Molecular Dynamics/Order Parameter eXtrapolation (MD/OPX) for Bionanosystem Simulations
Miao, Yinglong; Ortoleva, Peter J.
2009-01-01
A multiscale approach, Molecular Dynamics/Order Parameter eXtrapolation (MD/OPX), to the all-atom simulation of large bionanosystems is presented. The approach starts with the introduction of a set of order parameters (OPs) automatically generated with orthogonal polynomials to characterize the nanoscale features of bionanosystems. The OPs are shown to evolve slowly via Newton’s equations and the all-atom multiscale analysis (AMA) developed earlier1 demonstrates the existence of their stochas...
Multiscale principal component analysis
Principal component analysis (PCA) is an important tool in exploring data. The conventional approach to PCA leads to a solution which favours the structures with large variances. This is sensitive to outliers and could obfuscate interesting underlying structures. One of the equivalent definitions of PCA is that it seeks the subspaces that maximize the sum of squared pairwise distances between data projections. This definition opens up more flexibility in the analysis of principal components which is useful in enhancing PCA. In this paper we introduce scales into PCA by maximizing only the sum of pairwise distances between projections for pairs of datapoints with distances within a chosen interval of values [l,u]. The resulting principal component decompositions in Multiscale PCA depend on point (l,u) on the plane and for each point we define projectors onto principal components. Cluster analysis of these projectors reveals the structures in the data at various scales. Each structure is described by the eigenvectors at the medoid point of the cluster which represent the structure. We also use the distortion of projections as a criterion for choosing an appropriate scale especially for data with outliers. This method was tested on both artificial distribution of data and real data. For data with multiscale structures, the method was able to reveal the different structures of the data and also to reduce the effect of outliers in the principal component analysis
Multiscale Signal Analysis and Modeling
Zayed, Ahmed
2013-01-01
Multiscale Signal Analysis and Modeling presents recent advances in multiscale analysis and modeling using wavelets and other systems. This book also presents applications in digital signal processing using sampling theory and techniques from various function spaces, filter design, feature extraction and classification, signal and image representation/transmission, coding, nonparametric statistical signal processing, and statistical learning theory. This book also: Discusses recently developed signal modeling techniques, such as the multiscale method for complex time series modeling, multiscale positive density estimations, Bayesian Shrinkage Strategies, and algorithms for data adaptive statistics Introduces new sampling algorithms for multidimensional signal processing Provides comprehensive coverage of wavelets with presentations on waveform design and modeling, wavelet analysis of ECG signals and wavelet filters Reviews features extraction and classification algorithms for multiscale signal and image proce...
Multiscale analysis of neural spike trains.
Ramezan, Reza; Marriott, Paul; Chenouri, Shojaeddin
2014-01-30
This paper studies the multiscale analysis of neural spike trains, through both graphical and Poisson process approaches. We introduce the interspike interval plot, which simultaneously visualizes characteristics of neural spiking activity at different time scales. Using an inhomogeneous Poisson process framework, we discuss multiscale estimates of the intensity functions of spike trains. We also introduce the windowing effect for two multiscale methods. Using quasi-likelihood, we develop bootstrap confidence intervals for the multiscale intensity function. We provide a cross-validation scheme, to choose the tuning parameters, and study its unbiasedness. Studying the relationship between the spike rate and the stimulus signal, we observe that adjusting for the first spike latency is important in cross-validation. We show, through examples, that the correlation between spike trains and spike count variability can be multiscale phenomena. Furthermore, we address the modeling of the periodicity of the spike trains caused by a stimulus signal or by brain rhythms. Within the multiscale framework, we introduce intensity functions for spike trains with multiplicative and additive periodic components. Analyzing a dataset from the retinogeniculate synapse, we compare the fit of these models with the Bayesian adaptive regression splines method and discuss the limitations of the methodology. Computational efficiency, which is usually a challenge in the analysis of spike trains, is one of the highlights of these new models. In an example, we show that the reconstruction quality of a complex intensity function demonstrates the ability of the multiscale methodology to crack the neural code. PMID:23996238
Numerical Analysis of Multiscale Computations
Engquist, Björn; Tsai, Yen-Hsi R
2012-01-01
This book is a snapshot of current research in multiscale modeling, computations and applications. It covers fundamental mathematical theory, numerical algorithms as well as practical computational advice for analysing single and multiphysics models containing a variety of scales in time and space. Complex fluids, porous media flow and oscillatory dynamical systems are treated in some extra depth, as well as tools like analytical and numerical homogenization, and fast multipole method.
[Multiscale entropy analysis of electrocardiogram].
Wang, Jun; Ning, Xinbao; Li, Jin; Ma, Qianli; Xu, Yinlin; Bian, Chunhua
2007-10-01
Using the algorithm proposed by Costa M, et al., we studied the multiscale entropy (MSE) of electrocardiogram. The sample entropy (SampEn) of the healthy subjects was found to be higher than that of the subjects with coronary heart disease or myocardial infarction. The healthy subjects' complexity was found to be the highest. The SampEn of the subjects with coronary heart disease was noted to be only slightly higher than that of the subjects with myocardial infarction. These findings show that the complexity of the subjects with coronary heart disease or myocardial infarction is distinctly lower than the complexity of the healthy ones, and the subjects suffereing from coronary heart disease are liable to the onset of myocardial infarction. PMID:18027679
Multiscale analysis and computation for flows in heterogeneous media
Efendiev, Yalchin [Texas A & M Univ., College Station, TX (United States); Hou, T. Y. [California Inst. of Technology (CalTech), Pasadena, CA (United States); Durlofsky, L. J. [Stanford Univ., CA (United States); Tchelepi, H. [Stanford Univ., CA (United States)
2016-08-04
Our work in this project is aimed at making fundamental advances in multiscale methods for flow and transport in highly heterogeneous porous media. The main thrust of this research is to develop a systematic multiscale analysis and efficient coarse-scale models that can capture global effects and extend existing multiscale approaches to problems with additional physics and uncertainties. A key emphasis is on problems without an apparent scale separation. Multiscale solution methods are currently under active investigation for the simulation of subsurface flow in heterogeneous formations. These procedures capture the effects of fine-scale permeability variations through the calculation of specialized coarse-scale basis functions. Most of the multiscale techniques presented to date employ localization approximations in the calculation of these basis functions. For some highly correlated (e.g., channelized) formations, however, global effects are important and these may need to be incorporated into the multiscale basis functions. Other challenging issues facing multiscale simulations are the extension of existing multiscale techniques to problems with additional physics, such as compressibility, capillary effects, etc. In our project, we explore the improvement of multiscale methods through the incorporation of additional (single-phase flow) information and the development of a general multiscale framework for flows in the presence of uncertainties, compressible flow and heterogeneous transport, and geomechanics. We have considered (1) adaptive local-global multiscale methods, (2) multiscale methods for the transport equation, (3) operator-based multiscale methods and solvers, (4) multiscale methods in the presence of uncertainties and applications, (5) multiscale finite element methods for high contrast porous media and their generalizations, and (6) multiscale methods for geomechanics. Below, we present a brief overview of each of these contributions.
Multiscale Entropy Analysis on Human Operating Behavior
Junshan Pan; Hanping Hu; Xiang Liu; Yong Hu
2015-01-01
By exploiting the statistical analysis method, human dynamics provides new insights to the research of human behavior. In this paper, we analyze the characteristics of the computer operating behavior through a modified multiscale entropy algorithm with both the interval time series and the number series of individuals’ operating behavior been investigated. We also discuss the activity of individuals’ behavior from the three groups denoted as the retiree group, the student group and the worker...
Time Series Analysis Using Composite Multiscale Entropy
Kung-Yen Lee
2013-03-01
Full Text Available Multiscale entropy (MSE was recently developed to evaluate the complexity of time series over different time scales. Although the MSE algorithm has been successfully applied in a number of different fields, it encounters a problem in that the statistical reliability of the sample entropy (SampEn of a coarse-grained series is reduced as a time scale factor is increased. Therefore, in this paper, the concept of a composite multiscale entropy (CMSE is introduced to overcome this difficulty. Simulation results on both white noise and 1/f noise show that the CMSE provides higher entropy reliablity than the MSE approach for large time scale factors. On real data analysis, both the MSE and CMSE are applied to extract features from fault bearing vibration signals. Experimental results demonstrate that the proposed CMSE-based feature extractor provides higher separability than the MSE-based feature extractor.
Multiscale statistical analysis of coronal solar activity
Gamborino, Diana; Martinell, Julio J
2016-01-01
Multi-filter images from the solar corona are used to obtain temperature maps which are analyzed using techniques based on proper orthogonal decomposition (POD) in order to extract dynamical and structural information at various scales. Exploring active regions before and after a solar flare and comparing them with quiet regions we show that the multiscale behavior presents distinct statistical properties for each case that can be used to characterize the level of activity in a region. Information about the nature of heat transport is also be extracted from the analysis.
Multivariate multiscale entropy for brain consciousness analysis.
Ahmed, Mosabber Uddin; Li, Ling; Cao, Jianting; Mandic, Danilo P
2011-01-01
The recently introduced multiscale entropy (MSE) method accounts for long range correlations over multiple time scales and can therefore reveal the complexity of biological signals. The existing MSE algorithm deals with scalar time series whereas multivariate time series are common in experimental and biological systems. To that cause, in this paper the MSE method is extended to the multivariate case. This allows us to gain a greater insight into the complexity of the underlying signal generating system, producing multifaceted and more robust estimates than standard single channel MSE. Simulations on both synthetic data and brain consciousness analysis support the approach. PMID:22254434
Multiscale entropy analysis of electroseismic time series
L. Guzmán-Vargas; Ramírez-Rojas, A.; Angulo-Brown, F.
2008-01-01
In this work we use the multiscale entropy method to analyse the variability of geo-electric time series monitored in two sites located in Mexico. In our analysis we consider a period of time from January 1995 to December 1995. We systematically calculate the sample entropy of electroseismic time series. Important differences in the entropy profile for several time scales are observed in records from the same station. In particular, a complex behaviour is observed in the vicinity of a
The multiscale analysis between stock market time series
Shi, Wenbin; Shang, Pengjian
2015-11-01
This paper is devoted to multiscale cross-correlation analysis on stock market time series, where multiscale DCCA cross-correlation coefficient as well as multiscale cross-sample entropy (MSCE) is applied. Multiscale DCCA cross-correlation coefficient is a realization of DCCA cross-correlation coefficient on multiple scales. The results of this method present a good scaling characterization. More significantly, this method is able to group stock markets by areas. Compared to multiscale DCCA cross-correlation coefficient, MSCE presents a more remarkable scaling characterization and the value of each log return of financial time series decreases with the increasing of scale factor. But the results of grouping is not as good as multiscale DCCA cross-correlation coefficient.
Landform Mapping Using Multiscale Topographic Analysis
Bliss, N. B.
2008-12-01
Many ecological and agricultural processes depend on topographic relationships. Topography strongly influences microclimate, the types and productivity of plants, biomass, evapotranspiration rates, carbon storage rates, and fire fuel accumulation. These factors in turn influence the water cycle, stream flow, water quality, and soil formation. Most previous topographic analysis methods have focused on the elevation of a given grid cell (pixel) and very localized measures of slope and aspect (e.g., computed from elevation in a 3x3 window). Some measures have moved beyond a strictly local relationship, such as the compound topographic index, which can be used as a soil wetness index. I introduce a new method of multiscale topographic analysis which can be applied to digital elevation model (DEM) data of any resolution. The method calculates slope and curvature (change of slope) of the land not only in relation to adjacent grid cells but also for much larger distances downstream. The algorithm uses a flow direction grid to create a synthetic stream network as a set of connected line segments (a vector dataset). The multiscale measures are stored on a node attribute table, where the nodes are the endpoints of line segments connecting the original DEM grid cells. A pointer is computed for directly accessing data for nodes at selected distances down the stream network. Baseline distances are selected by counting cells down the flow path by each power of two (1, 2, 4, 8, ... cells downstream). Slope and curvature measures are defined for each of these baselines and are queried to distinguish multiscale topographic characteristics. Several applications of these methods have been tested. A floodplain measure identifies areas that are relatively low on the landscape, even as elevation changes while moving from plains into hills or mountains (study area: South Dakota). The landscape may be partitioned to provide zones for ecological analysis, including selection of field
Multiscale entropy analysis of biological signals.
Costa, Madalena; Goldberger, Ary L; Peng, C-K
2005-02-01
Traditional approaches to measuring the complexity of biological signals fail to account for the multiple time scales inherent in such time series. These algorithms have yielded contradictory findings when applied to real-world datasets obtained in health and disease states. We describe in detail the basis and implementation of the multiscale entropy (MSE) method. We extend and elaborate previous findings showing its applicability to the fluctuations of the human heartbeat under physiologic and pathologic conditions. The method consistently indicates a loss of complexity with aging, with an erratic cardiac arrhythmia (atrial fibrillation), and with a life-threatening syndrome (congestive heart failure). Further, these different conditions have distinct MSE curve profiles, suggesting diagnostic uses. The results support a general "complexity-loss" theory of aging and disease. We also apply the method to the analysis of coding and noncoding DNA sequences and find that the latter have higher multiscale entropy, consistent with the emerging view that so-called "junk DNA" sequences contain important biological information. PMID:15783351
Multiscale entropy analysis of electroseismic time series
L. Guzmán-Vargas
2008-08-01
Full Text Available In this work we use the multiscale entropy method to analyse the variability of geo-electric time series monitored in two sites located in Mexico. In our analysis we consider a period of time from January 1995 to December 1995. We systematically calculate the sample entropy of electroseismic time series. Important differences in the entropy profile for several time scales are observed in records from the same station. In particular, a complex behaviour is observed in the vicinity of a M=7.4 EQ occurred on 14 September 1995. Besides, we also compare the changes in the entropy of the original data with their corresponding shuffled version.
Multiscale Entropy Analysis on Human Operating Behavior
Junshan Pan
2015-12-01
Full Text Available By exploiting the statistical analysis method, human dynamics provides new insights to the research of human behavior. In this paper, we analyze the characteristics of the computer operating behavior through a modified multiscale entropy algorithm with both the interval time series and the number series of individuals’ operating behavior been investigated. We also discuss the activity of individuals’ behavior from the three groups denoted as the retiree group, the student group and the worker group based on the nature of their jobs. We find that the operating behavior of the retiree group exhibits more complex dynamics than the other two groups and further present a reasonable explanation for this phenomenon. Our findings offer new insights for the further understanding of individual behavior at different time scales.
Time Series Analysis Using Composite Multiscale Entropy
Kung-Yen Lee; Chun-Chieh Wang; Shiou-Gwo Lin; Chiu-Wen Wu; Shuen-De Wu
2013-01-01
Multiscale entropy (MSE) was recently developed to evaluate the complexity of time series over different time scales. Although the MSE algorithm has been successfully applied in a number of different fields, it encounters a problem in that the statistical reliability of the sample entropy (SampEn) of a coarse-grained series is reduced as a time scale factor is increased. Therefore, in this paper, the concept of a composite multiscale entropy (CMSE) is introduced to overcome this difficulty. S...
Multiscale Analysis of Heart Rate Dynamics: Entropy and Time Irreversibility Measures
Costa, Madalena D.; Peng, Chung-Kang; Ary L Goldberger
2008-01-01
Cardiovascular signals are largely analyzed using traditional time and frequency domain measures. However, such measures fail to account for important properties related to multiscale organization and nonequilibrium dynamics. The complementary role of conventional signal analysis methods and emerging multiscale techniques, is, therefore, an important frontier area of investigation. The key finding of this presentation is that two recently developed multiscale computational tools— multiscale e...
Multiscale Analysis and Optimisation of Photosynthetic Solar Energy Systems
Ringsmuth, Andrew K
2014-01-01
This work asks how light harvesting in photosynthetic systems can be optimised for economically scalable, sustainable energy production. Hierarchy theory is introduced as a system-analysis and optimisation tool better able to handle multiscale, multiprocess complexities in photosynthetic energetics compared with standard linear-process analysis. Within this framework, new insights are given into relationships between composition, structure and energetics at the scale of the thylakoid membrane, and also into how components at different scales cooperate under functional objectives of the whole photosynthetic system. Combining these reductionistic and holistic analyses creates a platform for modelling multiscale-optimal, idealised photosynthetic systems in silico.
Entropic Approach to Multiscale Clustering Analysis
Antonio Insolia; Manlio De Domenico
2012-01-01
Recently, a novel method has been introduced to estimate the statistical significance of clustering in the direction distribution of objects. The method involves a multiscale procedure, based on the Kullback–Leibler divergence and the Gumbel statistics of extreme values, providing high discrimination power, even in presence of strong background isotropic contamination. It is shown that the method is: (i) semi-analytical, drastically reducing computation time; (ii) very sensitive to small, med...
Multiscale power analysis for heart rate variability
Zeng, Peng; Liu, Hongxing; Ni, Huangjing; Zhou, Jing; Xia, Lan; Ning, Xinbao
2015-06-01
We first introduce multiscale power (MSP) method to assess the power distribution of physiological signals on multiple time scales. Simulation on synthetic data and experiments on heart rate variability (HRV) are tested to support the approach. Results show that both physical and psychological changes influence power distribution significantly. A quantitative parameter, termed power difference (PD), is introduced to evaluate the degree of power distribution alteration. We find that dynamical correlation of HRV will be destroyed completely when PD>0.7.
Multiscale Analysis of Information Dynamics for Linear Multivariate Processes
Faes, Luca; Stramaglia, Sebastiano; Nollo, Giandomenico; Stramaglia, Sebastiano
2016-01-01
In the study of complex physical and physiological systems represented by multivariate time series, an issue of great interest is the description of the system dynamics over a range of different temporal scales. While information-theoretic approaches to the multiscale analysis of complex dynamics are being increasingly used, the theoretical properties of the applied measures are poorly understood. This study introduces for the first time a framework for the analytical computation of information dynamics for linear multivariate stochastic processes explored at different time scales. After showing that the multiscale processing of a vector autoregressive (VAR) process introduces a moving average (MA) component, we describe how to represent the resulting VARMA process using state-space (SS) models and how to exploit the SS model parameters to compute analytical measures of information storage and information transfer for the original and rescaled processes. The framework is then used to quantify multiscale infor...
Humeau, Anne; Mahé, Guillaume; Chapeau-Blondeau, François; Rousseau, David; Abraham, Pierre
2011-10-01
Processes regulating the cardiovascular system (CVS) are numerous. Each possesses several temporal scales. Their interactions lead to interdependences across multiple scales. For the CVS analysis, different multiscale studies have been proposed, mostly performed on heart rate variability signals (HRV) reflecting the central CVS; only few were dedicated to data from the peripheral CVS, such as laser Doppler flowmetry (LDF) signals. Very recently, a study implemented the first computation of multiscale entropy for LDF signals. A nonmonotonic evolution of multiscale entropy with two distinctive scales was reported, leading to a markedly different behavior from the one of HRV. Our goal herein is to confirm these results and to go forward in the investigations on origins of this behavior. For this purpose, 12 LDF signals recorded simultaneously on the two forearms of six healthy subjects are processed. This is performed before and after application of physiological scales-based filters aiming at isolating previously found frequency bands linked to physiological activities. The results obtained with signals recorded simultaneously on two different sites of each subject show a probable central origin for the nonmonotonic behavior. The filtering results lead to the suggestion that origins of the distinctive scales could be dominated by the cardiac activity. PMID:21712149
Entropic Approach to Multiscale Clustering Analysis
Antonio Insolia
2012-05-01
Full Text Available Recently, a novel method has been introduced to estimate the statistical significance of clustering in the direction distribution of objects. The method involves a multiscale procedure, based on the Kullback–Leibler divergence and the Gumbel statistics of extreme values, providing high discrimination power, even in presence of strong background isotropic contamination. It is shown that the method is: (i semi-analytical, drastically reducing computation time; (ii very sensitive to small, medium and large scale clustering; (iii not biased against the null hypothesis. Applications to the physics of ultra-high energy cosmic rays, as a cosmological probe, are presented and discussed.
Multiscale analysis of subwavelength imaging with metal-dielectric multilayers
Kotynski, Rafal; Stefaniuk, Tomasz
2009-01-01
Imaging with a layered superlens is a spatial filtering operation characterized by the point spread function (PSF). We show that in the same optical system the image of a narrow sub-wavelength Gaussian incident field may be surprisingly dissimilar to the PSF, and the width of PSF is not a straightforward measure of resolution. FWHM or std. dev. of PSF give ambiguous information about the actual resolution, and imaging of objects smaller than the FWHM of PSF is possible. A multiscale analysis ...
Lifetime statistics of quantum chaos studied by a multiscale analysis
Di Falco, A.
2012-04-30
In a series of pump and probe experiments, we study the lifetime statistics of a quantum chaotic resonator when the number of open channels is greater than one. Our design embeds a stadium billiard into a two dimensional photonic crystal realized on a silicon-on-insulator substrate. We calculate resonances through a multiscale procedure that combines energy landscape analysis and wavelet transforms. Experimental data is found to follow the universal predictions arising from random matrix theory with an excellent level of agreement.
Non-linear coupled CNN models for multiscale image analysis
Corinto, Fernando; Biey, Mario; Gilli, Marco
2006-01-01
A CNN model of partial differential equations (PDEs) for image multiscale analysis is proposed. The model is based on a polynomial representation of the diffusivity function and defines a paradigm of polynomial CNNs,for approximating a large class of nonlinear isotropic and/or anisotropic PDEs. The global dynamics of spacediscrete polynomial CNN models is analyzed and compared with the dynamic behavior of the corresponding space-continuous PDE models. It is shown that in the isotropic case th...
Complexity of carbon market from multi-scale entropy analysis
Fan, Xinghua; Li, Shasha; Tian, Lixin
2016-06-01
Complexity of carbon market is the consequence of economic dynamics and extreme social political events in global carbon markets. The multi-scale entropy can measure the long-term structures in the daily price return time series. By using multi-scale entropy analysis, we explore the complexity of carbon market and mean reversion trend of daily price return. The logarithmic difference of data Dec16 from August 6, 2010 to May 22, 2015 is selected as the sample. The entropy is higher in small time scale, while lower in large. The dependence of the entropy on the time scale reveals the mean reversion of carbon prices return in the long run. A relatively great fluctuation over some short time period indicates that the complexity of carbon market evolves consistently with economic development track and the events of international climate conferences.
Multiscale analysis of heart rate variability in nonstationary environments
Jianbo eGao
2013-05-01
Full Text Available Heart rate variability (HRV is highly nonstationary, even if no perturbing influences can be identified during the recording of the data. The nonstationarity becomes more profound when HRV data are measured in intrinsically nonstationary environments, such as social stress. In general, HRV data measured in such situations are more difficult to analyze than those measured in constant environments. In this paper, we analyze HRV data measured during a social stress test using two multiscale approaches, the adaptive fractal analysis (AFA and scale-dependent Lyapunov exponent (SDLE, for the purpose of uncovering differences in HRV between chronic fatigue syndrome (CFS patients and their matched-controls. CFS is a debilitating, heterogeneous illness with no known biomarker. HRV has shown some promise recently as a non-invasive measure of subtle physiological disturbances and trauma that are otherwise difficult to assess. If the HRV in persons with CFS are significantly different from their healthy controls, then certain cardiac irregularities may constitute good candidate biomarkers for CFS. Our multiscale analyses show that there are notable differences in HRV between CFS and their matched controls before a social stress test, but these differences seem to diminish during the test. These analyses illustrate that the two employed multiscale approaches could be useful for the analysis of HRV measured in various environments, both stationary and nonstationary.
Multiscale analysis of subwavelength imaging with metal-dielectric multilayers.
Kotyński, Rafał; Stefaniuk, Tomasz
2010-04-15
Imaging with a layered superlens is a spatial filtering operation characterized by the point spread function (PSF). We show that in the same optical system the image of a narrow subwavelength Gaussian incident field may be surprisingly dissimilar to the PSF, and the width of the PSF is not a straightforward measure of the resolution. The FWHM or standard deviation of the PSF gives ambiguous information about the actual resolution, and imaging of objects smaller than the FWHM of the PSF is possible. A multiscale analysis of imaging gives good insight into the peculiar scale-dependent properties of subwavelength imaging. PMID:20410943
Multiscale recurrence analysis of spatio-temporal data
Riedl, M.; Marwan, N.; Kurths, J.
2015-12-01
The description and analysis of spatio-temporal dynamics is a crucial task in many scientific disciplines. In this work, we propose a method which uses the mapogram as a similarity measure between spatially distributed data instances at different time points. The resulting similarity values of the pairwise comparison are used to construct a recurrence plot in order to benefit from established tools of recurrence quantification analysis and recurrence network analysis. In contrast to other recurrence tools for this purpose, the mapogram approach allows the specific focus on different spatial scales that can be used in a multi-scale analysis of spatio-temporal dynamics. We illustrate this approach by application on mixed dynamics, such as traveling parallel wave fronts with additive noise, as well as more complicate examples, pseudo-random numbers and coupled map lattices with a semi-logistic mapping rule. Especially the complicate examples show the usefulness of the multi-scale consideration in order to take spatial pattern of different scales and with different rhythms into account. So, this mapogram approach promises new insights in problems of climatology, ecology, or medicine.
Dehazing method through polarimetric imaging and multi-scale analysis
Cao, Lei; Shao, Xiaopeng; Liu, Fei; Wang, Lin
2015-05-01
An approach for haze removal utilizing polarimetric imaging and multi-scale analysis has been developed to solve one problem that haze weather weakens the interpretation of remote sensing because of the poor visibility and short detection distance of haze images. On the one hand, the polarization effects of the airlight and the object radiance in the imaging procedure has been considered. On the other hand, one fact that objects and haze possess different frequency distribution properties has been emphasized. So multi-scale analysis through wavelet transform has been employed to make it possible for low frequency components that haze presents and high frequency coefficients that image details or edges occupy are processed separately. According to the measure of the polarization feather by Stokes parameters, three linear polarized images (0°, 45°, and 90°) have been taken on haze weather, then the best polarized image min I and the worst one max I can be synthesized. Afterwards, those two polarized images contaminated by haze have been decomposed into different spatial layers with wavelet analysis, and the low frequency images have been processed via a polarization dehazing algorithm while high frequency components manipulated with a nonlinear transform. Then the ultimate haze-free image can be reconstructed by inverse wavelet reconstruction. Experimental results verify that the dehazing method proposed in this study can strongly promote image visibility and increase detection distance through haze for imaging warning and remote sensing systems.
Romeny, Bart M Haar
2008-01-01
Front-End Vision and Multi-Scale Image Analysis is a tutorial in multi-scale methods for computer vision and image processing. It builds on the cross fertilization between human visual perception and multi-scale computer vision (`scale-space') theory and applications. The multi-scale strategies recognized in the first stages of the human visual system are carefully examined, and taken as inspiration for the many geometric methods discussed. All chapters are written in Mathematica, a spectacular high-level language for symbolic and numerical manipulations. The book presents a new and effective
Multiscale Analysis of Pebble Bed Reactors
Hans Gougar; Woo Yoon; Abderrafi Ougouag
2010-10-01
– The PEBBED code was developed at the Idaho National Laboratory for design and analysis of pebble-bed high temperature reactors. The diffusion-depletion-pebble-mixing algorithm of the original PEBBED code was enhanced through coupling with the THERMIX-KONVEK code for thermal fluid analysis and by the COMBINE code for online cross section generation. The COMBINE code solves the B-1 or B-3 approximations to the transport equation for neutron slowing down and resonance interactions in a homogeneous medium with simple corrections for shadowing and thermal self-shielding. The number densities of materials within specified regions of the core are averaged and transferred to COMBINE from PEBBED for updating during the burnup iteration. The simple treatment of self-shielding in previous versions of COMBINE led to inaccurate results for cross sections and unsatisfactory core performance calculations. A new version of COMBINE has been developed that treats all levels of heterogeneity using the 1D transport code ANISN. In a 3-stage calculation, slowing down is performed in 167 groups for each homogeneous subregion (kernel, particle layers, graphite shell, control rod absorber annulus, etc.) Particles in a local average pebble are homogenized using ANISN then passed to the next (pebble) stage. A 1D transport solution is again performed over the pebble geometry and the homogenized pebble cross sections are passed to a 1-d radial model of a wedge of the pebble bed core. This wedge may also include homogeneous reflector regions and a control rod region composed of annuli of different absorbing regions. Radial leakage effects are therefore captured with discrete ordinates transport while axial and azimuthal effects are captured with a transverse buckling term. In this paper, results of various PBR models will be compared with comparable models from literature. Performance of the code will be assessed.
Adaptive multiscale entropy analysis of multivariate neural data.
Hu, Meng; Liang, Hualou
2012-01-01
Multiscale entropy (MSE) has been widely used to quantify a system's complexity by taking into account the multiple time scales inherent in physiologic time series. The method, however, is biased toward the coarse scale, i.e., low-frequency components due to the progressive smoothing operations. In addition, the algorithm for extracting the different scales is not well adapted to nonlinear/nonstationary signals. In this letter, we introduce adaptive multiscale entropy (AME) measures in which the scales are adaptively derived directly from the data by virtue of recently developed multivariate empirical mode decomposition. Depending on the consecutive removal of low-frequency or high-frequency components, our AME can be estimated at either coarse-to-fine or fine-to-coarse scales over which the sample entropy is performed. Computer simulations are performed to verify the effectiveness of AME for analysis of the highly nonstationary data. Local field potentials collected from the visual cortex of macaque monkey while performing a generalized flash suppression task are used as an example to demonstrate the usefulness of our AME approach to reveal the underlying dynamics in complex neural data. PMID:21788182
Bednarcyk, Brett A.; Arnold, Steven M.
2012-01-01
A framework for the multiscale design and analysis of composite materials and structures is presented. The ImMAC software suite, developed at NASA Glenn Research Center, embeds efficient, nonlinear micromechanics capabilities within higher scale structural analysis methods such as finite element analysis. The result is an integrated, multiscale tool that relates global loading to the constituent scale, captures nonlinearities at this scale, and homogenizes local nonlinearities to predict their effects at the structural scale. Example applications of the multiscale framework are presented for the stochastic progressive failure of a SiC/Ti composite tensile specimen and the effects of microstructural variations on the nonlinear response of woven polymer matrix composites.
Jiang, Lijian
2010-08-01
In this paper, we discuss a numerical multiscale approach for solving wave equations with heterogeneous coefficients. Our interest comes from geophysics applications and we assume that there is no scale separation with respect to spatial variables. To obtain the solution of these multiscale problems on a coarse grid, we compute global fields such that the solution smoothly depends on these fields. We present a Galerkin multiscale finite element method using the global information and provide a convergence analysis when applied to solve the wave equations. We investigate the relation between the smoothness of the global fields and convergence rates of the global Galerkin multiscale finite element method for the wave equations. Numerical examples demonstrate that the use of global information renders better accuracy for wave equations with heterogeneous coefficients than the local multiscale finite element method. © 2010 IMACS.
Lin, Yen-Hung; Huang, Hui-Chun; Chang, Yi-Chung; Lin, Chen; Lo, Men-Tzung; Liu, Li-Yu Daisy; Tsai, Pi-Ru; Chen, Yih-Sharng; Ko, Wen-Je; Ho, Yi-Lwun; Chen, Ming-Fong; Peng, Chung-Kang; Buchman, Timothy G.
