Scale of association: hierarchical linear models and the measurement of ecological systems

Treesearch

Sean M. McMahon; Jeffrey M. Diez

2007-01-01

A fundamental challenge to understanding patterns in ecological systems lies in employing methods that can analyse, test and draw inference from measured associations between variables across scales. Hierarchical linear models (HLM) use advanced estimation algorithms to measure regression relationships and variance-covariance parameters in hierarchically structured...

Application of Hierarchical Linear Models/Linear Mixed-Effects Models in School Effectiveness Research

ERIC Educational Resources Information Center

Ker, H. W.

2014-01-01

Multilevel data are very common in educational research. Hierarchical linear models/linear mixed-effects models (HLMs/LMEs) are often utilized to analyze multilevel data nowadays. This paper discusses the problems of utilizing ordinary regressions for modeling multilevel educational data, compare the data analytic results from three regression…

Hierarchical structure in sharply divided phase space for the piecewise linear map

NASA Astrophysics Data System (ADS)

Akaishi, Akira; Aoki, Kazuki; Shudo, Akira

2017-05-01

We have studied a two-dimensional piecewise linear map to examine how the hierarchical structure of stable regions affects the slow dynamics in Hamiltonian systems. In the phase space there are infinitely many stable regions, each of which is polygonal-shaped, and the rest is occupied by chaotic orbits. By using symbolic representation of stable regions, a procedure to compute the edges of the polygons is presented. The stable regions are hierarchically distributed in phase space and the edges of the stable regions show the marginal instability. The cumulative distribution of the recurrence time obeys a power law as ˜t-2 , the same as the one for the system with phase space, which is composed of a single stable region and chaotic components. By studying the symbol sequence of recurrence trajectories, we show that the hierarchical structure of stable regions has no significant effect on the power-law exponent and that only the marginal instability on the boundary of stable regions is responsible for determining the exponent. We also discuss the relevance of the hierarchical structure to those in more generic chaotic systems.

To Aggregate or Not and Potentially Better Questions for Clustered Data: The Need for Hierarchical Linear Modeling in CTE Research

ERIC Educational Resources Information Center

Nimon, Kim

2012-01-01

Using state achievement data that are openly accessible, this paper demonstrates the application of hierarchical linear modeling within the context of career technical education research. Three prominent approaches to analyzing clustered data (i.e., modeling aggregated data, modeling disaggregated data, modeling hierarchical data) are discussed…

Bayesian Correction for Misclassification in Multilevel Count Data Models.

PubMed

Nelson, Tyler; Song, Joon Jin; Chin, Yoo-Mi; Stamey, James D

2018-01-01

Covariate misclassification is well known to yield biased estimates in single level regression models. The impact on hierarchical count models has been less studied. A fully Bayesian approach to modeling both the misclassified covariate and the hierarchical response is proposed. Models with a single diagnostic test and with multiple diagnostic tests are considered. Simulation studies show the ability of the proposed model to appropriately account for the misclassification by reducing bias and improving performance of interval estimators. A real data example further demonstrated the consequences of ignoring the misclassification. Ignoring misclassification yielded a model that indicated there was a significant, positive impact on the number of children of females who observed spousal abuse between their parents. When the misclassification was accounted for, the relationship switched to negative, but not significant. Ignoring misclassification in standard linear and generalized linear models is well known to lead to biased results. We provide an approach to extend misclassification modeling to the important area of hierarchical generalized linear models.

An Analysis of Turkey's PISA 2015 Results Using Two-Level Hierarchical Linear Modelling

ERIC Educational Resources Information Center

Atas, Dogu; Karadag, Özge

2017-01-01

In the field of education, most of the data collected are multi-level structured. Cities, city based schools, school based classes and finally students in the classrooms constitute a hierarchical structure. Hierarchical linear models give more accurate results compared to standard models when the data set has a structure going far as individuals,…

Determining Predictor Importance in Hierarchical Linear Models Using Dominance Analysis

ERIC Educational Resources Information Center

Luo, Wen; Azen, Razia

2013-01-01

Dominance analysis (DA) is a method used to evaluate the relative importance of predictors that was originally proposed for linear regression models. This article proposes an extension of DA that allows researchers to determine the relative importance of predictors in hierarchical linear models (HLM). Commonly used measures of model adequacy in…

The Advantages of Hierarchical Linear Modeling. ERIC/AE Digest.

ERIC Educational Resources Information Center

Osborne, Jason W.

This digest introduces hierarchical data structure, describes how hierarchical models work, and presents three approaches to analyzing hierarchical data. Hierarchical, or nested data, present several problems for analysis. People or creatures that exist within hierarchies tend to be more similar to each other than people randomly sampled from the…

Evaluating the Impacts of ICT Use: A Multi-Level Analysis with Hierarchical Linear Modeling

ERIC Educational Resources Information Center

Song, Hae-Deok; Kang, Taehoon

2012-01-01

The purpose of this study is to evaluate the impacts of ICT use on achievements by considering not only ICT use, but also the process and background variables that influence ICT use at both the student- and school-level. This study was conducted using data from the 2010 Survey of Seoul Education Longitudinal Research. A Hierarchical Linear…

A Hierarchical Linear Model for Estimating Gender-Based Earnings Differentials.

ERIC Educational Resources Information Center

Haberfield, Yitchak; Semyonov, Moshe; Addi, Audrey

1998-01-01

Estimates of gender earnings inequality in data from 116,431 Jewish workers were compared using a hierarchical linear model (HLM) and ordinary least squares model. The HLM allows estimation of the extent to which earnings inequality depends on occupational characteristics. (SK)

Avoiding Boundary Estimates in Hierarchical Linear Models through Weakly Informative Priors

ERIC Educational Resources Information Center

Chung, Yeojin; Rabe-Hesketh, Sophia; Gelman, Andrew; Dorie, Vincent; Liu, Jinchen

2012-01-01

Hierarchical or multilevel linear models are widely used for longitudinal or cross-sectional data on students nested in classes and schools, and are particularly important for estimating treatment effects in cluster-randomized trials, multi-site trials, and meta-analyses. The models can allow for variation in treatment effects, as well as…

In Which Ways and to What Extent Do English and Shanghai Students Understand Linear Function?

ERIC Educational Resources Information Center

Wang, Yuqian; Barmby, Patrick; Bolden, David

2017-01-01

This study investigates how students in England and Shanghai understand linear function. Understanding is defined theoretically in terms of five hierarchical levels: Dependent Relationship; Connecting Representations; Property Noticing; Object Analysis; and Inventising. A pilot study instrument presented a set of problems to both cohorts, showing…

A Closer Look at Charter Schools Using Hierarchical Linear Modeling. NCES 2006-460

ERIC Educational Resources Information Center

Braun, Henry; Jenkins, Frank; Grigg, Wendy

2006-01-01

Charter schools are a relatively new, but fast-growing, phenomenon in American public education. As such, they merit the attention of all parties interested in the education of the nation's youth. The present report comprises two separate analyses. The first is a "combined analysis" in which hierarchical linear models (HLMs) were…

Hierarchical Linear Modeling (HLM): An Introduction to Key Concepts within Cross-Sectional and Growth Modeling Frameworks. Technical Report #1308

ERIC Educational Resources Information Center

Anderson, Daniel

2012-01-01

This manuscript provides an overview of hierarchical linear modeling (HLM), as part of a series of papers covering topics relevant to consumers of educational research. HLM is tremendously flexible, allowing researchers to specify relations across multiple "levels" of the educational system (e.g., students, classrooms, schools, etc.).…

Personalized Medicine Enrichment Design for DHA Supplementation Clinical Trial.

PubMed

Lei, Yang; Mayo, Matthew S; Carlson, Susan E; Gajewski, Byron J

2017-03-01

Personalized medicine aims to match patient subpopulation to the most beneficial treatment. The purpose of this study is to design a prospective clinical trial in which we hope to achieve the highest level of confirmation in identifying and making treatment recommendations for subgroups, when the risk levels in the control arm can be ordered. This study was motivated by our goal to identify subgroups in a DHA (docosahexaenoic acid) supplementation trial to reduce preterm birth (gestational age<37 weeks) rate. We performed a meta-analysis to obtain informative prior distributions and simulated operating characteristics to ensure that overall Type I error rate was close to 0.05 in designs with three different models: independent, hierarchical, and dynamic linear models. We performed simulations and sensitivity analysis to examine the subgroup power of models and compared results to a chi-square test. We performed simulations under two hypotheses: a large overall treatment effect and a small overall treatment effect. Within each hypothesis, we designed three different subgroup effects scenarios where resulting subgroup rates are linear, flat, or nonlinear. When the resulting subgroup rates are linear or flat, dynamic linear model appeared to be the most powerful method to identify the subgroups with a treatment effect. It also outperformed other methods when resulting subgroup rates are nonlinear and the overall treatment effect is big. When the resulting subgroup rates are nonlinear and the overall treatment effect is small, hierarchical model and chi-square test did better. Compared to independent and hierarchical models, dynamic linear model tends to be relatively robust and powerful when the control arm has ordinal risk subgroups.

Predicting Longitudinal Change in Language Production and Comprehension in Individuals with Down Syndrome: Hierarchical Linear Modeling.

ERIC Educational Resources Information Center

Chapman, Robin S.; Hesketh, Linda J.; Kistler, Doris J.

2002-01-01

Longitudinal change in syntax comprehension and production skill, measured over six years, was modeled in 31 individuals (ages 5-20) with Down syndrome. The best fitting Hierarchical Linear Modeling model of comprehension uses age and visual and auditory short-term memory as predictors of initial status, and age for growth trajectory. (Contains…

Parameter Recovery for the 1-P HGLLM with Non-Normally Distributed Level-3 Residuals

ERIC Educational Resources Information Center

Kara, Yusuf; Kamata, Akihito

2017-01-01

A multilevel Rasch model using a hierarchical generalized linear model is one approach to multilevel item response theory (IRT) modeling and is referred to as a one-parameter hierarchical generalized linear logistic model (1-P HGLLM). Although it has the flexibility to model nested structure of data with covariates, the model assumes the normality…

A generalized linear factor model approach to the hierarchical framework for responses and response times.

PubMed

Molenaar, Dylan; Tuerlinckx, Francis; van der Maas, Han L J

2015-05-01

We show how the hierarchical model for responses and response times as developed by van der Linden (2007), Fox, Klein Entink, and van der Linden (2007), Klein Entink, Fox, and van der Linden (2009), and Glas and van der Linden (2010) can be simplified to a generalized linear factor model with only the mild restriction that there is no hierarchical model at the item side. This result is valuable as it enables all well-developed modelling tools and extensions that come with these methods. We show that the restriction we impose on the hierarchical model does not influence parameter recovery under realistic circumstances. In addition, we present two illustrative real data analyses to demonstrate the practical benefits of our approach. © 2014 The British Psychological Society.

Coevolution of dependency distance, hierarchical structure and word order. Comment on "Dependency distance: a new perspective on syntactic patterns in natural languages" by Haitao Liu et al.

NASA Astrophysics Data System (ADS)

Jing, Yingqi

2017-07-01

Exploring the relationships between structural rules and their linearization constraints have been a central issue in formal syntax and linguistic typology [1]. Liu et al. give a historical overview of the investigation of dependency distance minimization (DDM) in various fields, and specify its potential connections with the graphic patterns of syntactic structure and the linear ordering of words and constituents in real sentences [2]. This comment focuses on discussing the relations between dependency distance (DD), hierarchical structure and word order, and advocates further study on the coevolution of these traits in language histories.

Hypertext comprehension of deaf and hard-of-hearing students and students with specific language impairment.

PubMed

Blom, Helen; Segers, Eliane; Hermans, Daan; Knoors, Harry; Verhoeven, Ludo

2017-02-01

This paper provides insight into the reading comprehension of hierarchically structured hypertexts within D/HH students and students with SLI. To our knowledge, it is the first study on hypertext comprehension in D/HH students and students with SLI, and it also considers the role of working memory. We compared hypertext versus linear text comprehension in D/HH students and students with SLI versus younger students without language problems who had a similar level of decoding and vocabulary. The results demonstrated no difference in text comprehension between the hierarchically structured hypertext and the linear text. Text comprehension of D/HH students and students with SLI was comparable to that of the students without language problems. In addition, there was a similar positive predictive value of visuospatial and not verbal working memory on hypertext comprehension for all three groups. The findings implicate that educational settings can make use of hierarchically structured hypertexts as well as linear texts and that children can navigate in the digital world from young age on, even if language or working memory problems are present. Copyright © 2016 Elsevier Ltd. All rights reserved.

Hierarchical and non-hierarchical {lambda} elements for one dimensional problems with unknown strength of singularity

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

Wong, K.K.; Surana, K.S.

1996-10-01

This paper presents a new and general procedure for designing hierarchical and non-hierarchical special elements called {lambda} elements for one dimensional singular problems where the strength of the singularity is unknown. The {lambda} element formulations presented here permit correct numerical simulation of linear as well as non-linear singular problems without a priori knowledge of the strength of the singularity. A procedure is also presented for determining the exact strength of the singularity using the converged solution. It is shown that in special instances, the general formulation of {lambda} elements can also be made hierarchical. The {lambda} elements presented here aremore » of type C{sup 0} and provide C{sup 0} inter-element continuity with p-version elements. One dimensional steady state radial flow of an upper convected Maxwell fluid is considered as a sample problem. Since in this case {lambda}{sub i} are known, this problem provides a good example for investigating the performance of the formulation proposed here. Least squares approach (or Least Squares Finite Element Formulation: LSFEF) is used to construct the integral form (error functional I) from the differential equations. Numerical studies are presented for radially inward flow of an upper convected Maxwell fluid with inner radius r{sub i} = .1 and .01 etc. and Deborah number De = 2.« less

The Impact of Historically Black Colleges and Universities on the Academic Success of African-American Students

ERIC Educational Resources Information Center

Kim, Mikyong Minsun; Conrad, Clifton F.

2006-01-01

Anchored in national longitudinal data analyzed through hierarchical linear and non-linear modeling, this study found that African-American students have a similar probability of obtaining a BA degree whether they attended a historically Black college or university (HBCU) or a historically White college or university (HWCU). Among…

Hierarchical Linear Modeling Analyses of NEO-PI-R Scales In the Baltimore Longitudinal Study of Aging

PubMed Central

Terracciano, Antonio; McCrae, Robert R.; Brant, Larry J.; Costa, Paul T.

2009-01-01

We examined age trends in the five factors and 30 facets assessed by the Revised NEO Personality Inventory in Baltimore Longitudinal Study of Aging data (N = 1,944; 5,027 assessments) collected between 1989 and 2004. Consistent with cross-sectional results, Hierarchical Linear Modeling analyses showed gradual personality changes in adulthood: a decline up to age 80 in Neuroticism, stability and then decline in Extraversion, decline in Openness, increase in Agreeableness, and increase up to age 70 in Conscientiousness. Some facets showed different curves from the factor they define. Birth cohort effects were modest, and there were no consistent Gender × Age interactions. Significant non-normative changes were found for all five factors; they were not explained by attrition but might be due to genetic factors, disease, or life experience. PMID:16248708

Multicollinearity in hierarchical linear models.

PubMed

Yu, Han; Jiang, Shanhe; Land, Kenneth C

2015-09-01

This study investigates an ill-posed problem (multicollinearity) in Hierarchical Linear Models from both the data and the model perspectives. We propose an intuitive, effective approach to diagnosing the presence of multicollinearity and its remedies in this class of models. A simulation study demonstrates the impacts of multicollinearity on coefficient estimates, associated standard errors, and variance components at various levels of multicollinearity for finite sample sizes typical in social science studies. We further investigate the role multicollinearity plays at each level for estimation of coefficient parameters in terms of shrinkage. Based on these analyses, we recommend a top-down method for assessing multicollinearity in HLMs that first examines the contextual predictors (Level-2 in a two-level model) and then the individual predictors (Level-1) and uses the results for data collection, research problem redefinition, model re-specification, variable selection and estimation of a final model. Copyright © 2015 Elsevier Inc. All rights reserved.

Differentiation of Students' Reasoning on Linear and Quadratic Geometric Number Patterns

ERIC Educational Resources Information Center

Lin, Fou-Lai; Yang, Kai-Lin

2004-01-01

There are two purposes in this study. One is to compare how 7th and 8th graders reason on linear and quadratic geometric number patterns when they have not learned this kind of tasks in school. The other is to explore the hierarchical relations among the four components of reasoning on geometric number patterns: understanding, generalizing,…

Hierarchical flexural strength of enamel: transition from brittle to damage-tolerant behaviour

PubMed Central

Bechtle, Sabine; Özcoban, Hüseyin; Lilleodden, Erica T.; Huber, Norbert; Schreyer, Andreas; Swain, Michael V.; Schneider, Gerold A.

2012-01-01

Hard, biological materials are generally hierarchically structured from the nano- to the macro-scale in a somewhat self-similar manner consisting of mineral units surrounded by a soft protein shell. Considerable efforts are underway to mimic such materials because of their structurally optimized mechanical functionality of being hard and stiff as well as damage-tolerant. However, it is unclear how different hierarchical levels interact to achieve this performance. In this study, we consider dental enamel as a representative, biological hierarchical structure and determine its flexural strength and elastic modulus at three levels of hierarchy using focused ion beam (FIB) prepared cantilevers of micrometre size. The results are compared and analysed using a theoretical model proposed by Jäger and Fratzl and developed by Gao and co-workers. Both properties decrease with increasing hierarchical dimension along with a switch in mechanical behaviour from linear-elastic to elastic-inelastic. We found Gao's model matched the results very well. PMID:22031729

When linearity prevails over hierarchy in syntax

PubMed Central

Willer Gold, Jana; Arsenijević, Boban; Batinić, Mia; Becker, Michael; Čordalija, Nermina; Kresić, Marijana; Leko, Nedžad; Marušič, Franc Lanko; Milićev, Tanja; Milićević, Nataša; Mitić, Ivana; Peti-Stantić, Anita; Stanković, Branimir; Šuligoj, Tina; Tušek, Jelena; Nevins, Andrew

2018-01-01

Hierarchical structure has been cherished as a grammatical universal. We use experimental methods to show where linear order is also a relevant syntactic relation. An identical methodology and design were used across six research sites on South Slavic languages. Experimental results show that in certain configurations, grammatical production can in fact favor linear order over hierarchical structure. However, these findings are limited to coordinate structures and distinct from the kind of production errors found with comparable configurations such as “attraction” errors. The results demonstrate that agreement morphology may be computed in a series of steps, one of which is partly independent from syntactic hierarchy. PMID:29288218

A hierarchical model for estimating change in American Woodcock populations

USGS Publications Warehouse

Sauer, J.R.; Link, W.A.; Kendall, W.L.; Kelley, J.R.; Niven, D.K.

2008-01-01

The Singing-Ground Survey (SGS) is a primary source of information on population change for American woodcock (Scolopax minor). We analyzed the SGS using a hierarchical log-linear model and compared the estimates of change and annual indices of abundance to a route regression analysis of SGS data. We also grouped SGS routes into Bird Conservation Regions (BCRs) and estimated population change and annual indices using BCRs within states and provinces as strata. Based on the hierarchical model?based estimates, we concluded that woodcock populations were declining in North America between 1968 and 2006 (trend = -0.9%/yr, 95% credible interval: -1.2, -0.5). Singing-Ground Survey results are generally similar between analytical approaches, but the hierarchical model has several important advantages over the route regression. Hierarchical models better accommodate changes in survey efficiency over time and space by treating strata, years, and observers as random effects in the context of a log-linear model, providing trend estimates that are derived directly from the annual indices. We also conducted a hierarchical model analysis of woodcock data from the Christmas Bird Count and the North American Breeding Bird Survey. All surveys showed general consistency in patterns of population change, but the SGS had the shortest credible intervals. We suggest that population management and conservation planning for woodcock involving interpretation of the SGS use estimates provided by the hierarchical model.

Hierarchical clustering in chameleon f(R) gravity

NASA Astrophysics Data System (ADS)

Hellwing, Wojciech A.; Li, Baojiu; Frenk, Carlos S.; Cole, Shaun

2013-11-01

We use a suite of high-resolution state-of-the-art N-body dark matter simulations of chameleon f(R) gravity to study the higher order volume-averaged correlation functions overline{ξ _n} together with the hierarchical nth-order correlation amplitudes S_n=overline{ξ }_n/overline{ξ }_2^{n-1} and density distribution functions (PDF). We show that under the non-linear modifications of gravity the hierarchical scaling of the reduced cumulants is preserved. This is however characterized by significant changes in the values of both overline{ξ _n} and Sn and their scale dependence with respect to General Relativity gravity (GR). In addition, we measure a significant increase of the non-linear σ8 parameter reaching 14, 5 and 0.5 per cent in excess of the GR value for the three flavours of our f(R) models. We further note that the values of the reduced cumulants up to order n = 9 are significantly increased in f(R) gravity for all our models at small scales R ≲ 30 h-1 Mpc. In contrast, the values of the hierarchical amplitudes, Sn, are smaller in f(R) indicating that the modified gravity density distribution functions are deviating from the GR case. Furthermore, we find that the redshift evolution of relative deviations of the f(R) hierarchical correlation amplitudes is fastest at high and moderate redshifts 1 ≤ z ≤ 4. The growth of these deviations significantly slows down in the low-redshift universe. We also compute the PDFs and show that for scales below ˜20 h-1 Mpc, they are significantly shifted in f(R) gravity towards the low densities. Finally, we discuss the implications of our theoretical predictions for measurements of the hierarchical clustering in galaxy redshift surveys, including the important problems of the galaxy biasing and redshift space distortions.

Testing higher-order Lagrangian perturbation theory against numerical simulation. 1: Pancake models

NASA Technical Reports Server (NTRS)

Buchert, T.; Melott, A. L.; Weiss, A. G.

1993-01-01

We present results showing an improvement of the accuracy of perturbation theory as applied to cosmological structure formation for a useful range of quasi-linear scales. The Lagrangian theory of gravitational instability of an Einstein-de Sitter dust cosmogony investigated and solved up to the third order is compared with numerical simulations. In this paper we study the dynamics of pancake models as a first step. In previous work the accuracy of several analytical approximations for the modeling of large-scale structure in the mildly non-linear regime was analyzed in the same way, allowing for direct comparison of the accuracy of various approximations. In particular, the Zel'dovich approximation (hereafter ZA) as a subclass of the first-order Lagrangian perturbation solutions was found to provide an excellent approximation to the density field in the mildly non-linear regime (i.e. up to a linear r.m.s. density contrast of sigma is approximately 2). The performance of ZA in hierarchical clustering models can be greatly improved by truncating the initial power spectrum (smoothing the initial data). We here explore whether this approximation can be further improved with higher-order corrections in the displacement mapping from homogeneity. We study a single pancake model (truncated power-spectrum with power-spectrum with power-index n = -1) using cross-correlation statistics employed in previous work. We found that for all statistical methods used the higher-order corrections improve the results obtained for the first-order solution up to the stage when sigma (linear theory) is approximately 1. While this improvement can be seen for all spatial scales, later stages retain this feature only above a certain scale which is increasing with time. However, third-order is not much improvement over second-order at any stage. The total breakdown of the perturbation approach is observed at the stage, where sigma (linear theory) is approximately 2, which corresponds to the onset of hierarchical clustering. This success is found at a considerable higher non-linearity than is usual for perturbation theory. Whether a truncation of the initial power-spectrum in hierarchical models retains this improvement will be analyzed in a forthcoming work.

A Study on the Horizontal Stratification of Higher Education in South Korea

ERIC Educational Resources Information Center

Park, Hwanbo

2015-01-01

This study analyzed university and college graduates' income gap in South Korea to investigate factors influencing such disparities. Specifically, this study focused on types of higher education institutions and academic disciplines among the many factors affecting post-graduation income differences, using a hierarchical linear model. According to…

Hierarchical tone mapping for high dynamic range image visualization

NASA Astrophysics Data System (ADS)

Qiu, Guoping; Duan, Jiang

2005-07-01

In this paper, we present a computationally efficient, practically easy to use tone mapping techniques for the visualization of high dynamic range (HDR) images in low dynamic range (LDR) reproduction devices. The new method, termed hierarchical nonlinear linear (HNL) tone-mapping operator maps the pixels in two hierarchical steps. The first step allocates appropriate numbers of LDR display levels to different HDR intensity intervals according to the pixel densities of the intervals. The second step linearly maps the HDR intensity intervals to theirs allocated LDR display levels. In the developed HNL scheme, the assignment of LDR display levels to HDR intensity intervals is controlled by a very simple and flexible formula with a single adjustable parameter. We also show that our new operators can be used for the effective enhancement of ordinary images.

Using GOMS models and hypertext to create representations of medical procedures for online display

NASA Technical Reports Server (NTRS)

Gugerty, Leo; Halgren, Shannon; Gosbee, John; Rudisill, Marianne

1991-01-01

This study investigated two methods to improve organization and presentation of computer-based medical procedures. A literature review suggested that the GOMS (goals, operators, methods, and selecton rules) model can assist in rigorous task analysis, which can then help generate initial design ideas for the human-computer interface. GOMS model are hierarchical in nature, so this study also investigated the effect of hierarchical, hypertext interfaces. We used a 2 x 2 between subjects design, including the following independent variables: procedure organization - GOMS model based vs. medical-textbook based; navigation type - hierarchical vs. linear (booklike). After naive subjects studies the online procedures, measures were taken of their memory for the content and the organization of the procedures. This design was repeated for two medical procedures. For one procedure, subjects who studied GOMS-based and hierarchical procedures remembered more about the procedures than other subjects. The results for the other procedure were less clear. However, data for both procedures showed a 'GOMSification effect'. That is, when asked to do a free recall of a procedure, subjects who had studies a textbook procedure often recalled key information in a location inconsistent with the procedure they actually studied, but consistent with the GOMS-based procedure.

Unpacking the Complexity of Linear Equations from a Cognitive Load Theory Perspective

ERIC Educational Resources Information Center

Ngu, Bing Hiong; Phan, Huy P.

2016-01-01

The degree of element interactivity determines the complexity and therefore the intrinsic cognitive load of linear equations. The unpacking of linear equations at the level of operational and relational lines allows the classification of linear equations in a hierarchical level of complexity. Mapping similar operational and relational lines across…

Libraries for Software Use on Peregrine | High-Performance Computing | NREL

Science.gov Websites

-specific libraries. Libraries List Name Description BLAS Basic Linear Algebra Subroutines, libraries only managing hierarchically structured data. LAPACK Standard Netlib offering for computational linear algebra

Examining Elementary Social Studies Marginalization: A Multilevel Model

ERIC Educational Resources Information Center

Fitchett, Paul G.; Heafner, Tina L.; Lambert, Richard G.

2014-01-01

Utilizing data from the National Center for Education Statistics Schools and Staffing Survey (SASS), a multilevel model (Hierarchical Linear Model) was developed to examine the association of teacher/classroom and state level indicators on reported elementary social studies instructional time. Findings indicated that state testing policy was a…

Caregiver Life Satisfaction: Relationship to Youth Symptom Severity through Treatment

ERIC Educational Resources Information Center

Athay, M. Michele

2012-01-01

This study utilized the Satisfaction with Life Scale to investigate the life satisfaction of caregivers for youth receiving mental health services (N = 383). Specifically, this study assessed how caregiver life satisfaction relates to youth symptom severity throughout treatment. Hierarchical linear modeling with a time-varying covariate was used…

Accelerating Recovery from Poverty: Prevention Effects for Recently Separated Mothers

ERIC Educational Resources Information Center

Forgatch, Marion S.; DeGarmo, David S.

2007-01-01

This study evaluated benefits of a preventive intervention to the living standards of recently separated mothers. In the Oregon Divorce Study's randomized experimental design, data were collected 5 times over 30 months and evaluated with Hierarchical Linear Growth Models. Relative to their no-intervention control counterparts, experimental mothers…

Neighborhood Context and Police Vigor: A Multilevel Analysis

ERIC Educational Resources Information Center

Sobol, James J.; Wu, Yuning; Sun, Ivan Y.

2013-01-01

This study provides a partial test of Klinger's ecological theory of police behavior using hierarchical linear modeling on 1,677 suspects who had encounters with police within 24 beats. The current study used data from four sources originally collected by the Project on Policing Neighborhoods (POPN), including systematic social observation,…

School-Based Kindergarten Transition Practices and Child Outcomes: Revisiting the Issue

ERIC Educational Resources Information Center

Little, Michael H.

2017-01-01

The purpose of this study was to examine the relationship between school-based kindergarten transition practices and student achievement and executive functioning using recent, nationally representative data from the Early Childhood Longitudinal Study, Kindergarten Class of 2010-11. The analysis employed 3-level hierarchical linear models and…

Linear SFM: A hierarchical approach to solving structure-from-motion problems by decoupling the linear and nonlinear components

NASA Astrophysics Data System (ADS)

Zhao, Liang; Huang, Shoudong; Dissanayake, Gamini

2018-07-01

This paper presents a novel hierarchical approach to solving structure-from-motion (SFM) problems. The algorithm begins with small local reconstructions based on nonlinear bundle adjustment (BA). These are then joined in a hierarchical manner using a strategy that requires solving a linear least squares optimization problem followed by a nonlinear transform. The algorithm can handle ordered monocular and stereo image sequences. Two stereo images or three monocular images are adequate for building each initial reconstruction. The bulk of the computation involves solving a linear least squares problem and, therefore, the proposed algorithm avoids three major issues associated with most of the nonlinear optimization algorithms currently used for SFM: the need for a reasonably accurate initial estimate, the need for iterations, and the possibility of being trapped in a local minimum. Also, by summarizing all the original observations into the small local reconstructions with associated information matrices, the proposed Linear SFM manages to preserve all the information contained in the observations. The paper also demonstrates that the proposed problem formulation results in a sparse structure that leads to an efficient numerical implementation. The experimental results using publicly available datasets show that the proposed algorithm yields solutions that are very close to those obtained using a global BA starting with an accurate initial estimate. The C/C++ source code of the proposed algorithm is publicly available at https://github.com/LiangZhaoPKUImperial/LinearSFM.

A Multilevel Study of the Role of Environment in Adolescent Substance Use

ERIC Educational Resources Information Center

Steen, Julie A.

2010-01-01

The purpose of this study is to assess the relationships between county-level characteristics and adolescent use of alcohol, cigarettes, and marijuana. The study consisted of a hierarchical generalized linear analysis of secondary data from the Florida Youth Substance Abuse Survey. Variables on the county level included the percent of adolescents…

A Multilevel Analysis of Phase II of the Louisiana School Effectiveness Study.

ERIC Educational Resources Information Center

Kennedy, Eugene; And Others

This paper presents findings of a study that used conventional modeling strategies (student- and school-level) and a new multilevel modeling strategy, Hierarchical Linear Modeling, to investigate school effects on student-achievement outcomes for data collected as part of Phase 2 of the Louisiana School Effectiveness Study. The purpose was to…

A Multilevel Study of Students' Motivations of Studying Accounting: Implications for Employers

ERIC Educational Resources Information Center

Law, Philip; Yuen, Desmond

2012-01-01

Purpose: The purpose of this study is to examine the influence of factors affecting students' choice of accounting as a study major in Hong Kong. Design/methodology/approach: Multinomial logistic regression and Hierarchical Generalized Linear Modeling (HGLM) are used to analyze the survey data for the level one and level two data, which is the…

The effect of maternal psychopathology on parent-child agreement of child anxiety symptoms: A hierarchical linear modeling approach.

PubMed

Affrunti, Nicholas W; Woodruff-Borden, Janet

2015-05-01

The current study examined the effects of maternal anxiety, worry, depression, child age and gender on mother and child reports of child anxiety using hierarchical linear modeling. Participants were 73 mother-child dyads with children between the ages of 7 and 10 years. Reports of child anxiety symptoms, including symptoms of specific disorders (e.g., social phobia) were obtained using concordant versions of the Screen for Anxiety and Related Emotional Disorders (SCARED). Children reported significantly higher levels of anxiety symptoms relative to their mothers. Maternal worry and depression predicted for significantly lower levels of maternal-reported child anxiety and increasing discrepant reports. Maternal anxiety predicted for higher levels of maternal-reported child anxiety and decreasing discrepant reports. Maternal depression was associated with increased child-reported child anxiety symptoms. No significant effect of child age or gender was observed. Findings may inform inconsistencies in previous studies on reporter discrepancies. Implications and future directions are discussed. Copyright © 2015 Elsevier Ltd. All rights reserved.

Multistep hierarchical self-assembly of chiral nanopore arrays

PubMed Central

Kim, Hanim; Lee, Sunhee; Shin, Tae Joo; Korblova, Eva; Walba, David M.; Clark, Noel A.; Lee, Sang Bok; Yoon, Dong Ki

2014-01-01

A series of simple hierarchical self-assembly steps achieve self-organization from the centimeter to the subnanometer-length scales in the form of square-centimeter arrays of linear nanopores, each one having a single chiral helical nanofilament of large internal surface area and interfacial interactions based on chiral crystalline molecular arrangements. PMID:25246585

Estimation of annual energy production using dynamic wake meandering in combination with ambient CFD solutions

NASA Astrophysics Data System (ADS)

Hahn, S.; Machefaux, E.; Hristov, Y. V.; Albano, M.; Threadgill, R.

2016-09-01

In the present study, combination of the standalone dynamic wake meandering (DWM) model with Reynolds-averaged Navier-Stokes (RANS) CFD solutions for ambient ABL flows is introduced, and its predictive performance for annual energy production (AEP) is evaluated against Vestas’ SCADA data for six operating wind farms over semi-complex terrains under neutral conditions. The performances of conventional linear and quadratic wake superposition techniques are also compared, together with the in-house implemention of successive hierarchical merging approaches. As compared to our standard procedure based on the Jensen model in WindPRO, the overall results are promising, leading to a significant improvement in AEP accuracy for four of the six sites. While the conventional linear superposition shows the best performance for the improved four sites, the hierarchical square superposition shows the least deteriorated result for the other two sites.

An exploratory analysis of treatment completion and client and organizational factors using hierarchical linear modeling.

PubMed

Woodward, Albert; Das, Abhik; Raskin, Ira E; Morgan-Lopez, Antonio A

2006-11-01

Data from the Alcohol and Drug Services Study (ADSS) are used to analyze the structure and operation of the substance abuse treatment industry in the United States. Published literature contains little systematic empirical analysis of the interaction between organizational characteristics and treatment outcomes. This paper addresses that deficit. It develops and tests a hierarchical linear model (HLM) to address questions about the empirical relationship between treatment inputs (industry costs, types and use of counseling and medical personnel, diagnosis mix, patient demographics, and the nature and level of services used in substance abuse treatment), and patient outcomes (retention and treatment completion rates). The paper adds to the literature by demonstrating a direct and statistically significant link between treatment completion and the organizational and staffing structure of the treatment setting. Related reimbursement issues, questions for future analysis, and limitations of the ADSS for this analysis are discussed.

How Does District Principal Evaluation Affect Learning-Centered Principal Leadership? Evidence from Michigan School Districts

ERIC Educational Resources Information Center

Sun, Min; Youngs, Peter

2009-01-01

This study used Hierarchical Multivariate Linear models to investigate relationships between principals' behaviors and district principal evaluation purpose, focus, and assessed leadership activities in 13 school districts in Michigan. The study found that principals were more likely to engage in learning-centered leadership behaviors when the…

Early Childhood Family Structure and Mother-Child Interactions: Variation by Race and Ethnicity

ERIC Educational Resources Information Center

Gibson-Davis, Christina M.; Gassman-Pines, Anna

2010-01-01

With data from the Early Childhood Longitudinal Study-Birth Cohort (n = 6,449), a nationally representative sample of births in 2001, we used hierarchical linear modeling to analyze differences in observed interactions between married, cohabiting, never-married, and divorced mothers and their children. In contrast to previous studies, we…

What Is the Relationship between Teacher Quality and Student Achievement? An Exploratory Study

ERIC Educational Resources Information Center

Stronge, James H.; Ward, Thomas J.; Tucker, Pamela D.; Hindman, Jennifer L.

2007-01-01

The major purpose of the study was to examine what constitutes effective teaching as defined by measured increases in student learning with a focus on the instructional behaviors and practices. Ordinary least squares (OLS) regression analyses and hierarchical linear modeling (HLM) were used to identify teacher effectiveness levels while…

Testing the Intervention Effect in Single-Case Experiments: A Monte Carlo Simulation Study

ERIC Educational Resources Information Center

Heyvaert, Mieke; Moeyaert, Mariola; Verkempynck, Paul; Van den Noortgate, Wim; Vervloet, Marlies; Ugille, Maaike; Onghena, Patrick

2017-01-01

This article reports on a Monte Carlo simulation study, evaluating two approaches for testing the intervention effect in replicated randomized AB designs: two-level hierarchical linear modeling (HLM) and using the additive method to combine randomization test "p" values (RTcombiP). Four factors were manipulated: mean intervention effect,…

Community Context, Social Integration into Family, and Youth Violence

ERIC Educational Resources Information Center

Knoester, Chris; Haynie, Dana L.

2005-01-01

The purpose of this study is to analyze the extent to which neighborhood-level family structure and feelings of family integration are associated with acts of violence among 16,910 adolescents from the National Longitudinal Study of Adolescent Health. The results from our hierarchical linear models indicate that adolescents who live in…

Racial Differences in Perceptions of Social Support in Consumer-Centered Services

ERIC Educational Resources Information Center

Woodward, Amanda Toler; Mowbray, Carol T.; Holter, Mark C.; Bybee, Deborah

2007-01-01

The purpose of this study was to explore potential racial differences in the experience of support offered by consumer-centered services for adults with serious mental illness. The study used hierarchical linear modeling to examine the level of support consumers report receiving from programs and the extent to which program-level characteristics…

A Multilevel Analysis of Gender Differences in Psychological Distress over Time

ERIC Educational Resources Information Center

Botticello, Amanda L.

2009-01-01

Females have higher rates of depression than males, a disparity that emerges in adolescence and persists into adulthood. This study uses hierarchical linear modeling to assess the effects of school context on gender differences in depressive symptoms among adolescents based on two waves of data from the National Longitudinal Study of Adolescent…

The traveling salesman problem: a hierarchical model.

PubMed

Graham, S M; Joshi, A; Pizlo, Z

2000-10-01

Our review of prior literature on spatial information processing in perception, attention, and memory indicates that these cognitive functions involve similar mechanisms based on a hierarchical architecture. The present study extends the application of hierarchical models to the area of problem solving. First, we report results of an experiment in which human subjects were tested on a Euclidean traveling salesman problem (TSP) with 6 to 30 cities. The subject's solutions were either optimal or near-optimal in length and were produced in a time that was, on average, a linear function of the number of cities. Next, the performance of the subjects is compared with that of five representative artificial intelligence and operations research algorithms, that produce approximate solutions for Euclidean problems. None of these algorithms was found to be an adequate psychological model. Finally, we present a new algorithm for solving the TSP, which is based on a hierarchical pyramid architecture. The performance of this new algorithm is quite similar to the performance of the subjects.

Testing higher-order Lagrangian perturbation theory against numerical simulations. 2: Hierarchical models

NASA Technical Reports Server (NTRS)

Melott, A. L.; Buchert, T.; Weib, A. G.

1995-01-01

We present results showing an improvement of the accuracy of perturbation theory as applied to cosmological structure formation for a useful range of scales. The Lagrangian theory of gravitational instability of Friedmann-Lemaitre cosmogonies is compared with numerical simulations. We study the dynamics of hierarchical models as a second step. In the first step we analyzed the performance of the Lagrangian schemes for pancake models, the difference being that in the latter models the initial power spectrum is truncated. This work probed the quasi-linear and weakly non-linear regimes. We here explore whether the results found for pancake models carry over to hierarchical models which are evolved deeply into the non-linear regime. We smooth the initial data by using a variety of filter types and filter scales in order to determine the optimal performance of the analytical models, as has been done for the 'Zel'dovich-approximation' - hereafter TZA - in previous work. We find that for spectra with negative power-index the second-order scheme performs considerably better than TZA in terms of statistics which probe the dynamics, and slightly better in terms of low-order statistics like the power-spectrum. However, in contrast to the results found for pancake models, where the higher-order schemes get worse than TZA at late non-linear stages and on small scales, we here find that the second-order model is as robust as TZA, retaining the improvement at later stages and on smaller scales. In view of these results we expect that the second-order truncated Lagrangian model is especially useful for the modelling of standard dark matter models such as Hot-, Cold-, and Mixed-Dark-Matter.

High School Grade Inflation from 2004 to 2011. ACT Research Report Series, 2013 (3)

ERIC Educational Resources Information Center

Zhang, Qian; Sanchez, Edgar I.

2013-01-01

This study explores inflation in high school grade point average (HSGPA), defined as trend over time in the conditional average of HSGPA, given ACT® Composite score. The time period considered is 2004 to 2011. Using hierarchical linear modeling, the study updates a previous analysis of Woodruff and Ziomek (2004). The study also investigates…

Union Status and Faculty Job Satisfaction: Contemporary Evidence from the 2004 National Study of Postsecondary Faculty

ERIC Educational Resources Information Center

Myers, Carrie B.

2011-01-01

This study tests the association between union status and job satisfaction using 8,000+ U.S. faculty at four-year public institutions surveyed in the 2004 National Study of Postsecondary Faculty. The results from hierarchical linear models that included individual and institutional variables found that nonunion faculty reported significantly…

The Development of Internet Use for Communication among Undergraduate Students: A Multilevel Analysis

ERIC Educational Resources Information Center

Huang, Chiungjung

2011-01-01

As few studies utilized longitudinal design to examine the development of Internet use for communication, the purpose of this study was to examine the effects of gender and initial Internet use for communication on subsequent use. The study sample was 280 undergraduate students who were assessed at five time points. Hierarchical linear models were…

When Money Really Matters: Tying Resources of Specific Programmatic and Instructional Elements to Student Academic Growth

ERIC Educational Resources Information Center

Goetz, Michael Eric

2012-01-01

This study explores the cost-effectiveness ratios associated with individual tutoring, intensive reading/language arts instruction, and a focus on core subject areas. Using the Early Childhood Longitudinal Study (ECLS-K) database, this study analyzes these programs using a three-level hierarchical linear model (HLM) with a nationally…

Algorithm for solving of two-level hierarchical minimax program control problem of final state the regional socio-economic system in the presence of risks

NASA Astrophysics Data System (ADS)

Shorikov, A. F.

2017-10-01

In this paper we study the problem of optimization of guaranteed result for program control by the final state of regional social and economic system in the presence of risks. For this problem we propose a mathematical model in the form of two-level hierarchical minimax program control problem of the final state of this process with incomplete information. For solving of its problem we constructed the common algorithm that has a form of a recurrent procedure of solving a linear programming and a finite optimization problems.

Exploring Growth Trajectories of Problem Behavior in Young Children

ERIC Educational Resources Information Center

McCaffrey, Bethany L.

2012-01-01

Given the negative outcomes associated with problem behavior and the heightened risk for children with disabilities to display problematic behavior, the current study implemented hierarchical linear modeling to explore the growth trajectories of problem behavior in a nationally representative sample of preschool children with disabilities. Results…

Abstract Linguistic Structure Correlates with Temporal Activity during Naturalistic Comprehension

PubMed Central

Brennan, Jonathan R.; Stabler, Edward P.; Van Wagenen, Sarah E.; Luh, Wen-Ming; Hale, John T.

2016-01-01

Neurolinguistic accounts of sentence comprehension identify a network of relevant brain regions, but do not detail the information flowing through them. We investigate syntactic information. Does brain activity implicate a computation over hierarchical grammars or does it simply reflect linear order, as in a Markov chain? To address this question, we quantify the cognitive states implied by alternative parsing models. We compare processing-complexity predictions from these states against fMRI timecourses from regions that have been implicated in sentence comprehension. We find that hierarchical grammars independently predict timecourses from left anterior and posterior temporal lobe. Markov models are predictive in these regions and across a broader network that includes the inferior frontal gyrus. These results suggest that while linear effects are wide-spread across the language network, certain areas in the left temporal lobe deal with abstract, hierarchical syntactic representations. PMID:27208858

Improved Convergence and Robustness of USM3D Solutions on Mixed-Element Grids

NASA Technical Reports Server (NTRS)

Pandya, Mohagna J.; Diskin, Boris; Thomas, James L.; Frink, Neal T.

2016-01-01

Several improvements to the mixed-element USM3D discretization and defect-correction schemes have been made. A new methodology for nonlinear iterations, called the Hierarchical Adaptive Nonlinear Iteration Method, has been developed and implemented. The Hierarchical Adaptive Nonlinear Iteration Method provides two additional hierarchies around a simple and approximate preconditioner of USM3D. The hierarchies are a matrix-free linear solver for the exact linearization of Reynolds-averaged Navier-Stokes equations and a nonlinear control of the solution update. Two variants of the Hierarchical Adaptive Nonlinear Iteration Method are assessed on four benchmark cases, namely, a zero-pressure-gradient flat plate, a bump-in-channel configuration, the NACA 0012 airfoil, and a NASA Common Research Model configuration. The new methodology provides a convergence acceleration factor of 1.4 to 13 over the preconditioner-alone method representing the baseline solver technology.

Improved Convergence and Robustness of USM3D Solutions on Mixed-Element Grids

NASA Technical Reports Server (NTRS)

Pandya, Mohagna J.; Diskin, Boris; Thomas, James L.; Frinks, Neal T.

2016-01-01

Several improvements to the mixed-elementUSM3Ddiscretization and defect-correction schemes have been made. A new methodology for nonlinear iterations, called the Hierarchical Adaptive Nonlinear Iteration Method, has been developed and implemented. The Hierarchical Adaptive Nonlinear Iteration Method provides two additional hierarchies around a simple and approximate preconditioner of USM3D. The hierarchies are a matrix-free linear solver for the exact linearization of Reynolds-averaged Navier-Stokes equations and a nonlinear control of the solution update. Two variants of the Hierarchical Adaptive Nonlinear Iteration Method are assessed on four benchmark cases, namely, a zero-pressure-gradient flat plate, a bump-in-channel configuration, the NACA 0012 airfoil, and a NASA Common Research Model configuration. The new methodology provides a convergence acceleration factor of 1.4 to 13 over the preconditioner-alone method representing the baseline solver technology.

Hierarchical Bayes approach for subgroup analysis.

PubMed

Hsu, Yu-Yi; Zalkikar, Jyoti; Tiwari, Ram C

2017-01-01

In clinical data analysis, both treatment effect estimation and consistency assessment are important for a better understanding of the drug efficacy for the benefit of subjects in individual subgroups. The linear mixed-effects model has been used for subgroup analysis to describe treatment differences among subgroups with great flexibility. The hierarchical Bayes approach has been applied to linear mixed-effects model to derive the posterior distributions of overall and subgroup treatment effects. In this article, we discuss the prior selection for variance components in hierarchical Bayes, estimation and decision making of the overall treatment effect, as well as consistency assessment of the treatment effects across the subgroups based on the posterior predictive p-value. Decision procedures are suggested using either the posterior probability or the Bayes factor. These decision procedures and their properties are illustrated using a simulated example with normally distributed response and repeated measurements.

Hierarchical Rhetorical Sentence Categorization for Scientific Papers

NASA Astrophysics Data System (ADS)

Rachman, G. H.; Khodra, M. L.; Widyantoro, D. H.

2018-03-01

Important information in scientific papers can be composed of rhetorical sentences that is structured from certain categories. To get this information, text categorization should be conducted. Actually, some works in this task have been completed by employing word frequency, semantic similarity words, hierarchical classification, and the others. Therefore, this paper aims to present the rhetorical sentence categorization from scientific paper by employing TF-IDF and Word2Vec to capture word frequency and semantic similarity words and employing hierarchical classification. Every experiment is tested in two classifiers, namely Naïve Bayes and SVM Linear. This paper shows that hierarchical classifier is better than flat classifier employing either TF-IDF or Word2Vec, although it increases only almost 2% from 27.82% when using flat classifier until 29.61% when using hierarchical classifier. It shows also different learning model for child-category can be built by hierarchical classifier.

Resolving the Framework Position of Organic Structure-Directing Agents in Hierarchical Zeolites via Polarized Stimulated Raman Scattering.

PubMed

Fleury, Guillaume; Steele, Julian A; Gerber, Iann C; Jolibois, F; Puech, P; Muraoka, Koki; Keoh, Sye Hoe; Chaikittisilp, Watcharop; Okubo, Tatsuya; Roeffaers, Maarten B J

2018-04-05

The direct synthesis of hierarchically intergrown silicalite-1 can be achieved using a specific diquaternary ammonium agent. However, the location of these molecules in the zeolite framework, which is critical to understand the formation of the material, remains unclear. Where traditional characterization tools have previously failed, herein we use polarized stimulated Raman scattering (SRS) microscopy to resolve molecular organization inside few-micron-sized crystals. Through a combination of experiment and first-principles calculations, our investigation reveals the preferential location of the templating agent inside the linear pores of the MFI framework. Besides illustrating the attractiveness of SRS microscopy in the field of material science to study and spatially resolve local molecular distribution as well as orientation, these results can be exploited in the design of new templating agents for the preparation of hierarchical zeolites.

VR Employment Outcomes of Individuals with Autism Spectrum Disorders: A Decade in the Making

ERIC Educational Resources Information Center

Alverson, Charlotte Y.; Yamamoto, Scott H.

2018-01-01

This study utilized hierarchical linear modeling analysis of a 10-year extant dataset from Rehabilitation Services Administration to investigate significant predictors of employment outcomes for vocational rehabilitation (VR) clients with autism. Predictor variables were gender, ethnicity, attained education level, IEP status in high school,…

Hierarchy and Scope of Planning in Subject-Verb Agreement Production

ERIC Educational Resources Information Center

Gillespie, Maureen; Pearlmutter, Neal J.

2011-01-01

Two subject-verb agreement error elicitation studies tested the hierarchical feature-passing account of agreement computation in production and three timing-based alternatives: linear distance to the head noun, semantic integration, and a combined effect of both (a scope of planning account). In Experiment 1, participants completed subject noun…

Factors Affecting Online Groupwork Interest: A Multilevel Analysis

ERIC Educational Resources Information Center

Du, Jianxia; Xu, Jianzhong; Fan, Xitao

2013-01-01

The purpose of the present study is to examine the personal and contextual factors that may affect students' online groupwork interest. Using the data obtained from graduate students in an online course, both student- and group-level predictors for online groupwork interest were analyzed within the framework of hierarchical linear modeling…

Predicting South Korean University Students' Happiness through Social Support and Efficacy Beliefs

ERIC Educational Resources Information Center

Lee, Diane Sookyoung; Padilla, Amado M.

