Data driven innovations in structural health monitoring
Rosales, M. J.; Liyanapathirana, R.
2017-05-01
At present, substantial investments are being allocated to civil infrastructures also considered as valuable assets at a national or global scale. Structural Health Monitoring (SHM) is an indispensable tool required to ensure the performance and safety of these structures based on measured response parameters. The research to date on damage assessment has tended to focus on the utilization of wireless sensor networks (WSN) as it proves to be the best alternative over the traditional visual inspections and tethered or wired counterparts. Over the last decade, the structural health and behaviour of innumerable infrastructure has been measured and evaluated owing to several successful ventures of implementing these sensor networks. Various monitoring systems have the capability to rapidly transmit, measure, and store large capacities of data. The amount of data collected from these networks have eventually been unmanageable which paved the way to other relevant issues such as data quality, relevance, re-use, and decision support. There is an increasing need to integrate new technologies in order to automate the evaluation processes as well as to enhance the objectivity of data assessment routines. This paper aims to identify feasible methodologies towards the application of time-series analysis techniques to judiciously exploit the vast amount of readily available as well as the upcoming data resources. It continues the momentum of a greater effort to collect and archive SHM approaches that will serve as data-driven innovations for the assessment of damage through efficient algorithms and data analytics.
Flexibility of Data-driven Process Structures
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
Data-driven discovery of partial differential equations.
Rudy, Samuel H; Brunton, Steven L; Proctor, Joshua L; Kutz, J Nathan
2017-04-01
We propose a sparse regression method capable of discovering the governing partial differential equation(s) of a given system by time series measurements in the spatial domain. The regression framework relies on sparsity-promoting techniques to select the nonlinear and partial derivative terms of the governing equations that most accurately represent the data, bypassing a combinatorially large search through all possible candidate models. The method balances model complexity and regression accuracy by selecting a parsimonious model via Pareto analysis. Time series measurements can be made in an Eulerian framework, where the sensors are fixed spatially, or in a Lagrangian framework, where the sensors move with the dynamics. The method is computationally efficient, robust, and demonstrated to work on a variety of canonical problems spanning a number of scientific domains including Navier-Stokes, the quantum harmonic oscillator, and the diffusion equation. Moreover, the method is capable of disambiguating between potentially nonunique dynamical terms by using multiple time series taken with different initial data. Thus, for a traveling wave, the method can distinguish between a linear wave equation and the Korteweg-de Vries equation, for instance. The method provides a promising new technique for discovering governing equations and physical laws in parameterized spatiotemporal systems, where first-principles derivations are intractable.
The Structural Consequences of Big Data-Driven Education.
Zeide, Elana
2017-06-01
Educators and commenters who evaluate big data-driven learning environments focus on specific questions: whether automated education platforms improve learning outcomes, invade student privacy, and promote equality. This article puts aside separate unresolved-and perhaps unresolvable-issues regarding the concrete effects of specific technologies. It instead examines how big data-driven tools alter the structure of schools' pedagogical decision-making, and, in doing so, change fundamental aspects of America's education enterprise. Technological mediation and data-driven decision-making have a particularly significant impact in learning environments because the education process primarily consists of dynamic information exchange. In this overview, I highlight three significant structural shifts that accompany school reliance on data-driven instructional platforms that perform core school functions: teaching, assessment, and credentialing. First, virtual learning environments create information technology infrastructures featuring constant data collection, continuous algorithmic assessment, and possibly infinite record retention. This undermines the traditional intellectual privacy and safety of classrooms. Second, these systems displace pedagogical decision-making from educators serving public interests to private, often for-profit, technology providers. They constrain teachers' academic autonomy, obscure student evaluation, and reduce parents' and students' ability to participate or challenge education decision-making. Third, big data-driven tools define what "counts" as education by mapping the concepts, creating the content, determining the metrics, and setting desired learning outcomes of instruction. These shifts cede important decision-making to private entities without public scrutiny or pedagogical examination. In contrast to the public and heated debates that accompany textbook choices, schools often adopt education technologies ad hoc. Given education
Data-driven sensor placement from coherent fluid structures
Manohar, Krithika; Kaiser, Eurika; Brunton, Bingni W.; Kutz, J. Nathan; Brunton, Steven L.
2017-11-01
Optimal sensor placement is a central challenge in the prediction, estimation and control of fluid flows. We reinterpret sensor placement as optimizing discrete samples of coherent fluid structures for full state reconstruction. This permits a drastic reduction in the number of sensors required for faithful reconstruction, since complex fluid interactions can often be described by a small number of coherent structures. Our work optimizes point sensors using the pivoted matrix QR factorization to sample coherent structures directly computed from flow data. We apply this sampling technique in conjunction with various data-driven modal identification methods, including the proper orthogonal decomposition (POD) and dynamic mode decomposition (DMD). In contrast to POD-based sensors, DMD demonstrably enables the optimization of sensors for prediction in systems exhibiting multiple scales of dynamics. Finally, reconstruction accuracy from pivot sensors is shown to be competitive with sensors obtained using traditional computationally prohibitive optimization methods.
A data-driven alternative to the fractional Fokker–Planck equation
International Nuclear Information System (INIS)
Pressé, Steve
2015-01-01
Anomalous diffusion processes are ubiquitous in biology and arise in the transport of proteins, vesicles and other particles. Such anomalously diffusive behavior is attributed to a number of factors within the cell including heterogeneous environments, active transport processes and local trapping/binding. There are a number of microscopic principles—such as power law jump size and/or waiting time distributions—from which the fractional Fokker–Planck equation (FFPE) can be derived and used to provide mechanistic insight into the origins of anomalous diffusion. On the other hand, it is fair to ask if other microscopic principles could also have given rise to the evolution of an observed density profile that appears to be well fit by an FFPE. Here we discuss another possible mechanistic alternative that can give rise to densities like those generated by FFPEs. Rather than to fit a density (or concentration profile) using a solution to the spatial FFPE, we reconstruct the profile generated by an FFPE using a regular FPE with a spatial and time-dependent force. We focus on the special case of the spatial FFPE for superdiffusive processes. This special case is relevant to, for example, active transport in a biological context. We devise a prescription for extracting such forces on synthetically generated data and provide an interpretation to the forces extracted. In particular, the time-dependence of forces could tell us about ATP depletion or changes in the cell's metabolic activity. Modeling anomalous behavior with normal diffusion driven by these effective forces yields an alternative mechanistic picture that, ultimately, could help motivate future experiments. (paper)
Dynamic model reduction using data-driven Loewner-framework applied to thermally morphing structures
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.
Data-driven modelling of structured populations a practical guide to the integral projection model
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...
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 ...
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.
DeDaL: Cytoscape 3 app for producing and morphing data-driven and structure-driven network layouts.
Czerwinska, Urszula; Calzone, Laurence; Barillot, Emmanuel; Zinovyev, Andrei
2015-08-14
Visualization and analysis of molecular profiling data together with biological networks are able to provide new mechanistic insights into biological functions. Currently, it is possible to visualize high-throughput data on top of pre-defined network layouts, but they are not always adapted to a given data analysis task. A network layout based simultaneously on the network structure and the associated multidimensional data might be advantageous for data visualization and analysis in some cases. We developed a Cytoscape app, which allows constructing biological network layouts based on the data from molecular profiles imported as values of node attributes. DeDaL is a Cytoscape 3 app, which uses linear and non-linear algorithms of dimension reduction to produce data-driven network layouts based on multidimensional data (typically gene expression). DeDaL implements several data pre-processing and layout post-processing steps such as continuous morphing between two arbitrary network layouts and aligning one network layout with respect to another one by rotating and mirroring. The combination of all these functionalities facilitates the creation of insightful network layouts representing both structural network features and correlation patterns in multivariate data. We demonstrate the added value of applying DeDaL in several practical applications, including an example of a large protein-protein interaction network. DeDaL is a convenient tool for applying data dimensionality reduction methods and for designing insightful data displays based on data-driven layouts of biological networks, built within Cytoscape environment. DeDaL is freely available for downloading at http://bioinfo-out.curie.fr/projects/dedal/.
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 Equations and Causation
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.
Structural Equation Model Trees
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…
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.
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.
Yan, Wentao; Lin, Stephen; Kafka, Orion L.; Lian, Yanping; Yu, Cheng; Liu, Zeliang; Yan, Jinhui; Wolff, Sarah; Wu, Hao; Ndip-Agbor, Ebot; Mozaffar, Mojtaba; Ehmann, Kornel; Cao, Jian; Wagner, Gregory J.; Liu, Wing Kam
2018-01-01
Additive manufacturing (AM) possesses appealing potential for manipulating material compositions, structures and properties in end-use products with arbitrary shapes without the need for specialized tooling. Since the physical process is difficult to experimentally measure, numerical modeling is a powerful tool to understand the underlying physical mechanisms. This paper presents our latest work in this regard based on comprehensive material modeling of process-structure-property relationships for AM materials. The numerous influencing factors that emerge from the AM process motivate the need for novel rapid design and optimization approaches. For this, we propose data-mining as an effective solution. Such methods—used in the process-structure, structure-properties and the design phase that connects them—would allow for a design loop for AM processing and materials. We hope this article will provide a road map to enable AM fundamental understanding for the monitoring and advanced diagnostics of AM processing.
Yan, Wentao; Lin, Stephen; Kafka, Orion L.; Lian, Yanping; Yu, Cheng; Liu, Zeliang; Yan, Jinhui; Wolff, Sarah; Wu, Hao; Ndip-Agbor, Ebot; Mozaffar, Mojtaba; Ehmann, Kornel; Cao, Jian; Wagner, Gregory J.; Liu, Wing Kam
2018-05-01
Additive manufacturing (AM) possesses appealing potential for manipulating material compositions, structures and properties in end-use products with arbitrary shapes without the need for specialized tooling. Since the physical process is difficult to experimentally measure, numerical modeling is a powerful tool to understand the underlying physical mechanisms. This paper presents our latest work in this regard based on comprehensive material modeling of process-structure-property relationships for AM materials. The numerous influencing factors that emerge from the AM process motivate the need for novel rapid design and optimization approaches. For this, we propose data-mining as an effective solution. Such methods—used in the process-structure, structure-properties and the design phase that connects them—would allow for a design loop for AM processing and materials. We hope this article will provide a road map to enable AM fundamental understanding for the monitoring and advanced diagnostics of AM processing.
Tibaduiza, D.-A.; Torres-Arredondo, M.-A.; Mujica, L. E.; Rodellar, J.; Fritzen, C.-P.
2013-12-01
This article is concerned with the practical use of Multiway Principal Component Analysis (MPCA), Discrete Wavelet Transform (DWT), Squared Prediction Error (SPE) measures and Self-Organizing Maps (SOM) to detect and classify damages in mechanical structures. The formalism is based on a distributed piezoelectric active sensor network for the excitation and detection of structural dynamic responses. Statistical models are built using PCA when the structure is known to be healthy either directly from the dynamic responses or from wavelet coefficients at different scales representing Time-frequency information. Different damages on the tested structures are simulated by adding masses at different positions. The data from the structure in different states (damaged or not) are then projected into the different principal component models by each actuator in order to obtain the input feature vectors for a SOM from the scores and the SPE measures. An aircraft fuselage from an Airbus A320 and a multi-layered carbon fiber reinforced plastic (CFRP) plate are used as examples to test the approaches. Results are presented, compared and discussed in order to determine their potential in structural health monitoring. These results showed that all the simulated damages were detectable and the selected features proved capable of separating all damage conditions from the undamaged state for both approaches.
NMR data-driven structure determination using NMR-I-TASSER in the CASD-NMR experiment
Energy Technology Data Exchange (ETDEWEB)
Jang, Richard [Huazhong University of Science and Technology, School of Software Engineering (China); Wang, Yan [Huazhong University of Science and Technology, School of Life Science and Technology (China); Xue, Zhidong, E-mail: zdxue@hust.edu.cn [Huazhong University of Science and Technology, School of Software Engineering (China); Zhang, Yang, E-mail: zhng@umich.edu [University of Michigan, Department of Computational Medicine and Bioinformatics (United States)
2015-08-15
NMR-I-TASSER, an adaption of the I-TASSER algorithm combining NMR data for protein structure determination, recently joined the second round of the CASD-NMR experiment. Unlike many molecular dynamics-based methods, NMR-I-TASSER takes a molecular replacement-like approach to the problem by first threading the target through the PDB to identify structural templates which are then used for iterative NOE assignments and fragment structure assembly refinements. The employment of multiple templates allows NMR-I-TASSER to sample different topologies while convergence to a single structure is not required. Retroactive and blind tests of the CASD-NMR targets from Rounds 1 and 2 demonstrate that even without using NOE peak lists I-TASSER can generate correct structure topology with 15 of 20 targets having a TM-score above 0.5. With the addition of NOE-based distance restraints, NMR-I-TASSER significantly improved the I-TASSER models with all models having the TM-score above 0.5. The average RMSD was reduced from 5.29 to 2.14 Å in Round 1 and 3.18 to 1.71 Å in Round 2. There is no obvious difference in the modeling results with using raw and refined peak lists, indicating robustness of the pipeline to the NOE assignment errors. Overall, despite the low-resolution modeling the current NMR-I-TASSER pipeline provides a coarse-grained structure folding approach complementary to traditional molecular dynamics simulations, which can produce fast near-native frameworks for atomic-level structural refinement.
Structural equations in language learning
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
Data-Driven H∞ Control for Nonlinear Distributed Parameter Systems.
Luo, Biao; Huang, Tingwen; Wu, Huai-Ning; Yang, Xiong
2015-11-01
The data-driven H∞ control problem of nonlinear distributed parameter systems is considered in this paper. An off-policy learning method is developed to learn the H∞ control policy from real system data rather than the mathematical model. First, Karhunen-Loève decomposition is used to compute the empirical eigenfunctions, which are then employed to derive a reduced-order model (ROM) of slow subsystem based on the singular perturbation theory. The H∞ control problem is reformulated based on the ROM, which can be transformed to solve the Hamilton-Jacobi-Isaacs (HJI) equation, theoretically. To learn the solution of the HJI equation from real system data, a data-driven off-policy learning approach is proposed based on the simultaneous policy update algorithm and its convergence is proved. For implementation purpose, a neural network (NN)- based action-critic structure is developed, where a critic NN and two action NNs are employed to approximate the value function, control, and disturbance policies, respectively. Subsequently, a least-square NN weight-tuning rule is derived with the method of weighted residuals. Finally, the developed data-driven off-policy learning approach is applied to a nonlinear diffusion-reaction process, and the obtained results demonstrate its effectiveness.
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%.
Handbook of structural equation modeling
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
Data driven marketing for dummies
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
Data-driven execution of fast multipole methods
Ltaief, Hatem
2013-09-17
Fast multipole methods (FMMs) have O (N) complexity, are compute bound, and require very little synchronization, which makes them a favorable algorithm on next-generation supercomputers. Their most common application is to accelerate N-body problems, but they can also be used to solve boundary integral equations. When the particle distribution is irregular and the tree structure is adaptive, load balancing becomes a non-trivial question. A common strategy for load balancing FMMs is to use the work load from the previous step as weights to statically repartition the next step. The authors discuss in the paper another approach based on data-driven execution to efficiently tackle this challenging load balancing problem. The core idea consists of breaking the most time-consuming stages of the FMMs into smaller tasks. The algorithm can then be represented as a directed acyclic graph where nodes represent tasks and edges represent dependencies among them. The execution of the algorithm is performed by asynchronously scheduling the tasks using the queueing and runtime for kernels runtime environment, in a way such that data dependencies are not violated for numerical correctness purposes. This asynchronous scheduling results in an out-of-order execution. The performance results of the data-driven FMM execution outperform the previous strategy and show linear speedup on a quad-socket quad-core Intel Xeon system.Copyright © 2013 John Wiley & Sons, Ltd. Copyright © 2013 John Wiley & Sons, Ltd.
Structural Equation Modeling of Multivariate Time Series
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…
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...
A first course in structural equation modeling
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...
Linear causal modeling with structural equations
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
Rysavy, Steven J; Beck, David AC; Daggett, Valerie
2014-01-01
Protein function is intimately linked to protein structure and dynamics yet experimentally determined structures frequently omit regions within a protein due to indeterminate data, which is often due protein dynamics. We propose that atomistic molecular dynamics simulations provide a diverse sampling of biologically relevant structures for these missing segments (and beyond) to improve structural modeling and structure prediction. Here we make use of the Dynameomics data warehouse, which contains simulations of representatives of essentially all known protein folds. We developed novel computational methods to efficiently identify, rank and retrieve small peptide structures, or fragments, from this database. We also created a novel data model to analyze and compare large repositories of structural data, such as contained within the Protein Data Bank and the Dynameomics data warehouse. Our evaluation compares these structural repositories for improving loop predictions and analyzes the utility of our methods and models. Using a standard set of loop structures, containing 510 loops, 30 for each loop length from 4 to 20 residues, we find that the inclusion of Dynameomics structures in fragment-based methods improves the quality of the loop predictions without being dependent on sequence homology. Depending on loop length, ∼25–75% of the best predictions came from the Dynameomics set, resulting in lower main chain root-mean-square deviations for all fragment lengths using the combined fragment library. We also provide specific cases where Dynameomics fragments provide better predictions for NMR loop structures than fragments from crystal structures. Online access to these fragment libraries is available at http://www.dynameomics.org/fragments. PMID:25142412
Rysavy, Steven J; Beck, David A C; Daggett, Valerie
2014-11-01
Protein function is intimately linked to protein structure and dynamics yet experimentally determined structures frequently omit regions within a protein due to indeterminate data, which is often due protein dynamics. We propose that atomistic molecular dynamics simulations provide a diverse sampling of biologically relevant structures for these missing segments (and beyond) to improve structural modeling and structure prediction. Here we make use of the Dynameomics data warehouse, which contains simulations of representatives of essentially all known protein folds. We developed novel computational methods to efficiently identify, rank and retrieve small peptide structures, or fragments, from this database. We also created a novel data model to analyze and compare large repositories of structural data, such as contained within the Protein Data Bank and the Dynameomics data warehouse. Our evaluation compares these structural repositories for improving loop predictions and analyzes the utility of our methods and models. Using a standard set of loop structures, containing 510 loops, 30 for each loop length from 4 to 20 residues, we find that the inclusion of Dynameomics structures in fragment-based methods improves the quality of the loop predictions without being dependent on sequence homology. Depending on loop length, ∼ 25-75% of the best predictions came from the Dynameomics set, resulting in lower main chain root-mean-square deviations for all fragment lengths using the combined fragment library. We also provide specific cases where Dynameomics fragments provide better predictions for NMR loop structures than fragments from crystal structures. Online access to these fragment libraries is available at http://www.dynameomics.org/fragments. © 2014 The Protein Society.
Data-driven architectural production and operation
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
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.
Data-Driven Problems in Elasticity
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.
Consistent data-driven computational mechanics
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.
Structural equation modeling methods and applications
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
Directory of Open Access Journals (Sweden)
Gang Li
2016-09-01
Full Text Available The spatial–temporal correlation is an important feature of sensor data in wireless sensor networks (WSNs. Most of the existing works based on the spatial–temporal correlation can be divided into two parts: redundancy reduction and anomaly detection. These two parts are pursued separately in existing works. In this work, the combination of temporal data-driven sleep scheduling (TDSS and spatial data-driven anomaly detection is proposed, where TDSS can reduce data redundancy. The TDSS model is inspired by transmission control protocol (TCP congestion control. Based on long and linear cluster structure in the tunnel monitoring system, cooperative TDSS and spatial data-driven anomaly detection are then proposed. To realize synchronous acquisition in the same ring for analyzing the situation of every ring, TDSS is implemented in a cooperative way in the cluster. To keep the precision of sensor data, spatial data-driven anomaly detection based on the spatial correlation and Kriging method is realized to generate an anomaly indicator. The experiment results show that cooperative TDSS can realize non-uniform sensing effectively to reduce the energy consumption. In addition, spatial data-driven anomaly detection is quite significant for maintaining and improving the precision of sensor data.
Advanced structural equation modeling issues and techniques
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.
Multiplicity Control in Structural Equation Modeling
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…
Data-driven non-Markovian closure models
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
Principles and practice of structural equation modeling
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
Data-driven regionalization of housing markets
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
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...
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.
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....
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....
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.
Structural equation modeling and natural systems
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.
Meta-analytic structural equation modelling
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.
Structural equation models from paths to networks
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...
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)
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.
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.
A data driven nonlinear stochastic model for blood glucose dynamics.
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.
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.
Exploratory structural equation modeling of personality data.
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.
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...
Controller synthesis for negative imaginary systems: a data driven approach
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 discovery of Koopman eigenfunctions using deep learning
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.
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.
The algebraic structure of lax equations for infinite matrices
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
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...
Data driven modelling of vertical atmospheric radiation
International Nuclear Information System (INIS)
Antoch, Jaromir; Hlubinka, Daniel
2011-01-01
In the Czech Hydrometeorological Institute (CHMI) there exists a unique set of meteorological measurements consisting of the values of vertical atmospheric levels of beta and gamma radiation. In this paper a stochastic data-driven model based on nonlinear regression and on nonhomogeneous Poisson process is suggested. In the first part of the paper, growth curves were used to establish an appropriate nonlinear regression model. For comparison we considered a nonhomogeneous Poisson process with its intensity based on growth curves. In the second part both approaches were applied to the real data and compared. Computational aspects are briefly discussed as well. The primary goal of this paper is to present an improved understanding of the distribution of environmental radiation as obtained from the measurements of the vertical radioactivity profiles by the radioactivity sonde system. - Highlights: → We model vertical atmospheric levels of beta and gamma radiation. → We suggest appropriate nonlinear regression model based on growth curves. → We compare nonlinear regression modelling with Poisson process based modeling. → We apply both models to the real data.
Hamiltonian structure of the Lotka-Volterra equations
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.
Fitting ARMA Time Series by Structural Equation Models.
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)
A Structural Equation Modeling Analysis of Influences on Juvenile Delinquency
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…
Hierarchical regression analysis in structural Equation Modeling
de Jong, P.F.
1999-01-01
In a hierarchical or fixed-order regression analysis, the independent variables are entered into the regression equation in a prespecified order. Such an analysis is often performed when the extra amount of variance accounted for in a dependent variable by a specific independent variable is the main
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.
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.
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)
Analytic structure of solutions to multiconfiguration equations
Energy Technology Data Exchange (ETDEWEB)
Fournais, Soeren [Department of Mathematical Sciences, University of Aarhus, Ny Munkegade, Building 1530, DK-8000 Arhus C (Denmark); Hoffmann-Ostenhof, Maria [Fakultaet fuer Mathematik, Universitaet Wien, Nordbergstrasse 15, A-1090 Vienna (Austria); Hoffmann-Ostenhof, Thomas [Institut fuer Theoretische Chemie, Waehringerstrasse 17, Universitaet Wien, A-1090 Vienna (Austria); Soerensen, Thomas Oestergaard [Department of Mathematics, Imperial College London, Huxley Building, 180 Queen' s Gate, London SW7 2AZ (United Kingdom)], E-mail: fournais@imf.au.dk, E-mail: Maria.Hoffmann-Ostenhof@univie.ac.at, E-mail: thoffman@esi.ac.at, E-mail: t.sorensen@imperial.ac.uk
2009-08-07
We study the regularity at the positions of the (fixed) nuclei of solutions to (non-relativistic) multiconfiguration equations (including Hartree-Fock) of Coulomb systems. We prove the following: let {l_brace}{psi}{sub 1}, ..., {psi}{sub M}{r_brace} be any solution to the rank-M multiconfiguration equations for a molecule with L fixed nuclei at R{sub 1},...,R{sub L} element of R{sup 3}. Then, for any j in {l_brace}1, ..., M{r_brace}, k in {l_brace}1, ..., L{r_brace}, there exists a neighborhood U{sub j,k} subset or equal R{sup 3} of R{sub k}, and functions {psi}{sup (1)}{sub j,k}, {psi}{sup (2)}{sub j,k}, real analytic in U{sub j,k}, such that {phi}{sub j}(x)={phi}{sub j,k}{sup (1)}(x)+|x-R{sub k}|{phi}{sub j,k}{sup (2)}(x), x element of U{sub j,k}. A similar result holds for the corresponding electron density. The proof uses the Kustaanheimo-Stiefel transformation, as applied in [9] to the study of the eigenfunctions of the Schroedinger operator of atoms and molecules near two-particle coalescence points.
Meta-analysis a structural equation modeling approach
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
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.
Using Shape Memory Alloys: A Dynamic Data Driven Approach
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.
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...
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
Generalized latent variable modeling multilevel, longitudinal, and structural equation models
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.
Data-driven architectural design to production and operation
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
Data-Driven Methods to Diversify Knowledge of Human Psychology
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.
Thermodynamically consistent data-driven computational mechanics
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.
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
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
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).
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.
Dynamic Data Driven Applications Systems (DDDAS)
2013-03-06
detected Level 1 (L1) sensors: PIR & Piezoelectric Level 2 (L2) sensor: Overhead camera (UAV) Level 1.1 sensor: LIDAR Dynamic Influence Diagram ID1...Effects of Porous Shape Memory Alloys • Bayesian Computational Sensor Networks for Aircraft Structural Health Monitoring • Fluid SLAM and the Robotic...Structural Health Monitoring – PI: Thomas Henderson, U. of Utah • Fluid SLAM and the Robotic Reconstruction of Localized Atmospheric Phenomena – PI
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.
Data-driven in computational plasticity
Ibáñez, R.; Abisset-Chavanne, E.; Cueto, E.; Chinesta, F.
2018-05-01
Computational mechanics is taking an enormous importance in industry nowadays. On one hand, numerical simulations can be seen as a tool that allows the industry to perform fewer experiments, reducing costs. On the other hand, the physical processes that are intended to be simulated are becoming more complex, requiring new constitutive relationships to capture such behaviors. Therefore, when a new material is intended to be classified, an open question still remains: which constitutive equation should be calibrated. In the present work, the use of model order reduction techniques are exploited to identify the plastic behavior of a material, opening an alternative route with respect to traditional calibration methods. Indeed, the main objective is to provide a plastic yield function such that the mismatch between experiments and simulations is minimized. Therefore, once the experimental results just like the parameterization of the plastic yield function are provided, finding the optimal plastic yield function can be seen either as a traditional optimization or interpolation problem. It is important to highlight that the dimensionality of the problem is equal to the number of dimensions related to the parameterization of the yield function. Thus, the use of sparse interpolation techniques seems almost compulsory.