2014-01-01
Introduction: Extracorporeal life support (ECLS) can temporarily support cardiopulmonary function, and is occasionally used in resuscitation. Multi-scale entropy (MSE) derived from heart rate variability (HRV) is a powerful tool in outcome prediction of patients with cardiovascular diseases. Multi-scale symbolic entropy analysis (MSsE), a new method derived from MSE, mitigates the effect of arrhythmia on analysis. The objective is to evaluate the prognostic value of MSsE in patients receiving...
Lin, Yen-Hung; Huang, Hui-Chun; Chang, Yi-Chung; Lin, Chen; Lo, Men-Tzung; Liu, Li-Yu Daisy; Tsai, Pi-Ru; Chen, Yih-Sharng; Ko, Wen-Je; Ho, Yi-Lwun; Chen, Ming-Fong; Peng, Chung-Kang; Buchman, Timothy G.
2014-01-01
Introduction Extracorporeal life support (ECLS) can temporarily support cardiopulmonary function, and is occasionally used in resuscitation. Multi-scale entropy (MSE) derived from heart rate variability (HRV) is a powerful tool in outcome prediction of patients with cardiovascular diseases. Multi-scale symbolic entropy analysis (MSsE), a new method derived from MSE, mitigates the effect of arrhythmia on analysis. The objective is to evaluate the prognostic value of MSsE in patients receiving ...
Multivariate Multi-Scale Permutation Entropy for Complexity Analysis of Alzheimer’s Disease EEG
Isabella Palamara; Giuseppe Morabito; Alessia Bramanti; Fabio La Foresta; Domenico Labate; Francesco Carlo Morabito
2012-01-01
An original multivariate multi-scale methodology for assessing the complexity of physiological signals is proposed. The technique is able to incorporate the simultaneous analysis of multi-channel data as a unique block within a multi-scale framework. The basic complexity measure is done by using Permutation Entropy, a methodology for time series processing based on ordinal analysis. Permutation Entropy is conceptually simple, structurally robust to noise and artifacts, computationally very fa...
Chun-Chieh Wang; Tian-Yau Wu; Chiu-Wen Wu; Shuen-De Wu
2013-01-01
The objective of this research is to investigate the feasibility of utilizing the multi-scale analysis and support vector machine (SVM) classification scheme to diagnose the bearing faults in rotating machinery. For complicated signals, the characteristics of dynamic systems may not be apparently observed in a scale, particularly for the fault-related features of rotating machinery. In this research, the multi-scale analysis is employed to extract the possible fault-related features in differ...
Multiscale entropy analysis of different spontaneous motor unit discharge patterns.
Zhang, Xu; Chen, Xiang; Barkhaus, Paul E; Zhou, Ping
2013-03-01
This study explores a novel application of multiscale entropy (MSE) analysis for characterizing different patterns of spontaneous electromyogram (EMG) signals including sporadic, tonic and repetitive spontaneous motor unit discharges, and normal surface EMG baseline. Two algorithms for MSE analysis, namely, the standard MSE and the intrinsic mode entropy (IMEn) (based on the recently developed multivariate empirical mode decomposition method), were applied to different patterns of spontaneous EMG. Significant differences were observed in multiple scales of the standard MSE and IMEn analyses (p <; 0.001) for any two of the spontaneous EMG patterns, while such significance may not be observed from the single-scale entropy analysis. Compared to the standard MSE, the IMEn analysis facilitates usage of a relatively low scale number to discern entropy difference among various patterns of spontaneous EMG signals. The findings from this study contribute to our understanding of the nonlinear dynamic properties of different spontaneous EMG patterns, which may be related to spinal motoneuron or motor unit health. PMID:24235117
Multi-Scale Analysis Based Curve Feature Extraction in Reverse Engineering
YANG Hongjuan; ZHOU Yiqi; CHEN Chengjun; ZHAO Zhengxu
2006-01-01
A sectional curve feature extraction algorithm based on multi-scale analysis is proposed for reverse engineering. The algorithm consists of two parts: feature segmentation and feature classification. In the first part, curvature scale space is applied to multi-scale analysis and original feature detection. To obtain the primary and secondary curve primitives, feature fusion is realized by multi-scale feature detection information transmission. In the second part: projection height function is presented based on the area of quadrilateral, which improved criterions of sectional curve feature classification. Results of synthetic curves and practical scanned sectional curves are given to illustrate the efficiency of the proposed algorithm on feature extraction. The consistence between feature extraction based on multi-scale curvature analysis and curve primitives is verified.
Analysis of complex time series using refined composite multiscale entropy
Wu, Shuen-De; Wu, Chiu-Wen [Department of Mechatronic Technology, National Taiwan Normal University, Taipei 10610, Taiwan (China); Lin, Shiou-Gwo [Department of Communications, Navigation and Control Engineering, National Taiwan Ocean University, Keelung 20224, Taiwan (China); Lee, Kung-Yen [Department of Engineering Science and Ocean Engineering, National Taiwan University, Taipei 10617, Taiwan (China); Peng, Chung-Kang [College of Health Sciences and Technology, National Central University, Chung-Li 32001, Taiwan (China); Division of Interdisciplinary Medicine and Biotechnology, Beth Israel Deaconess Medical Center/Harvard Medical School, Boston (United States)
2014-04-01
Multiscale entropy (MSE) is an effective algorithm for measuring the complexity of a time series that has been applied in many fields successfully. However, MSE may yield an inaccurate estimation of entropy or induce undefined entropy because the coarse-graining procedure reduces the length of a time series considerably at large scales. Composite multiscale entropy (CMSE) was recently proposed to improve the accuracy of MSE, but it does not resolve undefined entropy. Here we propose a refined composite multiscale entropy (RCMSE) to improve CMSE. For short time series analyses, we demonstrate that RCMSE increases the accuracy of entropy estimation and reduces the probability of inducing undefined entropy.
Multi-scale analysis of lung computed tomography images
Gori, I.; Bagagli, F.; Fantacci, M. E.; Martinez, A. Preite; Retico, A.; De Mitri, I.; Donadio, S.; Fulcheri, C.; Gargano, G; Magro, R.; Santoro, M; Stumbo, S
2009-01-01
A computer-aided detection (CAD) system for the identification of lung internal nodules in low-dose multi-detector helical Computed Tomography (CT) images was developed in the framework of the MAGIC-5 project. The three modules of our lung CAD system, a segmentation algorithm for lung internal region identification, a multi-scale dot-enhancement filter for nodule candidate selection and a multi-scale neural technique for false positive finding reduction, are described. The results obtained on...
Dynamical glucometry: use of multiscale entropy analysis in diabetes.
Costa, Madalena D; Henriques, Teresa; Munshi, Medha N; Segal, Alissa R; Goldberger, Ary L
2014-09-01
Diabetes mellitus (DM) is one of the world's most prevalent medical conditions. Contemporary management focuses on lowering mean blood glucose values toward a normal range, but largely ignores the dynamics of glucose fluctuations. We probed analyte time series obtained from continuous glucose monitor (CGM) sensors. We show that the fluctuations in CGM values sampled every 5 min are not uncorrelated noise. Next, using multiscale entropy analysis, we quantified the complexity of the temporal structure of the CGM time series from a group of elderly subjects with type 2 DM and age-matched controls. We further probed the structure of these CGM time series using detrended fluctuation analysis. Our findings indicate that the dynamics of glucose fluctuations from control subjects are more complex than those of subjects with type 2 DM over time scales ranging from about 5 min to 5 h. These findings support consideration of a new framework, dynamical glucometry, to guide mechanistic research and to help assess and compare therapeutic interventions, which should enhance complexity of glucose fluctuations and not just lower mean and variance of blood glucose levels. PMID:25273219
Escudero, Javier; Acar, Evrim; Fernandez, Alberto; Bro, Rasmus
2015-01-01
Tensor factorisations have proven useful to model amplitude and spectral information of brain recordings. Here, we assess the usefulness of tensor factorisations in the multiway analysis of other brain signal features in the context of complexity measures recently proposed to inspect multiscale dynamics. We consider the “refined composite multiscale entropy” (rcMSE), which computes entropy “profiles” showing levels of physiological complexity over temporal scales for individual signals. We co...
Multiscale Analysis of Spreading in a Large Communication Network
Kivelä, Mikko; Kaski, Kimmo; Kertész, János; Saramäki, Jari; Karsai, Márton
2011-01-01
In temporal networks, both the topology of the underlying network and the timings of interaction events can be crucial in determining how some dynamic process mediated by the network unfolds. We have explored the limiting case of the speed of spreading in the SI model, set up such that an event between an infectious and susceptible individual always transmits the infection. The speed of this process sets an upper bound for the speed of any dynamic process that is mediated through the interaction events of the network. With the help of temporal networks derived from large scale time-stamped data on mobile phone calls, we extend earlier results that point out the slowing-down effects of burstiness and temporal inhomogeneities. In such networks, links are not permanently active, but dynamic processes are mediated by recurrent events taking place on the links at specific points in time. We perform a multi-scale analysis and pinpoint the importance of the timings of event sequences on individual links, their corre...
Multiscale Analysis of the Predictability of Stock Returns
Paweł Fiedor
2015-06-01
Full Text Available Due to the strong complexity of financial markets, economics does not have a unified theory of price formation in financial markets. The most common assumption is the Efficient-Market Hypothesis, which has been attacked by a number of researchers, using different tools. There were varying degrees to which these tools complied with the formal definitions of efficiency and predictability. In our earlier work, we analysed the predictability of stock returns at two time scales using the entropy rate, which can be directly linked to the mathematical definition of predictability. Nonetheless, none of the above-mentioned studies allow any general understanding of how the financial markets work, beyond disproving the Efficient-Market Hypothesis. In our previous study, we proposed the Maximum Entropy Production Principle, which uses the entropy rate to create a general principle underlying the price formation processes. Both of these studies show that the predictability of price changes is higher at the transaction level intraday scale than the scale of daily returns, but ignore all scales in between. In this study we extend these ideas using the multiscale entropy analysis framework to enhance our understanding of the predictability of price formation processes at various time scales.
Analysis of electroencephalograms in Alzheimer's disease patients with multiscale entropy.
Escudero, J; Abásolo, D; Hornero, R; Espino, P; López, M
2006-11-01
The aim of this study was to analyse the electroencephalogram (EEG) background activity of Alzheimer's disease (AD) patients using multiscale entropy (MSE). MSE is a recently developed method that quantifies the regularity of a signal on different time scales. These time scales are inspected by means of several coarse-grained sequences formed from the analysed signals. We recorded the EEGs from 19 scalp electrodes in 11 AD patients and 11 age-matched controls and estimated the MSE profile for each epoch of the EEG recordings. The shape of the MSE profiles reveals the EEG complexity, and it suggests that the EEG contains information in deeper scales than the smallest one. Moreover, the results showed that the EEG background activity is less complex in AD patients than control subjects. We found significant differences between both subject groups at electrodes F3, F7, Fp1, Fp2, T5, T6, P3, P4, O1 and O2 (p-value < 0.01, Student's t-test). These findings indicate that the EEG complexity analysis performed on deeper time scales by MSE may be a useful tool in order to increase our knowledge of AD. PMID:17028404
Multiscale Detrended Cross-Correlation Analysis of STOCK Markets
Yin, Yi; Shang, Pengjian
2014-06-01
In this paper, we employ the detrended cross-correlation analysis (DCCA) to investigate the cross-correlations between different stock markets. We report the results of cross-correlated behaviors in US, Chinese and European stock markets in period 1997-2012 by using DCCA method. The DCCA shows the cross-correlated behaviors of intra-regional and inter-regional stock markets in the short and long term which display the similarities and differences of cross-correlated behaviors simply and roughly and the persistence of cross-correlated behaviors of fluctuations. Then, because of the limitation and inapplicability of DCCA method, we propose multiscale detrended cross-correlation analysis (MSDCCA) method to avoid "a priori" selecting the ranges of scales over which two coefficients of the classical DCCA method are identified, and employ MSDCCA to reanalyze these cross-correlations to exhibit some important details such as the existence and position of minimum, maximum and bimodal distribution which are lost if the scale structure is described by two coefficients only and essential differences and similarities in the scale structures of cross-correlation of intra-regional and inter-regional markets. More statistical characteristics of cross-correlation obtained by MSDCCA method help us to understand how two different stock markets influence each other and to analyze the influence from thus two inter-regional markets on the cross-correlation in detail, thus we get a richer and more detailed knowledge of the complex evolutions of dynamics of the cross-correlations between stock markets. The application of MSDCCA is important to promote our understanding of the internal mechanisms and structures of financial markets and helps to forecast the stock indices based on our current results demonstrated the cross-correlations between stock indices. We also discuss the MSDCCA methods of secant rolling window with different sizes and, lastly, provide some relevant implications and
Multiscale analysis of spreading in a large communication network
In temporal networks, both the topology of the underlying network and the timings of interaction events can be crucial in determining how a dynamic process mediated by the network unfolds. We have explored the limiting case of the speed of spreading in the SI model, set up such that an event between an infectious and a susceptible individual always transmits the infection. The speed of this process sets an upper bound for the speed of any dynamic process that is mediated through the interaction events of the network. With the help of temporal networks derived from large-scale time-stamped data on mobile phone calls, we extend earlier results that indicate the slowing-down effects of burstiness and temporal inhomogeneities. In such networks, links are not permanently active, but dynamic processes are mediated by recurrent events taking place on the links at specific points in time. We perform a multiscale analysis and pinpoint the importance of the timings of event sequences on individual links, their correlations with neighboring sequences, and the temporal pathways taken by the network-scale spreading process. This is achieved by studying empirically and analytically different characteristic relay times of links, relevant to the respective scales, and a set of temporal reference models that allow for removing selected time-domain correlations one by one. Our analysis shows that for the spreading velocity, time-domain inhomogeneities are as important as the network topology, which indicates the need to take time-domain information into account when studying spreading dynamics. In particular, results for the different characteristic relay times underline the importance of the burstiness of individual links
Multi-scale analysis of lung computed tomography images
Gori, I; Fantacci, M E; Martinez, A Preite; Retico, A; De Mitri, I; Donadio, S; Fulcheri, C; Gargano, G; Magro, R; Santoro, M; Stumbo, S; 10.1088/1748-0221/2/09/P09007
2009-01-01
A computer-aided detection (CAD) system for the identification of lung internal nodules in low-dose multi-detector helical Computed Tomography (CT) images was developed in the framework of the MAGIC-5 project. The three modules of our lung CAD system, a segmentation algorithm for lung internal region identification, a multi-scale dot-enhancement filter for nodule candidate selection and a multi-scale neural technique for false positive finding reduction, are described. The results obtained on a dataset of low-dose and thin-slice CT scans are shown in terms of free response receiver operating characteristic (FROC) curves and discussed.
Multi-scale analysis of lung computed tomography images
Gori, I; Fantacci, M E; Preite Martinez, A; Retico, A; De Mitri, I; Donadio, S; Fulcheri, C
2007-01-01
A computer-aided detection (CAD) system for the identification of lung internal nodules in low-dose multi-detector helical Computed Tomography (CT) images was developed in the framework of the MAGIC-5 project. The three modules of our lung CAD system, a segmentation algorithm for lung internal region identification, a multi-scale dot-enhancement filter for nodule candidate selection and a multi-scale neural technique for false positive finding reduction, are described. The results obtained on a dataset of low-dose and thin-slice CT scans are shown in terms of free response receiver operating characteristic (FROC) curves and discussed.
The set of prime numbers: Multifractals and multiscale analysis
In this work we show that the prime numbers can be seen as the sides or angles of a multifractal polygon based on a hexagon. This also means that primes follow a multiscale distribution and can be generated in a beautiful deterministic fashion.
Analysis of the effects of different machining processes on sealing using multiscale topography
Deltombe, Raphael; Bigerelle, Maxence; Jourani, Abdeljalil
2016-03-01
This study characterizes seal performance using a multiscale analysis of surface topography. The performance of two surface morphologies is compared: the first one is obtained with machining only and leads to leakage while the second one is obtained with machining and superfinishing and prevents leakage. It is shown that conventional roughness analysis does not enable to identify the differences between both surfaces. Only the use of a new parameter, the order parameter, and the use of a multiscale analysis of surfaces enable to distinguish the studied surfaces and to identify leakage causes. These causes are checked using a numerical contact simulation. It is shown that microroughness plays a major role in leakage.
Implementation of Grid-computing Framework for Simulation in Multi-scale Structural Analysis
Data Iranata
2010-05-01
Full Text Available A new grid-computing framework for simulation in multi-scale structural analysis is presented. Two levels of parallel processing will be involved in this framework: multiple local distributed computing environments connected by local network to form a grid-based cluster-to-cluster distributed computing environment. To successfully perform the simulation, a large-scale structural system task is decomposed into the simulations of a simplified global model and several detailed component models using various scales. These correlated multi-scale structural system tasks are distributed among clusters and connected together in a multi-level hierarchy and then coordinated over the internet. The software framework for supporting the multi-scale structural simulation approach is also presented. The program architecture design allows the integration of several multi-scale models as clients and servers under a single platform. To check its feasibility, a prototype software system has been designed and implemented to perform the proposed concept. The simulation results show that the software framework can increase the speedup performance of the structural analysis. Based on this result, the proposed grid-computing framework is suitable to perform the simulation of the multi-scale structural analysis.
A New Method for Pitch Tracking and Voicing Decision Based on Spectral Multi-Scale Analysis
Aicha Bouzid
2009-11-01
Full Text Available This paper proposes a new voicing detection and pitch estimation method that is particularly robust for noisy speech. This method is based on the spectral analysis of the speech multi-scale product. The multi-scale product (MP consists of making the product of wavelet transform coefficients. The wavelet used is the quadratic spline function. We argue that the spectral of Multi-scale Product Analysis is capable of revealing an estimate of a pitch-harmonic more accurately even in a heavy noisy scenario. We evaluate our approach on the Keele database. The experimental results show the robustness of our method for noisy speech, and the good performance for clean speech in comparison with state-of-the-art algorithms.
Analysis of electroencephalograms in Alzheimer's disease patients with multiscale entropy
Escudero, J; Abasolo, D; Hornero, R.; Espino, P; M. Lopez
2006-01-01
The aim of this study was to analyse the electroencephalogram ( EEG) background activity of Alzheimer's disease ( AD) patients using multiscale entropy (MSE). MSE is a recently developed method that quantifies the regularity of a signal on different time scales. These time scales are inspected by means of several coarse-grained sequences formed from the analysed signals. We recorded the EEGs from 19 scalp electrodes in 11 AD patients and 11 age-matched controls and estimated the MSE profile f...
Creep behavior multi-scale analysis of zirconium alloys
Zirconium alloys are used in the nuclear industry as fuel cladding tubes in order to prevent radionuclide leaks into primary loop. They are also used to ensure an efficient heat transfer between nuclear fuel and primary water. Under operating conditions, these alloys are subjected to intense thermal-mechanical and neutronic solicitations. The mechanical wear of fuel rods is a key element in nuclear reactor safety. During this work, TEM investigations have been carried out by coworkers in order to quantify driving plasticity mechanisms in investigated stress and temperature range. The main result of this study is that the prismatic glide of screw dislocations remains the main plasticity mechanism. It is frequently associated with a cross slip mechanism on pyramidal planes. These experimental observations have been implemented into a multi-scale approach, thus providing a better description of the phenomenon. The results of a characterization campaign of the multiaxial creep behavior at 400 C of recrystallised Zircaloy-4 are used in this work and a micromechanical interpretation is proposed. The Elasto-Visco-Plastic (EVP) behavior is also characterized with relaxation tests between 280 C and 43 C. Analysis of experimental results indicates macroscopic effects of dynamic strain ageing. At the same time, Finite Element computations on polycrystalline aggregates were undertaken and a procedure for the statistical estimation of intra-phase mean mechanical fields is proposed. For a sufficient number of statistical realizations, it is shown that this estimator allows the precise determination of the analytical solution provided by the self consistent model within the framework of anisotropic elasticity. Thus, this procedure was applied to an EVP medium. It seems that rule allow a good description of numerical observations. However, EVP flow of quasi-elastic crystallographic phases remains difficult to describe. The aim of this PhD is to develop models of EVP behavior of
Multiscale and cross entropy analysis of auroral and polar cap indices during geomagnetic storms
Gopinath, Sumesh; Prince, P. R.
2016-01-01
In order to improve general monoscale information entropy methods like permutation and sample entropy in characterizing the irregularity of complex magnetospheric system, it is necessary to extend these entropy metrics to a multiscale paradigm. We propose novel multiscale and cross entropy method for the analysis of magnetospheric proxies such as auroral and polar cap indices during geomagnetic disturbance times. Such modified entropy metrics are certainly advantageous in classifying subsystems such as individual contributions of auroral electrojets and field aligned currents to high latitude magnetic perturbations during magnetic storm and polar substorm periods. We show that the multiscale entropy/cross entropy of geomagnetic indices vary with scale factor. These variations can be attributed to changes in multiscale dynamical complexity of non-equilibrium states present in the magnetospheric system. These types of features arise due to imbalance in injection and dissipation rates of energy with variations in magnetospheric response to solar wind. We also show that the multiscale entropy values of time series decrease during geomagnetic storm times which reveals an increase in temporal correlations as the system gradually shifts to a more orderly state. Such variations in entropy values can be interpreted as the signature of dynamical phase transitions which arise at the periods of geomagnetic storms and substorms that confirms several previously found results regarding emergence of cooperative dynamics, self-organization and non-Markovian nature of magnetosphere during disturbed periods.
Filter-based multiscale entropy analysis of complex physiological time series.
Xu, Yuesheng; Zhao, Liang
2013-08-01
Multiscale entropy (MSE) has been widely and successfully used in analyzing the complexity of physiological time series. We reinterpret the averaging process in MSE as filtering a time series by a filter of a piecewise constant type. From this viewpoint, we introduce filter-based multiscale entropy (FME), which filters a time series to generate multiple frequency components, and then we compute the blockwise entropy of the resulting components. By choosing filters adapted to the feature of a given time series, FME is able to better capture its multiscale information and to provide more flexibility for studying its complexity. Motivated by the heart rate turbulence theory, which suggests that the human heartbeat interval time series can be described in piecewise linear patterns, we propose piecewise linear filter multiscale entropy (PLFME) for the complexity analysis of the time series. Numerical results from PLFME are more robust to data of various lengths than those from MSE. The numerical performance of the adaptive piecewise constant filter multiscale entropy without prior information is comparable to that of PLFME, whose design takes prior information into account. PMID:24032873
Donald Estep; Michael Holst; Simon Tavener
2010-02-08
This project was concerned with the accurate computational error estimation for numerical solutions of multiphysics, multiscale systems that couple different physical processes acting across a large range of scales relevant to the interests of the DOE. Multiscale, multiphysics models are characterized by intimate interactions between different physics across a wide range of scales. This poses significant computational challenges addressed by the proposal, including: (1) Accurate and efficient computation; (2) Complex stability; and (3) Linking different physics. The research in this project focused on Multiscale Operator Decomposition methods for solving multiphysics problems. The general approach is to decompose a multiphysics problem into components involving simpler physics over a relatively limited range of scales, and then to seek the solution of the entire system through some sort of iterative procedure involving solutions of the individual components. MOD is a very widely used technique for solving multiphysics, multiscale problems; it is heavily used throughout the DOE computational landscape. This project made a major advance in the analysis of the solution of multiscale, multiphysics problems.
Multi-Scale Scratch Analysis in Qinghai-Tibet Plateau and its Geological Implications
Sun, Yanyun; Yang, Wencai; Yu, Changqing
2016-04-01
Multi-scale scratch analysis on a regional gravity field is a new data processing system for depicting three-dimensional density structures and tectonic features. It comprises four modules including the spectral analysis of potential fields, multi-scale wavelet analysis, density distribution inversion, and scratch analysis. The multi-scale scratch analysis method was applied to regional gravity data to extract information about the deformation belts in the Qinghai-Tibet Plateau, which can help reveal variations of the deformation belts and plane distribution features from the upper crust to the lower crust, provide evidence for the study of three-dimensional crustal structures, and define distribution of deformation belts and mass movement. Results show the variation of deformation belts from the upper crust to the lower crust. The deformation belts vary from dense and thin in the upper crust to coarse and thick in the lower crust, demonstrating that vertical distribution of deformation belts resembles a tree with a coarse and thick trunk in the lower part and dense and thin branches at the top. The dense and thin deformation areas in the upper crust correspond to crustal shortening areas, while the thick and continuous deformation belts in the lower crust indicate the structural framework of the plateau. Additionally, the lower crustal deformation belts recognized by the multi-scale scratch analysis coincide approximately with the crustal deformation belts recognized using single-scale scratch analysis. However, deformation belts recognized by the latter are somewhat rough while multi-scale scratch analysis can provide more detailed and accurate results.
Multiscale analysis of depth images from natural scenes: Scaling in the depth of the woods
We analyze an ensemble of images from outdoor natural scenes and consisting of pairs of a standard gray-level luminance image associated with a depth image of the same scene, delivered by a recently introduced low-cost sensor for joint imaging of depth and luminance. We specially focus on statistical analysis of multiscale and fractal properties in the natural images. Two methodologies are implemented for this purpose, and examining the distribution of contrast upon coarse-graining at increasing scales, and the orientationally averaged power spectrum tied to spatial frequencies. Both methodologies confirm, on another independent dataset here, the presence of fractal scale invariance in the luminance natural images, as previously reported. Both methodologies here also reveal the presence of fractal scale invariance in the novel data formed by depth images from natural scenes. The multiscale analysis is confronted on luminance images and on the novel depth images together with an analysis of their statistical correlation. The results, especially the new results on the multiscale analysis of depth images, consolidate the importance and extend the multiplicity of aspects of self-similarity and fractal scale invariance properties observable in the constitution of images from natural scenes. Such results are useful to better understanding and modeling of the (multiscale) structure of images from natural scenes, with relevance to image processing algorithms and to visual perception. The approach also contains potentialities for the fractal characterization of three-dimensional natural structures and their interaction with light
Analysis of the Spatial Distribution of Galaxies by Multiscale Methods
E. Saar
2005-09-01
Full Text Available Galaxies are arranged in interconnected walls and filaments forming a cosmic web encompassing huge, nearly empty, regions between the structures. Many statistical methods have been proposed in the past in order to describe the galaxy distribution and discriminate the different cosmological models. We present in this paper multiscale geometric transforms sensitive to clusters, sheets, and walls: the 3D isotropic undecimated wavelet transform, the 3D ridgelet transform, and the 3D beamlet transform. We show that statistical properties of transform coefficients measure in a coherent and statistically reliable way, the degree of clustering, filamentarity, sheetedness, and voidedness of a data set.
The set of prime numbers: Multiscale analysis and numeric accelerators
In this work, we show that the prime numbers follow a multiscale distribution. Indeed they can be classified thanks to tree structures, which are expressed in terms of two maximal subsets of N and using multilayer selection rules, acting on these sets of prime candidates. Consequently, the prime numbers follow a specific deterministic rules. Indeed, a numeric accelerator for generating primes can be realized in terms of the above mentioned specific rules. From the comparison with the Fibonacci numbers a beautiful harmony comes in terms of the Golden Mean which is relevant to high energy physics and E-Infinity theory too.
Multiscale entropy analysis of complex physiologic time series.
Costa, Madalena; Goldberger, Ary L; Peng, C-K
2002-08-01
There has been considerable interest in quantifying the complexity of physiologic time series, such as heart rate. However, traditional algorithms indicate higher complexity for certain pathologic processes associated with random outputs than for healthy dynamics exhibiting long-range correlations. This paradox may be due to the fact that conventional algorithms fail to account for the multiple time scales inherent in healthy physiologic dynamics. We introduce a method to calculate multiscale entropy (MSE) for complex time series. We find that MSE robustly separates healthy and pathologic groups and consistently yields higher values for simulated long-range correlated noise compared to uncorrelated noise. PMID:12190613
Timeline analysis and wavelet multiscale analysis of the AKARI All-Sky Survey at 90 micron
Wang, Lingyu; Yamamura, Issei; Shibai, Hiroshi; Savage, Rich; Oliver, Seb; Thomson, Matthew; Rahman, Nurur; Clements, Dave; Figueredo, Elysandra; Goto, Tomotsugu; Hasegawa, Sunao; Jeong, Woong-Seob; Matsuura, Shuji; Muller, Thomas G; Nakagawa, Takao; Pearson, Chris P; Serjeant, Stephen; Shirahata, Mai; White, Glenn J
2008-01-01
We present a careful analysis of the point source detection limit of the AKARI All-Sky Survey in the WIDE-S 90 $\\mu$m band near the North Ecliptic Pole (NEP). Timeline Analysis is used to detect IRAS sources and then a conversion factor is derived to transform the peak timeline signal to the interpolated 90 $\\mu$m flux of a source. Combined with a robust noise measurement, the point source flux detection limit at S/N $>5$ for a single detector row is $1.1\\pm0.1$ Jy which corresponds to a point source detection limit of the survey of $\\sim$0.4 Jy. Wavelet transform offers a multiscale representation of the Time Series Data (TSD). We calculate the continuous wavelet transform of the TSD and then search for significant wavelet coefficients considered as potential source detections. To discriminate real sources from spurious or moving objects, only sources with confirmation are selected. In our multiscale analysis, IRAS sources selected above $4\\sigma$ can be identified as the only real sources at the Point Sourc...
Quantitative Multiscale Analysis using Different Wavelets in 1D Voice Signal and 2D Image
Shakhakarmi, Niraj
2012-01-01
Mutiscale analysis represents multiresolution scrutiny of a signal to improve its signal quality. Multiresolution analysis of 1D voice signal and 2D image is conducted using DCT, FFT and different wavelets such as Haar, Deubachies, Morlet, Cauchy, Shannon, Biorthogonal, Symmlet and Coiflet deploying the cascaded filter banks based decomposition and reconstruction. The outstanding quantitative analysis of the specified wavelets is done to investigate the signal quality, mean square error, entropy and peak-to-peak SNR at multiscale stage-4 for both 1D voice signal and 2D image. In addition, the 2D image compression performance is significantly found 93.00% in DB-4, 93.68% in bior-4.4, 93.18% in Sym-4 and 92.20% in Coif-2 during the multiscale analysis.
Multiscale Topographical Analysis of Biogeochemically Reduced Hematite Surfaces
Rustad, J.; Rosso, K. M.; Dubuffet, F.; Yuen, D. A.
2001-12-01
Establishing the mechanisms and magnitudes of nano-mesoscale influences on interfacial chemical reactivity requires a multiscale description of the structure of the interfacial region. The identification of scaling relationships characterizing mineral surface structure in low-temperature environments is a first step in the construction structure-activity relationships that are potentially applicable over multiple length scales. Using wavelet image processing techniques and scaling relationships such as the evaluation of Hurst exponents and fractal dimension, we systematize and quantify mineral surface topography of a sample of hematite undergoing biochemically induced reductive dissolution. Image mosaicking methods commonly applied in remote sensing and medical imaging contexts are applied to AFM images to obtain large scale images for the evalution of scaling exponents. Gaussian wavelet methods are used to enhance and quantify structural features associated with the biogeochemically reduced surfaces.