2016-01-01

This study investigated the adversity and coping experiences of 198 South Korean university students and takes a cultural lens in understanding how social and individual factors shape their happiness. Hierarchical linear regression analyses suggest that Korean students' perceptions of social support significantly predicted their happiness,…

Effects of Preschool Intervention Strategies on School Readiness in Kindergarten

ERIC Educational Resources Information Center

Ma, Xin; Nelson, Regena F.; Shen, Jianping; Krenn, Huilan Y.

2015-01-01

Using hierarchical linear modeling, the present study aimed to examine whether targeted intervention strategies implemented individually during a preschool program exhibited any short-term and long-term effects on children's school readiness in kindergarten, utilizing data gathered through the Supporting Partnerships to Assure Ready Kids (SPARK)…

Nonresident Undergraduates' Performance in English Writing Classes-- Hierarchical Linear Modeling Analysis

ERIC Educational Resources Information Center

Vaughn, Allison A.; Bergman, Matthew; Fass-Holmes, Barry

2015-01-01

Do undergraduates whose native language is not English have writing deficiencies leading to academic struggles? The present study showed that the answer to this question was "no" at an American West Coast public university. This university's nonresident undergraduates on average earned B- to B+ in their colleges' English…

Effects of Technology Immersion on Middle School Students' Learning Opportunities and Achievement

ERIC Educational Resources Information Center

Shapley, Kelly; Sheehan, Daniel; Maloney, Catherine; Caranikas-Walker, Fanny

2011-01-01

An experimental study of the Technology Immersion model involved comparisons between 21 middle schools that received laptops for each teacher and student, instructional and learning resources, professional development, and technical and pedagogical support, and 21 control schools. Using hierarchical linear modeling to analyze longitudinal survey…

Working-Class Jobs and New Parents' Mental Health

ERIC Educational Resources Information Center

Perry-Jenkins, Maureen; Smith, JuliAnna Z.; Goldberg, Abbie E.; Logan, Jade

2011-01-01

Little research has explored linkages between work conditions and mental health in working-class employed parents. The current study aims to address this gap, employing hierarchical linear modeling techniques to examine how levels of and changes in job autonomy, job urgency, supervisor support, and coworker support predicted parents' depressive…

Predicting Homework Effort: Support for a Domain-Specific, Multilevel Homework Model

ERIC Educational Resources Information Center

Trautwein, Ulrich; Ludtke, Oliver; Schnyder, Inge; Niggli, Alois

2006-01-01

According to the domain-specific, multilevel homework model proposed in the present study, students' homework effort is influenced by expectancy and value beliefs, homework characteristics, parental homework behavior, and conscientiousness. The authors used structural equation modeling and hierarchical linear modeling analyses to test the model in…

A Multilevel Analysis on Student Learning in Colleges and Universities.

ERIC Educational Resources Information Center

Hu, Shouping; Kuh, George D.

This study tested a learning productivity model for undergraduates at four-year colleges and universities using hierarchical linear modeling. Student level data were from 44,328 full-time enrolled undergraduates from 120 four-year colleges and universities who completed the College Student Experiences Questionnaire between 1990 and 1997.…

Predictors of Burnout in Children's Residential Treatment Center Staff

ERIC Educational Resources Information Center

Lakin, Brittany L.; Leon, Scott C.; Miller, Steven A.

2008-01-01

This study explored burnout among frontline staff within a children's residential treatment center (RTC) population. Data were collected from 375 full-time, frontline, children's RTC staff employed at 21 RTCs in Illinois. Using hierarchical linear modeling (HLM), results indicated that frontline staff age, training, empathic concern, communicative…

Multilevel Correlates of Childhood Physical Aggression and Prosocial Behavior

ERIC Educational Resources Information Center

Romano, Elisa; Tremblay, Richard E.; Boulerice, Bernard; Swisher, Raymond

2005-01-01

The study identified independent individual, family, and neighborhood correlates of children's physical aggression and prosocial behavior. Participants were 2,745-11-year olds nested in 1,982 families, which were themselves nested in 96 Canadian neighborhoods. Hierarchical linear modeling showed that the total variation explained by the…

Romantic Relationships and Adjustment Problems in China: The Moderating Effect of Classroom Romantic Context

ERIC Educational Resources Information Center

Hou, Jinqin; Natsuaki, Misaki N.; Zhang, Jianxin; Guo, Fei; Huang, Zheng; Wang, Mianbo; Chen, Zhiyan

2013-01-01

Theoretical and empirical research has shown that adolescent romantic relationships are associated with a wide range of developmental outcomes, including adverse consequences. The present study used a hierarchical linear model to examine the moderating effect of classroom romantic context on the association between adolescent romantic…

Leader-Member Exchange, Learning Orientation and Innovative Work Behavior

ERIC Educational Resources Information Center

Atitumpong, Aungkhana; Badir, Yuosre F.

2018-01-01

Purpose: This study aims to examine the effects of leader-member exchange (LMX) and employee learning orientation on employee innovative work behavior (IWB) through creative self-efficacy. Design/methodology/approach: Data have been collected from 337 employees and 137 direct managers from manufacturing sector. A hierarchical linear model has been…

Unpacking the Inequality among Turkish Schools: Findings from PISA 2006

ERIC Educational Resources Information Center

Alacaci, Cengiz; Erbas, Ayhan Kursat

2010-01-01

The study investigates the effects of certain school characteristics on students' mathematics performances in Turkey in the PISA 2006 while controlling for family background and demographic characteristics. Three models of Hierarchical Linear Modeling (HLM) are constructed. The results reveal that 55% of the variance is attributable to…

Predicting Successful Mathematics Remediation among Latina/o Students

ERIC Educational Resources Information Center

Crisp, Gloria; Reyes, Nicole Alia Salis; Doran, Erin

2017-01-01

This study examines Latina/o students' remedial math needs and outcomes. Data were drawn from a national sample of Latina/o students. Hierarchical generalized linear modeling techniques were used to predict three successful remediation outcomes. Results highlight the importance of providing financial aid and academic support to Latina/o students,…

School Health Promotion Policies and Adolescent Risk Behaviors in Israel: A Multilevel Analysis

ERIC Educational Resources Information Center

Tesler, Riki; Harel-Fisch, Yossi; Baron-Epel, Orna

2016-01-01

Background: Health promotion policies targeting risk-taking behaviors are being implemented across schools in Israel. This study identified the most effective components of these policies influencing cigarette smoking and alcohol consumption among adolescents. Methods: Logistic hierarchical linear model (HLM) analysis of data for 5279 students in…

Universal Beliefs and Specific Practices: Students' Math Self-Efficacy and Related Factors in the United States and China

ERIC Educational Resources Information Center

Wu, Yin

2016-01-01

This study intends to compare and contrast student and school factors that are associated with students' mathematics self-efficacy in the United States and China. Using hierarchical linear regressions to analyze the Programme for International Student Assessment (PISA) 2012 data, this study compares math self-efficacy, achievement, and variables…

Does Access Matter? Time in General Education and Achievement for Students with Disabilities

ERIC Educational Resources Information Center

Cosier, Meghan; Causton-Theoharis, Julie; Theoharis, George

2013-01-01

This study examined the relationship between hours in general education and achievement in reading and mathematics for students with disabilities. The study population included more than 1,300 students between the ages of 6 and 9 years old within 180 school districts. Hierarchical linear modeling (HLM) was utilized with the Pre-Elementary…

Does High School Facility Quality Affect Student Achievement? A Two-Level Hierarchical Linear Model

ERIC Educational Resources Information Center

Bowers, Alex J.; Urick, Angela

2011-01-01

The purpose of this study is to isolate the independent effects of high school facility quality on student achievement using a large, nationally representative U.S. database of student achievement and school facility quality. Prior research on linking school facility quality to student achievement has been mixed. Studies that relate overall…

Building a Multicontextual Model of Latino College Enrollment: Student, School, and State-Level Effects

ERIC Educational Resources Information Center

Nunez, Anne-Marie; Kim, Dongbin

2012-01-01

Latinos' college enrollment rates, particularly in four-year institutions, have not kept pace with their population growth in the United States. Using three-level hierarchical generalized linear modeling, this study analyzes data from the Educational Longitudinal Study (ELS) to examine the influence of high school and state contexts, in addition…

Illustration of a Multilevel Model for Meta-Analysis

ERIC Educational Resources Information Center

de la Torre, Jimmy; Camilli, Gregory; Vargas, Sadako; Vernon, R. Fox

2007-01-01

In this article, the authors present a multilevel (or hierarchical linear) model that illustrates issues in the application of the model to data from meta-analytic studies. In doing so, several issues are discussed that typically arise in the course of a meta-analysis. These include the presence of non-zero between-study variability, how multiple…

The Influence of Classroom Disciplinary Climate of Schools on Reading Achievement: A Cross-Country Comparative Study

ERIC Educational Resources Information Center

Ning, Bo; Van Damme, Jan; Van Den Noortgate, Wim; Yang, Xiangdong; Gielen, Sarah

2015-01-01

Despite considerable interest in research and practice in the effect of classroom disciplinary climate of schools on academic achievement, little is known about the generalizability of this effect over countries. Using hierarchical linear analyses, the present study reveals that a better classroom disciplinary climate in a school is significantly…

Latino Adolescents' Civic Development in the United States: Research Results from the IEA Civic Education Study

ERIC Educational Resources Information Center

Torney-Purta, Judith; Barber, Carolyn H.; Wilkenfeld, Britt

2007-01-01

Many studies have reported gaps between Latino and non-Latino adolescents in academic and political outcomes. The current study presents possible explanations for such gaps, both at the individual and school level. Hierarchical linear modeling is employed to examine data from 2,811 American ninth graders (approximately 14 years of age) who had…

Longitudinal Change in Happiness during Aging: The Predictive Role of Positive Expectancies

ERIC Educational Resources Information Center

Holahan, Carole K.; Holahan, Charles J.; Velasquez, Katherine E.; North, Rebecca J.

2008-01-01

This study employed hierarchical linear modeling to document the time course of happiness across 20 years from average ages of 66 to 86 among 717 members of the Terman Study of the Gifted. In addition, the study examined the role of positive expectancies about aging, assessed at an average age of 61, in enhancing happiness in aging. The results…

Hierarchical Linear Modelling of Sixth-Grade Students' Socio-Economic Status and School Factors on Mathematics Achievement: Case Studies of Kenya and Zimbabwe

ERIC Educational Resources Information Center

Kanyongo, Gibbs Y.; Ayieko, Rachel

2017-01-01

This study investigated the relationship between socio-economic status, school-level variables and mathematics achievement of sixth graders in Kenya and Zimbabwe. The study is based on secondary data collected by the Southern and Eastern Africa Consortium for Monitoring Educational Quality (SACMEQ III). SACMEQ employed cluster-sampling procedures…

Contextual Differential Item Functioning: Examining the Validity of Teaching Self-Efficacy Instruments Using Hierarchical Generalized Linear Modeling

ERIC Educational Resources Information Center

Zhao, Jing

2012-01-01

The purpose of the study is to further investigate the validity of instruments used for collecting preservice teachers' perceptions of self-efficacy adapting the three-level IRT model described in Cheong's study (2006). The focus of the present study is to investigate whether the polytomously-scored items on the preservice teachers' self-efficacy…

Computational Tools for Probing Interactions in Multiple Linear Regression, Multilevel Modeling, and Latent Curve Analysis

ERIC Educational Resources Information Center

Preacher, Kristopher J.; Curran, Patrick J.; Bauer, Daniel J.

2006-01-01

Simple slopes, regions of significance, and confidence bands are commonly used to evaluate interactions in multiple linear regression (MLR) models, and the use of these techniques has recently been extended to multilevel or hierarchical linear modeling (HLM) and latent curve analysis (LCA). However, conducting these tests and plotting the…

Association between surgeon volume and hospitalisation costs for patients with oral cancer: a nationwide population base study in Taiwan.

PubMed

Lee, C-C; Ho, H-C; Jack, Lee C-C; Su, Y-C; Lee, M-S; Hung, S-K; Chou, Pesus

2010-02-01

Oral cancer leads to a considerable use of and expenditure on health care. Wide resection of the tumour and reconstruction with a pedicle flap/free flap is widely used. This study was conducted to explore the relationship between hospitalisation costs and surgeon case volume when this operation was performed. A population-based study. This study uses data for the years 2005-2006 obtained from the National Health Insurance Research Database published in the Taiwanese National Health Research Institute. From this population-based data, the authors selected a total of 2663 oral cancer patients who underwent tumour resection and reconstruction. Case volume relationships were based on the following criteria; low-, medium-, high-, very high-volume surgeons were defined by or= 56 resections with reconstruction, respectively. Hierarchical linear regression analysis was subsequently performed to explore the relationship between surgeon case volume and the cost and length of hospitalisation. The mean hospitalisation cost among the 2663 patients was US$ 9528 (all costs are given in US dollars). After adjusting for physician, hospital, and patient characteristics in a hierarchical linear regression model, the cost per patient for low-volume surgeons was found to be US$ 741 (P = 0.012) higher than that for medium-volume surgeons, US$ 1546 (P < 0.001) higher than that for high-volume surgeons, and US$ 1820 (P < 0.001) higher than that for very-high-volume surgeons. After adjustment for physician, hospital, and patient characteristics, the hierarchical linear regression model revealed that the mean length of stay per patient for low-volume surgeons was the highest (P < 0.001). After adjustment for physician, hospital, and patient characteristics, low-volume surgeons performing wide excision with reconstructive surgery in oral cancer patients incurred significantly higher costs and longer hospital stays per patient than did other surgeons. Treatment strategies adopted by high- and very-high-volume surgeons should be analysed further and utilised more widely.

Using hierarchical linear growth models to evaluate protective mechanisms that mediate science achievement

NASA Astrophysics Data System (ADS)

von Secker, Clare Elaine

The study of students at risk is a major topic of science education policy and discussion. Much research has focused on describing conditions and problems associated with the statistical risk of low science achievement among individuals who are members of groups characterized by problems such as poverty and social disadvantage. But outcomes attributed to these factors do not explain the nature and extent of mechanisms that account for differences in performance among individuals at risk. There is ample theoretical and empirical evidence that demographic differences should be conceptualized as social contexts, or collections of variables, that alter the psychological significance and social demands of life events, and affect subsequent relationships between risk and resilience. The hierarchical linear growth models used in this dissertation provide greater specification of the role of social context and the protective effects of attitude, expectations, parenting practices, peer influences, and learning opportunities on science achievement. While the individual influences of these protective factors on science achievement were small, their cumulative effect was substantial. Meta-analysis conducted on the effects associated with psychological and environmental processes that mediate risk mechanisms in sixteen social contexts revealed twenty-two significant differences between groups of students. Positive attitudes, high expectations, and more intense science course-taking had positive effects on achievement of all students, although these factors were not equally protective in all social contexts. In general, effects associated with authoritative parenting and peer influences were negative, regardless of social context. An evaluation comparing the performance and stability of hierarchical linear growth models with traditional repeated measures models is included as well.

Familial and Religious Influences on Adolescent Alcohol Use: A Multi-Level Study of Students and School Communities

ERIC Educational Resources Information Center

Bjarnason, Thoroddur; Thorlindsson, Thorolfur; Sigfusdottir, Inga D.; Welch, Michael R.

2005-01-01

A multi-level Durkheimian theory of familial and religious influences on adolescent alcohol use is developed and tested with hierarchical linear modeling of data from Icelandic schools and students. On the individual level, traditional family structure, parental monitoring, parental support, religious participation, and perceptions of divine…

Comparing Private Schools and Public Schools Using Hierarchical Linear Modeling. NCES 2006-461

ERIC Educational Resources Information Center

Braun, Henry; Jenkins, Frank; Grigg, Wendy

2006-01-01

The goal of the study was to examine differences in mean National Assessment of Educational Progress (NAEP) reading and mathematics scores between public and private schools when selected characteristics of students and/or schools were taken into account. Among the student characteristics considered were gender, race/ethnicity, disability status,…

Exclusionary Discipline of Students with Disabilities: Student and School Characteristics Predicting Suspension

ERIC Educational Resources Information Center

Sullivan, Amanda L.; Van Norman, Ethan R.; Klingbeil, David A.

2014-01-01

Given the negative outcomes associated with suspension, scholars and practitioners are concerned with discipline disparities. This study explored patterns and predictors of suspension in a sample of 2,750 students with disabilities in 39 schools in a Midwestern district. Hierarchical generalized linear modeling demonstrated that disability type,…

Motivations and Benefits for Attaining HR Certifications

ERIC Educational Resources Information Center

Lester, Scott W.; Dwyer, Dale J.

2012-01-01

Purpose: The aim of this paper is to examine the motivations and benefits for pursuing or not pursuing the PHR and SPHR. Design/methodology/approach: Using a sample of 1,862 participants, the study used multinomial logistic and hierarchical linear regression to test six hypotheses. Findings: Participants pursuing SPHR were more likely to report…

The Role of Evaluative Metadata in an Online Teacher Resource Exchange

ERIC Educational Resources Information Center

Abramovich, Samuel; Schunn, Christian D.; Correnti, Richard J.

2013-01-01

A large-scale online teacher resource exchange is studied to examine the ways in which metadata influence teachers' selection of resources. A hierarchical linear modeling approach was used to tease apart the simultaneous effects of resource features and author features. From a decision heuristics theoretical perspective, teachers appear to…

Injunctive and Descriptive Peer Group Norms and the Academic Adjustment of Rural Early Adolescents

ERIC Educational Resources Information Center

Hamm, Jill V.; Schmid, Lorrie; Farmer, Thomas W.; Locke, Belinda

2011-01-01

This study integrates diverse literatures on peer group influence by conceptualizing and examining the relationship of peer group injunctive norms to the academic adjustment of a large and ethnically diverse sample of rural early adolescents' academic adjustment. Results of three-level hierarchical linear modeling indicated that peer groups were…

A Multi-Level Examination of College and Its Influence on Ecumenical Worldview Development

ERIC Educational Resources Information Center

Mayhew, Matthew J.

2012-01-01

This multi-level, longitudinal study investigated the ecumenical worldview development of 13,932 students enrolled in one of 126 institutions. Results indicated that the final hierarchical linear model, consisting of institution-and-student-level predictors as well as slopes explaining the relationships among some of these predictors, explained…

Treatment Effects of a Relationship-Strengthening Intervention for Economically Disadvantaged New Parents

ERIC Educational Resources Information Center

Charles, Pajarita; Jones, Anne; Guo, Shenyang

2014-01-01

Objective: The purpose of the present study was to evaluate the treatment effects of a relationship skills and family strengthening intervention for n = 726 high-risk, disadvantaged new parents. Method: Hierarchical linear modeling and regression models were used to assess intervention treatment effects. These findings were subsequently verified…

Predictors of Tobacco and Alcohol Refusal Efficacy for Urban and Rural African-American Adolescents

ERIC Educational Resources Information Center

Nasim, Aashir; Belgrave, Faye Z.; Corona, Rosalie; Townsend, Tiffany G.

2009-01-01

This study sought to determine the relative contributions of individual, family, peer, and community risk and promotive factors in explaining alcohol and tobacco refusal attitudes among 227 African-American adolescents (ages 12 to 17) from urban and rural areas. Hierarchical linear regression (HLR) results revealed differences in the predictive…

Information Technology, Mathematics Achievement and Educational Equity in Developed Economies

ERIC Educational Resources Information Center

Tan, Cheng Yong; Hew, Khe Foon

2017-01-01

The present study examined how access to home and school IT resources impacted student mathematics achievement. Data comprised 144,395 secondary school students from 7,308 schools in 22 developed economies who participated in the Programme for International Student Assessment (PISA) 2012. Results of hierarchical linear modelling showed that after…

The Contributions of Teachers' Emotional Support to Children's Social Behaviors and Self-Regulatory Skills in First Grade

ERIC Educational Resources Information Center

Merritt, Eileen G.; Wanless, Shannon B.; Rimm-Kaufman, Sara E.; Cameron, Claire; Peugh, James L.

2012-01-01

The present observational study used hierarchical linear modeling to examine predictors of children's social and self-regulatory outcomes in first-grade classrooms. Specifically, goals were the following: (1) to explore relations between emotionally supportive teacher-child interactions and children's social behaviors (aggression with peers,…

Perceived Family Resources Based on Number of Members with ADHD

ERIC Educational Resources Information Center

Corwin, Melinda; Mulsow, Miriam; Feng, Du

2012-01-01

Objective: This study examines how the number of family members with ADHD affects other family members' perceived resources. Method: A total of 40 adolescents diagnosed with ADHD and their mothers, fathers, and adolescent siblings living in the household participated. Hierarchical linear modeling was used to analyze family-level data from a total…

Factors Predicting Science Achievement of Immigrant and Non-Immigrant Students: A Multilevel Analysis

ERIC Educational Resources Information Center

Areepattamannil, Shaljan; Kaur, Berinderjeet

2013-01-01

This study, employing hierarchical linear modeling (HLM), sought to investigate the student-level and school-level factors associated with the science achievement of immigrant and non-immigrant students among a national sample of 22,646 students from 896 schools in Canada. While student background characteristics such as home language, family…

An Investigation of Teacher Impact on Student Inquiry Science Performance Using a Hierarchical Linear Model

ERIC Educational Resources Information Center

Liu, Ou Lydia; Lee, Hee-Sun; Linn, Marcia C.

2010-01-01

Teachers play a central role in inquiry science classrooms. In this study, we investigate how seven teacher variables (i.e., gender, experience, perceived importance of inquiry and traditional teaching, workshop attendance, partner teacher, use of technology) affect student knowledge integration understanding of science topics drawing on previous…

Outdoor Behavioral Health Care: Client and Treatment Characteristics Effects on Young Adult Outcomes

ERIC Educational Resources Information Center

Roberts, Sean D.; Stroud, Daniel; Hoag, Matthew J.; Combs, Katie M.

2016-01-01

A lack of clarity exists regarding how different clients respond to outdoor behavioral health care (OBH). In this study, specific client and treatment characteristics were assessed for 186 young adults completing an OBH therapeutic wilderness program. Clinical outcomes were measured with the Outcome Questionnaire-45.2. Hierarchical linear modeling…

U. S. Fourth Graders' Informational Text Comprehension: Indicators from NAEP

ERIC Educational Resources Information Center

Schugar, Heather R.; Dreher, Miriam Jean

2017-01-01

This study is a secondary analysis of reading data collected from over 165,000 fourth graders as part of the U.S. National Assessment of Educational Progress. Using hierarchical linear modelling, the authors investigated factors associated with students' informational text comprehension, including out-of-school reading engagement, and in-school…

Differential Relations of Constructivist and Didactic Instruction to Students' Cognition, Motivation, and Achievement

ERIC Educational Resources Information Center

Nie, Youyan; Lau, Shun

2010-01-01

This study examined how constructivist and didactic instruction was related to students' cognitive, motivational, and achievement outcomes in English classrooms, using a sample of 3000 Grade 9 students from 108 classrooms in 39 secondary schools in Singapore. Results of hierarchical linear modeling showed differential cross-level relations. After…

Does Mission Matter? An Analysis of Private School Achievement Differences

ERIC Educational Resources Information Center

Boerema, Albert J.

2009-01-01

Using student achievement data from British Columbia, Canada, this study is an exploration of the differences that lie within the private school sector using hierarchical linear modeling to analyze the data. The analysis showed that when controlling for language, parents' level of educational attainment, and prior achievement, the private school…

The Role of Schools, Families, and Psychological Variables on Math Achievement of Black High School Students

ERIC Educational Resources Information Center

Strayhorn, Terrell L.

2010-01-01

Using data from the National Education Longitudinal Study (NELS;1988/2000), the author conducted hierarchical linear regression analyses, with a nested design, to estimate the influence of affective variables--parent involvement, teacher perceptions, and school environments--on Black students' math achievement in grade 10. Drawing on…

A Cluster Analysis of Personality Style in Adults with ADHD

ERIC Educational Resources Information Center

Robin, Arthur L.; Tzelepis, Angela; Bedway, Marquita

2008-01-01

Objective: The purpose of this study was to use hierarchical linear cluster analysis to examine the normative personality styles of adults with ADHD. Method: A total of 311 adults with ADHD completed the Millon Index of Personality Styles, which consists of 24 scales assessing motivating aims, cognitive modes, and interpersonal behaviors. Results:…

Student Engagement and Academic Performance in Mexico: Evidence and Puzzles from PISA

ERIC Educational Resources Information Center

Weiss, Christopher C.; García, Emma

2015-01-01

This paper investigates the relationship between student engagement--with teachers and schools--and academic performance in Mexico. It uses hierarchical linear models and data from the OECD 2003 PISA study to examine the relative importance of engagement in comparison to other educational inputs--school and family characteristics--as predictors of…

Just Another Club? The Distinctiveness of the Relation between Religious Service Attendance and Adolescent Psychosocial Adjustment

ERIC Educational Resources Information Center

Good, Marie; Willoughby, Teena; Fritjers, Jan

2009-01-01

This study used hierarchical linear modeling to compare longitudinal patterns of adolescent religious service attendance and club attendance, and to contrast the longitudinal relations between adolescent adjustment and religious service versus club attendance. Participants included 1050 students (47% girls) encompassing a school district in…

Complementary Roles of Care and Behavioral Control in Classroom Management: The Self-Determination Theory Perspective

ERIC Educational Resources Information Center

Nie, Youyan; Lau, Shun

2009-01-01

This study examined how classroom management practices--care and behavioral control--were differentially associated with students' engagement, misbehavior, and satisfaction with school, using a large representative sample of 3196 Grade 9 students from 117 classes in Singapore. Results of hierarchical linear modeling showed differential relations.…

An International Meta-Analysis of Reading Recovery

ERIC Educational Resources Information Center

D'Agostino, Jerome V.; Harmey, Sinéad J.

2016-01-01

Reading Recovery is one of the most researched literacy programs worldwide. Although there have been at least 4 quantitative reviews of its effectiveness, none have considered all rigorous group-comparison studies from all implementing nations from the late 1970s to 2015. Using a hierarchical linear modeling (HLM) v-known analysis, we examined if…

Student and School Factors Affecting Mathematics Achievement: International Comparisons between Korea, Japan and the USA

ERIC Educational Resources Information Center

Shin, Jongho; Lee, Hyunjoo; Kim, Yongnam

2009-01-01

The purpose of the study was to comparatively investigate student- and school-level factors affecting mathematics achievement of Korean, Japanese and American students. For international comparisons, the PISA 2003 data were analysed by using the Hierarchical Linear Modeling method. The variables of competitive-learning preference, instrumental…

The Relationship between School Collective Reflective Practice and Teacher Physiological Efficacy Sources

ERIC Educational Resources Information Center

Kennedy, Sheryl Y.; Smith, Julia B.

2013-01-01

This study used Hierarchical Linear Modeling to analyze the relationship between school organizational behaviors and practices (at the school level) on teachers' reports of internal and external physiological sources of efficacy. Six hundred sixty-one teachers from 42 schools in the United States were surveyed to measure both individual sources of…

Identification of multivariable nonlinear systems in the presence of colored noises using iterative hierarchical least squares algorithm.

PubMed

Jafari, Masoumeh; Salimifard, Maryam; Dehghani, Maryam

2014-07-01

This paper presents an efficient method for identification of nonlinear Multi-Input Multi-Output (MIMO) systems in the presence of colored noises. The method studies the multivariable nonlinear Hammerstein and Wiener models, in which, the nonlinear memory-less block is approximated based on arbitrary vector-based basis functions. The linear time-invariant (LTI) block is modeled by an autoregressive moving average with exogenous (ARMAX) model which can effectively describe the moving average noises as well as the autoregressive and the exogenous dynamics. According to the multivariable nature of the system, a pseudo-linear-in-the-parameter model is obtained which includes two different kinds of unknown parameters, a vector and a matrix. Therefore, the standard least squares algorithm cannot be applied directly. To overcome this problem, a Hierarchical Least Squares Iterative (HLSI) algorithm is used to simultaneously estimate the vector and the matrix of unknown parameters as well as the noises. The efficiency of the proposed identification approaches are investigated through three nonlinear MIMO case studies. Copyright © 2014 ISA. Published by Elsevier Ltd. All rights reserved.

Remarks on Hierarchic Control for a Linearized Micropolar Fluids System in Moving Domains

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

Jesus, Isaías Pereira de, E-mail: isaias@ufpi.edu.br

We study a Stackelberg strategy subject to the evolutionary linearized micropolar fluids equations in domains with moving boundaries, considering a Nash multi-objective equilibrium (non necessarily cooperative) for the “follower players” (as is called in the economy field) and an optimal problem for the leader player with approximate controllability objective. We will obtain the following main results: the existence and uniqueness of Nash equilibrium and its characterization, the approximate controllability of the linearized micropolar system with respect to the leader control and the existence and uniqueness of the Stackelberg–Nash problem, where the optimality system for the leader is given.

Effects of Prior Knowledge and Concept-Map Structure on Disorientation, Cognitive Load, and Learning

ERIC Educational Resources Information Center

Amadieu, Franck; van Gog, Tamara; Paas, Fred; Tricot, Andre; Marine, Claudette

2009-01-01

This study explored the effects of prior knowledge (high vs. low; HPK and LPK) and concept-map structure (hierarchical vs. network; HS and NS) on disorientation, cognitive load, and learning from non-linear documents on "the infection process of a retrograde virus (HIV)". Participants in the study were 24 adults. Overall subjective ratings of…

Seeking Effectiveness and Equity in a Large College Chemistry Course: An HLM Investigation of Peer-Led Guided Inquiry

ERIC Educational Resources Information Center

Lewis, Scott E.; Lewis, Jennifer E.

2008-01-01

This study employed hierarchical linear models (HLM) to investigate Peer-Led Guided Inquiry (PLGI), a teaching practice combining cooperative learning and inquiry and tailored for a large class. Ultimately, the study provided an example of the effective introduction of a reform pedagogical approach in a large class setting. In the narrative, the…

Assessing Validity of Measurement in Learning Disabilities Using Hierarchical Generalized Linear Modeling: The Roles of Anxiety and Motivation

ERIC Educational Resources Information Center

Sideridis, Georgios D.

2016-01-01

The purpose of the present studies was to test the hypothesis that the psychometric characteristics of ability scales may be significantly distorted if one accounts for emotional factors during test taking. Specifically, the present studies evaluate the effects of anxiety and motivation on the item difficulties of the Rasch model. In Study 1, the…

The Relation of Teachers' Emotional Intelligence and Students' Social Skills to Students' Emotional and Behavioral Difficulties: A Study of Preschool Teachers' Perceptions

ERIC Educational Resources Information Center

Poulou, Maria S.

2017-01-01

The present study used hierarchical linear modeling to examine predictors of students' emotional and behavioral difficulties in preschool classrooms. Specifically, the study examined (a) the link between teachers' perceptions of their own emotional intelligence and students' emotional and behavioral difficulties, (b) the link between teachers'…

Visual analysis of mass cytometry data by hierarchical stochastic neighbour embedding reveals rare cell types.

PubMed

van Unen, Vincent; Höllt, Thomas; Pezzotti, Nicola; Li, Na; Reinders, Marcel J T; Eisemann, Elmar; Koning, Frits; Vilanova, Anna; Lelieveldt, Boudewijn P F

2017-11-23

Mass cytometry allows high-resolution dissection of the cellular composition of the immune system. However, the high-dimensionality, large size, and non-linear structure of the data poses considerable challenges for the data analysis. In particular, dimensionality reduction-based techniques like t-SNE offer single-cell resolution but are limited in the number of cells that can be analyzed. Here we introduce Hierarchical Stochastic Neighbor Embedding (HSNE) for the analysis of mass cytometry data sets. HSNE constructs a hierarchy of non-linear similarities that can be interactively explored with a stepwise increase in detail up to the single-cell level. We apply HSNE to a study on gastrointestinal disorders and three other available mass cytometry data sets. We find that HSNE efficiently replicates previous observations and identifies rare cell populations that were previously missed due to downsampling. Thus, HSNE removes the scalability limit of conventional t-SNE analysis, a feature that makes it highly suitable for the analysis of massive high-dimensional data sets.

Assessing exposure to violence using multiple informants: application of hierarchical linear model.

PubMed

Kuo, M; Mohler, B; Raudenbush, S L; Earls, F J

2000-11-01

The present study assesses the effects of demographic risk factors on children's exposure to violence (ETV) and how these effects vary by informants. Data on exposure to violence of 9-, 12-, and 15-year-olds were collected from both child participants (N = 1880) and parents (N = 1776), as part of the assessment of the Project on Human Development in Chicago Neighborhoods (PHDCN). A two-level hierarchical linear model (HLM) with multivariate outcomes was employed to analyze information obtained from these two different groups of informants. The findings indicate that parents generally report less ETV than do their children and that associations of age, gender, and parent education with ETV are stronger in the self-reports than in the parent reports. The findings support a multivariate approach when information obtained from different sources is being integrated. The application of HLM allows an assessment of interactions between risk factors and informants and uses all available data, including data from one informant when data from the other informant is missing.

School system evaluation by value added analysis under endogeneity.

PubMed

Manzi, Jorge; San Martín, Ernesto; Van Bellegem, Sébastien

2014-01-01

Value added is a common tool in educational research on effectiveness. It is often modeled as a (prediction of a) random effect in a specific hierarchical linear model. This paper shows that this modeling strategy is not valid when endogeneity is present. Endogeneity stems, for instance, from a correlation between the random effect in the hierarchical model and some of its covariates. This paper shows that this phenomenon is far from exceptional and can even be a generic problem when the covariates contain the prior score attainments, a typical situation in value added modeling. Starting from a general, model-free definition of value added, the paper derives an explicit expression of the value added in an endogeneous hierarchical linear Gaussian model. Inference on value added is proposed using an instrumental variable approach. The impact of endogeneity on the value added and the estimated value added is calculated accurately. This is also illustrated on a large data set of individual scores of about 200,000 students in Chile.

A unified stochastic formulation of dissipative quantum dynamics. I. Generalized hierarchical equations

NASA Astrophysics Data System (ADS)

Hsieh, Chang-Yu; Cao, Jianshu

2018-01-01

We extend a standard stochastic theory to study open quantum systems coupled to a generic quantum environment. We exemplify the general framework by studying a two-level quantum system coupled bilinearly to the three fundamental classes of non-interacting particles: bosons, fermions, and spins. In this unified stochastic approach, the generalized stochastic Liouville equation (SLE) formally captures the exact quantum dissipations when noise variables with appropriate statistics for different bath models are applied. Anharmonic effects of a non-Gaussian bath are precisely encoded in the bath multi-time correlation functions that noise variables have to satisfy. Starting from the SLE, we devise a family of generalized hierarchical equations by averaging out the noise variables and expand bath multi-time correlation functions in a complete basis of orthonormal functions. The general hierarchical equations constitute systems of linear equations that provide numerically exact simulations of quantum dynamics. For bosonic bath models, our general hierarchical equation of motion reduces exactly to an extended version of hierarchical equation of motion which allows efficient simulation for arbitrary spectral densities and temperature regimes. Similar efficiency and flexibility can be achieved for the fermionic bath models within our formalism. The spin bath models can be simulated with two complementary approaches in the present formalism. (I) They can be viewed as an example of non-Gaussian bath models and be directly handled with the general hierarchical equation approach given their multi-time correlation functions. (II) Alternatively, each bath spin can be first mapped onto a pair of fermions and be treated as fermionic environments within the present formalism.

Measuring the hierarchy of feedforward networks

NASA Astrophysics Data System (ADS)

Corominas-Murtra, Bernat; Rodríguez-Caso, Carlos; Goñi, Joaquín; Solé, Ricard

2011-03-01

In this paper we explore the concept of hierarchy as a quantifiable descriptor of ordered structures, departing from the definition of three conditions to be satisfied for a hierarchical structure: order, predictability, and pyramidal structure. According to these principles, we define a hierarchical index taking concepts from graph and information theory. This estimator allows to quantify the hierarchical character of any system susceptible to be abstracted in a feedforward causal graph, i.e., a directed acyclic graph defined in a single connected structure. Our hierarchical index is a balance between this predictability and pyramidal condition by the definition of two entropies: one attending the onward flow and the other for the backward reversion. We show how this index allows to identify hierarchical, antihierarchical, and nonhierarchical structures. Our formalism reveals that departing from the defined conditions for a hierarchical structure, feedforward trees and the inverted tree graphs emerge as the only causal structures of maximal hierarchical and antihierarchical systems respectively. Conversely, null values of the hierarchical index are attributed to a number of different configuration networks; from linear chains, due to their lack of pyramid structure, to full-connected feedforward graphs where the diversity of onward pathways is canceled by the uncertainty (lack of predictability) when going backward. Some illustrative examples are provided for the distinction among these three types of hierarchical causal graphs.

Nonexpansiveness of a linearized augmented Lagrangian operator for hierarchical convex optimization

NASA Astrophysics Data System (ADS)

Yamagishi, Masao; Yamada, Isao

2017-04-01

Hierarchical convex optimization concerns two-stage optimization problems: the first stage problem is a convex optimization; the second stage problem is the minimization of a convex function over the solution set of the first stage problem. For the hierarchical convex optimization, the hybrid steepest descent method (HSDM) can be applied, where the solution set of the first stage problem must be expressed as the fixed point set of a certain nonexpansive operator. In this paper, we propose a nonexpansive operator that yields a computationally efficient update when it is plugged into the HSDM. The proposed operator is inspired by the update of the linearized augmented Lagrangian method. It is applicable to characterize the solution set of recent sophisticated convex optimization problems found in the context of inverse problems, where the sum of multiple proximable convex functions involving linear operators must be minimized to incorporate preferable properties into the minimizers. For such a problem formulation, there has not yet been reported any nonexpansive operator that yields an update free from the inversions of linear operators in cases where it is utilized in the HSDM. Unlike previously known nonexpansive operators, the proposed operator yields an inversion-free update in such cases. As an application of the proposed operator plugged into the HSDM, we also present, in the context of the so-called superiorization, an algorithmic solution to a convex optimization problem over the generalized convex feasible set where the intersection of the hard constraints is not necessarily simple.

Diagnostics for generalized linear hierarchical models in network meta-analysis.

PubMed

Zhao, Hong; Hodges, James S; Carlin, Bradley P

2017-09-01

Network meta-analysis (NMA) combines direct and indirect evidence comparing more than 2 treatments. Inconsistency arises when these 2 information sources differ. Previous work focuses on inconsistency detection, but little has been done on how to proceed after identifying inconsistency. The key issue is whether inconsistency changes an NMA's substantive conclusions. In this paper, we examine such discrepancies from a diagnostic point of view. Our methods seek to detect influential and outlying observations in NMA at a trial-by-arm level. These observations may have a large effect on the parameter estimates in NMA, or they may deviate markedly from other observations. We develop formal diagnostics for a Bayesian hierarchical model to check the effect of deleting any observation. Diagnostics are specified for generalized linear hierarchical NMA models and investigated for both published and simulated datasets. Results from our example dataset using either contrast- or arm-based models and from the simulated datasets indicate that the sources of inconsistency in NMA tend not to be influential, though results from the example dataset suggest that they are likely to be outliers. This mimics a familiar result from linear model theory, in which outliers with low leverage are not influential. Future extensions include incorporating baseline covariates and individual-level patient data. Copyright © 2017 John Wiley & Sons, Ltd.

A hierarchical preconditioner for the electric field integral equation on unstructured meshes based on primal and dual Haar bases

NASA Astrophysics Data System (ADS)

Adrian, S. B.; Andriulli, F. P.; Eibert, T. F.

2017-02-01

A new hierarchical basis preconditioner for the electric field integral equation (EFIE) operator is introduced. In contrast to existing hierarchical basis preconditioners, it works on arbitrary meshes and preconditions both the vector and the scalar potential within the EFIE operator. This is obtained by taking into account that the vector and the scalar potential discretized with loop-star basis functions are related to the hypersingular and the single layer operator (i.e., the well known integral operators from acoustics). For the single layer operator discretized with piecewise constant functions, a hierarchical preconditioner can easily be constructed. Thus the strategy we propose in this work for preconditioning the EFIE is the transformation of the scalar and the vector potential into operators equivalent to the single layer operator and to its inverse. More specifically, when the scalar potential is discretized with star functions as source and testing functions, the resulting matrix is a single layer operator discretized with piecewise constant functions and multiplied left and right with two additional graph Laplacian matrices. By inverting these graph Laplacian matrices, the discretized single layer operator is obtained, which can be preconditioned with the hierarchical basis. Dually, when the vector potential is discretized with loop functions, the resulting matrix can be interpreted as a hypersingular operator discretized with piecewise linear functions. By leveraging on a scalar Calderón identity, we can interpret this operator as spectrally equivalent to the inverse single layer operator. Then we use a linear-in-complexity, closed-form inverse of the dual hierarchical basis to precondition the hypersingular operator. The numerical results show the effectiveness of the proposed preconditioner and the practical impact of theoretical developments in real case scenarios.

Language Shift in the United States and Foreign-Born Older Mexican Heritage Individuals: Co-Ethnic Context for Language Resistance

ERIC Educational Resources Information Center

Siordia, Carlos; Diaz, Maria E.

2012-01-01

In this study, we investigate individual-level language shift in a population of Mexican origin Latinos/as aged 65 and up. By using data from the Hispanic Established Populations for the Epidemiologic Study of the Elderly, we investigate their English language use as the dependent variable in a hierarchical linear model. The microlevel independent…

A Multi-Level Simultaneous Analysis of How Student and School Characteristics Are Related to Students' English Language Achievement

ERIC Educational Resources Information Center

Güvendir, Emre

2015-01-01

This study examines how student and school characteristics are related to Turkish students' English language achievement in Evaluation of Student Achievement Test (ÖBBS) of 2009. The participants of the study involve 43707 ninth year students who were required to take ÖBBS in 2009. For data analysis two level hierarchical linear modeling was…

Equity in Educational Resources at the School Level in Korea

ERIC Educational Resources Information Center

Woo, Myung Suk

2010-01-01

This paper analyzed the equity of resources at the elementary school level in Korea using hierarchical linear modeling (HLM). The data included 2,327 Korean public elementary schools in 101 Local Governments within five Local Educational Offices (LEOs). This study found that schools in low property tax per resident areas receive fewer grants,…

Teacher-Child Relationship Quality and Academic Achievement in Elementary School: Does Gender Matter?

ERIC Educational Resources Information Center

McCormick, Meghan P.; O'Connor, Erin E.

2015-01-01

Using data from the National Institute of Child Health and Human Development Study of Early Child Care and Youth Development (N = 1,364) and 2-level hierarchical linear models with site fixed effects, we examined between- and within-child associations between teacher-child relationship closeness and conflict and standardized measures of children's…

A Multiple Risk Factors Model of the Development of Aggression among Early Adolescents from Urban Disadvantaged Neighborhoods

ERIC Educational Resources Information Center

Kim, Sangwon; Orpinas, Pamela; Kamphaus, Randy; Kelder, Steven H.

2011-01-01

This study empirically derived a multiple risk factors model of the development of aggression among middle school students in urban, low-income neighborhoods, using Hierarchical Linear Modeling (HLM). Results indicated that aggression increased from sixth to eighth grade. Additionally, the influences of four risk domains (individual, family,…

Trajectory of Life Satisfaction and Its Relationship with Subjective Economic Status and Successful Aging

ERIC Educational Resources Information Center

Hsu, Hui-Chuan

2010-01-01

The aim of the study was to explore the relationship between subjective economic status and indicators of successful aging to life satisfaction trajectories among the elderly in Taiwan. Data were from the four waves of "Survey of Health and Living Status of the Elderly in Taiwan". Hierarchical linear modeling was conducted. Subjective…

High Achievers from Low Socioeconomic Backgrounds: The Critical Role of Disciplinary Climate and Grit

ERIC Educational Resources Information Center

Huang, Haigen; Zhu, Hao

2017-01-01

The purpose of this study was to examine whether school disciplinary climate and grit predicted low socioeconomic status (SES) students being high achievers in mathematics and science with a representative sample of 15-year-old students in the United States. Our analysis, using a two-level logistic hierarchical linear model (HLM), indicated both…

Modeling Reader and Text Interactions during Narrative Comprehension: A Test of the Lexical Quality Hypothesis

ERIC Educational Resources Information Center

Hamilton, Stephen T.; Freed, Erin M.; Long, Debra L.

2013-01-01

The goal of this study was to examine predictions derived from the Lexical Quality Hypothesis regarding relations among word decoding, working-memory capacity, and the ability to integrate new concepts into a developing discourse representation. Hierarchical Linear Modeling was used to quantify the effects of three text properties (length,…

Psychosocial Development from College through Midlife: A 34-Year Sequential Study

ERIC Educational Resources Information Center

Whitbourne, Susan Krauss; Sneed, Joel R.; Sayer, Aline

2009-01-01

Two cohorts of alumni, leading-edge and trailing-edge baby boomers, first tested in their college years, were followed to ages 43 (N = 136) and 54 (N = 182) on a measure of Erikson's theory of psychosocial development. Hierarchical linear modeling was used to model the trajectory of growth for each psychosocial issue across middle adulthood. As…

Analysis of Student Performance in Large-Enrollment Life Science Courses

ERIC Educational Resources Information Center

Creech, Leah Renee; Sweeder, Ryan D.

2012-01-01

This study examined the historical performance of students at Michigan State University in 12 life sciences courses over 13 yr to find variables impacting student success. Hierarchical linear modeling predicted 25.0-62.8% of the variance in students' grades in the courses analyzed. The primary predictor of a student's course grade was his or her…

Predicting Change in Parenting Stress across Early Childhood: Child and Maternal Factors

ERIC Educational Resources Information Center

Williford, Amanda P.; Calkins, Susan D.; Keane, Susan P.

2007-01-01

This study examined maternal parenting stress in a sample of 430 boys and girls including those at risk for externalizing behavior problems. Children and their mothers were assessed when the children were ages 2, 4, and 5. Hierarchical linear modeling (HLM) was used to examine stability of parenting stress across early childhood and to examine…

Peer Victimization within the Ethnic Context of High School

ERIC Educational Resources Information Center

Felix, Erika D.; You, Sukkyung

2011-01-01

Risk for peer victimization varies by ethnicity, but few studies explore how the ethnic context of the school can affect this. Using a large sample of schools and high school students, we used hierarchical linear modeling to explore victimization risk by ethnicity within the ethnic context of the school. Models predicted total, physical, verbal,…

HLM in Cluster-Randomised Trials--Measuring Efficacy across Diverse Populations of Learners

ERIC Educational Resources Information Center

Hegedus, Stephen; Tapper, John; Dalton, Sara; Sloane, Finbarr

2013-01-01

We describe the application of Hierarchical Linear Modelling (HLM) in a cluster-randomised study to examine learning algebraic concepts and procedures in an innovative, technology-rich environment in the US. HLM is applied to measure the impact of such treatment on learning and on contextual variables. We provide a detailed description of such…

Effects of a Reform High School Mathematics Curriculum on Student Achievement: Whom Does It Benefit?

ERIC Educational Resources Information Center

Krupa, Erin E.; Confrey, Jere

2017-01-01

This study compared the effects of an integrated reform-based curriculum to a subject-specific curriculum on student learning of 19,526 high school algebra students. Using hierarchical linear modelling to account for variation in student achievement, the impact of the reform-based "Core-Plus Mathematics" curricular materials on student…

Parent Involvement and Children's Academic and Social Development in Elementary School

ERIC Educational Resources Information Center

El Nokali, Nermeen E.; Bachman, Heather J.; Votruba-Drzal, Elizabeth

2010-01-01

Data from the National Institute of Child Health and Human Development (NICHD) Study of Early Childcare and Youth Development (N = 1,364) were used to investigate children's trajectories of academic and social development across 1st, 3rd, and 5th grades. Hierarchical linear modeling was used to examine within- and between-child associations among…

Parental Characteristics and the Achievement Gap in Mathematics: Hierarchical Linear Modeling Analysis of Longitudinal Study of American Youth (LSAY)

ERIC Educational Resources Information Center

Shoraka, Mohammad; Arnold, Robert; Kim, Eun Sook; Salinitri, Geri; Kromrey, Jeffrey

2015-01-01

One of the most salient problems in education is the achievement gap. The researchers investigated the effects of parental education and parental occupations in science, technology, engineering, mathematics, or medical professions (STEMM) on the achievement gap in mathematics. Because students were nested within schools, two-level Hierarchical…

Relationships of Out-of-School-Time Mathematics Lessons to Mathematical Literacy in Singapore and Australia

ERIC Educational Resources Information Center

Kaur, Berinderjeet; Areepattamannil, Shaljan

2013-01-01

This study, drawing on date from the Programme for International Student Assessment (PISA) 2009, examined the relationships of out-of-school-time mathematics lessons to mathematical literacy in Singapore and Australia. Results of two-level hierarchical linear modelling (HLM) analyses revealed that out-of-school-time enrichment lessons in…

Testing the Adaptation to Poverty-Related Stress Model: Predicting Psychopathology Symptoms in Families Facing Economic Hardship

ERIC Educational Resources Information Center

Wadsworth, Martha E.; Raviv, Tali; Santiago, Catherine DeCarlo; Etter, Erica M.

2011-01-01

This study tested the Adaptation to Poverty-related Stress Model and its proposed relations between poverty-related stress, effortful and involuntary stress responses, and symptoms of psychopathology in an ethnically diverse sample of low-income children and their parents. Prospective Hierarchical Linear Modeling analyses conducted with 98…

Test Scores, Dropout Rates, and Transfer Rates as Alternative Indicators of High School Performance

ERIC Educational Resources Information Center

Rumberger, Russell W.; Palardy, Gregory J.

2005-01-01

This study investigated the relationships among several different indicators of high school performance: test scores, dropout rates, transfer rates, and attrition rates. Hierarchical linear models were used to analyze panel data from a sample of 14,199 students who took part in the National Education Longitudinal Survey of 1988. The results…

Increasing Equity and Achievement in Fifth Grade Mathematics: The Contribution of Content Exposure

ERIC Educational Resources Information Center

Ottmar, Erin R.; Konold, Timothy R.; Berry, Robert Q.; Grissmer, David W.; Cameron, Claire E.

