Tubular Initial Conditions and Ridge Formation

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M. S. Borysova

2013-01-01

Full Text Available The 2D azimuth and rapidity structure of the two-particle correlations in relativistic A+A collisions is altered significantly by the presence of sharp inhomogeneities in superdense matter formed in such processes. The causality constraints enforce one to associate the long-range longitudinal correlations observed in a narrow angular interval, the so-called (soft ridge, with peculiarities of the initial conditions of collision process. This study's objective is to analyze whether multiform initial tubular structures, undergoing the subsequent hydrodynamic evolution and gradual decoupling, can form the soft ridges. Motivated by the flux-tube scenarios, the initial energy density distribution contains the different numbers of high density tube-like boost-invariant inclusions that form a bumpy structure in the transverse plane. The influence of various structures of such initial conditions in the most central A+A events on the collective evolution of matter, resulting spectra, angular particle correlations and vn-coefficients is studied in the framework of the hydrokinetic model (HKM.

Multipoint propagators for non-Gaussian initial conditions

International Nuclear Information System (INIS)

Bernardeau, Francis; Sefusatti, Emiliano; Crocce, Martin

2010-01-01

We show here how renormalized perturbation theory calculations applied to the quasilinear growth of the large-scale structure can be carried on in presence of primordial non-Gaussian (PNG) initial conditions. It is explicitly demonstrated that the series reordering scheme proposed in Bernardeau, Crocce, and Scoccimarro [Phys. Rev. D 78, 103521 (2008)] is preserved for non-Gaussian initial conditions. This scheme applies to the power spectrum and higher-order spectra and is based on a reorganization of the contributing terms into the sum of products of multipoint propagators. In case of PNG, new contributing terms appear, the importance of which is discussed in the context of current PNG models. The properties of the building blocks of such resummation schemes, the multipoint propagators, are then investigated. It is first remarked that their expressions are left unchanged at one-loop order irrespective of statistical properties of the initial field. We furthermore show that the high-momentum limit of each of these propagators can be explicitly computed even for arbitrary initial conditions. They are found to be damped by an exponential cutoff whose expression is directly related to the moment generating function of the one-dimensional displacement field. This extends what had been established for multipoint propagators for Gaussian initial conditions. Numerical forms of the cutoff are shown for the so-called local model of PNG.

Cosmological constant and general isocurvature initial conditions

International Nuclear Information System (INIS)

Trotta, R.; Riazuelo, A.; Durrer, R.

2003-01-01

We investigate in detail the question of whether a nonvanishing cosmological constant is required by the present-day cosmic microwave background and large scale structure data when general isocurvature initial conditions are taken into account. We also discuss the differences between the usual Bayesian and the frequentist approaches in data analysis. We show that the Cosmic Background Explorer (COBE)-normalized matter power spectrum is dominated by the adiabatic mode and therefore breaks the degeneracy between initial conditions which is present in the cosmic microwave background anisotropies. We find that in a flat universe the Bayesian analysis requires Ω Λ =e0 to more than 3σ, while in the frequentist approach Ω Λ =0 is still within 3σ for a value of h≤0.48. Both conclusions hold regardless of the initial conditions

Initial conditions for cosmological perturbations

Science.gov (United States)

Ashtekar, Abhay; Gupt, Brajesh

2017-02-01

Penrose proposed that the big bang singularity should be constrained by requiring that the Weyl curvature vanishes there. The idea behind this past hypothesis is attractive because it constrains the initial conditions for the universe in geometric terms and is not confined to a specific early universe paradigm. However, the precise statement of Penrose’s hypothesis is tied to classical space-times and furthermore restricts only the gravitational degrees of freedom. These are encapsulated only in the tensor modes of the commonly used cosmological perturbation theory. Drawing inspiration from the underlying idea, we propose a quantum generalization of Penrose’s hypothesis using the Planck regime in place of the big bang, and simultaneously incorporating tensor as well as scalar modes. Initial conditions selected by this generalization constrain the universe to be as homogeneous and isotropic in the Planck regime as permitted by the Heisenberg uncertainty relations.

Initial conditions for cosmological perturbations

International Nuclear Information System (INIS)

Ashtekar, Abhay; Gupt, Brajesh

2017-01-01

Penrose proposed that the big bang singularity should be constrained by requiring that the Weyl curvature vanishes there. The idea behind this past hypothesis is attractive because it constrains the initial conditions for the universe in geometric terms and is not confined to a specific early universe paradigm. However, the precise statement of Penrose’s hypothesis is tied to classical space-times and furthermore restricts only the gravitational degrees of freedom. These are encapsulated only in the tensor modes of the commonly used cosmological perturbation theory. Drawing inspiration from the underlying idea, we propose a quantum generalization of Penrose’s hypothesis using the Planck regime in place of the big bang, and simultaneously incorporating tensor as well as scalar modes. Initial conditions selected by this generalization constrain the universe to be as homogeneous and isotropic in the Planck regime as permitted by the Heisenberg uncertainty relations . (paper)

The Initial Conditions of Fractional Calculus

International Nuclear Information System (INIS)

Trigeassou, J. C.; Maamri, N.

2011-01-01

During the past fifty years , Fractional Calculus has become an original and renowned mathematical tool for the modelling of diffusion Partial Differential Equations and the design of robust control algorithms. However, in spite of these celebrated results, some theoretical problems have not yet received a satisfying solution. The mastery of initial conditions, either for Fractional Differential Equations (FDEs) or for the Caputo and Riemann-Liouville fractional derivatives, remains an open research domain. The solution of this fundamental problem, also related to the long range memory property, is certainly the necessary prerequisite for a satisfying approach to modelling and control applications. The fractional integrator and its continuously frequency distributed differential model is a valuable tool for the simulation of fractional systems and the solution of initial condition problems. Indeed, the infinite dimensional state vector of fractional integrators allows the direct generalization to fractional calculus of the theoretical results of integer order systems. After a reminder of definitions and properties related to fractional derivatives and systems, this presentation is intended to show, based on the results of two recent publications [1,2], how the fractional integrator provides the solution of the initial condition problem of FDEs and of Caputo and Riemann-Liouville fractional derivatives. Numerical simulation examples illustrate and validate these new theoretical concepts.

Initial phase wall conditioning in KSTAR

International Nuclear Information System (INIS)

Hong, Suk-Ho; Kim, Kwang-Pyo; Kim, Sungwoo; Lee, Dong-Su; Kim, Kyung-Min; Lee, Kun-Su; Kim, Jong-Su; Park, Jae-Min; Kim, Woong-Chae; Kim, Hak-Kun; Park, Kap-Rai; Yang, Hyung-Lyeol; Sun, Jong-Ho; Woo, Hyun-Jong; Lee, Sang-Yong; Lee, Sang-Hwa; Park, Eun-Kyung; Park, Sang-Joon; Kim, Sun-Ho; Wang, Sun-Jung

2011-01-01

The initial phase wall conditioning in KSTAR is depicted. The KSTAR wall conditioning procedure consists of vessel baking, glow discharge cleaning (GDC), ICRH wall conditioning (ICWC) and boronization (Bz). Vessel baking is performed for the initial vacuum conditioning in order to remove various kinds of impurities including H 2 O, carbon and oxygen and for the plasma operation. The total outgassing rates after vessel baking in three successive KSTAR campaigns are compared. GDC is regularly performed as a standard wall cleaning procedure. Another cleaning technique is ICWC, which is useful for inter-shot wall conditioning under a strong magnetic field. In order to optimize the operation time and removal efficiency of ICWC, a parameter scan is performed. Bz is a standard technique to remove oxygen impurity from a vacuum vessel. KSTAR has used carborane powder which is a non-toxic boron-containing material. The KSTAR Bz has been successfully performed through two campaigns: water and oxygen levels in the vacuum vessel are reduced significantly. As a result, KSTAR has achieved its first L-H mode transition, although the input power was marginal for the L-H transition threshold. The characteristics of boron-containing thin films deposited for boronization are investigated.

ATMOSPHERIC CIRCULATION OF HOT JUPITERS: INSENSITIVITY TO INITIAL CONDITIONS

International Nuclear Information System (INIS)

Liu Beibei; Showman, Adam P.

2013-01-01

The ongoing characterization of hot Jupiters has motivated a variety of circulation models of their atmospheres. Such models must be integrated starting from an assumed initial state, which is typically taken to be a wind-free, rest state. Here, we investigate the sensitivity of hot-Jupiter atmospheric circulation to initial conditions with shallow-water models and full three-dimensional models. Those models are initialized with zonal jets, and we explore a variety of different initial jet profiles. We demonstrate that, in both classes of models, the final, equilibrated state is independent of initial condition—as long as frictional drag near the bottom of the domain and/or interaction with a specified planetary interior are included so that the atmosphere can adjust angular momentum over time relative to the interior. When such mechanisms are included, otherwise identical models initialized with vastly different initial conditions all converge to the same statistical steady state. In some cases, the models exhibit modest time variability; this variability results in random fluctuations about the statistical steady state, but we emphasize that, even in these cases, the statistical steady state itself does not depend on initial conditions. Although the outcome of hot-Jupiter circulation models depend on details of the radiative forcing and frictional drag, aspects of which remain uncertain, we conclude that the specification of initial conditions is not a source of uncertainty, at least over the parameter range explored in most current models.

Selection of the initial conditions in the tunneling time definition

International Nuclear Information System (INIS)

Zajchenko, A.K.

2004-01-01

The necessity of changing of the initial conditions in the Olkhovsky - Recami definition of the tunneling time is justified. The new initial conditions are proposed which adequately taking into account the irreversibility of the wave packets spreading. The expression for the tunneling time with the new initial conditions is reduced to the form which is convenient for the performing and controlling the accuracy of calculations

Solutions to the Cosmic Initial Entropy Problem without Equilibrium Initial Conditions

Directory of Open Access Journals (Sweden)

Vihan M. Patel

2017-08-01

Full Text Available The entropy of the observable universe is increasing. Thus, at earlier times the entropy was lower. However, the cosmic microwave background radiation reveals an apparently high entropy universe close to thermal and chemical equilibrium. A two-part solution to this cosmic initial entropy problem is proposed. Following Penrose, we argue that the evenly distributed matter of the early universe is equivalent to low gravitational entropy. There are two competing explanations for how this initial low gravitational entropy comes about. (1 Inflation and baryogenesis produce a virtually homogeneous distribution of matter with a low gravitational entropy. (2 Dissatisfied with explaining a low gravitational entropy as the product of a ‘special’ scalar field, some theorists argue (following Boltzmann for a “more natural” initial condition in which the entire universe is in an initial equilibrium state of maximum entropy. In this equilibrium model, our observable universe is an unusual low entropy fluctuation embedded in a high entropy universe. The anthropic principle and the fluctuation theorem suggest that this low entropy region should be as small as possible and have as large an entropy as possible, consistent with our existence. However, our low entropy universe is much larger than needed to produce observers, and we see no evidence for an embedding in a higher entropy background. The initial conditions of inflationary models are as natural as the equilibrium background favored by many theorists.

Initial conditions for turbulent mixing simulations

Directory of Open Access Journals (Sweden)

T. Kaman

2010-01-01

Full Text Available In the context of the classical Rayleigh-Taylor hydrodynamical instability, we examine the much debated question of models for initial conditions and the possible influence of unrecorded long wave length contributions to the instability growth rate α.

Crack initiation under generalized plane strain conditions

International Nuclear Information System (INIS)

Shum, D.K.M.; Merkle, J.G.

1991-01-01

A method for estimating the decrease in crack-initiation toughness, from a reference plane strain value, due to positive straining along the crack front of a circumferential flaw in a reactor pressure vessel is presented in this study. This method relates crack initiation under generalized plane strain conditions with material failure at points within a distance of a few crack-tip-opening displacements ahead of a crack front, and involves the formulation of a micromechanical crack-initiation model. While this study is intended to address concerns regarding the effects of positive out-of- plane straining on ductile crack initiation, the approach adopted in this work can be extended in a straightforward fashion to examine conditions of macroscopic cleavage crack initiation. Provided single- parameter dominance of near-tip fields exists in the flawed structure, results from this study could be used to examine the appropriateness of applying plane strain fracture toughness to the evaluation of circumferential flaws, in particular to those in ring-forged vessels which have no longitudinal welds. In addition, results from this study could also be applied toward the analysis of the effects of thermal streaming on the fracture resistance of circumferentially oriented flaws in a pressure vessel. 37 refs., 8 figs., 1 tab

Initial Conditions Corresponding to Optimal Ion Acceleration in the VINCY Cyclotron

International Nuclear Information System (INIS)

Ilic, A. Z.; Ristic-Djurovic, J. L.; Cirkovic, S. T.

2007-01-01

The quality of a beam in a cyclotron depends a lot on the choice of initial conditions for acceleration. The criteria defining optimal acceleration as well as the choice of the corresponding initial conditions have been outlined. The results of beam dynamics simulations with optimal and non-optimal initial conditions are compared. (author)

Tailoring discrete quantum walk dynamics via extended initial conditions

Energy Technology Data Exchange (ETDEWEB)

Valcarcel, German J de; Roldan, Eugenio [Departament d' Optica, Universitat de Valencia, Dr Moliner 50, 46100-Burjassot, Spain, EU (Spain); Romanelli, Alejandro, E-mail: german.valcarcel@uv.es, E-mail: eugenio.roldan@uv.es, E-mail: alejo@fing.edu.uy [Instituto de Fisica, Facultad de IngenierIa, Universidad de la Republica, CC 30, CP 11000, Montevideo (Uruguay)

2010-12-15

We study the evolution of initially extended distributions in the coined quantum walk (QW) on the line. By analysing the dispersion relation of the process, continuous wave equations are derived whose form depends on the initial distribution shape. In particular, for a class of initial conditions, the evolution is dictated by the Schroedinger equation of a free particle. As that equation also governs paraxial optical diffraction, all of the phenomenology of the latter can be implemented in the QW. This allows us, in particular, to devise an initially extended condition leading to a uniform probability distribution whose width increases linearly with time, with increasing homogeneity.

Tailoring discrete quantum walk dynamics via extended initial conditions

International Nuclear Information System (INIS)

Valcarcel, German J de; Roldan, Eugenio; Romanelli, Alejandro

2010-01-01

We study the evolution of initially extended distributions in the coined quantum walk (QW) on the line. By analysing the dispersion relation of the process, continuous wave equations are derived whose form depends on the initial distribution shape. In particular, for a class of initial conditions, the evolution is dictated by the Schroedinger equation of a free particle. As that equation also governs paraxial optical diffraction, all of the phenomenology of the latter can be implemented in the QW. This allows us, in particular, to devise an initially extended condition leading to a uniform probability distribution whose width increases linearly with time, with increasing homogeneity.

Sensitivity of a Simulated Derecho Event to Model Initial Conditions

Science.gov (United States)

Wang, Wei

2014-05-01

Since 2003, the MMM division at NCAR has been experimenting cloud-permitting scale weather forecasting using Weather Research and Forecasting (WRF) model. Over the years, we've tested different model physics, and tried different initial and boundary conditions. Not surprisingly, we found that the model's forecasts are more sensitive to the initial conditions than model physics. In 2012 real-time experiment, WRF-DART (Data Assimilation Research Testbed) at 15 km was employed to produce initial conditions for twice-a-day forecast at 3 km. On June 29, this forecast system captured one of the most destructive derecho event on record. In this presentation, we will examine forecast sensitivity to different model initial conditions, and try to understand the important features that may contribute to the success of the forecast.

On specification of initial conditions in turbulence models

Energy Technology Data Exchange (ETDEWEB)

Rollin, Bertrand [Los Alamos National Laboratory; Andrews, Malcolm J [Los Alamos National Laboratory

2010-12-01

Recent research has shown that initial conditions have a significant influence on the evolution of a flow towards turbulence. This important finding offers a unique opportunity for turbulence control, but also raises the question of how to properly specify initial conditions in turbulence models. We study this problem in the context of the Rayleigh-Taylor instability. The Rayleigh-Taylor instability is an interfacial fluid instability that leads to turbulence and turbulent mixing. It occurs when a light fluid is accelerated in to a heavy fluid because of misalignment between density and pressure gradients. The Rayleigh-Taylor instability plays a key role in a wide variety of natural and man-made flows ranging from supernovae to the implosion phase of Inertial Confinement Fusion (ICF). Our approach consists of providing the turbulence models with a predicted profile of its key variables at the appropriate time in accordance to the initial conditions of the problem.

Initial conditioning of the TFTR vacuum vessel

International Nuclear Information System (INIS)

Dylla, H.F.; Blanchard, W.R.; Krawchuk, R.B.; Hawryluk, R.J.; Owens, D.K.

1984-01-01

We report on the initial conditioning of the Tokamak Fusion Test Reactor (TFTR) vacuum vessel prior to the initiation of first plasma discharges, and during subsequent operation with high power ohmically-heated plasmas. Following evacuation of the 86 m 3 vessel with the 10 4 1/s high vacuum pumping system, the vessel was conditioned by a 15 A dc glow discharge in H 2 at a pressure of 5 mTorr. Rapid-pulse discharge cleaning was used subsequently to preferentially condition the graphite plasma limiters. The effectiveness of the discharge cleaning was monitored by measuring the exhaust rates of the primary discharge products (CO/C 2 H 4 , CH 4 , and H 2 O). After 175 hours of glow discharge treatment, the equivalent of 50 monolayers of C and O was removed from the vessel, and the partial pressures of impurity gases were reduced to the range of 10 -9 -10 -10 Torr

A Poisson-lognormal conditional-autoregressive model for multivariate spatial analysis of pedestrian crash counts across neighborhoods.

Science.gov (United States)

Wang, Yiyi; Kockelman, Kara M

2013-11-01

This work examines the relationship between 3-year pedestrian crash counts across Census tracts in Austin, Texas, and various land use, network, and demographic attributes, such as land use balance, residents' access to commercial land uses, sidewalk density, lane-mile densities (by roadway class), and population and employment densities (by type). The model specification allows for region-specific heterogeneity, correlation across response types, and spatial autocorrelation via a Poisson-based multivariate conditional auto-regressive (CAR) framework and is estimated using Bayesian Markov chain Monte Carlo methods. Least-squares regression estimates of walk-miles traveled per zone serve as the exposure measure. Here, the Poisson-lognormal multivariate CAR model outperforms an aspatial Poisson-lognormal multivariate model and a spatial model (without cross-severity correlation), both in terms of fit and inference. Positive spatial autocorrelation emerges across neighborhoods, as expected (due to latent heterogeneity or missing variables that trend in space, resulting in spatial clustering of crash counts). In comparison, the positive aspatial, bivariate cross correlation of severe (fatal or incapacitating) and non-severe crash rates reflects latent covariates that have impacts across severity levels but are more local in nature (such as lighting conditions and local sight obstructions), along with spatially lagged cross correlation. Results also suggest greater mixing of residences and commercial land uses is associated with higher pedestrian crash risk across different severity levels, ceteris paribus, presumably since such access produces more potential conflicts between pedestrian and vehicle movements. Interestingly, network densities show variable effects, and sidewalk provision is associated with lower severe-crash rates. Copyright © 2013 Elsevier Ltd. All rights reserved.

arXiv Initial Conditions for Critical Higgs Inflation

CERN Document Server

Salvio, Alberto

2018-05-10

It has been pointed out that a large non-minimal coupling ξ between the Higgs and the Ricci scalar can source higher derivative operators, which may change the predictions of Higgs inflation. A variant, called critical Higgs inflation, employs the near-criticality of the top mass to introduce an inflection point in the potential and lower drastically the value of ξ . We here study whether critical Higgs inflation can occur even if the pre-inflationary initial conditions do not satisfy the slow-roll behavior (retaining translation and rotation symmetries). A positive answer is found: inflation turns out to be an attractor and therefore no fine-tuning of the initial conditions is necessary. A very large initial Higgs time-derivative (as compared to the potential energy density) is compensated by a moderate increase in the initial field value. These conclusions are reached by solving the exact Higgs equation without using the slow-roll approximation. This also allows us to consistently treat the inflection poi...

On the initial condition of inflationary fluctuations

International Nuclear Information System (INIS)

Jiang, Hongliang; Wang, Yi; Zhou, Siyi

2016-01-01

It is usually assumed that the inflationary fluctuations start from the Bunch-Davies (BD) vacuum and the iε prescription is used when interactions are calculated. We show that those assumptions can be verified explicitly by calculating the loop corrections to the inflationary two-point and three-point correlation functions. Those loop corrections can be resummed to exponential factors, which suppress non-BD coefficients and behave as the iε factor for the case of the BD initial condition. A new technique of loop chain diagram resummation is developed for this purpose. For the non-BD initial conditions which is setup at finite time and has not fully decayed, explicit correction to the two-point and three-point correlation functions are calculated. Especially, non-Gaussianity in the folded limit is regularized due to the interactions.

Protostellar formation in rotating interstellar clouds. VI. Nonuniform initial conditions

International Nuclear Information System (INIS)

Boss, A.P.

1987-01-01

The collapse and fragmentation of rotating protostellar clouds is explored, starting from nonuniform density and nonuniform rotation initial conditions. Whether binary fragmentation occurs during the first dynamic collapse phase depends strongly on the initial density profile. Exponential clouds are only somewhat more resistant to fragmentation than uniform-density clouds, but power-law clouds do not undergo fragmentation for likely values of a relevant parameter. Because binary fragments start from profiles intermediate between uniform density and exponential clouds, minimum protostellar mass for population I stars should be increased to approximately 0.02 solar mass. The axisymmetric Terey et al. (1984) model should be stable with respect to nonaxisymmetric perturbations. Considering the observed binary frequency, collapse from power-law initial conditions appears to be less common than collapse from more uniform initial conditions. 34 references

Effect of Initial Conditions on Reproducibility of Scientific Research

Science.gov (United States)

Djulbegovic, Benjamin; Hozo, Iztok

2014-01-01

Background: It is estimated that about half of currently published research cannot be reproduced. Many reasons have been offered as explanations for failure to reproduce scientific research findings- from fraud to the issues related to design, conduct, analysis, or publishing scientific research. We also postulate a sensitive dependency on initial conditions by which small changes can result in the large differences in the research findings when attempted to be reproduced at later times. Methods: We employed a simple logistic regression equation to model the effect of covariates on the initial study findings. We then fed the input from the logistic equation into a logistic map function to model stability of the results in repeated experiments over time. We illustrate the approach by modeling effects of different factors on the choice of correct treatment. Results: We found that reproducibility of the study findings depended both on the initial values of all independent variables and the rate of change in the baseline conditions, the latter being more important. When the changes in the baseline conditions vary by about 3.5 to about 4 in between experiments, no research findings could be reproduced. However, when the rate of change between the experiments is ≤2.5 the results become highly predictable between the experiments. Conclusions: Many results cannot be reproduced because of the changes in the initial conditions between the experiments. Better control of the baseline conditions in-between the experiments may help improve reproducibility of scientific findings. PMID:25132705

Relativistic initial conditions for N-body simulations

Energy Technology Data Exchange (ETDEWEB)

Fidler, Christian [Catholic University of Louvain—Center for Cosmology, Particle Physics and Phenomenology (CP3) 2, Chemin du Cyclotron, B-1348 Louvain-la-Neuve (Belgium); Tram, Thomas; Crittenden, Robert; Koyama, Kazuya; Wands, David [Institute of Cosmology and Gravitation, University of Portsmouth, Portsmouth PO1 3FX (United Kingdom); Rampf, Cornelius, E-mail: christian.fidler@uclouvain.be, E-mail: thomas.tram@port.ac.uk, E-mail: rampf@thphys.uni-heidelberg.de, E-mail: robert.crittenden@port.ac.uk, E-mail: kazuya.koyama@port.ac.uk, E-mail: david.wands@port.ac.uk [Institut für Theoretische Physik, Universität Heidelberg, Philosophenweg 16, D–69120 Heidelberg (Germany)

2017-06-01

Initial conditions for (Newtonian) cosmological N-body simulations are usually set by re-scaling the present-day power spectrum obtained from linear (relativistic) Boltzmann codes to the desired initial redshift of the simulation. This back-scaling method can account for the effect of inhomogeneous residual thermal radiation at early times, which is absent in the Newtonian simulations. We analyse this procedure from a fully relativistic perspective, employing the recently-proposed Newtonian motion gauge framework. We find that N-body simulations for ΛCDM cosmology starting from back-scaled initial conditions can be self-consistently embedded in a relativistic space-time with first-order metric potentials calculated using a linear Boltzmann code. This space-time coincides with a simple ''N-body gauge'' for z < 50 for all observable modes. Care must be taken, however, when simulating non-standard cosmologies. As an example, we analyse the back-scaling method in a cosmology with decaying dark matter, and show that metric perturbations become large at early times in the back-scaling approach, indicating a breakdown of the perturbative description. We suggest a suitable ''forwards approach' for such cases.

Variation formulae for the solutions of delay differential equations with discontinuous initial conditions

International Nuclear Information System (INIS)

Kharatishvili, G L; Tadumadze, T A

2005-01-01

Variation formulae are proved for solutions of non-linear differential equations with variable delays and discontinuous initial conditions. The discontinuity of the initial condition means that at the initial moment of time the values of the initial function and the trajectory, generally speaking, do not coincide. The formulae obtained contain a new summand connected with the discontinuity of the initial condition and the variation of the initial moment.

A new equilibrium torus solution and GRMHD initial conditions

Science.gov (United States)

Penna, Robert F.; Kulkarni, Akshay; Narayan, Ramesh

2013-11-01

Context. General relativistic magnetohydrodynamic (GRMHD) simulations are providing influential models for black hole spin measurements, gamma ray bursts, and supermassive black hole feedback. Many of these simulations use the same initial condition: a rotating torus of fluid in hydrostatic equilibrium. A persistent concern is that simulation results sometimes depend on arbitrary features of the initial torus. For example, the Bernoulli parameter (which is related to outflows), appears to be controlled by the Bernoulli parameter of the initial torus. Aims: In this paper, we give a new equilibrium torus solution and describe two applications for the future. First, it can be used as a more physical initial condition for GRMHD simulations than earlier torus solutions. Second, it can be used in conjunction with earlier torus solutions to isolate the simulation results that depend on initial conditions. Methods: We assume axisymmetry, an ideal gas equation of state, constant entropy, and ignore self-gravity. We fix an angular momentum distribution and solve the relativistic Euler equations in the Kerr metric. Results: The Bernoulli parameter, rotation rate, and geometrical thickness of the torus can be adjusted independently. Our torus tends to be more bound and have a larger radial extent than earlier torus solutions. Conclusions: While this paper was in preparation, several GRMHD simulations appeared based on our equilibrium torus. We believe it will continue to provide a more realistic starting point for future simulations.

Conditional Probabilities in the Excursion Set Theory. Generic Barriers and non-Gaussian Initial Conditions

CERN Document Server

De Simone, Andrea; Riotto, Antonio

2011-01-01

The excursion set theory, where density perturbations evolve stochastically with the smoothing scale, provides a method for computing the dark matter halo mass function. The computation of the mass function is mapped into the so-called first-passage time problem in the presence of a moving barrier. The excursion set theory is also a powerful formalism to study other properties of dark matter halos such as halo bias, accretion rate, formation time, merging rate and the formation history of halos. This is achieved by computing conditional probabilities with non-trivial initial conditions, and the conditional two-barrier first-crossing rate. In this paper we use the recently-developed path integral formulation of the excursion set theory to calculate analytically these conditional probabilities in the presence of a generic moving barrier, including the one describing the ellipsoidal collapse, and for both Gaussian and non-Gaussian initial conditions. The non-Markovianity of the random walks induced by non-Gaussi...

Classical and quantum initial conditions for Higgs inflation

Directory of Open Access Journals (Sweden)

Alberto Salvio

2015-11-01

Full Text Available We investigate whether Higgs inflation can occur in the Standard Model starting from natural initial conditions or not. The Higgs has a non-minimal coupling to the Ricci scalar. We confine our attention to the regime where quantum Einstein gravity effects are small in order to have results that are independent of the ultraviolet completion of gravity. At the classical level we find no tuning is required to have successful Higgs inflation, provided the initial homogeneity condition is satisfied. On the other hand, at the quantum level we obtain that the renormalization for large non-minimal coupling requires an additional degree of freedom, unless a tuning of the initial values of the running parameters is made. In order to see that this effect may change the predictions we finally include such degree of freedom in the field content and show that Starobinsky's R2 inflation dominates over Higgs inflation.

Consistent initial conditions for the Saint-Venant equations in river network modeling

Directory of Open Access Journals (Sweden)

C.-W. Yu

2017-09-01

Full Text Available Initial conditions for flows and depths (cross-sectional areas throughout a river network are required for any time-marching (unsteady solution of the one-dimensional (1-D hydrodynamic Saint-Venant equations. For a river network modeled with several Strahler orders of tributaries, comprehensive and consistent synoptic data are typically lacking and synthetic starting conditions are needed. Because of underlying nonlinearity, poorly defined or inconsistent initial conditions can lead to convergence problems and long spin-up times in an unsteady solver. Two new approaches are defined and demonstrated herein for computing flows and cross-sectional areas (or depths. These methods can produce an initial condition data set that is consistent with modeled landscape runoff and river geometry boundary conditions at the initial time. These new methods are (1 the pseudo time-marching method (PTM that iterates toward a steady-state initial condition using an unsteady Saint-Venant solver and (2 the steady-solution method (SSM that makes use of graph theory for initial flow rates and solution of a steady-state 1-D momentum equation for the channel cross-sectional areas. The PTM is shown to be adequate for short river reaches but is significantly slower and has occasional non-convergent behavior for large river networks. The SSM approach is shown to provide a rapid solution of consistent initial conditions for both small and large networks, albeit with the requirement that additional code must be written rather than applying an existing unsteady Saint-Venant solver.

Multivariate performance reliability prediction in real-time

International Nuclear Information System (INIS)

Lu, S.; Lu, H.; Kolarik, W.J.

2001-01-01

This paper presents a technique for predicting system performance reliability in real-time considering multiple failure modes. The technique includes on-line multivariate monitoring and forecasting of selected performance measures and conditional performance reliability estimates. The performance measures across time are treated as a multivariate time series. A state-space approach is used to model the multivariate time series. Recursive forecasting is performed by adopting Kalman filtering. The predicted mean vectors and covariance matrix of performance measures are used for the assessment of system survival/reliability with respect to the conditional performance reliability. The technique and modeling protocol discussed in this paper provide a means to forecast and evaluate the performance of an individual system in a dynamic environment in real-time. The paper also presents an example to demonstrate the technique

Should tsunami models use a nonzero initial condition for horizontal velocity?

Science.gov (United States)

Nava, G.; Lotto, G. C.; Dunham, E. M.

