Relevant bibliographies by topics / Dynamic structural equation models

Academic literature on the topic 'Dynamic structural equation models'

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Published: 10 March 2023

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Journal articles on the topic "Dynamic structural equation models"

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Asparouhov, Tihomir, Ellen L. Hamaker, and Bengt Muthén. "Dynamic Structural Equation Models." Structural Equation Modeling: A Multidisciplinary Journal 25, no. 3 (December 27, 2017): 359–88. http://dx.doi.org/10.1080/10705511.2017.1406803.

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Grimm, Kevin J., and Nilam Ram. "Latent Growth and Dynamic Structural Equation Models." Annual Review of Clinical Psychology 14, no. 1 (May 7, 2018): 55–89. http://dx.doi.org/10.1146/annurev-clinpsy-050817-084840.

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Fontanella, Lara, Luigi Ippoliti, and Pasquale Valentini. "Environmental pollution analysis by dynamic structural equation models." Environmetrics 18, no. 3 (2007): 265–83. http://dx.doi.org/10.1002/env.835.

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Shina, Arya Fendha Ibnu. "ESTIMASI PARAMETER PADA SISTEM MODEL PERSAMAAN SIMULTAN DATA PANEL DINAMIS DENGAN METODE 2 SLS GMM-AB." MEDIA STATISTIKA 11, no. 2 (December 30, 2018): 79–91. http://dx.doi.org/10.14710/medstat.11.2.79-91.

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Single equation models ignore interdependencies or two-way relationships between response variables. The simultaneous equation model accommodates this two-way relationship form. Two Stage Least Square Generalized Methods of Moment Arellano and Bond (2 SLS GMM-AB) is used to estimate the parameters in the simultaneous system model of dynamic panel data if each structural equation is exactly identified or over identified. In the simultaneous equation system model with dynamic panel data, each structural equation and reduced form is a dynamic panel data regression equation. Estimation of structural equations and reduced form using Ordinary Least Square (OLS) resulted biased and inconsistent estimators. Arellano and Bond GMM method (GMM AB) estimator produces unbiased, consistent, and efficient estimators.The purpose of this paper is to explain the steps of 2 SLS GMM-AB method to estimate parameter in simultaneous equation model with dynamic panel data. Keywords:2 SLS GMM-AB, Arellano and Bond estimator, Dynamic Panel Data, Simultaneous Equations
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Cziráky, Dario. "Estimation of dynamic structural equation models with latent variables." Advances in Methodology and Statistics 1, no. 1 (January 1, 2004): 185–204. http://dx.doi.org/10.51936/toxt5757.

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The paper proposes a time series generalisation of the structural equation model with latent variables (SEM). An instrumental variable estimator is considered and its asymptotic properties are analysed. Special emphases are placed on the potential use of the lagged observed variables as instruments and consistency of such estimation is established under some general assumptions about the stochastic properties of the modelled variables. In addition, an identification procedure suitable both for static and dynamic structural equation models is described. The methods are illustrated in an empirical application to dynamic panel estimation of a consumption function using UK household data.
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McNeish, Daniel. "Two-Level Dynamic Structural Equation Models with Small Samples." Structural Equation Modeling: A Multidisciplinary Journal 26, no. 6 (March 28, 2019): 948–66. http://dx.doi.org/10.1080/10705511.2019.1578657.

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Wang, Yulin, Yu Luo, Hulin Wu, and Hongyu Miao. "Dynamic structural equation models for directed cyclic graphs: the structural identifiability problem." Statistics and Its Interface 12, no. 3 (2019): 365–75. http://dx.doi.org/10.4310/sii.2019.v12.n3.a2.

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Wang, Yulin, Yu Luo, Hulin Wu, and Hongyu Miao. "Dynamic structural equation models for directed cyclic graphs: the structural identifiability problem." Statistics and Its Interface 12, no. 3 (2019): 365–75. http://dx.doi.org/10.4310/18-sii550.

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Afonin, S. M. "Structural Schemes and Structural-Parametric Models of Electroelastic Actuators for Nanomechatronics Systems." Mekhatronika, Avtomatizatsiya, Upravlenie 20, no. 4 (April 10, 2019): 219–29. http://dx.doi.org/10.17587/mau.20.219-229.

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The parametric structural schemes, structural-the parametric models and the transfer functions of the electroelastic actuators for the nanomechatronics systems are obtained. The transfer functions of the piezoactuator are determined under the generalized piezoelectric effect. The changes in the elastic compliance and the stiffness of the piezoactuator are found, taking into account the type of control. The decision wave equation and the structural-parametric models of the electroelastic actuators are obtained. Effects of the geometric and physical parameters of the electroelastic actuators and the external load on its static and dynamic characteristics are determined. The parameteric structural schemes for the electroelastic actuators for the nanomechatronics systems are obtained. The transfer functions are determined. For calculation of the automatic control systems for the nanometric movements with the electroelastic actuators are obtained the parametric structural schemes and the transfer functions of actuators. Static and dynamic characteristics of the electroelastic actuators are determined. The application of electroelastic actuators solves problems of the precise matching in microelectronics and nanotechnology, compensation of temperature and gravitational deformations, atmospheric turbulence by wave front correction. By solving the wave equation with allowance for the corresponding equations of the piezoelectric effect, the boundary conditions on loaded working surfaces of the electroelastic actuator, the strains along the coordinate axes, it is possible to construct the structural parametric model of the actuator. The transfer functions and the parametric structural schemes of the electroelastic actuator are obtained from the equations describing the corresponding structural parametric models and taking into account the opposed electromotive force of the electroelastic actuator for the nanomechatronics systems.
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Sagan, Adam. "Dynamic Structural Equation Models in Momentary Assessment in Consumer Research." Marketing i Zarządzanie 54 (2018): 61–73. http://dx.doi.org/10.18276/miz.2018.54-05.