Dynamic Data Driven Applications Systems (DDDAS)
2012-05-03
response) – Earthquakes, hurricanes, tornados, wildfires, floods, landslides, tsunamis, … • Critical Infrastructure systems – Electric-powergrid...Multiphase Flow Weather and Climate Structural Mechanics Seismic Processing Aerodynamics Geophysical Fluids Quantum Chemistry Actinide Chemistry...Alloys • Approach and Objectives: Consider porous SMAs: similar macroscopic behavior but mass /weight is less, and thus attractive for
Dynamic Data-Driven UAV Network for Plume Characterization
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
Variables and equations in hybrid systems with structural changes
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
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)
Data-Driven Exercises for Chemistry: A New Digital Collection
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
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
Dynamically adaptive data-driven simulation of extreme hydrological flows
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
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
Data-Driven Education: Technologies and Directions
CERN. Geneva
2017-01-01
An educational program of study can be viewed as a knowledge graph consisting of learning units and relationships between them. Such a knowledge graph provides the core data structure for organizing and navigating learning experiences. We address three issues in this talk. First, how can we synthesize the knowledge graph, given a set of concepts to be covered in the study program. Next, how can we use data mining to identify and correct deficiencies in a knowledge graph. Finally, how can we use data mining to form study groups with the goal of maximizing overall learning. We conclude by pointing out some open research problems. BIOGRAPHY OF RAKESH AGRAWAL Rakesh Agrawal is the President and Founder of the Data Insights Laboratories, San Jose, USA. He is also the Rukmini Visiting Chair Professor at the Indian Institute of Science, Bangalore, India and an Invited Professor at EPFL, Lausanne, Switzerland. He is a member of the National Academy of Engineering, both USA and India, a...
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.
Data driven model generation based on computational intelligence
Gemmar, Peter; Gronz, Oliver; Faust, Christophe; Casper, Markus
2010-05-01
The simulation of discharges at a local gauge or the modeling of large scale river catchments are effectively involved in estimation and decision tasks of hydrological research and practical applications like flood prediction or water resource management. However, modeling such processes using analytical or conceptual approaches is made difficult by both complexity of process relations and heterogeneity of processes. It was shown manifold that unknown or assumed process relations can principally be described by computational methods, and that system models can automatically be derived from observed behavior or measured process data. This study describes the development of hydrological process models using computational methods including Fuzzy logic and artificial neural networks (ANN) in a comprehensive and automated manner. Methods We consider a closed concept for data driven development of hydrological models based on measured (experimental) data. The concept is centered on a Fuzzy system using rules of Takagi-Sugeno-Kang type which formulate the input-output relation in a generic structure like Ri : IFq(t) = lowAND...THENq(t+Δt) = ai0 +ai1q(t)+ai2p(t-Δti1)+ai3p(t+Δti2)+.... The rule's premise part (IF) describes process states involving available process information, e.g. actual outlet q(t) is low where low is one of several Fuzzy sets defined over variable q(t). The rule's conclusion (THEN) estimates expected outlet q(t + Δt) by a linear function over selected system variables, e.g. actual outlet q(t), previous and/or forecasted precipitation p(t ?Δtik). In case of river catchment modeling we use head gauges, tributary and upriver gauges in the conclusion part as well. In addition, we consider temperature and temporal (season) information in the premise part. By creating a set of rules R = {Ri|(i = 1,...,N)} the space of process states can be covered as concise as necessary. Model adaptation is achieved by finding on optimal set A = (aij) of conclusion
A Structural Equation Approach to Models with Spatial Dependence
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
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
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
Sensitivity Analysis in Structural Equation Models: Cases and Their Influence
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…
On the specification of structural equation models for ecological systems
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
A Structural Equation Model of Expertise in College Physics
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…
On the Use of Structural Equation Models in Marketing Modeling
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
Structural Equations and Causal Explanations: Some Challenges for Causal SEM
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…
A Structural Equation Model of Conceptual Change in Physics
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…
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)
Data-driven non-linear elasticity: constitutive manifold construction and problem discretization
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.
A data-driven approach for retrieving temperatures and abundances in brown dwarf atmospheres
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...
Annotated bibliography of structural equation modelling: technical work.
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).
Poisson structure of the equations of ideal multispecies fluid electrodynamics
International Nuclear Information System (INIS)
Spencer, R.G.
1984-01-01
The equations of the two- (or multi-) fluid model of plasma physics are recast in Hamiltonian form, following general methods of symplectic geometry. The dynamical variables are the fields of physical interest, but are noncanonical, so that the Poisson bracket in the theory is not the standard one. However, it is a skew-symmetric bilinear form which, from the method of derivation, automatically satisfies the Jacobi identity; therefore, this noncanonical structure has all the essential properties of a canonical Poisson bracket
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.
Structural Equation Modeling with Lisrel: An Initial Vision
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.
A data-driven approach for modeling post-fire debris-flow volumes and their uncertainty
Friedel, Michael J.
2011-01-01
This study demonstrates the novel application of genetic programming to evolve nonlinear post-fire debris-flow volume equations from variables associated with a data-driven conceptual model of the western United States. The search space is constrained using a multi-component objective function that simultaneously minimizes root-mean squared and unit errors for the evolution of fittest equations. An optimization technique is then used to estimate the limits of nonlinear prediction uncertainty associated with the debris-flow equations. In contrast to a published multiple linear regression three-variable equation, linking basin area with slopes greater or equal to 30 percent, burn severity characterized as area burned moderate plus high, and total storm rainfall, the data-driven approach discovers many nonlinear and several dimensionally consistent equations that are unbiased and have less prediction uncertainty. Of the nonlinear equations, the best performance (lowest prediction uncertainty) is achieved when using three variables: average basin slope, total burned area, and total storm rainfall. Further reduction in uncertainty is possible for the nonlinear equations when dimensional consistency is not a priority and by subsequently applying a gradient solver to the fittest solutions. The data-driven modeling approach can be applied to nonlinear multivariate problems in all fields of study.
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.
Data-Driven Learning: Reasonable Fears and Rational Reassurance
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 Regulation and Governance in Smart Cities
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
Data-Driven Planning: Using Assessment in Strategic Planning
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…
Data-Driven Model Order Reduction for Bayesian Inverse Problems
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.
Data mining, knowledge discovery and data-driven modelling
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
Scalable data-driven short-term traffic prediction
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
Data-driven analysis of blood glucose management effectiveness
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
Data-Driven Learning of Q-Matrix
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…
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
Developing Annotation Solutions for Online Data Driven Learning
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…
Data-driven modelling of LTI systems using symbolic regression
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
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)
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
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.
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.
Crystal structure optimisation using an auxiliary equation of state
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.
The Monge-Ampère equation: Hamiltonian and symplectic structures, recursions, and hierarchies
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 oscillatory differential equations II
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...
Structural Equation Models in a Redundancy Analysis Framework With Covariates.
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.
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.
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
Lagrangian structures, integrability and chaos for 3D dynamical equations
International Nuclear Information System (INIS)
Bustamante, Miguel D; Hojman, Sergio A
2003-01-01
In this paper, we consider the general setting for constructing action principles for three-dimensional first-order autonomous equations. We present the results for some integrable and non-integrable cases of the Lotka-Volterra equation, and show Lagrangian descriptions which are valid for systems satisfying Shil'nikov criteria on the existence of strange attractors, though chaotic behaviour has not been verified up to now. The Euler-Lagrange equations we get for these systems usually present 'time reparametrization' invariance, though other kinds of invariance may be found according to the kernel of the associated symplectic 2-form. The formulation of a Hamiltonian structure (Poisson brackets and Hamiltonians) for these systems from the Lagrangian viewpoint leads to a method of finding new constants of the motion starting from known ones, which is applied to some systems found in the literature known to possess a constant of the motion, to find the other and thus showing their integrability. In particular, we show that the so-called ABC system is completely integrable if it possesses one constant of the motion
Data-Driven Controller Design The H2 Approach
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...
Structural Equation Modeling with Mplus Basic Concepts, Applications, and Programming
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
Modeling the Informal Economy in Mexico. A Structural Equation Approach
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...
Satisfaction in border tourism: An analysis with structural equations
Directory of Open Access Journals (Sweden)
Juan Antonio Jimber del Río
2017-05-01
Full Text Available Border tourism is the temporary displacement of people to the dividing line between two countries contiguous areas. This activity promotes the economic development of these geographical regions. The aim of this research is to analyze visitors from the Dominican Republic and Haiti border. We propose the results of an empirical study with structural equations that show correlations between the attitude factor towards the border tourism, the value factors perceived by the tourist, satisfaction and loyalty of the visitor in the destination place.
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.
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 data-driven framework for investigating customer retention
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 ...
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.
Authoring Data-Driven Videos with DataClips.
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.
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
Predictive model for early math skills based on structural equations.
Aragón, Estíbaliz; Navarro, José I; Aguilar, Manuel; Cerda, Gamal; García-Sedeño, Manuel
2016-12-01
Early math skills are determined by higher cognitive processes that are particularly important for acquiring and developing skills during a child's early education. Such processes could be a critical target for identifying students at risk for math learning difficulties. Few studies have considered the use of a structural equation method to rationalize these relations. Participating in this study were 207 preschool students ages 59 to 72 months, 108 boys and 99 girls. Performance with respect to early math skills, early literacy, general intelligence, working memory, and short-term memory was assessed. A structural equation model explaining 64.3% of the variance in early math skills was applied. Early literacy exhibited the highest statistical significance (β = 0.443, p < 0.05), followed by intelligence (β = 0.286, p < 0.05), working memory (β = 0.220, p < 0.05), and short-term memory (β = 0.213, p < 0.05). Correlations between the independent variables were also significant (p < 0.05). According to the results, cognitive variables should be included in remedial intervention programs. © 2016 Scandinavian Psychological Associations and John Wiley & Sons Ltd.
Prior Sensitivity Analysis in Default Bayesian Structural Equation Modeling.
van Erp, Sara; Mulder, Joris; Oberski, Daniel L
2017-11-27
Bayesian structural equation modeling (BSEM) has recently gained popularity because it enables researchers to fit complex models and solve some of the issues often encountered in classical maximum likelihood estimation, such as nonconvergence and inadmissible solutions. An important component of any Bayesian analysis is the prior distribution of the unknown model parameters. Often, researchers rely on default priors, which are constructed in an automatic fashion without requiring substantive prior information. However, the prior can have a serious influence on the estimation of the model parameters, which affects the mean squared error, bias, coverage rates, and quantiles of the estimates. In this article, we investigate the performance of three different default priors: noninformative improper priors, vague proper priors, and empirical Bayes priors-with the latter being novel in the BSEM literature. Based on a simulation study, we find that these three default BSEM methods may perform very differently, especially with small samples. A careful prior sensitivity analysis is therefore needed when performing a default BSEM analysis. For this purpose, we provide a practical step-by-step guide for practitioners to conducting a prior sensitivity analysis in default BSEM. Our recommendations are illustrated using a well-known case study from the structural equation modeling literature, and all code for conducting the prior sensitivity analysis is available in the online supplemental materials. (PsycINFO Database Record (c) 2017 APA, all rights reserved).
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.
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.…
Generalized structured component analysis a component-based approach to structural equation modeling
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...
Parameterized data-driven fuzzy model based optimal control of a semi-batch reactor.
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.
General Purpose Data-Driven Monitoring for Space Operations
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
Data-driven algorithm to estimate friction in automobile engine
DEFF Research Database (Denmark)
Stotsky, Alexander A.
2010-01-01
Algorithms based on the oscillations of the engine angular rotational speed under fuel cutoff and no-load were proposed for estimation of the engine friction torque. The recursive algorithm to restore the periodic signal is used to calculate the amplitude of the engine speed signal at fuel cutoff....... The values of the friction torque in the corresponding table entries are updated at acquiring new measurements of the friction moment. A new, data-driven algorithm for table adaptation on the basis of stepwise regression was developed and verified using the six-cylinder Volvo engine....
Product design pattern based on big data-driven scenario
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...
Data-Driven Model Reduction and Transfer Operator Approximation
Klus, Stefan; Nüske, Feliks; Koltai, Péter; Wu, Hao; Kevrekidis, Ioannis; Schütte, Christof; Noé, Frank
2018-06-01
In this review paper, we will present different data-driven dimension reduction techniques for dynamical systems that are based on transfer operator theory as well as methods to approximate transfer operators and their eigenvalues, eigenfunctions, and eigenmodes. The goal is to point out similarities and differences between methods developed independently by the dynamical systems, fluid dynamics, and molecular dynamics communities such as time-lagged independent component analysis, dynamic mode decomposition, and their respective generalizations. As a result, extensions and best practices developed for one particular method can be carried over to other related methods.
Data-Driven Healthcare: Challenges and Opportunities for Interactive Visualization.
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.
Structural equation models of VMT growth in US urbanised areas.
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.
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.
Analisis Loyalitas Pelanggan Industri Jasa Pengiriman Menggunakan Structural Equation Modeling
Directory of Open Access Journals (Sweden)
Sarika Zuhri
2017-01-01
Full Text Available Customer loyalty is important for both product and service industries. A loyal customer keeps using the company’s product and services. For a shipping service company, retaining existing customers in order to remain faithful will certainly be very crucial. This study was to determine relationship between variables affecting customer loyalty at PT. Pos Indonesia-Banda Aceh, a shipping service industry. The research used Structural Equation Modeling (SEM and with samples of 153 questionnaires obtained through a non-probability sampling technique. By using AMOS software, it can be concluded that the perceived quality does affect customer satisfaction, perceived value has influence on the customer satisfaction, the customer satisfaction is influential to trust and the trust itself has positive influence on customer loyalty.
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.
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.
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...
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.
Data driven CAN node reliability assessment for manufacturing system
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.
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.
Dynamically adaptive data-driven simulation of extreme hydrological flows
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.
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.
Dynamically adaptive data-driven simulation of extreme hydrological flows
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.
Using structural equation modeling to investigate relationships among ecological variables
Malaeb, Z.A.; Kevin, Summers J.; Pugesek, B.H.
2000-01-01
Structural equation modeling is an advanced multivariate statistical process with which a researcher can construct theoretical concepts, test their measurement reliability, hypothesize and test a theory about their relationships, take into account measurement errors, and consider both direct and indirect effects of variables on one another. Latent variables are theoretical concepts that unite phenomena under a single term, e.g., ecosystem health, environmental condition, and pollution (Bollen, 1989). Latent variables are not measured directly but can be expressed in terms of one or more directly measurable variables called indicators. For some researchers, defining, constructing, and examining the validity of latent variables may be the end task of itself. For others, testing hypothesized relationships of latent variables may be of interest. We analyzed the correlation matrix of eleven environmental variables from the U.S. Environmental Protection Agency's (USEPA) Environmental Monitoring and Assessment Program for Estuaries (EMAP-E) using methods of structural equation modeling. We hypothesized and tested a conceptual model to characterize the interdependencies between four latent variables-sediment contamination, natural variability, biodiversity, and growth potential. In particular, we were interested in measuring the direct, indirect, and total effects of sediment contamination and natural variability on biodiversity and growth potential. The model fit the data well and accounted for 81% of the variability in biodiversity and 69% of the variability in growth potential. It revealed a positive total effect of natural variability on growth potential that otherwise would have been judged negative had we not considered indirect effects. That is, natural variability had a negative direct effect on growth potential of magnitude -0.3251 and a positive indirect effect mediated through biodiversity of magnitude 0.4509, yielding a net positive total effect of 0
On the specification of structural equation models for ecological systems
Grace, J.B.; Michael, Anderson T.; Han, O.; Scheiner, S.M.
2010-01-01
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 and some of the challenges for proper specification of theoretical ideas in structural equation models (SE models). In our presentation, we describe some of the requirements for classical latent variable models in which observed variables (indicators) are interpreted as the effects of underlying causes. We also describe alternative model specifications in which indicators are interpreted as having causal influences on the theoretical concepts. We suggest that this latter nonclassical specification (which involves another variable type-the composite) will often be appropriate for ecological studies because of the multifaceted nature of our theoretical concepts. In this paper, we employ the use of meta-models to aid the translation of theory into SE models and also to facilitate our ability to relate results back to our theories. We demonstrate our approach by showing how a synthetic theory of grassland biodiversity can be evaluated using SEM and data from a coastal grassland. In this example, the theory focuses on the responses of species richness to abiotic stress and disturbance, both directly and through intervening effects on community biomass. Models examined include both those based on classical forms (where each concept is represented using a single latent variable) and also ones in which the concepts are recognized to be multifaceted and modeled as such. To address the challenge of matching SE models with the conceptual level of our theory, two approaches are illustrated, compositing and aggregation. Both approaches are shown to have merits, with the former being preferable for cases where the multiple facets of a concept have widely differing effects in the
Solution of the stellar structure equations in Eulerian coordinates
International Nuclear Information System (INIS)
Deupree, R.G.
1976-01-01
The equations of hydrostatic and thermal equilibrium, assuming only radiative energy transport and spherical symmetry, are solved in Eulerian coordinates by a suitable modification of the Henyey method. An Eulerian approach may possibly be more suitably extended to more spatial dimensions than the usual Lagrangian procedure. The principle advantage of this method is that the equations of hydrostatic and thermal equilibrium and Poisson's equation may be solved simultaneously
Data-driven simulation methodology using DES 4-layer architecture
Directory of Open Access Journals (Sweden)
Aida Saez
2016-05-01
Full Text Available In this study, we present a methodology to build data-driven simulation models of manufacturing plants. We go further than other research proposals and we suggest focusing simulation model development under a 4-layer architecture (network, logic, database and visual reality. The Network layer includes system infrastructure. The Logic layer covers operations planning and control system, and material handling equipment system. The Database holds all the information needed to perform the simulation, the results used to analyze and the values that the Logic layer is using to manage the Plant. Finally, the Visual Reality displays an augmented reality system including not only the machinery and the movement but also blackboards and other Andon elements. This architecture provides numerous advantages as helps to build a simulation model that consistently considers the internal logistics, in a very flexible way.
Data driven approaches for diagnostics and optimization of NPP operation
International Nuclear Information System (INIS)
Pliska, J.; Machat, Z.
2014-01-01
The efficiency and heat rate is an important indicator of both the health of the power plant equipment and the quality of power plant operation. To achieve this challenges powerful tool is a statistical data processing of large data sets which are stored in data historians. These large data sets contain useful information about process quality and equipment and sensor health. The paper discusses data-driven approaches for model building of main power plant equipment such as condenser, cooling tower and the overall thermal cycle as well using multivariate regression techniques based on so called a regression triplet - data, model and method. Regression models comprise a base for diagnostics and optimization tasks. Diagnostics and optimization tasks are demonstrated on practical cases - diagnostics of main power plant equipment to early identify equipment fault, and optimization task of cooling circuit by cooling water flow control to achieve for a given boundary conditions the highest power output. (authors)
submitter Data-driven RBE parameterization for helium ion beams
Mairani, A; Dokic, I; Valle, S M; Tessonnier, T; Galm, R; Ciocca, M; Parodi, K; Ferrari, A; Jäkel, O; Haberer, T; Pedroni, P; Böhlen, T T
2016-01-01
Helium ion beams are expected to be available again in the near future for clinical use. A suitable formalism to obtain relative biological effectiveness (RBE) values for treatment planning (TP) studies is needed. In this work we developed a data-driven RBE parameterization based on published in vitro experimental values. The RBE parameterization has been developed within the framework of the linear-quadratic (LQ) model as a function of the helium linear energy transfer (LET), dose and the tissue specific parameter ${{(\\alpha /\\beta )}_{\\text{ph}}}$ of the LQ model for the reference radiation. Analytic expressions are provided, derived from the collected database, describing the $\\text{RB}{{\\text{E}}_{\\alpha}}={{\\alpha}_{\\text{He}}}/{{\\alpha}_{\\text{ph}}}$ and ${{\\text{R}}_{\\beta}}={{\\beta}_{\\text{He}}}/{{\\beta}_{\\text{ph}}}$ ratios as a function of LET. Calculated RBE values at 2 Gy photon dose and at 10% survival ($\\text{RB}{{\\text{E}}_{10}}$ ) are compared with the experimental ones. Pearson's correlati...
Data-driven forward model inference for EEG brain imaging
DEFF Research Database (Denmark)
Hansen, Sofie Therese; Hauberg, Søren; Hansen, Lars Kai
2016-01-01
Electroencephalography (EEG) is a flexible and accessible tool with excellent temporal resolution but with a spatial resolution hampered by volume conduction. Reconstruction of the cortical sources of measured EEG activity partly alleviates this problem and effectively turns EEG into a brain......-of-concept study, we show that, even when anatomical knowledge is unavailable, a suitable forward model can be estimated directly from the EEG. We propose a data-driven approach that provides a low-dimensional parametrization of head geometry and compartment conductivities, built using a corpus of forward models....... Combined with only a recorded EEG signal, we are able to estimate both the brain sources and a person-specific forward model by optimizing this parametrization. We thus not only solve an inverse problem, but also optimize over its specification. Our work demonstrates that personalized EEG brain imaging...
Data-Driven Predictive Direct Load Control of Refrigeration Systems
DEFF Research Database (Denmark)
Shafiei, Seyed Ehsan; Knudsen, Torben; Wisniewski, Rafal
2015-01-01
A predictive control using subspace identification is applied for the smart grid integration of refrigeration systems under a direct load control scheme. A realistic demand response scenario based on regulation of the electrical power consumption is considered. A receding horizon optimal control...... is proposed to fulfil two important objectives: to secure high coefficient of performance and to participate in power consumption management. Moreover, a new method for design of input signals for system identification is put forward. The control method is fully data driven without an explicit use of model...... against real data. The performance improvement results in a 22% reduction in the energy consumption. A comparative simulation is accomplished showing the superiority of the method over the existing approaches in terms of the load following performance....
Data-Driven Assistance Functions for Industrial Automation Systems
International Nuclear Information System (INIS)
Windmann, Stefan; Niggemann, Oliver
2015-01-01
The increasing amount of data in industrial automation systems overburdens the user in process control and diagnosis tasks. One possibility to cope with these challenges consists of using smart assistance systems that automatically monitor and optimize processes. This article deals with aspects of data-driven assistance systems such as assistance functions, process models and data acquisition. The paper describes novel approaches for self-diagnosis and self-optimization, and shows how these assistance functions can be integrated in different industrial environments. The considered assistance functions are based on process models that are automatically learned from process data. Fault detection and isolation is based on the comparison of observations of the real system with predictions obtained by application of the process models. The process models are further employed for energy efficiency optimization of industrial processes. Experimental results are presented for fault detection and energy efficiency optimization of a drive system. (paper)
Data-driven identification of potential Zika virus vectors
Evans, Michelle V; Dallas, Tad A; Han, Barbara A; Murdock, Courtney C; Drake, John M
2017-01-01
Zika is an emerging virus whose rapid spread is of great public health concern. Knowledge about transmission remains incomplete, especially concerning potential transmission in geographic areas in which it has not yet been introduced. To identify unknown vectors of Zika, we developed a data-driven model linking vector species and the Zika virus via vector-virus trait combinations that confer a propensity toward associations in an ecological network connecting flaviviruses and their mosquito vectors. Our model predicts that thirty-five species may be able to transmit the virus, seven of which are found in the continental United States, including Culex quinquefasciatus and Cx. pipiens. We suggest that empirical studies prioritize these species to confirm predictions of vector competence, enabling the correct identification of populations at risk for transmission within the United States. DOI: http://dx.doi.org/10.7554/eLife.22053.001 PMID:28244371
Objective, Quantitative, Data-Driven Assessment of Chemical Probes.
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.
Data-driven system to predict academic grades and dropout
Rovira, Sergi; Puertas, Eloi
2017-01-01
Nowadays, the role of a tutor is more important than ever to prevent students dropout and improve their academic performance. This work proposes a data-driven system to extract relevant information hidden in the student academic data and, thus, help tutors to offer their pupils a more proactive personal guidance. In particular, our system, based on machine learning techniques, makes predictions of dropout intention and courses grades of students, as well as personalized course recommendations. Moreover, we present different visualizations which help in the interpretation of the results. In the experimental validation, we show that the system obtains promising results with data from the degree studies in Law, Computer Science and Mathematics of the Universitat de Barcelona. PMID:28196078
Econophysics and Data Driven Modelling of Market Dynamics
Aoyama, Hideaki; Chakrabarti, Bikas; Chakraborti, Anirban; Ghosh, Asim; Econophysics and Data Driven Modelling of Market Dynamics
2015-01-01
This book presents the works and research findings of physicists, economists, mathematicians, statisticians, and financial engineers who have undertaken data-driven modelling of market dynamics and other empirical studies in the field of Econophysics. During recent decades, the financial market landscape has changed dramatically with the deregulation of markets and the growing complexity of products. The ever-increasing speed and decreasing costs of computational power and networks have led to the emergence of huge databases. The availability of these data should permit the development of models that are better founded empirically, and econophysicists have accordingly been advocating that one should rely primarily on the empirical observations in order to construct models and validate them. The recent turmoil in financial markets and the 2008 crash appear to offer a strong rationale for new models and approaches. The Econophysics community accordingly has an important future role to play in market modelling....
A Transition Towards a Data-Driven Business Model (DDBM)
DEFF Research Database (Denmark)
Zaki, Mohamed; Bøe-Lillegraven, Tor; Neely, Andy
2016-01-01
Nettavisen is a Norwegian online start-up that experienced a boost after the financial crisis of 2009. Since then, the firm has been able to increase its market share and profitability through the use of highly disruptive business models, allowing the relatively small staff to outcompete powerhouse...... legacy-publishing companies and new media players such as Facebook and Google. These disruptive business models have been successful, as Nettavisen captured a large market share in Norway early on, and was consistently one of the top-three online news sites in Norway. Capitalising on media data explosion...... and the recent acquisition of blogger network ‘Blog.no’, Nettavisen is moving towards a data-driven business model (DDBM). In particular, the firm aims to analyse huge volumes of user Web browsing and purchasing habits....
Helioseismic and neutrino data-driven reconstruction of solar properties
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.
A Data-driven Concept Schema for Defining Clinical Research Data Needs
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
Comparing Entrepreneurship Intention: A Multigroup Structural Equation Modeling Approach
Directory of Open Access Journals (Sweden)
Sabrina O. Sihombing
2012-04-01
Full Text Available Unemployment is one of the main social and economic problems that many countries face nowadays. One strategic way to overcome this problem is by fostering entrepreneurship spirit especially for unem ployment graduates. Entrepreneurship is becoming an alternative Job for students after they graduate. This is because entrepreneurship of-fers major benefits, such as setting up one’s own business and the pos-sibility of having significant financial rewards than working for others. Entrepreneurship is then offered by many universities. This research applies the theory of planned behavior (TPB by incorporating attitude toward success as an antecedent variable of the attitude to examine students’ intention to become an entrepreneur. The objective of this research is to compare entrepreneurship intention between business students and non-business students. A self-administered questionnaire was used to collect data for this study. Questionnaires were distributed to respondents by applying the drop-off/pick-up method. A number of 294 by questionnaires were used in the analysis. Data were analyzed by using structural equation modeling. Two out of four hypotheses were confirmed. These hypotheses are the relationship between the attitude toward becoming an entrepreneur and the intention to try becoming an entrepreneur, and the relationship perceived behavioral control and intention to try becoming an entrepreneur. This paper also provides a discussion and offers directions for future research.
Suicidal ideation in adolescents: A structural equation modeling approach.
Choi, Jung-Hyun; Yu, Mi; Kim, Kyoung-Eun
2014-06-19
The purpose of this study is to test a model linking adolescents' experience of violence and peer support to their happiness and suicidal ideation. The participants were high school students in Seoul, and in Kyungi, and Chungnam Provinces in Korea. The Conflict Tactics Scale, School Violence Scale, Oxford Happiness Inventory, and Suicidal Ideation Questionnaire were administered to just over 1000 adolescents. The model was tested using a path analysis technique within structural equation modeling. The model fit indices suggest that the revised model is a better fit for the data than the original hypothesized model. The experience of violence had a significant negative direct effect and peer support had a significant positive direct effect on their happiness. Happiness had a significant negative effect and the experience of violence had a significant positive effect on suicidal ideation. These findings demonstrate the fundamental importance of reducing exposure of violence to adolescents, and that increasing peer support and their happiness may be the key to adolescent suicidal ideation prevention. © 2014 Wiley Publishing Asia Pty Ltd.