ANALYSIS/MODEL COVER SHEET, MULTISCALE THERMOHYDROLOGIC MODEL
The purpose of the Multiscale Thermohydrologic Model (MSTHM) is to describe the thermohydrologic evolution of the near-field environment (NFE) and engineered barrier system (EBS) throughout the potential high-level nuclear waste repository at Yucca Mountain for a particular engineering design (CRWMS M andO 2000c). The process-level model will provide thermohydrologic (TH) information and data (such as in-drift temperature, relative humidity, liquid saturation, etc.) for use in other technical products. This data is provided throughout the entire repository area as a function of time. The MSTHM couples the Smeared-heat-source Drift-scale Thermal-conduction (SDT), Line-average-heat-source Drift-scale Thermohydrologic (LDTH), Discrete-heat-source Drift-scale Thermal-conduction (DDT), and Smeared-heat-source Mountain-scale Thermal-conduction (SMT) submodels such that the flow of water and water vapor through partially-saturated fractured rock is considered. The MSTHM accounts for 3-D drift-scale and mountain-scale heat flow, repository-scale variability of stratigraphy and infiltration flux, and waste package (WP)-to-WP variability in heat output from WPs. All submodels use the nonisothermal unsaturated-saturated flow and transport (NUFT) simulation code. The MSTHM is implemented in several data-processing steps. The four major steps are: (1) submodel input-file preparation, (2) execution of the four submodel families with the use of the NUFT code, (3) execution of the multiscale thermohydrologic abstraction code (MSTHAC), and (4) binning and post-processing (i.e., graphics preparation) of the output from MSTHAC. Section 6 describes the MSTHM in detail. The objectives of this Analyses and Model Report (AMR) are to investigate near field (NF) and EBS thermohydrologic environments throughout the repository area at various evolution periods, and to provide TH data that may be used in other process model reports
Traffic time series analysis by using multiscale time irreversibility and entropy
Wang, Xuejiao; Shang, Pengjian; Fang, Jintang
2014-09-01
Traffic systems, especially urban traffic systems, are regulated by different kinds of interacting mechanisms which operate across multiple spatial and temporal scales. Traditional approaches fail to account for the multiple time scales inherent in time series, such as empirical probability distribution function and detrended fluctuation analysis, which have lead to different results. The role of multiscale analytical method in traffic time series is a frontier area of investigation. In this paper, our main purpose is to introduce a new method—multiscale time irreversibility, which is helpful to extract information from traffic time series we studied. In addition, to analyse the complexity of traffic volume time series of Beijing Ring 2, 3, 4 roads between workdays and weekends, which are from August 18, 2012 to October 26, 2012, we also compare the results by this new method and multiscale entropy method we have known well. The results show that the higher asymmetry index we get, the higher traffic congestion level will be, and accord with those which are obtained by multiscale entropy.
Madalena D. Costa
2015-03-01
Full Text Available We introduce a generalization of multiscale entropy (MSE analysis. The method is termed MSEn, where the subscript denotes the moment used to coarse-grain a time series. MSEμ, described previously, uses the mean value (first moment. Here, we focus on MSEσ2 , which uses the second moment, i.e., the variance. MSEσ2 quantifies the dynamics of the volatility (variance of a signal over multiple time scales. We use the method to analyze the structure of heartbeat time series. We find that the dynamics of the volatility of heartbeat time series obtained from healthy young subjects is highly complex. Furthermore, we find that the multiscale complexity of the volatility, not only the multiscale complexity of the mean heart rate, degrades with aging and pathology. The “bursty” behavior of the dynamics may be related to intermittency in energy and information flows, as part of multiscale cycles of activation and recovery. Generalized MSE may also be useful in quantifying the dynamical properties of other physiologic and of non-physiologic time series.
Applying Multiscale Entropy to the Complexity Analysis of Rainfall-Runoff Relationships
Chien-Ming Chou
2012-01-01
This paper presents a novel framework for the complexity analysis of rainfall, runoff, and runoff coefficient (RC) time series using multiscale entropy (MSE). The MSE analysis of RC time series was used to investigate changes in the complexity of rainfall-runoff processes due to human activities. Firstly, a coarse graining process was applied to a time series. The sample entropy was then computed for each coarse-grained time series, and plotted as a function of the scale factor. The proposed ...
Escudero, J; Abasolo, D; Hornero, R.; Espino, P; M. Lopez
2007-01-01
We appreciate the interest of Dr Tang in our article. Certainly, our previous results should be taken with caution due to the small database size. Nevertheless, it must be noted that this limitation was clearly recognized in our article. Furthermore, our hypothesis is completely justified from the current state of the art in the analysis of electroencephalogram (EEG) signals from Alzheimer's disease (AD) patients. We evaluated whether the multiscale entropy (MSE) analysis of the EEG backgroun...
Takahashi, Tetsuya; Cho, Raymond Y; Mizuno, Tomoyuki; Kikuchi, Mitsuru; Murata, Tetsuhito; Takahashi, Koichi; Wada, Yuji
2010-01-01
Multiscale entropy (MSE) analysis is a novel entropy-based approach for measuring dynamical complexity in physiological systems over a range of temporal scales. To evaluate this analytic approach as an aid to elucidating the pathophysiologic mechanisms in schizophrenia, we examined MSE in EEG activity in drug-naïve schizophrenia subjects pre- and post-treatment with antipsychotics in comparison with traditional EEG analysis. We recorded eyes-closed resting state EEG from frontal, temporal, pa...
Multi-Scale Entropy Analysis of Different Spontaneous Motor Unit Discharge Patterns
Zhang, Xu; Chen, Xiang; Barkhaus, Paul E.; Zhou, Ping
2013-01-01
This study explores a novel application of multi-scale entropy (MSE) analysis for characterizing different patterns of spontaneous electromyogram (EMG) signals including sporadic, tonic and repetitive spontaneous motor unit discharges, and normal surface EMG baseline. Two algorithms for MSE analysis, namely the standard MSE and the intrinsic mode entropy (IMEn) (based on the recently developed multivariate empirical mode decomposition (MEMD) method), were applied to different patterns of spon...
Wavelet-Based Multi-Scale Entropy Analysis of Complex Rainfall Time Series
Chien-Ming Chou
2011-01-01
This paper presents a novel framework to determine the number of resolution levels in the application of a wavelet transformation to a rainfall time series. The rainfall time series are decomposed using the à trous wavelet transform. Then, multi-scale entropy (MSE) analysis that helps to elucidate some hidden characteristics of the original rainfall time series is applied to the decomposed rainfall time series. The analysis shows that the Mann-Kendall (MK) rank correlation test of MSE curves ...
Multi-scale Analysis for Rosseland Equation with Small Periodic Oscillating Coefficients
Qiao-fu, Zhang
2012-01-01
Rosseland equation is one of the most popular models of the conduction-radiation coupled heat transfer in the thermal protection system. The well-posedness, the corresponding mathematical theory and the Multi-scale analysis method for the Rosseland-type equations with small periodic oscillating coefficients are concerned, which provides a theoretical basis for the Multi-scale computation of the conduction-radiation coupled heat transfer in an optically thick medium with a small periodic structure. The global well-posedness of the Rosseland-type (parabolic) equations is given in the first part. The corresponding solving algorithms and their convergence analysis are presented in the second part. In the third part we study the well-posedness and the second-order two-scale asymptotic expansion of the Rosseland-type (elliptic) equations with small periodic oscillating coefficients. The convergence analysis of the second-order two-scale asymptotic expansion is studied in the last part.
Wavelet-based multiscale performance analysis: An approach to assess and improve hydrological models
Rathinasamy, Maheswaran; Khosa, Rakesh; Adamowski, Jan; ch, Sudheer; Partheepan, G.; Anand, Jatin; Narsimlu, Boini
2014-12-01
The temporal dynamics of hydrological processes are spread across different time scales and, as such, the performance of hydrological models cannot be estimated reliably from global performance measures that assign a single number to the fit of a simulated time series to an observed reference series. Accordingly, it is important to analyze model performance at different time scales. Wavelets have been used extensively in the area of hydrological modeling for multiscale analysis, and have been shown to be very reliable and useful in understanding dynamics across time scales and as these evolve in time. In this paper, a wavelet-based multiscale performance measure for hydrological models is proposed and tested (i.e., Multiscale Nash-Sutcliffe Criteria and Multiscale Normalized Root Mean Square Error). The main advantage of this method is that it provides a quantitative measure of model performance across different time scales. In the proposed approach, model and observed time series are decomposed using the Discrete Wavelet Transform (known as the à trous wavelet transform), and performance measures of the model are obtained at each time scale. The applicability of the proposed method was explored using various case studies--both real as well as synthetic. The synthetic case studies included various kinds of errors (e.g., timing error, under and over prediction of high and low flows) in outputs from a hydrologic model. The real time case studies investigated in this study included simulation results of both the process-based Soil Water Assessment Tool (SWAT) model, as well as statistical models, namely the Coupled Wavelet-Volterra (WVC), Artificial Neural Network (ANN), and Auto Regressive Moving Average (ARMA) methods. For the SWAT model, data from Wainganga and Sind Basin (India) were used, while for the Wavelet Volterra, ANN and ARMA models, data from the Cauvery River Basin (India) and Fraser River (Canada) were used. The study also explored the effect of the
Chia-Hsuan Lee; Tien-Lung Sun
2015-01-01
The goal of this study was to investigate the parameters affecting exergame performance using multi-scale entropy analysis, with the aim of informing the design of exergames for personalized balance training. Test subjects’ center of pressure (COP) displacement data were recorded during exergame play to examine their balance ability at varying difficulty levels of a balance-based exergame; the results of a multi-scale entropy-based analysis were then compared to traditional COP indicators. F...
Multiscale analysis of heart rate, blood pressure and respiration time series
Angelini, L; Marinazzo, D; Nitti, L; Pellicoro, M; Pinna, G D; Stramaglia, S; Tupputi, S A
2005-01-01
We present the multiscale entropy analysis of short term physiological time series of simultaneously acquired samples of heart rate, blood pressure and lung volume, from healthy subjects and from subjects with Chronic Heart Failure. Evaluating the complexity of signals at the multiple time scales inherent in physiologic dynamics, we find that healthy subjects show more complex time series at large time scales; on the other hand, at fast time scales, which are more influenced by respiration, the pathologic dynamics of blood pressure is the most random. These results robustly separate healthy and pathologic groups. We also propose a multiscale approach to evaluate interactions between time series, by performing a multivariate autoregressive modelling of the coarse grained time series: this analysis provides several new quantitative indicators which are statistically correlated with the pathology.
Learning Multiscale Active Facial Patches for Expression Analysis.
Zhong, Lin; Liu, Qingshan; Yang, Peng; Huang, Junzhou; Metaxas, Dimitris N
2015-08-01
In this paper, we present a new idea to analyze facial expression by exploring some common and specific information among different expressions. Inspired by the observation that only a few facial parts are active in expression disclosure (e.g., around mouth, eye), we try to discover the common and specific patches which are important to discriminate all the expressions and only a particular expression, respectively. A two-stage multitask sparse learning (MTSL) framework is proposed to efficiently locate those discriminative patches. In the first stage MTSL, expression recognition tasks are combined to located common patches. Each of the tasks aims to find dominant patches for each expression. Secondly, two related tasks, facial expression recognition and face verification tasks, are coupled to learn specific facial patches for individual expression. The two-stage patch learning is performed on patches sampled by multiscale strategy. Extensive experiments validate the existence and significance of common and specific patches. Utilizing these learned patches, we achieve superior performances on expression recognition compared to the state-of-the-arts. PMID:25291808
Launch Window Analysis for the Magnetospheric Multiscale Mission
Williams, Trevor W.
2012-01-01
The NASA Magnetospheric Multiscale (MMS) mission will fly four spinning spacecraft in formation in highly elliptical orbits to study the magnetosphere of the Earth. This paper describes the development of an MMS launch window tool that uses the orbitaveraged Variation of Parameter equations as the basis for a semi-analytic quantification of the dominant oblateness and lunisolar perturbation effects on the MMS orbit. This approach, coupled with a geometric interpretation of all of the MMS science and engineering constraints, allows a scan of 180(sup 2) = 32,400 different (RAAN, AOP) pairs to be carried out for a specified launch day in less than 10 s on a typical modern laptop. The resulting plot indicates the regions in (RAAN, AOP) space where each constraint is satisfied or violated: their intersection gives, in an easily interpreted graphical manner, the final solution space for the day considered. This tool, SWM76, is now used to provide launch conditions to the full fidelity (but far slower) MMS simulation code: very good agreement has been observed between the two methods.
Low-carbon building assessment and multi-scale input-output analysis
Chen, G. Q.; Chen, H.; Chen, Z. M.; Zhang, Bo; Shao, L.; Guo, S.; Zhou, S. Y.; Jiang, M. M.
2011-01-01
Presented as a low-carbon building evaluation framework in this paper are detailed carbon emission account procedures for the life cycle of buildings in terms of nine stages as building construction, fitment, outdoor facility construction, transportation, operation, waste treatment, property management, demolition, and disposal for buildings, supported by integrated carbon intensity databases based on multi-scale input-output analysis, essential for low-carbon planning, procurement and supply chain design, and logistics management.
Implementation of Grid-computing Framework for Simulation in Multi-scale Structural Analysis
Data Iranata
2010-01-01
A new grid-computing framework for simulation in multi-scale structural analysis is presented. Two levels of parallel processing will be involved in this framework: multiple local distributed computing environments connected by local network to form a grid-based cluster-to-cluster distributed computing environment. To successfully perform the simulation, a large-scale structural system task is decomposed into the simulations of a simplified global model and several detailed component models u...
Multiscale entropy analysis of biological signals: a fundamental bi-scaling law
Gao, Jianbo; Hu, Jing; Liu, Feiyan; Cao, Yinhe
2015-01-01
Since introduced in early 2000, multiscale entropy (MSE) has found many applications in biosignal analysis, and been extended to multivariate MSE. So far, however, no analytic results for MSE or multivariate MSE have been reported. This has severely limited our basic understanding of MSE. For example, it has not been studied whether MSE estimated using default parameter values and short data set is meaningful or not. Nor is it known whether MSE has any relation with other complexity measures,...
Analysis of Multi-Scale Fractal Dimension to Classify Human Motion
da Silva, Núbia Rosa; Bruno, Odemir Martinez
2012-01-01
In recent years there has been considerable interest in human action recognition. Several approaches have been developed in order to enhance the automatic video analysis. Although some developments have been achieved by the computer vision community, the properly classification of human motion is still a hard and challenging task. The objective of this study is to investigate the use of 3D multi-scale fractal dimension to recognize motion patterns in videos. In order to develop a robust strat...
Guo, Li; Guo, XiaoMing; Mi, ChangWen
2012-09-01
In this paper, we propose a concurrent multi-scale finite element (FE) model coupling equations of the degree of freedoms of meso-scale model of ITZs and macroscopic model of bulk pastes. The multi-scale model is subsequently implemented and integrated into ABAQUS resulting in easy application to complex concrete structures. A few benchmark numerical examples are performed to test both the accuracy and efficiency of the developed model in analyzing chloride diffusion in concrete. These examples clearly demonstrate that high diffusivity of ITZs, primarily because of its porous microstructure, tends to accelerate chloride penetration along concentration gradient. The proposed model provides new guidelines for the durability analysis of concrete structures under adverse operating conditions.
Nanosystem Self-Assembly Pathways Discovered via All-Atom Multiscale Analysis
Pankavich, Stephen
2014-01-01
We consider the self-assembly of composite structures from a group of nanocomponents, each consisting of particles within an $N$-atom system. Self-assembly pathways and rates for nanocomposites are derived via a multiscale analysis of the classical Liouville equation. From a reduced statistical framework, rigorous stochastic equations for population levels of beginning, intermediate, and final aggregates are also derived. It is shown that the definition of an assembly type is a self-consistency criterion that must strike a balance between precision and the need for population levels to be slowly varying relative to the time scale of atomic motion. The deductive multiscale approach is complemented by a qualitative notion of multicomponent association and the ensemble of exact atomic-level configurations consistent with them. In processes such as viral self-assembly from proteins and RNA or DNA, there are many possible intermediates, so that it is usually difficult to predict the most efficient assembly pathway...
A Spectral Multiscale Method for Wave Propagation Analysis: Atomistic-Continuum Coupled Simulation
Patra, Amit K; Ganguli, Ranjan
2014-01-01
In this paper, we present a new multiscale method which is capable of coupling atomistic and continuum domains for high frequency wave propagation analysis. The problem of non-physical wave reflection, which occurs due to the change in system description across the interface between two scales, can be satisfactorily overcome by the proposed method. We propose an efficient spectral domain decomposition of the total fine scale displacement along with a potent macroscale equation in the Laplace domain to eliminate the spurious interfacial reflection. We use Laplace transform based spectral finite element method to model the macroscale, which provides the optimum approximations for required dynamic responses of the outer atoms of the simulated microscale region very accurately. This new method shows excellent agreement between the proposed multiscale model and the full molecular dynamics (MD) results. Numerical experiments of wave propagation in a 1D harmonic lattice, a 1D lattice with Lennard-Jones potential, a ...
Lin, Aijing; Shang, Pengjian
2016-04-01
Considering the diverse application of multifractal techniques in natural scientific disciplines, this work underscores the versatility of multiscale multifractal detrended fluctuation analysis (MMA) method to investigate artificial and real-world data sets. The modified MMA method based on cumulative distribution function is proposed with the objective of quantifying the scaling exponent and multifractality of nonstationary time series. It is demonstrated that our approach can provide a more stable and faithful description of multifractal properties in comprehensive range rather than fixing the window length and slide length. Our analyzes based on CDF-MMA method reveal significant differences in the multifractal characteristics in the temporal dynamics between US and Chinese stock markets, suggesting that these two stock markets might be regulated by very different mechanism. The CDF-MMA method is important for evidencing the stable and fine structure of multiscale and multifractal scaling behaviors and can be useful to deepen and broaden our understanding of scaling exponents and multifractal characteristics.
Multiscale recurrence analysis of long-term nonlinear and nonstationary time series
Recurrence analysis is an effective tool to characterize and quantify the dynamics of complex systems, e.g., laminar, divergent or nonlinear transition behaviors. However, recurrence computation is highly expensive as the size of time series increases. Few, if any, previous approaches have been capable of quantifying the recurrence properties from a long-term time series, while which is often collected in the real-time monitoring of complex systems. This paper presents a novel multiscale framework to explore recurrence dynamics in complex systems and resolve computational issues for a large-scale dataset. As opposed to the traditional single-scale recurrence analysis, we characterize and quantify recurrence dynamics in multiple wavelet scales, which captures not only nonlinear but also nonstationary behaviors in a long-term time series. The proposed multiscale recurrence approach was utilized to identify heart failure subjects from the 24-h time series of heart rate variability (HRV). It was shown to identify the conditions of congestive heart failure with an average sensitivity of 92.1% and specificity of 94.7%. The proposed multiscale recurrence framework can be potentially extended to other nonlinear dynamic methods that are computationally expensive for large-scale datasets.
Multivariate multiscale entropy: a tool for complexity analysis of multichannel data.
Ahmed, Mosabber Uddin; Mandic, Danilo P
2011-12-01
This work generalizes the recently introduced univariate multiscale entropy (MSE) analysis to the multivariate case. This is achieved by introducing multivariate sample entropy (MSampEn) in a rigorous way, in order to account for both within- and cross-channel dependencies in multiple data channels, and by evaluating it over multiple temporal scales. The multivariate MSE (MMSE) method is shown to provide an assessment of the underlying dynamical richness of multichannel observations, and more degrees of freedom in the analysis than standard MSE. The benefits of the proposed approach are illustrated by simulations on complexity analysis of multivariate stochastic processes and on real-world multichannel physiological and environmental data. PMID:22304127
Wavelet multiscale analysis of a power system load variance
Avdakovic, Samir; Nuhanovic, Amir; Kusljugic, Mirza
2013-01-01
Wavelet transform (WT) represents a very attractive mathematical area for just more than 15 years of its research in applications in electrical engineering. This is mainly due to its advantages over other processing techniques and signal analysis, which is reflected in the time-frequency analysis, and so it has an important application in the processing and analysis of time series. In this paper, for example, the analysis of the hourly load of a real power system over the past few yea...
Analysis of individual brain activation maps using hierarchical description and multiscale detection
The authors propose a new method for the analysis of brain activation images that aims at detecting activated volumes rather than pixels. The method is based on Poisson process modeling, hierarchical description, and multiscale detection (MSD). Its performances have been assessed using both Monte Carlo simulated images and experimental PET brain activation data. As compared to other methods, the MSD approach shows enhanced sensitivity with a controlled overall type I error, and has the ability to provide an estimate of the spatial limits of the detected signals. It is applicable to any kind of difference image for which the spatial autocorrelation function can be approximated by a stationary Gaussian function
Costa, Madalena D.; Ary L Goldberger
2015-01-01
We introduce a generalization of multiscale entropy (MSE) analysis. The method is termed MSE n , where the subscript denotes the moment used to coarse-grain a time series. MSE μ , described previously, uses the mean value (first moment). Here, we focus on MSE σ 2 , which uses the second moment, i.e., the variance. MSE σ 2 quantifies the dynamics of the volatility (variance) of a signal over multiple time scales. We use the method to analyze the structure of heartbeat time series. We find that...
Multiscale entropy analysis of biological signals:a fundamental bi-scaling law
Jianbo eGao
2015-01-01
Multiscale entropy (MSE) analysis is an interesting method for analyzing biological signals.So far, however, few analytic results for MSE have been reported. This has severelylimited our basic understanding of MSE. To overcome this limitation, and more importantly,to guide more fruitful applications of MSE in various areas of life sciences, we derive, for timeseries with long memory, a fundamental bi-scaling law, one for the scale in the phase space,the other for the block size used for smoot...
Mood states modulate complexity in heartbeat dynamics: A multiscale entropy analysis
Valenza, G.; Nardelli, M.; Bertschy, G.; Lanata, A.; Scilingo, E. P.
2014-07-01
This paper demonstrates that heartbeat complex dynamics is modulated by different pathological mental states. Multiscale entropy analysis was performed on R-R interval series gathered from the electrocardiogram of eight bipolar patients who exhibited mood states among depression, hypomania, and euthymia, i.e., good affective balance. Three different methodologies for the choice of the sample entropy radius value were also compared. We show that the complexity level can be used as a marker of mental states being able to discriminate among the three pathological mood states, suggesting to use heartbeat complexity as a more objective clinical biomarker for mental disorders.
Arnold, Steven M.; Bednarcyk, Brett A.; Hussain, Aquila; Katiyar, Vivek
2010-01-01
A unified framework is presented that enables coupled multiscale analysis of composite structures and associated graphical pre- and postprocessing within the Abaqus/CAE environment. The recently developed, free, Finite Element Analysis--Micromechanics Analysis Code (FEAMAC) software couples NASA's Micromechanics Analysis Code with Generalized Method of Cells (MAC/GMC) with Abaqus/Standard and Abaqus/Explicit to perform micromechanics based FEA such that the nonlinear composite material response at each integration point is modeled at each increment by MAC/GMC. The Graphical User Interfaces (FEAMAC-Pre and FEAMAC-Post), developed through collaboration between SIMULIA Erie and the NASA Glenn Research Center, enable users to employ a new FEAMAC module within Abaqus/CAE that provides access to the composite microscale. FEA IAC-Pre is used to define and store constituent material properties, set-up and store composite repeating unit cells, and assign composite materials as sections with all data being stored within the CAE database. Likewise FEAMAC-Post enables multiscale field quantity visualization (contour plots, X-Y plots), with point and click access to the microscale i.e., fiber and matrix fields).
Escudero, Javier; Acar, Evrim; Fernández, Alberto; Bro, Rasmus
2015-10-01
Tensor factorisations have proven useful to model amplitude and spectral information of brain recordings. Here, we assess the usefulness of tensor factorisations in the multiway analysis of other brain signal features in the context of complexity measures recently proposed to inspect multiscale dynamics. We consider the "refined composite multiscale entropy" (rcMSE), which computes entropy "profiles" showing levels of physiological complexity over temporal scales for individual signals. We compute the rcMSE of resting-state magnetoencephalogram (MEG) recordings from 36 patients with Alzheimer's disease and 26 control subjects. Instead of traditional simple visual examinations, we organise the entropy profiles as a three-way tensor to inspect relationships across temporal and spatial scales and subjects with multiway data analysis techniques based on PARAFAC and PARAFAC2 factorisations. A PARAFAC2 model with two factors was appropriate to account for the interactions in the entropy tensor between temporal scales and MEG channels for all subjects. Moreover, the PARAFAC2 factors had information related to the subjects' diagnosis, achieving a cross-validated area under the ROC curve of 0.77. This confirms the suitability of tensor factorisations to represent electrophysiological brain data efficiently despite the unsupervised nature of these techniques. This article is part of a Special Issue entitled 'Neural data analysis'. PMID:25982737
Multiscale analysis of restoration priorities for marine shoreline planning.
Diefenderfer, Heida L; Sobocinski, Kathryn L; Thom, Ronald M; May, Christopher W; Borde, Amy B; Southard, Susan L; Vavrinec, John; Sather, Nichole K
2009-10-01
Planners are being called on to prioritize marine shorelines for conservation status and restoration action. This study documents an approach to determining the management strategy most likely to succeed based on current conditions at local and landscape scales. The conceptual framework based in restoration ecology pairs appropriate restoration strategies with sites based on the likelihood of producing long-term resilience given the condition of ecosystem structures and processes at three scales: the shorezone unit (site), the drift cell reach (nearshore marine landscape), and the watershed (terrestrial landscape). The analysis is structured by a conceptual ecosystem model that identifies anthropogenic impacts on targeted ecosystem functions. A scoring system, weighted by geomorphic class, is applied to available spatial data for indicators of stress and function using geographic information systems. This planning tool augments other approaches to prioritizing restoration, including historical conditions and change analysis and ecosystem valuation. PMID:19495862
Multiscale Embedded Gene Co-expression Network Analysis
Song, Won-Min; Zhang, Bin
2015-01-01
Gene co-expression network analysis has been shown effective in identifying functional co-expressed gene modules associated with complex human diseases. However, existing techniques to construct co-expression networks require some critical prior information such as predefined number of clusters, numerical thresholds for defining co-expression/interaction, or do not naturally reproduce the hallmarks of complex systems such as the scale-free degree distribution of small-worldness. Previously, a...
Multi-scale model analysis and hindcast of the 2013 Colorado Flood
Gochis, David; Yu, Wei; Sampson, Kevin; Dugger, Aubrey; McCreight, James; Zhang, Yongxin; Ikeda, Kyoko
2015-04-01
While the generation of most flood and flash flood events is fundamentally linked to the occurrence of heavy rainfall, the physical mechanisms responsible for translating rainfall into floods are complex and manifold. These runoff generation processes evolve over many spatial and temporal scales during the course of flooding events. As such robust flood and flash flood prediction systems need to account for multitude of terrestrial processes occurring over a wide range of space and time scales. One such extreme multiscale flood event was the 2013 Colorado Flood in which over 400 mm of rainfall fell along the Rock Mountain mountain front region over the course of a few days. The flooding impacts from this heavy rainfall event included not only high, fast flows in steep mountain streams but also included large areas of inundation on the adjacent plains and numerous soil saturation excess impacts such as hillslope failures and groundwater intrusions into domestic structures. A multi-scale and multi-process evaluation of this flood event is performed using the community WRF-Hydro modeling system. We incorporate several operational quantitative precipitation estimate and quantitative precipitation forecast products in the analysis and document the skill of multiple configurations of WRF-Hydro physics options across a range of contributing area length scales. Emphasis is placed on assessing how well the different model configurations capture the multi-scale streamflow response from small headwater catchments out to the entire South Platte River basin whose total contributing area exceeds 25,000 sq km. In addition to streamflow we also present evaluations of event simulations and hindcasts of soil saturation fraction, groundwater levels and inundated areas as a means of assessing different runoff generation mechanisms. Finally, results from a U.S. national-scale, fully-coupled hydrometeorological hindcast of the 2013 Colorado flood event using the combined WRF atmospheric
Multivariate Image Analysis in Gaussian Multi-Scale Space for Defect Detection
Dong-tai Liang; Wei-yan Deng; Xuan-yin Wang; Yang Zhang
2009-01-01
Inspired by the coarse-to-fine visual perception process of human vision system, a new approach based on Gaussian multi-scale space for defect detection of industrial products was proposed. By selecting different scale parameters of the Gaussian kernel, the multi-scale representation of the original image data could be obtained and used to constitute the multi-variate image, in which each channel could represent a perceptual observation of the original image from different scales. The Multivariate Image Analysis (MIA) techniques were used to extract defect features information. The MIA combined Principal Component Analysis (PCA) to obtain the principal component scores of the multivariate test image. The Q-statistic image, derived from the residuals after the extraction of the first principal component score and noise, could be used to efficiently reveal the surface defects with an appropriate threshold value decided by training images. Experimental results show that the proposed method performs better than the gray histogram-based method. It has less sensitivity to the inhomogeneous of illumination, and has more robustness and reliability of defect detection with lower pseudo reject rate.
Goïc, Gaëtan Le; Bigerelle, Maxence; Samper, Serge; Favrelière, Hugues; Pillet, Maurice
2016-01-01
This study investigates the correlations between the topography of different damaged rough surfaces and process conditions. Several surfaces are measured and compared to determine if they can be discriminated. The analysis is performed by using Gaussian Filtering, Wavelet Transform and a more recent approach named Discrete Modal Decomposition. Standardized 3D roughness parameters are computed for each multiscale method, filter (e.g., high-pass, low-pass and band-pass) and available scale. The relevance (i.e., the ability to discriminate surface topographies corresponding to different process conditions) is then investigated using a statistical analysis based on the MesRugTM expert system. The results indicate clear differences between the multiscale methods and show that the Wavelet approach is useful when characterizing localized surface defects while Gaussian Filtering is more appropriate for highly periodic morphological structures. For more complex topographies, this study also clearly shows that the Discrete Modal Decomposition exhibits compelling abilities that fall between those of the Gaussian and Wavelet approaches; this method is clearly more relevant than the Gaussian method in the case of localized defects and less relevant in the case of highly periodical structures and fractal surfaces (1 /fα spectrum). This can be explained by the modulated frequency/amplitude descriptors generated via the modal basis.
Multiscale analysis of heart beat interval increment series and its clinical significance
HUANG XiaoLin; NING XinBao; WANG XinLong
2009-01-01
Analysis of multiscale entropy (MSE) and multiscale standard deviation (MSD) are performed for both the heart rate interval series and the interval increment series. For the interval series, it is found that, it is impractical to discriminate the diseases of atrial fibrillation (AF) and congestive heart failure (CHF) unambiguously from the healthy. A clear discrimination from the healthy, both young and old, however, can be made in the MSE analysis of the increment series where we find that both CHF and AF sufferers have significantly low MSE values in the whole range of time scales investigated, which reveals that there are common dynamic characteristics underlying these two different diseases. In addition, we propose the sample entropy (SE) corresponding to time scale factor 4 of increment series as a diag-nosis index of both AF and CHF, and the reference threshold is recommended. Further indication that this index can help discriminate sensitively the mild heart failure (cardiac function classes 1 and 2) from the healthy gives a clue to early clinic diagnosis of CHF.