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Dissertations / Theses on the topic "Dynamic structural equation models"

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Ciraki, Dario. "Dynamic structural equation models : estimation and interference." Thesis, London School of Economics and Political Science (University of London), 2007. http://etheses.lse.ac.uk/2937/.

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The thesis focuses on estimation of dynamic structural equation models in which some or all variables might be unobservable (latent) or measured with error. Moreover, we consider the situation where latent variables can be measured with multiple observable indicators and where lagged values of latent variables might be included in the model. This situation leads to a dynamic structural equation model (DSEM), which can be viewed as dynamic generalisation of the structural equation model (SEM). Taking the mismeasurement problem into account aims at reducing or eliminating the errors-in-variables bias and hence at minimising the chance of obtaining incorrect coefficient estimates. Furthermore, such methods can be used to improve measurement of latent variables and to obtain more accurate forecasts. The thesis aims to make a contribution to the literature in four areas. Firstly, we propose a unifying theoretical framework for the analysis of dynamic structural equation models. Secondly, we provide analytical results for both panel and time series DSEM models along with the software implementation suggestions. Thirdly, we propose non-parametric estimation methods that can also be used for obtaining starting values in maximum likelihood estimation. Finally, we illustrate these methods on several real data examples demonstrating the capabilities of the currently available software as well as importance of good starting values.
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Jung, Kwang Hee. "Dynamic GSCA generalized structured component analysis: a structural equation model for analyzing effective connectivity in functional neuroimaging." Thesis, McGill University, 2012. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=106488.

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Structural equation modeling (SEM) is often used to investigate effective connectivity in functional neuroimaging studies. Modeling effective connectivity refers to an approach in which a number of specific brain regions, called regions of interest (ROIs), are selected according to some prior knowledge about the regions, and directional (causal) relationships between them are hypothesized and tested. Existing methods for SEM, however, have serious limitations in terms of their computational capacity and the range of models that can be specified. To alleviate these difficulties, I propose a new method of SEM for analysis of effective connectivity, called Dynamic GSCA (Generalized Structured Component Analysis). This method is a component-based method that combines the original GSCA and a multivariate autoregressive model to account for the dynamic nature of data taken over time. Dynamic GSCA can accommodate more elaborate structural models that describe relationships among ROIs and is less prone to computational difficulties, such as improper solutions and the lack of model identification, than the conventional methods of SEM. To illustrate the use of the proposed method, results of empirical studies based on synthetic and real data are reported. Further extensions of Dynamic GSCA are also discussed, including higher order components, multi-sample comparison, multilevel analysis, and latent interactions.
La Modélisation par Équations Structurelles (MES) est souvent utilisée dans les études d'imagerie cérébrales fonctionnelles afin d'investiguer la connectivité effective. La modélisation de connectivité effective est une approche dans laquelle certaines régions cérébrales, appelées régions d'intérêts (RIs), sont spécifiquement sélectionnées à partir de connaissances établies sur ces régions, et des hypothèses sur les possibles liens directionnels (causals) entre les RIs sont formulées et testées. Par contre, les méthodes de MES existantes sont sérieusement limitées par leur capacité computationelle et le nombre et l'étendue des modèles qui peuvent être spécifiés. Afin d'adresser ces difficultés, je propose ici une nouvelle méthode de MES afin d'analyser la connectivité effective, appelée Analyse en Composantes Structurée Généralisée (ACSG) Dynamique. Cette méthode est une méthode basée sur les composantes, combinant la version originale des ACSGs et un modèle auto-régresseur multi-variable afin de tenir compte de la nature dynamique des données recueillies à différent temps. Les ACSG Dynamiques peuvent accommoder des modèles structurels plus complexes pour décrire les relations entre les RIs. De plus, comparé aux méthodes traditionnelles de MES, les ACSG Dynamiques sont moins susceptible de succomber aux difficultés computationelles, comme les solutions inappropriées et l'échec d'identification de modèle. Afin d'illustrer l'utilisation de la méthode proposée, des résultats d'études empiriques basées sur des données synthétiques et réelles sont présentées. Des extensions possibles des ACSG Dynamiques sont aussi discutées, incluant des composantes de plus haut niveau, la comparaison de plusieurs échantillons, l'analyse multi-niveau, et les interactions latentes.
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Yang, Yang. "Two-dimensional dynamic analysis of functionally graded structures by using meshfree boundary-domain integral equation method." Thesis, University of Macau, 2015. http://umaclib3.umac.mo/record=b3335354.

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Zhou, Lixing. "Dynamic generalized (multiple-set) structured canonical correlation analysis (dynamic GCANO): a structural equation model for simultaneous analysis of multiple-subject effective connectivity in functional neuroimaging studies." Thesis, McGill University, 2014. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=123190.

