong-Mee; Stanick, Cameo

    2018-01-01

    The objective of this study was to create the Korean version of the Modified Practice Attitudes Scale (K-MPAS) to measure clinicians' attitudes toward evidence-based treatments (EBTs) in the Korean mental health system. Using 189 U.S. therapists and 283 members from the Korean mental health system, we examined the reliability and validity of the MPAS scores. We also conducted the first exploratory and confirmatory factor analysis on the MPAS and compared EBT attitudes across U.S. and Korean therapists. Results revealed that the inclusion of both "reversed-worded" and "non-reversed-worded" items introduced significant method effects that compromised the integrity of the one-factor MPAS model. Problems with the one-factor structure were resolved by eliminating the "non-reversed-worded" items. Reliability and validity were adequate among both Korean and U.S. therapists. Korean therapists also reported significantly more negative attitudes toward EBTs on the MPAS than U.S. therapists. The K-MPAS is the first questionnaire designed to measure Korean service providers' attitudes toward EBTs to help advance the dissemination of EBTs in Korea. The current study also demonstrated the negative impacts that can be introduced by incorporating oppositely worded items into a scale, particularly with respect to factor structure and detecting significant group differences.

  13. Confirmatory Factor Analysis of WAIS-IV in a Clinical Sample: Examining a Bi-Factor Model

    Directory of Open Access Journals (Sweden)

    Rachel Collinson

    2016-12-01

    Full Text Available There have been a number of studies that have examined the factor structure of the Wechsler Adult Intelligence Scale IV (WAIS-IV using the standardization sample. In this study, we investigate its factor structure on a clinical neuropsychology sample of mixed aetiology. Correlated factor, higher-order and bi-factor models are all tested. Overall, the results suggest that the WAIS-IV will be suitable for use with this population.

  14. Likelihood-based Dynamic Factor Analysis for Measurement and Forecasting

    NARCIS (Netherlands)

    Jungbacker, B.M.J.P.; Koopman, S.J.

    2015-01-01

    We present new results for the likelihood-based analysis of the dynamic factor model. The latent factors are modelled by linear dynamic stochastic processes. The idiosyncratic disturbance series are specified as autoregressive processes with mutually correlated innovations. The new results lead to

  15. Detection and analysis of unusual features in the structural model and structure-factor data of a birch pollen allergen

    International Nuclear Information System (INIS)

    Rupp, Bernhard

    2012-01-01

    The structure factors deposited with PDB entry 3k78 show properties inconsistent with experimentally observed diffraction data, and without uncertainty represent calculated structure factors. The refinement of the model against these structure factors leads to an isomorphous structure different from the deposited model with an implausibly small R value (0.019). Physically improbable features in the model of the birch pollen structure Bet v 1d are faithfully reproduced in electron density generated with the deposited structure factors, but these structure factors themselves exhibit properties that are characteristic of data calculated from a simple model and are inconsistent with the data and error model obtained through experimental measurements. The refinement of the model against these structure factors leads to an isomorphous structure different from the deposited model with an implausibly small R value (0.019). The abnormal refinement is compared with normal refinement of an isomorphous variant structure of Bet v 1l. A variety of analytical tools, including the application of Diederichs plots, Rσ plots and bulk-solvent analysis are discussed as promising aids in validation. The examination of the Bet v 1d structure also cautions against the practice of indicating poorly defined protein chain residues through zero occupancies. The recommendation to preserve diffraction images is amplified

  16. [Analysis of dietary pattern and diabetes mellitus influencing factors identified by classification tree model in adults of Fujian].

    Science.gov (United States)

    Yu, F L; Ye, Y; Yan, Y S

    2017-05-10

    Objective: To find out the dietary patterns and explore the relationship between environmental factors (especially dietary patterns) and diabetes mellitus in the adults of Fujian. Methods: Multi-stage sampling method were used to survey residents aged ≥18 years by questionnaire, physical examination and laboratory detection in 10 disease surveillance points in Fujian. Factor analysis was used to identify the dietary patterns, while logistic regression model was applied to analyze relationship between dietary patterns and diabetes mellitus, and classification tree model was adopted to identify the influencing factors for diabetes mellitus. Results: There were four dietary patterns in the population, including meat, plant, high-quality protein, and fried food and beverages patterns. The result of logistic analysis showed that plant pattern, which has higher factor loading of fresh fruit-vegetables and cereal-tubers, was a protective factor for non-diabetes mellitus. The risk of diabetes mellitus in the population at T2 and T3 levels of factor score were 0.727 (95 %CI: 0.561-0.943) times and 0.736 (95 %CI : 0.573-0.944) times higher, respectively, than those whose factor score was in lowest quartile. Thirteen influencing factors and eleven group at high-risk for diabetes mellitus were identified by classification tree model. The influencing factors were dyslipidemia, age, family history of diabetes, hypertension, physical activity, career, sex, sedentary time, abdominal adiposity, BMI, marital status, sleep time and high-quality protein pattern. Conclusion: There is a close association between dietary patterns and diabetes mellitus. It is necessary to promote healthy and reasonable diet, strengthen the monitoring and control of blood lipids, blood pressure and body weight, and have good lifestyle for the prevention and control of diabetes mellitus.

  17. Reliability Analysis of a Composite Wind Turbine Blade Section Using the Model Correction Factor Method: Numerical Study and Validation

    DEFF Research Database (Denmark)

    Dimitrov, Nikolay Krasimirov; Friis-Hansen, Peter; Berggreen, Christian

    2013-01-01

    by the composite failure criteria. Each failure mode has been considered in a separate component reliability analysis, followed by a system analysis which gives the total probability of failure of the structure. The Model Correction Factor method used in connection with FORM (First-Order Reliability Method) proved...

  18. A dynamic factor model of the evaluation of the financial crisis in Turkey.

    Science.gov (United States)

    Sezgin, F; Kinay, B

    2010-01-01

    Factor analysis has been widely used in economics and finance in situations where a relatively large number of variables are believed to be driven by few common causes of variation. Dynamic factor analysis (DFA) which is a combination of factor and time series analysis, involves autocorrelation matrices calculated from multivariate time series. Dynamic factor models were traditionally used to construct economic indicators, macroeconomic analysis, business cycles and forecasting. In recent years, dynamic factor models have become more popular in empirical macroeconomics. They have more advantages than other methods in various respects. Factor models can for instance cope with many variables without running into scarce degrees of freedom problems often faced in regression-based analysis. In this study, a model which determines the effect of the global crisis on Turkey is proposed. The main aim of the paper is to analyze how several macroeconomic quantities show an alteration before the evolution of the crisis and to decide if a crisis can be forecasted or not.

  19. A Solution to Modeling Multilevel Confirmatory Factor Analysis with Data Obtained from Complex Survey Sampling to Avoid Conflated Parameter Estimates

    Directory of Open Access Journals (Sweden)

    Jiun-Yu Wu

    2017-09-01

    Full Text Available The issue of equality in the between-and within-level structures in Multilevel Confirmatory Factor Analysis (MCFA models has been influential for obtaining unbiased parameter estimates and statistical inferences. A commonly seen condition is the inequality of factor loadings under equal level-varying structures. With mathematical investigation and Monte Carlo simulation, this study compared the robustness of five statistical models including two model-based (a true and a mis-specified models, one design-based, and two maximum models (two models where the full rank of variance-covariance matrix is estimated in between level and within level, respectively in analyzing complex survey measurement data with level-varying factor loadings. The empirical data of 120 3rd graders' (from 40 classrooms perceived Harter competence scale were modeled using MCFA and the parameter estimates were used as true parameters to perform the Monte Carlo simulation study. Results showed maximum models was robust to unequal factor loadings while the design-based and the miss-specified model-based approaches produced conflated results and spurious statistical inferences. We recommend the use of maximum models if researchers have limited information about the pattern of factor loadings and measurement structures. Measurement models are key components of Structural Equation Modeling (SEM; therefore, the findings can be generalized to multilevel SEM and CFA models. Mplus codes are provided for maximum models and other analytical models.

  20. A Markov Chain Monte Carlo Approach to Confirmatory Item Factor Analysis

    Science.gov (United States)

    Edwards, Michael C.

    2010-01-01

    Item factor analysis has a rich tradition in both the structural equation modeling and item response theory frameworks. The goal of this paper is to demonstrate a novel combination of various Markov chain Monte Carlo (MCMC) estimation routines to estimate parameters of a wide variety of confirmatory item factor analysis models. Further, I show…

  1. EXPLORATORY FACTOR ANALYSIS (EFA IN CONSUMER BEHAVIOR AND MARKETING RESEARCH

    Directory of Open Access Journals (Sweden)

    Marcos Pascual Soler

    2012-06-01

    Full Text Available Exploratory Factor Analysis (EFA is one of the most widely used statistical procedures in social research. The main objective of this work is to describe the most common practices used by researchers in the consumer behavior and marketing area. Through a literature review methodology the practices of AFE in five consumer behavior and marketing journals(2000-2010 were analyzed. Then, the choices made by the researchers concerning factor model, retention criteria, rotation, factors interpretation and other relevant issues to factor analysis were analized. The results suggest that researchers routinely conduct analyses using such questionable methods. Suggestions for improving the use of factor analysis and the reporting of results are presented and a checklist (Exploratory Factor Analysis Checklist, EFAC is provided to help editors, reviewers, and authors improve reporting exploratory factor analysis.

  2. Exploring the core factors and its dynamic effects on oil price: An application on path analysis and BVAR-TVP model

    International Nuclear Information System (INIS)

    Chai Jian; Guo, Ju-E.; Meng Lei; Wang Shouyang

    2011-01-01

    As the uncertainty of oil price increases, impacts of the influential factors on oil price vary over time. It is of great importance to explore the core factors and its time-varying influence on oil price. In view of this, based on the PATH-ANALYSIS model, this paper obtains the core factors, builds an oil price system VAR model, which uses demand, supply, price, and inventory as endogenous variables, and China's net imports as well as dollar index as exogenous variables. Then we set up a BVAR-TVP (Time varying parameter) model to analyze dynamic impacts of core factors on oil price. The results show that: (1) oil prices became more sensitive to oil supply changes, and the influence delays became shorter; (2) the impact of oil inventories on oil prices with a time lag of two quarters but has a downward trend; (3) the impact of oil consumption on oil prices with a time lag of two quarters, and this effect is increasingly greater; (4) the US dollar index is always the important factor of oil price and its control power increases gradually, and the financial crisis (occurred in 2008) further strengthens the influence of US dollar. - Highlights: ► We build an oil price VAR model based on the PATH-ANALYSIS results. ► The dynamic effects of core factors on oil price was studied by BVAR-TVP model. ► Oil prices became more sensitive to oil supply changes. ► The effect of oil consumption on oil prices is increasingly greater. ► Financial crisis further strengthens the influence of US dollar on oil price.

  3. Confirmatory Factor Analysis of the WISC-III with Child Psychiatric Inpatients.

    Science.gov (United States)

    Tupa, David J.; Wright, Margaret O'Dougherty; Fristad, Mary A.

    1997-01-01

    Factor models of the Wechsler Intelligence Scale for Children-Third Edition (WISC-III) for one, two, three, and four factors were tested using confirmatory factor analysis with a sample of 177 child psychiatric inpatients. The four-factor model proposed in the WISC-III manual provided the best fit to the data. (SLD)

  4. Confirmatory factor analysis of the female sexual function index.

    Science.gov (United States)

    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.

  5. An Evaluation on Factors Influencing Decision making for Malaysia Disaster Management: The Confirmatory Factor Analysis Approach

    Science.gov (United States)

    Zubir, S. N. A.; Thiruchelvam, S.; Mustapha, K. N. M.; Che Muda, Z.; Ghazali, A.; Hakimie, H.

    2017-12-01

    For the past few years, natural disaster has been the subject of debate in disaster management especially in flood disaster. Each year, natural disaster results in significant loss of life, destruction of homes and public infrastructure, and economic hardship. Hence, an effective and efficient flood disaster management would assure non-futile efforts for life saving. The aim of this article is to examine the relationship between approach, decision maker, influence factor, result, and ethic to decision making for flood disaster management in Malaysia. The key elements of decision making in the disaster management were studied based on the literature. Questionnaire surveys were administered among lead agencies at East Coast of Malaysia in the state of Kelantan and Pahang. A total of 307 valid responses had been obtained for further analysis. Exploratory Factor Analysis (EFA) and Confirmatory Factor Analysis (CFA) were carried out to analyse the measurement model involved in the study. The CFA for second-order reflective and first-order reflective measurement model indicates that approach, decision maker, influence factor, result, and ethic have a significant and direct effect on decision making during disaster. The results from this study showed that decision- making during disaster is an important element for disaster management to necessitate a successful collaborative decision making. The measurement model is accepted to proceed with further analysis known as Structural Equation Modeling (SEM) and can be assessed for the future research.

  6. Biosphere dose conversion Factor Importance and Sensitivity Analysis

    International Nuclear Information System (INIS)

    M. Wasiolek

    2004-01-01

    This report presents importance and sensitivity analysis for the environmental radiation model for Yucca Mountain, Nevada (ERMYN). ERMYN is a biosphere model supporting the total system performance assessment (TSPA) for the license application (LA) for the Yucca Mountain repository. This analysis concerns the output of the model, biosphere dose conversion factors (BDCFs) for the groundwater, and the volcanic ash exposure scenarios. It identifies important processes and parameters that influence the BDCF values and distributions, enhances understanding of the relative importance of the physical and environmental processes on the outcome of the biosphere model, includes a detailed pathway analysis for key radionuclides, and evaluates the appropriateness of selected parameter values that are not site-specific or have large uncertainty

  7. Confirmatory Factor Analysis of the Procrastination Assessment Scale for Students

    Directory of Open Access Journals (Sweden)

    Ronald D. Yockey

    2015-10-01

    Full Text Available The relative fit of one- and two-factor models of the Procrastination Assessment Scale for Students (PASS was investigated using confirmatory factor analysis on an ethnically diverse sample of 345 participants. The results indicated that although the two-factor model provided better fit to the data than the one-factor model, neither model provided optimal fit. However, a two-factor model which accounted for common item theme pairs used by Solomon and Rothblum in the creation of the scale provided good fit to the data. In addition, a significant difference by ethnicity was also found on the fear of failure subscale of the PASS, with Whites having significantly lower scores than Asian Americans or Latino/as. Implications of the results are discussed and recommendations made for future work with the scale.

  8. Moderating Factors of Video-Modeling with Other as Model: A Meta-Analysis of Single-Case Studies

    Science.gov (United States)

    Mason, Rose A.; Ganz, Jennifer B.; Parker, Richard I.; Burke, Mack D.; Camargo, Siglia P.

    2012-01-01

    Video modeling with other as model (VMO) is a more practical method for implementing video-based modeling techniques, such as video self-modeling, which requires significantly more editing. Despite this, identification of contextual factors such as participant characteristics and targeted outcomes that moderate the effectiveness of VMO has not…

  9. A methodology to incorporate organizational factors into human reliability analysis

    International Nuclear Information System (INIS)

    Li Pengcheng; Chen Guohua; Zhang Li; Xiao Dongsheng

    2010-01-01

    A new holistic methodology for Human Reliability Analysis (HRA) is proposed to model the effects of the organizational factors on the human reliability. Firstly, a conceptual framework is built, which is used to analyze the causal relationships between the organizational factors and human reliability. Then, the inference model for Human Reliability Analysis is built by combining the conceptual framework with Bayesian networks, which is used to execute the causal inference and diagnostic inference of human reliability. Finally, a case example is presented to demonstrate the specific application of the proposed methodology. The results show that the proposed methodology of combining the conceptual model with Bayesian Networks can not only easily model the causal relationship between organizational factors and human reliability, but in a given context, people can quantitatively measure the human operational reliability, and identify the most likely root causes or the prioritization of root causes caused human error. (authors)

  10. Loneliness and solitude in adolescence: A confirmatory factor analysis of alternative models

    DEFF Research Database (Denmark)

    Goossens, Luc; Lasgaard, Mathias; Luyckx, Koen

    2009-01-01

    completed by a sample of mid-adolescents (N = 534) from Grades 10 through 12 (aged 15-18 years) in the Dutch-speaking part of Belgium. As expected, the four-factor solution provided a better fit to the data than did alternative models that comprised just a single factor, or two and three factors. Use...

  11. Multi-Scale Factor Analysis of High-Dimensional Brain Signals

    KAUST Repository

    Ting, Chee-Ming; Ombao, Hernando; Salleh, Sh-Hussain

    2017-01-01

    In this paper, we develop an approach to modeling high-dimensional networks with a large number of nodes arranged in a hierarchical and modular structure. We propose a novel multi-scale factor analysis (MSFA) model which partitions the massive

  12. The Meaning of Higher-Order Factors in Reflective-Measurement Models

    Science.gov (United States)

    Eid, Michael; Koch, Tobias

    2014-01-01

    Higher-order factor analysis is a widely used approach for analyzing the structure of a multidimensional test. Whenever first-order factors are correlated researchers are tempted to apply a higher-order factor model. But is this reasonable? What do the higher-order factors measure? What is their meaning? Willoughby, Holochwost, Blanton, and Blair…

  13. Modular Open-Source Software for Item Factor Analysis

    Science.gov (United States)

    Pritikin, Joshua N.; Hunter, Micheal D.; Boker, Steven M.

    2015-01-01

    This article introduces an item factor analysis (IFA) module for "OpenMx," a free, open-source, and modular statistical modeling package that runs within the R programming environment on GNU/Linux, Mac OS X, and Microsoft Windows. The IFA module offers a novel model specification language that is well suited to programmatic generation…

  14. Beyond factor analysis: Multidimensionality and the Parkinson's Disease Sleep Scale-Revised.

    Directory of Open Access Journals (Sweden)

    Maria E Pushpanathan

    Full Text Available Many studies have sought to describe the relationship between sleep disturbance and cognition in Parkinson's disease (PD. The Parkinson's Disease Sleep Scale (PDSS and its variants (the Parkinson's disease Sleep Scale-Revised; PDSS-R, and the Parkinson's Disease Sleep Scale-2; PDSS-2 quantify a range of symptoms impacting sleep in only 15 items. However, data from these scales may be problematic as included items have considerable conceptual breadth, and there may be overlap in the constructs assessed. Multidimensional measurement models, accounting for the tendency for items to measure multiple constructs, may be useful more accurately to model variance than traditional confirmatory factor analysis. In the present study, we tested the hypothesis that a multidimensional model (a bifactor model is more appropriate than traditional factor analysis for data generated by these types of scales, using data collected using the PDSS-R as an exemplar. 166 participants diagnosed with idiopathic PD participated in this study. Using PDSS-R data, we compared three models: a unidimensional model; a 3-factor model consisting of sub-factors measuring insomnia, motor symptoms and obstructive sleep apnoea (OSA and REM sleep behaviour disorder (RBD symptoms; and, a confirmatory bifactor model with both a general factor and the same three sub-factors. Only the confirmatory bifactor model achieved satisfactory model fit, suggesting that PDSS-R data are multidimensional. There were differential associations between factor scores and patient characteristics, suggesting that some PDSS-R items, but not others, are influenced by mood and personality in addition to sleep symptoms. Multidimensional measurement models may also be a helpful tool in the PDSS and the PDSS-2 scales and may improve the sensitivity of these instruments.

  15. Multi-Scale Factor Analysis of High-Dimensional Brain Signals

    KAUST Repository

    Ting, Chee-Ming

    2017-05-18

    In this paper, we develop an approach to modeling high-dimensional networks with a large number of nodes arranged in a hierarchical and modular structure. We propose a novel multi-scale factor analysis (MSFA) model which partitions the massive spatio-temporal data defined over the complex networks into a finite set of regional clusters. To achieve further dimension reduction, we represent the signals in each cluster by a small number of latent factors. The correlation matrix for all nodes in the network are approximated by lower-dimensional sub-structures derived from the cluster-specific factors. To estimate regional connectivity between numerous nodes (within each cluster), we apply principal components analysis (PCA) to produce factors which are derived as the optimal reconstruction of the observed signals under the squared loss. Then, we estimate global connectivity (between clusters or sub-networks) based on the factors across regions using the RV-coefficient as the cross-dependence measure. This gives a reliable and computationally efficient multi-scale analysis of both regional and global dependencies of the large networks. The proposed novel approach is applied to estimate brain connectivity networks using functional magnetic resonance imaging (fMRI) data. Results on resting-state fMRI reveal interesting modular and hierarchical organization of human brain networks during rest.

  16. A review of the models for evaluating organizational factors in human reliability analysis

    International Nuclear Information System (INIS)

    Alvarenga, Marco Antonio Bayout; Fonseca, Renato Alves da; Melo, Paulo Fernando Ferreira Frutuoso e

    2009-01-01

    Human factors should be evaluated in three hierarchical levels. The first level should concern the cognitive behavior of human beings during the control of processes that occur through the man-machine interface. Here, one evaluates human errors through human reliability models of first and second generation, like THERP, ASEP and HCR (first generation) and ATHEANA and CREAM (second generation). In the second level, the focus is in the cognitive behavior of human beings when they work in groups, as in nuclear power plants. The focus here is in the anthropological aspects that govern the interaction among human beings. In the third level, one is interested in the influence that the organizational culture exerts on human beings as well as on the tasks being performed. Here, one adds to the factors of the second level the economical and political aspects that shape the company organizational culture. Nowadays, the methodologies of HRA incorporate organizational factors in the group and organization levels through performance shaping factors. This work makes a critical evaluation of the deficiencies concerning human factors and evaluates the potential of quantitative techniques that have been proposed in the last decade to model organizational factors, including the interaction among groups, with the intention of eliminating this chronic deficiency of HRA models. Two important techniques will be discussed in this context: STAMP, based on system theory and FRAM, which aims at modeling the nonlinearities of socio-technical systems. (author)

  17. A review of the models for evaluating organizational factors in human reliability analysis

    Energy Technology Data Exchange (ETDEWEB)

    Alvarenga, Marco Antonio Bayout; Fonseca, Renato Alves da [Comissao Nacional de Energia Nuclear (CNEN), Rio de Janeiro, RJ (Brazil)], e-mail: bayout@cnen.gov.br, e-mail: rfonseca@cnen.gov.br; Melo, Paulo Fernando Ferreira Frutuoso e [Coordenacao dos Programas de Pos-graduacao de Engenharia (COPPE/UFRJ), Rio de Janeiro, RJ (Brazil). Programa de Engenharia Nuclear], e-mail: frutuoso@con.ufrj.br

    2009-07-01

    Human factors should be evaluated in three hierarchical levels. The first level should concern the cognitive behavior of human beings during the control of processes that occur through the man-machine interface. Here, one evaluates human errors through human reliability models of first and second generation, like THERP, ASEP and HCR (first generation) and ATHEANA and CREAM (second generation). In the second level, the focus is in the cognitive behavior of human beings when they work in groups, as in nuclear power plants. The focus here is in the anthropological aspects that govern the interaction among human beings. In the third level, one is interested in the influence that the organizational culture exerts on human beings as well as on the tasks being performed. Here, one adds to the factors of the second level the economical and political aspects that shape the company organizational culture. Nowadays, the methodologies of HRA incorporate organizational factors in the group and organization levels through performance shaping factors. This work makes a critical evaluation of the deficiencies concerning human factors and evaluates the potential of quantitative techniques that have been proposed in the last decade to model organizational factors, including the interaction among groups, with the intention of eliminating this chronic deficiency of HRA models. Two important techniques will be discussed in this context: STAMP, based on system theory and FRAM, which aims at modeling the nonlinearities of socio-technical systems. (author)

  18. The scientific use of factor analysis in behavioral and life sciences

    National Research Council Canada - National Science Library

    Cattell, Raymond Bernard

    1978-01-01

    ...; the choice of procedures in experimentation; factor interpretation; the relationship of factor analysis to broadened psychometric concepts such as scaling, validity, and reliability, and to higher- strata models...

  19. Determining the Number of Factors in P-Technique Factor Analysis

    Science.gov (United States)

    Lo, Lawrence L.; Molenaar, Peter C. M.; Rovine, Michael

    2017-01-01

    Determining the number of factors is a critical first step in exploratory factor analysis. Although various criteria and methods for determining the number of factors have been evaluated in the usual between-subjects R-technique factor analysis, there is still question of how these methods perform in within-subjects P-technique factor analysis. A…

  20. Human factors review for Severe Accident Sequence Analysis (SASA)

    International Nuclear Information System (INIS)

    Krois, P.A.; Haas, P.M.; Manning, J.J.; Bovell, C.R.

    1984-01-01

    The paper will discuss work being conducted during this human factors review including: (1) support of the Severe Accident Sequence Analysis (SASA) Program based on an assessment of operator actions, and (2) development of a descriptive model of operator severe accident management. Research by SASA analysts on the Browns Ferry Unit One (BF1) anticipated transient without scram (ATWS) was supported through a concurrent assessment of operator performance to demonstrate contributions to SASA analyses from human factors data and methods. A descriptive model was developed called the Function Oriented Accident Management (FOAM) model, which serves as a structure for bridging human factors, operations, and engineering expertise and which is useful for identifying needs/deficiencies in the area of accident management. The assessment of human factors issues related to ATWS required extensive coordination with SASA analysts. The analysis was consolidated primarily to six operator actions identified in the Emergency Procedure Guidelines (EPGs) as being the most critical to the accident sequence. These actions were assessed through simulator exercises, qualitative reviews, and quantitative human reliability analyses. The FOAM descriptive model assumes as a starting point that multiple operator/system failures exceed the scope of procedures and necessitates a knowledge-based emergency response by the operators. The FOAM model provides a functionally-oriented structure for assembling human factors, operations, and engineering data and expertise into operator guidance for unconventional emergency responses to mitigate severe accident progression and avoid/minimize core degradation. Operators must also respond to potential radiological release beyond plant protective barriers. Research needs in accident management and potential uses of the FOAM model are described. 11 references, 1 figure

  1. Higher Education End-of-Course Evaluations: Assessing the Psychometric Properties Utilizing Exploratory Factor Analysis and Rasch Modeling Approaches

    Directory of Open Access Journals (Sweden)

    Kelly D. Bradley

    2016-07-01

    Full Text Available This paper offers a critical assessment of the psychometric properties of a standard higher education end-of-course evaluation. Using both exploratory factor analysis (EFA and Rasch modeling, the authors investigate the (a an overall assessment of dimensionality using EFA, (b a secondary assessment of dimensionality using a principal components analysis (PCA of the residuals when the items are fit to the Rasch model, and (c an assessment of item-level properties using item-level statistics provided when the items are fit to the Rasch model. The results support the usage of the scale as a supplement to high-stakes decision making such as tenure. However, the lack of precise targeting of item difficulty to person ability combined with the low person separation index renders rank-ordering professors according to minuscule differences in overall subscale scores a highly questionable practice.

  2. A Beginner’s Guide to Factor Analysis: Focusing on Exploratory Factor Analysis

    Directory of Open Access Journals (Sweden)

    An Gie Yong

    2013-10-01

    Full Text Available The following paper discusses exploratory factor analysis and gives an overview of the statistical technique and how it is used in various research designs and applications. A basic outline of how the technique works and its criteria, including its main assumptions are discussed as well as when it should be used. Mathematical theories are explored to enlighten students on how exploratory factor analysis works, an example of how to run an exploratory factor analysis on SPSS is given, and finally a section on how to write up the results is provided. This will allow readers to develop a better understanding of when to employ factor analysis and how to interpret the tables and graphs in the output.

  3. A general psychopathology factor (P factor) in children: Structural model analysis and external validation through familial risk and child global executive function.

    Science.gov (United States)

    Martel, Michelle M; Pan, Pedro M; Hoffmann, Maurício S; Gadelha, Ary; do Rosário, Maria C; Mari, Jair J; Manfro, Gisele G; Miguel, Eurípedes C; Paus, Tomás; Bressan, Rodrigo A; Rohde, Luis A; Salum, Giovanni A

    2017-01-01

    High rates of comorbidities and poor validity of disorder diagnostic criteria for mental disorders hamper advances in mental health research. Recent work has suggested the utility of continuous cross-cutting dimensions, including general psychopathology and specific factors of externalizing and internalizing (e.g., distress and fear) syndromes. The current study evaluated the reliability of competing structural models of psychopathology and examined external validity of the best fitting model on the basis of family risk and child global executive function (EF). A community sample of 8,012 families from Brazil with children ages 6-12 years completed structured interviews about the child and parental psychiatric syndromes, and a subsample of 2,395 children completed tasks assessing EF (i.e., working memory, inhibitory control, and time processing). Confirmatory factor analyses tested a series of structural models of psychopathology in both parents and children. The model with a general psychopathology factor ("P factor") with 3 specific factors (fear, distress, and externalizing) exhibited the best fit. The general P factor accounted for most of the variance in all models, with little residual variance explained by each of the 3 specific factors. In addition, associations between child and parental factors were mainly significant for the P factors and nonsignificant for the specific factors from the respective models. Likewise, the child P factor-but not the specific factors-was significantly associated with global child EF. Overall, our results provide support for a latent overarching P factor characterizing child psychopathology, supported by familial associations and child EF. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

  4. Applying total interpretive structural modeling to study factors affecting construction labour productivity

    Directory of Open Access Journals (Sweden)

    Sayali Shrikrishna Sandbhor

    2014-03-01

    Full Text Available Construction sector has always been dependent on manpower. Most of the activities carried out on any construction site are labour intensive. Since productivity of any project depends directly on productivity of labour, it is a prime responsibility of the employer to enhance labour productivity. Measures to improve the same depend on analysis of positive and negative factors affecting productivity. Major attention should be given to factors that decrease the productivity of labour. Factor analysis thus is an integral part of any study aiming to improve productivity.  Interpretive structural modeling is a methodology for identifying and summarizing relationships among factors which define an issue or problem. It provides a means to arrange the factors in an order as per their complexity. This study attempts to use the latest version of interpretive structural modeling i.e. total interpretive structural modeling to analyze factors negatively affecting construction labour productivity. It establishes interpretive relationship among these factors facilitating improvement in the overall productivity of construction site.

  5. Two Expectation-Maximization Algorithms for Boolean Factor Analysis

    Czech Academy of Sciences Publication Activity Database

    Frolov, A. A.; Húsek, Dušan; Polyakov, P.Y.

    2014-01-01

    Roč. 130, 23 April (2014), s. 83-97 ISSN 0925-2312 R&D Projects: GA ČR GAP202/10/0262 Grant - others:GA MŠk(CZ) ED1.1.00/02.0070; GA MŠk(CZ) EE.2.3.20.0073 Program:ED Institutional research plan: CEZ:AV0Z10300504 Keywords : Boolean Factor analysis * Binary Matrix factorization * Neural networks * Binary data model * Dimension reduction * Bars problem Subject RIV: IN - Informatics, Computer Science Impact factor: 2.083, year: 2014

  6. An easy guide to factor analysis

    CERN Document Server

    Kline, Paul

    2014-01-01

    Factor analysis is a statistical technique widely used in psychology and the social sciences. With the advent of powerful computers, factor analysis and other multivariate methods are now available to many more people. An Easy Guide to Factor Analysis presents and explains factor analysis as clearly and simply as possible. The author, Paul Kline, carefully defines all statistical terms and demonstrates step-by-step how to work out a simple example of principal components analysis and rotation. He further explains other methods of factor analysis, including confirmatory and path analysis, a

  7. A pragmatic approach to estimate alpha factors for common cause failure analysis

    International Nuclear Information System (INIS)

    Hassija, Varun; Senthil Kumar, C.; Velusamy, K.

    2014-01-01

    Highlights: • Estimation of coefficients in alpha factor model for common cause analysis. • A derivation of plant specific alpha factors is demonstrated. • We examine sensitivity of common cause contribution to total system failure. • We compare beta factor and alpha factor models for various redundant configurations. • The use of alpha factors is preferable, especially for large redundant systems. - Abstract: Most of the modern technological systems are deployed with high redundancy but still they fail mainly on account of common cause failures (CCF). Various models such as Beta Factor, Multiple Greek Letter, Binomial Failure Rate and Alpha Factor exists for estimation of risk from common cause failures. Amongst all, alpha factor model is considered most suitable for high redundant systems as it arrives at common cause failure probabilities from a set of ratios of failures and the total component failure probability Q T . In the present study, alpha factor model is applied for the assessment of CCF of safety systems deployed at two nuclear power plants. A method to overcome the difficulties in estimation of the coefficients viz., alpha factors in the model, importance of deriving plant specific alpha factors and sensitivity of common cause contribution to the total system failure probability with respect to hazard imposed by various CCF events is highlighted. An approach described in NUREG/CR-5500 is extended in this study to provide more explicit guidance for a statistical approach to derive plant specific coefficients for CCF analysis especially for high redundant systems. The procedure is expected to aid regulators for independent safety assessment

  8. Further insights on the French WISC-IV factor structure through Bayesian structural equation modeling.

    Science.gov (United States)

    Golay, Philippe; Reverte, Isabelle; Rossier, Jérôme; Favez, Nicolas; Lecerf, Thierry

    2013-06-01

    The interpretation of the Wechsler Intelligence Scale for Children--Fourth Edition (WISC-IV) is based on a 4-factor model, which is only partially compatible with the mainstream Cattell-Horn-Carroll (CHC) model of intelligence measurement. The structure of cognitive batteries is frequently analyzed via exploratory factor analysis and/or confirmatory factor analysis. With classical confirmatory factor analysis, almost all cross-loadings between latent variables and measures are fixed to zero in order to allow the model to be identified. However, inappropriate zero cross-loadings can contribute to poor model fit, distorted factors, and biased factor correlations; most important, they do not necessarily faithfully reflect theory. To deal with these methodological and theoretical limitations, we used a new statistical approach, Bayesian structural equation modeling (BSEM), among a sample of 249 French-speaking Swiss children (8-12 years). With BSEM, zero-fixed cross-loadings between latent variables and measures are replaced by approximate zeros, based on informative, small-variance priors. Results indicated that a direct hierarchical CHC-based model with 5 factors plus a general intelligence factor better represented the structure of the WISC-IV than did the 4-factor structure and the higher order models. Because a direct hierarchical CHC model was more adequate, it was concluded that the general factor should be considered as a breadth rather than a superordinate factor. Because it was possible for us to estimate the influence of each of the latent variables on the 15 subtest scores, BSEM allowed improvement of the understanding of the structure of intelligence tests and the clinical interpretation of the subtest scores. PsycINFO Database Record (c) 2013 APA, all rights reserved.

  9. Quantitative Analysis of Mixtures of Monoprotic Acids Applying Modified Model-Based Rank Annihilation Factor Analysis on Variation Matrices of Spectrophotometric Acid-Base Titrations

    Directory of Open Access Journals (Sweden)

    Ebrahim Ghorbani-Kalhor

    2015-04-01

    Full Text Available In the current work, a new version of rank annihilation factor analysis was developedto circumvent the rank deficiency problem in multivariate data measurements.Simultaneous determination of dissociation constant and concentration of monoprotic acids was performed by applying model-based rank annihilation factor analysis on variation matrices of spectrophotometric acid-base titrations data. Variation matrices can be obtained by subtracting first row of data matrix from all rows of the main data matrix. This method uses variation matrices instead of multivariate spectrophotometric acid-base titrations matrices to circumvent the rank deficiency problem in the rank quantitation step. The applicability of this approach was evaluated by simulated data at first stage, then the binary mixtures of ascorbic and sorbic acids as model compounds were investigated by the proposed method. At the end, the proposed method was successfully applied for resolving the ascorbic and sorbic acid in an orange juice real sample. Therefore, unique results were achieved by applying rank annihilation factor analysis on variation matrix and using hard soft model combination advantage without any problem and difficulty in rank determination. Normal 0 false false false EN-US X-NONE AR-SA /* Style Definitions */ table.MsoNormalTable {mso-style-name:"Table Normal"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-parent:""; mso-padding-alt:0cm 5.4pt 0cm 5.4pt; mso-para-margin-top:0cm; mso-para-margin-right:0cm; mso-para-margin-bottom:10.0pt; mso-para-margin-left:0cm; line-height:115%; mso-pagination:widow-orphan; font-size:11.0pt; font-family:"Calibri","sans-serif"; mso-ascii-font-family:Calibri; mso-ascii-theme-font:minor-latin; mso-hansi-font-family:Calibri; mso-hansi-theme-font:minor-latin; mso-bidi-font-family:Arial; mso-bidi-theme-font:minor-bidi; mso-bidi-language:AR-SA;}    

  10. Exploratory Factor Analysis With Small Samples and Missing Data.

    Science.gov (United States)

    McNeish, Daniel

    2017-01-01

    Exploratory factor analysis (EFA) is an extremely popular method for determining the underlying factor structure for a set of variables. Due to its exploratory nature, EFA is notorious for being conducted with small sample sizes, and recent reviews of psychological research have reported that between 40% and 60% of applied studies have 200 or fewer observations. Recent methodological studies have addressed small size requirements for EFA models; however, these models have only considered complete data, which are the exception rather than the rule in psychology. Furthermore, the extant literature on missing data techniques with small samples is scant, and nearly all existing studies focus on topics that are not of primary interest to EFA models. Therefore, this article presents a simulation to assess the performance of various missing data techniques for EFA models with both small samples and missing data. Results show that deletion methods do not extract the proper number of factors and estimate the factor loadings with severe bias, even when data are missing completely at random. Predictive mean matching is the best method overall when considering extracting the correct number of factors and estimating factor loadings without bias, although 2-stage estimation was a close second.

  11. Analysis on influencing factors of clinical teachers’ job satisfaction by structural equation model

    Directory of Open Access Journals (Sweden)

    Haiyi Jia

    2017-02-01

    Full Text Available [Research objective] Analyze the influencing factors of clinical teachers’ job satisfaction. [Research method] The ERG theory was used as the framework to design the questionnaires. Data were analyzed by structural equation model for investigating the influencing factors. [Research result] The modified model shows that factors of existence needs and growth needs have direct influence on the job satisfaction of clinical teachers, the influence coefficients are 0.540 and 0.380. The three influencing factors have positive effects on each other, and the correlation coefficients are 0.620, 0.400 and 0.330 respectively. [Research conclusion] Relevant departments should take active measures to improve job satisfaction of clinical teachers from two aspects: existence needs and growth needs, and to improve their work enthusiasm and teaching quality.

  12. Scalable group level probabilistic sparse factor analysis

    DEFF Research Database (Denmark)

    Hinrich, Jesper Løve; Nielsen, Søren Føns Vind; Riis, Nicolai Andre Brogaard

    2017-01-01

    Many data-driven approaches exist to extract neural representations of functional magnetic resonance imaging (fMRI) data, but most of them lack a proper probabilistic formulation. We propose a scalable group level probabilistic sparse factor analysis (psFA) allowing spatially sparse maps, component...... pruning using automatic relevance determination (ARD) and subject specific heteroscedastic spatial noise modeling. For task-based and resting state fMRI, we show that the sparsity constraint gives rise to components similar to those obtained by group independent component analysis. The noise modeling...... shows that noise is reduced in areas typically associated with activation by the experimental design. The psFA model identifies sparse components and the probabilistic setting provides a natural way to handle parameter uncertainties. The variational Bayesian framework easily extends to more complex...

  13. [A factor analysis method for contingency table data with unlimited multiple choice questions].

    Science.gov (United States)

    Toyoda, Hideki; Haiden, Reina; Kubo, Saori; Ikehara, Kazuya; Isobe, Yurie

    2016-02-01

    The purpose of this study is to propose a method of factor analysis for analyzing contingency tables developed from the data of unlimited multiple-choice questions. This method assumes that the element of each cell of the contingency table has a binominal distribution and a factor analysis model is applied to the logit of the selection probability. Scree plot and WAIC are used to decide the number of factors, and the standardized residual, the standardized difference between the sample, and the proportion ratio, is used to select items. The proposed method was applied to real product impression research data on advertised chips and energy drinks. Since the results of the analysis showed that this method could be used in conjunction with conventional factor analysis model, and extracted factors were fully interpretable, and suggests the usefulness of the proposed method in the study of psychology using unlimited multiple-choice questions.

  14. DISTANCE AS KEY FACTOR IN MODELLING STUDENTS’ RECRUITMENT BY UNIVERSITIES

    Directory of Open Access Journals (Sweden)

    SIMONA MĂLĂESCU

    2015-10-01

    Full Text Available Distance as Key Factor in Modelling Students’ Recruitment by Universities. In a previous paper analysing the challenge of keeping up with the current methodologies in the analysis and modelling of students’ recruitment by universities in the case of some ECE countries which still don’t register or develop key data to take advantage from the state of the art knowledge on the domain, we have promised to approach the factor distance in a future work due to the extent of the topic. This paper fulfill that promise bringing a review of the literature especially dealing with modelling the geographical area of recruiting students of an university, where combining distance with the proximate key factors previously reviewed, complete the meta-analysis of existing literature we have started a year ago. Beyond the theoretical benefit from a practical perspective, the metaanalysis aimed at synthesizing elements of good practice that can be applied to the local university system.

  15. Using structural equation modeling for network meta-analysis.

    Science.gov (United States)

    Tu, Yu-Kang; Wu, Yun-Chun

    2017-07-14

    Network meta-analysis overcomes the limitations of traditional pair-wise meta-analysis by incorporating all available evidence into a general statistical framework for simultaneous comparisons of several treatments. Currently, network meta-analyses are undertaken either within the Bayesian hierarchical linear models or frequentist generalized linear mixed models. Structural equation modeling (SEM) is a statistical method originally developed for modeling causal relations among observed and latent variables. As random effect is explicitly modeled as a latent variable in SEM, it is very flexible for analysts to specify complex random effect structure and to make linear and nonlinear constraints on parameters. The aim of this article is to show how to undertake a network meta-analysis within the statistical framework of SEM. We used an example dataset to demonstrate the standard fixed and random effect network meta-analysis models can be easily implemented in SEM. It contains results of 26 studies that directly compared three treatment groups A, B and C for prevention of first bleeding in patients with liver cirrhosis. We also showed that a new approach to network meta-analysis based on the technique of unrestricted weighted least squares (UWLS) method can also be undertaken using SEM. For both the fixed and random effect network meta-analysis, SEM yielded similar coefficients and confidence intervals to those reported in the previous literature. The point estimates of two UWLS models were identical to those in the fixed effect model but the confidence intervals were greater. This is consistent with results from the traditional pairwise meta-analyses. Comparing to UWLS model with common variance adjusted factor, UWLS model with unique variance adjusted factor has greater confidence intervals when the heterogeneity was larger in the pairwise comparison. The UWLS model with unique variance adjusted factor reflects the difference in heterogeneity within each comparison

  16. A Bayesian Belief Network modelling of organisational factors in risk analysis: A case study in maritime transportation

    International Nuclear Information System (INIS)

    Trucco, P.; Cagno, E.; Ruggeri, F.; Grande, O.

    2008-01-01

    The paper presents an innovative approach to integrate Human and Organisational Factors (HOF) into risk analysis. The approach has been developed and applied to a case study in the maritime industry, but it can also be utilised in other sectors. A Bayesian Belief Network (BBN) has been developed to model the Maritime Transport System (MTS), by taking into account its different actors (i.e., ship-owner, shipyard, port and regulator) and their mutual influences. The latter have been modelled by means of a set of dependent variables whose combinations express the relevant functions performed by each actor. The BBN model of the MTS has been used in a case study for the quantification of HOF in the risk analysis carried out at the preliminary design stage of High Speed Craft (HSC). The study has focused on a collision in open sea hazard carried out by means of an original method of integration of a Fault Tree Analysis (FTA) of technical elements with a BBN model of the influences of organisational functions and regulations, as suggested by the International Maritime Organisation's (IMO) Guidelines for Formal Safety Assessment (FSA). The approach has allowed the identification of probabilistic correlations between the basic events of a collision accident and the BBN model of the operational and organisational conditions. The linkage can be exploited in different ways, especially to support identification and evaluation of risk control options also at the organisational level. Conditional probabilities for the BBN have been estimated by means of experts' judgments, collected from an international panel of different European countries. Finally, a sensitivity analysis has been carried out over the model to identify configurations of the MTS leading to a significant reduction of accident probability during the operation of the HSC

  17. Human Reliability Analysis for Design: Using Reliability Methods for Human Factors Issues

    Energy Technology Data Exchange (ETDEWEB)

    Ronald Laurids Boring

    2010-11-01

    This paper reviews the application of human reliability analysis methods to human factors design issues. An application framework is sketched in which aspects of modeling typically found in human reliability analysis are used in a complementary fashion to the existing human factors phases of design and testing. The paper provides best achievable practices for design, testing, and modeling. Such best achievable practices may be used to evaluate and human system interface in the context of design safety certifications.

  18. Human Reliability Analysis for Design: Using Reliability Methods for Human Factors Issues

    International Nuclear Information System (INIS)

    Boring, Ronald Laurids

    2010-01-01

    This paper reviews the application of human reliability analysis methods to human factors design issues. An application framework is sketched in which aspects of modeling typically found in human reliability analysis are used in a complementary fashion to the existing human factors phases of design and testing. The paper provides best achievable practices for design, testing, and modeling. Such best achievable practices may be used to evaluate and human system interface in the context of design safety certifications.

  19. Analysis of Influence Factors of PM2.5 in Chengdu Based on VAR Model

    Science.gov (United States)

    Mingzhi, Luo

    2017-05-01

    Air pollution and smog are the serious harms to public health and has attracted public attention. Based on the vector auto-regressive (VAR) model, we analysed the influence factors of PM2.5 in Chengdu, investigated the effect of other kinds of air pollutants and meteorological factors onthe PM2.5 by using the methods of generalized impulse response function, variance decomposition analysis, Granger causality test and therelated daily data from December 1, 2013 to November 14, 2016 in Chengdu city to the empirical study. The resultsshow that the influence factors of PM2.5 were stable;the increase of nitrogen dioxide, ozone,precipitation and temperature difference led to the increase of PM2.5 concentration while the increase ofthe wind speed, PM10, sulphur dioxide and carbon monoxide resulted in the decrease of PM2.5 concentration.Climate conditions,nitrogen dioxide and ozone are Granger causes for PM2.5.It is suggestedthat the key for the control of PM2.5 must be based on the cause and formation rules of PM2.5.A further study on nitrogen dioxide and ozone may play an important role in finding out the real source and formation reasons of PM2.5.

  20. Generalized Linear Mixed Model Analysis of Urban-Rural Differences in Social and Behavioral Factors for Colorectal Cancer Screening

    Science.gov (United States)

    Wang, Ke-Sheng; Liu, Xuefeng; Ategbole, Muyiwa; Xie, Xin; Liu, Ying; Xu, Chun; Xie, Changchun; Sha, Zhanxin

    2017-09-27

    Objective: Screening for colorectal cancer (CRC) can reduce disease incidence, morbidity, and mortality. However, few studies have investigated the urban-rural differences in social and behavioral factors influencing CRC screening. The objective of the study was to investigate the potential factors across urban-rural groups on the usage of CRC screening. Methods: A total of 38,505 adults (aged ≥40 years) were selected from the 2009 California Health Interview Survey (CHIS) data - the latest CHIS data on CRC screening. The weighted generalized linear mixed-model (WGLIMM) was used to deal with this hierarchical structure data. Weighted simple and multiple mixed logistic regression analyses in SAS ver. 9.4 were used to obtain the odds ratios (ORs) and their 95% confidence intervals (CIs). Results: The overall prevalence of CRC screening was 48.1% while the prevalence in four residence groups - urban, second city, suburban, and town/rural, were 45.8%, 46.9%, 53.7% and 50.1%, respectively. The results of WGLIMM analysis showed that there was residence effect (pregression analysis revealed that age, race, marital status, education level, employment stats, binge drinking, and smoking status were associated with CRC screening (p<0.05). Stratified by residence regions, age and poverty level showed associations with CRC screening in all four residence groups. Education level was positively associated with CRC screening in second city and suburban. Infrequent binge drinking was associated with CRC screening in urban and suburban; while current smoking was a protective factor in urban and town/rural groups. Conclusions: Mixed models are useful to deal with the clustered survey data. Social factors and behavioral factors (binge drinking and smoking) were associated with CRC screening and the associations were affected by living areas such as urban and rural regions. Creative Commons Attribution License

  1. Tensor-Dictionary Learning with Deep Kruskal-Factor Analysis

    Energy Technology Data Exchange (ETDEWEB)

    Stevens, Andrew J.; Pu, Yunchen; Sun, Yannan; Spell, Gregory; Carin, Lawrence

    2017-04-20

    We introduce new dictionary learning methods for tensor-variate data of any order. We represent each data item as a sum of Kruskal decomposed dictionary atoms within the framework of beta-process factor analysis (BPFA). Our model is nonparametric and can infer the tensor-rank of each dictionary atom. This Kruskal-Factor Analysis (KFA) is a natural generalization of BPFA. We also extend KFA to a deep convolutional setting and develop online learning methods. We test our approach on image processing and classification tasks achieving state of the art results for 2D & 3D inpainting and Caltech 101. The experiments also show that atom-rank impacts both overcompleteness and sparsity.

  2. Final Technical Report: Advanced Measurement and Analysis of PV Derate Factors.

    Energy Technology Data Exchange (ETDEWEB)

    King, Bruce Hardison [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Burton, Patrick D. [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Hansen, Clifford [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Jones, Christian Birk [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)

    2015-12-01

    The Advanced Measurement and Analysis of PV Derate Factors project focuses on improving the accuracy and reducing the uncertainty of PV performance model predictions by addressing a common element of all PV performance models referred to as “derates”. Widespread use of “rules of thumb”, combined with significant uncertainty regarding appropriate values for these factors contribute to uncertainty in projected energy production.

  3. Analysis of transfer reactions: determination of spectroscopic factors

    Energy Technology Data Exchange (ETDEWEB)

    Keeley, N. [CEA Saclay, Dept. d' Astrophysique, de Physique des Particules de Physique Nucleaire et de l' Instrumentation Associee (DSM/DAPNIA/SPhN), 91- Gif sur Yvette (France); The Andrzej So an Institute for Nuclear Studies, Dept. of Nuclear Reactions, Warsaw (Poland)

    2007-07-01

    An overview of the most popular models used for the analysis of direct reaction data is given, concentrating on practical aspects. The 4 following models (in order of increasing sophistication): the distorted wave born approximation (DWBA), the adiabatic model, the coupled channels born approximation, and the coupled reaction channels are briefly described. As a concrete example, the C{sup 12}(d,p)C{sup 13} reaction at an incident deuteron energy of 30 MeV is analysed with progressively more physically sophisticated models. The effect of the choice of the reaction model on the spectroscopic information extracted from the data is investigated and other sources of uncertainty in the derived spectroscopic factors are discussed. We have showed that the choice of the reaction model can significantly influence the nuclear structure information, particularly the spectroscopic factors or amplitudes but occasionally also the spin-parity, that we wish to extract from direct reaction data. We have also demonstrated that the DWBA can fail to give a satisfactory description of transfer data but when the tenets of the theory are fulfilled DWBA can work very well and will yield the same results as most sophisticated models. The use of global rather than fitted optical potentials can also lead to important differences in the extracted spectroscopic factors.

  4. Confirmatory factor analysis of posttraumatic stress symptoms in sexually harassed women.

    Science.gov (United States)

    Palmieri, Patrick A; Fitzgerald, Louise F

    2005-12-01

    Posttraumatic stress disorder (PTSD) factor analytic research to date has not provided a clear consensus on the structure of posttraumatic stress symptoms. Seven hypothesized factor structures were evaluated using confirmatory factor analysis of the Posttraumatic Stress Disorder Checklist, a paper-and-pencil measure of posttraumatic stress symptom severity, in a sample of 1,218 women who experienced a broad range of workplace sexual harassment. The model specifying correlated re-experiencing, effortful avoidance, emotional numbing, and hyperarousal factors provided the best fit to the data. Virtually no support was obtained for the Diagnostic and Statistical Manual of Mental Disorders, fourth edition (DSM-IV; American Psychiatric Association, 1994) three-factor model of re-experiencing, avoidance, and hyperarousal factors. Different patterns of correlations with external variables were found for the avoidance and emotional numbing factors, providing further validation of the supported model.

  5. Factors accounting for youth suicide attempt in Hong Kong: a model building.

    Science.gov (United States)

    Wan, Gloria W Y; Leung, Patrick W L

    2010-10-01

    This study aimed at proposing and testing a conceptual model of youth suicide attempt. We proposed a model that began with family factors such as a history of physical abuse and parental divorce/separation. Family relationship, presence of psychopathology, life stressors, and suicide ideation were postulated as mediators, leading to youth suicide attempt. The stepwise entry of the risk factors to a logistic regression model defined their proximity as related to suicide attempt. Path analysis further refined our proposed model of youth suicide attempt. Our originally proposed model was largely confirmed. The main revision was dropping parental divorce/separation as a risk factor in the model due to lack of significant contribution when examined alongside with other risk factors. This model was cross-validated by gender. This study moved research on youth suicide from identification of individual risk factors to model building, integrating separate findings of the past studies.

  6. Verify Super Double-Heterogeneous Spherical Lattice Model for Equilibrium Fuel Cycle Analysis AND HTR Spherical Super Lattice Model for Equilibrium Fuel Cycle Analysis

    International Nuclear Information System (INIS)

    Gray S. Chang

    2005-01-01

    The currently being developed advanced High Temperature gas-cooled Reactors (HTR) is able to achieve a simplification of safety through reliance on innovative features and passive systems. One of the innovative features in these HTRs is reliance on ceramic-coated fuel particles to retain the fission products even under extreme accident conditions. Traditionally, the effect of the random fuel kernel distribution in the fuel pebble/block is addressed through the use of the Dancoff correction factor in the resonance treatment. However, the Dancoff correction factor is a function of burnup and fuel kernel packing factor, which requires that the Dancoff correction factor be updated during Equilibrium Fuel Cycle (EqFC) analysis. An advanced KbK-sph model and whole pebble super lattice model (PSLM), which can address and update the burnup dependent Dancoff effect during the EqFC analysis. The pebble homogeneous lattice model (HLM) is verified by the burnup characteristics with the double-heterogeneous KbK-sph lattice model results. This study summarizes and compares the KbK-sph lattice model and HLM burnup analyzed results. Finally, we discuss the Monte-Carlo coupling with a fuel depletion and buildup code--ORIGEN-2 as a fuel burnup analysis tool and its PSLM calculated results for the HTR EqFC burnup analysis

  7. Three-dimensional flow analysis and improvement of slip factor model for forward-curved blades centrifugal fan

    International Nuclear Information System (INIS)

    Guo, En Min; Kim, Kwang Yong

    2004-01-01

    This work developed improved slip factor model and correction method to predict flow through impeller in forward-curved centrifugal fan. Both steady and unsteady three-dimensional CFD analyses were performed to validate the slip factor model and the correction method. The results show that the improved slip factor model presented in this paper could provide more accurate predictions for forward-curved centrifugal impeller than the other slip factor models since the present model takes into account the effect of blade curvature. The correction method is provided to predict mass-averaged absolute circumferential velocity at the exit of impeller by taking account of blockage effects induced by the large-scale backflow near the front plate and flow separation within blade passage. The comparison with CFD results also shows that the improved slip factor model coupled with the present correction method provides accurate predictions for mass-averaged absolute circumferential velocity at the exit of impeller near and above the flow rate of peak total pressure coefficient

  8. The structure of PTSD symptoms: a test of alternative models using confirmatory factor analysis.

    Science.gov (United States)

    Elklit, Ask; Shevlin, Mark

    2007-09-01

    This study aimed to examine the structure of self-reported post-traumatic stress disorder (PTSD) symptoms. Based on previous factor analytic findings and the DSM-IV formulation, six confirmatory factor models were specified and estimated that reflected different symptom clusters. The analyses were based on responses from 1116 participants who had suffered whiplash injuries and screened for full or subclinical PTSD using the Harvard Trauma Questionnaire. A correlated four-factor model with re-experiencing, avoidance, dysphoria and arousal factors fitted the data very well. Correlations with criteria measures showed that these factors were associated with other trauma related variables in a theoretically predictable way and showed evidence of unique predictive utility. These results concur with previous research findings using different trauma populations but do not reflect the current DSM-IV symptom groupings.

  9. Phenotypic factor analysis of psychopathology reveals a new body-related transdiagnostic factor.

    Science.gov (United States)

    Pezzoli, Patrizia; Antfolk, Jan; Santtila, Pekka

    2017-01-01

    Comorbidity challenges the notion of mental disorders as discrete categories. An increasing body of literature shows that symptoms cut across traditional diagnostic boundaries and interact in shaping the latent structure of psychopathology. Using exploratory and confirmatory factor analysis, we reveal the latent sources of covariation among nine measures of psychopathological functioning in a population-based sample of 13024 Finnish twins and their siblings. By implementing unidimensional, multidimensional, second-order, and bifactor models, we illustrate the relationships between observed variables, specific, and general latent factors. We also provide the first investigation to date of measurement invariance of the bifactor model of psychopathology across gender and age groups. Our main result is the identification of a distinct "Body" factor, alongside the previously identified Internalizing and Externalizing factors. We also report relevant cross-disorder associations, especially between body-related psychopathology and trait anger, as well as substantial sex and age differences in observed and latent means. The findings expand the meta-structure of psychopathology, with implications for empirical and clinical practice, and demonstrate shared mechanisms underlying attitudes towards nutrition, self-image, sexuality and anger, with gender- and age-specific features.

  10. Factor analysis

    CERN Document Server

    Gorsuch, Richard L

    2013-01-01

    Comprehensive and comprehensible, this classic covers the basic and advanced topics essential for using factor analysis as a scientific tool in psychology, education, sociology, and related areas. Emphasizing the usefulness of the techniques, it presents sufficient mathematical background for understanding and sufficient discussion of applications for effective use. This includes not only theory but also the empirical evaluations of the importance of mathematical distinctions for applied scientific analysis.

