Perceived Social Relationships and Science Learning Outcomes for Taiwanese Eighth Graders: Structural Equation Modeling with a Complex Sampling Consideration

Science.gov (United States)

Jen, Tsung-Hau; Lee, Che-Di; Chien, Chin-Lung; Hsu, Ying-Shao; Chen, Kuan-Ming

2013-01-01

Based on the Trends in International Mathematics and Science Study 2007 study and a follow-up national survey, data for 3,901 Taiwanese grade 8 students were analyzed using structural equation modeling to confirm a social-relation-based affection-driven model (SRAM). SRAM hypothesized relationships among students' perceived social relationships in…

Travelling solitons in the parametrically driven nonlinear Schroedinger equation

International Nuclear Information System (INIS)

Barashenkov, I.V.; Zemlyanaya, E.V.; Baer, M.

2000-01-01

We show that the parametrically driven nonlinear Schroedinger equation has wide classes of travelling soliton solutions, some of which are stable. For small driving strengths stable nonpropagating and moving solitons co-exist while strongly forced solitons can only be stable when moving sufficiently fast

Momentum equation for arc-driven rail guns

International Nuclear Information System (INIS)

Batteh, J.H.

1984-01-01

In several models of arc-driven rail guns, the rails are assumed to be infinitely high to simplify the calculation of the electromagnetic fields which appear in the momentum equation for the arc. This assumption leads to overestimates of the arc pressures and accelerations by approximately a factor of 2 for typical rail-gun geometries. In this paper, we develop a simple method for modifying the momentum equation to account for the effect of finite-height rails on the performance of the rail gun and the properties of the arc. The modification is based on an integration of the Lorentz force across the arc cross section at each axial location in the arc. Application of this technique suggests that, for typical rail-gun geometries and moderately long arcs, the momentum equation appropriate for infinite-height rails can be retained provided that the magnetic pressure term in the equation is scaled by a factor which depends on the effective inductance of the gun. The analysis also indicates that the magnetic pressure gradient actually changes sign near the arc/projectile boundary because of the magnetic fields associated with the arc current

PC analysis of stochastic differential equations driven by Wiener noise

KAUST Repository

Le Maitre, Olivier; Knio, Omar

2015-01-01

A polynomial chaos (PC) analysis with stochastic expansion coefficients is proposed for stochastic differential equations driven by additive or multiplicative Wiener noise. It is shown that for this setting, a Galerkin formalism naturally leads

Data-driven non-linear elasticity: constitutive manifold construction and problem discretization

Science.gov (United States)

Ibañez, Ruben; Borzacchiello, Domenico; Aguado, Jose Vicente; Abisset-Chavanne, Emmanuelle; Cueto, Elias; Ladeveze, Pierre; Chinesta, Francisco

2017-11-01

The use of constitutive equations calibrated from data has been implemented into standard numerical solvers for successfully addressing a variety problems encountered in simulation-based engineering sciences (SBES). However, the complexity remains constantly increasing due to the need of increasingly detailed models as well as the use of engineered materials. Data-Driven simulation constitutes a potential change of paradigm in SBES. Standard simulation in computational mechanics is based on the use of two very different types of equations. The first one, of axiomatic character, is related to balance laws (momentum, mass, energy,\\ldots ), whereas the second one consists of models that scientists have extracted from collected, either natural or synthetic, data. Data-driven (or data-intensive) simulation consists of directly linking experimental data to computers in order to perform numerical simulations. These simulations will employ laws, universally recognized as epistemic, while minimizing the need of explicit, often phenomenological, models. The main drawback of such an approach is the large amount of required data, some of them inaccessible from the nowadays testing facilities. Such difficulty can be circumvented in many cases, and in any case alleviated, by considering complex tests, collecting as many data as possible and then using a data-driven inverse approach in order to generate the whole constitutive manifold from few complex experimental tests, as discussed in the present work.

Testing Mediation Using Multiple Regression and Structural Equation Modeling Analyses in Secondary Data

Science.gov (United States)

Li, Spencer D.

2011-01-01

Mediation analysis in child and adolescent development research is possible using large secondary data sets. This article provides an overview of two statistical methods commonly used to test mediated effects in secondary analysis: multiple regression and structural equation modeling (SEM). Two empirical studies are presented to illustrate the…

Meta-analysis a structural equation modeling approach

CERN Document Server

Cheung, Mike W-L

2015-01-01

Presents a novel approach to conducting meta-analysis using structural equation modeling. Structural equation modeling (SEM) and meta-analysis are two powerful statistical methods in the educational, social, behavioral, and medical sciences. They are often treated as two unrelated topics in the literature. This book presents a unified framework on analyzing meta-analytic data within the SEM framework, and illustrates how to conduct meta-analysis using the metaSEM package in the R statistical environment. Meta-Analysis: A Structural Equation Modeling Approach begins by introducing the impo

Data-driven non-Markovian closure models

Science.gov (United States)

Kondrashov, Dmitri; Chekroun, Mickaël D.; Ghil, Michael

2015-03-01

This paper has two interrelated foci: (i) obtaining stable and efficient data-driven closure models by using a multivariate time series of partial observations from a large-dimensional system; and (ii) comparing these closure models with the optimal closures predicted by the Mori-Zwanzig (MZ) formalism of statistical physics. Multilayer stochastic models (MSMs) are introduced as both a generalization and a time-continuous limit of existing multilevel, regression-based approaches to closure in a data-driven setting; these approaches include empirical model reduction (EMR), as well as more recent multi-layer modeling. It is shown that the multilayer structure of MSMs can provide a natural Markov approximation to the generalized Langevin equation (GLE) of the MZ formalism. A simple correlation-based stopping criterion for an EMR-MSM model is derived to assess how well it approximates the GLE solution. Sufficient conditions are derived on the structure of the nonlinear cross-interactions between the constitutive layers of a given MSM to guarantee the existence of a global random attractor. This existence ensures that no blow-up can occur for a broad class of MSM applications, a class that includes non-polynomial predictors and nonlinearities that do not necessarily preserve quadratic energy invariants. The EMR-MSM methodology is first applied to a conceptual, nonlinear, stochastic climate model of coupled slow and fast variables, in which only slow variables are observed. It is shown that the resulting closure model with energy-conserving nonlinearities efficiently captures the main statistical features of the slow variables, even when there is no formal scale separation and the fast variables are quite energetic. Second, an MSM is shown to successfully reproduce the statistics of a partially observed, generalized Lotka-Volterra model of population dynamics in its chaotic regime. The challenges here include the rarity of strange attractors in the model's parameter

Item response theory and structural equation modelling for ordinal data: Describing the relationship between KIDSCREEN and Life-H.

Science.gov (United States)

Titman, Andrew C; Lancaster, Gillian A; Colver, Allan F

2016-10-01

Both item response theory and structural equation models are useful in the analysis of ordered categorical responses from health assessment questionnaires. We highlight the advantages and disadvantages of the item response theory and structural equation modelling approaches to modelling ordinal data, from within a community health setting. Using data from the SPARCLE project focussing on children with cerebral palsy, this paper investigates the relationship between two ordinal rating scales, the KIDSCREEN, which measures quality-of-life, and Life-H, which measures participation. Practical issues relating to fitting models, such as non-positive definite observed or fitted correlation matrices, and approaches to assessing model fit are discussed. item response theory models allow properties such as the conditional independence of particular domains of a measurement instrument to be assessed. When, as with the SPARCLE data, the latent traits are multidimensional, structural equation models generally provide a much more convenient modelling framework. © The Author(s) 2013.

A Data-driven Concept Schema for Defining Clinical Research Data Needs

Science.gov (United States)

Hruby, Gregory W.; Hoxha, Julia; Ravichandran, Praveen Chandar; Mendonça, Eneida A.; Hanauer, David A; Weng, Chunhua

2016-01-01

OBJECTIVES The Patient, Intervention, Control/Comparison, and Outcome (PICO) framework is an effective technique for framing a clinical question. We aim to develop the counterpart of PICO to structure clinical research data needs. METHODS We use a data-driven approach to abstracting key concepts representing clinical research data needs by adapting and extending an expert-derived framework originally developed for defining cancer research data needs. We annotated clinical trial eligibility criteria, EHR data request logs, and data queries to electronic health records (EHR), to extract and harmonize concept classes representing clinical research data needs. We evaluated the class coverage, class preservation from the original framework, schema generalizability, schema understandability, and schema structural correctness through a semi-structured interview with eight multidisciplinary domain experts. We iteratively refined the schema based on the evaluations. RESULTS Our data-driven schema preserved 68% of the 63 classes from the original framework and covered 88% (73/82) of the classes proposed by evaluators. Class coverage for participants of different backgrounds ranged from 60% to 100% with a median value of 95% agreement among the individual evaluators. The schema was found understandable and structurally sound. CONCLUSIONS Our proposed schema may serve as the counterpart to PICO for improving the research data needs communication between researchers and informaticians. PMID:27185504

Analytical Solutions for Multi-Time Scale Fractional Stochastic Differential Equations Driven by Fractional Brownian Motion and Their Applications

Directory of Open Access Journals (Sweden)

Xiao-Li Ding

2018-01-01

Full Text Available In this paper, we investigate analytical solutions of multi-time scale fractional stochastic differential equations driven by fractional Brownian motions. We firstly decompose homogeneous multi-time scale fractional stochastic differential equations driven by fractional Brownian motions into independent differential subequations, and give their analytical solutions. Then, we use the variation of constant parameters to obtain the solutions of nonhomogeneous multi-time scale fractional stochastic differential equations driven by fractional Brownian motions. Finally, we give three examples to demonstrate the applicability of our obtained results.

Data-driven workflows for microservices

DEFF Research Database (Denmark)

Safina, Larisa; Mazzara, Manuel; Montesi, Fabrizio

2016-01-01

Microservices is an architectural style inspired by service-oriented computing that has recently started gainingpopularity. Jolie is a programming language based on the microservices paradigm: the main building block of Jolie systems are services, in contrast to, e.g., functions or objects....... The primitives offered by the Jolie language elicit many of the recurring patterns found in microservices, like load balancers and structured processes. However, Jolie still lacks some useful constructs for dealing with message types and data manipulation that are present in service-oriented computing......). We show the impact of our implementation on some of the typical scenarios found in microservice systems. This shows how computation can move from a process-driven to a data-driven approach, and leads to the preliminary identification of recurring communication patterns that can be shaped as design...

Parsimonious Structural Equation Models for Repeated Measures Data, with Application to the Study of Consumer Preferences

Science.gov (United States)

Elrod, Terry; Haubl, Gerald; Tipps, Steven W.

2012-01-01

Recent research reflects a growing awareness of the value of using structural equation models to analyze repeated measures data. However, such data, particularly in the presence of covariates, often lead to models that either fit the data poorly, are exceedingly general and hard to interpret, or are specified in a manner that is highly data…

Driven-dissipative Euler close-quote s equations for a rigid body: A chaotic system relevant to fluid dynamics

International Nuclear Information System (INIS)

Turner, L.

1996-01-01

Adhering to the lore that vorticity is a critical ingredient of fluid turbulence, a triad of coupled helicity (vorticity) states of the incompressible Navier-Stokes fluid are followed. Effects of the remaining states of the fluid on the triad are then modeled as a simple driving term. Numerical solution of the equations yield attractors that seem strange and chaotic. This suggests that the unpredictability of nonlinear fluid dynamics (i.e., turbulence) may be traced back to the most primordial structure of the Navier-Stokes equation; namely, the driven triadic interaction. copyright 1996 The American Physical Society

Structural equation modelling based data fusion for technology forecasting: A generic framework

CSIR Research Space (South Africa)

Staphorst, L

2013-07-01

Full Text Available to explain the variations in independent variables as functions (commonly referred to regression functions) of variations in dependent variables [13]. With this knowledge it is then possible to perform prediction and forecasting of the values that dependent....G.; “A General Method for Estimating a Linear Structural Equation System,” in Structural Equation Models in the Social Sciences, eds.: A.S. Goldberger and O. D. Duncan, New York: Seminar, 1973. [15] Steinberg, A.N. and Rogova, G.; "Situation...

Data driven information system for supervision of judicial open

Directory of Open Access Journals (Sweden)

Ming LI

2016-08-01

Full Text Available Aiming at the four outstanding problems of informationized supervision for judicial publicity, the judicial public data is classified based on data driven to form the finally valuable data. Then, the functional structure, technical structure and business structure of the data processing system are put forward, including data collection module, data reduction module, data analysis module, data application module and data security module, etc. The development of the data processing system based on these structures can effectively reduce work intensity of judicial open iformation management, summarize the work state, find the problems, and promote the level of judicial publicity.

Dynamic model reduction using data-driven Loewner-framework applied to thermally morphing structures

Science.gov (United States)

Phoenix, Austin A.; Tarazaga, Pablo A.

2017-05-01

The work herein proposes the use of the data-driven Loewner-framework for reduced order modeling as applied to dynamic Finite Element Models (FEM) of thermally morphing structures. The Loewner-based modeling approach is computationally efficient and accurately constructs reduced models using analytical output data from a FEM. This paper details the two-step process proposed in the Loewner approach. First, a random vibration FEM simulation is used as the input for the development of a Single Input Single Output (SISO) data-based dynamic Loewner state space model. Second, an SVD-based truncation is used on the Loewner state space model, such that the minimal, dynamically representative, state space model is achieved. For this second part, varying levels of reduction are generated and compared. The work herein can be extended to model generation using experimental measurements by replacing the FEM output data in the first step and following the same procedure. This method will be demonstrated on two thermally morphing structures, a rigidly fixed hexapod in multiple geometric configurations and a low mass anisotropic morphing boom. This paper is working to detail the method and identify the benefits of the reduced model methodology.

From patterns to causal understanding: Structural equation modeling (SEM) in soil ecology

Science.gov (United States)

Eisenhauer, Nico; Powell, Jeff R; Grace, James B.; Bowker, Matthew A.

2015-01-01

In this perspectives paper we highlight a heretofore underused statistical method in soil ecological research, structural equation modeling (SEM). SEM is commonly used in the general ecological literature to develop causal understanding from observational data, but has been more slowly adopted by soil ecologists. We provide some basic information on the many advantages and possibilities associated with using SEM and provide some examples of how SEM can be used by soil ecologists to shift focus from describing patterns to developing causal understanding and inspiring new types of experimental tests. SEM is a promising tool to aid the growth of soil ecology as a discipline, particularly by supporting research that is increasingly hypothesis-driven and interdisciplinary, thus shining light into the black box of interactions belowground.

Applications of Multilevel Structural Equation Modeling to Cross-Cultural Research

Science.gov (United States)

Cheung, Mike W.-L.; Au, Kevin

2005-01-01

Multilevel structural equation modeling (MSEM) has been proposed as an extension to structural equation modeling for analyzing data with nested structure. We have begun to see a few applications in cross-cultural research in which MSEM fits well as the statistical model. However, given that cross-cultural studies can only afford collecting data…

Data-driven batch schuduling

Energy Technology Data Exchange (ETDEWEB)

Bent, John [Los Alamos National Laboratory; Denehy, Tim [GOOGLE; Arpaci - Dusseau, Remzi [UNIV OF WISCONSIN; Livny, Miron [UNIV OF WISCONSIN; Arpaci - Dusseau, Andrea C [NON LANL

2009-01-01

In this paper, we develop data-driven strategies for batch computing schedulers. Current CPU-centric batch schedulers ignore the data needs within workloads and execute them by linking them transparently and directly to their needed data. When scheduled on remote computational resources, this elegant solution of direct data access can incur an order of magnitude performance penalty for data-intensive workloads. Adding data-awareness to batch schedulers allows a careful coordination of data and CPU allocation thereby reducing the cost of remote execution. We offer here new techniques by which batch schedulers can become data-driven. Such systems can use our analytical predictive models to select one of the four data-driven scheduling policies that we have created. Through simulation, we demonstrate the accuracy of our predictive models and show how they can reduce time to completion for some workloads by as much as 80%.

Data-Driven Problems in Elasticity

Science.gov (United States)

Conti, S.; Müller, S.; Ortiz, M.

2018-01-01

We consider a new class of problems in elasticity, referred to as Data-Driven problems, defined on the space of strain-stress field pairs, or phase space. The problem consists of minimizing the distance between a given material data set and the subspace of compatible strain fields and stress fields in equilibrium. We find that the classical solutions are recovered in the case of linear elasticity. We identify conditions for convergence of Data-Driven solutions corresponding to sequences of approximating material data sets. Specialization to constant material data set sequences in turn establishes an appropriate notion of relaxation. We find that relaxation within this Data-Driven framework is fundamentally different from the classical relaxation of energy functions. For instance, we show that in the Data-Driven framework the relaxation of a bistable material leads to material data sets that are not graphs.

Sensitivity of rocky planet structures to the equation of state

International Nuclear Information System (INIS)

Swift, D.C.

2009-01-01

Structures were calculated for Mercury, Venus, Earth, the Moon, and Mars, using a core-mantle model and adjusting the core radius to reproduce the observed mass and diameter of each body. Structures were calculated using Fe and basalt equations of state of different degrees of sophistication for the core and mantle. The choice of equation of state had a significant effect on the inferred structure. For each structure, the moment of inertia ratio was calculated and compared with observed values. Linear Grueneisen equations of state fitted to limited portions of shock data reproduced the observed moments of inertia significantly better than did more detailed equations of state incorporating phase transitions, presumably reflecting the actual compositions of the bodies. The linear Grueneisen equations of state and corresponding structures seem however to be a reasonable starting point for comparative simulations of large-scale astrophysical impacts.

Analytical Solutions for Multi-Time Scale Fractional Stochastic Differential Equations Driven by Fractional Brownian Motion and Their Applications

OpenAIRE

Xiao-Li Ding; Juan J. Nieto

2018-01-01

In this paper, we investigate analytical solutions of multi-time scale fractional stochastic differential equations driven by fractional Brownian motions. We firstly decompose homogeneous multi-time scale fractional stochastic differential equations driven by fractional Brownian motions into independent differential subequations, and give their analytical solutions. Then, we use the variation of constant parameters to obtain the solutions of nonhomogeneous multi-time scale fractional stochast...

Augmenting Bag-of-Words: Data-Driven Discovery of Temporal and Structural Information for Activity Recognition

OpenAIRE

Bettadapura, Vinay; Schindler, Grant; Plotz, Thomaz; Essa, Irfan

2015-01-01

We present data-driven techniques to augment Bag of Words (BoW) models, which allow for more robust modeling and recognition of complex long-term activities, especially when the structure and topology of the activities are not known a priori. Our approach specifically addresses the limitations of standard BoW approaches, which fail to represent the underlying temporal and causal information that is inherent in activity streams. In addition, we also propose the use of randomly sampled regular ...

Milstein Approximation for Advection-Diffusion Equations Driven by Multiplicative Noncontinuous Martingale Noises

International Nuclear Information System (INIS)

Barth, Andrea; Lang, Annika

2012-01-01

In this paper, the strong approximation of a stochastic partial differential equation, whose differential operator is of advection-diffusion type and which is driven by a multiplicative, infinite dimensional, càdlàg, square integrable martingale, is presented. A finite dimensional projection of the infinite dimensional equation, for example a Galerkin projection, with nonequidistant time stepping is used. Error estimates for the discretized equation are derived in L 2 and almost sure senses. Besides space and time discretizations, noise approximations are also provided, where the Milstein double stochastic integral is approximated in such a way that the overall complexity is not increased compared to an Euler–Maruyama approximation. Finally, simulations complete the paper.

A new lattice Boltzmann equation to simulate density-driven convection of carbon dioxide

KAUST Repository

Allen, Rebecca; Reis, Tim; Sun, Shuyu

2013-01-01

-driven convection becomes an important transport process to model. However, the challenge lies in simulating this transport process accurately with high spatial resolution and low CPU cost. This issue can be addressed by using the lattice Boltzmann equation (LBE

Structural Equations and Causation

OpenAIRE

Hall, Ned

2007-01-01

Structural equations have become increasingly popular in recent years as tools for understanding causation. But standard structural equations approaches to causation face deep problems. The most philosophically interesting of these consists in their failure to incorporate a distinction between default states of an object or system, and deviations therefrom. Exploring this problem, and how to fix it, helps to illuminate the central role this distinction plays in our causal thinking.

Coherence and chaos in the driven damped sine-Gordon equation: Measurement of the soliton spectrum

Energy Technology Data Exchange (ETDEWEB)

Overman, II, E A; McLaughlin, D W; Bishop, A R; Los Alamos National Lab., NM

1986-02-01

A numerical procedure is developed which measures the sine-Gordon soliton and radiation content of any field (PHI, PHIsub(t)) which is periodic in space. The procedure is applied to the field generated by a damped, driven sine-Gordon equation. This field can be either temporally periodic (locked to the driver) or chaotic. In either case the numerical measurement shows that the spatial structure can be described by only a few spatially localized (soliton wave-train) modes. The numerical procedure quantitatively identifies the presence, number and properties of these soliton wave-trains. For example, an increase of spatial symmetry is accompanied by the injection of additional solitons into the field. (orig.).

Fitting Data to Model: Structural Equation Modeling Diagnosis Using Two Scatter Plots

Science.gov (United States)

Yuan, Ke-Hai; Hayashi, Kentaro

2010-01-01

This article introduces two simple scatter plots for model diagnosis in structural equation modeling. One plot contrasts a residual-based M-distance of the structural model with the M-distance for the factor score. It contains information on outliers, good leverage observations, bad leverage observations, and normal cases. The other plot contrasts…

Data Driven Economic Model Predictive Control

Directory of Open Access Journals (Sweden)

Masoud Kheradmandi

2018-04-01

Full Text Available This manuscript addresses the problem of data driven model based economic model predictive control (MPC design. To this end, first, a data-driven Lyapunov-based MPC is designed, and shown to be capable of stabilizing a system at an unstable equilibrium point. The data driven Lyapunov-based MPC utilizes a linear time invariant (LTI model cognizant of the fact that the training data, owing to the unstable nature of the equilibrium point, has to be obtained from closed-loop operation or experiments. Simulation results are first presented demonstrating closed-loop stability under the proposed data-driven Lyapunov-based MPC. The underlying data-driven model is then utilized as the basis to design an economic MPC. The economic improvements yielded by the proposed method are illustrated through simulations on a nonlinear chemical process system example.

Authoring Data-Driven Videos with DataClips.

Science.gov (United States)

Amini, Fereshteh; Riche, Nathalie Henry; Lee, Bongshin; Monroy-Hernandez, Andres; Irani, Pourang

2017-01-01

Data videos, or short data-driven motion graphics, are an increasingly popular medium for storytelling. However, creating data videos is difficult as it involves pulling together a unique combination of skills. We introduce DataClips, an authoring tool aimed at lowering the barriers to crafting data videos. DataClips allows non-experts to assemble data-driven "clips" together to form longer sequences. We constructed the library of data clips by analyzing the composition of over 70 data videos produced by reputable sources such as The New York Times and The Guardian. We demonstrate that DataClips can reproduce over 90% of our data videos corpus. We also report on a qualitative study comparing the authoring process and outcome achieved by (1) non-experts using DataClips, and (2) experts using Adobe Illustrator and After Effects to create data-driven clips. Results indicated that non-experts are able to learn and use DataClips with a short training period. In the span of one hour, they were able to produce more videos than experts using a professional editing tool, and their clips were rated similarly by an independent audience.

Data-driven storytelling

CERN Document Server

Hurter, Christophe; Diakopoulos, Nicholas ed.; Carpendale, Sheelagh

2018-01-01

This book is an accessible introduction to data-driven storytelling, resulting from discussions between data visualization researchers and data journalists. This book will be the first to define the topic, present compelling examples and existing resources, as well as identify challenges and new opportunities for research.

Huginot data of plastic foams obtained from leaser-driven shocks

Czech Academy of Sciences Publication Activity Database

Dezulian, R.; Canova, F.; Barbanotti, S.; Orsenigo, F.; Redaelli, R.; Vinci, T.; Lucchini, G.; Batani, D.; Rus, Bedřich; Polan, Jiří; Kozlová, Michaela; Stupka, Michal; Präg R., Ansgar; Homer, Pavel; Havlíček, Tomáš; Soukup, Miroslav; Krouský, Eduard; Skála, Jiří; Dudžák, Roman; Pfeifer, Miroslav; Nishimura, H.; Nagai, K.; Ito, F.; Norimatsu, T.; Kilpio, A.; Shashkov, E.; Stuchebrukhov, I.; Vovchenko, V.; Chernomyrdin, V.; Krasuyk, I.

2006-01-01

Roč. 73, č. 4 (2006), 047401/1-047401/4 ISSN 1539-3755 R&D Projects: GA MŠk(CZ) LC528; GA MŠk(CZ) LN00A100 Grant - others:6th FP of the E.U.(XE) RII3-CT-2003-506350; RFBR(XE) 03-02-17549 Program:FP6 Institutional research plan: CEZ:AV0Z10100523 Keywords : equation of state * laser-driven shock * Huginot data Subject RIV: BH - Optics, Masers, Lasers Impact factor: 2.438, year: 2006

PC analysis of stochastic differential equations driven by Wiener noise

KAUST Repository

Le Maitre, Olivier

2015-03-01

A polynomial chaos (PC) analysis with stochastic expansion coefficients is proposed for stochastic differential equations driven by additive or multiplicative Wiener noise. It is shown that for this setting, a Galerkin formalism naturally leads to the definition of a hierarchy of stochastic differential equations governing the evolution of the PC modes. Under the mild assumption that the Wiener and uncertain parameters can be treated as independent random variables, it is also shown that the Galerkin formalism naturally separates parametric uncertainty and stochastic forcing dependences. This enables us to perform an orthogonal decomposition of the process variance, and consequently identify contributions arising from the uncertainty in parameters, the stochastic forcing, and a coupled term. Insight gained from this decomposition is illustrated in light of implementation to simplified linear and non-linear problems; the case of a stochastic bifurcation is also considered.

Exploratory laser-driven shock wave studies

International Nuclear Information System (INIS)

Solem, J.C.; Veeser, L.R.

1977-11-01

We show the results of a feasibility study for investigating shock structure and for measuring equation-of-state parameters using high-energy, short-pulse lasers. We discuss the temporal and spatial structure of the luminosity from laser-driven shock unloading in aluminum foils. We demonstrate that shock velocity can be measured by observing the time interval between shock emergence across two thicknesses and show data for shocks of 1.3 and 2.1 Mbar. The fact that we observe shock fronts cleanly breaking through steps as small as 3 μm indicates that the shock front thickness is very small in the few megabar region; this is the first experimental verification that these fronts are not more than a few micrometers thick. We present approximate measurements of free-surface velocity. Finally, we speculate on the use of these techniques to obtain detailed equation-of-state data

Principles and practice of structural equation modeling

CERN Document Server

Kline, Rex B

2015-01-01

Emphasizing concepts and rationale over mathematical minutiae, this is the most widely used, complete, and accessible structural equation modeling (SEM) text. Continuing the tradition of using real data examples from a variety of disciplines, the significantly revised fourth edition incorporates recent developments such as Pearl's graphing theory and the structural causal model (SCM), measurement invariance, and more. Readers gain a comprehensive understanding of all phases of SEM, from data collection and screening to the interpretation and reporting of the results. Learning is enhanced by ex

Consistent data-driven computational mechanics

Science.gov (United States)

González, D.; Chinesta, F.; Cueto, E.

2018-05-01

We present a novel method, within the realm of data-driven computational mechanics, to obtain reliable and thermodynamically sound simulation from experimental data. We thus avoid the need to fit any phenomenological model in the construction of the simulation model. This kind of techniques opens unprecedented possibilities in the framework of data-driven application systems and, particularly, in the paradigm of industry 4.0.

Exploratory structural equation modeling of personality data.

Science.gov (United States)

Booth, Tom; Hughes, David J

2014-06-01

The current article compares the use of exploratory structural equation modeling (ESEM) as an alternative to confirmatory factor analytic (CFA) models in personality research. We compare model fit, factor distinctiveness, and criterion associations of factors derived from ESEM and CFA models. In Sample 1 (n = 336) participants completed the NEO-FFI, the Trait Emotional Intelligence Questionnaire-Short Form, and the Creative Domains Questionnaire. In Sample 2 (n = 425) participants completed the Big Five Inventory and the depression and anxiety scales of the General Health Questionnaire. ESEM models provided better fit than CFA models, but ESEM solutions did not uniformly meet cutoff criteria for model fit. Factor scores derived from ESEM and CFA models correlated highly (.91 to .99), suggesting the additional factor loadings within the ESEM model add little in defining latent factor content. Lastly, criterion associations of each personality factor in CFA and ESEM models were near identical in both inventories. We provide an example of how ESEM and CFA might be used together in improving personality assessment. © The Author(s) 2014.

Regression Levels of Selected Affective Factors on Science Achievement: A Structural Equation Model with TIMSS 2011 Data

Science.gov (United States)

Akilli, Mustafa

2015-01-01

The aim of this study is to demonstrate the science success regression levels of chosen emotional features of 8th grade students using Structural Equation Model. The study was conducted by the analysis of students' questionnaires and science success in TIMSS 2011 data using SEM. Initially, the factors that are thought to have an effect on science…

Handbook of structural equation modeling

CERN Document Server

Hoyle, Rick H

2012-01-01

The first comprehensive structural equation modeling (SEM) handbook, this accessible volume presents both the mechanics of SEM and specific SEM strategies and applications. The editor, contributors, and editorial advisory board are leading methodologists who have organized the book to move from simpler material to more statistically complex modeling approaches. Sections cover the foundations of SEM; statistical underpinnings, from assumptions to model modifications; steps in implementation, from data preparation through writing the SEM report; and basic and advanced applications, inclu

Boltzmann-equation simulations of radio-frequency-driven, low-temperature plasmas

International Nuclear Information System (INIS)

Drallos, P.J.; Riley, M.E.

1995-01-01

We present a method for the numerical solution of the Boltzmann equation (BE) describing plasma electrons. We apply the method to a capacitively-coupled, radio-frequency-driven He discharge in parallel-plate (quasi-1D) geometry which contains time scales for physical processes spanning six orders of magnitude. Our BE solution procedure uses the method of characteristics for the Vlasov operator with interpolation in phase space at early time, allowing storage of the distribution function on a fixed phase-space grid. By alternating this BE method with a fluid description of the electrons, or with a novel time-cycle-average equation method, we compute the periodic steady state of a He plasma by time evolution from startup conditions. We find that the results compare favorably with measured current-voltage, plasma density, and ''cited state densities in the ''GEC'' Reference Cell. Our atomic He model includes five levels (some are summed composites), 15 electronic transitions, radiation trapping, and metastable-metastable collisions

Boltzmann-equation simulations of radio-frequency-driven, low-temperature plasmas

Energy Technology Data Exchange (ETDEWEB)

Drallos, P.J.; Riley, M.E.

1995-01-01

We present a method for the numerical solution of the Boltzmann equation (BE) describing plasma electrons. We apply the method to a capacitively-coupled, radio-frequency-driven He discharge in parallel-plate (quasi-1D) geometry which contains time scales for physical processes spanning six orders of magnitude. Our BE solution procedure uses the method of characteristics for the Vlasov operator with interpolation in phase space at early time, allowing storage of the distribution function on a fixed phase-space grid. By alternating this BE method with a fluid description of the electrons, or with a novel time-cycle-average equation method, we compute the periodic steady state of a He plasma by time evolution from startup conditions. We find that the results compare favorably with measured current-voltage, plasma density, and ``cited state densities in the ``GEC`` Reference Cell. Our atomic He model includes five levels (some are summed composites), 15 electronic transitions, radiation trapping, and metastable-metastable collisions.

A new lattice Boltzmann equation to simulate density-driven convection of carbon dioxide

KAUST Repository

Allen, Rebecca

2013-01-01

The storage of CO2 in fluid-filled geological formations has been carried out for more than a decade in locations around the world. After CO2 has been injected into the aquifer and has moved laterally under the aquifer\\'s cap-rock, density-driven convection becomes an important transport process to model. However, the challenge lies in simulating this transport process accurately with high spatial resolution and low CPU cost. This issue can be addressed by using the lattice Boltzmann equation (LBE) to formulate a model for a similar scenario when a solute diffuses into a fluid and density differences lead to convective mixing. The LBE is a promising alternative to the traditional methods of computational fluid dynamics. Rather than discretizing the system of partial differential equations of classical continuum mechanics directly, the LBE is derived from a velocity-space truncation of the Boltzmann equation of classical kinetic theory. We propose an extension to the LBE, which can accurately predict the transport of dissolved CO2 in water, as a step towards fluid-filled porous media simulations. This is achieved by coupling two LBEs, one for the fluid flow and one for the convection and diffusion of CO2. Unlike existing lattice Boltzmann equations for porous media flow, our model is derived from a system of moment equations and a Crank-Nicolson discretization of the velocity-truncated Boltzmann equation. The forcing terms are updated locally without the need for additional central difference approximation. Therefore our model preserves all the computational advantages of the single-phase lattice Boltzmann equation and is formally second-order accurate in both space and time. Our new model also features a novel implementation of boundary conditions, which is simple to implement and does not suffer from the grid-dependent error that is present in the standard "bounce-back" condition. The significance of using the LBE in this work lies in the ability to efficiently

Knowledge-Driven Versus Data-Driven Logics

Czech Academy of Sciences Publication Activity Database

Dubois, D.; Hájek, Petr; Prade, H.

2000-01-01

Roč. 9, č. 1 (2000), s. 65-89 ISSN 0925-8531 R&D Projects: GA AV ČR IAA1030601 Grant - others:CNRS(FR) 4008 Institutional research plan: AV0Z1030915 Keywords : epistemic logic * possibility theory * data-driven reasoning * deontic logic Subject RIV: BA - General Mathematics

Data-driven architectural production and operation

NARCIS (Netherlands)

Bier, H.H.; Mostafavi, S.

2014-01-01

Data-driven architectural production and operation as explored within Hyperbody rely heavily on system thinking implying that all parts of a system are to be understood in relation to each other. These relations are increasingly established bi-directionally so that data-driven architecture is not

Construction of alternative Hamiltonian structures for field equations

Energy Technology Data Exchange (ETDEWEB)

Herrera, Mauricio [Departamento de Fisica, Facultad de Ciencias Fisicas y Matematicas, Universidad de Chile, Santiago (Chile); Hojman, Sergio A. [Departamento de Fisica, Facultad de Ciencias, Universidad de Chile, Santiago (Chile); Facultad de Educacion, Universidad Nacional Andres Bello, Santiago (Chile); Centro de Recursos Educativos Avanzados, CREA, Santiago (Chile)

2001-08-10

We use symmetry vectors of nonlinear field equations to build alternative Hamiltonian structures. We construct such structures even for equations which are usually believed to be non-Hamiltonian such as heat, Burger and potential Burger equations. We improve on a previous version of the approach using recursion operators to increase the rank of the Poisson bracket matrices. Cole-Hopf and Miura-type transformations allow the mapping of these structures from one equation to another. (author)

Data-driven modelling of structured populations a practical guide to the integral projection model

CERN Document Server

Ellner, Stephen P; Rees, Mark

2016-01-01

This book is a “How To” guide for modeling population dynamics using Integral Projection Models (IPM) starting from observational data. It is written by a leading research team in this area and includes code in the R language (in the text and online) to carry out all computations. The intended audience are ecologists, evolutionary biologists, and mathematical biologists interested in developing data-driven models for animal and plant populations. IPMs may seem hard as they involve integrals. The aim of this book is to demystify IPMs, so they become the model of choice for populations structured by size or other continuously varying traits. The book uses real examples of increasing complexity to show how the life-cycle of the study organism naturally leads to the appropriate statistical analysis, which leads directly to the IPM itself. A wide range of model types and analyses are presented, including model construction, computational methods, and the underlying theory, with the more technical material in B...

Structural Equation Modeling of Multivariate Time Series

Science.gov (United States)

du Toit, Stephen H. C.; Browne, Michael W.

2007-01-01

The covariance structure of a vector autoregressive process with moving average residuals (VARMA) is derived. It differs from other available expressions for the covariance function of a stationary VARMA process and is compatible with current structural equation methodology. Structural equation modeling programs, such as LISREL, may therefore be…

Hamiltonian structure of the Lotka-Volterra equations

Science.gov (United States)

Nutku, Y.

1990-03-01

The Lotka-Volterra equations governing predator-prey relations are shown to admit Hamiltonian structure with respect to a generalized Poisson bracket. These equations provide an example of a system for which the naive criterion for the existence of Hamiltonian structure fails. We show further that there is a three-component generalization of the Lotka-Volterra equations which is a bi-Hamiltonian system.

GLOBAL LINEARIZATION OF DIFFERENTIAL EQUATIONS WITH SPECIAL STRUCTURES

Institute of Scientific and Technical Information of China (English)

无

2011-01-01

This paper introduces the global linearization of the differential equations with special structures.The function in the differential equation is unbounded.We prove that the differential equation with unbounded function can be topologically linearlized if it has a special structure.

Dynamic data analysis modeling data with differential equations

CERN Document Server

Ramsay, James

2017-01-01

This text focuses on the use of smoothing methods for developing and estimating differential equations following recent developments in functional data analysis and building on techniques described in Ramsay and Silverman (2005) Functional Data Analysis. The central concept of a dynamical system as a buffer that translates sudden changes in input into smooth controlled output responses has led to applications of previously analyzed data, opening up entirely new opportunities for dynamical systems. The technical level has been kept low so that those with little or no exposure to differential equations as modeling objects can be brought into this data analysis landscape. There are already many texts on the mathematical properties of ordinary differential equations, or dynamic models, and there is a large literature distributed over many fields on models for real world processes consisting of differential equations. However, a researcher interested in fitting such a model to data, or a statistician interested in...

Structural equations in language learning

NARCIS (Netherlands)

Moortgat, M.J.

In categorial systems with a fixed structural component, the learning problem comes down to finding the solution for a set of typeassignment equations. A hard-wired structural component is problematic if one want to address issues of structural variation. Our starting point is a type-logical

Combining density functional theory calculations, supercomputing, and data-driven methods to design new materials (Conference Presentation)

Science.gov (United States)

Jain, Anubhav

2017-04-01

Density functional theory (DFT) simulations solve for the electronic structure of materials starting from the Schrödinger equation. Many case studies have now demonstrated that researchers can often use DFT to design new compounds in the computer (e.g., for batteries, catalysts, and hydrogen storage) before synthesis and characterization in the lab. In this talk, I will focus on how DFT calculations can be executed on large supercomputing resources in order to generate very large data sets on new materials for functional applications. First, I will briefly describe the Materials Project, an effort at LBNL that has virtually characterized over 60,000 materials using DFT and has shared the results with over 17,000 registered users. Next, I will talk about how such data can help discover new materials, describing how preliminary computational screening led to the identification and confirmation of a new family of bulk AMX2 thermoelectric compounds with measured zT reaching 0.8. I will outline future plans for how such data-driven methods can be used to better understand the factors that control thermoelectric behavior, e.g., for the rational design of electronic band structures, in ways that are different from conventional approaches.

KNMI DataLab experiences in serving data-driven innovations

Science.gov (United States)

Noteboom, Jan Willem; Sluiter, Raymond

2016-04-01

Climate change research and innovations in weather forecasting rely more and more on (Big) data. Besides increasing data from traditional sources (such as observation networks, radars and satellites), the use of open data, crowd sourced data and the Internet of Things (IoT) is emerging. To deploy these sources of data optimally in our services and products, KNMI has established a DataLab to serve data-driven innovations in collaboration with public and private sector partners. Big data management, data integration, data analytics including machine learning and data visualization techniques are playing an important role in the DataLab. Cross-domain data-driven innovations that arise from public-private collaborative projects and research programmes can be explored, experimented and/or piloted by the KNMI DataLab. Furthermore, advice can be requested on (Big) data techniques and data sources. In support of collaborative (Big) data science activities, scalable environments are offered with facilities for data integration, data analysis and visualization. In addition, Data Science expertise is provided directly or from a pool of internal and external experts. At the EGU conference, gained experiences and best practices are presented in operating the KNMI DataLab to serve data-driven innovations for weather and climate applications optimally.

A data driven nonlinear stochastic model for blood glucose dynamics.

Science.gov (United States)

Zhang, Yan; Holt, Tim A; Khovanova, Natalia

2016-03-01

The development of adequate mathematical models for blood glucose dynamics may improve early diagnosis and control of diabetes mellitus (DM). We have developed a stochastic nonlinear second order differential equation to describe the response of blood glucose concentration to food intake using continuous glucose monitoring (CGM) data. A variational Bayesian learning scheme was applied to define the number and values of the system's parameters by iterative optimisation of free energy. The model has the minimal order and number of parameters to successfully describe blood glucose dynamics in people with and without DM. The model accounts for the nonlinearity and stochasticity of the underlying glucose-insulin dynamic process. Being data-driven, it takes full advantage of available CGM data and, at the same time, reflects the intrinsic characteristics of the glucose-insulin system without detailed knowledge of the physiological mechanisms. We have shown that the dynamics of some postprandial blood glucose excursions can be described by a reduced (linear) model, previously seen in the literature. A comprehensive analysis demonstrates that deterministic system parameters belong to different ranges for diabetes and controls. Implications for clinical practice are discussed. This is the first study introducing a continuous data-driven nonlinear stochastic model capable of describing both DM and non-DM profiles. Copyright © 2015 The Authors. Published by Elsevier Ireland Ltd.. All rights reserved.

An Improved Estimation Using Polya-Gamma Augmentation for Bayesian Structural Equation Models with Dichotomous Variables

Science.gov (United States)

Kim, Seohyun; Lu, Zhenqiu; Cohen, Allan S.

2018-01-01

Bayesian algorithms have been used successfully in the social and behavioral sciences to analyze dichotomous data particularly with complex structural equation models. In this study, we investigate the use of the Polya-Gamma data augmentation method with Gibbs sampling to improve estimation of structural equation models with dichotomous variables.…

Data-Driven and Expectation-Driven Discovery of Empirical Laws.

Science.gov (United States)

1982-10-10

occurred in small integer proportions to each other. In 1809, Joseph Gay- Lussac found evidence for his law of combining volumes, which stated that a...of Empirical Laws Patrick W. Langley Gary L. Bradshaw Herbert A. Simon T1he Robotics Institute Carnegie-Mellon University Pittsburgh, Pennsylvania...Subtitle) S. TYPE OF REPORT & PERIOD COVERED Data-Driven and Expectation-Driven Discovery Interim Report 2/82-10/82 of Empirical Laws S. PERFORMING ORG

Big Data: An Opportunity for Collaboration with Computer Scientists on Data-Driven Science

Science.gov (United States)

Baru, C.

2014-12-01

Big data technologies are evolving rapidly, driven by the need to manage ever increasing amounts of historical data; process relentless streams of human and machine-generated data; and integrate data of heterogeneous structure from extremely heterogeneous sources of information. Big data is inherently an application-driven problem. Developing the right technologies requires an understanding of the applications domain. Though, an intriguing aspect of this phenomenon is that the availability of the data itself enables new applications not previously conceived of! In this talk, we will discuss how the big data phenomenon creates an imperative for collaboration among domain scientists (in this case, geoscientists) and computer scientists. Domain scientists provide the application requirements as well as insights about the data involved, while computer scientists help assess whether problems can be solved with currently available technologies or require adaptaion of existing technologies and/or development of new technologies. The synergy can create vibrant collaborations potentially leading to new science insights as well as development of new data technologies and systems. The area of interface between geosciences and computer science, also referred to as geoinformatics is, we believe, a fertile area for interdisciplinary research.