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Effective connectivity in functional neuroimaging studies is defined as the time dependent causal influence that a certain brain region of interest (ROI) exerts on another. Structural equation modeling (SEM) is regularly employed to analyze effective connectivity. In recent years, various SEM methods have been proposed to model effective connectivity. However, there has been little attempt to develop SEM methods for analyzing common patterns of effective connectivity across subjects despite the prevalence of multiple-subject research in effective connectivity. This dissertation proposes a method that fills this gap. This method is called dynamic generalized (multiple-set) structured canonical correlation analysis (dynamic GCANO). It combines generalized (multiple-set) canonical correlation analysis (GCANO) with a multivariate autoregressive time series model in a unified framework. This dissertation begins with a brief review of existing SEM techniques, and points out their limitations in analyzing multiple-subject effective connectivity data, which serves as a motivation to develop dynamic GCANO. The technical underpinnings of the proposed method are then stated, including specifications of a modeling framework and an optimization criterion for parameter estimation, which is minimized by an alternating least squares algorithm. The effectiveness of dynamic GCANO is demonstrated by analyzing both synthetic and real data sets. The former reveals reasonably good parameter recoveries by the proposed method, while the latter shows the usefulness of the method in empirical research. Several features of dynamic GCANO are highlighted through these examples. The dissertation concludes with possible extensions of the proposed method.
Suivant les méthodes d'imagerie fonctionnelle cérébrale, une connectivité efficace est définie comme influence dépendant de causalité temporelle qu'une certaine région d'intérêt du cerveau (ROI) exerce sur une autre. La modélisation par équation structurelle (SEM) est régulièrement utilisée pour analyser la connectivité efficace. Ces dernières années, diverses méthodes de SEM ont été proposées pour la modélisation de la connectivité efficace. Cependant, il y a eu peu de tentative pour développer des méthodes de SEM pour analyser les modèles communs de connectivité efficace sur-sujets, malgré la prédominance de recherche sur des sujets multiples pour analyser la connectivité efficace. Cette thèse propose une méthode qui comble cette lacune. Cette méthode est appelée dynamique généralisée (multiples ensemble) structuré l'analyse de corrélation canonique (dynamique GCANO). Elle combine généralisée (multiples ensemble) structuré l'analyse de corrélation canonique (GCANO) avec multivariée des séries chronologiques autorégressif dans un cadre unifié. Cette thèse commence par un bref sommaire sur les techniques existantes de SEM et souligne leurs limites pour analyser les données de plusieurs sous réserve pour la connectivité efficace, ce qui a mené à développer la dynamique GCANO. Les techniques de base de la méthode proposée sont ensuite énumérées, y compris les spécifications du cadre de modélisation et un critère d'optimisation pour l'estimation de paramètres, qui est réduit par alternant algorithme des moindres carrés. L'efficacité du dynamique GCANO est démontrée par l'analyse des ensembles de données synthétiques et réels. Les données synthétiques montrent une récupération raisonnable de paramètre par la méthode proposée, alors que les données réelles montrent l'utilité de la méthode dans les recherches empiriques. Plusieurs fonctionnalités du dynamique sont mises en évidence par le biais de ces exemples. En conclusion, la thèse propose des extensions possibles de la méthode proposée.
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Hu, Shanshan. "AFFECT, MOTIVATION, AND ENGAGEMENT IN THE CONTEXT OF MATHEMATICS EDUCATION: TESTING A DYNAMIC MODEL OF INTERACTIVE RELATIONSHIPS." UKnowledge, 2018. https://uknowledge.uky.edu/edp_etds/71.

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The present study tested the interactive model of affect, motivation, and engagement (Linnenbrink, 2007) in mathematics education with a nationally representative sample. Self-efficacy, self-concept, and anxiety were indicators of pleasant and unpleasant affect. Intrinsic and extrinsic motivation were indicators of mastery and performance approach. Persistence and cognitive activation were indicators of behavioral and cognitive engagement. The 2012 Programme for International Student Assessment (PISA) supplied a sample of 4,978 students from the United States for structural equation modeling. The results indicated that PISA data overall supported the interactive model. Specifically, PISA data completely supported the specification of the relationship between motivation and affect, largely supported the specification of the relationship between affect and engagement, but failed to support the specification of the relationship between motivation and engagement. Finally, PISA data largely supported the specification of the mediation effects of affect on the relationship between motivation and engagement.
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Han, Sukho Brown D. Scott. "The impact analysis of structural change in Korean agriculture with respect to the Korean-United States free trade agreement dynamic simultaneous equation model approach /." Diss., Columbia, Mo. : University of Missouri--Columbia, 2009. http://hdl.handle.net/10355/6969.

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Title from PDF of title page (University of Missouri--Columbia, viewed on Feb 26, 2010). The entire thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file; a non-technical public abstract appears in the public.pdf file. Dissertation advisor: Dr. Scott Brown. Includes bibliographical references.
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Bocaccio, Alessandro Antunes. "A inteligência como capacidade dinâmica : uma relação entre processo de monitoramento de ambiente externo e vantagem competitiva." reponame:Biblioteca Digital de Teses e Dissertações da UFRGS, 2016. http://hdl.handle.net/10183/163858.