  11. Understanding influential factors on implementing green supply chain management practices: An interpretive structural modelling analysis.

    Science.gov (United States)

    Agi, Maher A N; Nishant, Rohit

    2017-03-01

    In this study, we establish a set of 19 influential factors on the implementation of Green Supply Chain Management (GSCM) practices and analyse the interaction between these factors and their effect on the implementation of GSCM practices using the Interpretive Structural Modelling (ISM) method and the "Matrice d'Impacts Croisés Multiplication Appliquée à un Classement" (MICMAC) analysis on data compiled from interviews with supply chain (SC) executives based in the Gulf countries (Middle East region). The study reveals a strong influence and driving power of the nature of the relationships between SC partners on the implementation of GSCM practices. We especially found that dependence, trust, and durability of the relationship with SC partners have a very high influence. In addition, the size of the company, the top management commitment, the implementation of quality management and the employees training and education exert a critical influence on the implementation of GSCM practices. Contextual elements such as the industry sector and region and their effect on the prominence of specific factors are also highlighted through our study. Finally, implications for research and practice are discussed. Copyright © 2016 Elsevier Ltd. All rights reserved.

  12. A Confirmatory Factor Analysis Model of Servant Leader of School Director Under the Office of the Vocational Education Commission in Thailand

    Directory of Open Access Journals (Sweden)

    Boonchan Sisan

    2017-11-01

    Full Text Available This research aims to develop and examine the Goodness-of-Fit Index of Confirmatory Factor Analysis (CFA in servant leader of school director under the Office of the Vocational Education Commission (OVEC. The result is based on the empirical data. The sample group consisted of 247 school directors under the OVEC. The samples were taken using Multi - Stage Sampling randomized technique. Research instrument was questionnaire which had 0.80 - 1.00 for item objective congruence, discriminative power with 0.46 - .80 , and reliability of .95. The data analysed by Confirmatory Factor Analysis (CFA. The study shows the servant leader of school director under the OVEC consists of six factors: Appreciating of Others, Developing Others, Developing Community, moral Expressions, Supporting Leadership, and Using Leadership Together. The results of examination of the Goodness-of-Fit Index of Confirmatory Factor Analysis (CFA found the model fit indexes based on the empirical data were =280.89; df=252; P-value=0.10204; Relative =1.11; RMSEA=0.022; NFI=0.98; RMR=0.016; SRMR=0.041; GFI=0.92; AGFI=0.89; NIF=0.98; IFI=1.00; CFI=1.00; CN=252.56. The factor loadings of six factors were from 0.73 – 0.94 and factor loadings of indicators were from -0.39 – 0.57.

  13. Factors Associated with Asthma ED Visit Rates among Medicaid-enrolled Children: A Structural Equation Modeling Approach

    Directory of Open Access Journals (Sweden)

    Luceta McRoy

    2017-02-01

    Full Text Available Background: Asthma is one of the leading causes of emergency department visits and school absenteeism among school-aged children in the United States, but there is significant local-area variation in emergency department visit rates, as well as significant differences across racial-ethnic groups. Analysis: We first calculated emergency department (ED visit rates among Medicaid-enrolled children age 5–12 with asthma using a multi-state dataset. We then performed exploratory factor analysis using over 226 variables to assess whether they clustered around three county-level conceptual factors (socioeconomic status, healthcare capacity, and air quality thought to be associated with variation in asthma ED visit rates. Measured variables (including ED visit rate as the outcome of interest were then standardized and tested in a simple conceptual model through confirmatory factor analysis. Results: County-level (contextual variables did cluster around factors declared a priori in the conceptual model. Structural equation models connecting the ED visit rates to socioeconomic status, air quality, and healthcare system professional capacity factors (consistent with our conceptual framework converged on a solution and achieved a reasonable goodness of fit on confirmatory factor analysis. Conclusion: Confirmatory factor analysis offers an approach for quantitatively testing conceptual models of local-area variation and racial disparities in asthma-related emergency department use.

  14. Skewed factor models using selection mechanisms

    KAUST Repository

    Kim, Hyoung-Moon

    2015-12-21

    Traditional factor models explicitly or implicitly assume that the factors follow a multivariate normal distribution; that is, only moments up to order two are involved. However, it may happen in real data problems that the first two moments cannot explain the factors. Based on this motivation, here we devise three new skewed factor models, the skew-normal, the skew-tt, and the generalized skew-normal factor models depending on a selection mechanism on the factors. The ECME algorithms are adopted to estimate related parameters for statistical inference. Monte Carlo simulations validate our new models and we demonstrate the need for skewed factor models using the classic open/closed book exam scores dataset.

  15. Skewed factor models using selection mechanisms

    KAUST Repository

    Kim, Hyoung-Moon; Maadooliat, Mehdi; Arellano-Valle, Reinaldo B.; Genton, Marc G.

    2015-01-01

    Traditional factor models explicitly or implicitly assume that the factors follow a multivariate normal distribution; that is, only moments up to order two are involved. However, it may happen in real data problems that the first two moments cannot explain the factors. Based on this motivation, here we devise three new skewed factor models, the skew-normal, the skew-tt, and the generalized skew-normal factor models depending on a selection mechanism on the factors. The ECME algorithms are adopted to estimate related parameters for statistical inference. Monte Carlo simulations validate our new models and we demonstrate the need for skewed factor models using the classic open/closed book exam scores dataset.

  16. Followee recommendation in microblog using matrix factorization model with structural regularization.

    Science.gov (United States)

    Yu, Yan; Qiu, Robin G

    2014-01-01

    Microblog that provides us a new communication and information sharing platform has been growing exponentially since it emerged just a few years ago. To microblog users, recommending followees who can serve as high quality information sources is a competitive service. To address this problem, in this paper we propose a matrix factorization model with structural regularization to improve the accuracy of followee recommendation in microblog. More specifically, we adapt the matrix factorization model in traditional item recommender systems to followee recommendation in microblog and use structural regularization to exploit structure information of social network to constrain matrix factorization model. The experimental analysis on a real-world dataset shows that our proposed model is promising.

  17. A factor analysis of Functional Independence and Functional Assessment Measure scores among focal and diffuse brain injury patients: The importance of bi-factor models.

    Science.gov (United States)

    Gunn, Sarah; Burgess, Gerald H; Maltby, John

    2018-04-28

    To explore the factor structure of the UK Functional Independence Measure and Functional Assessment Measure (FIM+FAM) among focal and diffuse acquired brain injury patients. Criterion standard. An NHS acute acquired brain injury inpatient rehabilitation hospital. Referred sample of 447 adults (835 cases after exclusions) admitted for inpatient treatment following an acquired brain injury significant enough to justify intensive inpatient neurorehabilitation. Not applicable. Functional Independence Measure and Functional Assessment Measure. Exploratory Factor Analysis suggested a two-factor structure to FIM+FAM scores, among both focal-proximate and diffuse-proximate acquired brain injury aetiologies. Confirmatory Factor Analysis suggested a three-factor bi-factor structure presented the best fit of the FIM+FAM score data across both aetiologies. However, across both analyses, a convergence was found towards a general factor, demonstrated by high correlations between factors in the Exploratory Factor Analysis, and by a general factor explaining the majority of the variance in scores on Confirmatory Factor Analysis. Our findings suggested that although factors describing specific functional domains can be derived from FIM+FAM item scores, there is a convergence towards a single factor describing overall functioning. This single factor informs the specific group factors (e.g. motor, psychosocial and communication function) following brain injury. Further research into the comparative value of the general and group factors as evaluative/prognostic measures is indicated. Copyright © 2018. Published by Elsevier Inc.

  18. Evaluating WAIS-IV structure through a different psychometric lens: structural causal model discovery as an alternative to confirmatory factor analysis.

    Science.gov (United States)

    van Dijk, Marjolein J A M; Claassen, Tom; Suwartono, Christiany; van der Veld, William M; van der Heijden, Paul T; Hendriks, Marc P H

    Since the publication of the WAIS-IV in the U.S. in 2008, efforts have been made to explore the structural validity by applying factor analysis to various samples. This study aims to achieve a more fine-grained understanding of the structure of the Dutch language version of the WAIS-IV (WAIS-IV-NL) by applying an alternative analysis based on causal modeling in addition to confirmatory factor analysis (CFA). The Bayesian Constraint-based Causal Discovery (BCCD) algorithm learns underlying network structures directly from data and assesses more complex structures than is possible with factor analysis. WAIS-IV-NL profiles of two clinical samples of 202 patients (i.e. patients with temporal lobe epilepsy and a mixed psychiatric outpatient group) were analyzed and contrasted with a matched control group (N = 202) selected from the Dutch standardization sample of the WAIS-IV-NL to investigate internal structure by means of CFA and BCCD. With CFA, the four-factor structure as proposed by Wechsler demonstrates acceptable fit in all three subsamples. However, BCCD revealed three consistent clusters (verbal comprehension, visual processing, and processing speed) in all three subsamples. The combination of Arithmetic and Digit Span as a coherent working memory factor could not be verified, and Matrix Reasoning appeared to be isolated. With BCCD, some discrepancies from the proposed four-factor structure are exemplified. Furthermore, these results fit CHC theory of intelligence more clearly. Consistent clustering patterns indicate these results are robust. The structural causal discovery approach may be helpful in better interpreting existing tests, the development of new tests, and aid in diagnostic instruments.

  19. Confirmatory factor analysis of the neck disability index in a whiplash population indicates a one-factor model is viable

    OpenAIRE

    Gabel, Charles P.; Cuesta-Vargas, Antonio I.; Barr, Sebastian; Winkeljohn Black, Stephanie; Osborne, Jason W.; Melloh, Markus

    2016-01-01

    Purpose The neck disability index (NDI) as a 10-item patient reported outcome (PRO) measure is the most commonly used whiplash associated disorders (WAD) assessment tool. However, statistical rigor and factor structure are not definitive. To date, confirmatory factor analysis (CFA) has not examined whether the factor structure generalizes across different groups (e.g., WAD versus non-WAD). This study aimed to determine the psychometric properties of the NDI in these population groups.

  20. Micropollutants throughout an integrated urban drainage model: Sensitivity and uncertainty analysis

    Science.gov (United States)

    Mannina, Giorgio; Cosenza, Alida; Viviani, Gaspare

    2017-11-01

    The paper presents the sensitivity and uncertainty analysis of an integrated urban drainage model which includes micropollutants. Specifically, a bespoke integrated model developed in previous studies has been modified in order to include the micropollutant assessment (namely, sulfamethoxazole - SMX). The model takes into account also the interactions between the three components of the system: sewer system (SS), wastewater treatment plant (WWTP) and receiving water body (RWB). The analysis has been applied to an experimental catchment nearby Palermo (Italy): the Nocella catchment. Overall, five scenarios, each characterized by different uncertainty combinations of sub-systems (i.e., SS, WWTP and RWB), have been considered applying, for the sensitivity analysis, the Extended-FAST method in order to select the key factors affecting the RWB quality and to design a reliable/useful experimental campaign. Results have demonstrated that sensitivity analysis is a powerful tool for increasing operator confidence in the modelling results. The approach adopted here can be used for blocking some non-identifiable factors, thus wisely modifying the structure of the model and reducing the related uncertainty. The model factors related to the SS have been found to be the most relevant factors affecting the SMX modeling in the RWB when all model factors (scenario 1) or model factors of SS (scenarios 2 and 3) are varied. If the only factors related to the WWTP are changed (scenarios 4 and 5), the SMX concentration in the RWB is mainly influenced (till to 95% influence of the total variance for SSMX,max) by the aerobic sorption coefficient. A progressive uncertainty reduction from the upstream to downstream was found for the soluble fraction of SMX in the RWB.

  1. The analysis of factors of management of safety of critical information infrastructure with use of dynamic models

    Science.gov (United States)

    Trostyansky, S. N.; Kalach, A. V.; Lavlinsky, V. V.; Lankin, O. V.

    2018-03-01

    Based on the analysis of the dynamic model of panel data by region, including fire statistics for surveillance sites and statistics of a set of regional socio-economic indicators, as well as the time of rapid response of the state fire service to fires, the probability of fires in the surveillance sites and the risk of human death in The result of such fires from the values of the corresponding indicators for the previous year, a set of regional social-economics factors, as well as regional indicators time rapid response of the state fire service in the fire. The results obtained are consistent with the results of the application to the fire risks of the model of a rational offender. Estimation of the economic equivalent of human life from data on surveillance objects for Russia, calculated on the basis of the analysis of the presented dynamic model of fire risks, correctly agrees with the known literary data. The results obtained on the basis of the econometric approach to fire risks allow us to forecast fire risks at the supervisory sites in the regions of Russia and to develop management solutions to minimize such risks.

  2. Separating form factor and nuclear model effects in quasielastic neutrino-nucleus scattering

    Science.gov (United States)

    Wieske, Joseph

    2017-09-01

    When studying neutrino oscillations an understanding of charged current quasielastic (CCQE) neutrino-nucleus scattering is imperative. This interaction depends on a nuclear model as well as knowledge of form factors. In the past, CCQE data from the MiniBooNE experiment was analyzed assuming the Relativistic Fermi Gas (RFG) nuclear model, an axial dipole form factor in, and using the the z-expansion for the axial form factor in. We present the first analysis that combines a non-RFG nuclear model, in particular the Correlated Fermi Gas nuclear model (CFG) of, and the z expansion for the axial form factor. This will allow us to separate form factor and nuclear model effects in CCQE scattering. This project was supported through the Wayne State University REU program under NSF Grant PHY-1460853 and by the DOE Grant DE-SC0007983.

  3. A Comparative Analysis of Ability of Mimicking Portfolios in Representing the Background Factors

    OpenAIRE

    Asgharian, Hossein

    2004-01-01

    Our aim is to give a comparative analysis of ability of different factor mimicking portfolios in representing the background factors. Our analysis contains a cross-sectional regression approach, a time-series regression approach and a portfolio approach for constructing factor mimicking portfolios. The focus of the analysis is the power of mimicking portfolios in the asset pricing models. We conclude that the time series regression approach, with the book-to-market sorted portfolios as the ba...

  4. Sensitivity analysis practices: Strategies for model-based inference

    Energy Technology Data Exchange (ETDEWEB)

    Saltelli, Andrea [Institute for the Protection and Security of the Citizen (IPSC), European Commission, Joint Research Centre, TP 361, 21020 Ispra (Vatican City State, Holy See,) (Italy)]. E-mail: andrea.saltelli@jrc.it; Ratto, Marco [Institute for the Protection and Security of the Citizen (IPSC), European Commission, Joint Research Centre, TP 361, 21020 Ispra (VA) (Italy); Tarantola, Stefano [Institute for the Protection and Security of the Citizen (IPSC), European Commission, Joint Research Centre, TP 361, 21020 Ispra (VA) (Italy); Campolongo, Francesca [Institute for the Protection and Security of the Citizen (IPSC), European Commission, Joint Research Centre, TP 361, 21020 Ispra (VA) (Italy)

    2006-10-15

    Fourteen years after Science's review of sensitivity analysis (SA) methods in 1989 (System analysis at molecular scale, by H. Rabitz) we search Science Online to identify and then review all recent articles having 'sensitivity analysis' as a keyword. In spite of the considerable developments which have taken place in this discipline, of the good practices which have emerged, and of existing guidelines for SA issued on both sides of the Atlantic, we could not find in our review other than very primitive SA tools, based on 'one-factor-at-a-time' (OAT) approaches. In the context of model corroboration or falsification, we demonstrate that this use of OAT methods is illicit and unjustified, unless the model under analysis is proved to be linear. We show that available good practices, such as variance based measures and others, are able to overcome OAT shortcomings and easy to implement. These methods also allow the concept of factors importance to be defined rigorously, thus making the factors importance ranking univocal. We analyse the requirements of SA in the context of modelling, and present best available practices on the basis of an elementary model. We also point the reader to available recipes for a rigorous SA.

  5. Sensitivity analysis practices: Strategies for model-based inference

    International Nuclear Information System (INIS)

    Saltelli, Andrea; Ratto, Marco; Tarantola, Stefano; Campolongo, Francesca

    2006-01-01

    Fourteen years after Science's review of sensitivity analysis (SA) methods in 1989 (System analysis at molecular scale, by H. Rabitz) we search Science Online to identify and then review all recent articles having 'sensitivity analysis' as a keyword. In spite of the considerable developments which have taken place in this discipline, of the good practices which have emerged, and of existing guidelines for SA issued on both sides of the Atlantic, we could not find in our review other than very primitive SA tools, based on 'one-factor-at-a-time' (OAT) approaches. In the context of model corroboration or falsification, we demonstrate that this use of OAT methods is illicit and unjustified, unless the model under analysis is proved to be linear. We show that available good practices, such as variance based measures and others, are able to overcome OAT shortcomings and easy to implement. These methods also allow the concept of factors importance to be defined rigorously, thus making the factors importance ranking univocal. We analyse the requirements of SA in the context of modelling, and present best available practices on the basis of an elementary model. We also point the reader to available recipes for a rigorous SA

  6. Factors affecting construction performance: exploratory factor analysis

    Science.gov (United States)

    Soewin, E.; Chinda, T.

    2018-04-01

    The present work attempts to develop a multidimensional performance evaluation framework for a construction company by considering all relevant measures of performance. Based on the previous studies, this study hypothesizes nine key factors, with a total of 57 associated items. The hypothesized factors, with their associated items, are then used to develop questionnaire survey to gather data. The exploratory factor analysis (EFA) was applied to the collected data which gave rise 10 factors with 57 items affecting construction performance. The findings further reveal that the items constituting ten key performance factors (KPIs) namely; 1) Time, 2) Cost, 3) Quality, 4) Safety & Health, 5) Internal Stakeholder, 6) External Stakeholder, 7) Client Satisfaction, 8) Financial Performance, 9) Environment, and 10) Information, Technology & Innovation. The analysis helps to develop multi-dimensional performance evaluation framework for an effective measurement of the construction performance. The 10 key performance factors can be broadly categorized into economic aspect, social aspect, environmental aspect, and technology aspects. It is important to understand a multi-dimension performance evaluation framework by including all key factors affecting the construction performance of a company, so that the management level can effectively plan to implement an effective performance development plan to match with the mission and vision of the company.

  7. The Factors Influencing Satisfaction with Public City Transport: A Structural Equation Modelling Approach

    Directory of Open Access Journals (Sweden)

    Pawlasova Pavlina

    2015-12-01

    Full Text Available Satisfaction is one of the key factors which influences customer loyalty. We assume that the satisfied customer will be willing to use the ssame service provider again. The overall passengers´ satisfaction with public city transport may be affected by the overall service quality. Frequency, punctuality, cleanliness in the vehicle, proximity, speed, fare, accessibility and safety of transport, information and other factors can influence passengers´ satisfaction. The aim of this paper is to quantify factors and identify the most important factors influencing customer satisfaction with public city transport within conditions of the Czech Republic. Two methods of analysis are applied in order to fulfil the aim. The method of factor analysis and the method Varimax were used in order to categorize variables according to their mutual relations. The method of structural equation modelling was used to evaluate the factors and validate the model. Then, the optimal model was found. The logistic parameters, including service continuity and frequency, and service, including information rate, station proximity and vehicle cleanliness, are the factors influencing passengers´ satisfaction on a large scale.

  8. Determinants of job stress in chemical process industry: A factor analysis approach.

    Science.gov (United States)

    Menon, Balagopal G; Praveensal, C J; Madhu, G

    2015-01-01

    Job stress is one of the active research domains in industrial safety research. The job stress can result in accidents and health related issues in workers in chemical process industries. Hence it is important to measure the level of job stress in workers so as to mitigate the same to avoid the worker's safety related problems in the industries. The objective of this study is to determine the job stress factors in the chemical process industry in Kerala state, India. This study also aims to propose a comprehensive model and an instrument framework for measuring job stress levels in the chemical process industries in Kerala, India. The data is collected through a questionnaire survey conducted in chemical process industries in Kerala. The collected data out of 1197 surveys is subjected to principal component and confirmatory factor analysis to develop the job stress factor structure. The factor analysis revealed 8 factors that influence the job stress in process industries. It is also found that the job stress in employees is most influenced by role ambiguity and the least by work environment. The study has developed an instrument framework towards measuring job stress utilizing exploratory factor analysis and structural equation modeling.

  9. A factor analysis to detect factors influencing building national brand

    Directory of Open Access Journals (Sweden)

    Naser Azad

    Full Text Available Developing a national brand is one of the most important issues for development of a brand. In this study, we present factor analysis to detect the most important factors in building a national brand. The proposed study uses factor analysis to extract the most influencing factors and the sample size has been chosen from two major auto makers in Iran called Iran Khodro and Saipa. The questionnaire was designed in Likert scale and distributed among 235 experts. Cronbach alpha is calculated as 84%, which is well above the minimum desirable limit of 0.70. The implementation of factor analysis provides six factors including “cultural image of customers”, “exciting characteristics”, “competitive pricing strategies”, “perception image” and “previous perceptions”.

  10. Modeling human reliability analysis using MIDAS

    International Nuclear Information System (INIS)

    Boring, R. L.

    2006-01-01

    This paper documents current efforts to infuse human reliability analysis (HRA) into human performance simulation. The Idaho National Laboratory is teamed with NASA Ames Research Center to bridge the SPAR-H HRA method with NASA's Man-machine Integration Design and Analysis System (MIDAS) for use in simulating and modeling the human contribution to risk in nuclear power plant control room operations. It is anticipated that the union of MIDAS and SPAR-H will pave the path for cost-effective, timely, and valid simulated control room operators for studying current and next generation control room configurations. This paper highlights considerations for creating the dynamic HRA framework necessary for simulation, including event dependency and granularity. This paper also highlights how the SPAR-H performance shaping factors can be modeled in MIDAS across static, dynamic, and initiator conditions common to control room scenarios. This paper concludes with a discussion of the relationship of the workload factors currently in MIDAS and the performance shaping factors in SPAR-H. (authors)

  11. Modeling Ability Differentiation in the Second-Order Factor Model

    Science.gov (United States)

    Molenaar, Dylan; Dolan, Conor V.; van der Maas, Han L. J.

    2011-01-01

    In this article we present factor models to test for ability differentiation. Ability differentiation predicts that the size of IQ subtest correlations decreases as a function of the general intelligence factor. In the Schmid-Leiman decomposition of the second-order factor model, we model differentiation by introducing heteroscedastic residuals,…

  12. Analysis of Learning Environment Factors Based on Maslow’s Hierarchy of Needs

    Directory of Open Access Journals (Sweden)

    Košir Katja

    2013-09-01

    Full Text Available This paper provides a new analysis of some learning environment factors from the point of view of one of the most established motivational models, i.e. Maslow’s hierarchy of needs. For a teacher, this model can represent a meaningful tool for the analysis of the potential factors of pupils’ inadequate school adjustment. Some psychological constructs that can be conceptualized as learning environment factors are presented at specific levels of needs. As regards the level of physiological needs, this paper provides an overview of research studies on ergonomic factors of learning environment. As for safety needs, the paper outlines the concepts of classroom management and peer-to-peer violence, and presents some main research findings in both fields. The analysis regarding the level of love and belonging includes aspects of positive classroom climate and the concept of pupils’ social acceptance. Contemporary findings about the development of pupil’s academic self-concept are presented within the self-esteem and achievements needs. Flow is considered to be one of key factors that help teacher satisfy the self-actualization needs and stimulate pupils’ personal development. On the basis of this analysis, some implications and recommendations are given to help teachers efficiently encourage an integrated approach to pupil development.

  13. Factor analysis of multivariate data

    Digital Repository Service at National Institute of Oceanography (India)

    Fernandes, A.A.; Mahadevan, R.

    A brief introduction to factor analysis is presented. A FORTRAN program, which can perform the Q-mode and R-mode factor analysis and the singular value decomposition of a given data matrix is presented in Appendix B. This computer program, uses...

  14. Factor Analysis of the Brazilian Version of UPPS Impulsive Behavior Scale

    Science.gov (United States)

    Sediyama, Cristina Y. N.; Moura, Ricardo; Garcia, Marina S.; da Silva, Antonio G.; Soraggi, Carolina; Neves, Fernando S.; Albuquerque, Maicon R.; Whiteside, Setephen P.; Malloy-Diniz, Leandro F.

    2017-01-01

    Objective: To examine the internal consistency and factor structure of the Brazilian adaptation of the UPPS Impulsive Behavior Scale. Methods: UPPS is a self-report scale composed by 40 items assessing four factors of impulsivity: (a) urgency, (b) lack of premeditation; (c) lack of perseverance; (d) sensation seeking. In the present study 384 participants (278 women and 106 men), who were recruited from schools, universities, leisure centers and workplaces fulfilled the UPPS scale. An exploratory factor analysis was performed by using Varimax factor rotation and Kaiser Normalization, and we also conducted two confirmatory analyses to test the independency of the UPPS components found in previous analysis. Results: Results showed a decrease in mean UPPS total scores with age and this analysis showed that the youngest participants (below 30 years) scored significantly higher than the other groups over 30 years. No difference in gender was found. Cronbach’s alpha, results indicated satisfactory values for all subscales, with similar high values for the subscales and confirmatory factor analysis indexes also indicated a poor model fit. The results of two exploratory factor analysis were satisfactory. Conclusion: Our results showed that the Portuguese version has the same four-factor structure of the original and previous translations of the UPPS. PMID:28484414

  15. Factor Analysis of the Brazilian Version of UPPS Impulsive Behavior Scale

    Directory of Open Access Journals (Sweden)

    Leandro F. Malloy-Diniz

    2017-04-01

    Full Text Available Objective: To examine the internal consistency and factor structure of the Brazilian adaptation of the UPPS Impulsive Behavior Scale.Methods: UPPS is a self-report scale composed by 40 items assessing four factors of impulsivity: (a urgency, (b lack of premeditation; (c lack of perseverance; (d sensation seeking. In the present study 384 participants (278 women and 106 men, who were recruited from schools, universities, leisure centers and workplaces fulfilled the UPPS scale. An exploratory factor analysis was performed by using Varimax factor rotation and Kaiser Normalization, and we also conducted two confirmatory analyses to test the independency of the UPPS components found in previous analysis.Results: Results showed a decrease in mean UPPS total scores with age and this analysis showed that the youngest participants (below 30 years scored significantly higher than the other groups over 30 years. No difference in gender was found. Cronbach’s alpha, results indicated satisfactory values for all subscales, with similar high values for the subscales and confirmatory factor analysis indexes also indicated a poor model fit. The results of two exploratory factor analysis were satisfactory.Conclusion: Our results showed that the Portuguese version has the same four-factor structure of the original and previous translations of the UPPS.

  16. Factor Analysis of the Brazilian Version of UPPS Impulsive Behavior Scale.

    Science.gov (United States)

    Sediyama, Cristina Y N; Moura, Ricardo; Garcia, Marina S; da Silva, Antonio G; Soraggi, Carolina; Neves, Fernando S; Albuquerque, Maicon R; Whiteside, Setephen P; Malloy-Diniz, Leandro F

    2017-01-01

    Objective: To examine the internal consistency and factor structure of the Brazilian adaptation of the UPPS Impulsive Behavior Scale. Methods: UPPS is a self-report scale composed by 40 items assessing four factors of impulsivity: (a) urgency, (b) lack of premeditation; (c) lack of perseverance; (d) sensation seeking. In the present study 384 participants (278 women and 106 men), who were recruited from schools, universities, leisure centers and workplaces fulfilled the UPPS scale. An exploratory factor analysis was performed by using Varimax factor rotation and Kaiser Normalization, and we also conducted two confirmatory analyses to test the independency of the UPPS components found in previous analysis. Results: Results showed a decrease in mean UPPS total scores with age and this analysis showed that the youngest participants (below 30 years) scored significantly higher than the other groups over 30 years. No difference in gender was found. Cronbach's alpha, results indicated satisfactory values for all subscales, with similar high values for the subscales and confirmatory factor analysis indexes also indicated a poor model fit. The results of two exploratory factor analysis were satisfactory. Conclusion: Our results showed that the Portuguese version has the same four-factor structure of the original and previous translations of the UPPS.

  17. A comprehensive probabilistic analysis model of oil pipelines network based on Bayesian network

    Science.gov (United States)

    Zhang, C.; Qin, T. X.; Jiang, B.; Huang, C.

    2018-02-01

    Oil pipelines network is one of the most important facilities of energy transportation. But oil pipelines network accident may result in serious disasters. Some analysis models for these accidents have been established mainly based on three methods, including event-tree, accident simulation and Bayesian network. Among these methods, Bayesian network is suitable for probabilistic analysis. But not all the important influencing factors are considered and the deployment rule of the factors has not been established. This paper proposed a probabilistic analysis model of oil pipelines network based on Bayesian network. Most of the important influencing factors, including the key environment condition and emergency response are considered in this model. Moreover, the paper also introduces a deployment rule for these factors. The model can be used in probabilistic analysis and sensitive analysis of oil pipelines network accident.

  18. Sensitivity analysis approaches applied to systems biology models.

    Science.gov (United States)

    Zi, Z

    2011-11-01

    With the rising application of systems biology, sensitivity analysis methods have been widely applied to study the biological systems, including metabolic networks, signalling pathways and genetic circuits. Sensitivity analysis can provide valuable insights about how robust the biological responses are with respect to the changes of biological parameters and which model inputs are the key factors that affect the model outputs. In addition, sensitivity analysis is valuable for guiding experimental analysis, model reduction and parameter estimation. Local and global sensitivity analysis approaches are the two types of sensitivity analysis that are commonly applied in systems biology. Local sensitivity analysis is a classic method that studies the impact of small perturbations on the model outputs. On the other hand, global sensitivity analysis approaches have been applied to understand how the model outputs are affected by large variations of the model input parameters. In this review, the author introduces the basic concepts of sensitivity analysis approaches applied to systems biology models. Moreover, the author discusses the advantages and disadvantages of different sensitivity analysis methods, how to choose a proper sensitivity analysis approach, the available sensitivity analysis tools for systems biology models and the caveats in the interpretation of sensitivity analysis results.

  19. Identifying the important factors in simulation models with many factors

    NARCIS (Netherlands)

    Bettonvil, B.; Kleijnen, J.P.C.

    1994-01-01

    Simulation models may have many parameters and input variables (together called factors), while only a few factors are really important (parsimony principle). For such models this paper presents an effective and efficient screening technique to identify and estimate those important factors. The

  20. Factor analysis and scintigraphy

    International Nuclear Information System (INIS)

    Di Paola, R.; Penel, C.; Bazin, J.P.; Berche, C.

    1976-01-01

    The goal of factor analysis is usually to achieve reduction of a large set of data, extracting essential features without previous hypothesis. Due to the development of computerized systems, the use of largest sampling, the possibility of sequential data acquisition and the increase of dynamic studies, the problem of data compression can be encountered now in routine. Thus, results obtained for compression of scintigraphic images were first presented. Then possibilities given by factor analysis for scan processing were discussed. At last, use of this analysis for multidimensional studies and specially dynamic studies were considered for compression and processing [fr

  1. A 3-factor model for the FACIT-Sp.

    Science.gov (United States)

    Canada, Andrea L; Murphy, Patricia E; Fitchett, George; Peterman, Amy H; Schover, Leslie R

    2008-09-01

    The 12-item Functional Assessment of Chronic Illness Therapy-Spiritual Well-being Scale (FACIT-Sp) is a popular measure of the religious/spiritual (R/S) components of quality of life (QoL) in patients with cancer. The original factor analyses of the FACIT-Sp supported two factors: Meaning/Peace and Faith. Because Meaning suggests a cognitive aspect of R/S and Peace an affective component, we hypothesized a 3-factor solution: Meaning, Peace, and Faith. Participants were 240 long-term female survivors of cancer who completed the FACIT-Sp, the SF-12, and the BSI 18. We used confirmatory factor analysis to compare the 2- and 3-factor models of the FACIT-Sp and subsequently assessed associations between the resulting solutions and QoL domains. Survivors averaged 44 years of age and 10 years post-diagnosis. A 3-factor solution of the FACIT-Sp significantly improved the fit of the model to the data over the original 2-factor structure (Delta chi(2)=72.36, df=2, p<0.001). Further adjustments to the 3-factor model resulted in a final solution with even better goodness-of-fit indices (chi(2)=59.11, df=1, p=0.13, CFI=1.00, SMRM=0.05).The original Meaning/Peace factor controlling for Faith was associated with mental (r=0.63, p<0.000) and physical (r=0.22, p<0.01) health on the SF-12, and the original Faith factor controlling for Meaning/Peace was negatively associated with mental health (r=-0.15, p<0.05). The 3-factor model was more informative. Specifically, using partial correlations, the Peace factor was only related to mental health (r=0.53, p<0.001); Meaning was related to both physical (r=0.18, p<0.01) and mental (r=0.17, p<0.01) health; and Faith was negatively associated with mental health (r=-0.17, p<0.05). The results of this study support a 3-factor solution of the FACIT-Sp. The new solution not only represents a psychometric improvement over the original, but also enables a more detailed examination of the contribution of different dimensions of R/S to QoL. (c

  2. Analysis of psychological factors for quality assessment of interactive multimodal service

    Science.gov (United States)

    Yamagishi, Kazuhisa; Hayashi, Takanori

    2005-03-01

    We proposed a subjective quality assessment model for interactive multimodal services. First, psychological factors of an audiovisual communication service were extracted by using the semantic differential (SD) technique and factor analysis. Forty subjects participated in subjective tests and performed point-to-point conversational tasks on a PC-based TV phone that exhibits various network qualities. The subjects assessed those qualities on the basis of 25 pairs of adjectives. Two psychological factors, i.e., an aesthetic feeling and a feeling of activity, were extracted from the results. Then, quality impairment factors affecting these two psychological factors were analyzed. We found that the aesthetic feeling is mainly affected by IP packet loss and video coding bit rate, and the feeling of activity depends on delay time and video frame rate. We then proposed an opinion model derived from the relationships among quality impairment factors, psychological factors, and overall quality. The results indicated that the estimation error of the proposed model is almost equivalent to the statistical reliability of the subjective score. Finally, using the proposed model, we discuss guidelines for quality design of interactive audiovisual communication services.

  3. Training department's role in human factor analysis during post-trip reviews

    International Nuclear Information System (INIS)

    Goodman, D.

    1987-01-01

    Provide training is a frequent corrective action specified in a post-trip review report. This corrective action is most often decided upon by technical and operational staff, not training staff, without a detailed analysis of whether training can resolve the immediate problem or enhance employees' future performance. A more specific human factor or performance problem analysis would often reveal that training cannot impact or resolve the concern to avoid future occurrences. This human factor analysis is similar to Thomas Gilbert's Behavior Engineering Model (Human Competence, McGraw-Hill, 1978) or Robert Mager's/Peter Pipe's Performance Analysis (Analyzing Performance Problems, Pitman Learning, 1984). At Palo Verde Nuclear Generating Station, training analysts participate in post-trip reviews in order to conduct or provide input to this type of human factor and performance problem analysis. Their goal is to keep provide training out of corrective action statements unless training can in fact impact or resolve the problem. The analysts follow a plant specific logic diagram to identify human factors and to identify whether changes to the environment or to the person would best resolve the concern

  4. Spatial econometric analysis of factors influencing regional energy efficiency in China.

    Science.gov (United States)

    Song, Malin; Chen, Yu; An, Qingxian

    2018-05-01

    Increased environmental pollution and energy consumption caused by the country's rapid development has raised considerable public concern, and has become the focus of the government and public. This study employs the super-efficiency slack-based model-data envelopment analysis (SBM-DEA) to measure the total factor energy efficiency of 30 provinces in China. The estimation model for the spatial interaction intensity of regional total factor energy efficiency is based on Wilson's maximum entropy model. The model is used to analyze the factors that affect the potential value of total factor energy efficiency using spatial dynamic panel data for 30 provinces during 2000-2014. The study found that there are differences and spatial correlations of energy efficiency among provinces and regions in China. The energy efficiency in the eastern, central, and western regions fluctuated significantly, and was mainly because of significant energy efficiency impacts on influences of industrial structure, energy intensity, and technological progress. This research is of great significance to China's energy efficiency and regional coordinated development.

  5. A Rasch and factor analysis of the Functional Assessment of Cancer Therapy-General (FACT-G

    Directory of Open Access Journals (Sweden)

    Selby Peter J

    2007-04-01

    Full Text Available Abstract Background Although the Functional Assessment of Cancer Therapy – General questionnaire (FACT-G has been validated few studies have explored the factor structure of the instrument, in particular using non-sample dependent measurement techniques, such as Rasch Models. Furthermore, few studies have explored the relationship between item fit to the Rasch Model and clinical utility. The aim of this study was to investigate the dimensionality and measurement properties of the FACT-G with Rasch Models and Factor analysis. Methods A factor analysis and Rasch analysis (Partial Credit Model was carried out on the FACT-G completed by a heterogeneous sample of cancer patients (n = 465. For the Rasch analysis item fit (infit mean squares ≥ 1.30, dimensionality and item invariance were assessed. The impact of removing misfitting items on the clinical utility of the subscales and FACT-G total scale was also assessed. Results The factor analysis demonstrated a four factor structure of the FACT-G which broadly corresponded to the four subscales of the instrument. Internal consistency for these four scales was very good (Cronbach's alpha 0.72 – 0.85. The Rasch analysis demonstrated that each of the subscales and the FACT-G total scale had misfitting items (infit means square ≥ 1.30. All these scales with the exception of the Social & Family Well-being Scale (SFWB were unidimensional. When misfitting items were removed, the effect sizes and the clinical utility of the instrument were maintained for the subscales and the total FACT-G scores. Conclusion The results of the traditional factor analysis and Rasch analysis of the FACT-G broadly agreed. Caution should be exercised when utilising the Social & Family Well-being scale and further work is required to determine whether this scale is best represented by two factors. Additionally, removing misfitting items from scales should be performed alongside an assessment of the impact on clinical utility.

  6. ANALYSIS OF FACTORS WHICH AFFECTING THE ECONOMIC GROWTH

    Directory of Open Access Journals (Sweden)

    Suparna Wijaya

    2017-03-01

    Full Text Available High economic growth and sustainable process are main conditions for sustainability of economic country development. They are also become measures of the success of the country's economy. Factors which tested in this study are economic and non-economic factors which impacting economic development. This study has a goal to explain the factors that influence on macroeconomic Indonesia. It used linear regression modeling approach. The analysis result showed that Tax Amnesty, Exchange Rate, Inflation, and interest rate, they jointly can bring effect which amounted to 77.6% on economic growth whereas the remaining 22.4% is the influenced by other variables which not observed in this study. Keywords: tax amnesty, exchange rates, inflation, SBI and economic growth

  7. Personality disorders in substance abusers: Validation of the DIP-Q through principal components factor analysis and canonical correlation analysis

    Directory of Open Access Journals (Sweden)

    Hesse Morten

    2005-05-01

    Full Text Available Abstract Background Personality disorders are common in substance abusers. Self-report questionnaires that can aid in the assessment of personality disorders are commonly used in assessment, but are rarely validated. Methods The Danish DIP-Q as a measure of co-morbid personality disorders in substance abusers was validated through principal components factor analysis and canonical correlation analysis. A 4 components structure was constructed based on 238 protocols, representing antagonism, neuroticism, introversion and conscientiousness. The structure was compared with (a a 4-factor solution from the DIP-Q in a sample of Swedish drug and alcohol abusers (N = 133, and (b a consensus 4-components solution based on a meta-analysis of published correlation matrices of dimensional personality disorder scales. Results It was found that the 4-factor model of personality was congruent across the Danish and Swedish samples, and showed good congruence with the consensus model. A canonical correlation analysis was conducted on a subset of the Danish sample with staff ratings of pathology. Three factors that correlated highly between the two variable sets were found. These variables were highly similar to the three first factors from the principal components analysis, antagonism, neuroticism and introversion. Conclusion The findings support the validity of the DIP-Q as a measure of DSM-IV personality disorders in substance abusers.

  8. Collapsing Factors in Multitrait-Multimethod Models: Examining Consequences of a Mismatch Between Measurement Design and Model

    Directory of Open Access Journals (Sweden)

    Christian eGeiser

    2015-08-01

    Full Text Available Models of confirmatory factor analysis (CFA are frequently applied to examine the convergent validity of scores obtained from multiple raters or methods in so-called multitrait-multimethod (MTMM investigations. Many applications of CFA-MTMM and similarly structured models result in solutions in which at least one method (or specific factor shows non-significant loading or variance estimates. Eid et al. (2008 distinguished between MTMM measurement designs with interchangeable (randomly selected versus structurally different (fixed methods and showed that each type of measurement design implies specific CFA-MTMM measurement models. In the current study, we hypothesized that some of the problems that are commonly seen in applications of CFA-MTMM models may be due to a mismatch between the underlying measurement design and fitted models. Using simulations, we found that models with M method factors (where M is the total number of methods and unconstrained loadings led to a higher proportion of solutions in which at least one method factor became empirically unstable when these models were fit to data generated from structurally different methods. The simulations also revealed that commonly used model goodness-of-fit criteria frequently failed to identify incorrectly specified CFA-MTMM models. We discuss implications of these findings for other complex CFA models in which similar issues occur, including nested (bifactor and latent state-trait models.

  9. Application of classification algorithms for analysis of road safety risk factor dependencies.

    Science.gov (United States)

    Kwon, Oh Hoon; Rhee, Wonjong; Yoon, Yoonjin

    2015-02-01

    Transportation continues to be an integral part of modern life, and the importance of road traffic safety cannot be overstated. Consequently, recent road traffic safety studies have focused on analysis of risk factors that impact fatality and injury level (severity) of traffic accidents. While some of the risk factors, such as drug use and drinking, are widely known to affect severity, an accurate modeling of their influences is still an open research topic. Furthermore, there are innumerable risk factors that are waiting to be discovered or analyzed. A promising approach is to investigate historical traffic accident data that have been collected in the past decades. This study inspects traffic accident reports that have been accumulated by the California Highway Patrol (CHP) since 1973 for which each accident report contains around 100 data fields. Among them, we investigate 25 fields between 2004 and 2010 that are most relevant to car accidents. Using two classification methods, the Naive Bayes classifier and the decision tree classifier, the relative importance of the data fields, i.e., risk factors, is revealed with respect to the resulting severity level. Performances of the classifiers are compared to each other and a binary logistic regression model is used as the basis for the comparisons. Some of the high-ranking risk factors are found to be strongly dependent on each other, and their incremental gains on estimating or modeling severity level are evaluated quantitatively. The analysis shows that only a handful of the risk factors in the data dominate the severity level and that dependency among the top risk factors is an imperative trait to consider for an accurate analysis. Copyright © 2014 Elsevier Ltd. All rights reserved.

  10. Functional dynamic factor models with application to yield curve forecasting

    KAUST Repository

    Hays, Spencer

    2012-09-01

    Accurate forecasting of zero coupon bond yields for a continuum of maturities is paramount to bond portfolio management and derivative security pricing. Yet a universal model for yield curve forecasting has been elusive, and prior attempts often resulted in a trade-off between goodness of fit and consistency with economic theory. To address this, herein we propose a novel formulation which connects the dynamic factor model (DFM) framework with concepts from functional data analysis: a DFM with functional factor loading curves. This results in a model capable of forecasting functional time series. Further, in the yield curve context we show that the model retains economic interpretation. Model estimation is achieved through an expectation- maximization algorithm, where the time series parameters and factor loading curves are simultaneously estimated in a single step. Efficient computing is implemented and a data-driven smoothing parameter is nicely incorporated. We show that our model performs very well on forecasting actual yield data compared with existing approaches, especially in regard to profit-based assessment for an innovative trading exercise. We further illustrate the viability of our model to applications outside of yield forecasting.

  11. Application of the Value Optimization Model of Key Factors Based on DSEM

    Directory of Open Access Journals (Sweden)

    Chao Su

    2016-01-01

    Full Text Available The key factors of the damping solvent extraction method (DSEM for the analysis of the unbounded medium are the size of bounded domain, the artificial damping ratio, and the finite element mesh density. To control the simulation accuracy and computational efficiency of the soil-structure interaction, this study establishes a value optimization model of key factors that is composed of the design variables, the objective function, and the constraint function system. Then the optimum solutions of key factors are obtained by the optimization model. According to some comparisons of the results provided by the different initial conditions, the value optimization model of key factors is feasible to govern the simulation accuracy and computational efficiency and to analyze the practical unbounded medium-structure interaction.

  12. Workplace Innovation: Exploratory and Confirmatory Factor Analysis for Construct Validation

    Directory of Open Access Journals (Sweden)

    Wipulanusat Warit

    2017-06-01

    Full Text Available Workplace innovation enables the development and improvement of products, processes and services leading simultaneously to improvement in organisational performance. This study has the purpose of examining the factor structure of workplace innovation. Survey data, extracted from the 2014 APS employee census, comprising 3,125 engineering professionals in the Commonwealth of Australia’s departments were analysed using exploratory factor analysis (EFA and confirmatory factor analysis (CFA. EFA returned a two-factor structure explaining 69.1% of the variance of the construct. CFA revealed that a two-factor structure was indicated as a validated model (GFI = 0.98, AGFI = 0.95, RMSEA = 0.08, RMR = 0.02, IFI = 0.98, NFI = 0.98, CFI = 0.98, and TLI = 0.96. Both factors showed good reliability of the scale (Individual creativity: α = 0.83, CR = 0.86, and AVE = 0.62; Team Innovation: α = 0.82, CR = 0.88, and AVE = 0.61. These results confirm that the two factors extracted for characterising workplace innovation included individual creativity and team innovation.

  13. An innovation resistance factor model

    Directory of Open Access Journals (Sweden)

    Siti Salwa Mohd Ishak

    2016-09-01

    Full Text Available The process and implementation strategy of information technology in construction is generally considered through the limiting prism of theoretical contexts generated from innovation diffusion and acceptance. This research argues that more attention should be given to understanding the positive effects of resistance. The study develops a theoretical framing for the Integrated Resistance Factor Model (IRFM. The framing uses a combination of diffusion of innovation theory, technology acceptance model and social network perspective. The model is tested to identify the most significant resistance factors using Partial Least Square (PLS technique. All constructs proposed in the model are found to be significant, valid and consistent with the theoretical framework. IRFM is shown to be an effective and appropriate model of user resistance factors. The most critical factors to influence technology resistance in the online project information management system (OPIMS context are: support from leaders and peers, complexity of the technology, compatibility with key work practices; and pre-trial of the technology before it is actually deployed. The study provides a new model for further research in technology innovation specific to the construction industry.

  14. Models of Anaylzing the Influence of Factors on Forming Profit Rate

    Directory of Open Access Journals (Sweden)

    Klara S. Jakovčević

    2014-04-01

    Full Text Available The analysis in this paper is focused on identifying the impact of individual factors on the elements of the profit rate. The primary aim of this work is a methodological overview of solutions for understanding the full content of the profit rate as a cause of economic quality as well as indicators of the results of reproduction. Application of model analysis of profit rate factors was performed in an enterprise from Serbia that manufactures construction materials from baked clay. The aim is of application is to test the range in determining elements and factors of economic success of the enterprise, and quantification of changes in its assumptions. The results are useful guideline for the management to take organizational measures to increase the economic success of the enterprise. This means eliminating the negative, emphasizing the positive impact of objectively, and organizational factors to make higher economic success. Based on empirical research, it could be concluded that the proposed quantitative models of analyzing the dynamics of enterprise business quality could be applied in practice.

  15. Functional dynamic factor models with application to yield curve forecasting

    KAUST Repository

    Hays, Spencer; Shen, Haipeng; Huang, Jianhua Z.

    2012-01-01

    resulted in a trade-off between goodness of fit and consistency with economic theory. To address this, herein we propose a novel formulation which connects the dynamic factor model (DFM) framework with concepts from functional data analysis: a DFM

  16. A Rasch and confirmatory factor analysis of the General Health Questionnaire (GHQ - 12

    Directory of Open Access Journals (Sweden)

    Velikova Galina

    2010-04-01

    Full Text Available Abstract Background The General Health Questionnaire (GHQ - 12 was designed as a short questionnaire to assess psychiatric morbidity. Despite the fact that studies have suggested a number of competing multidimensional factor structures, it continues to be largely used as a unidimensional instrument. This may have an impact on the identification of psychiatric morbidity in target populations. The aim of this study was to explore the dimensionality of the GHQ-12 and to evaluate a number of alternative models for the instrument. Methods The data were drawn from a large heterogeneous sample of cancer patients. The Partial Credit Model (Rasch was applied to the 12-item GHQ. Item misfit (infit mean square ≥ 1.3 was identified, misfitting items removed and unidimensionality and differential item functioning (age, gender, and treatment aims were assessed. The factor structures of the various alternative models proposed in the literature were explored and optimum model fit evaluated using Confirmatory Factor Analysis. Results The Rasch analysis of the 12-item GHQ identified six misfitting items. Removal of these items produced a six-item instrument which was not unidimensional. The Rasch analysis of an 8-item GHQ demonstrated two unidimensional structures corresponding to Anxiety/Depression and Social Dysfunction. No significant differential item functioning was observed by age, gender and treatment aims for the six- and eight-item GHQ. Two models competed for best fit from the confirmatory factor analysis, namely the GHQ-8 and Hankin's (2008 unidimensional model, however, the GHQ-8 produced the best overall fit statistics. Conclusions The results are consistent with the evidence that the GHQ-12 is a multi-dimensional instrument. Use of the summated scores for the GHQ-12 could potentially lead to an incorrect assessment of patients' psychiatric morbidity. Further evaluation of the GHQ-12 with different target populations is warranted.

  17. Confirmatory Factor Analysis of the Bases of Leader Power: First-Order Factor Model and Its Invariance Across Groups.

    Science.gov (United States)

    Rahim, M A; Magner, N R

    1996-10-01

    Confirmatory factor analyses of data (from five samples: N = 308 accountants and finance professionals, N = 578 management and non-management employees, and N = 588 employed management students in the U.S.; N = 728 management and non-management employees in S. Korea, N = 250 management and non-management bank employees in Bangladesh) on the 29 items of the Rahim Leader Power Inventory were performed with LISREL 7. The results provided support for the convergent and discriminant validities of the subscales measuring the five bases of leader power (coercive, reward, legitimate, expert, and referent), and the invariance of factor pattern and factor loadings across organizational levels and the three American samples. Additional analysis indicated that leader power profiles differed across the three national cultures represented in the study.

  18. Modelling human factor with Petri nets

    International Nuclear Information System (INIS)

    Bedreaga, Luminita; Constantinescu, Cristina; Guzun, Basarab

    2007-01-01

    The human contribution to risk and safety of nuclear power plant operation can be best understood, assessed and quantified using tools to evaluate human reliability. Human reliability analysis becomes an important part of every probabilistic safety assessment and it is used to demonstrate that nuclear power plants designed with different safety levels are prepared to cope with severe accidents. Human reliability analysis in context of probabilistic safety assessment consists in: identifying human-system interactions important to safety; quantifying probabilities appropriate with these interactions. Nowadays, the complex system functions can be modelled using special techniques centred either on states space adequate to system or on events appropriate to the system. Knowing that complex system model consists in evaluating the likelihood of success, in other words, in evaluating the possible value for that system being in some state, the inductive methods which are based on the system states can be applied also for human reliability modelling. Thus, switching to the system states taking into account the human interactions, the underlying basis of the Petri nets can be successfully applied and the likelihoods appropriate to these states can also derived. The paper presents the manner to assess the human reliability quantification using Petri nets approach. The example processed in the paper is from human reliability documentation without a detailed human factor analysis (qualitative). The obtained results by these two kinds of methods are in good agreement. (authors)

  19. Factoral analysis of the cost of preparing oil

    Energy Technology Data Exchange (ETDEWEB)

    Avdeyeva, L A; Kudoyarov, G Sh; Shmatova, M F

    1979-01-01

    Mathematical statistics methods (basically correlational and regression analysis) are used to study the factors which form the level of cost of preparing oil with consideration of the mutual influence of the factors. Selected as the claims for inclusion into a mathematical model was a group of five a priori justified factors: the water level of the oil being extracted (%); the specific expenditure of deemulsifiers; the volume of oil preparation; the quality of oil preparation (the salt content) and the level of use of the installations' capacities (%). To construct an economic and mathematical model of the cost of the technical preparation (SPP) of the oil, all the unions which make up the Ministry of the Oil Industry were divided into two comparable totalities. The first group included unions in which the oil SPP was lower than the branch average and the second, unions in which the SPP was higher than the branch wide cost. Using the coefficients of regression, special elasticity coefficients and the fluctuation indicators, the basic factors were finally identified which have the greatest influence on the formation of the oil SPP level separately for the first and second groups of unions.

  20. Variance-based sensitivity analysis for wastewater treatment plant modelling.

    Science.gov (United States)

    Cosenza, Alida; Mannina, Giorgio; Vanrolleghem, Peter A; Neumann, Marc B

    2014-02-01

    Global sensitivity analysis (GSA) is a valuable tool to support the use of mathematical models that characterise technical or natural systems. In the field of wastewater modelling, most of the recent applications of GSA use either regression-based methods, which require close to linear relationships between the model outputs and model factors, or screening methods, which only yield qualitative results. However, due to the characteristics of membrane bioreactors (MBR) (non-linear kinetics, complexity, etc.) there is an interest to adequately quantify the effects of non-linearity and interactions. This can be achieved with variance-based sensitivity analysis methods. In this paper, the Extended Fourier Amplitude Sensitivity Testing (Extended-FAST) method is applied to an integrated activated sludge model (ASM2d) for an MBR system including microbial product formation and physical separation processes. Twenty-one model outputs located throughout the different sections of the bioreactor and 79 model factors are considered. Significant interactions among the model factors are found. Contrary to previous GSA studies for ASM models, we find the relationship between variables and factors to be non-linear and non-additive. By analysing the pattern of the variance decomposition along the plant, the model factors having the highest variance contributions were identified. This study demonstrates the usefulness of variance-based methods in membrane bioreactor modelling where, due to the presence of membranes and different operating conditions than those typically found in conventional activated sludge systems, several highly non-linear effects are present. Further, the obtained results highlight the relevant role played by the modelling approach for MBR taking into account simultaneously biological and physical processes. © 2013.

  1. Constructing the Japanese version of the Maslach Burnout Inventory-Student Survey: Confirmatory factor analysis.

    Science.gov (United States)

    Tsubakita, Takashi; Shimazaki, Kazuyo

    2016-01-01

    To examine the factorial validity of the Maslach Burnout Inventory-Student Survey, using a sample of 2061 Japanese university students majoring in the medical and natural sciences (67.9% male, 31.8% female; Mage  = 19.6 years, standard deviation = 1.5). The back-translated scale used unreversed items to assess inefficacy. The inventory's descriptive properties and Cronbach's alphas were calculated using SPSS software. The present authors compared fit indices of the null, one factor, and default three factor models via confirmatory factor analysis with maximum-likelihood estimation using AMOS software, version 21.0. Intercorrelations between exhaustion, cynicism, and inefficacy were relatively higher than in prior studies. Cronbach's alphas were 0.76, 0.85, and 0.78, respectively. Although fit indices of the hypothesized three factor model did not meet the respective criteria, the model demonstrated better fit than did the null and one factor models. The present authors added four paths between error variables within items, but the modified model did not show satisfactory fit. Subsequent analysis revealed that a bi-factor model fit the data better than did the hypothesized or modified three factor models. The Japanese version of the Maslach Burnout Inventory-Student Survey needs minor changes to improve the fit of its three factor model, but the scale as a whole can be used to adequately assess overall academic burnout in Japanese university students. Although the scale was back-translated, two items measuring exhaustion whose expressions overlapped should be modified, and all items measuring inefficacy should be reversed in order to statistically clarify the factorial difference between the scale's three factors. © 2015 The Authors. Japan Journal of Nursing Science © 2015 Japan Academy of Nursing Science.

  2. The integration of expert-defined importance factors to enrich Bayesian Fault Tree Analysis

    International Nuclear Information System (INIS)

    Darwish, Molham; Almouahed, Shaban; Lamotte, Florent de

    2017-01-01

    This paper proposes an analysis of a hybrid Bayesian-Importance model for system designers to improve the quality of services related to Active Assisted Living Systems. The proposed model is based on two factors: failure probability measure of different service components and, an expert defined degree of importance that each component holds for the success of the corresponding service. The proposed approach advocates the integration of expert-defined importance factors to enrich the Bayesian Fault Tree Analysis (FTA) approach. The evaluation of the proposed approach is conducted using the Fault Tree Analysis formalism where the undesired state of a system is analyzed using Boolean logic mechanisms to combine a series of lower-level events.

