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A review of methods currently employed for modeling static and dynamic characteristics of fluid film bearings is provided. In nearly all cases, the literature discusses the use of dynamic coefficients for low-order rotor-dynamics models in which a single stiffness and damping matrix is used to connect an individual rotor node to an individual stator/bearing node along the shaft centerline. The focus of the present work is on developing dynamic coefficients for high-order structural finite element models, which use a distribution of the coefficients over the journal circumference. While the methods presented here are applicable to many bearing types, the focus is on the most common bearing, the plain and tilting pad bearing designs. A numerical study is provided to show the importance of properly implementing the bearing coefficients by comparing the results for two methods of coefficient implementation for a rotor model. The results show differences in rotor-to-stator transfer accelerance, resonance frequencies, and damping levels.

A review of methods currently employed for modeling static and dynamic characteristics of fluid film bearings is provided. In nearly all cases, the literature discusses the use of dynamic coefficients for low-order rotor-dynamics models in which a single stiffness and damping matrix is used to connect an individual rotor node to an individual stator/bearing node along the shaft centerline. The focus of the present work is on developing dynamic coefficients for high-order structural finite element models, which use a distribution of the coefficients over the journal circumference. While the methods presented here are applicable to many bearing types, the focus is on the most common bearing, the plain and tilting pad bearing designs. A numerical study is provided to show the importance of properly implementing the bearing coefficients by comparing the results for two methods of coefficient implementation for a rotor model. The results show differences in rotor-to-stator transfer accelerance, resonance frequencies, and damping levels.

Model-based design encourages architects to use customized architectural languages that precisely target their problem domains. These languages, coined domain-specific modeling languages (DSMLs), are essential components of model-driven architecture (MDA), platform-based design, and model-integrated computing (MIC). As a result, tools and methods for DSMLs have blossomed: Key research includes the heterogeneous composition of DSMLs, model transformations between DSMLs, approximations of formal properties within DSMLs, and reuse of DSML semantics. Despite this success, some key foundational issues concerning DSMLs have not been addressed. My presentation will address one of these issues; the structural semantics of DSMLs. The structural semantics of some DSML describes the set of structurally well-formed models that belong to that language. I will describe a formalization of structural semantics that takes into account current metamodeling techniques, provides a formal understanding of metacircularity, and allows us to algorithmically decide if two metamodels are equivalent. I will also describe how this theory has been used to provide a formal underpinning for existing metaprogrammable tools, providing proof-theoretic methods for evaluating DSML properties. ©2007 Microsoft Corporation. All rights reserved.

This article proposes a spatial dynamic structural equation model for the analysis of housing prices at the State level in the USA. The study contributes to the existing literature by extending the use of dynamic factor models to the econometric analysis of multivariate lattice data. One of the main advantages of our model formulation is that by modeling the spatial variation via spatially structured factor loadings, we entertain the possibility of identifying similarity "regions" that share common time series components. The factor loadings are modeled as conditionally independent multivariate Gaussian Markov Random Fields while the common components are modeled by latent dynamic factors. The general model is proposed in a state-space formulation where both stationary and nonstationary autoregressive distributed-lag processes for the latent factors are considered. For the latent factors which exhibit a common trend, and hence are cointegrated, an error correction specification of the (vector) autoregressive distributed-lag process is proposed. Full probabilistic inference for the model parameters is facilitated by adapting standard Markov chain Monte Carlo (MCMC) algorithms for dynamic linear models to our model formulation. The fit of the model is discussed for a data set of 48 States for which we model the relationship between housing prices and the macroeconomy, using state level unemployment and per capita personal income.

In order to improve our abilities to discriminate low yield nuclear explosions and to obtain accurate yield estimates, a study has been undertaken to infer detailed source and structural parameters by direct inversion of broad band seismic data using laterally homogeneous forward modeling methods. Three key problems have been identified which must be solved: the development of an accurate and efficient algorithm for computing differential seismograms, which are necessary for the inversion procedure, the development of better methods for incorporating anelastic attenuation into modal synthesis computations, and the determination of starting solutions that will produce synthetic seismograms which have the same general characteristics that we see in the data. An exact differential seismogram algorithm has been developed and implemented which produces accurate differential seismograms at arbitrary frequencies and phase velocities. An exact complex pole based algorithm has been developed to account for anelastic attenuation. A new representation of earth upper mantle and crustal structure has been used which provides an explainable, plausible and implementable method for modeling the features that we see in regional broad band data. Regional wave propagation, Differential seismograms, Seismic scattering.