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As organizações estão expostas a uma quantidade e variabilidade cada vez mais crescente de informações. A capacidade de se antecipar às tendências e se adequar ao ambiente passa a ser, além de fonte de vantagem competitiva, fator necessário para a sobrevivência. Nessa realidade, organizações frequentemente apresentam dificuldades de leitura de seu ambiente e adaptação ao meio. Acredita-se na necessidade de desenvolvimento de uma capacidade interna da organização para que o monitoramento do ambiente se estabeleça, bem como análise de oportunidades, planejamento de ações de melhoria e reconfiguração da organização. Este estudo buscou verificar a relação da Inteligência - enquanto processo de monitoramento do ambiente - como uma Capacidades Dinâmica, e de como esta pode contribuir com a geração de vantagem competitiva. Criou-se um modelo de pesquisa, utilizando-se dos modelos de Rios (2010) e Teece (2014), relacionando os conceitos de Inteligência e Capacidade Dinâmicas, e estas com a Vantagem Competitiva. Por meio de um questionário, realizou-se uma Pesquisa Survey, onde coletaram-se respostas de funcionários e/ou sócio de empresas brasileiras, independente de porte ou segmento. Para análise, utilizou-se da Modelagem de Equações Estruturais, e foi possível demonstrar que a Inteligência influencia positivamente nas Capacidades Dinâmicas do sub-grupo Transforming, na Estratégia e na Vantagem Competitiva. Dessa forma o modelo desenvolvido, tendo apresentado boa confiabilidade e aderência, pode também ser validado.
Organizations are exposed to an increasing amount and variability of information. The ability to anticipate trends and adapt to the environment becomes, besides a source of competitive advantage, a necessary factor for survival. In this reality, organizations frequently present difficulties in reading their environment and adapting to them. We believe in the need to develop an internal capacity of the organization for the monitoring of the environment to be established, as well as analysis of opportunities, planning of actions of improvement and reconfiguration of the organization. This study sought to verify the relationship of Intelligence - as a process of monitoring the environment - as a Dynamic Capabilities, and how this can contribute to the generation of competitive advantage. A research model was created, using the models of Rios (2010) and Teece (2014), relating the concepts of Dynamic Intelligence and Capacity, and these with the Competitive Advantage. By means of a questionnaire, a Survey Research was conducted, where responses were collected from employees and / or partners of Brazilian companies, regardless of size or segment. For the analysis, it was used the Modeling of Structural Equations, and it was possible to demonstrate that the Intelligence influences positively in the Dynamic Capacities of the Transforming subgroup, in the Strategy and the Competitive Advantage. In this way the developed model, having presented good reliability and adhesion, can also be validated.
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Konarski, Roman. "Sensitivity analysis for structural equation models." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1997. http://www.collectionscanada.ca/obj/s4/f2/dsk3/ftp04/nq22893.pdf.

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Cerqueira, Pedro Henrique Ramos. "Structural equation models applied to quantitative genetics." Universidade de São Paulo, 2015. http://www.teses.usp.br/teses/disponiveis/11/11134/tde-05112015-145419/.

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Causal models have been used in different areas of knowledge in order to comprehend the causal associations between variables. Over the past decades, the amount of studies using these models have been growing a lot, especially those related to biological systems where studying and learning causal relationships among traits are essential for predicting the consequences of interventions in such system. Graph analysis (GA) and structural equation modeling (SEM) are tools used to explore such associations. While GA allows searching causal structures that express qualitatively how variables are causally connected, fitting SEM with a known causal structure allows to infer the magnitude of causal effects. Also SEM can be viewed as multiple regression models in which response variables can be explanatory variables for others. In quantitative genetics studies, SEM aimed to study the direct and indirect genetic effects associated to individuals through information related to them, beyond the observed characteristics, such as the kinship relations. In those studies typically the assumptions of linear relationships among traits are made. However, in some scenarios, nonlinear relationships can be observed, which make unsuitable the mentioned assumptions. To overcome this limitation, this paper proposes to use a mixed effects polynomial structural equation model, second or superior degree, to model those nonlinear relationships. Two studies were developed, a simulation and an application to real data. The first study involved simulation of 50 data sets, with a fully recursive causal structure involving three characteristics in which linear and nonlinear causal relations between them were allowed. The second study involved the analysis of traits related to dairy cows of the Holstein breed. Phenotypic relationships between traits were calving difficulty, gestation length and also the proportion of perionatal death. We compare the model of multiple traits and polynomials structural equations models, under different polynomials degrees in order to assess the benefits of the SEM polynomial of second or higher degree. For some situations the inappropriate assumption of linearity results in poor predictions of the direct, indirect and total of the genetic variances and covariance, either overestimating, underestimating, or even assign opposite signs to covariances. Therefore, we conclude that the inclusion of a polynomial degree increases the SEM expressive power.
Modelos causais têm sido muitos utilizados em estudos em diferentes áreas de conhecimento, a fim de compreender as associações ou relações causais entre variáveis. Durante as últimas décadas, o uso desses modelos têm crescido muito, especialmente estudos relacionados à sistemas biológicos, uma vez que compreender as relações entre características são essenciais para prever quais são as consequências de intervenções em tais sistemas. Análise do grafo (AG) e os modelos de equações estruturais (MEE) são utilizados como ferramentas para explorar essas relações. Enquanto AG nos permite buscar por estruturas causais, que representam qualitativamente como as variáveis são causalmente conectadas, ajustando o MEE com uma estrutura causal conhecida nos permite inferir a magnitude dos efeitos causais. Os MEE também podem ser vistos como modelos de regressão múltipla em que uma variável resposta pode ser vista como explanatória para uma outra característica. Estudos utilizando MEE em genética quantitativa visam estudar os efeitos genéticos diretos e indiretos associados aos indivíduos por meio de informações realcionadas aos indivíduas, além das característcas observadas, como por exemplo o parentesco entre eles. Neste contexto, é tipicamente adotada a suposição que as características observadas são relacionadas linearmente. No entanto, para alguns cenários, relações não lineares são observadas, o que torna as suposições mencionadas inadequadas. Para superar essa limitação, este trabalho propõe o uso de modelos de equações estruturais de efeitos polinomiais mistos, de segundo grau ou seperior, para modelar relações não lineares. Neste trabalho foram desenvolvidos dois estudos, um de simulação e uma aplicação a dados reais. O primeiro estudo envolveu a simulação de 50 conjuntos de dados, com uma estrutura causal completamente recursiva, envolvendo 3 características, em que foram permitidas relações causais lineares e não lineares entre as mesmas. O segundo estudo envolveu a análise de características relacionadas ao gado leiteiro da raça Holandesa, foram utilizadas relações entre os seguintes fenótipos: dificuldade de parto, duração da gestação e a proporção de morte perionatal. Nós comparamos o modelo misto de múltiplas características com os modelos de equações estruturais polinomiais, com diferentes graus polinomiais, a fim de verificar os benefícios do MEE polinomial de segundo grau ou superior. Para algumas situações a suposição inapropriada de linearidade resulta em previsões pobres das variâncias e covariâncias genéticas diretas, indiretas e totais, seja por superestimar, subestimar, ou mesmo atribuir sinais opostos as covariâncias. Portanto, verificamos que a inclusão de um grau de polinômio aumenta o poder de expressão do MEE.
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Jung, Sunho. "Regularized structural equation models with latent variables." Thesis, McGill University, 2009. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=66858.