  3. Problems with the factor analysis of items: Solutions based on item response theory and item parcelling

    Directory of Open Access Journals (Sweden)

    Gideon P. De Bruin

    2004-10-01

    Full Text Available The factor analysis of items often produces spurious results in the sense that unidimensional scales appear multidimensional. This may be ascribed to failure in meeting the assumptions of linearity and normality on which factor analysis is based. Item response theory is explicitly designed for the modelling of the non-linear relations between ordinal variables and provides a strong alternative to the factor analysis of items. Items may also be combined in parcels that are more likely to satisfy the assumptions of factor analysis than do the items. The use of the Rasch rating scale model and the factor analysis of parcels is illustrated with data obtained with the Locus of Control Inventory. The results of these analyses are compared with the results obtained through the factor analysis of items. It is shown that the Rasch rating scale model and the factoring of parcels produce superior results to the factor analysis of items. Recommendations for the analysis of scales are made. Opsomming Die faktorontleding van items lewer dikwels misleidende resultate op, veral in die opsig dat eendimensionele skale as meerdimensioneel voorkom. Hierdie resultate kan dikwels daaraan toegeskryf word dat daar nie aan die aannames van lineariteit en normaliteit waarop faktorontleding berus, voldoen word nie. Itemresponsteorie, wat eksplisiet vir die modellering van die nie-liniêre verbande tussen ordinale items ontwerp is, bied ’n aantreklike alternatief vir die faktorontleding van items. Items kan ook in pakkies gegroepeer word wat meer waarskynlik aan die aannames van faktorontleding voldoen as individuele items. Die gebruik van die Rasch beoordelingskaalmodel en die faktorontleding van pakkies word aan die hand van data wat met die Lokus van Beheervraelys verkry is, gedemonstreer. Die resultate van hierdie ontledings word vergelyk met die resultate wat deur ‘n faktorontleding van die individuele items verkry is. Die resultate dui daarop dat die Rasch

  4. SHMF: Interest Prediction Model with Social Hub Matrix Factorization

    Directory of Open Access Journals (Sweden)

    Chaoyuan Cui

    2017-01-01

    Full Text Available With the development of social networks, microblog has become the major social communication tool. There is a lot of valuable information such as personal preference, public opinion, and marketing in microblog. Consequently, research on user interest prediction in microblog has a positive practical significance. In fact, how to extract information associated with user interest orientation from the constantly updated blog posts is not so easy. Existing prediction approaches based on probabilistic factor analysis use blog posts published by user to predict user interest. However, these methods are not very effective for the users who post less but browse more. In this paper, we propose a new prediction model, which is called SHMF, using social hub matrix factorization. SHMF constructs the interest prediction model by combining the information of blogs posts published by both user and direct neighbors in user’s social hub. Our proposed model predicts user interest by integrating user’s historical behavior and temporal factor as well as user’s friendships, thus achieving accurate forecasts of user’s future interests. The experimental results on Sina Weibo show the efficiency and effectiveness of our proposed model.

  5. The effects of motivational factors on car use: a multidisciplinary modelling approach

    Energy Technology Data Exchange (ETDEWEB)

    Steg, L.; Ras, M. [University of Groningen (Netherlands). Centre for Environmental and Traffic Psychology; Geurs, K. [National Institute of Public Health and Environment, Bilthoven (Netherlands)

    2001-11-01

    Current transport models usually do not take motivational factors into account, and if they do, it is only implicitly. This paper presents a modelling approach aimed at explicitly examining the effects of motivational factors on present and future car use in the Netherlands. A car-use forecasting model for the years 2010 and 2020 was constructed on the basis of (i) a multinominal regression analysis, which revealed the importance of a motivational variable (viz., problem awareness) in explaining current car-use behavior separate from socio-demographic and socio-economic variables, and (ii) a population model constructed to forecast the size and composition of the Dutch population. The results show that car use could be better explained by taking motivational factors explicitly into account, and that the level of car use forecast might change significantly if changes in motivations are assumed. The question on how motivational factors could be incorporated into current (Dutch) national transport models was also addressed. (author)

  6. A Confirmatory Factor Analysis of the Academic Motivation Scale with Black College Students

    Science.gov (United States)

    Cokley, Kevin

    2015-01-01

    The factor structure of the Academic Motivation Scale (AMS) was examined with a sample of 578 Black college students. A confirmatory factor analysis of the AMS was conducted. Results indicated that the hypothesized seven-factor model did not fit the data. Implications for future research with the AMS are discussed.

  7. Combining analysis of variance and three‐way factor analysis methods for studying additive and multiplicative effects in sensory panel data

    DEFF Research Database (Denmark)

    Romano, Rosaria; Næs, Tormod; Brockhoff, Per Bruun

    2015-01-01

    Data from descriptive sensory analysis are essentially three‐way data with assessors, samples and attributes as the three ways in the data set. Because of this, there are several ways that the data can be analysed. The paper focuses on the analysis of sensory characteristics of products while...... in the use of the scale with reference to the existing structure of relationships between sensory descriptors. The multivariate assessor model will be tested on a data set from milk. Relations between the proposed model and other multiplicative models like parallel factor analysis and analysis of variance...

  8. Exploratory Modelling of Financial Reporting and Analysis Practices in Small Growth Enterprises

    OpenAIRE

    Richard G. P. McMahon; Leslie G. Davies; Nicholas M. Bluhm

    1994-01-01

    This paper reports an exploratory study of statistical modelling of historical financial reporting and analysis in a sample of small growth enterprises. The study sought to identify those factors that determine whether particular financial reporting and analysis practices are undertaken, and to represent these explanatory factors in expressions that reflect their relative and combined influence. Dichotomous logistic regression is employed to model financial analysis and polytomous logistic re...

  9. Housing price forecastability: A factor analysis

    DEFF Research Database (Denmark)

    Bork, Lasse; Møller, Stig Vinther

    of the model stays high at longer horizons. The estimated factors are strongly statistically signi…cant according to a bootstrap resampling method which takes into account that the factors are estimated regressors. The simple three-factor model also contains substantial out-of-sample predictive power...

  10. Confirmatory factor analysis and structural equation modeling of socio-cultural constructs among chamorro and non-chamorro micronesian betel nut chewers.

    Science.gov (United States)

    Murphy, Kelle L; Liu, Min; Herzog, Thaddeus A

    2017-07-05

    Betel nut chewing is embedded within the cultures of South Asia, and Southeast Asia, and the Western Pacific. The determinants of betel nut consumption are complex. Ongoing consumption of betel nut is affected by cultural, social, and drug-specific effects (i.e. dependence). This study's first objective was to assess the psychometric properties (i.e. reliability and validity) of the socio-cultural constructs in a survey developed for betel nut chewers. The study's second objective was to investigate the influence of socio-cultural variables on betel nut chewing behaviors among Chamorro and non-Chamorro Micronesians in Guam. The current study was a secondary analysis of a larger study (N = 600; n = 375 chewers and n = 225 former chewers) that examined socio-cultural factors that influence why chewers chew betel nut, along with assessing chewing behaviors, perceptions of risks, probability of changing behaviors, and methods that could be used to reduce use or quit. The socio-cultural constructs of the survey were analyzed using confirmatory factor analysis and structural equation modeling. The socio-cultural factors were a sufficient fit with data and the instrument is reliable and valid, as indicated by various model fit indices (χ 2 (13) = 18.49 with p = .14, TLI = .99, CFI = 1.00, SRMR = .02, RMSEA = .03 with 90% CIs [.00,.07]). Cronbach's alpha, the sign and magnitude of the factor loadings, the inter-factor correlations, and the large proportion of variance extracted for each factor, all indicate that the instrument is reliable and valid. Additionally, multivariate analyses showed that socio-cultural reasons were important contributing or chewing betel nut. Participants cited chewing because their friends and family members chewed, the behavior is embedded within their culture, and it would be considered rude and disrespectful to not chew. Based on the findings, this study provides important implications pertaining to

  11. Sea level rise and the geoid: factor analysis approach

    Directory of Open Access Journals (Sweden)

    Alexey Sadovski

    2013-08-01

    Full Text Available Sea levels are rising around the world, and this is a particular concern along most of the coasts of the United States. A 1989 EPA report shows that sea levels rose 5-6 inches more than the global average along the Mid-Atlantic and Gulf Coasts in the last century. The main reason for this is coastal land subsidence. This sea level rise is considered more as relative sea level rise than global sea level rise. Thus, instead of studying sea level rise globally, this paper describes a statistical approach by using factor analysis of regional sea level rates of change. Unlike physical models and semi-empirical models that attempt to approach how much and how fast sea levels are changing, this methodology allows for a discussion of the factor(s that statistically affects sea level rates of change, and seeks patterns to explain spatial correlations.

  12. A confirmatory factor analysis of the Utrecht Work Engagement Scale for Students in a Chinese sample.

    Science.gov (United States)

    Meng, Lina; Jin, Yi

    2017-02-01

    Educational institutions play an important role in encouraging students' engagement with course work. Educators are finding instruments to measure students' engagement in order to develop strategies to improve it. Little is known about the factor structure of the Utrecht Work Engagement Scale for Students among Chinese nursing students. The aim of this research was to examine the factor structure of the Utrecht Work Engagement Scale for Students via confirmatory factor analysis. The study used a cross-sectional design. A sample of 480 students from a nursing school in one Chinese university completed the Utrecht Work Engagement Scale for Students. Factor analysis was used to analyze the resulting data. The overall results of internal consistency reliability and confirmatory factor analysis provided evidence supporting the reliability and three-factor structure of the Utrecht Work Engagement Scale for Students. The total internal consistency reliability coefficients were 0.91. Model comparison tests indicated that an oblique factors model that permitted correlations between pairs of error terms fitted the data better than other first-order models. In addition, due to the three strongly intercorrelated factors, a second-order model was found to fit the data well, providing support for the factorial structure of the Utrecht Work Engagement Scale for Students. The findings of confirmatory factor analysis provided evidence supporting the reliability and three-factor structure of the Utrecht Work Engagement Scale for Students when evaluated with a Chinese nursing student sample in this study. Thus, it is appropriate to use The Utrecht Work Engagement Scale for Students in for assessing the engagement among Chinese nursing students. Copyright © 2016 Elsevier Ltd. All rights reserved.

  13. The correlation analysis of tumor necrosis factor-alpha-308G/A polymorphism and venous thromboembolism risk: A meta-analysis.

    Science.gov (United States)

    Gao, Quangen; Zhang, Peijin; Wang, Wei; Ma, He; Tong, Yue; Zhang, Jing; Lu, Zhaojun

    2016-10-01

    Venous thromboembolism is a common complex disorder, being the resultant of gene-gene and gene-environment interactions. Tumor necrosis factor-alpha is a proinflammatory cytokine which has been implicated in venous thromboembolism risk. A promoter 308G/A polymorphism in the tumor necrosis factor-alpha gene has been suggested to modulate the risk for venous thromboembolism. However, the published findings remain inconsistent. In this study, we conducted a meta-analysis of all available data regarding this issue. Eligible studies were identified through search of Pubmed, EBSCO Medline, Web of Science, and China National Knowledge Infrastructure (CNKI, Chinese) databases up to June 2014. Pooled Odd ratios (ORs) with 95% confidence intervals were applied to estimating the strength of the genetic association in the random-effects model or fixed-effects model. A total of 10 studies involving 1999 venous thromboembolism cases and 2166 controls were included in this meta-analysis to evaluate the association between tumor necrosis factor-alpha-308G/A polymorphism and venous thromboembolism risk. Overall, no significantly increased risk venous thromboembolism was observed in all comparison models when all studies were pooled into the meta-analysis. However, in stratified analyses by ethnicity, there was a pronounced association with venous thromboembolism risk among West Asians in three genetic models (A vs. G: OR = 1.82, 95%CI = 1.13-2.94; GA vs. GG: OR = 1.82, 95%CI = 1.08-3.06; AA/GA vs. GG: OR = 1.88, 95%CI = 1.12-3.16). When stratifying by source of controls, no significant result was detected in all genetic models. This meta-analysis demonstrates that tumor necrosis factor-alpha 308G/A polymorphism may contribute to susceptibility to venous thromboembolism among West Asians. Studies are needed to ascertain these findings in larger samples and different racial groups. © The Author(s) 2015.

  14. Job Stress and Related Factors Among Iranian Male Staff Using a Path Analysis Model.

    Science.gov (United States)

    Azad-Marzabadi, Esfandiar; Gholami Fesharaki, Mohammad

    2016-06-01

    In recent years, job stress has been cited as a risk factor for some diseases. Given the importance of this subject, we established a new model for classifying job stress among Iranian male staff using path analysis. This cross-sectional study was done on male staff in Tehran, Iran, 2013. The participants in the study were selected using a proportional stratum sampling method. The tools used included nine questionnaires (1- HSE questionnaire; 2- GHQ questionnaire; 3- Beck depression inventory; 4- Framingham personality type; 5- Azad-Fesharaki's physical activity questionnaire; 6- Adult attachment style questionnaire; 7- Azad socioeconomic questionnaire; 8- Job satisfaction survey; and 9- demographic questionnaire). A total of 575 individuals (all male) were recruited for the study. Their mean (±SD) age was 33.49 (±8.9) and their mean job experience was 12.79 (±8.98) years. The pathway of job stress among Iranian male staff showed an adequate model fit (RMSEA=0.021, GFI=0.99, AGFI=0.97, P=0.136). In addition, the total effect of variables like personality type (β=0.283), job satisfaction (β=0.287), and age (β=0.108) showed a positive relationship with job stress, while variables like general health (β=-0.151) and depression (β=-0.242) showed the reverse effect on job stress. According to the results of this study, we can conclude that our suggested model is suited to explaining the pathways of stress among Iranian male staff.

  15. Forming Factors And Builder Indicators Of Brand Personality Models In Traditional Retail Traders

    Directory of Open Access Journals (Sweden)

    Yunelly Asra

    2017-12-01

    Full Text Available This study aims to find the factors forming and indicator builder model of brand personality of traditional retail traders through measuring the influence of retail mix and culture. The formation of brand personality uses Aaker brand personality dimension to 250 consumers in Bengkalis Regency. The type of research is causal research design. The research variables are brand personality Retail Mix and Brand Personality. Data collection is done by probability sampling with purposive method. Data analysis was done by perception analysis frequency distribution and multiple regression using SPSS version 21.0. The results of this study are The factor of retail mix partially has a positive and significant impact on the brand personality of traditional retail traders in Bengkalis Regency. Factor cultural partially does not affect the brand personality of traditional retail traders in Bengkalis Regency. Simultaneously retail mix and cultural have positive and significant influence on traditional brand traders brand personality in Bengkalis Regency. Initial forming factor of brand personality model of traditional retail traders in Bengkalis Regency is Retail Mix Factor. Indicator of the model of traditional traders brand personality builder in Bengkalis are sincerity excitement competence sophistication competence ruggedness.

  16. The Oswestry Disability Index, confirmatory factor analysis in a sample of 35,263 verifies a one-factor structure but practicality issues remain.

    Science.gov (United States)

    Gabel, Charles Philip; Cuesta-Vargas, Antonio; Qian, Meihua; Vengust, Rok; Berlemann, Ulrich; Aghayev, Emin; Melloh, Markus

    2017-08-01

    To analyze the factor structure of the Oswestry Disability Index (ODI) in a large symptomatic low back pain (LBP) population using exploratory (EFA) and confirmatory factor analysis (CFA). Analysis of pooled baseline ODI LBP patient data from the international Spine Tango registry of EUROSPINE, the Spine Society of Europe. The sample, with n = 35,263 (55.2% female; age 15-99, median 59 years), included 76.1% of patients with a degenerative disease, and 23.9% of the patients with various other spinal conditions. The initial EFA provided a hypothetical construct for consideration. Subsequent CFA was considered in three scenarios: the full sample and separate genders. Models were compared empirically for best fit. The EFA indicated a one-factor solution accounting for 54% of the total variance. The CFA analysis based on the full sample confirmed this one-factor structure. Sub-group analyses by gender achieved good model fit for configural and partial metric invariance, but not scalar invariance. A possible two-construct model solution as outlined by previous researchers: dynamic-activities (personal care, lifting, walking, sex and social) and static-activities (pain, sleep, standing, travelling and sitting) was not preferred. The ODI demonstrated a one-factor structure in a large LBP sample. A potential two-factor model was considered, but not found appropriate for constructs of dynamic and static activity. The use of the single summary score for the ODI is psychometrically supported. However, practicality limitations were reported for use in the clinical and research settings. Researchers are encouraged to consider a shift towards newer, more sensitive and robustly developed instruments.

  17. Modelling impulsive factors for electronics and restaurant coupons’ e-store display

    Science.gov (United States)

    Ariningsih, P. K.; Nainggolan, M.; Sandy, I. A.

    2018-04-01

    In many times, the increment of e-store visitors does not followed by sales increment. Most purchases through e-commerce are impulsive buying, however only small amount of study is available to understand impulsive factors of e-store display. This paper suggests a preliminary concept on understanding the impulsive factors in Electronics and Restaurant Coupons e-store display, which are two among few popular group products sold through e-commerce. By conducting literature study and survey, 31 attributes were identified as impulsive factors in electronics e-store display and 20 attributes were identified as impulsive factors for restaurant coupon e-store. The attributes were then grouped into comprehensive impulsive factors by factor analysis. Each group of impulsive attributes were generated into 3 factors. Accessibility Factors and Trust Factors appeared for each group products. The other factors are Internal Factors for electronics e-store and Marketing factors for restaurant coupons e-store. Structural Equation Model of the impulsive factors was developed for each type of e-store, which stated the covariance between Trust Factors and Accessibility Factors. Based on preliminary model, Internal Factor and Trust Factor are influencing impulsive buying in electronics store. Special factor for electronics e-store is Internal Factor, while for restaurant coupons e-store is Marketing Factor.

  18. Using a knowledge elicitation method to specify the business model of a human factors organization

    NARCIS (Netherlands)

    Schraagen, J.M.C.; Ven, J. van de; Hoffman, R.R.; Moon, B.M.

    2009-01-01

    Concept Mapping was used to structure knowledge elicitation interviews with a group of human factors specialists whose goal was to describe the business model of their Department. This novel use of cognitive task analysis to describe the business model of a human factors organization resulted in a

  19. Using a knowledge elicitation method to specify the business model of a human factors organization.

    NARCIS (Netherlands)

    Schraagen, Johannes Martinus Cornelis; van de Ven, Josine; Hoffman, Robert R.; Moon, Brian M.

    2009-01-01

    Concept Mapping was used to structure knowledge elicitation interviews with a group of human factors specialists whose goal was to describe the business model of their Department. This novel use of cognitive task analysis to describe the business model of a human factors organization resulted in a

  20. Examining the Determinants of China’s Inward FDI Using Grey Matrix Relational Analysis Model

    Directory of Open Access Journals (Sweden)

    Hang JIANG

    2017-12-01

    Full Text Available Grey relational analysis (GRA model is an important part of grey system theory, which is used to ascertain the relational grade between an influential factor and the major behavior factor. Most of GRA models are mainly applied to the field in which the behavior factor and influential factor are the cross-sectional or time series data in a given system. However, owing to the panel data contains plenty information including individual and time characteristics, the traditional GRA model cannot be applied to panel data analysis. To overcome this drawback, the grey matrix relational analysis model is applied to measure the similarity of panel data from two dimensions of individual and time on the basis of the definition of the matrix sequence of a discrete data sequence. This paper examines the determinants of inward foreign direct investment (IFDI in China using grey matrix relational analysis model. The study finds that the GDP per capita, enrollment of regular institutions of higher education, and internal expenditure on R&D are the key factors of IFDI.

  1. Bayesian switching factor analysis for estimating time-varying functional connectivity in fMRI.

    Science.gov (United States)

    Taghia, Jalil; Ryali, Srikanth; Chen, Tianwen; Supekar, Kaustubh; Cai, Weidong; Menon, Vinod

    2017-07-15

    There is growing interest in understanding the dynamical properties of functional interactions between distributed brain regions. However, robust estimation of temporal dynamics from functional magnetic resonance imaging (fMRI) data remains challenging due to limitations in extant multivariate methods for modeling time-varying functional interactions between multiple brain areas. Here, we develop a Bayesian generative model for fMRI time-series within the framework of hidden Markov models (HMMs). The model is a dynamic variant of the static factor analysis model (Ghahramani and Beal, 2000). We refer to this model as Bayesian switching factor analysis (BSFA) as it integrates factor analysis into a generative HMM in a unified Bayesian framework. In BSFA, brain dynamic functional networks are represented by latent states which are learnt from the data. Crucially, BSFA is a generative model which estimates the temporal evolution of brain states and transition probabilities between states as a function of time. An attractive feature of BSFA is the automatic determination of the number of latent states via Bayesian model selection arising from penalization of excessively complex models. Key features of BSFA are validated using extensive simulations on carefully designed synthetic data. We further validate BSFA using fingerprint analysis of multisession resting-state fMRI data from the Human Connectome Project (HCP). Our results show that modeling temporal dependencies in the generative model of BSFA results in improved fingerprinting of individual participants. Finally, we apply BSFA to elucidate the dynamic functional organization of the salience, central-executive, and default mode networks-three core neurocognitive systems with central role in cognitive and affective information processing (Menon, 2011). Across two HCP sessions, we demonstrate a high level of dynamic interactions between these networks and determine that the salience network has the highest temporal

  2. Model Correction Factor Method

    DEFF Research Database (Denmark)

    Christensen, Claus; Randrup-Thomsen, Søren; Morsing Johannesen, Johannes

    1997-01-01

    The model correction factor method is proposed as an alternative to traditional polynomial based response surface techniques in structural reliability considering a computationally time consuming limit state procedure as a 'black box'. The class of polynomial functions is replaced by a limit...... of the model correction factor method, is that in simpler form not using gradient information on the original limit state function or only using this information once, a drastic reduction of the number of limit state evaluation is obtained together with good approximations on the reliability. Methods...

  3. Shell model and spectroscopic factors

    International Nuclear Information System (INIS)

    Poves, P.

    2007-01-01

    In these lectures, I introduce the notion of spectroscopic factor in the shell model context. A brief review is given of the present status of the large scale applications of the Interacting Shell Model. The spectroscopic factors and the spectroscopic strength are discussed for nuclei in the vicinity of magic closures and for deformed nuclei. (author)

  4. A structural dynamic factor model for the effects of monetary policy estimated by the EM algorithm

    DEFF Research Database (Denmark)

    Bork, Lasse

    This paper applies the maximum likelihood based EM algorithm to a large-dimensional factor analysis of US monetary policy. Specifically, economy-wide effects of shocks to the US federal funds rate are estimated in a structural dynamic factor model in which 100+ US macroeconomic and financial time...... series are driven by the joint dynamics of the federal funds rate and a few correlated dynamic factors. This paper contains a number of methodological contributions to the existing literature on data-rich monetary policy analysis. Firstly, the identification scheme allows for correlated factor dynamics...... as opposed to the orthogonal factors resulting from the popular principal component approach to structural factor models. Correlated factors are economically more sensible and important for a richer monetary policy transmission mechanism. Secondly, I consider both static factor loadings as well as dynamic...

  5. Evaluation of chemical transport model predictions of primary organic aerosol for air masses classified by particle-component-based factor analysis

    OpenAIRE

    C. A. Stroud; M. D. Moran; P. A. Makar; S. Gong; W. Gong; J. Zhang; J. G. Slowik; J. P. D. Abbatt; G. Lu; J. R. Brook; C. Mihele; Q. Li; D. Sills; K. B. Strawbridge; M. L. McGuire

    2012-01-01

    Observations from the 2007 Border Air Quality and Meteorology Study (BAQS-Met 2007) in Southern Ontario, Canada, were used to evaluate predictions of primary organic aerosol (POA) and two other carbonaceous species, black carbon (BC) and carbon monoxide (CO), made for this summertime period by Environment Canada's AURAMS regional chemical transport model. Particle component-based factor analysis was applied to aerosol mass spectrometer measurements made at one urban site (Windsor, ON) and two...

  6. Analysis of Human Error Types and Performance Shaping Factors in the Next Generation Main Control Room

    International Nuclear Information System (INIS)

    Sin, Y. C.; Jung, Y. S.; Kim, K. H.; Kim, J. H.

    2008-04-01

    Main control room of nuclear power plants has been computerized and digitalized in new and modernized plants, as information and digital technologies make great progresses and become mature. Survey on human factors engineering issues in advanced MCRs: Model-based approach, Literature survey-based approach. Analysis of human error types and performance shaping factors is analysis of three human errors. The results of project can be used for task analysis, evaluation of human error probabilities, and analysis of performance shaping factors in the HRA analysis

  7. Critical success factors model developing for sustainable Kaizen implementation in manufactur-ing industry in Ethiopia

    Directory of Open Access Journals (Sweden)

    Haftu Hailu

    2017-12-01

    Full Text Available The purpose of the research is to identify critical success factors and model developing for sustaining kaizen implementation. Peacock shoe is one of the manufacturing industries in Ethiopia facing challenges on sustaining. The methodology followed is factor analysis and empirically testing hypothesis. A database was designed using SPSS version 20. The survey was validated using statistical validation using the Cronbach alpha index; the result is 0.908. The KMO index value was obtained for the 32 items and had a value of 0.642 with Bartlett's Test of Sphericity Approx. Chi-Square 4503.007, degree of freedom 496 and significance value 0.000. A factor analysis by principal components and varimax rotation was applied for finding the critical success factors. Finding designates that 32 items were merged into eight critical success factors. All the eight factors together explain for 76.941 % of the variance. Multiple regression model analysis has indicated that some of the critical success factors had relationship with success indicators. Due to constraint of time, the researcher focused only at peacock shoe manufacturing industry. Other limitation also includes the absence of any local research that shows the critical success factors at the moment.

  8. The Factors of Forming the National HR-Management Model

    Directory of Open Access Journals (Sweden)

    Elena P. Kostenko

    2017-12-01

    Full Text Available There are some factors considered in this article, which influence the forming of national HR-management model. The group-forming criterion is the nature of factors, that determine the system of HR-management as a system of corporate culture values, norms and rules of organizational behavior, ways of realization some important managing functions and dominating approaches to make decisions. This article shows that the plurality of combinations in different factors leads to forming the unique HR-management model. The geoclimatic factor influences the principles of the labor organization (orientation primarily on individual or collective forms of labor, attitude to the management experience of other countries, attitude to resources, etc., the distribution of labor resources, the level of labor mobility, and the psychosocial type of employee. Models of man's labor behavior are constituted In the process of historical development. Attention is focused on the formation of a national HR-model, such as the conducted socio-economic policy, the characteristics of the institutional environment, economic goals and priorities of the country's development, the level of development and the nature of the national productive forces and economic structures. Much attention was paid to the analysis of the historically formed value system and labor traditions, which influence the approaches to HR-management. As far as religion influences the model of person’s inclusion in labor, motives of labor behavior, management culture of a certain employee, preferred payment etc., we examined how the main traditional religions (Christianity, Islam, Judaism, Buddhism, Confucianism, Hinduism influence the HR-management system in different countries.

  9. Modeling of Iranian Cheetah Habitat using Ecological Niche Factor Analysis (Case Study: Dare Anjir Wildlife Refuge

    Directory of Open Access Journals (Sweden)

    N. Zamani

    2016-03-01

    Full Text Available Evaluation of habitat sustainability indexes is essential in wildlife management and conservation of rare species. Suitable habitats are required in wildlife managements and conservation also, they increase reproduction and survival rate of species. In this study in order to mapping habitat sustainability and recognizing habitat requirements of Iranian Cheetah (Acinonyx jubatus venaticus, field data from Dare Anjir  wildlife refuge were collected since autumn 2009 until summer 2011. Ecological Niche Factor Analysis approach has been used to develop habitat suitability model. In this method primary maps of  habitat variables including elevation, slope, aspect, vegetation cover, distance from water sources and environmental monitoring stations have been produced by Idrisi and Biomapper software and imported in Biomapper. The output scores obtained from the analysis showed that Iranian cheetah tends to mountain areas where has more topographical features for camouflage in order to hunting, and northern aspects which have more humidity, denser vegetation cover and more preys . Our result showed that the Iranian cheetah has medium niche width and prefer marginal habitats.

  10. Towards automatic analysis of dynamic radionuclide studies using principal-components factor analysis

    International Nuclear Information System (INIS)

    Nigran, K.S.; Barber, D.C.

    1985-01-01

    A method is proposed for automatic analysis of dynamic radionuclide studies using the mathematical technique of principal-components factor analysis. This method is considered as a possible alternative to the conventional manual regions-of-interest method widely used. The method emphasises the importance of introducing a priori information into the analysis about the physiology of at least one of the functional structures in a study. Information is added by using suitable mathematical models to describe the underlying physiological processes. A single physiological factor is extracted representing the particular dynamic structure of interest. Two spaces 'study space, S' and 'theory space, T' are defined in the formation of the concept of intersection of spaces. A one-dimensional intersection space is computed. An example from a dynamic 99 Tcsup(m) DTPA kidney study is used to demonstrate the principle inherent in the method proposed. The method requires no correction for the blood background activity, necessary when processing by the manual method. The careful isolation of the kidney by means of region of interest is not required. The method is therefore less prone to operator influence and can be automated. (author)

  11. Estimating Dynamic Connectivity States in fMRI Using Regime-Switching Factor Models

    KAUST Repository

    Ting, Chee-Ming

    2017-12-06

    We consider the challenges in estimating state-related changes in brain connectivity networks with a large number of nodes. Existing studies use sliding-window analysis or time-varying coefficient models which are unable to capture both smooth and abrupt changes simultaneously, and rely on ad-hoc approaches to the high-dimensional estimation. To overcome these limitations, we propose a Markov-switching dynamic factor model which allows the dynamic connectivity states in functional magnetic resonance imaging (fMRI) data to be driven by lower-dimensional latent factors. We specify a regime-switching vector autoregressive (SVAR) factor process to quantity the time-varying directed connectivity. The model enables a reliable, data-adaptive estimation of change-points of connectivity regimes and the massive dependencies associated with each regime. We develop a three-step estimation procedure: 1) extracting the factors using principal component analysis, 2) identifying connectivity regimes in a low-dimensional subspace based on the factor-based SVAR model, 3) constructing high-dimensional state connectivity metrics based on the subspace estimates. Simulation results show that our estimator outperforms K-means clustering of time-windowed coefficients, providing more accurate estimate of time-evolving connectivity. It achieves percentage of reduction in mean squared error by 60% when the network dimension is comparable to the sample size. When applied to resting-state fMRI data, our method successfully identifies modular organization in resting-state networks in consistency with other studies. It further reveals changes in brain states with variations across subjects and distinct large-scale directed connectivity patterns across states.

  12. Theoretical Assessment of the Impact of Climatic Factors in a Vibrio Cholerae Model.

    Science.gov (United States)

    Kolaye, G; Damakoa, I; Bowong, S; Houe, R; Békollè, D

    2018-05-04

    A mathematical model for Vibrio Cholerae (V. Cholerae) in a closed environment is considered, with the aim of investigating the impact of climatic factors which exerts a direct influence on the bacterial metabolism and on the bacterial reservoir capacity. We first propose a V. Cholerae mathematical model in a closed environment. A sensitivity analysis using the eFast method was performed to show the most important parameters of the model. After, we extend this V. cholerae model by taking account climatic factors that influence the bacterial reservoir capacity. We present the theoretical analysis of the model. More precisely, we compute equilibria and study their stabilities. The stability of equilibria was investigated using the theory of periodic cooperative systems with a concave nonlinearity. Theoretical results are supported by numerical simulations which further suggest the necessity to implement sanitation campaigns of aquatic environments by using suitable products against the bacteria during the periods of growth of aquatic reservoirs.

  13. Effect of abiotic and biotic stress factors analysis using machine learning methods in zebrafish.

    Science.gov (United States)

    Gutha, Rajasekar; Yarrappagaari, Suresh; Thopireddy, Lavanya; Reddy, Kesireddy Sathyavelu; Saddala, Rajeswara Reddy

    2018-03-01

    In order to understand the mechanisms underlying stress responses, meta-analysis of transcriptome is made to identify differentially expressed genes (DEGs) and their biological, molecular and cellular mechanisms in response to stressors. The present study is aimed at identifying the effect of abiotic and biotic stress factors, and it is found that several stress responsive genes are common for both abiotic and biotic stress factors in zebrafish. The meta-analysis of micro-array studies revealed that almost 4.7% i.e., 108 common DEGs are differentially regulated between abiotic and biotic stresses. This shows that there is a global coordination and fine-tuning of gene regulation in response to these two types of challenges. We also performed dimension reduction methods, principal component analysis, and partial least squares discriminant analysis which are able to segregate abiotic and biotic stresses into separate entities. The supervised machine learning model, recursive-support vector machine, could classify abiotic and biotic stresses with 100% accuracy using a subset of DEGs. Beside these methods, the random forests decision tree model classified five out of 8 stress conditions with high accuracy. Finally, Functional enrichment analysis revealed the different gene ontology terms, transcription factors and miRNAs factors in the regulation of stress responses. Copyright © 2017 Elsevier Inc. All rights reserved.

  14. Model of T-Type Fracture in Coal Fracturing and Analysis of Influence Factors of Fracture Morphology

    Directory of Open Access Journals (Sweden)

    Yuwei Li

    2018-05-01

    Full Text Available Special T-type fractures can be formed when coal is hydraulically fractured and there is currently no relevant theoretical model to calculate and describe them. This paper first establishes the height calculation model of vertical fractures in multi-layered formations and deduces the stress intensity factor (SIF at the upper and lower sides of the fracture in the process of vertical fracture extension. Combined with the fracture tip stress analysis method of fracture mechanics theory, the horizontal bedding is taken into account for tensile and shear failure, and the critical mechanical conditions for the formation of horizontal fracture in coal are obtained. Finally, the model of T-type fracture in coal fracturing is established, and it is verified by fracturing simulation experiments. The model calculation result shows that the increase of vertical fracture height facilitates the increase of horizontal fracture length. The fracture toughness of coal has a significant influence on the length of horizontal fracture and there is a threshold. When the fracture toughness is less than the threshold, the length of horizontal fracture remains unchanged, otherwise, the length of horizontal fracture increases rapidly with the increase of fracture toughness. When the shear strength of the interface between the coalbed and the interlayer increases, the length of the horizontal fracture of the T-type fracture rapidly decreases.

  15. Improved SVR Model for Multi-Layer Buildup Factor Calculation

    International Nuclear Information System (INIS)

    Trontl, K.; Pevec, D.; Smuc, T.

    2006-01-01

    The accuracy of point kernel method applied in gamma ray dose rate calculations in shielding design and radiation safety analysis is limited by the accuracy of buildup factors used in calculations. Although buildup factors for single-layer shields are well defined and understood, buildup factors for stratified shields represent a complex physical problem that is hard to express in mathematical terms. The traditional approach for expressing buildup factors of multi-layer shields is through semi-empirical formulas obtained by fitting the results of transport theory or Monte Carlo calculations. Such an approach requires an ad-hoc definition of the fitting function and often results with numerous and usually inadequately explained and defined correction factors added to the final empirical formula. Even more, finally obtained formulas are generally limited to a small number of predefined combinations of materials within relatively small range of gamma ray energies and shield thicknesses. Recently, a new approach has been suggested by the authors involving one of machine learning techniques called Support Vector Machines, i.e., Support Vector Regression (SVR). Preliminary investigations performed for double-layer shields revealed great potential of the method, but also pointed out some drawbacks of the developed model, mostly related to the selection of one of the parameters describing the problem (material atomic number), and the method in which the model was designed to evolve during the learning process. It is the aim of this paper to introduce a new parameter (single material buildup factor) that is to replace the existing material atomic number as an input parameter. The comparison of two models generated by different input parameters has been performed. The second goal is to improve the evolution process of learning, i.e., the experimental computational procedure that provides a framework for automated construction of complex regression models of predefined

  16. EMPLOYMENT LEVEL ANALYSIS FROM THE DETERMINANT FACTORS PERSPECTIVE

    Directory of Open Access Journals (Sweden)

    Elena Diana ŞERB

    2016-02-01

    Full Text Available Neglecting the human factor as part of the labor market causes losses for society as any activity that is initiated within it, has as a starting point, and also as a finishing point, the human intervention. The starting point of the article is represented by the projections made by the European    Commission in the Population Ageing Report in 2015 underlying assumptions and projections, and also by the projections of the United Nations report in 2015, and this resulted in many conclusions including the one that for the first time in Romania the average aging in 2015 exceeds the values measured by EU till present day, and this is reflected in the employment level (active aging population. The hypothesis behind the article is that the evolution of the population and migrants has repercussions on employment. Structured in three parts: knowledge status, the analysis of employment indicators and information about the intensity and direction of the link between a number of factors and employment level, this article aims to establish the determinant factors of employment through a research focused on the analysis of secondary sources, and also using the regression model. The most important lesson learned as a result of this research is that the labor market works with a variety of factors with a higher or lower influence, and in turn the labor market influences other factors.

  17. Mathematical models for prediction of safety factors for a simply ...

    African Journals Online (AJOL)

    From the results obtained, mathematical prediction models were developed using a least square regression analysis for bending, shear and deflection modes of failure considered in the study. The results showed that the safety factors for material, dead and live load are not unique, but they are influenced by safety index ...

  18. Comparing statistical and machine learning classifiers: alternatives for predictive modeling in human factors research.

    Science.gov (United States)

    Carnahan, Brian; Meyer, Gérard; Kuntz, Lois-Ann

    2003-01-01

    Multivariate classification models play an increasingly important role in human factors research. In the past, these models have been based primarily on discriminant analysis and logistic regression. Models developed from machine learning research offer the human factors professional a viable alternative to these traditional statistical classification methods. To illustrate this point, two machine learning approaches--genetic programming and decision tree induction--were used to construct classification models designed to predict whether or not a student truck driver would pass his or her commercial driver license (CDL) examination. The models were developed and validated using the curriculum scores and CDL exam performances of 37 student truck drivers who had completed a 320-hr driver training course. Results indicated that the machine learning classification models were superior to discriminant analysis and logistic regression in terms of predictive accuracy. Actual or potential applications of this research include the creation of models that more accurately predict human performance outcomes.

  19. Uncertainty analysis of hydrological modeling in a tropical area using different algorithms

    Science.gov (United States)

    Rafiei Emam, Ammar; Kappas, Martin; Fassnacht, Steven; Linh, Nguyen Hoang Khanh

    2018-01-01

    Hydrological modeling outputs are subject to uncertainty resulting from different sources of errors (e.g., error in input data, model structure, and model parameters), making quantification of uncertainty in hydrological modeling imperative and meant to improve reliability of modeling results. The uncertainty analysis must solve difficulties in calibration of hydrological models, which further increase in areas with data scarcity. The purpose of this study is to apply four uncertainty analysis algorithms to a semi-distributed hydrological model, quantifying different source of uncertainties (especially parameter uncertainty) and evaluate their performance. In this study, the Soil and Water Assessment Tools (SWAT) eco-hydrological model was implemented for the watershed in the center of Vietnam. The sensitivity of parameters was analyzed, and the model was calibrated. The uncertainty analysis for the hydrological model was conducted based on four algorithms: Generalized Likelihood Uncertainty Estimation (GLUE), Sequential Uncertainty Fitting (SUFI), Parameter Solution method (ParaSol) and Particle Swarm Optimization (PSO). The performance of the algorithms was compared using P-factor and Rfactor, coefficient of determination (R 2), the Nash Sutcliffe coefficient of efficiency (NSE) and Percent Bias (PBIAS). The results showed the high performance of SUFI and PSO with P-factor>0.83, R-factor 0.91, NSE>0.89, and 0.18analysis. Indeed, the uncertainty analysis must be accounted when the outcomes of the model use for policy or management decisions.

  20. Analyzing Mathematics Beliefs of Pre-Service Teachers Using Confirmatory Factor Analysis

    Directory of Open Access Journals (Sweden)

    Mazlini Adnan

    2011-12-01

    Full Text Available Mathematics beliefs play an important role in enhancing the quality and the effectiveness of teaching and learning. This study analyzes the mathematics beliefs of 317 pre-service teachers from six Higher Education Institutions (HEIs (Government Public Universities who were randomly selected to participate in this study. Questionnaires consisting of twenty three items were given to the respondents during the data collection process. The validation of the items was done by using confirmatory factor analysis (CFA. In order to obtain a model fit for the measurement model of mathematics beliefs, several fit index tests such as CMINDF, GFI, AGFI, IFI, NFI, CFI, TLI and RMSEA were used. Constructivist beliefs and traditional beliefs were identified as the contributing factors in the model. The analysis also revealed that mathematics beliefs consist of structures of two hidden variables. The correlation between the two variables (constructivist beliefs and traditional beliefs is at a moderate level. Hence, pre-service teachers should be able to recognize their type of mathematics beliefs in order to become effective mathematics teachers.

  1. Defining critical success factors in TOD implementation using rough set analysis

    NARCIS (Netherlands)

    Thomas, R.; Bertolini, L.

    2017-01-01

    This paper defines critical success conditions in transit-oriented development (TOD), evaluating the impact of practices, policies, and governance models on implementation. As part of a meta-analysis of 11 international case studies, 16 critical success factors were developed and validated using

  2. The Infinitesimal Jackknife with Exploratory Factor Analysis

    Science.gov (United States)

    Zhang, Guangjian; Preacher, Kristopher J.; Jennrich, Robert I.

    2012-01-01

    The infinitesimal jackknife, a nonparametric method for estimating standard errors, has been used to obtain standard error estimates in covariance structure analysis. In this article, we adapt it for obtaining standard errors for rotated factor loadings and factor correlations in exploratory factor analysis with sample correlation matrices. Both…

  3. Modeling and Finite Element Analysis of Load-Carrying Performance of a Wind Turbine Considering the Influence of Assembly Factors

    Directory of Open Access Journals (Sweden)

    Jianmei Wang

    2017-03-01

    Full Text Available In this work, a wind turbine shrink disk is used as the research object to investigate load-carrying performance of a multi-layer interference fit, and the theoretical model and finite element model are constructed. According to those models, a MW-level turbine shrink disk is designed, and a test device is developed to apply torque to this turbine shrink disk by hydraulic jack. Then, the circumferential slip between the contact surfaces is monitored and the slip of all contact surfaces is zero. This conclusion verifies the reasonability of the proposed models. The effect of the key influencing factors, such as machining deviation, assembly clearance and propel stroke, were analyzed. The contact pressure and load torque of the mating surfaces were obtained by building typical models with different parameters using finite element analysis (FEA. The results show that the minimum assembly clearance and the machining deviation within the machining range have little influence on load-carrying performance of multi-layer interference fit, while having a greater influence on the maximum assembly clearance and the propel stroke. The results also show that the load-carrying performance of a multiple-layer interference fit can be ensured only if the key factors are set within a reasonable design range. To avoid the abnormal operation of equipment caused by insufficient load torque, the propel stroke during practical assembly should be at least 0.95 times the designed propel stroke, which is significant in guiding the design and assembly of the multi-layer interference fit.

  4. A Comparison of Pseudo-Maximum Likelihood and Asymptotically Distribution-Free Dynamic Factor Analysis Parameter Estimation in Fitting Covariance-Structure Models to Block-Toeplitz Representing Single-Subject Multivariate Time-Series

    NARCIS (Netherlands)

    Molenaar, P.C.M.; Nesselroade, J.R.

    1998-01-01

    The study of intraindividual variability pervades empirical inquiry in virtually all subdisciplines of psychology. The statistical analysis of multivariate time-series data - a central product of intraindividual investigations - requires special modeling techniques. The dynamic factor model (DFM),

  5. Left ventricular wall motion abnormalities evaluated by factor analysis as compared with Fourier analysis

    International Nuclear Information System (INIS)

    Hirota, Kazuyoshi; Ikuno, Yoshiyasu; Nishikimi, Toshio

    1986-01-01

    Factor analysis was applied to multigated cardiac pool scintigraphy to evaluate its ability to detect left ventricular wall motion abnormalities in 35 patients with old myocardial infarction (MI), and in 12 control cases with normal left ventriculography. All cases were also evaluated by conventional Fourier analysis. In most cases with normal left ventriculography, the ventricular and atrial factors were extracted by factor analysis. In cases with MI, the third factor was obtained in the left ventricle corresponding to wall motion abnormality. Each case was scored according to the coincidence of findings of ventriculography and those of factor analysis or Fourier analysis. Scores were recorded for three items; the existence, location, and degree of asynergy. In cases of MI, the detection rate of asynergy was 94 % by factor analysis, 83 % by Fourier analysis, and the agreement in respect to location was 71 % and 66 %, respectively. Factor analysis had higher scores than Fourier analysis, but this was not significant. The interobserver error of factor analysis was less than that of Fourier analysis. Factor analysis can display locations and dynamic motion curves of asynergy, and it is regarded as a useful method for detecting and evaluating left ventricular wall motion abnormalities. (author)

  6. Path analysis of risk factors leading to premature birth.

    Science.gov (United States)

    Fields, S J; Livshits, G; Sirotta, L; Merlob, P

    1996-01-01

    The present study tested whether various sociodemographic, anthropometric, behavioral, and medical/physiological factors act in a direct or indirect manner on the risk of prematurity using path analysis on a sample of Israeli births. The path model shows that medical complications, primarily toxemia, chorioammionitis, and a previous low birth weight delivery directly and significantly act on the risk of prematurity as do low maternal pregnancy weight gain and ethnicity. Other medical complications, including chronic hypertension, preclampsia, and placental abruption, although significantly correlated with prematurity, act indirectly on prematurity through toxemia. The model further shows that the commonly accepted sociodemographic, anthropometric, and behavioral risk factors act by modifying the development of medical complications that lead to prematurity as opposed to having a direct effect on premature delivery. © 1996 Wiley-Liss, Inc. Copyright © 1996 Wiley-Liss, Inc.

  7. Investigating product development strategy in beverage industry using factor analysis

    Directory of Open Access Journals (Sweden)

    Naser Azad

    2013-03-01

    Full Text Available Selecting a product development strategy that is associated with the company's current service or product innovation, based on customers’ needs and changing environment, plays an important role in increasing demand, increasing market share, increasing sales and profits. Therefore, it is important to extract effective variables associated with product development to improve performance measurement of firms. This paper investigates important factors influencing product development strategies using factor analysis. The proposed model of this paper investigates 36 factors and, using factor analysis, we extract six most influential factors including information sharing, intelligence information, exposure strategy, differentiation, research and development strategy and market survey. The first strategy, partnership, includes five sub-factor including product development partnership, partnership with foreign firms, customers’ perception from competitors’ products, Customer involvement in product development, inter-agency coordination, customer-oriented approach to innovation and transmission of product development change where inter-agency coordination has been considered the most important factor. Internal strengths are the most influential factors impacting the second strategy, intelligence information. The third factor, introducing strategy, introducing strategy, includes four sub criteria and consumer buying behavior is the most influencing factor. Differentiation is the next important factor with five components where knowledge and expertise in product innovation is the most important one. Research and development strategy with four sub-criteria where reducing product development cycle plays the most influential factor and finally, market survey strategy is the last important factor with three factors and finding new market plays the most important role.

  8. Exploratory factor analysis in Rehabilitation Psychology: a content analysis.

    Science.gov (United States)

    Roberson, Richard B; Elliott, Timothy R; Chang, Jessica E; Hill, Jessica N

    2014-11-01

    Our objective was to examine the use and quality of exploratory factor analysis (EFA) in articles published in Rehabilitation Psychology. Trained raters examined 66 separate exploratory factor analyses in 47 articles published between 1999 and April 2014. The raters recorded the aim of the EFAs, the distributional statistics, sample size, factor retention method(s), extraction and rotation method(s), and whether the pattern coefficients, structure coefficients, and the matrix of association were reported. The primary use of the EFAs was scale development, but the most widely used extraction and rotation method was principle component analysis, with varimax rotation. When determining how many factors to retain, multiple methods (e.g., scree plot, parallel analysis) were used most often. Many articles did not report enough information to allow for the duplication of their results. EFA relies on authors' choices (e.g., factor retention rules extraction, rotation methods), and few articles adhered to all of the best practices. The current findings are compared to other empirical investigations into the use of EFA in published research. Recommendations for improving EFA reporting practices in rehabilitation psychology research are provided.

  9. Multicollinearity in prognostic factor analyses using the EORTC QLQ-C30: identification and impact on model selection.

    Science.gov (United States)

    Van Steen, Kristel; Curran, Desmond; Kramer, Jocelyn; Molenberghs, Geert; Van Vreckem, Ann; Bottomley, Andrew; Sylvester, Richard

    2002-12-30

    Clinical and quality of life (QL) variables from an EORTC clinical trial of first line chemotherapy in advanced breast cancer were used in a prognostic factor analysis of survival and response to chemotherapy. For response, different final multivariate models were obtained from forward and backward selection methods, suggesting a disconcerting instability. Quality of life was measured using the EORTC QLQ-C30 questionnaire completed by patients. Subscales on the questionnaire are known to be highly correlated, and therefore it was hypothesized that multicollinearity contributed to model instability. A correlation matrix indicated that global QL was highly correlated with 7 out of 11 variables. In a first attempt to explore multicollinearity, we used global QL as dependent variable in a regression model with other QL subscales as predictors. Afterwards, standard diagnostic tests for multicollinearity were performed. An exploratory principal components analysis and factor analysis of the QL subscales identified at most three important components and indicated that inclusion of global QL made minimal difference to the loadings on each component, suggesting that it is redundant in the model. In a second approach, we advocate a bootstrap technique to assess the stability of the models. Based on these analyses and since global QL exacerbates problems of multicollinearity, we therefore recommend that global QL be excluded from prognostic factor analyses using the QLQ-C30. The prognostic factor analysis was rerun without global QL in the model, and selected the same significant prognostic factors as before. Copyright 2002 John Wiley & Sons, Ltd.

  10. Perturbation analysis for Monte Carlo continuous cross section models

    International Nuclear Information System (INIS)

    Kennedy, Chris B.; Abdel-Khalik, Hany S.

    2011-01-01

    Sensitivity analysis, including both its forward and adjoint applications, collectively referred to hereinafter as Perturbation Analysis (PA), is an essential tool to complete Uncertainty Quantification (UQ) and Data Assimilation (DA). PA-assisted UQ and DA have traditionally been carried out for reactor analysis problems using deterministic as opposed to stochastic models for radiation transport. This is because PA requires many model executions to quantify how variations in input data, primarily cross sections, affect variations in model's responses, e.g. detectors readings, flux distribution, multiplication factor, etc. Although stochastic models are often sought for their higher accuracy, their repeated execution is at best computationally expensive and in reality intractable for typical reactor analysis problems involving many input data and output responses. Deterministic methods however achieve computational efficiency needed to carry out the PA analysis by reducing problem dimensionality via various spatial and energy homogenization assumptions. This however introduces modeling error components into the PA results which propagate to the following UQ and DA analyses. The introduced errors are problem specific and therefore are expected to limit the applicability of UQ and DA analyses to reactor systems that satisfy the introduced assumptions. This manuscript introduces a new method to complete PA employing a continuous cross section stochastic model and performed in a computationally efficient manner. If successful, the modeling error components introduced by deterministic methods could be eliminated, thereby allowing for wider applicability of DA and UQ results. Two MCNP models demonstrate the application of the new method - a Critical Pu Sphere (Jezebel), a Pu Fast Metal Array (Russian BR-1). The PA is completed for reaction rate densities, reaction rate ratios, and the multiplication factor. (author)

  11. Linear model analysis of the influencing factors of boar longevity in Southern China.

    Science.gov (United States)

    Wang, Chao; Li, Jia-Lian; Wei, Hong-Kui; Zhou, Yuan-Fei; Jiang, Si-Wen; Peng, Jian

    2017-04-15

    This study aimed to investigate the factors influencing the boar herd life month (BHLM) in Southern China. A total of 1630 records of culling boars from nine artificial insemination centers were collected from January 2013 to May 2016. A logistic regression model and two linear models were used to analyze the effects of breed, housing type, age at herd entry, and seed stock herd on boar removal reason and BHLM, respectively. Boar breed and the age at herd entry had significant effects on the removal reasons (P linear models (with or without removal reason including) showed boars raised individually in stalls exhibited shorter BHLM than those raised in pens (P introduction. Copyright © 2017. Published by Elsevier Inc.

  12. Spatial scale effects in environmental risk-factor modelling for diseases

    Directory of Open Access Journals (Sweden)

    Ram K. Raghavan

    2013-05-01

    Full Text Available Studies attempting to identify environmental risk factors for diseases can be seen to extract candidate variables from remotely sensed datasets, using a single buffer-zone surrounding locations from where disease status are recorded. A retrospective case-control study using canine leptospirosis data was conducted to verify the effects of changing buffer-zones (spatial extents on the risk factors derived. The case-control study included 94 case dogs predominantly selected based on positive polymerase chain reaction (PCR test for leptospires in urine, and 185 control dogs based on negative PCR. Land cover features from National Land Cover Dataset (NLCD and Kansas Gap Analysis Program (KS GAP around geocoded addresses of cases/controls were extracted using multiple buffers at every 500 m up to 5,000 m, and multivariable logistic models were used to estimate the risk of different land cover variables to dogs. The types and statistical significance of risk factors identified changed with an increase in spatial extent in both datasets. Leptospirosis status in dogs was significantly associated with developed high-intensity areas in models that used variables extracted from spatial extents of 500-2000 m, developed medium-intensity areas beyond 2,000 m and up to 3,000 m, and evergreen forests beyond 3,500 m and up to 5,000 m in individual models in the NLCD. Significant associations were seen in urban areas in models that used variables extracted from spatial extents of 500-2,500 m and forest/woodland areas beyond 2,500 m and up to 5,000 m in individual models in Kansas gap analysis programme datasets. The use of ad hoc spatial extents can be misleading or wrong, and the determination of an appropriate spatial extent is critical when extracting environmental variables for studies. Potential work-arounds for this problem are discussed.

  13. Lithuanian Population Aging Factors Analysis

    Directory of Open Access Journals (Sweden)

    Agnė Garlauskaitė

    2015-05-01

    Full Text Available The aim of this article is to identify the factors that determine aging of Lithuania’s population and to assess the influence of these factors. The article shows Lithuanian population aging factors analysis, which consists of two main parts: the first describes the aging of the population and its characteristics in theoretical terms. Second part is dedicated to the assessment of trends that influence the aging population and demographic factors and also to analyse the determinants of the aging of the population of Lithuania. After analysis it is concluded in the article that the decline in the birth rate and increase in the number of emigrants compared to immigrants have the greatest impact on aging of the population, so in order to show the aging of the population, a lot of attention should be paid to management of these demographic processes.

  14. A Confirmatory Factor Analysis on the Attitude Scale of Constructivist Approach for Science Teachers

    Directory of Open Access Journals (Sweden)

    E. Evrekli

    2010-11-01

    Full Text Available Underlining the importance of teachers for the constructivist approach, the present study attempts to develop “Attitude Scale of Construc¬tivist Approach for Science Teachers (ASCAST”. The pre-applications of the scale were administered to a total of 210 science teachers; however, the data obtained from 5 teachers were excluded from the analysis. As a result of the analysis of the data obtained from the pre-applications, it was found that the scale could have a single factor structure, which was tested using the confir¬matory factor analysis. As a result of the initial confirmatory factor analysis, the values of fit were examined and found to be low. Subsequently, by exam¬ining the modification indices, error covariance was added between items 23 and 24 and the model was tested once again. The added error covariance led to a significant improvement in the model, producing values of fit suitable for limit values. Thus, it was concluded that the scale could be employed with a single factor. The explained variance value for the scale developed with a sin¬gle factor structure was calculated to be 50.43% and its reliability was found to be .93. The results obtained suggest that the scale possesses reliable-valid characteristics and could be used in further studies.

  15. Global sensitivity analysis of a filtration model for submerged anaerobic membrane bioreactors (AnMBR).

    Science.gov (United States)

    Robles, A; Ruano, M V; Ribes, J; Seco, A; Ferrer, J

    2014-04-01

    The results of a global sensitivity analysis of a filtration model for submerged anaerobic MBRs (AnMBRs) are assessed in this paper. This study aimed to (1) identify the less- (or non-) influential factors of the model in order to facilitate model calibration and (2) validate the modelling approach (i.e. to determine the need for each of the proposed factors to be included in the model). The sensitivity analysis was conducted using a revised version of the Morris screening method. The dynamic simulations were conducted using long-term data obtained from an AnMBR plant fitted with industrial-scale hollow-fibre membranes. Of the 14 factors in the model, six were identified as influential, i.e. those calibrated using off-line protocols. A dynamic calibration (based on optimisation algorithms) of these influential factors was conducted. The resulting estimated model factors accurately predicted membrane performance. Copyright © 2014 Elsevier Ltd. All rights reserved.

  16. Linear factor copula models and their properties

    KAUST Repository

    Krupskii, Pavel; Genton, Marc G.

    2018-01-01

    We consider a special case of factor copula models with additive common factors and independent components. These models are flexible and parsimonious with O(d) parameters where d is the dimension. The linear structure allows one to obtain closed form expressions for some copulas and their extreme‐value limits. These copulas can be used to model data with strong tail dependencies, such as extreme data. We study the dependence properties of these linear factor copula models and derive the corresponding limiting extreme‐value copulas with a factor structure. We show how parameter estimates can be obtained for these copulas and apply one of these copulas to analyse a financial data set.

  17. Linear factor copula models and their properties

    KAUST Repository

    Krupskii, Pavel

    2018-04-25

    We consider a special case of factor copula models with additive common factors and independent components. These models are flexible and parsimonious with O(d) parameters where d is the dimension. The linear structure allows one to obtain closed form expressions for some copulas and their extreme‐value limits. These copulas can be used to model data with strong tail dependencies, such as extreme data. We study the dependence properties of these linear factor copula models and derive the corresponding limiting extreme‐value copulas with a factor structure. We show how parameter estimates can be obtained for these copulas and apply one of these copulas to analyse a financial data set.

  18. The Analysis of the Contribution of Human Factors to the In-Flight Loss of Control Accidents

    Science.gov (United States)

    Ancel, Ersin; Shih, Ann T.