Multiscale analysis of nonlinear systems using computational homology
Konstantin Mischaikow, Rutgers University/Georgia Institute of Technology, Michael Schatz, Georgia Institute of Technology, William Kalies, Florida Atlantic University, Thomas Wanner,George Mason University
2010-05-19
This is a collaborative project between the principal investigators. However, as is to be expected, different PIs have greater focus on different aspects of the project. This report lists these major directions of research which were pursued during the funding period: (1) Computational Homology in Fluids - For the computational homology effort in thermal convection, the focus of the work during the first two years of the funding period included: (1) A clear demonstration that homology can sensitively detect the presence or absence of an important flow symmetry, (2) An investigation of homology as a probe for flow dynamics, and (3) The construction of a new convection apparatus for probing the effects of large-aspect-ratio. (2) Computational Homology in Cardiac Dynamics - We have initiated an effort to test the use of homology in characterizing data from both laboratory experiments and numerical simulations of arrhythmia in the heart. Recently, the use of high speed, high sensitivity digital imaging in conjunction with voltage sensitive fluorescent dyes has enabled researchers to visualize electrical activity on the surface of cardiac tissue, both in vitro and in vivo. (3) Magnetohydrodynamics - A new research direction is to use computational homology to analyze results of large scale simulations of 2D turbulence in the presence of magnetic fields. Such simulations are relevant to the dynamics of black hole accretion disks. The complex flow patterns from simulations exhibit strong qualitative changes as a function of magnetic field strength. Efforts to characterize the pattern changes using Fourier methods and wavelet analysis have been unsuccessful. (4) Granular Flow - two experts in the area of granular media are studying 2D model experiments of earthquake dynamics where the stress fields can be measured; these stress fields from complex patterns of 'force chains' that may be amenable to analysis using computational homology. (5) Microstructure
Multiscale analysis of nonlinear systems using computational homology
Konstantin Mischaikow; Michael Schatz; William Kalies; Thomas Wanner
2010-05-24
This is a collaborative project between the principal investigators. However, as is to be expected, different PIs have greater focus on different aspects of the project. This report lists these major directions of research which were pursued during the funding period: (1) Computational Homology in Fluids - For the computational homology effort in thermal convection, the focus of the work during the first two years of the funding period included: (1) A clear demonstration that homology can sensitively detect the presence or absence of an important flow symmetry, (2) An investigation of homology as a probe for flow dynamics, and (3) The construction of a new convection apparatus for probing the effects of large-aspect-ratio. (2) Computational Homology in Cardiac Dynamics - We have initiated an effort to test the use of homology in characterizing data from both laboratory experiments and numerical simulations of arrhythmia in the heart. Recently, the use of high speed, high sensitivity digital imaging in conjunction with voltage sensitive fluorescent dyes has enabled researchers to visualize electrical activity on the surface of cardiac tissue, both in vitro and in vivo. (3) Magnetohydrodynamics - A new research direction is to use computational homology to analyze results of large scale simulations of 2D turbulence in the presence of magnetic fields. Such simulations are relevant to the dynamics of black hole accretion disks. The complex flow patterns from simulations exhibit strong qualitative changes as a function of magnetic field strength. Efforts to characterize the pattern changes using Fourier methods and wavelet analysis have been unsuccessful. (4) Granular Flow - two experts in the area of granular media are studying 2D model experiments of earthquake dynamics where the stress fields can be measured; these stress fields from complex patterns of 'force chains' that may be amenable to analysis using computational homology. (5) Microstructure
Multi-Scale Morphological Analysis of SDSS DR5 Survey using the Metric Space Technique
Wu, Yongfeng; Khalil, Andre
2008-01-01
Following novel development and adaptation of the Metric Space Technique (MST), a multi-scale morphological analysis of the Sloan Digital Sky Survey (SDSS) Data Release 5 (DR5) was performed. The technique was adapted to perform a space-scale morphological analysis by filtering the galaxy point distributions with a smoothing gaussian function, thus giving quantitative structural information on all size scales between 5 and 250 Mpc. The analysis was performed on a dozen slices of a volume of space containing many newly measured galaxies from the SDSS DR5 survey. Using the MST, observational data were compared to galaxy samples taken from N-body simulations with current best estimates of cosmological parameters and from random catalogs. By using the maximal ranking method among MST output functions we also develop a way to quantify the overall similarity of the observed samples with the simulated samples.
Effect of surface step on nanoindentation of thin films by multiscale analysis
Nanoindentation simulations on flat and stepped surfaces are respectively investigated using the quasicontinuum method based on the embedded-atom method potential. Effect of surface step considering indenter size and step height is studied. Results show that the critical load for the first dislocation emission will be decreased with the increase of step height. However, the effect of surface step will be weakened if the indenter size continues to increase. Initial atomistic structures after dislocation nucleation and emission are discussed systematically. The initial dislocations are not quite identically nucleated under the stepped surface. Stress distribution analysis reveals that the shear stress in the slip planes close to the step is much larger than the shear stress in the slip planes far from the step for nanoindentation on the stepped surface. The multiscale simulation results are consistent with experimental results and analytic solutions. The conclusions about step effect considering indenter size and step height are helpful for understanding the microscopic mechanism of nanoindentation tests on thin films with surface step. - Highlights: ►We modeled nanoindentation process into stepped surface by a multiscale method. ►Step effect considering indenter size and step height is investigated. ►The initial atomistic structures after deformation are discussed systematically. ►The critical load will be decreased with the increase of step height. ►Step effect will be weakened if the indenter size continues to increase.
Nature's functional surfaces are typically hierarchical multiscale structures. There are several techniques for producing such artificial structures on polymers but their mass production is not straightforward. We present here a simple and versatile method for manufacturing hierarchical multiscale polymer surface patterns. The microroughening technique permits the single-step production of multilevel three-dimensional surface architectures in a mechanically durable nickel mold. The molding technique is suitable for area-controlled fabrication of structures with various geometrical shapes on smooth and curved surfaces. The mold structures were transferred to polypropylene surfaces by means of injection molding. The fabricated surface structures were characterized by using a filtered power spectral density method which facilitated a quantitative study of the roughness distributions at different length scales of structure periodicities. Analysis showed that the microroughening technique is an appropriate tool for controlled production of surface roughness at a micro-nanometer scale. Roughness distribution values can be used for predicting surface structure-related properties such as wetting, and the distributions can also be simulated without an experimental preparation process. The work presents a suitable approach for mass production of hierarchical polymer surfaces at different length scales and provides a new route for designing surface structures with tunable wetting properties. (paper)
Multiscale Multiphysics-Based Modeling and Analysis on the Tool Wear in Micro Drilling
Niu, Zhichao; Cheng, Kai
2016-02-01
In micro-cutting processes, process variables including cutting force, cutting temperature and drill-workpiece interfacing conditions (lubrication and interaction, etc.) significantly affect the tool wear in a dynamic interactive in-process manner. The resultant tool life and cutting performance directly affect the component surface roughness, material removal rate and form accuracy control, etc. In this paper, a multiscale multiphysics oriented approach to modeling and analysis is presented particularly on tooling performance in micro drilling processes. The process optimization is also taken account based on establishing the intrinsic relationship between process parameters and cutting performance. The modeling and analysis are evaluated and validated through well-designed machining trials, and further supported by metrology measurements and simulations. The paper is concluded with a further discussion on the potential and application of the approach for broad micro manufacturing purposes.
Bornas, Xavier; Llabrés, Jordi; Noguera, Miquel; López, Ana M A; Gelabert, Joan Miquel; Vila, Irene
2006-10-01
In this study we explored the changes in the variability and complexity of the electrocardiogram (ECG) of flight phobics (N=61) and a matched non-phobic control group (N=58) when they performed a paced breathing task and were exposed to flight related stimuli. Lower complexity/entropy values were expected in phobics as compared to controls. The phobic system complexity as well as the heart rate variability (HRV) were expected to be reduced by the exposure to fearful stimuli. The multiscale entropy (MSE) analysis revealed lower entropy values in phobics during paced breathing and exposure, and a complexity loss was observed in phobics during exposure to threatening situations. The expected HRV decreases were not found in this study. The discussion is focused on the distinction between variability and complexity measures of the cardiac output, and on the usefulness of the MSE analysis in the field of anxiety disorders. PMID:16839658
Multi-scale dynamical analysis (MSDA) of sea level records versus PDO, AMO, and NAO indexes
Scafetta, Nicola
2013-01-01
Herein I propose a multi-scale dynamical analysis to facilitate the physical interpretation of tide gauge records. The technique uses graphical diagrams. It is applied to six secular-long tide gauge records representative of the world oceans: Sydney, Pacific coast of Australia; Fremantle, Indian Ocean coast of Australia; New York City, Atlantic coast of USA; Honolulu, U.S. state of Hawaii; San Diego, U.S. state of California; and Venice, Mediterranean Sea, Italy. For comparison, an equivalent analysis is applied to the Pacific Decadal Oscillation (PDO) index and to the Atlantic Multidecadal Oscillation (AMO) index. Finally, a global reconstruction of sea level and a reconstruction of the North Atlantic Oscillation (NAO) index are analyzed and compared: both sequences cover about three centuries from 1700 to 2000. The proposed methodology quickly highlights oscillations and teleconnections among the records at the decadal and multidecadal scales. At the secular time scales tide gauge records present relatively...
Sparse Image and Signal Processing: Wavelets and Related Geometric Multiscale Analysis
Starck, J-L.; Murtagh, Fionn; Fadili, J.
2010-01-01
This book presents the state of the art in sparse and multiscale image and signal processing, covering linear multiscale transforms, such as wavelet, ridgelet, or curvelet transforms, and non-linear multiscale transforms based on the median and mathematical morphology operators. Recent concepts of sparsity and morphological diversity are described and exploited for various problems such as denoising, inverse problem regularization, sparse signal decomposition, blind source separation, and com...
Valenza, G; Wendt, H; Kiyono, K; Hayano, J; Watanabe, E; Yamamoto, Y; Abry, P; Barbieri, R
2015-08-01
Multiscale analysis of human heartbeat dynamics has been proved effective in characterizeing cardiovascular control physiology in health and disease. However, estimation of multiscale properties can be affected by the interpolation procedure used to preprocess the unevenly sampled R-R intervals derived from the ECG. To this extent, in this study we propose the estimation of wavelet coefficients and wavelet leaders on the output of inhomogeneous point process models of heartbeat dynamics. The RR interval series is modeled using probability density functions (pdfs) characterizing and predicting the time until the next heartbeat event occurs, as a linear function of the past history. Multiscale analysis is then applied to the pdfs' instantaneous first order moment. The proposed approach is tested on experimental data gathered from 57 congestive heart failure (CHF) patients by evaluating the recognition accuracy in predicting survivor and non-survivor patients, and by comparing performances from the informative point-process based interpolation and non-informative spline-based interpolation. Results demonstrate that multiscale analysis of point-process high-resolution representations achieves the highest prediction accuracy of 65.45%, proving our method as a promising tool to assess risk prediction in CHF patients. PMID:26736666
Takahashi, Tetsuya; Cho, Raymond Y.; Mizuno, Tomoyuki; Kikuchi, Mitsuru; Murata, Tetsuhito; Takahashi, Koichi; Wada, Yuji
2010-01-01
Multiscale entropy (MSE) analysis is a novel entropy-based approach for measuring dynamical complexity in physiological systems over a range of temporal scales. To evaluate this analytic approach as an aid to elucidating the pathophysiologic mechanisms in schizophrenia, we examined MSE in EEG activity in drug-naïve schizophrenia subjects pre- and post-treatment with antipsychotics in comparison with traditional EEG analysis. We recorded eyes-closed resting state EEG from frontal, temporal, parietal and occipital regions in drug-naïve 22 schizophrenia and 24 age-matched healthy control subjects. Fifteen patients were re-evaluated within 2–8 weeks after the initiation of antipsychotic treatment. For each participant, MSE was calculated on one continuous 60 second epoch for each experimental session. Schizophrenia subjects showed significantly higher complexity at higher time scales (lower frequencies), than that of healthy controls in fronto-centro-temporal, but not in parieto-occipital regions. Post-treatment, this higher complexity decreased to healthy control subject levels selectively in fronto-central regions, while the increased complexity in temporal sites remained higher. Comparative power analysis identified spectral slowing in frontal regions in pre-treatment schizophrenia subjects, consistent with previous findings, whereas no antipsychotic treatment effect was observed. In summary, multiscale entropy measures identified abnormal dynamical EEG signal complexity in anterior brain areas in schizophrenia that normalized selectively in fronto-central areas with antipsychotic treatment. These findings show that entropy-based analytic methods may serve as a novel approach for characterizing and understanding abnormal cortical dynamics in schizophrenia, and elucidating the therapeutic mechanisms of antipsychotics. PMID:20149880
Takahashi, Tetsuya; Cho, Raymond Y; Mizuno, Tomoyuki; Kikuchi, Mitsuru; Murata, Tetsuhito; Takahashi, Koichi; Wada, Yuji
2010-05-15
Multiscale entropy (MSE) analysis is a novel entropy-based approach for measuring dynamical complexity in physiological systems over a range of temporal scales. To evaluate this analytic approach as an aid to elucidating the pathophysiologic mechanisms in schizophrenia, we examined MSE in EEG activity in drug-naive schizophrenia subjects pre- and post-treatment with antipsychotics in comparison with traditional EEG analysis. We recorded eyes-closed resting-state EEG from frontal, temporal, parietal, and occipital regions in drug-naive 22 schizophrenia and 24 age-matched healthy control subjects. Fifteen patients were re-evaluated within 2-8 weeks after the initiation of antipsychotic treatment. For each participant, MSE was calculated on one continuous 60-s epoch for each experimental session. Schizophrenia subjects showed significantly higher complexity at higher time scales (lower frequencies) than did healthy controls in fronto-centro-temporal, but not in parieto-occipital regions. Post-treatment, this higher complexity decreased to healthy control subject levels selectively in fronto-central regions, while the increased complexity in temporal sites remained higher. Comparative power analysis identified spectral slowing in frontal regions in pre-treatment schizophrenia subjects, consistent with previous findings, whereas no antipsychotic treatment effect was observed. In summary, multiscale entropy measures identified abnormal dynamical EEG signal complexity in anterior brain areas in schizophrenia that normalized selectively in fronto-central areas with antipsychotic treatment. These findings show that entropy-based analytic methods may serve as a novel approach for characterizing and understanding abnormal cortical dynamics in schizophrenia and elucidating the therapeutic mechanisms of antipsychotics. PMID:20149880
Multi-Scale Entropy Analysis as a Method for Time-Series Analysis of Climate Data
Heiko Balzter
2015-03-01
Full Text Available Evidence is mounting that the temporal dynamics of the climate system are changing at the same time as the average global temperature is increasing due to multiple climate forcings. A large number of extreme weather events such as prolonged cold spells, heatwaves, droughts and floods have been recorded around the world in the past 10 years. Such changes in the temporal scaling behaviour of climate time-series data can be difficult to detect. While there are easy and direct ways of analysing climate data by calculating the means and variances for different levels of temporal aggregation, these methods can miss more subtle changes in their dynamics. This paper describes multi-scale entropy (MSE analysis as a tool to study climate time-series data and to identify temporal scales of variability and their change over time in climate time-series. MSE estimates the sample entropy of the time-series after coarse-graining at different temporal scales. An application of MSE to Central European, variance-adjusted, mean monthly air temperature anomalies (CRUTEM4v is provided. The results show that the temporal scales of the current climate (1960–2014 are different from the long-term average (1850–1960. For temporal scale factors longer than 12 months, the sample entropy increased markedly compared to the long-term record. Such an increase can be explained by systems theory with greater complexity in the regional temperature data. From 1961 the patterns of monthly air temperatures are less regular at time-scales greater than 12 months than in the earlier time period. This finding suggests that, at these inter-annual time scales, the temperature variability has become less predictable than in the past. It is possible that climate system feedbacks are expressed in altered temporal scales of the European temperature time-series data. A comparison with the variance and Shannon entropy shows that MSE analysis can provide additional information on the
Lahousse, T.; Chang, K. T.; Lin, Y. H.
2011-10-01
We developed a multi-scale OBIA (object-based image analysis) landslide detection technique to map shallow landslides in the Baichi watershed, Taiwan, after the 2004 Typhoon Aere event. Our semi-automated detection method selected multiple scales through landslide size statistics analysis for successive classification rounds. The detection performance achieved a modified success rate (MSR) of 86.5% with the training dataset and 86% with the validation dataset. This performance level was due to the multi-scale aspect of our methodology, as the MSR for single scale classification was substantially lower, even after spectral difference segmentation, with a maximum of 74%. Our multi-scale technique was capable of detecting landslides of varying sizes, including very small landslides, up to 95 m2. The method presented certain limitations: the thresholds we established for classification were specific to the study area, to the landslide type in the study area, and to the spectral characteristics of the satellite image. Because updating site-specific and image-specific classification thresholds is easy with OBIA software, our multi-scale technique is expected to be useful for mapping shallow landslides at watershed level.
T. Lahousse
2011-10-01
Full Text Available We developed a multi-scale OBIA (object-based image analysis landslide detection technique to map shallow landslides in the Baichi watershed, Taiwan, after the 2004 Typhoon Aere event. Our semi-automated detection method selected multiple scales through landslide size statistics analysis for successive classification rounds. The detection performance achieved a modified success rate (MSR of 86.5% with the training dataset and 86% with the validation dataset. This performance level was due to the multi-scale aspect of our methodology, as the MSR for single scale classification was substantially lower, even after spectral difference segmentation, with a maximum of 74%. Our multi-scale technique was capable of detecting landslides of varying sizes, including very small landslides, up to 95 m^{2}. The method presented certain limitations: the thresholds we established for classification were specific to the study area, to the landslide type in the study area, and to the spectral characteristics of the satellite image. Because updating site-specific and image-specific classification thresholds is easy with OBIA software, our multi-scale technique is expected to be useful for mapping shallow landslides at watershed level.
Three-band decomposition analysis in multiscale FSI models of abdominal aortic aneurysms
Nestola, Maria G. C.; Gizzi, Alessio; Cherubini, Christian; Filippi, Simonetta
2016-07-01
Computational modeling plays an important role in biology and medicine to assess the effects of hemodynamic alterations in the onset and development of vascular pathologies. Synthetic analytic indices are of primary importance for a reliable and effective a priori identification of the risk. In this scenario, we propose a multiscale fluid-structure interaction (FSI) modeling approach of hemodynamic flows, extending the recently introduced three-band decomposition (TBD) analysis for moving domains. A quantitative comparison is performed with respect to the most common hemodynamic risk indicators in a systematic manner. We demonstrate the reliability of the TBD methodology also for deformable domains by assuming a hyperelastic formulation of the arterial wall and a Newtonian approximation of the blood flow. Numerical simulations are performed for physiologic and pathologic axially symmetric geometry models with particular attention to abdominal aortic aneurysms (AAAs). Risk assessment, limitations and perspectives are finally discussed.
Alzheimer's Disease Detection in Brain Magnetic Resonance Images Using Multiscale Fractal Analysis
We present a new automated system for the detection of brain magnetic resonance images (MRI) affected by Alzheimer's disease (AD). The MRI is analyzed by means of multiscale analysis (MSA) to obtain its fractals at six different scales. The extracted fractals are used as features to differentiate healthy brain MRI from those of AD by a support vector machine (SVM) classifier. The result of classifying 93 brain MRIs consisting of 51 images of healthy brains and 42 of brains affected by AD, using leave-one-out cross-validation method, yielded 99.18% ± 0.01 classification accuracy, 100% sensitivity, and 98.20% ± 0.02 specificity. These results and a processing time of 5.64 seconds indicate that the proposed approach may be an efficient diagnostic aid for radiologists in the screening for AD
Multi-scale reliability analysis and updating of complex systems by use of linear programming
Complex systems are characterized by large numbers of components, cut sets or link sets, or by statistical dependence between the component states. These measures of complexity render the computation of system reliability a challenging task. In this paper, a decomposition approach is described, which, together with a linear programming formulation, allows determination of bounds on the reliability of complex systems with manageable computational effort. The approach also facilitates multi-scale modeling and analysis of a system, whereby varying degrees of detail can be considered in the decomposed system. The paper also describes a method for computing bounds on conditional probabilities by use of linear programming, which can be used to update the system reliability for any given event. Applications to a power network demonstrate the methodology
Lin, Aijing; Ma, Hui; Shang, Pengjian
2015-10-01
Here we propose the new method DH-MMA, based on multiscale multifractal detrended fluctuation analysis(MMA), to investigate the scaling properties in stock markets. It is demonstrated that our approach can provide a more stable and faithful description of the scaling properties in comprehensive range rather than fixing the window length and slide length. It allows the assessment of more universal and subtle scaling characteristics. We illustrate DH-MMA by selecting power-law artificial data sets and six stock markets from US and China. The US stocks exhibit very strong multifractality for positive values of q, however, the Chinese stocks show stronger multifractality for negative q than positive q. In general, the US stock markets show similar behaviors, but Chinese stock markets display distinguishing characteristics.
Multiscale entropy analysis of biological signals:a fundamental bi-scaling law
Jianbo eGao
2015-06-01
Full Text Available Multiscale entropy (MSE analysis is an interesting method for analyzing biological signals.So far, however, few analytic results for MSE have been reported. This has severelylimited our basic understanding of MSE. To overcome this limitation, and more importantly,to guide more fruitful applications of MSE in various areas of life sciences, we derive, for timeseries with long memory, a fundamental bi-scaling law, one for the scale in the phase space,the other for the block size used for smoothing. We illustrate the usefulness of the approach byexamining heart rate variability (HRV data for the purpose of distinguishing healthy subjectsfrom patients with congestive heart failure, a life-threatening condition.1
Wang, Wen-Jing; Cui, Ling-Li; Chen, Dao-Yun
2016-04-01
Monitoring of potential bearing faults in operation is of critical importance to safe operation of high speed trains. One of the major challenges is how to differentiate relevant signals to operational conditions of bearings from noises emitted from the surrounding environment. In this work, we report a procedure for analyzing acoustic emission signals collected from rolling bearings for diagnosis of bearing health conditions by examining their morphological pattern spectrum (MPS) through a multi-scale morphology analysis procedure. The results show that acoustic emission signals resulted from a given type of bearing faults share rather similar MPS curves. Further examinations in terms of sample entropy and Lempel-Ziv complexity of MPS curves suggest that these two parameters can be utilized to determine damage modes.
Etournay, Raphaël; Merkel, Matthias; Popović, Marko; Brandl, Holger; Dye, Natalie A; Aigouy, Benoît; Salbreux, Guillaume; Eaton, Suzanne; Jülicher, Frank
2016-01-01
Segmentation and tracking of cells in long-term time-lapse experiments has emerged as a powerful method to understand how tissue shape changes emerge from the complex choreography of constituent cells. However, methods to store and interrogate the large datasets produced by these experiments are not widely available. Furthermore, recently developed methods for relating tissue shape changes to cell dynamics have not yet been widely applied by biologists because of their technical complexity. We therefore developed a database format that stores cellular connectivity and geometry information of deforming epithelial tissues, and computational tools to interrogate it and perform multi-scale analysis of morphogenesis. We provide tutorials for this computational framework, called TissueMiner, and demonstrate its capabilities by comparing cell and tissue dynamics in vein and inter-vein subregions of the Drosophila pupal wing. These analyses reveal an unexpected role for convergent extension in shaping wing veins. PMID:27228153
Multiscale Static Analysis of Notched and Unnotched Laminates Using the Generalized Method of Cells
Naghipour Ghezeljeh, Paria; Arnold, Steven M.; Pineda, Evan J.; Stier, Bertram; Hansen, Lucas; Bednarcyk, Brett A.; Waas, Anthony M.
2016-01-01
The generalized method of cells (GMC) is demonstrated to be a viable micromechanics tool for predicting the deformation and failure response of laminated composites, with and without notches, subjected to tensile and compressive static loading. Given the axial [0], transverse [90], and shear [+45/-45] response of a carbon/epoxy (IM7/977-3) system, the unnotched and notched behavior of three multidirectional layups (Layup 1: [0,45,90,-45](sub 2S), Layup 2: [0,60,0](sub 3S), and Layup 3: [30,60,90,-30, -60](sub 2S)) are predicted under both tensile and compressive static loading. Matrix nonlinearity is modeled in two ways. The first assumes all nonlinearity is due to anisotropic progressive damage of the matrix only, which is modeled, using the multiaxial mixed-mode continuum damage model (MMCDM) within GMC. The second utilizes matrix plasticity coupled with brittle final failure based on the maximum principle strain criteria to account for matrix nonlinearity and failure within the Finite Element Analysis--Micromechanics Analysis Code (FEAMAC) software multiscale framework. Both MMCDM and plasticity models incorporate brittle strain- and stress-based failure criteria for the fiber. Upon satisfaction of these criteria, the fiber properties are immediately reduced to a nominal value. The constitutive response for each constituent (fiber and matrix) is characterized using a combination of vendor data and the axial, transverse, and shear responses of unnotched laminates. Then, the capability of the multiscale methodology is assessed by performing blind predictions of the mentioned notched and unnotched composite laminates response under tensile and compressive loading. Tabulated data along with the detailed results (i.e., stress-strain curves as well as damage evolution states at various ratios of strain to failure) for all laminates are presented.
Bednarcyk, Brett A.; Arnold, Steven M.
2007-01-01
A framework is presented that enables coupled multiscale analysis of composite structures. The recently developed, free, Finite Element Analysis-Micromechanics Analysis Code (FEAMAC) software couples the Micromechanics Analysis Code with Generalized Method of Cells (MAC/GMC) with ABAQUS to perform micromechanics based FEA such that the nonlinear composite material response at each integration point is modeled at each increment by MAC/GMC. As a result, the stochastic nature of fiber breakage in composites can be simulated through incorporation of an appropriate damage and failure model that operates within MAC/GMC on the level of the fiber. Results are presented for the progressive failure analysis of a titanium matrix composite tensile specimen that illustrate the power and utility of the framework and address the techniques needed to model the statistical nature of the problem properly. In particular, it is shown that incorporating fiber strength randomness on multiple scales improves the quality of the simulation by enabling failure at locations other than those associated with structural level stress risers.
SIMULATION OF CRACK DIAGNOSIS OF ROTOR BASED ON MULTI-SCALE SINGULAR-SPECTRUM ANALYSIS
LI Ruqiang; LIU Yuanfeng
2006-01-01
In the diagnosis of rotor crack based on wavelet analysis, it is a painful task to find out an adaptive mother wavelet as many of them can be chosen and the analytic results of different mother wavelets are yet not the same. For this limitation of wavelet analysis, a novel diagnostic approach of rotor crack based on multi-scale singular-spectrum analysis (MS-SSA) is proposed. Firstly, a Jeffcott model of a cracked rotor is developed and the forth-order Runge-Kutta method is used to solve the motion equations of this rotor to obtain its time response (signals). Secondly, a comparatively simple approach of MS-SSA is presented and the empirical orthogonal functions of different orders in various scales are regarded as analyzing functions. At last, the signals of the cracked rotor and an uncracked rotor are analyzed using the proposed approach of MS-SSA, and the simulative results are compared. The results show that, the data-adaptive analyzing functions can capture many features of signals and the rotor crack can be identified and diagnosed effectively by comparing the analytic results of signals of the cracked rotor with those of the uncracked rotor using the analyzing functions of different orders.
Medical image retrieval and analysis by Markov random fields and multi-scale fractal dimension.
Backes, André Ricardo; Gerhardinger, Leandro Cavaleri; Batista Neto, João do Espírito Santo; Bruno, Odemir Martinez
2015-02-01
Many Content-based Image Retrieval (CBIR) systems and image analysis tools employ color, shape and texture (in a combined fashion or not) as attributes, or signatures, to retrieve images from databases or to perform image analysis in general. Among these attributes, texture has turned out to be the most relevant, as it allows the identification of a larger number of images of a different nature. This paper introduces a novel signature which can be used for image analysis and retrieval. It combines texture with complexity extracted from objects within the images. The approach consists of a texture segmentation step, modeled as a Markov Random Field process, followed by the estimation of the complexity of each computed region. The complexity is given by a Multi-scale Fractal Dimension. Experiments have been conducted using an MRI database in both pattern recognition and image retrieval contexts. The results show the accuracy of the proposed method in comparison with other traditional texture descriptors and also indicate how the performance changes as the level of complexity is altered. PMID:25586375
Medical image retrieval and analysis by Markov random fields and multi-scale fractal dimension
Many Content-based Image Retrieval (CBIR) systems and image analysis tools employ color, shape and texture (in a combined fashion or not) as attributes, or signatures, to retrieve images from databases or to perform image analysis in general. Among these attributes, texture has turned out to be the most relevant, as it allows the identification of a larger number of images of a different nature. This paper introduces a novel signature which can be used for image analysis and retrieval. It combines texture with complexity extracted from objects within the images. The approach consists of a texture segmentation step, modeled as a Markov Random Field process, followed by the estimation of the complexity of each computed region. The complexity is given by a Multi-scale Fractal Dimension. Experiments have been conducted using an MRI database in both pattern recognition and image retrieval contexts. The results show the accuracy of the proposed method in comparison with other traditional texture descriptors and also indicate how the performance changes as the level of complexity is altered. (paper)
Medical image retrieval and analysis by Markov random fields and multi-scale fractal dimension
Backes, André Ricardo; Cavaleri Gerhardinger, Leandro; do Espírito Santo Batista Neto, João; Martinez Bruno, Odemir
2015-02-01
Many Content-based Image Retrieval (CBIR) systems and image analysis tools employ color, shape and texture (in a combined fashion or not) as attributes, or signatures, to retrieve images from databases or to perform image analysis in general. Among these attributes, texture has turned out to be the most relevant, as it allows the identification of a larger number of images of a different nature. This paper introduces a novel signature which can be used for image analysis and retrieval. It combines texture with complexity extracted from objects within the images. The approach consists of a texture segmentation step, modeled as a Markov Random Field process, followed by the estimation of the complexity of each computed region. The complexity is given by a Multi-scale Fractal Dimension. Experiments have been conducted using an MRI database in both pattern recognition and image retrieval contexts. The results show the accuracy of the proposed method in comparison with other traditional texture descriptors and also indicate how the performance changes as the level of complexity is altered.
Chung, Chen-Chih; Kang, Jiunn-Horng; Yuan, Rey-Yue; Wu, Dean; Chen, Chih-Chung; Chi, Nai-Fang; Chen, Po-Chih; Hu, Chaur-Jong
2013-07-01
Sleep disorders are frequently seen in patients with Parkinson disease (PD), including rapid eye movement (REM) behavior disorder and periodic limb movement disorder. However, knowledge about changes in non-REM sleep in patients with PD is limited. This study explored the characteristics of electroencephalography (EEG) during sleep in patients with PD and non-PD controls. We further conducted multiscale entropy (MSE) analysis to evaluate and compare the complexity of sleep EEG for the 2 groups. There were 9 patients with PD (Hoehn-Yahr stage 1 or 2) and 11 non-PD controls. All participants underwent standard whole-night polysomnography (PSG), which included 23 channels, 6 of which were for EEG. The raw data of the EEG were extracted and subjected to MSE analysis. Patients with PD had a longer sleep onset time and a higher spontaneous EEG arousal index. Sleep stage-specific increased MSE was observed in patients with PD during non-REM sleep. The difference was more marked and significant at higher time scale factors (TSFs). In conclusion, increased biosignal complexity, as revealed by MSE analysis, was found in patients with PD during non-REM sleep at high TSFs. This finding might reflect a compensatory mechanism for early defects in neuronal network control machinery in PD. PMID:23545244
ESMAEILLOU, Bardia; Fitoussi, Joseph; Meraghni, Fodil; TCHARKHTCHI, Abbas
2014-01-01
The objective of this work is to identify and to analyze the main micro-mechanisms which govern the fatigue behavior of a short glass fiber reinforced polyamide 66 composite through a multi-scale experimental analysis. Tension-tension fatigue tests have been performed at different applied maximum stress and have been analyzed at both microscopic and macroscopic scale. Together with the progressive stiffness reduction, the temperature rise due to self-heating during cyclic loading has been mea...