Predictive Capability of the Compressible MRG Equation for an Explosively Driven Particle with Validation

Science.gov (United States)

Garno, Joshua; Ouellet, Frederick; Koneru, Rahul; Balachandar, Sivaramakrishnan; Rollin, Bertrand

2017-11-01

An analytic model to describe the hydrodynamic forces on an explosively driven particle is not currently available. The Maxey-Riley-Gatignol (MRG) particle force equation generalized for compressible flows is well-studied in shock-tube applications, and captures the evolution of particle force extracted from controlled shock-tube experiments. In these experiments only the shock-particle interaction was examined, and the effects of the contact line were not investigated. In the present work, the predictive capability of this model is considered for the case where a particle is explosively ejected from a rigid barrel into ambient air. Particle trajectory information extracted from simulations is compared with experimental data. This configuration ensures that both the shock and contact produced by the detonation will influence the motion of the particle. The simulations are carried out using a finite volume, Euler-Lagrange code using the JWL equation of state to handle the explosive products. This work was supported by the U.S. Department of Energy, National Nuclear Security Administration, Advanced Simulation and Computing Program, as a Cooperative Agreement under the Predictive Science Academic Alliance Program,under Contract No. DE-NA0002378.

Testing strong factorial invariance using three-level structural equation modeling

Directory of Open Access Journals (Sweden)

Suzanne eJak

2014-07-01

Full Text Available Within structural equation modeling, the most prevalent model to investigate measurement bias is the multigroup model. Equal factor loadings and intercepts across groups in a multigroup model represent strong factorial invariance (absence of measurement bias across groups. Although this approach is possible in principle, it is hardly practical when the number of groups is large or when the group size is relatively small. Jak, Oort and Dolan (2013 showed how strong factorial invariance across large numbers of groups can be tested in a multilevel structural equation modeling framework, by treating group as a random instead of a fixed variable. In the present study, this model is extended for use with three-level data. The proposed method is illustrated with an investigation of strong factorial invariance across 156 school classes and 50 schools in a Dutch dyscalculia test, using three-level structural equation modeling.

Quantile driven identification of structural derivatives

OpenAIRE

Andrew Chesher

2001-01-01

Conditions are derived under which there is local nonparametric identification of derivatives of structural equations in nonlinear triangular simultaneous equations systems. The attack on this problem is via conditional quantile functions and exploits local quantile independence conditions. The identification conditions include local analogues of the order and rank conditions familiar in the analysis of linear simultaneous equations models. The objects whose identification is sought are deriv...

Data and Dynamics Driven Approaches for Modelling and Forecasting the Red Sea Chlorophyll

KAUST Repository

Dreano, Denis

2017-05-31

Phytoplankton is at the basis of the marine food chain and therefore play a fundamental role in the ocean ecosystem. However, the large-scale phytoplankton dynamics of the Red Sea are not well understood yet, mainly due to the lack of historical in situ measurements. As a result, our knowledge in this area relies mostly on remotely-sensed observations and large-scale numerical marine ecosystem models. Models are very useful to identify the mechanisms driving the variations in chlorophyll concentration and have practical applications for fisheries operation and harmful algae blooms monitoring. Modelling approaches can be divided between physics- driven (dynamical) approaches, and data-driven (statistical) approaches. Dynamical models are based on a set of differential equations representing the transfer of energy and matter between different subsets of the biota, whereas statistical models identify relationships between variables based on statistical relations within the available data. The goal of this thesis is to develop, implement and test novel dynamical and statistical modelling approaches for studying and forecasting the variability of chlorophyll concentration in the Red Sea. These new models are evaluated in term of their ability to efficiently forecast and explain the regional chlorophyll variability. We also propose innovative synergistic strategies to combine data- and physics-driven approaches to further enhance chlorophyll forecasting capabilities and efficiency.

Migraine Subclassification via a Data-Driven Automated Approach Using Multimodality Factor Mixture Modeling of Brain Structure Measurements.

Science.gov (United States)

Schwedt, Todd J; Si, Bing; Li, Jing; Wu, Teresa; Chong, Catherine D

2017-07-01

The current subclassification of migraine is according to headache frequency and aura status. The variability in migraine symptoms, disease course, and response to treatment suggest the presence of additional heterogeneity or subclasses within migraine. The study objective was to subclassify migraine via a data-driven approach, identifying latent factors by jointly exploiting multiple sets of brain structural features obtained via magnetic resonance imaging (MRI). Migraineurs (n = 66) and healthy controls (n = 54) had brain MRI measurements of cortical thickness, cortical surface area, and volumes for 68 regions. A multimodality factor mixture model was used to subclassify MRIs and to determine the brain structural factors that most contributed to the subclassification. Clinical characteristics of subjects in each subgroup were compared. Automated MRI classification divided the subjects into two subgroups. Migraineurs in subgroup #1 had more severe allodynia symptoms during migraines (6.1 ± 5.3 vs. 3.6 ± 3.2, P = .03), more years with migraine (19.2 ± 11.3 years vs 13 ± 8.3 years, P = .01), and higher Migraine Disability Assessment (MIDAS) scores (25 ± 22.9 vs 15.7 ± 12.2, P = .04). There were not differences in headache frequency or migraine aura status between the two subgroups. Data-driven subclassification of brain MRIs based upon structural measurements identified two subgroups. Amongst migraineurs, the subgroups differed in allodynia symptom severity, years with migraine, and migraine-related disability. Since allodynia is associated with this imaging-based subclassification of migraine and prior publications suggest that allodynia impacts migraine treatment response and disease prognosis, future migraine diagnostic criteria could consider allodynia when defining migraine subgroups. © 2017 American Headache Society.

A first course in structural equation modeling

CERN Document Server

Raykov, Tenko

2012-01-01

In this book, authors Tenko Raykov and George A. Marcoulides introduce students to the basics of structural equation modeling (SEM) through a conceptual, nonmathematical approach. For ease of understanding, the few mathematical formulas presented are used in a conceptual or illustrative nature, rather than a computational one.Featuring examples from EQS, LISREL, and Mplus, A First Course in Structural Equation Modeling is an excellent beginner's guide to learning how to set up input files to fit the most commonly used types of structural equation models with these programs. The basic ideas and methods for conducting SEM are independent of any particular software.Highlights of the Second Edition include: Review of latent change (growth) analysis models at an introductory level Coverage of the popular Mplus program Updated examples of LISREL and EQS A CD that contains all of the text's LISREL, EQS, and Mplus examples.A First Course in Structural Equation Modeling is intended as an introductory book for students...

Bifurcation analysis of a neutral delay differential equation modelling the torsional motion of a driven drill-string

Energy Technology Data Exchange (ETDEWEB)

Balanov, A.G.; Janson, N.B. E-mail: n.janson@lancaster.ac.uk; McClintock, P.V.E.; Tucker, R.W.; Wang, C.H.T

2003-01-01

Using techniques from dynamical systems analysis we explore numerically the solution space, under parametric variation, of a neutral differential delay equation that arises naturally in the Cosserat description of torsional waves on a driven drill-string.

Bifurcation analysis of a neutral delay differential equation modelling the torsional motion of a driven drill-string

International Nuclear Information System (INIS)

Balanov, A.G.; Janson, N.B.; McClintock, P.V.E.; Tucker, R.W.; Wang, C.H.T.

2003-01-01

Using techniques from dynamical systems analysis we explore numerically the solution space, under parametric variation, of a neutral differential delay equation that arises naturally in the Cosserat description of torsional waves on a driven drill-string

Data-driven forecasting of high-dimensional chaotic systems with long short-term memory networks.

Science.gov (United States)

Vlachas, Pantelis R; Byeon, Wonmin; Wan, Zhong Y; Sapsis, Themistoklis P; Koumoutsakos, Petros

2018-05-01

We introduce a data-driven forecasting method for high-dimensional chaotic systems using long short-term memory (LSTM) recurrent neural networks. The proposed LSTM neural networks perform inference of high-dimensional dynamical systems in their reduced order space and are shown to be an effective set of nonlinear approximators of their attractor. We demonstrate the forecasting performance of the LSTM and compare it with Gaussian processes (GPs) in time series obtained from the Lorenz 96 system, the Kuramoto-Sivashinsky equation and a prototype climate model. The LSTM networks outperform the GPs in short-term forecasting accuracy in all applications considered. A hybrid architecture, extending the LSTM with a mean stochastic model (MSM-LSTM), is proposed to ensure convergence to the invariant measure. This novel hybrid method is fully data-driven and extends the forecasting capabilities of LSTM networks.

Strong plasma shock structures based on the Navier--Stokes equations

International Nuclear Information System (INIS)

Abe, K.

1975-01-01

The structure of a plasma collisional shock wave is examined on the basis of the Navier--Stokes equations and simultaneously on the basis of the Fokker--Planck equation. The resultant structures are compared to check the validity of the Navier--Stokes equations applied to the structures of strong shock waves. The Navier--Stokes equations give quite correct structures for weak shock waves. For the strong shock waves, the detailed structures obtained from the Navier--Stokes equations differ from the results of the Fokker--Planck equation, but the shock thicknesses of the two shock waves are in relatively close agreement

Prototype Development: Context-Driven Dynamic XML Ophthalmologic Data Capture Application

Science.gov (United States)

Schwei, Kelsey M; Kadolph, Christopher; Finamore, Joseph; Cancel, Efrain; McCarty, Catherine A; Okorie, Asha; Thomas, Kate L; Allen Pacheco, Jennifer; Pathak, Jyotishman; Ellis, Stephen B; Denny, Joshua C; Rasmussen, Luke V; Tromp, Gerard; Williams, Marc S; Vrabec, Tamara R; Brilliant, Murray H

2017-01-01

Background The capture and integration of structured ophthalmologic data into electronic health records (EHRs) has historically been a challenge. However, the importance of this activity for patient care and research is critical. Objective The purpose of this study was to develop a prototype of a context-driven dynamic extensible markup language (XML) ophthalmologic data capture application for research and clinical care that could be easily integrated into an EHR system. Methods Stakeholders in the medical, research, and informatics fields were interviewed and surveyed to determine data and system requirements for ophthalmologic data capture. On the basis of these requirements, an ophthalmology data capture application was developed to collect and store discrete data elements with important graphical information. Results The context-driven data entry application supports several features, including ink-over drawing capability for documenting eye abnormalities, context-based Web controls that guide data entry based on preestablished dependencies, and an adaptable database or XML schema that stores Web form specifications and allows for immediate changes in form layout or content. The application utilizes Web services to enable data integration with a variety of EHRs for retrieval and storage of patient data. Conclusions This paper describes the development process used to create a context-driven dynamic XML data capture application for optometry and ophthalmology. The list of ophthalmologic data elements identified as important for care and research can be used as a baseline list for future ophthalmologic data collection activities. PMID:28903894

OpenMx: An Open Source Extended Structural Equation Modeling Framework

Science.gov (United States)

Boker, Steven; Neale, Michael; Maes, Hermine; Wilde, Michael; Spiegel, Michael; Brick, Timothy; Spies, Jeffrey; Estabrook, Ryne; Kenny, Sarah; Bates, Timothy; Mehta, Paras; Fox, John

2011-01-01

OpenMx is free, full-featured, open source, structural equation modeling (SEM) software. OpenMx runs within the "R" statistical programming environment on Windows, Mac OS-X, and Linux computers. The rationale for developing OpenMx is discussed along with the philosophy behind the user interface. The OpenMx data structures are…

Extraction of dynamical equations from chaotic data

International Nuclear Information System (INIS)

Rowlands, G.; Sprott, J.C.

1991-02-01

A method is described for extracting from a chaotic time series a system of equations whose solution reproduces the general features of the original data even when these are contaminated with noise. The equations facilitate calculation of fractal dimension, Lyapunov exponents and short-term predictions. The method is applied to data derived from numerical solutions of the Logistic equation, the Henon equations, the Lorenz equations and the Roessler equations. 10 refs., 5 figs

Structure-driven turbulence in ``No man's Land''

Science.gov (United States)

Kosuga, Yusuke; Diamond, Patrick

2012-10-01

Structures are often observed in many physical systems. In tokamaks, for example, such structures are observed as density blobs and holes. Such density blobs and holes are generated at the tokamak edge, where strong gradient perturbations generate an outgoing blob and an incoming hole. Since density holes can propagate from the edge to the core, such structures may play an important role in understanding the phenomenology of the edge-core coupling region, so-called ``No Man's Land.'' In this work, we discuss the dynamics of such structures in real space. In particular, we consider the dynamics of density blobs and holes in the Hasegawa-Wakatani system. Specific questions addressed here include: i) how these structures extract free energy and enhance transport? how different is the relaxation driven by such structures from that driven by linear drift waves? ii) how these structures interact with shear flows? In particular, how these structures interact with a shear layer, which can absorb structures resonantly? iii) how can we calculate the coupled evolution of structures and shear flows? Implications for edge-core coupling problem are discussed as well.

Thermodynamically self-consistent integral equations and the structure of liquid metals

International Nuclear Information System (INIS)

Pastore, G.; Kahl, G.

1987-01-01

We discuss the application of the new thermodynamically self-consistent integral equations for the determination of the structural properties of liquid metals. We present a detailed comparison of the structure (S(q) and g(r)) for models of liquid alkali metals as obtained from two thermodynamically self-consistent integral equations and some published exact computer simulation results; the range of states extends from the triple point to the expanded metal. The theories which only impose thermodynamic self-consistency without any fitting of external data show an excellent agreement with the simulation results, thus demonstrating that this new type of integral equation is definitely superior to the conventional ones (hypernetted chain, Percus-Yevick, mean spherical approximation, etc). (author)

Data-driven modeling of nano-nose gas sensor arrays

DEFF Research Database (Denmark)

Alstrøm, Tommy Sonne; Larsen, Jan; Nielsen, Claus Højgård

2010-01-01

We present a data-driven approach to classification of Quartz Crystal Microbalance (QCM) sensor data. The sensor is a nano-nose gas sensor that detects concentrations of analytes down to ppm levels using plasma polymorized coatings. Each sensor experiment takes approximately one hour hence...... the number of available training data is limited. We suggest a data-driven classification model which work from few examples. The paper compares a number of data-driven classification and quantification schemes able to detect the gas and the concentration level. The data-driven approaches are based on state...

Dissipative Structures of the Kuramoto–Sivashinsky Equation

Directory of Open Access Journals (Sweden)

N. A. Kudryashov

2015-01-01

Full Text Available In the present work, we study the features of dissipative structures formation described by the periodic boundary value problem for the Kuramoto-Sivashinsky equation. The numerical algorithm which is based on the pseudospectral method is presented. We prove the efficiency and accuracy of the proposed numerical method on the exact solution of the equation considered. Using this approach, we performed the numerical simulation of dissipative structure formations described by the Kuramoto–Sivashinsky equation. The influence of the problem parameters on these processes are studied. The quantitative and qualitative characteristics of dissipative structure formations are described. We have shown that there is a value of the control parameter at which the processes of dissipative structure formation are observed. In particular, using the cyclic convolution we define the average value of this parameter. Also, we find the dependence of the amplitude of the structures on the value of control parameter.

Structural equations for Killing tensors of order two. II

International Nuclear Information System (INIS)

Hauser, I.; Malhiot, R.J.

1975-01-01

In a preceding paper, a new form of the structural equations for any Killing tensor of order two have been derived; these equations constitute a system analogous to the Killing vector equations Nabla/sub alpha/ K/sub beta/ = ω/sub alpha beta/ = -ω/sub beta alpha/ and Nabla/sub gamma/ ω/sub alpha beta = R/sub alpha beta gamma delta/ K/sup delta/. The first integrability condition for the Killing tensor structural equations is now derived. The structural equations and the integrability condition have forms which can readily be expressed in terms of a null tetrad to furnish a Killing tensor parallel of the Newman--Penrose equations; this is briefly described. The integrability condition implies the new result, for any given space--time, that the dimension of the set of second-order Killing tensors attains its maximum possible value of 50 only if the space--time is of constant curvature. Potential applications of the structural equations are discussed

Linear causal modeling with structural equations

CERN Document Server

Mulaik, Stanley A

2009-01-01

Emphasizing causation as a functional relationship between variables that describe objects, Linear Causal Modeling with Structural Equations integrates a general philosophical theory of causation with structural equation modeling (SEM) that concerns the special case of linear causal relations. In addition to describing how the functional relation concept may be generalized to treat probabilistic causation, the book reviews historical treatments of causation and explores recent developments in experimental psychology on studies of the perception of causation. It looks at how to perceive causal

Basic and Advanced Bayesian Structural Equation Modeling With Applications in the Medical and Behavioral Sciences

CERN Document Server

Lee, Sik-Yum

2012-01-01

This book provides clear instructions to researchers on how to apply Structural Equation Models (SEMs) for analyzing the inter relationships between observed and latent variables. Basic and Advanced Bayesian Structural Equation Modeling introduces basic and advanced SEMs for analyzing various kinds of complex data, such as ordered and unordered categorical data, multilevel data, mixture data, longitudinal data, highly non-normal data, as well as some of their combinations. In addition, Bayesian semiparametric SEMs to capture the true distribution of explanatory latent variables are introduce

Dynamic Data-Driven UAV Network for Plume Characterization

Science.gov (United States)

2016-05-23

AFRL-AFOSR-VA-TR-2016-0203 Dynamic Data-Driven UAV Network for Plume Characterization Kamran Mohseni UNIVERSITY OF FLORIDA Final Report 05/23/2016...AND SUBTITLE Dynamic Data-Driven UAV Network for Plume Characterization 5a.  CONTRACT NUMBER 5b.  GRANT NUMBER FA9550-13-1-0090 5c.  PROGRAM ELEMENT...studied a dynamic data driven (DDD) approach to operation of a heterogeneous team of unmanned aerial vehicles ( UAVs ) or micro/miniature aerial

Nonlinear structure formation in ion-temperature-gradient driven drift waves in pair-ion plasma with nonthermal electron distribution

Science.gov (United States)

Razzaq, Javaria; Haque, Q.; Khan, Majid; Bhatti, Adnan Mehmood; Kamran, M.; Mirza, Arshad M.

2018-02-01

Nonlinear structure formation in ion-temperature-gradient (ITG) driven waves is investigated in pair-ion plasma comprising ions and nonthermal electrons (kappa, Cairns). By using the transport equations of the Braginskii model, a new set of nonlinear equations are derived. A linear dispersion relation is obtained and discussed analytically as well as numerically. It is shown that the nonthermal population of electrons affects both the linear and nonlinear characteristics of the ITG mode in pair-ion plasma. This work will be useful in tokamaks and stellarators where non-Maxwellian population of electrons may exist due to resonant frequency heating, electron cyclotron heating, runaway electrons, etc.

PubChemQC Project: A Large-Scale First-Principles Electronic Structure Database for Data-Driven Chemistry.

Science.gov (United States)

Nakata, Maho; Shimazaki, Tomomi

2017-06-26

Large-scale molecular databases play an essential role in the investigation of various subjects such as the development of organic materials, in silico drug design, and data-driven studies with machine learning. We have developed a large-scale quantum chemistry database based on first-principles methods. Our database currently contains the ground-state electronic structures of 3 million molecules based on density functional theory (DFT) at the B3LYP/6-31G* level, and we successively calculated 10 low-lying excited states of over 2 million molecules via time-dependent DFT with the B3LYP functional and the 6-31+G* basis set. To select the molecules calculated in our project, we referred to the PubChem Project, which was used as the source of the molecular structures in short strings using the InChI and SMILES representations. Accordingly, we have named our quantum chemistry database project "PubChemQC" ( http://pubchemqc.riken.jp/ ) and placed it in the public domain. In this paper, we show the fundamental features of the PubChemQC database and discuss the techniques used to construct the data set for large-scale quantum chemistry calculations. We also present a machine learning approach to predict the electronic structure of molecules as an example to demonstrate the suitability of the large-scale quantum chemistry database.

Structural equation modeling methods and applications

CERN Document Server

Wang, Jichuan

2012-01-01

A reference guide for applications of SEM using Mplus Structural Equation Modeling: Applications Using Mplus is intended as both a teaching resource and a reference guide. Written in non-mathematical terms, this book focuses on the conceptual and practical aspects of Structural Equation Modeling (SEM). Basic concepts and examples of various SEM models are demonstrated along with recently developed advanced methods, such as mixture modeling and model-based power analysis and sample size estimate for SEM. The statistical modeling program, Mplus, is also featured and provides researchers with a

Data driven parallelism in experimental high energy physics applications

International Nuclear Information System (INIS)

Pohl, M.

1987-01-01

I present global design principles for the implementation of high energy physics data analysis code on sequential and parallel processors with mixed shared and local memory. Potential parallelism in the structure of high energy physics tasks is identified with granularity varying from a few times 10 8 instructions all the way down to a few times 10 4 instructions. It follows the hierarchical structure of detector and data acquisition systems. To take advantage of this - yet preserving the necessary portability of the code - I propose a computational model with purely data driven concurrency in Single Program Multiple Data (SPMD) mode. The task granularity is defined by varying the granularity of the central data structure manipulated. Concurrent processes coordiate themselves asynchroneously using simple lock constructs on parts of the data structure. Load balancing among processes occurs naturally. The scheme allows to map the internal layout of the data structure closely onto the layout of local and shared memory in a parallel architecture. It thus allows to optimize the application with respect to synchronization as well as data transport overheads. I present a coarse top level design for a portable implementation of this scheme on sequential machines, multiprocessor mainframes (e.g. IBM 3090), tightly coupled multiprocessors (e.g. RP-3) and loosely coupled processor arrays (e.g. LCAP, Emulating Processor Farms). (orig.)

Data driven parallelism in experimental high energy physics applications

Science.gov (United States)

Pohl, Martin

1987-08-01

I present global design principles for the implementation of High Energy Physics data analysis code on sequential and parallel processors with mixed shared and local memory. Potential parallelism in the structure of High Energy Physics tasks is identified with granularity varying from a few times 10 8 instructions all the way down to a few times 10 4 instructions. It follows the hierarchical structure of detector and data acquisition systems. To take advantage of this - yet preserving the necessary portability of the code - I propose a computational model with purely data driven concurrency in Single Program Multiple Data (SPMD) mode. The Task granularity is defined by varying the granularity of the central data structure manipulated. Concurrent processes coordinate themselves asynchroneously using simple lock constructs on parts of the data structure. Load balancing among processes occurs naturally. The scheme allows to map the internal layout of the data structure closely onto the layout of local and shared memory in a parallel architecture. It thus allows to optimize the application with respect to synchronization as well as data transport overheads. I present a coarse top level design for a portable implementation of this scheme on sequential machines, multiprocessor mainframes (e.g. IBM 3090), tightly coupled multiprocessors (e.g. RP-3) and loosely coupled processor arrays (e.g. LCAP, Emulating Processor Farms).

Integrable systems of partial differential equations determined by structure equations and Lax pair

International Nuclear Information System (INIS)

Bracken, Paul

2010-01-01

It is shown how a system of evolution equations can be developed both from the structure equations of a submanifold embedded in three-space as well as from a matrix SO(6) Lax pair. The two systems obtained this way correspond exactly when a constraint equation is selected and imposed on the system of equations. This allows for the possibility of selecting the coefficients in the second fundamental form in a general way.

Comparisons of Multilevel Modeling and Structural Equation Modeling Approaches to Actor-Partner Interdependence Model.

Science.gov (United States)

Hong, Sehee; Kim, Soyoung

2018-01-01

There are basically two modeling approaches applicable to analyzing an actor-partner interdependence model: the multilevel modeling (hierarchical linear model) and the structural equation modeling. This article explains how to use these two models in analyzing an actor-partner interdependence model and how these two approaches work differently. As an empirical example, marital conflict data were used to analyze an actor-partner interdependence model. The multilevel modeling and the structural equation modeling produced virtually identical estimates for a basic model. However, the structural equation modeling approach allowed more realistic assumptions on measurement errors and factor loadings, rendering better model fit indices.

New Jacobian Matrix and Equations of Motion for a 6 d.o.f Cable-Driven Robot

Directory of Open Access Journals (Sweden)

Ali Afshari

2007-03-01

Full Text Available In this paper, we introduce a new method and new motion variables to study kinematics and dynamics of a 6 d.o.f cable-driven robot. Using these new variables and Lagrange equations, we achieve new equations of motion which are different in appearance and several aspects from conventional equations usually used to study 6 d.o.f cable robots. Then, we introduce a new Jacobian matrix which expresses kinematical relations of the robot via a new approach and is basically different from the conventional Jacobian matrix. One of the important characteristics of the new method is computational efficiency in comparison with the conventional method. It is demonstrated that using the new method instead of the conventional one, significantly reduces the computation time required to determine workspace of the robot as well as the time required to solve the equations of motion.

Data mining, knowledge discovery and data-driven modelling

NARCIS (Netherlands)

Solomatine, D.P.; Velickov, S.; Bhattacharya, B.; Van der Wal, B.

2003-01-01

The project was aimed at exploring the possibilities of a new paradigm in modelling - data-driven modelling, often referred as "data mining". Several application areas were considered: sedimentation problems in the Port of Rotterdam, automatic soil classification on the basis of cone penetration

An ontology-driven tool for structured data acquisition using Web forms.

Science.gov (United States)

Gonçalves, Rafael S; Tu, Samson W; Nyulas, Csongor I; Tierney, Michael J; Musen, Mark A

2017-08-01

Structured data acquisition is a common task that is widely performed in biomedicine. However, current solutions for this task are far from providing a means to structure data in such a way that it can be automatically employed in decision making (e.g., in our example application domain of clinical functional assessment, for determining eligibility for disability benefits) based on conclusions derived from acquired data (e.g., assessment of impaired motor function). To use data in these settings, we need it structured in a way that can be exploited by automated reasoning systems, for instance, in the Web Ontology Language (OWL); the de facto ontology language for the Web. We tackle the problem of generating Web-based assessment forms from OWL ontologies, and aggregating input gathered through these forms as an ontology of "semantically-enriched" form data that can be queried using an RDF query language, such as SPARQL. We developed an ontology-based structured data acquisition system, which we present through its specific application to the clinical functional assessment domain. We found that data gathered through our system is highly amenable to automatic analysis using queries. We demonstrated how ontologies can be used to help structuring Web-based forms and to semantically enrich the data elements of the acquired structured data. The ontologies associated with the enriched data elements enable automated inferences and provide a rich vocabulary for performing queries.

Structure and Calibration of Constitutive Equations for Granular Soils

Directory of Open Access Journals (Sweden)

Sawicki Andrzej

2015-02-01

Full Text Available The form of incremental constitutive equations for granular soils is discussed for the triaxial configuration. The classical elasto-plastic approach and the semi-empirical model are discussed on the basis of constitutive relations determined directly from experimental data. First, the general structure of elasto-plastic constitutive equations is presented. Then, the structure of semiempirical constitutive equations is described, and a method of calibrating the model is presented. This calibration method is based on a single experiment, performed in the triaxial apparatus, which also involves a partial verification of the model, on an atypical stress path. The model is shown to give reasonable predictions. An important feature of the semi-empirical incremental model is the definition of loading and unloading, which is different from that assumed in elasto-plasticity. This definition distinguishes between spherical and deviatoric loading/unloading. The definition of deviatoric loading/unloading has been subject to some criticism. It was therefore discussed and clarified in this paper on the basis of the experiment presented.

Photonic Crystal Laser-Driven Accelerator Structures

International Nuclear Information System (INIS)

Cowan, B

2004-01-01

The authors discuss simulated photonic crystal structure designs for laser-driven particle acceleration. They focus on three-dimensional planar structures based on the so-called ''woodpile'' lattice, demonstrating guiding of a speed-of-light accelerating mode by a defect in the photonic crystal lattice. They introduce a candidate geometry and discuss the properties of the accelerating mode. They also discuss the linear beam dynamics in the structure present a novelmethod for focusing the beam. In addition they describe ongoing investigations of photonic crystal fiber-based structures

Structural Equation Modeling: Theory and Applications in Forest Management

Directory of Open Access Journals (Sweden)

Tzeng Yih Lam

2012-01-01

Full Text Available Forest ecosystem dynamics are driven by a complex array of simultaneous cause-and-effect relationships. Understanding this complex web requires specialized analytical techniques such as Structural Equation Modeling (SEM. The SEM framework and implementation steps are outlined in this study, and we then demonstrate the technique by application to overstory-understory relationships in mature Douglas-fir forests in the northwestern USA. A SEM model was formulated with (1 a path model representing the effects of successively higher layers of vegetation on late-seral herbs through processes such as light attenuation and (2 a measurement model accounting for measurement errors. The fitted SEM model suggested a direct negative effect of light attenuation on late-seral herbs cover but a direct positive effect of northern aspect. Moreover, many processes have indirect effects mediated through midstory vegetation. SEM is recommended as a forest management tool for designing silvicultural treatments and systems for attaining complex arrays of management objectives.

Structural Equation Model Trees

Science.gov (United States)

Brandmaier, Andreas M.; von Oertzen, Timo; McArdle, John J.; Lindenberger, Ulman

2013-01-01

In the behavioral and social sciences, structural equation models (SEMs) have become widely accepted as a modeling tool for the relation between latent and observed variables. SEMs can be seen as a unification of several multivariate analysis techniques. SEM Trees combine the strengths of SEMs and the decision tree paradigm by building tree…

Using Two Different Approaches to Assess Dietary Patterns: Hypothesis-Driven and Data-Driven Analysis

Directory of Open Access Journals (Sweden)

Ágatha Nogueira Previdelli

2016-09-01

Full Text Available The use of dietary patterns to assess dietary intake has become increasingly common in nutritional epidemiology studies due to the complexity and multidimensionality of the diet. Currently, two main approaches have been widely used to assess dietary patterns: data-driven and hypothesis-driven analysis. Since the methods explore different angles of dietary intake, using both approaches simultaneously might yield complementary and useful information; thus, we aimed to use both approaches to gain knowledge of adolescents’ dietary patterns. Food intake from a cross-sectional survey with 295 adolescents was assessed by 24 h dietary recall (24HR. In hypothesis-driven analysis, based on the American National Cancer Institute method, the usual intake of Brazilian Healthy Eating Index Revised components were estimated. In the data-driven approach, the usual intake of foods/food groups was estimated by the Multiple Source Method. In the results, hypothesis-driven analysis showed low scores for Whole grains, Total vegetables, Total fruit and Whole fruits, while, in data-driven analysis, fruits and whole grains were not presented in any pattern. High intakes of sodium, fats and sugars were observed in hypothesis-driven analysis with low total scores for Sodium, Saturated fat and SoFAA (calories from solid fat, alcohol and added sugar components in agreement, while the data-driven approach showed the intake of several foods/food groups rich in these nutrients, such as butter/margarine, cookies, chocolate powder, whole milk, cheese, processed meat/cold cuts and candies. In this study, using both approaches at the same time provided consistent and complementary information with regard to assessing the overall dietary habits that will be important in order to drive public health programs, and improve their efficiency to monitor and evaluate the dietary patterns of populations.

Structural Equation Modeling with the Smartpls

Directory of Open Access Journals (Sweden)

Christian M. Ringle

2014-05-01

Full Text Available The objective of this article is to present a didactic example of Structural Equation Modeling using the software SmartPLS 2.0 M3. The program mentioned uses the method of Partial Least Squares and seeks to address the following situations frequently observed in marketing research: Absence of symmetric distributions of variables measured by a theory still in its beginning phase or with little “consolidation”, formative models, and/or a limited amount of data. The growing use of SmartPLS has demonstrated its robustness and the applicability of the model in the areas that are being studied. 

Data-driven discovery of Koopman eigenfunctions using deep learning

Science.gov (United States)

Lusch, Bethany; Brunton, Steven L.; Kutz, J. Nathan

2017-11-01

Koopman operator theory transforms any autonomous non-linear dynamical system into an infinite-dimensional linear system. Since linear systems are well-understood, a mapping of non-linear dynamics to linear dynamics provides a powerful approach to understanding and controlling fluid flows. However, finding the correct change of variables remains an open challenge. We present a strategy to discover an approximate mapping using deep learning. Our neural networks find this change of variables, its inverse, and a finite-dimensional linear dynamical system defined on the new variables. Our method is completely data-driven and only requires measurements of the system, i.e. it does not require derivatives or knowledge of the governing equations. We find a minimal set of approximate Koopman eigenfunctions that are sufficient to reconstruct and advance the system to future states. We demonstrate the method on several dynamical systems.

Continuous Time Structural Equation Modeling with R Package ctsem

Directory of Open Access Journals (Sweden)

Charles C. Driver

2017-04-01

Full Text Available We introduce ctsem, an R package for continuous time structural equation modeling of panel (N > 1 and time series (N = 1 data, using full information maximum likelihood. Most dynamic models (e.g., cross-lagged panel models in the social and behavioural sciences are discrete time models. An assumption of discrete time models is that time intervals between measurements are equal, and that all subjects were assessed at the same intervals. Violations of this assumption are often ignored due to the difficulty of accounting for varying time intervals, therefore parameter estimates can be biased and the time course of effects becomes ambiguous. By using stochastic differential equations to estimate an underlying continuous process, continuous time models allow for any pattern of measurement occasions. By interfacing to OpenMx, ctsem combines the flexible specification of structural equation models with the enhanced data gathering opportunities and improved estimation of continuous time models. ctsem can estimate relationships over time for multiple latent processes, measured by multiple noisy indicators with varying time intervals between observations. Within and between effects are estimated simultaneously by modeling both observed covariates and unobserved heterogeneity. Exogenous shocks with different shapes, group differences, higher order diffusion effects and oscillating processes can all be simply modeled. We first introduce and define continuous time models, then show how to specify and estimate a range of continuous time models using ctsem.

On the Use of Structural Equation Models in Marketing Modeling

NARCIS (Netherlands)

Steenkamp, J.E.B.M.; Baumgartner, H.

2000-01-01

We reflect on the role of structural equation modeling (SEM) in marketing modeling and managerial decision making. We discuss some benefits provided by SEM and alert marketing modelers to several recent developments in SEM in three areas: measurement analysis, analysis of cross-sectional data, and

Fitting ARMA Time Series by Structural Equation Models.

Science.gov (United States)

van Buuren, Stef

1997-01-01

This paper outlines how the stationary ARMA (p,q) model (G. Box and G. Jenkins, 1976) can be specified as a structural equation model. Maximum likelihood estimates for the parameters in the ARMA model can be obtained by software for fitting structural equation models. The method is applied to three problem types. (SLD)

Fermionic covariant prolongation structure theory for supernonlinear evolution equation

International Nuclear Information System (INIS)

Cheng Jipeng; Wang Shikun; Wu Ke; Zhao Weizhong

2010-01-01

We investigate the superprincipal bundle and its associated superbundle. The super(nonlinear)connection on the superfiber bundle is constructed. Then by means of the connection theory, we establish the fermionic covariant prolongation structure theory of the supernonlinear evolution equation. In this geometry theory, the fermionic covariant fundamental equations determining the prolongation structure are presented. As an example, the supernonlinear Schroedinger equation is analyzed in the framework of this fermionic covariant prolongation structure theory. We obtain its Lax pairs and Baecklund transformation.

Data-driven architectural design to production and operation

NARCIS (Netherlands)

Bier, H.H.; Mostafavi, S.

2015-01-01

Data-driven architectural production and operation explored within Hyperbody rely heavily on system thinking implying that all parts of a system are to be understood in relation to each other. These relations are established bi-directionally so that data-driven architecture is not only produced

A predictive estimation method for carbon dioxide transport by data-driven modeling with a physically-based data model

Science.gov (United States)

Jeong, Jina; Park, Eungyu; Han, Weon Shik; Kim, Kue-Young; Jun, Seong-Chun; Choung, Sungwook; Yun, Seong-Taek; Oh, Junho; Kim, Hyun-Jun

2017-11-01

In this study, a data-driven method for predicting CO2 leaks and associated concentrations from geological CO2 sequestration is developed. Several candidate models are compared based on their reproducibility and predictive capability for CO2 concentration measurements from the Environment Impact Evaluation Test (EIT) site in Korea. Based on the data mining results, a one-dimensional solution of the advective-dispersive equation for steady flow (i.e., Ogata-Banks solution) is found to be most representative for the test data, and this model is adopted as the data model for the developed method. In the validation step, the method is applied to estimate future CO2 concentrations with the reference estimation by the Ogata-Banks solution, where a part of earlier data is used as the training dataset. From the analysis, it is found that the ensemble mean of multiple estimations based on the developed method shows high prediction accuracy relative to the reference estimation. In addition, the majority of the data to be predicted are included in the proposed quantile interval, which suggests adequate representation of the uncertainty by the developed method. Therefore, the incorporation of a reasonable physically-based data model enhances the prediction capability of the data-driven model. The proposed method is not confined to estimations of CO2 concentration and may be applied to various real-time monitoring data from subsurface sites to develop automated control, management or decision-making systems.

A predictive estimation method for carbon dioxide transport by data-driven modeling with a physically-based data model.

Science.gov (United States)

Jeong, Jina; Park, Eungyu; Han, Weon Shik; Kim, Kue-Young; Jun, Seong-Chun; Choung, Sungwook; Yun, Seong-Taek; Oh, Junho; Kim, Hyun-Jun

2017-11-01

In this study, a data-driven method for predicting CO 2 leaks and associated concentrations from geological CO 2 sequestration is developed. Several candidate models are compared based on their reproducibility and predictive capability for CO 2 concentration measurements from the Environment Impact Evaluation Test (EIT) site in Korea. Based on the data mining results, a one-dimensional solution of the advective-dispersive equation for steady flow (i.e., Ogata-Banks solution) is found to be most representative for the test data, and this model is adopted as the data model for the developed method. In the validation step, the method is applied to estimate future CO 2 concentrations with the reference estimation by the Ogata-Banks solution, where a part of earlier data is used as the training dataset. From the analysis, it is found that the ensemble mean of multiple estimations based on the developed method shows high prediction accuracy relative to the reference estimation. In addition, the majority of the data to be predicted are included in the proposed quantile interval, which suggests adequate representation of the uncertainty by the developed method. Therefore, the incorporation of a reasonable physically-based data model enhances the prediction capability of the data-driven model. The proposed method is not confined to estimations of CO 2 concentration and may be applied to various real-time monitoring data from subsurface sites to develop automated control, management or decision-making systems. Copyright © 2017 Elsevier B.V. All rights reserved.

Using Shape Memory Alloys: A Dynamic Data Driven Approach

KAUST Repository

Douglas, Craig C.

2013-06-01

Shape Memory Alloys (SMAs) are capable of changing their crystallographic structure due to changes of either stress or temperature. SMAs are used in a number of aerospace devices and are required in some devices in exotic environments. We are developing dynamic data driven application system (DDDAS) tools to monitor and change SMAs in real time for delivering payloads by aerospace vehicles. We must be able to turn on and off the sensors and heating units, change the stress on the SMA, monitor on-line data streams, change scales based on incoming data, and control what type of data is generated. The application must have the capability to be run and steered remotely as an unmanned feedback control loop.

Data-driven models of dominantly-inherited Alzheimer's disease progression.

Science.gov (United States)

Oxtoby, Neil P; Young, Alexandra L; Cash, David M; Benzinger, Tammie L S; Fagan, Anne M; Morris, John C; Bateman, Randall J; Fox, Nick C; Schott, Jonathan M; Alexander, Daniel C

2018-03-22

Dominantly-inherited Alzheimer's disease is widely hoped to hold the key to developing interventions for sporadic late onset Alzheimer's disease. We use emerging techniques in generative data-driven disease progression modelling to characterize dominantly-inherited Alzheimer's disease progression with unprecedented resolution, and without relying upon familial estimates of years until symptom onset. We retrospectively analysed biomarker data from the sixth data freeze of the Dominantly Inherited Alzheimer Network observational study, including measures of amyloid proteins and neurofibrillary tangles in the brain, regional brain volumes and cortical thicknesses, brain glucose hypometabolism, and cognitive performance from the Mini-Mental State Examination (all adjusted for age, years of education, sex, and head size, as appropriate). Data included 338 participants with known mutation status (211 mutation carriers in three subtypes: 163 PSEN1, 17 PSEN2, and 31 APP) and a baseline visit (age 19-66; up to four visits each, 1.1 ± 1.9 years in duration; spanning 30 years before, to 21 years after, parental age of symptom onset). We used an event-based model to estimate sequences of biomarker changes from baseline data across disease subtypes (mutation groups), and a differential equation model to estimate biomarker trajectories from longitudinal data (up to 66 mutation carriers, all subtypes combined). The two models concur that biomarker abnormality proceeds as follows: amyloid deposition in cortical then subcortical regions (∼24 ± 11 years before onset); phosphorylated tau (17 ± 8 years), tau and amyloid-β changes in cerebrospinal fluid; neurodegeneration first in the putamen and nucleus accumbens (up to 6 ± 2 years); then cognitive decline (7 ± 6 years), cerebral hypometabolism (4 ± 4 years), and further regional neurodegeneration. Our models predicted symptom onset more accurately than predictions that used familial estimates: root mean squared error of 1

Challenges of Data-driven Healthcare Management

DEFF Research Database (Denmark)

Bossen, Claus; Danholt, Peter; Ubbesen, Morten Bonde

This paper describes the new kind of data-work involved in developing data-driven healthcare based on two cases from Denmark: The first case concerns a governance infrastructure based on Diagnose-Related Groups (DRG), which was introduced in Denmark in the 1990s. The DRG-system links healthcare...... activity and financing and relies of extensive data entry, reporting and calculations. This has required the development of new skills, work and work roles. The second case concerns a New Governance project aimed at developing new performance indicators for healthcare delivery as an alternative to DRG....... Here, a core challenge is select indicators and actually being able to acquire data upon them. The two cases point out that data-driven healthcare requires more and new kinds of work for which new skills, functions and work roles have to be developed....

Prototype Development: Context-Driven Dynamic XML Ophthalmologic Data Capture Application.

Science.gov (United States)

Peissig, Peggy; Schwei, Kelsey M; Kadolph, Christopher; Finamore, Joseph; Cancel, Efrain; McCarty, Catherine A; Okorie, Asha; Thomas, Kate L; Allen Pacheco, Jennifer; Pathak, Jyotishman; Ellis, Stephen B; Denny, Joshua C; Rasmussen, Luke V; Tromp, Gerard; Williams, Marc S; Vrabec, Tamara R; Brilliant, Murray H

2017-09-13

The capture and integration of structured ophthalmologic data into electronic health records (EHRs) has historically been a challenge. However, the importance of this activity for patient care and research is critical. The purpose of this study was to develop a prototype of a context-driven dynamic extensible markup language (XML) ophthalmologic data capture application for research and clinical care that could be easily integrated into an EHR system. Stakeholders in the medical, research, and informatics fields were interviewed and surveyed to determine data and system requirements for ophthalmologic data capture. On the basis of these requirements, an ophthalmology data capture application was developed to collect and store discrete data elements with important graphical information. The context-driven data entry application supports several features, including ink-over drawing capability for documenting eye abnormalities, context-based Web controls that guide data entry based on preestablished dependencies, and an adaptable database or XML schema that stores Web form specifications and allows for immediate changes in form layout or content. The application utilizes Web services to enable data integration with a variety of EHRs for retrieval and storage of patient data. This paper describes the development process used to create a context-driven dynamic XML data capture application for optometry and ophthalmology. The list of ophthalmologic data elements identified as important for care and research can be used as a baseline list for future ophthalmologic data collection activities. ©Peggy Peissig, Kelsey M Schwei, Christopher Kadolph, Joseph Finamore, Efrain Cancel, Catherine A McCarty, Asha Okorie, Kate L Thomas, Jennifer Allen Pacheco, Jyotishman Pathak, Stephen B Ellis, Joshua C Denny, Luke V Rasmussen, Gerard Tromp, Marc S Williams, Tamara R Vrabec, Murray H Brilliant. Originally published in JMIR Medical Informatics (http://medinform.jmir.org), 13.09.2017.