    2012-01-01

    In-flight loss of control (LOC) is currently the leading cause of fatal accidents based on various commercial aircraft accident statistics. As the Next Generation Air Transportation System (NextGen) emerges, new contributing factors leading to LOC are anticipated. The NASA Aviation Safety Program (AvSP), along with other aviation agencies and communities are actively developing safety products to mitigate the LOC risk. This paper discusses the approach used to construct a generic integrated LOC accident framework (LOCAF) model based on a detailed review of LOC accidents over the past two decades. The LOCAF model is comprised of causal factors from the domain of human factors, aircraft system component failures, and atmospheric environment. The multiple interdependent causal factors are expressed in an Object-Oriented Bayesian belief network. In addition to predicting the likelihood of LOC accident occurrence, the system-level integrated LOCAF model is able to evaluate the impact of new safety technology products developed in AvSP. This provides valuable information to decision makers in strategizing NASA's aviation safety technology portfolio. The focus of this paper is on the analysis of human causal factors in the model, including the contributions from flight crew and maintenance workers. The Human Factors Analysis and Classification System (HFACS) taxonomy was used to develop human related causal factors. The preliminary results from the baseline LOCAF model are also presented.

  19. A Costing Analysis for Decision Making Grid Model in Failure-Based Maintenance

    Directory of Open Access Journals (Sweden)

    Burhanuddin M. A.

    2011-01-01

    Full Text Available Background. In current economic downturn, industries have to set good control on production cost, to maintain their profit margin. Maintenance department as an imperative unit in industries should attain all maintenance data, process information instantaneously, and subsequently transform it into a useful decision. Then act on the alternative to reduce production cost. Decision Making Grid model is used to identify strategies for maintenance decision. However, the model has limitation as it consider two factors only, that is, downtime and frequency of failures. We consider third factor, cost, in this study for failure-based maintenance. The objective of this paper is to introduce the formulae to estimate maintenance cost. Methods. Fish bone analysis conducted with Ishikawa model and Decision Making Grid methods are used in this study to reveal some underlying risk factors that delay failure-based maintenance. The goal of the study is to estimate the risk factor that is, repair cost to fit in the Decision Making Grid model. Decision Making grid model consider two variables, frequency of failure and downtime in the analysis. This paper introduces third variable, repair cost for Decision Making Grid model. This approaches give better result to categorize the machines, reduce cost, and boost the earning for the manufacturing plant. Results. We collected data from one of the food processing factories in Malaysia. From our empirical result, Machine C, Machine D, Machine F, and Machine I must be in the Decision Making Grid model even though their frequency of failures and downtime are less than Machine B and Machine N, based on the costing analysis. The case study and experimental results show that the cost analysis in Decision Making Grid model gives more promising strategies in failure-based maintenance. Conclusions. The improvement of Decision Making Grid model for decision analysis with costing analysis is our contribution in this paper for

  20. Factor Economic Analysis at Forestry Enterprises

    Directory of Open Access Journals (Sweden)

    M.Yu. Chik

    2018-03-01

    Full Text Available The article studies the importance of economic analysis according to the results of research of scientific works of domestic and foreign scientists. The calculation of the influence of factors on the change in the cost of harvesting timber products by cost items has been performed. The results of the calculation of the influence of factors on the change of costs on 1 UAH are determined using the full cost of sold products. The variable and fixed costs and their distribution are allocated that influences the calculation of the impact of factors on cost changes on 1 UAH of sold products. The paper singles out the general results of calculating the influence of factors on cost changes on 1 UAH of sold products. According to the results of the analysis, the list of reserves for reducing the cost of production at forest enterprises was proposed. The main sources of reserves for reducing the prime cost of forest products at forest enterprises are investigated based on the conducted factor analysis.

  1. The Five-Factor Model: General Overview

    Directory of Open Access Journals (Sweden)

    A A Vorobyeva

    2011-12-01

    Full Text Available The article describes the five-factor model (FFM, giving an overview of its history, basic dimensions, cross-cultural research conducted on the model and highlights some practical studies based on the FFM, including the studies on job performance, leader performance and daily social interactions. An overview of the recent five-factor theory is also provided. According to the theory, the five factors are encoded in human genes, therefore it is almost impossible to change the basic factors themselves, but a person's behavior might be changed due to characteristic adaptations which do not alter personality dimensions, only a person's behavior.

  2. Liquidity indicator for the Croatian economy – Factor analysis approach

    Directory of Open Access Journals (Sweden)

    Mirjana Čižmešija

    2014-12-01

    Full Text Available Croatian business surveys (BS are conducted in the manufacturing industry, retail trade and construction sector. In all of these sectors, manager´s assessments of liquidity are measured. The aim of the paper was to form a new composite liquidity indicator by including business survey liquidity measures from all three covered economic sectors in the Croatian economy mentioned above. In calculating the leading indicator, a factor analysis approach was used. However, this kind of indicator does not exist in a Croatia or in any other European economy. Furthermore, the issue of Croatian companies´ illiquidity is highly neglected in the literature. The empirical analysis consists of two parts. In the first part the new liquidity indicator was formed using factor analysis. One factor (representing the new liquidity indicator; LI was extracted out of the three liquidity variables in three economic sectors. This factor represents the new liquidity indicator. In the second part, econometric models were applied in order to investigate the forecasting properties of the new business survey liquidity indicator, when predicting the direction of changes in Croatian industrial production. The quarterly data used in the research covered the period from January 2000 to April 2013. Based on econometric analysis, it can be concluded that the LI is a leading indicator of Croatia’s industrial production with better forecasting properties then the standard liquidity indicators (formed in a manufacturing industry.

  3. Modeling the factors that influence knowledge transfer in mergers and acquisitions

    Institute of Scientific and Technical Information of China (English)

    YU Haiyan; LIANG Zhanping

    2010-01-01

    This paper constructs a model on the factors that influence knowledge transfer in mergers and acquisitions (M&A) and validates it via questionnaire surveys.Using 125 valid collected questionnaires,multiple linear regression analysis and hierarchical regression analysis showed that five out of the ten factors had a positive effect on knowledge transfer effect.The ranking of factor importance,from high to low,was knowledge explicitness,relationship quality,learning intent,advanced transfer activities,and learning capability,which is fairly consistent with positive factors observed in other interorganizational knowledge transfer researches.Our results also showed that one of the control variables (size of acquired firm) had neither a direct or indirect effect on knowledge transfer in M&A.Additionally,our research found that knowledge distance and degree of M&A integration had a positive influence on knowledge transfer effect at the early stage after M&A,but had a negative influence at the late stage.Based on this research,several suggestions for knowledge transfer in M&A are proposed.

  4. Modeling the factors that influence knowledge transfer in mergers and acquisitions

    Institute of Scientific and Technical Information of China (English)

    YU; Haiyan; LIANG; Zhanping

    2010-01-01

    This paper constructs a model on the factors that influence knowledge transfer in mergers and acquisitions(M&A)and validates it via questionnaire surveys.Using 125valid collected questionnaires,multiple linear regression analysis and hierarchical regression analysis showed that five out of the ten factors had a positive effect on knowledge transfer effect.The ranking of factor importance,from high to low,was knowledge explicitness,relationship quality,learning intent,advanced transfer activities,and learning capability,which is fairly consistent with positive factors observed in other interorganizational knowledge transfer researches.Our results also showed that one of the control variables(size of acquired firm)had neither a direct or indirect effect on knowledge transfer in M&A.Additionally,our research found that knowledge distance and degree of M&A integration had a positive influence on knowledge transfer effect at the early stage after M&A,but had a negative influence at the late stage.Based on this research,several suggestions for knowledge transfer in M&A are proposed.

  5. Identification of advanced human factors engineering analysis, design and evaluation methods

    International Nuclear Information System (INIS)

    Plott, C.; Ronan, A. M.; Laux, L.; Bzostek, J.; Milanski, J.; Scheff, S.

    2006-01-01

    NUREG-0711 Rev.2, 'Human Factors Engineering Program Review Model,' provides comprehensive guidance to the Nuclear Regulatory Commission (NRC) in assessing the human factors practices employed by license applicants for Nuclear Power Plant control room designs. As software based human-system interface (HSI) technologies supplant traditional hardware-based technologies, the NRC may encounter new HSI technologies or seemingly unconventional approaches to human factors design, analysis, and evaluation methods which NUREG-0711 does not anticipate. A comprehensive survey was performed to identify advanced human factors engineering analysis, design and evaluation methods, tools, and technologies that the NRC may encounter in near term future licensee applications. A review was conducted to identify human factors methods, tools, and technologies relevant to each review element of NUREG-0711. Additionally emerging trends in technology which have the potential to impact review elements, such as Augmented Cognition, and various wireless tools and technologies were identified. The purpose of this paper is to provide an overview of the survey results and to highlight issues that could be revised or adapted to meet with emerging trends. (authors)

  6. Global combustion sources of organic aerosols: model comparison with 84 AMS factor-analysis data sets

    Science.gov (United States)

    Tsimpidi, Alexandra P.; Karydis, Vlassis A.; Pandis, Spyros N.; Lelieveld, Jos

    2016-07-01

    Emissions of organic compounds from biomass, biofuel, and fossil fuel combustion strongly influence the global atmospheric aerosol load. Some of the organics are directly released as primary organic aerosol (POA). Most are emitted in the gas phase and undergo chemical transformations (i.e., oxidation by hydroxyl radical) and form secondary organic aerosol (SOA). In this work we use the global chemistry climate model ECHAM/MESSy Atmospheric Chemistry (EMAC) with a computationally efficient module for the description of organic aerosol (OA) composition and evolution in the atmosphere (ORACLE). The tropospheric burden of open biomass and anthropogenic (fossil and biofuel) combustion particles is estimated to be 0.59 and 0.63 Tg, respectively, accounting for about 30 and 32 % of the total tropospheric OA load. About 30 % of the open biomass burning and 10 % of the anthropogenic combustion aerosols originate from direct particle emissions, whereas the rest is formed in the atmosphere. A comprehensive data set of aerosol mass spectrometer (AMS) measurements along with factor-analysis results from 84 field campaigns across the Northern Hemisphere are used to evaluate the model results. Both the AMS observations and the model results suggest that over urban areas both POA (25-40 %) and SOA (60-75 %) contribute substantially to the overall OA mass, whereas further downwind and in rural areas the POA concentrations decrease substantially and SOA dominates (80-85 %). EMAC does a reasonable job in reproducing POA and SOA levels during most of the year. However, it tends to underpredict POA and SOA concentrations during winter indicating that the model misses wintertime sources of OA (e.g., residential biofuel use) and SOA formation pathways (e.g., multiphase oxidation).

  7. Adjusting for multiple prognostic factors in the analysis of randomised trials

    Science.gov (United States)

    2013-01-01

    Background When multiple prognostic factors are adjusted for in the analysis of a randomised trial, it is unclear (1) whether it is necessary to account for each of the strata, formed by all combinations of the prognostic factors (stratified analysis), when randomisation has been balanced within each stratum (stratified randomisation), or whether adjusting for the main effects alone will suffice, and (2) the best method of adjustment in terms of type I error rate and power, irrespective of the randomisation method. Methods We used simulation to (1) determine if a stratified analysis is necessary after stratified randomisation, and (2) to compare different methods of adjustment in terms of power and type I error rate. We considered the following methods of analysis: adjusting for covariates in a regression model, adjusting for each stratum using either fixed or random effects, and Mantel-Haenszel or a stratified Cox model depending on outcome. Results Stratified analysis is required after stratified randomisation to maintain correct type I error rates when (a) there are strong interactions between prognostic factors, and (b) there are approximately equal number of patients in each stratum. However, simulations based on real trial data found that type I error rates were unaffected by the method of analysis (stratified vs unstratified), indicating these conditions were not met in real datasets. Comparison of different analysis methods found that with small sample sizes and a binary or time-to-event outcome, most analysis methods lead to either inflated type I error rates or a reduction in power; the lone exception was a stratified analysis using random effects for strata, which gave nominal type I error rates and adequate power. Conclusions It is unlikely that a stratified analysis is necessary after stratified randomisation except in extreme scenarios. Therefore, the method of analysis (accounting for the strata, or adjusting only for the covariates) will not

  8. Incorporation of human factors into ship collision risk models focusing on human centred design aspects

    International Nuclear Information System (INIS)

    Sotiralis, P.; Ventikos, N.P.; Hamann, R.; Golyshev, P.; Teixeira, A.P.

    2016-01-01

    This paper presents an approach that more adequately incorporates human factor considerations into quantitative risk analysis of ship operation. The focus is on the collision accident category, which is one of the main risk contributors in ship operation. The approach is based on the development of a Bayesian Network (BN) model that integrates elements from the Technique for Retrospective and Predictive Analysis of Cognitive Errors (TRACEr) and focuses on the calculation of the collision accident probability due to human error. The model takes into account the human performance in normal, abnormal and critical operational conditions and implements specific tasks derived from the analysis of the task errors leading to the collision accident category. A sensitivity analysis is performed to identify the most important contributors to human performance and ship collision. Finally, the model developed is applied to assess the collision risk of a feeder operating in Dover strait using the collision probability estimated by the developed BN model and an Event tree model for calculation of human, economic and environmental risks. - Highlights: • A collision risk model for the incorporation of human factors into quantitative risk analysis is proposed. • The model takes into account the human performance in different operational conditions leading to the collision. • The most important contributors to human performance and ship collision are identified. • The model developed is applied to assess the collision risk of a feeder operating in Dover strait.

  9. A Study on Influencing Factors of Knowledge Management Systems Adoption: Models Comparison Approach

    OpenAIRE

    Mei-Chun Yeh; Ming-Shu Yuan

    2007-01-01

    Using Linear Structural Relation model (LISREL model) as analysis method and technology acceptance model and decomposed theory of planned behavior as research foundation, this study approachesmainly from the angle of behavioral intention to examine the influential factors of 421 employees adopting knowledge management systems and in the meantime to compare the two method models mentioned on the top. According to the research, there is no, in comparison with technology acceptance model anddeco...

  10. Confirmatory factor analysis reveals a latent cognitive structure common to bipolar disorder, schizophrenia, and normal controls.

    Science.gov (United States)

    Schretlen, David J; Peña, Javier; Aretouli, Eleni; Orue, Izaskun; Cascella, Nicola G; Pearlson, Godfrey D; Ojeda, Natalia

    2013-06-01

    We sought to determine whether a single hypothesized latent factor structure would characterize cognitive functioning in three distinct groups. We assessed 576 adults (340 community controls, 126 adults with bipolar disorder, and 110 adults with schizophrenia) using 15 measures derived from nine cognitive tests. Confirmatory factor analysis (CFA) was conducted to examine the fit of a hypothesized six-factor model. The hypothesized factors included attention, psychomotor speed, verbal memory, visual memory, ideational fluency, and executive functioning. The six-factor model provided an excellent fit for all three groups [for community controls, root mean square error of approximation (RMSEA) schizophrenia, RMSEA = 0.06 and CFI = 0.98]. Alternate models that combined fluency with processing speed or verbal and visual memory reduced the goodness of fit. Multi-group CFA results supported factor invariance across the three groups. Confirmatory factor analysis supported a single six-factor structure of cognitive functioning among patients with schizophrenia or bipolar disorder and community controls. While the three groups clearly differ in level of performance, they share a common underlying architecture of information processing abilities. These cognitive factors could provide useful targets for clinical trials of treatments that aim to enhance information processing in persons with neurological and neuropsychiatric disorders. © 2013 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

  11. Business Models For SMEs In Bandung: Swot Analysis

    Directory of Open Access Journals (Sweden)

    Senen Machmud

    2014-04-01

    Full Text Available The main objective of this study is to find the model business for small and medium-sized enterprises (SMEs with management strategy and business strategy approach. This research to help researchers, owners of SMEs and government in developing a framework for management strategy and business strategy on how the best result of business models. This study is valuable considering the limited among of empirical work previously done on the topic in question. The result of management strategies is internal and external factor analysis than analysis with strength, weakness, opportunities, and treatment (SWOT.

  12. Multivariate factor analysis of Girgentana goat milk composition

    Directory of Open Access Journals (Sweden)

    Pietro Giaccone

    2010-01-01

    Full Text Available The interpretation of the several variables that contribute to defining milk quality is difficult due to the high degree of  correlation among them. In this case, one of the best methods of statistical processing is factor analysis, which belongs  to the multivariate groups; for our study this particular statistical approach was employed.  A total of 1485 individual goat milk samples from 117 Girgentana goats, were collected fortnightly from January to July,  and analysed for physical and chemical composition, and clotting properties. Milk pH and tritable acidity were within the  normal range for fresh goat milk. Morning milk yield resulted 704 ± 323 g with 3.93 ± 1.23% and 3.48±0.38% for fat  and protein percentages, respectively. The milk urea content was 43.70 ± 8.28 mg/dl. The clotting ability of Girgentana  milk was quite good, with a renneting time equal to 16.96 ± 3.08 minutes, a rate of curd formation of 2.01 ± 1.63 min-  utes and a curd firmness of 25.08 ± 7.67 millimetres.  Factor analysis was performed by applying axis orthogonal rotation (rotation type VARIMAX; the analysis grouped the  milk components into three latent or common factors. The first, which explained 51.2% of the total covariance, was  defined as “slow milks”, because it was linked to r and pH. The second latent factor, which explained 36.2% of the total  covariance, was defined as “milk yield”, because it is positively correlated to the morning milk yield and to the urea con-  tent, whilst negatively correlated to the fat percentage. The third latent factor, which explained 12.6% of the total covari-  ance, was defined as “curd firmness,” because it is linked to protein percentage, a30 and titatrable acidity. With the aim  of evaluating the influence of environmental effects (stage of kidding, parity and type of kidding, factor scores were anal-  ysed with the mixed linear model. Results showed significant effects of the season of

  13. The joint return period analysis of natural disasters based on monitoring and statistical modeling of multidimensional hazard factors

    Energy Technology Data Exchange (ETDEWEB)

    Liu, Xueqin [State Key Laboratory of Earth Surface Processes and Resource Ecology, Beijing Normal University, Beijing 100875 (China); National Marine Environmental Monitoring Center, State Oceanic Administration, Dalian 116023 (China); School of Social Development and Public Policy, Beijing Normal University, Beijing 100875 (China); Li, Ning [State Key Laboratory of Earth Surface Processes and Resource Ecology, Beijing Normal University, Beijing 100875 (China); Yuan, Shuai, E-mail: syuan@nmemc.org.cn [National Marine Environmental Monitoring Center, State Oceanic Administration, Dalian 116023 (China); Xu, Ning; Shi, Wenqin; Chen, Weibin [National Marine Environmental Monitoring Center, State Oceanic Administration, Dalian 116023 (China)

    2015-12-15

    As a random event, a natural disaster has the complex occurrence mechanism. The comprehensive analysis of multiple hazard factors is important in disaster risk assessment. In order to improve the accuracy of risk analysis and forecasting, the formation mechanism of a disaster should be considered in the analysis and calculation of multi-factors. Based on the consideration of the importance and deficiencies of multivariate analysis of dust storm disasters, 91 severe dust storm disasters in Inner Mongolia from 1990 to 2013 were selected as study cases in the paper. Main hazard factors from 500-hPa atmospheric circulation system, near-surface meteorological system, and underlying surface conditions were selected to simulate and calculate the multidimensional joint return periods. After comparing the simulation results with actual dust storm events in 54 years, we found that the two-dimensional Frank Copula function showed the better fitting results at the lower tail of hazard factors and that three-dimensional Frank Copula function displayed the better fitting results at the middle and upper tails of hazard factors. However, for dust storm disasters with the short return period, three-dimensional joint return period simulation shows no obvious advantage. If the return period is longer than 10 years, it shows significant advantages in extreme value fitting. Therefore, we suggest the multivariate analysis method may be adopted in forecasting and risk analysis of serious disasters with the longer return period, such as earthquake and tsunami. Furthermore, the exploration of this method laid the foundation for the prediction and warning of other nature disasters. - Highlights: • A method to estimate the multidimensional joint return periods is presented. • 2D function allows better fitting results at the lower tail of hazard factors. • Three-dimensional simulation has obvious advantages in extreme value fitting. • Joint return periods are closer to the reality

  14. The joint return period analysis of natural disasters based on monitoring and statistical modeling of multidimensional hazard factors

    International Nuclear Information System (INIS)

    Liu, Xueqin; Li, Ning; Yuan, Shuai; Xu, Ning; Shi, Wenqin; Chen, Weibin

    2015-01-01

    As a random event, a natural disaster has the complex occurrence mechanism. The comprehensive analysis of multiple hazard factors is important in disaster risk assessment. In order to improve the accuracy of risk analysis and forecasting, the formation mechanism of a disaster should be considered in the analysis and calculation of multi-factors. Based on the consideration of the importance and deficiencies of multivariate analysis of dust storm disasters, 91 severe dust storm disasters in Inner Mongolia from 1990 to 2013 were selected as study cases in the paper. Main hazard factors from 500-hPa atmospheric circulation system, near-surface meteorological system, and underlying surface conditions were selected to simulate and calculate the multidimensional joint return periods. After comparing the simulation results with actual dust storm events in 54 years, we found that the two-dimensional Frank Copula function showed the better fitting results at the lower tail of hazard factors and that three-dimensional Frank Copula function displayed the better fitting results at the middle and upper tails of hazard factors. However, for dust storm disasters with the short return period, three-dimensional joint return period simulation shows no obvious advantage. If the return period is longer than 10 years, it shows significant advantages in extreme value fitting. Therefore, we suggest the multivariate analysis method may be adopted in forecasting and risk analysis of serious disasters with the longer return period, such as earthquake and tsunami. Furthermore, the exploration of this method laid the foundation for the prediction and warning of other nature disasters. - Highlights: • A method to estimate the multidimensional joint return periods is presented. • 2D function allows better fitting results at the lower tail of hazard factors. • Three-dimensional simulation has obvious advantages in extreme value fitting. • Joint return periods are closer to the reality

  15. Factors influencing crime rates: an econometric analysis approach

    Science.gov (United States)

    Bothos, John M. A.; Thomopoulos, Stelios C. A.

    2016-05-01

    The scope of the present study is to research the dynamics that determine the commission of crimes in the US society. Our study is part of a model we are developing to understand urban crime dynamics and to enhance citizens' "perception of security" in large urban environments. The main targets of our research are to highlight dependence of crime rates on certain social and economic factors and basic elements of state anticrime policies. In conducting our research, we use as guides previous relevant studies on crime dependence, that have been performed with similar quantitative analyses in mind, regarding the dependence of crime on certain social and economic factors using statistics and econometric modelling. Our first approach consists of conceptual state space dynamic cross-sectional econometric models that incorporate a feedback loop that describes crime as a feedback process. In order to define dynamically the model variables, we use statistical analysis on crime records and on records about social and economic conditions and policing characteristics (like police force and policing results - crime arrests), to determine their influence as independent variables on crime, as the dependent variable of our model. The econometric models we apply in this first approach are an exponential log linear model and a logit model. In a second approach, we try to study the evolvement of violent crime through time in the US, independently as an autonomous social phenomenon, using autoregressive and moving average time-series econometric models. Our findings show that there are certain social and economic characteristics that affect the formation of crime rates in the US, either positively or negatively. Furthermore, the results of our time-series econometric modelling show that violent crime, viewed solely and independently as a social phenomenon, correlates with previous years crime rates and depends on the social and economic environment's conditions during previous years.

  16. Analysis and optimization of dynamic model of eccentric shaft grinder

    Science.gov (United States)

    Gao, Yangjie; Han, Qiushi; Li, Qiguang; Peng, Baoying

    2018-04-01

    Eccentric shaft servo grinder is the core equipment in the process chain of machining eccentric shaft. The establishment of the movement model and the determination of the kinematic relation of the-axis in the grinding process directly affect the quality of the grinding process, and there are many error factors in grinding, and it is very important to analyze the influence of these factors on the work piece quality. The three-dimensional model of eccentric shaft grinder is drawn by Pro/E three-dimensional drawing software, the model is imported into ANSYS Workbench Finite element analysis software, and the finite element analysis is carried out, and then the variation and parameters of each component of the bed are obtained by the modal analysis result. The natural frequencies and formations of the first six steps of the eccentric shaft grinder are obtained by modal analysis, and the weak links of the parts of the grinder are found out, and a reference improvement method is proposed for the design of the eccentric shaft grinder in the future.

  17. Multiplication factor versus regression analysis in stature estimation from hand and foot dimensions.

    Science.gov (United States)

    Krishan, Kewal; Kanchan, Tanuj; Sharma, Abhilasha

    2012-05-01

    Estimation of stature is an important parameter in identification of human remains in forensic examinations. The present study is aimed to compare the reliability and accuracy of stature estimation and to demonstrate the variability in estimated stature and actual stature using multiplication factor and regression analysis methods. The study is based on a sample of 246 subjects (123 males and 123 females) from North India aged between 17 and 20 years. Four anthropometric measurements; hand length, hand breadth, foot length and foot breadth taken on the left side in each subject were included in the study. Stature was measured using standard anthropometric techniques. Multiplication factors were calculated and linear regression models were derived for estimation of stature from hand and foot dimensions. Derived multiplication factors and regression formula were applied to the hand and foot measurements in the study sample. The estimated stature from the multiplication factors and regression analysis was compared with the actual stature to find the error in estimated stature. The results indicate that the range of error in estimation of stature from regression analysis method is less than that of multiplication factor method thus, confirming that the regression analysis method is better than multiplication factor analysis in stature estimation. Copyright © 2012 Elsevier Ltd and Faculty of Forensic and Legal Medicine. All rights reserved.

  18. Analysis on the influence factors of Bitcoin's price based on VEC model

    OpenAIRE

    Zhu, Yechen; Dickinson, David; Li, Jianjun

    2017-01-01

    Background: Bitcoin, the most innovate digital currency as of now, created since 2008, even through experienced its ups and downs, still keeps drawing attentions to all parts of society. It relies on peer-to-peer network, achieved decentralization, anonymous and transparent. As the most representative digital currency, people curious to study how Bitcoin' price changes in the past. Methods: In this paper, we use monthly data from 2011 to 2016 to build a VEC model to exam how economic factors ...

  19. Applying Petri nets in modelling the human factor

    International Nuclear Information System (INIS)

    Bedreaga, Luminita; Constntinescu, Cristina; Guzun, Basarab

    2007-01-01

    Usually, in the reliability analysis performed for complex systems, we determine the success probability to work with other performance indices, i.e. the likelihood associated with a given state. The possible values assigned to system states can be derived using inductive methods. If one wants to calculate the probability to occur a particular event in the system, then deductive methods should be applied. In the particular case of the human reliability analysis, as part of probabilistic safety analysis, the international regulatory commission have developed specific guides and procedures to perform such assessments. The paper presents the modality to obtain the human reliability quantification using the Petri nets approach. This is an efficient means to assess reliability systems because of their specific features. The examples showed in the paper are from human reliability documentation without a detailed human factor analysis (qualitative). We present human action modelling using event trees and Petri nets approach. The obtained results by these two kinds of methods are in good concordance. (authors)

  20. Chou-Yang model and PHI form factor

    Energy Technology Data Exchange (ETDEWEB)

    Fazal-e-Aleem; Saleem, M.; Rafique, M.

    1988-03-01

    By using the deduced differential cross-section data for PHIp elastic scattering at 175 GeV/c in the Chou-Yang model, the PHI form factor has been computed and parametrized. Then in conjunction with the proton form factor this form factor is used in the pristine Chou-Yang model to obtain differential cross-section data at Fermilab energies. The theoretical results agree with the experimental measurements, endorsing the conjecture that the hadronic form factor of neutral particle is proportional to its magnetic form factor.

  1. PWSCC Growth Assessment Model Considering Stress Triaxiality Factor for Primary Alloy 600 Components

    Directory of Open Access Journals (Sweden)

    Jong-Sung Kim

    2016-08-01

    Full Text Available We propose a primary water stress corrosion cracking (PWSCC initiation model of Alloy 600 that considers the stress triaxiality factor to apply to finite element analysis. We investigated the correlation between stress triaxiality effects and PWSCC growth behavior in cold-worked Alloy 600 stream generator tubes, and identified an additional stress triaxiality factor that can be added to Garud's PWSCC initiation model. By applying the proposed PWSCC initiation model considering the stress triaxiality factor, PWSCC growth simulations based on the macroscopic phenomenological damage mechanics approach were carried out on the PWSCC growth tests of various cold-worked Alloy 600 steam generator tubes and compact tension specimens. As a result, PWSCC growth behavior results from the finite element prediction are in good agreement with the experimental results.

  2. Analysis of the flood extent extraction model and the natural flood influencing factors: A GIS-based and remote sensing analysis

    International Nuclear Information System (INIS)

    Lawal, D U; Matori, A N; Yusuf, K W; Hashim, A M; Balogun, A L

    2014-01-01

    Serious floods have hit the State of Perlis in 2005, 2010, as well as 2011. Perlis is situated in the northern part of Peninsula Malaysia. The floods caused great damage to properties and human lives. There are various methods used in an attempt to provide the most reliable ways to reduce the flood risk and damage to the optimum level by identifying the flood vulnerable zones. The purpose of this paper is to develop a flood extent extraction model based on Minimum Distance Algorithm and to overlay with the natural flood influencing factors considered herein in order to examine the effect of each factor in flood generation. GIS spatial database was created from a geological map, SPOT satellite image, and the topographical map. An attribute database was equally created from field investigations and historical flood areas reports of the study area. The results show a great correlation between the flood extent extraction model and the flood factors

  3. A retrospective analysis to identify the factors affecting infection in patients undergoing chemotherapy.

    Science.gov (United States)

    Park, Ji Hyun; Kim, Hyeon-Young; Lee, Hanna; Yun, Eun Kyoung

    2015-12-01

    This study compares the performance of the logistic regression and decision tree analysis methods for assessing the risk factors for infection in cancer patients undergoing chemotherapy. The subjects were 732 cancer patients who were receiving chemotherapy at K university hospital in Seoul, Korea. The data were collected between March 2011 and February 2013 and were processed for descriptive analysis, logistic regression and decision tree analysis using the IBM SPSS Statistics 19 and Modeler 15.1 programs. The most common risk factors for infection in cancer patients receiving chemotherapy were identified as alkylating agents, vinca alkaloid and underlying diabetes mellitus. The logistic regression explained 66.7% of the variation in the data in terms of sensitivity and 88.9% in terms of specificity. The decision tree analysis accounted for 55.0% of the variation in the data in terms of sensitivity and 89.0% in terms of specificity. As for the overall classification accuracy, the logistic regression explained 88.0% and the decision tree analysis explained 87.2%. The logistic regression analysis showed a higher degree of sensitivity and classification accuracy. Therefore, logistic regression analysis is concluded to be the more effective and useful method for establishing an infection prediction model for patients undergoing chemotherapy. Copyright © 2015 Elsevier Ltd. All rights reserved.

  4. Sensitivity analysis of numerical model of prestressed concrete containment

    Energy Technology Data Exchange (ETDEWEB)

    Bílý, Petr, E-mail: petr.bily@fsv.cvut.cz; Kohoutková, Alena, E-mail: akohout@fsv.cvut.cz

    2015-12-15

    Graphical abstract: - Highlights: • FEM model of prestressed concrete containment with steel liner was created. • Sensitivity analysis of changes in geometry and loads was conducted. • Steel liner and temperature effects are the most important factors. • Creep and shrinkage parameters are essential for the long time analysis. • Prestressing schedule is a key factor in the early stages. - Abstract: Safety is always the main consideration in the design of containment of nuclear power plant. However, efficiency of the design process should be also taken into consideration. Despite the advances in computational abilities in recent years, simplified analyses may be found useful for preliminary scoping or trade studies. In the paper, a study on sensitivity of finite element model of prestressed concrete containment to changes in geometry, loads and other factors is presented. Importance of steel liner, reinforcement, prestressing process, temperature changes, nonlinearity of materials as well as density of finite elements mesh is assessed in the main stages of life cycle of the containment. Although the modeling adjustments have not produced any significant changes in computation time, it was found that in some cases simplified modeling process can lead to significant reduction of work time without degradation of the results.

  5. Multiple factor analysis by example using R

    CERN Document Server

    Pagès, Jérôme

    2014-01-01

    Multiple factor analysis (MFA) enables users to analyze tables of individuals and variables in which the variables are structured into quantitative, qualitative, or mixed groups. Written by the co-developer of this methodology, Multiple Factor Analysis by Example Using R brings together the theoretical and methodological aspects of MFA. It also includes examples of applications and details of how to implement MFA using an R package (FactoMineR).The first two chapters cover the basic factorial analysis methods of principal component analysis (PCA) and multiple correspondence analysis (MCA). The

  6. The five-factor model of personality traits and organizational citizenship behaviors: a meta-analysis.

    Science.gov (United States)

    Chiaburu, Dan S; Oh, In-Sue; Berry, Christopher M; Li, Ning; Gardner, Richard G

    2011-11-01

    Using meta-analytic tests based on 87 statistically independent samples, we investigated the relationships between the five-factor model (FFM) of personality traits and organizational citizenship behaviors in both the aggregate and specific forms, including individual-directed, organization-directed, and change-oriented citizenship. We found that Emotional Stability, Extraversion, and Openness/Intellect have incremental validity for citizenship over and above Conscientiousness and Agreeableness, 2 well-established FFM predictors of citizenship. In addition, FFM personality traits predict citizenship over and above job satisfaction. Finally, we compared the effect sizes obtained in the current meta-analysis with the comparable effect sizes predicting task performance from previous meta-analyses. As a result, we found that Conscientiousness, Emotional Stability, and Extraversion have similar magnitudes of relationships with citizenship and task performance, whereas Openness and Agreeableness have stronger relationships with citizenship than with task performance. This lends some support to the idea that personality traits are (slightly) more important determinants of citizenship than of task performance. We conclude with proposed directions for future research on the relationships between FFM personality traits and specific forms of citizenship, based on the current findings. (c) 2011 APA, all rights reserved.

  7. Fatigue Analysis of Tubesheet/Shell Juncture Applying the Mitigation Factor for Over-conservatism

    International Nuclear Information System (INIS)

    Kang, Deog Ji; Kim, Kyu Hyoung; Lee, Jae Gon

    2009-01-01

    If the environmental fatigue requirements are applied to the primary components of a nuclear power plant, to which the present ASME Code fatigue curves are applied, some locations with high level CUF (Cumulative Usage Factor) are anticipated not to meet the code criteria. The application of environmental fatigue damage is still particularly controversial for plants with 60-year design lives. Therefore, it is need to develop a detailed fatigue analysis procedure to identify the conservatisms in the procedure and to lower the cumulative usage factor. Several factors are being considered to mitigate the conservatism such as three-dimensional finite element modeling. In the present analysis, actual pressure transient data instead of conservative maximum and minimum pressure data was applied as one of mitigation factors. Unlike in the general method, individual transient events were considered instead of the grouped transient events. The tubesheet/shell juncture in the steam generator assembly is the one of the weak locations and was, therefore, selected as a target to evaluate the mitigation factor in the present analysis

  8. Research on Human-Error Factors of Civil Aircraft Pilots Based On Grey Relational Analysis

    Directory of Open Access Journals (Sweden)

    Guo Yundong

    2018-01-01

    Full Text Available In consideration of the situation that civil aviation accidents involve many human-error factors and show the features of typical grey systems, an index system of civil aviation accident human-error factors is built using human factor analysis and classification system model. With the data of accidents happened worldwide between 2008 and 2011, the correlation between human-error factors can be analyzed quantitatively using the method of grey relational analysis. Research results show that the order of main factors affecting pilot human-error factors is preconditions for unsafe acts, unsafe supervision, organization and unsafe acts. The factor related most closely with second-level indexes and pilot human-error factors is the physical/mental limitations of pilots, followed by supervisory violations. The relevancy between the first-level indexes and the corresponding second-level indexes and the relevancy between second-level indexes can also be analyzed quantitatively.

  9. Improved Dynamic Modeling of the Cascade Distillation Subsystem and Analysis of Factors Affecting Its Performance

    Science.gov (United States)

    Perry, Bruce A.; Anderson, Molly S.

    2015-01-01

    The Cascade Distillation Subsystem (CDS) is a rotary multistage distiller being developed to serve as the primary processor for wastewater recovery during long-duration space missions. The CDS could be integrated with a system similar to the International Space Station Water Processor Assembly to form a complete water recovery system for future missions. A preliminary chemical process simulation was previously developed using Aspen Custom Modeler® (ACM), but it could not simulate thermal startup and lacked detailed analysis of several key internal processes, including heat transfer between stages. This paper describes modifications to the ACM simulation of the CDS that improve its capabilities and the accuracy of its predictions. Notably, the modified version can be used to model thermal startup and predicts the total energy consumption of the CDS. The simulation has been validated for both NaC1 solution and pretreated urine feeds and no longer requires retuning when operating parameters change. The simulation was also used to predict how internal processes and operating conditions of the CDS affect its performance. In particular, it is shown that the coefficient of performance of the thermoelectric heat pump used to provide heating and cooling for the CDS is the largest factor in determining CDS efficiency. Intrastage heat transfer affects CDS performance indirectly through effects on the coefficient of performance.

  10. Biosphere analysis - a complementary assessment of dose conversion factors for the Olkiluoto site

    International Nuclear Information System (INIS)

    Kylloenen, J.; Keto, V.

    2010-04-01

    The Olkiluoto site is currently the primary candidate for the final disposal site for spent nuclear fuel from the Olkiluoto and Loviisa NPPs. Safety analysis calculations must be performed to verify the compliance with the long-term safety requirements. The behaviour and distribution of radionuclides in the biosphere is of high importance in these calculations. The aim of this study was to perform a complementary assessment of dose conversion factors for the Olkiluoto site. Posiva has performed extensive analysis on the different ecosystems. In this work the biosphere analysis model of Fortum Nuclear Services (FNS) is used to give an independent estimate of biosphere dose conversion factors for the Olkiluoto site. The following nuclides are analysed: Cl-36, Ni-59, Se-79, Mo-93, Nb-94, Sn-126, I-129 and Cs-135. The FNS model is an equilibrium compartment model in which a steady annual release of 1 Bq of each radionuclide is distributed in different scenarios. The scenarios are the well scenario, which models a small agricultural ecosystem, the lake scenario which models a larger ecosystem with both agriculture and lake use, and sea and transition scenario, which models the behaviour of the radionuclides in marine environments. The scenarios are described and the transfer equations written for the lake scenario. The parameter values are taken from the FNS biosphere database, which has been used in the Finnish L/ILW waste repository safety analyses since mid 1990's. The results of the FNS analysis are compared to those presented in Posiva working report 2000-20 (POSIVA-WR-00-20). The results are of the same order of magnitude for all nuclides except I-129. Since the Posiva and FNS models were independently constructed, the results can be considered as convincing, and the compliance of the results give confidence to the modelling results. (orig.)

  11. Latent segmentation based count models: Analysis of bicycle safety in Montreal and Toronto.

    Science.gov (United States)

    Yasmin, Shamsunnahar; Eluru, Naveen

    2016-10-01

    The study contributes to literature on bicycle safety by building on the traditional count regression models to investigate factors affecting bicycle crashes at the Traffic Analysis Zone (TAZ) level. TAZ is a traffic related geographic entity which is most frequently used as spatial unit for macroscopic crash risk analysis. In conventional count models, the impact of exogenous factors is restricted to be the same across the entire region. However, it is possible that the influence of exogenous factors might vary across different TAZs. To accommodate for the potential variation in the impact of exogenous factors we formulate latent segmentation based count models. Specifically, we formulate and estimate latent segmentation based Poisson (LP) and latent segmentation based Negative Binomial (LNB) models to study bicycle crash counts. In our latent segmentation approach, we allow for more than two segments and also consider a large set of variables in segmentation and segment specific models. The formulated models are estimated using bicycle-motor vehicle crash data from the Island of Montreal and City of Toronto for the years 2006 through 2010. The TAZ level variables considered in our analysis include accessibility measures, exposure measures, sociodemographic characteristics, socioeconomic characteristics, road network characteristics and built environment. A policy analysis is also conducted to illustrate the applicability of the proposed model for planning purposes. This macro-level research would assist decision makers, transportation officials and community planners to make informed decisions to proactively improve bicycle safety - a prerequisite to promoting a culture of active transportation. Copyright © 2016 Elsevier Ltd. All rights reserved.

  12. A factor analytic investigation of the Tripartite model of affect in a clinical sample of young Australians

    Directory of Open Access Journals (Sweden)

    Cosgrave Elizabeth M

    2008-09-01

    Full Text Available Abstract Background The Mood and Anxiety Symptom Questionnaire (MASQ was designed to specifically measure the Tripartite model of affect and is proposed to offer a delineation between the core components of anxiety and depression. Factor analytic data from adult clinical samples has shown mixed results; however no studies employing confirmatory factor analysis (CFA have supported the predicted structure of distinct Depression, Anxiety and General Distress factors. The Tripartite model has not been validated in a clinical sample of older adolescents and young adults. The aim of the present study was to examine the validity of the Tripartite model using scale-level data from the MASQ and correlational and confirmatory factor analysis techniques. Methods 137 young people (M = 17.78, SD = 2.63 referred to a specialist mental health service for adolescents and young adults completed the MASQ and diagnostic interview. Results All MASQ scales were highly inter-correlated, with the lowest correlation between the depression- and anxiety-specific scales (r = .59. This pattern of correlations was observed for all participants rating for an Axis-I disorder but not for participants without a current disorder (r = .18. Confirmatory factor analyses were conducted to evaluate the model fit of a number of solutions. The predicted Tripartite structure was not supported. A 2-factor model demonstrated superior model fit and parsimony compared to 1- or 3-factor models. These broad factors represented Depression and Anxiety and were highly correlated (r = .88. Conclusion The present data lend support to the notion that the Tripartite model does not adequately explain the relationship between anxiety and depression in all clinical populations. Indeed, in the present study this model was found to be inappropriate for a help-seeking community sample of older adolescents and young adults.

  13. Confirmatory Factor Analysis of the Universiti Sains Malaysia Emotional Quotient Inventory Among Medical Students in Malaysia

    Directory of Open Access Journals (Sweden)

    Wan Nor Arifin

    2016-05-01

    Full Text Available The Universiti Sains Malaysia Emotional Quotient Inventory (USMEQ-i is a Malay-language emotional intelligence (EI inventory that was based on a mixed-model approach of EI. It was specifically developed and validated for use among medical course applicants. However, evidence to support its use among medical students is inadequate. This study aims to provide further construct validity evidence for the USMEQ-i among medical students through confirmatory factor analysis (CFA. A cross-sectional study was carried out on a sample of 479 medical students in Universiti Sains Malaysia (USM. After a preliminary analysis, data from only 317 respondents were found suitable for inclusion in CFA. CFA was performed using the maximum likelihood estimation method with bootstrapping due to the nonnormality of items at the multivariate level. The results of the analysis support the two-factor model of the EI component and the one-factor model of the faking component. However, the USMEQ-i should be administered with caution until further cross-validation studies are conducted among students in other medical schools in Malaysia.

  14. Risk factor analysis of equine strongyle resistance to anthelmintics

    Directory of Open Access Journals (Sweden)

    G. Sallé

    2017-12-01

    Full Text Available Intestinal strongyles are the most problematic endoparasites of equids as a result of their wide distribution and the spread of resistant isolates throughout the world. While abundant literature can be found on the extent of anthelmintic resistance across continents, empirical knowledge about associated risk factors is missing. This study brought together results from anthelmintic efficacy testing and risk factor analysis to provide evidence-based guidelines in the field. It involved 688 horses from 39 French horse farms and riding schools to both estimate Faecal Egg Count Reduction (FECR after anthelmintic treatment and to interview farm and riding school managers about their practices. Risk factors associated with reduced anthelmintic efficacy in equine strongyles were estimated across drugs using a marginal modelling approach. Results demonstrated ivermectin efficacy (96.3% ± 14.5% FECR, the inefficacy of fenbendazole (42.8% ± 33.4% FECR and an intermediate profile for pyrantel (90.3% ± 19.6% FECR. Risk factor analysis provided support to advocate for FEC-based treatment regimens combined with individual anthelmintic dosage and the enforcement of tighter biosecurity around horse introduction. The combination of these measures resulted in a decreased risk of drug resistance (relative risk of 0.57, p = 0.02. Premises falling under this typology also relied more on their veterinarians suggesting practitionners play an important role in the sustainability of anthelmintic usage. Similarly, drug resistance risk was halved in premises with frequent pasture rotation and with stocking rate below five horses/ha (relative risk of 0.53, p < 0.01. This is the first empirical risk factor analysis for anthelmintic resistance in equids. Our findings should guide the implementation of more sustained strongyle management in the field. Keywords: Horse, Nematode, Anthelmintic resistance, Strongyle, Cyathostomin

  15. Analysis of mineral phases in coal utilizing factor analysis

    International Nuclear Information System (INIS)

    Roscoe, B.A.; Hopke, P.K.

    1982-01-01

    The mineral phase inclusions of coal are discussed. The contribution of these to a coal sample are determined utilizing several techniques. Neutron activation analysis in conjunction with coal washability studies have produced some information on the general trends of elemental variation in the mineral phases. These results have been enhanced by the use of various statistical techniques. The target transformation factor analysis is specifically discussed and shown to be able to produce elemental profiles of the mineral phases in coal. A data set consisting of physically fractionated coal samples was generated. These samples were analyzed by neutron activation analysis and then their elemental concentrations examined using TTFA. Information concerning the mineral phases in coal can thus be acquired from factor analysis even with limited data. Additional data may permit the resolution of additional mineral phases as well as refinement of theose already identified

  16. Using Structured Additive Regression Models to Estimate Risk Factors of Malaria: Analysis of 2010 Malawi Malaria Indicator Survey Data

    Science.gov (United States)

    Chirombo, James; Lowe, Rachel; Kazembe, Lawrence

    2014-01-01

    Background After years of implementing Roll Back Malaria (RBM) interventions, the changing landscape of malaria in terms of risk factors and spatial pattern has not been fully investigated. This paper uses the 2010 malaria indicator survey data to investigate if known malaria risk factors remain relevant after many years of interventions. Methods We adopted a structured additive logistic regression model that allowed for spatial correlation, to more realistically estimate malaria risk factors. Our model included child and household level covariates, as well as climatic and environmental factors. Continuous variables were modelled by assuming second order random walk priors, while spatial correlation was specified as a Markov random field prior, with fixed effects assigned diffuse priors. Inference was fully Bayesian resulting in an under five malaria risk map for Malawi. Results Malaria risk increased with increasing age of the child. With respect to socio-economic factors, the greater the household wealth, the lower the malaria prevalence. A general decline in malaria risk was observed as altitude increased. Minimum temperatures and average total rainfall in the three months preceding the survey did not show a strong association with disease risk. Conclusions The structured additive regression model offered a flexible extension to standard regression models by enabling simultaneous modelling of possible nonlinear effects of continuous covariates, spatial correlation and heterogeneity, while estimating usual fixed effects of categorical and continuous observed variables. Our results confirmed that malaria epidemiology is a complex interaction of biotic and abiotic factors, both at the individual, household and community level and that risk factors are still relevant many years after extensive implementation of RBM activities. PMID:24991915

  17. Using Evidence Credibility Decay Model for dependence assessment in human reliability analysis

    International Nuclear Information System (INIS)

    Guo, Xingfeng; Zhou, Yanhui; Qian, Jin; Deng, Yong

    2017-01-01

    Highlights: • A new computational model is proposed for dependence assessment in HRA. • We combined three factors of “CT”, “TR” and “SP” within Dempster–Shafer theory. • The BBA of “SP” is reconstructed by discounting rate based on the ECDM. • Simulation experiments are illustrated to show the efficiency of the proposed method. - Abstract: Dependence assessment among human errors plays an important role in human reliability analysis. When dependence between two sequent tasks exists in human reliability analysis, if the preceding task fails, the failure probability of the following task is higher than success. Typically, three major factors are considered: “Closeness in Time” (CT), “Task Relatedness” (TR) and “Similarity of Performers” (SP). Assume TR is not changed, both SP and CT influence the degree of dependence level and SP is discounted by the time as the result of combine two factors in this paper. In this paper, a new computational model is proposed based on the Dempster–Shafer Evidence Theory (DSET) and Evidence Credibility Decay Model (ECDM) to assess the dependence between tasks in human reliability analysis. First, the influenced factors among human tasks are identified and the basic belief assignments (BBAs) of each factor are constructed based on expert evaluation. Then, the BBA of SP is discounted as the result of combining two factors and reconstructed by using the ECDM, the factors are integrated into a fused BBA. Finally, the dependence level is calculated based on fused BBA. Experimental results demonstrate that the proposed model not only quantitatively describe the fact that the input factors influence the dependence level, but also exactly show how the dependence level regular changes with different situations of input factors.

  18. Verification of Overall Safety Factors In Deterministic Design Of Model Tested Breakwaters

    DEFF Research Database (Denmark)

    Burcharth, H. F.

    2001-01-01

    The paper deals with concepts of safety implementation in design. An overall safety factor concept is evaluated on the basis of a reliability analysis of a model tested rubble mound breakwater with monolithic super structure. Also discussed are design load identification and failure mode limit...

  19. Modeling of Individual and Organizational Factors Affecting Traumatic Occupational Injuries Based on the Structural Equation Modeling: A Case Study in Large Construction Industries.

    Science.gov (United States)

    Mohammadfam, Iraj; Soltanzadeh, Ahmad; Moghimbeigi, Abbas; Akbarzadeh, Mehdi

    2016-09-01

    Individual and organizational factors are the factors influencing traumatic occupational injuries. The aim of the present study was the short path analysis of the severity of occupational injuries based on individual and organizational factors. The present cross-sectional analytical study was implemented on traumatic occupational injuries within a ten-year timeframe in 13 large Iranian construction industries. Modeling and data analysis were done using the structural equation modeling (SEM) approach and the IBM SPSS AMOS statistical software version 22.0, respectively. The mean age and working experience of the injured workers were 28.03 ± 5.33 and 4.53 ± 3.82 years, respectively. The portions of construction and installation activities of traumatic occupational injuries were 64.4% and 18.1%, respectively. The SEM findings showed that the individual, organizational and accident type factors significantly were considered as effective factors on occupational injuries' severity (P accidents' severity in large construction industries.

  20. Ranking factors of an investment in cogeneration: sensitivity analysis ranking the technical and economical factors

    International Nuclear Information System (INIS)

    Sundberg, Gunnel

    2001-01-01

    A deregulation of the electricity market in Europe will result in increased competition among the power-producing companies. They will therefore carefully estimate the financial risk in an investment in new power-producing capability. One part of the risk assessment is to perform a sensitivity analysis. This paper presents a sensitivity analysis using factorial design, resulting in an assessment of the most important technical and economical factors affecting an investment in gas turbine combined cycle and a steam cycle fired by wood chips. The study is performed using a simulation model that optimises the operation of existing power plants and potential new investments to fulfil the desired heat demand. The local utility system analysed is a Swedish district heating system with 655 GWh y -1 heat demand. The conclusion is that to understand which of the technical and economical factors affect the investment, it is not sufficient to investigate the parameters of the studied plant, but also the parameters related to the competing plants. Both the individual effects of the factors and the effect of their interaction should be investigated. For the energy system studied the price of natural gas, price of wood chips and investment cost have the major influence on the profitability of the investment. (Author)

  1. Validity of observer ratings of the five-factor model of personality traits: a meta-analysis.

    Science.gov (United States)

    Oh, In-Sue; Wang, Gang; Mount, Michael K

    2011-07-01

    Conclusions reached in previous research about the magnitude and nature of personality-performance linkages have been based almost exclusively on self-report measures of personality. The purpose of this study is to address this void in the literature by conducting a meta-analysis of the relationship between observer ratings of the five-factor model (FFM) personality traits and overall job performance. Our results show that the operational validities of FFM traits based on observer ratings are higher than those based on self-report ratings. In addition, the results show that when based on observer ratings, all FFM traits are significant predictors of overall performance. Further, observer ratings of FFM traits show meaningful incremental validity over self-reports of corresponding FFM traits in predicting overall performance, but the reverse is not true. We conclude that the validity of FFM traits in predicting overall performance is higher than previously believed, and our results underscore the importance of disentangling the validity of personality traits from the method of measurement of the traits.

  2. Evaluation of chemical transport model predictions of primary organic aerosol for air masses classified by particle component-based factor analysis

    Directory of Open Access Journals (Sweden)

    C. A. Stroud

    2012-09-01

    Full Text Available Observations from the 2007 Border Air Quality and Meteorology Study (BAQS-Met 2007 in Southern Ontario, Canada, were used to evaluate predictions of primary organic aerosol (POA and two other carbonaceous species, black carbon (BC and carbon monoxide (CO, made for this summertime period by Environment Canada's AURAMS regional chemical transport model. Particle component-based factor analysis was applied to aerosol mass spectrometer measurements made at one urban site (Windsor, ON and two rural sites (Harrow and Bear Creek, ON to derive hydrocarbon-like organic aerosol (HOA factors. A novel diagnostic model evaluation was performed by investigating model POA bias as a function of HOA mass concentration and indicator ratios (e.g. BC/HOA. Eight case studies were selected based on factor analysis and back trajectories to help classify model bias for certain POA source types. By considering model POA bias in relation to co-located BC and CO biases, a plausible story is developed that explains the model biases for all three species.

    At the rural sites, daytime mean PM1 POA mass concentrations were under-predicted compared to observed HOA concentrations. POA under-predictions were accentuated when the transport arriving at the rural sites was from the Detroit/Windsor urban complex and for short-term periods of biomass burning influence. Interestingly, the daytime CO concentrations were only slightly under-predicted at both rural sites, whereas CO was over-predicted at the urban Windsor site with a normalized mean bias of 134%, while good agreement was observed at Windsor for the comparison of daytime PM1 POA and HOA mean values, 1.1 μg m−3 and 1.2 μg m−3, respectively. Biases in model POA predictions also trended from positive to negative with increasing HOA values. Periods of POA over-prediction were most evident at the urban site on calm nights due to an overly-stable model surface layer

  3. Driven Factors Analysis of China’s Irrigation Water Use Efficiency by Stepwise Regression and Principal Component Analysis

    Directory of Open Access Journals (Sweden)

    Renfu Jia

    2016-01-01

    Full Text Available This paper introduces an integrated approach to find out the major factors influencing efficiency of irrigation water use in China. It combines multiple stepwise regression (MSR and principal component analysis (PCA to obtain more realistic results. In real world case studies, classical linear regression model often involves too many explanatory variables and the linear correlation issue among variables cannot be eliminated. Linearly correlated variables will cause the invalidity of the factor analysis results. To overcome this issue and reduce the number of the variables, PCA technique has been used combining with MSR. As such, the irrigation water use status in China was analyzed to find out the five major factors that have significant impacts on irrigation water use efficiency. To illustrate the performance of the proposed approach, the calculation based on real data was conducted and the results were shown in this paper.

  4. Confirmatory factors analysis of science teacher leadership in the Thailand world-class standard schools

    Science.gov (United States)

    Thawinkarn, Dawruwan

    2018-01-01

    This research aims to analyze factors of science teacher leadership in the Thailand World-Class Standard Schools. The research instrument was a five scale rating questionnaire with reliability 0.986. The sample group included 500 science teachers from World-Class Standard Schools who had been selected by using the stratified random sampling technique. Factor analysis of science teacher leadership in the Thailand World-Class Standard Schools was conducted by using M plus for Windows. The results are as follows: The results of confirmatory factor analysis on science teacher leadership in the Thailand World-Class Standard Schools revealed that the model significantly correlated with the empirical data. The consistency index value was x2 = 105.655, df = 88, P-Value = 0.086, TLI = 0.997, CFI = 0.999, RMSEA = 0.022, and SRMR = 0.019. The value of factor loading of science teacher leadership was positive, with statistical significance at the level of 0.01. The value of six factors was between 0.880-0.996. The highest factor loading was the professional learning community, followed by child-centered instruction, participation in development, the role model in teaching, transformational leaders, and self-development with factor loading at 0.996, 0.928, 0.911, 0.907, 0.901, and 0.871, respectively. The reliability of each factor was 99.1%, 86.0%, 83.0%, 82.2%, 81.0%, and 75.8%, respectively.

  5. Worry About Caregiving Performance: A Confirmatory Factor Analysis

    Directory of Open Access Journals (Sweden)

    Ruijie Li

    2018-03-01

    Full Text Available Recent studies on the Zarit Burden Interview (ZBI support the existence of a unique factor, worry about caregiving performance (WaP, beyond role and personal strain. Our current study aims to confirm the existence of WaP within the multidimensionality of ZBI and to determine if predictors of WaP differ from the role and personal strain. We performed confirmatory factor analysis (CFA on 466 caregiver-patient dyads to compare between one-factor (total score, two-factor (role/personal strain, three-factor (role/personal strain and WaP, and four-factor models (role strain split into two factors. We conducted linear regression analyses to explore the relationships between different ZBI factors with socio-demographic and disease characteristics, and investigated the stage-dependent differences between WaP with role and personal strain by dyadic relationship. The four-factor structure that incorporated WaP and split role strain into two factors yielded the best fit. Linear regression analyses reveal that different variables significantly predict WaP (adult child caregiver and Neuropsychiatric Inventory Questionnaire (NPI-Q severity from role/personal strain (adult child caregiver, instrumental activities of daily living, and NPI-Q distress. Unlike other factors, WaP was significantly endorsed in early cognitive impairment. Among spouses, WaP remained low across Clinical Dementia Rating (CDR stages until a sharp rise in CDR 3; adult child and sibling caregivers experience a gradual rise throughout the stages. Our results affirm the existence of WaP as a unique factor. Future research should explore the potential of WaP as a possible intervention target to improve self-efficacy in the milder stages of burden.

