Generalizing DTW to the multi-dimensional case requires an adaptive approach

PubMed Central

Hu, Bing; Jin, Hongxia; Wang, Jun; Keogh, Eamonn

2017-01-01

In recent years Dynamic Time Warping (DTW) has emerged as the distance measure of choice for virtually all time series data mining applications. For example, virtually all applications that process data from wearable devices use DTW as a core sub-routine. This is the result of significant progress in improving DTW’s efficiency, together with multiple empirical studies showing that DTW-based classifiers at least equal (and generally surpass) the accuracy of all their rivals across dozens of datasets. Thus far, most of the research has considered only the one-dimensional case, with practitioners generalizing to the multi-dimensional case in one of two ways, dependent or independent warping. In general, it appears the community believes either that the two ways are equivalent, or that the choice is irrelevant. In this work, we show that this is not the case. The two most commonly used multi-dimensional DTW methods can produce different classifications, and neither one dominates over the other. This seems to suggest that one should learn the best method for a particular application. However, we will show that this is not necessary; a simple, principled rule can be used on a case-by-case basis to predict which of the two methods we should trust at the time of classification. Our method allows us to ensure that classification results are at least as accurate as the better of the two rival methods, and, in many cases, our method is significantly more accurate. We demonstrate our ideas with the most extensive set of multi-dimensional time series classification experiments ever attempted. PMID:29104448

A general framework for time series data mining based on event analysis: application to the medical domains of electroencephalography and stabilometry.

PubMed

Lara, Juan A; Lizcano, David; Pérez, Aurora; Valente, Juan P

2014-10-01

There are now domains where information is recorded over a period of time, leading to sequences of data known as time series. In many domains, like medicine, time series analysis requires to focus on certain regions of interest, known as events, rather than analyzing the whole time series. In this paper, we propose a framework for knowledge discovery in both one-dimensional and multidimensional time series containing events. We show how our approach can be used to classify medical time series by means of a process that identifies events in time series, generates time series reference models of representative events and compares two time series by analyzing the events they have in common. We have applied our framework on time series generated in the areas of electroencephalography (EEG) and stabilometry. Framework performance was evaluated in terms of classification accuracy, and the results confirmed that the proposed schema has potential for classifying EEG and stabilometric signals. The proposed framework is useful for discovering knowledge from medical time series containing events, such as stabilometric and electroencephalographic time series. These results would be equally applicable to other medical domains generating iconographic time series, such as, for example, electrocardiography (ECG). Copyright © 2014 Elsevier Inc. All rights reserved.

An architecture for consolidating multidimensional time-series data onto a common coordinate grid

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

Shippert, Tim; Gaustad, Krista

Consolidating measurement data for use by data models or in inter-comparison studies frequently requires transforming the data onto a common grid. Standard methods for interpolating multidimensional data are often not appropriate for data with non-homogenous dimensionality, and are hard to implement in a consistent manner for different datastreams. These challenges are increased when dealing with the automated procedures necessary for use with continuous, operational datastreams. In this paper we introduce a method of applying a series of one-dimensional transformations to merge data onto a common grid, examine the challenges of ensuring consistent application of data consolidation methods, present a frameworkmore » for addressing those challenges, and describe the implementation of such a framework for the Atmospheric Radiation Measurement (ARM) program.« less

Revisiting the Fundamental Analytical Solutions of Heat and Mass Transfer: The Kernel of Multirate and Multidimensional Diffusion

NASA Astrophysics Data System (ADS)

Zhou, Quanlin; Oldenburg, Curtis M.; Rutqvist, Jonny; Birkholzer, Jens T.

2017-11-01

There are two types of analytical solutions of temperature/concentration in and heat/mass transfer through boundaries of regularly shaped 1-D, 2-D, and 3-D blocks. These infinite-series solutions with either error functions or exponentials exhibit highly irregular but complementary convergence at different dimensionless times, td. In this paper, approximate solutions were developed by combining the error-function-series solutions for early times and the exponential-series solutions for late times and by using time partitioning at the switchover time, td0. The combined solutions contain either the leading term of both series for normal-accuracy approximations (with less than 0.003 relative error) or the first two terms for high-accuracy approximations (with less than 10-7 relative error) for 1-D isotropic (spheres, cylinders, slabs) and 2-D/3-D rectangular blocks (squares, cubes, rectangles, and rectangular parallelepipeds). This rapid and uniform convergence for rectangular blocks was achieved by employing the same time partitioning with individual dimensionless times for different directions and the product of their combined 1-D slab solutions. The switchover dimensionless time was determined to minimize the maximum approximation errors. Furthermore, the analytical solutions of first-order heat/mass flux for 2-D/3-D rectangular blocks were derived for normal-accuracy approximations. These flux equations contain the early-time solution with a three-term polynomial in √td and the late-time solution with the limited-term exponentials for rectangular blocks. The heat/mass flux equations and the combined temperature/concentration solutions form the ultimate kernel for fast simulations of multirate and multidimensional heat/mass transfer in porous/fractured media with millions of low-permeability blocks of varying shapes and sizes.

Revisiting the Fundamental Analytical Solutions of Heat and Mass Transfer: The Kernel of Multirate and Multidimensional Diffusion

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

Zhou, Quanlin; Oldenburg, Curtis M.; Rutqvist, Jonny

There are two types of analytical solutions of temperature/concentration in and heat/mass transfer through boundaries of regularly shaped 1D, 2D, and 3D blocks. These infinite-series solutions with either error functions or exponentials exhibit highly irregular but complementary convergence at different dimensionless times, t d0. In this paper, approximate solutions were developed by combining the error-function-series solutions for early times and the exponential-series solutions for late times and by using time partitioning at the switchover time, t d0. The combined solutions contain either the leading term of both series for normal-accuracy approximations (with less than 0.003 relative error) or the firstmore » two terms for high-accuracy approximations (with less than 10-7 relative error) for 1D isotropic (spheres, cylinders, slabs) and 2D/3D rectangular blocks (squares, cubes, rectangles, and rectangular parallelepipeds). This rapid and uniform convergence for rectangular blocks was achieved by employing the same time partitioning with individual dimensionless times for different directions and the product of their combined 1D slab solutions. The switchover dimensionless time was determined to minimize the maximum approximation errors. Furthermore, the analytical solutions of first-order heat/mass flux for 2D/3D rectangular blocks were derived for normal-accuracy approximations. These flux equations contain the early-time solution with a three-term polynomial in √td and the late-time solution with the limited-term exponentials for rectangular blocks. The heat/mass flux equations and the combined temperature/concentration solutions form the ultimate kernel for fast simulations of multirate and multidimensional heat/mass transfer in porous/fractured media with millions of low-permeability blocks of varying shapes and sizes.« less

Revisiting the Fundamental Analytical Solutions of Heat and Mass Transfer: The Kernel of Multirate and Multidimensional Diffusion

DOE PAGES

Zhou, Quanlin; Oldenburg, Curtis M.; Rutqvist, Jonny; ...

2017-10-24

There are two types of analytical solutions of temperature/concentration in and heat/mass transfer through boundaries of regularly shaped 1D, 2D, and 3D blocks. These infinite-series solutions with either error functions or exponentials exhibit highly irregular but complementary convergence at different dimensionless times, t d0. In this paper, approximate solutions were developed by combining the error-function-series solutions for early times and the exponential-series solutions for late times and by using time partitioning at the switchover time, t d0. The combined solutions contain either the leading term of both series for normal-accuracy approximations (with less than 0.003 relative error) or the firstmore » two terms for high-accuracy approximations (with less than 10-7 relative error) for 1D isotropic (spheres, cylinders, slabs) and 2D/3D rectangular blocks (squares, cubes, rectangles, and rectangular parallelepipeds). This rapid and uniform convergence for rectangular blocks was achieved by employing the same time partitioning with individual dimensionless times for different directions and the product of their combined 1D slab solutions. The switchover dimensionless time was determined to minimize the maximum approximation errors. Furthermore, the analytical solutions of first-order heat/mass flux for 2D/3D rectangular blocks were derived for normal-accuracy approximations. These flux equations contain the early-time solution with a three-term polynomial in √td and the late-time solution with the limited-term exponentials for rectangular blocks. The heat/mass flux equations and the combined temperature/concentration solutions form the ultimate kernel for fast simulations of multirate and multidimensional heat/mass transfer in porous/fractured media with millions of low-permeability blocks of varying shapes and sizes.« less

An architecture for consolidating multidimensional time-series data onto a common coordinate grid

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

Shippert, Tim; Gaustad, Krista

In this paper, consolidating measurement data for use by data models or in inter-comparison studies frequently requires transforming the data onto a common grid. Standard methods for interpolating multidimensional data are often not appropriate for data with non-homogenous dimensionality, and are hard to implement in a consistent manner for different datastreams. In addition, these challenges are increased when dealing with the automated procedures necessary for use with continuous, operational datastreams. In this paper we introduce a method of applying a series of one-dimensional transformations to merge data onto a common grid, examine the challenges of ensuring consistent application of datamore » consolidation methods, present a framework for addressing those challenges, and describe the implementation of such a framework for the Atmospheric Radiation Measurement (ARM) program.« less

An architecture for consolidating multidimensional time-series data onto a common coordinate grid

DOE PAGES

Shippert, Tim; Gaustad, Krista

2016-12-16

In this paper, consolidating measurement data for use by data models or in inter-comparison studies frequently requires transforming the data onto a common grid. Standard methods for interpolating multidimensional data are often not appropriate for data with non-homogenous dimensionality, and are hard to implement in a consistent manner for different datastreams. In addition, these challenges are increased when dealing with the automated procedures necessary for use with continuous, operational datastreams. In this paper we introduce a method of applying a series of one-dimensional transformations to merge data onto a common grid, examine the challenges of ensuring consistent application of datamore » consolidation methods, present a framework for addressing those challenges, and describe the implementation of such a framework for the Atmospheric Radiation Measurement (ARM) program.« less

Identification of market trends with string and D2-brane maps

NASA Astrophysics Data System (ADS)

Bartoš, Erik; Pinčák, Richard

2017-08-01

The multidimensional string objects are introduced as a new alternative for an application of string models for time series forecasting in trading on financial markets. The objects are represented by open string with 2-endpoints and D2-brane, which are continuous enhancement of 1-endpoint open string model. We show how new object properties can change the statistics of the predictors, which makes them the candidates for modeling a wide range of time series systems. String angular momentum is proposed as another tool to analyze the stability of currency rates except the historical volatility. To show the reliability of our approach with application of string models for time series forecasting we present the results of real demo simulations for four currency exchange pairs.

Edgelist phase unwrapping algorithm for time series InSAR analysis.

PubMed

Shanker, A Piyush; Zebker, Howard

2010-03-01

We present here a new integer programming formulation for phase unwrapping of multidimensional data. Phase unwrapping is a key problem in many coherent imaging systems, including time series synthetic aperture radar interferometry (InSAR), with two spatial and one temporal data dimensions. The minimum cost flow (MCF) [IEEE Trans. Geosci. Remote Sens. 36, 813 (1998)] phase unwrapping algorithm describes a global cost minimization problem involving flow between phase residues computed over closed loops. Here we replace closed loops by reliable edges as the basic construct, thus leading to the name "edgelist." Our algorithm has several advantages over current methods-it simplifies the representation of multidimensional phase unwrapping, it incorporates data from external sources, such as GPS, where available to better constrain the unwrapped solution, and it treats regularly sampled or sparsely sampled data alike. It thus is particularly applicable to time series InSAR, where data are often irregularly spaced in time and individual interferograms can be corrupted with large decorrelated regions. We show that, similar to the MCF network problem, the edgelist formulation also exhibits total unimodularity, which enables us to solve the integer program problem by using efficient linear programming tools. We apply our method to a persistent scatterer-InSAR data set from the creeping section of the Central San Andreas Fault and find that the average creep rate of 22 mm/Yr is constant within 3 mm/Yr over 1992-2004 but varies systematically with ground location, with a slightly higher rate in 1992-1998 than in 1999-2003.

Harmonic oscillators and resonance series generated by a periodic unstable classical orbit

NASA Technical Reports Server (NTRS)

Kazansky, A. K.; Ostrovsky, Valentin N.

1995-01-01

The presence of an unstable periodic classical orbit allows one to introduce the decay time as a purely classical magnitude: inverse of the Lyapunov index which characterizes the orbit instability. The Uncertainty Relation gives the corresponding resonance width which is proportional to the Planck constant. The more elaborate analysis is based on the parabolic equation method where the problem is effectively reduced to the multidimensional harmonic oscillator with the time-dependent frequency. The resonances form series in the complex energy plane which is equidistant in the direction perpendicular to the real axis. The applications of the general approach to various problems in atomic physics are briefly exposed.

Dynamic correlations at different time-scales with empirical mode decomposition

NASA Astrophysics Data System (ADS)

Nava, Noemi; Di Matteo, T.; Aste, Tomaso

2018-07-01

We introduce a simple approach which combines Empirical Mode Decomposition (EMD) and Pearson's cross-correlations over rolling windows to quantify dynamic dependency at different time scales. The EMD is a tool to separate time series into implicit components which oscillate at different time-scales. We apply this decomposition to intraday time series of the following three financial indices: the S&P 500 (USA), the IPC (Mexico) and the VIX (volatility index USA), obtaining time-varying multidimensional cross-correlations at different time-scales. The correlations computed over a rolling window are compared across the three indices, across the components at different time-scales and across different time lags. We uncover a rich heterogeneity of interactions, which depends on the time-scale and has important lead-lag relations that could have practical use for portfolio management, risk estimation and investment decisions.

From analytical solutions of solute transport equations to multidimensional time-domain random walk (TDRW) algorithms

NASA Astrophysics Data System (ADS)

Bodin, Jacques

2015-03-01

In this study, new multi-dimensional time-domain random walk (TDRW) algorithms are derived from approximate one-dimensional (1-D), two-dimensional (2-D), and three-dimensional (3-D) analytical solutions of the advection-dispersion equation and from exact 1-D, 2-D, and 3-D analytical solutions of the pure-diffusion equation. These algorithms enable the calculation of both the time required for a particle to travel a specified distance in a homogeneous medium and the mass recovery at the observation point, which may be incomplete due to 2-D or 3-D transverse dispersion or diffusion. The method is extended to heterogeneous media, represented as a piecewise collection of homogeneous media. The particle motion is then decomposed along a series of intermediate checkpoints located on the medium interface boundaries. The accuracy of the multi-dimensional TDRW method is verified against (i) exact analytical solutions of solute transport in homogeneous media and (ii) finite-difference simulations in a synthetic 2-D heterogeneous medium of simple geometry. The results demonstrate that the method is ideally suited to purely diffusive transport and to advection-dispersion transport problems dominated by advection. Conversely, the method is not recommended for highly dispersive transport problems because the accuracy of the advection-dispersion TDRW algorithms degrades rapidly for a low Péclet number, consistent with the accuracy limit of the approximate analytical solutions. The proposed approach provides a unified methodology for deriving multi-dimensional time-domain particle equations and may be applicable to other mathematical transport models, provided that appropriate analytical solutions are available.

Forecasting and analyzing high O3 time series in educational area through an improved chaotic approach

NASA Astrophysics Data System (ADS)

Hamid, Nor Zila Abd; Adenan, Nur Hamiza; Noorani, Mohd Salmi Md

2017-08-01

Forecasting and analyzing the ozone (O3) concentration time series is important because the pollutant is harmful to health. This study is a pilot study for forecasting and analyzing the O3 time series in one of Malaysian educational area namely Shah Alam using chaotic approach. Through this approach, the observed hourly scalar time series is reconstructed into a multi-dimensional phase space, which is then used to forecast the future time series through the local linear approximation method. The main purpose is to forecast the high O3 concentrations. The original method performed poorly but the improved method addressed the weakness thereby enabling the high concentrations to be successfully forecast. The correlation coefficient between the observed and forecasted time series through the improved method is 0.9159 and both the mean absolute error and root mean squared error are low. Thus, the improved method is advantageous. The time series analysis by means of the phase space plot and Cao method identified the presence of low-dimensional chaotic dynamics in the observed O3 time series. Results showed that at least seven factors affect the studied O3 time series, which is consistent with the listed factors from the diurnal variations investigation and the sensitivity analysis from past studies. In conclusion, chaotic approach has been successfully forecast and analyzes the O3 time series in educational area of Shah Alam. These findings are expected to help stakeholders such as Ministry of Education and Department of Environment in having a better air pollution management.

Modeling Individual Cyclic Variation in Human Behavior.

PubMed

Pierson, Emma; Althoff, Tim; Leskovec, Jure

2018-04-01

Cycles are fundamental to human health and behavior. Examples include mood cycles, circadian rhythms, and the menstrual cycle. However, modeling cycles in time series data is challenging because in most cases the cycles are not labeled or directly observed and need to be inferred from multidimensional measurements taken over time. Here, we present Cyclic Hidden Markov Models (CyH-MMs) for detecting and modeling cycles in a collection of multidimensional heterogeneous time series data. In contrast to previous cycle modeling methods, CyHMMs deal with a number of challenges encountered in modeling real-world cycles: they can model multivariate data with both discrete and continuous dimensions; they explicitly model and are robust to missing data; and they can share information across individuals to accommodate variation both within and between individual time series. Experiments on synthetic and real-world health-tracking data demonstrate that CyHMMs infer cycle lengths more accurately than existing methods, with 58% lower error on simulated data and 63% lower error on real-world data compared to the best-performing baseline. CyHMMs can also perform functions which baselines cannot: they can model the progression of individual features/symptoms over the course of the cycle, identify the most variable features, and cluster individual time series into groups with distinct characteristics. Applying CyHMMs to two real-world health-tracking datasets-of human menstrual cycle symptoms and physical activity tracking data-yields important insights including which symptoms to expect at each point during the cycle. We also find that people fall into several groups with distinct cycle patterns, and that these groups differ along dimensions not provided to the model. For example, by modeling missing data in the menstrual cycles dataset, we are able to discover a medically relevant group of birth control users even though information on birth control is not given to the model.

Modeling Individual Cyclic Variation in Human Behavior

PubMed Central

Pierson, Emma; Althoff, Tim; Leskovec, Jure

2018-01-01

Cycles are fundamental to human health and behavior. Examples include mood cycles, circadian rhythms, and the menstrual cycle. However, modeling cycles in time series data is challenging because in most cases the cycles are not labeled or directly observed and need to be inferred from multidimensional measurements taken over time. Here, we present Cyclic Hidden Markov Models (CyH-MMs) for detecting and modeling cycles in a collection of multidimensional heterogeneous time series data. In contrast to previous cycle modeling methods, CyHMMs deal with a number of challenges encountered in modeling real-world cycles: they can model multivariate data with both discrete and continuous dimensions; they explicitly model and are robust to missing data; and they can share information across individuals to accommodate variation both within and between individual time series. Experiments on synthetic and real-world health-tracking data demonstrate that CyHMMs infer cycle lengths more accurately than existing methods, with 58% lower error on simulated data and 63% lower error on real-world data compared to the best-performing baseline. CyHMMs can also perform functions which baselines cannot: they can model the progression of individual features/symptoms over the course of the cycle, identify the most variable features, and cluster individual time series into groups with distinct characteristics. Applying CyHMMs to two real-world health-tracking datasets—of human menstrual cycle symptoms and physical activity tracking data—yields important insights including which symptoms to expect at each point during the cycle. We also find that people fall into several groups with distinct cycle patterns, and that these groups differ along dimensions not provided to the model. For example, by modeling missing data in the menstrual cycles dataset, we are able to discover a medically relevant group of birth control users even though information on birth control is not given to the model. PMID:29780976

Evidence for a Multidimensional Self-Efficacy for Exercise Scale

ERIC Educational Resources Information Center

Rodgers, W. M.; Wilson, P. M.; Hall, C. R.; Fraser, S. N.; Murray, T. C.

2008-01-01

This series of three studies considers the multidimensionality of exercise self-efficacy by examining the psychometric characteristics of an instrument designed to assess three behavioral subdomains: task, scheduling, and coping. In Study 1, exploratory factor analysis revealed the expected factor structure in a sample of 395 students.…

Application of information-retrieval methods to the classification of physical data

NASA Technical Reports Server (NTRS)

Mamotko, Z. N.; Khorolskaya, S. K.; Shatrovskiy, L. I.

1975-01-01

Scientific data received from satellites are characterized as a multi-dimensional time series, whose terms are vector functions of a vector of measurement conditions. Information retrieval methods are used to construct lower dimensional samples on the basis of the condition vector, in order to obtain these data and to construct partial relations. The methods are applied to the joint Soviet-French Arkad project.

Nonuniform sampling and non-Fourier signal processing methods in multidimensional NMR

PubMed Central

Mobli, Mehdi; Hoch, Jeffrey C.

2017-01-01

Beginning with the introduction of Fourier Transform NMR by Ernst and Anderson in 1966, time domain measurement of the impulse response (the free induction decay, FID) consisted of sampling the signal at a series of discrete intervals. For compatibility with the discrete Fourier transform (DFT), the intervals are kept uniform, and the Nyquist theorem dictates the largest value of the interval sufficient to avoid aliasing. With the proposal by Jeener of parametric sampling along an indirect time dimension, extension to multidimensional experiments employed the same sampling techniques used in one dimension, similarly subject to the Nyquist condition and suitable for processing via the discrete Fourier transform. The challenges of obtaining high-resolution spectral estimates from short data records using the DFT were already well understood, however. Despite techniques such as linear prediction extrapolation, the achievable resolution in the indirect dimensions is limited by practical constraints on measuring time. The advent of non-Fourier methods of spectrum analysis capable of processing nonuniformly sampled data has led to an explosion in the development of novel sampling strategies that avoid the limits on resolution and measurement time imposed by uniform sampling. The first part of this review discusses the many approaches to data sampling in multidimensional NMR, the second part highlights commonly used methods for signal processing of such data, and the review concludes with a discussion of other approaches to speeding up data acquisition in NMR. PMID:25456315

Multidimensional Aspects of Motivation in Cross-Cultural Settings and Ways of Researching This.

ERIC Educational Resources Information Center

McInerney, Dennis M.

This paper reports on a series of studies that examine the multidimensional nature of achievement motivation across a number of cultural groups, the determinants of this achievement motivation, and the relationship of achievement motivation to criteria of school success, such as attendance, retention, achievement, further education, and…

Coherent changes of multifractal properties of continuous acoustic emission at failure of heterogeneous materials

NASA Astrophysics Data System (ADS)

Panteleev, Ivan; Bayandin, Yuriy; Naimark, Oleg

2017-12-01

This work performs a correlation analysis of the statistical properties of continuous acoustic emission recorded in different parts of marble and fiberglass laminate samples under quasi-static deformation. A spectral coherent measure of time series, which is a generalization of the squared coherence spectrum on a multidimensional series, was chosen. The spectral coherent measure was estimated in a sliding time window for two parameters of the acoustic emission multifractal singularity spectrum: the spectrum width and the generalized Hurst exponent realizing the maximum of the singularity spectrum. It is shown that the preparation of the macrofracture focus is accompanied by the synchronization (coherent behavior) of the statistical properties of acoustic emission in allocated frequency intervals.

Benchmarking the Multidimensional Stellar Implicit Code MUSIC

NASA Astrophysics Data System (ADS)

Goffrey, T.; Pratt, J.; Viallet, M.; Baraffe, I.; Popov, M. V.; Walder, R.; Folini, D.; Geroux, C.; Constantino, T.

2017-04-01

We present the results of a numerical benchmark study for the MUltidimensional Stellar Implicit Code (MUSIC) based on widely applicable two- and three-dimensional compressible hydrodynamics problems relevant to stellar interiors. MUSIC is an implicit large eddy simulation code that uses implicit time integration, implemented as a Jacobian-free Newton Krylov method. A physics based preconditioning technique which can be adjusted to target varying physics is used to improve the performance of the solver. The problems used for this benchmark study include the Rayleigh-Taylor and Kelvin-Helmholtz instabilities, and the decay of the Taylor-Green vortex. Additionally we show a test of hydrostatic equilibrium, in a stellar environment which is dominated by radiative effects. In this setting the flexibility of the preconditioning technique is demonstrated. This work aims to bridge the gap between the hydrodynamic test problems typically used during development of numerical methods and the complex flows of stellar interiors. A series of multidimensional tests were performed and analysed. Each of these test cases was analysed with a simple, scalar diagnostic, with the aim of enabling direct code comparisons. As the tests performed do not have analytic solutions, we verify MUSIC by comparing it to established codes including ATHENA and the PENCIL code. MUSIC is able to both reproduce behaviour from established and widely-used codes as well as results expected from theoretical predictions. This benchmarking study concludes a series of papers describing the development of the MUSIC code and provides confidence in future applications.

Informatics in radiology (infoRAD): navigating the fifth dimension: innovative interface for multidimensional multimodality image navigation.

PubMed

Rosset, Antoine; Spadola, Luca; Pysher, Lance; Ratib, Osman

2006-01-01

The display and interpretation of images obtained by combining three-dimensional data acquired with two different modalities (eg, positron emission tomography and computed tomography) in the same subject require complex software tools that allow the user to adjust the image parameters. With the current fast imaging systems, it is possible to acquire dynamic images of the beating heart, which add a fourth dimension of visual information-the temporal dimension. Moreover, images acquired at different points during the transit of a contrast agent or during different functional phases add a fifth dimension-functional data. To facilitate real-time image navigation in the resultant large multidimensional image data sets, the authors developed a Digital Imaging and Communications in Medicine-compliant software program. The open-source software, called OsiriX, allows the user to navigate through multidimensional image series while adjusting the blending of images from different modalities, image contrast and intensity, and the rate of cine display of dynamic images. The software is available for free download at http://homepage.mac.com/rossetantoine/osirix. (c) RSNA, 2006.

Cumulative area of peaks in a multidimensional high performance liquid chromatogram.

PubMed

Stevenson, Paul G; Guiochon, Georges

2013-09-20

An algorithm was developed to recognize peaks in a multidimensional separation and calculate their cumulative peak area. To find the retention times of peaks in a one dimensional chromatogram, the Savitzky-Golay smoothing filter was used to smooth and find the first through third derivatives of the experimental profiles. Close examination of the shape of these curves informs on the number of peaks that are present and provides starting values for fitting theoretical profiles. Due to the nature of comprehensive multidimensional HPLC, adjacent cut fractions may contain compounds common to more than one cut fraction. The algorithm determines which components were common in adjacent cuts and subsequently calculates the area of a two-dimensional peak profile by interpolating the surface of the 2D peaks between adjacent peaks. This algorithm was tested by calculating the cumulative peak area of a series of 2D-HPLC separations of alkylbenzenes, phenol and caffeine with varied concentrations. A good relationship was found between the concentration and the cumulative peak area. Copyright © 2013 Elsevier B.V. All rights reserved.

Nonuniform sampling and non-Fourier signal processing methods in multidimensional NMR.

PubMed

Mobli, Mehdi; Hoch, Jeffrey C

2014-11-01

Beginning with the introduction of Fourier Transform NMR by Ernst and Anderson in 1966, time domain measurement of the impulse response (the free induction decay, FID) consisted of sampling the signal at a series of discrete intervals. For compatibility with the discrete Fourier transform (DFT), the intervals are kept uniform, and the Nyquist theorem dictates the largest value of the interval sufficient to avoid aliasing. With the proposal by Jeener of parametric sampling along an indirect time dimension, extension to multidimensional experiments employed the same sampling techniques used in one dimension, similarly subject to the Nyquist condition and suitable for processing via the discrete Fourier transform. The challenges of obtaining high-resolution spectral estimates from short data records using the DFT were already well understood, however. Despite techniques such as linear prediction extrapolation, the achievable resolution in the indirect dimensions is limited by practical constraints on measuring time. The advent of non-Fourier methods of spectrum analysis capable of processing nonuniformly sampled data has led to an explosion in the development of novel sampling strategies that avoid the limits on resolution and measurement time imposed by uniform sampling. The first part of this review discusses the many approaches to data sampling in multidimensional NMR, the second part highlights commonly used methods for signal processing of such data, and the review concludes with a discussion of other approaches to speeding up data acquisition in NMR. Copyright © 2014 Elsevier B.V. All rights reserved.

Historical Development of Learning Style Inventories from Dichotomous Cognitive Concepts of Field Dependence and Field Independence to Multi-Dimensional Assessment.

ERIC Educational Resources Information Center

Tamaoka, Katsuo

The historical development of learning style inventories is examined from the dichotomous concepts of cognitive styles to multidimensional assessment. Based on a series of experiments on vertical perception, H. A. Witkin formed the concepts of field-dependent and field-independent cognitive styles. Using the term "learning styles"…

Modification and Validation of the Multidimensional Scale of Perceived Social Support for Chinese School Teachers

ERIC Educational Resources Information Center

Ho, Sammy K.; Chan, Edmund S.

2017-01-01

This study aims to investigate the psychometric properties of the revised multidimensional scale of perceived social support (R-MSPSS) for Chinese school teachers. A questionnaire comprising the R-MSPSS and other psychological measures was administered to a sample of 539 school teachers in Hong Kong. A series of confirmatory factor analysis was…

Coronal Mass Ejection Data Clustering and Visualization of Decision Trees

NASA Astrophysics Data System (ADS)

Ma, Ruizhe; Angryk, Rafal A.; Riley, Pete; Filali Boubrahimi, Soukaina

2018-05-01

Coronal mass ejections (CMEs) can be categorized as either “magnetic clouds” (MCs) or non-MCs. Features such as a large magnetic field, low plasma-beta, and low proton temperature suggest that a CME event is also an MC event; however, so far there is neither a definitive method nor an automatic process to distinguish the two. Human labeling is time-consuming, and results can fluctuate owing to the imprecise definition of such events. In this study, we approach the problem of MC and non-MC distinction from a time series data analysis perspective and show how clustering can shed some light on this problem. Although many algorithms exist for traditional data clustering in the Euclidean space, they are not well suited for time series data. Problems such as inadequate distance measure, inaccurate cluster center description, and lack of intuitive cluster representations need to be addressed for effective time series clustering. Our data analysis in this work is twofold: clustering and visualization. For clustering we compared the results from the popular hierarchical agglomerative clustering technique to a distance density clustering heuristic we developed previously for time series data clustering. In both cases, dynamic time warping will be used for similarity measure. For classification as well as visualization, we use decision trees to aggregate single-dimensional clustering results to form a multidimensional time series decision tree, with averaged time series to present each decision. In this study, we achieved modest accuracy and, more importantly, an intuitive interpretation of how different parameters contribute to an MC event.

A New Time-varying Concept of Risk in a Changing Climate.

PubMed

Sarhadi, Ali; Ausín, María Concepción; Wiper, Michael P

2016-10-20

In a changing climate arising from anthropogenic global warming, the nature of extreme climatic events is changing over time. Existing analytical stationary-based risk methods, however, assume multi-dimensional extreme climate phenomena will not significantly vary over time. To strengthen the reliability of infrastructure designs and the management of water systems in the changing environment, multidimensional stationary risk studies should be replaced with a new adaptive perspective. The results of a comparison indicate that current multi-dimensional stationary risk frameworks are no longer applicable to projecting the changing behaviour of multi-dimensional extreme climate processes. Using static stationary-based multivariate risk methods may lead to undesirable consequences in designing water system infrastructures. The static stationary concept should be replaced with a flexible multi-dimensional time-varying risk framework. The present study introduces a new multi-dimensional time-varying risk concept to be incorporated in updating infrastructure design strategies under changing environments arising from human-induced climate change. The proposed generalized time-varying risk concept can be applied for all stochastic multi-dimensional systems that are under the influence of changing environments.

Emotion Recognition from EEG Signals Using Multidimensional Information in EMD Domain.

PubMed

Zhuang, Ning; Zeng, Ying; Tong, Li; Zhang, Chi; Zhang, Hanming; Yan, Bin

2017-01-01

This paper introduces a method for feature extraction and emotion recognition based on empirical mode decomposition (EMD). By using EMD, EEG signals are decomposed into Intrinsic Mode Functions (IMFs) automatically. Multidimensional information of IMF is utilized as features, the first difference of time series, the first difference of phase, and the normalized energy. The performance of the proposed method is verified on a publicly available emotional database. The results show that the three features are effective for emotion recognition. The role of each IMF is inquired and we find that high frequency component IMF1 has significant effect on different emotional states detection. The informative electrodes based on EMD strategy are analyzed. In addition, the classification accuracy of the proposed method is compared with several classical techniques, including fractal dimension (FD), sample entropy, differential entropy, and discrete wavelet transform (DWT). Experiment results on DEAP datasets demonstrate that our method can improve emotion recognition performance.

Cluster Analysis and Gaussian Mixture Estimation of Correlated Time-Series by Means of Multi-dimensional Scaling

NASA Astrophysics Data System (ADS)

Ibuki, Takero; Suzuki, Sei; Inoue, Jun-ichi

We investigate cross-correlations between typical Japanese stocks collected through Yahoo!Japan website ( http://finance.yahoo.co.jp/ ). By making use of multi-dimensional scaling (MDS) for the cross-correlation matrices, we draw two-dimensional scattered plots in which each point corresponds to each stock. To make a clustering for these data plots, we utilize the mixture of Gaussians to fit the data set to several Gaussian densities. By minimizing the so-called Akaike Information Criterion (AIC) with respect to parameters in the mixture, we attempt to specify the best possible mixture of Gaussians. It might be naturally assumed that all the two-dimensional data points of stocks shrink into a single small region when some economic crisis takes place. The justification of this assumption is numerically checked for the empirical Japanese stock data, for instance, those around 11 March 2011.

Reconstructing latent dynamical noise for better forecasting observables

NASA Astrophysics Data System (ADS)

Hirata, Yoshito

2018-03-01

I propose a method for reconstructing multi-dimensional dynamical noise inspired by the embedding theorem of Muldoon et al. [Dyn. Stab. Syst. 13, 175 (1998)] by regarding multiple predictions as different observables. Then, applying the embedding theorem by Stark et al. [J. Nonlinear Sci. 13, 519 (2003)] for a forced system, I produce time series forecast by supplying the reconstructed past dynamical noise as auxiliary information. I demonstrate the proposed method on toy models driven by auto-regressive models or independent Gaussian noise.

Aggregate Measures of Watershed Health from Reconstructed ...

EPA Pesticide Factsheets

Risk-based indices such as reliability, resilience, and vulnerability (R-R-V), have the potential to serve as watershed health assessment tools. Recent research has demonstrated the applicability of such indices for water quality (WQ) constituents such as total suspended solids and nutrients on an individual basis. However, the calculations can become tedious when time-series data for several WQ constituents have to be evaluated individually. Also, comparisons between locations with different sets of constituent data can prove difficult. In this study, data reconstruction using relevance vector machine algorithm was combined with dimensionality reduction via variational Bayesian noisy principal component analysis to reconstruct and condense sparse multidimensional WQ data sets into a single time series. The methodology allows incorporation of uncertainty in both the reconstruction and dimensionality-reduction steps. The R-R-V values were calculated using the aggregate time series at multiple locations within two Indiana watersheds. Results showed that uncertainty present in the reconstructed WQ data set propagates to the aggregate time series and subsequently to the aggregate R-R-V values as well. serving as motivating examples. Locations with different WQ constituents and different standards for impairment were successfully combined to provide aggregate measures of R-R-V values. Comparisons with individual constituent R-R-V values showed that v

Statistical Downscaling in Multi-dimensional Wave Climate Forecast

NASA Astrophysics Data System (ADS)

Camus, P.; Méndez, F. J.; Medina, R.; Losada, I. J.; Cofiño, A. S.; Gutiérrez, J. M.

2009-04-01

Wave climate at a particular site is defined by the statistical distribution of sea state parameters, such as significant wave height, mean wave period, mean wave direction, wind velocity, wind direction and storm surge. Nowadays, long-term time series of these parameters are available from reanalysis databases obtained by numerical models. The Self-Organizing Map (SOM) technique is applied to characterize multi-dimensional wave climate, obtaining the relevant "wave types" spanning the historical variability. This technique summarizes multi-dimension of wave climate in terms of a set of clusters projected in low-dimensional lattice with a spatial organization, providing Probability Density Functions (PDFs) on the lattice. On the other hand, wind and storm surge depend on instantaneous local large-scale sea level pressure (SLP) fields while waves depend on the recent history of these fields (say, 1 to 5 days). Thus, these variables are associated with large-scale atmospheric circulation patterns. In this work, a nearest-neighbors analog method is used to predict monthly multi-dimensional wave climate. This method establishes relationships between the large-scale atmospheric circulation patterns from numerical models (SLP fields as predictors) with local wave databases of observations (monthly wave climate SOM PDFs as predictand) to set up statistical models. A wave reanalysis database, developed by Puertos del Estado (Ministerio de Fomento), is considered as historical time series of local variables. The simultaneous SLP fields calculated by NCEP atmospheric reanalysis are used as predictors. Several applications with different size of sea level pressure grid and with different temporal domain resolution are compared to obtain the optimal statistical model that better represents the monthly wave climate at a particular site. In this work we examine the potential skill of this downscaling approach considering perfect-model conditions, but we will also analyze the suitability of this methodology to be used for seasonal forecast and for long-term climate change scenario projection of wave climate.

rasdaman Array Database: current status

NASA Astrophysics Data System (ADS)

Merticariu, George; Toader, Alexandru

2015-04-01

rasdaman (Raster Data Manager) is a Free Open Source Array Database Management System which provides functionality for storing and processing massive amounts of raster data in the form of multidimensional arrays. The user can access, process and delete the data using SQL. The key features of rasdaman are: flexibility (datasets of any dimensionality can be processed with the help of SQL queries), scalability (rasdaman's distributed architecture enables it to seamlessly run on cloud infrastructures while offering an increase in performance with the increase of computation resources), performance (real-time access, processing, mixing and filtering of arrays of any dimensionality) and reliability (legacy communication protocol replaced with a new one based on cutting edge technology - Google Protocol Buffers and ZeroMQ). Among the data with which the system works, we can count 1D time series, 2D remote sensing imagery, 3D image time series, 3D geophysical data, and 4D atmospheric and climate data. Most of these representations cannot be stored only in the form of raw arrays, as the location information of the contents is also important for having a correct geoposition on Earth. This is defined by ISO 19123 as coverage data. rasdaman provides coverage data support through the Petascope service. Extensions were added on top of rasdaman in order to provide support for the Geoscience community. The following OGC standards are currently supported: Web Map Service (WMS), Web Coverage Service (WCS), and Web Coverage Processing Service (WCPS). The Web Map Service is an extension which provides zoom and pan navigation over images provided by a map server. Starting with version 9.1, rasdaman supports WMS version 1.3. The Web Coverage Service provides capabilities for downloading multi-dimensional coverage data. Support is also provided for several extensions of this service: Subsetting Extension, Scaling Extension, and, starting with version 9.1, Transaction Extension, which defines request types for inserting, updating and deleting coverages. A web client, designed for both novice and experienced users, is also available for the service and its extensions. The client offers an intuitive interface that allows users to work with multi-dimensional coverages by abstracting the specifics of the standard definitions of the requests. The Web Coverage Processing Service defines a language for on-the-fly processing and filtering multi-dimensional raster coverages. rasdaman exposes this service through the WCS processing extension. Demonstrations are provided online via the Earthlook website (earthlook.org) which presents use-cases from a wide variety of application domains, using the rasdaman system as processing engine.

Hierarchical Aligned Cluster Analysis for Temporal Clustering of Human Motion.

PubMed

Zhou, Feng; De la Torre, Fernando; Hodgins, Jessica K

2013-03-01

Temporal segmentation of human motion into plausible motion primitives is central to understanding and building computational models of human motion. Several issues contribute to the challenge of discovering motion primitives: the exponential nature of all possible movement combinations, the variability in the temporal scale of human actions, and the complexity of representing articulated motion. We pose the problem of learning motion primitives as one of temporal clustering, and derive an unsupervised hierarchical bottom-up framework called hierarchical aligned cluster analysis (HACA). HACA finds a partition of a given multidimensional time series into m disjoint segments such that each segment belongs to one of k clusters. HACA combines kernel k-means with the generalized dynamic time alignment kernel to cluster time series data. Moreover, it provides a natural framework to find a low-dimensional embedding for time series. HACA is efficiently optimized with a coordinate descent strategy and dynamic programming. Experimental results on motion capture and video data demonstrate the effectiveness of HACA for segmenting complex motions and as a visualization tool. We also compare the performance of HACA to state-of-the-art algorithms for temporal clustering on data of a honey bee dance. The HACA code is available online.

Density-based clustering: A 'landscape view' of multi-channel neural data for inference and dynamic complexity analysis.

PubMed

Baglietto, Gabriel; Gigante, Guido; Del Giudice, Paolo

2017-01-01

Two, partially interwoven, hot topics in the analysis and statistical modeling of neural data, are the development of efficient and informative representations of the time series derived from multiple neural recordings, and the extraction of information about the connectivity structure of the underlying neural network from the recorded neural activities. In the present paper we show that state-space clustering can provide an easy and effective option for reducing the dimensionality of multiple neural time series, that it can improve inference of synaptic couplings from neural activities, and that it can also allow the construction of a compact representation of the multi-dimensional dynamics, that easily lends itself to complexity measures. We apply a variant of the 'mean-shift' algorithm to perform state-space clustering, and validate it on an Hopfield network in the glassy phase, in which metastable states are largely uncorrelated from memories embedded in the synaptic matrix. In this context, we show that the neural states identified as clusters' centroids offer a parsimonious parametrization of the synaptic matrix, which allows a significant improvement in inferring the synaptic couplings from the neural activities. Moving to the more realistic case of a multi-modular spiking network, with spike-frequency adaptation inducing history-dependent effects, we propose a procedure inspired by Boltzmann learning, but extending its domain of application, to learn inter-module synaptic couplings so that the spiking network reproduces a prescribed pattern of spatial correlations; we then illustrate, in the spiking network, how clustering is effective in extracting relevant features of the network's state-space landscape. Finally, we show that the knowledge of the cluster structure allows casting the multi-dimensional neural dynamics in the form of a symbolic dynamics of transitions between clusters; as an illustration of the potential of such reduction, we define and analyze a measure of complexity of the neural time series.

Spectral Eclipse Timing

NASA Astrophysics Data System (ADS)

Dobbs-Dixon, Ian; Agol, Eric; Deming, Drake

2015-12-01

We utilize multi-dimensional simulations of varying equatorial jet strength to predict wavelength-dependent variations in the eclipse times of gas-giant planets. A displaced hot spot introduces an asymmetry in the secondary eclipse light curve that manifests itself as a measured offset in the timing of the center of eclipse. A multi-wavelength observation of secondary eclipse, one probing the timing of barycentric eclipse at short wavelengths and another probing at longer wavelengths, will reveal the longitudinal displacement of the hot spot and break the degeneracy between this effect and that associated with the asymmetry due to an eccentric orbit. The effect of time offsets was first explored in the IRAC wavebands by Williams et al. Here we improve upon their methodology, extend to a broad range of wavelengths, and demonstrate our technique on a series of multi-dimensional radiative-hydrodynamical simulations of HD 209458b with varying equatorial jet strength and hot-spot displacement. Simulations with the largest hot-spot displacement result in timing offsets of up to 100 s in the infrared. Though we utilize a particular radiative hydrodynamical model to demonstrate this effect, the technique is model independent. This technique should allow a much larger survey of hot-spot displacements with the James Webb Space Telescope than currently accessible with time-intensive phase curves, hopefully shedding light on the physical mechanisms associated with thermal energy advection in irradiated gas giants.

Localized motion in random matrix decomposition of complex financial systems

NASA Astrophysics Data System (ADS)

Jiang, Xiong-Fei; Zheng, Bo; Ren, Fei; Qiu, Tian

2017-04-01

With the random matrix theory, we decompose the multi-dimensional time series of complex financial systems into a set of orthogonal eigenmode functions, which are classified into the market mode, sector mode, and random mode. In particular, the localized motion generated by the business sectors, plays an important role in financial systems. Both the business sectors and their impact on the stock market are identified from the localized motion. We clarify that the localized motion induces different characteristics of the time correlations for the stock-market index and individual stocks. With a variation of a two-factor model, we reproduce the return-volatility correlations of the eigenmodes.

Enabling Web-Based Analysis of CUAHSI HIS Hydrologic Data Using R and Web Processing Services

NASA Astrophysics Data System (ADS)

Ames, D. P.; Kadlec, J.; Bayles, M.; Seul, M.; Hooper, R. P.; Cummings, B.

2015-12-01

The CUAHSI Hydrologic Information System (CUAHSI HIS) provides open access to a large number of hydrological time series observation and modeled data from many parts of the world. Several software tools have been designed to simplify searching and access to the CUAHSI HIS datasets. These software tools include: Desktop client software (HydroDesktop, HydroExcel), developer libraries (WaterML R Package, OWSLib, ulmo), and the new interactive search website, http://data.cuahsi.org. An issue with using the time series data from CUAHSI HIS for further analysis by hydrologists (for example for verification of hydrological and snowpack models) is the large heterogeneity of the time series data. The time series may be regular or irregular, contain missing data, have different time support, and be recorded in different units. R is a widely used computational environment for statistical analysis of time series and spatio-temporal data that can be used to assess fitness and perform scientific analyses on observation data. R includes the ability to record a data analysis in the form of a reusable script. The R script together with the input time series dataset can be shared with other users, making the analysis more reproducible. The major goal of this study is to examine the use of R as a Web Processing Service for transforming time series data from the CUAHSI HIS and sharing the results on the Internet within HydroShare. HydroShare is an online data repository and social network for sharing large hydrological data sets such as time series, raster datasets, and multi-dimensional data. It can be used as a permanent cloud storage space for saving the time series analysis results. We examine the issues associated with running R scripts online: including code validation, saving of outputs, reporting progress, and provenance management. An explicit goal is that the script which is run locally should produce exactly the same results as the script run on the Internet. Our design can be used as a model for other studies that need to run R scripts on the web.

Manipulating Environmental Time Series in Python/Numpy: the Scikits.Timeseries Package and its Applications.

NASA Astrophysics Data System (ADS)

Gerard-Marchant, P. G.

2008-12-01

Numpy is a free, open source C/Python interface designed for the fast and convenient manipulation of multidimensional numerical arrays. The base object, ndarray, can also be easily be extended to define new objects meeting specific needs. Thanks to its simplicity, efficiency and modularity, numpy and its companion library Scipy have become increasingly popular in the scientific community over the last few years, with application ranging from astronomy and engineering to finances and statistics. Its capacity to handle missing values is particularly appealing when analyzing environmental time series, where irregular data sampling might be an issue. After reviewing the main characteristics of numpy objects and the mechanism of subclassing, we will present the scikits.timeseries package, developed to manipulate single- and multi-variable arrays indexed in time. We will illustrate some typical applications of this package by introducing climpy, a set of extensions designed to help analyzing the impacts of climate variability on environmental data such as precipitations or streamflows.

Rapid determination of thermodynamic parameters from one-dimensional programmed-temperature gas chromatography for use in retention time prediction in comprehensive multidimensional chromatography.

PubMed

McGinitie, Teague M; Ebrahimi-Najafabadi, Heshmatollah; Harynuk, James J

2014-01-17

A new method for estimating the thermodynamic parameters of ΔH(T0), ΔS(T0), and ΔCP for use in thermodynamic modeling of GC×GC separations has been developed. The method is an alternative to the traditional isothermal separations required to fit a three-parameter thermodynamic model to retention data. Herein, a non-linear optimization technique is used to estimate the parameters from a series of temperature-programmed separations using the Nelder-Mead simplex algorithm. With this method, the time required to obtain estimates of thermodynamic parameters a series of analytes is significantly reduced. This new method allows for precise predictions of retention time with the average error being only 0.2s for 1D separations. Predictions for GC×GC separations were also in agreement with experimental measurements; having an average relative error of 0.37% for (1)tr and 2.1% for (2)tr. Copyright © 2013 Elsevier B.V. All rights reserved.

Efficient multidimensional regularization for Volterra series estimation

NASA Astrophysics Data System (ADS)

Birpoutsoukis, Georgios; Csurcsia, Péter Zoltán; Schoukens, Johan

2018-05-01

This paper presents an efficient nonparametric time domain nonlinear system identification method. It is shown how truncated Volterra series models can be efficiently estimated without the need of long, transient-free measurements. The method is a novel extension of the regularization methods that have been developed for impulse response estimates of linear time invariant systems. To avoid the excessive memory needs in case of long measurements or large number of estimated parameters, a practical gradient-based estimation method is also provided, leading to the same numerical results as the proposed Volterra estimation method. Moreover, the transient effects in the simulated output are removed by a special regularization method based on the novel ideas of transient removal for Linear Time-Varying (LTV) systems. Combining the proposed methodologies, the nonparametric Volterra models of the cascaded water tanks benchmark are presented in this paper. The results for different scenarios varying from a simple Finite Impulse Response (FIR) model to a 3rd degree Volterra series with and without transient removal are compared and studied. It is clear that the obtained models capture the system dynamics when tested on a validation dataset, and their performance is comparable with the white-box (physical) models.

Algorithm for Compressing Time-Series Data

NASA Technical Reports Server (NTRS)

Hawkins, S. Edward, III; Darlington, Edward Hugo

2012-01-01

An algorithm based on Chebyshev polynomials effects lossy compression of time-series data or other one-dimensional data streams (e.g., spectral data) that are arranged in blocks for sequential transmission. The algorithm was developed for use in transmitting data from spacecraft scientific instruments to Earth stations. In spite of its lossy nature, the algorithm preserves the information needed for scientific analysis. The algorithm is computationally simple, yet compresses data streams by factors much greater than two. The algorithm is not restricted to spacecraft or scientific uses: it is applicable to time-series data in general. The algorithm can also be applied to general multidimensional data that have been converted to time-series data, a typical example being image data acquired by raster scanning. However, unlike most prior image-data-compression algorithms, this algorithm neither depends on nor exploits the two-dimensional spatial correlations that are generally present in images. In order to understand the essence of this compression algorithm, it is necessary to understand that the net effect of this algorithm and the associated decompression algorithm is to approximate the original stream of data as a sequence of finite series of Chebyshev polynomials. For the purpose of this algorithm, a block of data or interval of time for which a Chebyshev polynomial series is fitted to the original data is denoted a fitting interval. Chebyshev approximation has two properties that make it particularly effective for compressing serial data streams with minimal loss of scientific information: The errors associated with a Chebyshev approximation are nearly uniformly distributed over the fitting interval (this is known in the art as the "equal error property"); and the maximum deviations of the fitted Chebyshev polynomial from the original data have the smallest possible values (this is known in the art as the "min-max property").

Risk patterns and correlated brain activities. Multidimensional statistical analysis of FMRI data in economic decision making study.

PubMed

van Bömmel, Alena; Song, Song; Majer, Piotr; Mohr, Peter N C; Heekeren, Hauke R; Härdle, Wolfgang K

2014-07-01

Decision making usually involves uncertainty and risk. Understanding which parts of the human brain are activated during decisions under risk and which neural processes underly (risky) investment decisions are important goals in neuroeconomics. Here, we analyze functional magnetic resonance imaging (fMRI) data on 17 subjects who were exposed to an investment decision task from Mohr, Biele, Krugel, Li, and Heekeren (in NeuroImage 49, 2556-2563, 2010b). We obtain a time series of three-dimensional images of the blood-oxygen-level dependent (BOLD) fMRI signals. We apply a panel version of the dynamic semiparametric factor model (DSFM) presented in Park, Mammen, Wolfgang, and Borak (in Journal of the American Statistical Association 104(485), 284-298, 2009) and identify task-related activations in space and dynamics in time. With the panel DSFM (PDSFM) we can capture the dynamic behavior of the specific brain regions common for all subjects and represent the high-dimensional time-series data in easily interpretable low-dimensional dynamic factors without large loss of variability. Further, we classify the risk attitudes of all subjects based on the estimated low-dimensional time series. Our classification analysis successfully confirms the estimated risk attitudes derived directly from subjects' decision behavior.

Fast multidimensional ensemble empirical mode decomposition for the analysis of big spatio-temporal datasets.

PubMed

Wu, Zhaohua; Feng, Jiaxin; Qiao, Fangli; Tan, Zhe-Min

2016-04-13

In this big data era, it is more urgent than ever to solve two major issues: (i) fast data transmission methods that can facilitate access to data from non-local sources and (ii) fast and efficient data analysis methods that can reveal the key information from the available data for particular purposes. Although approaches in different fields to address these two questions may differ significantly, the common part must involve data compression techniques and a fast algorithm. This paper introduces the recently developed adaptive and spatio-temporally local analysis method, namely the fast multidimensional ensemble empirical mode decomposition (MEEMD), for the analysis of a large spatio-temporal dataset. The original MEEMD uses ensemble empirical mode decomposition to decompose time series at each spatial grid and then pieces together the temporal-spatial evolution of climate variability and change on naturally separated timescales, which is computationally expensive. By taking advantage of the high efficiency of the expression using principal component analysis/empirical orthogonal function analysis for spatio-temporally coherent data, we design a lossy compression method for climate data to facilitate its non-local transmission. We also explain the basic principles behind the fast MEEMD through decomposing principal components instead of original grid-wise time series to speed up computation of MEEMD. Using a typical climate dataset as an example, we demonstrate that our newly designed methods can (i) compress data with a compression rate of one to two orders; and (ii) speed-up the MEEMD algorithm by one to two orders. © 2016 The Authors.

Approximation Methods in Multidimensional Filter Design and Related Problems Encountered in Multidimensional System Design.

DTIC Science & Technology

1983-03-21

zero , it is necessary that B M(0) be nonzero. In the case considered here, B M(0) is taken to be nonsingula and withot loss of generality it may be set...452. (c.51 D. Levin, " General order Padd type rational approximants defined from a double power series," J. Inst. Maths. Applics., 18, 1976, pp. 1-8...common zeros in the closed unit bidisc, U- 2 . The 2-D setting provides a nice theoretical framework for generalization of these stabilization results to

A longitudinal model of the dynamics between HMOs' consumer-friendliness and preventive health care utilization.

PubMed

Xiao, Qian; Savage, Grant T; Zhuang, Weiling

2014-01-01

This study aims at replicating and extending Xiao and Savage's (2008) research to understand the multidimensional aspect of HMOs distinguished by HMOs' consumer-friendliness, and their relationship to consumers' preventive care utilization. This study develops a dynamic model to consider both concurrent and time lagging effects of HMOs' consumer-friendliness. Our data analysis discloses similar relationship patterns as revealed by Xiao and Savage. Additionally, our findings reveal the time-series changes of the influence of HMOs' consumer-friendliness that either the effects of early experienced HMOs' consumer-friendliness wear out totally or HMOs' consumer-friendly characteristics on the concurrent term contain most of the explanatory power.

Automatic construction of a recurrent neural network based classifier for vehicle passage detection

NASA Astrophysics Data System (ADS)

Burnaev, Evgeny; Koptelov, Ivan; Novikov, German; Khanipov, Timur

2017-03-01

Recurrent Neural Networks (RNNs) are extensively used for time-series modeling and prediction. We propose an approach for automatic construction of a binary classifier based on Long Short-Term Memory RNNs (LSTM-RNNs) for detection of a vehicle passage through a checkpoint. As an input to the classifier we use multidimensional signals of various sensors that are installed on the checkpoint. Obtained results demonstrate that the previous approach to handcrafting a classifier, consisting of a set of deterministic rules, can be successfully replaced by an automatic RNN training on an appropriately labelled data.

Discovering significant evolution patterns from satellite image time series.

PubMed

Petitjean, François; Masseglia, Florent; Gançarski, Pierre; Forestier, Germain

2011-12-01

Satellite Image Time Series (SITS) provide us with precious information on land cover evolution. By studying these series of images we can both understand the changes of specific areas and discover global phenomena that spread over larger areas. Changes that can occur throughout the sensing time can spread over very long periods and may have different start time and end time depending on the location, which complicates the mining and the analysis of series of images. This work focuses on frequent sequential pattern mining (FSPM) methods, since this family of methods fits the above-mentioned issues. This family of methods consists of finding the most frequent evolution behaviors, and is actually able to extract long-term changes as well as short term ones, whenever the change may start and end. However, applying FSPM methods to SITS implies confronting two main challenges, related to the characteristics of SITS and the domain's constraints. First, satellite images associate multiple measures with a single pixel (the radiometric levels of different wavelengths corresponding to infra-red, red, etc.), which makes the search space multi-dimensional and thus requires specific mining algorithms. Furthermore, the non evolving regions, which are the vast majority and overwhelm the evolving ones, challenge the discovery of these patterns. We propose a SITS mining framework that enables discovery of these patterns despite these constraints and characteristics. Our proposal is inspired from FSPM and provides a relevant visualization principle. Experiments carried out on 35 images sensed over 20 years show the proposed approach makes it possible to extract relevant evolution behaviors.

Empirical Identification of Hierarchies.

ERIC Educational Resources Information Center

McCormick, Douglas; And Others

Outlining a cluster procedure which maximizes specific criteria while building scales from binary measures using a sequential, agglomerative, overlapping, non-hierarchic method results in indices giving truer results than exploratory facotr analyses or multidimensional scaling. In a series of eleven figures, patterns within cluster histories…

Scientific Visualization and Simulation for Multi-dimensional Marine Environment Data

NASA Astrophysics Data System (ADS)

Su, T.; Liu, H.; Wang, W.; Song, Z.; Jia, Z.

2017-12-01

As higher attention on the ocean and rapid development of marine detection, there are increasingly demands for realistic simulation and interactive visualization of marine environment in real time. Based on advanced technology such as GPU rendering, CUDA parallel computing and rapid grid oriented strategy, a series of efficient and high-quality visualization methods, which can deal with large-scale and multi-dimensional marine data in different environmental circumstances, has been proposed in this paper. Firstly, a high-quality seawater simulation is realized by FFT algorithm, bump mapping and texture animation technology. Secondly, large-scale multi-dimensional marine hydrological environmental data is virtualized by 3d interactive technologies and volume rendering techniques. Thirdly, seabed terrain data is simulated with improved Delaunay algorithm, surface reconstruction algorithm, dynamic LOD algorithm and GPU programming techniques. Fourthly, seamless modelling in real time for both ocean and land based on digital globe is achieved by the WebGL technique to meet the requirement of web-based application. The experiments suggest that these methods can not only have a satisfying marine environment simulation effect, but also meet the rendering requirements of global multi-dimension marine data. Additionally, a simulation system for underwater oil spill is established by OSG 3D-rendering engine. It is integrated with the marine visualization method mentioned above, which shows movement processes, physical parameters, current velocity and direction for different types of deep water oil spill particle (oil spill particles, hydrates particles, gas particles, etc.) dynamically and simultaneously in multi-dimension. With such application, valuable reference and decision-making information can be provided for understanding the progress of oil spill in deep water, which is helpful for ocean disaster forecasting, warning and emergency response.

Analysis of microvascular perfusion with multi-dimensional complete ensemble empirical mode decomposition with adaptive noise algorithm: Processing of laser speckle contrast images recorded in healthy subjects, at rest and during acetylcholine stimulation.

PubMed

Humeau-Heurtier, Anne; Marche, Pauline; Dubois, Severine; Mahe, Guillaume

2015-01-01

Laser speckle contrast imaging (LSCI) is a full-field imaging modality to monitor microvascular blood flow. It is able to give images with high temporal and spatial resolutions. However, when the skin is studied, the interpretation of the bidimensional data may be difficult. This is why an averaging of the perfusion values in regions of interest is often performed and the result is followed in time, reducing the data to monodimensional time series. In order to avoid such a procedure (that leads to a loss of the spatial resolution), we propose to extract patterns from LSCI data and to compare these patterns for two physiological states in healthy subjects: at rest and at the peak of acetylcholine-induced perfusion peak. For this purpose, the recent multi-dimensional complete ensemble empirical mode decomposition with adaptive noise (MCEEMDAN) algorithm is applied to LSCI data. The results show that the intrinsic mode functions and residue given by MCEEMDAN show different patterns for the two physiological states. The images, as bidimensional data, can therefore be processed to reveal microvascular perfusion patterns, hidden in the images themselves. This work is therefore a feasibility study before analyzing data in patients with microvascular dysfunctions.

Detection of bifurcations in noisy coupled systems from multiple time series

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

Williamson, Mark S., E-mail: m.s.williamson@exeter.ac.uk; Lenton, Timothy M.

We generalize a method of detecting an approaching bifurcation in a time series of a noisy system from the special case of one dynamical variable to multiple dynamical variables. For a system described by a stochastic differential equation consisting of an autonomous deterministic part with one dynamical variable and an additive white noise term, small perturbations away from the system's fixed point will decay slower the closer the system is to a bifurcation. This phenomenon is known as critical slowing down and all such systems exhibit this decay-type behaviour. However, when the deterministic part has multiple coupled dynamical variables, themore » possible dynamics can be much richer, exhibiting oscillatory and chaotic behaviour. In our generalization to the multi-variable case, we find additional indicators to decay rate, such as frequency of oscillation. In the case of approaching a homoclinic bifurcation, there is no change in decay rate but there is a decrease in frequency of oscillations. The expanded method therefore adds extra tools to help detect and classify approaching bifurcations given multiple time series, where the underlying dynamics are not fully known. Our generalisation also allows bifurcation detection to be applied spatially if one treats each spatial location as a new dynamical variable. One may then determine the unstable spatial mode(s). This is also something that has not been possible with the single variable method. The method is applicable to any set of time series regardless of its origin, but may be particularly useful when anticipating abrupt changes in the multi-dimensional climate system.« less

Detection of bifurcations in noisy coupled systems from multiple time series

NASA Astrophysics Data System (ADS)

Williamson, Mark S.; Lenton, Timothy M.

2015-03-01

We generalize a method of detecting an approaching bifurcation in a time series of a noisy system from the special case of one dynamical variable to multiple dynamical variables. For a system described by a stochastic differential equation consisting of an autonomous deterministic part with one dynamical variable and an additive white noise term, small perturbations away from the system's fixed point will decay slower the closer the system is to a bifurcation. This phenomenon is known as critical slowing down and all such systems exhibit this decay-type behaviour. However, when the deterministic part has multiple coupled dynamical variables, the possible dynamics can be much richer, exhibiting oscillatory and chaotic behaviour. In our generalization to the multi-variable case, we find additional indicators to decay rate, such as frequency of oscillation. In the case of approaching a homoclinic bifurcation, there is no change in decay rate but there is a decrease in frequency of oscillations. The expanded method therefore adds extra tools to help detect and classify approaching bifurcations given multiple time series, where the underlying dynamics are not fully known. Our generalisation also allows bifurcation detection to be applied spatially if one treats each spatial location as a new dynamical variable. One may then determine the unstable spatial mode(s). This is also something that has not been possible with the single variable method. The method is applicable to any set of time series regardless of its origin, but may be particularly useful when anticipating abrupt changes in the multi-dimensional climate system.

Invited Article: Single-shot THz detection techniques optimized for multidimensional THz spectroscopy

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

Teo, Stephanie M.; Ofori-Okai, Benjamin K.; Werley, Christopher A.

Multidimensional spectroscopy at visible and infrared frequencies has opened a window into the transfer of energy and quantum coherences at ultrafast time scales. For these measurements to be performed in a manageable amount of time, one spectral axis is typically recorded in a single laser shot. An analogous rapid-scanning capability for THz measurements will unlock the multidimensional toolkit in this frequency range. Here, we first review the merits of existing single-shot THz schemes and discuss their potential in multidimensional THz spectroscopy. We then introduce improved experimental designs and noise suppression techniques for the two most promising methods: frequency-to-time encoding withmore » linear spectral interferometry and angle-to-time encoding with dual echelons. Both methods, each using electro-optic detection in the linear regime, were able to reproduce the THz temporal waveform acquired with a traditional scanning delay line. Although spectral interferometry had mediocre performance in terms of signal-to-noise, the dual echelon method was easily implemented and achieved the same level of signal-to-noise as the scanning delay line in only 4.5% of the laser pulses otherwise required (or 22 times faster). This reduction in acquisition time will compress day-long scans to hours and hence provides a practical technique for multidimensional THz measurements.« less

Invited Article: Single-shot THz detection techniques optimized for multidimensional THz spectroscopy.

PubMed

Teo, Stephanie M; Ofori-Okai, Benjamin K; Werley, Christopher A; Nelson, Keith A

2015-05-01

Multidimensional spectroscopy at visible and infrared frequencies has opened a window into the transfer of energy and quantum coherences at ultrafast time scales. For these measurements to be performed in a manageable amount of time, one spectral axis is typically recorded in a single laser shot. An analogous rapid-scanning capability for THz measurements will unlock the multidimensional toolkit in this frequency range. Here, we first review the merits of existing single-shot THz schemes and discuss their potential in multidimensional THz spectroscopy. We then introduce improved experimental designs and noise suppression techniques for the two most promising methods: frequency-to-time encoding with linear spectral interferometry and angle-to-time encoding with dual echelons. Both methods, each using electro-optic detection in the linear regime, were able to reproduce the THz temporal waveform acquired with a traditional scanning delay line. Although spectral interferometry had mediocre performance in terms of signal-to-noise, the dual echelon method was easily implemented and achieved the same level of signal-to-noise as the scanning delay line in only 4.5% of the laser pulses otherwise required (or 22 times faster). This reduction in acquisition time will compress day-long scans to hours and hence provides a practical technique for multidimensional THz measurements.