How motivation affects academic performance: a structural equation modelling analysis.
Kusurkar, R A; Ten Cate, Th J; Vos, C M P; Westers, P; Croiset, G
2013-03-01
Few studies in medical education have studied effect of quality of motivation on performance. Self-Determination Theory based on quality of motivation differentiates between Autonomous Motivation (AM) that originates within an individual and Controlled Motivation (CM) that originates from external sources. To determine whether Relative Autonomous Motivation (RAM, a measure of the balance between AM and CM) affects academic performance through good study strategy and higher study effort and compare this model between subgroups: males and females; students selected via two different systems namely qualitative and weighted lottery selection. Data on motivation, study strategy and effort was collected from 383 medical students of VU University Medical Center Amsterdam and their academic performance results were obtained from the student administration. Structural Equation Modelling analysis technique was used to test a hypothesized model in which high RAM would positively affect Good Study Strategy (GSS) and study effort, which in turn would positively affect academic performance in the form of grade point averages. This model fit well with the data, Chi square = 1.095, df = 3, p = 0.778, RMSEA model fit = 0.000. This model also fitted well for all tested subgroups of students. Differences were found in the strength of relationships between the variables for the different subgroups as expected. In conclusion, RAM positively correlated with academic performance through deep strategy towards study and higher study effort. This model seems valid in medical education in subgroups such as males, females, students selected by qualitative and weighted lottery selection.
Local fit evaluation of structural equation models using graphical criteria.
Thoemmes, Felix; Rosseel, Yves; Textor, Johannes
2018-03-01
Evaluation of model fit is critically important for every structural equation model (SEM), and sophisticated methods have been developed for this task. Among them are the χ² goodness-of-fit test, decomposition of the χ², derived measures like the popular root mean square error of approximation (RMSEA) or comparative fit index (CFI), or inspection of residuals or modification indices. Many of these methods provide a global approach to model fit evaluation: A single index is computed that quantifies the fit of the entire SEM to the data. In contrast, graphical criteria like d-separation or trek-separation allow derivation of implications that can be used for local fit evaluation, an approach that is hardly ever applied. We provide an overview of local fit evaluation from the viewpoint of SEM practitioners. In the presence of model misfit, local fit evaluation can potentially help in pinpointing where the problem with the model lies. For models that do fit the data, local tests can identify the parts of the model that are corroborated by the data. Local tests can also be conducted before a model is fitted at all, and they can be used even for models that are globally underidentified. We discuss appropriate statistical local tests, and provide applied examples. We also present novel software in R that automates this type of local fit evaluation. (PsycINFO Database Record (c) 2018 APA, all rights reserved).
Comparing Entrepreneurship Intention: A Multigroup Structural Equation Modeling Approach
Directory of Open Access Journals (Sweden)
Sabrina O. Sihombing
2012-04-01
Full Text Available Unemployment is one of the main social and economic problems that many countries face nowadays. One strategic way to overcome this problem is by fostering entrepreneurship spirit especially for unem-ployment graduates. Entrepreneurship is becoming an alternative Job for students after they graduate. This is because entrepreneurship of fers major benefits, such as setting up one’s own business and the pos sibility of having significant financial rewards than working for others. Entrepreneurship is then offered by many universities. This research applies the theory of planned behavior (TPB by incorporating attitude toward success as an antecedent variable of the attitude to examine students’ intention to become an entrepreneur. The objective of this research is to compare entrepreneurship intention between business students and non-business students. A self-administered questionnaire was used to collect data for this study. Questionnaires were distributed to respondents by applying the drop-off/pick-up method. A number of 294 by questionnaires were used in the analysis. Data were analyzed by using structural equation modeling. Two out of four hypotheses were confirmed. These hypotheses are the relationship between the attitude toward becoming an entrepreneur and the intention to try becoming an entrepreneur, and the relationship perceived behavioral control and intention to try becoming an entrepreneur. This paper also provides a discussion and offers directions for future research.
Structural equation modeling with EQS basic concepts, applications, and programming
Byrne, Barbara M
2013-01-01
Readers who want a less mathematical alternative to the EQS manual will find exactly what they're looking for in this practical text. Written specifically for those with little to no knowledge of structural equation modeling (SEM) or EQS, the author's goal is to provide a non-mathematical introduction to the basic concepts of SEM by applying these principles to EQS, Version 6.1. The book clearly demonstrates a wide variety of SEM/EQS applications that include confirmatory factor analytic and full latent variable models. Written in a "user-friendly" style, the author "walks" the reader through the varied steps involved in the process of testing SEM models: model specification and estimation, assessment of model fit, EQS output, and interpretation of findings. Each of the book's applications is accompanied by: a statement of the hypothesis being tested, a schematic representation of the model, explanations of the EQS input and output files, tips on how to use the pull-down menus, and the data file upon which ...
Parenting Stress, Mental Health, Dyadic Adjustment: A Structural Equation Model
Directory of Open Access Journals (Sweden)
Luca Rollè
2017-05-01
Full Text Available Objective: In the 1st year of the post-partum period, parenting stress, mental health, and dyadic adjustment are important for the wellbeing of both parents and the child. However, there are few studies that analyze the relationship among these three dimensions. The aim of this study is to investigate the relationships between parenting stress, mental health (depressive and anxiety symptoms, and dyadic adjustment among first-time parents.Method: We studied 268 parents (134 couples of healthy babies. At 12 months post-partum, both parents filled out, in a counterbalanced order, the Parenting Stress Index-Short Form, the Edinburgh Post-natal Depression Scale, the State-Trait Anxiety Inventory, and the Dyadic Adjustment Scale. Structural equation modeling was used to analyze the potential mediating effects of mental health on the relationship between parenting stress and dyadic adjustment.Results: Results showed the full mediation effect of mental health between parenting stress and dyadic adjustment. A multi-group analysis further found that the paths did not differ across mothers and fathers.Discussion: The results suggest that mental health is an important dimension that mediates the relationship between parenting stress and dyadic adjustment in the transition to parenthood.
The ACTIVE conceptual framework as a structural equation model
Gross, Alden L.; Payne, Brennan R.; Casanova, Ramon; Davoudzadeh, Pega; Dzierzewski, Joseph M.; Farias, Sarah; Giovannetti, Tania; Ip, Edward H.; Marsiske, Michael; Rebok, George W.; Schaie, K. Warner; Thomas, Kelsey; Willis, Sherry; Jones, Richard N.
2018-01-01
Background/Study Context Conceptual frameworks are analytic models at a high level of abstraction. Their operationalization can inform randomized trial design and sample size considerations. Methods The Advanced Cognitive Training for Independent and Vital Elderly (ACTIVE) conceptual framework was empirically tested using structural equation modeling (N=2,802). ACTIVE was guided by a conceptual framework for cognitive training in which proximal cognitive abilities (memory, inductive reasoning, speed of processing) mediate treatment-related improvement in primary outcomes (everyday problem-solving, difficulty with activities of daily living, everyday speed, driving difficulty), which in turn lead to improved secondary outcomes (health-related quality of life, health service utilization, mobility). Measurement models for each proximal, primary, and secondary outcome were developed and tested using baseline data. Each construct was then combined in one model to evaluate fit (RMSEA, CFI, normalized residuals of each indicator). To expand the conceptual model and potentially inform future trials, evidence of modification of structural model parameters was evaluated by age, years of education, sex, race, and self-rated health status. Results Preconceived measurement models for memory, reasoning, speed of processing, everyday problem-solving, instrumental activities of daily living (IADL) difficulty, everyday speed, driving difficulty, and health-related quality of life each fit well to the data (all RMSEA .95). Fit of the full model was excellent (RMSEA = .038; CFI = .924). In contrast with previous findings from ACTIVE regarding who benefits from training, interaction testing revealed associations between proximal abilities and primary outcomes are stronger on average by nonwhite race, worse health, older age, and less education (p conceptual model. Findings suggest that the types of people who show intervention effects on cognitive performance potentially may be
The ACTIVE conceptual framework as a structural equation model.
Gross, Alden L; Payne, Brennan R; Casanova, Ramon; Davoudzadeh, Pega; Dzierzewski, Joseph M; Farias, Sarah; Giovannetti, Tania; Ip, Edward H; Marsiske, Michael; Rebok, George W; Schaie, K Warner; Thomas, Kelsey; Willis, Sherry; Jones, Richard N
2018-01-01
Background/Study Context: Conceptual frameworks are analytic models at a high level of abstraction. Their operationalization can inform randomized trial design and sample size considerations. The Advanced Cognitive Training for Independent and Vital Elderly (ACTIVE) conceptual framework was empirically tested using structural equation modeling (N=2,802). ACTIVE was guided by a conceptual framework for cognitive training in which proximal cognitive abilities (memory, inductive reasoning, speed of processing) mediate treatment-related improvement in primary outcomes (everyday problem-solving, difficulty with activities of daily living, everyday speed, driving difficulty), which in turn lead to improved secondary outcomes (health-related quality of life, health service utilization, mobility). Measurement models for each proximal, primary, and secondary outcome were developed and tested using baseline data. Each construct was then combined in one model to evaluate fit (RMSEA, CFI, normalized residuals of each indicator). To expand the conceptual model and potentially inform future trials, evidence of modification of structural model parameters was evaluated by age, years of education, sex, race, and self-rated health status. Preconceived measurement models for memory, reasoning, speed of processing, everyday problem-solving, instrumental activities of daily living (IADL) difficulty, everyday speed, driving difficulty, and health-related quality of life each fit well to the data (all RMSEA .95). Fit of the full model was excellent (RMSEA = .038; CFI = .924). In contrast with previous findings from ACTIVE regarding who benefits from training, interaction testing revealed associations between proximal abilities and primary outcomes are stronger on average by nonwhite race, worse health, older age, and less education (p conceptual model. Findings suggest that the types of people who show intervention effects on cognitive performance potentially may be different from
A Hamiltonian structure for the linearized Einstein vacuum field equations
International Nuclear Information System (INIS)
Torres del Castillo, G.F.
1991-01-01
By considering the Einstein vacuum field equations linearized about the Minkowski metric, the evolution equations for the gauge-invariant quantities characterizing the gravitational field are written in a Hamiltonian form. A Poisson bracket between functionals of the field, compatible with the constraints satisfied by the field variables, is obtained (Author)
Applications of Multilevel Structural Equation Modeling to Cross-Cultural Research
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…
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
Half-trek criterion for generic identifiability of linear structural equation models
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
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
Generalized structural equations improve sexual-selection analyses.
Directory of Open Access Journals (Sweden)
Sonia Lombardi
Full Text Available Sexual selection is an intense evolutionary force, which operates through competition for the access to breeding resources. There are many cases where male copulatory success is highly asymmetric, and few males are able to sire most females. Two main hypotheses were proposed to explain this asymmetry: "female choice" and "male dominance". The literature reports contrasting results. This variability may reflect actual differences among studied populations, but it may also be generated by methodological differences and statistical shortcomings in data analysis. A review of the statistical methods used so far in lek studies, shows a prevalence of Linear Models (LM and Generalized Linear Models (GLM which may be affected by problems in inferring cause-effect relationships; multi-collinearity among explanatory variables and erroneous handling of non-normal and non-continuous distributions of the response variable. In lek breeding, selective pressure is maximal, because large numbers of males and females congregate in small arenas. We used a dataset on lekking fallow deer (Dama dama, to contrast the methods and procedures employed so far, and we propose a novel approach based on Generalized Structural Equations Models (GSEMs. GSEMs combine the power and flexibility of both SEM and GLM in a unified modeling framework. We showed that LMs fail to identify several important predictors of male copulatory success and yields very imprecise parameter estimates. Minor variations in data transformation yield wide changes in results and the method appears unreliable. GLMs improved the analysis, but GSEMs provided better results, because the use of latent variables decreases the impact of measurement errors. Using GSEMs, we were able to test contrasting hypotheses and calculate both direct and indirect effects, and we reached a high precision of the estimates, which implies a high predictive ability. In synthesis, we recommend the use of GSEMs in studies on
Using structural equation modeling for network meta-analysis.
Tu, Yu-Kang; Wu, Yun-Chun
2017-07-14
Network meta-analysis overcomes the limitations of traditional pair-wise meta-analysis by incorporating all available evidence into a general statistical framework for simultaneous comparisons of several treatments. Currently, network meta-analyses are undertaken either within the Bayesian hierarchical linear models or frequentist generalized linear mixed models. Structural equation modeling (SEM) is a statistical method originally developed for modeling causal relations among observed and latent variables. As random effect is explicitly modeled as a latent variable in SEM, it is very flexible for analysts to specify complex random effect structure and to make linear and nonlinear constraints on parameters. The aim of this article is to show how to undertake a network meta-analysis within the statistical framework of SEM. We used an example dataset to demonstrate the standard fixed and random effect network meta-analysis models can be easily implemented in SEM. It contains results of 26 studies that directly compared three treatment groups A, B and C for prevention of first bleeding in patients with liver cirrhosis. We also showed that a new approach to network meta-analysis based on the technique of unrestricted weighted least squares (UWLS) method can also be undertaken using SEM. For both the fixed and random effect network meta-analysis, SEM yielded similar coefficients and confidence intervals to those reported in the previous literature. The point estimates of two UWLS models were identical to those in the fixed effect model but the confidence intervals were greater. This is consistent with results from the traditional pairwise meta-analyses. Comparing to UWLS model with common variance adjusted factor, UWLS model with unique variance adjusted factor has greater confidence intervals when the heterogeneity was larger in the pairwise comparison. The UWLS model with unique variance adjusted factor reflects the difference in heterogeneity within each comparison
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)
Data-driven forecasting of high-dimensional chaotic systems with long short-term memory networks.
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.
A Data-Driven Approach to Realistic Shape Morphing
Gao, Lin; Lai, Yu-Kun; Huang, Qi-Xing; Hu, Shi-Min
2013-01-01
Morphing between 3D objects is a fundamental technique in computer graphics. Traditional methods of shape morphing focus on establishing meaningful correspondences and finding smooth interpolation between shapes. Such methods however only take geometric information as input and thus cannot in general avoid producing unnatural interpolation, in particular for large-scale deformations. This paper proposes a novel data-driven approach for shape morphing. Given a database with various models belonging to the same category, we treat them as data samples in the plausible deformation space. These models are then clustered to form local shape spaces of plausible deformations. We use a simple metric to reasonably represent the closeness between pairs of models. Given source and target models, the morphing problem is casted as a global optimization problem of finding a minimal distance path within the local shape spaces connecting these models. Under the guidance of intermediate models in the path, an extended as-rigid-as-possible interpolation is used to produce the final morphing. By exploiting the knowledge of plausible models, our approach produces realistic morphing for challenging cases as demonstrated by various examples in the paper. © 2013 The Eurographics Association and Blackwell Publishing Ltd.
Selection of the Sample for Data-Driven $Z \\to \
Krauss, Martin
2009-01-01
The topic of this study was to improve the selection of the sample for data-driven Z → ν ν background estimation, which is a major contribution in supersymmetric searches in ̄ a no-lepton search mode. The data is based on Z → + − samples using data created with ATLAS simulation software. This method works if two leptons are reconstructed, but using cuts that are typical for SUSY searches reconstruction efficiency for electrons and muons is rather low. For this reason it was tried to enhance the data sample. Therefore events were considered, where only one electron was reconstructed. In this case the invariant mass for the electron and each jet was computed to select the jet with the best match for the Z boson mass as not reconstructed electron. This way the sample can be extended but significantly looses purity because of also reconstructed background events. To improve this method other variables have to be considered which were not available for this study. Applying a similar method to muons using ...
A data-driven approach to quality risk management.
Alemayehu, Demissie; Alvir, Jose; Levenstein, Marcia; Nickerson, David
2013-10-01
An effective clinical trial strategy to ensure patient safety as well as trial quality and efficiency involves an integrated approach, including prospective identification of risk factors, mitigation of the risks through proper study design and execution, and assessment of quality metrics in real-time. Such an integrated quality management plan may also be enhanced by using data-driven techniques to identify risk factors that are most relevant in predicting quality issues associated with a trial. In this paper, we illustrate such an approach using data collected from actual clinical trials. Several statistical methods were employed, including the Wilcoxon rank-sum test and logistic regression, to identify the presence of association between risk factors and the occurrence of quality issues, applied to data on quality of clinical trials sponsored by Pfizer. ONLY A SUBSET OF THE RISK FACTORS HAD A SIGNIFICANT ASSOCIATION WITH QUALITY ISSUES, AND INCLUDED: Whether study used Placebo, whether an agent was a biologic, unusual packaging label, complex dosing, and over 25 planned procedures. Proper implementation of the strategy can help to optimize resource utilization without compromising trial integrity and patient safety.
A data-driven approach to quality risk management
Directory of Open Access Journals (Sweden)
Demissie Alemayehu
2013-01-01
Full Text Available Aim: An effective clinical trial strategy to ensure patient safety as well as trial quality and efficiency involves an integrated approach, including prospective identification of risk factors, mitigation of the risks through proper study design and execution, and assessment of quality metrics in real-time. Such an integrated quality management plan may also be enhanced by using data-driven techniques to identify risk factors that are most relevant in predicting quality issues associated with a trial. In this paper, we illustrate such an approach using data collected from actual clinical trials. Materials and Methods: Several statistical methods were employed, including the Wilcoxon rank-sum test and logistic regression, to identify the presence of association between risk factors and the occurrence of quality issues, applied to data on quality of clinical trials sponsored by Pfizer. Results: Only a subset of the risk factors had a significant association with quality issues, and included: Whether study used Placebo, whether an agent was a biologic, unusual packaging label, complex dosing, and over 25 planned procedures. Conclusion: Proper implementation of the strategy can help to optimize resource utilization without compromising trial integrity and patient safety.
ATLAS job transforms: a data driven workflow engine
International Nuclear Information System (INIS)
Stewart, G A; Breaden-Madden, W B; Maddocks, H J; Harenberg, T; Sandhoff, M; Sarrazin, B
2014-01-01
The need to run complex workflows for a high energy physics experiment such as ATLAS has always been present. However, as computing resources have become even more constrained, compared to the wealth of data generated by the LHC, the need to use resources efficiently and manage complex workflows within a single grid job have increased. In ATLAS, a new Job Transform framework has been developed that we describe in this paper. This framework manages the multiple execution steps needed to 'transform' one data type into another (e.g., RAW data to ESD to AOD to final ntuple) and also provides a consistent interface for the ATLAS production system. The new framework uses a data driven workflow definition which is both easy to manage and powerful. After a transform is defined, jobs are expressed simply by specifying the input data and the desired output data. The transform infrastructure then executes only the necessary substeps to produce the final data products. The global execution cost of running the job is minimised and the transform can adapt to scenarios where data can be produced along different execution paths. Transforms for specific physics tasks which support up to 60 individual substeps have been successfully run. As the new transforms infrastructure has been deployed in production many features have been added to the framework which improve reliability, quality of error reporting and also provide support for multi-process jobs.
Human body segmentation via data-driven graph cut.
Li, Shifeng; Lu, Huchuan; Shao, Xingqing
2014-11-01
Human body segmentation is a challenging and important problem in computer vision. Existing methods usually entail a time-consuming training phase for prior knowledge learning with complex shape matching for body segmentation. In this paper, we propose a data-driven method that integrates top-down body pose information and bottom-up low-level visual cues for segmenting humans in static images within the graph cut framework. The key idea of our approach is first to exploit human kinematics to search for body part candidates via dynamic programming for high-level evidence. Then, by using the body parts classifiers, obtaining bottom-up cues of human body distribution for low-level evidence. All the evidence collected from top-down and bottom-up procedures are integrated in a graph cut framework for human body segmentation. Qualitative and quantitative experiment results demonstrate the merits of the proposed method in segmenting human bodies with arbitrary poses from cluttered backgrounds.
Data-driven classification of patients with primary progressive aphasia.
Hoffman, Paul; Sajjadi, Seyed Ahmad; Patterson, Karalyn; Nestor, Peter J
2017-11-01
Current diagnostic criteria classify primary progressive aphasia into three variants-semantic (sv), nonfluent (nfv) and logopenic (lv) PPA-though the adequacy of this scheme is debated. This study took a data-driven approach, applying k-means clustering to data from 43 PPA patients. The algorithm grouped patients based on similarities in language, semantic and non-linguistic cognitive scores. The optimum solution consisted of three groups. One group, almost exclusively those diagnosed as svPPA, displayed a selective semantic impairment. A second cluster, with impairments to speech production, repetition and syntactic processing, contained a majority of patients with nfvPPA but also some lvPPA patients. The final group exhibited more severe deficits to speech, repetition and syntax as well as semantic and other cognitive deficits. These results suggest that, amongst cases of non-semantic PPA, differentiation mainly reflects overall degree of language/cognitive impairment. The observed patterns were scarcely affected by inclusion/exclusion of non-linguistic cognitive scores. Copyright © 2017 The Authors. Published by Elsevier Inc. All rights reserved.
A Data-Driven Approach to Realistic Shape Morphing
Gao, Lin
2013-05-01
Morphing between 3D objects is a fundamental technique in computer graphics. Traditional methods of shape morphing focus on establishing meaningful correspondences and finding smooth interpolation between shapes. Such methods however only take geometric information as input and thus cannot in general avoid producing unnatural interpolation, in particular for large-scale deformations. This paper proposes a novel data-driven approach for shape morphing. Given a database with various models belonging to the same category, we treat them as data samples in the plausible deformation space. These models are then clustered to form local shape spaces of plausible deformations. We use a simple metric to reasonably represent the closeness between pairs of models. Given source and target models, the morphing problem is casted as a global optimization problem of finding a minimal distance path within the local shape spaces connecting these models. Under the guidance of intermediate models in the path, an extended as-rigid-as-possible interpolation is used to produce the final morphing. By exploiting the knowledge of plausible models, our approach produces realistic morphing for challenging cases as demonstrated by various examples in the paper. © 2013 The Eurographics Association and Blackwell Publishing Ltd.
Data driven profiting from your most important business asset
Redman, Thomas C
2008-01-01
Your company's data has the potential to add enormous value to every facet of the organization -- from marketing and new product development to strategy to financial management. Yet if your company is like most, it's not using its data to create strategic advantage. Data sits around unused -- or incorrect data fouls up operations and decision making. In Data Driven, Thomas Redman, the "Data Doc," shows how to leverage and deploy data to sharpen your company's competitive edge and enhance its profitability. The author reveals: · The special properties that make data such a powerful asset · The hidden costs of flawed, outdated, or otherwise poor-quality data · How to improve data quality for competitive advantage · Strategies for exploiting your data to make better business decisions · The many ways to bring data to market · Ideas for dealing with political struggles over data and concerns about privacy rights Your company's data is a key business asset, and you need to manage it aggressively and professi...
Data driven processor 'Vertex Trigger' for B experiments
International Nuclear Information System (INIS)
Hartouni, E.P.
1993-01-01
Data Driven Processors (DDP's) are specialized computation engines configured to solve specific numerical problems, such as vertex reconstruction. The architecture of the DDP which is the subject of this talk was designed and implemented by W. Sippach and B.C. Knapp at Nevis Lab. in the early 1980's. This particular implementation allows multiple parallel streams of data to provide input to a heterogenous collection of simple operators whose interconnection form an algorithm. The local data flow control allows this device to execute algorithms extremely quickly provided that care is taken in the layout of the algorithm. I/O rates of several hundred megabytes/second are routinely achieved thus making DDP's attractive candidates for complex online calculations. The original question was open-quote can a DDP reconstruct tracks in a Silicon Vertex Detector, find events with a separated vertex and do it fast enough to be used as an online trigger?close-quote Restating this inquiry as three questions and describing the answers to the questions will be the subject of this talk. The three specific questions are: (1) Can an algorithm be found which reconstructs tracks in a planar geometry and no magnetic field; (2) Can separated vertices be recognized in some way; (3) Can the algorithm be implemented in the Nevis-UMass and DDP and execute in 10-20 μs?
EXPLORING DATA-DRIVEN SPECTRAL MODELS FOR APOGEE M DWARFS
Lua Birky, Jessica; Hogg, David; Burgasser, Adam J.; Jessica Birky
2018-01-01
The Cannon (Ness et al. 2015; Casey et al. 2016) is a flexible, data-driven spectral modeling and parameter inference framework, demonstrated on high-resolution Apache Point Galactic Evolution Experiment (APOGEE; λ/Δλ~22,500, 1.5-1.7µm) spectra of giant stars to estimate stellar labels (Teff, logg, [Fe/H], and chemical abundances) to precisions higher than the model-grid pipeline. The lack of reliable stellar parameters reported by the APOGEE pipeline for temperatures less than ~3550K, motivates extension of this approach to M dwarf stars. Using a training set of 51 M dwarfs with spectral types ranging M0-M9 obtained from SDSS optical spectra, we demonstrate that the Cannon can infer spectral types to a precision of +/-0.6 types, making it an effective tool for classifying high-resolution near-infrared spectra. We discuss the potential for extending this work to determine the physical stellar labels Teff, logg, and [Fe/H].This work is supported by the SDSS Faculty and Student (FAST) initiative.
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
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)
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...
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.…
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…
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
Full information estimations of a system of simultaneous equations with error component structure
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
Data-driven approach for creating synthetic electronic medical records.
Buczak, Anna L; Babin, Steven; Moniz, Linda
2010-10-14
New algorithms for disease outbreak detection are being developed to take advantage of full electronic medical records (EMRs) that contain a wealth of patient information. However, due to privacy concerns, even anonymized EMRs cannot be shared among researchers, resulting in great difficulty in comparing the effectiveness of these algorithms. To bridge the gap between novel bio-surveillance algorithms operating on full EMRs and the lack of non-identifiable EMR data, a method for generating complete and synthetic EMRs was developed. This paper describes a novel methodology for generating complete synthetic EMRs both for an outbreak illness of interest (tularemia) and for background records. The method developed has three major steps: 1) synthetic patient identity and basic information generation; 2) identification of care patterns that the synthetic patients would receive based on the information present in real EMR data for similar health problems; 3) adaptation of these care patterns to the synthetic patient population. We generated EMRs, including visit records, clinical activity, laboratory orders/results and radiology orders/results for 203 synthetic tularemia outbreak patients. Validation of the records by a medical expert revealed problems in 19% of the records; these were subsequently corrected. We also generated background EMRs for over 3000 patients in the 4-11 yr age group. Validation of those records by a medical expert revealed problems in fewer than 3% of these background patient EMRs and the errors were subsequently rectified. A data-driven method was developed for generating fully synthetic EMRs. The method is general and can be applied to any data set that has similar data elements (such as laboratory and radiology orders and results, clinical activity, prescription orders). The pilot synthetic outbreak records were for tularemia but our approach may be adapted to other infectious diseases. The pilot synthetic background records were in the 4
Evidence-based and data-driven road safety management
Directory of Open Access Journals (Sweden)
Fred Wegman
2015-07-01
Full Text Available Over the past decades, road safety in highly-motorised countries has made significant progress. Although we have a fair understanding of the reasons for this progress, we don't have conclusive evidence for this. A new generation of road safety management approaches has entered road safety, starting when countries decided to guide themselves by setting quantitative targets (e.g. 50% less casualties in ten years' time. Setting realistic targets, designing strategies and action plans to achieve these targets and monitoring progress have resulted in more scientific research to support decision-making on these topics. Three subjects are key in this new approach of evidence-based and data-driven road safety management: ex-post and ex-ante evaluation of both individual interventions and intervention packages in road safety strategies, and transferability (external validity of the research results. In this article, we explore these subjects based on recent experiences in four jurisdictions (Western Australia, the Netherlands, Sweden and Switzerland. All four apply similar approaches and tools; differences are considered marginal. It is concluded that policy-making and political decisions were influenced to a great extent by the results of analysis and research. Nevertheless, to compensate for a relatively weak theoretical basis and to improve the power of this new approach, a number of issues will need further research. This includes ex-post and ex-ante evaluation, a better understanding of extrapolation of historical trends and the transferability of research results. This new approach cannot be realized without high-quality road safety data. Good data and knowledge are indispensable for this new and very promising approach.