Meta-analytic structural equation modelling

CERN Document Server

Jak, Suzanne

2015-01-01

This book explains how to employ MASEM, the combination of meta-analysis (MA) and structural equation modelling (SEM). It shows how by using MASEM, a single model can be tested to explain the relationships between a set of variables in several studies. This book gives an introduction to MASEM, with a focus on the state of the art approach: the two stage approach of Cheung and Cheung & Chan. Both, the fixed and the random approach to MASEM are illustrated with two applications to real data. All steps that have to be taken to perform the analyses are discussed extensively. All data and syntax files are available online, so that readers can imitate all analyses. By using SEM for meta-analysis, this book shows how to benefit from all available information from all available studies, even if few or none of the studies report about all relationships that feature in the full model of interest.

Data to Decisions: Creating a Culture of Model-Driven Drug Discovery.

Science.gov (United States)

Brown, Frank K; Kopti, Farida; Chang, Charlie Zhenyu; Johnson, Scott A; Glick, Meir; Waller, Chris L

2017-09-01

Merck & Co., Inc., Kenilworth, NJ, USA, is undergoing a transformation in the way that it prosecutes R&D programs. Through the adoption of a "model-driven" culture, enhanced R&D productivity is anticipated, both in the form of decreased attrition at each stage of the process and by providing a rational framework for understanding and learning from the data generated along the way. This new approach focuses on the concept of a "Design Cycle" that makes use of all the data possible, internally and externally, to drive decision-making. These data can take the form of bioactivity, 3D structures, genomics, pathway, PK/PD, safety data, etc. Synthesis of high-quality data into models utilizing both well-established and cutting-edge methods has been shown to yield high confidence predictions to prioritize decision-making and efficiently reposition resources within R&D. The goal is to design an adaptive research operating plan that uses both modeled data and experiments, rather than just testing, to drive project decision-making. To support this emerging culture, an ambitious information management (IT) program has been initiated to implement a harmonized platform to facilitate the construction of cross-domain workflows to enable data-driven decision-making and the construction and validation of predictive models. These goals are achieved through depositing model-ready data, agile persona-driven access to data, a unified cross-domain predictive model lifecycle management platform, and support for flexible scientist-developed workflows that simplify data manipulation and consume model services. The end-to-end nature of the platform, in turn, not only supports but also drives the culture change by enabling scientists to apply predictive sciences throughout their work and over the lifetime of a project. This shift in mindset for both scientists and IT was driven by an early impactful demonstration of the potential benefits of the platform, in which expert-level early discovery

Structure preserving transformations for Newtonian Lie-admissible equations

International Nuclear Information System (INIS)

Cantrijn, F.

1979-01-01

Recently, a new formulation of non-conservative mechanics has been presented in terms of Hamilton-admissible equations which constitute a generalization of the conventional Hamilton equations. The algebraic structure entering the Hamilton-admissible description of a non-conservative system is that of a Lie-admissible algebra. The corresponding geometrical treatment is related to the existence of a so-called symplectic-admissible form. The transformation theory for Hamilton-admissible systems is currently investigated. The purpose of this paper is to describe one aspect of this theory by identifying the class of transformations which preserve the structure of Hamilton-admissible equations. Necessary and sufficient conditions are established for a transformation to be structure preserving. Some particular cases are discussed and an example is worked out

Structural Equation and Mei Conserved Quantity of Mei Symmetry for Appell Equations in Holonomic Systems with Unilateral Constraints

International Nuclear Information System (INIS)

Jia Liqun; Cui Jinchao; Zhang Yaoyu; Luo Shaokai

2009-01-01

Structural equation and Mei conserved quantity of Mei symmetry for Appell equations in holonomic systems with unilateral constraints are investigated. Appell equations and differential equations of motion for holonomic mechanic systems with unilateral constraints are established. The definition and the criterion of Mei symmetry for Appell equations in holonomic systems with unilateral constraints under the infinitesimal transformations of groups are also given. The expressions of the structural equation and Mei conserved quantity of Mei symmetry for Appell equations in holonomic systems with unilateral constraints expressed by Appell functions are obtained. An example is given to illustrate the application of the results. (general)

A perspective on the structural studies of inner membrane electrochemical potential-driven transporters.

Science.gov (United States)

Lemieux, M Joanne

2008-09-01

Electrochemical potential-driven transporters represent a vast array of proteins with varied substrate specificities. While diverse in size and substrate specificity, they are all driven by electrochemical potentials. Over the past five years there have been increasing numbers of X-ray structures reported for this family of transporters. Structural information is available for five subfamilies of electrochemical potential-driven transporters. No structural information exists for the remaining 91 subfamilies. In this review, the various subfamilies of electrochemical potential-driven transporters are discussed. The seven reported structures for the electrochemical potential-driven transporters and the methods for their crystallization are also presented. With a few exceptions, overall crystallization trends have been very similar for the transporters despite their differences in substrate specificity and topology. Also discussed is why the structural studies on these transporters were successful while others are not as fruitful. With the plethora of transporters with unknown structures, this review provides incentive for crystallization of transporters in the remaining subfamilies for which no structural information exists.

The Interplay of School Readiness and Teacher Readiness for Educational Technology Integration: A Structural Equation Model

Science.gov (United States)

Petko, Dominik; Prasse, Doreen; Cantieni, Andrea

2018-01-01

Decades of research have shown that technological change in schools depends on multiple interrelated factors. Structural equation models explaining the interplay of factors often suffer from high complexity and low coherence. To reduce complexity, a more robust structural equation model was built with data from a survey of 349 Swiss primary school…

Controller synthesis for negative imaginary systems: a data driven approach

KAUST Repository

Mabrok, Mohamed

2016-02-17

The negative imaginary (NI) property occurs in many important applications. For instance, flexible structure systems with collocated force actuators and position sensors can be modelled as negative imaginary systems. In this study, a data-driven controller synthesis methodology for NI systems is presented. In this approach, measured frequency response data of the plant is used to construct the controller frequency response at every frequency by minimising a cost function. Then, this controller response is used to identify the controller transfer function using system identification methods. © The Institution of Engineering and Technology 2016.

Data driven marketing for dummies

CERN Document Server

Semmelroth, David

2013-01-01

Embrace data and use it to sell and market your products Data is everywhere and it keeps growing and accumulating. Companies need to embrace big data and make it work harder to help them sell and market their products. Successful data analysis can help marketing professionals spot sales trends, develop smarter marketing campaigns, and accurately predict customer loyalty. Data Driven Marketing For Dummies helps companies use all the data at their disposal to make current customers more satisfied, reach new customers, and sell to their most important customer segments more efficiently. Identifyi

Somatic Expression of Psychological Problems (Somatization: Examination with Structural Equation Model

Directory of Open Access Journals (Sweden)

Tugba Seda Çolak

2014-09-01

Full Text Available The main purpose of the research is to define which psychological symptoms (somatization, depression, obsessive ‐ compulsive, hostility, interpersonal sensitivity, anxiety, phobic anxiety, paranoid ideation and psychoticism cause somatic reactions at most. Total effect of these psychological symptoms on somatic symptoms had been investigated. Study was carried out with structural equation model to research the relation between the psychological symptoms and somatization. The main material of the research is formed by the data obtained from 492 people. SCL‐90‐R scale was used in order to obtain the data. As a result of the structural equation analysis, it has been found that 1Psychoticism, phobic anxiety, and paranoid ideation do not predict somatic symptoms.2There is a negative relation between interpersonal sensitivity level mand somatic reactions.3Anxiety symptoms had been found as causative to occur the highest level of somatic reactions.

Flexibility of Data-driven Process Structures

NARCIS (Netherlands)

Muller, Dominic; Reichert, Manfred; Herbst, Joachim; Eder, Johann; Dustdar, Schahram

2006-01-01

The coordination of complex process structures is a fundamental task for enterprises, such as in the automotive industry. Usually, such process structures consist of several (sub-)processes whose execution must be coordinated and synchronized. Effecting this manually is both ineffective and

Hamiltonian structures of some non-linear evolution equations

International Nuclear Information System (INIS)

Tu, G.Z.

1983-06-01

The Hamiltonian structure of the O(2,1) non-linear sigma model, generalized AKNS equations, are discussed. By reducing the O(2,1) non-linear sigma model to its Hamiltonian form some new conservation laws are derived. A new hierarchy of non-linear evolution equations is proposed and shown to be generalized Hamiltonian equations with an infinite number of conservation laws. (author)

Mediation in dyadic data at the level of the dyads: a Structural Equation Modeling approach.

Science.gov (United States)

Ledermann, Thomas; Macho, Siegfried

2009-10-01

An extended version of the Common Fate Model (CFM) is presented to estimate and test mediation in dyadic data. The model can be used for distinguishable dyad members (e.g., heterosexual couples) or indistinguishable dyad members (e.g., homosexual couples) if (a) the variables measure characteristics of the dyadic relationship or shared external influences that affect both partners; if (b) the causal associations between the variables should be analyzed at the dyadic level; and if (c) the measured variables are reliable indicators of the latent variables. To assess mediation using Structural Equation Modeling, a general three-step procedure is suggested. The first is a selection of a good fitting model, the second a test of the direct effects, and the third a test of the mediating effect by means of bootstrapping. The application of the model along with the procedure for assessing mediation is illustrated using data from 184 couples on marital problems, communication, and marital quality. Differences with the Actor-Partner Interdependence Model and the analysis of longitudinal mediation by using the CFM are discussed.

Constitutive equations for describing high-temperature inelastic behavior of structural alloys

International Nuclear Information System (INIS)

Robinson, D.N.; Pugh, C.E.; Corum, J.M.

1976-01-01

This paper addresses constitutive equations for the description of inelastic behavior of LMFBR structural alloys at elevated temperatures. Both elastic-plastic (time-independent) and creep (time-dependent) deformations are considered for types 304 and 316 stainless steel and 2 1 / 4 Cr--1 Mo steel. The constitutive equations identified for interim use in design analyses are described along with the assumptions and data on which they are based. Areas where improvements are needed are identified, and some alternate theories that are being pursued are outlined

Dynamic modeling of interfacial structures via interfacial area transport equation

International Nuclear Information System (INIS)

Seungjin, Kim; Mamoru, Ishii

2004-01-01

Full text of publication follows:In the current thermal-hydraulic system analysis codes using the two-fluid model, the empirical correlations that are based on the two-phase flow regimes and regime transition criteria are being employed as closure relations for the interfacial transfer terms. Due to its inherent shortcomings, however, such static correlations are inaccurate and present serious problems in the numerical analysis. In view of this, a new dynamic approach employing the interfacial area transport equation has been studied. The interfacial area transport equation dynamically models the two-phase flow regime transitions and predicts continuous change of the interfacial area concentration along the flow field. Hence, when employed in the thermal-hydraulic system analysis codes, it eliminates artificial bifurcations stemming from the use of the static flow regime transition criteria. Therefore, the interfacial area transport equation can make a leapfrog improvement in the current capability of the two-fluid model from both scientific and practical point of view. Accounting for the substantial differences in the transport phenomena of various sizes of bubbles, the two-group interfacial area transport equations have been developed. The group 1 equation describes the transport of small-dispersed bubbles that are either distorted or spherical in shapes, and the group 2 equation describes the transport of large cap, slug or churn-turbulent bubbles. The source and sink terms in the right hand-side of the transport equations have been established by mechanistically modeling the creation and destruction of bubbles due to major bubble interaction mechanisms. The coalescence mechanisms include the random collision driven by turbulence, and the entrainment of trailing bubbles in the wake region of the preceding bubble. The disintegration mechanisms include the break-up by turbulence impact, shearing-off at the rim of large cap bubbles and the break-up of large cap

New Formulation of the Governing Equations for Analyzing Outrigger Structures

International Nuclear Information System (INIS)

Er, G.-K.

2010-01-01

In this paper, an easily comprehensible solution procedure is proposed for the analysis of outrigger-braced structures. The idea is based on the compatibility of the columns' axial deformation. The unknowns are selected to be the axial forces in the columns. The resulted governing equations and the equations for the optimum analysis of the outrigger locations are different from the conventional ones, but numerical analysis shows that the results obtained with the new equations are same as those obtained with conventional equations. The relations between the new equations and the conventional ones are also figured out. The new procedure of formulating the governing equations can be easily extended to more complicated cases of outrigger-braced structures.

Modeling alcohol use disorder severity: an integrative structural equation modeling approach

Directory of Open Access Journals (Sweden)

Nathasha R Moallem

2013-07-01

Full Text Available Background: Alcohol dependence is a complex psychological disorder whose phenomenology changes as the disorder progresses. Neuroscience has provided a variety of theories and evidence for the development, maintenance, and severity of addiction; however, clinically, it has been difficult to evaluate alcohol use disorder (AUD severity. Objective: This study seeks to evaluate and validate a data-driven approach to capturing alcohol severity in a community sample. Method: Participants were non-treatment seeking problem drinkers (n = 283. A structural equation modeling (SEM approach was used to (a verify the latent factor structure of the indices of AUD severity; and (b test the relationship between the AUD severity factor and measures of alcohol use, affective symptoms, and motivation to change drinking. Results: The model was found to fit well, with all chosen indices of AUD severity loading significantly and positively onto the severity factor. In addition, the paths from the alcohol use, motivation, and affective factors accounted for 68% of the variance in AUD severity. Greater AUD severity was associated with greater alcohol use, increased affective symptoms, and higher motivation to change.Conclusions: Unlike the categorical diagnostic criteria, the AUD severity factor is comprised of multiple quantitative dimensions of impairment observed across the progression of the disorder. The AUD severity factor was validated by testing it in relation to other outcomes such as alcohol use, affective symptoms, and motivation for change. Clinically, this approach to AUD severity can be used to inform treatment planning and ultimately to improve outcomes.

The algebraic structure of lax equations for infinite matrices

NARCIS (Netherlands)

Helminck, G.F.

2002-01-01

In this paper we discuss the algebraic structure of the tower of differential difference equations that one can associate with any commutative subalgebra of $M_k(\\mathbb{C})$. These equations can be formulated conveniently in so-called Lax equations for infinite upper- resp. lowertriangular matrices

A Structural Equation Modelling Approach for Massive Blended Synchronous Teacher Training

Science.gov (United States)

Kannan, Kalpana; Narayanan, Krishnan

2015-01-01

This paper presents a structural equation modelling (SEM) approach for blended synchronous teacher training workshop. It examines the relationship among various factors that influence the Satisfaction (SAT) of participating teachers. Data were collected with the help of a questionnaire from about 500 engineering college teachers. These teachers…

Investigating the Theoretical Structure of the DAS-II Core Battery at School Age Using Bayesian Structural Equation Modeling

Science.gov (United States)

Dombrowski, Stefan C.; Golay, Philippe; McGill, Ryan J.; Canivez, Gary L.

2018-01-01

Bayesian structural equation modeling (BSEM) was used to investigate the latent structure of the Differential Ability Scales-Second Edition core battery using the standardization sample normative data for ages 7-17. Results revealed plausibility of a three-factor model, consistent with publisher theory, expressed as either a higher-order (HO) or a…

Facilitating Data Driven Business Model Innovation - A Case study

DEFF Research Database (Denmark)

Bjerrum, Torben Cæsar Bisgaard; Andersen, Troels Christian; Aagaard, Annabeth

2016-01-01

. The businesses interdisciplinary capabilities come into play in the BMI process, where knowledge from the facilitation strategy and knowledge from phases of the BMI process needs to be present to create new knowledge, hence new BMs and innovations. Depending on the environment and shareholders, this also exposes......This paper aims to understand the barriers that businesses meet in understanding their current business models (BM) and in their attempt at innovating new data driven business models (DDBM) using data. The interdisciplinary challenge of knowledge exchange occurring outside and/or inside businesses......, that gathers knowledge is of great importance. The SMEs have little, if no experience, within data handling, data analytics, and working with structured Business Model Innovation (BMI), that relates to both new and conventional products, processes and services. This new frontier of data and BMI will have...

Generalized structured component analysis a component-based approach to structural equation modeling

CERN Document Server

Hwang, Heungsun

2014-01-01

Winner of the 2015 Sugiyama Meiko Award (Publication Award) of the Behaviormetric Society of Japan Developed by the authors, generalized structured component analysis is an alternative to two longstanding approaches to structural equation modeling: covariance structure analysis and partial least squares path modeling. Generalized structured component analysis allows researchers to evaluate the adequacy of a model as a whole, compare a model to alternative specifications, and conduct complex analyses in a straightforward manner. Generalized Structured Component Analysis: A Component-Based Approach to Structural Equation Modeling provides a detailed account of this novel statistical methodology and its various extensions. The authors present the theoretical underpinnings of generalized structured component analysis and demonstrate how it can be applied to various empirical examples. The book enables quantitative methodologists, applied researchers, and practitioners to grasp the basic concepts behind this new a...

Initial Results from an Energy-Aware Airborne Dynamic, Data-Driven Application System Performing Sampling in Coherent Boundary-Layer Structures

Science.gov (United States)

Frew, E.; Argrow, B. M.; Houston, A. L.; Weiss, C.

2014-12-01

The energy-aware airborne dynamic, data-driven application system (EA-DDDAS) performs persistent sampling in complex atmospheric conditions by exploiting wind energy using the dynamic data-driven application system paradigm. The main challenge for future airborne sampling missions is operation with tight integration of physical and computational resources over wireless communication networks, in complex atmospheric conditions. The physical resources considered here include sensor platforms, particularly mobile Doppler radar and unmanned aircraft, the complex conditions in which they operate, and the region of interest. Autonomous operation requires distributed computational effort connected by layered wireless communication. Onboard decision-making and coordination algorithms can be enhanced by atmospheric models that assimilate input from physics-based models and wind fields derived from multiple sources. These models are generally too complex to be run onboard the aircraft, so they need to be executed in ground vehicles in the field, and connected over broadband or other wireless links back to the field. Finally, the wind field environment drives strong interaction between the computational and physical systems, both as a challenge to autonomous path planning algorithms and as a novel energy source that can be exploited to improve system range and endurance. Implementation details of a complete EA-DDDAS will be provided, along with preliminary flight test results targeting coherent boundary-layer structures.

On establishing constitutive equations for use in design of high-temperature fast-reactor structures

International Nuclear Information System (INIS)

Pugh, C.E.

1978-01-01

The presentation describes the approach being used to establish constitutive equations for wide use in the design of fast breeder reactor (FBR) components in the US. The approach combines exploratory experiments, constitutive model studies, studies of computational techniques, and tests of simple structural configurations. Short-time (elastic-plastic) behavior, long-time (creep) behavior, and their interactions are considered, and some of the background to equations now identified for use in current FBR design applications involving three structural alloys is discussed. Comments are also given on current efforts aimed at identifying improved constitutive equations for these alloys and on properties data required for design applications. References are cited which have addressed the status of the process at various times. (Auth.)

Scenario driven data modelling: a method for integrating diverse sources of data and data streams

Science.gov (United States)

2011-01-01

Background Biology is rapidly becoming a data intensive, data-driven science. It is essential that data is represented and connected in ways that best represent its full conceptual content and allows both automated integration and data driven decision-making. Recent advancements in distributed multi-relational directed graphs, implemented in the form of the Semantic Web make it possible to deal with complicated heterogeneous data in new and interesting ways. Results This paper presents a new approach, scenario driven data modelling (SDDM), that integrates multi-relational directed graphs with data streams. SDDM can be applied to virtually any data integration challenge with widely divergent types of data and data streams. In this work, we explored integrating genetics data with reports from traditional media. SDDM was applied to the New Delhi metallo-beta-lactamase gene (NDM-1), an emerging global health threat. The SDDM process constructed a scenario, created a RDF multi-relational directed graph that linked diverse types of data to the Semantic Web, implemented RDF conversion tools (RDFizers) to bring content into the Sematic Web, identified data streams and analytical routines to analyse those streams, and identified user requirements and graph traversals to meet end-user requirements. Conclusions We provided an example where SDDM was applied to a complex data integration challenge. The process created a model of the emerging NDM-1 health threat, identified and filled gaps in that model, and constructed reliable software that monitored data streams based on the scenario derived multi-relational directed graph. The SDDM process significantly reduced the software requirements phase by letting the scenario and resulting multi-relational directed graph define what is possible and then set the scope of the user requirements. Approaches like SDDM will be critical to the future of data intensive, data-driven science because they automate the process of converting

Prolongation Structure of Semi-discrete Nonlinear Evolution Equations

International Nuclear Information System (INIS)

Bai Yongqiang; Wu Ke; Zhao Weizhong; Guo Hanying

2007-01-01

Based on noncommutative differential calculus, we present a theory of prolongation structure for semi-discrete nonlinear evolution equations. As an illustrative example, a semi-discrete model of the nonlinear Schroedinger equation is discussed in terms of this theory and the corresponding Lax pairs are also given.

Fault Detection for Nonlinear Process With Deterministic Disturbances: A Just-In-Time Learning Based Data Driven Method.

Science.gov (United States)

Yin, Shen; Gao, Huijun; Qiu, Jianbin; Kaynak, Okyay

2017-11-01

Data-driven fault detection plays an important role in industrial systems due to its applicability in case of unknown physical models. In fault detection, disturbances must be taken into account as an inherent characteristic of processes. Nevertheless, fault detection for nonlinear processes with deterministic disturbances still receive little attention, especially in data-driven field. To solve this problem, a just-in-time learning-based data-driven (JITL-DD) fault detection method for nonlinear processes with deterministic disturbances is proposed in this paper. JITL-DD employs JITL scheme for process description with local model structures to cope with processes dynamics and nonlinearity. The proposed method provides a data-driven fault detection solution for nonlinear processes with deterministic disturbances, and owns inherent online adaptation and high accuracy of fault detection. Two nonlinear systems, i.e., a numerical example and a sewage treatment process benchmark, are employed to show the effectiveness of the proposed method.

A data-driven approach for retrieving temperatures and abundances in brown dwarf atmospheres

OpenAIRE

Line, MR; Fortney, JJ; Marley, MS; Sorahana, S

2014-01-01

© 2014. The American Astronomical Society. All rights reserved. Brown dwarf spectra contain a wealth of information about their molecular abundances, temperature structure, and gravity. We present a new data driven retrieval approach, previously used in planetary atmosphere studies, to extract the molecular abundances and temperature structure from brown dwarf spectra. The approach makes few a priori physical assumptions about the state of the atmosphere. The feasibility of the approach is fi...

Data-Driven Methods to Diversify Knowledge of Human Psychology

OpenAIRE

Jack, Rachael E.; Crivelli, Carlos; Wheatley, Thalia

2017-01-01

open access article Psychology aims to understand real human behavior. However, cultural biases in the scientific process can constrain knowledge. We describe here how data-driven methods can relax these constraints to reveal new insights that theories can overlook. To advance knowledge we advocate a symbiotic approach that better combines data-driven methods with theory.

Evaluating Neighborhoods Livability in Nigeria: A Structural Equation Modelling (SEM Approach

Directory of Open Access Journals (Sweden)

Sule Abass Iyanda

2018-01-01

Full Text Available There is a growing concern about city livability around the world and of particular concern is the aspects of the person-environment relationship which encompasses many characteristics suffice to make a place livable. Extant literature provides livability dimensions such as housing unit characteristics, neighborhood facilities, economic vitality and safety environment. These livability dimensions as well as their attributes found in the extant literature have been reported to have high reliability measurement level. Although, various methods have been applied to examine relationships among the variables however structural equation modeling (SEM has been found more holistic as a modeling technique to understand and explain the relationships that may exist among variable measurements. Structural equation modeling simultaneously performs multivariate analysis including multiple regression, path and factor analysis in the cause-effect relationships between latent constructs. Therefore, this study investigates the key factors of livability of planned residential neighborhoods in Minna, Nigeria with the research objectives of – (a to study the livability level of the selected residential neighborhoods, (b to determine the dimensions and indicators which most influence the level of livability in the selected residential neighborhoods, and (c to reliably test the efficacy of structural equation modeling (SEM in the assessment of livability. The methodology adopted in this study includes- Data collection with the aid of structured questionnaire survey administered to the residents of the study area based on stratified random sampling. The data collected was analyzed with the aid of the Statistical Package for Social Sciences (SPSS 22.0 and AMOS 22.0 software for structural equation modeling (a second-order factor. The study revealed that livability as a second-order factor is indicated by economic vitality, safety environment, neighborhood facilities

Process-Structure Linkages Using a Data Science Approach: Application to Simulated Additive Manufacturing Data

International Nuclear Information System (INIS)

Popova, Evdokia; Rodgers, Theron M.; Gong, Xinyi; Cecen, Ahmet; Madison, Jonathan D.; Kalidindi, Surya R.

2017-01-01

A novel data science workflow is developed and demonstrated to extract process-structure linkages (i.e., reduced-order model) for microstructure evolution problems when the final microstructure depends on (simulation or experimental) processing parameters. Our workflow consists of four main steps: data pre-processing, microstructure quantification, dimensionality reduction, and extraction/validation of process-structure linkages. These methods that can be employed within each step vary based on the type and amount of available data. In this paper, this data-driven workflow is applied to a set of synthetic additive manufacturing microstructures obtained using the Potts-kinetic Monte Carlo (kMC) approach. Additive manufacturing techniques inherently produce complex microstructures that can vary significantly with processing conditions. Using the developed workflow, a low-dimensional data-driven model was established to correlate process parameters with the predicted final microstructure. In addition, the modular workflows developed and presented in this work facilitate easy dissemination and curation by the broader community.

Analysis of directly driven ICF targets

International Nuclear Information System (INIS)

Velarde, G.; Aragones, J.M.; Gago, J.A.

1986-01-01

The current capabilities at DENIM for the analysis of directly driven targets are presented. These include theoretical, computational and applied physical studies and developments of detailed simulation models for the most relevant processes in ICF. The simulation of directly driven ICF targets is carried out with the one-dimensional NORCLA code developed at DENIM. This code contains two main segments: NORMA and CLARA, able to work fully coupled and in an iterative manner. NORMA solves the hydrodynamic equations in a lagrangian mesh. It has modular programs couple to it to treat the laser or particle beam interaction with matter. Equations of state, opacities and conductivities are taken from a DENIM atomic data library, generated externally with other codes that will also be explained in this work. CLARA solves the transport equation for neutrons, as well as for charged particles, and suprathermal electrons using discrete ordinates and finite element methods in the computational procedure. Parametric calculations of multilayered single-shell targets driven by heavy ion beams are also analyzed. Finally, conclusions are focused on the ongoing developments in the areas of interest such as: radiation transport, atomic physics, particle in cell method, charged particle transport, two-dimensional calculations and instabilities. (author)

Parameterized data-driven fuzzy model based optimal control of a semi-batch reactor.

Science.gov (United States)

Kamesh, Reddi; Rani, K Yamuna

2016-09-01

A parameterized data-driven fuzzy (PDDF) model structure is proposed for semi-batch processes, and its application for optimal control is illustrated. The orthonormally parameterized input trajectories, initial states and process parameters are the inputs to the model, which predicts the output trajectories in terms of Fourier coefficients. Fuzzy rules are formulated based on the signs of a linear data-driven model, while the defuzzification step incorporates a linear regression model to shift the domain from input to output domain. The fuzzy model is employed to formulate an optimal control problem for single rate as well as multi-rate systems. Simulation study on a multivariable semi-batch reactor system reveals that the proposed PDDF modeling approach is capable of capturing the nonlinear and time-varying behavior inherent in the semi-batch system fairly accurately, and the results of operating trajectory optimization using the proposed model are found to be comparable to the results obtained using the exact first principles model, and are also found to be comparable to or better than parameterized data-driven artificial neural network model based optimization results. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.

Data-Driven Innovation through Open Government Data

DEFF Research Database (Denmark)

Jetzek, Thorhildur; Avital, Michel; Bjørn-Andersen, Niels

2014-01-01

The exponentially growing production of data and the social trend towards openness and sharing are power-ful forces that are changing the global economy and society. Governments around the world have become active participants in this evolution, opening up their data for access and reuse by public...... and private agents alike. The phenomenon of Open Government Data has spread around the world in the last four years, driven by the widely held belief that use of Open Government Data has the ability to generate both economic and social value. However, a cursory review of the popular press, as well...... as an investigation of academic research and empirical data, reveals the need to further understand the relationship between Open Government Data and value. In this paper, we focus on how use of Open Government Data can bring about new innovative solutions that can generate social and economic value. We apply...

A non-linear dimension reduction methodology for generating data-driven stochastic input models

Science.gov (United States)

Ganapathysubramanian, Baskar; Zabaras, Nicholas

2008-06-01

Stochastic analysis of random heterogeneous media (polycrystalline materials, porous media, functionally graded materials) provides information of significance only if realistic input models of the topology and property variations are used. This paper proposes a framework to construct such input stochastic models for the topology and thermal diffusivity variations in heterogeneous media using a data-driven strategy. Given a set of microstructure realizations (input samples) generated from given statistical information about the medium topology, the framework constructs a reduced-order stochastic representation of the thermal diffusivity. This problem of constructing a low-dimensional stochastic representation of property variations is analogous to the problem of manifold learning and parametric fitting of hyper-surfaces encountered in image processing and psychology. Denote by M the set of microstructures that satisfy the given experimental statistics. A non-linear dimension reduction strategy is utilized to map M to a low-dimensional region, A. We first show that M is a compact manifold embedded in a high-dimensional input space Rn. An isometric mapping F from M to a low-dimensional, compact, connected set A⊂Rd(d≪n) is constructed. Given only a finite set of samples of the data, the methodology uses arguments from graph theory and differential geometry to construct the isometric transformation F:M→A. Asymptotic convergence of the representation of M by A is shown. This mapping F serves as an accurate, low-dimensional, data-driven representation of the property variations. The reduced-order model of the material topology and thermal diffusivity variations is subsequently used as an input in the solution of stochastic partial differential equations that describe the evolution of dependant variables. A sparse grid collocation strategy (Smolyak algorithm) is utilized to solve these stochastic equations efficiently. We showcase the methodology by constructing low

A non-linear dimension reduction methodology for generating data-driven stochastic input models

International Nuclear Information System (INIS)

Ganapathysubramanian, Baskar; Zabaras, Nicholas

2008-01-01

Stochastic analysis of random heterogeneous media (polycrystalline materials, porous media, functionally graded materials) provides information of significance only if realistic input models of the topology and property variations are used. This paper proposes a framework to construct such input stochastic models for the topology and thermal diffusivity variations in heterogeneous media using a data-driven strategy. Given a set of microstructure realizations (input samples) generated from given statistical information about the medium topology, the framework constructs a reduced-order stochastic representation of the thermal diffusivity. This problem of constructing a low-dimensional stochastic representation of property variations is analogous to the problem of manifold learning and parametric fitting of hyper-surfaces encountered in image processing and psychology. Denote by M the set of microstructures that satisfy the given experimental statistics. A non-linear dimension reduction strategy is utilized to map M to a low-dimensional region, A. We first show that M is a compact manifold embedded in a high-dimensional input space R n . An isometric mapping F from M to a low-dimensional, compact, connected set A is contained in R d (d<< n) is constructed. Given only a finite set of samples of the data, the methodology uses arguments from graph theory and differential geometry to construct the isometric transformation F:M→A. Asymptotic convergence of the representation of M by A is shown. This mapping F serves as an accurate, low-dimensional, data-driven representation of the property variations. The reduced-order model of the material topology and thermal diffusivity variations is subsequently used as an input in the solution of stochastic partial differential equations that describe the evolution of dependant variables. A sparse grid collocation strategy (Smolyak algorithm) is utilized to solve these stochastic equations efficiently. We showcase the methodology

A Structural Equation Modeling Analysis of Influences on Juvenile Delinquency

Science.gov (United States)

Barrett, David E.; Katsiyannis, Antonis; Zhang, Dalun; Zhang, Dake

2014-01-01

This study examined influences on delinquency and recidivism using structural equation modeling. The sample comprised 199,204 individuals: 99,602 youth whose cases had been processed by the South Carolina Department of Juvenile Justice and a matched control group of 99,602 youth without juvenile records. Structural equation modeling for the…

Validation of an employee satisfaction model: A structural equation model approach

OpenAIRE

Ophillia Ledimo; Nico Martins

2015-01-01

The purpose of this study was to validate an employee satisfaction model and to determine the relationships between the different dimensions of the concept, using the structural equation modelling approach (SEM). A cross-sectional quantitative survey design was used to collect data from a random sample of (n=759) permanent employees of a parastatal organisation. Data was collected using the Employee Satisfaction Survey (ESS) to measure employee satisfaction dimensions. Following the steps of ...

Invalidity of the spectral Fokker-Planck equation forCauchy noise driven Langevin equation

DEFF Research Database (Denmark)

Ditlevsen, Ove Dalager

2004-01-01

-called alpha-stable noise (or Levy noise) the Fokker-Planck equation no longer exists as a partial differential equation for the probability density because the property of finite variance is lost. In stead it has been attempted to formulate an equation for the characteristic function (the Fourier transform...

On Robust Stability of Differential-Algebraic Equations with Structured Uncertainty

Directory of Open Access Journals (Sweden)

A. Kononov

2018-03-01

Full Text Available We consider a linear time-invariant system of differential-algebraic equations (DAE, which can be written as a system of ordinary differential equations with non-invertible coefficients matrices. An important characteristic of DAE is the unsolvability index, which reflects the complexity of the internal structure of the system. The question of the asymptotic stability of DAE containing the uncertainty given by the matrix norm is investigated. We consider a perturbation in the structured uncertainty case. It is assumed that the initial nominal system is asymptotically stable. For the analysis, the original equation is reduced to the structural form, in which the differential and algebraic subsystems are separated. This structural form is equivalent to the input system in the sense of coincidence of sets of solutions, and the operator transforming the DAE into the structural form possesses the inverse operator. The conversion to structural form does not use a change of variables. Regularity of matrix pencil of the source equation is the necessary and sufficient condition of structural form existence. Sufficient conditions have been obtained that perturbations do not break the internal structure of the nominal system. Under these conditions robust stability of the DAE with structured uncertainty is investigated. Estimates for the stability radius of the perturbed DAE system are obtained. The text of the article is from the simpler case, in which the perturbation is present only for an unknown function, to a more complex one, under which the perturbation is also present in the derivative of the unknown function. We used values of the real and the complex stability radii of explicit ordinary differential equations for obtaining the results. We consider the example illustrating the obtained results.

Discovering governing equations from data by sparse identification of nonlinear dynamical systems.

Science.gov (United States)

Brunton, Steven L; Proctor, Joshua L; Kutz, J Nathan

2016-04-12

Extracting governing equations from data is a central challenge in many diverse areas of science and engineering. Data are abundant whereas models often remain elusive, as in climate science, neuroscience, ecology, finance, and epidemiology, to name only a few examples. In this work, we combine sparsity-promoting techniques and machine learning with nonlinear dynamical systems to discover governing equations from noisy measurement data. The only assumption about the structure of the model is that there are only a few important terms that govern the dynamics, so that the equations are sparse in the space of possible functions; this assumption holds for many physical systems in an appropriate basis. In particular, we use sparse regression to determine the fewest terms in the dynamic governing equations required to accurately represent the data. This results in parsimonious models that balance accuracy with model complexity to avoid overfitting. We demonstrate the algorithm on a wide range of problems, from simple canonical systems, including linear and nonlinear oscillators and the chaotic Lorenz system, to the fluid vortex shedding behind an obstacle. The fluid example illustrates the ability of this method to discover the underlying dynamics of a system that took experts in the community nearly 30 years to resolve. We also show that this method generalizes to parameterized systems and systems that are time-varying or have external forcing.

Bending of Euler-Bernoulli nanobeams based on the strain-driven and stress-driven nonlocal integral models: a numerical approach

Science.gov (United States)

Oskouie, M. Faraji; Ansari, R.; Rouhi, H.

2018-04-01

Eringen's nonlocal elasticity theory is extensively employed for the analysis of nanostructures because it is able to capture nanoscale effects. Previous studies have revealed that using the differential form of the strain-driven version of this theory leads to paradoxical results in some cases, such as bending analysis of cantilevers, and recourse must be made to the integral version. In this article, a novel numerical approach is developed for the bending analysis of Euler-Bernoulli nanobeams in the context of strain- and stress-driven integral nonlocal models. This numerical approach is proposed for the direct solution to bypass the difficulties related to converting the integral governing equation into a differential equation. First, the governing equation is derived based on both strain-driven and stress-driven nonlocal models by means of the minimum total potential energy. Also, in each case, the governing equation is obtained in both strong and weak forms. To solve numerically the derived equations, matrix differential and integral operators are constructed based upon the finite difference technique and trapezoidal integration rule. It is shown that the proposed numerical approach can be efficiently applied to the strain-driven nonlocal model with the aim of resolving the mentioned paradoxes. Also, it is able to solve the problem based on the strain-driven model without inconsistencies of the application of this model that are reported in the literature.

Data Driven Constraints for the SVM

DEFF Research Database (Denmark)

Darkner, Sune; Clemmensen, Line Katrine Harder

2012-01-01

We propose a generalized data driven constraint for support vector machines exemplified by classification of paired observations in general and specifically on the human ear canal. This is particularly interesting in dynamic cases such as tissue movement or pathologies developing over time. Assum...

Reformulation of Maxwell's equations to incorporate near-solute solvent structure.

Science.gov (United States)

Yang, Pei-Kun; Lim, Carmay

2008-09-04

Maxwell's equations, which treat electromagnetic interactions between macroscopic charged objects in materials, have explained many phenomena and contributed to many applications in our lives. Derived in 1861 when no methods were available to determine the atomic structure of macromolecules, Maxwell's equations assume the solvent to be a structureless continuum. However, near-solute solvent molecules are highly structured, unlike far-solute bulk solvent molecules. Current methods cannot treat both the near-solute solvent structure and time-dependent electromagnetic interactions in a macroscopic system. Here, we derive "microscopic" electrodynamics equations that can treat macroscopic time-dependent electromagnetic field problems like Maxwell's equations and reproduce the solvent molecular and dipole density distributions observed in molecular dynamics simulations. These equations greatly reduce computational expense by not having to include explicit solvent molecules, yet they treat the solvent electrostatic and van der Waals effects more accurately than continuum models. They provide a foundation to study electromagnetic interactions between molecules in a macroscopic system that are ubiquitous in biology, bioelectromagnetism, and nanotechnology. The general strategy presented herein to incorporate the near-solute solvent structure would enable studies on how complex cellular protein-ligand interactions are affected by electromagnetic radiation, which could help to prevent harmful electromagnetic spectra or find potential therapeutic applications.

Structural equation modeling and natural systems

Science.gov (United States)

Grace, James B.

2006-01-01

This book, first published in 2006, presents an introduction to the methodology of structural equation modeling, illustrates its use, and goes on to argue that it has revolutionary implications for the study of natural systems. A major theme of this book is that we have, up to this point, attempted to study systems primarily using methods (such as the univariate model) that were designed only for considering individual processes. Understanding systems requires the capacity to examine simultaneous influences and responses. Structural equation modeling (SEM) has such capabilities. It also possesses many other traits that add strength to its utility as a means of making scientific progress. In light of the capabilities of SEM, it can be argued that much of ecological theory is currently locked in an immature state that impairs its relevance. It is further argued that the principles of SEM are capable of leading to the development and evaluation of multivariate theories of the sort vitally needed for the conservation of natural systems.

PHYCAA: Data-driven measurement and removal of physiological noise in BOLD fMRI

DEFF Research Database (Denmark)

Churchill, Nathan W.; Yourganov, Grigori; Spring, Robyn

2012-01-01

, autocorrelated physiological noise sources with reproducible spatial structure, using an adaptation of Canonical Correlation Analysis performed in a split-half resampling framework. The technique is able to identify physiological effects with vascular-linked spatial structure, and an intrinsic dimensionality...... with physiological noise, and real data-driven model prediction and reproducibility, for both block and event-related task designs. This is demonstrated compared to no physiological noise correction, and to the widely used RETROICOR (Glover et al., 2000) physiological denoising algorithm, which uses externally...

On the Boussinesq-Burgers equations driven by dynamic boundary conditions

Science.gov (United States)

Zhu, Neng; Liu, Zhengrong; Zhao, Kun

2018-02-01

We study the qualitative behavior of the Boussinesq-Burgers equations on a finite interval subject to the Dirichlet type dynamic boundary conditions. Assuming H1 ×H2 initial data which are compatible with boundary conditions and utilizing energy methods, we show that under appropriate conditions on the dynamic boundary data, there exist unique global-in-time solutions to the initial-boundary value problem, and the solutions converge to the boundary data as time goes to infinity, regardless of the magnitude of the initial data.

Guidelines for a graph-theoretic implementation of structural equation modeling

Science.gov (United States)

Grace, James B.; Schoolmaster, Donald R.; Guntenspergen, Glenn R.; Little, Amanda M.; Mitchell, Brian R.; Miller, Kathryn M.; Schweiger, E. William

2012-01-01

Structural equation modeling (SEM) is increasingly being chosen by researchers as a framework for gaining scientific insights from the quantitative analyses of data. New ideas and methods emerging from the study of causality, influences from the field of graphical modeling, and advances in statistics are expanding the rigor, capability, and even purpose of SEM. Guidelines for implementing the expanded capabilities of SEM are currently lacking. In this paper we describe new developments in SEM that we believe constitute a third-generation of the methodology. Most characteristic of this new approach is the generalization of the structural equation model as a causal graph. In this generalization, analyses are based on graph theoretic principles rather than analyses of matrices. Also, new devices such as metamodels and causal diagrams, as well as an increased emphasis on queries and probabilistic reasoning, are now included. Estimation under a graph theory framework permits the use of Bayesian or likelihood methods. The guidelines presented start from a declaration of the goals of the analysis. We then discuss how theory frames the modeling process, requirements for causal interpretation, model specification choices, selection of estimation method, model evaluation options, and use of queries, both to summarize retrospective results and for prospective analyses. The illustrative example presented involves monitoring data from wetlands on Mount Desert Island, home of Acadia National Park. Our presentation walks through the decision process involved in developing and evaluating models, as well as drawing inferences from the resulting prediction equations. In addition to evaluating hypotheses about the connections between human activities and biotic responses, we illustrate how the structural equation (SE) model can be queried to understand how interventions might take advantage of an environmental threshold to limit Typha invasions. The guidelines presented provide for

Data-driven regionalization of housing markets

NARCIS (Netherlands)

Helbich, M.; Brunauer, W.; Hagenauer, J.; Leitner, M.

2013-01-01

This article presents a data-driven framework for housing market segmentation. Local marginal house price surfaces are investigated by means of mixed geographically weighted regression and are reduced to a set of principal component maps, which in turn serve as input for spatial regionalization. The

An Application of Structural Equation Modeling for Developing Good Teaching Characteristics Ontology

Science.gov (United States)

Phiakoksong, Somjin; Niwattanakul, Suphakit; Angskun, Thara

2013-01-01

Ontology is a knowledge representation technique which aims to make knowledge explicit by defining the core concepts and their relationships. The Structural Equation Modeling (SEM) is a statistical technique which aims to explore the core factors from empirical data and estimates the relationship between these factors. This article presents an…

Locative media and data-driven computing experiments

Directory of Open Access Journals (Sweden)

Sung-Yueh Perng

2016-06-01

Full Text Available Over the past two decades urban social life has undergone a rapid and pervasive geocoding, becoming mediated, augmented and anticipated by location-sensitive technologies and services that generate and utilise big, personal, locative data. The production of these data has prompted the development of exploratory data-driven computing experiments that seek to find ways to extract value and insight from them. These projects often start from the data, rather than from a question or theory, and try to imagine and identify their potential utility. In this paper, we explore the desires and mechanics of data-driven computing experiments. We demonstrate how both locative media data and computing experiments are ‘staged’ to create new values and computing techniques, which in turn are used to try and derive possible futures that are ridden with unintended consequences. We argue that using computing experiments to imagine potential urban futures produces effects that often have little to do with creating new urban practices. Instead, these experiments promote Big Data science and the prospect that data produced for one purpose can be recast for another and act as alternative mechanisms of envisioning urban futures.