Method for tracking the location of mobile agents using stand-off detection technique

DOEpatents

Schmitt, Randal L [Tijeras, NM; Bender, Susan Fae Ann [Tijeras, NM; Rodacy, Philip J [Albuquerque, NM; Hargis, Jr., Philip J.; Johnson, Mark S [Albuquerque, NM

2006-12-26

A method for tracking the movement and position of mobile agents using light detection and ranging (LIDAR) as a stand-off optical detection technique. The positions of the agents are tracked by analyzing the time-history of a series of optical measurements made over the field of view of the optical system. This provides a (time+3-D) or (time+2-D) mapping of the location of the mobile agents. Repeated pulses of a laser beam impinge on a mobile agent, such as a bee, and are backscattered from the agent into a LIDAR detection system. Alternatively, the incident laser pulses excite fluorescence or phosphorescence from the agent, which is detected using a LIDAR system. Analysis of the spatial location of signals from the agents produced by repeated pulses generates a multidimensional map of agent location.

Extracting Leading Nonlinear Modes of Changing Climate From Global SST Time Series

NASA Astrophysics Data System (ADS)

Mukhin, D.; Gavrilov, A.; Loskutov, E. M.; Feigin, A. M.; Kurths, J.

2017-12-01

Data-driven modeling of climate requires adequate principal variables extracted from observed high-dimensional data. For constructing such variables it is needed to find spatial-temporal patterns explaining a substantial part of the variability and comprising all dynamically related time series from the data. The difficulties of this task rise from the nonlinearity and non-stationarity of the climate dynamical system. The nonlinearity leads to insufficiency of linear methods of data decomposition for separating different processes entangled in the observed time series. On the other hand, various forcings, both anthropogenic and natural, make the dynamics non-stationary, and we should be able to describe the response of the system to such forcings in order to separate the modes explaining the internal variability. The method we present is aimed to overcome both these problems. The method is based on the Nonlinear Dynamical Mode (NDM) decomposition [1,2], but takes into account external forcing signals. An each mode depends on hidden, unknown a priori, time series which, together with external forcing time series, are mapped onto data space. Finding both the hidden signals and the mapping allows us to study the evolution of the modes' structure in changing external conditions and to compare the roles of the internal variability and forcing in the observed behavior. The method is used for extracting of the principal modes of SST variability on inter-annual and multidecadal time scales accounting the external forcings such as CO2, variations of the solar activity and volcanic activity. The structure of the revealed teleconnection patterns as well as their forecast under different CO2 emission scenarios are discussed.[1] Mukhin, D., Gavrilov, A., Feigin, A., Loskutov, E., & Kurths, J. (2015). Principal nonlinear dynamical modes of climate variability. Scientific Reports, 5, 15510. [2] Gavrilov, A., Mukhin, D., Loskutov, E., Volodin, E., Feigin, A., & Kurths, J. (2016). Method for reconstructing nonlinear modes with adaptive structure from multidimensional data. Chaos: An Interdisciplinary Journal of Nonlinear Science, 26(12), 123101.

Treating Comorbid Anxiety in Adolescents With ADHD Using a Cognitive Behavior Therapy Program Approach.

PubMed

Houghton, Stephen; Alsalmi, Nadiyah; Tan, Carol; Taylor, Myra; Durkin, Kevin

2017-11-01

To evaluate an 8-week cognitive behavior therapy (CBT) treatment specifically designed for adolescents with ADHD and comorbid anxiety. Using a multiple baseline design, nine adolescents (13 years to 16 years 9 months) received a weekly CBT, which focused on four identified anxiety-arousing times. Participants self-recorded their levels of anxiety for each of the four times during baseline, intervention, and a maintenance phase. Anxiety was also assessed using the Multidimensional Anxiety Scale for Children (MASC). Paired samples t tests supported the success of the intervention. Interrupted time-series data for each participant revealed varying rates of success across the four times, however. The MASC data revealed significant reductions in Physical Symptoms of Anxiety, Social Anxiety, Separation Anxiety, Harm Avoidance, and Total Anxiety. The data demonstrate the efficacy of a CBT program for the treatment of comorbid anxiety in adolescents with ADHD.

Data-adaptive harmonic spectra and multilayer Stuart-Landau models

NASA Astrophysics Data System (ADS)

Chekroun, Mickaël D.; Kondrashov, Dmitri

2017-09-01

Harmonic decompositions of multivariate time series are considered for which we adopt an integral operator approach with periodic semigroup kernels. Spectral decomposition theorems are derived that cover the important cases of two-time statistics drawn from a mixing invariant measure. The corresponding eigenvalues can be grouped per Fourier frequency and are actually given, at each frequency, as the singular values of a cross-spectral matrix depending on the data. These eigenvalues obey, furthermore, a variational principle that allows us to define naturally a multidimensional power spectrum. The eigenmodes, as far as they are concerned, exhibit a data-adaptive character manifested in their phase which allows us in turn to define a multidimensional phase spectrum. The resulting data-adaptive harmonic (DAH) modes allow for reducing the data-driven modeling effort to elemental models stacked per frequency, only coupled at different frequencies by the same noise realization. In particular, the DAH decomposition extracts time-dependent coefficients stacked by Fourier frequency which can be efficiently modeled—provided the decay of temporal correlations is sufficiently well-resolved—within a class of multilayer stochastic models (MSMs) tailored here on stochastic Stuart-Landau oscillators. Applications to the Lorenz 96 model and to a stochastic heat equation driven by a space-time white noise are considered. In both cases, the DAH decomposition allows for an extraction of spatio-temporal modes revealing key features of the dynamics in the embedded phase space. The multilayer Stuart-Landau models (MSLMs) are shown to successfully model the typical patterns of the corresponding time-evolving fields, as well as their statistics of occurrence.

Computer-Aided Discovery Tools for Volcano Deformation Studies with InSAR and GPS

NASA Astrophysics Data System (ADS)

Pankratius, V.; Pilewskie, J.; Rude, C. M.; Li, J. D.; Gowanlock, M.; Bechor, N.; Herring, T.; Wauthier, C.

2016-12-01

We present a Computer-Aided Discovery approach that facilitates the cloud-scalable fusion of different data sources, such as GPS time series and Interferometric Synthetic Aperture Radar (InSAR), for the purpose of identifying the expansion centers and deformation styles of volcanoes. The tools currently developed at MIT allow the definition of alternatives for data processing pipelines that use various analysis algorithms. The Computer-Aided Discovery system automatically generates algorithmic and parameter variants to help researchers explore multidimensional data processing search spaces efficiently. We present first application examples of this technique using GPS data on volcanoes on the Aleutian Islands and work in progress on combined GPS and InSAR data in Hawaii. In the model search context, we also illustrate work in progress combining time series Principal Component Analysis with InSAR augmentation to constrain the space of possible model explanations on current empirical data sets and achieve a better identification of deformation patterns. This work is supported by NASA AIST-NNX15AG84G and NSF ACI-1442997 (PI: V. Pankratius).

Decomposing Time Series Data by a Non-negative Matrix Factorization Algorithm with Temporally Constrained Coefficients

PubMed Central

Cheung, Vincent C. K.; Devarajan, Karthik; Severini, Giacomo; Turolla, Andrea; Bonato, Paolo

2017-01-01

The non-negative matrix factorization algorithm (NMF) decomposes a data matrix into a set of non-negative basis vectors, each scaled by a coefficient. In its original formulation, the NMF assumes the data samples and dimensions to be independently distributed, making it a less-than-ideal algorithm for the analysis of time series data with temporal correlations. Here, we seek to derive an NMF that accounts for temporal dependencies in the data by explicitly incorporating a very simple temporal constraint for the coefficients into the NMF update rules. We applied the modified algorithm to 2 multi-dimensional electromyographic data sets collected from the human upper-limb to identify muscle synergies. We found that because it reduced the number of free parameters in the model, our modified NMF made it possible to use the Akaike Information Criterion to objectively identify a model order (i.e., the number of muscle synergies composing the data) that is more functionally interpretable, and closer to the numbers previously determined using ad hoc measures. PMID:26737046

Classification of damage in structural systems using time series analysis and supervised and unsupervised pattern recognition techniques

NASA Astrophysics Data System (ADS)

Omenzetter, Piotr; de Lautour, Oliver R.

2010-04-01

Developed for studying long, periodic records of various measured quantities, time series analysis methods are inherently suited and offer interesting possibilities for Structural Health Monitoring (SHM) applications. However, their use in SHM can still be regarded as an emerging application and deserves more studies. In this research, Autoregressive (AR) models were used to fit experimental acceleration time histories from two experimental structural systems, a 3- storey bookshelf-type laboratory structure and the ASCE Phase II SHM Benchmark Structure, in healthy and several damaged states. The coefficients of the AR models were chosen as damage sensitive features. Preliminary visual inspection of the large, multidimensional sets of AR coefficients to check the presence of clusters corresponding to different damage severities was achieved using Sammon mapping - an efficient nonlinear data compression technique. Systematic classification of damage into states based on the analysis of the AR coefficients was achieved using two supervised classification techniques: Nearest Neighbor Classification (NNC) and Learning Vector Quantization (LVQ), and one unsupervised technique: Self-organizing Maps (SOM). This paper discusses the performance of AR coefficients as damage sensitive features and compares the efficiency of the three classification techniques using experimental data.

Understanding vasopressor intervention and weaning: risk prediction in a public heterogeneous clinical time series database.

PubMed

Wu, Mike; Ghassemi, Marzyeh; Feng, Mengling; Celi, Leo A; Szolovits, Peter; Doshi-Velez, Finale

2017-05-01

The widespread adoption of electronic health records allows us to ask evidence-based questions about the need for and benefits of specific clinical interventions in critical-care settings across large populations. We investigated the prediction of vasopressor administration and weaning in the intensive care unit. Vasopressors are commonly used to control hypotension, and changes in timing and dosage can have a large impact on patient outcomes. We considered a cohort of 15 695 intensive care unit patients without orders for reduced care who were alive 30 days post-discharge. A switching-state autoregressive model (SSAM) was trained to predict the multidimensional physiological time series of patients before, during, and after vasopressor administration. The latent states from the SSAM were used as predictors of vasopressor administration and weaning. The unsupervised SSAM features were able to predict patient vasopressor administration and successful patient weaning. Features derived from the SSAM achieved areas under the receiver operating curve of 0.92, 0.88, and 0.71 for predicting ungapped vasopressor administration, gapped vasopressor administration, and vasopressor weaning, respectively. We also demonstrated many cases where our model predicted weaning well in advance of a successful wean. Models that used SSAM features increased performance on both predictive tasks. These improvements may reflect an underlying, and ultimately predictive, latent state detectable from the physiological time series. © The Author 2016. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For Permissions, please email: journals.permissions@oup.com

Domestic Violence: Issues and Dynamics. Informal Series No. 7.

ERIC Educational Resources Information Center

D'Oyley, Vincent, Ed.

The proceedings from the Toronto Conference present an overview of domestic violence, the roles of the police and judicial system, male/female relationships in domestic violence, the clinical treatment of domestic violence, domestic violence and education, social services, and a bibliography on battered wives. The multi-dimensionality of, and the…

Portable laser synthesizer for high-speed multi-dimensional spectroscopy

DOEpatents

Demos, Stavros G [Livermore, CA; Shverdin, Miroslav Y [Sunnyvale, CA; Shirk, Michael D [Brentwood, CA

2012-05-29

Portable, field-deployable laser synthesizer devices designed for multi-dimensional spectrometry and time-resolved and/or hyperspectral imaging include a coherent light source which simultaneously produces a very broad, energetic, discrete spectrum spanning through or within the ultraviolet, visible, and near infrared wavelengths. The light output is spectrally resolved and each wavelength is delayed with respect to each other. A probe enables light delivery to a target. For multidimensional spectroscopy applications, the probe can collect the resulting emission and deliver this radiation to a time gated spectrometer for temporal and spectral analysis.

Multidimensional brain activity dictated by winner-take-all mechanisms.

PubMed

Tozzi, Arturo; Peters, James F

2018-06-21

A novel demon-based architecture is introduced to elucidate brain functions such as pattern recognition during human perception and mental interpretation of visual scenes. Starting from the topological concepts of invariance and persistence, we introduce a Selfridge pandemonium variant of brain activity that takes into account a novel feature, namely, demons that recognize short straight-line segments, curved lines and scene shapes, such as shape interior, density and texture. Low-level representations of objects can be mapped to higher-level views (our mental interpretations): a series of transformations can be gradually applied to a pattern in a visual scene, without affecting its invariant properties. This makes it possible to construct a symbolic multi-dimensional representation of the environment. These representations can be projected continuously to an object that we have seen and continue to see, thanks to the mapping from shapes in our memory to shapes in Euclidean space. Although perceived shapes are 3-dimensional (plus time), the evaluation of shape features (volume, color, contour, closeness, texture, and so on) leads to n-dimensional brain landscapes. Here we discuss the advantages of our parallel, hierarchical model in pattern recognition, computer vision and biological nervous system's evolution. Copyright © 2018 Elsevier B.V. All rights reserved.

An improved method for nonlinear parameter estimation: a case study of the Rössler model

NASA Astrophysics Data System (ADS)

He, Wen-Ping; Wang, Liu; Jiang, Yun-Di; Wan, Shi-Quan

2016-08-01

Parameter estimation is an important research topic in nonlinear dynamics. Based on the evolutionary algorithm (EA), Wang et al. (2014) present a new scheme for nonlinear parameter estimation and numerical tests indicate that the estimation precision is satisfactory. However, the convergence rate of the EA is relatively slow when multiple unknown parameters in a multidimensional dynamical system are estimated simultaneously. To solve this problem, an improved method for parameter estimation of nonlinear dynamical equations is provided in the present paper. The main idea of the improved scheme is to use all of the known time series for all of the components in some dynamical equations to estimate the parameters in single component one by one, instead of estimating all of the parameters in all of the components simultaneously. Thus, we can estimate all of the parameters stage by stage. The performance of the improved method was tested using a classic chaotic system—Rössler model. The numerical tests show that the amended parameter estimation scheme can greatly improve the searching efficiency and that there is a significant increase in the convergence rate of the EA, particularly for multiparameter estimation in multidimensional dynamical equations. Moreover, the results indicate that the accuracy of parameter estimation and the CPU time consumed by the presented method have no obvious dependence on the sample size.

Bridging the gap between Hydrologic and Atmospheric communities through a standard based framework

NASA Astrophysics Data System (ADS)

Boldrini, E.; Salas, F.; Maidment, D. R.; Mazzetti, P.; Santoro, M.; Nativi, S.; Domenico, B.

2012-04-01

Data interoperability in the study of Earth sciences is essential to performing interdisciplinary multi-scale multi-dimensional analyses (e.g. hydrologic impacts of global warming, regional urbanization, global population growth etc.). This research aims to bridge the existing gap between hydrologic and atmospheric communities both at semantic and technological levels. Within the context of hydrology, scientists are usually concerned with data organized as time series: a time series can be seen as a variable measured at a particular point in space over a period of time (e.g. the stream flow values as periodically measured by a buoy sensor in a river); atmospheric scientists instead usually organize their data as coverages: a coverage can be seen as a multidimensional data array (e.g. satellite images acquired through time). These differences make non-trivial the set up of a common framework to perform data discovery and access. A set of web services specifications and implementations is already in place in both the scientific communities to allow data discovery and access in the different domains. The CUAHSI-Hydrologic Information System (HIS) service stack lists different services types and implementations: - a metacatalog (implemented as a CSW) used to discover metadata services by distributing the query to a set of catalogs - time series catalogs (implemented as CSW) used to discover datasets published by the feature services - feature services (implemented as WFS) containing features with data access link - sensor observation services (implemented as SOS) enabling access to the stream of acquisitions Within the Unidata framework, there lies a similar service stack for atmospheric data: - the broker service (implemented as a CSW) distributes a user query to a set of heterogeneous services (i.e. catalogs services, but also inventory and access services) - the catalog service (implemented as a CSW) is able to harvest the available metadata offered by THREDDS services, and executes complex queries against the available metadata. - inventory service (implemented as a THREDDS) being able to hierarchically organize and publish a local collection of multi-dimensional arrays (e.g. NetCDF, GRIB files), as well as publish auxiliary standard services to realize the actual data access and visualization (e.g. WCS, OPeNDAP, WMS). The approach followed in this research is to build on top of the existing standards and implementations, by setting up a standard-aware interoperable framework, able to deal with the existing heterogeneity in an organic way. As a methodology, interoperability tests against real services were performed; existing problems were thus highlighted and possibly solved. The use of flexible tools, able to deal in a smart way with heterogeneity has proven to be successful, in particular experiments were carried on with both GI-cat broker and ESRI GeoPortal frameworks. GI-cat discovery broker was proven successful at implementing the CSW interface, as well as federating heterogeneous resources, such as THREDDS and WCS services published by Unidata, HydroServer, WFS and SOS services published by CUAHSI. Experiments with ESRI GeoPortal were also successful: the GeoPortal was used to deploy a web interface able to distribute searches amongst catalog implementations from both the hydrologic and the atmospheric communities, including HydroServers and GI-cat, combining results from both the domains in a seamless way.

The Multidimensional Structure of University Absenteeism: An Exploratory Study

ERIC Educational Resources Information Center

López-Bonilla, Jesús Manuel; López-Bonilla, Luis Miguel

2015-01-01

Absenteeism has been a common and very extended problem in university spheres for several years. This problem has become a permanent feature in academic studies in general, yet it has received scarce empirical research attention. This work is focused on the analysis of the factors that determine university absenteeism. It evaluates a series of…

A Two-Decision Model for Responses to Likert-Type Items

ERIC Educational Resources Information Center

Thissen-Roe, Anne; Thissen, David

2013-01-01

Extreme response set, the tendency to prefer the lowest or highest response option when confronted with a Likert-type response scale, can lead to misfit of item response models such as the generalized partial credit model. Recently, a series of intrinsically multidimensional item response models have been hypothesized, wherein tendency toward…

What's a Good Job? The Importance of Employment Relationships. CPRN Study. Changing Employment Relationships Series.

ERIC Educational Resources Information Center

Lowe, Graham S.; Schellenberg, Grant

The Changing Employment Relationships Project examined the importance of good employment relationships for workers, employers, and public policy. A nationally representative sample of 2,500 employed Canadians was surveyed, and 8 focus groups were conducted. The research findings were analyzed to explain the multidimensional nature of the…

Students' Proficiency Scores within Multitrait Item Response Theory

ERIC Educational Resources Information Center

Scott, Terry F.; Schumayer, Daniel

2015-01-01

In this paper we present a series of item response models of data collected using the Force Concept Inventory. The Force Concept Inventory (FCI) was designed to poll the Newtonian conception of force viewed as a multidimensional concept, that is, as a complex of distinguishable conceptual dimensions. Several previous studies have developed…

Positive Classroom Motivational Environments : Convergence between Mastery Goal Structure and Classroom Social Climate

ERIC Educational Resources Information Center

Patrick, Helen; Kaplan, Avi; Ryan, Allison M.

2011-01-01

In a series of 4 studies we investigated the relations of mastery goal structure and 4 dimensions of the classroom social climate (teacher academic support, teacher emotional support, classroom mutual respect, task-related interaction). We conducted multidimensional scaling with separate adolescent samples that differed considerably (i.e., by…

Multidimensional Riemann problem with self-similar internal structure - part III - a multidimensional analogue of the HLLI Riemann solver for conservative hyperbolic systems

NASA Astrophysics Data System (ADS)

Balsara, Dinshaw S.; Nkonga, Boniface

2017-10-01

Just as the quality of a one-dimensional approximate Riemann solver is improved by the inclusion of internal sub-structure, the quality of a multidimensional Riemann solver is also similarly improved. Such multidimensional Riemann problems arise when multiple states come together at the vertex of a mesh. The interaction of the resulting one-dimensional Riemann problems gives rise to a strongly-interacting state. We wish to endow this strongly-interacting state with physically-motivated sub-structure. The fastest way of endowing such sub-structure consists of making a multidimensional extension of the HLLI Riemann solver for hyperbolic conservation laws. Presenting such a multidimensional analogue of the HLLI Riemann solver with linear sub-structure for use on structured meshes is the goal of this work. The multidimensional MuSIC Riemann solver documented here is universal in the sense that it can be applied to any hyperbolic conservation law. The multidimensional Riemann solver is made to be consistent with constraints that emerge naturally from the Galerkin projection of the self-similar states within the wave model. When the full eigenstructure in both directions is used in the present Riemann solver, it becomes a complete Riemann solver in a multidimensional sense. I.e., all the intermediate waves are represented in the multidimensional wave model. The work also presents, for the very first time, an important analysis of the dissipation characteristics of multidimensional Riemann solvers. The present Riemann solver results in the most efficient implementation of a multidimensional Riemann solver with sub-structure. Because it preserves stationary linearly degenerate waves, it might also help with well-balancing. Implementation-related details are presented in pointwise fashion for the one-dimensional HLLI Riemann solver as well as the multidimensional MuSIC Riemann solver.

Graphite Ablation and Thermal Response Simulation Under Arc-Jet Flow Conditions

NASA Technical Reports Server (NTRS)

Chen, Y.-K.; Milos, F. S.; Reda, D. C.; Stewart, D. A.; Venkatapathy, Ethiraj (Technical Monitor)

2002-01-01

The Two-dimensional Implicit Thermal Response and Ablation program, TITAN, was developed and integrated with a Navier-Stokes solver, GIANTS, for multidimensional ablation and shape change simulation of thermal protection systems in hypersonic flow environments. The governing equations in both codes are demoralized using the same finite-volume approximation with a general body-fitted coordinate system. Time-dependent solutions are achieved by an implicit time marching technique using Gauess-Siedel line relaxation with alternating sweeps. As the first part of a code validation study, this paper compares TITAN-GIANTS predictions with thermal response and recession data obtained from arc-jet tests recently conducted in the Interaction Heating Facility (IHF) at NASA Ames Research Center. The test models are graphite sphere-cones. Graphite was selected as a test material to minimize the uncertainties from material properties. Recession and thermal response data were obtained from two separate arc-jet test series. The first series was at a heat flux where graphite ablation is mainly due to sublimation, and the second series was at a relatively low heat flux where recession is the result of diffusion-controlled oxidation. Ablation and thermal response solutions for both sets of conditions, as calculated by TITAN-GIANTS, are presented and discussed in detail. Predicted shape change and temperature histories generally agree well with the data obtained from the arc-jet tests.

Digital Images on the DIME

NASA Technical Reports Server (NTRS)

2003-01-01

With NASA on its side, Positive Systems, Inc., of Whitefish, Montana, is veering away from the industry standards defined for producing and processing remotely sensed images. A top developer of imaging products for geographic information system (GIS) and computer-aided design (CAD) applications, Positive Systems is bucking traditional imaging concepts with a cost-effective and time-saving software tool called Digital Images Made Easy (DIME(trademark)). Like piecing a jigsaw puzzle together, DIME can integrate a series of raw aerial or satellite snapshots into a single, seamless panoramic image, known as a 'mosaic.' The 'mosaicked' images serve as useful backdrops to GIS maps - which typically consist of line drawings called 'vectors' - by allowing users to view a multidimensional map that provides substantially more geographic information.

A review of snapshot multidimensional optical imaging: measuring photon tags in parallel

PubMed Central

Gao, Liang; Wang, Lihong V.

2015-01-01

Multidimensional optical imaging has seen remarkable growth in the past decade. Rather than measuring only the two-dimensional spatial distribution of light, as in conventional photography, multidimensional optical imaging captures light in up to nine dimensions, providing unprecedented information about incident photons’ spatial coordinates, emittance angles, wavelength, time, and polarization. Multidimensional optical imaging can be accomplished either by scanning or parallel acquisition. Compared with scanning-based imagers, parallel acquisition—also dubbed snapshot imaging—has a prominent advantage in maximizing optical throughput, particularly when measuring a datacube of high dimensions. Here, we first categorize snapshot multidimensional imagers based on their acquisition and image reconstruction strategies, then highlight the snapshot advantage in the context of optical throughput, and finally we discuss their state-of-the-art implementations and applications. PMID:27134340

Construct validity of the Swedish version of the revised piper fatigue scale in an oncology sample--a Rasch analysis.

PubMed

Lundgren-Nilsson, Asa; Dencker, Anna; Jakobsson, Sofie; Taft, Charles; Tennant, Alan

2014-06-01

Fatigue is a common and distressing symptom in cancer patients due to both the disease and its treatments. The concept of fatigue is multidimensional and includes both physical and mental components. The 22-item Revised Piper Fatigue Scale (RPFS) is a multidimensional instrument developed to assess cancer-related fatigue. This study reports on the construct validity of the Swedish version of the RPFS from the perspective of Rasch measurement. The Swedish version of the RPFS was answered by 196 cancer patients fatigued after 4 to 5 weeks of curative radiation therapy. Data from the scale were fitted to the Rasch measurement model. This involved testing a series of assumptions, including the stochastic ordering of items, local response dependency, and unidimensionality. A series of fit statistics were computed, differential item functioning (DIF) was tested, and local response dependency was accommodated through testlets. The Behavioral, Affective and Sensory domains all satisfied the Rasch model expectations. No DIF was observed, and all domains were found to be unidimensional. The Mood/Cognitive scale failed to fit the model, and substantial multidimensionality was found. Splitting the scale between Mood and Cognitive items resolved fit to the Rasch model, and new domains were unidimensional without DIF. The current Rasch analyses add to the evidence of measurement properties of the scale and show that the RPFS has good psychometric properties and works well to measure fatigue. The original four-factor structure, however, was not supported. Copyright © 2014 International Society for Pharmacoeconomics and Outcomes Research (ISPOR). Published by Elsevier Inc. All rights reserved.

Computations of M sub 2 and K sub 1 ocean tidal perturbations in satellite elements

NASA Technical Reports Server (NTRS)

Estes, R. H.

1974-01-01

Semi-analytic perturbation equations for the influence of M2 and K1 ocean tidal constituents on satellite motion are expanded into multi-dimensional Fourier series and calculations made for the BE-C satellite. Perturbation in the orbital elements are compared to those of the long period solid earth tides.

The PEPR GeneChip data warehouse, and implementation of a dynamic time series query tool (SGQT) with graphical interface.

PubMed

Chen, Josephine; Zhao, Po; Massaro, Donald; Clerch, Linda B; Almon, Richard R; DuBois, Debra C; Jusko, William J; Hoffman, Eric P

2004-01-01

Publicly accessible DNA databases (genome browsers) are rapidly accelerating post-genomic research (see http://www.genome.ucsc.edu/), with integrated genomic DNA, gene structure, EST/ splicing and cross-species ortholog data. DNA databases have relatively low dimensionality; the genome is a linear code that anchors all associated data. In contrast, RNA expression and protein databases need to be able to handle very high dimensional data, with time, tissue, cell type and genes, as interrelated variables. The high dimensionality of microarray expression profile data, and the lack of a standard experimental platform have complicated the development of web-accessible databases and analytical tools. We have designed and implemented a public resource of expression profile data containing 1024 human, mouse and rat Affymetrix GeneChip expression profiles, generated in the same laboratory, and subject to the same quality and procedural controls (Public Expression Profiling Resource; PEPR). Our Oracle-based PEPR data warehouse includes a novel time series query analysis tool (SGQT), enabling dynamic generation of graphs and spreadsheets showing the action of any transcript of interest over time. In this report, we demonstrate the utility of this tool using a 27 time point, in vivo muscle regeneration series. This data warehouse and associated analysis tools provides access to multidimensional microarray data through web-based interfaces, both for download of all types of raw data for independent analysis, and also for straightforward gene-based queries. Planned implementations of PEPR will include web-based remote entry of projects adhering to quality control and standard operating procedure (QC/SOP) criteria, and automated output of alternative probe set algorithms for each project (see http://microarray.cnmcresearch.org/pgadatatable.asp).

The PEPR GeneChip data warehouse, and implementation of a dynamic time series query tool (SGQT) with graphical interface

PubMed Central

Chen, Josephine; Zhao, Po; Massaro, Donald; Clerch, Linda B.; Almon, Richard R.; DuBois, Debra C.; Jusko, William J.; Hoffman, Eric P.

2004-01-01

Publicly accessible DNA databases (genome browsers) are rapidly accelerating post-genomic research (see http://www.genome.ucsc.edu/), with integrated genomic DNA, gene structure, EST/ splicing and cross-species ortholog data. DNA databases have relatively low dimensionality; the genome is a linear code that anchors all associated data. In contrast, RNA expression and protein databases need to be able to handle very high dimensional data, with time, tissue, cell type and genes, as interrelated variables. The high dimensionality of microarray expression profile data, and the lack of a standard experimental platform have complicated the development of web-accessible databases and analytical tools. We have designed and implemented a public resource of expression profile data containing 1024 human, mouse and rat Affymetrix GeneChip expression profiles, generated in the same laboratory, and subject to the same quality and procedural controls (Public Expression Profiling Resource; PEPR). Our Oracle-based PEPR data warehouse includes a novel time series query analysis tool (SGQT), enabling dynamic generation of graphs and spreadsheets showing the action of any transcript of interest over time. In this report, we demonstrate the utility of this tool using a 27 time point, in vivo muscle regeneration series. This data warehouse and associated analysis tools provides access to multidimensional microarray data through web-based interfaces, both for download of all types of raw data for independent analysis, and also for straightforward gene-based queries. Planned implementations of PEPR will include web-based remote entry of projects adhering to quality control and standard operating procedure (QC/SOP) criteria, and automated output of alternative probe set algorithms for each project (see http://microarray.cnmcresearch.org/pgadatatable.asp). PMID:14681485

The Four Faces of Competition: The Development of the Multidimensional Competitive Orientation Inventory

PubMed Central

Orosz, Gábor; Tóth-Király, István; Büki, Noémi; Ivaskevics, Krisztián; Bőthe, Beáta; Fülöp, Márta

2018-01-01

To date, no short scale exists with established factor structure that can assess individual differences in competition. The aim of the present study was to uncover and operationalize the facets of competitive orientations with theoretical underpinning and strong psychometric properties. A total of 2676 respondents were recruited for four studies. The items were constructed based on qualitative research in different cultural contexts. A combined method of exploratory structural equation modeling (ESEM) and confirmatory factor analysis (CFA) was employed. ESEM resulted in a four-factor structure of the competitive orientations and this structure was supported by a series of CFAs on different comprehensive samples. The Multidimensional Competitive Orientation Inventory (MCOI) included 12 items and four factors: hypercompetitive orientation, self-developmental competitive orientation, anxiety-driven competition avoidance, and lack of interest toward competition. Strong gender invariance was established. The four facets of competition have differentiated relationship patterns with adaptive and maladaptive personality and motivational constructs. The MCOI can assess the adaptive and maladaptive facets of competitive orientations with a short, reliable, valid and theoretically underlined multidimensional measure. PMID:29872415

Some applications of the multi-dimensional fractional order for the Riemann-Liouville derivative

NASA Astrophysics Data System (ADS)

Ahmood, Wasan Ajeel; Kiliçman, Adem

2017-01-01

In this paper, the aim of this work is to study theorem for the one-dimensional space-time fractional deriative, generalize some function for the one-dimensional fractional by table represents the fractional Laplace transforms of some elementary functions to be valid for the multi-dimensional fractional Laplace transform and give the definition of the multi-dimensional fractional Laplace transform. This study includes that, dedicate the one-dimensional fractional Laplace transform for functions of only one independent variable and develop of the one-dimensional fractional Laplace transform to multi-dimensional fractional Laplace transform based on the modified Riemann-Liouville derivative.

Multidimensional gray-wavelet processing in interferometric fiber-optic gyroscopes

NASA Astrophysics Data System (ADS)

Yang, Yi; Wang, Zinan; Peng, Chao; Li, Zhengbin

2013-11-01

A multidimensional signal processing method for a single interferometric fiber-optic gyroscope (IFOG) is proposed, to the best of our knowledge, for the first time. The proposed method, based on a novel IFOG structure with quadrature demodulation, combines a multidimensional gray model (GM) and a wavelet compression technique for noise suppression and sensitivity enhancement. In the IFOG, two series of measured rotation rates are obtained simultaneously: an in-phase component and a quadrature component. Together with the traditionally measured rate, the three measured rates are processed by the combined gray-wavelet method. Simulations show that the intensity noise and non-reciprocal phase fluctuations are effectively suppressed by this method. Experimental comparisons with a one-dimensional GM(1, 1) model show that the proposed three-dimensional method achieves much better denoising performance. This advantage is validated by the Allan variance analysis: in a low-SNR (signal-to-noise ratio) experiment, our method reduces the angle random walk (ARW) and the bias instability (BI) from 1 × 10-2 deg h-1/2 and 3 × 10-2 deg h-1 to 1 × 10-3 deg h-1/2 and 3 × 10-3 deg h-1, respectively; in a high-SNR experiment, our method reduces the ARW and the BI from 9 × 10-4 deg h-1/2 and 5 × 10-3 deg h-1 to 4 × 10-4 deg h-1/2 and 3 × 10-3 deg h-1, respectively. Further, our method increases the dimension of the state-of-the-art IFOG technique from one to three, thus obtaining higher IFOG sensitivity and stability by exploiting the increase in available information.

On the importance of variable soil depth and process representation in the modeling of shallow landslide initiation

NASA Astrophysics Data System (ADS)

Fatichi, S.; Burlando, P.; Anagnostopoulos, G.

2014-12-01

Sub-surface hydrology has a dominant role on the initiation of rainfall-induced landslides, since changes in the soil water potential affect soil shear strength and thus apparent cohesion. Especially on steep slopes and shallow soils, loss of shear strength can lead to failure even in unsaturated conditions. A process based model, HYDROlisthisis, characterized by high resolution in space and, time is developed to investigate the interactions between surface and subsurface hydrology and shallow landslide initiation. Specifically, 3D variably saturated flow conditions, including soil hydraulic hysteresis and preferential flow, are simulated for the subsurface flow, coupled with a surface runoff routine. Evapotranspiration and specific root water uptake are taken into account for continuous simulations of soil water content during storm and inter-storm periods. The geotechnical component of the model is based on a multidimensional limit equilibrium analysis, which takes into account the basic principles of unsaturated soil mechanics. The model is applied to a small catchment in Switzerland historically prone to rainfall-triggered landslides. A series of numerical simulations were carried out with various boundary conditions (soil depths) and using hydrological and geotechnical components of different complexity. Specifically, the sensitivity to the inclusion of preferential flow and soil hydraulic hysteresis was tested together with the replacement of the infinite slope assumption with a multi-dimensional limit equilibrium analysis. The effect of the different model components on model performance was assessed using accuracy statistics and Receiver Operating Characteristic (ROC) curve. The results show that boundary conditions play a crucial role in the model performance and that the introduced hydrological (preferential flow and soil hydraulic hysteresis) and geotechnical components (multidimensional limit equilibrium analysis) considerably improve predictive capabilities in the presented case study.

Predicting adverse hemodynamic events in critically ill patients.

PubMed

Yoon, Joo H; Pinsky, Michael R

2018-06-01

The art of predicting future hemodynamic instability in the critically ill has rapidly become a science with the advent of advanced analytical processed based on computer-driven machine learning techniques. How these methods have progressed beyond severity scoring systems to interface with decision-support is summarized. Data mining of large multidimensional clinical time-series databases using a variety of machine learning tools has led to our ability to identify alert artifact and filter it from bedside alarms, display real-time risk stratification at the bedside to aid in clinical decision-making and predict the subsequent development of cardiorespiratory insufficiency hours before these events occur. This fast evolving filed is primarily limited by linkage of high-quality granular to physiologic rationale across heterogeneous clinical care domains. Using advanced analytic tools to glean knowledge from clinical data streams is rapidly becoming a reality whose clinical impact potential is great.

Multidimensional Poverty and Health Status as a Predictor of Chronic Income Poverty.

PubMed

Callander, Emily J; Schofield, Deborah J

2015-12-01

Longitudinal analysis of Wave 5 to 10 of the nationally representative Household, Income and Labour Dynamics in Australia dataset was undertaken to assess whether multidimensional poverty status can predict chronic income poverty. Of those who were multidimensionally poor (low income plus poor health or poor health and insufficient education attainment) in 2007, and those who were in income poverty only (no other forms of disadvantage) in 2007, a greater proportion of those in multidimensional poverty continued to be in income poverty for the subsequent 5 years through to 2012. People who were multidimensionally poor in 2007 had 2.17 times the odds of being in income poverty each year through to 2012 than those who were in income poverty only in 2005 (95% CI: 1.23-3.83). Multidimensional poverty measures are a useful tool for policymakers to identify target populations for policies aiming to improve equity and reduce chronic disadvantage. Copyright © 2014 John Wiley & Sons, Ltd.

Multidimensional upwind hydrodynamics on unstructured meshes using graphics processing units - I. Two-dimensional uniform meshes

NASA Astrophysics Data System (ADS)

Paardekooper, S.-J.

2017-08-01

We present a new method for numerical hydrodynamics which uses a multidimensional generalization of the Roe solver and operates on an unstructured triangular mesh. The main advantage over traditional methods based on Riemann solvers, which commonly use one-dimensional flux estimates as building blocks for a multidimensional integration, is its inherently multidimensional nature, and as a consequence its ability to recognize multidimensional stationary states that are not hydrostatic. A second novelty is the focus on graphics processing units (GPUs). By tailoring the algorithms specifically to GPUs, we are able to get speedups of 100-250 compared to a desktop machine. We compare the multidimensional upwind scheme to a traditional, dimensionally split implementation of the Roe solver on several test problems, and we find that the new method significantly outperforms the Roe solver in almost all cases. This comes with increased computational costs per time-step, which makes the new method approximately a factor of 2 slower than a dimensionally split scheme acting on a structured grid.

A Method for Generating Reduced-Order Linear Models of Multidimensional Supersonic Inlets

NASA Technical Reports Server (NTRS)

Chicatelli, Amy; Hartley, Tom T.

1998-01-01

Simulation of high speed propulsion systems may be divided into two categories, nonlinear and linear. The nonlinear simulations are usually based on multidimensional computational fluid dynamics (CFD) methodologies and tend to provide high resolution results that show the fine detail of the flow. Consequently, these simulations are large, numerically intensive, and run much slower than real-time. ne linear simulations are usually based on large lumping techniques that are linearized about a steady-state operating condition. These simplistic models often run at or near real-time but do not always capture the detailed dynamics of the plant. Under a grant sponsored by the NASA Lewis Research Center, Cleveland, Ohio, a new method has been developed that can be used to generate improved linear models for control design from multidimensional steady-state CFD results. This CFD-based linear modeling technique provides a small perturbation model that can be used for control applications and real-time simulations. It is important to note the utility of the modeling procedure; all that is needed to obtain a linear model of the propulsion system is the geometry and steady-state operating conditions from a multidimensional CFD simulation or experiment. This research represents a beginning step in establishing a bridge between the controls discipline and the CFD discipline so that the control engineer is able to effectively use multidimensional CFD results in control system design and analysis.

Pathways into chronic multidimensional poverty amongst older people: a longitudinal study.

PubMed

Callander, Emily J; Schofield, Deborah J

2016-03-07

The use of multidimensional poverty measures is becoming more common for measuring the living standards of older people. However, the pathways into poverty are relatively unknown, nor is it known how this affects the length of time people are in poverty for. Using Waves 1 to 12 of the nationally representative Household, Income and Labour Dynamics in Australia (HILDA) survey, longitudinal analysis was undertaken to identify the order that key forms of disadvantage develop - poor health, low income and insufficient education attainment - amongst Australians aged 65 years and over in multidimensional poverty, and the relationship this has with chronic poverty. Path analysis and linear regression models were used. For all older people with at least a Year 10 level of education attainment earlier mental health was significantly related to later household income (p = 0.001) and wealth (p = 0.017). For all older people with at less than a Year 10 level of education attainment earlier household income was significantly related to later mental health (p = 0.021). When limited to those in multidimensional poverty who were in income poverty and also had poor health, older people generally fell into income poverty first and then developed poor health. The order in which income poverty and poor health were developed had a significant influence on the length of time older people with less than a Year 10 level of education attainment were in multidimensional poverty for. Those who developed poor health first then fell into income poverty spend significantly less time in multidimensional poverty (-4.90, p < .0001) than those who fell into income poverty then developed poor health. Knowing the order that different forms of disadvantage develop, and the influence this has on poverty entrenchment, is of use to policy makers wishing to provide interventions to prevent older people being in long-term multidimensional poverty.

The assessment of function. Part II: clinical perspective of a javelin thrower with low back and groin pain

PubMed Central

Reiman, Michael P; Manske, Robert C

2012-01-01

Assessment of an individual’s functional ability can be complex. This assessment should also be individualized and adaptable to changes in functional status. In the first article of this series, we operationally defined function, discussed the construct of function, examined the evidence as it relates to assessment methods of various aspects of function, and explored the multi-dimensional nature of the concept of function. In this case report, we aim to demonstrate the utilization of a multi-dimensional assessment method (functional performance testing) as it relates to a high-level athlete presenting with pain in the low back and groin. It is our intent to demonstrate how the clinician should continually adapt their assessment dependent on the current functional abilities of the patients. PMID:23633887

Influence of Multidimensionality on Convergence of Sampling in Protein Simulation

NASA Astrophysics Data System (ADS)

Metsugi, Shoichi

2005-06-01

We study the problem of convergence of sampling in protein simulation originating in the multidimensionality of protein’s conformational space. Since several important physical quantities are given by second moments of dynamical variables, we attempt to obtain the time of simulation necessary for their sufficient convergence. We perform a molecular dynamics simulation of a protein and the subsequent principal component (PC) analysis as a function of simulation time T. As T increases, PC vectors with smaller amplitude of variations are identified and their amplitudes are equilibrated before identifying and equilibrating vectors with larger amplitude of variations. This sequential identification and equilibration mechanism makes protein simulation a useful method although it has an intrinsic multidimensional nature.

Artifacts in time-resolved NUS: A case study of NOE build-up curves from 2D NOESY.

PubMed

Dass, Rupashree; Kasprzak, Paweł; Koźmiński, Wiktor; Kazimierczuk, Krzysztof

2016-04-01

Multidimensional NMR spectroscopy requires time-consuming sampling of indirect dimensions and so is usually used to study stable samples. However, dynamically changing compounds or their mixtures commonly occur in problems of natural science. Monitoring them requires the use multidimensional NMR in a time-resolved manner - in other words, a series of quick spectra must be acquired at different points in time. Among the many solutions that have been proposed to achieve this goal, time-resolved non-uniform sampling (TR-NUS) is one of the simplest. In a TR-NUS experiment, the signal is sampled using a shuffled random schedule and then divided into overlapping subsets. These subsets are then processed using one of the NUS reconstruction methods, for example compressed sensing (CS). The resulting stack of spectra forms a temporal "pseudo-dimension" that shows the changes caused by the process occurring in the sample. CS enables the use of small subsets of data, which minimizes the averaging of the effects studied. Yet, even within these limited timeframes, the sample undergoes certain changes. In this paper we discuss the effect of varying signal amplitude in a TR-NUS experiment. Our theoretical calculations show that the variations within the subsets lead to t1-noise, which is dependent on the rate of change of the signal amplitude. We verify these predictions experimentally. As a model case we choose a novel 2D TR-NOESY experiment in which mixing time is varied in parallel with shuffled NUS in the indirect dimension. The experiment, performed on a sample of strychnine, provides a near-continuous NOE build-up curve, whose shape closely reflects the t1-noise level. 2D TR-NOESY reduces the measurement time compared to the conventional approach and makes it possible to verify the theoretical predictions about signal variations during TR-NUS. Copyright © 2016 Elsevier Inc. All rights reserved.

Print and Non-Print Materials: Adapting for Classroom Use. The ACTFL Foreign Language Education Series, Vol. 10.

ERIC Educational Resources Information Center

Mollica, Anthony S.

It has been said that the classroom must be a multidimensional environment where learning takes place and students are encouraged to realize their own potential. How all the relationships between the student and the components of the environment are cultivated will in large part determine the effectiveness of the teaching-learning process. This…

Finite element techniques in computational time series analysis of turbulent flows

NASA Astrophysics Data System (ADS)

Horenko, I.

2009-04-01

In recent years there has been considerable increase of interest in the mathematical modeling and analysis of complex systems that undergo transitions between several phases or regimes. Such systems can be found, e.g., in weather forecast (transitions between weather conditions), climate research (ice and warm ages), computational drug design (conformational transitions) and in econometrics (e.g., transitions between different phases of the market). In all cases, the accumulation of sufficiently detailed time series has led to the formation of huge databases, containing enormous but still undiscovered treasures of information. However, the extraction of essential dynamics and identification of the phases is usually hindered by the multidimensional nature of the signal, i.e., the information is "hidden" in the time series. The standard filtering approaches (like f.~e. wavelets-based spectral methods) have in general unfeasible numerical complexity in high-dimensions, other standard methods (like f.~e. Kalman-filter, MVAR, ARCH/GARCH etc.) impose some strong assumptions about the type of the underlying dynamics. Approach based on optimization of the specially constructed regularized functional (describing the quality of data description in terms of the certain amount of specified models) will be introduced. Based on this approach, several new adaptive mathematical methods for simultaneous EOF/SSA-like data-based dimension reduction and identification of hidden phases in high-dimensional time series will be presented. The methods exploit the topological structure of the analysed data an do not impose severe assumptions on the underlying dynamics. Special emphasis will be done on the mathematical assumptions and numerical cost of the constructed methods. The application of the presented methods will be first demonstrated on a toy example and the results will be compared with the ones obtained by standard approaches. The importance of accounting for the mathematical assumptions used in the analysis will be pointed up in this example. Finally, applications to analysis of meteorological and climate data will be presented.

Positivity-preserving numerical schemes for multidimensional advection

NASA Technical Reports Server (NTRS)

Leonard, B. P.; Macvean, M. K.; Lock, A. P.

1993-01-01

This report describes the construction of an explicit, single time-step, conservative, finite-volume method for multidimensional advective flow, based on a uniformly third-order polynomial interpolation algorithm (UTOPIA). Particular attention is paid to the problem of flow-to-grid angle-dependent, anisotropic distortion typical of one-dimensional schemes used component-wise. The third-order multidimensional scheme automatically includes certain cross-difference terms that guarantee good isotropy (and stability). However, above first-order, polynomial-based advection schemes do not preserve positivity (the multidimensional analogue of monotonicity). For this reason, a multidimensional generalization of the first author's universal flux-limiter is sought. This is a very challenging problem. A simple flux-limiter can be found; but this introduces strong anisotropic distortion. A more sophisticated technique, limiting part of the flux and then restoring the isotropy-maintaining cross-terms afterwards, gives more satisfactory results. Test cases are confined to two dimensions; three-dimensional extensions are briefly discussed.

Light at Night Markup Language (LANML): XML Technology for Light at Night Monitoring Data

NASA Astrophysics Data System (ADS)

Craine, B. L.; Craine, E. R.; Craine, E. M.; Crawford, D. L.

2013-05-01

Light at Night Markup Language (LANML) is a standard, based upon XML, useful in acquiring, validating, transporting, archiving and analyzing multi-dimensional light at night (LAN) datasets of any size. The LANML standard can accommodate a variety of measurement scenarios including single spot measures, static time-series, web based monitoring networks, mobile measurements, and airborne measurements. LANML is human-readable, machine-readable, and does not require a dedicated parser. In addition LANML is flexible; ensuring future extensions of the format will remain backward compatible with analysis software. The XML technology is at the heart of communicating over the internet and can be equally useful at the desktop level, making this standard particularly attractive for web based applications, educational outreach and efficient collaboration between research groups.

A review on battery thermal management in electric vehicle application

NASA Astrophysics Data System (ADS)

Xia, Guodong; Cao, Lei; Bi, Guanglong

2017-11-01

The global issues of energy crisis and air pollution have offered a great opportunity to develop electric vehicles. However, so far, cycle life of power battery, environment adaptability, driving range and charging time seems far to compare with the level of traditional vehicles with internal combustion engine. Effective battery thermal management (BTM) is absolutely essential to relieve this situation. This paper reviews the existing literature from two levels that are cell level and battery module level. For single battery, specific attention is paid to three important processes which are heat generation, heat transport, and heat dissipation. For large format cell, multi-scale multi-dimensional coupled models have been developed. This will facilitate the investigation on factors, such as local irreversible heat generation, thermal resistance, current distribution, etc., that account for intrinsic temperature gradients existing in cell. For battery module based on air and liquid cooling, series, series-parallel and parallel cooling configurations are discussed. Liquid cooling strategies, especially direct liquid cooling strategies, are reviewed and they may advance the battery thermal management system to a new generation.

Accessing Multi-Dimensional Images and Data Cubes in the Virtual Observatory

NASA Astrophysics Data System (ADS)

Tody, Douglas; Plante, R. L.; Berriman, G. B.; Cresitello-Dittmar, M.; Good, J.; Graham, M.; Greene, G.; Hanisch, R. J.; Jenness, T.; Lazio, J.; Norris, P.; Pevunova, O.; Rots, A. H.

2014-01-01

Telescopes across the spectrum are routinely producing multi-dimensional images and datasets, such as Doppler velocity cubes, polarization datasets, and time-resolved “movies.” Examples of current telescopes producing such multi-dimensional images include the JVLA, ALMA, and the IFU instruments on large optical and near-infrared wavelength telescopes. In the near future, both the LSST and JWST will also produce such multi-dimensional images routinely. High-energy instruments such as Chandra produce event datasets that are also a form of multi-dimensional data, in effect being a very sparse multi-dimensional image. Ensuring that the data sets produced by these telescopes can be both discovered and accessed by the community is essential and is part of the mission of the Virtual Observatory (VO). The Virtual Astronomical Observatory (VAO, http://www.usvao.org/), in conjunction with its international partners in the International Virtual Observatory Alliance (IVOA), has developed a protocol and an initial demonstration service designed for the publication, discovery, and access of arbitrarily large multi-dimensional images. The protocol describing multi-dimensional images is the Simple Image Access Protocol, version 2, which provides the minimal set of metadata required to characterize a multi-dimensional image for its discovery and access. A companion Image Data Model formally defines the semantics and structure of multi-dimensional images independently of how they are serialized, while providing capabilities such as support for sparse data that are essential to deal effectively with large cubes. A prototype data access service has been deployed and tested, using a suite of multi-dimensional images from a variety of telescopes. The prototype has demonstrated the capability to discover and remotely access multi-dimensional data via standard VO protocols. The prototype informs the specification of a protocol that will be submitted to the IVOA for approval, with an operational data cube service to be delivered in mid-2014. An associated user-installable VO data service framework will provide the capabilities required to publish VO-compatible multi-dimensional images or data cubes.

The Impact of Time Perspective Latent Profiles on College Drinking: A Multidimensional Approach

PubMed Central

Braitman, Abby L.; Henson, James M.

2015-01-01

Background Zimbardo and Boyd’s1 time perspective, or the temporal framework individuals use to process information, has been shown to predict health behaviors such as alcohol use. Previous studies supported the predictive validity of individual dimensions of time perspective, with some dimensions acting as protective factors and others as risk factors. However, some studies produced findings contrary to the general body of literature. In addition, time perspective is a multidimensional construct, and the combination of perspectives may be more predictive than individual dimensions in isolation; consequently, multidimensional profiles are a more accurate measure of individual differences and more appropriate for predicting health behaviors. Objectives The current study identified naturally occurring profiles of time perspective and examined their association with risky alcohol use. Methods Data were collected from a college student sample (n = 431, mean age = 20.41 years) using an online survey. Time perspective profiles were identified using latent profile analysis. Results Bootstrapped regression models identified a protective class that engaged in significantly less overall drinking (β = −0.254) as well as engaging in significantly less episodic high risk drinking (β = −0.274). There was also emerging evidence of a high risk time perspective profile that was linked to more overall drinking (β = 0.198) and engaging in more high risk drinking (β = 0.245), though these differences were not significant. Conclusions/Importance These findings support examining time perspective in a multidimensional framework rather than individual dimensions in isolation. Implications include identifying students most in need of interventions, and tailoring interventions to target temporal framing in decision-making. PMID:25607806

The impact of time perspective latent profiles on college drinking: a multidimensional approach.

PubMed

Braitman, Abby L; Henson, James M

2015-04-01

Zimbardo and Boyd's(1) time perspective, or the temporal framework individuals use to process information, has been shown to predict health behaviors such as alcohol use. Previous studies supported the predictive validity of individual dimensions of time perspective, with some dimensions acting as protective factors and others as risk factors. However, some studies produced findings contrary to the general body of literature. In addition, time perspective is a multidimensional construct, and the combination of perspectives may be more predictive than individual dimensions in isolation; consequently, multidimensional profiles are a more accurate measure of individual differences and more appropriate for predicting health behaviors. The current study identified naturally occurring profiles of time perspective and examined their association with risky alcohol use. Data were collected from a college student sample (n = 431, mean age = 20.41 years) using an online survey. Time perspective profiles were identified using latent profile analysis. Bootstrapped regression models identified a protective class that engaged in significantly less overall drinking (β = -0.254) as well as engaging in significantly less episodic high risk drinking (β = -0.274). There was also emerging evidence of a high risk time perspective profile that was linked to more overall drinking (β = 0.198) and engaging in more high risk drinking (β = 0.245), though these differences were not significant. CONCLUSIONS/IMPORTANCE: These findings support examining time perspective in a multidimensional framework rather than individual dimensions in isolation. Implications include identifying students most in need of interventions, and tailoring interventions to target temporal framing in decision-making.

Managing Chronic Pain in Adults with or in Recovery from Substance Use Disorders. Treatment Improvement Protocol (TIP) Series 54

ERIC Educational Resources Information Center

Substance Abuse and Mental Health Services Administration, 2012

2012-01-01

Chronic noncancer pain (CNCP) is common in the general population as well as in people who have a substance use disorder (SUD) (Exhibit 1-1). Chronic pain is not harmless; it has physiological, social, and psychological dimensions that can seriously harm health, functioning, and well-being. As a multidimensional condition with both objective and…

A Multidimensional Perspective of College Readiness: Relating Student and School Characteristics to Performance on the ACT®. ACT Research Report Series 2015 (6)

ERIC Educational Resources Information Center

McNeish, Daniel M.; Radunzel, Justine; Sanchez, Edgar

2015-01-01

This study examined the contributions of students' noncognitive characteristics toward explaining performance on the ACT® test, over and above traditional predictors such as high school grade point average (HSGPA), coursework taken, and school characteristics. The sample consisted of 6,440 high school seniors from 4,541 schools who took the ACT in…

Spectral Analysis: From Additive Perspective to Multiplicative Perspective

NASA Astrophysics Data System (ADS)

Wu, Z.

2017-12-01

The early usage of trigonometric functions can be traced back to at least 17th century BC. It was Bhaskara II of the 12th century CE who first proved the mathematical equivalence between the sum of two trigonometric functions of any given angles and the product of two trigonometric functions of related angles, which has been taught these days in middle school classroom. The additive perspective of trigonometric functions led to the development of the Fourier transform that is used to express any functions as the sum of a set of trigonometric functions and opened a new mathematical field called harmonic analysis. Unfortunately, Fourier's sum cannot directly express nonlinear interactions between trigonometric components of different periods, and thereby lacking the capability of quantifying nonlinear interactions in dynamical systems. In this talk, the speaker will introduce the Huang transform and Holo-spectrum which were pioneered by Norden Huang and emphasizes the multiplicative perspective of trigonometric functions in expressing any function. Holo-spectrum is a multi-dimensional spectral expression of a time series that explicitly identifies the interactions among different scales and quantifies nonlinear interactions hidden in a time series. Along with this introduction, the developing concepts of physical, rather than mathematical, analysis of data will be explained. Various enlightening applications of Holo-spectrum analysis in atmospheric and climate studies will also be presented.

Multidimensional effects in nonadiabatic statistical theories of spin- forbidden kinetics. A case study of 3O + CO → CO 2

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

Jasper, Ahren

2015-04-14

The appropriateness of treating crossing seams of electronic states of different spins as nonadiabatic transition states in statistical calculations of spin-forbidden reaction rates is considered. We show that the spin-forbidden reaction coordinate, the nuclear coordinate perpendicular to the crossing seam, is coupled to the remaining nuclear degrees of freedom. We found that this coupling gives rise to multidimensional effects that are not typically included in statistical treatments of spin-forbidden kinetics. Three qualitative categories of multidimensional effects may be identified: static multidimensional effects due to the geometry-dependence of the local shape of the crossing seam and of the spin–orbit coupling, dynamicalmore » multidimensional effects due to energy exchange with the reaction coordinate during the seam crossing, and nonlocal(history-dependent) multidimensional effects due to interference of the electronic variables at second, third, and later seam crossings. Nonlocal multidimensional effects are intimately related to electronic decoherence, where electronic dephasing acts to erase the history of the system. A semiclassical model based on short-time full-dimensional trajectories that includes all three multidimensional effects as well as a model for electronic decoherence is presented. The results of this multidimensional nonadiabatic statistical theory (MNST) for the 3O + CO → CO 2 reaction are compared with the results of statistical theories employing one-dimensional (Landau–Zener and weak coupling) models for the transition probability and with those calculated previously using multistate trajectories. The MNST method is shown to accurately reproduce the multistate decay-of-mixing trajectory results, so long as consistent thresholds are used. Furthermore, the MNST approach has several advantages over multistate trajectory approaches and is more suitable in chemical kinetics calculations at low temperatures and for complex systems. The error in statistical calculations that neglect multidimensional effects is shown to be as large as a factor of 2 for this system, with static multidimensional effects identified as the largest source of error.« less

Dynamic State Estimation of Terrestrial and Solar Plasmas

NASA Astrophysics Data System (ADS)

Kamalabadi, Farzad

A pervasive problem in virtually all branches of space science is the estimation of multi-dimensional state parameters of a dynamical system from a collection of indirect, often incomplete, and imprecise measurements. Subsequent scientific inference is predicated on rigorous analysis, interpretation, and understanding of physical observations and on the reliability of the associated quantitative statistical bounds and performance characteristics of the algorithms used. In this work, we focus on these dynamic state estimation problems and illustrate their importance in the context of two timely activities in space remote sensing. First, we discuss the estimation of multi-dimensional ionospheric state parameters from UV spectral imaging measurements anticipated to be acquired the recently selected NASA Heliophysics mission, Ionospheric Connection Explorer (ICON). Next, we illustrate that similar state-space formulations provide the means for the estimation of 3D, time-dependent densities and temperatures in the solar corona from a series of white-light and EUV measurements. We demonstrate that, while a general framework for the stochastic formulation of the state estimation problem is suited for systematic inference of the parameters of a hidden Markov process, several challenges must be addressed in the assimilation of an increasing volume and diversity of space observations. These challenges are: (1) the computational tractability when faced with voluminous and multimodal data, (2) the inherent limitations of the underlying models which assume, often incorrectly, linear dynamics and Gaussian noise, and (3) the unavailability or inaccuracy of transition probabilities and noise statistics. We argue that pursuing answers to these questions necessitates cross-disciplinary research that enables progress toward systematically reconciling observational and theoretical understanding of the space environment.

Application of comprehensive multidimensional gas chromatography combined with time-of-flight mass spectrometry (GC x GC-TOFMS) for high resolution analysis of hop essential oil.

PubMed

Roberts, Mark T; Dufour, Jean-Pierre; Lewis, Alastair C

2004-04-01

The selection and quality of hops is a major determinant in beer flavour. Brewers acknowledge that distinctive characteristics of different hop varieties can be traced to the composition of their essential oils. The difficulty in characterising complex mixtures such as hop oil using 1-D chromatography is that many compounds co-elute. With the introduction of comprehensive multidimensional capillary gas chromatography (GC x GC), there is a tremendous improvement in the separation power or peak capacity. Recent work using GC x GC with flame ionisation detection has suggested that there may be over 1,000 compounds in hop oil. This work describes the use of GC x GC combined with TOFMS detection (Leco Pegasus 4D instrument) to analyse Target hop oil. The TOFMS spectral acquisition rate of 60 Hz provided sufficient spectra per peak (2-D peak base width of 0.1-0.2 s) for identification (119 components were identified with 45 previously unreported compounds). When analysing results, an advantage of GC x GC coupled to TOFMS is that 2-D chromatograms can be viewed for individual masses that are characteristic of particular functional groups. This allows the analyst to view the various homologous series of compounds although in certain cases coelution may still be present as shown by the esters with mass 75.

On the Use of Social Clocks for the Monitoring of Multidimensional Social Development

ERIC Educational Resources Information Center

Mueller, Georg P.

2011-01-01

This article describes a new methodology for monitoring multidimensional social development using social clocks: comparisons with so called reference trajectories make it possible to establish the development stage of a country along a number of independent time axes, thus affording new opportunities for analyzing leads, lags, and asynchronies…

Multidimensional Social Control Variables as Predictors of Drunkenness among French Adolescents

ERIC Educational Resources Information Center

Begue, Laurent; Roche, Sebastian

2009-01-01

Background: Previous studies of the determinants of drunkenness among youth investigated the contribution of a limited range of variables measuring social control. For the first time in France, this study including 1295 participants aged 14-19 years aimed at assessing the relative contribution of a broad range of multidimensional variables…

Development and Validation of the Frost Multidimensional Perfectionism Scale--Brief

ERIC Educational Resources Information Center

Burgess, Alexandra M.; Frost, Randy O.; DiBartolo, Patricia Marten

2016-01-01

Twenty-five years ago, one of the first empirically validated measures of perfectionism, the Frost et al. Multidimensional Perfectionism Scale (F-MPS) was published. Since that time, psychometric studies of the original F-MPS have provided a plethora of evidence to support the potential development of a shorter yet still psychometrically robust…

Multidimensional Homophily in Friendship Networks1

PubMed Central

Block, Per; Grund, Thomas

2014-01-01

Homophily – the tendency for individuals to associate with similar others – is one of the most persistent findings in social network analysis. Its importance is established along the lines of a multitude of sociologically relevant dimensions, e.g. sex, ethnicity and social class. Existing research, however, mostly focuses on one dimension at a time. But people are inherently multidimensional, have many attributes and are members of multiple groups. In this article, we explore such multidimensionality further in the context of network dynamics. Are friendship ties increasingly likely to emerge and persist when individuals have an increasing number of attributes in common? We analyze eleven friendship networks of adolescents, draw on stochastic actor-oriented network models and focus on the interaction of established homophily effects. Our results indicate that main effects for homophily on various dimensions are positive. At the same time, the interaction of these homophily effects is negative. There seems to be a diminishing effect for having more than one attribute in common. We conclude that studies of homophily and friendship formation need to address such multidimensionality further. PMID:25525503

Experimental Mathematics and Mathematical Physics

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

Bailey, David H.; Borwein, Jonathan M.; Broadhurst, David

2009-06-26

One of the most effective techniques of experimental mathematics is to compute mathematical entities such as integrals, series or limits to high precision, then attempt to recognize the resulting numerical values. Recently these techniques have been applied with great success to problems in mathematical physics. Notable among these applications are the identification of some key multi-dimensional integrals that arise in Ising theory, quantum field theory and in magnetic spin theory.

Compressed Semi-Discrete Central-Upwind Schemes for Hamilton-Jacobi Equations

NASA Technical Reports Server (NTRS)

Bryson, Steve; Kurganov, Alexander; Levy, Doron; Petrova, Guergana

2003-01-01

We introduce a new family of Godunov-type semi-discrete central schemes for multidimensional Hamilton-Jacobi equations. These schemes are a less dissipative generalization of the central-upwind schemes that have been recently proposed in series of works. We provide the details of the new family of methods in one, two, and three space dimensions, and then verify their expected low-dissipative property in a variety of examples.

Exploration of multidimensional interactive classroom teaching for CCD principle and application course

NASA Astrophysics Data System (ADS)

Fu, Xinghu; Tan, Ailing; Zhang, Baojun; Fu, Guangwei; Bi, Weihong

2017-08-01

The CCD principle and application course is professional and comprehensive. It involves many subject contents. The course content includes eight aspects. In order to complete the teaching tasks within a limited time, improve the classroom teaching quality and prompt students master the course content faster and better, so the multidimensional interactive classroom teaching is proposed. In the teaching practice, the interactive relationship between the frontier science, scientific research project, living example and classroom content is researched detailedly. Finally, it has been proved practically that the proposed multidimensional interactive classroom teaching can achieved good teaching effect.

Application of spectral methods for high-frequency financial data to quantifying states of market participants

NASA Astrophysics Data System (ADS)

Sato, Aki-Hiro

2008-06-01

Empirical analysis of the foreign exchange market is conducted based on methods to quantify similarities among multi-dimensional time series with spectral distances introduced in [A.-H. Sato, Physica A 382 (2007) 258-270]. As a result it is found that the similarities among currency pairs fluctuate with the rotation of the earth, and that the similarities among best quotation rates are associated with those among quotation frequencies. Furthermore, it is shown that the Jensen-Shannon spectral divergence is proportional to a mean of the Kullback-Leibler spectral distance both empirically and numerically. It is confirmed that these spectral distances are connected with distributions for behavioural parameters of the market participants from numerical simulation. This concludes that spectral distances of representative quantities of financial markets are related into diversification of behavioural parameters of the market participants.

Random Matrix Theory in molecular dynamics analysis.

PubMed

Palese, Luigi Leonardo

2015-01-01

It is well known that, in some situations, principal component analysis (PCA) carried out on molecular dynamics data results in the appearance of cosine-shaped low index projections. Because this is reminiscent of the results obtained by performing PCA on a multidimensional Brownian dynamics, it has been suggested that short-time protein dynamics is essentially nothing more than a noisy signal. Here we use Random Matrix Theory to analyze a series of short-time molecular dynamics experiments which are specifically designed to be simulations with high cosine content. We use as a model system the protein apoCox17, a mitochondrial copper chaperone. Spectral analysis on correlation matrices allows to easily differentiate random correlations, simply deriving from the finite length of the process, from non-random signals reflecting the intrinsic system properties. Our results clearly show that protein dynamics is not really Brownian also in presence of the cosine-shaped low index projections on principal axes. Copyright © 2014 Elsevier B.V. All rights reserved.