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In structural equation models with latent variables, maximum likelihood (ML) estimation is currently the most prevailing estimation method. However, the ML method fails to provide accurate solutions in a number of situations including those involving small sample sizes, nonnormality, and model misspecification. To over come these difficulties, regularized extensions of two-stage least squares estimation are proposed that incorporate a ridge type of regularization in the estimation of parameters. Two simulation studies and two empirical applications demonstrate that the proposed method is a promising alternative to both the maximum likelihood and non-regularized two-stage least squares estimation methods. An optimal value of the regularization parameter is found by the K-fold cross validation technique. A nonparametric bootstrap method is used to evaluate the stability of solutions. A goodness-of-fit measure is used for assessing the overall fit.
Dans les modèles d'équations structurales avec des variables latentes, l'estimation demaximum devraisemblance est la méthode d'estimation la plus utilisée. Par contre, la méthode de maximum devraisemblance souvent ne réussit pas á fournir des solutions exactes, par exemple lorsque les échantillons sont petits, les données ne sont pas normale, ou lorsque le modèle est mal specifié. L'estimation des moindres carrés á deux-phases est asymptotiquement sans distribution et robuste contre mauvaises spécifications, mais elle manque de robustesse quand les chantillons sont petits. Afin de surmonter les trois difficultés mentionnés ci-dessus et d'obtenir une estimation plus exacte, des extensions régularisées des moindres carrés á deux phases sont proposé á qui incorporent directement un type de régularisation dans les modèles d'équations structurales avec des variables latentes. Deux études de simulation et deux applications empiriques démontrent que la méthode propose est une alternative prometteuse aux méthodes de maximum vraisemblance et de l'estimation des moindres carrés á deux-phases. Un paramètre de régularisation valeur optimale a été trouvé par la technique de validation croisé d'ordre K. Une méthode non-paramétrique Bootstrap est utilisée afin d'évaluer la stabilité des solutions. Une mesure d'adéquation est utilisée pour estimer l'adéquation globale.
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Books on the topic "Dynamic structural equation models"

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Westland, J. Christopher. Structural Equation Models. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-12508-0.

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Westland, J. Christopher. Structural Equation Models. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-16507-3.

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A, Bollen Kenneth, and Long J. Scott, eds. Testing structural equation models. Newbury Park: Sage Publications, 1993.

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van Montfort, Kees, Johan Oud, and Albert Satorra, eds. Recent Developments on Structural Equation Models. Dordrecht: Springer Netherlands, 2004. http://dx.doi.org/10.1007/978-1-4020-1958-6.

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Stronge, W. J. Dynamic models for structural plasticity. London: Springer Verlag, 1993.

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Stronge, William James, and Tongxi Yu. Dynamic Models for Structural Plasticity. London: Springer London, 1993. http://dx.doi.org/10.1007/978-1-4471-0397-4.

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P, Wang B., American Society of Mechanical Engineers. Applied Mechanics Division., and Symposium on Reanalysis of Structural Dynamic Models (1986 : Anaheim, Calif.), eds. Reanalysis of structural dynamic models. New York, N.Y. (345 E. 47th St., New York 10017): ASME, 1986.

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1941-, Yu T. X., ed. Dynamic models for structural plasticity. London: Springer-Verlag, 1993.

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McArdle, John J., and John R. Nesselroade. Longitudinal data analysis using structural equation models. Washington: American Psychological Association, 2014. http://dx.doi.org/10.1037/14440-000.

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1965-, Curran Patrick J., ed. Latent curve models: A structural equation perspective. Hoboken, NJ: John Wiley & Sons, 2005.

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Book chapters on the topic "Dynamic structural equation models"

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McArdle, John J., and John R. Nesselroade. "Dynamic processes over groups." In Longitudinal data analysis using structural equation models., 307–14. Washington: American Psychological Association, 2014. http://dx.doi.org/10.1037/14440-027.

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McArdle, John J., and John R. Nesselroade. "Dynamic influences over groups." In Longitudinal data analysis using structural equation models., 315–17. Washington: American Psychological Association, 2014. http://dx.doi.org/10.1037/14440-028.

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Saccomani, Maria Pia, and Karl Thomaseth. "Structural vs Practical Identifiability of Nonlinear Differential Equation Models in Systems Biology." In Dynamics of Mathematical Models in Biology, 31–41. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-45723-9_3.

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Li, Ze-yu, Xue-bo Chen, and Qiubai Sun. "Dynamic Analysis of Enterprise Security System Based on Multi-level Analysis and Structural Equation Model." In Advances in Intelligent Systems and Computing, 217–26. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-94589-7_22.

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Hilbert, Sven, and Matthias Stadler. "Structural Equation Models." In Encyclopedia of Personality and Individual Differences, 5253–61. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-319-24612-3_1285.

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Bauldry, Shawn. "Structural Equation Models." In Encyclopedia of Gerontology and Population Aging, 1–3. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-319-69892-2_566-1.