In recent years research on credit risk modelling has mainly focused on default probabilities. Recovery rates are usually modelled independently, quite often they are even assumed constant. Then, however, the structural connection between recovery rates and default probabilities is lost and the tails of the loss distribution can be underestimated considerably. The problem of underestimating tail losses becomes even more severe, when calibration issues are taken into account. To demonstrate this we choose a Merton-type structural model as our reference system. Diffusion and jump-diffusion are considered as underlying processes. We run Monte Carlo simulations of this model and calibrate different recovery models to the simulation data. For simplicity, we take the default probabilities directly from the simulation data. We compare a reduced-form model for recoveries with a constant recovery approach. In addition, we consider a functional dependence between recovery rates and default probabilities. This dependence can be derived analytically for the diffusion case. We find that the constant recovery approach drastically and systematically underestimates the tail of the loss distribution. The reduced-form recovery model shows better results, when all simulation data is used for calibration. However, if we restrict the simulation data used for calibration, the results for the reduced-form model deteriorate. We find the most reliable and stable results, when we make use of the functional dependence between recovery rates and default probabilities.

We employ Monte Carlo simulations to study the relaxation properties of the two-dimensional Coulomb glass in disordered semiconductors and the three-dimensional Bose glass in type-II superconductors in the presence of extended linear defects. We investigate the effects of adding non-zero random on-site energies from different distributions on the properties of the correlation-induced Coulomb gap in the density of states (DOS) and on the non-equilibrium aging kinetics highlighted by the density autocorrelation functions. We also probe the sensitivity of the system's equilibrium and non-equilibrium relaxation properties to instantaneous changes in the density of charge carriers in the Coulomb glass or flux lines in the Bose glass.

Evaluation of counterfactual queries (e.g., 'If A were true, would C have been true?') is important to fault diagnosis, planning, determination of liability, and policy analysis. We present a method for evaluating counter-factuals when the underlying causal model is represented by structural models a nonlinear generalization of the simultaneous equations models commonly used in econometrics and social sciences. This new method provides a coherent means for evaluating policies involving the control of variables which, prior to enacting the policy were influenced by other variables in the system.

Pearl and Mackenzie's excellent "Book of Why" contains an important example showing why learning from data alone does not properly compute 'potential outcomes' - which are the answers to counterfactual questions. To do so, we need to incorporate causal knowledge. This video presents Pearl's solution using structural equation models, but also demonstrates a more powerful and flexible solution using Bayesian networks (in AgenaRisk) that more fully exploits the underlying causal structure of the problem.

Pearl and Mackenzie's excellent "Book of Why" contains an important example showing why learning from data alone does not properly compute 'potential outcomes' - which are the answers to counterfactual questions. To do so, we need to incorporate causal knowledge. This video presents Pearl's solution using structural equation models, but also demonstrates a more powerful and flexible solution using Bayesian networks (in AgenaRisk) that more fully exploits the underlying causal structure of the problem.

This article is from Algorithms for Molecular Biology : AMB , volume 7 . Abstract Background: We present a way to compute the minimal semi-positive invariants of a Petri net representing a biological reaction system, as resolution of a Constraint Satisfaction Problem. The use of Petri nets to manipulate Systems Biology models and make available a variety of tools is quite old, and recently analyses based on invariant computation for biological models have become more and more frequent, for instance in the context of module decomposition. Results: In our case, this analysis brings both qualitative and quantitative information on the models, in the form of conservation laws, consistency checking, etc. thanks to finite domain constraint programming. It is noticeable that some of the most recent optimizations of standard invariant computation techniques in Petri nets correspond to well-known techniques in constraint solving, like symmetry-breaking. Moreover, we show that the simple and natural encoding proposed is not only efficient but also flexible enough to encompass sub/sur-invariants, siphons/traps, etc., i.e., other Petri net structural properties that lead to supplementary insight on the dynamics of the biochemical system under study. Conclusions: A simple implementation based on GNU-Prolog's finite domain solver, and including symmetry detection and breaking, was incorporated into the BIOCHAM modelling environment and in the independent tool Nicotine. Some illustrative examples and benchmarks are provided.

This article is from The European Journal of Neuroscience , volume 36 . Abstract Modern imaging methods allow a non-invasive assessment of both structural and functional brain connectivity. This has lead to the identification of disease-related alterations affecting functional connectivity. The mechanism of how such alterations in functional connectivity arise in a structured network of interacting neural populations is as yet poorly understood. Here we use a modeling approach to explore the way in which this can arise and to highlight the important role that local population dynamics can have in shaping emergent spatial functional connectivity patterns. The local dynamics for a neural population is taken to be of the Wilson–Cowan type, whilst the structural connectivity patterns used, describing long-range anatomical connections, cover both realistic scenarios (from the CoComac database) and idealized ones that allow for more detailed theoretical study. We have calculated graph–theoretic measures of functional network topology from numerical simulations of model networks. The effect of the form of local dynamics on the observed network state is quantified by examining the correlation between structural and functional connectivity. We document a profound and systematic dependence of the simulated functional connectivity patterns on the parameters controlling the dynamics. Importantly, we show that a weakly coupled oscillator theory explaining these correlations and their variation across parameter space can be developed. This theoretical development provides a novel way to characterize the mechanisms for the breakdown of functional connectivity in diseases through changes in local dynamics.

