Human Factors Model

NASA Technical Reports Server (NTRS)

1993-01-01

Jack is an advanced human factors software package that provides a three dimensional model for predicting how a human will interact with a given system or environment. It can be used for a broad range of computer-aided design applications. Jack was developed by the computer Graphics Research Laboratory of the University of Pennsylvania with assistance from NASA's Johnson Space Center, Ames Research Center and the Army. It is the University's first commercial product. Jack is still used for academic purposes at the University of Pennsylvania. Commercial rights were given to Transom Technologies, Inc.

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

PubMed

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.

Bayes Factor Covariance Testing in Item Response Models.

PubMed

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.

Why College Students Cheat: A Conceptual Model of Five Factors

ERIC Educational Resources Information Center

Yu, Hongwei; Glanzer, Perry L.; Johnson, Byron R.; Sriram, Rishi; Moore, Brandon

2018-01-01

Though numerous studies have identified factors associated with academic misconduct, few have proposed conceptual models that could make sense of multiple factors. In this study, we used structural equation modeling (SEM) to test a conceptual model of five factors using data from a relatively large sample of 2,503 college students. The results…

Default Bayes Factors for Model Selection in Regression

ERIC Educational Resources Information Center

Rouder, Jeffrey N.; Morey, Richard D.

2012-01-01

In this article, we present a Bayes factor solution for inference in multiple regression. Bayes factors are principled measures of the relative evidence from data for various models or positions, including models that embed null hypotheses. In this regard, they may be used to state positive evidence for a lack of an effect, which is not possible…

A 3-factor model for the FACIT-Sp.

PubMed

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

Tree-Structured Infinite Sparse Factor Model

PubMed Central

Zhang, XianXing; Dunson, David B.; Carin, Lawrence

2013-01-01

A tree-structured multiplicative gamma process (TMGP) is developed, for inferring the depth of a tree-based factor-analysis model. This new model is coupled with the nested Chinese restaurant process, to nonparametrically infer the depth and width (structure) of the tree. In addition to developing the model, theoretical properties of the TMGP are addressed, and a novel MCMC sampler is developed. The structure of the inferred tree is used to learn relationships between high-dimensional data, and the model is also applied to compressive sensing and interpolation of incomplete images. PMID:25279389

Relating Factor Models for Longitudinal Data to Quasi-Simplex and NARMA Models

ERIC Educational Resources Information Center

Rovine, Michael J.; Molenaar, Peter C. M.

2005-01-01

In this article we show the one-factor model can be rewritten as a quasi-simplex model. Using this result along with addition theorems from time series analysis, we describe a common general model, the nonstationary autoregressive moving average (NARMA) model, that includes as a special case, any latent variable model with continuous indicators…

Taking the Error Term of the Factor Model into Account: The Factor Score Predictor Interval

ERIC Educational Resources Information Center

Beauducel, Andre

2013-01-01

The problem of factor score indeterminacy implies that the factor and the error scores cannot be completely disentangled in the factor model. It is therefore proposed to compute Harman's factor score predictor that contains an additive combination of factor and error variance. This additive combination is discussed in the framework of classical…

Model of a ternary complex between activated factor VII, tissue factor and factor IX.

PubMed

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.

Comparisons of Four Methods for Estimating a Dynamic Factor Model

ERIC Educational Resources Information Center

Zhang, Zhiyong; Hamaker, Ellen L.; Nesselroade, John R.

2008-01-01

Four methods for estimating a dynamic factor model, the direct autoregressive factor score (DAFS) model, are evaluated and compared. The first method estimates the DAFS model using a Kalman filter algorithm based on its state space model representation. The second one employs the maximum likelihood estimation method based on the construction of a…

Lessons from the Specific Factors Model of International Trade.

ERIC Educational Resources Information Center

Tohamy, Soumaya M.; Mixon, J. Wilson, Jr.

2003-01-01

Uses the Specific Factors model to illustrate the meaning of economic efficiency, how complex economies simultaneously determine prices and quantities, and how changes in demand conditions or technology can affect income distribution among owners of factors of production. Employs spreadsheets to help students see how the model works. (JEH)

Determination of effective loss factors in reduced SEA models

NASA Astrophysics Data System (ADS)

Chimeno Manguán, M.; Fernández de las Heras, M. J.; Roibás Millán, E.; Simón Hidalgo, F.

2017-01-01

The definition of Statistical Energy Analysis (SEA) models for large complex structures is highly conditioned by the classification of the structure elements into a set of coupled subsystems and the subsequent determination of the loss factors representing both the internal damping and the coupling between subsystems. The accurate definition of the complete system can lead to excessively large models as the size and complexity increases. This fact can also rise practical issues for the experimental determination of the loss factors. This work presents a formulation of reduced SEA models for incomplete systems defined by a set of effective loss factors. This reduced SEA model provides a feasible number of subsystems for the application of the Power Injection Method (PIM). For structures of high complexity, their components accessibility can be restricted, for instance internal equipments or panels. For these cases the use of PIM to carry out an experimental SEA analysis is not possible. New methods are presented for this case in combination with the reduced SEA models. These methods allow defining some of the model loss factors that could not be obtained through PIM. The methods are validated with a numerical analysis case and they are also applied to an actual spacecraft structure with accessibility restrictions: a solar wing in folded configuration.

The Effects of Autocorrelation on the Curve-of-Factors Growth Model

ERIC Educational Resources Information Center

Murphy, Daniel L.; Beretvas, S. Natasha; Pituch, Keenan A.

2011-01-01

This simulation study examined the performance of the curve-of-factors model (COFM) when autocorrelation and growth processes were present in the first-level factor structure. In addition to the standard curve-of factors growth model, 2 new models were examined: one COFM that included a first-order autoregressive autocorrelation parameter, and a…

Components of Mathematics Anxiety: Factor Modeling of the MARS30-Brief.

PubMed

Pletzer, Belinda; Wood, Guilherme; Scherndl, Thomas; Kerschbaum, Hubert H; Nuerk, Hans-Christoph

2016-01-01

Mathematics anxiety involves feelings of tension, discomfort, high arousal, and physiological reactivity interfering with number manipulation and mathematical problem solving. Several factor analytic models indicate that mathematics anxiety is rather a multidimensional than unique construct. However, the factor structure of mathematics anxiety has not been fully clarified by now. This issue shall be addressed in the current study. The Mathematics Anxiety Rating Scale (MARS) is a reliable measure of mathematics anxiety (Richardson and Suinn, 1972), for which several reduced forms have been developed. Most recently, a shortened version of the MARS (MARS30-brief) with comparable reliability was published. Different studies suggest that mathematics anxiety involves up to seven different factors. Here we examined the factor structure of the MARS30-brief by means of confirmatory factor analysis. The best model fit was obtained by a six-factor model, dismembering the known two general factors "Mathematical Test Anxiety" (MTA) and "Numerical Anxiety" (NA) in three factors each. However, a more parsimonious 5-factor model with two sub-factors for MTA and three for NA fitted the data comparably well. Factors were differentially susceptible to sex differences and differences between majors. Measurement invariance for sex was established.

Factor models for cancer signatures

NASA Astrophysics Data System (ADS)

Kakushadze, Zura; Yu, Willie

2016-11-01

We present a novel method for extracting cancer signatures by applying statistical risk models (http://ssrn.com/abstract=2732453) from quantitative finance to cancer genome data. Using 1389 whole genome sequenced samples from 14 cancers, we identify an ;overall; mode of somatic mutational noise. We give a prescription for factoring out this noise and source code for fixing the number of signatures. We apply nonnegative matrix factorization (NMF) to genome data aggregated by cancer subtype and filtered using our method. The resultant signatures have substantially lower variability than those from unfiltered data. Also, the computational cost of signature extraction is cut by about a factor of 10. We find 3 novel cancer signatures, including a liver cancer dominant signature (96% contribution) and a renal cell carcinoma signature (70% contribution). Our method accelerates finding new cancer signatures and improves their overall stability. Reciprocally, the methods for extracting cancer signatures could have interesting applications in quantitative finance.

A Twin Factor Mixture Modeling Approach to Childhood Temperament: Differential Heritability

ERIC Educational Resources Information Center

Scott, Brandon G.; Lemery-Chalfant, Kathryn; Clifford, Sierra; Tein, Jenn-Yun; Stoll, Ryan; Goldsmith, H.Hill

2016-01-01

Twin factor mixture modeling was used to identify temperament profiles while simultaneously estimating a latent factor model for each profile with a sample of 787 twin pairs (M[subscript age] = 7.4 years, SD = 0.84; 49% female; 88.3% Caucasian), using mother- and father-reported temperament. A four-profile, one-factor model fit the data well.…

Satisfiers and Dissatisfiers: A Two-Factor Model for Website Design and Evaluation.

ERIC Educational Resources Information Center

Zhang, Ping; von Dran, Gisela M.

2000-01-01

Investigates Web site design factors and their impact from a theoretical perspective. Presents a two-factor model that can guide Web site design and evaluation. According to the model, there are two types of design factors: hygiene and motivator. Results showed that the two-factor model provides a means for Web-user interface studies. Provides…

Components of Mathematics Anxiety: Factor Modeling of the MARS30-Brief

PubMed Central

Pletzer, Belinda; Wood, Guilherme; Scherndl, Thomas; Kerschbaum, Hubert H.; Nuerk, Hans-Christoph

2016-01-01

Mathematics anxiety involves feelings of tension, discomfort, high arousal, and physiological reactivity interfering with number manipulation and mathematical problem solving. Several factor analytic models indicate that mathematics anxiety is rather a multidimensional than unique construct. However, the factor structure of mathematics anxiety has not been fully clarified by now. This issue shall be addressed in the current study. The Mathematics Anxiety Rating Scale (MARS) is a reliable measure of mathematics anxiety (Richardson and Suinn, 1972), for which several reduced forms have been developed. Most recently, a shortened version of the MARS (MARS30-brief) with comparable reliability was published. Different studies suggest that mathematics anxiety involves up to seven different factors. Here we examined the factor structure of the MARS30-brief by means of confirmatory factor analysis. The best model fit was obtained by a six-factor model, dismembering the known two general factors “Mathematical Test Anxiety” (MTA) and “Numerical Anxiety” (NA) in three factors each. However, a more parsimonious 5-factor model with two sub-factors for MTA and three for NA fitted the data comparably well. Factors were differentially susceptible to sex differences and differences between majors. Measurement invariance for sex was established. PMID:26924996

Factors of collaborative working: a framework for a collaboration model.

PubMed

Patel, Harshada; Pettitt, Michael; Wilson, John R

2012-01-01

The ability of organisations to support collaborative working environments is of increasing importance as they move towards more distributed ways of working. Despite the attention collaboration has received from a number of disparate fields, there is a lack of a unified understanding of the component factors of collaboration. As part of our work on a European Integrated Project, CoSpaces, collaboration and collaborative working and the factors which define it were examined through the literature and new empirical work with a number of partner user companies in the aerospace, automotive and construction sectors. This was to support development of a descriptive human factors model of collaboration - the CoSpaces Collaborative Working Model (CCWM). We identified seven main categories of factors involved in collaboration: Context, Support, Tasks, Interaction Processes, Teams, Individuals, and Overarching Factors, and summarised these in a framework which forms a basis for the model. We discuss supporting evidence for the factors which emerged from our fieldwork with user partners, and use of the model in activities such as collaboration readiness profiling. Copyright © 2011 Elsevier Ltd and The Ergonomics Society. All rights reserved.

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

PubMed

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.

The Barrett-Crane model: asymptotic measure factor

NASA Astrophysics Data System (ADS)

Kamiński, Wojciech; Steinhaus, Sebastian

2014-04-01

The original spin foam model construction for 4D gravity by Barrett and Crane suffers from a few troubling issues. In the simple examples of the vertex amplitude they can be summarized as the existence of contributions to the asymptotics from non-geometric configurations. Even restricted to geometric contributions the amplitude is not completely worked out. While the phase is known to be the Regge action, the so-called measure factor has remained mysterious for a decade. In the toy model case of the 6j symbol this measure factor has a nice geometric interpretation of V-1/2 leading to speculations that a similar interpretation should be possible also in the 4D case. In this paper we provide the first geometric interpretation of the geometric part of the asymptotic for the spin foam consisting of two glued 4-simplices (decomposition of the 4-sphere) in the Barrett-Crane model in the large internal spin regime.

Consumer's Online Shopping Influence Factors and Decision-Making Model

NASA Astrophysics Data System (ADS)

Yan, Xiangbin; Dai, Shiliang

Previous research on online consumer behavior has mostly been confined to the perceived risk which is used to explain those barriers for purchasing online. However, perceived benefit is another important factor which influences consumers’ decision when shopping online. As a result, an integrated consumer online shopping decision-making model is developed which contains three elements—Consumer, Product, and Web Site. This model proposed relative factors which influence the consumers’ intention during the online shopping progress, and divided them into two different dimensions—mentally level and material level. We tested those factors with surveys, from both online volunteers and offline paper surveys with more than 200 samples. With the help of SEM, the experimental results show that the proposed model and method can be used to analyze consumer’s online shopping decision-making process effectively.

On the Relations among Regular, Equal Unique Variances, and Image Factor Analysis Models.

ERIC Educational Resources Information Center

Hayashi, Kentaro; Bentler, Peter M.

2000-01-01

Investigated the conditions under which the matrix of factor loadings from the factor analysis model with equal unique variances will give a good approximation to the matrix of factor loadings from the regular factor analysis model. Extends the results to the image factor analysis model. Discusses implications for practice. (SLD)

Scale-model charge-transfer technique for measuring enhancement factors

NASA Technical Reports Server (NTRS)

Kositsky, J.; Nanevicz, J. E.

1991-01-01

Determination of aircraft electric field enhancement factors is crucial when using airborne field mill (ABFM) systems to accurately measure electric fields aloft. SRI used the scale model charge transfer technique to determine enhancement factors of several canonical shapes and a scale model Learjet 36A. The measured values for the canonical shapes agreed with known analytic solutions within about 6 percent. The laboratory determined enhancement factors for the aircraft were compared with those derived from in-flight data gathered by a Learjet 36A outfitted with eight field mills. The values agreed to within experimental error (approx. 15 percent).

Computational Thermochemistry: Scale Factor Databases and Scale Factors for Vibrational Frequencies Obtained from Electronic Model Chemistries.

PubMed

Alecu, I M; Zheng, Jingjing; Zhao, Yan; Truhlar, Donald G

2010-09-14

Optimized scale factors for calculating vibrational harmonic and fundamental frequencies and zero-point energies have been determined for 145 electronic model chemistries, including 119 based on approximate functionals depending on occupied orbitals, 19 based on single-level wave function theory, three based on the neglect-of-diatomic-differential-overlap, two based on doubly hybrid density functional theory, and two based on multicoefficient correlation methods. Forty of the scale factors are obtained from large databases, which are also used to derive two universal scale factor ratios that can be used to interconvert between scale factors optimized for various properties, enabling the derivation of three key scale factors at the effort of optimizing only one of them. A reduced scale factor optimization model is formulated in order to further reduce the cost of optimizing scale factors, and the reduced model is illustrated by using it to obtain 105 additional scale factors. Using root-mean-square errors from the values in the large databases, we find that scaling reduces errors in zero-point energies by a factor of 2.3 and errors in fundamental vibrational frequencies by a factor of 3.0, but it reduces errors in harmonic vibrational frequencies by only a factor of 1.3. It is shown that, upon scaling, the balanced multicoefficient correlation method based on coupled cluster theory with single and double excitations (BMC-CCSD) can lead to very accurate predictions of vibrational frequencies. With a polarized, minimally augmented basis set, the density functionals with zero-point energy scale factors closest to unity are MPWLYP1M (1.009), τHCTHhyb (0.989), BB95 (1.012), BLYP (1.013), BP86 (1.014), B3LYP (0.986), MPW3LYP (0.986), and VSXC (0.986).

Modelling oxygen transfer using dynamic alpha factors.

PubMed

Jiang, Lu-Man; Garrido-Baserba, Manel; Nolasco, Daniel; Al-Omari, Ahmed; DeClippeleir, Haydee; Murthy, Sudhir; Rosso, Diego

2017-11-01

Due to the importance of wastewater aeration in meeting treatment requirements and due to its elevated energy intensity, it is important to describe the real nature of an aeration system to improve design and specification, performance prediction, energy consumption, and process sustainability. Because organic loadings drive aeration efficiency to its lowest value when the oxygen demand (energy) is the highest, the implications of considering their dynamic nature on energy costs are of utmost importance. A dynamic model aimed at identifying conservation opportunities is presented. The model developed describes the correlation between the COD concentration and the α factor in activated sludge. Using the proposed model, the aeration efficiency is calculated as a function of the organic loading (i.e. COD). This results in predictions of oxygen transfer values that are more realistic than the traditional method of assuming constant α values. The model was applied to two water resource recovery facilities, and was calibrated and validated with time-sensitive databases. Our improved aeration model structure increases the quality of prediction of field data through the recognition of the dynamic nature of the alpha factor (α) as a function of the applied oxygen demand. For the cases presented herein, the model prediction of airflow improved by 20-35% when dynamic α is used. The proposed model offers a quantitative tool for the prediction of energy demand and for minimizing aeration design uncertainty. Copyright © 2017 Elsevier Ltd. All rights reserved.

An Introduction to Human Factors and Combat Models

DTIC Science & Technology

1989-03-01

Combat Models by Timothy F. Schroth Captain, United States Army B . A., Temple University, 1982 Submitted in partial fulfillment of the requirements for...INTRODUCTION .......... ................. 4 B . DEFINING HUMAN FACTORS - AN HISTORICAL APPROACH 4 C. BEFORE/AFTER THE BATTLE ...... ........... 8 1. Culture...16 III. COMBAT MODELS ....... .................. 18 A. INTRODUCTION ....... ................. 18 B . PURPOSE OF COMBAT MODELS ... ........... 20 1

On the Relation between the Linear Factor Model and the Latent Profile Model

ERIC Educational Resources Information Center

Halpin, Peter F.; Dolan, Conor V.; Grasman, Raoul P. P. P.; De Boeck, Paul

2011-01-01

The relationship between linear factor models and latent profile models is addressed within the context of maximum likelihood estimation based on the joint distribution of the manifest variables. Although the two models are well known to imply equivalent covariance decompositions, in general they do not yield equivalent estimates of the…

Detecting Social Desirability Bias Using Factor Mixture Models

ERIC Educational Resources Information Center

Leite, Walter L.; Cooper, Lou Ann

2010-01-01

Based on the conceptualization that social desirable bias (SDB) is a discrete event resulting from an interaction between a scale's items, the testing situation, and the respondent's latent trait on a social desirability factor, we present a method that makes use of factor mixture models to identify which examinees are most likely to provide…

Moving in Parallel Toward a Modern Modeling Epistemology: Bayes Factors and Frequentist Modeling Methods.

PubMed

Rodgers, Joseph Lee

2016-01-01

The Bayesian-frequentist debate typically portrays these statistical perspectives as opposing views. However, both Bayesian and frequentist statisticians have expanded their epistemological basis away from a singular focus on the null hypothesis, to a broader perspective involving the development and comparison of competing statistical/mathematical models. For frequentists, statistical developments such as structural equation modeling and multilevel modeling have facilitated this transition. For Bayesians, the Bayes factor has facilitated this transition. The Bayes factor is treated in articles within this issue of Multivariate Behavioral Research. The current presentation provides brief commentary on those articles and more extended discussion of the transition toward a modern modeling epistemology. In certain respects, Bayesians and frequentists share common goals.

Factoring vs linear modeling in rate estimation: a simulation study of relative accuracy.

PubMed

Maldonado, G; Greenland, S

1998-07-01

A common strategy for modeling dose-response in epidemiology is to transform ordered exposures and covariates into sets of dichotomous indicator variables (that is, to factor the variables). Factoring tends to increase estimation variance, but it also tends to decrease bias and thus may increase or decrease total accuracy. We conducted a simulation study to examine the impact of factoring on the accuracy of rate estimation. Factored and unfactored Poisson regression models were fit to follow-up study datasets that were randomly generated from 37,500 population model forms that ranged from subadditive to supramultiplicative. In the situations we examined, factoring sometimes substantially improved accuracy relative to fitting the corresponding unfactored model, sometimes substantially decreased accuracy, and sometimes made little difference. The difference in accuracy between factored and unfactored models depended in a complicated fashion on the difference between the true and fitted model forms, the strength of exposure and covariate effects in the population, and the study size. It may be difficult in practice to predict when factoring is increasing or decreasing accuracy. We recommend, therefore, that the strategy of factoring variables be supplemented with other strategies for modeling dose-response.

HIGH DIMENSIONAL COVARIANCE MATRIX ESTIMATION IN APPROXIMATE FACTOR MODELS.

PubMed

Fan, Jianqing; Liao, Yuan; Mincheva, Martina

2011-01-01

The variance covariance matrix plays a central role in the inferential theories of high dimensional factor models in finance and economics. Popular regularization methods of directly exploiting sparsity are not directly applicable to many financial problems. Classical methods of estimating the covariance matrices are based on the strict factor models, assuming independent idiosyncratic components. This assumption, however, is restrictive in practical applications. By assuming sparse error covariance matrix, we allow the presence of the cross-sectional correlation even after taking out common factors, and it enables us to combine the merits of both methods. We estimate the sparse covariance using the adaptive thresholding technique as in Cai and Liu (2011), taking into account the fact that direct observations of the idiosyncratic components are unavailable. The impact of high dimensionality on the covariance matrix estimation based on the factor structure is then studied.

QCD dipole model and k T factorization

NASA Astrophysics Data System (ADS)

Bialas, A.; Navelet, H.; Peschanski, R.

2001-01-01

It is shown that the colour dipole approach to hard scattering at high energy is fully compatible with k T factorization at the leading logarithm approximation (in - logx Bj). The relations between the dipole amplitudes and unintegrated diagonal and non-diagonal gluon distributions are given. It is also shown that including the exact gluon kinematics in the k T factorization formula destroys the conservation of transverse position vectors and thus is incompatible with the dipole model for both elastic and diffractive amplitudes.

Dynamic Factor Analysis Models with Time-Varying Parameters

ERIC Educational Resources Information Center

Chow, Sy-Miin; Zu, Jiyun; Shifren, Kim; Zhang, Guangjian

2011-01-01

Dynamic factor analysis models with time-varying parameters offer a valuable tool for evaluating multivariate time series data with time-varying dynamics and/or measurement properties. We use the Dynamic Model of Activation proposed by Zautra and colleagues (Zautra, Potter, & Reich, 1997) as a motivating example to construct a dynamic factor…

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

ERIC Educational Resources Information Center

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…

Bayes factors for the linear ballistic accumulator model of decision-making.

PubMed

Evans, Nathan J; Brown, Scott D

2018-04-01

Evidence accumulation models of decision-making have led to advances in several different areas of psychology. These models provide a way to integrate response time and accuracy data, and to describe performance in terms of latent cognitive processes. Testing important psychological hypotheses using cognitive models requires a method to make inferences about different versions of the models which assume different parameters to cause observed effects. The task of model-based inference using noisy data is difficult, and has proven especially problematic with current model selection methods based on parameter estimation. We provide a method for computing Bayes factors through Monte-Carlo integration for the linear ballistic accumulator (LBA; Brown and Heathcote, 2008), a widely used evidence accumulation model. Bayes factors are used frequently for inference with simpler statistical models, and they do not require parameter estimation. In order to overcome the computational burden of estimating Bayes factors via brute force integration, we exploit general purpose graphical processing units; we provide free code for this. This approach allows estimation of Bayes factors via Monte-Carlo integration within a practical time frame. We demonstrate the method using both simulated and real data. We investigate the stability of the Monte-Carlo approximation, and the LBA's inferential properties, in simulation studies.

Dimensional models of personality: the five-factor model and the DSM-5

PubMed Central

Trull, Timothy J.; Widiger, Thomas A.

2013-01-01

It is evident that the classification of personality disorder is shifting toward a dimensional trait model and, more specifically, the five-factor model (FFM). The purpose of this paper is to provide an overview of the FFM of personality disorder. It will begin with a description of this dimensional model of normal and abnormal personality functioning, followed by a comparison with a proposal for future revisions to DSM-5 and a discussion of its potential advantages as an integrative hierarchical model of normal and abnormal personality structure. PMID:24174888

Predictive Modeling of Risk Factors and Complications of Cataract Surgery

PubMed Central

Gaskin, Gregory L; Pershing, Suzann; Cole, Tyler S; Shah, Nigam H

2016-01-01

Purpose To quantify the relationship between aggregated preoperative risk factors and cataract surgery complications, as well as to build a model predicting outcomes on an individual-level—given a constellation of demographic, baseline, preoperative, and intraoperative patient characteristics. Setting Stanford Hospital and Clinics between 1994 and 2013. Design Retrospective cohort study Methods Patients age 40 or older who received cataract surgery between 1994 and 2013. Risk factors, complications, and demographic information were extracted from the Electronic Health Record (EHR), based on International Classification of Diseases, 9th edition (ICD-9) codes, Current Procedural Terminology (CPT) codes, drug prescription information, and text data mining using natural language processing. We used a bootstrapped least absolute shrinkage and selection operator (LASSO) model to identify highly-predictive variables. We built random forest classifiers for each complication to create predictive models. Results Our data corroborated existing literature on postoperative complications—including the association of intraoperative complications, complex cataract surgery, black race, and/or prior eye surgery with an increased risk of any postoperative complications. We also found a number of other, less well-described risk factors, including systemic diabetes mellitus, young age (<60 years old), and hyperopia as risk factors for complex cataract surgery and intra- and post-operative complications. Our predictive models based on aggregated outperformed existing published models. Conclusions The constellations of risk factors and complications described here can guide new avenues of research and provide specific, personalized risk assessment for a patient considering cataract surgery. The predictive capacity of our models can enable risk stratification of patients, which has utility as a teaching tool as well as informing quality/value-based reimbursements. PMID:26692059

HIGH DIMENSIONAL COVARIANCE MATRIX ESTIMATION IN APPROXIMATE FACTOR MODELS

PubMed Central

Fan, Jianqing; Liao, Yuan; Mincheva, Martina

2012-01-01

The variance covariance matrix plays a central role in the inferential theories of high dimensional factor models in finance and economics. Popular regularization methods of directly exploiting sparsity are not directly applicable to many financial problems. Classical methods of estimating the covariance matrices are based on the strict factor models, assuming independent idiosyncratic components. This assumption, however, is restrictive in practical applications. By assuming sparse error covariance matrix, we allow the presence of the cross-sectional correlation even after taking out common factors, and it enables us to combine the merits of both methods. We estimate the sparse covariance using the adaptive thresholding technique as in Cai and Liu (2011), taking into account the fact that direct observations of the idiosyncratic components are unavailable. The impact of high dimensionality on the covariance matrix estimation based on the factor structure is then studied. PMID:22661790

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

PubMed

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

2018-05-01

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

A Twin Factor Mixture Modeling Approach to Childhood Temperament: Differential Heritability

PubMed Central

Scott, Brandon G.; Lemery-Chalfant, Kathryn; Clifford, Sierra; Tein, Jenn-Yun; Stoll, Ryan; Goldsmith, H. Hill

2016-01-01

Twin factor mixture modeling was used to identify temperament profiles, while simultaneously estimating a latent factor model for each profile with a sample of 787 twin pairs (Mage =7.4 years; SD = .84; 49% female; 88.3% Caucasian), using mother- and father-reported temperament. A 4-profile, 1-factor model fit the data well. Profiles included ‘Regulated, Typical Reactive’, ‘Well-regulated, Positive Reactive’, ‘Regulated, Surgent’, and ‘Dysregulated, Negative Reactive.’ All profiles were heritable, with heritability lower and shared environment also contributing to membership in the ‘Regulated, Typical Reactive’ and ‘Dysregulated, Negative Reactive’ profiles. PMID:27291568

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

NASA Astrophysics Data System (ADS)

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.

Modeling wind adjustment factor and midflame wind speed for Rothermel's surface fire spread model

Treesearch

Patricia L. Andrews

2012-01-01

Rothermel's surface fire spread model was developed to use a value for the wind speed that affects surface fire, called midflame wind speed. Models have been developed to adjust 20-ft wind speed to midflame wind speed for sheltered and unsheltered surface fuel. In this report, Wind Adjustment Factor (WAF) model equations are given, and the BehavePlus fire modeling...

High-Dimensional Sparse Factor Modeling: Applications in Gene Expression Genomics

PubMed Central

Carvalho, Carlos M.; Chang, Jeffrey; Lucas, Joseph E.; Nevins, Joseph R.; Wang, Quanli; West, Mike

2010-01-01

We describe studies in molecular profiling and biological pathway analysis that use sparse latent factor and regression models for microarray gene expression data. We discuss breast cancer applications and key aspects of the modeling and computational methodology. Our case studies aim to investigate and characterize heterogeneity of structure related to specific oncogenic pathways, as well as links between aggregate patterns in gene expression profiles and clinical biomarkers. Based on the metaphor of statistically derived “factors” as representing biological “subpathway” structure, we explore the decomposition of fitted sparse factor models into pathway subcomponents and investigate how these components overlay multiple aspects of known biological activity. Our methodology is based on sparsity modeling of multivariate regression, ANOVA, and latent factor models, as well as a class of models that combines all components. Hierarchical sparsity priors address questions of dimension reduction and multiple comparisons, as well as scalability of the methodology. The models include practically relevant non-Gaussian/nonparametric components for latent structure, underlying often quite complex non-Gaussianity in multivariate expression patterns. Model search and fitting are addressed through stochastic simulation and evolutionary stochastic search methods that are exemplified in the oncogenic pathway studies. Supplementary supporting material provides more details of the applications, as well as examples of the use of freely available software tools for implementing the methodology. PMID:21218139

An ensemble model of competitive multi-factor binding of the genome

PubMed Central

Wasson, Todd; Hartemink, Alexander J.

2009-01-01

Hundreds of different factors adorn the eukaryotic genome, binding to it in large number. These DNA binding factors (DBFs) include nucleosomes, transcription factors (TFs), and other proteins and protein complexes, such as the origin recognition complex (ORC). DBFs compete with one another for binding along the genome, yet many current models of genome binding do not consider different types of DBFs together simultaneously. Additionally, binding is a stochastic process that results in a continuum of binding probabilities at any position along the genome, but many current models tend to consider positions as being either binding sites or not. Here, we present a model that allows a multitude of DBFs, each at different concentrations, to compete with one another for binding sites along the genome. The result is an “occupancy profile,” a probabilistic description of the DNA occupancy of each factor at each position. We implement our model efficiently as the software package COMPETE. We demonstrate genome-wide and at specific loci how modeling nucleosome binding alters TF binding, and vice versa, and illustrate how factor concentration influences binding occupancy. Binding cooperativity between nearby TFs arises implicitly via mutual competition with nucleosomes. Our method applies not only to TFs, but also recapitulates known occupancy profiles of a well-studied replication origin with and without ORC binding. Importantly, the sequence preferences our model takes as input are derived from in vitro experiments. This ensures that the calculated occupancy profiles are the result of the forces of competition represented explicitly in our model and the inherent sequence affinities of the constituent DBFs. PMID:19720867

Classroom Factors Affecting Students: Self-Evaluation: An Interactional Model.

ERIC Educational Resources Information Center

Marshall, Hermine H.; Weinstein, Rhona S.

1984-01-01

A complex interactional model of classroom factors that contribute to the development of students' self-evaluations is presented. Factors described are: (1) task structure; (2) grouping practices; (3) feedback and evaluation procedures and information about ability; (4) motivational strategies; (5) locus of responsibility for learning; and (6) the…

Extracting the Evaluations of Stereotypes: Bi-factor Model of the Stereotype Content Structure

PubMed Central

Sayans-Jiménez, Pablo; Cuadrado, Isabel; Rojas, Antonio J.; Barrada, Juan R.

2017-01-01

Stereotype dimensions—competence, morality and sociability—are fundamental to studying the perception of other groups. These dimensions have shown moderate/high positive correlations with each other that do not reflect the theoretical expectations. The explanation for this (e.g., halo effect) undervalues the utility of the shared variance identified. In contrast, in this work we propose that this common variance could represent the global evaluation of the perceived group. Bi-factor models are proposed to improve the internal structure and to take advantage of the information representing the shared variance among dimensions. Bi-factor models were compared with first order models and other alternative models in three large samples (300–309 participants). The relationships among the global and specific bi-factor dimensions with a global evaluation dimension (measured through a semantic differential) were estimated. The results support the use of bi-factor models rather than first order models (and other alternative models). Bi-factor models also show a greater utility to directly and more easily explore the stereotype content including its evaluative content. PMID:29085313

An Investigation of the Factor Structure and Convergent and Discriminant Validity of the Five-Factor Model Rating Form

ERIC Educational Resources Information Center

Samuel, Douglas B.; Mullins-Sweatt, Stephanie N.; Widiger, Thomas A.

2013-01-01

The Five-Factor Model Rating Form (FFMRF) is a one-page measure designed to provide an efficient assessment of the higher order domains of the Five Factor Model (FFM) as well as the more specific, lower order facets proposed by McCrae and Costa. Although previous research has suggested that the FFMRF's assessment of the lower order facets converge…

Rethinking "Harmonious Parenting" Using a Three-Factor Discipline Model

ERIC Educational Resources Information Center

Greenspan, Stephen

2006-01-01

Diana Baumrind's typology of parenting is based on a two-factor model of "control" and "warmth". Her recommended discipline style, labeled "authoritative parenting", was constructed by taking high scores on these two factors. A problem with authoritative parenting is that it does not allow for flexible and differentiated responses to discipline…

Human Modeling for Ground Processing Human Factors Engineering Analysis

NASA Technical Reports Server (NTRS)

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

Indigenous Chinese Personality Constructs: Is the Five-Factor Model Complete?

ERIC Educational Resources Information Center

Cheung, Fanny M.; Leung, Kwok; Zhang, Jian-Xin; Sun, Hai-Fa; Gan, Yi-Qun; Song, Wei-Zhen; Xie, Dong

2001-01-01

Three studies involving Chinese respondents from China and Hong Kong and diverse respondents from Hawaii compared the Chinese Personality Assessment Inventory factor structure with the Revised NEO Personality Inventory (NEO-PI-R) and NEO-Five Factor Inventory. Results supported the universality of the five-factor model, the validity of NEO-PI-R,…

Four Factors of Clinical Decision Making: A Teaching Model.

ERIC Educational Resources Information Center

Leist, James C.; Konen, Joseph C.

1996-01-01

Four factors of clinical decision making identified by medical students include quality of care, cost, ethics, and legal concerns. This paper argues that physicians have two responsibilities in the clinical decision-making model: to be the primary advocate for quality health care and to ensure balance among the four factors, working in partnership…

Maximum Likelihood Dynamic Factor Modeling for Arbitrary "N" and "T" Using SEM

ERIC Educational Resources Information Center

Voelkle, Manuel C.; Oud, Johan H. L.; von Oertzen, Timo; Lindenberger, Ulman

2012-01-01

This article has 3 objectives that build on each other. First, we demonstrate how to obtain maximum likelihood estimates for dynamic factor models (the direct autoregressive factor score model) with arbitrary "T" and "N" by means of structural equation modeling (SEM) and compare the approach to existing methods. Second, we go beyond standard time…

Personality Correlates of Midlife Cardiometabolic Risk: The Explanatory Role of Higher-Order Factors of the Five Factor Model

PubMed Central

Dermody, Sarah S.; Wright, Aidan G.C.; Cheong, JeeWon; Miller, Karissa G.; Muldoon, Matthew F.; Flory, Janine D.; Gianaros, Peter J.; Marsland, Anna L.; Manuck, Stephen B.

2015-01-01

Objective Varying associations are reported between Five Factor Model (FFM) personality traits and cardiovascular diseaabolic risk within a hierarchical model of personality that posits higherse risk. Here, we further examine dispositional correlates of cardiomet -order traits of Stability (shared variance of Agreeableness, Conscientiousness, inverse Neuroticism) and Plasticity (Extraversion, Openness), and test hypothesized mediation via biological and behavioral factors. Method In an observational study of 856 community volunteers aged 30–54 years (46% male, 86% Caucasian), latent variable FFM traits (using multiple-informant reports) and aggregated cardiometabolic risk (indicators: insulin resistance, dyslipidemia, blood pressure, adiposity) were estimated using confirmatory factor analysis (CFA). The cardiometabolic factor was regressed on each personality factor or higher-order trait. Cross-sectional indirect effects via systemic inflammation, cardiac autonomic control, and physical activity were tested. Results CFA models confirmed the Stability “meta-trait,” but not Plasticity. Lower Stability was associated with heightened cardiometabolic risk. This association was accounted for by inflammation, autonomic function, and physical activity. Among FFM traits, only Openness was associated with risk over and above Stability and, unlike Stablity, this relationship was unexplained by the intervening variables. Conclusions A Stability meta-trait covaries with midlife cardiometabolic risk, and this association is accounted for by three candidate biological and behavioral factors. PMID:26249259

Clinical application of the five-factor model.

PubMed

Widiger, Thomas A; Presnall, Jennifer Ruth

2013-12-01

The Five-Factor Model (FFM) has become the predominant dimensional model of general personality structure. The purpose of this paper is to suggest a clinical application. A substantial body of research indicates that the personality disorders included within the American Psychiatric Association's (APA) Diagnostic and Statistical Manual of Mental Disorders (DSM) can be understood as extreme and/or maladaptive variants of the FFM (the acronym "DSM" refers to any particular edition of the APA DSM). In addition, the current proposal for the forthcoming fifth edition of the DSM (i.e., DSM-5) is shifting closely toward an FFM dimensional trait model of personality disorder. Advantages of this shifting conceptualization are discussed, including treatment planning. © 2012 Wiley Periodicals, Inc.

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

PubMed

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.

Positive Matrix Factorization Model for environmental data analyses

EPA Pesticide Factsheets

Positive Matrix Factorization is a receptor model developed by EPA to provide scientific support for current ambient air quality standards and implement those standards by identifying and quantifying the relative contributions of air pollution sources.

An Illumination Modeling System for Human Factors Analyses

NASA Technical Reports Server (NTRS)

Huynh, Thong; Maida, James C.; Bond, Robert L. (Technical Monitor)

2002-01-01

Seeing is critical to human performance. Lighting is critical for seeing. Therefore, lighting is critical to human performance. This is common sense, and here on earth, it is easily taken for granted. However, on orbit, because the sun will rise or set every 45 minutes on average, humans working in space must cope with extremely dynamic lighting conditions. Contrast conditions of harsh shadowing and glare is also severe. The prediction of lighting conditions for critical operations is essential. Crew training can factor lighting into the lesson plans when necessary. Mission planners can determine whether low-light video cameras are required or whether additional luminaires need to be flown. The optimization of the quantity and quality of light is needed because of the effects on crew safety, on electrical power and on equipment maintainability. To address all of these issues, an illumination modeling system has been developed by the Graphics Research and Analyses Facility (GRAF) and Lighting Environment Test Facility (LETF) in the Space Human Factors Laboratory at NASA Johnson Space Center. The system uses physically based ray tracing software (Radiance) developed at Lawrence Berkeley Laboratories, a human factors oriented geometric modeling system (PLAID) and an extensive database of humans and environments. Material reflectivity properties of major surfaces and critical surfaces are measured using a gonio-reflectometer. Luminaires (lights) are measured for beam spread distribution, color and intensity. Video camera performances are measured for color and light sensitivity. 3D geometric models of humans and the environment are combined with the material and light models to form a system capable of predicting lighting conditions and visibility conditions in space.

Space station crew safety: Human factors interaction model

NASA Technical Reports Server (NTRS)

Cohen, M. M.; Junge, M. K.

1985-01-01

A model of the various human factors issues and interactions that might affect crew safety is developed. The first step addressed systematically the central question: How is this space station different from all other spacecraft? A wide range of possible issue was identified and researched. Five major topics of human factors issues that interacted with crew safety resulted: Protocols, Critical Habitability, Work Related Issues, Crew Incapacitation and Personal Choice. Second, an interaction model was developed that would show some degree of cause and effect between objective environmental or operational conditions and the creation of potential safety hazards. The intermediary steps between these two extremes of causality were the effects on human performance and the results of degraded performance. The model contains three milestones: stressor, human performance (degraded) and safety hazard threshold. Between these milestones are two countermeasure intervention points. The first opportunity for intervention is the countermeasure against stress. If this countermeasure fails, performance degrades. The second opportunity for intervention is the countermeasure against error. If this second countermeasure fails, the threshold of a potential safety hazard may be crossed.

Identification and synthetic modeling of factors affecting American black duck populations

USGS Publications Warehouse

Conroy, Michael J.; Miller, Mark W.; Hines, James E.

2002-01-01

We reviewed the literature on factors potentially affecting the population status of American black ducks (Anas rupribes). Our review suggests that there is some support for the influence of 4 major, continental-scope factors in limiting or regulating black duck populations: 1) loss in the quantity or quality of breeding habitats; 2) loss in the quantity or quality of wintering habitats; 3) harvest, and 4) interactions (competition, hybridization) with mallards (Anas platyrhychos) during the breeding and/or wintering periods. These factors were used as the basis of an annual life cycle model in which reproduction rates and survival rates were modeled as functions of the above factors, with parameters of the model describing the strength of these relationships. Variation in the model parameter values allows for consideration of scientific uncertainty as to the degree each of these factors may be contributing to declines in black duck populations, and thus allows for the investigation of the possible effects of management (e.g., habitat improvement, harvest reductions) under different assumptions. We then used available, historical data on black duck populations (abundance, annual reproduction rates, and survival rates) and possible driving factors (trends in breeding and wintering habitats, harvest rates, and abundance of mallards) to estimate model parameters. Our estimated reproduction submodel included parameters describing negative density feedback of black ducks, positive influence of breeding habitat, and negative influence of mallard densities; our survival submodel included terms for positive influence of winter habitat on reproduction rates, and negative influences of black duck density (i.e., compensation to harvest mortality). Individual models within each group (reproduction, survival) involved various combinations of these factors, and each was given an information theoretic weight for use in subsequent prediction. The reproduction model with highest

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

PubMed

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.