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Raghunathan, Trivellore, Patricia A. Berglund, and Peter W. Solenberger. "Structural Equation Models." In Multiple Imputation in Practice, 110–19. Boca Raton, Florida : CRC Press, [2019] | Authors have developed a software for analyzing mathematical data, IVEware.: Chapman and Hall/CRC, 2018. http://dx.doi.org/10.1201/9781315154275-7.

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Hershberger, Scott L. "Structural Equation Models." In International Encyclopedia of Statistical Science, 1552–55. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-04898-2_576.

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Hilbert, Sven, and Matthias Stadler. "Structural Equation Models." In Encyclopedia of Personality and Individual Differences, 1–9. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-28099-8_1285-1.

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Bauldry, Shawn. "Structural Equation Models." In Encyclopedia of Gerontology and Population Aging, 4789–91. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-22009-9_566.

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Conference papers on the topic "Dynamic structural equation models"

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Baingana, Brian, Gonzalo Mateos, and Georgios B. Giannakis. "Dynamic structural equation models for tracking topologies of social networksy." In 2013 IEEE 5th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP). IEEE, 2013. http://dx.doi.org/10.1109/camsap.2013.6714065.

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Baingana, Brian, and Georgios B. Giannakis. "Switched dynamic structural equation models for tracking social network topologies." In 2015 IEEE Global Conference on Signal and Information Processing (GlobalSIP). IEEE, 2015. http://dx.doi.org/10.1109/globalsip.2015.7418283.

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Leishear, Robert A., and Jeffrey H. Morehouse. "Dynamic Stresses During Structural Impacts." In ASME 2003 International Mechanical Engineering Congress and Exposition. ASMEDC, 2003. http://dx.doi.org/10.1115/imece2003-55475.

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Dynamic stresses that occur when an object strikes a structure can be described by considering both vibration theory and conservation of energy principles. An object striking a simple beam is used as one of several examples. The complete stresses are found by adding the stresses obtained from a vibration equation to the stresses found by using a conservation of energy equation. When combined, these two stresses establish the maximum stress in a structure subject to impact. This solution assumes that each point within the beam acts as a linear system, reacting to the dynamic application of a load to the structure. When struck, the surface of the beam is momentarily compressed, and the beam bends. The equations describe both the localized stresses at the point of impact and the bending stresses in the beam following impact. These equations are then extended to an elastoplastic case, using a bilinear model to describe the dual natured linear elastic and linear plastic material behavior. The general solution technique is applicable to cases other than the simple beam.
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Akhavan, S., and H. Soltanian-Zadeh. "Topology tracking of static and dynamic networks based on structural equation models." In 2017 Artificial Intelligence and Signal Processing Conference (AISP). IEEE, 2017. http://dx.doi.org/10.1109/aisp.2017.8324119.

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Hemez, Francois. "Identifying Models of Truncation Error When Modified Equation Analysis is Intractable." In 50th AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials Conference. Reston, Virigina: American Institute of Aeronautics and Astronautics, 2009. http://dx.doi.org/10.2514/6.2009-2281.

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Lund, Erik, Henrik Møller, and Lars Aaes Jakobsen. "Shape Optimization of Fluid-Structure Interaction Problems Using Two-Equation Turbulence Models." In 43rd AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials Conference. Reston, Virigina: American Institute of Aeronautics and Astronautics, 2002. http://dx.doi.org/10.2514/6.2002-1478.

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Feng, Zhipeng, Qian Huang, Shuai Liu, Fengchun Cai, Xi Lv, and Xiaozhou Jiang. "Study on Dynamic Characteristics and Flow Induced Vibration of Tube Bundles Based on the Fluid Structure Coupling Method." In 2018 26th International Conference on Nuclear Engineering. American Society of Mechanical Engineers, 2018. http://dx.doi.org/10.1115/icone26-81342.

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In order to study the dynamic characteristics and fluid structure interactions of tubular structures under the action of fluid in reactor, such as fuel rod bundles and heat transfer tube bundle of steam generator, the dynamic equations and the acoustic wave equations of structures are discretized by finite element method. The acoustic wave equations are simplified from continuity equation and momentum equation of fluid field. Based on the fluid structure coupling method, the dynamic characteristics of the tube under the internal flow, external flow and combined action of internal and external flow are calculated respectively. The influence of flow field domain, element type and grid number on the dynamic characteristics of the tube is also analyzed. Secondly, based on the computational fluid dynamics and computational structural dynamics, the interaction between the two physical fields of fluid and structure is considered simultaneously. The finite volume method is used to discretize the fluid control equations and the turbulent flow is investigated using the large eddy simulation method (LES). The Newmark algorithm is used to solve the structural dynamic equations. Combined with the dynamic mesh control technique, a numerical model for flow induced vibration of three-dimensional flexible tube is established. Finally, the flow induced vibration of a three dimensional flexible single tube and a square arrangement tube bundle is calculated using the numerical model. By comparing with the existing research results, it is found that the numerical simulation results are in good agreement with the experimental results. Thus, the correctness of the model is verified. It is also shown that the numerical model established in this paper can be used to simulate the dynamic characteristics and flow induced vibration of tubular structures.
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Liu, Baixi, Hongzhao Liu, and Daning Yuan. "Five Parameters Structural Damping Constitution and Its Application." In ASME 7th Biennial Conference on Engineering Systems Design and Analysis. ASMEDC, 2004. http://dx.doi.org/10.1115/esda2004-58047.