Data-Driven Model Uncertainty Estimation in Hydrologic Data Assimilation
Pathiraja, S.; Moradkhani, H.; Marshall, L.; Sharma, A.; Geenens, G.
2018-02-01
The increasing availability of earth observations necessitates mathematical methods to optimally combine such data with hydrologic models. Several algorithms exist for such purposes, under the umbrella of data assimilation (DA). However, DA methods are often applied in a suboptimal fashion for complex real-world problems, due largely to several practical implementation issues. One such issue is error characterization, which is known to be critical for a successful assimilation. Mischaracterized errors lead to suboptimal forecasts, and in the worst case, to degraded estimates even compared to the no assimilation case. Model uncertainty characterization has received little attention relative to other aspects of DA science. Traditional methods rely on subjective, ad hoc tuning factors or parametric distribution assumptions that may not always be applicable. We propose a novel data-driven approach (named SDMU) to model uncertainty characterization for DA studies where (1) the system states are partially observed and (2) minimal prior knowledge of the model error processes is available, except that the errors display state dependence. It includes an approach for estimating the uncertainty in hidden model states, with the end goal of improving predictions of observed variables. The SDMU is therefore suited to DA studies where the observed variables are of primary interest. Its efficacy is demonstrated through a synthetic case study with low-dimensional chaotic dynamics and a real hydrologic experiment for one-day-ahead streamflow forecasting. In both experiments, the proposed method leads to substantial improvements in the hidden states and observed system outputs over a standard method involving perturbation with Gaussian noise.
Data-driven motion correction in brain SPECT
International Nuclear Information System (INIS)
Kyme, A.Z.; Hutton, B.F.; Hatton, R.L.; Skerrett, D.W.
2002-01-01
Patient motion can cause image artifacts in SPECT despite restraining measures. Data-driven detection and correction of motion can be achieved by comparison of acquired data with the forward-projections. By optimising the orientation of the reconstruction, parameters can be obtained for each misaligned projection and applied to update this volume using a 3D reconstruction algorithm. Digital and physical phantom validation was performed to investigate this approach. Noisy projection data simulating at least one fully 3D patient head movement during acquisition were constructed by projecting the digital Huffman brain phantom at various orientations. Motion correction was applied to the reconstructed studies. The importance of including attenuation effects in the estimation of motion and the need for implementing an iterated correction were assessed in the process. Correction success was assessed visually for artifact reduction, and quantitatively using a mean square difference (MSD) measure. Physical Huffman phantom studies with deliberate movements introduced during the acquisition were also acquired and motion corrected. Effective artifact reduction in the simulated corrupt studies was achieved by motion correction. Typically the MSD ratio between the corrected and reference studies compared to the corrupted and reference studies was > 2. Motion correction could be achieved without inclusion of attenuation effects in the motion estimation stage, providing simpler implementation and greater efficiency. Moreover the additional improvement with multiple iterations of the approach was small. Improvement was also observed in the physical phantom data, though the technique appeared limited here by an object symmetry. Copyright (2002) The Australian and New Zealand Society of Nuclear Medicine Inc
Architectural Strategies for Enabling Data-Driven Science at Scale
Crichton, D. J.; Law, E. S.; Doyle, R. J.; Little, M. M.
2017-12-01
architectural strategies, including a 2015-2016 NASA AIST Study on Big Data, for evolving scientific research towards massively distributed data-driven discovery. It will include example use cases across earth science, planetary science, and other disciplines.
SIDEKICK: Genomic data driven analysis and decision-making framework
Directory of Open Access Journals (Sweden)
Yoon Kihoon
2010-12-01
Full Text Available Abstract Background Scientists striving to unlock mysteries within complex biological systems face myriad barriers in effectively integrating available information to enhance their understanding. While experimental techniques and available data sources are rapidly evolving, useful information is dispersed across a variety of sources, and sources of the same information often do not use the same format or nomenclature. To harness these expanding resources, scientists need tools that bridge nomenclature differences and allow them to integrate, organize, and evaluate the quality of information without extensive computation. Results Sidekick, a genomic data driven analysis and decision making framework, is a web-based tool that provides a user-friendly intuitive solution to the problem of information inaccessibility. Sidekick enables scientists without training in computation and data management to pursue answers to research questions like "What are the mechanisms for disease X" or "Does the set of genes associated with disease X also influence other diseases." Sidekick enables the process of combining heterogeneous data, finding and maintaining the most up-to-date data, evaluating data sources, quantifying confidence in results based on evidence, and managing the multi-step research tasks needed to answer these questions. We demonstrate Sidekick's effectiveness by showing how to accomplish a complex published analysis in a fraction of the original time with no computational effort using Sidekick. Conclusions Sidekick is an easy-to-use web-based tool that organizes and facilitates complex genomic research, allowing scientists to explore genomic relationships and formulate hypotheses without computational effort. Possible analysis steps include gene list discovery, gene-pair list discovery, various enrichments for both types of lists, and convenient list manipulation. Further, Sidekick's ability to characterize pairs of genes offers new ways to
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.
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 approach for creating synthetic electronic medical records
Directory of Open Access Journals (Sweden)
Moniz Linda
2010-10-01
Full Text Available Abstract Background New algorithms for disease outbreak detection are being developed to take advantage of full electronic medical records (EMRs that contain a wealth of patient information. However, due to privacy concerns, even anonymized EMRs cannot be shared among researchers, resulting in great difficulty in comparing the effectiveness of these algorithms. To bridge the gap between novel bio-surveillance algorithms operating on full EMRs and the lack of non-identifiable EMR data, a method for generating complete and synthetic EMRs was developed. Methods This paper describes a novel methodology for generating complete synthetic EMRs both for an outbreak illness of interest (tularemia and for background records. The method developed has three major steps: 1 synthetic patient identity and basic information generation; 2 identification of care patterns that the synthetic patients would receive based on the information present in real EMR data for similar health problems; 3 adaptation of these care patterns to the synthetic patient population. Results We generated EMRs, including visit records, clinical activity, laboratory orders/results and radiology orders/results for 203 synthetic tularemia outbreak patients. Validation of the records by a medical expert revealed problems in 19% of the records; these were subsequently corrected. We also generated background EMRs for over 3000 patients in the 4-11 yr age group. Validation of those records by a medical expert revealed problems in fewer than 3% of these background patient EMRs and the errors were subsequently rectified. Conclusions A data-driven method was developed for generating fully synthetic EMRs. The method is general and can be applied to any data set that has similar data elements (such as laboratory and radiology orders and results, clinical activity, prescription orders. The pilot synthetic outbreak records were for tularemia but our approach may be adapted to other infectious
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...
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.
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
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)
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.
Data-Driven Based Asynchronous Motor Control for Printing Servo Systems
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.
A non-linear dimension reduction methodology for generating data-driven stochastic input models
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
First-principles data-driven discovery of transition metal oxides for artificial photosynthesis
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.
Nonlinear structures for extended Korteweg–de Vries equation in ...
Indian Academy of Sciences (India)
The presence of immobile nanodust grains changes the general properties of the ...... rational-type solutions, which may be helpful to explain the creation of very .... investigate the behaviour of nonlinear structures in the Earth's ionosphere ...
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...
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.
The HADDOCK web server for data-driven biomolecular docking
de Vries, S.J.|info:eu-repo/dai/nl/304837717; van Dijk, M.|info:eu-repo/dai/nl/325811113; Bonvin, A.M.J.J.|info:eu-repo/dai/nl/113691238
2010-01-01
Computational docking is the prediction or modeling of the three-dimensional structure of a biomolecular complex, starting from the structures of the individual molecules in their free, unbound form. HADDOC K is a popular docking program that takes a datadriven approach to docking, with support for
Quality of peas modelled by a structural equation system
DEFF Research Database (Denmark)
Bech, A. C.; Juhl, H. J.; Hansen, M.
2000-01-01
in a PLS structural model with the Total Food Quality Model as starting point. The results show that texture and flavour do have approximately the same effect on consumers' perception of overall quality. Quality development goals for plant breeders would be to optimse perceived flavour directly...
Design of a data-driven predictive controller for start-up process of AMT vehicles.
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 Participation: Algorithms, Cities, Citizens, and Corporate Control
Directory of Open Access Journals (Sweden)
Matthew Tenney
2016-07-01
Full Text Available In this paper, we critically explore the interplay of algorithms and civic participation in visions of a city governed by equation, sensor and tweet. We begin by discussing the rhetoric surrounding techno-enabled paths to participatory democracy. This leads to us interrogating how the city is impacted by a discourse that promises to harness social/human capital through data science. We move to a praxis level and examine the motivations of local planners to adopt and increasingly automate forms of VGI as a form of citizen engagement. We ground theory and praxis with a report on the uneven impacts of algorithmic civic participation underway in the Canadian city of Toronto.
Data-driven modeling, control and tools for cyber-physical energy systems
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
Golay, Philippe; Reverte, Isabelle; Rossier, Jérôme; Favez, Nicolas; Lecerf, Thierry
2013-06-01
The interpretation of the Wechsler Intelligence Scale for Children--Fourth Edition (WISC-IV) is based on a 4-factor model, which is only partially compatible with the mainstream Cattell-Horn-Carroll (CHC) model of intelligence measurement. The structure of cognitive batteries is frequently analyzed via exploratory factor analysis and/or confirmatory factor analysis. With classical confirmatory factor analysis, almost all cross-loadings between latent variables and measures are fixed to zero in order to allow the model to be identified. However, inappropriate zero cross-loadings can contribute to poor model fit, distorted factors, and biased factor correlations; most important, they do not necessarily faithfully reflect theory. To deal with these methodological and theoretical limitations, we used a new statistical approach, Bayesian structural equation modeling (BSEM), among a sample of 249 French-speaking Swiss children (8-12 years). With BSEM, zero-fixed cross-loadings between latent variables and measures are replaced by approximate zeros, based on informative, small-variance priors. Results indicated that a direct hierarchical CHC-based model with 5 factors plus a general intelligence factor better represented the structure of the WISC-IV than did the 4-factor structure and the higher order models. Because a direct hierarchical CHC model was more adequate, it was concluded that the general factor should be considered as a breadth rather than a superordinate factor. Because it was possible for us to estimate the influence of each of the latent variables on the 15 subtest scores, BSEM allowed improvement of the understanding of the structure of intelligence tests and the clinical interpretation of the subtest scores. PsycINFO Database Record (c) 2013 APA, all rights reserved.
Using Shape Memory Alloys: A Dynamic Data Driven Approach
Douglas, Craig C.; Calo, Victor M.; Cerwinsky, Derrick; Deng, Li; Efendiev, Yalchin R.
2013-01-01
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
Developing a Data Driven Process-Based Model for Remote Sensing of Ecosystem Production
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
Data-driven models of dominantly-inherited Alzheimer's disease progression.
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
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.
Characterizing wood-plastic composites via data-driven methodologies
John G. Michopoulos; John C. Hermanson; Robert Badaliance
2007-01-01
The recent increase of wood-plastic composite materials in various application areas has underlined the need for an efficient and robust methodology to characterize their nonlinear anisotropic constitutive behavior. In addition, the multiplicity of various loading conditions in structures utilizing these materials further increases the need for a characterization...
Data-driven design optimization for composite material characterization
John G. Michopoulos; John C. Hermanson; Athanasios Iliopoulos; Samuel G. Lambrakos; Tomonari Furukawa
2011-06-01
The main goal of the present paper is to demonstrate the value of design optimization beyond its use for structural shape determination in the realm of the constitutive characterization of anisotropic material systems such as polymer matrix composites with or without damage. The approaches discussed are based on the availability of massive experimental data...
Parameter Estimation of Structural Equation Modeling Using Bayesian Approach
Directory of Open Access Journals (Sweden)
Dewi Kurnia Sari
2016-05-01
Full Text Available Leadership is a process of influencing, directing or giving an example of employees in order to achieve the objectives of the organization and is a key element in the effectiveness of the organization. In addition to the style of leadership, the success of an organization or company in achieving its objectives can also be influenced by the commitment of the organization. Where organizational commitment is a commitment created by each individual for the betterment of the organization. The purpose of this research is to obtain a model of leadership style and organizational commitment to job satisfaction and employee performance, and determine the factors that influence job satisfaction and employee performance using SEM with Bayesian approach. This research was conducted at Statistics FNI employees in Malang, with 15 people. The result of this study showed that the measurement model, all significant indicators measure each latent variable. Meanwhile in the structural model, it was concluded there are a significant difference between the variables of Leadership Style and Organizational Commitment toward Job Satisfaction directly as well as a significant difference between Job Satisfaction on Employee Performance. As for the influence of Leadership Style and variable Organizational Commitment on Employee Performance directly declared insignificant.
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
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.
On the selection of user-defined parameters in data-driven stochastic subspace identification
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.
Data-Driven Property Estimation for Protective Clothing
2014-09-01
reliable predictions falls under the rubric “machine learning”. Inspired by the applications of machine learning in pharmaceutical drug design and...using genetic algorithms, for instance— descriptor selection can be automated as well. A well-known structured learning technique—Artificial Neural...descriptors automatically, by iteration, e.g., using a genetic algorithm [49]. 4.2.4 Avoiding Overfitting A peril of all regression—least squares as
Taylor, Lawrence W., Jr.; Rajiyah, H.
1991-01-01
Partial differential equations for modeling the structural dynamics and control systems of flexible spacecraft are applied here in order to facilitate systems analysis and optimization of these spacecraft. Example applications are given, including the structural dynamics of SCOLE, the Solar Array Flight Experiment, the Mini-MAST truss, and the LACE satellite. The development of related software is briefly addressed.
Fitting Data to Model: Structural Equation Modeling Diagnosis Using Two Scatter Plots
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…
International Nuclear Information System (INIS)
Chudnovsky, D.V.; Chudnovsky, G.V.
1980-01-01
We consider semi-classical approximation to factorized S-matrices. We show that this new class of matrices, called s-matrices, defines Hamiltonian structures for isospectral deformation equations. Concrete examples of factorized s-matrices are constructed and they are used to define Hamiltonian structure for general two-dimensional isospectral deformation systems. (orig.)
OpenMx: An Open Source Extended Structural Equation Modeling Framework
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…
Application of Exploratory Structural Equation Modeling to Evaluate the Academic Motivation Scale
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…
Filamentary structures of the cosmic web and the nonlinear Schroedinger type equation
International Nuclear Information System (INIS)
Tigrak, E; Weygaert, R van de; Jones, B J T
2011-01-01
We show that the filamentary type structures of the cosmic web can be modeled as solitonic waves by solving the reaction diffusion system which is the hydrodynamical analogous of the nonlinear Schroedinger type equation. We find the analytical solution of this system by applying the Hirota direct method which produces the dissipative soliton solutions to formulate the dynamical evolution of the nonlinear structure formation.
Structural interactions in ionic liquids linked to higher-order Poisson-Boltzmann equations
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.
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
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 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
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…
Data-Driven Visualization and Group Analysis of Multichannel EEG Coherence with Functional Units
Caat, Michael ten; Maurits, Natasha M.; Roerdink, Jos B.T.M.
2008-01-01
A typical data- driven visualization of electroencephalography ( EEG) coherence is a graph layout, with vertices representing electrodes and edges representing significant coherences between electrode signals. A drawback of this layout is its visual clutter for multichannel EEG. To reduce clutter,
Estimating the Probability of Wind Ramping Events: A Data-driven Approach
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.
Autonomous Soil Assessment System: A Data-Driven Approach to Planetary Mobility Hazard Detection
Raimalwala, K.; Faragalli, M.; Reid, E.
2018-04-01
The Autonomous Soil Assessment System predicts mobility hazards for rovers. Its development and performance are presented, with focus on its data-driven models, machine learning algorithms, and real-time sensor data fusion for predictive analytics.
National Aeronautics and Space Administration — Among various approaches for implementing prognostic algorithms data-driven algorithms are popular in the industry due to their intuitive nature and relatively fast...
Short-term stream flow forecasting at Australian river sites using data-driven regression techniques
CSIR Research Space (South Africa)
Steyn, Melise
2017-09-01
Full Text Available This study proposes a computationally efficient solution to stream flow forecasting for river basins where historical time series data are available. Two data-driven modeling techniques are investigated, namely support vector regression...
Fluid/Structure Interaction Studies of Aircraft Using High Fidelity Equations on Parallel Computers
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.
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....
Data-Driven Cyber-Physical Systems via Real-Time Stream Analytics and Machine Learning
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...
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.
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
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.
Reformulation of Maxwell's equations to incorporate near-solute solvent structure.
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.
RNA motif search with data-driven element ordering.
Rampášek, Ladislav; Jimenez, Randi M; Lupták, Andrej; Vinař, Tomáš; Brejová, Broňa
2016-05-18
In this paper, we study the problem of RNA motif search in long genomic sequences. This approach uses a combination of sequence and structure constraints to uncover new distant homologs of known functional RNAs. The problem is NP-hard and is traditionally solved by backtracking algorithms. We have designed a new algorithm for RNA motif search and implemented a new motif search tool RNArobo. The tool enhances the RNAbob descriptor language, allowing insertions in helices, which enables better characterization of ribozymes and aptamers. A typical RNA motif consists of multiple elements and the running time of the algorithm is highly dependent on their ordering. By approaching the element ordering problem in a principled way, we demonstrate more than 100-fold speedup of the search for complex motifs compared to previously published tools. We have developed a new method for RNA motif search that allows for a significant speedup of the search of complex motifs that include pseudoknots. Such speed improvements are crucial at a time when the rate of DNA sequencing outpaces growth in computing. RNArobo is available at http://compbio.fmph.uniba.sk/rnarobo .
DeRuntz Jr., John A.
2005-01-01
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 equa...
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.
Scidac-Data: Enabling Data Driven Modeling of Exascale Computing
Mubarak, Misbah; Ding, Pengfei; Aliaga, Leo; Tsaris, Aristeidis; Norman, Andrew; Lyon, Adam; Ross, Robert
2017-10-01
The SciDAC-Data project is a DOE-funded initiative to analyze and exploit two decades of information and analytics that have been collected by the Fermilab data center on the organization, movement, and consumption of high energy physics (HEP) data. The project analyzes the analysis patterns and data organization that have been used by NOvA, MicroBooNE, MINERvA, CDF, D0, and other experiments to develop realistic models of HEP analysis workflows and data processing. The SciDAC-Data project aims to provide both realistic input vectors and corresponding output data that can be used to optimize and validate simulations of HEP analysis. These simulations are designed to address questions of data handling, cache optimization, and workflow structures that are the prerequisites for modern HEP analysis chains to be mapped and optimized to run on the next generation of leadership-class exascale computing facilities. We present the use of a subset of the SciDAC-Data distributions, acquired from analysis of approximately 71,000 HEP workflows run on the Fermilab data center and corresponding to over 9 million individual analysis jobs, as the input to detailed queuing simulations that model the expected data consumption and caching behaviors of the work running in high performance computing (HPC) and high throughput computing (HTC) environments. In particular we describe how the Sequential Access via Metadata (SAM) data-handling system in combination with the dCache/Enstore-based data archive facilities has been used to develop radically different models for analyzing the HEP data. We also show how the simulations may be used to assess the impact of design choices in archive facilities.
Data-driven probability concentration and sampling on manifold
Energy Technology Data Exchange (ETDEWEB)
Soize, C., E-mail: christian.soize@univ-paris-est.fr [Université Paris-Est, Laboratoire Modélisation et Simulation Multi-Echelle, MSME UMR 8208 CNRS, 5 bd Descartes, 77454 Marne-La-Vallée Cedex 2 (France); Ghanem, R., E-mail: ghanem@usc.edu [University of Southern California, 210 KAP Hall, Los Angeles, CA 90089 (United States)
2016-09-15
A new methodology is proposed for generating realizations of a random vector with values in a finite-dimensional Euclidean space that are statistically consistent with a dataset of observations of this vector. The probability distribution of this random vector, while a priori not known, is presumed to be concentrated on an unknown subset of the Euclidean space. A random matrix is introduced whose columns are independent copies of the random vector and for which the number of columns is the number of data points in the dataset. The approach is based on the use of (i) the multidimensional kernel-density estimation method for estimating the probability distribution of the random matrix, (ii) a MCMC method for generating realizations for the random matrix, (iii) the diffusion-maps approach for discovering and characterizing the geometry and the structure of the dataset, and (iv) a reduced-order representation of the random matrix, which is constructed using the diffusion-maps vectors associated with the first eigenvalues of the transition matrix relative to the given dataset. The convergence aspects of the proposed methodology are analyzed and a numerical validation is explored through three applications of increasing complexity. The proposed method is found to be robust to noise levels and data complexity as well as to the intrinsic dimension of data and the size of experimental datasets. Both the methodology and the underlying mathematical framework presented in this paper contribute new capabilities and perspectives at the interface of uncertainty quantification, statistical data analysis, stochastic modeling and associated statistical inverse problems.
Energy Technology Data Exchange (ETDEWEB)
Surdoval, Wayne A. [National Energy Technology Lab. (NETL), Pittsburgh, PA, (United States); Berry, David A. [National Energy Technology Lab. (NETL), Morgantown, WV (United States); Shultz, Travis R. [National Energy Technology Lab. (NETL), Morgantown, WV (United States)
2018-03-09
A set of equations are presented for calculating atomic principal spectral lines and fine-structure energy splits for single and multi-electron atoms. Calculated results are presented and compared to the National Institute of Science and Technology database demonstrating very good accuracy. The equations do not require fitted parameters. The only experimental parameter required is the Ionization energy for the electron of interest. The equations have comparable accuracy and broader applicability than the single electron Dirac equation. Three Appendices discuss the origin of the new equations and present calculated results. New insights into the special relativistic nature of the Dirac equation and its relationship to the new equations are presented.
The structure of solutions of the matrix linear unilateral polynomial equation with two variables
Directory of Open Access Journals (Sweden)
N. S. Dzhaliuk
2017-07-01
Full Text Available We investigate the structure of solutions of the matrix linear polynomial equation $A(\\lambdaX(\\lambda+B(\\lambdaY(\\lambda=C(\\lambda,$ in particular, possible degrees of the solutions. The solving of this equation is reduced to the solving of the equivalent matrix polynomial equation with matrix coefficients in triangular forms with invariant factors on the main diagonals, to which the matrices $A (\\lambda, B(\\lambda$ \\ and \\ $C(\\lambda$ are reduced by means of semiscalar equivalent transformations. On the basis of it, we have pointed out the bounds of the degrees of the matrix polynomial equation solutions. Necessary and sufficient conditions for the uniqueness of a solution with a minimal degree are established. An effective method for constructing minimal degree solutions of the equations is suggested. In this article, unlike well-known results about the estimations of the degrees of the solutions of the matrix polynomial equations in which both matrix coefficients are regular or at least one of them is regular, we have considered the case when the matrix polynomial equation has arbitrary matrix coefficients $A(\\lambda$ and $B(\\lambda.$
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
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...
Comparing direct and iterative equation solvers in a large structural analysis software system
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.
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
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
Spinor-electron wave guided modes in coupled quantum wells structures by solving the Dirac equation
International Nuclear Information System (INIS)
Linares, Jesus; Nistal, Maria C.
2009-01-01
A quantum analysis based on the Dirac equation of the propagation of spinor-electron waves in coupled quantum wells, or equivalently coupled electron waveguides, is presented. The complete optical wave equations for Spin-Up (SU) and Spin-Down (SD) spinor-electron waves in these electron guides couplers are derived from the Dirac equation. The relativistic amplitudes and dispersion equations of the spinor-electron wave-guided modes in a planar quantum coupler formed by two coupled quantum wells, or equivalently by two coupled slab electron waveguides, are exactly derived. The main outcomes related to the spinor modal structure, such as the breaking of the non-relativistic degenerate spin states, the appearance of phase shifts associated with the spin polarization and so on, are shown.
Bifurcation structure of localized states in the Lugiato-Lefever equation with anomalous dispersion
Parra-Rivas, P.; Gomila, D.; Gelens, L.; Knobloch, E.
2018-04-01
The origin, stability, and bifurcation structure of different types of bright localized structures described by the Lugiato-Lefever equation are studied. This mean field model describes the nonlinear dynamics of light circulating in fiber cavities and microresonators. In the case of anomalous group velocity dispersion and low values of the intracavity phase detuning these bright states are organized in a homoclinic snaking bifurcation structure. We describe how this bifurcation structure is destroyed when the detuning is increased across a critical value, and determine how a bifurcation structure known as foliated snaking emerges.
Data-driven analysis of functional brain interactions during free listening to music and speech.
Fang, Jun; Hu, Xintao; Han, Junwei; Jiang, Xi; Zhu, Dajiang; Guo, Lei; Liu, Tianming
2015-06-01
Natural stimulus functional magnetic resonance imaging (N-fMRI) such as fMRI acquired when participants were watching video streams or listening to audio streams has been increasingly used to investigate functional mechanisms of the human brain in recent years. One of the fundamental challenges in functional brain mapping based on N-fMRI is to model the brain's functional responses to continuous, naturalistic and dynamic natural stimuli. To address this challenge, in this paper we present a data-driven approach to exploring functional interactions in the human brain during free listening to music and speech streams. Specifically, we model the brain responses using N-fMRI by measuring the functional interactions on large-scale brain networks with intrinsically established structural correspondence, and perform music and speech classification tasks to guide the systematic identification of consistent and discriminative functional interactions when multiple subjects were listening music and speech in multiple categories. The underlying premise is that the functional interactions derived from N-fMRI data of multiple subjects should exhibit both consistency and discriminability. Our experimental results show that a variety of brain systems including attention, memory, auditory/language, emotion, and action networks are among the most relevant brain systems involved in classic music, pop music and speech differentiation. Our study provides an alternative approach to investigating the human brain's mechanism in comprehension of complex natural music and speech.
A priori data-driven multi-clustered reservoir generation algorithm for echo state network.