Using Amazon Web Services (AWS) to enable real-time, remote sensing of biophysical and anthropogenic conditions in green infrastructure systems in Philadelphia, an ultra-urban application of the Internet of Things (IoT)

NASA Astrophysics Data System (ADS)

Montalto, F. A.; Yu, Z.; Soldner, K.; Israel, A.; Fritch, M.; Kim, Y.; White, S.

2017-12-01

Urban stormwater utilities are increasingly using decentralized "green" infrastructure (GI) systems to capture stormwater and achieve compliance with regulations. Because environmental conditions, and design varies by GSI facility, monitoring of GSI systems under a range of conditions is essential. Conventional monitoring efforts can be costly because in-field data logging requires intense data transmission rates. The Internet of Things (IoT) can be used to more cost-effectively collect, store, and publish GSI monitoring data. Using 3G mobile networks, a cloud-based database was built on an Amazon Web Services (AWS) EC2 virtual machine to store and publish data collected with environmental sensors deployed in the field. This database can store multi-dimensional time series data, as well as photos and other observations logged by citizen scientists through a public engagement mobile app through a new Application Programming Interface (API). Also on the AWS EC2 virtual machine, a real-time QAQC flagging algorithm was developed to validate the sensor data streams.

An advanced process-based distributed model for the investigation of rainfall-induced landslides: The effect of process representation and boundary conditions

NASA Astrophysics Data System (ADS)

Anagnostopoulos, Grigorios G.; Fatichi, Simone; Burlando, Paolo

2015-09-01

Extreme rainfall events are the major driver of shallow landslide occurrences in mountainous and steep terrain regions around the world. Subsurface hydrology has a dominant role on the initiation of rainfall-induced shallow landslides, since changes in the soil water content affect significantly the soil shear strength. Rainfall infiltration produces an increase of soil water potential, which is followed by a rapid drop in apparent cohesion. Especially on steep slopes of shallow soils, this loss of shear strength can lead to failure even in unsaturated conditions before positive water pressures are developed. We present HYDROlisthisis, a process-based model, fully distributed in space with fine time resolution, in order to investigate the interactions between surface and subsurface hydrology and shallow landslides initiation. Fundamental elements of the approach are the dependence of shear strength on the three-dimensional (3-D) field of soil water potential, as well as the temporal evolution of soil water potential during the wetting and drying phases. Specifically, 3-D variably saturated flow conditions, including soil hydraulic hysteresis and preferential flow phenomena, are simulated for the subsurface flow, coupled with a surface runoff routine based on the kinematic wave approximation. The geotechnical component of the model is based on a multidimensional limit equilibrium analysis, which takes into account the basic principles of unsaturated soil mechanics. A series of numerical simulations were carried out with various boundary conditions and using different hydrological and geotechnical components. Boundary conditions in terms of distributed soil depth were generated using both empirical and process-based models. The effect of including preferential flow and soil hydraulic hysteresis was tested together with the replacement of the infinite slope assumption with the multidimensional limit equilibrium analysis. The results show that boundary conditions play a crucial role in the model performance and that the introduced hydrological (preferential flow and soil hydraulic hysteresis) and geotechnical components (multidimensional limit equilibrium analysis) significantly improve predictive capabilities in the presented case study.

Multidimensional assessment of patient condition and mutational analysis in peripheral blood, as tools to improve outcome prediction in myelodysplastic syndromes: A prospective study of the Spanish MDS group.

PubMed

Ramos, Fernando; Robledo, Cristina; Pereira, Arturo; Pedro, Carmen; Benito, Rocío; de Paz, Raquel; Del Rey, Mónica; Insunza, Andrés; Tormo, Mar; Díez-Campelo, María; Xicoy, Blanca; Salido, Eduardo; Sánchez-Del-Real, Javier; Arenillas, Leonor; Florensa, Lourdes; Luño, Elisa; Del Cañizo, Consuelo; Sanz, Guillermo F; María Hernández-Rivas, Jesús

2017-09-01

The International Prognostic Scoring System and its revised form (IPSS-R) are the most widely used indices for prognostic assessment of patients with myelodysplastic syndromes (MDS), but can only partially account for the observed variation in patient outcomes. This study aimed to evaluate the relative contribution of patient condition and mutational status in peripheral blood when added to the IPSS-R, for estimating overall survival and the risk of leukemic transformation in patients with MDS. A prospective cohort (2006-2015) of 200 consecutive patients with MDS were included in the study series and categorized according to the IPSS-R. Patients were further stratified according to patient condition (assessed using the multidimensional Lee index for older adults) and genetic mutations (peripheral blood samples screened using next-generation sequencing). The change in likelihood-ratio was tested in Cox models after adding individual covariates. The addition of the Lee index to the IPSS-R significantly improved prediction of overall survival [hazard ratio (HR) 3.02, 95% confidence interval (CI) 1.96-4.66, P < 0.001), and mutational analysis significantly improved prediction of leukemic evolution (HR 2.64, 1.56-4.46, P < 0.001). Non-leukemic death was strongly linked to patient condition (HR 2.71, 1.72-4.25, P < 0.001), but not to IPSS-R score (P = 0.35) or mutational status (P = 0.75). Adjustment for exposure to disease-modifying therapy, evaluated as a time-dependent covariate, had no effect on the proposed model's predictive ability. In conclusion, patient condition, assessed by the multidimensional Lee index and patient mutational status can improve the prediction of clinical outcomes of patients with MDS already stratified by IPSS-R. © 2017 Wiley Periodicals, Inc.

The use of minimal spanning trees in particle physics

DOE PAGES

Rainbolt, J. Lovelace; Schmitt, M.

2017-02-14

Minimal spanning trees (MSTs) have been used in cosmology and astronomy to distinguish distributions of points in a multi-dimensional space. They are essentially unknown in particle physics, however. We briefly define MSTs and illustrate their properties through a series of examples. We show how they might be applied to study a typical event sample from a collider experiment and conclude that MSTs may prove useful in distinguishing different classes of events.

The use of minimal spanning trees in particle physics

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

Rainbolt, J. Lovelace; Schmitt, M.

Minimal spanning trees (MSTs) have been used in cosmology and astronomy to distinguish distributions of points in a multi-dimensional space. They are essentially unknown in particle physics, however. We briefly define MSTs and illustrate their properties through a series of examples. We show how they might be applied to study a typical event sample from a collider experiment and conclude that MSTs may prove useful in distinguishing different classes of events.

Multidimensional Latent Markov Models in a Developmental Study of Inhibitory Control and Attentional Flexibility in Early Childhood

ERIC Educational Resources Information Center

Bartolucci, Francesco; Solis-Trapala, Ivonne L.

2010-01-01

We demonstrate the use of a multidimensional extension of the latent Markov model to analyse data from studies with repeated binary responses in developmental psychology. In particular, we consider an experiment based on a battery of tests which was administered to pre-school children, at three time periods, in order to measure their inhibitory…

Visual modeling in an analysis of multidimensional data

NASA Astrophysics Data System (ADS)

Zakharova, A. A.; Vekhter, E. V.; Shklyar, A. V.; Pak, A. J.

2018-01-01

The article proposes an approach to solve visualization problems and the subsequent analysis of multidimensional data. Requirements to the properties of visual models, which were created to solve analysis problems, are described. As a perspective direction for the development of visual analysis tools for multidimensional and voluminous data, there was suggested an active use of factors of subjective perception and dynamic visualization. Practical results of solving the problem of multidimensional data analysis are shown using the example of a visual model of empirical data on the current state of studying processes of obtaining silicon carbide by an electric arc method. There are several results of solving this problem. At first, an idea of possibilities of determining the strategy for the development of the domain, secondly, the reliability of the published data on this subject, and changes in the areas of attention of researchers over time.

Multi-dimensional scores to predict mortality in patients with idiopathic pulmonary fibrosis undergoing lung transplantation assessment.

PubMed

Fisher, Jolene H; Al-Hejaili, Faris; Kandel, Sonja; Hirji, Alim; Shapera, Shane; Mura, Marco

2017-04-01

The heterogeneous progression of idiopathic pulmonary fibrosis (IPF) makes prognostication difficult and contributes to high mortality on the waitlist for lung transplantation (LTx). Multi-dimensional scores (Composite Physiologic index [CPI], [Gender-Age-Physiology [GAP]; RIsk Stratification scorE [RISE]) demonstrated enhanced predictive power towards outcome in IPF. The lung allocation score (LAS) is a multi-dimensional tool commonly used to stratify patients assessed for LTx. We sought to investigate whether IPF-specific multi-dimensional scores predict mortality in patients with IPF assessed for LTx. The study included 302 patients with IPF who underwent a LTx assessment (2003-2014). Multi-dimensional scores were calculated. The primary outcome was 12-month mortality after assessment. LTx was considered as competing event in all analyses. At the end of the observation period, there were 134 transplants, 63 deaths, and 105 patients were alive without LTx. Multi-dimensional scores predicted mortality with accuracy similar to LAS, and superior to that of individual variables: area under the curve (AUC) for LAS was 0.78 (sensitivity 71%, specificity 86%); CPI 0.75 (sensitivity 67%, specificity 82%); GAP 0.67 (sensitivity 59%, specificity 74%); RISE 0.78 (sensitivity 71%, specificity 84%). A separate analysis conducted only in patients actively listed for LTx (n = 247; 50 deaths) yielded similar results. In patients with IPF assessed for LTx as well as in those actually listed, multi-dimensional scores predict mortality better than individual variables, and with accuracy similar to the LAS. If validated, multi-dimensional scores may serve as inexpensive tools to guide decisions on the timing of referral and listing for LTx. Copyright © 2017 Elsevier Ltd. All rights reserved.

Interactions across Multiple Stimulus Dimensions in Primary Auditory Cortex.

PubMed

Sloas, David C; Zhuo, Ran; Xue, Hongbo; Chambers, Anna R; Kolaczyk, Eric; Polley, Daniel B; Sen, Kamal

2016-01-01

Although sensory cortex is thought to be important for the perception of complex objects, its specific role in representing complex stimuli remains unknown. Complex objects are rich in information along multiple stimulus dimensions. The position of cortex in the sensory hierarchy suggests that cortical neurons may integrate across these dimensions to form a more gestalt representation of auditory objects. Yet, studies of cortical neurons typically explore single or few dimensions due to the difficulty of determining optimal stimuli in a high dimensional stimulus space. Evolutionary algorithms (EAs) provide a potentially powerful approach for exploring multidimensional stimulus spaces based on real-time spike feedback, but two important issues arise in their application. First, it is unclear whether it is necessary to characterize cortical responses to multidimensional stimuli or whether it suffices to characterize cortical responses to a single dimension at a time. Second, quantitative methods for analyzing complex multidimensional data from an EA are lacking. Here, we apply a statistical method for nonlinear regression, the generalized additive model (GAM), to address these issues. The GAM quantitatively describes the dependence between neural response and all stimulus dimensions. We find that auditory cortical neurons in mice are sensitive to interactions across dimensions. These interactions are diverse across the population, indicating significant integration across stimulus dimensions in auditory cortex. This result strongly motivates using multidimensional stimuli in auditory cortex. Together, the EA and the GAM provide a novel quantitative paradigm for investigating neural coding of complex multidimensional stimuli in auditory and other sensory cortices.

Constraints on the explosion mechanism and progenitors of Type Ia supernovae

NASA Astrophysics Data System (ADS)

Dessart, Luc; Blondin, Stéphane; Hillier, D. John; Khokhlov, Alexei

2014-06-01

Observations of SN 2011fe at early times reveal an evolution analogous to a fireball model of constant colour. In contrast, our unmixed delayed detonations of Chandrasekhar-mass white dwarfs (DDC series) exhibit a faster brightening concomitant with a shift in colour to the blue. In this paper, we study the origin of these discrepancies. We find that strong chemical mixing largely resolves the photometric mismatch at early times, but it leads to an enhanced line broadening that contrasts, for example, with the markedly narrow Si II 6355 Å line of SN 2011fe. We also explore an alternative configuration with pulsational-delayed detonations (PDDEL model series). Because of the pulsation, PDDEL models retain more unburnt carbon, have little mass at high velocity, and have a much hotter outer ejecta after the explosion. The pulsation does not influence the inner ejecta, so PDDEL and DDC models exhibit similar radiative properties beyond maximum. However, at early times, PDDEL models show bluer optical colours and a higher luminosity, even for weak mixing. Their early-time radiation is derived primarily from the initial shock-deposited energy in the outer ejecta rather than radioactive-decay heating. Furthermore, PDDEL models show short-lived C II lines, reminiscent of SN 2013dy. They typically exhibit lines that are weaker, narrower, and of near-constant width, reminiscent of SN 2011fe. In addition to multidimensional effects, varying configurations for such `pulsations' offer a source of spectral diversity amongst Type Ia supernovae (SNe Ia). PDDEL and DDC models also provide one explanation for low- and high-velocity-gradient SNe Ia.

Reconstructing the temporal ordering of biological samples using microarray data.

PubMed

Magwene, Paul M; Lizardi, Paul; Kim, Junhyong

2003-05-01

Accurate time series for biological processes are difficult to estimate due to problems of synchronization, temporal sampling and rate heterogeneity. Methods are needed that can utilize multi-dimensional data, such as those resulting from DNA microarray experiments, in order to reconstruct time series from unordered or poorly ordered sets of observations. We present a set of algorithms for estimating temporal orderings from unordered sets of sample elements. The techniques we describe are based on modifications of a minimum-spanning tree calculated from a weighted, undirected graph. We demonstrate the efficacy of our approach by applying these techniques to an artificial data set as well as several gene expression data sets derived from DNA microarray experiments. In addition to estimating orderings, the techniques we describe also provide useful heuristics for assessing relevant properties of sample datasets such as noise and sampling intensity, and we show how a data structure called a PQ-tree can be used to represent uncertainty in a reconstructed ordering. Academic implementations of the ordering algorithms are available as source code (in the programming language Python) on our web site, along with documentation on their use. The artificial 'jelly roll' data set upon which the algorithm was tested is also available from this web site. The publicly available gene expression data may be found at http://genome-www.stanford.edu/cellcycle/ and http://caulobacter.stanford.edu/CellCycle/.

Robust Period Estimation Using Mutual Information for Multiband Light Curves in the Synoptic Survey Era

NASA Astrophysics Data System (ADS)

Huijse, Pablo; Estévez, Pablo A.; Förster, Francisco; Daniel, Scott F.; Connolly, Andrew J.; Protopapas, Pavlos; Carrasco, Rodrigo; Príncipe, José C.

2018-05-01

The Large Synoptic Survey Telescope (LSST) will produce an unprecedented amount of light curves using six optical bands. Robust and efficient methods that can aggregate data from multidimensional sparsely sampled time-series are needed. In this paper we present a new method for light curve period estimation based on quadratic mutual information (QMI). The proposed method does not assume a particular model for the light curve nor its underlying probability density and it is robust to non-Gaussian noise and outliers. By combining the QMI from several bands the true period can be estimated even when no single-band QMI yields the period. Period recovery performance as a function of average magnitude and sample size is measured using 30,000 synthetic multiband light curves of RR Lyrae and Cepheid variables generated by the LSST Operations and Catalog simulators. The results show that aggregating information from several bands is highly beneficial in LSST sparsely sampled time-series, obtaining an absolute increase in period recovery rate up to 50%. We also show that the QMI is more robust to noise and light curve length (sample size) than the multiband generalizations of the Lomb–Scargle and AoV periodograms, recovering the true period in 10%–30% more cases than its competitors. A python package containing efficient Cython implementations of the QMI and other methods is provided.

Analysis of recurrent neural networks for short-term energy load forecasting

NASA Astrophysics Data System (ADS)

Di Persio, Luca; Honchar, Oleksandr

2017-11-01

Short-term forecasts have recently gained an increasing attention because of the rise of competitive electricity markets. In fact, short-terms forecast of possible future loads turn out to be fundamental to build efficient energy management strategies as well as to avoid energy wastage. Such type of challenges are difficult to tackle both from a theoretical and applied point of view. Latter tasks require sophisticated methods to manage multidimensional time series related to stochastic phenomena which are often highly interconnected. In the present work we first review novel approaches to energy load forecasting based on recurrent neural network, focusing our attention on long/short term memory architectures (LSTMs). Such type of artificial neural networks have been widely applied to problems dealing with sequential data such it happens, e.g., in socio-economics settings, for text recognition purposes, concerning video signals, etc., always showing their effectiveness to model complex temporal data. Moreover, we consider different novel variations of basic LSTMs, such as sequence-to-sequence approach and bidirectional LSTMs, aiming at providing effective models for energy load data. Last but not least, we test all the described algorithms on real energy load data showing not only that deep recurrent networks can be successfully applied to energy load forecasting, but also that this approach can be extended to other problems based on time series prediction.

LC-IMS-MS Feature Finder. Detecting Multidimensional Liquid Chromatography, Ion Mobility, and Mass Spectrometry Features in Complex Datasets

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

Crowell, Kevin L.; Slysz, Gordon W.; Baker, Erin Shammel

2013-09-05

We introduce a command line software application LC-IMS-MS Feature Finder that searches for molecular ion signatures in multidimensional liquid chromatography-ion mobility spectrometry-mass spectrometry (LC-IMS-MS) data by clustering deisotoped peaks with similar monoisotopic mass, charge state, LC elution time, and ion mobility drift time values. The software application includes an algorithm for detecting and quantifying co-eluting chemical species, including species that exist in multiple conformations that may have been separated in the IMS dimension.

Overcoming Multidimensional Marginality: The Significance of Higher Education for Traditionally Reared Single Mothers Living in the Outer Periphery

ERIC Educational Resources Information Center

Greenberg, Zeev; Shenaar-Golan, Vered

2017-01-01

The current study gives voice to a group of remarkable returning college students whose lives are defined by multidimensional marginality. These students are single mothers who grew up in traditional families in the outer periphery of Northern Israel, where they still lived at the time of this study. Drawing on the women's life stories gathered…

Reducing seed dependent variability of non-uniformly sampled multidimensional NMR data

NASA Astrophysics Data System (ADS)

Mobli, Mehdi

2015-07-01

The application of NMR spectroscopy to study the structure, dynamics and function of macromolecules requires the acquisition of several multidimensional spectra. The one-dimensional NMR time-response from the spectrometer is extended to additional dimensions by introducing incremented delays in the experiment that cause oscillation of the signal along "indirect" dimensions. For a given dimension the delay is incremented at twice the rate of the maximum frequency (Nyquist rate). To achieve high-resolution requires acquisition of long data records sampled at the Nyquist rate. This is typically a prohibitive step due to time constraints, resulting in sub-optimal data records to the detriment of subsequent analyses. The multidimensional NMR spectrum itself is typically sparse, and it has been shown that in such cases it is possible to use non-Fourier methods to reconstruct a high-resolution multidimensional spectrum from a random subset of non-uniformly sampled (NUS) data. For a given acquisition time, NUS has the potential to improve the sensitivity and resolution of a multidimensional spectrum, compared to traditional uniform sampling. The improvements in sensitivity and/or resolution achieved by NUS are heavily dependent on the distribution of points in the random subset acquired. Typically, random points are selected from a probability density function (PDF) weighted according to the NMR signal envelope. In extreme cases as little as 1% of the data is subsampled. The heavy under-sampling can result in poor reproducibility, i.e. when two experiments are carried out where the same number of random samples is selected from the same PDF but using different random seeds. Here, a jittered sampling approach is introduced that is shown to improve random seed dependent reproducibility of multidimensional spectra generated from NUS data, compared to commonly applied NUS methods. It is shown that this is achieved due to the low variability of the inherent sensitivity of the random subset chosen from a given PDF. Finally, it is demonstrated that metrics used to find optimal NUS distributions are heavily dependent on the inherent sensitivity of the random subset, and such optimisation is therefore less critical when using the proposed sampling scheme.

CELL5M: A geospatial database of agricultural indicators for Africa South of the Sahara.

PubMed

Koo, Jawoo; Cox, Cindy M; Bacou, Melanie; Azzarri, Carlo; Guo, Zhe; Wood-Sichra, Ulrike; Gong, Queenie; You, Liangzhi

2016-01-01

Recent progress in large-scale georeferenced data collection is widening opportunities for combining multi-disciplinary datasets from biophysical to socioeconomic domains, advancing our analytical and modeling capacity. Granular spatial datasets provide critical information necessary for decision makers to identify target areas, assess baseline conditions, prioritize investment options, set goals and targets and monitor impacts. However, key challenges in reconciling data across themes, scales and borders restrict our capacity to produce global and regional maps and time series. This paper provides overview, structure and coverage of CELL5M-an open-access database of geospatial indicators at 5 arc-minute grid resolution-and introduces a range of analytical applications and case-uses. CELL5M covers a wide set of agriculture-relevant domains for all countries in Africa South of the Sahara and supports our understanding of multi-dimensional spatial variability inherent in farming landscapes throughout the region.

Transition Characteristic Analysis of Traffic Evolution Process for Urban Traffic Network

PubMed Central

Chen, Hong; Li, Yang

2014-01-01

The characterization of the dynamics of traffic states remains fundamental to seeking for the solutions of diverse traffic problems. To gain more insights into traffic dynamics in the temporal domain, this paper explored temporal characteristics and distinct regularity in the traffic evolution process of urban traffic network. We defined traffic state pattern through clustering multidimensional traffic time series using self-organizing maps and construct a pattern transition network model that is appropriate for representing and analyzing the evolution progress. The methodology is illustrated by an application to data flow rate of multiple road sections from Network of Shenzhen's Nanshan District, China. Analysis and numerical results demonstrated that the methodology permits extracting many useful traffic transition characteristics including stability, preference, activity, and attractiveness. In addition, more information about the relationships between these characteristics was extracted, which should be helpful in understanding the complex behavior of the temporal evolution features of traffic patterns. PMID:24982969

Local/non-local regularized image segmentation using graph-cuts: application to dynamic and multispectral MRI.

PubMed

Hanson, Erik A; Lundervold, Arvid

2013-11-01

Multispectral, multichannel, or time series image segmentation is important for image analysis in a wide range of applications. Regularization of the segmentation is commonly performed using local image information causing the segmented image to be locally smooth or piecewise constant. A new spatial regularization method, incorporating non-local information, was developed and tested. Our spatial regularization method applies to feature space classification in multichannel images such as color images and MR image sequences. The spatial regularization involves local edge properties, region boundary minimization, as well as non-local similarities. The method is implemented in a discrete graph-cut setting allowing fast computations. The method was tested on multidimensional MRI recordings from human kidney and brain in addition to simulated MRI volumes. The proposed method successfully segment regions with both smooth and complex non-smooth shapes with a minimum of user interaction.

Frequency-Comb Based Double-Quantum Two-Dimensional Spectrum Identifies Collective Hyperfine Resonances in Atomic Vapor Induced by Dipole-Dipole Interactions

NASA Astrophysics Data System (ADS)

Lomsadze, Bachana; Cundiff, Steven T.

2018-06-01

Frequency-comb based multidimensional coherent spectroscopy is a novel optical method that enables high-resolution measurement in a short acquisition time. The method's resolution makes multidimensional coherent spectroscopy relevant for atomic systems that have narrow resonances. We use double-quantum multidimensional coherent spectroscopy to reveal collective hyperfine resonances in rubidium vapor at 100 °C induced by dipole-dipole interactions. We observe tilted and elongated line shapes in the double-quantum 2D spectra, which have never been reported for Doppler-broadened systems. The elongated line shapes suggest that the signal is predominately from the interacting atoms that have a near zero relative velocity.

ComVisMD - compact visualization of multidimensional data: experimenting with cricket players data

NASA Astrophysics Data System (ADS)

Dandin, Shridhar B.; Ducassé, Mireille

2018-03-01

Database information is multidimensional and often displayed in tabular format (row/column display). Presented in aggregated form, multidimensional data can be used to analyze the records or objects. Online Analytical database Processing (OLAP) proposes mechanisms to display multidimensional data in aggregated forms. A choropleth map is a thematic map in which areas are colored in proportion to the measurement of a statistical variable being displayed, such as population density. They are used mostly for compact graphical representation of geographical information. We propose a system, ComVisMD inspired by choropleth map and the OLAP cube to visualize multidimensional data in a compact way. ComVisMD displays multidimensional data like OLAP Cube, where we are mapping an attribute a (first dimension, e.g. year started playing cricket) in vertical direction, object coloring based on b (second dimension, e.g. batting average), mapping varying-size circles based on attribute c (third dimension, e.g. highest score), mapping numbers based on attribute d (fourth dimension, e.g. matches played). We illustrate our approach on cricket players data, namely on two tables Country and Player. They have a large number of rows and columns: 246 rows and 17 columns for players of one country. ComVisMD’s visualization reduces the size of the tabular display by a factor of about 4, allowing users to grasp more information at a time than the bare table display.

A multidimensional evaluation of a nursing information-literacy program.

PubMed Central

Fox, L M; Richter, J M; White, N E

1996-01-01

The goal of an information-literacy program is to develop student skills in locating, evaluating, and applying information for use in critical thinking and problem solving. This paper describes a multidimensional evaluation process for determining nursing students' growth in cognitive and affective domains. Results indicate improvement in student skills as a result of a nursing information-literacy program. Multidimensional evaluation produces a well-rounded picture of student progress based on formal measurement as well as informal feedback. Developing new educational programs can be a time-consuming challenge. It is important, when expending so much effort, to ensure that the goals of the new program are achieved and benefits to students demonstrated. A multidimensional approach to evaluation can help to accomplish those ends. In 1988, The University of Northern Colorado School of Nursing began working with a librarian to integrate an information-literacy component, entitled Pathways to Information Literacy, into the curriculum. This article describes the program and discusses how a multidimensional evaluation process was used to assess program effectiveness. The evaluation process not only helped to measure the effectiveness of the program but also allowed the instructors to use several different approaches to evaluation. PMID:8826621

Multidimensional Separation of Natural Products Using Liquid Chromatography Coupled to Hadamard Transform Ion Mobility Mass Spectrometry

NASA Astrophysics Data System (ADS)

Liu, Wenjie; Zhang, Xing; Knochenmuss, Richard; Siems, William F.; Hill, Herbert H.

2016-05-01

A high performance liquid chromatograph (HPLC)was interfaced to an atmospheric drift tube ion mobility time of flight mass spectrometry. The power of multidimensional separation was demonstrated using chili pepper extracts. The ambient pressure drift tube ion mobility provided high resolving powers up to 166 for the HPLC eluent. With implementation of Hadamard transform (HT), the duty cycle for the ion mobility drift tube was increased from less than 1% to 50%, and the ion transmission efficiency was improved by over 200 times compared with pulsed mode, improving signal to noise ratio 10 times. HT ion mobility and TOF mass spectrometry provide an additional dimension of separation for complex samples without increasing the analysis time compared with conventional HPLC.

Multi-Dimensional Deprivation in India during and after the Reforms: Do the Household Expenditure and the Family Health Surveys Present Consistent Evidence?

ERIC Educational Resources Information Center

Mishra, Ankita; Ray, Ranjan

2013-01-01

This paper uses the recent approach of multidimensional deprivation measures to provide a comprehensive and wide ranging assessment of changes to living standards in India during the period, 1992/93-2004/5. This covers the reforms and the immediate post reforms time periods. The study is the first to be based on the simultaneous use of two…

Analysis of the time structure of synchronization in multidimensional chaotic systems

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

Makarenko, A. V., E-mail: avm.science@mail.ru

2015-05-15

A new approach is proposed to the integrated analysis of the time structure of synchronization of multidimensional chaotic systems. The method allows one to diagnose and quantitatively evaluate the intermittency characteristics during synchronization of chaotic oscillations in the T-synchronization mode. A system of two identical logistic mappings with unidirectional coupling that operate in the developed chaos regime is analyzed. It is shown that the widely used approach, in which only synchronization patterns are subjected to analysis while desynchronization areas are considered as a background signal and removed from analysis, should be regarded as methodologically incomplete.

MODA: a new algorithm to compute optical depths in multidimensional hydrodynamic simulations

NASA Astrophysics Data System (ADS)

Perego, Albino; Gafton, Emanuel; Cabezón, Rubén; Rosswog, Stephan; Liebendörfer, Matthias

2014-08-01

Aims: We introduce the multidimensional optical depth algorithm (MODA) for the calculation of optical depths in approximate multidimensional radiative transport schemes, equally applicable to neutrinos and photons. Motivated by (but not limited to) neutrino transport in three-dimensional simulations of core-collapse supernovae and neutron star mergers, our method makes no assumptions about the geometry of the matter distribution, apart from expecting optically transparent boundaries. Methods: Based on local information about opacities, the algorithm figures out an escape route that tends to minimize the optical depth without assuming any predefined paths for radiation. Its adaptivity makes it suitable for a variety of astrophysical settings with complicated geometry (e.g., core-collapse supernovae, compact binary mergers, tidal disruptions, star formation, etc.). We implement the MODA algorithm into both a Eulerian hydrodynamics code with a fixed, uniform grid and into an SPH code where we use a tree structure that is otherwise used for searching neighbors and calculating gravity. Results: In a series of numerical experiments, we compare the MODA results with analytically known solutions. We also use snapshots from actual 3D simulations and compare the results of MODA with those obtained with other methods, such as the global and local ray-by-ray method. It turns out that MODA achieves excellent accuracy at a moderate computational cost. In appendix we also discuss implementation details and parallelization strategies.

Multidimensional Compressed Sensing MRI Using Tensor Decomposition-Based Sparsifying Transform

PubMed Central

Yu, Yeyang; Jin, Jin; Liu, Feng; Crozier, Stuart

2014-01-01

Compressed Sensing (CS) has been applied in dynamic Magnetic Resonance Imaging (MRI) to accelerate the data acquisition without noticeably degrading the spatial-temporal resolution. A suitable sparsity basis is one of the key components to successful CS applications. Conventionally, a multidimensional dataset in dynamic MRI is treated as a series of two-dimensional matrices, and then various matrix/vector transforms are used to explore the image sparsity. Traditional methods typically sparsify the spatial and temporal information independently. In this work, we propose a novel concept of tensor sparsity for the application of CS in dynamic MRI, and present the Higher-order Singular Value Decomposition (HOSVD) as a practical example. Applications presented in the three- and four-dimensional MRI data demonstrate that HOSVD simultaneously exploited the correlations within spatial and temporal dimensions. Validations based on cardiac datasets indicate that the proposed method achieved comparable reconstruction accuracy with the low-rank matrix recovery methods and, outperformed the conventional sparse recovery methods. PMID:24901331

Comparison of Degrees of Potential-Energy-Surface Anharmonicity for Complexes and Clusters with Hydrogen Bonds

NASA Astrophysics Data System (ADS)

Kozlovskaya, E. N.; Doroshenko, I. Yu.; Pogorelov, V. E.; Vaskivskyi, Ye. V.; Pitsevich, G. A.

2018-01-01

Previously calculated multidimensional potential-energy surfaces of the MeOH monomer and dimer, water dimer, malonaldehyde, formic acid dimer, free pyridine-N-oxide/trichloroacetic acid complex, and protonated water dimer were analyzed. The corresponding harmonic potential-energy surfaces near the global minima were constructed for series of clusters and complexes with hydrogen bonds of different strengths based on the behavior of the calculated multidimensional potential-energy surfaces. This enabled the introduction of an obvious anharmonicity parameter for the calculated potential-energy surfaces. The anharmonicity parameter was analyzed as functions of the size of the analyzed area near the energy minimum, the number of points over which energies were compared, and the dimensionality of the solved vibrational problem. Anharmonicity parameters for potential-energy surfaces in complexes with strong, medium, and weak H-bonds were calculated under identical conditions. The obtained anharmonicity parameters were compared with the corresponding diagonal anharmonicity constants for stretching vibrations of the bridging protons and the lengths of the hydrogen bridges.

Finite element concepts in computational aerodynamics

NASA Technical Reports Server (NTRS)

Baker, A. J.

1978-01-01

Finite element theory was employed to establish an implicit numerical solution algorithm for the time averaged unsteady Navier-Stokes equations. Both the multidimensional and a time-split form of the algorithm were considered, the latter of particular interest for problem specification on a regular mesh. A Newton matrix iteration procedure is outlined for solving the resultant nonlinear algebraic equation systems. Multidimensional discretization procedures are discussed with emphasis on automated generation of specific nonuniform solution grids and accounting of curved surfaces. The time-split algorithm was evaluated with regards to accuracy and convergence properties for hyperbolic equations on rectangular coordinates. An overall assessment of the viability of the finite element concept for computational aerodynamics is made.

Studying Climate Response to Forcing by the Nonlinear Dynamical Mode Decomposition

NASA Astrophysics Data System (ADS)

Mukhin, Dmitry; Gavrilov, Andrey; Loskutov, Evgeny; Feigin, Alexander

2017-04-01

An analysis of global climate response to external forcing, both anthropogenic (mainly, CO2 and aerosol) and natural (solar and volcanic), is needed for adequate predictions of global climate change. Being complex dynamical system, the climate reacts to external perturbations exciting feedbacks (both positive and negative) making the response non-trivial and poorly predictable. Thus an extraction of internal modes of climate system, investigation of their interaction with external forcings and further modeling and forecast of their dynamics, are all the problems providing the success of climate modeling. In the report the new method for principal mode extraction from climate data is presented. The method is based on the Nonlinear Dynamical Mode (NDM) expansion [1,2], but takes into account a number of external forcings applied to the system. Each NDM is represented by hidden time series governing the observed variability, which, together with external forcing time series, are mapped onto data space. While forcing time series are considered to be known, the hidden unknown signals underlying the internal climate dynamics are extracted from observed data by the suggested method. In particular, it gives us an opportunity to study the evolution of principal system's mode structure in changing external conditions and separate the internal climate variability from trends forced by external perturbations. Furthermore, the modes so obtained can be extrapolated beyond the observational time series, and long-term prognosis of modes' structure including characteristics of interconnections and responses to external perturbations, can be carried out. In this work the method is used for reconstructing and studying the principal modes of climate variability on inter-annual and decadal time scales accounting the external forcings such as anthropogenic emissions, variations of the solar activity and volcanic activity. The structure of the obtained modes as well as their response to external factors, e.g. forecast their change in 21 century under different CO2 emission scenarios, are discussed. [1] Mukhin, D., Gavrilov, A., Feigin, A., Loskutov, E., & Kurths, J. (2015). Principal nonlinear dynamical modes of climate variability. Scientific Reports, 5, 15510. http://doi.org/10.1038/srep15510 [2] Gavrilov, A., Mukhin, D., Loskutov, E., Volodin, E., Feigin, A., & Kurths, J. (2016). Method for reconstructing nonlinear modes with adaptive structure from multidimensional data. Chaos: An Interdisciplinary Journal of Nonlinear Science, 26(12), 123101. http://doi.org/10.1063/1.4968852

InSAR Time Series to Characterize Landslide Ground Deformations in a Tropical Urban Environment: Focus on Bukavu, East African Rift System (RD Congo).

NASA Astrophysics Data System (ADS)

Nobile, A.; d'Oreye, N.; Monsieurs, E.; Dewitte, O.; Kervyn, F.

2016-12-01

The western branch of the East African Rift System, in Central Africa, is a region naturally prone to landslides due to factors such as heavy rainfall, tectonic activity and steep topography. In addition, sensibility to slope instability is expected to increase in the future in response to increasing demographic pressure and land use/land cover changes. The Rift flanks west of Lake Kivu (DRC) are one of the Congolese regions most affected by landslides. Although heavy rainfall periods and earthquakes are the main triggering factors, nothing is known on their potential role on the current dynamics of existing landslides Here we used InSAR time series to monitor ground deformations associated to large slow-moving landslides that continuously affect highly populated slopes in the city of Bukavu (DRC). Bukavu is located within the Rift, on the southern shore of Lake Kivu, in a tropical environment. Using >100 Cosmo-SkyMed SAR images, acquired between March 2015 and June 2016 with a mean revisiting time of 8 days per orbit (ascending and descending), we produce displacement-rate maps and ground deformation time series using different techniques: Persistent Scatter (PS), Small Baseline Subset (SBAS) and Multidimensional Small Baseline Subsets (MSBAS). The three techniques provides similar results in areas with relatively small displacements (few mm per months). However, in areas where displacements are much higher and where coherence is lost by traditional techniques, MSBAS, that process concurrently the two satellite orbits improving temporal resolution, is more efficient. It allows to measure higher ground deformation rates by keeping the coherence. For one specific landslide where intense field mapping was done, the results show clearly the pattern of the deformations that divides the landslide in blocks that move with different velocity (up to 20 cm/yr). This pattern is consistent with field observations and possibly related to the anthropic activity. Furthermore, DGPS measurements, taken at 21 benchmarks in the area during the same period, allow validating the InSAR results. The combination of InSAR data with rainfall and seismic monitoring, and field observations should help us, when longer time-series will be available, to better understand the mechanisms (both natural and human) that affect this landslide.

Time-space and cognition-space transformations for transportation network analysis based on multidimensional scaling and self-organizing map

NASA Astrophysics Data System (ADS)

Hong, Zixuan; Bian, Fuling

2008-10-01

Geographic space, time space and cognition space are three fundamental and interrelated spaces in geographic information systems for transportation. However, the cognition space and its relationships to the time space and geographic space are often neglected. This paper studies the relationships of these three spaces in urban transportation system from a new perspective and proposes a novel MDS-SOM transformation method which takes the advantages of the techniques of multidimensional scaling (MDS) and self-organizing map (SOM). The MDS-SOM transformation framework includes three kinds of mapping: the geographic-time transformation, the cognition-time transformation and the time-cognition transformation. The transformations in our research provide a better understanding of the interactions of these three spaces and beneficial knowledge is discovered to help the transportation analysis and decision supports.

Rethinking language in autism.

PubMed

Sterponi, Laura; de Kirby, Kenton; Shankey, Jennifer

2015-07-01

In this article, we invite a rethinking of traditional perspectives of language in autism. We advocate a theoretical reappraisal that offers a corrective to the dominant and largely tacitly held view that language, in its essence, is a referential system and a reflection of the individual's cognition. Drawing on scholarship in Conversation Analysis and linguistic anthropology, we present a multidimensional view of language, showing how it also functions as interactional accomplishment, social action, and mode of experience. From such a multidimensional perspective, we revisit data presented by other researchers that include instances of prototypical features of autistic speech, giving them a somewhat different-at times complementary, at times alternative-interpretation. In doing so, we demonstrate that there is much at stake in the view of language that we as researchers bring to our analysis of autistic speech. Ultimately, we argue that adopting a multidimensional view of language has wide ranging implications, deepening our understanding of autism's core features and developmental trajectory. © The Author(s) 2014.

Discrete models for the numerical analysis of time-dependent multidimensional gas dynamics

NASA Technical Reports Server (NTRS)

Roe, P. L.

1984-01-01

A possible technique is explored for extending to multidimensional flows some of the upwind-differencing methods that are highly successful in the one-dimensional case. Emphasis is on the two-dimensional case, and the flow domain is assumed to be divided into polygonal computational elements. Inside each element, the flow is represented by a local superposition of elementary solutions consisting of plane waves not necessarily aligned with the element boundaries.

A New Time-Space Accurate Scheme for Hyperbolic Problems. 1; Quasi-Explicit Case

NASA Technical Reports Server (NTRS)

Sidilkover, David

1998-01-01

This paper presents a new discretization scheme for hyperbolic systems of conservations laws. It satisfies the TVD property and relies on the new high-resolution mechanism which is compatible with the genuinely multidimensional approach proposed recently. This work can be regarded as a first step towards extending the genuinely multidimensional approach to unsteady problems. Discontinuity capturing capabilities and accuracy of the scheme are verified by a set of numerical tests.

Principal component analysis of MSBAS DInSAR time series from Campi Flegrei, Italy

NASA Astrophysics Data System (ADS)

Tiampo, Kristy F.; González, Pablo J.; Samsonov, Sergey; Fernández, Jose; Camacho, Antonio

2017-09-01

Because of its proximity to the city of Naples and with a population of nearly 1 million people within its caldera, Campi Flegrei is one of the highest risk volcanic areas in the world. Since the last major eruption in 1538, the caldera has undergone frequent episodes of ground subsidence and uplift accompanied by seismic activity that has been interpreted as the result of a stationary, deeper source below the caldera that feeds shallower eruptions. However, the location and depth of the deeper source is not well-characterized and its relationship to current activity is poorly understood. Recently, a significant increase in the uplift rate has occurred, resulting in almost 13 cm of uplift by 2013 (De Martino et al., 2014; Samsonov et al., 2014b; Di Vito et al., 2016). Here we apply a principal component decomposition to high resolution time series from the region produced by the advanced Multidimensional SBAS DInSAR technique in order to better delineate both the deeper source and the recent shallow activity. We analyzed both a period of substantial subsidence (1993-1999) and a second of significant uplift (2007-2013) and inverted the associated vertical surface displacement for the most likely source models. Results suggest that the underlying dynamics of the caldera changed in the late 1990s, from one in which the primary signal arises from a shallow deflating source above a deeper, expanding source to one dominated by a shallow inflating source. In general, the shallow source lies between 2700 and 3400 m below the caldera while the deeper source lies at 7600 m or more in depth. The combination of principal component analysis with high resolution MSBAS time series data allows for these new insights and confirms the applicability of both to areas at risk from dynamic natural hazards.

Communication: Heterogeneous water dynamics on a clathrate hydrate lattice detected by multidimensional oxygen nuclear magnetic resonance

NASA Astrophysics Data System (ADS)

Adjei-Acheamfour, Mischa; Storek, Michael; Böhmer, Roland

2017-05-01

Previous deuteron nuclear magnetic resonance studies revealed conflicting evidence regarding the possible motional heterogeneity of the water dynamics on the hydrate lattice of an ice-like crystal. Using oxygen-17 nuclei as a sensitive quadrupolar probe, the reorientational two-time correlation function displays a clear nonexponentiality. To check whether this dispersive behavior is a consequence of dynamic heterogeneity or rather of an intrinsic nonexponentiality, a multidimensional, four-time magnetic resonance experiment was devised that is generally applicable to strongly quadrupolarly perturbed half-integer nuclei such as oxygen-17. Measurements of an appropriate four-time function demonstrate that it is possible to select a subensemble of slow water molecules. Its mean time scale is compared to theoretical predictions and evidence for significant motional heterogeneity is found.

Nonlinear dynamical modes of climate variability: from curves to manifolds

NASA Astrophysics Data System (ADS)

Gavrilov, Andrey; Mukhin, Dmitry; Loskutov, Evgeny; Feigin, Alexander

2016-04-01

The necessity of efficient dimensionality reduction methods capturing dynamical properties of the system from observed data is evident. Recent study shows that nonlinear dynamical mode (NDM) expansion is able to solve this problem and provide adequate phase variables in climate data analysis [1]. A single NDM is logical extension of linear spatio-temporal structure (like empirical orthogonal function pattern): it is constructed as nonlinear transformation of hidden scalar time series to the space of observed variables, i. e. projection of observed dataset onto a nonlinear curve. Both the hidden time series and the parameters of the curve are learned simultaneously using Bayesian approach. The only prior information about the hidden signal is the assumption of its smoothness. The optimal nonlinearity degree and smoothness are found using Bayesian evidence technique. In this work we do further extension and look for vector hidden signals instead of scalar with the same smoothness restriction. As a result we resolve multidimensional manifolds instead of sum of curves. The dimension of the hidden manifold is optimized using also Bayesian evidence. The efficiency of the extension is demonstrated on model examples. Results of application to climate data are demonstrated and discussed. The study is supported by Government of Russian Federation (agreement #14.Z50.31.0033 with the Institute of Applied Physics of RAS). 1. Mukhin, D., Gavrilov, A., Feigin, A., Loskutov, E., & Kurths, J. (2015). Principal nonlinear dynamical modes of climate variability. Scientific Reports, 5, 15510. http://doi.org/10.1038/srep15510

New approach to evaluate rotation of cervical vertebrae

NASA Astrophysics Data System (ADS)

Hahn, Matthias

2001-07-01

Functional deficits after whiplash injury can be analyzed with a quite novel radiologic method by examination of joint-blocks in C0/1 and C1/2. Thereto the movability of C0, C1 and C2 is determined with three spiral CT-scans of the patient's cervical spine. One series in neutral and one in maximal active lateral right and left rotation each. Previous methods were slice based and time consuming when manually evaluated. We propose a new approach to a computation of these angles in 3D. After a threshold segmentation of bone tissue, a rough 2D classification takes place for C0, C1 and C2 in each rotation series. The center of an axial rotation for each vertebra is gained from the approximation of its center of gravity. The rotation itself is estimated by a cross-correlation of the radial distance functions. From the previous rotation the results are taken to initialize a 3D matching algorithm based on the sum of squared differences in intensity. The optimal match of the vertebrae is computed by means of the multidimensional Powell minimization algorithm. The three translational and three rotational components build a six-dimensional search-space. The vertebrae detection and rotation computation is done fully automatic.

Real-time object recognition in multidimensional images based on joined extended structural tensor and higher-order tensor decomposition methods

NASA Astrophysics Data System (ADS)

Cyganek, Boguslaw; Smolka, Bogdan

2015-02-01

In this paper a system for real-time recognition of objects in multidimensional video signals is proposed. Object recognition is done by pattern projection into the tensor subspaces obtained from the factorization of the signal tensors representing the input signal. However, instead of taking only the intensity signal the novelty of this paper is first to build the Extended Structural Tensor representation from the intensity signal that conveys information on signal intensities, as well as on higher-order statistics of the input signals. This way the higher-order input pattern tensors are built from the training samples. Then, the tensor subspaces are built based on the Higher-Order Singular Value Decomposition of the prototype pattern tensors. Finally, recognition relies on measurements of the distance of a test pattern projected into the tensor subspaces obtained from the training tensors. Due to high-dimensionality of the input data, tensor based methods require high memory and computational resources. However, recent achievements in the technology of the multi-core microprocessors and graphic cards allows real-time operation of the multidimensional methods as is shown and analyzed in this paper based on real examples of object detection in digital images.

Investigation of multidimensional control systems in the state space and wavelet medium

NASA Astrophysics Data System (ADS)

Fedosenkov, D. B.; Simikova, A. A.; Fedosenkov, B. A.

2018-05-01

The notions are introduced of “one-dimensional-point” and “multidimensional-point” automatic control systems. To demonstrate the joint use of approaches based on the concepts of state space and wavelet transforms, a method for optimal control in a state space medium represented in the form of time-frequency representations (maps), is considered. The computer-aided control system is formed on the basis of the similarity transformation method, which makes it possible to exclude the use of reduced state variable observers. 1D-material flow signals formed by primary transducers are converted by means of wavelet transformations into multidimensional concentrated-at-a point variables in the form of time-frequency distributions of Cohen’s class. The algorithm for synthesizing a stationary controller for feeding processes is given here. The conclusion is made that the formation of an optimal control law with time-frequency distributions available contributes to the improvement of transient processes quality in feeding subsystems and the mixing unit. Confirming the efficiency of the method presented is illustrated by an example of the current registration of material flows in the multi-feeding unit. The first section in your paper.

Disentangling multidimensional spatio-temporal data into their common and aberrant responses

DOE PAGES

Chang, Young Hwan; Korkola, James; Amin, Dhara N.; ...

2015-04-22

With the advent of high-throughput measurement techniques, scientists and engineers are starting to grapple with massive data sets and encountering challenges with how to organize, process and extract information into meaningful structures. Multidimensional spatio-temporal biological data sets such as time series gene expression with various perturbations over different cell lines, or neural spike trains across many experimental trials, have the potential to acquire insight about the dynamic behavior of the system. For this potential to be realized, we need a suitable representation to understand the data. A general question is how to organize the observed data into meaningful structures andmore » how to find an appropriate similarity measure. A natural way of viewing these complex high dimensional data sets is to examine and analyze the large-scale features and then to focus on the interesting details. Since the wide range of experiments and unknown complexity of the underlying system contribute to the heterogeneity of biological data, we develop a new method by proposing an extension of Robust Principal Component Analysis (RPCA), which models common variations across multiple experiments as the lowrank component and anomalies across these experiments as the sparse component. We show that the proposed method is able to find distinct subtypes and classify data sets in a robust way without any prior knowledge by separating these common responses and abnormal responses. Thus, the proposed method provides us a new representation of these data sets which has the potential to help users acquire new insight from data.« less

Predicting Energy Consumption for Potential Effective Use in Hybrid Vehicle Powertrain Management Using Driver Prediction

NASA Astrophysics Data System (ADS)

Magnuson, Brian

A proof-of-concept software-in-the-loop study is performed to assess the accuracy of predicted net and charge-gaining energy consumption for potential effective use in optimizing powertrain management of hybrid vehicles. With promising results of improving fuel efficiency of a thermostatic control strategy for a series, plug-ing, hybrid-electric vehicle by 8.24%, the route and speed prediction machine learning algorithms are redesigned and implemented for real- world testing in a stand-alone C++ code-base to ingest map data, learn and predict driver habits, and store driver data for fast startup and shutdown of the controller or computer used to execute the compiled algorithm. Speed prediction is performed using a multi-layer, multi-input, multi- output neural network using feed-forward prediction and gradient descent through back- propagation training. Route prediction utilizes a Hidden Markov Model with a recurrent forward algorithm for prediction and multi-dimensional hash maps to store state and state distribution constraining associations between atomic road segments and end destinations. Predicted energy is calculated using the predicted time-series speed and elevation profile over the predicted route and the road-load equation. Testing of the code-base is performed over a known road network spanning 24x35 blocks on the south hill of Spokane, Washington. A large set of training routes are traversed once to add randomness to the route prediction algorithm, and a subset of the training routes, testing routes, are traversed to assess the accuracy of the net and charge-gaining predicted energy consumption. Each test route is traveled a random number of times with varying speed conditions from traffic and pedestrians to add randomness to speed prediction. Prediction data is stored and analyzed in a post process Matlab script. The aggregated results and analysis of all traversals of all test routes reflect the performance of the Driver Prediction algorithm. The error of average energy gained through charge-gaining events is 31.3% and the error of average net energy consumed is 27.3%. The average delta and average standard deviation of the delta of predicted energy gained through charge-gaining events is 0.639 and 0.601 Wh respectively for individual time-series calculations. Similarly, the average delta and average standard deviation of the delta of the predicted net energy consumed is 0.567 and 0.580 Wh respectively for individual time-series calculations. The average delta and standard deviation of the delta of the predicted speed is 1.60 and 1.15 respectively also for the individual time-series measurements. The percentage of accuracy of route prediction is 91%. Overall, test routes are traversed 151 times for a total test distance of 276.4 km.

Multidimensional scaling of musical time estimations.

PubMed

Cocenas-Silva, Raquel; Bueno, José Lino Oliveira; Molin, Paul; Bigand, Emmanuel

2011-06-01

The aim of this study was to identify the psycho-musical factors that govern time evaluation in Western music from baroque, classic, romantic, and modern repertoires. The excerpts were previously found to represent variability in musical properties and to induce four main categories of emotions. 48 participants (musicians and nonmusicians) freely listened to 16 musical excerpts (lasting 20 sec. each) and grouped those that seemed to have the same duration. Then, participants associated each group of excerpts to one of a set of sine wave tones varying in duration from 16 to 24 sec. Multidimensional scaling analysis generated a two-dimensional solution for these time judgments. Musical excerpts with high arousal produced an overestimation of time, and affective valence had little influence on time perception. The duration was also overestimated when tempo and loudness were higher, and to a lesser extent, timbre density. In contrast, musical tension had little influence.

Reconstruction of multidimensional carbon hosts with combined 0D, 1D and 2D networks for enhanced lithium-sulfur batteries

NASA Astrophysics Data System (ADS)

Li, S. H.; Xia, X. H.; Wang, Y. D.; Wang, X. L.; Tu, J. P.

2017-02-01

It is a core task to find solutions to suppress the "shuttle effect" of polysulfides and improve high rate capability at the sulfur cathode of lithium sulfur batteries. Herein we first time propose a concept of multileveled blocking "dams" to suppress the diffusion of polysulfides. We report a facile and effective strategy to construct multidimensional conductive carbon hosts for accommodation of active sulfur. Multidimensional ternary carbon networks (MTCNs) with 0D nanospheres, 1D nanotubes and 2D nanoflakes are organically combined together to provide multileveled conductive channels to reserve active sulfur and promote stable sustained reactions. In the light of enhanced conductivity and multileveled blocking "dams" for polysulfides, the designed MTCNs/S cathode has been demonstrated with noticeable improvement in discharge capacity (1472 mAh g-1 at 0.l C) and long-term cycling stability (65% retention at 5.0 C after 500 cycles). Our research may provide a new insight in the gradient blocking of polysulfides with the help of multidimensional carbon networks.

Measuring change for a multidimensional test using a generalized explanatory longitudinal item response model.

PubMed

Cho, Sun-Joo; Athay, Michele; Preacher, Kristopher J

2013-05-01

Even though many educational and psychological tests are known to be multidimensional, little research has been done to address how to measure individual differences in change within an item response theory framework. In this paper, we suggest a generalized explanatory longitudinal item response model to measure individual differences in change. New longitudinal models for multidimensional tests and existing models for unidimensional tests are presented within this framework and implemented with software developed for generalized linear models. In addition to the measurement of change, the longitudinal models we present can also be used to explain individual differences in change scores for person groups (e.g., learning disabled students versus non-learning disabled students) and to model differences in item difficulties across item groups (e.g., number operation, measurement, and representation item groups in a mathematics test). An empirical example illustrates the use of the various models for measuring individual differences in change when there are person groups and multiple skill domains which lead to multidimensionality at a time point. © 2012 The British Psychological Society.

Analytical prediction with multidimensional computer programs and experimental verification of the performance, at a variety of operating conditions, of two traveling wave tubes with depressed collectors

NASA Technical Reports Server (NTRS)

Dayton, J. A., Jr.; Kosmahl, H. G.; Ramins, P.; Stankiewicz, N.

1979-01-01

Experimental and analytical results are compared for two high performance, octave bandwidth TWT's that use depressed collectors (MDC's) to improve the efficiency. The computations were carried out with advanced, multidimensional computer programs that are described here in detail. These programs model the electron beam as a series of either disks or rings of charge and follow their multidimensional trajectories from the RF input of the ideal TWT, through the slow wave structure, through the magnetic refocusing system, to their points of impact in the depressed collector. Traveling wave tube performance, collector efficiency, and collector current distribution were computed and the results compared with measurements for a number of TWT-MDC systems. Power conservation and correct accounting of TWT and collector losses were observed. For the TWT's operating at saturation, very good agreement was obtained between the computed and measured collector efficiencies. For a TWT operating 3 and 6 dB below saturation, excellent agreement between computed and measured collector efficiencies was obtained in some cases but only fair agreement in others. However, deviations can largely be explained by small differences in the computed and actual spent beam energy distributions. The analytical tools used here appear to be sufficiently refined to design efficient collectors for this class of TWT. However, for maximum efficiency, some experimental optimization (e.g., collector voltages and aperture sizes) will most likely be required.

Exactly Solvable Multidimensional Nonlinear Equations and Inverse Scattering,

DTIC Science & Technology

1986-12-01

time dimension. Here the prototype euQation is 1 the Kadomtsev - Petviashvili (K-P) equation : .0 6u , x , x - )3,:’u ,’ which is the cop,patliil ity...AD-R193 274 EXACTLY SOLVABLE MULTIDIMENSIONAL NONLINEAR EQUATIONS L/1 AND INVERSE SCATTERING(U) CLARKSON UNIV POTSDAM MY A J MBLOUITZ DEC 86 NSOSI4...ecuations by associating thnm with appropriate compatible linear equations , -ne of which is identified as a Scattering prooD,, ne others(s) serves to

Multi-Dimensional Asymptotically Stable 4th Order Accurate Schemes for the Diffusion Equation

NASA Technical Reports Server (NTRS)

Abarbanel, Saul; Ditkowski, Adi

1996-01-01

An algorithm is presented which solves the multi-dimensional diffusion equation on co mplex shapes to 4th-order accuracy and is asymptotically stable in time. This bounded-error result is achieved by constructing, on a rectangular grid, a differentiation matrix whose symmetric part is negative definite. The differentiation matrix accounts for the Dirichlet boundary condition by imposing penalty like terms. Numerical examples in 2-D show that the method is effective even where standard schemes, stable by traditional definitions fail.

Novel Visualization Approaches in Environmental Mineralogy

NASA Astrophysics Data System (ADS)

Anderson, C. D.; Lopano, C. L.; Hummer, D. R.; Heaney, P. J.; Post, J. E.; Kubicki, J. D.; Sofo, J. O.

2006-05-01

Communicating the complexities of atomic scale reactions between minerals and fluids is fraught with intrinsic challenges. For example, an increasing number of techniques are now available for the interrogation of dynamical processes at the mineral-fluid interface. However, the time-dependent behavior of atomic interactions between a solid and a liquid is often not adequately captured by two-dimensional line drawings or images. At the same time, the necessity for describing these reactions to general audiences is growing more urgent, as funding agencies are amplifying their encouragement to scientists to reach across disciplines and to justify their studies to public audiences. To overcome the shortcomings of traditional graphical representations, the Center for Environmental Kinetics Analysis is creating three-dimensional visualizations of experimental and simulated mineral reactions. These visualizations are then displayed on a stereo 3D projection system called the GeoWall. Made possible (and affordable) by recent improvements in computer and data projector technology, the GeoWall system uses a combination of computer software and hardware, polarizing filters and polarizing glasses, to present visualizations in true 3D. The three-dimensional views greatly improve comprehension of complex multidimensional data, and animations of time series foster better understanding of the underlying processes. The visualizations also offer an effective means to communicate the complexities of environmental mineralogy to colleagues, students and the public. Here we present three different kinds of datasets that demonstrate the effectiveness of the GeoWall in clarifying complex environmental reactions at the atomic scale. First, a time-resolved series of diffraction patterns obtained during the hydrothermal synthesis of metal oxide phases from precursor solutions can be viewed as a surface with interactive controls for peak scaling and color mapping. Second, the results of Rietveld analysis of cation exchange reactions in Mn oxides has provided three-dimensional difference Fourier maps. When stitched together in a temporal series, these offer an animated view of changes in atomic configurations during the process of exchange. Finally, molecular dynamical simulations are visualized as three-dimensional reactions between vibrating atoms in both the solid and the aqueous phases.

Time-varying nonstationary multivariate risk analysis using a dynamic Bayesian copula

NASA Astrophysics Data System (ADS)

Sarhadi, Ali; Burn, Donald H.; Concepción Ausín, María.; Wiper, Michael P.

2016-03-01

A time-varying risk analysis is proposed for an adaptive design framework in nonstationary conditions arising from climate change. A Bayesian, dynamic conditional copula is developed for modeling the time-varying dependence structure between mixed continuous and discrete multiattributes of multidimensional hydrometeorological phenomena. Joint Bayesian inference is carried out to fit the marginals and copula in an illustrative example using an adaptive, Gibbs Markov Chain Monte Carlo (MCMC) sampler. Posterior mean estimates and credible intervals are provided for the model parameters and the Deviance Information Criterion (DIC) is used to select the model that best captures different forms of nonstationarity over time. This study also introduces a fully Bayesian, time-varying joint return period for multivariate time-dependent risk analysis in nonstationary environments. The results demonstrate that the nature and the risk of extreme-climate multidimensional processes are changed over time under the impact of climate change, and accordingly the long-term decision making strategies should be updated based on the anomalies of the nonstationary environment.

Illiquidity premium and expected stock returns in the UK: A new approach

NASA Astrophysics Data System (ADS)

Chen, Jiaqi; Sherif, Mohamed

2016-09-01

This study examines the relative importance of liquidity risk for the time-series and cross-section of stock returns in the UK. We propose a simple way to capture the multidimensionality of illiquidity. Our analysis indicates that existing illiquidity measures have considerable asset specific components, which justifies our new approach. Further, we use an alternative test of the Amihud (2002) measure and parametric and non-parametric methods to investigate whether liquidity risk is priced in the UK. We find that the inclusion of the illiquidity factor in the capital asset pricing model plays a significant role in explaining the cross-sectional variation in stock returns, in particular with the Fama-French three-factor model. Further, using Hansen-Jagannathan non-parametric bounds, we find that the illiquidity-augmented capital asset pricing models yield a small distance error, other non-liquidity based models fail to yield economically plausible distance values. Our findings have important implications for managing the liquidity risk of equity portfolios.

Data-Driven Engineering of Social Dynamics: Pattern Matching and Profit Maximization

PubMed Central

Peng, Huan-Kai; Lee, Hao-Chih; Pan, Jia-Yu; Marculescu, Radu

2016-01-01

In this paper, we define a new problem related to social media, namely, the data-driven engineering of social dynamics. More precisely, given a set of observations from the past, we aim at finding the best short-term intervention that can lead to predefined long-term outcomes. Toward this end, we propose a general formulation that covers two useful engineering tasks as special cases, namely, pattern matching and profit maximization. By incorporating a deep learning model, we derive a solution using convex relaxation and quadratic-programming transformation. Moreover, we propose a data-driven evaluation method in place of the expensive field experiments. Using a Twitter dataset, we demonstrate the effectiveness of our dynamics engineering approach for both pattern matching and profit maximization, and study the multifaceted interplay among several important factors of dynamics engineering, such as solution validity, pattern-matching accuracy, and intervention cost. Finally, the method we propose is general enough to work with multi-dimensional time series, so it can potentially be used in many other applications. PMID:26771830

Data-Driven Engineering of Social Dynamics: Pattern Matching and Profit Maximization.

PubMed

Peng, Huan-Kai; Lee, Hao-Chih; Pan, Jia-Yu; Marculescu, Radu

2016-01-01

In this paper, we define a new problem related to social media, namely, the data-driven engineering of social dynamics. More precisely, given a set of observations from the past, we aim at finding the best short-term intervention that can lead to predefined long-term outcomes. Toward this end, we propose a general formulation that covers two useful engineering tasks as special cases, namely, pattern matching and profit maximization. By incorporating a deep learning model, we derive a solution using convex relaxation and quadratic-programming transformation. Moreover, we propose a data-driven evaluation method in place of the expensive field experiments. Using a Twitter dataset, we demonstrate the effectiveness of our dynamics engineering approach for both pattern matching and profit maximization, and study the multifaceted interplay among several important factors of dynamics engineering, such as solution validity, pattern-matching accuracy, and intervention cost. Finally, the method we propose is general enough to work with multi-dimensional time series, so it can potentially be used in many other applications.

Automated Spatiotemporal Analysis of Fibrils and Coronal Rain Using the Rolling Hough Transform

NASA Astrophysics Data System (ADS)

Schad, Thomas

2017-09-01

A technique is presented that automates the direction characterization of curvilinear features in multidimensional solar imaging datasets. It is an extension of the Rolling Hough Transform (RHT) technique presented by Clark, Peek, and Putman ( Astrophys. J. 789, 82, 2014), and it excels at rapid quantification of spatial and spatiotemporal feature orientation even for applications with a low signal-to-noise ratio. It operates on a pixel-by-pixel basis within a dataset and reliably quantifies orientation even for locations not centered on a feature ridge, which is used here to derive a quasi-continuous map of the chromospheric fine-structure projection angle. For time-series analysis, a procedure is developed that uses a hierarchical application of the RHT to automatically derive the apparent motion of coronal rain observed off-limb. Essential to the success of this technique is the formulation presented in this article for the RHT error analysis as it provides a means to properly filter results.

How significant is the slope of the sea-side boundary for modelling seawater intrusion in coastal aquifers?

NASA Astrophysics Data System (ADS)

Walther, Marc; Graf, Thomas; Kolditz, Olaf; Liedl, Rudolf; Post, Vincent

2017-08-01

Application of numerical models is a common method to assess groundwater resources. The versatility of these models allows consideration of different levels of complexity, but the accuracy of the outcomes hinges upon a proper description of the system behaviour. In seawater intrusion assessment, the implementation of the sea-side boundary condition is of particular importance. We evaluate the influence of the slope of the sea-side boundary on the simulation results of seawater intrusion in a freshwater aquifer by employing a series of slope variations together with a sensitivity analysis by varying additional sensitive parameters (freshwater inflow and longitudinal and transverse dispersivities). Model results reveal a multi-dimensional dependence of the investigated variables with an increasing relevance of the sea-side boundary slope for seawater intrusion (decrease of up to 32%), submarine groundwater discharge zone (reduction of up to 55%), and turnover times (increase of up to 730%) with increasing freshwater inflow or dispersivity values.

Analysing spatio-temporal land degradation dynamics in dry rangelands using landscape metrics and satellite time series data

NASA Astrophysics Data System (ADS)

von Keyserlingk, Jennifer; Paton, Eva Nora; Förster, Saskia; Bronstert, Axel

2017-04-01

Many of the dry rangelands of Southern Europe are threatened by land degradation. This process not only reduces the land's ecological functioning, but also its capacity to provide ecosystem goods and services for local land users. In rangelands, one important aspect is vegetation degradation, which reduces the land's capacity to support livestock. Thus, there is an urgent need to understand the complex dynamics and drivers of land degradation. In the past, both have been difficult to study due to the extensive spatial and temporal scales involved. In the last decade, a large number of remotely sensed imageries has become available for free, which enables a new approach to this topic. The aim of this research is to study land degradation as a multidimensional process incorporating its spatial and temporal components. We developed a methodological approach that makes use of long-term satellite Landsat data. Here, we use imagery of a typical degraded Mediterranean rangeland in Southern Cyprus (Randi Forest) for the years 1998-2015. We have chosen the NDVI as a proxy for vegetation greenness and applied different spatial landscape metrics to calculate changes in vegetation patterns over time. Further, we applied a time-series based approach (BFAST) on selected pixels, to look for sudden changes and trends in the vegetation dynamics. The results promoted our knowledge on how land degradation dynamics in Mediterranean rangelands can be captured through spatio-temporal vegetation dynamics and allowed us to select the most suitable metrics for further analysis. In the long-term, we aim at using Landsat satellite data covering 30 years. To gain a functional understanding of land degradation, we want to overlay our results from the remotely sensed data with results of an eco-hydrological model (SWAT).