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In this paper, the five parameters model of viscoelastic theory is introduced as the constitutive equation for damping alloy. Based on the experiment data, the five parameters are fitted by using an optimization algorithm. The finite element dynamic equations are derived through the established five parameters constitution. For the convenience of the computation, the established dynamic equations containing convolution integration are changed into ordinary differential equations. By means of the Kineto-Elastodynamic theory, the system dynamic equation of elastic linkage mechanism is gained. In order to solve the high order differential equations, the state space method is employed. An example is given to show that the model proposed in this paper is more accurate and stable than the three parameters damping model.
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MORADI, SARVIN, SAEED (YASHAR) EFTEKHAR AZAM, and MASSOOD MOFID. "PHYSICS-INFORMED NEURAL NETWORK APPROACH FOR IDENTIFICATION OF DYNAMIC SYSTEMS." In Structural Health Monitoring 2021. Destech Publications, Inc., 2022. http://dx.doi.org/10.12783/shm2021/36352.

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In this study, a novel method for online and real-time identification of dynamic systems is presented. This method is based on the newly introduced algorithm Physics Informed Neural Network (PINN). In order to find the dynamic characteristics of the system, sparse displacement measurements are fed to the Artificial Neural Network (ANN); By introducing the classic vibration equation of the system to the ANN as a physics constraint, the PINN estimates both dynamic characteristic and state of the system. The proposed framework is evaluated by several numerical studies with different system properties, noise levels, architecture, and training data. On that account, four structural systems are presented: (1) single-degree-of-freedom (SDOF) systems with different properties and noise levels, as basis model with an accurate analytical solution (2) a three-degree-of-freedom (3-DOF) system with both complete and sparse measurements, representing the structural model of the n-story shear frames (3) a simple supported beam subjected to an initial displacement with several NNs architecture and sensor numbers, and (4) a Pure Cubic Oscillator (PCO) as a nonlinear dynamic system. The results of the proposed platform for the PINN are compared to a mutual ANN in all cases to emphasize the superiority of the PINN in both determining the dynamic characteristics and state estimation of dynamic systems. In addition, the performance of both NNs is examined with different training data to ensure the resilience of the algorithm and affirm the role of the added criteria, physics constraint, in reducing the dependency on the training data. The proposed algorithm can accurately estimate the dynamic characteristics of different dynamic systems with sparse, noisy measurements; by means of the classic dynamic equations and smartly selection of the hidden layer numbers, the PINN will be a powerful predictive tool for the dynamic analysis in the absence of any prior knowledge of the dynamic systems.
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Zaman, Bakht, Luis Miguel Lopez Ramos, and Baltasar Beferull-Lozano. "Dynamic Regret Analysis for Online Tracking of Time-varying Structural Equation Model Topologies." In 2020 15th IEEE Conference on Industrial Electronics and Applications (ICIEA). IEEE, 2020. http://dx.doi.org/10.1109/iciea48937.2020.9248365.

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Reports on the topic "Dynamic structural equation models"

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Chen, Le-Yu. Identification of structural dynamic discrete choice models. Institute for Fiscal Studies, May 2009. http://dx.doi.org/10.1920/wp.cem.2009.0809.

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Kuether, Robert J., Jonel Ortiz, and Mark Chen. Model Order Reduction of Nonviscously Damped Structural Dynamic Models. Office of Scientific and Technical Information (OSTI), September 2018. http://dx.doi.org/10.2172/1475503.

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Canova, Fabio, and Filippo Ferroni. Mind the gap! Stylized Dynamic Facts and Structural Models. Federal Reserve Bank of Chicago, 2020. http://dx.doi.org/10.21033/wp-2020-29.

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Kalouptsidi, Myrto, Paul Scott, and Eduardo Souza-Rodrigues. Linear IV Regression Estimators for Structural Dynamic Discrete Choice Models. Cambridge, MA: National Bureau of Economic Research, October 2018. http://dx.doi.org/10.3386/w25134.

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Xin, Yi, and Yingyao Hu. Identification and estimation of dynamic structural models with unobserved choices. The IFS, June 2019. http://dx.doi.org/10.1920/wp.cem.2019.3519.

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Biezad, Daniel J. Investigation of Dynamic Structural Models Suitable for the Simulation of Large Aircraft. Fort Belvoir, VA: Defense Technical Information Center, November 1999. http://dx.doi.org/10.21236/ada383217.

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Campbell, R. L. Fluid Film Bearing Dynamic Coefficients and Their Application to Structural Finite Element Models. Fort Belvoir, VA: Defense Technical Information Center, August 2003. http://dx.doi.org/10.21236/ada465781.

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Kimhi, Ayal, Barry Goodwin, Ashok Mishra, Avner Ahituv, and Yoav Kislev. The dynamics of off-farm employment, farm size, and farm structure. United States Department of Agriculture, September 2006. http://dx.doi.org/10.32747/2006.7695877.bard.