This article is from The European Journal of Neuroscience , volume 36 . Abstract Modern imaging methods allow a non-invasive assessment of both structural and functional brain connectivity. This has lead to the identification of disease-related alterations affecting functional connectivity. The mechanism of how such alterations in functional connectivity arise in a structured network of interacting neural populations is as yet poorly understood. Here we use a modeling approach to explore the way in which this can arise and to highlight the important role that local population dynamics can have in shaping emergent spatial functional connectivity patterns. The local dynamics for a neural population is taken to be of the Wilson–Cowan type, whilst the structural connectivity patterns used, describing long-range anatomical connections, cover both realistic scenarios (from the CoComac database) and idealized ones that allow for more detailed theoretical study. We have calculated graph–theoretic measures of functional network topology from numerical simulations of model networks. The effect of the form of local dynamics on the observed network state is quantified by examining the correlation between structural and functional connectivity. We document a profound and systematic dependence of the simulated functional connectivity patterns on the parameters controlling the dynamics. Importantly, we show that a weakly coupled oscillator theory explaining these correlations and their variation across parameter space can be developed. This theoretical development provides a novel way to characterize the mechanisms for the breakdown of functional connectivity in diseases through changes in local dynamics.

There is an increasing need to develop Parameter Identification methods for improving structural dynamic models, based on the inability of engineers to produce mathematical models which correlate with experimental data. This research explores the efficiency of combining Component Mode Synthesis (substructuring) methods with Parameter Identification procedures in order to improve analytical modeling of structural components and their connections. Improvements are computed in terms of physical stiffness and damping parameters in order that the physical characteristics of the model can be better understood. Connections involving both viscous and friction damping are investigated. Substructuring methods are utilized to reduce the complexity of the identification problem. Component and inter-component structural connection properties are evaluated and identified independently, thus simplifying the identification problem. It is shown that modal test data is effective for identifying modeling problems associated with structural components, and for determining the stiffness and damping properties of intercomponent connections. In general, Parameter Identification is improved when greater quantities of experimental data are available.

Previously the Exploration Concepts Branch of NASA Langley Research Center has developed techniques for automating the preliminary design level of launch vehicle airframe structural analysis for purposes of enhancing historical regression based mass estimating relationships. This past work was useful and greatly reduced design time, however its application area was very narrow in terms of being able to handle a large variety in structural and vehicle general arrangement alternatives. Implementation of the analysis approach presented herein also incorporates some newly developed computer programs. Loft is a program developed to create analysis meshes and simultaneously define structural element design regions. A simple component defining ASCII file is read by Loft to begin the design process. HSLoad is a Visual Basic implementation of the HyperSizer Application Programming Interface, which automates the structural element design process. Details of these two programs and their use are explained in this paper. A feature which falls naturally out of the above analysis paradigm is the concept of "reference models". The flexibility of the FEA based JAVA processing procedures and associated process control classes coupled with the general utility of Loft and HSLoad make it possible to create generic program template files for analysis of components ranging from something as simple as a stiffened flat panel, to curved panels, fuselage and cryogenic tank components, flight control surfaces, wings, through full air and space vehicle general arrangements.

The excellent mechanical properties of laminated composite structures make them prime candidates for wide variety of applications in aerospace, mechanical and other branches of engineering. The enormous design flexibility of advanced composites is obtained at the cost of large number of design parameters. Due to complexity of the systems and lack of complete design based informations, designers tend to be conservative in their design. Furthermore, any new design is extensively evaluated experimentally until it achieves the necessary reliability, performance and safety. However, the experimental evaluation of composite structures are costly and time consuming. Consequently, it is extremely useful if a full-scale structure can be replaced by a similar scaled-down model which is much easier to work with. Furthermore, a dramatic reduction in cost and time can be achieved, if available experimental data of a specific structure can be used to predict the behavior of a group of similar systems. This study investigates problems associated with the design of scaled models. Such study is important since it provides the necessary scaling laws, and the factors which affect the accuracy of the scale models. Similitude theory is employed to develop the necessary similarity conditions (scaling laws). Scaling laws provide relationship between a full-scale structure and its scale model, and can be used to extrapolate the experimental data of a small, inexpensive, and testable model into design information for a large prototype. Due to large number of design parameters, the identification of the principal scaling laws by conventional method (dimensional analysis) is tedious. Similitude theory based on governing equations of the structural system is more direct and simpler in execution. The difficulty of making completely similar scale models often leads to accept certain type of distortion from exact duplication of the prototype (partial similarity). Both complete and partial similarity are discussed. The procedure consists of systematically observing the effect of each parameter and corresponding scaling laws. Then acceptable intervals and limitations for these parameters and scaling laws are discussed. In each case, a set of valid scaling factors and corresponding response scaling laws that accurately predict the response of prototypes from experimental models is introduced. The examples used include rectangular laminated plates under destabilizing loads, applied individually, vibrational characteristics of same plates, as well as cylindrical bending of beam-plates.