Antrett, Philipp
2011-01-01
This thesis describes a multidisciplinary, multiscale approach to the analysis of tight gas reservoirs. It focused initially on the facies architecture of a Permian tight gas field in the Southern Permian Basin (SPB), East Frisia, Northern Germany. To improve field development, 3D seismic data, wireline and core data were compared to a reservoir analogue in the Panamint Valley, California, United States. Depositional environments of the Permian Upper Rotliegend II included perennial saline la...
Saether, Erik; Hochhalter, Jacob D.; Glaessgen, Edward H.; Mishin, Yuri
2014-01-01
A multiscale modeling methodology is developed for structurally-graded material microstructures. Molecular dynamic (MD) simulations are performed at the nanoscale to determine fundamental failure mechanisms and quantify material constitutive parameters. These parameters are used to calibrate material processes at the mesoscale using discrete dislocation dynamics (DD). Different grain boundary interactions with dislocations are analyzed using DD to predict grain-size dependent stress-strain behavior. These relationships are mapped into crystal plasticity (CP) parameters to develop a computationally efficient finite element-based DD/CP model for continuum-level simulations and complete the multiscale analysis by predicting the behavior of macroscopic physical specimens. The present analysis is focused on simulating the behavior of a graded microstructure in which grain sizes are on the order of nanometers in the exterior region and transition to larger, multi-micron size in the interior domain. This microstructural configuration has been shown to offer improved mechanical properties over homogeneous coarse-grained materials by increasing yield stress while maintaining ductility. Various mesoscopic polycrystal models of structurally-graded microstructures are generated, analyzed and used as a benchmark for comparison between multiscale DD/CP model and DD predictions. A final series of simulations utilize the DD/CP analysis method exclusively to study macroscopic models that cannot be analyzed by MD or DD methods alone due to the model size.
Multi codes and multi-scale analysis for void fraction prediction in hot channel for VVER-1000/V392
Recently, an approach of multi codes and multi-scale analysis is widely applied to study core thermal hydraulic behavior such as void fraction prediction. Better results are achieved by using multi codes or coupling codes such as PARCS and RELAP5. The advantage of multi-scale analysis is zooming of the interested part in the simulated domain for detail investigation. Therefore, in this study, the multi codes between MCNP5, RELAP5, CTF and also the multi-scale analysis based RELAP5 and CTF are applied to investigate void fraction in hot channel of VVER-1000/V392 reactor. Since VVER-1000/V392 reactor is a typical advanced reactor that can be considered as the base to develop later VVER-1200 reactor, then understanding core behavior in transient conditions is necessary in order to investigate VVER technology. It is shown that the item of near wall boiling, Γw in RELAP5 proposed by Lahey mechanistic method may not give enough accuracy of void fraction prediction as smaller scale code as CTF. (author)
Pineda, Evan J.; Waas, Anthony M.; Berdnarcyk, Brett A.; Arnold, Steven M.; Collier, Craig S.
2009-01-01
This preliminary report demonstrates the capabilities of the recently developed software implementation that links the Generalized Method of Cells to explicit finite element analysis by extending a previous development which tied the generalized method of cells to implicit finite elements. The multiscale framework, which uses explicit finite elements at the global-scale and the generalized method of cells at the microscale is detailed. This implementation is suitable for both dynamic mechanics problems and static problems exhibiting drastic and sudden changes in material properties, which often encounter convergence issues with commercial implicit solvers. Progressive failure analysis of stiffened and un-stiffened fiber-reinforced laminates subjected to normal blast pressure loads was performed and is used to demonstrate the capabilities of this framework. The focus of this report is to document the development of the software implementation; thus, no comparison between the results of the models and experimental data is drawn. However, the validity of the results are assessed qualitatively through the observation of failure paths, stress contours, and the distribution of system energies.
Johan Debayle
2011-05-01
Full Text Available An image analysis method has been developed in order to compute the velocity field of a granular medium (sand grains, mean diameter 600 μm submitted to different kinds of mechanical stresses. The differential method based on optical flow conservation consists in describing a dense motion field with vectors associated to each pixel. A multiscale, coarse-to-fine, analytical approach through tailor sized windows yields the best compromise between accuracy and robustness of the results, while enabling an acceptable computation time. The corresponding algorithmis presented and its validation discussed through different tests. The results of the validation tests of the proposed approach show that the method is satisfactory when attributing specific values to parameters in association with the size of the image analysis window. An application in the case of vibrated sand has been studied. An instrumented laboratory device provides sinusoidal vibrations and enables external optical observations of sand motion in 3D transparent boxes. At 50 Hz, by increasing the relative acceleration G, the onset and development of two convective rolls can be observed. An ultra fast camera records the grain avalanches, and several pairs of images are analysed by the proposed method. The vertical velocity profiles are deduced and allow to precisely quantify the dimensions of the fluidized region as a function of G.
Baumert, Mathias; Javorka, Michal; Seeck, Andrea; Faber, Renaldo; Sanders, Prashanthan; Voss, Andreas
2012-03-01
Pregnancy leads to physiological changes in various parameters of the cardiovascular system. The aim of this study was to investigate longitudinal changes in the structure and complexity of heart rate variability (HRV) and QT interval variability during the second half of normal gestation. We analysed 30-min high-resolution ECGs recorded monthly in 32 pregnant women, starting from the 20th week of gestation. Heart rate and QT variability were quantified using multiscale entropy (MSE) and detrended fluctuation analyses (DFA). DFA of HRV showed significantly higher scaling exponents towards the end of gestation (p<0.0001). MSE analysis showed a significant decrease in sample entropy of HRV with progressing gestation on scales 1-4 (p<0.05). MSE analysis and DFA of QT interval time series revealed structures significantly different from those of HRV with no significant alteration during the second half of gestation. In conclusion, pregnancy is associated with increases in long-term correlations and regularity of HRV, but it does not affect QT variability. The structure of QT time series is significantly different from that of RR time series, despite its close physiological dependence. PMID:21530956
Liu, An-Bang; Wu, Hsien-Tsai; Liu, Chun-Wei; Liu, Cyuan-Cin; Tang, Chieh-Ju; Tsai, I-Ting; Sun, Cheuk-Kwan
2015-01-01
We applied multiscale entropy (MSE) to assess variation in crest time (CT), a parameter in arterial waveform analysis, in diagnosing patients with diabetes. Data on digital volume pulse were obtained from 93 individuals in three groups [Healthy young (Group 1, 20 40, n = 30), and diabetic (Group 3, n = 33) subjects]. Crest time, normalized crest time, crest time ratio (CTR), small- and large-scale MSE on CT [MSESS(CT) and MSELS(CT), respectively] were computed and correlated with anthropometric (i.e., body weight/height, waist circumference), hemodynamic (i.e., blood pressure), and biochemical parameters (i.e., serum triglyceride, high-density lipoprotein, fasting blood sugar, and glycosylated hemoglobin). The results demonstrated higher variability in CT in healthy subjects (Groups 1 and 2) compared with that in diabetic patients (Group 3) as reflected in significantly elevated MSESS(CT) and MSELS(CT) in the former (p < 0.003 and p < 0.001, respectively). MSELS(CT) also showed significant association with waist circumference and fasting blood sugar (i.e., two diagnostic criteria of metabolic syndrome) as well as glycosylated hemoglobin concentration. In conclusion, using MSE analysis for assessing CT variation successfully distinguished diabetic patients from healthy subjects. MSESS(CT) and MSELS(CT) therefore may serve as noninvasive tools for identifying subjects with diabetes and those at risk. PMID:25351478
Wen-Yao Pan
2015-01-01
Full Text Available Obstructive sleep apnea (OSA is an independent cardiovascular risk factor to which autonomic nervous dysfunction has been reported to be an important contributor. Ninety subjects recruited from the sleep center of a single medical center were divided into four groups: normal snoring subjects without OSA (apnea hypopnea index, AHI < 5, n = 11, mild OSA (5 ≤ AHI < 15, n = 10, moderate OSA (15 ≤ AHI < 30, n = 24, and severe OSA (AHI ≥ 30, n = 45. Demographic (i.e., age, gender, anthropometric (i.e., body mass index, neck circumference, and polysomnographic (PSG data were recorded and compared among the different groups. For each subject, R-R intervals (RRI from 10 segments of 10-minute electrocardiogram recordings during non-rapid eye movement sleep at stage N2 were acquired and analyzed for heart rate variability (HRV and sample entropy using multiscale entropy index (MEI that was divided into small scale (MEISS, scale 1–5 and large scale (MEILS, scale 6–10. Our results not only demonstrated that MEISS could successfully distinguish normal snoring subjects and those with mild OSA from those with moderate and severe disease, but also revealed good correlation between MEISS and AHI with Spearman correlation analysis (r = −0.684, p < 0.001. Therefore, using the two parameters of EEG and ECG, MEISS may serve as a simple preliminary screening tool for assessing the severity of OSA before proceeding to PSG analysis.
Multiscale integral analysis of a HT leakage in a fusion nuclear power plant
Velarde, M.; Fradera, J.; Perlado, J. M.; Zamora, I.; Martínez-Saban, E.; Colomer, C.; Briani, P.
2016-05-01
The present work presents an example of the application of an integral methodology based on a multiscale analysis that covers the whole tritium cycle within a nuclear fusion power plant, from a micro scale, analyzing key components where tritium is leaked through permeation, to a macro scale, considering its atmospheric transport. A leakage from the Nuclear Power Plants, (NPP) primary to the secondary side of a heat exchanger (HEX) is considered for the present example. Both primary and secondary loop coolants are assumed to be He. Leakage is placed inside the HEX, leaking tritium in elementary tritium (HT) form to the secondary loop where it permeates through the piping structural material to the exterior. The Heating Ventilation and Air Conditioning (HVAC) system removes the leaked tritium towards the NPP exhaust. The HEX is modelled with system codes and coupled to Computational Fluid Dynamic (CFD) to account for tritium dispersion inside the nuclear power plants buildings and in site environment. Finally, tritium dispersion is calculated with an atmospheric transport code and a dosimetry analysis is carried out. Results show how the implemented methodology is capable of assessing the impact of tritium from the microscale to the atmospheric scale including the dosimetric aspect.
Multi-scale Analysis of MEMS Sensors Subject to Drop Impacts
Sarah Zerbini
2007-09-01
Full Text Available The effect of accidental drops on MEMS sensors are examined within the frame-work of a multi-scale finite element approach. With specific reference to a polysilicon MEMSaccelerometer supported by a naked die, the analysis is decoupled into macro-scale (at dielength-scale and meso-scale (at MEMS length-scale simulations, accounting for the verysmall inertial contribution of the sensor to the overall dynamics of the device. Macro-scaleanalyses are adopted to get insights into the link between shock waves caused by the impactagainst a target surface and propagating inside the die, and the displacement/acceleration his-tories at the MEMS anchor points. Meso-scale analyses are adopted to detect the most stresseddetails of the sensor and to assess whether the impact can lead to possible localized failures.Numerical results show that the acceleration at sensor anchors cannot be considered an ob-jective indicator for drop severity. Instead, accurate analyses at sensor level are necessary toestablish how MEMS can fail because of drops.
Multi-scale Analysis of MEMS Sensors Subject to Drop Impacts
Mariani, Stefano; Ghisi, Aldo; Corigliano, Alberto; Zerbini, Sarah
2007-01-01
The effect of accidental drops on MEMS sensors are examined within the framework of a multi-scale finite element approach. With specific reference to a polysilicon MEMS accelerometer supported by a naked die, the analysis is decoupled into macro-scale (at die length-scale) and meso-scale (at MEMS length-scale) simulations, accounting for the very small inertial contribution of the sensor to the overall dynamics of the device. Macro-scale analyses are adopted to get insights into the link between shock waves caused by the impact against a target surface and propagating inside the die, and the displacement/acceleration histories at the MEMS anchor points. Meso-scale analyses are adopted to detect the most stressed details of the sensor and to assess whether the impact can lead to possible localized failures. Numerical results show that the acceleration at sensor anchors cannot be considered an objective indicator for drop severity. Instead, accurate analyses at sensor level are necessary to establish how MEMS can fail because of drops.
Multiscale analysis of heat transfer in coated fuel particle compacts – Application to the HTTR
Highlights: • Heat diffusion in heterogeneous materials is modeled using multiple-scales analysis. • High order expansion is performed, and enables the evaluation of macro- and micro-scale features. • Non-linear effects due to the temperature-dependent thermal conductivities are considered. • The accuracy is validated: weak scale separation and different micro-scale lattice are investigated. • Results are presented for fuel compacts at various operating temperatures and fuel EFPDs. - Abstract: Heat conduction in coated fuel particles compacts is modeled for a nuclear application. In order to take into account the multi-scale features of the heterogeneous material, the homogenization method is used to evaluate macro- as well as micro-scale temperature fluctuations. The method is validated against direct computations of the temperature field for simple configurations, and is shown to be robust with respect to the micro-scale lattice used in the homogenization procedure. Non-linear effects due to the temperature dependence of thermal conductivities are taken into account in the model
Brian J. Gow
2015-11-01
Full Text Available Multiscale entropy (MSE is a widely used metric for characterizing the nonlinear dynamics of physiological processes. Significant variability, however, exists in the methodological approaches to MSE which may ultimately impact results and their interpretations. Using publications focused on balance-related center of pressure (COP dynamics, we highlight sources of methodological heterogeneity that can impact study findings. Seventeen studies were systematically identified that employed MSE for characterizing COP displacement dynamics. We identified five key methodological procedures that varied significantly between studies: (1 data length; (2 frequencies of the COP dynamics analyzed; (3 sampling rate; (4 point matching tolerance and sequence length; and (5 filtering of displacement changes from drifts, fidgets, and shifts. We discuss strengths and limitations of the various approaches employed and supply flowcharts to assist in the decision making process regarding each of these procedures. Our guidelines are intended to more broadly inform the design and analysis of future studies employing MSE for continuous time series, such as COP.
Multiscale entropy analysis of biological signals: a fundamental bi-scaling law.
Gao, Jianbo; Hu, Jing; Liu, Feiyan; Cao, Yinhe
2015-01-01
Since introduced in early 2000, multiscale entropy (MSE) has found many applications in biosignal analysis, and been extended to multivariate MSE. So far, however, no analytic results for MSE or multivariate MSE have been reported. This has severely limited our basic understanding of MSE. For example, it has not been studied whether MSE estimated using default parameter values and short data set is meaningful or not. Nor is it known whether MSE has any relation with other complexity measures, such as the Hurst parameter, which characterizes the correlation structure of the data. To overcome this limitation, and more importantly, to guide more fruitful applications of MSE in various areas of life sciences, we derive a fundamental bi-scaling law for fractal time series, one for the scale in phase space, the other for the block size used for smoothing. We illustrate the usefulness of the approach by examining two types of physiological data. One is heart rate variability (HRV) data, for the purpose of distinguishing healthy subjects from patients with congestive heart failure, a life-threatening condition. The other is electroencephalogram (EEG) data, for the purpose of distinguishing epileptic seizure EEG from normal healthy EEG. PMID:26082711
Applying Multiscale Entropy to the Complexity Analysis of Rainfall-Runoff Relationships
Chien-Ming Chou
2012-05-01
Full Text Available This paper presents a novel framework for the complexity analysis of rainfall, runoff, and runoff coefficient (RC time series using multiscale entropy (MSE. The MSE analysis of RC time series was used to investigate changes in the complexity of rainfall-runoff processes due to human activities. Firstly, a coarse graining process was applied to a time series. The sample entropy was then computed for each coarse-grained time series, and plotted as a function of the scale factor. The proposed method was tested in a case study of daily rainfall and runoff data for the upstream Wu–Tu watershed. Results show that the entropy measures of rainfall time series are higher than those of runoff time series at all scale factors. The entropy measures of the RC time series are between the entropy measures of the rainfall and runoff time series at various scale factors. Results also show that the entropy values of rainfall, runoff, and RC time series increase as scale factors increase. The changes in the complexity of RC time series indicate the changes of rainfall-runoff relations due to human activities and provide a reference for the selection of rainfall-runoff models that are capable of dealing with great complexity and take into account of obvious self-similarity can be suggested to the modeling of rainfall-runoff processes. Moreover, the robustness of the MSE results were tested to confirm that MSE analysis is consistent and the same results when removing 25% data, making this approach suitable for the complexity analysis of rainfall, runoff, and RC time series.
Multi-scale complexity analysis of muscle coactivation during gait in children with cerebral palsy
Xu Zhang
2015-07-01
Full Text Available The objective of this study is to characterize complexity of lower-extremity muscle coactivation and coordination during gait in children with cerebral palsy (CP, children with typical development (TD and healthy adults, by applying recently developed multivariate multi-scale entropy (MMSE analysis to surface EMG signals. Eleven CP children (CP group, eight TD children and seven healthy adults (consider as an entire control group were asked to walk while surface EMG signals were collected from 5 thigh muscles and 3 lower leg muscles on each leg (16 EMG channels in total. The 16-channel surface EMG data, recorded during a series of consecutive gait cycles, were simultaneously processed by multivariate empirical mode decomposition (MEMD, to generate fully aligned data scales for subsequent MMSE analysis. In order to conduct extensive examination of muscle coactivation complexity using the MEMD-enhanced MMSE, 14 data analysis schemes were designed by varying partial muscle combinations and time durations of data segments. Both TD children and healthy adults showed almost consistent MMSE curves over multiple scales for all the 14 schemes, without any significant difference (p > 0.09. However, quite diversity in MMSE curve was observed in the CP group when compared with those in the control group. There appears to be diverse neuropathological processes in CP that may affect dynamical complexity of muscle coactivation and coordination during gait. The abnormal complexity patterns emerging in CP group can be attributed to different factors such as motor control impairments, loss of muscle couplings, and spasticity or paralysis in individual muscles. All these findings expand our knowledge of neuropathology of CP from a novel point of view of muscle co-activation complexity, also indicating the potential to derive a quantitative index for assessing muscle activation characteristics as well as motor function in CP.
Bhattacharjee, Satyaki; Matouš, Karel
2016-05-01
A new manifold-based reduced order model for nonlinear problems in multiscale modeling of heterogeneous hyperelastic materials is presented. The model relies on a global geometric framework for nonlinear dimensionality reduction (Isomap), and the macroscopic loading parameters are linked to the reduced space using a Neural Network. The proposed model provides both homogenization and localization of the multiscale solution in the context of computational homogenization. To construct the manifold, we perform a number of large three-dimensional simulations of a statistically representative unit cell using a parallel finite strain finite element solver. The manifold-based reduced order model is verified using common principles from the machine-learning community. Both homogenization and localization of the multiscale solution are demonstrated on a large three-dimensional example and the local microscopic fields as well as the homogenized macroscopic potential are obtained with acceptable engineering accuracy.
Multiscale multifractal detrended-fluctuation analysis of two-dimensional surfaces
Wang, Fang; Fan, Qingju; Stanley, H. Eugene
2016-04-01
Two-dimensional (2D) multifractal detrended fluctuation analysis (MF-DFA) has been used to study monofractality and multifractality on 2D surfaces, but when it is used to calculate the generalized Hurst exponent in a fixed time scale, the presence of crossovers can bias the outcome. To solve this problem, multiscale multifractal analysis (MMA) was recent employed in a one-dimensional case. MMA produces a Hurst surface h (q ,s ) that provides a spectrum of local scaling exponents at different scale ranges such that the positions of the crossovers can be located. We apply this MMA method to a 2D surface and identify factors that influence the results. We generate several synthesized surfaces and find that crossovers are consistently present, which means that their fractal properties differ at different scales. We apply MMA to the surfaces, and the results allow us to observe these differences and accurately estimate the generalized Hurst exponents. We then study eight natural texture images and two real-world images and find (i) that the moving window length (WL) and the slide length (SL) are the key parameters in the MMA method, that the WL more strongly influences the Hurst surface than the SL, and that the combination of WL =4 and SL =4 is optimal for a 2D image; (ii) that the robustness of h (2 ,s ) to four common noises is high at large scales but variable at small scales; and (iii) that the long-term correlations in the images weaken as the intensity of Gaussian noise and salt and pepper noise is increased. Our findings greatly improve the performance of the MMA method on 2D surfaces.
Bayesian hierarchical multi-subject multiscale analysis of functional MRI data.
Sanyal, Nilotpal; Ferreira, Marco A R
2012-11-15
We develop a methodology for Bayesian hierarchical multi-subject multiscale analysis of functional Magnetic Resonance Imaging (fMRI) data. We begin by modeling the brain images temporally with a standard general linear model. After that, we transform the resulting estimated standardized regression coefficient maps through a discrete wavelet transformation to obtain a sparse representation in the wavelet space. Subsequently, we assign to the wavelet coefficients a prior that is a mixture of a point mass at zero and a Gaussian white noise. In this mixture prior for the wavelet coefficients, the mixture probabilities are related to the pattern of brain activity across different resolutions. To incorporate this information, we assume that the mixture probabilities for wavelet coefficients at the same location and level are common across subjects. Furthermore, we assign for the mixture probabilities a prior that depends on a few hyperparameters. We develop an empirical Bayes methodology to estimate the hyperparameters and, as these hyperparameters are shared by all subjects, we obtain precise estimated values. Then we carry out inference in the wavelet space and obtain smoothed images of the regression coefficients by applying the inverse wavelet transform to the posterior means of the wavelet coefficients. An application to computer simulated synthetic data has shown that, when compared to single-subject analysis, our multi-subject methodology performs better in terms of mean squared error. Finally, we illustrate the utility and flexibility of our multi-subject methodology with an application to an event-related fMRI dataset generated by Postle (2005) through a multi-subject fMRI study of working memory related brain activation. PMID:22951257
Chenxi, Li; Chen, Yanni; Li, Youjun; Wang, Jue; Liu, Tian
2016-06-01
The multiscale entropy (MSE) is a novel method for quantifying the intrinsic dynamical complexity of physiological systems over several scales. To evaluate this method as a promising way to explore the neural mechanisms in ADHD, we calculated the MSE in EEG activity during the designed task. EEG data were collected from 13 outpatient boys with a confirmed diagnosis of ADHD and 13 age- and gender-matched normal control children during their doing multi-source interference task (MSIT). We estimated the MSE by calculating the sample entropy values of delta, theta, alpha and beta frequency bands over twenty time scales using coarse-grained procedure. The results showed increased complexity of EEG data in delta and theta frequency bands and decreased complexity in alpha frequency bands in ADHD children. The findings of this study revealed aberrant neural connectivity of kids with ADHD during interference task. The results showed that MSE method may be a new index to identify and understand the neural mechanism of ADHD. PMID:26995277
A Mathematical Analysis of Atomistic-to-Continuum (AtC) Multiscale Coupling Methods
Gunzburger, Max
2013-11-13
We have worked on several projects aimed at improving the efficiency and understanding of multiscale methods, especially those applicable to problems involving atomistic-to-continuum coupling. Activities include blending methods for AtC coupling and efficient quasi-continuum methods for problems with long-range interactions.
Escudero, Javier; Evrim, Acar Ataman; Fernández, Alberto;
2015-01-01
dynamics. We consider the "refined composite multiscale entropy" (rcMSE), which computes entropy "profiles" showing levels of physiological complexity over temporal scales for individual signals. We compute the rcMSE of resting-state magnetoencephalogram (MEG) recordings from 36 patients with Alzheimer...
Flow pattern map and multi-scale entropy analysis in 3 × 3 rod bundle channel
Highlights: • Flow patterns of steam–water two-phase flow in a 3 × 3 rod bundle were visualized. • The flow pattern map obtained for the rod bundle was compared with some existing flow pattern maps. • Multi-scale entropy was used to characterize the dynamic characteristics of the different flow patterns. • Rate of multi-scale entropy can effectively identify flow patterns in a rod bundle channel. - Abstract: The characteristics of the heat transfer and two-phase flow resistance in a rod bundle channel are closely related to flow patterns. In the present study, two-phase flow patterns for vapor–water flows in a 3 by 3 rod bundle channel were obtained at atmospheric pressure and relatively low mass flow condition. Under the current experimental conditions, slug flow found in circular tubes was not observed. Comparisons with some existing flow pattern maps and transition criteria were likewise conducted. Results show that the transition boundary of circular tube deviated from the transition boundary of the boiling two-phase flow of the rod bundle channel. Using the differential pressure signal of the vapor–liquid two-phase flow, multi-scale entropy algorithm is employed to reveal the dynamic characteristics of the different flow patterns in the rod bundle. The multi-scale entropy rate could effectively classify the flow patterns in the rod bundle channel. Results suggest that multi-scale entropy can be an effective method to reveal the dynamic details of macro and local vantages
Multi-physics and multi-scale methods used in nuclear reactor analysis
Highlights: • Review of techniques to perform neutronic/thermal–hydraulic coupled calculations. • Operator-Splitting and Jacobian-Free Newton methods for neutronic/T–H coupling. • Brief review of pin-power reconstruction techniques used in neutronic calculations. • Indicative coupled neutronic/T–H calculations with emphasis on the coupling scheme. - Abstract: In an operating nuclear reactor core, various physical phenomena of different nature are interrelated. Multi-physics calculations that account for the interrelated nature of the neutronic and thermal–hydraulic phenomena are of major importance in reactor safety and design and as a result a special effort is developed within the nuclear engineering scientific community to improve their efficiency and accuracy. In addition, the strongly heterogeneous nature of reactor cores involves phenomena of different scales. The interaction between different scales is a specificity of these systems, since a local perturbation might influence the behavior of the whole core, or a global perturbation can influence the properties of the media on all scales. As a consequence, multi-scale calculations are required in order to take the reactor core multi-scale nature into account. It should be mentioned that the multi-physics nature of a nuclear reactor cannot be separated from the multi-scale one in the framework of computational nuclear engineering as reactor design and safety require computational tools which are able to examine globally the complicated nature of a nuclear reactor in various scales. In this work a global overview of the current status of two-physics (neutronic/thermal–hydraulic) and multi-scale neutronic calculations techniques is presented with reference to their applications in different nuclear reactor concepts. Finally an effort to extract the main remaining challenges in the field of multi-physics and multi-scale calculations is made
Multiscale Entropy Analysis of the Portevin-Le Chatelier Effect in an Al-2.5%Mg Alloy
Sarkar, A.; Barat, P.; Mukherjee, P.
2006-01-01
The complexity of the Portevin-Le Chatelier effect in Al-2.5%Mg polycrystalline samples subjected to uniaxial tensile tests is quantified. Multiscale entropy analysis is carried out on the stress time series data observed during jerky flow to quantify the complexity of the distinct spatiotemporal dynamical regimes. It is shown that for the static type C band, the entropy is very low for all the scales compared to the hopping type B and the propagating type A bands. The results are interpreted...
Huo, Chengyu; Huang, Xiaolin; Zhuang, Jianjun; Hou, Fengzhen; Ni, Huangjing; Ning, Xinbao
2013-09-01
The Poincaré plot is one of the most important approaches in human cardiac rhythm analysis. However, further investigations are still needed to concentrate on techniques that can characterize the dispersion of the points displayed by a Poincaré plot. Based on a modified Poincaré plot, we provide a novel measurement named distribution entropy (DE) and propose a quadrantal multi-scale distribution entropy analysis (QMDE) for the quantitative descriptions of the scatter distribution patterns in various regions and temporal scales. We apply this method to the heartbeat interval series derived from healthy subjects and congestive heart failure (CHF) sufferers, respectively, and find that the discriminations between them are most significant in the first quadrant, which implies significant impacts on vagal regulation brought about by CHF. We also investigate the day-night differences of young healthy people, and it is shown that the results present a clearly circadian rhythm, especially in the first quadrant. In addition, the multi-scale analysis indicates that the results of healthy subjects and CHF sufferers fluctuate in different trends with variation of the scale factor. The same phenomenon also appears in circadian rhythm investigations of young healthy subjects, which implies that the cardiac dynamic system is affected differently in various temporal scales by physiological or pathological factors.
Multi-scale analysis of deformation behavior at SCC crack tip (3) (Contract research)
In recent years, incidents of the stress corrosion cracking (SCC) were frequently reported that occurred to the various components of domestic boiling water reactors (BWR), and the cause investigation and measure become the present important issue. By the Japan nuclear energy safety organization (JNES), a research project on the intergranular SCC (IGSCC) in nuclear grade stainless steels (henceforth, IGSCC project) is under enforcement from a point of view to secure safety and reliability of BWR, and SCC growth data of low carbon stainless steels are being accumulated for the weld part or the work-hardened region adjacent to the weld metal. In the project, it has been an important subject to guarantee the validity of accumulated SCC data. At a crack tip of SCC in compact tension (CT) type specimen used for the SCC propagation test, a macroscopic plastic region is formed where heterogeneity of microstructure developed by microscopic sliding and dislocations is observed. However, there is little quantitative information on the plastic region, and therefore, to assess the data of macroscopic SCC growth rate and the validity of propagation test method, it is essentially required to investigate the plastic region at the crack tip in detail from a microscopic viewpoint. This report describes a result of the research conducted by the Japan Atomic Energy Agency and the National Institute for Materials Science under contract with JNES that was concerned with a multi-scale analysis of plastic deformation behavior at the crack tip of SCC. The research was carried out to evaluate the validity of the SCC growth data acquired in the IGSCC project based on a mechanistic understanding of SCC. For the purpose, in this research, analyses of the plastic deformation behavior and microstructure around the crack tip were performed in a nano-order scale. The hardness measured in nano, meso and macro scales was employed as a common index of the strength, and the essential data necessary
Dai, Gaoming; Mishnaevsky, Leon, Jr.
2014-01-01
3D numerical simulations of fatigue damage of multiscale fiber reinforced polymer composites with secondary nanoclay reinforcement are carried out. Macro–micro FE models of the multiscale composites are generated automatically using Python based software. The effect of the nanoclay reinforcement...... (localized in the fiber/matrix interface (fiber sizing) and distributed throughout the matrix) on the crack path, damage mechanisms and fatigue behavior is investigated in numerical experiments. It was observed that the composites with secondary nanoreinforcement localized in the fiber sizing ensure higher...... lifetime and damage resistance than those with nanoreinforcement dispersed throughout the matrix. Crack bridging by nanoparticles was observed mainly in composites with randomly oriented nanoplatelets and clusters, while the crack path deviation was strongest in the composites with aligned nanoplatelets...