2017-12-01

Tsunami propagation in the open ocean is most commonly modeled by solving the shallow water wave equations. These equations require two initial conditions: one on sea surface height and another on depth-averaged horizontal particle velocity or, equivalently, horizontal momentum. While most modelers assume that initial velocity is zero, Y.T. Song and collaborators have argued for nonzero initial velocity, claiming that horizontal displacement of a sloping seafloor imparts significant horizontal momentum to the ocean. They show examples in which this effect increases the resulting tsunami height by a factor of two or more relative to models in which initial velocity is zero. We test this claim with a "full-physics" integrated dynamic rupture and tsunami model that couples the elastic response of the Earth to the linearized acoustic-gravitational response of a compressible ocean with gravity; the model self-consistently accounts for seismic waves in the solid Earth, acoustic waves in the ocean, and tsunamis (with dispersion at short wavelengths). We run several full-physics simulations of subduction zone megathrust ruptures and tsunamis in geometries with a sloping seafloor, using both idealized structures and a more realistic Tohoku structure. Substantial horizontal momentum is imparted to the ocean, but almost all momentum is carried away in the form of ocean acoustic waves. We compare tsunami propagation in each full-physics simulation to that predicted by an equivalent shallow water wave simulation with varying assumptions regarding initial conditions. We find that the initial horizontal velocity conditions proposed by Song and collaborators consistently overestimate the tsunami amplitude and predict an inconsistent wave profile. Finally, we determine tsunami initial conditions that are rigorously consistent with our full-physics simulations by isolating the tsunami waves (from ocean acoustic and seismic waves) at some final time, and backpropagating the tsunami

Influence of changes in initial conditions for the simulation of dynamic systems

Energy Technology Data Exchange (ETDEWEB)

Kotyrba, Martin [Department of Informatics and Computers, University of Ostrava, 30 dubna 22, Ostrava (Czech Republic)

2015-03-10

Chaos theory is a field of study in mathematics, with applications in several disciplines including meteorology, sociology, physics, engineering, economics, biology, and philosophy. Chaos theory studies the behavior of dynamical systems that are highly sensitive to initial conditions—a paradigm popularly referred to as the butterfly effect. Small differences in initial conditions field widely diverging outcomes for such dynamical systems, rendering long-term prediction impossible in general. This happens even though these systems are deterministic, meaning that their future behavior is fully determined by their initial conditions, with no random elements involved. In this paperinfluence of changes in initial conditions will be presented for the simulation of Lorenz system.

Quantifying uncertainty in Gulf of Mexico forecasts stemming from uncertain initial conditions

KAUST Repository

Iskandarani, Mohamed; Le Hé naff, Matthieu; Srinivasan, Ashwanth; Knio, Omar

2016-01-01

Polynomial Chaos (PC) methods are used to quantify the impacts of initial conditions uncertainties on oceanic forecasts of the Gulf of Mexico circulation. Empirical Orthogonal Functions are used as initial conditions perturbations with their modal

Effects of the initial conditions on cosmological $N$-body simulations

OpenAIRE

L'Huillier, Benjamin; Park, Changbom; Kim, Juhan

2014-01-01

Cosmology is entering an era of percent level precision due to current large observational surveys. This precision in observation is now demanding more accuracy from numerical methods and cosmological simulations. In this paper, we study the accuracy of $N$-body numerical simulations and their dependence on changes in the initial conditions and in the simulation algorithms. For this purpose, we use a series of cosmological $N$-body simulations with varying initial conditions. We test the infl...

Asymptotics for the Conditional-Sum-of-Squares Estimator in Multivariate Fractional Time-Series Models

DEFF Research Database (Denmark)

Ørregård Nielsen, Morten

2015-01-01

the multivariate non-cointegrated fractional autoregressive integrated moving average (ARIMA) model. The novelty of the consistency result, in particular, is that it applies to a multivariate model and to an arbitrarily large set of admissible parameter values, for which the objective function does not converge...

MUSIC: MUlti-Scale Initial Conditions

Science.gov (United States)

Hahn, Oliver; Abel, Tom

2013-11-01

MUSIC generates multi-scale initial conditions with multiple levels of refinements for cosmological ‘zoom-in’ simulations. The code uses an adaptive convolution of Gaussian white noise with a real-space transfer function kernel together with an adaptive multi-grid Poisson solver to generate displacements and velocities following first- (1LPT) or second-order Lagrangian perturbation theory (2LPT). MUSIC achieves rms relative errors of the order of 10-4 for displacements and velocities in the refinement region and thus improves in terms of errors by about two orders of magnitude over previous approaches. In addition, errors are localized at coarse-fine boundaries and do not suffer from Fourier space-induced interference ringing.

Ductile Crack Initiation Criterion with Mismatched Weld Joints Under Dynamic Loading Conditions.

Science.gov (United States)

An, Gyubaek; Jeong, Se-Min; Park, Jeongung

2018-03-01

Brittle failure of high toughness steel structures tends to occur after ductile crack initiation/propagation. Damages to steel structures were reported in the Hanshin Great Earthquake. Several brittle failures were observed in beam-to-column connection zones with geometrical discontinuity. It is widely known that triaxial stresses accelerate the ductile fracture of steels. The study examined the effects of geometrical heterogeneity and strength mismatches (both of which elevate plastic constraints due to heterogeneous plastic straining) and loading rate on critical conditions initiating ductile fracture. This involved applying the two-parameter criterion (involving equivalent plastic strain and stress triaxiality) to estimate ductile cracking for strength mismatched specimens under static and dynamic tensile loading conditions. Ductile crack initiation testing was conducted under static and dynamic loading conditions using circumferentially notched specimens (Charpy type) with/without strength mismatches. The results indicated that the condition for ductile crack initiation using the two parameter criterion was a transferable criterion to evaluate ductile crack initiation independent of the existence of strength mismatches and loading rates.

Optimisation of resolution in micellar electrokinetic chromatography by multivariate evaluation of electrolytes.

Science.gov (United States)

Mikaeli, S; Thorsén, G; Karlberg, B

2001-01-12

A novel approach to multivariate evaluation of separation electrolytes for micellar electrokinetic chromatography is presented. An initial screening of the experimental parameters is performed using a Plackett-Burman design. Significant parameters are further evaluated using full factorial designs. The total resolution of the separation is calculated and used as response. The proposed scheme has been applied to the optimisation of the separation of phenols and the chiral separation of (+)-1-(9-anthryl)-2-propyl chloroformate-derivatized amino acids. A total of eight experimental parameters were evaluated and optimal conditions found in less than 48 experiments.

Initial conditions of radiative shock experiments

International Nuclear Information System (INIS)

Kuranz, C. C.; Drake, R. P.; Krauland, C. M.; Marion, D. C.; Grosskopf, M. J.; Rutter, E.; Torralva, B.; Holloway, J. P.; Bingham, D.; Goh, J.; Boehly, T. R.; Sorce, A. T.

2013-01-01

We performed experiments at the Omega Laser Facility to characterize the initial, laser-driven state of a radiative shock experiment. These experiments aimed to measure the shock breakout time from a thin, laser-irradiated Be disk. The data are then used to inform a range of valid model parameters, such as electron flux limiter and polytropic γ, used when simulating radiative shock experiments using radiation hydrodynamics codes. The characterization experiment and the radiative shock experiment use a laser irradiance of ∼7 × 10 14 W cm −2 to launch a shock in the Be disk. A velocity interferometer and a streaked optical pyrometer were used to infer the amount of time for the shock to move through the Be disk. The experimental results were compared with simulation results from the Hyades code, which can be used to model the initial conditions of a radiative shock system using the CRASH code

Initial Cladding Condition

International Nuclear Information System (INIS)

Siegmann, E.

2000-01-01

The purpose of this analysis is to describe the condition of commercial Zircaloy clad fuel as it is received at the Yucca Mountain Project (YMP) site. Most commercial nuclear fuel is encased in Zircaloy cladding. This analysis is developed to describe cladding degradation from the expected failure modes. This includes reactor operation impacts including incipient failures, potential degradation after reactor operation during spent fuel storage in pool and dry storage and impacts due to transportation. Degradation modes include cladding creep, and delayed hydride cracking during dry storage and transportation. Mechanical stresses from fuel handling and transportation vibrations are also included. This Analysis and Model Report (AMR) does not address any potential damage to assemblies that might occur at the YMP surface facilities. Ranges and uncertainties have been defined. This analysis will be the initial boundary condition for the analysis of cladding degradation inside the repository. In accordance with AP-2.13Q, ''Technical Product Development Planning'', a work plan (CRWMS M andO 2000c) was developed, issued, and utilized in the preparation of this document. There are constraints, caveats and limitations to this analysis. This cladding degradation analysis is based on commercial Pressurized Water Reactor (PWR) fuel with Zircaloy cladding but is applicable to Boiling Water Reactor (BWR) fuel. Reactor operating experience for both PWRs and BWRs is used to establish fuel reliability from reactor operation. It is limited to fuel exposed to normal operation and anticipated operational occurrences (i.e. events which are anticipated to occur within a reactor lifetime), and not to fuel that has been exposed to severe accidents. Fuel burnup projections have been limited to the current commercial reactor licensing environment with restrictions on fuel enrichment, oxide coating thickness and rod plenum pressures. The information provided in this analysis will be used in

Multivariate Term Structure Models with Level and Heteroskedasticity Effects

DEFF Research Database (Denmark)

Christiansen, Charlotte

2005-01-01

The paper introduces and estimates a multivariate level-GARCH model for the long rate and the term-structure spread where the conditional volatility is proportional to the ãth power of the variable itself (level effects) and the conditional covariance matrix evolves according to a multivariate GA...... and the level model. GARCH effects are more important than level effects. The results are robust to the maturity of the interest rates. Udgivelsesdato: MAY...

Influence of initial conditions on rod behaviour during boiling crisis phase following a reactivity initiated accident

International Nuclear Information System (INIS)

Georgenthum, V.; Sugiyama, T.

2010-01-01

In the frame of their research programs on high burn-up fuel safety, the French Institute for Radioprotection and Nuclear Safety (IRSN) and the Japan Atomic Energy Agency (JAEA) performed a large set of tests devoted to the study of PWR fuel rod behavior during Reactivity Initiated Accident (RIA) respectively in the CABRI reactor and in the NSRR reactor. The reactor test conditions are different in terms of coolant nature, temperature and pressure. In the CABRI reactor, tests were performed until now with sodium coolant at 280 Celsius degrees and 3 bar. In the NSRR reactor most of the tests were performed with stagnant water at 20 C. degrees and atmospheric pressure but recently a new high temperature high pressure capsule has been developed which allows to performed tests at up to 280 Celsius degrees and 70 bar. The paper discusses the influence of test conditions on rod behaviour during boiling phase, based on tests results and SCANAIR code calculations. The study shows that when the boiling crisis is reached, the initial inner and outer rod pressure have an essential impact on the clad straining and possible ballooning. The analysis of the different test conditions makes it possible to discriminate the influence of initial conditions on the different phases of the transient and is useful for modelling and code development. (authors)

Robustness of inflation to inhomogeneous initial conditions

Energy Technology Data Exchange (ETDEWEB)

Clough, Katy; Lim, Eugene A. [Theoretical Particle Physics and Cosmology Group, Physics Department, Kings College London, Strand, London WC2R 2LS (United Kingdom); DiNunno, Brandon S.; Fischler, Willy; Flauger, Raphael; Paban, Sonia, E-mail: katy.clough@kcl.ac.uk, E-mail: eugene.a.lim@gmail.com, E-mail: bsd86@physics.utexas.edu, E-mail: fischler@physics.utexas.edu, E-mail: flauger@physics.utexas.edu, E-mail: paban@physics.utexas.edu [Department of Physics, The University of Texas at Austin, Austin, TX, 78712 (United States)

2017-09-01

We consider the effects of inhomogeneous initial conditions in both the scalar field profile and the extrinsic curvature on different inflationary models. In particular, we compare the robustness of small field inflation to that of large field inflation, using numerical simulations with Einstein gravity in 3+1 dimensions. We find that small field inflation can fail in the presence of subdominant gradient energies, suggesting that it is much less robust to inhomogeneities than large field inflation, which withstands dominant gradient energies. However, we also show that small field inflation can be successful even if some regions of spacetime start out in the region of the potential that does not support inflation. In the large field case, we confirm previous results that inflation is robust if the inflaton occupies the inflationary part of the potential. Furthermore, we show that increasing initial scalar gradients will not form sufficiently massive inflation-ending black holes if the initial hypersurface is approximately flat. Finally, we consider the large field case with a varying extrinsic curvature K , such that some regions are initially collapsing. We find that this may again lead to local black holes, but overall the spacetime remains inflationary if the spacetime is open, which confirms previous theoretical studies.

Chaotic inflation as an attractor in initial-condition space

International Nuclear Information System (INIS)

Kung, J.H.; Brandenberger, R.H.

1990-01-01

We study the evolution of scalar field inhomogeneities in the preinflationary phase of an inflationary universe. We decompose the scalar field configuration in Fourier modes and consider initial conditions in which more than one mode is excited. We find that the long-wavelength modes are stable against perturbations due to short-wavelength excitations and that chaotic inflation results even if at the initial time the short waves contain most of the energy density

Linear models of coregionalization for multivariate lattice data: Order-dependent and order-free cMCARs.

Science.gov (United States)

MacNab, Ying C

2016-08-01

This paper concerns with multivariate conditional autoregressive models defined by linear combination of independent or correlated underlying spatial processes. Known as linear models of coregionalization, the method offers a systematic and unified approach for formulating multivariate extensions to a broad range of univariate conditional autoregressive models. The resulting multivariate spatial models represent classes of coregionalized multivariate conditional autoregressive models that enable flexible modelling of multivariate spatial interactions, yielding coregionalization models with symmetric or asymmetric cross-covariances of different spatial variation and smoothness. In the context of multivariate disease mapping, for example, they facilitate borrowing strength both over space and cross variables, allowing for more flexible multivariate spatial smoothing. Specifically, we present a broadened coregionalization framework to include order-dependent, order-free, and order-robust multivariate models; a new class of order-free coregionalized multivariate conditional autoregressives is introduced. We tackle computational challenges and present solutions that are integral for Bayesian analysis of these models. We also discuss two ways of computing deviance information criterion for comparison among competing hierarchical models with or without unidentifiable prior parameters. The models and related methodology are developed in the broad context of modelling multivariate data on spatial lattice and illustrated in the context of multivariate disease mapping. The coregionalization framework and related methods also present a general approach for building spatially structured cross-covariance functions for multivariate geostatistics. © The Author(s) 2016.

Determination of Initial Conditions for the Safety Analysis by Random Sampling of Operating Parameters

International Nuclear Information System (INIS)

Jeong, Hae-Yong; Park, Moon-Ghu

2015-01-01

In most existing evaluation methodologies, which follow a conservative approach, the most conservative initial conditions are searched for each transient scenario through tremendous assessment for wide operating windows or limiting conditions for operation (LCO) allowed by the operating guidelines. In this procedure, a user effect could be involved and a remarkable time and human resources are consumed. In the present study, we investigated a more effective statistical method for the selection of the most conservative initial condition by the use of random sampling of operating parameters affecting the initial conditions. A method for the determination of initial conditions based on random sampling of plant design parameters is proposed. This method is expected to be applied for the selection of the most conservative initial plant conditions in the safety analysis using a conservative evaluation methodology. In the method, it is suggested that the initial conditions of reactor coolant flow rate, pressurizer level, pressurizer pressure, and SG level are adjusted by controlling the pump rated flow, setpoints of PLCS, PPCS, and FWCS, respectively. The proposed technique is expected to contribute to eliminate the human factors introduced in the conventional safety analysis procedure and also to reduce the human resources invested in the safety evaluation of nuclear power plants

Fitting Nonlinear Ordinary Differential Equation Models with Random Effects and Unknown Initial Conditions Using the Stochastic Approximation Expectation-Maximization (SAEM) Algorithm.

Science.gov (United States)

Chow, Sy-Miin; Lu, Zhaohua; Sherwood, Andrew; Zhu, Hongtu

2016-03-01

The past decade has evidenced the increased prevalence of irregularly spaced longitudinal data in social sciences. Clearly lacking, however, are modeling tools that allow researchers to fit dynamic models to irregularly spaced data, particularly data that show nonlinearity and heterogeneity in dynamical structures. We consider the issue of fitting multivariate nonlinear differential equation models with random effects and unknown initial conditions to irregularly spaced data. A stochastic approximation expectation-maximization algorithm is proposed and its performance is evaluated using a benchmark nonlinear dynamical systems model, namely, the Van der Pol oscillator equations. The empirical utility of the proposed technique is illustrated using a set of 24-h ambulatory cardiovascular data from 168 men and women. Pertinent methodological challenges and unresolved issues are discussed.

Effect of initial conditions on combustion generated loads

International Nuclear Information System (INIS)

Tieszen, S.R.

1993-01-01

This analytical study examines the effect of initial thermodynamic conditions on the loads generated by the combustion of homogeneous hydrogen-air-steam mixtures. The effect of initial temperature, pressure, hydrogen concentration, and steam concentration is evaluated for two cases, (1) constant volume and (2) constant initial pressure. For each case, the Adiabatic, Isochoric, Complete Combustion (AICC), Chapman-Jouguet (CJ), and normally reflected CJ pressures are calculated for a range of hydrogen and steam concentrations representative of the entire flammable regime. For detonation loads, pressure profiles and time-histories are also evaluated in one-dimensional Cartesian geometry. The results show that to a first approximation, the AICC and CJ pressures are directly proportional to the initial density. Increasing the hydrogen concentration up to stoichiometric concentrations significantly increases the AICC, CJ, and reflected CJ pressures. For the constant volume case, the AICC, CJ, and reflected CJ pressures increase with increasing hydrogen concentration on the rich side of stoichiometric concentrations. For the constant initial pressure case, the AICC, CJ, and reflected CJ pressures decrease with increasing hydrogen concentration on the rich side of stoichiometric values. The addition of steam decreases the AICC, CJ, and reflected CJ pressures for the constant initial pressure case, but increases them for the constant volume case. For detonations, the pressure time-histories can be normalized with the AICC pressure and the reverberation time for Cartesion geometry. (orig.)

No-hair conjectures, primordial shear and protoinflationary initial conditions

CERN Document Server

Giovannini, Massimo

2014-01-01

Anisotropic inflationary background geometries are analyzed in the context of an extended gauge action where the electric and magnetic susceptibilities are not bound to coincide and depend on the inflaton field. After deriving various classes of solutions with electric and magnetic hairs, we discuss the problem of the initial boundary conditions of the shear parameter and consider a globally neutral plasma as a possible relic of a preinflationary stage of expansion. While electric hairs are washed out by the finite value of the protoinflationary conductivity, magnetic hairs can persist and introduce a tiny amount of shear causing a different inflationary rate of expansion along orthogonal spatial directions. The plasma interactions are a necessary criterion to discriminate between physical and unphysical initial conditions but they are not strictly sufficient to warrant the stability of a given magnetic solution.

Predicting a contact's sensitivity to initial conditions using metrics of frictional coupling

International Nuclear Information System (INIS)

Flicek, Robert C.; Hills, David A.; Brake, Matthew Robert W.

2016-01-01

This paper presents a method for predicting how sensitive a frictional contact’s steady-state behavior is to its initial conditions. Previous research has proven that if a contact is uncoupled, i.e. if slip displacements do not influence the contact pressure distribution, then its steady-state response is independent of initial conditions, but if the contact is coupled, the steady-state response depends on initial conditions. In this paper, two metrics for quantifying coupling in discrete frictional systems are examined. These metrics suggest that coupling is dominated by material dissimilarity due to Dundurs’ composite material parameter β when β ≥ 0.2, but geometric mismatch becomes the dominant source of coupling for smaller values of β. Based on a large set of numerical simulations with different contact geometries, material combinations, and friction coefficients, a contact’s sensitivity to initial conditions is found to be correlated with the product of the coupling metric and the friction coefficient. For cyclic shear loading, this correlation is maintained for simulations with different contact geometries, material combinations, and friction coefficients. Furthermore, for cyclic bulk loading, the correlation is only maintained when the contact edge angle is held constant.

Predictors for the initiation of a basal supported oral therapy (BOT) in type 2 diabetic patients under real-life conditions in Germany.

Science.gov (United States)

Kostev, Karel; Dippel, Franz-Werner

2012-12-01

To assess the predictors for the initiation of a basal supported oral therapy (BOT) in type 2 diabetic patients under real-life conditions in Germany. A historical cohort study based on representative German real life data (IMS(®) Disease Analyzer) was performed. The study included patients with type 2 diabetes who started an oral antidiabetic drug (OAD) treatment between 01/1995 and 12/2011. Patients with consecutive treatment data for at least 12 months before the initiation of an OAD treatment were eligible for the analysis. The time-dependent rate of patients starting an insulin therapy with a long-acting insulin was calculated by use of the Kaplan-Meier method. Multivariate Cox regression analyses were applied to identify associated factors. The study included 194,967 patients with type 2 diabetes mellitus being on OAD therapy. 24,964 patients were switched to BOT during the observational period. The probability of switching to insulin therapy was associated with three main predictors such as (1) poor metabolic control, (2) midlife age and (3) number and type of the OAD before insulinization. The variation of the HbA1c threshold to HbA1c≥7.5 leads to comparable outcomes with significant HR. The highest probability of initiating a basal supported oral therapy (BOT) under real life conditions was found for patients with poor metabolic control, midlife age and pre-treatment with specific OADs such as SU, GLI or AGI before initiation of insulin therapy. Copyright © 2012 Primary Care Diabetes Europe. Published by Elsevier Ltd. All rights reserved.

Star formation in mergers with comologically motivated initial conditions

NARCIS (Netherlands)

Karman, Wouter; Macciò, Andrea V.; Kannan, Rahul; Moster, Benjamin P.; Somerville, Rachel S.

2015-01-01

We use semi-analytic models and cosmological merger trees to provide the initial conditions for multimerger numerical hydrodynamic simulations, and exploit these simulations to explore the effect of galaxy interaction and merging on star formation (SF). We compute numerical realizations of 12 merger

Elliptic flow from non-equilibrium initial condition with a saturation scale

International Nuclear Information System (INIS)

Ruggieri, M.; Scardina, F.; Plumari, S.; Greco, V.

2013-01-01

A current goal of relativistic heavy-ion collisions experiments is the search for a Color Glass Condensate (CGC) as the limiting state of QCD matter at very high density. In viscous hydrodynamics simulations, a standard Glauber initial condition leads to estimate 4πη/s∼1, while employing the Kharzeev–Levin–Nardi (KLN) modeling of the glasma leads to at least a factor of 2 larger η/s. Within a kinetic theory approach based on a relativistic Boltzmann-like transport simulation, our main result is that the out-of-equilibrium initial distribution reduces the efficiency in building-up the elliptic flow. At RHIC energy we find the available data on v 2 are in agreement with a 4πη/s∼1 also for KLN initial conditions. More generally, our study shows that the initial non-equilibrium in p-space can have a significant impact on the build-up of anisotropic flow

MULTIVARIATE CURVE RESOLUTION OF NMR SPECTROSCOPY METABONOMIC DATA

Science.gov (United States)

Sandia National Laboratories is working with the EPA to evaluate and develop mathematical tools for analysis of the collected NMR spectroscopy data. Initially, we have focused on the use of Multivariate Curve Resolution (MCR) also known as molecular factor analysis (MFA), a tech...

(Anti)symmetric multivariate exponential functions and corresponding Fourier transforms

International Nuclear Information System (INIS)

Klimyk, A U; Patera, J

2007-01-01

We define and study symmetrized and antisymmetrized multivariate exponential functions. They are defined as determinants and antideterminants of matrices whose entries are exponential functions of one variable. These functions are eigenfunctions of the Laplace operator on the corresponding fundamental domains satisfying certain boundary conditions. To symmetric and antisymmetric multivariate exponential functions there correspond Fourier transforms. There are three types of such Fourier transforms: expansions into the corresponding Fourier series, integral Fourier transforms and multivariate finite Fourier transforms. Eigenfunctions of the integral Fourier transforms are found

Adaptive estimation of multivariate functions using conditionally Gaussian tensor-product spline priors

NARCIS (Netherlands)

Jonge, de R.; Zanten, van J.H.

2012-01-01

We investigate posterior contraction rates for priors on multivariate functions that are constructed using tensor-product B-spline expansions. We prove that using a hierarchical prior with an appropriate prior distribution on the partition size and Gaussian prior weights on the B-spline

Stochastic coalescence in finite systems: an algorithm for the numerical solution of the multivariate master equation.

Science.gov (United States)

Alfonso, Lester; Zamora, Jose; Cruz, Pedro

2015-04-01

The stochastic approach to coagulation considers the coalescence process going in a system of a finite number of particles enclosed in a finite volume. Within this approach, the full description of the system can be obtained from the solution of the multivariate master equation, which models the evolution of the probability distribution of the state vector for the number of particles of a given mass. Unfortunately, due to its complexity, only limited results were obtained for certain type of kernels and monodisperse initial conditions. In this work, a novel numerical algorithm for the solution of the multivariate master equation for stochastic coalescence that works for any type of kernels and initial conditions is introduced. The performance of the method was checked by comparing the numerically calculated particle mass spectrum with analytical solutions obtained for the constant and sum kernels, with an excellent correspondence between the analytical and numerical solutions. In order to increase the speedup of the algorithm, software parallelization techniques with OpenMP standard were used, along with an implementation in order to take advantage of new accelerator technologies. Simulations results show an important speedup of the parallelized algorithms. This study was funded by a grant from Consejo Nacional de Ciencia y Tecnologia de Mexico SEP-CONACYT CB-131879. The authors also thanks LUFAC® Computacion SA de CV for CPU time and all the support provided.

Hierarchy of temporal responses of multivariate self-excited epidemic processes

Science.gov (United States)

Saichev, Alexander; Maillart, Thomas; Sornette, Didier

2013-04-01

Many natural and social systems are characterized by bursty dynamics, for which past events trigger future activity. These systems can be modelled by so-called self-excited Hawkes conditional Poisson processes. It is generally assumed that all events have similar triggering abilities. However, some systems exhibit heterogeneity and clusters with possibly different intra- and inter-triggering, which can be accounted for by generalization into the "multivariate" self-excited Hawkes conditional Poisson processes. We develop the general formalism of the multivariate moment generating function for the cumulative number of first-generation and of all generation events triggered by a given mother event (the "shock") as a function of the current time t. This corresponds to studying the response function of the process. A variety of different systems have been analyzed. In particular, for systems in which triggering between events of different types proceeds through a one-dimension directed or symmetric chain of influence in type space, we report a novel hierarchy of intermediate asymptotic power law decays ˜ 1/ t 1-( m+1) θ of the rate of triggered events as a function of the distance m of the events to the initial shock in the type space, where 0 < θ < 1 for the relevant long-memory processes characterizing many natural and social systems. The richness of the generated time dynamics comes from the cascades of intermediate events of possibly different kinds, unfolding via random changes of types genealogy.

Weak convergence of marked point processes generated by crossings of multivariate jump processes

DEFF Research Database (Denmark)

Tamborrino, Massimiliano; Sacerdote, Laura; Jacobsen, Martin

2014-01-01

We consider the multivariate point process determined by the crossing times of the components of a multivariate jump process through a multivariate boundary, assuming to reset each component to an initial value after its boundary crossing. We prove that this point process converges weakly...... process converging to a multivariate Ornstein–Uhlenbeck process is discussed as a guideline for applying diffusion limits for jump processes. We apply our theoretical findings to neural network modeling. The proposed model gives a mathematical foundation to the generalization of the class of Leaky...

Modeling Covariance Breakdowns in Multivariate GARCH

OpenAIRE

Jin, Xin; Maheu, John M

2014-01-01

This paper proposes a flexible way of modeling dynamic heterogeneous covariance breakdowns in multivariate GARCH (MGARCH) models. During periods of normal market activity, volatility dynamics are governed by an MGARCH specification. A covariance breakdown is any significant temporary deviation of the conditional covariance matrix from its implied MGARCH dynamics. This is captured through a flexible stochastic component that allows for changes in the conditional variances, covariances and impl...

Quantification of discreteness effects in cosmological N-body simulations: Initial conditions

International Nuclear Information System (INIS)

Joyce, M.; Marcos, B.

2007-01-01

The relation between the results of cosmological N-body simulations, and the continuum theoretical models they simulate, is currently not understood in a way which allows a quantification of N dependent effects. In this first of a series of papers on this issue, we consider the quantification of such effects in the initial conditions of such simulations. A general formalism developed in [A. Gabrielli, Phys. Rev. E 70, 066131 (2004).] allows us to write down an exact expression for the power spectrum of the point distributions generated by the standard algorithm for generating such initial conditions. Expanded perturbatively in the amplitude of the input (i.e. theoretical, continuum) power spectrum, we obtain at linear order the input power spectrum, plus two terms which arise from discreteness and contribute at large wave numbers. For cosmological type power spectra, one obtains as expected, the input spectrum for wave numbers k smaller than that characteristic of the discreteness. The comparison of real space correlation properties is more subtle because the discreteness corrections are not as strongly localized in real space. For cosmological type spectra the theoretical mass variance in spheres and two-point correlation function are well approximated above a finite distance. For typical initial amplitudes this distance is a few times the interparticle distance, but it diverges as this amplitude (or, equivalently, the initial redshift of the cosmological simulation) goes to zero, at fixed particle density. We discuss briefly the physical significance of these discreteness terms in the initial conditions, in particular, with respect to the definition of the continuum limit of N-body simulations

Persistence of Initial Conditions in Continental Scale Air Quality Simulations

Data.gov (United States)

U.S. Environmental Protection Agency — This dataset contains the data used in Figures 1 – 6 and Table 2 of the technical note "Persistence of Initial Conditions in Continental Scale Air Quality...

Optimal control of distributed parameter system with incomplete information about the initial condition

International Nuclear Information System (INIS)

Kotarski, W.; Kowalewski, A.

1982-03-01

In this paper we consider an optimal control problem for a system described by a linear partial differential equation of the parabolic type with Dirichlet's boundary condition. We impose some constraints on the control. The performance functional has the integral form. The control time T is fixed. The initial condition is not given by a known function but belongs to a certain set (incomplete information about the initial state). The problem formulated in this paper describes the process of optimal heating, of which we do not have exact information about the initial temperature on the heated object. We present an example in which the set of admissible controls and one of initial conditions are given by means of the norm constraints too. The application of the well-known projective gradient method in the Hilbert space allows us to obtain the numerical solution for our optimization problem. (author)

Initial conditions and ENSO prediction using a coupled ocean-atmosphere model

Science.gov (United States)

Larow, T. E.; Krishnamurti, T. N.

1998-01-01

A coupled ocean-atmosphere initialization scheme using Newtonian relaxation has been developed for the Florida State University coupled ocean-atmosphere global general circulation model. The initialization scheme is used to initialize the coupled model for seasonal forecasting the boreal summers of 1987 and 1988. The atmosphere model is a modified version of the Florida State University global spectral model, resolution T-42. The ocean general circulation model consists of a slightly modified version of the Hamburg's climate group model described in Latif (1987) and Latif et al. (1993). The coupling is synchronous with information exchanged every two model hours. Using ECMWF atmospheric daily analysis and observed monthly mean SSTs, two, 1-year, time-dependent, Newtonian relaxation were performed using the coupled model prior to conducting the seasonal forecasts. The coupled initializations were conducted from 1 June 1986 to 1 June 1987 and from 1 June 1987 to 1 June 1988. Newtonian relaxation was applied to the prognostic atmospheric vorticity, divergence, temperature and dew point depression equations. In the ocean model the relaxation was applied to the surface temperature. Two, 10-member ensemble integrations were conducted to examine the impact of the coupled initialization on the seasonal forecasts. The initial conditions used for the ensembles are the ocean's final state after the initialization and the atmospheric initial conditions are ECMWF analysis. Examination of the SST root mean square error and anomaly correlations between observed and forecasted SSTs in the Niño-3 and Niño-4 regions for the 2 seasonal forecasts, show closer agreement between the initialized forecast than two, 10-member non-initialized ensemble forecasts. The main conclusion here is that a single forecast with the coupled initialization outperforms, in SST anomaly prediction, against each of the control forecasts (members of the ensemble) which do not include such an initialization

Can multivariate models based on MOAKS predict OA knee pain? Data from the Osteoarthritis Initiative

Science.gov (United States)

Luna-Gómez, Carlos D.; Zanella-Calzada, Laura A.; Galván-Tejada, Jorge I.; Galván-Tejada, Carlos E.; Celaya-Padilla, José M.