[Psychosocial factors at work and cardiovascular diseases: contribution of the Effort-Reward Imbalance model].

PubMed

Niedhammer, I; Siegrist, J

1998-11-01

The effect of psychosocial factors at work on health, especially cardiovascular health, has given rise to growing concern in occupational epidemiology over the last few years. Two theoretical models, Karasek's model and the Effort-Reward Imbalance model, have been developed to evaluate psychosocial factors at work within specific conceptual frameworks in an attempt to take into account the serious methodological difficulties inherent in the evaluation of such factors. Karasek's model, the most widely used model, measures three factors: psychological demands, decision latitude and social support at work. Many studies have shown the predictive effects of these factors on cardiovascular diseases independently of well-known cardiovascular risk factors. More recently, the Effort-Reward Imbalance model takes into account the role of individual coping characteristics which was neglected in the Karasek model. The effort-reward imbalance model focuses on the reciprocity of exchange in occupational life where high-cost/low-gain conditions are considered particularly stressful. Three dimensions of rewards are distinguished: money, esteem and gratifications in terms of promotion prospects and job security. Some studies already support that high-effort/low reward-conditions are predictive of cardiovascular diseases.

Unidimensional factor models imply weaker partial correlations than zero-order correlations.

PubMed

van Bork, Riet; Grasman, Raoul P P P; Waldorp, Lourens J

2018-06-01

In this paper we present a new implication of the unidimensional factor model. We prove that the partial correlation between two observed variables that load on one factor given any subset of other observed variables that load on this factor lies between zero and the zero-order correlation between these two observed variables. We implement this result in an empirical bootstrap test that rejects the unidimensional factor model when partial correlations are identified that are either stronger than the zero-order correlation or have a different sign than the zero-order correlation. We demonstrate the use of the test in an empirical data example with data consisting of fourteen items that measure extraversion.

Using exploratory regression to identify optimal driving factors for cellular automaton modeling of land use change.

PubMed

Feng, Yongjiu; Tong, Xiaohua

2017-09-22

Defining transition rules is an important issue in cellular automaton (CA)-based land use modeling because these models incorporate highly correlated driving factors. Multicollinearity among correlated driving factors may produce negative effects that must be eliminated from the modeling. Using exploratory regression under pre-defined criteria, we identified all possible combinations of factors from the candidate factors affecting land use change. Three combinations that incorporate five driving factors meeting pre-defined criteria were assessed. With the selected combinations of factors, three logistic regression-based CA models were built to simulate dynamic land use change in Shanghai, China, from 2000 to 2015. For comparative purposes, a CA model with all candidate factors was also applied to simulate the land use change. Simulations using three CA models with multicollinearity eliminated performed better (with accuracy improvements about 3.6%) than the model incorporating all candidate factors. Our results showed that not all candidate factors are necessary for accurate CA modeling and the simulations were not sensitive to changes in statistically non-significant driving factors. We conclude that exploratory regression is an effective method to search for the optimal combinations of driving factors, leading to better land use change models that are devoid of multicollinearity. We suggest identification of dominant factors and elimination of multicollinearity before building land change models, making it possible to simulate more realistic outcomes.

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

ERIC Educational Resources Information Center

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…

Determining factors influencing survival of breast cancer by fuzzy logistic regression model.

PubMed

Nikbakht, Roya; Bahrampour, Abbas

2017-01-01

Fuzzy logistic regression model can be used for determining influential factors of disease. This study explores the important factors of actual predictive survival factors of breast cancer's patients. We used breast cancer data which collected by cancer registry of Kerman University of Medical Sciences during the period of 2000-2007. The variables such as morphology, grade, age, and treatments (surgery, radiotherapy, and chemotherapy) were applied in the fuzzy logistic regression model. Performance of model was determined in terms of mean degree of membership (MDM). The study results showed that almost 41% of patients were in neoplasm and malignant group and more than two-third of them were still alive after 5-year follow-up. Based on the fuzzy logistic model, the most important factors influencing survival were chemotherapy, morphology, and radiotherapy, respectively. Furthermore, the MDM criteria show that the fuzzy logistic regression have a good fit on the data (MDM = 0.86). Fuzzy logistic regression model showed that chemotherapy is more important than radiotherapy in survival of patients with breast cancer. In addition, another ability of this model is calculating possibilistic odds of survival in cancer patients. The results of this study can be applied in clinical research. Furthermore, there are few studies which applied the fuzzy logistic models. Furthermore, we recommend using this model in various research areas.

Probabilistic Usage of the Multi-Factor Interaction Model

NASA Technical Reports Server (NTRS)

Chamis, Christos C.

2008-01-01

A Multi-Factor Interaction Model (MFIM) is used to predict the insulating foam mass expulsion during the ascending of a space vehicle. The exponents in the MFIM are evaluated by an available approach which consists of least squares and an optimization algorithm. These results were subsequently used to probabilistically evaluate the effects of the uncertainties in each participating factor in the mass expulsion. The probabilistic results show that the surface temperature dominates at high probabilities and the pressure which causes the mass expulsion at low probabil

The Kroll-Lee-Zumino Model and Pion Form Factors

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

Dominguez, C. A.; Loewe, M.

2010-08-04

At the one loop level, we make use of the renormalizable Abelian quantum field theory model of Kroll, Lee, and Zumino (KLZ) in order to compute the vertex corrections to the tree-level, Vector Meson Dominance (VMD) electromagnetic pion form factor. This result, together with the one-loop vacuum polarization contribution, implies an electromagnetic pion form factor which is in outstanding agreement with data in the whole range of accessible momentum transfers in the space-like region. The time-like form factor, which reproduces the Gounaris-Sakurai formula at and near the rho-meson peak, remains unaffected by the vertex correction at order O(g{sup 2}). Wemore » also use the KLZ model to compute the pion scalar radius at the one loop level, finding S = 0.40 fm{sup 2}. From this value we find for the low energy constant of chiral perturbation theory l{sub 4} = 3.4.« less

Correlation of causal factors that influence construction safety performance: A model.

PubMed

Rodrigues, F; Coutinho, A; Cardoso, C

2015-01-01

The construction sector has presented positive development regarding the decrease in occupational accident rates in recent years. Regardless, the construction sector stands out systematically from other industries due to its high number of fatalities. The aim of this paper is to deeply understand the causality of construction accidents from the early design phase through a model. This study reviewed several research papers presenting various analytical models that correlate the contributing factors to occupational accidents in this sector. This study also analysed different construction projects and conducted a survey of design and site supervision teams. This paper proposes a model developed from the analysis of existing ones, which correlates the causal factors through all the construction phases. It was concluded that effective risk prevention can only be achieved by a global correlation of causal factors including not only production ones but also client requirements, financial climate, design team competence, project and risk management, financial capacity, health and safety policy and early planning. Accordingly, a model is proposed.

Two Empirical Models for Land-falling Hurricane Gust Factors

NASA Technical Reports Server (NTRS)

Merceret, Franics J.

2008-01-01

Gaussian and lognormal models for gust factors as a function of height and mean windspeed in land-falling hurricanes are presented. The models were empirically derived using data from 2004 hurricanes Frances and Jeanne and independently verified using data from 2005 hurricane Wilma. The data were collected from three wind towers at Kennedy Space Center and Cape Canaveral Air Force Station with instrumentation at multiple levels from 12 to 500 feet above ground level. An additional 200-foot tower was available for the verification. Mean wind speeds from 15 to 60 knots were included in the data. The models provide formulas for the mean and standard deviation of the gust factor given the mean windspeed and height above ground. These statistics may then be used to assess the probability of exceeding a specified peak wind threshold of operational significance given a specified mean wind speed.

Multi-factor energy price models and exotic derivatives pricing

NASA Astrophysics Data System (ADS)

Hikspoors, Samuel

The high pace at which many of the world's energy markets have gradually been opened to competition have generated a significant amount of new financial activity. Both academicians and practitioners alike recently started to develop the tools of energy derivatives pricing/hedging as a quantitative topic of its own. The energy contract structures as well as their underlying asset properties set the energy risk management industry apart from its more standard equity and fixed income counterparts. This thesis naturally contributes to these broad market developments in participating to the advances of the mathematical tools aiming at a better theory of energy contingent claim pricing/hedging. We propose many realistic two-factor and three-factor models for spot and forward price processes that generalize some well known and standard modeling assumptions. We develop the associated pricing methodologies and propose stable calibration algorithms that motivate the application of the relevant modeling schemes.

The Five-Factor Model of personality disorder and DSM-5.

PubMed

Trull, Timothy J

2012-12-01

The Five-Factor Model of personality disorders (FFMPD; Widiger & Mullins-Sweatt, ) developed from the recognition that the popular Five-Factor Model (FFM) of personality could be used to describe and understand the official personality disorder (PD) constructs from the American Psychiatric Association's (APA) diagnostic manuals (e.g., DSM-IV-TR, APA, ). This article provides an overview of the FFM, highlighting its validity and utility in characterizing PDs as well as its ability to provide a comprehensive account of personality pathology in general. In 2013, DSM-5 is scheduled to appear, and the "hybrid" PD proposal will emphasize a 25-personality trait model. I present the current version of this new model, compare it to the FFMPD, and discuss issues related to the implementation of the FFMPD. © 2012 The Author. Journal of Personality © 2012, Wiley Periodicals, Inc.

Transcription factors in pancreatic development. Animal models.

PubMed

Martin, Merce; Hauer, Viviane; Messmer, Mélanie; Orvain, Christophe; Gradwohl, Gérard

2007-01-01

Through the analysis of genetically modified mice a hierarchy of transcription factors regulating pancreas specification, endocrine destiny as well as endocrine subtype specification and differentiation has been established. In addition to conventional approaches such as transgenic technologies and gene targeting, recombinase fate mapping in mice has been key in establishing the lineage relationship between progenitor cells and their progeny in understanding pancreas formation. Moreover, the design of specific mouse models to conditionally express transcription factors in different populations of progenitor cells has revealed to what extent transcription factors required for islet cell development are also sufficient to induce endocrine differentiation and the importance of the competence of progenitor cells to respond to the genetic program implemented by these factors. Taking advantage of this basic science knowledge acquired in rodents, immature insulin-producing cells have recently been differentiated in vitro from human embryonic stem cells. Taken together these major advances emphasize the need to gain further in-depth knowledge of the molecular and cellular mechanisms controlling beta-cell differentiation in mice to generate functional beta-cells in the future that could be used for cell therapy in diabetes.

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

NASA Astrophysics Data System (ADS)

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.

A combined model of human erythropoiesis and granulopoiesis under growth factor and chemotherapy treatment

PubMed Central

2014-01-01

Background Haematotoxicity of conventional chemotherapies often results in delays of treatment or reduction of chemotherapy dose. To ameliorate these side-effects, patients are routinely treated with blood transfusions or haematopoietic growth factors such as erythropoietin (EPO) or granulocyte colony-stimulating factor (G-CSF). For the latter ones, pharmaceutical derivatives are available, which differ in absorption kinetics, pharmacokinetic and -dynamic properties. Due to the complex interaction of cytotoxic effects of chemotherapy and the stimulating effects of different growth factor derivatives, optimal treatment is a non-trivial task. In the past, we developed mathematical models of thrombopoiesis, granulopoiesis and erythropoiesis under chemotherapy and growth-factor applications which can be used to perform clinically relevant predictions regarding the feasibility of chemotherapy schedules and cytopenia prophylaxis with haematopoietic growth factors. However, interactions of lineages and growth-factors were ignored so far. Results To close this gap, we constructed a hybrid model of human granulopoiesis and erythropoiesis under conventional chemotherapy, G-CSF and EPO applications. This was achieved by combining our single lineage models of human erythropoiesis and granulopoiesis with a common stem cell model. G-CSF effects on erythropoiesis were also implemented. Pharmacodynamic models are based on ordinary differential equations describing proliferation and maturation of haematopoietic cells. The system is regulated by feedback loops partly mediated by endogenous and exogenous EPO and G-CSF. Chemotherapy is modelled by depletion of cells. Unknown model parameters were determined by fitting the model predictions to time series data of blood counts and cytokine profiles. Data were extracted from literature or received from cooperating clinical study groups. Our model explains dynamics of mature blood cells and cytokines after growth-factor applications in

Alternative Factor Models and Heritability of the Short Leyton Obsessional Inventory--Children's Version

ERIC Educational Resources Information Center

Moore, Janette; Smith, Gillian W.; Shevlin, Mark; O'Neill, Francis A.

2010-01-01

An alternative models framework was used to test three confirmatory factor analytic models for the Short Leyton Obsessional Inventory-Children's Version (Short LOI-CV) in a general population sample of 517 young adolescent twins (11-16 years). A one-factor model as implicit in current classification systems of Obsessive-Compulsive Disorder (OCD),…

Assessing risk factors for dental caries: a statistical modeling approach.

PubMed

Trottini, Mario; Bossù, Maurizio; Corridore, Denise; Ierardo, Gaetano; Luzzi, Valeria; Saccucci, Matteo; Polimeni, Antonella

2015-01-01

The problem of identifying potential determinants and predictors of dental caries is of key importance in caries research and it has received considerable attention in the scientific literature. From the methodological side, a broad range of statistical models is currently available to analyze dental caries indices (DMFT, dmfs, etc.). These models have been applied in several studies to investigate the impact of different risk factors on the cumulative severity of dental caries experience. However, in most of the cases (i) these studies focus on a very specific subset of risk factors; and (ii) in the statistical modeling only few candidate models are considered and model selection is at best only marginally addressed. As a result, our understanding of the robustness of the statistical inferences with respect to the choice of the model is very limited; the richness of the set of statistical models available for analysis in only marginally exploited; and inferences could be biased due the omission of potentially important confounding variables in the model's specification. In this paper we argue that these limitations can be overcome considering a general class of candidate models and carefully exploring the model space using standard model selection criteria and measures of global fit and predictive performance of the candidate models. Strengths and limitations of the proposed approach are illustrated with a real data set. In our illustration the model space contains more than 2.6 million models, which require inferences to be adjusted for 'optimism'.

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

NASA Astrophysics Data System (ADS)

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.

The Common Factors Discrimination Model: An Integrated Approach to Counselor Supervision

ERIC Educational Resources Information Center

Crunk, A. Elizabeth; Barden, Sejal M.

2017-01-01

Numerous models of clinical supervision have been developed; however, there is little empirical support indicating that any one model is superior. Therefore, common factors approaches to supervision integrate essential components that are shared among counseling and supervision models. The purpose of this paper is to present an innovative model of…

Gaussian and Lognormal Models of Hurricane Gust Factors

NASA Technical Reports Server (NTRS)

Merceret, Frank

2009-01-01

A document describes a tool that predicts the likelihood of land-falling tropical storms and hurricanes exceeding specified peak speeds, given the mean wind speed at various heights of up to 500 feet (150 meters) above ground level. Empirical models to calculate mean and standard deviation of the gust factor as a function of height and mean wind speed were developed in Excel based on data from previous hurricanes. Separate models were developed for Gaussian and offset lognormal distributions for the gust factor. Rather than forecasting a single, specific peak wind speed, this tool provides a probability of exceeding a specified value. This probability is provided as a function of height, allowing it to be applied at a height appropriate for tall structures. The user inputs the mean wind speed, height, and operational threshold. The tool produces the probability from each model that the given threshold will be exceeded. This application does have its limits. They were tested only in tropical storm conditions associated with the periphery of hurricanes. Winds of similar speed produced by non-tropical system may have different turbulence dynamics and stability, which may change those winds statistical characteristics. These models were developed along the Central Florida seacoast, and their results may not accurately extrapolate to inland areas, or even to coastal sites that are different from those used to build the models. Although this tool cannot be generalized for use in different environments, its methodology could be applied to those locations to develop a similar tool tuned to local conditions.

[Mathematic concept model of accumulation of functional disorders associated with environmental factors].

PubMed

Zaĭtseva, N V; Trusov, P V; Kir'ianov, D A

2012-01-01

The mathematic concept model presented describes accumulation of functional disorders associated with environmental factors, plays predictive role and is designed for assessments of possible effects caused by heterogenous factors with variable exposures. Considering exposure changes with self-restoration process opens prospects of using the model to evaluate, analyse and manage occupational risks. To develop current theoretic approaches, the authors suggested a model considering age-related body peculiarities, systemic interactions of organs, including neuro-humoral regulation, accumulation of functional disorders due to external factors, rehabilitation of functions during treatment. General objective setting covers defining over a hundred unknow coefficients that characterize speed of various processes within the body. To solve this problem, the authors used iteration approach, successive identification, that starts from the certain primary approximation of the model parameters and processes subsequent updating on the basis of new theoretic and empirical knowledge.

On the Performance Characteristics of Latent-Factor and Knowledge Tracing Models

ERIC Educational Resources Information Center

Klingler, Severin; Käser, Tanja; Solenthaler, Barbara; Gross, Markus

2015-01-01

Modeling student knowledge is a fundamental task of an intelligent tutoring system. A popular approach for modeling the acquisition of knowledge is Bayesian Knowledge Tracing (BKT). Various extensions to the original BKT model have been proposed, among them two novel models that unify BKT and Item Response Theory (IRT). Latent Factor Knowledge…

Factor weighting in DRASTIC modeling.

PubMed

Pacheco, F A L; Pires, L M G R; Santos, R M B; Sanches Fernandes, L F

2015-02-01

Evaluation of aquifer vulnerability comprehends the integration of very diverse data, including soil characteristics (texture), hydrologic settings (recharge), aquifer properties (hydraulic conductivity), environmental parameters (relief), and ground water quality (nitrate contamination). It is therefore a multi-geosphere problem to be handled by a multidisciplinary team. The DRASTIC model remains the most popular technique in use for aquifer vulnerability assessments. The algorithm calculates an intrinsic vulnerability index based on a weighted addition of seven factors. In many studies, the method is subject to adjustments, especially in the factor weights, to meet the particularities of the studied regions. However, adjustments made by different techniques may lead to markedly different vulnerabilities and hence to insecurity in the selection of an appropriate technique. This paper reports the comparison of 5 weighting techniques, an enterprise not attempted before. The studied area comprises 26 aquifer systems located in Portugal. The tested approaches include: the Delphi consensus (original DRASTIC, used as reference), Sensitivity Analysis, Spearman correlations, Logistic Regression and Correspondence Analysis (used as adjustment techniques). In all cases but Sensitivity Analysis, adjustment techniques have privileged the factors representing soil characteristics, hydrologic settings, aquifer properties and environmental parameters, by leveling their weights to ≈4.4, and have subordinated the factors describing the aquifer media by downgrading their weights to ≈1.5. Logistic Regression predicts the highest and Sensitivity Analysis the lowest vulnerabilities. Overall, the vulnerability indices may be separated by a maximum value of 51 points. This represents an uncertainty of 2.5 vulnerability classes, because they are 20 points wide. Given this ambiguity, the selection of a weighting technique to integrate a vulnerability index may require additional

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

PubMed

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.

Probabilistic Multi-Factor Interaction Model for Complex Material Behavior

NASA Technical Reports Server (NTRS)

Abumeri, Galib H.; Chamis, Christos C.

2010-01-01

Complex material behavior is represented by a single equation of product form to account for interaction among the various factors. The factors are selected by the physics of the problem and the environment that the model is to represent. For example, different factors will be required for each to represent temperature, moisture, erosion, corrosion, etc. It is important that the equation represent the physics of the behavior in its entirety accurately. The Multi-Factor Interaction Model (MFIM) is used to evaluate the divot weight (foam weight ejected) from the external launch tanks. The multi-factor has sufficient degrees of freedom to evaluate a large number of factors that may contribute to the divot ejection. It also accommodates all interactions by its product form. Each factor has an exponent that satisfies only two points - the initial and final points. The exponent describes a monotonic path from the initial condition to the final. The exponent values are selected so that the described path makes sense in the absence of experimental data. In the present investigation, the data used were obtained by testing simulated specimens in launching conditions. Results show that the MFIM is an effective method of describing the divot weight ejected under the conditions investigated. The problem lies in how to represent the divot weight with a single equation. A unique solution to this problem is a multi-factor equation of product form. Each factor is of the following form (1 xi/xf)ei, where xi is the initial value, usually at ambient conditions, xf the final value, and ei the exponent that makes the curve represented unimodal that meets the initial and final values. The exponents are either evaluated by test data or by technical judgment. A minor disadvantage may be the selection of exponents in the absence of any empirical data. This form has been used successfully in describing the foam ejected in simulated space environmental conditions. Seven factors were required

Qualitative models of seat discomfort including static and dynamic factors.

PubMed

Ebe, K; Griffin, M J

2000-06-01

Judgements of overall seating comfort in dynamic conditions sometimes correlate better with the static characteristics of a seat than with measures of the dynamic environment. This study developed qualitative models of overall seat discomfort to include both static and dynamic seat characteristics. A dynamic factor that reflected how vibration discomfort increased as vibration magnitude increased was combined with a static seat factor which reflected seating comfort without vibration. The ability of the model to predict the relative and overall importance of dynamic and static seat characteristics on comfort was tested in two experiments. A paired comparison experiment, using four polyurethane foam cushions (50, 70, 100, 120 mm thick), provided different static and dynamic comfort when 12 subjects were exposed to one-third octave band random vertical vibration with centre frequencies of 2.5 and 5.5 Hz, at magnitudes of 0.00, 0.25 and 0.50 m x s(-2) rms measured beneath the foam samples. Subject judgements of the relative discomfort of the different conditions depended on both static and dynamic characteristics in a manner consistent with the model. The effect of static and dynamic seat factors on overall seat discomfort was investigated by magnitude estimation using three foam cushions (of different hardness) and a rigid wooden seat at six vibration magnitudes with 20 subjects. Static seat factors (i.e. cushion stiffness) affected the manner in which vibration influenced the overall discomfort: cushions with lower stiffness were more comfortable and more sensitive to changes in vibration magnitude than those with higher stiffness. The experiments confirm that judgements of overall seat discomfort can be affected by both the static and dynamic characteristics of a seat, with the effect depending on vibration magnitude: when vibration magnitude was low, discomfort was dominated by static seat factors; as the vibration magnitude increased, discomfort became dominated

Adolescent Personality: A Five-Factor Model Construct Validation

ERIC Educational Resources Information Center

Baker, Spencer T.; Victor, James B.; Chambers, Anthony L.; Halverson, Jr., Charles F.

2004-01-01

The purpose of this study was to investigate convergent and discriminant validity of the five-factor model of adolescent personality in a school setting using three different raters (methods): self-ratings, peer ratings, and teacher ratings. The authors investigated validity through a multitrait-multimethod matrix and a confirmatory factor…

Five-Factor Model of Personality and Career Exploration

ERIC Educational Resources Information Center

Reed, Mary Beth; Bruch, Monroe A.; Haase, Richard F.

2004-01-01

This study investigates whether the dimensions of the five-factor model (FFM) of personality are related to specific career exploration variables. Based on the FFM, predictions were made about the relevance of particular traits to career exploration variables. Results from a canonical correlation analysis showed that variable loadings on three…

Probabilistic Multi-Factor Interaction Model for Complex Material Behavior

NASA Technical Reports Server (NTRS)

Chamis, Christos C.; Abumeri, Galib H.

2008-01-01

The Multi-Factor Interaction Model (MFIM) is used to evaluate the divot weight (foam weight ejected) from the launch external tanks. The multi-factor has sufficient degrees of freedom to evaluate a large number of factors that may contribute to the divot ejection. It also accommodates all interactions by its product form. Each factor has an exponent that satisfies only two points, the initial and final points. The exponent describes a monotonic path from the initial condition to the final. The exponent values are selected so that the described path makes sense in the absence of experimental data. In the present investigation the data used was obtained by testing simulated specimens in launching conditions. Results show that the MFIM is an effective method of describing the divot weight ejected under the conditions investigated.

Probabilistic Multi-Factor Interaction Model for Complex Material Behavior

NASA Technical Reports Server (NTRS)

Chamis, Christos C.; Abumeri, Galib H.

2008-01-01

The Multi-Factor Interaction Model (MFIM) is used to evaluate the divot weight (foam weight ejected) from the launch external tanks. The multi-factor has sufficient degrees of freedom to evaluate a large number of factors that may contribute to the divot ejection. It also accommodates all interactions by its product form. Each factor has an exponent that satisfies only two points the initial and final points. The exponent describes a monotonic path from the initial condition to the final. The exponent values are selected so that the described path makes sense in the absence of experimental data. In the present investigation, the data used was obtained by testing simulated specimens in launching conditions. Results show that the MFIM is an effective method of describing the divot weight ejected under the conditions investigated.

Dietary Factors Modulate Helicobacter-Associated Gastric Cancer in Rodent Models

PubMed Central

Fox, James G.; Wang, Timothy C.

2014-01-01

Since its discovery in 1982, the global importance of H. pylori-induced disease, particularly in developing countries, remains high. The use of rodent models particularly mice, and the unanticipated usefulness of the gerbil to study H. pylori pathogenesis have been used extensively to study the interactions of the host, the pathogen and the environmental conditions influencing the outcome of persistent H. pylori infection. Dietary factors in humans are increasingly recognized as being important factors in modulating progression and severity of H. pylori-induced gastric cancer. Studies using rodent models to verify and help explain mechanisms whereby various dietary ingredients impact disease outcome should continue to be extremely productive. PMID:24301796

Comparing the Fit of Item Response Theory and Factor Analysis Models

ERIC Educational Resources Information Center

Maydeu-Olivares, Alberto; Cai, Li; Hernandez, Adolfo

2011-01-01

Linear factor analysis (FA) models can be reliably tested using test statistics based on residual covariances. We show that the same statistics can be used to reliably test the fit of item response theory (IRT) models for ordinal data (under some conditions). Hence, the fit of an FA model and of an IRT model to the same data set can now be…

The Manifest Association Structure of the Single-Factor Model: Insights from Partial Correlations

ERIC Educational Resources Information Center

Salgueiro, Maria de Fatima; Smith, Peter W. F.; McDonald, John W.

2008-01-01

The association structure between manifest variables arising from the single-factor model is investigated using partial correlations. The additional insights to the practitioner provided by partial correlations for detecting a single-factor model are discussed. The parameter space for the partial correlations is presented, as are the patterns of…

Thermal form-factor approach to dynamical correlation functions of integrable lattice models

NASA Astrophysics Data System (ADS)

Göhmann, Frank; Karbach, Michael; Klümper, Andreas; Kozlowski, Karol K.; Suzuki, Junji

2017-11-01

We propose a method for calculating dynamical correlation functions at finite temperature in integrable lattice models of Yang-Baxter type. The method is based on an expansion of the correlation functions as a series over matrix elements of a time-dependent quantum transfer matrix rather than the Hamiltonian. In the infinite Trotter-number limit the matrix elements become time independent and turn into the thermal form factors studied previously in the context of static correlation functions. We make this explicit with the example of the XXZ model. We show how the form factors can be summed utilizing certain auxiliary functions solving finite sets of nonlinear integral equations. The case of the XX model is worked out in more detail leading to a novel form-factor series representation of the dynamical transverse two-point function.

Modeling the factors affecting unsafe behavior in the construction industry from safety supervisors' perspective.

PubMed

Khosravi, Yahya; Asilian-Mahabadi, Hassan; Hajizadeh, Ebrahim; Hassanzadeh-Rangi, Narmin; Bastani, Hamid; Khavanin, Ali; Mortazavi, Seyed Bagher

2014-01-01

There can be little doubt that the construction is the most hazardous industry in the worldwide. This study was designed to modeling the factors affecting unsafe behavior from the perspective of safety supervisors. The qualitative research was conducted to extract a conceptual model. A structural model was then developed based on a questionnaire survey (n=266) by two stage Structural Equation Model (SEM) approach. An excellent confirmed 12-factors structure explained about 62% of variances unsafe behavior in the construction industry. A good fit structural model indicated that safety climate factors were positively correlated with safety individual factors (P<0.001) and workplace safety condition (P<0.001). The workplace safety condition was found to play a strong mediating role in linking the safety climate and construction workers' engagement in safe or unsafe behavior. In order to improve construction safety performance, more focus on the workplace condition is required.

Bayesian Estimation of Random Coefficient Dynamic Factor Models

ERIC Educational Resources Information Center

Song, Hairong; Ferrer, Emilio

2012-01-01

Dynamic factor models (DFMs) have typically been applied to multivariate time series data collected from a single unit of study, such as a single individual or dyad. The goal of DFMs application is to capture dynamics of multivariate systems. When multiple units are available, however, DFMs are not suited to capture variations in dynamics across…

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

PubMed

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

2013-01-01

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

The Structure of Musical Preferences: A Five-Factor Model

PubMed Central

Rentfrow, Peter J.; Goldberg, Lewis R.; Levitin, Daniel J.

2011-01-01

Music is a cross-cultural universal, a ubiquitous activity found in every known human culture. Individuals demonstrate manifestly different preferences in music, and yet relatively little is known about the underlying structure of those preferences. Here, we introduce a model of musical preferences based on listeners’ affective reactions to excerpts of music from a wide variety of musical genres. The findings from three independent studies converged to suggest that there exists a latent five-factor structure underlying music preferences that is genre-free, and reflects primarily emotional/affective responses to music. We have interpreted and labeled these factors as: 1) a Mellow factor comprising smooth and relaxing styles; 2) an Urban factor defined largely by rhythmic and percussive music, such as is found in rap, funk, and acid jazz; 3) a Sophisticated factor that includes classical, operatic, world, and jazz; 4) an Intense factor defined by loud, forceful, and energetic music; and 5) a Campestral factor comprising a variety of different styles of direct, and rootsy music such as is often found in country and singer-songwriter genres. The findings from a fourth study suggest that preferences for the MUSIC factors are affected by both the social and auditory characteristics of the music. PMID:21299309

The structure of musical preferences: a five-factor model.

PubMed

Rentfrow, Peter J; Goldberg, Lewis R; Levitin, Daniel J

2011-06-01

Music is a cross-cultural universal, a ubiquitous activity found in every known human culture. Individuals demonstrate manifestly different preferences in music, and yet relatively little is known about the underlying structure of those preferences. Here, we introduce a model of musical preferences based on listeners' affective reactions to excerpts of music from a wide variety of musical genres. The findings from 3 independent studies converged to suggest that there exists a latent 5-factor structure underlying music preferences that is genre free and reflects primarily emotional/affective responses to music. We have interpreted and labeled these factors as (a) a Mellow factor comprising smooth and relaxing styles; (b) an Unpretentious factor comprising a variety of different styles of sincere and rootsy music such as is often found in country and singer-songwriter genres; (c) a Sophisticated factor that includes classical, operatic, world, and jazz; (d) an Intense factor defined by loud, forceful, and energetic music; and (e) a Contemporary factor defined largely by rhythmic and percussive music, such as is found in rap, funk, and acid jazz. The findings from a fourth study suggest that preferences for the MUSIC factors are affected by both the social and the auditory characteristics of the music. 2011 APA, all rights reserved

The Four-Factor Model of Depressive Symptoms in Dementia Caregivers: A Structural Equation Model of Ethnic Differences

PubMed Central

Roth, David L.; Ackerman, Michelle L.; Okonkwo, Ozioma C.; Burgio, Louis D.

2008-01-01

Previous studies have suggested that 4 latent constructs (depressed affect, well-being, interpersonal problems, somatic symptoms) underlie the item responses on the Center for Epidemiological Studies Depression (CES-D) Scale. This instrument has been widely used in dementia caregiving research, but the fit of this multifactor model and the explanatory contributions of multifactor models have not been sufficiently examined for caregiving samples. The authors subjected CES-D data (N = 1,183) from the initial Resources for Enhancing Alzheimer’s Caregiver Health Study to confirmatory factor analysis methods and found that the 4-factor model provided excellent fit to the observed data. Invariance analyses suggested only minimal item-loading differences across race subgroups and supported the validity of race comparisons on the latent factors. Significant race differences were found on 3 of the 4 latent factors both before and after controlling for demographic covariates. African Americans reported less depressed affect and better well-being than White caregivers, who reported better well-being and fewer interpersonal problems than Hispanic caregivers. These findings clarify and extend previous studies of race differences in depression among diverse samples of dementia caregivers. PMID:18808246

Finding of Correction Factor and Dimensional Error in Bio-AM Model by FDM Technique

NASA Astrophysics Data System (ADS)

Manmadhachary, Aiamunoori; Ravi Kumar, Yennam; Krishnanand, Lanka

2018-06-01

Additive Manufacturing (AM) is the swift manufacturing process, in which input data can be provided from various sources like 3-Dimensional (3D) Computer Aided Design (CAD), Computed Tomography (CT), Magnetic Resonance Imaging (MRI) and 3D scanner data. From the CT/MRI data can be manufacture Biomedical Additive Manufacturing (Bio-AM) models. The Bio-AM model gives a better lead on preplanning of oral and maxillofacial surgery. However manufacturing of the accurate Bio-AM model is one of the unsolved problems. The current paper demonstrates error between the Standard Triangle Language (STL) model to Bio-AM model of dry mandible and found correction factor in Bio-AM model with Fused Deposition Modelling (FDM) technique. In the present work dry mandible CT images are acquired by CT scanner and supplied into a 3D CAD model in the form of STL model. Further the data is sent to FDM machine for fabrication of Bio-AM model. The difference between Bio-AM to STL model dimensions is considered as dimensional error and the ratio of STL to Bio-AM model dimensions considered as a correction factor. This correction factor helps to fabricate the AM model with accurate dimensions of the patient anatomy. These true dimensional Bio-AM models increasing the safety and accuracy in pre-planning of oral and maxillofacial surgery. The correction factor for Dimension SST 768 FDM AM machine is 1.003 and dimensional error is limited to 0.3 %.

Finding of Correction Factor and Dimensional Error in Bio-AM Model by FDM Technique

NASA Astrophysics Data System (ADS)

Manmadhachary, Aiamunoori; Ravi Kumar, Yennam; Krishnanand, Lanka

2016-06-01

Additive Manufacturing (AM) is the swift manufacturing process, in which input data can be provided from various sources like 3-Dimensional (3D) Computer Aided Design (CAD), Computed Tomography (CT), Magnetic Resonance Imaging (MRI) and 3D scanner data. From the CT/MRI data can be manufacture Biomedical Additive Manufacturing (Bio-AM) models. The Bio-AM model gives a better lead on preplanning of oral and maxillofacial surgery. However manufacturing of the accurate Bio-AM model is one of the unsolved problems. The current paper demonstrates error between the Standard Triangle Language (STL) model to Bio-AM model of dry mandible and found correction factor in Bio-AM model with Fused Deposition Modelling (FDM) technique. In the present work dry mandible CT images are acquired by CT scanner and supplied into a 3D CAD model in the form of STL model. Further the data is sent to FDM machine for fabrication of Bio-AM model. The difference between Bio-AM to STL model dimensions is considered as dimensional error and the ratio of STL to Bio-AM model dimensions considered as a correction factor. This correction factor helps to fabricate the AM model with accurate dimensions of the patient anatomy. These true dimensional Bio-AM models increasing the safety and accuracy in pre-planning of oral and maxillofacial surgery. The correction factor for Dimension SST 768 FDM AM machine is 1.003 and dimensional error is limited to 0.3 %.

A Model of Factors Contributing to STEM Learning and Career Orientation

ERIC Educational Resources Information Center

Nugent, Gwen; Barker, Bradley; Welch, Greg; Grandgenett, Neal; Wu, ChaoRong; Nelson, Carl

2015-01-01

The purpose of this research was to develop and test a model of factors contributing to science, technology, engineering, and mathematics (STEM) learning and career orientation, examining the complex paths and relationships among social, motivational, and instructional factors underlying these outcomes for middle school youth. Social cognitive…

Estimating safety effects of pavement management factors utilizing Bayesian random effect models.

PubMed

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

Rotation in the Dynamic Factor Modeling of Multivariate Stationary Time Series.

ERIC Educational Resources Information Center

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

2001-01-01

Proposes a special rotation procedure for the exploratory dynamic factor model for stationary multivariate time series. The rotation procedure applies separately to each univariate component series of a q-variate latent factor series and transforms such a component, initially represented as white noise, into a univariate moving-average.…

Mobile computing acceptance factors in the healthcare industry: a structural equation model.

PubMed

Wu, Jen-Her; Wang, Shu-Ching; Lin, Li-Min

2007-01-01

This paper presents a revised technology acceptance model to examine what determines mobile healthcare systems (MHS) acceptance by healthcare professionals. Conformation factor analysis was performed to test the reliability and validity of the measurement model. The structural equation modeling technique was used to evaluate the causal model. The results indicated that compatibility, perceived usefulness and perceived ease of use significantly affected healthcare professional behavioral intent. MHS self-efficacy had strong indirect impact on healthcare professional behavioral intent through the mediators of perceived usefulness and perceived ease of use. Yet, the hypotheses for technical support and training effects on the perceived usefulness and perceived ease of use were not supported. This paper provides initial insights into factors that are likely to be significant antecedents of planning and implementing mobile healthcare to enhance professionals' MHS acceptance. The proposed model variables explained 70% of the variance in behavioral intention to use MHS; further study is needed to explore extra significant antecedents of new IT/IS acceptance for mobile healthcare. Such as privacy and security issue, system and information quality, limitations of mobile devices; the above may be other interesting factors for implementing mobile healthcare and could be conducted by qualitative research.

A Joint Model for Vitamin K-Dependent Clotting Factors and Anticoagulation Proteins.

PubMed

Ooi, Qing Xi; Wright, Daniel F B; Tait, R Campbell; Isbister, Geoffrey K; Duffull, Stephen B

2017-12-01

Warfarin acts by inhibiting the reduction of vitamin K (VK) to its active form, thereby decreasing the production of VK-dependent coagulation proteins. The aim of this research is to develop a joint model for the VK-dependent clotting factors II, VII, IX and X, and the anticoagulation proteins, proteins C and S, during warfarin initiation. Data from 18 patients with atrial fibrillation who had warfarin therapy initiated were available for analysis. Nine blood samples were collected from each subject at baseline, and at 1-5, 8, 15 and 29 days after warfarin initiation and assayed for factors II, VII, IX and X, and proteins C and S. Warfarin concentration-time data were not available. The coagulation proteins data were modelled in a stepwise manner using NONMEM ® Version 7.2. In the first stage, each of the coagulation proteins was modelled independently using a kinetic-pharmacodynamic model. In the subsequent step, the six kinetic-pharmacodynamic models were combined into a single joint model. One patient was administered VK and was excluded from the analysis. Each kinetic-pharmacodynamic model consisted of two parts: (1) a common one-compartment pharmacokinetic model with first-order absorption and elimination for warfarin; and (2) an inhibitory E max model linked to a turnover model for coagulation proteins. In the joint model, an unexpected pharmacodynamic lag was identified and the estimated degradation half-life of VK-dependent coagulation proteins were in agreement with previously published values. The model provided an adequate fit to the observed data. The joint model represents the first work to quantify the influence of warfarin on all six VK-dependent coagulation proteins simultaneously. Future work will expand the model to predict the influence of exogenously administered VK on the time course of clotting factor concentrations after warfarin overdose and during perioperative warfarin reversal procedures.

Emotional intelligence is a second-stratum factor of intelligence: evidence from hierarchical and bifactor models.

PubMed

MacCann, Carolyn; Joseph, Dana L; Newman, Daniel A; Roberts, Richard D

2014-04-01

This article examines the status of emotional intelligence (EI) within the structure of human cognitive abilities. To evaluate whether EI is a 2nd-stratum factor of intelligence, data were fit to a series of structural models involving 3 indicators each for fluid intelligence, crystallized intelligence, quantitative reasoning, visual processing, and broad retrieval ability, as well as 2 indicators each for emotion perception, emotion understanding, and emotion management. Unidimensional, multidimensional, hierarchical, and bifactor solutions were estimated in a sample of 688 college and community college students. Results suggest adequate fit for 2 models: (a) an oblique 8-factor model (with 5 traditional cognitive ability factors and 3 EI factors) and (b) a hierarchical solution (with cognitive g at the highest level and EI representing a 2nd-stratum factor that loads onto g at λ = .80). The acceptable relative fit of the hierarchical model confirms the notion that EI is a group factor of cognitive ability, marking the expression of intelligence in the emotion domain. The discussion proposes a possible expansion of Cattell-Horn-Carroll theory to include EI as a 2nd-stratum factor of similar standing to factors such as fluid intelligence and visual processing.

Parental Expression of Disappointment: Should It Be a Factor in Hoffman's Model of Parental Discipline?

ERIC Educational Resources Information Center

Patrick, Renee B.; Gibbs, John C.