2013-01-01

This study uses a large nationally representative data set (ECLS-K) of 5,181 students to examine the extent to which exposure to content and instructional practice contributes to mathematics achievement in fifth grade. Using hierarchical linear modeling, results suggest that more exposure to content beyond numbers and operations (i.e., geometry,…

Bullying Victimization and Student Engagement in Elementary, Middle, and High Schools: Moderating Role of School Climate

ERIC Educational Resources Information Center

Yang, Chunyan; Sharkey, Jill D.; Reed, Lauren A.; Chen, Chun; Dowdy, Erin

2018-01-01

Bullying is the most common form of school violence and is associated with a range of negative outcomes, including traumatic responses. This study used hierarchical linear modeling to examine the multilevel moderating effects of school climate and school level (i.e., elementary, middle, and high schools) on the association between bullying…

Examining the Variability of Mathematics Performance and Its Correlates Using Data from TIMSS '95 and TIMSS '99

ERIC Educational Resources Information Center

O'Dwyer, Laura M.

2005-01-01

International studies in education provide researchers with opportunities to examine how students with both similar and dissimilar formal education systems perform on a single test and provide rich information about the relationships among student outcomes and the factors that affect them. Using hierarchical linear regression techniques and TIMSS…

Obesity, High-Calorie Food Intake, and Academic Achievement Trends among U.S. School Children

ERIC Educational Resources Information Center

Li, Jian; O'Connell, Ann A.

2012-01-01

The authors investigated children's self-reported high-calorie food intake in Grade 5 and its relationship to trends in obesity status and academic achievement over the first 6 years of school. They used 3-level hierarchical linear models in the large-scale database (the Early Childhood Longitudinal Study--Kindergarten Cohort). Findings indicated…

A Longitudinal Study of Depressive Symptoms and Marijuana Use in a Sample of Inner-City African Americans

ERIC Educational Resources Information Center

Repetto, Paula B.; Zimmerman, Marc A.; Caldwell, Cleopatra H.

2008-01-01

The association between marijuana use and depressive symptoms was examined longitudinally in a sample of 622 African American youth, interviewed on six occasions, using hierarchical linear modeling (HLM). We considered whether depressive symptoms predicted changes in marijuana use and vice versa from high school through the transition into young…

Embodied linearity of speed control in Drosophila melanogaster.

PubMed

Medici, V; Fry, S N

2012-12-07

Fruitflies regulate flight speed by adjusting their body angle. To understand how low-level posture control serves an overall linear visual speed control strategy, we visually induced free-flight acceleration responses in a wind tunnel and measured the body kinematics using high-speed videography. Subsequently, we reverse engineered the transfer function mapping body pitch angle onto flight speed. A linear model is able to reproduce the behavioural data with good accuracy. Our results show that linearity in speed control is realized already at the level of body posture-mediated speed control and is therefore embodied at the level of the complex aerodynamic mechanisms of body and wings. Together with previous results, this study reveals the existence of a linear hierarchical control strategy, which can provide relevant control principles for biomimetic implementations, such as autonomous flying micro air vehicles.

Embodied linearity of speed control in Drosophila melanogaster

PubMed Central

Medici, V.; Fry, S. N.

2012-01-01

Fruitflies regulate flight speed by adjusting their body angle. To understand how low-level posture control serves an overall linear visual speed control strategy, we visually induced free-flight acceleration responses in a wind tunnel and measured the body kinematics using high-speed videography. Subsequently, we reverse engineered the transfer function mapping body pitch angle onto flight speed. A linear model is able to reproduce the behavioural data with good accuracy. Our results show that linearity in speed control is realized already at the level of body posture-mediated speed control and is therefore embodied at the level of the complex aerodynamic mechanisms of body and wings. Together with previous results, this study reveals the existence of a linear hierarchical control strategy, which can provide relevant control principles for biomimetic implementations, such as autonomous flying micro air vehicles. PMID:22933185

LIMO EEG: a toolbox for hierarchical LInear MOdeling of ElectroEncephaloGraphic data.

PubMed

Pernet, Cyril R; Chauveau, Nicolas; Gaspar, Carl; Rousselet, Guillaume A

2011-01-01

Magnetic- and electric-evoked brain responses have traditionally been analyzed by comparing the peaks or mean amplitudes of signals from selected channels and averaged across trials. More recently, tools have been developed to investigate single trial response variability (e.g., EEGLAB) and to test differences between averaged evoked responses over the entire scalp and time dimensions (e.g., SPM, Fieldtrip). LIMO EEG is a Matlab toolbox (EEGLAB compatible) to analyse evoked responses over all space and time dimensions, while accounting for single trial variability using a simple hierarchical linear modelling of the data. In addition, LIMO EEG provides robust parametric tests, therefore providing a new and complementary tool in the analysis of neural evoked responses.

LIMO EEG: A Toolbox for Hierarchical LInear MOdeling of ElectroEncephaloGraphic Data

PubMed Central

Pernet, Cyril R.; Chauveau, Nicolas; Gaspar, Carl; Rousselet, Guillaume A.

2011-01-01

Magnetic- and electric-evoked brain responses have traditionally been analyzed by comparing the peaks or mean amplitudes of signals from selected channels and averaged across trials. More recently, tools have been developed to investigate single trial response variability (e.g., EEGLAB) and to test differences between averaged evoked responses over the entire scalp and time dimensions (e.g., SPM, Fieldtrip). LIMO EEG is a Matlab toolbox (EEGLAB compatible) to analyse evoked responses over all space and time dimensions, while accounting for single trial variability using a simple hierarchical linear modelling of the data. In addition, LIMO EEG provides robust parametric tests, therefore providing a new and complementary tool in the analysis of neural evoked responses. PMID:21403915

Towards Stability Analysis of Jump Linear Systems with State-Dependent and Stochastic Switching

NASA Technical Reports Server (NTRS)

Tejada, Arturo; Gonzalez, Oscar R.; Gray, W. Steven

2004-01-01

This paper analyzes the stability of hierarchical jump linear systems where the supervisor is driven by a Markovian stochastic process and by the values of the supervised jump linear system s states. The stability framework for this class of systems is developed over infinite and finite time horizons. The framework is then used to derive sufficient stability conditions for a specific class of hybrid jump linear systems with performance supervision. New sufficient stochastic stability conditions for discrete-time jump linear systems are also presented.

Posterior propriety for hierarchical models with log-likelihoods that have norm bounds

DOE PAGES

Michalak, Sarah E.; Morris, Carl N.

2015-07-17

Statisticians often use improper priors to express ignorance or to provide good frequency properties, requiring that posterior propriety be verified. Our paper addresses generalized linear mixed models, GLMMs, when Level I parameters have Normal distributions, with many commonly-used hyperpriors. It provides easy-to-verify sufficient posterior propriety conditions based on dimensions, matrix ranks, and exponentiated norm bounds, ENBs, for the Level I likelihood. Since many familiar likelihoods have ENBs, which is often verifiable via log-concavity and MLE finiteness, our novel use of ENBs permits unification of posterior propriety results and posterior MGF/moment results for many useful Level I distributions, including those commonlymore » used with multilevel generalized linear models, e.g., GLMMs and hierarchical generalized linear models, HGLMs. Furthermore, those who need to verify existence of posterior distributions or of posterior MGFs/moments for a multilevel generalized linear model given a proper or improper multivariate F prior as in Section 1 should find the required results in Sections 1 and 2 and Theorem 3 (GLMMs), Theorem 4 (HGLMs), or Theorem 5 (posterior MGFs/moments).« less

Asymptotic analysis of hierarchical martensitic microstructure

NASA Astrophysics Data System (ADS)

Cesana, Pierluigi; Porta, Marcel; Lookman, Turab

2014-12-01

We consider a hierarchical nested microstructure, which also contains a point of singularity (disclination) at the origin, observed in lead orthovanadate. We show how to exactly compute the energy cost and associated displacement field within linearized elasticity by enforcing geometric compatibility of strains across interfaces of the three-phase mixture of distortions (variants) in the microstructure. We prove that the mechanical deformation is purely elastic and discuss the behavior of the system close to the origin.

Hierarchical hybrid film of MnO2 nanoparticles/multi-walled fullerene nanotubes-graphene for highly selective sensing of hydrogen peroxide.

PubMed

Pan, Yang; Hou, Zhaohui; Yi, Wei; Zhu, Wei; Zeng, Fanyan; Liu, You-Nian

2015-08-15

Hierarchical hybrid films of MnO2 nanoparticles/multi-walled fullerene nanotubes-graphene (MNPs/MWFNTs-GS) have been prepared via a simple wet-chemical method. For this purpose, MWFNTs (~300nm in length) are fabricated from tailoring multi-walled carbon nanotubes (MWCNTs), and then inserted into GS to pile up into a hierarchical hybrid film with the in situ formative MNPs. Scanning electron microscope, transmission electron microscope and X-ray diffraction are used to confirm the morphology and structure of the as-obtained film. The electrochemical studies reveal that MNPs/MWFNTs-GS exhibit significantly enhanced electrocatalytic activity compared with MNPs/GS, and show a rapid response to H2O2 over a wide linear range of 2.0μM-8.44mM with a high sensitivity of 206.3μA mM(-1)cm(-2) and an excellent selectivity. These favorable electrochemical detection properties may be mainly attributed to the introduction of MWFNTs, which helps to promote the electron/ion transport between MNPs and GS and form the hierarchical film structure. Copyright © 2015 Elsevier B.V. All rights reserved.

Examining the Big-Fish-Little-Pond Effect on Students' Self-Concept of Learning Science in Taiwan Based on the TIMSS Databases

ERIC Educational Resources Information Center

Liou, Pey-Yan

2014-01-01

The purpose of this study is to examine the relationship between student self-concept and achievement in science in Taiwan based on the big-fish-little-pond effect (BFLPE) model using the Trends in International Mathematics and Science Study (TIMSS) 2003 and 2007 databases. Hierarchical linear modeling was used to examine the effects of the…

Efficient Actor-Critic Algorithm with Hierarchical Model Learning and Planning

PubMed Central

Fu, QiMing

2016-01-01

To improve the convergence rate and the sample efficiency, two efficient learning methods AC-HMLP and RAC-HMLP (AC-HMLP with ℓ 2-regularization) are proposed by combining actor-critic algorithm with hierarchical model learning and planning. The hierarchical models consisting of the local and the global models, which are learned at the same time during learning of the value function and the policy, are approximated by local linear regression (LLR) and linear function approximation (LFA), respectively. Both the local model and the global model are applied to generate samples for planning; the former is used only if the state-prediction error does not surpass the threshold at each time step, while the latter is utilized at the end of each episode. The purpose of taking both models is to improve the sample efficiency and accelerate the convergence rate of the whole algorithm through fully utilizing the local and global information. Experimentally, AC-HMLP and RAC-HMLP are compared with three representative algorithms on two Reinforcement Learning (RL) benchmark problems. The results demonstrate that they perform best in terms of convergence rate and sample efficiency. PMID:27795704

Efficient Actor-Critic Algorithm with Hierarchical Model Learning and Planning.

PubMed

Zhong, Shan; Liu, Quan; Fu, QiMing

2016-01-01

To improve the convergence rate and the sample efficiency, two efficient learning methods AC-HMLP and RAC-HMLP (AC-HMLP with ℓ 2 -regularization) are proposed by combining actor-critic algorithm with hierarchical model learning and planning. The hierarchical models consisting of the local and the global models, which are learned at the same time during learning of the value function and the policy, are approximated by local linear regression (LLR) and linear function approximation (LFA), respectively. Both the local model and the global model are applied to generate samples for planning; the former is used only if the state-prediction error does not surpass the threshold at each time step, while the latter is utilized at the end of each episode. The purpose of taking both models is to improve the sample efficiency and accelerate the convergence rate of the whole algorithm through fully utilizing the local and global information. Experimentally, AC-HMLP and RAC-HMLP are compared with three representative algorithms on two Reinforcement Learning (RL) benchmark problems. The results demonstrate that they perform best in terms of convergence rate and sample efficiency.

Understanding Disproportionate Representation in Special Education by Examining Group Differences in Behavior Ratings

ERIC Educational Resources Information Center

Peters, Christina D.; Kranzler, John H.; Algina, James; Smith, Stephen W.; Daunic, Ann P.

2014-01-01

The aim of the current study was to examine mean-group differences on behavior rating scales and variables that may predict such differences. Sixty-five teachers completed the Clinical Assessment of Behavior-Teacher Form (CAB-T) for a sample of 982 students. Four outcome variables from the CAB-T were assessed. Hierarchical linear modeling was used…

Revisiting the Impact of NCLB High-Stakes School Accountability, Capacity, and Resources: State NAEP 1990-2009 Reading and Math Achievement Gaps and Trends

ERIC Educational Resources Information Center

Lee, Jaekyung; Reeves, Todd

2012-01-01

This study examines the impact of high-stakes school accountability, capacity, and resources under NCLB on reading and math achievement outcomes through comparative interrupted time-series analyses of 1990-2009 NAEP state assessment data. Through hierarchical linear modeling latent variable regression with inverse probability of treatment…

Are Principal Background and School Processes Related to Teacher Job Satisfaction? A Multilevel Study Using Schools and Staffing Survey 2003-04

ERIC Educational Resources Information Center

Shen, Jianping; Leslie, Jeffrey M.; Spybrook, Jessaca K.; Ma, Xin

2012-01-01

Using nationally representative samples for public school teachers and principals, the authors inquired into whether principal background and school processes are related to teacher job satisfaction. Employing hierarchical linear modeling (HLM), the authors were able to control for background characteristics at both the teacher and school levels.…

The Influence of Socioeconomic, Parental, and District Factors on the 2013 MCAS Grade 4 Language Arts and Mathematics Scores

ERIC Educational Resources Information Center

Caldwell, Dale G.

2017-01-01

This correlational, explanatory study utilized multiple linear and hierarchical regression to examine the predictive power of socioeconomic, parental and district factors on the total percentage of students who scored Proficient or Advanced Proficient on the 2013 MCAS Grade 4 language arts and mathematics test. The population for this study…

Using HLM to Explore the Effects of Perceptions of Learning Environments and Assessments on Students' Test Performance

ERIC Educational Resources Information Center

Chu, Man-Wai; Babenko, Oksana; Cui, Ying; Leighton, Jacqueline P.

2014-01-01

The study examines the role that perceptions or impressions of learning environments and assessments play in students' performance on a large-scale standardized test. Hierarchical linear modeling (HLM) was used to test aspects of the Learning Errors and Formative Feedback model to determine how much variation in students' performance was explained…

Associations of Home and Classroom Environments with Head Start Children's Code-Related and Oral Language Skills

ERIC Educational Resources Information Center

Han, Jisu; Schlieber, Marisa; Gregory, Bradley

2017-01-01

This study used data from the Head Start Family and Child Experiences Survey (FACES) 2009 4-year-old cohort to examine associations among family characteristics, home and classroom environments, and the emergent literacy skills of Head Start children. Results from hierarchical linear models suggest that both family and classroom contexts play a…

Educational Leadership as Best Practice in Highly Effective Schools in the Autonomous Region of the Basque County (Spain)

ERIC Educational Resources Information Center

Intxausti, Nahia; Joaristi, Luis; Lizasoain, Luis

2016-01-01

This study presents part of a research project currently underway which aims to characterise the best practices of highly effective schools in the Autonomous Region of the Basque Country (Spain). Multilevel statistical modelling and hierarchical linear models were used to select 32 highly effective schools, with highly effective being taken to…

The Protective Role of Group Identity: Sectarian Antisocial Behavior and Adolescent Emotion Problems

ERIC Educational Resources Information Center

Merrilees, Christine E.; Taylor, Laura K.; Goeke-Morey, Marcie C.; Shirlow, Peter; Cummings, E. Mark; Cairns, Ed

2014-01-01

The protective role of strength of group identity was examined for youth in a context of protracted political conflict. Participants included 814 adolescents (M[subscript age] = 13.61, SD = 1.99 at Time 1) participating in a longitudinal study in Belfast, Northern Ireland. Utilizing hierarchical linear modeling, the results show that the effect of…

The Development of Science Achievement in Middle and High School: Individual Differences and School Effects.

ERIC Educational Resources Information Center

Ma, Xin; Wilkins, Jessie L. M.

2002-01-01

Used hierarchical linear models with data from the Longitudinal Study of American Youth to model the growth of student science achievement in biology, physical science, and environmental science during middle and high school. Growth was quadratic across all areas, with rapid growth at the beginning of middle school and slow growth at the ending…

Student Engagement in Two Countries: A Comparative Study Using National Survey of Student Engagement (NSSE) Data

ERIC Educational Resources Information Center

Kandiko, C. B.

2008-01-01

To compare college and university student engagement in two countries with different responses to global forces, Canada and the United States (US), a series of hierarchical linear regression (HLM) models were developed to analyse data from the 2006 administration of the National Survey of Student Engagement (NSSE). Overall, students in the U.S.…

What Motivates High School Students to Want to Be Teachers? The Role of Salary, Working Conditions, and Societal Evaluations about Occupations in a Comparative Perspective

ERIC Educational Resources Information Center

Han, Seong Won; Borgonovi, Francesca; Guerriero, Sonia

2018-01-01

This study examines between-country differences in the degree to which teachers' working conditions, salaries, and societal evaluations about desirable job characteristics are associated with students' teaching career expectations. Three-level hierarchical generalized linear models are employed to analyze cross-national data from the Programme for…

Social determinants of childhood asthma symptoms: an ecological study in urban Latin America.

PubMed

Fattore, Gisel L; Santos, Carlos A T; Barreto, Mauricio L

2014-04-01

Asthma is an important public health problem in urban Latin America. This study aimed to analyze the role of socioeconomic and environmental factors as potential determinants of asthma symptoms prevalence in children from Latin American (LA) urban centers. We selected 31 LA urban centers with complete data, and an ecological analysis was performed. According to our theoretical framework, the explanatory variables were classified in three levels: distal, intermediate, and proximate. The association between variables in the three levels and prevalence of asthma symptoms was examined by bivariate and multivariate linear regression analysis weighed by sample size. In a second stage, we fitted several linear regression models introducing sequentially the variables according to the predefined hierarchy. In the final hierarchical model Gini Index, crowding, sanitation, variation in infant mortality rates and homicide rates, explained great part of the variance in asthma prevalence between centers (R(2) = 75.0 %). We found a strong association between socioeconomic and environmental variables and prevalence of asthma symptoms in LA urban children, and according to our hierarchical framework and the results found we suggest that social inequalities (measured by the Gini Index) is a central determinant to explain high prevalence of asthma in LA.

Control Strategies for Guided Collective Motion

DTIC Science & Technology

2015-02-27

Rorres and H. Anton , “ Elementary linear algebra applications version,” 9th Edition, Wiley India Pvt. Ltd., 2011. [20] S.H. Strogatz, “From Kuramoto to... linear cyclic pursuit in which an agent pursues its leader with an angle of deviation. The sufficient conditions for the stability of such systems are...Generalized Hierarchical Cyclic Pursuit 6. D. Mukherjee and D. Ghose: Deviated Linear Cyclic Pursuit 7. D. Mukherjee and D. Ghose; On Synchronous and

Multiscale Hierarchical Design of a Flexible Piezoresistive Pressure Sensor with High Sensitivity and Wide Linearity Range.

PubMed

Shi, Jidong; Wang, Liu; Dai, Zhaohe; Zhao, Lingyu; Du, Mingde; Li, Hongbian; Fang, Ying

2018-05-30

Flexible piezoresistive pressure sensors have been attracting wide attention for applications in health monitoring and human-machine interfaces because of their simple device structure and easy-readout signals. For practical applications, flexible pressure sensors with both high sensitivity and wide linearity range are highly desirable. Herein, a simple and low-cost method for the fabrication of a flexible piezoresistive pressure sensor with a hierarchical structure over large areas is presented. The piezoresistive pressure sensor consists of arrays of microscale papillae with nanoscale roughness produced by replicating the lotus leaf's surface and spray-coating of graphene ink. Finite element analysis (FEA) shows that the hierarchical structure governs the deformation behavior and pressure distribution at the contact interface, leading to a quick and steady increase in contact area with loads. As a result, the piezoresistive pressure sensor demonstrates a high sensitivity of 1.2 kPa -1 and a wide linearity range from 0 to 25 kPa. The flexible pressure sensor is applied for sensitive monitoring of small vibrations, including wrist pulse and acoustic waves. Moreover, a piezoresistive pressure sensor array is fabricated for mapping the spatial distribution of pressure. These results highlight the potential applications of the flexible piezoresistive pressure sensor for health monitoring and electronic skin. © 2018 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

A Spreadsheet for a 2 x 3 x 2 Log-Linear Analysis. AIR 1991 Annual Forum Paper.

ERIC Educational Resources Information Center

Saupe, Joe L.

This paper describes a personal computer spreadsheet set up to carry out hierarchical log-linear analyses, a type of analysis useful for institutional research into multidimensional frequency tables formed from categorical variables such as faculty rank, student class level, gender, or retention status. The spreadsheet provides a concrete vehicle…

Micro-Raman spectroscopy of natural and synthetic indigo samples.

PubMed

Vandenabeele, Peter; Moens, Luc

2003-02-01

In this work indigo samples from three different sources are studied by using Raman spectroscopy: the synthetic pigment and pigments from the woad (Isatis tinctoria) and the indigo plant (Indigofera tinctoria). 21 samples were obtained from 8 suppliers; for each sample 5 Raman spectra were recorded and used for further chemometrical analysis. Principal components analysis (PCA) was performed as data reduction method before applying hierarchical cluster analysis. Linear discriminant analysis (LDA) was implemented as a non-hierarchical supervised pattern recognition method to build a classification model. In order to avoid broad-shaped interferences from the fluorescence background, the influence of 1st and 2nd derivatives on the classification was studied by using cross-validation. Although chemically identical, it is shown that Raman spectroscopy in combination with suitable chemometric methods has the potential to discriminate between synthetic and natural indigo samples.

A study of microindentation hardness tests by mechanism-based strain gradient plasticity

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

Huang, Y.; Xue, Z.; Gao, H.

2000-08-01

We recently proposed a theory of mechanism-based strain gradient (MSG) plasticity to account for the size dependence of plastic deformation at micron- and submicron-length scales. The MSG plasticity theory connects micron-scale plasticity to dislocation theories via a multiscale, hierarchical framework linking Taylor's dislocation hardening model to strain gradient plasticity. Here we show that the theory of MSG plasticity, when used to study micro-indentation, indeed reproduces the linear dependence observed in experiments, thus providing an important self-consistent check of the theory. The effects of pileup, sink-in, and the radius of indenter tip have been taken into account in the indentation model.more » In accomplishing this objective, we have generalized the MSG plasticity theory to include the elastic deformation in the hierarchical framework. (c) 2000 Materials Research Society.« less

Stochastic lumping analysis for linear kinetics and its application to the fluctuation relations between hierarchical kinetic networks.

PubMed

Deng, De-Ming; Chang, Cheng-Hung

2015-05-14

Conventional studies of biomolecular behaviors rely largely on the construction of kinetic schemes. Since the selection of these networks is not unique, a concern is raised whether and under which conditions hierarchical schemes can reveal the same experimentally measured fluctuating behaviors and unique fluctuation related physical properties. To clarify these questions, we introduce stochasticity into the traditional lumping analysis, generalize it from rate equations to chemical master equations and stochastic differential equations, and extract the fluctuation relations between kinetically and thermodynamically equivalent networks under intrinsic and extrinsic noises. The results provide a theoretical basis for the legitimate use of low-dimensional models in the studies of macromolecular fluctuations and, more generally, for exploring stochastic features in different levels of contracted networks in chemical and biological kinetic systems.

Sensory processing and world modeling for an active ranging device

NASA Technical Reports Server (NTRS)

Hong, Tsai-Hong; Wu, Angela Y.

1991-01-01

In this project, we studied world modeling and sensory processing for laser range data. World Model data representation and operation were defined. Sensory processing algorithms for point processing and linear feature detection were designed and implemented. The interface between world modeling and sensory processing in the Servo and Primitive levels was investigated and implemented. In the primitive level, linear features detectors for edges were also implemented, analyzed and compared. The existing world model representations is surveyed. Also presented is the design and implementation of the Y-frame model, a hierarchical world model. The interfaces between the world model module and the sensory processing module are discussed as well as the linear feature detectors that were designed and implemented.

Non-linear clustering in the cold plus hot dark matter model

NASA Astrophysics Data System (ADS)

Bonometto, Silvio A.; Borgani, Stefano; Ghigna, Sebastiano; Klypin, Anatoly; Primack, Joel R.

1995-03-01

The main aim of this work is to find out if hierarchical scaling, observed in galaxy clustering, can be dynamically explained by studying N-body simulations. Previous analyses of dark matter (DM) particle distributions indicated heavy distortions with respect to the hierarchical pattern. Here, we shall describe how such distortions are to be interpreted and why they can be fully reconciled with the observed galaxy clustering. This aim is achieved by using high-resolution (512^3 grid-points) particle-mesh (PM) N-body simulations to follow the development of non-linear clustering in a Omega=1 universe, dominated either by cold dark matter (CDM) or by a mixture of cold+hot dark matter (CHDM) with Omega_cold=0.6, and Omega_hot=0.3 and Omega_baryon=0.1 a simulation box of side 100 Mpc (h=0.5) is used. We analyse two CHDM realizations with biasing factor b=1.5 (COBE normalization), starting from different initial random numbers, and compare them with CDM simulations with b=1 (COBE-compatible) and b=1.5. We evaluate high-order correlation functions and the void probability function (VPF). Correlation functions are obtained from both counts in cells and counts of neighbours. The analysis is carried out for DM particles and for galaxies identified as massive haloes of the evolved density field. We confirm that clustering of DM particles systematically exhibits deviations from hierarchical scaling, although the deviation increases somewhat in redshift space. Deviations from the hierarchical scaling of DM particles are found to be related to the spectrum shape, in a way that indicates that such distortions arise from finite sampling effects. We identify galaxy positions in the simulations and show that, quite differently from the DM particle background, galaxies follow hierarchical scaling (S_q=xi_q/& xgr^q-1_2=consta nt) far more closely, with reduced skewness and kurtosis coefficients S_3~2.5 and S_4~7.5, in general agreement with observational results. Unlike DM, the scaling of galaxy clustering is must marginally affected by redshift distortions and is obtained for both CDM and CHDM models. Hierarchical scaling in simulations is confirmed by VPF analysis. Also in this case, we find substantial agreement with observational findings.

The Impact of Salient Role Stress on Trajectories of Health in Late Life among Survivors of a Seven-Year Panel Study: Analyses of Individual Growth Curves

ERIC Educational Resources Information Center

Shaw, Benjamin A.; Krause, Neal

2002-01-01

The purpose of this study is twofold: 1) to model changes in health over time among older adults; and 2) to assess the degree to which stress arising in salient social roles accounts for individual variation in these changes. Individual growth curve analyses using Hierarchical Linear Modeling (HLM) software were employed with longitudinal data…

Microfluidic Droplet-Facilitated Hierarchical Assembly for Dual Cargo Loading and Synergistic Delivery.

PubMed

Yu, Ziyi; Zheng, Yu; Parker, Richard M; Lan, Yang; Wu, Yuchao; Coulston, Roger J; Zhang, Jing; Scherman, Oren A; Abell, Chris

2016-04-06

Bottom-up hierarchical assembly has emerged as an elaborate and energy-efficient strategy for the fabrication of smart materials. Herein, we present a hierarchical assembly process, whereby linear amphiphilic block copolymers are self-assembled into micelles, which in turn are accommodated at the interface of microfluidic droplets via cucurbit[8]uril-mediated host-guest chemistry to form supramolecular microcapsules. The monodisperse microcapsules can be used for simultaneous carriage of both organic (Nile Red) and aqueous-soluble (fluorescein isothiocyanate-dextran) cargo. Furthermore, the well-defined compartmentalized structure benefits from the dynamic nature of the supramolecular interaction and offers synergistic delivery of cargos with triggered release or through photocontrolled porosity. This demonstration of premeditated hierarchical assembly, where interactions from the molecular to microscale are designed, illustrates the power of this route toward accessing the next generation of functional materials and encapsulation strategies.

Feeling Good, Feeling Bad: Influences of Maternal Perceptions of the Child and Marital Adjustment on Well-Being in Mothers of Children with an Autism Spectrum Disorder

ERIC Educational Resources Information Center

Lickenbrock, Diane M.; Ekas, Naomi V.; Whitman, Thomas L.

2011-01-01

Mothers of children with an autism spectrum disorder (n = 49) participated in a 30-day diary study which examined associations between mothers' positive and negative perceptions of their children, marital adjustment, and maternal well-being. Hierarchical linear modeling results revealed that marital adjustment mediated associations between…

Predicting Urban Elementary Student Success and Passage on Ohio's High-Stakes Achievement Measures Using DIBELS Oral Reading Fluency and Informal Math Concepts and Applications: An Exploratory Study Employing Hierarchical Linear Modeling

ERIC Educational Resources Information Center

Merkle, Erich Robert

2011-01-01

Contemporary education is experiencing substantial reform across legislative, pedagogical, and assessment dimensions. The increase in school-based accountability systems has brought forth a culture where states, school districts, teachers, and individual students are required to demonstrate their efficacy towards improvement of the educational…

Gene-Environment Contributions to the Development of Infant Vagal Reactivity: The Interaction of Dopamine and Maternal Sensitivity

ERIC Educational Resources Information Center

Propper, Cathi; Moore, Ginger A.; Mills-Koonce, W. Roger; Halpern, Carolyn Tucker; Hill-Soderlund, Ashley L.; Calkins, Susan D.; Carbone, Mary Anna; Cox, Martha

2008-01-01

This study investigated dopamine receptor genes ("DRD2" and "DRD4") and maternal sensitivity as predictors of infant respiratory sinus arrhythmia (RSA) and RSA reactivity, purported indices of vagal tone and vagal regulation, in a challenge task at 3, 6, and 12 months in 173 infant-mother dyads. Hierarchical linear modeling (HLM) revealed that at…

How Many Teachers Does It Take to Support a Student? Examining the Relationship between Teacher Support and Adverse Health Outcomes in High-Performing, Pressure-Cooker High Schools

ERIC Educational Resources Information Center

Conner, Jerusha O.; Miles, Sarah B.; Pope, Denise C.

2014-01-01

Although considerable research has demonstrated the importance of supportive teacher-student relationships to students' academic and nonacademic outcomes, few studies have explored these relationships in the context of high-performing high schools. Hierarchical linear modeling with a sample of 5,557 students from 14 different high-performing…

Effects of Traditional and Nontraditional Forms of Parental Involvement on School-Level Achievement Outcome: An HLM Study Using SASS 2007-2008

ERIC Educational Resources Information Center

Shen, Jianping; Washington, Alandra L.; Bierlein Palmer, Louann; Xia, Jiangang

2014-01-01

The authors examined parental involvement's (PI) impact on school performance. The hierarchical linear modeling method was applied to national Schools and Staffing Survey 2007-2008 data. They found that PI variables explained significant variance for the outcomes of (a) meeting adequate yearly progress (AYP) and (b) being free from sanctions. The…

The effect of perceptions of support for autonomy, relatedness, and competence on interest in camp for adolescent girls

Treesearch

Ann Gillard; Clifton E. Watts; Peter A. Witt

2008-01-01

Understanding whether young people perceive camp to be supportive of their needs for autonomy, relatedness, and competence (ARC) is important for camp administrators and staff as they plan and structure opportunities for positive youth development. We conducted a study during the summer of 2006 at a Girl Scout camp in Pennsylvania. Through hierarchical linear...

Making a Way to Success: Self-Authorship and Academic Achievement of First-Year African American Students at Historically Black Colleges

ERIC Educational Resources Information Center

Strayhorn, Terrell L.

2014-01-01

The purpose of the study was to estimate the relationship between academic achievement in college, as defined by first-year grade point average (GPA), and self-authorship among African American first-year students at an HBCU (N = 140), using hierarchical linear regression techniques. A single research question guided this investigation: What is…

Kinetic Rate Kernels via Hierarchical Liouville-Space Projection Operator Approach.

PubMed

Zhang, Hou-Dao; Yan, YiJing

2016-05-19

Kinetic rate kernels in general multisite systems are formulated on the basis of a nonperturbative quantum dissipation theory, the hierarchical equations of motion (HEOM) formalism, together with the Nakajima-Zwanzig projection operator technique. The present approach exploits the HEOM-space linear algebra. The quantum non-Markovian site-to-site transfer rate can be faithfully evaluated via projected HEOM dynamics. The developed method is exact, as evident by the comparison to the direct HEOM evaluation results on the population evolution.

Efficient Parallel Formulations of Hierarchical Methods and Their Applications

NASA Astrophysics Data System (ADS)

Grama, Ananth Y.

1996-01-01

Hierarchical methods such as the Fast Multipole Method (FMM) and Barnes-Hut (BH) are used for rapid evaluation of potential (gravitational, electrostatic) fields in particle systems. They are also used for solving integral equations using boundary element methods. The linear systems arising from these methods are dense and are solved iteratively. Hierarchical methods reduce the complexity of the core matrix-vector product from O(n^2) to O(n log n) and the memory requirement from O(n^2) to O(n). We have developed highly scalable parallel formulations of a hybrid FMM/BH method that are capable of handling arbitrarily irregular distributions. We apply these formulations to astrophysical simulations of Plummer and Gaussian galaxies. We have used our parallel formulations to solve the integral form of the Laplace equation. We show that our parallel hierarchical mat-vecs yield high efficiency and overall performance even on relatively small problems. A problem containing approximately 200K nodes takes under a second to compute on 256 processors and yet yields over 85% efficiency. The efficiency and raw performance is expected to increase for bigger problems. For the 200K node problem, our code delivers about 5 GFLOPS of performance on a 256 processor T3D. This is impressive considering the fact that the problem has floating point divides and roots, and very little locality resulting in poor cache performance. A dense matrix-vector product of the same dimensions would require about 0.5 TeraBytes of memory and about 770 TeraFLOPS of computing speed. Clearly, if the loss in accuracy resulting from the use of hierarchical methods is acceptable, our code yields significant savings in time and memory. We also study the convergence of a GMRES solver built around this mat-vec. We accelerate the convergence of the solver using three preconditioning techniques: diagonal scaling, block-diagonal preconditioning, and inner-outer preconditioning. We study the performance and parallel efficiency of these preconditioned solvers. Using this solver, we solve dense linear systems with hundreds of thousands of unknowns. Solving a 105K unknown problem takes about 10 minutes on a 64 processor T3D. Until very recently, boundary element problems of this magnitude could not even be generated, let alone solved.

Social structure in a family group of Guanaco (Lama guanicoe, Ungulate): is female hierarchy based on 'prior attributes' or 'social dynamics'?

PubMed

Correa, Loreto A; Zapata, Beatriz; Samaniego, Horacio; Soto-Gamboa, Mauricio

2013-09-01

Social life involves costs and benefits mostly associated with how individuals interact with each other. The formation of hierarchies inside social groups has evolved as a common strategy to avoid high costs stemming from social interactions. Hierarchical relationships seem to be associated with different features such as body size, body condition and/or age, which determine dominance ability ('prior attributes' hypothesis). In contrast, the 'social dynamic' hypothesis suggests that an initial social context is a determinant in the formation of the hierarchy, more so than specific individual attributes. Hierarchical rank places individuals in higher positions, which presumably increases resource accessibility to their benefit, including opportunities for reproduction. We evaluate the maintenance of hierarchy in a family group of guanacos (Lama guanicoe) and evaluate the possible mechanisms involved in the stability of these interactions and their consequences. We estimate the linearity of social hierarchy and their dynamics. We find evidence of the formation of a highly linear hierarchy among females with males positioned at the bottom of the hierarchy. This hierarchy is not affected by physical characteristics or age, suggesting that it is established only through intra-group interactions. Rank is not related with calves' weight gain either; however, subordinated females, with lower rank, exhibit higher rates of allosuckling. We found no evidence of hierarchical structure in calves suggesting that hierarchical relationship in guanacos could be established during the formation of the family group. Hence, our results suggest that hierarchical dynamics could be related more to social dynamics than to prior attributes. We finally discuss the importance of hierarchies established by dominance and their role in minimizing social costs of interactions. Copyright © 2013 Elsevier B.V. All rights reserved.

Clinical time series prediction: Toward a hierarchical dynamical system framework.

PubMed

Liu, Zitao; Hauskrecht, Milos

2015-09-01

Developing machine learning and data mining algorithms for building temporal models of clinical time series is important for understanding of the patient condition, the dynamics of a disease, effect of various patient management interventions and clinical decision making. In this work, we propose and develop a novel hierarchical framework for modeling clinical time series data of varied length and with irregularly sampled observations. Our hierarchical dynamical system framework for modeling clinical time series combines advantages of the two temporal modeling approaches: the linear dynamical system and the Gaussian process. We model the irregularly sampled clinical time series by using multiple Gaussian process sequences in the lower level of our hierarchical framework and capture the transitions between Gaussian processes by utilizing the linear dynamical system. The experiments are conducted on the complete blood count (CBC) panel data of 1000 post-surgical cardiac patients during their hospitalization. Our framework is evaluated and compared to multiple baseline approaches in terms of the mean absolute prediction error and the absolute percentage error. We tested our framework by first learning the time series model from data for the patients in the training set, and then using it to predict future time series values for the patients in the test set. We show that our model outperforms multiple existing models in terms of its predictive accuracy. Our method achieved a 3.13% average prediction accuracy improvement on ten CBC lab time series when it was compared against the best performing baseline. A 5.25% average accuracy improvement was observed when only short-term predictions were considered. A new hierarchical dynamical system framework that lets us model irregularly sampled time series data is a promising new direction for modeling clinical time series and for improving their predictive performance. Copyright © 2014 Elsevier B.V. All rights reserved.

Predictability of extremes in non-linear hierarchically organized systems

NASA Astrophysics Data System (ADS)

Kossobokov, V. G.; Soloviev, A.

2011-12-01

Understanding the complexity of non-linear dynamics of hierarchically organized systems progresses to new approaches in assessing hazard and risk of the extreme catastrophic events. In particular, a series of interrelated step-by-step studies of seismic process along with its non-stationary though self-organized behaviors, has led already to reproducible intermediate-term middle-range earthquake forecast/prediction technique that has passed control in forward real-time applications during the last two decades. The observed seismic dynamics prior to and after many mega, great, major, and strong earthquakes demonstrate common features of predictability and diverse behavior in course durable phase transitions in complex hierarchical non-linear system of blocks-and-faults of the Earth lithosphere. The confirmed fractal nature of earthquakes and their distribution in space and time implies that many traditional estimations of seismic hazard (from term-less to short-term ones) are usually based on erroneous assumptions of easy tractable analytical models, which leads to widespread practice of their deceptive application. The consequences of underestimation of seismic hazard propagate non-linearly into inflicted underestimation of risk and, eventually, into unexpected societal losses due to earthquakes and associated phenomena (i.e., collapse of buildings, landslides, tsunamis, liquefaction, etc.). The studies aimed at forecast/prediction of extreme events (interpreted as critical transitions) in geophysical and socio-economical systems include: (i) large earthquakes in geophysical systems of the lithosphere blocks-and-faults, (ii) starts and ends of economic recessions, (iii) episodes of a sharp increase in the unemployment rate, (iv) surge of the homicides in socio-economic systems. These studies are based on a heuristic search of phenomena preceding critical transitions and application of methodologies of pattern recognition of infrequent events. Any study of rare phenomena of highly complex origin, by their nature, implies using problem oriented methods, which design breaks the limits of classical statistical or econometric applications. The unambiguously designed forecast/prediction algorithms of the "yes or no" variety, analyze the observable quantitative integrals and indicators available to a given date, then provides unambiguous answer to the question whether a critical transition should be expected or not in the next time interval. Since the predictability of an originating non-linear dynamical system is limited in principle, the probabilistic component of forecast/prediction algorithms is represented by the empirical probabilities of alarms, on one side, and failures-to-predict, on the other, estimated on control sets achieved in the retro- and prospective experiments. Predicting in advance is the only decisive test of forecast/predictions and the relevant on-going experiments are conducted in the case seismic extremes, recessions, and increases of unemployment rate. The results achieved in real-time testing keep being encouraging and confirm predictability of the extremes.

Hierarchical Bayesian sparse image reconstruction with application to MRFM.

PubMed

Dobigeon, Nicolas; Hero, Alfred O; Tourneret, Jean-Yves

2009-09-01

This paper presents a hierarchical Bayesian model to reconstruct sparse images when the observations are obtained from linear transformations and corrupted by an additive white Gaussian noise. Our hierarchical Bayes model is well suited to such naturally sparse image applications as it seamlessly accounts for properties such as sparsity and positivity of the image via appropriate Bayes priors. We propose a prior that is based on a weighted mixture of a positive exponential distribution and a mass at zero. The prior has hyperparameters that are tuned automatically by marginalization over the hierarchical Bayesian model. To overcome the complexity of the posterior distribution, a Gibbs sampling strategy is proposed. The Gibbs samples can be used to estimate the image to be recovered, e.g., by maximizing the estimated posterior distribution. In our fully Bayesian approach, the posteriors of all the parameters are available. Thus, our algorithm provides more information than other previously proposed sparse reconstruction methods that only give a point estimate. The performance of the proposed hierarchical Bayesian sparse reconstruction method is illustrated on synthetic data and real data collected from a tobacco virus sample using a prototype MRFM instrument.

A new fast direct solver for the boundary element method

NASA Astrophysics Data System (ADS)

Huang, S.; Liu, Y. J.

2017-09-01

A new fast direct linear equation solver for the boundary element method (BEM) is presented in this paper. The idea of the new fast direct solver stems from the concept of the hierarchical off-diagonal low-rank matrix. The hierarchical off-diagonal low-rank matrix can be decomposed into the multiplication of several diagonal block matrices. The inverse of the hierarchical off-diagonal low-rank matrix can be calculated efficiently with the Sherman-Morrison-Woodbury formula. In this paper, a more general and efficient approach to approximate the coefficient matrix of the BEM with the hierarchical off-diagonal low-rank matrix is proposed. Compared to the current fast direct solver based on the hierarchical off-diagonal low-rank matrix, the proposed method is suitable for solving general 3-D boundary element models. Several numerical examples of 3-D potential problems with the total number of unknowns up to above 200,000 are presented. The results show that the new fast direct solver can be applied to solve large 3-D BEM models accurately and with better efficiency compared with the conventional BEM.

Hierarchical Bayesian Markov switching models with application to predicting spawning success of shovelnose sturgeon

USGS Publications Warehouse

Holan, S.H.; Davis, G.M.; Wildhaber, M.L.; DeLonay, A.J.; Papoulias, D.M.

2009-01-01

The timing of spawning in fish is tightly linked to environmental factors; however, these factors are not very well understood for many species. Specifically, little information is available to guide recruitment efforts for endangered species such as the sturgeon. Therefore, we propose a Bayesian hierarchical model for predicting the success of spawning of the shovelnose sturgeon which uses both biological and behavioural (longitudinal) data. In particular, we use data that were produced from a tracking study that was conducted in the Lower Missouri River. The data that were produced from this study consist of biological variables associated with readiness to spawn along with longitudinal behavioural data collected by using telemetry and archival data storage tags. These high frequency data are complex both biologically and in the underlying behavioural process. To accommodate such complexity we developed a hierarchical linear regression model that uses an eigenvalue predictor, derived from the transition probability matrix of a two-state Markov switching model with generalized auto-regressive conditional heteroscedastic dynamics. Finally, to minimize the computational burden that is associated with estimation of this model, a parallel computing approach is proposed. ?? Journal compilation 2009 Royal Statistical Society.

Linear-scaling density-functional simulations of charged point defects in Al2O3 using hierarchical sparse matrix algebra.

PubMed

Hine, N D M; Haynes, P D; Mostofi, A A; Payne, M C

2010-09-21

We present calculations of formation energies of defects in an ionic solid (Al(2)O(3)) extrapolated to the dilute limit, corresponding to a simulation cell of infinite size. The large-scale calculations required for this extrapolation are enabled by developments in the approach to parallel sparse matrix algebra operations, which are central to linear-scaling density-functional theory calculations. The computational cost of manipulating sparse matrices, whose sizes are determined by the large number of basis functions present, is greatly improved with this new approach. We present details of the sparse algebra scheme implemented in the ONETEP code using hierarchical sparsity patterns, and demonstrate its use in calculations on a wide range of systems, involving thousands of atoms on hundreds to thousands of parallel processes.

A policy-capturing study of the simultaneous effects of fit with jobs, groups, and organizations.

PubMed

Kristof-Brown, Amy L; Jansen, Karen J; Colbert, Amy E

2002-10-01

The authors report an experimental policy-capturing study that examines the simultaneous impact of person-job (PJ), person-group (PG), and person-organization (PO) fit on work satisfaction. Using hierarchical linear modeling, the authors determined that all 3 types of fit had important, independent effects on satisfaction. Work experience explained systematic differences in how participants weighted each type of fit. Multiple interactions also showed participants used complex strategies for combining fit cues.

Detecting multiple outliers in linear functional relationship model for circular variables using clustering technique

NASA Astrophysics Data System (ADS)

Mokhtar, Nurkhairany Amyra; Zubairi, Yong Zulina; Hussin, Abdul Ghapor

2017-05-01

Outlier detection has been used extensively in data analysis to detect anomalous observation in data and has important application in fraud detection and robust analysis. In this paper, we propose a method in detecting multiple outliers for circular variables in linear functional relationship model. Using the residual values of the Caires and Wyatt model, we applied the hierarchical clustering procedure. With the use of tree diagram, we illustrate the graphical approach of the detection of outlier. A simulation study is done to verify the accuracy of the proposed method. Also, an illustration to a real data set is given to show its practical applicability.

Technical Adequacy of the easyCBM Primary-Level Reading Measures (Grades K-1), 2009-2010 Version. Technical Report #1003

ERIC Educational Resources Information Center

Lai, Cheng-Fei; Nese, Joseph F. T.; Jamgochian, Elisa M.; Alonzo, Julie; Tindal, Gerald

2010-01-01

In this technical report, we provide the results of a series of studies on the technical adequacy of the early reading measures available on the easyCBM[R] assessment system. The results from the two-level hierarchical linear growth model analyses suggest that the reliability of the slope estimates for the easyCBM[R] reading measures are strong,…

Growing Readers: A Hierarchical Linear Model of Children's Reading Growth During the First 2 Years of School

ERIC Educational Resources Information Center

McCoach, D. Betsy; O'Connell, Ann A.; Reis, Sally M.; Levitt, Heather A.

2006-01-01

Using the first 4 waves of data from the Early Childhood Longitudinal Study-Kindergarten cohort (ECLS-K), this piecewise 3-level (time-student-school) growth-curve model provides a portrait of students' reading growth over the first 2 years of school. On average, students make much greater reading gains in 1st grade than they do in kindergarten.…

Analysis of Student and School Level Variables Related to Mathematics Self-Efficacy Level Based on PISA 2012 Results for China-Shanghai, Turkey, and Greece

ERIC Educational Resources Information Center

Usta, H. Gonca

2016-01-01

This study aims to analyze the student and school level variables that affect students' self-efficacy levels in mathematics in China-Shanghai, Turkey, and Greece based on PISA 2012 results. In line with this purpose, the hierarchical linear regression model (HLM) was employed. The interschool variability is estimated at approximately 17% in…

Using Hierarchical Linear Modeling to Examine How Individual SLPs Differentially Contribute to Children's Language and Literacy Gains in Public Schools.

PubMed

Farquharson, Kelly; Tambyraja, Sherine R; Logan, Jessica; Justice, Laura M; Schmitt, Mary Beth

2015-08-01

The purpose of this study was twofold: (a) to determine the unique contributions in children's language and literacy gains, over 1 academic year, that are attributable to the individual speech-language pathologist (SLP) and (b) to explore possible child- and SLP-level factors that may further explain SLPs' contributions to children's language and literacy gains. Participants were 288 kindergarten and 1st-grade children with language impairment who were currently receiving school-based language intervention from SLPs. Using hierarchical linear modeling, we partitioned the variance in children's gains in language (i.e., grammar, vocabulary) and literacy (i.e., word decoding) that could be attributed to their individual SLP. Results revealed a significant contribution of individual SLPs to children's gains in grammar, vocabulary, and word decoding. Children's fall language scores and grade were significant predictors of SLPs' contributions, although no SLP-level predictors were significant. The present study makes a first step toward incorporating implementation science and suggests that, for children receiving school-based language intervention, variance in child language and literacy gains in an academic year is at least partially attributable to SLPs. Continued work in this area should examine the possible SLP-level characteristics that may further explicate the relative contributions of SLPs.

On the use of a PM2.5 exposure simulator to explain birthweight

PubMed Central

Berrocal, Veronica J.; Gelfand, Alan E.; Holland, David M.; Burke, Janet; Miranda, Marie Lynn

2010-01-01

In relating pollution to birth outcomes, maternal exposure has usually been described using monitoring data. Such characterization provides a misrepresentation of exposure as it (i) does not take into account the spatial misalignment between an individual’s residence and monitoring sites, and (ii) it ignores the fact that individuals spend most of their time indoors and typically in more than one location. In this paper, we break with previous studies by using a stochastic simulator to describe personal exposure (to particulate matter) and then relate simulated exposures at the individual level to the health outcome (birthweight) rather than aggregating to a selected spatial unit. We propose a hierarchical model that, at the first stage, specifies a linear relationship between birthweight and personal exposure, adjusting for individual risk factors and introduces random spatial effects for the census tract of maternal residence. At the second stage, our hierarchical model specifies the distribution of each individual’s personal exposure using the empirical distribution yielded by the stochastic simulator as well as a model for the spatial random effects. We have applied our framework to analyze birthweight data from 14 counties in North Carolina in years 2001 and 2002. We investigate whether there are certain aspects and time windows of exposure that are more detrimental to birthweight by building different exposure metrics which we incorporate, one by one, in our hierarchical model. To assess the difference in relating ambient exposure to birthweight versus personal exposure to birthweight, we compare estimates of the effect of air pollution obtained from hierarchical models that linearly relate ambient exposure and birthweight versus those obtained from our modeling framework. Our analysis does not show a significant effect of PM2.5 on birthweight for reasons which we discuss. However, our modeling framework serves as a template for analyzing the relationship between personal exposure and longer term health endpoints. PMID:21691413

Linear regulator design for stochastic systems by a multiple time scales method

NASA Technical Reports Server (NTRS)

Teneketzis, D.; Sandell, N. R., Jr.

1976-01-01

A hierarchically-structured, suboptimal controller for a linear stochastic system composed of fast and slow subsystems is considered. The controller is optimal in the limit as the separation of time scales of the subsystems becomes infinite. The methodology is illustrated by design of a controller to suppress the phugoid and short period modes of the longitudinal dynamics of the F-8 aircraft.