2017-03-01

Osteoarthritis is the most common rheumatic disease in the world. Knee pain is the most disabling symptom in the disease, the prediction of pain is one of the targets in preventive medicine, this can be applied to new therapies or treatments. Using the magnetic resonance imaging and the grading scales, a multivariate model based on genetic algorithms is presented. Using a predictive model can be useful to associate minor structure changes in the joint with the future knee pain. Results suggest that multivariate models can be predictive with future knee chronic pain. All models; T0, T1 and T2, were statistically significant, all p values were 0.60.

The shear-free condition and constant-mean-curvature hyperboloidal initial data

International Nuclear Information System (INIS)

Allen, Paul T; Allen, Iva Stavrov; Isenberg, James; Lee, John M

2016-01-01

We consider the Einstein–Maxwell-fluid constraint equations, and make use of the conformal method to construct and parametrize constant-mean-curvature hyperboloidal initial data sets that satisfy the shear-free condition. This condition is known to be necessary in order that a spacetime development admit a regular conformal boundary at future null infinity; see (Andersson and Chruściel 1994 Commun. Math. Phys. 161 533–68). We work with initial data sets in a variety of regularity classes, primarily considering those data sets whose geometries are weakly asymptotically hyperbolic , as defined in (Allen et al 2015 arXiv:1506.03399). These metrics are C 1,1 conformally compact, but not necessarily C 2 conformally compact. In order to ensure that the data sets we construct are indeed shear-free, we make use of the conformally covariant traceless Hessian introduced in (Allen et al 2015 arXiv:1506.03399). We furthermore construct a class of initial data sets with weakly asymptotically hyerbolic metrics that may be only C 0,1 conformally compact; these data sets are insufficiently regular to make sense of the shear-free condition. (paper)

Conditional stability in determination of initial data for stochastic parabolic equations

International Nuclear Information System (INIS)

Yuan, Ganghua

2017-01-01

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

Conditional stability in determination of initial data for stochastic parabolic equations

Science.gov (United States)

Yuan, Ganghua

2017-03-01

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

On the initial conditions for a universe with variable G and c

International Nuclear Information System (INIS)

Gomide, F.M.; Uehara, M.

1978-01-01

Initial conditions are discussed for a closed universe with matter injection process and with time varying G and c. It is shown that the usual interpretation for the cosmic microwave background radiation can be accepted for this model universe, provided a certain initial condition is imposed on the Pryce-Hoyle field. The time varying c is responsible for two red-shift laws one for wave-length and the other for the frequency. If radiation temperature obeys the red-shift law for frequency, the primordial Planck spectrum can be reproduced along cosmic expansion [pt

3-D simulations to investigate initial condition effects on the growth of Rayleigh-Taylor mixing

Energy Technology Data Exchange (ETDEWEB)

Andrews, Malcolm J [Los Alamos National Laboratory

2008-01-01

The effect of initial conditions on the growth rate of turbulent Rayleigh-Taylor (RT) mixing has been studied using carefully formulated numerical simulations. An integrated large-eddy simulation (ILES) that uses a finite-volume technique was employed to solve the three-dimensional incompressible Euler equations with numerical dissipation. The initial conditions were chosen to test the dependence of the RT growth parameters ({alpha}{sub b}, {alpha}{sub s}) on variations in (a) the spectral bandwidth, (b) the spectral shape, and (c) discrete banded spectra. Our findings support the notion that the overall growth of the RT mixing is strongly dependent on initial conditions. Variation in spectral shapes and bandwidths are found to have a complex effect of the late time development of the RT mixing layer, and raise the question of whether we can design RT transition and turbulence based on our choice of initial conditions. In addition, our results provide a useful database for the initialization and development of closures describing RT transition and turbulence.

Charging conditions research to increase the initial projected velocity at different initial charge temperatures

Science.gov (United States)

Ishchenko, Aleksandr; Burkin, Viktor; Kasimov, Vladimir; Samorokova, Nina; Zykova, Angelica; Diachkovskii, Alexei

2017-11-01

The problems of the defense industry occupy the most important place in the constantly developing modern world. The daily development of defense technology does not stop, nor do studies on internal ballistics. The scientists of the whole world are faced with the task of managing the main characteristics of a ballistic experiment. The main characteristics of the ballistic experiment are the maximum pressure in the combustion chamber Pmax and the projected velocity at the time of barrel leaving UM. During the work the combustion law of the new high-energy fuel was determined in a ballistic experiment for different initial temperatures. This combustion law was used for a parametric study of depending Pmax and UM from a powder charge mass and a traveling charge was carried out. The optimal conditions for loading were obtained for improving the initial velocity at pressures up to 600 MPa for different initial temperatures. In this paper, one of the most promising schemes of throwing is considered, as well as a method for increasing the muzzle velocity of a projected element to 3317 m/s.

Chemiluminescence-based multivariate sensing of local equivalence ratios in premixed atmospheric methane-air flames

Energy Technology Data Exchange (ETDEWEB)

Tripathi, Markandey M.; Krishnan, Sundar R.; Srinivasan, Kalyan K.; Yueh, Fang-Yu; Singh, Jagdish P.

2011-09-07

Chemiluminescence emissions from OH*, CH*, C2, and CO2 formed within the reaction zone of premixed flames depend upon the fuel-air equivalence ratio in the burning mixture. In the present paper, a new partial least square regression (PLS-R) based multivariate sensing methodology is investigated and compared with an OH*/CH* intensity ratio-based calibration model for sensing equivalence ratio in atmospheric methane-air premixed flames. Five replications of spectral data at nine different equivalence ratios ranging from 0.73 to 1.48 were used in the calibration of both models. During model development, the PLS-R model was initially validated with the calibration data set using the leave-one-out cross validation technique. Since the PLS-R model used the entire raw spectral intensities, it did not need the nonlinear background subtraction of CO2 emission that is required for typical OH*/CH* intensity ratio calibrations. An unbiased spectral data set (not used in the PLS-R model development), for 28 different equivalence ratio conditions ranging from 0.71 to 1.67, was used to predict equivalence ratios using the PLS-R and the intensity ratio calibration models. It was found that the equivalence ratios predicted with the PLS-R based multivariate calibration model matched the experimentally measured equivalence ratios within 7%; whereas, the OH*/CH* intensity ratio calibration grossly underpredicted equivalence ratios in comparison to measured equivalence ratios, especially under rich conditions ( > 1.2). The practical implications of the chemiluminescence-based multivariate equivalence ratio sensing methodology are also discussed.

Effects of initial conditions on self-similarity in a co-flowing axi-symmetric round jet

International Nuclear Information System (INIS)

Uddin, M.; Pollard, A.

2004-01-01

The effect of initial conditions of a spatially developing coflowing jet is investigated using an LES at Re D = 7,300. A co-flow velocity to initial jet centerline velocity ratio of 1:11 and a co-flow to initial jet diameter ratio of 35:1 are used to match the flow cases of Reference 11. The 35D x 135D simulation volume is divided into 1024 x 256 x 128 control volumes in the longitudinal, radial and azimuthal directions respectively. Time averaged results of the effect of initial conditions on mean flow, the decay of jet centreline velocity, growth of the jet and the distribution of Reynolds stresses in the near, and far field of the shear layer is presented. These quantities show good agreement with the measurements of Reference 11. Our results suggest that the first order moments, e.g., decay of centreline velocity excess, the radial mean velocity profiles, have little dependence on the initial conditions. As well, the Reynolds shear stress appears to have lesser sensitivity to the variation of initial velocity profiles. However, initial conditions have pronounced effect on the self-similarity of normal stresses. Additionally, the computations indicate little Reynolds number dependency, which is consistent with Townsend's school of thought. (author)

Robust multivariate analysis

CERN Document Server

J Olive, David

2017-01-01

This text presents methods that are robust to the assumption of a multivariate normal distribution or methods that are robust to certain types of outliers. Instead of using exact theory based on the multivariate normal distribution, the simpler and more applicable large sample theory is given.  The text develops among the first practical robust regression and robust multivariate location and dispersion estimators backed by theory.   The robust techniques  are illustrated for methods such as principal component analysis, canonical correlation analysis, and factor analysis.  A simple way to bootstrap confidence regions is also provided. Much of the research on robust multivariate analysis in this book is being published for the first time. The text is suitable for a first course in Multivariate Statistical Analysis or a first course in Robust Statistics. This graduate text is also useful for people who are familiar with the traditional multivariate topics, but want to know more about handling data sets with...

SCC of cold-worked austenitic stainless steels exposed to PWR primary water conditions: susceptibility to initiation

International Nuclear Information System (INIS)

Herms, E.; Raquet, O.; Sejourne, L.; Vaillant, F.

2009-01-01

Heavily cold-worked austenitic stainless steels (AISI 304L and 316L types) could be significantly susceptible to Stress Corrosion Cracking (SCC) when exposed to PWR nominal primary water conditions even in absence of any pollutants. Susceptibility to SCC was shown to be related with some conditions such as initial hardness, procedure of cold-work or dynamic straining. A dedicated program devoted to better understand the initiation stage on CW austenitic stainless steels in PWR water is presented. Initiation is studied thanks to SCC test conditions leading to an intergranular cracking propagation mode on a CW austenitic stainless steel which is the mode generally reported after field experience. SCC tests are carried out in typical primary water conditions (composition 1000 ppm B and 2 ppm Li) and for temperature in the range 290 - 340 C. Material selected is 316L cold-worked essentially by rolling (reduction in thickness of 40%). Initiation tests are carried out under various stress levels with the aim to investigate the evolution of the initiation period versus the value of applied stress. SCC tests are performed on cylindrical notched specimens in order to increase the applied stress and allow accelerated testing without modify the exposure conditions to strictly nominal hydrogenated PWR water. Respective influences of cyclic/dynamic conditions on SCC initiation are presented and discussed. Dedicated interrupted tests help to investigate the behaviour of the crack initiation process. These SCC tests have shown that crack initiation could be obtained after a very short time under dynamic loading conditions on heavily pre-strained austenitic stainless steels. Actual results show that the most limiting stage of the cracking process on CW 316L seems to be the transition from slow transgranular propagation of surface initiated cracks to intergranular fast propagation through the thickness of the sample. The duration of this stage during crack initiation tests is

Accurate initial conditions in mixed Dark Matter--Baryon simulations

CERN Document Server

Valkenburg, Wessel

2017-06-01

We quantify the error in the results of mixed baryon--dark-matter hydrodynamic simulations, stemming from outdated approximations for the generation of initial conditions. The error at redshift 0 in contemporary large simulations, is of the order of few to ten percent in the power spectra of baryons and dark matter, and their combined total-matter power spectrum. After describing how to properly assign initial displacements and peculiar velocities to multiple species, we review several approximations: (1) {using the total-matter power spectrum to compute displacements and peculiar velocities of both fluids}, (2) scaling the linear redshift-zero power spectrum back to the initial power spectrum using the Newtonian growth factor ignoring homogeneous radiation, (3) using longitudinal-gauge velocities with synchronous-gauge densities, and (4) ignoring the phase-difference in the Fourier modes for the offset baryon grid, relative to the dark-matter grid. Three of these approximations do not take into account that ...

Bayesian recovery of the initial condition for the heat equation

NARCIS (Netherlands)

Knapik, B.T.; Vaart, van der A.W.; Zanten, van J.H.

2011-01-01

We study a Bayesian approach to recovering the initial condition for the heat equation from noisy observations of the solution at a later time. We consider a class of prior distributions indexed by a parameter quantifying "smoothness" and show that the corresponding posterior distributions contract

An alternative phase-space distribution to sample initial conditions for classical dynamics simulations

International Nuclear Information System (INIS)

Garcia-Vela, A.

2002-01-01

A new quantum-type phase-space distribution is proposed in order to sample initial conditions for classical trajectory simulations. The phase-space distribution is obtained as the modulus of a quantum phase-space state of the system, defined as the direct product of the coordinate and momentum representations of the quantum initial state. The distribution is tested by sampling initial conditions which reproduce the initial state of the Ar-HCl cluster prepared by ultraviolet excitation, and by simulating the photodissociation dynamics by classical trajectories. The results are compared with those of a wave packet calculation, and with a classical simulation using an initial phase-space distribution recently suggested. A better agreement is found between the classical and the quantum predictions with the present phase-space distribution, as compared with the previous one. This improvement is attributed to the fact that the phase-space distribution propagated classically in this work resembles more closely the shape of the wave packet propagated quantum mechanically

Constraints on rapidity-dependent initial conditions from charged-particle pseudorapidity densities and two-particle correlations

Science.gov (United States)

Ke, Weiyao; Moreland, J. Scott; Bernhard, Jonah E.; Bass, Steffen A.

2017-10-01

We study the initial three-dimensional spatial configuration of the quark-gluon plasma (QGP) produced in relativistic heavy-ion collisions using centrality and pseudorapidity-dependent measurements of the medium's charged particle density and two-particle correlations. A cumulant-generating function is first used to parametrize the rapidity dependence of local entropy deposition and extend arbitrary boost-invariant initial conditions to nonzero beam rapidities. The model is then compared to p +Pb and Pb + Pb charged-particle pseudorapidity densities and two-particle pseudorapidity correlations and systematically optimized using Bayesian parameter estimation to extract high-probability initial condition parameters. The optimized initial conditions are then compared to a number of experimental observables including the pseudorapidity-dependent anisotropic flows, event-plane decorrelations, and flow correlations. We find that the form of the initial local longitudinal entropy profile is well constrained by these experimental measurements.

Multivariate log-skew-elliptical distributions with applications to precipitation data

KAUST Repository

Marchenko, Yulia V.

2009-07-13

We introduce a family of multivariate log-skew-elliptical distributions, extending the list of multivariate distributions with positive support. We investigate their probabilistic properties such as stochastic representations, marginal and conditional distributions, and existence of moments, as well as inferential properties. We demonstrate, for example, that as for the log-t distribution, the positive moments of the log-skew-t distribution do not exist. Our emphasis is on two special cases, the log-skew-normal and log-skew-t distributions, which we use to analyze US national (univariate) and regional (multivariate) monthly precipitation data. © 2009 John Wiley & Sons, Ltd.

Multivariate log-skew-elliptical distributions with applications to precipitation data

KAUST Repository

Marchenko, Yulia V.; Genton, Marc G.

2009-01-01

We introduce a family of multivariate log-skew-elliptical distributions, extending the list of multivariate distributions with positive support. We investigate their probabilistic properties such as stochastic representations, marginal and conditional distributions, and existence of moments, as well as inferential properties. We demonstrate, for example, that as for the log-t distribution, the positive moments of the log-skew-t distribution do not exist. Our emphasis is on two special cases, the log-skew-normal and log-skew-t distributions, which we use to analyze US national (univariate) and regional (multivariate) monthly precipitation data. © 2009 John Wiley & Sons, Ltd.

Prospective surveillance of multivariate spatial disease data

Science.gov (United States)

Corberán-Vallet, A

2012-01-01

Surveillance systems are often focused on more than one disease within a predefined area. On those occasions when outbreaks of disease are likely to be correlated, the use of multivariate surveillance techniques integrating information from multiple diseases allows us to improve the sensitivity and timeliness of outbreak detection. In this article, we present an extension of the surveillance conditional predictive ordinate to monitor multivariate spatial disease data. The proposed surveillance technique, which is defined for each small area and time period as the conditional predictive distribution of those counts of disease higher than expected given the data observed up to the previous time period, alerts us to both small areas of increased disease incidence and the diseases causing the alarm within each area. We investigate its performance within the framework of Bayesian hierarchical Poisson models using a simulation study. An application to diseases of the respiratory system in South Carolina is finally presented. PMID:22534429

Linear stochastic differential equations with anticipating initial conditions

DEFF Research Database (Denmark)

Khalifa, Narjess; Kuo, Hui-Hsiung; Ouerdiane, Habib

In this paper we use the new stochastic integral introduced by Ayed and Kuo (2008) and the results obtained by Kuo et al. (2012b) to find a solution to a drift-free linear stochastic differential equation with anticipating initial condition. Our solution is based on well-known results from...... classical Itô theory and anticipative Itô formula results from Kue et al. (2012b). We also show that the solution obtained by our method is consistent with the solution obtained by the methods of Malliavin calculus, e.g. Buckdahn and Nualart (1994)....

Workshop on initiation of stress corrosion cracking under LWR conditions: Proceedings

International Nuclear Information System (INIS)

Nelson, J.L.; Cubicciotti, D.; Licina, G.J.

1988-05-01

A workshop titled ''Initiation of Stress Corrosion Cracking under LWR Conditions'' was held in Palo Alto, California on November 13, 1986, hosted by the Electric Power Research Institute. Participants were experts on the topic from nuclear steam supply and component manufacturers, public and private research laboratories, and university environments. Presentations included discussions on the definition of crack initiation, the effects of environmental and electrochemical variables on cracking susceptibility, and detection methods for the determination of crack initiation events and measurement of critical environmental and stress parameters. Examination of the questions related to crack initiation and its relative importance to the overall question of cracking of LWR materials from these perspectives provided inputs to EPRI project managers on the future direction of research efforts designed to prevent and control cracking. Thirteen reports have been cataloged separately

Predictability of Japan/East Sea (JES) System to Uncertain Initial/Lateral Boundary Conditions and Surface Winds

National Research Council Canada - National Science Library

Fang, Chin-Lung

2003-01-01

.... Change in either initial or boundary condition leads to a variety of model solutions. It is necessary to specify realistic initial and boundary conditions to achieve better understanding and prediction of the ocean behavior...

Identification of spatially-localized initial conditions via sparse PCA

Science.gov (United States)

Dwivedi, Anubhav; Jovanovic, Mihailo

2017-11-01

Principal Component Analysis involves maximization of a quadratic form subject to a quadratic constraint on the initial flow perturbations and it is routinely used to identify the most energetic flow structures. For general flow configurations, principal components can be efficiently computed via power iteration of the forward and adjoint governing equations. However, the resulting flow structures typically have a large spatial support leading to a question of physical realizability. To obtain spatially-localized structures, we modify the quadratic constraint on the initial condition to include a convex combination with an additional regularization term which promotes sparsity in the physical domain. We formulate this constrained optimization problem as a nonlinear eigenvalue problem and employ an inverse power-iteration-based method to solve it. The resulting solution is guaranteed to converge to a nonlinear eigenvector which becomes increasingly localized as our emphasis on sparsity increases. We use several fluids examples to demonstrate that our method indeed identifies the most energetic initial perturbations that are spatially compact. This work was supported by Office of Naval Research through Grant Number N00014-15-1-2522.

Maximum run-up behavior of tsunamis under non-zero initial velocity condition

Directory of Open Access Journals (Sweden)

Baran AYDIN

2018-03-01

Full Text Available The tsunami run-up problem is solved non-linearly under the most general initial conditions, that is, for realistic initial waveforms such as N-waves, as well as standard initial waveforms such as solitary waves, in the presence of initial velocity. An initial-boundary value problem governed by the non-linear shallow-water wave equations is solved analytically utilizing the classical separation of variables technique, which proved to be not only fast but also accurate analytical approach for this type of problems. The results provide important information on maximum tsunami run-up qualitatively. We observed that, although the calculated maximum run-ups increase significantly, going as high as double that of the zero-velocity case, initial waves having non-zero fluid velocity exhibit the same run-up behavior as waves without initial velocity, for all wave types considered in this study.

Effect of Initial Hydraulic Conditions on Capillary Rise in a Porous Medium: Pore-Network Modeling

KAUST Repository

Joekar-Niasar, V.

2012-01-01

The dynamics of capillary rise in a porous medium have been mostly studied in initially dry systems. As initial saturation and initial hydraulic conditions in many natural and industrial porous media can be variable, it is important to investigate the influence of initial conditions on the dynamics of the process. In this study, using dynamic pore-network modeling, we simulated capillary rise in a porous medium for different initial saturations (and consequently initial capillary pressures). Furthermore, the effect of hydraulic connectivity of the wetting phase in corners on the height and velocity of the wetting front was studied. Our simulation results show that there is a trade-off between capillary forces and trapping due to snap-off, which leads to a nonlinear dependence of wetting front velocity on initial saturation at the pore scale. This analysis may provide a possible answer to the experimental observations in the literature showing a non-monotonic dependency between initial saturation and the macroscopic front velocity. © Soil Science Society of America.

Abstract fractional integro-differential equations involving nonlocal initial conditions in α-norm

Directory of Open Access Journals (Sweden)

Wang Rong-Nian

2011-01-01

Full Text Available Abstract In the present paper, we deal with the Cauchy problems of abstract fractional integro-differential equations involving nonlocal initial conditions in α-norm, where the operator A in the linear part is the generator of a compact analytic semigroup. New criterions, ensuring the existence of mild solutions, are established. The results are obtained by using the theory of operator families associated with the function of Wright type and the semigroup generated by A, Krasnoselkii's fixed point theorem and Schauder's fixed point theorem. An application to a fractional partial integro-differential equation with nonlocal initial condition is also considered. Mathematics subject classification (2000 26A33, 34G10, 34G20

Multivariate extended skew-t distributions and related families

KAUST Repository

Arellano-Valle, Reinaldo B.

2010-12-01

A class of multivariate extended skew-t (EST) distributions is introduced and studied in detail, along with closely related families such as the subclass of extended skew-normal distributions. Besides mathematical tractability and modeling flexibility in terms of both skewness and heavier tails than the normal distribution, the most relevant properties of the EST distribution include closure under conditioning and ability to model lighter tails as well. The first part of the present paper examines probabilistic properties of the EST distribution, such as various stochastic representations, marginal and conditional distributions, linear transformations, moments and in particular Mardia’s measures of multivariate skewness and kurtosis. The second part of the paper studies statistical properties of the EST distribution, such as likelihood inference, behavior of the profile log-likelihood, the score vector and the Fisher information matrix. Especially, unlike the extended skew-normal distribution, the Fisher information matrix of the univariate EST distribution is shown to be non-singular when the skewness is set to zero. Finally, a numerical application of the conditional EST distribution is presented in the context of confidential data perturbation.

Multivariate extended skew-t distributions and related families

KAUST Repository

Arellano-Valle, Reinaldo B.; Genton, Marc G.

2010-01-01

A class of multivariate extended skew-t (EST) distributions is introduced and studied in detail, along with closely related families such as the subclass of extended skew-normal distributions. Besides mathematical tractability and modeling flexibility in terms of both skewness and heavier tails than the normal distribution, the most relevant properties of the EST distribution include closure under conditioning and ability to model lighter tails as well. The first part of the present paper examines probabilistic properties of the EST distribution, such as various stochastic representations, marginal and conditional distributions, linear transformations, moments and in particular Mardia’s measures of multivariate skewness and kurtosis. The second part of the paper studies statistical properties of the EST distribution, such as likelihood inference, behavior of the profile log-likelihood, the score vector and the Fisher information matrix. Especially, unlike the extended skew-normal distribution, the Fisher information matrix of the univariate EST distribution is shown to be non-singular when the skewness is set to zero. Finally, a numerical application of the conditional EST distribution is presented in the context of confidential data perturbation.

THE ANALYSIS OF THE COMMODITY PRICE FORECASTING SUCCESS CONSIDERING DIFFERENT LENGTHS OF THE INITIAL CONDITION DRIFT

Directory of Open Access Journals (Sweden)

Marcela Lascsáková

2015-09-01

Full Text Available In the paper the numerical model based on the exponential approximation of commodity stock exchanges was derived. The price prognoses of aluminium on the London Metal Exchange were determined as numerical solution of the Cauchy initial problem for the 1st order ordinary differential equation. To make the numerical model more accurate the idea of the modification of the initial condition value by the stock exchange was realized. By having analyzed the forecasting success of the chosen initial condition drift types, the initial condition drift providing the most accurate prognoses for the commodity price movements was determined. The suggested modification of the original model made the commodity price prognoses more accurate.

Models and Inference for Multivariate Spatial Extremes

KAUST Repository

Vettori, Sabrina

2017-12-07

The development of flexible and interpretable statistical methods is necessary in order to provide appropriate risk assessment measures for extreme events and natural disasters. In this thesis, we address this challenge by contributing to the developing research field of Extreme-Value Theory. We initially study the performance of existing parametric and non-parametric estimators of extremal dependence for multivariate maxima. As the dimensionality increases, non-parametric estimators are more flexible than parametric methods but present some loss in efficiency that we quantify under various scenarios. We introduce a statistical tool which imposes the required shape constraints on non-parametric estimators in high dimensions, significantly improving their performance. Furthermore, by embedding the tree-based max-stable nested logistic distribution in the Bayesian framework, we develop a statistical algorithm that identifies the most likely tree structures representing the data\\'s extremal dependence using the reversible jump Monte Carlo Markov Chain method. A mixture of these trees is then used for uncertainty assessment in prediction through Bayesian model averaging. The computational complexity of full likelihood inference is significantly decreased by deriving a recursive formula for the nested logistic model likelihood. The algorithm performance is verified through simulation experiments which also compare different likelihood procedures. Finally, we extend the nested logistic representation to the spatial framework in order to jointly model multivariate variables collected across a spatial region. This situation emerges often in environmental applications but is not often considered in the current literature. Simulation experiments show that the new class of multivariate max-stable processes is able to detect both the cross and inner spatial dependence of a number of extreme variables at a relatively low computational cost, thanks to its Bayesian hierarchical

Effect of Initial Hydraulic Conditions on Capillary Rise in a Porous Medium: Pore-Network Modeling

KAUST Repository

Joekar-Niasar, V.; Hassanizadeh, S. M.

2012-01-01

The dynamics of capillary rise in a porous medium have been mostly studied in initially dry systems. As initial saturation and initial hydraulic conditions in many natural and industrial porous media can be variable, it is important to investigate

Transcriptome and Multivariable Data Analysis of Corynebacterium glutamicum under Different Dissolved Oxygen Conditions in Bioreactors

Science.gov (United States)

Sun, Yang; Guo, Wenwen; Wang, Fen; Peng, Feng; Yang, Yankun; Dai, Xiaofeng; Liu, Xiuxia; Bai, Zhonghu

2016-01-01

Dissolved oxygen (DO) is an important factor in the fermentation process of Corynebacterium glutamicum, which is a widely used aerobic microbe in bio-industry. Herein, we described RNA-seq for C. glutamicum under different DO levels (50%, 30% and 0%) in 5 L bioreactors. Multivariate data analysis (MVDA) models were used to analyze the RNA-seq and metabolism data to investigate the global effect of DO on the transcriptional distinction of the substance and energy metabolism of C. glutamicum. The results showed that there were 39 and 236 differentially expressed genes (DEGs) under the 50% and 0% DO conditions, respectively, compared to the 30% DO condition. Key genes and pathways affected by DO were analyzed, and the result of the MVDA and RNA-seq revealed that different DO levels in the fermenter had large effects on the substance and energy metabolism and cellular redox balance of C. glutamicum. At low DO, the glycolysis pathway was up-regulated, and TCA was shunted by the up-regulation of the glyoxylate pathway and over-production of amino acids, including valine, cysteine and arginine. Due to the lack of electron-acceptor oxygen, 7 genes related to the electron transfer chain were changed, causing changes in the intracellular ATP content at 0% and 30% DO. The metabolic flux was changed to rebalance the cellular redox. This study applied deep sequencing to identify a wealth of genes and pathways that changed under different DO conditions and provided an overall comprehensive view of the metabolism of C. glutamicum. The results provide potential ways to improve the oxygen tolerance of C. glutamicum and to modify the metabolic flux for amino acid production and heterologous protein expression. PMID:27907077

Multivariate Analysis and Prediction of Dioxin-Furan ...

Science.gov (United States)

Peer Review Draft of Regional Methods Initiative Final Report Dioxins, which are bioaccumulative and environmentally persistent, pose an ongoing risk to human and ecosystem health. Fish constitute a significant source of dioxin exposure for humans and fish-eating wildlife. Current dioxin analytical methods are costly, time-consuming, and produce hazardous by-products. A Danish team developed a novel, multivariate statistical methodology based on the covariance of dioxin-furan congener Toxic Equivalences (TEQs) and fatty acid methyl esters (FAMEs) and applied it to North Atlantic Ocean fishmeal samples. The goal of the current study was to attempt to extend this Danish methodology to 77 whole and composite fish samples from three trophic groups: predator (whole largemouth bass), benthic (whole flathead and channel catfish) and forage fish (composite bluegill, pumpkinseed and green sunfish) from two dioxin contaminated rivers (Pocatalico R. and Kanawha R.) in West Virginia, USA. Multivariate statistical analyses, including, Principal Components Analysis (PCA), Hierarchical Clustering, and Partial Least Squares Regression (PLS), were used to assess the relationship between the FAMEs and TEQs in these dioxin contaminated freshwater fish from the Kanawha and Pocatalico Rivers. These three multivariate statistical methods all confirm that the pattern of Fatty Acid Methyl Esters (FAMEs) in these freshwater fish covaries with and is predictive of the WHO TE

A New Iteration Multivariate Pad e´ Approximation Technique for ...

African Journals Online (AJOL)

In this paper, the Laplace transform, the New iteration method and the Multivariate Pade´ approximation technique are employed to solve nonlinear fractional partial differential equations whose fractional derivatives are described in the sense of Caputo. The Laplace transform is used to ”fully” determine the initial iteration ...

Assessment of metals bioavailability to vegetables under field conditions using DGT, single extractions and multivariate statistics

Science.gov (United States)

2012-01-01

Background The metals bioavailability in soils is commonly assessed by chemical extractions; however a generally accepted method is not yet established. In this study, the effectiveness of Diffusive Gradients in Thin-films (DGT) technique and single extractions in the assessment of metals bioaccumulation in vegetables, and the influence of soil parameters on phytoavailability were evaluated using multivariate statistics. Soil and plants grown in vegetable gardens from mining-affected rural areas, NW Romania, were collected and analysed. Results Pseudo-total metal content of Cu, Zn and Cd in soil ranged between 17.3-146 mg kg-1, 141–833 mg kg-1 and 0.15-2.05 mg kg-1, respectively, showing enriched contents of these elements. High degrees of metals extractability in 1M HCl and even in 1M NH4Cl were observed. Despite the relatively high total metal concentrations in soil, those found in vegetables were comparable to values typically reported for agricultural crops, probably due to the low concentrations of metals in soil solution (Csoln) and low effective concentrations (CE), assessed by DGT technique. Among the analysed vegetables, the highest metal concentrations were found in carrots roots. By applying multivariate statistics, it was found that CE, Csoln and extraction in 1M NH4Cl, were better predictors for metals bioavailability than the acid extractions applied in this study. Copper transfer to vegetables was strongly influenced by soil organic carbon (OC) and cation exchange capacity (CEC), while pH had a higher influence on Cd transfer from soil to plants. Conclusions The results showed that DGT can be used for general evaluation of the risks associated to soil contamination with Cu, Zn and Cd in field conditions. Although quantitative information on metals transfer from soil to vegetables was not observed. PMID:23079133

Multivariate analysis with LISREL

CERN Document Server

Jöreskog, Karl G; Y Wallentin, Fan

2016-01-01

This book traces the theory and methodology of multivariate statistical analysis and shows how it can be conducted in practice using the LISREL computer program. It presents not only the typical uses of LISREL, such as confirmatory factor analysis and structural equation models, but also several other multivariate analysis topics, including regression (univariate, multivariate, censored, logistic, and probit), generalized linear models, multilevel analysis, and principal component analysis. It provides numerous examples from several disciplines and discusses and interprets the results, illustrated with sections of output from the LISREL program, in the context of the example. The book is intended for masters and PhD students and researchers in the social, behavioral, economic and many other sciences who require a basic understanding of multivariate statistical theory and methods for their analysis of multivariate data. It can also be used as a textbook on various topics of multivariate statistical analysis.