Multiscale Analysis of Foreign Exchange Order Flows and Technical Trading Profitability
Nikola Gradojevic; Camillo Lento
2012-01-01
This paper investigates the multiscale (frequency-dependent) relationship between technical trading profitability and feedback trading effects in the Canada/U.S. dollar foreign exchange market. The results suggest weak evidence that technical trading activities of financial and non-financial customers drive frequent violations of the FX market microstructure assumption that exchange rate movements are driven by order flow. After controlling for transaction costs, we find that the contribution...
Xavier Blanc; Claude Le Bris; Prédéric Legol
2007-01-01
In order to describe a solid which deforms smoothly in some region,but non smoothly in some other region,many multiscale methods have been recently proposed that aim at coupling an atonfistic model (discrete mechanics)with a macroscopic model(continuum mechanics).We provide here a theoretical basis for such a coupling in a one-dimensional setting,in the case of convex energy.
Gow, Brian J.; Chung-Kang Peng; Wayne, Peter M.; Andrew C Ahn
2015-01-01
Multiscale entropy (MSE) is a widely used metric for characterizing the nonlinear dynamics of physiological processes. Significant variability, however, exists in the methodological approaches to MSE which may ultimately impact results and their interpretations. Using publications focused on balance-related center of pressure (COP) dynamics, we highlight sources of methodological heterogeneity that can impact study findings. Seventeen studies were systematically identified that employed MSE f...
Performance Analysis of Multiscale Entropy for the Assessment of ECG Signal Quality
Zhang, Yatao; Wei, Shoushui; Long, Yutao; Liu, Chengyu
2015-01-01
This study explored the performance of multiscale entropy (MSE) for the assessment of mobile ECG signal quality, aiming to provide a reasonable application guideline. Firstly, the MSE for the typical noises, that is, high frequency (HF) noise, low frequency (LF) noise, and power-line (PL) noise, was analyzed. The sensitivity of MSE to the signal to noise ratio (SNR) of the synthetic artificial ECG plus different noises was further investigated. The results showed that the MSE values could ref...
Pei-Feng Lin
Full Text Available The heart begins to beat before the brain is formed. Whether conventional hierarchical central commands sent by the brain to the heart alone explain all the interplay between these two organs should be reconsidered. Here, we demonstrate correlations between the signal complexity of brain and cardiac activity. Eighty-seven geriatric outpatients with healthy hearts and varied cognitive abilities each provided a 24-hour electrocardiography (ECG and a 19-channel eye-closed routine electroencephalography (EEG. Multiscale entropy (MSE analysis was applied to three epochs (resting-awake state, photic stimulation of fast frequencies (fast-PS, and photic stimulation of slow frequencies (slow-PS of EEG in the 1-58 Hz frequency range, and three RR interval (RRI time series (awake-state, sleep and that concomitant with the EEG for each subject. The low-to-high frequency power (LF/HF ratio of RRI was calculated to represent sympatho-vagal balance. With statistics after Bonferroni corrections, we found that: (a the summed MSE value on coarse scales of the awake RRI (scales 11-20, RRI-MSE-coarse were inversely correlated with the summed MSE value on coarse scales of the resting-awake EEG (scales 6-20, EEG-MSE-coarse at Fp2, C4, T6 and T4; (b the awake RRI-MSE-coarse was inversely correlated with the fast-PS EEG-MSE-coarse at O1, O2 and C4; (c the sleep RRI-MSE-coarse was inversely correlated with the slow-PS EEG-MSE-coarse at Fp2; (d the RRI-MSE-coarse and LF/HF ratio of the awake RRI were correlated positively to each other; (e the EEG-MSE-coarse at F8 was proportional to the cognitive test score; (f the results conform to the cholinergic hypothesis which states that cognitive impairment causes reduction in vagal cardiac modulation; (g fast-PS significantly lowered the EEG-MSE-coarse globally. Whether these heart-brain correlations could be fully explained by the central autonomic network is unknown and needs further exploration.
Lin, Pei-Feng; Lo, Men-Tzung; Tsao, Jenho; Chang, Yi-Chung; Lin, Chen; Ho, Yi-Lwun
2014-01-01
The heart begins to beat before the brain is formed. Whether conventional hierarchical central commands sent by the brain to the heart alone explain all the interplay between these two organs should be reconsidered. Here, we demonstrate correlations between the signal complexity of brain and cardiac activity. Eighty-seven geriatric outpatients with healthy hearts and varied cognitive abilities each provided a 24-hour electrocardiography (ECG) and a 19-channel eye-closed routine electroencephalography (EEG). Multiscale entropy (MSE) analysis was applied to three epochs (resting-awake state, photic stimulation of fast frequencies (fast-PS), and photic stimulation of slow frequencies (slow-PS)) of EEG in the 1-58 Hz frequency range, and three RR interval (RRI) time series (awake-state, sleep and that concomitant with the EEG) for each subject. The low-to-high frequency power (LF/HF) ratio of RRI was calculated to represent sympatho-vagal balance. With statistics after Bonferroni corrections, we found that: (a) the summed MSE value on coarse scales of the awake RRI (scales 11-20, RRI-MSE-coarse) were inversely correlated with the summed MSE value on coarse scales of the resting-awake EEG (scales 6-20, EEG-MSE-coarse) at Fp2, C4, T6 and T4; (b) the awake RRI-MSE-coarse was inversely correlated with the fast-PS EEG-MSE-coarse at O1, O2 and C4; (c) the sleep RRI-MSE-coarse was inversely correlated with the slow-PS EEG-MSE-coarse at Fp2; (d) the RRI-MSE-coarse and LF/HF ratio of the awake RRI were correlated positively to each other; (e) the EEG-MSE-coarse at F8 was proportional to the cognitive test score; (f) the results conform to the cholinergic hypothesis which states that cognitive impairment causes reduction in vagal cardiac modulation; (g) fast-PS significantly lowered the EEG-MSE-coarse globally. Whether these heart-brain correlations could be fully explained by the central autonomic network is unknown and needs further exploration. PMID:24498375
Multi-scale analysis of deformation behavior at SCC crack tip (Contract research)
In recent years, incidents of the stress corrosion cracking (SCC) were frequently reported that occurred to the various components of domestic boiling water reactors (BWR), and the cause investigation and measure become the present important issue. By the Japan nuclear energy safety organization (JNES), a research project on the intergranular SCC (IGSCC) in nuclear grade stainless steels (henceforth, IGSCC project) is under enforcement from a point of view to secure safety and reliability of BWR, and SCC growth data of low carbon stainless steels are being accumulated for the weld part or the work-hardened region adjacent to the weld metal. In the project, it has been an important subject to guarantee the validity of accumulated SCC data. At a crack tip of SCC in compact tension (CT) type specimen used for the SCC propagation test, a macroscopic plastic region is formed where heterogeneity of microstructure developed by microscopic sliding and dislocations is observed. However, there is little quantitative information on the plastic region, and therefore, to assess the data of macroscopic SCC growth rate and the validity of propagation test method, it is essentially required to investigate the plastic region at crack tip in detail from a microscopic viewpoint. This report describes a result of the research conducted by the Japan Atomic Energy Research Institute and the National Institute for Materials Science under contract with JNES that was concerned with a multi-scale analysis of plastic deformation behavior at the crack tip of SCC. The research was carried out to evaluate the validity of the SCC growth data acquired in the IGSCC project based on a mechanistic understanding of SCC. For the purpose, in this research, analyses of the plastic deformation behavior and microstructure around the crack tip were performed in a nano-order scale. The hardness measured in nano, meso and macro scales was employed as a common index of the strength, and the essential data
Chia-Hsuan Lee
2015-11-01
Full Text Available The goal of this study was to investigate the parameters affecting exergame performance using multi-scale entropy analysis, with the aim of informing the design of exergames for personalized balance training. Test subjects’ center of pressure (COP displacement data were recorded during exergame play to examine their balance ability at varying difficulty levels of a balance-based exergame; the results of a multi-scale entropy-based analysis were then compared to traditional COP indicators. For games involving static posture frames, variation in posture frame travel time was found to significantly affect the complexity of both the anterior-posterior (MSE-AP and medio-lateral (MSE-ML components of balancing movements. However, in games involving dynamic posture frames, only MSE-AP was found to be sensitive to the variation of parameters, namely foot-lifting speed. Findings were comparable to the COP data published by Sun et al., indicating that the use of complexity data is a feasible means of distinguishing between different parameter sets and of understanding how human design considerations must be taken into account in exergame development. Not only can this method be used as another assessment index in the future, it can also be used in the optimization of parameters within the virtual environments of exergames.
Luo, Shihua; Guo, Fan; Lai, Dejian; Yan, Fang; Tang, Feilai
2015-09-01
Hurst exponent is an important measure of nonlinearity of dynamical time series. In this paper, using rescaled-range (R/S) analysis, multi-fractal detrended fluctuation analysis (MF-DFA) methods, the multiscale Hurst exponent (MHE) and the multiscale generalized Hurst exponent (MGHE) of coarse-grained silicon content ([Si]) time series in blast furnace (BF) hot metal were calculated. First, we collected these [Si] time series from No. 1 BF of Nanchang Iron and Steel Co. and No. 10 BF of Xinyu Iron and Steel Co. in Jiangxi Province, China. Then, we analyzed and compared the estimated Hurst exponents and the generalized Hurst exponent of these observed time series with some simulated time series. Our results show that the observed time series from these BFs have negative correlation with the Hurst exponent less than 0.5, the generalized Hurst exponent H(q) is a nonlinear function of q, and such negative correlation and local various structure persist in their moving averages of the observed time series up to lag 5 or 10.
Silva, Luiz Eduardo Virgilio; Lataro, Renata Maria; Castania, Jaci Airton; da Silva, Carlos Alberto Aguiar; Valencia, Jose Fernando; Murta, Luiz Otavio; Salgado, Helio Cesar; Fazan, Rubens; Porta, Alberto
2016-07-01
The analysis of heart rate variability (HRV) by nonlinear methods has been gaining increasing interest due to their ability to quantify the complexity of cardiovascular regulation. In this study, multiscale entropy (MSE) and refined MSE (RMSE) were applied to track the complexity of HRV as a function of time scale in three pathological conscious animal models: rats with heart failure (HF), spontaneously hypertensive rats (SHR), and rats with sinoaortic denervation (SAD). Results showed that HF did not change HRV complexity, although there was a tendency to decrease the entropy in HF animals. On the other hand, SHR group was characterized by reduced complexity at long time scales, whereas SAD animals exhibited a smaller short- and long-term irregularity. We propose that short time scales (1 to 4), accounting for fast oscillations, are more related to vagal and respiratory control, whereas long time scales (5 to 20), accounting for slow oscillations, are more related to sympathetic control. The increased sympathetic modulation is probably the main reason for the lower entropy observed at high scales for both SHR and SAD groups, acting as a negative factor for the cardiovascular complexity. This study highlights the contribution of the multiscale complexity analysis of HRV for understanding the physiological mechanisms involved in cardiovascular regulation. PMID:27225948
A coronary artery segmentation method based on multiscale analysis and region growing.
Kerkeni, Asma; Benabdallah, Asma; Manzanera, Antoine; Bedoui, Mohamed Hedi
2016-03-01
Accurate coronary artery segmentation is a fundamental step in various medical imaging applications such as stenosis detection, 3D reconstruction and cardiac dynamics assessing. In this paper, a multiscale region growing (MSRG) method for coronary artery segmentation in 2D X-ray angiograms is proposed. First, a region growing rule incorporating both vesselness and direction information in a unique way is introduced. Then an iterative multiscale search based on this criterion is performed. Selected points in each step are considered as seeds for the following step. By combining vesselness and direction information in the growing rule, this method is able to avoid blockage caused by low vesselness values in vascular regions, which in turn, yields continuous vessel tree. Performing the process in a multiscale fashion helps to extract thin and peripheral vessels often missed by other segmentation methods. Quantitative evaluation performed on real angiography images shows that the proposed segmentation method identifies about 80% of the total coronary artery tree in relatively easy images and 70% in challenging cases with a mean precision of 82% and outperforms others segmentation methods in terms of sensitivity. The MSRG segmentation method was also implemented with different enhancement filters and it has been shown that the Frangi filter gives better results. The proposed segmentation method has proven to be tailored for coronary artery segmentation. It keeps an acceptable performance when dealing with challenging situations such as noise, stenosis and poor contrast. PMID:26748040
Wang, Y. D.; Liu, K. Y.; Yang, Y. S.; Ren, Y. Q.; Hu, T.; Deng, B.; Xiao, T. Q.
2016-04-01
Three dimensional (3D) characterization of shales has recently attracted wide attentions in relation to the growing importance of shale oil and gas. Obtaining a complete 3D compositional distribution of shale has proven to be challenging due to its multi-scale characteristics. A combined multi-energy X-ray micro-CT technique and data-constrained modelling (DCM) approach has been used to quantitatively investigate the multi-scale mineral and porosity distributions of a heterogeneous shale from the Junger Basin, northwestern China by sub-sampling. The 3D sub-resolution structures of minerals and pores in the samples are quantitatively obtained as the partial volume fraction distributions, with colours representing compositions. The shale sub-samples from two areas have different physical structures for minerals and pores, with the dominant minerals being feldspar and dolomite, respectively. Significant heterogeneities have been observed in the analysis. The sub-voxel sized pores form large interconnected clusters with fractal structures. The fractal dimensions of the largest clusters for both sub-samples were quantitatively calculated and found to be 2.34 and 2.86, respectively. The results are relevant in quantitative modelling of gas transport in shale reservoirs.
Despite the recognized importance of the multi-scale spatio-temporal organization of proteins, most computational tools can only access a limited spectrum of time and spatial scales, thereby ignoring the effects on protein behavior of the intricate coupling between the different scales. Starting from a physico-chemical atomistic network of interactions that encodes the structure of the protein, we introduce a methodology based on multi-scale graph partitioning that can uncover partitions and levels of organization of proteins that span the whole range of scales, revealing biological features occurring at different levels of organization and tracking their effect across scales. Additionally, we introduce a measure of robustness to quantify the relevance of the partitions through the generation of biochemically-motivated surrogate random graph models. We apply the method to four distinct conformations of myosin tail interacting protein, a protein from the molecular motor of the malaria parasite, and study properties that have been experimentally addressed such as the closing mechanism, the presence of conserved clusters, and the identification through computational mutational analysis of key residues for binding
Cash, Philip; Hicks, Ben; Culley, Steve
2015-01-01
This paper contributes to improving our understanding of design activity. Specifically the paper uses Activity Theory to enable a multi-scale analysis of the activity of three engineering designers over a period of one month. Correspondingly, this paper represents the first work that explicitly i...
Oluoch, Kevin; Marwan, Norbert; Trauth, Martin; Kurths, Juergen
2013-04-01
Evolving complex networks analysis is a very recent and very promising attempt to describe, in the most realistic ways, complex systems or multi-system dynamics. The Earth system is comprised of many attractors that are multi-scaled, multi-complexity non-linear systems of systems. Space time propagations responsible for precipitation is one example in which the interactions between the aforementioned properties of complex systems can be applied; especially the spatio-temporal wave likeness of spatial patterning and temporal recurrences representative of the underlying dynamics. Tobler's first law of geography states: "Everything is related to everything else, but near things are more related than distant things" (Waldo Tobler, 1970 Economic Geography 46: 234-40). Most time-series analysis are pairwise correlations and even when faced with gridded data, the neighborhood characteristics is never used as an input variable. In our point of view, such analysis ignore vital information on the multi-scale non-linear spatial patterns of the continuities and singularities possibly resulting from underlying random processes. This work in progress is an application, mainly inspired by wave theory and non-linear dynamics. It is a systematic method of methods, which exploits the nonlinear multi-scale wave nature of virtually everything in nature including financial data, disease dynamics et cetera and applies it to climate through complex network analysis of rainfall data. The method uses a continuous spatial wavelet transform for non-linear multi-scale decomposition. Such an output carries all vital information pertaining the singularity structures in the data. Similarity measures are obtained by considering the multi-fractal nature of the distribution of discontinuities. The more similar the point-wise generalized dimensions are in-terms of their continuity, fractal, entropy, information and correlation dimensions, the higher the chance that they characterize similar
Refined Multiscale Fuzzy Entropy based on Standard Deviation for Biomedical Signal Analysis
Azami, Hamed; Fernandez, Alberto; Escudero, Javier
2016-01-01
Multiscale entropy (MSE) has been a prevalent algorithm to quantify the complexity of fluctuations in the local mean value of biomedical time series. Recent developments in the field have tried to improve the MSE by reducing its variability in large scale factors. On the other hand, there has been recent interest in using other statistical moments than the mean, i.e. variance, in the coarse-graining step of the MSE. Building on these trends, here we introduce the so-called refined composite m...
An integrated multi-scale risk analysis procedure for pluvial flooding
Tader, Andreas; Mergili, Martin; Jäger, Stefan; Glade, Thomas; Neuhold, Clemens; Stiefelmeyer, Heinz
2016-04-01
Mitigation of or adaptation to the negative impacts of natural processes on society requires a better understanding of the spatio-temporal distribution not only of the processes themselves, but also of the elements at risk. Information on their values, exposures and vulnerabilities towards the expected impact magnitudes/intensities of the relevant processes is needed. GIS-supported methods are particularly useful for integrated spatio-temporal analyses of natural processes and their potential consequences. Hereby, pluvial floods are of particular concern for many parts of Austria. The overall aim of the present study is to calculate the hazards emanating from pluvial floods, to determine the exposure of given elements at risk, to determine their vulnerabilities towards given pluvial flood hazards and to analyze potential consequences in terms of monetary losses. The whole approach builds on data available on a national scale. We introduce an integrated, multi-scale risk analysis procedure with regard to pluvial flooding. Focusing on the risk to buildings, we firstly exemplify this procedure with a well-documented event in the city of Graz (Austria), in order to highlight the associated potentials and limitations. Secondly, we attempt to predict the possible consequences of pluvial flooding triggered by rainfall events with recurrence intervals of 30, 100 and 300 years. (i) We compute spatially distributed inundation depths using the software FloodArea. Infiltration capacity and surface roughness are estimated from the land cover units given by the official cadastre. Various assumptions are tested with regard to the inflow to the urban sewer system. (ii) Based on the inundation depths and the official building register, we employ a set of rules and functions to deduce the exposure, vulnerability and risk for each building. A risk indicator for each building, expressed as the expected damage associated to a given event, is derived by combining the building value and
Blood Flow Multiscale Phenomena
Agić, Ante; Mijović, Budimir; Nikolić, Tatjana
2007-01-01
The cardiovascular disease is one of most frequent cause deaths in modern society. The objective of this work is analyse the effect of dynamic vascular geometry (curvature, torsion,bifurcation) and pulsatile blood nature on secondary flow, wall shear stress and platelet deposition. The problem was examined as multi-scale physical phenomena using perturbation analysis and numerical modelling. The secondary flow determined as influence pulsatile pressure, vascular tube time-dependen...
Yanchuk, Serhiy; Giacomelli, Giovanni
2015-10-01
Dynamical systems with multiple, hierarchically long-delayed feedback are introduced and studied extending our previous work [Yanchuk and Giacomelli, Phys. Rev. Lett. 112, 174103 (2014)]. Focusing on the phenomenological model of a Stuart-Landau oscillator with two feedbacks, we show the multiscale properties of its dynamics and demonstrate them by means of a space-time representation. For sufficiently long delays, we derive a normal form describing the system close to the destabilization. The space and temporal variables, which are involved in the space-time representation, correspond to suitable time scales of the original system. The physical meaning of the results, together with the interpretation of the description at different scales, is presented and discussed. In particular, it is shown how this representation uncovers hidden multiscale patterns such as spirals or spatiotemporal chaos. The effect of the delay size and the features of the transition between small and large delays is also analyzed. Finally, we comment on the application of the method and on its extension to an arbitrary, but finite, number of delayed feedback terms. PMID:26565300
Giampietro, Mario; Sorman, Alevgül H
2013-01-01
The vast majority of the countries of the world are now facing an imminent energy crisis, particularly the USA, China, India, Japan and EU countries, but also developing countries having to boost their economic growth precisely when more powerful economies will prevent them from using the limited supply of fossil energy. Despite this crisis, current protocols of energy accounting have been developed for dealing with fossil energy exclusively and are therefore not useful for the analysis of alternative energy sources. The first part of the book illustrates the weakness of existing analyses of energy problems: the science of energy was born and developed neglecting the issue of scale. The authors argue that it is necessary to adopt more complex protocols of accounting and analysis in order to generate robust energy scenarios and effective assessments of the quality of alternative energy sources. The second part of the book introduces the concept of energetic metabolism of modern societies and uses empirical res...
Hamano, H.; Nakayama, T.; Fujita, T.; Hori, H.; Tagami, H.
2009-12-01
It is necessary to reduce Greenhouse gases (GHG) emissions drastically to stabilize climate change, and Japan is also required to assess its long-term global warming policy. In achieving the low carbon society and sustainable cities, the numerical evaluation of environmental impacts of the application of different technologies and policies was preliminarily examined by utilizing integrative urban environmental model. This research aims to develop the multi-scale model for urban climate analysis and to evaluate the urban greening effects on energy consumption from household and business sectors. It developed the multi-scale model combined the process-based NIES integrated catchment-based eco-hydrology (NICE) model with the meso-scale meteorological model (Regional Atmospheric Modeling System : RAMS) and urban canopy model to estimate the urban climate mitigation effects by introduction of urban heat environmental mitigation technology and scenario. The numerical simulation conducted with the multi-scale level horizontally consisting regional scale (260×260km with 2km grid) and urban area scale (36×26km with 0.2km grid) against the objective area, Kawasaki city of Japan. The urban canopy model predicts the three dimensional atmospheric conditions including anthropogenic heat effect from household, business and factory sectors. Furthermore the tile method applied into the urban canopy model for the improvement of numerical accuracy and detailed land use information in each grid. The validation of this model was conducted by comparison with the observed air temperature of 29 points in entire Kawasaki area from 1st to 31th of August, 2006. From the quantitative validation of model performance, the coefficient of correlation was 0.72 and the root mean square error was 2.99C. The introduction of patch method into urban canopy model made it possible to calculate the each land use effect, and the accuracy of predicted results was improved against the land use area
Speck, D E
2008-04-28
The Multiscale Epidemiologic/Economic Simulation and Analysis (MESA) Decision Support System (DSS) is the product of investments that began in FY05 by the Department of Homeland Security (DHS) Science and Technology Directorate and continue today with joint funding by both DHS and the US Department of Agriculture (USDA). The DSS consists of a coupled epidemiologic/economic model, a standalone graphical user interface (GUI) that supports both model setup and post-analysis, and a Scenario Bank archive to store all content related to foreign animal disease (FAD) studies. The MESA epi model is an object-oriented, agent-based, stochastic, spatio-temporal simulator that parametrically models FAD outbreaks and response strategies from initial disease introduction to conclusion over local, regional, and national scales. Through its output database, the epi model couples to an economic model that calculates farm-level impacts from animal infections, responsive control strategies and loss of trade. The MESA architecture contains a variety of internal models that implement the major components of the epi simulation, including disease introduction, intra-herd spread, inter-herd spread (direct and indirect), detection, and various control strategies (movement restrictions, culling, vaccination) in a highly configurable and extensible fashion. MESA development was originally focused to support investigations into the economic and agricultural industry impacts associated with Foot-and-Mouth Disease (FMD outbreaks). However, it has been adapted to other FADs such has Highly Pathogenic Avian Influenza (HPAI), Classical Swine Fever (CSF) and Exotic Newcastle Disease (END). The MESA model is highly parameterized and employs an extensible architecture that permits straightforward addition of new component models (e.g., alternative disease spread approaches) when necessary. Since its inception, MESA has been developed with a requirement to enable simulation of the very large scale
Adjoint Based A Posteriori Analysis of Multiscale Mortar Discretizations with Multinumerics
Tavener, Simon
2013-01-01
In this paper we derive a posteriori error estimates for linear functionals of the solution to an elliptic problem discretized using a multiscale nonoverlapping domain decomposition method. The error estimates are based on the solution of an appropriately defined adjoint problem. We present a general framework that allows us to consider both primal and mixed formulations of the forward and adjoint problems within each subdomain. The primal subdomains are discretized using either an interior penalty discontinuous Galerkin method or a continuous Galerkin method with weakly imposed Dirichlet conditions. The mixed subdomains are discretized using Raviart- Thomas mixed finite elements. The a posteriori error estimate also accounts for the errors due to adjoint-inconsistent subdomain discretizations. The coupling between the subdomain discretizations is achieved via a mortar space. We show that the numerical discretization error can be broken down into subdomain and mortar components which may be used to drive adaptive refinement.Copyright © by SIAM.
Borgdorff, J.; Bona-Casas, C.; Mamonski, M.; Kurowski, K.; Piontek, T.; Bosak, B.; Rycerz, K.; Ciepiela, E.; Gubala, T.; Harezlak, D.; Bubak, M.; Lorenz, E.; Hoekstra, A.G.
2012-01-01
Nature is observed at all scales; with multiscale modeling, scientists bring together several scales for a holistic analysis of a phenomenon. The models on these different scales may require significant but also heterogeneous computational resources, creating the need for distributed multiscale computing. A particularly demanding type of multiscale models, tightly coupled, brings with it a number of theoretical and practical issues. In this contribution, a tightly coupled model of in-stent re...
Morales-Navarrete, Hernán; Segovia-Miranda, Fabián; Klukowski, Piotr; Meyer, Kirstin; Nonaka, Hidenori; Marsico, Giovanni; Chernykh, Mikhail; Kalaidzidis, Alexander; Zerial, Marino; Kalaidzidis, Yannis
2015-01-01
A prerequisite for the systems biology analysis of tissues is an accurate digital three-dimensional reconstruction of tissue structure based on images of markers covering multiple scales. Here, we designed a flexible pipeline for the multi-scale reconstruction and quantitative morphological analysis of tissue architecture from microscopy images. Our pipeline includes newly developed algorithms that address specific challenges of thick dense tissue reconstruction. Our implementation allows for a flexible workflow, scalable to high-throughput analysis and applicable to various mammalian tissues. We applied it to the analysis of liver tissue and extracted quantitative parameters of sinusoids, bile canaliculi and cell shapes, recognizing different liver cell types with high accuracy. Using our platform, we uncovered an unexpected zonation pattern of hepatocytes with different size, nuclei and DNA content, thus revealing new features of liver tissue organization. The pipeline also proved effective to analyse lung and kidney tissue, demonstrating its generality and robustness. DOI: http://dx.doi.org/10.7554/eLife.11214.001 PMID:26673893
Multiscale Simulation Framework for Coupled Fluid Flow and Mechanical Deformation
Hou, Thomas [California Inst. of Technology (CalTech), Pasadena, CA (United States); Efendiev, Yalchin [Stanford Univ., CA (United States); Tchelepi, Hamdi [Texas A & M Univ., College Station, TX (United States); Durlofsky, Louis [Stanford Univ., CA (United States)
2016-05-24
Our work in this project is aimed at making fundamental advances in multiscale methods for flow and transport in highly heterogeneous porous media. The main thrust of this research is to develop a systematic multiscale analysis and efficient coarse-scale models that can capture global effects and extend existing multiscale approaches to problems with additional physics and uncertainties. A key emphasis is on problems without an apparent scale separation. Multiscale solution methods are currently under active investigation for the simulation of subsurface flow in heterogeneous formations. These procedures capture the effects of fine-scale permeability variations through the calculation of specialized coarse-scale basis functions. Most of the multiscale techniques presented to date employ localization approximations in the calculation of these basis functions. For some highly correlated (e.g., channelized) formations, however, global effects are important and these may need to be incorporated into the multiscale basis functions. Other challenging issues facing multiscale simulations are the extension of existing multiscale techniques to problems with additional physics, such as compressibility, capillary effects, etc. In our project, we explore the improvement of multiscale methods through the incorporation of additional (single-phase flow) information and the development of a general multiscale framework for flows in the presence of uncertainties, compressible flow and heterogeneous transport, and geomechanics. We have considered (1) adaptive local-global multiscale methods, (2) multiscale methods for the transport equation, (3) operator-based multiscale methods and solvers, (4) multiscale methods in the presence of uncertainties and applications, (5) multiscale finite element methods for high contrast porous media and their generalizations, and (6) multiscale methods for geomechanics.
MRI-Based Multiscale Model for Electromagnetic Analysis in the Human Head with Implanted DBS
Maria Ida Iacono
2013-01-01
Full Text Available Deep brain stimulation (DBS is an established procedure for the treatment of movement and affective disorders. Patients with DBS may benefit from magnetic resonance imaging (MRI to evaluate injuries or comorbidities. However, the MRI radio-frequency (RF energy may cause excessive tissue heating particularly near the electrode. This paper studies how the accuracy of numerical modeling of the RF field inside a DBS patient varies with spatial resolution and corresponding anatomical detail of the volume surrounding the electrodes. A multiscale model (MS was created by an atlas-based segmentation using a 1 mm3 head model (mRes refined in the basal ganglia by a 200 μm2 ex-vivo dataset. Four DBS electrodes targeting the left globus pallidus internus were modeled. Electromagnetic simulations at 128 MHz showed that the peak of the electric field of the MS doubled (18.7 kV/m versus 9.33 kV/m and shifted 6.4 mm compared to the mRes model. Additionally, the MS had a sixfold increase over the mRes model in peak-specific absorption rate (SAR of 43.9 kW/kg versus 7 kW/kg. The results suggest that submillimetric resolution and improved anatomical detail in the model may increase the accuracy of computed electric field and local SAR around the tip of the implant.
Time-Varying, Multi-Scale Adaptive System Reliability Analysis of Lifeline Infrastructure Networks
Gearhart, Jared Lee [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Kurtz, Nolan Scot [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
2014-09-01
The majority of current societal and economic needs world-wide are met by the existing networked, civil infrastructure. Because the cost of managing such infrastructure is high and increases with time, risk-informed decision making is essential for those with management responsibilities for these systems. To address such concerns, a methodology that accounts for new information, deterioration, component models, component importance, group importance, network reliability, hierarchical structure organization, and efficiency concerns has been developed. This methodology analyzes the use of new information through the lens of adaptive Importance Sampling for structural reliability problems. Deterioration, multi-scale bridge models, and time-variant component importance are investigated for a specific network. Furthermore, both bridge and pipeline networks are studied for group and component importance, as well as for hierarchical structures in the context of specific networks. Efficiency is the primary driver throughout this study. With this risk-informed approach, those responsible for management can address deteriorating infrastructure networks in an organized manner.
Analysis of Fiber Clustering in Composite Materials Using High-Fidelity Multiscale Micromechanics
Bednarcyk, Brett A.; Aboudi, Jacob; Arnold, Steven M.
2015-01-01
A new multiscale micromechanical approach is developed for the prediction of the behavior of fiber reinforced composites in presence of fiber clustering. The developed method is based on a coupled two-scale implementation of the High-Fidelity Generalized Method of Cells theory, wherein both the local and global scales are represented using this micromechanical method. Concentration tensors and effective constitutive equations are established on both scales and linked to establish the required coupling, thus providing the local fields throughout the composite as well as the global properties and effective nonlinear response. Two nondimensional parameters, in conjunction with actual composite micrographs, are used to characterize the clustering of fibers in the composite. Based on the predicted local fields, initial yield and damage envelopes are generated for various clustering parameters for a polymer matrix composite with both carbon and glass fibers. Nonlinear epoxy matrix behavior is also considered, with results in the form of effective nonlinear response curves, with varying fiber clustering and for two sets of nonlinear matrix parameters.