Extreme close approaches in hierarchical triple systems with comparable masses

NASA Astrophysics Data System (ADS)

Haim, Niv; Katz, Boaz

2018-06-01

We study close approaches in hierarchical triple systems with comparable masses using full N-body simulations, motivated by a recent model for type Ia supernovae involving direct collisions of white dwarfs (WDs). For stable hierarchical systems where the inner binary components have equal masses, we show that the ability of the inner binary to achieve very close approaches, where the separation between the components of the inner binary reaches values which are orders of magnitude smaller than the semi-major axis, can be analytically predicted from initial conditions. The rate of close approaches is found to be roughly linear with the mass of the tertiary. The rate increases in systems with unequal inner binaries by a marginal factor of ≲ 2 for mass ratios 0.5 ≤ m1/m2 ≤ 1 relevant for the inner white-dwarf binaries. For an average tertiary mass of ˜0.3M⊙ which is representative of typical M-dwarfs, the chance for clean collisions is ˜1% setting challenging constraints on the collisional model for type Ia's.

MATHEMATICAL MODELING OF PESTICIDES IN THE ENVIRONMENT: CURRENT AND FUTURE DEVELOPMENTS

EPA Science Inventory

Transport models, total ecosystem models with aggregated linear approximations, evaluative models, hierarchical models, and influence analysis methods are mathematical techniques that are particularly applicable to the problems encountered when characterizing pesticide chemicals ...

Applying an Integrative Framework of Executive Function to Preschoolers With Specific Language Impairment.

PubMed

Kapa, Leah L; Plante, Elena; Doubleday, Kevin

2017-08-16

The first goal of this research was to compare verbal and nonverbal executive function abilities between preschoolers with and without specific language impairment (SLI). The second goal was to assess the group differences on 4 executive function components in order to determine if the components may be hierarchically related as suggested within a developmental integrative framework of executive function. This study included 26 4- and 5-year-olds diagnosed with SLI and 26 typically developing age- and sex-matched peers. Participants were tested on verbal and nonverbal measures of sustained selective attention, working memory, inhibition, and shifting. The SLI group performed worse compared with typically developing children on both verbal and nonverbal measures of sustained selective attention and working memory, the verbal inhibition task, and the nonverbal shifting task. Comparisons of standardized group differences between executive function measures revealed a linear increase with the following order: working memory, inhibition, shifting, and sustained selective attention. The pattern of results suggests that preschoolers with SLI have deficits in executive functioning compared with typical peers, and deficits are not limited to verbal tasks. A significant linear relationship between group differences across executive function components supports the possibility of a hierarchical relationship between executive function skills.

Bayesian analysis of non-linear differential equation models with application to a gut microbial ecosystem.

PubMed

Lawson, Daniel J; Holtrop, Grietje; Flint, Harry

2011-07-01

Process models specified by non-linear dynamic differential equations contain many parameters, which often must be inferred from a limited amount of data. We discuss a hierarchical Bayesian approach combining data from multiple related experiments in a meaningful way, which permits more powerful inference than treating each experiment as independent. The approach is illustrated with a simulation study and example data from experiments replicating the aspects of the human gut microbial ecosystem. A predictive model is obtained that contains prediction uncertainty caused by uncertainty in the parameters, and we extend the model to capture situations of interest that cannot easily be studied experimentally. Copyright © 2011 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Hierarchical additive modeling of nonlinear association with spatial correlations--an application to relate alcohol outlet density and neighborhood assault rates.

PubMed

Yu, Qingzhao; Li, Bin; Scribner, Richard Allen

2009-06-30

Previous studies have suggested a link between alcohol outlets and assaults. In this paper, we explore the effects of alcohol availability on assaults at the census tract level over time. In addition, we use a natural experiment to check whether a sudden loss of alcohol outlets is associated with deeper decreasing in assault violence. Several features of the data raise statistical challenges: (1) the association between covariates (for example, the alcohol outlet density of each census tract) and the assault rates may be complex and therefore cannot be described using a linear model without covariates transformation, (2) the covariates may be highly correlated with each other, (3) there are a number of observations that have missing inputs, and (4) there is spatial association in assault rates at the census tract level. We propose a hierarchical additive model, where the nonlinear correlations and the complex interaction effects are modeled using the multiple additive regression trees and the residual spatial association in the assault rates that cannot be explained in the model are smoothed using a conditional autoregressive (CAR) method. We develop a two-stage algorithm that connects the nonparametric trees with CAR to look for important covariates associated with the assault rates, while taking into account the spatial association of assault rates in adjacent census tracts. The proposed method is applied to the Los Angeles assault data (1990-1999). To assess the efficiency of the method, the results are compared with those obtained from a hierarchical linear model. Copyright (c) 2009 John Wiley & Sons, Ltd.

Multilevel modelling: Beyond the basic applications.

PubMed

Wright, Daniel B; London, Kamala

2009-05-01

Over the last 30 years statistical algorithms have been developed to analyse datasets that have a hierarchical/multilevel structure. Particularly within developmental and educational psychology these techniques have become common where the sample has an obvious hierarchical structure, like pupils nested within a classroom. We describe two areas beyond the basic applications of multilevel modelling that are important to psychology: modelling the covariance structure in longitudinal designs and using generalized linear multilevel modelling as an alternative to methods from signal detection theory (SDT). Detailed code for all analyses is described using packages for the freeware R.

Using the social cognitive theory to understand physical activity among dialysis patients.

PubMed

Patterson, Megan S; Umstattd Meyer, M Renée; Beaujean, A Alexander; Bowden, Rodney G

2014-08-01

The purpose of this study was to use the social cognitive theory (SCT) constructs self-efficacy, outcome expectations, and self-regulation to better understand associations of physical activity (PA) behaviors among dialysis patients after controlling for demographic and health-related factors. This study was cross-sectional in design. Participants (N = 115; mean age = 61.51 years, SD = 14.01) completed self-report questionnaires during a regularly scheduled dialysis treatment session. Bivariate and hierarchical linear regression analyses were conducted to examine relationships among SCT constructs and PA. Significant relationships between PA and self-efficacy (r = .336), self-regulation (r = .280), and outcome expectations (r = .265) were detected among people on dialysis in bivariate analyses. Hierarchical linear regression revealed significant increases in variance explained for the addition of self-efficacy, self-regulation, and covariates (p < .01). Younger age, self-efficacy, and self-regulation were associated (p < .10) with greater participation in physical activity in the final model (R² = .272). Conclusion/Implication: This research supports the use of SCT in understanding PA among people undergoing dialysis treatment. The findings of this study can help health educators and health care practitioners better understand PA and how to promote it among this population. Future research should further investigate which activities dialysis patients participate in across the life span of their disease. Future PA programs should focus on increasing a patient's self-efficacy and self-regulation.

An Airlift Hub-and-Spoke Location-Routing Model with Time Windows: Case Study of the CONUS-to-Korea Airlift Problem

DTIC Science & Technology

1998-03-01

a point of embarkation to a point of debarkation. This study develops an alternative hub-and-spoke combined location-routing integer linear...programming prototype model, and uses this model to determine what advantages a hub-and-spoke system offers, and in which scenarios it is better-suited than the...extension on the following works: the hierarchical model of Perl and Daskin (1983), time windows features of Chan (1991), combining subtour-breaking and range

Organizational culture predicts job satisfaction and perceived clinical effectiveness in pediatric primary care practices.

PubMed

Brazil, Kevin; Wakefield, Dorothy B; Cloutier, Michelle M; Tennen, Howard; Hall, Charles B

2010-01-01

In recent years, there has been a growing understanding that organizational culture is related to an organization's performance. However, few studies have examined organizational culture in medical group practices. The purpose of this study was to examine the relationship of organizational culture on provider job satisfaction and perceived clinical effectiveness in primary care pediatric practices. This cross-sectional study included 36 primary care pediatric practices located in Connecticut. There were 374 participants in this study, which included 127 clinicians and 247 nonclinicians. Office managers completed a questionnaire that recorded staff and practice characteristics; all participants completed the Organizational Culture Scale, a questionnaire that assessed the practice on four cultural domains (i.e., group, developmental, rational, and hierarchical), and the Primary Care Organizational Questionnaire that evaluated perceived effectiveness and job satisfaction. Hierarchical linear models using a restricted maximum likelihood estimation method were used to evaluate whether the practice culture types predicted job satisfaction and perceived effectiveness. Group culture was positively associated with both satisfaction and perceived effectiveness. In contrast, hierarchical and rational culture were negatively associated with both job satisfaction and perceived effectiveness. These relationships were true for clinicians, nonclinicians, and the practice as a whole. Our study demonstrates that practice culture is associated with job satisfaction and perceived clinical effectiveness and that a group culture was associated with high job satisfaction and perceived effectiveness.

Hierarchical 3-dimensional nickel-iron nanosheet arrays on carbon fiber paper as a novel electrode for non-enzymatic glucose sensing.

PubMed

Kannan, Palanisamy; Maiyalagan, Thandavarayan; Marsili, Enrico; Ghosh, Srabanti; Niedziolka-Jönsson, Joanna; Jönsson-Niedziolka, Martin

2016-01-14

Three-dimensional nickel-iron (3-D/Ni-Fe) nanostructures are exciting candidates for various applications because they produce more reaction-active sites than 1-D and 2-D nanostructured materials and exhibit attractive optical, electrical and catalytic properties. In this work, freestanding 3-D/Ni-Fe interconnected hierarchical nanosheets, hierarchical nanospheres, and porous nanospheres are directly grown on a flexible carbon fiber paper (CFP) substrate by a single-step hydrothermal process. Among the nanostructures, 3-D/Ni-Fe interconnected hierarchical nanosheets show excellent electrochemical properties because of its high conductivity, large specific active surface area, and mesopores on its walls (vide infra). The 3-D/Ni-Fe hierarchical nanosheet array modified CFP substrate is further explored as a novel electrode for electrochemical non-enzymatic glucose sensor application. The 3-D/Ni-Fe hierarchical nanosheet arrays exhibit significant catalytic activity towards the electrochemical oxidation of glucose, as compared to the 3-D/Ni-Fe hierarchical nanospheres, and porous nanospheres. The 3-D/Ni-Fe hierarchical nanosheet arrays can access a large amount of glucose molecules on their surface (mesopore walls) for an efficient electrocatalytic oxidation process. Moreover, 3-D/Ni-Fe hierarchical nanosheet arrays showed higher sensitivity (7.90 μA μM(-1) cm(-2)) with wide linear glucose concentration ranging from 0.05 μM to 0.2 mM, and the low detection limit (LOD) of 0.031 μM (S/N = 3) is achieved by the amperometry method. Further, the 3-D/Ni-Fe hierarchical nanosheet array modified CFP electrode can be demonstrated to have excellent selectivity towards the detection of glucose in the presence of 500-fold excess of major important interferents. All these results indicate that 3-D/Ni-Fe hierarchical nanosheet arrays are promising candidates for non-enzymatic glucose sensing.

Novel Catalyst for the Chirality Selective Synthesis of Single Walled Carbon Nanotubes

DTIC Science & Technology

2015-05-12

hierarchical structures comprising nitrogen- doped reduced GO (rGO) and acid- oxidized SWCNTs was produced using a linear hydrothermal microreactor. Fiber...structures comprising nitrogen- doped reduced GO (rGO) and acidoxidized SWCNTs was produced using a linear hydrothermal microreactor. Fiber micro... doped into Co/SiO2 catalysts to change their chirality selectivity. Further, enrichment of (9,8) nanotubes was carried out by extraction using fluorene

Formation Flying With Decentralized Control in Libration Point Orbits

NASA Technical Reports Server (NTRS)

Folta, David; Carpenter, J. Russell; Wagner, Christoph

2000-01-01

A decentralized control framework is investigated for applicability of formation flying control in libration orbits. The decentralized approach, being non-hierarchical, processes only direct measurement data, in parallel with the other spacecraft. Control is accomplished via linearization about a reference libration orbit with standard control using a Linear Quadratic Regulator (LQR) or the GSFC control algorithm. Both are linearized about the current state estimate as with the extended Kalman filter. Based on this preliminary work, the decentralized approach appears to be feasible for upcoming libration missions using distributed spacecraft.

Word Order and Voice Influence the Timing of Verb Planning in German Sentence Production.

PubMed

Sauppe, Sebastian

2017-01-01

Theories of incremental sentence production make different assumptions about when speakers encode information about described events and when verbs are selected, accordingly. An eye tracking experiment on German testing the predictions from linear and hierarchical incrementality about the timing of event encoding and verb planning is reported. In the experiment, participants described depictions of two-participant events with sentences that differed in voice and word order. Verb-medial active sentences and actives and passives with sentence-final verbs were compared. Linear incrementality predicts that sentences with verbs placed early differ from verb-final sentences because verbs are assumed to only be planned shortly before they are articulated. By contrast, hierarchical incrementality assumes that speakers start planning with relational encoding of the event. A weak version of hierarchical incrementality assumes that only the action is encoded at the outset of formulation and selection of lexical verbs only occurs shortly before they are articulated, leading to the prediction of different fixation patterns for verb-medial and verb-final sentences. A strong version of hierarchical incrementality predicts no differences between verb-medial and verb-final sentences because it assumes that verbs are always lexically selected early in the formulation process. Based on growth curve analyses of fixations to agent and patient characters in the described pictures, and the influence of character humanness and the lack of an influence of the visual salience of characters on speakers' choice of active or passive voice, the current results suggest that while verb planning does not necessarily occur early during formulation, speakers of German always create an event representation early.

Intelligence and Accidents: A Multilevel Model

DTIC Science & Technology

2006-05-06

individuals with low scores. Analysis Procedures The HLM 6 computer program (Raudenbush, Bryk, Cheong, & Congdon , 2004) was employed to conduct the...Cheong, Y. F., & Congdon , R. (2004). HLM 6: Hierarchical linear and nonlinear modeling. Chicago: Scientific Software International. Reynolds, D. H

Multicriterion problem of allocation of resources in the heterogeneous distributed information processing systems

NASA Astrophysics Data System (ADS)

Antamoshkin, O. A.; Kilochitskaya, T. R.; Ontuzheva, G. A.; Stupina, A. A.; Tynchenko, V. S.

2018-05-01

This study reviews the problem of allocation of resources in the heterogeneous distributed information processing systems, which may be formalized in the form of a multicriterion multi-index problem with the linear constraints of the transport type. The algorithms for solution of this problem suggest a search for the entire set of Pareto-optimal solutions. For some classes of hierarchical systems, it is possible to significantly speed up the procedure of verification of a system of linear algebraic inequalities for consistency due to the reducibility of them to the stream models or the application of other solution schemes (for strongly connected structures) that take into account the specifics of the hierarchies under consideration.

Preoperative factors affecting cost and length of stay for isolated off-pump coronary artery bypass grafting: hierarchical linear model analysis.

PubMed

Shinjo, Daisuke; Fushimi, Kiyohide

2015-11-17

To determine the effect of preoperative patient and hospital factors on resource use, cost and length of stay (LOS) among patients undergoing off-pump coronary artery bypass grafting (OPCAB). Observational retrospective study. Data from the Japanese Administrative Database. Patients who underwent isolated, elective OPCAB between April 2011 and March 2012. The primary outcomes of this study were inpatient cost and LOS associated with OPCAB. A two-level hierarchical linear model was used to examine the effects of patient and hospital characteristics on inpatient costs and LOS. The independent variables were patient and hospital factors. We identified 2491 patients who underwent OPCAB at 268 hospitals. The mean cost of OPCAB was $40 665 ±7774, and the mean LOS was 23.4±8.2 days. The study found that select patient factors and certain comorbidities were associated with a high cost and long LOS. A high hospital OPCAB volume was associated with a low cost (-6.6%; p=0.024) as well as a short LOS (-17.6%, p<0.001). The hospital OPCAB volume is associated with efficient resource use. The findings of the present study indicate the need to focus on hospital elective OPCAB volume in Japan in order to improve cost and LOS. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/

Multi-length scale tomography for the determination and optimization of the effective microstructural properties in novel hierarchical solid oxide fuel cell anodes

NASA Astrophysics Data System (ADS)

Lu, Xuekun; Taiwo, Oluwadamilola O.; Bertei, Antonio; Li, Tao; Li, Kang; Brett, Dan J. L.; Shearing, Paul R.

2017-11-01

Effective microstructural properties are critical in determining the electrochemical performance of solid oxide fuel cells (SOFCs), particularly when operating at high current densities. A novel tubular SOFC anode with a hierarchical microstructure, composed of self-organized micro-channels and sponge-like regions, has been fabricated by a phase inversion technique to mitigate concentration losses. However, since pore sizes span over two orders of magnitude, the determination of the effective transport parameters using image-based techniques remains challenging. Pioneering steps are made in this study to characterize and optimize the microstructure by coupling multi-length scale 3D tomography and modeling. The results conclusively show that embedding finger-like micro-channels into the tubular anode can improve the mass transport by 250% and the permeability by 2-3 orders of magnitude. Our parametric study shows that increasing the porosity in the spongy layer beyond 10% enhances the effective transport parameters of the spongy layer at an exponential rate, but linearly for the full anode. For the first time, local and global mass transport properties are correlated to the microstructure, which is of wide interest for rationalizing the design optimization of SOFC electrodes and more generally for hierarchical materials in batteries and membranes.

Clinical time series prediction: towards a hierarchical dynamical system framework

PubMed Central

Liu, Zitao; Hauskrecht, Milos

2014-01-01

Objective Developing machine learning and data mining algorithms for building temporal models of clinical time series is important for understanding of the patient condition, the dynamics of a disease, effect of various patient management interventions and clinical decision making. In this work, we propose and develop a novel hierarchical framework for modeling clinical time series data of varied length and with irregularly sampled observations. Materials and methods Our hierarchical dynamical system framework for modeling clinical time series combines advantages of the two temporal modeling approaches: the linear dynamical system and the Gaussian process. We model the irregularly sampled clinical time series by using multiple Gaussian process sequences in the lower level of our hierarchical framework and capture the transitions between Gaussian processes by utilizing the linear dynamical system. The experiments are conducted on the complete blood count (CBC) panel data of 1000 post-surgical cardiac patients during their hospitalization. Our framework is evaluated and compared to multiple baseline approaches in terms of the mean absolute prediction error and the absolute percentage error. Results We tested our framework by first learning the time series model from data for the patient in the training set, and then applying the model in order to predict future time series values on the patients in the test set. We show that our model outperforms multiple existing models in terms of its predictive accuracy. Our method achieved a 3.13% average prediction accuracy improvement on ten CBC lab time series when it was compared against the best performing baseline. A 5.25% average accuracy improvement was observed when only short-term predictions were considered. Conclusion A new hierarchical dynamical system framework that lets us model irregularly sampled time series data is a promising new direction for modeling clinical time series and for improving their predictive performance. PMID:25534671

Transfer Student Success: Educationally Purposeful Activities Predictive of Undergraduate GPA

ERIC Educational Resources Information Center

Fauria, Renee M.; Fuller, Matthew B.

2015-01-01

Researchers evaluated the effects of Educationally Purposeful Activities (EPAs) on transfer and nontransfer students' cumulative GPAs. Hierarchical, linear, and multiple regression models yielded seven statistically significant educationally purposeful items that influenced undergraduate student GPAs. Statistically significant positive EPAs for…

Practical Assessment, Research & Evaluation, 2000-2001.

ERIC Educational Resources Information Center

Rudner, Lawrence M., Ed.; Schafer, William D., Ed.

2001-01-01

This document consists of papers published in the electronic journal "Practical Assessment, Research & Evaluation" during 2000-2001: (1) "Advantages of Hierarchical Linear Modeling" (Jason W. Osborne); (2) "Prediction in Multiple Regression" (Jason W. Osborne); (3) Scoring Rubrics: What, When, and How?"…

A Structural Approach to the Validation of Hierarchical Training Sequences

DTIC Science & Technology

1981-06-01

consideration as can be examinedI -11- within existing constraints on time and resources. Second, for each con- nection to be studied, identify groups ... one group of learners who are taught all skills in a linear sequence whereas many groups learning different skills are needed to implement the White...meability. For example, suppose that a group of item sets were used to assess performance on three academic tasks, A, B, and C. Assume that task A was

Factors influencing the occupational injuries of physical therapists in Taiwan: A hierarchical linear model approach.

PubMed

Tao, Yu-Hui; Wu, Yu-Lung; Huang, Wan-Yun

2017-01-01

The evidence literature suggests that physical therapy practitioners are subjected to a high probability of acquiring work-related injuries, but only a few studies have specifically investigated Taiwanese physical therapy practitioners. This study was conducted to determine the relationships among individual and group hospital-level factors that contribute to the medical expenses for the occupational injuries of physical therapy practitioners in Taiwan. Physical therapy practitioners in Taiwan with occupational injuries were selected from the 2013 National Health Insurance Research Databases (NHIRD). The age, gender, job title, hospitals attributes, and outpatient data of physical therapy practitioners who sustained an occupational injury in 2013 were obtained with SAS 9.3. SPSS 20.0 and HLM 7.01 were used to conduct descriptive and hierarchical linear model analyses, respectively. The job title of physical therapy practitioners at the individual level and the hospital type at the group level exert positive effects on per person medical expenses. Hospital hierarchy moderates the individual-level relationships of age and job title with the per person medical expenses. Considering that age, job title, and hospital hierarchy affect medical expenses for the occupational injuries of physical therapy practitioners, we suggest strengthening related safety education and training and elevating the self-awareness of the risk of occupational injuries of physical therapy practitioners to reduce and prevent the occurrence of such injuries.

Assessment of Gait Characteristics in Total Knee Arthroplasty Patients Using a Hierarchical Partial Least Squares Method.

PubMed

Wang, Wei; Ackland, David C; McClelland, Jodie A; Webster, Kate E; Halgamuge, Saman

2018-01-01

Quantitative gait analysis is an important tool in objective assessment and management of total knee arthroplasty (TKA) patients. Studies evaluating gait patterns in TKA patients have tended to focus on discrete data such as spatiotemporal information, joint range of motion and peak values of kinematics and kinetics, or consider selected principal components of gait waveforms for analysis. These strategies may not have the capacity to capture small variations in gait patterns associated with each joint across an entire gait cycle, and may ultimately limit the accuracy of gait classification. The aim of this study was to develop an automatic feature extraction method to analyse patterns from high-dimensional autocorrelated gait waveforms. A general linear feature extraction framework was proposed and a hierarchical partial least squares method derived for discriminant analysis of multiple gait waveforms. The effectiveness of this strategy was verified using a dataset of joint angle and ground reaction force waveforms from 43 patients after TKA surgery and 31 healthy control subjects. Compared with principal component analysis and partial least squares methods, the hierarchical partial least squares method achieved generally better classification performance on all possible combinations of waveforms, with the highest classification accuracy . The novel hierarchical partial least squares method proposed is capable of capturing virtually all significant differences between TKA patients and the controls, and provides new insights into data visualization. The proposed framework presents a foundation for more rigorous classification of gait, and may ultimately be used to evaluate the effects of interventions such as surgery and rehabilitation.

Hierarchical model analysis of the Atlantic Flyway Breeding Waterfowl Survey

USGS Publications Warehouse

Sauer, John R.; Zimmerman, Guthrie S.; Klimstra, Jon D.; Link, William A.

2014-01-01

We used log-linear hierarchical models to analyze data from the Atlantic Flyway Breeding Waterfowl Survey. The survey has been conducted by state biologists each year since 1989 in the northeastern United States from Virginia north to New Hampshire and Vermont. Although yearly population estimates from the survey are used by the United States Fish and Wildlife Service for estimating regional waterfowl population status for mallards (Anas platyrhynchos), black ducks (Anas rubripes), wood ducks (Aix sponsa), and Canada geese (Branta canadensis), they are not routinely adjusted to control for time of day effects and other survey design issues. The hierarchical model analysis permits estimation of year effects and population change while accommodating the repeated sampling of plots and controlling for time of day effects in counting. We compared population estimates from the current stratified random sample analysis to population estimates from hierarchical models with alternative model structures that describe year to year changes as random year effects, a trend with random year effects, or year effects modeled as 1-year differences. Patterns of population change from the hierarchical model results generally were similar to the patterns described by stratified random sample estimates, but significant visibility differences occurred between twilight to midday counts in all species. Controlling for the effects of time of day resulted in larger population estimates for all species in the hierarchical model analysis relative to the stratified random sample analysis. The hierarchical models also provided a convenient means of estimating population trend as derived statistics from the analysis. We detected significant declines in mallard and American black ducks and significant increases in wood ducks and Canada geese, a trend that had not been significant for 3 of these 4 species in the prior analysis. We recommend using hierarchical models for analysis of the Atlantic Flyway Breeding Waterfowl Survey.

Using a matrix-analytical approach to synthesizing evidence solved incompatibility problem in the hierarchy of evidence.

PubMed

Walach, Harald; Loef, Martin

2015-11-01

The hierarchy of evidence presupposes linearity and additivity of effects, as well as commutativity of knowledge structures. It thereby implicitly assumes a classical theoretical model. This is an argumentative article that uses theoretical analysis based on pertinent literature and known facts to examine the standard view of methodology. We show that the assumptions of the hierarchical model are wrong. The knowledge structures gained by various types of studies are not sequentially indifferent, that is, do not commute. External validity and internal validity are at least partially incompatible concepts. Therefore, one needs a different theoretical structure, typical of quantum-type theories, to model this situation. The consequence of this situation is that the implicit assumptions of the hierarchical model are wrong, if generalized to the concept of evidence in total. The problem can be solved by using a matrix-analytical approach to synthesizing evidence. Here, research methods that produce different types of evidence that complement each other are synthesized to yield the full knowledge. We show by an example how this might work. We conclude that the hierarchical model should be complemented by a broader reasoning in methodology. Copyright © 2015 Elsevier Inc. All rights reserved.

Methodological quality and reporting of generalized linear mixed models in clinical medicine (2000-2012): a systematic review.

PubMed

Casals, Martí; Girabent-Farrés, Montserrat; Carrasco, Josep L

2014-01-01

Modeling count and binary data collected in hierarchical designs have increased the use of Generalized Linear Mixed Models (GLMMs) in medicine. This article presents a systematic review of the application and quality of results and information reported from GLMMs in the field of clinical medicine. A search using the Web of Science database was performed for published original articles in medical journals from 2000 to 2012. The search strategy included the topic "generalized linear mixed models","hierarchical generalized linear models", "multilevel generalized linear model" and as a research domain we refined by science technology. Papers reporting methodological considerations without application, and those that were not involved in clinical medicine or written in English were excluded. A total of 443 articles were detected, with an increase over time in the number of articles. In total, 108 articles fit the inclusion criteria. Of these, 54.6% were declared to be longitudinal studies, whereas 58.3% and 26.9% were defined as repeated measurements and multilevel design, respectively. Twenty-two articles belonged to environmental and occupational public health, 10 articles to clinical neurology, 8 to oncology, and 7 to infectious diseases and pediatrics. The distribution of the response variable was reported in 88% of the articles, predominantly Binomial (n = 64) or Poisson (n = 22). Most of the useful information about GLMMs was not reported in most cases. Variance estimates of random effects were described in only 8 articles (9.2%). The model validation, the method of covariate selection and the method of goodness of fit were only reported in 8.0%, 36.8% and 14.9% of the articles, respectively. During recent years, the use of GLMMs in medical literature has increased to take into account the correlation of data when modeling qualitative data or counts. According to the current recommendations, the quality of reporting has room for improvement regarding the characteristics of the analysis, estimation method, validation, and selection of the model.

Hierarchical NiCo2O4 Hollow Sphere as a Peroxidase Mimetic for Colorimetric Detection of H2O2 and Glucose

PubMed Central

Huang, Wei; Lin, Tianye; Cao, Yang; Lai, Xiaoyong; Peng, Juan; Tu, Jinchun

2017-01-01

In this work, the hierarchical NiCo2O4 hollow sphere synthesized via a “coordinating etching and precipitating” process was demonstrated to exhibit intrinsic peroxidase-like activity. The peroxidase-like activity of NiCo2O4, NiO, and Co3O4 hollow spheres were comparatively studied by the catalytic oxidation reaction of 3,3,5,5-tetramethylbenzidine (TMB) in presence of H2O2, and a superior peroxidase-like activity of NiCo2O4 was confirmed by stronger absorbance at 652 nm. Furthermore, the proposed sensing platform showed commendable response to H2O2 with a linear range from 10 μM to 400 μM, and a detection limit of 0.21 μM. Cooperated with GOx, the developed novel colorimetric and visual glucose-sensing platform exhibited high selectivity, favorable reproducibility, satisfactory applicability, wide linear range (from 0.1 mM to 4.5 mM), and a low detection limit of 5.31 μM. In addition, the concentration-dependent color change would offer a better and handier way for detection of H2O2 and glucose by naked eye. PMID:28124997

Analyzing Multilevel Factors Underlying Adolescent Smoking Behaviors: The Roles of Friendship Network, Family Relations, and School Environment.

PubMed

Kim, Harris H-S; Chun, JongSerl

2018-06-01

This study investigates the extent to which friendship network, family relations, and school context are related to adolescent cigarette smoking. Friendship network is measured in terms of delinquent peers; family relations in terms of parental supervision; and school environment in terms of objective (eg, antismoking policy) and subjective (eg, school attachment) characteristics. Findings are based on the secondary analysis of the health behavior in school-aged children, 2009-2010. Two-level hierarchical generalized linear models are estimated using hierarchical linear modeling 7. At the student level, ties to delinquent friends is significantly related to higher odds of smoking, while greater parental supervision is associated with lower odds. At the school level, antismoking policy and curriculum independently lower smoking behavior. Better within-class peer relations, greater school attachment, and higher academic performance are also negatively related to smoking. Last, the positive association between delinquent friends and smoking is weaker in schools with a formally enacted antismoking policy. However, this association is stronger in schools with better peer relations. Adolescent smoking behavior is embedded in a broader ecological setting. This research reveals that a proper understanding of it requires comprehensive analysis that incorporates factors measured at individual (student) and contextual (school) levels. © 2018, American School Health Association.

Nonreciprocity in the dynamics of coupled oscillators with nonlinearity, asymmetry, and scale hierarchy

NASA Astrophysics Data System (ADS)

Moore, Keegan J.; Bunyan, Jonathan; Tawfick, Sameh; Gendelman, Oleg V.; Li, Shuangbao; Leamy, Michael; Vakakis, Alexander F.

2018-01-01

In linear time-invariant dynamical and acoustical systems, reciprocity holds by the Onsager-Casimir principle of microscopic reversibility, and this can be broken only by odd external biases, nonlinearities, or time-dependent properties. A concept is proposed in this work for breaking dynamic reciprocity based on irreversible nonlinear energy transfers from large to small scales in a system with nonlinear hierarchical internal structure, asymmetry, and intentional strong stiffness nonlinearity. The resulting nonreciprocal large-to-small scale energy transfers mimic analogous nonlinear energy transfer cascades that occur in nature (e.g., in turbulent flows), and are caused by the strong frequency-energy dependence of the essentially nonlinear small-scale components of the system considered. The theoretical part of this work is mainly based on action-angle transformations, followed by direct numerical simulations of the resulting system of nonlinear coupled oscillators. The experimental part considers a system with two scales—a linear large-scale oscillator coupled to a small scale by a nonlinear spring—and validates the theoretical findings demonstrating nonreciprocal large-to-small scale energy transfer. The proposed study promotes a paradigm for designing nonreciprocal acoustic materials harnessing strong nonlinearity, which in a future application will be implemented in designing lattices incorporating nonlinear hierarchical internal structures, asymmetry, and scale mixing.

Applying an Integrative Framework of Executive Function to Preschoolers With Specific Language Impairment

PubMed Central

Plante, Elena; Doubleday, Kevin

2017-01-01

Purpose The first goal of this research was to compare verbal and nonverbal executive function abilities between preschoolers with and without specific language impairment (SLI). The second goal was to assess the group differences on 4 executive function components in order to determine if the components may be hierarchically related as suggested within a developmental integrative framework of executive function. Method This study included 26 4- and 5-year-olds diagnosed with SLI and 26 typically developing age- and sex-matched peers. Participants were tested on verbal and nonverbal measures of sustained selective attention, working memory, inhibition, and shifting. Results The SLI group performed worse compared with typically developing children on both verbal and nonverbal measures of sustained selective attention and working memory, the verbal inhibition task, and the nonverbal shifting task. Comparisons of standardized group differences between executive function measures revealed a linear increase with the following order: working memory, inhibition, shifting, and sustained selective attention. Conclusion The pattern of results suggests that preschoolers with SLI have deficits in executive functioning compared with typical peers, and deficits are not limited to verbal tasks. A significant linear relationship between group differences across executive function components supports the possibility of a hierarchical relationship between executive function skills. PMID:28724132

Mixed effect Poisson log-linear models for clinical and epidemiological sleep hypnogram data

PubMed Central

Swihart, Bruce J.; Caffo, Brian S.; Crainiceanu, Ciprian; Punjabi, Naresh M.

2013-01-01

Bayesian Poisson log-linear multilevel models scalable to epidemiological studies are proposed to investigate population variability in sleep state transition rates. Hierarchical random effects are used to account for pairings of subjects and repeated measures within those subjects, as comparing diseased to non-diseased subjects while minimizing bias is of importance. Essentially, non-parametric piecewise constant hazards are estimated and smoothed, allowing for time-varying covariates and segment of the night comparisons. The Bayesian Poisson regression is justified through a re-derivation of a classical algebraic likelihood equivalence of Poisson regression with a log(time) offset and survival regression assuming exponentially distributed survival times. Such re-derivation allows synthesis of two methods currently used to analyze sleep transition phenomena: stratified multi-state proportional hazards models and log-linear models with GEE for transition counts. An example data set from the Sleep Heart Health Study is analyzed. Supplementary material includes the analyzed data set as well as the code for a reproducible analysis. PMID:22241689

Modeling place field activity with hierarchical slow feature analysis

PubMed Central

Schönfeld, Fabian; Wiskott, Laurenz

2015-01-01

What are the computational laws of hippocampal activity? In this paper we argue for the slowness principle as a fundamental processing paradigm behind hippocampal place cell firing. We present six different studies from the experimental literature, performed with real-life rats, that we replicated in computer simulations. Each of the chosen studies allows rodents to develop stable place fields and then examines a distinct property of the established spatial encoding: adaptation to cue relocation and removal; directional dependent firing in the linear track and open field; and morphing and scaling the environment itself. Simulations are based on a hierarchical Slow Feature Analysis (SFA) network topped by a principal component analysis (ICA) output layer. The slowness principle is shown to account for the main findings of the presented experimental studies. The SFA network generates its responses using raw visual input only, which adds to its biological plausibility but requires experiments performed in light conditions. Future iterations of the model will thus have to incorporate additional information, such as path integration and grid cell activity, in order to be able to also replicate studies that take place during darkness. PMID:26052279

Hierarchical drivers of reef-fish metacommunity structure.

PubMed

MacNeil, M Aaron; Graham, Nicholas A J; Polunin, Nicholas V C; Kulbicki, Michel; Galzin, René; Harmelin-Vivien, Mireille; Rushton, Steven P

2009-01-01

Coral reefs are highly complex ecological systems, where multiple processes interact across scales in space and time to create assemblages of exceptionally high biodiversity. Despite the increasing frequency of hierarchically structured sampling programs used in coral-reef science, little progress has been made in quantifying the relative importance of processes operating across multiple scales. The vast majority of reef studies are conducted, or at least analyzed, at a single spatial scale, ignoring the implicitly hierarchical structure of the overall system in favor of small-scale experiments or large-scale observations. Here we demonstrate how alpha (mean local number of species), beta diversity (degree of species dissimilarity among local sites), and gamma diversity (overall species richness) vary with spatial scale, and using a hierarchical, information-theoretic approach, we evaluate the relative importance of site-, reef-, and atoll-level processes driving the fish metacommunity structure among 10 atolls in French Polynesia. Process-based models, representing well-established hypotheses about drivers of reef-fish community structure, were assembled into a candidate set of 12 hierarchical linear models. Variation in fish abundance, biomass, and species richness were unevenly distributed among transect, reef, and atoll levels, establishing the relative contribution of variation at these spatial scales to the structure of the metacommunity. Reef-fish biomass, species richness, and the abundance of most functional-groups corresponded primarily with transect-level habitat diversity and atoll-lagoon size, whereas detritivore and grazer abundances were largely correlated with potential covariates of larval dispersal. Our findings show that (1) within-transect and among-atoll factors primarily drive the relationship between alpha and gamma diversity in this reef-fish metacommunity; (2) habitat is the primary correlate with reef-fish metacommunity structure at multiple spatial scales; and (3) inter-atoll connectedness was poorly correlated with the nonrandom clustering of reef-fish species. These results demonstrate the importance of modeling hierarchical data and processes in understanding reef-fish metacommunity structure.

Nonlinear changes in brain activity during continuous word repetition: an event-related multiparametric functional MR imaging study.

PubMed

Hagenbeek, R E; Rombouts, S A R B; Veltman, D J; Van Strien, J W; Witter, M P; Scheltens, P; Barkhof, F

2007-10-01

Changes in brain activation as a function of continuous multiparametric word recognition have not been studied before by using functional MR imaging (fMRI), to our knowledge. Our aim was to identify linear changes in brain activation and, what is more interesting, nonlinear changes in brain activation as a function of extended word repetition. Fifteen healthy young right-handed individuals participated in this study. An event-related extended continuous word-recognition task with 30 target words was used to study the parametric effect of word recognition on brain activation. Word-recognition-related brain activation was studied as a function of 9 word repetitions. fMRI data were analyzed with a general linear model with regressors for linearly changing signal intensity and nonlinearly changing signal intensity, according to group average reaction time (RT) and individual RTs. A network generally associated with episodic memory recognition showed either constant or linearly decreasing brain activation as a function of word repetition. Furthermore, both anterior and posterior cingulate cortices and the left middle frontal gyrus followed the nonlinear curve of the group RT, whereas the anterior cingulate cortex was also associated with individual RT. Linear alteration in brain activation as a function of word repetition explained most changes in blood oxygen level-dependent signal intensity. Using a hierarchically orthogonalized model, we found evidence for nonlinear activation associated with both group and individual RTs.

Hierarchical Boltzmann simulations and model error estimation

NASA Astrophysics Data System (ADS)

Torrilhon, Manuel; Sarna, Neeraj

2017-08-01

A hierarchical simulation approach for Boltzmann's equation should provide a single numerical framework in which a coarse representation can be used to compute gas flows as accurately and efficiently as in computational fluid dynamics, but a subsequent refinement allows to successively improve the result to the complete Boltzmann result. We use Hermite discretization, or moment equations, for the steady linearized Boltzmann equation for a proof-of-concept of such a framework. All representations of the hierarchy are rotationally invariant and the numerical method is formulated on fully unstructured triangular and quadrilateral meshes using a implicit discontinuous Galerkin formulation. We demonstrate the performance of the numerical method on model problems which in particular highlights the relevance of stability of boundary conditions on curved domains. The hierarchical nature of the method allows also to provide model error estimates by comparing subsequent representations. We present various model errors for a flow through a curved channel with obstacles.

Real-Time Detector of Human Fatigue: Detecting Lapses in Alertness

DTIC Science & Technology

2008-02-15

These coefficients and their variances, covariances and standard errors were computed simultaneously using HLM 6 (Raudenbush, Bryk, Cheong, & Congdon ...CA: Sage. Raudenbush, S. W., Bryk, A. S., Cheong, Y. F., & Congdon , R. T. (2004). HLM6: Hierarchical Linear and Nonlinear Modeling [Computer software

Bonding Social Capital in Low-Income Neighborhoods

ERIC Educational Resources Information Center

Brisson, Daniel S.; Usher, Charles L.

2005-01-01

Social capital has recently become a guiding theoretical framework for family interventions in low-income neighborhoods. In the context of the Annie E. Casey Foundation's Making Connections initiative, this research uses hierarchical linear modeling to examine how neighborhood characteristics and resident participation affect bonding social…

Indigenous Intelligence: Have We Lost Our Indigenous Mind?

ERIC Educational Resources Information Center

Dumont, Jim

2002-01-01

Eurocentric intelligence is restricted to rational, linear, competitive, and hierarchical thinking. Indigenous intelligence encompasses the body, mind, heart, and experience in total responsiveness and total relationship to the whole environment, which includes the seven generations past and future. Implementation of major changes to indigenous…

The Relationship between Counselors' Multicultural Counseling Competence and Poverty Beliefs

ERIC Educational Resources Information Center

Clark, Madeline; Moe, Jeff; Hays, Danica G.

2017-01-01

The authors explored the relationship between counselors' multicultural counseling competence (MCC), poverty beliefs, and select demographic factors. Results of hierarchical linear regressions indicate that MCC is predictive of counselor individualistic and structural poverty beliefs. Implications for counselor multicultural training and immersion…

Genetic mixed linear models for twin survival data.

PubMed

Ha, Il Do; Lee, Youngjo; Pawitan, Yudi

2007-07-01

Twin studies are useful for assessing the relative importance of genetic or heritable component from the environmental component. In this paper we develop a methodology to study the heritability of age-at-onset or lifespan traits, with application to analysis of twin survival data. Due to limited period of observation, the data can be left truncated and right censored (LTRC). Under the LTRC setting we propose a genetic mixed linear model, which allows general fixed predictors and random components to capture genetic and environmental effects. Inferences are based upon the hierarchical-likelihood (h-likelihood), which provides a statistically efficient and unified framework for various mixed-effect models. We also propose a simple and fast computation method for dealing with large data sets. The method is illustrated by the survival data from the Swedish Twin Registry. Finally, a simulation study is carried out to evaluate its performance.

Ultrametric properties of the attractor spaces for random iterated linear function systems

NASA Astrophysics Data System (ADS)

Buchovets, A. G.; Moskalev, P. V.

2018-03-01

We investigate attractors of random iterated linear function systems as independent spaces embedded in the ordinary Euclidean space. The introduction on the set of attractor points of a metric that satisfies the strengthened triangle inequality makes this space ultrametric. Then inherent in ultrametric spaces the properties of disconnectedness and hierarchical self-similarity make it possible to define an attractor as a fractal. We note that a rigorous proof of these properties in the case of an ordinary Euclidean space is very difficult.

STABILITY OF FMRI STRIATAL RESPONSE TO ALCOHOL CUES: A HIERARCHICAL LINEAR MODELING APPROACH

PubMed Central

Schacht, Joseph P.; Anton, Raymond F.; Randall, Patrick K.; Li, Xingbao; Henderson, Scott; Myrick, Hugh

2011-01-01

In functional magnetic resonance imaging (fMRI) studies of alcohol-dependent individuals, alcohol cues elicit activation of the ventral and dorsal aspects of the striatum (VS and DS), which are believed to underlie aspects of reward learning critical to the initiation and maintenance of alcohol dependence. Cue-elicited striatal activation may represent a biological substrate through which treatment efficacy may be measured. However, to be useful for this purpose, VS or DS activation must first demonstrate stability across time. Using hierarchical linear modeling (HLM), this study tested the stability of cue-elicited activation in anatomically and functionally defined regions of interest in bilateral VS and DS. Nine non-treatment-seeking alcohol-dependent participants twice completed an alcohol cue reactivity task during two fMRI scans separated by 14 days. HLM analyses demonstrated that, across all participants, alcohol cues elicited significant activation in each of the regions of interest. At the group level, these activations attenuated slightly between scans, but session-wise differences were not significant. Within-participants stability was best in the anatomically defined right VS and DS and in a functionally defined region that encompassed right caudate and putamen (intraclass correlation coefficients of .75, .81, and .76, respectively). Thus, within this small sample, alcohol cue-elicited fMRI activation had good reliability in the right striatum, though a larger sample is necessary to ensure generalizability and further evaluate stability. This study also demonstrates the utility of HLM analytic techniques for serial fMRI studies, in which separating within-participants variance (individual changes in activation) from between-participants factors (time or treatment) is critical. PMID:21316465

Factors Associated with Job Satisfaction among University Teachers in Northeastern Region of China: A Cross-Sectional Study.

PubMed

Pan, Bochen; Shen, Xue; Liu, Li; Yang, Yilong; Wang, Lie

2015-10-14

Teachers' job satisfaction is one of the key factors in institutional dynamics and is generally considered to be the primary variable by which the effectiveness of an organization's human resource is evaluated. The objectives of this study were to assess the level of job satisfaction among university teachers and to clarify the associated factors. A cross-sectional study was conducted between November 2013 and January 2014. Teachers from six universities in Shenyang, China were randomly sampled. The job satisfaction scale Minnesota Satisfaction Questionnaire (MSQ), perceived organizational support (POS), psychological capital questionnaire (PCQ-24), and effort-reward imbalance scale (ERI) together with questions about demographic and working factors were administered in questionnaires distributed to 1500 university teachers. Hierarchical linear regression analyses were performed to explore the related factors. 1210 effective responses were obtained (effective respondent rate 80.7%). The average score of overall job satisfaction was 69.71. Hierarchical linear regression analysis revealed that turnover intention, occupational stress and chronic disease all had negative impacts on job satisfaction, whereas perceived organizational support, psychological capital and higher monthly income were positively associated with job satisfaction among the university teachers. Age was also linked to the level of job satisfaction. All the variables explained 60.7% of the variance in job satisfaction. Chinese university teachers had a moderate level of job satisfaction. Demographic and working characteristics were associated factors for job satisfaction. Perceived organizational support showed the strongest association with job satisfaction. RESULTS of the study indicate that improving the perceived organizational support may increase the level of job satisfaction for university teachers.

Notes sur les mouvements recursifs (Notes on Regressive Moves).

ERIC Educational Resources Information Center

Auchlin, Antoine; And Others

1981-01-01

Examines the phenomenon of regressive moves (retro-interpretation) in the light of a hypothesis according to which the formation of complex and hierarchically organized conversation units is subordinated to the linearity of discourse. Analyzes a transactional exchange, describing the interplay of integration, anticipation, and retro-interpretation…

Improving satellite-based PM2.5 estimates in China using Gaussian processes modeling in a Bayesian hierarchical setting.

PubMed

Yu, Wenxi; Liu, Yang; Ma, Zongwei; Bi, Jun

2017-08-01

Using satellite-based aerosol optical depth (AOD) measurements and statistical models to estimate ground-level PM 2.5 is a promising way to fill the areas that are not covered by ground PM 2.5 monitors. The statistical models used in previous studies are primarily Linear Mixed Effects (LME) and Geographically Weighted Regression (GWR) models. In this study, we developed a new regression model between PM 2.5 and AOD using Gaussian processes in a Bayesian hierarchical setting. Gaussian processes model the stochastic nature of the spatial random effects, where the mean surface and the covariance function is specified. The spatial stochastic process is incorporated under the Bayesian hierarchical framework to explain the variation of PM 2.5 concentrations together with other factors, such as AOD, spatial and non-spatial random effects. We evaluate the results of our model and compare them with those of other, conventional statistical models (GWR and LME) by within-sample model fitting and out-of-sample validation (cross validation, CV). The results show that our model possesses a CV result (R 2  = 0.81) that reflects higher accuracy than that of GWR and LME (0.74 and 0.48, respectively). Our results indicate that Gaussian process models have the potential to improve the accuracy of satellite-based PM 2.5 estimates.

Hierarchical Bayesian modelling of mobility metrics for hazard model input calibration

NASA Astrophysics Data System (ADS)

Calder, Eliza; Ogburn, Sarah; Spiller, Elaine; Rutarindwa, Regis; Berger, Jim

2015-04-01

In this work we present a method to constrain flow mobility input parameters for pyroclastic flow models using hierarchical Bayes modeling of standard mobility metrics such as H/L and flow volume etc. The advantage of hierarchical modeling is that it can leverage the information in global dataset for a particular mobility metric in order to reduce the uncertainty in modeling of an individual volcano, especially important where individual volcanoes have only sparse datasets. We use compiled pyroclastic flow runout data from Colima, Merapi, Soufriere Hills, Unzen and Semeru volcanoes, presented in an open-source database FlowDat (https://vhub.org/groups/massflowdatabase). While the exact relationship between flow volume and friction varies somewhat between volcanoes, dome collapse flows originating from the same volcano exhibit similar mobility relationships. Instead of fitting separate regression models for each volcano dataset, we use a variation of the hierarchical linear model (Kass and Steffey, 1989). The model presents a hierarchical structure with two levels; all dome collapse flows and dome collapse flows at specific volcanoes. The hierarchical model allows us to assume that the flows at specific volcanoes share a common distribution of regression slopes, then solves for that distribution. We present comparisons of the 95% confidence intervals on the individual regression lines for the data set from each volcano as well as those obtained from the hierarchical model. The results clearly demonstrate the advantage of considering global datasets using this technique. The technique developed is demonstrated here for mobility metrics, but can be applied to many other global datasets of volcanic parameters. In particular, such methods can provide a means to better contain parameters for volcanoes for which we only have sparse data, a ubiquitous problem in volcanology.

Mapping brucellosis increases relative to elk density using hierarchical Bayesian models

USGS Publications Warehouse

Cross, Paul C.; Heisey, Dennis M.; Scurlock, Brandon M.; Edwards, William H.; Brennan, Angela; Ebinger, Michael R.

2010-01-01

The relationship between host density and parasite transmission is central to the effectiveness of many disease management strategies. Few studies, however, have empirically estimated this relationship particularly in large mammals. We applied hierarchical Bayesian methods to a 19-year dataset of over 6400 brucellosis tests of adult female elk (Cervus elaphus) in northwestern Wyoming. Management captures that occurred from January to March were over two times more likely to be seropositive than hunted elk that were killed in September to December, while accounting for site and year effects. Areas with supplemental feeding grounds for elk had higher seroprevalence in 1991 than other regions, but by 2009 many areas distant from the feeding grounds were of comparable seroprevalence. The increases in brucellosis seroprevalence were correlated with elk densities at the elk management unit, or hunt area, scale (mean 2070 km2; range = [95–10237]). The data, however, could not differentiate among linear and non-linear effects of host density. Therefore, control efforts that focus on reducing elk densities at a broad spatial scale were only weakly supported. Additional research on how a few, large groups within a region may be driving disease dynamics is needed for more targeted and effective management interventions. Brucellosis appears to be expanding its range into new regions and elk populations, which is likely to further complicate the United States brucellosis eradication program. This study is an example of how the dynamics of host populations can affect their ability to serve as disease reservoirs.