Writing through Big Data: New Challenges and Possibilities for Data-Driven Arguments

Science.gov (United States)

Beveridge, Aaron

2017-01-01

As multimodal writing continues to shift and expand in the era of Big Data, writing studies must confront the new challenges and possibilities emerging from data mining, data visualization, and data-driven arguments. Often collected under the broad banner of "data literacy," students' experiences of data visualization and data-driven…

Multiplicity Control in Structural Equation Modeling

Science.gov (United States)

Cribbie, Robert A.

2007-01-01

Researchers conducting structural equation modeling analyses rarely, if ever, control for the inflated probability of Type I errors when evaluating the statistical significance of multiple parameters in a model. In this study, the Type I error control, power and true model rates of famsilywise and false discovery rate controlling procedures were…

Random-Effects Models for Meta-Analytic Structural Equation Modeling: Review, Issues, and Illustrations

Science.gov (United States)

Cheung, Mike W.-L.; Cheung, Shu Fai

2016-01-01

Meta-analytic structural equation modeling (MASEM) combines the techniques of meta-analysis and structural equation modeling for the purpose of synthesizing correlation or covariance matrices and fitting structural equation models on the pooled correlation or covariance matrix. Both fixed-effects and random-effects models can be defined in MASEM.…

Retrospective data-driven respiratory gating for PET/CT

International Nuclear Information System (INIS)

Schleyer, Paul J; O'Doherty, Michael J; Barrington, Sally F; Marsden, Paul K

2009-01-01

Respiratory motion can adversely affect both PET and CT acquisitions. Respiratory gating allows an acquisition to be divided into a series of motion-reduced bins according to the respiratory signal, which is typically hardware acquired. In order that the effects of motion can potentially be corrected for, we have developed a novel, automatic, data-driven gating method which retrospectively derives the respiratory signal from the acquired PET and CT data. PET data are acquired in listmode and analysed in sinogram space, and CT data are acquired in cine mode and analysed in image space. Spectral analysis is used to identify regions within the CT and PET data which are subject to respiratory motion, and the variation of counts within these regions is used to estimate the respiratory signal. Amplitude binning is then used to create motion-reduced PET and CT frames. The method was demonstrated with four patient datasets acquired on a 4-slice PET/CT system. To assess the accuracy of the data-derived respiratory signal, a hardware-based signal was acquired for comparison. Data-driven gating was successfully performed on PET and CT datasets for all four patients. Gated images demonstrated respiratory motion throughout the bin sequences for all PET and CT series, and image analysis and direct comparison of the traces derived from the data-driven method with the hardware-acquired traces indicated accurate recovery of the respiratory signal.

Towards efficient data exchange and sharing for big-data driven materials science: metadata and data formats

Science.gov (United States)

Ghiringhelli, Luca M.; Carbogno, Christian; Levchenko, Sergey; Mohamed, Fawzi; Huhs, Georg; Lüders, Martin; Oliveira, Micael; Scheffler, Matthias

2017-11-01

With big-data driven materials research, the new paradigm of materials science, sharing and wide accessibility of data are becoming crucial aspects. Obviously, a prerequisite for data exchange and big-data analytics is standardization, which means using consistent and unique conventions for, e.g., units, zero base lines, and file formats. There are two main strategies to achieve this goal. One accepts the heterogeneous nature of the community, which comprises scientists from physics, chemistry, bio-physics, and materials science, by complying with the diverse ecosystem of computer codes and thus develops "converters" for the input and output files of all important codes. These converters then translate the data of each code into a standardized, code-independent format. The other strategy is to provide standardized open libraries that code developers can adopt for shaping their inputs, outputs, and restart files, directly into the same code-independent format. In this perspective paper, we present both strategies and argue that they can and should be regarded as complementary, if not even synergetic. The represented appropriate format and conventions were agreed upon by two teams, the Electronic Structure Library (ESL) of the European Center for Atomic and Molecular Computations (CECAM) and the NOvel MAterials Discovery (NOMAD) Laboratory, a European Centre of Excellence (CoE). A key element of this work is the definition of hierarchical metadata describing state-of-the-art electronic-structure calculations.

Intermittency for stochastic partial differential equations driven by strongly inhomogeneous space-time white noises

Science.gov (United States)

Xie, Bin

2018-01-01

In this paper, the main topic is to investigate the intermittent property of the one-dimensional stochastic heat equation driven by an inhomogeneous Brownian sheet, which is a noise deduced from the study of the catalytic super-Brownian motion. Under some proper conditions on the catalytic measure of the inhomogeneous Brownian sheet, we show that the solution is weakly full intermittent based on the estimates of moments of the solution. In particular, it is proved that the second moment of the solution grows at the exponential rate. The novelty is that the catalytic measure relative to the inhomogeneous noise is not required to be absolutely continuous with respect to the Lebesgue measure on R.

ANALISIS STRUCTURAL EQUATION MODELING PADA PENGARUH KEBIASAAN MENGAKSES FACEBOOK TERHADAP KUALITAS HIDUP DAN PRESTASI AKADEMIK MAHASISWA

Directory of Open Access Journals (Sweden)

Nalim Nalim

2014-02-01

Full Text Available This study tried to determine the effect on quality of life Facebook and students' academic achievement. A total of 210 samples were taken from three universities with proportional multistage random sampling method, while data analysis was conducted using Structural Equation Modeling (SEM with software lisrel 8.80 (student version. The results showed, although according to the investigators alleged that Facebook had a negative impact on quality of life, but the effect was not significant. This is evident from the t value of -1.90 (less than 1.96. Similarly, the structural equation generated, quality of life and Facebook together provide significant influence on academic achievement (with values of t are respectively 0.69 and -0.92. Keywords: structural equation modeling, custom facebook access, quality of life, student academic achievement

Input variable selection for data-driven models of Coriolis flowmeters for two-phase flow measurement

International Nuclear Information System (INIS)

Wang, Lijuan; Yan, Yong; Wang, Xue; Wang, Tao

2017-01-01

Input variable selection is an essential step in the development of data-driven models for environmental, biological and industrial applications. Through input variable selection to eliminate the irrelevant or redundant variables, a suitable subset of variables is identified as the input of a model. Meanwhile, through input variable selection the complexity of the model structure is simplified and the computational efficiency is improved. This paper describes the procedures of the input variable selection for the data-driven models for the measurement of liquid mass flowrate and gas volume fraction under two-phase flow conditions using Coriolis flowmeters. Three advanced input variable selection methods, including partial mutual information (PMI), genetic algorithm-artificial neural network (GA-ANN) and tree-based iterative input selection (IIS) are applied in this study. Typical data-driven models incorporating support vector machine (SVM) are established individually based on the input candidates resulting from the selection methods. The validity of the selection outcomes is assessed through an output performance comparison of the SVM based data-driven models and sensitivity analysis. The validation and analysis results suggest that the input variables selected from the PMI algorithm provide more effective information for the models to measure liquid mass flowrate while the IIS algorithm provides a fewer but more effective variables for the models to predict gas volume fraction. (paper)

Structural redundancy of data from wastewater treatment systems. Determination of individual balance equations.

Science.gov (United States)

Spindler, A

2014-06-15

Although data reconciliation is intensely applied in process engineering, almost none of its powerful methods are employed for validation of operational data from wastewater treatment plants. This is partly due to some prerequisites that are difficult to meet including steady state, known variances of process variables and absence of gross errors. However, an algorithm can be derived from the classical approaches to data reconciliation that allows to find a comprehensive set of equations describing redundancy in the data when measured and unmeasured variables (flows and concentrations) are defined. This is a precondition for methods of data validation based on individual mass balances such as CUSUM charts. The procedure can also be applied to verify the necessity of existing or additional measurements with respect to the improvement of the data's redundancy. Results are given for a large wastewater treatment plant. The introduction aims at establishing a link between methods known from data reconciliation in process engineering and their application in wastewater treatment. Copyright © 2014 Elsevier Ltd. All rights reserved.

[Kinematics Modeling and Analysis of Central-driven Robot for Upper Limb Rehabilitation after Stroke].

Science.gov (United States)

Yi, Jinhua; Yu, Hongliu; Zhang, Ying; Hu, Xin; Shi, Ping

2015-12-01

The present paper proposed a central-driven structure of upper limb rehabilitation robot in order to reduce the volume of the robotic arm in the structure, and also to reduce the influence of motor noise, radiation and other adverse factors on upper limb dysfunction patient. The forward and inverse kinematics equations have been obtained with using the Denavit-Hartenberg (D-H) parameter method. The motion simulation has been done to obtain the angle-time curve of each joint and the position-time curve of handle under setting rehabilitation path by using Solid Works software. Experimental results showed that the rationality with the central-driven structure design had been verified by the fact that the handle could move under setting rehabilitation path. The effectiveness of kinematics equations had been proved, and the error was less than 3° by comparing the angle-time curves obtained from calculation with those from motion simulation.

Data-Driven Learning of Q-Matrix

Science.gov (United States)

Liu, Jingchen; Xu, Gongjun; Ying, Zhiliang

2012-01-01

The recent surge of interests in cognitive assessment has led to developments of novel statistical models for diagnostic classification. Central to many such models is the well-known "Q"-matrix, which specifies the item-attribute relationships. This article proposes a data-driven approach to identification of the "Q"-matrix and estimation of…

Structural equation models from paths to networks

CERN Document Server

Westland, J Christopher

2015-01-01

This compact reference surveys the full range of available structural equation modeling (SEM) methodologies.  It reviews applications in a broad range of disciplines, particularly in the social sciences where many key concepts are not directly observable.  This is the first book to present SEM’s development in its proper historical context–essential to understanding the application, strengths and weaknesses of each particular method.  This book also surveys the emerging path and network approaches that complement and enhance SEM, and that will grow in importance in the near future.  SEM’s ability to accommodate unobservable theory constructs through latent variables is of significant importance to social scientists.  Latent variable theory and application are comprehensively explained, and methods are presented for extending their power, including guidelines for data preparation, sample size calculation, and the special treatment of Likert scale data.  Tables of software, methodologies and fit st...

Evolution of spin-dependent structure functions from DGLAP equations in leading order and next to leading order

International Nuclear Information System (INIS)

Baishya, R.; Jamil, U.; Sarma, J. K.

2009-01-01

In this paper the spin-dependent singlet and nonsinglet structure functions have been obtained by solving Dokshitzer, Gribov, Lipatov, Altarelli, Parisi evolution equations in leading order and next to leading order in the small x limit. Here we have used Taylor series expansion and then the method of characteristics to solve the evolution equations. We have also calculated t and x evolutions of deuteron structure functions, and the results are compared with the SLAC E-143 Collaboration data.

Half-trek criterion for generic identifiability of linear structural equation models

NARCIS (Netherlands)

Foygel, R.; Draisma, J.; Drton, M.

2012-01-01

A linear structural equation model relates random variables of interest and corresponding Gaussian noise terms via a linear equation system. Each such model can be represented by a mixed graph in which directed edges encode the linear equations, and bidirected edges indicate possible correlations

Half-trek criterion for generic identifiability of linear structural equation models

NARCIS (Netherlands)

Foygel, R.; Draisma, J.; Drton, M.

2011-01-01

A linear structural equation model relates random variables of interest and corresponding Gaussian noise terms via a linear equation system. Each such model can be represented by a mixed graph in which directed edges encode the linear equations, and bidirected edges indicate possible correlations

Data-Driven Based Asynchronous Motor Control for Printing Servo Systems

Science.gov (United States)

Bian, Min; Guo, Qingyun

Modern digital printing equipment aims to the environmental-friendly industry with high dynamic performances and control precision and low vibration and abrasion. High performance motion control system of printing servo systems was required. Control system of asynchronous motor based on data acquisition was proposed. Iterative learning control (ILC) algorithm was studied. PID control was widely used in the motion control. However, it was sensitive to the disturbances and model parameters variation. The ILC applied the history error data and present control signals to approximate the control signal directly in order to fully track the expect trajectory without the system models and structures. The motor control algorithm based on the ILC and PID was constructed and simulation results were given. The results show that data-driven control method is effective dealing with bounded disturbances for the motion control of printing servo systems.

Imaging of the internal structure of comet 67P/Churyumov-Gerasimenko from radiotomography CONSERT Data (Rosetta Mission) through a full 3D regularized inversion of the Helmholtz equations on functional spaces

Science.gov (United States)

Barriot, Jean-Pierre; Serafini, Jonathan; Sichoix, Lydie; Benna, Mehdi; Kofman, Wlodek; Herique, Alain

We investigate the inverse problem of imaging the internal structure of comet 67P/ Churyumov-Gerasimenko from radiotomography CONSERT data by using a coupled regularized inversion of the Helmholtz equations. A first set of Helmholtz equations, written w.r.t a basis of 3D Hankel functions describes the wave propagation outside the comet at large distances, a second set of Helmholtz equations, written w.r.t. a basis of 3D Zernike functions describes the wave propagation throughout the comet with avariable permittivity. Both sets are connected by continuity equations over a sphere that surrounds the comet. This approach, derived from GPS water vapor tomography of the atmosphere,will permit a full 3D inversion of the internal structure of the comet, contrary to traditional approaches that use a discretization of space at a fraction of the radiowave wavelength.

Modeling the Informal Economy in Mexico. A Structural Equation Approach

OpenAIRE

Brambila Macias, Jose

2008-01-01

This paper uses annual data for the period 1970-2006 in order to estimate and investigate the evolution of the Mexican informal economy. In order to do so, we model the informal economy as a latent variable and try to explain it through relationships between possible cause and indicator variables using structural equation modeling (SEM). Our results indicate that the Mexican informal sector at the beginning of the 1970’s initially accounted for 40 percent of GDP while slightly decreasing to s...

Materials data base and design equations for the UCLA solid breeder blanket

International Nuclear Information System (INIS)

Sharafat, S.; Amodeo, R.; Ghoniem, N.M.

1986-01-01

The need for a complete and coherent material data base for fusion reactor systems has been an important issue for some time now. Since the choices for materials used in fusion reactors are becoming more apparent, it is important to be able to quickly access this data to facilitate reactor design. The philosophy of a data base is one of expansion and modification. This will lead to a constantly growing collection of most recently acquired information. Based on this philosophy special care has been given to the structure, the accessibility and ease of modification. The data base is developed primarily for use on Personal Computers (PC's). In Section 10.2. materials and properties investigated for this blanket study are listed. Section 10.3. is a list of phenomenological equations and mathematical fits for all materials and properties considered. Section 10.4. describes the authors efforts to develop a swelling equations based on the few experimental data points available for breeder materials. In Section 10.5. the sintering phenomena for ceramics is investigated

Reduction of structured population models to threshold-type delay equations and functional differential equations: A case study

Energy Technology Data Exchange (ETDEWEB)

Smith, H.L. (Arizona State Univ., Tempe (United States))

1993-01-01

It is shown by way of a simple example that certain structured population models lead naturally to differential delay equations of the threshold type and that these equations can be transformed in a natural way to functional differential equations. The model examined can be viewed as a model of competition between adults and juveniles of a single population. The results indicate the possibility that this competition leads to instability. 28 refs., 2 figs.

Virtuous organization: A structural equation modeling approach

Directory of Open Access Journals (Sweden)

Majid Zamahani

2013-02-01

Full Text Available For years, the idea of virtue was unfavorable among researchers and virtues were traditionally considered as culture-specific, relativistic and they were supposed to be associated with social conservatism, religious or moral dogmatism, and scientific irrelevance. Virtue and virtuousness have been recently considered seriously among organizational researchers. The proposed study of this paper examines the relationships between leadership, organizational culture, human resource, structure and processes, care for community and virtuous organization. Structural equation modeling is employed to investigate the effects of each variable on other components. The data used in this study consists of questionnaire responses from employees in Payam e Noor University in Yazd province. A total of 250 questionnaires were sent out and a total of 211 valid responses were received. Our results have revealed that all the five variables have positive and significant impacts on virtuous organization. Among the five variables, organizational culture has the most direct impact (0.80 and human resource has the most total impact (0.844 on virtuous organization.

Photonic Crystal Laser-Driven Accelerator Structures

International Nuclear Information System (INIS)

Cowan, Benjamin M.

2007-01-01

Laser-driven acceleration holds great promise for significantly improving accelerating gradient. However, scaling the conventional process of structure-based acceleration in vacuum down to optical wavelengths requires a substantially different kind of structure. We require an optical waveguide that (1) is constructed out of dielectric materials, (2) has transverse size on the order of a wavelength, and (3) supports a mode with speed-of-light phase velocity in vacuum. Photonic crystals---structures whose electromagnetic properties are spatially periodic---can meet these requirements. We discuss simulated photonic crystal accelerator structures and describe their properties. We begin with a class of two-dimensional structures which serves to illustrate the design considerations and trade-offs involved. We then present a three-dimensional structure, and describe its performance in terms of accelerating gradient and efficiency. We discuss particle beam dynamics in this structure, demonstrating a method for keeping a beam confined to the waveguide. We also discuss material and fabrication considerations. Since accelerating gradient is limited by optical damage to the structure, the damage threshold of the dielectric is a critical parameter. We experimentally measure the damage threshold of silicon for picosecond pulses in the infrared, and determine that our structure is capable of sustaining an accelerating gradient of 300 MV/m at 1550 nm. Finally, we discuss possibilities for manufacturing these structures using common microfabrication techniques

At the Frontiers of Modeling Intensive Longitudinal Data: Dynamic Structural Equation Models for the Affective Measurements from the COGITO Study.

Science.gov (United States)

Hamaker, E L; Asparouhov, T; Brose, A; Schmiedek, F; Muthén, B

2018-04-06

With the growing popularity of intensive longitudinal research, the modeling techniques and software options for such data are also expanding rapidly. Here we use dynamic multilevel modeling, as it is incorporated in the new dynamic structural equation modeling (DSEM) toolbox in Mplus, to analyze the affective data from the COGITO study. These data consist of two samples of over 100 individuals each who were measured for about 100 days. We use composite scores of positive and negative affect and apply a multilevel vector autoregressive model to allow for individual differences in means, autoregressions, and cross-lagged effects. Then we extend the model to include random residual variances and covariance, and finally we investigate whether prior depression affects later depression scores through the random effects of the daily diary measures. We end with discussing several urgent-but mostly unresolved-issues in the area of dynamic multilevel modeling.

Annotated bibliography of structural equation modelling: technical work.

Science.gov (United States)

Austin, J T; Wolfle, L M

1991-05-01

Researchers must be familiar with a variety of source literature to facilitate the informed use of structural equation modelling. Knowledge can be acquired through the study of an expanding literature found in a diverse set of publishing forums. We propose that structural equation modelling publications can be roughly classified into two groups: (a) technical and (b) substantive applications. Technical materials focus on the procedures rather than substantive conclusions derived from applications. The focus of this article is the former category; included are foundational/major contributions, minor contributions, critical and evaluative reviews, integrations, simulations and computer applications, precursor and historical material, and pedagogical textbooks. After a brief introduction, we annotate 294 articles in the technical category dating back to Sewall Wright (1921).

Data-Intensive Science meets Inquiry-Driven Pedagogy: Interactive Big Data Exploration, Threshold Concepts, and Liminality

Science.gov (United States)

Ramachandran, Rahul; Word, Andrea; Nair, Udasysankar

2014-01-01

Threshold concepts in any discipline are the core concepts an individual must understand in order to master a discipline. By their very nature, these concepts are troublesome, irreversible, integrative, bounded, discursive, and reconstitutive. Although grasping threshold concepts can be extremely challenging for each learner as s/he moves through stages of cognitive development relative to a given discipline, the learner's grasp of these concepts determines the extent to which s/he is prepared to work competently and creatively within the field itself. The movement of individuals from a state of ignorance of these core concepts to one of mastery occurs not along a linear path but in iterative cycles of knowledge creation and adjustment in liminal spaces - conceptual spaces through which learners move from the vaguest awareness of concepts to mastery, accompanied by understanding of their relevance, connectivity, and usefulness relative to questions and constructs in a given discipline. For example, challenges in the teaching and learning of atmospheric science can be traced to threshold concepts in fluid dynamics. In particular, Dynamic Meteorology is one of the most challenging courses for graduate students and undergraduates majoring in Atmospheric Science. Dynamic Meteorology introduces threshold concepts - those that prove troublesome for the majority of students but that are essential, associated with fundamental relationships between forces and motion in the atmosphere and requiring the application of basic classical statics, dynamics, and thermodynamic principles to the three dimensionally varying atmospheric structure. With the explosive growth of data available in atmospheric science, driven largely by satellite Earth observations and high-resolution numerical simulations, paradigms such as that of dataintensive science have emerged. These paradigm shifts are based on the growing realization that current infrastructure, tools and processes will not allow

Interpreting experimental data on egg production--applications of dynamic differential equations.

Science.gov (United States)

France, J; Lopez, S; Kebreab, E; Dijkstra, J

2013-09-01

This contribution focuses on applying mathematical models based on systems of ordinary first-order differential equations to synthesize and interpret data from egg production experiments. Models based on linear systems of differential equations are contrasted with those based on nonlinear systems. Regression equations arising from analytical solutions to linear compartmental schemes are considered as candidate functions for describing egg production curves, together with aspects of parameter estimation. Extant candidate functions are reviewed, a role for growth functions such as the Gompertz equation suggested, and a function based on a simple new model outlined. Structurally, the new model comprises a single pool with an inflow and an outflow. Compartmental simulation models based on nonlinear systems of differential equations, and thus requiring numerical solution, are next discussed, and aspects of parameter estimation considered. This type of model is illustrated in relation to development and evaluation of a dynamic model of calcium and phosphorus flows in layers. The model consists of 8 state variables representing calcium and phosphorus pools in the crop, stomachs, plasma, and bone. The flow equations are described by Michaelis-Menten or mass action forms. Experiments that measure Ca and P uptake in layers fed different calcium concentrations during shell-forming days are used to evaluate the model. In addition to providing a useful management tool, such a simulation model also provides a means to evaluate feeding strategies aimed at reducing excretion of potential pollutants in poultry manure to the environment.

Prolongation structure and linear eigenvalue equations for Einstein-Maxwell fields

International Nuclear Information System (INIS)

Kramer, D.; Neugebauer, G.

1981-01-01

The Einstein-Maxwell equations for stationary axisymmetric exterior fields are shown to be the integrability conditions of a set of linear eigenvalue equations for pseudopotentials. Using the method of Wahlquist and Estabrook (J. Math Phys.; 16:1 (1975)) it is shown that the prolongation structure of the Einstein-Maxwell equations contains the SU(2,1) Lie algebra. A new mapping of known solutions to other solutions has been found. (author)

A Model-Driven Methodology for Big Data Analytics-as-a-Service

OpenAIRE

Damiani, Ernesto; Ardagna, Claudio Agostino; Ceravolo, Paolo; Bellandi, Valerio; Bezzi, Michele; Hebert, Cedric

2017-01-01

The Big Data revolution has promised to build a data-driven ecosystem where better decisions are supported by enhanced analytics and data management. However, critical issues still need to be solved in the road that leads to commodization of Big Data Analytics, such as the management of Big Data complexity and the protection of data security and privacy. In this paper, we focus on the first issue and propose a methodology based on Model Driven Engineering (MDE) that aims to substantially lowe...

General Purpose Data-Driven Monitoring for Space Operations

Science.gov (United States)

Iverson, David L.; Martin, Rodney A.; Schwabacher, Mark A.; Spirkovska, Liljana; Taylor, William McCaa; Castle, Joseph P.; Mackey, Ryan M.

2009-01-01

As modern space propulsion and exploration systems improve in capability and efficiency, their designs are becoming increasingly sophisticated and complex. Determining the health state of these systems, using traditional parameter limit checking, model-based, or rule-based methods, is becoming more difficult as the number of sensors and component interactions grow. Data-driven monitoring techniques have been developed to address these issues by analyzing system operations data to automatically characterize normal system behavior. System health can be monitored by comparing real-time operating data with these nominal characterizations, providing detection of anomalous data signatures indicative of system faults or failures. The Inductive Monitoring System (IMS) is a data-driven system health monitoring software tool that has been successfully applied to several aerospace applications. IMS uses a data mining technique called clustering to analyze archived system data and characterize normal interactions between parameters. The scope of IMS based data-driven monitoring applications continues to expand with current development activities. Successful IMS deployment in the International Space Station (ISS) flight control room to monitor ISS attitude control systems has led to applications in other ISS flight control disciplines, such as thermal control. It has also generated interest in data-driven monitoring capability for Constellation, NASA's program to replace the Space Shuttle with new launch vehicles and spacecraft capable of returning astronauts to the moon, and then on to Mars. Several projects are currently underway to evaluate and mature the IMS technology and complementary tools for use in the Constellation program. These include an experiment on board the Air Force TacSat-3 satellite, and ground systems monitoring for NASA's Ares I-X and Ares I launch vehicles. The TacSat-3 Vehicle System Management (TVSM) project is a software experiment to integrate fault

A Data-Driven Stochastic Reactive Power Optimization Considering Uncertainties in Active Distribution Networks and Decomposition Method

DEFF Research Database (Denmark)

Ding, Tao; Yang, Qingrun; Yang, Yongheng

2018-01-01

To address the uncertain output of distributed generators (DGs) for reactive power optimization in active distribution networks, the stochastic programming model is widely used. The model is employed to find an optimal control strategy with minimum expected network loss while satisfying all......, in this paper, a data-driven modeling approach is introduced to assume that the probability distribution from the historical data is uncertain within a confidence set. Furthermore, a data-driven stochastic programming model is formulated as a two-stage problem, where the first-stage variables find the optimal...... control for discrete reactive power compensation equipment under the worst probability distribution of the second stage recourse. The second-stage variables are adjusted to uncertain probability distribution. In particular, this two-stage problem has a special structure so that the second-stage problem...

New Hamiltonian structure of the fractional C-KdV soliton equation hierarchy

International Nuclear Information System (INIS)

Yu Fajun; Zhang Hongqing

2008-01-01

A generalized Hamiltonian structure of the fractional soliton equation hierarchy is presented by using of differential forms and exterior derivatives of fractional orders. Example of the fractional Hamiltonian system of the C-KdV soliton equation hierarchy is constructed, which is a new Hamiltonian structure

Combinatorial Dyson-Schwinger equations and inductive data types

Science.gov (United States)

Kock, Joachim

2016-06-01

The goal of this contribution is to explain the analogy between combinatorial Dyson-Schwinger equations and inductive data types to a readership of mathematical physicists. The connection relies on an interpretation of combinatorial Dyson-Schwinger equations as fixpoint equations for polynomial functors (established elsewhere by the author, and summarised here), combined with the now-classical fact that polynomial functors provide semantics for inductive types. The paper is expository, and comprises also a brief introduction to type theory.

The Monge-Ampère equation: Hamiltonian and symplectic structures, recursions, and hierarchies

NARCIS (Netherlands)

Kersten, P.H.M.; Krasil'shchik, I.; Verbovetsky, A.V.

2004-01-01

Using methods of geometry and cohomology developed recently, we study the Monge-Ampère equation, arising as the first nontrivial equation in the associativity equations, or WDVV equations. We describe Hamiltonian and symplectic structures as well as recursion operators for this equation in its

Structure-preserving algorithms for the Duffing equation

International Nuclear Information System (INIS)

Gang Tieqiang; Mei Fengxiang; Xie Jiafang

2008-01-01

In this paper, the dissipative and the forced terms of the Duffing equation are considered as the perturbations of nonlinear Hamiltonian equations and the perturbational effect is indicated by parameter ε. Firstly, based on the gradient-Hamiltonian decomposition theory of vector fields, by using splitting methods, this paper constructs structure-preserving algorithms (SPAs) for the Duffing equation. Then, according to the Liouville formula, it proves that the Jacobian matrix determinants of the SPAs are equal to that of the exact flow of the Duffing equation. However, considering the explicit Runge–Kutta methods, this paper finds that there is an error term of order p+1 for the Jacobian matrix determinants. The volume evolution law of a given region in phase space is discussed for different algorithms, respectively. As a result, the sum of Lyapunov exponents is exactly invariable for the SPAs proposed in this paper. Finally, through numerical experiments, relative norm errors and absolute energy errors of phase trajectories of the SPAs and the Heun method (a second-order Runge–Kutta method) are compared. Computational results illustrate that the SPAs are evidently better than the Heun method when ε is small or equal to zero. (general)

Scalable data-driven short-term traffic prediction

NARCIS (Netherlands)

Friso, K.; Wismans, L. J.J.; Tijink, M. B.

2017-01-01

Short-term traffic prediction has a lot of potential for traffic management. However, most research has traditionally focused on either traffic models-which do not scale very well to large networks, computationally-or on data-driven methods for freeways, leaving out urban arterials completely. Urban

Solutions to nonlinear Schrodinger equations for special initial data

Directory of Open Access Journals (Sweden)

Takeshi Wada

2015-11-01

Full Text Available This article concerns the solvability of the nonlinear Schrodinger equation with gauge invariant power nonlinear term in one space dimension. The well-posedness of this equation is known only for $H^s$ with $s\\ge 0$. Under some assumptions on the nonlinearity, this paper shows that this equation is uniquely solvable for special but typical initial data, namely the linear combinations of $\\delta(x$ and p.v. (1/x, which belong to $H^{-1/2-0}$. The proof in this article allows $L^2$-perturbations on the initial data.

Retrospective cost adaptive Reynolds-averaged Navier-Stokes k-ω model for data-driven unsteady turbulent simulations

Science.gov (United States)

Li, Zhiyong; Hoagg, Jesse B.; Martin, Alexandre; Bailey, Sean C. C.

2018-03-01

This paper presents a data-driven computational model for simulating unsteady turbulent flows, where sparse measurement data is available. The model uses the retrospective cost adaptation (RCA) algorithm to automatically adjust the closure coefficients of the Reynolds-averaged Navier-Stokes (RANS) k- ω turbulence equations to improve agreement between the simulated flow and the measurements. The RCA-RANS k- ω model is verified for steady flow using a pipe-flow test case and for unsteady flow using a surface-mounted-cube test case. Measurements used for adaptation of the verification cases are obtained from baseline simulations with known closure coefficients. These verification test cases demonstrate that the RCA-RANS k- ω model can successfully adapt the closure coefficients to improve agreement between the simulated flow field and a set of sparse flow-field measurements. Furthermore, the RCA-RANS k- ω model improves agreement between the simulated flow and the baseline flow at locations at which measurements do not exist. The RCA-RANS k- ω model is also validated with experimental data from 2 test cases: steady pipe flow, and unsteady flow past a square cylinder. In both test cases, the adaptation improves agreement with experimental data in comparison to the results from a non-adaptive RANS k- ω model that uses the standard values of the k- ω closure coefficients. For the steady pipe flow, adaptation is driven by mean stream-wise velocity measurements at 24 locations along the pipe radius. The RCA-RANS k- ω model reduces the average velocity error at these locations by over 35%. For the unsteady flow over a square cylinder, adaptation is driven by time-varying surface pressure measurements at 2 locations on the square cylinder. The RCA-RANS k- ω model reduces the average surface-pressure error at these locations by 88.8%.

Thermodynamically consistent data-driven computational mechanics

Science.gov (United States)

González, David; Chinesta, Francisco; Cueto, Elías

2018-05-01

In the paradigm of data-intensive science, automated, unsupervised discovering of governing equations for a given physical phenomenon has attracted a lot of attention in several branches of applied sciences. In this work, we propose a method able to avoid the identification of the constitutive equations of complex systems and rather work in a purely numerical manner by employing experimental data. In sharp contrast to most existing techniques, this method does not rely on the assumption on any particular form for the model (other than some fundamental restrictions placed by classical physics such as the second law of thermodynamics, for instance) nor forces the algorithm to find among a predefined set of operators those whose predictions fit best to the available data. Instead, the method is able to identify both the Hamiltonian (conservative) and dissipative parts of the dynamics while satisfying fundamental laws such as energy conservation or positive production of entropy, for instance. The proposed method is tested against some examples of discrete as well as continuum mechanics, whose accurate results demonstrate the validity of the proposed approach.

Generalized internal long wave equations: construction, hamiltonian structure and conservation laws

International Nuclear Information System (INIS)

Lebedev, D.R.

1982-01-01

Some aspects of the theory of the internal long-wave equations (ILW) are considered. A general class of the ILW type equations is constructed by means of the Zakharov-Shabat ''dressing'' method. Hamiltonian structure and infinite numbers of conservation laws are introduced. The considered equations are shown to be Hamiltonian in the so-called second Hamiltonian structu

Obtaining off-Hugoniot equation of state data on solid metals at extreme pressures via pulsed-power driven cylindrical liner implosions

Science.gov (United States)

Lemke, Raymond

2015-06-01

The focus of this talk is on magnetically driven, liner implosion experiments on the Z machine (Z) in which a solid, metal tube is shocklessly compressed to multi-megabar pressure. The goal of the experiments is to collect velocimetry data that can be used in conjunction with a new optimization based analysis technique to infer the principal isentrope of the tube material over a range of pressures. For the past decade, shock impact and ramp loading experiments on Z have used planar platforms exclusively. While producing state-of-the-art results for material science, it is difficult to produce drive pressures greater than 6 Mbar in the divergent planar geometry. In contrast, a cylindrical liner implosion is convergent; magnetic drive pressures approaching 50 Mbar are possible with the available current on Z (~ 20 MA). In our cylindrical experiments, the liner comprises an inner tube composed of the sample material (e.g., Ta) of unknown equation of state, and an outer tube composed of aluminum (Al) that serves as the current carrying cathode. Internal to the sample are fielded multiple PDV (Photonic Doppler Velocimetry) probes that measure velocity of the inner free surface of the imploding sample. External to the composite liner, at much larger radius, is an Al tube that is the return current anode. VISAR (velocity interferometry system for any reflector) probes measure free surface velocity of the exploding anode. Using the latter, MHD and optimization codes are employed to solve an inverse problem that yields the current driving the liner implosion. Then, the drive current, PDV velocity, MHD and optimization codes, are used to solve another inverse problem that yields pressure vs. density on approximately the principal isentrope of the sample material. Results for Ta, Re, and Cu compressed to ~ 10 Mbar are presented. Sandia National Laboratories is a multiprogram laboratory managed and operated by Sandia Corporation, a wholly owned subsidiary of Lockheed Martin

Structural Equation Modeling with Mplus Basic Concepts, Applications, and Programming

CERN Document Server

Byrne, Barbara M

2011-01-01

Modeled after Barbara Byrne's other best-selling structural equation modeling (SEM) books, this practical guide reviews the basic concepts and applications of SEM using Mplus Versions 5 & 6. The author reviews SEM applications based on actual data taken from her own research. Using non-mathematical language, it is written for the novice SEM user. With each application chapter, the author "walks" the reader through all steps involved in testing the SEM model including: an explanation of the issues addressed illustrated and annotated testing of the hypothesized and post hoc models expl

Data-Driven Planning: Using Assessment in Strategic Planning

Science.gov (United States)

Bresciani, Marilee J.

2010-01-01

Data-driven planning or evidence-based decision making represents nothing new in its concept. For years, business leaders have claimed they have implemented planning informed by data that have been strategically and systematically gathered. Within higher education and student affairs, there may be less evidence of the actual practice of…

Crystal structure optimisation using an auxiliary equation of state

Science.gov (United States)

Jackson, Adam J.; Skelton, Jonathan M.; Hendon, Christopher H.; Butler, Keith T.; Walsh, Aron

2015-11-01

Standard procedures for local crystal-structure optimisation involve numerous energy and force calculations. It is common to calculate an energy-volume curve, fitting an equation of state around the equilibrium cell volume. This is a computationally intensive process, in particular, for low-symmetry crystal structures where each isochoric optimisation involves energy minimisation over many degrees of freedom. Such procedures can be prohibitive for non-local exchange-correlation functionals or other "beyond" density functional theory electronic structure techniques, particularly where analytical gradients are not available. We present a simple approach for efficient optimisation of crystal structures based on a known equation of state. The equilibrium volume can be predicted from one single-point calculation and refined with successive calculations if required. The approach is validated for PbS, PbTe, ZnS, and ZnTe using nine density functionals and applied to the quaternary semiconductor Cu2ZnSnS4 and the magnetic metal-organic framework HKUST-1.

Crystal structure optimisation using an auxiliary equation of state

International Nuclear Information System (INIS)

Jackson, Adam J.; Skelton, Jonathan M.; Hendon, Christopher H.; Butler, Keith T.; 3 Institute and Department of Materials Science and Engineering, Yonsei University, Seoul 120-749 (Korea, Republic of))" data-affiliation=" (Centre for Sustainable Chemical Technologies and Department of Chemistry, University of Bath, Claverton Down, Bath BA2 7AY (United Kingdom); Global E3 Institute and Department of Materials Science and Engineering, Yonsei University, Seoul 120-749 (Korea, Republic of))" >Walsh, Aron

2015-01-01

Standard procedures for local crystal-structure optimisation involve numerous energy and force calculations. It is common to calculate an energy–volume curve, fitting an equation of state around the equilibrium cell volume. This is a computationally intensive process, in particular, for low-symmetry crystal structures where each isochoric optimisation involves energy minimisation over many degrees of freedom. Such procedures can be prohibitive for non-local exchange-correlation functionals or other “beyond” density functional theory electronic structure techniques, particularly where analytical gradients are not available. We present a simple approach for efficient optimisation of crystal structures based on a known equation of state. The equilibrium volume can be predicted from one single-point calculation and refined with successive calculations if required. The approach is validated for PbS, PbTe, ZnS, and ZnTe using nine density functionals and applied to the quaternary semiconductor Cu 2 ZnSnS 4 and the magnetic metal-organic framework HKUST-1

Crystal structure optimisation using an auxiliary equation of state

Energy Technology Data Exchange (ETDEWEB)

Jackson, Adam J.; Skelton, Jonathan M.; Hendon, Christopher H.; Butler, Keith T. [Centre for Sustainable Chemical Technologies and Department of Chemistry, University of Bath, Claverton Down, Bath BA2 7AY (United Kingdom); Walsh, Aron, E-mail: a.walsh@bath.ac.uk [Centre for Sustainable Chemical Technologies and Department of Chemistry, University of Bath, Claverton Down, Bath BA2 7AY (United Kingdom); Global E" 3 Institute and Department of Materials Science and Engineering, Yonsei University, Seoul 120-749 (Korea, Republic of)

2015-11-14

Standard procedures for local crystal-structure optimisation involve numerous energy and force calculations. It is common to calculate an energy–volume curve, fitting an equation of state around the equilibrium cell volume. This is a computationally intensive process, in particular, for low-symmetry crystal structures where each isochoric optimisation involves energy minimisation over many degrees of freedom. Such procedures can be prohibitive for non-local exchange-correlation functionals or other “beyond” density functional theory electronic structure techniques, particularly where analytical gradients are not available. We present a simple approach for efficient optimisation of crystal structures based on a known equation of state. The equilibrium volume can be predicted from one single-point calculation and refined with successive calculations if required. The approach is validated for PbS, PbTe, ZnS, and ZnTe using nine density functionals and applied to the quaternary semiconductor Cu{sub 2}ZnSnS{sub 4} and the magnetic metal-organic framework HKUST-1.

Enabling Data-Driven Methodologies Across the Data Lifecycle and Ecosystem

Science.gov (United States)

Doyle, R. J.; Crichton, D.

2017-12-01

NASA has unlocked unprecedented scientific knowledge through exploration of the Earth, our solar system, and the larger universe. NASA is generating enormous amounts of data that are challenging traditional approaches to capturing, managing, analyzing and ultimately gaining scientific understanding from science data. New architectures, capabilities and methodologies are needed to span the entire observing system, from spacecraft to archive, while integrating data-driven discovery and analytic capabilities. NASA data have a definable lifecycle, from remote collection point to validated accessibility in multiple archives. Data challenges must be addressed across this lifecycle, to capture opportunities and avoid decisions that may limit or compromise what is achievable once data arrives at the archive. Data triage may be necessary when the collection capacity of the sensor or instrument overwhelms data transport or storage capacity. By migrating computational and analytic capability to the point of data collection, informed decisions can be made about which data to keep; in some cases, to close observational decision loops onboard, to enable attending to unexpected or transient phenomena. Along a different dimension than the data lifecycle, scientists and other end-users must work across an increasingly complex data ecosystem, where the range of relevant data is rarely owned by a single institution. To operate effectively, scalable data architectures and community-owned information models become essential. NASA's Planetary Data System is having success with this approach. Finally, there is the difficult challenge of reproducibility and trust. While data provenance techniques will be part of the solution, future interactive analytics environments must support an ability to provide a basis for a result: relevant data source and algorithms, uncertainty tracking, etc., to assure scientific integrity and to enable confident decision making. Advances in data science offer

Peculiar symmetry structure of some known discrete nonautonomous equations

International Nuclear Information System (INIS)

Garifullin, R N; Habibullin, I T; Yamilov, R I

2015-01-01

We study the generalized symmetry structure of three known discrete nonautonomous equations. One of them is the semidiscrete dressing chain of Shabat. Two others are completely discrete equations defined on the square lattice. The first one is a discrete analogue of the dressing chain introduced by Levi and Yamilov. The second one is a nonautonomous generalization of the potential discrete KdV equation or, in other words, the H1 equation of the well-known Adler−Bobenko−Suris list. We demonstrate that these equations have generalized symmetries in both directions if and only if their coefficients, depending on the discrete variables, are periodic. The order of the simplest generalized symmetry in at least one direction depends on the period and may be arbitrarily high. We substantiate this picture by some theorems in the case of small periods. In case of an arbitrarily large period, we show that it is possible to construct two hierarchies of generalized symmetries and conservation laws. The same picture should take place in case of any nonautonomous equation of the Adler−Bobenko−Suris list. (paper)

Fluid/Structure Interaction Studies of Aircraft Using High Fidelity Equations on Parallel Computers

Science.gov (United States)

Guruswamy, Guru; VanDalsem, William (Technical Monitor)

1994-01-01

Abstract Aeroelasticity which involves strong coupling of fluids, structures and controls is an important element in designing an aircraft. Computational aeroelasticity using low fidelity methods such as the linear aerodynamic flow equations coupled with the modal structural equations are well advanced. Though these low fidelity approaches are computationally less intensive, they are not adequate for the analysis of modern aircraft such as High Speed Civil Transport (HSCT) and Advanced Subsonic Transport (AST) which can experience complex flow/structure interactions. HSCT can experience vortex induced aeroelastic oscillations whereas AST can experience transonic buffet associated structural oscillations. Both aircraft may experience a dip in the flutter speed at the transonic regime. For accurate aeroelastic computations at these complex fluid/structure interaction situations, high fidelity equations such as the Navier-Stokes for fluids and the finite-elements for structures are needed. Computations using these high fidelity equations require large computational resources both in memory and speed. Current conventional super computers have reached their limitations both in memory and speed. As a result, parallel computers have evolved to overcome the limitations of conventional computers. This paper will address the transition that is taking place in computational aeroelasticity from conventional computers to parallel computers. The paper will address special techniques needed to take advantage of the architecture of new parallel computers. Results will be illustrated from computations made on iPSC/860 and IBM SP2 computer by using ENSAERO code that directly couples the Euler/Navier-Stokes flow equations with high resolution finite-element structural equations.