A multiscale analysis of coral reef topographic complexity using lidar-derived bathymetry
Zawada, D.G.; Brock, J.C.
2009-01-01
Coral reefs represent one of the most irregular substrates in the marine environment. This roughness or topographic complexity is an important structural characteristic of reef habitats that affects a number of ecological and environmental attributes, including species diversity and water circulation. Little is known about the range of topographic complexity exhibited within a reef or between different reef systems. The objective of this study was to quantify topographic complexity for a 5-km x 5-km reefscape along the northern Florida Keys reef tract, over spatial scales ranging from meters to hundreds of meters. The underlying dataset was a 1-m spatial resolution, digital elevation model constructed from lidar measurements. Topographic complexity was quantified using a fractal algorithm, which provided a multi-scale characterization of reef roughness. The computed fractal dimensions (D) are a measure of substrate irregularity and are bounded between values of 2 and 3. Spatial patterns in D were positively correlated with known reef zonation in the area. Landward regions of the study site contain relatively smooth (D ??? 2.35) flat-topped patch reefs, which give way to rougher (D ??? 2.5), deep, knoll-shaped patch reefs. The seaward boundary contains a mixture of substrate features, including discontinuous shelf-edge reefs, and exhibits a corresponding range of roughness values (2.28 ??? D ??? 2.61). ?? 2009 Coastal Education and Research Foundation.
Wu, Hsien-Tsai; Hsu, Po-Chun; Lin, Cheng-Feng; Wang, Hou-Jun; Sun, Cheuk-Kwan; Liu, An-Bang; Lo, Men-Tzung; Tang, Chieh-Ju
2011-10-01
This study proposed a dynamic pulse wave velocity (PWV)-based biomedical parameter in assessing the degree of atherosclerosis for the aged and diabetic populations. Totally, 91 subjects were recruited from a single medical institution between July 2009 and October 2010. The subjects were divided into four groups: young healthy adults (Group 1, n = 22), healthy upper middle-aged adults (Group 2, n = 28), type 2 diabetics with satisfactory blood sugar control (Group 3, n = 21), and unsatisfactory blood sugar control (Group 4, n = 20). A self-developed six-channel electrocardiography (ECG)-PWV-based equipment was used to acquire 1000 successive recordings of PWV(foot) values within 30 min. The data, thus, obtained were analyzed with multiscale entropy (MSE). Large-scale MSE index (MEI(LS)) was chosen as the assessment parameter. Not only did MEI(LS) successfully differentiate between subjects in Groups 1 and 2, but it also showed a significant difference between Groups 3 and 4. Compared with the conventional parameter of PWV(foot) and MEI on R-R interval [i.e., MEI(RRI)] in evaluating the degree of atherosclerotic change, the dynamic parameter, MEI(LS) (PWV), could better reflect the impact of age and blood sugar control on the progression of atherosclerosis. PMID:21693413
Performance Analysis of Multiscale Entropy for the Assessment of ECG Signal Quality
Yatao Zhang
2015-01-01
Full Text Available This study explored the performance of multiscale entropy (MSE for the assessment of mobile ECG signal quality, aiming to provide a reasonable application guideline. Firstly, the MSE for the typical noises, that is, high frequency (HF noise, low frequency (LF noise, and power-line (PL noise, was analyzed. The sensitivity of MSE to the signal to noise ratio (SNR of the synthetic artificial ECG plus different noises was further investigated. The results showed that the MSE values could reflect content level of various noises contained in the ECG signals. For the synthetic ECG plus LF noise, the MSE was sensitive to SNR within higher range of scale factor. However, for the synthetic ECG plus HF noise, the MSE was sensitive to SNR within lower range of scale factor. Thus, a recommended scale factor range within 5 to 10 was given. Finally, the results were verified on the real ECG signals, which were derived from MIT-BIH Arrhythmia Database and Noise Stress Test Database. In all, MSE could effectively assess the noise level on the real ECG signals, and this study provided a valuable reference for applying MSE method to the practical signal quality assessment of mobile ECG.
Multiscale Multiphysics Caprock Seal Analysis: A Case Study of the Farnsworth Unit, Texas, USA
Heath, J. E.; Dewers, T. A.; Mozley, P.
2015-12-01
Caprock sealing behavior depends on coupled processes that operate over a variety of length and time scales. Capillary sealing behavior depends on nanoscale pore throats and interfacial fluid properties. Larger-scale sedimentary architecture, fractures, and faults may govern properties of potential "seal-bypass" systems. We present the multiscale multiphysics investigation of sealing integrity of the caprock system that overlies the Morrow Sandstone reservoir, Farnsworth Unit, Texas. The Morrow Sandstone is the target injection unit for an on-going combined enhanced oil recovery-CO2 storage project by the Southwest Regional Partnership on Carbon Sequestration (SWP). Methods include small-to-large scale measurement techniques, including: focused ion beam-scanning electron microscopy; laser scanning confocal microscopy; electron and optical petrography; core examinations of sedimentary architecture and fractures; geomechanical testing; and a noble gas profile through sealing lithologies into the reservoir, as preserved from fresh core. The combined data set is used as part of a performance assessment methodology. The authors gratefully acknowledge the U.S. Department of Energy's (DOE) National Energy Technology Laboratory for sponsoring this project through the SWP under Award No. DE-FC26-05NT42591. Sandia National Laboratories is a multi-program laboratory managed and operated by Sandia Corporation, a wholly owned subsidiary of Lockheed Martin Corporation, for the U.S. Department of Energy's National Nuclear Security Administration under contract DE-AC04-94AL85000.
Multi-scale and multi-fractal analysis of pressure fluctuation in slurry bubble column bed reactor
无
2007-01-01
The Daubechies second order wavelet was applied to decompose pressure fluctuation signals with the gas flux varying from 0.18 to 0.90 m3/h and the solid mass fraction from 0 to 20% and scales 1-9 detail signals and the 9th scale approximation signals. The pressure signals were studied by multi-scale and R/S analysis method. Hurst analysis method was applied to analyze multi-fractal characteristics of different scale signals. The results show that the characteristics of mono-fractal under scale 1 and scale 2, and bi-fractal under scale 3-9 are effective in deducing the hydrodynamics in slurry bubbling flow system. The measured pressure signals are decomposed to micro-scale signals, meso-scale signals and macro-scale signals. Micro-scale and macro-scale signals are of mono-fractal characteristics, and meso-scale signals are of bi-fractal characteristics. By analyzing energy distribution of different scale signals, it is shown that pressure fluctuations mainly reflects meso-scale interaction between the particles and the bubble.
Li, Rui; Wang, Jun
2016-01-01
A financial price model is developed based on the voter interacting system in this work. The Lempel-Ziv complexity is introduced to analyze the complex behaviors of the stock market. Some stock market stylized facts including fat tails, absence of autocorrelation and volatility clustering are investigated for the proposed price model firstly. Then the complexity of fluctuation behaviors of the real stock markets and the proposed price model are mainly explored by Lempel-Ziv complexity (LZC) analysis and multi-scale weighted-permutation entropy (MWPE) analysis. A series of LZC analyses of the returns and the absolute returns of daily closing prices and moving average prices are performed. Moreover, the complexity of the returns, the absolute returns and their corresponding intrinsic mode functions (IMFs) derived from the empirical mode decomposition (EMD) with MWPE is also investigated. The numerical empirical study shows similar statistical and complex behaviors between the proposed price model and the real stock markets, which exhibits that the proposed model is feasible to some extent.
Bush Encroachment Mapping for Africa - Multi-Scale Analysis with Remote Sensing and GIS
Graw, V. A. M.; Oldenburg, C.; Dubovyk, O.
2015-12-01
Bush encroachment describes a global problem which is especially facing the savanna ecosystem in Africa. Livestock is directly affected by decreasing grasslands and inedible invasive species which defines the process of bush encroachment. For many small scale farmers in developing countries livestock represents a type of insurance in times of crop failure or drought. Among that bush encroachment is also a problem for crop production. Studies on the mapping of bush encroachment so far focus on small scales using high-resolution data and rarely provide information beyond the national level. Therefore a process chain was developed using a multi-scale approach to detect bush encroachment for whole Africa. The bush encroachment map is calibrated with ground truth data provided by experts in Southern, Eastern and Western Africa. By up-scaling location specific information on different levels of remote sensing imagery - 30m with Landsat images and 250m with MODIS data - a map is created showing potential and actual areas of bush encroachment on the African continent and thereby provides an innovative approach to map bush encroachment on the regional scale. A classification approach links location data based on GPS information from experts to the respective pixel in the remote sensing imagery. Supervised classification is used while actual bush encroachment information represents the training samples for the up-scaling. The classification technique is based on Random Forests and regression trees, a machine learning classification approach. Working on multiple scales and with the help of field data an innovative approach can be presented showing areas affected by bush encroachment on the African continent. This information can help to prevent further grassland decrease and identify those regions where land management strategies are of high importance to sustain livestock keeping and thereby also secure livelihoods in rural areas.
Multiscale Analysis for Field-Effect Penetration through Two-Dimensional Materials.
Tian, Tian; Rice, Peter; Santos, Elton J G; Shih, Chih-Jen
2016-08-10
Gate-tunable two-dimensional (2D) materials-based quantum capacitors (QCs) and van der Waals heterostructures involve tuning transport or optoelectronic characteristics by the field effect. Recent studies have attributed the observed gate-tunable characteristics to the change of the Fermi level in the first 2D layer adjacent to the dielectrics, whereas the penetration of the field effect through the one-molecule-thick material is often ignored or oversimplified. Here, we present a multiscale theoretical approach that combines first-principles electronic structure calculations and the Poisson-Boltzmann equation methods to model penetration of the field effect through graphene in a metal-oxide-graphene-semiconductor (MOGS) QC, including quantifying the degree of "transparency" for graphene two-dimensional electron gas (2DEG) to an electric displacement field. We find that the space charge density in the semiconductor layer can be modulated by gating in a nonlinear manner, forming an accumulation or inversion layer at the semiconductor/graphene interface. The degree of transparency is determined by the combined effect of graphene quantum capacitance and the semiconductor capacitance, which allows us to predict the ranking for a variety of monolayer 2D materials according to their transparency to an electric displacement field as follows: graphene > silicene > germanene > WS2 > WTe2 > WSe2 > MoS2 > phosphorene > MoSe2 > MoTe2, when the majority carrier is electron. Our findings reveal a general picture of operation modes and design rules for the 2D-materials-based QCs. PMID:27409143
Microstructural analysis of TRISO particles using multi-scale X-ray computed tomography
TRISO particles, a composite nuclear fuel built up by ceramic and graphitic layers, have outstanding high temperature resistance. TRISO fuel is the key technology for High Temperature Reactors (HTRs) and the Generation IV Very High Temperature Reactor (VHTR) variant. TRISO offers unparalleled containment of fission products and is extremely robust during accident conditions. An understanding of the thermal performance and mechanical properties of TRISO fuel requires a detailed knowledge of pore sizes, their distribution and interconnectivity. Here 50 nm, nano-, and 1 μm resolution, micro-computed tomography (CT), have been used to quantify non-destructively porosity of a surrogate TRISO particle at the 0.3–10 μm and 3–100 μm scales respectively. This indicates that pore distributions can reliably be measured down to a size approximately 3 times the pixel size which is consistent with the segmentation process. Direct comparison with Scanning Electron Microscopy (SEM) sections indicates that destructive sectioning can introduce significant levels of coarse damage, especially in the pyrolytic carbon layers. Further comparative work is required to identify means of minimizing such damage for SEM studies. Finally since it is non-destructive, multi-scale time-lapse X-ray CT opens the possibility of intermittently tracking the degradation of TRISO structure under thermal cycles or radiation conditions in order to validate models of degradation such as kernel movement. X-ray CT in-situ experimentation of TRISO particles under load and temperature could also be used to understand the internal changes that occur in the particles under accident conditions
Microstructural analysis of TRISO particles using multi-scale X-ray computed tomography
Lowe, T.; Bradley, R. S.; Yue, S.; Barii, K.; Gelb, J.; Rohbeck, N.; Turner, J.; Withers, P. J.
2015-06-01
TRISO particles, a composite nuclear fuel built up by ceramic and graphitic layers, have outstanding high temperature resistance. TRISO fuel is the key technology for High Temperature Reactors (HTRs) and the Generation IV Very High Temperature Reactor (VHTR) variant. TRISO offers unparalleled containment of fission products and is extremely robust during accident conditions. An understanding of the thermal performance and mechanical properties of TRISO fuel requires a detailed knowledge of pore sizes, their distribution and interconnectivity. Here 50 nm, nano-, and 1 μm resolution, micro-computed tomography (CT), have been used to quantify non-destructively porosity of a surrogate TRISO particle at the 0.3-10 μm and 3-100 μm scales respectively. This indicates that pore distributions can reliably be measured down to a size approximately 3 times the pixel size which is consistent with the segmentation process. Direct comparison with Scanning Electron Microscopy (SEM) sections indicates that destructive sectioning can introduce significant levels of coarse damage, especially in the pyrolytic carbon layers. Further comparative work is required to identify means of minimizing such damage for SEM studies. Finally since it is non-destructive, multi-scale time-lapse X-ray CT opens the possibility of intermittently tracking the degradation of TRISO structure under thermal cycles or radiation conditions in order to validate models of degradation such as kernel movement. X-ray CT in-situ experimentation of TRISO particles under load and temperature could also be used to understand the internal changes that occur in the particles under accident conditions.
Microstructural analysis of TRISO particles using multi-scale X-ray computed tomography
Lowe, T., E-mail: tristan.lowe@manchester.ac.uk [Manchester X-ray Imaging Facility, School of Materials, University of Manchester, M13 9PL (United Kingdom); Bradley, R.S. [Manchester X-ray Imaging Facility, School of Materials, University of Manchester, M13 9PL (United Kingdom); Yue, S. [Manchester X-ray Imaging Facility, School of Materials, University of Manchester, M13 9PL (United Kingdom); The Research Complex at Harwell, Rutherford Appleton Laboratory, Didcot, Oxfordshire OX11 0FA (United Kingdom); Barii, K. [School of Mechanical Engineering, University of Manchester, M13 9PL (United Kingdom); Gelb, J. [Zeiss Xradia Inc., Pleasanton, CA (United States); Rohbeck, N. [Manchester X-ray Imaging Facility, School of Materials, University of Manchester, M13 9PL (United Kingdom); Turner, J. [School of Mechanical Engineering, University of Manchester, M13 9PL (United Kingdom); Withers, P.J. [Manchester X-ray Imaging Facility, School of Materials, University of Manchester, M13 9PL (United Kingdom); The Research Complex at Harwell, Rutherford Appleton Laboratory, Didcot, Oxfordshire OX11 0FA (United Kingdom)
2015-06-15
TRISO particles, a composite nuclear fuel built up by ceramic and graphitic layers, have outstanding high temperature resistance. TRISO fuel is the key technology for High Temperature Reactors (HTRs) and the Generation IV Very High Temperature Reactor (VHTR) variant. TRISO offers unparalleled containment of fission products and is extremely robust during accident conditions. An understanding of the thermal performance and mechanical properties of TRISO fuel requires a detailed knowledge of pore sizes, their distribution and interconnectivity. Here 50 nm, nano-, and 1 μm resolution, micro-computed tomography (CT), have been used to quantify non-destructively porosity of a surrogate TRISO particle at the 0.3–10 μm and 3–100 μm scales respectively. This indicates that pore distributions can reliably be measured down to a size approximately 3 times the pixel size which is consistent with the segmentation process. Direct comparison with Scanning Electron Microscopy (SEM) sections indicates that destructive sectioning can introduce significant levels of coarse damage, especially in the pyrolytic carbon layers. Further comparative work is required to identify means of minimizing such damage for SEM studies. Finally since it is non-destructive, multi-scale time-lapse X-ray CT opens the possibility of intermittently tracking the degradation of TRISO structure under thermal cycles or radiation conditions in order to validate models of degradation such as kernel movement. X-ray CT in-situ experimentation of TRISO particles under load and temperature could also be used to understand the internal changes that occur in the particles under accident conditions.
J. Borgdorff; C. Bona-Casas; M. Mamonski; K. Kurowski; T. Piontek; B. Bosak; K. Rycerz; E. Ciepiela; T. Gubala; D. Harezlak; M. Bubak; E. Lorenz; A.G. Hoekstra
2012-01-01
Nature is observed at all scales; with multiscale modeling, scientists bring together several scales for a holistic analysis of a phenomenon. The models on these different scales may require significant but also heterogeneous computational resources, creating the need for distributed multiscale comp
Bari, Vlasta; Valencia, Jose F; Vallverdu, Montserrat; Girardengo, Giulia; Bassani, Tito; Marchi, Andrea; Calvillo, Laura; Caminal, Pere; Cerutti, Sergio; Brink, Paul A; Crotti, Lia; Schwartz, Peter J; Porta, Alberto
2013-01-01
This study assesses complexity of cardiovascular control in patients affected by type-1 variant of long QT (LQT1) syndrome. Complexity was assessed by refined multiscale entropy of heart period (HP) and QT interval variabilities. HP was taken as the time distance between two consecutive R peaks (RR) and QT interval was approximated as the time distance between the R-peak and T-wave apex (RTa) and between R-peak and T-wave end (RTe). RR, RTa and RTe intervals were automatically extracted from 24h Holter recordings and the daytime period was analyzed (from 02:00 to 06:00 PM). Non mutation carrier (NMC) individuals (n=11), utilized as a control group, were taken from the same family line of the mutation carrier (MC) subjects (n=26). We found that, while NMC and MC groups were indistinguishable based on time domain and complexity analyses of RR dynamics, complexity analysis of RTa and RTe variabilities clearly separates the two populations and suggests an impairment in the cardiac control mechanisms acting on the ventricles. PMID:24110995
Multiscale convexity analysis of certain fractal binary objects-like 8-segment Koch quadric, Koch triadic, and random Koch quadric and triadic islands-is performed via (i) morphologic openings with respect to recursively changing the size of a template, and (ii) construction of convex hulls through half-plane closings. Based on scale vs convexity measure relationship, transition levels between the morphologic regimes are determined as crossover scales. These crossover scales are taken as the basis to segment binary fractal objects into various morphologically prominent zones. Each segmented zone is characterized through normalized morphologic complexity measures. Despite the fact that there is no notably significant relationship between the zone-wise complexity measures and fractal dimensions computed by conventional box counting method, fractal objects-whether they are generated deterministically or by introducing randomness-possess morphologically significant sub-zones with varied degrees of spatial complexities. Classification of realistic fractal sets and/or fields according to sub-zones possessing varied degrees of spatial complexities provides insight to explore links with the physical processes involved in the formation of fractal-like phenomena.
A Multi-scale Approach for CO2 Accounting and Risk Analysis in CO2 Enhanced Oil Recovery Sites
Dai, Z.; Viswanathan, H. S.; Middleton, R. S.; Pan, F.; Ampomah, W.; Yang, C.; Jia, W.; Lee, S. Y.; McPherson, B. J. O. L.; Grigg, R.; White, M. D.
2015-12-01
Using carbon dioxide in enhanced oil recovery (CO2-EOR) is a promising technology for emissions management because CO2-EOR can dramatically reduce carbon sequestration costs in the absence of greenhouse gas emissions policies that include incentives for carbon capture and storage. This study develops a multi-scale approach to perform CO2 accounting and risk analysis for understanding CO2 storage potential within an EOR environment at the Farnsworth Unit of the Anadarko Basin in northern Texas. A set of geostatistical-based Monte Carlo simulations of CO2-oil-water flow and transport in the Marrow formation are conducted for global sensitivity and statistical analysis of the major risk metrics: CO2 injection rate, CO2 first breakthrough time, CO2 production rate, cumulative net CO2 storage, cumulative oil and CH4 production, and water injection and production rates. A global sensitivity analysis indicates that reservoir permeability, porosity, and thickness are the major intrinsic reservoir parameters that control net CO2 injection/storage and oil/CH4 recovery rates. The well spacing (the distance between the injection and production wells) and the sequence of alternating CO2 and water injection are the major operational parameters for designing an effective five-spot CO2-EOR pattern. The response surface analysis shows that net CO2 injection rate increases with the increasing reservoir thickness, permeability, and porosity. The oil/CH4 production rates are positively correlated to reservoir permeability, porosity and thickness, but negatively correlated to the initial water saturation. The mean and confidence intervals are estimated for quantifying the uncertainty ranges of the risk metrics. The results from this study provide useful insights for understanding the CO2 storage potential and the corresponding risks of commercial-scale CO2-EOR fields.
Chen, Jin-Long; Chen, Pin-Fan; Wang, Hung-Ming
2014-07-15
Parameters of glucose dynamics recorded by the continuous glucose monitoring system (CGMS) could help in the control of glycemic fluctuations, which is important in diabetes management. Multiscale entropy (MSE) analysis has recently been developed to measure the complexity of physical and physiological time sequences. A reduced MSE complexity index indicates the increased repetition patterns of the time sequence, and, thus, a decreased complexity in this system. No study has investigated the MSE analysis of glucose dynamics in diabetes. This study was designed to compare the complexity of glucose dynamics between the diabetic patients (n = 17) and the control subjects (n = 13), who were matched for sex, age, and body mass index via MSE analysis using the CGMS data. Compared with the control subjects, the diabetic patients revealed a significant increase (P < 0.001) in the mean (diabetic patients 166.0 ± 10.4 vs. control subjects 93.3 ± 1.5 mg/dl), the standard deviation (51.7 ± 4.3 vs. 11.1 ± 0.5 mg/dl), and the mean amplitude of glycemic excursions (127.0 ± 9.2 vs. 27.7 ± 1.3 mg/dl) of the glucose levels; and a significant decrease (P < 0.001) in the MSE complexity index (5.09 ± 0.23 vs. 7.38 ± 0.28). In conclusion, the complexity of glucose dynamics is decreased in diabetes. This finding implies the reactivity of glucoregulation is impaired in the diabetic patients. Such impairment presenting as an increased regularity of glycemic fluctuating pattern could be detected by MSE analysis. Thus, the MSE complexity index could potentially be used as a biomarker in the monitoring of diabetes. PMID:24808497
Gao, Zhong-Ke; Yang, Yu-Xuan; Zhai, Lu-Sheng; Dang, Wei-Dong; Yu, Jia-Liang; Jin, Ning-De
2016-02-01
High water cut and low velocity vertical upward oil-water two-phase flow is a typical complex system with the features of multiscale, unstable and non-homogenous. We first measure local flow information by using distributed conductance sensor and then develop a multivariate multiscale complex network (MMCN) to reveal the dispersed oil-in-water local flow behavior. Specifically, we infer complex networks at different scales from multi-channel measurements for three typical vertical oil-in-water flow patterns. Then we characterize the generated multiscale complex networks in terms of network clustering measure. The results suggest that the clustering coefficient entropy from the MMCN not only allows indicating the oil-in-water flow pattern transition but also enables to probe the dynamical flow behavior governing the transitions of vertical oil-water two-phase flow.
Dombroski, M; Melius, C; Edmunds, T; Banks, L E; Bates, T; Wheeler, R
2008-09-24
This study uses the Multi-scale Epidemiologic Simulation and Analysis (MESA) system developed for foreign animal diseases to assess consequences of nationwide human infectious disease outbreaks. A literature review identified the state of the art in both small-scale regional models and large-scale nationwide models and characterized key aspects of a nationwide epidemiological model. The MESA system offers computational advantages over existing epidemiological models and enables a broader array of stochastic analyses of model runs to be conducted because of those computational advantages. However, it has only been demonstrated on foreign animal diseases. This paper applied the MESA modeling methodology to human epidemiology. The methodology divided 2000 US Census data at the census tract level into school-bound children, work-bound workers, elderly, and stay at home individuals. The model simulated mixing among these groups by incorporating schools, workplaces, households, and long-distance travel via airports. A baseline scenario with fixed input parameters was run for a nationwide influenza outbreak using relatively simple social distancing countermeasures. Analysis from the baseline scenario showed one of three possible results: (1) the outbreak burned itself out before it had a chance to spread regionally, (2) the outbreak spread regionally and lasted a relatively long time, although constrained geography enabled it to eventually be contained without affecting a disproportionately large number of people, or (3) the outbreak spread through air travel and lasted a long time with unconstrained geography, becoming a nationwide pandemic. These results are consistent with empirical influenza outbreak data. The results showed that simply scaling up a regional small-scale model is unlikely to account for all the complex variables and their interactions involved in a nationwide outbreak. There are several limitations of the methodology that should be explored in future
Wagner, Gregory John (Sandia National Laboratories, Livermore, CA); Collis, Samuel Scott; Templeton, Jeremy Alan (Sandia National Laboratories, Livermore, CA); Lehoucq, Richard B.; Parks, Michael L.; Jones, Reese E. (Sandia National Laboratories, Livermore, CA); Silling, Stewart Andrew; Scovazzi, Guglielmo; Bochev, Pavel B.
2007-10-01
This report is a collection of documents written as part of the Laboratory Directed Research and Development (LDRD) project A Mathematical Framework for Multiscale Science and Engineering: The Variational Multiscale Method and Interscale Transfer Operators. We present developments in two categories of multiscale mathematics and analysis. The first, continuum-to-continuum (CtC) multiscale, includes problems that allow application of the same continuum model at all scales with the primary barrier to simulation being computing resources. The second, atomistic-to-continuum (AtC) multiscale, represents applications where detailed physics at the atomistic or molecular level must be simulated to resolve the small scales, but the effect on and coupling to the continuum level is frequently unclear.
Multiscale remote sensing analysis to monitor riparian and upland semiarid vegetation
Nguyen, Uyen
The health of natural vegetation communities is of concern due to observed changes in the climatic-hydrological regime and land cover changes particularly in arid and semiarid regions. Monitoring vegetation at multi temporal and spatial scales can be the most informative approach for detecting change and inferring causal agents of change and remediation strategies. Riparian communities are tightly linked to annual stream hydrology, ground water elevations and sediment transport. These processes are subject to varying magnitudes of disturbance overtime and are candidates for multi-scale monitoring. My first research objective focused on the response of vegetation in the Upper San Pedro River, Arizona, to reduced base flows and climate change. I addressed the correlation between riparian vegetation and hydro-climate variables during the last three decades in one of the remaining undammed rivers in the southwestern U.S. Its riparian forest is threatened by the diminishing base flows, attributed by different studies either to increases in evapotranspiration (ET) due to conversion of grasslands to mesquite shrublands in the adjacent uplands, or to increased regional groundwater pumping to serve growing populations in surrounding urban areas and or to some interactions of those causes. Landsat 5 imagery was acquired for pre- monsoon period, when riparian trees had leafed out but before the arrival of summer monsoon rains in July. The result has showed Normalized Difference Vegetation Index (NDVI) values from both Landsat and Moderate Resolution Imaging Spectrometer (MODIS) had significant decreases which positively correlated to river flows, which decreased over the study period, and negatively correlated with air temperatures, which have increased by about 1.4°C from 1904 to the present. The predictions from other studies that decreased river flows could negatively impact the riparian forest were supported by this study. The pre-monsoon Normalized Different Vegetation
Fenderson, Lindsey E; Kovach, Adrienne I; Litvaitis, John A; O'Brien, Kathleen M; Boland, Kelly M; Jakubas, Walter J
2014-05-01
Landscape features of anthropogenic or natural origin can influence organisms' dispersal patterns and the connectivity of populations. Understanding these relationships is of broad interest in ecology and evolutionary biology and provides key insights for habitat conservation planning at the landscape scale. This knowledge is germane to restoration efforts for the New England cottontail (Sylvilagus transitionalis), an early successional habitat specialist of conservation concern. We evaluated local population structure and measures of genetic diversity of a geographically isolated population of cottontails in the northeastern United States. We also conducted a multiscale landscape genetic analysis, in which we assessed genetic discontinuities relative to the landscape and developed several resistance models to test hypotheses about landscape features that promote or inhibit cottontail dispersal within and across the local populations. Bayesian clustering identified four genetically distinct populations, with very little migration among them, and additional substructure within one of those populations. These populations had private alleles, low genetic diversity, critically low effective population sizes (3.2-36.7), and evidence of recent genetic bottlenecks. Major highways and a river were found to limit cottontail dispersal and to separate populations. The habitat along roadsides, railroad beds, and utility corridors, on the other hand, was found to facilitate cottontail movement among patches. The relative importance of dispersal barriers and facilitators on gene flow varied among populations in relation to landscape composition, demonstrating the complexity and context dependency of factors influencing gene flow and highlighting the importance of replication and scale in landscape genetic studies. Our findings provide information for the design of restoration landscapes for the New England cottontail and also highlight the dual influence of roads, as both
Multi-scale similarity entropy as a complexity descriptor to discriminate healthy to distress foetus
Girault, Jean Marc; Oudjemia, Souad; Voicu, Iulian
2013-01-01
International audience This paper deals with the discrimination between suffering foetuses and normal foetuses by means of a multi-scale similarity entropy. Sample entropy and similarity entropy are evaluated in multi-scale analysis on foetal heart rate signals. Without multi-scale analysis, our results show that only the similarity entropy differentiate suffering foetuses to normal foetuses. Furthermore with the multi-scale analysis, our results show that both the sample entropy and the s...
Francisco de Assis Salviano de Sousa
2009-03-01
Full Text Available The variations of the rainfall in a region of the Mundaú river watershed, at state of Alagoas, Brazil, had been studied using the rainfall anomaly index (RAI and the Wavelet Analysis. This method involves transformation of a one-dimensional series in a time space and frequency, allowing determining the dominant scales of variability and its secular variations. The results had shown that the precipitation variability in the two regions is defined by located secular multi-scales in certain intervals of time. However, on inter-annual variability to the ENSO cycle and the decadal variability of the scales had influenced in the local pluviometric variability.