Contextual Effects on Kindergarten Teachers' Intention to Report Child Abuse

ERIC Educational Resources Information Center

Feng, Jui-Ying; Wu, Yow-Wu B.; Fetzer, Susan; Chang, Hsin-Yi

2012-01-01

Child abuse is underreported for children with socioeconomic inequalities. The impact of geographic location combined with sociocultural characteristics on teachers' reports of child abuse remains unclear. A national survey of 572 kindergarten teachers from 79 schools in Taiwan used hierarchical linear modeling to investigate the contribution of…

Pessimism, Trauma, Risky Sex: Covariates of Depression in College Students

ERIC Educational Resources Information Center

Swanholm, Eric; Vosvick, Mark; Chng, Chwee-Lye

2009-01-01

Objective: To explain variance in depression in students (N = 648) using a model incorporating sexual trauma, pessimism, and risky sex. Method: Survey data collected from undergraduate students receiving credit for participation. Results: Controlling for demographics, a hierarchical linear regression analysis [Adjusted R[superscript 2] = 0.34,…

Using Hierarchical Linear Modeling for Proformative Evaluation: A Case Example

ERIC Educational Resources Information Center

Coryn, Chris L. S.

2007-01-01

Proformative evaluation--first introduced in Scriven's (2006) "The great enigma: An evaluation design puzzle"--"is motivated, like formative evaluation, by the intention to improve something that is still developing, but unlike formative, the improvement is only possible by taking action, hence proactive instead of reactive, hence both, hence…

Individual and School Predictors of Middle School Aggression

ERIC Educational Resources Information Center

Reis, Janet; Trockel, Mickey; Mulhall, Peter

2007-01-01

Hierarchical linear modeling is used to assess individual student, family, and school predictors of aggression in 111,662 students in sixth, seventh, and eighth grades. Nine measures of problem-solving strategies, quality of family and peer interaction, and perceptions of school climate are analyzed at the individual student level. Eight measures…

The Effects of Academic Optimism on Elementary Reading Achievement

ERIC Educational Resources Information Center

Bevel, Raymona K.; Mitchell, Roxanne M.

2012-01-01

Purpose: The purpose of this paper is to explore the relationship between academic optimism (AO) and elementary reading achievement (RA). Design/methodology/approach: Using correlation and hierarchical linear regression, the authors examined school-level effects of AO on fifth grade reading achievement in 29 elementary schools in Alabama.…

Centering Effects in HLM Level-1 Predictor Variables.

ERIC Educational Resources Information Center

Schumacker, Randall E.; Bembry, Karen

Research has suggested that important research questions can be addressed with meaningful interpretations using hierarchical linear modeling (HLM). The proper interpretation of results, however, is invariably linked to the choice of centering for the Level-1 predictor variables that produce the outcome measure for the Level-2 regression analysis.…

Substance Use, Anxiety, and Depressive Symptoms among College Students

ERIC Educational Resources Information Center

Walters, Kenneth S.; Bulmer, Sandra Minor; Troiano, Peter F.; Obiaka, Uzoma; Bonhomme, Rebecca

2018-01-01

Research on college substance use and mental illness is limited and inconsistent. Measures of substance use, and anxiety and depressive symptoms, were completed by 1,316 undergraduates within a major drug transportation corridor. Hierarchical linear regressions were used to test associations between anxious and depressive symptoms and substance…

Racial Identity and Academic Achievement in the Neighborhood Context: A Multilevel Analysis

ERIC Educational Resources Information Center

Byrd, Christy M.; Chavous, Tabbye M.

2009-01-01

Increasingly, researchers have found relationships between a strong, positive sense of racial identity and academic achievement among African American youth. Less attention, however, has been given to the roles and functions of racial identity among youth experiencing different social and economic contexts. Using hierarchical linear modeling, the…

Psychopathology and Marital Satisfaction: The Importance of Evaluating Both Partners

ERIC Educational Resources Information Center

Whisman, Mark A.; Uebelacker, Lisa A.; Weinstock, Lauren M.

2004-01-01

Using path analysis and hierarchical linear modeling, the authors evaluated the associations between both partners' level of depression and anxiety, as measured by Minnesota Multiphasic Personality Inventory-2 (MMPI-2) content scales, and both partners' level of marital satisfaction among married couples (N = 774) that participated in the MMPI…

Contributions of Child's Physiology and Maternal Behavior to Children's Trajectories of Temperamental Reactivity

ERIC Educational Resources Information Center

Blandon, Alysia Y.; Calkins, Susan D.; Keane, Susan P.; O'brien, Marion

2010-01-01

Trajectories of children's temperamental reactivity (negative affectivity and surgency) were examined in a community sample of 370 children across the ages of 4 to 7 with hierarchical linear modeling. Children's physiological reactivity (respiratory sinus arrhythmia [RSA]), physiological regulation ([delta]RSA), and maternal parenting behavior…

Handling Correlations between Covariates and Random Slopes in Multilevel Models

ERIC Educational Resources Information Center

Bates, Michael David; Castellano, Katherine E.; Rabe-Hesketh, Sophia; Skrondal, Anders

2014-01-01

This article discusses estimation of multilevel/hierarchical linear models that include cluster-level random intercepts and random slopes. Viewing the models as structural, the random intercepts and slopes represent the effects of omitted cluster-level covariates that may be correlated with included covariates. The resulting correlations between…

Dealing with Dependence (Part II): A Gentle Introduction to Hierarchical Linear Modeling

ERIC Educational Resources Information Center

McCoach, D. Betsy

2010-01-01

In education, most naturally occurring data are clustered within contexts. Students are clustered within classrooms, classrooms are clustered within schools, and schools are clustered within districts. When people are clustered within naturally occurring organizational units such as schools, classrooms, or districts, the responses of people from…

Diversity and Educational Benefits: Moving Beyond Self-Reported Questionnaire Data

ERIC Educational Resources Information Center

Herzog, Serge

2007-01-01

Effects of ethnic/racial diversity among students and faculty on cognitive growth of undergraduate students are estimated via a series of hierarchical linear and multinomial logistic regression models. Using objective measures of compositional, curricular, and interactional diversity based on actuarial course enrollment records of over 6,000…

Predicting Children's Depressive Symptoms from Community and Individual Risk Factors

ERIC Educational Resources Information Center

Dallaire, Danielle H.; Cole, David A.; Smith, Thomas M.; Ciesla, Jeffrey A.; LaGrange, Beth; Jacquez, Farrah M.; Pineda, Ashley Q.; Truss, Alanna E.; Folmer, Amy S.

2008-01-01

Community, demographic, familial, and personal risk factors of childhood depressive symptoms were examined from an ecological theoretical approach using hierarchical linear modeling. Individual-level data were collected from an ethnically diverse (73% African-American) community sample of 197 children and their parents; community-level data were…

Neighborhood Disadvantage and Variations in Blood Pressure

ERIC Educational Resources Information Center

Cathorall, Michelle L.; Xin, Huaibo; Peachey, Andrew; Bibeau, Daniel L.; Schulz, Mark; Aronson, Robert

2015-01-01

Purpose: To examine the extent to which neighborhood disadvantage accounts for variation in blood pressure. Methods: Demographic, biometric, and self-reported data from 19,261 health screenings were used. Addresses of participants were geocoded and located within census block groups (n = 14,510, 75.3%). Three hierarchical linear models were…

The Effects of High School Organization on Dropping Out: An Exploratory Investigation.

ERIC Educational Resources Information Center

Bryk, Anthony S.; Thum, Yeow Meng

1989-01-01

A hierarchical linear model analysis investigated the effects of structural and normative features of schools on absenteeism and the probability of dropping out. Subjects included 4,450 sophomores in 160 Catholic and public high schools from the High School and Beyond 1980 cohort. (SLD)

Departmental Contexts and Faculty Research Activity in Norway

ERIC Educational Resources Information Center

Smeby, Jens-Christian; Try, Sverre

2005-01-01

The aim of the paper is to examine the relationship between departmental attributes and university faculty research activity. Since individual and departmental factors are highly interrelated, individual attributes are included in a hierarchical linear model taking into consideration the nested structure of the data. Research activity is measured…

The Effect of the Presence of Others on Caloric Intake in Homebound Older Adults

PubMed Central

Locher, Julie L.; Robinson, Caroline O.; Roth, David L.; Ritchie, Christine S.; Burgio, Kathryn L.

2008-01-01

Background Undernutrition in homebound older adults is a significant problem. The purpose of this study was to investigate the effect of the presence of others, both within the household and during meals, on caloric intake in homebound older adults. Methods In-depth interviews and three 24-hour dietary recalls were obtained from 50 older adults who were receiving home health services. Descriptive statistics were used to characterize participants, and hierarchical linear modeling was performed to evaluate predictors of caloric intake per meal. Results Participants’ mean age was 77. Females composed 65% and African Americans composed 42% of the sample. Analyses are based on 553 meal observations. The majority (84%) of participants consumed all meals for each of the 3 days of data collection; however, they consumed an average of only 1305 calories per day. Hierarchical linear modeling analysis indicated that persons who had others present during meals consumed an average of 114.0 calories more per meal than those who ate alone (p = .009) and that women consumed 76.7 fewer calories per meal than did men (p = .045). The presence of others within the household had no effect on caloric intake. Conclusion This research suggests that a simple and inexpensive way to increase caloric intake in homebound older adults is to make arrangements for family members or caregivers to eat with them. PMID:16339337

Multilevel modeling and panel data analysis in educational research (Case study: National examination data senior high school in West Java)

NASA Astrophysics Data System (ADS)

Zulvia, Pepi; Kurnia, Anang; Soleh, Agus M.

2017-03-01

Individual and environment are a hierarchical structure consist of units grouped at different levels. Hierarchical data structures are analyzed based on several levels, with the lowest level nested in the highest level. This modeling is commonly call multilevel modeling. Multilevel modeling is widely used in education research, for example, the average score of National Examination (UN). While in Indonesia UN for high school student is divided into natural science and social science. The purpose of this research is to develop multilevel and panel data modeling using linear mixed model on educational data. The first step is data exploration and identification relationships between independent and dependent variable by checking correlation coefficient and variance inflation factor (VIF). Furthermore, we use a simple model approach with highest level of the hierarchy (level-2) is regency/city while school is the lowest of hierarchy (level-1). The best model was determined by comparing goodness-of-fit and checking assumption from residual plots and predictions for each model. Our finding that for natural science and social science, the regression with random effects of regency/city and fixed effects of the time i.e multilevel model has better performance than the linear mixed model in explaining the variability of the dependent variable, which is the average scores of UN.

HDDM: Hierarchical Bayesian estimation of the Drift-Diffusion Model in Python.

PubMed

Wiecki, Thomas V; Sofer, Imri; Frank, Michael J

2013-01-01

The diffusion model is a commonly used tool to infer latent psychological processes underlying decision-making, and to link them to neural mechanisms based on response times. Although efficient open source software has been made available to quantitatively fit the model to data, current estimation methods require an abundance of response time measurements to recover meaningful parameters, and only provide point estimates of each parameter. In contrast, hierarchical Bayesian parameter estimation methods are useful for enhancing statistical power, allowing for simultaneous estimation of individual subject parameters and the group distribution that they are drawn from, while also providing measures of uncertainty in these parameters in the posterior distribution. Here, we present a novel Python-based toolbox called HDDM (hierarchical drift diffusion model), which allows fast and flexible estimation of the the drift-diffusion model and the related linear ballistic accumulator model. HDDM requires fewer data per subject/condition than non-hierarchical methods, allows for full Bayesian data analysis, and can handle outliers in the data. Finally, HDDM supports the estimation of how trial-by-trial measurements (e.g., fMRI) influence decision-making parameters. This paper will first describe the theoretical background of the drift diffusion model and Bayesian inference. We then illustrate usage of the toolbox on a real-world data set from our lab. Finally, parameter recovery studies show that HDDM beats alternative fitting methods like the χ(2)-quantile method as well as maximum likelihood estimation. The software and documentation can be downloaded at: http://ski.clps.brown.edu/hddm_docs/

Chunking dynamics: heteroclinics in mind

PubMed Central

Rabinovich, Mikhail I.; Varona, Pablo; Tristan, Irma; Afraimovich, Valentin S.

2014-01-01

Recent results of imaging technologies and non-linear dynamics make possible to relate the structure and dynamics of functional brain networks to different mental tasks and to build theoretical models for the description and prediction of cognitive activity. Such models are non-linear dynamical descriptions of the interaction of the core components—brain modes—participating in a specific mental function. The dynamical images of different mental processes depend on their temporal features. The dynamics of many cognitive functions are transient. They are often observed as a chain of sequentially changing metastable states. A stable heteroclinic channel (SHC) consisting of a chain of saddles—metastable states—connected by unstable separatrices is a mathematical image for robust transients. In this paper we focus on hierarchical chunking dynamics that can represent several forms of transient cognitive activity. Chunking is a dynamical phenomenon that nature uses to perform information processing of long sequences by dividing them in shorter information items. Chunking, for example, makes more efficient the use of short-term memory by breaking up long strings of information (like in language where one can see the separation of a novel on chapters, paragraphs, sentences, and finally words). Chunking is important in many processes of perception, learning, and cognition in humans and animals. Based on anatomical information about the hierarchical organization of functional brain networks, we propose a cognitive network architecture that hierarchically chunks and super-chunks switching sequences of metastable states produced by winnerless competitive heteroclinic dynamics. PMID:24672469

Chunking dynamics: heteroclinics in mind.

PubMed

Rabinovich, Mikhail I; Varona, Pablo; Tristan, Irma; Afraimovich, Valentin S

2014-01-01

Recent results of imaging technologies and non-linear dynamics make possible to relate the structure and dynamics of functional brain networks to different mental tasks and to build theoretical models for the description and prediction of cognitive activity. Such models are non-linear dynamical descriptions of the interaction of the core components-brain modes-participating in a specific mental function. The dynamical images of different mental processes depend on their temporal features. The dynamics of many cognitive functions are transient. They are often observed as a chain of sequentially changing metastable states. A stable heteroclinic channel (SHC) consisting of a chain of saddles-metastable states-connected by unstable separatrices is a mathematical image for robust transients. In this paper we focus on hierarchical chunking dynamics that can represent several forms of transient cognitive activity. Chunking is a dynamical phenomenon that nature uses to perform information processing of long sequences by dividing them in shorter information items. Chunking, for example, makes more efficient the use of short-term memory by breaking up long strings of information (like in language where one can see the separation of a novel on chapters, paragraphs, sentences, and finally words). Chunking is important in many processes of perception, learning, and cognition in humans and animals. Based on anatomical information about the hierarchical organization of functional brain networks, we propose a cognitive network architecture that hierarchically chunks and super-chunks switching sequences of metastable states produced by winnerless competitive heteroclinic dynamics.

Hierarchical ensemble of global and local classifiers for face recognition.

PubMed

Su, Yu; Shan, Shiguang; Chen, Xilin; Gao, Wen

2009-08-01

In the literature of psychophysics and neurophysiology, many studies have shown that both global and local features are crucial for face representation and recognition. This paper proposes a novel face recognition method which exploits both global and local discriminative features. In this method, global features are extracted from the whole face images by keeping the low-frequency coefficients of Fourier transform, which we believe encodes the holistic facial information, such as facial contour. For local feature extraction, Gabor wavelets are exploited considering their biological relevance. After that, Fisher's linear discriminant (FLD) is separately applied to the global Fourier features and each local patch of Gabor features. Thus, multiple FLD classifiers are obtained, each embodying different facial evidences for face recognition. Finally, all these classifiers are combined to form a hierarchical ensemble classifier. We evaluate the proposed method using two large-scale face databases: FERET and FRGC version 2.0. Experiments show that the results of our method are impressively better than the best known results with the same evaluation protocol.

Problem of two-level hierarchical minimax program control the final state of regional social and economic system with incomplete information

NASA Astrophysics Data System (ADS)

Shorikov, A. F.

2016-12-01

In this article we consider a discrete-time dynamical system consisting of a set a controllable objects (region and forming it municipalities). The dynamics each of these is described by the corresponding linear or nonlinear discrete-time recurrent vector relations and its control system consist from two levels: basic level (control level I) that is dominating level and auxiliary level (control level II) that is subordinate level. Both levels have different criterions of functioning and united by information and control connections which defined in advance. In this article we study the problem of optimization of guaranteed result for program control by the final state of regional social and economic system in the presence of risks vectors. For this problem we propose a mathematical model in the form of two-level hierarchical minimax program control problem of the final states of this system with incomplete information and the general scheme for its solving.

Hierarchical Nearest-Neighbor Gaussian Process Models for Large Geostatistical Datasets.

PubMed

Datta, Abhirup; Banerjee, Sudipto; Finley, Andrew O; Gelfand, Alan E

2016-01-01

Spatial process models for analyzing geostatistical data entail computations that become prohibitive as the number of spatial locations become large. This article develops a class of highly scalable nearest-neighbor Gaussian process (NNGP) models to provide fully model-based inference for large geostatistical datasets. We establish that the NNGP is a well-defined spatial process providing legitimate finite-dimensional Gaussian densities with sparse precision matrices. We embed the NNGP as a sparsity-inducing prior within a rich hierarchical modeling framework and outline how computationally efficient Markov chain Monte Carlo (MCMC) algorithms can be executed without storing or decomposing large matrices. The floating point operations (flops) per iteration of this algorithm is linear in the number of spatial locations, thereby rendering substantial scalability. We illustrate the computational and inferential benefits of the NNGP over competing methods using simulation studies and also analyze forest biomass from a massive U.S. Forest Inventory dataset at a scale that precludes alternative dimension-reducing methods. Supplementary materials for this article are available online.

Hierarchical Nearest-Neighbor Gaussian Process Models for Large Geostatistical Datasets

PubMed Central

Datta, Abhirup; Banerjee, Sudipto; Finley, Andrew O.; Gelfand, Alan E.

2018-01-01

Spatial process models for analyzing geostatistical data entail computations that become prohibitive as the number of spatial locations become large. This article develops a class of highly scalable nearest-neighbor Gaussian process (NNGP) models to provide fully model-based inference for large geostatistical datasets. We establish that the NNGP is a well-defined spatial process providing legitimate finite-dimensional Gaussian densities with sparse precision matrices. We embed the NNGP as a sparsity-inducing prior within a rich hierarchical modeling framework and outline how computationally efficient Markov chain Monte Carlo (MCMC) algorithms can be executed without storing or decomposing large matrices. The floating point operations (flops) per iteration of this algorithm is linear in the number of spatial locations, thereby rendering substantial scalability. We illustrate the computational and inferential benefits of the NNGP over competing methods using simulation studies and also analyze forest biomass from a massive U.S. Forest Inventory dataset at a scale that precludes alternative dimension-reducing methods. Supplementary materials for this article are available online. PMID:29720777

Hierarchical Time-Lagged Independent Component Analysis: Computing Slow Modes and Reaction Coordinates for Large Molecular Systems.

PubMed

Pérez-Hernández, Guillermo; Noé, Frank

2016-12-13

Analysis of molecular dynamics, for example using Markov models, often requires the identification of order parameters that are good indicators of the rare events, i.e. good reaction coordinates. Recently, it has been shown that the time-lagged independent component analysis (TICA) finds the linear combinations of input coordinates that optimally represent the slow kinetic modes and may serve in order to define reaction coordinates between the metastable states of the molecular system. A limitation of the method is that both computing time and memory requirements scale with the square of the number of input features. For large protein systems, this exacerbates the use of extensive feature sets such as the distances between all pairs of residues or even heavy atoms. Here we derive a hierarchical TICA (hTICA) method that approximates the full TICA solution by a hierarchical, divide-and-conquer calculation. By using hTICA on distances between heavy atoms we identify previously unknown relaxation processes in the bovine pancreatic trypsin inhibitor.

Parameterizations for ensemble Kalman inversion

NASA Astrophysics Data System (ADS)

Chada, Neil K.; Iglesias, Marco A.; Roininen, Lassi; Stuart, Andrew M.

2018-05-01

The use of ensemble methods to solve inverse problems is attractive because it is a derivative-free methodology which is also well-adapted to parallelization. In its basic iterative form the method produces an ensemble of solutions which lie in the linear span of the initial ensemble. Choice of the parameterization of the unknown field is thus a key component of the success of the method. We demonstrate how both geometric ideas and hierarchical ideas can be used to design effective parameterizations for a number of applied inverse problems arising in electrical impedance tomography, groundwater flow and source inversion. In particular we show how geometric ideas, including the level set method, can be used to reconstruct piecewise continuous fields, and we show how hierarchical methods can be used to learn key parameters in continuous fields, such as length-scales, resulting in improved reconstructions. Geometric and hierarchical ideas are combined in the level set method to find piecewise constant reconstructions with interfaces of unknown topology.

An Amperometric Acetylcholinesterase Sensor Based on the Bio-templated Synthesis of Hierarchical Mesoporous Bioactive Glass Microspheres

NASA Astrophysics Data System (ADS)

Lv, Zhuo; Luo, Ruiping; Xi, Lijuan; Chen, Yang; Wang, Hongsu

2017-11-01

This work describes the synthesis of three-dimensional hollow hierarchical mesoporous bioactive glass (HMBG) microspheres based on Herba leonuri pollen grains via a hydrothermal method. The HMBG microspheres perfectly copied the hierarchical porous structure and inner hollow structure constituting the double-layer surface of the natural Herba leonuri pollen grains. This structural mimicry of the pollen grains resulted in a higher degree of adsorption of acetylcholinesterase (AChE) on HMBG microspheres in comparison with mesoporous bioactive glass. Subsequently, an amperometric biosensor for the detection of Malathion was fabricated by immobilizing AChE onto an HMBG microspheres-modified carbon paste electrode. The biosensor response exhibited two good linear ranges during an incubation time of 10 min in the malathion concentration ranges of 0.02-50 ppb and 50-600 ppb, with a detection limit of 0.0135 ppb ( S/ N = 3). Overall, the prepared enzymatic biosensor showed high sensitivity in the rapid detection of Malathion and could be applied to detect pesticide residues in vegetable matter.

Nanoporous platinum-cobalt alloy for electrochemical sensing for ethanol, hydrogen peroxide, and glucose.

PubMed

Xu, Caixia; Sun, Fenglei; Gao, Hua; Wang, Jinping

2013-05-30

Nanoporous platinum-cobalt (NP-PtCo) alloy with hierarchical nanostructure is straightforwardly fabricated by dealloying PtCoAl alloy in a mild alkaline solution. Selectively etching Al resulted in a hierarchical three-dimensional network nanostructure with a narrow size distribution at 3 nm. The as-prepared NP-PtCo alloy shows superior performance toward ethanol and hydrogen peroxide (H2O2) with highly sensitive response due to its unique electrocatalytic activity. In addition, NP-PtCo also exhibits excellent amperometric durability and long-term stability for H2O2 as well as a good anti-interference toward ascorbic acid, uric acid, and dopamine. The hierarchical nanoporous architecture in PtCo alloy is also highly active for glucose sensing electrooxidation and sensing in a wide linear range. The NP-PtCo alloy holds great application potential for electrochemical sensing with simple preparation, unique catalytic activity, and high structure stability. Copyright © 2013 Elsevier B.V. All rights reserved.

Superposition-Based Analysis of First-Order Probabilistic Timed Automata

NASA Astrophysics Data System (ADS)

Fietzke, Arnaud; Hermanns, Holger; Weidenbach, Christoph

This paper discusses the analysis of first-order probabilistic timed automata (FPTA) by a combination of hierarchic first-order superposition-based theorem proving and probabilistic model checking. We develop the overall semantics of FPTAs and prove soundness and completeness of our method for reachability properties. Basically, we decompose FPTAs into their time plus first-order logic aspects on the one hand, and their probabilistic aspects on the other hand. Then we exploit the time plus first-order behavior by hierarchic superposition over linear arithmetic. The result of this analysis is the basis for the construction of a reachability equivalent (to the original FPTA) probabilistic timed automaton to which probabilistic model checking is finally applied. The hierarchic superposition calculus required for the analysis is sound and complete on the first-order formulas generated from FPTAs. It even works well in practice. We illustrate the potential behind it with a real-life DHCP protocol example, which we analyze by means of tool chain support.

Microfibres and macroscopic films from the coordination-driven hierarchical self-assembly of cylindrical micelles

PubMed Central

Lunn, David J.; Gould, Oliver E. C.; Whittell, George R.; Armstrong, Daniel P.; Mineart, Kenneth P.; Winnik, Mitchell A.; Spontak, Richard J.; Pringle, Paul G.; Manners, Ian

2016-01-01

Anisotropic nanoparticles prepared from block copolymers are of growing importance as building blocks for the creation of synthetic hierarchical materials. However, the assembly of these structural units is generally limited to the use of amphiphilic interactions. Here we report a simple, reversible coordination-driven hierarchical self-assembly strategy for the preparation of micron-scale fibres and macroscopic films based on monodisperse cylindrical block copolymer micelles. Coordination of Pd(0) metal centres to phosphine ligands immobilized within the soluble coronas of block copolymer micelles is found to induce intermicelle crosslinking, affording stable linear fibres comprised of micelle subunits in a staggered arrangement. The mean length of the fibres can be varied by altering the micelle concentration, reaction stoichiometry or aspect ratio of the micelle building blocks. Furthermore, the fibres aggregate on drying to form robust, self-supporting macroscopic micelle-based thin films with useful mechanical properties that are analogous to crosslinked polymer networks, but on a longer length scale. PMID:27538877

Classroom Age Composition and Developmental Change in 70 Urban Preschool Classrooms

ERIC Educational Resources Information Center

Moller, Arlen C.; Forbes-Jones, Emma; Hightower, A. Dirk

2008-01-01

A multilevel modeling approach was used to investigate the influence of age composition in 70 urban preschool classrooms. A series of hierarchical linear models demonstrated that greater variance in classroom age composition was negatively related to development on the Child Observation Record (COR) Cognitive, Motor, and Social subscales. This was…

A Three-Level Hierarchical Linear Model Using Student Growth Curve Modeling and Contextual Data

ERIC Educational Resources Information Center

Giorgio, Dorian

2012-01-01

Educational experts have criticized status models of school accountability, as required by the No Child Left Behind Act (NCLB), describing them as ineffectual in measuring achievement because their one-time assessment of student knowledge ignores student growth. Research on student achievement has instead identified growth models as superior…

Item Response Theory Using Hierarchical Generalized Linear Models

ERIC Educational Resources Information Center

Ravand, Hamdollah

2015-01-01

Multilevel models (MLMs) are flexible in that they can be employed to obtain item and person parameters, test for differential item functioning (DIF) and capture both local item and person dependence. Papers on the MLM analysis of item response data have focused mostly on theoretical issues where applications have been add-ons to simulation…

Analyzing Hierarchical Relationships Among Modes of Cognitive Reasoning and Integrated Science Process Skills.

ERIC Educational Resources Information Center

Yeany, Russell H.; And Others

1986-01-01

Searched for a learning hierarchy among skills comprising formal operations and integrated science processes. Ordering, theoretic, and probabilistic latent structure methods were used to analyze data collected from 700 science students. Both linear and branching relationships were identified within and across the two sets of skills. (Author/JN)

Trajectories of Family Management Practices and Early Adolescent Behavioral Outcomes

ERIC Educational Resources Information Center

Wang, Ming-Te; Dishion, Thomas J.; Stormshak, Elizabeth A.; Willett, John B.

2011-01-01

Stage-environment fit theory was used to examine the reciprocal lagged relations between family management practices and early adolescent problem behavior during the middle school years. In addition, the potential moderating roles of family structure and of gender were explored. Hierarchical linear modeling was used to describe patterns of growth…

Marriage, Cohabitation, and Happiness: A Cross-National Analysis of 27 Countries

ERIC Educational Resources Information Center

Lee, Kristen Schultz; Ono, Hiroshi

2012-01-01

The authors investigated how the reported happiness of married and cohabiting individuals varies cross-nationally with societal gender beliefs and religious context. They used the 2002 International Social Survey Programme data from 27 countries (N = 36,889) and specified hierarchical linear models with macro-micro level interactions in order to…

Do Nondomestic Undergraduates Choose a Major Field in Order to Maximize Grade Point Averages?

ERIC Educational Resources Information Center

Bergman, Matthew E.; Fass-Holmes, Barry

2016-01-01

The authors investigated whether undergraduates attending an American West Coast public university who were not U.S. citizens (nondomestic) maximized their grade point averages (GPA) through their choice of major field. Multiple regression hierarchical linear modeling analyses showed that major field's effect size was small for these…

To Enter Stone, Be Water: Situating Literacy Coaching as Rhizomatic

ERIC Educational Resources Information Center

Reilly, Mary Ann

2014-01-01

Reilly leans on the metaphor of rhizomes to remind readers that the work of a coach is not linear or hierarchical, but fluid and dynamic. Reilly frames literacy coaches as rhizomatic agents in schools and urges coaches to appreciate resistance and interruptions as critical and necessary for transformative teaching and learning.

Rural Compared to Urban Home Community Settings as Predictors of First-Year Students' Adjustment to University

ERIC Educational Resources Information Center

Ames, Megan E.; Wintre, Maxine G.; Prancer, S. Mark; Pratt, Michael W.; Birnie-Lefcovitch, Shelly; Polivy, Janet; Adams, Gerald R.

2014-01-01

Undergraduates (N = 2,823) at 6 universities were surveyed longitudinally to examine the relevance of student home setting on the transition to university. Preliminary results indicated that rural students seem less likely to attend large, ethnically diverse universities. Hierarchical linear models revealed that "proximal rural" students…

Hierarchical Bio-Inspired Cooperative Control for Nonlinear Dynamical Systems and Hardware Demonstration

DTIC Science & Technology

2013-04-03

cooperative control, LEGO robotic testbed, non-linear dynamics 16. SECURITY CLASSIFICATION OF: 17. LIMITATION OF ABSTRACT 18. NUMBER OF PAGES...testbed The architecture of the LEGO robots (® LEGO is a trademark and/or copyright of the LEGO Group) used in tests were based off the quick-start

Suppressor Variables and Multilevel Mixture Modelling

ERIC Educational Resources Information Center

Darmawan, I Gusti Ngurah; Keeves, John P.

2006-01-01

A major issue in educational research involves taking into consideration the multilevel nature of the data. Since the late 1980s, attempts have been made to model social science data that conform to a nested structure. Among other models, two-level structural equation modelling or two-level path modelling and hierarchical linear modelling are two…

Student Motivation in Low-Stakes Assessment Contexts: An Exploratory Analysis in Engineering Mechanics

ERIC Educational Resources Information Center

Musekamp, Frank; Pearce, Jacob

2016-01-01

The goal of this paper is to examine the relationship of student motivation and achievement in low-stakes assessment contexts. Using Pearson product-moment correlations and hierarchical linear regression modelling to analyse data on 794 tertiary students who undertook a low-stakes engineering mechanics assessment (along with the questionnaire of…

Principals' Leadership Behaviors as Perceived by Teachers in At-Risk Middle Schools

ERIC Educational Resources Information Center

Johnson, R. Anthony

2011-01-01

A need for greater understanding of teachers' (N = 530) perceptions of the leadership behaviors of principals in Title I middle schools (n = 13) is prevalent exists. The researcher used the "Audit of Principal Effectiveness" survey to collect data. The researcher also used Hierarchical Linear Modeling as the quantitative analysis.…

Family Structure States and Transitions: Associations with Children's Well-Being during Middle Childhood

ERIC Educational Resources Information Center

Magnuson, Katherine; Berger, Lawrence M.

2009-01-01

Using longitudinal data from the Maternal and Child Supplement of the National Longitudinal Survey of Youth (N = 3,862) and Hierarchical Linear Models, we estimated associations of family structure states and transitions with children's achievement and behavior trajectories during middle childhood. Results suggest that residing in a single-mother…

Falling Off Track: How Teacher-Student Relationships Predict Early High School Failure Rates.

ERIC Educational Resources Information Center

Miller, Shazia Rafiullah

This paper examines the relationship between the climate of teacher-student relations within a school and individual student's likelihood of freshman year success. Using administrative data from the Chicago Public Schools and survey data, researchers used hierarchical linear modeling to determine whether teacher-student climate predicts students'…

Collective Socialization and Child Conduct Problems: A Multilevel Analysis with an African American Sample

ERIC Educational Resources Information Center

Simons, Leslie Gordon; Simons, Ronald L.; Conger, Rand D.; Brody, Gene H.

2004-01-01

This article uses hierarchical linear modeling with a sample of African American children and their primary caregivers to examine the association between various community factors and child conduct problems. The analysis revealed a rather strong inverse association between level of collective socialization and conduct problems. This relationship…

Bottom-Up Analysis of Single-Case Research Designs

ERIC Educational Resources Information Center

Parker, Richard I.; Vannest, Kimberly J.

2012-01-01

This paper defines and promotes the qualities of a "bottom-up" approach to single-case research (SCR) data analysis. Although "top-down" models, for example, multi-level or hierarchical linear models, are gaining momentum and have much to offer, interventionists should be cautious about analyses that are not easily understood, are not governed by…

Assessment and Innovation: One Darn Thing Leads to Another

ERIC Educational Resources Information Center

Rutz, Carol; Lauer-Glebov, Jacqulyn

2005-01-01

Using recent experience at Carleton College in Minnesota as a case history, the authors offer a model for assessment that provides more flexibility than the well-known assessment feedback loop, which assumes a linear progression within a hierarchical administrative structure. The proposed model is based on a double helix, with values and feedback…

Hierarchical Linear Modeling Meta-Analysis of Single-Subject Design Research

ERIC Educational Resources Information Center

Gage, Nicholas A.; Lewis, Timothy J.

2014-01-01

The identification of evidence-based practices continues to provoke issues of disagreement across multiple fields. One area of contention is the role of single-subject design (SSD) research in providing scientific evidence. The debate about SSD's utility centers on three issues: sample size, effect size, and serial dependence. One potential…

3D deformable image matching: a hierarchical approach over nested subspaces

NASA Astrophysics Data System (ADS)

Musse, Olivier; Heitz, Fabrice; Armspach, Jean-Paul

2000-06-01

This paper presents a fast hierarchical method to perform dense deformable inter-subject matching of 3D MR Images of the brain. To recover the complex morphological variations in neuroanatomy, a hierarchy of 3D deformations fields is estimated, by minimizing a global energy function over a sequence of nested subspaces. The nested subspaces, generated from a single scaling function, consist of deformation fields constrained at different scales. The highly non linear energy function, describing the interactions between the target and the source images, is minimized using a coarse-to-fine continuation strategy over this hierarchy. The resulting deformable matching method shows low sensitivity to local minima and is able to track large non-linear deformations, with moderate computational load. The performances of the approach are assessed both on simulated 3D transformations and on a real data base of 3D brain MR Images from different individuals. The method has shown efficient in putting into correspondence the principle anatomical structures of the brain. An application to atlas-based MRI segmentation, by transporting a labeled segmentation map on patient data, is also presented.

Hierarchical design of an electro-hydraulic actuator based on robust LPV methods

NASA Astrophysics Data System (ADS)

Németh, Balázs; Varga, Balázs; Gáspár, Péter

2015-08-01

The paper proposes a hierarchical control design of an electro-hydraulic actuator, which is used to improve the roll stability of vehicles. The purpose of the control system is to generate a reference torque, which is required by the vehicle dynamic control. The control-oriented model of the actuator is formulated in two subsystems. The high-level hydromotor is described in a linear form, while the low-level spool valve is a polynomial system. These subsystems require different control strategies. At the high level, a linear parameter-varying control is used to guarantee performance specifications. At the low level, a control Lyapunov-function-based algorithm, which creates discrete control input values of the valve, is proposed. The interaction between the two subsystems is guaranteed by the spool displacement, which is control input at the high level and must be tracked at the low-level control. The spool displacement has physical constraints, which must also be incorporated into the control design. The robust design of the high-level control incorporates the imprecision of the low-level control as an uncertainty of the system.

Factors Associated with Job Satisfaction among University Teachers in Northeastern Region of China: A Cross-Sectional Study

PubMed Central

Pan, Bochen; Shen, Xue; Liu, Li; Yang, Yilong; Wang, Lie

2015-01-01

Objective: Teachers’ job satisfaction is one of the key factors in institutional dynamics and is generally considered to be the primary variable by which the effectiveness of an organization’s human resource is evaluated. The objectives of this study were to assess the level of job satisfaction among university teachers and to clarify the associated factors. Method: A cross-sectional study was conducted between November 2013 and January 2014. Teachers from six universities in Shenyang, China were randomly sampled. The job satisfaction scale Minnesota Satisfaction Questionnaire (MSQ), perceived organizational support (POS), psychological capital questionnaire (PCQ-24), and effort-reward imbalance scale (ERI) together with questions about demographic and working factors were administered in questionnaires distributed to 1500 university teachers. Hierarchical linear regression analyses were performed to explore the related factors. Results: 1210 effective responses were obtained (effective respondent rate 80.7%). The average score of overall job satisfaction was 69.71. Hierarchical linear regression analysis revealed that turnover intention, occupational stress and chronic disease all had negative impacts on job satisfaction, whereas perceived organizational support, psychological capital and higher monthly income were positively associated with job satisfaction among the university teachers. Age was also linked to the level of job satisfaction. All the variables explained 60.7% of the variance in job satisfaction. Conclusions: Chinese university teachers had a moderate level of job satisfaction. Demographic and working characteristics were associated factors for job satisfaction. Perceived organizational support showed the strongest association with job satisfaction. Results of the study indicate that improving the perceived organizational support may increase the level of job satisfaction for university teachers. PMID:26473906

Community turnover of wood-inhabiting fungi across hierarchical spatial scales.

PubMed

Abrego, Nerea; García-Baquero, Gonzalo; Halme, Panu; Ovaskainen, Otso; Salcedo, Isabel

2014-01-01

For efficient use of conservation resources it is important to determine how species diversity changes across spatial scales. In many poorly known species groups little is known about at which spatial scales the conservation efforts should be focused. Here we examined how the community turnover of wood-inhabiting fungi is realised at three hierarchical levels, and how much of community variation is explained by variation in resource composition and spatial proximity. The hierarchical study design consisted of management type (fixed factor), forest site (random factor, nested within management type) and study plots (randomly placed plots within each study site). To examine how species richness varied across the three hierarchical scales, randomized species accumulation curves and additive partitioning of species richness were applied. To analyse variation in wood-inhabiting species and dead wood composition at each scale, linear and Permanova modelling approaches were used. Wood-inhabiting fungal communities were dominated by rare and infrequent species. The similarity of fungal communities was higher within sites and within management categories than among sites or between the two management categories, and it decreased with increasing distance among the sampling plots and with decreasing similarity of dead wood resources. However, only a small part of community variation could be explained by these factors. The species present in managed forests were in a large extent a subset of those species present in natural forests. Our results suggest that in particular the protection of rare species requires a large total area. As managed forests have only little additional value complementing the diversity of natural forests, the conservation of natural forests is the key to ecologically effective conservation. As the dissimilarity of fungal communities increases with distance, the conserved natural forest sites should be broadly distributed in space, yet the individual conserved areas should be large enough to ensure local persistence.

Community Turnover of Wood-Inhabiting Fungi across Hierarchical Spatial Scales

PubMed Central

Abrego, Nerea; García-Baquero, Gonzalo; Halme, Panu; Ovaskainen, Otso; Salcedo, Isabel

2014-01-01

For efficient use of conservation resources it is important to determine how species diversity changes across spatial scales. In many poorly known species groups little is known about at which spatial scales the conservation efforts should be focused. Here we examined how the community turnover of wood-inhabiting fungi is realised at three hierarchical levels, and how much of community variation is explained by variation in resource composition and spatial proximity. The hierarchical study design consisted of management type (fixed factor), forest site (random factor, nested within management type) and study plots (randomly placed plots within each study site). To examine how species richness varied across the three hierarchical scales, randomized species accumulation curves and additive partitioning of species richness were applied. To analyse variation in wood-inhabiting species and dead wood composition at each scale, linear and Permanova modelling approaches were used. Wood-inhabiting fungal communities were dominated by rare and infrequent species. The similarity of fungal communities was higher within sites and within management categories than among sites or between the two management categories, and it decreased with increasing distance among the sampling plots and with decreasing similarity of dead wood resources. However, only a small part of community variation could be explained by these factors. The species present in managed forests were in a large extent a subset of those species present in natural forests. Our results suggest that in particular the protection of rare species requires a large total area. As managed forests have only little additional value complementing the diversity of natural forests, the conservation of natural forests is the key to ecologically effective conservation. As the dissimilarity of fungal communities increases with distance, the conserved natural forest sites should be broadly distributed in space, yet the individual conserved areas should be large enough to ensure local persistence. PMID:25058128

Extension of mixture-of-experts networks for binary classification of hierarchical data.

PubMed

Ng, Shu-Kay; McLachlan, Geoffrey J

2007-09-01

For many applied problems in the context of medically relevant artificial intelligence, the data collected exhibit a hierarchical or clustered structure. Ignoring the interdependence between hierarchical data can result in misleading classification. In this paper, we extend the mechanism for mixture-of-experts (ME) networks for binary classification of hierarchical data. Another extension is to quantify cluster-specific information on data hierarchy by random effects via the generalized linear mixed-effects model (GLMM). The extension of ME networks is implemented by allowing for correlation in the hierarchical data in both the gating and expert networks via the GLMM. The proposed model is illustrated using a real thyroid disease data set. In our study, we consider 7652 thyroid diagnosis records from 1984 to early 1987 with complete information on 20 attribute values. We obtain 10 independent random splits of the data into a training set and a test set in the proportions 85% and 15%. The test sets are used to assess the generalization performance of the proposed model, based on the percentage of misclassifications. For comparison, the results obtained from the ME network with independence assumption are also included. With the thyroid disease data, the misclassification rate on test sets for the extended ME network is 8.9%, compared to 13.9% for the ME network. In addition, based on model selection methods described in Section 2, a network with two experts is selected. These two expert networks can be considered as modeling two groups of patients with high and low incidence rates. Significant variation among the predicted cluster-specific random effects is detected in the patient group with low incidence rate. It is shown that the extended ME network outperforms the ME network for binary classification of hierarchical data. With the thyroid disease data, useful information on the relative log odds of patients with diagnosed conditions at different periods can be evaluated. This information can be taken into consideration for the assessment of treatment planning of the disease. The proposed extended ME network thus facilitates a more general approach to incorporate data hierarchy mechanism in network modeling.

Linear scaling computation of the Fock matrix. VI. Data parallel computation of the exchange-correlation matrix

NASA Astrophysics Data System (ADS)

Gan, Chee Kwan; Challacombe, Matt

2003-05-01

Recently, early onset linear scaling computation of the exchange-correlation matrix has been achieved using hierarchical cubature [J. Chem. Phys. 113, 10037 (2000)]. Hierarchical cubature differs from other methods in that the integration grid is adaptive and purely Cartesian, which allows for a straightforward domain decomposition in parallel computations; the volume enclosing the entire grid may be simply divided into a number of nonoverlapping boxes. In our data parallel approach, each box requires only a fraction of the total density to perform the necessary numerical integrations due to the finite extent of Gaussian-orbital basis sets. This inherent data locality may be exploited to reduce communications between processors as well as to avoid memory and copy overheads associated with data replication. Although the hierarchical cubature grid is Cartesian, naive boxing leads to irregular work loads due to strong spatial variations of the grid and the electron density. In this paper we describe equal time partitioning, which employs time measurement of the smallest sub-volumes (corresponding to the primitive cubature rule) to load balance grid-work for the next self-consistent-field iteration. After start-up from a heuristic center of mass partitioning, equal time partitioning exploits smooth variation of the density and grid between iterations to achieve load balance. With the 3-21G basis set and a medium quality grid, equal time partitioning applied to taxol (62 heavy atoms) attained a speedup of 61 out of 64 processors, while for a 110 molecule water cluster at standard density it achieved a speedup of 113 out of 128. The efficiency of equal time partitioning applied to hierarchical cubature improves as the grid work per processor increases. With a fine grid and the 6-311G(df,p) basis set, calculations on the 26 atom molecule α-pinene achieved a parallel efficiency better than 99% with 64 processors. For more coarse grained calculations, superlinear speedups are found to result from reduced computational complexity associated with data parallelism.

Thought suppression across time: Change in frequency and duration of thought recurrence.

PubMed

Lambert, Ann E; Hu, Yueqin; Magee, Joshua C; Beadel, Jessica R; Teachman, Bethany A

2014-01-01

Some studies have found that trying to suppress thoughts increases their long-term recurrence, a phenomenon associated with psychopathology, particularly obsessive-compulsive disorder. However, effect sizes in thought suppression studies have often been small and inconsistent. The present study sought to improve thought suppression conceptualization and measurement by examining two distinct dimensions of thought recurrence - frequency and duration of a thought's return - and how they evolve over time. After a thought focus period, 100 adults were assigned to either suppress or monitor the recurrence of an unpleasant thought for 4 min. Then, during a second four-minute period, all participants were asked to monitor the thought's recurrence. Hierarchical linear modeling indicated that thought frequency declined across time and the rate of decline slowed as time went on. Initially, the extent of thought duration remained short and stable for those asked to suppress, and increased linearly over time for those asked to monitor. Later, this pattern reversed. Duration increased linearly for those initially asked to suppress but was short and stable for those who initially monitored. Accounting for change over time and means of measuring recurrence (frequency vs. duration) may help elucidate past mixed findings, and improve thought suppression research methodology.

Linear and Non-Linear Visual Feature Learning in Rat and Humans

PubMed Central

Bossens, Christophe; Op de Beeck, Hans P.

2016-01-01

The visual system processes visual input in a hierarchical manner in order to extract relevant features that can be used in tasks such as invariant object recognition. Although typically investigated in primates, recent work has shown that rats can be trained in a variety of visual object and shape recognition tasks. These studies did not pinpoint the complexity of the features used by these animals. Many tasks might be solved by using a combination of relatively simple features which tend to be correlated. Alternatively, rats might extract complex features or feature combinations which are nonlinear with respect to those simple features. In the present study, we address this question by starting from a small stimulus set for which one stimulus-response mapping involves a simple linear feature to solve the task while another mapping needs a well-defined nonlinear combination of simpler features related to shape symmetry. We verified computationally that the nonlinear task cannot be trivially solved by a simple V1-model. We show how rats are able to solve the linear feature task but are unable to acquire the nonlinear feature. In contrast, humans are able to use the nonlinear feature and are even faster in uncovering this solution as compared to the linear feature. The implications for the computational capabilities of the rat visual system are discussed. PMID:28066201

Job stress models, depressive disorders and work performance of engineers in microelectronics industry.

PubMed

Chen, Sung-Wei; Wang, Po-Chuan; Hsin, Ping-Lung; Oates, Anthony; Sun, I-Wen; Liu, Shen-Ing

2011-01-01

Microelectronic engineers are considered valuable human capital contributing significantly toward economic development, but they may encounter stressful work conditions in the context of a globalized industry. The study aims at identifying risk factors of depressive disorders primarily based on job stress models, the Demand-Control-Support and Effort-Reward Imbalance models, and at evaluating whether depressive disorders impair work performance in microelectronics engineers in Taiwan. The case-control study was conducted among 678 microelectronics engineers, 452 controls and 226 cases with depressive disorders which were defined by a score 17 or more on the Beck Depression Inventory and a psychiatrist's diagnosis. The self-administered questionnaires included the Job Content Questionnaire, Effort-Reward Imbalance Questionnaire, demography, psychosocial factors, health behaviors and work performance. Hierarchical logistic regression was applied to identify risk factors of depressive disorders. Multivariate linear regressions were used to determine factors affecting work performance. By hierarchical logistic regression, risk factors of depressive disorders are high demands, low work social support, high effort/reward ratio and low frequency of physical exercise. Combining the two job stress models may have better predictive power for depressive disorders than adopting either model alone. Three multivariate linear regressions provide similar results indicating that depressive disorders are associated with impaired work performance in terms of absence, role limitation and social functioning limitation. The results may provide insight into the applicability of job stress models in a globalized high-tech industry considerably focused in non-Western countries, and the design of workplace preventive strategies for depressive disorders in Asian electronics engineering population.

Bayesian Hierarchical Random Intercept Model Based on Three Parameter Gamma Distribution

NASA Astrophysics Data System (ADS)

Wirawati, Ika; Iriawan, Nur; Irhamah

2017-06-01

Hierarchical data structures are common throughout many areas of research. Beforehand, the existence of this type of data was less noticed in the analysis. The appropriate statistical analysis to handle this type of data is the hierarchical linear model (HLM). This article will focus only on random intercept model (RIM), as a subclass of HLM. This model assumes that the intercept of models in the lowest level are varied among those models, and their slopes are fixed. The differences of intercepts were suspected affected by some variables in the upper level. These intercepts, therefore, are regressed against those upper level variables as predictors. The purpose of this paper would demonstrate a proven work of the proposed two level RIM of the modeling on per capita household expenditure in Maluku Utara, which has five characteristics in the first level and three characteristics of districts/cities in the second level. The per capita household expenditure data in the first level were captured by the three parameters Gamma distribution. The model, therefore, would be more complex due to interaction of many parameters for representing the hierarchical structure and distribution pattern of the data. To simplify the estimation processes of parameters, the computational Bayesian method couple with Markov Chain Monte Carlo (MCMC) algorithm and its Gibbs Sampling are employed.

Multilevel Hierarchical Modeling of Benthic Macroinvertebrate Responses to Urbanization in Nine Metropolitan Regions across the Conterminous United States

USGS Publications Warehouse

Kashuba, Roxolana; Cha, YoonKyung; Alameddine, Ibrahim; Lee, Boknam; Cuffney, Thomas F.

2010-01-01

Multilevel hierarchical modeling methodology has been developed for use in ecological data analysis. The effect of urbanization on stream macroinvertebrate communities was measured across a gradient of basins in each of nine metropolitan regions across the conterminous United States. The hierarchical nature of this dataset was harnessed in a multi-tiered model structure, predicting both invertebrate response at the basin scale and differences in invertebrate response at the region scale. Ordination site scores, total taxa richness, Ephemeroptera, Plecoptera, Trichoptera (EPT) taxa richness, and richness-weighted mean tolerance of organisms at a site were used to describe invertebrate responses. Percentage of urban land cover was used as a basin-level predictor variable. Regional mean precipitation, air temperature, and antecedent agriculture were used as region-level predictor variables. Multilevel hierarchical models were fit to both levels of data simultaneously, borrowing statistical strength from the complete dataset to reduce uncertainty in regional coefficient estimates. Additionally, whereas non-hierarchical regressions were only able to show differing relations between invertebrate responses and urban intensity separately for each region, the multilevel hierarchical regressions were able to explain and quantify those differences within a single model. In this way, this modeling approach directly establishes the importance of antecedent agricultural conditions in masking the response of invertebrates to urbanization in metropolitan regions such as Milwaukee-Green Bay, Wisconsin; Denver, Colorado; and Dallas-Fort Worth, Texas. Also, these models show that regions with high precipitation, such as Atlanta, Georgia; Birmingham, Alabama; and Portland, Oregon, start out with better regional background conditions of invertebrates prior to urbanization but experience faster negative rates of change with urbanization. Ultimately, this urbanization-invertebrate response example is used to detail the multilevel hierarchical construction methodology, showing how the result is a set of models that are both statistically more rigorous and ecologically more interpretable than simple linear regression models.