Directory of Open Access Journals (Sweden)
Xiumin Li
Full Text Available Echo state networks (ESNs with multi-clustered reservoir topology perform better in reservoir computing and robustness than those with random reservoir topology. However, these ESNs have a complex reservoir topology, which leads to difficulties in reservoir generation. This study focuses on the reservoir generation problem when ESN is used in environments with sufficient priori data available. Accordingly, a priori data-driven multi-cluster reservoir generation algorithm is proposed. The priori data in the proposed algorithm are used to evaluate reservoirs by calculating the precision and standard deviation of ESNs. The reservoirs are produced using the clustering method; only the reservoir with a better evaluation performance takes the place of a previous one. The final reservoir is obtained when its evaluation score reaches the preset requirement. The prediction experiment results obtained using the Mackey-Glass chaotic time series show that the proposed reservoir generation algorithm provides ESNs with extra prediction precision and increases the structure complexity of the network. Further experiments also reveal the appropriate values of the number of clusters and time window size to obtain optimal performance. The information entropy of the reservoir reaches the maximum when ESN gains the greatest precision.
A data-driven approach for evaluating multi-modal therapy in traumatic brain injury.
Haefeli, Jenny; Ferguson, Adam R; Bingham, Deborah; Orr, Adrienne; Won, Seok Joon; Lam, Tina I; Shi, Jian; Hawley, Sarah; Liu, Jialing; Swanson, Raymond A; Massa, Stephen M
2017-02-16
Combination therapies targeting multiple recovery mechanisms have the potential for additive or synergistic effects, but experimental design and analyses of multimodal therapeutic trials are challenging. To address this problem, we developed a data-driven approach to integrate and analyze raw source data from separate pre-clinical studies and evaluated interactions between four treatments following traumatic brain injury. Histologic and behavioral outcomes were measured in 202 rats treated with combinations of an anti-inflammatory agent (minocycline), a neurotrophic agent (LM11A-31), and physical therapy consisting of assisted exercise with or without botulinum toxin-induced limb constraint. Data was curated and analyzed in a linked workflow involving non-linear principal component analysis followed by hypothesis testing with a linear mixed model. Results revealed significant benefits of the neurotrophic agent LM11A-31 on learning and memory outcomes after traumatic brain injury. In addition, modulations of LM11A-31 effects by co-administration of minocycline and by the type of physical therapy applied reached statistical significance. These results suggest a combinatorial effect of drug and physical therapy interventions that was not evident by univariate analysis. The study designs and analytic techniques applied here form a structured, unbiased, internally validated workflow that may be applied to other combinatorial studies, both in animals and humans.
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.)
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)
Liu, An; Noback, Inge
2011-01-01
The paper analyses the determinants of female labour participation. Structural equation modelling is used to handle theoretical concepts and to solve the typical problem of multicollinearity. The proposed methodology is applied to a dataset for the year 2002 made up of a sample of 278 municipalities
A Robust Bayesian Approach for Structural Equation Models with Missing Data
Lee, Sik-Yum; Xia, Ye-Mao
2008-01-01
In this paper, normal/independent distributions, including but not limited to the multivariate t distribution, the multivariate contaminated distribution, and the multivariate slash distribution, are used to develop a robust Bayesian approach for analyzing structural equation models with complete or missing data. In the context of a nonlinear…
Kawano, Yu; Cao, Ming
2017-01-01
We define and then study the structural observability for a class of complex networks whose dynamics are governed by the nonlinear balance equations. Although related notions of observability of such complex networks have been studied before and in particular, necessary conditions have been reported
Residuals and the Residual-Based Statistic for Testing Goodness of Fit of Structural Equation Models
Foldnes, Njal; Foss, Tron; Olsson, Ulf Henning
2012-01-01
The residuals obtained from fitting a structural equation model are crucial ingredients in obtaining chi-square goodness-of-fit statistics for the model. The authors present a didactic discussion of the residuals, obtaining a geometrical interpretation by recognizing the residuals as the result of oblique projections. This sheds light on the…
Czech Academy of Sciences Publication Activity Database
Nooijen, M.; Demel, Ondřej; Datta, D.; Kong, L.; Shamasundar, K. R.; Lotrich, V.; Huntington, L. M.; Neese, F.
2014-01-01
Roč. 140, č. 8 (2014), 081102 ISSN 0021-9606 R&D Projects: GA ČR GPP208/10/P041; GA ČR GAP208/11/2222 Institutional support: RVO:61388955 Keywords : Electronic states * Electronic structure * Equations of motion Subject RIV: CF - Physical ; Theoretical Chemistry Impact factor: 2.952, year: 2014
Dijkstra, T.K.; Henseler, J.
2011-01-01
The recent advent of nonlinear structural equation models with indices poses a new challenge to the measurement of scientific constructs. We discuss, exemplify and add to a family of statistical methods aimed at creating linear indices, and compare their suitability in a complex path model with
Horzum, Mehmet Baris; Kaymak, Zeliha Demir; Gungoren, Ozlem Canan
2015-01-01
The relationship between online learning readiness, academic motivations, and perceived learning was investigated via structural equation modeling in the research. The population of the research consisted of 750 students who studied using the online learning programs of Sakarya University. 420 of the students who volunteered for the research and…
Anti-Transgender Prejudice: A Structural Equation Model of Associated Constructs
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.…
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…
A Methodological Review of Structural Equation Modelling in Higher Education Research
Green, Teegan
2016-01-01
Despite increases in the number of articles published in higher education journals using structural equation modelling (SEM), research addressing their statistical sufficiency, methodological appropriateness and quantitative rigour is sparse. In response, this article provides a census of all covariance-based SEM articles published up until 2013…
The Effect of Authentic Leadership on School Culture: A Structural Equation Model
Karadag, Engin; Oztekin-Bayir, Ozge
2018-01-01
In the study, the effect of school principals' authentic leadership behaviors on teachers' perceptions of school culture was tested with the structural equation model. The study was carried out with the correlation research design. Authentic leadership behavior was taken as the independent variable, and school culture was taken as the dependent…
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…
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…
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…
Performance and scaling of locally-structured grid methods forpartial differential equations
Energy Technology Data Exchange (ETDEWEB)
Colella, Phillip; Bell, John; Keen, Noel; Ligocki, Terry; Lijewski, Michael; Van Straalen, Brian
2007-07-19
In this paper, we discuss some of the issues in obtaining high performance for block-structured adaptive mesh refinement software for partial differential equations. We show examples in which AMR scales to thousands of processors. We also discuss a number of metrics for performance and scalability that can provide a basis for understanding the advantages and disadvantages of this approach.
An Application of Structural Equation Modeling for Developing Good Teaching Characteristics Ontology
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…
Chen, Wei; Wang, Long; Zhang, Xing-Li; Shi, Jian-Nong
2012-01-01
The objective of this study was to investigate the impact of trauma exposure on the posttraumatic stress symptomatology (PTSS) of children who resided near the epicenter of the 2008 Wenchuan earthquake. The mechanisms of this impact were explored via structural equation models with self-esteem and coping strategies included as mediators. The…
Directory of Open Access Journals (Sweden)
Mai, L.W.
2006-01-01
Full Text Available Analyses the relationship between satisfaction with mail-order speciality food attributes, overall satisfaction, and likelihood of future purchase using a structural equation model. The results indicate that customer satisfaction is associated with both service and product features of mail order speciality food.
Prescriptive Statements and Educational Practice: What Can Structural Equation Modeling (SEM) Offer?
Martin, Andrew J.
2011-01-01
Longitudinal structural equation modeling (SEM) can be a basis for making prescriptive statements on educational practice and offers yields over "traditional" statistical techniques under the general linear model. The extent to which prescriptive statements can be made will rely on the appropriate accommodation of key elements of research design,…
Folmer, Henk; Dutta, Subrata; Oud, Han
2010-01-01
This article presents a structural equations model of rural industrial entrepreneurship (RIE) among farmers in the Bardhaman district, West Bengal, India. It identifies the determinants of RIE but also analyzes impacts of RIE on its endogenous determinants. Age, education, marital status, number of
Folmer, H.; Dutta, S.; Oud, J.H.L.
2010-01-01
This article presents a structural equations model of rural industrial entrepreneurship (RIE) among farmers in the Bardhaman district, West Bengal, India. It identifies the determinants of RIE but also analyzes impacts of RIE on its endogenous determinants. Age, education, marital status, number of
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…
Estimating structural equation models with non-normal variables by using transformations
Montfort, van K.; Mooijaart, A.; Meijerink, F.
2009-01-01
We discuss structural equation models for non-normal variables. In this situation the maximum likelihood and the generalized least-squares estimates of the model parameters can give incorrect estimates of the standard errors and the associated goodness-of-fit chi-squared statistics. If the sample
Karadag, Engin; Kilicoglu, Gökhan; Yilmaz, Derya
2014-01-01
The purpose of this study is to explain constructed theoretical models that organizational cynicism perceptions of primary school teachers affect school culture and academic achievement, by using structural equation modeling. With the assumption that there is a cause-effect relationship between three main variables, the study was constructed with…
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.
A Structural Equation Modelling Approach for Massive Blended Synchronous Teacher Training
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…
Testing strong factorial invariance using three-level structural equation modeling
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
Cheung, Mike W. L.; Chan, Wai
2009-01-01
Structural equation modeling (SEM) is widely used as a statistical framework to test complex models in behavioral and social sciences. When the number of publications increases, there is a need to systematically synthesize them. Methodology of synthesizing findings in the context of SEM is known as meta-analytic SEM (MASEM). Although correlation…
A Structural Equation Modelling of the Academic Self-Concept Scale
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…
DEFF Research Database (Denmark)
Salarzadeh Jenatabadi, Hashem; Babashamsi, Peyman; Khajeheian, Datis
2016-01-01
There are many factors which could inﬂuence the sustainability of airlines. The main purpose of this study is to introduce a framework for a financial sustainability index and model it based on structural equation modeling (SEM) with maximum likelihood and Bayesian predictors. The introduced...
From Ordinary Differential Equations to Structural Causal Models: the deterministic case
Mooij, J.M.; Janzing, D.; Schölkopf, B.; Nicholson, A.; Smyth, P.
2013-01-01
We show how, and under which conditions, the equilibrium states of a first-order Ordinary Differential Equation (ODE) system can be described with a deterministic Structural Causal Model (SCM). Our exposition sheds more light on the concept of causality as expressed within the framework of
Recent developments in structure-preserving algorithms for oscillatory differential equations
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...
Family Environment and Childhood Obesity: A New Framework with Structural Equation Modeling
Huang, Hui; Wan Mohamed Radzi, Che Wan Jasimah bt; Salarzadeh Jenatabadi, Hashem
2017-01-01
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 ...
International Nuclear Information System (INIS)
Nagirnyi, T.S.
1993-01-01
Studies of the coupled processes in electrically conducting nonferromagnetic viscoelastic bodies usually begin with a system of equations that accounts for the influence of rheology on the mechanical and temperature fields. In this context, rheology is understood as the course of certain internal processes in the body that are reflected when the relaxation time and the defects of thermomechanical moduli are specified. In this work, the methods of continuum mechanics are used to state a system of equations for the quantitative description of coupled mechanical, thermal, and electromagnetic processes taking account of structural transformations in the context of the model of a rheologically simple electrically conducting nonferromagnetic body
Hamiltonian structures and integrability for a discrete coupled KdV-type equation hierarchy
International Nuclear Information System (INIS)
Zhao Haiqiong; Zhu Zuonong; Zhang Jingli
2011-01-01
Coupled Korteweg-de Vries (KdV) systems have many important physical applications. By considering a 4 × 4 spectral problem, we derive a discrete coupled KdV-type equation hierarchy. Our hierarchy includes the coupled Volterra system proposed by Lou et al. (e-print arXiv: 0711.0420) as the first member which is a discrete version of the coupled KdV equation. We also investigate the integrability in the Liouville sense and the multi-Hamiltonian structures for the obtained hierarchy. (authors)
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
International Nuclear Information System (INIS)
Abourabia, A.M.; El-Danaf, T.S.; Morad, A.M.
2008-01-01
The problem under consideration are related to wave propagation in micro structured materials, characterized by higher-order nonlinear and higher-order dispersive effects; particularly, the wave propagation in dilatant granular materials. In the present paper the model equation is solved analytically by exact method called Jacobi elliptic method. The types of solutions are defined and discussed over a wide range of material parameters (two dispersion parameters and one microstructure parameter). The dispersion properties and the relation between group and phase velocities of the model equation are studied. The diagrams are drawn to illustrate the physical properties of the exact solutions
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.
Data-driven design of fault diagnosis and fault-tolerant control systems
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...
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...
Data-driven remaining useful life prognosis techniques stochastic models, methods and applications
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...
Nonlinear model of a rotating hub-beams structure: Equations of motion
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.
The performance effect of the Lean package – a survey study using a structural equation model
DEFF Research Database (Denmark)
Kristensen, Thomas Borup; Israelsen, Poul
Purpose - Our aim is to test and validate a system-wide approach using mediating relationships in a structural equation model in order to understand how the practices of Lean affect performance. Design/methodology/approach – A cross-sectional survey with 200 responding companies indicating...... that they use Lean. This is analyzed in a structural quation model setting. Findings - Previous quantitative research has shown mixed results for the performance of Lean because they have not addressed the system-wide mediating relations between Lean practices. We find that Companies using a system...... practices in creating improved performance. Hence, we develop a new systemwide structural equation model approach with multiple mediations, and we validate this with substantial tests....
Transport equations, Level Set and Eulerian mechanics. Application to fluid-structure coupling
International Nuclear Information System (INIS)
Maitre, E.
2008-11-01
My works were devoted to numerical analysis of non-linear elliptic-parabolic equations, to neutron transport equation and to the simulation of fabrics draping. More recently I developed an Eulerian method based on a level set formulation of the immersed boundary method to deal with fluid-structure coupling problems arising in bio-mechanics. Some of the more efficient algorithms to solve the neutron transport equation make use of the splitting of the transport operator taking into account its characteristics. In the present work we introduced a new algorithm based on this splitting and an adaptation of minimal residual methods to infinite dimensional case. We present the case where the velocity space is of dimension 1 (slab geometry) and 2 (plane geometry) because the splitting is simpler in the former
Decoupling of the Leading Order DGLAP Evolution Equation with Spin Dependent Structure Functions
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.
Solution of quadratic matrix equations for free vibration analysis of structures.
Gupta, K. K.
1973-01-01
An efficient digital computer procedure and the related numerical algorithm are presented herein for the solution of quadratic matrix equations associated with free vibration analysis of structures. Such a procedure enables accurate and economical analysis of natural frequencies and associated modes of discretized structures. The numerically stable algorithm is based on the Sturm sequence method, which fully exploits the banded form of associated stiffness and mass matrices. The related computer program written in FORTRAN V for the JPL UNIVAC 1108 computer proves to be substantially more accurate and economical than other existing procedures of such analysis. Numerical examples are presented for two structures - a cantilever beam and a semicircular arch.
Directory of Open Access Journals (Sweden)
Diana Puspita Sari
2015-11-01
Full Text Available Kereta api merupakan salah satu moda transportasi dengan jumlah penumpang yang besar. Pengoperasian kereta api Tawang Jaya merupakan salah satu upaya untuk mengakomodir rute perjalanan Semarang – Jakarta dan sebaliknya. Studi pendahuluan mencatat bahwa telah terjadi ketidaknyamanan pada kereta api Tawang Jaya yang diiringi dengan penurunan jumlah penumpang. Penurunan jumlah penumpang mungkin disebabkan karena ketidaknyamanan, sehingga banyak penumpang yang beraliih menggunakan moda transportasi lain atau kereta jenis lain. Hal ini dibuktikan dengan kenaikan jumlah penumpang kereta kelas bisnis dan eksekutif pada waktu yang bersamaan. Studi ini bertujuan untuk mengetahui persepsi kenyamanan pada perjalanan kereta api Tawang Jaya dengan menggunakan Structural Equation Modelling (SEM menggunakan software AMOS versi 21.0. Pendekatan Structural Equation Modelling (SEM digunakan untuk menjelaskan secara sistematis kenyamanan pelanggan kereta api melalui beberapa faktor (suasana kabin, tempat duduk, gejala mabuk perjalanan, faktor terowongan, keletihan dan kenyamanan berkendara serta menghitung pengaruh dari faktor-faktor tersebut terhadap kenyamanan. Hasil dari penelitian menunjukkan bahwa faktor yang secara langsung berpengaruh signifikan terhadap kenyamanan berkendara adalah suasana kabin dengan bobot sebesar 0.60. Sementara itu faktor yang secara tidak langsung berpengaruh signifikan terhadap kenyamanan berkendara melalui mediasi keletihan adalah tempat duduk dengan bobot sebesar 0.5394. Kata kunci: kenyamanan; kereta api; structural equation modelling (SEM Abstract Railway is one of the transportation vehicle with a large number of passenger. The operation of Tawang Jaya Train is one way to accomodate the route of Semarang – Jakarta and reversed. The pilot study notes that discomfort problem has occured in Tawang Jaya Train and caused decreasing the number of passenger. Decrease in the number of passengers may be caused because of
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
Data-driven reverse engineering of signaling pathways using ensembles of dynamic models.
Directory of Open Access Journals (Sweden)
David Henriques
2017-02-01
Full Text Available Despite significant efforts and remarkable progress, the inference of signaling networks from experimental data remains very challenging. The problem is particularly difficult when the objective is to obtain a dynamic model capable of predicting the effect of novel perturbations not considered during model training. The problem is ill-posed due to the nonlinear nature of these systems, the fact that only a fraction of the involved proteins and their post-translational modifications can be measured, and limitations on the technologies used for growing cells in vitro, perturbing them, and measuring their variations. As a consequence, there is a pervasive lack of identifiability. To overcome these issues, we present a methodology called SELDOM (enSEmbLe of Dynamic lOgic-based Models, which builds an ensemble of logic-based dynamic models, trains them to experimental data, and combines their individual simulations into an ensemble prediction. It also includes a model reduction step to prune spurious interactions and mitigate overfitting. SELDOM is a data-driven method, in the sense that it does not require any prior knowledge of the system: the interaction networks that act as scaffolds for the dynamic models are inferred from data using mutual information. We have tested SELDOM on a number of experimental and in silico signal transduction case-studies, including the recent HPN-DREAM breast cancer challenge. We found that its performance is highly competitive compared to state-of-the-art methods for the purpose of recovering network topology. More importantly, the utility of SELDOM goes beyond basic network inference (i.e. uncovering static interaction networks: it builds dynamic (based on ordinary differential equation models, which can be used for mechanistic interpretations and reliable dynamic predictions in new experimental conditions (i.e. not used in the training. For this task, SELDOM's ensemble prediction is not only consistently better
Dynamic Data-Driven Prediction of Lean Blowout in a Swirl-Stabilized Combustor
Directory of Open Access Journals (Sweden)
Soumalya Sarkar
2015-09-01
Full Text Available This paper addresses dynamic data-driven prediction of lean blowout (LBO phenomena in confined combustion processes, which are prevalent in many physical applications (e.g., land-based and aircraft gas-turbine engines. The underlying concept is built upon pattern classification and is validated for LBO prediction with time series of chemiluminescence sensor data from a laboratory-scale swirl-stabilized dump combustor. The proposed method of LBO prediction makes use of the theory of symbolic dynamics, where (finite-length time series data are partitioned to produce symbol strings that, in turn, generate a special class of probabilistic finite state automata (PFSA. These PFSA, called D-Markov machines, have a deterministic algebraic structure and their states are represented by symbol blocks of length D or less, where D is a positive integer. The D-Markov machines are constructed in two steps: (i state splitting, i.e., the states are split based on their information contents, and (ii state merging, i.e., two or more states (of possibly different lengths are merged together to form a new state without any significant loss of the embedded information. The modeling complexity (e.g., number of states of a D-Markov machine model is observed to be drastically reduced as the combustor approaches LBO. An anomaly measure, based on Kullback-Leibler divergence, is constructed to predict the proximity of LBO. The problem of LBO prediction is posed in a pattern classification setting and the underlying algorithms have been tested on experimental data at different extents of fuel-air premixing and fuel/air ratio. It is shown that, over a wide range of fuel-air premixing, D-Markov machines with D > 1 perform better as predictors of LBO than those with D = 1.
Data-Driven Modeling of Complex Systems by means of a Dynamical ANN
Seleznev, A.; Mukhin, D.; Gavrilov, A.; Loskutov, E.; Feigin, A.
2017-12-01
The data-driven methods for modeling and prognosis of complex dynamical systems become more and more popular in various fields due to growth of high-resolution data. We distinguish the two basic steps in such an approach: (i) determining the phase subspace of the system, or embedding, from available time series and (ii) constructing an evolution operator acting in this reduced subspace. In this work we suggest a novel approach combining these two steps by means of construction of an artificial neural network (ANN) with special topology. The proposed ANN-based model, on the one hand, projects the data onto a low-dimensional manifold, and, on the other hand, models a dynamical system on this manifold. Actually, this is a recurrent multilayer ANN which has internal dynamics and capable of generating time series. Very important point of the proposed methodology is the optimization of the model allowing us to avoid overfitting: we use Bayesian criterion to optimize the ANN structure and estimate both the degree of evolution operator nonlinearity and the complexity of nonlinear manifold which the data are projected on. The proposed modeling technique will be applied to the analysis of high-dimensional dynamical systems: Lorenz'96 model of atmospheric turbulence, producing high-dimensional space-time chaos, and quasi-geostrophic three-layer model of the Earth's atmosphere with the natural orography, describing the dynamics of synoptical vortexes as well as mesoscale blocking systems. The possibility of application of the proposed methodology to analyze real measured data is also discussed. The study was supported by the Russian Science Foundation (grant #16-12-10198).
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.
Numerical solution of quadratic matrix equations for free vibration analysis of structures
Gupta, K. K.
1975-01-01
This paper is concerned with the efficient and accurate solution of the eigenvalue problem represented by quadratic matrix equations. Such matrix forms are obtained in connection with the free vibration analysis of structures, discretized by finite 'dynamic' elements, resulting in frequency-dependent stiffness and inertia matrices. The paper presents a new numerical solution procedure of the quadratic matrix equations, based on a combined Sturm sequence and inverse iteration technique enabling economical and accurate determination of a few required eigenvalues and associated vectors. An alternative procedure based on a simultaneous iteration procedure is also described when only the first few modes are the usual requirement. The employment of finite dynamic elements in conjunction with the presently developed eigenvalue routines results in a most significant economy in the dynamic analysis of structures.
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.
A STRUCTURAL EQUATION MODEL-II FOR WORK-LIFE BALANCE OF IT PROFESSIONALS IN CHENNAI
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...
A STRUCTURAL EQUATION MODEL: GREECEâ€™S TOURISM DEMAND FOR TOURIST DESTINATION
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 Greeceâ€™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...
A Structural Equation Model: India’s International Tourism Demand for Tourist Destination
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...
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 ...
Che Wan Jasimah bt Wan Mohamed Radzi; Huang Hui; Nur Anisah Binti Mohamed @ A. Rahman; Hashem Salarzadeh Jenatabadi
2017-01-01
Structural Equation Modeling (SEM) has been used extensively in sustainability studies to model relationships among latent and manifest variables. This paper provides a tutorial exposition of the SEM approach in food security studies and introduces a basic framework based on family food security and children’s environment sustainability. This framework includes family food security and three main concepts representing children’s environment, including children’s BMI, health, and school perfor...
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...
Structural equation models to estimate risk of infection and tolerance to bovine mastitis
Detilleux, Johann; Theron, Léonard; Duprez, Jean-Noël; Reding, Edouard; Humblet, Marie-France; Planchon, Viviane; Delfosse, Camille; Bertozzi, Carlo; Mainil, Jacques; Hanzen, Christian
2013-01-01
Background 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. Methods We propose structural equation models with latent variables (1) to quantify the latent risk of infection and to identify, amon...
Analyzing Sport Consumer Behaviour toward Sportswear Store: A Structural Equation Modelling Approach
Hafedh Ibrahim; Faouzi Najjar
2014-01-01
The aim of this study is to elucidate in sportswear store setting the relationships among psychological traits, loyalty to salesperson and behavioural intentions in three different sport consumers according to their switching behaviour. By means of structural equation modelling, we find a clear difference in the behaviour of the three groups. The results show that loyalty to salesperson is more influenced by need for social affiliation for the stayer customers. Whereas, for the dissatisfied a...
Validation of an employee satisfaction model: A structural equation model approach
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 ...
A Structural Equation Model of Risk Perception of Rockfall for Revisit Intention
Ya-Fen Lee; Yun-Yao Chi
2014-01-01
The study aims to explore the relationship between risk perception of rockfall and revisit intention using a Structural Equation Modeling (SEM) analysis. A total of 573 valid questionnaires are collected from travelers to Taroko National Park, Taiwan. The findings show the majority of travelers have the medium perception of rockfall risk, and are willing to revisit the Taroko National Park. The revisit intention to Taroko National Park is influenced by hazardous preferences, willingness-to-pa...
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...
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...
Ability Grouping and Differentiated Instruction in an Era of Data-Driven Decision Making
Park, Vicki; Datnow, Amanda
2017-01-01
Despite data-driven decision making being a ubiquitous part of policy and school reform efforts, little is known about how teachers use data for instructional decision making. Drawing on data from a qualitative case study of four elementary schools, we examine the logic and patterns of teacher decision making about differentiation and ability…
Data-driven diagnostics of terrestrial carbon dynamics over North America
Jingfeng Xiao; Scott V. Ollinger; Steve Frolking; George C. Hurtt; David Y. Hollinger; Kenneth J. Davis; Yude Pan; Xiaoyang Zhang; Feng Deng; Jiquan Chen; Dennis D. Baldocchi; Bevery E. Law; M. Altaf Arain; Ankur R. Desai; Andrew D. Richardson; Ge Sun; Brian Amiro; Hank Margolis; Lianhong Gu; Russell L. Scott; Peter D. Blanken; Andrew E. Suyker
2014-01-01
The exchange of carbon dioxide is a key measure of ecosystem metabolism and a critical intersection between the terrestrial biosphere and the Earth's climate. Despite the general agreement that the terrestrial ecosystems in North America provide a sizeable carbon sink, the size and distribution of the sink remain uncertain. We use a data-driven approach to upscale...
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
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
Perspectives of data-driven LPV modeling of high-purity distillation columns
Bachnas, A.A.; Toth, R.; Mesbah, A.; Ludlage, J.H.A.
2013-01-01
Abstract—This paper investigates data-driven, Linear- Parameter-Varying (LPV) modeling of a high-purity distillation column. Two LPV modeling approaches are studied: a local approach, corresponding to the interpolation of Linear Time- Invariant (LTI) models identified at steady-state purity levels,
The Role of Guided Induction in Paper-Based Data-Driven Learning
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…
Design and evaluation of a data-driven scenario generation framework for game-based training
Luo, L.; Yin, H.; Cai, W.; Zhong, J.; Lees, M.
Generating suitable game scenarios that can cater for individual players has become an emerging challenge in procedural content generation. In this paper, we propose a data-driven scenario generation framework for game-based training. An evolutionary scenario generation process is designed with a
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...
Teacher Talk about Student Ability and Achievement in the Era of Data-Driven Decision Making
Datnow, Amanda; Choi, Bailey; Park, Vicki; St. John, Elise
2018-01-01
Background: Data-driven decision making continues to be a common feature of educational reform agendas across the globe. In many U.S. schools, the teacher team meeting is a key setting in which data use is intended to take place, with the aim of planning instruction to address students' needs. However, most prior research has not examined how the…
Big-Data-Driven Stem Cell Science and Tissue Engineering: Vision and Unique Opportunities.