The excellent mechanical properties of laminated composite structures make them prime candidates for wide variety of applications in aerospace, mechanical and other branches of engineering. The enormous design flexibility of advanced composites is obtained at the cost of large number of design parameters. Due to complexity of the systems and lack of complete design based informations, designers tend to be conservative in their design. Furthermore, any new design is extensively evaluated experimentally until it achieves the necessary reliability, performance and safety. However, the experimental evaluation of composite structures are costly and time consuming. Consequently, it is extremely useful if a full-scale structure can be replaced by a similar scaled-down model which is much easier to work with. Furthermore, a dramatic reduction in cost and time can be achieved, if available experimental data of a specific structure can be used to predict the behavior of a group of similar systems. This study investigates problems associated with the design of scaled models. Such study is important since it provides the necessary scaling laws, and the factors which affect the accuracy of the scale models. Similitude theory is employed to develop the necessary similarity conditions (scaling laws). Scaling laws provide relationship between a full-scale structure and its scale model, and can be used to extrapolate the experimental data of a small, inexpensive, and testable model into design information for a large prototype. Due to large number of design parameters, the identification of the principal scaling laws by conventional method (dimensional analysis) is tedious. Similitude theory based on governing equations of the structural system is more direct and simpler in execution. The difficulty of making completely similar scale models often leads to accept certain type of distortion from exact duplication of the prototype (partial similarity). Both complete and partial similarity are discussed. The procedure consists of systematically observing the effect of each parameter and corresponding scaling laws. Then acceptable intervals and limitations for these parameters and scaling laws are discussed. In each case, a set of valid scaling factors and corresponding response scaling laws that accurately predict the response of prototypes from experimental models is introduced. The examples used include rectangular laminated plates under destabilizing loads, applied individually, vibrational characteristics of same plates, as well as cylindrical bending of beam-plates.

This paper deals simultaneously with linear structural and functional error-in-variables models (SEIVM and FEIVM), revisiting in this context generalized and modified least squares estimators of the slope and intercept, and some methods of moments estimators of unknown variances of the measurement errors. New joint central limit theorems (CLT's) are established for these estimators in the SEIVM and FEIVM under some first time, so far the most general, respective conditions on the explanatory variables, and under the existence of four moments of the measurement errors. Moreover, due to them being in Studentized forms to begin with, the obtained CLT's are a priori nearly, or completely, data-based, and free of unknown parameters of the distribution of the errors and any parameters associated with the explanatory variables. In contrast, in related CLT's in the literature so far, the covariance matrices of the limiting normal distributions are, in general, complicated and depend on various, typically unknown parameters that are hard to estimate. In addition, the very forms of the CLT's in the present paper are universal for the SEIVM and FEIVM. This extends a previously known interplay between a SEIVM and a FEIVM. Moreover, though the particular methods and details of the proofs of the CLT's in the SEIVM and FEIVM that are established in this paper are quite different, a unified general scheme of these proofs is constructed for the two models herewith.

Current theories hold that brain function is highly related to long-range physical connections through axonal bundles, namely extrinsic connectiv-ity. However, obtaining a groupwise cortical parcellation based on extrinsic connectivity remains challenging. Current parcellation methods are compu-tationally expensive; need tuning of several parameters or rely on ad-hoc constraints. Furthermore, none of these methods present a model for the cortical extrinsic connectivity of the cortex. To tackle these problems, we propose a parsimonious model for the extrinsic connectivity and an efficient parceling technique based on clustering of tractograms. Our technique allows the creation of single subject and groupwise parcellations of the whole cortex. The parcellations obtained with our technique are in agreement with structural and functional parcellations in the literature. In particular, the motor and sensory cortex are subdivided in agreement with the human ho-munculus of Penfield. We illustrate this by comparing our resulting parcels with the motor strip mapping included in the Human Connectome Project data.

Schwarzschild models of a Plummer sphere were constructed and compared with analytical results to test the robustness of Schwarzschild's method and its efficiency in probing the degeneracy of the solution space. The method was then used to construct and study the nonuniqueness of triaxial equilibria with central density cusps, for which no analytical solutions are known. The stability of some models was also tested via N-body simulations.

We study structural transitions in a system of interacting particles arranged as a crystalline bilayer, as a function of the density $\rho$ and the distance $d$ between the layers. As $d$ is decreased a sequence of transitions involving triangular, rhombic, square and centered rectangular lattices is observed. The sequence of phases and the order of transitions depends on the nature of interactions.

We provide two fundamental results on the population (infinite-sample) likelihood function of Gaussian mixture models with $M \geq 3$ components. Our first main result shows that the population likelihood function has bad local maxima even in the special case of equally-weighted mixtures of well-separated and spherical Gaussians. We prove that the log-likelihood value of these bad local maxima can be arbitrarily worse than that of any global optimum, thereby resolving an open question of Srebro (2007). Our second main result shows that the EM algorithm (or a first-order variant of it) with random initialization will converge to bad critical points with probability at least $1-e^{-\Omega(M)}$. We further establish that a first-order variant of EM will not converge to strict saddle points almost surely, indicating that the poor performance of the first-order method can be attributed to the existence of bad local maxima rather than bad saddle points. Overall, our results highlight the necessity of careful initialization when using the EM algorithm in practice, even when applied in highly favorable settings.