What predicts recovery orientation in county departments of mental health? A pilot study.

PubMed

Brown, Timothy T; Mahoney, Christine B; Adams, Neal; Felton, Mistique; Pareja, Candy

2010-09-01

In this pilot study we examined the determinants of recovery orientation among employees and influential stakeholders in a sample of 12 county departments of mental health in California. A two-level hierarchical linear model with random intercepts was estimated. Analyses show that recovery orientation has a U-shaped relationship with the age of staff/influential stakeholders and is negatively related to the difference between the desired level of adhocracy and the current level of adhocracy. Recovery orientation is positively related to the education level of staff/influential stakeholders, satisfying transformational leadership outcomes, and larger mental health budgets per capita. Policy implications are discussed.

A Hierarchical Linear Modeling Analysis of Working Memory and Implicit Prosody in the Resolution of Adjunct Attachment Ambiguity

ERIC Educational Resources Information Center

Traxler, Matthew J.

2009-01-01

An eye-movement monitoring experiment investigated readers' response to temporarily ambiguous sentences. The sentences were ambiguous because a relative clause could attach to one of two preceding nouns. Semantic information disambiguated the sentences. Working memory considerations predict an overall preference for the second of the two nouns, as…

Toward Addressing the Issues of Site Selection in District Effectiveness Research: A Two-Level Hierarchical Linear Growth Model

ERIC Educational Resources Information Center

Bowers, Alex J.

2010-01-01

Purpose: District effectiveness research (DER) is an emerging field concerned with identifying the organizational structures, administration, and leadership practices at the school district level that help districts find success with all of their students across the schools within the system. This work has mirrored much of the early school…

Science Teaching Reform through Professional Development: Teachers' Use of a Scientific Classroom Discourse Community Model

ERIC Educational Resources Information Center

Lewis, Elizabeth B.; Baker, Dale R.; Helding, Brandon A.

2015-01-01

This report outlines a 2-year investigation into how secondary science teachers used professional development (PD) to build scientific classroom discourse communities (SCDCs). Observation data, teacher, student, and school demographic information were used to build a hierarchical linear model. The length of time that teachers received PD was the…

Individual Differences in Trajectories of Emotion Regulation Processes: The Effects of Maternal Depressive Symptomatology and Children's Physiological Regulation

ERIC Educational Resources Information Center

Blandon, Alysia Y.; Calkins, Susan D.; Keane, Susan P.; O'Brien, Marion

2008-01-01

Trajectories of emotion regulation processes were examined in a community sample of 269 children across the ages of 4 to 7 using hierarchical linear modeling. Maternal depressive symptomatology (Symptom Checklist-90) and children's physiological reactivity (respiratory sinus arrhythmia [RSA]) and vagal regulation ([delta]RSA) were explored as…

Classification Based on Hierarchical Linear Models: The Need for Incorporation of Social Contexts in Classification Analysis

ERIC Educational Resources Information Center

Vaughn, Brandon K.; Wang, Qui

2009-01-01

Many areas in educational and psychological research involve the use of classification statistical analysis. For example, school districts might be interested in attaining variables that provide optimal prediction of school dropouts. In psychology, a researcher might be interested in the classification of a subject into a particular psychological…

Connecting the Dots: How Connectedness to Multiple Contexts Influences the Psychological and Academic Adjustment of Urban Youth

ERIC Educational Resources Information Center

Witherspoon, Dawn; Schotland, Marieka; Way, Niobe; Hughes, Diane

2009-01-01

Cluster analyses and hierarchical linear modeling were used to investigate the impact of perceptions of connectedness to family, school, and neighborhood contexts on academic and psycho-social outcomes for 437 urban ethnically diverse adolescents. Five profiles of connectedness to family, school, and neighborhood were identified. Two profiles were…

The Impact of School Environment and Grade Level on Student Delinquency: A Multilevel Modeling Approach

ERIC Educational Resources Information Center

Lo, Celia C.; Kim, Young S.; Allen, Thomas M.; Allen, Andrea N.; Minugh, P. Allison; Lomuto, Nicoletta

2011-01-01

Effects on delinquency made by grade level, school type (based on grade levels accommodated), and prosocial school climate were assessed, controlling for individual-level risk and protective factors. Data were obtained from the Substance Abuse Services Division of Alabama's state mental health agency and analyzed via hierarchical linear modeling,…

Adolescents' as Active Agents in the Socialization Process: Legitimacy of Parental Authority and Obligation to Obey as Predictors of Obedience

ERIC Educational Resources Information Center

Darling, Nancy; Cumsille, Patricio; Loreto Martinez, M.

2007-01-01

Adolescents' agreement with parental standards and beliefs about the legitimacy of parental authority and their own obligation to obey were used to predict adolescents' obedience, controlling for parental monitoring, rules, and rule enforcement. Hierarchical linear models were used to predict both between-adolescent and within-adolescent,…

Concordance of Interests in Dynamic Models of Social Partnership in the System of Continuing Professional Education

ERIC Educational Resources Information Center

Tarasenko, Larissa V.; Ougolnitsky, Guennady A.; Usov, Anatoly B.; Vaskov, Maksim A.; Kirik, Vladimir A.; Astoyanz, Margarita S.; Angel, Olga Y.

2016-01-01

A dynamic game theoretic model of concordance of interests in the process of social partnership in the system of continuing professional education is proposed. Non-cooperative, cooperative, and hierarchical setups are examined. Analytical solution for a linear state version of the model is provided. Nash equilibrium algorithms (for non-cooperative…

Modeling Signal-Noise Processes Supports Student Construction of a Hierarchical Image of Sample

ERIC Educational Resources Information Center

Lehrer, Richard

2017-01-01

Grade 6 (modal age 11) students invented and revised models of the variability generated as each measured the perimeter of a table in their classroom. To construct models, students represented variability as a linear composite of true measure (signal) and multiple sources of random error. Students revised models by developing sampling…

The Role of Social Relationships in Predicting Loneliness: The National Social Life, Health, and Aging Project

ERIC Educational Resources Information Center

Shiovitz-Ezra, Sharon; Leitsch, Sara A.

2010-01-01

The authors explore associations between objective and subjective social network characteristics and loneliness in later life, using data from the National Social Life, Health, and Aging Project, a nationally representative sample of individuals ages 57 to 85 in the United States. Hierarchical linear regression was used to examine the associations…

Division of Labor in German Dual-Earner Families: Testing Equity Theoretical Hypotheses

ERIC Educational Resources Information Center

Klumb, Petra; Hoppmann, Christiane; Staats, Melanie

2006-01-01

On the basis of 52 German dual-earner couples with at least 1 child younger than 5 years, we tested the effects of an unequal division of labor on relationship satisfaction. We analyzed diary reports of time allocated to productive activities according to the actor-partner-interdependence model. Hierarchical linear models showed that rather than…

Marginal and Random Intercepts Models for Longitudinal Binary Data with Examples from Criminology

ERIC Educational Resources Information Center

Long, Jeffrey D.; Loeber, Rolf; Farrington, David P.

2009-01-01

Two models for the analysis of longitudinal binary data are discussed: the marginal model and the random intercepts model. In contrast to the linear mixed model (LMM), the two models for binary data are not subsumed under a single hierarchical model. The marginal model provides group-level information whereas the random intercepts model provides…

For Richer, for Poorer: Money as a Topic of Marital Conflict in the Home

ERIC Educational Resources Information Center

Papp, Lauren M.; Cummings, E. Mark; Goeke-Morey, Marcie C.

2009-01-01

Guided by a family stress perspective, we examined the hypothesis that discussing money would be associated with the handling of marital conflict in the home. Analyses were based on dyadic hierarchical linear modeling of 100 husbands' and 100 wives' diary reports of 748 conflict instances. Contrary to findings from previous laboratory-based…

Effect Sizes for Growth-Modeling Analysis for Controlled Clinical Trials in the Same Metric as for Classical Analysis

ERIC Educational Resources Information Center

Feingold, Alan

2009-01-01

The use of growth-modeling analysis (GMA)--including hierarchical linear models, latent growth models, and general estimating equations--to evaluate interventions in psychology, psychiatry, and prevention science has grown rapidly over the last decade. However, an effect size associated with the difference between the trajectories of the…

Bilingual Education in an Aboriginal Context: Examining the Transfer of Language Skills from Inuktitut to English or French

ERIC Educational Resources Information Center

Usborne, Esther; Caouette, Julie; Qumaaluk, Qiallak; Taylor, Donald M.

2009-01-01

Bilingual education is thought to be one of the principal means of simultaneously revitalizing threatened language and preparing students for success in mainstream society. However, little research has examined, in a comprehensive and longitudinal fashion, bilingual programs in Aboriginal contexts. Hierarchical linear modeling was used to conduct…

The Challenge of Separating Effects of Simultaneous Education Projects on Student Achievement

ERIC Educational Resources Information Center

Ma, Xin; Ma, Lingling

2009-01-01

When multiple education projects operate in an overlapping or rear-ended manner, it is always a challenge to separate unique project effects on schooling outcomes. Our analysis represents a first attempt to address this challenge. A three-level hierarchical linear model (HLM) was presented as a general analytical framework to separate program…

Aspirations, Progress and Perceptions of Boys from a Single Sex School Following the Changeover to Coeducation

ERIC Educational Resources Information Center

Yates, Shirley M.

2004-01-01

Career and further education aspirations, educational progress and perceptions of the learning environment were measured annually over three years in primary and secondary boys from a single sex non-government school, following the changeover to coeducation. Hierarchical Linear Modelling analyses revealed the significant role played by the career…

Making Sense of Students' Actions in an Open-Ended Virtual Laboratory Environment

ERIC Educational Resources Information Center

Gal, Ya'akov; Uzan, Oriel; Belford, Robert; Karabinos, Michael; Yaron, David

2015-01-01

A process for analyzing log files collected from open-ended learning environments is developed and tested on a virtual lab problem involving reaction stoichiometry. The process utilizes a set of visualization tools that, by grouping student actions in a hierarchical manner, helps experts make sense of the linear list of student actions recorded in…

FRIT characterized hierarchical kernel memory arrangement for multiband palmprint recognition

NASA Astrophysics Data System (ADS)

Kisku, Dakshina R.; Gupta, Phalguni; Sing, Jamuna K.

2015-10-01

In this paper, we present a hierarchical kernel associative memory (H-KAM) based computational model with Finite Ridgelet Transform (FRIT) representation for multispectral palmprint recognition. To characterize a multispectral palmprint image, the Finite Ridgelet Transform is used to achieve a very compact and distinctive representation of linear singularities while it also captures the singularities along lines and edges. The proposed system makes use of Finite Ridgelet Transform to represent multispectral palmprint image and it is then modeled by Kernel Associative Memories. Finally, the recognition scheme is thoroughly tested with a benchmarking multispectral palmprint database CASIA. For recognition purpose a Bayesian classifier is used. The experimental results exhibit robustness of the proposed system under different wavelengths of palm image.

Minimax terminal approach problem in two-level hierarchical nonlinear discrete-time dynamical system

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

Shorikov, A. F., E-mail: afshorikov@mail.ru

We consider a discrete–time dynamical system consisting of three controllable objects. The motions of all objects are given by the corresponding vector nonlinear or linear discrete–time recurrent vector relations, and control system for its has two levels: basic (first or I level) that is dominating and subordinate level (second or II level) and both have different criterions of functioning and united a priori by determined informational and control connections defined in advance. For the dynamical system in question, we propose a mathematical formalization in the form of solving a multistep problem of two-level hierarchical minimax program control over the terminalmore » approach process with incomplete information and give a general scheme for its solving.« less

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

Chen, Chao; Pouransari, Hadi; Rajamanickam, Sivasankaran

We present a parallel hierarchical solver for general sparse linear systems on distributed-memory machines. For large-scale problems, this fully algebraic algorithm is faster and more memory-efficient than sparse direct solvers because it exploits the low-rank structure of fill-in blocks. Depending on the accuracy of low-rank approximations, the hierarchical solver can be used either as a direct solver or as a preconditioner. The parallel algorithm is based on data decomposition and requires only local communication for updating boundary data on every processor. Moreover, the computation-to-communication ratio of the parallel algorithm is approximately the volume-to-surface-area ratio of the subdomain owned by everymore » processor. We also provide various numerical results to demonstrate the versatility and scalability of the parallel algorithm.« less

On the usefulness of 'what' and 'where' pathways in vision.

PubMed

de Haan, Edward H F; Cowey, Alan

2011-10-01

The primate visual brain is classically portrayed as a large number of separate 'maps', each dedicated to the processing of specific visual cues, such as colour, motion or faces and their many features. In order to understand this fractionated architecture, the concept of cortical 'pathways' or 'streams' was introduced. In the currently prevailing view, the different maps are organised hierarchically into two major pathways, one involved in recognition and memory (the ventral stream or 'what' pathway) and the other in the programming of action (the dorsal stream or 'where' pathway). In this review, we question this heuristically influential but potentially misleading linear hierarchical pathway model and argue instead for a 'patchwork' or network model. Copyright © 2011 Elsevier Ltd. All rights reserved.

Chemical Fingerprint and Quantitative Analysis for the Quality Evaluation of Platycladi cacumen by Ultra-performance Liquid Chromatography Coupled with Hierarchical Cluster Analysis.

PubMed

Shan, Mingqiu; Li, Sam Fong Yau; Yu, Sheng; Qian, Yan; Guo, Shuchen; Zhang, Li; Ding, Anwei

2018-01-01

Platycladi cacumen (dried twigs and leaves of Platycladus orientalis (L.) Franco) is a frequently utilized Chinese medicinal herb. To evaluate the quality of the phytomedcine, an ultra-performance liquid chromatographic method with diode array detection was established for chemical fingerprinting and quantitative analysis. In this study, 27 batches of P. cacumen from different regions were collected for analysis. A chemical fingerprint with 20 common peaks was obtained using Similarity Evaluation System for Chromatographic Fingerprint of Traditional Chinese Medicine (Version 2004A). Among these 20 components, seven flavonoids (myricitrin, isoquercitrin, quercitrin, afzelin, cupressuflavone, amentoflavone and hinokiflavone) were identified and determined simultaneously. In the method validation, the seven analytes showed good regressions (R ≥ 0.9995) within linear ranges and good recoveries from 96.4% to 103.3%. Furthermore, with the contents of these seven flavonoids, hierarchical clustering analysis was applied to distinguish the 27 batches into five groups. The chemometric results showed that these groups were almost consistent with geographical positions and climatic conditions of the production regions. Integrating fingerprint analysis, simultaneous determination and hierarchical clustering analysis, the established method is rapid, sensitive, accurate and readily applicable, and also provides a significant foundation for quality control of P. cacumen efficiently. © The Author 2017. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Heuristic Identification of Biological Architectures for Simulating Complex Hierarchical Genetic Interactions

PubMed Central

Moore, Jason H; Amos, Ryan; Kiralis, Jeff; Andrews, Peter C

2015-01-01

Simulation plays an essential role in the development of new computational and statistical methods for the genetic analysis of complex traits. Most simulations start with a statistical model using methods such as linear or logistic regression that specify the relationship between genotype and phenotype. This is appealing due to its simplicity and because these statistical methods are commonly used in genetic analysis. It is our working hypothesis that simulations need to move beyond simple statistical models to more realistically represent the biological complexity of genetic architecture. The goal of the present study was to develop a prototype genotype–phenotype simulation method and software that are capable of simulating complex genetic effects within the context of a hierarchical biology-based framework. Specifically, our goal is to simulate multilocus epistasis or gene–gene interaction where the genetic variants are organized within the framework of one or more genes, their regulatory regions and other regulatory loci. We introduce here the Heuristic Identification of Biological Architectures for simulating Complex Hierarchical Interactions (HIBACHI) method and prototype software for simulating data in this manner. This approach combines a biological hierarchy, a flexible mathematical framework, a liability threshold model for defining disease endpoints, and a heuristic search strategy for identifying high-order epistatic models of disease susceptibility. We provide several simulation examples using genetic models exhibiting independent main effects and three-way epistatic effects. PMID:25395175

Longitudinal data analyses using linear mixed models in SPSS: concepts, procedures and illustrations.

PubMed

Shek, Daniel T L; Ma, Cecilia M S

2011-01-05

Although different methods are available for the analyses of longitudinal data, analyses based on generalized linear models (GLM) are criticized as violating the assumption of independence of observations. Alternatively, linear mixed models (LMM) are commonly used to understand changes in human behavior over time. In this paper, the basic concepts surrounding LMM (or hierarchical linear models) are outlined. Although SPSS is a statistical analyses package commonly used by researchers, documentation on LMM procedures in SPSS is not thorough or user friendly. With reference to this limitation, the related procedures for performing analyses based on LMM in SPSS are described. To demonstrate the application of LMM analyses in SPSS, findings based on six waves of data collected in the Project P.A.T.H.S. (Positive Adolescent Training through Holistic Social Programmes) in Hong Kong are presented.

Longitudinal Data Analyses Using Linear Mixed Models in SPSS: Concepts, Procedures and Illustrations

PubMed Central

Shek, Daniel T. L.; Ma, Cecilia M. S.

2011-01-01

Although different methods are available for the analyses of longitudinal data, analyses based on generalized linear models (GLM) are criticized as violating the assumption of independence of observations. Alternatively, linear mixed models (LMM) are commonly used to understand changes in human behavior over time. In this paper, the basic concepts surrounding LMM (or hierarchical linear models) are outlined. Although SPSS is a statistical analyses package commonly used by researchers, documentation on LMM procedures in SPSS is not thorough or user friendly. With reference to this limitation, the related procedures for performing analyses based on LMM in SPSS are described. To demonstrate the application of LMM analyses in SPSS, findings based on six waves of data collected in the Project P.A.T.H.S. (Positive Adolescent Training through Holistic Social Programmes) in Hong Kong are presented. PMID:21218263

Hierarchical cortical transcriptome disorganization in autism.

PubMed

Lombardo, Michael V; Courchesne, Eric; Lewis, Nathan E; Pramparo, Tiziano

2017-01-01

Autism spectrum disorders (ASD) are etiologically heterogeneous and complex. Functional genomics work has begun to identify a diverse array of dysregulated transcriptomic programs (e.g., synaptic, immune, cell cycle, DNA damage, WNT signaling, cortical patterning and differentiation) potentially involved in ASD brain abnormalities during childhood and adulthood. However, it remains unclear whether such diverse dysregulated pathways are independent of each other or instead reflect coordinated hierarchical systems-level pathology. Two ASD cortical transcriptome datasets were re-analyzed using consensus weighted gene co-expression network analysis (WGCNA) to identify common co-expression modules across datasets. Linear mixed-effect models and Bayesian replication statistics were used to identify replicable differentially expressed modules. Eigengene network analysis was then utilized to identify between-group differences in how co-expression modules interact and cluster into hierarchical meta-modular organization. Protein-protein interaction analyses were also used to determine whether dysregulated co-expression modules show enhanced interactions. We find replicable evidence for 10 gene co-expression modules that are differentially expressed in ASD cortex. Rather than being independent non-interacting sources of pathology, these dysregulated co-expression modules work in synergy and physically interact at the protein level. These systems-level transcriptional signals are characterized by downregulation of synaptic processes coordinated with upregulation of immune/inflammation, response to other organism, catabolism, viral processes, translation, protein targeting and localization, cell proliferation, and vasculature development. Hierarchical organization of meta-modules (clusters of highly correlated modules) is also highly affected in ASD. These findings highlight that dysregulation of the ASD cortical transcriptome is characterized by the dysregulation of multiple coordinated transcriptional programs producing synergistic systems-level effects that cannot be fully appreciated by studying the individual component biological processes in isolation.

Network dysfunction of emotional and cognitive processes in those at genetic risk of bipolar disorder.

PubMed

Breakspear, Michael; Roberts, Gloria; Green, Melissa J; Nguyen, Vinh T; Frankland, Andrew; Levy, Florence; Lenroot, Rhoshel; Mitchell, Philip B

2015-11-01

The emotional and cognitive vulnerabilities that precede the development of bipolar disorder are poorly understood. The inferior frontal gyrus-a key cortical hub for the integration of cognitive and emotional processes-exhibits both structural and functional changes in bipolar disorder, and is also functionally impaired in unaffected first-degree relatives, showing diminished engagement during inhibition of threat-related emotional stimuli. We hypothesized that this functional impairment of the inferior frontal gyrus in those at genetic risk of bipolar disorder reflects the dysfunction of broader network dynamics underlying the coordination of emotion perception and cognitive control. To test this, we studied effective connectivity in functional magnetic resonance imaging data acquired from 41 first-degree relatives of patients with bipolar disorder, 45 matched healthy controls and 55 participants with established bipolar disorder. Dynamic causal modelling was used to model the neuronal interaction between key regions associated with fear perception (the anterior cingulate), inhibition (the left dorsolateral prefrontal cortex) and the region upon which these influences converge, namely the inferior frontal gyrus. Network models that embodied non-linear, hierarchical relationships were the most strongly supported by data from our healthy control and bipolar participants. We observed a marked difference in the hierarchical influence of the anterior cingulate on the effective connectivity from the dorsolateral prefrontal cortex to the inferior frontal gyrus that is unique to the at-risk cohort. Non-specific, non-hierarchical mechanisms appear to compensate for this network disturbance. We thus establish a specific network disturbance suggesting dysfunction in the processes that support hierarchical relationships between emotion and cognitive control in those at high genetic risk for bipolar disorder. © The Author (2015). Published by Oxford University Press on behalf of the Guarantors of Brain. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Characteristics of Tables for Disseminating Biobehavioral Results.

PubMed

Schneider, Barbara St Pierre; Nagelhout, Ed; Feng, Du

2018-01-01

To report the complexity and richness of study variables within biological nursing research, authors often use tables; however, the ease with which consumers understand, synthesize, evaluate, and build upon findings depends partly upon table design. To assess and compare table characteristics within research and review articles published in Biological Research for Nursing and Nursing Research. A total of 10 elements in tables from 48 biobehavioral or biological research or review articles were analyzed. To test six hypotheses, a two-level hierarchical linear model was used for each of the continuous table elements, and a two-level hierarchical generalized linear model was used for each of the categorical table elements. Additionally, the inclusion of probability values in statistical tables was examined. The mean number of tables per article was 3. Tables in research articles were more likely to contain quantitative content, while tables in review articles were more likely to contain both quantitative and qualitative content. Tables in research articles had a greater number of rows, columns, and column-heading levels than tables in review articles. More than one half of statistical tables in research articles had a separate probability column or had probability values within the table, whereas approximately one fourth had probability notes. Authors and journal editorial staff may be generating tables that better depict biobehavioral content than those identified in specific style guidelines. However, authors and journal editorial staff may want to consider table design in terms of audience, including alternative visual displays.

Prevalence of health literacy and its correlates among patients with type II diabetes in Kuwait: A population based study.

PubMed

Hussein, Shaimaa H; Almajran, Abdullah; Albatineh, Ahmed N

2018-05-03

The purpose of this study is to estimate the prevalence of health literacy among patients with type II diabetes and investigate its association with several covariates. No studies were conducted in the Arabian Gulf region characterizing such factors for this population. A cross sectional study was implemented in which 359 type II diabetes patients were recruited from diabetes centers across Kuwait. Health literacy was measured by STOFHLA. Multivariate linear regression was applied to investigate the relationship between health literacy and several covariates. About 44.5% had inadequate, 19.5% marginal, and 35.5% adequate health literacy. Patients with inadequate health literacy were more likely to be older, females, widowed, low education, with income less than 500 KD/month. Multivariate linear regression indicated residence, nationality, education level, and age were significantly associated with health literacy. Adding marital status and gender, hierarchical linear regression revealed that 43.4% of the variability was accounted for. Inadequate health literacy is high in Kuwait. Interventions should be implemented to improve health literacy. This will reduce the prevalence of diabetes-related complications, produce better diabetes outcomes, and improve patients' quality-of-life. Health literacy should be an integral part to health promotion and chronic diseases' management programs in Kuwait. Copyright © 2018 Elsevier B.V. All rights reserved.

Decentralization, stabilization, and estimation of large-scale linear systems

NASA Technical Reports Server (NTRS)

Siljak, D. D.; Vukcevic, M. B.

1976-01-01

In this short paper we consider three closely related aspects of large-scale systems: decentralization, stabilization, and estimation. A method is proposed to decompose a large linear system into a number of interconnected subsystems with decentralized (scalar) inputs or outputs. The procedure is preliminary to the hierarchic stabilization and estimation of linear systems and is performed on the subsystem level. A multilevel control scheme based upon the decomposition-aggregation method is developed for stabilization of input-decentralized linear systems Local linear feedback controllers are used to stabilize each decoupled subsystem, while global linear feedback controllers are utilized to minimize the coupling effect among the subsystems. Systems stabilized by the method have a tolerance to a wide class of nonlinearities in subsystem coupling and high reliability with respect to structural perturbations. The proposed output-decentralization and stabilization schemes can be used directly to construct asymptotic state estimators for large linear systems on the subsystem level. The problem of dimensionality is resolved by constructing a number of low-order estimators, thus avoiding a design of a single estimator for the overall system.

Markerless human motion tracking using hierarchical multi-swarm cooperative particle swarm optimization.

PubMed

Saini, Sanjay; Zakaria, Nordin; Rambli, Dayang Rohaya Awang; Sulaiman, Suziah

2015-01-01

The high-dimensional search space involved in markerless full-body articulated human motion tracking from multiple-views video sequences has led to a number of solutions based on metaheuristics, the most recent form of which is Particle Swarm Optimization (PSO). However, the classical PSO suffers from premature convergence and it is trapped easily into local optima, significantly affecting the tracking accuracy. To overcome these drawbacks, we have developed a method for the problem based on Hierarchical Multi-Swarm Cooperative Particle Swarm Optimization (H-MCPSO). The tracking problem is formulated as a non-linear 34-dimensional function optimization problem where the fitness function quantifies the difference between the observed image and a projection of the model configuration. Both the silhouette and edge likelihoods are used in the fitness function. Experiments using Brown and HumanEva-II dataset demonstrated that H-MCPSO performance is better than two leading alternative approaches-Annealed Particle Filter (APF) and Hierarchical Particle Swarm Optimization (HPSO). Further, the proposed tracking method is capable of automatic initialization and self-recovery from temporary tracking failures. Comprehensive experimental results are presented to support the claims.

Discriminative Hierarchical K-Means Tree for Large-Scale Image Classification.

PubMed

Chen, Shizhi; Yang, Xiaodong; Tian, Yingli

2015-09-01

A key challenge in large-scale image classification is how to achieve efficiency in terms of both computation and memory without compromising classification accuracy. The learning-based classifiers achieve the state-of-the-art accuracies, but have been criticized for the computational complexity that grows linearly with the number of classes. The nonparametric nearest neighbor (NN)-based classifiers naturally handle large numbers of categories, but incur prohibitively expensive computation and memory costs. In this brief, we present a novel classification scheme, i.e., discriminative hierarchical K-means tree (D-HKTree), which combines the advantages of both learning-based and NN-based classifiers. The complexity of the D-HKTree only grows sublinearly with the number of categories, which is much better than the recent hierarchical support vector machines-based methods. The memory requirement is the order of magnitude less than the recent Naïve Bayesian NN-based approaches. The proposed D-HKTree classification scheme is evaluated on several challenging benchmark databases and achieves the state-of-the-art accuracies, while with significantly lower computation cost and memory requirement.

Chassis integrated control for active suspension, active front steering and direct yaw moment systems using hierarchical strategy

NASA Astrophysics Data System (ADS)

Zhao, Jing; Wong, Pak Kin; Ma, Xinbo; Xie, Zhengchao

2017-01-01

This paper proposes a novel integrated controller with three-layer hierarchical structure to coordinate the interactions among active suspension system (ASS), active front steering (AFS) and direct yaw moment control (DYC). First of all, a 14-degree-of-freedom nonlinear vehicle dynamic model is constructed. Then, an upper layer is designed to calculate the total corrected moment for ASS and intermediate layer based on linear moment distribution. By considering the working regions of the AFS and DYC, the intermediate layer is functionalised to determine the trigger signal for the lower layer with corresponding weights. The lower layer is utilised to separately trace the desired value of each local controller and achieve the local control objectives of each subsystem. Simulation results show that the proposed three-layer hierarchical structure is effective in handling the working region of the AFS and DYC, while the quasi-experimental result shows that the proposed integrated controller is able to improve the lateral and vertical dynamics of the vehicle effectively as compared with a conventional electronic stability controller.

Efficient Record Linkage Algorithms Using Complete Linkage Clustering.

PubMed

Mamun, Abdullah-Al; Aseltine, Robert; Rajasekaran, Sanguthevar

2016-01-01

Data from different agencies share data of the same individuals. Linking these datasets to identify all the records belonging to the same individuals is a crucial and challenging problem, especially given the large volumes of data. A large number of available algorithms for record linkage are prone to either time inefficiency or low-accuracy in finding matches and non-matches among the records. In this paper we propose efficient as well as reliable sequential and parallel algorithms for the record linkage problem employing hierarchical clustering methods. We employ complete linkage hierarchical clustering algorithms to address this problem. In addition to hierarchical clustering, we also use two other techniques: elimination of duplicate records and blocking. Our algorithms use sorting as a sub-routine to identify identical copies of records. We have tested our algorithms on datasets with millions of synthetic records. Experimental results show that our algorithms achieve nearly 100% accuracy. Parallel implementations achieve almost linear speedups. Time complexities of these algorithms do not exceed those of previous best-known algorithms. Our proposed algorithms outperform previous best-known algorithms in terms of accuracy consuming reasonable run times.

Efficient Record Linkage Algorithms Using Complete Linkage Clustering

PubMed Central

Mamun, Abdullah-Al; Aseltine, Robert; Rajasekaran, Sanguthevar

2016-01-01

Data from different agencies share data of the same individuals. Linking these datasets to identify all the records belonging to the same individuals is a crucial and challenging problem, especially given the large volumes of data. A large number of available algorithms for record linkage are prone to either time inefficiency or low-accuracy in finding matches and non-matches among the records. In this paper we propose efficient as well as reliable sequential and parallel algorithms for the record linkage problem employing hierarchical clustering methods. We employ complete linkage hierarchical clustering algorithms to address this problem. In addition to hierarchical clustering, we also use two other techniques: elimination of duplicate records and blocking. Our algorithms use sorting as a sub-routine to identify identical copies of records. We have tested our algorithms on datasets with millions of synthetic records. Experimental results show that our algorithms achieve nearly 100% accuracy. Parallel implementations achieve almost linear speedups. Time complexities of these algorithms do not exceed those of previous best-known algorithms. Our proposed algorithms outperform previous best-known algorithms in terms of accuracy consuming reasonable run times. PMID:27124604

Synthesis of Pt@TiO2@CNTs Hierarchical Structure Catalyst by Atomic Layer Deposition and Their Photocatalytic and Photoelectrochemical Activity.

PubMed

Liao, Shih-Yun; Yang, Ya-Chu; Huang, Sheng-Hsin; Gan, Jon-Yiew

2017-04-29

Pt@TiO2@CNTs hierarchical structures were prepared by first functionalizing carbon nanotubes (CNTs) with nitric acid at 140 °C. Coating of TiO2 particles on the CNTs at 300 °C was then conducted by atomic layer deposition (ALD). After the TiO2@CNTs structure was fabricated, Pt particles were deposited on the TiO2 surface as co-catalyst by plasma-enhanced ALD. The saturated deposition rates of TiO2 on a-CNTs were 1.5 Å/cycle and 0.4 Å/cycle for substrate-enhanced process and linear process, respectively. The saturated deposition rate of Pt on TiO2 was 0.39 Å/cycle. The photocatalytic activities of Pt@TiO2@CNTs hierarchical structures were higher than those without Pt co-catalyst. The particle size of Pt on TiO2@CNTs was a key factor to determine the efficiency of methylene blue (MB) degradation. The Pt@TiO2@CNTs of 2.41 ± 0.27 nm exhibited the best efficiency of MB degradation.

Highly conductive ribbons prepared by stick-slip assembly of organosoluble gold nanoparticles.

PubMed

Lawrence, Jimmy; Pham, Jonathan T; Lee, Dong Yun; Liu, Yujie; Crosby, Alfred J; Emrick, Todd

2014-02-25

Precisely positioning and assembling nanoparticles (NPs) into hierarchical nanostructures is opening opportunities in a wide variety of applications. Many techniques employed to produce hierarchical micrometer and nanoscale structures are limited by complex fabrication of templates and difficulties with scalability. Here we describe the fabrication and characterization of conductive nanoparticle ribbons prepared from surfactant-free organosoluble gold nanoparticles (Au NPs). We used a flow-coating technique in a controlled, stick-slip assembly to regulate the deposition of Au NPs into densely packed, multilayered structures. This affords centimeter-scale long, high-resolution Au NP ribbons with precise periodic spacing in a rapid manner, up to 2 orders-of-magnitude finer and faster than previously reported methods. These Au NP ribbons exhibit linear ohmic response, with conductivity that varies by changing the binding headgroup of the ligands. Controlling NP percolation during sintering (e.g., by adding polymer to retard rapid NP coalescence) enables the formation of highly conductive ribbons, similar to thermally sintered conductive adhesives. Hierarchical, conductive Au NP ribbons represent a promising platform to enable opportunities in sensing, optoelectronics, and electromechanical devices.

The analysis of thin walled composite laminated helicopter rotor with hierarchical warping functions and finite element method

NASA Astrophysics Data System (ADS)

Zhu, Dechao; Deng, Zhongmin; Wang, Xingwei

2001-08-01

In the present paper, a series of hierarchical warping functions is developed to analyze the static and dynamic problems of thin walled composite laminated helicopter rotors composed of several layers with single closed cell. This method is the development and extension of the traditional constrained warping theory of thin walled metallic beams, which had been proved very successful since 1940s. The warping distribution along the perimeter of each layer is expanded into a series of successively corrective warping functions with the traditional warping function caused by free torsion or free bending as the first term, and is assumed to be piecewise linear along the thickness direction of layers. The governing equations are derived based upon the variational principle of minimum potential energy for static analysis and Rayleigh Quotient for free vibration analysis. Then the hierarchical finite element method is introduced to form a numerical algorithm. Both static and natural vibration problems of sample box beams are analyzed with the present method to show the main mechanical behavior of the thin walled composite laminated helicopter rotor.

Synthesis of Pt@TiO2@CNTs Hierarchical Structure Catalyst by Atomic Layer Deposition and Their Photocatalytic and Photoelectrochemical Activity

PubMed Central

Liao, Shih-Yun; Yang, Ya-Chu; Huang, Sheng-Hsin; Gan, Jon-Yiew

2017-01-01

Pt@TiO2@CNTs hierarchical structures were prepared by first functionalizing carbon nanotubes (CNTs) with nitric acid at 140 °C. Coating of TiO2 particles on the CNTs at 300 °C was then conducted by atomic layer deposition (ALD). After the TiO2@CNTs structure was fabricated, Pt particles were deposited on the TiO2 surface as co-catalyst by plasma-enhanced ALD. The saturated deposition rates of TiO2 on a-CNTs were 1.5 Å/cycle and 0.4 Å/cycle for substrate-enhanced process and linear process, respectively. The saturated deposition rate of Pt on TiO2 was 0.39 Å/cycle. The photocatalytic activities of Pt@TiO2@CNTs hierarchical structures were higher than those without Pt co-catalyst. The particle size of Pt on TiO2@CNTs was a key factor to determine the efficiency of methylene blue (MB) degradation. The Pt@TiO2@CNTs of 2.41 ± 0.27 nm exhibited the best efficiency of MB degradation. PMID:28468248

The Relationship of School-Based Parental Involvement with Student Achievement: A Comparison of Principal and Parent Survey Reports from PISA 2012

ERIC Educational Resources Information Center

Sebastian, James; Moon, Jeong-Mi; Cunningham, Matt

2017-01-01

This paper explores parental involvement using principal and parent survey reports to examine whether parents' involvement in their children's schools predicts academic achievement. Survey data from principals and parents of seven countries from the PISA 2012 database and hierarchical linear modelling were used to analyse between- and within-…

Plant functional group responses to fire frequency and tree canopy cover gradients in oak savannas and woodlands.

Treesearch

D.W. Peterson; P.B. Reich; K.J. Wrage

2007-01-01

We measured plant functional group cover and tree canopy cover on permanent plots within a long-term prescribed fire frequency experiment and used hierarchical linear modeling to assess plant functional group responses to fire frequency and tree canopy cover. Understory woody plant cover was highest in unburned woodlands and was negatively correlated with fire...

Effect of School and Home Factors on Learning Outcomes at Elementary School Level: A Hierarchical Linear Model

ERIC Educational Resources Information Center

Singh, Jai

2016-01-01

India is a democratic, socialistic republic that is committed to providing high quality elementary education to all children. This research paper examines and analyses the effects of school, teacher and home factors on learning outcomes in elementary schools in the urban slum areas of Varanasi city and assesses the learning outcomes of students of…

What Works after School? The Relationship between After-School Program Quality, Program Attendance, and Academic Outcomes

ERIC Educational Resources Information Center

Leos-Urbel, Jacob

2015-01-01

This article examines the relationship between after-school program quality, program attendance, and academic outcomes for a sample of low-income after-school program participants. Regression and hierarchical linear modeling analyses use a unique longitudinal data set including 29 after-school programs that served 5,108 students in Grades 4 to 8…

Helping Struggling Adolescent Readers: Is Implementation of Different Components of Scholastic's READ 180 Associated with Differences in Student Achievement Gains?

ERIC Educational Resources Information Center

Coffey, Debra J.

2013-01-01

This dissertation uses data from the evaluation of a Striving Readers project to examine the associations between levels of implementation of different components of Scholastic's "READ 180" and student achievement as measured on the Iowa Test of Basic Skills (ITBS) reading assessment. The approach was hierarchical linear modeling using…

Effects of Inquiry-Based Science Instruction on Science Achievement and Interest in Science: Evidence from Qatar

ERIC Educational Resources Information Center

Areepattamannil, Shaljan

2012-01-01

The author sought to investigate the effects of inquiry-based science instruction on science achievement and interest in science of 5,120 adolescents from 85 schools in Qatar. Results of hierarchical linear modeling analyses revealed the substantial positive effects of science teaching and learning with a focus on model or applications and…

Early Reading Achievement of Children in Immigrant Families: Is There an Immigrant Paradox?

ERIC Educational Resources Information Center

Palacios, Natalia; Guttmanova, Katarina; Chase-Lansdale, P. Lindsay

2008-01-01

This article examines whether longitudinal reading trajectories vary by the generational status of immigrant children as they begin formal schooling through the 3rd grade. The results of the hierarchical linear model indicated that 1st and 2nd generation children (i.e., those born in a foreign country and those born in the United States to…

School Effects on Educational Achievement in Mathematics and Science: 1985-86. National Assessment of Educational Progress. Research and Development Report.

ERIC Educational Resources Information Center

Arnold, Carolyn L.; Kaufman, Phillip D.

This report examines the effects of both student and school characteristics on mathematics and science achievement levels in the third, seventh, and eleventh grades using data from the 1985-86 National Assessment of Educational Progress (NAEP). Analyses feature hierarchical linear models (HLM), a regression-like statistical technique that…

Unpacking the Gender Gap in Postsecondary Participation among African Americans and Caucasians Using Hierarchical Generalized Linear Modeling

ERIC Educational Resources Information Center

Tekleselassie, Abebayehu; Mallery, Coretta; Choi, Jaehwa

2013-01-01

National reports recognize a growing gender gap in postsecondary enrollment as a major challenge impacting the lives of young men, particularly African Americans. Previous gender and race specific research is largely inconclusive. It is, for example, unclear from previous research how persistent the gender gap is across various school contexts,…

Interaction of 5-HTTLPR and Idiographic Stressors Predicts Prospective Depressive Symptoms Specifically among Youth in a Multiwave Design

ERIC Educational Resources Information Center

Hankin, Benjamin L.; Jenness, Jessica; Abela, John R. Z.; Smolen, Andrew

2011-01-01

5-HTTLPR, episodic stressors, depressive and anxious symptoms were assessed prospectively (child and parent report) every 3 months over 1 year (5 waves of data) among community youth ages 9 to 15 (n = 220). Lagged hierarchical linear modeling analyses showed 5-HTTLPR interacted with idiographic stressors (increases relative to the child's own…

The Relationship among School Safety, School Liking, and Students' Self-Esteem: Based on a Multilevel Mediation Model

ERIC Educational Resources Information Center

Zhang, Xinghui; Xuan, Xin; Chen, Fumei; Zhang, Cai; Luo, Yuhan; Wang, Yun

2016-01-01

Background: Perceptions of school safety have an important effect on students' development. Based on the model of "context-process-outcomes," we examined school safety as a context variable to explore how school safety at the school level affected students' self-esteem. Methods: We used hierarchical linear modeling to examine the link…

Peer Assessment in the Digital Age: A Meta-Analysis Comparing Peer and Teacher Ratings

ERIC Educational Resources Information Center

Li, Hongli; Xiong, Yao; Zang, Xiaojiao; Kornhaber, Mindy L.; Lyu, Youngsun; Chung, Kyung Sun; Suen, Hoi K.

2016-01-01

Given the wide use of peer assessment, especially in higher education, the relative accuracy of peer ratings compared to teacher ratings is a major concern for both educators and researchers. This concern has grown with the increase of peer assessment in digital platforms. In this meta-analysis, using a variance-known hierarchical linear modelling…

The Influences of Inductive Instruction and Resources on Students' Attitudes toward Reading: Evidence from PISA 2009

ERIC Educational Resources Information Center

Jhang, Fang-Hua

2014-01-01

The declining trend in the positive reading attitude of students' has concerned scholars. This paper aims to apply a 3-level hierarchical linear model to analyse how inductive instruction and resources influence both students' positive and negative attitudes towards reading. Approximately 470,000 15-year-old students, and their school principals,…

Revisiting Fixed- and Random-Effects Models: Some Considerations for Policy-Relevant Education Research

ERIC Educational Resources Information Center

Clarke, Paul; Crawford, Claire; Steele, Fiona; Vignoles, Anna

2015-01-01

The use of fixed (FE) and random effects (RE) in two-level hierarchical linear regression is discussed in the context of education research. We compare the robustness of FE models with the modelling flexibility and potential efficiency of those from RE models. We argue that the two should be seen as complementary approaches. We then compare both…

Predictors affecting personal health information management skills.

PubMed

Kim, Sujin; Abner, Erin

2016-01-01

This study investigated major factors affecting personal health records (PHRs) management skills associated with survey respondents' health information management related activities. A self-report survey was used to assess individuals' personal characteristics, health knowledge, PHR skills, and activities. Factors underlying respondents' current PHR-related activities were derived using principal component analysis (PCA). Scale scores were calculated based on the results of the PCA, and hierarchical linear regression analyses were used to identify respondent characteristics associated with the scale scores. Internal consistency of the derived scale scores was assessed with Cronbach's α. Among personal health information activities surveyed (N = 578 respondents), the four extracted factors were subsequently grouped and labeled as: collecting skills (Cronbach's α = 0.906), searching skills (Cronbach's α = 0.837), sharing skills (Cronbach's α = 0.763), and implementing skills (Cronbach's α = 0.908). In the hierarchical regression analyses, education and computer knowledge significantly increased the explanatory power of the models. Health knowledge (β = 0.25, p < 0.001) emerged as a positive predictor of PHR collecting skills. This study confirmed that PHR training and learning should consider a full spectrum of information management skills including collection, utilization and distribution to support patients' care and prevention continua.

Bayesian hierarchical piecewise regression models: a tool to detect trajectory divergence between groups in long-term observational studies.

PubMed

Buscot, Marie-Jeanne; Wotherspoon, Simon S; Magnussen, Costan G; Juonala, Markus; Sabin, Matthew A; Burgner, David P; Lehtimäki, Terho; Viikari, Jorma S A; Hutri-Kähönen, Nina; Raitakari, Olli T; Thomson, Russell J

2017-06-06

Bayesian hierarchical piecewise regression (BHPR) modeling has not been previously formulated to detect and characterise the mechanism of trajectory divergence between groups of participants that have longitudinal responses with distinct developmental phases. These models are useful when participants in a prospective cohort study are grouped according to a distal dichotomous health outcome. Indeed, a refined understanding of how deleterious risk factor profiles develop across the life-course may help inform early-life interventions. Previous techniques to determine between-group differences in risk factors at each age may result in biased estimate of the age at divergence. We demonstrate the use of Bayesian hierarchical piecewise regression (BHPR) to generate a point estimate and credible interval for the age at which trajectories diverge between groups for continuous outcome measures that exhibit non-linear within-person response profiles over time. We illustrate our approach by modeling the divergence in childhood-to-adulthood body mass index (BMI) trajectories between two groups of adults with/without type 2 diabetes mellitus (T2DM) in the Cardiovascular Risk in Young Finns Study (YFS). Using the proposed BHPR approach, we estimated the BMI profiles of participants with T2DM diverged from healthy participants at age 16 years for males (95% credible interval (CI):13.5-18 years) and 21 years for females (95% CI: 19.5-23 years). These data suggest that a critical window for weight management intervention in preventing T2DM might exist before the age when BMI growth rate is naturally expected to decrease. Simulation showed that when using pairwise comparison of least-square means from categorical mixed models, smaller sample sizes tended to conclude a later age of divergence. In contrast, the point estimate of the divergence time is not biased by sample size when using the proposed BHPR method. BHPR is a powerful analytic tool to model long-term non-linear longitudinal outcomes, enabling the identification of the age at which risk factor trajectories diverge between groups of participants. The method is suitable for the analysis of unbalanced longitudinal data, with only a limited number of repeated measures per participants and where the time-related outcome is typically marked by transitional changes or by distinct phases of change over time.

Loneliness and depression among the elderly in an agricultural settlement: mediating effects of social support.

PubMed

Wan Mohd Azam, Wan Mohd Yunus; Din, Normah Che; Ahmad, Mahadir; Ghazali, Shazli Ezzat; Ibrahim, Norhayati; Said, Zaini; Ghazali, Ahmad Rohi; Shahar, Suzana; Razali, Rosdinom; Maniam, T

2013-04-01

Loneliness has long been known to have strong association with depression. The relationship between loneliness and depression, however, has been associated with other risk factors including social support. The aim of this paper is to describe the role of social support in the association between loneliness and depression. This cross-sectional study examined the mediating effects of social support among 161 community-based elderly in agricultural settlement of a rural area in Sungai Tengi, Malaysia. Subjects were investigated with De Jong Gierveld Loneliness Scale, Geriatric Depression Scale and Medical Outcome Survey Social Support Survey. Data were analyzed using Pearson correlation, linear and hierarchical regression. Results indicated that social support partially mediated the relationship between loneliness and depression. This suggests that social support affects the linear association between loneliness and depression in the elderly. Copyright © 2013 Wiley Publishing Asia Pty Ltd.

Solving Graph Laplacian Systems Through Recursive Bisections and Two-Grid Preconditioning

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

Ponce, Colin; Vassilevski, Panayot S.

2016-02-18

We present a parallelizable direct method for computing the solution to graph Laplacian-based linear systems derived from graphs that can be hierarchically bipartitioned with small edge cuts. For a graph of size n with constant-size edge cuts, our method decomposes a graph Laplacian in time O(n log n), and then uses that decomposition to perform a linear solve in time O(n log n). We then use the developed technique to design a preconditioner for graph Laplacians that do not have this property. Finally, we augment this preconditioner with a two-grid method that accounts for much of the preconditioner's weaknesses. Wemore » present an analysis of this method, as well as a general theorem for the condition number of a general class of two-grid support graph-based preconditioners. Numerical experiments illustrate the performance of the studied methods.« less

Understanding the Positive Role of Neighborhood Socioeconomic Advantage in Achievement: The Contribution of the Home, Child Care and School Environments

PubMed Central

Dupéré, Véronique; Leventhal, Tama; Crosnoe, Robert; Dion, Éric

2011-01-01

The goal of this study was to examine the mechanisms underlying associations between neighborhood socioeconomic advantage and children’s achievement trajectories between 54 months and 15 years old. Results of hierarchical linear growth models based on a diverse sample of 1,364 children indicate that neighborhood socioeconomic advantage was non-linearly associated with youths’ initial vocabulary and reading scores, such that the presence of educated, affluent professionals in the neighborhood had a favorable association with children’s achievement among those in less advantaged neighborhoods until it leveled off at moderate levels of advantage. A similar tendency was observed for math achievement. The quality of the home and child care environments as well as school advantage partially explained these associations. The findings suggest that multiple environments need to be considered simultaneously for understanding neighborhood-achievement links. PMID:20822235

United States Medical Licensing Examination and American Board of Pediatrics Certification Examination Results: Does the Residency Program Contribute to Trainee Achievement.

PubMed

Welch, Thomas R; Olson, Brad G; Nelsen, Elizabeth; Beck Dallaghan, Gary L; Kennedy, Gloria A; Botash, Ann

2017-09-01

To determine whether training site or prior examinee performance on the US Medical Licensing Examination (USMLE) step 1 and step 2 might predict pass rates on the American Board of Pediatrics (ABP) certifying examination. Data from graduates of pediatric residency programs completing the ABP certifying examination between 2009 and 2013 were obtained. For each, results of the initial ABP certifying examination were obtained, as well as results on National Board of Medical Examiners (NBME) step 1 and step 2 examinations. Hierarchical linear modeling was used to nest first-time ABP results within training programs to isolate program contribution to ABP results while controlling for USMLE step 1 and step 2 scores. Stepwise linear regression was then used to determine which of these examinations was a better predictor of ABP results. A total of 1110 graduates of 15 programs had complete testing results and were subject to analysis. Mean ABP scores for these programs ranged from 186.13 to 214.32. The hierarchical linear model suggested that the interaction of step 1 and 2 scores predicted ABP performance (F[1,1007.70] = 6.44, P = .011). By conducting a multilevel model by training program, both USMLE step examinations predicted first-time ABP results (b = .002, t = 2.54, P = .011). Linear regression analyses indicated that step 2 results were a better predictor of ABP performance than step 1 or a combination of the two USMLE scores. Performance on the USMLE examinations, especially step 2, predicts performance on the ABP certifying examination. The contribution of training site to ABP performance was statistically significant, though contributed modestly to the effect compared with prior USMLE scores. Copyright © 2017 Elsevier Inc. All rights reserved.