Del Sol, Antonio; Thiesen, Hans J; Imitola, Jaime; Carazo Salas, Rafael E
2017-02-02
Achieving the promises of stem cell science to generate precise disease models and designer cell samples for personalized therapeutics will require harnessing pheno-genotypic cell-level data quantitatively and predictively in the lab and clinic. Those requirements could be met by developing a Big-Data-driven stem cell science strategy and community. Copyright © 2017 Elsevier Inc. All rights reserved.
Exploring Techniques of Developing Writing Skill in IELTS Preparatory Courses: A Data-Driven Study
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…
A framework for the automated data-driven constitutive characterization of composites
J.G. Michopoulos; John Hermanson; T. Furukawa; A. Iliopoulos
2010-01-01
We present advances on the development of a mechatronically and algorithmically automated framework for the data-driven identification of constitutive material models based on energy density considerations. These models can capture both the linear and nonlinear constitutive response of multiaxially loaded composite materials in a manner that accounts for progressive...
Writing through Big Data: New Challenges and Possibilities for Data-Driven Arguments
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…
Data-driven directions for effective footwear provision for the high-risk diabetic foot
Arts, M. L. J.; de Haart, M.; Waaijman, R.; Dahmen, R.; Berendsen, H.; Nollet, F.; Bus, S. A.
2015-01-01
Custom-made footwear is used to offload the diabetic foot to prevent plantar foot ulcers. This prospective study evaluates the offloading effects of modifying custom-made footwear and aims to provide data-driven directions for the provision of effectively offloading footwear in clinical practice.
Toward Data-Driven Design of Educational Courses: A Feasibility Study
Agrawal, Rakesh; Golshan, Behzad; Papalexakis, Evangelos
2016-01-01
A study plan is the choice of concepts and the organization and sequencing of the concepts to be covered in an educational course. While a good study plan is essential for the success of any course offering, the design of study plans currently remains largely a manual task. We present a novel data-driven method, which given a list of concepts can…
Retesting the Limits of Data-Driven Learning: Feedback and Error Correction
Crosthwaite, Peter
2017-01-01
An increasing number of studies have looked at the value of corpus-based data-driven learning (DDL) for second language (L2) written error correction, with generally positive results. However, a potential conundrum for language teachers involved in the process is how to provide feedback on students' written production for DDL. The study looks at…
Articulatory Distinctiveness of Vowels and Consonants: A Data-Driven Approach
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
Data-Driven Hint Generation in Vast Solution Spaces: A Self-Improving Python Programming Tutor
Rivers, Kelly; Koedinger, Kenneth R.
2017-01-01
To provide personalized help to students who are working on code-writing problems, we introduce a data-driven tutoring system, ITAP (Intelligent Teaching Assistant for Programming). ITAP uses state abstraction, path construction, and state reification to automatically generate personalized hints for students, even when given states that have not…
International Nuclear Information System (INIS)
Iovane, G.; Giordano, P.
2005-01-01
In this work we introduce the hypersingular integral equations and analyze a realistic model of gravitational waveguides on a cantorian space-time. A waveguiding effect is considered with respect to the large scale structure of the Universe, where the structure formation appears as if it were a classically self-similar random process at all astrophysical scales. The result is that it seems we live in an El Naschie's o (∞) Cantorian space-time, where gravitational lensing and waveguiding effects can explain the appearing Universe. In particular, we consider filamentary and planar large scale structures as possible refraction channels for electromagnetic radiation coming from cosmological structures. From this vision the Universe appears like a large self-similar adaptive mirrors set, thanks to three numerical simulations. Consequently, an infinite Universe is just an optical illusion that is produced by mirroring effects connected with the large scale structure of a finite and not a large Universe
Tanaka, Yoichi; Nishi, Yuki; Nishi, Yuki; Osumi, Michihiro; Morioka, Shu
2017-01-01
Pain is a subjective emotional experience that is influenced by psychosociological factors such as social skills, which are defined as problem-solving abilities in social interactions. This study aimed to reveal the relationships among pain, social skills, and other psychosociological factors by using structural equation modeling. A total of 101 healthy volunteers (41 men and 60 women; mean age: 36.6±12.7 years) participated in this study. To evoke participants' sense of inner pain, we showed them images of painful scenes on a PC screen and asked them to evaluate the pain intensity by using the visual analog scale (VAS). We examined the correlation between social skills and VAS, constructed a hypothetical model based on results from previous studies and the current correlational analysis results, and verified the model's fit using structural equation modeling. We found significant positive correlations between VAS and total social skills values, as well as between VAS and the "start of relationships" subscales. Structural equation modeling revealed that the values for "start of relationships" had a direct effect on VAS values (path coefficient =0.32, p social support. The results indicated that extroverted people are more sensitive to inner pain and tend to get more social support and maintain a better psychological condition.
A data-driven emulation framework for representing water-food nexus in a changing cold region
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
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.
A Structural Equation Modelling for CRM Development in rural Tourism in the Catalan Pyrenees
Directory of Open Access Journals (Sweden)
José Mª Prat Forga
2014-12-01
Full Text Available This paper investigates the interrelationships between customer relationship management development in rural tourism, information and communication technologies level in the territory, perceived economic impacts and rural tourism development. A total of 76 respondents completed a survey conducted in the Spanish Pyrenees Mountains in order to examine the structural effects of these impact factors. The results reveal that the support for customer relationship management development in rural tourism shown by rural tourism workers mainly depends on the level of development of information and communication technologies. A confirmatory factor analysis and structural equation modelling procedure were performed, respectively, using the AMOS software.
Subdiffusive master equation with space-dependent anomalous exponent and structural instability
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.
The quantum group, Harper equation and structure of Bloch eigenstates on a honeycomb lattice
International Nuclear Information System (INIS)
Eliashvili, M; Tsitsishvili, G; Japaridze, G I
2012-01-01
The tight-binding model of quantum particles on a honeycomb lattice is investigated in the presence of a homogeneous magnetic field. Provided the magnetic flux per unit hexagon is a rational of the elementary flux, the one-particle Hamiltonian is expressed in terms of the generators of the quantum group U q (sl 2 ). Employing the functional representation of the quantum group U q (sl 2 ), the Harper equation is rewritten as a system of two coupled functional equations in the complex plane. For the special values of quasi-momentum, the entangled system admits solutions in terms of polynomials. The system is shown to exhibit a certain symmetry allowing us to resolve the entanglement, and a basic single equation determining the eigenvalues and eigenstates (polynomials) is obtained. Equations specifying the locations of the roots of polynomials in the complex plane are found. Employing numerical analysis, the roots of polynomials corresponding to different eigenstates are solved and diagrams exhibiting the ordered structure of one-particle eigenstates are depicted. (paper)
Directory of Open Access Journals (Sweden)
G. K. Parks
1999-12-01
Full Text Available An electrostatic analyser (ESA onboard the Equator-S spacecraft operating in coordination with a potential control device (PCD has obtained the first accurate electron energy spectrum with energies ≈7 eV–100 eV in the vicinity of the magnetopause. On 8 January, 1998, a solar wind pressure increase pushed the magnetopause inward, leaving the Equator-S spacecraft in the magnetosheath. On the return into the magnetosphere approximately 80 min later, the magnetopause was observed by the ESA and the solid state telescopes (the SSTs detected electrons and ions with energies ≈20–300 keV. The high time resolution (3 s data from ESA and SST show the boundary region contains of multiple plasma sources that appear to evolve in space and time. We show that electrons with energies ≈7 eV–100 eV permeate the outer regions of the magnetosphere, from the magnetopause to ≈6Re. Pitch-angle distributions of ≈20–300 keV electrons show the electrons travel in both directions along the magnetic field with a peak at 90° indicating a trapped configuration. The IMF during this interval was dominated by Bx and By components with a small Bz.Key words. Magnetospheric physics (magnetopause · cusp · and boundary layers; magnetospheric configuration and dynamics; solar wind · magnetosphere interactions
Phase structure of hot and/or dense QCD with the Schwinger-Dyson equation
Energy Technology Data Exchange (ETDEWEB)
Takagi, Satoshi [Nagoya Univ., Nagoya, Aichi (Japan)
2002-09-01
We investigate the phase structure of the hot and/or dense QCD using the Schwinger-Dyson equation (SDE) with the improved ladder approximation in the Landau gauge. We solve the coupled SDE for the Majorana masses of the quark and antiquark (separately from the SDE for the Dirac mass) in the finite temperature and/or chemical potential region. The resultant phase structure is rather different from those by other analyses. In addition to this analysis we investigate the phase structure with the different two types of the SDE, in one of which the Majorana mass gap of the antiquark is neglected, while in the other of which the Majorana mass gap of the quark and that of the antiquark are set to be equal. The effect of the Debye mass of the gluon on the phase structure is also investigated. (author)
The Hubble law and the spiral structures of galaxies from equations of motion in general relativity
International Nuclear Information System (INIS)
Sachs, M.
1975-01-01
Fully exploiting the Lie group that characterizes the underlying symmetry of general relativity theory, Einstein's tensor formalism factorizes, yielding a generalized (16-component) quaternion field formalism. The associated generalized geodesic equation, taken as the equation of motion of a star, predicts the Hubble law from one approximation for the generally covariant equations of motion, and the spiral structure of galaxies from another approximation. These results depend on the imposition of appropriate boundary conditions. The Hubble law follows when the boundary conditions derive from the oscillating model cosmology, and not from the other cosmological models. The spiral structures of the galaxies follow from the same boundary conditions, but with a different time scale than for the whole universe. The solutions that imply the spiral motion are Fresnel integrals. These predict the star's motion to be along the 'Cornu Spiral'. The part of this spiral in the first quadrant is the imploding phase of the galaxy, corresponding to a motion with continually decreasing radii, approaching the galactic center as time increases. The part of the Cornu Spiral' in the third quadrant is the exploding phase, corresponding to continually increasing radii, as the star moves out from the hub. The spatial origin in the coordinate system of this curve is the inflection point, where the explosion changes to implosion. The two- (or many-) armed spiral galaxies are explained here in terms of two (or many) distinct explosions occurring at displaced times, in the domain of the rotating, planar galaxy. (author)
Energy Technology Data Exchange (ETDEWEB)
Iles-Smith, Jake, E-mail: Jakeilessmith@gmail.com [Controlled Quantum Dynamics Theory, Imperial College London, London SW7 2PG (United Kingdom); Photon Science Institute and School of Physics and Astronomy, The University of Manchester, Oxford Road, Manchester M13 9PL (United Kingdom); Department of Photonics Engineering, DTU Fotonik, Ørsteds Plads, 2800 Kongens Lyngby (Denmark); Dijkstra, Arend G. [Max Planck Institute for the Structure and Dynamics of Matter, Luruper Chaussee 149, 22761 Hamburg (Germany); Lambert, Neill [CEMS, RIKEN, Saitama 351-0198 (Japan); Nazir, Ahsan, E-mail: ahsan.nazir@manchester.ac.uk [Photon Science Institute and School of Physics and Astronomy, The University of Manchester, Oxford Road, Manchester M13 9PL (United Kingdom)
2016-01-28
We explore excitonic energy transfer dynamics in a molecular dimer system coupled to both structured and unstructured oscillator environments. By extending the reaction coordinate master equation technique developed by Iles-Smith et al. [Phys. Rev. A 90, 032114 (2014)], we go beyond the commonly used Born-Markov approximations to incorporate system-environment correlations and the resultant non-Markovian dynamical effects. We obtain energy transfer dynamics for both underdamped and overdamped oscillator environments that are in perfect agreement with the numerical hierarchical equations of motion over a wide range of parameters. Furthermore, we show that the Zusman equations, which may be obtained in a semiclassical limit of the reaction coordinate model, are often incapable of describing the correct dynamical behaviour. This demonstrates the necessity of properly accounting for quantum correlations generated between the system and its environment when the Born-Markov approximations no longer hold. Finally, we apply the reaction coordinate formalism to the case of a structured environment comprising of both underdamped (i.e., sharply peaked) and overdamped (broad) components simultaneously. We find that though an enhancement of the dimer energy transfer rate can be obtained when compared to an unstructured environment, its magnitude is rather sensitive to both the dimer-peak resonance conditions and the relative strengths of the underdamped and overdamped contributions.
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.
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
Attractor of Beam Equation with Structural Damping under Nonlinear Boundary Conditions
Directory of Open Access Journals (Sweden)
Danxia Wang
2015-01-01
Full Text Available Simultaneously, considering the viscous effect of material, damping of medium, and rotational inertia, we study a kind of more general Kirchhoff-type extensible beam equation utt-uxxtt+uxxxx-σ(∫0l(ux2dxuxx-ϕ(∫0l(ux2dxuxxt=q(x, in [0,L]×R+ with the structural damping and the rotational inertia term. Little attention is paid to the longtime behavior of the beam equation under nonlinear boundary conditions. In this paper, under nonlinear boundary conditions, we prove not only the existence and uniqueness of global solutions by prior estimates combined with some inequality skills, but also the existence of a global attractor by the existence of an absorbing set and asymptotic compactness of corresponding solution semigroup. In addition, the same results also can be proved under the other nonlinear boundary conditions.
International Nuclear Information System (INIS)
Williams, A.G.
1998-01-01
There is a need for covariant solutions of bound state equations in order to construct realistic QCD based models of mesons and baryons. Furthermore, we ideally need to know the structure of these bound states in all kinematical regimes, which makes a direct solution in Minkowski space (without any 3-dimensional reductions) desirable. The Bethe-Salpeter equation (BSE) for bound states in scalar theories is reformulated and solved for arbitrary scattering kernels in terms of a generalized spectral representation directly in Minkowski space. This differs from the conventional Euclidean approach, where the BSE can only be solved in ladder approximation after a Wick rotation. An application of covariant Bethe-Salpeter solutions to a quark-diquark model of the nucleon is also briefly discussed. (orig.)
Directional approach to spatial structure of solutions to the Navier–Stokes equations in the plane
International Nuclear Information System (INIS)
Konieczny, P; Mucha, P B
2011-01-01
We investigate a steady flow of incompressible fluid in the plane. The motion is governed by the Navier–Stokes equations with prescribed velocity u ∞ at infinity. The main result shows the existence of unique solutions for arbitrary force, provided sufficient largeness of u ∞ . Furthermore a spatial structure of the solution is obtained in comparison with the Oseen flow. A key element of our new approach is based on a setting which treats the direction of the flow as the time direction. The analysis is done in the framework of the Fourier transform taken in one (perpendicular) direction and a special choice of function spaces which take into account the inhomogeneous character of the symbol of the Oseen system. From that point of view our technique can be used as an effective tool in examining spatial asymptotics of solutions to other systems modelled by elliptic equations
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
Directory of Open Access Journals (Sweden)
Thamo Sutharssan
2015-07-01
Full Text Available Prognostics and health management (PHM has become an important component of many engineering systems and products, where algorithms are used to detect anomalies, diagnose faults and predict remaining useful lifetime (RUL. PHM can provide many advantages to users and maintainers. Although primary goals are to ensure the safety, provide state of the health and estimate RUL of the components and systems, there are also financial benefits such as operational and maintenance cost reductions and extended lifetime. This study aims at reviewing the current status of algorithms and methods used to underpin different existing PHM approaches. The focus is on providing a structured and comprehensive classification of the existing state-of-the-art PHM approaches, data-driven approaches and algorithms.
DG-FEM solution for nonlinear wave-structure interaction using Boussinesq-type equations
DEFF Research Database (Denmark)
Engsig-Karup, Allan Peter; Hesthaven, Jan; Bingham, Harry B.
2008-01-01
equations in complex and curvilinear geometries which amends the application range of previous numerical models that have been based on structured Cartesian grids. The Boussinesq method provides the basis for the accurate description of fully nonlinear and dispersive water waves in both shallow and deep...... waters within the breaking limit. To demonstrate the current applicability of the model both linear and mildly nonlinear test cases are considered in two horizontal dimensions where the water waves interact with bottom-mounted fully reflecting structures. It is established that, by simple symmetry...... considerations combined with a mirror principle, it is possible to impose weak slip boundary conditions for both structured and general curvilinear wall boundaries while maintaining the accuracy of the scheme. As is standard for current high-order Boussinesq-type models, arbitrary waves can be generated...
An Open Framework for Dynamic Big-data-driven Application Systems (DBDDAS) Development
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.
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.
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...
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.
Data-driven modeling and real-time distributed control for energy efficient manufacturing systems
International Nuclear Information System (INIS)
Zou, Jing; Chang, Qing; Arinez, Jorge; Xiao, Guoxian
2017-01-01
As manufacturers face the challenges of increasing global competition and energy saving requirements, it is imperative to seek out opportunities to reduce energy waste and overall cost. In this paper, a novel data-driven stochastic manufacturing system modeling method is proposed to identify and predict energy saving opportunities and their impact on production. A real-time distributed feedback production control policy, which integrates the current and predicted system performance, is established to improve the overall profit and energy efficiency. A case study is presented to demonstrate the effectiveness of the proposed control policy. - Highlights: • A data-driven stochastic manufacturing system model is proposed. • Real-time system performance and energy saving opportunity identification method is developed. • Prediction method for future potential system performance and energy saving opportunity is developed. • A real-time distributed feedback control policy is established to improve energy efficiency and overall system profit.
An Open Framework for Dynamic Big-data-driven Application Systems (DBDDAS) Development
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.
Structural equation models to estimate risk of infection and tolerance to bovine mastitis.
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
Expert Strategies in Solving Algebraic Structure Sense Problems: The Case of Quadratic Equations
Jupri, Al; Sispiyati, R.
2017-02-01
Structure sense, an intuitive ability towards symbolic expressions, including skills to interpret, to manipulate, and to perceive symbols in different roles, is considered as a key success in learning algebra. In this article, we report results of three phases of a case study on solving algebraic structure sense problems aiming at testing the appropriateness of algebraic structure sense tasks and at investigating expert strategies dealing with the tasks. First, we developed three tasks on quadratic equations based on the characteristics of structure sense for high school algebra. Next, we validated the tasks to seven experts. In the validation process, we requested these experts to solve each task using two different strategies. Finally, we analyzing expert solution strategies in the light of structure sense characteristics. We found that even if eventual expert strategies are in line with the characteristics of structure sense; some of their initial solution strategies used standard procedures which might pay less attention to algebraic structures. This finding suggests that experts have reconsidered their procedural work and have provided more efficient solution strategies. For further investigation, we consider to test the tasks to high school algebra students and to see whether they produce similar results as experts.
Electromagnetic scattering of large structures in layered earths using integral equations
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.
A Novel Online Data-Driven Algorithm for Detecting UAV Navigation Sensor Faults
Rui Sun; Qi Cheng; Guanyu Wang; Washington Yotto Ochieng
2017-01-01
The use of Unmanned Aerial Vehicles (UAVs) has increased significantly in recent years. On-board integrated navigation sensors are a key component of UAVs’ flight control systems and are essential for flight safety. In order to ensure flight safety, timely and effective navigation sensor fault detection capability is required. In this paper, a novel data-driven Adaptive Neuron Fuzzy Inference System (ANFIS)-based approach is presented for the detection of on-board navigation sensor faults in ...
Data Driven Exploratory Attacks on Black Box Classifiers in Adversarial Domains
Sethi, Tegjyot Singh; Kantardzic, Mehmed
2017-01-01
While modern day web applications aim to create impact at the civilization level, they have become vulnerable to adversarial activity, where the next cyber-attack can take any shape and can originate from anywhere. The increasing scale and sophistication of attacks, has prompted the need for a data driven solution, with machine learning forming the core of many cybersecurity systems. Machine learning was not designed with security in mind, and the essential assumption of stationarity, requiri...
Data Driven Marketing in Apple and Back to School Campaign 2011
Bernátek, Martin
2011-01-01
Out of the campaign analysis the most important contribution is that Data-Driven Marketing makes sense only once it is already part of the marketing plan. So the team preparing the marketing plan defines the goals and sets the proper measurement matrix according to those goals. It enables to adjust the marketing plan to extract more value, watch the execution and do adjustments if necessary and evaluate at the end of the campaign.
Data-driven automatic parking constrained control for four-wheeled mobile vehicles
Wenxu Yan; Jing Deng; Dezhi Xu
2016-01-01
In this article, a novel data-driven constrained control scheme is proposed for automatic parking systems. The design of the proposed scheme only depends on the steering angle and the orientation angle of the car, and it does not involve any model information of the car. Therefore, the proposed scheme-based automatic parking system is applicable to different kinds of cars. In order to further reduce the desired trajectory coordinate tracking errors, a coordinates compensation algorithm is als...
Extension of a data-driven gating technique to 3D, whole body PET studies
International Nuclear Information System (INIS)
Schleyer, Paul J; O'Doherty, Michael J; Marsden, Paul K
2011-01-01
Respiratory gating can be used to separate a PET acquisition into a series of near motion-free bins. This is typically done using additional gating hardware; however, software-based methods can derive the respiratory signal from the acquired data itself. The aim of this work was to extend a data-driven respiratory gating method to acquire gated, 3D, whole body PET images of clinical patients. The existing method, previously demonstrated with 2D, single bed-position data, uses a spectral analysis to find regions in raw PET data which are subject to respiratory motion. The change in counts over time within these regions is then used to estimate the respiratory signal of the patient. In this work, the gating method was adapted to only accept lines of response from a reduced set of axial angles, and the respiratory frequency derived from the lung bed position was used to help identify the respiratory frequency in all other bed positions. As the respiratory signal does not identify the direction of motion, a registration-based technique was developed to align the direction for all bed positions. Data from 11 clinical FDG PET patients were acquired, and an optical respiratory monitor was used to provide a hardware-based signal for comparison. All data were gated using both the data-driven and hardware methods, and reconstructed. The centre of mass of manually defined regions on gated images was calculated, and the overall displacement was defined as the change in the centre of mass between the first and last gates. The mean displacement was 10.3 mm for the data-driven gated images and 9.1 mm for the hardware gated images. No significant difference was found between the two gating methods when comparing the displacement values. The adapted data-driven gating method was demonstrated to successfully produce respiratory gated, 3D, whole body, clinical PET acquisitions.
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.
Data-Driven and Expectation-Driven Discovery of Empirical Laws.
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
Effective equations for fluid-structure interaction with applications to poroelasticity
Brown, Donald; Popov, Peter V.; Efendiev, Yalchin R.
2012-01-01
Modeling of fluid-solid interactions in porous media is a challenging and computationally demanding task. Due to the multiscale nature of the problem, simulating the flow and mechanics by direct numerical simulation is often not feasible and an effective model is preferred. In this work, we formally derive an effective model for Fluid-Structure Interaction (FSI). In earlier work, assuming infinitesimal pore-scale deformations, an effective poroelastic model of Biot was derived. We extend this model to a nonlinear Biot model that includes pore-scale deformation into the effective description. The main challenge is the difference in coordinate systems of the fluid and solid equations. This is circumvented by utilizing the Arbitrary Lagrange-Eulerian (ALE) formulation of the FSI equations, giving a unified frame in which to apply two-scale asymptotic techniques. In the derived nonlinear Biot model, the local cell problem are coupled to the macroscopic equations via the effective coefficients. These coefficients may be viewed as tabular functions of the macroscopic parameters. After simplifying this dependence, we assume the coefficients depend on macroscopic pressure only. Using a three dimensional pore geometry we calculate, as a proof-of-concept example, the effective permeability and Biot coefficients for various values or pressure. We observe that, for this geometry, a stronger pressure dependence on flow quantities than on mechanically based effective quantities. © 2014 Taylor & Francis Group, LLC.
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.
Effective equations for fluid-structure interaction with applications to poroelasticity
Brown, Donald
2012-11-05
Modeling of fluid-solid interactions in porous media is a challenging and computationally demanding task. Due to the multiscale nature of the problem, simulating the flow and mechanics by direct numerical simulation is often not feasible and an effective model is preferred. In this work, we formally derive an effective model for Fluid-Structure Interaction (FSI). In earlier work, assuming infinitesimal pore-scale deformations, an effective poroelastic model of Biot was derived. We extend this model to a nonlinear Biot model that includes pore-scale deformation into the effective description. The main challenge is the difference in coordinate systems of the fluid and solid equations. This is circumvented by utilizing the Arbitrary Lagrange-Eulerian (ALE) formulation of the FSI equations, giving a unified frame in which to apply two-scale asymptotic techniques. In the derived nonlinear Biot model, the local cell problem are coupled to the macroscopic equations via the effective coefficients. These coefficients may be viewed as tabular functions of the macroscopic parameters. After simplifying this dependence, we assume the coefficients depend on macroscopic pressure only. Using a three dimensional pore geometry we calculate, as a proof-of-concept example, the effective permeability and Biot coefficients for various values or pressure. We observe that, for this geometry, a stronger pressure dependence on flow quantities than on mechanically based effective quantities. © 2014 Taylor & Francis Group, LLC.
International Nuclear Information System (INIS)
Arms, J.M.; Marsden, J.E.; Moncrief, V.
1982-01-01
The space of solutions of Einstein's vacuum equations is shown to have conical singularities at each spacetime possessing a compact Cauchy surface of constant mean curvature and a nontrivial set of Killing fields. Similar results are shown for the coupled Einstein-Yang-Mills system. Combined with an appropriate slice theorem, the results show that the space of geometrically equivalent solutions is a stratified manifold with each stratum being a symplectic manifold characterized by the symmetry type of its members. Contents: Introduction 1. The Kuranishi map and its properties. 2. The momentum constraints. 3. The Hamiltonian constraints. 4. The Einstein-Yang-Mills system. 5. Discussion and examples
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.
Critical Factors Analysis for Offshore Software Development Success by Structural Equation Modeling
Wada, Yoshihisa; Tsuji, Hiroshi
In order to analyze the success/failure factors in offshore software development service by the structural equation modeling, this paper proposes to follow two approaches together; domain knowledge based heuristic analysis and factor analysis based rational analysis. The former works for generating and verifying of hypothesis to find factors and causalities. The latter works for verifying factors introduced by theory to build the model without heuristics. Following the proposed combined approaches for the responses from skilled project managers of the questionnaire, this paper found that the vendor property has high causality for the success compared to software property and project property.
International Nuclear Information System (INIS)
Iagolnitzer, D.
1983-11-01
Recent axiomatic results on the (non holonomic) analytic structure of the multiparticle S matrix and Green functions are reviewed and related general conjectures are described: (i) formal expansions of Green functions in terms of (holonomic) Feynman-type integrals in which each vertex represents an irreducible kernel, and (ii) ''graph by graph unitarity'' and other discontinuity formulae of the latter. These conjectures are closely linked with unitarity or asymptotic completeness equations, which they yield in a formal sense. In constructive field theory, a direct proof of the first conjecture (together with an independent proof of the second) would thus imply, as a first step, asymptotic completeness in that sense
Hussain, Nur Farahin Mee; Zahid, Zalina
2014-12-01
Nowadays, in the job market demand, graduates are expected not only to have higher performance in academic but they must also be excellent in soft skill. Problem-Based Learning (PBL) has a number of distinct advantages as a learning method as it can deliver graduates that will be highly prized by industry. This study attempts to determine the satisfaction level of engineering students on the PBL Approach and to evaluate their determinant factors. The Structural Equation Modeling (SEM) was used to investigate how the factors of Good Teaching Scale, Clear Goals, Student Assessment and Levels of Workload affected the student satisfaction towards PBL approach.