2007-01-01

The authors addressed whether parental expression of disappointment should be included as a distinct factor in M. L. Hoffman's (2000) well-established typology of parenting styles (induction, love withdrawal, power assertion). Hoffman's 3-factor model, along with a more inclusive 4-factor model (induction, love withdrawal, power assertion, and…

Item Parameter Estimation for the MIRT Model: Bias and Precision of Confirmatory Factor Analysis-Based Models

ERIC Educational Resources Information Center

Finch, Holmes

2010-01-01

The accuracy of item parameter estimates in the multidimensional item response theory (MIRT) model context is one that has not been researched in great detail. This study examines the ability of two confirmatory factor analysis models specifically for dichotomous data to properly estimate item parameters using common formulae for converting factor…

Talent identification model for sprinter using discriminant factor

NASA Astrophysics Data System (ADS)

Kusnanik, N. W.; Hariyanto, A.; Herdyanto, Y.; Satia, A.

2018-01-01

The main purpose of this study was to identify young talented sprinter using discriminant factor. The research was conducted in 3 steps including item pool, screening of item pool, and trial of instruments at the small and big size of samples. 315 male elementary school students participated in this study with mean age of 11-13 years old. Data were collected by measuring anthropometry (standing height, sitting height, body mass, and leg length); testing physical fitness (40m sprint for speed, shuttle run for agility, standing broad jump for power, multistage fitness test for endurance). Data were analyzed using discriminant factor. The result of this study found that there were 5 items that selected as an instrument to identify young talented sprinter: sitting height, body mass, leg length, sprint 40m, and multistage fitness test. Model of Discriminant for talent identification in sprinter was D = -24,497 + (0,155 sitting height) + (0,080 body mass) + (0,148 leg length) + (-1,225 Sprint 40m) + (0,563 MFT). The conclusion of this study: instrument tests that have been selected and discriminant model that have been found can be applied to identify young talented as a sprinter.

Modeling the factors associating with health-related habits among Japanese students.

PubMed

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.

Modelling of Field-Reversed Configuration Experiment with Large Safety Factor

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

Steinhauer, L; Guo, H; Hoffman, A

2005-11-28

The Translation-Confinement-Sustainment facility has been operated in the 'translation-formation' mode in which a plasma is ejected at high-speed from a {theta}-pinch-like source into a confinement chamber where it settles into a field-reversed-configuration state. Measurements of the poloidal and toroidal field have been the basis of modeling to infer the safety factor. It is found that the edge safety factor exceeds two, and that there is strong forward magnetic shear. The high-q arises because the large elongation compensates for the modest ratio of toroidal-to-poloidal field in the plasma. This is the first known instance of a very high-{beta} plasma with amore » safety factor greater than unity. Two-fluid modeling of the measurements also indicate several other significant features: a broad 'transition layer' at the plasma boundary with probable line-tying effects, complex high-speed flows, and the appearance of a two-fluid minimum-energy state in the plasma core. All these features may contribute to both the stability and good confinement of the plasma.« less

[Individual tree diameter increment model for natural Betula platyphylla forests based on meteorological factors].

PubMed

Zhang, Hai Ping; Li, Feng Ri; Dong, Li Hu; Liu, Qiang

2017-06-18

Based on the 212 re-measured permanent plots for natural Betula platyphylla fore-sts in Daxing'an Mountains and Xiaoxing'an Mountains and 30 meteorological stations data, an individual tree growth model based on meteorological factors was constructed. The differences of stand and meteorological factors between Daxing'an Mountains and Xiaoxing'an Mountains were analyzed and the diameter increment model including the regional effects was developed by dummy variable approach. The results showed that the minimum temperature (T g min ) and mean precipitation (P g m ) in growing season were the main meteorological factors which affected the diameter increment in the two study areas. T g min and P g m were positively correlated with the diameter increment, but the influence strength of T g min was obviously different between the two research areas. The adjusted coefficient of determination (R a 2 ) of the diameter increment model with meteorological factors was 0.56 and had an 11% increase compared to the one without meteorological factors. It was concluded that meteorological factors could well explain the diameter increment of B. platyphylla. R a 2 of the model with regional effects was 0.59, and increased by 18% compared to the one without regional effects, and effectively solved the incompatible problem of parameters between the two research areas. The validation results showed that the individual tree diameter growth model with regional effect had the best prediction accuracy in estimating the diameter increment of B. platyphylla. The mean error, mean absolute error, mean error percent and mean prediction error percent were 0.0086, 0.4476, 5.8% and 20.0%, respectively. Overall, dummy variable model of individual tree diameter increment based on meteorological factors could well describe the diameter increment process of natural B. platyphylla in Daxing'an Mountains and Xiaoxing'an Mountains.

Robust Programming Problems Based on the Mean-Variance Model Including Uncertainty Factors

NASA Astrophysics Data System (ADS)

Hasuike, Takashi; Ishii, Hiroaki

2009-01-01

This paper considers robust programming problems based on the mean-variance model including uncertainty sets and fuzzy factors. Since these problems are not well-defined problems due to fuzzy factors, it is hard to solve them directly. Therefore, introducing chance constraints, fuzzy goals and possibility measures, the proposed models are transformed into the deterministic equivalent problems. Furthermore, in order to solve these equivalent problems efficiently, the solution method is constructed introducing the mean-absolute deviation and doing the equivalent transformations.

The contribution of an animal model toward uncovering biological risk factors for PTSD.

PubMed

Cohen, Hagit; Matar, Michael A; Richter-Levin, Gal; Zohar, Joseph

2006-07-01

Clinical studies of posttraumatic stress disorder (PTSD) have elicited proposed risk factors for developing PTSD in the aftermath of stress exposure. Generally, these risk factors have arisen from retrospective analysis of premorbid characteristics of study populations. A valid animal model of PTSD can complement clinical studies and help to elucidate issues, such as the contribution of proposed risk factors, in ways which are not practicable in the clinical arena. Important qualities of animal models include the possibility to conduct controlled prospective studies, easy access to postmortem brains, and the availability of genetically manipulated subjects, which can be tailored to specific needs. When these qualities are further complemented by an approach which defines phenomenologic criteria to address the variance in individual response pattern and magnitude, enabling the animal subjects to be classified into definable groups for focused study, the model acquires added validity. This article presents an overview of a series of studies in such an animal model which examine the contribution of two proposed risk factors and the value of two early postexposure pharmacological manipulations on the prevalence rates of subjects displaying an extreme magnitude of behavioral response to a predator stress paradigm.

SCL-90-R emotional distress ratings in substance use and impulse control disorders: One-factor, oblique first-order, higher-order, and bi-factor models compared.

PubMed

Arrindell, Willem A; Urbán, Róbert; Carrozzino, Danilo; Bech, Per; Demetrovics, Zsolt; Roozen, Hendrik G

2017-09-01

To fully understand the dimensionality of an instrument in a certain population, rival bi-factor models should be routinely examined and tested against oblique first-order and higher-order structures. The present study is among the very few studies that have carried out such a comparison in relation to the Symptom Checklist-90-R. In doing so, it utilized a sample comprising 2593 patients with substance use and impulse control disorders. The study also included a test of a one-dimensional model of general psychological distress. Oblique first-order factors were based on the original a priori 9-dimensional model advanced by Derogatis (1977); and on an 8-dimensional model proposed by Arrindell and Ettema (2003)-Agoraphobia, Anxiety, Depression, Somatization, Cognitive-performance deficits, Interpersonal sensitivity and mistrust, Acting-out hostility, and Sleep difficulties. Taking individual symptoms as input, three higher-order models were tested with at the second-order levels either (1) General psychological distress; (2) 'Panic with agoraphobia', 'Depression' and 'Extra-punitive behavior'; or (3) 'Irritable-hostile depression' and 'Panic with agoraphobia'. In line with previous studies, no support was found for the one-factor model. Bi-factor models were found to fit the dataset best relative to the oblique first-order and higher-order models. However, oblique first-order and higher-order factor models also fit the data fairly well in absolute terms. Higher-order solution (2) provided support for R.F. Krueger's empirical model of psychopathology which distinguishes between fear, distress, and externalizing factors (Krueger, 1999). The higher-order model (3), which combines externalizing and distress factors (Irritable-hostile depression), fit the data numerically equally well. Overall, findings were interpreted as supporting the hypothesis that the prevalent forms of symptomatology addressed have both important common and unique features. Proposals were made to

The Five-Factor Model of Personality and Career Success.

ERIC Educational Resources Information Center

Seibert, Scott E.; Kraimer, Maria L.

2001-01-01

Measures of career success and an inventory of the Five-Factor Model of Personality were completed by 496 workers. Extraversion was related positively to salary, promotion, and career satisfaction; neuroticism was related negatively to satisfaction. A significant negative relationship between agreeableness and salary was found for workers in…

Make the most of your samples: Bayes factor estimators for high-dimensional models of sequence evolution.

PubMed

Baele, Guy; Lemey, Philippe; Vansteelandt, Stijn

2013-03-06

Accurate model comparison requires extensive computation times, especially for parameter-rich models of sequence evolution. In the Bayesian framework, model selection is typically performed through the evaluation of a Bayes factor, the ratio of two marginal likelihoods (one for each model). Recently introduced techniques to estimate (log) marginal likelihoods, such as path sampling and stepping-stone sampling, offer increased accuracy over the traditional harmonic mean estimator at an increased computational cost. Most often, each model's marginal likelihood will be estimated individually, which leads the resulting Bayes factor to suffer from errors associated with each of these independent estimation processes. We here assess the original 'model-switch' path sampling approach for direct Bayes factor estimation in phylogenetics, as well as an extension that uses more samples, to construct a direct path between two competing models, thereby eliminating the need to calculate each model's marginal likelihood independently. Further, we provide a competing Bayes factor estimator using an adaptation of the recently introduced stepping-stone sampling algorithm and set out to determine appropriate settings for accurately calculating such Bayes factors, with context-dependent evolutionary models as an example. While we show that modest efforts are required to roughly identify the increase in model fit, only drastically increased computation times ensure the accuracy needed to detect more subtle details of the evolutionary process. We show that our adaptation of stepping-stone sampling for direct Bayes factor calculation outperforms the original path sampling approach as well as an extension that exploits more samples. Our proposed approach for Bayes factor estimation also has preferable statistical properties over the use of individual marginal likelihood estimates for both models under comparison. Assuming a sigmoid function to determine the path between two competing

Bayesian model reveals latent atrophy factors with dissociable cognitive trajectories in Alzheimer's disease.

PubMed

Zhang, Xiuming; Mormino, Elizabeth C; Sun, Nanbo; Sperling, Reisa A; Sabuncu, Mert R; Yeo, B T Thomas

2016-10-18

We used a data-driven Bayesian model to automatically identify distinct latent factors of overlapping atrophy patterns from voxelwise structural MRIs of late-onset Alzheimer's disease (AD) dementia patients. Our approach estimated the extent to which multiple distinct atrophy patterns were expressed within each participant rather than assuming that each participant expressed a single atrophy factor. The model revealed a temporal atrophy factor (medial temporal cortex, hippocampus, and amygdala), a subcortical atrophy factor (striatum, thalamus, and cerebellum), and a cortical atrophy factor (frontal, parietal, lateral temporal, and lateral occipital cortices). To explore the influence of each factor in early AD, atrophy factor compositions were inferred in beta-amyloid-positive (Aβ+) mild cognitively impaired (MCI) and cognitively normal (CN) participants. All three factors were associated with memory decline across the entire clinical spectrum, whereas the cortical factor was associated with executive function decline in Aβ+ MCI participants and AD dementia patients. Direct comparison between factors revealed that the temporal factor showed the strongest association with memory, whereas the cortical factor showed the strongest association with executive function. The subcortical factor was associated with the slowest decline for both memory and executive function compared with temporal and cortical factors. These results suggest that distinct patterns of atrophy influence decline across different cognitive domains. Quantification of this heterogeneity may enable the computation of individual-level predictions relevant for disease monitoring and customized therapies. Factor compositions of participants and code used in this article are publicly available for future research.

Object recognition in images via a factor graph model

NASA Astrophysics Data System (ADS)

He, Yong; Wang, Long; Wu, Zhaolin; Zhang, Haisu

2018-04-01

Object recognition in images suffered from huge search space and uncertain object profile. Recently, the Bag-of- Words methods are utilized to solve these problems, especially the 2-dimension CRF(Conditional Random Field) model. In this paper we suggest the method based on a general and flexible fact graph model, which can catch the long-range correlation in Bag-of-Words by constructing a network learning framework contrasted from lattice in CRF. Furthermore, we explore a parameter learning algorithm based on the gradient descent and Loopy Sum-Product algorithms for the factor graph model. Experimental results on Graz 02 dataset show that, the recognition performance of our method in precision and recall is better than a state-of-art method and the original CRF model, demonstrating the effectiveness of the proposed method.

An Explanatory Model of Dating Violence Risk Factors in Spanish Adolescents.

PubMed

Aizpitarte, Alazne; Alonso-Arbiol, Itziar; Van de Vijver, Fons J R

2017-12-01

Dating violence is a serious public health issue that needs further understanding in terms of risk factors that may be involved in it. The main goal of this study was to test a mediational model of dating violence risk factors. The sample was composed of 477 secondary and college students from Spain (59% females). A dynamic developmental explanatory model considering aggressiveness, insecure attachment, interparental conflict, and peer dating violence was tested using a multigroup structural equation model. Aggressiveness partially mediated the relation between anxious attachment and dating violence and fully mediated the association between interparental conflict resolution and dating violence. Furthermore, perceived peer dating violence was a direct predictor of dating violence. Implications for prevention and intervention plans are discussed. © 2017 The Authors. Journal of Research on Adolescence © 2017 Society for Research on Adolescence.

Factors affecting species distribution predictions: A simulation modeling experiment

Treesearch

Gordon C. Reese; Kenneth R. Wilson; Jennifer A. Hoeting; Curtis H. Flather

2005-01-01

Geospatial species sample data (e.g., records with location information from natural history museums or annual surveys) are rarely collected optimally, yet are increasingly used for decisions concerning our biological heritage. Using computer simulations, we examined factors that could affect the performance of autologistic regression (ALR) models that predict species...

Tumor Secreted Autocrine Motility Factor (AMF): Causal Role in an Animal Model of Cachexia

DTIC Science & Technology

2005-08-01

AD Award Number: DAMD17-02-1-0586 TITLE: Tumor Secreted Autocrine Motility Factor ( AMF ): Causal Role in an Animal Model of Cachexia PRINCIPAL...5a. CONTRACT NUMBER Tumor Secreted Autocrine Motility Factor ( AMF ): Causal Role in an Animal Model of Cachexia 5b. GRANT NUMBER DAM D1 7-02-1-0586 5c...quality of life and postpone mortality. We proposed that autocrine motility factor ( AMF ) is released into the bloodstream from cancer sites and

Coopersmith Self-Esteem: Two Different Hypothesized Factor Models--Both Acceptable for the Same Data Structure.

ERIC Educational Resources Information Center

Hofmann, Rich; Sherman, Larry

Using data from 135 sixth-, seventh-, and eighth-graders between 11 and 15 years old attending a middle school in a suburban Southwest Ohio school district, two hypothesized models of the factor structures for the Coopersmith Self-Esteem Inventory were tested. One model represents the original Coopersmith factor structure, and the other model is…

Testing Measurement Invariance in the Target Rotated Multigroup Exploratory Factor Model

ERIC Educational Resources Information Center

Dolan, Conor V.; Oort, Frans J.; Stoel, Reinoud D.; Wicherts, Jelte M.

2009-01-01

We propose a method to investigate measurement invariance in the multigroup exploratory factor model, subject to target rotation. We consider both oblique and orthogonal target rotation. This method has clear advantages over other approaches, such as the use of congruence measures. We demonstrate that the model can be implemented readily in the…

Make the most of your samples: Bayes factor estimators for high-dimensional models of sequence evolution

PubMed Central

2013-01-01

Background Accurate model comparison requires extensive computation times, especially for parameter-rich models of sequence evolution. In the Bayesian framework, model selection is typically performed through the evaluation of a Bayes factor, the ratio of two marginal likelihoods (one for each model). Recently introduced techniques to estimate (log) marginal likelihoods, such as path sampling and stepping-stone sampling, offer increased accuracy over the traditional harmonic mean estimator at an increased computational cost. Most often, each model’s marginal likelihood will be estimated individually, which leads the resulting Bayes factor to suffer from errors associated with each of these independent estimation processes. Results We here assess the original ‘model-switch’ path sampling approach for direct Bayes factor estimation in phylogenetics, as well as an extension that uses more samples, to construct a direct path between two competing models, thereby eliminating the need to calculate each model’s marginal likelihood independently. Further, we provide a competing Bayes factor estimator using an adaptation of the recently introduced stepping-stone sampling algorithm and set out to determine appropriate settings for accurately calculating such Bayes factors, with context-dependent evolutionary models as an example. While we show that modest efforts are required to roughly identify the increase in model fit, only drastically increased computation times ensure the accuracy needed to detect more subtle details of the evolutionary process. Conclusions We show that our adaptation of stepping-stone sampling for direct Bayes factor calculation outperforms the original path sampling approach as well as an extension that exploits more samples. Our proposed approach for Bayes factor estimation also has preferable statistical properties over the use of individual marginal likelihood estimates for both models under comparison. Assuming a sigmoid function to

Integral Counseling and a Three-Factor Model of Defenses

ERIC Educational Resources Information Center

Pearson, Willow

2007-01-01

This article introduces 3 major factors of K. Wilber's (1999b) "all-quadrants, all-levels" model of defenses: the category of defense, the level of self development and defense, and the threat zone against which the self is defending. Through theoretical discussion and select clinical examples differentiating healthy and unhealthy expressions of…

A Two-Factor Model Better Explains Heterogeneity in Negative Symptoms: Evidence from the Positive and Negative Syndrome Scale.

PubMed

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.

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

ERIC Educational Resources Information Center

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,…

In silico mining and PCR-based approaches to transcription factor discovery in non-model plants: gene discovery of the WRKY transcription factors in conifers.

PubMed

Liu, Jun-Jun; Xiang, Yu

2011-01-01

WRKY transcription factors are key regulators of numerous biological processes in plant growth and development, as well as plant responses to abiotic and biotic stresses. Research on biological functions of plant WRKY genes has focused in the past on model plant species or species with largely characterized transcriptomes. However, a variety of non-model plants, such as forest conifers, are essential as feed, biofuel, and wood or for sustainable ecosystems. Identification of WRKY genes in these non-model plants is equally important for understanding the evolutionary and function-adaptive processes of this transcription factor family. Because of limited genomic information, the rarity of regulatory gene mRNAs in transcriptomes, and the sequence divergence to model organism genes, identification of transcription factors in non-model plants using methods similar to those generally used for model plants is difficult. This chapter describes a gene family discovery strategy for identification of WRKY transcription factors in conifers by a combination of in silico-based prediction and PCR-based experimental approaches. Compared to traditional cDNA library screening or EST sequencing at transcriptome scales, this integrated gene discovery strategy provides fast, simple, reliable, and specific methods to unveil the WRKY gene family at both genome and transcriptome levels in non-model plants.

Factor structure of a conceptual model of oral health tested among 65-year olds in Norway and Sweden.

PubMed

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

Prognostic factors in multiple myeloma: selection using Cox's proportional hazard model.

PubMed

Pasqualetti, P; Collacciani, A; Maccarone, C; Casale, R

1996-01-01

The pretreatment characteristics of 210 patients with multiple myeloma, observed between 1980 and 1994, were evaluated as potential prognostic factors for survival. Multivariate analysis according to Cox's proportional hazard model identified in the 160 dead patients with myeloma, among 26 different single prognostic variables, the following factors in order of importance: beta 2-microglobulin; bone marrow plasma cell percentage, hemoglobinemia, degree of lytic bone lesions, serum creatinine, and serum albumin. By analysis of these variables a prognostic index (PI), that considers the regression coefficients derived by Cox's model of all significant factors, was obtained. Using this it was possible to separate the whole patient group into three stages: stage I (PI < 1.485, 67 patients), stage II (PI: 1.485-2.090, 76 patients), and stage III (PI > 2.090, 67 patients), with a median survivals of 68, 36 and 13 months (P < 0.0001), respectively. Also the responses to therapy (P < 0.0001) and the survival curves (P < 0.00001) presented significant differences among the three subgroups. Knowledge of these factors could be of value in predicting prognosis and in planning therapy in patients with multiple myeloma.

Parental expression of disappointment: should it be a factor in Hoffman's model of parental discipline?

PubMed

Patrick, Renee B; Gibbs, John C

2007-06-01

The authors addressed whether parental expression of disappointment should be included as a distinct factor in M. L. Hoffman's well-established typology of parenting styles (induction, love withdrawal, power assertion). Hoffman's 3-factor model, along with a more inclusive 4-factor model (induction, love withdrawal, power assertion, and expressions of disappointment), were respectively evaluated in exploratory factor analyses. The analysis utilized extant data comprised of responses by children (N = 73) and their mothers (N = 67) to an adaptation of M. L. Hoffman and H. D. Saltzstein's parental discipline measure. The findings supported Hoffman's original model. Disappointment may be reducible to love withdrawal or induction, although disappointment may be a more appropriate induction for adolescents.

Entrance and exit region friction factor models for annular seal analysis. Ph.D. Thesis

NASA Technical Reports Server (NTRS)

Elrod, David Alan

1988-01-01

The Mach number definition and boundary conditions in Nelson's nominally-centered, annular gas seal analysis are revised. A method is described for determining the wall shear stress characteristics of an annular gas seal experimentally. Two friction factor models are developed for annular seal analysis; one model is based on flat-plate flow theory; the other uses empirical entrance and exit region friction factors. The friction factor predictions of the models are compared to experimental results. Each friction model is used in an annular gas seal analysis. The seal characteristics predicted by the two seal analyses are compared to experimental results and to the predictions of Nelson's analysis. The comparisons are for smooth-rotor seals with smooth and honeycomb stators. The comparisons show that the analysis which uses empirical entrance and exit region shear stress models predicts the static and stability characteristics of annular gas seals better than the other analyses. The analyses predict direct stiffness poorly.

Assessing Cognitive and Affective Empathy Through the Interpersonal Reactivity Index: An Argument Against a Two-Factor Model.

PubMed

Chrysikou, Evangelia G; Thompson, W Jake

2016-12-01

One aspect of higher order social cognition is empathy, a psychological construct comprising a cognitive (recognizing emotions) and an affective (responding to emotions) component. The complex nature of empathy complicates the accurate measurement of these components. The most widely used measure of empathy is the Interpersonal Reactivity Index (IRI). However, the factor structure of the IRI as it is predominantly used in the psychological literature differs from Davis's original four-factor model in that it arbitrarily combines the subscales to form two factors: cognitive and affective empathy. This two-factor model of the IRI, although popular, has yet to be examined for psychometric support. In the current study, we examine, for the first time, the validity of this alternative model. A confirmatory factor analysis showed poor model fit for this two-factor structure. Additional analyses offered support for the original four-factor model, as well as a hierarchical model for the scale. In line with previous findings, females scored higher on the IRI than males. Our findings indicate that the IRI, as it is currently used in the literature, does not accurately measure cognitive and affective empathy and highlight the advantages of using the original four-factor structure of the scale for empathy assessments. © The Author(s) 2015.

A conceptual model of psychosocial risk and protective factors for excessive gestational weight gain.

PubMed

Hill, Briony; Skouteris, Helen; McCabe, Marita; Milgrom, Jeannette; Kent, Bridie; Herring, Sharon J; Hartley-Clark, Linda; Gale, Janette

2013-02-01

nearly half of all women exceed the guideline recommended pregnancy weight gain for their Body Mass Index (BMI) category. Excessive gestational weight gain (GWG) is correlated positively with postpartum weight retention and is a predictor of long-term, higher BMI in mothers and their children. Psychosocial factors are generally not targeted in GWG behaviour change interventions, however, multifactorial, conceptual models that include these factors, may be useful in determining the pathways that contribute to excessive GWG. We propose a conceptual model, underpinned by health behaviour change theory, which outlines the psychosocial determinants of GWG, including the role of motivation and self-efficacy towards healthy behaviours. This model is based on a review of the existing literature in this area. there is increasing evidence to show that psychosocial factors, such as increased depressive symptoms, anxiety, lower self-esteem and body image dissatisfaction, are associated with excessive GWG. What is less known is how these factors might lead to excessive GWG. Our conceptual model proposes a pathway of factors that affect GWG, and may be useful for understanding the mechanisms by which interventions impact on weight management during pregnancy. This involves tracking the relationships among maternal psychosocial factors, including body image concerns, motivation to adopt healthy lifestyle behaviours, confidence in adopting healthy lifestyle behaviours for the purposes of weight management, and actual behaviour changes. health-care providers may improve weight gain outcomes in pregnancy if they assess and address psychosocial factors in pregnancy. Copyright © 2011 Elsevier Ltd. All rights reserved.

Vulnerability and Resilience in Women with Arthritis: Test of a Two-Factor Model

ERIC Educational Resources Information Center

Smith, Bruce W.; Zautra, Alex J.

2008-01-01

The purpose of this study was to test a 2-factor model of affective health in women with rheumatoid arthritis (RA; n = 82) or osteoarthritis (OA; n = 88). Positive and negative social interactions and affect were assessed for 11 consecutive weeks. For each participant, Vulnerability and Resilience factors were created from factor analyses of…

Quality of life among people with multiple sclerosis: Replication of a three-factor prediction model.

PubMed

Bishop, Malachy; Rumrill, Phillip D; Roessler, Richard T

2015-01-01

This article presents a replication of Rumrill, Roessler, and Fitzgerald's 2004 analysis of a three-factor model of the impact of multiple sclerosis (MS) on quality of life (QOL). The three factors in the original model included illness-related, employment-related, and psychosocial adjustment factors. To test hypothesized relationships between QOL and illness-related, employment-related, and psychosocial variables using data from a survey of the employment concerns of Americans with MS (N = 1,839). An ex post facto, multiple correlational design was employed incorporating correlational and multiple regression analyses. QOL was positively related to educational level, employment status, job satisfaction, and job-match, and negatively related to number of symptoms, severity of symptoms, and perceived stress level. The three-factor model explained approximately 37 percent of the variance in QOL scores. The results of this replication confirm the continuing value of the three-factor model for predicting the QOL of adults with MS, and demonstrate the importance of medical, mental health, and vocational rehabilitation interventions and services in promoting QOL.

Multilevel poisson regression modelling for determining factors of dengue fever cases in bandung

NASA Astrophysics Data System (ADS)

Arundina, Davila Rubianti; Tantular, Bertho; Pontoh, Resa Septiani

2017-03-01

Scralatina or Dengue Fever is a kind of fever caused by serotype virus which Flavivirus genus and be known as Dengue Virus. Dengue Fever caused by Aedes Aegipty Mosquito bites who infected by a dengue virus. The study was conducted in 151 villages in Bandung. Health Analysts believes that there are two factors that affect the dengue cases, Internal factor (individual) and external factor (environment). The data who used in this research is hierarchical data. The method is used for hierarchical data modelling is multilevel method. Which is, the level 1 is village and level 2 is sub-district. According exploration data analysis, the suitable Multilevel Method is Random Intercept Model. Penalized Quasi Likelihood (PQL) approach on multilevel Poisson is a proper analysis to determine factors that affecting dengue cases in the city of Bandung. Clean and Healthy Behavior factor from the village level have an effect on the number of cases of dengue fever in the city of Bandung. Factor from the sub-district level has no effect.

Indirect estimation of emission factors for phosphate surface mining using air dispersion modeling.

PubMed

Tartakovsky, Dmitry; Stern, Eli; Broday, David M

2016-06-15

To date, phosphate surface mining suffers from lack of reliable emission factors. Due to complete absence of data to derive emissions factors, we developed a methodology for estimating them indirectly by studying a range of possible emission factors for surface phosphate mining operations and comparing AERMOD calculated concentrations to concentrations measured around the mine. We applied this approach for the Khneifiss phosphate mine, Syria, and the Al-Hassa and Al-Abyad phosphate mines, Jordan. The work accounts for numerous model unknowns and parameter uncertainties by applying prudent assumptions concerning the parameter values. Our results suggest that the net mining operations (bulldozing, grading and dragline) contribute rather little to ambient TSP concentrations in comparison to phosphate processing and transport. Based on our results, the common practice of deriving the emission rates for phosphate mining operations from the US EPA emission factors for surface coal mining or from the default emission factor of the EEA seems to be reasonable. Yet, since multiple factors affect dispersion from surface phosphate mines, a range of emission factors, rather than only a single value, was found to satisfy the model performance. Copyright © 2016 Elsevier B.V. All rights reserved.

Validation of a Four-Factor Model of Career Indecision

ERIC Educational Resources Information Center

Brown, Steven D.; Hacker, Jason; Abrams, Matthew; Carr, Andrea; Rector, Christopher; Lamp, Kristen; Telander, Kyle; Siena, Anne

2012-01-01

Two studies were designed to explore whether a meta-analytically derived four-factor model of career indecision (Brown & Rector, 2008) could be replicated at the primary and secondary data levels. In the first study, an initial pool of 167 items was written based on 35 different instruments whose scores had loaded saliently on at least one…

Liberal bias and the five-factor model.

PubMed

Charney, Evan

2015-01-01

Duarte et al. draw attention to the "embedding of liberal values and methods" in social psychological research. They note how these biases are often invisible to the researchers themselves. The authors themselves fall prey to these "invisible biases" by utilizing the five-factor model of personality and the trait of openness to experience as one possible explanation for the under-representation of political conservatives in social psychology. I show that the manner in which the trait of openness to experience is conceptualized and measured is a particularly blatant example of the very liberal bias the authors decry.

Impact of Lead Time and Safety Factor in Mixed Inventory Models with Backorder Discounts

NASA Astrophysics Data System (ADS)

Lo, Ming-Cheng; Chao-Hsien Pan, Jason; Lin, Kai-Cing; Hsu, Jia-Wei

This study investigates the impact of safety factor on the continuous review inventory model involving controllable lead time with mixture of backorder discount and partial lost sales. The objective is to minimize the expected total annual cost with respect to order quantity, backorder price discount, safety factor and lead time. A model with normal demand is also discussed. Numerical examples are presented to illustrate the procedures of the algorithms and the effects of parameters on the result of the proposed models are analyzed.

Molecular modelling of the Norrie disease protein predicts a cystine knot growth factor tertiary structure.

PubMed

Meitinger, T; Meindl, A; Bork, P; Rost, B; Sander, C; Haasemann, M; Murken, J

1993-12-01

The X-lined gene for Norrie disease, which is characterized by blindness, deafness and mental retardation has been cloned recently. This gene has been thought to code for a putative extracellular factor; its predicted amino acid sequence is homologous to the C-terminal domain of diverse extracellular proteins. Sequence pattern searches and three-dimensional modelling now suggest that the Norrie disease protein (NDP) has a tertiary structure similar to that of transforming growth factor beta (TGF beta). Our model identifies NDP as a member of an emerging family of growth factors containing a cystine knot motif, with direct implications for the physiological role of NDP. The model also sheds light on sequence related domains such as the C-terminal domain of mucins and of von Willebrand factor.

Analysis of Korean Students' International Mobility by 2-D Model: Driving Force Factor and Directional Factor

ERIC Educational Resources Information Center

Park, Elisa L.

2009-01-01

The purpose of this study is to understand the dynamics of Korean students' international mobility to study abroad by using the 2-D Model. The first D, "the driving force factor," explains how and what components of the dissatisfaction with domestic higher education perceived by Korean students drives students' outward mobility to seek…

Comparison of Survival Models for Analyzing Prognostic Factors in Gastric Cancer Patients

PubMed

Habibi, Danial; Rafiei, Mohammad; Chehrei, Ali; Shayan, Zahra; Tafaqodi, Soheil

2018-03-27

Objective: There are a number of models for determining risk factors for survival of patients with gastric cancer. This study was conducted to select the model showing the best fit with available data. Methods: Cox regression and parametric models (Exponential, Weibull, Gompertz, Log normal, Log logistic and Generalized Gamma) were utilized in unadjusted and adjusted forms to detect factors influencing mortality of patients. Comparisons were made with Akaike Information Criterion (AIC) by using STATA 13 and R 3.1.3 softwares. Results: The results of this study indicated that all parametric models outperform the Cox regression model. The Log normal, Log logistic and Generalized Gamma provided the best performance in terms of AIC values (179.2, 179.4 and 181.1, respectively). On unadjusted analysis, the results of the Cox regression and parametric models indicated stage, grade, largest diameter of metastatic nest, largest diameter of LM, number of involved lymph nodes and the largest ratio of metastatic nests to lymph nodes, to be variables influencing the survival of patients with gastric cancer. On adjusted analysis, according to the best model (log normal), grade was found as the significant variable. Conclusion: The results suggested that all parametric models outperform the Cox model. The log normal model provides the best fit and is a good substitute for Cox regression. Creative Commons Attribution License

The Hierarchical Factor Model of ADHD: Invariant across Age and National Groupings?

ERIC Educational Resources Information Center

Toplak, Maggie E.; Sorge, Geoff B.; Flora, David B.; Chen, Wai; Banaschewski, Tobias; Buitelaar, Jan; Ebstein, Richard; Eisenberg, Jacques; Franke, Barbara; Gill, Michael; Miranda, Ana; Oades, Robert D.; Roeyers, Herbert; Rothenberger, Aribert; Sergeant, Joseph; Sonuga-Barke, Edmund; Steinhausen, Hans-Christoph; Thompson, Margaret; Tannock, Rosemary; Asherson, Philip; Faraone, Stephen V.

2012-01-01

Objective: To examine the factor structure of attention-deficit/hyperactivity disorder (ADHD) in a clinical sample of 1,373 children and adolescents with ADHD and their 1,772 unselected siblings recruited from different countries across a large age range. Hierarchical and correlated factor analytic models were compared separately in the ADHD and…

Structural Model of psychological risk and protective factors affecting on quality of life in patients with coronary heart disease: A psychocardiology model

PubMed Central

Nekouei, Zohreh Khayyam; Yousefy, Alireza; Doost, Hamid Taher Neshat; Manshaee, Gholamreza; Sadeghei, Masoumeh

2014-01-01

Background: Conducted researches show that psychological factors may have a very important role in the etiology, continuity and consequences of coronary heart diseases. This study has drawn the psychological risk and protective factors and their effects in patients with coronary heart diseases (CHD) in a structural model. It aims to determine the structural relations between psychological risk and protective factors with quality of life in patients with coronary heart disease. Materials and Methods: The present cross-sectional and correlational studies were conducted using structural equation modeling. The study sample included 398 patients of coronary heart disease in the university referral Hospital, as well as other city health care centers in Isfahan city. They were selected based on random sampling method. Then, in case, they were executed the following questionnaires: Coping with stressful situations (CISS- 21), life orientation (LOT-10), general self-efficacy (GSE-10), depression, anxiety and stress (DASS-21), perceived stress (PSS-14), multidimensional social support (MSPSS-12), alexithymia (TAS-20), spiritual intelligence (SQ-23) and quality of life (WHOQOL-26). Results: The results showed that protective and risk factors could affect the quality of life in patients with CHD with factor loadings of 0.35 and −0.60, respectively. Moreover, based on the values of the framework of the model such as relative chi-square (CMIN/DF = 3.25), the Comparative Fit Index (CFI = 0.93), the Parsimony Comparative Fit Index (PCFI = 0.68), the Root Mean Square Error of Approximation (RMSEA = 0.07) and details of the model (significance of the relationships) it has been confirmed that the psychocardiological structural model of the study is the good fitting model. Conclusion: This study was among the first to research the different psychological risk and protective factors of coronary heart diseases in the form of a structural model. The results of this study have

Effects of source shape on the numerical aperture factor with a geometrical-optics model.

PubMed

Wan, Der-Shen; Schmit, Joanna; Novak, Erik

2004-04-01

We study the effects of an extended light source on the calibration of an interference microscope, also referred to as an optical profiler. Theoretical and experimental numerical aperture (NA) factors for circular and linear light sources along with collimated laser illumination demonstrate that the shape of the light source or effective aperture cone is critical for a correct NA factor calculation. In practice, more-accurate results for the NA factor are obtained when a linear approximation to the filament light source shape is used in a geometric model. We show that previously measured and derived NA factors show some discrepancies because a circular rather than linear approximation to the filament source was used in the modeling.

Modeling and projection of dengue fever cases in Guangzhou based on variation of weather factors.

PubMed

Li, Chenlu; Wang, Xiaofeng; Wu, Xiaoxu; Liu, Jianing; Ji, Duoying; Du, Juan

2017-12-15

Dengue fever is one of the most serious vector-borne infectious diseases, especially in Guangzhou, China. Dengue viruses and their vectors Aedes albopictus are sensitive to climate change primarily in relation to weather factors. Previous research has mainly focused on identifying the relationship between climate factors and dengue cases, or developing dengue case models with some non-climate factors. However, there has been little research addressing the modeling and projection of dengue cases only from the perspective of climate change. This study considered this topic using long time series data (1998-2014). First, sensitive weather factors were identified through meta-analysis that included literature review screening, lagged analysis, and collinear analysis. Then, key factors that included monthly average temperature at a lag of two months, and monthly average relative humidity and monthly average precipitation at lags of three months were determined. Second, time series Poisson analysis was used with the generalized additive model approach to develop a dengue model based on key weather factors for January 1998 to December 2012. Data from January 2013 to July 2014 were used to validate that the model was reliable and reasonable. Finally, future weather data (January 2020 to December 2070) were input into the model to project the occurrence of dengue cases under different climate scenarios (RCP 2.6 and RCP 8.5). Longer time series analysis and scientifically selected weather variables were used to develop a dengue model to ensure reliability. The projections suggested that seasonal disease control (especially in summer and fall) and mitigation of greenhouse gas emissions could help reduce the incidence of dengue fever. The results of this study hope to provide a scientifically theoretical basis for the prevention and control of dengue fever in Guangzhou. Copyright © 2017 Elsevier B.V. All rights reserved.

FACTOR ANALYTIC MODELS OF CLUSTERED MULTIVARIATE DATA WITH INFORMATIVE CENSORING

EPA Science Inventory

This paper describes a general class of factor analytic models for the analysis of clustered multivariate data in the presence of informative missingness. We assume that there are distinct sets of cluster-level latent variables related to the primary outcomes and to the censorin...

Stochastic model of transcription factor-regulated gene expression

NASA Astrophysics Data System (ADS)

Karmakar, Rajesh; Bose, Indrani

2006-09-01

We consider a stochastic model of transcription factor (TF)-regulated gene expression. The model describes two genes, gene A and gene B, which synthesize the TFs and the target gene proteins, respectively. We show through analytic calculations that the TF fluctuations have a significant effect on the distribution of the target gene protein levels when the mean TF level falls in the highest sensitive region of the dose-response curve. We further study the effect of reducing the copy number of gene A from two to one. The enhanced TF fluctuations yield results different from those in the deterministic case. The probability that the target gene protein level exceeds a threshold value is calculated with the knowledge of the probability density functions associated with the TF and target gene protein levels. Numerical simulation results for a more detailed stochastic model are shown to be in agreement with those obtained through analytic calculations. The relevance of these results in the context of the genetic disorder haploinsufficiency is pointed out. Some experimental observations on the haploinsufficiency of the tumour suppressor gene, Nkx 3.1, are explained with the help of the stochastic model of TF-regulated gene expression.

Analytic model for academic research productivity having factors, interactions and implications

PubMed Central

2011-01-01

Financial support is dear in academia and will tighten further. How can the research mission be accomplished within new restraints? A model is presented for evaluating source components of academic research productivity. It comprises six factors: funding; investigator quality; efficiency of the research institution; the research mix of novelty, incremental advancement, and confirmatory studies; analytic accuracy; and passion. Their interactions produce output and patterned influences between factors. Strategies for optimizing output are enabled. PMID:22130145

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

NASA Astrophysics Data System (ADS)

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.

Predicting dimensions of personality disorder from domains and facets of the Five-Factor Model.

PubMed

Reynolds, S K; Clark, L A

2001-04-01

We compared the utility of several trait models for describing personality disorder in a heterogeneous clinical sample (N = 94). Participants completed the Schedule for Nonadaptive and Adaptive Personality (SNAP; Clark, 1993b), a self-report measure that assesses traits relevant to personality disorder, and two measures of the Five-Factor Model: the Revised NEO Personality Inventory (NEO-PI-R; Costa and McCrae, 1992) and the Big Five Inventory (BFI; John, Donahue, & Kentle, 1991). Regression analyses indicated substantial overlap between the SNAP scales and the NEO-PI-R facets. In addition, use of the NEO-PI-R facets afforded substantial improvement over the Five-Factor Model domains in predicting interview-based ratings of DSM-IV personality disorder (American Psychiatric Association, 1994), such that the NEO facets and the SNAP scales demonstrated roughly equivalent levels of predictive power. Results support assessment of the full range of NEO-PI-R facets over the Five-Factor Model domains for both research and clinical use.

Factors Expressed in an Animal Model of Anteroinferior Glenohumeral Instability

PubMed Central

Mulcahey, Mary K.; Marshall, Mindy; Gallacher, Stacey E.; Kaback, Lee A.; Blaine, Theodore A.