The structural default model of Lipton and Sepp, 2009 is generalized for a set of banks with mutual interbank liabilities whose assets are driven by correlated Levy processes with idiosyncratic and common components. The multi-dimensional problem is made tractable via a novel computational method, which generalizes the one-dimensional fractional partial differential equation method of Itkin, 2014 to the two- and three-dimensional cases. This method is unconditionally stable and of the second order of approximation in space and time; in addition, for many popular Levy models it has linear complexity in each dimension. Marginal and joint survival probabilities for two and three banks with mutual liabilities are computed. The effects of mutual liabilities are discussed, and numerical examples are given to illustrate these effects.

The use of continuum models for the analysis of discrete built-up complex aerospace structures is an attractive idea especially at the conceptual and preliminary design stages. But the diversity of available continuum models and hard-to-use qualities of these models have prevented them from finding wide applications. In this regard, Artificial Neural Networks (ANN or NN) may have a great potential as these networks are universal approximators that can realize any continuous mapping, and can provide general mechanisms for building models from data whose input-output relationship can be highly nonlinear. The ultimate aim of the present work is to be able to build high fidelity continuum models for complex aerospace structures using the ANN. As a first step, the concepts and features of ANN are familiarized through the MATLAB NN Toolbox by simulating some representative mapping examples, including some problems in structural engineering. Then some further aspects and lessons learned about the NN training are discussed, including the performances of Feed-Forward and Radial Basis Function NN when dealing with noise-polluted data and the technique of cross-validation. Finally, as an example of using NN in continuum models, a lattice structure with repeating cells is represented by a continuum beam whose properties are provided by neural networks.

The use of instrumental variables for estimating the effect of an exposure on an outcome is popular in econometrics, and increasingly so in epidemiology. This increasing popularity may be attributed to the natural occurrence of instrumental variables in observational studies that incorporate elements of randomization, either by design or by nature (e.g., random inheritance of genes). Instrumental variables estimation of exposure effects is well established for continuous outcomes and to some extent for binary outcomes. It is, however, largely lacking for time-to-event outcomes because of complications due to censoring and survivorship bias. In this paper, we make a novel proposal under a class of structural cumulative survival models which parameterize time-varying effects of a point exposure directly on the scale of the survival function; these models are essentially equivalent with a semi-parametric variant of the instrumental variables additive hazards model. We propose a class of recursive instrumental variable estimators for these exposure effects, and derive their large sample properties along with inferential tools. We examine the performance of the proposed method in simulation studies and illustrate it in a Mendelian randomization study to evaluate the effect of diabetes on mortality using data from the Health and Retirement Study. We further use the proposed method to investigate potential benefit from breast cancer screening on subsequent breast cancer mortality based on the HIP-study.

Cosmological singularity and asymptotic behaviour of scale factor of generalized cosmological models are analyzed in respect of their structural stability. It is shown, that cosmological singularity is structurally unstable for the majority of models with barotropic perfect fluid with strong energy condition. Inclusion of Lambda-term extends the set of structurally stable cosmological models.

In this paper, we address the problem of identifying linear structural equation models. We first extend the edge set half-trek criterion to cover a broader class of models. We then show that any semi-Markovian linear model can be recursively decomposed into simpler sub-models, resulting in improved identification power. Finally, we show that, unlike the existing methods developed for linear models, the resulting method subsumes the identification algorithm of non-parametric models.

The use of continuum models for the analysis of discrete built-up complex aerospace structures is an attractive idea especially at the conceptual and preliminary design stages. But the diversity of available continuum models and hard-to-use qualities of these models have prevented them from finding wide applications. In this regard, Artificial Neural Networks (ANN or NN) may have a great potential as these networks are universal approximators that can realize any continuous mapping, and can provide general mechanisms for building models from data whose input-output relationship can be highly nonlinear. The ultimate aim of the present work is to be able to build high fidelity continuum models for complex aerospace structures using the ANN. As a first step, the concepts and features of ANN are familiarized through the MATLAB NN Toolbox by simulating some representative mapping examples, including some problems in structural engineering. Then some further aspects and lessons learned about the NN training are discussed, including the performances of Feed-Forward and Radial Basis Function NN when dealing with noise-polluted data and the technique of cross-validation. Finally, as an example of using NN in continuum models, a lattice structure with repeating cells is represented by a continuum beam whose properties are provided by neural networks.

The use of continuum models for the analysis of discrete built-up complex aerospace structures is an attractive idea especially at the conceptual and preliminary design stages. But the diversity of available continuum models and hard-to-use qualities of these models have prevented them from finding wide applications. In this regard, Artificial Neural Networks (ANN or NN) may have a great potential as these networks are universal approximators that can realize any continuous mapping, and can provide general mechanisms for building models from data whose input-output relationship can be highly nonlinear. The ultimate aim of the present work is to be able to build high fidelity continuum models for complex aerospace structures using the ANN. As a first step, the concepts and features of ANN are familiarized through the MATLAB NN Toolbox by simulating some representative mapping examples, including some problems in structural engineering. Then some further aspects and lessons learned about the NN training are discussed, including the performances of Feed-Forward and Radial Basis Function NN when dealing with noise-polluted data and the technique of cross-validation. Finally, as an example of using NN in continuum models, a lattice structure with repeating cells is represented by a continuum beam whose properties are provided by neural networks.