Psychometric properties of the Intrinsic Motivation Inventory in a competitive sport setting: a confirmatory factor analysis.

PubMed

McAuley, E; Duncan, T; Tammen, V V

1989-03-01

The present study was designed to assess selected psychometric properties of the Intrinsic Motivation Inventory (IMI) (Ryan, 1982), a multidimensional measure of subjects' experience with regard to experimental tasks. Subjects (N = 116) competed in a basketball free-throw shooting game, following which they completed the IMI. The LISREL VI computer program was employed to conduct a confirmatory factor analysis to assess the tenability of a five factor hierarchical model representing four first-order factors or dimensions and a second-order general factor representing intrinsic motivation. Indices of model acceptability tentatively suggest that the sport data adequately fit the hypothesized five factor hierarchical model. Alternative models were tested but did not result in significant improvements in the goodness-of-fit indices, suggesting the proposed model to be the most accurate of the models tested. Coefficient alphas for the four dimensions and the overall scale indicated adequate reliability. The results are discussed with regard to the importance of accurate assessment of psychological constructs and the use of linear structural equations in confirming the factor structures of measures.

Facile preparation of branched hierarchical ZnO nanowire arrays with enhanced photocatalytic activity: A photodegradation kinetic model

NASA Astrophysics Data System (ADS)

Ebrahimi, M.; Yousefzadeh, S.; Samadi, M.; Dong, Chunyang; Zhang, Jinlong; Moshfegh, A. Z.

2018-03-01

Branched hierarchical zinc oxide nanowires (BH-ZnO NWs) were fabricated successfully by a facile and rapid synthesis using two-step growth process. Initially, ZnO NWs have been prepared by anodizing zinc foil at room temperature and followed by annealing treatment. Then, the BH- ZnO NWs were grown on the ZnO NWs by a solution based method at very low temperature (31 oC). The BH- ZnO NWs with different aspect ratio were obtained by varying reaction time (0.5, 2, 5, 10 h). Photocatalytic activity of the samples was studied under both UV and visible light. The results indicated that the optimized BH-ZnO NWs (5 h) as a photocatalyst exhibited the highest photoactivity with about 3 times higher than the ZnO NWs under UV light. In addition, it was also determined that photodegradation rate constant (k) for the BH- ZnO NWs surface obeys a linear function with the branch length (l) and their correlation was described by using a proposed kinetic model.

Identifying organizational cultures that promote patient safety.

PubMed

Singer, Sara J; Falwell, Alyson; Gaba, David M; Meterko, Mark; Rosen, Amy; Hartmann, Christine W; Baker, Laurence

2009-01-01

Safety climate refers to shared perceptions of what an organization is like with regard to safety, whereas safety culture refers to employees' fundamental ideology and orientation and explains why safety is pursued in the manner exhibited within a particular organization. Although research has sought to identify opportunities for improving safety outcomes by studying patterns of variation in safety climate, few empirical studies have examined the impact of organizational characteristics such as culture on hospital safety climate. This study explored how aspects of general organizational culture relate to hospital patient safety climate. In a stratified sample of 92 U.S. hospitals, we sampled 100% of senior managers and physicians and 10% of other hospital workers. The Patient Safety Climate in Healthcare Organizations and the Zammuto and Krakower organizational culture surveys measured safety climate and group, entrepreneurial, hierarchical, and production orientation of hospitals' culture, respectively. We administered safety climate surveys to 18,361 personnel and organizational culture surveys to a 5,894 random subsample between March 2004 and May 2005. Secondary data came from the 2004 American Hospital Association Annual Hospital Survey and Dun & Bradstreet. Hierarchical linear regressions assessed relationships between organizational culture and safety climate measures. Aspects of general organizational culture were strongly related to safety climate. A higher level of group culture correlated with a higher level of safety climate, but more hierarchical culture was associated with lower safety climate. Aspects of organizational culture accounted for more than threefold improvement in measures of model fit compared with models with controls alone. A mix of culture types, emphasizing group culture, seemed optimal for safety climate. Safety climate and organizational culture are positively related. Results support strategies that promote group orientation and reduced hierarchy, including use of multidisciplinary team training, continuous quality improvement tools, and human resource practices and policies.

A Study of Hierarchical Classification in Concrete and Formal Thought.

ERIC Educational Resources Information Center

Lowell, Walter E.

This researcher investigated the relationship of hierarchical classification processes in subjects categorized as to developmental level as defined by Piaget's theory, and explored the validity of the hierarchical model and test used in the study. A hierarchical classification test and a battery of four Piaget-type tasks were administered…

Aesthetic perception of visual textures: a holistic exploration using texture analysis, psychological experiment, and perception modeling.

PubMed

Liu, Jianli; Lughofer, Edwin; Zeng, Xianyi

2015-01-01

Modeling human aesthetic perception of visual textures is important and valuable in numerous industrial domains, such as product design, architectural design, and decoration. Based on results from a semantic differential rating experiment, we modeled the relationship between low-level basic texture features and aesthetic properties involved in human aesthetic texture perception. First, we compute basic texture features from textural images using four classical methods. These features are neutral, objective, and independent of the socio-cultural context of the visual textures. Then, we conduct a semantic differential rating experiment to collect from evaluators their aesthetic perceptions of selected textural stimuli. In semantic differential rating experiment, eights pairs of aesthetic properties are chosen, which are strongly related to the socio-cultural context of the selected textures and to human emotions. They are easily understood and connected to everyday life. We propose a hierarchical feed-forward layer model of aesthetic texture perception and assign 8 pairs of aesthetic properties to different layers. Finally, we describe the generation of multiple linear and non-linear regression models for aesthetic prediction by taking dimensionality-reduced texture features and aesthetic properties of visual textures as dependent and independent variables, respectively. Our experimental results indicate that the relationships between each layer and its neighbors in the hierarchical feed-forward layer model of aesthetic texture perception can be fitted well by linear functions, and the models thus generated can successfully bridge the gap between computational texture features and aesthetic texture properties.

Hierarchical Graphene coating for highly sensitive solid phase microextraction of organochlorine pesticides.

PubMed

Wang, Fuxin; Liu, Shuqin; Yang, Hao; Zheng, Juan; Qiu, Junlang; Xu, Jianqiao; Tong, Yexiang; Zhu, Fang; Ouyang, Gangfeng

2016-11-01

Graphene, a novel class of carbon nanostructures, has received great attention as sorbents due to its fascinating structures, ultrahigh specific surface area, and good extraction ability. In this paper, a new type of hierarchical graphene was synthesized through employing a mild and environment-friendly method. Such 3D interconnected graphene own a high specific surface area up to 524m(2)g(-1), which is about 2.5 fold larger than the graphene, since the synthetic material has interlayer pores between nanosheets and in-plane pores. Then a superior solid-phase microextraction fiber was fabricated by sequentially coating the stainless steel fiber with silicone sealant film and hierarchical graphene powder. Since the novel hierarchical graphene possessed large surface area and good adsorption property, the as-prepared fiber exhibited good extraction properties of the organochlorine pesticides (OCPs). As for the analytical performance, the as-prepared fiber achieved low detection limits (0.08-0.80ngL(-1)) and wide linearity (10-30,000ngL(-1)) under the optimal conditions. The repeatability (n=5) for single fiber were between 5.1% and 11%, while the reproducibility (n=3) of fiber-to-fiber were range from 6.2% to14%. Moreover, the fiber was successfully applied to the analysis of OCPs in the Pearl River water. Copyright © 2016 Elsevier B.V. All rights reserved.

Exploring the cross-level impact of market orientation on nursing innovation in hospitals.

PubMed

Weng, Rhay-Hung; Huang, Ching-Yuan; Lin, Tzu-En

2013-01-01

Recently, many hospitals have been enthusiastically encouraging nurses to pursue nursing innovation to improve health care quality and increase nursing productivity by proposing innovative training methods, products, services, care skills, and care methods. This study tried to explore the cross-level impact of market orientation on nursing innovation. In our study, 3 to 7 nurses and 1 manager were selected from each nursing team to act as respondents. The questionnaire survey began after the managers of each nursing team and the nurses had been anonymously coded and paired up in Taiwan in 2009-2010. A total of 808 valid questionnaires were collected, including 172 valid teams. Hierarchical linear modeling was used for the analysis. Nursing innovation is the sum of knowledge creation, innovation behavior, and innovation diffusion displayed by the nurses during nursing care. The level of knowledge creation, as perceived by the nurses, was the highest, whereas the level of innovation diffusion was the lowest. Results of hierarchical linear modeling showed that only competitor orientation yielded a significant positive influence on knowledge creation, innovation behavior, or innovation diffusion. The r values were 0.53, 0.49, and 0.61, respectively. Customer orientation and interfunctional coordination did not have significant effects on nursing innovation. Hospital nurses exhibited better performance in knowledge creation than in innovation behavior and diffusion. Only competitor orientation had a significantly positive and cross-level influence on nursing innovation. However, competitor orientation was observed to be the lowest dimension of market orientation, which indicates that this factor should be the focus when improving nursing innovations in the future. Therefore, managers should continually understand the strategies, advantages, and methods of their competitors.

The relationship between victimization and mental health functioning in homeless youth and adults.

PubMed

Rattelade, Stephanie; Farrell, Susan; Aubry, Tim; Klodawsky, Fran

2014-06-01

This study examined the relationship between victimization and mental health functioning in homeless individuals. Homeless populations experience higher levels of victimization than the general population, which in turn have a detrimental effect on their mental health. A sample of 304 homeless adults and youth completed one-on-one interviews, answering questions on mental health, past victimization, and recent victimization experiences. A hierarchical linear regression showed that experiences of childhood sexual abuse predicted lower mental health functioning after controlling for the sex and age of individuals. The study findings are applicable to current support programs for victims in the homeless population and are relevant to future research on homelessness and victimization.

Depression, Control, and Climate: An Examination of Factors Impacting Teaching Quality in Preschool Classrooms.

PubMed

Sandilos, Lia E; Cycyk, Lauren M; Hammer, Carol Scheffner; Sawyer, Brook E; López, Lisa; Blair, Clancy

This study investigated the relationship of preschool teachers' self-reported depressive symptomatology, perception of classroom control, and perception of school climate to classroom quality as measured by the Classroom Assessment Scoring System Pre-K. The sample consisted of 59 urban preschool classrooms serving low-income and linguistically diverse students in the northeastern and southeastern United States. Results of hierarchical linear modeling revealed that teachers' individual reports of depressive symptomatology were significantly and negatively predictive of the observed quality of their instructional support and classroom organization. The findings of this study have implications for increasing access to mental health supports for teachers in an effort to minimize depressive symptoms and potentially improve classroom quality.

Escaping the snare of chronological growth and launching a free curve alternative: general deviance as latent growth model.

PubMed

Wood, Phillip Karl; Jackson, Kristina M

2013-08-01

Researchers studying longitudinal relationships among multiple problem behaviors sometimes characterize autoregressive relationships across constructs as indicating "protective" or "launch" factors or as "developmental snares." These terms are used to indicate that initial or intermediary states of one problem behavior subsequently inhibit or promote some other problem behavior. Such models are contrasted with models of "general deviance" over time in which all problem behaviors are viewed as indicators of a common linear trajectory. When fit of the "general deviance" model is poor and fit of one or more autoregressive models is good, this is taken as support for the inhibitory or enhancing effect of one construct on another. In this paper, we argue that researchers consider competing models of growth before comparing deviance and time-bound models. Specifically, we propose use of the free curve slope intercept (FCSI) growth model (Meredith & Tisak, 1990) as a general model to typify change in a construct over time. The FCSI model includes, as nested special cases, several statistical models often used for prospective data, such as linear slope intercept models, repeated measures multivariate analysis of variance, various one-factor models, and hierarchical linear models. When considering models involving multiple constructs, we argue the construct of "general deviance" can be expressed as a single-trait multimethod model, permitting a characterization of the deviance construct over time without requiring restrictive assumptions about the form of growth over time. As an example, prospective assessments of problem behaviors from the Dunedin Multidisciplinary Health and Development Study (Silva & Stanton, 1996) are considered and contrasted with earlier analyses of Hussong, Curran, Moffitt, and Caspi (2008), which supported launch and snare hypotheses. For antisocial behavior, the FCSI model fit better than other models, including the linear chronometric growth curve model used by Hussong et al. For models including multiple constructs, a general deviance model involving a single trait and multimethod factors (or a corresponding hierarchical factor model) fit the data better than either the "snares" alternatives or the general deviance model previously considered by Hussong et al. Taken together, the analyses support the view that linkages and turning points cannot be contrasted with general deviance models absent additional experimental intervention or control.

Escaping the snare of chronological growth and launching a free curve alternative: General deviance as latent growth model

PubMed Central

WOOD, PHILLIP KARL; JACKSON, KRISTINA M.

2014-01-01

Researchers studying longitudinal relationships among multiple problem behaviors sometimes characterize autoregressive relationships across constructs as indicating “protective” or “launch” factors or as “developmental snares.” These terms are used to indicate that initial or intermediary states of one problem behavior subsequently inhibit or promote some other problem behavior. Such models are contrasted with models of “general deviance” over time in which all problem behaviors are viewed as indicators of a common linear trajectory. When fit of the “general deviance” model is poor and fit of one or more autoregressive models is good, this is taken as support for the inhibitory or enhancing effect of one construct on another. In this paper, we argue that researchers consider competing models of growth before comparing deviance and time-bound models. Specifically, we propose use of the free curve slope intercept (FCSI) growth model (Meredith & Tisak, 1990) as a general model to typify change in a construct over time. The FCSI model includes, as nested special cases, several statistical models often used for prospective data, such as linear slope intercept models, repeated measures multivariate analysis of variance, various one-factor models, and hierarchical linear models. When considering models involving multiple constructs, we argue the construct of “general deviance” can be expressed as a single-trait multimethod model, permitting a characterization of the deviance construct over time without requiring restrictive assumptions about the form of growth over time. As an example, prospective assessments of problem behaviors from the Dunedin Multidisciplinary Health and Development Study (Silva & Stanton, 1996) are considered and contrasted with earlier analyses of Hussong, Curran, Moffitt, and Caspi (2008), which supported launch and snare hypotheses. For antisocial behavior, the FCSI model fit better than other models, including the linear chronometric growth curve model used by Hussong et al. For models including multiple constructs, a general deviance model involving a single trait and multimethod factors (or a corresponding hierarchical factor model) fit the data better than either the “snares” alternatives or the general deviance model previously considered by Hussong et al. Taken together, the analyses support the view that linkages and turning points cannot be contrasted with general deviance models absent additional experimental intervention or control. PMID:23880389

Positive predictors of quality of life for postpartum mothers with a history of childhood maltreatment.

PubMed

Irwin, Jessica L; Beeghly, Marjorie; Rosenblum, Katherine L; Muzik, Maria

2016-12-01

The postpartum period brings a host of biopsychosocial, familial, and economic changes, which may be challenging for new mothers, especially those with trauma histories. Trauma-exposed women are at heightened risk for psychiatric symptomatology and reduced quality of life. The current study sought to evaluate whether a set of hypothesized promotive factors assessed during the first 18 months postpartum (positive parenting, family cohesion, and maternal resilience) are associated with life satisfaction in this population, after controlling for income and postpartum psychiatric symptoms. Analyses were based on data collected for 266 mother-infant dyads from a longitudinal cohort study, Maternal Anxiety during the Childbearing Years (MACY), of women oversampled for childhood maltreatment history. Hierarchical linear regression was used to evaluate the study hypotheses. Consistent with prior work, greater postpartum psychiatric symptoms and less income predicted poor perceptions of life quality. In hierarchical regressions controlling for income and psychiatric symptoms, positive parenting and family cohesion predicted unique variance in mothers' positive perceptions of life quality, and resilience was predictive beyond all other factors. Factors from multiple levels of analysis (maternal, dyadic, and familial) may serve as promotive factors predicting positive perceptions of life quality among women with childhood trauma histories, even those struggling with high levels of psychiatric or economic distress.

Mental and Physical Health Correlates among Family Caregivers of Patients with Newly-Diagnosed Incurable Cancer: A Hierarchical Linear Regression Analysis

PubMed Central

Shaffer, Kelly M.; Jacobs, Jamie M.; Nipp, Ryan D.; Carr, Alaina; Jackson, Vicki A.; Park, Elyse R.; Pirl, William F.; El-Jawahri, Areej; Gallagher, Emily R.; Greer, Joseph A.; Temel, Jennifer S.

2016-01-01

Purpose Caregiver, relational, and patient factors have been associated with the health of family members and friends providing care to patients with early-stage cancer. Little research has examined whether findings extend to family caregivers of patients with incurable cancer, who experience unique and substantial caregiving burdens. We examined correlates of mental and physical health among caregivers of patients with newly-diagnosed incurable lung or non-colorectal gastrointestinal cancer. Methods At baseline for a trial of early palliative care, caregivers of participating patients (N=275) reported their mental and physical health (Medical Outcome Survey-Short Form-36); patients reported their quality of life (Functional Assessment of Cancer Therapy-General). Analyses used hierarchical linear regression with two-tailed significance tests. Results Caregivers’ mental health was worse than the U.S. national population (M=44.31, p<.001), yet their physical health was better (M=56.20, p<.001). Hierarchical regression analyses testing caregiver, relational, and patient factors simultaneously revealed that younger (B=0.31, p=.001), spousal caregivers (B=−8.70, p=.003), who cared for patients reporting low emotional well-being (B=0.51, p=.01) reported worse mental health; older (B=−0.17, p=.01) caregivers with low educational attainment (B=4.36, p<.001) who cared for patients reporting low social well-being (B=0.35, p=.05) reported worse physical health. Conclusions In this large sample of family caregivers of patients with incurable cancer, caregiver demographics, relational factors, and patient-specific factors were all related to caregiver mental health, while caregiver demographics were primarily associated with caregiver physical health. These findings help identify characteristics of family caregivers at highest risk of poor mental and physical health who may benefit from greater supportive care. PMID:27866337

Right-Sizing Statistical Models for Longitudinal Data

PubMed Central

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

2015-01-01

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

Right-sizing statistical models for longitudinal data.

PubMed

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

2015-12-01

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

Ultrasensitive non-enzymatic glucose sensor based on three-dimensional network of ZnO-CuO hierarchical nanocomposites by electrospinning

PubMed Central

Zhou, Chunyang; Xu, Lin; Song, Jian; Xing, Ruiqing; Xu, Sai; Liu, Dali; Song, Hongwei

2014-01-01

Three-dimensional (3D) porous ZnO–CuO hierarchical nanocomposites (HNCs) nonenzymatic glucose electrodes with different thicknesses were fabricated by coelectrospinning and compared with 3D mixed ZnO/CuO nanowires (NWs) and pure CuO NWs electrodes. The structural characterization revealed that the ZnO–CuO HNCs were composed of the ZnO and CuO mixed NWs trunk (~200 nm), whose outer surface was attached with small CuO nanoparticles (NPs). Moreover, a good synergetic effect between CuO and ZnO was confirmed. The nonenzymatic biosensing properties of as prepared 3D porous electrodes based on fluorine doped tin oxide (FTO) were studied and the results indicated that the sensing properties of 3D porous ZnO–CuO HNCs electrodes were significantly improved and depended strongly on the thickness of the HNCs. At an applied potential of + 0.7 V, the optimum ZnO–CuO HNCs electrode presented a high sensitivity of 3066.4 μAmM−1cm−2, the linear range up to 1.6 mM, and low practical detection limit of 0.21 μM. It also showed outstanding long term stability, good reproducibility, excellent selectivity and accurate measurement in real serum sample. The formation of special hierarchical heterojunction and the well-constructed 3D structure were the main reasons for the enhanced nonenzymatic biosensing behavior. PMID:25488502

Detecting a Change in School Performance: A Bayesian Analysis for a Multilevel Join Point Problem. CSE Technical Report 542.

ERIC Educational Resources Information Center

Thum, Yeow Meng; Bhattacharya, Suman Kumar

To better describe individual behavior within a system, this paper uses a sample of longitudinal test scores from a large urban school system to consider hierarchical Bayes estimation of a multilevel linear regression model in which each individual regression slope of test score on time switches at some unknown point in time, "kj."…

Daily Stress and Emotional Well-Being among Asian American Adolescents: Same-Day, Lagged, and Chronic Associations

ERIC Educational Resources Information Center

Kiang, Lisa; Buchanan, Christy M.

2014-01-01

Daily-diary data from 180 Asian American 9th-10th graders (58% female, 75% second generation; "M" age = 14.97 years) were used to investigate how family, school, and peer stress are each associated with same-day and next-day (lagged) well-being, and vice versa. Hierarchical linear modeling provided support for reciprocal links when…

Transitioning to Middle School in the Sixth Grade: A Hierarchical Linear Modeling (HLM) Analysis of Substance Use, Violence, and Suicidal Thoughts

ERIC Educational Resources Information Center

Gunter, Whitney D.; Bakken, Nicholas W.

2010-01-01

In recent years, public schools have moved away from traditional grade configurations with junior high schools and have shifted toward integrating sixth-grade students into middle schools. It has been argued that the effect this will have on students is to allow for additional freedom and earlier social growth. However, the counterargument to this…

Determinants of Salary Growth in Shenzhen, China: An Analysis of Formal Education, On-the-Job Training, and Adult Education with a Three-Level Model.

ERIC Educational Resources Information Center

Xiao, Jin

2002-01-01

Uses hierarchical linear model to estimate the effects of three forms of human capital on employee salary in China: Formal education, employer-provided on-the-job training, and adult education. Finds, for example, that employees' experience in changing production technology and on-the-job training are positively associated with salary increases…

Developmental Course of Impulsivity and Capability from Age 10 to Age 25 as Related to Trajectory of Suicide Attempt in a Community Cohort

ERIC Educational Resources Information Center

Kasen, Stephanie; Cohen, Patricia; Chen, Henian

2011-01-01

Hierarchical linear models were used to examine trajectories of impulsivity and capability between ages 10 and 25 in relation to suicide attempt in 770 youths followed longitudinally: intercepts were set at age 17. The impulsivity measure assessed features of urgency (e.g., poor control, quick provocation, and disregard for external constraints);…

The Relationship between Teachers' and Principals' Decision-Making Power: Is It a Win-Win Situation or a Zero-Sum Game?

ERIC Educational Resources Information Center

Shen, Jianping; Xia, Jiangang

2012-01-01

Is the power relationship between public school teachers and principals a win-win situation or a zero-sum game? By applying hierarchical linear modeling to the 1999-2000 nationally representative Schools and Staffing Survey data, we found that both the win-win and zero-sum-game theories had empirical evidence. The decision-making areas…

Motivation, Classroom Environment, and Learning in Introductory Geology: A Hierarchical Linear Model

NASA Astrophysics Data System (ADS)

Gilbert, L. A.; Hilpert, J. C.; Van Der Hoeven Kraft, K.; Budd, D.; Jones, M. H.; Matheney, R.; Mcconnell, D. A.; Perkins, D.; Stempien, J. A.; Wirth, K. R.

2013-12-01

Prior research has indicated that highly motivated students perform better and that learning increases in innovative, reformed classrooms, but untangling the student effects from the instructor effects is essential to understanding how to best support student learning. Using a hierarchical linear model, we examine these effects separately and jointly. We use data from nearly 2,000 undergraduate students surveyed by the NSF-funded GARNET (Geoscience Affective Research NETwork) project in 65 different introductory geology classes at research universities, public masters-granting universities, liberal arts colleges and community colleges across the US. Student level effects were measured as increases in expectancy and self-regulation using the Motivated Strategies for Learning Questionnaire (MSLQ; Pintrich et al., 1991). Instructor level effects were measured using the Reformed Teaching Observation Protocol, (RTOP; Sawada et al., 2000), with higher RTOP scores indicating a more reformed, student-centered classroom environment. Learning was measured by learning gains on a Geology Concept Inventory (GCI; Libarkin and Anderson, 2005) and normalized final course grade. The hierarchical linear model yielded significant results at several levels. At the student level, increases in expectancy and self-regulation are significantly and positively related to higher grades regardless of instructor; the higher the increase, the higher the grade. At the instructor level, RTOP scores are positively related to normalized average GCI learning gains. The higher the RTOP score, the higher the average class GCI learning gains. Across both levels, average class GCI learning gains are significantly and positively related to student grades; the higher the GCI learning gain, the higher the grade. Further, the RTOP scores are significantly and negatively related to the relationship between expectancy and course grade. The lower the RTOP score, the higher the correlation between change in expectancy and grade. As such, students with low motivation show higher grades and greater learning gains in high RTOP (student-centered) classrooms than in low RTOP ones. These results support the recommendation of student-centered practices in the classroom and consideration of student motivation in our approach to the future of geoscience education.

Hierarchical hollow microsphere and flower-like indium oxide: Controllable synthesis and application as H{sub 2}S cataluminescence sensing materials

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

Cai, Pingyang, E-mail: cpyxx@163.com; Bai, Wei, E-mail: weibaiscu@gmail.com; Zhang, Lichun, E-mail: lichun0203@yahoo.cn

Graphical abstract: Hierarchical hollow microsphere and flower-like In{sub 2}O{sub 3} were controllable fabricated through a novel and simple hydrothermal process, and the former showed superior cataluminescence sensing performance to H{sub 2}S. Highlights: ► In{sub 2}O{sub 3} hierarchical hollow sphere were prepared via a hydrothermal route. ► The growth process of In{sub 2}O{sub 3} hierarchical hollow sphere has been investigated. ► The sensor based on prepared In{sub 2}O{sub 3} shows good sensing performance to H{sub 2}S. -- Abstract: In the present work, In{sub 2}O{sub 3} hierarchical hollow microsphere and flower-like microstructure were achieved controllably by a hydrothermal process in the sodiummore » dodecyl sulfate (SDS)-N,N-dimethyl-formamide (DMF) system. XRD, SEM, HRTEM and N{sub 2} adsorption measurements were used to characterize the as-prepared indium oxide materials and the possible mechanism for the microstructures formation was briefly discussed. The cataluminescence gas sensor based on the as-prepared In{sub 2}O{sub 3} was utilized to detect H{sub 2}S concentrations in flowing air. Comparative gas sensing results revealed that the sensor based on hierarchical hollow microsphere exhibited much higher sensing sensitivity in detecting H{sub 2}S gas than the sensor based on flower-like microstructure. The present gas sensor had a fast response time of 5 s and a recovery time of less than 25 s, furthermore, the cataluminescence intensity vs. H{sub 2}S concentration was linear in range of 2–20 μg mL{sup −1} with a detection limit of 0.5 μg mL{sup −1}. The present highly sensitive, fast-responding, and low-cost In{sub 2}O{sub 3}-based gas sensor for H{sub 2}S would have many practical applications.« less

Managerial and environmental factors in the continuity of mental health care across institutions.

PubMed

Greenberg, Greg A; Rosenheck, Robert A

2003-04-01

The authors examined the association of continuity of care with factors assumed to be under the control of health care administrators and environmental factors not under managerial control. The authors used a facility-level administrative data set for 139 Department of Veterans Affairs medical centers over a six-year period and supplemental data on environmental factors to conduct two types of analysis. First, simple correlations were used to examine bivariate associations between eight continuity-of-care measures and nine measures of the institutional environment and the social context. Second, to control for potential autocorrelation, multivariate hierarchical linear models with all nine independent measures were created. The strongest predictors of continuity of care were per capita outpatient expenditure and the degree of emphasis on outpatient care as measured by the percentage of all mental health expenditures devoted to outpatient care. The former was significantly associated with greater continuity of care on six of eight measures and the latter on seven of eight measures. The environmental factor of social capital (the degree of civic involvement and trust at the state level) was associated with greater continuity of care on five measures. The degree to which non-VA mental health services were funded in a state was unexpectedly found to be positively associated with greater continuity of care. In multivariate analysis using hierarchical linear modeling, significant relationships with continuity of care remained for per capita outpatient expenditures, overall outpatient emphasis, and social capital, but not for non-VA mental health funding. A linear term representing the year was positively and significantly associated with six of the eight examined continuity-of-care measures, indicating improvement in continuity of care for the period under study, although the explanation for this trend over time is unclear. Several factors potentially under managerial control are associated with increased mental health continuity of care.

Environmental correlates to behavioral health outcomes in Alzheimer's special care units.

PubMed

Zeisel, John; Silverstein, Nina M; Hyde, Joan; Levkoff, Sue; Lawton, M Powell; Holmes, William

2003-10-01

We systematically measured the associations between environmental design features of nursing home special care units and the incidence of aggression, agitation, social withdrawal, depression, and psychotic problems among persons living there who have Alzheimer's disease or a related disorder. We developed and tested a model of critical health-related environmental design features in settings for people with Alzheimer's disease. We used hierarchical linear modeling statistical techniques to assess associations between seven environmental design features and behavioral health measures for 427 residents in 15 special care units. Behavioral health measures included the Cohen-Mansfield physical agitation, verbal agitation, and aggressive behavior scales, the Multidimensional Observation Scale for Elderly Subjects depression and social withdrawal scales, and BEHAVE-AD (psychotic symptom list) misidentification and paranoid delusions scales. Statistical controls were included for the influence of, among others, cognitive status, need for assistance with activities of daily living, prescription drug use, amount of Alzheimer's staff training, and staff-to-resident ratio. Although hierarchical linear modeling minimizes the risk of Type II-false positive-error, this exploratory study also pays special attention to avoiding Type I error-the failure to recognize possible relationships between behavioral health characteristics and independent variables. We found associations between each behavioral health measure and particular environmental design features, as well as between behavioral health measures and both resident and nonenvironmental facility variables. This research demonstrates the potential that environment has for contributing to the improvement of Alzheimer's symptoms. A balanced combination of pharmacologic, behavioral, and environmental approaches is likely to be most effective in improving the health, behavior, and quality of life of people with Alzheimer's disease.

Temperament and job stress in Japanese company employees.

PubMed

Sakai, Y; Akiyama, T; Miyake, Y; Kawamura, Y; Tsuda, H; Kurabayashi, L; Tominaga, M; Noda, T; Akiskal, K; Akiskal, H

2005-03-01

This study aims to demonstrate the relevance of temperament to job stress. The subjects were 848 male and 366 female Japanese company employees. Temperament Evaluation of Memphis, Pisa, Paris and San Diego-Autoquestionnaire version (TEMPS-A) and Munich Personality Test (MPT) were administered to assess temperaments, and the NIOSH Generic Job Stress Questionnaire (GJSQ) to assess job stress. We used hierarchical multiple linear regression analysis in order to demonstrate whether temperament variables added any unique variance after controlling the effects of other predictors such as gender, age and job rank. In all subscales of the GJSQ, temperament predicted a large share of the variance in job stress. Remarkably, for interpersonal relationship stressors, the temperament variables added greater variance than that predicted by gender, age and job rank. Summary of the hierarchical linear regression analysis showed that the irritable temperament was associated with the most prominent vulnerability, followed by cyclothymic and anxious temperaments. The schizoid temperament had difficulty in the area of social support. On the other hand, the hyperthymic temperament displayed significant robustness in facing most job stressors; the melancholic type showed a similar pattern to a lesser degree. The findings may be different in a clinical Japanese sample, or a cohort of healthy employees from a different cultural background. Temperament influences job stress significantly-indeed, it impacts on such stress with greater magnitude than age, gender and job rank in most areas examined. Temperament influences interpersonal relationship stressors more than workload-related stressors. Interestingly, in line with previous clinical and theoretical formulations, the hyperthymic and melancholic types actually appear to be "hyper-adapted" to the workplace.

Decision Making Processes and Outcomes

PubMed Central

Hicks Patrick, Julie; Steele, Jenessa C.; Spencer, S. Melinda

2013-01-01

The primary aim of this study was to examine the contributions of individual characteristics and strategic processing to the prediction of decision quality. Data were provided by 176 adults, ages 18 to 93 years, who completed computerized decision-making vignettes and a battery of demographic and cognitive measures. We examined the relations among age, domain-specific experience, working memory, and three measures of strategic information search to the prediction of solution quality using a 4-step hierarchical linear regression analysis. Working memory and two measures of strategic processing uniquely contributed to the variance explained. Results are discussed in terms of potential advances to both theory and intervention efforts. PMID:24282638

Influence of Leaders' Psychological Capital on Their Followers: Multilevel Mediation Effect of Organizational Identification

PubMed Central

Chen, Qishan; Wen, Zhonglin; Kong, Yurou; Niu, Jun; Hau, Kit-Tai

2017-01-01

We investigated the relationships between leaders' and their followers' psychological capital and organizational identification in a Chinese community. Participants included 423 followers on 34 work teams, each with its respective team leader. Hierarchical linear models (HLM) were used in the analyses to delineate the relationships among participants' demographic background (gender, age, marital status, and educational level), human capital, and tenure. The results revealed that leaders' psychological capital positively influenced their followers' psychological capital through the mediation effect of enhancing followers' organizational identification. The implications of these findings, the study's limitations, and directions for future research are discussed. PMID:29075218

Variability aware compact model characterization for statistical circuit design optimization

NASA Astrophysics Data System (ADS)

Qiao, Ying; Qian, Kun; Spanos, Costas J.

2012-03-01

Variability modeling at the compact transistor model level can enable statistically optimized designs in view of limitations imposed by the fabrication technology. In this work we propose an efficient variabilityaware compact model characterization methodology based on the linear propagation of variance. Hierarchical spatial variability patterns of selected compact model parameters are directly calculated from transistor array test structures. This methodology has been implemented and tested using transistor I-V measurements and the EKV-EPFL compact model. Calculation results compare well to full-wafer direct model parameter extractions. Further studies are done on the proper selection of both compact model parameters and electrical measurement metrics used in the method.

Daily spillover to and from binge eating in first-year university females.

PubMed

Barker, Erin T; Williams, Rebecca L; Galambos, Nancy L

2006-01-01

Coping models of binge eating propose that stress and/or negative affect trigger binge eating, which serves to shift attention to the binge and its consequences. The current study tested these general assumptions using 14-day daily diary data collected from 66 first-year university females. Hierarchical Generalized Linear Modeling results showed that increased stress, negative affect, and weight concerns were associated with an increased likelihood of reporting symptoms of binge eating within days. Elevated weight concerns predicted next-day binge eating and binge eating predicted greater next-day negative affect. Discussion focuses on implications for coping models of binge eating.

Rare-event statistics and modular invariance

NASA Astrophysics Data System (ADS)

Nechaev, S. K.; Polovnikov, K.

2018-01-01

Simple geometric arguments based on constructing the Euclid orchard are presented, which explain the equivalence of various types of distributions that result from rare-event statistics. In particular, the spectral density of the exponentially weighted ensemble of linear polymer chains is examined for its number-theoretic properties. It can be shown that the eigenvalue statistics of the corresponding adjacency matrices in the sparse regime show a peculiar hierarchical structure and are described by the popcorn (Thomae) function discontinuous in the dense set of rational numbers. Moreover, the spectral edge density distribution exhibits Lifshitz tails, reminiscent of 1D Anderson localization. Finally, a continuous approximation for the popcorn function is suggested based on the Dedekind η-function, and the hierarchical ultrametric structure of the popcorn-like distributions is demonstrated to be related to hidden SL(2,Z) modular symmetry.

Composite Robust $$H_\\infty$$ Control for Uncertain Stochastic Nonlinear Systems with State Delay via Disturbance Observer

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

Liu, Yunlong; Wang, Hong; Guo, Lei

Here in this note, the robust stochastic stabilization and robust H_infinity control problems are investigated for uncertain stochastic time-delay systems with nonlinearity and multiple disturbances. By estimating the disturbance, which can be described by an exogenous model, a composite hierarchical control scheme is proposed that integrates the output of the disturbance observer with state feedback control law. Sufficient conditions for the existence of the disturbance observer and composite hierarchical controller are established in terms of linear matrix inequalities, which ensure the mean-square asymptotic stability of the resulting closed-loop system and the disturbance attenuation. It has been shown that the disturbancemore » rejection performance can also be achieved. A numerical example is provided to show the potential of the proposed techniques and encouraging results have been obtained.« less

Composite Robust $$H_\\infty$$ Control for Uncertain Stochastic Nonlinear Systems with State Delay via Disturbance Observer

DOE PAGES

Liu, Yunlong; Wang, Hong; Guo, Lei

2018-03-26

Here in this note, the robust stochastic stabilization and robust H_infinity control problems are investigated for uncertain stochastic time-delay systems with nonlinearity and multiple disturbances. By estimating the disturbance, which can be described by an exogenous model, a composite hierarchical control scheme is proposed that integrates the output of the disturbance observer with state feedback control law. Sufficient conditions for the existence of the disturbance observer and composite hierarchical controller are established in terms of linear matrix inequalities, which ensure the mean-square asymptotic stability of the resulting closed-loop system and the disturbance attenuation. It has been shown that the disturbancemore » rejection performance can also be achieved. A numerical example is provided to show the potential of the proposed techniques and encouraging results have been obtained.« less

Inter-Ethnic/Racial Facial Variations: A Systematic Review and Bayesian Meta-Analysis of Photogrammetric Studies

PubMed Central

Wen, Yi Feng; Wong, Hai Ming; Lin, Ruitao; Yin, Guosheng; McGrath, Colman

2015-01-01

Background Numerous facial photogrammetric studies have been published around the world. We aimed to critically review these studies so as to establish population norms for various angular and linear facial measurements; and to determine inter-ethnic/racial facial variations. Methods and Findings A comprehensive and systematic search of PubMed, ISI Web of Science, Embase, and Scopus was conducted to identify facial photogrammetric studies published before December, 2014. Subjects of eligible studies were either Africans, Asians or Caucasians. A Bayesian hierarchical random effects model was developed to estimate posterior means and 95% credible intervals (CrI) for each measurement by ethnicity/race. Linear contrasts were constructed to explore inter-ethnic/racial facial variations. We identified 38 eligible studies reporting 11 angular and 18 linear facial measurements. Risk of bias of the studies ranged from 0.06 to 0.66. At the significance level of 0.05, African males were found to have smaller nasofrontal angle (posterior mean difference: 8.1°, 95% CrI: 2.2°–13.5°) compared to Caucasian males and larger nasofacial angle (7.4°, 0.1°–13.2°) compared to Asian males. Nasolabial angle was more obtuse in Caucasian females than in African (17.4°, 0.2°–35.3°) and Asian (9.1°, 0.4°–17.3°) females. Additional inter-ethnic/racial variations were revealed when the level of statistical significance was set at 0.10. Conclusions A comprehensive database for angular and linear facial measurements was established from existing studies using the statistical model and inter-ethnic/racial variations of facial features were observed. The results have implications for clinical practice and highlight the need and value for high quality photogrammetric studies. PMID:26247212

Inter-Ethnic/Racial Facial Variations: A Systematic Review and Bayesian Meta-Analysis of Photogrammetric Studies.

PubMed

Wen, Yi Feng; Wong, Hai Ming; Lin, Ruitao; Yin, Guosheng; McGrath, Colman

2015-01-01

Numerous facial photogrammetric studies have been published around the world. We aimed to critically review these studies so as to establish population norms for various angular and linear facial measurements; and to determine inter-ethnic/racial facial variations. A comprehensive and systematic search of PubMed, ISI Web of Science, Embase, and Scopus was conducted to identify facial photogrammetric studies published before December, 2014. Subjects of eligible studies were either Africans, Asians or Caucasians. A Bayesian hierarchical random effects model was developed to estimate posterior means and 95% credible intervals (CrI) for each measurement by ethnicity/race. Linear contrasts were constructed to explore inter-ethnic/racial facial variations. We identified 38 eligible studies reporting 11 angular and 18 linear facial measurements. Risk of bias of the studies ranged from 0.06 to 0.66. At the significance level of 0.05, African males were found to have smaller nasofrontal angle (posterior mean difference: 8.1°, 95% CrI: 2.2°-13.5°) compared to Caucasian males and larger nasofacial angle (7.4°, 0.1°-13.2°) compared to Asian males. Nasolabial angle was more obtuse in Caucasian females than in African (17.4°, 0.2°-35.3°) and Asian (9.1°, 0.4°-17.3°) females. Additional inter-ethnic/racial variations were revealed when the level of statistical significance was set at 0.10. A comprehensive database for angular and linear facial measurements was established from existing studies using the statistical model and inter-ethnic/racial variations of facial features were observed. The results have implications for clinical practice and highlight the need and value for high quality photogrammetric studies.

Predictive Coding: A Possible Explanation of Filling-In at the Blind Spot

PubMed Central

Raman, Rajani; Sarkar, Sandip

2016-01-01

Filling-in at the blind spot is a perceptual phenomenon in which the visual system fills the informational void, which arises due to the absence of retinal input corresponding to the optic disc, with surrounding visual attributes. It is known that during filling-in, nonlinear neural responses are observed in the early visual area that correlates with the perception, but the knowledge of underlying neural mechanism for filling-in at the blind spot is far from complete. In this work, we attempted to present a fresh perspective on the computational mechanism of filling-in process in the framework of hierarchical predictive coding, which provides a functional explanation for a range of neural responses in the cortex. We simulated a three-level hierarchical network and observe its response while stimulating the network with different bar stimulus across the blind spot. We find that the predictive-estimator neurons that represent blind spot in primary visual cortex exhibit elevated non-linear response when the bar stimulated both sides of the blind spot. Using generative model, we also show that these responses represent the filling-in completion. All these results are consistent with the finding of psychophysical and physiological studies. In this study, we also demonstrate that the tolerance in filling-in qualitatively matches with the experimental findings related to non-aligned bars. We discuss this phenomenon in the predictive coding paradigm and show that all our results could be explained by taking into account the efficient coding of natural images along with feedback and feed-forward connections that allow priors and predictions to co-evolve to arrive at the best prediction. These results suggest that the filling-in process could be a manifestation of the general computational principle of hierarchical predictive coding of natural images. PMID:26959812

Three-level global resource allocation model for hiv control: A hierarchical decision system approach.

PubMed

Kassa, Semu Mitiku

2018-02-01

Funds from various global organizations, such as, The Global Fund, The World Bank, etc. are not directly distributed to the targeted risk groups. Especially in the so-called third-world-countries, the major part of the fund in HIV prevention programs comes from these global funding organizations. The allocations of these funds usually pass through several levels of decision making bodies that have their own specific parameters to control and specific objectives to achieve. However, these decisions are made mostly in a heuristic manner and this may lead to a non-optimal allocation of the scarce resources. In this paper, a hierarchical mathematical optimization model is proposed to solve such a problem. Combining existing epidemiological models with the kind of interventions being on practice, a 3-level hierarchical decision making model in optimally allocating such resources has been developed and analyzed. When the impact of antiretroviral therapy (ART) is included in the model, it has been shown that the objective function of the lower level decision making structure is a non-convex minimization problem in the allocation variables even if all the production functions for the intervention programs are assumed to be linear.

Partitioning degrees of freedom in hierarchical and other richly-parameterized models.

PubMed

Cui, Yue; Hodges, James S; Kong, Xiaoxiao; Carlin, Bradley P

2010-02-01

Hodges & Sargent (2001) developed a measure of a hierarchical model's complexity, degrees of freedom (DF), that is consistent with definitions for scatterplot smoothers, interpretable in terms of simple models, and that enables control of a fit's complexity by means of a prior distribution on complexity. DF describes complexity of the whole fitted model but in general it is unclear how to allocate DF to individual effects. We give a new definition of DF for arbitrary normal-error linear hierarchical models, consistent with Hodges & Sargent's, that naturally partitions the n observations into DF for individual effects and for error. The new conception of an effect's DF is the ratio of the effect's modeled variance matrix to the total variance matrix. This gives a way to describe the sizes of different parts of a model (e.g., spatial clustering vs. heterogeneity), to place DF-based priors on smoothing parameters, and to describe how a smoothed effect competes with other effects. It also avoids difficulties with the most common definition of DF for residuals. We conclude by comparing DF to the effective number of parameters p(D) of Spiegelhalter et al (2002). Technical appendices and a dataset are available online as supplemental materials.

Spatial variability of the effect of air pollution on term birth weight: evaluating influential factors using Bayesian hierarchical models.

PubMed

Li, Lianfa; Laurent, Olivier; Wu, Jun

2016-02-05

Epidemiological studies suggest that air pollution is adversely associated with pregnancy outcomes. Such associations may be modified by spatially-varying factors including socio-demographic characteristics, land-use patterns and unaccounted exposures. Yet, few studies have systematically investigated the impact of these factors on spatial variability of the air pollution's effects. This study aimed to examine spatial variability of the effects of air pollution on term birth weight across Census tracts and the influence of tract-level factors on such variability. We obtained over 900,000 birth records from 2001 to 2008 in Los Angeles County, California, USA. Air pollution exposure was modeled at individual level for nitrogen dioxide (NO2) and nitrogen oxides (NOx) using spatiotemporal models. Two-stage Bayesian hierarchical non-linear models were developed to (1) quantify the associations between air pollution exposure and term birth weight within each tract; and (2) examine the socio-demographic, land-use, and exposure-related factors contributing to the between-tract variability of the associations between air pollution and term birth weight. Higher air pollution exposure was associated with lower term birth weight (average posterior effects: -14.7 (95 % CI: -19.8, -9.7) g per 10 ppb increment in NO2 and -6.9 (95 % CI: -12.9, -0.9) g per 10 ppb increment in NOx). The variation of the association across Census tracts was significantly influenced by the tract-level socio-demographic, exposure-related and land-use factors. Our models captured the complex non-linear relationship between these factors and the associations between air pollution and term birth weight: we observed the thresholds from which the influence of the tract-level factors was markedly exacerbated or attenuated. Exacerbating factors might reflect additional exposure to environmental insults or lower socio-economic status with higher vulnerability, whereas attenuating factors might indicate reduced exposure or higher socioeconomic status with lower vulnerability. Our Bayesian models effectively combined a priori knowledge with training data to infer the posterior association of air pollution with term birth weight and to evaluate the influence of the tract-level factors on spatial variability of such association. This study contributes new findings about non-linear influences of socio-demographic factors, land-use patterns, and unaccounted exposures on spatial variability of the effects of air pollution.

Functional Behavioral Assessment-Based Interventions for Students with or at Risk for Emotional and/or Behavioral Disorders in School: A Hierarchical Linear Modeling Meta-Analysis

ERIC Educational Resources Information Center

Gage, Nicholas A.; Lewis, Timothy J.; Stichter, Janine P.

2012-01-01

Of the myriad practices currently utilized for students with disabilities, particularly students with or at risk for emotional and/or behavioral disorder (EBD), functional behavior assessment (FBA) is a practice with an emerging solid research base. However, the FBA research base relies on single-subject design (SSD) and synthesis has relied on…

The Case of Chichewa and English in Malawi: The Impact of First Language Reading and Writing on Learning English as a Second Language

ERIC Educational Resources Information Center

Shin, Jaran; Sailors, Misty; McClung, Nicola; Pearson, P. David; Hoffman, James V.; Chilimanjira, Margaret

2015-01-01

We investigated the relationship between Chichewa (L1) and English (L2) literacies in Malawi. Through our use of hierarchical linear modeling, we found that cross-language literacy transfer between Chichewa and English did occur, but that the pattern and the strength of the relationships varied depending on the literacy domain (i.e., reading or…

Integration Framework of Process Planning based on Resource Independent Operation Summary to Support Collaborative Manufacturing

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

Kulvatunyou, Boonserm; Wysk, Richard A.; Cho, Hyunbo

2004-06-01

In today's global manufacturing environment, manufacturing functions are distributed as never before. Design, engineering, fabrication, and assembly of new products are done routinely in many different enterprises scattered around the world. Successful business transactions require the sharing of design and engineering data on an unprecedented scale. This paper describes a framework that facilitates the collaboration of engineering tasks, particularly process planning and analysis, to support such globalized manufacturing activities. The information models of data and the software components that integrate those information models are described. The integration framework uses an Integrated Product and Process Data (IPPD) representation called a Resourcemore » Independent Operation Summary (RIOS) to facilitate the communication of business and manufacturing requirements. Hierarchical process modeling, process planning decomposition and an augmented AND/OR directed graph are used in this representation. The Resource Specific Process Planning (RSPP) module assigns required equipment and tools, selects process parameters, and determines manufacturing costs based on two-level hierarchical RIOS data. The shop floor knowledge (resource and process knowledge) and a hybrid approach (heuristic and linear programming) to linearize the AND/OR graph provide the basis for the planning. Finally, a prototype system is developed and demonstrated with an exemplary part. Java and XML (Extensible Markup Language) are used to ensure software and information portability.« less

Estimating mono- and bi-phasic regression parameters using a mixture piecewise linear Bayesian hierarchical model

PubMed Central

Zhao, Rui; Catalano, Paul; DeGruttola, Victor G.; Michor, Franziska

2017-01-01

The dynamics of tumor burden, secreted proteins or other biomarkers over time, is often used to evaluate the effectiveness of therapy and to predict outcomes for patients. Many methods have been proposed to investigate longitudinal trends to better characterize patients and to understand disease progression. However, most approaches assume a homogeneous patient population and a uniform response trajectory over time and across patients. Here, we present a mixture piecewise linear Bayesian hierarchical model, which takes into account both population heterogeneity and nonlinear relationships between biomarkers and time. Simulation results show that our method was able to classify subjects according to their patterns of treatment response with greater than 80% accuracy in the three scenarios tested. We then applied our model to a large randomized controlled phase III clinical trial of multiple myeloma patients. Analysis results suggest that the longitudinal tumor burden trajectories in multiple myeloma patients are heterogeneous and nonlinear, even among patients assigned to the same treatment cohort. In addition, between cohorts, there are distinct differences in terms of the regression parameters and the distributions among categories in the mixture. Those results imply that longitudinal data from clinical trials may harbor unobserved subgroups and nonlinear relationships; accounting for both may be important for analyzing longitudinal data. PMID:28723910

Neural Mechanisms Underlying the Computation of Hierarchical Tree Structures in Mathematics

PubMed Central

Nakai, Tomoya; Sakai, Kuniyoshi L.