2015-01-01

Background: There is little information on the molecular factors important in healing and changes that occur in the glenoid labrum in response to injury. Using a novel animal model of acute anterior shoulder dislocation, this study characterizes the factors expressed in the glenoid labrum in response to injury and correlates their expression to glenohumeral stability. Purpose: To study the response of the glenoid labrum to injury both biomechanically and with immunohistochemical testing. Methods: An injury to the anteroinferior labrum was surgically induced in 50 male Lewis rats. Rats were sacrificed at 3, 7, 14, 28, or 42 days. Immunolocalization experiments were performed to localize the expression of growth factors and cytokines. For biomechanical testing, dynamic stiffness for anterior and posterior laxity, load to failure, stiffness, and maximum load were recorded. Statistical differences were determined at P < .05. Study Design: Descriptive laboratory study. Results: Expression of interleukin–1 beta (IL-1β), transforming growth factor–beta 1 (TGF-β1), matrix metalloproteinase 3 (MMP3), and matrix metalloproteinase 13 (MMP13) were increased in injured compared with uninjured specimens. Collagen III expression was increased early and decreased with time. Biomechanical testing verified instability by demonstrating increased anterior displacement and decreased stiffness in injured shoulders at all time points. Conclusion: This novel animal model of acute anterior shoulder dislocation showed increased expression of IL-1β, TGF-β1, MMP3, MMP13, and collagen III in the injured labral tissue at early time points. Increased anterior laxity and decreased stiffness and maximum load to failure were seen after anterior labral injury, supporting the model’s ability to re-create anterior glenohumeral instability. These data provide important information on the temporal changes occurring in a rat model of anterior glenohumeral dislocation. Clinical Relevance

The application of Global Sensitivity Analysis to quantify the dominant input factors for hydraulic model simulations

NASA Astrophysics Data System (ADS)

Savage, James; Pianosi, Francesca; Bates, Paul; Freer, Jim; Wagener, Thorsten

2015-04-01

Predicting flood inundation extents using hydraulic models is subject to a number of critical uncertainties. For a specific event, these uncertainties are known to have a large influence on model outputs and any subsequent analyses made by risk managers. Hydraulic modellers often approach such problems by applying uncertainty analysis techniques such as the Generalised Likelihood Uncertainty Estimation (GLUE) methodology. However, these methods do not allow one to attribute which source of uncertainty has the most influence on the various model outputs that inform flood risk decision making. Another issue facing modellers is the amount of computational resource that is available to spend on modelling flood inundations that are 'fit for purpose' to the modelling objectives. Therefore a balance needs to be struck between computation time, realism and spatial resolution, and effectively characterising the uncertainty spread of predictions (for example from boundary conditions and model parameterisations). However, it is not fully understood how much of an impact each factor has on model performance, for example how much influence changing the spatial resolution of a model has on inundation predictions in comparison to other uncertainties inherent in the modelling process. Furthermore, when resampling fine scale topographic data in the form of a Digital Elevation Model (DEM) to coarser resolutions, there are a number of possible coarser DEMs that can be produced. Deciding which DEM is then chosen to represent the surface elevations in the model could also influence model performance. In this study we model a flood event using the hydraulic model LISFLOOD-FP and apply Sobol' Sensitivity Analysis to estimate which input factor, among the uncertainty in model boundary conditions, uncertain model parameters, the spatial resolution of the DEM and the choice of resampled DEM, have the most influence on a range of model outputs. These outputs include whole domain maximum

The accuracy of climate models' simulated season lengths and the effectiveness of grid scale correction factors

DOE PAGES

Winterhalter, Wade E.

2011-09-01

Global climate change is expected to impact biological populations through a variety of mechanisms including increases in the length of their growing season. Climate models are useful tools for predicting how season length might change in the future. However, the accuracy of these models tends to be rather low at regional geographic scales. Here, I determined the ability of several atmosphere and ocean general circulating models (AOGCMs) to accurately simulate historical season lengths for a temperate ectotherm across the continental United States. I also evaluated the effectiveness of regional-scale correction factors to improve the accuracy of these models. I foundmore » that both the accuracy of simulated season lengths and the effectiveness of the correction factors to improve the model's accuracy varied geographically and across models. These results suggest that regional specific correction factors do not always adequately remove potential discrepancies between simulated and historically observed environmental parameters. As such, an explicit evaluation of the correction factors' effectiveness should be included in future studies of global climate change's impact on biological populations.« less

Drug development costs when financial risk is measured using the Fama-French three-factor model.

PubMed

Vernon, John A; Golec, Joseph H; Dimasi, Joseph A

2010-08-01

In a widely cited article, DiMasi, Hansen, and Grabowski (2003) estimate the average pre-tax cost of bringing a new molecular entity to market. Their base case estimate, excluding post-marketing studies, was $802 million (in $US 2000). Strikingly, almost half of this cost (or $399 million) is the cost of capital (COC) used to fund clinical development expenses to the point of FDA marketing approval. The authors used an 11% real COC computed using the capital asset pricing model (CAPM). But the CAPM is a single factor risk model, and multi-factor risk models are the current state of the art in finance. Using the Fama-French three factor model we find that the cost of drug development to be higher than the earlier estimate. Copyright (c) 2009 John Wiley & Sons, Ltd.

Factors associated with therapeutic inertia in hypertension: validation of a predictive model.

PubMed

Redón, Josep; Coca, Antonio; Lázaro, Pablo; Aguilar, Ma Dolores; Cabañas, Mercedes; Gil, Natividad; Sánchez-Zamorano, Miguel Angel; Aranda, Pedro

2010-08-01

To study factors associated with therapeutic inertia in treating hypertension and to develop a predictive model to estimate the probability of therapeutic inertia in a given medical consultation, based on variables related to the consultation, patient, physician, clinical characteristics, and level of care. National, multicentre, observational, cross-sectional study in primary care and specialist (hospital) physicians who each completed a questionnaire on therapeutic inertia, provided professional data and collected clinical data on four patients. Therapeutic inertia was defined as a consultation in which treatment change was indicated (i.e., SBP >or= 140 or DBP >or= 90 mmHg in all patients; SBP >or= 130 or DBP >or= 80 in patients with diabetes or stroke), but did not occur. A predictive model was constructed and validated according to the factors associated with therapeutic inertia. Data were collected on 2595 patients and 13,792 visits. Therapeutic inertia occurred in 7546 (75%) of the 10,041 consultations in which treatment change was indicated. Factors associated with therapeutic inertia were primary care setting, male sex, older age, SPB and/or DBP values close to normal, treatment with more than one antihypertensive drug, treatment with an ARB II, and more than six visits/year. Physician characteristics did not weigh heavily in the association. The predictive model was valid internally and externally, with acceptable calibration, discrimination and reproducibility, and explained one-third of the variability in therapeutic inertia. Although therapeutic inertia is frequent in the management of hypertension, the factors explaining it are not completely clear. Whereas some aspects of the consultations were associated with therapeutic inertia, physician characteristics were not a decisive factor.

Finite mixture modeling approach for developing crash modification factors in highway safety analysis.

PubMed

Park, Byung-Jung; Lord, Dominique; Wu, Lingtao

2016-10-28

This study aimed to investigate the relative performance of two models (negative binomial (NB) model and two-component finite mixture of negative binomial models (FMNB-2)) in terms of developing crash modification factors (CMFs). Crash data on rural multilane divided highways in California and Texas were modeled with the two models, and crash modification functions (CMFunctions) were derived. The resultant CMFunction estimated from the FMNB-2 model showed several good properties over that from the NB model. First, the safety effect of a covariate was better reflected by the CMFunction developed using the FMNB-2 model, since the model takes into account the differential responsiveness of crash frequency to the covariate. Second, the CMFunction derived from the FMNB-2 model is able to capture nonlinear relationships between covariate and safety. Finally, following the same concept as those for NB models, the combined CMFs of multiple treatments were estimated using the FMNB-2 model. The results indicated that they are not the simple multiplicative of single ones (i.e., their safety effects are not independent under FMNB-2 models). Adjustment Factors (AFs) were then developed. It is revealed that current Highway Safety Manual's method could over- or under-estimate the combined CMFs under particular combination of covariates. Safety analysts are encouraged to consider using the FMNB-2 models for developing CMFs and AFs. Copyright © 2016 Elsevier Ltd. All rights reserved.

Food hygiene practices and its associated factors among model and non model households in Abobo district, southwestern Ethiopia: Comparative cross-sectional study.

PubMed

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.

[Systematization and hygienic standardization of environmental factors on the basis of common graphic models].

PubMed

Galkin, A A

2012-01-01

On the basis of graphic models of the human response to environmental factors, two main types of complex quantitative influence as well as interrelation between determined effects at the level of an individual, and stochastic effects on population were revealed. Two main kinds of factors have been suggested to be distinguished. They are essential factors and accidental factors. The essential factors are common for environment. The accidental factors are foreign for environment. The above two kinds are different in approaches of hygienic standardization Accidental factors need a dot-like approach, whereas a two-level range approach is suitable for the essential factors.

Examining the Reliability and Validity of Clinician Ratings on the Five-Factor Model Score Sheet

ERIC Educational Resources Information Center

Few, Lauren R.; Miller, Joshua D.; Morse, Jennifer Q.; Yaggi, Kirsten E.; Reynolds, Sarah K.; Pilkonis, Paul A.

2010-01-01

Despite substantial research use, measures of the five-factor model (FFM) are infrequently used in clinical settings due, in part, to issues related to administration time and a reluctance to use self-report instruments. The current study examines the reliability and validity of the Five-Factor Model Score Sheet (FFMSS), which is a 30-item…

Two-vehicle injury severity models based on integration of pavement management and traffic engineering factors.

PubMed

Jiang, Ximiao; Huang, Baoshan; Yan, Xuedong; Zaretzki, Russell L; Richards, Stephen

2013-01-01

The severity of traffic-related injuries has been studied by many researchers in recent decades. However, the evaluation of many factors is still in dispute and, until this point, few studies have taken into account pavement management factors as points of interest. The objective of this article is to evaluate the combined influences of pavement management factors and traditional traffic engineering factors on the injury severity of 2-vehicle crashes. This study examines 2-vehicle rear-end, sideswipe, and angle collisions that occurred on Tennessee state routes from 2004 to 2008. Both the traditional ordered probit (OP) model and Bayesian ordered probit (BOP) model with weak informative prior were fitted for each collision type. The performances of these models were evaluated based on the parameter estimates and deviances. The results indicated that pavement management factors played identical roles in all 3 collision types. Pavement serviceability produces significant positive effects on the severity of injuries. The pavement distress index (PDI), rutting depth (RD), and rutting depth difference between right and left wheels (RD_df) were not significant in any of these 3 collision types. The effects of traffic engineering factors varied across collision types, except that a few were consistently significant in all 3 collision types, such as annual average daily traffic (AADT), rural-urban location, speed limit, peaking hour, and light condition. The findings of this study indicated that improved pavement quality does not necessarily lessen the severity of injuries when a 2-vehicle crash occurs. The effects of traffic engineering factors are not universal but vary by the type of crash. The study also found that the BOP model with a weak informative prior can be used as an alternative but was not superior to the traditional OP model in terms of overall performance.

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

PubMed

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.

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

PubMed

Villar, Oscar Armando Esparza-Del; Montañez-Alvarado, Priscila; Gutiérrez-Vega, Marisela; Carrillo-Saucedo, Irene Concepción; Gurrola-Peña, Gloria Margarita; Ruvalcaba-Romero, Norma Alicia; García-Sánchez, María Dolores; Ochoa-Alcaraz, Sergio Gabriel

2017-03-01

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. 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. 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. The EHBMS is a validated scale that can be used to measure exercise based on the HBM in Mexican populations.

Exploring critical factors influencing physicians' acceptance of mobile electronic medical records based on the dual-factor model: a validation in Taiwan.

PubMed

Liu, Chung-Feng; Cheng, Tain-Junn

2015-02-07

With respect to information management, most of the previous studies on the acceptance of healthcare information technologies were analyzed from "positive" perspectives. However, such acceptance is always influenced by both positive and negative factors and it is necessary to validate both in order to get a complete understanding. This study aims to explore physicians' acceptance of mobile electronic medical records based on the dual-factor model, which is comprised of inhibitors and enablers, to explain an individual's technology usage. Following an earlier healthcare study in the USA, the researchers conducted a similar survey for an Eastern country (Taiwan) to validate whether perceived threat to professional autonomy acts as a critical inhibitor. In addition, perceived mobility, which is regarded as a critical feature of mobile services, was also evaluated as a common antecedent variable in the model. Physicians from three branch hospitals of a medical group were invited to participate and complete questionnaires. Partial least squares, a structural equation modeling technique, was used to evaluate the proposed model for explanatory power and hypotheses testing. 158 valid questionnaires were collected, yielding a response rate of 33.40%. As expected, the inhibitor of perceived threat has a significant impact on the physicians' perceptions of usefulness as well as their intention to use. The enablers of perceived ease of use and perceived usefulness were also significant. In addition, as expected, perceived mobility was confirmed to have a significant impact on perceived ease of use, perceived usefulness and perceived threat. It was confirmed that the dual-factor model is a comprehensive method for exploring the acceptance of healthcare information technologies, both in Western and Eastern countries. Furthermore, perceived mobility was proven to be an effective antecedent variable in the model. The researchers believe that the results of this study will

Scale Factor Study for 1:30 Local Scour Model

DTIC Science & Technology

2016-08-01

railway crossing on the Santa Ana River near Corona , CA. Data from the scale factor study provide an adjustment for applying documented scour...upstream and downstream of the test section respectively. Discharge into the model came from three recirculation pumps with a total capacity of ERDC...upstream baffle blocks. The total discharge was measured by reading the differential from a manometer across a venturi meter and verified with a total

Measuring the effects of socioeconomic factors on mental health among migrants in urban China: a multiple indicators multiple causes model.

PubMed

Guan, Ming

2017-01-01

Since 1978, rural-urban migrants mainly contribute Chinese urbanization. The purpose of this paper is to examine the effects of socioeconomic factors on mental health of them. Their mental health was measured by 12-item general health questionnaire (GHQ-12). The study sample comprised 5925 migrants obtained from the 2009 rural-to-urban migrants survey (RUMiC). The relationships among the instruments were assessed by the correlation analysis. The one-factor (overall items), two-factor (positive vs. negative items), and model conducted by principal component analysis were tested in the confirmatory factor analysis (CFA). On the basis of three CFA models, the three multiple indicators multiple causes (MIMIC) models with age, gender, marriage, ethnicity, and employment were constructed to investigate the concurrent associations between socioeconomic factors and GHQ-12. Of the sample, only 1.94% were of ethnic origin and mean age was 31.63 (SD = ±10.43) years. The one-factor, two-factor, and three-factor structure (i.e. semi-positive/negative/independent usefulness) had good model fits in the CFA analysis and gave order (i.e. 2 factor>3 factor>1 factor), which suggests that the three models can be used to assess psychological symptoms of migrants in urban China. All MIMIC models had acceptable fit and gave order (i.e. one-dimensional model>two-dimensional model>three-dimensional model). There were weak associations of socioeconomic factors with mental health among migrants in urban China. Policy discussion suggested that improvement of socioeconomic status of rural-urban migrants and mental health systems in urban China should be highlighted and strengthened.

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

PubMed

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.

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

NASA Astrophysics Data System (ADS)

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.

Subglottic injury, gastric juice, corticosteroids, and peptide growth factors in a porcine model.

PubMed

Yellon, R F; Szeremeta, W; Grandis, J R; Diguisseppe, P; Dickman, P S

1998-06-01

To study the effects of mucosal injury, gastric juice, and corticosteroids and to determine the presence of peptide growth factors in the subglottic mucosa in a porcine model. Prospective cohort animal study. In this model of subglottic injury, five groups (n = 5 each) of piglets were used. Injury was induced by electrocautery (acute), electrocautery plus repeated saline application (chronic), electrocautery plus repeated gastric juice application (chronic plus gastric juice), or repeated gastric juice application (gastric). Control piglets had normal saline applied repeatedly. Histopathologic findings for the gastric juice group included basal cell hyperplasia (80%), squamous metaplasia (80%), and mucosal ulceration (40%). Control piglets showed squamous metaplasia (80%) but no basilar hyperplasia or ulceration. Immunohistochemistry detected peptide growth factors and epidermal growth factor receptor (EGFR) in all groups. Decreased staining was most frequent in the acute injury group. Quantitative reverse transcriptase polymerase chain reaction (RT-PCR) documented lower expression of EGFR in the gastric juice group (P = .01). These findings suggest that peptide growth factors and EGFR are part of normal subglottic mucosal turnover. Noxious stimuli decrease production of these factors. Gastric juice had adverse effects documented by histopathology and molecular techniques.

Overall Preference of Running Shoes Can Be Predicted by Suitable Perception Factors Using a Multiple Regression Model.

PubMed

Tay, Cheryl Sihui; Sterzing, Thorsten; Lim, Chen Yen; Ding, Rui; Kong, Pui Wah

2017-05-01

This study examined (a) the strength of four individual footwear perception factors to influence the overall preference of running shoes and (b) whether these perception factors satisfied the nonmulticollinear assumption in a regression model. Running footwear must fulfill multiple functional criteria to satisfy its potential users. Footwear perception factors, such as fit and cushioning, are commonly used to guide shoe design and development, but it is unclear whether running-footwear users are able to differentiate one factor from another. One hundred casual runners assessed four running shoes on a 15-cm visual analogue scale for four footwear perception factors (fit, cushioning, arch support, and stability) as well as for overall preference during a treadmill running protocol. Diagnostic tests showed an absence of multicollinearity between factors, where values for tolerance ranged from .36 to .72, corresponding to variance inflation factors of 2.8 to 1.4. The multiple regression model of these four footwear perception variables accounted for 77.7% to 81.6% of variance in overall preference, with each factor explaining a unique part of the total variance. Casual runners were able to rate each footwear perception factor separately, thus assigning each factor a true potential to improve overall preference for the users. The results also support the use of a multiple regression model of footwear perception factors to predict overall running shoe preference. Regression modeling is a useful tool for running-shoe manufacturers to more precisely evaluate how individual factors contribute to the subjective assessment of running footwear.

Factors affecting strategic plan implementation using interpretive structural modeling (ISM).

PubMed

Bahadori, Mohammadkarim; Teymourzadeh, Ehsan; Tajik, Hamidreza; Ravangard, Ramin; Raadabadi, Mehdi; Hosseini, Seyed Mojtaba

2018-06-11

Purpose Strategic planning is the best tool for managers seeking an informed presence and participation in the market without surrendering to changes. Strategic planning enables managers to achieve their organizational goals and objectives. Hospital goals, such as improving service quality and increasing patient satisfaction cannot be achieved if agreed strategies are not implemented. The purpose of this paper is to investigate the factors affecting strategic plan implementation in one teaching hospital using interpretive structural modeling (ISM). Design/methodology/approach The authors used a descriptive study involving experts and senior managers; 16 were selected as the study sample using a purposive sampling method. Data were collected using a questionnaire designed and prepared based on previous studies. Data were analyzed using ISM. Findings Five main factors affected strategic plan implementation. Although all five variables and factors are top level, "senior manager awareness and participation in the strategic planning process" and "creating and maintaining team participation in the strategic planning process" had maximum drive power. "Organizational structure effects on the strategic planning process" and "Organizational culture effects on the strategic planning process" had maximum dependence power. Practical implications Identifying factors affecting strategic plan implementation is a basis for healthcare quality improvement by analyzing the relationship among factors and overcoming the barriers. Originality/value The authors used ISM to analyze the relationship between factors affecting strategic plan implementation.

Predictive model of thrombospondin-1 and vascular endothelial growth factor in breast tumor tissue.

PubMed

Rohrs, Jennifer A; Sulistio, Christopher D; Finley, Stacey D

2016-01-01

Angiogenesis, the formation of new blood capillaries from pre-existing vessels, is a hallmark of cancer. Thus far, strategies for reducing tumor angiogenesis have focused on inhibiting pro-angiogenic factors, while less is known about the therapeutic effects of mimicking the actions of angiogenesis inhibitors. Thrombospondin-1 (TSP1) is an important endogenous inhibitor of angiogenesis that has been investigated as an anti-angiogenic agent. TSP1 impedes the growth of new blood vessels in many ways, including crosstalk with pro-angiogenic factors. Due to the complexity of TSP1 signaling, a predictive systems biology model would provide quantitative understanding of the angiogenic balance in tumor tissue. Therefore, we have developed a molecular-detailed, mechanistic model of TSP1 and vascular endothelial growth factor (VEGF), a promoter of angiogenesis, in breast tumor tissue. The model predicts the distribution of the angiogenic factors in tumor tissue, revealing that TSP1 is primarily in an inactive, cleaved form due to the action of proteases, rather than bound to its cellular receptors or to VEGF. The model also predicts the effects of enhancing TSP1's interactions with its receptors and with VEGF. To provide additional predictions that can guide the development of new anti-angiogenic drugs, we simulate administration of exogenous TSP1 mimetics that bind specific targets. The model predicts that the CD47-binding TSP1 mimetic dramatically decreases the ratio of receptor-bound VEGF to receptor-bound TSP1, in favor of anti-angiogenesis. Thus, we have established a model that provides a quantitative framework to study the response to TSP1 mimetics.

Neuropathology and Animal Models of Autism: Genetic and Environmental Factors

PubMed Central

Gadad, Bharathi S.; Young, Keith A.; German, Dwight C.

2013-01-01

Autism is a heterogeneous behaviorally defined neurodevelopmental disorder. It is defined by the presence of marked social deficits, specific language abnormalities, and stereotyped repetitive patterns of behavior. Because of the variability in the behavioral phenotype of the disorder among patients, the term autism spectrum disorder has been established. In the first part of this review, we provide an overview of neuropathological findings from studies of autism postmortem brains and identify the cerebellum as one of the key brain regions that can play a role in the autism phenotype. We review research findings that indicate possible links between the environment and autism including the role of mercury and immune-related factors. Because both genes and environment can alter the structure of the developing brain in different ways, it is not surprising that there is heterogeneity in the behavioral and neuropathological phenotypes of autism spectrum disorders. Finally, we describe animal models of autism that occur following insertion of different autism-related genes and exposure to environmental factors, highlighting those models which exhibit both autism-like behavior and neuropathology. PMID:24151553

Circadian typology and the Alternative Five-Factor Model of personality.

PubMed

Tonetti, Lorenzo; Pascalis, Vilfredo De; Fabbri, Marco; Martoni, Monica; Russo, Paolo Maria; Natale, Vincenzo

2016-10-01

Two studies were carried out to explore the relationship between circadian typology and the Alternative Five-Factor Model of personality. In the first study, 379 participants (232 females) were administered the reduced version of the Morningness-Eveningness Questionnaire and the Zuckerman-Kuhlman Personality Questionnaire. Evening types reported higher impulsive sensation-seeking scores than morning and intermediate types, whereas morning types scored higher than evening types on activity factor. In the second study, the association between morningness and activity personality factor was verified through the objective-actigraphic monitoring of the rest-activity cycle. Actigraphy allowed us to operationalise both circadian typology, through the computing of midpoint of sleep (early values, expressed in hours and minutes, correspond to an advanced phase of the sleep/wake cycle), and activity factor by the means of motor activity recording. Fifty-one individuals (30 females) wore an actigraph on the nondominant wrist continuously for 1 week. A negative correlation was observed between midpoint of sleep and mean diurnal motor activity, demonstrating that an early phase of the sleep/wake cycle (i.e. morningness preference) was related to higher diurnal motor activity. Assessed both subjectively and objectively, the results of both studies highlight a significant relationship between morningness and activity personality factor. © 2015 International Union of Psychological Science.

How do various maize crop models vary in their responses to climate change factors?

USDA-ARS?s Scientific Manuscript database

Potential consequences of climate change on crop production can be studied using mechanistic crop simulation models. While a broad variety of maize simulation models exist, it is not known whether different models give similar grain yield responses to changes in climatic factors, or whether they agr...

Learning a generative model of images by factoring appearance and shape.

PubMed

Le Roux, Nicolas; Heess, Nicolas; Shotton, Jamie; Winn, John

2011-03-01

Computer vision has grown tremendously in the past two decades. Despite all efforts, existing attempts at matching parts of the human visual system's extraordinary ability to understand visual scenes lack either scope or power. By combining the advantages of general low-level generative models and powerful layer-based and hierarchical models, this work aims at being a first step toward richer, more flexible models of images. After comparing various types of restricted Boltzmann machines (RBMs) able to model continuous-valued data, we introduce our basic model, the masked RBM, which explicitly models occlusion boundaries in image patches by factoring the appearance of any patch region from its shape. We then propose a generative model of larger images using a field of such RBMs. Finally, we discuss how masked RBMs could be stacked to form a deep model able to generate more complicated structures and suitable for various tasks such as segmentation or object recognition.

Effects of leukemia inhibitory factor and basic fibroblast growth factor on free radicals and endogenous stem cell proliferation in a mouse model of cerebral infarction.

PubMed

Huang, Weihui; Li, Yadan; Lin, Yufeng; Ye, Xue; Zang, Dawei

2012-07-05

The present study established a mouse model of cerebral infarction by middle cerebral artery occlusion, and monitored the effect of 25 μg/kg leukemia inhibitory factor and (or) basic fibroblast growth factor administration 2 hours after model establishment. Results showed that following administration, the number of endogenous neural stem cells in the infarct area significantly increased, malondialdehyde content in brain tissue homogenates significantly decreased, nitric oxide content, glutathione peroxidase and superoxide dismutase activity significantly elevated, and mouse motor function significantly improved as confirmed by the rotarod and bar grab tests. In particular, the effect of leukemia inhibitory factor in combination with basic fibroblast growth factor was the most significant. Results indicate that leukemia inhibitory factor and basic fibroblast growth factor can improve the microenvironment after cerebral infarction by altering free radical levels, improving the quantity of endogenous neural stem cells, and promoting neurological function of mice with cerebral infarction.

Risk Factors for Addiction and Their Association with Model-Based Behavioral Control.

PubMed

Reiter, Andrea M F; Deserno, Lorenz; Wilbertz, Tilmann; Heinze, Hans-Jochen; Schlagenhauf, Florian

2016-01-01

Addiction shows familial aggregation and previous endophenotype research suggests that healthy relatives of addicted individuals share altered behavioral and cognitive characteristics with individuals suffering from addiction. In this study we asked whether impairments in behavioral control proposed for addiction, namely a shift from goal-directed, model-based toward habitual, model-free control, extends toward an unaffected sample (n = 20) of adult children of alcohol-dependent fathers as compared to a sample without any personal or family history of alcohol addiction (n = 17). Using a sequential decision-making task designed to investigate model-free and model-based control combined with a computational modeling analysis, we did not find any evidence for altered behavioral control in individuals with a positive family history of alcohol addiction. Independent of family history of alcohol dependence, we however observed that the interaction of two different risk factors of addiction, namely impulsivity and cognitive capacities, predicts the balance of model-free and model-based behavioral control. Post-hoc tests showed a positive association of model-based behavior with cognitive capacity in the lower, but not in the higher impulsive group of the original sample. In an independent sample of particularly high- vs. low-impulsive individuals, we confirmed the interaction effect of cognitive capacities and high vs. low impulsivity on model-based control. In the confirmation sample, a positive association of omega with cognitive capacity was observed in highly impulsive individuals, but not in low impulsive individuals. Due to the moderate sample size of the study, further investigation of the association of risk factors for addiction with model-based behavior in larger sample sizes is warranted.

The Impact of Sample Size and Other Factors When Estimating Multilevel Logistic Models

ERIC Educational Resources Information Center

Schoeneberger, Jason A.

2016-01-01

The design of research studies utilizing binary multilevel models must necessarily incorporate knowledge of multiple factors, including estimation method, variance component size, or number of predictors, in addition to sample sizes. This Monte Carlo study examined the performance of random effect binary outcome multilevel models under varying…

A Model of Factors Contributing to STEM Learning and Career Orientation

NASA Astrophysics Data System (ADS)

Nugent, Gwen; Barker, Bradley; Welch, Greg; Grandgenett, Neal; Wu, ChaoRong; Nelson, Carl

2015-05-01

The purpose of this research was to develop and test a model of factors contributing to science, technology, engineering, and mathematics (STEM) learning and career orientation, examining the complex paths and relationships among social, motivational, and instructional factors underlying these outcomes for middle school youth. Social cognitive career theory provided the foundation for the research because of its emphasis on explaining mechanisms which influence both career orientations and academic performance. Key constructs investigated were youth STEM interest, self-efficacy, and career outcome expectancy (consequences of particular actions). The study also investigated the effects of prior knowledge, use of problem-solving learning strategies, and the support and influence of informal educators, family members, and peers. A structural equation model was developed, and structural equation modeling procedures were used to test proposed relationships between these constructs. Results showed that educators, peers, and family-influenced youth STEM interest, which in turn predicted their STEM self-efficacy and career outcome expectancy. STEM career orientation was fostered by youth-expected outcomes for such careers. Results suggest that students' pathways to STEM careers and learning can be largely explained by these constructs, and underscore the importance of youth STEM interest.

Use of loglinear models to assess factors influencing concern for the natural environment.

PubMed

Lakhan, V Chris; Lavalle, Placido D

2002-07-01

Since it is necessary to isolate the most significant factors influencing personal concern for the environment, this paper utilizes loglinear models for identifying the interactions and interrelationships underlying multidimensional environmental survey data. A field study in Guyana conducted face-to-face interviews with 1600 citizens. Acquired categorical data were then subjected to loglinear modeling techniques to determine what significance the factors education, age, residential location, and gender have on personal concern for the environment. The loglinear models obtained from the five-dimensional contingency table suggest that there is a direct relationship between education and personal concern for the environment. Age has an interaction with education, and some influence on environmental concern, with younger respondents expressing higher concern for the environment than older respondents. Other results from the loglinear model demonstrate that residential location and the gender of the respondents do not have any statistically significant association with personal concern for the environment.

Modeling Vascularized Bone Regeneration Within a Porous Biodegradable CaP Scaffold Loaded with Growth Factors

PubMed Central

Sun, X; Kang, Y; Bao, J; Zhang, Y; Yang, Y; Zhou, X

2013-01-01

Osteogenetic microenvironment is a complex constitution in which extracellular matrix (ECM) molecules, stem cells and growth factors each interact to direct the coordinate regulation of bone tissue development. Importantly, angiogenesis improvement and revascularization are critical for osteogenesis during bone tissue regeneration processes. In this study, we developed a three-dimensional (3D) multi-scale system model to study cell response to growth factors released from a 3D biodegradable porous calcium phosphate (CaP) scaffold. Our model reconstructed the 3D bone regeneration system and examined the effects of pore size and porosity on bone formation and angiogenesis. The results suggested that scaffold porosity played a more dominant role in affecting bone formation and angiogenesis compared with pore size, while the pore size could be controlled to tailor the growth factor release rate and release fraction. Furthermore, a combination of gradient VEGF with BMP2 and Wnt released from the multi-layer scaffold promoted angiogenesis and bone formation more readily than single growth factors. These results demonstrated that the developed model can be potentially applied to predict vascularized bone regeneration with specific scaffold and growth factors. PMID:23566802

[Spatial differentiation and impact factors of Yutian Oasis's soil surface salt based on GWR model].

PubMed

Yuan, Yu Yun; Wahap, Halik; Guan, Jing Yun; Lu, Long Hui; Zhang, Qin Qin

2016-10-01

In this paper, topsoil salinity data gathered from 24 sampling sites in the Yutian Oasis were used, nine different kinds of environmental variables closely related to soil salinity were selec-ted as influencing factors, then, the spatial distribution characteristics of topsoil salinity and spatial heterogeneity of influencing factors were analyzed by combining the spatial autocorrelation with traditional regression analysis and geographically weighted regression model. Results showed that the topsoil salinity in Yutian Oasis was not of random distribution but had strong spatial dependence, and the spatial autocorrelation index for topsoil salinity was 0.479. Groundwater salinity, groundwater depth, elevation and temperature were the main factors influencing topsoil salt accumulation in arid land oases and they were spatially heterogeneous. The nine selected environmental variables except soil pH had significant influences on topsoil salinity with spatial disparity. GWR model was superior to the OLS model on interpretation and estimation of spatial non-stationary data, also had a remarkable advantage in visualization of modeling parameters.

The HEXACO and Five-Factor Models of Personality in Relation to RIASEC Vocational Interests

ERIC Educational Resources Information Center

McKay, Derek A.; Tokar, David M.

2012-01-01

The current study extended the empirical research on the overlap of vocational interests and personality by (a) testing hypothesized relations between RIASEC interests and the personality dimensions of the HEXACO model, and (b) exploring the HEXACO personality model's predictive advantage over the five-factor model (FFM) in capturing RIASEC…

Predictive models and prognostic factors for upper tract urothelial carcinoma: a comprehensive review of the literature.

PubMed

Mbeutcha, Aurélie; Mathieu, Romain; Rouprêt, Morgan; Gust, Kilian M; Briganti, Alberto; Karakiewicz, Pierre I; Shariat, Shahrokh F

2016-10-01

In the context of customized patient care for upper tract urothelial carcinoma (UTUC), decision-making could be facilitated by risk assessment and prediction tools. The aim of this study was to provide a critical overview of existing predictive models and to review emerging promising prognostic factors for UTUC. A literature search of articles published in English from January 2000 to June 2016 was performed using PubMed. Studies on risk group stratification models and predictive tools in UTUC were selected, together with studies on predictive factors and biomarkers associated with advanced-stage UTUC and oncological outcomes after surgery. Various predictive tools have been described for advanced-stage UTUC assessment, disease recurrence and cancer-specific survival (CSS). Most of these models are based on well-established prognostic factors such as tumor stage, grade and lymph node (LN) metastasis, but some also integrate newly described prognostic factors and biomarkers. These new prediction tools seem to reach a high level of accuracy, but they lack external validation and decision-making analysis. The combinations of patient-, pathology- and surgery-related factors together with novel biomarkers have led to promising predictive tools for oncological outcomes in UTUC. However, external validation of these predictive models is a prerequisite before their introduction into daily practice. New models predicting response to therapy are urgently needed to allow accurate and safe individualized management in this heterogeneous disease.

A Model Incorporating Some of the Mechanical and Biochemical Factors Underlying Clot Formation and Dissolution in Flowing Blood

DOE PAGES

Anand, M.; Rajagopal, K.; Rajagopal, K. R.

2003-01-01

Multiple interacting mechanisms control the formation and dissolution of clots to maintain blood in a state of delicate balance. In addition to a myriad of biochemical reactions, rheological factors also play a crucial role in modulating the response of blood to external stimuli. To date, a comprehensive model for clot formation and dissolution, that takes into account the biochemical, medical and rheological factors, has not been put into place, the existing models emphasizing either one or the other of the factors. In this paper, after discussing the various biochemical, physiologic and rheological factors at some length, we develop a modelmore » for clot formation and dissolution that incorporates many of the relevant crucial factors that have a bearing on the problem. The model, though just a first step towards understanding a complex phenomenon, goes further than previous models in integrating the biochemical, physiologic and rheological factors that come into play.« less

Factors Predicting Sustainability of the Schoolwide Positive Behavior Intervention Support Model

ERIC Educational Resources Information Center

Chitiyo, Jonathan; May, Michael E.

2018-01-01

The Schoolwide Positive Behavior Intervention Support model (SWPBIS) continues to gain widespread use across schools in the United States and abroad. Despite its widespread implementation, little research has examined factors that influence its sustainability. Informed by Rogers's diffusion theory, this study examined school personnel's…

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

PubMed

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.

Granularity as a Cognitive Factor in the Effectiveness of Business Process Model Reuse

NASA Astrophysics Data System (ADS)

Holschke, Oliver; Rake, Jannis; Levina, Olga

Reusing design models is an attractive approach in business process modeling as modeling efficiency and quality of design outcomes may be significantly improved. However, reusing conceptual models is not a cost-free effort, but has to be carefully designed. While factors such as psychological anchoring and task-adequacy in reuse-based modeling tasks have been investigated, information granularity as a cognitive concept has not been at the center of empirical research yet. We hypothesize that business process granularity as a factor in design tasks under reuse has a significant impact on the effectiveness of resulting business process models. We test our hypothesis in a comparative study employing high and low granularities. The reusable processes provided were taken from widely accessible reference models for the telecommunication industry (enhanced Telecom Operations Map). First experimental results show that Recall in tasks involving coarser granularity is lower than in cases of finer granularity. These findings suggest that decision makers in business process management should be considerate with regard to the implementation of reuse mechanisms of different granularities. We realize that due to our small sample size results are not statistically significant, but this preliminary run shows that it is ready for running on a larger scale.

A Bayesian Multi-Level Factor Analytic Model of Consumer Price Sensitivities across Categories

ERIC Educational Resources Information Center

Duvvuri, Sri Devi; Gruca, Thomas S.

2010-01-01

Identifying price sensitive consumers is an important problem in marketing. We develop a Bayesian multi-level factor analytic model of the covariation among household-level price sensitivities across product categories that are substitutes. Based on a multivariate probit model of category incidence, this framework also allows the researcher to…

Assessment of the five-factor model of personality.

PubMed

Widiger, T A; Trull, T J

1997-04-01

The five-factor model (FFM) of personality is obtaining construct validation, recognition, and practical consideration across a broad domain of fields, including clinical psychology, industrial-organizational psychology, and health psychology. As a result, an array of instruments have been developed and existing instruments are being modified to assess the FFM. In this article, we present an overview and critique of five such instruments (the Goldberg Big Five Markers, the revised NEO Personality Inventory, the Interpersonal Adjective Scales-Big Five, the Personality Psychopathology-Five, and the Hogan Personality Inventory), focusing in particular on their representation of the lexical FFM and their practical application.

A single factor underlies the metabolic syndrome: a confirmatory factor analysis.

PubMed

Pladevall, Manel; Singal, Bonita; Williams, L Keoki; Brotons, Carlos; Guyer, Heidi; Sadurni, Josep; Falces, Carles; Serrano-Rios, Manuel; Gabriel, Rafael; Shaw, Jonathan E; Zimmet, Paul Z; Haffner, Steven

2006-01-01

Confirmatory factor analysis (CFA) was used to test the hypothesis that the components of the metabolic syndrome are manifestations of a single common factor. Three different datasets were used to test and validate the model. The Spanish and Mauritian studies included 207 men and 203 women and 1,411 men and 1,650 women, respectively. A third analytical dataset including 847 men was obtained from a previously published CFA of a U.S. population. The one-factor model included the metabolic syndrome core components (central obesity, insulin resistance, blood pressure, and lipid measurements). We also tested an expanded one-factor model that included uric acid and leptin levels. Finally, we used CFA to compare the goodness of fit of one-factor models with the fit of two previously published four-factor models. The simplest one-factor model showed the best goodness-of-fit indexes (comparative fit index 1, root mean-square error of approximation 0.00). Comparisons of one-factor with four-factor models in the three datasets favored the one-factor model structure. The selection of variables to represent the different metabolic syndrome components and model specification explained why previous exploratory and confirmatory factor analysis, respectively, failed to identify a single factor for the metabolic syndrome. These analyses support the current clinical definition of the metabolic syndrome, as well as the existence of a single factor that links all of the core components.

Modelling the factor structure of the Child Depression Inventory in a population of apparently healthy adolescents in Nigeria.

PubMed

Olorunju, Samson Bamidele; Akpa, Onoja Matthew; Afolabi, Rotimi Felix

2018-01-01

Childhood and adolescent depression is common and often persists into adulthood with negative implications for school performances, peer relationship and behavioural functioning. The Child Depression Inventory (CDI) has been used to assess depression among adolescents in many countries including Nigeria but it is uncertain if the theoretical structure of CDI appropriately fits the experiences of adolescents in Nigeria. This study assessed varying theoretical modelling structure of the CDI in a population of apparently healthy adolescents in Benue state, Nigeria. Data was extracted on CDI scale and demographic information from a total of 1, 963 adolescents (aged 10-19 years), who participated in a state wide study assessing adolescent psychosocial functioning. In addition to descriptive statistics and reliability tests, Exploratory Factor Analysis (EFA) and Confirmatory Factor analysis (CFA) were used to model the underlying factor structure and its adequacy. The suggested new model was compared with existing CDI models as well as the CDI's original theoretical model. A model is considered better, if it has minimum Root Mean Square Error of Approximation (RMSEA<0.05), Minimum value of Discrepancy (CMIN/DF<3.0) and Akaike information criteria. All analyses were performed at 95% confidence level, using the version 21 of AMOS and the R software. Participants were 14.7±2.1 years and mostly male (54.3%), from Monogamous homes (67.9%) and lived in urban areas (52.2%). The measure of the overall internal consistency of the 2-factor CDI was α = 0.84. The 2-factor model had the minimum RMSEA (0.044), CMIN/DF (2.87) and least AIC (1037.996) compared to the other five CDI models. The child depression inventory has a 2-factor structure in a non-clinical general population of adolescents in Nigeria. Future use of the CDI in related setting may consider the 2-factor model.

Modeling Shear Induced Von Willebrand Factor Binding to Collagen

NASA Astrophysics Data System (ADS)

Dong, Chuqiao; Wei, Wei; Morabito, Michael; Webb, Edmund; Oztekin, Alparslan; Zhang, Xiaohui; Cheng, Xuanhong

2017-11-01

Von Willebrand factor (vWF) is a blood glycoprotein that binds with platelets and collagen on injured vessel surfaces to form clots. VWF bioactivity is shear flow induced: at low shear, binding between VWF and other biological entities is suppressed; for high shear rate conditions - as are found near arterial injury sites - VWF elongates, activating its binding with platelets and collagen. Based on parameters derived from single molecule force spectroscopy experiments, we developed a coarse-grain molecular model to simulate bond formation probability as a function of shear rate. By introducing a binding criterion that depends on the conformation of a sub-monomer molecular feature of our model, the model predicts shear-induced binding, even for conditions where binding is highly energetically favorable. We further investigate the influence of various model parameters on the ability to predict shear-induced binding (vWF length, collagen site density and distribution, binding energy landscape, and slip/catch bond length) and demonstrate parameter ranges where the model provides good agreement with existing experimental data. Our results may be important for understanding vWF activity and also for achieving targeted drug therapy via biomimetic synthetic molecules. National Science Foundation (NSF),Division of Mathematical Sciences (DMS).