The performed project focused on a new paradigm of planning systems that are based on a combination of Bayesian networks and structural equation models. We focused on theoretical issues that surround combining the two in a practical planning system, developing the foundations for, and building a prototype of such system. The approach and the system built allow for efficient, yet normatively correct, treatment of various types of information, uncertainty, and utility. It is especially powerful in complex situations where the available information is heterogeneous and consists of a mixture of deterministic and uncertain relationships among discrete and continuous variables. Our main contributions are: (1) several fast state of the art stochastic sampling algorithms for approximate inference in graphical models, (2) treatment of reversible causal mechanisms for causal reasoning in graphical models, (3) a scheme for interactive construction of causal graphical models based on causal mechanisms, (4) an algorithm for learning graphical models from data, and (5) a prototype of the system, used by over 2,300 people world-wide.

This paper illustrates first how estimated Structural Equation Modeling (SEM) measurement error variances are actually estimates of score reliabilities. The major advantage of SEM over other analytic methods is that it accounts for measurement error. Score reliabilities are estimated as part of structural modeling, so that structural models test both substantive hypothesis and measurement models. The paper also demonstrates how changing the estimated reliabilities of the SEM observed variables not only changes estimated reliabilities, but can also change all the other parameters throughout the analysis. A heuristic data set is used to illustrate these changes. One appendix contains the Statistical Package for the Social Sciences syntax used for the illustration, and the other shows data from a previous study used for illustrative purposes. (Contains 4 tables, 6 figures, and 33 references.) (Author/SLD)

This paper illustrates first how estimated Structural Equation Modeling (SEM) measurement error variances are actually estimates of score reliabilities. The major advantage of SEM over other analytic methods is that it accounts for measurement error. Score reliabilities are estimated as part of structural modeling, so that structural models test both substantive hypothesis and measurement models. The paper also demonstrates how changing the estimated reliabilities of the SEM observed variables not only changes estimated reliabilities, but can also change all the other parameters throughout the analysis. A heuristic data set is used to illustrate these changes. One appendix contains the Statistical Package for the Social Sciences syntax used for the illustration, and the other shows data from a previous study used for illustrative purposes. (Contains 4 tables, 6 figures, and 33 references.) (Author/SLD)

Modern combustion methods and the constant wish for an increase in the specific performance results in high thermal and mechanical loads in modern combustion engines. In order to shorten the development time, an interactive, simultaneous execution of design (CAD) and calculation activities (CAE) is necessary. In this the product must be looked at in detail and be optimized in various disciplines and in a harmonized way of procedure to the development progress. In the initial proactive phase the efficiency of virtual CAE- methods is high and quickly decreases in the second half of the development, which shows strongly reactive features. Therefore a combined HCF (High-Cycle-Fatigue) Failure Analysis and Optimization and TMF (Thermo-Mechanical-Fatigue) Failure Analysis and Optimization model is discussed in this paper for the reduction in the analysis time.

This article is from Bioinformatics , volume 30 . Abstract Motivation: Membrane proteins are an important class of biological macromolecules involved in many cellular key processes including signalling and transport. They account for one third of genes in the human genome and >50% of current drug targets. Despite their importance, experimental structural data are sparse, resulting in high expectations for computational modelling tools to help fill this gap. However, as many empirical methods have been trained on experimental structural data, which is biased towards soluble globular proteins, their accuracy for transmembrane proteins is often limited.Results: We developed a local model quality estimation method for membrane proteins (‘QMEANBrane’) by combining statistical potentials trained on membrane protein structures with a per-residue weighting scheme. The increasing number of available experimental membrane protein structures allowed us to train membrane-specific statistical potentials that approach statistical saturation. We show that reliable local quality estimation of membrane protein models is possible, thereby extending local quality estimation to these biologically relevant molecules.Availability and implementation: Source code and datasets are available on request.Contact:[email protected] Information:Supplementary data are available at Bioinformatics online.

This article is from Bioinformatics , volume 30 . Abstract Motivation: Membrane proteins are an important class of biological macromolecules involved in many cellular key processes including signalling and transport. They account for one third of genes in the human genome and >50% of current drug targets. Despite their importance, experimental structural data are sparse, resulting in high expectations for computational modelling tools to help fill this gap. However, as many empirical methods have been trained on experimental structural data, which is biased towards soluble globular proteins, their accuracy for transmembrane proteins is often limited.Results: We developed a local model quality estimation method for membrane proteins (‘QMEANBrane’) by combining statistical potentials trained on membrane protein structures with a per-residue weighting scheme. The increasing number of available experimental membrane protein structures allowed us to train membrane-specific statistical potentials that approach statistical saturation. We show that reliable local quality estimation of membrane protein models is possible, thereby extending local quality estimation to these biologically relevant molecules.Availability and implementation: Source code and datasets are available on request.Contact:[email protected] Information:Supplementary data are available at Bioinformatics online.