  6. Are Fit Indices Biased in Favor of Bi-Factor Models in Cognitive Ability Research?: A Comparison of Fit in Correlated Factors, Higher-Order, and Bi-Factor Models via Monte Carlo Simulations

    Directory of Open Access Journals (Sweden)

    Grant B. Morgan

    2015-02-01

    Full Text Available Bi-factor confirmatory factor models have been influential in research on cognitive abilities because they often better fit the data than correlated factors and higher-order models. They also instantiate a perspective that differs from that offered by other models. Motivated by previous work that hypothesized an inherent statistical bias of fit indices favoring the bi-factor model, we compared the fit of correlated factors, higher-order, and bi-factor models via Monte Carlo methods. When data were sampled from a true bi-factor structure, each of the approximate fit indices was more likely than not to identify the bi-factor solution as the best fitting. When samples were selected from a true multiple correlated factors structure, approximate fit indices were more likely overall to identify the correlated factors solution as the best fitting. In contrast, when samples were generated from a true higher-order structure, approximate fit indices tended to identify the bi-factor solution as best fitting. There was extensive overlap of fit values across the models regardless of true structure. Although one model may fit a given dataset best relative to the other models, each of the models tended to fit the data well in absolute terms. Given this variability, models must also be judged on substantive and conceptual grounds.

  7. Selection of asset investment models by hospitals: examination of influencing factors, using Switzerland as an example.

    Science.gov (United States)

    Eicher, Bernhard

    2016-10-01

    Hospitals are responsible for a remarkable part of the annual increase in healthcare expenditure. This article examines one of the major cost drivers, the expenditure for investment in hospital assets. The study, conducted in Switzerland, identifies factors that influence hospitals' investment decisions. A suggestion on how to categorize asset investment models is presented based on the life cycle of an asset, and its influencing factors defined based on transaction cost economics. The influence of five factors (human asset specificity, physical asset specificity, uncertainty, bargaining power, and privacy of ownership) on the selection of an asset investment model is examined using a two-step fuzzy-set Qualitative Comparative Analysis. The research shows that outsourcing-oriented asset investment models are particularly favored in the presence of two combinations of influencing factors: First, if technological uncertainty is high and both human asset specificity and bargaining power of a hospital are low. Second, if assets are very specific, technological uncertainty is high and there is a private hospital with low bargaining power, outsourcing-oriented asset investment models are favored too. Using Qualitative Comparative Analysis, it can be demonstrated that investment decisions of hospitals do not depend on isolated influencing factors but on a combination of factors. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

  8. Model-based human reliability analysis: prospects and requirements

    International Nuclear Information System (INIS)

    Mosleh, A.; Chang, Y.H.

    2004-01-01

    Major limitations of the conventional methods for human reliability analysis (HRA), particularly those developed for operator response analysis in probabilistic safety assessments (PSA) of nuclear power plants, are summarized as a motivation for the need and a basis for developing requirements for the next generation HRA methods. It is argued that a model-based approach that provides explicit cognitive causal links between operator behaviors and directly or indirectly measurable causal factors should be at the core of the advanced methods. An example of such causal model is briefly reviewed, where due to the model complexity and input requirements can only be currently implemented in a dynamic PSA environment. The computer simulation code developed for this purpose is also described briefly, together with current limitations in the models, data, and the computer implementation

  9. Global Sensitivity Analysis of Environmental Models: Convergence, Robustness and Validation

    Science.gov (United States)

    Sarrazin, Fanny; Pianosi, Francesca; Khorashadi Zadeh, Farkhondeh; Van Griensven, Ann; Wagener, Thorsten

    2015-04-01

    Global Sensitivity Analysis aims to characterize the impact that variations in model input factors (e.g. the parameters) have on the model output (e.g. simulated streamflow). In sampling-based Global Sensitivity Analysis, the sample size has to be chosen carefully in order to obtain reliable sensitivity estimates while spending computational resources efficiently. Furthermore, insensitive parameters are typically identified through the definition of a screening threshold: the theoretical value of their sensitivity index is zero but in a sampling-base framework they regularly take non-zero values. There is little guidance available for these two steps in environmental modelling though. The objective of the present study is to support modellers in making appropriate choices, regarding both sample size and screening threshold, so that a robust sensitivity analysis can be implemented. We performed sensitivity analysis for the parameters of three hydrological models with increasing level of complexity (Hymod, HBV and SWAT), and tested three widely used sensitivity analysis methods (Elementary Effect Test or method of Morris, Regional Sensitivity Analysis, and Variance-Based Sensitivity Analysis). We defined criteria based on a bootstrap approach to assess three different types of convergence: the convergence of the value of the sensitivity indices, of the ranking (the ordering among the parameters) and of the screening (the identification of the insensitive parameters). We investigated the screening threshold through the definition of a validation procedure. The results showed that full convergence of the value of the sensitivity indices is not necessarily needed to rank or to screen the model input factors. Furthermore, typical values of the sample sizes that are reported in the literature can be well below the sample sizes that actually ensure convergence of ranking and screening.

  10. Comparison of Transcription Factor Binding Site Models

    KAUST Repository

    Bhuyan, Sharifulislam

    2012-05-01

    Modeling of transcription factor binding sites (TFBSs) and TFBS prediction on genomic sequences are important steps to elucidate transcription regulatory mechanism. Dependency of transcription regulation on a great number of factors such as chemical specificity, molecular structure, genomic and epigenetic characteristics, long distance interaction, makes this a challenging problem. Different experimental procedures generate evidence that DNA-binding domains of transcription factors show considerable DNA sequence specificity. Probabilistic modeling of TFBSs has been moderately successful in identifying patterns from a family of sequences. In this study, we compare performances of different probabilistic models and try to estimate their efficacy over experimental TFBSs data. We build a pipeline to calculate sensitivity and specificity from aligned TFBS sequences for several probabilistic models, such as Markov chains, hidden Markov models, Bayesian networks. Our work, containing relevant statistics and evaluation for the models, can help researchers to choose the most appropriate model for the problem at hand.

  11. Factor structure and internal reliability of an exercise health belief model scale in a Mexican population

    Directory of Open Access Journals (Sweden)

    Oscar Armando Esparza-Del Villar

    2017-03-01

    Full Text Available Abstract Background Mexico is one of the countries with the highest rates of overweight and obesity around the world, with 68.8% of men and 73% of women reporting both. This is a public health problem since there are several health related consequences of not exercising, like having cardiovascular diseases or some types of cancers. All of these problems can be prevented by promoting exercise, so it is important to evaluate models of health behaviors to achieve this goal. Among several models the Health Belief Model is one of the most studied models to promote health related behaviors. This study validates the first exercise scale based on the Health Belief Model (HBM in Mexicans with the objective of studying and analyzing this model in Mexico. Methods Items for the scale called the Exercise Health Belief Model Scale (EHBMS were developed by a health research team, then the items were applied to a sample of 746 participants, male and female, from five cities in Mexico. The factor structure of the items was analyzed with an exploratory factor analysis and the internal reliability with Cronbach’s alpha. Results The exploratory factor analysis reported the expected factor structure based in the HBM. The KMO index (0.92 and the Barlett’s sphericity test (p < 0.01 indicated an adequate and normally distributed sample. Items had adequate factor loadings, ranging from 0.31 to 0.92, and the internal consistencies of the factors were also acceptable, with alpha values ranging from 0.67 to 0.91. Conclusions The EHBMS is a validated scale that can be used to measure exercise based on the HBM in Mexican populations.

  12. An analysis of a three-factor model proposed by the Danish Society of Actuaries for forecasting and risk analysis

    DEFF Research Database (Denmark)

    Jørgensen, Peter Løchte; Slipsager, Søren Kærgaard

    2016-01-01

    This paper provides the explicit solution to the three-factor diffusion model recently proposed by the Danish Society of Actuaries to the Danish industry of life insurance and pensions. The solution is obtained by use of the known general solution to multidimensional linear stochastic differential...

  13. Parametric Analysis of Flexible Logic Control Model

    Directory of Open Access Journals (Sweden)

    Lihua Fu

    2013-01-01

    Full Text Available Based on deep analysis about the essential relation between two input variables of normal two-dimensional fuzzy controller, we used universal combinatorial operation model to describe the logic relationship and gave a flexible logic control method to realize the effective control for complex system. In practical control application, how to determine the general correlation coefficient of flexible logic control model is a problem for further studies. First, the conventional universal combinatorial operation model has been limited in the interval [0,1]. Consequently, this paper studies a kind of universal combinatorial operation model based on the interval [a,b]. And some important theorems are given and proved, which provide a foundation for the flexible logic control method. For dealing reasonably with the complex relations of every factor in complex system, a kind of universal combinatorial operation model with unequal weights is put forward. Then, this paper has carried out the parametric analysis of flexible logic control model. And some research results have been given, which have important directive to determine the values of the general correlation coefficients in practical control application.

  14. Analysis of factors important for the occurrence of Campylobacter in Danish broiler flocks

    DEFF Research Database (Denmark)

    Sommer, Helle Mølgaard; Heuer, Ole Eske; Sørensen, Anna Irene Vedel

    2013-01-01

    a multivariate analysis including all 43 variables. A multivariate analysis was conducted using a generalized linear model, and the correlations between the houses from the same farms were accounted for by adding a variance structure to the model. The procedures for analyses included backward elimination...... of positive flocks/total number of flocks delivered over the 2-year period).The following factors were found to be significantly associated with the occurrence of Campylobacter in the broiler flocks: old broiler houses, late introduction of whole wheat in the feed, relatively high broiler age at slaughter...

  15. Reformulation of Crop and Management Factor in ANSWERS Model

    Directory of Open Access Journals (Sweden)

    Yayat Hidayat

    2008-05-01

    Full Text Available Crop and management factor value is significantly corelated with outputs of ANSWERS model especially on soil erosion. Using daily crop and management factors (daily C factors, the ANSWERS model performs well in predicting soil erosion which is showed by determination coeffient (R2 = 0.89, model efficiency (0.86, and average of percentage model deviations (24.1%. Whereas using USLE C factor (2 cropping systems, predicted is much higher than measured soil erosion (over estimate. Output of the model is not statisfy; it is represented by model coefficient (0.40 and average of percentage model deviations (63.6%.

  16. Factor analysis for imperfect maintenance planning at nuclear power plants by cognitive task analysis

    International Nuclear Information System (INIS)

    Takagawa, Kenichi; Iida, Hiroyasu

    2011-01-01

    Imperfect maintenance planning was frequently identified in domestic nuclear power plants. To prevent such an event, we analyzed causal factors in maintenance planning stages and showed the directionality of countermeasures in this study. There is a pragmatic limit in finding the causal factors from the items based on report descriptions. Therefore, the idea of the systemic accident model, which is used to monitor the performance variability in normal circumstances, is taken as a new concept instead of investigating negative factors. As an actual method for analyzing usual activities, cognitive task analysis (CTA) was applied. Persons who experienced various maintenance activities at one electric power company were interviewed about sources related to decision making during maintenance planning, and then usual factors affecting planning were extracted as performance variability factors. The tendency of domestic events was analyzed using the classification item of those factors, and the directionality of countermeasures was shown. The following are critical for preventing imperfect maintenance planning: the persons in charge should fully understand the situation of the equipment for which they are responsible in the work planning and maintenance evaluation stages, and they should definitely understand, for example, the maintenance bases of that equipment. (author)

  17. Phoenix – A model-based Human Reliability Analysis methodology: Qualitative Analysis Procedure

    International Nuclear Information System (INIS)

    Ekanem, Nsimah J.; Mosleh, Ali; Shen, Song-Hua

    2016-01-01

    Phoenix method is an attempt to address various issues in the field of Human Reliability Analysis (HRA). Built on a cognitive human response model, Phoenix incorporates strong elements of current HRA good practices, leverages lessons learned from empirical studies, and takes advantage of the best features of existing and emerging HRA methods. Its original framework was introduced in previous publications. This paper reports on the completed methodology, summarizing the steps and techniques of its qualitative analysis phase. The methodology introduces the “Crew Response Tree” which provides a structure for capturing the context associated with Human Failure Events (HFEs), including errors of omission and commission. It also uses a team-centered version of the Information, Decision and Action cognitive model and “macro-cognitive” abstractions of crew behavior, as well as relevant findings from cognitive psychology literature and operating experience, to identify potential causes of failures and influencing factors during procedure-driven and knowledge-supported crew-plant interactions. The result is the set of identified HFEs and likely scenarios leading to each. The methodology itself is generic in the sense that it is compatible with various quantification methods, and can be adapted for use across different environments including nuclear, oil and gas, aerospace, aviation, and healthcare. - Highlights: • Produces a detailed, consistent, traceable, reproducible and properly documented HRA. • Uses “Crew Response Tree” to capture context associated with Human Failure Events. • Models dependencies between Human Failure Events and influencing factors. • Provides a human performance model for relating context to performance. • Provides a framework for relating Crew Failure Modes to its influencing factors.

  18. Model of separated form factors for unilamellar vesicles

    International Nuclear Information System (INIS)

    Kiselev, M.A.; Aksenov, V.L.; Lesieur, P.; Lombardo, D.; Kiselev, A.M.

    2001-01-01

    A new model of separated form factors is proposed for the evaluation of small-angle neutron scattering curves from large unilamellar vesicles. The validity of the model was checked via comparison with the model of a hollow sphere. The model of separated form factors and the hollow sphere model give a reasonable agreement in the evaluation of vesicle parameters

  19. PENGUJIAN FAMA-FRENCH THREE-FACTOR MODEL DI INDONESIA

    Directory of Open Access Journals (Sweden)

    Damar Hardianto

    2017-03-01

    Full Text Available This study empirically examined the Fama-French three factor model of stock returnsfor Indonesia over the period 2000-2004. We found evidence for pervasive market, size, andbook-to-market factors in Indonesian stock returns. We found that cross-sectional mean returnswere explained by exposures to these three factors, and not by the market factor alone. Theempirical results were reasonably consistent with the Fama-French three factor model.

  20. Confirmatory Factor Analysis of the School-Based Assessment Evaluation Scale Among Teachers

    Directory of Open Access Journals (Sweden)

    Nor Hasnida Che Md. Ghazali

    2016-09-01

    Full Text Available The school-based assessment (SBA system is a holistic assessment system that is conducted in schools by subject teachers in assessing the students cognitive (intellect, affective (emotional and spiritual and psychomotor (physical aspects. It is in line with the National Philosophy of Education and the Standards-based School Curriculum in Malaysia. In order to evaluate the implementation of SBA, a measurement scale was validated. Questionnaire was used as an instrument for data collection. 776 primary and secondary school teachers were selected as respondents using stratified random sampling. The data was analyzed with SPSS and AMOS version 18. The aim of this paper was to explore different factor structures of the SBA evaluation scale by using the second-order Confirmatory Factor Analysis. Results indicated that the SBA evaluation model was a valid and reliable scale. The input measurement model was validated with two factors (personnel qualifications and physical infrastructure, process measurement model was validated with six factors (‘attitude’, ‘understanding’, ‘skills’, ‘challenges’, ‘moderation’ and ‘monitoring’ and product measurement model was validated with two factors (‘students’ attitude’ and ‘students’ motivation’. This study provides support for using a valid instrument in evaluating the implementation of SBA in schools. Furthermore, the CFA procedures used supported the conceptual framework set out earlier. Thus, it presents clearly the importance of the evaluation process of any education system to follow all the dimensions outlined in the evaluation model proposed by Daniel Stufflebeam.       Sistem Penilaian Berbasis Sekolah (SBA adalah sistem penilaian holistik yang dilakukan di sekolah-sekolah oleh guru mata pelajaran dalam menilai kognitif (kecerdasan, afektif (emosional dan spiritual dan psikomotorik (fisik siswa. Hal ini sejalan dengan Filsafat Pendidikan Nasional dan Kurikulum

  1. The Five-Factor Model of Personality and Borderline Personality Disorder: A Genetic Analysis of Comorbidity

    NARCIS (Netherlands)

    Distel, M.A.; Trull, T.J.; Willemsen, G.; Vink, J.M.; Derom, C.A.; Lynskey, M.; Martin, N.G.; Boomsma, D.I.

    2009-01-01

    Background: Recently, the nature of personality disorders and their relationship with normal personality traits has received extensive attention. The five-factor model (FFM) of personality, consisting of the personality traits neuroticism, extraversion, openness to experience, agreeableness, and

  2. Global sensitivity analysis applied to drying models for one or a population of granules

    DEFF Research Database (Denmark)

    Mortier, Severine Therese F. C.; Gernaey, Krist; Thomas, De Beer

    2014-01-01

    The development of mechanistic models for pharmaceutical processes is of increasing importance due to a noticeable shift toward continuous production in the industry. Sensitivity analysis is a powerful tool during the model building process. A global sensitivity analysis (GSA), exploring sensitiv......The development of mechanistic models for pharmaceutical processes is of increasing importance due to a noticeable shift toward continuous production in the industry. Sensitivity analysis is a powerful tool during the model building process. A global sensitivity analysis (GSA), exploring...... sensitivity in a broad parameter space, is performed to detect the most sensitive factors in two models, that is, one for drying of a single granule and one for the drying of a population of granules [using population balance model (PBM)], which was extended by including the gas velocity as extra input...... compared to our earlier work. beta(2) was found to be the most important factor for the single particle model which is useful information when performing model calibration. For the PBM-model, the granule radius and gas temperature were found to be most sensitive. The former indicates that granulator...

  3. Factor Copula Models for Replicated Spatial Data

    KAUST Repository

    Krupskii, Pavel

    2016-12-19

    We propose a new copula model that can be used with replicated spatial data. Unlike the multivariate normal copula, the proposed copula is based on the assumption that a common factor exists and affects the joint dependence of all measurements of the process. Moreover, the proposed copula can model tail dependence and tail asymmetry. The model is parameterized in terms of a covariance function that may be chosen from the many models proposed in the literature, such as the Matérn model. For some choice of common factors, the joint copula density is given in closed form and therefore likelihood estimation is very fast. In the general case, one-dimensional numerical integration is needed to calculate the likelihood, but estimation is still reasonably fast even with large data sets. We use simulation studies to show the wide range of dependence structures that can be generated by the proposed model with different choices of common factors. We apply the proposed model to spatial temperature data and compare its performance with some popular geostatistics models.

  4. Factor Copula Models for Replicated Spatial Data

    KAUST Repository

    Krupskii, Pavel; Huser, Raphaë l; Genton, Marc G.

    2016-01-01

    We propose a new copula model that can be used with replicated spatial data. Unlike the multivariate normal copula, the proposed copula is based on the assumption that a common factor exists and affects the joint dependence of all measurements of the process. Moreover, the proposed copula can model tail dependence and tail asymmetry. The model is parameterized in terms of a covariance function that may be chosen from the many models proposed in the literature, such as the Matérn model. For some choice of common factors, the joint copula density is given in closed form and therefore likelihood estimation is very fast. In the general case, one-dimensional numerical integration is needed to calculate the likelihood, but estimation is still reasonably fast even with large data sets. We use simulation studies to show the wide range of dependence structures that can be generated by the proposed model with different choices of common factors. We apply the proposed model to spatial temperature data and compare its performance with some popular geostatistics models.

  5. Assessment of slip factor models at off-design condition

    International Nuclear Information System (INIS)

    Yoon, Sung Ho; Baek, Je Hyun

    2000-01-01

    Slip factor is defined as an empirical factor being multiplied to theoretical energy transfer for the estimation of real work input of a centrifugal compressor. Researchers have tried to develop a simple empirical model, for a century, to predict a slip factor. However most these models were developed on the condition of design point assuming inviscid flow. So these models often fail to predict a correct slip factor at off-design condition. In this study, we summarized various slip factor models and compared these models with experimental and numerical data at off-design condition. As a result of this study, Wiesner's and Paeng and Chung's models are applicable for radial impeller, but all the models are not suitable for backswept impeller. Finally, the essential avenues for future study is discussed

  6. Dynamic Factor Analysis of Nonstationary Multivariate Time Series.

    Science.gov (United States)

    Molenaar, Peter C. M.; And Others

    1992-01-01

    The dynamic factor model proposed by P. C. Molenaar (1985) is exhibited, and a dynamic nonstationary factor model (DNFM) is constructed with latent factor series that have time-varying mean functions. The use of a DNFM is illustrated using data from a television viewing habits study. (SLD)

  7. The modeling and analysis of the word-of-mouth marketing

    Science.gov (United States)

    Li, Pengdeng; Yang, Xiaofan; Yang, Lu-Xing; Xiong, Qingyu; Wu, Yingbo; Tang, Yuan Yan

    2018-03-01

    As compared to the traditional advertising, word-of-mouth (WOM) communications have striking advantages such as significantly lower cost and much faster propagation, and this is especially the case with the popularity of online social networks. This paper focuses on the modeling and analysis of the WOM marketing. A dynamic model, known as the SIPNS model, capturing the WOM marketing processes with both positive and negative comments is established. On this basis, a measure of the overall profit of a WOM marketing campaign is proposed. The SIPNS model is shown to admit a unique equilibrium, and the equilibrium is determined. The impact of different factors on the equilibrium of the SIPNS model is illuminated through theoretical analysis. Extensive experimental results suggest that the equilibrium is much likely to be globally attracting. Finally, the influence of different factors on the expected overall profit of a WOM marketing campaign is ascertained both theoretically and experimentally. Thereby, some promotion strategies are recommended. To our knowledge, this is the first time the WOM marketing is treated in this way.

  8. The Butterfly Effect: Correlations Between Modeling in Nuclear-Particle Physics and Socioeconomic Factors

    CERN Document Server

    Pia, Maria Grazia; Bell, Zane W.; Dressendorfer, Paul V.

    2010-01-01

    A scientometric analysis has been performed on selected physics journals to estimate the presence of simulation and modeling in physics literature in the past fifty years. Correlations between the observed trends and several social and economical factors have been evaluated.

  9. Latent class factor and cluster models, bi-plots and tri-plots and related graphical displays

    NARCIS (Netherlands)

    Magidson, J.; Vermunt, J.K.

    2001-01-01

    We propose an alternative method of conducting exploratory latent class analysis that utilizes latent class factor models, and compare it to the more traditional approach based on latent class cluster models. We show that when formulated in terms of R mutually independent, dichotomous latent

  10. The recovery factors analysis of the human errors for research reactors

    International Nuclear Information System (INIS)

    Farcasiu, M.; Nitoi, M.; Apostol, M.; Turcu, I.; Florescu, Ghe.

    2006-01-01

    The results of many Probabilistic Safety Assessment (PSA) studies show a very significant contribution of human errors to systems unavailability of the nuclear installations. The treatment of human interactions is considered one of the major limitations in the context of PSA. To identify those human actions that can have an effect on system reliability or availability applying the Human Reliability Analysis (HRA) is necessary. The recovery factors analysis of the human action is an important step in HRA. This paper presents how can be reduced the human errors probabilities (HEP) using those elements that have the capacity to recovery human error. The recovery factors modeling is marked to identify error likelihood situations or situations that conduct at development of the accident. This analysis is realized by THERP method. The necessary information was obtained from the operating experience of the research reactor TRIGA of the INR Pitesti. The required data were obtained from generic databases. (authors)

  11. Confirmation of the three-factor model of problematic internet use on off-line adolescent and adult samples.

    Science.gov (United States)

    Koronczai, Beatrix; Urbán, Róbert; Kökönyei, Gyöngyi; Paksi, Borbála; Papp, Krisztina; Kun, Bernadette; Arnold, Petra; Kállai, János; Demetrovics, Zsolt

    2011-11-01

    As the Internet became widely used, problems associated with its excessive use became increasingly apparent. Although for the assessment of these problems several models and related questionnaires have been elaborated, there has been little effort made to confirm them. The aim of the present study was to test the three-factor model of the previously created Problematic Internet Use Questionnaire (PIUQ) by data collection methods formerly not applied (off-line group and face-to-face settings), on the one hand, and by testing on different age groups (adolescent and adult representative samples), on the other hand. Data were collected from 438 high-school students (44.5 percent boys; mean age: 16.0 years; standard deviation=0.7 years) and also from 963 adults (49.9 percent males; mean age: 33.6 years; standard deviation=11.8 years). We applied confirmatory factor analysis to confirm the measurement model of problematic Internet use. The results of the analyses carried out inevitably support the original three-factor model over the possible one-factor solution. Using latent profile analysis, we identified 11 percent of adults and 18 percent of adolescent users characterized by problematic use. Based on exploratory factor analysis, we also suggest a short form of the PIUQ consisting of nine items. Both the original 18-item version of PIUQ and its short 9-item form have satisfactory reliability and validity characteristics, and thus, they are suitable for the assessment of problematic Internet use in future studies.

  12. Modeling and Analysis of the Motivations of Fast Fashion Consumers in Relation to Innovativeness

    Directory of Open Access Journals (Sweden)

    Saricam Canan

    2016-12-01

    Full Text Available In this study, fast fashion concept is investigated in order to understand the motivations of the consumers that make them adopt these products because of their willingness for the innovativeness. The relationship between the motivational factors which were named as “Social or status image” and “Uniqueness” as expressions of individuality, “Conformity” and the willingness for “Innovativeness” is analyzed using a conceptual model. Exploratory factor analysis, confirmatory factor analysis and structural equation modeling were used to analyze and validate the model. The data used for the study was obtained from 244 people living in Turkey. The findings showed that the motivational factors “Social or status image” and “Uniqueness” as expressions of individuality are influential on the consumers’ willingness for “Innovativeness”.

  13. Confirmatory Factor Analysis of U.S. Merit Systems Protection Board's Survey of Sexual Harassment: The Fit of a Three-Factor Model.

    Science.gov (United States)

    Stockdale, Margaret S.; Hope, Kathryn G.

    1997-01-01

    Factor analysis of data from 1,070 federal employees, 575 undergraduates and 575 graduate students, faculty, and staff uncovered some weaknesses in the Merit Systems Protection Board's sexual harassment survey instrument. This type of survey does not adequately measure sexual coercion or quid pro quo forms of harassment. (SK)

  14. ANALYSIS MODEL FOR INVENTORY MANAGEMENT

    Directory of Open Access Journals (Sweden)

    CAMELIA BURJA

    2010-01-01

    Full Text Available The inventory represents an essential component for the assets of the enterprise and the economic analysis gives them special importance because their accurate management determines the achievement of the activity object and the financial results. The efficient management of inventory requires ensuring an optimum level for them, which will guarantee the normal functioning of the activity with minimum inventory expenses and funds which are immobilised. The paper presents an analysis model for inventory management based on their rotation speed and the correlation with the sales volume illustrated in an adequate study. The highlighting of the influence factors on the efficient inventory management ensures the useful information needed to justify managerial decisions, which will lead to a balancedfinancial position and to increased company performance.

  15. Dispersion-theoretical analysis of the nucleon electromagnetic form factors

    Energy Technology Data Exchange (ETDEWEB)

    Belushkin, M.

    2007-09-29

    The structure of the proton and the neutron is of fundamental importance for the study of the strong interaction dynamics over a wide range of momentum transfers. The nucleon form factors encode information on the internal structure of the nucleon as probed by the electromagnetic interaction, and, to a certain extent, reflect the charge and magnetisation distributions within the proton and the neutron. In this thesis we report on our investigation of the electromagnetic form factors of the proton and the neutron with dispersion relation techniques, including known experimental input on the {pi}{pi}, K anti K and the {rho}{pi} continua and perturbative QCD constraints. We include new experimental data on the pion form factor and the nucleon form factors in our simultaneous analysis of all four form factors in both the space- and the timelike regions for all momentum transfers, and perform Monte- Carlo sampling in order to obtain theoretical uncertainty bands. Finally, we discuss the implications of our results on the pion cloud of the nucleon, the nucleon radii and the Okubo-Zweig-Iizuka rule, and present our results of a model-independent approach to estimating two-photon effects in elastic electron-proton scattering. (orig.)

  16. Dispersion-theoretical analysis of the nucleon electromagnetic form factors

    International Nuclear Information System (INIS)

    Belushkin, M.

    2007-01-01

    The structure of the proton and the neutron is of fundamental importance for the study of the strong interaction dynamics over a wide range of momentum transfers. The nucleon form factors encode information on the internal structure of the nucleon as probed by the electromagnetic interaction, and, to a certain extent, reflect the charge and magnetisation distributions within the proton and the neutron. In this thesis we report on our investigation of the electromagnetic form factors of the proton and the neutron with dispersion relation techniques, including known experimental input on the ππ, K anti K and the ρπ continua and perturbative QCD constraints. We include new experimental data on the pion form factor and the nucleon form factors in our simultaneous analysis of all four form factors in both the space- and the timelike regions for all momentum transfers, and perform Monte- Carlo sampling in order to obtain theoretical uncertainty bands. Finally, we discuss the implications of our results on the pion cloud of the nucleon, the nucleon radii and the Okubo-Zweig-Iizuka rule, and present our results of a model-independent approach to estimating two-photon effects in elastic electron-proton scattering. (orig.)

  17. The effect of choosing three different C factor formulae derived from NDVI on a fully raster-based erosion modelling

    Science.gov (United States)

    Sulistyo, Bambang

    2016-11-01

    The research was aimed at studying the efect of choosing three different C factor formulae derived from NDVI on a fully raster-based erosion modelling of The USLE using remote sensing data and GIS technique. Methods applied was by analysing all factors affecting erosion such that all data were in the form of raster. Those data were R, K, LS, C and P factors. Monthly R factor was evaluated based on formula developed by Abdurachman. K factor was determined using modified formula used by Ministry of Forestry based on soil samples taken in the field. LS factor was derived from Digital Elevation Model. Three C factors used were all derived from NDVI and developed by Suriyaprasit (non-linear) and by Sulistyo (linear and non-linear). P factor was derived from the combination between slope data and landcover classification interpreted from Landsat 7 ETM+. Another analysis was the creation of map of Bulk Density used to convert erosion unit. To know the model accuracy, model validation was done by applying statistical analysis and by comparing Emodel with Eactual. A threshold value of ≥ 0.80 or ≥ 80% was chosen to justify. The research result showed that all Emodel using three formulae of C factors have coeeficient of correlation value of > 0.8. The results of analysis of variance showed that there was significantly difference between Emodel and Eactual when using C factor formula developed by Suriyaprasit and Sulistyo (non-linear). Among the three formulae, only Emodel using C factor formula developed by Sulistyo (linear) reached the accuracy of 81.13% while the other only 56.02% as developed by Sulistyo (nonlinear) and 4.70% as developed by Suriyaprasit, respectively.

  18. Sensitivity analysis of the terrestrial food chain model FOOD III

    International Nuclear Information System (INIS)

    Zach, Reto.

    1980-10-01

    As a first step in constructing a terrestrial food chain model suitable for long-term waste management situations, a numerical sensitivity analysis of FOOD III was carried out to identify important model parameters. The analysis involved 42 radionuclides, four pathways, 14 food types, 93 parameters and three percentages of parameter variation. We also investigated the importance of radionuclides, pathways and food types. The analysis involved a simple contamination model to render results from individual pathways comparable. The analysis showed that radionuclides vary greatly in their dose contribution to each of the four pathways, but relative contributions to each pathway are very similar. Man's and animals' drinking water pathways are much more important than the leaf and root pathways. However, this result depends on the contamination model used. All the pathways contain unimportant food types. Considering the number of parameters involved, FOOD III has too many different food types. Many of the parameters of the leaf and root pathway are important. However, this is true for only a few of the parameters of animals' drinking water pathway, and for neither of the two parameters of mans' drinking water pathway. The radiological decay constant increases the variability of these results. The dose factor is consistently the most important variable, and it explains most of the variability of radionuclide doses within pathways. Consideration of the variability of dose factors is important in contemporary as well as long-term waste management assessment models, if realistic estimates are to be made. (auth)

  19. Bayesian inference of the number of factors in gene-expression analysis: application to human virus challenge studies

    Directory of Open Access Journals (Sweden)

    Hero Alfred

    2010-11-01

    Full Text Available Abstract Background Nonparametric Bayesian techniques have been developed recently to extend the sophistication of factor models, allowing one to infer the number of appropriate factors from the observed data. We consider such techniques for sparse factor analysis, with application to gene-expression data from three virus challenge studies. Particular attention is placed on employing the Beta Process (BP, the Indian Buffet Process (IBP, and related sparseness-promoting techniques to infer a proper number of factors. The posterior density function on the model parameters is computed using Gibbs sampling and variational Bayesian (VB analysis. Results Time-evolving gene-expression data are considered for respiratory syncytial virus (RSV, Rhino virus, and influenza, using blood samples from healthy human subjects. These data were acquired in three challenge studies, each executed after receiving institutional review board (IRB approval from Duke University. Comparisons are made between several alternative means of per-forming nonparametric factor analysis on these data, with comparisons as well to sparse-PCA and Penalized Matrix Decomposition (PMD, closely related non-Bayesian approaches. Conclusions Applying the Beta Process to the factor scores, or to the singular values of a pseudo-SVD construction, the proposed algorithms infer the number of factors in gene-expression data. For real data the "true" number of factors is unknown; in our simulations we consider a range of noise variances, and the proposed Bayesian models inferred the number of factors accurately relative to other methods in the literature, such as sparse-PCA and PMD. We have also identified a "pan-viral" factor of importance for each of the three viruses considered in this study. We have identified a set of genes associated with this pan-viral factor, of interest for early detection of such viruses based upon the host response, as quantified via gene-expression data.

  20. Bayesian inference of the number of factors in gene-expression analysis: application to human virus challenge studies.

    Science.gov (United States)

    Chen, Bo; Chen, Minhua; Paisley, John; Zaas, Aimee; Woods, Christopher; Ginsburg, Geoffrey S; Hero, Alfred; Lucas, Joseph; Dunson, David; Carin, Lawrence

    2010-11-09

    Nonparametric Bayesian techniques have been developed recently to extend the sophistication of factor models, allowing one to infer the number of appropriate factors from the observed data. We consider such techniques for sparse factor analysis, with application to gene-expression data from three virus challenge studies. Particular attention is placed on employing the Beta Process (BP), the Indian Buffet Process (IBP), and related sparseness-promoting techniques to infer a proper number of factors. The posterior density function on the model parameters is computed using Gibbs sampling and variational Bayesian (VB) analysis. Time-evolving gene-expression data are considered for respiratory syncytial virus (RSV), Rhino virus, and influenza, using blood samples from healthy human subjects. These data were acquired in three challenge studies, each executed after receiving institutional review board (IRB) approval from Duke University. Comparisons are made between several alternative means of per-forming nonparametric factor analysis on these data, with comparisons as well to sparse-PCA and Penalized Matrix Decomposition (PMD), closely related non-Bayesian approaches. Applying the Beta Process to the factor scores, or to the singular values of a pseudo-SVD construction, the proposed algorithms infer the number of factors in gene-expression data. For real data the "true" number of factors is unknown; in our simulations we consider a range of noise variances, and the proposed Bayesian models inferred the number of factors accurately relative to other methods in the literature, such as sparse-PCA and PMD. We have also identified a "pan-viral" factor of importance for each of the three viruses considered in this study. We have identified a set of genes associated with this pan-viral factor, of interest for early detection of such viruses based upon the host response, as quantified via gene-expression data.

  1. Assessing the Credit Risk of Corporate Bonds Based on Factor Analysis and Logistic Regress Analysis Techniques: Evidence from New Energy Enterprises in China

    Directory of Open Access Journals (Sweden)

    Yuanxin Liu

    2018-05-01

    Full Text Available In recent years, new energy sources have ushered in tremendous opportunities for development. The difficulties to finance new energy enterprises (NEEs can be estimated through issuing corporate bonds. However, there are few scientific and reasonable methods to assess the credit risk of NEE bonds, which is not conducive to the healthy development of NEEs. Based on this, this paper analyzes the advantages and risks of NEEs issuing bonds and the main factors affecting the credit risk of NEE bonds, constructs a hybrid model for assessing the credit risk of NEE bonds based on factor analysis and logistic regress analysis techniques, and verifies the applicability and effectiveness of the model employing relevant data from 46 Chinese NEEs. The results show that the main factors affecting the credit risk of NEE bonds are internal factors involving the company’s profitability, solvency, operational ability, growth potential, asset structure and viability, and external factors including macroeconomic environment and energy policy support. Based on the empirical results and the exact situation of China’s NEE bonds, this article finally puts forward several targeted recommendations.

  2. Efficient fully 3D list-mode TOF PET image reconstruction using a factorized system matrix with an image domain resolution model

    International Nuclear Information System (INIS)

    Zhou, Jian; Qi, Jinyi

    2014-01-01

    A factorized system matrix utilizing an image domain resolution model is attractive in fully 3D time-of-flight PET image reconstruction using list-mode data. In this paper, we study a factored model based on sparse matrix factorization that is comprised primarily of a simplified geometrical projection matrix and an image blurring matrix. Beside the commonly-used Siddon’s ray-tracer, we propose another more simplified geometrical projector based on the Bresenham’s ray-tracer which further reduces the computational cost. We discuss in general how to obtain an image blurring matrix associated with a geometrical projector, and provide theoretical analysis that can be used to inspect the efficiency in model factorization. In simulation studies, we investigate the performance of the proposed sparse factorization model in terms of spatial resolution, noise properties and computational cost. The quantitative results reveal that the factorization model can be as efficient as a non-factored model, while its computational cost can be much lower. In addition we conduct Monte Carlo simulations to identify the conditions under which the image resolution model can become more efficient in terms of image contrast recovery. We verify our observations using the provided theoretical analysis. The result offers a general guide to achieve the optimal reconstruction performance based on a sparse factorization model with an image domain resolution model. (paper)

  3. A model for website analysis and\tconception: the Website Canvas Model applied to\tEldiario.es

    Directory of Open Access Journals (Sweden)

    Carles Sanabre Vives

    2015-11-01

    Full Text Available This article presents the model of ideation and analysis called Website CanvasModel. It allows identifying the key aspects for a website to be successful, and shows how ithas been applied to Eldiario.es. As a result, the key factors prompting the success of thisdigital newspaper have been identified.

  4. A Second-Order Confirmatory Factor Analysis of the Moral Distress Scale-Revised for Nurses.

    Science.gov (United States)

    Sharif Nia, Hamid; Shafipour, Vida; Allen, Kelly-Ann; Heidari, Mohammad Reza; Yazdani-Charati, Jamshid; Zareiyan, Armin

    2017-01-01

    Moral distress is a growing problem for healthcare professionals that may lead to dissatisfaction, resignation, or occupational burnout if left unattended, and nurses experience different levels of this phenomenon. This study aims to investigate the factor structure of the Persian version of the Moral Distress Scale-Revised in intensive care and general nurses. This methodological research was conducted with 771 nurses from eight hospitals in the Mazandaran Province of Iran in 2017. Participants completed the Moral Distress Scale-Revised, data collected, and factor structure assessed using the construct, convergent, and divergent validity methods. The reliability of the scale was assessed using internal consistency (Cronbach's alpha, Theta, and McDonald's omega coefficients) and construct reliability. Ethical considerations: This study was approved by the Ethics Committee of Mazandaran University of Medical Sciences. The exploratory factor analysis ( N = 380) showed that the Moral Distress Scale-Revised has five factors: lack of professional competence at work, ignoring ethical issues and patient conditions, futile care, carrying out the physician's orders without question and unsafe care, and providing care under personal and organizational pressures, which explained 56.62% of the overall variance. The confirmatory factor analysis ( N = 391) supported the five-factor solution and the second-order latent factor model. The first-order model did not show a favorable convergent and divergent validity. Ultimately, the Moral Distress Scale-Revised was found to have a favorable internal consistency and construct reliability. The Moral Distress Scale-Revised was found to be a multidimensional construct. The data obtained confirmed the hypothesis of the factor structure model with a latent second-order variable. Since the convergent and divergent validity of the scale were not confirmed in this study, further assessment is necessary in future studies.

  5. Sparse Probabilistic Parallel Factor Analysis for the Modeling of PET and Task-fMRI Data

    DEFF Research Database (Denmark)

    Beliveau, Vincent; Papoutsakis, Georgios; Hinrich, Jesper Løve

    2017-01-01

    Modern datasets are often multiway in nature and can contain patterns common to a mode of the data (e.g. space, time, and subjects). Multiway decomposition such as parallel factor analysis (PARAFAC) take into account the intrinsic structure of the data, and sparse versions of these methods improv...

  6. Logistic regression analysis of risk factors for postoperative recurrence of spinal tumors and analysis of prognostic factors.

    Science.gov (United States)

    Zhang, Shanyong; Yang, Lili; Peng, Chuangang; Wu, Minfei

    2018-02-01

    The aim of the present study was to investigate the risk factors for postoperative recurrence of spinal tumors by logistic regression analysis and analysis of prognostic factors. In total, 77 male and 48 female patients with spinal tumor were selected in our hospital from January, 2010 to December, 2015 and divided into the benign (n=76) and malignant groups (n=49). All the patients underwent microsurgical resection of spinal tumors and were reviewed regularly 3 months after operation. The McCormick grading system was used to evaluate the postoperative spinal cord function. Data were subjected to statistical analysis. Of the 125 cases, 63 cases showed improvement after operation, 50 cases were stable, and deterioration was found in 12 cases. The improvement rate of patients with cervical spine tumor, which reached 56.3%, was the highest. Fifty-two cases of sensory disturbance, 34 cases of pain, 30 cases of inability to exercise, 26 cases of ataxia, and 12 cases of sphincter disorders were found after operation. Seventy-two cases (57.6%) underwent total resection, 18 cases (14.4%) received subtotal resection, 23 cases (18.4%) received partial resection, and 12 cases (9.6%) were only treated with biopsy/decompression. Postoperative recurrence was found in 57 cases (45.6%). The mean recurrence time of patients in the malignant group was 27.49±6.09 months, and the mean recurrence time of patients in the benign group was 40.62±4.34. The results were significantly different (Pregression analysis of total resection-related factors showed that total resection should be the preferred treatment for patients with benign tumors, thoracic and lumbosacral tumors, and lower McCormick grade, as well as patients without syringomyelia and intramedullary tumors. Logistic regression analysis of recurrence-related factors revealed that the recurrence rate was relatively higher in patients with malignant, cervical, thoracic and lumbosacral, intramedullary tumors, and higher Mc

  7. Testing alternative factor models of PTSD and the robustness of the dysphoria factor.

    Science.gov (United States)

    Elklit, Ask; Armour, Cherie; Shevlin, Mark

    2010-01-01

    This study first aimed to examine the structure of self-reported posttraumatic stress disorder (PTSD) symptoms using three different samples. The second aim of the paper was to test the robustness of the factor analytic model when depression scores were controlled for. Based on previous factor analytic findings and the DSM-IV formulation, six confirmatory factor models were specified and estimated that reflected different symptom clusters. The best fitting model was subsequently re-fitted to the data after including a depression variable. The analyses were based on responses from 973 participants across three samples. Sample 1 consisted of 633 parents who were members of 'The National Association of Infant Death' and who had lost a child. Sample 2 consisted of 227 victims of rape, who completed a questionnaire within 4 weeks of the rape. Each respondent had been in contact with the Centre for Rape Victims (CRV) at the Aarhus University Hospital, Denmark. Sample 3 consisted of 113 refugees resident in Denmark. All participants had been referred to a treatment centre which focused on rehabilitating refugees through treatment for psychosocial integration problems (RRCF: Rehabliterings og Revliderings Centre for Flygtninge). In total 500 participants received a diagnosis of PTSD/sub-clinical PTSD (Sample 1, N=214; 2, N=176; 3, N=110). A correlated four-factor model with re-experiencing, avoidance, dysphoria, and arousal factors provided the best fit to the sample data. The average attenuation in the factor loadings was highest for the dysphoria factor (M=-.26, SD=.11) compared to the re-experiencing (M=-.14, SD=.18), avoidance (M=-.10, SD=.21), and arousal (M=-.09, SD=.13) factors. With regards to the best fitting factor model these results concur with previous research findings using different trauma populations but do not reflect the current DSM-IV symptom groupings. The attenuation of dysphoria factor loadings suggests that dysphoria is a non-specific component of

  8. The asset pricing model of musharakah factors

    Science.gov (United States)

    Simon, Shahril; Omar, Mohd; Lazam, Norazliani Md

    2015-02-01

    The existing three-factor model developed by Fama and French for conventional investment was formulated based on risk-free rates element in which contradict with Shariah principles. We note that the underlying principles that govern Shariah investment were mutual risk and profit sharing between parties, the assurance of fairness for all and that transactions were based on an underlying asset. In addition, the three-factor model did not exclude stock that was not permissible by Shariah such as financial services based on riba (interest), gambling operator, manufacture or sale of non-halal products or related products and other activities deemed non-permissible according to Shariah. Our approach to construct the factor model for Shariah investment was based on the basic tenets of musharakah in tabulating the factors. We start by noting that Islamic stocks with similar characteristics should have similar returns and risks. This similarity between Islamic stocks was defined by the similarity of musharakah attributes such as business, management, profitability and capital. These attributes define factor exposures (or betas) to factors. The main takeaways were that musharakah attributes we chose had explain stock returns well in cross section and were significant in different market environments. The management factor seemed to be responsible for the general dynamics of the explanatory power.

  9. Physics Metacognition Inventory Part II: Confirmatory factor analysis and Rasch analysis

    Science.gov (United States)

    Taasoobshirazi, Gita; Bailey, MarLynn; Farley, John

    2015-11-01

    The Physics Metacognition Inventory was developed to measure physics students' metacognition for problem solving. In one of our earlier studies, an exploratory factor analysis provided evidence of preliminary construct validity, revealing six components of students' metacognition when solving physics problems including knowledge of cognition, planning, monitoring, evaluation, debugging, and information management. The college students' scores on the inventory were found to be reliable and related to students' physics motivation and physics grade. However, the results of the exploratory factor analysis indicated that the questionnaire could be revised to improve its construct validity. The goal of this study was to revise the questionnaire and establish its construct validity through a confirmatory factor analysis. In addition, a Rasch analysis was applied to the data to better understand the psychometric properties of the inventory and to further evaluate the construct validity. Results indicated that the final, revised inventory is a valid, reliable, and efficient tool for assessing student metacognition for physics problem solving.

  10. Familial aggregation and linkage analysis with covariates for metabolic syndrome risk factors.

    Science.gov (United States)

    Naseri, Parisa; Khodakarim, Soheila; Guity, Kamran; Daneshpour, Maryam S

    2018-06-15

    Mechanisms of metabolic syndrome (MetS) causation are complex, genetic and environmental factors are important factors for the pathogenesis of MetS In this study, we aimed to evaluate familial and genetic influences on metabolic syndrome risk factor and also assess association between FTO (rs1558902 and rs7202116) and CETP(rs1864163) genes' single nucleotide polymorphisms (SNP) with low HDL_C in the Tehran Lipid and Glucose Study (TLGS). The design was a cross-sectional study of 1776 members of 227 randomly-ascertained families. Selected families contained at least one affected metabolic syndrome and at least two members of the family had suffered a loss of HDL_C according to ATP III criteria. In this study, after confirming the familial aggregation with intra-trait correlation coefficients (ICC) of Metabolic syndrome (MetS) and the quantitative lipid traits, the genetic linkage analysis of HDL_C was performed using conditional logistic method with adjusted sex and age. The results of the aggregation analysis revealed a higher correlation between siblings than between parent-offspring pairs representing the role of genetic factors in MetS. In addition, the conditional logistic model with covariates showed that the linkage results between HDL_C and three marker, rs1558902, rs7202116 and rs1864163 were significant. In summary, a high risk of MetS was found in siblings confirming the genetic influences of metabolic syndrome risk factor. Moreover, the power to detect linkage increases in the one parameter conditional logistic model regarding the use of age and sex as covariates. Copyright © 2018. Published by Elsevier B.V.

  11. The ATLAS Analysis Model

    CERN Multimedia

    Amir Farbin

    The ATLAS Analysis Model is a continually developing vision of how to reconcile physics analysis requirements with the ATLAS offline software and computing model constraints. In the past year this vision has influenced the evolution of the ATLAS Event Data Model, the Athena software framework, and physics analysis tools. These developments, along with the October Analysis Model Workshop and the planning for CSC analyses have led to a rapid refinement of the ATLAS Analysis Model in the past few months. This article introduces some of the relevant issues and presents the current vision of the future ATLAS Analysis Model. Event Data Model The ATLAS Event Data Model (EDM) consists of several levels of details, each targeted for a specific set of tasks. For example the Event Summary Data (ESD) stores calorimeter cells and tracking system hits thereby permitting many calibration and alignment tasks, but will be only accessible at particular computing sites with potentially large latency. In contrast, the Analysis...

  12. Estimating the approximation error when fixing unessential factors in global sensitivity analysis

    Energy Technology Data Exchange (ETDEWEB)

    Sobol' , I.M. [Institute for Mathematical Modelling of the Russian Academy of Sciences, Moscow (Russian Federation); Tarantola, S. [Joint Research Centre of the European Commission, TP361, Institute of the Protection and Security of the Citizen, Via E. Fermi 1, 21020 Ispra (Italy)]. E-mail: stefano.tarantola@jrc.it; Gatelli, D. [Joint Research Centre of the European Commission, TP361, Institute of the Protection and Security of the Citizen, Via E. Fermi 1, 21020 Ispra (Italy)]. E-mail: debora.gatelli@jrc.it; Kucherenko, S.S. [Imperial College London (United Kingdom); Mauntz, W. [Department of Biochemical and Chemical Engineering, Dortmund University (Germany)

    2007-07-15

    One of the major settings of global sensitivity analysis is that of fixing non-influential factors, in order to reduce the dimensionality of a model. However, this is often done without knowing the magnitude of the approximation error being produced. This paper presents a new theorem for the estimation of the average approximation error generated when fixing a group of non-influential factors. A simple function where analytical solutions are available is used to illustrate the theorem. The numerical estimation of small sensitivity indices is discussed.

  13. Confirmatory factor analysis of the Neuropsychological Assessment Battery of the LADIS study

    DEFF Research Database (Denmark)

    Moleiro, Carla; Madureira, Sofia; Verdelho, Ana

    2013-01-01

    analysis (CFA) was used to investigate the dimensions of a structured set of neuropsychological tests administered to a multicenter, international sample of independent older adults (LADIS study). Six hundred and thirty-eight older adults completed baseline neuropsychological, clinical, functional...... and motor assessments, which were repeated each year for a 3-year follow-up. CFA provided support for a 3-factor model. These factors involve the dimensions of executive functions, memory functions, and speed and motor control abilities. Performance decreased in most neuropsychological measures. Results...

  14. Model analysis of the world data on the pion transition form factor

    International Nuclear Information System (INIS)

    Noguera, S.; Vento, V.

    2012-01-01

    We discuss the impact of recent Belle data on our description of the pion transition form factor based on the assumption that a perturbative formalism and a nonperturbative one can be matched in a physically acceptable manner at a certain hadronic scale Q 0 . We discuss the implications of the different parameters of the model in comparing with world data and conclude that within experimental errors our description remains valid. Thus we can assert that the low Q 2 nonperturbative description together with an additional 1/Q 2 term at the matching scale have a strong influence on the Q 2 behavior up to very high values of Q 2 . (orig.)

  15. Uncertainty modelling and analysis of volume calculations based on a regular grid digital elevation model (DEM)

    Science.gov (United States)

    Li, Chang; Wang, Qing; Shi, Wenzhong; Zhao, Sisi

    2018-05-01

    The accuracy of earthwork calculations that compute terrain volume is critical to digital terrain analysis (DTA). The uncertainties in volume calculations (VCs) based on a DEM are primarily related to three factors: 1) model error (ME), which is caused by an adopted algorithm for a VC model, 2) discrete error (DE), which is usually caused by DEM resolution and terrain complexity, and 3) propagation error (PE), which is caused by the variables' error. Based on these factors, the uncertainty modelling and analysis of VCs based on a regular grid DEM are investigated in this paper. Especially, how to quantify the uncertainty of VCs is proposed by a confidence interval based on truncation error (TE). In the experiments, the trapezoidal double rule (TDR) and Simpson's double rule (SDR) were used to calculate volume, where the TE is the major ME, and six simulated regular grid DEMs with different terrain complexity and resolution (i.e. DE) were generated by a Gauss synthetic surface to easily obtain the theoretical true value and eliminate the interference of data errors. For PE, Monte-Carlo simulation techniques and spatial autocorrelation were used to represent DEM uncertainty. This study can enrich uncertainty modelling and analysis-related theories of geographic information science.

  16. The development of human factors technologies -The development of human behaviour analysis techniques-

    Energy Technology Data Exchange (ETDEWEB)

    Lee, Jung Woon; Lee, Yong Heui; Park, Keun Ok; Chun, Se Woo; Suh, Sang Moon; Park, Jae Chang [Korea Atomic Energy Research Institute, Taejon (Korea, Republic of)

    1995-07-01

    In order to contribute to human error reduction through the studies on human-machine interaction in nuclear power plants, this project has objectives to develop SACOM(Simulation Analyzer with a Cognitive Operator Model) and techniques for human error analysis and application. In this year, we studied the followings: (1) Site investigation of operator tasks, (2) Development of operator task micro structure and revision of micro structure, (3) Development of knowledge representation software and SACOM prototype, (4) Development of performance assessment methodologies in task simulation and analysis of the effects of performance shaping factors. analysis and application techniques> (1) Classification of error shaping factors(ESFs) and development of software for ESF evaluation, (2) Analysis of human error occurrences and revision of analysis procedure, (3) Experiment for human error data collection using a compact nuclear simulator, (4) Development of a prototype data base system of the analyzed information on trip cases. 55 figs, 23 tabs, 33 refs. (Author).

  17. Visual modeling in an analysis of multidimensional data

    Science.gov (United States)

    Zakharova, A. A.; Vekhter, E. V.; Shklyar, A. V.; Pak, A. J.

    2018-01-01

    The article proposes an approach to solve visualization problems and the subsequent analysis of multidimensional data. Requirements to the properties of visual models, which were created to solve analysis problems, are described. As a perspective direction for the development of visual analysis tools for multidimensional and voluminous data, there was suggested an active use of factors of subjective perception and dynamic visualization. Practical results of solving the problem of multidimensional data analysis are shown using the example of a visual model of empirical data on the current state of studying processes of obtaining silicon carbide by an electric arc method. There are several results of solving this problem. At first, an idea of possibilities of determining the strategy for the development of the domain, secondly, the reliability of the published data on this subject, and changes in the areas of attention of researchers over time.

  18. Hierarchical and coupling model of factors influencing vessel traffic flow.

    Science.gov (United States)

    Liu, Zhao; Liu, Jingxian; Li, Huanhuan; Li, Zongzhi; Tan, Zhirong; Liu, Ryan Wen; Liu, Yi

    2017-01-01

    Understanding the characteristics of vessel traffic flow is crucial in maintaining navigation safety, efficiency, and overall waterway transportation management. Factors influencing vessel traffic flow possess diverse features such as hierarchy, uncertainty, nonlinearity, complexity, and interdependency. To reveal the impact mechanism of the factors influencing vessel traffic flow, a hierarchical model and a coupling model are proposed in this study based on the interpretative structural modeling method. The hierarchical model explains the hierarchies and relationships of the factors using a graph. The coupling model provides a quantitative method that explores interaction effects of factors using a coupling coefficient. The coupling coefficient is obtained by determining the quantitative indicators of the factors and their weights. Thereafter, the data obtained from Port of Tianjin is used to verify the proposed coupling model. The results show that the hierarchical model of the factors influencing vessel traffic flow can explain the level, structure, and interaction effect of the factors; the coupling model is efficient in analyzing factors influencing traffic volumes. The proposed method can be used for analyzing increases in vessel traffic flow in waterway transportation system.

  19. Analysis of the Factors Affecting the Interval between Blood Donations Using Log-Normal Hazard Model with Gamma Correlated Frailties.

    Science.gov (United States)

    Tavakol, Najmeh; Kheiri, Soleiman; Sedehi, Morteza

    2016-01-01

    Time to donating blood plays a major role in a regular donor to becoming continues one. The aim of this study was to determine the effective factors on the interval between the blood donations. In a longitudinal study in 2008, 864 samples of first-time donors in Shahrekord Blood Transfusion Center,  capital city of Chaharmahal and Bakhtiari Province, Iran were selected by a systematic sampling and were followed up for five years. Among these samples, a subset of 424 donors who had at least two successful blood donations were chosen for this study and the time intervals between their donations were measured as response variable. Sex, body weight, age, marital status, education, stay and job were recorded as independent variables. Data analysis was performed based on log-normal hazard model with gamma correlated frailty. In this model, the frailties are sum of two independent components assumed a gamma distribution. The analysis was done via Bayesian approach using Markov Chain Monte Carlo algorithm by OpenBUGS. Convergence was checked via Gelman-Rubin criteria using BOA program in R. Age, job and education were significant on chance to donate blood (Pdonation for the higher-aged donors, clericals, workers, free job, students and educated donors were higher and in return, time intervals between their blood donations were shorter. Due to the significance effect of some variables in the log-normal correlated frailty model, it is necessary to plan educational and cultural program to encourage the people with longer inter-donation intervals to donate more frequently.

  20. Analysis of significance of environmental factors in landslide susceptibility modeling: Case study Jemma drainage network, Ethiopia

    Directory of Open Access Journals (Sweden)

    Vít Maca

    2017-06-01

    Full Text Available Aim of the paper is to describe methodology for calculating significance of environmental factors in landslide susceptibility modeling and present result of selected one. As a study area part of a Jemma basin in Ethiopian Highland is used. This locality is highly affected by mass movement processes. In the first part all major factors and their influence are described briefly. Majority of the work focuses on research of other methodologies used in susceptibility models and design of own methodology. This method is unlike most of the methods used completely objective, therefore it is not possible to intervene in the results. In article all inputs and outputs of the method are described as well as all stages of calculations. Results are illustrated on specific examples. In study area most important factor for landslide susceptibility is slope, on the other hand least important is land cover. At the end of article landslide susceptibility map is created. Part of the article is discussion of results and possible improvements of the methodology.