Multidimensional analysis of fast-spectrum material replacement measurements for systematic estimation of cross section uncertainties

NASA Technical Reports Server (NTRS)

Klann, P. G.; Lantz, E.; Mayo, W. T.

1973-01-01

A series of central core and core-reflector interface sample replacement experiments for 16 materials performed in the NASA heavy-metal-reflected, fast spectrum critical assembly (NCA) were analyzed in four and 13 groups using the GAM 2 cross-section set. The individual worths obtained by TDSN and DOT multidimensional transport theory calculations showed significant differences from the experimental results. These were attributed to cross-section uncertainties in the GAM 2 cross sections. Simultaneous analysis of the measured and calculated sample worths permitted separation of the worths into capture and scattering components which systematically provided fast spectrum averaged correction factors to the magnitudes of the GAM 2 absorption and scattering cross sections. Several Los Alamos clean critical assemblies containing Oy, Ta, and Mo as well as one of the NCA compositions were reanalyzed using the corrected cross sections. In all cases the eigenvalues were significantly improved and were recomputed to within 1 percent of the experimental eigenvalue. A comparable procedure may be used for ENDF cross sections when these are available.

Towards an Entropy Stable Spectral Element Framework for Computational Fluid Dynamics

NASA Technical Reports Server (NTRS)

Carpenter, Mark H.; Parsani, Matteo; Fisher, Travis C.; Nielsen, Eric J.

2016-01-01

Entropy stable (SS) discontinuous spectral collocation formulations of any order are developed for the compressible Navier-Stokes equations on hexahedral elements. Recent progress on two complementary efforts is presented. The first effort is a generalization of previous SS spectral collocation work to extend the applicable set of points from tensor product, Legendre-Gauss-Lobatto (LGL) to tensor product Legendre-Gauss (LG) points. The LG and LGL point formulations are compared on a series of test problems. Although being more costly to implement, it is shown that the LG operators are significantly more accurate on comparable grids. Both the LGL and LG operators are of comparable efficiency and robustness, as is demonstrated using test problems for which conventional FEM techniques suffer instability. The second effort generalizes previous SS work to include the possibility of p-refinement at non-conforming interfaces. A generalization of existing entropy stability machinery is developed to accommodate the nuances of fully multi-dimensional summation-by-parts (SBP) operators. The entropy stability of the compressible Euler equations on non-conforming interfaces is demonstrated using the newly developed LG operators and multi-dimensional interface interpolation operators.

Validation of a systems-actuarial computer process for multidimensional classification of child psychopathology.

PubMed

McDermott, P A; Hale, R L

1982-07-01

Tested diagnostic classifications of child psychopathology produced by a computerized technique known as multidimensional actuarial classification (MAC) against the criterion of expert psychological opinion. The MAC program applies series of statistical decision rules to assess the importance of and relationships among several dimensions of classification, i.e., intellectual functioning, academic achievement, adaptive behavior, and social and behavioral adjustment, to perform differential diagnosis of children's mental retardation, specific learning disabilities, behavioral and emotional disturbance, possible communication or perceptual-motor impairment, and academic under- and overachievement in reading and mathematics. Classifications rendered by MAC are compared to those offered by two expert child psychologists for cases of 73 children referred for psychological services. Experts' agreement with MAC was significant for all classification areas, as was MAC's agreement with the experts held as a conjoint reference standard. Whereas the experts' agreement with MAC averaged 86.0% above chance, their agreement with one another averaged 76.5% above chance. Implications of the findings are explored and potential advantages of the systems-actuarial approach are discussed.

A second-order cell-centered Lagrangian ADER-MOOD finite volume scheme on multidimensional unstructured meshes for hydrodynamics

NASA Astrophysics Data System (ADS)

Boscheri, Walter; Dumbser, Michael; Loubère, Raphaël; Maire, Pierre-Henri

2018-04-01

In this paper we develop a conservative cell-centered Lagrangian finite volume scheme for the solution of the hydrodynamics equations on unstructured multidimensional grids. The method is derived from the Eucclhyd scheme discussed in [47,43,45]. It is second-order accurate in space and is combined with the a posteriori Multidimensional Optimal Order Detection (MOOD) limiting strategy to ensure robustness and stability at shock waves. Second-order of accuracy in time is achieved via the ADER (Arbitrary high order schemes using DERivatives) approach. A large set of numerical test cases is proposed to assess the ability of the method to achieve effective second order of accuracy on smooth flows, maintaining an essentially non-oscillatory behavior on discontinuous profiles, general robustness ensuring physical admissibility of the numerical solution, and precision where appropriate.

Changes in frontal plane dynamics and the loading response phase of the gait cycle are characteristic of severe knee osteoarthritis application of a multidimensional analysis technique.

PubMed

Astephen, J L; Deluzio, K J

2005-02-01

Osteoarthritis of the knee is related to many correlated mechanical factors that can be measured with gait analysis. Gait analysis results in large data sets. The analysis of these data is difficult due to the correlated, multidimensional nature of the measures. A multidimensional model that uses two multivariate statistical techniques, principal component analysis and discriminant analysis, was used to discriminate between the gait patterns of the normal subject group and the osteoarthritis subject group. Nine time varying gait measures and eight discrete measures were included in the analysis. All interrelationships between and within the measures were retained in the analysis. The multidimensional analysis technique successfully separated the gait patterns of normal and knee osteoarthritis subjects with a misclassification error rate of <6%. The most discriminatory feature described a static and dynamic alignment factor. The second most discriminatory feature described a gait pattern change during the loading response phase of the gait cycle. The interrelationships between gait measures and between the time instants of the gait cycle can provide insight into the mechanical mechanisms of pathologies such as knee osteoarthritis. These results suggest that changes in frontal plane loading and alignment and the loading response phase of the gait cycle are characteristic of severe knee osteoarthritis gait patterns. Subsequent investigations earlier in the disease process may suggest the importance of these factors to the progression of knee osteoarthritis.

Comprehensive lipidomic analysis of human plasma using multidimensional liquid- and gas-phase separations: Two-dimensional liquid chromatography-mass spectrometry vs. liquid chromatography-trapped-ion-mobility-mass spectrometry.

PubMed

Baglai, Anna; Gargano, Andrea F G; Jordens, Jan; Mengerink, Ynze; Honing, Maarten; van der Wal, Sjoerd; Schoenmakers, Peter J

2017-12-29

Recent advancements in separation science have resulted in the commercialization of multidimensional separation systems that provide higher peak capacities and, hence, enable a more-detailed characterization of complex mixtures. In particular, two powerful analytical tools are increasingly used by analytical scientists, namely online comprehensive two-dimensional liquid chromatography (LC×LC, having a second-dimension separation in the liquid phase) and liquid chromatography-ion mobility-spectrometry (LC-IMS, second dimension separation in the gas phase). The goal of the current study was a general assessment of the liquid-chromatography-trapped-ion-mobility-mass spectrometry (LC-TIMS-MS) and comprehensive two-dimensional liquid chromatography-mass spectrometry (LC×LC-MS) platforms for untargeted lipid mapping in human plasma. For the first time trapped-ion-mobility spectrometry (TIMS) was employed for the separation of the major lipid classes and ion-mobility-derived collision-cross-section values were determined for a number of lipid standards. The general effects of a number of influencing parameters have been inspected and possible directions for improvements are discussed. We aimed to provide a general indication and practical guidelines for the analyst to choose an efficient multidimensional separation platform according to the particular requirements of the application. Analysis time, orthogonality, peak capacity, and an indicative measure for the resolving power are discussed as main characteristics for multidimensional separation systems. Copyright © 2017 Elsevier B.V. All rights reserved.

Mental workload and cognitive task automaticity: an evaluation of subjective and time estimation metrics.

PubMed

Liu, Y; Wickens, C D

1994-11-01

The evaluation of mental workload is becoming increasingly important in system design and analysis. The present study examined the structure and assessment of mental workload in performing decision and monitoring tasks by focusing on two mental workload measurements: subjective assessment and time estimation. The task required the assignment of a series of incoming customers to the shortest of three parallel service lines displayed on a computer monitor. The subject was either in charge of the customer assignment (manual mode) or was monitoring an automated system performing the same task (automatic mode). In both cases, the subjects were required to detect the non-optimal assignments that they or the computer had made. Time pressure was manipulated by the experimenter to create fast and slow conditions. The results revealed a multi-dimensional structure of mental workload and a multi-step process of subjective workload assessment. The results also indicated that subjective workload was more influenced by the subject's participatory mode than by the factor of task speed. The time estimation intervals produced while performing the decision and monitoring tasks had significantly greater length and larger variability than those produced while either performing no other tasks or performing a well practised customer assignment task. This result seemed to indicate that time estimation was sensitive to the presence of perceptual/cognitive demands, but not to response related activities to which behavioural automaticity has developed.

Goniochromatic and sparkle properties of effect pigmented samples in multidimensional configuration

NASA Astrophysics Data System (ADS)

Höpe, Andreas; Hauer, Kai-Olaf; Teichert, Sven; Hünerhoff, Dirk; Strothkämper, Christian

2015-03-01

The effects of goniochromatism and sparkle are gaining more and more interest for surface refinement applications driven by demanding requirements from such different branches as automotive, cosmetics, printing and packaging industry. The common background and intention in all of these implementations is improvement of the visual appearance of the related commercial products. Goniochromatic materials show strong angular-dependent reflection characteristics and hence a color impression depending on the effective spatial arrangement of illumination and observation relative to the surface of the artifact. Sparkle is a texture related effect giving a surface which is irradiated directionally, like direct sun light, a bright glittering effect, similar to twinkling stars at the night sky. The prototype for this new effect is the Xirallic® pigment of MERCK KGaA, Germany. The same pigment shows in diffuse irradiation, like on a cloudy day, a different visual effect called graininess (coarseness) which appears as a granular structure of the surface. Both effects were studied on especially manufactured samples of a dilution series in pigment concentration and a tonality series with carbon black. The experiments were carried out with the robot-based gonioreflectometer and integrating sphere facilities at Physikalisch-Technische Bundesanstalt (PTB) in multidimensional configurations of directional and diffuse irradiation. The research is part of the European Metrology Research Program (EMRP), which is a metrology-focused program of coordinated Research & Development (R&D) funded by the European Commission and participating countries within the European Association of National Metrology Institutes (EURAMET). More information and updated news concerning the project can be found on the xD-Reflect website http://www.xdreflect.eu/.

Recognition Memory for Realistic Synthetic Faces

PubMed Central

Yotsumoto, Yuko; Kahana, Michael J.; Wilson, Hugh R.; Sekuler, Robert

2006-01-01

A series of experiments examined short-term recognition memory for trios of briefly-presented, synthetic human faces derived from three real human faces. The stimuli were graded series of faces, which differed by varying known amounts from the face of the average female. Faces based on each of the three real faces were transformed so as to lie along orthogonal axes in a 3-D face space. Experiment 1 showed that the synthetic faces' perceptual similarity stucture strongly influenced recognition memory. Results were fit by NEMo, a noisy exemplar model of perceptual recognition memory. The fits revealed that recognition memory was influenced both by the similarity of the probe to series items, and by the similarities among the series items themselves. Non-metric multi-dimensional scaling (MDS) showed that faces' perceptual representations largely preserved the 3-D space in which the face stimuli were arrayed. NEMo gave a better account of the results when similarity was defined as perceptual, MDS similarity rather than physical proximity of one face to another. Experiment 2 confirmed the importance of within-list homogeneity directly, without mediation of a model. We discuss the affinities and differences between visual memory for synthetic faces and memory for simpler stimuli. PMID:17948069

Lab-X-ray multidimensional imaging of processes inside porous media

NASA Astrophysics Data System (ADS)

Godinho, Jose

2017-04-01

Time-lapse and other multidimensional X-ray imaging techniques have mostly been applied using synchrotron radiation, which limits accessibility and complicates data analysis. Here, we present new time-lapse imaging approaches using laboratory X-ray computed microtomography (CT) to study transformations inside porous media. Specifically, three methods will be presented: 1) Quantitative time-lapse radiography to study sub-second processes. For example to study the penetration of particles into fractures and pores, which is essential to understand how proppants keep fractures opened during hydraulic fracturing and how filter cakes form during borehole drilling. 2) Combination of time-lapse CT with diffraction tomography to study the transformation between bio-inspired polymorphs in 6D, e.g. mineral phase transformation between ACC, Vaterite and Calcite - CaCO3, and between ACS, Anhydrite and Gypsum - CaSO4. Crystals can be resolved in nanopores down to 7 nm (over 100 times smaller than the resolution of CT), which allows studying the effect of confinement on phase stability and growth rates. 3) Fast iterative helical micro-CT scanning to study samples of high ratio height to width (e.g. long cores) with optimal resolution. Here we show how this can be useful to study the distribution of the products from fluid-mediated mineral reactions throughout longer reaction paths and more representative volumes. Using state of the art reconstruction algorithms allows reducing the scanning times from over ten hours to below two hours enabling time-lapse studies. It is expected that these new techniques will open new possibilities for time-lapse imaging of a wider range of geological processes using laboratory X-ray CT, thereby increasing the accessibility of multidimensional imaging to a larger number of users and applications in geology.

Scalable Earth-observation Analytics for Geoscientists: Spacetime Extensions to the Array Database SciDB

NASA Astrophysics Data System (ADS)

Appel, Marius; Lahn, Florian; Pebesma, Edzer; Buytaert, Wouter; Moulds, Simon

2016-04-01

Today's amount of freely available data requires scientists to spend large parts of their work on data management. This is especially true in environmental sciences when working with large remote sensing datasets, such as obtained from earth-observation satellites like the Sentinel fleet. Many frameworks like SpatialHadoop or Apache Spark address the scalability but target programmers rather than data analysts, and are not dedicated to imagery or array data. In this work, we use the open-source data management and analytics system SciDB to bring large earth-observation datasets closer to analysts. Its underlying data representation as multidimensional arrays fits naturally to earth-observation datasets, distributes storage and computational load over multiple instances by multidimensional chunking, and also enables efficient time-series based analyses, which is usually difficult using file- or tile-based approaches. Existing interfaces to R and Python furthermore allow for scalable analytics with relatively little learning effort. However, interfacing SciDB and file-based earth-observation datasets that come as tiled temporal snapshots requires a lot of manual bookkeeping during ingestion, and SciDB natively only supports loading data from CSV-like and custom binary formatted files, which currently limits its practical use in earth-observation analytics. To make it easier to work with large multi-temporal datasets in SciDB, we developed software tools that enrich SciDB with earth observation metadata and allow working with commonly used file formats: (i) the SciDB extension library scidb4geo simplifies working with spatiotemporal arrays by adding relevant metadata to the database and (ii) the Geospatial Data Abstraction Library (GDAL) driver implementation scidb4gdal allows to ingest and export remote sensing imagery from and to a large number of file formats. Using added metadata on temporal resolution and coverage, the GDAL driver supports time-based ingestion of imagery to existing multi-temporal SciDB arrays. While our SciDB plugin works directly in the database, the GDAL driver has been specifically developed using a minimum amount of external dependencies (i.e. CURL). Source code for both tools is available from github [1]. We present these tools in a case-study that demonstrates the ingestion of multi-temporal tiled earth-observation data to SciDB, followed by a time-series analysis using R and SciDBR. Through the exclusive use of open-source software, our approach supports reproducibility in scalable large-scale earth-observation analytics. In the future, these tools can be used in an automated way to let scientists only work on ready-to-use SciDB arrays to significantly reduce the data management workload for domain scientists. [1] https://github.com/mappl/scidb4geo} and \\url{https://github.com/mappl/scidb4gdal

Variational calculation of macrostate transition rates

NASA Astrophysics Data System (ADS)

Ulitsky, Alex; Shalloway, David

1998-08-01

We develop the macrostate variational method (MVM) for computing reaction rates of diffusive conformational transitions in multidimensional systems by a variational coarse-grained "macrostate" decomposition of the Smoluchowski equation. MVM uses multidimensional Gaussian packets to identify and focus computational effort on the "transition region," a localized, self-consistently determined region in conformational space positioned roughly between the macrostates. It also determines the "transition direction" which optimally specifies the projected potential of mean force for mean first-passage time calculations. MVM is complementary to variational transition state theory in that it can efficiently solve multidimensional problems but does not accommodate memory-friction effects. It has been tested on model 1- and 2-dimensional potentials and on the 12-dimensional conformational transition between the isoforms of a microcluster of six-atoms having only van der Waals interactions. Comparison with Brownian dynamics calculations shows that MVM obtains equivalent results at a fraction of the computational cost.

Integrating Scientific Array Processing into Standard SQL

NASA Astrophysics Data System (ADS)

Misev, Dimitar; Bachhuber, Johannes; Baumann, Peter

2014-05-01

We live in a time that is dominated by data. Data storage is cheap and more applications than ever accrue vast amounts of data. Storing the emerging multidimensional data sets efficiently, however, and allowing them to be queried by their inherent structure, is a challenge many databases have to face today. Despite the fact that multidimensional array data is almost always linked to additional, non-array information, array databases have mostly developed separately from relational systems, resulting in a disparity between the two database categories. The current SQL standard and SQL DBMS supports arrays - and in an extension also multidimensional arrays - but does so in a very rudimentary and inefficient way. This poster demonstrates the practicality of an SQL extension for array processing, implemented in a proof-of-concept multi-faceted system that manages a federation of array and relational database systems, providing transparent, efficient and scalable access to the heterogeneous data in them.

An empirical study of multidimensional fidelity of COMPASS consultation.

PubMed

Wong, Venus; Ruble, Lisa A; McGrew, John H; Yu, Yue

2018-06-01

Consultation is essential to the daily practice of school psychologists (National Association of School Psychologist, 2010). Successful consultation requires fidelity at both the consultant (implementation) and consultee (intervention) levels. We applied a multidimensional, multilevel conception of fidelity (Dunst, Trivette, & Raab, 2013) to a consultative intervention called the Collaborative Model for Promoting Competence and Success (COMPASS) for students with autism. The study provided 3 main findings. First, multidimensional, multilevel fidelity is a stable construct and increases over time with consultation support. Second, mediation analyses revealed that implementation-level fidelity components had distant, indirect effects on student Individualized Education Program (IEP) outcomes. Third, 3 fidelity components correlated with IEP outcomes: teacher coaching responsiveness at the implementation level, and teacher quality of delivery and student responsiveness at the intervention levels. Implications and future directions are discussed. (PsycINFO Database Record (c) 2018 APA, all rights reserved).

A Multidimensional Data Warehouse for Community Health Centers

PubMed Central

Kunjan, Kislaya; Toscos, Tammy; Turkcan, Ayten; Doebbeling, Brad N.

2015-01-01

Community health centers (CHCs) play a pivotal role in healthcare delivery to vulnerable populations, but have not yet benefited from a data warehouse that can support improvements in clinical and financial outcomes across the practice. We have developed a multidimensional clinic data warehouse (CDW) by working with 7 CHCs across the state of Indiana and integrating their operational, financial and electronic patient records to support ongoing delivery of care. We describe in detail the rationale for the project, the data architecture employed, the content of the data warehouse, along with a description of the challenges experienced and strategies used in the development of this repository that may help other researchers, managers and leaders in health informatics. The resulting multidimensional data warehouse is highly practical and is designed to provide a foundation for wide-ranging healthcare data analytics over time and across the community health research enterprise. PMID:26958297

A Multidimensional Data Warehouse for Community Health Centers.

PubMed

Kunjan, Kislaya; Toscos, Tammy; Turkcan, Ayten; Doebbeling, Brad N

2015-01-01

Community health centers (CHCs) play a pivotal role in healthcare delivery to vulnerable populations, but have not yet benefited from a data warehouse that can support improvements in clinical and financial outcomes across the practice. We have developed a multidimensional clinic data warehouse (CDW) by working with 7 CHCs across the state of Indiana and integrating their operational, financial and electronic patient records to support ongoing delivery of care. We describe in detail the rationale for the project, the data architecture employed, the content of the data warehouse, along with a description of the challenges experienced and strategies used in the development of this repository that may help other researchers, managers and leaders in health informatics. The resulting multidimensional data warehouse is highly practical and is designed to provide a foundation for wide-ranging healthcare data analytics over time and across the community health research enterprise.

A cross-diffusion system derived from a Fokker-Planck equation with partial averaging

NASA Astrophysics Data System (ADS)

Jüngel, Ansgar; Zamponi, Nicola

2017-02-01

A cross-diffusion system for two components with a Laplacian structure is analyzed on the multi-dimensional torus. This system, which was recently suggested by P.-L. Lions, is formally derived from a Fokker-Planck equation for the probability density associated with a multi-dimensional Itō process, assuming that the diffusion coefficients depend on partial averages of the probability density with exponential weights. A main feature is that the diffusion matrix of the limiting cross-diffusion system is generally neither symmetric nor positive definite, but its structure allows for the use of entropy methods. The global-in-time existence of positive weak solutions is proved and, under a simplifying assumption, the large-time asymptotics is investigated.

Improved surface-wave retrieval from ambient seismic noise by multi-dimensional deconvolution

NASA Astrophysics Data System (ADS)

Wapenaar, Kees; Ruigrok, Elmer; van der Neut, Joost; Draganov, Deyan

2011-01-01

The methodology of surface-wave retrieval from ambient seismic noise by crosscorrelation relies on the assumption that the noise field is equipartitioned. Deviations from equipartitioning degrade the accuracy of the retrieved surface-wave Green's function. A point-spread function, derived from the same ambient noise field, quantifies the smearing in space and time of the virtual source of the Green's function. By multidimensionally deconvolving the retrieved Green's function by the point-spread function, the virtual source becomes better focussed in space and time and hence the accuracy of the retrieved surface-wave Green's function may improve significantly. We illustrate this at the hand of a numerical example and discuss the advantages and limitations of this new methodology.

Students' proficiency scores within multitrait item response theory

NASA Astrophysics Data System (ADS)

Scott, Terry F.; Schumayer, Daniel

2015-12-01

In this paper we present a series of item response models of data collected using the Force Concept Inventory. The Force Concept Inventory (FCI) was designed to poll the Newtonian conception of force viewed as a multidimensional concept, that is, as a complex of distinguishable conceptual dimensions. Several previous studies have developed single-trait item response models of FCI data; however, we feel that multidimensional models are also appropriate given the explicitly multidimensional design of the inventory. The models employed in the research reported here vary in both the number of fitting parameters and the number of underlying latent traits assumed. We calculate several model information statistics to ensure adequate model fit and to determine which of the models provides the optimal balance of information and parsimony. Our analysis indicates that all item response models tested, from the single-trait Rasch model through to a model with ten latent traits, satisfy the standard requirements of fit. However, analysis of model information criteria indicates that the five-trait model is optimal. We note that an earlier factor analysis of the same FCI data also led to a five-factor model. Furthermore the factors in our previous study and the traits identified in the current work match each other well. The optimal five-trait model assigns proficiency scores to all respondents for each of the five traits. We construct a correlation matrix between the proficiencies in each of these traits. This correlation matrix shows strong correlations between some proficiencies, and strong anticorrelations between others. We present an interpretation of this correlation matrix.

Development of ITSASGIS-5D: seeking interoperability between Marine GIS layers and scientific multidimensional data using open source tools and OGC services for multidisciplinary research.

NASA Astrophysics Data System (ADS)

Sagarminaga, Y.; Galparsoro, I.; Reig, R.; Sánchez, J. A.

2012-04-01

Since 2000, an intense effort was conducted in AZTI's Marine Research Division to set up a data management system which could gather all the marine datasets that were being produced by different in-house research projects. For that, a corporative GIS was designed that included a data and metadata repository, a database, a layer catalog & search application and an internet map viewer. Several layers, mostly dealing with physical, chemical and biological in-situ sampling, and basic and thematic cartography including bathymetry, geomorphology, different species habitat maps, and human pressure and activities maps, were successfully gathered in this system. Very soon, it was realised that new marine technologies yielding continuous multidimensional data, sometimes called FES (Fluid Earth System) data, were difficult to handle in this structure. The data affected, mainly included numerical oceanographic and meteorological models, remote sensing data, coastal RADAR data, and some in-situ observational systems such as CTD's casts, moored or lagrangian buoys, etc. A management system for gridded multidimensional data was developed using standardized formats (netcdf using CF conventions) and tools such as THREDDS catalog (UNIDATA/UCAR) providing web services such as OPENDAP, NCSS, and WCS, as well as ncWMS service developed by the Reading e-science Center. At present, a system (ITSASGIS-5D) is being developed, based on OGC standards and open-source tools to allow interoperability between all the data types mentioned before. This system includes, in the server side, postgresql/postgis databases and geoserver for GIS layers, and THREDDS/Opendap and ncWMS services for FES gridded data. Moreover, an on-line client is being developed to allow joint access, user configuration, data visualisation & query and data distribution. This client is using mapfish, ExtJS - GeoEXT, and openlayers libraries. Through this presentation the elements of the first released version of this system will be described and showed, together with the new topics to be developed in new versions that include among others, the integration of geoNetwork libraries and tools for both FES and GIS metadata management, and the use of new OGC Sensor Observation Services (SOS) to integrate non gridded multidimensional data such as time series, depth profiles or trajectories provided by different observational systems. The final aim of this approach is to contribute to the multidisciplinary access and use of marine data for management and research activities, and facilitate the implementation of integrated ecosystem based approaches in the fields of fisheries advice and management, marine spatial planning, or the implementation of the European policies such as the Water Framework Directive, the Marine Strategy Framework Directive or the Habitat Framework Directive.

Rapid acquisition of data dense solid-state CPMG NMR spectral sets using multi-dimensional statistical analysis

DOE PAGES

Mason, H. E.; Uribe, E. C.; Shusterman, J. A.

2018-01-01

Tensor-rank decomposition methods have been applied to variable contact time 29 Si{ 1 H} CP/CPMG NMR data sets to extract NMR dynamics information and dramatically decrease conventional NMR acquisition times.

Rapid acquisition of data dense solid-state CPMG NMR spectral sets using multi-dimensional statistical analysis

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

Mason, H. E.; Uribe, E. C.; Shusterman, J. A.

Tensor-rank decomposition methods have been applied to variable contact time 29 Si{ 1 H} CP/CPMG NMR data sets to extract NMR dynamics information and dramatically decrease conventional NMR acquisition times.

Application of musical timbre discrimination features to active sonar classification

NASA Astrophysics Data System (ADS)

Young, Victor W.; Hines, Paul C.; Pecknold, Sean

2005-04-01

In musical acoustics significant effort has been devoted to uncovering the physical basis of timbre perception. Most investigations into timbre rely on multidimensional scaling (MDS), in which different musical sounds are arranged as points in multidimensional space. The Euclidean distance between points corresponds to the perceptual distance between sounds and the multidimensional axes are linked to measurable properties of the sounds. MDS has identified numerous temporal and spectral features believed to be important to timbre perception. There is reason to believe that some of these features may have wider application in the disparate field of underwater acoustics, since anecdotal evidence suggests active sonar returns from metallic objects sound different than natural clutter returns when auralized by human operators. This is particularly encouraging since attempts to develop robust automatic classifiers capable of target-clutter discrimination over a wide range of operational conditions have met with limited success. Spectral features relevant to target-clutter discrimination are believed to include click-pitch and envelope irregularity; relevant temporal features are believed to include duration, sub-band attack/decay time, and time separation pitch. Preliminary results from an investigation into the role of these timbre features in target-clutter discrimination will be presented. [Work supported by NSERC and GDC.

Multidimensional poverty, household environment and short-term morbidity in India.

PubMed

Dehury, Bidyadhar; Mohanty, Sanjay K

2017-01-01

Using the unit data from the second round of the Indian Human Development Survey (IHDS-II), 2011-2012, which covered 42,152 households, this paper examines the association between multidimensional poverty, household environmental deprivation and short-term morbidities (fever, cough and diarrhoea) in India. Poverty is measured in a multidimensional framework that includes the dimensions of education, health and income, while household environmental deprivation is defined as lack of access to improved sanitation, drinking water and cooking fuel. A composite index combining multidimensional poverty and household environmental deprivation has been computed, and households are classified as follows: multidimensional poor and living in a poor household environment, multidimensional non-poor and living in a poor household environment, multidimensional poor and living in a good household environment and multidimensional non-poor and living in a good household environment. Results suggest that about 23% of the population belonging to multidimensional poor households and living in a poor household environment had experienced short-term morbidities in a reference period of 30 days compared to 20% of the population belonging to multidimensional non-poor households and living in a poor household environment, 19% of the population belonging to multidimensional poor households and living in a good household environment and 15% of the population belonging to multidimensional non-poor households and living in a good household environment. Controlling for socioeconomic covariates, the odds of short-term morbidity was 1.47 [CI 1.40-1.53] among the multidimensional poor and living in a poor household environment, 1.28 [CI 1.21-1.37] among the multidimensional non-poor and living in a poor household environment and 1.21 [CI 1.64-1.28] among the multidimensional poor and living in a good household environment compared to the multidimensional non-poor and living in a good household environment. Results are robust across states and hold good for each of the three morbidities: fever, cough and diarrhoea. This establishes that along with poverty, household environmental conditions have a significant bearing on short-term morbidities in India. Public investment in sanitation, drinking water and cooking fuel can reduce the morbidity and improve the health of the population.

Fast acquisition of multidimensional NMR spectra of solids and mesophases using alternative sampling methods.

PubMed

Lesot, Philippe; Kazimierczuk, Krzysztof; Trébosc, Julien; Amoureux, Jean-Paul; Lafon, Olivier

2015-11-01

Unique information about the atom-level structure and dynamics of solids and mesophases can be obtained by the use of multidimensional nuclear magnetic resonance (NMR) experiments. Nevertheless, the acquisition of these experiments often requires long acquisition times. We review here alternative sampling methods, which have been proposed to circumvent this issue in the case of solids and mesophases. Compared to the spectra of solutions, those of solids and mesophases present some specificities because they usually display lower signal-to-noise ratios, non-Lorentzian line shapes, lower spectral resolutions and wider spectral widths. We highlight herein the advantages and limitations of these alternative sampling methods. A first route to accelerate the acquisition time of multidimensional NMR spectra consists in the use of sparse sampling schemes, such as truncated, radial or random sampling ones. These sparsely sampled datasets are generally processed by reconstruction methods differing from the Discrete Fourier Transform (DFT). A host of non-DFT methods have been applied for solids and mesophases, including the G-matrix Fourier transform, the linear least-square procedures, the covariance transform, the maximum entropy and the compressed sensing. A second class of alternative sampling consists in departing from the Jeener paradigm for multidimensional NMR experiments. These non-Jeener methods include Hadamard spectroscopy as well as spatial or orientational encoding of the evolution frequencies. The increasing number of high field NMR magnets and the development of techniques to enhance NMR sensitivity will contribute to widen the use of these alternative sampling methods for the study of solids and mesophases in the coming years. Copyright © 2015 John Wiley & Sons, Ltd.

Measuring Future Time Perspective across Adulthood: Development and Evaluation of a Brief Multidimensional Questionnaire

PubMed Central

Brothers, Allyson; Chui, Helena; Diehl, Manfred

2014-01-01

Purpose of the Study: Despite calls for the consideration of future time perspective (FTP) as a multidimensional construct, mostly unidimensional measurement instruments have been used. This study had two objectives: (a) to develop a brief multidimensional questionnaire for assessing FTP in adulthood and evaluate its psychometric properties; and (b) to examine age associations and age-group differences of the dimensions of FTP. Design and Methods: Data were collected from 625 community-residing adults between the ages of 18 and 93, representing young, middle-aged, and older adults. The psychometric evaluation involved exploratory factor analyses (EFA) and confirmatory FA (CFA), reliability and validity analyses, and measurement invariance testing. Zero-order and partial correlations were used to examine the association of the dimensions of FTP with age, and multivariate analysis of variance was used to examine age-group differences. Results: EFA and CFA supported a three-factor solution: Future as Open, Future as Limited, and Future as Ambiguous. Metric measurement invariance for this factor structure was confirmed across the three age groups. Reliability and validity analyses provided evidence of sound psychometric properties of the brief questionnaire. Age was negatively associated with Future as Open and positively associated with Future as Limited. Young adults exhibited significantly greater ambiguity toward the future than middle-aged or older adults. Implications: This study provides evidence in support of the psychometric properties of a new brief multidimensional FTP scale. It also provides evidence for a pattern of age associations and age-group differences consistent with life-span developmental theory. PMID:25063938

Effective rotational correlation times of proteins from NMR relaxation interference

NASA Astrophysics Data System (ADS)

Lee, Donghan; Hilty, Christian; Wider, Gerhard; Wüthrich, Kurt

2006-01-01

Knowledge of the effective rotational correlation times, τc, for the modulation of anisotropic spin-spin interactions in macromolecules subject to Brownian motion in solution is of key interest for the practice of NMR spectroscopy in structural biology. The value of τc enables an estimate of the NMR spin relaxation rates, and indicates possible aggregation of the macromolecular species. This paper reports a novel NMR pulse scheme, [ 15N, 1H]-TRACT, which is based on transverse relaxation-optimized spectroscopy and permits to determine τc for 15N- 1H bonds without interference from dipole-dipole coupling of the amide proton with remote protons. [ 15N, 1H]-TRACT is highly efficient since only a series of one-dimensional NMR spectra need to be recorded. Its use is suggested for a quick estimate of the rotational correlation time, to monitor sample quality and to determine optimal parameters for complex multidimensional NMR experiments. Practical applications are illustrated with the 110 kDa 7,8-dihydroneopterin aldolase from Staphylococcus aureus, the uniformly 15N-labeled Escherichia coli outer membrane protein X (OmpX) in 60 kDa mixed OmpX/DHPC micelles with approximately 90 molecules of unlabeled 1,2-dihexanoyl- sn-glycero-3-phosphocholine (DHPC), and the 16 kDa pheromone-binding protein from Bombyx mori, which cover a wide range of correlation times.

Multidimensional chromatography in food analysis.

PubMed

Herrero, Miguel; Ibáñez, Elena; Cifuentes, Alejandro; Bernal, Jose

2009-10-23

In this work, the main developments and applications of multidimensional chromatographic techniques in food analysis are reviewed. Different aspects related to the existing couplings involving chromatographic techniques are examined. These couplings include multidimensional GC, multidimensional LC, multidimensional SFC as well as all their possible combinations. Main advantages and drawbacks of each coupling are critically discussed and their key applications in food analysis described.

Time and change in health care.

PubMed

Waterworth, Susan

2017-10-02

Purpose The purpose of this paper is to explore the dimensions of temporality that are rarely considered in the literature on leading change. Design/methodology/approach The analysis is informed by Adams' (1995) social theory of time encompassing temporality, timing and tempo. This will illustrate the complexities of time as they relate to the individual, teams and organisation. Findings This paper demonstrates the multidimensional nature of time: temporality, timing and tempo, and how each of these can contribute to our understanding of the temporal nature and complexity of change within the health system. A framework to inform much-needed research in the area of time and change is presented. Practical implications Challenging assumptions that there is only one common time, that is clock time, can provide opportunities for further discussion and understanding of how various people view time and the influence this has on leading and participating in change in health care. Originality/value There is limited literature on the temporal dimensions of change at an organisational, team and individual level. The perspective offered in this paper presents the multidimensional nature of time and the influence this has on understanding the temporal nature of change and critically identifies some key areas for future research.

Theoretical and numerical studies of chaotic mixing

NASA Astrophysics Data System (ADS)

Kim, Ho Jun

Theoretical and numerical studies of chaotic mixing are performed to circumvent the difficulties of efficient mixing, which come from the lack of turbulence in microfluidic devices. In order to carry out efficient and accurate parametric studies and to identify a fully chaotic state, a spectral element algorithm for solution of the incompressible Navier-Stokes and species transport equations is developed. Using Taylor series expansions in time marching, the new algorithm employs an algebraic factorization scheme on multi-dimensional staggered spectral element grids, and extends classical conforming Galerkin formulations to nonconforming spectral elements. Lagrangian particle tracking methods are utilized to study particle dispersion in the mixing device using spectral element and fourth order Runge-Kutta discretizations in space and time, respectively. Comparative studies of five different techniques commonly employed to identify the chaotic strength and mixing efficiency in microfluidic systems are presented to demonstrate the competitive advantages and shortcomings of each method. These are the stirring index based on the box counting method, Poincare sections, finite time Lyapunov exponents, the probability density function of the stretching field, and mixing index inverse, based on the standard deviation of scalar species distribution. Series of numerical simulations are performed by varying the Peclet number (Pe) at fixed kinematic conditions. The mixing length (lm) is characterized as function of the Pe number, and lm ∝ ln(Pe) scaling is demonstrated for fully chaotic cases. Employing the aforementioned techniques, optimum kinematic conditions and the actuation frequency of the stirrer that result in the highest mixing/stirring efficiency are identified in a zeta potential patterned straight micro channel, where a continuous flow is generated by superposition of a steady pressure driven flow and time periodic electroosmotic flow induced by a stream-wise AC electric field. Finally, it is shown that the invariant manifold of hyperbolic periodic point determines the geometry of fast mixing zones in oscillatory flows in two-dimensional cavity.

Multidimensional spectrometer

DOEpatents

Zanni, Martin Thomas; Damrauer, Niels H.

2010-07-20

A multidimensional spectrometer for the infrared, visible, and ultraviolet regions of the electromagnetic spectrum, and a method for making multidimensional spectroscopic measurements in the infrared, visible, and ultraviolet regions of the electromagnetic spectrum. The multidimensional spectrometer facilitates measurements of inter- and intra-molecular interactions.

Non-negative Tensor Factorization for Robust Exploratory Big-Data Analytics

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

Alexandrov, Boian; Vesselinov, Velimir Valentinov; Djidjev, Hristo Nikolov

Currently, large multidimensional datasets are being accumulated in almost every field. Data are: (1) collected by distributed sensor networks in real-time all over the globe, (2) produced by large-scale experimental measurements or engineering activities, (3) generated by high-performance simulations, and (4) gathered by electronic communications and socialnetwork activities, etc. Simultaneous analysis of these ultra-large heterogeneous multidimensional datasets is often critical for scientific discoveries, decision-making, emergency response, and national and global security. The importance of such analyses mandates the development of the next-generation of robust machine learning (ML) methods and tools for bigdata exploratory analysis.

Multidimensional FEM-FCT schemes for arbitrary time stepping

NASA Astrophysics Data System (ADS)

Kuzmin, D.; Möller, M.; Turek, S.

2003-05-01

The flux-corrected-transport paradigm is generalized to finite-element schemes based on arbitrary time stepping. A conservative flux decomposition procedure is proposed for both convective and diffusive terms. Mathematical properties of positivity-preserving schemes are reviewed. A nonoscillatory low-order method is constructed by elimination of negative off-diagonal entries of the discrete transport operator. The linearization of source terms and extension to hyperbolic systems are discussed. Zalesak's multidimensional limiter is employed to switch between linear discretizations of high and low order. A rigorous proof of positivity is provided. The treatment of non-linearities and iterative solution of linear systems are addressed. The performance of the new algorithm is illustrated by numerical examples for the shock tube problem in one dimension and scalar transport equations in two dimensions.

Pointillist, Cyclical, and Overlapping: Multidimensional Facets of Time in Online Learning

ERIC Educational Resources Information Center

Ihanainen, Pekka; Moravec, John W.

2011-01-01

A linear, sequential time conception based on in-person meetings and pedagogical activities is not enough for those who practice and hope to enhance contemporary education, particularly where online interactions are concerned. In this article, we propose a new model for understanding time in pedagogical contexts. Conceptual parts of the model will…

Application of WHO recommendations in outcome assessment of clinical series published in hand surgery journals with five years interval.

PubMed

Alarab, H; Dubert, T

2015-02-01

Outcome measurement is becoming increasingly important in hand surgery. The International classification of functioning, disability and health (ICF), is a WHO multi-dimensional approach to health condition including three domains: body "functions and structures", activities and participation. The aim of this study was to measure how often these three ICF domains were included in outcome measurements of the clinical series published in the American, European and French hand surgery journals. Our study included clinical series published in 2007 and 2012 in the American Journal Of Hand Surgery, European Journal Of Hand Surgery and Chirurgie de la Main. Analysis of each of these publications was done in two steps. First, we checked the presence or absence of the three domains of ICF in outcome measurement without considering the way it was measured. Then, we reported the use of evaluation instruments and/or quantitative measurement for each domain. We included 54 series in 2007 and 119 in 2012. The number of series reporting on the three domains and using at least one quantitative measurement for each domain represents 6% of articles in 2007 and 10% in 2012. This study shows that the quality of outcome measurement has improved over these 5 years, but remains poor according to the ICF recommendation. Copyright © 2015 Elsevier Masson SAS. All rights reserved.

Measures for a multidimensional multiverse

NASA Astrophysics Data System (ADS)

Chung, Hyeyoun

2015-04-01

We explore the phenomenological implications of generalizing the causal patch and fat geodesic measures to a multidimensional multiverse, where the vacua can have differing numbers of large dimensions. We consider a simple model in which the vacua are nucleated from a D -dimensional parent spacetime through dynamical compactification of the extra dimensions, and compute the geometric contribution to the probability distribution of observations within the multiverse for each measure. We then study how the shape of this probability distribution depends on the time scales for the existence of observers, for vacuum domination, and for curvature domination (tobs,tΛ , and tc, respectively.) In this work we restrict ourselves to bubbles with positive cosmological constant, Λ . We find that in the case of the causal patch cutoff, when the bubble universes have p +1 large spatial dimensions with p ≥2 , the shape of the probability distribution is such that we obtain the coincidence of time scales tobs˜tΛ˜tc . Moreover, the size of the cosmological constant is related to the size of the landscape. However, the exact shape of the probability distribution is different in the case p =2 , compared to p ≥3 . In the case of the fat geodesic measure, the result is even more robust: the shape of the probability distribution is the same for all p ≥2 , and we once again obtain the coincidence tobs˜tΛ˜tc . These results require only very mild conditions on the prior probability of the distribution of vacua in the landscape. Our work shows that the observed double coincidence of time scales is a robust prediction even when the multiverse is generalized to be multidimensional; that this coincidence is not a consequence of our particular Universe being (3 +1 )-dimensional; and that this observable cannot be used to preferentially select one measure over another in a multidimensional multiverse.

Band warping, band non-parabolicity, and Dirac points in electronic and lattice structures

NASA Astrophysics Data System (ADS)

Resca, Lorenzo; Mecholsky, Nicholas A.; Pegg, Ian L.

2017-10-01

We illustrate at a fundamental level the physical and mathematical origins of band warping and band non-parabolicity in electronic and vibrational structures. We point out a robust presence of pairs of topologically induced Dirac points in a primitive-rectangular lattice using a p-type tight-binding approximation. We analyze two-dimensional primitive-rectangular and square Bravais lattices with implications that are expected to generalize to more complex structures. Band warping is shown to arise at the onset of a singular transition to a crystal lattice with a larger symmetry group, which allows the possibility of irreducible representations of higher dimensions, hence band degeneracy, at special symmetry points in reciprocal space. Band warping is incompatible with a multi-dimensional Taylor series expansion, whereas band non-parabolicities are associated with multi-dimensional Taylor series expansions to all orders. Still band non-parabolicities may merge into band warping at the onset of a larger symmetry group. Remarkably, while still maintaining a clear connection with that merging, band non-parabolicities may produce pairs of conical intersections at relatively low-symmetry points. Apparently, such conical intersections are robustly maintained by global topology requirements, rather than any local symmetry protection. For two p-type tight-binding bands, we find such pairs of conical intersections drifting along the edges of restricted Brillouin zones of primitive-rectangular Bravais lattices as lattice constants vary relatively to each other, until these conical intersections merge into degenerate warped bands at high-symmetry points at the onset of a square lattice. The conical intersections that we found appear to have similar topological characteristics as Dirac points extensively studied in graphene and other topological insulators, even though our conical intersections have none of the symmetry complexity and protection afforded by the latter more complex structures.

Implementation of an ADME enabling selection and visualization tool for drug discovery.

PubMed

Stoner, Chad L; Gifford, Eric; Stankovic, Charles; Lepsy, Christopher S; Brodfuehrer, Joanne; Prasad, J V N Vara; Surendran, Narayanan

2004-05-01

The pharmaceutical industry has large investments in compound library enrichment, high throughput biological screening, and biopharmaceutical (ADME) screening. As the number of compounds submitted for in vitro ADME screens increases, data analysis, interpretation, and reporting will become rate limiting in providing ADME-structure-activity relationship information to guide the synthetic strategy for chemical series. To meet these challenges, a software tool was developed and implemented that enables scientists to explore in vitro and in silico ADME and chemistry data in a multidimensional framework. The present work integrates physicochemical and ADME data, encompassing results for Caco-2 permeability, human liver microsomal half-life, rat liver microsomal half-life, kinetic solubility, measured log P, rule of 5 descriptors (molecular weight, hydrogen bond acceptors, hydrogen bond donors, calculated log P), polar surface area, chemical stability, and CYP450 3A4 inhibition. To facilitate interpretation of this data, a semicustomized software solution using Spotfire was designed that allows for multidimensional data analysis and visualization. The solution also enables simultaneous viewing and export of chemical structures with the corresponding ADME properties, enabling a more facile analysis of ADME-structure-activity relationship. In vitro and in silico ADME data were generated for 358 compounds from a series of human immunodeficiency virus protease inhibitors, resulting in a data set of 5370 experimental values which were subsequently analyzed and visualized using the customized Spotfire application. Implementation of this analysis and visualization tool has accelerated the selection of molecules for further development based on optimum ADME characteristics, and provided medicinal chemistry with specific, data driven structural recommendations for improvements in the ADME profile. Copyright 2004 Wiley-Liss, Inc. and the American Pharmacists Association J Pharm Sci 93: 1131-1141, 2004

Scientific Visualization Tools for Enhancement of Undergraduate Research

NASA Astrophysics Data System (ADS)

Rodriguez, W. J.; Chaudhury, S. R.

2001-05-01

Undergraduate research projects that utilize remote sensing satellite instrument data to investigate atmospheric phenomena pose many challenges. A significant challenge is processing large amounts of multi-dimensional data. Remote sensing data initially requires mining; filtering of undesirable spectral, instrumental, or environmental features; and subsequently sorting and reformatting to files for easy and quick access. The data must then be transformed according to the needs of the investigation(s) and displayed for interpretation. These multidimensional datasets require views that can range from two-dimensional plots to multivariable-multidimensional scientific visualizations with animations. Science undergraduate students generally find these data processing tasks daunting. Generally, researchers are required to fully understand the intricacies of the dataset and write computer programs or rely on commercially available software, which may not be trivial to use. In the time that undergraduate researchers have available for their research projects, learning the data formats, programming languages, and/or visualization packages is impractical. When dealing with large multi-dimensional data sets appropriate Scientific Visualization tools are imperative in allowing students to have a meaningful and pleasant research experience, while producing valuable scientific research results. The BEST Lab at Norfolk State University has been creating tools for multivariable-multidimensional analysis of Earth Science data. EzSAGE and SAGE4D have been developed to sort, analyze and visualize SAGE II (Stratospheric Aerosol and Gas Experiment) data with ease. Three- and four-dimensional visualizations in interactive environments can be produced. EzSAGE provides atmospheric slices in three-dimensions where the researcher can change the scales in the three-dimensions, color tables and degree of smoothing interactively to focus on particular phenomena. SAGE4D provides a navigable four-dimensional interactive environment. These tools allow students to make higher order decisions based on large multidimensional sets of data while diminishing the level of frustration that results from dealing with the details of processing large data sets.

Numeric invariants from multidimensional persistence

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

Skryzalin, Jacek; Carlsson, Gunnar

2017-05-19

In this paper, we analyze the space of multidimensional persistence modules from the perspectives of algebraic geometry. We first build a moduli space of a certain subclass of easily analyzed multidimensional persistence modules, which we construct specifically to capture much of the information which can be gained by using multidimensional persistence over one-dimensional persistence. We argue that the global sections of this space provide interesting numeric invariants when evaluated against our subclass of multidimensional persistence modules. Lastly, we extend these global sections to the space of all multidimensional persistence modules and discuss how the resulting numeric invariants might be usedmore » to study data.« less

Measuring future time perspective across adulthood: development and evaluation of a brief multidimensional questionnaire.

PubMed

Brothers, Allyson; Chui, Helena; Diehl, Manfred

2014-12-01

Despite calls for the consideration of future time perspective (FTP) as a multidimensional construct, mostly unidimensional measurement instruments have been used. This study had two objectives: (a) to develop a brief multidimensional questionnaire for assessing FTP in adulthood and evaluate its psychometric properties; and (b) to examine age associations and age-group differences of the dimensions of FTP. Data were collected from 625 community-residing adults between the ages of 18 and 93, representing young, middle-aged, and older adults. The psychometric evaluation involved exploratory factor analyses (EFA) and confirmatory FA (CFA), reliability and validity analyses, and measurement invariance testing. Zero-order and partial correlations were used to examine the association of the dimensions of FTP with age, and multivariate analysis of variance was used to examine age-group differences. EFA and CFA supported a three-factor solution: Future as Open, Future as Limited, and Future as Ambiguous. Metric measurement invariance for this factor structure was confirmed across the three age groups. Reliability and validity analyses provided evidence of sound psychometric properties of the brief questionnaire. Age was negatively associated with Future as Open and positively associated with Future as Limited. Young adults exhibited significantly greater ambiguity toward the future than middle-aged or older adults. This study provides evidence in support of the psychometric properties of a new brief multidimensional FTP scale. It also provides evidence for a pattern of age associations and age-group differences consistent with life-span developmental theory. © The Author 2014. Published by Oxford University Press on behalf of The Gerontological Society of America. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Multidimensional Sexual Perfectionism and Female Sexual Function: A Longitudinal Investigation.

PubMed

Stoeber, Joachim; Harvey, Laura N

2016-11-01

Research on multidimensional sexual perfectionism differentiates four forms: self-oriented, partner-oriented, partner-prescribed, and socially prescribed. Self-oriented sexual perfectionism reflects perfectionistic standards people apply to themselves as sexual partners; partner-oriented sexual perfectionism reflects perfectionistic standards people apply to their sexual partner; partner-prescribed sexual perfectionism reflects people's beliefs that their sexual partner imposes perfectionistic standards on them; and socially prescribed sexual perfectionism reflects people's beliefs that society imposes such standards on them. Previous studies found partner-prescribed and socially prescribed sexual perfectionism to be maladaptive forms of sexual perfectionism associated with a negative sexual self-concept and problematic sexual behaviors, but only examined cross-sectional relationships. The present article presents the first longitudinal study examining whether multidimensional sexual perfectionism predicts changes in sexual self-concept and sexual function over time. A total of 366 women aged 17-69 years completed measures of multidimensional sexual perfectionism, sexual esteem, sexual anxiety, sexual problem self-blame, and sexual function (cross-sectional data). Three to six months later, 164 of the women completed the same measures again (longitudinal data). Across analyses, partner-prescribed sexual perfectionism emerged as the most maladaptive form of sexual perfectionism. In the cross-sectional data, partner-prescribed sexual perfectionism showed positive relationships with sexual anxiety, sexual problem self-blame, and intercourse pain, and negative relationships with sexual esteem, desire, arousal, lubrication, and orgasmic function. In the longitudinal data, partner-prescribed sexual perfectionism predicted increases in sexual anxiety and decreases in sexual esteem, arousal, and lubrication over time. The findings suggest that partner-prescribed sexual perfectionism contributes to women's negative sexual self-concept and female sexual dysfunction.

Knowledge-based nonuniform sampling in multidimensional NMR.

PubMed

Schuyler, Adam D; Maciejewski, Mark W; Arthanari, Haribabu; Hoch, Jeffrey C

2011-07-01

The full resolution afforded by high-field magnets is rarely realized in the indirect dimensions of multidimensional NMR experiments because of the time cost of uniformly sampling to long evolution times. Emerging methods utilizing nonuniform sampling (NUS) enable high resolution along indirect dimensions by sampling long evolution times without sampling at every multiple of the Nyquist sampling interval. While the earliest NUS approaches matched the decay of sampling density to the decay of the signal envelope, recent approaches based on coupled evolution times attempt to optimize sampling by choosing projection angles that increase the likelihood of resolving closely-spaced resonances. These approaches employ knowledge about chemical shifts to predict optimal projection angles, whereas prior applications of tailored sampling employed only knowledge of the decay rate. In this work we adapt the matched filter approach as a general strategy for knowledge-based nonuniform sampling that can exploit prior knowledge about chemical shifts and is not restricted to sampling projections. Based on several measures of performance, we find that exponentially weighted random sampling (envelope matched sampling) performs better than shift-based sampling (beat matched sampling). While shift-based sampling can yield small advantages in sensitivity, the gains are generally outweighed by diminished robustness. Our observation that more robust sampling schemes are only slightly less sensitive than schemes highly optimized using prior knowledge about chemical shifts has broad implications for any multidimensional NMR study employing NUS. The results derived from simulated data are demonstrated with a sample application to PfPMT, the phosphoethanolamine methyltransferase of the human malaria parasite Plasmodium falciparum.

Implementation and evaluation of a hypercube-based method for spatiotemporal exploration and analysis

NASA Astrophysics Data System (ADS)

Marchand, Pierre; Brisebois, Alexandre; Bédard, Yvan; Edwards, Geoffrey

This paper presents the results obtained with a new type of spatiotemporal topological dimension implemented within a hypercube, i.e., within a multidimensional database (MDDB) structure formed by the conjunction of several thematic, spatial and temporal dimensions. Our goal is to support efficient SpatioTemporal Exploration and Analysis (STEA) in the context of Automatic Position Reporting System (APRS), the worldwide amateur radio system for position report transmission. Mobile APRS stations are equipped with GPS navigation systems to provide real-time positioning reports. Previous research about the multidimensional approach has proved good potential for spatiotemporal exploration and analysis despite a lack of explicit topological operators (spatial, temporal and spatiotemporal). Our project implemented such operators through a hierarchy of operators that are applied to pairs of instances of objects. At the top of the hierarchy, users can use simple operators such as "same place", "same time" or "same time, same place". As they drill down into the hierarchy, more detailed topological operators are made available such as "adjacent immediately after", "touch during" or more detailed operators. This hierarchy is structured according to four levels of granularity based on cognitive models, generalized relationships and formal models of topological relationships. In this paper, we also describe the generic approach which allows efficient STEA within the multidimensional approach. Finally, we demonstrate that such an implementation offers query run times which permit to maintain a "train-of-thought" during exploration and analysis operations as they are compatible with Newell's cognitive band (query runtime<10 s) (Newell, A., 1990. Unified theories of cognition. Harvard University Press, Cambridge MA, 549 p.).

Local Difference Measures between Complex Networks for Dynamical System Model Evaluation

PubMed Central

Lange, Stefan; Donges, Jonathan F.; Volkholz, Jan; Kurths, Jürgen

2015-01-01

A faithful modeling of real-world dynamical systems necessitates model evaluation. A recent promising methodological approach to this problem has been based on complex networks, which in turn have proven useful for the characterization of dynamical systems. In this context, we introduce three local network difference measures and demonstrate their capabilities in the field of climate modeling, where these measures facilitate a spatially explicit model evaluation. Building on a recent study by Feldhoff et al. [1] we comparatively analyze statistical and dynamical regional climate simulations of the South American monsoon system. Three types of climate networks representing different aspects of rainfall dynamics are constructed from the modeled precipitation space-time series. Specifically, we define simple graphs based on positive as well as negative rank correlations between rainfall anomaly time series at different locations, and such based on spatial synchronizations of extreme rain events. An evaluation against respective networks built from daily satellite data provided by the Tropical Rainfall Measuring Mission 3B42 V7 reveals far greater differences in model performance between network types for a fixed but arbitrary climate model than between climate models for a fixed but arbitrary network type. We identify two sources of uncertainty in this respect. Firstly, climate variability limits fidelity, particularly in the case of the extreme event network; and secondly, larger geographical link lengths render link misplacements more likely, most notably in the case of the anticorrelation network; both contributions are quantified using suitable ensembles of surrogate networks. Our model evaluation approach is applicable to any multidimensional dynamical system and especially our simple graph difference measures are highly versatile as the graphs to be compared may be constructed in whatever way required. Generalizations to directed as well as edge- and node-weighted graphs are discussed. PMID:25856374

Patterns of Irregular Burials in Western Europe (1st-5th Century A.D.)

PubMed Central

Milella, Marco; Mariotti, Valentina; Belcastro, Maria Giovanna; Knüsel, Christopher J.

2015-01-01

Background Irregular burials (IB—burials showing features that contrast with the majority of others in their geographic and chronological context) have been the focus of archaeological study because of their relative rarity and enigmatic appearance. Interpretations of IB often refer to supposed fear of the dead or to social processes taking place in time-specific contexts. However, a comprehensive and quantitative analysis of IB for various geographical contexts is still lacking, a fact that hampers any discussion of these burials on a larger scale. Methods Here, we collected a bibliographic dataset of 375 IB from both Britain and Continental Europe, altogether spanning a time period from the 1st to the 5th century AD. Each burial has been coded according to ten dichotomous variables, further analyzed by means of chi-squared tests on absolute frequencies, non-metric multidimensional scaling, and cluster analysis. Results Even acknowledging the limits of this study, and in particular the bias represented by the available literature, our results point to interesting patterns. Geographically, IB show a contrast between Britain and Continental Europe, possibly related to historical processes specific to these regions. Different types of IB (especially prone depositions and depositions with the cephalic extremity displaced) present a series of characteristics and associations between features that permit a more detailed conceptualization of these occurrences from a socio-cultural perspective that aids to elucidate their funerary meaning. Conclusions and Significance Altogether, the present work stresses the variability of IB, and the need to contextualize them in a proper archaeological and historical context. It contributes to the discussion of IB by providing a specific geographic and chronological frame of reference that supports a series of hypotheses about the cultural processes possibly underlying their occurrence. PMID:26115408

Attributed graph distance measure for automatic detection of attention deficit hyperactive disordered subjects.

PubMed

Dey, Soumyabrata; Rao, A Ravishankar; Shah, Mubarak

2014-01-01

Attention Deficit Hyperactive Disorder (ADHD) is getting a lot of attention recently for two reasons. First, it is one of the most commonly found childhood disorders and second, the root cause of the problem is still unknown. Functional Magnetic Resonance Imaging (fMRI) data has become a popular tool for the analysis of ADHD, which is the focus of our current research. In this paper we propose a novel framework for the automatic classification of the ADHD subjects using their resting state fMRI (rs-fMRI) data of the brain. We construct brain functional connectivity networks for all the subjects. The nodes of the network are constructed with clusters of highly active voxels and edges between any pair of nodes represent the correlations between their average fMRI time series. The activity level of the voxels are measured based on the average power of their corresponding fMRI time-series. For each node of the networks, a local descriptor comprising of a set of attributes of the node is computed. Next, the Multi-Dimensional Scaling (MDS) technique is used to project all the subjects from the unknown graph-space to a low dimensional space based on their inter-graph distance measures. Finally, the Support Vector Machine (SVM) classifier is used on the low dimensional projected space for automatic classification of the ADHD subjects. Exhaustive experimental validation of the proposed method is performed using the data set released for the ADHD-200 competition. Our method shows promise as we achieve impressive classification accuracies on the training (70.49%) and test data sets (73.55%). Our results reveal that the detection rates are higher when classification is performed separately on the male and female groups of subjects.

Mapping model behaviour using Self-Organizing Maps

NASA Astrophysics Data System (ADS)

Herbst, M.; Gupta, H. V.; Casper, M. C.

2009-03-01

Hydrological model evaluation and identification essentially involves extracting and processing information from model time series. However, the type of information extracted by statistical measures has only very limited meaning because it does not relate to the hydrological context of the data. To overcome this inadequacy we exploit the diagnostic evaluation concept of Signature Indices, in which model performance is measured using theoretically relevant characteristics of system behaviour. In our study, a Self-Organizing Map (SOM) is used to process the Signatures extracted from Monte-Carlo simulations generated by the distributed conceptual watershed model NASIM. The SOM creates a hydrologically interpretable mapping of overall model behaviour, which immediately reveals deficits and trade-offs in the ability of the model to represent the different functional behaviours of the watershed. Further, it facilitates interpretation of the hydrological functions of the model parameters and provides preliminary information regarding their sensitivities. Most notably, we use this mapping to identify the set of model realizations (among the Monte-Carlo data) that most closely approximate the observed discharge time series in terms of the hydrologically relevant characteristics, and to confine the parameter space accordingly. Our results suggest that Signature Index based SOMs could potentially serve as tools for decision makers inasmuch as model realizations with specific Signature properties can be selected according to the purpose of the model application. Moreover, given that the approach helps to represent and analyze multi-dimensional distributions, it could be used to form the basis of an optimization framework that uses SOMs to characterize the model performance response surface. As such it provides a powerful and useful way to conduct model identification and model uncertainty analyses.

Mapping model behaviour using Self-Organizing Maps

NASA Astrophysics Data System (ADS)

Herbst, M.; Gupta, H. V.; Casper, M. C.

2008-12-01

Hydrological model evaluation and identification essentially depends on the extraction of information from model time series and its processing. However, the type of information extracted by statistical measures has only very limited meaning because it does not relate to the hydrological context of the data. To overcome this inadequacy we exploit the diagnostic evaluation concept of Signature Indices, in which model performance is measured using theoretically relevant characteristics of system behaviour. In our study, a Self-Organizing Map (SOM) is used to process the Signatures extracted from Monte-Carlo simulations generated by a distributed conceptual watershed model. The SOM creates a hydrologically interpretable mapping of overall model behaviour, which immediately reveals deficits and trade-offs in the ability of the model to represent the different functional behaviours of the watershed. Further, it facilitates interpretation of the hydrological functions of the model parameters and provides preliminary information regarding their sensitivities. Most notably, we use this mapping to identify the set of model realizations (among the Monte-Carlo data) that most closely approximate the observed discharge time series in terms of the hydrologically relevant characteristics, and to confine the parameter space accordingly. Our results suggest that Signature Index based SOMs could potentially serve as tools for decision makers inasmuch as model realizations with specific Signature properties can be selected according to the purpose of the model application. Moreover, given that the approach helps to represent and analyze multi-dimensional distributions, it could be used to form the basis of an optimization framework that uses SOMs to characterize the model performance response surface. As such it provides a powerful and useful way to conduct model identification and model uncertainty analyses.

Effectiveness of a multidimensional home nurse led heart failure disease management program--a French nationwide time-series comparison.

PubMed

Agrinier, Nelly; Altieri, Christelle; Alla, François; Jay, Nicolas; Dobre, Daniela; Thilly, Nathalie; Zannad, Faiez

2013-10-09

The purpose of this study was to assess the effectiveness of a disease management program (DMP) in heart failure (HF) on the incidence of HF hospitalizations and related costs in a real-world population-based setting. Insuffisance CArdiaque en LORraine (ICALOR), a DMP for HF was established in 2006 in the French region of Lorraine. Patients were enrolled after an index HF hospitalization. They received educational and home-visit monitoring programs by HF-trained nurses. General physicians received automatic alerts about patients' significant clinical or biological changes. We used the ICALOR and the national diagnostic related group databases to conduct a comparison of time-series trends in HF hospitalizations in France. The economic impact was obtained using the national scale of costs in France. The median age of the 1222 patients recruited before 2010 was 76 years, and 65% were male. Upon enrollment, patients essentially presented with NYHA class II (n=537, 48%) or class III (n=359, 32%) symptoms. One-year mortality rate was 20.3%. The implementation of the ICALOR program was associated with a reduction in HF hospitalizations in Lorraine estimated by an absolute difference between the number of hospitalizations observed in the Lorraine region and that expected had it been similar to that observed in the whole country of -7.19% in 2010. The estimated annual hospital cost saved by ICALOR was €1,927,648 in 2010. Coordinated DMP of HF might improve outcome cost-effectively when implemented in a real-world population setting, and was associated in Lorraine with a substantial modification of the trend of HF hospitalizations. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

Patterns of Irregular Burials in Western Europe (1st-5th Century A.D.).

PubMed

Milella, Marco; Mariotti, Valentina; Belcastro, Maria Giovanna; Knüsel, Christopher J

2015-01-01

Irregular burials (IB--burials showing features that contrast with the majority of others in their geographic and chronological context) have been the focus of archaeological study because of their relative rarity and enigmatic appearance. Interpretations of IB often refer to supposed fear of the dead or to social processes taking place in time-specific contexts. However, a comprehensive and quantitative analysis of IB for various geographical contexts is still lacking, a fact that hampers any discussion of these burials on a larger scale. Here, we collected a bibliographic dataset of 375 IB from both Britain and Continental Europe, altogether spanning a time period from the 1st to the 5th century AD. Each burial has been coded according to ten dichotomous variables, further analyzed by means of chi-squared tests on absolute frequencies, non-metric multidimensional scaling, and cluster analysis. Even acknowledging the limits of this study, and in particular the bias represented by the available literature, our results point to interesting patterns. Geographically, IB show a contrast between Britain and Continental Europe, possibly related to historical processes specific to these regions. Different types of IB (especially prone depositions and depositions with the cephalic extremity displaced) present a series of characteristics and associations between features that permit a more detailed conceptualization of these occurrences from a socio-cultural perspective that aids to elucidate their funerary meaning. Altogether, the present work stresses the variability of IB, and the need to contextualize them in a proper archaeological and historical context. It contributes to the discussion of IB by providing a specific geographic and chronological frame of reference that supports a series of hypotheses about the cultural processes possibly underlying their occurrence.