We test the reproducibility of X-ray reflectometry(XRR) measurements and optimizations using an National Metrology Institute of Japan (NMIJ)/National Institute of Advanced Industrial Science and Technology (AIST) pre-standard. Based on bootstrap analysis of repeated refinements, using several CPU-years of time, we provide concrete recommendations of best practices for ensuring the reproducibility of XRR model fitting results. These recommendations can be used to study both instrument repeatability and cross-instrument reproducibility. Because the recommendations used optimizations methods available in commonly used commercial software, they can quickly be applied both in research and analytical laboratories, as well as fabrication environments.

Structural equation modeling (SEM) applications routinely employ a trilogy of significance tests that includes the likelihood ratio test, Wald test, and score test or modification index. Researchers use these tests to assess global model fit, evaluate whether individual estimates differ from zero, and identify potential sources of local misfit, respectively. This full cadre of significance testing options is not yet available for multiply imputed data sets, as methodologists have yet to develop a general score test for this context. Thus, the goal of this paper is to outline a new score test for multiply imputed data. Consistent with its complete-data counterpart, this imputation-based score test provides an estimate of the familiar expected parameter change statistic. The new procedure is available in the R package semTools and naturally suited for identifying local misfit in SEM applications (i.e., a model modification index). The article uses a simulation study to assess the performance (Type I error rate, power) of the proposed score test relative to the score test produced by full information maximum likelihood (FIML) estimation. Owing to the two-stage nature of multiple imputation, the score test exhibited slightly lower power than the corresponding FIML statistic in some situations but was generally well-calibrated. [The paper was published in "Psychological Methods."]

Posterior Predictive Model Checking (PPMC) is a Bayesian model checking method that compares the observed data to (plausible) future observations from the posterior predictive distribution. We propose an alternative to PPMC in the context of structural equation modeling, which we term the Poor Persons PPMC (PP-PPMC), for the situation wherein one cannot afford (or is unwilling) to draw samples from the full posterior. Using only byproducts of likelihood-based estimation (maximum likelihood estimate and information matrix), the PP-PPMC offers a natural method to handle parameter uncertainty in model fit assessment. In particular, a coupling relationship between the classical p-values from the model fit chi-square test and the predictive p-values from the PP-PPMC method is carefully examined, suggesting that PP-PPMC may offer an alternative, principled approach for model fit assessment. We also illustrate the flexibility of the PP-PPMC approach by applying it to case-influence diagnostics. [This paper was published in "Structural Equation Modeling" v23 n2 p206-220 2016.]

A feasibility study was performed to examine the advisability of incorporating a set of Programmed Test Inputs (PTIs) during the Space Launch System (SLS) vehicle flight. The intent of these inputs is to provide validation to the preflight models for control system stability margins, aerodynamics, and structural dynamics. During October 2009, Ares I-X program was successful in carrying out a series of PTI maneuvers which provided a significant amount of valuable data for post-flight analysis. The resulting data comparisons showed excellent agreement with the preflight linear models across the frequency spectrum of interest. However unlike Ares I-X, the structural dynamics associated with the SLS boost phase configuration are far more complex and highly coupled in all three axes. This presents a challenge when implementing this similar system identification technique to SLS. Preliminary simulation results show noticeable mismatches between PTI validation and analytical linear models in the frequency range of the structural dynamics. An alternate approach was examined which demonstrates the potential for better overall characterization of the system frequency response as well as robustness of the control design.

In the design and development of dynamically similar structural models a distorted model of the panel is used. The panel thickness is made larger than that dictated by geometric scaling, and the mass of the panel is decreased by adding mass to the surface of the panel to counteract the additional stiffness obtained by the thickness increase.

Using ReactionKinetics, a Mathematica based package a few dozen detailed models for combustion of hydrogen, carbon monoxide and methanol are investigated. Essential structural characteristics are pulled out, and similarities and differences of the mechanisms are highlighted. These investigations can be used before or parallel with usual numerical investigations, such as pathway analysis, sensitivity analysis, parameter estimation, or simulation.

These secondary data analyses of the MTA longitudinal follow-up examine long-term substance use risk associated with cumulative exposure to stimulant medication within a causal modeling framework, adjusting for a comprehensive list of sociodemographic and clinical covariates.

Under the sponsorship of AFOSR, USU carried out a challenging research project to develop efficient high-fidelity, geometrically exact, multi physics structural models for synergetic exploitations of static or dynamic large nonlinear structural deformations under coupled thermal, electromagnetic, and mechanical loads. The approach uses the concept of decomposition of the rotation tensor to systematically capture all geometrical nonlinearities, and uses the variational asymptotic method to achieve an excellent tradeoff between accuracy and efficiency. In the first year, we have carried out a critical assessment of the proposed approach and developed models for functionally graded plates. In the second year, we developed models for functionally graded smart plates responsive to electromagnetical fields in addition to mechanical fields. We also developed a new classical plate model for heterogeneous plates through simultaneous homogenization and dimensional reduction. In the third year, we initiated the development for a geometrically exact plate analysis, a refined plate model for heterogeneous plates, and used the finite element method to solve the multi physics plate model and confirmed our analytical solutions.