Multivariate supOU processes

DEFF Research Database (Denmark)

Barndorff-Nielsen, Ole Eiler; Stelzer, Robert

Univariate superpositions of Ornstein-Uhlenbeck (OU) type processes, called supOU processes, provide a class of continuous time processes capable of exhibiting long memory behaviour. This paper introduces multivariate supOU processes and gives conditions for their existence and finiteness...... of moments. Moreover, the second order moment structure is explicitly calculated, and examples exhibit the possibility of long range dependence. Our supOU processes are defined via homogeneous and factorisable Lévy bases. We show that the behaviour of supOU processes is particularly nice when the mean...... reversion parameter is restricted to normal matrices and especially to strictly negative definite ones.For finite variation Lévy bases we are able to give conditions for supOU processes to have locally bounded càdlàg paths of finite variation and to show an analogue of the stochastic differential equation...

Multivariate supOU processes

DEFF Research Database (Denmark)

Barndorff-Nielsen, Ole Eiler; Stelzer, Robert

2011-01-01

Univariate superpositions of Ornstein–Uhlenbeck-type processes (OU), called supOU processes, provide a class of continuous time processes capable of exhibiting long memory behavior. This paper introduces multivariate supOU processes and gives conditions for their existence and finiteness of moments....... Moreover, the second-order moment structure is explicitly calculated, and examples exhibit the possibility of long-range dependence. Our supOU processes are defined via homogeneous and factorizable Lévy bases. We show that the behavior of supOU processes is particularly nice when the mean reversion...... parameter is restricted to normal matrices and especially to strictly negative definite ones. For finite variation Lévy bases we are able to give conditions for supOU processes to have locally bounded càdlàg paths of finite variation and to show an analogue of the stochastic differential equation of OU...

Correlated initial condition for an embedded process by time partitioning

Czech Academy of Sciences Publication Activity Database

Velický, Bedřich; Kalvová, Anděla; Špička, Václav

2010-01-01

Roč. 81, č. 23 (2010), 235116/1-235116/12 ISSN 1098-0121 R&D Projects: GA ČR GA202/08/0361 Institutional research plan: CEZ:AV0Z10100520; CEZ:AV0Z10100521 Keywords : non-equilibrium * Initial conditions * decay of correlations * Green's functions * quantum transport equations Subject RIV: BM - Solid Matter Physics ; Magnetism Impact factor: 3.772, year: 2010

Curvature profiles as initial conditions for primordial black hole formation

International Nuclear Information System (INIS)

Polnarev, Alexander G; Musco, Ilia

2007-01-01

This work is part of an ongoing research programme to study possible primordial black hole (PBH) formation during the radiation-dominated era of the early universe. Working within spherical symmetry, we specify an initial configuration in terms of a curvature profile, which represents initial conditions for the large amplitude metric perturbations, away from the homogeneous Friedmann-Robertson-Walker model, which are required for PBH formation. Using an asymptotic quasi-homogeneous solution, we relate the curvature profile with the density and velocity fields, which at an early enough time, when the length scale of the configuration is much larger than the cosmological horizon, can be treated as small perturbations of the background values. We present general analytic solutions for the density and velocity profiles. These solutions enable us to consider in a self-consistent way the formation of PBHs in a wide variety of cosmological situations with the cosmological fluid being treated as an arbitrary mixture of different components with different equations of state. We obtain the analytical solutions for the density and velocity profiles as functions of the initial time. We then use two different parametrizations for the curvature profile and follow numerically the evolution of initial configurations

Rotation in the dynamic factor modeling of multivariate stationary time series.

NARCIS (Netherlands)

Molenaar, P.C.M.; Nesselroade, J.R.

2001-01-01

A special rotation procedure is proposed for the exploratory dynamic factor model for stationary multivariate time series. The rotation procedure applies separately to each univariate component series of a q-variate latent factor series and transforms such a component, initially represented as white

Data classification and MTBF prediction with a multivariate analysis approach

International Nuclear Information System (INIS)

Braglia, Marcello; Carmignani, Gionata; Frosolini, Marco; Zammori, Francesco

2012-01-01

The paper presents a multivariate statistical approach that supports the classification of mechanical components, subjected to specific operating conditions, in terms of the Mean Time Between Failure (MTBF). Assessing the influence of working conditions and/or environmental factors on the MTBF is a prerequisite for the development of an effective preventive maintenance plan. However, this task may be demanding and it is generally performed with ad-hoc experimental methods, lacking of statistical rigor. To solve this common problem, a step by step multivariate data classification technique is proposed. Specifically, a set of structured failure data are classified in a meaningful way by means of: (i) cluster analysis, (ii) multivariate analysis of variance, (iii) feature extraction and (iv) predictive discriminant analysis. This makes it possible not only to define the MTBF of the analyzed components, but also to identify the working parameters that explain most of the variability of the observed data. The approach is finally demonstrated on 126 centrifugal pumps installed in an oil refinery plant; obtained results demonstrate the quality of the final discrimination, in terms of data classification and failure prediction.

Effect of initial conditions and Mach number on the Richtmyer-Meshkov instability in ICF like conditions

Science.gov (United States)

Rao, Pooja; She, Dan; Lim, Hyunkyung; Glimm, James

2015-11-01

The qualitative and quantitative effect of initial conditions (linear and non-linear) and high Mach number (1.3 and 1.45) is studied on the turbulent mixing induced by the Richtmyer-Meshkov instability in idealized ICF conditions. The Richtmyer-Meshkov instability seeds Rayleigh-taylor instabilities in ICF experiments and is one of the factors that contributes to reduced performance of ICF experiments. Its also found in collapsing cores of stars and supersonic combustion. We use the Stony Brook University code, FronTier, which is verified via a code comparison study against the AMR multiphysics code FLASH, and validated against vertical shock tube experiments done by the LANL Extreme Fluids Team. These simulations are designed as a step towards simulating more realistic ICF conditions and quantifying the detrimental effects of mixing on the yield.

Corrosion fatigue initiation and short crack growth behaviour of austenitic stainless steels under light water reactor conditions

International Nuclear Information System (INIS)

Seifert, H.P.; Ritter, S.; Leber, H.J.

2012-01-01

Highlights: ► Corrosion fatigue in austenitic stainless steels under light water reactor conditions. ► Identification of major parameters of influence on initiation and short crack growth. ► Critical system conditions for environmental reduction of fatigue initiation life. ► Comparison with the environmental factor (F env ) approach. - Abstract: The corrosion fatigue initiation and short crack growth behaviour of different wrought low-carbon and stabilised austenitic stainless steels was characterised under simulated boiling water reactor and pressurised water reactor primary water conditions by cyclic fatigue tests with sharply notched fracture mechanics specimens. The special emphasis was placed to the behaviour at low corrosion potentials and, in particular, to hydrogen water chemistry conditions. The major parameter effects and critical conjoint threshold conditions, which result in relevant environmental reduction and acceleration of fatigue initiation life and subsequent short crack growth, respectively, are discussed and summarised. The observed corrosion fatigue behaviour is compared with the fatigue evaluation procedures in codes and regulatory guidelines.

The Matter Bispectrum in N-body Simulations with non-Gaussian Initial Conditions

OpenAIRE

Sefusatti, Emiliano; Crocce, Martin; Desjacques, Vincent

2010-01-01

We present measurements of the dark matter bispectrum in N-body simulations with non-Gaussian initial conditions of the local kind for a large variety of triangular configurations and compare them with predictions from Eulerian perturbation theory up to one-loop corrections. We find that the effects of primordial non-Gaussianity at large scales, when compared to perturbation theory, are well described by the initial component of the matter bispectrum, linearly extrapolated at the redshift of ...

Integrating Supplementary Application-Based Tutorials in the Multivariable Calculus Course

Science.gov (United States)

Verner, I. M.; Aroshas, S.; Berman, A.

2008-01-01

This article presents a study in which applications were integrated in the Multivariable Calculus course at the Technion in the framework of supplementary tutorials. The purpose of the study was to test the opportunity of extending the conventional curriculum by optional applied problem-solving activities and get initial evidence on the possible…

Influence of the initial conditions for the numerical simulation of two-phase slug flow

Energy Technology Data Exchange (ETDEWEB)

Pachas Napa, Alex A.; Morales, Rigoberto E.M.; Medina, Cesar D. Perea

2010-07-01

Multiphase flows in pipelines commonly show several patterns depending on the flow rate, geometry and physical properties of the phases. In oil production, the slug flow pattern is the most common among the others. This flow pattern is characterized by an intermittent succession in space and time of an aerated liquid slug and an elongated gas bubble with a liquid film. Slug flow is studied through the slug tracking model described as one-dimensional and Lagrangian frame referenced. In the model, the mass and the momentum balance equations are applied in control volumes constituted by the gas bubble and the liquid slug. Initial conditions must be determined, which need to reproduce the intermittence of the flow pattern. These initial conditions are given by a sequence of flow properties for each unit cell. Properties of the unit cell in initial conditions should reflect the intermittence, for which they can be analyzed in statistical terms. Therefore, statistical distributions should be obtained for the slug flow variables. Distributions are complemented with the mass balance and the bubble design model. The objective of the present work is to obtain initial conditions for the slug tracking model that reproduce a better adjustment of the fluctuating properties for different pipe inclinations (horizontal, vertical or inclined). The numerical results are compared with experimental data obtained by PFG/FEM/UNICAMP for air-water flow at 0 deg, 45 deg and 90 deg and good agreement is observed. (author)

Multivariate statistical methods a primer

CERN Document Server

Manly, Bryan FJ

2004-01-01

THE MATERIAL OF MULTIVARIATE ANALYSISExamples of Multivariate DataPreview of Multivariate MethodsThe Multivariate Normal DistributionComputer ProgramsGraphical MethodsChapter SummaryReferencesMATRIX ALGEBRAThe Need for Matrix AlgebraMatrices and VectorsOperations on MatricesMatrix InversionQuadratic FormsEigenvalues and EigenvectorsVectors of Means and Covariance MatricesFurther Reading Chapter SummaryReferencesDISPLAYING MULTIVARIATE DATAThe Problem of Displaying Many Variables in Two DimensionsPlotting index VariablesThe Draftsman's PlotThe Representation of Individual Data P:ointsProfiles o

Quantifying uncertainty in Gulf of Mexico forecasts stemming from uncertain initial conditions

KAUST Repository

Iskandarani, Mohamed

2016-06-09

Polynomial Chaos (PC) methods are used to quantify the impacts of initial conditions uncertainties on oceanic forecasts of the Gulf of Mexico circulation. Empirical Orthogonal Functions are used as initial conditions perturbations with their modal amplitudes considered as uniformly distributed uncertain random variables. These perturbations impact primarily the Loop Current system and several frontal eddies located in its vicinity. A small ensemble is used to sample the space of the modal amplitudes and to construct a surrogate for the evolution of the model predictions via a nonintrusive Galerkin projection. The analysis of the surrogate yields verification measures for the surrogate\\'s reliability and statistical information for the model output. A variance analysis indicates that the sea surface height predictability in the vicinity of the Loop Current is limited to about 20 days. © 2016. American Geophysical Union. All Rights Reserved.

Interpretability of Multivariate Brain Maps in Linear Brain Decoding: Definition, and Heuristic Quantification in Multivariate Analysis of MEG Time-Locked Effects.

Science.gov (United States)

Kia, Seyed Mostafa; Vega Pons, Sandro; Weisz, Nathan; Passerini, Andrea

2016-01-01

Brain decoding is a popular multivariate approach for hypothesis testing in neuroimaging. Linear classifiers are widely employed in the brain decoding paradigm to discriminate among experimental conditions. Then, the derived linear weights are visualized in the form of multivariate brain maps to further study spatio-temporal patterns of underlying neural activities. It is well known that the brain maps derived from weights of linear classifiers are hard to interpret because of high correlations between predictors, low signal to noise ratios, and the high dimensionality of neuroimaging data. Therefore, improving the interpretability of brain decoding approaches is of primary interest in many neuroimaging studies. Despite extensive studies of this type, at present, there is no formal definition for interpretability of multivariate brain maps. As a consequence, there is no quantitative measure for evaluating the interpretability of different brain decoding methods. In this paper, first, we present a theoretical definition of interpretability in brain decoding; we show that the interpretability of multivariate brain maps can be decomposed into their reproducibility and representativeness. Second, as an application of the proposed definition, we exemplify a heuristic for approximating the interpretability in multivariate analysis of evoked magnetoencephalography (MEG) responses. Third, we propose to combine the approximated interpretability and the generalization performance of the brain decoding into a new multi-objective criterion for model selection. Our results, for the simulated and real MEG data, show that optimizing the hyper-parameters of the regularized linear classifier based on the proposed criterion results in more informative multivariate brain maps. More importantly, the presented definition provides the theoretical background for quantitative evaluation of interpretability, and hence, facilitates the development of more effective brain decoding algorithms

Multivariate Receptor Models for Spatially Correlated Multipollutant Data

KAUST Repository

Jun, Mikyoung

2013-08-01

The goal of multivariate receptor modeling is to estimate the profiles of major pollution sources and quantify their impacts based on ambient measurements of pollutants. Traditionally, multivariate receptor modeling has been applied to multiple air pollutant data measured at a single monitoring site or measurements of a single pollutant collected at multiple monitoring sites. Despite the growing availability of multipollutant data collected from multiple monitoring sites, there has not yet been any attempt to incorporate spatial dependence that may exist in such data into multivariate receptor modeling. We propose a spatial statistics extension of multivariate receptor models that enables us to incorporate spatial dependence into estimation of source composition profiles and contributions given the prespecified number of sources and the model identification conditions. The proposed method yields more precise estimates of source profiles by accounting for spatial dependence in the estimation. More importantly, it enables predictions of source contributions at unmonitored sites as well as when there are missing values at monitoring sites. The method is illustrated with simulated data and real multipollutant data collected from eight monitoring sites in Harris County, Texas. Supplementary materials for this article, including data and R code for implementing the methods, are available online on the journal web site. © 2013 Copyright Taylor and Francis Group, LLC.

Control Multivariable por Desacoplo

Directory of Open Access Journals (Sweden)

Fernando Morilla

2013-01-01

results obtained by the authors after several years of research giving priority to the problem generalization and practical issues like easiness of implementation and utilization of PID controllers as elementary blocks. This combination of interests makes difficult to obtain perfect decoupling in all cases; although it is possible to achieve an important interaction reduction at the basic level of the control pyramid in such a way that other control systems at higher hierarchical levels benefit of this fact. This article summarizes the main aspects of decoupling control and presents its application to two illustrative examples: an experimental quadruple tank process and a 4×4 model of a heat, ventilation and air conditioning system. Palabras clave: Control de procesos, Control multivariable, Control por desacoplo, Control PID, Keywords: Process control, multivariable control, decoupling control, PID control

Fractal analysis on a classical hard-wall billiard with openings using a two-dimensional set of initial conditions

International Nuclear Information System (INIS)

Ree, Suhan

2003-01-01

Fractal analysis is performed to measure the chaoticity of a classical hard-wall billiard with openings. We use the circular billiard with a straight cut with two openings, and a two-dimensional (2D) set of initial conditions that produce all possible trajectories of a particle injected from one opening. We numerically compute the fractal dimension of singular points of the function that maps an initial condition to the number of collisions with the wall before the exit, using the box-counting algorithm that uses uniformly distributed points inside the 2D set of initial conditions. Finally, the classical chaotic properties are observed while the parameters of the billiard are varied, and the results are compared with those with the one-dimensional set of initial conditions

Rotation in the Dynamic Factor Modeling of Multivariate Stationary Time Series.

Science.gov (United States)

Molenaar, Peter C. M.; Nesselroade, John R.

2001-01-01

Proposes a special rotation procedure for the exploratory dynamic factor model for stationary multivariate time series. The rotation procedure applies separately to each univariate component series of a q-variate latent factor series and transforms such a component, initially represented as white noise, into a univariate moving-average.…

Multivariate return periods of sea storms for coastal erosion risk assessment

Directory of Open Access Journals (Sweden)

S. Corbella

2012-08-01

Full Text Available The erosion of a beach depends on various storm characteristics. Ideally, the risk associated with a storm would be described by a single multivariate return period that is also representative of the erosion risk, i.e. a 100 yr multivariate storm return period would cause a 100 yr erosion return period. Unfortunately, a specific probability level may be associated with numerous combinations of storm characteristics. These combinations, despite having the same multivariate probability, may cause very different erosion outcomes. This paper explores this ambiguity problem in the context of copula based multivariate return periods and using a case study at Durban on the east coast of South Africa. Simulations were used to correlate multivariate return periods of historical events to return periods of estimated storm induced erosion volumes. In addition, the relationship of the most-likely design event (Salvadori et al., 2011 to coastal erosion was investigated. It was found that the multivariate return periods for wave height and duration had the highest correlation to erosion return periods. The most-likely design event was found to be an inadequate design method in its current form. We explore the inclusion of conditions based on the physical realizability of wave events and the use of multivariate linear regression to relate storm parameters to erosion computed from a process based model. Establishing a link between storm statistics and erosion consequences can resolve the ambiguity between multivariate storm return periods and associated erosion return periods.

Anomalous transport in cellular flows: The role of initial conditions and aging

Science.gov (United States)

Pöschke, Patrick; Sokolov, Igor M.; Nepomnyashchy, Alexander A.; Zaks, Michael A.

2016-09-01

We consider the diffusion-advection problem in two simple cellular flow models (often invoked as examples of subdiffusive tracer motion) and concentrate on the intermediate time range, in which the tracer motion indeed may show subdiffusion. We perform extensive numerical simulations of the systems under different initial conditions and show that the pure intermediate-time subdiffusion regime is only evident when the particles start at the border between different cells, i.e., at the separatrix, and is less pronounced or absent for other initial conditions. The motion moreover shows quite peculiar aging properties, which are also mirrored in the behavior of the time-averaged mean squared displacement for single trajectories. This kind of behavior is due to the complex motion of tracers trapped inside the cell and is absent in classical models based on continuous-time random walks with no dynamics in the trapped state.

Non-Gaussian initial conditions in ΛCDM: Newtonian, relativistic, and primordial contributions

International Nuclear Information System (INIS)

Bruni, Marco; Hidalgo, Juan Carlos; Meures, Nikolai; Wands, David

2014-01-01

The goal of the present paper is to set initial conditions for structure formation at nonlinear order, consistent with general relativity, while also allowing for primordial non-Gaussianity. We use the nonlinear continuity and Raychaudhuri equations, which together with the nonlinear energy constraint, determine the evolution of the matter density fluctuation in general relativity. We solve this equations at first and second order in a perturbative expansion, recovering and extending previous results derived in the matter-dominated limit and in the Newtonian regime. We present a second-order solution for the comoving density contrast in a ΛCDM universe, identifying nonlinear contributions coming from the Newtonian growing mode, primordial non-Gaussianity and intrinsic non-Gaussianity, due to the essential nonlinearity of the relativistic constraint equations. We discuss the application of these results to initial conditions in N-body simulations, showing that relativistic corrections mimic a non-zero nonlinear parameter f NL

The initial representation in reasoning towards an interpretation of conditional sentences.

Science.gov (United States)

Schroyens, Walter; Braem, Senne

2011-02-01

All accounts of human reasoning (whether presented at the symbolic or subsymbolic level) have to reckon with the temporal organization of the human processing systems and the ephemeral nature of the representations it uses. We present three new empirical tests for the hypothesis that people commence the interpretational process by constructing a minimal initial representation. In the case of if A then C the initial representation captures the occurrence of the consequent, C, within the context of the antecedent, A. Conditional inference problems are created by a categorical premise that affirms or denies A or C. The initial representation allows an inference when the explicitly represented information matches (e.g., the categorical premise A affirms the antecedent "A") but not when it mismatches (e.g., "not-A" denies A). Experiments 1 and 2 confirmed that people tend to accept the conclusion that "nothing follows" for the denial problems, as indeed they do not have a determinate initial-model conclusion. Experiment 3 demonstrated the other way round that the effect of problem type (affirmation versus denial) is reduced when we impede the possibility of inferring a determinate conclusion on the basis of the initial representation of both the affirmation and the denial problems.

Multi-Scale Initial Conditions For Cosmological Simulations

Energy Technology Data Exchange (ETDEWEB)

Hahn, Oliver; /KIPAC, Menlo Park; Abel, Tom; /KIPAC, Menlo Park /ZAH, Heidelberg /HITS, Heidelberg

2011-11-04

We discuss a new algorithm to generate multi-scale initial conditions with multiple levels of refinements for cosmological 'zoom-in' simulations. The method uses an adaptive convolution of Gaussian white noise with a real-space transfer function kernel together with an adaptive multi-grid Poisson solver to generate displacements and velocities following first- (1LPT) or second-order Lagrangian perturbation theory (2LPT). The new algorithm achieves rms relative errors of the order of 10{sup -4} for displacements and velocities in the refinement region and thus improves in terms of errors by about two orders of magnitude over previous approaches. In addition, errors are localized at coarse-fine boundaries and do not suffer from Fourier-space-induced interference ringing. An optional hybrid multi-grid and Fast Fourier Transform (FFT) based scheme is introduced which has identical Fourier-space behaviour as traditional approaches. Using a suite of re-simulations of a galaxy cluster halo our real-space-based approach is found to reproduce correlation functions, density profiles, key halo properties and subhalo abundances with per cent level accuracy. Finally, we generalize our approach for two-component baryon and dark-matter simulations and demonstrate that the power spectrum evolution is in excellent agreement with linear perturbation theory. For initial baryon density fields, it is suggested to use the local Lagrangian approximation in order to generate a density field for mesh-based codes that is consistent with the Lagrangian perturbation theory instead of the current practice of using the Eulerian linearly scaled densities.

Singularity, initial conditions and quantum tunneling in modern cosmology

International Nuclear Information System (INIS)

Khalatnikov, I M; Kamenshchik, A Yu

1998-01-01

The key problems of modern cosmology, such as the cosmological singularity, initial conditions, and the quantum tunneling hypothesis, are discussed. The relationship between the latest cosmological trends and L D Landau's old ideas is analyzed. Particular attention is given to the oscillatory approach to singularity; quantum tunneling processes determining wave function of the Universe in the presence of a compex scalar field; and the role of quantum corrections in these processes. The classical dynamics of closed models with a real scalar field is investigated from the standpoint of chaotic, fractal, and singularity-avoiding properties. (special issue)

Perturbations in the initial soil moisture conditions: Impacts on hydrologic simulation in a large river basin

Science.gov (United States)

Niroula, Sundar; Halder, Subhadeep; Ghosh, Subimal

2018-06-01

Real time hydrologic forecasting requires near accurate initial condition of soil moisture; however, continuous monitoring of soil moisture is not operational in many regions, such as, in Ganga basin, extended in Nepal, India and Bangladesh. Here, we examine the impacts of perturbation/error in the initial soil moisture conditions on simulated soil moisture and streamflow in Ganga basin and its propagation, during the summer monsoon season (June to September). This provides information regarding the required minimum duration of model simulation for attaining the model stability. We use the Variable Infiltration Capacity model for hydrological simulations after validation. Multiple hydrologic simulations are performed, each of 21 days, initialized on every 5th day of the monsoon season for deficit, surplus and normal monsoon years. Each of these simulations is performed with the initial soil moisture condition obtained from long term runs along with positive and negative perturbations. The time required for the convergence of initial errors is obtained for all the cases. We find a quick convergence for the year with high rainfall as well as for the wet spells within a season. We further find high spatial variations in the time required for convergence; the region with high precipitation such as Lower Ganga basin attains convergence at a faster rate. Furthermore, deeper soil layers need more time for convergence. Our analysis is the first attempt on understanding the sensitivity of hydrological simulations of Ganga basin on initial soil moisture conditions. The results obtained here may be useful in understanding the spin-up requirements for operational hydrologic forecasts.

Non-fragile multivariable PID controller design via system augmentation

Science.gov (United States)

Liu, Jinrong; Lam, James; Shen, Mouquan; Shu, Zhan

2017-07-01

In this paper, the issue of designing non-fragile H∞ multivariable proportional-integral-derivative (PID) controllers with derivative filters is investigated. In order to obtain the controller gains, the original system is associated with an extended system such that the PID controller design can be formulated as a static output-feedback control problem. By taking the system augmentation approach, the conditions with slack matrices for solving the non-fragile H∞ multivariable PID controller gains are established. Based on the results, linear matrix inequality -based iterative algorithms are provided to compute the controller gains. Simulations are conducted to verify the effectiveness of the proposed approaches.

Symbolic computation of analytic approximate solutions for nonlinear differential equations with initial conditions

Science.gov (United States)

Lin, Yezhi; Liu, Yinping; Li, Zhibin

2012-01-01

The Adomian decomposition method (ADM) is one of the most effective methods for constructing analytic approximate solutions of nonlinear differential equations. In this paper, based on the new definition of the Adomian polynomials, and the two-step Adomian decomposition method (TSADM) combined with the Padé technique, a new algorithm is proposed to construct accurate analytic approximations of nonlinear differential equations with initial conditions. Furthermore, a MAPLE package is developed, which is user-friendly and efficient. One only needs to input a system, initial conditions and several necessary parameters, then our package will automatically deliver analytic approximate solutions within a few seconds. Several different types of examples are given to illustrate the validity of the package. Our program provides a helpful and easy-to-use tool in science and engineering to deal with initial value problems. Program summaryProgram title: NAPA Catalogue identifier: AEJZ_v1_0 Program summary URL:http://cpc.cs.qub.ac.uk/summaries/AEJZ_v1_0.html Program obtainable from: CPC Program Library, Queen's University, Belfast, N. Ireland Licensing provisions: Standard CPC licence, http://cpc.cs.qub.ac.uk/licence/licence.html No. of lines in distributed program, including test data, etc.: 4060 No. of bytes in distributed program, including test data, etc.: 113 498 Distribution format: tar.gz Programming language: MAPLE R13 Computer: PC Operating system: Windows XP/7 RAM: 2 Gbytes Classification: 4.3 Nature of problem: Solve nonlinear differential equations with initial conditions. Solution method: Adomian decomposition method and Padé technique. Running time: Seconds at most in routine uses of the program. Special tasks may take up to some minutes.

42 CFR 433.130 - Waiver of conditions of initial operation and approval.

Science.gov (United States)

2010-10-01

... Claims Processing and Information Retrieval Systems § 433.130 Waiver of conditions of initial operation... system will not significantly improve the efficiency of the administration of the State plan. (c) If CMS... waiver, that a system would significantly improve the administration of the State Medicaid program, CMS...

Multivariate statistics exercises and solutions

CERN Document Server

Härdle, Wolfgang Karl

2015-01-01

The authors present tools and concepts of multivariate data analysis by means of exercises and their solutions. The first part is devoted to graphical techniques. The second part deals with multivariate random variables and presents the derivation of estimators and tests for various practical situations. The last part introduces a wide variety of exercises in applied multivariate data analysis. The book demonstrates the application of simple calculus and basic multivariate methods in real life situations. It contains altogether more than 250 solved exercises which can assist a university teacher in setting up a modern multivariate analysis course. All computer-based exercises are available in the R language. All R codes and data sets may be downloaded via the quantlet download center  www.quantlet.org or via the Springer webpage. For interactive display of low-dimensional projections of a multivariate data set, we recommend GGobi.

On the initial condition problem of the time domain PMCHWT surface integral equation

KAUST Repository

Uysal, Ismail Enes; Bagci, Hakan; Ergin, A. Arif; Ulku, H. Arda

2017-01-01

Non-physical, linearly increasing and constant current components are induced in marching on-in-time solution of time domain surface integral equations when initial conditions on time derivatives of (unknown) equivalent currents are not enforced

Advances in statistical monitoring of complex multivariate processes with applications in industrial process control

CERN Document Server

Kruger, Uwe

2012-01-01

The development and application of multivariate statistical techniques in process monitoring has gained substantial interest over the past two decades in academia and industry alike.  Initially developed for monitoring and fault diagnosis in complex systems, such techniques have been refined and applied in various engineering areas, for example mechanical and manufacturing, chemical, electrical and electronic, and power engineering.  The recipe for the tremendous interest in multivariate statistical techniques lies in its simplicity and adaptability for developing monitoring applica

Multivariable controller for a 600 MWe CANDU nuclear power plant

International Nuclear Information System (INIS)

Mensah, S.

1982-11-01

The problems of designing a multivariable regulator for a nuclear power station of the Gentilly-2 type are studied. A reduced model, G2LDM, linearized around steady state operating conditions, is derived from the non-linear model G2SIM. The resulting linear model is described by state-space equations. Good agreement is demonstrated between the transient responses of both models. Properties of G2LDM are assessed by performing controllability and observability tests, cyclicity and rank tests, and eigenanalysis. A comprehensive set of application-orinented algorithms which allow multivariable controller design with closed-loop pole-assignment techniques are implemented in a computer-aided design package via several modules. A general scheme for the implementation of a multivariable controller in G2SIM is designed, and simulation tests show satisfactory performance of the controller [fr

Transients from initial conditions based on Lagrangian perturbation theory in N-body simulations II: the effect of the transverse mode

International Nuclear Information System (INIS)

Tatekawa, Takayuki

2014-01-01

We study the initial conditions for cosmological N-body simulations for precision cosmology. In general, Zel'dovich approximation has been applied for the initial conditions of N-body simulations for a long time. These initial conditions provide incorrect higher-order growth. These error caused by setting up the initial conditions by perturbation theory is called transients. We investigated the impact of transient on non-Gaussianity of density field by performing cosmological N-body simulations with initial conditions based on first-, second-, and third-order Lagrangian perturbation theory in previous paper. In this paper, we evaluates the effect of the transverse mode in the third-order Lagrangian perturbation theory for several statistical quantities such as power spectrum and non-Gaussianty. Then we clarified that the effect of the transverse mode in the third-order Lagrangian perturbation theory is quite small

STAR FORMATION AT VERY LOW METALLICITY. V. THE GREATER IMPORTANCE OF INITIAL CONDITIONS COMPARED TO METALLICITY THRESHOLDS

International Nuclear Information System (INIS)

Jappsen, Anne-Katharina; Low, Mordecai-Mark Mac; Glover, Simon C. O.; Klessen, Ralf S.; Kitsionas, Spyridon

2009-01-01

The formation of the first stars out of metal-free gas appears to result in stars at least an order of magnitude more massive than in the present-day case. We here consider what controls the transition from a primordial to a modern initial mass function. It has been proposed that this occurs when effective metal line cooling occurs at a metallicity threshold of Z/Z sun > 10 -3.5 . We study the influence of low levels of metal enrichment on the cooling and collapse of initially ionized gas in small protogalactic halos using three-dimensional, smoothed particle hydrodynamics simulations with particle splitting. Our initial conditions represent protogalaxies forming within a previously ionized H II region that has not yet had time to cool and recombine. These differ considerably from those used in simulations predicting a metallicity threshold, where the gas was initially cold and only partially ionized. In the centrally condensed potential that we study here, a wide variety of initial conditions for the gas yields a monolithic central collapse. Our models show no fragmentation during collapse to number densities as high as 10 5 cm -3 , for metallicities reaching as high as 10 -1 Z sun , far above the threshold suggested by previous work. Rotation allows for the formation of gravitationally stable gas disks over large fractions of the local Hubble time. Turbulence slows the growth of the central density slightly, but both spherically symmetric and turbulent initial conditions collapse and form a single sink particle. We therefore argue that fragmentation at moderate density depends on the initial conditions for star formation more than on the metal abundances present. The actual initial conditions to be considered still need to be determined in detail by observation and modeling of galaxy formation. Metal abundance may still drive fragmentation at very high densities due to dust cooling, perhaps giving an alternative metallicity threshold.