Local difference measures between complex networks for dynamical system model evaluation.

PubMed

Lange, Stefan; Donges, Jonathan F; Volkholz, Jan; Kurths, Jürgen

2015-01-01

A faithful modeling of real-world dynamical systems necessitates model evaluation. A recent promising methodological approach to this problem has been based on complex networks, which in turn have proven useful for the characterization of dynamical systems. In this context, we introduce three local network difference measures and demonstrate their capabilities in the field of climate modeling, where these measures facilitate a spatially explicit model evaluation.Building on a recent study by Feldhoff et al. [8] we comparatively analyze statistical and dynamical regional climate simulations of the South American monsoon system [corrected]. types of climate networks representing different aspects of rainfall dynamics are constructed from the modeled precipitation space-time series. Specifically, we define simple graphs based on positive as well as negative rank correlations between rainfall anomaly time series at different locations, and such based on spatial synchronizations of extreme rain events. An evaluation against respective networks built from daily satellite data provided by the Tropical Rainfall Measuring Mission 3B42 V7 reveals far greater differences in model performance between network types for a fixed but arbitrary climate model than between climate models for a fixed but arbitrary network type. We identify two sources of uncertainty in this respect. Firstly, climate variability limits fidelity, particularly in the case of the extreme event network; and secondly, larger geographical link lengths render link misplacements more likely, most notably in the case of the anticorrelation network; both contributions are quantified using suitable ensembles of surrogate networks. Our model evaluation approach is applicable to any multidimensional dynamical system and especially our simple graph difference measures are highly versatile as the graphs to be compared may be constructed in whatever way required. Generalizations to directed as well as edge- and node-weighted graphs are discussed.

Tau lepton production and decays: perspective of multi-dimensional distributions and Monte Carlo methods

NASA Astrophysics Data System (ADS)

Was, Z.

2017-06-01

Status of τ lepton decay Monte Carlo generator TAUOLA, its main applications and recent developments are reviewed. It is underlined, that in recent efforts on development of new hadronic currents, the multi-dimensional nature of distributions of the experimental data must be taken with a great care: lesson from comparison and fits to the BaBar and Belle data is recalled. It was found, that as in the past at a time of comparisons with CLEO and ALEPH data, proper fitting, to as detailed as possible representation of the experimental data, is essential for appropriate developments of models of τ decay dynamic. This multi-dimensional nature of distributions is also important for observables where τ leptons are used to constrain experimental data. In later part of the presentation, use of the TAUOLA program for phenomenology of W, Z, H decays at LHC is addressed, in particular in the context of the Higgs boson parity measurements. Some new results, relevant for QED lepton pair emission are mentioned as well.

[Multidimensional measurement of precarious employment: social distribution and its association with health in Catalonia (Spain)].

PubMed

Benach, Joan; Julià, Mireia; Tarafa, Gemma; Mir, Jordi; Molinero, Emilia; Vives, Alejandra

2015-01-01

To show the prevalence of precarious employment in Catalonia (Spain) for the first time and its association with mental and self-rated health, measured with a multidimensional scale. A cross-sectional study was conducted using data from the II Catalan Working Conditions Survey (2010) with a subsample of employed workers with a contract. The prevalence of precarious employment using a multidimensional scale and its association with health was calculated using multivariate log-binomial regression stratified by gender. The prevalence of precarious employment in Catalonia was high (42.6%). We found higher precariousness in women, youth, immigrants, and manual and less educated workers. There was a positive gradient in the association between precarious employment and poor health. Precarious employment is associated with poor health in the working population. Working conditions surveys should include questions on precarious employment and health indicators, which would allow monitoring and subsequent analyses of health inequalities. Copyright © 2015 SESPAS. Published by Elsevier Espana. All rights reserved.

Multidimensional Modeling of Coronal Rain Dynamics

NASA Astrophysics Data System (ADS)

Fang, X.; Xia, C.; Keppens, R.

2013-07-01

We present the first multidimensional, magnetohydrodynamic simulations that capture the initial formation and long-term sustainment of the enigmatic coronal rain phenomenon. We demonstrate how thermal instability can induce a spectacular display of in situ forming blob-like condensations which then start their intimate ballet on top of initially linear force-free arcades. Our magnetic arcades host a chromospheric, transition region, and coronal plasma. Following coronal rain dynamics for over 80 minutes of physical time, we collect enough statistics to quantify blob widths, lengths, velocity distributions, and other characteristics which directly match modern observational knowledge. Our virtual coronal rain displays the deformation of blobs into V-shaped features, interactions of blobs due to mostly pressure-mediated levitations, and gives the first views of blobs that evaporate in situ or are siphoned over the apex of the background arcade. Our simulations pave the way for systematic surveys of coronal rain showers in true multidimensional settings to connect parameterized heating prescriptions with rain statistics, ultimately allowing us to quantify the coronal heating input.

Med Diet 4.0: the Mediterranean diet with four sustainable benefits.

PubMed

Dernini, S; Berry, E M; Serra-Majem, L; La Vecchia, C; Capone, R; Medina, F X; Aranceta-Bartrina, J; Belahsen, R; Burlingame, B; Calabrese, G; Corella, D; Donini, L M; Lairon, D; Meybeck, A; Pekcan, A G; Piscopo, S; Yngve, A; Trichopoulou, A

2017-05-01

To characterize the multiple dimensions and benefits of the Mediterranean diet as a sustainable diet, in order to revitalize this intangible food heritage at the country level; and to develop a multidimensional framework - the Med Diet 4.0 - in which four sustainability benefits of the Mediterranean diet are presented in parallel: major health and nutrition benefits, low environmental impacts and richness in biodiversity, high sociocultural food values, and positive local economic returns. A narrative review was applied at the country level to highlight the multiple sustainable benefits of the Mediterranean diet into a single multidimensional framework: the Med Diet 4.0. Setting/subjects We included studies published in English in peer-reviewed journals that contained data on the characterization of sustainable diets and of the Mediterranean diet. The methodological framework approach was finalized through a series of meetings, workshops and conferences where the framework was presented, discussed and ultimately refined. The Med Diet 4.0 provides a conceptual multidimensional framework to characterize the Mediterranean diet as a sustainable diet model, by applying principles of sustainability to the Mediterranean diet. By providing a broader understanding of the many sustainable benefits of the Mediterranean diet, the Med Diet 4.0 can contribute to the revitalization of the Mediterranean diet by improving its current perception not only as a healthy diet but also a sustainable lifestyle model, with country-specific and culturally appropriate variations. It also takes into account the identity and diversity of food cultures and systems, expressed within the notion of the Mediterranean diet, across the Mediterranean region and in other parts of the world. Further multidisciplinary studies are needed for the assessment of the sustainability of the Mediterranean diet to include these new dimensions.

Gradient-based multiconfiguration Shepard interpolation for generating potential energy surfaces for polyatomic reactions.

PubMed

Tishchenko, Oksana; Truhlar, Donald G

2010-02-28

This paper describes and illustrates a way to construct multidimensional representations of reactive potential energy surfaces (PESs) by a multiconfiguration Shepard interpolation (MCSI) method based only on gradient information, that is, without using any Hessian information from electronic structure calculations. MCSI, which is called multiconfiguration molecular mechanics (MCMM) in previous articles, is a semiautomated method designed for constructing full-dimensional PESs for subsequent dynamics calculations (classical trajectories, full quantum dynamics, or variational transition state theory with multidimensional tunneling). The MCSI method is based on Shepard interpolation of Taylor series expansions of the coupling term of a 2 x 2 electronically diabatic Hamiltonian matrix with the diagonal elements representing nonreactive analytical PESs for reactants and products. In contrast to the previously developed method, these expansions are truncated in the present version at the first order, and, therefore, no input of electronic structure Hessians is required. The accuracy of the interpolated energies is evaluated for two test reactions, namely, the reaction OH+H(2)-->H(2)O+H and the hydrogen atom abstraction from a model of alpha-tocopherol by methyl radical. The latter reaction involves 38 atoms and a 108-dimensional PES. The mean unsigned errors averaged over a wide range of representative nuclear configurations (corresponding to an energy range of 19.5 kcal/mol in the former case and 32 kcal/mol in the latter) are found to be within 1 kcal/mol for both reactions, based on 13 gradients in one case and 11 in the other. The gradient-based MCMM method can be applied for efficient representations of multidimensional PESs in cases where analytical electronic structure Hessians are too expensive or unavailable, and it provides new opportunities to employ high-level electronic structure calculations for dynamics at an affordable cost.

A Comparative Study of Online Item Calibration Methods in Multidimensional Computerized Adaptive Testing

ERIC Educational Resources Information Center

Chen, Ping

2017-01-01

Calibration of new items online has been an important topic in item replenishment for multidimensional computerized adaptive testing (MCAT). Several online calibration methods have been proposed for MCAT, such as multidimensional "one expectation-maximization (EM) cycle" (M-OEM) and multidimensional "multiple EM cycles"…

Best Design for Multidimensional Computerized Adaptive Testing with the Bifactor Model

ERIC Educational Resources Information Center

Seo, Dong Gi; Weiss, David J.

2015-01-01

Most computerized adaptive tests (CATs) have been studied using the framework of unidimensional item response theory. However, many psychological variables are multidimensional and might benefit from using a multidimensional approach to CATs. This study investigated the accuracy, fidelity, and efficiency of a fully multidimensional CAT algorithm…

Multidimensional Measurement of Poverty among Women in Sub-Saharan Africa

ERIC Educational Resources Information Center

Batana, Yele Maweki

2013-01-01

Since the seminal work of Sen, poverty has been recognized as a multidimensional phenomenon. The recent availability of relevant databases renewed the interest in this approach. This paper estimates multidimensional poverty among women in fourteen Sub-Saharan African countries using the Alkire and Foster multidimensional poverty measures, whose…

The Efficacy of Multidimensional Constraint Keys in Database Query Performance

ERIC Educational Resources Information Center

Cardwell, Leslie K.

2012-01-01

This work is intended to introduce a database design method to resolve the two-dimensional complexities inherent in the relational data model and its resulting performance challenges through abstract multidimensional constructs. A multidimensional constraint is derived and utilized to implement an indexed Multidimensional Key (MK) to abstract a…

Dynamic analysis, transformation, dissemination and applications of scientific multidimensional data in ArcGIS Platform

NASA Astrophysics Data System (ADS)

Shrestha, S. R.; Collow, T. W.; Rose, B.

2016-12-01

Scientific datasets are generated from various sources and platforms but they are typically produced either by earth observation systems or by modelling systems. These are widely used for monitoring, simulating, or analyzing measurements that are associated with physical, chemical, and biological phenomena over the ocean, atmosphere, or land. A significant subset of scientific datasets stores values directly as rasters or in a form that can be rasterized. This is where a value exists at every cell in a regular grid spanning the spatial extent of the dataset. Government agencies like NOAA, NASA, EPA, USGS produces large volumes of near real-time, forecast, and historical data that drives climatological and meteorological studies, and underpins operations ranging from weather prediction to sea ice loss. Modern science is computationally intensive because of the availability of an enormous amount of scientific data, the adoption of data-driven analysis, and the need to share these dataset and research results with the public. ArcGIS as a platform is sophisticated and capable of handling such complex domain. We'll discuss constructs and capabilities applicable to multidimensional gridded data that can be conceptualized as a multivariate space-time cube. Building on the concept of a two-dimensional raster, a typical multidimensional raster dataset could contain several "slices" within the same spatial extent. We will share a case from the NOAA Climate Forecast Systems Reanalysis (CFSR) multidimensional data as an example of how large collections of rasters can be efficiently organized and managed through a data model within a geodatabase called "Mosaic dataset" and dynamically transformed and analyzed using raster functions. A raster function is a lightweight, raster-valued transformation defined over a mixed set of raster and scalar input. That means, just like any tool, you can provide a raster function with input parameters. It enables dynamic processing of only the data that's being displayed on the screen or requested by an application. We will present the dynamic processing and analysis of CFSR data using the chains of raster function and share it as dynamic multidimensional image service. This workflow and capabilities can be easily applied to any scientific data formats that are supported in mosaic dataset.

A WENO-Limited, ADER-DT, Finite-Volume Scheme for Efficient, Robust, and Communication-Avoiding Multi-Dimensional Transport

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

Norman, Matthew R

2014-01-01

The novel ADER-DT time discretization is applied to two-dimensional transport in a quadrature-free, WENO- and FCT-limited, Finite-Volume context. Emphasis is placed on (1) the serial and parallel computational properties of ADER-DT and this framework and (2) the flexibility of ADER-DT and this framework in efficiently balancing accuracy with other constraints important to transport applications. This study demonstrates a range of choices for the user when approaching their specific application while maintaining good parallel properties. In this method, genuine multi-dimensionality, single-step and single-stage time stepping, strict positivity, and a flexible range of limiting are all achieved with only one parallel synchronizationmore » and data exchange per time step. In terms of parallel data transfers per simulated time interval, this improves upon multi-stage time stepping and post-hoc filtering techniques such as hyperdiffusion. This method is evaluated with standard transport test cases over a range of limiting options to demonstrate quantitatively and qualitatively what a user should expect when employing this method in their application.« less

A Multilayer Naïve Bayes Model for Analyzing User's Retweeting Sentiment Tendency.

PubMed

Wang, Mengmeng; Zuo, Wanli; Wang, Ying

2015-01-01

Today microblogging has increasingly become a means of information diffusion via user's retweeting behavior. Since retweeting content, as context information of microblogging, is an understanding of microblogging, hence, user's retweeting sentiment tendency analysis has gradually become a hot research topic. Targeted at online microblogging, a dynamic social network, we investigate how to exploit dynamic retweeting sentiment features in retweeting sentiment tendency analysis. On the basis of time series of user's network structure information and published text information, we first model dynamic retweeting sentiment features. Then we build Naïve Bayes models from profile-, relationship-, and emotion-based dimensions, respectively. Finally, we build a multilayer Naïve Bayes model based on multidimensional Naïve Bayes models to analyze user's retweeting sentiment tendency towards a microblog. Experiments on real-world dataset demonstrate the effectiveness of the proposed framework. Further experiments are conducted to understand the importance of dynamic retweeting sentiment features and temporal information in retweeting sentiment tendency analysis. What is more, we provide a new train of thought for retweeting sentiment tendency analysis in dynamic social networks.

Stories we live, identities we build: how are elementary teachers' science identities shaped by their lived experiences?

NASA Astrophysics Data System (ADS)

Avraamidou, Lucy

2018-02-01

The aim of this multiple case study was to uncover a series of critical events and experiences related to the formation of the science identities of four beginning elementary female teachers, through a life-history approach and a conceptualization of teacher identity as lived experience. Grounded within the theoretical framework of Figured Worlds, the study used qualitative, interpretive methods for data collection (interviews, biographies, teaching philosophies) and analysis. The analysis shed light on the ways in which various experiences situated within different Figured Worlds (science, family and childhood, schooling, out-of-school, university, professional) impacted the participants' identity trajectories. The findings provided three main insights that contribute to science identity research and have implications for elementary teacher preparation: (a) science teacher identity is multidimensional and extends beyond cognitive domains of becoming to include affective dimensions; (b) science teacher identity is relational, linked and shaped by various other constructs or sub-identities; (c) place and time, defined as a space with meaning created by experiences, and science teacher identity are inextricably bound to one another.

Multidimensional assessment of acute confusion after traumatic brain injury.

PubMed

Sherer, Mark; Nakase-Thompson, Risa; Yablon, Stuart A; Gontkovsky, Samuel T

2005-05-01

To describe the phenomenology of posttraumatic confusional state (PTCS) and to provide preliminary validation of a new procedure, the Confusion Assessment Protocol (CAP), for assessing PTCS. Criterion standard investigation. Inpatient traumatic brain injury (TBI) rehabilitation program. Two consecutive series of patients (n=62, n=93) with TBI admitted for inpatient rehabilitation. Not applicable. Clinical diagnosis of delirium based on Diagnostic and Statistical Manual of Mental Disorders, 4th Edition (DSM-IV) criteria, classification of posttraumatic amnesia (PTA) based on the Galveston Orientation and Amnesia Test (GOAT), and Disability Rating Scale score at time of rehabilitation hospital discharge. Results Agreement between the diagnosis of PTCS with the CAP and DSM-IV classification of delirium was 87%, and agreement between PTCS and PTA using GOAT criteria was 90%. Patients classified as in PTCS sustained more severe injuries and required longer rehabilitation stays. Confusion status was associated with poorer functional status at rehabilitation discharge. The CAP is a brief, structured, repeatable measure of multiple neurobehavioral aspects of PTCS. Confusion status as determined by CAP assessment contributed to prediction of outcome at rehabilitation discharge after adjustment for other potential predictors.

Structural changes in cross-border liabilities: A multidimensional approach

NASA Astrophysics Data System (ADS)

Araújo, Tanya; Spelta, Alessandro

2014-01-01

We study the international interbank market through a geometric analysis of empirical data. The geometric analysis of the time series of cross-country liabilities shows that the systematic information of the interbank international market is contained in a space of small dimension. Geometric spaces of financial relations across countries are developed, for which the space volume, multivariate skewness and multivariate kurtosis are computed. The behavior of these coefficients reveals an important modification acting in the financial linkages since 1997 and allows us to relate the shape of the geometric space that emerges in recent years to the globally turbulent period that has characterized financial systems since the late 1990s. Here we show that, besides a persistent decrease in the volume of the geometric space since 1997, the observation of a generalized increase in the values of the multivariate skewness and kurtosis sheds some light on the behavior of cross-border interdependencies during periods of financial crises. This was found to occur in such a systematic fashion, that these coefficients may be used as a proxy for systemic risk.

Comprehensive analysis of a multidimensional liquid chromatography mass spectrometry dataset acquired on a quadrupole selecting, quadrupole collision cell, time-of-flight mass spectrometer: I. How much of the data is theoretically interpretable by search engines?

PubMed

Chalkley, Robert J; Baker, Peter R; Hansen, Kirk C; Medzihradszky, Katalin F; Allen, Nadia P; Rexach, Michael; Burlingame, Alma L

2005-08-01

An in-depth analysis of a multidimensional chromatography-mass spectrometry dataset acquired on a quadrupole selecting, quadrupole collision cell, time-of-flight (QqTOF) geometry instrument was carried out. A total of 3269 CID spectra were acquired. Through manual verification of database search results and de novo interpretation of spectra 2368 spectra could be confidently determined as predicted tryptic peptides. A detailed analysis of the non-matching spectra was also carried out, highlighting what the non-matching spectra in a database search typically are composed of. The results of this comprehensive dataset study demonstrate that QqTOF instruments produce information-rich data of which a high percentage of the data is readily interpretable.

Multidimensional Time-Resolved Spectroscopy of Vibrational Coherence in Biopolyenes

NASA Astrophysics Data System (ADS)

Buckup, Tiago; Motzkus, Marcus

2014-04-01

Multidimensional femtosecond time-resolved vibrational coherence spectroscopy allows one to investigate the evolution of vibrational coherence in electronic excited states. Methods such as pump-degenerate four-wave mixing and pump-impulsive vibrational spectroscopy combine an initial ultrashort laser pulse with a nonlinear probing sequence to reinduce vibrational coherence exclusively in the excited states. By carefully exploiting specific electronic resonances, one can detect vibrational coherence from 0 cm-1 to over 2,000 cm-1 and map its evolution. This review focuses on the observation and mapping of high-frequency vibrational coherence for all-trans biological polyenes such as β-carotene, lycopene, retinal, and retinal Schiff base. We discuss the role of molecular symmetry in vibrational coherence activity in the S1 electronic state and the interplay of coupling between electronic states and vibrational coherence.

Information granules in image histogram analysis.

PubMed

Wieclawek, Wojciech

2018-04-01

A concept of granular computing employed in intensity-based image enhancement is discussed. First, a weighted granular computing idea is introduced. Then, the implementation of this term in the image processing area is presented. Finally, multidimensional granular histogram analysis is introduced. The proposed approach is dedicated to digital images, especially to medical images acquired by Computed Tomography (CT). As the histogram equalization approach, this method is based on image histogram analysis. Yet, unlike the histogram equalization technique, it works on a selected range of the pixel intensity and is controlled by two parameters. Performance is tested on anonymous clinical CT series. Copyright © 2017 Elsevier Ltd. All rights reserved.

Using ADOPT Algorithm and Operational Data to Discover Precursors to Aviation Adverse Events

NASA Technical Reports Server (NTRS)

Janakiraman, Vijay; Matthews, Bryan; Oza, Nikunj

2018-01-01

The US National Airspace System (NAS) is making its transition to the NextGen system and assuring safety is one of the top priorities in NextGen. At present, safety is managed reactively (correct after occurrence of an unsafe event). While this strategy works for current operations, it may soon become ineffective for future airspace designs and high density operations. There is a need for proactive management of safety risks by identifying hidden and "unknown" risks and evaluating the impacts on future operations. To this end, NASA Ames has developed data mining algorithms that finds anomalies and precursors (high-risk states) to safety issues in the NAS. In this paper, we describe a recently developed algorithm called ADOPT that analyzes large volumes of data and automatically identifies precursors from real world data. Precursors help in detecting safety risks early so that the operator can mitigate the risk in time. In addition, precursors also help identify causal factors and help predict the safety incident. The ADOPT algorithm scales well to large data sets and to multidimensional time series, reduce analyst time significantly, quantify multiple safety risks giving a holistic view of safety among other benefits. This paper details the algorithm and includes several case studies to demonstrate its application to discover the "known" and "unknown" safety precursors in aviation operation.

Real-time monitoring and visualization of the multi-dimensional motion of an anisotropic nanoparticle

NASA Astrophysics Data System (ADS)

Go, Gi-Hyun; Heo, Seungjin; Cho, Jong-Hoi; Yoo, Yang-Seok; Kim, Minkwan; Park, Chung-Hyun; Cho, Yong-Hoon

2017-03-01

As interest in anisotropic particles has increased in various research fields, methods of tracking such particles have become increasingly desirable. Here, we present a new and intuitive method to monitor the Brownian motion of a nanowire, which can construct and visualize multi-dimensional motion of a nanowire confined in an optical trap, using a dual particle tracking system. We measured the isolated angular fluctuations and translational motion of the nanowire in the optical trap, and determined its physical properties, such as stiffness and torque constants, depending on laser power and polarization direction. This has wide implications in nanoscience and nanotechnology with levitated anisotropic nanoparticles.

A multidimensional representation model of geographic features

USGS Publications Warehouse

Usery, E. Lynn; Timson, George; Coletti, Mark

2016-01-28

A multidimensional model of geographic features has been developed and implemented with data from The National Map of the U.S. Geological Survey. The model, programmed in C++ and implemented as a feature library, was tested with data from the National Hydrography Dataset demonstrating the capability to handle changes in feature attributes, such as increases in chlorine concentration in a stream, and feature geometry, such as the changing shoreline of barrier islands over time. Data can be entered directly, from a comma separated file, or features with attributes and relationships can be automatically populated in the model from data in the Spatial Data Transfer Standard format.

Electrochemical force microscopy

DOEpatents

Kalinin, Sergei V.; Jesse, Stephen; Collins, Liam F.; Rodriguez, Brian J.

2017-01-10

A system and method for electrochemical force microscopy are provided. The system and method are based on a multidimensional detection scheme that is sensitive to forces experienced by a biased electrode in a solution. The multidimensional approach allows separation of fast processes, such as double layer charging, and charge relaxation, and slow processes, such as diffusion and faradaic reactions, as well as capturing the bias dependence of the response. The time-resolved and bias measurements can also allow probing both linear (small bias range) and non-linear (large bias range) electrochemical regimes and potentially the de-convolution of charge dynamics and diffusion processes from steric effects and electrochemical reactivity.

Gender Identity and Adjustment: Understanding the Impact of Individual and Normative Differences in Sex Typing

PubMed Central

Lurye, Leah E.; Zosuls, Kristina M.; Ruble, Diane N.

2009-01-01

The relationship among gender identity, sex typing, and adjustment has attracted the attention of social and developmental psychologists for many years. However, they have explored this issue with different assumptions and different approaches. Generally the approaches differ regarding whether sex typing is considered adaptive versus maladaptive, measured as an individual or normative difference, and whether gender identity is regarded as a unidimensional or multidimensional construct. In this chapter, we consider both perspectives and suggest that the developmental timing and degree of sex typing, as well as the multidimensionality of gender identity, be considered when examining their relationship to adjustment. PMID:18521861

Gender identity and adjustment: understanding the impact of individual and normative differences in sex typing.

PubMed

Lurye, Leah E; Zosuls, Kristina M; Ruble, Diane N

2008-01-01

The relationship among gender identity, sex typing, and adjustment has attracted the attention of social and developmental psychologists for many years. However, they have explored this issue with different assumptions and different approaches. Generally the approaches differ regarding whether sex typing is considered adaptive versus maladaptive, measured as an individual or normative difference, and whether gender identity is regarded as a unidimensional or multidimensional construct. In this chapter, we consider both perspectives and suggest that the developmental timing and degree of sex typing, as well as the multidimensionality of gender identity, be considered when examining their relationship to adjustment.

Multidimensional Visualization of MHD and Turbulence in Fusion Plasmas [Multi-dimensional Visualization of Turbulence in Fusion Plasmas

DOE PAGES

Muscatello, Christopher M.; Domier, Calvin W.; Hu, Xing; ...

2014-08-13

Here, quasi-optical imaging at sub-THz frequencies has had a major impact on fusion plasma diagnostics. Mm-wave imaging reflectometry utilizes microwaves to actively probe fusion plasmas, inferring the local properties of electron density fluctuations. Electron cyclotron emission imaging is a multichannel radiometer that passively measures the spontaneous emission of microwaves from the plasma to infer local properties of electron temperature fluctuations. These imaging diagnostics work together to diagnose the characteristics of turbulence. Important quantities such as amplitude and wavenumber of coherent fluctuations, correlation lengths and decor relation times of turbulence, and poloidal flow velocity of the plasma are readily inferred.

Framework for analyzing ecological trait-based models in multidimensional niche spaces

NASA Astrophysics Data System (ADS)

Biancalani, Tommaso; DeVille, Lee; Goldenfeld, Nigel

2015-05-01

We develop a theoretical framework for analyzing ecological models with a multidimensional niche space. Our approach relies on the fact that ecological niches are described by sequences of symbols, which allows us to include multiple phenotypic traits. Ecological drivers, such as competitive exclusion, are modeled by introducing the Hamming distance between two sequences. We show that a suitable transform diagonalizes the community interaction matrix of these models, making it possible to predict the conditions for niche differentiation and, close to the instability onset, the asymptotically long time population distributions of niches. We exemplify our method using the Lotka-Volterra equations with an exponential competition kernel.

Validation of Brief Multidimensional Spirituality/Religiousness Inventory (BMMRS) in Italian Adult Participants and in Participants with Medical Diseases.

PubMed

Vespa, Anna; Giulietti, Maria Velia; Spatuzzi, Roberta; Fabbietti, Paolo; Meloni, Cristina; Gattafoni, Pisana; Ottaviani, Marica

2017-06-01

This study aimed at assessing the reliability and construct validity of Brief Multidimensional Measure of Religiousness/Spirituality (BMMRS) on Italian sample. 353 Italian participants: 58.9% affected by different diseases and 41.1% healthy subjects. The results of descriptive statistics of internal consistency reliabilities (Chronbach's coefficient) of the BMMRS revealed a remarkable consistency and reliability of different scales DSE, SpC, SC, CSC, VB, SPY-WELL and a good Inter-Class Correlations ≥70 maintaining a good stability of the measures over the time. BMMRS is a useful inventory for the evaluation of the principal spiritual dimensions.

Ultrafast-based projection-reconstruction three-dimensional nuclear magnetic resonance spectroscopy.

PubMed

Mishkovsky, Mor; Kupce, Eriks; Frydman, Lucio

2007-07-21

Recent years have witnessed increased efforts toward the accelerated acquisition of multidimensional nuclear magnetic resonance (nD NMR) spectra. Among the methods proposed to speed up these NMR experiments is "projection reconstruction," a scheme based on the acquisition of a reduced number of two-dimensional (2D) NMR data sets constituting cross sections of the nD time domain being sought. Another proposition involves "ultrafast" spectroscopy, capable of completing nD NMR acquisitions within a single scan. Potential limitations of these approaches include the need for a relatively slow 2D-type serial data collection procedure in the former case, and a need for at least n high-performance, linearly independent gradients and a sufficiently high sensitivity in the latter. The present study introduces a new scheme that comes to address these limitations, by combining the basic features of the projection reconstruction and the ultrafast approaches into a single, unified nD NMR experiment. In the resulting method each member within the series of 2D cross sections required by projection reconstruction to deliver the nD NMR spectrum being sought, is acquired within a single scan with the aid of the 2D ultrafast protocol. Full nD NMR spectra can thus become available by backprojecting a small number of 2D sets, collected using a minimum number of scans. Principles, opportunities, and limitations of the resulting approach, together with demonstrations of its practical advantages, are here discussed and illustrated with a series of three-dimensional homo- and heteronuclear NMR correlation experiments.

Multidimensional Unfolding by Nonmetric Multidimensional Scaling of Spearman Distances in the Extended Permutation Polytope

ERIC Educational Resources Information Center

Van Deun, Katrijn; Heiser, Willem J.; Delbeke, Luc

2007-01-01

A multidimensional unfolding technique that is not prone to degenerate solutions and is based on multidimensional scaling of a complete data matrix is proposed: distance information about the unfolding data and about the distances both among judges and among objects is included in the complete matrix. The latter information is derived from the…

Improved p-type conductivity in Al-rich AlGaN using multidimensional Mg-doped superlattices

PubMed Central

Zheng, T. C.; Lin, W.; Liu, R.; Cai, D. J.; Li, J. C.; Li, S. P.; Kang, J. Y.

2016-01-01

A novel multidimensional Mg-doped superlattice (SL) is proposed to enhance vertical hole conductivity in conventional Mg-doped AlGaN SL which generally suffers from large potential barrier for holes. Electronic structure calculations within the first-principle theoretical framework indicate that the densities of states (DOS) of the valence band nearby the Fermi level are more delocalized along the c-axis than that in conventional SL, and the potential barrier significantly decreases. Hole concentration is greatly enhanced in the barrier of multidimensional SL. Detailed comparisons of partial charges and decomposed DOS reveal that the improvement of vertical conductance may be ascribed to the stronger pz hybridization between Mg and N. Based on the theoretical analysis, highly conductive p-type multidimensional Al0.63Ga0.37N/Al0.51Ga0.49N SLs are grown with identified steps via metalorganic vapor-phase epitaxy. The hole concentration reaches up to 3.5 × 1018 cm−3, while the corresponding resistivity reduces to 0.7 Ω cm at room temperature, which is tens times improvement in conductivity compared with that of conventional SLs. High hole concentration can be maintained even at 100 K. High p-type conductivity in Al-rich structural material is an important step for the future design of superior AlGaN-based deep ultraviolet devices. PMID:26906334

The Effect of Multidimensional Motivation Interventions on Cognitive and Behavioral Components of Motivation: Testing Martin's Model

PubMed Central

Pooragha Roodbarde, Fatemeh; Talepasand, Siavash; Rahimian Boogar, Issac

2017-01-01

Objective: The present study aimed at examining the effect of multidimensional motivation interventions based on Martin's model on cognitive and behavioral components of motivation. Method: The research design was prospective with pretest, posttest, and follow-up, and 2 experimental groups. In this study, 90 students (45 participants in the experimental group and 45 in the control group) constituted the sample of the study, and they were selected by available sampling method. Motivation interventions were implemented for fifteen 60-minute sessions 3 times a week, which lasted for about 2 months. Data were analyzed using repeated measures multivariate variance analysis test. Results: The findings revealed that multidimensional motivation interventions resulted in a significant increase in the scores of cognitive components such as self-efficacy, mastery goal, test anxiety, and feeling of lack of control, and behavioral components such as task management. The results of one-month follow-up indicated the stability of the created changes in test anxiety and cognitive strategies; however, no significant difference was found between the 2 groups at the follow-up in self-efficacy, mastery goals, source of control, and motivation. Conclusion: The research evidence indicated that academic motivation is a multidimensional component and is affected by cognitive and behavioral factors; therefore, researchers, teachers, and other authorities should attend to these factors to increase academic motivation. PMID:28659984

The Effect of Multidimensional Motivation Interventions on Cognitive and Behavioral Components of Motivation: Testing Martin's Model.

PubMed

Pooragha Roodbarde, Fatemeh; Talepasand, Siavash; Rahimian Boogar, Issac

2017-04-01

Objective: The present study aimed at examining the effect of multidimensional motivation interventions based on Martin's model on cognitive and behavioral components of motivation. Method: The research design was prospective with pretest, posttest, and follow-up, and 2 experimental groups. In this study, 90 students (45 participants in the experimental group and 45 in the control group) constituted the sample of the study, and they were selected by available sampling method. Motivation interventions were implemented for fifteen 60-minute sessions 3 times a week, which lasted for about 2 months. Data were analyzed using repeated measures multivariate variance analysis test. Results: The findings revealed that multidimensional motivation interventions resulted in a significant increase in the scores of cognitive components such as self-efficacy, mastery goal, test anxiety, and feeling of lack of control, and behavioral components such as task management. The results of one-month follow-up indicated the stability of the created changes in test anxiety and cognitive strategies; however, no significant difference was found between the 2 groups at the follow-up in self-efficacy, mastery goals, source of control, and motivation. Conclusion: The research evidence indicated that academic motivation is a multidimensional component and is affected by cognitive and behavioral factors; therefore, researchers, teachers, and other authorities should attend to these factors to increase academic motivation.

L2 Utterance Fluency Development Before, During, and after Residence Abroad: A Multidimensional Investigation

ERIC Educational Resources Information Center

Huensch, Amanda; Tracy-Ventura, Nicole

2017-01-01

This study investigated second language fluency development over a nearly 2-year period which included an academic year abroad and the year immediately following the participants' return to their home university. Data from 24 L1 English learners of Spanish were collected 6 times: once before, 3 times during, and 2 times after a 9-month stay…

Short-Term Memory Scanning Viewed as Exemplar-Based Categorization

ERIC Educational Resources Information Center

Nosofsky, Robert M.; Little, Daniel R.; Donkin, Christopher; Fific, Mario

2011-01-01

Exemplar-similarity models such as the exemplar-based random walk (EBRW) model (Nosofsky & Palmeri, 1997b) were designed to provide a formal account of multidimensional classification choice probabilities and response times (RTs). At the same time, a recurring theme has been to use exemplar models to account for old-new item recognition and to…

Spanish Zimbardo Time Perspective Inventory Construction and Validity among Higher Education Students

ERIC Educational Resources Information Center

Usart, Mireia; Romero, Margarida

2014-01-01

Introduction: The study of "Time Orientation" (TO) has been focused on how to measure this construct and its effects on human behavior. Defined as a fundamental psychological variable, TO is multidimensional, sensible to cultural differences and age. Although its relation to learning, it deserves further study in the different Higher…

Real-Time Data Warehousing and On-Line Analytical Processing at Aberdeen Test Center’s Distributed Center

DTIC Science & Technology

2005-12-01

data collected via on-board instrumentation -VxWorks based computer. Each instrument produces a continuous time history record of up to 250...data in multidimensional hierarchies and views. UGC 2005 Institute a high performance data warehouse • PostgreSQL 7.4 installed on dedicated filesystem

On the complex quantification of risk: systems-based perspective on terrorism.

PubMed

Haimes, Yacov Y

2011-08-01

This article highlights the complexity of the quantification of the multidimensional risk function, develops five systems-based premises on quantifying the risk of terrorism to a threatened system, and advocates the quantification of vulnerability and resilience through the states of the system. The five premises are: (i) There exists interdependence between a specific threat to a system by terrorist networks and the states of the targeted system, as represented through the system's vulnerability, resilience, and criticality-impact. (ii) A specific threat, its probability, its timing, the states of the targeted system, and the probability of consequences can be interdependent. (iii) The two questions in the risk assessment process: "What is the likelihood?" and "What are the consequences?" can be interdependent. (iv) Risk management policy options can reduce both the likelihood of a threat to a targeted system and the associated likelihood of consequences by changing the states (including both vulnerability and resilience) of the system. (v) The quantification of risk to a vulnerable system from a specific threat must be built on a systemic and repeatable modeling process, by recognizing that the states of the system constitute an essential step to construct quantitative metrics of the consequences based on intelligence gathering, expert evidence, and other qualitative information. The fact that the states of all systems are functions of time (among other variables) makes the time frame pivotal in each component of the process of risk assessment, management, and communication. Thus, risk to a system, caused by an initiating event (e.g., a threat) is a multidimensional function of the specific threat, its probability and time frame, the states of the system (representing vulnerability and resilience), and the probabilistic multidimensional consequences. © 2011 Society for Risk Analysis.

Particle Filtering for Model-Based Anomaly Detection in Sensor Networks

NASA Technical Reports Server (NTRS)

Solano, Wanda; Banerjee, Bikramjit; Kraemer, Landon

2012-01-01

A novel technique has been developed for anomaly detection of rocket engine test stand (RETS) data. The objective was to develop a system that postprocesses a csv file containing the sensor readings and activities (time-series) from a rocket engine test, and detects any anomalies that might have occurred during the test. The output consists of the names of the sensors that show anomalous behavior, and the start and end time of each anomaly. In order to reduce the involvement of domain experts significantly, several data-driven approaches have been proposed where models are automatically acquired from the data, thus bypassing the cost and effort of building system models. Many supervised learning methods can efficiently learn operational and fault models, given large amounts of both nominal and fault data. However, for domains such as RETS data, the amount of anomalous data that is actually available is relatively small, making most supervised learning methods rather ineffective, and in general met with limited success in anomaly detection. The fundamental problem with existing approaches is that they assume that the data are iid, i.e., independent and identically distributed, which is violated in typical RETS data. None of these techniques naturally exploit the temporal information inherent in time series data from the sensor networks. There are correlations among the sensor readings, not only at the same time, but also across time. However, these approaches have not explicitly identified and exploited such correlations. Given these limitations of model-free methods, there has been renewed interest in model-based methods, specifically graphical methods that explicitly reason temporally. The Gaussian Mixture Model (GMM) in a Linear Dynamic System approach assumes that the multi-dimensional test data is a mixture of multi-variate Gaussians, and fits a given number of Gaussian clusters with the help of the wellknown Expectation Maximization (EM) algorithm. The parameters thus learned are used for calculating the joint distribution of the observations. However, this GMM assumption is essentially an approximation and signals the potential viability of non-parametric density estimators. This is the key idea underlying the new approach.

Fast multi-dimensional NMR by minimal sampling

NASA Astrophysics Data System (ADS)

Kupče, Ēriks; Freeman, Ray

2008-03-01

A new scheme is proposed for very fast acquisition of three-dimensional NMR spectra based on minimal sampling, instead of the customary step-wise exploration of all of evolution space. The method relies on prior experiments to determine accurate values for the evolving frequencies and intensities from the two-dimensional 'first planes' recorded by setting t1 = 0 or t2 = 0. With this prior knowledge, the entire three-dimensional spectrum can be reconstructed by an additional measurement of the response at a single location (t1∗,t2∗) where t1∗ and t2∗ are fixed values of the evolution times. A key feature is the ability to resolve problems of overlap in the acquisition dimension. Applied to a small protein, agitoxin, the three-dimensional HNCO spectrum is obtained 35 times faster than systematic Cartesian sampling of the evolution domain. The extension to multi-dimensional spectroscopy is outlined.

A lock-free priority queue design based on multi-dimensional linked lists

DOE PAGES

Dechev, Damian; Zhang, Deli

2015-04-03

The throughput of concurrent priority queues is pivotal to multiprocessor applications such as discrete event simulation, best-first search and task scheduling. Existing lock-free priority queues are mostly based on skiplists, which probabilistically create shortcuts in an ordered list for fast insertion of elements. The use of skiplists eliminates the need of global rebalancing in balanced search trees and ensures logarithmic sequential search time on average, but the worst-case performance is linear with respect to the input size. In this paper, we propose a quiescently consistent lock-free priority queue based on a multi-dimensional list that guarantees worst-case search time of O(logN)more » for key universe of size N. The novel multi-dimensional list (MDList) is composed of nodes that contain multiple links to child nodes arranged by their dimensionality. The insertion operation works by first injectively mapping the scalar key to a high-dimensional vector, then uniquely locating the target position by using the vector as coordinates. Nodes in MDList are ordered by their coordinate prefixes and the ordering property of the data structure is readily maintained during insertion without rebalancing nor randomization. Furthermore, in our experimental evaluation using a micro-benchmark, our priority queue achieves an average of 50% speedup over the state of the art approaches under high concurrency.« less

A multidimensional unified gas-kinetic scheme for radiative transfer equations on unstructured mesh

NASA Astrophysics Data System (ADS)

Sun, Wenjun; Jiang, Song; Xu, Kun

2017-12-01

In order to extend the unified gas kinetic scheme (UGKS) to solve radiative transfer equations in a complex geometry, a multidimensional asymptotic preserving implicit method on unstructured mesh is constructed in this paper. With an implicit formulation, the CFL condition for the determination of the time step in UGKS can be much relaxed, and a large time step is used in simulations. Differently from previous direction-by-direction UGKS on orthogonal structured mesh, on unstructured mesh the interface flux transport takes into account multi-dimensional effect, where gradients of radiation intensity and material temperature in both normal and tangential directions of a cell interface are included in the flux evaluation. The multiple scale nature makes the UGKS be able to capture the solutions in both optically thin and thick regions seamlessly. In the optically thick region the condition of cell size being less than photon's mean free path is fully removed, and the UGKS recovers a solver for diffusion equation in such a limit on unstructured mesh. For a distorted quadrilateral mesh, the UGKS goes to a nine-point scheme for the diffusion equation, and it naturally reduces to the standard five-point scheme for a orthogonal quadrilateral mesh. Numerical computations covering a wide range of transport regimes on unstructured and distorted quadrilateral meshes will be presented to validate the current approach.

A lock-free priority queue design based on multi-dimensional linked lists

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

Dechev, Damian; Zhang, Deli

The throughput of concurrent priority queues is pivotal to multiprocessor applications such as discrete event simulation, best-first search and task scheduling. Existing lock-free priority queues are mostly based on skiplists, which probabilistically create shortcuts in an ordered list for fast insertion of elements. The use of skiplists eliminates the need of global rebalancing in balanced search trees and ensures logarithmic sequential search time on average, but the worst-case performance is linear with respect to the input size. In this paper, we propose a quiescently consistent lock-free priority queue based on a multi-dimensional list that guarantees worst-case search time of O(logN)more » for key universe of size N. The novel multi-dimensional list (MDList) is composed of nodes that contain multiple links to child nodes arranged by their dimensionality. The insertion operation works by first injectively mapping the scalar key to a high-dimensional vector, then uniquely locating the target position by using the vector as coordinates. Nodes in MDList are ordered by their coordinate prefixes and the ordering property of the data structure is readily maintained during insertion without rebalancing nor randomization. Furthermore, in our experimental evaluation using a micro-benchmark, our priority queue achieves an average of 50% speedup over the state of the art approaches under high concurrency.« less

A quantitative study on magnesium alloy stent biodegradation.

PubMed

Gao, Yuanming; Wang, Lizhen; Gu, Xuenan; Chu, Zhaowei; Guo, Meng; Fan, Yubo

2018-06-06

Insufficient scaffolding time in the process of rapid corrosion is the main problem of magnesium alloy stent (MAS). Finite element method had been used to investigate corrosion of MAS. However, related researches mostly described all elements suffered corrosion in view of one-dimensional corrosion. Multi-dimensional corrosions significantly influence mechanical integrity of MAS structures such as edges and corners. In this study, the effects of multi-dimensional corrosion were studied using experiment quantitatively, then a phenomenological corrosion model was developed to consider these effects. We implemented immersion test with magnesium alloy (AZ31B) cubes, which had different numbers of exposed surfaces to analyze differences of dimension. It was indicated that corrosion rates of cubes are almost proportional to their exposed-surface numbers, especially when pitting corrosions are not marked. The cubes also represented the hexahedron elements in simulation. In conclusion, corrosion rate of every element accelerates by increasing corrosion-surface numbers in multi-dimensional corrosion. The damage ratios among elements with the same size are proportional to the ratios of corrosion-surface numbers under uniform corrosion. The finite element simulation using proposed model provided more details of changes of morphology and mechanics in scaffolding time by removing 25.7% of elements of MAS. The proposed corrosion model reflected the effects of multi-dimension on corrosions. It would be used to predict degradation process of MAS quantitatively. Copyright © 2018 Elsevier Ltd. All rights reserved.

Application of a multidimensional gas chromatography system with simultaneous mass spectrometric and flame ionization detection to the analysis of sandalwood oil.

PubMed

Sciarrone, Danilo; Costa, Rosaria; Ragonese, Carla; Tranchida, Peter Quinto; Tedone, Laura; Santi, Luca; Dugo, Paola; Dugo, Giovanni; Joulain, Daniel; Mondello, Luigi

2011-01-07

The production and trade of Indian sandalwood oil is strictly regulated, due to the impoverishment of the plantations; for such a reason, Australian sandalwood oil has been evaluated as a possible substitute of the Indian type. International directives report, for both the genuine essential oils, specific ranges for the sesquiterpene alcohols (santalols). In the present investigation, a multidimensional gas chromatographic system (MDGC), equipped with simultaneous flame ionization and mass spectrometric detection (FID/MS), has been successfully applied to the analysis of a series of sandalwood oils of different origin. A detailed description of the system utilized is reported. Three santalol isomers, (Z)-α-trans-bergamotol, (E,E)-farnesol, (Z)-nuciferol, epi-α-bisabolol and (Z)-lanceol have been quantified. LoD (MS) and LoQ (FID) values were determined for (E,E)-farnesol, used as representative of the oxygenated sesquiterpenic group, showing levels equal to 0.002% and 0.003%, respectively. A great advantage of the instrumental configuration herein discussed, is represented by the fact that identification and quantitation of target analytes are carried out in one step, without the need to perform two separate analyses. Copyright © 2010 Elsevier B.V. All rights reserved.

On the Need for Multidimensional Stirling Simulations

NASA Technical Reports Server (NTRS)

Dyson, Rodger W.; Wilson, Scott D.; Tew, Roy C.; Demko, Rikako

2005-01-01

Given the cost and complication of simulating Stirling convertors, do we really need multidimensional modeling when one-dimensional capabilities exist? This paper provides a comprehensive description of when and why multidimensional simulation is needed.

Flame-Generated Vorticity Production in Premixed Flame-Vortex Interactions

NASA Technical Reports Server (NTRS)

Patnaik, G.; Kailasanath, K.

2003-01-01

In this study, we use detailed time-dependent, multi-dimensional numerical simulations to investigate the relative importance of the processes leading to FGV in flame-vortex interactions in normal gravity and microgravity and to determine if the production of vorticity in flames in gravity is the same as that in zero gravity except for the contribution of the gravity term. The numerical simulations will be performed using the computational model developed at NRL, FLAME3D. FLAME3D is a parallel, multi-dimensional (either two- or three-dimensional) flame model based on FLIC2D, which has been used extensively to study the structure and stability of premixed hydrogen and methane flames.

An Environmentally Sensitive Fluorescent Dye as a Multidimensional Probe of Amyloid Formation

PubMed Central

2016-01-01

We have explored amyloid formation using poly(amino acid) model systems in which differences in peptide secondary structure and hydrophobicity can be introduced in a controlled manner. We show that an environmentally sensitive fluorescent dye, dapoxyl, is able to identify β-sheet structure and hydrophobic surfaces, structural features likely to be related to toxicity, as a result of changes in its excitation and emission profiles and its relative quantum yield. These results show that dapoxyl is a multidimensional probe of the time dependence of amyloid aggregation, which provides information about the presence and nature of metastable aggregation intermediates that is inaccessible to the conventional probes that rely on changes in quantum yield alone. PMID:26865546

Application of random coherence order selection in gradient-enhanced multidimensional NMR

NASA Astrophysics Data System (ADS)

Bostock, Mark J.; Nietlispach, Daniel

2016-03-01

Development of multidimensional NMR is essential to many applications, for example in high resolution structural studies of biomolecules. Multidimensional techniques enable separation of NMR signals over several dimensions, improving signal resolution, whilst also allowing identification of new connectivities. However, these advantages come at a significant cost. The Fourier transform theorem requires acquisition of a grid of regularly spaced points to satisfy the Nyquist criterion, while frequency discrimination and acquisition of a pure phase spectrum require acquisition of both quadrature components for each time point in every indirect (non-acquisition) dimension, adding a factor of 2 N -1 to the number of free- induction decays which must be acquired, where N is the number of dimensions. Compressed sensing (CS) ℓ 1-norm minimisation in combination with non-uniform sampling (NUS) has been shown to be extremely successful in overcoming the Nyquist criterion. Previously, maximum entropy reconstruction has also been used to overcome the limitation of frequency discrimination, processing data acquired with only one quadrature component at a given time interval, known as random phase detection (RPD), allowing a factor of two reduction in the number of points for each indirect dimension (Maciejewski et al. 2011 PNAS 108 16640). However, whilst this approach can be easily applied in situations where the quadrature components are acquired as amplitude modulated data, the same principle is not easily extended to phase modulated (P-/N-type) experiments where data is acquired in the form exp (iωt) or exp (-iωt), and which make up many of the multidimensional experiments used in modern NMR. Here we demonstrate a modification of the CS ℓ 1-norm approach to allow random coherence order selection (RCS) for phase modulated experiments; we generalise the nomenclature for RCS and RPD as random quadrature detection (RQD). With this method, the power of RQD can be extended to the full suite of experiments available to modern NMR spectroscopy, allowing resolution enhancements for all indirect dimensions; alone or in combination with NUS, RQD can be used to improve experimental resolution, or shorten experiment times, of considerable benefit to the challenging applications undertaken by modern NMR.

Multidimensional measures validated for home health needs of older persons: A systematic review.

PubMed

de Rossi Figueiredo, Daniela; Paes, Lucilene Gama; Warmling, Alessandra Martins; Erdmann, Alacoque Lorenzini; de Mello, Ana Lúcia Schaefer Ferreira

2018-01-01

To conduct a systematic review of the literature on valid and reliable multidimensional instruments to assess home health needs of older persons. Systematic review. Electronic databases, PubMed/Medline, Web of Science, Scopus, Cumulative Index to Nursing and Allied Health Literature, Scientific Electronic Library Online and the Latin American and Caribbean Health Sciences Information. All English, Portuguese and Spanish literature which included studies of reliability and validity of instruments that assessed at least two dimensions: physical, psychological, social support and functional independence, self-rated health behaviors and contextual environment and if such instruments proposed interventions after evaluation and/or monitoring changes over a period of time. Older persons aged 60 years or older. Of the 2397 studies identified, 32 were considered eligible. Two-thirds of the instruments proposed the physical, psychological, social support and functional independence dimensions. Inter-observer and intra-observer reliability and internal consistency values were 0.7 or above. More than two-thirds of the studies included validity (n=26) and more than one validity was tested in 15% (n=4) of these. Only 7% (n=2) proposed interventions after evaluation and/or monitoring changes over a period of time. Although the multidimensional assessment was performed, and the reliability values of the reviewed studies were satisfactory, different validity tests were not present in several studies. A gap at the instrument conception was observed related to interventions after evaluation and/or monitoring changes over a period of time. Further studies with this purpose are necessary for home health needs of the older persons. Copyright © 2017 Elsevier Ltd. All rights reserved.

Influence of fusion dynamics on fission observables: A multidimensional analysis

NASA Astrophysics Data System (ADS)

Schmitt, C.; Mazurek, K.; Nadtochy, P. N.

2018-01-01

An attempt to unfold the respective influence of the fusion and fission stages on typical fission observables, and namely the neutron prescission multiplicity, is proposed. A four-dimensional dynamical stochastic Langevin model is used to calculate the decay by fission of excited compound nuclei produced in a wide set of heavy-ion collisions. The comparison of the results from such a calculation and experimental data is discussed, guided by predictions of the dynamical deterministic HICOL code for the compound-nucleus formation time. While the dependence of the latter on the entrance-channel properties can straigthforwardly explain some observations, a complex interplay between the various parameters of the reaction is found to occur in other cases. A multidimensional analysis of the respective role of these parameters, including entrance-channel asymmetry, bombarding energy, compound-nucleus fissility, angular momentum, and excitation energy, is proposed. It is shown that, depending on the size of the system, apparent inconsistencies may be deduced when projecting onto specific ordering parameters. The work suggests the possibility of delicate compensation effects in governing the measured fission observables, thereby highlighting the necessity of a multidimensional discussion.

Imaging a multidimensional multichannel potential energy surface: Photodetachment of H(-)(NH3) and NH4 (.).

PubMed

Hu, Qichi; Song, Hongwei; Johnson, Christopher J; Li, Jun; Guo, Hua; Continetti, Robert E

2016-06-28

Probes of the Born-Oppenheimer potential energy surfaces governing polyatomic molecules often rely on spectroscopy for the bound regions or collision experiments in the continuum. A combined spectroscopic and half-collision approach to image nuclear dynamics in a multidimensional and multichannel system is reported here. The Rydberg radical NH4 and the double Rydberg anion NH4 (-) represent a polyatomic system for benchmarking electronic structure and nine-dimensional quantum dynamics calculations. Photodetachment of the H(-)(NH3) ion-dipole complex and the NH4 (-) DRA probes different regions on the neutral NH4 PES. Photoelectron energy and angular distributions at photon energies of 1.17, 1.60, and 2.33 eV compare well with quantum dynamics. Photoelectron-photofragment coincidence experiments indicate dissociation of the nascent NH4 Rydberg radical occurs to H + NH3 with a peak kinetic energy of 0.13 eV, showing the ground state of NH4 to be unstable, decaying by tunneling-induced dissociation on a time scale beyond the present scope of multidimensional quantum dynamics.

Multidimensional Scaling in the Poincare Disk

DTIC Science & Technology

2011-05-01

REPORT Multidimensional Scaling in the Poincare Dis 14. ABSTRACT 16. SECURITY CLASSIFICATION OF: Multidimensional scaling (MDS) is a class of projective...DATES COVERED (From - To) Standard Form 298 (Rev 8/98) Prescribed by ANSI Std. Z39.18 - Multidimensional Scaling in the Poincare Dis Report Title... plane . Our construction is based on an approximate hyperbolic line search and exempli?es some of the particulars that need to be addressed when

Investigating fast enzyme-DNA kinetics using multidimensional fluorescence imaging and microfluidics

NASA Astrophysics Data System (ADS)

Robinson, Tom; Manning, Hugh B.; Dunsby, Christopher; Neil, Mark A. A.; Baldwin, Geoff S.; de Mello, Andrew J.; French, Paul M. W.

2010-02-01

We have developed a rapid microfluidic mixing device to image fast kinetics. To verify the performance of the device it was simulated using computational fluid dynamics (CFD) and the results were directly compared to experimental fluorescence lifetime imaging (FLIM) measurements. The theoretical and measured mixing times of the device were found to be in agreement over a range of flow rates. This mixing device is being developed with the aim of analysing fast enzyme kinetics in the sub-millisecond time domain, which cannot be achieved with conventional macro-stopped flow devices. Here we have studied the binding of a DNA repair enzyme, uracil DNA glycosylase (UDG), to a fluorescently labelled DNA substrate. Bulk phase fluorescence measurements have been used to measure changes on binding: it was found that the fluorescence lifetime increased along with an increase in the polarisation anisotropy and rotational correlation time. Analysis of the same reaction in the microfluidic mixer by CFD enabled us to predict the mixing time of the device to be 46 μs, more than 20 times faster than current stopped-flow techniques. We also demonstrate that it is possible to image UDG-DNA interactions within the micromixer using the signal changes observed from the multidimensional spectrofluorometer.

Scaling Laws for the Multidimensional Burgers Equation with Quadratic External Potential

NASA Astrophysics Data System (ADS)

Leonenko, N. N.; Ruiz-Medina, M. D.

2006-07-01

The reordering of the multidimensional exponential quadratic operator in coordinate-momentum space (see X. Wang, C.H. Oh and L.C. Kwek (1998). J. Phys. A.: Math. Gen. 31:4329-4336) is applied to derive an explicit formulation of the solution to the multidimensional heat equation with quadratic external potential and random initial conditions. The solution to the multidimensional Burgers equation with quadratic external potential under Gaussian strongly dependent scenarios is also obtained via the Hopf-Cole transformation. The limiting distributions of scaling solutions to the multidimensional heat and Burgers equations with quadratic external potential are then obtained under such scenarios.

Models of multidimensional discrete distribution of probabilities of random variables in information systems

NASA Astrophysics Data System (ADS)

Gromov, Yu Yu; Minin, Yu V.; Ivanova, O. G.; Morozova, O. N.

2018-03-01

Multidimensional discrete distributions of probabilities of independent random values were received. Their one-dimensional distribution is widely used in probability theory. Producing functions of those multidimensional distributions were also received.

Improving Personality Facet Scores with Multidimensional Computer Adaptive Testing: An Illustration with the Neo Pi-R

ERIC Educational Resources Information Center

Makransky, Guido; Mortensen, Erik Lykke; Glas, Cees A. W.

2013-01-01

Narrowly defined personality facet scores are commonly reported and used for making decisions in clinical and organizational settings. Although these facets are typically related, scoring is usually carried out for a single facet at a time. This method can be ineffective and time consuming when personality tests contain many highly correlated…

Differences in Student Evaluations of Limited-Term Lecturers and Full-Time Faculty

ERIC Educational Resources Information Center

Cho, Jeong-Il; Otani, Koichiro; Kim, B. Joon

2014-01-01

This study compared student evaluations of teaching (SET) for limited-term lecturers (LTLs) and full-time faculty (FTF) using a Likert-scaled survey administered to students (N = 1,410) at the end of university courses. Data were analyzed using a general linear regression model to investigate the influence of multi-dimensional evaluation items on…

The horizontal and vertical attributes of individualism and collectivism in a Spanish population.

PubMed

Gouveia, Valdiney V; Clemente, Miguel; Espinosa, Pablo

2003-02-01

The authors examined the dimensionality and factorial structure of individualism and collectivism in Spanish participants (N = 526). A series of confirmatory factor analyses were performed on responses to the 32-item individualism-collectivism measure reported by T. M. Singelis, H. C. Triandis, D. S. Bhawuk, and M. Gelfand (1995). Consistent with earlier data, the best fitting model was multidimensional: a vertical versus a horizontal attribute crossed with individualism and collectivism dimensions. Whereas the overall fit of the data to a LISREL model was moderate, additional self-report data on respondents' interpersonal experiences supported the construct validity of the 4 factors. The authors suggest that the additional complexity is useful in explaining Spanish social behavior.

Moral attentiveness: who pays attention to the moral aspects of life?

PubMed

Reynolds, Scott J

2008-09-01

This research draws from social cognitive theory to develop a construct known as moral attentiveness, the extent to which an individual chronically perceives and considers morality and moral elements in his or her experiences, and proposes that moral attentiveness affects a variety of behaviors. A series of 5 studies with undergraduates, MBA students, and managers were conducted to create and validate a reliable multidimensional scale and to provide evidence that moral attentiveness is associated with (a) the recall and reporting of self- and others' morality-related behaviors, (b) moral awareness, and (c) moral behavior. Results of the studies suggest that moral attentiveness has a significant effect on how individuals understand and act in their moral worlds.

Multidimensional Knowledge Structures.

ERIC Educational Resources Information Center

Schuh, Kathy L.

Multidimensional knowledge structures, described from a constructivist perspective and aligned with the "Mind as Rhizome" metaphor, provide support for constructivist learning strategies. This qualitative study was conducted to seek empirical support for a description of multidimensional knowledge structures, focusing on the…

Multidimensional quantum entanglement with large-scale integrated optics.

PubMed

Wang, Jianwei; Paesani, Stefano; Ding, Yunhong; Santagati, Raffaele; Skrzypczyk, Paul; Salavrakos, Alexia; Tura, Jordi; Augusiak, Remigiusz; Mančinska, Laura; Bacco, Davide; Bonneau, Damien; Silverstone, Joshua W; Gong, Qihuang; Acín, Antonio; Rottwitt, Karsten; Oxenløwe, Leif K; O'Brien, Jeremy L; Laing, Anthony; Thompson, Mark G

2018-04-20

The ability to control multidimensional quantum systems is central to the development of advanced quantum technologies. We demonstrate a multidimensional integrated quantum photonic platform able to generate, control, and analyze high-dimensional entanglement. A programmable bipartite entangled system is realized with dimensions up to 15 × 15 on a large-scale silicon photonics quantum circuit. The device integrates more than 550 photonic components on a single chip, including 16 identical photon-pair sources. We verify the high precision, generality, and controllability of our multidimensional technology, and further exploit these abilities to demonstrate previously unexplored quantum applications, such as quantum randomness expansion and self-testing on multidimensional states. Our work provides an experimental platform for the development of multidimensional quantum technologies. Copyright © 2018 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works.

Validity and reliability of the multidimensional assessment of fatigue scale in Iranian patients with relapsing-remitting subtype of multiple sclerosis.

PubMed

Behrangrad, Shabnam; Kordi Yoosefinejad, Amin

2018-03-01

The purpose of this study is to investigate the validity and reliability of the Persian version of the Multidimensional Assessment of Fatigue Scale (MAFS) in an Iranian population with multiple sclerosis. A self-reported survey on fatigue including the MAFS, Fatigue Impact Scale and demographic measures was completed by 130 patients with multiple sclerosis and 60 healthy persons sampled with a convenience method. Test-retest reliability and validity were evaluated 3 days apart. Construct validity of the MAFS was assessed with the Fatigue Impact Scale. The MAFS had high internal consistency (Cronbach's alpha >0.9) and 3-d test-retest reliability (intraclass correlation coefficient = 0.99). Correlation between the Fatigue Impact Scale and MAFS was high (r = 0.99). Correlation between MAFS scores and the Expanded Disability Status Scale was also strong (r = 0.85). Questionnaire items showed acceptable item-scale correlation (0.968-0.993). The Persian version of the MAFS appears to be a valid and reliable questionnaire. It is an appropriate short multidimensional instrument to assess fatigue in patients with multiple sclerosis in clinical practice and research. Implications for Rehabilitation The Persian version of Multidimensional Assessment of Fatigue is a valid and reliable instrument for the assessment and monitoring the fatigue in Persian-language patients with multiple sclerosis. It is very easy to administer and a time efficient scale in comparison to other instruments evaluating fatigue in patients with multiple sclerosis.

SAGE - MULTIDIMENSIONAL SELF-ADAPTIVE GRID CODE

NASA Technical Reports Server (NTRS)

Davies, C. B.

1994-01-01

SAGE, Self Adaptive Grid codE, is a flexible tool for adapting and restructuring both 2D and 3D grids. Solution-adaptive grid methods are useful tools for efficient and accurate flow predictions. In supersonic and hypersonic flows, strong gradient regions such as shocks, contact discontinuities, shear layers, etc., require careful distribution of grid points to minimize grid error and produce accurate flow-field predictions. SAGE helps the user obtain more accurate solutions by intelligently redistributing (i.e. adapting) the original grid points based on an initial or interim flow-field solution. The user then computes a new solution using the adapted grid as input to the flow solver. The adaptive-grid methodology poses the problem in an algebraic, unidirectional manner for multi-dimensional adaptations. The procedure is analogous to applying tension and torsion spring forces proportional to the local flow gradient at every grid point and finding the equilibrium position of the resulting system of grid points. The multi-dimensional problem of grid adaption is split into a series of one-dimensional problems along the computational coordinate lines. The reduced one dimensional problem then requires a tridiagonal solver to find the location of grid points along a coordinate line. Multi-directional adaption is achieved by the sequential application of the method in each coordinate direction. The tension forces direct the redistribution of points to the strong gradient region. To maintain smoothness and a measure of orthogonality of grid lines, torsional forces are introduced that relate information between the family of lines adjacent to one another. The smoothness and orthogonality constraints are direction-dependent, since they relate only the coordinate lines that are being adapted to the neighboring lines that have already been adapted. Therefore the solutions are non-unique and depend on the order and direction of adaption. Non-uniqueness of the adapted grid is acceptable since it makes possible an overall and local error reduction through grid redistribution. SAGE includes the ability to modify the adaption techniques in boundary regions, which substantially improves the flexibility of the adaptive scheme. The vectorial approach used in the analysis also provides flexibility. The user has complete choice of adaption direction and order of sequential adaptions without concern for the computational data structure. Multiple passes are available with no restraint on stepping directions; for each adaptive pass the user can choose a completely new set of adaptive parameters. This facility, combined with the capability of edge boundary control, enables the code to individually adapt multi-dimensional multiple grids. Zonal grids can be adapted while maintaining continuity along the common boundaries. For patched grids, the multiple-pass capability enables complete adaption. SAGE is written in FORTRAN 77 and is intended to be machine independent; however, it requires a FORTRAN compiler which supports NAMELIST input. It has been successfully implemented on Sun series computers, SGI IRIS's, DEC MicroVAX computers, HP series computers, the Cray YMP, and IBM PC compatibles. Source code is provided, but no sample input and output files are provided. The code reads three datafiles: one that contains the initial grid coordinates (x,y,z), one that contains corresponding flow-field variables, and one that contains the user control parameters. It is assumed that the first two datasets are formatted as defined in the plotting software package PLOT3D. Several machine versions of PLOT3D are available from COSMIC. The amount of main memory is dependent on the size of the matrix. The standard distribution medium for SAGE is a 5.25 inch 360K MS-DOS format diskette. It is also available on a .25 inch streaming magnetic tape cartridge in UNIX tar format or on a 9-track 1600 BPI ASCII CARD IMAGE format magnetic tape. SAGE was developed in 1989, first released as a 2D version in 1991 and updated to 3D in 1993.