  1. Causal Analysis for Performance Modeling of Computer Programs

    Directory of Open Access Journals (Sweden)

    Jan Lemeire

    2007-01-01

    Full Text Available Causal modeling and the accompanying learning algorithms provide useful extensions for in-depth statistical investigation and automation of performance modeling. We enlarged the scope of existing causal structure learning algorithms by using the form-free information-theoretic concept of mutual information and by introducing the complexity criterion for selecting direct relations among equivalent relations. The underlying probability distribution of experimental data is estimated by kernel density estimation. We then reported on the benefits of a dependency analysis and the decompositional capacities of causal models. Useful qualitative models, providing insight into the role of every performance factor, were inferred from experimental data. This paper reports on the results for a LU decomposition algorithm and on the study of the parameter sensitivity of the Kakadu implementation of the JPEG-2000 standard. Next, the analysis was used to search for generic performance characteristics of the applications.

  2. Behavioral determinants of cardiovascular diseases risk factors: A qualitative directed content analysis.

    Science.gov (United States)

    Sabzmakan, Leila; Morowatisharifabad, Mohammad Ali; Mohammadi, Eesa; Mazloomy-Mahmoodabad, Seid Saied; Rabiei, Katayoun; Naseri, Mohammad Hassan; Shakibazadeh, Elham; Mirzaei, Masoud

    2014-03-01

    The PRECEDE model is a useful tool for planers to assess health problems, the behavioral and environmental causes of the problems, and their determinants. This study aims to understand the experiences of patients and health care providers about the behavioral causes of cardiovascular diseases (CVDs) risk factors and their determinants. This qualitative study utilized content analysis approach based on the PRECEDE model. The study was conducted for over 6 months in 2012 at the diabetes units of health centers associated with Alborz University of Medical Sciences, which is located in Karaj, Iran. Data were collected using individual semi-structured interviews with 50 patients and 12 health care providers. Data analysis was performed simultaneously with data collection using the content analysis directed method. Stress, unhealthy eating, and physical inactivity were the behaviors, which predict the risk factors for CVD. Most of the patients considered stress as the most important underlying cause of their illness. In this study, 110 of the primary codes were categorized into seven subcategories, including knowledge, attitude, perceived susceptibility, severity, perceived benefits, barriers, and self-efficacy, which were located in the predisposing category of the PRECEDE model. Among these determinants, perceived barriers and self-efficacy for the mentioned behaviors seemed to be of great importance. Identifying behavioral determinants will help the planners design future programs and select the most appropriate methods and applications to address these determinants in order to reduce risky behaviors.

  3. Analysis of Traffic Crashes Involving Pedestrians Using Big Data: Investigation of Contributing Factors and Identification of Hotspots.

    Science.gov (United States)

    Xie, Kun; Ozbay, Kaan; Kurkcu, Abdullah; Yang, Hong

    2017-08-01

    This study aims to explore the potential of using big data in advancing the pedestrian risk analysis including the investigation of contributing factors and the hotspot identification. Massive amounts of data of Manhattan from a variety of sources were collected, integrated, and processed, including taxi trips, subway turnstile counts, traffic volumes, road network, land use, sociodemographic, and social media data. The whole study area was uniformly split into grid cells as the basic geographical units of analysis. The cell-structured framework makes it easy to incorporate rich and diversified data into risk analysis. The cost of each crash, weighted by injury severity, was assigned to the cells based on the relative distance to the crash site using a kernel density function. A tobit model was developed to relate grid-cell-specific contributing factors to crash costs that are left-censored at zero. The potential for safety improvement (PSI) that could be obtained by using the actual crash cost minus the cost of "similar" sites estimated by the tobit model was used as a measure to identify and rank pedestrian crash hotspots. The proposed hotspot identification method takes into account two important factors that are generally ignored, i.e., injury severity and effects of exposure indicators. Big data, on the one hand, enable more precise estimation of the effects of risk factors by providing richer data for modeling, and on the other hand, enable large-scale hotspot identification with higher resolution than conventional methods based on census tracts or traffic analysis zones. © 2017 Society for Risk Analysis.

  4. Overview of Building Information Modelling (BIM) adoption factors for construction organisations

    Science.gov (United States)

    Mohammad, W. N. S. Wan; Abdullah, M. R.; Ismail, S.; Takim, R.

    2018-04-01

    Improvement and innovation in building visualization, project coordination and communication are the major benefits generated by Building Information Modelling (BIM) for construction organisations. Thus, as many firms across the world would adopt BIM, however they do not know the clear direction in which path they are moving as there is no specific reference available for them to refer to. Hence, the paper seeks to identify the factors of BIM adoption from previous research. The methodology used in this paper is based on literature review from various sources such as conference articles and journals. Then, the findings were analysed using content analysis. The findings show that there are 24 factors found from literature that influence the adoption of BIM and four (4) factors such as vendor, organisational vision, knowledge, and implementation plan are among the least factors mentioned by previous researchers.

  5. Classification analysis of organization factors related to system safety

    International Nuclear Information System (INIS)

    Liu Huizhen; Zhang Li; Zhang Yuling; Guan Shihua

    2009-01-01

    This paper analyzes the different types of organization factors which influence the system safety. The organization factor can be divided into the interior organization factor and exterior organization factor. The latter includes the factors of political, economical, technical, law, social culture and geographical, and the relationships among different interest groups. The former includes organization culture, communication, decision, training, process, supervision and management and organization structure. This paper focuses on the description of the organization factors. The classification analysis of the organization factors is the early work of quantitative analysis. (authors)

  6. Analysis of vector boson production within TMD factorization

    International Nuclear Information System (INIS)

    Scimemi, Ignazio; Vladimirov, Alexey

    2018-01-01

    We present a comprehensive analysis and extraction of the unpolarized transverse momentum dependent (TMD) parton distribution functions, which are fundamental constituents of the TMD factorization theorem. We provide a general review of the theory of TMD distributions, and present a new scheme of scale fixation. This scheme, called the ζ-prescription, allows to minimize the impact of perturbative logarithms in a large range of scales and does not generate undesired power corrections. Within ζ-prescription we consistently include the perturbatively calculable parts up to next-to-next-to-leading order (NNLO), and perform the global fit of the Drell-Yan and Z-boson production, which include the data of E288, Tevatron and LHC experiments. The non-perturbative part of the TMDs are explored checking a variety of models. We support the obtained results by a study of theoretical uncertainties, perturbative convergence, and a dedicated study of the range of applicability of the TMD factorization theorem. The considered non-perturbative models present significant differences in the fitting behavior, which allow us to clearly disfavor most of them. The numerical evaluations are provided by the arTeMiDe code, which is introduced in this work and that can be used for current/future TMD phenomenology. (orig.)

  7. Analysis of vector boson production within TMD factorization

    Energy Technology Data Exchange (ETDEWEB)

    Scimemi, Ignazio [Universidad Complutense de Madrid, Departamento de Fisica Teorica, Madrid (Spain); Vladimirov, Alexey [Universitaet Regensburg, Institut fuer Theoretische Physik, Regensburg (Germany)

    2018-02-15

    We present a comprehensive analysis and extraction of the unpolarized transverse momentum dependent (TMD) parton distribution functions, which are fundamental constituents of the TMD factorization theorem. We provide a general review of the theory of TMD distributions, and present a new scheme of scale fixation. This scheme, called the ζ-prescription, allows to minimize the impact of perturbative logarithms in a large range of scales and does not generate undesired power corrections. Within ζ-prescription we consistently include the perturbatively calculable parts up to next-to-next-to-leading order (NNLO), and perform the global fit of the Drell-Yan and Z-boson production, which include the data of E288, Tevatron and LHC experiments. The non-perturbative part of the TMDs are explored checking a variety of models. We support the obtained results by a study of theoretical uncertainties, perturbative convergence, and a dedicated study of the range of applicability of the TMD factorization theorem. The considered non-perturbative models present significant differences in the fitting behavior, which allow us to clearly disfavor most of them. The numerical evaluations are provided by the arTeMiDe code, which is introduced in this work and that can be used for current/future TMD phenomenology. (orig.)

  8. Analysis of algae growth mechanism and water bloom prediction under the effect of multi-affecting factor.

    Science.gov (United States)

    Wang, Li; Wang, Xiaoyi; Jin, Xuebo; Xu, Jiping; Zhang, Huiyan; Yu, Jiabin; Sun, Qian; Gao, Chong; Wang, Lingbin

    2017-03-01

    The formation process of algae is described inaccurately and water blooms are predicted with a low precision by current methods. In this paper, chemical mechanism of algae growth is analyzed, and a correlation analysis of chlorophyll-a and algal density is conducted by chemical measurement. Taking into account the influence of multi-factors on algae growth and water blooms, the comprehensive prediction method combined with multivariate time series and intelligent model is put forward in this paper. Firstly, through the process of photosynthesis, the main factors that affect the reproduction of the algae are analyzed. A compensation prediction method of multivariate time series analysis based on neural network and Support Vector Machine has been put forward which is combined with Kernel Principal Component Analysis to deal with dimension reduction of the influence factors of blooms. Then, Genetic Algorithm is applied to improve the generalization ability of the BP network and Least Squares Support Vector Machine. Experimental results show that this method could better compensate the prediction model of multivariate time series analysis which is an effective way to improve the description accuracy of algae growth and prediction precision of water blooms.

  9. A comparison study on detection of key geochemical variables and factors through three different types of factor analysis

    Science.gov (United States)

    Hoseinzade, Zohre; Mokhtari, Ahmad Reza

    2017-10-01

    Large numbers of variables have been measured to explain different phenomena. Factor analysis has widely been used in order to reduce the dimension of datasets. Additionally, the technique has been employed to highlight underlying factors hidden in a complex system. As geochemical studies benefit from multivariate assays, application of this method is widespread in geochemistry. However, the conventional protocols in implementing factor analysis have some drawbacks in spite of their advantages. In the present study, a geochemical dataset including 804 soil samples collected from a mining area in central Iran in order to search for MVT type Pb-Zn deposits was considered to outline geochemical analysis through various fractal methods. Routine factor analysis, sequential factor analysis, and staged factor analysis were applied to the dataset after opening the data with (additive logratio) alr-transformation to extract mineralization factor in the dataset. A comparison between these methods indicated that sequential factor analysis has more clearly revealed MVT paragenesis elements in surface samples with nearly 50% variation in F1. In addition, staged factor analysis has given acceptable results while it is easy to practice. It could detect mineralization related elements while larger factor loadings are given to these elements resulting in better pronunciation of mineralization.

  10. Students' motivation to study dentistry in Malaysia: an analysis using confirmatory factor analysis.

    Science.gov (United States)

    Musa, Muhd Firdaus Che; Bernabé, Eduardo; Gallagher, Jennifer E

    2015-06-12

    Malaysia has experienced a significant expansion of dental schools over the past decade. Research into students' motivation may inform recruitment and retention of the future dental workforce. The objectives of this study were to explore students' motivation to study dentistry and whether that motivation varied by students' and school characteristics. All 530 final-year students in 11 dental schools (6 public and 5 private) in Malaysia were invited to participate at the end of 2013. The self-administered questionnaire, developed at King's College London, collected information on students' motivation to study dentistry and demographic background. Responses on students' motivation were collected using five-point ordinal scales. Confirmatory factor analysis (CFA) was used to evaluate the underlying structure of students' motivation to study dentistry. Multivariate analysis of variance (MANOVA) was used to compare factor scores for overall motivation and sub-domains by students' and school characteristics. Three hundred and fifty-six final-year students in eight schools (all public and two private) participated in the survey, representing an 83% response rate for these schools and 67% of all final-year students nationally. The majority of participants were 24 years old (47%), female (70%), Malay (56%) and from middle-income families (41%) and public schools (78%). CFA supported a model with five first-order factors (professional job, healthcare and people, academic, careers advising and family and friends) which were linked to a single second-order factor representing overall students' motivation. Academic factors and healthcare and people had the highest standardized factor loadings (0.90 and 0.71, respectively), suggesting they were the main motivation to study dentistry. MANOVA showed that students from private schools had higher scores for healthcare and people than those in public schools whereas Malay students had lower scores for family and friends than those

  11. Dynamic Multi-Factor Credit Risk Model with Fat-Tailed Factors

    Czech Academy of Sciences Publication Activity Database

    Gapko, Petr; Šmíd, Martin

    2012-01-01

    Roč. 62, č. 2 (2012), s. 125-140 ISSN 0015-1920 R&D Projects: GA ČR GD402/09/H045; GA ČR GA402/09/0965 Grant - others:Univerzita Karlova(CZ) GAUK 46108 Institutional research plan: CEZ:AV0Z10750506 Keywords : credit risk * probability of default * loss given default * credit loss * credit loss distribution * Basel II Subject RIV: AH - Economics Impact factor: 0.340, year: 2012 http://library.utia.cas.cz/separaty/2012/E/smid-dynamic multi-factor credit risk model with fat-tailed factors.pdf

  12. Worldwide analysis of marine oil spill cleanup cost factors

    International Nuclear Information System (INIS)

    Etkin, D.S.

    2000-01-01

    The many factors that influence oil spill response costs were discussed with particular emphasis on how spill responses differ around the world because of differing cultural values, socio-economic factors and labor costs. This paper presented an analysis of marine oil spill cleanup costs based on the country, proximity to shoreline, spill size, oil type, degree of shoreline oiling and cleanup methodology. The objective was to determine how each factor impacts per-unit cleanup costs. Near-shore spills and in-port spills were found to be 4-5 times more expensive to clean than offshore spills. Responses to spills of heavy fuels also cost 10 times more than for lighter crudes and diesel. Spill responses for spills under 30 tonnes are 10 times more costly than on a per-unit basis, for spills of 300 tonnes. A newly developed modelling technique that can be used on different types of marine spills was described. It is based on updated cost data acquired from case studies of more than 300 spills in 40 countries. The model determines a per-unit cleanup cost estimation by taking into consideration oil type, location, spill size, cleanup methodology, and shoreline oiling. It was concluded that the actual spill costs are totally dependent on the actual circumstances of the spill. 13 refs., 10 tabs., 3 figs

  13. Empirical Study on Total Factor Productive Energy Efficiency in Beijing-Tianjin-Hebei Region-Analysis based on Malmquist Index and Window Model

    Science.gov (United States)

    Xu, Qiang; Ding, Shuai; An, Jingwen

    2017-12-01

    This paper studies the energy efficiency of Beijing-Tianjin-Hebei region and to finds out the trend of energy efficiency in order to improve the economic development quality of Beijing-Tianjin-Hebei region. Based on Malmquist index and window analysis model, this paper estimates the total factor energy efficiency in Beijing-Tianjin-Hebei region empirically by using panel data in this region from 1991 to 2014, and provides the corresponding political recommendations. The empirical result shows that, the total factor energy efficiency in Beijing-Tianjin-Hebei region increased from 1991 to 2014, mainly relies on advances in energy technology or innovation, and obvious regional differences in energy efficiency to exist. Throughout the window period of 24 years, the regional differences of energy efficiency in Beijing-Tianjin-Hebei region shrank. There has been significant convergent trend in energy efficiency after 2000, mainly depends on the diffusion and spillover of energy technologies.

  14. Hierarchical and coupling model of factors influencing vessel traffic flow.

    Directory of Open Access Journals (Sweden)

    Zhao Liu

    Full Text Available Understanding the characteristics of vessel traffic flow is crucial in maintaining navigation safety, efficiency, and overall waterway transportation management. Factors influencing vessel traffic flow possess diverse features such as hierarchy, uncertainty, nonlinearity, complexity, and interdependency. To reveal the impact mechanism of the factors influencing vessel traffic flow, a hierarchical model and a coupling model are proposed in this study based on the interpretative structural modeling method. The hierarchical model explains the hierarchies and relationships of the factors using a graph. The coupling model provides a quantitative method that explores interaction effects of factors using a coupling coefficient. The coupling coefficient is obtained by determining the quantitative indicators of the factors and their weights. Thereafter, the data obtained from Port of Tianjin is used to verify the proposed coupling model. The results show that the hierarchical model of the factors influencing vessel traffic flow can explain the level, structure, and interaction effect of the factors; the coupling model is efficient in analyzing factors influencing traffic volumes. The proposed method can be used for analyzing increases in vessel traffic flow in waterway transportation system.

  15. Chi-squared Automatic Interaction Detection Decision Tree Analysis of Risk Factors for Infant Anemia in Beijing, China.

    Science.gov (United States)

    Ye, Fang; Chen, Zhi-Hua; Chen, Jie; Liu, Fang; Zhang, Yong; Fan, Qin-Ying; Wang, Lin

    2016-05-20

    In the past decades, studies on infant anemia have mainly focused on rural areas of China. With the increasing heterogeneity of population in recent years, available information on infant anemia is inconclusive in large cities of China, especially with comparison between native residents and floating population. This population-based cross-sectional study was implemented to determine the anemic status of infants as well as the risk factors in a representative downtown area of Beijing. As useful methods to build a predictive model, Chi-squared automatic interaction detection (CHAID) decision tree analysis and logistic regression analysis were introduced to explore risk factors of infant anemia. A total of 1091 infants aged 6-12 months together with their parents/caregivers living at Heping Avenue Subdistrict of Beijing were surveyed from January 1, 2013 to December 31, 2014. The prevalence of anemia was 12.60% with a range of 3.47%-40.00% in different subgroup characteristics. The CHAID decision tree model has demonstrated multilevel interaction among risk factors through stepwise pathways to detect anemia. Besides the three predictors identified by logistic regression model including maternal anemia during pregnancy, exclusive breastfeeding in the first 6 months, and floating population, CHAID decision tree analysis also identified the fourth risk factor, the maternal educational level, with higher overall classification accuracy and larger area below the receiver operating characteristic curve. The infant anemic status in metropolis is complex and should be carefully considered by the basic health care practitioners. CHAID decision tree analysis has demonstrated a better performance in hierarchical analysis of population with great heterogeneity. Risk factors identified by this study might be meaningful in the early detection and prompt treatment of infant anemia in large cities.

  16. Reliability Analysis of Offshore Jacket Structures with Wave Load on Deck using the Model Correction Factor Method

    DEFF Research Database (Denmark)

    Sørensen, John Dalsgaard; Friis-Hansen, P.; Nielsen, J.S.

    2006-01-01

    failure/collapse of jacket type platforms with wave in deck loads using the so-called Model Correction Factor Method (MCFM). A simple representative model for the RSR measure is developed and used in the MCFM technique. A realistic example is evaluated and it is seen that it is possible to perform...

  17. An alternative method for centrifugal compressor loading factor modelling

    Science.gov (United States)

    Galerkin, Y.; Drozdov, A.; Rekstin, A.; Soldatova, K.

    2017-08-01

    The loading factor at design point is calculated by one or other empirical formula in classical design methods. Performance modelling as a whole is out of consideration. Test data of compressor stages demonstrates that loading factor versus flow coefficient at the impeller exit has a linear character independent of compressibility. Known Universal Modelling Method exploits this fact. Two points define the function - loading factor at design point and at zero flow rate. The proper formulae include empirical coefficients. A good modelling result is possible if the choice of coefficients is based on experience and close analogs. Earlier Y. Galerkin and K. Soldatova had proposed to define loading factor performance by the angle of its inclination to the ordinate axis and by the loading factor at zero flow rate. Simple and definite equations with four geometry parameters were proposed for loading factor performance calculated for inviscid flow. The authors of this publication have studied the test performance of thirteen stages of different types. The equations are proposed with universal empirical coefficients. The calculation error lies in the range of plus to minus 1,5%. The alternative model of a loading factor performance modelling is included in new versions of the Universal Modelling Method.

  18. Development of interpretation models for PFN uranium log analysis

    International Nuclear Information System (INIS)

    Barnard, R.W.

    1980-11-01

    This report presents the models for interpretation of borehole logs for the PFN (Prompt Fission Neutron) uranium logging system. Two models have been developed, the counts-ratio model and the counts/dieaway model. Both are empirically developed, but can be related to the theoretical bases for PFN analysis. The models try to correct for the effects of external factors (such as probe or formation parameters) in the calculation of uranium grade. The theoretical bases and calculational techniques for estimating uranium concentration from raw PFN data and other parameters are discussed. Examples and discussions of borehole logs are included

  19. Using BMDP and SPSS for a Q factor analysis.

    Science.gov (United States)

    Tanner, B A; Koning, S M

    1980-12-01

    While Euclidean distances and Q factor analysis may sometimes be preferred to correlation coefficients and cluster analysis for developing a typology, commercially available software does not always facilitate their use. Commands are provided for using BMDP and SPSS in a Q factor analysis with Euclidean distances.

  20. Pedestrian-Vehicle Accidents Reconstruction with PC-Crash®: Sensibility Analysis of Factors Variation

    Energy Technology Data Exchange (ETDEWEB)

    Martinez Gala, F.

    2016-07-01

    This paper describes the main findings of a study performed by INSIA-UPM about the improvement of the reconstruction process of real world vehicle-pedestrian accidents using PC-Crash® software, aimed to develop a software tool for the estimation of the variability of the collision speed due to the lack of real values of some parameters required during the reconstruction task. The methodology has been based on a sensibility analysis of the factors variation. A total of 9 factors have been analyzed with the objective of identifying which ones were significant. Four of them (pedestrian height, collision angle, hood height and pedestrian-road friction coefficient) were significant and were included in a full factorial experiment with the collision speed as an additional factor in order to obtain a regression model with up to third level interactions. Two different factorial experiments with the same structure have been performed because of pedestrian gender differences. The tool has been created as a collision speed predictor based on the regression models obtained, using the 4 significant factors and the projection distance measured or estimated in the accident site. The tool has been used on the analysis of real-world reconstructed accidents occurred in the city of Madrid (Spain). The results have been adequate in most cases with less than 10% of deviation between the predicted speed and the one estimated in the reconstructions. (Author)

  1. Evidence-Based Practice Questionnaire: A Confirmatory Factor Analysis in a Social Work Sample

    Directory of Open Access Journals (Sweden)

    Karen Rice

    2010-10-01

    Full Text Available This study examined the psychometric properties of the Evidence-Based Practice Questionnaire (EBPQ. The 24-item EBPQ was developed to measure health professionals’ attitudes toward, knowledge of, and use of evidence-based practice (EBP. A confirmatory factor analysis was performed on the EBPQ given to a random sample of National Association of Social Work members (N = 167. The coefficient alpha of the EBPQ was .93. The study supported a 23-item 3-factor model with acceptable model fit indices (χ² = 469.04; RMSEA = .081; SRMR = .068; CFI = .900. This study suggests a slightly modified EBPQ may be a useful tool to assess social workers’ attitudes toward, knowledge of, and use of EBP.

  2. Confirmatory Factor Analysis of the French Version of the Anticipatory and Consummatory Interpersonal Pleasure Scale

    Directory of Open Access Journals (Sweden)

    Joséphine Chaix

    2017-07-01

    Full Text Available The Anticipatory and Consummatory Interpersonal Pleasure Scale (ACIPS, a measure specifically designed to assess hedonic capacity for social and interpersonal pleasure, was used to evaluate the presence of social anhedonia in patients as well as the general population. The first goal of this study was to validate the structure of the French version of the ACIPS. The second objective was to verify whether a one, two or three factor solution is most appropriate for the ACIPS scale. The French version of the ACIPS was tested on 263 French-speaking pre-graduate students or professional volunteers. For the confirmatory factor analysis, data were treated as categorical ordinal and all the models were estimated using a robust weighted least squares estimator with adjustments for the mean and variance. Three models were estimated. A one-factor model representing a general undifferentiated “pleasure” latent construct was first tested on the 17 ACIPS items. A two-factor model distinguishing anticipatory-pleasure and consummatory-pleasure was tested next. Finally, a three-factor model including subdomains of intimate social interactions, group social interactions, and social bonding was tested. The one and two-factor models showed a somewhat poor fit to the data. However, the goodness of fit of the three factor model was adequate. These results suggest that individuals who enjoyed interaction in one of these three subdomains were more likely to enjoy doing so in the two other domains. However, on the basis of the comparison between the one and three factor models, these three types of interactions may not be considered as indistinguishable. Rather, they represent distinct and theoretically meaningful dimensions. These results show the French version of the ACIPS is a useful and valid scale to measure the capacity of savoring different kinds of social relationships.

  3. Exploring Technostress: Results of a Large Sample Factor Analysis

    Directory of Open Access Journals (Sweden)

    Steponas Jonušauskas

    2016-06-01

    Full Text Available With reference to the results of a large sample factor analysis, the article aims to propose the frame examining technostress in a population. The survey and principal component analysis of the sample consisting of 1013 individuals who use ICT in their everyday work was implemented in the research. 13 factors combine 68 questions and explain 59.13 per cent of the answers dispersion. Based on the factor analysis, questionnaire was reframed and prepared to reasonably analyze the respondents’ answers, revealing technostress causes and consequences as well as technostress prevalence in the population in a statistically validated pattern. A key elements of technostress based on factor analysis can serve for the construction of technostress measurement scales in further research.

  4. Analyzing Factors Influencing Teaching as a Career Choice Using Structural Equation Modeling

    Directory of Open Access Journals (Sweden)

    Budhinath Padhy

    2015-02-01

    Full Text Available The purpose of the study is to analyze factors influencing students’ perceptions of teaching as a career choice using structural equation modeling with the goal of shaping a teacher education recruitment program. In this study, 458 students from a Midwestern university in the United States responded to an online survey about career-related factors they value, their expectation that teaching would offer those factors, and any social-influence factors that might encourage them to choose a teaching career. The effect of 10 exogenous motivation variables (value-environment, value-intrinsic, value-extrinsic, value-altruistic, expectancy-environment, expectancy-intrinsic, expectancy-extrinsic, social-media-education, social-prior-experience, and social-suggestions on choosing a teaching career was examined. Results of our analysis showed that the factors related to expectancy-environment, expectancy-intrinsic, social-media-education, social-prior-experience, and social-suggestions were found to be significant, whereas value-related factors and expectancy-extrinsic factors were found to be insignificant.

  5. Hydrochemical analysis of groundwater using a tree-based model

    Science.gov (United States)

    Litaor, M. Iggy; Brielmann, H.; Reichmann, O.; Shenker, M.

    2010-06-01

    SummaryHydrochemical indices are commonly used to ascertain aquifer characteristics, salinity problems, anthropogenic inputs and resource management, among others. This study was conducted to test the applicability of a binary decision tree model to aquifer evaluation using hydrochemical indices as input. The main advantage of the tree-based model compared to other commonly used statistical procedures such as cluster and factor analyses is the ability to classify groundwater samples with assigned probability and the reduction of a large data set into a few significant variables without creating new factors. We tested the model using data sets collected from headwater springs of the Jordan River, Israel. The model evaluation consisted of several levels of complexity, from simple separation between the calcium-magnesium-bicarbonate water type of karstic aquifers to the more challenging separation of calcium-sodium-bicarbonate water type flowing through perched and regional basaltic aquifers. In all cases, the model assigned measures for goodness of fit in the form of misclassification errors and singled out the most significant variable in the analysis. The model proceeded through a sequence of partitions providing insight into different possible pathways and changing lithology. The model results were extremely useful in constraining the interpretation of geological heterogeneity and constructing a conceptual flow model for a given aquifer. The tree model clearly identified the hydrochemical indices that were excluded from the analysis, thus providing information that can lead to a decrease in the number of routinely analyzed variables and a significant reduction in laboratory cost.

  6. Linear mixed-effects modeling approach to FMRI group analysis.

    Science.gov (United States)

    Chen, Gang; Saad, Ziad S; Britton, Jennifer C; Pine, Daniel S; Cox, Robert W

    2013-06-01

    Conventional group analysis is usually performed with Student-type t-test, regression, or standard AN(C)OVA in which the variance-covariance matrix is presumed to have a simple structure. Some correction approaches are adopted when assumptions about the covariance structure is violated. However, as experiments are designed with different degrees of sophistication, these traditional methods can become cumbersome, or even be unable to handle the situation at hand. For example, most current FMRI software packages have difficulty analyzing the following scenarios at group level: (1) taking within-subject variability into account when there are effect estimates from multiple runs or sessions; (2) continuous explanatory variables (covariates) modeling in the presence of a within-subject (repeated measures) factor, multiple subject-grouping (between-subjects) factors, or the mixture of both; (3) subject-specific adjustments in covariate modeling; (4) group analysis with estimation of hemodynamic response (HDR) function by multiple basis functions; (5) various cases of missing data in longitudinal studies; and (6) group studies involving family members or twins. Here we present a linear mixed-effects modeling (LME) methodology that extends the conventional group analysis approach to analyze many complicated cases, including the six prototypes delineated above, whose analyses would be otherwise either difficult or unfeasible under traditional frameworks such as AN(C)OVA and general linear model (GLM). In addition, the strength of the LME framework lies in its flexibility to model and estimate the variance-covariance structures for both random effects and residuals. The intraclass correlation (ICC) values can be easily obtained with an LME model with crossed random effects, even at the presence of confounding fixed effects. The simulations of one prototypical scenario indicate that the LME modeling keeps a balance between the control for false positives and the sensitivity

  7. Exploring Technostress: Results of a Large Sample Factor Analysis

    OpenAIRE

    Jonušauskas, Steponas; Raišienė, Agota Giedrė

    2016-01-01

    With reference to the results of a large sample factor analysis, the article aims to propose the frame examining technostress in a population. The survey and principal component analysis of the sample consisting of 1013 individuals who use ICT in their everyday work was implemented in the research. 13 factors combine 68 questions and explain 59.13 per cent of the answers dispersion. Based on the factor analysis, questionnaire was reframed and prepared to reasonably analyze the respondents’ an...

  8. Time-dependent reliability analysis of nuclear reactor operators using probabilistic network models

    International Nuclear Information System (INIS)

    Oka, Y.; Miyata, K.; Kodaira, H.; Murakami, S.; Kondo, S.; Togo, Y.

    1987-01-01

    Human factors are very important for the reliability of a nuclear power plant. Human behavior has essentially a time-dependent nature. The details of thinking and decision making processes are important for detailed analysis of human reliability. They have, however, not been well considered by the conventional methods of human reliability analysis. The present paper describes the models for the time-dependent and detailed human reliability analysis. Recovery by an operator is taken into account and two-operators models are also presented

  9. Bayes factor between Student t and Gaussian mixed models within an animal breeding context

    Directory of Open Access Journals (Sweden)

    García-Cortés Luis

    2008-07-01

    Full Text Available Abstract The implementation of Student t mixed models in animal breeding has been suggested as a useful statistical tool to effectively mute the impact of preferential treatment or other sources of outliers in field data. Nevertheless, these additional sources of variation are undeclared and we do not know whether a Student t mixed model is required or if a standard, and less parameterized, Gaussian mixed model would be sufficient to serve the intended purpose. Within this context, our aim was to develop the Bayes factor between two nested models that only differed in a bounded variable in order to easily compare a Student t and a Gaussian mixed model. It is important to highlight that the Student t density converges to a Gaussian process when degrees of freedom tend to infinity. The twomodels can then be viewed as nested models that differ in terms of degrees of freedom. The Bayes factor can be easily calculated from the output of a Markov chain Monte Carlo sampling of the complex model (Student t mixed model. The performance of this Bayes factor was tested under simulation and on a real dataset, using the deviation information criterion (DIC as the standard reference criterion. The two statistical tools showed similar trends along the parameter space, although the Bayes factor appeared to be the more conservative. There was considerable evidence favoring the Student t mixed model for data sets simulated under Student t processes with limited degrees of freedom, and moderate advantages associated with using the Gaussian mixed model when working with datasets simulated with 50 or more degrees of freedom. For the analysis of real data (weight of Pietrain pigs at six months, both the Bayes factor and DIC slightly favored the Student t mixed model, with there being a reduced incidence of outlier individuals in this population.

  10. A comprehensive analysis of factors influencing the injury severity of large-truck crashes.

    Science.gov (United States)

    Zhu, Xiaoyu; Srinivasan, Sivaramakrishnan

    2011-01-01

    Given the importance of trucking to the economic well being of a country and the safety concerns posed by the trucks, a study of large-truck crashes is critical. This paper contributes by undertaking an extensive analysis of the empirical factors affecting injury severity of large-truck crashes. Data from a recent, nationally representative sample of large-truck crashes are examined to determine the factors affecting the overall injury severity of these crashes. The explanatory factors include the characteristics of the crash, vehicle(s), and the driver(s). The injury severity was modeled using two measures. Several similarities and some differences were observed across the two models which underscore the need for improved accuracy in the assessment of injury severity of crashes. The estimated models capture the marginal effects of a variety of explanatory factors simultaneously. In particular, the models indicate the impacts of several driver behavior variables on the severity of the crashes, after controlling for a variety of other factors. For example, driver distraction (truck drivers), alcohol use (car drivers), and emotional factors (car drivers) are found to be associated with higher severity crashes. A further interesting finding is the strong statistical significance of several dummy variables that indicate missing data - these reflect how the nature of the crash itself could affect the completeness of the data. Future efforts should seek to collect such data more comprehensively so that the true effects of these aspects on the crash severity can be determined. Copyright © 2010 Elsevier Ltd. All rights reserved.

  11. Mars approach for global sensitivity analysis of differential equation models with applications to dynamics of influenza infection.

    Science.gov (United States)

    Lee, Yeonok; Wu, Hulin

    2012-01-01

    Differential equation models are widely used for the study of natural phenomena in many fields. The study usually involves unknown factors such as initial conditions and/or parameters. It is important to investigate the impact of unknown factors (parameters and initial conditions) on model outputs in order to better understand the system the model represents. Apportioning the uncertainty (variation) of output variables of a model according to the input factors is referred to as sensitivity analysis. In this paper, we focus on the global sensitivity analysis of ordinary differential equation (ODE) models over a time period using the multivariate adaptive regression spline (MARS) as a meta model based on the concept of the variance of conditional expectation (VCE). We suggest to evaluate the VCE analytically using the MARS model structure of univariate tensor-product functions which is more computationally efficient. Our simulation studies show that the MARS model approach performs very well and helps to significantly reduce the computational cost. We present an application example of sensitivity analysis of ODE models for influenza infection to further illustrate the usefulness of the proposed method.

  12. Statictical Analysis Of The Conditioning Factors Of Urban Electric Consumption

    International Nuclear Information System (INIS)

    Segura D'Rouville, Juan Joel; Suárez Carreño, Franyelit María

    2017-01-01

    This research work presents the analysis of the most important factors that condition the urban residential electricity consumption. This study shows the quantitative parameters conditioning the electricity consumption. This sector of analysis has been chosen because there is disaggregated information of which are the main social and technological factors that determine its behavior, growth, with the objective of elaborating policies in the management of the electric consumption. The electrical demand considered as the sum of the powers of all the equipment that are used in each of the instants of a full day, is related to the electrical consumption, which is not but the value of the power demanded by a determined consumer Multiplied by the time in which said demand is maintained. In this report we propose the design of a probabilistic model of prediction of electricity consumption, taking into account mainly influential social and technological factors. The statistical process of this database is done through the Stat Graphics software version 4.1, for its extensive didactic in the accomplishment of calculations and associated methods. Finally, the correlation of the variables was performed to classify the determinants in a specific way and thus to determine the consumption of the dwellings. (author)

  13. The development of human factors technologies -The development of human behaviour analysis techniques-

    International Nuclear Information System (INIS)

    Lee, Jung Woon; Lee, Yong Heui; Park, Keun Ok; Chun, Se Woo; Suh, Sang Moon; Park, Jae Chang

    1995-07-01

    In order to contribute to human error reduction through the studies on human-machine interaction in nuclear power plants, this project has objectives to develop SACOM(Simulation Analyzer with a Cognitive Operator Model) and techniques for human error analysis and application. In this year, we studied the followings: 1) Site investigation of operator tasks, 2) Development of operator task micro structure and revision of micro structure, 3) Development of knowledge representation software and SACOM prototype, 4) Development of performance assessment methodologies in task simulation and analysis of the effects of performance shaping factors. 1) Classification of error shaping factors(ESFs) and development of software for ESF evaluation, 2) Analysis of human error occurrences and revision of analysis procedure, 3) Experiment for human error data collection using a compact nuclear simulator, 4) Development of a prototype data base system of the analyzed information on trip cases. 55 figs, 23 tabs, 33 refs. (Author)

  14. Water Use Efficiency and Its Influencing Factors in China: Based on the Data Envelopment Analysis (DEA—Tobit Model

    Directory of Open Access Journals (Sweden)

    Shuqiao Wang

    2018-06-01

    Full Text Available Water resources are important and irreplaceable natural and economic resources. Achieving a balance between economic prosperity and protection of water resource environments is a major issue in China. This article develops a data envelopment analysis (DEA approach with undesirable outputs by using Seiford’s linear converting method to estimate water use efficiencies for 30 provinces in China, from 2008–2016,and then analyzes the influencing factors while using a DEA-Tobit model. The findings show that the overall water use efficiency of the measured Chinese provinces, when considering sewage emissions as the undesirable output, is 0.582. Thus, most regions still need improvement. Provinces with the highest water efficiency are located in economically developed Eastern China. The spatial pattern of water use efficiency in China is consistent with the general pattern of regional economic development. This study implies that factors like export dependence, technical progress, and educational value have a positive influence on water use efficiency. Further, while industrial structure has had a negative impact, government intervention has had little impact on water use efficiency. These research results will provide a scientific basis for the government to make plans for water resource development, and it may be helpful in improving regional sustainable development.

  15. Factor analysis improves the selection of prescribing indicators

    DEFF Research Database (Denmark)

    Rasmussen, Hanne Marie Skyggedal; Søndergaard, Jens; Sokolowski, Ineta

    2006-01-01

    OBJECTIVE: To test a method for improving the selection of indicators of general practitioners' prescribing. METHODS: We conducted a prescription database study including all 180 general practices in the County of Funen, Denmark, approximately 472,000 inhabitants. Principal factor analysis was us...... appropriate and inappropriate prescribing, as revealed by the correlation of the indicators in the first factor. CONCLUSION: Correlation and factor analysis is a feasible method that assists the selection of indicators and gives better insight into prescribing patterns....

  16. A Two-Factor Model Better Explains Heterogeneity in Negative Symptoms: Evidence from the Positive and Negative Syndrome Scale.

    Science.gov (United States)

    Jang, Seon-Kyeong; Choi, Hye-Im; Park, Soohyun; Jaekal, Eunju; Lee, Ga-Young; Cho, Young Il; Choi, Kee-Hong

    2016-01-01

    Acknowledging separable factors underlying negative symptoms may lead to better understanding and treatment of negative symptoms in individuals with schizophrenia. The current study aimed to test whether the negative symptoms factor (NSF) of the Positive and Negative Syndrome Scale (PANSS) would be better represented by expressive and experiential deficit factors, rather than by a single factor model, using confirmatory factor analysis (CFA). Two hundred and twenty individuals with schizophrenia spectrum disorders completed the PANSS; subsamples additionally completed the Brief Negative Symptom Scale (BNSS) and the Motivation and Pleasure Scale-Self-Report (MAP-SR). CFA results indicated that the two-factor model fit the data better than the one-factor model; however, latent variables were closely correlated. The two-factor model's fit was significantly improved by accounting for correlated residuals between N2 (emotional withdrawal) and N6 (lack of spontaneity and flow of conversation), and between N4 (passive social withdrawal) and G16 (active social avoidance), possibly reflecting common method variance. The two NSF factors exhibited differential patterns of correlation with subdomains of the BNSS and MAP-SR. These results suggest that the PANSS NSF would be better represented by a two-factor model than by a single-factor one, and support the two-factor model's adequate criterion-related validity. Common method variance among several items may be a potential source of measurement error under a two-factor model of the PANSS NSF.

  17. Analysis of factors affecting satisfaction level on problem based learning approach using structural equation modeling

    Science.gov (United States)

    Hussain, Nur Farahin Mee; Zahid, Zalina

    2014-12-01

    Nowadays, in the job market demand, graduates are expected not only to have higher performance in academic but they must also be excellent in soft skill. Problem-Based Learning (PBL) has a number of distinct advantages as a learning method as it can deliver graduates that will be highly prized by industry. This study attempts to determine the satisfaction level of engineering students on the PBL Approach and to evaluate their determinant factors. The Structural Equation Modeling (SEM) was used to investigate how the factors of Good Teaching Scale, Clear Goals, Student Assessment and Levels of Workload affected the student satisfaction towards PBL approach.

  18. Estimating safety effects of pavement management factors utilizing Bayesian random effect models.

    Science.gov (United States)

    Jiang, Ximiao; Huang, Baoshan; Zaretzki, Russell L; Richards, Stephen; Yan, Xuedong

    2013-01-01

    Previous studies of pavement management factors that relate to the occurrence of traffic-related crashes are rare. Traditional research has mostly employed summary statistics of bidirectional pavement quality measurements in extended longitudinal road segments over a long time period, which may cause a loss of important information and result in biased parameter estimates. The research presented in this article focuses on crash risk of roadways with overall fair to good pavement quality. Real-time and location-specific data were employed to estimate the effects of pavement management factors on the occurrence of crashes. This research is based on the crash data and corresponding pavement quality data for the Tennessee state route highways from 2004 to 2009. The potential temporal and spatial correlations among observations caused by unobserved factors were considered. Overall 6 models were built accounting for no correlation, temporal correlation only, and both the temporal and spatial correlations. These models included Poisson, negative binomial (NB), one random effect Poisson and negative binomial (OREP, ORENB), and two random effect Poisson and negative binomial (TREP, TRENB) models. The Bayesian method was employed to construct these models. The inference is based on the posterior distribution from the Markov chain Monte Carlo (MCMC) simulation. These models were compared using the deviance information criterion. Analysis of the posterior distribution of parameter coefficients indicates that the pavement management factors indexed by Present Serviceability Index (PSI) and Pavement Distress Index (PDI) had significant impacts on the occurrence of crashes, whereas the variable rutting depth was not significant. Among other factors, lane width, median width, type of terrain, and posted speed limit were significant in affecting crash frequency. The findings of this study indicate that a reduction in pavement roughness would reduce the likelihood of traffic

  19. Human factors analysis of incident/accident report

    International Nuclear Information System (INIS)

    Kuroda, Isao

    1992-01-01

    Human factors analysis of accident/incident has different kinds of difficulties in not only technical, but also psychosocial background. This report introduces some experiments of 'Variation diagram method' which is able to extend to operational and managemental factors. (author)

  20. Confirmatory factor analysis of the Malay version comprehensive feeding practices questionnaire tested among mothers of primary school children in Malaysia.

    Science.gov (United States)

    Shohaimi, Shamarina; Wei, Wong Yoke; Shariff, Zalilah Mohd

    2014-01-01

    Comprehensive feeding practices questionnaire (CFPQ) is an instrument specifically developed to evaluate parental feeding practices. It has been confirmed among children in America and applied to populations in France, Norway, and New Zealand. In order to extend the application of CFPQ, we conducted a factor structure validation of the translated version of CFPQ (CFPQ-M) using confirmatory factor analysis among mothers of primary school children (N = 397) in Malaysia. Several items were modified for cultural adaptation. Of 49 items, 39 items with loading factors >0.40 were retained in the final model. The confirmatory factor analysis revealed that the final model (twelve-factor model with 39 items and 2 error covariances) displayed the best fit for our sample (Chi-square = 1147; df = 634; P Malaysia. The present study extends the usability of the CFPQ and enables researchers and parents to better understand the relationships between parental feeding practices and related problems such as childhood obesity.

  1. Factors predicting early postpartum glucose intolerance in Japanese women with gestational diabetes mellitus: decision-curve analysis.

    Science.gov (United States)

    Kondo, M; Nagao, Y; Mahbub, M H; Tanabe, Tsuyoshi; Tanizawa, Y

    2018-04-29

    To identify factors predicting early postpartum glucose intolerance in Japanese women with gestational diabetes mellitus, using decision-curve analysis. A retrospective cohort study was performed. The participants were 123 Japanese women with gestational diabetes who underwent 75-g oral glucose tolerance tests at 8-12 weeks after delivery. They were divided into a glucose intolerance and a normal glucose tolerance group based on postpartum oral glucose tolerance test results. Analysis of the pregnancy oral glucose tolerance test results showed predictive factors for postpartum glucose intolerance. We also evaluated the clinical usefulness of the prediction model based on decision-curve analysis. Of 123 women, 78 (63.4%) had normoglycaemia and 45 (36.6%) had glucose intolerance. Multivariable logistic regression analysis showed insulinogenic index/fasting immunoreactive insulin and summation of glucose levels, assessed during pregnancy oral glucose tolerance tests (total glucose), to be independent risk factors for postpartum glucose intolerance. Evaluating the regression models, the best discrimination (area under the curve 0.725) was obtained using the basic model (i.e. age, family history of diabetes, BMI ≥25 kg/m 2 and use of insulin during pregnancy) plus insulinogenic index/fasting immunoreactive insulin intolerance. Insulinogenic index/fasting immunoreactive insulin calculated using oral glucose tolerance test results during pregnancy is potentially useful for predicting early postpartum glucose intolerance in Japanese women with gestational diabetes. This article is protected by copyright. All rights reserved. This article is protected by copyright. All rights reserved.

  2. Analysis of technological, institutional and socioeconomic factors ...

    African Journals Online (AJOL)

    Analysis of technological, institutional and socioeconomic factors that influences poor reading culture among secondary school students in Nigeria. ... Proliferation and availability of smart phones, chatting culture and social media were identified as technological factors influencing poor reading culture among secondary ...

  3. Modeling the factors associating with health-related habits among Japanese students.

    Science.gov (United States)

    Mato, Mie; Tsukasaki, Keiko

    2017-11-23

    The aim of the present study was to clarify the structural relationship between health-related habits and psychosocial factors during adolescence/early adulthood. An anonymous, self-administered questionnaire was provided to 1141 third- and fourth-year students at eight academic departments from six universities in regional Japanese cities. Surveys included items addressing participants' demographic characteristics, psychosocial factors (individual-level social capital, self-efficacy, mental health (from health-related quality of life SF-36v2), and sense of coherence (SOC)), and health-related habits. A multiple indicator analysis based on structural equation modeling was conducted to examine the structural relationship between health-related habits and these factors. Valid responses were obtained from 952 participants. The final model demonstrated a high level of goodness of fit. While the path from SOC to health-related habits was significant, those from self-efficacy to health-related habits and from mental health to health-related habits were not significant. The path coefficient from SOC to health-related habits was greater than the path coefficient from background characteristics. In the multiple population comparison that considered gender, a nearly identical model was supported for men and women. Psychosocial factors related to health-related habits were social capital, self-efficacy, mental health, and SOC. Furthermore, it was suggested that SOC functions as an intervening factor for maintaining a healthy lifestyle. It was observed that individual psychosocial factors influence health-related habits more than their background characteristics. Findings highlight that supporting the building of social relationships and social environments is essential to promote a healthy lifestyle among university students. © The Author 2017. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

  4. Exploratory Analysis of the Factors Affecting Consumer Choice in E-Commerce: Conjoint Analysis

    Directory of Open Access Journals (Sweden)

    Elena Mazurova

    2017-05-01

    Full Text Available According to previous studies of online consumer behaviour, three factors are the most influential on purchasing behavior - brand, colour and position of the product on the screen. However, a simultaneous influence of these three factors on the consumer decision making process has not been investigated previously. In this particular work we aim to execute a comprehensive study of the influence of these three factors. In order to answer our main research questions, we conducted an experiment with 96 different combinations of the three attributes, and using statistical analysis, such as conjoint analysis, t-test analysis and Kendall analysis we identified that the most influential factor to the online consumer decision making process is brand, the second most important attribute is the colour, which was estimated half as important as brand, and the least important attribute is the position on the screen. Additionally, we identified the main differences regarding consumers stated and revealed preferences regarding these three attributes.

  5. Analysis of CD45- [CD34+/KDR+] endothelial progenitor cells as juvenile protective factors in a rat model of ischemic-hemorrhagic stroke.

    Directory of Open Access Journals (Sweden)

    Julius L Decano

    Full Text Available Identification of juvenile protective factors (JPFs which are altered with age and contribute to adult-onset diseases could identify novel pathways for reversing the effects of age, an accepted non-modifiable risk factor to adult-onset diseases. Since endothelial progenitor cells (EPCs have been observed to be altered in stroke, hypertension and hypercholesterolemia, said EPCs are candidate JPFs for adult-onset stroke. A priori, if EPC aging plays a 'master-switch JPF-role' in stroke pathogenesis, juvenile EPC therapy alone should delay stroke-onset. Using a hypertensive, transgenic-hyperlipidemic rat model of spontaneous ischemic-hemorrhagic stroke, spTg25, we tested the hypothesis that freshly isolated juvenile EPCs are JPFs that can attenuate stroke progression and delay stroke onset.FACS analysis revealed that CD45- [CD34+/KDR+] EPCs decrease with progression to stroke in spTg25 rats, exhibit differential expression of the dual endodthelin-1/VEGFsp receptor (DEspR and undergo differential DEspR-subtype specific changes in number and in vitro angiogenic tube-incorporation. In vivo EPC infusion of male, juvenile non-expanded cd45-[CD34+/KDR+] EPCs into female stroke-prone rats prior to stroke attenuated progression and delayed stroke onset (P<0.003. Detection of Y-chromosome DNA in brain microvessels of EPC-treated female spTg25 rats indicates integration of male EPCs into female rat brain microvessels. Gradient-echo MRI showed delay of ischemic-hemorrhagic lesions in EPC-treated rats. Real-time RT-PCR pathway-specific array-analysis revealed age-associated gene expression changes in CD45-[CD34+/KDR]EPC subtypes, which were accelerated in stroke-prone rats. Pro-angiogenic genes implicated in intimal hyperplasia were increased in stroke-prone rat EPCs (P<0.0001, suggesting a maladaptive endothelial repair system which acts like a double-edged sword repairing while predisposing to age-associated intimal hyperplasia.Altogether, the data

  6. A Parametric Factor Model of the Term Structure of Mortality

    DEFF Research Database (Denmark)

    Haldrup, Niels; Rosenskjold, Carsten Paysen T.

    The prototypical Lee-Carter mortality model is characterized by a single common time factor that loads differently across age groups. In this paper we propose a factor model for the term structure of mortality where multiple factors are designed to influence the age groups differently via...... on the loading functions, the factors are not designed to be orthogonal but can be dependent and can possibly cointegrate when the factors have unit roots. We suggest two estimation procedures similar to the estimation of the dynamic Nelson-Siegel term structure model. First, a two-step nonlinear least squares...... procedure based on cross-section regressions together with a separate model to estimate the dynamics of the factors. Second, we suggest a fully specified model estimated by maximum likelihood via the Kalman filter recursions after the model is put on state space form. We demonstrate the methodology for US...

  7. A factor analysis to find critical success factors in retail brand

    Directory of Open Access Journals (Sweden)

    Naser Azad

    2013-03-01

    Full Text Available The present exploratory study aims to find critical components of retail brand among some retail stores. The study seeks to build a brand name in retail level and looks to find important factors affecting it. Customer behavior is largely influenced when the first retail customer experience is formed. These factors have direct impacts on customer experience and satisfaction in retail industry. The proposed study performs an empirical investigation on two well-known retain stores located in city of Tehran, Iran. Using a sample of 265 people from regular customers, the study uses factor analysis and extracts four main factors including related brand, product benefits, customer welfare strategy and corporate profits using the existing 31 factors in the literature.

  8. Sustainability Perceptions in Romanian Non-Profit Organizations: An Exploratory Study Using Success Factor Analysis

    Directory of Open Access Journals (Sweden)

    Sebastian Ion Ceptureanu

    2018-01-01

    Full Text Available This paper analyses sustainability perceptions in Romanian non-profits by investigating 81 non-profits managers and board members. Using a multidimensional sustainability measurement framework, Success Factor Analysis, as a conceptual model, we measured perceptions on 5 critical sustainability factors: People, Business Model, Operations, Strategy and Culture and concluded that there are significant differences in the perceptions of sustainability depending on respondents’ previous failure experiences. While those which previously experienced failure adopt a long-term approach based on marketization, clear accountability standards and rely on strategy, while the others prefer a short-term approach, focused more on non-profits operations and focus on human resources.

  9. [Habitat factor analysis for Torreya grandis cv. Merrillii based on spatial information technology].

    Science.gov (United States)

    Wang, Xiao-ming; Wang, Ke; Ao, Wei-jiu; Deng, Jin-song; Han, Ning; Zhu, Xiao-yun

    2008-11-01

    Torreya grandis cv. Merrillii, a tertiary survival plant, is a rare tree species of significant economic value and expands rapidly in China. Its special habitat factor analysis has the potential value to provide guide information for its planting, management, and sustainable development, because the suitable growth conditions for this tree species are special and strict. In this paper, the special habitat factors for T. grandis cv. Merrillii in its core region, i.e., in seven villages of Zhuji City, Zhejiang Province were analyzed with Principal Component Analysis (PCA) and a series of data, such as IKONOS image, Digital Elevation Model (DEM), and field survey data supported by the spatial information technology. The results showed that T. grandis cv. Merrillii exhibited high selectivity of environmental factors such as elevation, slope, and aspect. 96.22% of T. grandis cv. Merrillii trees were located at the elevation from 300 to 600 m, 97.52% of them were found to present on the areas whose slope was less than 300, and 74.43% of them distributed on sunny and half-sunny slopes. The results of PCA analysis indicated that the main environmental factors affecting the habitat of T. grandis cv. Merrillii were moisture, heat, and soil nutrients, and moisture might be one of the most important ecological factors for T. grandis cv. Merrillii due to the unique biological and ecological characteristics of the tree species.

  10. Modeling and Stability Analysis of Wedge Clutch System

    Directory of Open Access Journals (Sweden)

    Jian Yao

    2014-01-01

    Full Text Available A wedge clutch with unique features of self-reinforcement and small actuation force was designed. Its self-reinforcement feature, associated with different factors such as the wedge angle and friction coefficient, brings different dynamics and unstable problem with improper parameters. To analyze this system, a complete mathematical model of the actuation system is built, which includes the DC motor, the wedge mechanism, and the actuated clutch pack. By considering several nonlinear factors, such as the slip-stick friction and the contact or not of the clutch plates, the system is piecewise linear. Through the stability analysis of the linearized system in clutch slipping phase, the stable condition of the designed parameters is obtained as α>arctan⁡(μc. The mathematical model of the actuation system is validated by prototype testing. And with the validated model, the system dynamics in both stable and unstable conditions is investigated and discussed in engineering side.

  11. Factor analysis of the Mayo-Portland Adaptability Inventory: structure and validity.

    Science.gov (United States)

    Bohac, D L; Malec, J F; Moessner, A M

    1997-07-01

    Principal-components (PC) factor analysis of the Mayo-Portland Adaptability Inventory (MPAI) was conducted using a sample of outpatients (n = 189) with acquired brain injury (ABI) to evaluate whether outcome after ABI is multifactorial or unifactorial in nature. An eight-factor model was derived which explained 64-4% of the total variance. The eight factors were interpreted as representing Activities of Daily Living, Social Initiation, Cognition, Impaired-Self-awareness/Distress, Social Skills/ Support, Independence, Visuoperceptual, and Psychiatric, respectively. Validation of the Cognition factor was supported when factor scores were correlated with various neuropsychological measures. In addition, 117 patient self-rating total scores were used to evaluate the Impaired Self-awareness/Distress factor. An inverse relationship was observed, supporting this factor's ability to capture the two-dimensional phenomena of diminished self-awareness or enhanced emotional distress. A new subscale structure is suggested, that may allow greater clinical utility in understanding how ABI manifests in patients, and may provide clinicians with a better structure for implementing treatment strategies to address specific areas of impairment and disability for specific patients. Additionally, more precise measurement of treatment outcomes may be afforded by this reorganization.

  12. A Monte Carlo study comparing PIV, ULS and DWLS in the estimation of dichotomous confirmatory factor analysis.

    Science.gov (United States)

    Nestler, Steffen

    2013-02-01

    We conducted a Monte Carlo study to investigate the performance of the polychoric instrumental variable estimator (PIV) in comparison to unweighted least squares (ULS) and diagonally weighted least squares (DWLS) in the estimation of a confirmatory factor analysis model with dichotomous indicators. The simulation involved 144 conditions (1,000 replications per condition) that were defined by a combination of (a) two types of latent factor models, (b) four sample sizes (100, 250, 500, 1,000), (c) three factor loadings (low, moderate, strong), (d) three levels of non-normality (normal, moderately, and extremely non-normal), and (e) whether the factor model was correctly specified or misspecified. The results showed that when the model was correctly specified, PIV produced estimates that were as accurate as ULS and DWLS. Furthermore, the simulation showed that PIV was more robust to structural misspecifications than ULS and DWLS. © 2012 The British Psychological Society.

  13. [Factors and validity analysis of Mini-Mental State Examination in Chinese elderly people].

    Science.gov (United States)

    Gao, Ming-yue; Yang, Min; Kuang, Wei-hong; Qiu, Pei-yuan

    2015-06-18

    To examine factors that may have impact on the Mini-Mental State Examination (MMSE) screening validity, which could lead to further establishing the general model of the MMSE score in Chinese health elderly and to improve the screening accuracy of the existing MMSE reference. Based on the data of the Chinese Longitudinal Healthy Longevity Survey (CLHLS), the MMSE scores of 19,117 normal elderly and 137 dementia patients who met the inclusion criteria were used for the analysis. The area under the curve (AUC) and validity indexes were used to compare the screening accuracy of various criteria. Multiple linear regression was used to identify factors that had impact on the MMSE score for both the normal and dementia elderly. Descriptive analysis was performed for differences in the MMSE scores by age trends and gender between the normal and dementia elderly. The AUC of MMSE was ≥0.75(Pvalidity of MMSE in CLHLS is not affected by educational level. The analysis of factors that may impact on the MMSE screening validity are gender, age, vision and residence which with validity identification. These four factors can be used as assist tool of MMSE in the screening of dementia to improve the screening accuracy.