CONFOLD2: improved contact-driven ab initio protein structure modeling.

Science.gov (United States)

Adhikari, Badri; Cheng, Jianlin

2018-01-25

Contact-guided protein structure prediction methods are becoming more and more successful because of the latest advances in residue-residue contact prediction. To support contact-driven structure prediction, effective tools that can quickly build tertiary structural models of good quality from predicted contacts need to be developed. We develop an improved contact-driven protein modelling method, CONFOLD2, and study how it may be effectively used for ab initio protein structure prediction with predicted contacts as input. It builds models using various subsets of input contacts to explore the fold space under the guidance of a soft square energy function, and then clusters the models to obtain the top five models. CONFOLD2 obtains an average reconstruction accuracy of 0.57 TM-score for the 150 proteins in the PSICOV contact prediction dataset. When benchmarked on the CASP11 contacts predicted using CONSIP2 and CASP12 contacts predicted using Raptor-X, CONFOLD2 achieves a mean TM-score of 0.41 on both datasets. CONFOLD2 allows to quickly generate top five structural models for a protein sequence when its secondary structures and contacts predictions at hand. The source code of CONFOLD2 is publicly available at https://github.com/multicom-toolbox/CONFOLD2/ .

Variables and equations in hybrid systems with structural changes

NARCIS (Netherlands)

Beek, van D.A.

2001-01-01

In many models of physical systems, structural changes are common. Such structural changes may cause a variable to change from a differential variable to an algebraic variable, or to a variable that is not defined by an equation at all. Most hybrid modelling languages either restrict the kind of

Relationships among Adolescents' Leisure Motivation, Leisure Involvement, and Leisure Satisfaction: A Structural Equation Model

Science.gov (United States)

Chen, Ying-Chieh; Li, Ren-Hau; Chen, Sheng-Hwang

2013-01-01

The purpose of this cross-sectional study was to test a cause-and-effect model of factors affecting leisure satisfaction among Taiwanese adolescents. A structural equation model was proposed in which the relationships among leisure motivation, leisure involvement, and leisure satisfaction were explored. The study collected data from 701 adolescent…

Data Driven Tuning of Inventory Controllers

DEFF Research Database (Denmark)

Huusom, Jakob Kjøbsted; Santacoloma, Paloma Andrade; Poulsen, Niels Kjølstad

2007-01-01

A systematic method for criterion based tuning of inventory controllers based on data-driven iterative feedback tuning is presented. This tuning method circumvent problems with modeling bias. The process model used for the design of the inventory control is utilized in the tuning...... as an approximation to reduce time required on experiments. The method is illustrated in an application with a multivariable inventory control implementation on a four tank system....

A Structural Equation Model: India’s International Tourism Demand for Tourist Destination

OpenAIRE

N. Rangaswamy; Chukiat Chaiboonsri; Prasert Chaitip

2008-01-01

Structural equation modeling (LISREL 8) was used to test the causal relationships between tourist travel motivations (travel cost satisfaction) and tourist destination (tourism product, tourism product attributes, and tourism product management). A survey containing Likert-type scales was used in collecting data from 100 international tourists who had traveled to India. Using factor analysis, dimensions were identified for scales used in the study: travel cost satisfaction, tourism product, t...

Decoupling of the Leading Order DGLAP Evolution Equation with Spin Dependent Structure Functions

Science.gov (United States)

Azadbakht, F. Teimoury; Boroun, G. R.

2018-02-01

We propose an analytical solution for DGLAP evolution equations with polarized splitting functions at the Leading Order (LO) approximation based on the Laplace transform method. It is shown that the DGLAP evolution equations can be decoupled completely into two second order differential equations which then are solved analytically by using the initial conditions δ FS(x,Q2)=F[partial δ FS0(x), δ FS0(x)] and {δ G}(x,Q2)=G[partial δ G0(x), δ G0(x)]. We used this method to obtain the polarized structure function of the proton as well as the polarized gluon distribution function inside the proton and compared the numerical results with experimental data of COMPASS, HERMES, and AAC'08 Collaborations. It was found that there is a good agreement between our predictions and the experiments.

How the 2SLS/IV estimator can handle equality constraints in structural equation models: a system-of-equations approach.

Science.gov (United States)

Nestler, Steffen

2014-05-01

Parameters in structural equation models are typically estimated using the maximum likelihood (ML) approach. Bollen (1996) proposed an alternative non-iterative, equation-by-equation estimator that uses instrumental variables. Although this two-stage least squares/instrumental variables (2SLS/IV) estimator has good statistical properties, one problem with its application is that parameter equality constraints cannot be imposed. This paper presents a mathematical solution to this problem that is based on an extension of the 2SLS/IV approach to a system of equations. We present an example in which our approach was used to examine strong longitudinal measurement invariance. We also investigated the new approach in a simulation study that compared it with ML in the examination of the equality of two latent regression coefficients and strong measurement invariance. Overall, the results show that the suggested approach is a useful extension of the original 2SLS/IV estimator and allows for the effective handling of equality constraints in structural equation models. © 2013 The British Psychological Society.

Well-posedness of Prandtl equations with non-compatible data

International Nuclear Information System (INIS)

Cannone, M; Lombardo, M C; Sammartino, M

2013-01-01

In this paper we shall be concerned with Prandtl's equations with incompatible data, i.e. with initial data that, in general, do not fulfil the boundary conditions imposed on the solution. Under the hypothesis of analyticity in the streamwise variable, we shall prove that Prandtl's equations, on the half-plane or on the half-space, are well posed for a short time. (paper)

Advanced structural equation modeling issues and techniques

CERN Document Server

Marcoulides, George A

2013-01-01

By focusing primarily on the application of structural equation modeling (SEM) techniques in example cases and situations, this book provides an understanding and working knowledge of advanced SEM techniques with a minimum of mathematical derivations. The book was written for a broad audience crossing many disciplines, assumes an understanding of graduate level multivariate statistics, including an introduction to SEM.

Integrability and Poisson Structures of Three Dimensional Dynamical Systems and Equations of Hydrodynamic Type

Science.gov (United States)

Gumral, Hasan

Poisson structure of completely integrable 3 dimensional dynamical systems can be defined in terms of an integrable 1-form. We take advantage of this fact and use the theory of foliations in discussing the geometrical structure underlying complete and partial integrability. We show that the Halphen system can be formulated in terms of a flat SL(2,R)-valued connection and belongs to a non-trivial Godbillon-Vey class. On the other hand, for the Euler top and a special case of 3-species Lotka-Volterra equations which are contained in the Halphen system as limiting cases, this structure degenerates into the form of globally integrable bi-Hamiltonian structures. The globally integrable bi-Hamiltonian case is a linear and the sl_2 structure is a quadratic unfolding of an integrable 1-form in 3 + 1 dimensions. We complete the discussion of the Hamiltonian structure of 2-component equations of hydrodynamic type by presenting the Hamiltonian operators for Euler's equation and a continuum limit of Toda lattice. We present further infinite sequences of conserved quantities for shallow water equations and show that their generalizations by Kodama admit bi-Hamiltonian structure. We present a simple way of constructing the second Hamiltonian operators for N-component equations admitting some scaling properties. The Kodama reduction of the dispersionless-Boussinesq equations and the Lax reduction of the Benney moment equations are shown to be equivalent by a symmetry transformation. They can be cast into the form of a triplet of conservation laws which enable us to recognize a non-trivial scaling symmetry. The resulting bi-Hamiltonian structure generates three infinite sequences of conserved densities.

Noise-sustained structure, Intermittency, and the Ginzburg--Landau equation

International Nuclear Information System (INIS)

Deissler, R.J.

1985-01-01

The time-dependent generalized Ginzburg--Landau equation is an equation that is related to many physical systems. Solutions of this equation in the presence of low-level external noise are studied. Numerical solutions of this equation in the stationary frame of refernce and with nonzero group velocity that is greater than a critical velocity exhibit a selective spatial amplification of noise resulting in spatially growing waves. These waves in turn result in the formation of a dynamic structure. It is found that the microscopic noise plays an importuant role in the macroscopic dynamics of the system. For certain parameter values the system exhibits intermittent turbulent behavior in which the random nature of the external noise plays a crucial role. A mechanism which may be responsible for the intermittent turbulence occurring in some fluid systems is suggested

Recent developments in structure-preserving algorithms for oscillatory differential equations

CERN Document Server

Wu, Xinyuan

2018-01-01

The main theme of this book is recent progress in structure-preserving algorithms for solving initial value problems of oscillatory differential equations arising in a variety of research areas, such as astronomy, theoretical physics, electronics, quantum mechanics and engineering. It systematically describes the latest advances in the development of structure-preserving integrators for oscillatory differential equations, such as structure-preserving exponential integrators, functionally fitted energy-preserving integrators, exponential Fourier collocation methods, trigonometric collocation methods, and symmetric and arbitrarily high-order time-stepping methods. Most of the material presented here is drawn from the recent literature. Theoretical analysis of the newly developed schemes shows their advantages in the context of structure preservation. All the new methods introduced in this book are proven to be highly effective compared with the well-known codes in the scientific literature. This book also addre...

Model Servqual Dengan Pendekatan Structural Equation Modeling (Studi Pada Mahasiswa Sistem Informasi)

OpenAIRE

Nurfaizal, Yusmedi

2015-01-01

Penelitian ini berjudul “MODEL SERVQUAL DENGAN PENDEKATAN STRUCTURAL EQUATION MODELING (Studi Pada Mahasiswa Sistem Informasi)”. Tujuan penelitian ini adalah untuk mengetahui model Servqual dengan pendekatan Structural Equation Modeling pada mahasiswa sistem informasi. Peneliti memutuskan untuk mengambil sampel sebanyak 100 responden. Untuk menguji model digunakan analisis SEM. Hasil penelitian menunjukkan bahwa tangibility, reliability responsiveness, assurance dan emphaty mempunyai pengaruh...

Multilevel structural equation models for assessing moderation within and across levels of analysis.

Science.gov (United States)

Preacher, Kristopher J; Zhang, Zhen; Zyphur, Michael J

2016-06-01

Social scientists are increasingly interested in multilevel hypotheses, data, and statistical models as well as moderation or interactions among predictors. The result is a focus on hypotheses and tests of multilevel moderation within and across levels of analysis. Unfortunately, existing approaches to multilevel moderation have a variety of shortcomings, including conflated effects across levels of analysis and bias due to using observed cluster averages instead of latent variables (i.e., "random intercepts") to represent higher-level constructs. To overcome these problems and elucidate the nature of multilevel moderation effects, we introduce a multilevel structural equation modeling (MSEM) logic that clarifies the nature of the problems with existing practices and remedies them with latent variable interactions. This remedy uses random coefficients and/or latent moderated structural equations (LMS) for unbiased tests of multilevel moderation. We describe our approach and provide an example using the publicly available High School and Beyond data with Mplus syntax in Appendix. Our MSEM method eliminates problems of conflated multilevel effects and reduces bias in parameter estimates while offering a coherent framework for conceptualizing and testing multilevel moderation effects. (PsycINFO Database Record (c) 2016 APA, all rights reserved).

Development of High-Gradient Dielectric Laser-Driven Particle Accelerator Structures

Energy Technology Data Exchange (ETDEWEB)

Byer, Robert L. [Stanford Univ., CA (United States). Edward L. Ginzton Lab.

2013-11-07

The thrust of Stanford's program is to conduct research on high-gradient dielectric accelerator structures driven with high repetition-rate, tabletop infrared lasers. The close collaboration between Stanford and SLAC (Stanford Linear Accelerator Center) is critical to the success of this project, because it provides a unique environment where prototype dielectric accelerator structures can be rapidly fabricated and tested with a relativistic electron beam.

Spectrum Analysis of Inertial and Subinertial Motions Based on Analyzed Winds and Wind-Driven Currents from a Primitive Equation General Ocean Circulation Model.

Science.gov (United States)

1982-12-01

1Muter.Te Motions Based on Ana lyzed Winds and wind-driven December 1982 Currents from. a Primitive Squat ion General a.OW -love"*..* Oean Circulation...mew se"$ (comeS.... do oISN..u am ae~ 00do OWaor NUN Fourier and Rotary Spc , Analysis Modeled Inertial and Subinrtial Motion 4 Primitive Equation

Bus Travel Time Deviation Analysis Using Automatic Vehicle Location Data and Structural Equation Modeling

Directory of Open Access Journals (Sweden)

Xiaolin Gong

2015-01-01

Full Text Available To investigate the influences of causes of unreliability and bus schedule recovery phenomenon on microscopic segment-level travel time variance, this study adopts Structural Equation Modeling (SEM to specify, estimate, and measure the theoretical proposed models. The SEM model establishes and verifies hypotheses for interrelationships among travel time deviations, departure delays, segment lengths, dwell times, and number of traffic signals and access connections. The finally accepted model demonstrates excellent fitness. Most of the hypotheses are supported by the sample dataset from bus Automatic Vehicle Location system. The SEM model confirms the bus schedule recovery phenomenon. The departure delays at bus terminals and upstream travel time deviations indeed have negative impacts on travel time fluctuation of buses en route. Meanwhile, the segment length directly and negatively impacts travel time variability and inversely positively contributes to the schedule recovery process; this exogenous variable also indirectly and positively influences travel times through the existence of signalized intersections and access connections. This study offers a rational approach to analyzing travel time deviation feature. The SEM model structure and estimation results facilitate the understanding of bus service performance characteristics and provide several implications for bus service planning, management, and operation.

Crossover integral equation theory for the liquid structure study

International Nuclear Information System (INIS)

Lai, S.K.; Chen, H.C.

1994-08-01

The main purpose of this work is to report on a calculation that describes the role of the long-range bridge function [H. Iyetomi and S. Ichimaru, Phys. Rev. A 25, 2434 (1982)] as applied to the study of structure of simple liquid metals. It was found here that this bridge function accounts pretty well for the major part of long-range interactions but is physically inadequate for describing the short-range part of liquid structure. To improve on the theory we have drawn attention to the crossover integral equation method which, in essence, amounts to adding to the above bridge function a short-range correction of bridge diagrams. The suggested crossover procedure has been tested for the case of liquid metal Cs. Remarkably good agreement with experiment was obtained confirming our conjecture that the crossover integral equation approach as stressed in this work is potentially an appropriate theory for an accurate study of liquid structure possibly for the supercooled liquid regime. (author). 21 refs, 3 figs

First-principles data-driven discovery of transition metal oxides for artificial photosynthesis

Science.gov (United States)

Yan, Qimin

We develop a first-principles data-driven approach for rapid identification of transition metal oxide (TMO) light absorbers and photocatalysts for artificial photosynthesis using the Materials Project. Initially focusing on Cr, V, and Mn-based ternary TMOs in the database, we design a broadly-applicable multiple-layer screening workflow automating density functional theory (DFT) and hybrid functional calculations of bulk and surface electronic and magnetic structures. We further assess the electrochemical stability of TMOs in aqueous environments from computed Pourbaix diagrams. Several promising earth-abundant low band-gap TMO compounds with desirable band edge energies and electrochemical stability are identified by our computational efforts and then synergistically evaluated using high-throughput synthesis and photoelectrochemical screening techniques by our experimental collaborators at Caltech. Our joint theory-experiment effort has successfully identified new earth-abundant copper and manganese vanadate complex oxides that meet highly demanding requirements for photoanodes, substantially expanding the known space of such materials. By integrating theory and experiment, we validate our approach and develop important new insights into structure-property relationships for TMOs for oxygen evolution photocatalysts, paving the way for use of first-principles data-driven techniques in future applications. This work is supported by the Materials Project Predictive Modeling Center and the Joint Center for Artificial Photosynthesis through the U.S. Department of Energy, Office of Basic Energy Sciences, Materials Sciences and Engineering Division, under Contract No. DE-AC02-05CH11231. Computational resources also provided by the Department of Energy through the National Energy Supercomputing Center.

The Application of Structural Equation Modeling to Maternal Ratings of Twins' Behavior and Emotional Problems.

Science.gov (United States)

Silberg, Judy L.; And Others

1994-01-01

Applied structural equation modeling to twin data to assess impact of genetic and environmental factors on children's behavioral and emotional functioning. Applied models to maternal ratings of behavior of 515 monozygotic and 749 dizygotic twin pairs. Importance of genetic, shared, and specific environmental factors for explaining variation was…

Data-driven modeling, control and tools for cyber-physical energy systems

Science.gov (United States)

Behl, Madhur

Energy systems are experiencing a gradual but substantial change in moving away from being non-interactive and manually-controlled systems to utilizing tight integration of both cyber (computation, communications, and control) and physical representations guided by first principles based models, at all scales and levels. Furthermore, peak power reduction programs like demand response (DR) are becoming increasingly important as the volatility on the grid continues to increase due to regulation, integration of renewables and extreme weather conditions. In order to shield themselves from the risk of price volatility, end-user electricity consumers must monitor electricity prices and be flexible in the ways they choose to use electricity. This requires the use of control-oriented predictive models of an energy system's dynamics and energy consumption. Such models are needed for understanding and improving the overall energy efficiency and operating costs. However, learning dynamical models using grey/white box approaches is very cost and time prohibitive since it often requires significant financial investments in retrofitting the system with several sensors and hiring domain experts for building the model. We present the use of data-driven methods for making model capture easy and efficient for cyber-physical energy systems. We develop Model-IQ, a methodology for analysis of uncertainty propagation for building inverse modeling and controls. Given a grey-box model structure and real input data from a temporary set of sensors, Model-IQ evaluates the effect of the uncertainty propagation from sensor data to model accuracy and to closed-loop control performance. We also developed a statistical method to quantify the bias in the sensor measurement and to determine near optimal sensor placement and density for accurate data collection for model training and control. Using a real building test-bed, we show how performing an uncertainty analysis can reveal trends about

Structural invariance of the Schroedinger equation and chronoprojective geometry

International Nuclear Information System (INIS)

Burdet, G.; Perrin, M.

1983-07-01

We describe an extension of the chronoprojective geometry and show how its automorphisms are related to the invariance properties of the Schroedinger equation describing a quantum test particle in any Newton-Cartan structure

[A Structural Equation Model on Family Strength of Married Working Women].

Science.gov (United States)

Hong, Yeong Seon; Han, Kuem Sun

2015-12-01

The purpose of this study was to identify the effect of predictive factors related to family strength and develop a structural equation model that explains family strength among married working women. A hypothesized model was developed based on literature reviews and predictors of family strength by Yoo. This constructed model was built of an eight pathway form. Two exogenous variables included in this model were ego-resilience and family support. Three endogenous variables included in this model were functional couple communication, family stress and family strength. Data were collected using a self-report questionnaire from 319 married working women who were 30~40 of age and lived in cities of Chungnam province in Korea. Data were analyzed with PASW/WIN 18.0 and AMOS 18.0 programs. Family support had a positive direct, indirect and total effect on family strength. Family stress had a negative direct, indirect and total effect on family strength. Functional couple communication had a positive direct and total effect on family strength. These predictive variables of family strength explained 61.8% of model. The results of the study show a structural equation model for family strength of married working women and that predicting factors for family strength are family support, family stress, and functional couple communication. To improve family strength of married working women, the results of this study suggest nursing access and mediative programs to improve family support and functional couple communication, and reduce family stress.

A Knowledge-Driven Geospatially Enabled Framework for Geological Big Data

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Liang Wu

2017-06-01

Full Text Available Geologic survey procedures accumulate large volumes of structured and unstructured data. Fully exploiting the knowledge and information that are included in geological big data and improving the accessibility of large volumes of data are important endeavors. In this paper, which is based on the architecture of the geological survey information cloud-computing platform (GSICCP and big-data-related technologies, we split geologic unstructured data into fragments and extract multi-dimensional features via geological domain ontology. These fragments are reorganized into a NoSQL (Not Only SQL database, and then associations between the fragments are added. A specific class of geological questions was analyzed and transformed into workflow tasks according to the predefined rules and associations between fragments to identify spatial information and unstructured content. We establish a knowledge-driven geologic survey information smart-service platform (GSISSP based on previous work, and we detail a study case for our research. The study case shows that all the content that has known relationships or semantic associations can be mined with the assistance of multiple ontologies, thereby improving the accuracy and comprehensiveness of geological information discovery.

The Cauchy problem for the Pavlov equation with large data

Science.gov (United States)

Wu, Derchyi

2017-08-01

We prove a local solvability of the Cauchy problem for the Pavlov equation with large initial data by the inverse scattering method. The Pavlov equation arises in studies Einstein-Weyl geometries and dispersionless integrable models. Our theory yields a local solvability of Cauchy problems for a quasi-linear wave equation with a characteristic initial hypersurface.

The evaporation of oil spills: prediction of equations using distillation data

International Nuclear Information System (INIS)

Fingas, M.

1997-01-01

The evaporative characteristics of 19 different crude oils and petroleum products were studied . Best-fit equation parameters were determined for percentage loss by time and absolute weight loss. Except in three cases, all oils were found to fit logarithmic curves. The equation constants were correlated with oil distillation data. Relationships enabling calculation of evaporation equations directly from distillation data have been developed. The high correlation of distillation data and evaporation data suggests that the two processes are analogous and that evaporation, like distillation, is largely governed by intrinsic oil properties rather than environmental properties such as boundary-layer factors

Data-driven Regulation and Governance in Smart Cities

NARCIS (Netherlands)

Ranchordás, Sofia; Klop, Abram; Mak, Vanessa; Berlee, Anna; Tjong Tjin Tai, Eric

2018-01-01

This chapter discusses the concept of data-driven regulation and governance in the context of smart cities by describing how these urban centres harness these technologies to collect and process information about citizens, traffic, urban planning or waste production. It describes how several smart

Statistical Data Processing with R – Metadata Driven Approach

Directory of Open Access Journals (Sweden)

Rudi SELJAK

2016-06-01

Full Text Available In recent years the Statistical Office of the Republic of Slovenia has put a lot of effort into re-designing its statistical process. We replaced the classical stove-pipe oriented production system with general software solutions, based on the metadata driven approach. This means that one general program code, which is parametrized with process metadata, is used for data processing for a particular survey. Currently, the general program code is entirely based on SAS macros, but in the future we would like to explore how successfully statistical software R can be used for this approach. Paper describes the metadata driven principle for data validation, generic software solution and main issues connected with the use of statistical software R for this approach.

Identifying Time Periods of Minimal Thermal Gradient for Temperature-Driven Structural Health Monitoring

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John Reilly

2018-03-01

Full Text Available Temperature changes play a large role in the day to day structural behavior of structures, but a smaller direct role in most contemporary Structural Health Monitoring (SHM analyses. Temperature-Driven SHM will consider temperature as the principal driving force in SHM, relating a measurable input temperature to measurable output generalized strain (strain, curvature, etc. and generalized displacement (deflection, rotation, etc. to create three-dimensional signatures descriptive of the structural behavior. Identifying time periods of minimal thermal gradient provides the foundation for the formulation of the temperature–deformation–displacement model. Thermal gradients in a structure can cause curvature in multiple directions, as well as non-linear strain and stress distributions within the cross-sections, which significantly complicates data analysis and interpretation, distorts the signatures, and may lead to unreliable conclusions regarding structural behavior and condition. These adverse effects can be minimized if the signatures are evaluated at times when thermal gradients in the structure are minimal. This paper proposes two classes of methods based on the following two metrics: (i the range of raw temperatures on the structure, and (ii the distribution of the local thermal gradients, for identifying time periods of minimal thermal gradient on a structure with the ability to vary the tolerance of acceptable thermal gradients. The methods are tested and validated with data collected from the Streicker Bridge on campus at Princeton University.

Identifying Time Periods of Minimal Thermal Gradient for Temperature-Driven Structural Health Monitoring.

Science.gov (United States)

Reilly, John; Glisic, Branko

2018-03-01

Temperature changes play a large role in the day to day structural behavior of structures, but a smaller direct role in most contemporary Structural Health Monitoring (SHM) analyses. Temperature-Driven SHM will consider temperature as the principal driving force in SHM, relating a measurable input temperature to measurable output generalized strain (strain, curvature, etc.) and generalized displacement (deflection, rotation, etc.) to create three-dimensional signatures descriptive of the structural behavior. Identifying time periods of minimal thermal gradient provides the foundation for the formulation of the temperature-deformation-displacement model. Thermal gradients in a structure can cause curvature in multiple directions, as well as non-linear strain and stress distributions within the cross-sections, which significantly complicates data analysis and interpretation, distorts the signatures, and may lead to unreliable conclusions regarding structural behavior and condition. These adverse effects can be minimized if the signatures are evaluated at times when thermal gradients in the structure are minimal. This paper proposes two classes of methods based on the following two metrics: (i) the range of raw temperatures on the structure, and (ii) the distribution of the local thermal gradients, for identifying time periods of minimal thermal gradient on a structure with the ability to vary the tolerance of acceptable thermal gradients. The methods are tested and validated with data collected from the Streicker Bridge on campus at Princeton University.

Data-driven design of fault diagnosis and fault-tolerant control systems

CERN Document Server

Ding, Steven X

2014-01-01

Data-driven Design of Fault Diagnosis and Fault-tolerant Control Systems presents basic statistical process monitoring, fault diagnosis, and control methods, and introduces advanced data-driven schemes for the design of fault diagnosis and fault-tolerant control systems catering to the needs of dynamic industrial processes. With ever increasing demands for reliability, availability and safety in technical processes and assets, process monitoring and fault-tolerance have become important issues surrounding the design of automatic control systems. This text shows the reader how, thanks to the rapid development of information technology, key techniques of data-driven and statistical process monitoring and control can now become widely used in industrial practice to address these issues. To allow for self-contained study and facilitate implementation in real applications, important mathematical and control theoretical knowledge and tools are included in this book. Major schemes are presented in algorithm form and...

Product design pattern based on big data-driven scenario

Directory of Open Access Journals (Sweden)

Conggang Yu

2016-07-01

Full Text Available This article discusses about new product design patterns in the big data era, gives designer a new rational thinking way, and is a new way to understand the design of the product. Based on the key criteria of the product design process, category, element, and product are used to input the data, which comprises concrete data and abstract data as an enlargement of the criteria of product design process for the establishment of a big data-driven product design pattern’s model. Moreover, an experiment and a product design case are conducted to verify the feasibility of the new pattern. Ultimately, we will conclude that the data-driven product design has two patterns: one is the concrete data supporting the product design, namely “product–data–product” pattern, and the second is based on the value of the abstract data for product design, namely “data–product–data” pattern. Through the data, users are involving themselves in the design development process. Data and product form a huge network, and data plays a role of connection or node. So the essence of the design is to find a new connection based on element, and to find a new node based on category.

Discrete integration of continuous Kalman filtering equations for time invariant second-order structural systems

Science.gov (United States)

Park, K. C.; Belvin, W. Keith

1990-01-01

A general form for the first-order representation of the continuous second-order linear structural-dynamics equations is introduced to derive a corresponding form of first-order continuous Kalman filtering equations. Time integration of the resulting equations is carried out via a set of linear multistep integration formulas. It is shown that a judicious combined selection of computational paths and the undetermined matrices introduced in the general form of the first-order linear structural systems leads to a class of second-order discrete Kalman filtering equations involving only symmetric sparse N x N solution matrices.

Data-driven remaining useful life prognosis techniques stochastic models, methods and applications

CERN Document Server

Si, Xiao-Sheng; Hu, Chang-Hua

2017-01-01

This book introduces data-driven remaining useful life prognosis techniques, and shows how to utilize the condition monitoring data to predict the remaining useful life of stochastic degrading systems and to schedule maintenance and logistics plans. It is also the first book that describes the basic data-driven remaining useful life prognosis theory systematically and in detail. The emphasis of the book is on the stochastic models, methods and applications employed in remaining useful life prognosis. It includes a wealth of degradation monitoring experiment data, practical prognosis methods for remaining useful life in various cases, and a series of applications incorporated into prognostic information in decision-making, such as maintenance-related decisions and ordering spare parts. It also highlights the latest advances in data-driven remaining useful life prognosis techniques, especially in the contexts of adaptive prognosis for linear stochastic degrading systems, nonlinear degradation modeling based pro...

ODEion--a software module for structural identification of ordinary differential equations.

Science.gov (United States)

Gennemark, Peter; Wedelin, Dag

2014-02-01

In the systems biology field, algorithms for structural identification of ordinary differential equations (ODEs) have mainly focused on fixed model spaces like S-systems and/or on methods that require sufficiently good data so that derivatives can be accurately estimated. There is therefore a lack of methods and software that can handle more general models and realistic data. We present ODEion, a software module for structural identification of ODEs. Main characteristic features of the software are: • The model space is defined by arbitrary user-defined functions that can be nonlinear in both variables and parameters, such as for example chemical rate reactions. • ODEion implements computationally efficient algorithms that have been shown to efficiently handle sparse and noisy data. It can run a range of realistic problems that previously required a supercomputer. • ODEion is easy to use and provides SBML output. We describe the mathematical problem, the ODEion system itself, and provide several examples of how the system can be used. Available at: http://www.odeidentification.org.

Matrix Methods for Solving Hartree-Fock Equations in Atomic Structure Calculations and Line Broadening

Directory of Open Access Journals (Sweden)

Thomas Gomez

2018-04-01

Full Text Available Atomic structure of N-electron atoms is often determined by solving the Hartree-Fock equations, which are a set of integro-differential equations. The integral part of the Hartree-Fock equations treats electron exchange, but the Hartree-Fock equations are not often treated as an integro-differential equation. The exchange term is often approximated as an inhomogeneous or an effective potential so that the Hartree-Fock equations become a set of ordinary differential equations (which can be solved using the usual shooting methods. Because the Hartree-Fock equations are an iterative-refinement method, the inhomogeneous term relies on the previous guess of the wavefunction. In addition, there are numerical complications associated with solving inhomogeneous differential equations. This work uses matrix methods to solve the Hartree-Fock equations as an integro-differential equation. It is well known that a derivative operator can be expressed as a matrix made of finite-difference coefficients; energy eigenvalues and eigenvectors can be obtained by using linear-algebra packages. The integral (exchange part of the Hartree-Fock equation can be approximated as a sum and written as a matrix. The Hartree-Fock equations can be solved as a matrix that is the sum of the differential and integral matrices. We compare calculations using this method against experiment and standard atomic structure calculations. This matrix method can also be used to solve for free-electron wavefunctions, thus improving how the atoms and free electrons interact. This technique is important for spectral line broadening in two ways: it improves the atomic structure calculations, and it improves the motion of the plasma electrons that collide with the atom.

Design of a data-driven predictive controller for start-up process of AMT vehicles.

Science.gov (United States)

Lu, Xiaohui; Chen, Hong; Wang, Ping; Gao, Bingzhao

2011-12-01

In this paper, a data-driven predictive controller is designed for the start-up process of vehicles with automated manual transmissions (AMTs). It is obtained directly from the input-output data of a driveline simulation model constructed by the commercial software AMESim. In order to obtain offset-free control for the reference input, the predictor equation is gained with incremental inputs and outputs. Because of the physical characteristics, the input and output constraints are considered explicitly in the problem formulation. The contradictory requirements of less friction losses and less driveline shock are included in the objective function. The designed controller is tested under nominal conditions and changed conditions. The simulation results show that, during the start-up process, the AMT clutch with the proposed controller works very well, and the process meets the control objectives: fast clutch lockup time, small friction losses, and the preservation of driver comfort, i.e., smooth acceleration of the vehicle. At the same time, the closed-loop system has the ability to reject uncertainties, such as the vehicle mass and road grade.

Data-driven analysis of blood glucose management effectiveness

NARCIS (Netherlands)

Nannings, B.; Abu-Hanna, A.; Bosman, R. J.

2005-01-01

The blood-glucose-level (BGL) of Intensive Care (IC) patients requires close monitoring and control. In this paper we describe a general data-driven analytical method for studying the effectiveness of BGL management. The method is based on developing and studying a clinical outcome reflecting the

Turbulent mixed buoyancy driven flow and heat transfer in lid driven enclosure

International Nuclear Information System (INIS)

Mishra, Ajay Kumar; Sharma, Anil Kumar

2015-01-01

Turbulent mixed buoyancy driven flow and heat transfer of air in lid driven rectangular enclosure has been investigated for Grashof number in the range of 10 8 to 10 11 and for Richardson number 0.1, 1 and 10. Steady two dimensional Reynolds-Averaged-Navier-Stokes equations and conservation equations of mass and energy, coupled with the Boussinesq approximation, are solved. The spatial derivatives in the equations are discretized using the finite-element method. The SIMPLE algorithm is used to resolve pressure-velocity coupling. Turbulence is modeled with the k-ω closure model with physical boundary conditions along with the Boussinesq approximation, for the flow and heat transfer. The predicted results are validated against benchmark solutions reported in literature. The results include stream lines and temperature fields are presented to understand flow and heat transfer characteristics. There is a marked reduction in mean Nusselt number (about 58%) as the Richardson number increases from 0.1 to 10 for the case of Ra=10 10 signifying the effect of reduction of top lid velocity resulting in reduction of turbulent mixing. (author)

Data-Driven Controller Design The H2 Approach

CERN Document Server

Sanfelice Bazanella, Alexandre; Eckhard, Diego

2012-01-01

Data-driven methodologies have recently emerged as an important paradigm alternative to model-based controller design and several such methodologies are formulated as an H2 performance optimization. This book presents a comprehensive theoretical treatment of the H2 approach to data-driven control design. The fundamental properties implied by the H2 problem formulation are analyzed in detail, so that common features to all solutions are identified. Direct methods (VRFT) and iterative methods (IFT, DFT, CbT) are put under a common theoretical framework. The choice of the reference model, the experimental conditions, the optimization method to be used, and several other designer’s choices are crucial to the quality of the final outcome, and firm guidelines for all these choices are derived from the theoretical analysis presented. The practical application of the concepts in the book is illustrated with a large number of practical designs performed for different classes of processes: thermal, fluid processing a...

Pipe break prediction based on evolutionary data-driven methods with brief recorded data

International Nuclear Information System (INIS)

Xu Qiang; Chen Qiuwen; Li Weifeng; Ma Jinfeng

2011-01-01

Pipe breaks often occur in water distribution networks, imposing great pressure on utility managers to secure stable water supply. However, pipe breaks are hard to detect by the conventional method. It is therefore necessary to develop reliable and robust pipe break models to assess the pipe's probability to fail and then to optimize the pipe break detection scheme. In the absence of deterministic physical models for pipe break, data-driven techniques provide a promising approach to investigate the principles underlying pipe break. In this paper, two data-driven techniques, namely Genetic Programming (GP) and Evolutionary Polynomial Regression (EPR) are applied to develop pipe break models for the water distribution system of Beijing City. The comparison with the recorded pipe break data from 1987 to 2005 showed that the models have great capability to obtain reliable predictions. The models can be used to prioritize pipes for break inspection and then improve detection efficiency.

Time-Dependent Thermally-Driven Interfacial Flows in Multilayered Fluid Structures

Science.gov (United States)

Haj-Hariri, Hossein; Borhan, A.

1996-01-01

A computational study of thermally-driven convection in multilayered fluid structures will be performed to examine the effect of interactions among deformable fluid-fluid interfaces on the structure of time-dependent flow in these systems. Multilayered fluid structures in two models configurations will be considered: the differentially heated rectangular cavity with a free surface, and the encapsulated cylindrical liquid bridge. An extension of a numerical method developed as part of our recent NASA Fluid Physics grant will be used to account for finite deformations of fluid-fluid interfaces.

Maximum Principle for General Controlled Systems Driven by Fractional Brownian Motions

International Nuclear Information System (INIS)

Han Yuecai; Hu Yaozhong; Song Jian

2013-01-01

We obtain a maximum principle for stochastic control problem of general controlled stochastic differential systems driven by fractional Brownian motions (of Hurst parameter H>1/2). This maximum principle specifies a system of equations that the optimal control must satisfy (necessary condition for the optimal control). This system of equations consists of a backward stochastic differential equation driven by both fractional Brownian motions and the corresponding underlying standard Brownian motions. In addition to this backward equation, the maximum principle also involves the Malliavin derivatives. Our approach is to use conditioning and Malliavin calculus. To arrive at our maximum principle we need to develop some new results of stochastic analysis of the controlled systems driven by fractional Brownian motions via fractional calculus. Our approach of conditioning and Malliavin calculus is also applied to classical system driven by standard Brownian motions while the controller has only partial information. As a straightforward consequence, the classical maximum principle is also deduced in this more natural and simpler way.

Data-driven asthma endotypes defined from blood biomarker and gene expression data.

Directory of Open Access Journals (Sweden)

Barbara Jane George

Full Text Available The diagnosis and treatment of childhood asthma is complicated by its mechanistically distinct subtypes (endotypes driven by genetic susceptibility and modulating environmental factors. Clinical biomarkers and blood gene expression were collected from a stratified, cross-sectional study of asthmatic and non-asthmatic children from Detroit, MI. This study describes four distinct asthma endotypes identified via a purely data-driven method. Our method was specifically designed to integrate blood gene expression and clinical biomarkers in a way that provides new mechanistic insights regarding the different asthma endotypes. For example, we describe metabolic syndrome-induced systemic inflammation as an associated factor in three of the four asthma endotypes. Context provided by the clinical biomarker data was essential in interpreting gene expression patterns and identifying putative endotypes, which emphasizes the importance of integrated approaches when studying complex disease etiologies. These synthesized patterns of gene expression and clinical markers from our research may lead to development of novel serum-based biomarker panels.

Parameter estimation in stochastic differential equations

CERN Document Server

Bishwal, Jaya P N

2008-01-01

Parameter estimation in stochastic differential equations and stochastic partial differential equations is the science, art and technology of modelling complex phenomena and making beautiful decisions. The subject has attracted researchers from several areas of mathematics and other related fields like economics and finance. This volume presents the estimation of the unknown parameters in the corresponding continuous models based on continuous and discrete observations and examines extensively maximum likelihood, minimum contrast and Bayesian methods. Useful because of the current availability of high frequency data is the study of refined asymptotic properties of several estimators when the observation time length is large and the observation time interval is small. Also space time white noise driven models, useful for spatial data, and more sophisticated non-Markovian and non-semimartingale models like fractional diffusions that model the long memory phenomena are examined in this volume.

Developing a Data Driven Process-Based Model for Remote Sensing of Ecosystem Production

Science.gov (United States)

Elmasri, B.; Rahman, A. F.

2010-12-01

Estimating ecosystem carbon fluxes at various spatial and temporal scales is essential for quantifying the global carbon cycle. Numerous models have been developed for this purpose using several environmental variables as well as vegetation indices derived from remotely sensed data. Here we present a data driven modeling approach for gross primary production (GPP) that is based on a process based model BIOME-BGC. The proposed model was run using available remote sensing data and it does not depend on look-up tables. Furthermore, this approach combines the merits of both empirical and process models, and empirical models were used to estimate certain input variables such as light use efficiency (LUE). This was achieved by using remotely sensed data to the mathematical equations that represent biophysical photosynthesis processes in the BIOME-BGC model. Moreover, a new spectral index for estimating maximum photosynthetic activity, maximum photosynthetic rate index (MPRI), is also developed and presented here. This new index is based on the ratio between the near infrared and the green bands (ρ858.5/ρ555). The model was tested and validated against MODIS GPP product and flux measurements from two eddy covariance flux towers located at Morgan Monroe State Forest (MMSF) in Indiana and Harvard Forest in Massachusetts. Satellite data acquired by the Advanced Microwave Scanning Radiometer (AMSR-E) and MODIS were used. The data driven model showed a strong correlation between the predicted and measured GPP at the two eddy covariance flux towers sites. This methodology produced better predictions of GPP than did the MODIS GPP product. Moreover, the proportion of error in the predicted GPP for MMSF and Harvard forest was dominated by unsystematic errors suggesting that the results are unbiased. The analysis indicated that maintenance respiration is one of the main factors that dominate the overall model outcome errors and improvement in maintenance respiration estimation

Algebraic models for the hierarchy structure of evolution equations at small x

International Nuclear Information System (INIS)

Rembiesa, P.; Stasto, A.M.

2005-01-01

We explore several models of QCD evolution equations simplified by considering only the rapidity dependence of dipole scattering amplitudes, while provisionally neglecting their dependence on transverse coordinates. Our main focus is on the equations that include the processes of pomeron splittings. We examine the algebraic structures of the governing equation hierarchies, as well as the asymptotic behavior of their solutions in the large-rapidity limit

Iterative Systems Biology for Medicine – time for advancing from network signature to mechanistic equations

KAUST Repository

Gomez-Cabrero, David

2017-05-09

The rise and growth of Systems Biology following the sequencing of the human genome has been astounding. Early on, an iterative wet-dry methodology was formulated which turned out as a successful approach in deciphering biological complexity. Such type of analysis effectively identified and associated molecular network signatures operative in biological processes across different systems. Yet, it has proven difficult to distinguish between causes and consequences, thus making it challenging to attack medical questions where we require precise causative drug targets and disease mechanisms beyond a web of associated markers. Here we review principal advances with regard to identification of structure, dynamics, control, and design of biological systems, following the structure in the visionary review from 2002 by Dr. Kitano. Yet, here we find that the underlying challenge of finding the governing mechanistic system equations enabling precision medicine remains open thus rendering clinical translation of systems biology arduous. However, stunning advances in raw computational power, generation of high-precision multi-faceted biological data, combined with powerful algorithms hold promise to set the stage for data-driven identification of equations implicating a fundamental understanding of living systems during health and disease.

Cultural, Social, and Economic Capital Constructs in International Assessments: An Evaluation Using Exploratory Structural Equation Modeling

Science.gov (United States)

Caro, Daniel H.; Sandoval-Hernández, Andrés; Lüdtke, Oliver

2014-01-01

The article employs exploratory structural equation modeling (ESEM) to evaluate constructs of economic, cultural, and social capital in international large-scale assessment (LSA) data from the Progress in International Reading Literacy Study (PIRLS) 2006 and the Programme for International Student Assessment (PISA) 2009. ESEM integrates the…

Considerations of ion temperature gradient driven turbulence

International Nuclear Information System (INIS)

Cowley, S.C.; Kulsrud, R.M.

1991-02-01

The ion temperature gradient driven instability is considered in this paper. Physical pictures are presented to clarify the nature of the instability. The saturation of a single eddy is modeled by a simple nonlinear equation. We show that eddies which are elongated in the direction of the temperature gradient are the most unstable and have the highest saturation amplitudes. In a sheared magnetic field, such elongated eddies twist with the field lines. This structure is shown to be alternative to the usual Fourier mode picture in which the mode is localized around the surface where k parallel = 0. We show how these elongated twisting eddies, which are an integral part of the ''ballooning mode'' structure, could survive in a torus. The elongated eddies are shown to be unstable to secondary instabilities that are driven by the large gradients in the long eddy. We argue that this mechanism isotropizes ion temperature gradient turbulence. We further argue that the ''mixing length'' is set by this nonlinear process, not by a linear eigenmode width. 17 refs., 6 figs

Applied structural equation modelling for researchers and practitioners using R and Stata for behavioural research

CERN Document Server

Ramlall, Indranarain

2016-01-01

This book explains in a rigorous, concise and practical manner all the vital components embedded in structural equation modelling. Focusing on R and stata to implement and perform various structural equation models.