Multidimensional Perfectionism and the Self

ERIC Educational Resources Information Center

Ward, Andrew M.; Ashby, Jeffrey S.

2008-01-01

This study examined multidimensional perfectionism and self-development. Two hundred seventy-one undergraduates completed a measure of multidimensional perfectionism and two Kohutian measures designed to measure aspects of self-development including social connectedness, social assurance, goal instability (idealization), and grandiosity. The…

Chemical space visualization: transforming multidimensional chemical spaces into similarity-based molecular networks.

PubMed

de la Vega de León, Antonio; Bajorath, Jürgen

2016-09-01

The concept of chemical space is of fundamental relevance for medicinal chemistry and chemical informatics. Multidimensional chemical space representations are coordinate-based. Chemical space networks (CSNs) have been introduced as a coordinate-free representation. A computational approach is presented for the transformation of multidimensional chemical space into CSNs. The design of transformation CSNs (TRANS-CSNs) is based upon a similarity function that directly reflects distance relationships in original multidimensional space. TRANS-CSNs provide an immediate visualization of coordinate-based chemical space and do not require the use of dimensionality reduction techniques. At low network density, TRANS-CSNs are readily interpretable and make it possible to evaluate structure-activity relationship information originating from multidimensional chemical space.

The solution of large multi-dimensional Poisson problems

NASA Technical Reports Server (NTRS)

Stone, H. S.

1974-01-01

The Buneman algorithm for solving Poisson problems can be adapted to solve large Poisson problems on computers with a rotating drum memory so that the computation is done with very little time lost due to rotational latency of the drum.

Multi-Dimensional Signal Processing Research Program

DTIC Science & Technology

1981-09-30

applications to real-time image processing and analysis. A specific long-range application is the automated processing of aerial reconnaissance imagery...Non-supervised image segmentation is a potentially im- portant operation in the automated processing of aerial reconnaissance pho- tographs since it

Near Real-time Scientific Data Analysis and Visualization with the ArcGIS Platform

NASA Astrophysics Data System (ADS)

Shrestha, S. R.; Viswambharan, V.; Doshi, A.

2017-12-01

Scientific multidimensional data are generated from a variety of sources and platforms. These datasets are mostly produced by earth observation and/or modeling systems. Agencies like NASA, NOAA, USGS, and ESA produce large volumes of near real-time observation, forecast, and historical data that drives fundamental research and its applications in larger aspects of humanity from basic decision making to disaster response. A common big data challenge for organizations working with multidimensional scientific data and imagery collections is the time and resources required to manage and process such large volumes and varieties of data. The challenge of adopting data driven real-time visualization and analysis, as well as the need to share these large datasets, workflows, and information products to wider and more diverse communities, brings an opportunity to use the ArcGIS platform to handle such demand. In recent years, a significant effort has put in expanding the capabilities of ArcGIS to support multidimensional scientific data across the platform. New capabilities in ArcGIS to support scientific data management, processing, and analysis as well as creating information products from large volumes of data using the image server technology are becoming widely used in earth science and across other domains. We will discuss and share the challenges associated with big data by the geospatial science community and how we have addressed these challenges in the ArcGIS platform. We will share few use cases, such as NOAA High Resolution Refresh Radar (HRRR) data, that demonstrate how we access large collections of near real-time data (that are stored on-premise or on the cloud), disseminate them dynamically, process and analyze them on-the-fly, and serve them to a variety of geospatial applications. We will also share how on-the-fly processing using raster functions capabilities, can be extended to create persisted data and information products using raster analytics capabilities that exploit distributed computing in an enterprise environment.

Multidimensional poverty and catastrophic health spending in the mountainous regions of Myanmar, Nepal and India.

PubMed

Mohanty, Sanjay K; Agrawal, Nand Kishor; Mahapatra, Bidhubhusan; Choudhury, Dhrupad; Tuladhar, Sabarnee; Holmgren, E Valdemar

2017-01-18

Economic burden to households due to out-of-pocket expenditure (OOPE) is large in many Asian countries. Though studies suggest increasing household poverty due to high OOPE in developing countries, studies on association of multidimensional poverty and household health spending is limited. This paper tests the hypothesis that the multidimensionally poor are more likely to incur catastrophic health spending cutting across countries. Data from the Poverty and Vulnerability Assessment (PVA) Survey carried out by the International Center for Integrated Mountain Development (ICIMOD) has been used in the analyses. The PVA survey was a comprehensive household survey that covered the mountainous regions of India, Nepal and Myanmar. A total of 2647 households from India, 2310 households in Nepal and 4290 households in Myanmar covered under the PVA survey. Poverty is measured in a multidimensional framework by including the dimensions of education, income and energy, water and sanitation using the Alkire and Foster method. Health shock is measured using the frequency of illness, family sickness and death of any family member in a reference period of one year. Catastrophic health expenditure is defined as 40% above the household's capacity to pay. Results suggest that about three-fifths of the population in Myanmar, two-fifths of the population in Nepal and one-third of the population in India are multidimensionally poor. About 47% of the multidimensionally poor in India had incurred catastrophic health spending compared to 35% of the multidimensionally non-poor and the pattern was similar in both Nepal and Myanmar. The odds of incurring catastrophic health spending was 56% more among the multidimensionally poor than among the multidimensionally non-poor [95% CI: 1.35-1.76]. While health shocks to households are consistently significant predictors of catastrophic health spending cutting across country of residence, the educational attainment of the head of the household is not significant. The multidimensionally poor in the poorer regions are more likely to face health shocks and are less likely to afford professional health services. Increasing government spending on health and increasing households' access to health insurance can reduce catastrophic health spending and multidimensional poverty.

Morphological variation among late holocene Mexicans: Implications for discussions about the human occupation of the Americas.

PubMed

Herrera, Brianne; Peart, Daniel; Hernandez, Nicole; Spradley, Kate; Hubbe, Mark

2017-05-01

Cranial morphology has previously been used to estimate phylogenetic relationships among populations, and has been an important tool in the reconstruction of ancient human dispersals across the planet. In the Americas, previous morphological studies support a scenario of people entering the Americas and dispersing from North America into South America through Meso America, making the Mexican territory the natural funnel through which biological diversity entered South America. We explore the cranial morphological affinities of three late Holocene Mexican series, in relation to ancient and modern crania from North and South America, Australo-Melanesia, and East Asia. Morphological affinities were assessed through Mahalanobis Distances, and represented via Multidimensional Scaling and Ward's Linkage Cluster analysis. Minimum F ST values were also calculated for each series. Our results show Mexican groups share morphological affinities with the Native American series, but do not cluster together as would be expected. The minimum F ST estimates show between-group variation in the Americas is higher than the Asian or Australo-Melanesian populations, and that Mexican series have high between-group variance (F ST  = 0.124), compared to the geographically larger South America (F ST  = 0.116) and North America (F ST  = 0.076). These results show that the Mexican series share morphological affinities with the East Asian series, but maintains high levels of between-group variation, similar to South America. This supports the suggestion that the high phenotypic variation seen the Americas is not a result of its size, as it can be found in more constricted areas, such as the Mexican territory. © 2017 Wiley Periodicals, Inc.

An introduction to multidimensional measurement using Rasch models.

PubMed

Briggs, Derek C; Wilson, Mark

2003-01-01

The act of constructing a measure requires a number of important assumptions. Principle among these assumptions is that the construct is unidimensional. In practice there are many instances when the assumption of unidimensionality does not hold, and where the application of a multidimensional measurement model is both technically appropriate and substantively advantageous. In this paper we illustrate the usefulness of a multidimensional approach to measurement with the Multidimensional Random Coefficient Multinomial Logit (MRCML) model, an extension of the unidimensional Rasch model. An empirical example is taken from a collection of embedded assessments administered to 541 students enrolled in middle school science classes with a hands-on science curriculum. Student achievement on these assessments are multidimensional in nature, but can also be treated as consecutive unidimensional estimates, or as is most common, as a composite unidimensional estimate. Structural parameters are estimated for each model using ConQuest, and model fit is compared. Student achievement in science is also compared across models. The multidimensional approach has the best fit to the data, and provides more reliable estimates of student achievement than under the consecutive unidimensional approach. Finally, at an interpretational level, the multidimensional approach may well provide richer information to the classroom teacher about the nature of student achievement.

Multi-Dimensional Calibration of Impact Dynamic Models

NASA Technical Reports Server (NTRS)

Horta, Lucas G.; Reaves, Mercedes C.; Annett, Martin S.; Jackson, Karen E.

2011-01-01

NASA Langley, under the Subsonic Rotary Wing Program, recently completed two helicopter tests in support of an in-house effort to study crashworthiness. As part of this effort, work is on-going to investigate model calibration approaches and calibration metrics for impact dynamics models. Model calibration of impact dynamics problems has traditionally assessed model adequacy by comparing time histories from analytical predictions to test at only a few critical locations. Although this approach provides for a direct measure of the model predictive capability, overall system behavior is only qualitatively assessed using full vehicle animations. In order to understand the spatial and temporal relationships of impact loads as they migrate throughout the structure, a more quantitative approach is needed. In this work impact shapes derived from simulated time history data are used to recommend sensor placement and to assess model adequacy using time based metrics and orthogonality multi-dimensional metrics. An approach for model calibration is presented that includes metric definitions, uncertainty bounds, parameter sensitivity, and numerical optimization to estimate parameters to reconcile test with analysis. The process is illustrated using simulated experiment data.

The risk of falling into poverty after developing heart disease: a survival analysis.

PubMed

Callander, Emily J; Schofield, Deborah J

2016-07-15

Those with a low income are known to have a higher risk of developing heart disease. However, the inverse relationship - falling into income poverty after developing heart disease has not been explored with longitudinal data. This paper aims to determine if those with heart disease have an elevated risk of falling into poverty. Survival analysis was conducted using the longitudinal Household Income and Labour Dynamics in Australia survey, between the years 2007 and 2012. The study focused on the Australian population aged 21 years and over in 2007 who were not already in poverty and did not already have heart disease, who were followed from 2007 to 2012. Cox regression models adjusting for age, sex and time-varying co-variates (marital status, home ownership and remoteness of area of residence) were constructed to assess the risk of falling into poverty. For those aged 20 who developed heart disease, the hazard ratio for falling into income poverty was 9.24 (95 % CI: 8.97-9.51) and for falling into multidimensional poverty the hazard ratio was 14.21 (95 % CI: 13.76-14.68); for those aged 40 the hazard ratio for falling into income poverty was 3.45 (95 % CI: 3.39-3.51) and for multidimensional poverty, 5.20 (95 % CI: 5.11-5.29); and for those aged 60 the hazard ratio for falling into income poverty was 1.29 (95 % CI: 1.28-1.30) and for multidimensional poverty, 1.52 (95 % CI: 1.51-1.54), relative those who never developed heart disease. The risk for both income and multidimensional poverty decreases with age up to the age of 70, over which, those who developed heart disease had a reduced risk of poverty. For those under the age of 70, developing heart disease is associated with an increased risk of falling into both income poverty and multidimensional poverty.

Data matching for free-surface multiple attenuation by multidimensional deconvolution

NASA Astrophysics Data System (ADS)

van der Neut, Joost; Frijlink, Martijn; van Borselen, Roald

2012-09-01

A common strategy for surface-related multiple elimination of seismic data is to predict multiples by a convolutional model and subtract these adaptively from the input gathers. Problems can be posed by interfering multiples and primaries. Removing multiples by multidimensional deconvolution (MDD) (inversion) does not suffer from these problems. However, this approach requires data to be consistent, which is often not the case, especially not at interpolated near-offsets. A novel method is proposed to improve data consistency prior to inversion. This is done by backpropagating first-order multiples with a time-gated reference primary event and matching these with early primaries in the input gather. After data matching, multiple elimination by MDD can be applied with a deterministic inversion scheme.

Multidimensional Models of Type Ia Supernova Nebular Spectra: Strong Emission Lines from Stripped Companion Gas Rule Out Classic Single-degenerate Systems

NASA Astrophysics Data System (ADS)

Botyánszki, János; Kasen, Daniel; Plewa, Tomasz

2018-01-01

The classic single-degenerate model for the progenitors of Type Ia supernova (SN Ia) predicts that the supernova ejecta should be enriched with solar-like abundance material stripped from the companion star. Spectroscopic observations of normal SNe Ia at late times, however, have not resulted in definite detection of hydrogen. In this Letter, we study line formation in SNe Ia at nebular times using non-LTE spectral modeling. We present, for the first time, multidimensional radiative transfer calculations of SNe Ia with stripped material mixed in the ejecta core, based on hydrodynamical simulations of ejecta–companion interaction. We find that interaction models with main-sequence companions produce significant Hα emission at late times, ruling out these types of binaries being viable progenitors of SNe Ia. We also predict significant He I line emission at optical and near-infrared wavelengths for both hydrogen-rich or helium-rich material, providing an additional observational probe of stripped ejecta. We produce models with reduced stripped masses and find a more stringent mass limit of M st ≲ 1 × 10‑4 M ⊙ of stripped companion material for SN 2011fe.

Fast and high-order numerical algorithms for the solution of multidimensional nonlinear fractional Ginzburg-Landau equation

NASA Astrophysics Data System (ADS)

Mohebbi, Akbar

2018-02-01

In this paper we propose two fast and accurate numerical methods for the solution of multidimensional space fractional Ginzburg-Landau equation (FGLE). In the presented methods, to avoid solving a nonlinear system of algebraic equations and to increase the accuracy and efficiency of method, we split the complex problem into simpler sub-problems using the split-step idea. For a homogeneous FGLE, we propose a method which has fourth-order of accuracy in time component and spectral accuracy in space variable and for nonhomogeneous one, we introduce another scheme based on the Crank-Nicolson approach which has second-order of accuracy in time variable. Due to using the Fourier spectral method for fractional Laplacian operator, the resulting schemes are fully diagonal and easy to code. Numerical results are reported in terms of accuracy, computational order and CPU time to demonstrate the accuracy and efficiency of the proposed methods and to compare the results with the analytical solutions. The results show that the present methods are accurate and require low CPU time. It is illustrated that the numerical results are in good agreement with the theoretical ones.

Identification, synthesis, and characterization of novel sulfur-containing volatile compounds from the in-depth analysis of Lisbon lemon peels (Citrus limon L. Burm. f. cv. Lisbon).

PubMed

Cannon, Robert J; Kazimierski, Arkadiusz; Curto, Nicole L; Li, Jing; Trinnaman, Laurence; Jańczuk, Adam J; Agyemang, David; Da Costa, Neil C; Chen, Michael Z

2015-02-25

Lemons (Citrus limon) are a desirable citrus fruit grown and used globally in a wide range of applications. The main constituents of this sour-tasting fruit have been well quantitated and characterized. However, additional research is still necessary to better understand the trace volatile compounds that may contribute to the overall aroma of the fruit. In this study, Lisbon lemons (C. limon L. Burm. f. cv. Lisbon) were purchased from a grove in California, USA, and extracted by liquid-liquid extraction. Fractionation and multidimensional gas chromatography-mass spectrometry were utilized to separate, focus, and enhance unidentified compounds. In addition, these methods were employed to more accurately assign flavor dilution factors by aroma extract dilution analysis. Numerous compounds were identified for the first time in lemons, including a series of branched aliphatic aldehydes and several novel sulfur-containing structures. Rarely reported in citrus peels, sulfur compounds are known to contribute significantly to the aroma profile of the fruit and were found to be aroma-active in this particular study on lemons. This paper discusses the identification, synthesis, and organoleptic properties of these novel volatile sulfur compounds.

Spatial and temporal variability in the structure of invertebrate assemblages in control stream mesocosms.

PubMed

Wong, Diana C L; Maltby, Lorraine; Whittle, Don; Warren, Philip; Dorn, Philip B

2004-01-01

Outdoor stream mesocosm studies conducted between 1992 and 1996 at two facilities enabled the investigation of structural variability in invertebrate assemblages within and between studies. Temporal variability of benthic invertebrate assemblages between eight replicate streams within a study was assessed in a 28-day mesocosm study without chemical treatment. Cluster analysis, non-metric multidimensional scaling, and principal component analysis each showed the untreated assemblages as structurally distinct groups on the three sampling days. The assemblages between the eight replicate streams showed >88% Bray-Curtis similarity at any one time during the study. In addition, pre-treatment data from a series of four studies conducted at one facility were used to examine structural variability in the starting benthic invertebrate assemblages between studies. Invertebrate assemblages were structurally distinct at the start of each mesocosm study conducted in different years at the same facility and the taxa responsible for differences in the assemblages were also different each year. The implications of temporal and spatial variability in benthic invertebrate assemblages within and between mesocosm studies with regards to species sensitivity and study repeatability should be considered when results of such studies are used in risk assessment.

High Fidelity Modeling of Turbulent Mixing and Chemical Kinetics Interactions in a Post-Detonation Flow Field

NASA Astrophysics Data System (ADS)

Sinha, Neeraj; Zambon, Andrea; Ott, James; Demagistris, Michael

2015-06-01

Driven by the continuing rapid advances in high-performance computing, multi-dimensional high-fidelity modeling is an increasingly reliable predictive tool capable of providing valuable physical insight into complex post-detonation reacting flow fields. Utilizing a series of test cases featuring blast waves interacting with combustible dispersed clouds in a small-scale test setup under well-controlled conditions, the predictive capabilities of a state-of-the-art code are demonstrated and validated. Leveraging physics-based, first principle models and solving large system of equations on highly-resolved grids, the combined effects of finite-rate/multi-phase chemical processes (including thermal ignition), turbulent mixing and shock interactions are captured across the spectrum of relevant time-scales and length scales. Since many scales of motion are generated in a post-detonation environment, even if the initial ambient conditions are quiescent, turbulent mixing plays a major role in the fireball afterburning as well as in dispersion, mixing, ignition and burn-out of combustible clouds in its vicinity. Validating these capabilities at the small scale is critical to establish a reliable predictive tool applicable to more complex and large-scale geometries of practical interest.

Evaluating impact of a multi-dimensional education programme on perceived performance of primary care professionals in diabetes care.

PubMed

Parekh, Sanjoti; Bush, Robert; Cook, Susan; Grant, Phillipa

2015-11-01

The purpose of this study is to evaluate an educational programme, 'Diabetes Connect: Connecting Professions', which was developed to enhance communication across primary care networks, to support best practice in clinical interventions and progress multidisciplinary team work to benefit patients in diabetes care. A total of 26 workshops were successfully delivered for 309 primary care professionals across the state of Queensland in Australia from November 2011. It consists of two separate, but complementary training elements: a series of online clinical education training modules and state-wide interprofessional learning workshops developed to enhance professional competencies. The evaluation design included completion of online surveys by the participants at two time points: first upon registering for the online modules or workshops; second, one week after attending a workshop. The survey included questions to evaluate the change in role performance measures. Overall, significant increases in participants' current knowledge, perceived ability to adopt this knowledge at work and willingness to change professional behaviour in the short term were observed. The study suggests that for maximum benefit both, workshop and online training, should be combined and made available widely. Future programmes should use a randomised trial design to test the delivery model.

The Tunneling Method for Global Optimization in Multidimensional Scaling.

ERIC Educational Resources Information Center

Groenen, Patrick J. F.; Heiser, Willem J.

1996-01-01

A tunneling method for global minimization in multidimensional scaling is introduced and adjusted for multidimensional scaling with general Minkowski distances. The method alternates a local search step with a tunneling step in which a different configuration is sought with the same STRESS implementation. (SLD)

High resolution 4-D spectroscopy with sparse concentric shell sampling and FFT-CLEAN.

PubMed

Coggins, Brian E; Zhou, Pei

2008-12-01

Recent efforts to reduce the measurement time for multidimensional NMR experiments have fostered the development of a variety of new procedures for sampling and data processing. We recently described concentric ring sampling for 3-D NMR experiments, which is superior to radial sampling as input for processing by a multidimensional discrete Fourier transform. Here, we report the extension of this approach to 4-D spectroscopy as Randomized Concentric Shell Sampling (RCSS), where sampling points for the indirect dimensions are positioned on concentric shells, and where random rotations in the angular space are used to avoid coherent artifacts. With simulations, we show that RCSS produces a very low level of artifacts, even with a very limited number of sampling points. The RCSS sampling patterns can be adapted to fine rectangular grids to permit use of the Fast Fourier Transform in data processing, without an apparent increase in the artifact level. These artifacts can be further reduced to the noise level using the iterative CLEAN algorithm developed in radioastronomy. We demonstrate these methods on the high resolution 4-D HCCH-TOCSY spectrum of protein G's B1 domain, using only 1.2% of the sampling that would be needed conventionally for this resolution. The use of a multidimensional FFT instead of the slow DFT for initial data processing and for subsequent CLEAN significantly reduces the calculation time, yielding an artifact level that is on par with the level of the true spectral noise.

High Resolution 4-D Spectroscopy with Sparse Concentric Shell Sampling and FFT-CLEAN

PubMed Central

Coggins, Brian E.; Zhou, Pei

2009-01-01

SUMMARY Recent efforts to reduce the measurement time for multidimensional NMR experiments have fostered the development of a variety of new procedures for sampling and data processing. We recently described concentric ring sampling for 3-D NMR experiments, which is superior to radial sampling as input for processing by a multidimensional discrete Fourier transform. Here, we report the extension of this approach to 4-D spectroscopy as Randomized Concentric Shell Sampling (RCSS), where sampling points for the indirect dimensions are positioned on concentric shells, and where random rotations in the angular space are used to avoid coherent artifacts. With simulations, we show that RCSS produces a very low level of artifacts, even with a very limited number of sampling points. The RCSS sampling patterns can be adapted to fine rectangular grids to permit use of the Fast Fourier Transform in data processing, without an apparent increase in the artifact level. These artifacts can be further reduced to the noise level using the iterative CLEAN algorithm developed in radioastronomy. We demonstrate these methods on the high resolution 4-D HCCH-TOCSY spectrum of protein G's B1 domain, using only 1.2% of the sampling that would be needed conventionally for this resolution. The use of a multidimensional FFT instead of the slow DFT for initial data processing and for subsequent CLEAN significantly reduces the calculation time, yielding an artifact level that is on par with the level of the true spectral noise. PMID:18853260

Concurrent and convergent validity of the mobility- and multidimensional-hierarchical disability categorization models with physical performance in community older adults.

PubMed

Hu, Ming-Hsia; Yeh, Chih-Jun; Chen, Tou-Rong; Wang, Ching-Yi

2014-01-01

A valid, time-efficient and easy-to-use instrument is important for busy clinical settings, large scale surveys, or community screening use. The purpose of this study was to validate the mobility hierarchical disability categorization model (an abbreviated model) by investigating its concurrent validity with the multidimensional hierarchical disability categorization model (a comprehensive model) and triangulating both models with physical performance measures in older adults. 604 community-dwelling older adults of at least 60 years in age volunteered to participate. Self-reported function on mobility, instrumental activities of daily living (IADL) and activities of daily living (ADL) domains were recorded and then the disability status determined based on both the multidimensional hierarchical categorization model and the mobility hierarchical categorization model. The physical performance measures, consisting of grip strength and usual and fastest gait speeds (UGS, FGS), were collected on the same day. Both categorization models showed high correlation (γs = 0.92, p < 0.001) and agreement (kappa = 0.61, p < 0.0001). Physical performance measures demonstrated significant different group means among the disability subgroups based on both categorization models. The results of multiple regression analysis indicated that both models individually explain similar amount of variance on all physical performances, with adjustments for age, sex, and number of comorbidities. Our results found that the mobility hierarchical disability categorization model is a valid and time efficient tool for large survey or screening use.

The Role of Mothers' and Fathers' Religiosity in African American Adolescents' Religious Beliefs and Practices

PubMed Central

Halgunseth, Linda C.; Jensen, Alexander C.; Sakuma, Kari-Lyn; McHale, Susan M.

2015-01-01

Objectives To advance understanding of youth religiosity in its sociocultural context, this study examined the associations between parents' and adolescents' religious beliefs and practices and tested the roles of parent and youth gender and youth ethnic identity in these linkages. Methods The sample included 130, two-parent, African American families. Adolescents (49% female) averaged 14.43 years old. Mothers, fathers, and adolescents were interviewed in their homes about their family and personal characteristics, including their religious beliefs. In a series of seven nightly phone calls, adolescents reported on their daily practices , including time spent in religious practices (e.g., attending services, prayer), and parents reported on their time spent in religious practices with their adolescents. Results Findings indicated that mothers' beliefs were linked to the beliefs of sons and daughters, but fathers' beliefs were only associated with the beliefs of sons. Mothers' practices were associated with youths' practices, but the link was stronger when mothers' held moderately strong religious beliefs. Fathers' practices were also linked to youth practices, but the association was stronger for daughters than for sons. Conclusions Findings highlight the understudied role of fathers in African American families, the importance of examining religiosity as a multidimensional construct, and the utility of ethnic homogeneous designs for illuminating the implications of sociocultural factors in the development of African American youth. PMID:26414002

Visual Analysis of Cloud Computing Performance Using Behavioral Lines.

PubMed

Muelder, Chris; Zhu, Biao; Chen, Wei; Zhang, Hongxin; Ma, Kwan-Liu

2016-02-29

Cloud computing is an essential technology to Big Data analytics and services. A cloud computing system is often comprised of a large number of parallel computing and storage devices. Monitoring the usage and performance of such a system is important for efficient operations, maintenance, and security. Tracing every application on a large cloud system is untenable due to scale and privacy issues. But profile data can be collected relatively efficiently by regularly sampling the state of the system, including properties such as CPU load, memory usage, network usage, and others, creating a set of multivariate time series for each system. Adequate tools for studying such large-scale, multidimensional data are lacking. In this paper, we present a visual based analysis approach to understanding and analyzing the performance and behavior of cloud computing systems. Our design is based on similarity measures and a layout method to portray the behavior of each compute node over time. When visualizing a large number of behavioral lines together, distinct patterns often appear suggesting particular types of performance bottleneck. The resulting system provides multiple linked views, which allow the user to interactively explore the data by examining the data or a selected subset at different levels of detail. Our case studies, which use datasets collected from two different cloud systems, show that this visual based approach is effective in identifying trends and anomalies of the systems.

Using ToxCast data to reconstruct dynamic cell state ...

EPA Pesticide Factsheets

AbstractBackground. High-throughput in vitro screening is an important tool for evaluating the potential biological activity of the thousands of existing chemicals in commerce and the hundreds more introduced each year. Among the assay technologies available, high-content imaging (HCI) allows multiplexed measurements of cellular phenotypic changes induced by chemical exposures. For a large chemical inventory having limited concentration-time series data, the deconvolution of cellular response profiles into transitive or irrevocable state trajectories is an important consideration. Objectives. Our goal was to analyze temporal and concentration-related cellular changes measured using HCI to identify the “tipping point” at which the cells did not show recovery towards a normal phenotypic state. Methods. The effects of 976 chemicals (ToxCast Phase I and II) were evaluated using HCI as a function of concentration and time in HepG2 cells over a 72-hr exposure period to concentrations ranging from 0.4- to 200 µM. The cellular endpoints included nuclear p53 accumulation, JNK, markers of oxidative stress, cytoskeletal changes, mitochondrial energization and density, cell viability and cell cycle progression. A novel computational model was developed to interpret dynamic multidimensional system responses as cell-state trajectories. Results. Analysis of cell-state trajectories showed that HepG2 cells were resilient to the effects of 178 chemicals up to the highest co

The role of mothers' and fathers' religiosity in African American adolescents' religious beliefs and practices.

PubMed

Halgunseth, Linda C; Jensen, Alexander C; Sakuma, Kari-Lyn; McHale, Susan M

2016-07-01

To advance understanding of youth religiosity in its sociocultural context, this study examined the associations between parents' and adolescents' religious beliefs and practices and tested the roles of parent and youth gender and youth ethnic identity in these linkages. The sample included 130 two-parent, African American families. Adolescents (49% female) averaged 14.43 years old. Mothers, fathers, and adolescents were interviewed in their homes about their family and personal characteristics, including their religious beliefs. In a series of 7 nightly phone calls, adolescents reported on their daily practices, including time spent in religious practices (e.g., attending services, prayer), and parents reported on their time spent in religious practices with their adolescents. Findings indicated that mothers' beliefs were linked to the beliefs of sons and daughters, but fathers' beliefs were only associated with the beliefs of sons. Mothers' practices were associated with youths' practices, but the link was stronger when mothers' held moderately strong religious beliefs. Fathers' practices were also linked to youth practices, but the association was stronger for daughters than for sons. Findings highlight the understudied role of fathers in African American families, the importance of examining religiosity as a multidimensional construct, and the utility of ethnic homogeneous designs for illuminating the implications of sociocultural factors in the development of African American youth. (PsycINFO Database Record (c) 2016 APA, all rights reserved).

Longitudinal sex and stress hormone profiles among reproductive age and post-menopausal women after severe TBI: A case series analysis.

PubMed

Ranganathan, Prerna; Kumar, Raj G; Davis, Kendra; McCullough, Emily H; Berga, Sarah L; Wagner, Amy K

2016-01-01

To describe hormone profiles for pre-/post-menopausal women, to monitor time to resumption of menstruation among pre-menopausal women and to describe cortisol associated LH suppression and phasic variation in other sex hormones over timeMethods and procedures: This study determined amenorrhea duration and characterized acute (days 0-7) and chronic (months 1-6) gonadotropins [luteinizing hormone and follicle stimulating hormone (LH, FSH)], sex hormones (progesterone, estradiol) and stress hormone (cortisol) profiles. Women were pre-menopausal (n = 3) or post-menopausal (n = 3). Among pre-menopausal women, menstrual cycle resolution and phase association (luteal/follicular) was monitored using self-report monthly reproductive history questionnaires. This study compared post-TBI hormone profiles, stratified by menopausal status, to hormone levels from seven controls and described 6- and 12-month outcomes for these women. Consistent with functional hypothalamic amenorrhea (FHA), menstruation resumption among pre-menopausal women occurred when serum cortisol normalized to luteal phase control levels. For post-menopausal women, serum cortisol reductions corresponded with resolution of suppressed LH levels. The stress of TBI results in anovulation and central hypothalamic-pituitary-ovarian (HPG) axis suppression. Future work will examine acute/chronic consequences of post-TBI hypercortisolemia and associated HPG suppression, the temporal association of HPG suppression with other neuroendocrine adaptations and how HPG suppression impacts multidimensional recovery for women with TBI.

Optimizability of OGC Standards Implementations - a Case Study

NASA Astrophysics Data System (ADS)

Misev, D.; Baumann, P.

2012-04-01

Why do we shop at Amazon? Because they have a unique offering that is nowhere else available? Certainly not. Rather, Amazon offers (i) simple, yet effective search; (ii) very simple payment; (iii) extremely rapid delivery. This is how scientific services will be distinguished in future: not for their data holding (there will be manifold choice), but for their service quality. We are facing the transition from data stewardship to service stewardship. One of the OGC standards which particularly enables flexible retrieval is the Web Coverage Processing Service (WCPS). It defines a high-level query language on large, multi-dimensional raster data, such as 1D timeseries, 2D EO imagery, 3D x/y/t image time series and x/y/z geophysical data, 4D x/y/z/t climate and ocean data. We have implemented WCPS based on an Array Database Management System, rasdaman, which is available in open source. In this demonstration, we study WCPS queries on 2D, 3D, and 4D data sets. Particular emphasis is placed on the computational load queries generate in such on-demand processing and filtering. We look at different techniques and their impact on performance, such as adaptive storage partitioning, query rewriting, and just-in-time compilation. Results show that there is significant potential for effective server-side optimization once a query language is sufficiently high-level and declarative.

Soil and plant factors driving the community of soil-borne microorganisms across chronosequences of secondary succession of chalk grasslands with a neutral pH.

PubMed

Kuramae, Eiko; Gamper, Hannes; van Veen, Johannes; Kowalchuk, George

2011-08-01

Although soil pH has been shown to be an important factor driving microbial communities, relatively little is known about the other potentially important factors that shape soil-borne microbial community structure. This study examined plant and microbial communities across a series of neutral pH fields (pH=7.0-7.5) representing a chronosequence of secondary succession after former arable fields were taken out of production. These fields ranged from 17 to >66 years since the time of abandonment, and an adjacent arable field was included as a reference. Hierarchical clustering analysis, nonmetric multidimensional scaling and analysis of similarity of 52 different plant species showed that the plant community composition was significantly different in the different chronosequences, and that plant species richness and diversity increased with time since abandonment. The microbial community structure, as analyzed by phylogenetic microarrays (PhyloChips), was significantly different in arable field and the early succession stage, but no distinct microbial communities were observed for the intermediate and the late succession stages. The most determinant factors in shaping the soil-borne microbial communities were phosphorous and NH(4)(+). Plant community composition and diversity did not have a significant effect on the belowground microbial community structure or diversity. © 2011 Federation of European Microbiological Societies. Published by Blackwell Publishing Ltd. All rights reserved.

Relevance in the science classroom: A multidimensional analysis

NASA Astrophysics Data System (ADS)

Hartwell, Matthew F.

While perceived relevance is considered a fundamental component of adaptive learning, the experience of relevance and its conceptual definition have not been well described. The mixed-methods research presented in this dissertation aimed to clarify the conceptual meaning of relevance by focusing on its phenomenological experience from the students' perspective. Following a critical literature review, I propose an identity-based model of perceived relevance that includes three components: a contextual target, an identity target, and a connection type, or lens. An empirical investigation of this model that consisted of two general phases was implemented in four 9th grade-biology classrooms. Participants in Phase 1 (N = 118) completed a series of four open-ended writing activities focused on eliciting perceived personal connections to academic content. Exploratory qualitative content analysis of a 25% random sample of the student responses was used to identify the main meaning-units of the proposed model as well as different dimensions of student relevance perceptions. These meaning-units and dimensions provided the basis for the construction of a conceptual mapping sentence capturing students' perceived relevance, which was then applied in a confirmatory analysis to all other student responses. Participants in Phase 2 (N = 139) completed a closed survey designed based on the mapping sentence to assess their perceived relevance of a biology unit. The survey also included scales assessing other domain-level motivational processes. Exploratory factor analysis and non-metric multidimensional scaling indicated a coherent conceptual structure, which included a primary interpretive relevance dimension. Comparison of the conceptual structure across various groups (randomly-split sample, gender, academic level, domain-general motivational profiles) provided support for its ubiquity and insight into variation in the experience of perceived relevance among students of different groups. The findings combine to support a multidimensional perspective of relevance in the 9th grade biology classroom; offering researchers a useful model for future investigation and educators with insights into the students' classroom experience.

Enhancing Student Motivation and Engagement: The Effects of a Multidimensional Intervention

ERIC Educational Resources Information Center

Martin, Andrew J.

2008-01-01

The present study sought to investigate the effects of a multidimensional educational intervention on high school students' motivation and engagement. The intervention incorporated: (a) multidimensional targets of motivation and engagement, (b) empirically derived intervention methodology, (c) research-based risk and protective factors, (d)…

Multi-Dimensional Analysis of Dynamic Human Information Interaction

ERIC Educational Resources Information Center

Park, Minsoo

2013-01-01

Introduction: This study aims to understand the interactions of perception, effort, emotion, time and performance during the performance of multiple information tasks using Web information technologies. Method: Twenty volunteers from a university participated in this study. Questionnaires were used to obtain general background information and…

Improving the Accessibility and Use of NASA Earth Science Data

NASA Technical Reports Server (NTRS)

Tisdale, Matthew; Tisdale, Brian

2015-01-01

Many of the NASA Langley Atmospheric Science Data Center (ASDC) Distributed Active Archive Center (DAAC) multidimensional tropospheric and atmospheric chemistry data products are stored in HDF4, HDF5 or NetCDF format, which traditionally have been difficult to analyze and visualize with geospatial tools. With the rising demand from the diverse end-user communities for geospatial tools to handle multidimensional products, several applications, such as ArcGIS, have refined their software. Many geospatial applications now have new functionalities that enable the end user to: Store, serve, and perform analysis on each individual variable, its time dimension, and vertical dimension. Use NetCDF, GRIB, and HDF raster data formats across applications directly. Publish output within REST image services or WMS for time and space enabled web application development. During this webinar, participants will learn how to leverage geospatial applications such as ArcGIS, OPeNDAP and ncWMS in the production of Earth science information, and in increasing data accessibility and usability.

A psychometric study of the multidimensional fatigue inventory to assess fatigue in patients with schizophrenia spectrum disorders.

PubMed

Hedlund, Lena; Gyllensten, Amanda Lundvik; Hansson, Lars

2015-04-01

Fatigue is frequently reported by patients with mental illness. The multidimensional fatigue inventory (MFI-20) is a self-assessment instrument with 20 items including five dimensions of fatigue. The purpose of this study was to examine the test-retest reliability, internal consistency, convergent construct validity and feasibility of using MFI-20 in patients with schizophrenia spectrum disorders. Patients completed two self-assessment instruments, MFI-20 (n = 93) and Visual Analogue Scale (n = 79), twice within 1 week ± 2 days. Fifty-three patients also rated the feasibility of responding to the MFI-20 with a Likert scale. The test-retest reliability and validity were analysed by using Spearman's correlations and internal consistency by calculating Cronbach's α. The test-retest showed a correlation between .66 and .91 for all subscales of MFI. The internal consistency was .92. The analysis of convergent construct validity showed a correlation of .68 (time 1) and .77 (time 2). No item was systematically identified as being difficult to answer.

Numerical simulation of two-dimensional flow over a heated carbon surface with coupled heterogeneous and homogeneous reactions

NASA Astrophysics Data System (ADS)

Johnson, Ryan Federick; Chelliah, Harsha Kumar

2017-01-01

For a range of flow and chemical timescales, numerical simulations of two-dimensional laminar flow over a reacting carbon surface were performed to understand further the complex coupling between heterogeneous and homogeneous reactions. An open-source computational package (OpenFOAM®) was used with previously developed lumped heterogeneous reaction models for carbon surfaces and a detailed homogeneous reaction model for CO oxidation. The influence of finite-rate chemical kinetics was explored by varying the surface temperatures from 1800 to 2600 K, while flow residence time effects were explored by varying the free-stream velocity up to 50 m/s. The reacting boundary layer structure dependence on the residence time was analysed by extracting the ratio of chemical source and species diffusion terms. The important contributions of radical species reactions on overall carbon removal rate, which is often neglected in multi-dimensional simulations, are highlighted. The results provide a framework for future development and validation of lumped heterogeneous reaction models based on multi-dimensional reacting flow configurations.

Report order and identification of multidimensional stimuli: a study of event-related brain potentials.

PubMed

Shieh, Kong-King; Shen, I-Hsuan

2004-06-01

An experiment was conducted to investigate the effect of order of report on multidimensional stimulus identification. Subjects were required to identify each two-dimensional symbol by pushing corresponding buttons on the keypad on which there were two columns representing the two dimensions. Order of report was manipulated for the dimension represented by the left or right column. Both behavioral data and event-related potentials were recorded from 14 college students. Behavioral data analysis showed that order of report had a significant effect on response times. Such results were consistent with those of previous studies. Analysis of event-related brain potentials showed significant differences in peak amplitude and mean amplitude at time windows of 120-250 msec. at Fz, F3, and F4 and of 350-750 msec. at Fz, F3, F4, Cz, and Pz. Data provided neurophysiological evidence that reporting dimensional values according to natural language habits was appropriate and less cognitively demanding.

Heteronuclear Multidimensional Protein NMR in a Teaching Laboratory

ERIC Educational Resources Information Center

Wright, Nathan T.

2016-01-01

Heteronuclear multidimensional NMR techniques are commonly used to study protein structure, function, and dynamics, yet they are rarely taught at the undergraduate level. Here, we describe a senior undergraduate laboratory where students collect, process, and analyze heteronuclear multidimensional NMR experiments using an unstudied Ig domain (Ig2…

Compressed Continuous Computation v. 12/20/2016

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

Gorodetsky, Alex

2017-02-17

A library for performing numerical computation with low-rank functions. The (C3) library enables performing continuous linear and multilinear algebra with multidimensional functions. Common tasks include taking "matrix" decompositions of vector- or matrix-valued functions, approximating multidimensional functions in low-rank format, adding or multiplying functions together, integrating multidimensional functions.

The Discriminating Power of Items that Measure More than One Dimension.

ERIC Educational Resources Information Center

Reckase, Mark D.

The work presented in this paper defined conceptually the concepts of multidimensional discrimination and information, derived mathematical expressions for the concepts for a particular multidimensional item response theory (IRT) model, and applied the concepts to actual test data. Multidimensional discrimination was defined as a function of the…

Multidimensional Computerized Adaptive Testing for Indonesia Junior High School Biology

ERIC Educational Resources Information Center

Kuo, Bor-Chen; Daud, Muslem; Yang, Chih-Wei

2015-01-01

This paper describes a curriculum-based multidimensional computerized adaptive test that was developed for Indonesia junior high school Biology. In adherence to the Indonesian curriculum of different Biology dimensions, 300 items was constructed, and then tested to 2238 students. A multidimensional random coefficients multinomial logit model was…

Supervised and Unsupervised Learning of Multidimensional Acoustic Categories

ERIC Educational Resources Information Center

Goudbeek, Martijn; Swingley, Daniel; Smits, Roel

2009-01-01

Learning to recognize the contrasts of a language-specific phonemic repertoire can be viewed as forming categories in a multidimensional psychophysical space. Research on the learning of distributionally defined visual categories has shown that categories defined over 1 dimension are easy to learn and that learning multidimensional categories is…

Health, Wealth and Wisdom: Exploring Multidimensional Inequality in a Developing Country

ERIC Educational Resources Information Center

Nilsson, Therese

2010-01-01

Despite a broad theoretical literature on multidimensional inequality and a widespread belief that welfare is not synonymous to income--not the least in a developing context--empirical inequality examinations rarely includes several welfare attributes. We explore three techniques on how to evaluate multidimensional inequality using Zambian…

Multidimensional Physical Self-Concept of Athletes with Physical Disabilities

ERIC Educational Resources Information Center

Shapiro, Deborah R.; Martin, Jeffrey J.

2010-01-01

The purposes of this investigation were first to predict reported PA (physical activity) behavior and self-esteem using a multidimensional physical self-concept model and second to describe perceptions of multidimensional physical self-concept (e.g., strength, endurance, sport competence) among athletes with physical disabilities. Athletes (N =…

Imaging a multidimensional multichannel potential energy surface: Photodetachment of H{sup −}(NH{sub 3}) and NH{sub 4}{sup −}

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

Hu, Qichi; Johnson, Christopher J.; Continetti, Robert E., E-mail: hguo@umn.edu, E-mail: rcontinetti@ucsd.edu

2016-06-28

Probes of the Born-Oppenheimer potential energy surfaces governing polyatomic molecules often rely on spectroscopy for the bound regions or collision experiments in the continuum. A combined spectroscopic and half-collision approach to image nuclear dynamics in a multidimensional and multichannel system is reported here. The Rydberg radical NH{sub 4} and the double Rydberg anion NH{sub 4}{sup −} represent a polyatomic system for benchmarking electronic structure and nine-dimensional quantum dynamics calculations. Photodetachment of the H{sup −}(NH{sub 3}) ion-dipole complex and the NH{sub 4}{sup −} DRA probes different regions on the neutral NH{sub 4} PES. Photoelectron energy and angular distributions at photon energiesmore » of 1.17, 1.60, and 2.33 eV compare well with quantum dynamics. Photoelectron-photofragment coincidence experiments indicate dissociation of the nascent NH{sub 4} Rydberg radical occurs to H + NH{sub 3} with a peak kinetic energy of 0.13 eV, showing the ground state of NH{sub 4} to be unstable, decaying by tunneling-induced dissociation on a time scale beyond the present scope of multidimensional quantum dynamics.« less

A Reduced-Order Model for Efficient Simulation of Synthetic Jet Actuators

NASA Technical Reports Server (NTRS)

Yamaleev, Nail K.; Carpenter, Mark H.

2003-01-01

A new reduced-order model of multidimensional synthetic jet actuators that combines the accuracy and conservation properties of full numerical simulation methods with the efficiency of simplified zero-order models is proposed. The multidimensional actuator is simulated by solving the time-dependent compressible quasi-1-D Euler equations, while the diaphragm is modeled as a moving boundary. The governing equations are approximated with a fourth-order finite difference scheme on a moving mesh such that one of the mesh boundaries coincides with the diaphragm. The reduced-order model of the actuator has several advantages. In contrast to the 3-D models, this approach provides conservation of mass, momentum, and energy. Furthermore, the new method is computationally much more efficient than the multidimensional Navier-Stokes simulation of the actuator cavity flow, while providing practically the same accuracy in the exterior flowfield. The most distinctive feature of the present model is its ability to predict the resonance characteristics of synthetic jet actuators; this is not practical when using the 3-D models because of the computational cost involved. Numerical results demonstrating the accuracy of the new reduced-order model and its limitations are presented.

Understanding the operational environment: implications for advanced visualizations

NASA Astrophysics Data System (ADS)

Aleva, Denise; Fitzhugh, Elisabeth; Dixon, Sharon

2009-05-01

With the changing character of warfare, information superiority is a high priority. Given the complexity of current and future operating environments, analysts, strategists and planners need a multidimensional understanding of the battlespace. Asymmetric warfare necessitates that our strategists look beyond targets-based operations, where we simply identify and destroy enemy entities. Effects-based operations models the enemy as a system which reacts to our actions. This requires the capability to predict the adversary response to a selected action. Actions may be diplomatic, information, military or economic (DIME). Effects may be political, military, economic, social, information or infrastructure (PMESII). Timing must be explicitly considered and effects dynamically assessed. Visualizations of intelligence information are needed which will promote full understanding of all aspects of adversary strengths and weaknesses by providing the extensive data about adversary forces, organic essentials, infrastructure, leadership, population, and science and technology in an easily accessible and understandable format. This will enhance Effectsbased operations, and therefore, the capability to predict and counter adversary courses of action. This paper outlines a systems engineering approach to designing visualizations which convey the multidimensional information to decision makers. Visualization issues inherent in understanding the multidimensional operational environment will be discussed.

Method of data mining including determining multidimensional coordinates of each item using a predetermined scalar similarity value for each item pair

DOEpatents

Meyers, Charles E.; Davidson, George S.; Johnson, David K.; Hendrickson, Bruce A.; Wylie, Brian N.

1999-01-01

A method of data mining represents related items in a multidimensional space. Distance between items in the multidimensional space corresponds to the extent of relationship between the items. The user can select portions of the space to perceive. The user also can interact with and control the communication of the space, focusing attention on aspects of the space of most interest. The multidimensional spatial representation allows more ready comprehension of the structure of the relationships among the items.

A Conceptual Model for Multidimensional Analysis of Documents

NASA Astrophysics Data System (ADS)

Ravat, Franck; Teste, Olivier; Tournier, Ronan; Zurlfluh, Gilles

Data warehousing and OLAP are mainly used for the analysis of transactional data. Nowadays, with the evolution of Internet, and the development of semi-structured data exchange format (such as XML), it is possible to consider entire fragments of data such as documents as analysis sources. As a consequence, an adapted multidimensional analysis framework needs to be provided. In this paper, we introduce an OLAP multidimensional conceptual model without facts. This model is based on the unique concept of dimensions and is adapted for multidimensional document analysis. We also provide a set of manipulation operations.

A multidimensional subdiffusion model: An arbitrage-free market

NASA Astrophysics Data System (ADS)

Li, Guo-Hua; Zhang, Hong; Luo, Mao-Kang

2012-12-01

To capture the subdiffusive characteristics of financial markets, the subordinated process, directed by the inverse α-stale subordinator Sα(t) for 0 < α < 1, has been employed as the model of asset prices. In this article, we introduce a multidimensional subdiffusion model that has a bond and K correlated stocks. The stock price process is a multidimensional subdiffusion process directed by the inverse α-stable subordinator. This model describes the period of stagnation for each stock and the behavior of the dependency between multiple stocks. Moreover, we derive the multidimensional fractional backward Kolmogorov equation for the subordinated process using the Laplace transform technique. Finally, using a martingale approach, we prove that the multidimensional subdiffusion model is arbitrage-free, and also gives an arbitrage-free pricing rule for contingent claims associated with the martingale measure.

Progress in multi-dimensional upwind differencing

NASA Technical Reports Server (NTRS)

Vanleer, Bram

1992-01-01

Multi-dimensional upwind-differencing schemes for the Euler equations are reviewed. On the basis of the first-order upwind scheme for a one-dimensional convection equation, the two approaches to upwind differencing are discussed: the fluctuation approach and the finite-volume approach. The usual extension of the finite-volume method to the multi-dimensional Euler equations is not entirely satisfactory, because the direction of wave propagation is always assumed to be normal to the cell faces. This leads to smearing of shock and shear waves when these are not grid-aligned. Multi-directional methods, in which upwind-biased fluxes are computed in a frame aligned with a dominant wave, overcome this problem, but at the expense of robustness. The same is true for the schemes incorporating a multi-dimensional wave model not based on multi-dimensional data but on an 'educated guess' of what they could be. The fluctuation approach offers the best possibilities for the development of genuinely multi-dimensional upwind schemes. Three building blocks are needed for such schemes: a wave model, a way to achieve conservation, and a compact convection scheme. Recent advances in each of these components are discussed; putting them all together is the present focus of a worldwide research effort. Some numerical results are presented, illustrating the potential of the new multi-dimensional schemes.

On large time step TVD scheme for hyperbolic conservation laws and its efficiency evaluation

NASA Astrophysics Data System (ADS)

Qian, ZhanSen; Lee, Chun-Hian

2012-08-01

A large time step (LTS) TVD scheme originally proposed by Harten is modified and further developed in the present paper and applied to Euler equations in multidimensional problems. By firstly revealing the drawbacks of Harten's original LTS TVD scheme, and reasoning the occurrence of the spurious oscillations, a modified formulation of its characteristic transformation is proposed and a high resolution, strongly robust LTS TVD scheme is formulated. The modified scheme is proven to be capable of taking larger number of time steps than the original one. Following the modified strategy, the LTS TVD schemes for Yee's upwind TVD scheme and Yee-Roe-Davis's symmetric TVD scheme are constructed. The family of the LTS schemes is then extended to multidimensional by time splitting procedure, and the associated boundary condition treatment suitable for the LTS scheme is also imposed. The numerical experiments on Sod's shock tube problem, inviscid flows over NACA0012 airfoil and ONERA M6 wing are performed to validate the developed schemes. Computational efficiencies for the respective schemes under different CFL numbers are also evaluated and compared. The results reveal that the improvement is sizable as compared to the respective single time step schemes, especially for the CFL number ranging from 1.0 to 4.0.

Impact of a multidimensional infection control approach on catheter-associated urinary tract infection rates in an adult intensive care unit in Lebanon: International Nosocomial Infection Control Consortium (INICC) findings.

PubMed

Kanj, Souha S; Zahreddine, Nada; Rosenthal, Victor Daniel; Alamuddin, Lamia; Kanafani, Zeina; Molaeb, Bassel

2013-09-01

The objective of this study was to assess the impact of a multidimensional infection control approach for the reduction of catheter-associated urinary tract infection (CAUTI) in an adult intensive care unit (ICU) of a hospital member of the International Nosocomial Infection Control Consortium (INICC) in Lebanon. A before-after prospective active surveillance study was carried out to determine rates of CAUTI in 1506 ICU patients, hospitalized during 10 291 bed-days. The study period was divided into two phases: phase 1 (baseline) and phase 2 (intervention). During phase 1, surveillance was performed applying the definitions of the US Centers for Disease Control and Prevention National Healthcare Safety Network (CDC/NHSN). In phase 2, we adopted a multidimensional approach that included: (1) a bundle of infection control interventions, (2) education, (3) surveillance of CAUTI rates, (4) feedback on CAUTI rates, (5) process surveillance, and (6) performance feedback. We used random effects Poisson regression to account for clustering of CAUTI rates across time-periods. We recorded a total of 9829 urinary catheter-days: 306 in phase 1 and 9523 in phase 2. The rate of CAUTI was 13.07 per 1000 urinary catheter-days in phase 1, and was decreased by 83% in phase 2 to 2.21 per 1000 urinary catheter-days (risk ratio 0.17; 95% confidence interval 0.06-0.5; p=0.0002). Our multidimensional approach was associated with a significant reduction in the CAUTI rate. Copyright © 2013. Published by Elsevier Ltd.

Devaney chaos, Li-Yorke chaos, and multi-dimensional Li-Yorke chaos for topological dynamics

NASA Astrophysics Data System (ADS)

Dai, Xiongping; Tang, Xinjia

2017-11-01

Let π : T × X → X, written T↷π X, be a topological semiflow/flow on a uniform space X with T a multiplicative topological semigroup/group not necessarily discrete. We then prove: If T↷π X is non-minimal topologically transitive with dense almost periodic points, then it is sensitive to initial conditions. As a result of this, Devaney chaos ⇒ Sensitivity to initial conditions, for this very general setting. Let R+↷π X be a C0-semiflow on a Polish space; then we show: If R+↷π X is topologically transitive with at least one periodic point p and there is a dense orbit with no nonempty interior, then it is multi-dimensional Li-Yorke chaotic; that is, there is a uncountable set Θ ⊆ X such that for any k ≥ 2 and any distinct points x1 , … ,xk ∈ Θ, one can find two time sequences sn → ∞ ,tn → ∞ with Moreover, let X be a non-singleton Polish space; then we prove: Any weakly-mixing C0-semiflow R+↷π X is densely multi-dimensional Li-Yorke chaotic. Any minimal weakly-mixing topological flow T↷π X with T abelian is densely multi-dimensional Li-Yorke chaotic. Any weakly-mixing topological flow T↷π X is densely Li-Yorke chaotic. We in addition construct a completely Li-Yorke chaotic minimal SL (2 , R)-acting flow on the compact metric space R ∪ { ∞ }. Our various chaotic dynamics are sensitive to the choices of the topology of the phase semigroup/group T.

Joint mapping of genes and conditions via multidimensional unfolding analysis

PubMed Central

Van Deun, Katrijn; Marchal, Kathleen; Heiser, Willem J; Engelen, Kristof; Van Mechelen, Iven

2007-01-01

Background Microarray compendia profile the expression of genes in a number of experimental conditions. Such data compendia are useful not only to group genes and conditions based on their similarity in overall expression over profiles but also to gain information on more subtle relations between genes and conditions. Getting a clear visual overview of all these patterns in a single easy-to-grasp representation is a useful preliminary analysis step: We propose to use for this purpose an advanced exploratory method, called multidimensional unfolding. Results We present a novel algorithm for multidimensional unfolding that overcomes both general problems and problems that are specific for the analysis of gene expression data sets. Applying the algorithm to two publicly available microarray compendia illustrates its power as a tool for exploratory data analysis: The unfolding analysis of a first data set resulted in a two-dimensional representation which clearly reveals temporal regulation patterns for the genes and a meaningful structure for the time points, while the analysis of a second data set showed the algorithm's ability to go beyond a mere identification of those genes that discriminate between different patient or tissue types. Conclusion Multidimensional unfolding offers a useful tool for preliminary explorations of microarray data: By relying on an easy-to-grasp low-dimensional geometric framework, relations among genes, among conditions and between genes and conditions are simultaneously represented in an accessible way which may reveal interesting patterns in the data. An additional advantage of the method is that it can be applied to the raw data without necessitating the choice of suitable genewise transformations of the data. PMID:17550582

Parsimony and goodness-of-fit in multi-dimensional NMR inversion

NASA Astrophysics Data System (ADS)

Babak, Petro; Kryuchkov, Sergey; Kantzas, Apostolos

2017-01-01

Multi-dimensional nuclear magnetic resonance (NMR) experiments are often used for study of molecular structure and dynamics of matter in core analysis and reservoir evaluation. Industrial applications of multi-dimensional NMR involve a high-dimensional measurement dataset with complicated correlation structure and require rapid and stable inversion algorithms from the time domain to the relaxation rate and/or diffusion domains. In practice, applying existing inverse algorithms with a large number of parameter values leads to an infinite number of solutions with a reasonable fit to the NMR data. The interpretation of such variability of multiple solutions and selection of the most appropriate solution could be a very complex problem. In most cases the characteristics of materials have sparse signatures, and investigators would like to distinguish the most significant relaxation and diffusion values of the materials. To produce an easy to interpret and unique NMR distribution with the finite number of the principal parameter values, we introduce a new method for NMR inversion. The method is constructed based on the trade-off between the conventional goodness-of-fit approach to multivariate data and the principle of parsimony guaranteeing inversion with the least number of parameter values. We suggest performing the inversion of NMR data using the forward stepwise regression selection algorithm. To account for the trade-off between goodness-of-fit and parsimony, the objective function is selected based on Akaike Information Criterion (AIC). The performance of the developed multi-dimensional NMR inversion method and its comparison with conventional methods are illustrated using real data for samples with bitumen, water and clay.

Development, Content Validity, and User Review of a Web-based Multidimensional Pain Diary for Adolescent and Young Adults With Sickle Cell Disease.

PubMed

Bakshi, Nitya; Stinson, Jennifer N; Ross, Diana; Lukombo, Ines; Mittal, Nonita; Joshi, Saumya V; Belfer, Inna; Krishnamurti, Lakshmanan

2015-06-01

Vaso-occlusive pain, the hallmark of sickle cell disease (SCD), is a major contributor to morbidity, poor health-related quality of life, and health care utilization associated with this disease. There is wide variation in the burden, frequency, and severity of pain experienced by patients with SCD. As compared with health care utilization for pain, a daily pain diary captures the breadth of the pain experience and is a superior measure of pain burden and its impact on patients. Electronic pain diaries based on real-time data capture methods overcome methodological barriers and limitations of paper pain diaries, but their psychometric properties have not been formally established in patients with SCD. To develop and establish the content validity of a web-based multidimensional pain diary for adolescents and young adults with SCD and conduct an end-user review to refine the prototype. Following identification of items, a conceptual model was developed. Interviews with adolescents and young adults with SCD were conducted. Subsequently, end-user review with use of the electronic pain diary prototype was conducted. Two iterative cycles of in-depth cognitive interviews in adolescents and young adults with SCD informed the design and guided the addition, removal, and modification of items in the multidimensional pain diary. Potential end-users provided positive feedback on the design and prototype of the electronic diary. A multidimensional web-based electronic pain diary for adolescents and young adults with SCD has been developed and content validity and initial end-user reviews have been completed.

Thinking about online sources: Exploring students' epistemic cognition in internet-based chemistry learning

NASA Astrophysics Data System (ADS)

Dai, Ting

This dissertation investigated the relation between epistemic cognition---epistemic aims and source beliefs---and learning outcome in an Internet--based research context. Based on a framework of epistemic cognition (Chinn, Buckland, & Samarapungavan, 2011), a context--specific epistemic aims and source beliefs questionnaire (CEASBQ) was developed and administered to 354 students from college--level introductory chemistry courses. A series of multitrait--multimethod model comparisons provided evidence for construct convergent and discriminant validity for three epistemic aims--- true beliefs, justified beliefs, explanatory connection, which were all distinguished from, yet correlated with, mastery goals. Students' epistemic aims were specific to the chemistry topics in research. Multidimensional scaling results indicated that students' source evaluation was based on two dimensions--- professional expertise and first--hand knowledge, suggesting a multidimensional structure of source beliefs. Most importantly, online learning outcome was found to be significantly associated with two epistemic aims---justified beliefs and explanatory connection: The more students sought justifications in the online research, the lower they tended to score on the learning outcome measure, whereas the more students sought explanatory connections between information, the higher they scored on the outcome measure. There was a significant but small positive association between source beliefs and learning outcome. The influences of epistemic aims and source beliefs on learning outcome were found to be above and beyond the effects of a number of covariates, including prior knowledge and perceived ability with online sources.

Visualizing Science Dissections in 3D: Contextualizing Student Responses to Multidimensional Learning Materials in Science Dissections

NASA Astrophysics Data System (ADS)

Walker, Robin Annette

A series of dissection tasks was developed in this mixed-methods study of student self-explanations of their learning using actual and virtual multidimensional science dissections and visuo-spatial instruction. Thirty-five seventh-grade students from a science classroom (N = 20 Female/15 Male, Age =13 years) were assigned to three dissection environments instructing them to: (a) construct static paper designs of frogs, (b) perform active dissections with formaldehyde specimens, and (c) engage with interactive 3D frog visualizations and virtual simulations. This multi-methods analysis of student engagement with anchored dissection materials found learning gains on labeling exercises and lab assessments among most students. Data revealed that students who correctly utilized multimedia text and diagrams, individually and collaboratively, manipulated 3D tools more effectively and were better able to self-explain and complete their dissection work. Student questionnaire responses corroborated that they preferred learning how to dissect a frog using 3D multimedia instruction. The data were used to discuss the impact of 3D technologies, programs, and activities on student learning, spatial reasoning, and their interest in science. Implications were drawn regarding how to best integrate 3D visualizations into science curricula as innovative learning options for students, as instructional alternatives for teachers, and as mandated dissection choices for those who object to physical dissections in schools.

Methods to measure olfactory behavior in mice

PubMed Central

Zou, Junhui; Wang, Wenbin; Pan, Yung-Wei; Lu, Song; Xia, Zhengui

2015-01-01

Mice rely on the sense of olfaction to detect food sources, recognize social and mating partners, and avoid predators. Many behaviors of mice including learning and memory, social interaction, fear, and anxiety are closely associated with their function of olfaction, and behavior tasks designed to evaluate those brain functions may use odors as cues. Accurate assessment of olfaction is not only essential for the study of olfactory system but also critical for proper interpretation of various mouse behaviors especially learning and memory, emotionality and affect, and sociality. Here we describe a series of behavior experiments that offer multidimensional and quantitative assessments for mouse’s olfactory function, including olfactory habituation, discrimination, odor preference, odor detection sensitivity, and olfactory memory, to both social and nonsocial odors. PMID:25645244

Real-Time Visualization of an HPF-based CFD Simulation

NASA Technical Reports Server (NTRS)

Kremenetsky, Mark; Vaziri, Arsi; Haimes, Robert; Chancellor, Marisa K. (Technical Monitor)

1996-01-01

Current time-dependent CFD simulations produce very large multi-dimensional data sets at each time step. The visual analysis of computational results are traditionally performed by post processing the static data on graphics workstations. We present results from an alternate approach in which we analyze the simulation data in situ on each processing node at the time of simulation. The locally analyzed results, usually more economical and in a reduced form, are then combined and sent back for visualization on a graphics workstation.

Multiple quantum coherence spectroscopy.

PubMed

Mathew, Nathan A; Yurs, Lena A; Block, Stephen B; Pakoulev, Andrei V; Kornau, Kathryn M; Wright, John C

2009-08-20

Multiple quantum coherences provide a powerful approach for studies of complex systems because increasing the number of quantum states in a quantum mechanical superposition state increases the selectivity of a spectroscopic measurement. We show that frequency domain multiple quantum coherence multidimensional spectroscopy can create these superposition states using different frequency excitation pulses. The superposition state is created using two excitation frequencies to excite the symmetric and asymmetric stretch modes in a rhodium dicarbonyl chelate and the dynamic Stark effect to climb the vibrational ladders involving different overtone and combination band states. A monochromator resolves the free induction decay of different coherences comprising the superposition state. The three spectral dimensions provide the selectivity required to observe 19 different spectral features associated with fully coherent nonlinear processes involving up to 11 interactions with the excitation fields. The different features act as spectroscopic probes of the diagonal and off-diagonal parts of the molecular potential energy hypersurface. This approach can be considered as a coherent pump-probe spectroscopy where the pump is a series of excitation pulses that prepares a multiple quantum coherence and the probe is another series of pulses that creates the output coherence.

Equating Multidimensional Tests under a Random Groups Design: A Comparison of Various Equating Procedures

ERIC Educational Resources Information Center

Lee, Eunjung

2013-01-01

The purpose of this research was to compare the equating performance of various equating procedures for the multidimensional tests. To examine the various equating procedures, simulated data sets were used that were generated based on a multidimensional item response theory (MIRT) framework. Various equating procedures were examined, including…

Effect Size Measures for Differential Item Functioning in a Multidimensional IRT Model

ERIC Educational Resources Information Center

Suh, Youngsuk

2016-01-01

This study adapted an effect size measure used for studying differential item functioning (DIF) in unidimensional tests and extended the measure to multidimensional tests. Two effect size measures were considered in a multidimensional item response theory model: signed weighted P-difference and unsigned weighted P-difference. The performance of…

On Multidimensional Item Response Theory: A Coordinate-Free Approach. Research Report. ETS RR-07-30

ERIC Educational Resources Information Center

Antal, Tamás

2007-01-01

A coordinate-free definition of complex-structure multidimensional item response theory (MIRT) for dichotomously scored items is presented. The point of view taken emphasizes the possibilities and subtleties of understanding MIRT as a multidimensional extension of the classical unidimensional item response theory models. The main theorem of the…

The Definition of Difficulty and Discrimination for Multidimensional Item Response Theory Models.

ERIC Educational Resources Information Center

Reckase, Mark D.; McKinley, Robert L.