2014-01-01

Whether mathematical and linguistic processes share the same neural mechanisms has been a matter of controversy. By examining various sentence structures, we recently demonstrated that activations in the left inferior frontal gyrus (L. IFG) and left supramarginal gyrus (L. SMG) were modulated by the Degree of Merger (DoM), a measure for the complexity of tree structures. In the present study, we hypothesize that the DoM is also critical in mathematical calculations, and clarify whether the DoM in the hierarchical tree structures modulates activations in these regions. We tested an arithmetic task that involved linear and quadratic sequences with recursive computation. Using functional magnetic resonance imaging, we found significant activation in the L. IFG, L. SMG, bilateral intraparietal sulcus (IPS), and precuneus selectively among the tested conditions. We also confirmed that activations in the L. IFG and L. SMG were free from memory-related factors, and that activations in the bilateral IPS and precuneus were independent from other possible factors. Moreover, by fitting parametric models of eight factors, we found that the model of DoM in the hierarchical tree structures was the best to explain the modulation of activations in these five regions. Using dynamic causal modeling, we showed that the model with a modulatory effect for the connection from the L. IPS to the L. IFG, and with driving inputs into the L. IFG, was highly probable. The intrinsic, i.e., task-independent, connection from the L. IFG to the L. IPS, as well as that from the L. IPS to the R. IPS, would provide a feedforward signal, together with negative feedback connections. We indicate that mathematics and language share the network of the L. IFG and L. IPS/SMG for the computation of hierarchical tree structures, and that mathematics recruits the additional network of the L. IPS and R. IPS. PMID:25379713

An adaptive sparse-grid high-order stochastic collocation method for Bayesian inference in groundwater reactive transport modeling

NASA Astrophysics Data System (ADS)

Zhang, Guannan; Lu, Dan; Ye, Ming; Gunzburger, Max; Webster, Clayton

2013-10-01

Bayesian analysis has become vital to uncertainty quantification in groundwater modeling, but its application has been hindered by the computational cost associated with numerous model executions required by exploring the posterior probability density function (PPDF) of model parameters. This is particularly the case when the PPDF is estimated using Markov Chain Monte Carlo (MCMC) sampling. In this study, a new approach is developed to improve the computational efficiency of Bayesian inference by constructing a surrogate of the PPDF, using an adaptive sparse-grid high-order stochastic collocation (aSG-hSC) method. Unlike previous works using first-order hierarchical basis, this paper utilizes a compactly supported higher-order hierarchical basis to construct the surrogate system, resulting in a significant reduction in the number of required model executions. In addition, using the hierarchical surplus as an error indicator allows locally adaptive refinement of sparse grids in the parameter space, which further improves computational efficiency. To efficiently build the surrogate system for the PPDF with multiple significant modes, optimization techniques are used to identify the modes, for which high-probability regions are defined and components of the aSG-hSC approximation are constructed. After the surrogate is determined, the PPDF can be evaluated by sampling the surrogate system directly without model execution, resulting in improved efficiency of the surrogate-based MCMC compared with conventional MCMC. The developed method is evaluated using two synthetic groundwater reactive transport models. The first example involves coupled linear reactions and demonstrates the accuracy of our high-order hierarchical basis approach in approximating high-dimensional posteriori distribution. The second example is highly nonlinear because of the reactions of uranium surface complexation, and demonstrates how the iterative aSG-hSC method is able to capture multimodal and non-Gaussian features of PPDF caused by model nonlinearity. Both experiments show that aSG-hSC is an effective and efficient tool for Bayesian inference.

Distributed mixed-integer fuzzy hierarchical programming for municipal solid waste management. Part I: System identification and methodology development.

PubMed

Cheng, Guanhui; Huang, Guohe; Dong, Cong; Xu, Ye; Chen, Xiujuan; Chen, Jiapei

2017-03-01

Due to the existence of complexities of heterogeneities, hierarchy, discreteness, and interactions in municipal solid waste management (MSWM) systems such as Beijing, China, a series of socio-economic and eco-environmental problems may emerge or worsen and result in irredeemable damages in the following decades. Meanwhile, existing studies, especially ones focusing on MSWM in Beijing, could hardly reflect these complexities in system simulations and provide reliable decision support for management practices. Thus, a framework of distributed mixed-integer fuzzy hierarchical programming (DMIFHP) is developed in this study for MSWM under these complexities. Beijing is selected as a representative case. The Beijing MSWM system is comprehensively analyzed in many aspects such as socio-economic conditions, natural conditions, spatial heterogeneities, treatment facilities, and system complexities, building a solid foundation for system simulation and optimization. Correspondingly, the MSWM system in Beijing is discretized as 235 grids to reflect spatial heterogeneity. A DMIFHP model which is a nonlinear programming problem is constructed to parameterize the Beijing MSWM system. To enable scientific solving of it, a solution algorithm is proposed based on coupling of fuzzy programming and mixed-integer linear programming. Innovations and advantages of the DMIFHP framework are discussed. The optimal MSWM schemes and mechanism revelations will be discussed in another companion paper due to length limitation.

Associating quantitative behavioral traits with gene expression in the brain: searching for diamonds in the hay.

PubMed

Reiner-Benaim, Anat; Yekutieli, Daniel; Letwin, Noah E; Elmer, Gregory I; Lee, Norman H; Kafkafi, Neri; Benjamini, Yoav

2007-09-01

Gene expression and phenotypic functionality can best be associated when they are measured quantitatively within the same experiment. The analysis of such a complex experiment is presented, searching for associations between measures of exploratory behavior in mice and gene expression in brain regions. The analysis of such experiments raises several methodological problems. First and foremost, the size of the pool of potential discoveries being screened is enormous yet only few biologically relevant findings are expected, making the problem of multiple testing especially severe. We present solutions based on screening by testing related hypotheses, then testing the hypotheses of interest. In one variant the subset is selected directly, in the other one a tree of hypotheses is tested hierarchical; both variants control the False Discovery Rate (FDR). Other problems in such experiments are in the fact that the level of data aggregation may be different for the quantitative traits (one per animal) and gene expression measurements (pooled across animals); in that the association may not be linear; and in the resolution of interest only few replications exist. We offer solutions to these problems as well. The hierarchical FDR testing strategies presented here can serve beyond the structure of our motivating example study to any complex microarray study. Supplementary data are available at Bioinformatics online.

Depression, Control, and Climate: An Examination of Factors Impacting Teaching Quality in Preschool Classrooms

PubMed Central

Sandilos, Lia E.; Cycyk, Lauren M.; Hammer, Carol Scheffner; Sawyer, Brook E.; López, Lisa; Blair, Clancy

2015-01-01

Research Findings This study investigated the relationship of preschool teachers' self-reported depressive symptomatology, perception of classroom control, and perception of school climate to classroom quality as measured by the Classroom Assessment Scoring System Pre-K. The sample consisted of 59 urban preschool classrooms serving low-income and linguistically diverse students in the northeastern and southeastern United States. Results of hierarchical linear modeling revealed that teachers' individual reports of depressive symptomatology were significantly and negatively predictive of the observed quality of their instructional support and classroom organization. Practice or Policy The findings of this study have implications for increasing access to mental health supports for teachers in an effort to minimize depressive symptoms and potentially improve classroom quality. PMID:26924914

Visual feature extraction from voxel-weighted averaging of stimulus images in 2 fMRI studies.

PubMed

Hart, Corey B; Rose, William J

2013-11-01

Multiple studies have provided evidence for distributed object representation in the brain, with several recent experiments leveraging basis function estimates for partial image reconstruction from fMRI data. Using a novel combination of statistical decomposition, generalized linear models, and stimulus averaging on previously examined image sets and Bayesian regression of recorded fMRI activity during presentation of these data sets, we identify a subset of relevant voxels that appear to code for covarying object features. Using a technique we term "voxel-weighted averaging," we isolate image filters that these voxels appear to implement. The results, though very cursory, appear to have significant implications for hierarchical and deep-learning-type approaches toward the understanding of neural coding and representation.

Multimorbidity and health-related quality of life (HRQoL) in a nationally representative population sample: implications of count versus cluster method for defining multimorbidity on HRQoL.

PubMed

Wang, Lili; Palmer, Andrew J; Cocker, Fiona; Sanderson, Kristy

2017-01-09

No universally accepted definition of multimorbidity (MM) exists, and implications of different definitions have not been explored. This study examined the performance of the count and cluster definitions of multimorbidity on the sociodemographic profile and health-related quality of life (HRQoL) in a general population. Data were derived from the nationally representative 2007 Australian National Survey of Mental Health and Wellbeing (n = 8841). The HRQoL scores were measured using the Assessment of Quality of Life (AQoL-4D) instrument. The simple count (2+ & 3+ conditions) and hierarchical cluster methods were used to define/identify clusters of multimorbidity. Linear regression was used to assess the associations between HRQoL and multimorbidity as defined by the different methods. The assessment of multimorbidity, which was defined using the count method, resulting in the prevalence of 26% (MM2+) and 10.1% (MM3+). Statistically significant clusters identified through hierarchical cluster analysis included heart or circulatory conditions (CVD)/arthritis (cluster-1, 9%) and major depressive disorder (MDD)/anxiety (cluster-2, 4%). A sensitivity analysis suggested that the stability of the clusters resulted from hierarchical clustering. The sociodemographic profiles were similar between MM2+, MM3+ and cluster-1, but were different from cluster-2. HRQoL was negatively associated with MM2+ (β: -0.18, SE: -0.01, p < 0.001), MM3+ (β: -0.23, SE: -0.02, p < 0.001), cluster-1 (β: -0.10, SE: 0.01, p < 0.001) and cluster-2 (β: -0.36, SE: 0.01, p < 0.001). Our findings confirm the existence of an inverse relationship between multimorbidity and HRQoL in the Australian population and indicate that the hierarchical clustering approach is validated when the outcome of interest is HRQoL from this head-to-head comparison. Moreover, a simple count fails to identify if there are specific conditions of interest that are driving poorer HRQoL. Researchers should exercise caution when selecting a definition of multimorbidity because it may significantly influence the study outcomes.

Apathy, not depressive symptoms, as a predictor of semantic and phonemic fluency task performance in stroke and transient ischemic attack.

PubMed

Fishman, Keera N; Ashbaugh, Andrea R; Lanctôt, Krista L; Cayley, Megan L; Herrmann, Nathan; Murray, Brian J; Sicard, Michelle; Lien, Karen; Sahlas, Demetrios J; Swartz, Richard H

2018-06-01

This study examined the relationship between apathy and cognition in patients with cerebrovascular disease. Apathy may result from damage to frontal subcortical circuits causing dysexecutive syndromes, but apathy is also related to depression. We assessed the ability of apathy to predict phonemic fluency and semantic fluency performance after controlling for depressive symptoms in 282 individuals with stroke and/or transient ischemic attack. Participants (N = 282) completed the Phonemic Fluency Test, Semantic Fluency Test, Center for Epidemiologic Studies Depression Scale, and Apathy Evaluation Scale. A cross-sectional correlational design was utilized. Using hierarchical linear regressions, apathy scores significantly predicted semantic fluency performance (β = -.159, p = .020), but not phonemic fluency performance (β = -.112, p = .129) after scaling scores by age and years of education and controlling for depressive symptoms. Depressive symptoms entered into the first step of both hierarchical linear regressions did not predict semantic fluency (β = -.035, p = .554) or phonemic fluency (β = -.081, p = .173). Apathy and depressive symptoms were moderately correlated, r(280) = .58, p < .001. The results of this study are consistent with research supporting a differentiation between phonemic and semantic fluency tasks, whereby phonemic fluency tasks primarily involve frontal regions, and semantic fluency tasks involve recruitment of more extended networks. The results also highlight a distinction between apathy and depressive symptoms and suggest that apathy may be a more reliable predictor of cognitive deficits than measures of mood in individuals with cerebrovascular disease. Apathy may also be more related to cognition due to overlapping motivational and cognitive frontal subcortical circuitry. Future research should explore whether treatments for apathy could be a novel target for improving cognitive outcomes after stroke.

Teacher and Paraeducator Perceptions of a Hierarchical to Hierarchical-Collaborative Relationship

ERIC Educational Resources Information Center

Dalley, Anna Maria

2017-01-01

This qualitative, descriptive single-case study explored the transition from a hierarchical to a hierarchical-collaborative relationship between special education teachers (SET) and special education paraeducators (SEP). Sixteen participants, including eight veteran SET and eight SEP working in one county located in the Mid-Atlantic region of the…

Working models of attachment to parents and partners: implications for emotional behavior between partners.

PubMed

Mehta, Neera; Cowan, Philip A; Cowan, Carolyn P

2009-12-01

This study examined whether working models of attachment are associated with observed positive emotion, sadness, and anger during marital conflict. Individuals (n = 176) from a longitudinal study of families participated in the current cross-sectional study. Narrative interviews assessed the unique and combined contribution of attachment representations based on parents (adult attachment) and partner (couple attachment). The influence of partner's attachment, depression symptoms, and sex of participant was also examined. Hierarchical linear models demonstrated that one's couple attachment security predicts one's observed positive emotion, whereas the partner's couple attachment security predicts one's observed negative emotion. Partner's depression symptoms moderated the effects of partner's couple attachment. Adult attachment was not related to observed emotional behavior between partners. These findings have important clinical implications for individual, couple, and family therapy.

Posttraumatic stress symptoms in police staff 12-18 months after the Canterbury earthquakes.

PubMed

Surgenor, Lois J; Snell, Deborah L; Dorahy, Martin J

2015-04-01

Understanding posttraumatic stress disorder (PTSD) symptoms in police first-responders is an underdeveloped field. Using a cross-sectional survey, this study investigated demographic and occupational characteristics, coping resources and processes, along with first-responder roles and consequences 18 months following a disaster. Hierarchical linear regression (N = 576) showed that greater symptom levels were significantly positively associated with negative emotional coping (β = .31), a communications role (β = .08) and distress following exposure to resource losses (β = .14), grotesque scenes (β = .21), personal harm (β = .14), and concern for significant others (β = .17). Optimism alone was negatively associated (β = -.15), with the overall model being a modest fit (adjusted R(2) = .39). The findings highlight variables for further study in police. Copyright © 2015 International Society for Traumatic Stress Studies.

Child Maltreatment and Children's Developmental Trajectories in Early- to Middle-Childhood

PubMed Central

Font, Sarah A.; Berger, Lawrence M.

2014-01-01

Associations between experiencing child maltreatment and adverse developmental outcomes are widely studied, yet conclusions regarding the extent to which effects are bidirectional, and whether they are likely causal, remain elusive. This study uses the Fragile Families and Child Well-Being study, a birth cohort of 4,898 children followed from birth through age 9. Hierarchical linear modeling and structural equation modeling are employed to estimate associations of maltreatment with cognitive and social-emotional well-being. Results suggest that effects of early childhood maltreatment emerge immediately, though developmental outcomes are also affected by newly occurring maltreatment over time. Additionally, findings indicate that children's early developmental scores predict their subsequent probability of experiencing maltreatment, though to a lesser extent than early maltreatment predicts subsequent developmental outcomes. PMID:25521556

State-space models’ dirty little secrets: even simple linear Gaussian models can have estimation problems

NASA Astrophysics Data System (ADS)

Auger-Méthé, Marie; Field, Chris; Albertsen, Christoffer M.; Derocher, Andrew E.; Lewis, Mark A.; Jonsen, Ian D.; Mills Flemming, Joanna

2016-05-01

State-space models (SSMs) are increasingly used in ecology to model time-series such as animal movement paths and population dynamics. This type of hierarchical model is often structured to account for two levels of variability: biological stochasticity and measurement error. SSMs are flexible. They can model linear and nonlinear processes using a variety of statistical distributions. Recent ecological SSMs are often complex, with a large number of parameters to estimate. Through a simulation study, we show that even simple linear Gaussian SSMs can suffer from parameter- and state-estimation problems. We demonstrate that these problems occur primarily when measurement error is larger than biological stochasticity, the condition that often drives ecologists to use SSMs. Using an animal movement example, we show how these estimation problems can affect ecological inference. Biased parameter estimates of a SSM describing the movement of polar bears (Ursus maritimus) result in overestimating their energy expenditure. We suggest potential solutions, but show that it often remains difficult to estimate parameters. While SSMs are powerful tools, they can give misleading results and we urge ecologists to assess whether the parameters can be estimated accurately before drawing ecological conclusions from their results.

Buckling and postbuckling of composite panels with cutouts subjected to combined edge shear and temperature change

NASA Technical Reports Server (NTRS)

Noor, Ahmed K.; Kim, Yong H.

1995-01-01

The results of a detailed study of the buckling and postbuckling responses of composite panels with central circular cutouts are presented. The panels are subjected to combined edge shear and temperature change. The panels are discretized by using a two-field degenerate solid element with each of the displacement components having a linear variation throughout the thickness of the panel. The fundamental unknowns consist of the average mechanical strains through the thickness and the displacement components. The effects of geometric nonlinearities and laminated anisotropic material behavior are included. The stability boundary, postbuckling response and the hierarchical sensitivity coefficients are evaluated. The hierarchical sensitivity coefficients measure the sensitivity of the buckling and postbuckling responses to variations in the panel stiffnesses, and the material properties of both the individual layers and the constituents (fibers and matrix). Numerical results are presented for composite panels with central circular cutouts subjected to combined edge shear and temperature change, showing the effects of variations in the hole diameter, laminate stacking sequence and fiber orientation, on the stability boundary and postbuckling response and their sensitivity to changes in the various panel parameters.

Predicting coin flips: using resampling and hierarchical models to help untangle the NHL's shoot-out.

PubMed

Lopez, Michael J; Schuckers, Michael

2017-05-01

Roughly 14% of regular season National Hockey League games since the 2005-06 season have been decided by a shoot-out, and the resulting allocation of points has impacted play-off races each season. But despite interest from fans, players and league officials, there is little in the way of published research on team or individual shoot-out performance. This manuscript attempts to fill that void. We present both generalised linear mixed model and Bayesian hierarchical model frameworks to model shoot-out outcomes, with results suggesting that there are (i) small but statistically significant talent gaps between shooters, (ii) marginal differences in performance among netminders and (iii) few, if any, predictors of player success after accounting for individual talent. We also provide a resampling strategy to highlight a selection bias with respect to shooter assignment, in which coaches choose their most skilled offensive players early in shoot-out rounds and are less likely to select players with poor past performances. Finally, given that per-shot data for shoot-outs do not currently exist in a single location for public use, we provide both our data and source code for other researchers interested in studying shoot-out outcomes.

The impact of task demand on visual word recognition.

PubMed

Yang, J; Zevin, J

2014-07-11

The left occipitotemporal cortex has been found sensitive to the hierarchy of increasingly complex features in visually presented words, from individual letters to bigrams and morphemes. However, whether this sensitivity is a stable property of the brain regions engaged by word recognition is still unclear. To address the issue, the current study investigated whether different task demands modify this sensitivity. Participants viewed real English words and stimuli with hierarchical word-likeness while performing a lexical decision task (i.e., to decide whether each presented stimulus is a real word) and a symbol detection task. General linear model and independent component analysis indicated strong activation in the fronto-parietal and temporal regions during the two tasks. Furthermore, the bilateral inferior frontal gyrus and insula showed significant interaction effects between task demand and stimulus type in the pseudoword condition. The occipitotemporal cortex showed strong main effects for task demand and stimulus type, but no sensitivity to the hierarchical word-likeness was found. These results suggest that different task demands on semantic, phonological and orthographic processes can influence the involvement of the relevant regions during visual word recognition. Copyright © 2014 IBRO. Published by Elsevier Ltd. All rights reserved.

A sustainable system of systems approach: a new HFE paradigm.

PubMed

Thatcher, Andrew; Yeow, Paul H P

2016-01-01

Sustainability issues such as natural resource depletion, pollution and poor working conditions have no geographical boundaries in our interconnected world. To address these issues requires a paradigm shift within human factors and ergonomics (HFE), to think beyond a bounded, linear model understanding towards a broader systems framework. For this reason, we introduce a sustainable system of systems model that integrates the current hierarchical conceptualisation of possible interventions (i.e., micro-, meso- and macro-ergonomics) with important concepts from the sustainability literature, including the triple bottom line approach and the notion of time frames. Two practical examples from the HFE literature are presented to illustrate the model. The implications of this paradigm shift for HFE researchers and practitioners are discussed and include the long-term sustainability of the HFE community and comprehensive solutions to problems that consider the emergent issues that arise from this interconnected world. A sustainable world requires a broader systems thinking than that which currently exists in ergonomics. This study proposes a sustainable system of systems model that incorporates ideas from the ecological sciences, notably a nested hierarchy of systems and a hierarchical time dimension. The implications for sustainable design and the sustainability of the HFE community are considered.

Multiscale imaging of bone microdamage

PubMed Central

Poundarik, Atharva A.; Vashishth, Deepak

2015-01-01

Bone is a structural and hierarchical composite that exhibits remarkable ability to sustain complex mechanical loading and resist fracture. Bone quality encompasses various attributes of bone matrix from the quality of its material components (type-I collagen, mineral and non-collagenous matrix proteins) and cancellous microarchitecture, to the nature and extent of bone microdamage. Microdamage, produced during loading, manifests in multiple forms across the scales of hierarchy in bone and functions to dissipate energy and avert fracture. Microdamage formation is a key determinant of bone quality, and through a range of biological and physical mechanisms, accumulates with age and disease. Accumulated microdamage in bone decreases bone strength and increases bone’s propensity to fracture. Thus, a thorough assessment of microdamage, across the hierarchical levels of bone, is crucial to better understand bone quality and bone fracture. This review article details multiple imaging modalities that have been used to study and characterize microdamage; from bulk staining techniques originally developed by Harold Frost to assess linear microcracks, to atomic force microscopy, a modality that revealed mechanistic insights into the formation diffuse damage at the ultrastructural level in bone. New automated techniques using imaging modalities such as microcomputed tomography are also presented for a comprehensive overview. PMID:25664772

Theory of mind and executive function: working-memory capacity and inhibitory control as predictors of false-belief task performance.

PubMed

Mutter, Brigitte; Alcorn, Mark B; Welsh, Marilyn

2006-06-01

This study of the relationship between theory of mind and executive function examined whether on the false-belief task age differences between 3 and 5 ears of age are related to development of working-memory capacity and inhibitory processes. 72 children completed tasks measuring false belief, working memory, and inhibition. Significant age effects were observed for false-belief and working-memory performance, as well as for the false-alarm and perseveration measures of inhibition. A simultaneous multiple linear regression specified the contribution of age, inhibition, and working memory to the prediction of false-belief performance. This model was significant, explaining a total of 36% of the variance. To examine the independent contributions of the working-memory and inhibition variables, after controlling for age, two hierarchical multiple linear regressions were conducted. These multiple regression analyses indicate that working memory and inhibition make small, overlapping contributions to false-belief performance after accounting for age, but that working memory, as measured in this study, is a somewhat better predictor of false-belief understanding than is inhibition.

Job Crafting, Employee Well-being, and Quality of Care.

PubMed

Yepes-Baldó, Montserrat; Romeo, Marina; Westerberg, Kristina; Nordin, Maria

2018-01-01

The main objective is to study the effects of job crafting activities of elder care and nursing home employees on their perceived well-being and quality of care in two European countries, Spain and Sweden. The Job Crafting, the General Health, and the Quality of Care questionnaires were administered to 530 employees. Correlations and hierarchical regression analyses were performed. Results confirm the effects of job crafting on quality of care ( r = .291, p < .01; β = .261, p < .01; Δ R 2 = .065, p < .01) and employees' well-being ( r = .201, p < .01; β = .171, p < .01; Δ R 2 = .028, p < .01). A positive linear relationship was found between job crafting and well-being in Spain and Sweden and with quality of care in Spain. On the contrary, in Sweden, the relationship between job crafting and well-being was not linear. Job crafting contributes significantly to employees' and residents' well-being. Management should promote job crafting to co-create meaningful and productive work. Cultural effects are proposed to explain the differences found.

In silico screening of drug-membrane thermodynamics reveals linear relations between bulk partitioning and the potential of mean force

NASA Astrophysics Data System (ADS)

Menichetti, Roberto; Kanekal, Kiran H.; Kremer, Kurt; Bereau, Tristan

2017-09-01

The partitioning of small molecules in cell membranes—a key parameter for pharmaceutical applications—typically relies on experimentally available bulk partitioning coefficients. Computer simulations provide a structural resolution of the insertion thermodynamics via the potential of mean force but require significant sampling at the atomistic level. Here, we introduce high-throughput coarse-grained molecular dynamics simulations to screen thermodynamic properties. This application of physics-based models in a large-scale study of small molecules establishes linear relationships between partitioning coefficients and key features of the potential of mean force. This allows us to predict the structure of the insertion from bulk experimental measurements for more than 400 000 compounds. The potential of mean force hereby becomes an easily accessible quantity—already recognized for its high predictability of certain properties, e.g., passive permeation. Further, we demonstrate how coarse graining helps reduce the size of chemical space, enabling a hierarchical approach to screening small molecules.

Sampling-free Bayesian inversion with adaptive hierarchical tensor representations

NASA Astrophysics Data System (ADS)

Eigel, Martin; Marschall, Manuel; Schneider, Reinhold

2018-03-01

A sampling-free approach to Bayesian inversion with an explicit polynomial representation of the parameter densities is developed, based on an affine-parametric representation of a linear forward model. This becomes feasible due to the complete treatment in function spaces, which requires an efficient model reduction technique for numerical computations. The advocated perspective yields the crucial benefit that error bounds can be derived for all occuring approximations, leading to provable convergence subject to the discretization parameters. Moreover, it enables a fully adaptive a posteriori control with automatic problem-dependent adjustments of the employed discretizations. The method is discussed in the context of modern hierarchical tensor representations, which are used for the evaluation of a random PDE (the forward model) and the subsequent high-dimensional quadrature of the log-likelihood, alleviating the ‘curse of dimensionality’. Numerical experiments demonstrate the performance and confirm the theoretical results.

Hierarchical Address Event Routing for Reconfigurable Large-Scale Neuromorphic Systems.

PubMed

Park, Jongkil; Yu, Theodore; Joshi, Siddharth; Maier, Christoph; Cauwenberghs, Gert

2017-10-01

We present a hierarchical address-event routing (HiAER) architecture for scalable communication of neural and synaptic spike events between neuromorphic processors, implemented with five Xilinx Spartan-6 field-programmable gate arrays and four custom analog neuromophic integrated circuits serving 262k neurons and 262M synapses. The architecture extends the single-bus address-event representation protocol to a hierarchy of multiple nested buses, routing events across increasing scales of spatial distance. The HiAER protocol provides individually programmable axonal delay in addition to strength for each synapse, lending itself toward biologically plausible neural network architectures, and scales across a range of hierarchies suitable for multichip and multiboard systems in reconfigurable large-scale neuromorphic systems. We show approximately linear scaling of net global synaptic event throughput with number of routing nodes in the network, at 3.6×10 7 synaptic events per second per 16k-neuron node in the hierarchy.

Hierarchical Feature Extraction With Local Neural Response for Image Recognition.

PubMed

Li, Hong; Wei, Yantao; Li, Luoqing; Chen, C L P

2013-04-01

In this paper, a hierarchical feature extraction method is proposed for image recognition. The key idea of the proposed method is to extract an effective feature, called local neural response (LNR), of the input image with nontrivial discrimination and invariance properties by alternating between local coding and maximum pooling operation. The local coding, which is carried out on the locally linear manifold, can extract the salient feature of image patches and leads to a sparse measure matrix on which maximum pooling is carried out. The maximum pooling operation builds the translation invariance into the model. We also show that other invariant properties, such as rotation and scaling, can be induced by the proposed model. In addition, a template selection algorithm is presented to reduce computational complexity and to improve the discrimination ability of the LNR. Experimental results show that our method is robust to local distortion and clutter compared with state-of-the-art algorithms.

Fuzzy object modeling

NASA Astrophysics Data System (ADS)

Udupa, Jayaram K.; Odhner, Dewey; Falcao, Alexandre X.; Ciesielski, Krzysztof C.; Miranda, Paulo A. V.; Vaideeswaran, Pavithra; Mishra, Shipra; Grevera, George J.; Saboury, Babak; Torigian, Drew A.

2011-03-01

To make Quantitative Radiology (QR) a reality in routine clinical practice, computerized automatic anatomy recognition (AAR) becomes essential. As part of this larger goal, we present in this paper a novel fuzzy strategy for building bodywide group-wise anatomic models. They have the potential to handle uncertainties and variability in anatomy naturally and to be integrated with the fuzzy connectedness framework for image segmentation. Our approach is to build a family of models, called the Virtual Quantitative Human, representing normal adult subjects at a chosen resolution of the population variables (gender, age). Models are represented hierarchically, the descendents representing organs contained in parent organs. Based on an index of fuzziness of the models, 32 thorax data sets, and 10 organs defined in them, we found that the hierarchical approach to modeling can effectively handle the non-linear relationships in position, scale, and orientation that exist among organs in different patients.

Tuning the Size of Nanoassembles: A Hierarchical Transfer of Information from Dendrimers to Polyion Complexes.

PubMed

Amaral, Sandra P; Tawara, Maun H; Fernandez-Villamarin, Marcos; Borrajo, Erea; Martínez-Costas, José; Vidal, Anxo; Riguera, Ricardo; Fernandez-Megia, Eduardo

2018-05-04

The generation of dendrimers is a powerful tool in the control of the size and biodistribution of polyion complexes (PIC). Using a combinatorial screening of six dendrimers (18-243 terminal groups) and five oppositely charged PEGylated copolymers, a dendrimer-to-PIC hierarchical transfer of structural information was revealed with PIC diameters that increased from 80 to 500 nm on decreasing the dendrimer generation. This rise in size, which was also accompanied by a micelle-to-vesicle transition, is interpreted according to a cone- to rod-shaped progression in the architecture of the unit PIC (uPIC). This precise size tuning enabled dendritic PICs to act as nanorulers for controlled biodistribution. Overall, a domino-like control of the size and biological properties of PIC that is not attainable with linear polymers is feasible through dendrimer generation. © 2018 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.

A Bayesian hierarchical model with spatial variable selection: the effect of weather on insurance claims

PubMed Central

Scheel, Ida; Ferkingstad, Egil; Frigessi, Arnoldo; Haug, Ola; Hinnerichsen, Mikkel; Meze-Hausken, Elisabeth

2013-01-01

Climate change will affect the insurance industry. We develop a Bayesian hierarchical statistical approach to explain and predict insurance losses due to weather events at a local geographic scale. The number of weather-related insurance claims is modelled by combining generalized linear models with spatially smoothed variable selection. Using Gibbs sampling and reversible jump Markov chain Monte Carlo methods, this model is fitted on daily weather and insurance data from each of the 319 municipalities which constitute southern and central Norway for the period 1997–2006. Precise out-of-sample predictions validate the model. Our results show interesting regional patterns in the effect of different weather covariates. In addition to being useful for insurance pricing, our model can be used for short-term predictions based on weather forecasts and for long-term predictions based on downscaled climate models. PMID:23396890

Presenting a new kinetic model for methanol to light olefins reactions over a hierarchical SAPO-34 catalyst using the Langmuir-Hinshelwood-Hougen-Watson mechanism

NASA Astrophysics Data System (ADS)

Javad Azarhoosh, Mohammad; Halladj, Rouein; Askari, Sima

2017-10-01

In this study, a new kinetic model for methanol to light olefins (MTO) reactions over a hierarchical SAPO-34 catalyst using the Langmuir-Hinshelwood-Hougen-Watson (LHHW) mechanism was presented and the kinetic parameters was obtained using a genetic algorithm (GA) and genetic programming (GP). Several kinetic models for the MTO reactions have been presented. However, due to the complexity of the reactions, most reactions are considered lumped and elementary, which cannot be deemed a completely accurate kinetic model of the process. Therefore, in this study, the LHHW mechanism is presented as kinetic models of MTO reactions. Because of the non-linearity of the kinetic models and existence of many local optimal points, evolutionary algorithms (GA and GP) are used in this study to estimate the kinetic parameters in the rate equations. Via the simultaneous connection of the code related to modelling the reactor and the GA and GP codes in the MATLAB R2013a software, optimization of the kinetic models parameters was performed such that the least difference between the results from the kinetic models and experiential results was obtained and the best kinetic parameters of MTO process reactions were achieved. A comparison of the results from the model with experiential results showed that the present model possesses good accuracy.

Emergence of the interplay between hierarchy and contact splitting in biological adhesion highlighted through a hierarchical shear lag model.

PubMed

Brely, Lucas; Bosia, Federico; Pugno, Nicola M

2018-06-20

Contact unit size reduction is a widely studied mechanism as a means to improve adhesion in natural fibrillar systems, such as those observed in beetles or geckos. However, these animals also display complex structural features in the way the contact is subdivided in a hierarchical manner. Here, we study the influence of hierarchical fibrillar architectures on the load distribution over the contact elements of the adhesive system, and the corresponding delamination behaviour. We present an analytical model to derive the load distribution in a fibrillar system loaded in shear, including hierarchical splitting of contacts, i.e. a "hierarchical shear-lag" model that generalizes the well-known shear-lag model used in mechanics. The influence on the detachment process is investigated introducing a numerical procedure that allows the derivation of the maximum delamination force as a function of the considered geometry, including statistical variability of local adhesive energy. Our study suggests that contact splitting generates improved adhesion only in the ideal case of extremely compliant contacts. In real cases, to produce efficient adhesive performance, contact splitting needs to be coupled with hierarchical architectures to counterbalance high load concentrations resulting from contact unit size reduction, generating multiple delamination fronts and helping to avoid detrimental non-uniform load distributions. We show that these results can be summarized in a generalized adhesion scaling scheme for hierarchical structures, proving the beneficial effect of multiple hierarchical levels. The model can thus be used to predict the adhesive performance of hierarchical adhesive structures, as well as the mechanical behaviour of composite materials with hierarchical reinforcements.

Analyzing longitudinal data with the linear mixed models procedure in SPSS.

PubMed

West, Brady T

2009-09-01

Many applied researchers analyzing longitudinal data share a common misconception: that specialized statistical software is necessary to fit hierarchical linear models (also known as linear mixed models [LMMs], or multilevel models) to longitudinal data sets. Although several specialized statistical software programs of high quality are available that allow researchers to fit these models to longitudinal data sets (e.g., HLM), rapid advances in general purpose statistical software packages have recently enabled analysts to fit these same models when using preferred packages that also enable other more common analyses. One of these general purpose statistical packages is SPSS, which includes a very flexible and powerful procedure for fitting LMMs to longitudinal data sets with continuous outcomes. This article aims to present readers with a practical discussion of how to analyze longitudinal data using the LMMs procedure in the SPSS statistical software package.

Sampling schemes and parameter estimation for nonlinear Bernoulli-Gaussian sparse models

NASA Astrophysics Data System (ADS)

Boudineau, Mégane; Carfantan, Hervé; Bourguignon, Sébastien; Bazot, Michael

2016-06-01

We address the sparse approximation problem in the case where the data are approximated by the linear combination of a small number of elementary signals, each of these signals depending non-linearly on additional parameters. Sparsity is explicitly expressed through a Bernoulli-Gaussian hierarchical model in a Bayesian framework. Posterior mean estimates are computed using Markov Chain Monte-Carlo algorithms. We generalize the partially marginalized Gibbs sampler proposed in the linear case in [1], and build an hybrid Hastings-within-Gibbs algorithm in order to account for the nonlinear parameters. All model parameters are then estimated in an unsupervised procedure. The resulting method is evaluated on a sparse spectral analysis problem. It is shown to converge more efficiently than the classical joint estimation procedure, with only a slight increase of the computational cost per iteration, consequently reducing the global cost of the estimation procedure.

A unified stochastic formulation of dissipative quantum dynamics. II. Beyond linear response of spin baths

NASA Astrophysics Data System (ADS)

Hsieh, Chang-Yu; Cao, Jianshu

2018-01-01

We use the "generalized hierarchical equation of motion" proposed in Paper I [C.-Y. Hsieh and J. Cao, J. Chem. Phys. 148, 014103 (2018)] to study decoherence in a system coupled to a spin bath. The present methodology allows a systematic incorporation of higher-order anharmonic effects of the bath in dynamical calculations. We investigate the leading order corrections to the linear response approximations for spin bath models. Two kinds of spin-based environments are considered: (1) a bath of spins discretized from a continuous spectral density and (2) a bath of localized nuclear or electron spins. The main difference resides with how the bath frequency and the system-bath coupling parameters are distributed in an environment. When discretized from a continuous spectral density, the system-bath coupling typically scales as ˜1 /√{NB } where NB is the number of bath spins. This scaling suppresses the non-Gaussian characteristics of the spin bath and justifies the linear response approximations in the thermodynamic limit. For the nuclear/electron spin bath models, system-bath couplings are directly deduced from spin-spin interactions and do not necessarily obey the 1 /√{NB } scaling. It is not always possible to justify the linear response approximations in this case. Furthermore, if the spin-spin Hamiltonian is highly symmetrical, there exist additional constraints that generate highly non-Markovian and persistent dynamics that is beyond the linear response treatments.

Motivational Antecedents of Preventive Proactivity in Late Life: Linking Future Orientation and Exercise1

PubMed Central

Kahana, Eva; Kahana, Boaz; Zhang, Jianping

2007-01-01

Future orientation is considered as a motivational antecedent of late-life proactivity. In a panel study of 453 old-old adults, we linked future orientation to exercise, a key component of late-life proactivity. Findings based on hierarchical linear modeling reveal that future orientation at baseline predicts changes in exercise during the subsequent four years. Whereas exercise behavior generally declined over time, future orientation and female gender were associated with smaller decline. These results suggest that future-oriented thinking has a lasting impact on health promotion behavior. Future orientation thus represents a dispositional antecedent of preventive proactivity as proposed in our successful aging model. PMID:18080009

VR Employment Outcomes of Individuals with Autism Spectrum Disorders: A Decade in the Making.

PubMed

Alverson, Charlotte Y; Yamamoto, Scott H

2018-01-01

This study utilized hierarchical linear modeling analysis of a 10-year extant dataset from Rehabilitation Services Administration to investigate significant predictors of employment outcomes for vocational rehabilitation (VR) clients with autism. Predictor variables were gender, ethnicity, attained education level, IEP status in high school, secondary disability status, and total number of VR services. Competitive employment was the criterion variable. Only one predictor variable, Total Number of VR Services, was significant across all 10 years. IEP status in high school was not significant in any year. The remaining predictors were significant in one or more years. Further research and implications for researchers and practitioners are included.

Deployment cycle stressors and post-traumatic stress symptoms in Army National Guard women: the mediating effect of resilience.

PubMed

Wooten, Nikki R

2012-01-01

This study examined the associations between deployment cycle stressors, post-traumatic stress symptoms (PTSS), and resilience in Army National Guard (ARNG) women deployed to Operations Enduring Freedom and Iraqi Freedom. Resilience was also tested as a mediator. Hierarchical linear regression indicated that deployment and post-deployment stressors were positively associated, and resilience was negatively associated with PTSS. Resilience fully mediated the association between post-deployment stressors and PTSS. Findings suggest assessing deployment and post-deployment stressors in ARNG women may be helpful in identifying those at risk for severe PTSS; and highlight the potential of individual-level resilient characteristics in mitigating the adverse impact of post-deployment stressors.

Two-Level Hierarchical FEM Method for Modeling Passive Microwave Devices

NASA Astrophysics Data System (ADS)

Polstyanko, Sergey V.; Lee, Jin-Fa

1998-03-01

In recent years multigrid methods have been proven to be very efficient for solving large systems of linear equations resulting from the discretization of positive definite differential equations by either the finite difference method or theh-version of the finite element method. In this paper an iterative method of the multiple level type is proposed for solving systems of algebraic equations which arise from thep-version of the finite element analysis applied to indefinite problems. A two-levelV-cycle algorithm has been implemented and studied with a Gauss-Seidel iterative scheme used as a smoother. The convergence of the method has been investigated, and numerical results for a number of numerical examples are presented.

Iron status as a covariate in methylmercury-associated neurotoxicity risk.

PubMed

Fonseca, Márlon de Freitas; De Souza Hacon, Sandra; Grandjean, Philippe; Choi, Anna Lai; Bastos, Wanderley Rodrigues

2014-04-01

Intrauterine methylmercury exposure and prenatal iron deficiency negatively affect offspring's brain development. Since fish is a major source of both methylmercury and iron, occurrence of negative confounding may affect the interpretation of studies concerning cognition. We assessed relationships between methylmercury exposure and iron-status in childbearing females from a population naturally exposed to methylmercury through fish intake (Amazon). We concluded a census (refuse <20%) collecting samples from 274 healthy females (12-49 years) for hair-mercury determination and assessed iron-status through red cell tests and determination of serum ferritin and iron. Reactive C protein and thyroid hormones was used for excluding inflammation and severe thyroid dysfunctions that could affect results. We assessed the association between iron-status and hair-mercury by bivariate correlation analysis and also by different multivariate models: linear regression (to check trends); hierarchical agglomerative clustering method (groups of variables correlated with each other); and factor analysis (to examine redundancy or duplication from a set of correlated variables). Hair-mercury correlated weakly with mean corpuscular volume (r=.141; P=.020) and corpuscular hemoglobin (r=.132; .029), but not with the best biomarker of iron-status, ferritin (r=.037; P=.545). In the linear regression analysis, methylmercury exposure showed weak association with age-adjusted ferritin; age had a significant coefficient (Beta=.015; 95% CI: .003-.027; P=.016) but ferritin did not (Beta=.034; 95% CI: -.147 to .216; P=.711). In the hierarchical agglomerative clustering method, hair-mercury and iron-status showed the smallest similarities. Regarding factor analysis, iron-status and hair-mercury loaded different uncorrelated components. We concluded that iron-status and methylmercury exposure probably occur in an independent way. Copyright © 2013 Elsevier Ltd. All rights reserved.

Exploring the Link Between Alcohol and Marijuana Use and Teen Dating Violence Victimization Among High School Students: The Influence of School Context.

PubMed

Parker, Elizabeth M; Debnam, Katrina; Pas, Elise T; Bradshaw, Catherine P

2016-10-01

Adolescence is a developmental period when dating behavior is first initiated and when the risk of abuse by or against a dating partner begins to emerge. It is also one in which experimentation with alcohol and illicit substances typically begins. The current study examined the association between recent alcohol use and recent marijuana use and the experience of physical and verbal teen dating violence (TDV) victimization while considering the potential influence of school contextual variables. Data came from 27,758 high school students attending 58 Maryland public high schools. Hierarchical linear modeling was used to identify student- and school-level predictors associated with TDV. Results indicated that approximately 11% of students reported experiencing physical TDV and 11% of students reported experiencing verbal TDV over the past year. In addition, 33% of students reported recent alcohol use and 21% reported recent marijuana use. Hierarchical linear modeling results revealed that students who reported frequent recent alcohol or recent marijuana use were at increased odds of experiencing physical (adjusted odds ratio [AOR]alcohol = 2.80, p < .001; AORmarijuana = 2.03, p < .001) or verbal TDV (AORalcohol = 2.63, p < .001; AORmarijuana = 2.20, p < .001) victimization compared to students who reported little or no alcohol or marijuana use. School support was a protective factor for both physical TDV (AOR = 0.74, p < .001) and verbal TDV (AOR = 0.76, p < .001) victimization. Findings suggested that prevention efforts to address alcohol and marijuana use may have an effect on TDV victimization. Results also highlight the potential utility of enhancing student perceptions of school support as an approach for reducing TDV victimization. © 2015 Society for Public Health Education.

The Spike-and-Slab Lasso Generalized Linear Models for Prediction and Associated Genes Detection.

PubMed

Tang, Zaixiang; Shen, Yueping; Zhang, Xinyan; Yi, Nengjun

2017-01-01

Large-scale "omics" data have been increasingly used as an important resource for prognostic prediction of diseases and detection of associated genes. However, there are considerable challenges in analyzing high-dimensional molecular data, including the large number of potential molecular predictors, limited number of samples, and small effect of each predictor. We propose new Bayesian hierarchical generalized linear models, called spike-and-slab lasso GLMs, for prognostic prediction and detection of associated genes using large-scale molecular data. The proposed model employs a spike-and-slab mixture double-exponential prior for coefficients that can induce weak shrinkage on large coefficients, and strong shrinkage on irrelevant coefficients. We have developed a fast and stable algorithm to fit large-scale hierarchal GLMs by incorporating expectation-maximization (EM) steps into the fast cyclic coordinate descent algorithm. The proposed approach integrates nice features of two popular methods, i.e., penalized lasso and Bayesian spike-and-slab variable selection. The performance of the proposed method is assessed via extensive simulation studies. The results show that the proposed approach can provide not only more accurate estimates of the parameters, but also better prediction. We demonstrate the proposed procedure on two cancer data sets: a well-known breast cancer data set consisting of 295 tumors, and expression data of 4919 genes; and the ovarian cancer data set from TCGA with 362 tumors, and expression data of 5336 genes. Our analyses show that the proposed procedure can generate powerful models for predicting outcomes and detecting associated genes. The methods have been implemented in a freely available R package BhGLM (http://www.ssg.uab.edu/bhglm/). Copyright © 2017 by the Genetics Society of America.

The effects of guided inquiry instruction on student achievement in high school biology

NASA Astrophysics Data System (ADS)

Vass, Laszlo

The purpose of this quantitative, quasi-experimental study was to measure the effect of a student-centered instructional method called guided inquiry on the achievement of students in a unit of study in high school biology. The study used a non-random sample of 109 students, the control group of 55 students enrolled in high school one, received teacher centered instruction while the experimental group of 54 students enrolled at high school two received student-centered, guided inquiry instruction. The pretest-posttest design of the study analyzed scores using an independent t-test, a dependent t-test (p = <.001), an ANCOVA (p = .007), mixed method ANOVA (p = .024) and hierarchical linear regression (p = <.001). The experimental group that received guided inquiry instruction had statistically significantly higher achievement than the control group.

A Bayesian, generalized frailty model for comet assays.

PubMed

Ghebretinsae, Aklilu Habteab; Faes, Christel; Molenberghs, Geert; De Boeck, Marlies; Geys, Helena

2013-05-01

This paper proposes a flexible modeling approach for so-called comet assay data regularly encountered in preclinical research. While such data consist of non-Gaussian outcomes in a multilevel hierarchical structure, traditional analyses typically completely or partly ignore this hierarchical nature by summarizing measurements within a cluster. Non-Gaussian outcomes are often modeled using exponential family models. This is true not only for binary and count data, but also for, example, time-to-event outcomes. Two important reasons for extending this family are for (1) the possible occurrence of overdispersion, meaning that the variability in the data may not be adequately described by the models, which often exhibit a prescribed mean-variance link, and (2) the accommodation of a hierarchical structure in the data, owing to clustering in the data. The first issue is dealt with through so-called overdispersion models. Clustering is often accommodated through the inclusion of random subject-specific effects. Though not always, one conventionally assumes such random effects to be normally distributed. In the case of time-to-event data, one encounters, for example, the gamma frailty model (Duchateau and Janssen, 2007 ). While both of these issues may occur simultaneously, models combining both are uncommon. Molenberghs et al. ( 2010 ) proposed a broad class of generalized linear models accommodating overdispersion and clustering through two separate sets of random effects. Here, we use this method to model data from a comet assay with a three-level hierarchical structure. Although a conjugate gamma random effect is used for the overdispersion random effect, both gamma and normal random effects are considered for the hierarchical random effect. Apart from model formulation, we place emphasis on Bayesian estimation. Our proposed method has an upper hand over the traditional analysis in that it (1) uses the appropriate distribution stipulated in the literature; (2) deals with the complete hierarchical nature; and (3) uses all information instead of summary measures. The fit of the model to the comet assay is compared against the background of more conventional model fits. Results indicate the toxicity of 1,2-dimethylhydrazine dihydrochloride at different dose levels (low, medium, and high).

Two and three-dimensional segmentation of hyperpolarized 3He magnetic resonance imaging of pulmonary gas distribution

NASA Astrophysics Data System (ADS)

Heydarian, Mohammadreza; Kirby, Miranda; Wheatley, Andrew; Fenster, Aaron; Parraga, Grace

2012-03-01

A semi-automated method for generating hyperpolarized helium-3 (3He) measurements of individual slice (2D) or whole lung (3D) gas distribution was developed. 3He MRI functional images were segmented using two-dimensional (2D) and three-dimensional (3D) hierarchical K-means clustering of the 3He MRI signal and in addition a seeded region-growing algorithm was employed for segmentation of the 1H MRI thoracic cavity volume. 3He MRI pulmonary function measurements were generated following two-dimensional landmark-based non-rigid registration of the 3He and 1H pulmonary images. We applied this method to MRI of healthy subjects and subjects with chronic obstructive lung disease (COPD). The results of hierarchical K-means 2D and 3D segmentation were compared to an expert observer's manual segmentation results using linear regression, Pearson correlations and the Dice similarity coefficient. 2D hierarchical K-means segmentation of ventilation volume (VV) and ventilation defect volume (VDV) was strongly and significantly correlated with manual measurements (VV: r=0.98, p<.0001 VDV: r=0.97, p<.0001) and mean Dice coefficients were greater than 92% for all subjects. 3D hierarchical K-means segmentation of VV and VDV was also strongly and significantly correlated with manual measurements (VV: r=0.98, p<.0001 VDV: r=0.64, p<.0001) and the mean Dice coefficients were greater than 91% for all subjects. Both 2D and 3D semi-automated segmentation of 3He MRI gas distribution provides a way to generate novel pulmonary function measurements.

Hierarchical mutual information for the comparison of hierarchical community structures in complex networks

NASA Astrophysics Data System (ADS)

Perotti, Juan Ignacio; Tessone, Claudio Juan; Caldarelli, Guido

2015-12-01

The quest for a quantitative characterization of community and modular structure of complex networks produced a variety of methods and algorithms to classify different networks. However, it is not clear if such methods provide consistent, robust, and meaningful results when considering hierarchies as a whole. Part of the problem is the lack of a similarity measure for the comparison of hierarchical community structures. In this work we give a contribution by introducing the hierarchical mutual information, which is a generalization of the traditional mutual information and makes it possible to compare hierarchical partitions and hierarchical community structures. The normalized version of the hierarchical mutual information should behave analogously to the traditional normalized mutual information. Here the correct behavior of the hierarchical mutual information is corroborated on an extensive battery of numerical experiments. The experiments are performed on artificial hierarchies and on the hierarchical community structure of artificial and empirical networks. Furthermore, the experiments illustrate some of the practical applications of the hierarchical mutual information, namely the comparison of different community detection methods and the study of the consistency, robustness, and temporal evolution of the hierarchical modular structure of networks.

A Hierarchical Modeling Approach to Data Analysis and Study Design in a Multi-Site Experimental fMRI Study

ERIC Educational Resources Information Center

Zhou, Bo; Konstorum, Anna; Duong, Thao; Tieu, Kinh H.; Wells, William M.; Brown, Gregory G.; Stern, Hal S.; Shahbaba, Babak

2013-01-01

We propose a hierarchical Bayesian model for analyzing multi-site experimental fMRI studies. Our method takes the hierarchical structure of the data (subjects are nested within sites, and there are multiple observations per subject) into account and allows for modeling between-site variation. Using posterior predictive model checking and model…