Highlights: → Advanced analysis and design techniques for innovative reactors are addressed. → Detailed investigation of a 3 pass core design with a multi-physics-scales tool. → Coupled 40-group neutron transport/equivalent channels TH core analyses methods. → Multi-scale capabilities: from equivalent channels to sub-channel pin-by-pin study. → High fidelity approach: reduction of conservatism involved in core simulations. - Abstract: The High Performance Light Water Reactor (HPLWR) is a thermal spectrum nuclear reactor cooled and moderated with light water operated at supercritical pressure. It is an innovative reactor concept, which requires developing and applying advanced analysis tools as described in the paper. The relevant water density reduction associated with the heat-up, together with the multi-pass core design, results in a pronounced coupling between neutronic and thermal-hydraulic analyses, which takes into account the strong natural influence of the in-core distribution of power generation and water properties. The neutron flux gradients within the multi-pass core, together with the pronounced dependence of water properties on the temperature, require to consider a fine spatial resolution in which the individual fuel pins are resolved to provide precise evaluation of the clad temperature, currently considered as one of the crucial design criteria. These goals have been achieved considering an advanced analysis method based on the usage of existing codes which have been coupled with developed interfaces. Initially neutronic and thermal-hydraulic full core calculations have been iterated until a consistent solution is found to determine the steady state full power condition of the HPLWR core. Results of few group neutronic analyses might be less reliable in case of HPLWR 3-pass core than for conventional LWRs because of considerable changes of the neutron spectrum within the core, hence 40 groups transport theory has been preferred to the
Multi-scale uncertainty and sensitivity analysis of the TALL-3D experiment
Highlights: • The ATHLET-CFX model of the TALL-3D facility behaves in a monotonic way regarding the propagation of the modeling uncertainty. • The biggest variations are observed in the temperature behavior. • A screening analysis identifies the most influential parameters. - Abstract: Over the last decades, the increase of the computational power has made feasible the computer modeling of complex thermal-hydraulic phenomena. These complex models use physical models to account for specific thermal-hydraulic phenomena. Each physical model requires a set of model input data. For several reasons (e.g. measurement uncertainties for stationary and time-dependent values, cost of the measurement campaign), the input data for the physical models cannot always be determined with precision. This lack of accuracy can significantly impair the model results. The analysis of the influence of these input uncertainties is therefore a key step to understand the model behavior and possibly improve its accuracy. The TALL-3D facility, built by KTH in the scope of the THINS project, aims at investigating challenging phenomena in a facility filled with lead–bismuth eutectic (LBE) containing a pool. The experimental data will be used for the validation of the models developed by the project partners. Based on the coupling between ANSYS CFX (CFD) and ATHLET (system code) implemented by the GRS, TUM performed an uncertainty and sensitivity analysis on the model of the TALL-3D facility. This analysis investigates the uncertainty in the output which is due to the uncertainty on the input (uncertainty analysis) and assesses the influence of the uncertain parameters (sensitivity analysis)
Multi-scale uncertainty and sensitivity analysis of the TALL-3D experiment
Geffray, Clotaire, E-mail: clotaire.geffray@ntech.mw.tum.de; Macián-Juan, Rafael
2015-08-15
Highlights: • The ATHLET-CFX model of the TALL-3D facility behaves in a monotonic way regarding the propagation of the modeling uncertainty. • The biggest variations are observed in the temperature behavior. • A screening analysis identifies the most influential parameters. - Abstract: Over the last decades, the increase of the computational power has made feasible the computer modeling of complex thermal-hydraulic phenomena. These complex models use physical models to account for specific thermal-hydraulic phenomena. Each physical model requires a set of model input data. For several reasons (e.g. measurement uncertainties for stationary and time-dependent values, cost of the measurement campaign), the input data for the physical models cannot always be determined with precision. This lack of accuracy can significantly impair the model results. The analysis of the influence of these input uncertainties is therefore a key step to understand the model behavior and possibly improve its accuracy. The TALL-3D facility, built by KTH in the scope of the THINS project, aims at investigating challenging phenomena in a facility filled with lead–bismuth eutectic (LBE) containing a pool. The experimental data will be used for the validation of the models developed by the project partners. Based on the coupling between ANSYS CFX (CFD) and ATHLET (system code) implemented by the GRS, TUM performed an uncertainty and sensitivity analysis on the model of the TALL-3D facility. This analysis investigates the uncertainty in the output which is due to the uncertainty on the input (uncertainty analysis) and assesses the influence of the uncertain parameters (sensitivity analysis)
MammoViewer - CAD application based on effective Multiscale Image Analysis
The paper presents the use of a software package for the analysis of medical images. MammoViewer, which has been designed to help in mammographical diagnosis, is also applied to processing images of other modalities. Algorithms of data multiresolution analysis by a selection of wavelet filters, several kinds of decomposition (dyadic, packets, undecimated wavelets, 2D kernels on a hexagonal grid), visualization and correction of coefficient distribution, constitute important elements of the package, aimed at improving perception of potential lesions and their detection efficacy. (author)
Steed, Chad A [ORNL; Beaver, Justin M [ORNL; BogenII, Paul L. [Google Inc.; Drouhard, Margaret MEG G [ORNL; Pyle, Joshua M [ORNL
2015-01-01
In this paper, we introduce a new visual analytics system, called Matisse, that allows exploration of global trends in textual information streams with specific application to social media platforms. Despite the potential for real-time situational awareness using these services, interactive analysis of such semi-structured textual information is a challenge due to the high-throughput and high-velocity properties. Matisse addresses these challenges through the following contributions: (1) robust stream data management, (2) automated sen- timent/emotion analytics, (3) inferential temporal, geospatial, and term-frequency visualizations, and (4) a flexible drill-down interaction scheme that progresses from macroscale to microscale views. In addition to describing these contributions, our work-in-progress paper concludes with a practical case study focused on the analysis of Twitter 1% sample stream information captured during the week of the Boston Marathon bombings.
Analysis of multi-scale systemic risk in Brazil's financial market
Adriana Bruscato Bortoluzzo; Andrea Maria Accioly Fonseca Minardi; Bruno Caio Fernando Passos
2014-01-01
This work analyzes whether the relationship between risk and returns predicted by the Capital Asset Pricing Model (CAPM) is valid in the Brazilian stock market. The analysis is based on discrete wavelet decomposition on different time scales. This technique allows to analyze the relationship between different time horizons, since the short-term ones (2 to 4 days) up to the long-term ones (64 to 128 days). The results indicate that there is a negative or null relationship between systemic risk...
Analysis and Visualization of Multi-Scale Astrophysical Simulations using Python and NumPy
Turk, M.; /KIPAC, Menlo Park
2008-09-30
The study the origins of cosmic structure requires large-scale computer simulations beginning with well-constrained, observationally-determined, initial conditions. We use Adaptive Mesh Refinement to conduct multi-resolution simulations spanning twelve orders of magnitude in spatial dimensions and over twenty orders of magnitude in density. These simulations must be analyzed and visualized in a manner that is fast, accurate, and reproducible. I present 'yt,' a cross-platform analysis toolkit written in Python. 'yt' consists of a data-management layer for transporting and tracking simulation outputs, a plotting layer, a parallel analysis layer for handling mesh-based and particle-based data, as well as several interfaces. I demonstrate how the origins of cosmic structure--from the scale of clusters of galaxies down to the formation of individual stars--can be analyzed and visualized using a NumPy-based toolkit. Additionally, I discuss efforts to port this analysis code to other adaptive mesh refinement data formats, enabling direct comparison of data between research groups using different methods to simulate the same objects.
Multidisciplinary approach and multi-scale elemental analysis and separation chemistry
The development of methods for the analysis of trace elements is an important component of my research activities either for a radiometric measure or mass spectrometric detection. Many studies raise the question of the chemical signature of a sample or a process: eruptive behavior of a volcano, indicator of pollution, ion exchange in vectors vesicles of active principles,... Each time, highly sensitive analytical procedures, accurate and multi-elementary as well as the development of specific protocols were needed. Neutron activation analysis has often been used as reference procedure and allowed to validate the chemical lixiviation and the measurement by ICP-MS. Analysis of radioactive samples requires skills in analysis of trace but also separation chemistry. Two separation methods occupy an important place in the separation chemistry of radionuclides: chromatography and liquid-liquid extraction. The study of extraction of Lanthanide (III) by the oxide octyl (phenyl)-n, N-diisobutyl-carbamoylmethyl phosphine (CMPO) and a calixarene-CMPO led to better understand and quantify the influence of operating conditions on their performance of extraction and selectivity. The high concentration of salts in aqueous solutions required to reason in terms of thermodynamic activities in relying on a comprehensive approach to quantification of deviations from ideality. In order to reduce the amount of waste generated and costs, alternatives to the hydrometallurgical extraction processes were considered using ionic liquids at low temperatures as alternative solvents in biphasic processes. Remaining in this logic of effluent reduction, miniaturization of the liquid-liquid extraction is also study so as to exploit the characteristics of microscopic scale (very large specific surface, short diffusion distances). The miniaturization of chromatographic separations carries the same ambitions of gain of volumes of wastes and reagents. The miniaturization of the separation Uranium
Czech, Christopher
The field of meta-materials engineering has largely expanded mechanical design possibilities over the last two decades; some notable design advances include the systematic engineering of negative Poisson's ratio materials and functionally graded materials, materials designed for optimal electronic and thermo-mechanical performances, and the design of materials under uncertainty. With these innovations, the systematic engineering of materials for design-specific uses is becoming more common in industrial and military uses. The motivation for this body of research is the design of the shear beam for a non-pneumatic wheel. Previously, a design optimization of a finite element model of the non-pneumatic wheel was completed, where a linear elastic material was simulated in the shear beam to reduce hysteretic energy losses. As part of the optimization, a set of optimal orthotropic material properties and other geometric properties were identified for the shear beam. Given that no such natural linear elastic material exists, a meta-material can be engineered that meets these properties using the aforementioned tools. However, manufacturing constraints prevent the use of standard homogenization analysis and optimization tools in the engineering of the shear beam due to limitations in the accuracy of the homogenization process for thin materials. In this research, the more general volume averaging analysis is shown to be an accurate tool for meta-material analysis for engineering thin-layered materials. Given an accurate analysis method, several optimization formulations are proposed, and optimality conditions are derived to determine the most mathematically feasible and numerically reliable formulation for topology optimization of a material design problem using a continuous material interpolation over the design domain. This formulation is implemented to engineer meta-materials for problems using the volume averaging analysis, which includes the use of variable linking
New methodologies for multi-scale time-variant reliability analysis of complex lifeline networks
Kurtz, Nolan Scot
The cost of maintaining existing civil infrastructure is enormous. Since the livelihood of the public depends on such infrastructure, its state must be managed appropriately using quantitative approaches. Practitioners must consider not only which components are most fragile to hazard, e.g. seismicity, storm surge, hurricane winds, etc., but also how they participate on a network level using network analysis. Focusing on particularly damaged components does not necessarily increase network functionality, which is most important to the people that depend on such infrastructure. Several network analyses, e.g. S-RDA, LP-bounds, and crude-MCS, and performance metrics, e.g. disconnection bounds and component importance, are available for such purposes. Since these networks are existing, the time state is also important. If networks are close to chloride sources, deterioration may be a major issue. Information from field inspections may also have large impacts on quantitative models. To address such issues, hazard risk analysis methodologies for deteriorating networks subjected to seismicity, i.e. earthquakes, have been created from analytics. A bridge component model has been constructed for these methodologies. The bridge fragilities, which were constructed from data, required a deeper level of analysis as these were relevant for specific structures. Furthermore, chloride-induced deterioration network effects were investigated. Depending on how mathematical models incorporate new information, many approaches are available, such as Bayesian model updating. To make such procedures more flexible, an adaptive importance sampling scheme was created for structural reliability problems. Additionally, such a method handles many kinds of system and component problems with singular or multiple important regions of the limit state function. These and previously developed analysis methodologies were found to be strongly sensitive to the network size. Special network topologies may
Koch, Steven E.; Golus, Robert E.
1988-01-01
This paper presents a statistical analysis of the characteristics of the wavelike activity that occurred over the north-central United States on July 11-12, 1981, using data from the Cooperative Convective Precipitation Experiment in Montana. In particular, two distinct wave episodes of about 8-h duration within a longer (33 h) period of wave activity were studied in detail. It is demonstrated that the observed phenomena display features consistent with those of mesoscale gravity waves. The principles of statistical methods used to detect and track mesoscale gravity waves are discussed together with their limitations.
M. S. Jouini
2011-12-01
Full Text Available Pore spaces heterogeneity in carbonates rocks has long been identified as an important factor impacting reservoir productivity. In this paper, we study the heterogeneity of carbonate rocks pore spaces based on the image analysis of scanning electron microscopy (SEM data acquired at various magnifications. Sixty images of twelve carbonate samples from a reservoir in the Middle East were analyzed. First, pore spaces were extracted from SEM images using a segmentation technique based on watershed algorithm. Pores geometries revealed a multifractal behavior at various magnifications from 800x to 12 000x. In addition, the singularity spectrum provided quantitative values that describe the degree of heterogeneity in the carbonates samples. Moreover, for the majority of the analyzed samples, we found low variations (around 5% in the multifractal dimensions for magnifications between 1700x and 12 000x. Finally, these results demonstrate that multifractal analysis could be an appropriate tool for characterizing quantitatively the heterogeneity of carbonate pore spaces geometries. However, our findings show that magnification has an impact on multifractal dimensions, revealing the limit of applicability of multifractal descriptions for these natural structures.
Multi-scale AM-FM motion analysis of ultrasound videos of carotid artery plaques
Murillo, Sergio; Murray, Victor; Loizou, C. P.; Pattichis, C. S.; Pattichis, Marios; Barriga, E. Simon
2012-03-01
An estimated 82 million American adults have one or more type of cardiovascular diseases (CVD). CVD is the leading cause of death (1 of every 3 deaths) in the United States. When considered separately from other CVDs, stroke ranks third among all causes of death behind diseases of the heart and cancer. Stroke accounts for 1 out of every 18 deaths and is the leading cause of serious long-term disability in the United States. Motion estimation of ultrasound videos (US) of carotid artery (CA) plaques provides important information regarding plaque deformation that should be considered for distinguishing between symptomatic and asymptomatic plaques. In this paper, we present the development of verifiable methods for the estimation of plaque motion. Our methodology is tested on a set of 34 (5 symptomatic and 29 asymptomatic) ultrasound videos of carotid artery plaques. Plaque and wall motion analysis provides information about plaque instability and is used in an attempt to differentiate between symptomatic and asymptomatic cases. The final goal for motion estimation and analysis is to identify pathological conditions that can be detected from motion changes due to changes in tissue stiffness.
Multiscale Trend Analysis for Pampa Grasslands Using Ground Data and Vegetation Sensor Imagery.
Scottá, Fernando C; da Fonseca, Eliana L
2015-01-01
This study aimed to evaluate changes in the aboveground net primary productivity (ANPP) of grasslands in the Pampa biome by using experimental plots and changes in the spectral responses of similar vegetation communities obtained by remote sensing and to compare both datasets with meteorological variations to validate the transition scales of the datasets. Two different geographic scales were considered in this study. At the local scale, an analysis of the climate and its direct influences on grassland ANPP was performed using data from a long-term experiment. At the regional scale, the influences of climate on the grassland reflectance patterns were determined using vegetation sensor imagery data. Overall, the monthly variations of vegetation canopy growth analysed using environmental changes (air temperature, total rainfall and total evapotranspiration) were similar. The results from the ANPP data and the NDVI data showed the that variations in grassland growth were similar and independent of the analysis scale, which indicated that local data and the relationships of local data with climate can be considered at the regional scale in the Pampa biome by using remote sensing. PMID:26197320
A Multi-Scaler Recording System and its Application to Radiometric ''Off-Line'' Analysis
In large complex reprocessing plants a great deal has been done over the past few years to provide in-line instrumentation for the contemporary analysis of process stream content and characteristics. However, these instruments have a qualitative rather than a quantitative part to play in the overall control of the plant. Quantitative information, which must be obtained for control and accounting purposes, demands and relies upon the efficient use of laboratory techniques and instrumentation for the precise analysis of representative samples taken from the process streams. These techniques, in particular those involving pulse counting systems, can be made automatic with modern instrumentation, such as will be described, in which the data is obtained in digital form in electronic stores (scalers). To support a large plant there will be many separate counting systems of this kind, independently controlled and therefore having no time correlation between them. The automatic recording system described in the paper provides a common data read-out facility for more than 50 independently operating counting systems, recording scaler data, together with associated sample and system identification and the absolute time occurrence of each read-out. The data can be recorded, in forms suitable for subsequent processing by a computer, on a variety of tape and card punches, serial and parallel printers or magnetic tape. In addition, the whole recording system, including the scalers in any one system, can be checked for correct operation on an automatic routine basis which does not interfere with the operation of other counting systems. It is concluded that the effective quantitative control of a plant rests on a rapid efficient sample analysis under laboratory conditions. It is probable that future developments of ''off-line'' facilities rather than on-line instrumentation will be possible and more worthwhile. The desirable characteristics of instrumentation for such a laboratory
Georgopoulos, Panos G.; Isukapalli, Sastry S.
2009-01-01
A conceptual framework is presented for multiscale field/network/agent-based modeling to support human and ecological health risk assessments. This framework is based on the representation of environmental dynamics in terms of interacting networks, agents that move across different networks, fields representing spatiotemporal distributions of physical properties, rules governing constraints and interactions, and actors that make decisions affecting the state of the system. Different determini...
Quan Liu; Yi-Feng Chen; Shou-Zen Fan; Maysam F. Abbod; Jiann-Shing Shieh
2015-01-01
In order to build a reliable index to monitor the depth of anesthesia (DOA), many algorithms have been proposed in recent years, one of which is sample entropy (SampEn), a commonly used and important tool to measure the regularity of data series. However, SampEn only estimates the complexity of signals on one time scale. In this study, a new approach is introduced using multiscale entropy (MSE) considering the structure information over different time scales. The entropy values over different...
Kang, Xiaoxu; Jia, Xiaofeng; Geocadin, Romergryko G.; Thakor, Nitish V.; Maybhate, Anil
2009-01-01
Neurological complications after cardiac arrest (CA) can be fatal. Although hypothermia has been shown to be beneficial, understanding the mechanism and establishing neurological outcomes remains challenging because effects of CA and hypothermia are not well characterized. This paper aims to analyze EEG (and the α-rhythms) using multiscale entropy (MSE) to demonstrate the ability of MSE in tracking changes due to hypothermia and compare MSE during early recovery with long-term neurological ex...
de Aranzabal, Itziar; Schmitz, María F; Pineda, Francisco D
2009-11-01
Tourism and landscape are interdependent concepts. Nature- and culture-based tourism are now quite well developed activities and can constitute an excellent way of exploiting the natural resources of certain areas, and should therefore be considered as key objectives in landscape planning and management in a growing number of countries. All of this calls for careful evaluation of the effects of tourism on the territory. This article focuses on an integrated spatial method for landscape analysis aimed at quantifying the relationship between preferences of visitors and landscape features. The spatial expression of the model relating types of leisure and recreational preferences to the potential capacity of the landscape to meet them involves a set of maps showing degrees of potential visitor satisfaction. The method constitutes a useful tool for the design of tourism planning and management strategies, with landscape conservation as a reference. PMID:19760454
Wu, Guo Rong; Chen, Fuyong; Kang, Dezhi; Zhang, Xiangyang; Marinazzo, Daniele; Chen, Huafu
2011-11-01
Multivariate Granger causality is a well-established approach for inferring information flow in complex systems, and it is being increasingly applied to map brain connectivity. Traditional Granger causality is based on vector autoregressive (AR) or mixed autoregressive moving average (ARMA) model, which are potentially affected by errors in parameter estimation and may be contaminated by zero-lag correlation, notably when modeling neuroimaging data. To overcome this issue, we present here an extended canonical correlation approach to measure multivariate Granger causal interactions among time series. The procedure includes a reduced rank step for calculating canonical correlation analysis (CCA), and extends the definition of causality including instantaneous effects, thus avoiding the potential estimation problems of AR (or ARMA) models. We tested this approach on simulated data and confirmed its practical utility by exploring local network connectivity at different scales in the epileptic brain analyzing scalp and depth-EEG data during an interictal period. PMID:21788178
Multiscale Analysis of Surface Topography from Single Point Incremental Forming using an Acetal Tool
Single point incremental forming (SPIF) is a sheet metal manufacturing process that forms a part by incrementally applying point loads to the material to achieve the desired deformations and final part geometry. This paper investigates the differences in surface topography between a carbide tool and an acetal-tipped tool. Area-scale analysis is performed on the confocal areal surface measurements per ASME B46. The objective of this paper is to determine at which scales surfaces formed by two different tool materials can be differentiated. It is found that the surfaces in contact with the acetal forming tool have greater relative areas at all scales greater than 5 × 104 μm2 than the surfaces in contact with the carbide tools. The surfaces not in contact with the tools during forming, also referred to as the free surface, are unaffected by the tool material
de Aranzabal, Itziar; Schmitz, María F.; Pineda, Francisco D.
2009-11-01
Tourism and landscape are interdependent concepts. Nature- and culture-based tourism are now quite well developed activities and can constitute an excellent way of exploiting the natural resources of certain areas, and should therefore be considered as key objectives in landscape planning and management in a growing number of countries. All of this calls for careful evaluation of the effects of tourism on the territory. This article focuses on an integrated spatial method for landscape analysis aimed at quantifying the relationship between preferences of visitors and landscape features. The spatial expression of the model relating types of leisure and recreational preferences to the potential capacity of the landscape to meet them involves a set of maps showing degrees of potential visitor satisfaction. The method constitutes a useful tool for the design of tourism planning and management strategies, with landscape conservation as a reference.
Beguet, Benoit; Guyon, Dominique; Boukir, Samia; Chehata, Nesrine
2014-10-01
The main goal of this study is to design a method to describe the structure of forest stands from Very High Resolution satellite imagery, relying on some typical variables such as crown diameter, tree height, trunk diameter, tree density and tree spacing. The emphasis is placed on the automatization of the process of identification of the most relevant image features for the forest structure retrieval task, exploiting both spectral and spatial information. Our approach is based on linear regressions between the forest structure variables to be estimated and various spectral and Haralick's texture features. The main drawback of this well-known texture representation is the underlying parameters which are extremely difficult to set due to the spatial complexity of the forest structure. To tackle this major issue, an automated feature selection process is proposed which is based on statistical modeling, exploring a wide range of parameter values. It provides texture measures of diverse spatial parameters hence implicitly inducing a multi-scale texture analysis. A new feature selection technique, we called Random PRiF, is proposed. It relies on random sampling in feature space, carefully addresses the multicollinearity issue in multiple-linear regression while ensuring accurate prediction of forest variables. Our automated forest variable estimation scheme was tested on Quickbird and Pléiades panchromatic and multispectral images, acquired at different periods on the maritime pine stands of two sites in South-Western France. It outperforms two well-established variable subset selection techniques. It has been successfully applied to identify the best texture features in modeling the five considered forest structure variables. The RMSE of all predicted forest variables is improved by combining multispectral and panchromatic texture features, with various parameterizations, highlighting the potential of a multi-resolution approach for retrieving forest structure
Yu-Ching Shih
2014-04-01
Full Text Available Micro-cantilever sensors are widely used to detect biomolecules, chemical gases, and ionic species. However, the theoretical descriptions and predictive modeling of these devices are not well developed, and lag behind advances in fabrication and applications. In this paper, we present a novel multiscale simulation framework for nanomechanical sensors. This framework, combining density functional theory (DFT calculations and finite element method (FEM analysis, is capable of analyzing molecular adsorption-induced deformation and stress fields in the sensors from the molecular scale to the device scale. Adsorption of alkanethiolate self-assembled monolayer (SAM on the Au(111 surface of the micro-cantilever sensor is studied in detail to demonstrate the applicability of this framework. DFT calculations are employed to investigate the molecular adsorption-induced surface stress upon the gold surface. The 3D shell elements with initial stresses obtained from the DFT calculations serve as SAM domains in the adsorption layer, while FEM is employed to analyze the deformation and stress of the sensor devices. We find that the micro-cantilever tip deflection has a linear relationship with the coverage of the SAM domains. With full coverage, the tip deflection decreases as the molecular chain length increases. The multiscale simulation framework provides a quantitative analysis of the displacement and stress fields, and can be used to predict the response of nanomechanical sensors subjected to complex molecular adsorption.
Natoli, J. Y.; Wagner, F.; Ciapponi, A.; Capoulade, J.; Gallais, L.; Commandré, M.
2010-11-01
The mechanism of laser induced damage in optical materials under high power nanosecond laser irradiation is commonly attributed to the presence of precursor centers. Depending on material and laser source, the precursors could have different origins. Some of them are clearly extrinsic, such as impurities or structural defects linked to the fabrication conditions. In most cases the center size ranging from sub-micrometer to nanometer scale does not permit an easy detection by optical techniques before irradiation. Most often, only a post mortem observation of optics permits to proof the local origin of breakdown. Multi-scale analyzes by changing irradiation beam size have been performed to investigate the density, size and nature of laser damage precursors. Destructive methods such as raster scan, laser damage probability plot and morphology studies permit to deduce the precursor densities. Another experimental way to get information on nature of precursors is to use non destructive methods such as photoluminescence and absorption measurements. The destructive and non destructive multiscale studies are also motivated for practical reasons. Indeed LIDT studies of large optics as those used in LMJ or NIF projects are commonly performed on small samples and with table top lasers whose characteristics change from one to another. In these conditions, it is necessary to know exactly the influence of the different experimental parameters and overall the spot size effect on the final data. In this paper, we present recent developments in multiscale characterization and results obtained on optical coatings (surface case) and KDP crystal (bulk case).
A multiscale analysis and model of vegetation change in a semiarid landscape
Francis, Joyce Marie
The slopes of the San Francisco Peaks of northern Arizona provide a steep environmental gradient that can be used to investigate the effects of changing ecological conditions on spatial pattern and distribution of vegetation. I use this gradient to: (1) examine the changes in spatial pattern of vegetation along an semiarid environmental gradient; (2) characterize the relationship between spatial scale and pattern across this landscape; and, (3) investigate potential changes in vegetation distribution along the gradient in response to climatic change. Lacunarity analysis was used to determine the spatial pattern of trees and shrubs at five sites along the gradient. The intra- and interspecific associations were determined using join count statistics coupled with a Monte Carlo simulation. Tree stems approach a random distribution at all sites. Canopies and biomass displayed increasing levels of aggregation with increasing moisture availability. Join count analysis revealed that although Pinus edulis and Juniperous monosperma are not segregated at any of the sites, they are segregated at the edges of their range from shrub species and from Pinus ponderosa. Data from a high resolution thematic mapper simulator (NS001) were aggregated and combined with Landsat thematic mapper data to provide a continuum of grain sizes from 5 m to 30 m on a side. The spatial pattern of each image was analyzed to explore the effects of grain size on various patch and landscape level metrics. Finer grained images appeared to be more fragmented than coarse grained images. The metrics varied smoothly as a function of grain size and were fitted to nonlinear models. These models failed to accurately predict the metrics for a second, independent landscape but did display similar scaling patterns for both landscapes. The effects of climate change may be most drastic along environmental gradients. A fine scale model of changes in vegetation distribution in response to climate change was developed
Drought analysis using multi-scale standardized precipitation index in the Han River Basin, China
Yue-ping XU; Sheng-ji LIN; Yan HUANG; Qin-qing ZHANG; Qi-hua RAN
2011-01-01
Regional drought analysis provides useful information for sustainable water resources management. In this paper, a standardized precipitation index (SPI) at multiple time scales was used to investigate the spatial patterns and trends of drought in the Han River Basin, one of the largest tributaries of Yangtze River, China. It was found that, in terms of drought severity, the upper basin of the Hart River is the least, while the growing trend is the most conspicuous; a less conspicuous growing trend can be observed in the middle basin; and there is an insignificant decreasing trend in the lower basin. Meanwhile, the impact of drought on the Middle Route of the South-to-North Water Transfer Project was investigated, and it is suggested that water intake must be reduced in times of drought, particularly when successive or simultaneous droughts in the upper and middle basins of the Han River Basin occur. The results can provide substantial information for future water allocation schemes of the South-to-North Water Transfer Project.
Djath, Bughsin'; Verron, Jacques; Melet, Angelique; Gourdeau, Lionel; Barnier, Bernard; Molines, Jean-Marc
2014-09-01
A high 1/36° resolution numerical model is used to study the ocean circulation in the Solomon Sea. An evaluation of the model with (the few) available observation shows that the 1/36° resolution model realistically simulates the Solomon Sea circulations. The model notably reproduces the high levels of mesoscale eddy activity observed in the Solomon Sea. With regard to previous simulations at 1/12° resolution, the average eddy kinetic energy levels are increased by up to ˜30-40% in the present 1/36° simulation, and the enhancement extends at depth. At the surface, the eddy kinetic energy level is maximum in March-April-May and is minimum in December-January-February. The high subsurface variability is related to the variability of the western boundary current (New Guinea Coastal Undercurrent). Moreover, the emergence of submesoscales is clearly apparent in the present simulations. A spectral analysis is conducted in order to evidence and characterize the modeled submesoscale dynamics and to provide a spectral view of scales interactions. The corresponding spectral slopes show a strong consistency with the Surface Quasi-Geostrophic turbulence theory.
Analysis of multi-scale systemic risk in Brazil's financial market
Adriana Bruscato Bortoluzzo
2014-06-01
Full Text Available This work analyzes whether the relationship between risk and returns predicted by the Capital Asset Pricing Model (CAPM is valid in the Brazilian stock market. The analysis is based on discrete wavelet decomposition on different time scales. This technique allows to analyze the relationship between different time horizons, since the short-term ones (2 to 4 days up to the long-term ones (64 to 128 days. The results indicate that there is a negative or null relationship between systemic risk and returns for Brazil from 2004 to 2007. As the average excess return of a market portfolio in relation to a risk-free asset during that period was positive, it would be expected this relationship to be positive. That is, higher systematic risk should result in higher excess returns, which did not occur. Therefore, during that period, appropriate compensation for systemic risk was not observed in the Brazilian market. The scales that proved to be most significant to the risk-return relation were the first three, which corresponded to short-term time horizons. When treating differently, year-by-year, and consequently separating positive and negative premiums, some relevance is found, during some years, in the risk/return relation predicted by the CAPM. However, this pattern did not persist throughout the years. Therefore, there is not any evidence strong enough confirming that the asset pricing follows the model.
Multiscale Computing with the Multiscale Modeling Library and Runtime Environment
Borgdorff J.; Mamonski M.; Bosak B.; Groen D.; Ben Belgacem M.; Kurowski K.; Hoekstra A.G.
2013-01-01
We introduce a software tool to simulate multiscale models: The Multiscale Coupling Library and Environment 2 (MUSCLE 2). MUSCLE 2 is a component-based modeling tool inspired by the multiscale modeling and simulation framework, with an easy-to-use API which supports Java, C++, C, and Fortran. We present MUSCLE 2's runtime features, such as its distributed computing capabilities, and its benefits to multiscale modelers. We also describe two multiscale models that use MUSCLE 2 to do distributed...
Semi-convection in the ocean and in stars: A multi-scale analysis
Friedrich Kupka
2015-04-01
Full Text Available Fluid stratified by gravitation can be subject to a number of instabilities which eventually lead to a flow that causes enhanced mixing and transport of heat. The special case where a destabilizing temperature gradient counteracts the action of a stabilizing gradient in molecular weight is of interest to astrophysics (inside stars and giant planets and geophysics (lakes, oceans as well as to some engineering applications. The detailed dynamics of such a system depend on the molecular diffusivities of heat, momentum, and solute as well as system parameters including the ratio of the two gradients to each other. Further important properties are the formation and merging of well-defined layers in the fluid which cannot be derived from linear stability analysis. Moreover, the physical processes operate on a vast range of length and time scales. This has made the case of semi-convection, where a mean temperature gradient destabilizes the stratification while at the same time the mean molecular gradient tends to stabilize it, a challenge to physical modelling and to numerical hydrodynamical simulation. During the MetStröm project the simulation codes ANTARES and MITgcm have been extended such that they can be used for the simulations of such flows. We present a comparison of effective diffusivities derived from direct numerical simulations. For both stars and the oceanic regimes, the Nusselt numbers (scaled diffusivities follow similar relationships. Semi-convection quickly becomes inefficient, because the formation of layers limits vertical mixing. In contrast to the complementary saltfingering, these layers tend to damp instabilities so that effective diffusivities of salinity (concentration are up to two orders of magnitudes smaller than in the former case.