A study was undertaken to develop guidelines for the interpretation of the parameters of three multidimensional item response theory models and to determine the relationship between the parameters and traditional concepts of item difficulty and discrimination. The three models considered were multidimensional extensions of the one-, two-, and…

Bifactor Approach to Modeling Multidimensionality of Physical Self-Perception Profile

ERIC Educational Resources Information Center

Chung, ChihMing; Liao, Xiaolan; Song, Hairong; Lee, Taehun

2016-01-01

The multi-dimensionality of Physical Self-Perception Profile (PSPP) has been acknowledged by the use of correlated-factor model and second-order model. In this study, the authors critically endorse the bifactor model, as a substitute to address the multi-dimensionality of PSPP. To cross-validate the models, analyses are conducted first in…

Item Vector Plots for the Multidimensional Three-Parameter Logistic Model

ERIC Educational Resources Information Center

Bryant, Damon; Davis, Larry

2011-01-01

This brief technical note describes how to construct item vector plots for dichotomously scored items fitting the multidimensional three-parameter logistic model (M3PLM). As multidimensional item response theory (MIRT) shows promise of being a very useful framework in the test development life cycle, graphical tools that facilitate understanding…

Deriving Multidimensional Poverty Indicators: Methodological Issues and an Empirical Analysis for Italy

ERIC Educational Resources Information Center

Coromaldi, Manuela; Zoli, Mariangela

2012-01-01

Theoretical and empirical studies have recently adopted a multidimensional concept of poverty. There is considerable debate about the most appropriate degree of multidimensionality to retain in the analysis. In this work we add to the received literature in two ways. First, we derive indicators of multiple deprivation by applying a particular…

Evaluating Item Fit for Multidimensional Item Response Models

ERIC Educational Resources Information Center

Zhang, Bo; Stone, Clement A.

2008-01-01

This research examines the utility of the s-x[superscript 2] statistic proposed by Orlando and Thissen (2000) in evaluating item fit for multidimensional item response models. Monte Carlo simulation was conducted to investigate both the Type I error and statistical power of this fit statistic in analyzing two kinds of multidimensional test…

A Multidimensional Scaling Approach to Dimensionality Assessment for Measurement Instruments Modeled by Multidimensional Item Response Theory

ERIC Educational Resources Information Center

Toro, Maritsa

2011-01-01

The statistical assessment of dimensionality provides evidence of the underlying constructs measured by a survey or test instrument. This study focuses on educational measurement, specifically tests comprised of items described as multidimensional. That is, items that require examinee proficiency in multiple content areas and/or multiple cognitive…

Perceptual Salience and Children's Multidimensional Problem Solving

ERIC Educational Resources Information Center

Odom, Richard D.; Corbin, David W.

1973-01-01

Uni- and multidimensional processing of 6- to 9-year olds was studied using recall tasks in which an array of stimuli was reconstructed to match a model array. Results indicated that both age groups were able to solve multidimensional problems, but that solution rate was retarded by the unidimensional processing of highly salient dimensions.…

The Multidimensional Attitudes Scale toward Persons with Disabilities (MAS): Construction and Validation

ERIC Educational Resources Information Center

Findler, Liora; Vilchinsky, Noa; Werner, Shirli

2007-01-01

This study presents the development of a new instrument, the "Multidimensional Attitudes Scale Toward Persons With Disabilities" (MAS). Based on the multidimensional approach, it posits that attitudes are composed of three dimensions: affect, cognition, and behavior. The scale was distributed to a sample of 132 people along with a…

The Extraction of One-Dimensional Flow Properties from Multi-Dimensional Data Sets

NASA Technical Reports Server (NTRS)

Baurle, Robert A.; Gaffney, Richard L., Jr.

2007-01-01

The engineering design and analysis of air-breathing propulsion systems relies heavily on zero- or one-dimensional properties (e.g. thrust, total pressure recovery, mixing and combustion efficiency, etc.) for figures of merit. The extraction of these parameters from experimental data sets and/or multi-dimensional computational data sets is therefore an important aspect of the design process. A variety of methods exist for extracting performance measures from multi-dimensional data sets. Some of the information contained in the multi-dimensional flow is inevitably lost when any one-dimensionalization technique is applied. Hence, the unique assumptions associated with a given approach may result in one-dimensional properties that are significantly different than those extracted using alternative approaches. The purpose of this effort is to examine some of the more popular methods used for the extraction of performance measures from multi-dimensional data sets, reveal the strengths and weaknesses of each approach, and highlight various numerical issues that result when mapping data from a multi-dimensional space to a space of one dimension.

The Art of Extracting One-Dimensional Flow Properties from Multi-Dimensional Data Sets

NASA Technical Reports Server (NTRS)

Baurle, R. A.; Gaffney, R. L.

2007-01-01

The engineering design and analysis of air-breathing propulsion systems relies heavily on zero- or one-dimensional properties (e:g: thrust, total pressure recovery, mixing and combustion efficiency, etc.) for figures of merit. The extraction of these parameters from experimental data sets and/or multi-dimensional computational data sets is therefore an important aspect of the design process. A variety of methods exist for extracting performance measures from multi-dimensional data sets. Some of the information contained in the multi-dimensional flow is inevitably lost when any one-dimensionalization technique is applied. Hence, the unique assumptions associated with a given approach may result in one-dimensional properties that are significantly different than those extracted using alternative approaches. The purpose of this effort is to examine some of the more popular methods used for the extraction of performance measures from multi-dimensional data sets, reveal the strengths and weaknesses of each approach, and highlight various numerical issues that result when mapping data from a multi-dimensional space to a space of one dimension.

The Cognitive Visualization System with the Dynamic Projection of Multidimensional Data

NASA Astrophysics Data System (ADS)

Gorohov, V.; Vitkovskiy, V.

2008-08-01

The phenomenon of cognitive machine drawing consists in the generation on the screen the special graphic representations, which create in the brain of human operator entertainment means. These means seem man by aesthetically attractive and, thus, they stimulate its descriptive imagination, closely related to the intuitive mechanisms of thinking. The essence of cognitive effect lies in the fact that man receives the moving projection as pseudo-three-dimensional object characterizing multidimensional means in the multidimensional space. After the thorough qualitative study of the visual aspects of multidimensional means with the aid of the enumerated algorithms appears the possibility, using algorithms of standard machine drawing to paint the interesting user separate objects or the groups of objects. Then it is possible to again return to the dynamic behavior of the rotation of means for the purpose of checking the intuitive ideas of user about the clusters and the connections in multidimensional data. Is possible the development of the methods of cognitive machine drawing in combination with other information technologies, first of all with the packets of digital processing of images and multidimensional statistical analysis.

On Involvement.

ERIC Educational Resources Information Center

Greene, Michael B.

Involvement Ratings In Settings (IRIS), a multi-dimensional non-verbal scale of involvement adaptable to a time-sampling method of data collection, was constructed with the aid of the videotapes of second-grade Follow Through classrooms made by CCEP. Scales were defined through observations of involved and alienated behavior, and the IRIS was…

Toward a Multidimensional Perspective on Teacher-Coach Role Conflict

ERIC Educational Resources Information Center

Richards, K. Andrew R.; Templin, Thomas J.

2012-01-01

Research grounded in role theory and occupational socialization theory point to the potential consequences of occupying the roles of physical education teacher and athletic coach concurrently. Specifically, time constraints and inconsistencies in role requirements, organization, rewards, and modes of accountability in teaching and coaching create…

ADJOINT-DERIVED LOCATION AND TRAVEL TIME PROBABILITIES FOR A MULTI-DIMENSIONAL GROUNDWATER SYSTEM. (U915324)

EPA Science Inventory

The perspectives, information and conclusions conveyed in research project abstracts, progress reports, final reports, journal abstracts and journal publications convey the viewpoints of the principal investigator and may not represent the views and policies of ORD and EPA. Concl...

Multidimensional NMR approaches towards highly resolved, sensitive and high-throughput quantitative metabolomics.

PubMed

Marchand, Jérémy; Martineau, Estelle; Guitton, Yann; Dervilly-Pinel, Gaud; Giraudeau, Patrick

2017-02-01

Multi-dimensional NMR is an appealing approach for dealing with the challenging complexity of biological samples in metabolomics. This article describes how spectroscopists have recently challenged their imagination in order to make 2D NMR a powerful tool for quantitative metabolomics, based on innovative pulse sequences combined with meticulous analytical chemistry approaches. Clever time-saving strategies have also been explored to make 2D NMR a high-throughput tool for metabolomics, relying on alternative data acquisition schemes such as ultrafast NMR. Currently, much work is aimed at drastically boosting the NMR sensitivity thanks to hyperpolarisation techniques, which have been used in combination with fast acquisition methods and could greatly expand the application potential of NMR metabolomics. Copyright © 2016 Elsevier Ltd. All rights reserved.

Detecting Shielded Special Nuclear Materials Using Multi-Dimensional Neutron Source and Detector Geometries

NASA Astrophysics Data System (ADS)

Santarius, John; Navarro, Marcos; Michalak, Matthew; Fancher, Aaron; Kulcinski, Gerald; Bonomo, Richard

2016-10-01

A newly initiated research project will be described that investigates methods for detecting shielded special nuclear materials by combining multi-dimensional neutron sources, forward/adjoint calculations modeling neutron and gamma transport, and sparse data analysis of detector signals. The key tasks for this project are: (1) developing a radiation transport capability for use in optimizing adaptive-geometry, inertial-electrostatic confinement (IEC) neutron source/detector configurations for neutron pulses distributed in space and/or phased in time; (2) creating distributed-geometry, gas-target, IEC fusion neutron sources; (3) applying sparse data and noise reduction algorithms, such as principal component analysis (PCA) and wavelet transform analysis, to enhance detection fidelity; and (4) educating graduate and undergraduate students. Funded by DHS DNDO Project 2015-DN-077-ARI095.

Multidimensional motion measurement of a bimorph-type piezoelectric actuator using a diffraction grating target

NASA Astrophysics Data System (ADS)

Kim, Jong-Ahn; Bae, Eui Won; Kim, Soo Hyun; Kwak, Yoon Keun

2001-09-01

Precision actuators, such as pick-up actuators for HDDs or CD-ROMs, mostly show multidimensional motion. So, to evaluate them more completely, multidimensional measurement is required. Through structural variation and optimization of the design index, the performance of a measurement system can be improved to satisfy the requirement of this application, and so the resolution of each axis is higher than 0.1 μm for translation and 0.5 arcsec for rotation. Using this measurement system, the multidimensional motion and frequency transfer functions of a bimorph-type piezoelectric actuator are obtained.

Multidimensional poverty and child survival in India.

PubMed

Mohanty, Sanjay K

2011-01-01

Though the concept of multidimensional poverty has been acknowledged cutting across the disciplines (among economists, public health professionals, development thinkers, social scientists, policy makers and international organizations) and included in the development agenda, its measurement and application are still limited. OBJECTIVES AND METHODOLOGY: Using unit data from the National Family and Health Survey 3, India, this paper measures poverty in multidimensional space and examine the linkages of multidimensional poverty with child survival. The multidimensional poverty is measured in the dimension of knowledge, health and wealth and the child survival is measured with respect to infant mortality and under-five mortality. Descriptive statistics, principal component analyses and the life table methods are used in the analyses. The estimates of multidimensional poverty are robust and the inter-state differentials are large. While infant mortality rate and under-five mortality rate are disproportionately higher among the abject poor compared to the non-poor, there are no significant differences in child survival among educationally, economically and health poor at the national level. State pattern in child survival among the education, economical and health poor are mixed. Use of multidimensional poverty measures help to identify abject poor who are unlikely to come out of poverty trap. The child survival is significantly lower among abject poor compared to moderate poor and non-poor. We urge to popularize the concept of multiple deprivations in research and program so as to reduce poverty and inequality in the population.

Biodiversity as a multidimensional construct: a review, framework and case study of herbivory's impact on plant biodiversity

PubMed Central

Naeem, S.; Prager, Case; Weeks, Brian; Varga, Alex; Flynn, Dan F. B.; Griffin, Kevin; Muscarella, Robert; Palmer, Matthew; Wood, Stephen; Schuster, William

2016-01-01

Biodiversity is inherently multidimensional, encompassing taxonomic, functional, phylogenetic, genetic, landscape and many other elements of variability of life on the Earth. However, this fundamental principle of multidimensionality is rarely applied in research aimed at understanding biodiversity's value to ecosystem functions and the services they provide. This oversight means that our current understanding of the ecological and environmental consequences of biodiversity loss is limited primarily to what unidimensional studies have revealed. To address this issue, we review the literature, develop a conceptual framework for multidimensional biodiversity research based on this review and provide a case study to explore the framework. Our case study specifically examines how herbivory by whitetail deer (Odocoileus virginianus) alters the multidimensional influence of biodiversity on understory plant cover at Black Rock Forest, New York. Using three biodiversity dimensions (taxonomic, functional and phylogenetic diversity) to explore our framework, we found that herbivory alters biodiversity's multidimensional influence on plant cover; an effect not observable through a unidimensional approach. Although our review, framework and case study illustrate the advantages of multidimensional over unidimensional approaches, they also illustrate the statistical and empirical challenges such work entails. Meeting these challenges, however, where data and resources permit, will be important if we are to better understand and manage the consequences we face as biodiversity continues to decline in the foreseeable future. PMID:27928041

Biodiversity as a multidimensional construct: a review, framework and case study of herbivory's impact on plant biodiversity.

PubMed

Naeem, S; Prager, Case; Weeks, Brian; Varga, Alex; Flynn, Dan F B; Griffin, Kevin; Muscarella, Robert; Palmer, Matthew; Wood, Stephen; Schuster, William

2016-12-14

Biodiversity is inherently multidimensional, encompassing taxonomic, functional, phylogenetic, genetic, landscape and many other elements of variability of life on the Earth. However, this fundamental principle of multidimensionality is rarely applied in research aimed at understanding biodiversity's value to ecosystem functions and the services they provide. This oversight means that our current understanding of the ecological and environmental consequences of biodiversity loss is limited primarily to what unidimensional studies have revealed. To address this issue, we review the literature, develop a conceptual framework for multidimensional biodiversity research based on this review and provide a case study to explore the framework. Our case study specifically examines how herbivory by whitetail deer (Odocoileus virginianus) alters the multidimensional influence of biodiversity on understory plant cover at Black Rock Forest, New York. Using three biodiversity dimensions (taxonomic, functional and phylogenetic diversity) to explore our framework, we found that herbivory alters biodiversity's multidimensional influence on plant cover; an effect not observable through a unidimensional approach. Although our review, framework and case study illustrate the advantages of multidimensional over unidimensional approaches, they also illustrate the statistical and empirical challenges such work entails. Meeting these challenges, however, where data and resources permit, will be important if we are to better understand and manage the consequences we face as biodiversity continues to decline in the foreseeable future. © 2016 The Authors.

The reality of disability: Multidimensional poverty of people with disability and their families in Latin America.

PubMed

Pinilla-Roncancio, Mónica

2017-12-30

Disability and poverty are interconnected and although this relationship has been recognised, there is a lack of empirical evidence to support any possible causal relationship in this topic, particularly in the context of Latin America (LA). This study tests the hypothesis "Disability increases the risk of multidimensional poverty of people living with disabilities and their families". Using national census data from Brazil, Chile, Colombia, Costa Rica and Mexico, the Global Multidimensional Poverty Index (Global MPI) was calculated with the aim of measuring and comparing the levels of multidimensional poverty of people living in households with and without disabled members in the five countries. We found that in the five countries people with disabilities and their families had higher incidence, intensity and levels of multidimensional poverty compared with people living in other households. Their levels of deprivation were also higher for all the indicators included in the Global MPI and the contribution of this group to the national MPI was higher than their share of the population, thus people with disabilities and their families are overrepresented in those living in multidimensional poverty. People with disabilities and their families are in worse conditions than poor households without disabled members and social policies should aim to reduce their high levels of multidimensional poverty and deprivation. Copyright © 2017 Elsevier Inc. All rights reserved.

Uncertainty visualisation in the Model Web

NASA Astrophysics Data System (ADS)

Gerharz, L. E.; Autermann, C.; Hopmann, H.; Stasch, C.; Pebesma, E.

2012-04-01

Visualisation of geospatial data as maps is a common way to communicate spatially distributed information. If temporal and furthermore uncertainty information are included in the data, efficient visualisation methods are required. For uncertain spatial and spatio-temporal data, numerous visualisation methods have been developed and proposed, but only few tools for visualisation of data in a standardised way exist. Furthermore, usually they are realised as thick clients, and lack functionality of handling data coming from web services as it is envisaged in the Model Web. We present an interactive web tool for visualisation of uncertain spatio-temporal data developed in the UncertWeb project. The client is based on the OpenLayers JavaScript library. OpenLayers provides standard map windows and navigation tools, i.e. pan, zoom in/out, to allow interactive control for the user. Further interactive methods are implemented using jStat, a JavaScript library for statistics plots developed in UncertWeb, and flot. To integrate the uncertainty information into existing standards for geospatial data, the Uncertainty Markup Language (UncertML) was applied in combination with OGC Observations&Measurements 2.0 and JavaScript Object Notation (JSON) encodings for vector and NetCDF for raster data. The client offers methods to visualise uncertain vector and raster data with temporal information. Uncertainty information considered for the tool are probabilistic and quantified attribute uncertainties which can be provided as realisations or samples, full probability distributions functions and statistics. Visualisation is supported for uncertain continuous and categorical data. In the client, the visualisation is realised using a combination of different methods. Based on previously conducted usability studies, a differentiation between expert (in statistics or mapping) and non-expert users has been indicated as useful. Therefore, two different modes are realised together in the tool: (i) adjacent maps showing data and uncertainty separately, and (ii) multidimensional mapping providing different visualisation methods in combination to explore the spatial, temporal and uncertainty distribution of the data. Adjacent maps allow a simpler visualisation by separating value and uncertainty maps for non-experts and a first overview. The multidimensional approach allows a more complex exploration of the data for experts by browsing through the different dimensions. It offers the visualisation of maps, statistic plots and time series in different windows and sliders to interactively move through time, space and uncertainty (thresholds).

A Multidimensional Partial Credit Model with Associated Item and Test Statistics: An Application to Mixed-Format Tests

ERIC Educational Resources Information Center

Yao, Lihua; Schwarz, Richard D.

2006-01-01

Multidimensional item response theory (IRT) models have been proposed for better understanding the dimensional structure of data or to define diagnostic profiles of student learning. A compensatory multidimensional two-parameter partial credit model (M-2PPC) for constructed-response items is presented that is a generalization of those proposed to…

Self-Concepts in Reading, Writing, Listening, and Speaking: A Multidimensional and Hierarchical Structure and Its Generalizability across Native and Foreign Languages

ERIC Educational Resources Information Center

Arens, A. Katrin; Jansen, Malte

2016-01-01

Academic self-concept has been conceptualized as a multidimensional and hierarchical construct. Previous research has mostly focused on its multidimensionality, distinguishing between verbal and mathematical self-concept domains, and only a few studies have examined the factorial structure within specific self-concept domains. The present study…

Assessing Construct Validity Using Multidimensional Item Response Theory.

ERIC Educational Resources Information Center

Ackerman, Terry A.

The concept of a user-specified validity sector is discussed. The idea of the validity sector combines the work of M. D. Reckase (1986) and R. Shealy and W. Stout (1991). Reckase developed a methodology to represent an item in a multidimensional latent space as a vector. Item vectors are computed using multidimensional item response theory item…

Motivation and Engagement in the Workplace: Examining a Multidimensional Framework and Instrument from a Measurement and Evaluation Perspective

ERIC Educational Resources Information Center

Martin, Andrew J.

2009-01-01

This investigation conducts measurement and evaluation of a multidimensional model of workplace motivation and engagement from a construct validation perspective. Two studies were conducted, one using the multi-item multidimensional Motivation and Engagement Scale-Work (N = 637 school personnel) and one using a parallel short form (N = 574 school…

Effects of Multidimensional Concept Maps on Fourth Graders' Learning in Web-Based Computer Course

ERIC Educational Resources Information Center

Huang, Hwa-Shan; Chiou, Chei-Chang; Chiang, Heien-Kun; Lai, Sung-Hsi; Huang, Chiun-Yen; Chou, Yin-Yu

2012-01-01

This study explores the effect of multidimensional concept mapping instruction on students' learning performance in a web-based computer course. The subjects consisted of 103 fourth graders from an elementary school in central Taiwan. They were divided into three groups: multidimensional concept map (MCM) instruction group, Novak concept map (NCM)…

Evaluating the Invariance of Cognitive Profile Patterns Derived from Profile Analysis via Multidimensional Scaling (PAMS): A Bootstrapping Approach

ERIC Educational Resources Information Center

Kim, Se-Kang

2010-01-01

The aim of the current study is to validate the invariance of major profile patterns derived from multidimensional scaling (MDS) by bootstrapping. Profile Analysis via Multidimensional Scaling (PAMS) was employed to obtain profiles and bootstrapping was used to construct the sampling distributions of the profile coordinates and the empirical…

Psychometric Properties of the Frost Multidimensional Perfectionism Scale with Australian Adolescent Girls: Clarification of Multidimensionality and Perfectionist Typology

ERIC Educational Resources Information Center

Hawkins, Colleen C.; Watt, Helen M. G.; Sinclair, Kenneth E.

2006-01-01

The psychometric properties of the Frost, Marten, Lahart, and Rosenblate Multidimensional Perfectionism Scale (1990) are investigated to determine its usefulness as a measurement of perfectionism with Australian secondary school girls and to find empirical support for the existence of both healthy and unhealthy types of perfectionist students.…

Story Re-Visions: Tales for the Future.

ERIC Educational Resources Information Center

Gray, Jacqueline W.

Over the years many different psychologists and psychoanalysts have found value in the concept of the self-narrative, or the "life story." Narrative thought demands an appreciation of the particulars of time and place and a focus on multidimensional understanding of events, people, emotion, and motivation. By using the life story or…

"Blueprint III:" Is the Third Time the Charm?

ERIC Educational Resources Information Center

Kaniuka, Maureen

2009-01-01

The vision of training school psychologists to serve a broad, preventative role has evolved through "Blueprint I," "Blueprint II," and the 2002 Conference on the Future of School Psychology with limited impact on the daily functioning of practitioners. "Blueprint III" is a multidimensional and integrated model for the training of school…

Analyzing Longitudinal Item Response Data via the Pairwise Fitting Method

ERIC Educational Resources Information Center

Fu, Zhi-Hui; Tao, Jian; Shi, Ning-Zhong; Zhang, Ming; Lin, Nan

2011-01-01

Multidimensional item response theory (MIRT) models can be applied to longitudinal educational surveys where a group of individuals are administered different tests over time with some common items. However, computational problems typically arise as the dimension of the latent variables increases. This is especially true when the latent variable…

Exposure-time based modeling of nonlinear reactive transport in porous media subject to physical and geochemical heterogeneity.

PubMed

Sanz-Prat, Alicia; Lu, Chuanhe; Amos, Richard T; Finkel, Michael; Blowes, David W; Cirpka, Olaf A

2016-09-01

Transport of reactive solutes in groundwater is affected by physical and chemical heterogeneity of the porous medium, leading to complex spatio-temporal patterns of concentrations and reaction rates. For certain cases of bioreactive transport, it could be shown that the concentrations of reactive constituents in multi-dimensional domains are approximately aligned with isochrones, that is, lines of identical travel time, provided that the chemical properties of the matrix are uniform. We extend this concept to combined physical and chemical heterogeneity by additionally considering the time that a water parcel has been exposed to reactive materials, the so-called exposure time. We simulate bioreactive transport in a one-dimensional domain as function of time and exposure time, rather than space. Subsequently, we map the concentrations to multi-dimensional heterogeneous domains by means of the mean exposure time at each location in the multi-dimensional domain. Differences in travel and exposure time at a given location are accounted for as time difference. This approximation simplifies reactive-transport simulations significantly under conditions of steady-state flow when reactions are restricted to specific locations. It is not expected to be exact in realistic applications because the underlying assumption, such as neglecting transverse mixing altogether, may not hold. We quantify the error introduced by the approximation for the hypothetical case of a two-dimensional, binary aquifer made of highly-permeable, non-reactive and low-permeable, reactive materials releasing dissolved organic matter acting as electron donor for aerobic respiration and denitrification. The kinetically controlled reactions are catalyzed by two non-competitive bacteria populations, enabling microbial growth. Even though the initial biomass concentrations were uniform, the interplay between transport, non-uniform electron-donor supply, and bio-reactions led to distinct spatial patterns of the two types of biomass at late times. Results obtained by mapping the exposure-time based results to the two-dimensional domain are compared with simulations based on the two-dimensional, spatially explicit advection-dispersion-reaction equation. Once quasi-steady state has been reached, we find a good agreement in terms of the chemical-compound concentrations between the two approaches inside the reactive zones, whereas the exposure-time based model is not able to capture reactions occurring in the zones with zero electron-donor release. We conclude that exposure-time models provide good approximations of nonlinear bio-reactive transport when transverse mixing is not the overall controlling process and all reactions are essentially restricted to distinct reactive zones. Copyright © 2016 Elsevier B.V. All rights reserved.

PedsQL™ Multidimensional Fatigue Scale in sickle cell disease: feasibility, reliability, and validity.

PubMed

Panepinto, Julie A; Torres, Sylvia; Bendo, Cristiane B; McCavit, Timothy L; Dinu, Bogdan; Sherman-Bien, Sandra; Bemrich-Stolz, Christy; Varni, James W

2014-01-01

Sickle cell disease (SCD) is an inherited blood disorder characterized by a chronic hemolytic anemia that can contribute to fatigue and global cognitive impairment in patients. The study objective was to report on the feasibility, reliability, and validity of the PedsQL™ Multidimensional Fatigue Scale in SCD for pediatric patient self-report ages 5-18 years and parent proxy-report for ages 2-18 years. This was a cross-sectional multi-site study whereby 240 pediatric patients with SCD and 303 parents completed the 18-item PedsQL™ Multidimensional Fatigue Scale. Participants also completed the PedsQL™ 4.0 Generic Core Scales. The PedsQL™ Multidimensional Fatigue Scale evidenced excellent feasibility, excellent reliability for the Total Scale Scores (patient self-report α = 0.90; parent proxy-report α = 0.95), and acceptable reliability for the three individual scales (patient self-report α = 0.77-0.84; parent proxy-report α = 0.90-0.97). Intercorrelations of the PedsQL™ Multidimensional Fatigue Scale with the PedsQL™ Generic Core Scales were predominantly in the large (≥0.50) range, supporting construct validity. PedsQL™ Multidimensional Fatigue Scale Scores were significantly worse with large effects sizes (≥0.80) for patients with SCD than for a comparison sample of healthy children, supporting known-groups discriminant validity. Confirmatory factor analysis demonstrated an acceptable to excellent model fit in SCD. The PedsQL™ Multidimensional Fatigue Scale demonstrated acceptable to excellent measurement properties in SCD. The results demonstrate the relative severity of fatigue symptoms in pediatric patients with SCD, indicating the potential clinical utility of multidimensional assessment of fatigue in patients with SCD in clinical research and practice. © 2013 Wiley Periodicals, Inc.

PedsQL™ Multidimensional Fatigue Scale in Sickle Cell Disease: Feasibility, Reliability and Validity

PubMed Central

Panepinto, Julie A.; Torres, Sylvia; Bendo, Cristiane B.; McCavit, Timothy L.; Dinu, Bogdan; Sherman-Bien, Sandra; Bemrich-Stolz, Christy; Varni, James W.

2013-01-01

Background Sickle cell disease (SCD) is an inherited blood disorder characterized by a chronic hemolytic anemia that can contribute to fatigue and global cognitive impairment in patients. The study objective was to report on the feasibility, reliability, and validity of the PedsQL™ Multidimensional Fatigue Scale in SCD for pediatric patient self-report ages 5–18 years and parent proxy-report for ages 2–18 years. Procedure This was a cross-sectional multi-site study whereby 240 pediatric patients with SCD and 303 parents completed the 18-item PedsQL™ Multidimensional Fatigue Scale. Participants also completed the PedsQL™ 4.0 Generic Core Scales. Results The PedsQL™ Multidimensional Fatigue Scale evidenced excellent feasibility, excellent reliability for the Total Scale Scores (patient self-report α = 0.90; parent proxy-report α = 0.95), and acceptable reliability for the three individual scales (patient self-report α = 0.77–0.84; parent proxy-report α = 0.90–0.97). Intercorrelations of the PedsQL™ Multidimensional Fatigue Scale with the PedsQL™ Generic Core Scales were predominantly in the large (≥ 0.50) range, supporting construct validity. PedsQL™ Multidimensional Fatigue Scale Scores were significantly worse with large effects sizes (≥0.80) for patients with SCD than for a comparison sample of healthy children, supporting known-groups discriminant validity. Confirmatory factor analysis demonstrated an acceptable to excellent model fit in SCD. Conclusions The PedsQL™ Multidimensional Fatigue Scale demonstrated acceptable to excellent measurement properties in SCD. The results demonstrate the relative severity of fatigue symptoms in pediatric patients with SCD, indicating the potential clinical utility of multidimensional assessment of fatigue in patients with SCD in clinical research and practice. PMID:24038960

Development of multi-dimensional body image scale for malaysian female adolescents

PubMed Central

Taib, Mohd Nasir Mohd; Shariff, Zalilah Mohd; Khor, Geok Lin

2008-01-01

The present study was conducted to develop a Multi-dimensional Body Image Scale for Malaysian female adolescents. Data were collected among 328 female adolescents from a secondary school in Kuantan district, state of Pahang, Malaysia by using a self-administered questionnaire and anthropometric measurements. The self-administered questionnaire comprised multiple measures of body image, Eating Attitude Test (EAT-26; Garner & Garfinkel, 1979) and Rosenberg Self-esteem Inventory (Rosenberg, 1965). The 152 items from selected multiple measures of body image were examined through factor analysis and for internal consistency. Correlations between Multi-dimensional Body Image Scale and body mass index (BMI), risk of eating disorders and self-esteem were assessed for construct validity. A seven factor model of a 62-item Multi-dimensional Body Image Scale for Malaysian female adolescents with construct validity and good internal consistency was developed. The scale encompasses 1) preoccupation with thinness and dieting behavior, 2) appearance and body satisfaction, 3) body importance, 4) muscle increasing behavior, 5) extreme dieting behavior, 6) appearance importance, and 7) perception of size and shape dimensions. Besides, a multidimensional body image composite score was proposed to screen negative body image risk in female adolescents. The result found body image was correlated with BMI, risk of eating disorders and self-esteem in female adolescents. In short, the present study supports a multi-dimensional concept for body image and provides a new insight into its multi-dimensionality in Malaysian female adolescents with preliminary validity and reliability of the scale. The Multi-dimensional Body Image Scale can be used to identify female adolescents who are potentially at risk of developing body image disturbance through future intervention programs. PMID:20126371

Development of multi-dimensional body image scale for malaysian female adolescents.

PubMed

Chin, Yit Siew; Taib, Mohd Nasir Mohd; Shariff, Zalilah Mohd; Khor, Geok Lin

2008-01-01

The present study was conducted to develop a Multi-dimensional Body Image Scale for Malaysian female adolescents. Data were collected among 328 female adolescents from a secondary school in Kuantan district, state of Pahang, Malaysia by using a self-administered questionnaire and anthropometric measurements. The self-administered questionnaire comprised multiple measures of body image, Eating Attitude Test (EAT-26; Garner & Garfinkel, 1979) and Rosenberg Self-esteem Inventory (Rosenberg, 1965). The 152 items from selected multiple measures of body image were examined through factor analysis and for internal consistency. Correlations between Multi-dimensional Body Image Scale and body mass index (BMI), risk of eating disorders and self-esteem were assessed for construct validity. A seven factor model of a 62-item Multi-dimensional Body Image Scale for Malaysian female adolescents with construct validity and good internal consistency was developed. The scale encompasses 1) preoccupation with thinness and dieting behavior, 2) appearance and body satisfaction, 3) body importance, 4) muscle increasing behavior, 5) extreme dieting behavior, 6) appearance importance, and 7) perception of size and shape dimensions. Besides, a multidimensional body image composite score was proposed to screen negative body image risk in female adolescents. The result found body image was correlated with BMI, risk of eating disorders and self-esteem in female adolescents. In short, the present study supports a multi-dimensional concept for body image and provides a new insight into its multi-dimensionality in Malaysian female adolescents with preliminary validity and reliability of the scale. The Multi-dimensional Body Image Scale can be used to identify female adolescents who are potentially at risk of developing body image disturbance through future intervention programs.

Numeric invariants from multidimensional persistence

DOE PAGES

Skryzalin, Jacek; Carlsson, Gunnar

2017-05-19

Topological data analysis is the study of data using techniques from algebraic topology. Often, one begins with a finite set of points representing data and a “filter” function which assigns a real number to each datum. Using both the data and the filter function, one can construct a filtered complex for further analysis. For example, applying the homology functor to the filtered complex produces an algebraic object known as a “one-dimensional persistence module”, which can often be interpreted as a finite set of intervals representing various geometric features in the data. If one runs the above process incorporating multiple filtermore » functions simultaneously, one instead obtains a multidimensional persistence module. Unfortunately, these are much more difficult to interpret. In this article, we analyze the space of multidimensional persistence modules from the perspective of algebraic geometry. First we build a moduli space of a certain subclass of easily analyzed multidimensional persistence modules, which we construct specifically to capture much of the information which can be gained by using multidimensional persistence instead of one-dimensional persistence. Fruthermore, we argue that the global sections of this space provide interesting numeric invariants when evaluated against our subclass of multidimensional persistence modules. Finally, we extend these global sections to the space of all multidimensional persistence modules and discuss how the resulting numeric invariants might be used to study data. This paper extends the results of Adcock et al. (Homol Homotopy Appl 18(1), 381–402, 2016) by constructing numeric invariants from the computation of a multidimensional persistence module as given by Carlsson et al. (J Comput Geom 1(1), 72–100, 2010).« less

Effectiveness of Multidimensional Cancer Survivor Rehabilitation and Cost-Effectiveness of Cancer Rehabilitation in General: A Systematic Review

PubMed Central

Mewes, Janne C.; IJzerman, Maarten J.; van Harten, Wim H.

2012-01-01

Introduction. Many cancer survivors suffer from a combination of disease- and treatment-related morbidities and complaints after primary treatment. There is a growing evidence base for the effectiveness of monodimensional rehabilitation interventions; in practice, however, patients often participate in multidimensional programs. This study systematically reviews evidence regarding effectiveness of multidimensional rehabilitation programs for cancer survivors and cost-effectiveness of cancer rehabilitation in general. Methods. The published literature was systematically reviewed. Data were extracted using standardized forms and were summarized narratively. Results. Sixteen effectiveness and six cost-effectiveness studies were included. Multidimensional rehabilitation programs were found to be effective, but not more effective than monodimensional interventions, and not on all outcome measures. Effect sizes for quality of life were in the range of −0.12 (95% confidence interval [CI], −0.45–0.20) to 0.98 (95% CI, 0.69–1.29). Incremental cost-effectiveness ratios ranged from −€16,976, indicating cost savings, to €11,057 per quality-adjusted life year. Conclusions. The evidence for multidimensional interventions and the economic impact of rehabilitation studies is scarce and dominated by breast cancer studies. Studies published so far report statistically significant benefits for multidimensional interventions over usual care, most notably for the outcomes fatigue and physical functioning. An additional benefit of multidimensional over monodimensional rehabilitation was not found, but this was also sparsely reported on. Available economic evaluations assessed very different rehabilitation interventions. Yet, despite low comparability, all showed favorable cost-effectiveness ratios. Future studies should focus their designs on the comparative effectiveness and cost-effectiveness of multidimensional programs. PMID:22982580

The Multidimensional Assessment of Interoceptive Awareness (MAIA)

PubMed Central

Mehling, Wolf E.; Price, Cynthia; Daubenmier, Jennifer J.; Acree, Mike; Bartmess, Elizabeth; Stewart, Anita

2012-01-01

This paper describes the development of a multidimensional self-report measure of interoceptive body awareness. The systematic mixed-methods process involved reviewing the current literature, specifying a multidimensional conceptual framework, evaluating prior instruments, developing items, and analyzing focus group responses to scale items by instructors and patients of body awareness-enhancing therapies. Following refinement by cognitive testing, items were field-tested in students and instructors of mind-body approaches. Final item selection was achieved by submitting the field test data to an iterative process using multiple validation methods, including exploratory cluster and confirmatory factor analyses, comparison between known groups, and correlations with established measures of related constructs. The resulting 32-item multidimensional instrument assesses eight concepts. The psychometric properties of these final scales suggest that the Multidimensional Assessment of Interoceptive Awareness (MAIA) may serve as a starting point for research and further collaborative refinement. PMID:23133619

Benefits of Multidimensional Measures of Child Well Being in China

PubMed Central

Gatenio Gabel, Shirley; Zhang, Yiwei

2017-01-01

In recent decades, measures of child well-being have evolved from single dimension to multidimensional measures. Multi-dimensional measures deepen and broaden our understanding of child well-being and inform us of areas of neglect. Child well-being in China today is measured through proxy measures of household need. This paper discusses the evolution of child well-being measures more generally, explores the benefits of positive indicators and multiple dimensions in formulating policy, and then reviews efforts to date by the Chinese government, researchers, and non-governmental and intergovernmental organizations to develop comprehensive multidimensional measures of child well-being in China. The domains and their potential interactions, as well as data sources and availability, are presented. The authors believe that child well-being in China would benefit from the development of a multidimensional index and that there is sufficient data to develop such an index. PMID:29113121

Benefits of Multidimensional Measures of Child Well Being in China.

PubMed

Gatenio Gabel, Shirley; Zhang, Yiwei

2017-11-06

In recent decades, measures of child well-being have evolved from single dimension to multidimensional measures. Multi-dimensional measures deepen and broaden our understanding of child well-being and inform us of areas of neglect. Child well-being in China today is measured through proxy measures of household need. This paper discusses the evolution of child well-being measures more generally, explores the benefits of positive indicators and multiple dimensions in formulating policy, and then reviews efforts to date by the Chinese government, researchers, and non-governmental and intergovernmental organizations to develop comprehensive multidimensional measures of child well-being in China. The domains and their potential interactions, as well as data sources and availability, are presented. The authors believe that child well-being in China would benefit from the development of a multidimensional index and that there is sufficient data to develop such an index.

Testing the multidimensionality of the inventory of school motivation in a Dutch student sample.

PubMed

Korpershoek, Hanke; Xu, Kun; Mok, Magdalena Mo Ching; McInerney, Dennis M; van der Werf, Greetje

2015-01-01

A factor analytic and a Rasch measurement approach were applied to evaluate the multidimensional nature of the school motivation construct among more than 7,000 Dutch secondary school students. The Inventory of School Motivation (McInerney and Ali, 2006) was used, which intends to measure four motivation dimensions (mastery, performance, social, and extrinsic motivation), each comprising of two first-order factors. One unidimensional model and three multidimensional models (4-factor, 8-factor, higher order) were fit to the data. Results of both approaches showed that the multidimensional models validly represented the school motivation among Dutch secondary school pupils, whereas model fit of the unidimensional model was poor. The differences in model fit between the three multidimensional models were small, although a different model was favoured by the two approaches. The need for improvement of some of the items and the need to increase measurement precision of several first-order factors are discussed.

An information model for managing multi-dimensional gridded data in a GIS

NASA Astrophysics Data System (ADS)

Xu, H.; Abdul-Kadar, F.; Gao, P.

2016-04-01

Earth observation agencies like NASA and NOAA produce huge volumes of historical, near real-time, and forecasting data representing terrestrial, atmospheric, and oceanic phenomena. The data drives climatological and meteorological studies, and underpins operations ranging from weather pattern prediction and forest fire monitoring to global vegetation analysis. These gridded data sets are distributed mostly as files in HDF, GRIB, or netCDF format and quantify variables like precipitation, soil moisture, or sea surface temperature, along one or more dimensions like time and depth. Although the data cube is a well-studied model for storing and analyzing multi-dimensional data, the GIS community remains in need of a solution that simplifies interactions with the data, and elegantly fits with existing database schemas and dissemination protocols. This paper presents an information model that enables Geographic Information Systems (GIS) to efficiently catalog very large heterogeneous collections of geospatially-referenced multi-dimensional rasters—towards providing unified access to the resulting multivariate hypercubes. We show how the implementation of the model encapsulates format-specific variations and provides unified access to data along any dimension. We discuss how this framework lends itself to familiar GIS concepts like image mosaics, vector field visualization, layer animation, distributed data access via web services, and scientific computing. Global data sources like MODIS from USGS and HYCOM from NOAA illustrate how one would employ this framework for cataloging, querying, and intuitively visualizing such hypercubes. ArcGIS—an established platform for processing, analyzing, and visualizing geospatial data—serves to demonstrate how this integration brings the full power of GIS to the scientific community.

Lithium Depletion in Solar-like Stars: Effect of Overshooting Based on Realistic Multi-dimensional Simulations

NASA Astrophysics Data System (ADS)

Baraffe, I.; Pratt, J.; Goffrey, T.; Constantino, T.; Folini, D.; Popov, M. V.; Walder, R.; Viallet, M.

2017-08-01

We study lithium depletion in low-mass and solar-like stars as a function of time, using a new diffusion coefficient describing extra-mixing taking place at the bottom of a convective envelope. This new form is motivated by multi-dimensional fully compressible, time-implicit hydrodynamic simulations performed with the MUSIC code. Intermittent convective mixing at the convective boundary in a star can be modeled using extreme value theory, a statistical analysis frequently used for finance, meteorology, and environmental science. In this Letter, we implement this statistical diffusion coefficient in a one-dimensional stellar evolution code, using parameters calibrated from multi-dimensional hydrodynamic simulations of a young low-mass star. We propose a new scenario that can explain observations of the surface abundance of lithium in the Sun and in clusters covering a wide range of ages, from ˜50 Myr to ˜4 Gyr. Because it relies on our physical model of convective penetration, this scenario has a limited number of assumptions. It can explain the observed trend between rotation and depletion, based on a single additional assumption, namely, that rotation affects the mixing efficiency at the convective boundary. We suggest the existence of a threshold in stellar rotation rate above which rotation strongly prevents the vertical penetration of plumes and below which rotation has small effects. In addition to providing a possible explanation for the long-standing problem of lithium depletion in pre-main-sequence and main-sequence stars, the strength of our scenario is that its basic assumptions can be tested by future hydrodynamic simulations.

Lithium Depletion in Solar-like Stars: Effect of Overshooting Based on Realistic Multi-dimensional Simulations

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

Baraffe, I.; Pratt, J.; Goffrey, T.

We study lithium depletion in low-mass and solar-like stars as a function of time, using a new diffusion coefficient describing extra-mixing taking place at the bottom of a convective envelope. This new form is motivated by multi-dimensional fully compressible, time-implicit hydrodynamic simulations performed with the MUSIC code. Intermittent convective mixing at the convective boundary in a star can be modeled using extreme value theory, a statistical analysis frequently used for finance, meteorology, and environmental science. In this Letter, we implement this statistical diffusion coefficient in a one-dimensional stellar evolution code, using parameters calibrated from multi-dimensional hydrodynamic simulations of a youngmore » low-mass star. We propose a new scenario that can explain observations of the surface abundance of lithium in the Sun and in clusters covering a wide range of ages, from ∼50 Myr to ∼4 Gyr. Because it relies on our physical model of convective penetration, this scenario has a limited number of assumptions. It can explain the observed trend between rotation and depletion, based on a single additional assumption, namely, that rotation affects the mixing efficiency at the convective boundary. We suggest the existence of a threshold in stellar rotation rate above which rotation strongly prevents the vertical penetration of plumes and below which rotation has small effects. In addition to providing a possible explanation for the long-standing problem of lithium depletion in pre-main-sequence and main-sequence stars, the strength of our scenario is that its basic assumptions can be tested by future hydrodynamic simulations.« less

Interaction and common ground in dementia: Communication across linguistic and cultural diversity in a residential dementia care setting.

PubMed

Strandroos, Lisa; Antelius, Eleonor

2017-09-01

Previous research concerning bilingual people with a dementia disease has mainly focused on the importance of sharing a spoken language with caregivers. While acknowledging this, this article addresses the multidimensional character of communication and interaction. As using spoken language is made difficult as a consequence of the dementia disease, this multidimensionality becomes particularly important. The article is based on a qualitative analysis of ethnographic fieldwork at a dementia care facility. It presents ethnographic examples of different communicative forms, with particular focus on bilingual interactions. Interaction is understood as a collective and collaborative activity. The text finds that a shared spoken language is advantageous, but is not the only source of, nor a guarantee for, creating common ground and understanding. Communicative resources other than spoken language are for example body language, embodiment, artefacts and time. Furthermore, forms of communication are not static but develop, change and are created over time. Ability to communicate is thus not something that one has or has not, but is situationally and collaboratively created. To facilitate this, time and familiarity are central resources, and the results indicate the importance of continuity in interpersonal relations.

Effectiveness of electronic guideline-based implementation systems in ambulatory care settings - a systematic review

PubMed Central

2009-01-01

Background Electronic guideline-based decision support systems have been suggested to successfully deliver the knowledge embedded in clinical practice guidelines. A number of studies have already shown positive findings for decision support systems such as drug-dosing systems and computer-generated reminder systems for preventive care services. Methods A systematic literature search (1990 to December 2008) of the English literature indexed in the Medline database, Embase, the Cochrane Central Register of Controlled Trials, and CRD (DARE, HTA and NHS EED databases) was conducted to identify evaluation studies of electronic multi-step guideline implementation systems in ambulatory care settings. Important inclusion criterions were the multidimensionality of the guideline (the guideline needed to consist of several aspects or steps) and real-time interaction with the system during consultation. Clinical decision support systems such as one-time reminders for preventive care for which positive findings were shown in earlier reviews were excluded. Two comparisons were considered: electronic multidimensional guidelines versus usual care (comparison one) and electronic multidimensional guidelines versus other guideline implementation methods (comparison two). Results Twenty-seven publications were selected for analysis in this systematic review. Most designs were cluster randomized controlled trials investigating process outcomes more than patient outcomes. With success defined as at least 50% of the outcome variables being significant, none of the studies were successful in improving patient outcomes. Only seven of seventeen studies that investigated process outcomes showed improvements in process of care variables compared with the usual care group (comparison one). No incremental effect of the electronic implementation over the distribution of paper versions of the guideline was found, neither for the patient outcomes nor for the process outcomes (comparison two). Conclusions There is little evidence at the moment for the effectiveness of an increasingly used and commercialised instrument such as electronic multidimensional guidelines. After more than a decade of development of numerous electronic systems, research on the most effective implementation strategy for this kind of guideline-based decision support systems is still lacking. This conclusion implies a considerable risk towards inappropriate investments in ineffective implementation interventions and in suboptimal care. PMID:20042070

Two multidimensional chromatographic methods for enantiomeric analysis of o,p'-DDT and o,p'-DDD in contaminated soil and air in a malaria area of South Africa.

PubMed

Naudé, Yvette; Rohwer, Egmont R

2012-06-12

In rural parts of South Africa the organochlorine insecticide DDT (1,1,1-trichloro-2,2-bis(p-chlorophenyl)ethane) is still used for malaria vector control where traditional dwellings are sprayed on the inside with small quantities of technical DDT. Since o,p'-DDT may show enantioselective oestrogenicity and biodegradability, it is important to analyse enantiomers of o,p'-DDT and its chiral degradation product, o,p'-DDD, for both health and environmental-forensic considerations. Generally, chiral analysis is performed using heart-cut multidimensional gas chromatography (MDGC) and, more recently, comprehensive two-dimensional gas chromatography (GC×GC). We developed an off-line gas chromatographic fraction collection (heart-cut) procedure for the selective capturing of the appropriate isomers from a first apolar column, followed by reinjection and separation on a second chiral column. Only the o,p'-isomers of DDT and DDD fractions from the first dimension complex chromatogram (achiral apolar GC column separation) were selectively collected onto a polydimethylsiloxane (PDMS) multichannel open tubular silicone rubber trap by simply placing the latter device on the flame tip of an inactivated flame ionisation detector (FID). The multichannel trap containing the o,p'-heart-cuts was then thermally desorbed into a GC with time-of-flight mass spectrometry detection (GC-TOFMS) for second dimension enantioselective separation on a chiral column (β-cyclodextrin-based). By selectively capturing only the o,p'-isomers from the complex sample chromatogram, (1)D separation of ultra-trace level enantiomers could be achieved on the second chiral column without matrix interference. Here, we present solventless concentration techniques for extraction of DDT from contaminated soil and air, and report enantiomeric fraction (EF) values of o,p'-DDT and o,p'-DDD obtained by a new multidimensional approach for heart-cut gas chromatographic fraction collection for off-line second dimension enantiomeric separation by (1)D GC-TOFMS of selected isomers. This multidimensional method is compared to the complementary technique of comprehensive GC×GC-TOFMS using the same enantioselective column, this time as the first dimension of separation. Copyright © 2012 Elsevier B.V. All rights reserved.

Multidimensional CAT Item Selection Methods for Domain Scores and Composite Scores with Item Exposure Control and Content Constraints

ERIC Educational Resources Information Center

Yao, Lihua

2014-01-01

The intent of this research was to find an item selection procedure in the multidimensional computer adaptive testing (CAT) framework that yielded higher precision for both the domain and composite abilities, had a higher usage of the item pool, and controlled the exposure rate. Five multidimensional CAT item selection procedures (minimum angle;…

Reliability and Validity of the Chinese Version of the Multidimensional Anxiety Scale for Children among Chinese Secondary School Students

ERIC Educational Resources Information Center

Yao, Shuqiao; Zou, Tao; Zhu, Xiongzhao; Abela, John R. Z.; Auerbach, Randy P.; Tong, Xi

2007-01-01

The objective of the current study was to develop a Chinese translation of the Multidimensional Anxiety Scale for Children (MASC) [March (1997) Multidimensional anxiety scale for children: Technical manual, Multi health systems, Toronto, ON], and to evaluate its reliability and validity. The original version of the MASC was translated into Chinese…

Self Esteem, Locus of Control and Multidimensional Perfectionism as the Predictors of Subjective Well Being

ERIC Educational Resources Information Center

Karatas, Zeynep; Tagay, Ozlem

2012-01-01

The purpose of this study is to determine whether there is a relationship between self-esteem, locus of control and multidimensional perfectionism, and the extent to which the variables of self-esteem, locus of control and multidimensional perfectionism contribute to the prediction of subjective well-being. The study was carried out with 318 final…

Pedagogical Factors Stimulating the Self-Development of Students' Multi-Dimensional Thinking in Terms of Subject-Oriented Teaching

ERIC Educational Resources Information Center

Andreev, Valentin I.

2014-01-01

The main aim of this research is to disclose the essence of students' multi-dimensional thinking, also to reveal the rating of factors which stimulate the raising of effectiveness of self-development of students' multi-dimensional thinking in terms of subject-oriented teaching. Subject-oriented learning is characterized as a type of learning where…

Applying Multidimensional Item Response Theory Models in Validating Test Dimensionality: An Example of K-12 Large-Scale Science Assessment

ERIC Educational Resources Information Center

Li, Ying; Jiao, Hong; Lissitz, Robert W.

2012-01-01

This study investigated the application of multidimensional item response theory (IRT) models to validate test structure and dimensionality. Multiple content areas or domains within a single subject often exist in large-scale achievement tests. Such areas or domains may cause multidimensionality or local item dependence, which both violate the…

A Multidimensional Ideal Point Item Response Theory Model for Binary Data

ERIC Educational Resources Information Center

Maydeu-Olivares, Albert; Hernandez, Adolfo; McDonald, Roderick P.

2006-01-01

We introduce a multidimensional item response theory (IRT) model for binary data based on a proximity response mechanism. Under the model, a respondent at the mode of the item response function (IRF) endorses the item with probability one. The mode of the IRF is the ideal point, or in the multidimensional case, an ideal hyperplane. The model…

A Canonical Correlation Analysis of the Influence of Social Comparison, Gender, and Grade Level on the Multidimensional Self-Concepts of Gifted Adolescents

ERIC Educational Resources Information Center

Rinn, Anne N.; Jamieson, Kelly M.; Gross, Candace M.; McQueen, Kand S.

2009-01-01

This study examines the effects of social comparison, gender, and grade level on gifted adolescents' multidimensional self-concept. Participants include 248 gifted adolescents who had completed the sixth through tenth grade during the previous academic year. Multidimensional self-concept was measured using the Self Description Questionnaire II…

Multidimensional scaling for evolutionary algorithms--visualization of the path through search space and solution space using Sammon mapping.

PubMed

Pohlheim, Hartmut

2006-01-01

Multidimensional scaling as a technique for the presentation of high-dimensional data with standard visualization techniques is presented. The technique used is often known as Sammon mapping. We explain the mathematical foundations of multidimensional scaling and its robust calculation. We also demonstrate the use of this technique in the area of evolutionary algorithms. First, we present the visualization of the path through the search space of the best individuals during an optimization run. We then apply multidimensional scaling to the comparison of multiple runs regarding the variables of individuals and multi-criteria objective values (path through the solution space).

Best Design for Multidimensional Computerized Adaptive Testing With the Bifactor Model

PubMed Central

Seo, Dong Gi; Weiss, David J.

2015-01-01

Most computerized adaptive tests (CATs) have been studied using the framework of unidimensional item response theory. However, many psychological variables are multidimensional and might benefit from using a multidimensional approach to CATs. This study investigated the accuracy, fidelity, and efficiency of a fully multidimensional CAT algorithm (MCAT) with a bifactor model using simulated data. Four item selection methods in MCAT were examined for three bifactor pattern designs using two multidimensional item response theory models. To compare MCAT item selection and estimation methods, a fixed test length was used. The Ds-optimality item selection improved θ estimates with respect to a general factor, and either D- or A-optimality improved estimates of the group factors in three bifactor pattern designs under two multidimensional item response theory models. The MCAT model without a guessing parameter functioned better than the MCAT model with a guessing parameter. The MAP (maximum a posteriori) estimation method provided more accurate θ estimates than the EAP (expected a posteriori) method under most conditions, and MAP showed lower observed standard errors than EAP under most conditions, except for a general factor condition using Ds-optimality item selection. PMID:29795848

A Multidimensional Study of Vocal Function Following Radiation Therapy for Laryngeal Cancers.

PubMed

Angadi, Vrushali; Dressler, Emily; Stemple, Joseph

2017-06-01

Radiation therapy (XRT) has proven to be an effective curative modality in the treatment of laryngeal cancers. However, XRT also has deleterious effects on vocal function. To demonstrate the multidimensional nature of deficits in vocal function as a result of radiation therapy for laryngeal cancer. Cohort study. Vocal function parameters were chosen from the 5 domains of voice assessment to complete a multidimensional assessment battery. Adults irradiated (XRT group) for laryngeal cancers were compared to a control group of individuals with no history of head and neck cancers or radiation therapy. The control group was matched in age, sex, and pack years of smoking. Eighteen participants were recruited for the study. The XRT group demonstrated significantly worse clinical values as compared to the control group across select parameters in the each of the 5 domains of voice assessment. Radiation therapy for laryngeal cancers results in multidimensional deficits in vocal function. Notably, these deficits persist long term. In the present study sample, multidimensional deficits were persistent 2 to 7 years following completion of XRT. The observed multidimensional persistent vocal difficulties highlight the importance of vocal rehabilitation in the irradiated larynx cancer population.

Time-Accurate Local Time Stepping and High-Order Time CESE Methods for Multi-Dimensional Flows Using Unstructured Meshes

NASA Technical Reports Server (NTRS)

Chang, Chau-Lyan; Venkatachari, Balaji Shankar; Cheng, Gary

2013-01-01

With the wide availability of affordable multiple-core parallel supercomputers, next generation numerical simulations of flow physics are being focused on unsteady computations for problems involving multiple time scales and multiple physics. These simulations require higher solution accuracy than most algorithms and computational fluid dynamics codes currently available. This paper focuses on the developmental effort for high-fidelity multi-dimensional, unstructured-mesh flow solvers using the space-time conservation element, solution element (CESE) framework. Two approaches have been investigated in this research in order to provide high-accuracy, cross-cutting numerical simulations for a variety of flow regimes: 1) time-accurate local time stepping and 2) highorder CESE method. The first approach utilizes consistent numerical formulations in the space-time flux integration to preserve temporal conservation across the cells with different marching time steps. Such approach relieves the stringent time step constraint associated with the smallest time step in the computational domain while preserving temporal accuracy for all the cells. For flows involving multiple scales, both numerical accuracy and efficiency can be significantly enhanced. The second approach extends the current CESE solver to higher-order accuracy. Unlike other existing explicit high-order methods for unstructured meshes, the CESE framework maintains a CFL condition of one for arbitrarily high-order formulations while retaining the same compact stencil as its second-order counterpart. For large-scale unsteady computations, this feature substantially enhances numerical efficiency. Numerical formulations and validations using benchmark problems are discussed in this paper along with realistic examples.

Impact of an International Nosocomial Infection Control Consortium multidimensional approach on catheter-associated urinary tract infections in adult intensive care units in the Philippines: International Nosocomial Infection Control Consortium (INICC) findings.

PubMed

Navoa-Ng, Josephine Anne; Berba, Regina; Rosenthal, Victor D; Villanueva, Victoria D; Tolentino, María Corazon V; Genuino, Glenn Angelo S; Consunji, Rafael J; Mantaring, Jacinto Blas V

2013-10-01

To assess the impact of a multidimensional infection control approach on the reduction of catheter-associated urinary tract infection (CAUTI) rates in adult intensive care units (AICUs) in two hospitals in the Philippines that are members of the International Nosocomial Infection Control Consortium. This was a before-after prospective active surveillance study to determine the rates of CAUTI in 3183 patients hospitalized in 4 ICUS over 14,426 bed-days. The study was divided into baseline and intervention periods. During baseline, surveillance was performed using the definitions of the US Centers for Disease Control and Prevention and the National Healthcare Safety Network (CDC/NHSN). During intervention, we implemented a multidimensional approach that included: (1) a bundle of infection control interventions, (2) education, (3) surveillance of CAUTI rates, (4) feedback on CAUTI rates, (5) process surveillance and (6) performance feedback. We used random effects Poisson regression to account for the clustering of CAUTI rates across time. We recorded 8720 urinary catheter (UC)-days: 819 at baseline and 7901 during intervention. The rate of CAUTI was 11.0 per 1000 UC-days at baseline and was decreased by 76% to 2.66 per 1000 UC-days during intervention [rate ratio [RR], 0.24; 95% confidence interval [CI], 0.11-0.53; P-value, 0.0001]. Our multidimensional approach was associated with a significant reduction in the CAUTI rates in the ICU setting of a limited-resource country. Copyright © 2013 King Saud Bin Abdulaziz University for Health Sciences. Published by Elsevier Ltd. All rights reserved.

Mergeomics: a web server for identifying pathological pathways, networks, and key regulators via multidimensional data integration.

PubMed

Arneson, Douglas; Bhattacharya, Anindya; Shu, Le; Mäkinen, Ville-Petteri; Yang, Xia

2016-09-09

Human diseases are commonly the result of multidimensional changes at molecular, cellular, and systemic levels. Recent advances in genomic technologies have enabled an outpour of omics datasets that capture these changes. However, separate analyses of these various data only provide fragmented understanding and do not capture the holistic view of disease mechanisms. To meet the urgent needs for tools that effectively integrate multiple types of omics data to derive biological insights, we have developed Mergeomics, a computational pipeline that integrates multidimensional disease association data with functional genomics and molecular networks to retrieve biological pathways, gene networks, and central regulators critical for disease development. To make the Mergeomics pipeline available to a wider research community, we have implemented an online, user-friendly web server ( http://mergeomics. idre.ucla.edu/ ). The web server features a modular implementation of the Mergeomics pipeline with detailed tutorials. Additionally, it provides curated genomic resources including tissue-specific expression quantitative trait loci, ENCODE functional annotations, biological pathways, and molecular networks, and offers interactive visualization of analytical results. Multiple computational tools including Marker Dependency Filtering (MDF), Marker Set Enrichment Analysis (MSEA), Meta-MSEA, and Weighted Key Driver Analysis (wKDA) can be used separately or in flexible combinations. User-defined summary-level genomic association datasets (e.g., genetic, transcriptomic, epigenomic) related to a particular disease or phenotype can be uploaded and computed real-time to yield biologically interpretable results, which can be viewed online and downloaded for later use. Our Mergeomics web server offers researchers flexible and user-friendly tools to facilitate integration of multidimensional data into holistic views of disease mechanisms in the form of tissue-specific key regulators, biological pathways, and gene networks.

The PedsQL multidimensional fatigue scale in pediatric obesity: feasibility, reliability and validity.

PubMed

Varni, James W; Limbers, Christine A; Bryant, William P; Wilson, Don P

2010-01-01

The PedsQL (Pediatric Quality of Life Inventory) is a modular instrument designed to measure health-related quality of life (HRQOL) and disease-specific symptoms in children and adolescents. The PedsQL Multidimensional Fatigue Scale was designed as a child self-report and parent proxy-report generic symptom-specific instrument to measure fatigue in pediatric patients. The objective of the present study was to determine the feasibility, reliability, and validity of the PedsQL Multidimensional Fatigue Scale in pediatric obesity. The 18-item PedsQL Multidimensional Fatigue Scale (General Fatigue, Sleep/Rest Fatigue, and Cognitive Fatigue domains) and the PedsQL 4.0 Generic Core Scales were completed by 41 pediatric patients with a physician-diagnosis of obesity and 43 parents from a hospital-based Pediatric Endocrinology Clinic. The PedsQL Multidimensional Fatigue Scale evidenced minimal missing responses (1.6%, child report; 0.5%, parent report), achieved excellent reliability for the Total Fatigue Scale Score (alpha = 0.90 child report, 0.90 parent report), distinguished between pediatric patients with obesity and healthy children, and was significantly correlated with the PedsQL 4.0 Generic Core Scales supporting construct validity. Pediatric patients with obesity experienced fatigue comparable with pediatric patients receiving cancer treatment, demonstrating the relative severity of their fatigue symptoms. The results demonstrate the measurement properties of the PedsQL Multidimensional Fatigue Scale in pediatric obesity. The findings suggest that the PedsQL Multidimensional Fatigue Scale may be utilized in the standardized evaluation of fatigue in pediatric patients with obesity.

Multidimensional Poverty and Child Survival in India

PubMed Central

Mohanty, Sanjay K.

2011-01-01

Background Though the concept of multidimensional poverty has been acknowledged cutting across the disciplines (among economists, public health professionals, development thinkers, social scientists, policy makers and international organizations) and included in the development agenda, its measurement and application are still limited. Objectives and Methodology Using unit data from the National Family and Health Survey 3, India, this paper measures poverty in multidimensional space and examine the linkages of multidimensional poverty with child survival. The multidimensional poverty is measured in the dimension of knowledge, health and wealth and the child survival is measured with respect to infant mortality and under-five mortality. Descriptive statistics, principal component analyses and the life table methods are used in the analyses. Results The estimates of multidimensional poverty are robust and the inter-state differentials are large. While infant mortality rate and under-five mortality rate are disproportionately higher among the abject poor compared to the non-poor, there are no significant differences in child survival among educationally, economically and health poor at the national level. State pattern in child survival among the education, economical and health poor are mixed. Conclusion Use of multidimensional poverty measures help to identify abject poor who are unlikely to come out of poverty trap. The child survival is significantly lower among abject poor compared to moderate poor and non-poor. We urge to popularize the concept of multiple deprivations in research and program so as to reduce poverty and inequality in the population. PMID:22046384

The theory of n-scales

NASA Astrophysics Data System (ADS)

Dündar, Furkan Semih

2018-01-01

We provide a theory of n-scales previously called as n dimensional time scales. In previous approaches to the theory of time scales, multi-dimensional scales were taken as product space of two time scales [1, 2]. n-scales make the mathematical structure more flexible and appropriate to real world applications in physics and related fields. Here we define an n-scale as an arbitrary closed subset of ℝn. Modified forward and backward jump operators, Δ-derivatives and Δ-integrals on n-scales are defined.

Trajectories and traversal times in quantum tunneling

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

Huang, Zhi Hong.

1989-01-01

The classical concepts of trajectories and traversal times applied to quantum tunneling are discussed. By using the Wentzel-Kramers-Brillouin approximation, it is found that in a forbidden region of a multidimensional space the wave function can be described by two sets of trajectories, or equivalently by two sets of wave fronts. The trajectories belonging to different sets are mutually orthogonal. An extended Huygens construction is proposed to determine these wave fronts and trajectories. In contrast to the classical results in the allowed region, these trajectories couple to each other. However, if the incident wave is normal to the turning surface, themore » trajectories are found to be independent and can be determined by Newton's equations of motion with inverted potential and energy. The multidimensional tunneling theory is then applied to the scanning tunneling microscope to calculate the current density distribution and to derive the expressions for the lateral resolution and the surface corrugation amplitude. The traversal time in quantum tunneling, i.e. tunneling time, is found to depend on model calculations and simulations. Computer simulation of a wave packet tunneling through a square barrier is performed. Several approaches, including the phase method, Larmor clock, and time-dependent barrier model, are investigated. For a square barrier, two characteristic times are found: One is equal to the barrier width divided by the magnitude of the imaginary velocity; the other is equal to the decay length divided by the incident velocity. It is believed that the tunneling time can only be defined operationally.« less

Random phase detection in multidimensional NMR.

PubMed

Maciejewski, Mark W; Fenwick, Matthew; Schuyler, Adam D; Stern, Alan S; Gorbatyuk, Vitaliy; Hoch, Jeffrey C

2011-10-04

Despite advances in resolution accompanying the development of high-field superconducting magnets, biomolecular applications of NMR require multiple dimensions in order to resolve individual resonances, and the achievable resolution is typically limited by practical constraints on measuring time. In addition to the need for measuring long evolution times to obtain high resolution, the need to distinguish the sign of the frequency constrains the ability to shorten measuring times. Sign discrimination is typically accomplished by sampling the signal with two different receiver phases or by selecting a reference frequency outside the range of frequencies spanned by the signal and then sampling at a higher rate. In the parametrically sampled (indirect) time dimensions of multidimensional NMR experiments, either method imposes an additional factor of 2 sampling burden for each dimension. We demonstrate that by using a single detector phase at each time sample point, but randomly altering the phase for different points, the sign ambiguity that attends fixed single-phase detection is resolved. Random phase detection enables a reduction in experiment time by a factor of 2 for each indirect dimension, amounting to a factor of 8 for a four-dimensional experiment, albeit at the cost of introducing sampling artifacts. Alternatively, for fixed measuring time, random phase detection can be used to double resolution in each indirect dimension. Random phase detection is complementary to nonuniform sampling methods, and their combination offers the potential for additional benefits. In addition to applications in biomolecular NMR, random phase detection could be useful in magnetic resonance imaging and other signal processing contexts.