  14. Factor analysis for exercise stress radionuclide ventriculography

    International Nuclear Information System (INIS)

    Hirota, Kazuyoshi; Yasuda, Mitsutaka; Oku, Hisao; Ikuno, Yoshiyasu; Takeuchi, Kazuhide; Takeda, Tadanao; Ochi, Hironobu

    1987-01-01

    Using factor analysis, a new image processing in exercise stress radionuclide ventriculography, changes in factors associated with exercise were evaluated in 14 patients with angina pectoris or old myocardial infarction. The patients were imaged in the left anterior oblique projection, and three factor images were presented on a color coded scale. Abnormal factors (AF) were observed in 6 patients before exercise, 13 during exercise, and 4 after exercise. In 7 patients, the occurrence of AF was associated with exercise. Five of them became free from AF after exercise. Three patients showing AF before exercise had aggravation of AF during exercise. Overall, the occurrence or aggravation of AF was associated with exercise in ten (71 %) of the patients. The other three patients, however, had disappearance of AF during exercise. In the last patient, none of the AF was observed throughout the study. In view of a high incidence of AF associated with exercise, the factor analysis may have the potential in evaluating cardiac reverse from the viewpoint of left ventricular wall motion abnormality. (Namekawa, K.)

  15. Confirmatory factor analysis of teaching and learning guiding principles instrument among teacher educators in higher education institutions

    Science.gov (United States)

    Masuwai, Azwani; Tajudin, Nor'ain Mohd; Saad, Noor Shah

    2017-05-01

    The purpose of this study is to develop and establish the validity and reliability of an instrument to generate teaching and learning guiding principles using Teaching and Learning Guiding Principles Instrument (TLGPI). Participants consisted of 171 Malaysian teacher educators. It is an essential instrument to reflect in generating the teaching and learning guiding principles in higher education level in Malaysia. Confirmatory Factor Analysis has validated all 19 items of TLGPI whereby all items indicated high reliability and internal consistency. A Confirmatory Factor Analysis also confirmed that a single factor model was used to generate teaching and learning guiding principles.

  16. Condensation in models with factorized and pair-factorized stationary states

    International Nuclear Information System (INIS)

    Evans, M R; Waclaw, B

    2015-01-01

    Non-equilibrium real-space condensation is a phenomenon in which a finite fraction of some conserved quantity (mass, particles, etc) becomes spatially localized. We review two popular stochastic models of hopping particles that lead to condensation and whose stationary states assume a factorized form: the zero-range process and the misanthrope process, and their various generalizations. We also introduce a new model—a misanthrope process with parallel dynamics—that exhibits condensation and has a pair-factorized stationary state

  17. Attenuation Factors for B(E2) in the Microscopic Description of Multiphonon States ---A Simple Model Analysis---

    Science.gov (United States)

    Matsuyanagi, K.

    1982-05-01

    With an exactly solvable O(4) model of Piepenbring, Silvestre-Brac and Szymanski, we demonstrate that the attenuation factor for the B(E2) values, derived by the lowest-order approximation of the multiphonon method, takes excellent care of the kinematical anharmonicity effects, if multiphonon states are defined in the intrinsic subspace orthogonal to the pairing rotation. It is also shown that the other attenuation effect characterizing the interacting boson model is not a dominant effect in the model analysed here.

  18. Hand function evaluation: a factor analysis study.

    Science.gov (United States)

    Jarus, T; Poremba, R

    1993-05-01

    The purpose of this study was to investigate hand function evaluations. Factor analysis with varimax rotation was used to assess the fundamental characteristics of the items included in the Jebsen Hand Function Test and the Smith Hand Function Evaluation. The study sample consisted of 144 subjects without disabilities and 22 subjects with Colles fracture. Results suggest a four factor solution: Factor I--pinch movement; Factor II--grasp; Factor III--target accuracy; and Factor IV--activities of daily living. These categories differentiated the subjects without Colles fracture from the subjects with Colles fracture. A hand function evaluation consisting of these four factors would be useful. Such an evaluation that can be used for current clinical purposes is provided.

  19. Salivary SPECT and factor analysis in Sjoegren's syndrome

    International Nuclear Information System (INIS)

    Nakamura, T.; Oshiumi, Y.; Yonetsu, K.; Muranaka, T.; Sakai, K.; Kanda, S.; National Fukuoka Central Hospital

    1991-01-01

    Salivary SPECT and factor analysis in Sjoegren's syndrome were performed in 17 patients and 6 volunteers as controls. The ability of SPECT to detect small differences in the level of uptake can be used to separate glands from background even when uptake is reduced as in the patients with Sjoegren's syndrome. In control and probable Sjoegren's syndrome groups the uptake ratio of the submandibular gland to parotid gland on salivary SPECT (S/P ratio) was less than 1.0. However, in the definite Sjoergren's syndrome group, the ratio was more than 1.0. Moreover, the ratio in all patients with sialectasia, which is characteristic of Sjoegren's syndrome, was more than 1.0. Salivary factor analysis of normal parotid glands showed slowly increasing patterns of uptake and normal submandibular glands had rapidly increasing patterns of uptake. However, in the definite Sjoegren's syndrome group, the factor analysis patterns were altered, with slowly increasing patterns dominating both in the parotid and submandibular glands. These results suggest that the S/P ratio in salivary SPECT and salivary factor analysis provide additional radiologic criteria in diagnosing Sjoegren's syndrome. (orig.)

  20. Urban Saturated Power Load Analysis Based on a Novel Combined Forecasting Model

    Directory of Open Access Journals (Sweden)

    Huiru Zhao

    2015-03-01

    Full Text Available Analysis of urban saturated power loads is helpful to coordinate urban power grid construction and economic social development. There are two different kinds of forecasting models: the logistic curve model focuses on the growth law of the data itself, while the multi-dimensional forecasting model considers several influencing factors as the input variables. To improve forecasting performance, a novel combined forecasting model for saturated power load analysis was proposed in this paper, which combined the above two models. Meanwhile, the weights of these two models in the combined forecasting model were optimized by employing a fruit fly optimization algorithm. Using Hubei Province as the example, the effectiveness of the proposed combined forecasting model was verified, demonstrating a higher forecasting accuracy. The analysis result shows that the power load of Hubei Province will reach saturation in 2039, and the annual maximum power load will reach about 78,630 MW. The results obtained from this proposed hybrid urban saturated power load analysis model can serve as a reference for sustainable development for urban power grids, regional economies, and society at large.

  1. The Fear of Pain Questionnaire-III and the Fear of Pain Questionnaire-Short Form: a confirmatory factor analysis

    DEFF Research Database (Denmark)

    Vambheim, Sara M.; Lyby, Peter Solvoll; Aslaksen, Per M.

    2017-01-01

    .Aims and methods: The purpose of the study was to investigate the model fit, reliability and validity of the FPQ-III and the FPQ-SF in a Norwegian nonclinical sample, using confirmatory factor analysis (CFA). The second aim was to explore the model fit of the two scales in male and female subgroups separately...... the questionnaires, the model fit, validity and reliability were compared across sex using CFA.Results: The results revealed that both models' original factor structures had poor fit. However, the FPQ-SF had a better fit overall, compared to the FPQ-III. The model fit of the two models differed across sex...

  2. The performance shaping factors influence analysis on the human reliability for NPP operation

    International Nuclear Information System (INIS)

    Farcasiu, M.; Nitoi, M.; Apostol, M.; Florescu, G.

    2008-01-01

    The Human Reliability Analysis (HRA) is an important step in Probabilistic Safety Assessment (PSA) studies and offers an advisability for concrete improvement of the man - machine - organization interfaces, reliability and safety. The goals of this analysis are to obtain sufficient details in order to understand and document all-important factors that affect human performance. The purpose of this paper is to estimate the human errors probabilities in view of the negative or positive effect of the human performance shaping factors (PSFs) for the mitigation of the initiating events which could occur in Nuclear Power Plant (NPP). Using THERP and SPAR-H methods, an analysis model of PSFs influence on the human reliability is performed. This model is applied to more important activities, that are necessary to mitigate 'one steam generator tube failure' event at Cernavoda NPP. The results are joint human error probabilities (JHEP) values estimated for the following situations: without regarding to PSFs influence; with PSFs in specific conditions; with PSFs which could have only positive influence and with PSFs which could have only negative influence. In addition, PSFs with negative influence were identified and using the DOE method, the necessary activities for changing negative influence were assigned. (authors)

  3. Bayes Factor Covariance Testing in Item Response Models.

    Science.gov (United States)

    Fox, Jean-Paul; Mulder, Joris; Sinharay, Sandip

    2017-12-01

    Two marginal one-parameter item response theory models are introduced, by integrating out the latent variable or random item parameter. It is shown that both marginal response models are multivariate (probit) models with a compound symmetry covariance structure. Several common hypotheses concerning the underlying covariance structure are evaluated using (fractional) Bayes factor tests. The support for a unidimensional factor (i.e., assumption of local independence) and differential item functioning are evaluated by testing the covariance components. The posterior distribution of common covariance components is obtained in closed form by transforming latent responses with an orthogonal (Helmert) matrix. This posterior distribution is defined as a shifted-inverse-gamma, thereby introducing a default prior and a balanced prior distribution. Based on that, an MCMC algorithm is described to estimate all model parameters and to compute (fractional) Bayes factor tests. Simulation studies are used to show that the (fractional) Bayes factor tests have good properties for testing the underlying covariance structure of binary response data. The method is illustrated with two real data studies.

  4. Emotional Intelligence and Nurse Recruitment: Rasch and confirmatory factor analysis of the trait emotional intelligence questionnaire short form.

    Science.gov (United States)

    Snowden, Austyn; Watson, Roger; Stenhouse, Rosie; Hale, Claire

    2015-12-01

    To examine the construct validity of the Trait Emotional Intelligence Questionnaire Short form. Emotional intelligence involves the identification and regulation of our own emotions and the emotions of others. It is therefore a potentially useful construct in the investigation of recruitment and retention in nursing and many questionnaires have been constructed to measure it. Secondary analysis of existing dataset of responses to Trait Emotional Intelligence Questionnaire Short form using concurrent application of Rasch analysis and confirmatory factor analysis. First year undergraduate nursing and computing students completed Trait Emotional Intelligence Questionnaire-Short Form in September 2013. Responses were analysed by synthesising results of Rasch analysis and confirmatory factor analysis. Participants (N = 938) completed Trait Emotional Intelligence Questionnaire Short form. Rasch analysis showed the majority of the Trait Emotional Intelligence Questionnaire-Short Form items made a unique contribution to the latent trait of emotional intelligence. Five items did not fit the model and differential item functioning (gender) accounted for this misfit. Confirmatory factor analysis revealed a four-factor structure consisting of: self-confidence, empathy, uncertainty and social connection. All five misfitting items from the Rasch analysis belonged to the 'social connection' factor. The concurrent use of Rasch and factor analysis allowed for novel interpretation of Trait Emotional Intelligence Questionnaire Short form. Much of the response variation in Trait Emotional Intelligence Questionnaire Short form can be accounted for by the social connection factor. Implications for practice are discussed. © 2015 John Wiley & Sons Ltd.

  5. Validation of the Malay Version of the Parental Bonding Instrument among Malaysian Youths Using Exploratory Factor Analysis

    Science.gov (United States)

    MUHAMMAD, Noor Azimah; SHAMSUDDIN, Khadijah; OMAR, Khairani; SHAH, Shamsul Azhar; MOHD AMIN, Rahmah

    2014-01-01

    Background: Parenting behaviour is culturally sensitive. The aims of this study were (1) to translate the Parental Bonding Instrument into Malay (PBI-M) and (2) to determine its factorial structure and validity among the Malaysian population. Methods: The PBI-M was generated from a standard translation process and comprehension testing. The validation study of the PBI-M was administered to 248 college students aged 18 to 22 years. Results: Participants in the comprehension testing had difficulty understanding negative items. Five translated double negative items were replaced with five positive items with similar meanings. Exploratory factor analysis showed a three-factor model for the PBI-M with acceptable reliability. Four negative items (items 3, 4, 8, and 16) and item 19 were omitted from the final PBI-M list because of incorrect placement or low factor loading (overprotection factor. All the items loaded positively on their respective factors. Conclusion: The Malaysian population favoured positive items in answering questions. The PBI-M confirmed the three-factor model that consisted of care, autonomy and overprotection. The PBI-M is a valid and reliable instrument to assess the Malaysian parenting style. Confirmatory factor analysis may further support this finding. Keywords: Malaysia, parenting, questionnaire, validity PMID:25977634

  6. Projects Delay Factors of Saudi Arabia Construction Industry Using PLS-SEM Path Modelling Approach

    Directory of Open Access Journals (Sweden)

    Abdul Rahman Ismail

    2016-01-01

    Full Text Available This paper presents the development of PLS-SEM Path Model of delay factors of Saudi Arabia construction industry focussing on Mecca City. The model was developed and assessed using SmartPLS v3.0 software and it consists of 37 factors/manifests in 7 groups/independent variables and one dependent variable which is delay of the construction projects. The model was rigorously assessed at measurement and structural components and the outcomes found that the model has achieved the required threshold values. At structural level of the model, among the seven groups, the client and consultant group has the highest impact on construction delay with path coefficient β-value of 0.452 and the project management and contract administration group is having the least impact to the construction delay with β-value of 0.016. The overall model has moderate explaining power ability with R2 value of 0.197 for Saudi Arabia construction industry representation. This model will able to assist practitioners in Mecca city to pay more attention in risk analysis for potential construction delay.

  7. [A prediction model for internet game addiction in adolescents: using a decision tree analysis].

    Science.gov (United States)

    Kim, Ki Sook; Kim, Kyung Hee

    2010-06-01

    This study was designed to build a theoretical frame to provide practical help to prevent and manage adolescent internet game addiction by developing a prediction model through a comprehensive analysis of related factors. The participants were 1,318 students studying in elementary, middle, and high schools in Seoul and Gyeonggi Province, Korea. Collected data were analyzed using the SPSS program. Decision Tree Analysis using the Clementine program was applied to build an optimum and significant prediction model to predict internet game addiction related to various factors, especially parent related factors. From the data analyses, the prediction model for factors related to internet game addiction presented with 5 pathways. Causative factors included gender, type of school, siblings, economic status, religion, time spent alone, gaming place, payment to Internet café, frequency, duration, parent's ability to use internet, occupation (mother), trust (father), expectations regarding adolescent's study (mother), supervising (both parents), rearing attitude (both parents). The results suggest preventive and managerial nursing programs for specific groups by path. Use of this predictive model can expand the role of school nurses, not only in counseling addicted adolescents but also, in developing and carrying out programs with parents and approaching adolescents individually through databases and computer programming.

  8. An analysis of a three-factor model proposed by the Danish Society of Actuaries for forecasting and risk analysis

    OpenAIRE

    Jørgensen, Peter Løchte; Slipsager, Søren Kærgaard

    2016-01-01

    This paper provides the explicit solution to the three-factor diffusion model recently proposed by the Danish Society of Actuaries to the Danish industry of life insurance and pensions. The solution is obtained by use of the known general solution to multidimensional linear stochastic differential equation systems. With offset in the explicit solution, we establish the conditional distribution of the future state variables which allows for exact simulation. Using exact simulation, we illustra...

  9. Analysis of Epidermal Growth Factor Receptor Related Gene Expression Changes in a Cellular and Animal Model of Parkinson’s Disease

    Directory of Open Access Journals (Sweden)

    In-Su Kim

    2017-02-01

    Full Text Available We employed transcriptome analysis of epidermal growth factor receptor related gene expression changes in cellular and animal models of Parkinson’s disease (PD. We used a well-known Parkinsonian toxin 1-methyl-4-phenylpyridine (MPP+ to induce neuronal apoptosis in the human neuroblastoma SH-SY5Y cell line. The MPP+-treatment of SH-SY5Y cells was capable of inducing neuro-apoptosis, but it remains unclear what kinds of transcriptional genes are affected by MPP+ toxicity. Therefore the pathways that were significantly perturbed in MPP+ treated human neuroblastoma SH-SY5Y cells were identified based on genome-wide gene expression data at two time points (24 and 48 h. We found that the Epidermal Growth Factor Receptor (EGFR pathway-related genes showed significantly differential expression at all time points. The EGFR pathway has been linked to diverse cellular events such as proliferation, differentiation, and apoptosis. Further, to evaluate the functional significance of the altered EGFR related gene expression observed in MPP+-treated SH-SY5Y cells, the EGFR related GJB2 (Cx26 gene expression was analyzed in an MPP+-intoxicated animal PD model. Our findings identify that the EGFR signaling pathway and its related genes, such as Cx26, might play a significant role in dopaminergic (DAergic neuronal cell death during the process of neuro-apoptosis and therefore can be focused on as potential targets for therapeutic intervention.

  10. Using exploratory factor analysis in personality research: Best-practice recommendations

    Directory of Open Access Journals (Sweden)

    Sumaya Laher

    2010-11-01

    Research purpose: This article presents more objective methods to determine the number of factors, most notably parallel analysis and Velicer’s minimum average partial (MAP. The benefits of rotation are also discussed. The article argues for more consistent use of Procrustes rotation and congruence coefficients in factor analytic studies. Motivation for the study: Exploratory factor analysis is often criticised for not being rigorous and objective enough in terms of the methods used to determine the number of factors, the rotations to be used and ultimately the validity of the factor structure. Research design, approach and method: The article adopts a theoretical stance to discuss the best-practice recommendations for factor analytic research in the field of psychology. Following this, an example located within personality assessment and using the NEO-PI-R specifically is presented. A total of 425 students at the University of the Witwatersrand completed the NEO-PI-R. These responses were subjected to a principal components analysis using varimax rotation. The rotated solution was subjected to a Procrustes rotation with Costa and McCrae’s (1992 matrix as the target matrix. Congruence coefficients were also computed. Main findings: The example indicates the use of the methods recommended in the article and demonstrates an objective way of determining the number of factors. It also provides an example of Procrustes rotation with coefficients of agreement as an indication of how factor analytic results may be presented more rigorously in local research. Practical/managerial implications: It is hoped that the recommendations in this article will have best-practice implications for both researchers and practitioners in the field who employ factor analysis regularly. Contribution/value-add: This article will prove useful to all researchers employing factor analysis and has the potential to set the trend for better use of factor analysis in the South African context.

  11. What Factors Lead Companies to Adopt Social Media in their processes: Proposal and Test of a Measurement Model

    Directory of Open Access Journals (Sweden)

    Jozé Braz de Araújo

    2016-01-01

    Full Text Available The objective of this study was to understand which factors lead companies to use social media to achieve results. For that, a theoretical model was proposed and tested. Data was collected using a survey of 237 companies. In the analysis we analysis used the structural eq uation modeling technique. The results show that the social media relative advantage and its observability were important factors to social media organizational adoption. We also found that big companies with more formalized organizational structure (OS t end to adopt social media more than small ones with no formal OS. The companies studied showed strong organizational disposition for innovation adoption.

  12. Confirmatory Factor Analysis of the ISB - Burnout Syndrome Inventory

    Directory of Open Access Journals (Sweden)

    Ana Maria T. Benevides-Pereira

    2017-05-01

    Full Text Available AimBurnout is a dysfunctional reaction to chronic occupational stress. The present study analysis the psychometric qualities of the Burnout Syndrome Inventory (ISB through Confirmatory Factor Analysis (CFA.MethodEmpirical study in a multi-centre and multi-occupational sample (n = 701 using the ISB. The Part I assesses antecedent factors: Positive Organizational Conditions (PC and Negative Organizational Conditions (NC. The Part II assesses the syndrome: Emotional Exhaustion (EE, Dehumanization (DE, Emotional Distancing (ED and Personal Accomplishment (PA.ResultsThe highest means occurred in the positive scales CP (M = 23.29, SD = 5.89 and PA (M = 14.84, SD = 4.71. Negative conditions showed the greatest variability (SD = 6.03. Reliability indexes were reasonable, with the lowest rate at .77 for DE and the highest rate .91 for PA. The CFA revealed RMSEA = .057 and CFI = .90 with all scales regressions showing significant values (β = .73 until β = .92.ConclusionThe ISB showed a plausible instrument to evaluate burnout. The two sectors maintained the initial model and confirmed the theoretical presupposition. This instrument makes possible a more comprehensive idea of the labour context, and one or another part may be used separately according to the needs and the aims of the assessor.

  13. Development and Initial Validation of the Five-Factor Model Adolescent Personality Questionnaire (FFM-APQ).

    Science.gov (United States)

    Rogers, Mary E; Glendon, A Ian

    2018-01-01

    This research reports on the 4-phase development of the 25-item Five-Factor Model Adolescent Personality Questionnaire (FFM-APQ). The purpose was to develop and determine initial evidence for validity of a brief adolescent personality inventory using a vocabulary that could be understood by adolescents up to 18 years old. Phase 1 (N = 48) consisted of item generation and expert (N = 5) review of items; Phase 2 (N = 179) involved item analyses; in Phase 3 (N = 496) exploratory factor analysis assessed the underlying structure; in Phase 4 (N = 405) confirmatory factor analyses resulted in a 25-item inventory with 5 subscales.

  14. Logistic regression for risk factor modelling in stuttering research.

    Science.gov (United States)

    Reed, Phil; Wu, Yaqionq

    2013-06-01

    To outline the uses of logistic regression and other statistical methods for risk factor analysis in the context of research on stuttering. The principles underlying the application of a logistic regression are illustrated, and the types of questions to which such a technique has been applied in the stuttering field are outlined. The assumptions and limitations of the technique are discussed with respect to existing stuttering research, and with respect to formulating appropriate research strategies to accommodate these considerations. Finally, some alternatives to the approach are briefly discussed. The way the statistical procedures are employed are demonstrated with some hypothetical data. Research into several practical issues concerning stuttering could benefit if risk factor modelling were used. Important examples are early diagnosis, prognosis (whether a child will recover or persist) and assessment of treatment outcome. After reading this article you will: (a) Summarize the situations in which logistic regression can be applied to a range of issues about stuttering; (b) Follow the steps in performing a logistic regression analysis; (c) Describe the assumptions of the logistic regression technique and the precautions that need to be checked when it is employed; (d) Be able to summarize its advantages over other techniques like estimation of group differences and simple regression. Copyright © 2012 Elsevier Inc. All rights reserved.

  15. TEST OF THE FAMA-FRENCH THREE-FACTOR MODEL IN CROATIA

    Directory of Open Access Journals (Sweden)

    Denis Dolinar

    2013-06-01

    Full Text Available This paper empirically examines the Fama-French three-factor model of stock returns for Croatia. In contrast to the results of Fama and French (1993 for the U.S. stock market, their three-factor model did not show so successful when describing risk-return relation of Croatian stocks. This paper shows that the Fama-French three-factor model is a valid pricing model, since it explains cross-section of average returns on stocks in Croatia, and that has a greater explanatory power in comparison to the CAPM. In the case of Croatian stock market, size and B/M factors are not always significant, but on average they individually have certain marginal explanatory power. Namely, they capture small common variation in returns that is missed by the market factor. Moreover, B/M factor has shown as a stronger common risk proxy in relation to size factor. Finally, there is still a large portion of common variation in stock return that may be explained by other factors. Because emerging capital markets bear their own specificity, special care needs to be taken when applying existing or developing new pricing models.

  16. Estimation of physiological parameters using knowledge-based factor analysis of dynamic nuclear medicine image sequences

    International Nuclear Information System (INIS)

    Yap, J.T.; Chen, C.T.; Cooper, M.

    1995-01-01

    The authors have previously developed a knowledge-based method of factor analysis to analyze dynamic nuclear medicine image sequences. In this paper, the authors analyze dynamic PET cerebral glucose metabolism and neuroreceptor binding studies. These methods have shown the ability to reduce the dimensionality of the data, enhance the image quality of the sequence, and generate meaningful functional images and their corresponding physiological time functions. The new information produced by the factor analysis has now been used to improve the estimation of various physiological parameters. A principal component analysis (PCA) is first performed to identify statistically significant temporal variations and remove the uncorrelated variations (noise) due to Poisson counting statistics. The statistically significant principal components are then used to reconstruct a noise-reduced image sequence as well as provide an initial solution for the factor analysis. Prior knowledge such as the compartmental models or the requirement of positivity and simple structure can be used to constrain the analysis. These constraints are used to rotate the factors to the most physically and physiologically realistic solution. The final result is a small number of time functions (factors) representing the underlying physiological processes and their associated weighting images representing the spatial localization of these functions. Estimation of physiological parameters can then be performed using the noise-reduced image sequence generated from the statistically significant PCs and/or the final factor images and time functions. These results are compared to the parameter estimation using standard methods and the original raw image sequences. Graphical analysis was performed at the pixel level to generate comparable parametric images of the slope and intercept (influx constant and distribution volume)

  17. Sensitivity Analysis of Corrosion Rate Prediction Models Utilized for Reinforced Concrete Affected by Chloride

    Science.gov (United States)

    Siamphukdee, Kanjana; Collins, Frank; Zou, Roger

    2013-06-01

    Chloride-induced reinforcement corrosion is one of the major causes of premature deterioration in reinforced concrete (RC) structures. Given the high maintenance and replacement costs, accurate modeling of RC deterioration is indispensable for ensuring the optimal allocation of limited economic resources. Since corrosion rate is one of the major factors influencing the rate of deterioration, many predictive models exist. However, because the existing models use very different sets of input parameters, the choice of model for RC deterioration is made difficult. Although the factors affecting corrosion rate are frequently reported in the literature, there is no published quantitative study on the sensitivity of predicted corrosion rate to the various input parameters. This paper presents the results of the sensitivity analysis of the input parameters for nine selected corrosion rate prediction models. Three different methods of analysis are used to determine and compare the sensitivity of corrosion rate to various input parameters: (i) univariate regression analysis, (ii) multivariate regression analysis, and (iii) sensitivity index. The results from the analysis have quantitatively verified that the corrosion rate of steel reinforcement bars in RC structures is highly sensitive to corrosion duration time, concrete resistivity, and concrete chloride content. These important findings establish that future empirical models for predicting corrosion rate of RC should carefully consider and incorporate these input parameters.

  18. Dependent defaults and losses with factor copula models

    Directory of Open Access Journals (Sweden)

    Ackerer Damien

    2017-12-01

    Full Text Available We present a class of flexible and tractable static factor models for the term structure of joint default probabilities, the factor copula models. These high-dimensional models remain parsimonious with paircopula constructions, and nest many standard models as special cases. The loss distribution of a portfolio of contingent claims can be exactly and efficiently computed when individual losses are discretely supported on a finite grid. Numerical examples study the key features affecting the loss distribution and multi-name credit derivatives prices. An empirical exercise illustrates the flexibility of our approach by fitting credit index tranche prices.

  19. Confirmatory Factor Analysis of the Malay Version Comprehensive Feeding Practices Questionnaire Tested among Mothers of Primary School Children in Malaysia

    Directory of Open Access Journals (Sweden)

    Shamarina Shohaimi

    2014-01-01

    Full Text Available Comprehensive feeding practices questionnaire (CFPQ is an instrument specifically developed to evaluate parental feeding practices. It has been confirmed among children in America and applied to populations in France, Norway, and New Zealand. In order to extend the application of CFPQ, we conducted a factor structure validation of the translated version of CFPQ (CFPQ-M using confirmatory factor analysis among mothers of primary school children (N = 397 in Malaysia. Several items were modified for cultural adaptation. Of 49 items, 39 items with loading factors >0.40 were retained in the final model. The confirmatory factor analysis revealed that the final model (twelve-factor model with 39 items and 2 error covariances displayed the best fit for our sample (Chi-square = 1147; df = 634; P<0.05; CFI = 0.900; RMSEA = 0.045; SRMR = 0.0058. The instrument with some modifications was confirmed among mothers of school children in Malaysia. The present study extends the usability of the CFPQ and enables researchers and parents to better understand the relationships between parental feeding practices and related problems such as childhood obesity.

  20. Study on visibility evaluation model which is considered field factors; Field factor wo koryoshita shininsei hyoka model ni kansuru kenkyu

    Energy Technology Data Exchange (ETDEWEB)

    Nakanishi, M; Hagiwara, T [Hokkaido University, Sapporo (Japan)

    1997-10-01

    The present study proposes a model to evaluate visual performance of road traffic facilities required for drivers. Two factors were employed to obtain the suitable contrast for drivers under driving situation. One factor is a suitable luminance range, which is derived from minimum required luminance and glare luminance. Another is a field. The model showed capability of providing visibility range in some cases. 8 refs., 4 figs., 2 tabs.

  1. Numerical modeling of polymorphic transformation of oleic acid via near-infrared spectroscopy and factor analysis

    Science.gov (United States)

    Liu, Ling; Cheng, Yuliang; Sun, Xiulan; Pi, Fuwei

    2018-05-01

    Near-infrared (NIR) spectroscopy as a tool for direct and quantitatively screening the minute polymorphic transitions of bioactive fatty acids was assessed basing on a thermal heating process of oleic acid. Temperature-dependent NIR spectral profiles indicate that dynamical variances of COOH group dominate its γ → α phase transition, while the transition from active α to β phase mainly relates to the conformational transfer of acyl chain. Through operating multivariate curve resolution-alternating least squares with factor analysis, instantaneous contribution of each active polymorph during the transition process was illustrated for displaying the progressive evolutions of functional groups. Calculated contributions reveal that the α phase of oleic acid initially is present at around -18 °C, but sharply grows up around -2.2 °C from the transformation of γ phase and finally disappears at the melting point. On the other hand, the β phase of oleic acid is sole self-generation after melt even it embryonically appears at -2.2 °C. Such mathematical approach based on NIR spectroscopy and factor analysis calculation provides a volatile strategy in quantitatively exploring the transition processes of bioactive fatty acids; meanwhile, it maintains promising possibility for instantaneous quantifying each active polymorph of lipid materials.

  2. An analysis of predictive factors for concurrent acute-on-chronic liver failure and hepatorenal syndrome

    Directory of Open Access Journals (Sweden)

    CHEN Yanfang

    2015-09-01

    Full Text Available ObjectiveTo learn the clinical characteristics of concurrent acute-on-chronic liver failure (ACLF and hepatorenal syndrome (HRS, and to investigate the predictive factors for HRS in patients with ACLF. MethodsA total of 806 patients with ACLF who were admitted to our hospital from January 2012 to May 2014 were selected and divided into two groups according to the incidence of concurrent HRS. Clinical indices and laboratory test results were analyzed in the two groups, and the multivariate logistic regression analysis was used to figure out independent indices for the prediction of HRS in patients with ACLF. A prediction model was established and the receiver operating characteristic curve was drawn to evaluate the accuracy of the prediction model. Comparison of continuous data between the two groups was made by t test, and comparison of categorical data between the two groups was made by χ2 test. ResultsIn all patients with ACLF, 229 had HRS and 577 had no HRS. The univariate logistic regression analysis showed that hepatic encephalopathy, peritonitis, infection, age, cystatin C (Cys-C, serum creatinine (SCr, blood urea nitrogen, albumin, prealbumin, total bilirubin, direct bilirubin, total cholesterol, K+, Na+, phosphorus, Ca2+, prothrombin time, prothrombin activity, international normalized ratio, and hematocrit were significant predictive factors for HRS. The multivariate logistic regression analysis showed that concurrent peritonitis, Cys-C, SCr, and HCO3- were independent predictive factors for HRS in patients with ACLF (OR=3.155, P<0.01; OR=30.773, P<0.01; OR=1062, P<0.01; OR=0.915, P<0.05. The model was proved of great value in prediction. ConclusionConcurrent peritonitis, Cys-C, SCr, and HCO3- are effective predictive factors for HRS in patients with ACLF.

  3. Using sensitivity analysis to identify key factors for the propagation of a plant epidemic.

    Science.gov (United States)

    Rimbaud, Loup; Bruchou, Claude; Dallot, Sylvie; Pleydell, David R J; Jacquot, Emmanuel; Soubeyrand, Samuel; Thébaud, Gaël

    2018-01-01

    Identifying the key factors underlying the spread of a disease is an essential but challenging prerequisite to design management strategies. To tackle this issue, we propose an approach based on sensitivity analyses of a spatiotemporal stochastic model simulating the spread of a plant epidemic. This work is motivated by the spread of sharka, caused by plum pox virus , in a real landscape. We first carried out a broad-range sensitivity analysis, ignoring any prior information on six epidemiological parameters, to assess their intrinsic influence on model behaviour. A second analysis benefited from the available knowledge on sharka epidemiology and was thus restricted to more realistic values. The broad-range analysis revealed that the mean duration of the latent period is the most influential parameter of the model, whereas the sharka-specific analysis uncovered the strong impact of the connectivity of the first infected orchard. In addition to demonstrating the interest of sensitivity analyses for a stochastic model, this study highlights the impact of variation ranges of target parameters on the outcome of a sensitivity analysis. With regard to sharka management, our results suggest that sharka surveillance may benefit from paying closer attention to highly connected patches whose infection could trigger serious epidemics.

  4. Analysis and optimization of the TWINKLE factoring device

    NARCIS (Netherlands)

    Lenstra, A.K.; Shamir, A.; Preneel, B.

    2000-01-01

    We describe an enhanced version of the TWINKLE factoring device and analyse to what extent it can be expected to speed up the sieving step of the Quadratic Sieve and Number Field Sieve factoring al- gorithms. The bottom line of our analysis is that the TWINKLE-assisted factorization of 768-bit

  5. Transmission risk assessment of invasive fluke Fascioloides magna using GIS-modelling and multicriteria analysis methods

    Directory of Open Access Journals (Sweden)

    Juhásová L.

    2017-06-01

    Full Text Available The combination of multicriteria analysis (MCA, particularly analytic hierarchy process (AHP and geographic information system (GIS were applied for transmission risk assessment of Fascioloides magna (Trematoda; Fasciolidae in south-western Slovakia. Based on the details on F. magna life cycle, the following risk factors (RF of parasite transmission were determined: intermediate (RFIH and final hosts (RFFH (biological factors, annual precipitation (RFAP, land use (RFLU, flooded area (RFFA, and annual mean air temperature (RFAT (environmental factors. Two types of risk analyses were modelled: (1 potential risk analysis was focused on the determination of the potential risk of parasite transmission into novel territories (data on F. magna occurrence were excluded; (2 actual risk analysis considered also the summary data on F. magna occurrence in the model region (risk factor parasite occurrence RFPO included in the analysis. The results of the potential risk analysis provided novel distribution pattern and revealed new geographical area as the potential risk zone of F. magna occurrence. Although the actual risk analysis revealed all four risk zones of F. magna transmission (acceptable, moderate, undesirable and unacceptable, its outputs were significantly affected by the data on parasite occurrence what reduced the informative value of the actual transmission risk assessment.

  6. A Bayesian Nonparametric Approach to Factor Analysis

    DEFF Research Database (Denmark)

    Piatek, Rémi; Papaspiliopoulos, Omiros

    2018-01-01

    This paper introduces a new approach for the inference of non-Gaussian factor models based on Bayesian nonparametric methods. It relaxes the usual normality assumption on the latent factors, widely used in practice, which is too restrictive in many settings. Our approach, on the contrary, does no...

  7. Transforming Rubrics Using Factor Analysis

    Science.gov (United States)

    Baryla, Ed; Shelley, Gary; Trainor, William

    2012-01-01

    Student learning and program effectiveness is often assessed using rubrics. While much time and effort may go into their creation, it is equally important to assess how effective and efficient the rubrics actually are in terms of measuring competencies over a number of criteria. This study demonstrates the use of common factor analysis to identify…

  8. INNOVATIVE PRACTICES IN TOURISM. APOSSIBLE MODEL BY FOSTERING SHADOW FACTORS

    Directory of Open Access Journals (Sweden)

    Ada Mirela TOMESCU

    2015-08-01

    Full Text Available The paper is the result of an empirical research, a study that includes a theoretical framework. The data used to test our hypotheses come from 60 small tourism firms from Bihor County, Romania. The research conducted has revealed that actions focusing on innovation must be based on a solid analysis, supported by the knowledge and the understanding of the contextual factors (environment, culture as a mental programming, values also based on the organizational factors (the management commitment, systemic perspective, learning and practice of experimentation, rapid transfer of knowledge within the organization. For the purpose of this work, the contextual factors that are exogenous represent the shadow factors. The studies performed in three European projects implemented in tourism SMEs of Bihor County have allowed us to advance the idea that contextual and organizational factors, that are identified as the source of innovation are based on rationality, which is enlarged by affectivity and imagination. The identified correlations may be considered, in our opinion an element of novelty and originality. Finally, the purpose of this paper is to provide a possible model, based on the idea of building an innovative firm, the one that has learned how to determine their own employees to be innovative. O03, L2, L26

  9. Schistosoma mansoni reinfection: Analysis of risk factors by classification and regression tree (CART modeling.

    Directory of Open Access Journals (Sweden)

    Andréa Gazzinelli

    Full Text Available Praziquantel (PZQ is an effective chemotherapy for schistosomiasis mansoni and a mainstay for its control and potential elimination. However, it does not prevent against reinfection, which can occur rapidly in areas with active transmission. A guide to ranking the risk factors for Schistosoma mansoni reinfection would greatly contribute to prioritizing resources and focusing prevention and control measures to prevent rapid reinfection. The objective of the current study was to explore the relationship among the socioeconomic, demographic, and epidemiological factors that can influence reinfection by S. mansoni one year after successful treatment with PZQ in school-aged children in Northeastern Minas Gerais state Brazil. Parasitological, socioeconomic, demographic, and water contact information were surveyed in 506 S. mansoni-infected individuals, aged 6 to 15 years, resident in these endemic areas. Eligible individuals were treated with PZQ until they were determined to be negative by the absence of S. mansoni eggs in the feces on two consecutive days of Kato-Katz fecal thick smear. These individuals were surveyed again 12 months from the date of successful treatment with PZQ. A classification and regression tree modeling (CART was then used to explore the relationship between socioeconomic, demographic, and epidemiological variables and their reinfection status. The most important risk factor identified for S. mansoni reinfection was their "heavy" infection at baseline. Additional analyses, excluding heavy infection status, showed that lower socioeconomic status and a lower level of education of the household head were also most important risk factors for S. mansoni reinfection. Our results provide an important contribution toward the control and possible elimination of schistosomiasis by identifying three major risk factors that can be used for targeted treatment and monitoring of reinfection. We suggest that control measures that target

  10. Using a latent variable model with non-constant factor loadings to examine PM2.5 constituents related to secondary inorganic aerosols.

    Science.gov (United States)

    Zhang, Zhenzhen; O'Neill, Marie S; Sánchez, Brisa N

    2016-04-01

    Factor analysis is a commonly used method of modelling correlated multivariate exposure data. Typically, the measurement model is assumed to have constant factor loadings. However, from our preliminary analyses of the Environmental Protection Agency's (EPA's) PM 2.5 fine speciation data, we have observed that the factor loadings for four constituents change considerably in stratified analyses. Since invariance of factor loadings is a prerequisite for valid comparison of the underlying latent variables, we propose a factor model that includes non-constant factor loadings that change over time and space using P-spline penalized with the generalized cross-validation (GCV) criterion. The model is implemented using the Expectation-Maximization (EM) algorithm and we select the multiple spline smoothing parameters by minimizing the GCV criterion with Newton's method during each iteration of the EM algorithm. The algorithm is applied to a one-factor model that includes four constituents. Through bootstrap confidence bands, we find that the factor loading for total nitrate changes across seasons and geographic regions.

  11. ANALYSIS MODEL FOR RETURN ON CAPITAL EMPLOYED

    Directory of Open Access Journals (Sweden)

    BURJA CAMELIA

    2013-02-01

    Full Text Available At the microeconomic level, the appreciation of the capitals’ profitability is a very complex action which is ofinterest for stakeholders. This study has as main purpose to extend the traditional analysis model for the capitals’profitability, based on the ratio “Return on capital employed”. In line with it the objectives of this work aim theidentification of factors that exert an influence on the capital’s profitability utilized by a company and the measurementof their contribution in the manifestation of the phenomenon. The proposed analysis model is validated on the use caseof a representative company from the agricultural sector. The results obtained reveal that in a company there are somefactors which can act positively on the capitals’ profitability: capital turnover, sales efficiency, increase the share ofsales in the total revenues, improvement of the expenses’ efficiency. The findings are useful both for the decisionmakingfactors in substantiating the economic strategies and for the capital owners who are interested in efficiency oftheir investments.

  12. Factor structure of a conceptual model of oral health tested among 65-year olds in Norway and Sweden.

    Science.gov (United States)

    Astrøm, Anne Nordrehaug; Ekbäck, Gunnar; Ordell, Sven

    2010-04-01

    No studies have tested oral health-related quality of life models in dentate older adults across different populations. To test the factor structure of oral health outcomes within Gilbert's conceptual model among 65-year olds in Sweden and Norway. It was hypothesized that responses to 14 observed indicators could be explained by three correlated factors, symptom status, functional limitations and oral disadvantages, that each observed oral health indicator would associate more strongly with the factor it is supposed to measure than with competing factors and that the proposed 3-factor structure would possess satisfactory cross-national stability with 65-year olds in Norway and Sweden. In 2007, 6078 Swedish- and 4062 Norwegian adults borne in 1942 completed mailed questionnaires including oral symptoms, functional limitations and the eight item Oral Impacts on Daily Performances inventory. Model generation analysis was restricted to the Norwegian study group and the model achieved was tested without modifications in Swedish 65-year olds. A modified 3-factor solution with cross-loadings, improved the fit to the data compared with a 2-factor- and the initially proposed 3-factor model among the Norwegian [comparative fit index (CFI) = 0.97] and Swedish (CFI = 0.98) participants. All factor loadings for the modified 3-factor model were in the expected direction and were statistically significant at CR > 1. Multiple group confirmatory factor analyses, with Norwegian and Swedish data simultaneously revealed acceptable fit for the unconstrained model (CFI = 0.97), whereas unconstrained and constrained models were statistically significant different in nested model comparison. Within construct validity of Gilbert's model was supported with Norwegian and Swedish 65-year olds, indicating that the 14-item questionnaire reflected three constructs; symptom status, functional limitation and oral disadvantage. Measurement invariance was confirmed at the level of factor structure

  13. A Multinomial Probit Model with Latent Factors

    DEFF Research Database (Denmark)

    Piatek, Rémi; Gensowski, Miriam

    2017-01-01

    be meaningfully linked to an economic model. We provide sufficient conditions that make this structure identified and interpretable. For inference, we design a Markov chain Monte Carlo sampler based on marginal data augmentation. A simulation exercise shows the good numerical performance of our sampler......We develop a parametrization of the multinomial probit model that yields greater insight into the underlying decision-making process, by decomposing the error terms of the utilities into latent factors and noise. The latent factors are identified without a measurement system, and they can...

  14. A Factor Analysis of the BSRI and the PAQ.

    Science.gov (United States)

    Edwards, Teresa A.; And Others

    Factor analysis of the Bem Sex Role Inventory (BSRI) and the Personality Attributes Questionnaire (PAQ) was undertaken to study the independence of the masculine and feminine scales within each instrument. Both instruments were administered to undergraduate education majors. Analysis of primary first and second order factors of the BSRI indicated…

  15. An evaluation of energy-environment-economic efficiency for EU, APEC and ASEAN countries: Design of a Target-Oriented DFM model with fixed factors in Data Envelopment Analysis

    International Nuclear Information System (INIS)

    Suzuki, Soushi; Nijkamp, Peter

    2016-01-01

    This paper aims to offer an advanced assessment methodology for sustainable national energy-environment-economic efficiency strategies, based on an extended Data Envelopment Analysis (DEA). The use of novel efficiency-improving approaches based on DEA originates from the so-called Distance Friction Minimisation (DFM) method. To design a feasible improvement strategy for low-efficiency DMUs, we develop here a Target-Oriented (TO) DFM model. However, in many real-world cases input factors may not be flexibly adjusted in the short run. In this study, we integrate the TO-DFM model with a fixed (inflexible) factor (FF) approach to cope with such more realistic circumstances. Super-efficiency DEA is next used in our comparative study on the efficiency assessment of energy-environment-economic targets for the EU, APEC and ASEAN (A&A) countries, employing appropriate data sets from the years 2003 to 2012. We consider two inputs (primary energy consumption and population) and two outputs (CO 2 and GDP), including a fixed input factor (viz. population). On the basis of our DEA analysis results, EU countries appear to exhibit generally a higher efficiency than A&A countries. The above-mentioned TO-DFM-FF projection model is able to address realistic circumstances and requirements in an operational sustainability strategy for efficiency improvement in inefficient countries in the A&A region. - Highlights: • We examine energy-environment-economic efficiency in European and A&A countries. • We present an operational efficiency improvement strategy using DEA. • European countries tend to have higher energy efficiency than A&A countries. • The study provides efficiency-enhancing strategic paths for inefficient countries.

  16. Critical success factors model developing for sustainable Kaizen implementation in manufactur-ing industry in Ethiopia

    OpenAIRE

    Haftu Hailu; Abdelkadir Kedir; Getachew Bassa; Kassu Jilcha

    2017-01-01

    The purpose of the research is to identify critical success factors and model developing for sustaining kaizen implementation. Peacock shoe is one of the manufacturing industries in Ethiopia facing challenges on sustaining. The methodology followed is factor analysis and empirically testing hypothesis. A database was designed using SPSS version 20. The survey was validated using statistical validation using the Cronbach alpha index; the result is 0.908. The KMO index value was obtained for th...

  17. Identification of noise in linear data sets by factor analysis

    International Nuclear Information System (INIS)

    Roscoe, B.A.; Hopke, Ph.K.

    1982-01-01

    A technique which has the ability to identify bad data points, after the data has been generated, is classical factor analysis. The ability of classical factor analysis to identify two different types of data errors make it ideally suited for scanning large data sets. Since the results yielded by factor analysis indicate correlations between parameters, one must know something about the nature of the data set and the analytical techniques used to obtain it to confidentially isolate errors. (author)

  18. 241-SY-101 strain concentration factor development via nonlinear analysis. Volume 1 of 1

    International Nuclear Information System (INIS)

    1997-01-01

    The 241-SY-101 waste storage tank at the Hanford-Site has been known to accumulate and release significant quantities of hydrogen gas. An analysis was performed to assess the tank's structural integrity when subjected to postulated hydrogen deflagration loads. The analysis addressed many nonlinearities and appealed to a strain-based failure criteria. The model used to predict the global response of the tank was not refined enough to confidently predict local peak strains. Strain concentration factors were applied at structural discontinuities that were based on steel-lined reinforced-concrete containment studies. The discontinuities included large penetrations, small penetrations, springline geometries, stud/liner connections, and the 1/2 inch to 3/8 inch liner thickness transition. The only tank specific strain concentration factor applied in the evaluation was for the 1/2 inch to 3/8 inch liner thickness change in the dome. Review of the tank drawings reveals the possibility that a 4 inches Sch. 40 pipe penetrates the dome thickness transition region. It is not obvious how to combine the strain concentration factors for a small penetration with that of a thickness transition to arrive at a composite strain concentration factor. It is the goal of this effort to make an approximate determination of the relative significance of the 4 inch penetration and the 1/2 inch to 3/8 inch thickness transition in the 241-SY-101 dome geometry. This is accomplished by performing a parametric study with three general finite-element models. The first represents the thickness transition only, the second represents a 4 inch penetration only, and the third combines the thickness transition with a penetration model

  19. A Dynamic Multi-Level Factor Model with Long-Range Dependence

    DEFF Research Database (Denmark)

    Ergemen, Yunus Emre; Rodríguez-Caballero, Carlos Vladimir

    A dynamic multi-level factor model with stationary or nonstationary global and regional factors is proposed. In the model, persistence in global and regional common factors as well as innovations allows for the study of fractional cointegrating relationships. Estimation of global and regional...

  20. Potential barriers to the application of multi-factor portfolio analysis in public hospitals: evidence from a pilot study in the Netherlands.

    Science.gov (United States)

    Pavlova, Milena; Tsiachristas, Apostolos; Vermaeten, Gerhard; Groot, Wim

    2009-01-01

    Portfolio analysis is a business management tool that can assist health care managers to develop new organizational strategies. The application of portfolio analysis to US hospital settings has been frequently reported. In Europe however, the application of this technique has received little attention, especially concerning public hospitals. Therefore, this paper examines the peculiarities of portfolio analysis and its applicability to the strategic management of European public hospitals. The analysis is based on a pilot application of a multi-factor portfolio analysis in a Dutch university hospital. The nature of portfolio analysis and the steps in a multi-factor portfolio analysis are reviewed along with the characteristics of the research setting. Based on these data, a multi-factor portfolio model is developed and operationalized. The portfolio model is applied in a pilot investigation to analyze the market attractiveness and hospital strengths with regard to the provision of three orthopedic services: knee surgery, hip surgery, and arthroscopy. The pilot portfolio analysis is discussed to draw conclusions about potential barriers to the overall adoption of portfolio analysis in the management of a public hospital. Copyright (c) 2008 John Wiley & Sons, Ltd.

  1. Leak-Path Factor Analysis for the Nuclear Materials Storage Facility

    International Nuclear Information System (INIS)

    Shaffer, C.; Leonard, M.

    1999-01-01

    Leak-path factors (LPFs) were calculated for the Nuclear Materials Storage Facility (NMSF) located in the Plutonium Facility, Building 41 at the Los Alamos National Laboratory Technical Area 55. In the unlikely event of an accidental fire powerful enough to fail a container holding actinides, the subsequent release of oxides, modeled as PuO 2 aerosols, from the facility and into the surrounding environment was predicted. A 1-h nondestructive assay (NDA) laboratory fire accident was simulated with the MELCOR severe accident analysis code. Fire-driven air movement along with wind-driven air infiltration transported a portion of these actinides from the building. This fraction is referred to as the leak-path factor. The potential effect of smoke aerosol on the transport of the actinides was investigated to verify the validity of neglecting the smoke as conservative. The input model for the NMSF consisted of a system of control volumes, flow pathways, and surfaces sufficient to model the thermal-hydraulic conditions within the facility and the aerosol transport data necessary to simulate the transport of PuO 2 particles. The thermal-hydraulic, heat-transfer, and aerosol-transport models are solved simultaneously with data being exchanged between models. A MELCOR input model was designed such that it would reproduce the salient features of the fire per the corresponding CFAST calculation. Air infiltration into and out of the facility would be affected strongly by wind-driven differential pressures across the building. Therefore, differential pressures were applied to each side of the building according to guidance found in the ASHRAE handbook using a standard-velocity head equation with a leading multiplier to account for the orientation of the wind with the building. The model for the transport of aerosols considered all applicable transport processes, but the deposition within the building clearly was dominated by gravitational settling

  2. "Factor Analysis Using ""R"""

    Directory of Open Access Journals (Sweden)

    A. Alexander Beaujean

    2013-02-01

    Full Text Available R (R Development Core Team, 2011 is a very powerful tool to analyze data, that is gaining in popularity due to its costs (its free and flexibility (its open-source. This article gives a general introduction to using R (i.e., loading the program, using functions, importing data. Then, using data from Canivez, Konold, Collins, and Wilson (2009, this article walks the user through how to use the program to conduct factor analysis, from both an exploratory and confirmatory approach.

  3. Human factors engineering program review model

    International Nuclear Information System (INIS)

    1994-07-01

    The staff of the Nuclear Regulatory Commission is performing nuclear power plant design certification reviews based on a design process plan that describes the human factors engineering (HFE) program elements that are necessary and sufficient to develop an acceptable detailed design specification and an acceptable implemented design. There are two principal reasons for this approach. First, the initial design certification applications submitted for staff review did not include detailed design information. Second, since human performance literature and industry experiences have shown that many significant human factors issues arise early in the design process, review of the design process activities and results is important to the evaluation of an overall design. However, current regulations and guidance documents do not address the criteria for design process review. Therefore, the HFE Program Review Model (HFE PRM) was developed as a basis for performing design certification reviews that include design process evaluations as well as review of the final design. A central tenet of the HFE PRM is that the HFE aspects of the plant should be developed, designed, and evaluated on the basis of a structured top-down system analysis using accepted HFE principles. The HFE PRM consists of ten component elements. Each element in divided into four sections: Background, Objective, Applicant Submittals, and Review Criteria. This report describes the development of the HFE PRM and gives a detailed description of each HFE review element

  4. Research & development and growth: A Bayesian model averaging analysis

    Czech Academy of Sciences Publication Activity Database

    Horváth, Roman

    2011-01-01

    Roč. 28, č. 6 (2011), s. 2669-2673 ISSN 0264-9993. [Society for Non-linear Dynamics and Econometrics Annual Conferencen. Washington DC, 16.03.2011-18.03.2011] R&D Projects: GA ČR GA402/09/0965 Institutional research plan: CEZ:AV0Z10750506 Keywords : Research and development * Growth * Bayesian model averaging Subject RIV: AH - Economic s Impact factor: 0.701, year: 2011 http://library.utia.cas.cz/separaty/2011/E/horvath-research & development and growth a bayesian model averaging analysis.pdf

  5. Uncertainty and sensitivity analysis of environmental transport models

    International Nuclear Information System (INIS)

    Margulies, T.S.; Lancaster, L.E.

    1985-01-01

    An uncertainty and sensitivity analysis has been made of the CRAC-2 (Calculations of Reactor Accident Consequences) atmospheric transport and deposition models. Robustness and uncertainty aspects of air and ground deposited material and the relative contribution of input and model parameters were systematically studied. The underlying data structures were investigated using a multiway layout of factors over specified ranges generated via a Latin hypercube sampling scheme. The variables selected in our analysis include: weather bin, dry deposition velocity, rain washout coefficient/rain intensity, duration of release, heat content, sigma-z (vertical) plume dispersion parameter, sigma-y (crosswind) plume dispersion parameter, and mixing height. To determine the contributors to the output variability (versus distance from the site) step-wise regression analyses were performed on transformations of the spatial concentration patterns simulated. 27 references, 2 figures, 3 tables

  6. HOCOMOCO: expansion and enhancement of the collection of transcription factor binding sites models

    KAUST Repository

    Kulakovskiy, Ivan V.

    2015-11-19

    Models of transcription factor (TF) binding sites provide a basis for a wide spectrum of studies in regulatory genomics, from reconstruction of regulatory networks to functional annotation of transcripts and sequence variants. While TFs may recognize different sequence patterns in different conditions, it is pragmatic to have a single generic model for each particular TF as a baseline for practical applications. Here we present the expanded and enhanced version of HOCOMOCO (http://hocomoco.autosome.ru and http://www.cbrc.kaust.edu.sa/hocomoco10), the collection of models of DNA patterns, recognized by transcription factors. HOCOMOCO now provides position weight matrix (PWM) models for binding sites of 601 human TFs and, in addition, PWMs for 396 mouse TFs. Furthermore, we introduce the largest up to date collection of dinucleotide PWM models for 86 (52) human (mouse) TFs. The update is based on the analysis of massive ChIP-Seq and HT-SELEX datasets, with the validation of the resulting models on in vivo data. To facilitate a practical application, all HOCOMOCO models are linked to gene and protein databases (Entrez Gene, HGNC, UniProt) and accompanied by precomputed score thresholds. Finally, we provide command-line tools for PWM and diPWM threshold estimation and motif finding in nucleotide sequences.

  7. Linear and nonlinear models for predicting fish bioconcentration factors for pesticides.

    Science.gov (United States)

    Yuan, Jintao; Xie, Chun; Zhang, Ting; Sun, Jinfang; Yuan, Xuejie; Yu, Shuling; Zhang, Yingbiao; Cao, Yunyuan; Yu, Xingchen; Yang, Xuan; Yao, Wu

    2016-08-01

    This work is devoted to the applications of the multiple linear regression (MLR), multilayer perceptron neural network (MLP NN) and projection pursuit regression (PPR) to quantitative structure-property relationship analysis of bioconcentration factors (BCFs) of pesticides tested on Bluegill (Lepomis macrochirus). Molecular descriptors of a total of 107 pesticides were calculated with the DRAGON Software and selected by inverse enhanced replacement method. Based on the selected DRAGON descriptors, a linear model was built by MLR, nonlinear models were developed using MLP NN and PPR. The robustness of the obtained models was assessed by cross-validation and external validation using test set. Outliers were also examined and deleted to improve predictive power. Comparative results revealed that PPR achieved the most accurate predictions. This study offers useful models and information for BCF prediction, risk assessment, and pesticide formulation. Copyright © 2016 Elsevier Ltd. All rights reserved.