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

PubMed

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.

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

PubMed Central

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

Which psychological factors exacerbate irritable bowel syndrome? Development of a comprehensive model.

PubMed

van Tilburg, Miranda A L; Palsson, Olafur S; Whitehead, William E

2013-06-01

There is evidence that psychological factors affect the onset, severity and duration of irritable bowel syndrome (IBS). However, it is not clear which psychological factors are the most important and how they interact. The aims of the current study are to identify the most important psychological factors predicting IBS symptom severity and to investigate how these psychological variables are related to each other. Study participants were 286 IBS patients who completed a battery of psychological questionnaires including neuroticism, abuse history, life events, anxiety, somatization and catastrophizing. IBS severity measured by the IBS Severity Scale was the dependent variable. Path analysis was performed to determine the associations among the psychological variables, and IBS severity. Although the hypothesized model showed adequate fit, post hoc model modifications were performed to increase prediction. The final model was significant (Chi(2)=2.2; p=0.82; RMSEA<.05) predicting 36% of variance in IBS severity. Catastrophizing (standardized coefficient (β)=0.33; p<.001) and somatization (β=0.20; p<.001) were the only two psychological variables directly associated with IBS severity. Anxiety had an indirect effect on IBS symptoms through catastrophizing (β=0.80; p<.001); as well as somatization (β=0.37; p<.001). Anxiety, in turn, was predicted by neuroticism (β=0.66; p<.001) and stressful life events (β=0.31; p<.001). While cause-and-effect cannot be determined from these cross-sectional data, the outcomes suggest that the most fruitful approach to curb negative effects of psychological factors on IBS is to reduce catastrophizing and somatization. Copyright © 2013 Elsevier Inc. All rights reserved.

Assessing posttraumatic stress disorder's latent structure in elderly bereaved European trauma survivors: evidence for a five-factor dysphoric and anxious arousal model.

PubMed

Armour, Cherie; O'Connor, Maja; Elklit, Ask; Elhai, Jon D

2013-10-01

The three-factor structure of posttraumatic stress disorder (PTSD) specified by the Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition, is not supported in the empirical literature. Two alternative four-factor models have received a wealth of empirical support. However, a consensus regarding which is superior has not been reached. A recent five-factor model has been shown to provide superior fit over the existing four-factor models. The present study investigated the fit of the five-factor model against the existing four-factor models and assessed the resultant factors' association with depression in a bereaved European trauma sample (N = 325). The participants were assessed for PTSD via the Harvard Trauma Questionnaire and depression via the Beck Depression Inventory. The five-factor model provided superior fit to the data compared with the existing four-factor models. In the dysphoric arousal model, depression was equally related to both dysphoric arousal and emotional numbing, whereas depression was more related to dysphoric arousal than to anxious arousal.

Five Factor Model personality disorder scales: An introduction to a special section on assessment of maladaptive variants of the five factor model.

PubMed

Bagby, R Michael; Widiger, Thomas A

2018-01-01

The Five-Factor Model (FFM) is a dimensional model of general personality structure, consisting of the domains of neuroticism (or emotional instability), extraversion versus introversion, openness (or unconventionality), agreeableness versus antagonism, and conscientiousness (or constraint). The FFM is arguably the most commonly researched dimensional model of general personality structure. However, a notable limitation of existing measures of the FFM has been a lack of coverage of its maladaptive variants. A series of self-report inventories has been developed to assess for the maladaptive personality traits that define Diagnostic and Statistical Manual of Mental Disorders (fifth edition; DSM-5) Section II personality disorders (American Psychiatric Association [APA], 2013) from the perspective of the FFM. In this paper, we provide an introduction to this Special Section, presenting the rationale and empirical support for these measures and placing them in the historical context of the recent revision to the APA diagnostic manual. This introduction is followed by 5 papers that provide further empirical support for these measures and address current issues within the personality assessment literature. (PsycINFO Database Record (c) 2018 APA, all rights reserved).

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

PubMed

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.

Evaluation of finite-element models and stress-intensity factors for surface cracks emanating from stress concentrations

NASA Technical Reports Server (NTRS)

Tan, P. W.; Raju, I. S.; Shivakumar, K. N.; Newman, J. C., Jr.

1990-01-01

A re-evaluation of the 3-D finite-element models and methods used to analyze surface crack at stress concentrations is presented. Previous finite-element models used by Raju and Newman for surface and corner cracks at holes were shown to have ill-shaped elements at the intersection of the hole and crack boundaries. Improved models, without these ill-shaped elements, were developed for a surface crack at a circular hole and at a semi-circular edge notch. Stress-intensity factors were calculated by both the nodal-force and virtual-crack-closure methods. Comparisons made between the previously developed stress-intensity factor equations and the results from the improved models agreed well except for configurations with large notch-radii-to-plate-thickness ratios. Stress-intensity factors for a semi-elliptical surface crack located at the center of a semi-circular edge notch in a plate subjected to remote tensile loadings were calculated using the improved models.

Assessing the contribution of different factors in RegCM4.3 regional climate model projections using the Factor Separation method over the Med-CORDEX domain

NASA Astrophysics Data System (ADS)

Zsolt Torma, Csaba; Giorgi, Filippo

2014-05-01

A set of regional climate model (RCM) simulations applying dynamical downscaling of global climate model (GCM) simulations over the Mediterranean domain specified by the international initiative Coordinated Regional Downscaling Experiment (CORDEX) were completed with the Regional Climate Model RegCM, version RegCM4.3. Two GCMs were selected from the Coupled Model Intercomparison Project Phase 5 (CMIP5) ensemble to provide the driving fields for the RegCM: HadGEM2-ES (HadGEM) and MPI-ESM-MR (MPI). The simulations consist of an ensemble including multiple physics configurations and different "Reference Concentration Pathways" (RCP4.5 and RCP8.5). In total 15 simulations were carried out with 7 model physics configurations with varying convection and land surface schemes. The horizontal grid spacing of the RCM simulations is 50 km and the simulated period in all cases is 1970-2100 (1970-2099 in case of HadGEM driven simulations). This ensemble includes a combination of experiments in which different model components are changed individually and in combination, and thus lends itself optimally to the application of the Factor Separation (FS) method. This study applies the FS method to investigate the contributions of different factors, along with their synergy, on a set of regional climate model (RCM) projections for the Mediterranean region. The FS method is applied to 6 projections for the period 1970-2100 performed with the regional model RegCM4.3 over the Med-CORDEX domain. Two different sets of factors are intercompared, namely the driving global climate model (HadGEM and MPI) boundary conditions against two model physics settings (convection scheme and irrigation). We find that both the GCM driving conditions and the model physics provide important contributions, depending on the variable analyzed (surface air temperature and precipitation), season (winter vs. summer) and time horizon into the future, while the synergy term mostly tends to counterbalance the

The Benefits of Including Clinical Factors in Rectal Normal Tissue Complication Probability Modeling After Radiotherapy for Prostate Cancer

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

Defraene, Gilles, E-mail: gilles.defraene@uzleuven.be; Van den Bergh, Laura; Al-Mamgani, Abrahim

2012-03-01

Purpose: To study the impact of clinical predisposing factors on rectal normal tissue complication probability modeling using the updated results of the Dutch prostate dose-escalation trial. Methods and Materials: Toxicity data of 512 patients (conformally treated to 68 Gy [n = 284] and 78 Gy [n = 228]) with complete follow-up at 3 years after radiotherapy were studied. Scored end points were rectal bleeding, high stool frequency, and fecal incontinence. Two traditional dose-based models (Lyman-Kutcher-Burman (LKB) and Relative Seriality (RS) and a logistic model were fitted using a maximum likelihood approach. Furthermore, these model fits were improved by including themore » most significant clinical factors. The area under the receiver operating characteristic curve (AUC) was used to compare the discriminating ability of all fits. Results: Including clinical factors significantly increased the predictive power of the models for all end points. In the optimal LKB, RS, and logistic models for rectal bleeding and fecal incontinence, the first significant (p = 0.011-0.013) clinical factor was 'previous abdominal surgery.' As second significant (p = 0.012-0.016) factor, 'cardiac history' was included in all three rectal bleeding fits, whereas including 'diabetes' was significant (p = 0.039-0.048) in fecal incontinence modeling but only in the LKB and logistic models. High stool frequency fits only benefitted significantly (p = 0.003-0.006) from the inclusion of the baseline toxicity score. For all models rectal bleeding fits had the highest AUC (0.77) where it was 0.63 and 0.68 for high stool frequency and fecal incontinence, respectively. LKB and logistic model fits resulted in similar values for the volume parameter. The steepness parameter was somewhat higher in the logistic model, also resulting in a slightly lower D{sub 50}. Anal wall DVHs were used for fecal incontinence, whereas anorectal wall dose best described the other two endpoints. Conclusions

Multi-domain models of risk factors for depression and anxiety symptoms in preschoolers: evidence for common and specific factors.

PubMed

Hopkins, Joyce; Lavigne, John V; Gouze, Karen R; LeBailly, Susan A; Bryant, Fred B

2013-07-01

Relatively few studies have examined multiple pathways by which risk factors from different domains are related to symptoms of anxiety and depression in young children; even fewer have assessed risks for these symptoms specifically, rather than for internalizing symptoms in general. We examined a theoretically- and empirically-based model of variables associated with these symptom types in a diverse community sample of 796 4-year-olds (391 boys, 405 girls) that included factors from the following domains: contextual (SES, stress and family conflict); parent characteristics (parental depression); parenting (support/engagement, hostility and scaffolding); and child characteristics including negative affect (NA) effortful control (EC) sensory regulation (SR), inhibitory control (IC) and attachment. We also compared the models to determine which variables contribute to a common correlates of symptoms of anxiety or depression, and which correlates differentiate between those symptom types. In the best-fitting model for these symptom types (a) SES, stress and conflict had indirect effects on both symptom types via long-chain paths; (b) caregiver depression had direct effects and indirect ones (mediated through parenting and child effortful control) on both symptom types; (c) parenting had direct and indirect effects (via temperament and SR); and temperament had direct effects on both symptom types. These data provide evidence of common risk factors, as well as indicate some specific pathways/mediators for the different symptom types. EC was related to anxiety, but not depression symptoms, suggesting that strategies to improve child EC may be particularly effective for treatment of anxiety symptoms in young children.

Measurement Invariance of Second-Order Factor Model of the Multifactor Leadership Questionnaire (MLQ) across K-12 Principal Gender

ERIC Educational Resources Information Center

Xu, Lihua; Wubbena, Zane; Stewart, Trae

2016-01-01

Purpose: The purpose of this paper is to investigate the factor structure and the measurement invariance of the Multifactor Leadership Questionnaire (MLQ) across gender of K-12 school principals (n=6,317) in the USA. Design/methodology/approach: Nine first-order factor models and four second-order factor models were tested using confirmatory…

Baryon octet electromagnetic form factors in a confining NJL model

NASA Astrophysics Data System (ADS)

Carrillo-Serrano, Manuel E.; Bentz, Wolfgang; Cloët, Ian C.; Thomas, Anthony W.

2016-08-01

Electromagnetic form factors of the baryon octet are studied using a Nambu-Jona-Lasinio model which utilizes the proper-time regularization scheme to simulate aspects of colour confinement. In addition, the model also incorporates corrections to the dressed quarks from vector meson correlations in the t-channel and the pion cloud. Comparison with recent chiral extrapolations of lattice QCD results shows a remarkable level of consistency. For the charge radii we find the surprising result that rEp < rEΣ+ and | rEn | < | rEΞ0 |, whereas the magnetic radii have a pattern largely consistent with a naive expectation based on the dressed quark masses.

A Model of Factors Determining Students' Ability to Interpret External Representations in Biochemistry

ERIC Educational Resources Information Center

Schonborn, Konrad J.; Anderson, Trevor R.

2009-01-01

The aim of this research was to develop a model of factors affecting students' ability to interpret external representations (ERs) in biochemistry. The study was qualitative in design and was guided by the modelling framework of Justi and Gilbert. Application of the process outlined by the framework, and consultation with relevant literature, led…

Logistic regression models of factors influencing the location of bioenergy and biofuels plants

Treesearch

T.M. Young; R.L. Zaretzki; J.H. Perdue; F.M. Guess; X. Liu

2011-01-01

Logistic regression models were developed to identify significant factors that influence the location of existing wood-using bioenergy/biofuels plants and traditional wood-using facilities. Logistic models provided quantitative insight for variables influencing the location of woody biomass-using facilities. Availability of "thinnings to a basal area of 31.7m2/ha...

Moderating factors of video-modeling with other as model: a meta-analysis of single-case studies.

PubMed

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 previously been explored. The purpose of this study was to meta-analytically evaluate the evidence base of VMO with individuals with disabilities to determine if participant characteristics and targeted outcomes moderate the effectiveness of the intervention. Findings indicate that VMO is highly effective for participants with autism spectrum disorder (IRD=.83) and moderately effective for participants with developmental disabilities (IRD=.68). However, differential effects are indicated across levels of moderators for diagnoses and targeted outcomes. Implications for practice and future research are discussed. Copyright © 2012 Elsevier Ltd. All rights reserved.

Human Factors Research for Space Exploration: Measurement, Modeling, and Mitigation

NASA Technical Reports Server (NTRS)

Kaiser, Mary K.; Allen, Christopher S.; Barshi, Immanuel; Billman, Dorrit; Holden, Kritina L.

2010-01-01

As part of NASA's Human Research Program, the Space Human Factors Engineering Project serves as the bridge between Human Factors research and Human Spaceflight applications. Our goal is to be responsive to the operational community while addressing issues at a sufficient level of abstraction to ensure that our tools and solutions generalize beyond the point design. In this panel, representatives from four of our research domains will discuss the challenges they face in solving current problems while also enabling future capabilities. Historically, engineering-dominated organizations have tended to view good Human Factors (HF) as a desire rather than a requirement in system design and development. Our field has made significant gains in the past decade, however; the Department of Defense, for example, now recognizes Human-System Integration (HSI), of which HF is a component, as an integral part of their divisions hardware acquisition processes. And our own agency was far more accepting of HF/HSI requirements during the most recent vehicle systems definition than in any prior cycle. Nonetheless, HF subject matter experts at NASA often find themselves in catch up mode... coping with legacy systems (hardware and software) and procedures that were designed with little regard for the human element, and too often with an attitude of we can deal with any operator issues during training. Our challenge, then, is to segregate the true knowledge gaps in Space Human Factors from the prior failures to incorporate best (or even good) HF design principles. Further, we strive to extract the overarching core HF issues from the point-design-specific concerns that capture the operators (and managers) attention. Generally, our approach embraces a 3M approach to Human Factors: Measurement, Modeling, and Mitigation. Our first step is to measure human performance, to move from subjective anecdotes to objective, quantified data. Next we model the phenomenon, using appropriate methods in

A Protective Factors Model for Alcohol Abuse and Suicide Prevention among Alaska Native Youth

PubMed Central

Allen, James; Mohatt, Gerald V.; Fok, Carlotta Ching Ting; Henry, David; Burkett, Rebekah

2014-01-01

This study provides an empirical test of a culturally grounded theoretical model for prevention of alcohol abuse and suicide risk with Alaska Native youth, using a promising set of culturally appropriate measures for the study of the process of change and outcome. This model is derived from qualitative work that generated an heuristic model of protective factors from alcohol (Allen at al., 2006; Mohatt, Hazel et al., 2004; Mohatt, Rasmus et al., 2004). Participants included 413 rural Alaska Native youth ages 12-18 who assisted in testing a predictive model of Reasons for Life and Reflective Processes about alcohol abuse consequences as co-occurring outcomes. Specific individual, family, peer, and community level protective factor variables predicted these outcomes. Results suggest prominent roles for these predictor variables as intermediate prevention strategy target variables in a theoretical model for a multilevel intervention. The model guides understanding of underlying change processes in an intervention to increase the ultimate outcome variables of Reasons for Life and Reflective Processes regarding the consequences of alcohol abuse. PMID:24952249

Cross-cultural confirmation of bi-factor models of a symptom distress measure: Symptom Checklist-90-Revised in clinical samples.

PubMed

Urbán, Róbert; Arrindell, Willem A; Demetrovics, Zsolt; Unoka, Zsolt; Timman, Reinier

2016-05-30

Four decades have elapsed since the introduction for clinical and research purposes of the Symptom Checklist-90(-R). Yet, its underlying dimensional structure has not been clearly delineated. A shift has been observed in the methods utilized-from predominantly exploratory factor analytic in nature in the first two decades or so to different confirmatory methods in recent years. A need remains to search for a structure that remains invariant across samples and nations. In that context, the present study attempted to replicate and extend recent findings yielded in a Hungarian general population sample (N=2,874) with two psychiatric patient samples from Hungary (N=972) and The Netherlands (N=1,902). In doing so, four models were contrasted: the one-factor model, Derogatis' nine factor model, a second-ordered factor model, and a bi-factor model. The bi-factor model was shown to yield the closest fit to the data in both countries. Further studies are needed to determine the stable number and kind of subscale scores that reflect the specific (primary) symptoms best, that is, those subscales with minimal shared variance with the overall general psychological distress dimension. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

QSPR model for bioconcentration factors of nonpolar organic compounds using molecular electronegativity distance vector descriptors.

PubMed

Qin, Li-Tang; Liu, Shu-Shen; Liu, Hai-Ling

2010-02-01

A five-variable model (model M2) was developed for the bioconcentration factors (BCFs) of nonpolar organic compounds (NPOCs) by using molecular electronegativity distance vector (MEDV) to characterize the structures of NPOCs and variable selection and modeling based on prediction (VSMP) to select the optimum descriptors. The estimated correlation coefficient (r (2)) and the leave-one-out cross-validation correlation coefficients (q (2)) of model M2 were 0.9271 and 0.9171, respectively. The model was externally validated by splitting the whole data set into a representative training set of 85 chemicals and a validation set of 29 chemicals. The results show that the main structural factors influencing the BCFs of NPOCs are -cCc, cCcc, -Cl, and -Br (where "-" refers to a single bond and "c" refers to a conjugated bond). The quantitative structure-property relationship (QSPR) model can effectively predict the BCFs of NPOCs, and the predictions of the model can also extend the current BCF database of experimental values.

A two-factor error model for quantitative steganalysis

NASA Astrophysics Data System (ADS)

Böhme, Rainer; Ker, Andrew D.

2006-02-01

Quantitative steganalysis refers to the exercise not only of detecting the presence of hidden stego messages in carrier objects, but also of estimating the secret message length. This problem is well studied, with many detectors proposed but only a sparse analysis of errors in the estimators. A deep understanding of the error model, however, is a fundamental requirement for the assessment and comparison of different detection methods. This paper presents a rationale for a two-factor model for sources of error in quantitative steganalysis, and shows evidence from a dedicated large-scale nested experimental set-up with a total of more than 200 million attacks. Apart from general findings about the distribution functions found in both classes of errors, their respective weight is determined, and implications for statistical hypothesis tests in benchmarking scenarios or regression analyses are demonstrated. The results are based on a rigorous comparison of five different detection methods under many different external conditions, such as size of the carrier, previous JPEG compression, and colour channel selection. We include analyses demonstrating the effects of local variance and cover saturation on the different sources of error, as well as presenting the case for a relative bias model for between-image error.

Optimal experimental design in an epidermal growth factor receptor signalling and down-regulation model.

PubMed

Casey, F P; Baird, D; Feng, Q; Gutenkunst, R N; Waterfall, J J; Myers, C R; Brown, K S; Cerione, R A; Sethna, J P

2007-05-01

We apply the methods of optimal experimental design to a differential equation model for epidermal growth factor receptor signalling, trafficking and down-regulation. The model incorporates the role of a recently discovered protein complex made up of the E3 ubiquitin ligase, Cbl, the guanine exchange factor (GEF), Cool-1 (beta -Pix) and the Rho family G protein Cdc42. The complex has been suggested to be important in disrupting receptor down-regulation. We demonstrate that the model interactions can accurately reproduce the experimental observations, that they can be used to make predictions with accompanying uncertainties, and that we can apply ideas of optimal experimental design to suggest new experiments that reduce the uncertainty on unmeasurable components of the system.

A Multivariate Dynamic Spatial Factor Model for Speciated Pollutants and Adverse Birth Outcomes

DOE PAGES

Kaufeld, Kimberly Ann; Fuentes, Montse; Reich, Brian J.; ...

2017-09-11

Evidence suggests that exposure to elevated concentrations of air pollution during pregnancy is associated with increased risks of birth defects and other adverse birth outcomes. While current regulations put limits on total PM2.5 concentrations, there are many speciated pollutants within this size class that likely have distinct effects on perinatal health. However, due to correlations between these speciated pollutants, it can be difficult to decipher their effects in a model for birth outcomes. To combat this difficulty, we develop a multivariate spatio-temporal Bayesian model for speciated particulate matter using dynamic spatial factors. These spatial factors can then be interpolated tomore » the pregnant women’s homes to be used to model birth defects. The birth defect model allows the impact of pollutants to vary across different weeks of the pregnancy in order to identify susceptible periods. Here, the proposed methodology is illustrated using pollutant monitoring data from the Environmental Protection Agency and birth records from the National Birth Defect Prevention Study.« less

A Multivariate Dynamic Spatial Factor Model for Speciated Pollutants and Adverse Birth Outcomes

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

Kaufeld, Kimberly Ann; Fuentes, Montse; Reich, Brian J.

Evidence suggests that exposure to elevated concentrations of air pollution during pregnancy is associated with increased risks of birth defects and other adverse birth outcomes. While current regulations put limits on total PM2.5 concentrations, there are many speciated pollutants within this size class that likely have distinct effects on perinatal health. However, due to correlations between these speciated pollutants, it can be difficult to decipher their effects in a model for birth outcomes. To combat this difficulty, we develop a multivariate spatio-temporal Bayesian model for speciated particulate matter using dynamic spatial factors. These spatial factors can then be interpolated tomore » the pregnant women’s homes to be used to model birth defects. The birth defect model allows the impact of pollutants to vary across different weeks of the pregnancy in order to identify susceptible periods. Here, the proposed methodology is illustrated using pollutant monitoring data from the Environmental Protection Agency and birth records from the National Birth Defect Prevention Study.« less

Comparison of Cox's Regression Model and Parametric Models in Evaluating the Prognostic Factors for Survival after Liver Transplantation in Shiraz during 2000-2012.

PubMed

Adelian, R; Jamali, J; Zare, N; Ayatollahi, S M T; Pooladfar, G R; Roustaei, N

2015-01-01

Identification of the prognostic factors for survival in patients with liver transplantation is challengeable. Various methods of survival analysis have provided different, sometimes contradictory, results from the same data. To compare Cox's regression model with parametric models for determining the independent factors for predicting adults' and pediatrics' survival after liver transplantation. This study was conducted on 183 pediatric patients and 346 adults underwent liver transplantation in Namazi Hospital, Shiraz, southern Iran. The study population included all patients undergoing liver transplantation from 2000 to 2012. The prognostic factors sex, age, Child class, initial diagnosis of the liver disease, PELD/MELD score, and pre-operative laboratory markers were selected for survival analysis. Among 529 patients, 346 (64.5%) were adult and 183 (34.6%) were pediatric cases. Overall, the lognormal distribution was the best-fitting model for adult and pediatric patients. Age in adults (HR=1.16, p<0.05) and weight (HR=2.68, p<0.01) and Child class B (HR=2.12, p<0.05) in pediatric patients were the most important factors for prediction of survival after liver transplantation. Adult patients younger than the mean age and pediatric patients weighing above the mean and Child class A (compared to those with classes B or C) had better survival. Parametric regression model is a good alternative for the Cox's regression model.

The use of the bi-factor model to test the uni-dimensionality of a battery of reasoning tests.

PubMed

Primi, Ricardo; Rocha da Silva, Marjorie Cristina; Rodrigues, Priscila; Muniz, Monalisa; Almeida, Leandro S

2013-02-01

The Battery of Reasoning Tests 5 (BPR-5) aims to assess the reasoning ability of individuals, using sub-tests with different formats and contents that require basic processes of inductive and deductive reasoning for their resolution. The BPR has three sequential forms: BPR-5i (for children from first to fifth grade), BPR-5 - Form A (for children from sixth to eighth grade) and BPR-5 - form B (for high school and undergraduate students). The present study analysed 412 questionnaires concerning BPR-5i, 603 questionnaires concerning BPR-5 - Form A and 1748 questionnaires concerning BPR-5 - Form B. The main goal was to test the uni-dimensionality of the battery and its tests in relation to items using the bi-factor model. Results suggest that the g factor loadings (extracted by the uni-dimensional model) do not change when the data is adjusted for a more flexible multi-factor model (bi-factor model). A general reasoning factor underlying different contents items is supported.

Examining the latent structure of anxiety sensitivity in adolescents using factor mixture modeling.

PubMed

Allan, Nicholas P; MacPherson, Laura; Young, Kevin C; Lejuez, Carl W; Schmidt, Norman B

2014-09-01

Anxiety sensitivity has been implicated as an important risk factor, generalizable to most anxiety disorders. In adults, factor mixture modeling has been used to demonstrate that anxiety sensitivity is best conceptualized as categorical between individuals. That is, whereas most adults appear to possess normative levels of anxiety sensitivity, a small subset of the population appears to possess abnormally high levels of anxiety sensitivity. Further, those in the high anxiety sensitivity group are at increased risk of having high levels of anxiety and of having an anxiety disorder. This study was designed to determine whether these findings extend to adolescents. Factor mixture modeling was used to examine the best fitting model of anxiety sensitivity in a sample of 277 adolescents (M age = 11.0 years, SD = 0.81). Consistent with research in adults, the best fitting model consisted of 2 classes, 1 containing adolescents with high levels of anxiety sensitivity (n = 25) and another containing adolescents with normative levels of anxiety sensitivity (n = 252). Examination of anxiety sensitivity subscales revealed that the social concerns subscale was not important for classification of individuals. Convergent and discriminant validity of anxiety sensitivity classes were found in that membership in the high anxiety sensitivity class was associated with higher mean levels of anxiety symptoms, controlling for depression and externalizing problems, and was not associated with higher mean levels of depression or externalizing symptoms controlling for anxiety problems. PsycINFO Database Record (c) 2014 APA, all rights reserved.

Examining the Latent Structure of Anxiety Sensitivity in Adolescents using Factor Mixture Modeling

PubMed Central

Allan, Nicholas P.; MacPherson, Laura; Young, Kevin C.; Lejuez, Carl W.; Schmidt, Norman B.

2014-01-01

Anxiety sensitivity has been implicated as an important risk factor, generalizable to most anxiety disorders. In adults, factor mixture modeling has been used to demonstrate that anxiety sensitivity is best conceptualized as categorical between individuals. That is, whereas most adults appear to possess normative levels of anxiety sensitivity, a small subset of the population appears to possess abnormally high levels of anxiety sensitivity. Further, those in the high anxiety sensitivity group are at increased risk of having high levels of anxiety and of having an anxiety disorder. This study was designed to determine whether these findings extend to adolescents. Factor mixture modeling was used to examine the best fitting model of anxiety sensitivity in a sample of 277 adolescents (M age = 11.0, SD = .81). Consistent with research in adults, the best fitting model consisted of two classes, one containing adolescents with high levels of anxiety sensitivity (n = 25), and another containing adolescents with normative levels of anxiety sensitivity (n = 252). Examination of anxiety sensitivity subscales revealed that the social concerns subscale was not important for classification of individuals. Convergent and discriminant validity of anxiety sensitivity classes were found in that membership in the high anxiety sensitivity class was associated with higher mean levels of anxiety symptoms, controlling for depression and externalizing problems, and was not associated with higher mean levels of depression or externalizing symptoms controlling for anxiety problems. PMID:24749756

Modeling silicon diode energy response factors for use in therapeutic photon beams.

PubMed

Eklund, Karin; Ahnesjö, Anders

2009-10-21

Silicon diodes have good spatial resolution, which makes them advantageous over ionization chambers for dosimetry in fields with high dose gradients. However, silicon diodes overrespond to low-energy photons, that are more abundant in scatter which increase with large fields and larger depths. We present a cavity-theory-based model for a general response function for silicon detectors at arbitrary positions within photon fields. The model uses photon and electron spectra calculated from fluence pencil kernels. The incident photons are treated according to their energy through a bipartition of the primary beam photon spectrum into low- and high-energy components. Primary electrons from the high-energy component are treated according to Spencer-Attix cavity theory. Low-energy primary photons together with all scattered photons are treated according to large cavity theory supplemented with an energy-dependent factor K(E) to compensate for energy variations in the electron equilibrium. The depth variation of the response for an unshielded silicon detector has been calculated for 5 x 5 cm(2), 10 x 10 cm(2) and 20 x 20 cm(2) fields in 6 and 15 MV beams and compared with measurements showing that our model calculates response factors with deviations less than 0.6%. An alternative method is also proposed, where we show that one can use a correlation with the scatter factor to determine the detector response of silicon diodes with an error of less than 3% in 6 MV and 15 MV photon beams.

Modeling invasive breast cancer: growth factors propel progression of HER2-positive premalignant lesions

PubMed Central

Pradeep, C-R; Zeisel, A; Köstler, WJ; Lauriola, M; Jacob-Hirsch, J; Haibe-Kains, B; Amariglio, N; Ben-Chetrit, N; Emde, A; Solomonov, I; Neufeld, G; Piccart, M; Sagi, I; Sotiriou, C; Rechavi, G; Domany, E; Desmedt, C; Yarden, Y

2013-01-01

The HER2/neu oncogene encodes a receptor-like tyrosine kinase whose overexpression in breast cancer predicts poor prognosis and resistance to conventional therapies. However, the mechanisms underlying aggressiveness of HER2 (human epidermal growth factor receptor 2)-overexpressing tumors remain incompletely understood. Because it assists epidermal growth factor (EGF) and neuregulin receptors, we overexpressed HER2 in MCF10A mammary cells and applied growth factors. HER2-overexpressing cells grown in extracellular matrix formed filled spheroids, which protruded outgrowths upon growth factor stimulation. Our transcriptome analyses imply a two-hit model for invasive growth: HER2-induced proliferation and evasion from anoikis generate filled structures, which are morphologically and transcriptionally analogous to preinvasive patients’ lesions. In the second hit, EGF escalates signaling and transcriptional responses leading to invasive growth. Consistent with clinical relevance, a gene expression signature based on the HER2/EGF-activated transcriptional program can predict poorer prognosis of a subgroup of HER2-overexpressing patients. In conclusion, the integration of a three-dimensional cellular model and clinical data attributes progression of HER2-overexpressing lesions to EGF-like growth factors acting in the context of the tumor's microenvironment. PMID:22139081

Posttraumatic growth in bereaved parents: A multidimensional model of associated factors.

PubMed

Albuquerque, Sara; Narciso, Isabel; Pereira, Marco

2018-03-01

Although the death of a child is a devastating event, recent evidence shows that personal growth is a relevant outcome of parents' grief. This study aimed to examine the factors associated with posttraumatic growth (PTG) and to propose a multidimensional model consisting of sociodemographic, situational, and intrapersonal and interpersonal factors. A sample (N = 197; 89.8% female; mean age = 39.44 years) of bereaved parents completed the Post-Traumatic Growth Inventory-Short Form, the 14-Item Resilience Scale, the Continuing Bonds Scale, and the Dyadic Coping Inventory. The final model consisted of sociodemographic, situational, intrapersonal, and interpersonal factors of PTG, which accounted for 36.7% of the variance. Higher levels of PTG were generally associated with female sex, younger age of the child, higher levels of resilience, higher levels of internalized continuing bonds (i.e., internal representation of the child, maintaining psychological proximity), and higher levels of stress communication by the partner (communicating the stress experience and requesting emotional or practical support). In clinical practice, health professionals assisting bereaved parents should pay attention to men and parents of older children, who might be at higher risk of difficulties in developing PTG. Additionally, promoting a more internalized bond with the child, resilience and dyadic coping, especially stress communication, can constitute important therapeutic goals. (PsycINFO Database Record (c) 2018 APA, all rights reserved).

The risk factors of laryngeal pathology in Korean adults using a decision tree model.

PubMed

Byeon, Haewon

2015-01-01

The purpose of this study was to identify risk factors affecting laryngeal pathology in the Korean population and to evaluate the derived prediction model. Cross-sectional study. Data were drawn from the 2008 Korea National Health and Nutritional Examination Survey. The subjects were 3135 persons (1508 male and 2114 female) aged 19 years and older living in the community. The independent variables were age, sex, occupation, smoking, alcohol drinking, and self-reported voice problems. A decision tree analysis was done to identify risk factors for predicting a model of laryngeal pathology. The significant risk factors of laryngeal pathology were age, gender, occupation, smoking, and self-reported voice problem in decision tree model. Four significant paths were identified in the decision tree model for the prediction of laryngeal pathology. Those identified as high risk groups for laryngeal pathology included those who self-reported a voice problem, those who were males in their 50s who did not recognize a voice problem, those who were not economically active males in their 40s, and male workers aged 19 and over and under 50 or 60 and over who currently smoked. The results of this study suggest that individual risk factors, such as age, sex, occupation, health behavior, and self-reported voice problem, affect the onset of laryngeal pathology in a complex manner. Based on the results of this study, early management of the high-risk groups is needed for the prevention of laryngeal pathology. Copyright © 2015 The Voice Foundation. Published by Elsevier Inc. All rights reserved.

UTOPIAN: user-driven topic modeling based on interactive nonnegative matrix factorization.

PubMed

Choo, Jaegul; Lee, Changhyun; Reddy, Chandan K; Park, Haesun

2013-12-01

Topic modeling has been widely used for analyzing text document collections. Recently, there have been significant advancements in various topic modeling techniques, particularly in the form of probabilistic graphical modeling. State-of-the-art techniques such as Latent Dirichlet Allocation (LDA) have been successfully applied in visual text analytics. However, most of the widely-used methods based on probabilistic modeling have drawbacks in terms of consistency from multiple runs and empirical convergence. Furthermore, due to the complicatedness in the formulation and the algorithm, LDA cannot easily incorporate various types of user feedback. To tackle this problem, we propose a reliable and flexible visual analytics system for topic modeling called UTOPIAN (User-driven Topic modeling based on Interactive Nonnegative Matrix Factorization). Centered around its semi-supervised formulation, UTOPIAN enables users to interact with the topic modeling method and steer the result in a user-driven manner. We demonstrate the capability of UTOPIAN via several usage scenarios with real-world document corpuses such as InfoVis/VAST paper data set and product review data sets.

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

PubMed

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.

[Psychosocial factors as predictors of atherosclerosis and cardiovascular events: contribution from animal models].

PubMed

Alboni, Paolo; Alboni, Marco

2006-11-01

Conventional risk factors (abnormal lipids, hypertension, etc.) are independent predictors of atherosclerosis and cardiovascular events; however, these factors are not specific since about half patients with acute myocardial infarction paradoxically result at low cardiovascular risk. Recent prospective studies provide convincing evidence that some psychosocial factors are independent predictors of atherosclerosis and cardiovascular events, as well. Psychosocial factors that promote atherosclerosis can be divided into two general categories: chronic stressors, including social isolation/low social support and work stress (subordination without job control) and emotional factors, including affective disorders such as depression, severe anxiety and hostility/anger. The emotional factors, such as the chronic stressors, activate the biological mechanisms of chronic stress: increased activity of the hypothalamic-pituitary-adrenal axis, sympathetic system and inflammation processes, which have atherogenic effects, and an increase in blood coagulation. In spite of the amount of published data, psychosocial factors receive little attention in the medical setting. About 30 years ago, Kuller defined the criteria for a causal relation between a risk factor and atherosclerosis and cardiac events. The first of these criteria states that experimental research should demonstrate that any new factor would increase the extent of atherosclerosis or its complications in suitable animal models. We carried out a bibliographic research in order to investigate whether the results of the studies dealing with animal examination and experimentation support the psychosocial factors as predictors of atherosclerosis. Contributions related to some of the psychosocial factors such as social isolation, subordination and hostility/anger have been found. In these studies atherosclerotic extension has been evaluated at necroscopy; however, the incidence of cardiovascular events has not been

A re-evaluation of finite-element models and stress-intensity factors for surface cracks emanating from stress concentrations

NASA Technical Reports Server (NTRS)

Tan, P. W.; Raju, I. S.; Shivakumar, K. N.; Newman, J. C., Jr.

1988-01-01

A re-evaluation of the 3-D finite-element models and methods used to analyze surface crack at stress concentrations is presented. Previous finite-element models used by Raju and Newman for surface and corner cracks at holes were shown to have ill-shaped elements at the intersection of the hole and crack boundaries. These ill-shaped elements tended to make the model too stiff and, hence, gave lower stress-intensity factors near the hole-crack intersection than models without these elements. Improved models, without these ill-shaped elements, were developed for a surface crack at a circular hole and at a semi-circular edge notch. Stress-intensity factors were calculated by both the nodal-force and virtual-crack-closure methods. Both methods and different models gave essentially the same results. Comparisons made between the previously developed stress-intensity factor equations and the results from the improved models agreed well except for configurations with large notch-radii-to-plate-thickness ratios. Stress-intensity factors for a semi-elliptical surface crack located at the center of a semi-circular edge notch in a plate subjected to remote tensile loadings were calculated using the improved models. The ratio of crack depth to crack length ranged form 0.4 to 2; the ratio of crack depth to plate thickness ranged from 0.2 to 0.8; and the ratio of notch radius to the plate thickness ranged from 1 to 3. The models had about 15,000 degrees-of-freedom. Stress-intensity factors were calculated by using the nodal-force method.

Baryon octet electromagnetic form factors in a confining NJL model

DOE PAGES

Carrillo-Serrano, Manuel E.; Bentz, Wolfgang; Cloet, Ian C.; ...

2016-05-25

Electromagnetic form factors of the baryon octet are studied using a Nambu–Jona-Lasinio model which utilizes the proper-time regularization scheme to simulate aspects of colour confinement. In addition, the model also incorporates corrections to the dressed quarks from vector meson correlations in the t-channel and the pion cloud. Here, comparison with recent chiral extrapolations of lattice QCD results shows a remarkable level of consistency. For the charge radii we find the surprising result that r p E < r Σ+ E and |r n E| < |r Ξ0 E|, whereas the magnetic radii have a pattern largely consistent with a naivemore » expectation based on the dressed quark masses.« less

Using a latent variable model with non-constant factor loadings to examine PM2.5 constituents related to secondary inorganic aerosols.

PubMed

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.

Temperament factors and dimensional, latent bifactor models of child psychopathology: Transdiagnostic and specific associations in two youth samples.

PubMed

Hankin, Benjamin L; Davis, Elysia Poggi; Snyder, Hannah; Young, Jami F; Glynn, Laura M; Sandman, Curt A

2017-06-01

Common emotional and behavioral symptoms co-occur and are associated with core temperament factors. This study investigated links between temperament and dimensional, latent psychopathology factors, including a general common psychopathology factor (p factor) and specific latent internalizing and externalizing liabilities, as captured by a bifactor model, in two independent samples of youth. Specifically, we tested the hypothesis that temperament factors of negative affectivity (NA), positive affectivity (PA), and effortful control (EC) could serve as both transdiagnostic and specific risks in relation to recent bifactor models of child psychopathology. Sample 1 included 571 youth (average age 13.6, SD =2.37, range 9.3-17.5) with both youth and parent report. Sample 2 included 554 preadolescent children (average age 7.7, SD =1.35, range =5-11 years) with parent report. Structural equation modeling showed that the latent bifactor models fit in both samples. Replicated in both samples, the p factor was associated with lower EC and higher NA (transdiagnostic risks). Several specific risks replicated in both samples after controlling for co-occurring symptoms via the p factor: internalizing was associated with higher NA and lower PA, lower EC related to externalizing problems. Copyright © 2017 Elsevier Ireland Ltd. All rights reserved.

Factors associated with adoption of health information technology: a conceptual model based on a systematic review.

PubMed

Kruse, Clemens Scott; DeShazo, Jonathan; Kim, Forest; Fulton, Lawrence

2014-05-23

The Health Information Technology for Economic and Clinical Health Act (HITECH) allocated $19.2 billion to incentivize adoption of the electronic health record (EHR). Since 2009, Meaningful Use Criteria have dominated information technology (IT) strategy. Health care organizations have struggled to meet expectations and avoid penalties to reimbursements from the Center for Medicare and Medicaid Services (CMS). Organizational theories attempt to explain factors that influence organizational change, and many theories address changes in organizational strategy. However, due to the complexities of the health care industry, existing organizational theories fall short of demonstrating association with significant health care IT implementations. There is no organizational theory for health care that identifies, groups, and analyzes both internal and external factors of influence for large health care IT implementations like adoption of the EHR. The purpose of this systematic review is to identify a full-spectrum of both internal organizational and external environmental factors associated with the adoption of health information technology (HIT), specifically the EHR. The result is a conceptual model that is commensurate with the complexity of with the health care sector. We performed a systematic literature search in PubMed (restricted to English), EBSCO Host, and Google Scholar for both empirical studies and theory-based writing from 1993-2013 that demonstrated association between influential factors and three modes of HIT: EHR, electronic medical record (EMR), and computerized provider order entry (CPOE). We also looked at published books on organizational theories. We made notes and noted trends on adoption factors. These factors were grouped as adoption factors associated with various versions of EHR adoption. The resulting conceptual model summarizes the diversity of independent variables (IVs) and dependent variables (DVs) used in articles, editorials, books, as

A HUMAN FACTORS META MODEL FOR U.S. NUCLEAR POWER PLANT CONTROL ROOM MODERNIZATION

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

Joe, Jeffrey C.