Multivariate covariance generalized linear models

DEFF Research Database (Denmark)

Bonat, W. H.; Jørgensen, Bent

2016-01-01

are fitted by using an efficient Newton scoring algorithm based on quasi-likelihood and Pearson estimating functions, using only second-moment assumptions. This provides a unified approach to a wide variety of types of response variables and covariance structures, including multivariate extensions......We propose a general framework for non-normal multivariate data analysis called multivariate covariance generalized linear models, designed to handle multivariate response variables, along with a wide range of temporal and spatial correlation structures defined in terms of a covariance link...... function combined with a matrix linear predictor involving known matrices. The method is motivated by three data examples that are not easily handled by existing methods. The first example concerns multivariate count data, the second involves response variables of mixed types, combined with repeated...

Flow behaviour in a CANDU horizontal fuel channel from stagnant subcooled initial conditions

International Nuclear Information System (INIS)

Caplan, M.Z.; Gulshani, P.; Holmes, R.W.; Wright, A.C.D.

1984-01-01

The flow behaviour in a CANDU primary system with horizontal fuel channels is described following a small inlet header break. With the primary pumps running, emergency coolant injection is in the forward direction so that the channel outlet feeders remain warmer than the inlet thereby promoting forward natural circulation. However, the break force opposes the forward driving force. Should the primary pumps run down after the circuit has refilled, there is a break size for which the natural circulation force is balanced by the break force and channels could, theoretically, stagnate. Result of visualization and of full-size channel tests on channel flow behaviour from an initially stagnant channel condition are discussed. After a channel stagnation, the decay power heats the coolant to saturation. Steam is then formed and the coolant stratifies. The steam expands into the subcooled water in the end fitting in a chugging type of flow regime due to steam condensation. After the end fitting reaches the saturation temperature, steam is able to penetrate into the vertical feeder thereby initiating a large buoyancy induced flow which refills the channel. The duration of stagnation is shown to be sensitive to small asymmetries in the initial conditions. A small initial flow can significantly shorten the occurrence and/or duration of boiling as has been confirmed by reactor experience. (author)

Model Forecast Skill and Sensitivity to Initial Conditions in the Seasonal Sea Ice Outlook

Science.gov (United States)

Blanchard-Wrigglesworth, E.; Cullather, R. I.; Wang, W.; Zhang, J.; Bitz, C. M.

2015-01-01

We explore the skill of predictions of September Arctic sea ice extent from dynamical models participating in the Sea Ice Outlook (SIO). Forecasts submitted in August, at roughly 2 month lead times, are skillful. However, skill is lower in forecasts submitted to SIO, which began in 2008, than in hindcasts (retrospective forecasts) of the last few decades. The multimodel mean SIO predictions offer slightly higher skill than the single-model SIO predictions, but neither beats a damped persistence forecast at longer than 2 month lead times. The models are largely unsuccessful at predicting each other, indicating a large difference in model physics and/or initial conditions. Motivated by this, we perform an initial condition sensitivity experiment with four SIO models, applying a fixed -1 m perturbation to the initial sea ice thickness. The significant range of the response among the models suggests that different model physics make a significant contribution to forecast uncertainty.

New multivariable capabilities of the INCA program

Science.gov (United States)

Bauer, Frank H.; Downing, John P.; Thorpe, Christopher J.

1989-01-01

The INteractive Controls Analysis (INCA) program was developed at NASA's Goddard Space Flight Center to provide a user friendly, efficient environment for the design and analysis of control systems, specifically spacecraft control systems. Since its inception, INCA has found extensive use in the design, development, and analysis of control systems for spacecraft, instruments, robotics, and pointing systems. The (INCA) program was initially developed as a comprehensive classical design analysis tool for small and large order control systems. The latest version of INCA, expected to be released in February of 1990, was expanded to include the capability to perform multivariable controls analysis and design.

Multivariate Statistical Process Control Charts: An Overview

OpenAIRE

Bersimis, Sotiris; Psarakis, Stelios; Panaretos, John

2006-01-01

In this paper we discuss the basic procedures for the implementation of multivariate statistical process control via control charting. Furthermore, we review multivariate extensions for all kinds of univariate control charts, such as multivariate Shewhart-type control charts, multivariate CUSUM control charts and multivariate EWMA control charts. In addition, we review unique procedures for the construction of multivariate control charts, based on multivariate statistical techniques such as p...

Estimating the decomposition of predictive information in multivariate systems

Science.gov (United States)

Faes, Luca; Kugiumtzis, Dimitris; Nollo, Giandomenico; Jurysta, Fabrice; Marinazzo, Daniele

2015-03-01

In the study of complex systems from observed multivariate time series, insight into the evolution of one system may be under investigation, which can be explained by the information storage of the system and the information transfer from other interacting systems. We present a framework for the model-free estimation of information storage and information transfer computed as the terms composing the predictive information about the target of a multivariate dynamical process. The approach tackles the curse of dimensionality employing a nonuniform embedding scheme that selects progressively, among the past components of the multivariate process, only those that contribute most, in terms of conditional mutual information, to the present target process. Moreover, it computes all information-theoretic quantities using a nearest-neighbor technique designed to compensate the bias due to the different dimensionality of individual entropy terms. The resulting estimators of prediction entropy, storage entropy, transfer entropy, and partial transfer entropy are tested on simulations of coupled linear stochastic and nonlinear deterministic dynamic processes, demonstrating the superiority of the proposed approach over the traditional estimators based on uniform embedding. The framework is then applied to multivariate physiologic time series, resulting in physiologically well-interpretable information decompositions of cardiovascular and cardiorespiratory interactions during head-up tilt and of joint brain-heart dynamics during sleep.

Methods of Multivariate Analysis

CERN Document Server

Rencher, Alvin C

2012-01-01

Praise for the Second Edition "This book is a systematic, well-written, well-organized text on multivariate analysis packed with intuition and insight . . . There is much practical wisdom in this book that is hard to find elsewhere."-IIE Transactions Filled with new and timely content, Methods of Multivariate Analysis, Third Edition provides examples and exercises based on more than sixty real data sets from a wide variety of scientific fields. It takes a "methods" approach to the subject, placing an emphasis on how students and practitioners can employ multivariate analysis in real-life sit

Continuous multivariate exponential extension

International Nuclear Information System (INIS)

Block, H.W.

1975-01-01

The Freund-Weinman multivariate exponential extension is generalized to the case of nonidentically distributed marginal distributions. A fatal shock model is given for the resulting distribution. Results in the bivariate case and the concept of constant multivariate hazard rate lead to a continuous distribution related to the multivariate exponential distribution (MVE) of Marshall and Olkin. This distribution is shown to be a special case of the extended Freund-Weinman distribution. A generalization of the bivariate model of Proschan and Sullo leads to a distribution which contains both the extended Freund-Weinman distribution and the MVE

Kinetics of gamma quanta initiated difluoroethane chlorization in inert solvent in dynamic conditions

International Nuclear Information System (INIS)

Begishev, I.R.; Poluehktov, V.A.

1979-01-01

Studied is the kinetics of asymmetric difluoroethane chlorination under dynamic conditions where difluoroethane and chlorine are passed through liquid tetrachloromethane layer. It is shown that initiating chlorination of asymmetric difluoroethane by gamma quanta in the dose and temperature ranges of 2-50 rad/s and -30-100 deg C respectively brings to the significant increase of the reaction rate and unusually high trend for halogenated hydrocarbons, i.e. practically 1, 1, 1 - difluorochloroethane with quantitative yield is formed. In this case radiation chemical yield G=10 4 -10 5 is achieved. It is shown that the chlorination process under dynamic conditions, complicated by the transport of initial reagents from a gas phase to a liquid one and reaction products from a liquid phase to a gas one, is described satisfactorily by the model of ideal substitution reactor

Avoiding the blue spectrum and the fine-tuning of initial conditions in hybrid inflation

International Nuclear Information System (INIS)

Clesse, Sebastien; Rocher, Jonathan

2009-01-01

Hybrid inflation faces two well-known problems: the blue spectrum of the nonsupersymmetric version of the model and the fine-tuning of the initial conditions of the fields leading to sufficient inflation to account for the standard cosmological problems. They are investigated by studying the exact two-fields dynamics instead of assuming slow-roll. When the field values are restricted to be less than the reduced Planck mass, a non-negligible part of the initial condition space (around 15% depending on potential parameters) leads to successful inflation. Most of it is located outside the usual inflationary valley and organized in continuous patterns instead of being isolated as previously found. Their existence is explained and their properties are studied. This shows that no excessive fine-tuning is required for successful hybrid inflation. Moreover, by extending the initial condition space to Planckian-like or super-Planckian values, inflation becomes generically sufficiently long and can produce a red-tilted scalar power spectrum due to slow-roll violations. The robustness of these properties is confirmed by conducting our analysis on three other models of hybrid-type inflation in various framework: 'smooth' and 'shifted' inflation in SUSY and SUGRA, and 'radion assisted' gauge inflation. A high percentage of successful inflation for smooth hybrid inflation (up to 80%) is observed.

Analysis of grain growth process in melt spun Fe-B alloys under the initial saturated grain boundary segregation condition

International Nuclear Information System (INIS)

Chen, Z.; Liu, F.; Yang, X.Q.; Fan, Y.; Shen, C.J.

2012-01-01

Highlights: → We compared pure kinetic, pure thermodynamic and extended thermo-kinetic models. → An initial saturated GB segregation condition of nanoscale Fe-B alloys was determined. → The controlled-mechanism was proposed using two characteristic times (t 1 and t 2 ). - Abstract: A grain growth process in the melt spun low-solid-solubility Fe-B alloys was analyzed under the initial saturated grain boundary (GB) segregation condition. Applying melt spinning technique, single-phase supersaturated nanograins were prepared. Grain growth behavior of the single-phase supersaturated nanograins was investigated by performing isothermal annealing at 700 deg. C. Combined with the effect of GB segregation on the initial GB excess amount, the thermo-kinetic model [Chen et al., Acta Mater. 57 (2009) 1466] was extended to describe the initial GB segregation condition of nanoscale Fe-B alloys. In comparison of pure kinetic model, pure thermodynamic model and the extended thermo-kinetic model, an initial saturated GB segregation condition was determined. The controlled-mechanism of grain growth under initial saturated GB segregation condition was proposed using two characteristic annealing times (t 1 and t 2 ), which included a mainly kinetic-controlled process (t ≤ t 1 ), a transition from kinetic-mechanism to thermodynamic-mechanism (t 1 2 ) and pure thermodynamic-controlled process (t ≥ t 2 ).

Surface-Initiated Graft Atom Transfer Radical Polymerization of Methyl Methacrylate from Chitin Nanofiber Macroinitiator under Dispersion Conditions

Directory of Open Access Journals (Sweden)

Ryo Endo

2015-08-01

Full Text Available Surface-initiated graft atom transfer radical polymerization (ATRP of methyl methacrylate (MMA from self-assembled chitin nanofibers (CNFs was performed under dispersion conditions. Self-assembled CNFs were initially prepared by regeneration from a chitin ion gel with 1-allyl-3-methylimidazolium bromide using methanol; the product was then converted into the chitin nanofiber macroinitiator by reaction with α-bromoisobutyryl bromide in a dispersion containing N,N-dimethylformamide. Surface-initiated graft ATRP of MMA from the initiating sites on the CNFs was subsequently carried out under dispersion conditions, followed by filtration to obtain the CNF-graft-polyMMA film. Analysis of the product confirmed the occurrence of the graft ATRP on the surface of the CNFs.

A new Method for the Estimation of Initial Condition Uncertainty Structures in Mesoscale Models

Science.gov (United States)

Keller, J. D.; Bach, L.; Hense, A.

2012-12-01

The estimation of fast growing error modes of a system is a key interest of ensemble data assimilation when assessing uncertainty in initial conditions. Over the last two decades three methods (and variations of these methods) have evolved for global numerical weather prediction models: ensemble Kalman filter, singular vectors and breeding of growing modes (or now ensemble transform). While the former incorporates a priori model error information and observation error estimates to determine ensemble initial conditions, the latter two techniques directly address the error structures associated with Lyapunov vectors. However, in global models these structures are mainly associated with transient global wave patterns. When assessing initial condition uncertainty in mesoscale limited area models, several problems regarding the aforementioned techniques arise: (a) additional sources of uncertainty on the smaller scales contribute to the error and (b) error structures from the global scale may quickly move through the model domain (depending on the size of the domain). To address the latter problem, perturbation structures from global models are often included in the mesoscale predictions as perturbed boundary conditions. However, the initial perturbations (when used) are often generated with a variant of an ensemble Kalman filter which does not necessarily focus on the large scale error patterns. In the framework of the European regional reanalysis project of the Hans-Ertel-Center for Weather Research we use a mesoscale model with an implemented nudging data assimilation scheme which does not support ensemble data assimilation at all. In preparation of an ensemble-based regional reanalysis and for the estimation of three-dimensional atmospheric covariance structures, we implemented a new method for the assessment of fast growing error modes for mesoscale limited area models. The so-called self-breeding is development based on the breeding of growing modes technique

EARLY DYNAMICAL EVOLUTION OF THE SOLAR SYSTEM: PINNING DOWN THE INITIAL CONDITIONS OF THE NICE MODEL

International Nuclear Information System (INIS)

Batygin, Konstantin; Brown, Michael E.

2010-01-01

In the recent years, the 'Nice' model of solar system formation has attained an unprecedented level of success in reproducing much of the observed orbital architecture of the solar system by evolving the planets to their current locations from a more compact configuration. Within the context of this model, the formation of the classical Kuiper Belt requires a phase during which the ice giants have a high eccentricity. An outstanding question of this model is the initial configuration from which the solar system started out. Recent work has shown that multi-resonant initial conditions can serve as good candidates, as they naturally prevent vigorous type-II migration. In this paper, we use analytical arguments, as well as self-consistent numerical N-body simulations to identify fully resonant initial conditions, whose dynamical evolution is characterized by an eccentric phase of the ice giants, as well as planetary scattering. We find a total of eight such initial conditions. Four of these primordial states are compatible with the canonical 'Nice' model, while the others imply slightly different evolutions. The results presented here should prove useful in further development of a comprehensive model for solar system formation.

Cosmological Simulations with Scale-Free Initial Conditions. I. Adiabatic Hydrodynamics

International Nuclear Information System (INIS)

Owen, J.M.; Weinberg, D.H.; Evrard, A.E.; Hernquist, L.; Katz, N.

1998-01-01

We analyze hierarchical structure formation based on scale-free initial conditions in an Einstein endash de Sitter universe, including a baryonic component with Ω bary = 0.05. We present three independent, smoothed particle hydrodynamics (SPH) simulations, performed at two resolutions (32 3 and 64 3 dark matter and baryonic particles) and with two different SPH codes (TreeSPH and P3MSPH). Each simulation is based on identical initial conditions, which consist of Gaussian-distributed initial density fluctuations that have a power spectrum P(k) ∝ k -1 . The baryonic material is modeled as an ideal gas subject only to shock heating and adiabatic heating and cooling; radiative cooling and photoionization heating are not included. The evolution is expected to be self-similar in time, and under certain restrictions we identify the expected scalings for many properties of the distribution of collapsed objects in all three realizations. The distributions of dark matter masses, baryon masses, and mass- and emission-weighted temperatures scale quite reliably. However, the density estimates in the central regions of these structures are determined by the degree of numerical resolution. As a result, mean gas densities and Bremsstrahlung luminosities obey the expected scalings only when calculated within a limited dynamic range in density contrast. The temperatures and luminosities of the groups show tight correlations with the baryon masses, which we find can be well represented by power laws. The Press-Schechter (PS) approximation predicts the distribution of group dark matter and baryon masses fairly well, though it tends to overestimate the baryon masses. Combining the PS mass distribution with the measured relations for T(M) and L(M) predicts the temperature and luminosity distributions fairly accurately, though there are some discrepancies at high temperatures/luminosities. In general the three simulations agree well for the properties of resolved groups, where a group

Computer code calculations of the TMI-2 accident: initial and boundary conditions

International Nuclear Information System (INIS)

Behling, S.R.

1985-05-01

Initial and boundary conditions during the Three Mile Island Unit 2 (TMI-2) accident are described and detailed. A brief description of the TMI-2 plant configuration is given. Important contributions to the progression of the accident in the reactor coolant system are discussed. Sufficient information is provided to allow calculation of the TMI-2 accident with computer codes

Estimating correlation between multivariate longitudinal data in the presence of heterogeneity.

Science.gov (United States)

Gao, Feng; Philip Miller, J; Xiong, Chengjie; Luo, Jingqin; Beiser, Julia A; Chen, Ling; Gordon, Mae O

2017-08-17

Estimating correlation coefficients among outcomes is one of the most important analytical tasks in epidemiological and clinical research. Availability of multivariate longitudinal data presents a unique opportunity to assess joint evolution of outcomes over time. Bivariate linear mixed model (BLMM) provides a versatile tool with regard to assessing correlation. However, BLMMs often assume that all individuals are drawn from a single homogenous population where the individual trajectories are distributed smoothly around population average. Using longitudinal mean deviation (MD) and visual acuity (VA) from the Ocular Hypertension Treatment Study (OHTS), we demonstrated strategies to better understand the correlation between multivariate longitudinal data in the presence of potential heterogeneity. Conditional correlation (i.e., marginal correlation given random effects) was calculated to describe how the association between longitudinal outcomes evolved over time within specific subpopulation. The impact of heterogeneity on correlation was also assessed by simulated data. There was a significant positive correlation in both random intercepts (ρ = 0.278, 95% CI: 0.121-0.420) and random slopes (ρ = 0.579, 95% CI: 0.349-0.810) between longitudinal MD and VA, and the strength of correlation constantly increased over time. However, conditional correlation and simulation studies revealed that the correlation was induced primarily by participants with rapid deteriorating MD who only accounted for a small fraction of total samples. Conditional correlation given random effects provides a robust estimate to describe the correlation between multivariate longitudinal data in the presence of unobserved heterogeneity (NCT00000125).

A review of multivariate analyses in imaging genetics

Directory of Open Access Journals (Sweden)

Jingyu eLiu

2014-03-01

Full Text Available Recent advances in neuroimaging technology and molecular genetics provide the unique opportunity to investigate genetic influence on the variation of brain attributes. Since the year 2000, when the initial publication on brain imaging and genetics was released, imaging genetics has been a rapidly growing research approach with increasing publications every year. Several reviews have been offered to the research community focusing on various study designs. In addition to study design, analytic tools and their proper implementation are also critical to the success of a study. In this review, we survey recent publications using data from neuroimaging and genetics, focusing on methods capturing multivariate effects accommodating the large number of variables from both imaging data and genetic data. We group the analyses of genetic or genomic data into either a prior driven or data driven approach, including gene-set enrichment analysis, multifactor dimensionality reduction, principal component analysis, independent component analysis (ICA, and clustering. For the analyses of imaging data, ICA and extensions of ICA are the most widely used multivariate methods. Given detailed reviews of multivariate analyses of imaging data available elsewhere, we provide a brief summary here that includes a recently proposed method known as independent vector analysis. Finally, we review methods focused on bridging the imaging and genetic data by establishing multivariate and multiple genotype-phenotype associations, including sparse partial least squares, sparse canonical correlation analysis, sparse reduced rank regression and parallel ICA. These methods are designed to extract latent variables from both genetic and imaging data, which become new genotypes and phenotypes, and the links between the new genotype-phenotype pairs are maximized using different cost functions. The relationship between these methods along with their assumptions, advantages, and

On Perturbative Cubic Nonlinear Schrodinger Equations under Complex Nonhomogeneities and Complex Initial Conditions

Directory of Open Access Journals (Sweden)

Magdy A. El-Tawil

2009-01-01

Full Text Available A perturbing nonlinear Schrodinger equation is studied under general complex nonhomogeneities and complex initial conditions for zero boundary conditions. The perturbation method together with the eigenfunction expansion and variational parameters methods are used to introduce an approximate solution for the perturbative nonlinear case for which a power series solution is proved to exist. Using Mathematica, the symbolic solution algorithm is tested through computing the possible approximations under truncation procedures. The method of solution is illustrated through case studies and figures.

Bayesian Modeling of Air Pollution Extremes Using Nested Multivariate Max-Stable Processes

KAUST Repository

Vettori, Sabrina; Huser, Raphaë l; Genton, Marc G.

2018-01-01

Capturing the potentially strong dependence among the peak concentrations of multiple air pollutants across a spatial region is crucial for assessing the related public health risks. In order to investigate the multivariate spatial dependence properties of air pollution extremes, we introduce a new class of multivariate max-stable processes. Our proposed model admits a hierarchical tree-based formulation, in which the data are conditionally independent given some latent nested $\\alpha$-stable random factors. The hierarchical structure facilitates Bayesian inference and offers a convenient and interpretable characterization. We fit this nested multivariate max-stable model to the maxima of air pollution concentrations and temperatures recorded at a number of sites in the Los Angeles area, showing that the proposed model succeeds in capturing their complex tail dependence structure.

Bayesian Modeling of Air Pollution Extremes Using Nested Multivariate Max-Stable Processes

KAUST Repository

Vettori, Sabrina

2018-03-18

Capturing the potentially strong dependence among the peak concentrations of multiple air pollutants across a spatial region is crucial for assessing the related public health risks. In order to investigate the multivariate spatial dependence properties of air pollution extremes, we introduce a new class of multivariate max-stable processes. Our proposed model admits a hierarchical tree-based formulation, in which the data are conditionally independent given some latent nested $\\\\alpha$-stable random factors. The hierarchical structure facilitates Bayesian inference and offers a convenient and interpretable characterization. We fit this nested multivariate max-stable model to the maxima of air pollution concentrations and temperatures recorded at a number of sites in the Los Angeles area, showing that the proposed model succeeds in capturing their complex tail dependence structure.

Centrifuge model tests of rainfall-induced slope failures for the investigation of the initiation conditions

Science.gov (United States)

Matziaris, Vasileios; Marshall, Alec; Yu, Hai-Sui

2015-04-01

Rainfall-induced landslides are very common natural disasters which cause damage to properties and infrastructure and may result in the loss of human lives. These phenomena often take place in unsaturated soil slopes and are triggered by the saturation of the soil profile, due to rain infiltration, which leads to a loss of shear strength. The aim of this study is to determine rainfall thresholds for the initiation of landslides under different initial conditions. Model tests of rainfall-induced landslides are conducted in the Nottingham Centre for Geomechanics 50g-T geotechnical centrifuge. Initially unsaturated plane-strain slope models made with fine silica sand are prepared at varying densities at 1g and accommodated within a climatic chamber which provides controlled environmental conditions. During the centrifuge flight at 60g, rainfall events of varying intensity and duration are applied to the slope models causing the initiation of slope failure. The impact of soil state properties and rainfall characteristics on the landslide initiation process are discussed. The variation of pore water pressures within the slope before, during and after simulated rainfall events is recorded using miniature pore pressure transducers buried in the soil model. Slope deformation is determined by using a high-speed camera and digital image analysis techniques.

An overview of multivariate gamma distributions as seen from a (multivariate) matrix exponential perspective

DEFF Research Database (Denmark)

Bladt, Mogens; Nielsen, Bo Friis

2012-01-01

Laplace transform. In a longer perspective stochastic and statistical analysis for MVME will in particular apply to any of the previously defined distributions. Multivariate gamma distributions have been used in a variety of fields like hydrology, [11], [10], [6], space (wind modeling) [9] reliability [3......Numerous definitions of multivariate exponential and gamma distributions can be retrieved from the literature [4]. These distribtuions belong to the class of Multivariate Matrix-- Exponetial Distributions (MVME) whenever their joint Laplace transform is a rational function. The majority...... of these distributions further belongs to an important subclass of MVME distributions [5, 1] where the multivariate random vector can be interpreted as a number of simultaneously collected rewards during sojourns in a the states of a Markov chain with one absorbing state, the rest of the states being transient. We...

Multivariate Birkhoff interpolation

CERN Document Server

Lorentz, Rudolph A

1992-01-01

The subject of this book is Lagrange, Hermite and Birkhoff (lacunary Hermite) interpolation by multivariate algebraic polynomials. It unifies and extends a new algorithmic approach to this subject which was introduced and developed by G.G. Lorentz and the author. One particularly interesting feature of this algorithmic approach is that it obviates the necessity of finding a formula for the Vandermonde determinant of a multivariate interpolation in order to determine its regularity (which formulas are practically unknown anyways) by determining the regularity through simple geometric manipulations in the Euclidean space. Although interpolation is a classical problem, it is surprising how little is known about its basic properties in the multivariate case. The book therefore starts by exploring its fundamental properties and its limitations. The main part of the book is devoted to a complete and detailed elaboration of the new technique. A chapter with an extensive selection of finite elements follows as well a...

Discretizing LTI Descriptor (Regular Differential Input Systems with Consistent Initial Conditions

Directory of Open Access Journals (Sweden)

Athanasios D. Karageorgos

2010-01-01

Full Text Available A technique for discretizing efficiently the solution of a Linear descriptor (regular differential input system with consistent initial conditions, and Time-Invariant coefficients (LTI is introduced and fully discussed. Additionally, an upper bound for the error ‖x¯(kT−x¯k‖ that derives from the procedure of discretization is also provided. Practically speaking, we are interested in such kind of systems, since they are inherent in many physical, economical and engineering phenomena.

Multivariate multiscale entropy of financial markets

Science.gov (United States)

Lu, Yunfan; Wang, Jun

2017-11-01

In current process of quantifying the dynamical properties of the complex phenomena in financial market system, the multivariate financial time series are widely concerned. In this work, considering the shortcomings and limitations of univariate multiscale entropy in analyzing the multivariate time series, the multivariate multiscale sample entropy (MMSE), which can evaluate the complexity in multiple data channels over different timescales, is applied to quantify the complexity of financial markets. Its effectiveness and advantages have been detected with numerical simulations with two well-known synthetic noise signals. For the first time, the complexity of four generated trivariate return series for each stock trading hour in China stock markets is quantified thanks to the interdisciplinary application of this method. We find that the complexity of trivariate return series in each hour show a significant decreasing trend with the stock trading time progressing. Further, the shuffled multivariate return series and the absolute multivariate return series are also analyzed. As another new attempt, quantifying the complexity of global stock markets (Asia, Europe and America) is carried out by analyzing the multivariate returns from them. Finally we utilize the multivariate multiscale entropy to assess the relative complexity of normalized multivariate return volatility series with different degrees.

The Use of Graphs in Specific Situations of the Initial Conditions of Linear Differential Equations

Science.gov (United States)

Buendía, Gabriela; Cordero, Francisco

2013-01-01

In this article, we present a discussion on the role of graphs and its significance in the relation between the number of initial conditions and the order of a linear differential equation, which is known as the initial value problem. We propose to make a functional framework for the use of graphs that intends to broaden the explanations of the…

Multivariate analysis of prognostic factors for idiopathic sudden sensorineural hearing loss in children.

Science.gov (United States)

Chung, Jae Ho; Cho, Seok Hyun; Jeong, Jin Hyeok; Park, Chul Won; Lee, Seung Hwan

2015-09-01

To evaluate clinical characteristics and possible associated factors of idiopathic sudden sensorineural hearing loss (ISSNHL) in children using univariate and multivariate analyses. A retrospective case series with comparisons. From January 2007 to December 2013, medical records of 37 pediatric ISSNHL patients were reviewed to assess hearing recovery rate and examine factors associated with prognosis (gender; side of hearing loss; opposite side hearing loss; treatment onset; presence of vertigo, tinnitus, and ear fullness; initial hearing threshold), using univariate and multivariate analysis, and compare them with 276 adult ISSNHL patients. Pediatric patients comprised only 6.6% of pediatric/adult cases of ISSNHL, and those below 10 years old were only 0.7%. The overall recovery rates (complete and partial) of the pediatric and adult patients were 57.4% and 47.2%, respectively. The complete recovery rate of the pediatric group (46.6%) was higher than that of the adult group (30.8%, P = .040). According to multivariate analysis, absence of tinnitus, later onset of treatment, and higher hearing threshold at initial presentation were associated with a poor prognosis in pediatric ISSNHL. The recovery rate of ISSNHL in pediatric patients is higher than in adults, and the presence of tinnitus and earlier treatment onset is associated with favorable outcomes. 4. © 2015 The American Laryngological, Rhinological and Otological Society, Inc.

Multivariate geomorphic analysis of forest streams: Implications for assessment of land use impacts on channel condition

Science.gov (United States)

Richard. D. Wood-Smith; John M. Buffington

1996-01-01

Multivariate statistical analyses of geomorphic variables from 23 forest stream reaches in southeast Alaska result in successful discrimination between pristine streams and those disturbed by land management, specifically timber harvesting and associated road building. Results of discriminant function analysis indicate that a three-variable model discriminates 10...

On a nonlinear integrodifferential evolution inclusion with nonlocal initial conditions in Banach spaces

Directory of Open Access Journals (Sweden)

Zuomao Yan

2012-01-01

Full Text Available In this paper, we discuss the existence results for a class of nnlinear integrodifferential evolution inclusions with nonlocal initial conditions in Banach spaces. Our results are based on a fixed point theorem for condensing maps due to Martelli and the resolvent operators combined with approximation techniques.

Initial growth of Bauhinia variegata trees under different colored shade nets and light conditions

Directory of Open Access Journals (Sweden)

Renata Bachin Mazzini-Guedes

2014-12-01

Full Text Available Bauhinia variegata and B. variegata var. candida, commonly known as orchid trees, are small sized trees widely used for urban forestry and landscaping. Adult plants grow under full sun; in Brazil, however, seedlings are generally cultivated in commercial nurseries under natural half-shading. The objective of this study was to evaluate the influence of different colored shade nets and light conditions on the initial growth of B. variegata and B. variegata var. candida. The influence of six light conditions (red net with 50% shading; blue net with 50% shading; black net with 70% shading; black net with 50% shading; black net with 30% shading; and full sun on the initial growth of B. variegata and B. variegata var. candida were evaluated along 160 days, and growth relationships were calculated. Seedlings showed more efficiency on the use of photoassimilated compounds when grown under full sun. Such condition is the most appropriate for seedling production of B. variegata and B. variegata var. candida, contradicting what has been performed in practice.

Multivariate process monitoring of EAFs

Energy Technology Data Exchange (ETDEWEB)

Sandberg, E.; Lennox, B.; Marjanovic, O.; Smith, K.