  8. Matrix factorizations, minimal models and Massey products

    International Nuclear Information System (INIS)

    Knapp, Johanna; Omer, Harun

    2006-01-01

    We present a method to compute the full non-linear deformations of matrix factorizations for ADE minimal models. This method is based on the calculation of higher products in the cohomology, called Massey products. The algorithm yields a polynomial ring whose vanishing relations encode the obstructions of the deformations of the D-branes characterized by these matrix factorizations. This coincides with the critical locus of the effective superpotential which can be computed by integrating these relations. Our results for the effective superpotential are in agreement with those obtained from solving the A-infinity relations. We point out a relation to the superpotentials of Kazama-Suzuki models. We will illustrate our findings by various examples, putting emphasis on the E 6 minimal model

  9. A load factor based mean-variance analysis for fuel diversification

    Energy Technology Data Exchange (ETDEWEB)

    Gotham, Douglas; Preckel, Paul; Ruangpattana, Suriya [State Utility Forecasting Group, Purdue University, West Lafayette, IN (United States); Muthuraman, Kumar [McCombs School of Business, University of Texas, Austin, TX (United States); Rardin, Ronald [Department of Industrial Engineering, University of Arkansas, Fayetteville, AR (United States)

    2009-03-15

    Fuel diversification implies the selection of a mix of generation technologies for long-term electricity generation. The goal is to strike a good balance between reduced costs and reduced risk. The method of analysis that has been advocated and adopted for such studies is the mean-variance portfolio analysis pioneered by Markowitz (Markowitz, H., 1952. Portfolio selection. Journal of Finance 7(1) 77-91). However the standard mean-variance methodology, does not account for the ability of various fuels/technologies to adapt to varying loads. Such analysis often provides results that are easily dismissed by regulators and practitioners as unacceptable, since load cycles play critical roles in fuel selection. To account for such issues and still retain the convenience and elegance of the mean-variance approach, we propose a variant of the mean-variance analysis using the decomposition of the load into various types and utilizing the load factors of each load type. We also illustrate the approach using data for the state of Indiana and demonstrate the ability of the model in providing useful insights. (author)

  10. Risk factors and visual fatigue of baggage X-ray security screeners: a structural equation modelling analysis.

    Science.gov (United States)

    Yu, Rui-Feng; Yang, Lin-Dong; Wu, Xin

    2017-05-01

    This study identified the risk factors influencing visual fatigue in baggage X-ray security screeners and estimated the strength of correlations between those factors and visual fatigue using structural equation modelling approach. Two hundred and five X-ray security screeners participated in a questionnaire survey. The result showed that satisfaction with the VDT's physical features and the work environment conditions were negatively correlated with the intensity of visual fatigue, whereas job stress and job burnout had direct positive influences. The path coefficient between the image quality of VDT and visual fatigue was not significant. The total effects of job burnout, job stress, the VDT's physical features and the work environment conditions on visual fatigue were 0.471, 0.469, -0.268 and -0.251 respectively. These findings indicated that both extrinsic factors relating to VDT and workplace environment and psychological factors including job burnout and job stress should be considered in the workplace design and work organisation of security screening tasks to reduce screeners' visual fatigue. Practitioner Summary: This study identified the risk factors influencing visual fatigue in baggage X-ray security screeners and estimated the strength of correlations between those factors and visual fatigue. The findings were of great importance to the workplace design and the work organisation of security screening tasks to reduce screeners' visual fatigue.

  11. Model of a ternary complex between activated factor VII, tissue factor and factor IX.

    Science.gov (United States)

    Chen, Shu-wen W; Pellequer, Jean-Luc; Schved, Jean-François; Giansily-Blaizot, Muriel

    2002-07-01

    Upon binding to tissue factor, FVIIa triggers coagulation by activating vitamin K-dependent zymogens, factor IX (FIX) and factor X (FX). To understand recognition mechanisms in the initiation step of the coagulation cascade, we present a three-dimensional model of the ternary complex between FVIIa:TF:FIX. This model was built using a full-space search algorithm in combination with computational graphics. With the known crystallographic complex FVIIa:TF kept fixed, the FIX docking was performed first with FIX Gla-EGF1 domains, followed by the FIX protease/EGF2 domains. Because the FIXa crystal structure lacks electron density for the Gla domain, we constructed a chimeric FIX molecule that contains the Gla-EGF1 domains of FVIIa and the EGF2-protease domains of FIXa. The FVIIa:TF:FIX complex has been extensively challenged against experimental data including site-directed mutagenesis, inhibitory peptide data, haemophilia B database mutations, inhibitor antibodies and a novel exosite binding inhibitor peptide. This FVIIa:TF:FIX complex provides a powerful tool to study the regulation of FVIIa production and presents new avenues for developing therapeutic inhibitory compounds of FVIIa:TF:substrate complex.

  12. Confirmatory factor analysis of the Competitive State Anxiety Inventory-2.

    Science.gov (United States)

    Lane, A M; Sewell, D F; Terry, P C; Bartram, D; Nesti, M S

    1999-06-01

    The aim of this study was to evaluate the factor structure of the Competitive State Anxiety Inventory-2 (CSAI-2) using confirmatory factor analysis. Volunteer participants (n = 1213) completed the CSAI-2 approximately 1 h before competition and the data were analysed in two samples. The hypothesized model showed poor fit indices in both samples independently (Robust Comparative Fit Index: sample A = 0.82, sample B = 0.84) and simultaneously (Comparative Fit Index = 0.83), suggesting that the factor structure proposed by Martens et al. is flawed. Our findings suggest that a limitation of the Cognitive Anxiety scale derives from phrasing items around the word 'concerned' rather than 'worried'. We suggest that being concerned about an impending performance does not necessarily mean that an athlete is experiencing negative thoughts, but that the athlete is acknowledging the importance and difficulty of the challenge and is attempting to mobilize resources to cope. The present results question the use of the CSAI-2 as a valid measure of competitive state anxiety.

  13. Human factor analysis and preventive countermeasures in nuclear power plant

    International Nuclear Information System (INIS)

    Li Ye

    2010-01-01

    Based on the human error analysis theory and the characteristics of maintenance in a nuclear power plant, human factors of maintenance in NPP are divided into three different areas: human, technology, and organization. Which is defined as individual factors, including psychological factors, physiological characteristics, health status, level of knowledge and interpersonal skills; The technical factors including technology, equipment, tools, working order, etc.; The organizational factors including management, information exchange, education, working environment, team building and leadership management,etc The analysis found that organizational factors can directly or indirectly affect the behavior of staff and technical factors, is the most basic human error factor. Based on this nuclear power plant to reduce human error and measures the response. (authors)

  14. A finite element model for the stress and flexibility analysis of curved pipes

    International Nuclear Information System (INIS)

    Guerreiro, J.N.C.

    1987-03-01

    We present a finite element model for the analysis of pipe bends with flanged ends or flanged tangents. Comments are made on the consideration of the internal pressure load. Flexibility and stress instensification factores obtained with the present model are compared with others available. (Author) [pt

  15. A hierarchical model for ordinal matrix factorization

    DEFF Research Database (Denmark)

    Paquet, Ulrich; Thomson, Blaise; Winther, Ole

    2012-01-01

    This paper proposes a hierarchical probabilistic model for ordinal matrix factorization. Unlike previous approaches, we model the ordinal nature of the data and take a principled approach to incorporating priors for the hidden variables. Two algorithms are presented for inference, one based...

  16. Data analysis and approximate models model choice, location-scale, analysis of variance, nonparametric regression and image analysis

    CERN Document Server

    Davies, Patrick Laurie

    2014-01-01

    Introduction IntroductionApproximate Models Notation Two Modes of Statistical AnalysisTowards One Mode of Analysis Approximation, Randomness, Chaos, Determinism ApproximationA Concept of Approximation Approximation Approximating a Data Set by a Model Approximation Regions Functionals and EquivarianceRegularization and Optimality Metrics and DiscrepanciesStrong and Weak Topologies On Being (almost) Honest Simulations and Tables Degree of Approximation and p-values ScalesStability of Analysis The Choice of En(α, P) Independence Procedures, Approximation and VaguenessDiscrete Models The Empirical Density Metrics and Discrepancies The Total Variation Metric The Kullback-Leibler and Chi-Squared Discrepancies The Po(λ) ModelThe b(k, p) and nb(k, p) Models The Flying Bomb Data The Student Study Times Data OutliersOutliers, Data Analysis and Models Breakdown Points and Equivariance Identifying Outliers and Breakdown Outliers in Multivariate Data Outliers in Linear Regression Outliers in Structured Data The Location...

  17. Cloud Computing Adoption Business Model Factors: Does Enterprise Size Matter?

    OpenAIRE

    Bogataj Habjan, Kristina; Pucihar, Andreja

    2017-01-01

    This paper presents the results of research investigating the impact of business model factors on cloud computing adoption. The introduced research model consists of 40 cloud computing business model factors, grouped into eight factor groups. Their impact and importance for cloud computing adoption were investigated among enterpirses in Slovenia. Furthermore, differences in opinion according to enterprise size were investigated. Research results show no statistically significant impacts of in...

  18. Factor and Rasch analysis of the Fonseca anamnestic index for the diagnosis of myogenous temporomandibular disorder.

    Science.gov (United States)

    Rodrigues-Bigaton, Delaine; de Castro, Ester M; Pires, Paulo F

    Rasch analysis has been used in recent studies to test the psychometric properties of a questionnaire. The conditions for use of the Rasch model are one-dimensionality (assessed via prior factor analysis) and local independence (the probability of getting a particular item right or wrong should not be conditioned upon success or failure in another). To evaluate the dimensionality and the psychometric properties of the Fonseca anamnestic index (FAI), such as the fit of the data to the model, the degree of difficulty of the items, and the ability to respond in patients with myogenous temporomandibular disorder (TMD). The sample consisted of 94 women with myogenous TMD, diagnosed by the Research Diagnostic Criteria for Temporomandibular Disorders (RDC/TMD), who answered the FAI. For the factor analysis, we applied the Kaiser-Meyer-Olkin test, Bartlett's sphericity, Spearman's correlation, and the determinant of the correlation matrix. For extraction of the factors/dimensions, an eigenvalue >1.0 was used, followed by oblique oblimin rotation. The Rasch analysis was conducted on the dimension that showed the highest proportion of variance explained. Adequate sample "n" and FAI multidimensionality were observed. Dimension 1 (primary) consisted of items 1, 2, 3, 6, and 7. All items of dimension 1 showed adequate fit to the model, being observed according to the degree of difficulty (from most difficult to easiest), respectively, items 2, 1, 3, 6, and 7. The FAI presented multidimensionality with its main dimension consisting of five reliable items with adequate fit to the composition of its structure. Copyright © 2017 Associação Brasileira de Pesquisa e Pós-Graduação em Fisioterapia. Publicado por Elsevier Editora Ltda. All rights reserved.

  19. Cardiometabolic risk clustering in spinal cord injury: results of exploratory factor analysis.

    Science.gov (United States)

    Libin, Alexander; Tinsley, Emily A; Nash, Mark S; Mendez, Armando J; Burns, Patricia; Elrod, Matt; Hamm, Larry F; Groah, Suzanne L

    2013-01-01

    Evidence suggests an elevated prevalence of cardiometabolic risks among persons with spinal cord injury (SCI); however, the unique clustering of risk factors in this population has not been fully explored. The purpose of this study was to describe unique clustering of cardiometabolic risk factors differentiated by level of injury. One hundred twenty-one subjects (mean 37 ± 12 years; range, 18-73) with chronic C5 to T12 motor complete SCI were studied. Assessments included medical histories, anthropometrics and blood pressure, and fasting serum lipids, glucose, insulin, and hemoglobin A1c (HbA1c). The most common cardiometabolic risk factors were overweight/obesity, high levels of low-density lipoprotein (LDL-C), and low levels of high-density lipoprotein (HDL-C). Risk clustering was found in 76.9% of the population. Exploratory principal component factor analysis using varimax rotation revealed a 3-factor model in persons with paraplegia (65.4% variance) and a 4-factor solution in persons with tetraplegia (73.3% variance). The differences between groups were emphasized by the varied composition of the extracted factors: Lipid Profile A (total cholesterol [TC] and LDL-C), Body Mass-Hypertension Profile (body mass index [BMI], systolic blood pressure [SBP], and fasting insulin [FI]); Glycemic Profile (fasting glucose and HbA1c), and Lipid Profile B (TG and HDL-C). BMI and SBP formed a separate factor only in persons with tetraplegia. Although the majority of the population with SCI has risk clustering, the composition of the risk clusters may be dependent on level of injury, based on a factor analysis group comparison. This is clinically plausible and relevant as tetraplegics tend to be hypo- to normotensive and more sedentary, resulting in lower HDL-C and a greater propensity toward impaired carbohydrate metabolism.

  20. Lungworm Infections in German dairy cattle herds--seroprevalence and GIS-supported risk factor analysis.

    Directory of Open Access Journals (Sweden)

    Anne-Marie Schunn

    Full Text Available In November 2008, a total of 19,910 bulk tank milk (BTM samples were obtained from dairy farms from all over Germany, corresponding to about 20% of all German dairy herds, and analysed for antibodies against the bovine lungworm Dictyocaulus viviparus by use of the recombinant MSP-ELISA. A total number of 3,397 (17.1%; n = 19,910 BTM samples tested seropositive. The prevalences in individual German federal states varied between 0.0% and 31.2% positive herds. A geospatial map was drawn to show the distribution of seropositive and seronegative herds per postal code area. ELISA results were further analysed for associations with land-use and climate data. Bivariate statistical analysis was used to identify potential spatial risk factors for dictyocaulosis. Statistically significant positive associations were found between lungworm seropositive herds and the proportion of water bodies and grassed area per postal code area. Variables that showed a statistically significant association with a positive BTM test were included in a logistic regression model, which was further refined by controlled stepwise selection of variables. The low Pseudo R(2 values (0.08 for the full model and 0.06 for the final model and further evaluation of the model by ROC analysis indicate that additional, unrecorded factors (e.g. management factors or random effects may substantially contribute to lungworm infections in dairy cows. Veterinarians should include lungworms in the differential diagnosis of respiratory disease in dairy cattle, particularly those at pasture. Monitoring of herds through BTM screening for antibodies can help farmers and veterinarians plan and implement appropriate control measures.

  1. Skyrme-model πNN form factor and nucleon-nucleon interaction

    International Nuclear Information System (INIS)

    Holzwarth, G.; Machleidt, R.

    1997-01-01

    We apply the strong πNN form factor, which emerges from the Skyrme model, in the two-nucleon system using a one-boson-exchange (OBE) model for the nucleon-nucleon (NN) interaction. Deuteron properties and phase parameters of NN scattering are reproduced well. In contrast to the form factor of monopole shape that is traditionally used in OBE models, the Skyrme form factor leaves low-momentum transfers essentially unaffected while it suppresses the high-momentum region strongly. It turns out that this behavior is very appropriate for models of the NN interaction and makes it possible to use a soft pion form factor in the NN system. As a consequence, the πN and the NN systems can be described using the same πNN form factor, which is impossible with the monopole. copyright 1997 The American Physical Society

  2. Dissecting high-dimensional phenotypes with bayesian sparse factor analysis of genetic covariance matrices.

    Science.gov (United States)

    Runcie, Daniel E; Mukherjee, Sayan

    2013-07-01

    Quantitative genetic studies that model complex, multivariate phenotypes are important for both evolutionary prediction and artificial selection. For example, changes in gene expression can provide insight into developmental and physiological mechanisms that link genotype and phenotype. However, classical analytical techniques are poorly suited to quantitative genetic studies of gene expression where the number of traits assayed per individual can reach many thousand. Here, we derive a Bayesian genetic sparse factor model for estimating the genetic covariance matrix (G-matrix) of high-dimensional traits, such as gene expression, in a mixed-effects model. The key idea of our model is that we need consider only G-matrices that are biologically plausible. An organism's entire phenotype is the result of processes that are modular and have limited complexity. This implies that the G-matrix will be highly structured. In particular, we assume that a limited number of intermediate traits (or factors, e.g., variations in development or physiology) control the variation in the high-dimensional phenotype, and that each of these intermediate traits is sparse - affecting only a few observed traits. The advantages of this approach are twofold. First, sparse factors are interpretable and provide biological insight into mechanisms underlying the genetic architecture. Second, enforcing sparsity helps prevent sampling errors from swamping out the true signal in high-dimensional data. We demonstrate the advantages of our model on simulated data and in an analysis of a published Drosophila melanogaster gene expression data set.

  3. Empirical Research on China’s Carbon Productivity Decomposition Model Based on Multi-Dimensional Factors

    Directory of Open Access Journals (Sweden)

    Jianchang Lu

    2015-04-01

    Full Text Available Based on the international community’s analysis of the present CO2 emissions situation, a Log Mean Divisia Index (LMDI decomposition model is proposed in this paper, aiming to reflect the decomposition of carbon productivity. The model is designed by analyzing the factors that affect carbon productivity. China’s contribution to carbon productivity is analyzed from the dimensions of influencing factors, regional structure and industrial structure. It comes to the conclusions that: (a economic output, the provincial carbon productivity and energy structure are the most influential factors, which are consistent with China’s current actual policy; (b the distribution patterns of economic output, carbon productivity and energy structure in different regions have nothing to do with the Chinese traditional sense of the regional economic development patterns; (c considering the regional protectionism, regional actual situation need to be considered at the same time; (d in the study of the industrial structure, the contribution value of industry is the most prominent factor for China’s carbon productivity, while the industrial restructuring has not been done well enough.

  4. Confirmatory Analysis of Simultaneous, Sequential, and Achievement Factors on the K-ABC at 11 Age Levels Ranging from 2 1/2 to 12 1/2 years.

    Science.gov (United States)

    Willson, Victor L.; And Others

    1985-01-01

    Presents results of confirmatory factor analysis of the Kaufman Assessment Battery for children which is based on the underlying theoretical model of sequential, simultaneous, and achievement factors. Found support for the two-factor, simultaneous and sequential processing model. (MCF)

  5. Dynamic Factor Models for the Volatility Surface

    DEFF Research Database (Denmark)

    van der Wel, Michel; Ozturk, Sait R.; Dijk, Dick van

    The implied volatility surface is the collection of volatilities implied by option contracts for different strike prices and time-to-maturity. We study factor models to capture the dynamics of this three-dimensional implied volatility surface. Three model types are considered to examine desirable...

  6. Metroplex Optimization Model Expansion and Analysis: The Airline Fleet, Route, and Schedule Optimization Model (AFRS-OM)

    Science.gov (United States)

    Sherry, Lance; Ferguson, John; Hoffman, Karla; Donohue, George; Beradino, Frank

    2012-01-01

    This report describes the Airline Fleet, Route, and Schedule Optimization Model (AFRS-OM) that is designed to provide insights into airline decision-making with regards to markets served, schedule of flights on these markets, the type of aircraft assigned to each scheduled flight, load factors, airfares, and airline profits. The main inputs to the model are hedged fuel prices, airport capacity limits, and candidate markets. Embedded in the model are aircraft performance and associated cost factors, and willingness-to-pay (i.e. demand vs. airfare curves). Case studies demonstrate the application of the model for analysis of the effects of increased capacity and changes in operating costs (e.g. fuel prices). Although there are differences between airports (due to differences in the magnitude of travel demand and sensitivity to airfare), the system is more sensitive to changes in fuel prices than capacity. Further, the benefits of modernization in the form of increased capacity could be undermined by increases in hedged fuel prices

  7. Analysis of Increased Information Technology Outsourcing Factors

    Directory of Open Access Journals (Sweden)

    Brcar Franc

    2013-01-01

    Full Text Available The study explores the field of IT outsourcing. The narrow field of research is to build a model of IT outsourcing based on influential factors. The purpose of this research is to determine the influential factors on IT outsourcing expansion. A survey was conducted with 141 large-sized Slovenian companies. Data were statistically analyzed using binary logistic regression. The final model contains five factors: (1 management’s support; (2 knowledge on IT outsourcing; (3 improvement of efficiency and effectiveness; (4 quality improvement of IT services; and (5 innovation improvement of IT. Managers immediately can use the results of this research in their decision-making. Increased performance of each individual organization is to the benefit of the entire society. The examination of IT outsourcing with the methods used is the first such research in Slovenia.

  8. A Sociological Analysis on the Effective factors in Tends to Hijab

    Directory of Open Access Journals (Sweden)

    Mahmood Sharepour

    2012-12-01

    Full Text Available From a sociological perspective. Hjjab is formed in the context of social relations in which the framework. women's issues with cultural. social. political. Economic and religious factors. spiritual. personality and behavior that is different from the paradigms and perspectives is worthy. The purpose of this study is social factors associated with the tendency of female students to wear veil. The statistical population of female students studying at the University make up the number in 1390 was equal to 13,000. 560 of whom have a multi-stage cluster sampling method was selected to the questionnaire with reliability 0.74 responded Results of multivariable regression analysis showed that the most Important variable influencing the direction and lends 10 veil has been the attitudes 10 feminism variable. Other variables affecting status, lifestyle and location. Analytical model explained only 33% of the factors affecting be as two conflicting tendencies. as a moderate 10 strong in university student.

  9. Analysis of an innovative business model

    OpenAIRE

    Picquendaele, Laetitia

    2016-01-01

    This master thesis will investigate the freemium business model, raising on the questions: “Why is the freemium business model innovative and what are its success factors?” The aim is to analyse this business model by confronting theory and practice. Therefore, the document begins with a description discussion of the freemium business model. The literature review concludes by determining the success factors of the business model innovation and of the freemium model. The theory in this first p...

  10. TEST OF THE CHEN-ROLL-ROSS MACROECONOMIC FACTOR MODEL: EVIDENCE FROM CROATIAN STOCK MARKET

    Directory of Open Access Journals (Sweden)

    Denis Dolinar

    2015-12-01

    Full Text Available This paper empirically examines the well-known Chen-Roll-Ross model on the Croatian stock market. Modifications of definitions of the Chen-Roll-Ross model variables showed as necessary because of doubtful availability and quality of input data needed. Namely, some macroeconomic and market variables are not available in the originally defined form or do not exist. In that sense this paper gives some alternative definitions for some model variables. Also, in order to improve statistical analysis, in this paper we have modified Fama-MacBeth technique in the way that second-pass regression was substituted with panel regression analysis. Based on the two-pass regression analysis of returns of 34 Croatian stocks on 4 macroeconomic variables during the seven-and-half-year observation period the following conclusion is made. In contrast to the results of Chen, Roll and Ross (1986 for the U.S. stock market, their model is not successful when describing a risk-return relation of Croatian stocks. Nevertheless, one observed version of the Chen-RollRoss model showed certain statistical significance. Namely, two risk factors in that version of the model were statistically significant: default premium, measured as risk premium for the corporate short-term bank loan financing, and term structure premium, measured on short-run basis.

  11. Parametric sensitivity analysis of an agro-economic model of management of irrigation water

    Science.gov (United States)

    El Ouadi, Ihssan; Ouazar, Driss; El Menyari, Younesse

    2015-04-01

    The current work aims to build an analysis and decision support tool for policy options concerning the optimal allocation of water resources, while allowing a better reflection on the issue of valuation of water by the agricultural sector in particular. Thus, a model disaggregated by farm type was developed for the rural town of Ait Ben Yacoub located in the east Morocco. This model integrates economic, agronomic and hydraulic data and simulates agricultural gross margin across in this area taking into consideration changes in public policy and climatic conditions, taking into account the competition for collective resources. To identify the model input parameters that influence over the results of the model, a parametric sensitivity analysis is performed by the "One-Factor-At-A-Time" approach within the "Screening Designs" method. Preliminary results of this analysis show that among the 10 parameters analyzed, 6 parameters affect significantly the objective function of the model, it is in order of influence: i) Coefficient of crop yield response to water, ii) Average daily gain in weight of livestock, iii) Exchange of livestock reproduction, iv) maximum yield of crops, v) Supply of irrigation water and vi) precipitation. These 6 parameters register sensitivity indexes ranging between 0.22 and 1.28. Those results show high uncertainties on these parameters that can dramatically skew the results of the model or the need to pay particular attention to their estimates. Keywords: water, agriculture, modeling, optimal allocation, parametric sensitivity analysis, Screening Designs, One-Factor-At-A-Time, agricultural policy, climate change.

  12. Research on the relationship between the elements and pharmacological activities in velvet antler using factor analysis and cluster analysis

    Science.gov (United States)

    Zhou, Libing

    2017-04-01

    Velvet antler has certain effect on improving the body's immune cells and the regulation of immune system function, nervous system, anti-stress, anti-aging and osteoporosis. It has medicinal applications to treat a wide range of diseases such as tissue wound healing, anti-tumor, cardiovascular disease, et al. Therefore, the research on the relationship between pharmacological activities and elements in velvet antler is of great significance. The objective of this study was to comprehensively evaluate 15 kinds of elements in different varieties of velvet antlers and study on the relationship between the elements and traditional Chinese medicine efficacy for the human. The factor analysis and the factor cluster analysis methods were used to analyze the data of elements in the sika velvet antler, cervus elaphus linnaeus, flower horse hybrid velvet antler, apiti (elk) velvet antler, male reindeer velvet antler and find out the relationship between 15 kinds of elements including Ca, P, Mg, Na, K, Fe, Cu, Mn, Al, Ba, Co, Sr, Cr, Zn and Ni. Combining with MATLAB2010 and SPSS software, the chemometrics methods were made on the relationship between the elements in velvet antler and the pharmacological activities. The first commonality factor F1 had greater load on the indexes of Ca, P, Mg, Co, Sr and Ni, and the second commonality factor F2 had greater load on the indexes of K, Mn, Zn and Cr, and the third commonality factor F3 had greater load on the indexes of Na, Cu and Ba, and the fourth commonality factor F4 had greater load on the indexes of Fe and Al. 15 kinds of elements in velvet antler in the order were elk velvet antler>flower horse hybrid velvet antler>cervus elaphus linnaeus>sika velvet antler>male reindeer velvet antler. Based on the factor analysis and the factor cluster analysis, a model for evaluating traditional Chinese medicine quality was constructed. These studies provide the scientific base and theoretical foundation for the future large-scale rational

  13. Meson form factors and covariant three-dimensional formulation of composite model

    International Nuclear Information System (INIS)

    Skachkov, N.B.; Solovtsov, I.L.

    1978-01-01

    An approach is developed which is applied in the framework of the relativistic quark model to obtain explicit expressions for meson form factors in terms of covariant wave functions of the two-quark system. These wave functions obey the two-particle quasipotential equation in which the relative motion of quarks is singled out in a covariant way. The exact form of the wave functions is found using the transition to the relativistic configurational representation with the help of the harmonic analysis on the Lorentz group instead of the usual Fourier expansion and then solving the relativistic difference equation thus obtained. The expressions found for form factors are transformed into the three-dimensional covariant form which is a direct geometrical relativistic generalization of analogous expressions of the nonrelativistic quantum mechanics and provides the decrease of the meson form factor by the Fsub(π)(t) approximately t -1 law as -t infinity, in the Coulomb field

  14. Integrating human factors into process hazard analysis

    International Nuclear Information System (INIS)

    Kariuki, S.G.; Loewe, K.

    2007-01-01

    A comprehensive process hazard analysis (PHA) needs to address human factors. This paper describes an approach that systematically identifies human error in process design and the human factors that influence its production and propagation. It is deductive in nature and therefore considers human error as a top event. The combinations of different factors that may lead to this top event are analysed. It is qualitative in nature and is used in combination with other PHA methods. The method has an advantage because it does not look at the operator error as the sole contributor to the human failure within a system but a combination of all underlying factors

  15. Integrating the strengths of cognitive emotion models with traditional HCI analysis tools

    OpenAIRE

    Springett, Mark; Law, Effie Lai-Chong; Coulson, Mark

    2015-01-01

    This paper reports an attempt to integrate key concepts from cognitive models of emotion to cognitive models of interaction established in HCI literature. The aim is to transfer the strengths of interaction models to analysis of affect-critical systems in games, e-commerce and education, thereby increasing their usefulness in these systems where affect is increasingly recognised as a key success factor. Concepts from Scherer’s appraisal model and stimulation evaluation checks, along with a fr...

  16. Weighing up the weighted case mix tool (WCMT): a psychometric investigation using confirmatory factor analysis.

    Science.gov (United States)

    Duane, B G; Humphris, G; Richards, D; Okeefe, E J; Gordon, K; Freeman, R

    2014-12-01

    To assess the use of the WCMT in two Scottish health boards and to consider the impact of simplifying the tool to improve efficient use. A retrospective analysis of routine WCMT data (47,276 cases). Public Dental Service (PDS) within NHS Lothian and Highland. The WCMT consists of six criteria. Each criterion is measured independently on a four-point scale to assess patient complexity and the dental care for the disabled/impaired patient. Psychometric analyses on the data-set were conducted. Conventional internal consistency coefficients were calculated. Latent variable modelling was performed to assess the 'fit' of the raw data to a pre-specified measurement model. A Confirmatory Factor Analysis (CFA) was used to test three potential changes to the existing WCMT that included, the removal of the oral risk factor question, the removal of original weightings for scoring the Tool, and collapsing the 4-point rating scale to three categories. The removal of the oral risk factor question had little impact on the reliability of the proposed simplified CMT to discriminate between levels of patient complexity. The removal of weighting and collapsing each item's rating scale to three categories had limited impact on reliability of the revised tool. The CFA analysis provided strong evidence that a new, proposed simplified Case Mix Tool (sCMT) would operate closely to the pre-specified measurement model (the WMCT). A modified sCMT can demonstrate, without reducing reliability, a useful measure of the complexity of patient care. The proposed sCMT may be implemented within primary care dentistry to record patient complexity as part of an oral health assessment.

  17. Analysis and modelling of the factors controlling seed oil concentration in sunflower: a review

    Directory of Open Access Journals (Sweden)

    Andrianasolo Fety Nambinina

    2016-03-01

    Full Text Available Sunflower appears as a potentially highly competitive crop, thanks to the diversification of its market and the richness of its oil. However, seed oil concentration (OC – a commercial criterion for crushing industry – is subjected to genotypic and environmental effects that make it sometimes hardly predictable. It is assumed that more understanding of oil physiology combined with the use of crop models should permit to improve prediction and management of grain quality for various end-users. Main effects of temperature, water, nitrogen, plant density and fungal diseases were reviewed in this paper. Current generic and specific crop models which simulate oil concentration were found to be empirical and to lack of proper evaluation processes. Recently two modeling approaches integrating ecophysiological knowledge were developed by Andrianasolo (2014, Statistical and dynamic modelling of sunflower (Helianthus annuus L. grain composition as a function of agronomic and environmental factors, Ph.D. Thesis, INP Toulouse: (i a statistical approach relating OC to a range of explanatory variables (potential OC, temperature, water and nitrogen stress indices, intercepted radiation, plant density which resulted in prediction quality from 1.9 to 2.5 oil points depending on the nature of the models; (ii a dynamic approach, based on “source-sink” relationships involving leaves, stems, receptacles (as sources and hulls, proteins and oil (as sinks and using priority rules for carbon and nitrogen allocation. The latter model reproduced dynamic patterns of all source and sink components faithfully, but tended to overestimate OC. A better description of photosynthesis and nitrogen uptake, as well as genotypic parameters is expected to improve its performance.

  18. Modelling optimization involving different types of elements in finite element analysis

    International Nuclear Information System (INIS)

    Wai, C M; Rivai, Ahmad; Bapokutty, Omar

    2013-01-01

    Finite elements are used to express the mechanical behaviour of a structure in finite element analysis. Therefore, the selection of the elements determines the quality of the analysis. The aim of this paper is to compare and contrast 1D element, 2D element, and 3D element used in finite element analysis. A simple case study was carried out on a standard W460x74 I-beam. The I-beam was modelled and analyzed statically with 1D elements, 2D elements and 3D elements. The results for the three separate finite element models were compared in terms of stresses, deformation and displacement of the I-beam. All three finite element models yield satisfactory results with acceptable errors. The advantages and limitations of these elements are discussed. 1D elements offer simplicity although lacking in their ability to model complicated geometry. 2D elements and 3D elements provide more detail yet sophisticated results which require more time and computer memory in the modelling process. It is also found that the choice of element in finite element analysis is influence by a few factors such as the geometry of the structure, desired analysis results, and the capability of the computer

  19. Cerebrolysin modulates pronerve growth factor/nerve growth factor ratio and ameliorates the cholinergic deficit in a transgenic model of Alzheimer's disease.

    Science.gov (United States)

    Ubhi, Kiren; Rockenstein, Edward; Vazquez-Roque, Ruben; Mante, Michael; Inglis, Chandra; Patrick, Christina; Adame, Anthony; Fahnestock, Margaret; Doppler, Edith; Novak, Philip; Moessler, Herbert; Masliah, Eliezer

    2013-02-01

    Alzheimer's disease (AD) is characterized by degeneration of neocortex, limbic system, and basal forebrain, accompanied by accumulation of amyloid-β and tangle formation. Cerebrolysin (CBL), a peptide mixture with neurotrophic-like effects, is reported to improve cognition and activities of daily living in patients with AD. Likewise, CBL reduces synaptic and behavioral deficits in transgenic (tg) mice overexpressing the human amyloid precursor protein (hAPP). The neuroprotective effects of CBL may involve multiple mechanisms, including signaling regulation, control of APP metabolism, and expression of neurotrophic factors. We investigate the effects of CBL in the hAPP tg model of AD on levels of neurotrophic factors, including pro-nerve growth factor (NGF), NGF, brain-derived neurotrophic factor (BDNF), neurotropin (NT)-3, NT4, and ciliary neurotrophic factor (CNTF). Immunoblot analysis demonstrated that levels of pro-NGF were increased in saline-treated hAPP tg mice. In contrast, CBL-treated hAPP tg mice showed levels of pro-NGF comparable to control and increased levels of mature NGF. Consistently with these results, immunohistochemical analysis demonstrated increased NGF immunoreactivity in the hippocampus of CBL-treated hAPP tg mice. Protein levels of other neurotrophic factors, including BDNF, NT3, NT4, and CNTF, were unchanged. mRNA levels of NGF and other neurotrophins were also unchanged. Analysis of neurotrophin receptors showed preservation of the levels of TrKA and p75(NTR) immunoreactivity per cell in the nucleus basalis. Cholinergic cells in the nucleus basalis were reduced in the saline-treated hAPP tg mice, and treatment with CBL reduced these cholinergic deficits. These results suggest that the neurotrophic effects of CBL might involve modulation of the pro-NGF/NGF balance and a concomitant protection of cholinergic neurons. Copyright © 2012 Wiley Periodicals, Inc.

  20. A Novel Clustering Model Based on Set Pair Analysis for the Energy Consumption Forecast in China

    Directory of Open Access Journals (Sweden)

    Mingwu Wang

    2014-01-01

    Full Text Available The energy consumption forecast is important for the decision-making of national economic and energy policies. But it is a complex and uncertainty system problem affected by the outer environment and various uncertainty factors. Herein, a novel clustering model based on set pair analysis (SPA was introduced to analyze and predict energy consumption. The annual dynamic relative indicator (DRI of historical energy consumption was adopted to conduct a cluster analysis with Fisher’s optimal partition method. Combined with indicator weights, group centroids of DRIs for influence factors were transferred into aggregating connection numbers in order to interpret uncertainty by identity-discrepancy-contrary (IDC analysis. Moreover, a forecasting model based on similarity to group centroid was discussed to forecast energy consumption of a certain year on the basis of measured values of influence factors. Finally, a case study predicting China’s future energy consumption as well as comparison with the grey method was conducted to confirm the reliability and validity of the model. The results indicate that the method presented here is more feasible and easier to use and can interpret certainty and uncertainty of development speed of energy consumption and influence factors as a whole.

  1. Applications of factor analysis to electron and ion beam surface techniques

    International Nuclear Information System (INIS)

    Solomon, J.S.

    1987-01-01

    Factor analysis, a mathematical technique for extracting chemical information from matrices of data, is used to enhance Auger electron spectroscopy (AES), core level electron energy loss spectroscopy (EELS), ion scattering spectroscopy (ISS), and secondary ion mass spectroscopy (SIMS) in studies of interfaces, thin films, and surfaces. Several examples of factor analysis enhancement of chemical bonding variations in thin films and at interfaces studied with AES and SIMS are presented. Factor analysis is also shown to be of great benefit in quantifying electron and ion beam doses required to induce surface damage. Finally, examples are presented of the use of factor analysis to reconstruct elemental profiles when peaks of interest overlap each other during the course of depth profile analysis. (author)

  2. An Analysis of Decision Factors on the Price of South Korea’s Certified Emission Reductions in Use of Vector Error Correction Model

    Directory of Open Access Journals (Sweden)

    Sumin Park

    2017-09-01

    Full Text Available This study analyzes factors affecting the price of South Korea’s Certified Emission Reduction (CER using statistical methods. CER refers to the transaction price for the amount of carbon emitted. Analysis of results found a co-integration relationship among the price of South Korea’s CER, oil price (WTI, and South Korea’s maximum electric power demand, which means that there is a long-term relationship among the three variables. Based on this result, VECM (vector error correction model analysis, impulse response function, and variance decomposition were performed. As the oil price (WTI increases, the demand for gas in power generation in Korea declines while the demand for coal increases. This leads to increased greenhouse gas (GHG; e.g., CO2 emissions and increased price of South Korea’s CERs. In addition, rising oil prices (WTI cause a decline in demand for oil products such as kerosene, which results in an increase in South Korea’s maximum power demand.

  3. An SPSSR -Menu for Ordinal Factor Analysis

    Directory of Open Access Journals (Sweden)

    Mario Basto

    2012-01-01

    Full Text Available Exploratory factor analysis is a widely used statistical technique in the social sciences. It attempts to identify underlying factors that explain the pattern of correlations within a set of observed variables. A statistical software package is needed to perform the calculations. However, there are some limitations with popular statistical software packages, like SPSS. The R programming language is a free software package for statistical and graphical computing. It offers many packages written by contributors from all over the world and programming resources that allow it to overcome the dialog limitations of SPSS. This paper offers an SPSS dialog written in theR programming language with the help of some packages, so that researchers with little or no knowledge in programming, or those who are accustomed to making their calculations based on statistical dialogs, have more options when applying factor analysis to their data and hence can adopt a better approach when dealing with ordinal, Likert-type data.

  4. Multi-Factor Impact Analysis of Agricultural Production in Bangladesh with Climate Change

    Science.gov (United States)

    Ruane, Alex C.; Major, David C.; Yu, Winston H.; Alam, Mozaharul; Hussain, Sk. Ghulam; Khan, Abu Saleh; Hassan, Ahmadul; Al Hossain, Bhuiya Md. Tamim; Goldberg, Richard; Horton, Radley M.; hide

    2012-01-01

    Diverse vulnerabilities of Bangladesh's agricultural sector in 16 sub-regions are assessed using experiments designed to investigate climate impact factors in isolation and in combination. Climate information from a suite of global climate models (GCMs) is used to drive models assessing the agricultural impact of changes in temperature, precipitation, carbon dioxide concentrations, river floods, and sea level rise for the 2040-2069 period in comparison to a historical baseline. Using the multi-factor impacts analysis framework developed in Yu et al. (2010), this study provides new sub-regional vulnerability analyses and quantifies key uncertainties in climate and production. Rice (aman, boro, and aus seasons) and wheat production are simulated in each sub-region using the biophysical Crop Environment REsource Synthesis (CERES) models. These simulations are then combined with the MIKE BASIN hydrologic model for river floods in the Ganges-Brahmaputra-Meghna (GBM) Basins, and the MIKE21Two-Dimensional Estuary Model to determine coastal inundation under conditions of higher mean sea level. The impacts of each factor depend on GCM configurations, emissions pathways, sub-regions, and particular seasons and crops. Temperature increases generally reduce production across all scenarios. Precipitation changes can have either a positive or a negative impact, with a high degree of uncertainty across GCMs. Carbon dioxide impacts on crop production are positive and depend on the emissions pathway. Increasing river flood areas reduce production in affected sub-regions. Precipitation uncertainties from different GCMs and emissions scenarios are reduced when integrated across the large GBM Basins' hydrology. Agriculture in Southern Bangladesh is severely affected by sea level rise even when cyclonic surges are not fully considered, with impacts increasing under the higher emissions scenario.

  5. Profile and Risk Factor Analysis of Unintentional Injuries in Children.

    Science.gov (United States)

    Bhamkar, Rahul; Seth, Bageshree; Setia, Maninder Singh

    2016-10-01

    To study the profile and various risk factors associated with unintentional injuries in children. The study is a cross sectional analysis of data collected from 351 children presenting with unintentional injury to a tertiary care hospital in Navi Mumbai, India. Data were collected about variables based on Haddon Phase Factor Matrix - host, environment and agent factors. Proportions for categorical variables across various groups were compared using Chi square test or Fisher's exact test. Logistic regression model was used to evaluate the factors. Falls (36 %) were the most common injuries followed by bites (23 %). Majority of children were school going children (38 %) followed by preschool children (29 %). Forty-seven percent were from lower socioeconomic class. Commonest place of injury was home (48 %) and the commonest time was evening (49 %). Though there was male predominance in injuries, the difference across gender did not vary significantly (p = 0.15). Poisonings were significantly more common in infants and toddlers and in rural population (p risk of bites compared to urban (p Profile of injuries varies widely as per the variations in agent, host and environmental factors. Socio-environmental, economic conditions and infancy-toddler age groups are predisposing risk factors for bites and poisoning. Although rural areas and lower socioeconomic class population are more vulnerable to serious types of injuries, they still lack essential basic medical care.

  6. MATHEMATICAL RISK ANALYSIS: VIA NICHOLAS RISK MODEL AND BAYESIAN ANALYSIS

    Directory of Open Access Journals (Sweden)

    Anass BAYAGA

    2010-07-01

    Full Text Available The objective of this second part of a two-phased study was to explorethe predictive power of quantitative risk analysis (QRA method andprocess within Higher Education Institution (HEI. The method and process investigated the use impact analysis via Nicholas risk model and Bayesian analysis, with a sample of hundred (100 risk analysts in a historically black South African University in the greater Eastern Cape Province.The first findings supported and confirmed previous literature (KingIII report, 2009: Nicholas and Steyn, 2008: Stoney, 2007: COSA, 2004 that there was a direct relationship between risk factor, its likelihood and impact, certiris paribus. The second finding in relation to either controlling the likelihood or the impact of occurrence of risk (Nicholas risk model was that to have a brighter risk reward, it was important to control the likelihood ofoccurrence of risks as compared with its impact so to have a direct effect on entire University. On the Bayesian analysis, thus third finding, the impact of risk should be predicted along three aspects. These aspects included the human impact (decisions made, the property impact (students and infrastructural based and the business impact. Lastly, the study revealed that although in most business cases, where as business cycles considerably vary dependingon the industry and or the institution, this study revealed that, most impacts in HEI (University was within the period of one academic.The recommendation was that application of quantitative risk analysisshould be related to current legislative framework that affects HEI.

  7. Multinomial Response Models, for Modeling and Determining Important Factors in Different Contraceptive Methods in Women

    Directory of Open Access Journals (Sweden)

    E Haji Nejad

    2001-06-01

    Full Text Available Difference aspects of multinomial statistical modelings and its classifications has been studied so far. In these type of problems Y is the qualitative random variable with T possible states which are considered as classifications. The goal is prediction of Y based on a random Vector X ? IR^m. Many methods for analyzing these problems were considered. One of the modern and general method of classification is Classification and Regression Trees (CART. Another method is recursive partitioning techniques which has a strange relationship with nonparametric regression. Classical discriminant analysis is a standard method for analyzing these type of data. Flexible discriminant analysis method which is a combination of nonparametric regression and discriminant analysis and classification using spline that includes least square regression and additive cubic splines. Neural network is an advanced statistical method for analyzing these types of data. In this paper properties of multinomial logistics regression were investigated and this method was used for modeling effective factors in selecting contraceptive methods in Ghom province for married women age 15-49. The response variable has a tetranomial distibution. The levels of this variable are: nothing, pills, traditional and a collection of other contraceptive methods. A collection of significant independent variables were: place, age of women, education, history of pregnancy and family size. Menstruation age and age at marriage were not statistically significant.

  8. Forecasting malaria cases using climatic factors in delhi, India: a time series analysis.

    Science.gov (United States)

    Kumar, Varun; Mangal, Abha; Panesar, Sanjeet; Yadav, Geeta; Talwar, Richa; Raut, Deepak; Singh, Saudan

    2014-01-01

    Background. Malaria still remains a public health problem in developing countries and changing environmental and climatic factors pose the biggest challenge in fighting against the scourge of malaria. Therefore, the study was designed to forecast malaria cases using climatic factors as predictors in Delhi, India. Methods. The total number of monthly cases of malaria slide positives occurring from January 2006 to December 2013 was taken from the register maintained at the malaria clinic at Rural Health Training Centre (RHTC), Najafgarh, Delhi. Climatic data of monthly mean rainfall, relative humidity, and mean maximum temperature were taken from Regional Meteorological Centre, Delhi. Expert modeler of SPSS ver. 21 was used for analyzing the time series data. Results. Autoregressive integrated moving average, ARIMA (0,1,1) (0,1,0)(12), was the best fit model and it could explain 72.5% variability in the time series data. Rainfall (P value = 0.004) and relative humidity (P value = 0.001) were found to be significant predictors for malaria transmission in the study area. Seasonal adjusted factor (SAF) for malaria cases shows peak during the months of August and September. Conclusion. ARIMA models of time series analysis is a simple and reliable tool for producing reliable forecasts for malaria in Delhi, India.

  9. Patient Safety Culture Survey in Pediatric Complex Care Settings: A Factor Analysis.

    Science.gov (United States)

    Hessels, Amanda J; Murray, Meghan; Cohen, Bevin; Larson, Elaine L

    2017-04-19

    Children with complex medical needs are increasing in number and demanding the services of pediatric long-term care facilities (pLTC), which require a focus on patient safety culture (PSC). However, no tool to measure PSC has been tested in this unique hybrid acute care-residential setting. The objective of this study was to evaluate the psychometric properties of the Nursing Home Survey on Patient Safety Culture tool slightly modified for use in the pLTC setting. Factor analyses were performed on data collected from 239 staff at 3 pLTC in 2012. Items were screened by principal axis factoring, and the original structure was tested using confirmatory factor analysis. Exploratory factor analysis was conducted to identify the best model fit for the pLTC data, and factor reliability was assessed by Cronbach alpha. The extracted, rotated factor solution suggested items in 4 (staffing, nonpunitive response to mistakes, communication openness, and organizational learning) of the original 12 dimensions may not be a good fit for this population. Nevertheless, in the pLTC setting, both the original and the modified factor solutions demonstrated similar reliabilities to the published consistencies of the survey when tested in adult nursing homes and the items factored nearly identically as theorized. This study demonstrates that the Nursing Home Survey on Patient Safety Culture with minimal modification may be an appropriate instrument to measure PSC in pLTC settings. Additional psychometric testing is recommended to further validate the use of this instrument in this setting, including examining the relationship to safety outcomes. Increased use will yield data for benchmarking purposes across these specialized settings to inform frontline workers and organizational leaders of areas of strength and opportunity for improvement.

  10. Uncertainty analysis in estimating Japanese ingestion of global fallout Cs-137 using health risk evaluation model

    International Nuclear Information System (INIS)

    Shimada, Yoko; Morisawa, Shinsuke

    1998-01-01

    Most of model estimation of the environmental contamination includes some uncertainty associated with the parameter uncertainty in the model. In this study, the uncertainty was analyzed in a model for evaluating the ingestion of radionuclide caused by the long-term global low-level radioactive contamination by using various uncertainty analysis methods: the percentile estimate, the robustness analysis and the fuzzy estimate. The model is mainly composed of five sub-models, which include their own uncertainty; we also analyzed the uncertainty. The major findings obtained in this study include that the possibility of the discrepancy between predicted value by the model simulation and the observed data is less than 10%; the uncertainty of the predicted value is higher before 1950 and after 1980; the uncertainty of the predicted value can be reduced by decreasing the uncertainty of some environmental parameters in the model; the reliability of the model can definitively depend on the following environmental factors: direct foliar absorption coefficient, transfer factor of radionuclide from stratosphere down to troposphere, residual rate by food processing and cooking, transfer factor of radionuclide in ocean and sedimentation in ocean. (author)

  11. New JLS-Factor Model versus the Standard JLS Model: A Case Study on Chinese Stock Bubbles

    Directory of Open Access Journals (Sweden)

    Zongyi Hu

    2017-01-01

    Full Text Available In this paper, we extend the Johansen-Ledoit-Sornette (JLS model by introducing fundamental economic factors in China (including the interest rate and deposit reserve rate and the historical volatilities of targeted and US equity indices into the original model, which is a flexible tool to detect bubbles and predict regime changes in financial markets. We then derive a general method to incorporate these selected factors in addition to the log-periodic power law signature of herding and compare the prediction accuracy of the critical time between the original and the new JLS models (termed the JLS-factor model by applying these two models to fit two well-known Chinese stock indices in three bubble periods. The results show that the JLS-factor model with Chinese characteristics successfully depicts the evolutions of bubbles and “antibubbles” and constructs efficient end-of-bubble signals for all bubbles in Chinese stock markets. In addition, the results of standard statistical tests demonstrate the excellent explanatory power of these additive factors and confirm that the new JLS model provides useful improvements over the standard JLS model.

  12. Modeling Factors with Influence on Sustainable University Management

    Directory of Open Access Journals (Sweden)

    Oana Dumitrascu

    2015-01-01

    Full Text Available The main objective of this paper is to present the factors with influence on the sustainable university management and the relationships between them. In the scientific approach we begin from a graphical model, according to which the extracurricular activities together with internal environmental factors influence students’ involvement in such activities, the university attractiveness, their academic performance and their integration into the socially-economic and natural environment (components related with sustainable development. The model emphasizes that individual performances, related to students’ participation in extracurricular activities, have a positive influence on the sustainability of university management. The results of the study have shown that the university sustainability may be influenced by a number of factors, such as students’ performance, students’ involvement in extracurricular activities or university’s attractiveness and can in turn influence implicitly also the sustainability of university management. The originality of the paper consists in the relationships study using the modeling method in general and informatics tools of modeling in particular, as well as through graphical visualization of some influences, on the sustainability university management.

  13. Risk factors for new onset diabetes mellitus after liver transplantation: A meta-analysis.

    Science.gov (United States)

    Li, Da-Wei; Lu, Tian-Fei; Hua, Xiang-Wei; Dai, Hui-Juan; Cui, Xiao-Lan; Zhang, Jian-Jian; Xia, Qiang

    2015-05-28

    To determine the risk factors for new-onset diabetes mellitus (NODM) after liver transplantation by conducting a systematic review and meta-analysis. We electronically searched the databases of MEDLINE, EMBASE and the Cochrane Library from January 1980 to December 2013 to identify relevant studies reporting risk factors for NODM after liver transplantation. Two authors independently assessed the trials for inclusion and extracted the data. Discrepancies were resolved in consultation with a third reviewer. All statistical analyses were performed with the RevMan5.0 software (The Cochrane Collaboration, Oxford, United Kingdom). Pooled odds ratios (OR) or weighted mean differences (WMD) with 95% confidence intervals (CIs) were calculated using either a fixed effects or a random effects model, based on the presence (I (2) 50%) of significant heterogeneity. Twenty studies with 4580 patients were included in the meta-analysis, all of which were retrospective. The meta-analysis identified the following significant risk factors: hepatitis C virus (HCV) infection (OR = 2.68; 95%CI: 1.92-3.72); a family history of diabetes (OR = 1.69, 95%CI: 1.09-2.63, P diabetes (OR = 1.69; 95%CI: 1.09-2.63; P = 0.02); use of tacrolimus (OR = 1.34; 95%CI: 1.03-1.76; P = 0.03) and body mass index (BMI)(WMD = 1.19, 95%CI: 0.69-1.68, P diabetes, male gender, tacrolimus and BMI are risk factors for NODM after liver transplantation.

  14. Analyzing Multiple-Choice Questions by Model Analysis and Item Response Curves

    Science.gov (United States)

    Wattanakasiwich, P.; Ananta, S.

    2010-07-01

    In physics education research, the main goal is to improve physics teaching so that most students understand physics conceptually and be able to apply concepts in solving problems. Therefore many multiple-choice instruments were developed to probe students' conceptual understanding in various topics. Two techniques including model analysis and item response curves were used to analyze students' responses from Force and Motion Conceptual Evaluation (FMCE). For this study FMCE data from more than 1000 students at Chiang Mai University were collected over the past three years. With model analysis, we can obtain students' alternative knowledge and the probabilities for students to use such knowledge in a range of equivalent contexts. The model analysis consists of two algorithms—concentration factor and model estimation. This paper only presents results from using the model estimation algorithm to obtain a model plot. The plot helps to identify a class model state whether it is in the misconception region or not. Item response curve (IRC) derived from item response theory is a plot between percentages of students selecting a particular choice versus their total score. Pros and cons of both techniques are compared and discussed.

  15. Choice Model and Influencing Factor Analysis of Travel Mode for Migrant Workers: Case Study in Xi’an, China

    OpenAIRE

    Hong Chen; Zuo-xian Gan; Yu-ting He

    2015-01-01

    Based on the basic theory and methods of disaggregate choice model, the influencing factors in travel mode choice for migrant workers are analyzed, according to 1366 data samples of Xi’an migrant workers. Walking, bus, subway, and taxi are taken as the alternative parts of travel modes for migrant workers, and a multinomial logit (MNL) model of travel mode for migrant workers is set up. The validity of the model is verified by the hit rate, and the hit rates of four travel modes are all great...

  16. Models and data requirements for human reliability analysis

    International Nuclear Information System (INIS)

    1989-03-01

    It has been widely recognised for many years that the safety of the nuclear power generation depends heavily on the human factors related to plant operation. This has been confirmed by the accidents at Three Mile Island and Chernobyl. Both these cases revealed how human actions can defeat engineered safeguards and the need for special operator training to cover the possibility of unexpected plant conditions. The importance of the human factor also stands out in the analysis of abnormal events and insights from probabilistic safety assessments (PSA's), which reveal a large proportion of cases having their origin in faulty operator performance. A consultants' meeting, organized jointly by the International Atomic Energy Agency (IAEA) and the International Institute for Applied Systems Analysis (IIASA) was held at IIASA in Laxenburg, Austria, December 7-11, 1987, with the aim of reviewing existing models used in Probabilistic Safety Assessment (PSA) for Human Reliability Analysis (HRA) and of identifying the data required. The report collects both the contributions offered by the members of the Expert Task Force and the findings of the extensive discussions that took place during the meeting. Refs, figs and tabs

  17. Food hygiene practices and its associated factors among model and non model households in Abobo district, southwestern Ethiopia: Comparative cross-sectional study.

    Science.gov (United States)

    Okugn, Akoma; Woldeyohannes, Demelash

    2018-01-01

    In developing country most of human infectious diseases are caused by eating contaminated food. Estimated nine out ten of the diarrheal disease is attributable to the environment and associated with risk factors of poor food hygiene practice. Understanding the risk of eating unsafe food is the major concern to prevent and control food borne diseases. The main goal of this study was to assessing food hygiene practices and its associated factors among model and non model households at Abobo district. This study was conducted from 18 October 2013 to 13 June 2014. A community-based comparative cross-sectional study design was used. Pretested structured questionnaire was used to collect data. A total of 1247 households (417 model and 830 non model households) were included in the study from Abobo district. Bivariate and multivariate logistic regression analysis was used to identify factors associated with outcome variable. The study revealed that good food hygiene practice was 51%, of which 79% were model and 36.70% were non model households. Type of household [AOR: 2.07, 95% CI: (1.32-3.39)], sex of household head [AOR: 1.63, 95% CI: (1.06-2.48)], Availability of liquid wastes disposal pit [AOR: 2.23, 95% CI: (1.39,3.63)], Knowledge of liquid waste to cause diseases [AOR: 1.95, 95% (1.23,3.08)], and availability of functional hand washing facility [AOR: 3.61, 95% CI: (1.86-7.02)] were the factors associated with food handling practices. This study revealed that good food handling practice is low among model and non model households. While type of household (model versus non model households), sex, knowledge of solid waste to cause diseases, availability of functional hand washing facility, and availability of liquid wastes disposal pit were the factors associated with outcome variable. Health extension workers should play a great role in educating households regarding food hygiene practices to improve their knowledge and practices of the food hygiene.

  18. A replication of a factor analysis of motivations for trapping

    Science.gov (United States)

    Schroeder, Susan; Fulton, David C.

    2015-01-01

    Using a 2013 sample of Minnesota trappers, we employed confirmatory factor analysis to replicate an exploratory factor analysis of trapping motivations conducted by Daigle, Muth, Zwick, and Glass (1998).  We employed the same 25 items used by Daigle et al. and tested the same five-factor structure using a recent sample of Minnesota trappers. We also compared motivations in our sample to those reported by Daigle et el.

  19. A human factor analysis of a radiotherapy accident

    International Nuclear Information System (INIS)

    Thellier, S.

    2009-01-01

    Since September 2005, I.R.S.N. studies activities of radiotherapy treatment from the angle of the human and organizational factors to improve the reliability of treatment in radiotherapy. Experienced in nuclear industry incidents analysis, I.R.S.N. analysed and diffused in March 2008, for the first time in France, the detailed study of a radiotherapy accident from the angle of the human and organizational factors. The method used for analysis is based on interviews and documents kept by the hospital. This analysis aimed at identifying the causes of the difference recorded between the dose prescribed by the radiotherapist and the dose effectively received by the patient. Neither verbal nor written communication (intra-service meetings and protocols of treatment) allowed information to be transmitted correctly in order to permit radiographers to adjust the irradiation zones correctly. This analysis highlighted the fact that during the preparation and the carrying out of the treatment, various factors led planned controls to not be performed. Finally, this analysis highlighted the fact that unsolved areas persist in the report over this accident. This is due to a lack of traceability of a certain number of key actions. The article concluded that there must be improvement in three areas: cooperation between the practitioners, control of the actions and traceability of the actions. (author)

  20. Generalized Efficient Inference on Factor Models with Long-Range Dependence

    DEFF Research Database (Denmark)

    Ergemen, Yunus Emre

    . Short-memory dynamics are allowed in the common factor structure and possibly heteroskedastic error term. In the estimation, a generalized version of the principal components (PC) approach is proposed to achieve efficiency. Asymptotics for efficient common factor and factor loading as well as long......A dynamic factor model is considered that contains stochastic time trends allowing for stationary and nonstationary long-range dependence. The model nests standard I(0) and I(1) behaviour smoothly in common factors and residuals, removing the necessity of a priori unit-root and stationarity testing...