NIST Manuscript Publication Search

In the first part of this talk, I will present a spam filtering method based on statistical data compression models. The nature of these models allows them to be employed as Bayesian text classifiers based on character sequences. The models are fast to construct and incrementally updateable. I will present experimental results indicating that this method performs well in comparison to established spam filters, and that the method is extremely robust to noise, which should make it difficult for spammers to defeat. I will also give some examples, which show that the method is capable of picking up interesting, non-trivial patterns that are indicative of spam/ham. The second part of this talk describes how to exploit structural information for document categorization. Classifier stacking can be used to exploit the structure of semi-structured documents for improved text categorization performance. In this approach, a meta-classifier is used to combine predictions based on different structural elements. It will be shown that this approach consistently outperforms a flat-text linear SVM on a number of standard text categorization datasets, often by a wide margin. I will present selected nomograms that visualize the resulting meta-classifier and give interesting insight into the characteristics of the datasets. ©2005 Microsoft Corporation. All rights reserved.

This study examined the effect of type of correlation matrix on the robustness of LISREL maximum likelihood and unweighted least squares structural parameter estimates for models with categorical manifest variables. Two types of correlation matrices were analyzed; one containing Pearson product-moment correlations and one containing tetrachoric, polyserial, and product-moment correlations as appropriate. Using continuous variables generated according to the equations defining the population model, three cases were considered by dichotomizing some of the variables with varying degrees of skewness. When Pearson product-moment correlations were used to estimate associations involving dichotomous variables, the structural parameter estimates were biased when skewness was present in the dichotomous variables. Moreover, the degree of bias was consistent for both the maximum likelihood and unweighted least squares estimates. The analysis of mixed matrices produced average estimates that more closely approximated the model parameters except in the case where the dichotomous variables were skewed in opposite directions. (Author/BS)

There have been many challenges associated with modeling some of NASA KSC's largest structures. Given the size of the welded structures here at KSC, it was critically important to properly organize model struc.ture and carefully manage references. Additionally, because of the amount of hardware to be installed on these structures, it was very important to have a means to coordinate between different design teams and organizations, check for interferences, produce consistent drawings, and allow for simple release processes. Facing these challenges, the modeling team developed a unique reference management methodology and model fidelity methodology. This presentation will describe the techniques and methodologies that were developed for these projects. The attendees will learn about KSC's reference management and model fidelity methodologies for large structures. The attendees will understand the goals of these methodologies. The attendees will appreciate the advantages of developing a reference management methodology.

Structural equation models (SEMs) have been widely adopted for inference of causal interactions in complex networks. Recent examples include unveiling topologies of hidden causal networks over which processes such as spreading diseases, or rumors propagate. The appeal of SEMs in these settings stems from their simplicity and tractability, since they typically assume linear dependencies among observable variables. Acknowledging the limitations inherent to adopting linear models, the present paper advocates nonlinear SEMs, which account for (possible) nonlinear dependencies among network nodes. The advocated approach leverages kernels as a powerful encompassing framework for nonlinear modeling, and an efficient estimator with affordable tradeoffs is put forth. Interestingly, pursuit of the novel kernel-based approach yields a convex regularized estimator that promotes edge sparsity, and is amenable to proximal-splitting optimization methods. To this end, solvers with complementary merits are developed by leveraging the alternating direction method of multipliers, and proximal gradient iterations. Experiments conducted on simulated data demonstrate that the novel approach outperforms linear SEMs with respect to edge detection errors. Furthermore, tests on a real gene expression dataset unveil interesting new edges that were not revealed by linear SEMs, which could shed more light on regulatory behavior of human genes.

Structural equation models (SEMs) and vector autoregressive models (VARMs) are two broad families of approaches that have been shown useful in effective brain connectivity studies. While VARMs postulate that a given region of interest in the brain is directionally connected to another one by virtue of time-lagged influences, SEMs assert that causal dependencies arise due to contemporaneous effects, and may even be adopted when nodal measurements are not necessarily multivariate time series. To unify these complementary perspectives, linear structural vector autoregressive models (SVARMs) that leverage both contemporaneous and time-lagged nodal data have recently been put forth. Albeit simple and tractable, linear SVARMs are quite limited since they are incapable of modeling nonlinear dependencies between neuronal time series. To this end, the overarching goal of the present paper is to considerably broaden the span of linear SVARMs by capturing nonlinearities through kernels, which have recently emerged as a powerful nonlinear modeling framework in canonical machine learning tasks, e.g., regression, classification, and dimensionality reduction. The merits of kernel-based methods are extended here to the task of learning the effective brain connectivity, and an efficient regularized estimator is put forth to leverage the edge sparsity inherent to real-world complex networks. Judicious kernel choice from a preselected dictionary of kernels is also addressed using a data-driven approach. Extensive numerical tests on ECoG data captured through a study on epileptic seizures demonstrate that it is possible to unveil previously unknown causal links between brain regions of interest.