2005-06-01

Improved knowledge of the effect of scrap grades on the electric steelmaking process and optimised scrap loading practices increase the potential for process automation. As part of an ongoing programme, process data from four Scandinavian EAFs have been analysed, using the multivariate process monitoring approach, to develop predictive models for end point conditions such as chemical composition, yield and energy consumption. The models developed generally predict final Cr, Ni and Mo and tramp element contents well, but electrical energy consumption, yield and content of oxidisable and impurity elements (C, Si, Mn, P, S) are at present more difficult to predict. Potential scrap management applications of the prediction models are also presented. (author)

Multivariate meta-analysis: Potential and promise

Science.gov (United States)

Jackson, Dan; Riley, Richard; White, Ian R

2011-01-01

The multivariate random effects model is a generalization of the standard univariate model. Multivariate meta-analysis is becoming more commonly used and the techniques and related computer software, although continually under development, are now in place. In order to raise awareness of the multivariate methods, and discuss their advantages and disadvantages, we organized a one day ‘Multivariate meta-analysis’ event at the Royal Statistical Society. In addition to disseminating the most recent developments, we also received an abundance of comments, concerns, insights, critiques and encouragement. This article provides a balanced account of the day's discourse. By giving others the opportunity to respond to our assessment, we hope to ensure that the various view points and opinions are aired before multivariate meta-analysis simply becomes another widely used de facto method without any proper consideration of it by the medical statistics community. We describe the areas of application that multivariate meta-analysis has found, the methods available, the difficulties typically encountered and the arguments for and against the multivariate methods, using four representative but contrasting examples. We conclude that the multivariate methods can be useful, and in particular can provide estimates with better statistical properties, but also that these benefits come at the price of making more assumptions which do not result in better inference in every case. Although there is evidence that multivariate meta-analysis has considerable potential, it must be even more carefully applied than its univariate counterpart in practice. Copyright © 2011 John Wiley & Sons, Ltd. PMID:21268052

TRANSFORMATION OF ECONOMY IN THE CONDITIONS OF FORMING THE NATIONAL TECHNOLOGICAL INITIATIVE

Directory of Open Access Journals (Sweden)

Elena Sibirskaya

2017-12-01

Full Text Available The relevance of the study is conditioned by the need for a clear idea about the upcoming transformation of the Russian economy for implementing the national technology initiative (NTI. Today, Russia is facing a “challenge of development”, which determines the necessity of transition to breakthrough scientific and technological development as a major strategic objective for the future, which definitely determines further socio-economic development. In his address to the Federal Assembly of December 4, 2014, Russian President Vladimir Putin outlined the National technology initiative to be one of the priorities of state policy. “On the basis of long-term forecasting it is necessary to understand what challenges Russia will face in 10-15 years, what advanced solutions will be required in order to ensure national security, high quality of life, development of new technological order” (from the message to the Federal Assembly [6]. The response to this challenge is the National technology initiative, aimed at developing a robust creative and business environment that allows to convert technological breakthroughs to new markets into an element of the system of continuous reproduction of income, human and technological capital. Information base of the research includes legal documents of the Government of the Russian Federation, the official publications under the project office of STI, reports of Russian Academy of Sciences, developments of the Expert Council under the government of the Russian Federation and Agency for strategic initiatives, Federal Agency of scientific organizations, research groups, individual scientists and specialists, Internet resources and the authors’ own developments. The first systematic and methodologically coherent statement of the ideology of economy transformation in the conditions of forming national technological initiative is presented in the report “Framework of the National technology initiative” of

Evaluation of herbicides photodegradation by photo-Fenton process using multivariate analysis

Energy Technology Data Exchange (ETDEWEB)

Paterlini, W.C.; Nogueira, R.F.P. [Inst. of Chemistry, Sao Paulo State Univ., R. Prof. Francisco Degni s/n, Araraquara, SP (Brazil)

2003-07-01

The photodegradation of herbicides in aqueous medium by photo-Fenton process using ferrioxalate complex (FeOx) as a source of Fe{sup 2+} was evaluated under blacklight irradiation. The commercial products of the herbicides tebuthiuron, 2,4-D and diuron were used. Multivariate analysis was used to evaluate the role of two variables in the photodegradation process, FeOx and hydrogen peroxide concentrations, and to define the concentration ranges that result in the most efficient photodegradation of the herbicides. The photodegradation of the herbicides was followed by monitoring the decrease of the original compounds concentration by HPLC, by the determination of remaining total organic carbon content (TOC), and by the chloride ion release. Under optimised conditions, 20 minutes irradiation was enough to remove 92.7% of TOC for 2,4 D and 89.5% for diuron. Complete dechlorination of these compounds was achieved after 10 minutes of irradiation. It was observed that the initial concentration of these compounds and tebuthiuron was reduced to less than 15% after only 1 minute of irradiation. (orig.)

Multiple unit root tests under uncertainty over the initial condition : some powerful modifications

NARCIS (Netherlands)

Hanck, C.

We modify the union-of-rejection unit root test of Harvey et al. "Unit Root Testing in Practice: Dealing with Uncertainty over the Trend and Initial Condition" (Harvey, Econom Theory 25:587-636, 2009). This test rejects if either of two different unit root tests rejects but controls the inherent

Neural plasticity and its initiating conditions in tinnitus.

Science.gov (United States)

Roberts, L E

2018-03-01

Deafferentation caused by cochlear pathology (which can be hidden from the audiogram) activates forms of neural plasticity in auditory pathways, generating tinnitus and its associated conditions including hyperacusis. This article discusses tinnitus mechanisms and suggests how these mechanisms may relate to those involved in normal auditory information processing. Research findings from animal models of tinnitus and from electromagnetic imaging of tinnitus patients are reviewed which pertain to the role of deafferentation and neural plasticity in tinnitus and hyperacusis. Auditory neurons compensate for deafferentation by increasing their input/output functions (gain) at multiple levels of the auditory system. Forms of homeostatic plasticity are believed to be responsible for this neural change, which increases the spontaneous and driven activity of neurons in central auditory structures in animals expressing behavioral evidence of tinnitus. Another tinnitus correlate, increased neural synchrony among the affected neurons, is forged by spike-timing-dependent neural plasticity in auditory pathways. Slow oscillations generated by bursting thalamic neurons verified in tinnitus animals appear to modulate neural plasticity in the cortex, integrating tinnitus neural activity with information in brain regions supporting memory, emotion, and consciousness which exhibit increased metabolic activity in tinnitus patients. The latter process may be induced by transient auditory events in normal processing but it persists in tinnitus, driven by phantom signals from the auditory pathway. Several tinnitus therapies attempt to suppress tinnitus through plasticity, but repeated sessions will likely be needed to prevent tinnitus activity from returning owing to deafferentation as its initiating condition.

Multivariate refined composite multiscale entropy analysis

International Nuclear Information System (INIS)

Humeau-Heurtier, Anne

2016-01-01

Multiscale entropy (MSE) has become a prevailing method to quantify signals complexity. MSE relies on sample entropy. However, MSE may yield imprecise complexity estimation at large scales, because sample entropy does not give precise estimation of entropy when short signals are processed. A refined composite multiscale entropy (RCMSE) has therefore recently been proposed. Nevertheless, RCMSE is for univariate signals only. The simultaneous analysis of multi-channel (multivariate) data often over-performs studies based on univariate signals. We therefore introduce an extension of RCMSE to multivariate data. Applications of multivariate RCMSE to simulated processes reveal its better performances over the standard multivariate MSE. - Highlights: • Multiscale entropy quantifies data complexity but may be inaccurate at large scale. • A refined composite multiscale entropy (RCMSE) has therefore recently been proposed. • Nevertheless, RCMSE is adapted to univariate time series only. • We herein introduce an extension of RCMSE to multivariate data. • It shows better performances than the standard multivariate multiscale entropy.

Multivariate Generalized Multiscale Entropy Analysis

Directory of Open Access Journals (Sweden)

Anne Humeau-Heurtier

2016-11-01

Full Text Available Multiscale entropy (MSE was introduced in the 2000s to quantify systems’ complexity. MSE relies on (i a coarse-graining procedure to derive a set of time series representing the system dynamics on different time scales; (ii the computation of the sample entropy for each coarse-grained time series. A refined composite MSE (rcMSE—based on the same steps as MSE—also exists. Compared to MSE, rcMSE increases the accuracy of entropy estimation and reduces the probability of inducing undefined entropy for short time series. The multivariate versions of MSE (MMSE and rcMSE (MrcMSE have also been introduced. In the coarse-graining step used in MSE, rcMSE, MMSE, and MrcMSE, the mean value is used to derive representations of the original data at different resolutions. A generalization of MSE was recently published, using the computation of different moments in the coarse-graining procedure. However, so far, this generalization only exists for univariate signals. We therefore herein propose an extension of this generalized MSE to multivariate data. The multivariate generalized algorithms of MMSE and MrcMSE presented herein (MGMSE and MGrcMSE, respectively are first analyzed through the processing of synthetic signals. We reveal that MGrcMSE shows better performance than MGMSE for short multivariate data. We then study the performance of MGrcMSE on two sets of short multivariate electroencephalograms (EEG available in the public domain. We report that MGrcMSE may show better performance than MrcMSE in distinguishing different types of multivariate EEG data. MGrcMSE could therefore supplement MMSE or MrcMSE in the processing of multivariate datasets.

Regularized multivariate regression models with skew-t error distributions

KAUST Repository

Chen, Lianfu

2014-06-01

We consider regularization of the parameters in multivariate linear regression models with the errors having a multivariate skew-t distribution. An iterative penalized likelihood procedure is proposed for constructing sparse estimators of both the regression coefficient and inverse scale matrices simultaneously. The sparsity is introduced through penalizing the negative log-likelihood by adding L1-penalties on the entries of the two matrices. Taking advantage of the hierarchical representation of skew-t distributions, and using the expectation conditional maximization (ECM) algorithm, we reduce the problem to penalized normal likelihood and develop a procedure to minimize the ensuing objective function. Using a simulation study the performance of the method is assessed, and the methodology is illustrated using a real data set with a 24-dimensional response vector. © 2014 Elsevier B.V.

Critical elements on fitting the Bayesian multivariate Poisson Lognormal model

Science.gov (United States)

Zamzuri, Zamira Hasanah binti

2015-10-01

Motivated by a problem on fitting multivariate models to traffic accident data, a detailed discussion of the Multivariate Poisson Lognormal (MPL) model is presented. This paper reveals three critical elements on fitting the MPL model: the setting of initial estimates, hyperparameters and tuning parameters. These issues have not been highlighted in the literature. Based on simulation studies conducted, we have shown that to use the Univariate Poisson Model (UPM) estimates as starting values, at least 20,000 iterations are needed to obtain reliable final estimates. We also illustrated the sensitivity of the specific hyperparameter, which if it is not given extra attention, may affect the final estimates. The last issue is regarding the tuning parameters where they depend on the acceptance rate. Finally, a heuristic algorithm to fit the MPL model is presented. This acts as a guide to ensure that the model works satisfactorily given any data set.

Univariate and multivariate skewness and kurtosis for measuring nonnormality: Prevalence, influence and estimation.

Science.gov (United States)

Cain, Meghan K; Zhang, Zhiyong; Yuan, Ke-Hai

2017-10-01

Nonnormality of univariate data has been extensively examined previously (Blanca et al., Methodology: European Journal of Research Methods for the Behavioral and Social Sciences, 9(2), 78-84, 2013; Miceeri, Psychological Bulletin, 105(1), 156, 1989). However, less is known of the potential nonnormality of multivariate data although multivariate analysis is commonly used in psychological and educational research. Using univariate and multivariate skewness and kurtosis as measures of nonnormality, this study examined 1,567 univariate distriubtions and 254 multivariate distributions collected from authors of articles published in Psychological Science and the American Education Research Journal. We found that 74 % of univariate distributions and 68 % multivariate distributions deviated from normal distributions. In a simulation study using typical values of skewness and kurtosis that we collected, we found that the resulting type I error rates were 17 % in a t-test and 30 % in a factor analysis under some conditions. Hence, we argue that it is time to routinely report skewness and kurtosis along with other summary statistics such as means and variances. To facilitate future report of skewness and kurtosis, we provide a tutorial on how to compute univariate and multivariate skewness and kurtosis by SAS, SPSS, R and a newly developed Web application.

Multivariate pattern dependence.

Directory of Open Access Journals (Sweden)

Stefano Anzellotti

2017-11-01

Full Text Available When we perform a cognitive task, multiple brain regions are engaged. Understanding how these regions interact is a fundamental step to uncover the neural bases of behavior. Most research on the interactions between brain regions has focused on the univariate responses in the regions. However, fine grained patterns of response encode important information, as shown by multivariate pattern analysis. In the present article, we introduce and apply multivariate pattern dependence (MVPD: a technique to study the statistical dependence between brain regions in humans in terms of the multivariate relations between their patterns of responses. MVPD characterizes the responses in each brain region as trajectories in region-specific multidimensional spaces, and models the multivariate relationship between these trajectories. We applied MVPD to the posterior superior temporal sulcus (pSTS and to the fusiform face area (FFA, using a searchlight approach to reveal interactions between these seed regions and the rest of the brain. Across two different experiments, MVPD identified significant statistical dependence not detected by standard functional connectivity. Additionally, MVPD outperformed univariate connectivity in its ability to explain independent variance in the responses of individual voxels. In the end, MVPD uncovered different connectivity profiles associated with different representational subspaces of FFA: the first principal component of FFA shows differential connectivity with occipital and parietal regions implicated in the processing of low-level properties of faces, while the second and third components show differential connectivity with anterior temporal regions implicated in the processing of invariant representations of face identity.

Initial Development of Four Forest Species in Different Shading Conditions

Directory of Open Access Journals (Sweden)

C. C. Silva

2013-07-01

Full Text Available Abstract: Evaluated the initial development through destructive and non-destructive sampling, forest species Adenanthera pavonina, Cassia fistula, Parkia pendula and Hymenolobium petraeum, propagated by seeds at different levels of shading screens black poliefinas (0, 50 and 65% , in the region of Sinop, MT. There were no significant interactions between time and level of shading to any variable. Changes in fresh and dry weight at all levels of shading occurred from 30 DAT. The highest rates of growth were observed in 50% shading to A. pavonina, P. pendula and H. petraeum and 65% shading for C. fistula.Keywords: seedling, growth, physiology, climatic conditions.

Initial conditions of urban permeable surfaces in rainfall-runoff models using Horton’s infiltration

DEFF Research Database (Denmark)

Davidsen, Steffen; Löwe, Roland; Høegh Ravn, Nanna

2017-01-01

Infiltration is a key process controlling runoff, but varies depending on antecedent conditions. This study provides estimates on initial conditions for urban permeable surfaces via continuous simulation of the infiltration capacity using historical rain data. An analysis of historical rainfall...... records show that accumulated rainfall prior to large rain events does not depend on the return period of the event. Using an infiltration-runoff model we found that for a typical large rain storm, antecedent conditions in general lead to reduced infiltration capacity both for sandy and clayey soils...... and that there is substantial runoff for return periods above 1–10 years....

Multivariate Max-Stable Spatial Processes

KAUST Repository

Genton, Marc G.

2014-01-06

Analysis of spatial extremes is currently based on univariate processes. Max-stable processes allow the spatial dependence of extremes to be modelled and explicitly quantified, they are therefore widely adopted in applications. For a better understanding of extreme events of real processes, such as environmental phenomena, it may be useful to study several spatial variables simultaneously. To this end, we extend some theoretical results and applications of max-stable processes to the multivariate setting to analyze extreme events of several variables observed across space. In particular, we study the maxima of independent replicates of multivariate processes, both in the Gaussian and Student-t cases. Then, we define a Poisson process construction in the multivariate setting and introduce multivariate versions of the Smith Gaussian extremevalue, the Schlather extremal-Gaussian and extremal-t, and the BrownResnick models. Inferential aspects of those models based on composite likelihoods are developed. We present results of various Monte Carlo simulations and of an application to a dataset of summer daily temperature maxima and minima in Oklahoma, U.S.A., highlighting the utility of working with multivariate models in contrast to the univariate case. Based on joint work with Simone Padoan and Huiyan Sang.

Multivariate Max-Stable Spatial Processes

KAUST Repository

Genton, Marc G.

2014-01-01

Analysis of spatial extremes is currently based on univariate processes. Max-stable processes allow the spatial dependence of extremes to be modelled and explicitly quantified, they are therefore widely adopted in applications. For a better understanding of extreme events of real processes, such as environmental phenomena, it may be useful to study several spatial variables simultaneously. To this end, we extend some theoretical results and applications of max-stable processes to the multivariate setting to analyze extreme events of several variables observed across space. In particular, we study the maxima of independent replicates of multivariate processes, both in the Gaussian and Student-t cases. Then, we define a Poisson process construction in the multivariate setting and introduce multivariate versions of the Smith Gaussian extremevalue, the Schlather extremal-Gaussian and extremal-t, and the BrownResnick models. Inferential aspects of those models based on composite likelihoods are developed. We present results of various Monte Carlo simulations and of an application to a dataset of summer daily temperature maxima and minima in Oklahoma, U.S.A., highlighting the utility of working with multivariate models in contrast to the univariate case. Based on joint work with Simone Padoan and Huiyan Sang.

Characteristics of patients initiating raloxifene compared to those initiating bisphosphonates

Directory of Open Access Journals (Sweden)

Wang Sara

2008-12-01

Full Text Available Abstract Background Both raloxifene and bisphosphonates are indicated for the prevention and treatment of postmenopausal osteoporosis, however these medications have different efficacy and safety profiles. It is plausible that physicians would prescribe these agents to optimize the benefit/risk profile for individual patients. The objective of this study was to compare demographic and clinical characteristics of patients initiating raloxifene with those of patients initiating bisphosphonates for the prevention and treatment of osteoporosis. Methods This study was conducted using a retrospective cohort design. Female beneficiaries (45 years and older with at least one claim for raloxifene or a bisphosphonate in 2003 through 2005 and continuous enrollment in the previous 12 months and subsequent 6 months were identified using a collection of large national commercial, Medicare supplemental, and Medicaid administrative claims databases (MarketScan®. Patients were divided into two cohorts, a combined commercial/Medicare cohort and a Medicaid cohort. Within each cohort, characteristics (demographic, clinical, and resource utilization of patients initiating raloxifene were compared to those of patients initiating bisphosphonate therapy. Group comparisons were made using chi-square tests for proportions of categorical measures and Wilcoxon rank-sum tests for continuous variables. Logistic regression was used to simultaneously examine factors independently associated with initiation of raloxifene versus a bisphosphonate. Results Within both the commercial/Medicare and Medicaid cohorts, raloxifene patients were younger, had fewer comorbid conditions, and fewer pre-existing fractures than bisphosphonate patients. Raloxifene patients in both cohorts were less likely to have had a bone mineral density (BMD screening in the previous year than were bisphosphonate patients, and were also more likely to have used estrogen or estrogen/progestin therapy in the

A primer of multivariate statistics

CERN Document Server

Harris, Richard J

2014-01-01

Drawing upon more than 30 years of experience in working with statistics, Dr. Richard J. Harris has updated A Primer of Multivariate Statistics to provide a model of balance between how-to and why. This classic text covers multivariate techniques with a taste of latent variable approaches. Throughout the book there is a focus on the importance of describing and testing one's interpretations of the emergent variables that are produced by multivariate analysis. This edition retains its conversational writing style while focusing on classical techniques. The book gives the reader a feel for why

Exploring Proposals for Resolving the Initial Conditions and Multiverse Problems in Inflation

Science.gov (United States)

Panithanpaisal, Nondh; Steinhardt, Paul

2018-01-01

The theory of cosmic inflation with the plateau-like potentials for the scalar field is very successful in predicting standard cosmological parameters. However, if the quantum effects are included, the theory inherently contains serious problems, namely, the multiverse problem and the initial conditions problem. It has been suggested in Mukhanov 2015 and Deen et al. 2016 to add a potential wall to the potential, so that the field never reaches the self-reproduction point. We examine these two proposals by varying the positions of the potential wall as well as varying the initial ratios of kinetic energy, potential energy and curvature. We demonstrate that both proposals are fine-tuned, at best, as they suffer from the drift in the predictions of the spectral tilt (ns) and the tensor-to-scalar ratio (r).

Initial conditions for slow-roll inflation in a random Gaussian landscape

Energy Technology Data Exchange (ETDEWEB)

Masoumi, Ali; Vilenkin, Alexander; Yamada, Masaki, E-mail: ali@cosmos.phy.tufts.edu, E-mail: vilenkin@cosmos.phy.tufts.edu, E-mail: Masaki.Yamada@tufts.edu [Institute of Cosmology, Department of Physics and Astronomy, Tufts University, Medford, MA 02155 (United States)

2017-07-01

In the landscape perspective, our Universe begins with a quantum tunneling from an eternally-inflating parent vacuum, followed by a period of slow-roll inflation. We investigate the tunneling process and calculate the probability distribution for the initial conditions and for the number of e-folds of slow-roll inflation, modeling the landscape by a small-field one-dimensional random Gaussian potential. We find that such a landscape is fully consistent with observations, but the probability for future detection of spatial curvature is rather low, P ∼ 10{sup −3}.

Multivariate stochastic simulation with subjective multivariate normal distributions

Science.gov (United States)

P. J. Ince; J. Buongiorno

1991-01-01

In many applications of Monte Carlo simulation in forestry or forest products, it may be known that some variables are correlated. However, for simplicity, in most simulations it has been assumed that random variables are independently distributed. This report describes an alternative Monte Carlo simulation technique for subjectively assesed multivariate normal...

Model Checking Multivariate State Rewards

DEFF Research Database (Denmark)

Nielsen, Bo Friis; Nielson, Flemming; Nielson, Hanne Riis

2010-01-01

We consider continuous stochastic logics with state rewards that are interpreted over continuous time Markov chains. We show how results from multivariate phase type distributions can be used to obtain higher-order moments for multivariate state rewards (including covariance). We also generalise...

Initial conditions and robust Newton-Raphson for harmonic balance analysis of free-running oscillators

NARCIS (Netherlands)

Virtanen, J.E.; Maten, ter E.J.W.; Beelen, T.G.J.; Honkala, M.; Hulkkonen, M.

2011-01-01

Poor initial conditions for Harmonic Balance (HB) analysis of freerunning oscillators may lead to divergence of the direct Newton-Raphson method or may prevent to find the solution within an optimization approach. We exploit time integration to obtain estimates for the oscillation frequency and for

Assessment of initial soil moisture conditions for event-based rainfall-runoff modelling

OpenAIRE

Tramblay, Yves; Bouvier, Christophe; Martin, C.; Didon-Lescot, J. F.; Todorovik, D.; Domergue, J. M.

2010-01-01

Flash floods are the most destructive natural hazards that occur in the Mediterranean region. Rainfall-runoff models can be very useful for flash flood forecasting and prediction. Event-based models are very popular for operational purposes, but there is a need to reduce the uncertainties related to the initial moisture conditions estimation prior to a flood event. This paper aims to compare several soil moisture indicators: local Time Domain Reflectometry (TDR) measurements of soil moisture,...

Greenland Regional and Ice Sheet-wide Geometry Sensitivity to Boundary and Initial conditions

Science.gov (United States)

Logan, L. C.; Narayanan, S. H. K.; Greve, R.; Heimbach, P.

2017-12-01

Ice sheet and glacier model outputs require inputs from uncertainly known initial and boundary conditions, and other parameters. Conservation and constitutive equations formalize the relationship between model inputs and outputs, and the sensitivity of model-derived quantities of interest (e.g., ice sheet volume above floatation) to model variables can be obtained via the adjoint model of an ice sheet. We show how one particular ice sheet model, SICOPOLIS (SImulation COde for POLythermal Ice Sheets), depends on these inputs through comprehensive adjoint-based sensitivity analyses. SICOPOLIS discretizes the shallow-ice and shallow-shelf approximations for ice flow, and is well-suited for paleo-studies of Greenland and Antarctica, among other computational domains. The adjoint model of SICOPOLIS was developed via algorithmic differentiation, facilitated by the source transformation tool OpenAD (developed at Argonne National Lab). While model sensitivity to various inputs can be computed by costly methods involving input perturbation simulations, the time-dependent adjoint model of SICOPOLIS delivers model sensitivities to initial and boundary conditions throughout time at lower cost. Here, we explore both the sensitivities of the Greenland Ice Sheet's entire and regional volumes to: initial ice thickness, precipitation, basal sliding, and geothermal flux over the Holocene epoch. Sensitivity studies such as described here are now accessible to the modeling community, based on the latest version of SICOPOLIS that has been adapted for OpenAD to generate correct and efficient adjoint code.

Initial conditions and robust Newton-Raphson for harmonic balance analysis of free-running oscillators

NARCIS (Netherlands)

Virtanen, J.E.; Maten, ter E.J.W.; Honkala, M.; Hulkkonen, M.; Günther, M.; Bartel, A.; Brunk, M.; Schoeps, S.; Striebel, M.

2012-01-01

Poor initial conditions for Harmonic Balance (HB) analysis of free-running oscillators may lead to divergence of the direct Newton-Raphson method or may prevent to find the solution within an optimization approach. We exploit time integration to obtain estimates for the oscillation frequency and for

Path dependence, initial conditions, and routines in organizations : The Toyota production system re-examined

NARCIS (Netherlands)

Dolfsma, W.A.; van Driel, H.

2009-01-01

Purpose - The purpose of this paper is to disentangle and elaborate on the constitutive elements of the concept of path dependence (initial conditions and lock-in) for a concerted and in-depth application to the study of organizational change. Design/methodology/approach - The approach takes the

Initial conditions for cosmological N-body simulations of the scalar sector of theories of Newtonian, Relativistic and Modified Gravity

International Nuclear Information System (INIS)

Valkenburg, Wessel; Hu, Bin

2015-01-01

We present a description for setting initial particle displacements and field values for simulations of arbitrary metric theories of gravity, for perfect and imperfect fluids with arbitrary characteristics. We extend the Zel'dovich Approximation to nontrivial theories of gravity, and show how scale dependence implies curved particle paths, even in the entirely linear regime of perturbations. For a viable choice of Effective Field Theory of Modified Gravity, initial conditions set at high redshifts are affected at the level of up to 5% at Mpc scales, which exemplifies the importance of going beyond Λ-Cold Dark Matter initial conditions for modifications of gravity outside of the quasi-static approximation. In addition, we show initial conditions for a simulation where a scalar modification of gravity is modelled in a Lagrangian particle-like description. Our description paves the way for simulations and mock galaxy catalogs under theories of gravity beyond the standard model, crucial for progress towards precision tests of gravity and cosmology

Choice of initial conditions in dynamical calculations of distributions of nuclear fission fragments

International Nuclear Information System (INIS)

Kosenko, G.I.

1993-01-01

The distribution function in the coordinates and momenta for a fissioning system traversing a barrier is determined in terms of Langevin fluctuation-dissipation dynamics. It is shown that this distribution is best described by the Kramers distribution. The equilibrium distribution can be used as the initial condition, provided that the system is in the overdamping regime. 28 refs., 5 figs., 3 tabs

Multivariant design and multiple criteria analysis of building refurbishments

Energy Technology Data Exchange (ETDEWEB)

Kaklauskas, A.; Zavadskas, E. K.; Raslanas, S. [Faculty of Civil Engineering, Vilnius Gediminas Technical University, Vilnius (Lithuania)

2005-07-01

In order to design and realize an efficient building refurbishment, it is necessary to carry out an exhaustive investigation of all solutions that form it. The efficiency level of the considered building's refurbishment depends on a great many of factors, including: cost of refurbishment, annual fuel economy after refurbishment, tentative pay-back time, harmfulness to health of the materials used, aesthetics, maintenance properties, functionality, comfort, sound insulation and longevity, etc. Solutions of an alternative character allow for a more rational and realistic assessment of economic, ecological, legislative, climatic, social and political conditions, traditions and for better the satisfaction of customer requirements. They also enable one to cut down on refurbishment costs. In carrying out the multivariant design and multiple criteria analysis of a building refurbishment much data was processed and evaluated. Feasible alternatives could be as many as 100,000. How to perform a multivariant design and multiple criteria analysis of alternate alternatives based on the enormous amount of information became the problem. Method of multivariant design and multiple criteria of a building refurbishment's analysis were developed by the authors to solve the above problems. In order to demonstrate the developed method, a practical example is presented in this paper. (author)

Multivariate Self-Exciting Threshold Autoregressive Models with eXogenous Input

OpenAIRE

Addo, Peter Martey

2014-01-01

This study defines a multivariate Self--Exciting Threshold Autoregressive with eXogenous input (MSETARX) models and present an estimation procedure for the parameters. The conditions for stationarity of the nonlinear MSETARX models is provided. In particular, the efficiency of an adaptive parameter estimation algorithm and LSE (least squares estimate) algorithm for this class of models is then provided via simulations.

The halo bispectrum in N-body simulations with non-Gaussian initial conditions

Science.gov (United States)

Sefusatti, E.; Crocce, M.; Desjacques, V.

2012-10-01

We present measurements of the bispectrum of dark matter haloes in numerical simulations with non-Gaussian initial conditions of local type. We show, in the first place, that the overall effect of primordial non-Gaussianity on the halo bispectrum is larger than on the halo power spectrum when all measurable configurations are taken into account. We then compare our measurements with a tree-level perturbative prediction, finding good agreement at large scales when the constant Gaussian bias parameter, both linear and quadratic, and their constant non-Gaussian corrections are fitted for. The best-fitting values of the Gaussian bias factors and their non-Gaussian, scale-independent corrections are in qualitative agreement with the peak-background split expectations. In particular, we show that the effect of non-Gaussian initial conditions on squeezed configurations is fairly large (up to 30 per cent for fNL = 100 at redshift z = 0.5) and results from contributions of similar amplitude induced by the initial matter bispectrum, scale-dependent bias corrections as well as from non-linear matter bispectrum corrections. We show, in addition, that effects at second order in fNL are irrelevant for the range of values allowed by cosmic microwave background and galaxy power spectrum measurements, at least on the scales probed by our simulations (k > 0.01 h Mpc-1). Finally, we present a Fisher matrix analysis to assess the possibility of constraining primordial non-Gaussianity with future measurements of the galaxy bispectrum. We find that a survey with a volume of about 10 h-3 Gpc3 at mean redshift z ≃ 1 could provide an error on fNL of the order of a few. This shows the relevance of a joint analysis of galaxy power spectrum and bispectrum in future redshift surveys.

INITIAL TEST WELL CONDITIONING AT NOPAL I URANIUM DEPOSIT, SIERRA PENA BLANCA, CHIHUAHUA, MEXICO

Energy Technology Data Exchange (ETDEWEB)

R.D. Oliver; J.C. Dinsmoor; S.J. Goldstein; I. Reyes; R. De La Garza

2005-07-11

Three test wells, PB-1, PB-2, and PB-3, were drilled at the Nopal I uranium deposit as part of a natural analogue study to evaluate radionuclide transport processes during March-April 2003. The initial pumping to condition the wells was completed during December 2003. The PB-1 well, drilled immediately adjacent to the Nopal I ore body, was continuously cored to a depth of 250 m, terminating 20 m below the top of the measured water level. The PB-2 and PB-3 wells, which were drilled on opposite sides of PB-1 at a radial distance of approximately 40 to 50 m outside of the remaining projected ore body, were also drilled to about 20 m below the top of the measured water level. Each test well was completed with 4-inch (10.2-cm) diameter PVC casing with a slotted liner below the water table. Initial conditioning of all three wells using a submersible pump at low pump rates [less than 1 gallon (3.8 1) per minute] resulted in measurable draw down and recoveries. The greatest drawdown ({approx}15 m) was observed in PB-2, whereas only minor (<1 m) drawdown occurred in PB-3. For PB-1 and PB-2, the water turbidity decreased as the wells were pumped and the pH values decreased, indicating that the contamination from the drilling fluid was reduced as the wells were conditioned. Test wells PB-1 and PB-2 showed increased inflow after several borehole volumes of fluid were removed, but their inflow rates remained less that the pumping rate. Test well PB-3 showed the smallest drawdown and least change in pH and conductivity during initial pumping and quickest recovery with a rise in measured water level after conditioning. The 195 gallons (750 l) of water pumped from PB-3 during conditioning was discharged through a household sponge. That sponge showed measurable gamma radiation, which decayed to background values in less than 12 hours. Preliminary interpretations include filtration of a radioisotope source with a short half-life or of a radioisotope that volatized as the sponge

INITIAL TEST WELL CONDITIONING AT NOPAL I URANIUM DEPOSIT, SIERRA PENA BLANCA, CHIHUAHUA, MEXICO

International Nuclear Information System (INIS)

Oliver, R.D.; Dinsmoor, J.C.; Goldstein, S.J.; Reyes, I.; De La Garza, R.