Pain assessment in cats undergoing ovariohysterectomy by midline or lateral celiotomy through use of a previously validated multidimensional composite pain scale.

PubMed

Oliveira, Jéssica Pecene; Mencalha, Rodrigo; Sousa, Carlos Augusto dos Santos; Abidu-Figueiredo, Marcelo; Jorge, Síria da Fonseca

2014-10-01

To assess pain in the immediate postoperative period in cats submitted into two different celiotomy techniques for ovariohysterectomy. Fourteen healthy female cats up to three years old with a mean weight 2.75 kg, without breed specification, were used in this double blind experiment. The animals were randomly assigned to two treatments: I- ovariohysterectomy by lateral approach (LA) or II - by midline approach (MA). The anesthesia consisted of acepromazine (0.1 mg.kg-1) and midazolam (0.25mg.kg-1) followed isoflurane vaporization to induce and maintain hypnosis. A bolus of fentanyl (5 μg.kg-1) was administered intravenously to provide intraoperative analgesia. After surgery, pain scores were assessed through a multidimensional composite pain scale at four different times. Generally all factors related to psychomotor changes and pain expression showed higher scores in cats neutered by LA, but only psychomotor changes and total pain score presented statistical differences (p<0.05). The animals that underwent lateral celiotomy showed higher pain scores, at 1, 4 and 6 hours after surgery. Multidimensional analgesic scales were highly reliable. There was a tendency for the cats neutered by lateral approach to suffer more postoperative pain, including requiring a large number of analgesic rescues.

An integrated approach for the knowledge discovery in computer simulation models with a multi-dimensional parameter space

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

Khawli, Toufik Al; Eppelt, Urs; Hermanns, Torsten

2016-06-08

In production industries, parameter identification, sensitivity analysis and multi-dimensional visualization are vital steps in the planning process for achieving optimal designs and gaining valuable information. Sensitivity analysis and visualization can help in identifying the most-influential parameters and quantify their contribution to the model output, reduce the model complexity, and enhance the understanding of the model behavior. Typically, this requires a large number of simulations, which can be both very expensive and time consuming when the simulation models are numerically complex and the number of parameter inputs increases. There are three main constituent parts in this work. The first part ismore » to substitute the numerical, physical model by an accurate surrogate model, the so-called metamodel. The second part includes a multi-dimensional visualization approach for the visual exploration of metamodels. In the third part, the metamodel is used to provide the two global sensitivity measures: i) the Elementary Effect for screening the parameters, and ii) the variance decomposition method for calculating the Sobol indices that quantify both the main and interaction effects. The application of the proposed approach is illustrated with an industrial application with the goal of optimizing a drilling process using a Gaussian laser beam.« less

An integrated approach for the knowledge discovery in computer simulation models with a multi-dimensional parameter space

NASA Astrophysics Data System (ADS)

Khawli, Toufik Al; Gebhardt, Sascha; Eppelt, Urs; Hermanns, Torsten; Kuhlen, Torsten; Schulz, Wolfgang

2016-06-01

In production industries, parameter identification, sensitivity analysis and multi-dimensional visualization are vital steps in the planning process for achieving optimal designs and gaining valuable information. Sensitivity analysis and visualization can help in identifying the most-influential parameters and quantify their contribution to the model output, reduce the model complexity, and enhance the understanding of the model behavior. Typically, this requires a large number of simulations, which can be both very expensive and time consuming when the simulation models are numerically complex and the number of parameter inputs increases. There are three main constituent parts in this work. The first part is to substitute the numerical, physical model by an accurate surrogate model, the so-called metamodel. The second part includes a multi-dimensional visualization approach for the visual exploration of metamodels. In the third part, the metamodel is used to provide the two global sensitivity measures: i) the Elementary Effect for screening the parameters, and ii) the variance decomposition method for calculating the Sobol indices that quantify both the main and interaction effects. The application of the proposed approach is illustrated with an industrial application with the goal of optimizing a drilling process using a Gaussian laser beam.

Psychometric Validation of a Multidimensional Schema of Substance Use Topology: Discrimination of High and Low Risk Youth and Prediction of Substance Use Disorder.

ERIC Educational Resources Information Center

Kirisci, Levent; Tarter, Ralph E.

2001-01-01

Designs and evaluates a multidimensional schema for the assessment of alcohol, tobacco and other drug use topology. Findings illustrate the value of multidimensional assessment for identifying youth at high risk for substance use disorder (SUD) as well as for elucidating the factors contributing to the transition to suprathreshold SUD. (Contains…

Development of a Computerized Adaptive Testing for Diagnosing the Cognitive Process of Grade 7 Students in Learning Algebra, Using Multidimensional Item Response Theory

ERIC Educational Resources Information Center

Senarat, Somprasong; Tayraukham, Sombat; Piyapimonsit, Chatsiri; Tongkhambanjong, Sakesan

2013-01-01

The purpose of this research is to develop a multidimensional computerized adaptive test for diagnosing the cognitive process of grade 7 students in learning algebra by applying multidimensional item response theory. The research is divided into 4 steps: 1) the development of item bank of algebra, 2) the development of the multidimensional…

Dissociation of performance and subjective measures of workload

NASA Technical Reports Server (NTRS)

Yeh, Yei-Yu; Wickens, Christopher D.

1988-01-01

A theory is presented to identify sources that produce dissociations between performance and subjective measures of workload. The theory states that performance is determined by (1) amount of resources invested, (2) resource efficiency, and (3) degree of competition for common resources in a multidimensional space described in the multiple-resources model. Subjective perception of workload, multidimensional in nature, increases with greater amounts of resource investment and with greater demands on working memory. Performance and subjective workload measures dissociate when greater resources are invested to improve performance of a resource-limited task; when demands on working memory are increased by time-sharing between concurrent tasks or between display elements; and when performance is sensitive to resource competition and subjective measures are more sensitive to total investment. These dissociation findings and their implications are discussed and directions for future research are suggested.

Building a symbolic computer algebra toolbox to compute 2D Fourier transforms in polar coordinates.

PubMed

Dovlo, Edem; Baddour, Natalie

2015-01-01

The development of a symbolic computer algebra toolbox for the computation of two dimensional (2D) Fourier transforms in polar coordinates is presented. Multidimensional Fourier transforms are widely used in image processing, tomographic reconstructions and in fact any application that requires a multidimensional convolution. By examining a function in the frequency domain, additional information and insights may be obtained. The advantages of our method include: •The implementation of the 2D Fourier transform in polar coordinates within the toolbox via the combination of two significantly simpler transforms.•The modular approach along with the idea of lookup tables implemented help avoid the issue of indeterminate results which may occur when attempting to directly evaluate the transform.•The concept also helps prevent unnecessary computation of already known transforms thereby saving memory and processing time.

Efficient multidimensional free energy calculations for ab initio molecular dynamics using classical bias potentials

NASA Astrophysics Data System (ADS)

VandeVondele, Joost; Rothlisberger, Ursula

2000-09-01

We present a method for calculating multidimensional free energy surfaces within the limited time scale of a first-principles molecular dynamics scheme. The sampling efficiency is enhanced using selected terms of a classical force field as a bias potential. This simple procedure yields a very substantial increase in sampling accuracy while retaining the high quality of the underlying ab initio potential surface and can thus be used for a parameter free calculation of free energy surfaces. The success of the method is demonstrated by the applications to two gas phase molecules, ethane and peroxynitrous acid, as test case systems. A statistical analysis of the results shows that the entire free energy landscape is well converged within a 40 ps simulation at 500 K, even for a system with barriers as high as 15 kcal/mol.

Multidimensional fractional Schrödinger equation

NASA Astrophysics Data System (ADS)

Rodrigues, M. M.; Vieira, N.

2012-11-01

This work is intended to investigate the multi-dimensional space-time fractional Schrödinger equation of the form (CDt0+αu)(t,x) = iħ/2m(C∇βu)(t,x), with ħ the Planck's constant divided by 2π, m is the mass and u(t,x) is a wave function of the particle. Here (CDt0+α,C∇β are operators of the Caputo fractional derivatives, where α ∈]0,1] and β ∈]1,2]. The wave function is obtained using Laplace and Fourier transforms methods and a symbolic operational form of solutions in terms of the Mittag-Leffler functions is exhibited. It is presented an expression for the wave function and for the quantum mechanical probability density. Using Banach fixed point theorem, the existence and uniqueness of solutions is studied for this kind of fractional differential equations.

Time-varying trends of global vegetation activity

NASA Astrophysics Data System (ADS)

Pan, N.; Feng, X.; Fu, B.

2016-12-01

Vegetation plays an important role in regulating the energy change, water cycle and biochemical cycle in terrestrial ecosystems. Monitoring the dynamics of vegetation activity and understanding their driving factors have been an important issue in global change research. Normalized Difference Vegetation Index (NDVI), an indicator of vegetation activity, has been widely used in investigating vegetation changes at regional and global scales. Most studies utilized linear regression or piecewise linear regression approaches to obtain an averaged changing rate over a certain time span, with an implicit assumption that the trend didn't change over time during that period. However, no evidence shows that this assumption is right for the non-linear and non-stationary NDVI time series. In this study, we adopted the multidimensional ensemble empirical mode decomposition (MEEMD) method to extract the time-varying trends of NDVI from original signals without any a priori assumption of their functional form. Our results show that vegetation trends are spatially and temporally non-uniform during 1982-2013. Most vegetated area exhibited greening trends in the 1980s. Nevertheless, the area with greening trends decreased over time since the early 1990s, and the greening trends have stalled or even reversed in many places. Regions with browning trends were mainly located in southern low latitudes in the 1980s, whose area decreased before the middle 1990s and then increased at an accelerated rate. The greening-to-browning reversals were widespread across all continents except Oceania (43% of the vegetated areas), most of which happened after the middle 1990s. In contrast, the browning-to-greening reversals occurred in smaller area and earlier time. The area with monotonic greening and browning trends accounted for 33% and 5% of the vegetated area, respectively. By performing partial correlation analyses between NDVI and climatic elements (temperature, precipitation and cloud cover) and analyzing the MEEMD-extracted trends of these climatic elements, we discussed possible driving factors of the time-varying trends of NDVI in several specific regions where trend reversals occurred.

Multi-dimensional quantum state sharing based on quantum Fourier transform

NASA Astrophysics Data System (ADS)

Qin, Huawang; Tso, Raylin; Dai, Yuewei

2018-03-01

A scheme of multi-dimensional quantum state sharing is proposed. The dealer performs the quantum SUM gate and the quantum Fourier transform to encode a multi-dimensional quantum state into an entanglement state. Then the dealer distributes each participant a particle of the entanglement state, to share the quantum state among n participants. In the recovery, n-1 participants measure their particles and supply their measurement results; the last participant performs the unitary operation on his particle according to these measurement results and can reconstruct the initial quantum state. The proposed scheme has two merits: It can share the multi-dimensional quantum state and it does not need the entanglement measurement.

Some theorems and properties of multi-dimensional fractional Laplace transforms

NASA Astrophysics Data System (ADS)

Ahmood, Wasan Ajeel; Kiliçman, Adem

2016-06-01

The aim of this work is to study theorems and properties for the one-dimensional fractional Laplace transform, generalize some properties for the one-dimensional fractional Lapalce transform to be valid for the multi-dimensional fractional Lapalce transform and is to give the definition of the multi-dimensional fractional Lapalce transform. This study includes: dedicate the one-dimensional fractional Laplace transform for functions of only one independent variable with some of important theorems and properties and develop of some properties for the one-dimensional fractional Laplace transform to multi-dimensional fractional Laplace transform. Also, we obtain a fractional Laplace inversion theorem after a short survey on fractional analysis based on the modified Riemann-Liouville derivative.

Poverty and Wellbeing at the "Grassroots"--How Much Is Visible to Researchers?

ERIC Educational Resources Information Center

Tiwari, Meera

2009-01-01

This paper discusses the grassroots level understanding of poverty and wellbeing. There is rich debate and ever expanding literature on the meaning of wellbeing and poverty and their relationship in developing countries. In recent times wellbeing and poverty have been scrutinised within the discourse on multidimensionality of poverty. Most…

Methods for evaluating information in managing the enterprise on the basis of a hybrid three-tier system

NASA Astrophysics Data System (ADS)

Vasil'ev, V. A.; Dobrynina, N. V.

2017-01-01

The article presents data on the influence of information upon the functioning of complex systems in the process of ensuring their effective management. Ways and methods for evaluating multidimensional information that reduce time and resources, improve the validity of the studied system management decisions, were proposed.

Problematic Internet Use among Turkish University Students: A Multidimensional Investigation Based on Demographics and Internet Activities

ERIC Educational Resources Information Center

Tekinarslan, Erkan; Gurer, Melih Derya

2011-01-01

This study investigated the Turkish undergraduate university students' problematic Internet use (PIU) levels on different dimensions based on demographics (e.g., gender, Internet use by time of day), and Internet activities (e.g., chat, entertainment, social networking, information searching, etc.). Moreover, the study explored some predictors of…

The Multi-Dimensional Nature of Emergency Communications Management

ERIC Educational Resources Information Center

Staman, E. Michael; Katsouros, Mark; Hach, Richard

2009-01-01

Within an incredibly short period--perhaps less than twenty-four months--the need for emergency preparedness has risen to a higher level of urgency than at any other time in the history of academe. Large or small, public or private, higher education institutions are seriously considering the dual problems of notification and communications…

Tracking Poverty Reduction in Bhutan: Income Deprivation Alongside Deprivation in Other Sources of Happiness

ERIC Educational Resources Information Center

Santos, Maria Emma

2013-01-01

This paper analyses poverty reduction in Bhutan between two points in time--2003 and 2007--from a multidimensional perspective. The measures estimated include consumption expenditure as well as other indicators which are directly (when possible) or indirectly associated to valuable functionings, namely, health, education, access to electricity,…

Locus of Control as a Measure of Ineffectiveness in Anorexia Nervosa.

ERIC Educational Resources Information Center

Hood, Jane; And Others

1982-01-01

Using a multidimensional locus of control scale, scores of female anorexia nervosa patients (N=54) were compared to norms. Younger anorexic patients demonstrated higher internal control compared to norms on times related to fatalism and social-system control. Scores for older patients could not be differentiated from the norms. (Author)

Social Contexts of Regular Smoking in Adolescence: Towards a Multidimensional Ecological Model

ERIC Educational Resources Information Center

Wen, Ming; Van Duker, Heather; Olson, Lenora M.

2009-01-01

Using data from the Add Health, this study examined multilevel factors of adolescent smoking after controlling for the baseline smoking behavior and individual characteristics. Results showed that peer, family and school were all important life domains contextually influencing subsequent smoking behavior among adolescents. Time spent with peers,…

Changing Locus of Control: Steelworkers Adjusting to Forced Unemployment

ERIC Educational Resources Information Center

Legerski, Elizabeth Miklya; Cornwall, Marie; O'Neil, Brock

2006-01-01

Using an abbreviated version of Levenson's (1981) locus of control scale, we examine change over time in the locus of control of displaced steelworkers. The first data collection occurred approximately six months after plant shutdown, the second occurred a year later. Utilizing a multidimensional measurement model, we test the major assumption…

LC-IMS-MS Feature Finder: detecting multidimensional liquid chromatography, ion mobility and mass spectrometry features in complex datasets.

PubMed

Crowell, Kevin L; Slysz, Gordon W; Baker, Erin S; LaMarche, Brian L; Monroe, Matthew E; Ibrahim, Yehia M; Payne, Samuel H; Anderson, Gordon A; Smith, Richard D

2013-11-01

The addition of ion mobility spectrometry to liquid chromatography-mass spectrometry experiments requires new, or updated, software tools to facilitate data processing. We introduce a command line software application LC-IMS-MS Feature Finder that searches for molecular ion signatures in multidimensional liquid chromatography-ion mobility spectrometry-mass spectrometry (LC-IMS-MS) data by clustering deisotoped peaks with similar monoisotopic mass, charge state, LC elution time and ion mobility drift time values. The software application includes an algorithm for detecting and quantifying co-eluting chemical species, including species that exist in multiple conformations that may have been separated in the IMS dimension. LC-IMS-MS Feature Finder is available as a command-line tool for download at http://omics.pnl.gov/software/LC-IMS-MS_Feature_Finder.php. The Microsoft.NET Framework 4.0 is required to run the software. All other dependencies are included with the software package. Usage of this software is limited to non-profit research to use (see README). rds@pnnl.gov. Supplementary data are available at Bioinformatics online.

Quantification of polychlorinated dibenzo-p-dioxins and dibenzofurans by direct injection of sample extract into the comprehensive multidimensional gas chromatograph/high-resolution time-of-flight mass spectrometer.

PubMed

Shunji, Hashimoto; Yoshikatsu, Takazawa; Akihiro, Fushimi; Hiroyasu, Ito; Kiyoshi, Tanabe; Yasuyuki, Shibata; Masa-aki, Ubukata; Akihiko, Kusai; Kazuo, Tanaka; Hideyuki, Otsuka; Katsunori, Anezaki

2008-01-18

Polychlorinated dibenzo-p-dioxins and dibenzofurans in crude extracts of fly ash and flue gas from municipal waste incinerators were quantified using a comprehensive multidimensional gas chromatograph (GC x GC) coupled to a high-resolution time-of-flight mass spectrometer (HR-TOFMS). For identification and quantification, we developed our own program to prepare 3D chromatograms of selected mass numbers from the data of the GC x GC/HR-TOFMS. Isolation of all congeners with a TCDD toxic equivalency factor from the other isomers by only one injection was confirmed. The instrumental detection limit of TCDD on the GC x GC/HR-TOFMS was 0.9 pg by the relative calibration method. Quantification of these substances in the crude extracts was achieved by direct injection to the GC x GC/HR-TOFMS. The results agree with the values obtained using a generic gas chromatography/high-resolution mass spectrometry (GC/HRMS) system. It was confirmed that measurement by high-resolution TOFMS and GC x GC effectively reduces interference from other chemicals.

PACE-90 water and solute transport calculations for 0.01, 0.1, and 0. 5 mm/yr infiltration into Yucca Mountain; Yucca Mountain Site Characterization Project

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

Dykhuizen, R.C.; Eaton, R.R.; Hopkins, P.L.

1991-12-01

Numerical results are presented for the Performance Assessment Calculational Exercise (PACE-90). One- and two-dimensional water and solute transport are presented for steady infiltration into Yucca Mountain. Evenly distributed infiltration rates of 0.01, 0.1, and 0.5 mm/yr were considered. The calculations of solute transport show that significant amounts of radionuclides can reach the water table over 100,000 yr at the 0.5 mm/yr rate. For time periods less than 10,000 yr or infiltrations less than 0.1 mm/yr very little solute reaches the water table. The numerical simulations clearly demonstrate that multi-dimensional effects can result in significant decreases in the travel time ofmore » solute through the modeled domain. Dual continuum effects are shown to be negligible for the low steady state fluxes considered. However, material heterogeneities may cause local amplification of the flux level in multi-dimensional flows. These higher flux levels may then require modeling of a dual continuum porous medium.« less

Experimental demonstration of a flexible time-domain quantum channel.

PubMed

Xing, Xingxing; Feizpour, Amir; Hayat, Alex; Steinberg, Aephraim M

2014-10-20

We present an experimental realization of a flexible quantum channel where the Hilbert space dimensionality can be controlled electronically. Using electro-optical modulators (EOM) and narrow-band optical filters, quantum information is encoded and decoded in the temporal degrees of freedom of photons from a long-coherence-time single-photon source. Our results demonstrate the feasibility of a generic scheme for encoding and transmitting multidimensional quantum information over the existing fiber-optical telecommunications infrastructure.

The Necessity-Concerns-Framework: A Multidimensional Theory Benefits from Multidimensional Analysis

PubMed Central

Phillips, L. Alison; Diefenbach, Michael; Kronish, Ian M.; Negron, Rennie M.; Horowitz, Carol R.

2014-01-01

Background Patients’ medication-related concerns and necessity-beliefs predict adherence. Evaluation of the potentially complex interplay of these two dimensions has been limited because of methods that reduce them to a single dimension (difference scores). Purpose We use polynomial regression to assess the multidimensional effect of stroke-event survivors’ medication-related concerns and necessity-beliefs on their adherence to stroke-prevention medication. Methods Survivors (n=600) rated their concerns, necessity-beliefs, and adherence to medication. Confirmatory and exploratory polynomial regression determined the best-fitting multidimensional model. Results As posited by the Necessity-Concerns Framework (NCF), the greatest and lowest adherence was reported by those with strong necessity-beliefs/weak concerns and strong concerns/weak necessity-beliefs, respectively. However, as could not be assessed using a difference-score model, patients with ambivalent beliefs were less adherent than those exhibiting indifference. Conclusions Polynomial regression allows for assessment of the multidimensional nature of the NCF. Clinicians/Researchers should be aware that concerns and necessity dimensions are not polar opposites. PMID:24500078

The necessity-concerns framework: a multidimensional theory benefits from multidimensional analysis.

PubMed

Phillips, L Alison; Diefenbach, Michael A; Kronish, Ian M; Negron, Rennie M; Horowitz, Carol R

2014-08-01

Patients' medication-related concerns and necessity-beliefs predict adherence. Evaluation of the potentially complex interplay of these two dimensions has been limited because of methods that reduce them to a single dimension (difference scores). We use polynomial regression to assess the multidimensional effect of stroke-event survivors' medication-related concerns and necessity beliefs on their adherence to stroke-prevention medication. Survivors (n = 600) rated their concerns, necessity beliefs, and adherence to medication. Confirmatory and exploratory polynomial regression determined the best-fitting multidimensional model. As posited by the necessity-concerns framework (NCF), the greatest and lowest adherence was reported by those necessity weak concerns and strong concerns/weak Necessity-Beliefs, respectively. However, as could not be assessed using a difference-score model, patients with ambivalent beliefs were less adherent than those exhibiting indifference. Polynomial regression allows for assessment of the multidimensional nature of the NCF. Clinicians/Researchers should be aware that concerns and necessity dimensions are not polar opposites.

The moderating effects of gender on the associations between multidimensional hostility and psychosomatic symptoms: a Chinese case.

PubMed

Weng, Chia-Ying; Lin, I-Mei; Jiang, Ding-Yu

2010-08-01

The purpose of this study was to examine the effects of gender on the relationship between multidimensional hostility and psychosomatic symptoms in Chinese culture. The participants in this study were 398 Chinese college students (40% female) recruited from Taiwan. Four dimensions of multidimensional hostility-hostility cognition, hostility affect, expressive hostility behavior, and suppressive hostility behavior-were measured by the Chinese Hostility Inventory. After controlling for the effects of depression and anxiety, the results of path analysis revealed that the multidimensional hostility predicted psychosomatic symptoms directly, and predicted psychosomatic symptoms indirectly through negative health behavior. Furthermore, gender moderated the relationships between multidimensional hostility and health outcomes. Expressive hostility exacerbated psychosomatic symptom in females but buffered it in males, while affective hostility exacerbated psychosomatic symptoms in males. Additionally, suppressive hostility behavior was correlated to psychosomatic symptoms indirectly through negative health behavior in females. Moreover, expressive hostility was correlated to psychosomatic symptoms indirectly through negative health behavior more in males than in females.

GPS Technologies as a Tool to Detect the Pre-Earthquake Signals Associated with Strong Earthquakes

NASA Astrophysics Data System (ADS)

Pulinets, S. A.; Krankowski, A.; Hernandez-Pajares, M.; Liu, J. Y. G.; Hattori, K.; Davidenko, D.; Ouzounov, D.

2015-12-01

The existence of ionospheric anomalies before earthquakes is now widely accepted. These phenomena started to be considered by GPS community to mitigate the GPS signal degradation over the territories of the earthquake preparation. The question is still open if they could be useful for seismology and for short-term earthquake forecast. More than decade of intensive studies proved that ionospheric anomalies registered before earthquakes are initiated by processes in the boundary layer of atmosphere over earthquake preparation zone and are induced in the ionosphere by electromagnetic coupling through the Global Electric Circuit. Multiparameter approach based on the Lithosphere-Atmosphere-Ionosphere Coupling model demonstrated that earthquake forecast is possible only if we consider the final stage of earthquake preparation in the multidimensional space where every dimension is one from many precursors in ensemble, and they are synergistically connected. We demonstrate approaches developed in different countries (Russia, Taiwan, Japan, Spain, and Poland) within the framework of the ISSI and ESA projects) to identify the ionospheric precursors. They are also useful to determine the all three parameters necessary for the earthquake forecast: impending earthquake epicenter position, expectation time and magnitude. These parameters are calculated using different technologies of GPS signal processing: time series, correlation, spectral analysis, ionospheric tomography, wave propagation, etc. Obtained results from different teams demonstrate the high level of statistical significance and physical justification what gives us reason to suggest these methodologies for practical validation.

Community-level phenological response to climate change

PubMed Central

Ovaskainen, Otso; Skorokhodova, Svetlana; Yakovleva, Marina; Sukhov, Alexander; Kutenkov, Anatoliy; Kutenkova, Nadezhda; Shcherbakov, Anatoliy; Meyke, Evegeniy; Delgado, Maria del Mar

2013-01-01

Climate change may disrupt interspecies phenological synchrony, with adverse consequences to ecosystem functioning. We present here a 40-y-long time series on 10,425 dates that were systematically collected in a single Russian locality for 97 plant, 78 bird, 10 herptile, 19 insect, and 9 fungal phenological events, as well as for 77 climatic events related to temperature, precipitation, snow, ice, and frost. We show that species are shifting their phenologies at dissimilar rates, partly because they respond to different climatic factors, which in turn are shifting at dissimilar rates. Plants have advanced their spring phenology even faster than average temperature has increased, whereas migratory birds have shown more divergent responses and shifted, on average, less than plants. Phenological events of birds and insects were mainly triggered by climate cues (variation in temperature and snow and ice cover) occurring over the course of short periods, whereas many plants, herptiles, and fungi were affected by long-term climatic averages. Year-to-year variation in plants, herptiles, and insects showed a high degree of synchrony, whereas the phenological timing of fungi did not correlate with any other taxonomic group. In many cases, species that are synchronous in their year-to-year dynamics have also shifted in congruence, suggesting that climate change may have disrupted phenological synchrony less than has been previously assumed. Our results illustrate how a multidimensional change in the physical environment has translated into a community-level change in phenology. PMID:23901098

On new physics searches with multidimensional differential shapes

NASA Astrophysics Data System (ADS)

Ferreira, Felipe; Fichet, Sylvain; Sanz, Veronica

2018-03-01

In the context of upcoming new physics searches at the LHC, we investigate the impact of multidimensional differential rates in typical LHC analyses. We discuss the properties of shape information, and argue that multidimensional rates bring limited information in the scope of a discovery, but can have a large impact on model discrimination. We also point out subtleties about systematic uncertainties cancellations and the Cauchy-Schwarz bound on interference terms.

Measuring the Perception of the Teachers' Autonomy-Supportive Behavior in Physical Education: Development and Initial Validation of a Multi-Dimensional Instrument

ERIC Educational Resources Information Center

Tilga, Henri; Hein, Vello; Koka, Andre

2017-01-01

This research aimed to develop and validate an instrument to assess the students' perceptions of the teachers' autonomy-supportive behavior by the multi-dimensional scale (Multi-Dimensional Perceived Autonomy Support Scale for Physical Education). The participants were 1,476 students aged 12- to 15-years-old. In Study 1, a pool of 37 items was…

Multidimensional modulation for next-generation transmission systems

NASA Astrophysics Data System (ADS)

Millar, David S.; Koike-Akino, Toshiaki; Kojima, Keisuke; Parsons, Kieran

2017-01-01

Recent research in multidimensional modulation has shown great promise in long reach applications. In this work, we will investigate the origins of this gain, the different approaches to multidimensional constellation design, and different performance metrics for coded modulation. We will also discuss the reason that such coded modulation schemes seem to have limited application at shorter distances, and the potential for other coded modulation schemes in future transmission systems.

Multidimensional Data Modeling for Business Process Analysis

NASA Astrophysics Data System (ADS)

Mansmann, Svetlana; Neumuth, Thomas; Scholl, Marc H.

The emerging area of business process intelligence attempts to enhance the analytical capabilities of business process management systems by employing data warehousing and mining technologies. This paper presents an approach to re-engineering the business process modeling in conformity with the multidimensional data model. Since the business process and the multidimensional model are driven by rather different objectives and assumptions, there is no straightforward solution to converging these models.

The Psychometric Properties of an Arabic version of the PedsQL Multidimensional Fatigue Scale Tested for Children with Cancer.

PubMed

Al-Gamal, Ekhlas; Long, Tony

2017-09-01

Fatigue is considered to be one of the most reported symptoms experienced by children with cancer. A major aim of this study was to develop an Arabic version of the Pediatric Quality of Life (PedsQL) Multidimensional Fatigue Scale (child report) and to test its psychometric proprieties for the assessment of fatigue in Arabic children with cancer. The PedsQL Multidimensional Fatigue Scale (Arabic version) and the PedsQL TM 4.0 Generic Core scale (existing Arabic version) were completed by 70 Jordanian children with cancer. Cronbach's alpha coefficients were found to be 0.90 for the total PedsQL Multidimensional Fatigue Scale (Arabic version), 0.94 for the general fatigue subscale, 0.67 for the sleep/rest fatigue subscale, and 0.87 for the cognitive fatigue subscale. The PedsQL Multidimensional Fatigue Scale scores correlated significantly with the PedsQL TM 4.0 Generic Core scale and demonstrated good construct validity. The results demonstrate excellent reliability and good validity of the PedsQL Multidimensional Fatigue Scale (Arabic version) for children with cancer. This is the first validated scale that assesses fatigue in Arabic children with cancer. The English scale has been used with several pediatric clinical populations, so this Arabic version may be equally useful beyond the field of cancer.

DEVELOPMENT AND PSYCHOMETRIC TESTING OF A MULTIDIMENSIONAL INSTRUMENT OF PERCEIVED DISCRIMINATION AMONG AFRICAN AMERICANS IN THE JACKSON HEART STUDY

PubMed Central

Sims, Mario; Wyatt, Sharon B.; Gutierrez, Mary Lou; Taylor, Herman A.; Williams, David R.

2009-01-01

Objective Assessing the discrimination-health disparities hypothesis requires psychometrically sound, multidimensional measures of discrimination. Among the available discrimination measures, few are multidimensional and none have adequate psychometric testing in a large, African American sample. We report the development and psychometric testing of the multidimensional Jackson Heart Study Discrimination (JHSDIS) Instrument. Methods A multidimensional measure assessing the occurrence, frequency, attribution, and coping responses to perceived everyday and lifetime discrimination; lifetime burden of discrimination; and effect of skin color was developed and tested in the 5302-member cohort of the Jackson Heart Study. Internal consistency was calculated by using Cronbach α. coefficient. Confirmatory factor analysis established the dimensions, and intercorrelation coefficients assessed the discriminant validity of the instrument. Setting Tri-county area of the Jackson, MS metropolitan statistical area. Results The JHSDIS was psychometrically sound (overall α=.78, .84 and .77, respectively, for the everyday and lifetime subscales). Confirmatory factor analysis yielded 11 factors, which confirmed the a priori dimensions represented. Conclusions The JHSDIS combined three scales into a single multidimensional instrument with good psychometric properties in a large sample of African Americans. This analysis lays the foundation for using this instrument in research that will examine the association between perceived discrimination and CVD among African Americans. PMID:19341164

The multidimensional perturbation value: a single metric to measure similarity and activity of treatments in high-throughput multidimensional screens.

PubMed

Hutz, Janna E; Nelson, Thomas; Wu, Hua; McAllister, Gregory; Moutsatsos, Ioannis; Jaeger, Savina A; Bandyopadhyay, Somnath; Nigsch, Florian; Cornett, Ben; Jenkins, Jeremy L; Selinger, Douglas W

2013-04-01

Screens using high-throughput, information-rich technologies such as microarrays, high-content screening (HCS), and next-generation sequencing (NGS) have become increasingly widespread. Compared with single-readout assays, these methods produce a more comprehensive picture of the effects of screened treatments. However, interpreting such multidimensional readouts is challenging. Univariate statistics such as t-tests and Z-factors cannot easily be applied to multidimensional profiles, leaving no obvious way to answer common screening questions such as "Is treatment X active in this assay?" and "Is treatment X different from (or equivalent to) treatment Y?" We have developed a simple, straightforward metric, the multidimensional perturbation value (mp-value), which can be used to answer these questions. Here, we demonstrate application of the mp-value to three data sets: a multiplexed gene expression screen of compounds and genomic reagents, a microarray-based gene expression screen of compounds, and an HCS compound screen. In all data sets, active treatments were successfully identified using the mp-value, and simulations and follow-up analyses supported the mp-value's statistical and biological validity. We believe the mp-value represents a promising way to simplify the analysis of multidimensional data while taking full advantage of its richness.

Why didn't Box-Jenkins win (again)

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

Pack, D.J.; Downing, D.J.

This paper focuses on the forecasting performance of the Box-Jenkins methodology applied to the 111 time series of the Makridakis competition. It considers the influence of the following factors: (1) time series length, (2) time-series information (autocorrelation) content, (3) time-series outliers or structural changes, (4) averaging results over time series, and (5) forecast time origin choice. It is found that the 111 time series contain substantial numbers of very short series, series with obvious structural change, and series whose histories are relatively uninformative. If these series are typical of those that one must face in practice, the real message ofmore » the competition is that univariate time series extrapolations will frequently fail regardless of the methodology employed to produce them.« less

Multifractal analysis of visibility graph-based Ito-related connectivity time series.

PubMed

Czechowski, Zbigniew; Lovallo, Michele; Telesca, Luciano

2016-02-01

In this study, we investigate multifractal properties of connectivity time series resulting from the visibility graph applied to normally distributed time series generated by the Ito equations with multiplicative power-law noise. We show that multifractality of the connectivity time series (i.e., the series of numbers of links outgoing any node) increases with the exponent of the power-law noise. The multifractality of the connectivity time series could be due to the width of connectivity degree distribution that can be related to the exit time of the associated Ito time series. Furthermore, the connectivity time series are characterized by persistence, although the original Ito time series are random; this is due to the procedure of visibility graph that, connecting the values of the time series, generates persistence but destroys most of the nonlinear correlations. Moreover, the visibility graph is sensitive for detecting wide "depressions" in input time series.

The PedsQL Multidimensional Fatigue Scale in type 1 diabetes: feasibility, reliability, and validity.

PubMed

Varni, James W; Limbers, Christine A; Bryant, William P; Wilson, Don P

2009-08-01

The Pediatric Quality of Life Inventory (PedsQL, Mapi Research Trust, Lyon, France; www.pedsql.org) is a modular instrument designed to measure health-related quality of life and disease-specific symptoms in children and adolescents. The PedsQL Multidimensional Fatigue Scale was designed as a child self-report and parent proxy-report generic symptom-specific instrument to measure fatigue in pediatric patients. The objective of the present study was to determine the feasibility, reliability, and validity of the PedsQL Multidimensional Fatigue Scale in type 1 diabetes. The 18-item PedsQL Multidimensional Fatigue Scale (General Fatigue, Sleep/Rest Fatigue, and Cognitive Fatigue domains) and the PedsQL 4.0 Generic Core Scales were administered to 83 pediatric patients with type 1 diabetes and 84 parents. The PedsQL Multidimensional Fatigue Scale evidenced minimal missing responses (0.3% child report and 0.3% parent report), achieved excellent reliability for the Total Fatigue Scale score (alpha= 0.92 child report, 0.94 parent report), distinguished between pediatric patients with diabetes and healthy children, and was significantly correlated with the PedsQL 4.0 Generic Core Scales supporting construct validity. Pediatric patients with diabetes experienced fatigue that was comparable to pediatric patients with cancer on treatment, demonstrating the relative severity of their fatigue symptoms. The results demonstrate the measurement properties of the PedsQL Multidimensional Fatigue Scale in type 1 diabetes. The findings suggest that the PedsQL Multidimensional Fatigue Scale may be utilized in the standardized evaluation of fatigue in pediatric patients with type 1 diabetes.

The PedsQL Multidimensional Fatigue Scale in young adults: feasibility, reliability and validity in a University student population.

PubMed

Varni, James W; Limbers, Christine A

2008-02-01

The PedsQL (Pediatric Quality of Life Inventory) is a modular instrument designed to measure health-related quality of life (HRQOL) and disease-specific symptoms in children and adolescents ages 2-18. The PedsQL Multidimensional Fatigue Scale was designed as a generic symptom-specific instrument to measure fatigue in pediatric patients ages 2-18. Since a sizeable number of pediatric patients prefer to remain with their pediatric providers after age 18, the objective of the present study was to determine the feasibility, reliability, and validity of the PedsQL Multidimensional Fatigue Scale in young adults. The 18-item PedsQL Multidimensional Fatigue Scale (General Fatigue, Sleep/Rest Fatigue, and Cognitive Fatigue domains), the PedsQL 4.0 Generic Core Scales Young Adult Version, and the SF-8 Health Survey were completed by 423 university students ages 18-25. The PedsQL Multidimensional Fatigue Scale evidenced minimal missing responses, achieved excellent reliability for the Total Scale Score (alpha = 0.90), distinguished between healthy young adults and young adults with chronic health conditions, was significantly correlated with the relevant PedsQL 4.0 Generic Core Scales and the SF-8 standardized scores, and demonstrated a factor-derived structure largely consistent with the a priori conceptual model. The results demonstrate the measurement properties of the PedsQL Multidimensional Fatigue Scale in a convenience sample of young adult university students. The findings suggest that the PedsQL Multidimensional Fatigue Scale may be utilized in the evaluation of fatigue for a broad age range.

Fatigue and multidimensional disease severity in chronic obstructive pulmonary disease.

PubMed

Inal-Ince, Deniz; Savci, Sema; Saglam, Melda; Calik, Ebru; Arikan, Hulya; Bosnak-Guclu, Meral; Vardar-Yagli, Naciye; Coplu, Lutfi

2010-06-30

Fatigue is associated with longitudinal ratings of health in patients with chronic obstructive pulmonary disease (COPD). Although the degree of airflow obstruction is often used to grade disease severity in patients with COPD, multidimensional grading systems have recently been developed. The aim of this study was to investigate the relationship between perceived and actual fatigue level and multidimensional disease severity in patients with COPD. Twenty-two patients with COPD (aged 52-74 years) took part in the study. Multidimensional disease severity was measured using the SAFE and BODE indices. Perceived fatigue was assessed using the Fatigue Severity Scale (FSS) and the Fatigue Impact Scale (FIS). Peripheral muscle endurance was evaluated using the number of sit-ups, squats, and modified push-ups that each patient could do. Thirteen patients (59%) had severe fatigue, and their St George's Respiratory Questionnaire scores were significantly higher (p < 0.05). The SAFE index score was significantly correlated with the number of sit-ups, number of squats, FSS score and FIS score (p < 0.05). The BODE index was significantly associated with the numbers of sit-ups, squats and modified push-ups, and with the FSS and FIS scores (p < 0.05). Peripheral muscle endurance and fatigue perception in patients with COPD was related to multidimensional disease severity measured with both the SAFE and BODE indices. Improvements in perceived and actual fatigue levels may positively affect multidimensional disease severity and health status in COPD patients. Further research is needed to investigate the effects of fatigue perception and exercise training on patients with different stages of multidimensional COPD severity.

Nonparametric functional data estimation applied to ozone data: prediction and extreme value analysis.

PubMed

Quintela-del-Río, Alejandro; Francisco-Fernández, Mario

2011-02-01

The study of extreme values and prediction of ozone data is an important topic of research when dealing with environmental problems. Classical extreme value theory is usually used in air-pollution studies. It consists in fitting a parametric generalised extreme value (GEV) distribution to a data set of extreme values, and using the estimated distribution to compute return levels and other quantities of interest. Here, we propose to estimate these values using nonparametric functional data methods. Functional data analysis is a relatively new statistical methodology that generally deals with data consisting of curves or multi-dimensional variables. In this paper, we use this technique, jointly with nonparametric curve estimation, to provide alternatives to the usual parametric statistical tools. The nonparametric estimators are applied to real samples of maximum ozone values obtained from several monitoring stations belonging to the Automatic Urban and Rural Network (AURN) in the UK. The results show that nonparametric estimators work satisfactorily, outperforming the behaviour of classical parametric estimators. Functional data analysis is also used to predict stratospheric ozone concentrations. We show an application, using the data set of mean monthly ozone concentrations in Arosa, Switzerland, and the results are compared with those obtained by classical time series (ARIMA) analysis. Copyright © 2010 Elsevier Ltd. All rights reserved.

Type Ia Supernovae: Nature's Grandest Thermonuclear Explosions

NASA Astrophysics Data System (ADS)

Woosley, Stan

2003-10-01

When carbon fusion ignites near the center of an accreting white dwarf near the Chandrasekhar Mass, an irreversible series of events is initiated that ultimately leads to the complete disruption of the star and a bright display powered by radioactive decay. Though studied for over 40 years, the details of how this happens remain elusive. Three areas of uncertainty will be discussed: 1) the "ignition" of the bomb: one point or many, central or off-center; 2) the propagation of an unconfined Rayleigh-Taylor unstable burning front - does a robust transition to detonation occur as the front moves into a regime of ``distributed burning"? 3) the generation of the light curve and the Philipps relation. Studies of question 1), equally critical to the other two, suggest a dipolar flow at ignition time and off-center ignition. There may be an uncontrolable degree of chaos in the peak brightness resulting from the ignition process that will affect how SN Ia are used for cosmology. Question 2) is a frontier subject in the chemical combustion community. Results of new multi-dimensional studies will be presented. Question 3) involves atomic physics more than hydrodynamics, but current views suggest that SN Ia light curves should be characterized, at some level, by more than a single parameter.

Ecohydrological perspective of phytogenic organic and inorganic components in Greek lignites: a quantitative reinterpretation

NASA Astrophysics Data System (ADS)

Mulder, Christian; Sakorafa, Vasiliki; Burragato, Francesco; Visscher, Henk

2000-06-01

A consensus about the development of freshwater wetlands in relation to time and space is urgently required. Our study aims to address this issue by providing additional data for a fine-scaled comparison of local depositional settings of Greek mires during the Pliocene and Pleistocene. Lignite profiles exhibit phytogenic organic components (macerals) that have been used to investigate the past peat-forming vegetation structure and their succession series. The organic petrology of lignite samples from the opencast mines of Komanos (Ptolemais) and Choremi (Megalopolis) was achieved to assess the water supply, wetland type, nutrient status and vegetation physiognomy. A holistic approach (a study of ecosystems as complete entities) was carried out for a paleoecological reconstruction of the mires. Huminite, liptinite and inertinite were traced by means of their chemical and morphological differences together with the morphogenic and taphonomic affinities. The problem of combining independent information from different approaches in a multivariate calibration setup has been considered. Linear regression, non-metric multidimensional scaling and one-way analysis of variance tested the occurrence of palynological and petrological proxies. Although the lignite formation and deposition are less related to humid periods than expected, the resulting differences occurring in the reconstructed development stages appear to be related to astronomically forced climate fluctuations.

Characterizing the variability of benthic foraminifera in the northeastern Gulf of Mexico following the Deepwater Horizon event (2010-2012).

PubMed

Schwing, P T; O'Malley, B J; Romero, I C; Martínez-Colón, M; Hastings, D W; Glabach, M A; Hladky, E M; Greco, A; Hollander, D J

2017-01-01

Following the Deepwater Horizon (DWH) event in 2010 subsurface hydrocarbon intrusions (1000-1300 m) and an order of magnitude increase in flocculent hydrocarbon deposition caused increased concentrations of hydrocarbons in continental slope sediments. This study sought to characterize the variability [density, Fisher's alpha (S), equitability (E), Shannon (H)] of benthic foraminifera following the DWH event. A series of sediment cores were collected at two sites in the northeastern Gulf of Mexico from 2010 to 2012. At each site, three cores were utilized for benthic faunal analysis, organic geochemistry, and redox metal chemistry, respectively. The surface intervals (∼0-10 mm) of the sedimentary records collected in December 2010 at DSH08 and February 2011 at PCB06 were characterized by significant decreases in foraminiferal density, S, E, and H, relative to the down-core intervals as well as previous surveys. Non-metric multidimensional scaling (nMDS) analysis suggested that a 3-fold increase in polycyclic aromatic hydrocarbon (PAH) concentration in the surface interval, relative to the down-core interval, was the environmental driver of benthic foraminiferal variability. These records suggested that the benthic foraminiferal recovery time, following an event such as the DWH, was on the order of 1-2 years.

Separation of the atmospheric variability into non-Gaussian multidimensional sources by projection pursuit techniques

NASA Astrophysics Data System (ADS)

Pires, Carlos A. L.; Ribeiro, Andreia F. S.

2017-02-01

We develop an expansion of space-distributed time series into statistically independent uncorrelated subspaces (statistical sources) of low-dimension and exhibiting enhanced non-Gaussian probability distributions with geometrically simple chosen shapes (projection pursuit rationale). The method relies upon a generalization of the principal component analysis that is optimal for Gaussian mixed signals and of the independent component analysis (ICA), optimized to split non-Gaussian scalar sources. The proposed method, supported by information theory concepts and methods, is the independent subspace analysis (ISA) that looks for multi-dimensional, intrinsically synergetic subspaces such as dyads (2D) and triads (3D), not separable by ICA. Basically, we optimize rotated variables maximizing certain nonlinear correlations (contrast functions) coming from the non-Gaussianity of the joint distribution. As a by-product, it provides nonlinear variable changes `unfolding' the subspaces into nearly Gaussian scalars of easier post-processing. Moreover, the new variables still work as nonlinear data exploratory indices of the non-Gaussian variability of the analysed climatic and geophysical fields. The method (ISA, followed by nonlinear unfolding) is tested into three datasets. The first one comes from the Lorenz'63 three-dimensional chaotic model, showing a clear separation into a non-Gaussian dyad plus an independent scalar. The second one is a mixture of propagating waves of random correlated phases in which the emergence of triadic wave resonances imprints a statistical signature in terms of a non-Gaussian non-separable triad. Finally the method is applied to the monthly variability of a high-dimensional quasi-geostrophic (QG) atmospheric model, applied to the Northern Hemispheric winter. We find that quite enhanced non-Gaussian dyads of parabolic shape, perform much better than the unrotated variables in which concerns the separation of the four model's centroid regimes (positive and negative phases of the Arctic Oscillation and of the North Atlantic Oscillation). Triads are also likely in the QG model but of weaker expression than dyads due to the imposed shape and dimension. The study emphasizes the existence of nonlinear dyadic and triadic nonlinear teleconnections.

Regenerating time series from ordinal networks.

PubMed

McCullough, Michael; Sakellariou, Konstantinos; Stemler, Thomas; Small, Michael

2017-03-01

Recently proposed ordinal networks not only afford novel methods of nonlinear time series analysis but also constitute stochastic approximations of the deterministic flow time series from which the network models are constructed. In this paper, we construct ordinal networks from discrete sampled continuous chaotic time series and then regenerate new time series by taking random walks on the ordinal network. We then investigate the extent to which the dynamics of the original time series are encoded in the ordinal networks and retained through the process of regenerating new time series by using several distinct quantitative approaches. First, we use recurrence quantification analysis on traditional recurrence plots and order recurrence plots to compare the temporal structure of the original time series with random walk surrogate time series. Second, we estimate the largest Lyapunov exponent from the original time series and investigate the extent to which this invariant measure can be estimated from the surrogate time series. Finally, estimates of correlation dimension are computed to compare the topological properties of the original and surrogate time series dynamics. Our findings show that ordinal networks constructed from univariate time series data constitute stochastic models which approximate important dynamical properties of the original systems.

Regenerating time series from ordinal networks

NASA Astrophysics Data System (ADS)

McCullough, Michael; Sakellariou, Konstantinos; Stemler, Thomas; Small, Michael

2017-03-01

Recently proposed ordinal networks not only afford novel methods of nonlinear time series analysis but also constitute stochastic approximations of the deterministic flow time series from which the network models are constructed. In this paper, we construct ordinal networks from discrete sampled continuous chaotic time series and then regenerate new time series by taking random walks on the ordinal network. We then investigate the extent to which the dynamics of the original time series are encoded in the ordinal networks and retained through the process of regenerating new time series by using several distinct quantitative approaches. First, we use recurrence quantification analysis on traditional recurrence plots and order recurrence plots to compare the temporal structure of the original time series with random walk surrogate time series. Second, we estimate the largest Lyapunov exponent from the original time series and investigate the extent to which this invariant measure can be estimated from the surrogate time series. Finally, estimates of correlation dimension are computed to compare the topological properties of the original and surrogate time series dynamics. Our findings show that ordinal networks constructed from univariate time series data constitute stochastic models which approximate important dynamical properties of the original systems.

GPS Position Time Series @ JPL

NASA Technical Reports Server (NTRS)

Owen, Susan; Moore, Angelyn; Kedar, Sharon; Liu, Zhen; Webb, Frank; Heflin, Mike; Desai, Shailen

2013-01-01

Different flavors of GPS time series analysis at JPL - Use same GPS Precise Point Positioning Analysis raw time series - Variations in time series analysis/post-processing driven by different users. center dot JPL Global Time Series/Velocities - researchers studying reference frame, combining with VLBI/SLR/DORIS center dot JPL/SOPAC Combined Time Series/Velocities - crustal deformation for tectonic, volcanic, ground water studies center dot ARIA Time Series/Coseismic Data Products - Hazard monitoring and response focused center dot ARIA data system designed to integrate GPS and InSAR - GPS tropospheric delay used for correcting InSAR - Caltech's GIANT time series analysis uses GPS to correct orbital errors in InSAR - Zhen Liu's talking tomorrow on InSAR Time Series analysis

Modeling change from large-scale high-dimensional spatio-temporal array data

NASA Astrophysics Data System (ADS)

Lu, Meng; Pebesma, Edzer

2014-05-01

The massive data that come from Earth observation satellite and other sensors provide significant information for modeling global change. At the same time, the high dimensionality of the data has brought challenges in data acquisition, management, effective querying and processing. In addition, the output of earth system modeling tends to be data intensive and needs methodologies for storing, validation, analyzing and visualization, e.g. as maps. An important proportion of earth system observations and simulated data can be represented as multi-dimensional array data, which has received increasingly attention in big data management and spatial-temporal analysis. Study cases will be developed in natural science such as climate change, hydrological modeling, sediment dynamics, from which the addressing of big data problems is necessary. Multi-dimensional array-based database management and analytics system such as Rasdaman, SciDB, and R will be applied to these cases. From these studies will hope to learn the strengths and weaknesses of these systems, how they might work together or how semantics of array operations differ, through addressing the problems associated with big data. Research questions include: • How can we reduce dimensions spatially and temporally, or thematically? • How can we extend existing GIS functions to work on multidimensional arrays? • How can we combine data sets of different dimensionality or different resolutions? • Can map algebra be extended to an intelligible array algebra? • What are effective semantics for array programming of dynamic data driven applications? • In which sense are space and time special, as dimensions, compared to other properties? • How can we make the analysis of multi-spectral, multi-temporal and multi-sensor earth observation data easy?

A quick aphasia battery for efficient, reliable, and multidimensional assessment of language function.

PubMed

Wilson, Stephen M; Eriksson, Dana K; Schneck, Sarah M; Lucanie, Jillian M

2018-01-01

This paper describes a quick aphasia battery (QAB) that aims to provide a reliable and multidimensional assessment of language function in about a quarter of an hour, bridging the gap between comprehensive batteries that are time-consuming to administer, and rapid screening instruments that provide limited detail regarding individual profiles of deficits. The QAB is made up of eight subtests, each comprising sets of items that probe different language domains, vary in difficulty, and are scored with a graded system to maximize the informativeness of each item. From the eight subtests, eight summary measures are derived, which constitute a multidimensional profile of language function, quantifying strengths and weaknesses across core language domains. The QAB was administered to 28 individuals with acute stroke and aphasia, 25 individuals with acute stroke but no aphasia, 16 individuals with chronic post-stroke aphasia, and 14 healthy controls. The patients with chronic post-stroke aphasia were tested 3 times each and scored independently by 2 raters to establish test-retest and inter-rater reliability. The Western Aphasia Battery (WAB) was also administered to these patients to assess concurrent validity. We found that all QAB summary measures were sensitive to aphasic deficits in the two groups with aphasia. All measures showed good or excellent test-retest reliability (overall summary measure: intraclass correlation coefficient (ICC) = 0.98), and excellent inter-rater reliability (overall summary measure: ICC = 0.99). Sensitivity and specificity for diagnosis of aphasia (relative to clinical impression) were 0.91 and 0.95 respectively. All QAB measures were highly correlated with corresponding WAB measures where available. Individual patients showed distinct profiles of spared and impaired function across different language domains. In sum, the QAB efficiently and reliably characterized individual profiles of language deficits.

A quick aphasia battery for efficient, reliable, and multidimensional assessment of language function

PubMed Central

Eriksson, Dana K.; Schneck, Sarah M.; Lucanie, Jillian M.

2018-01-01

This paper describes a quick aphasia battery (QAB) that aims to provide a reliable and multidimensional assessment of language function in about a quarter of an hour, bridging the gap between comprehensive batteries that are time-consuming to administer, and rapid screening instruments that provide limited detail regarding individual profiles of deficits. The QAB is made up of eight subtests, each comprising sets of items that probe different language domains, vary in difficulty, and are scored with a graded system to maximize the informativeness of each item. From the eight subtests, eight summary measures are derived, which constitute a multidimensional profile of language function, quantifying strengths and weaknesses across core language domains. The QAB was administered to 28 individuals with acute stroke and aphasia, 25 individuals with acute stroke but no aphasia, 16 individuals with chronic post-stroke aphasia, and 14 healthy controls. The patients with chronic post-stroke aphasia were tested 3 times each and scored independently by 2 raters to establish test-retest and inter-rater reliability. The Western Aphasia Battery (WAB) was also administered to these patients to assess concurrent validity. We found that all QAB summary measures were sensitive to aphasic deficits in the two groups with aphasia. All measures showed good or excellent test-retest reliability (overall summary measure: intraclass correlation coefficient (ICC) = 0.98), and excellent inter-rater reliability (overall summary measure: ICC = 0.99). Sensitivity and specificity for diagnosis of aphasia (relative to clinical impression) were 0.91 and 0.95 respectively. All QAB measures were highly correlated with corresponding WAB measures where available. Individual patients showed distinct profiles of spared and impaired function across different language domains. In sum, the QAB efficiently and reliably characterized individual profiles of language deficits. PMID:29425241

Central Schemes for Multi-Dimensional Hamilton-Jacobi Equations

NASA Technical Reports Server (NTRS)

Bryson, Steve; Levy, Doron; Biegel, Bryan (Technical Monitor)

2002-01-01

We present new, efficient central schemes for multi-dimensional Hamilton-Jacobi equations. These non-oscillatory, non-staggered schemes are first- and second-order accurate and are designed to scale well with an increasing dimension. Efficiency is obtained by carefully choosing the location of the evolution points and by using a one-dimensional projection step. First-and second-order accuracy is verified for a variety of multi-dimensional, convex and non-convex problems.

Associations between modifiable lifestyle factors and multidimensional cognitive health among community-dwelling old adults: stratified by educational level.

PubMed

Yuan, Manqiong; Chen, Jia; Han, Yaofeng; Wei, Xingliang; Ye, Zirong; Zhang, Liangwen; Hong, Y Alicia; Fang, Ya

2018-02-15

Cognition is multidimensional, and each domain plays a unique and crucial part in successful daily life engagement. However, less attention has been paid to multi-domain cognitive health for the elderly, and the role of lifestyle factors in each domain remains unclear. We conducted a cross-sectional study of 3,230 older adults aged 60+ years in Xiamen, China, in 2016. The Montreal Cognitive Assessment (MoCA) was used to measure general cognition and six specific sub-domains. To account for educational effects, we adjusted the MoCA score and divided respondents into three education-specific groups (low, moderate, and high education groups with ≤5, 6~8, and ≥9 years of education, respectively). A series of proportional odds models were used to detect the associations between two categories of lifestyle factors - substance abuse (cigarette and alcohol) and leisure activity (TV watching, reading, smartphone use, social activity, and exercise) - and general cognition and the six sub-domains in those three groups. Among the 3,230 respondents, 2,617 eligible participants were included with a mean age of 69.05 ± 7.07 years. Previous or current smoking/drinking was not associated with MoCA scores in the whole population, but unexpectedly, the ex-smokers in the low education group performed better in general cognition (OR = 2.22) and attention (OR = 2.05) than their never-smoking counterparts. Modest TV watching, reading, and smartphone use also contributed to better cognition among elderly participants in the low education group. For the highly educated elderly, comparatively longer reading (>3.5 hours/week) was inversely associated with general cognition (OR = 0.53), memory (OR = 0.59), and language (OR = 0.54), while adequate exercise (5~7 days/week) was positively related to these factors with OR = 1.48, OR = 1.49, and OR = 1.53, respectively. For the moderately educated elderly, only modest reading was significantly beneficial. Lifestyle factors play different roles in multidimensional cognitive health in different educational groups, indicating that individual intervention strategies should be designed according to specific educational groups and different cognitive sub-domains.

An analysis of the processing requirements of a complex perceptual-motor task

NASA Technical Reports Server (NTRS)

Kramer, A. F.; Wickens, C. D.; Donchin, E.

1983-01-01

Current concerns in the assessment of mental workload are discussed, and the event-related brain potential (ERP) is introduced as a promising mental-workload index. Subjects participated in a series of studies in which they were required to perform a target acquisition task while also covertly counting either auditory or visual probes. The effects of several task-difficulty manipulations on the P300 component of the ERP elicited by the counted stimulus probes were investigated. With sufficiently practiced subjects the amplitude of the P300 was found to decrease with increases in task difficulty. The second experiment also provided evidence that the P300 is selectively sensitive to task-relevant attributes. A third experiment demonstrated a convergence in the amplitude of the P300s elicited in the simple and difficult versions of the tracking task. The amplitude of the P300 was also found to covary with the measures of tracking performance. The results of the series of three experiments illustrate the sensitivity of the P300 to the processing requirements of a complex target acquisition task. The findings are discussed in terms of the multidimensional nature of processing resources.

Divergence-free MHD on unstructured meshes using high order finite volume schemes based on multidimensional Riemann solvers

NASA Astrophysics Data System (ADS)

Balsara, Dinshaw S.; Dumbser, Michael

2015-10-01

Several advances have been reported in the recent literature on divergence-free finite volume schemes for Magnetohydrodynamics (MHD). Almost all of these advances are restricted to structured meshes. To retain full geometric versatility, however, it is also very important to make analogous advances in divergence-free schemes for MHD on unstructured meshes. Such schemes utilize a staggered Yee-type mesh, where all hydrodynamic quantities (mass, momentum and energy density) are cell-centered, while the magnetic fields are face-centered and the electric fields, which are so useful for the time update of the magnetic field, are centered at the edges. Three important advances are brought together in this paper in order to make it possible to have high order accurate finite volume schemes for the MHD equations on unstructured meshes. First, it is shown that a divergence-free WENO reconstruction of the magnetic field can be developed for unstructured meshes in two and three space dimensions using a classical cell-centered WENO algorithm, without the need to do a WENO reconstruction for the magnetic field on the faces. This is achieved via a novel constrained L2-projection operator that is used in each time step as a postprocessor of the cell-centered WENO reconstruction so that the magnetic field becomes locally and globally divergence free. Second, it is shown that recently-developed genuinely multidimensional Riemann solvers (called MuSIC Riemann solvers) can be used on unstructured meshes to obtain a multidimensionally upwinded representation of the electric field at each edge. Third, the above two innovations work well together with a high order accurate one-step ADER time stepping strategy, which requires the divergence-free nonlinear WENO reconstruction procedure to be carried out only once per time step. The resulting divergence-free ADER-WENO schemes with MuSIC Riemann solvers give us an efficient and easily-implemented strategy for divergence-free MHD on unstructured meshes. Several stringent two- and three-dimensional problems are shown to work well with the methods presented here.

Reducing acquisition times in multidimensional NMR with a time-optimized Fourier encoding algorithm

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

Zhang, Zhiyong; Department of Electronic Science, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, Xiamen University, Xiamen, Fujian 361005; Smith, Pieter E. S.

Speeding up the acquisition of multidimensional nuclear magnetic resonance (NMR) spectra is an important topic in contemporary NMR, with central roles in high-throughput investigations and analyses of marginally stable samples. A variety of fast NMR techniques have been developed, including methods based on non-uniform sampling and Hadamard encoding, that overcome the long sampling times inherent to schemes based on fast-Fourier-transform (FFT) methods. Here, we explore the potential of an alternative fast acquisition method that leverages a priori knowledge, to tailor polychromatic pulses and customized time delays for an efficient Fourier encoding of the indirect domain of an NMR experiment. Bymore » porting the encoding of the indirect-domain to the excitation process, this strategy avoids potential artifacts associated with non-uniform sampling schemes and uses a minimum number of scans equal to the number of resonances present in the indirect dimension. An added convenience is afforded by the fact that a usual 2D FFT can be used to process the generated data. Acquisitions of 2D heteronuclear correlation NMR spectra on quinine and on the anti-inflammatory drug isobutyl propionic phenolic acid illustrate the new method's performance. This method can be readily automated to deal with complex samples such as those occurring in metabolomics, in in-cell as well as in in vivo NMR applications, where speed and temporal stability are often primary concerns.« less

A mass, momentum, and energy conserving, fully implicit, scalable algorithm for the multi-dimensional, multi-species Rosenbluth-Fokker-Planck equation

NASA Astrophysics Data System (ADS)

Taitano, W. T.; Chacón, L.; Simakov, A. N.; Molvig, K.

2015-09-01

In this study, we demonstrate a fully implicit algorithm for the multi-species, multidimensional Rosenbluth-Fokker-Planck equation which is exactly mass-, momentum-, and energy-conserving, and which preserves positivity. Unlike most earlier studies, we base our development on the Rosenbluth (rather than Landau) form of the Fokker-Planck collision operator, which reduces complexity while allowing for an optimal fully implicit treatment. Our discrete conservation strategy employs nonlinear constraints that force the continuum symmetries of the collision operator to be satisfied upon discretization. We converge the resulting nonlinear system iteratively using Jacobian-free Newton-Krylov methods, effectively preconditioned with multigrid methods for efficiency. Single- and multi-species numerical examples demonstrate the advertised accuracy properties of the scheme, and the superior algorithmic performance of our approach. In particular, the discretization approach is numerically shown to be second-order accurate in time and velocity space and to exhibit manifestly positive entropy production. That is, H-theorem behavior is indicated for all the examples we have tested. The solution approach is demonstrated to scale optimally with respect to grid refinement (with CPU time growing linearly with the number of mesh points), and timestep (showing very weak dependence of CPU time with time-step size). As a result, the proposed algorithm delivers several orders-of-magnitude speedup vs. explicit algorithms.

A multidimensional approach to examine student interdisciplinary learning in science and engineering in higher education

NASA Astrophysics Data System (ADS)

Spelt, Elisabeth Jacoba Hendrika; Luning, Pieternelleke Arianne; van Boekel, Martinus A. J. S.; Mulder, Martin

2017-11-01

Preparing science and engineering students to work in interdisciplinary teams necessitates research on teaching and learning of interdisciplinary thinking. A multidimensional approach was taken to examine student interdisciplinary learning in a master course on food quality management. The collected 615 student experiences were analysed for the cognitive, emotional, and social learning dimensions using the learning theory of Illeris. Of these 615 experiences, the analysis showed that students reported 214, 194, and 207 times on, respectively, the emotional, the cognitive, and the social dimension. Per learning dimension, key learning experiences featuring interdisciplinary learning were identified such as 'frustrations in selecting and matching disciplinary knowledge to complex problems' (emotional), 'understanding how to apply theoretical models or concepts to real-world situations' (cognitive), and 'socially engaging with peers to recognise similarities in perceptions and experiences' (social). Furthermore, the results showed that students appreciated the cognitive dimension relatively more than the emotional and social dimensions.

Compact stochastic models for multidimensional quasiballistic thermal transport

NASA Astrophysics Data System (ADS)

Vermeersch, Bjorn

2016-11-01

The Boltzmann transport equation (BTE) has proven indispensable in elucidating quasiballistic heat dynamics. The experimental observations of nondiffusive thermal transients, however, are interpreted almost exclusively through purely diffusive formalisms that merely extract "effective" Fourier conductivities. Here, we build upon stochastic transport theory to provide a characterisation framework that blends the rich physics contained within the BTE solutions with the convenience of conventional analyses. The multidimensional phonon dynamics are described in terms of an isotropic Poissonian flight process with a rigorous Fourier-Laplace single pulse response P (ξ → ,s )=1 /[s +ψ(∥ ξ → ∥ )] . The spatial propagator ψ(∥ ξ → ∥ ) , unlike commonly reconstructed mean free path spectra κΣ(Λ) , serves as a genuine thermal blueprint of the medium that can be identified in a compact form directly from the raw measurement signals. Practical illustrations for transient thermal grating and time domain thermoreflectance experiments on respectively GaAs and InGaAs are provided.