Dynamically adaptive data-driven simulation of extreme hydrological flows

KAUST Repository

Kumar Jain, Pushkar; Mandli, Kyle; Hoteit, Ibrahim; Knio, Omar; Dawson, Clint

2017-01-01

evacuation in real-time and through the development of resilient infrastructure based on knowledge of how systems respond to extreme events. Data-driven computational modeling is a critical technology underpinning these efforts. This investigation focuses

A STRUCTURAL EQUATION MODEL: GREECE’S TOURISM DEMAND FOR TOURIST DESTINATION

OpenAIRE

Chaitip, Prasert; Chaiboonsri, Chukiat; Kovacs, Sandor; Balogh, Peter

2010-01-01

Structural equation model (LISREL 8) was applied to test the causal relationships between tourist travel motivations and tourist destination. A survey containing Likert scale questions was conducted to collect data from 100 tourists who had travelled to Greece’s tourist destination. With the help of factor analysis, four dimensions were identified for scales used in the study: travel cost satisfaction, tourism product, tourism product attributes, and tourism product management. Results indi...

Family Environment and Childhood Obesity: A New Framework with Structural Equation Modeling

Directory of Open Access Journals (Sweden)

Hui Huang

2017-02-01

Full Text Available The main purpose of the current article is to introduce a framework of the complexity of childhood obesity based on the family environment. A conceptual model that quantifies the relationships and interactions among parental socioeconomic status, family food security level, child’s food intake and certain aspects of parental feeding behaviour is presented using the structural equation modeling (SEM concept. Structural models are analysed in terms of the direct and indirect connections among latent and measurement variables that lead to the child weight indicator. To illustrate the accuracy, fit, reliability and validity of the introduced framework, real data collected from 630 families from Urumqi (Xinjiang, China were considered. The framework includes two categories of data comprising the normal body mass index (BMI range and obesity data. The comparison analysis between two models provides some evidence that in obesity modeling, obesity data must be extracted from the dataset and analysis must be done separately from the normal BMI range. This study may be helpful for researchers interested in childhood obesity modeling based on family environment.

Data-Driven Learning: Reasonable Fears and Rational Reassurance

Science.gov (United States)

Boulton, Alex

2009-01-01

Computer corpora have many potential applications in teaching and learning languages, the most direct of which--when the learners explore a corpus themselves--has become known as data-driven learning (DDL). Despite considerable enthusiasm in the research community and interest in higher education, the approach has not made major inroads to…

Data-Driven Exercises for Chemistry: A New Digital Collection

Science.gov (United States)

Grubbs, W. Tandy

2007-01-01

The analysis presents a new digital collection for various data-driven exercises that are used for teaching chemistry to the students. Such methods are expected to help the students to think in a more scientific manner.

Data-Driven Model Order Reduction for Bayesian Inverse Problems

KAUST Repository

Cui, Tiangang; Youssef, Marzouk; Willcox, Karen

2014-01-01

One of the major challenges in using MCMC for the solution of inverse problems is the repeated evaluation of computationally expensive numerical models. We develop a data-driven projection- based model order reduction technique to reduce

Data-Intensive Science Meets Inquiry-Driven Pedagogy: Interactive Big Data Exploration, Threshold Concepts, and Liminality

Science.gov (United States)

Ramachandran, R.; Nair, U. S.; Word, A.

2014-12-01

Threshold concepts in any discipline are the core concepts an individual must understand in order to master a discipline. By their very nature, these concepts are troublesome, irreversible, integrative, bounded, discursive, and reconstitutive. Although grasping threshold concepts can be extremely challenging for each learner as s/he moves through stages of cognitive development relative to a given discipline, the learner's grasp of these concepts determines the extent to which s/he is prepared to work competently and creatively within the field itself. The movement of individuals from a state of ignorance of these core concepts to one of mastery occurs not along a linear path but in iterative cycles of knowledge creation and adjustment in liminal spaces - conceptual spaces through which learners move from the vaguest awareness of concepts to mastery, accompanied by understanding of their relevance, connectivity, and usefulness relative to questions and constructs in a given discipline. With the explosive growth of data available in atmospheric science, driven largely by satellite Earth observations and high-resolution numerical simulations, paradigms such as that of data-intensive science have emerged. These paradigm shifts are based on the growing realization that current infrastructure, tools and processes will not allow us to analyze and fully utilize the complex and voluminous data that is being gathered. In this emerging paradigm, the scientific discovery process is driven by knowledge extracted from large volumes of data. In this presentation, we contend that this paradigm naturally lends to inquiry-driven pedagogy where knowledge is discovered through inductive engagement with large volumes of data rather than reached through traditional, deductive, hypothesis-driven analyses. In particular, data-intensive techniques married with an inductive methodology allow for exploration on a scale that is not possible in the traditional classroom with its typical

Nonlinear model of a rotating hub-beams structure: Equations of motion

Science.gov (United States)

Warminski, Jerzy

2018-01-01

Dynamics of a rotating structure composed of a rigid hub and flexible beams is presented in the paper. A nonlinear model of a beam takes into account bending, extension and nonlinear curvature. The influence of geometric nonlinearity and nonconstant angular velocity on dynamics of the rotating structure is presented. The exact equations of motion and associated boundary conditions are derived on the basis of the Hamilton's principle. The simplification of the exact nonlinear mathematical model is proposed taking into account the second order approximation. The reduced partial differential equations of motion together with associated boundary conditions can be used to study natural or forced vibrations of a rotating structure considering constant or nonconstant angular speed of a rigid hub and an arbitrary number of flexible blades.

Solutions of the KPI equation with smooth initial data

Science.gov (United States)

Boiti, M.; Pempinelli, F.; Pogrebkov, A.

1994-06-01

The solution $u(t,x,y)$ of the Kadomtsev--Petviashvili I (KPI) equation with given initial data $u(0,x,y)$ belonging to the Schwartz space is considered. No additional special constraints, usually considered in literature, as $\\int\\!dx\\,u(0,x,y)=0$ are required to be satisfied by the initial data. The problem is completely solved in the framework of the spectral transform theory and it is shown that $u(t,x,y)$ satisfies a special evolution version of the KPI equation and that, in general, $\\partial_t u(t,x,y)$ has different left and right limits at the initial time $t=0$. The conditions of the type $\\int\\!dx\\,u(t,x,y)=0$, $\\int\\!dx\\,xu_y(t,x,y)=0$ and so on (first, second, etc. `constraints') are dynamically generated by the evolution equation for $t\

A Structural Equation Approach to Models with Spatial Dependence

NARCIS (Netherlands)

Oud, Johan H. L.; Folmer, Henk

We introduce the class of structural equation models (SEMs) and corresponding estimation procedures into a spatial dependence framework. SEM allows both latent and observed variables within one and the same (causal) model. Compared with models with observed variables only, this feature makes it

A structural equation approach to models with spatial dependence

NARCIS (Netherlands)

Oud, J.H.L.; Folmer, H.

2008-01-01

We introduce the class of structural equation models (SEMs) and corresponding estimation procedures into a spatial dependence framework. SEM allows both latent and observed variables within one and the same (causal) model. Compared with models with observed variables only, this feature makes it

A Structural Equation Approach to Models with Spatial Dependence

NARCIS (Netherlands)

Oud, J.H.L.; Folmer, H.

2008-01-01

We introduce the class of structural equation models (SEMs) and corresponding estimation procedures into a spatial dependence framework. SEM allows both latent and observed variables within one and the same (causal) model. Compared with models with observed variables only, this feature makes it

Ion-temperature-gradient-driven modes in bi-ion magnetoplasma

Energy Technology Data Exchange (ETDEWEB)

Batool, Nazia; Mirza, Arshad M [Theoretical Plasma Physics Group, Department of Physics, Quaid-i-Azam University, Islamabad 45320 (Pakistan); Qamar, Anisa [Department of Physics, Peshawar University, NWFP 25120 (Pakistan)], E-mail: nazia.batool@ncp.edu.pk

2008-12-15

The toroidal ion-temperature-gradient (ITG)-driven electrostatic drift waves are investigated for bi-ion plasmas with equilibrium density, temperature and magnetic field gradients. Using Braginskii's transport equations for the ions and Boltzmann distributed electrons, the mode coupling equations are derived. New ITG-driven modes are shown to exist. The results of the present study should be helpful to understand several wave phenomena in space and tokamak plasmas.

Developing Annotation Solutions for Online Data Driven Learning

Science.gov (United States)

Perez-Paredes, Pascual; Alcaraz-Calero, Jose M.

2009-01-01

Although "annotation" is a widely-researched topic in Corpus Linguistics (CL), its potential role in Data Driven Learning (DDL) has not been addressed in depth by Foreign Language Teaching (FLT) practitioners. Furthermore, most of the research in the use of DDL methods pays little attention to annotation in the design and implementation…

Nuclear data requirements for accelerator driven sub-critical systems

Indian Academy of Sciences (India)

The development of accelerator driven sub-critical systems (ADSS) require significant amount of new nuclear data in extended energy regions as well as for a variety of new materials. This paper reviews these perspectives in the Indian context.

Data-driven methods towards learning the highly nonlinear inverse kinematics of tendon-driven surgical manipulators.

Science.gov (United States)

Xu, Wenjun; Chen, Jie; Lau, Henry Y K; Ren, Hongliang

2017-09-01

Accurate motion control of flexible surgical manipulators is crucial in tissue manipulation tasks. The tendon-driven serpentine manipulator (TSM) is one of the most widely adopted flexible mechanisms in minimally invasive surgery because of its enhanced maneuverability in torturous environments. TSM, however, exhibits high nonlinearities and conventional analytical kinematics model is insufficient to achieve high accuracy. To account for the system nonlinearities, we applied a data driven approach to encode the system inverse kinematics. Three regression methods: extreme learning machine (ELM), Gaussian mixture regression (GMR) and K-nearest neighbors regression (KNNR) were implemented to learn a nonlinear mapping from the robot 3D position states to the control inputs. The performance of the three algorithms was evaluated both in simulation and physical trajectory tracking experiments. KNNR performed the best in the tracking experiments, with the lowest RMSE of 2.1275 mm. The proposed inverse kinematics learning methods provide an alternative and efficient way to accurately model the tendon driven flexible manipulator. Copyright © 2016 John Wiley & Sons, Ltd.

Structure of parallel-velocity-shear-driven mode in toroidal plasmas

International Nuclear Information System (INIS)

Dong, J.Q.; Xu, W.B.; Zhang, Y.Z.; Horton, W.

1998-01-01

It is shown that the Fourier-ballooning representation is appropriate for the study of short-wavelength drift-like perturbation in toroidal plasmas with a parallel velocity shear (PVS). The radial structure of the mode driven by a PVS is investigated in a torus. The Reynolds stress created by PVS turbulence, and proposed as one of the sources for a sheared poloidal plasma rotation, is analyzed. It is demonstrated that a finite ion temperature may strongly enhance the Reynolds stress creation ability from PVS-driven turbulence. The correlation of this observation with the requirement that ion heating power be higher than a threshold value for the formation of an internal transport barrier is discussed. copyright 1998 American Institute of Physics

On Mixed Data and Event Driven Design for Adaptive-Critic-Based Nonlinear $H_{\\infty}$ Control.

Science.gov (United States)

Wang, Ding; Mu, Chaoxu; Liu, Derong; Ma, Hongwen

2018-04-01

In this paper, based on the adaptive critic learning technique, the control for a class of unknown nonlinear dynamic systems is investigated by adopting a mixed data and event driven design approach. The nonlinear control problem is formulated as a two-player zero-sum differential game and the adaptive critic method is employed to cope with the data-based optimization. The novelty lies in that the data driven learning identifier is combined with the event driven design formulation, in order to develop the adaptive critic controller, thereby accomplishing the nonlinear control. The event driven optimal control law and the time driven worst case disturbance law are approximated by constructing and tuning a critic neural network. Applying the event driven feedback control, the closed-loop system is built with stability analysis. Simulation studies are conducted to verify the theoretical results and illustrate the control performance. It is significant to observe that the present research provides a new avenue of integrating data-based control and event-triggering mechanism into establishing advanced adaptive critic systems.

Data and Dynamics Driven Approaches for Modelling and Forecasting the Red Sea Chlorophyll

KAUST Repository

Dreano, Denis

2017-01-01

concentration and have practical applications for fisheries operation and harmful algae blooms monitoring. Modelling approaches can be divided between physics- driven (dynamical) approaches, and data-driven (statistical) approaches. Dynamical models are based

Data-driven Discovery: A New Era of Exploiting the Literature and Data

Directory of Open Access Journals (Sweden)

Ying Ding

2016-11-01

Full Text Available In the current data-intensive era, the traditional hands-on method of conducting scientific research by exploring related publications to generate a testable hypothesis is well on its way of becoming obsolete within just a year or two. Analyzing the literature and data to automatically generate a hypothesis might become the de facto approach to inform the core research efforts of those trying to master the exponentially rapid expansion of publications and datasets. Here, viewpoints are provided and discussed to help the understanding of challenges of data-driven discovery.

Modeling of Two-Wheeled Self-Balancing Robot Driven by DC Gearmotors

Science.gov (United States)

Frankovský, P.; Dominik, L.; Gmiterko, A.; Virgala, I.; Kurylo, P.; Perminova, O.

2017-08-01

This paper is aimed at modelling a two-wheeled self-balancing robot driven by the geared DC motors. A mathematical model consists of two main parts, the model of robot's mechanical structure and the model of the actuator. Linearized equations of motion are derived and the overall model of the two-wheeled self-balancing robot is represented in state-space realization for the purpose of state feedback controller design.

semPLS: Structural Equation Modeling Using Partial Least Squares

Directory of Open Access Journals (Sweden)

Armin Monecke

2012-05-01

Full Text Available Structural equation models (SEM are very popular in many disciplines. The partial least squares (PLS approach to SEM offers an alternative to covariance-based SEM, which is especially suited for situations when data is not normally distributed. PLS path modelling is referred to as soft-modeling-technique with minimum demands regarding mea- surement scales, sample sizes and residual distributions. The semPLS package provides the capability to estimate PLS path models within the R programming environment. Different setups for the estimation of factor scores can be used. Furthermore it contains modular methods for computation of bootstrap confidence intervals, model parameters and several quality indices. Various plot functions help to evaluate the model. The well known mobile phone dataset from marketing research is used to demonstrate the features of the package.

A one-step method for modelling longitudinal data with differential equations.

Science.gov (United States)

Hu, Yueqin; Treinen, Raymond

2018-04-06

Differential equation models are frequently used to describe non-linear trajectories of longitudinal data. This study proposes a new approach to estimate the parameters in differential equation models. Instead of estimating derivatives from the observed data first and then fitting a differential equation to the derivatives, our new approach directly fits the analytic solution of a differential equation to the observed data, and therefore simplifies the procedure and avoids bias from derivative estimations. A simulation study indicates that the analytic solutions of differential equations (ASDE) approach obtains unbiased estimates of parameters and their standard errors. Compared with other approaches that estimate derivatives first, ASDE has smaller standard error, larger statistical power and accurate Type I error. Although ASDE obtains biased estimation when the system has sudden phase change, the bias is not serious and a solution is also provided to solve the phase problem. The ASDE method is illustrated and applied to a two-week study on consumers' shopping behaviour after a sale promotion, and to a set of public data tracking participants' grammatical facial expression in sign language. R codes for ASDE, recommendations for sample size and starting values are provided. Limitations and several possible expansions of ASDE are also discussed. © 2018 The British Psychological Society.

Articulatory Distinctiveness of Vowels and Consonants: A Data-Driven Approach

Science.gov (United States)

Wang, Jun; Green, Jordan R.; Samal, Ashok; Yunusova, Yana

2013-01-01

Purpose: To quantify the articulatory distinctiveness of 8 major English vowels and 11 English consonants based on tongue and lip movement time series data using a data-driven approach. Method: Tongue and lip movements of 8 vowels and 11 consonants from 10 healthy talkers were

Constructing a Measurement in Service Quality for Indian Banks: Structural Equation Modeling Approach

OpenAIRE

Anil Kumar; Manoj Kumar Dash

2013-01-01

The aim of this paper is to construct a measure in service quality for Indian banks and establishes a causal relationship of service attributes performance with customer satisfaction. The SERVQUAL model is used. The quantification of service quality led to the attempt to construct an index. The index is constructed using Structural Equation Modeling (SEM) and American Customer Satisfaction Index (ACSI) as the underlying frameworks. The analysis is based on data of 200 bank customers from the ...

Symplectic and Hamiltonian structures of nonlinear evolution equations

International Nuclear Information System (INIS)

Dorfman, I.Y.

1993-01-01

A Hamiltonian structure on a finite-dimensional manifold can be introduced either by endowing it with a (pre)symplectic structure, or by describing the Poisson bracket with the help of a tensor with two upper indices named the Poisson structure. Under the assumption of nondegeneracy, the Poisson structure is nothing else than the inverse of the symplectic structure. Also in the degenerate case the distinction between the two approaches is almost insignificant, because both presymplectic and Poisson structures split into symplectic structures on leaves of appropriately chosen foliations. Hamiltonian structures that arise in the theory of evolution equations demonstrate something new in this respect: trying to operate in local terms, one is induced to develop both approaches independently. Hamiltonian operators, being the infinite-dimensional counterparts of Poisson structures, were the first to become the subject of investigations. A considerable period of time passed before the papers initiated research in the theory of symplectic operators, being the counterparts of presymplectic structures. In what follows, we focus on the main achievements in this field

Data-driven modeling and predictive control for boiler-turbine unit using fuzzy clustering and subspace methods.

Science.gov (United States)

Wu, Xiao; Shen, Jiong; Li, Yiguo; Lee, Kwang Y

2014-05-01

This paper develops a novel data-driven fuzzy modeling strategy and predictive controller for boiler-turbine unit using fuzzy clustering and subspace identification (SID) methods. To deal with the nonlinear behavior of boiler-turbine unit, fuzzy clustering is used to provide an appropriate division of the operation region and develop the structure of the fuzzy model. Then by combining the input data with the corresponding fuzzy membership functions, the SID method is extended to extract the local state-space model parameters. Owing to the advantages of the both methods, the resulting fuzzy model can represent the boiler-turbine unit very closely, and a fuzzy model predictive controller is designed based on this model. As an alternative approach, a direct data-driven fuzzy predictive control is also developed following the same clustering and subspace methods, where intermediate subspace matrices developed during the identification procedure are utilized directly as the predictor. Simulation results show the advantages and effectiveness of the proposed approach. Copyright © 2014 ISA. Published by Elsevier Ltd. All rights reserved.

Small data global solutions for the Camassa–Choi equations

Science.gov (United States)

Harrop-Griffiths, Benjamin; Marzuola, Jeremy L.

2018-05-01

We consider solutions to the Cauchy problem for an internal-wave model derived by Camassa–Choi (1996 J. Fluid Mech. 313 83–103). This model is a natural generalization of the Benjamin–Ono and intermediate long wave equations for weak transverse effects as in the case of the Kadomtsev–Petviashvili equations for the Korteweg-de Vries equation. For that reason they are often referred to as the KP-ILW or the KP–Benjamin–Ono equations regarding finite or infinite depth respectively. We prove the existence and long-time dynamics of global solutions from small, smooth, spatially localized initial data on . The techniques applied here involve testing by wave packet techniques developed by Ifrim and Tataru in (2015 Nonlinearity 28 2661–75 2016 Bull. Soc. Math. France 144 369–94).

Building Data-Driven Pathways From Routinely Collected Hospital Data: A Case Study on Prostate Cancer

Science.gov (United States)

Clark, Jeremy; Cooper, Colin S; Mills, Robert; Rayward-Smith, Victor J; de la Iglesia, Beatriz

2015-01-01

Background Routinely collected data in hospitals is complex, typically heterogeneous, and scattered across multiple Hospital Information Systems (HIS). This big data, created as a byproduct of health care activities, has the potential to provide a better understanding of diseases, unearth hidden patterns, and improve services and cost. The extent and uses of such data rely on its quality, which is not consistently checked, nor fully understood. Nevertheless, using routine data for the construction of data-driven clinical pathways, describing processes and trends, is a key topic receiving increasing attention in the literature. Traditional algorithms do not cope well with unstructured processes or data, and do not produce clinically meaningful visualizations. Supporting systems that provide additional information, context, and quality assurance inspection are needed. Objective The objective of the study is to explore how routine hospital data can be used to develop data-driven pathways that describe the journeys that patients take through care, and their potential uses in biomedical research; it proposes a framework for the construction, quality assessment, and visualization of patient pathways for clinical studies and decision support using a case study on prostate cancer. Methods Data pertaining to prostate cancer patients were extracted from a large UK hospital from eight different HIS, validated, and complemented with information from the local cancer registry. Data-driven pathways were built for each of the 1904 patients and an expert knowledge base, containing rules on the prostate cancer biomarker, was used to assess the completeness and utility of the pathways for a specific clinical study. Software components were built to provide meaningful visualizations for the constructed pathways. Results The proposed framework and pathway formalism enable the summarization, visualization, and querying of complex patient-centric clinical information, as well as the

Product design pattern based on big data-driven scenario

OpenAIRE

Conggang Yu; Lusha Zhu

2016-01-01

This article discusses about new product design patterns in the big data era, gives designer a new rational thinking way, and is a new way to understand the design of the product. Based on the key criteria of the product design process, category, element, and product are used to input the data, which comprises concrete data and abstract data as an enlargement of the criteria of product design process for the establishment of a big data-driven product design pattern’s model. Moreover, an exper...

Ginzburg-Landau vortices driven by the Landau-Lifshitz-Gilbert equation

Energy Technology Data Exchange (ETDEWEB)

Kurzke, Matthias; Melcher, Christof; Moser, Roger; Spirn, Daniel

2009-06-15

A simplified model for the energy of the magnetization of a thin ferromagnetic film gives rise to a version of the theory of Ginzburg-Landau vortices for sphere-valued maps. In particular we have the development of vortices as a certain parameter tends to 0. The dynamics of the magnetization is ruled by the Landau-Lifshitz-Gilbert equation, which combines characteristic properties of a nonlinear Schroedinger equation and a gradient flow. This paper studies the motion of the vortex centers under this evolution equation. (orig.)

Ginzburg-Landau vortices driven by the Landau-Lifshitz-Gilbert equation

International Nuclear Information System (INIS)

Kurzke, Matthias; Melcher, Christof; Moser, Roger; Spirn, Daniel

2009-01-01

A simplified model for the energy of the magnetization of a thin ferromagnetic film gives rise to a version of the theory of Ginzburg-Landau vortices for sphere-valued maps. In particular we have the development of vortices as a certain parameter tends to 0. The dynamics of the magnetization is ruled by the Landau-Lifshitz-Gilbert equation, which combines characteristic properties of a nonlinear Schroedinger equation and a gradient flow. This paper studies the motion of the vortex centers under this evolution equation. (orig.)

Data-Driven Cyber-Physical Systems via Real-Time Stream Analytics and Machine Learning

OpenAIRE

Akkaya, Ilge

2016-01-01

Emerging distributed cyber-physical systems (CPSs) integrate a wide range of heterogeneous components that need to be orchestrated in a dynamic environment. While model-based techniques are commonly used in CPS design, they be- come inadequate in capturing the complexity as systems become larger and extremely dynamic. The adaptive nature of the systems makes data-driven approaches highly desirable, if not necessary.Traditionally, data-driven systems utilize large volumes of static data sets t...

Augmentation of DAA Staggered – Solution Equations in Underwater Shock Problems for Singular Structural Mass Matrices

Directory of Open Access Journals (Sweden)

John A. DeRuntz Jr.

2005-01-01

Full Text Available The numerical solution of underwater shock fluid – structure interaction problems using boundary element/finite element techniques became tractable through the development of the family of Doubly Asymptotic Approximations (DAA. Practical implementation of the method has relied on the so-called augmentation of the DAA equations. The fluid and structural systems are respectively coupled by the structural acceleration vector in the surface normal direction on the right hand side of the DAA equations, and the total pressure applied to the structural equations on its right hand side. By formally solving for the acceleration vector from the structural system and substituting it into its place in the DAA equations, the augmentation introduces a term involving the inverse of the structural mass matrix. However there exist at least two important classes of problems in which the structural mass matrix is singular. This paper develops a method to carry out the augmentation for such problems using a generalized inverse technique.

On global structure of general solution of the Chew-Sow equations

International Nuclear Information System (INIS)

Gerdt, V.P.

1981-01-01

The Chew-Low equations for static p-wave πN-scattering are considered. The equations are formulated in the form of a system of three nonlinear difference equations of the first order which have the general solution depending on three arbitrary periodic functions. An approach to the global construction of the general solution is suggested which is based on the series expansion in powers of one of the arbitrary functions C(ω) determining the structure of the invariant curve for the Chew-Low equations. It is shown that the initial nonlinear problem is reduced to the linear one in every order in C(ω). By means of solving the linear problem the general solution is found in the first-order approximation in C(ω) [ru

Information structure design for databases a practical guide to data modelling

CERN Document Server

Mortimer, Andrew J

2014-01-01

Computer Weekly Professional Series: Information Structure Design for Databases: A Practical Guide to Data modeling focuses on practical data modeling covering business and information systems. The publication first offers information on data and information, business analysis, and entity relationship model basics. Discussions cover degree of relationship symbols, relationship rules, membership markers, types of information systems, data driven systems, cost and value of information, importance of data modeling, and quality of information. The book then takes a look at entity relationship mode

Estimating the Probability of Wind Ramping Events: A Data-driven Approach

OpenAIRE

Wang, Cheng; Wei, Wei; Wang, Jianhui; Qiu, Feng

2016-01-01

This letter proposes a data-driven method for estimating the probability of wind ramping events without exploiting the exact probability distribution function (PDF) of wind power. Actual wind data validates the proposed method.

Hidden physics models: Machine learning of nonlinear partial differential equations

Science.gov (United States)

Raissi, Maziar; Karniadakis, George Em

2018-03-01

While there is currently a lot of enthusiasm about "big data", useful data is usually "small" and expensive to acquire. In this paper, we present a new paradigm of learning partial differential equations from small data. In particular, we introduce hidden physics models, which are essentially data-efficient learning machines capable of leveraging the underlying laws of physics, expressed by time dependent and nonlinear partial differential equations, to extract patterns from high-dimensional data generated from experiments. The proposed methodology may be applied to the problem of learning, system identification, or data-driven discovery of partial differential equations. Our framework relies on Gaussian processes, a powerful tool for probabilistic inference over functions, that enables us to strike a balance between model complexity and data fitting. The effectiveness of the proposed approach is demonstrated through a variety of canonical problems, spanning a number of scientific domains, including the Navier-Stokes, Schrödinger, Kuramoto-Sivashinsky, and time dependent linear fractional equations. The methodology provides a promising new direction for harnessing the long-standing developments of classical methods in applied mathematics and mathematical physics to design learning machines with the ability to operate in complex domains without requiring large quantities of data.

Generalized latent variable modeling multilevel, longitudinal, and structural equation models

CERN Document Server

Skrondal, Anders; Rabe-Hesketh, Sophia

2004-01-01

This book unifies and extends latent variable models, including multilevel or generalized linear mixed models, longitudinal or panel models, item response or factor models, latent class or finite mixture models, and structural equation models.

Exploring Techniques of Developing Writing Skill in IELTS Preparatory Courses: A Data-Driven Study

Science.gov (United States)

Ostovar-Namaghi, Seyyed Ali; Safaee, Seyyed Esmail

2017-01-01

Being driven by the hypothetico-deductive mode of inquiry, previous studies have tested the effectiveness of theory-driven interventions under controlled experimental conditions to come up with universally applicable generalizations. To make a case in the opposite direction, this data-driven study aims at uncovering techniques and strategies…

Observer and data-driven model based fault detection in Power Plant Coal Mills

DEFF Research Database (Denmark)

Fogh Odgaard, Peter; Lin, Bao; Jørgensen, Sten Bay

2008-01-01

model with motor power as the controlled variable, data-driven methods for fault detection are also investigated. Regression models that represent normal operating conditions (NOCs) are developed with both static and dynamic principal component analysis and partial least squares methods. The residual...... between process measurement and the NOC model prediction is used for fault detection. A hybrid approach, where a data-driven model is employed to derive an optimal unknown input observer, is also implemented. The three methods are evaluated with case studies on coal mill data, which includes a fault......This paper presents and compares model-based and data-driven fault detection approaches for coal mill systems. The first approach detects faults with an optimal unknown input observer developed from a simplified energy balance model. Due to the time-consuming effort in developing a first principles...

Statistical Methods for Stochastic Differential Equations

CERN Document Server

Kessler, Mathieu; Sorensen, Michael

2012-01-01

The seventh volume in the SemStat series, Statistical Methods for Stochastic Differential Equations presents current research trends and recent developments in statistical methods for stochastic differential equations. Written to be accessible to both new students and seasoned researchers, each self-contained chapter starts with introductions to the topic at hand and builds gradually towards discussing recent research. The book covers Wiener-driven equations as well as stochastic differential equations with jumps, including continuous-time ARMA processes and COGARCH processes. It presents a sp

Data-driven importance distributions for articulated tracking

DEFF Research Database (Denmark)

Hauberg, Søren; Pedersen, Kim Steenstrup

2011-01-01

We present two data-driven importance distributions for particle filterbased articulated tracking; one based on background subtraction, another on depth information. In order to keep the algorithms efficient, we represent human poses in terms of spatial joint positions. To ensure constant bone le...... filter, where they improve both accuracy and efficiency of the tracker. In fact, they triple the effective number of samples compared to the most commonly used importance distribution at little extra computational cost....

A Structural Equation Model of Expertise in College Physics

Science.gov (United States)

Taasoobshirazi, Gita; Carr, Martha

2009-01-01

A model of expertise in physics was tested on a sample of 374 college students in 2 different level physics courses. Structural equation modeling was used to test hypothesized relationships among variables linked to expert performance in physics including strategy use, pictorial representation, categorization skills, and motivation, and these…

A Structural Equation Model of Conceptual Change in Physics

Science.gov (United States)

Taasoobshirazi, Gita; Sinatra, Gale M.

2011-01-01

A model of conceptual change in physics was tested on introductory-level, college physics students. Structural equation modeling was used to test hypothesized relationships among variables linked to conceptual change in physics including an approach goal orientation, need for cognition, motivation, and course grade. Conceptual change in physics…

Dynamically adaptive data-driven simulation of extreme hydrological flows

Science.gov (United States)

Kumar Jain, Pushkar; Mandli, Kyle; Hoteit, Ibrahim; Knio, Omar; Dawson, Clint

2018-02-01

Hydrological hazards such as storm surges, tsunamis, and rainfall-induced flooding are physically complex events that are costly in loss of human life and economic productivity. Many such disasters could be mitigated through improved emergency evacuation in real-time and through the development of resilient infrastructure based on knowledge of how systems respond to extreme events. Data-driven computational modeling is a critical technology underpinning these efforts. This investigation focuses on the novel combination of methodologies in forward simulation and data assimilation. The forward geophysical model utilizes adaptive mesh refinement (AMR), a process by which a computational mesh can adapt in time and space based on the current state of a simulation. The forward solution is combined with ensemble based data assimilation methods, whereby observations from an event are assimilated into the forward simulation to improve the veracity of the solution, or used to invert for uncertain physical parameters. The novelty in our approach is the tight two-way coupling of AMR and ensemble filtering techniques. The technology is tested using actual data from the Chile tsunami event of February 27, 2010. These advances offer the promise of significantly transforming data-driven, real-time modeling of hydrological hazards, with potentially broader applications in other science domains.

Dynamically adaptive data-driven simulation of extreme hydrological flows

KAUST Repository

Kumar Jain, Pushkar

2017-12-27

Hydrological hazards such as storm surges, tsunamis, and rainfall-induced flooding are physically complex events that are costly in loss of human life and economic productivity. Many such disasters could be mitigated through improved emergency evacuation in real-time and through the development of resilient infrastructure based on knowledge of how systems respond to extreme events. Data-driven computational modeling is a critical technology underpinning these efforts. This investigation focuses on the novel combination of methodologies in forward simulation and data assimilation. The forward geophysical model utilizes adaptive mesh refinement (AMR), a process by which a computational mesh can adapt in time and space based on the current state of a simulation. The forward solution is combined with ensemble based data assimilation methods, whereby observations from an event are assimilated into the forward simulation to improve the veracity of the solution, or used to invert for uncertain physical parameters. The novelty in our approach is the tight two-way coupling of AMR and ensemble filtering techniques. The technology is tested using actual data from the Chile tsunami event of February 27, 2010. These advances offer the promise of significantly transforming data-driven, real-time modeling of hydrological hazards, with potentially broader applications in other science domains.

Instantaneous current and field structure of a gun-driven spheromak for two gun polarities

International Nuclear Information System (INIS)

Woodruff, S; Nagata, M

2002-01-01

The instantaneous plasma structure of the SPHEX spheromak is determined here by numerically processing data from insertable Rogowski and magnetic field probes. Data is presented and compared for two modes of gun operation: with the central electrode biased positively and negatively. It is found that while the mean-, or even instantaneous-, field structure would give the impression of a roughly axisymmetric spheromak, the instantaneous current structure does not. Hundred per cent variations in J measured at the magnetic axis can be explained by the rotation of a current filament that has a width equal to half of the radius of the flux-conserving first wall. In positive gun operation, current leaves the filament in the confinement region leading to high wall current there. In negative gun operation, wall current remains low as all injected current returns to the gun through the plasma. The plasma, in either instance, is strongly asymmetric. We discuss evidence for the existence of the current filament in other gun-driven spheromaks and coaxial plasma thrusters

An Open Framework for Dynamic Big-data-driven Application Systems (DBDDAS) Development

KAUST Repository

Douglas, Craig

2014-01-01

In this paper, we outline key features that dynamic data-driven application systems (DDDAS) have. A DDDAS is an application that has data assimilation that can change the models and/or scales of the computation and that the application controls the data collection based on the computational results. The term Big Data (BD) has come into being in recent years that is highly applicable to most DDDAS since most applications use networks of sensors that generate an overwhelming amount of data in the lifespan of the application runs. We describe what a dynamic big-data-driven application system (DBDDAS) toolkit must have in order to provide all of the essential building blocks that are necessary to easily create new DDDAS without re-inventing the building blocks.

An Open Framework for Dynamic Big-data-driven Application Systems (DBDDAS) Development

KAUST Repository

Douglas, Craig

2014-06-06

In this paper, we outline key features that dynamic data-driven application systems (DDDAS) have. A DDDAS is an application that has data assimilation that can change the models and/or scales of the computation and that the application controls the data collection based on the computational results. The term Big Data (BD) has come into being in recent years that is highly applicable to most DDDAS since most applications use networks of sensors that generate an overwhelming amount of data in the lifespan of the application runs. We describe what a dynamic big-data-driven application system (DBDDAS) toolkit must have in order to provide all of the essential building blocks that are necessary to easily create new DDDAS without re-inventing the building blocks.

Modeling of Two-Wheeled Self-Balancing Robot Driven by DC Gearmotors

Directory of Open Access Journals (Sweden)

Frankovský P.

2017-08-01

Full Text Available This paper is aimed at modelling a two-wheeled self-balancing robot driven by the geared DC motors. A mathematical model consists of two main parts, the model of robot’s mechanical structure and the model of the actuator. Linearized equations of motion are derived and the overall model of the two-wheeled self-balancing robot is represented in state-space realization for the purpose of state feedback controller design.

Model Driven Development of Data Sensitive Systems

DEFF Research Database (Denmark)

Olsen, Petur

2014-01-01

storage systems, where the actual values of the data is not relevant for the behavior of the system. For many systems the values are important. For instance the control flow of the system can be dependent on the input values. We call this type of system data sensitive, as the execution is sensitive...... to the values of variables. This theses strives to improve model-driven development of such data-sensitive systems. This is done by addressing three research questions. In the first we combine state-based modeling and abstract interpretation, in order to ease modeling of data-sensitive systems, while allowing...... efficient model-checking and model-based testing. In the second we develop automatic abstraction learning used together with model learning, in order to allow fully automatic learning of data-sensitive systems to allow learning of larger systems. In the third we develop an approach for modeling and model-based...

Data-Driven Security-Constrained OPF

DEFF Research Database (Denmark)

Thams, Florian; Halilbasic, Lejla; Pinson, Pierre

2017-01-01

considerations, while being less conservative than current approaches. Our approach can be scalable for large systems, accounts explicitly for power system security, and enables the electricity market to identify a cost-efficient dispatch avoiding redispatching actions. We demonstrate the performance of our......In this paper we unify electricity market operations with power system security considerations. Using data-driven techniques, we address both small signal stability and steady-state security, derive tractable decision rules in the form of line flow limits, and incorporate the resulting constraints...... in market clearing algorithms. Our goal is to minimize redispatching actions, and instead allow the market to determine the most cost-efficient dispatch while considering all security constraints. To maintain tractability of our approach we perform our security assessment offline, examining large datasets...

A STRUCTURAL EQUATION MODEL-II FOR WORK-LIFE BALANCE OF IT PROFESSIONALS IN CHENNAI

OpenAIRE

Rashida A. Banu

2016-01-01

The study developed and tested a model of work life balance of IT professionals employing structural equation modeling (SEM) to analyze the relationship between work place support (WPS) and work interference with personal life (WIPL), personal life interference with work (PLIW), satisfaction with work-life balance (SWLB) and improved effectiveness at work (IEW). The model fit the data well and hypotheses are generally supported. WPS and SWLB are negatively related to WIPL and P...

Data-Driven Model Order Reduction for Bayesian Inverse Problems

KAUST Repository

Cui, Tiangang

2014-01-06

One of the major challenges in using MCMC for the solution of inverse problems is the repeated evaluation of computationally expensive numerical models. We develop a data-driven projection- based model order reduction technique to reduce the computational cost of numerical PDE evaluations in this context.

Robust Data-Driven Inference for Density-Weighted Average Derivatives

DEFF Research Database (Denmark)

Cattaneo, Matias D.; Crump, Richard K.; Jansson, Michael

This paper presents a new data-driven bandwidth selector compatible with the small bandwidth asymptotics developed in Cattaneo, Crump, and Jansson (2009) for density- weighted average derivatives. The new bandwidth selector is of the plug-in variety, and is obtained based on a mean squared error...

Data-driven modelling of LTI systems using symbolic regression

NARCIS (Netherlands)

Khandelwal, D.; Toth, R.; Van den Hof, P.M.J.

2017-01-01

The aim of this project is to automate the task of data-driven identification of dynamical systems. The underlying goal is to develop an identification tool that models a physical system without distinguishing between classes of systems such as linear, nonlinear or possibly even hybrid systems. Such

Structural interactions in ionic liquids linked to higher-order Poisson-Boltzmann equations

Science.gov (United States)

Blossey, R.; Maggs, A. C.; Podgornik, R.

2017-06-01

We present a derivation of generalized Poisson-Boltzmann equations starting from classical theories of binary fluid mixtures, employing an approach based on the Legendre transform as recently applied to the case of local descriptions of the fluid free energy. Under specific symmetry assumptions, and in the linearized regime, the Poisson-Boltzmann equation reduces to a phenomenological equation introduced by Bazant et al. [Phys. Rev. Lett. 106, 046102 (2011)], 10.1103/PhysRevLett.106.046102, whereby the structuring near the surface is determined by bulk coefficients.

A performance measurement using balanced scorecard and structural equation modeling

Directory of Open Access Journals (Sweden)

Rosha Makvandi

2014-02-01

Full Text Available During the past few years, balanced scorecard (BSC has been widely used as a promising method for performance measurement. BSC studies organizations in terms of four perspectives including customer, internal processes, learning and growth and financial figures. This paper presents a hybrid of BSC and structural equation modeling (SEM to measure the performance of an Iranian university in province of Alborz, Iran. The proposed study of this paper uses this conceptual method, designs a questionnaire and distributes it among some university students and professors. Using SEM technique, the survey analyzes the data and the results indicate that the university did poorly in terms of all four perspectives. The survey extracts necessary target improvement by presenting necessary attributes for performance improvement.

Analogy between optically driven injection-locked laser diodes and driven damped linear oscillators

International Nuclear Information System (INIS)

Murakami, Atsushi; Shore, K. Alan

2006-01-01

An analytical study of optically driven laser diodes (LDs) has been undertaken to meet the requirement for a theoretical treatment for chaotic drive and synchronization occurring in the injection-locked LDs with strong injection. A small-signal analysis is performed for the sets of rate equations for the injection-locked LDs driven by a sinusoidal optical signal. In particular, as a model of chaotic driving signals from LD dynamics, an optical signal caused by direct modulation to the master LD is assumed, oscillating both in field amplitude and phase as is the case with chaotic driving signals. Consequently, we find conditions that allow reduction in the degrees of freedom of the driven LD. Under these conditions, the driven response is approximated to a simple form which is found to be equivalent to driven damped linear oscillators. The validity of the application of this theory to previous work on the synchronization of chaos and related phenomena occurring in the injection-locked LDs is demonstrated

Fork-join and data-driven execution models on multi-core architectures: Case study of the FMM

KAUST Repository

Amer, Abdelhalim

2013-01-01

Extracting maximum performance of multi-core architectures is a difficult task primarily due to bandwidth limitations of the memory subsystem and its complex hierarchy. In this work, we study the implications of fork-join and data-driven execution models on this type of architecture at the level of task parallelism. For this purpose, we use a highly optimized fork-join based implementation of the FMM and extend it to a data-driven implementation using a distributed task scheduling approach. This study exposes some limitations of the conventional fork-join implementation in terms of synchronization overheads. We find that these are not negligible and their elimination by the data-driven method, with a careful data locality strategy, was beneficial. Experimental evaluation of both methods on state-of-the-art multi-socket multi-core architectures showed up to 22% speed-ups of the data-driven approach compared to the original method. We demonstrate that a data-driven execution of FMM not only improves performance by avoiding global synchronization overheads but also reduces the memory-bandwidth pressure caused by memory-intensive computations. © 2013 Springer-Verlag.

Structural Equation Modeling with Lisrel: An Initial Vision

OpenAIRE

Naresh K Malhotra; Evandro Luiz Lopes; Ricardo Teixeira Veiga

2014-01-01

LISREL is considered one of the most robust software packages for Structural Equation Modeling with covariance matrices, while it is also considered complex and difficult to use. In this special issue of the Brazilian Journal of Marketing, we aim to present the main functions of LISREL, its features and, through a didactic example, reduce the perceived difficulty of using it. We also provide helpful guidelines to properly using this technique.

Testing strong factorial invariance using three-level structural equation modeling

NARCIS (Netherlands)

Jak, Suzanne

Within structural equation modeling, the most prevalent model to investigate measurement bias is the multigroup model. Equal factor loadings and intercepts across groups in a multigroup model represent strong factorial invariance (absence of measurement bias) across groups. Although this approach is

On the selection of user-defined parameters in data-driven stochastic subspace identification

Science.gov (United States)

Priori, C.; De Angelis, M.; Betti, R.

2018-02-01

The paper focuses on the time domain output-only technique called Data-Driven Stochastic Subspace Identification (DD-SSI); in order to identify modal models (frequencies, damping ratios and mode shapes), the role of its user-defined parameters is studied, and rules to determine their minimum values are proposed. Such investigation is carried out using, first, the time histories of structural responses to stationary excitations, with a large number of samples, satisfying the hypothesis on the input imposed by DD-SSI. Then, the case of non-stationary seismic excitations with a reduced number of samples is considered. In this paper, partitions of the data matrix different from the one proposed in the SSI literature are investigated, together with the influence of different choices of the weighting matrices. The study is carried out considering two different applications: (1) data obtained from vibration tests on a scaled structure and (2) in-situ tests on a reinforced concrete building. Referring to the former, the identification of a steel frame structure tested on a shaking table is performed using its responses in terms of absolute accelerations to a stationary (white noise) base excitation and to non-stationary seismic excitations of low intensity. Black-box and modal models are identified in both cases and the results are compared with those from an input-output subspace technique. With regards to the latter, the identification of a complex hospital building is conducted using data obtained from ambient vibration tests.