2005-01-01

Three test wells, PB-1, PB-2, and PB-3, were drilled at the Nopal I uranium deposit as part of a natural analogue study to evaluate radionuclide transport processes during March-April 2003. The initial pumping to condition the wells was completed during December 2003. The PB-1 well, drilled immediately adjacent to the Nopal I ore body, was continuously cored to a depth of 250 m, terminating 20 m below the top of the measured water level. The PB-2 and PB-3 wells, which were drilled on opposite sides of PB-1 at a radial distance of approximately 40 to 50 m outside of the remaining projected ore body, were also drilled to about 20 m below the top of the measured water level. Each test well was completed with 4-inch (10.2-cm) diameter PVC casing with a slotted liner below the water table. Initial conditioning of all three wells using a submersible pump at low pump rates [less than 1 gallon (3.8 1) per minute] resulted in measurable draw down and recoveries. The greatest drawdown (∼15 m) was observed in PB-2, whereas only minor (<1 m) drawdown occurred in PB-3. For PB-1 and PB-2, the water turbidity decreased as the wells were pumped and the pH values decreased, indicating that the contamination from the drilling fluid was reduced as the wells were conditioned. Test wells PB-1 and PB-2 showed increased inflow after several borehole volumes of fluid were removed, but their inflow rates remained less that the pumping rate. Test well PB-3 showed the smallest drawdown and least change in pH and conductivity during initial pumping and quickest recovery with a rise in measured water level after conditioning. The 195 gallons (750 l) of water pumped from PB-3 during conditioning was discharged through a household sponge. That sponge showed measurable gamma radiation, which decayed to background values in less than 12 hours. Preliminary interpretations include filtration of a radioisotope source with a short half-life or of a radioisotope that volatized as the sponge dried

Soil erosion and effluent particle size distribution under different initial conditions and rock fragment coverage

Science.gov (United States)

Jomaa, S.; Barry, D. A.; Brovelli, A.; Heng, B. C. P.; Sander, G. C.; Parlange, J.-Y.

2012-04-01

It is well known that the presence of rock fragments on the soil surface and the soil's initial characteristics (moisture content, surface roughness, bulk density, etc.) are key factors influencing soil erosion dynamics and sediment delivery. In addition, the interaction of these factors increases the complexity of soil erosion patterns and makes predictions more difficult. The aim of this study was (i) to investigate the effect of soil initial conditions and rock fragment coverage on soil erosion yields and effluent particle size distribution and (ii) to evaluate to what extent the rock fragment coverage controls this relationship. Three laboratory flume experiments with constant precipitation rate of 74 mm/h on a loamy soil parcel with a 2% slope were performed. Experiments with duration of 2 h were conducted using the 6-m × 2-m EPFL erosion flume. During each experiment two conditions were considered, a bare soil and a rock fragment-protected (with 40% coverage) soil. The initial soil surface state was varied between the three experiments, from a freshly re-ploughed and almost dry condition to a compacted soil with a well-developed shield layer and high moisture content. Experiments were designed so that rain splash was the primary driver of soil erosion. Results showed that the amount of eroded mass was highly controlled by the initial soil conditions and whether the steady-state equilibrium was un-, partially- or fully- developed during the previous event. Additionally, results revealed that sediment yields and particle size composition in the initial part of an erosion event are more sensitive to the erosion history than the long-time behaviour. This latter appears to be mainly controlled by rainfall intensity. If steady-state was achieved for a previous event, then the next event consistently produced concentrations for each size class that peaked rapidly, and then declined gradually to steady-state equilibrium. If steady state was not obtained, then

Multivariate analysis: models and method

International Nuclear Information System (INIS)

Sanz Perucha, J.

1990-01-01

Data treatment techniques are increasingly used since computer methods result of wider access. Multivariate analysis consists of a group of statistic methods that are applied to study objects or samples characterized by multiple values. A final goal is decision making. The paper describes the models and methods of multivariate analysis

Multivariate strategies in functional magnetic resonance imaging

DEFF Research Database (Denmark)

Hansen, Lars Kai

2007-01-01

We discuss aspects of multivariate fMRI modeling, including the statistical evaluation of multivariate models and means for dimensional reduction. In a case study we analyze linear and non-linear dimensional reduction tools in the context of a `mind reading' predictive multivariate fMRI model....

Applied multivariate statistical analysis

CERN Document Server

Härdle, Wolfgang Karl

2015-01-01

Focusing on high-dimensional applications, this 4th edition presents the tools and concepts used in multivariate data analysis in a style that is also accessible for non-mathematicians and practitioners.  It surveys the basic principles and emphasizes both exploratory and inferential statistics; a new chapter on Variable Selection (Lasso, SCAD and Elastic Net) has also been added.  All chapters include practical exercises that highlight applications in different multivariate data analysis fields: in quantitative financial studies, where the joint dynamics of assets are observed; in medicine, where recorded observations of subjects in different locations form the basis for reliable diagnoses and medication; and in quantitative marketing, where consumers’ preferences are collected in order to construct models of consumer behavior.  All of these examples involve high to ultra-high dimensions and represent a number of major fields in big data analysis. The fourth edition of this book on Applied Multivariate ...

A Review of Multivariate Distributions for Count Data Derived from the Poisson Distribution.

Science.gov (United States)

Inouye, David; Yang, Eunho; Allen, Genevera; Ravikumar, Pradeep

2017-01-01

The Poisson distribution has been widely studied and used for modeling univariate count-valued data. Multivariate generalizations of the Poisson distribution that permit dependencies, however, have been far less popular. Yet, real-world high-dimensional count-valued data found in word counts, genomics, and crime statistics, for example, exhibit rich dependencies, and motivate the need for multivariate distributions that can appropriately model this data. We review multivariate distributions derived from the univariate Poisson, categorizing these models into three main classes: 1) where the marginal distributions are Poisson, 2) where the joint distribution is a mixture of independent multivariate Poisson distributions, and 3) where the node-conditional distributions are derived from the Poisson. We discuss the development of multiple instances of these classes and compare the models in terms of interpretability and theory. Then, we empirically compare multiple models from each class on three real-world datasets that have varying data characteristics from different domains, namely traffic accident data, biological next generation sequencing data, and text data. These empirical experiments develop intuition about the comparative advantages and disadvantages of each class of multivariate distribution that was derived from the Poisson. Finally, we suggest new research directions as explored in the subsequent discussion section.

Reduction of damage initiation density in fused silica optics via UV laser conditioning

Science.gov (United States)

Peterson, John E.; Maricle, Stephen M.; Brusasco, Raymond M.; Penetrante, Bernardino M.

2004-03-16

The present invention provides a method for reducing the density of sites on the surface of fused silica optics that are prone to the initiation of laser-induced damage, resulting in optics which have far fewer catastrophic defects and are better capable of resisting optical deterioration upon exposure for a long period of time to a high-power laser beam having a wavelength of about 360 nm or less. The initiation of laser-induced damage is reduced by conditioning the optic at low fluences below levels that normally lead to catastrophic growth of damage. When the optic is then irradiated at its high fluence design limit, the concentration of catastrophic damage sites that form on the surface of the optic is greatly reduced.

Multivariate Bonferroni-type inequalities theory and applications

CERN Document Server

Chen, John

2014-01-01

Multivariate Bonferroni-Type Inequalities: Theory and Applications presents a systematic account of research discoveries on multivariate Bonferroni-type inequalities published in the past decade. The emergence of new bounding approaches pushes the conventional definitions of optimal inequalities and demands new insights into linear and Fréchet optimality. The book explores these advances in bounding techniques with corresponding innovative applications. It presents the method of linear programming for multivariate bounds, multivariate hybrid bounds, sub-Markovian bounds, and bounds using Hamil

A kernel version of multivariate alteration detection

DEFF Research Database (Denmark)

Nielsen, Allan Aasbjerg; Vestergaard, Jacob Schack

2013-01-01

Based on the established methods kernel canonical correlation analysis and multivariate alteration detection we introduce a kernel version of multivariate alteration detection. A case study with SPOT HRV data shows that the kMAD variates focus on extreme change observations.......Based on the established methods kernel canonical correlation analysis and multivariate alteration detection we introduce a kernel version of multivariate alteration detection. A case study with SPOT HRV data shows that the kMAD variates focus on extreme change observations....

The effect of coronae on leader initiation and development under thunderstorm conditions and in long air gaps

International Nuclear Information System (INIS)

Aleksandrov, N.L.; Bazelyan, E.M.; Carpenter Jr, R.B.; Drabkin, M.M.; Raizer, Yu P.

2001-01-01

The initiation and development of a leader is theoretically studied by considering an electrode which is embedded in a cloud of space charge injected by a corona discharge. The focus is on the initiation of upward lightning from a stationary grounded object in a thundercloud electric field. The main results are also applicable to the leader process in long laboratory air gaps at direct voltage. Simple physical models of non-stationary coronae developing in free space near a solitary stressed sphere and of a leader propagating in the space charge cloud of coronae are suggested. It is shown that the electric field redistribution due to the space charge released by the long corona discharge near the top of a high object hinders the initiation and development of an upward leader from the object in a thundercloud electric field. The conditions for the formation of corona streamers that are required to initiate a leader are derived. The criteria are obtained for a leader to be initiated and propagate in the space charge cloud. A hypothesis is proposed that the streamers are never initiated near the top of a high object under thunderstorm conditions if at ground level there is only a slowly-varying electric field of the thundercloud. The streamers may be induced by the fast-rising electric field of distant downward leaders or intracloud discharges. (author)

Multivariate Matrix-Exponential Distributions

DEFF Research Database (Denmark)

Bladt, Mogens; Nielsen, Bo Friis

2010-01-01

be written as linear combinations of the elements in the exponential of a matrix. For this reason we shall refer to multivariate distributions with rational Laplace transform as multivariate matrix-exponential distributions (MVME). The marginal distributions of an MVME are univariate matrix......-exponential distributions. We prove a characterization that states that a distribution is an MVME distribution if and only if all non-negative, non-null linear combinations of the coordinates have a univariate matrix-exponential distribution. This theorem is analog to a well-known characterization theorem...

Multivariate analysis methods in physics

International Nuclear Information System (INIS)

Wolter, M.

2007-01-01

A review of multivariate methods based on statistical training is given. Several multivariate methods useful in high-energy physics analysis are discussed. Selected examples from current research in particle physics are discussed, both from the on-line trigger selection and from the off-line analysis. Also statistical training methods are presented and some new application are suggested [ru

Method for statistical data analysis of multivariate observations

CERN Document Server

Gnanadesikan, R

1997-01-01

A practical guide for multivariate statistical techniques-- now updated and revised In recent years, innovations in computer technology and statistical methodologies have dramatically altered the landscape of multivariate data analysis. This new edition of Methods for Statistical Data Analysis of Multivariate Observations explores current multivariate concepts and techniques while retaining the same practical focus of its predecessor. It integrates methods and data-based interpretations relevant to multivariate analysis in a way that addresses real-world problems arising in many areas of inte

Multivariate survival analysis and competing risks

CERN Document Server

Crowder, Martin J

2012-01-01

Multivariate Survival Analysis and Competing Risks introduces univariate survival analysis and extends it to the multivariate case. It covers competing risks and counting processes and provides many real-world examples, exercises, and R code. The text discusses survival data, survival distributions, frailty models, parametric methods, multivariate data and distributions, copulas, continuous failure, parametric likelihood inference, and non- and semi-parametric methods. There are many books covering survival analysis, but very few that cover the multivariate case in any depth. Written for a graduate-level audience in statistics/biostatistics, this book includes practical exercises and R code for the examples. The author is renowned for his clear writing style, and this book continues that trend. It is an excellent reference for graduate students and researchers looking for grounding in this burgeoning field of research.

The value of multivariate model sophistication

DEFF Research Database (Denmark)

Rombouts, Jeroen; Stentoft, Lars; Violante, Francesco

2014-01-01

We assess the predictive accuracies of a large number of multivariate volatility models in terms of pricing options on the Dow Jones Industrial Average. We measure the value of model sophistication in terms of dollar losses by considering a set of 444 multivariate models that differ in their spec....... In addition to investigating the value of model sophistication in terms of dollar losses directly, we also use the model confidence set approach to statistically infer the set of models that delivers the best pricing performances.......We assess the predictive accuracies of a large number of multivariate volatility models in terms of pricing options on the Dow Jones Industrial Average. We measure the value of model sophistication in terms of dollar losses by considering a set of 444 multivariate models that differ...

Chimera states and the interplay between initial conditions and non-local coupling

Science.gov (United States)

Kalle, Peter; Sawicki, Jakub; Zakharova, Anna; Schöll, Eckehard

2017-03-01

Chimera states are complex spatio-temporal patterns that consist of coexisting domains of coherent and incoherent dynamics. We study chimera states in a network of non-locally coupled Stuart-Landau oscillators. We investigate the impact of initial conditions in combination with non-local coupling. Based on an analytical argument, we show how the coupling phase and the coupling strength are linked to the occurrence of chimera states, flipped profiles of the mean phase velocity, and the transition from a phase- to an amplitude-mediated chimera state.

Multivariate Generalizations of Student's t-Distribution. ONR Technical Report. [Biometric Lab Report No. 90-3.

Science.gov (United States)

Gibbons, Robert D.; And Others

In the process of developing a conditionally-dependent item response theory (IRT) model, the problem arose of modeling an underlying multivariate normal (MVN) response process with general correlation among the items. Without the assumption of conditional independence, for which the underlying MVN cdf takes on comparatively simple forms and can be…

A Hybrid ICA-SVM Approach for Determining the Quality Variables at Fault in a Multivariate Process

Directory of Open Access Journals (Sweden)

Yuehjen E. Shao

2012-01-01

Full Text Available The monitoring of a multivariate process with the use of multivariate statistical process control (MSPC charts has received considerable attention. However, in practice, the use of MSPC chart typically encounters a difficulty. This difficult involves which quality variable or which set of the quality variables is responsible for the generation of the signal. This study proposes a hybrid scheme which is composed of independent component analysis (ICA and support vector machine (SVM to determine the fault quality variables when a step-change disturbance existed in a multivariate process. The proposed hybrid ICA-SVM scheme initially applies ICA to the Hotelling T2 MSPC chart to generate independent components (ICs. The hidden information of the fault quality variables can be identified in these ICs. The ICs are then served as the input variables of the classifier SVM for performing the classification process. The performance of various process designs is investigated and compared with the typical classification method. Using the proposed approach, the fault quality variables for a multivariate process can be accurately and reliably determined.

Initiation and inhibition of pitting corrosion on reinforcing steel under natural corrosion conditions

Energy Technology Data Exchange (ETDEWEB)

Abd El Wanees, S., E-mail: s_wanees@yahoo.com [Chemistry Department, Faculty of Science, University of Tabuk, Tabuk (Saudi Arabia); Chemistry Department, Faculty of Science, Zagazig University, Zagazig 44519 (Egypt); Bahgat Radwan, A. [Center for Advanced Materials, Qatar University, Doha 2713 (Qatar); Alsharif, M.A. [Chemistry Department, Faculty of Science, University of Tabuk, Tabuk (Saudi Arabia); Abd El Haleem, S.M. [Chemistry Department, Faculty of Science, Zagazig University, Zagazig 44519 (Egypt)

2017-04-01

Initiation and inhibition of pitting corrosion on reinforcing steel in saturated, naturally aerated Ca(OH){sub 2} solutions, under natural corrosion conditions, are followed through measurements of corrosion current, electrochemical impedance spectroscopy and SEM investigation. Induction period for pit initiation and limiting corrosion current for pit propagation are found to depend on aggressive salt anion and cation-types, as well as, concentration. Ammonium chlorides and sulfates are more corrosive than the corresponding sodium salts. Benzotriazole and two of its derivatives are found to be good inhibitors for pitting corrosion of reinforcing steel. Adsorption of these compounds follows a Langmuir adsorption isotherm. The thermodynamic functions ΔE{sup ∗}, ΔH{sup ∗} and ΔS{sup ∗} for pitting corrosion processes in the absence and presence of inhibitor are calculated and discussed. - Highlights: • Cl{sup −} and SO{sub 4} {sup 2-} induce pitting corrosion on passive reinforcing steel. • Initiation and propagation of pitting depend on cation and anion types. • Inhibition is based on adsorption according to Langmuir isotherm.

Decoupling in an expanding universe boundary RG-flow affects initial conditions for inflation

CERN Document Server

Schalm, K; Van der Schaar, J P; Schalm, Koenraad; Shiu, Gary; Schaar, Jan Pieter van der

2004-01-01

We study decoupling in FRW spacetimes, emphasizing a Lagrangian description throughout. To account for the vacuum choice ambiguity in cosmological settings, we introduce an arbitrary boundary action representing the initial conditions. RG flow in these spacetimes naturally affects the boundary interactions. As a consequence the boundary conditions are sensitive to high-energy physics through irrelevant terms in the boundary action. Using scalar field theory as an example, we derive the leading dimension four irrelevant boundary operators. We discuss how the known vacuum choices, e.g. the Bunch-Davies vacuum, appear in the Lagrangian description and square with decoupling. For all choices of boundary conditions encoded by relevant boundary operators, of which the known ones are a subset, backreaction is under control. All, moreover, will generically feel the influence of high-energy physics through irrelevant (dimension four) boundary corrections. Having established a coherent effective field theory framework ...

Rescattering effects on intensity interferometry and initial conditions in relativistic heavy ion collisions

Science.gov (United States)

Li, Yang

The properties of the quark-gluon plasma are being thoroughly studied by utilizing relativistic heavy ion collisions. After its invention in astronomy in the 1950s, intensity interferometry was found to be a robust method to probe the spatial and temporal information of the nuclear collisions also. Although rescattering effects are negligible in elementary particle collisions, it may be very important for heavy ion collisions at RHIC and in the future LHC. Rescattering after production will modify the measured correlation function and make it harder to extract the dynamical information from data. To better understand the data which are dimmed by this final state process, we derive a general formula for intensity interferometry which can calculate rescattering effects easily. The formula can be used both non-relativistically and relativistically. Numerically, we found that rescattering effects on kaon interferometry for RHIC experiments can modify the measured ratio of the outward radius to the sideward radius, which is a sensitive probe to the equation of state, by as large as 15%. It is a nontrivial contribution which should be included to understand the data more accurately. The second part of this thesis is on the initial conditions in relativistic heavy ion collisions. Although relativistic hydrodynamics is successful in explaining many aspects of the data, it is only valid after some finite time after nuclear contact. The results depend on the choice of initial conditions which, so far, have been very uncertain. I describe a formula based on the McLerran-Venugopalan model to compute the initial energy density. The soft gluon fields produced immediately after the overlap of the nuclei can be expanded as a power series of the proper time t. Solving Yang-Mills equations with color current conservation can give us the analytical formulas for the fields. The local color charges on the transverse plane are stochastic variables and have to be taken care of by random

Finer discrimination of brain activation with local multivariate distance

Institute of Scientific and Technical Information of China (English)

无

2007-01-01

The organization of human brain function is diverse on different spatial scales.Various cognitive states are alwavs represented as distinct activity patterns across the specific brain region on fine scales.Conventional univariate analysis of functional MRI data seeks to determine how a particular cognitive state is encoded in brain activity by analyzing each voxel separately without considering the fine-scale patterns information contained in the local brain regions.In this paper,a local multivariate distance mapping(LMDM)technique is proposed to detect the brain activation and to map the fine-scale brain activity patterns.LMDM directly represents the local brain activity with the patterns across multiple voxels rather than individual voxels,and it employs the multivariate distance between different patterns to discriminate the brain state on fine scales.Experiments with simulated and real fMRI data demonstrate that LMDM technique can dramatically increase the sensitivity of the detection for the fine-scale brain activity pettems which contain the subtle information of the experimental conditions.

Multivariate statistical methods a first course

CERN Document Server

Marcoulides, George A

2014-01-01

Multivariate statistics refer to an assortment of statistical methods that have been developed to handle situations in which multiple variables or measures are involved. Any analysis of more than two variables or measures can loosely be considered a multivariate statistical analysis. An introductory text for students learning multivariate statistical methods for the first time, this book keeps mathematical details to a minimum while conveying the basic principles. One of the principal strategies used throughout the book--in addition to the presentation of actual data analyses--is poin

Stochastic conditional intensity processes

DEFF Research Database (Denmark)

Bauwens, Luc; Hautsch, Nikolaus

2006-01-01

model allows for a wide range of (cross-)autocorrelation structures in multivariate point processes. The model is estimated by simulated maximum likelihood (SML) using the efficient importance sampling (EIS) technique. By modeling price intensities based on NYSE trading, we provide significant evidence......In this article, we introduce the so-called stochastic conditional intensity (SCI) model by extending Russell’s (1999) autoregressive conditional intensity (ACI) model by a latent common dynamic factor that jointly drives the individual intensity components. We show by simulations that the proposed...... for a joint latent factor and show that its inclusion allows for an improved and more parsimonious specification of the multivariate intensity process...

Incidence of WHO stage 3 and 4 conditions following initiation of anti-retroviral therapy in resource limited settings.

Directory of Open Access Journals (Sweden)

Andrea J Curtis

Full Text Available OBJECTIVES: To determine the incidence of WHO clinical stage 3 and 4 conditions during early anti-retroviral therapy (ART in resource limited settings (RLS. DESIGN/SETTING: A descriptive analysis of routine program data collected prospectively from 25 Médecins Sans Frontières supported HIV treatment programs in eight countries between 2002 and 2010. SUBJECTS/PARTICIPANTS: 35,349 study participants with median follow-up on ART of 1.33 years (IQR 0.51-2.41. OUTCOME MEASURES: Incidence in 100 person-years of WHO stage 3 or 4 conditions during 5 periods after ART initiation. Diagnoses of conditions were made according to WHO criteria and relied upon clinical assessments supported by basic laboratory investigations. RESULTS: The incidence of any WHO clinical stage 3 or 4 condition over 3 years was 40.02 per 100 person-years (31.77 for stage 3 and 8.25 for stage 4. The incidence of stage 3 and 4 conditions fell by over 97% between months 0-3 and months 25-36 (77.81 to 2.40 for stage 3 and 28.70 to 0.64 for stage 4. During months 0-3 pulmonary tuberculosis was the most common condition diagnosed in adults (incidence 22.24 per 100 person-years and children aged 5-14 years (25.76 and oral candidiasis was the most common in children <5 years (25.79. Overall incidences were higher in Africa compared with Asia (43.98 versus 12.97 for stage 3 and 8.98 versus 7.05 for stage 4 conditions, p<0.001. Pulmonary tuberculosis, weight loss, oral and oesophageal candidiasis, chronic diarrhoea, HIV wasting syndrome and severe bacterial infections were more common in Africa. Extra-pulmonary tuberculosis, non-tuberculous mycobacterial infection, cryptococcosis, penicilliosis and toxoplasmosis were more common in Asia. CONCLUSIONS: The incidence of WHO stage 3 and 4 conditions during the early period after ART initiation in RLS is high, but greatly reduces over time. This is likely due to both the benefits of ART and deaths of the sickest patients occurring shortly

Multivariable control in nuclear power stations

International Nuclear Information System (INIS)

Parent, M.; McMorran, P.D.

1982-11-01

Multivariable methods have the potential to improve the control of large systems such as nuclear power stations. Linear-quadratic optimal control is a multivariable method based on the minimization of a cost function. A related technique leads to the Kalman filter for estimation of plant state from noisy measurements. A design program for optimal control and Kalman filtering has been developed as part of a computer-aided design package for multivariable control systems. The method is demonstrated on a model of a nuclear steam generator, and simulated results are presented

Reaction-diffusion fronts with inhomogeneous initial conditions

Energy Technology Data Exchange (ETDEWEB)

Bena, I [Departement de Physique Theorique, Universite de Geneve, CH-1211 Geneva 4 (Switzerland); Droz, M [Departement de Physique Theorique, Universite de Geneve, CH-1211 Geneva 4 (Switzerland); Martens, K [Departement de Physique Theorique, Universite de Geneve, CH-1211 Geneva 4 (Switzerland); Racz, Z [Institute for Theoretical Physics, Eoetvoes University, 1117 Budapest (Hungary)

2007-02-14

Properties of reaction zones resulting from A+B {yields} C type reaction-diffusion processes are investigated by analytical and numerical methods. The reagents A and B are separated initially and, in addition, there is an initial macroscopic inhomogeneity in the distribution of the B species. For simple two-dimensional geometries, exact analytical results are presented for the time evolution of the geometric shape of the front. We also show using cellular automata simulations that the fluctuations can be neglected both in the shape and in the width of the front.

Assessing initial conditions for chloride transport across low-permeability argillaceous rocks, Wellenberg, Switzerland

International Nuclear Information System (INIS)

Waber, H.N.; Hobbs, M.Y.; Frape, S.K.

2013-01-01

Information about fluid evolution and solute transport in a low-permeability metamorphic rock sequence has been obtained by comparing chloride concentrations and chlorine isotope ratios of pore water, groundwater, and fluid inclusions. The similarity of δ 37 Cl values in fluid inclusions and groundwater suggests a closed-system evolution during the metamorphic overprint, and signatures established at this time appear to form the initial conditions for chloride transport after exhumation of the rock sequence. (authors)

Multivariate and semiparametric kernel regression

OpenAIRE

Härdle, Wolfgang; Müller, Marlene

1997-01-01

The paper gives an introduction to theory and application of multivariate and semiparametric kernel smoothing. Multivariate nonparametric density estimation is an often used pilot tool for examining the structure of data. Regression smoothing helps in investigating the association between covariates and responses. We concentrate on kernel smoothing using local polynomial fitting which includes the Nadaraya-Watson estimator. Some theory on the asymptotic behavior and bandwidth selection is pro...

Assessing initial conditions for chloride transport across low-permeability argillaceous rocks, Wellenberg, Switzerland

Energy Technology Data Exchange (ETDEWEB)

Waber, H.N. [Rock-Water Interaction Group, Institute of Geological Sciences, University of Bern, Baltzerstrasse 1-3, 3012 Bern (Switzerland); Hobbs, M.Y. [Rock-Water Interaction Group, Institute of Geological Sciences, University of Bern, Baltzerstrasse 1-3, 3012 Bern (Switzerland); Nuclear Waste Management Organization (NWMO), 22 St. Clair Avenue East, M4T 2S3 Toronto, Ontario (Canada); Frape, S.K. [Department of Earth and Environmental Sciences, University of Waterloo, Waterloo, Ontario (Canada)

2013-07-01

Information about fluid evolution and solute transport in a low-permeability metamorphic rock sequence has been obtained by comparing chloride concentrations and chlorine isotope ratios of pore water, groundwater, and fluid inclusions. The similarity of δ{sup 37}Cl values in fluid inclusions and groundwater suggests a closed-system evolution during the metamorphic overprint, and signatures established at this time appear to form the initial conditions for chloride transport after exhumation of the rock sequence. (authors)

Multivariate GARCH models

DEFF Research Database (Denmark)

Silvennoinen, Annastiina; Teräsvirta, Timo

This article contains a review of multivariate GARCH models. Most common GARCH models are presented and their properties considered. This also includes nonparametric and semiparametric models. Existing specification and misspecification tests are discussed. Finally, there is an empirical example...

Applied multivariate statistics with R

CERN Document Server

Zelterman, Daniel

2015-01-01

This book brings the power of multivariate statistics to graduate-level practitioners, making these analytical methods accessible without lengthy mathematical derivations. Using the open source, shareware program R, Professor Zelterman demonstrates the process and outcomes for a wide array of multivariate statistical applications. Chapters cover graphical displays, linear algebra, univariate, bivariate and multivariate normal distributions, factor methods, linear regression, discrimination and classification, clustering, time series models, and additional methods. Zelterman uses practical examples from diverse disciplines to welcome readers from a variety of academic specialties. Those with backgrounds in statistics will learn new methods while they review more familiar topics. Chapters include exercises, real data sets, and R implementations. The data are interesting, real-world topics, particularly from health and biology-related contexts. As an example of the approach, the text examines a sample from the B...

Multivariate semi-logistic distribution and processes | Umar | Journal ...

African Journals Online (AJOL)

Multivariate semi-logistic distribution is introduced and studied. Some characterizations properties of multivariate semi-logistic distribution are presented. First order autoregressive minification processes and its generalization to kth order autoregressive minification processes with multivariate semi-logistic distribution as ...

Multivariate Pareto Minification Processes | Umar | Journal of the ...

African Journals Online (AJOL)

Autoregressive (AR) and autoregressive moving average (ARMA) processes with multivariate exponential (ME) distribution are presented and discussed. The theory of positive dependence is used to show that in many cases, multivariate exponential autoregressive (MEAR) and multivariate autoregressive moving average ...

Skill of a global seasonal streamflow forecasting system, relative roles of initial conditions and meteorological forcing

NARCIS (Netherlands)

Candogan Yossef, N.; Winsemius, H.C.; Weerts, A.; Van Beek, R.; Bierkens, M.F.P.

2013-01-01

We investigate the relative contributions of initial conditions (ICs) and meteorological forcing (MF) to the skill of the global seasonal streamflow forecasting system FEWS-World, using the global hydrological model PCRaster Global Water Balance. Potential improvement in forecasting skill through

Exact solution for four-order acousto-optic Bragg diffraction with arbitrary initial conditions.

Science.gov (United States)

Pieper, Ron; Koslover, Deborah; Poon, Ting-Chung

2009-03-01

An exact solution to the four-order acousto-optic (AO) Bragg diffraction problem with arbitrary initial conditions compatible with exact Bragg angle incident light is developed. The solution, obtained by solving a 4th-order differential equation, is formalized into a transition matrix operator predicting diffracted light orders at the exit of the AO cell in terms of the same diffracted light orders at the entrance. It is shown that the transition matrix is unitary and that this unitary matrix condition is sufficient to guarantee energy conservation. A comparison of analytical solutions with numerical predictions validates the formalism. Although not directly related to the approach used to obtain the solution, it was discovered that all four generated eigenvalues from the four-order AO differential matrix operator are expressed simply in terms of Euclid's Divine Proportion.

Multivariate wavelet frames

CERN Document Server

Skopina, Maria; Protasov, Vladimir

2016-01-01

This book presents a systematic study of multivariate wavelet frames with matrix dilation, in particular, orthogonal and bi-orthogonal bases, which are a special case of frames. Further, it provides algorithmic methods for the construction of dual and tight wavelet frames with a desirable approximation order, namely compactly supported wavelet frames, which are commonly required by engineers. It particularly focuses on methods of constructing them. Wavelet bases and frames are actively used in numerous applications such as audio and graphic signal processing, compression and transmission of information. They are especially useful in image recovery from incomplete observed data due to the redundancy of frame systems. The construction of multivariate wavelet frames, especially bases, with desirable properties remains a challenging problem as although a general scheme of construction is well known, its practical implementation in the multidimensional setting is difficult. Another important feature of wavelet is ...