Over the last several years, the United States (U.S.) Department of Energy (DOE) has sponsored human factors research and development (R&D) and human factors engineering (HFE) activities through its Light Water Reactor Sustainability (LWRS) program to modernize the main control rooms (MCR) of commercial nuclear power plants (NPP). Idaho National Laboratory (INL), in partnership with numerous commercial nuclear utilities, has conducted some of this R&D to enable the life extension of NPPs (i.e., provide the technical basis for the long-term reliability, productivity, safety, and security of U.S. NPPs). From these activities performed to date, a human factors meta model formore » U.S. NPP control room modernization can now be formulated. This paper discusses this emergent HFE meta model for NPP control room modernization, with the goal of providing an integrated high level roadmap and guidance on how to perform human factors R&D and HFE for those in the U.S. nuclear industry that are engaging in the process of upgrading their MCRs.« less

msCentipede: Modeling Heterogeneity across Genomic Sites and Replicates Improves Accuracy in the Inference of Transcription Factor Binding

PubMed Central

Gilad, Yoav; Pritchard, Jonathan K.; Stephens, Matthew

2015-01-01

Understanding global gene regulation depends critically on accurate annotation of regulatory elements that are functional in a given cell type. CENTIPEDE, a powerful, probabilistic framework for identifying transcription factor binding sites from tissue-specific DNase I cleavage patterns and genomic sequence content, leverages the hypersensitivity of factor-bound chromatin and the information in the DNase I spatial cleavage profile characteristic of each DNA binding protein to accurately infer functional factor binding sites. However, the model for the spatial profile in this framework fails to account for the substantial variation in the DNase I cleavage profiles across different binding sites. Neither does it account for variation in the profiles at the same binding site across multiple replicate DNase I experiments, which are increasingly available. In this work, we introduce new methods, based on multi-scale models for inhomogeneous Poisson processes, to account for such variation in DNase I cleavage patterns both within and across binding sites. These models account for the spatial structure in the heterogeneity in DNase I cleavage patterns for each factor. Using DNase-seq measurements assayed in a lymphoblastoid cell line, we demonstrate the improved performance of this model for several transcription factors by comparing against the Chip-seq peaks for those factors. Finally, we explore the effects of DNase I sequence bias on inference of factor binding using a simple extension to our framework that allows for a more flexible background model. The proposed model can also be easily applied to paired-end ATAC-seq and DNase-seq data. msCentipede, a Python implementation of our algorithm, is available at http://rajanil.github.io/msCentipede. PMID:26406244

msCentipede: Modeling Heterogeneity across Genomic Sites and Replicates Improves Accuracy in the Inference of Transcription Factor Binding.

PubMed

Raj, Anil; Shim, Heejung; Gilad, Yoav; Pritchard, Jonathan K; Stephens, Matthew

2015-01-01

Understanding global gene regulation depends critically on accurate annotation of regulatory elements that are functional in a given cell type. CENTIPEDE, a powerful, probabilistic framework for identifying transcription factor binding sites from tissue-specific DNase I cleavage patterns and genomic sequence content, leverages the hypersensitivity of factor-bound chromatin and the information in the DNase I spatial cleavage profile characteristic of each DNA binding protein to accurately infer functional factor binding sites. However, the model for the spatial profile in this framework fails to account for the substantial variation in the DNase I cleavage profiles across different binding sites. Neither does it account for variation in the profiles at the same binding site across multiple replicate DNase I experiments, which are increasingly available. In this work, we introduce new methods, based on multi-scale models for inhomogeneous Poisson processes, to account for such variation in DNase I cleavage patterns both within and across binding sites. These models account for the spatial structure in the heterogeneity in DNase I cleavage patterns for each factor. Using DNase-seq measurements assayed in a lymphoblastoid cell line, we demonstrate the improved performance of this model for several transcription factors by comparing against the Chip-seq peaks for those factors. Finally, we explore the effects of DNase I sequence bias on inference of factor binding using a simple extension to our framework that allows for a more flexible background model. The proposed model can also be easily applied to paired-end ATAC-seq and DNase-seq data. msCentipede, a Python implementation of our algorithm, is available at http://rajanil.github.io/msCentipede.

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

PubMed

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 < 0.001), thus establishing that MLM data analysis was necessary. Site-level had effects on PD and CAL reduction; those variables could explain 77-78% of PD reduction and 70-80% of CAL reduction at 3, 6, and 12 months. Other levels only

Utilizing interview and self-report assessment of the Five-Factor Model to examine convergence with the alternative model for personality disorders.

PubMed

Helle, Ashley C; Trull, Timothy J; Widiger, Thomas A; Mullins-Sweatt, Stephanie N

2017-07-01

An alternative model for personality disorders is included in Section III (Emerging Models and Measures) of Diagnostic and Statistical Manual of Mental Disorders, (5th ed.; DSM-5). The DSM-5 dimensional trait model is an extension of the Five-Factor Model (FFM; American Psychiatric Association, 2013). The Personality Inventory for DSM-5 (PID-5) assesses the 5 domains and 25 traits in the alternative model. The current study expands on recent research to examine the relationship of the PID-5 with an interview measure of the FFM. The Structured Interview for the Five Factor Model of Personality (SIFFM) assesses the 5 bipolar domains and 30 facets of the FFM. Research has indicated that the SIFFM captures maladaptive aspects of personality (as well as adaptive). The SIFFM, NEO PI-R, and PID-5 were administered to participants to examine their respective convergent and discriminant validity. Results provide evidence for the convergence of the 2 models using self-report and interview measures of the FFM. Clinical implications and future directions are discussed, particularly a call for the development of a structured interview for the assessment of the DSM-5 dimensional trait model. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

A protective factors model for alcohol abuse and suicide prevention among Alaska Native youth.

PubMed

Allen, James; Mohatt, Gerald V; Fok, Carlotta Ching Ting; Henry, David; Burkett, Rebekah

2014-09-01

This study provides an empirical test of a culturally grounded theoretical model for prevention of alcohol abuse and suicide risk with Alaska Native youth, using a promising set of culturally appropriate measures for the study of the process of change and outcome. This model is derived from qualitative work that generated an heuristic model of protective factors from alcohol (Allen et al. in J Prev Interv Commun 32:41-59, 2006; Mohatt et al. in Am J Commun Psychol 33:263-273, 2004a; Harm Reduct 1, 2004b). Participants included 413 rural Alaska Native youth ages 12-18 who assisted in testing a predictive model of Reasons for Life and Reflective Processes about alcohol abuse consequences as co-occurring outcomes. Specific individual, family, peer, and community level protective factor variables predicted these outcomes. Results suggest prominent roles for these predictor variables as intermediate prevention strategy target variables in a theoretical model for a multilevel intervention. The model guides understanding of underlying change processes in an intervention to increase the ultimate outcome variables of Reasons for Life and Reflective Processes regarding the consequences of alcohol abuse.

Critical Success Factors for E-Learning Acceptance: Confirmatory Factor Models

ERIC Educational Resources Information Center

Selim, Hassan M.

2007-01-01

E-learning, one of the tools emerged from information technology, has been integrated in many university programs. There are several factors that need to be considered while developing or implementing university curriculums that offer e-learning based courses. This paper is intended to specify e-learning critical success factors (CSFs) as…

Biomechanical factors associated with mandibular cantilevers: analysis with three-dimensional finite element models.

PubMed

Gonda, Tomoya; Yasuda, Daiisa; Ikebe, Kazunori; Maeda, Yoshinobu

2014-01-01

Although the risks of using a cantilever to treat missing teeth have been described, the mechanisms remain unclear. This study aimed to reveal these mechanisms from a biomechanical perspective. The effects of various implant sites, number of implants, and superstructural connections on stress distribution in the marginal bone were analyzed with three-dimensional finite element models based on mandibular computed tomography data. Forces from the masseter, temporalis, and internal pterygoid were applied as vectors. Two three-dimensional finite element models were created with the edentulous mandible showing severe and relatively modest residual ridge resorption. Cantilevers of the premolar and molar were simulated in the superstructures in the models. The following conditions were also included as factors in the models to investigate changes: poor bone quality, shortened dental arch, posterior occlusion, lateral occlusion, double force of the masseter, and short implant. Multiple linear regression analysis with a forced-entry method was performed with stress values as the objective variable and the factors as the explanatory variable. When bone mass was high, stress around the implant caused by differences in implantation sites was reduced. When bone mass was low, the presence of a cantilever was a possible risk factor. The stress around the implant increased significantly if bone quality was poor or if increased force (eg, bruxism) was applied. The addition of a cantilever to the superstructure increased stress around implants. When large muscle forces were applied to a superstructure with cantilevers or if bone quality was poor, stress around the implants increased.

Testing for Two-Way Interactions in the Multigroup Common Factor Model

ERIC Educational Resources Information Center

van Smeden, Maarten; Hessen, David J.

2013-01-01

In this article, a 2-way multigroup common factor model (MG-CFM) is presented. The MG-CFM can be used to estimate interaction effects between 2 grouping variables on 1 or more hypothesized latent variables. For testing the significance of such interactions, a likelihood ratio test is presented. In a simulation study, the robustness of the…

Psychometric Properties of an Abbreviated Instrument of the Five-Factor Model

ERIC Educational Resources Information Center

Mullins-Sweatt, Stephanie N.; Jamerson, Janetta E.; Samuel, Douglas B.; Olson, David R.; Widiger, Thomas A.

2006-01-01

Brief measures of the five-factor model (FFM) have been developed but none include an assessment of facets within each domain. The purpose of this study was to examine the validity of a simple, one-page, facet-level description of the FFM. Five data collections were completed to assess the reliability and the convergent and discriminant validity…

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

PubMed

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.

Phenylalanine ab initio models for the simulation of skin natural moisturizing factor

NASA Astrophysics Data System (ADS)

Carvalho, B. G.; Raniero, L. J.; Martin, A. A.; Favero, P. P.

2013-04-01

In this study, we evaluated models that can be used to simulate amino acids in biological environments via density functional theory (DFT). The goal was to obtain realistic representations that combine computational economy and result quality when compared to experimental data. We increased the complexity of the models by using a model of an amino acid in a vacuum, followed by a water-solvated amino acid model. To consider pH variation, we simulated zwitterionic and nonionic amino acid configurations. The amino acid chosen for testing was phenylalanine, an aromatic amino acid present in high concentrations in the natural moisturizing factor of skin that plays a fundamental role in ultraviolet protection and vitiligo disease. To validate the models, vibrational modes and electronic properties were calculated and compared to experimental results.

Assessing a five factor model of PTSD: is dysphoric arousal a unique PTSD construct showing differential relationships with anxiety and depression?

PubMed

Armour, Cherie; Elhai, Jon D; Richardson, Don; Ractliffe, Kendra; Wang, Li; Elklit, Ask

2012-03-01

Posttraumatic stress disorder's (PTSD) latent structure has been widely debated. To date, two four-factor models (Numbing and Dysphoria) have received the majority of factor analytic support. Recently, Elhai et al. (2011) proposed and supported a revised (five-factor) Dysphoric Arousal model. Data were gathered from two separate samples; War veterans and Primary Care medical patients. The three models were compared and the resultant factors of the Dysphoric Arousal model were validated against external constructs of depression and anxiety. The Dysphoric Arousal model provided significantly better fit than the Numbing and Dysphoria models across both samples. When differentiating between factors, the current results support the idea that Dysphoric Arousal can be differentiated from Anxious Arousal but not from Emotional Numbing when correlated with depression. In conclusion, the Dysphoria model may be a more parsimonious representation of PTSD's latent structure in these trauma populations despite superior fit of the Dysphoric Arousal model. Copyright © 2011 Elsevier Ltd. All rights reserved.

The two-factor model of psychopathic personality: evidence from the psychopathic personality inventory.

PubMed

Marcus, David K; Fulton, Jessica J; Edens, John F

2013-01-01

Psychopathy or psychopathic personality disorder represents a constellation of traits characterized by superficial charm, egocentricity, irresponsibility, fearlessness, persistent violation of social norms, and a lack of empathy, guilt, and remorse. Factor analyses of the Psychopathic Personality Inventory (PPI)typically yield two factors: Fearless Dominance (FD) and Self-Centered Impulsivity (SCI). Additionally, the Coldheartedness (CH) subscale typically does not load on either factor. The current paper includes a meta-analysis of studies that have examined theoretically important correlates of the two PPI factors and CH. Results suggest that (a) FD and SCI are orthogonal or weakly correlated, (b) each factor predicts distinct (and sometimes opposite) correlates, and (c) the FD factor is not highly correlated with most other measures of psychopathy. This pattern of results raises important questions about the relation between FD and SCI and the role of FD in conceptualizations of psychopathy. Our findings also indicate the need for future studies using the two-factor model of the PPI to conduct moderational analyses to examine potential interactions between FD and SCI in the prediction of important criterion measures.

Factors affecting regional per-capita carbon emissions in China based on an LMDI factor decomposition model.

PubMed

Dong, Feng; Long, Ruyin; Chen, Hong; Li, Xiaohui; Yang, Qingliang

2013-01-01

China is considered to be the main carbon producer in the world. The per-capita carbon emissions indicator is an important measure of the regional carbon emissions situation. This study used the LMDI factor decomposition model-panel co-integration test two-step method to analyze the factors that affect per-capita carbon emissions. The main results are as follows. (1) During 1997, Eastern China, Central China, and Western China ranked first, second, and third in the per-capita carbon emissions, while in 2009 the pecking order changed to Eastern China, Western China, and Central China. (2) According to the LMDI decomposition results, the key driver boosting the per-capita carbon emissions in the three economic regions of China between 1997 and 2009 was economic development, and the energy efficiency was much greater than the energy structure after considering their effect on restraining increased per-capita carbon emissions. (3) Based on the decomposition, the factors that affected per-capita carbon emissions in the panel co-integration test showed that Central China had the best energy structure elasticity in its regional per-capita carbon emissions. Thus, Central China was ranked first for energy efficiency elasticity, while Western China was ranked first for economic development elasticity.

Three-factor models versus time series models: quantifying time-dependencies of interactions between stimuli in cell biology and psychobiology for short longitudinal data.

PubMed

Frank, Till D; Kiyatkin, Anatoly; Cheong, Alex; Kholodenko, Boris N

2017-06-01

Signal integration determines cell fate on the cellular level, affects cognitive processes and affective responses on the behavioural level, and is likely to be involved in psychoneurobiological processes underlying mood disorders. Interactions between stimuli may subjected to time effects. Time-dependencies of interactions between stimuli typically lead to complex cell responses and complex responses on the behavioural level. We show that both three-factor models and time series models can be used to uncover such time-dependencies. However, we argue that for short longitudinal data the three factor modelling approach is more suitable. In order to illustrate both approaches, we re-analysed previously published short longitudinal data sets. We found that in human embryonic kidney 293 cells cells the interaction effect in the regulation of extracellular signal-regulated kinase (ERK) 1 signalling activation by insulin and epidermal growth factor is subjected to a time effect and dramatically decays at peak values of ERK activation. In contrast, we found that the interaction effect induced by hypoxia and tumour necrosis factor-alpha for the transcriptional activity of the human cyclo-oxygenase-2 promoter in HEK293 cells is time invariant at least in the first 12-h time window after stimulation. Furthermore, we applied the three-factor model to previously reported animal studies. In these studies, memory storage was found to be subjected to an interaction effect of the beta-adrenoceptor agonist clenbuterol and certain antagonists acting on the alpha-1-adrenoceptor / glucocorticoid-receptor system. Our model-based analysis suggests that only if the antagonist drug is administer in a critical time window, then the interaction effect is relevant. © The authors 2016. Published by Oxford University Press on behalf of the Institute of Mathematics and its Applications. All rights reserved.

Linear and nonlinear models for predicting fish bioconcentration factors for pesticides.

PubMed

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.

Exploring the Different Trajectories of Analytical Thinking Ability Factors: An Application of the Second-Order Growth Curve Factor Model

ERIC Educational Resources Information Center

Saengprom, Narumon; Erawan, Waraporn; Damrongpanit, Suntonrapot; Sakulku, Jaruwan

2015-01-01

The purposes of this study were 1) Compare analytical thinking ability by testing the same sets of students 5 times 2) Develop and verify whether analytical thinking ability of students corresponds to second-order growth curve factors model. Samples were 1,093 eighth-grade students. The results revealed that 1) Analytical thinking ability scores…

A GRAPHICAL DIAGNOSTIC METHOD FOR ASSESSING THE ROTATION IN FACTOR ANALYTICAL MODELS OF ATMOSPHERIC POLLUTION. (R831078)

EPA Science Inventory

Factor analytic tools such as principal component analysis (PCA) and positive matrix factorization (PMF), suffer from rotational ambiguity in the results: different solutions (factors) provide equally good fits to the measured data. The PMF model imposes non-negativity of both...

Factors Affecting Career Decision-Making: Further Validation of the O'Neil Career-Sex Role Model and the Career Factor Checklist.

ERIC Educational Resources Information Center

Meinecke, Christine; O'Neil, James M.

Many correlates of vocational choice have been suggested by career development theorists. A career decision-making model developed by O'Neil, Meeker, and Borgers suggests six factors (individual, societal, familial, socioeconomic, situational, psychosocial-emotional) that affect both sex role socialization and career decision-making. The validity…

How Do Various Maize Crop Models Vary in Their Responses to Climate Change Factors?

NASA Technical Reports Server (NTRS)

Bassu, Simona; Brisson, Nadine; Grassini, Patricio; Durand, Jean-Louis; Boote, Kenneth; Lizaso, Jon; Jones, James W.; Rosenzweig, Cynthia; Ruane, Alex C.; Adam, Myriam;

How do various maize crop models vary in their responses to climate change factors?

PubMed

Bassu, Simona; Brisson, Nadine; Durand, Jean-Louis; Boote, Kenneth; Lizaso, Jon; Jones, James W; Rosenzweig, Cynthia; Ruane, Alex C; Adam, Myriam; Baron, Christian; Basso, Bruno; Biernath, Christian; Boogaard, Hendrik; Conijn, Sjaak; Corbeels, Marc; Deryng, Delphine; De Sanctis, Giacomo; Gayler, Sebastian; Grassini, Patricio; Hatfield, Jerry; Hoek, Steven; Izaurralde, Cesar; Jongschaap, Raymond; Kemanian, Armen R; Kersebaum, K Christian; Kim, Soo-Hyung; Kumar, Naresh S; Makowski, David; Müller, Christoph; Nendel, Claas; Priesack, Eckart; Pravia, Maria Virginia; Sau, Federico; Shcherbak, Iurii; Tao, Fulu; Teixeira, Edmar; Timlin, Dennis; Waha, Katharina

2014-07-01

Potential consequences of climate change on crop production can be studied using mechanistic crop simulation models. While a broad variety of maize simulation models exist, it is not known whether different models diverge on grain yield responses to changes in climatic factors, or whether they agree in their general trends related to phenology, growth, and yield. With the goal of analyzing the sensitivity of simulated yields to changes in temperature and atmospheric carbon dioxide concentrations [CO2 ], we present the largest maize crop model intercomparison to date, including 23 different models. These models were evaluated for four locations representing a wide range of maize production conditions in the world: Lusignan (France), Ames (USA), Rio Verde (Brazil) and Morogoro (Tanzania). While individual models differed considerably in absolute yield simulation at the four sites, an ensemble of a minimum number of models was able to simulate absolute yields accurately at the four sites even with low data for calibration, thus suggesting that using an ensemble of models has merit. Temperature increase had strong negative influence on modeled yield response of roughly -0.5 Mg ha(-1) per °C. Doubling [CO2 ] from 360 to 720 μmol mol(-1) increased grain yield by 7.5% on average across models and the sites. That would therefore make temperature the main factor altering maize yields at the end of this century. Furthermore, there was a large uncertainty in the yield response to [CO2 ] among models. Model responses to temperature and [CO2 ] did not differ whether models were simulated with low calibration information or, simulated with high level of calibration information. © 2014 John Wiley & Sons Ltd.

The SAM framework: modeling the effects of management factors on human behavior in risk analysis.

PubMed

Murphy, D M; Paté-Cornell, M E

1996-08-01

Complex engineered systems, such as nuclear reactors and chemical plants, have the potential for catastrophic failure with disastrous consequences. In recent years, human and management factors have been recognized as frequent root causes of major failures in such systems. However, classical probabilistic risk analysis (PRA) techniques do not account for the underlying causes of these errors because they focus on the physical system and do not explicitly address the link between components' performance and organizational factors. This paper describes a general approach for addressing the human and management causes of system failure, called the SAM (System-Action-Management) framework. Beginning with a quantitative risk model of the physical system, SAM expands the scope of analysis to incorporate first the decisions and actions of individuals that affect the physical system. SAM then links management factors (incentives, training, policies and procedures, selection criteria, etc.) to those decisions and actions. The focus of this paper is on four quantitative models of action that describe this last relationship. These models address the formation of intentions for action and their execution as a function of the organizational environment. Intention formation is described by three alternative models: a rational model, a bounded rationality model, and a rule-based model. The execution of intentions is then modeled separately. These four models are designed to assess the probabilities of individual actions from the perspective of management, thus reflecting the uncertainties inherent to human behavior. The SAM framework is illustrated for a hypothetical case of hazardous materials transportation. This framework can be used as a tool to increase the safety and reliability of complex technical systems by modifying the organization, rather than, or in addition to, re-designing the physical system.

Using structured additive regression models to estimate risk factors of malaria: analysis of 2010 Malawi malaria indicator survey data.

PubMed

Chirombo, James; Lowe, Rachel; Kazembe, Lawrence

2014-01-01

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. 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. 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. 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.

The five-factor model of impulsivity-like traits and emotional lability in aggressive behavior.

PubMed

Dvorak, Robert D; Pearson, Matthew R; Kuvaas, Nicholas J

2013-01-01

Factors that increase automatic psychological processes may result in impulsive action and, consequently, aggressive behavior. The current cross-sectional study examined the association between the five-factor model of impulsivity-like traits (negative urgency, positive urgency, premeditation, perseverance, and sensation seeking), emotional lability, and physically aggressive behaviors among college students (n = 481) in a negative binomial hurdle model. In the logistic portion of the model, emotional lability was related to a higher likelihood of engaging in aggressive acts in the past 6 months. The association between emotional lability and the likelihood of aggressive behavior was moderated by two impulsivity-like traits: negative urgency and positive urgency. Specifically, emotional lability was related to engaging in aggressive acts among those with high negative urgency, and among those with low positive urgency. In the count portion of the model, emotional lability was uniquely related to the number of aggressive acts in the past 6 months. Our results indicate that emotional lability and facets of impulsivity interactively relate to engagement in aggressive behavior, suggesting that these variables be integrated into models of aggression. © 2013 Wiley Periodicals, Inc.

Replica Analysis for Portfolio Optimization with Single-Factor Model

NASA Astrophysics Data System (ADS)

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.

Assessing factors related to waist circumference and obesity: application of a latent variable model.

PubMed

Dalvand, Sahar; Koohpayehzadeh, Jalil; Karimlou, Masoud; Asgari, Fereshteh; Rafei, Ali; Seifi, Behjat; Niksima, Seyed Hassan; Bakhshi, Enayatollah

2015-01-01

Because the use of BMI (Body Mass Index) alone as a measure of adiposity has been criticized, in the present study our aim was to fit a latent variable model to simultaneously examine the factors that affect waist circumference (continuous outcome) and obesity (binary outcome) among Iranian adults. Data included 18,990 Iranian individuals aged 20-65 years that are derived from the third National Survey of Noncommunicable Diseases Risk Factors in Iran. Using latent variable model, we estimated the relation of two correlated responses (waist circumference and obesity) with independent variables including age, gender, PR (Place of Residence), PA (physical activity), smoking status, SBP (Systolic Blood Pressure), DBP (Diastolic Blood Pressure), CHOL (cholesterol), FBG (Fasting Blood Glucose), diabetes, and FHD (family history of diabetes). All variables were related to both obesity and waist circumference (WC). Older age, female sex, being an urban resident, physical inactivity, nonsmoking, hypertension, hypercholesterolemia, hyperglycemia, diabetes, and having family history of diabetes were significant risk factors that increased WC and obesity. Findings from this study of Iranian adult settings offer more insights into factors associated with high WC and high prevalence of obesity in this population.

Regression-Based Norms for a Bi-factor Model for Scoring the Brief Test of Adult Cognition by Telephone (BTACT).

PubMed

Gurnani, Ashita S; John, Samantha E; Gavett, Brandon E

2015-05-01

The current study developed regression-based normative adjustments for a bi-factor model of the The Brief Test of Adult Cognition by Telephone (BTACT). Archival data from the Midlife Development in the United States-II Cognitive Project were used to develop eight separate linear regression models that predicted bi-factor BTACT scores, accounting for age, education, gender, and occupation-alone and in various combinations. All regression models provided statistically significant fit to the data. A three-predictor regression model fit best and accounted for 32.8% of the variance in the global bi-factor BTACT score. The fit of the regression models was not improved by gender. Eight different regression models are presented to allow the user flexibility in applying demographic corrections to the bi-factor BTACT scores. Occupation corrections, while not widely used, may provide useful demographic adjustments for adult populations or for those individuals who have attained an occupational status not commensurate with expected educational attainment. © The Author 2015. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Identifying the environmental factors that effect within canopy BVOC loss using a multilevel canopy model

NASA Astrophysics Data System (ADS)

Chan, W. S.; Fuentes, J. D.; Lerdau, M.

2010-12-01

This presentation will provide research findings to evaluate the hypothesis that the loss of biogenic volatile organic compound (BVOC) within plant canopies is dynamic and depends on factors such as plant canopy architecture (height and leaf area distribution), atmospheric turbulence, concentration of oxidants (OH, O3, NO3), and the reactivity of BVOC species. Results will be presented from a new one dimensional, multilevel canopy model that couples algorithms for canopy microclimate, leaf physiology, BVOC emission, turbulent transport, and atmospheric chemistry to investigate the relative importance of factors that impact BVOC loss within a forest canopy. Model sensitivity tests will be presented and discussed to identify factors driving canopy loss. Results show isoprene and monoterpene canopy losses as high as 9 and 18%, respectively, for tall canopies during the daytime. We hypothesize that canopy height and wind speed (i.e. canopy residence time) may be the most important in dictating within-canopy loss. This work will reduce the error in bottom-up flux estimates of BVOCs and ultimately improve parameterizations of BVOC sources in air quality models by accounting for within canopy processes.

The Five-Factor Model Personality Assessment for Improved Student Design Team Performance

ERIC Educational Resources Information Center

Ogot, Madara; Okudan, Gul E.

2006-01-01

Researchers have long noted the correlation of various personality traits and team performance. Studies relating aggregate team personality traits to team performance are scattered in the literature and may not always be relevant to engineering design teams. This paper synthesizes the results from applicable Five-Factor Model (FFM)-based…

Multilevel Factor Analysis by Model Segregation: New Applications for Robust Test Statistics

ERIC Educational Resources Information Center

Schweig, Jonathan

2014-01-01

Measures of classroom environments have become central to policy efforts that assess school and teacher quality. This has sparked a wide interest in using multilevel factor analysis to test measurement hypotheses about classroom-level variables. One approach partitions the total covariance matrix and tests models separately on the…

Human factors model concerning the man-machine interface of mining crewstations

NASA Technical Reports Server (NTRS)

Rider, James P.; Unger, Richard L.

1989-01-01

The U.S. Bureau of Mines is developing a computer model to analyze the human factors aspect of mining machine operator compartments. The model will be used as a research tool and as a design aid. It will have the capability to perform the following: simulated anthropometric or reach assessment, visibility analysis, illumination analysis, structural analysis of the protective canopy, operator fatigue analysis, and computation of an ingress-egress rating. The model will make extensive use of graphics to simplify data input and output. Two dimensional orthographic projections of the machine and its operator compartment are digitized and the data rebuilt into a three dimensional representation of the mining machine. Anthropometric data from either an individual or any size population may be used. The model is intended for use by equipment manufacturers and mining companies during initial design work on new machines. In addition to its use in machine design, the model should prove helpful as an accident investigation tool and for determining the effects of machine modifications made in the field on the critical areas of visibility and control reach ability.

Modeling structural, dyadic, and individual factors: the inclusion and exclusion model of HIV related behavior.

PubMed

Albarracin, Dolores; Tannenbaum, Melanie B; Glasman, Laura R; Rothman, Alexander J

2010-12-01

Changing HIV-related behaviors requires addressing the individual, dyadic, and structural influences that shape them. This supplement of AIDS & Behavior presents frameworks that integrate these three influences on behavior. Concepts from these frameworks were selected to model the processes by which structural factors affect individual HIV-related behavior. In the Inclusion/Exclusion Model, material and symbolic inclusions and exclusions (sharing versus denying resources) regulate individuals' ability and motivation to detect, prevent, and treat HIV. Structural interventions create inclusions that increase one's ability or motivation to perform these behaviors or exclusions that hinder one's ability or motivation to execute counterproductive behaviors. The need to expand research regarding multilevel influences on HIV-related behavior is also discussed, particularly concerning further understanding of sustained behavior change and effective dissemination of evidence-based intervention strategies.

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

PubMed

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 < 0.05). Path analysis of occupational injuries based on the SEM reveals that individual and organizational factors and their indicator variables are very influential on the severity of traumatic occupational injuries. So, these should be considered to reduce occupational accidents' severity in large construction industries.

Suicide risk factors for young adults: testing a model across ethnicities.

PubMed

Gutierrez, P M; Rodriguez, P J; Garcia, P

2001-06-01

A general path model based on existing suicide risk research was developed to test factors contributing to current suicidal ideation in young adults. A sample of 673 undergraduate students completed a packet of questionnaires containing the Beck Depression Inventory, Adult Suicidal Ideation Questionnaire, and Multi-Attitude Suicide Tendency Scale. They also provided information on history of suicidality and exposure to attempted and completed suicide in others. Structural equation modeling was used to test the fit of the data to the hypothesized model. Goodness-of-fit indices were adequate and supported the interactive effects of exposure, repulsion by life, depression, and history of self-harm on current ideation. Model fit for three subgroups based on race/ethnicity (i.e., White, Black, and Hispanic) determined that repulsion by life and depression function differently across groups. Implications of these findings for current methods of suicide risk assessment and future research are discussed in the context of the importance of culture.

Evaluation of factors important in modeling plasma concentrations of tetracycline hydrochloride administered in water in swine.

PubMed

Mason, Sharon E; Almond, Glen W; Riviere, Jim E; Baynes, Ronald E

2012-10-01

To model the plasma tetracycline concentrations in swine (Sus scrofa domestica) treated with medication administered in water and determine the factors that contribute to the most accurate predictions of measured plasma drug concentrations. Plasma tetracycline concentrations measured in blood samples from 3 populations of swine. Data from previous studies provided plasma tetracycline concentrations that were measured in blood samples collected from 1 swine population at 0, 4, 8, 12, 24, 32, 48, 56, 72, 80, 96, and 104 hours and from 2 swine populations at 0, 12, 24, 48, and 72 hours hours during administration of tetracycline hydrochloride dissolved in water. A 1-compartment pharmacostatistical model was used to analyze 5 potential covariate schemes and determine factors most important in predicting the plasma concentrations of tetracycline in swine. 2 models most accurately predicted the tetracycline plasma concentrations in the 3 populations of swine. Factors of importance were body weight or age of pig, ambient temperature, concentration of tetracycline in water, and water use per unit of time. The factors found to be of importance, combined with knowledge of the individual pharmacokinetic and chemical properties of medications currently approved for administration in water, may be useful in more prudent administration of approved medications administered to swine. Factors found to be important in pharmacostatistical models may allow prediction of plasma concentrations of tetracycline or other commonly used medications administered in water. The ability to predict in vivo concentrations of medication in a population of food animals can be combined with bacterial minimum inhibitory concentrations to decrease the risk of developing antimicrobial resistance.

A Two-Factor Model of Relapse/Recurrence Vulnerability in Unipolar Depression

PubMed Central

Farb, Norman A. S.; Irving, Julie A.; Anderson, Adam K.; Segal, Zindel V.

2015-01-01

The substantial health burden associated with Major Depressive Disorder is a product of both its high prevalence and the significant risk of relapse, recurrence and chronicity. Establishing recurrence vulnerability factors (VFs) could improve the long-term management of MDD by identifying the need for further intervention in seemingly recovered patients. We present a model of sensitization in depression vulnerability, with an emphasis on the integration of behavioral and neural systems accounts. Evidence suggests that VFs fall into two categories: dysphoric attention and dysphoric elaboration. Dysphoric attention is driven by fixation on negative life events, and is characterized behaviorally by reduced executive control, and neurally by elevated activity in the brain’s salience network. Dysphoric elaboration is driven by rumination that promotes over-general self and contextual appraisals, and is characterized behaviorally by dysfunctional attitudes, and neurally by elevated connectivity within normally-distinct prefrontal brain networks. While, at present, few prospective VF studies exist from which to catalogue a definitive neurobehavioral account, extant data support the value of the proposed two-factor model. Measuring the continued presence of these two VFs during recovery may more accurately identify remitted patients who would benefit from targeted prophylactic intervention. PMID:25688431

Maximizing the Information and Validity of a Linear Composite in the Factor Analysis Model for Continuous Item Responses

ERIC Educational Resources Information Center

Ferrando, Pere J.

2008-01-01

This paper develops results and procedures for obtaining linear composites of factor scores that maximize: (a) test information, and (b) validity with respect to external variables in the multiple factor analysis (FA) model. I treat FA as a multidimensional item response theory model, and use Ackerman's multidimensional information approach based…

Taste phenotype associates with cardiovascular disease risk factors via diet quality in multivariate modeling.

PubMed

Sharafi, Mastaneh; Rawal, Shristi; Fernandez, Maria Luz; Huedo-Medina, Tania B; Duffy, Valerie B

2018-05-08

Sensations from foods and beverages drive dietary choices, which in turn, affect risk of diet-related diseases. Perception of these sensation varies with environmental and genetic influences. This observational study aimed to examine associations between chemosensory phenotype, diet and cardiovascular disease (CVD) risk. Reportedly healthy women (n = 110, average age 45 ± 9 years) participated in laboratory-based measures of chemosensory phenotype (taste and smell function, propylthiouracil (PROP) bitterness) and CVD risk factors (waist circumference, blood pressure, serum lipids). Diet variables included preference and intake of sweet/high-fat foods, dietary restraint, and diet quality based on reported preference (Healthy Eating Preference Index-HEPI) and intake (Healthy Eating Index-HEI). We found that females who reported high preference yet low consumption of sweet/high-fat foods had the highest dietary restraint and depressed quinine taste function. PROP nontasters were more likely to report lower diet quality; PROP supertasters more likely to consume but not like a healthy diet. Multivariate structural models were fitted to identify predictors of CVD risk factors. Reliable latent taste (quinine taste function, PROP tasting) and smell (odor intensity) variables were identified, with taste explaining more variance in the CVD risk factors. Lower bitter taste perception was associated with elevated risk. In multivariate models, the HEPI completely mediated the taste-adiposity and taste-HDL associations and partially mediated the taste-triglyceride or taste-systolic blood pressure associations. The taste-LDL pathway was significant and direct. The HEI could not replace HEPI in adequate models. However, using a latent diet quality variable with HEPI and HEI, increased the strength of association between diet quality and adiposity or CVD risk factors. In conclusion, bitter taste phenotype was associated with CVD risk factors via diet quality

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

NASA Astrophysics Data System (ADS)

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.

A path analysis model of factors influencing children's requests for unhealthy foods.

PubMed

Pettigrew, Simone; Jongenelis, Michelle; Miller, Caroline; Chapman, Kathy

2017-01-01

Little is known about the complex combination of factors influencing the extent to which children request unhealthy foods from their parents. The aim of this study was to develop a comprehensive model of influencing factors to provide insight into potential methods of reducing these requests. A web panel provider was used to administer a national online survey to a sample of 1302 Australian parent-child dyads (total sample n=2604). Initial univariate analyses identified potential predictors of children's requests for and consumption of unhealthy foods. The identified variables were subsequently incorporated into a path analysis model that included both parents' and children's reports of children's requests for unhealthy foods. The resulting model accounted for a substantial 31% of the variance in parent-reported food request frequency and 27% of the variance in child-reported request frequency. The variable demonstrating the strongest direct association with both parents' and children's reports of request frequency was the frequency of children's current intake of unhealthy foods. Parents' and children's exposure to food advertising and television viewing time were also positively associated with children's unhealthy food requests. The results highlight the need to break the habitual provision of unhealthy foods to avoid a vicious cycle of requests resulting in consumption. Copyright © 2016 Elsevier Ltd. All rights reserved.

Tree-based modeling of complex interactions of phosphorus loadings and environmental factors.

PubMed

Grunwald, S; Daroub, S H; Lang, T A; Diaz, O A

2009-06-01

Phosphorus (P) enrichment has been observed in the historic oligotrophic Greater Everglades in Florida mainly due to P influx from upstream, agriculturally dominated, low relief drainage basins of the Everglades Agricultural Area (EAA). Our specific objectives were to: (1) investigate relationships between various environmental factors and P loads in 10 farm basins within the EAA, (2) identify those environmental factors that impart major effects on P loads using three different tree-based modeling approaches, and (3) evaluate predictive models to assess P loads. We assembled thirteen environmental variable sets for all 10 sub-basins characterizing water level management, cropping practices, soils, hydrology, and farm-specific properties. Drainage flow and P concentrations were measured at each sub-basin outlet from 1992-2002 and aggregated to derive monthly P loads. We used three different tree-based models including single regression trees (ST), committee trees in Bagging (CTb) and ARCing (CTa) modes and ten-fold cross-validation to test prediction performances. The monthly P loads (MPL) during the monitoring period showed a maximum of 2528 kg (mean: 103 kg) and maximum monthly unit area P loads (UAL) of 4.88 kg P ha(-1) (mean: 0.16 kg P ha(-1)). Our results suggest that hydrologic/water management properties are the major controlling variables to predict MPL and UAL in the EAA. Tree-based modeling was successful in identifying relationships between P loads and environmental predictor variables on 10 farms in the EAA indicated by high R(2) (>0.80) and low prediction errors. Committee trees in ARCing mode generated the best performing models to predict P loads and P loads per unit area. Tree-based models had the ability to analyze complex, non-linear relationships between P loads and multiple variables describing hydrologic/water management, cropping practices, soil and farm-specific properties within the EAA.

Human Factors Engineering Program Review Model

DTIC Science & Technology

2004-02-01

Institute, 1993). ANSI HFS-100: American National Standard for Human Factors Engineering of Visual Display Terminal Workstations (American National... American National Standard for Human Factors Engineering of Visual Display Terminal Workstations (ANSI HFS-100-1988). Santa Monica, California

Macro-economic factors influencing the architectural business model shift in the pharmaceutical industry.

PubMed

Dierks, Raphaela Marie Louisa; Bruyère, Olivier; Reginster, Jean-Yves; Richy, Florent-Frederic

2016-10-01

Technological innovations, new regulations, increasing costs of drug productions and new demands are only few key drivers of a projected alternation in the pharmaceutical industry. The purpose of this review is to understand the macro economic factors responsible for the business model revolution to possess a competitive advantage over market players. Areas covered: Existing literature on macro-economic factors changing the pharmaceutical landscape has been reviewed to present a clear image of the current market environment. Expert commentary: Literature shows that pharmaceutical companies are facing an architectural alteration, however the evidence on the rationale driving the transformation is outstanding. Merger & Acquisitions (M&A) deals and collaborations are headlining the papers. Q1 2016 did show a major slowdown in M&A deals by volume since 2013 (with deal cancellations of Pfizer and Allergan, or the downfall of Valeant), but pharmaceutical analysts remain confident that this shortfall was a consequence of the equity market volatility. It seems likely that the shift to an M&A model will become apparent during the remainder of 2016, with deal announcements of Abbott Laboratories, AbbVie and Sanofi worth USD 45billion showing the appetite of big pharma companies to shift from the fully vertical integrated business model to more horizontal business models.

Individual Differences in Response to Automation: The Five Factor Model of Personality

ERIC Educational Resources Information Center

Szalma, James L.; Taylor, Grant S.

2011-01-01

This study examined the relationship of operator personality (Five Factor Model) and characteristics of the task and of adaptive automation (reliability and adaptiveness--whether the automation was well-matched to changes in task demand) to operator performance, workload, stress, and coping. This represents the first investigation of how the Five…

Looking beyond Psychopathology: The Dual-Factor Model of Mental Health in Youth

ERIC Educational Resources Information Center

Suldo, Shannon M.; Shaffer, Emily J.