Pairing and seniority in an equations-of-motion approach to nuclear structure theory
International Nuclear Information System (INIS)
Covello, A.; Andreozzi, F.; Gargano, A.; Porrino, A.
1990-01-01
In this paper, some achievements of an equations-of-motion approach to nuclear structure theory are discussed. As an introduction to the main subject, a brief survey of some early work is given. We then describe a formalism for treating the pairing-force problem and show, by numerical appllications, that at the lowest order of approximation it provides an advantageous alternative to the BCS method. Finally, we discuss how to treat a general shell-model Hamiltonian within the framework of the seniority scheme. This makes it possible to further reduce seniority-truncated shell-model spaces, as is illustrated by examples. (orig.)
Statistical Power Analysis with Missing Data A Structural Equation Modeling Approach
Davey, Adam
2009-01-01
Statistical power analysis has revolutionized the ways in which we conduct and evaluate research. Similar developments in the statistical analysis of incomplete (missing) data are gaining more widespread applications. This volume brings statistical power and incomplete data together under a common framework, in a way that is readily accessible to those with only an introductory familiarity with structural equation modeling. It answers many practical questions such as: How missing data affects the statistical power in a study How much power is likely with different amounts and types
Structural equation modeling in pediatric psychology: overview and review of applications.
Nelson, Timothy D; Aylward, Brandon S; Steele, Ric G
2008-08-01
To describe the use of structural equation modeling (SEM) in the Journal of Pediatric Psychology (JPP) and to discuss the usefulness of SEM applications in pediatric psychology research. The use of SEM in JPP between 1997 and 2006 was examined and compared to leading journals in clinical psychology, clinical child psychology, and child development. SEM techniques were used in psychology research, although investigations employing these methods are becoming more prevalent. Despite its infrequent use to date, SEM is a potentially useful tool for advancing pediatric psychology research with a number of advantages over traditional statistical methods.
Bassett, Hideko Hamada; Denham, Susanne; Mincic, Melissa; Graling, Kelly
2012-01-01
Research Findings: A theory-based 2-factor structure of preschoolers' emotion knowledge (i.e., recognition of emotional expression and understanding of emotion-eliciting situations) was tested using confirmatory factor analysis. Compared to 1- and 3-factor models, the 2-factor model showed a better fit to the data. The model was found to be…
Data-Driven Anomaly Detection Performance for the Ares I-X Ground Diagnostic Prototype
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.
Directory of Open Access Journals (Sweden)
Chang-Hee Han
2016-01-01
Full Text Available It has frequently been reported that some users of conventional neurofeedback systems can experience only a small portion of the total feedback range due to the large interindividual variability of EEG features. In this study, we proposed a data-driven neurofeedback strategy considering the individual variability of electroencephalography (EEG features to permit users of the neurofeedback system to experience a wider range of auditory or visual feedback without a customization process. The main idea of the proposed strategy is to adjust the ranges of each feedback level using the density in the offline EEG database acquired from a group of individuals. Twenty-two healthy subjects participated in offline experiments to construct an EEG database, and five subjects participated in online experiments to validate the performance of the proposed data-driven user feedback strategy. Using the optimized bin sizes, the number of feedback levels that each individual experienced was significantly increased to 139% and 144% of the original results with uniform bin sizes in the offline and online experiments, respectively. Our results demonstrated that the use of our data-driven neurofeedback strategy could effectively increase the overall range of feedback levels that each individual experienced during neurofeedback training.
KNMI DataLab experiences in serving data-driven innovations
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.
Han, Chang-Hee; Lim, Jeong-Hwan; Lee, Jun-Hak; Kim, Kangsan; Im, Chang-Hwan
2016-01-01
It has frequently been reported that some users of conventional neurofeedback systems can experience only a small portion of the total feedback range due to the large interindividual variability of EEG features. In this study, we proposed a data-driven neurofeedback strategy considering the individual variability of electroencephalography (EEG) features to permit users of the neurofeedback system to experience a wider range of auditory or visual feedback without a customization process. The main idea of the proposed strategy is to adjust the ranges of each feedback level using the density in the offline EEG database acquired from a group of individuals. Twenty-two healthy subjects participated in offline experiments to construct an EEG database, and five subjects participated in online experiments to validate the performance of the proposed data-driven user feedback strategy. Using the optimized bin sizes, the number of feedback levels that each individual experienced was significantly increased to 139% and 144% of the original results with uniform bin sizes in the offline and online experiments, respectively. Our results demonstrated that the use of our data-driven neurofeedback strategy could effectively increase the overall range of feedback levels that each individual experienced during neurofeedback training.
Structure of the amplitude equation of the climate; Struktur der Amplitudengleichung des Klimas
Energy Technology Data Exchange (ETDEWEB)
Hauschild, A. [Freie Univ. Berlin (Germany). Inst. fuer Meteorologie
1999-04-01
The structure of the `amplitude equation`, a new dynamic equation on the seasonal time scale is derived, in which the weather scales may be treated statistically. The elsewhere-introduced climate-specific seasonally smoothed amplitudes and phases of the Fourier spectral representation are used as new prognostic variables. For the vorticity it is shown, that the still unsolved problem of the parameterisation of subscale transports may be solved in the `amplitude equation`. The approach could be successful because of the empirically derived statistical properties of the amplitudes (Poisson distribution and ergodicity) and of the phases (equipartition) of sub-planetary waves could be used. They allow a scale separation of weather and climate and lead to a tremendous reduction of the number of the horizontal degree of freedom of the amplitude equation to be between 10{sup 3} and 10{sup 4}. (orig.) [Deutsch] Es wird die Struktur der `Amplitudengleichung`, einer neuen dynamischen Gleichung auf der saisonalen Zeitskala abgeleitet. Anhand analysierter Daten des EZMW wird gezeigt, dass in der `Amplitudengleichung` die explizite Dynamik des Wetters tatsaechlich statistisch behandelt werden kann. Als prognostische Variablen der Gleichung werden die woanders neu eingefuehrten, klimaspezifischen, saisonal geglaetteten Amplituden und Phasen der Fourier-Spektraldarstellung verwendet. Am Beispiel der Vorticity wird gezeigt, dass das bisher ungeloeste Problem der Behandlung der subskaligen Transporte in der `Amplitudengleichung` grundsaetzlich geloest werden kann. Dies gelingt durch Ausnutzung der ebenfalls empirisch abgeleiteten besonderen statistischen Eigenschaften der Amplituden (Poissonverteilung und Ergodizitaet) und Phasen (Gleichverteilung) der subplanetaren Wellen, die eine Skalentrennung von Wetter und Klima ermoeglichen. Dies fuehrt zur erheblichen Reduktion der Zahl der horizontalen Freiheitsgrade der Amplitudengleichung auf 10{sup 3} bis 10{sup 4}. Die Ableitung
Post-1-Newtonian equations of motion for systems of arbitrarily structured bodies
International Nuclear Information System (INIS)
Racine, Etienne; Flanagan, Eanna E.
2005-01-01
We give a surface-integral derivation of post-1-Newtonian translational equations of motion for a system of arbitrarily structured bodies, including the coupling to all the bodies' mass and current multipole moments. The derivation requires only that the post-1-Newtonian vacuum field equations are satisfied in weak field regions between the bodies; the bodies' internal gravity can be arbitrarily strong. In particular, black holes are not excluded. The derivation extends previous results due to Damour, Soffel, and Xu (DSX) for weakly self-gravitating bodies in which the post-1-Newtonian field equations are satisfied everywhere. The derivation consists of a number of steps: (i) The definition of each body's current and mass multipole moments and center-of-mass world line in terms of the behavior of the metric in a weak field region surrounding the body. (ii) The definition for each body of a set of gravitoelectric and gravitomagnetic tidal moments that act on that body, again in terms of the behavior of the metric in a weak field region surrounding the body. For the special case of weakly self-gravitating bodies, our definitions of these multipole and tidal moments agree with definitions given previously by DSX. (iii) The derivation of a formula, for any given body, of the second time derivative of its mass dipole moment in terms of its other multipole and tidal moments and their time derivatives. This formula was obtained previously by DSX for weakly self-gravitating bodies. (iv) A derivation of the relation between the tidal moments acting on each body and the multipole moments and center-of-mass world lines of all the other bodies. A formalism to compute this relation was developed by DSX; we simplify their formalism and compute the relation explicitly. (v) The deduction from the previous steps of the explicit translational equations of motion, whose form has not been previously derived
Post-1-Newtonian equations of motion for systems of arbitrarily structured bodies
Racine, Étienne; Flanagan, Éanna É.
2005-02-01
We give a surface-integral derivation of post-1-Newtonian translational equations of motion for a system of arbitrarily structured bodies, including the coupling to all the bodies' mass and current multipole moments. The derivation requires only that the post-1-Newtonian vacuum field equations are satisfied in weak field regions between the bodies; the bodies' internal gravity can be arbitrarily strong. In particular, black holes are not excluded. The derivation extends previous results due to Damour, Soffel, and Xu (DSX) for weakly self-gravitating bodies in which the post-1-Newtonian field equations are satisfied everywhere. The derivation consists of a number of steps: (i) The definition of each body’s current and mass multipole moments and center-of-mass world line in terms of the behavior of the metric in a weak field region surrounding the body. (ii) The definition for each body of a set of gravitoelectric and gravitomagnetic tidal moments that act on that body, again in terms of the behavior of the metric in a weak field region surrounding the body. For the special case of weakly self-gravitating bodies, our definitions of these multipole and tidal moments agree with definitions given previously by DSX. (iii) The derivation of a formula, for any given body, of the second time derivative of its mass dipole moment in terms of its other multipole and tidal moments and their time derivatives. This formula was obtained previously by DSX for weakly self-gravitating bodies. (iv) A derivation of the relation between the tidal moments acting on each body and the multipole moments and center-of-mass world lines of all the other bodies. A formalism to compute this relation was developed by DSX; we simplify their formalism and compute the relation explicitly. (v) The deduction from the previous steps of the explicit translational equations of motion, whose form has not been previously derived.
Leibov Roman
2017-01-01
This paper presents a bilinear approach to nonlinear differential equations system approximation problem. Sometimes the nonlinear differential equations right-hand sides linearization is extremely difficult or even impossible. Then piecewise-linear approximation of nonlinear differential equations can be used. The bilinear differential equations allow to improve piecewise-linear differential equations behavior and reduce errors on the border of different linear differential equations systems ...
International Nuclear Information System (INIS)
Yan, Z.; Zhang, H.
2001-01-01
In this paper, an isospectral problem and one associated with a new hierarchy of nonlinear evolution equations are presented. As a reduction, a representative system of new generalized derivative nonlinear Schroedinger equations in the hierarchy is given. It is shown that the hierarchy possesses bi-Hamiltonian structures by using the trace identity method and is Liouville integrable. The spectral problem is non linearized as a finite-dimensional completely integrable Hamiltonian system under a constraint between the potentials and spectral functions. Finally, the involutive solutions of the hierarchy of equations are obtained. In particular, the involutive solutions of the system of new generalized derivative nonlinear Schroedinger equations are developed
External radioactive markers for PET data-driven respiratory gating in positron emission tomography.
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
Guidelines for a graph-theoretic implementation of structural equation modeling
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
Grace, J.B.; Bollen, K.A.
2008-01-01
Structural equation modeling (SEM) holds the promise of providing natural scientists the capacity to evaluate complex multivariate hypotheses about ecological systems. Building on its predecessors, path analysis and factor analysis, SEM allows for the incorporation of both observed and unobserved (latent) variables into theoretically-based probabilistic models. In this paper we discuss the interface between theory and data in SEM and the use of an additional variable type, the composite. In simple terms, composite variables specify the influences of collections of other variables and can be helpful in modeling heterogeneous concepts of the sort commonly of interest to ecologists. While long recognized as a potentially important element of SEM, composite variables have received very limited use, in part because of a lack of theoretical consideration, but also because of difficulties that arise in parameter estimation when using conventional solution procedures. In this paper we present a framework for discussing composites and demonstrate how the use of partially-reduced-form models can help to overcome some of the parameter estimation and evaluation problems associated with models containing composites. Diagnostic procedures for evaluating the most appropriate and effective use of composites are illustrated with an example from the ecological literature. It is argued that an ability to incorporate composite variables into structural equation models may be particularly valuable in the study of natural systems, where concepts are frequently multifaceted and the influence of suites of variables are often of interest. ?? Springer Science+Business Media, LLC 2007.
Study The role of latent variables in lost working days by Structural Equation Modeling Approach
Directory of Open Access Journals (Sweden)
Meysam Heydari
2016-12-01
Full Text Available Background: Based on estimations, each year about 250 million work-related injuries and many temporary or permanent disabilities occur which most are preventable. Oil and Gas industries are among industries with high incidence of injuries in the world. The aim of this study has investigated the role and effect of different risk management variables on lost working days (LWD in the seismic projects. Methods: This study was a retrospective, cross-sectional and systematic analysis, which was carried out on occupational accidents between 2008-2015(an 8 years period in different seismic projects for oilfield exploration at Dana Energy (Iranian Seismic Company. The preliminary sample size of the study were 487accidents. A systems analysis approach were applied by using root case analysis (RCA and structural equation modeling (SEM. Tools for the data analysis were included, SPSS23 and AMOS23 software. Results: The mean of lost working days (LWD, was calculated 49.57, the final model of structural equation modeling showed that latent variables of, safety and health training factor(-0.33, risk assessment factor(-0.55 and risk control factor (-0.61 as direct causes significantly affected of lost working days (LWD in the seismic industries (p< 0.05. Conclusion: The finding of present study revealed that combination of variables affected in lost working days (LWD. Therefore,the role of these variables in accidents should be investigated and suitable programs should be considered for them.
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.
Multilevel structural equation models for assessing moderation within and across levels of analysis.
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).
De Las Cuevas, Carlos; de Leon, Jose; Peñate, Wenceslao; Betancort, Moisés
2017-01-01
To evaluate pathways through which sociodemographic, clinical, attitudinal, and perceived health control variables impact psychiatric patients' adherence to psychopharmacological medications. A sample of 966 consecutive psychiatric outpatients was studied. The variables were sociodemographic (age, gender, and education), clinical (diagnoses, drug treatment, and treatment duration), attitudinal (attitudes toward psychopharmacological medication and preferences regarding participation in decision-making), perception of control over health (health locus of control, self-efficacy, and psychological reactance), and level of adherence to psychopharmacological medications. Structural equation modeling was applied to examine the nonstraightforward relationships and the interactive effects among the analyzed variables. Structural equation modeling demonstrated that psychiatric patients' treatment adherence was associated: 1) negatively with cognitive psychological reactance (adherence decreased as cognitive psychological reactance increased), 2) positively with patients' trust in their psychiatrists (doctors' subscale), 3) negatively with patients' belief that they are in control of their mental health and that their mental health depends on their own actions (internal subscale), and 4) positively (although weakly) with age. Self-efficacy indirectly influenced treatment adherence through internal health locus of control. This study provides support for the hypothesis that perceived health control variables play a relevant role in psychiatric patients' adherence to psychopharmacological medications. The findings highlight the importance of considering prospective studies of patients' psychological reactance and health locus of control as they may be clinically relevant factors contributing to adherence to psychopharmacological medications.
Martinez, Sydney A; Beebe, Laura A; Thompson, David M; Wagener, Theodore L; Terrell, Deirdra R; Campbell, Janis E
2018-01-01
The inverse association between socioeconomic status and smoking is well established, yet the mechanisms that drive this relationship are unclear. We developed and tested four theoretical models of the pathways that link socioeconomic status to current smoking prevalence using a structural equation modeling (SEM) approach. Using data from the 2013 National Health Interview Survey, we selected four indicator variables (poverty ratio, personal earnings, educational attainment, and employment status) that we hypothesize underlie a latent variable, socioeconomic status. We measured direct, indirect, and total effects of socioeconomic status on smoking on four pathways through four latent variables representing social cohesion, financial strain, sleep disturbance, and psychological distress. Results of the model indicated that the probability of being a smoker decreased by 26% of a standard deviation for every one standard deviation increase in socioeconomic status. The direct effects of socioeconomic status on smoking accounted for the majority of the total effects, but the overall model also included significant indirect effects. Of the four mediators, sleep disturbance and psychological distress had the largest total effects on current smoking. We explored the use of structural equation modeling in epidemiology to quantify effects of socioeconomic status on smoking through four social and psychological factors to identify potential targets for interventions. A better understanding of the complex relationship between socioeconomic status and smoking is critical as we continue to reduce the burden of tobacco and eliminate health disparities related to smoking.
Directory of Open Access Journals (Sweden)
Kovačić Nataša
2015-11-01
Full Text Available The paper addresses the effect of external integration (EI with transport suppliers on the efficiency of travel agencies in the tourism sector supply chains. The main aim is the comparison of different estimation methods used in the structural equation modeling (SEM, applied to discover possible relationships between EIs and efficiencies. The latter are calculated by the means of data envelopment analysis (DEA. While designing the structural equation model, the exploratory and confirmatory factor analyses are also used as preliminary statistical procedures. For the estimation of parameters of SEM model, three different methods are explained, analyzed and compared: maximum likelihood (ML method, Bayesian Markov Chain Monte Carlo (BMCMC method, and unweighted least squares (ULS method. The study reveals that all estimation methods calculate comparable estimated parameters. The results also give an evidence of good model fit performance. Besides, the research confirms that the amplified external integration with transport providers leads to increased efficiency of travel agencies, which might be a very interesting finding for the operational management.
[A Structural Equation Model on Family Strength of Married Working Women].
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.
International Nuclear Information System (INIS)
Singh, Parampreet; Soni, S K
2016-01-01
The problem of obtaining canonical Hamiltonian structures from the equations of motion, without any knowledge of the action, is studied in the context of the spatially flat Friedmann, ‘Robertson’, and Walker models. Modifications to the Raychaudhuri equation are implemented independently as quadratic and cubic terms of energy density without introducing additional degrees of freedom. Depending on their sign, modifications make gravity repulsive above a curvature scale for matter satisfying strong energy conditions, or more attractive than in the classical theory. The canonical structure of the modified theories is determined by demanding that the total Hamiltonian be a linear combination of gravity and matter Hamiltonians. In the quadratic repulsive case, the modified canonical phase space of gravity is a polymerized phase space with canonical momentum as inverse a trigonometric function of the Hubble rate; the canonical Hamiltonian can be identified with the effective Hamiltonian in loop quantum cosmology. The repulsive cubic modification results in a ‘generalized polymerized’ canonical phase space. Both the repulsive modifications are found to yield singularity avoidance. In contrast, the quadratic and cubic attractive modifications result in a canonical phase space in which canonical momentum is nontrigonometric and singularities persist. Our results hint at connections between the repulsive/attractive nature of modifications to gravity arising from the gravitational sector and polymerized/non polymerized gravitational phase space. (paper)
Powers, Ráchael A; Cochran, John K; Maskaly, Jon; Sellers, Christine S
2017-05-01
The purpose of this study is to examine the applicability of Akers's Social Learning Theory (SLT) to explain intimate partner violence (IPV) victimization. In doing so, we draw on the Intergenerational Transmission of Violence Theory (IGT) to extend the scope of SLT to the explanation of victimization and for a consideration of uniquely gendered pathways in its causal structure. Using a structural equation modeling approach with self-report data from a sample of college students, the present study tests the extent to which SLT can effectively explain and predict IPV victimization and the degree, if any, to which the social learning model is gender invariant. Although our findings are largely supportive of SLT and, thus, affirm its extension to victimization as well as perpetration, the findings are also somewhat mixed. More significantly, in line with IGT literature, we find that the social learning process is not gender invariant. The implications of the latter are discussed.
Energy Technology Data Exchange (ETDEWEB)
Brear, D.J. [Power Reactor and Nuclear Fuel Development Corp., Oarai, Ibaraki (Japan). Oarai Engineering Center
1998-01-01
When liquid fuel makes contact with steel structure the liquid can freeze as a crust and the structure can melt at the surface. The melting and freezing processes that occur can influence the mode of fuel freezing and hence fuel relocation. Furthermore the temperature gradients established in the fuel and steel phases determine the rate at which heat is transferred from fuel to steel. In this memo the 1-D transient heat conduction equations are applied to the case of initially liquid UO{sub 2} brought into contact with solid steel using up-to-date materials properties. The solutions predict criteria for fuel crust formation and steel melting and provide a simple algorithm to determine the interface temperature when one or both of the materials is undergoing phase change. The predicted steel melting criterion is compared with available experimental results. (author)
International Nuclear Information System (INIS)
Brear, D.J.
1998-01-01
When liquid fuel makes contact with steel structure the liquid can freeze as a crust and the structure can melt at the surface. The melting and freezing processes that occur can influence the mode of fuel freezing and hence fuel relocation. Furthermore the temperature gradients established in the fuel and steel phases determine the rate at which heat is transferred from fuel to steel. In this memo the 1-D transient heat conduction equations are applied to the case of initially liquid UO 2 brought into contact with solid steel using up-to-date materials properties. The solutions predict criteria for fuel crust formation and steel melting and provide a simple algorithm to determine the interface temperature when one or both of the materials is undergoing phase change. The predicted steel melting criterion is compared with available experimental results. (author)
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.
Lee, Jeong-Won; Lee, Kyung-Eun; Park, Dong-Jin; Kim, Seong-Ho; Nah, Seong-Su; Lee, Ji Hyun; Kim, Seong-Kyu; Lee, Yeon-Ah; Hong, Seung-Jae; Kim, Hyun-Sook; Lee, Hye-Soon; Kim, Hyoun Ah; Joung, Chung-Il; Kim, Sang-Hyon; Lee, Shin-Seok
2017-01-01
Health-related quality of life (HRQOL) in patients with fibromyalgia (FM) is lower than in patients with other chronic diseases and the general population. Although various factors affect HRQOL, no study has examined a structural equation model of HRQOL as an outcome variable in FM patients. The present study assessed relationships among physical function, social factors, psychological factors, and HRQOL, and the effects of these variables on HRQOL in a hypothesized model using structural equation modeling (SEM). HRQOL was measured using SF-36, and the Fibromyalgia Impact Questionnaire (FIQ) was used to assess physical dysfunction. Social and psychological statuses were assessed using the Beck Depression Inventory (BDI), the State-Trait Anxiety Inventory (STAI), the Arthritis Self-Efficacy Scale (ASES), and the Social Support Scale. SEM analysis was used to test the structural relationships of the model using the AMOS software. Of the 336 patients, 301 (89.6%) were women with an average age of 47.9±10.9 years. The SEM results supported the hypothesized structural model (χ2 = 2.336, df = 3, p = 0.506). The final model showed that Physical Component Summary (PCS) was directly related to self-efficacy and inversely related to FIQ, and that Mental Component Summary (MCS) was inversely related to FIQ, BDI, and STAI. In our model of FM patients, HRQOL was affected by physical, social, and psychological variables. In these patients, higher levels of physical function and self-efficacy can improve the PCS of HRQOL, while physical function, depression, and anxiety negatively affect the MCS of HRQOL.
Directory of Open Access Journals (Sweden)
Jeong-Won Lee
Full Text Available Health-related quality of life (HRQOL in patients with fibromyalgia (FM is lower than in patients with other chronic diseases and the general population. Although various factors affect HRQOL, no study has examined a structural equation model of HRQOL as an outcome variable in FM patients. The present study assessed relationships among physical function, social factors, psychological factors, and HRQOL, and the effects of these variables on HRQOL in a hypothesized model using structural equation modeling (SEM.HRQOL was measured using SF-36, and the Fibromyalgia Impact Questionnaire (FIQ was used to assess physical dysfunction. Social and psychological statuses were assessed using the Beck Depression Inventory (BDI, the State-Trait Anxiety Inventory (STAI, the Arthritis Self-Efficacy Scale (ASES, and the Social Support Scale. SEM analysis was used to test the structural relationships of the model using the AMOS software.Of the 336 patients, 301 (89.6% were women with an average age of 47.9±10.9 years. The SEM results supported the hypothesized structural model (χ2 = 2.336, df = 3, p = 0.506. The final model showed that Physical Component Summary (PCS was directly related to self-efficacy and inversely related to FIQ, and that Mental Component Summary (MCS was inversely related to FIQ, BDI, and STAI.In our model of FM patients, HRQOL was affected by physical, social, and psychological variables. In these patients, higher levels of physical function and self-efficacy can improve the PCS of HRQOL, while physical function, depression, and anxiety negatively affect the MCS of HRQOL.
Brehm, Christoph; Barad, Michael F.; Kiris, Cetin C.
2016-01-01
An immersed boundary method for the compressible Navier-Stokes equation and the additional infrastructure that is needed to solve moving boundary problems and fully coupled fluid-structure interaction is described. All the methods described in this paper were implemented in NASA's LAVA solver framework. The underlying immersed boundary method is based on the locally stabilized immersed boundary method that was previously introduced by the authors. In the present paper this method is extended to account for all aspects that are involved for fluid structure interaction simulations, such as fast geometry queries and stencil computations, the treatment of freshly cleared cells, and the coupling of the computational fluid dynamics solver with a linear structural finite element method. The current approach is validated for moving boundary problems with prescribed body motion and fully coupled fluid structure interaction problems in 2D and 3D. As part of the validation procedure, results from the second AIAA aeroelastic prediction workshop are also presented. The current paper is regarded as a proof of concept study, while more advanced methods for fluid structure interaction are currently being investigated, such as geometric and material nonlinearities, and advanced coupling approaches.
Li, Qianxiao; Dietrich, Felix; Bollt, Erik M; Kevrekidis, Ioannis G
2017-10-01
Numerical approximation methods for the Koopman operator have advanced considerably in the last few years. In particular, data-driven approaches such as dynamic mode decomposition (DMD) 51 and its generalization, the extended-DMD (EDMD), are becoming increasingly popular in practical applications. The EDMD improves upon the classical DMD by the inclusion of a flexible choice of dictionary of observables which spans a finite dimensional subspace on which the Koopman operator can be approximated. This enhances the accuracy of the solution reconstruction and broadens the applicability of the Koopman formalism. Although the convergence of the EDMD has been established, applying the method in practice requires a careful choice of the observables to improve convergence with just a finite number of terms. This is especially difficult for high dimensional and highly nonlinear systems. In this paper, we employ ideas from machine learning to improve upon the EDMD method. We develop an iterative approximation algorithm which couples the EDMD with a trainable dictionary represented by an artificial neural network. Using the Duffing oscillator and the Kuramoto Sivashinsky partical differential equation as examples, we show that our algorithm can effectively and efficiently adapt the trainable dictionary to the problem at hand to achieve good reconstruction accuracy without the need to choose a fixed dictionary a priori. Furthermore, to obtain a given accuracy, we require fewer dictionary terms than EDMD with fixed dictionaries. This alleviates an important shortcoming of the EDMD algorithm and enhances the applicability of the Koopman framework to practical problems.