Evidence for a temperature-driven structural transformation in liquid bismuth

International Nuclear Information System (INIS)

Greenberg, Y.; Dariel, M.P.; Greenberg, Y.; Yahel, E.; Caspi, E.N.; Makov, G.; Benmore, C.; Beuneu, B.

2009-01-01

The thermodynamic properties of liquid bismuth have been explored from the melting point to 1100 C degrees by high-resolution measurements of the density, the heat capacity and the static structure factor. These physical properties display a number of anomalies. In particular, we have observed evidence for the presence of a temperature-driven liquid-liquid structural transformation that takes place at ambient pressure. The latter is characterized by a density discontinuity that occurs at 740 C degrees. Differential thermal analysis measurements revealed the endo-thermal nature of this transformation. A rearrangement of liquid bismuth structure was found by neutron diffraction measurements, supporting the existence of a liquid-liquid transformation far above the liquidus. (authors)

Wing-Body Aeroelasticity Using Finite-Difference Fluid/Finite-Element Structural Equations on Parallel Computers

Science.gov (United States)

Byun, Chansup; Guruswamy, Guru P.; Kutler, Paul (Technical Monitor)

1994-01-01

In recent years significant advances have been made for parallel computers in both hardware and software. Now parallel computers have become viable tools in computational mechanics. Many application codes developed on conventional computers have been modified to benefit from parallel computers. Significant speedups in some areas have been achieved by parallel computations. For single-discipline use of both fluid dynamics and structural dynamics, computations have been made on wing-body configurations using parallel computers. However, only a limited amount of work has been completed in combining these two disciplines for multidisciplinary applications. The prime reason is the increased level of complication associated with a multidisciplinary approach. In this work, procedures to compute aeroelasticity on parallel computers using direct coupling of fluid and structural equations will be investigated for wing-body configurations. The parallel computer selected for computations is an Intel iPSC/860 computer which is a distributed-memory, multiple-instruction, multiple data (MIMD) computer with 128 processors. In this study, the computational efficiency issues of parallel integration of both fluid and structural equations will be investigated in detail. The fluid and structural domains will be modeled using finite-difference and finite-element approaches, respectively. Results from the parallel computer will be compared with those from the conventional computers using a single processor. This study will provide an efficient computational tool for the aeroelastic analysis of wing-body structures on MIMD type parallel computers.

A structural equation modelling of the academic self-concept scale

Directory of Open Access Journals (Sweden)

Musa Matovu

2014-03-01

Full Text Available The study aimed at validating the academic self-concept scale by Liu and Wang (2005 in measuring academic self-concept among university students. Structural equation modelling was used to validate the scale which was composed of two subscales; academic confidence and academic effort. The study was conducted on university students; males and females from different levels of study and faculties. In this study the influence of academic self-concept on academic achievement was assessed, tested whether the hypothesised model fitted the data, analysed the invariance of the path coefficients among the moderating variables, and also, highlighted whether academic confidence and academic effort measured academic selfconcept. The results from the model revealed that academic self-concept influenced academic achievement and the hypothesised model fitted the data. The results also supported the model as the causal structure was not sensitive to gender, levels of study, and faculties of students; hence, applicable to all the groups taken as moderating variables. It was also noted that academic confidence and academic effort are a measure of academic self-concept. According to the results the academic self-concept scale by Liu and Wang (2005 was deemed adequate in collecting information about academic self-concept among university students.

Consumer choice of on-demand mHealth app services: Context and contents values using structural equation modeling.

Science.gov (United States)

Lee, Euehun; Han, Semi; Jo, Sang Hyun

2017-01-01

As smartphone penetration increases and the technology advances, various mobile services have reached the market. mHealth Applications are specifically highlighted for phenomena such as global aging & well-being, but the technology-driven mHealth services have not been successful in the market because consumer needs have not been reflected in the services properly. This study developed a research model consisting of context/contents values to explain the intention of consumers over the age of 40 in using mHealth Applications. To carry out this research, an online survey was conducted of mHealth Application users and recognizers in South Korea who are over 40 years old. 313 respondents gave usable data; those data were analyzed via a structural equation model. Context values (health stress, epistemic) produce an effect on contents values and contents values (convenience, usefulness), excepting reassurance and enjoyment, positively affect the intention to use mHealth Applications. The findings indicate that people who are stressed out about their health and are interested in new ways to control their health think that mHealth Applications are very convenient and useful because people can manage their health at home or at the office, even when they cannot go to a hospital. However, they feel that the current level of service does not provide reassurance. The level of service is behind people's expectations. Hence, a market-oriented approach that can determine user needs, specifically in terms of the reassurance value in the mHealth service field, is needed to develop mHealth Applications. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Stochastic Evolution Equations Driven by Fractional Noises

Science.gov (United States)

2016-11-28

paper is to establish the weak convergence, in the topology of the Skorohod space, of the ν-symmetric Riemann sums for functionals of the fractional...stochastic heat equation with fractional-colored noise: existence of the solution. ALEA Lat. Am. J. Probab. Math . Stat. 4 (2008), 57–87. [8] P. Carmona, Y...Hu: Strong disorder implies strong localization for directed polymers in a random environment. ALEA Lat. Am. J. Probab. Math . Stat. 2 (2006), 217

Ultra Deep Wave Equation Imaging and Illumination

Energy Technology Data Exchange (ETDEWEB)

Alexander M. Popovici; Sergey Fomel; Paul Sava; Sean Crawley; Yining Li; Cristian Lupascu

2006-09-30

In this project we developed and tested a novel technology, designed to enhance seismic resolution and imaging of ultra-deep complex geologic structures by using state-of-the-art wave-equation depth migration and wave-equation velocity model building technology for deeper data penetration and recovery, steeper dip and ultra-deep structure imaging, accurate velocity estimation for imaging and pore pressure prediction and accurate illumination and amplitude processing for extending the AVO prediction window. Ultra-deep wave-equation imaging provides greater resolution and accuracy under complex geologic structures where energy multipathing occurs, than what can be accomplished today with standard imaging technology. The objective of the research effort was to examine the feasibility of imaging ultra-deep structures onshore and offshore, by using (1) wave-equation migration, (2) angle-gathers velocity model building, and (3) wave-equation illumination and amplitude compensation. The effort consisted of answering critical technical questions that determine the feasibility of the proposed methodology, testing the theory on synthetic data, and finally applying the technology for imaging ultra-deep real data. Some of the questions answered by this research addressed: (1) the handling of true amplitudes in the downward continuation and imaging algorithm and the preservation of the amplitude with offset or amplitude with angle information required for AVO studies, (2) the effect of several imaging conditions on amplitudes, (3) non-elastic attenuation and approaches for recovering the amplitude and frequency, (4) the effect of aperture and illumination on imaging steep dips and on discriminating the velocities in the ultra-deep structures. All these effects were incorporated in the final imaging step of a real data set acquired specifically to address ultra-deep imaging issues, with large offsets (12,500 m) and long recording time (20 s).

Data-Driven Iterative Vibration Signal Enhancement Strategy Using Alpha Stable Distribution

Directory of Open Access Journals (Sweden)

Grzegorz Żak

2017-01-01

Full Text Available The authors propose a novel procedure for enhancement of the signal to noise ratio in vibration data acquired from machines working in mining industry environment. Proposed method allows performing data-driven reduction of the deterministic, high energy, and low frequency components. Furthermore, it provides a way to enhance signal of interest. Procedure incorporates application of the time-frequency decomposition, α-stable distribution based signal modeling, and stability parameter in the time domain as a stoppage criterion for iterative part of the procedure. An advantage of the proposed algorithm is data-driven, automative detection of the informative frequency band as well as band with high energy due to the properties of the used distribution. Furthermore, there is no need to have knowledge regarding kinematics, speed, and so on. The proposed algorithm is applied towards real data acquired from the belt conveyor pulley drive’s gearbox.

The two modes extension to the Berk-Breizman equation: Delayed differential equations and asymptotic solutions

International Nuclear Information System (INIS)

Marczynski, Slawomir

2011-01-01

The integro-differential Berk-Breizman (BB) equation, describing the evolution of particle-driven wave mode is transformed into a simple delayed differential equation form ν∂a(τ)/∂τ=a(τ) -a 2 (τ- 1) a(τ- 2). This transformation is also applied to the two modes extension of the BB theory. The obtained solutions are presented together with the derived asymptotic analytical solutions and the numerical results.

Structural Equation Modeling with Lisrel: An Initial Vision

Directory of Open Access Journals (Sweden)

Naresh K Malhotra

2014-05-01

Full Text Available LISREL is considered one of the most robust software packages for Structural Equation Modeling with covariance matrices, while it is also considered complex and difficult to use. In this special issue of the Brazilian Journal of Marketing, we aim to present the main functions of LISREL, its features and, through a didactic example, reduce the perceived difficulty of using it. We also provide helpful guidelines to properly using this technique.

Controller synthesis for negative imaginary systems: a data driven approach

KAUST Repository

Mabrok, Mohamed; Petersen, Ian R.

2016-01-01

-driven controller synthesis methodology for NI systems is presented. In this approach, measured frequency response data of the plant is used to construct the controller frequency response at every frequency by minimising a cost function. Then, this controller

Full information estimations of a system of simultaneous equations with error component structure

OpenAIRE

Balestra, Pietro; Krishnakumar, Jaya

1987-01-01

In this paper we develop full information methods for estimating the parameters of a system of simultaneous equations with error component struc-ture and establish relationships between the various structural estimat

Combining engineering and data-driven approaches

DEFF Research Database (Denmark)

Fischer, Katharina; De Sanctis, Gianluca; Kohler, Jochen

2015-01-01

Two general approaches may be followed for the development of a fire risk model: statistical models based on observed fire losses can support simple cost-benefit studies but are usually not detailed enough for engineering decision-making. Engineering models, on the other hand, require many assump...... to the calibration of a generic fire risk model for single family houses to Swiss insurance data. The example demonstrates that the bias in the risk estimation can be strongly reduced by model calibration.......Two general approaches may be followed for the development of a fire risk model: statistical models based on observed fire losses can support simple cost-benefit studies but are usually not detailed enough for engineering decision-making. Engineering models, on the other hand, require many...... assumptions that may result in a biased risk assessment. In two related papers we show how engineering and data-driven modelling can be combined by developing generic risk models that are calibrated to statistical data on observed fire events. The focus of the present paper is on the calibration procedure...

Pengembangan Data Warehouse Menggunakan Pendekatan Data-Driven untuk Membantu Pengelolaan SDM

Directory of Open Access Journals (Sweden)

Mujiono Mujiono

2016-01-01

Full Text Available The basis of bureaucratic reform is the reform of human resources management. One supporting factor is the development of an employee database. To support the management of human resources required including data warehouse and business intelligent tools. The data warehouse is an integrated concept of reliable data storage to provide support to all the needs of the data analysis. In this study developed a data warehouse using the data-driven approach to the source data comes from SIMPEG, SAPK and electronic presence. Data warehouses are designed using the nine steps methodology and unified modeling language (UML notation. Extract transform load (ETL is done by using Pentaho Data Integration by applying transformation maps. Furthermore, to help human resource management, the system is built to perform online analytical processing (OLAP to facilitate web-based information. In this study generated BI application development framework with Model-View-Controller (MVC architecture and OLAP operations are built using the dynamic query generation, PivotTable, and HighChart to present information about PNS, CPNS, Retirement, Kenpa and Presence

Automated Creation of Datamarts from a Clinical Data Warehouse, Driven by an Active Metadata Repository

Science.gov (United States)

Rogerson, Charles L.; Kohlmiller, Paul H.; Stutman, Harris

1998-01-01

A methodology and toolkit are described which enable the automated metadata-driven creation of datamarts from clinical data warehouses. The software uses schema-to-schema transformation driven by an active metadata repository. Tools for assessing datamart data quality are described, as well as methods for assessing the feasibility of implementing specific datamarts. A methodology for data remediation and the re-engineering of operational data capture is described.

NOvA Event Building, Buffering and Data-Driven Triggering From Within the DAQ System

International Nuclear Information System (INIS)

Fischler, M; Rechenmacher, R; Green, C; Kowalkowski, J; Norman, A; Paterno, M

2012-01-01

The NOvA experiment is a long baseline neutrino experiment design to make precision probes of the structure of neutrino mixing. The experiment features a unique deadtimeless data acquisition system that is capable acquiring and building an event data stream from the continuous readout of the more than 360,000 far detector channels. In order to achieve its physics goals the experiment must be able to buffer, correlate and extract the data in this stream with the beam-spills that occur that Fermilab. In addition the NOvA experiment seeks to enhance its data collection efficiencies for rare class of event topologies that are valuable for calibration through the use of data driven triggering. The NOvA-DDT is a prototype Data-Driven Triggering system. NOvA-DDT has been developed using the Fermilab artdaq generic DAQ/Event-building toolkit. This toolkit provides the advantages of sharing online software infrastructure with other Intensity Frontier experiments, and of being able to use any offline analysis module-unchanged-as a component of the online triggering decisions. We have measured the performance and overhead of NOvA-DDT framework using a Hough transform based trigger decision module developed for the NOvA detector to identify cosmic rays. The results of these tests which were run on the NOvA prototype near detector, yielded a mean processing time of 98 ms per event, while consuming only 1/16th of the available processing capacity. These results provide a proof of concept that a NOvA-DDT based processing system is a viable strategy for data acquisition and triggering for the NOvA far detector.

Data-driven haemodynamic response function extraction using Fourier-wavelet regularised deconvolution

NARCIS (Netherlands)

Wink, Alle Meije; Hoogduin, Hans; Roerdink, Jos B.T.M.

2008-01-01

Background: We present a simple, data-driven method to extract haemodynamic response functions (HRF) from functional magnetic resonance imaging (fMRI) time series, based on the Fourier-wavelet regularised deconvolution (ForWaRD) technique. HRF data are required for many fMRI applications, such as

Data-driven haemodynamic response function extraction using Fourier-wavelet regularised deconvolution

NARCIS (Netherlands)

Wink, Alle Meije; Hoogduin, Hans; Roerdink, Jos B.T.M.

2010-01-01

Background: We present a simple, data-driven method to extract haemodynamic response functions (HRF) from functional magnetic resonance imaging (fMRI) time series, based on the Fourier-wavelet regularised deconvolution (ForWaRD) technique. HRF data are required for many fMRI applications, such as

Efficient Feature-Driven Visualization of Large-Scale Scientific Data

Energy Technology Data Exchange (ETDEWEB)

Lu, Aidong

2012-12-12

Very large, complex scientific data acquired in many research areas creates critical challenges for scientists to understand, analyze, and organize their data. The objective of this project is to expand the feature extraction and analysis capabilities to develop powerful and accurate visualization tools that can assist domain scientists with their requirements in multiple phases of scientific discovery. We have recently developed several feature-driven visualization methods for extracting different data characteristics of volumetric datasets. Our results verify the hypothesis in the proposal and will be used to develop additional prototype systems.

Do Test Design and Uses Influence Test Preparation? Testing a Model of Washback with Structural Equation Modeling

Science.gov (United States)

Xie, Qin; Andrews, Stephen

2013-01-01

This study introduces Expectancy-value motivation theory to explain the paths of influences from perceptions of test design and uses to test preparation as a special case of washback on learning. Based on this theory, two conceptual models were proposed and tested via Structural Equation Modeling. Data collection involved over 870 test takers of…

On the specification of structural equation models for ecological systems

NARCIS (Netherlands)

Grace, James B.; Anderson, T. Michael; Olff, Han; Scheiner, Samuel M.

The use of structural equation modeling (SEM) is often motivated by its utility for investigating complex networks of relationships, but also because of its promise as a means of representing theoretical Concepts using latent variables. In this paper, we discuss characteristics of ecological theory

Structural Equations and Causal Explanations: Some Challenges for Causal SEM

Science.gov (United States)

Markus, Keith A.

2010-01-01

One common application of structural equation modeling (SEM) involves expressing and empirically investigating causal explanations. Nonetheless, several aspects of causal explanation that have an impact on behavioral science methodology remain poorly understood. It remains unclear whether applications of SEM should attempt to provide complete…

The issue of statistical power for overall model fit in evaluating structural equation models

Directory of Open Access Journals (Sweden)

Richard HERMIDA

2015-06-01

Full Text Available Statistical power is an important concept for psychological research. However, examining the power of a structural equation model (SEM is rare in practice. This article provides an accessible review of the concept of statistical power for the Root Mean Square Error of Approximation (RMSEA index of overall model fit in structural equation modeling. By way of example, we examine the current state of power in the literature by reviewing studies in top Industrial-Organizational (I/O Psychology journals using SEMs. Results indicate that in many studies, power is very low, which implies acceptance of invalid models. Additionally, we examined methodological situations which may have an influence on statistical power of SEMs. Results showed that power varies significantly as a function of model type and whether or not the model is the main model for the study. Finally, results indicated that power is significantly related to model fit statistics used in evaluating SEMs. The results from this quantitative review imply that researchers should be more vigilant with respect to power in structural equation modeling. We therefore conclude by offering methodological best practices to increase confidence in the interpretation of structural equation modeling results with respect to statistical power issues.

Quantum tunneling in the driven SU(2) model

International Nuclear Information System (INIS)

Kaminski, P.; Ploszajczak, M.; Arvieu, R.

1992-01-01

The tunneling rate is investigated in the quantum and classical limits using an exactly soluble driven SU(2) model. The tunneling rate is obtained by solving the time-dependent Schroedinger equation and projecting the exact wave-function on the space of coherent states using the Husimi distribution. The presence of the classical chaotic structures leads to the enormous growth in the tunneling rate. The results suggest the existence of a new mechanism of quantum tunneling, involving transport of the wave-function between stable regions of the classical phase-space due to a coupling with 'chaotic' levels. (author) 17 refs., 13 figs

The advancement of the built environment research through employment of structural equation modeling (SEM)

Science.gov (United States)

Wasilah, S.; Fahmyddin, T.

2018-03-01

The employment of structural equation modeling (SEM) in research has taken an increasing attention in among researchers in built environment. There is a gap to understand the attributes, application, and importance of this approach in data analysis in built environment study. This paper intends to provide fundamental comprehension of SEM method in data analysis, unveiling attributes, employment and significance and bestow cases to assess associations amongst variables and constructs. The study uses some main literature to grasp the essence of SEM regarding with built environment research. The better acknowledgment of this analytical tool may assist the researcher in the built environment to analyze data under complex research questions and to test multivariate models in a single study.

Testing students' e-learning via Facebook through Bayesian structural equation modeling.

Science.gov (United States)

Salarzadeh Jenatabadi, Hashem; Moghavvemi, Sedigheh; Wan Mohamed Radzi, Che Wan Jasimah Bt; Babashamsi, Parastoo; Arashi, Mohammad

2017-01-01

Learning is an intentional activity, with several factors affecting students' intention to use new learning technology. Researchers have investigated technology acceptance in different contexts by developing various theories/models and testing them by a number of means. Although most theories/models developed have been examined through regression or structural equation modeling, Bayesian analysis offers more accurate data analysis results. To address this gap, the unified theory of acceptance and technology use in the context of e-learning via Facebook are re-examined in this study using Bayesian analysis. The data (S1 Data) were collected from 170 students enrolled in a business statistics course at University of Malaya, Malaysia, and tested with the maximum likelihood and Bayesian approaches. The difference between the two methods' results indicates that performance expectancy and hedonic motivation are the strongest factors influencing the intention to use e-learning via Facebook. The Bayesian estimation model exhibited better data fit than the maximum likelihood estimator model. The results of the Bayesian and maximum likelihood estimator approaches are compared and the reasons for the result discrepancy are deliberated.

Testing students' e-learning via Facebook through Bayesian structural equation modeling.

Directory of Open Access Journals (Sweden)

Hashem Salarzadeh Jenatabadi

Full Text Available Learning is an intentional activity, with several factors affecting students' intention to use new learning technology. Researchers have investigated technology acceptance in different contexts by developing various theories/models and testing them by a number of means. Although most theories/models developed have been examined through regression or structural equation modeling, Bayesian analysis offers more accurate data analysis results. To address this gap, the unified theory of acceptance and technology use in the context of e-learning via Facebook are re-examined in this study using Bayesian analysis. The data (S1 Data were collected from 170 students enrolled in a business statistics course at University of Malaya, Malaysia, and tested with the maximum likelihood and Bayesian approaches. The difference between the two methods' results indicates that performance expectancy and hedonic motivation are the strongest factors influencing the intention to use e-learning via Facebook. The Bayesian estimation model exhibited better data fit than the maximum likelihood estimator model. The results of the Bayesian and maximum likelihood estimator approaches are compared and the reasons for the result discrepancy are deliberated.

Microwave-Driven Multifunctional Capability of Membrane Structures

Science.gov (United States)

Choi, Sang H.; Chu, Sang-Hyong; Song, Kyo D.; King, Glen C.

2002-01-01

A large, ultra lightweight space structure, such as solar sails and Gossamer spacecrafts, requires a distributed power source to alleviate wire networks, unlike the localized on-board power infrastructures typically found in most small spacecrafts. The concept of microwave-driven multifunctional capability for membrane structures is envisioned as the best option to alleviate the complexity associated with hard-wired control circuitry and on-board power infrastructures. A rectenna array based on a patch configuration for high voltage output was developed to drive membrane actuators, sensors, probes, or other devices. Networked patch rectenna array receives and converts microwave power into a DC power for an array of smart actuators. To use microwave power effectively, the concept of a power allocation and distribution (PAD) circuit is adopted for networking a rectenna/actuator patch array. The use of patch rectennas adds a significant amount of rigidity to membrane flexibility and they are relatively heavy. A dipole rectenna array (DRA) appears to be ideal for thin-film membrane structures, since DRA is flexible and light. Preliminary design and fabrication of PAD circuitry that consists of a few nodal elements were made for laboratory testing. The networked actuators were tested to correlate the network coupling effect, power allocation and distribution, and response time.

A data-driven prediction method for fast-slow systems

Science.gov (United States)

Groth, Andreas; Chekroun, Mickael; Kondrashov, Dmitri; Ghil, Michael

2016-04-01

In this work, we present a prediction method for processes that exhibit a mixture of variability on low and fast scales. The method relies on combining empirical model reduction (EMR) with singular spectrum analysis (SSA). EMR is a data-driven methodology for constructing stochastic low-dimensional models that account for nonlinearity and serial correlation in the estimated noise, while SSA provides a decomposition of the complex dynamics into low-order components that capture spatio-temporal behavior on different time scales. Our study focuses on the data-driven modeling of partial observations from dynamical systems that exhibit power spectra with broad peaks. The main result in this talk is that the combination of SSA pre-filtering with EMR modeling improves, under certain circumstances, the modeling and prediction skill of such a system, as compared to a standard EMR prediction based on raw data. Specifically, it is the separation into "fast" and "slow" temporal scales by the SSA pre-filtering that achieves the improvement. We show, in particular that the resulting EMR-SSA emulators help predict intermittent behavior such as rapid transitions between specific regions of the system's phase space. This capability of the EMR-SSA prediction will be demonstrated on two low-dimensional models: the Rössler system and a Lotka-Volterra model for interspecies competition. In either case, the chaotic dynamics is produced through a Shilnikov-type mechanism and we argue that the latter seems to be an important ingredient for the good prediction skills of EMR-SSA emulators. Shilnikov-type behavior has been shown to arise in various complex geophysical fluid models, such as baroclinic quasi-geostrophic flows in the mid-latitude atmosphere and wind-driven double-gyre ocean circulation models. This pervasiveness of the Shilnikow mechanism of fast-slow transition opens interesting perspectives for the extension of the proposed EMR-SSA approach to more realistic situations.

Data-driven Inference and Investigation of Thermosphere Dynamics and Variations

Science.gov (United States)

Mehta, P. M.; Linares, R.

2017-12-01

This paper presents a methodology for data-driven inference and investigation of thermosphere dynamics and variations. The approach uses data-driven modal analysis to extract the most energetic modes of variations for neutral thermospheric species using proper orthogonal decomposition, where the time-independent modes or basis represent the dynamics and the time-depedent coefficients or amplitudes represent the model parameters. The data-driven modal analysis approach combined with sparse, discrete observations is used to infer amplitues for the dynamic modes and to calibrate the energy content of the system. In this work, two different data-types, namely the number density measurements from TIMED/GUVI and the mass density measurements from CHAMP/GRACE are simultaneously ingested for an accurate and self-consistent specification of the thermosphere. The assimilation process is achieved with a non-linear least squares solver and allows estimation/tuning of the model parameters or amplitudes rather than the driver. In this work, we use the Naval Research Lab's MSIS model to derive the most energetic modes for six different species, He, O, N2, O2, H, and N. We examine the dominant drivers of variations for helium in MSIS and observe that seasonal latitudinal variation accounts for about 80% of the dynamic energy with a strong preference of helium for the winter hemisphere. We also observe enhanced helium presence near the poles at GRACE altitudes during periods of low solar activity (Feb 2007) as previously deduced. We will also examine the storm-time response of helium derived from observations. The results are expected to be useful in tuning/calibration of the physics-based models.

Health Promotion Behavior of Chinese International Students in Korea Including Acculturation Factors: A Structural Equation Model.

Science.gov (United States)

Kim, Sun Jung; Yoo, Il Young

2016-03-01

The purpose of this study was to explain the health promotion behavior of Chinese international students in Korea using a structural equation model including acculturation factors. A survey using self-administered questionnaires was employed. Data were collected from 272 Chinese students who have resided in Korea for longer than 6 months. The data were analyzed using structural equation modeling. The p value of final model is .31. The fitness parameters of the final model such as goodness of fit index, adjusted goodness of fit index, normed fit index, non-normed fit index, and comparative fit index were more than .95. Root mean square of residual and root mean square error of approximation also met the criteria. Self-esteem, perceived health status, acculturative stress and acculturation level had direct effects on health promotion behavior of the participants and the model explained 30.0% of variance. The Chinese students in Korea with higher self-esteem, perceived health status, acculturation level, and lower acculturative stress reported higher health promotion behavior. The findings can be applied to develop health promotion strategies for this population. Copyright © 2016. Published by Elsevier B.V.

Structure-preserving algorithms for oscillatory differential equations II

CERN Document Server

Wu, Xinyuan; Shi, Wei

2015-01-01

This book describes a variety of highly effective and efficient structure-preserving algorithms for second-order oscillatory differential equations. Such systems arise in many branches of science and engineering, and the examples in the book include systems from quantum physics, celestial mechanics and electronics. To accurately simulate the true behavior of such systems, a numerical algorithm must preserve as much as possible their key structural properties: time-reversibility, oscillation, symplecticity, and energy and momentum conservation. The book describes novel advances in RKN methods, ERKN methods, Filon-type asymptotic methods, AVF methods, and trigonometric Fourier collocation methods.  The accuracy and efficiency of each of these algorithms are tested via careful numerical simulations, and their structure-preserving properties are rigorously established by theoretical analysis. The book also gives insights into the practical implementation of the methods. This book is intended for engineers and sc...

Comparing direct and iterative equation solvers in a large structural analysis software system

Science.gov (United States)

Poole, E. L.

1991-01-01

Two direct Choleski equation solvers and two iterative preconditioned conjugate gradient (PCG) equation solvers used in a large structural analysis software system are described. The two direct solvers are implementations of the Choleski method for variable-band matrix storage and sparse matrix storage. The two iterative PCG solvers include the Jacobi conjugate gradient method and an incomplete Choleski conjugate gradient method. The performance of the direct and iterative solvers is compared by solving several representative structural analysis problems. Some key factors affecting the performance of the iterative solvers relative to the direct solvers are identified.

Post-partum blues among Korean mothers: a structural equation modelling approach.

Science.gov (United States)

Chung, Sung Suk; Yoo, Il Young; Joung, Kyoung Hwa

2013-08-01

The objective of this study was to propose the post-partum blues (PPB) model and to estimate the effects of self-esteem, social support, antenatal depression, and stressful events during pregnancy on PPB. Data were collected from 249 women post-partum during their stay in the maternity units of three hospitals in Korea using a self-administered questionnaire. A structural equation modelling approach using the Analysis of Moments Structure program was used to identify the direct and indirect effects of the variables on PPB. The full model had a good fit and accounted for 70.3% of the variance of PPB. Antenatal depression and stressful events during pregnancy had strong direct effects on PPB. Household income showed indirect effects on PPB via self-esteem and antenatal depression. Social support indirectly affected PPB via self-esteem, antenatal depression, and stressful events during pregnancy. © 2012 The Authors; International Journal of Mental Health Nursing © 2012 Australian College of Mental Health Nurses Inc.

Data-driven HR how to use analytics and metrics to drive performance

CERN Document Server

Marr, Bernard

2018-01-01

Traditionally seen as a purely people function unconcerned with numbers, HR is now uniquely placed to use company data to drive performance, both of the people in the organization and the organization as a whole. Data-driven HR is a practical guide which enables HR practitioners to leverage the value of the vast amount of data available at their fingertips. Covering how to identify the most useful sources of data, how to collect information in a transparent way that is in line with data protection requirements and how to turn this data into tangible insights, this book marks a turning point for the HR profession. Covering all the key elements of HR including recruitment, employee engagement, performance management, wellbeing and training, Data-driven HR examines the ways data can contribute to organizational success by, among other things, optimizing processes, driving performance and improving HR decision making. Packed with case studies and real-life examples, this is essential reading for all HR profession...

Data Driven Modelling of the Dynamic Wake Between Two Wind Turbines

DEFF Research Database (Denmark)

Knudsen, Torben; Bak, Thomas

2012-01-01

turbine. This paper establishes flow models relating the wind speeds at turbines in a farm. So far, research in this area has been mainly based on first principles static models and the data driven modelling done has not included the loading of the upwind turbine and its impact on the wind speed downwind......Wind turbines in a wind farm, influence each other through the wind flow. Downwind turbines are in the wake of upwind turbines and the wind speed experienced at downwind turbines is hence a function of the wind speeds at upwind turbines but also the momentum extracted from the wind by the upwind....... This paper is the first where modern commercial mega watt turbines are used for data driven modelling including the upwind turbine loading by changing power reference. Obtaining the necessary data is difficult and data is therefore limited. A simple dynamic extension to the Jensen wake model is tested...

Using of Structural Equation Modeling Techniques in Cognitive Levels Validation

Directory of Open Access Journals (Sweden)

Natalija Curkovic

2012-10-01

Full Text Available When constructing knowledge tests, cognitive level is usually one of the dimensions comprising the test specifications with each item assigned to measure a particular level. Recently used taxonomies of the cognitive levels most often represent some modification of the original Bloom’s taxonomy. There are many concerns in current literature about existence of predefined cognitive levels. The aim of this article is to investigate can structural equation modeling techniques confirm existence of different cognitive levels. For the purpose of the research, a Croatian final high-school Mathematics exam was used (N = 9626. Confirmatory factor analysis and structural regression modeling were used to test three different models. Structural equation modeling techniques did not support existence of different cognitive levels in this case. There is more than one possible explanation for that finding. Some other techniques that take into account nonlinear behaviour of the items as well as qualitative techniques might be more useful for the purpose of the cognitive levels validation. Furthermore, it seems that cognitive levels were not efficient descriptors of the items and so improvements are needed in describing the cognitive skills measured by items.

A copula-based sampling method for data-driven prognostics

International Nuclear Information System (INIS)

Xi, Zhimin; Jing, Rong; Wang, Pingfeng; Hu, Chao

2014-01-01

This paper develops a Copula-based sampling method for data-driven prognostics. The method essentially consists of an offline training process and an online prediction process: (i) the offline training process builds a statistical relationship between the failure time and the time realizations at specified degradation levels on the basis of off-line training data sets; and (ii) the online prediction process identifies probable failure times for online testing units based on the statistical model constructed in the offline process and the online testing data. Our contributions in this paper are three-fold, namely the definition of a generic health index system to quantify the health degradation of an engineering system, the construction of a Copula-based statistical model to learn the statistical relationship between the failure time and the time realizations at specified degradation levels, and the development of a simulation-based approach for the prediction of remaining useful life (RUL). Two engineering case studies, namely the electric cooling fan health prognostics and the 2008 IEEE PHM challenge problem, are employed to demonstrate the effectiveness of the proposed methodology. - Highlights: • We develop a novel mechanism for data-driven prognostics. • A generic health index system quantifies health degradation of engineering systems. • Off-line training model is constructed based on the Bayesian Copula model. • Remaining useful life is predicted from a simulation-based approach

COMPARISON THEOREM OF BACKWARD DOUBLY STOCHASTIC DIFFERENTIAL EQUATIONS

Institute of Scientific and Technical Information of China (English)

无

2010-01-01

This paper is devoted to deriving a comparison theorem of solutions to backward doubly stochastic differential equations driven by Brownian motion and backward It-Kunita integral. By the application of this theorem, we give an existence result of the solutions to these equations with continuous coefficients.

Towards consumer driven and innovative fruit supply chains

NARCIS (Netherlands)

Wiersinga, R.C.; Hiller, S.R.C.H.; Zimmerman, K.L.

2012-01-01

This paper aims to identify supply chain organization and management structures that maximize innovation in consumer driven fruit supply chains with the ultimate goal of increasing European fruit consumption. Data was collected on the chain organization, management structures and innovativeness of

A multilevel cross-lagged structural equation analysis for reciprocal relationship between social capital and health

OpenAIRE

Sessions, John; Yu, Ge; Fu, Yu; Wall, Matin

2015-01-01

We investigated the reciprocal relationship between individual social capital and perceived mental and physical health in the UK. Using data from the British Household Panel Survey from 1991 to 2008, we fitted cross-lagged structural equation models that include three indicators of social capital vis. social participation, social network, and loneliness. Given that multiple measurement points (level 1) are nested within individuals (level 2), we also applied a multilevel model to allow for re...

Subdiffusive master equation with space-dependent anomalous exponent and structural instability

Science.gov (United States)

Fedotov, Sergei; Falconer, Steven

2012-03-01

We derive the fractional master equation with space-dependent anomalous exponent. We analyze the asymptotic behavior of the corresponding lattice model both analytically and by Monte Carlo simulation. We show that the subdiffusive fractional equations with constant anomalous exponent μ in a bounded domain [0,L] are not structurally stable with respect to the nonhomogeneous variations of parameter μ. In particular, the Gibbs-Boltzmann distribution is no longer the stationary solution of the fractional Fokker-Planck equation whatever the space variation of the exponent might be. We analyze the random distribution of μ in space and find that in the long-time limit, the probability distribution is highly intermediate in space and the behavior is completely dominated by very unlikely events. We show that subdiffusive fractional equations with the nonuniform random distribution of anomalous exponent is an illustration of a “Black Swan,” the low probability event of the small value of the anomalous exponent that completely dominates the long-time behavior of subdiffusive systems.

Data driven CAN node reliability assessment for manufacturing system

Science.gov (United States)

Zhang, Leiming; Yuan, Yong; Lei, Yong

2017-01-01

The reliability of the Controller Area Network(CAN) is critical to the performance and safety of the system. However, direct bus-off time assessment tools are lacking in practice due to inaccessibility of the node information and the complexity of the node interactions upon errors. In order to measure the mean time to bus-off(MTTB) of all the nodes, a novel data driven node bus-off time assessment method for CAN network is proposed by directly using network error information. First, the corresponding network error event sequence for each node is constructed using multiple-layer network error information. Then, the generalized zero inflated Poisson process(GZIP) model is established for each node based on the error event sequence. Finally, the stochastic model is constructed to predict the MTTB of the node. The accelerated case studies with different error injection rates are conducted on a laboratory network to demonstrate the proposed method, where the network errors are generated by a computer controlled error injection system. Experiment results show that the MTTB of nodes predicted by the proposed method agree well with observations in the case studies. The proposed data driven node time to bus-off assessment method for CAN networks can successfully predict the MTTB of nodes by directly using network error event data.

FDTD for Hydrodynamic Electron Fluid Maxwell Equations

Directory of Open Access Journals (Sweden)

Yingxue Zhao

2015-05-01

Full Text Available In this work, we develop a numerical method for solving the three dimensional hydrodynamic electron fluid Maxwell equations that describe the electron gas dynamics driven by an external electromagnetic wave excitation. Our numerical approach is based on the Finite-Difference Time-Domain (FDTD method for solving the Maxwell’s equations and an explicit central finite difference method for solving the hydrodynamic electron fluid equations containing both electron density and current equations. Numerical results show good agreement with the experiment of studying the second-harmonic generation (SHG from metallic split-ring resonator (SRR.

Sensitivity Analysis in Structural Equation Models: Cases and Their Influence

Science.gov (United States)

Pek, Jolynn; MacCallum, Robert C.

2011-01-01

The detection of outliers and influential observations is routine practice in linear regression. Despite ongoing extensions and development of case diagnostics in structural equation models (SEM), their application has received limited attention and understanding in practice. The use of case diagnostics informs analysts of the uncertainty of model…

Anti-Transgender Prejudice: A Structural Equation Model of Associated Constructs

Science.gov (United States)

Tebbe, Esther N.; Moradi, Bonnie

2012-01-01

This study aimed to identify theoretically relevant key correlates of anti-transgender prejudice. Specifically, structural equation modeling was used to test the unique relations of anti-lesbian, gay, and bisexual (LGB) prejudice; traditional gender role attitudes; need for closure; and social dominance orientation with anti-transgender prejudice.…

Discrete variational derivative method a structure-preserving numerical method for partial differential equations

CERN Document Server

Furihata, Daisuke

2010-01-01

Nonlinear Partial Differential Equations (PDEs) have become increasingly important in the description of physical phenomena. Unlike Ordinary Differential Equations, PDEs can be used to effectively model multidimensional systems. The methods put forward in Discrete Variational Derivative Method concentrate on a new class of ""structure-preserving numerical equations"" which improves the qualitative behaviour of the PDE solutions and allows for stable computing. The authors have also taken care to present their methods in an accessible manner, which means that the book will be useful to engineer

Semi-structured data extraction and modelling: the WIA Project

Directory of Open Access Journals (Sweden)

Alessandro Mosca

2013-09-01

Full Text Available Over the last decades, the amount of data of all kinds available electronically has increased dramatically. Data are accessible through a range of interfaces including Web browsers, database query languages, application-specific interfaces, built on top of a number of different data exchange formats. All these data span from un-structured to highly structured data. Very often, some of them have structure even if the structure is implicit, and not as rigid or regular as that found in standard database systems. Spreadsheet documents are prototypical in this respect. Spreadsheets are the lightweight technology able to supply companies with easy to build business management and business intelligence applications, and business people largely adopt spreadsheets as smart vehicles for data files generation and sharing. Actually, the more spreadsheets grow in complexity (e.g., their use in product development plans and quoting, the more their arrangement, maintenance, and analysis appear as a knowledge-driven activity. The algorithmic approach to the problem of automatic data structure extraction from spreadsheet documents (i.e., grid-structured and free topological-related data emerges from the WIA project: Worksheets Intelligent Analyser. The WIA-algorithm shows how to provide a description of spreadsheet contents in terms of higher level of abstractions or conceptualisations. In particular, the WIA-algorithm target is about the extraction of i the calculus work-flow implemented in the spreadsheets formulas and ii the logical role played by the data which take part into the calculus. The aim of the resulting conceptualisations is to provide spreadsheets with abstract representations useful for further model refinements and optimizations through evolutionary algorithms computations.

Hamilton-Jacobi-Bellman equations for quantum control | Ogundiran ...

African Journals Online (AJOL)

The aim of this work is to study Hamilton-Jacobi-Bellman equation for quantum control driven by quantum noises. These noises are annhihilation, creation and gauge processes. We shall consider the solutions of Hamilton-Jacobi-Bellman equation via the Hamiltonian system measurable in time. JONAMP Vol. 11 2007: pp.

Data-Driven Healthcare: Challenges and Opportunities for Interactive Visualization.

Science.gov (United States)

Gotz, David; Borland, David

2016-01-01

The healthcare industry's widespread digitization efforts are reshaping one of the largest sectors of the world's economy. This transformation is enabling systems that promise to use ever-improving data-driven evidence to help doctors make more precise diagnoses, institutions identify at risk patients for intervention, clinicians develop more personalized treatment plans, and researchers better understand medical outcomes within complex patient populations. Given the scale and complexity of the data required to achieve these goals, advanced data visualization tools have the potential to play a critical role. This article reviews a number of visualization challenges unique to the healthcare discipline.

A computational method for direct integration of motion equations of structural systems

International Nuclear Information System (INIS)

Brusa, L.; Ciacci, R.; Creco, A.; Rossi, F.

1975-01-01

The dynamic analysis of structural systems requires the solution of the matrix equations: Md 2 delta/dt(t) + Cddelta/dt(t) + Kdelta(t) = F(t). Many numerical methods are available for direct integration of this equation and their efficiency is due to the fulfillment of the following requirements: A reasonable order of accuracy must be obtained for the approximation of the response relevant to the first modes: the model contributions relevant to the eigenvalues with large real part must be essentially neglected. This paper presents a step-by-step numerical scheme for the integration of this equation which satisfies the requirements previously mentioned. (Auth.)

A STRUCTURAL EQUATION MODEL-II FOR WORK-LIFE BALANCE OF IT PROFESSIONALS IN CHENNAI

Directory of Open Access Journals (Sweden)

Rashida A. Banu

2016-05-01

Full Text Available The study developed and tested a model of work life balance of IT professionals employing structural equation modeling (SEM to analyze the relationship between work place support (WPS and work interference with personal life (WIPL, personal life interference with work (PLIW, satisfaction with work-life balance (SWLB and improved effectiveness at work (IEW. The model fit the data well and hypotheses are generally supported. WPS and SWLB are negatively related to WIPL and PLIW. However, there is a positive relationship between SWLB and IEW.

Application of Exploratory Structural Equation Modeling to Evaluate the Academic Motivation Scale

Science.gov (United States)

Guay, Frédéric; Morin, Alexandre J. S.; Litalien, David; Valois, Pierre; Vallerand, Robert J.

2015-01-01

In this research, the authors examined the construct validity of scores of the Academic Motivation Scale using exploratory structural equation modeling. Study 1 and Study 2 involved 1,416 college students and 4,498 high school students, respectively. First, results of both studies indicated that the factor structure tested with exploratory…

A data-driven framework for investigating customer retention

OpenAIRE

Mgbemena, Chidozie Simon

2016-01-01

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University London. This study presents a data-driven simulation framework in order to understand customer behaviour and therefore improve customer retention. The overarching system design methodology used for this study is aligned with the design science paradigm. The Social Media Domain Analysis (SoMeDoA) approach is adopted and evaluated to build a model on the determinants of customer satisfaction ...

Trade-FDI Linkages in a System of Gravity Equations for German Regional Data

DEFF Research Database (Denmark)

Mitze, Timo; Alecke, Björn; Untiedt, Gerhard

We analyse the nature of German trade-FDI linkages within the EU27 based on a simultaneous equation gravity approach for imports, exports, in- and outward FDI stocks.We adopt both a Hausman-Taylor (1981) IV approach (3SLS-GMM) and rival non-IV estimation (the system extension to the Fixed Effects...... substitutive links between trade flows and outward FDI in line with earlier empirical evidence for Germany. Building upon German state level data we are also able to analyse the sensitivity of the results for regional sub-samples. The latter disaggregation hints at structural differences among the trade...

A data-driven emulation framework for representing water-food nexus in a changing cold region

Science.gov (United States)

Nazemi, A.; Zandmoghaddam, S.; Hatami, S.