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Objectives: (1) Preparing panel data sets for both the United States and Israel that contain a rich set of farm attributes, such as size, specialization, and output composition, and farmers’ characteristics such as off-farm employment status, education, and family composition. (2) Developing an empirical framework for the joint analysis of all the endogenous variables of interest in a dynamic setting. (3) Estimating simultaneous equations of the endogenous variables using the panel data sets from both countries. (4) Analyzing, using the empirical results, the possible effects of economic policies and institutional changes on the dynamics of the farm sector. An added objective is analyzing structural changes in farm sectors in additional countries. Background: Farm sectors in developed countries, including the U.S. and Israel, have experienced a sharp decline in their size and importance during the second half of the 20th century. The overall trend is towards fewer and larger farms that rely less on family labor. These structural changes have been a reaction to changes in technology, in government policies, and in market conditions: decreasing terms of trade, increasing alternative opportunities, and urbanization pressures. As these factors continue to change, so does the structure of the agricultural sector. Conclusions: We have shown that all major dimensions of structural changes in agriculture are closely interlinked. These include farm efficiency, farm scale, farm scope (diversification), and off-farm labor. We have also shown that these conclusions hold and perhaps even become stronger whenever dynamic aspects of structural adjustments are explicitly modeled using longitudinal data. While the results vary somewhat in the different applications, several common features are observed for both the U.S. and Israel. First, the trend towards the concentration of farm production in a smaller number of larger farm enterprises is likely to continue. Second, at the micro level, increased farm size is negatively associated with increased off-farm labor, with the causality going both ways. Third, the increase in farm size is mostly achieved by diversifying farm production into additional activities (crops or livestock). All these imply that the farm sector converges towards a bi-modal farm distribution, with some farms becoming commercial while the remaining farm households either exit farming altogether or continue producing but rely heavily on off-farm income. Implications: The primary scientific implication of this project is that one should not analyze a specific farm attribute in isolation. We have shown that controlling for the joint determination of the various farm and household attributes is crucial for obtaining meaningful empirical results. The policy implications are to some extent general but could be different in the two countries. The general implication is that farm policy is an important determinant of structural changes in the farm sector. For the U.S., we have shown the different effects of coupled and decoupled (direct) farm payments on the various farm attributes, and also shown that it is important to take into account the joint farm-household decisions in order to conduct a meaningful policy analysis. Only this kind of analysis explains the indirect effect of direct farm payments on farm production decisions. For Israel, we concluded that farm policy (or lack of farm policy) has contributed to the fast structural changes we observed over the last 25 years. The sharp change of direction in farm policy that started in the early 1980s has accelerated structural changes that could have been smoother otherwise. These accelerated structural changes most likely lead to welfare losses in rural areas.
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Snyder, Victor A., Dani Or, Amos Hadas, and S. Assouline. Characterization of Post-Tillage Soil Fragmentation and Rejoining Affecting Soil Pore Space Evolution and Transport Properties. United States Department of Agriculture, April 2002. http://dx.doi.org/10.32747/2002.7580670.bard.

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Tillage modifies soil structure, altering conditions for plant growth and transport processes through the soil. However, the resulting loose structure is unstable and susceptible to collapse due to aggregate fragmentation during wetting and drying cycles, and coalescense of moist aggregates by internal capillary forces and external compactive stresses. Presently, limited understanding of these complex processes often leads to consideration of the soil plow layer as a static porous medium. With the purpose of filling some of this knowledge gap, the objectives of this Project were to: 1) Identify and quantify the major factors causing breakdown of primary soil fragments produced by tillage into smaller secondary fragments; 2) Identify and quantify the. physical processes involved in the coalescence of primary and secondary fragments and surfaces of weakness; 3) Measure temporal changes in pore-size distributions and hydraulic properties of reconstructed aggregate beds as a function of specified initial conditions and wetting/drying events; and 4) Construct a process-based model of post-tillage changes in soil structural and hydraulic properties of the plow layer and validate it against field experiments. A dynamic theory of capillary-driven plastic deformation of adjoining aggregates was developed, where instantaneous rate of change in geometry of aggregates and inter-aggregate pores was related to current geometry of the solid-gas-liquid system and measured soil rheological functions. The theory and supporting data showed that consolidation of aggregate beds is largely an event-driven process, restricted to a fairly narrow range of soil water contents where capillary suction is great enough to generate coalescence but where soil mechanical strength is still low enough to allow plastic deforn1ation of aggregates. The theory was also used to explain effects of transient external loading on compaction of aggregate beds. A stochastic forInalism was developed for modeling soil pore space evolution, based on the Fokker Planck equation (FPE). Analytical solutions for the FPE were developed, with parameters which can be measured empirically or related to the mechanistic aggregate deformation model. Pre-existing results from field experiments were used to illustrate how the FPE formalism can be applied to field data. Fragmentation of soil clods after tillage was observed to be an event-driven (as opposed to continuous) process that occurred only during wetting, and only as clods approached the saturation point. The major mechanism of fragmentation of large aggregates seemed to be differential soil swelling behind the wetting front. Aggregate "explosion" due to air entrapment seemed limited to small aggregates wetted simultaneously over their entire surface. Breakdown of large aggregates from 11 clay soils during successive wetting and drying cycles produced fragment size distributions which differed primarily by a scale factor l (essentially equivalent to the Van Bavel mean weight diameter), so that evolution of fragment size distributions could be modeled in terms of changes in l. For a given number of wetting and drying cycles, l decreased systematically with increasing plasticity index. When air-dry soil clods were slightly weakened by a single wetting event, and then allowed to "age" for six weeks at constant high water content, drop-shatter resistance in aged relative to non-aged clods was found to increase in proportion to plasticity index. This seemed consistent with the rheological model, which predicts faster plastic coalescence around small voids and sharp cracks (with resulting soil strengthening) in soils with low resistance to plastic yield and flow. A new theory of crack growth in "idealized" elastoplastic materials was formulated, with potential application to soil fracture phenomena. The theory was preliminarily (and successfully) tested using carbon steel, a ductile material which closely approximates ideal elastoplastic behavior, and for which the necessary fracture data existed in the literature.
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Oden, J. T. Computational Methods for Nonlinear Dynamics Problems in Solid and Structural Mechanics: Models of Dynamic Frictional Phenomena in Metallic Structures. Fort Belvoir, VA: Defense Technical Information Center, March 1986. http://dx.doi.org/10.21236/ada174585.

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