Different physical structures of solutions for two related Zakharov-Kuznetsov equations
International Nuclear Information System (INIS)
Lai Shaoyong; Yin Jun; Wu Yonghong
2008-01-01
The auxiliary differential equation approach and the symbolic computation system Maple are employed to investigate two types of related Zakharov-Kuznetsov equations with variable coefficients. The exact solutions to the equations are constructed analytically under certain circumstances. It is shown that the variable coefficients of the derivative terms of the equations result in their semi-travelling wave solutions
Enciso, Alberto; Poyato, David; Soler, Juan
2018-05-01
Strong Beltrami fields, that is, vector fields in three dimensions whose curl is the product of the field itself by a constant factor, have long played a key role in fluid mechanics and magnetohydrodynamics. In particular, they are the kind of stationary solutions of the Euler equations where one has been able to show the existence of vortex structures (vortex tubes and vortex lines) of arbitrarily complicated topology. On the contrary, there are very few results about the existence of generalized Beltrami fields, that is, divergence-free fields whose curl is the field times a non-constant function. In fact, generalized Beltrami fields (which are also stationary solutions to the Euler equations) have been recently shown to be rare, in the sense that for "most" proportionality factors there are no nontrivial Beltrami fields of high enough regularity (e.g., of class {C^{6,α}}), not even locally. Our objective in this work is to show that, nevertheless, there are "many" Beltrami fields with non-constant factor, even realizing arbitrarily complicated vortex structures. This fact is relevant in the study of turbulent configurations. The core results are an "almost global" stability theorem for strong Beltrami fields, which ensures that a global strong Beltrami field with suitable decay at infinity can be perturbed to get "many" Beltrami fields with non-constant factor of arbitrarily high regularity and defined in the exterior of an arbitrarily small ball, and a "local" stability theorem for generalized Beltrami fields, which is an analogous perturbative result which is valid for any kind of Beltrami field (not just with a constant factor) but only applies to small enough domains. The proof relies on an iterative scheme of Grad-Rubin type. For this purpose, we study the Neumann problem for the inhomogeneous Beltrami equation in exterior domains via a boundary integral equation method and we obtain Hölder estimates, a sharp decay at infinity and some compactness
Structural Analysis of Port Brand Equity Using Structural Equation Modeling1
Directory of Open Access Journals (Sweden)
Taehwee Lee
2014-12-01
Full Text Available Port competition, especially in the Northeast Asia (NEA region, can be described as a price war. In this price competition, it is necessary to build up the brand concept to acquire higher market share. This paper aims to provide structural relationships for port brand equity (PBE and explore the PBE stages statistically. The stages are divided into three steps: port service quality as the precedent of PBE, the PBE dimensions (brand awareness [BA] and brand loyalty [BL], and the antecedent of PBE (overall value of brand equity [OVBE]. From a survey conducted with port users in Korea, the empirical results revealed several significant relationship: between tangibles (TA dimension of port service quality and BL, between the empathy (EMP dimension of port service quality and both BA and BL, and between BA and BL and OVBE. From the empirical analysis, this study suggests both managerial and academic contributions for port managers and scholars for further policy development and research in this important area.
Data-Driven Inference on Sign Restrictions in Bayesian Structural Vector Autoregression
DEFF Research Database (Denmark)
Lanne, Markku; Luoto, Jani
a genuinely noninformative prior and thus learning from the data about the impulse responses. While the shocks are statistically identified, they carry no economic meaning as such, and we propose a procedure for labeling them by their probabilities of satisfying each of the given sign restrictions...
Data-driven gating in PET: Influence of respiratory signal noise on motion resolution.
Büther, Florian; Ernst, Iris; Frohwein, Lynn Johann; Pouw, Joost; Schäfers, Klaus Peter; Stegger, Lars
2018-05-21
Data-driven gating (DDG) approaches for positron emission tomography (PET) are interesting alternatives to conventional hardware-based gating methods. In DDG, the measured PET data themselves are utilized to calculate a respiratory signal, that is, subsequently used for gating purposes. The success of gating is then highly dependent on the statistical quality of the PET data. In this study, we investigate how this quality determines signal noise and thus motion resolution in clinical PET scans using a center-of-mass-based (COM) DDG approach, specifically with regard to motion management of target structures in future radiotherapy planning applications. PET list mode datasets acquired in one bed position of 19 different radiotherapy patients undergoing pretreatment [ 18 F]FDG PET/CT or [ 18 F]FDG PET/MRI were included into this retrospective study. All scans were performed over a region with organs (myocardium, kidneys) or tumor lesions of high tracer uptake and under free breathing. Aside from the original list mode data, datasets with progressively decreasing PET statistics were generated. From these, COM DDG signals were derived for subsequent amplitude-based gating of the original list mode file. The apparent respiratory shift d from end-expiration to end-inspiration was determined from the gated images and expressed as a function of signal-to-noise ratio SNR of the determined gating signals. This relation was tested against additional 25 [ 18 F]FDG PET/MRI list mode datasets where high-precision MR navigator-like respiratory signals were available as reference signal for respiratory gating of PET data, and data from a dedicated thorax phantom scan. All original 19 high-quality list mode datasets demonstrated the same behavior in terms of motion resolution when reducing the amount of list mode events for DDG signal generation. Ratios and directions of respiratory shifts between end-respiratory gates and the respective nongated image were constant over all
International Nuclear Information System (INIS)
Torres-Arredondo, M-A; Sierra-Pérez, Julián; Cabanes, Guénaël
2016-01-01
The process of measuring and analysing the data from a distributed sensor network all over a structural system in order to quantify its condition is known as structural health monitoring (SHM). For the design of a trustworthy health monitoring system, a vast amount of information regarding the inherent physical characteristics of the sources and their propagation and interaction across the structure is crucial. Moreover, any SHM system which is expected to transition to field operation must take into account the influence of environmental and operational changes which cause modifications in the stiffness and damping of the structure and consequently modify its dynamic behaviour. On that account, special attention is paid in this paper to the development of an efficient SHM methodology where robust signal processing and pattern recognition techniques are integrated for the correct interpretation of complex ultrasonic waves within the context of damage detection and identification. The methodology is based on an acousto-ultrasonics technique where the discrete wavelet transform is evaluated for feature extraction and selection, linear principal component analysis for data-driven modelling and self-organising maps for a two-level clustering under the principle of local density. At the end, the methodology is experimentally demonstrated and results show that all the damages were detectable and identifiable. (paper)
Torres-Arredondo, M.-A.; Sierra-Pérez, Julián; Cabanes, Guénaël
2016-05-01
The process of measuring and analysing the data from a distributed sensor network all over a structural system in order to quantify its condition is known as structural health monitoring (SHM). For the design of a trustworthy health monitoring system, a vast amount of information regarding the inherent physical characteristics of the sources and their propagation and interaction across the structure is crucial. Moreover, any SHM system which is expected to transition to field operation must take into account the influence of environmental and operational changes which cause modifications in the stiffness and damping of the structure and consequently modify its dynamic behaviour. On that account, special attention is paid in this paper to the development of an efficient SHM methodology where robust signal processing and pattern recognition techniques are integrated for the correct interpretation of complex ultrasonic waves within the context of damage detection and identification. The methodology is based on an acousto-ultrasonics technique where the discrete wavelet transform is evaluated for feature extraction and selection, linear principal component analysis for data-driven modelling and self-organising maps for a two-level clustering under the principle of local density. At the end, the methodology is experimentally demonstrated and results show that all the damages were detectable and identifiable.
Atomistic modeling of structure II gas hydrate mechanics: Compressibility and equations of state
Directory of Open Access Journals (Sweden)
Thomas M. Vlasic
2016-08-01
Full Text Available This work uses density functional theory (DFT to investigate the poorly characterized structure II gas hydrates, for various guests (empty, propane, butane, ethane-methane, propane-methane, at the atomistic scale to determine key structure and mechanical properties such as equilibrium lattice volume and bulk modulus. Several equations of state (EOS for solids (Murnaghan, Birch-Murnaghan, Vinet, Liu were fitted to energy-volume curves resulting from structure optimization simulations. These EOS, which can be used to characterize the compressional behaviour of gas hydrates, were evaluated in terms of their robustness. The three-parameter Vinet EOS was found to perform just as well if not better than the four-parameter Liu EOS, over the pressure range in this study. As expected, the Murnaghan EOS proved to be the least robust. Furthermore, the equilibrium lattice volumes were found to increase with guest size, with double-guest hydrates showing a larger increase than single-guest hydrates, which has significant implications for the widely used van der Waals and Platteeuw thermodynamic model for gas hydrates. Also, hydrogen bonds prove to be the most likely factor contributing to the resistance of gas hydrates to compression; bulk modulus was found to increase linearly with hydrogen bond density, resulting in a relationship that could be used predictively to determine the bulk modulus of various structure II gas hydrates. Taken together, these results fill a long existing gap in the material chemical physics of these important clathrates.
Atomistic modeling of structure II gas hydrate mechanics: Compressibility and equations of state
Energy Technology Data Exchange (ETDEWEB)
Vlasic, Thomas M.; Servio, Phillip; Rey, Alejandro D., E-mail: alejandro.rey@mcgill.ca [Department of Chemical Engineering, McGill University, Montreal H3A 0C5 (Canada)
2016-08-15
This work uses density functional theory (DFT) to investigate the poorly characterized structure II gas hydrates, for various guests (empty, propane, butane, ethane-methane, propane-methane), at the atomistic scale to determine key structure and mechanical properties such as equilibrium lattice volume and bulk modulus. Several equations of state (EOS) for solids (Murnaghan, Birch-Murnaghan, Vinet, Liu) were fitted to energy-volume curves resulting from structure optimization simulations. These EOS, which can be used to characterize the compressional behaviour of gas hydrates, were evaluated in terms of their robustness. The three-parameter Vinet EOS was found to perform just as well if not better than the four-parameter Liu EOS, over the pressure range in this study. As expected, the Murnaghan EOS proved to be the least robust. Furthermore, the equilibrium lattice volumes were found to increase with guest size, with double-guest hydrates showing a larger increase than single-guest hydrates, which has significant implications for the widely used van der Waals and Platteeuw thermodynamic model for gas hydrates. Also, hydrogen bonds prove to be the most likely factor contributing to the resistance of gas hydrates to compression; bulk modulus was found to increase linearly with hydrogen bond density, resulting in a relationship that could be used predictively to determine the bulk modulus of various structure II gas hydrates. Taken together, these results fill a long existing gap in the material chemical physics of these important clathrates.
Atomistic modeling of structure II gas hydrate mechanics: Compressibility and equations of state
Vlasic, Thomas M.; Servio, Phillip; Rey, Alejandro D.
2016-08-01
This work uses density functional theory (DFT) to investigate the poorly characterized structure II gas hydrates, for various guests (empty, propane, butane, ethane-methane, propane-methane), at the atomistic scale to determine key structure and mechanical properties such as equilibrium lattice volume and bulk modulus. Several equations of state (EOS) for solids (Murnaghan, Birch-Murnaghan, Vinet, Liu) were fitted to energy-volume curves resulting from structure optimization simulations. These EOS, which can be used to characterize the compressional behaviour of gas hydrates, were evaluated in terms of their robustness. The three-parameter Vinet EOS was found to perform just as well if not better than the four-parameter Liu EOS, over the pressure range in this study. As expected, the Murnaghan EOS proved to be the least robust. Furthermore, the equilibrium lattice volumes were found to increase with guest size, with double-guest hydrates showing a larger increase than single-guest hydrates, which has significant implications for the widely used van der Waals and Platteeuw thermodynamic model for gas hydrates. Also, hydrogen bonds prove to be the most likely factor contributing to the resistance of gas hydrates to compression; bulk modulus was found to increase linearly with hydrogen bond density, resulting in a relationship that could be used predictively to determine the bulk modulus of various structure II gas hydrates. Taken together, these results fill a long existing gap in the material chemical physics of these important clathrates.
Li, F; Harmer, P
1996-12-01
Self-determination theory (Deci & Ryan, 1985) suggests that motivational orientation or regulatory styles with respect to various behaviors can be conceptualized along a continuum ranging from low (a motivation) to high (intrinsic motivation) levels of self-determination. This pattern is manifested in the rank order of correlations among these regulatory styles (i.e., adjacent correlations are expected to be higher than those more distant) and is known as a simplex structure. Using responses from the Sport Motivation Scale (Pelletier et al., 1995) obtained from a sample of 857 college students (442 men, 415 women), the present study tested the simplex structure underlying SMS subscales via structural equation modeling. Results confirmed the simplex model structure, indicating that the various motivational constructs are empirically organized from low to high self-determination. The simplex pattern was further found to be invariant across gender. Findings from this study support the construct validity of the SMS and have important implications for studies focusing on the influence of motivational orientation in sport.
Development of uncertainty-based work injury model using Bayesian structural equation modelling.
Chatterjee, Snehamoy
2014-01-01
This paper proposed a Bayesian method-based structural equation model (SEM) of miners' work injury for an underground coal mine in India. The environmental and behavioural variables for work injury were identified and causal relationships were developed. For Bayesian modelling, prior distributions of SEM parameters are necessary to develop the model. In this paper, two approaches were adopted to obtain prior distribution for factor loading parameters and structural parameters of SEM. In the first approach, the prior distributions were considered as a fixed distribution function with specific parameter values, whereas, in the second approach, prior distributions of the parameters were generated from experts' opinions. The posterior distributions of these parameters were obtained by applying Bayesian rule. The Markov Chain Monte Carlo sampling in the form Gibbs sampling was applied for sampling from the posterior distribution. The results revealed that all coefficients of structural and measurement model parameters are statistically significant in experts' opinion-based priors, whereas, two coefficients are not statistically significant when fixed prior-based distributions are applied. The error statistics reveals that Bayesian structural model provides reasonably good fit of work injury with high coefficient of determination (0.91) and less mean squared error as compared to traditional SEM.
Data-driven Inference and Investigation of Thermosphere Dynamics and Variations
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.
On formal structure of constitutive equations for materials exhibiting shape memory effects
International Nuclear Information System (INIS)
Dobovsek, I.
2000-01-01
A derivation of constitutive equations in a general three-dimensional setting is described, based on an additive decomposition of the rate of deformation tensor. The rate of deformation tensor is assumed to consist of an elastic part, a thermoelastic part, a plastic part, a part due to shape memory transformation, and a part due to phase transformation. The thermoelastic part due to thermoelastic coupling accounts for the influence of temperature near phase transformation, while the plastic part is taken in the form of classical J 2 flow theory of plasticity with combined isotropic and kinematic hardening, where the back stress represents a tensor of orientational microstresses. It is assumed that the phase transformation part depends on the first and the second invariant of the tensor of crystallographic distortion, on the deviatoric part of the stress tensor, and on a special evolution parameter describing the rate of forming of a new phase. The elastic part of the rate of deformation tensor is connected with the objective rate of Cauchy stress through the tensor of elastic compliance. As a result, a general form of derived constitutive equations exhibits a similar structure as constitutive relations in finite deformation plasticity. (orig.)
Lyapunov stability and poisson structure of the thermal TDHF and RPA equations
International Nuclear Information System (INIS)
Balian, R.; Veneroni, M.
1989-01-01
The thermal TDHF equation is analyzed in the Liouville representation of quantum mechanics, where the matrix elements of the single-particle (s.p) density ρ behave as classical dynamical variables. By introducing the Lie--Poisson bracket associated with the unitary group of the s.p. Hilbert space, we show that TDHF has a Hamiltonian, but non-canonical, classical form. Within this Poisson structure, either the s.p. energy or the s.p. grand potential Ω(ρ) act as a Hamilton function. The Lyapunov stability of both the TDHF and RPA equations around a HF state then follows, since the HF approximation for thermal equilibrium is determined by minimizing Ω(ρ). The RPA matrix in the Liouville space is expressed as the product of the Poisson tensor with the HF stability matrix, interpreted as a metric tensor generated by the entropy. This factorization displays the roles of the energy and entropy terms arising from Ω(ρ) in the RPA dynamics, and it helps to construct the RPA modes. Several extensions are considered. copyright 1989 Academic Press, Inc
Lyapunov stability and Poisson structure of the thermal TDHF and RPA equations
International Nuclear Information System (INIS)
Veneroni, M.; Balian, R.
1989-01-01
The thermal TDHF equation is analyzed in the Liouville representation of quantum mechanics, where the matrix elements of the single-particle (s.p.) density ρ behave as classical dynamical variables. By introducing the Lie-Poisson bracket associated with the unitary group of the s.p. Hilbert space, we show that TDHF has a hamiltonian, but non-canonical, classical form. Within this Poisson structure, either the s.p. energy or the s.p. grand potential Ω(ρ) act as a Hamilton function. The Lyapunov stability of both the TDHF and RPA equations around a HF state then follows, since the HF approximation for thermal equilibrium is determined by minimizing Ω(ρ). The RPA matrix in the Liouville space is expressed as the product of the Poisson tensor with the HF stability matrix, interpreted as a metric tensor generated by the entropy. This factorization displays the roles of the energy and entropy terms arising from Ω(ρ) in the RPA dynamics, and it helps to construct the RPA modes. Several extensions are considered
Data-driven criteria to assess fear remission and phenotypic variability of extinction in rats.
Shumake, Jason; Jones, Carolyn; Auchter, Allison; Monfils, Marie-Hélène
2018-03-19
Fear conditioning is widely employed to examine the mechanisms that underlie dysregulations of the fear system. Various manipulations are often used following fear acquisition to attenuate fear memories. In rodent studies, freezing is often the main output measure to quantify 'fear'. Here, we developed data-driven criteria for defining a standard benchmark that indicates remission from conditioned fear and for identifying subgroups with differential treatment responses. These analyses will enable a better understanding of individual differences in treatment responding.This article is part of a discussion meeting issue 'Of mice and mental health: facilitating dialogue between basic and clinical neuroscientists'. © 2018 The Author(s).
Applying Data-driven Imaging Biomarker in Mammography for Breast Cancer Screening: Preliminary Study
Kim, Eun-Kyung; Kim, Hyo-Eun; Han, Kyunghwa; Kang, Bong Joo; Sohn, Yu-Mee; Woo, Ok Hee; Lee, Chan Wha
2018-01-01
We assessed the feasibility of a data-driven imaging biomarker based on weakly supervised learning (DIB; an imaging biomarker derived from large-scale medical image data with deep learning technology) in mammography (DIB-MG). A total of 29,107 digital mammograms from five institutions (4,339 cancer cases and 24,768 normal cases) were included. After matching patients’ age, breast density, and equipment, 1,238 and 1,238 cases were chosen as validation and test sets, respectively, and the remai...
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
Classification Systems, their Digitization and Consequences for Data-Driven Decision Making
DEFF Research Database (Denmark)
Stein, Mari-Klara; Newell, Sue; Galliers, Robert D.
2013-01-01
Classification systems are foundational in many standardized software tools. This digitization of classification systems gives them a new ‘materiality’ that, jointly with the social practices of information producers/consumers, has significant consequences on the representational quality of such ...... and the foundational role of representational quality in understanding the success and consequences of data-driven decision-making.......-narration and meta-narration), and three different information production/consumption situations. We contribute to the relational theorization of representational quality and extend classification systems research by drawing explicit attention to the importance of ‘materialization’ of classification systems...
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.
A data driven method to measure electron charge mis-identification rate
Bakhshiansohi, Hamed
2009-01-01
Electron charge mis-measurement is an important challenge in analyses which depend on the charge of electron. To estimate the probability of {\\it electron charge mis-measurement} a data driven method is introduced and a good agreement with MC based methods is achieved.\\\\ The third moment of $\\phi$ distribution of hits in electron SuperCluster is studied. The correlation between this variable and the electron charge is also investigated. Using this `new' variable and some other variables the electron charge measurement is improved by two different approaches.
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
Zhang, Bo; Wang, Jianjun; Liu, Zhiping; Zhang, Xianren
2014-01-01
The application of Cassie equation to microscopic droplets is recently under intense debate because the microdroplet dimension is often of the same order of magnitude as the characteristic size of substrate heterogeneities, and the mechanism to describe the contact angle of microdroplets is not clear. By representing real surfaces statistically as an ensemble of patterned surfaces with randomly or regularly distributed heterogeneities (patches), lattice Boltzmann simulations here show that the contact angle of microdroplets has a wide distribution, either continuous or discrete, depending on the patch size. The origin of multiple contact angles observed is ascribed to the contact line pinning effect induced by substrate heterogeneities. We demonstrate that the local feature of substrate structure near the contact line determines the range of contact angles that can be stabilized, while the certain contact angle observed is closely related to the contact line width. PMID:25059292
Keith, Timothy Z
2014-01-01
Multiple Regression and Beyond offers a conceptually oriented introduction to multiple regression (MR) analysis and structural equation modeling (SEM), along with analyses that flow naturally from those methods. By focusing on the concepts and purposes of MR and related methods, rather than the derivation and calculation of formulae, this book introduces material to students more clearly, and in a less threatening way. In addition to illuminating content necessary for coursework, the accessibility of this approach means students are more likely to be able to conduct research using MR or SEM--and more likely to use the methods wisely. Covers both MR and SEM, while explaining their relevance to one another Also includes path analysis, confirmatory factor analysis, and latent growth modeling Figures and tables throughout provide examples and illustrate key concepts and techniques For additional resources, please visit: http://tzkeith.com/.
International Nuclear Information System (INIS)
Schloesser, R.; Wagner, G.; Koehler, S.; Sauer, H.
2005-01-01
Aside from characteristic psychopathological symptoms, cognitive deficits are a core feature of schizophrenia. These deficits can only be addressed within the context of widespread functional interactions among different brain areas. To examine these interactions, structural equation modeling (SEM) was used for the analysis of fMRI datasets. In a series of studies, both in antipsychotic-treated and drug-free schizophrenic patients, a pattern of enhanced thalamocortical functional connectivity could be observed as an indicator for possible disruptions of frontostriatal thalamocortical circuitry. Moreover, drug-free patients and those receiving typical antipsychotic drugs were characterized by reduced interhemispheric corticocortical connectivity. This difference relative to normal controls was less in patients under atypical antipsychotic drugs. The results could be interpreted as a beneficial effect of atypical antipsychotic drugs on information processing in schizophrenic patients. The present findings are consistent with the model of schizophrenia as a disconnection syndrome and earlier concepts of ''cognitive dysmetria'' in schizophrenia. (orig.) [de
Katharaki, Maria; Daskalakis, Stelios; Mantas, John
2010-01-01
The objective of this paper is to assess the future adaptability of e-Learning platforms within postgraduate modules. An ongoing empirical assessment was conducted amongst postgraduate students, based on the Technology Acceptance Model (TAM). The current paper presents the outcomes from the second phase of a survey, involving fifty six participants. Data analysis was performed using a structural equation model, based on partial least squares. Results highlighted the very strong effect of perceived usefulness and perceived ease of use to attitude towards using e-Learning platforms. Consequently, attitude towards use proved to be a very strong predictor of behavioral intention. Perceived usefulness, on the contrary, did not prove to have an effect to behavioral intention. Implications on the potential of using e-Learning platforms are discussed along with limitations and future directions of the study.
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.
Institute of Scientific and Technical Information of China (English)
DING Zhi-kun; NG Fung-fai; WANG Jia-yuan
2009-01-01
This paper describes a structural equation modeling (SEM) analysis of factors influencing architects' trust in project design teams. We undertook a survey of architects, during which we distributed 193 questionnaires in 29 A-level architectural We used Amos 6.0 for SEM to identify significant personal construct based factors affecting interpersonal trust. The results show that only social interaction between architects significantly affects their interpersonal trust. The explained variance of trust is not very high in the model. Therefore, future research should add more factors into the current model. The practical implication is that team managers should promote the social interactions between team members such that the interpersonal trust level between team members can be improved.
Marino, Teresa L; Negy, Charles; Hammons, Mary E; McKinney, Cliff; Asberg, Kia
2007-11-01
Despite a general consensus in the United States that overtly racist acts are unacceptable, many ambiguous situations in everyday life raise questions of whether racism has influenced a person's behavior in an interracial encounter. The authors of the present study sought to (a) examine simultaneously an array of variables thought to be related to perceived racism and (b) investigate how the contribution of these variables may differ with respect to the asymmetry hypothesis, which suggests that acts of discrimination from a dominant person toward a subordinate person will be viewed as more biased than if the situation were reversed. The authors used a dual structural equation modeling approach. Results indicated that ethnic identity significantly predicted perceived racism. In addition, the extent to which cognitive interpretation style significantly predicted perceived racism depended on the ethnicity of participants involved in the interaction.
Directory of Open Access Journals (Sweden)
Hayduk Leslie A
2012-10-01
Full Text Available Abstract Background Structural equation modeling developed as a statistical melding of path analysis and factor analysis that obscured a fundamental tension between a factor preference for multiple indicators and path modeling’s openness to fewer indicators. Discussion Multiple indicators hamper theory by unnecessarily restricting the number of modeled latents. Using the few best indicators – possibly even the single best indicator of each latent – encourages development of theoretically sophisticated models. Additional latent variables permit stronger statistical control of potential confounders, and encourage detailed investigation of mediating causal mechanisms. Summary We recommend the use of the few best indicators. One or two indicators are often sufficient, but three indicators may occasionally be helpful. More than three indicators are rarely warranted because additional redundant indicators provide less research benefit than single indicators of additional latent variables. Scales created from multiple indicators can introduce additional problems, and are prone to being less desirable than either single or multiple indicators.
Directory of Open Access Journals (Sweden)
Mina Beig
2012-08-01
Full Text Available Information technology plays an important role on increasing productivity in many organizations. The primary objective of the present survey is to study the impact of information technology on productivity and find a positive and significant relationship between these two factors. Structural equations technique and LISREL software are used for analysis of the questionnaires distributed among managers and some employees of Iran Behnoush Company. Organizations try to improve their performance by investment in information technology. However, many of the previous studies indicate insignificance of the impact of information technology on productivity of the organizations. The present survey studies the impact of information technology on organizations' productivity through the collected data from the above company. Results confirm existence of a positive relationship between information technology and productivity.
Martínez-Córcoles, Mario; Schöbel, Markus; Gracia, Francisco J; Tomás, Inés; Peiró, José M
2012-07-01
Safety participation is of paramount importance in guaranteeing the safe running of nuclear power plants. The present study examined the effects of empowering leadership on safety participation. Based on a sample of 495 employees from two Spanish nuclear power plants, structural equation modeling showed that empowering leadership has a significant relationship with safety participation, which is mediated by collaborative team learning. In addition, the results revealed that the relationship between empowering leadership and collaborative learning is partially mediated by the promotion of dialogue and open communication. The implications of these findings for safety research and their practical applications are outlined. An empowering leadership style enhances workers' safety performance, particularly safety participation behaviors. Safety participation is recommended to detect possible rule inconsistencies or misunderstood procedures and make workers aware of critical safety information and issues. Crown Copyright © 2012. Published by Elsevier Ltd. All rights reserved.
Analysis on influencing factors of clinical teachers’ job satisfaction by structural equation model
Directory of Open Access Journals (Sweden)
Haiyi Jia
2017-02-01
Full Text Available [Research objective] Analyze the influencing factors of clinical teachers’ job satisfaction. [Research method] The ERG theory was used as the framework to design the questionnaires. Data were analyzed by structural equation model for investigating the influencing factors. [Research result] The modified model shows that factors of existence needs and growth needs have direct influence on the job satisfaction of clinical teachers, the influence coefficients are 0.540 and 0.380. The three influencing factors have positive effects on each other, and the correlation coefficients are 0.620, 0.400 and 0.330 respectively. [Research conclusion] Relevant departments should take active measures to improve job satisfaction of clinical teachers from two aspects: existence needs and growth needs, and to improve their work enthusiasm and teaching quality.
From patterns to causal understanding: Structural equation modeling (SEM) in soil ecology
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.