2017-12-01

Water resource systems are under increasing pressure globally. Growing population along with competition between water demands and emerging effects of climate change have caused enormous vulnerabilities in water resource management across many regions. Diagnosing such vulnerabilities and provision of effective adaptation strategies requires the availability of simulation tools that can adequately represent the interactions between competing water demands for limiting water resources and inform decision makers about the critical vulnerability thresholds under a range of potential natural and anthropogenic conditions. Despite a significant progress in integrated modeling of water resource systems, regional models are often unable to fully represent the contemplating dynamics within the key elements of water resource systems locally. Here we propose a data-driven approach to emulate a complex regional water resource system model developed for Oldman River Basin in southern Alberta, Canada. The aim of the emulation is to provide a detailed understanding of the trade-offs and interaction at the Oldman Reservoir, which is the key to flood control and irrigated agriculture in this over-allocated semi-arid cold region. Different surrogate models are developed to represent the dynamic of irrigation demand and withdrawal as well as reservoir evaporation and release individually. The nan-falsified offline models are then integrated through the water balance equation at the reservoir location to provide a coupled model for representing the dynamic of reservoir operation and water allocation at the local scale. The performance of individual and integrated models are rigorously examined and sources of uncertainty are highlighted. To demonstrate the practical utility of such surrogate modeling approach, we use the integrated data-driven model for examining the trade-off in irrigation water supply, reservoir storage and release under a range of changing climate, upstream

Trapped surfaces in monopole-like Cauchy data of Einstein-Yang-Mills-Higgs equations

International Nuclear Information System (INIS)

Malec, E.; Koc, P.

1989-08-01

We choose the nonabelian monopole solution of Bogomolny, Prasad and Sommerfield as a part of Cauchy data for the evolution of Einstein-Yang-Mills-Higgs equations. Momentarily static spherically symmetric data for gravitational fields are obtained numerically via the Lichnerowicz equation. In the case of generic scaling of fields we have found initial data with trapped surfaces. (author). 13 refs

Structural equation models of VMT growth in US urbanised areas.

Science.gov (United States)

Ewing, Reid; Hamidi, Shima; Gallivan, Frank; Nelson, Arthur C.; Grace, James B.

2014-01-01

Vehicle miles travelled (VMT) is a primary performance indicator for land use and transportation, bringing with it both positive and negative externalities. This study updates and refines previous work on VMT in urbanised areas, using recent data, additional metrics and structural equation modelling (SEM). In a cross-sectional model for 2010, population, income and freeway capacity are positively related to VMT, while gasoline prices, development density and transit service levels are negatively related. Findings of the cross-sectional model are generally confirmed in a more tightly controlled longitudinal study of changes in VMT between 2000 and 2010, the first model of its kind. The cross-sectional and longitudinal models together, plus the transportation literature generally, give us a basis for generalising across studies to arrive at elasticity values of VMT with respect to different urban variables.

Data-driven Development of ROTEM and TEG Algorithms for the Management of Trauma Hemorrhage

DEFF Research Database (Denmark)

Baksaas-Aasen, Kjersti; Van Dieren, Susan; Balvers, Kirsten

2018-01-01

for ROTEM, TEG, and CCTs to be used in addition to ratio driven transfusion and tranexamic acid. CONCLUSIONS: We describe a systematic approach to define threshold parameters for ROTEM and TEG. These parameters were incorporated into algorithms to support data-driven adjustments of resuscitation...

Exact soliton-like solutions of perturbed phi4-equation

International Nuclear Information System (INIS)

Gonzalez, J.A.

1986-05-01

Exact soliton-like solutions of damped, driven phi 4 -equation are found. The exact expressions for the velocities of solitons are given. It is non-perturbatively proved that the perturbed phi 4 -equation has stable kink-like solutions of a new type. (author)

PEPSI-Dock: a detailed data-driven protein-protein interaction potential accelerated by polar Fourier correlation.

Science.gov (United States)

Neveu, Emilie; Ritchie, David W; Popov, Petr; Grudinin, Sergei

2016-09-01

Docking prediction algorithms aim to find the native conformation of a complex of proteins from knowledge of their unbound structures. They rely on a combination of sampling and scoring methods, adapted to different scales. Polynomial Expansion of Protein Structures and Interactions for Docking (PEPSI-Dock) improves the accuracy of the first stage of the docking pipeline, which will sharpen up the final predictions. Indeed, PEPSI-Dock benefits from the precision of a very detailed data-driven model of the binding free energy used with a global and exhaustive rigid-body search space. As well as being accurate, our computations are among the fastest by virtue of the sparse representation of the pre-computed potentials and FFT-accelerated sampling techniques. Overall, this is the first demonstration of a FFT-accelerated docking method coupled with an arbitrary-shaped distance-dependent interaction potential. First, we present a novel learning process to compute data-driven distant-dependent pairwise potentials, adapted from our previous method used for rescoring of putative protein-protein binding poses. The potential coefficients are learned by combining machine-learning techniques with physically interpretable descriptors. Then, we describe the integration of the deduced potentials into a FFT-accelerated spherical sampling provided by the Hex library. Overall, on a training set of 163 heterodimers, PEPSI-Dock achieves a success rate of 91% mid-quality predictions in the top-10 solutions. On a subset of the protein docking benchmark v5, it achieves 44.4% mid-quality predictions in the top-10 solutions when starting from bound structures and 20.5% when starting from unbound structures. The method runs in 5-15 min on a modern laptop and can easily be extended to other types of interactions. https://team.inria.fr/nano-d/software/PEPSI-Dock sergei.grudinin@inria.fr. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please e

The Role of Guided Induction in Paper-Based Data-Driven Learning

Science.gov (United States)

Smart, Jonathan

2014-01-01

This study examines the role of guided induction as an instructional approach in paper-based data-driven learning (DDL) in the context of an ESL grammar course during an intensive English program at an American public university. Specifically, it examines whether corpus-informed grammar instruction is more effective through inductive, data-driven…

External radioactive markers for PET data-driven respiratory gating in positron emission tomography.

Science.gov (United States)

Büther, Florian; Ernst, Iris; Hamill, James; Eich, Hans T; Schober, Otmar; Schäfers, Michael; Schäfers, Klaus P

2013-04-01

Respiratory gating is an established approach to overcoming respiration-induced image artefacts in PET. Of special interest in this respect are raw PET data-driven gating methods which do not require additional hardware to acquire respiratory signals during the scan. However, these methods rely heavily on the quality of the acquired PET data (statistical properties, data contrast, etc.). We therefore combined external radioactive markers with data-driven respiratory gating in PET/CT. The feasibility and accuracy of this approach was studied for [(18)F]FDG PET/CT imaging in patients with malignant liver and lung lesions. PET data from 30 patients with abdominal or thoracic [(18)F]FDG-positive lesions (primary tumours or metastases) were included in this prospective study. The patients underwent a 10-min list-mode PET scan with a single bed position following a standard clinical whole-body [(18)F]FDG PET/CT scan. During this scan, one to three radioactive point sources (either (22)Na or (18)F, 50-100 kBq) in a dedicated holder were attached the patient's abdomen. The list mode data acquired were retrospectively analysed for respiratory signals using established data-driven gating approaches and additionally by tracking the motion of the point sources in sinogram space. Gated reconstructions were examined qualitatively, in terms of the amount of respiratory displacement and in respect of changes in local image intensity in the gated images. The presence of the external markers did not affect whole-body PET/CT image quality. Tracking of the markers led to characteristic respiratory curves in all patients. Applying these curves for gated reconstructions resulted in images in which motion was well resolved. Quantitatively, the performance of the external marker-based approach was similar to that of the best intrinsic data-driven methods. Overall, the gain in measured tumour uptake from the nongated to the gated images indicating successful removal of respiratory motion

Protein engineering of Bacillus acidopullulyticus pullulanase for enhanced thermostability using in silico data driven rational design methods.

Science.gov (United States)

Chen, Ana; Li, Yamei; Nie, Jianqi; McNeil, Brian; Jeffrey, Laura; Yang, Yankun; Bai, Zhonghu

2015-10-01

Thermostability has been considered as a requirement in the starch processing industry to maintain high catalytic activity of pullulanase under high temperatures. Four data driven rational design methods (B-FITTER, proline theory, PoPMuSiC-2.1, and sequence consensus approach) were adopted to identify the key residue potential links with thermostability, and 39 residues of Bacillus acidopullulyticus pullulanase were chosen as mutagenesis targets. Single mutagenesis followed by combined mutagenesis resulted in the best mutant E518I-S662R-Q706P, which exhibited an 11-fold half-life improvement at 60 °C and a 9.5 °C increase in Tm. The optimum temperature of the mutant increased from 60 to 65 °C. Fluorescence spectroscopy results demonstrated that the tertiary structure of the mutant enzyme was more compact than that of the wild-type (WT) enzyme. Structural change analysis revealed that the increase in thermostability was most probably caused by a combination of lower stability free-energy and higher hydrophobicity of E518I, more hydrogen bonds of S662R, and higher rigidity of Q706P compared with the WT. The findings demonstrated the effectiveness of combined data-driven rational design approaches in engineering an industrial enzyme to improve thermostability. Copyright © 2015 Elsevier Inc. All rights reserved.

Kac limit and thermodynamic characterization of stochastic dynamics driven by Poisson-Kac fluctuations

Science.gov (United States)

Giona, Massimiliano; Brasiello, Antonio; Crescitelli, Silvestro

2017-07-01

We analyze the thermodynamic properties of stochastic differential equations driven by smooth Poisson-Kac fluctuations, and their convergence, in the Kac limit, towards Wiener-driven Langevin equations. Using a Markovian embedding of the stochastic work variable, it is proved that the Kac-limit convergence implies a Stratonovich formulation of the limit Langevin equations, in accordance with the Wong-Zakai theorem. Exact moment analysis applied to the case of a purely frictional system shows the occurrence of different regimes and crossover phenomena in the parameter space.

Structural Equation Model of Smartphone Addiction Based on Adult Attachment Theory: Mediating Effects of Loneliness and Depression

OpenAIRE

EunYoung Kim, PhD; Inhyo Cho, PhD; Eun Joo Kim, PhD

2017-01-01

Purpose: This study investigated the mediating effects of loneliness and depression on the relationship between adult attachment and smartphone addiction in university students. Methods: A total of 200 university students participated in this study. The data was analysed using descriptive statistics, correlation analysis, and structural equation modeling. Results: There were significant positive relationships between attachment anxiety, loneliness, depression, and smartphone addiction. ...

Electromagnetic scattering of large structures in layered earths using integral equations

Science.gov (United States)

Xiong, Zonghou; Tripp, Alan C.

1995-07-01

An electromagnetic scattering algorithm for large conductivity structures in stratified media has been developed and is based on the method of system iteration and spatial symmetry reduction using volume electric integral equations. The method of system iteration divides a structure into many substructures and solves the resulting matrix equation using a block iterative method. The block submatrices usually need to be stored on disk in order to save computer core memory. However, this requires a large disk for large structures. If the body is discretized into equal-size cells it is possible to use the spatial symmetry relations of the Green's functions to regenerate the scattering impedance matrix in each iteration, thus avoiding expensive disk storage. Numerical tests show that the system iteration converges much faster than the conventional point-wise Gauss-Seidel iterative method. The numbers of cells do not significantly affect the rate of convergency. Thus the algorithm effectively reduces the solution of the scattering problem to an order of O(N2), instead of O(N3) as with direct solvers.

Derivation of stochastic differential equations for scrape-off layer plasma fluctuations from experimentally measured statistics

Energy Technology Data Exchange (ETDEWEB)

Mekkaoui, Abdessamad [IEK-4 Forschungszentrum Juelich 52428 (Germany)

2013-07-01

A method to derive stochastic differential equations for intermittent plasma density dynamics in magnetic fusion edge plasma is presented. It uses a measured first four moments (mean, variance, Skewness and Kurtosis) and the correlation time of turbulence to write a Pearson equation for the probability distribution function of fluctuations. The Fokker-Planck equation is then used to derive a Langevin equation for the plasma density fluctuations. A theoretical expectations are used as a constraints to fix the nonlinearity structure of the stochastic differential equation. In particular when the quadratically nonlinear dynamics is assumed, then it is shown that the plasma density is driven by a multiplicative Wiener process and evolves on the turbulence correlation time scale, while the linear growth is quadratically damped by the fluctuation level. Strong criteria for statistical discrimination of experimental time series are proposed as an alternative to the Kurtosis-Skewness scaling. This scaling is broadly used in contemporary literature to characterize edge turbulence, but it is inappropriate because a large family of distributions could share this scaling. Strong criteria allow us to focus on the relevant candidate distribution and approach a nonlinear structure of edge turbulence model.

A Unified Equation of State on a Microscopic Basis : Implications for Neutron Stars Structure and Cooling

Science.gov (United States)

Burgio, G. F.

2018-03-01

We discuss the structure of Neutron Stars by modelling the homogeneous nuclear matter of the core by a suitable microscopic Equation of State, based on the Brueckner-Hartree-Fock many-body theory, and the crust, including the pasta phase, by the BCPM energy density functional which is based on the same Equation of State. This allows for a uni ed description of the Neutron Star matter over a wide density range. A comparison with other uni ed approaches is discussed. With the same Equation of State, which features strong direct Urca processes and using consistent nuclear pairing gaps as well as effective masses, we model neutron star cooling, in particular the current rapid cooldown of the neutron star Cas A. We nd that several scenarios are possible to explain the features of Cas A, but only large and extended proton 1 S 0 gaps and small neutron 3 PF 2 gaps can accommodate also the major part of the complete current cooling data.

A Structural Equation Modelling of the Academic Self-Concept Scale

Science.gov (United States)

Matovu, Musa

2014-01-01

The study aimed at validating the academic self-concept scale by Liu and Wang (2005) in measuring academic self-concept among university students. Structural equation modelling was used to validate the scale which was composed of two subscales; academic confidence and academic effort. The study was conducted on university students; males and…

Helioseismic and neutrino data-driven reconstruction of solar properties

Science.gov (United States)

Song, Ningqiang; Gonzalez-Garcia, M. C.; Villante, Francesco L.; Vinyoles, Nuria; Serenelli, Aldo

2018-06-01

In this work, we use Bayesian inference to quantitatively reconstruct the solar properties most relevant to the solar composition problem using as inputs the information provided by helioseismic and solar neutrino data. In particular, we use a Gaussian process to model the functional shape of the opacity uncertainty to gain flexibility and become as free as possible from prejudice in this regard. With these tools we first readdress the statistical significance of the solar composition problem. Furthermore, starting from a composition unbiased set of standard solar models (SSMs) we are able to statistically select those with solar chemical composition and other solar inputs which better describe the helioseismic and neutrino observations. In particular, we are able to reconstruct the solar opacity profile in a data-driven fashion, independently of any reference opacity tables, obtaining a 4 per cent uncertainty at the base of the convective envelope and 0.8 per cent at the solar core. When systematic uncertainties are included, results are 7.5 per cent and 2 per cent, respectively. In addition, we find that the values of most of the other inputs of the SSMs required to better describe the helioseismic and neutrino data are in good agreement with those adopted as the standard priors, with the exception of the astrophysical factor S11 and the microscopic diffusion rates, for which data suggests a 1 per cent and 30 per cent reduction, respectively. As an output of the study we derive the corresponding data-driven predictions for the solar neutrino fluxes.

Accelerator driven systems for energy production and waste incineration: Physics, design and related nuclear data

International Nuclear Information System (INIS)

Herman, M.; Stanculescu, A.; Paver, N.

2003-01-01

This volume contains the notes of lectures given at the workshops 'Hybrid Nuclear Systems for Energy Production, Utilisation of Actinides and Transmutation of Long-lived Radioactive Waste' and 'Nuclear Data for Science and Technology: Accelerator Driven Waste Incineration', held at the Abdus Salam ICTP in September 2001. The subject of the first workshop was focused on the so-called Accelerator Driven Systems, and covered the most important physics and technological aspects of this innovative field. The second workshop was devoted to an exhaustive survey on the acquisition, evaluation, retrieval and validation of the nuclear data relevant to the design of Accelerator Driven Systems

Accelerator driven systems for energy production and waste incineration: Physics, design and related nuclear data

Energy Technology Data Exchange (ETDEWEB)

Herman, M; Stanculescu, A [International Atomic Energy Agency, Vienna (Austria); Paver, N [University of Trieste and INFN, Trieste (Italy)

2003-06-15

This volume contains the notes of lectures given at the workshops 'Hybrid Nuclear Systems for Energy Production, Utilisation of Actinides and Transmutation of Long-lived Radioactive Waste' and 'Nuclear Data for Science and Technology: Accelerator Driven Waste Incineration', held at the Abdus Salam ICTP in September 2001. The subject of the first workshop was focused on the so-called Accelerator Driven Systems, and covered the most important physics and technological aspects of this innovative field. The second workshop was devoted to an exhaustive survey on the acquisition, evaluation, retrieval and validation of the nuclear data relevant to the design of Accelerator Driven Systems.

Data driven propulsion system weight prediction model

Science.gov (United States)

Gerth, Richard J.

1994-10-01

The objective of the research was to develop a method to predict the weight of paper engines, i.e., engines that are in the early stages of development. The impetus for the project was the Single Stage To Orbit (SSTO) project, where engineers need to evaluate alternative engine designs. Since the SSTO is a performance driven project the performance models for alternative designs were well understood. The next tradeoff is weight. Since it is known that engine weight varies with thrust levels, a model is required that would allow discrimination between engines that produce the same thrust. Above all, the model had to be rooted in data with assumptions that could be justified based on the data. The general approach was to collect data on as many existing engines as possible and build a statistical model of the engines weight as a function of various component performance parameters. This was considered a reasonable level to begin the project because the data would be readily available, and it would be at the level of most paper engines, prior to detailed component design.

Structural Equation Models in a Redundancy Analysis Framework With Covariates.

Science.gov (United States)

Lovaglio, Pietro Giorgio; Vittadini, Giorgio

2014-01-01

A recent method to specify and fit structural equation modeling in the Redundancy Analysis framework based on so-called Extended Redundancy Analysis (ERA) has been proposed in the literature. In this approach, the relationships between the observed exogenous variables and the observed endogenous variables are moderated by the presence of unobservable composites, estimated as linear combinations of exogenous variables. However, in the presence of direct effects linking exogenous and endogenous variables, or concomitant indicators, the composite scores are estimated by ignoring the presence of the specified direct effects. To fit structural equation models, we propose a new specification and estimation method, called Generalized Redundancy Analysis (GRA), allowing us to specify and fit a variety of relationships among composites, endogenous variables, and external covariates. The proposed methodology extends the ERA method, using a more suitable specification and estimation algorithm, by allowing for covariates that affect endogenous indicators indirectly through the composites and/or directly. To illustrate the advantages of GRA over ERA we propose a simulation study of small samples. Moreover, we propose an application aimed at estimating the impact of formal human capital on the initial earnings of graduates of an Italian university, utilizing a structural model consistent with well-established economic theory.

Simulation of electrically driven jet using Chebyshev collocation method

Institute of Scientific and Technical Information of China (English)

无

2011-01-01

The model of electrically driven jet is governed by a series of quasi 1D dimensionless partial differential equations(PDEs).Following the method of lines,the Chebyshev collocation method is employed to discretize the PDEs and obtain a system of differential-algebraic equations(DAEs).By differentiating constrains in DAEs twice,the system is transformed into a set of ordinary differential equations(ODEs) with invariants.Then the implicit differential equations solver "ddaskr" is used to solve the ODEs and ...

Structural stability and chaotic solutions of perturbed Benjamin-Ono equations

International Nuclear Information System (INIS)

Birnir, B.; Morrison, P.J.

1986-11-01

A method for proving chaos in partial differential equations is discussed and applied to the Benjamin-Ono equation subject to perturbations. The perturbations are of two types: one that corresponds to viscous dissipation, the so-called Burger's term, and one that involves the Hilbert transform and has been used to model Landau damping. The method proves chaos in the PDE by proving temporal chaos in its pole solutions. The spatial structure of the pole solutions remains intact, but their positions are chaotic in time. Melnikov's method is invoked to show this temporal chaos. It is discovered that the pole behavior is very sensitive to the Burger's perturbation, but is quite insensitive to the perturbation involving the Hilbert transform

qPortal: A platform for data-driven biomedical research.

Science.gov (United States)

Mohr, Christopher; Friedrich, Andreas; Wojnar, David; Kenar, Erhan; Polatkan, Aydin Can; Codrea, Marius Cosmin; Czemmel, Stefan; Kohlbacher, Oliver; Nahnsen, Sven

2018-01-01

Modern biomedical research aims at drawing biological conclusions from large, highly complex biological datasets. It has become common practice to make extensive use of high-throughput technologies that produce big amounts of heterogeneous data. In addition to the ever-improving accuracy, methods are getting faster and cheaper, resulting in a steadily increasing need for scalable data management and easily accessible means of analysis. We present qPortal, a platform providing users with an intuitive way to manage and analyze quantitative biological data. The backend leverages a variety of concepts and technologies, such as relational databases, data stores, data models and means of data transfer, as well as front-end solutions to give users access to data management and easy-to-use analysis options. Users are empowered to conduct their experiments from the experimental design to the visualization of their results through the platform. Here, we illustrate the feature-rich portal by simulating a biomedical study based on publically available data. We demonstrate the software's strength in supporting the entire project life cycle. The software supports the project design and registration, empowers users to do all-digital project management and finally provides means to perform analysis. We compare our approach to Galaxy, one of the most widely used scientific workflow and analysis platforms in computational biology. Application of both systems to a small case study shows the differences between a data-driven approach (qPortal) and a workflow-driven approach (Galaxy). qPortal, a one-stop-shop solution for biomedical projects offers up-to-date analysis pipelines, quality control workflows, and visualization tools. Through intensive user interactions, appropriate data models have been developed. These models build the foundation of our biological data management system and provide possibilities to annotate data, query metadata for statistics and future re-analysis on

Langevin dynamics for vector variables driven by multiplicative white noise: A functional formalism

Science.gov (United States)

Moreno, Miguel Vera; Arenas, Zochil González; Barci, Daniel G.

2015-04-01

We discuss general multidimensional stochastic processes driven by a system of Langevin equations with multiplicative white noise. In particular, we address the problem of how time reversal diffusion processes are affected by the variety of conventions available to deal with stochastic integrals. We present a functional formalism to build up the generating functional of correlation functions without any type of discretization of the Langevin equations at any intermediate step. The generating functional is characterized by a functional integration over two sets of commuting variables, as well as Grassmann variables. In this representation, time reversal transformation became a linear transformation in the extended variables, simplifying in this way the complexity introduced by the mixture of prescriptions and the associated calculus rules. The stochastic calculus is codified in our formalism in the structure of the Grassmann algebra. We study some examples such as higher order derivative Langevin equations and the functional representation of the micromagnetic stochastic Landau-Lifshitz-Gilbert equation.

Application of data science tools to quantify and distinguish between structures and models in molecular dynamics datasets.

Science.gov (United States)

Kalidindi, Surya R; Gomberg, Joshua A; Trautt, Zachary T; Becker, Chandler A

2015-08-28

Structure quantification is key to successful mining and extraction of core materials knowledge from both multiscale simulations as well as multiscale experiments. The main challenge stems from the need to transform the inherently high dimensional representations demanded by the rich hierarchical material structure into useful, high value, low dimensional representations. In this paper, we develop and demonstrate the merits of a data-driven approach for addressing this challenge at the atomic scale. The approach presented here is built on prior successes demonstrated for mesoscale representations of material internal structure, and involves three main steps: (i) digital representation of the material structure, (ii) extraction of a comprehensive set of structure measures using the framework of n-point spatial correlations, and (iii) identification of data-driven low dimensional measures using principal component analyses. These novel protocols, applied on an ensemble of structure datasets output from molecular dynamics (MD) simulations, have successfully classified the datasets based on several model input parameters such as the interatomic potential and the temperature used in the MD simulations.

Application of data science tools to quantify and distinguish between structures and models in molecular dynamics datasets

International Nuclear Information System (INIS)

Kalidindi, Surya R; Gomberg, Joshua A; Trautt, Zachary T; Becker, Chandler A

2015-01-01

Structure quantification is key to successful mining and extraction of core materials knowledge from both multiscale simulations as well as multiscale experiments. The main challenge stems from the need to transform the inherently high dimensional representations demanded by the rich hierarchical material structure into useful, high value, low dimensional representations. In this paper, we develop and demonstrate the merits of a data-driven approach for addressing this challenge at the atomic scale. The approach presented here is built on prior successes demonstrated for mesoscale representations of material internal structure, and involves three main steps: (i) digital representation of the material structure, (ii) extraction of a comprehensive set of structure measures using the framework of n-point spatial correlations, and (iii) identification of data-driven low dimensional measures using principal component analyses. These novel protocols, applied on an ensemble of structure datasets output from molecular dynamics (MD) simulations, have successfully classified the datasets based on several model input parameters such as the interatomic potential and the temperature used in the MD simulations. (paper)

Structural equation models to estimate risk of infection and tolerance to bovine mastitis.

Science.gov (United States)

Detilleux, Johann; Theron, Léonard; Duprez, Jean-Noël; Reding, Edouard; Humblet, Marie-France; Planchon, Viviane; Delfosse, Camille; Bertozzi, Carlo; Mainil, Jacques; Hanzen, Christian

2013-03-06

One method to improve durably animal welfare is to select, as reproducers, animals with the highest ability to resist or tolerate infection. To do so, it is necessary to distinguish direct and indirect mechanisms of resistance and tolerance because selection on these traits is believed to have different epidemiological and evolutionary consequences. We propose structural equation models with latent variables (1) to quantify the latent risk of infection and to identify, among the many potential mediators of infection, the few ones that influence it significantly and (2) to estimate direct and indirect levels of tolerance of animals infected naturally with pathogens. We applied the method to two surveys of bovine mastitis in the Walloon region of Belgium, in which we recorded herd management practices, mastitis frequency, and results of bacteriological analyses of milk samples. Structural equation models suggested that, among more than 35 surveyed herd characteristics, only nine (age, addition of urea in the rations, treatment of subclinical mastitis, presence of dirty liner, cows with hyperkeratotic teats, machine stripping, pre- and post-milking teat disinfection, and housing of milking cows in cubicles) were directly and significantly related to a latent measure of bovine mastitis, and that treatment of subclinical mastitis was involved in the pathway between post-milking teat disinfection and latent mastitis. These models also allowed the separation of direct and indirect effects of bacterial infection on milk productivity. Results suggested that infected cows were tolerant but not resistant to mastitis pathogens. We revealed the advantages of structural equation models, compared to classical models, for dissecting measurements of resistance and tolerance to infectious diseases, here bovine mastitis. Using our method, we identified nine major risk factors that were directly associated with an increased risk of mastitis and suggested that cows were tolerant but

Dynamic modeling of interfacial structures via interfacial area transport equation

International Nuclear Information System (INIS)

Seungjin, Kim; Mamoru, Ishii

2005-01-01

The interfacial area transport equation dynamically models the two-phase flow regime transitions and predicts continuous change of the interfacial area concentration along the flow field. Hence, when employed in the numerical thermal-hydraulic system analysis codes, it eliminates artificial bifurcations stemming from the use of the static flow regime transition criteria. Accounting for the substantial differences in the transport phenomena of various sizes of bubbles, the two-group interfacial area transport equations have been developed. The group 1 equation describes the transport of small-dispersed bubbles that are either distorted or spherical in shapes, and the group 2 equation describes the transport of large cap, slug or churn-turbulent bubbles. The source and sink terms in the right-hand-side of the transport equations have been established by mechanistically modeling the creation and destruction of bubbles due to major bubble interaction mechanisms. In the present paper, the interfacial area transport equations currently available are reviewed to address the feasibility and reliability of the model along with extensive experimental results. These include the data from adiabatic upward air-water two-phase flow in round tubes of various sizes, from a rectangular duct, and from adiabatic co-current downward air-water two-phase flow in round pipes of two sizes. (authors)

Laser driven single shock compression of fluid deuterium from 45 to 220 GPa

Energy Technology Data Exchange (ETDEWEB)

Hicks, D; Boehly, T; Celliers, P; Eggert, J; Moon, S; Meyerhofer, D; Collins, G

2008-03-23

The compression {eta} of liquid deuterium between 45 and 220 GPa under laser-driven shock loading has been measured using impedance matching to an aluminum (Al) standard. An Al impedance match model derived from a best fit to absolute Hugoniot data has been used to quantify and minimize the systematic errors caused by uncertainties in the high-pressure Al equation of state. In deuterium below 100 GPa results show that {eta} {approx_equal} 4.2, in agreement with previous impedance match data from magnetically-driven flyer and convergent-explosive shock wave experiments; between 100 and 220 GPa {eta} reaches a maximum of {approx}5.0, less than the 6-fold compression observed on the earliest laser-shock experiments but greater than expected from simple extrapolations of lower pressure data. Previous laser-driven double-shock results are found to be in good agreement with these single-shock measurements over the entire range under study. Both sets of laser-shock data indicate that deuterium undergoes an abrupt increase in compression at around 110 GPa.

Distributed adaptive diagnosis of sensor faults using structural response data

Science.gov (United States)

Dragos, Kosmas; Smarsly, Kay

2016-10-01

The reliability and consistency of wireless structural health monitoring (SHM) systems can be compromised by sensor faults, leading to miscalibrations, corrupted data, or even data loss. Several research approaches towards fault diagnosis, referred to as ‘analytical redundancy’, have been proposed that analyze the correlations between different sensor outputs. In wireless SHM, most analytical redundancy approaches require centralized data storage on a server for data analysis, while other approaches exploit the on-board computing capabilities of wireless sensor nodes, analyzing the raw sensor data directly on board. However, using raw sensor data poses an operational constraint due to the limited power resources of wireless sensor nodes. In this paper, a new distributed autonomous approach towards sensor fault diagnosis based on processed structural response data is presented. The inherent correlations among Fourier amplitudes of acceleration response data, at peaks corresponding to the eigenfrequencies of the structure, are used for diagnosis of abnormal sensor outputs at a given structural condition. Representing an entirely data-driven analytical redundancy approach that does not require any a priori knowledge of the monitored structure or of the SHM system, artificial neural networks (ANN) are embedded into the sensor nodes enabling cooperative fault diagnosis in a fully decentralized manner. The distributed analytical redundancy approach is implemented into a wireless SHM system and validated in laboratory experiments, demonstrating the ability of wireless sensor nodes to self-diagnose sensor faults accurately and efficiently with minimal data traffic. Besides enabling distributed autonomous fault diagnosis, the embedded ANNs are able to adapt to the actual condition of the structure, thus ensuring accurate and efficient fault diagnosis even in case of structural changes.

Nuclear data for accelerator-driven transmutation. Annual report 2000 / 2001

International Nuclear Information System (INIS)

Blomgren, J.; Johansson, C.; Klug, J.; Olsson, N.; Pomp, S.; Renberg, P.U.

2001-09-01

The present project, supported as a research task agreement by SKI, SKB, Barsebaeck Kraft AB and Vattenfall AB, started 1998-07-01. From 1999-01-01 the project also receives support from the Defence Research Establishment. The primary objective from the supporting organizations is to promote research and research education of relevance for development of the national competence within nuclear energy. The aim of the project is in short to: promote development of the competence within nuclear physics and nuclear technology by supporting licentiate and PhD students, push forward the international research front regarding fundamental nuclear data within the presently highlighted research area 'accelerator-driven transmutation', strengthen the Swedish influence within the mentioned research area by expanding the international contact network, constitute a basis for Swedish participation in the nuclear data activities at IAEA and OECD/NEA. The project is run by the Department of Neutron Research at Uppsala University, and is utilizing the unique neutron beam facility at the national The Svedberg Laboratory. In this document, we give a status report after the third year (2000-07-01--2001-06-30) of the project. The annual report also includes a report with the title: Charge-exchange giant resonances as probes of nuclear structure. This report is indexed separately

Data driven fault detection and isolation: a wind turbine scenario

Directory of Open Access Journals (Sweden)

Rubén Francisco Manrique Piramanrique

2015-04-01

Full Text Available One of the greatest drawbacks in wind energy generation is the high maintenance cost associated to mechanical faults. This problem becomes more evident in utility scale wind turbines, where the increased size and nominal capacity comes with additional problems associated with structural vibrations and aeroelastic effects in the blades. Due to the increased operation capability, it is imperative to detect system degradation and faults in an efficient manner, maintaining system integrity, reliability and reducing operation costs. This paper presents a comprehensive comparison of four different Fault Detection and Isolation (FDI filters based on “Data Driven” (DD techniques. In order to enhance FDI performance, a multi-level strategy is used where:  the first level detects the occurrence of any given fault (detection, while  the second identifies the source of the fault (isolation. Four different DD classification techniques (namely Support Vector Machines, Artificial Neural Networks, K Nearest Neighbors and Gaussian Mixture Models were studied and compared for each of the proposed classification levels. The best strategy at each level could be selected to build the final data driven FDI system. The performance of the proposed scheme is evaluated on a benchmark model of a commercial wind turbine. 

Conditional stability in determination of initial data for stochastic parabolic equations

International Nuclear Information System (INIS)

Yuan, Ganghua

2017-01-01

In this paper, we solve two kinds of inverse problems in determination of the initial data for stochastic parabolic equations. One is determination of the initial data by lateral boundary observation on arbitrary portion of the boundary, the second one is determination of the initial data by internal observation in a subregion inside the domain. We obtain conditional stability for the two kinds of inverse problems. To prove the results, we estimate the initial data by a terminal observation near the initial time, then we estimate this terminal observation by lateral boundary observation on arbitrary portion of the boundary or internal observation in a subregion inside the domain. To achieve those goals, we derive several new Carleman estimates for stochastic parabolic equations in this paper. (paper)

Conditional stability in determination of initial data for stochastic parabolic equations

Science.gov (United States)

Yuan, Ganghua

2017-03-01

In this paper, we solve two kinds of inverse problems in determination of the initial data for stochastic parabolic equations. One is determination of the initial data by lateral boundary observation on arbitrary portion of the boundary, the second one is determination of the initial data by internal observation in a subregion inside the domain. We obtain conditional stability for the two kinds of inverse problems. To prove the results, we estimate the initial data by a terminal observation near the initial time, then we estimate this terminal observation by lateral boundary observation on arbitrary portion of the boundary or internal observation in a subregion inside the domain. To achieve those goals, we derive several new Carleman estimates for stochastic parabolic equations in this paper.

Development of a restricted state space stochastic differential equation model for bacterial growth in rich media

DEFF Research Database (Denmark)

Møller, Jan Kloppenborg; Philipsen, Kirsten Riber; Christiansen, Lasse Engbo

2012-01-01

In the present study, bacterial growth in a rich media is analysed in a Stochastic Differential Equation (SDE) framework. It is demonstrated that the SDE formulation and smoothened state estimates provide a systematic framework for data driven model improvements, using random walk hidden states...

Data-Driven Anomaly Detection Performance for the Ares I-X Ground Diagnostic Prototype

Science.gov (United States)

Martin, Rodney A.; Schwabacher, Mark A.; Matthews, Bryan L.

2010-01-01

In this paper, we will assess the performance of a data-driven anomaly detection algorithm, the Inductive Monitoring System (IMS), which can be used to detect simulated Thrust Vector Control (TVC) system failures. However, the ability of IMS to detect these failures in a true operational setting may be related to the realistic nature of how they are simulated. As such, we will investigate both a low fidelity and high fidelity approach to simulating such failures, with the latter based upon the underlying physics. Furthermore, the ability of IMS to detect anomalies that were previously unknown and not previously simulated will be studied in earnest, as well as apparent deficiencies or misapplications that result from using the data-driven paradigm. Our conclusions indicate that robust detection performance of simulated failures using IMS is not appreciably affected by the use of a high fidelity simulation. However, we have found that the inclusion of a data-driven algorithm such as IMS into a suite of deployable health management technologies does add significant value.

Dromion-like structures and stability analysis in the variable coefficients complex Ginzburg–Landau equation

International Nuclear Information System (INIS)

Wong, Pring; Pang, Li-Hui; Huang, Long-Gang; Li, Yan-Qing; Lei, Ming; Liu, Wen-Jun

2015-01-01

The study of the complex Ginzburg–Landau equation, which can describe the fiber laser system, is of significance for ultra-fast laser. In this paper, dromion-like structures for the complex Ginzburg–Landau equation are considered due to their abundant nonlinear dynamics. Via the modified Hirota method and simplified assumption, the analytic dromion-like solution is obtained. The partial asymmetry of structure is particularly discussed, which arises from asymmetry of nonlinear and dispersion terms. Furthermore, the stability of dromion-like structures is analyzed. Oscillation structure emerges to exhibit strong interference when the dispersion loss is perturbed. Through the appropriate modulation of modified exponent parameter, the oscillation structure is transformed into two dromion-like structures. It indicates that the dromion-like structure is unstable, and the coherence intensity is affected by the modified exponent parameter. Results in this paper may be useful in accounting for some nonlinear phenomena in fiber laser systems, and understanding the essential role of modified Hirota method

Service and Data Driven Multi Business Model Platform in a World of Persuasive Technologies

DEFF Research Database (Denmark)

Andersen, Troels Christian; Bjerrum, Torben Cæsar Bisgaard

2016-01-01

companies in establishing a service organization that delivers, creates and captures value through service and data driven business models by utilizing their network, resources and customers and/or users. Furthermore, based on literature and collaboration with the case company, the suggestion of a new...... framework provides the necessary construction of how the manufac- turing companies can evolve their current business to provide multi service and data driven business models, using the same resources, networks and customers....

On the well-posedness of the stochastic Allen–Cahn equation in two dimensions

International Nuclear Information System (INIS)

Ryser, Marc D.; Nigam, Nilima; Tupper, Paul F.

2012-01-01

White noise-driven nonlinear stochastic partial differential equations (SPDEs) of parabolic type are frequently used to model physical systems in space dimensions d = 1, 2, 3. Whereas existence and uniqueness of weak solutions to these equations are well established in one dimension, the situation is different for d ⩾ 2. Despite their popularity in the applied sciences, higher dimensional versions of these SPDE models are generally assumed to be ill-posed by the mathematics community. We study this discrepancy on the specific example of the two dimensional Allen–Cahn equation driven by additive white noise. Since it is unclear how to define the notion of a weak solution to this equation, we regularize the noise and introduce a family of approximations. Based on heuristic arguments and numerical experiments, we conjecture that these approximations exhibit divergent behavior in the continuum limit. The results strongly suggest that shrinking the mesh size in simulations of the two-dimensional white noise-driven Allen–Cahn equation does not lead to the recovery of a physically meaningful limit.

A novel hierarchy of differential—integral equations and their generalized bi-Hamiltonian structures

International Nuclear Information System (INIS)

Zhai Yun-Yun; Geng Xian-Guo; He Guo-Liang

2014-01-01

With the aid of the zero-curvature equation, a novel integrable hierarchy of nonlinear evolution equations associated with a 3 × 3 matrix spectral problem is proposed. By using the trace identity, the bi-Hamiltonian structures of the hierarchy are established with two skew-symmetric operators. Based on two linear spectral problems, we obtain the infinite many conservation laws of the first member in the hierarchy

Objective, Quantitative, Data-Driven Assessment of Chemical Probes.

Science.gov (United States)

Antolin, Albert A; Tym, Joseph E; Komianou, Angeliki; Collins, Ian; Workman, Paul; Al-Lazikani, Bissan

2018-02-15

Chemical probes are essential tools for understanding biological systems and for target validation, yet selecting probes for biomedical research is rarely based on objective assessment of all potential compounds. Here, we describe the Probe Miner: Chemical Probes Objective Assessment resource, capitalizing on the plethora of public medicinal chemistry data to empower quantitative, objective, data-driven evaluation of chemical probes. We assess >1.8 million compounds for their suitability as chemical tools against 2,220 human targets and dissect the biases and limitations encountered. Probe Miner represents a valuable resource to aid the identification of potential chemical probes, particularly when used alongside expert curation. Copyright © 2017 The Authors. Published by Elsevier Ltd.. All rights reserved.

A Data-Driven Response Virtual Sensor Technique with Partial Vibration Measurements Using Convolutional Neural Network

Science.gov (United States)

Sun, Shan-Bin; He, Yuan-Yuan; Zhou, Si-Da; Yue, Zhen-Jiang

2017-01-01

Measurement of dynamic responses plays an important role in structural health monitoring, damage detection and other fields of research. However, in aerospace engineering, the physical sensors are limited in the operational conditions of spacecraft, due to the severe environment in outer space. This paper proposes a virtual sensor model with partial vibration measurements using a convolutional neural network. The transmissibility function is employed as prior knowledge. A four-layer neural network with two convolutional layers, one fully connected layer, and an output layer is proposed as the predicting model. Numerical examples of two different structural dynamic systems demonstrate the performance of the proposed approach. The excellence of the novel technique is further indicated using a simply supported beam experiment comparing to a modal-model-based virtual sensor, which uses modal parameters, such as mode shapes, for estimating the responses of the faulty sensors. The results show that the presented data-driven response virtual sensor technique can predict structural response with high accuracy. PMID:29231868

A Data-Driven Response Virtual Sensor Technique with Partial Vibration Measurements Using Convolutional Neural Network.

Science.gov (United States)

Sun, Shan-Bin; He, Yuan-Yuan; Zhou, Si-Da; Yue, Zhen-Jiang

2017-12-12

Measurement of dynamic responses plays an important role in structural health monitoring, damage detection and other fields of research. However, in aerospace engineering, the physical sensors are limited in the operational conditions of spacecraft, due to the severe environment in outer space. This paper proposes a virtual sensor model with partial vibration measurements using a convolutional neural network. The transmissibility function is employed as prior knowledge. A four-layer neural network with two convolutional layers, one fully connected layer, and an output layer is proposed as the predicting model. Numerical examples of two different structural dynamic systems demonstrate the performance of the proposed approach. The excellence of the novel technique is further indicated using a simply supported beam experiment comparing to a modal-model-based virtual sensor, which uses modal parameters, such as mode shapes, for estimating the responses of the faulty sensors. The results show that the presented data-driven response virtual sensor technique can predict structural response with high accuracy.

Privacy in Sensor-Driven Human Data Collection: A Guide for Practitioners

OpenAIRE

Stopczynski, Arkadiusz; Pietri, Riccardo; Pentland, Alex; Lazer, David; Lehmann, Sune

2014-01-01

In recent years, the amount of information collected about human beings has increased dramatically. This development has been partially driven by individuals posting and storing data about themselves and friends using online social networks or collecting their data for self-tracking purposes (quantified-self movement). Across the sciences, researchers conduct studies collecting data with an unprecedented resolution and scale. Using computational power combined with mathematical models, such r...

Semilinear hyperbolic systems and equations with singular initial data

International Nuclear Information System (INIS)

Gramchev, T.

1991-07-01

We study the weak limits of solutions u ε (t, ·) for ε→0 to semilinear strictly hyperbolic systems and wave equations with initial data u ε (0, ·) approximating a distribution κ, 0 ε (t, ·) for ε→0 exists. 13 refs

Analisis Structural Equation Modeling Pada Pengaruh Kebiasaan Mengakses Facebook Terhadap Kualitas Hidup Dan Prestasi Akademik Mahasiswa

OpenAIRE

Nalim, Nalim

2014-01-01

This study tried to determine the effect on quality of life Facebook and students' academic achievement. A total of 210 samples were taken from three universities with proportional multistage random sampling method, while data analysis was conducted using Structural Equation Modeling (SEM) with software lisrel 8.80 (student version). The results showed, although according to the investigators alleged that Facebook had a negative impact on quality of life, but the effect was not...