2008-01-01

In a dual-factor model of mental health (cf. Greenspoon & Saklofske, 2001), assessments of positive indicators of wellness (i.e., subjective well-being--SWB) are coupled with traditional negative indicators of illness (i.e., psychopathology) to comprehensively measure mental health. The current study examined the existence and utility of a…

Heeeere's Johnny: A Case Study in the Five Factor Model of Personality

ERIC Educational Resources Information Center

Miserandino, Marianne

2007-01-01

I describe an assignment for personality psychology or introduction to psychology classes in which students used the Five Factor Model of personality to analyze the personality of entertainer Johnny Carson through his The New York Times obituary. Students evaluated this assignment highly: A majority indicated that the assignment was interesting,…

Job strain (demands and control model) as a predictor of cardiovascular risk factors among petrochemical personnel

PubMed Central

Habibi, Ehsanollah; Poorabdian, Siamak; Shakerian, Mahnaz

2015-01-01

Background: One of the practical models for the assessment of stressful working conditions due to job strain is job demand and control model, which explains how physical and psychological adverse consequences, including cardiovascular risk factors can be established due to high work demands (the amount of workload, in addition to time limitations to complete that work) and low control of the worker on his/her work (lack of decision making) in the workplace. The aim of this study was to investigate how certain cardiovascular risk factors (including body mass index [BMI], heart rate, blood pressure, cholesterol and smoking) and the job demand and job control are related to each other. Materials and Methods: This prospective cohort study was conducted on 500 workers of the petrochemical industry in south of Iran, 2009. The study population was selected using simple random statistical method. They completed job demand and control questionnaire. The cardiovascular risk factors data was extracted from the workers hygiene profiles. Chi-square (χ2) test and hypothesis test (η) were used to assess the possible relationship between different quantified variables, individual demographic and cardiovascular risk factors. Results: The results of this study revealed that a significant relationship can be found between job demand control model and cardiovascular risk factors. Chi-square test result for the heart rate showed the highest (χ2 = 145.078) relationship, the corresponding results for smoking and BMI were χ2 = 85.652 and χ2 = 30.941, respectively. Subsequently, hypothesis testing results for cholesterol and hypertension was 0.469 and 0.684, respectively. Discussion: Job strain is likely to be associated with an increased risk of cardiovascular risk factors among male staff in a petrochemical company in Iran. The parameters illustrated in the Job demands and control model can act as acceptable predictors for the probability of job stress occurrence followed by showing

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

PubMed Central

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

Markov model of the loan portfolio dynamics considering influence of management and external economic factors

NASA Astrophysics Data System (ADS)

Bozhalkina, Yana; Timofeeva, Galina

2016-12-01

Mathematical model of loan portfolio in the form of a controlled Markov chain with discrete time is considered. It is assumed that coefficients of migration matrix depend on corrective actions and external factors. Corrective actions include process of receiving applications, interaction with existing solvent and insolvent clients. External factors are macroeconomic indicators, such as inflation and unemployment rates, exchange rates, consumer price indices, etc. Changes in corrective actions adjust the intensity of transitions in the migration matrix. The mathematical model for forecasting the credit portfolio structure taking into account a cumulative impact of internal and external changes is obtained.

Structural equation model of interactions between risk factors and work-related musculoskeletal complaints among Iranian hospital nurses.

PubMed

Mehralizadeh, Semira; Dehdashti, Alireza; Motalebi Kashani, Masoud

2017-01-01

Statistics indicate a high risk of developing work-related musculoskeletal disorders among hospital nurses. The challenge is to understand the associations between musculoskeletal symptoms and various individual and occupational risk factors. This study examined the direct and indirect interactions of various risk factors with musculoskeletal complaints in hospital nurses. In a cross-sectional design, Iranian hospital nurses from Semnan University of Medical Sciences participated in a questionnaire survey reporting their perceived perceptions of various work-related risk factors and musculoskeletal symptoms. We tested our proposed structural equation model to evaluate the relations between latent and observed concepts and the relative importance and strength of exogenous variables in explaining endogenous musculoskeletal complaints. Measurement model fits the data relatively acceptable. Our findings showed direct effects of psychological, role-related and work posture stressors on musculoskeletal complaints. Fatigue mediated the adverse indirect relations of psychological, role-related, work posture and individual factors with musculoskeletal complaints. Structural equation modeling may provide methodological opportunities in occupational health research with a potential to explain the complexity of interactions among risk factors. Prevention of work-related musculoskeletal disorders among nurses must account for physical and psychosocial conditions.

The Dual-Factor Model of Mental Health: Further Study of the Determinants of Group Differences

ERIC Educational Resources Information Center

Lyons, Michael D.; Huebner, E. Scott; Hills, Kimberly J.; Shinkareva, Svetlana V.

2012-01-01

Consistent with a positive psychology framework, this study examined the contributions of personality, environmental, and perceived social support variables in classifying adolescents using Greenspoon and Saklofske's Dual-Factor model of mental health. This model incorporates information about positive subjective well-being (SWB), along with…

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

NASA Astrophysics Data System (ADS)

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.

Development and application of a complex numerical model and software for the computation of dose conversion factors for radon progenies.

PubMed

Farkas, Árpád; Balásházy, Imre

2015-04-01

A more exact determination of dose conversion factors associated with radon progeny inhalation was possible due to the advancements in epidemiological health risk estimates in the last years. The enhancement of computational power and the development of numerical techniques allow computing dose conversion factors with increasing reliability. The objective of this study was to develop an integrated model and software based on a self-developed airway deposition code, an own bronchial dosimetry model and the computational methods accepted by International Commission on Radiological Protection (ICRP) to calculate dose conversion coefficients for different exposure conditions. The model was tested by its application for exposure and breathing conditions characteristic of mines and homes. The dose conversion factors were 8 and 16 mSv WLM(-1) for homes and mines when applying a stochastic deposition model combined with the ICRP dosimetry model (named PM-A model), and 9 and 17 mSv WLM(-1) when applying the same deposition model combined with authors' bronchial dosimetry model and the ICRP bronchiolar and alveolar-interstitial dosimetry model (called PM-B model). User friendly software for the computation of dose conversion factors has also been developed. The software allows one to compute conversion factors for a large range of exposure and breathing parameters and to perform sensitivity analyses. © The Author 2014. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

[Risk factors of eating disorders in the narratives of fashion models].

PubMed

Bogár, Nikolett; Túry, Ferenc

2017-01-01

The risk of eating disorders is high in populations who are exposed to slimness ideal, so among fashion models. The present qualitative study evaluates the risk factors of eating disorders in a group of fashion models with semistructured interview. Moreover, the aim of the study was to examine the impact of professional requirements on the health of models. The study group was internationally heterogeneous. The models were involved by personal professional relationship. A semistructured questionnaire was used by e-mail containing anthropometric data and different aspects of the model profession. 29 female and three male models, three agents, two designers, three fotographers, one personal trainer and one stylist answered the questionnaire. Transient bulimic symptoms were reported by six female models (21%). Moreover, five female models fulfilled the DSM-5 criteria of anorexia nervosa or bulimia nervosa. Four of them were anorexic (body mass index: 13.9-15.3), one was bulimic. The symptoms of three persons began before the model career, those of two models after it. 17 models reported that the model profession intensively increased the bodily preoccupations. The study corroborates the effect of the model profession on the increase of the risk for eating disorders. In the case of the models, whose eating disorder began after stepping into the model profession, the role of the representants of the fashion industry can be suggested as a form of psychological abuse. As the models or in the case of underages their parents accepted the strong requirement of slimness, an unconscious collusion is probable. Our date highlight the health impact of cultural ideals, and call the attention to prevention strategies.

78 FR 14533 - Official Release of EMFAC2011 Motor Vehicle Emission Factor Model for Use in the State of California

Federal Register 2010, 2011, 2012, 2013, 2014

2013-03-06

... testing of interim versions of the model with air districts and Metropolitan Planning Organizations (MPOs... Motor Vehicle Emission Factor Model for Use in the State of California AGENCY: Environmental Protection... of the latest version of the California EMFAC model (short for EMission FACtor) for use in state...

East meets West: the influence of racial, ethnic and cultural risk factors on cardiac surgical risk model performance.

PubMed

Soo-Hoo, Sarah; Nemeth, Samantha; Baser, Onur; Argenziano, Michael; Kurlansky, Paul

2018-01-01

To explore the impact of racial and ethnic diversity on the performance of cardiac surgical risk models, the Chinese SinoSCORE was compared with the Society of Thoracic Surgeons (STS) risk model in a diverse American population. The SinoSCORE risk model was applied to 13 969 consecutive coronary artery bypass surgery patients from twelve American institutions. SinoSCORE risk factors were entered into a logistic regression to create a 'derived' SinoSCORE whose performance was compared with that of the STS risk model. Observed mortality was 1.51% (66% of that predicted by STS model). The SinoSCORE 'low-risk' group had a mortality of 0.15%±0.04%, while the medium-risk and high-risk groups had mortalities of 0.35%±0.06% and 2.13%±0.14%, respectively. The derived SinoSCORE model had a relatively good discrimination (area under of the curve (AUC)=0.785) compared with that of the STS risk score (AUC=0.811; P=0.18 comparing the two). However, specific factors that were significant in the original SinoSCORE but that lacked significance in our derived model included body mass index, preoperative atrial fibrillation and chronic obstructive pulmonary disease. SinoSCORE demonstrated limited discrimination when applied to an American population. The derived SinoSCORE had a discrimination comparable with that of the STS, suggesting underlying similarities of physiological substrate undergoing surgery. However, differential influence of various risk factors suggests that there may be varying degrees of importance and interactions between risk factors. Clinicians should exercise caution when applying risk models across varying populations due to potential differences that racial, ethnic and geographic factors may play in cardiac disease and surgical outcomes.

Cosmological models with a hybrid scale factor in an extended gravity theory

NASA Astrophysics Data System (ADS)

Mishra, B.; Tripathy, S. K.; Tarai, Sankarsan

2018-03-01

A general formalism to investigate Bianchi type V Ih universes is developed in an extended theory of gravity. A minimally coupled geometry and matter field is considered with a rescaled function of f(R,T) substituted in place of the Ricci scalar R in the geometrical action. Dynamical aspects of the models are discussed by using a hybrid scale factor (HSF) that behaves as power law in an initial epoch and as an exponential form at late epoch. The power law behavior and the exponential behavior appear as two extreme cases of the present model.

Comparison of Cox’s Regression Model and Parametric Models in Evaluating the Prognostic Factors for Survival after Liver Transplantation in Shiraz during 2000–2012

PubMed Central

Adelian, R.; Jamali, J.; Zare, N.; Ayatollahi, S. M. T.; Pooladfar, G. R.; Roustaei, N.

2015-01-01

Background: Identification of the prognostic factors for survival in patients with liver transplantation is challengeable. Various methods of survival analysis have provided different, sometimes contradictory, results from the same data. Objective: To compare Cox’s regression model with parametric models for determining the independent factors for predicting adults’ and pediatrics’ survival after liver transplantation. Method: This study was conducted on 183 pediatric patients and 346 adults underwent liver transplantation in Namazi Hospital, Shiraz, southern Iran. The study population included all patients undergoing liver transplantation from 2000 to 2012. The prognostic factors sex, age, Child class, initial diagnosis of the liver disease, PELD/MELD score, and pre-operative laboratory markers were selected for survival analysis. Result: Among 529 patients, 346 (64.5%) were adult and 183 (34.6%) were pediatric cases. Overall, the lognormal distribution was the best-fitting model for adult and pediatric patients. Age in adults (HR=1.16, p<0.05) and weight (HR=2.68, p<0.01) and Child class B (HR=2.12, p<0.05) in pediatric patients were the most important factors for prediction of survival after liver transplantation. Adult patients younger than the mean age and pediatric patients weighing above the mean and Child class A (compared to those with classes B or C) had better survival. Conclusion: Parametric regression model is a good alternative for the Cox’s regression model. PMID:26306158

Risk factors for pressure ulcer development in critically Ill patients: a conceptual model to guide research.

PubMed

Benoit, Richard; Mion, Lorraine

2012-08-01

This paper presents a proposed conceptual model to guide research on pressure ulcer risk in critically ill patients, who are at high risk for pressure ulcer development. However, no conceptual model exists that guides risk assessment in this population. Results from a review of prospective studies were evaluated for design quality and level of statistical reporting. Multivariate findings from studies having high or medium design quality by the National Institute of Health and Clinical Excellence standards were conceptually grouped. The conceptual groupings were integrated into Braden and Bergstrom's (Braden and Bergstrom [1987] Rehabilitation Nursing, 12, 8-12, 16) conceptual model, retaining their original constructs and augmenting their concept of intrinsic factors for tissue tolerance. The model could enhance consistency in research on pressure ulcer risk factors. Copyright © 2012 Wiley Periodicals, Inc.

Applicability of the Dual-Factor Model of Mental Health for College Students

ERIC Educational Resources Information Center

Eklund, Katie; Dowdy, Erin; Jones, Camille; Furlong, Michael

2011-01-01

This study explores the utility of a dual-factor model of mental health in which the concepts of mental illness and mental wellness are integrated. Life satisfaction, emotional symptoms, personal adjustment, and clinical symptoms were assessed with a sample of 240 college students. Participants were organized into four groups based on levels of…

Towards a Model for Research on the Effects of School Organizational Health Factors on Primary School Performance in Trinidad & Tobago

ERIC Educational Resources Information Center

Ramdass, Mala; Lewis, Theodore

2012-01-01

This article presents a model for research on the effects of school organizational heath factors on primary school academic achievement in Trinidad and Tobago. The model can be applicable for evaluating schools in other developing countries. As proposed, the model hypothesizes relationships between external factors (exogenous variables),…

Brief Report: Bifactor Modeling of General vs. Specific Factors of Religiousness Differentially Predicting Substance Use Risk in Adolescence

PubMed Central

Kim-Spoon, Jungmeen; Longo, Gregory S.; Holmes, Christopher J.

2015-01-01

Religiousness is important to adolescents in the U.S., and the significant link between high religiousness and low substance use is well known. There is a debate between multidimensional and unidimensional perspectives of religiousness (Gorsuch, 1984); yet, no empirical study has tested this hierarchical model of religiousness related to adolescent health outcomes. The current study presents the first attempt to test a bifactor model of religiousness related to substance use among adolescents (N = 220, 45% female). Our bifactor model using structural equation modeling suggested the multidimensional nature of religiousness as well as the presence of a superordinate general religiousness factor directly explaining the covariation among the specific factors including organizational and personal religiousness and religious social support. The general religiousness factor was inversely related to substance use. After accounting for the contribution of the general religiousness factor, high organizational religiousness related to low substance use, whereas personal religiousness and religious support were positively related to substance use. The findings present the first evidence that supports hierarchical structures of adolescent religiousness that contribute differentially to adolescent substance use. PMID:26043168

Modelling long-term fire occurrence factors in Spain by accounting for local variations with geographically weighted regression

NASA Astrophysics Data System (ADS)

Martínez-Fernández, J.; Chuvieco, E.; Koutsias, N.

2013-02-01

Humans are responsible for most forest fires in Europe, but anthropogenic factors behind these events are still poorly understood. We tried to identify the driving factors of human-caused fire occurrence in Spain by applying two different statistical approaches. Firstly, assuming stationary processes for the whole country, we created models based on multiple linear regression and binary logistic regression to find factors associated with fire density and fire presence, respectively. Secondly, we used geographically weighted regression (GWR) to better understand and explore the local and regional variations of those factors behind human-caused fire occurrence. The number of human-caused fires occurring within a 25-yr period (1983-2007) was computed for each of the 7638 Spanish mainland municipalities, creating a binary variable (fire/no fire) to develop logistic models, and a continuous variable (fire density) to build standard linear regression models. A total of 383 657 fires were registered in the study dataset. The binary logistic model, which estimates the probability of having/not having a fire, successfully classified 76.4% of the total observations, while the ordinary least squares (OLS) regression model explained 53% of the variation of the fire density patterns (adjusted R2 = 0.53). Both approaches confirmed, in addition to forest and climatic variables, the importance of variables related with agrarian activities, land abandonment, rural population exodus and developmental processes as underlying factors of fire occurrence. For the GWR approach, the explanatory power of the GW linear model for fire density using an adaptive bandwidth increased from 53% to 67%, while for the GW logistic model the correctly classified observations improved only slightly, from 76.4% to 78.4%, but significantly according to the corrected Akaike Information Criterion (AICc), from 3451.19 to 3321.19. The results from GWR indicated a significant spatial variation in the local

Critical factors influencing physicians' intention to use computerized clinical practice guidelines: an integrative model of activity theory and the technology acceptance model.

PubMed

Hsiao, Ju-Ling; Chen, Rai-Fu

2016-01-16

With the widespread use of information communication technologies, computerized clinical practice guidelines are developed and considered as effective decision supporting tools in assisting the processes of clinical activities. However, the development of computerized clinical practice guidelines in Taiwan is still at the early stage and acceptance level among major users (physicians) of computerized clinical practice guidelines is not satisfactory. This study aims to investigate critical factors influencing physicians' intention to computerized clinical practice guideline use through an integrative model of activity theory and the technology acceptance model. The survey methodology was employed to collect data from physicians of the investigated hospitals that have implemented computerized clinical practice guidelines. A total of 505 questionnaires were sent out, with 238 completed copies returned, indicating a valid response rate of 47.1 %. The collected data was then analyzed by structural equation modeling technique. The results showed that attitudes toward using computerized clinical practice guidelines (γ = 0.451, p < 0.001), organizational support (γ = 0.285, p < 0.001), perceived usefulness of computerized clinical practice guidelines (γ = 0.219, p < 0.05), and social influence (γ = 0.213, p < 0.05) were critical factors influencing physicians' intention to use computerized clinical practice guidelines, and these factors can explain 68.6 % of the variance in intention to use computerized clinical practice guidelines. This study confirmed that some subject (human) factors, environment (organization) factors, tool (technology) factors mentioned in the activity theory should be carefully considered when introducing computerized clinical practice guidelines. Managers should pay much attention on those identified factors and provide adequate resources and incentives to help the promotion and use of computerized clinical

A mouse model of severe halothane hepatitis based on human risk factors.

PubMed

Dugan, Christine M; MacDonald, Allen E; Roth, Robert A; Ganey, Patricia E

2010-05-01

Halothane (2-bromo-2-chloro-1,1,1-trifluoro-ethane) is an inhaled anesthetic that induces severe, idiosyncratic liver injury, i.e., "halothane hepatitis," in approximately 1 in 20,000 human patients. We used known human risk factors (female sex, adult age, and genetics) as well as probable risk factors (fasting and inflammatory stress) to develop a murine model with characteristics of human halothane hepatitis. Female and male BALB/cJ mice treated with halothane developed dose-dependent liver injury within 24 h; however, the liver injury was severe only in females. Livers had extensive centrilobular necrosis, inflammatory cell infiltrate, and steatosis. Fasting rendered mice more sensitive to halothane hepatotoxicity, and 8-week-old female mice were more sensitive than males of the same age or than younger (4-week-old) females. C57BL/6 mice were insensitive to halothane, suggesting a strong genetic predisposition. In halothane-treated females, plasma concentration of tumor necrosis factor-alpha was greater than in males, and neutrophils were recruited to liver more rapidly and to a greater extent. Anti-CD18 serum attenuated halothane-induced liver injury in female mice, suggesting that neutrophil migration, activation, or both are required for injury. Coexposure of halothane-treated male mice to lipopolysaccharide to induce modest inflammatory stress converted their mild hepatotoxic response to a pronounced, female-like response. This is the first animal model of an idiosyncratic adverse drug reaction that is based on human risk factors and produces reproducible, severe hepatitis from halothane exposure with lesions characteristic of human halothane hepatitis. Moreover, these results suggest that a more robust innate immune response underlies the predisposition of female mice to halothane hepatitis.

[Apnoea in infants with bronchiolitis: Incidence and risk factors for a prediction model].

PubMed

Ramos-Fernández, José Miguel; Moreno-Pérez, David; Gutiérrez-Bedmar, Mario; Ramírez-Álvarez, María; Martínez García, Yasmina; Artacho-González, Lourdes; Urda-Cardona, Antonio

2018-03-01

The presence of apnoea in acute bronchiolitis (AB) varies between 1.2% and 28.8%, depending on the series, and is one of its most fearsome complications. The aim of this study is to determine the incidence of apnoea in hospitalised patients diagnosed with AB, and to define their associated risk factors in order to construct a prediction model. A retrospective observational study of patients admitted to a tertiary hospital in the last 5 years with a diagnosis of AB, according to the classic criteria. Data was collected on the frequency of apnoea and related clinical variables to find risk factors in a binary logistic regression model for the prediction of apnoea. A ROC curve was developed with the model. Apnoea was recorded during the admission of 53 (4.4%) patients out of a total 1,197 cases found. The risk factors included in the equation were: Female (OR 0.6, 95% CI: 0.27-1.37), Caesarean delivery (OR: 3.44, 95% CI: 1.5-7.7), Postmenstrual age ≤43 weeks (OR: 6.62, 95% CI: 2.38-18.7), Fever (OR: 0.33, 95% CI: 0.09-1.97), Low birth weight (OR: 5.93, 95% CI: 2.23-7.67), Apnoea observed by caregivers before admission (OR: 5.93, 95% CI: 2.64-13.3), and severe bacterial infection (OR: 3.98, 95% CI: 1.68-9.46). The optimal sensitivity and specificity of the model in the ROC curve was 0.842 and 0.846, respectively (P<.001). The incidence of apnoea during admission was 4.4 per 100 admissions of AB and year. The estimated prediction model equation may be of help to the clinician in order to classify patients with increased risk of apnoea during admission due to AB. Copyright © 2017 Asociación Española de Pediatría. Publicado por Elsevier España, S.L.U. All rights reserved.

Development and Initial Validation of the Five-Factor Model Adolescent Personality Questionnaire (FFM-APQ).

PubMed

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.

Personality traits measured by the Swedish universities Scales of Personality: factor structure and position within the five-factor model in an Estonian sample.

PubMed

Aluoja, Anu; Voogne, Helina; Maron, Eduard; Gustavsson, J Petter; Võhma, Ulle; Shlik, Jakov

2009-01-01

The study aims to test the reliability and validity of the Estonian version of the Swedish universities Scales of Personality (SSP), and to characterize the position of the SSP-measured traits within the basic personality dimensions of the five-factor model. A total of 529 participants completed the Estonian version of the SSP. A subsample of 197 persons completed the SSP together with the Revised NEO Personality Inventory (NEO-PI-R). The internal consistency of the SSP scales was satisfactory. Principal component analysis yielded three factors representing neuroticism, aggression and disinhibition. The factor solution obtained in the Estonian sample was similar to the original SSP study in the Swedish normative sample. NEO-PI-R Neuroticism had highest correlations with SSP neuroticism factor scales. Extraversion had strongest relationship with adventure seeking and low detachment. Agreeableness correlated positively with SSP social desirability and negatively to aggression-irritability scales. Conscientiousness facet Deliberation correlated with Impulsiveness. The Estonian SSP showed acceptable reliability and validity, which confirms that SSP is applicable in different social and cultural background. The SSP measures traits that correspond to the major personality models. The SSP characterizes three broad dimensions of personality, namely neuroticism, disinhibition and aggression, which are useful in assessment of personality correlates of mental disorders.

Biophysically inspired model for functionalized nanocarrier adhesion to cell surface: roles of protein expression and mechanical factors

NASA Astrophysics Data System (ADS)

Ramakrishnan, N.; Tourdot, Richard W.; Eckmann, David M.; Ayyaswamy, Portonovo S.; Muzykantov, Vladimir R.; Radhakrishnan, Ravi

2016-06-01

In order to achieve selective targeting of affinity-ligand coated nanoparticles to the target tissue, it is essential to understand the key mechanisms that govern their capture by the target cell. Next-generation pharmacokinetic (PK) models that systematically account for proteomic and mechanical factors can accelerate the design, validation and translation of targeted nanocarriers (NCs) in the clinic. Towards this objective, we have developed a computational model to delineate the roles played by target protein expression and mechanical factors of the target cell membrane in determining the avidity of functionalized NCs to live cells. Model results show quantitative agreement with in vivo experiments when specific and non-specific contributions to NC binding are taken into account. The specific contributions are accounted for through extensive simulations of multivalent receptor-ligand interactions, membrane mechanics and entropic factors such as membrane undulations and receptor translation. The computed NC avidity is strongly dependent on ligand density, receptor expression, bending mechanics of the target cell membrane, as well as entropic factors associated with the membrane and the receptor motion. Our computational model can predict the in vivo targeting levels of the intracellular adhesion molecule-1 (ICAM1)-coated NCs targeted to the lung, heart, kidney, liver and spleen of mouse, when the contributions due to endothelial capture are accounted for. The effect of other cells (such as monocytes, etc.) do not improve the model predictions at steady state. We demonstrate the predictive utility of our model by predicting partitioning coefficients of functionalized NCs in mice and human tissues and report the statistical accuracy of our model predictions under different scenarios.

Using Factor Mixture Models to Evaluate the Type A/B Classification of Alcohol Use Disorders in a Heterogeneous Treatment Sample

PubMed Central

Hildebrandt, Tom; Epstein, Elizabeth E.; Sysko, Robyn; Bux, Donald A.

2017-01-01

Background The type A/B classification model for alcohol use disorders (AUDs) has received considerable empirical support. However, few studies examine the underlying latent structure of this subtyping model, which has been challenged as a dichotomization of a single drinking severity dimension. Type B, relative to type A, alcoholics represent those with early age of onset, greater familial risk, and worse outcomes from alcohol use. Method We examined the latent structure of the type A/B model using categorical, dimensional, and factor mixture models in a mixed gender community treatment-seeking sample of adults with an AUD. Results Factor analytic models identified 2-factors (drinking severity/externalizing psychopathology and internalizing psychopathology) underlying the type A/B indicators. A factor mixture model with 2-dimensions and 3-classes emerged as the best overall fitting model. The classes reflected a type A class and two type B classes (B1 and B2) that differed on the respective level of drinking severity/externalizing pathology and internalizing pathology. Type B1 had a greater prevalence of women and more internalizing pathology and B2 had a greater prevalence of men and more drinking severity/externalizing pathology. The 2-factor, 3-class model also exhibited predictive validity by explaining significant variance in 12-month drinking and drug use outcomes. Conclusions The model identified in the current study may provide a basis for examining different sources of heterogeneity in the course and outcome of AUDs. PMID:28247423

Measurement Invariance and the Five-Factor Model of Personality: Asian International and Euro American Cultural Groups.

PubMed

Rollock, David; Lui, P Priscilla

2016-10-01

This study examined measurement invariance of the NEO Five-Factor Inventory (NEO-FFI), assessing the five-factor model (FFM) of personality among Euro American (N = 290) and Asian international (N = 301) students (47.8% women, Mage = 19.69 years). The full 60-item NEO-FFI data fit the expected five-factor structure for both groups using exploratory structural equation modeling, and achieved configural invariance. Only 37 items significantly loaded onto the FFM-theorized factors for both groups and demonstrated metric invariance. Threshold invariance was not supported with this reduced item set. Groups differed the most in the item-factor relationships for Extraversion and Agreeableness, as well as in response styles. Asian internationals were more likely to use midpoint responses than Euro Americans. While the FFM can characterize broad nomothetic patterns of personality traits, metric invariance with only the subset of NEO-FFI items identified limits direct group comparisons of correlation coefficients among personality domains and with other constructs, and of mean differences on personality domains. © The Author(s) 2015.

Optimization Scheduling Model for Wind-thermal Power System Considering the Dynamic penalty factor

NASA Astrophysics Data System (ADS)

PENG, Siyu; LUO, Jianchun; WANG, Yunyu; YANG, Jun; RAN, Hong; PENG, Xiaodong; HUANG, Ming; LIU, Wanyu

2018-03-01

In this paper, a new dynamic economic dispatch model for power system is presented.Objective function of the proposed model presents a major novelty in the dynamic economic dispatch including wind farm: introduced the “Dynamic penalty factor”, This factor could be computed by using fuzzy logic considering both the variable nature of active wind power and power demand, and it could change the wind curtailment cost according to the different state of the power system. Case studies were carried out on the IEEE30 system. Results show that the proposed optimization model could mitigate the wind curtailment and the total cost effectively, demonstrate the validity and effectiveness of the proposed model.

Confirmation of the Three-Factor Model of Problematic Internet Use on Off-Line Adolescent and Adult Samples

PubMed Central

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

2011-01-01

Abstract 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. PMID:21711129

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

PubMed

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.

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

PubMed Central

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

2016-01-01

Background Individual and organizational factors are the factors influencing traumatic occupational injuries. Objectives The aim of the present study was the short path analysis of the severity of occupational injuries based on individual and organizational factors. Materials and Methods 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. Results 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 < 0.05). Conclusions Path analysis of occupational injuries based on the SEM reveals that individual and organizational factors and their indicator variables are very influential on the severity of traumatic occupational injuries. So, these should be considered to reduce occupational accidents’ severity in large construction industries. PMID:27800465

Predicting knee replacement damage in a simulator machine using a computational model with a consistent wear factor.

PubMed

Zhao, Dong; Sakoda, Hideyuki; Sawyer, W Gregory; Banks, Scott A; Fregly, Benjamin J

2008-02-01

Wear of ultrahigh molecular weight polyethylene remains a primary factor limiting the longevity of total knee replacements (TKRs). However, wear testing on a simulator machine is time consuming and expensive, making it impractical for iterative design purposes. The objectives of this paper were first, to evaluate whether a computational model using a wear factor consistent with the TKR material pair can predict accurate TKR damage measured in a simulator machine, and second, to investigate how choice of surface evolution method (fixed or variable step) and material model (linear or nonlinear) affect the prediction. An iterative computational damage model was constructed for a commercial knee implant in an AMTI simulator machine. The damage model combined a dynamic contact model with a surface evolution model to predict how wear plus creep progressively alter tibial insert geometry over multiple simulations. The computational framework was validated by predicting wear in a cylinder-on-plate system for which an analytical solution was derived. The implant damage model was evaluated for 5 million cycles of simulated gait using damage measurements made on the same implant in an AMTI machine. Using a pin-on-plate wear factor for the same material pair as the implant, the model predicted tibial insert wear volume to within 2% error and damage depths and areas to within 18% and 10% error, respectively. Choice of material model had little influence, while inclusion of surface evolution affected damage depth and area but not wear volume predictions. Surface evolution method was important only during the initial cycles, where variable step was needed to capture rapid geometry changes due to the creep. Overall, our results indicate that accurate TKR damage predictions can be made with a computational model using a constant wear factor obtained from pin-on-plate tests for the same material pair, and furthermore, that surface evolution method matters only during the initial

Exploring Environmental Factors in Nursing Workplaces That Promote Psychological Resilience: Constructing a Unified Theoretical Model.

PubMed

Cusack, Lynette; Smith, Morgan; Hegney, Desley; Rees, Clare S; Breen, Lauren J; Witt, Regina R; Rogers, Cath; Williams, Allison; Cross, Wendy; Cheung, Kin

2016-01-01

Building nurses' resilience to complex and stressful practice environments is necessary to keep skilled nurses in the workplace and ensuring safe patient care. A unified theoretical framework titled Health Services Workplace Environmental Resilience Model (HSWERM), is presented to explain the environmental factors in the workplace that promote nurses' resilience. The framework builds on a previously-published theoretical model of individual resilience, which identified the key constructs of psychological resilience as self-efficacy, coping and mindfulness, but did not examine environmental factors in the workplace that promote nurses' resilience. This unified theoretical framework was developed using a literary synthesis drawing on data from international studies and literature reviews on the nursing workforce in hospitals. The most frequent workplace environmental factors were identified, extracted and clustered in alignment with key constructs for psychological resilience. Six major organizational concepts emerged that related to a positive resilience-building workplace and formed the foundation of the theoretical model. Three concepts related to nursing staff support (professional, practice, personal) and three related to nursing staff development (professional, practice, personal) within the workplace environment. The unified theoretical model incorporates these concepts within the workplace context, linking to the nurse, and then impacting on personal resilience and workplace outcomes, and its use has the potential to increase staff retention and quality of patient care.

Exploring Environmental Factors in Nursing Workplaces That Promote Psychological Resilience: Constructing a Unified Theoretical Model

PubMed Central

Cusack, Lynette; Smith, Morgan; Hegney, Desley; Rees, Clare S.; Breen, Lauren J.; Witt, Regina R.; Rogers, Cath; Williams, Allison; Cross, Wendy; Cheung, Kin

2016-01-01

Building nurses' resilience to complex and stressful practice environments is necessary to keep skilled nurses in the workplace and ensuring safe patient care. A unified theoretical framework titled Health Services Workplace Environmental Resilience Model (HSWERM), is presented to explain the environmental factors in the workplace that promote nurses' resilience. The framework builds on a previously-published theoretical model of individual resilience, which identified the key constructs of psychological resilience as self-efficacy, coping and mindfulness, but did not examine environmental factors in the workplace that promote nurses' resilience. This unified theoretical framework was developed using a literary synthesis drawing on data from international studies and literature reviews on the nursing workforce in hospitals. The most frequent workplace environmental factors were identified, extracted and clustered in alignment with key constructs for psychological resilience. Six major organizational concepts emerged that related to a positive resilience-building workplace and formed the foundation of the theoretical model. Three concepts related to nursing staff support (professional, practice, personal) and three related to nursing staff development (professional, practice, personal) within the workplace environment. The unified theoretical model incorporates these concepts within the workplace context, linking to the nurse, and then impacting on personal resilience and workplace outcomes, and its use has the potential to increase staff retention and quality of patient care. PMID:27242567

Zero-Inflated Poisson Modeling of Fall Risk Factors in Community-Dwelling Older Adults.

PubMed

Jung, Dukyoo; Kang, Younhee; Kim, Mi Young; Ma, Rye-Won; Bhandari, Pratibha

2016-02-01

The aim of this study was to identify risk factors for falls among community-dwelling older adults. The study used a cross-sectional descriptive design. Self-report questionnaires were used to collect data from 658 community-dwelling older adults and were analyzed using logistic and zero-inflated Poisson (ZIP) regression. Perceived health status was a significant factor in the count model, and fall efficacy emerged as a significant predictor in the logistic models. The findings suggest that fall efficacy is important for predicting not only faller and nonfaller status but also fall counts in older adults who may or may not have experienced a previous fall. The fall predictors identified in this study--perceived health status and fall efficacy--indicate the need for fall-prevention programs tailored to address both the physical and psychological issues unique to older adults. © The Author(s) 2014.

How to Construct More Accurate Student Models: Comparing and Optimizing Knowledge Tracing and Performance Factor Analysis

ERIC Educational Resources Information Center

Gong, Yue; Beck, Joseph E.; Heffernan, Neil T.

2011-01-01

Student modeling is a fundamental concept applicable to a variety of intelligent tutoring systems (ITS). However, there is not a lot of practical guidance on how to construct and train such models. This paper compares two approaches for student modeling, Knowledge Tracing (KT) and Performance Factors Analysis (PFA), by evaluating their predictive…

External factors in hospital information system (HIS) adoption model: a case on Malaysia.

PubMed

Lee, Heng Wei; Ramayah, Thurasamy; Zakaria, Nasriah

2012-08-01

Studies related to healthcare ICT integration in Malaysia are relatively little, thus this paper provide a literature review of the integration of information and communication technologies (ICT) in the healthcare sector in Malaysia through the hospital information system (HIS). Our study emphasized on secondary data to investigate the factors related to ICT integration in healthcare through HIS. Therefore this paper aimed to gather an in depth understanding of issues related to HIS adoption, and contributing in fostering HIS adoption in Malaysia and other countries. This paper provides a direction for future research to study the correlation of factors affecting HIS adoption. Finally a research model is proposed using current adoption theories and external factors from human, technology, and organization perspectives.

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

NASA Astrophysics Data System (ADS)

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.

Capturing the DSM-5 Alternative Personality Disorder Model Traits in the Five-Factor Model's Nomological Net.

PubMed

Suzuki, Takakuni; Griffin, Sarah A; Samuel, Douglas B

2017-04-01

Several studies have shown structural and statistical similarities between the Diagnostic and Statistical Manual of Mental Disorders (5th ed.; DSM-5) alternative personality disorder model and the Five-Factor Model (FFM). However, no study to date has evaluated the nomological network similarities between the two models. The relations of the Revised NEO Personality Inventory (NEO PI-R) and the Personality Inventory for DSM-5 (PID-5) with relevant criterion variables were examined in a sample of 336 undergraduate students (M age  = 19.4; 59.8% female). The resulting profiles for each instrument were statistically compared for similarity. Four of the five domains of the two models have highly similar nomological networks, with the exception being FFM Openness to Experience and PID-5 Psychoticism. Further probing of that pair suggested that the NEO PI-R domain scores obscured meaningful similarity between PID-5 Psychoticism and specific aspects and lower-order facets of Openness. The results support the notion that the DSM-5 alternative personality disorder model trait domains represent variants of the FFM domains. Similarities of Openness and Psychoticism domains were supported when the lower-order aspects and facets of Openness domain were considered. The findings support the view that the DSM-5 trait model represents an instantiation of the FFM. © 2015 Wiley Periodicals, Inc.

Groundwater source contamination mechanisms: Physicochemical profile clustering, risk factor analysis and multivariate modelling

NASA Astrophysics Data System (ADS)

Hynds, Paul; Misstear, Bruce D.; Gill, Laurence W.; Murphy, Heather M.

2014-04-01

An integrated domestic well sampling and "susceptibility assessment" programme was undertaken in the Republic of Ireland from April 2008 to November 2010. Overall, 211 domestic wells were sampled, assessed and collated with local climate data. Based upon groundwater physicochemical profile, three clusters have been identified and characterised by source type (borehole or hand-dug well) and local geological setting. Statistical analysis indicates that cluster membership is significantly associated with the prevalence of bacteria (p = 0.001), with mean Escherichia coli presence within clusters ranging from 15.4% (Cluster-1) to 47.6% (Cluster-3). Bivariate risk factor analysis shows that on-site septic tank presence was the only risk factor significantly associated (p < 0.05) with bacterial presence within all clusters. Point agriculture adjacency was significantly associated with both borehole-related clusters. Well design criteria were associated with hand-dug wells and boreholes in areas characterised by high permeability subsoils, while local geological setting was significant for hand-dug wells and boreholes in areas dominated by low/moderate permeability subsoils. Multivariate susceptibility models were developed for all clusters, with predictive accuracies of 84% (Cluster-1) to 91% (Cluster-2) achieved. Septic tank setback was a common variable within all multivariate models, while agricultural sources were also significant, albeit to a lesser degree. Furthermore, well liner clearance was a significant factor in all models, indicating that direct surface ingress is a significant well contamination mechanism. Identification and elucidation of cluster-specific contamination mechanisms may be used to develop improved overall risk management and wellhead protection strategies, while also informing future remediation and maintenance efforts.

Development of biomechanical models for human factors evaluations

NASA Technical Reports Server (NTRS)

Woolford, Barbara; Pandya, Abhilash; Maida, James

1993-01-01

Computer aided design (CAD) techniques are now well established and have become the norm in many aspects of aerospace engineering. They enable analytical studies, such as finite element analysis, to be performed to measure performance characteristics of the aircraft or spacecraft long before a physical model is built. However, because of the complexity of human performance, CAD systems for human factors are not in widespread use. The purpose of such a program would be to analyze the performance capability of a crew member given a particular environment and task. This requires the design capabilities to describe the environment's geometry and to describe the task's requirements, which may involve motion and strength. This in turn requires extensive data on human physical performance which can be generalized to many different physical configurations. PLAID is developing into such a program. Begun at Johnson Space Center in 1977, it was started to model only the geometry of the environment. The physical appearance of a human body was generated, and the tool took on a new meaning as fit, access, and reach could be checked. Specification of fields-of-view soon followed. This allowed PLAID to be used to predict what the Space Shuttle cameras or crew could see from a given point.

A proposed model of factors influencing hydrogen fuel cell vehicle acceptance

NASA Astrophysics Data System (ADS)

Imanina, N. H. Noor; Kwe Lu, Tan; Fadhilah, A. R.

2016-03-01

Issues such as environmental problem and energy insecurity keep worsening as a result of energy use from household to huge industries including automotive industry. Recently, a new type of zero emission vehicle, hydrogen fuel cell vehicle (HFCV) has received attention. Although there are argues on the feasibility of hydrogen as the future fuel, there is another important issue, which is the acceptance of HFCV. The study of technology acceptance in the early stage is a vital key for a successful introduction and penetration of a technology. This paper proposes a model of factors influencing green vehicle acceptance, specifically HFCV. This model is built base on two technology acceptance theories and other empirical studies of vehicle acceptance. It aims to provide a base for finding the key factors influencing new sustainable energy fuelled vehicle, HFCV acceptance which is achieved by explaining intention to accept HFCV. Intention is influenced by attitude, subjective norm and perceived behavioural control from Theory of Planned Behaviour and personal norm from Norm Activation Theory. In the framework, attitude is influenced by perceptions of benefits and risks, and social trust. Perceived behavioural control is influenced by government interventions. Personal norm is influenced by outcome efficacy and problem awareness.

Psychosocial factors, musculoskeletal disorders and work-related fatigue amongst nurses in Brunei: structural equation model approach.

PubMed

Abdul Rahman, Hanif; Abdul-Mumin, Khadizah; Naing, Lin

2017-09-01

Psychosocial factors, musculoskeletal disorders and work-related fatigue have adverse effects on individual nurses and place a substantial financial burden on health care. Evidence of an association has been reported in the literature, but no theoretical explanation has been published to date. To explore and develop a structural model to provide a theoretical explanation for this relationship. A cross-sectional study using data from 201 valid samples of emergency and critical care nurses across public hospitals in Brunei was performed via self-administered questionnaire. The structural equation model was assessed using partial least squares analysis. A valid and robust structural model was constructed. This revealed that 61.5% of the variance in chronic fatigue could be explained by psychosocial factors and musculoskeletal disorders pathways. Among the psychosocial factors, work-family conflict was identified as a key mediator for progression of musculoskeletal problems and subsequent fatigue through stress and burnout. This report provides a novel theoretical contribution to understanding the relationship between psychosocial factors, musculoskeletal disorders and work-related fatigue. These preliminary results may be useful for future studies on the development of work-related fatigue and musculoskeletal disorders, particularly the central role of work-family conflict. Copyright © 2017 Elsevier Ltd. All rights reserved.

Uncovering the influence of social skills and psychosociological factors on pain sensitivity using structural equation modeling.

PubMed

Tanaka, Yoichi; Nishi, Yuki; Nishi, Yuki; Osumi, Michihiro; Morioka, Shu

2017-01-01

Pain is a subjective emotional experience that is influenced by psychosociological factors such as social skills, which are defined as problem-solving abilities in social interactions. This study aimed to reveal the relationships among pain, social skills, and other psychosociological factors by using structural equation modeling. A total of 101 healthy volunteers (41 men and 60 women; mean age: 36.6±12.7 years) participated in this study. To evoke participants' sense of inner pain, we showed them images of painful scenes on a PC screen and asked them to evaluate the pain intensity by using the visual analog scale (VAS). We examined the correlation between social skills and VAS, constructed a hypothetical model based on results from previous studies and the current correlational analysis results, and verified the model's fit using structural equation modeling. We found significant positive correlations between VAS and total social skills values, as well as between VAS and the "start of relationships" subscales. Structural equation modeling revealed that the values for "start of relationships" had a direct effect on VAS values (path coefficient =0.32, p <0.01). In addition, the "start of relationships" had both a direct and an indirect effect on psychological factors via social support. The results indicated that extroverted people are more sensitive to inner pain and tend to get more social support and maintain a better psychological condition.

A conceptual model of factors contributing to the development of muscle dysmorphia.

PubMed

Grieve, Frederick G

2007-01-01

Muscle dysmorphia is a recently described subcategory of Body Dysmorphic Disorder. It is most prevalent in males and has a number of cognitive, behavioral, socioenviornmental, emotional, and psychological factors that influence its expression. An etiological model describing these influences is presented for evaluation. Nine variables (body mass, media influences, ideal body internalization, low self-esteem, body dissatisfaction, health locus of control, negative affect, perfectionism, and body distortion) were identified through the use of extant literature on muscle dysmorphia and through extrapolation from literature involving women and eating disorders. The functional relationships among these variables are described and implications of the model are discussed.

FACTORS INFLUENCING THE DESIGN OF BIOACCUMULATION FACTOR AND BIOTA-SEDIMENT ACCUMULATION FACTOR FIELD STUDIES

EPA Science Inventory

A series of modeling simulations were performed to develop an understanding of the underlying factors and principles involved in developing field sampling designs for measuring bioaccumulation factors (BAFs) and biota-sediment accumulation factors (BSAFs. These simulations reveal...

The relationships between behavioral addictions and the five-factor model of personality.

PubMed

Andreassen, Cecilie Schou; Griffiths, Mark D; Gjertsen, Siri Renate; Krossbakken, Elfrid; Kvam, Siri; Pallesen, Ståle

2013-06-01

Aims Although relationships between addiction and personality have previously been explored, no study has ever simultaneously investigated the interrelationships between several behavioral addictions, and related these to the main dimensions of the five-factor model of personality. Methods In this study, 218 university students completed questionnaires assessing seven different behavioral addictions (i.e., Facebook addiction, video game addiction, Internet addiction, exercise addiction, mobile phone addiction, compulsive buying, and study addiction) as well as an instrument assessing the main dimensions of the five-factor model of personality. Results Of the 21 bivariate intercorrelations between the seven behavioral addictions, all were positive (and nine significantly). The results also showed that (i) Neuroticism was positively associated with Internet addiction, exercise addiction, compulsive buying, and study addiction, (ii) Extroversion was positively associated with Facebook addiction, exercise addiction, mobile phone addiction, and compulsive buying, (iii) Openness to experience was negatively associated with Facebook addiction and mobile phone addiction, (iv) Agreeableness was negatively associated with Internet addiction, exercise addiction, mobile phone addiction, and compulsive buying, and (v) Conscientiousness was negatively associated with Facebook addiction, video game addiction, Internet addiction, and compulsive buying and positively associated with exercise addiction and study addiction. Conclusions The positive associations between the seven behavioral addictions suggest one or several underlying pathological factors. Hierarchical multiple regressions showed that personality traits explained between 6% and 17% of the variance in the seven behavioral addictions, suggesting that personality to a varying degree explains scores on measures of addictive behaviors.

Rule-Based Models of the Interplay between Genetic and Environmental Factors in Childhood Allergy

PubMed Central

Melén, Erik; Bergström, Anna; Torabi Moghadam, Behrooz; Pulkkinen, Ville; Acevedo, Nathalie; Orsmark Pietras, Christina; Ege, Markus; Braun-Fahrländer, Charlotte; Riedler, Josef; Doekes, Gert; Kabesch, Michael; van Hage, Marianne; Kere, Juha; Scheynius, Annika; Söderhäll, Cilla; Pershagen, Göran; Komorowski, Jan

2013-01-01

Both genetic and environmental factors are important for the development of allergic diseases. However, a detailed understanding of how such factors act together is lacking. To elucidate the interplay between genetic and environmental factors in allergic diseases, we used a novel bioinformatics approach that combines feature selection and machine learning. In two materials, PARSIFAL (a European cross-sectional study of 3113 children) and BAMSE (a Swedish birth-cohort including 2033 children), genetic variants as well as environmental and lifestyle factors were evaluated for their contribution to allergic phenotypes. Monte Carlo feature selection and rule based models were used to identify and rank rules describing how combinations of genetic and environmental factors affect the risk of allergic diseases. Novel interactions between genes were suggested and replicated, such as between ORMDL3 and RORA, where certain genotype combinations gave odds ratios for current asthma of 2.1 (95% CI 1.2-3.6) and 3.2 (95% CI 2.0-5.0) in the BAMSE and PARSIFAL children, respectively. Several combinations of environmental factors appeared to be important for the development of allergic disease in children. For example, use of baby formula and antibiotics early in life was associated with an odds ratio of 7.4 (95% CI 4.5-12.0) of developing asthma. Furthermore, genetic variants together with environmental factors seemed to play a role for allergic diseases, such as the use of antibiotics early in life and COL29A1 variants for asthma, and farm living and NPSR1 variants for allergic eczema. Overall, combinations of environmental and life style factors appeared more frequently in the models than combinations solely involving genes. In conclusion, a new bioinformatics approach is described for analyzing complex data, including extensive genetic and environmental information. Interactions identified with this approach could provide useful hints for further in-depth studies of etiological

Brief report: Bifactor modeling of general vs. specific factors of religiousness differentially predicting substance use risk in adolescence.

PubMed

Kim-Spoon, Jungmeen; Longo, Gregory S; Holmes, Christopher J

2015-08-01

Religiousness is important to adolescents in the U.S., and the significant link between high religiousness and low substance use is well known. There is a debate between multidimensional and unidimensional perspectives of religiousness (Gorsuch, 1984); yet, no empirical study has tested this hierarchical model of religiousness related to adolescent health outcomes. The current study presents the first attempt to test a bifactor model of religiousness related to substance use among adolescents (N = 220, 45% female). Our bifactor model using structural equation modeling suggested the multidimensional nature of religiousness as well as the presence of a superordinate general religiousness factor directly explaining the covariation among the specific factors including organizational and personal religiousness and religious social support. The general religiousness factor was inversely related to substance use. After accounting for the contribution of the general religiousness factor, high organizational religiousness related to low substance use, whereas personal religiousness and religious support were positively related to substance use. The findings present the first evidence that supports hierarchical structures of adolescent religiousness that contribute differentially to adolescent substance use. Copyright © 2015 The Foundation for Professionals in Services for Adolescents. Published by Elsevier Ltd. All rights reserved.

A dynamic social systems model for considering structural factors in HIV prevention and detection

PubMed Central

Latkin, Carl; Weeks, Margaret; Glasman, Laura; Galletly, Carol; Albarracin, Dolores

2010-01-01

We present a model for HIV-related behaviors that emphasizes the dynamic and social nature of the structural factors that influence HIV prevention and detection. Key structural dimensions of the model include resources, science and technology, formal social control, informal social influences and control, social interconnectedness, and settings. These six dimensions can be conceptualized on macro, meso, and micro levels. Given the inherent complexity of structural factors and their interrelatedness, HIV prevention interventions may focus on different levels and dimensions. We employ a systems perspective to describe the interconnected and dynamic processes of change among social systems and their components. The topics of HIV testing and safer injection facilities are analyzed using this structural framework. Finally, we discuss methodological issues in the development and evaluation of structural interventions for HIV prevention and detection. PMID:20838871

Continuous Release of Tumor-Derived Factors Improves the Modeling of Cachexia in Muscle Cell Culture.

PubMed

Jackman, Robert W; Floro, Jess; Yoshimine, Rei; Zitin, Brian; Eiampikul, Maythita; El-Jack, Kahlid; Seto, Danielle N; Kandarian, Susan C

2017-01-01

Cachexia is strongly associated with a poor prognosis in cancer patients but the biological trigger is unknown and therefore no therapeutics exist. The loss of skeletal muscle is the most deleterious aspect of cachexia and it appears to depend on secretions from tumor cells. Models for studying wasting in cell culture consist of experiments where skeletal muscle cells are incubated with medium conditioned by tumor cells. This has led to candidates for cachectic factors but some of the features of cachexia in vivo are not yet well-modeled in cell culture experiments. Mouse myotube atrophy measured by myotube diameter in response to medium conditioned by mouse colon carcinoma cells (C26) is consistently less than what is seen in muscles of mice bearing C26 tumors with moderate to severe cachexia. One possible reason for this discrepancy is that in vivo the C26 tumor and skeletal muscle share a circulatory system exposing the muscle to tumor factors in a constant and increasing way. We have applied Transwell®-adapted cell culture conditions to more closely simulate conditions found in vivo where muscle is exposed to the ongoing kinetics of constant tumor secretion of active factors. C26 cells were incubated on a microporous membrane (a Transwell® insert) that constitutes the upper compartment of wells containing plated myotubes. In this model, myotubes are exposed to a constant supply of cancer cell secretions in the medium but without direct contact with the cancer cells, analogous to a shared circulation of muscle and cancer cells in tumor-bearing animals. The results for myotube diameter support the idea that the use of Transwell® inserts serves as a more physiological model of the muscle wasting associated with cancer cachexia than the bolus addition of cancer cell conditioned medium. The Transwell® model supports the notion that the dose and kinetics of cachectic factor delivery to muscle play a significant role in the extent of pathology.

Quantifying the importance of spatial resolution and other factors through global sensitivity analysis of a flood inundation model

NASA Astrophysics Data System (ADS)

Thomas Steven Savage, James; Pianosi, Francesca; Bates, Paul; Freer, Jim; Wagener, Thorsten

2016-11-01

Where high-resolution topographic data are available, modelers are faced with the decision of whether it is better to spend computational resource on resolving topography at finer resolutions or on running more simulations to account for various uncertain input factors (e.g., model parameters). In this paper we apply global sensitivity analysis to explore how influential the choice of spatial resolution is when compared to uncertainties in the Manning's friction coefficient parameters, the inflow hydrograph, and those stemming from the coarsening of topographic data used to produce Digital Elevation Models (DEMs). We apply the hydraulic model LISFLOOD-FP to produce several temporally and spatially variable model outputs that represent different aspects of flood inundation processes, including flood extent, water depth, and time of inundation. We find that the most influential input factor for flood extent predictions changes during the flood event, starting with the inflow hydrograph during the rising limb before switching to the channel friction parameter during peak flood inundation, and finally to the floodplain friction parameter during the drying phase of the flood event. Spatial resolution and uncertainty introduced by resampling topographic data to coarser resolutions are much more important for water depth predictions, which are also sensitive to different input factors spatially and temporally. Our findings indicate that the sensitivity of LISFLOOD-FP predictions is more complex than previously thought. Consequently, the input factors that modelers should prioritize will differ depending on the model output assessed, and the location and time of when and where this output is most relevant.

Repeated measurements of transfer factor in rabbits: an animal model suitable for evaluation of short-term exposure.

PubMed

Dahlqvist, M; Lagerstrand, L; Nilsen, A

1994-01-01

Acute temporary changes in lung function may be of use as a biological exposure indicator. However, studies of humans occupationally exposed to complex airborne irritants are often expensive and time demanding. Therefore, an animal model could be a valuable complement. A rabbit model has been evaluated where transfer factor was measured twice during the same day, and with the rabbit awake and available for exposure, in between. Anaesthesia and intubation in 22 rabbits (2.6 [0.2] kg [Mean (SD)]) were immediately followed by two measurements of transfer factor and alveolar volume. Transfer factor was estimated by the single breath CO-technique used in humans. The samples were analysed for CO and He on a gas chromatograph. After one pair of measurements the rabbit was allowed to wake up and after 5 h the duplicate measurements were repeated. The mean values of transfer factor, alveolar volume and transfer constant were 0.50 (0.09) mmol min-1 kPa-1, 127 (8) ml and 3.9 (0.6) mmol min-1 kPa-1 l-1, respectively. The intraindividual coefficients of variation were 7.3%, 5.3% and 6.7%, respectively. Five hours later when the duplicate measurements were repeated, transfer factor, alveolar volume and transfer constant were unchanged still. The results suggest that relatively small changes in transfer factor may be detected without losing power, and thus that this model could be used as a biological exposure indicator.

Studies on kinetics of water quality factors to establish water transparency model in Neijiang River, China.

PubMed

Li, Ronghui; Pan, Wei; Guo, Jinchuan; Pang, Yong; Wu, Jianqiang; Li, Yiping; Pan, Baozhu; Ji, Yong; Ding, Ling

2014-05-01

The basis for submerged plant restoration in surface water is to research the complicated dynamic mechanism of water transparency. In this paper, through the impact factor analysis of water transparency, the suspended sediment, dissolved organic matter, algae were determined as three main impactfactors for water transparency of Neijiang River in Eastern China. And the multiple regression equation of water transparency and sediment concentration, permanganate index, chlorophyll-a concentration was developed. Considering the complicated transport and transformation of suspended sediment, dissolved organic matter and algae, numerical model of them were developed respectively for simulating the dynamic process. Water transparency numerical model was finally developed by coupling the sediment, water quality, and algae model. These results showed that suspended sediment was a key factor influencing water transparency of Neijiang River, the influence of water quality indicated by chemical oxygen demand and algal concentration indicated by chlorophyll a were indeterminate when their concentrations were lower, the influence was more obvious when high concentrations are available, such three factors showed direct influence on water transparency.

Job Satisfaction and Personality: The Utility of the Five-Factor Model of Personality

DTIC Science & Technology

1999-03-01

represented by E, symbolizes the focus on the P-E interaction (Dawis, 1992). Even prior to Lewin, Parsons (1909) emphasized this same concept in the...Personality structure: Emergence of the five-factor model. Annual Review of Psychology. 41. 417-440. Ekehammar, B. (1974). Interactionism in personality

Bi-Factor Multidimensional Item Response Theory Modeling for Subscores Estimation, Reliability, and Classification

ERIC Educational Resources Information Center

Md Desa, Zairul Nor Deana

2012-01-01

In recent years, there has been increasing interest in estimating and improving subscore reliability. In this study, the multidimensional item response theory (MIRT) and the bi-factor model were combined to estimate subscores, to obtain subscores reliability, and subscores classification. Both the compensatory and partially compensatory MIRT…

Lord-Wingersky Algorithm Version 2.0 for Hierarchical Item Factor Models with Applications in Test Scoring, Scale Alignment, and Model Fit Testing.

PubMed

Cai, Li

2015-06-01

Lord and Wingersky's (Appl Psychol Meas 8:453-461, 1984) recursive algorithm for creating summed score based likelihoods and posteriors has a proven track record in unidimensional item response theory (IRT) applications. Extending the recursive algorithm to handle multidimensionality is relatively simple, especially with fixed quadrature because the recursions can be defined on a grid formed by direct products of quadrature points. However, the increase in computational burden remains exponential in the number of dimensions, making the implementation of the recursive algorithm cumbersome for truly high-dimensional models. In this paper, a dimension reduction method that is specific to the Lord-Wingersky recursions is developed. This method can take advantage of the restrictions implied by hierarchical item factor models, e.g., the bifactor model, the testlet model, or the two-tier model, such that a version of the Lord-Wingersky recursive algorithm can operate on a dramatically reduced set of quadrature points. For instance, in a bifactor model, the dimension of integration is always equal to 2, regardless of the number of factors. The new algorithm not only provides an effective mechanism to produce summed score to IRT scaled score translation tables properly adjusted for residual dependence, but leads to new applications in test scoring, linking, and model fit checking as well. Simulated and empirical examples are used to illustrate the new applications.

[Multiple risk factors models of patients with acute coronary syndromes of different genders].

PubMed

Sun, Wanglexian; Hu, Tiemin; Huang, Xiansheng; Zhang, Ying; Guo, Jinrui; Wang, Wenfeng; Shi, Fei; Wang, Pengfei; Wang, Huarong; Sun, Jing; Li, Chunhua

2014-12-23

To establish the multiple risk factors models for patients with acute coronary syndromes (ACS) of different genders and quantitatively assess the pathopoiesis of all factors. A total of 2 308 consecutive ACS inpatients and a control group of 256 cases with normal coronary artery from January 2010 to December 2012 were enrolled and divided into 4 groups of female ACS (n = 970), male ACS (n = 1 338), female control (n = 136) and male control (n = 120). All demographic and clinical data were collected by the physicians and master degree candidates in the division of cardiology. The Logistic regression models of multiple risk factors were established for ACS by different genders. More than 45 years of age, dyslipidemia, type 2 diabetes mellitus, obesity and hypertension were all independent risk factors of ACS for different genders (P < 0.05). However, the same risk factors had different pathogenic effects on ACS between genders. The odds ratio (OR) was markedly different for females and males: per 5-year increase aged over 45 years (1.45 vs 1.13), dyslipidemia (3.45 vs 1.68), type 2 diabetes mellitus (4.06 vs 2.33), obesity (2.93 vs 1.91) and hypertension (1.78 vs 3.80) respectively (all P < 0.05). In addition, current smoking increased the risk of ACS attack in males by 5.49 (P < 0.05) while not statistically significant in females. Particularly cerebral ischemic stroke increased the risk of ACS attack by 5.49 folds in males other than females (P < 0.05). Type 2 diabetes mellitus, dyslipidemia and obesity may present higher risks of ACS attack for females than males. And smoking and hypertension are much more dangerous for males. Males with cerebral infarction are more susceptible for ACS than females.

Martian Atmospheric Modeling of Scale Factors for MarsGRAM 2005 and the MAVEN Project

NASA Technical Reports Server (NTRS)

McCullough, Chris

2011-01-01

For spacecraft missions to Mars, especially the navigation of Martian orbiters and landers, an extensive knowledge of the Martian atmosphere is extremely important. The generally-accepted NASA standard for modeling (MarsGRAM), which was developed at Marshall Space Flight Center. MarsGRAM is useful for task such as aerobraking, performance analysis and operations planning for aerobraking, entry descent and landing, and aerocapture. Unfortunately, the densities for the Martian atmosphere in MarsGRAM are based on table look-up and not on an analytical algorithm. Also, these values can vary drastically from the densities actually experienced by the spacecraft. This does not have much of an impact on simple integrations but drastically affects its usefulness in other applications, especially those in navigation. For example, the navigation team for the Mars Atmosphere Volatile Environment (MAVEN) Project uses MarsGRAM to target the desired atmospheric density for the orbiter's pariapse passage, its closet approach to the planet. After the satellite's passage through pariapsis the computed density is compared to the MarsGRAM model and a scale factor is assigned to the model to account for the difference. Therefore, large variations in the atmosphere from the model can cause unexpected deviations from the spacecraft's planned trajectory. In order to account for this, an analytic stochastic model of the scale factor's behavior is desired. The development of this model will allow for the MAVEN navigation team to determine the probability of various Martian atmospheric variations and their effects on the spacecraft.

Factors influencing protein tyrosine nitration--structure-based predictive models.

PubMed

Bayden, Alexander S; Yakovlev, Vasily A; Graves, Paul R; Mikkelsen, Ross B; Kellogg, Glen E

2011-03-15

Models for exploring tyrosine nitration in proteins have been created based on 3D structural features of 20 proteins for which high-resolution X-ray crystallographic or NMR data are available and for which nitration of 35 total tyrosines has been experimentally proven under oxidative stress. Factors suggested in previous work to enhance nitration were examined with quantitative structural descriptors. The role of neighboring acidic and basic residues is complex: for the majority of tyrosines that are nitrated the distance to the heteroatom of the closest charged side chain corresponds to the distance needed for suspected nitrating species to form hydrogen bond bridges between the tyrosine and that charged amino acid. This suggests that such bridges play a very important role in tyrosine nitration. Nitration is generally hindered for tyrosines that are buried and for those tyrosines for which there is insufficient space for the nitro group. For in vitro nitration, closed environments with nearby heteroatoms or unsaturated centers that can stabilize radicals are somewhat favored. Four quantitative structure-based models, depending on the conditions of nitration, have been developed for predicting site-specific tyrosine nitration. The best model, relevant for both in vitro and in vivo cases, predicts 30 of 35 tyrosine nitrations (positive predictive value) and has a sensitivity of 60/71 (11 false positives). Copyright © 2010 Elsevier Inc. All rights reserved.

FACTORS INFLUENCING THE DESIGN OF BIOACCUMULATION FACTOR AND BIOTA-SEDIMENT ACCUMULATION FACTOR FIELD STUDIES

EPA Science Inventory

General guidance for designing field studies to measure bioaccumulation factors (BAFs) and biota-sediment accumulation factors (BSAFs) is not available. To develop such guidance, a series of modeling simulations were performed to evaluate the underlying factors and principles th...

A Multi-Domain Model of Risk Factors for ODD Symptoms in a Community Sample of 4-Year-Olds

ERIC Educational Resources Information Center

Lavigne, John V.; Gouze, Karen R.; Hopkins, Joyce; Bryant, Fred B.; LeBailly, Susan A.

2012-01-01

Few studies have been designed to assess the pathways by which risk factors are associated with symptoms of psychopathology across multiple domains, including contextual factors, parental depression, parenting, and child characteristics. The present study examines a cross-sectional model of risk factors for symptoms of Oppositional Defiant…

Review of Railgun Modeling Techniques: The Computation of Railgun Force and Other Key Factors

NASA Astrophysics Data System (ADS)

Eckert, Nathan James

Currently, railgun force modeling either uses the simple "railgun force equation" or finite element methods. It is proposed here that a middle ground exists that does not require the solution of partial differential equations, is more readily implemented than finite element methods, and is more accurate than the traditional force equation. To develop this method, it is necessary to examine the core railgun factors: power supply mechanisms, the distribution of current in the rails and in the projectile which slides between them (called the armature), the magnetic field created by the current flowing through these rails, the inductance gradient (a key factor in simplifying railgun analysis, referred to as L'), the resultant Lorentz force, and the heating which accompanies this action. Common power supply technologies are investigated, and the shape of their current pulses are modeled. The main causes of current concentration are described, and a rudimentary method for computing current distribution in solid rails and a rectangular armature is shown to have promising accuracy with respect to outside finite element results. The magnetic field is modeled with two methods using the Biot-Savart law, and generally good agreement is obtained with respect to finite element methods (5.8% error on average). To get this agreement, a factor of 2 is added to the original formulation after seeing a reliable offset with FEM results. Three inductance gradient calculations are assessed, and though all agree with FEM results, the Kerrisk method and a regression analysis method developed by Murugan et al. (referred to as the LRM here) perform the best. Six railgun force computation methods are investigated, including the traditional railgun force equation, an equation produced by Waindok and Piekielny, and four methods inspired by the work of Xu et al. Overall, good agreement between the models and outside data is found, but each model's accuracy varies significantly between

A Confirmatory Factor Analysis of the Structure of Statistics Anxiety Measure: An examination of four alternative models

PubMed Central

Vahedi, Shahram; Farrokhi, Farahman

2011-01-01

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. PMID:22952530

Validation of a multifactorial risk factor model used for predicting future caries risk with Nevada adolescents.

PubMed

Ditmyer, Marcia M; Dounis, Georgia; Howard, Katherine M; Mobley, Connie; Cappelli, David

2011-05-20

The objective of this study was to measure the validity and reliability of a multifactorial Risk Factor Model developed for use in predicting future caries risk in Nevada adolescents in a public health setting. This study examined retrospective data from an oral health surveillance initiative that screened over 51,000 students 13-18 years of age, attending public/private schools in Nevada across six academic years (2002/2003-2007/2008). The Risk Factor Model included ten demographic variables: exposure to fluoridation in the municipal water supply, environmental smoke exposure, race, age, locale (metropolitan vs. rural), tobacco use, Body Mass Index, insurance status, sex, and sealant application. Multiple regression was used in a previous study to establish which significantly contributed to caries risk. Follow-up logistic regression ascertained the weight of contribution and odds ratios of the ten variables. Researchers in this study computed sensitivity, specificity, positive predictive value (PVP), negative predictive value (PVN), and prevalence across all six years of screening to assess the validity of the Risk Factor Model. Subjects' overall mean caries prevalence across all six years was 66%. Average sensitivity across all six years was 79%; average specificity was 81%; average PVP was 89% and average PVN was 67%. Overall, the Risk Factor Model provided a relatively constant, valid measure of caries that could be used in conjunction with a comprehensive risk assessment in population-based screenings by school nurses/nurse practitioners, health educators, and physicians to guide them in assessing potential future caries risk for use in prevention and referral practices.

How do microclimate factors affect the risk for superficial pressure ulcers: a mathematical modeling study.

PubMed

Gefen, Amit

2011-08-01

In this study, a mathematical model is developed for analyzing the effects of the microclimate on skin tolerance to superficial pressure ulcers (SPUs). The modeling identified the following factors as such that decrease the tolerance of skin to SPUs: (i) increase in the skin temperature, (ii) increase in the ambient temperature, (iii) increase in the relative humidity, (iv) increase in the skin-support (or skin-clothing-support) contact pressures, and (v) decrease in permeabilities of the materials contacting the skin or being close to it, e.g. the covering sheet of the support and clothing. The modeling is consistent with relevant empirical findings and clinical observations documented in the literature, explains them from a basic science aspect, and can be further developed for design of interventions, safer patient clothing and supports that consider the optimization of microclimate factors. Copyright © 2010 Tissue Viability Society. Published by Elsevier Ltd. All rights reserved.

Associations between five-factor model of the Positive and Negative Syndrome Scale and plasma levels of monoamine metabolite in patients with schizophrenia.

PubMed

Watanabe, Kenya; Miura, Itaru; Kanno-Nozaki, Keiko; Horikoshi, Sho; Mashiko, Hirobumi; Niwa, Shin-Ichi; Yabe, Hirooki

2015-12-15

The five-factor model of the Positive and Negative Syndrome Scale (PANSS) for schizophrenia symptoms is the most common multiple-factor model used in analyses; its use may improve evaluation of symptoms in schizophrenia patients. Plasma monoamine metabolite levels are possible indicators of clinical symptoms or response to antipsychotics in schizophrenia. We investigated the association between five-factor model components and plasma monoamine metabolites levels to explore the model's biological basis. Plasma levels of homovanillic acid (HVA), 3-methoxy-4-hydroxyphenylglycol (MHPG), and 5-hydroxyindoleacetic acid (5-HIAA) were measured using high-performance liquid chromatography in 65 Japanese patients with schizophrenia. Significant negative correlation between plasma 5-HIAA levels and the depression/anxiety component was found. Furthermore, significant positive correlation was found between plasma MHPG levels and the excitement component. Plasma HVA levels were not correlated with any five-factor model component. These results suggest that the five-factor model of the PANSS may have a biological basis, and may be useful for elucidating the psychopathology of schizophrenia. Assessment using the five-factor model may enable understanding of monoaminergic dysfunction, possibly allowing more appropriate medication selection. Further studies of a larger number of first-episode schizophrenia patients are needed to confirm and extend these results. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

In vitro oral drug permeation models: the importance of taking physiological and physico-chemical factors into consideration.

PubMed

Joubert, Ruan; Steyn, Johan Dewald; Heystek, Hendrik Jacobus; Steenekamp, Jan Harm; Du Preez, Jan Lourens; Hamman, Josias Hendrik

2017-02-01

The assessment of intestinal membrane permeability properties of new chemical entities is a crucial step in the drug discovery and development process and a variety of in vitro models, methods and techniques are available to estimate the extent of oral drug absorption in humans. However, variations in certain physiological and physico-chemical factors are often not reflected in the results and the complex dynamic interplay between these factors is sometimes oversimplified with in vitro models. Areas covered: In vitro models to evaluate drug pharmacokinetics are briefly outlined, while both physiological and physico-chemical factors that may have an influence on these techniques are critically reviewed. The shortcomings identified for some of the in vitro techniques are discussed in conjunction with novel ways to improve and thereby overcome some challenges. Expert opinion: Although conventional in vitro methods and theories are used as basic guidelines to predict drug absorption, critical evaluations have identified some shortcomings. Advancements in technology have made it possible to investigate and understand the role of physiological and physico-chemical factors in drug delivery more clearly, which can be used to improve and refine the techniques to more closely mimic the in vivo environment.

Risk factors for the treatment outcome of retreated pulmonary tuberculosis patients in China: an optimized prediction model.

PubMed

Wang, X-M; Yin, S-H; Du, J; Du, M-L; Wang, P-Y; Wu, J; Horbinski, C M; Wu, M-J; Zheng, H-Q; Xu, X-Q; Shu, W; Zhang, Y-J

2017-07-01

Retreatment of tuberculosis (TB) often fails in China, yet the risk factors associated with the failure remain unclear. To identify risk factors for the treatment failure of retreated pulmonary tuberculosis (PTB) patients, we analyzed the data of 395 retreated PTB patients who received retreatment between July 2009 and July 2011 in China. PTB patients were categorized into 'success' and 'failure' groups by their treatment outcome. Univariable and multivariable logistic regression were used to evaluate the association between treatment outcome and socio-demographic as well as clinical factors. We also created an optimized risk score model to evaluate the predictive values of these risk factors on treatment failure. Of 395 patients, 99 (25·1%) were diagnosed as retreatment failure. Our results showed that risk factors associated with treatment failure included drug resistance, low education level, low body mass index (6 months), standard treatment regimen, retreatment type, positive culture result after 2 months of treatment, and the place where the first medicine was taken. An Optimized Framingham risk model was then used to calculate the risk scores of these factors. Place where first medicine was taken (temporary living places) received a score of 6, which was highest among all the factors. The predicted probability of treatment failure increases as risk score increases. Ten out of 359 patients had a risk score >9, which corresponded to an estimated probability of treatment failure >70%. In conclusion, we have identified multiple clinical and socio-demographic factors that are associated with treatment failure of retreated PTB patients. We also created an optimized risk score model that was effective in predicting the retreatment failure. These results provide novel insights for the prognosis and improvement of treatment for retreated PTB patients.

[Relative factors in home safety evaluated by multilevel statistical models].

PubMed

Wang, Shu-mei; Zou, Jin-liang; Xu, Wen-yan; Yin, Ming-min

2010-09-01

To understand the situations of home establishments and the related domestic behaviors that causing injuries. A total of 9760 families with regular residents in a community in Shanghai were investigated. Questionnaire was designed based on the Guidelines for Conducting Community Surveys on Injuries and Violence and International Classification of External Causes of Injuries. Inside the home settings, relative factors were found on issues as fire protection and using of electricity. In terms of household settings, 14.85% of the families had smoke alarm systems in the kitchen; 40.75% had no windows set for emergence. 50% to 70% of the residents had the idea of safe behaviors, including 35.93% of the families stored cleaning products, other chemicals or medical substances in alternative containers, such as used drinking bottle. Only 1.81% of the people being investigated thought that home was also a high risk place where injury might occur and it was placed number 9 in a multiple choice questionnaire. Data from the multiple level model analysis showed that factors as number of family members, space, education, profession etc. were closely related to the situation of home settings and their resident's behaviors. Many injury related factors were found related to home establishments and people's daily behaviors at home which called for interventions to be taken.

Understanding Self-Sufficiency of Welfare Leavers in Illinois: Elaborating Models with Psychosocial Factors.

ERIC Educational Resources Information Center

Julnes, George; Fan, Xitao; Hayashi, Kentaro

2001-01-01

Used survey (for 1,001 adults) and administrative data (for 137,330 first-exit cases) in structural equation modeling to examine psychological and social factors as determinants of welfare dependency and self-sufficiency. Findings show well-being to be a predictor of low recidivism and high employment. (SLD)

Conceptualizations of Personality Disorders with the Five Factor Model-Count and Empathy Traits

ERIC Educational Resources Information Center

Kajonius, Petri J.; Dåderman, Anna M.

2017-01-01

Previous research has long advocated that emotional and behavioral disorders are related to general personality traits, such as the Five Factor Model (FFM). The addition of section III in the latest "Diagnostic and Statistical Manual of Mental Disorders" (DSM) recommends that extremity in personality traits together with maladaptive…

Identifying environmental variables explaining genotype-by-environment interaction for body weight of rainbow trout (Onchorynchus mykiss): reaction norm and factor analytic models.

PubMed

Sae-Lim, Panya; Komen, Hans; Kause, Antti; Mulder, Han A

2014-02-26

Identifying the relevant environmental variables that cause GxE interaction is often difficult when they cannot be experimentally manipulated. Two statistical approaches can be applied to address this question. When data on candidate environmental variables are available, GxE interaction can be quantified as a function of specific environmental variables using a reaction norm model. Alternatively, a factor analytic model can be used to identify the latent common factor that explains GxE interaction. This factor can be correlated with known environmental variables to identify those that are relevant. Previously, we reported a significant GxE interaction for body weight at harvest in rainbow trout reared on three continents. Here we explore their possible causes. Reaction norm and factor analytic models were used to identify which environmental variables (age at harvest, water temperature, oxygen, and photoperiod) may have caused the observed GxE interaction. Data on body weight at harvest was recorded on 8976 offspring reared in various locations: (1) a breeding environment in the USA (nucleus), (2) a recirculating aquaculture system in the Freshwater Institute in West Virginia, USA, (3) a high-altitude farm in Peru, and (4) a low-water temperature farm in Germany. Akaike and Bayesian information criteria were used to compare models. The combination of days to harvest multiplied with daily temperature (Day*Degree) and photoperiod were identified by the reaction norm model as the environmental variables responsible for the GxE interaction. The latent common factor that was identified by the factor analytic model showed the highest correlation with Day*Degree. Day*Degree and photoperiod were the environmental variables that differed most between Peru and other environments. Akaike and Bayesian information criteria indicated that the factor analytical model was more parsimonious than the reaction norm model. Day*Degree and photoperiod were identified as environmental

Identifying environmental variables explaining genotype-by-environment interaction for body weight of rainbow trout (Onchorynchus mykiss): reaction norm and factor analytic models

PubMed Central

2014-01-01

Background Identifying the relevant environmental variables that cause GxE interaction is often difficult when they cannot be experimentally manipulated. Two statistical approaches can be applied to address this question. When data on candidate environmental variables are available, GxE interaction can be quantified as a function of specific environmental variables using a reaction norm model. Alternatively, a factor analytic model can be used to identify the latent common factor that explains GxE interaction. This factor can be correlated with known environmental variables to identify those that are relevant. Previously, we reported a significant GxE interaction for body weight at harvest in rainbow trout reared on three continents. Here we explore their possible causes. Methods Reaction norm and factor analytic models were used to identify which environmental variables (age at harvest, water temperature, oxygen, and photoperiod) may have caused the observed GxE interaction. Data on body weight at harvest was recorded on 8976 offspring reared in various locations: (1) a breeding environment in the USA (nucleus), (2) a recirculating aquaculture system in the Freshwater Institute in West Virginia, USA, (3) a high-altitude farm in Peru, and (4) a low-water temperature farm in Germany. Akaike and Bayesian information criteria were used to compare models. Results The combination of days to harvest multiplied with daily temperature (Day*Degree) and photoperiod were identified by the reaction norm model as the environmental variables responsible for the GxE interaction. The latent common factor that was identified by the factor analytic model showed the highest correlation with Day*Degree. Day*Degree and photoperiod were the environmental variables that differed most between Peru and other environments. Akaike and Bayesian information criteria indicated that the factor analytical model was more parsimonious than the reaction norm model. Conclusions Day*Degree and

[Risk factors on the recurrence of ischemic stroke and the establishment of a Cox's regression model].

PubMed

An, Ya-chen; Chen, Yun-xia; Wang, Yu-xun; Zhao, Xiao-jing; Wang, Yan; Zhang, Jiang; Li, Chun-ling; Peng, Yan-bo; Gao, Su-ling; Chang, Li-sha; Zhang, Li; Xue, Xin-hong; Chen, Rui-ying; Wang, Da-li

2011-08-01

To investigate the risk factors and establish the Cox's regression model on the recurrence of ischemic stroke. We retrospectively reviewed consecutive patients with ischemic stroke admitted to the Neurology Department of the Hebei United University Affiliated Hospital between January 1, 2008 and December 31, 2009. Cases had been followed since the onset of ischemic stroke. The follow-up program was finished in June 30, 2010. Kaplan-Meier methods were used to describe the recurrence rate. Monovariant and multivariate Cox's proportional hazard regression model were used to analyze the risk factors associated to the episodes of recurrence. And then, a recurrence model was set up. During the period of follow-up program, 79 cases were relapsed, with the recurrence rates as 12.75% in one year and 18.87% in two years. Monovariant and multivariate Cox's proportional hazard regression model showed that the independent risk factors that were associated with the recurrence appeared to be age (X₁) (RR = 1.025, 95%CI: 1.003 - 1.048), history of hypertension (X₂) (RR = 1.976, 95%CI: 1.014 - 3.851), history of family strokes (X₃) (RR = 2.647, 95%CI: 1.175 - 5.961), total cholesterol amount (X₄) (RR = 1.485, 95%CI: 1.214 - 1.817), ESRS total scores (X₅) (RR = 1.327, 95%CI: 1.057 - 1.666) and progression of the disease (X₆) (RR = 1.889, 95%CI: 1.123 - 3.178). Personal prognosis index (PI) of the recurrence model was as follows: PI = 0.025X₁ + 0.681X₂ + 0.973X₃ + 0.395X₄ + 0.283X₅ + 0.636X₆. The smaller the personal prognosis index was, the lower the recurrence risk appeared, while the bigger the personal prognosis index was, the higher the recurrence risk appeared. Age, history of hypertension, total cholesterol amount, total scores of ESRS, together with the disease progression were the independent risk factors associated with the recurrence episodes of ischemic stroke. Both recurrence model and the personal prognosis index equation were successful

An evaluation of evaluative personality terms: a comparison of the big seven and five-factor model in predicting psychopathology.

PubMed

Durrett, Christine; Trull, Timothy J

2005-09-01

Two personality models are compared regarding their relationship with personality disorder (PD) symptom counts and with lifetime Axis I diagnoses. These models share 5 similar domains, and the Big 7 model also includes 2 domains assessing self-evaluation: positive and negative valence. The Big 7 model accounted for more variance in PDs than the 5-factor model, primarily because of the association of negative valence with most PDs. Although low-positive valence was associated with most Axis I diagnoses, the 5-factor model generally accounted for more variance in Axis I diagnoses than the Big 7 model. Some predicted associations between self-evaluation and psychopathology were not found, and unanticipated associations emerged. These findings are discussed regarding the utility of evaluative terms in clinical assessment.

Human factors evaluation of the HL-20 full-scale model

NASA Astrophysics Data System (ADS)

Willshire, Kelli F.; Simonsen, Lisa C.; Willshire, William L., Jr.

1993-09-01

The human factors testing of the HL-20 personnel launch system full-scale model was conducted in both the vertical and horizontal positions at NASA Langley Research Center. Three main areas of testing were considered: an anthropometric fit evaluation, the ingress and egress of a 10-person crew, and pilot viewing. The subjects, ranging from the 5th to 95th percentile size, had sufficient clearance in the model, with the exception of the last two rows of seats and the cockpit area. Adjustable seat heights and/or placement of the seats farther forward would provide more headroom. In the horizontal position, the model's seat placement and aisle width allowed a quick and orderly 10-person egress for the no-keel (a structural support running the length on the aisle), 6-in.-high keel, and 12-in.-high keel conditions. Egress times were less than 20 s. For the vertical position, the model's long cylindrical shape with the ladder in the ceiling allowed a quick and orderly egress with average times less than 30 s. Ingress and egress procedures were demonstrated using shuttle partial-pressure suits. The reduced mobility experienced while wearing the suits did increase egress times, although they still remained acceptable. The window arrangement for pilot viewing was found to be reasonably acceptable, although slight modifications, such as an increased downward view, is desirable.

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

NASA Astrophysics Data System (ADS)

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.

Manufacturing and Cost Considerations in Multidisciplinary Aircraft Design (Research on Mathematical Modeling of Manufacturability Factors)

NASA Technical Reports Server (NTRS)

Rais-Rohani, Masoud

1996-01-01

The identification of airframe Manufacturability Factors/Cost Drivers (MFCD) and the method by which the relationships between MFCD and designer-controlled parameters could be properly modeled are described.

Community-Engaged Modeling of Geographic and Demographic Patterns of Multiple Public Health Risk Factors