Hydrometeorological threshold conditions for debris flow initiation in Norway

Directory of Open Access Journals (Sweden)

N. K. Meyer

2012-10-01

Full Text Available Debris flows, triggered by extreme precipitation events and rapid snow melt, cause considerable damage to the Norwegian infrastructure every year. To define intensity-duration (ID thresholds for debris flow initiation critical water supply conditions arising from intensive rainfall or snow melt were assessed on the basis of daily hydro-meteorological information for 502 documented debris flow events. Two threshold types were computed: one based on absolute ID relationships and one using ID relationships normalized by the local precipitation day normal (PDN. For each threshold type, minimum, medium and maximum threshold values were defined by fitting power law curves along the 10th, 50th and 90th percentiles of the data population. Depending on the duration of the event, the absolute threshold intensities needed for debris flow initiation vary between 15 and 107 mm day⁻¹. Since the PDN changes locally, the normalized thresholds show spatial variations. Depending on location, duration and threshold level, the normalized threshold intensities vary between 6 and 250 mm day⁻¹. The thresholds obtained were used for a frequency analysis of over-threshold events giving an estimation of the exceedance probability and thus potential for debris flow events in different parts of Norway. The absolute thresholds are most often exceeded along the west coast, while the normalized thresholds are most frequently exceeded on the west-facing slopes of the Norwegian mountain ranges. The minimum thresholds derived in this study are in the range of other thresholds obtained for regions with a climate comparable to Norway. Statistics reveal that the normalized threshold is more reliable than the absolute threshold as the former shows no spatial clustering of debris flows related to water supply events captured by the threshold.

Procesoptimerende multivariable regulatorer til kraftværkskedler. Process Optimizing Multivariable Controllers for Powerplant Boilers

DEFF Research Database (Denmark)

Hansen, T.

The purpose of this Ph.D. thesis is twofold: The first purpose is to devise a new method for application of multivariable controllers in boiler control systems in which they act as optional process optimizing extensions to conventional control systems and in such a way that the safety measures...... mentioned, the concept is applicable to new as well as existing plants. The seccond purpose is to suggest specific methods for experimental modelling and multivariable controller design which are possible to use under the conceptual framework, implement them and test them in a boiler application....

A system to build distributed multivariate models and manage disparate data sharing policies: implementation in the scalable national network for effectiveness research.

Science.gov (United States)

Meeker, Daniella; Jiang, Xiaoqian; Matheny, Michael E; Farcas, Claudiu; D'Arcy, Michel; Pearlman, Laura; Nookala, Lavanya; Day, Michele E; Kim, Katherine K; Kim, Hyeoneui; Boxwala, Aziz; El-Kareh, Robert; Kuo, Grace M; Resnic, Frederic S; Kesselman, Carl; Ohno-Machado, Lucila

2015-11-01

Centralized and federated models for sharing data in research networks currently exist. To build multivariate data analysis for centralized networks, transfer of patient-level data to a central computation resource is necessary. The authors implemented distributed multivariate models for federated networks in which patient-level data is kept at each site and data exchange policies are managed in a study-centric manner. The objective was to implement infrastructure that supports the functionality of some existing research networks (e.g., cohort discovery, workflow management, and estimation of multivariate analytic models on centralized data) while adding additional important new features, such as algorithms for distributed iterative multivariate models, a graphical interface for multivariate model specification, synchronous and asynchronous response to network queries, investigator-initiated studies, and study-based control of staff, protocols, and data sharing policies. Based on the requirements gathered from statisticians, administrators, and investigators from multiple institutions, the authors developed infrastructure and tools to support multisite comparative effectiveness studies using web services for multivariate statistical estimation in the SCANNER federated network. The authors implemented massively parallel (map-reduce) computation methods and a new policy management system to enable each study initiated by network participants to define the ways in which data may be processed, managed, queried, and shared. The authors illustrated the use of these systems among institutions with highly different policies and operating under different state laws. Federated research networks need not limit distributed query functionality to count queries, cohort discovery, or independently estimated analytic models. Multivariate analyses can be efficiently and securely conducted without patient-level data transport, allowing institutions with strict local data storage

Multivariate data analysis

DEFF Research Database (Denmark)

Hansen, Michael Adsetts Edberg

Interest in statistical methodology is increasing so rapidly in the astronomical community that accessible introductory material in this area is long overdue. This book fills the gap by providing a presentation of the most useful techniques in multivariate statistics. A wide-ranging annotated set...

An Exact Confidence Region in Multivariate Calibration

OpenAIRE

Mathew, Thomas; Kasala, Subramanyam

1994-01-01

In the multivariate calibration problem using a multivariate linear model, an exact confidence region is constructed. It is shown that the region is always nonempty and is invariant under nonsingular transformations.

Multivariate rational data fitting

Science.gov (United States)

Cuyt, Annie; Verdonk, Brigitte

1992-12-01

Sections 1 and 2 discuss the advantages of an object-oriented implementation combined with higher floating-point arithmetic, of the algorithms available for multivariate data fitting using rational functions. Section 1 will in particular explain what we mean by "higher arithmetic". Section 2 will concentrate on the concepts of "object orientation". In sections 3 and 4 we shall describe the generality of the data structure that can be dealt with: due to some new results virtually every data set is acceptable right now, with possible coalescence of coordinates or points. In order to solve the multivariate rational interpolation problem the data sets are fed to different algorithms depending on the structure of the interpolation points in then-variate space.

Multivariate missing data in hydrology - Review and applications

Science.gov (United States)

Ben Aissia, Mohamed-Aymen; Chebana, Fateh; Ouarda, Taha B. M. J.

2017-12-01

Water resources planning and management require complete data sets of a number of hydrological variables, such as flood peaks and volumes. However, hydrologists are often faced with the problem of missing data (MD) in hydrological databases. Several methods are used to deal with the imputation of MD. During the last decade, multivariate approaches have gained popularity in the field of hydrology, especially in hydrological frequency analysis (HFA). However, treating the MD remains neglected in the multivariate HFA literature whereas the focus has been mainly on the modeling component. For a complete analysis and in order to optimize the use of data, MD should also be treated in the multivariate setting prior to modeling and inference. Imputation of MD in the multivariate hydrological framework can have direct implications on the quality of the estimation. Indeed, the dependence between the series represents important additional information that can be included in the imputation process. The objective of the present paper is to highlight the importance of treating MD in multivariate hydrological frequency analysis by reviewing and applying multivariate imputation methods and by comparing univariate and multivariate imputation methods. An application is carried out for multiple flood attributes on three sites in order to evaluate the performance of the different methods based on the leave-one-out procedure. The results indicate that, the performance of imputation methods can be improved by adopting the multivariate setting, compared to mean substitution and interpolation methods, especially when using the copula-based approach.

Modeling conditional correlations of asset returns

DEFF Research Database (Denmark)

Silvennoinen, Annastiina; Teräsvirta, Timo

2015-01-01

In this paper we propose a new multivariate GARCH model with time-varying conditional correlation structure. The time-varying conditional correlations change smoothly between two extreme states of constant correlations according to a predetermined or exogenous transition variable. An LM-test is d......In this paper we propose a new multivariate GARCH model with time-varying conditional correlation structure. The time-varying conditional correlations change smoothly between two extreme states of constant correlations according to a predetermined or exogenous transition variable. An LM......-test is derived to test the constancy of correlations and LM- and Wald tests to test the hypothesis of partially constant correlations. Analytical expressions for the test statistics and the required derivatives are provided to make computations feasible. An empirical example based on daily return series of five...

Modeling rainfall-runoff relationship using multivariate GARCH model

Science.gov (United States)

Modarres, R.; Ouarda, T. B. M. J.

2013-08-01

The traditional hydrologic time series approaches are used for modeling, simulating and forecasting conditional mean of hydrologic variables but neglect their time varying variance or the second order moment. This paper introduces the multivariate Generalized Autoregressive Conditional Heteroscedasticity (MGARCH) modeling approach to show how the variance-covariance relationship between hydrologic variables varies in time. These approaches are also useful to estimate the dynamic conditional correlation between hydrologic variables. To illustrate the novelty and usefulness of MGARCH models in hydrology, two major types of MGARCH models, the bivariate diagonal VECH and constant conditional correlation (CCC) models are applied to show the variance-covariance structure and cdynamic correlation in a rainfall-runoff process. The bivariate diagonal VECH-GARCH(1,1) and CCC-GARCH(1,1) models indicated both short-run and long-run persistency in the conditional variance-covariance matrix of the rainfall-runoff process. The conditional variance of rainfall appears to have a stronger persistency, especially long-run persistency, than the conditional variance of streamflow which shows a short-lived drastic increasing pattern and a stronger short-run persistency. The conditional covariance and conditional correlation coefficients have different features for each bivariate rainfall-runoff process with different degrees of stationarity and dynamic nonlinearity. The spatial and temporal pattern of variance-covariance features may reflect the signature of different physical and hydrological variables such as drainage area, topography, soil moisture and ground water fluctuations on the strength, stationarity and nonlinearity of the conditional variance-covariance for a rainfall-runoff process.

Comparing the accuracy of copula-based multivariate density forecasts in selected regions of support

NARCIS (Netherlands)

Diks, C.; Panchenko, V.; Sokolinskiy, O.; van Dijk, D.

2013-01-01

This paper develops a testing framework for comparing the predictive accuracy of copula-based multivariate density forecasts, focusing on a specific part of the joint distribution. The test is framed in the context of the Kullback-Leibler Information Criterion, but using (out-of-sample) conditional

Multivariate statistics high-dimensional and large-sample approximations

CERN Document Server

Fujikoshi, Yasunori; Shimizu, Ryoichi

2010-01-01

A comprehensive examination of high-dimensional analysis of multivariate methods and their real-world applications Multivariate Statistics: High-Dimensional and Large-Sample Approximations is the first book of its kind to explore how classical multivariate methods can be revised and used in place of conventional statistical tools. Written by prominent researchers in the field, the book focuses on high-dimensional and large-scale approximations and details the many basic multivariate methods used to achieve high levels of accuracy. The authors begin with a fundamental presentation of the basic

Exploratory multivariate analysis by example using R

CERN Document Server

Husson, Francois; Pages, Jerome

2010-01-01

Full of real-world case studies and practical advice, Exploratory Multivariate Analysis by Example Using R focuses on four fundamental methods of multivariate exploratory data analysis that are most suitable for applications. It covers principal component analysis (PCA) when variables are quantitative, correspondence analysis (CA) and multiple correspondence analysis (MCA) when variables are categorical, and hierarchical cluster analysis.The authors take a geometric point of view that provides a unified vision for exploring multivariate data tables. Within this framework, they present the prin

The Multivariate Largest Lyapunov Exponent as an Age-Related Metric of Quiet Standing Balance

Directory of Open Access Journals (Sweden)

Kun Liu

2015-01-01

Full Text Available The largest Lyapunov exponent has been researched as a metric of the balance ability during human quiet standing. However, the sensitivity and accuracy of this measurement method are not good enough for clinical use. The present research proposes a metric of the human body’s standing balance ability based on the multivariate largest Lyapunov exponent which can quantify the human standing balance. The dynamic multivariate time series of ankle, knee, and hip were measured by multiple electrical goniometers. Thirty-six normal people of different ages participated in the test. With acquired data, the multivariate largest Lyapunov exponent was calculated. Finally, the results of the proposed approach were analysed and compared with the traditional method, for which the largest Lyapunov exponent and power spectral density from the centre of pressure were also calculated. The following conclusions can be obtained. The multivariate largest Lyapunov exponent has a higher degree of differentiation in differentiating balance in eyes-closed conditions. The MLLE value reflects the overall coordination between multisegment movements. Individuals of different ages can be distinguished by their MLLE values. The standing stability of human is reduced with the increment of age.

Multivariable control in nuclear power stations -survey of design methods

International Nuclear Information System (INIS)

Mcmorran, P.D.

1979-12-01

The development of larger nuclear generating stations increases the importance of dynamic interaction between controllers, because each control action may affect several plant outputs. Multivariable control provides the techniques to design controllers which perform well under these conditions. This report is a foundation for further work on the application of multivariable control in AECL. It covers the requirements of control and the fundamental mathematics used, then reviews the most important linear methods, based on both state-space and frequency-response concepts. State-space methods are derived from analysis of the system differential equations, while frequency-response methods use the input-output transfer function. State-space methods covered include linear-quadratic optimal control, pole shifting, and the theory of state observers and estimators. Frequency-response methods include the inverse Nyquist array method, and classical non-interactive techniques. Transfer-function methods are particularly emphasized since they can incorporate ill-defined design criteria. The underlying concepts, and the application strengths and weaknesses of each design method are presented. A review of significant applications is also given. It is concluded that the inverse Nyquist array method, a frequency-response technique based on inverse transfer-function matrices, is preferred for the design of multivariable controllers for nuclear power plants. This method may be supplemented by information obtained from a modal analysis of the plant model. (auth)

Ellipsoidal prediction regions for multivariate uncertainty characterization

DEFF Research Database (Denmark)

Golestaneh, Faranak; Pinson, Pierre; Azizipanah-Abarghooee, Rasoul

2018-01-01

, for classes of decision-making problems based on robust, interval chance-constrained optimization, necessary inputs take the form of multivariate prediction regions rather than scenarios. The current literature is at very primitive stage of characterizing multivariate prediction regions to be employed...... in these classes of optimization problems. To address this issue, we introduce a new class of multivariate forecasts which form as multivariate ellipsoids for non-Gaussian variables. We propose a data-driven systematic framework to readily generate and evaluate ellipsoidal prediction regions, with predefined...... probability guarantees and minimum conservativeness. A skill score is proposed for quantitative assessment of the quality of prediction ellipsoids. A set of experiments is used to illustrate the discrimination ability of the proposed scoring rule for potential misspecification of ellipsoidal prediction regions...

Gravity with free initial conditions: A solution to the cosmological constant problem testable by CMB B -mode polarization

Science.gov (United States)

Totani, Tomonori

2017-10-01

In standard general relativity the universe cannot be started with arbitrary initial conditions, because four of the ten components of the Einstein's field equations (EFE) are constraints on initial conditions. In the previous work it was proposed to extend the gravity theory to allow free initial conditions, with a motivation to solve the cosmological constant problem. This was done by setting four constraints on metric variations in the action principle, which is reasonable because the gravity's physical degrees of freedom are at most six. However, there are two problems about this theory; the three constraints in addition to the unimodular condition were introduced without clear physical meanings, and the flat Minkowski spacetime is unstable against perturbations. Here a new set of gravitational field equations is derived by replacing the three constraints with new ones requiring that geodesic paths remain geodesic against metric variations. The instability problem is then naturally solved. Implications for the cosmological constant Λ are unchanged; the theory converges into EFE with nonzero Λ by inflation, but Λ varies on scales much larger than the present Hubble horizon. Then galaxies are formed only in small Λ regions, and the cosmological constant problem is solved by the anthropic argument. Because of the increased degrees of freedom in metric dynamics, the theory predicts new non-oscillatory modes of metric anisotropy generated by quantum fluctuation during inflation, and CMB B -mode polarization would be observed differently from the standard predictions by general relativity.

The cluster index of regularly varying sequences with applications to limit theory for functions of multivariate Markov chains

DEFF Research Database (Denmark)

Mikosch, Thomas Valentin; Wintenberger, Olivier

2014-01-01

We introduce the cluster index of a multivariate stationary sequence and characterize the index in terms of the spectral tail process. This index plays a major role in limit theory for partial sums of sequences. We illustrate the use of the cluster index by characterizing infinite variance stable...... limit distributions and precise large deviation results for sums of multivariate functions acting on a stationary Markov chain under a drift condition....

Multivariate and Spatial Visualisation of Archaeological Assemblages

Directory of Open Access Journals (Sweden)

Martin Sterry

2018-05-01

Full Text Available Multivariate analyses, in particular correspondence analysis (CA, have become a standard exploratory tool for analysing and interpreting variance in archaeological assemblages. While they have greatly helped analysts, they unfortunately remain abstract to the viewer, all the more so if the viewer has little or no experience with multivariate statistics. A second issue with these analyses can arise from the detachment of archaeological material from its geo-referenced location and typically considered only in terms of arbitrary classifications (e.g. North Europe, Central Europe, South Europe instead of the full range of local conditions (e.g. proximity to other assemblages, relationships with other spatial phenomena. This article addresses these issues by presenting a novel method for spatially visualising CA so that these analyses can be interpreted intuitively. The method works by transforming the resultant bi-plots of the CA into colour maps using the HSV colour model, in which the similarity and difference between assemblages directly corresponds to the similarity and difference of the colours used to display them. Utilising two datasets – ceramics from the excavations of the Roman fortress of Vetera I, and terra sigillata forms collected as part of 'The Samian Project' – the article demonstrates how the method is applied and how it can be used to draw out spatial and temporal trends.

Multivariate realised kernels

DEFF Research Database (Denmark)

Barndorff-Nielsen, Ole; Hansen, Peter Reinhard; Lunde, Asger

We propose a multivariate realised kernel to estimate the ex-post covariation of log-prices. We show this new consistent estimator is guaranteed to be positive semi-definite and is robust to measurement noise of certain types and can also handle non-synchronous trading. It is the first estimator...

Condition Help: A Patient- and Family-Initiated Rapid Response System.

Science.gov (United States)

Eden, Elizabeth L; Rack, Laurie L; Chen, Ling-Wan; Bump, Gregory M

2017-03-01

Rapid response teams (RRTs) help in delivering safe, timely care. Typically they are activated by clinicians using specific parameters. Allowing patients and families to activate RRTs is a novel intervention. The University of Pittsburgh Medical Center developed and implemented a patient- and family-initiated rapid response system called Condition Help (CH). When the CH system is activated, a patient care liaison or an on-duty administrator meets bedside with the unit charge nurse to address the patient's concerns. In this study, we collected demographic data, call reasons, call designations (safety or nonsafety), and outcome information for all CH calls made during the period January 2012 through June 2015. Two hundred forty patients/family members made 367 CH calls during the study period. Most calls were made by patients (76.8%) rather than family members (21.8%). Of the 240 patients, 43 (18%) made multiple calls; their calls accounted for 46.3% of all calls (170/367). Inadequate pain control was the reason for the call in most cases (48.2%), followed by dissatisfaction with staff (12.5%). The majority of calls involved nonsafety issues (83.4%) rather than safety issues (11.4%). In 41.4% of cases, a change in care was made. Patient- and family-initiated RRTs are designed to engage patients and families in providing safer care. In the CH system, safety issues are identified, but the majority of calls involve nonsafety issues. Journal of Hospital Medicine 2017;12:157-161. © 2017 Society of Hospital Medicine

Unsupervised classification of multivariate geostatistical data: Two algorithms

Science.gov (United States)

Romary, Thomas; Ors, Fabien; Rivoirard, Jacques; Deraisme, Jacques

2015-12-01

With the increasing development of remote sensing platforms and the evolution of sampling facilities in mining and oil industry, spatial datasets are becoming increasingly large, inform a growing number of variables and cover wider and wider areas. Therefore, it is often necessary to split the domain of study to account for radically different behaviors of the natural phenomenon over the domain and to simplify the subsequent modeling step. The definition of these areas can be seen as a problem of unsupervised classification, or clustering, where we try to divide the domain into homogeneous domains with respect to the values taken by the variables in hand. The application of classical clustering methods, designed for independent observations, does not ensure the spatial coherence of the resulting classes. Image segmentation methods, based on e.g. Markov random fields, are not adapted to irregularly sampled data. Other existing approaches, based on mixtures of Gaussian random functions estimated via the expectation-maximization algorithm, are limited to reasonable sample sizes and a small number of variables. In this work, we propose two algorithms based on adaptations of classical algorithms to multivariate geostatistical data. Both algorithms are model free and can handle large volumes of multivariate, irregularly spaced data. The first one proceeds by agglomerative hierarchical clustering. The spatial coherence is ensured by a proximity condition imposed for two clusters to merge. This proximity condition relies on a graph organizing the data in the coordinates space. The hierarchical algorithm can then be seen as a graph-partitioning algorithm. Following this interpretation, a spatial version of the spectral clustering algorithm is also proposed. The performances of both algorithms are assessed on toy examples and a mining dataset.

Effect of initial conditions on two-dimensional Rayleigh-Taylor instability and transition to turbulence in planar blast-wave-driven systems

International Nuclear Information System (INIS)

Miles, A.R.; Edwards, M.J.; Greenough, J.A.

2004-01-01

Perturbations on an interface driven by a strong blast wave grow in time due to a combination of Rayleigh-Taylor, Richtmyer-Meshkov, and decompression effects. In this paper, the results from a computational study of such a system under drive conditions to be attainable on the National Ignition Facility [E. M. Campbell, Laser Part. Beams 9, 209 (1991)] are presented. Using the multiphysics, adaptive mesh refinement, higher order Godunov Eulerian hydrocode, Raptor [L. H. Howell and J. A. Greenough, J. Comput. Phys. 184, 53 (2003)], the late nonlinear instability evolution for multiple amplitude and phase realizations of a variety of multimode spectral types is considered. Compressibility effects preclude the emergence of a regime of self-similar instability growth independent of the initial conditions by allowing for memory of the initial conditions to be retained in the mix-width at all times. The loss of transverse spectral information is demonstrated, however, along with the existence of a quasi-self-similar regime over short time intervals. Certain aspects of the initial conditions, including the rms amplitude, are shown to have a strong effect on the time to transition to the quasi-self-similar regime

Changes in cod muscle proteins during frozen storage revealed by proteome analysis and multivariate data analysis

DEFF Research Database (Denmark)

Kjærsgård, Inger Vibeke Holst; Nørrelykke, M.R.; Jessen, Flemming

2006-01-01

Multivariate data analysis has been combined with proteomics to enhance the recovery of information from 2-DE of cod muscle proteins during different storage conditions. Proteins were extracted according to 11 different storage conditions and samples were resolved by 2-DE. Data generated by 2-DE...... was subjected to principal component analysis (PCA) and discriminant partial least squares regression (DPLSR). Applying PCA to 2-DE data revealed the samples to form groups according to frozen storage time, whereas differences due to different storage temperatures or chilled storage in modified atmosphere...... light chain 1, 2 and 3, triose-phosphate isomerase, glyceraldehyde-3-phosphate dehydrogenase, aldolase A and two ?-actin fragments, and a nuclease diphosphate kinase B fragment to change in concentration, during frozen storage. Application of proteomics, multivariate data analysis and MS/MS to analyse...

Multivariate Time Series Search

Data.gov (United States)

National Aeronautics and Space Administration — Multivariate Time-Series (MTS) are ubiquitous, and are generated in areas as disparate as sensor recordings in aerospace systems, music and video streams, medical...

Intelligent multivariate process supervision

International Nuclear Information System (INIS)

Visuri, Pertti.

1986-01-01

This thesis addresses the difficulties encountered in managing large amounts of data in supervisory control of complex systems. Some previous alarm and disturbance analysis concepts are reviewed and a method for improving the supervision of complex systems is presented. The method, called multivariate supervision, is based on adding low level intelligence to the process control system. By using several measured variables linked together by means of deductive logic, the system can take into account the overall state of the supervised system. Thus, it can present to the operators fewer messages with higher information content than the conventional control systems which are based on independent processing of each variable. In addition, the multivariate method contains a special information presentation concept for improving the man-machine interface. (author)

Graphics for the multivariate two-sample problem

International Nuclear Information System (INIS)

Friedman, J.H.; Rafsky, L.C.

1981-01-01

Some graphical methods for comparing multivariate samples are presented. These methods are based on minimal spanning tree techniques developed for multivariate two-sample tests. The utility of these methods is illustrated through examples using both real and artificial data

Necessary conditions for the initiation and propagation of nuclear-detonation waves in plane atmospheres

International Nuclear Information System (INIS)

Weaver, T.A.; Wood, L.

1979-01-01

The basic conditions for the initiation of a nuclear-detonation wave in an atmosphere having plane symmetry (e.g., a thin, layered fluid envelope on a planet or star) are developed. Two classes of such a detonation are identified: those in which the temperature of the plasma is comparable to that of the electromagnetic radiation permeating it, and those in which the temperature of the plasma is much higher. Necessary conditions are developed for the propagation of such detonation waves for an arbitrarily great distance. The contribution of fusion chain reactions to these processes is evaluated. By means of these considerations, it is shown that neither the atmosphere nor oceans of the Earth may be made to undergo propagating nuclear detonation under any circumstances

Biodegradable blends of starch/polyvinyl alcohol/glycerol: multivariate analysis of the mechanical properties

Directory of Open Access Journals (Sweden)

Juliano Zanela

Full Text Available Abstract The aim of the work was to study the mechanical properties of extruded starch/polyvinyl alcohol (PVA/glycerol biodegradable blends using multivariate analysis. The blends were produced as cylindrical strands by extrusion using PVAs with different hydrolysis degrees and viscosities, at two extrusion temperature profiles (90/170/170/170/170 °C and 90/170/200/200/200 °C and three conditioning relative humidities of the samples (33, 53, and 75%. The mechanical properties showed a great variability according to PVA type, as well as the extrusion temperature profile and the conditioning relative humidity; the tensile strength ranged from 0.42 to 5.40 MPa, elongation at break ranged from 10 to 404% and Young’s modulus ranged from 0.93 to 13.81 MPa. The multivariate analysis was a useful methodology to study the mechanical properties behavior of starch/PVA/glycerol blends, and it can be used as an exploratory technique to select of the more suitable PVA type and extrusion temperature to produce biodegradable materials.

Adaptation to high throughput batch chromatography enhances multivariate screening.

Science.gov (United States)

Barker, Gregory A; Calzada, Joseph; Herzer, Sibylle; Rieble, Siegfried

2015-09-01

High throughput process development offers unique approaches to explore complex process design spaces with relatively low material consumption. Batch chromatography is one technique that can be used to screen chromatographic conditions in a 96-well plate. Typical batch chromatography workflows examine variations in buffer conditions or comparison of multiple resins in a given process, as opposed to the assessment of protein loading conditions in combination with other factors. A modification to the batch chromatography paradigm is described here where experimental planning, programming, and a staggered loading approach increase the multivariate space that can be explored with a liquid handling system. The iterative batch chromatography (IBC) approach is described, which treats every well in a 96-well plate as an individual experiment, wherein protein loading conditions can be varied alongside other factors such as wash and elution buffer conditions. As all of these factors are explored in the same experiment, the interactions between them are characterized and the number of follow-up confirmatory experiments is reduced. This in turn improves statistical power and throughput. Two examples of the IBC method are shown and the impact of the load conditions are assessed in combination with the other factors explored. Copyright © 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

The simultaneous use of several pseudo-random binary sequences in the identification of linear multivariable dynamic systems

International Nuclear Information System (INIS)

Cummins, J.D.

1965-02-01

With several white noise sources the various transmission paths of a linear multivariable system may be determined simultaneously. This memorandum considers the restrictions on pseudo-random two state sequences to effect simultaneous identification of several transmission paths and the consequential rejection of cross-coupled signals in linear multivariable systems. The conditions for simultaneous identification are established by an example, which shows that the integration time required is large i.e. tends to infinity, as it does when white noise sources are used. (author)

Multivariate Analysis and Machine Learning in Cerebral Palsy Research.

Science.gov (United States)

Zhang, Jing

2017-01-01

Cerebral palsy (CP), a common pediatric movement disorder, causes the most severe physical disability in children. Early diagnosis in high-risk infants is critical for early intervention and possible early recovery. In recent years, multivariate analytic and machine learning (ML) approaches have been increasingly used in CP research. This paper aims to identify such multivariate studies and provide an overview of this relatively young field. Studies reviewed in this paper have demonstrated that multivariate analytic methods are useful in identification of risk factors, detection of CP, movement assessment for CP prediction, and outcome assessment, and ML approaches have made it possible to automatically identify movement impairments in high-risk infants. In addition, outcome predictors for surgical treatments have been identified by multivariate outcome studies. To make the multivariate and ML approaches useful in clinical settings, further research with large samples is needed to verify and improve these multivariate methods in risk factor identification, CP detection, movement assessment, and outcome evaluation or prediction. As multivariate analysis, ML and data processing technologies advance in the era of Big Data of this century, it is expected that multivariate analysis and ML will play a bigger role in improving the diagnosis and treatment of CP to reduce mortality and morbidity rates, and enhance patient care for children with CP.

Multivariable biorthogonal continuous--discrete Wilson and Racah polynomials

International Nuclear Information System (INIS)

Tratnik, M.V.

1990-01-01

Several families of multivariable, biorthogonal, partly continuous and partly discrete, Wilson polynomials are presented. These yield limit cases that are purely continuous in some of the variables and purely discrete in the others, or purely discrete in all the variables. The latter are referred to as the multivariable biorthogonal Racah polynomials. Interesting further limit cases include the multivariable biorthogonal Hahn and dual Hahn polynomials

Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD): The TRIPOD Statement.

Science.gov (United States)

Collins, Gary S; Reitsma, Johannes B; Altman, Douglas G; Moons, Karel G M

2015-06-01

Prediction models are developed to aid health care providers in estimating the probability or risk that a specific disease or condition is present (diagnostic models) or that a specific event will occur in the future (prognostic models), to inform their decision making. However, the overwhelming evidence shows that the quality of reporting of prediction model studies is poor. Only with full and clear reporting of information on all aspects of a prediction model can risk of bias and potential usefulness of prediction models be adequately assessed. The Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) Initiative developed a set of recommendations for the reporting of studies developing, validating, or updating a prediction model, whether for diagnostic or prognostic purposes. This article describes how the TRIPOD Statement was developed. An extensive list of items based on a review of the literature was created, which was reduced after a Web-based survey and revised during a 3-day meeting in June 2011 with methodologists, health care professionals, and journal editors. The list was refined during several meetings of the steering group and in e-mail discussions with the wider group of TRIPOD contributors. The resulting TRIPOD Statement is a checklist of 22 items, deemed essential for transparent reporting of a prediction model study. The TRIPOD Statement aims to improve the transparency of the reporting of a prediction model study regardless of the study methods used. The TRIPOD Statement is best used in conjunction with the TRIPOD explanation and elaboration document. To aid the editorial process and readers of prediction model studies, it is recommended that authors include a completed checklist in their submission (also available at www.tripod-statement.org). The Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) Initiative developed a set of recommendations

Calculus of multivariate functions: it's application in business | Awen ...

African Journals Online (AJOL)

Multivariate functions can be applied to situations in business organizations like ... of capital invested in the plant, the size of the labour force and the cost of raw ... of multivariate functions and has considered types of multivariate differentiation ...

Multivariate calculus and geometry

CERN Document Server

Dineen, Seán

2014-01-01

Multivariate calculus can be understood best by combining geometric insight, intuitive arguments, detailed explanations and mathematical reasoning. This textbook has successfully followed this programme. It additionally provides a solid description of the basic concepts, via familiar examples, which are then tested in technically demanding situations. In this new edition the introductory chapter and two of the chapters on the geometry of surfaces have been revised. Some exercises have been replaced and others provided with expanded solutions. Familiarity with partial derivatives and a course in linear algebra are essential prerequisites for readers of this book. Multivariate Calculus and Geometry is aimed primarily at higher level undergraduates in the mathematical sciences. The inclusion of many practical examples involving problems of several variables will appeal to mathematics, science and engineering students.

Multivariable Feedback Control of Nuclear Reactors

Directory of Open Access Journals (Sweden)

Rune Moen

1982-07-01

Full Text Available Multivariable feedback control has been adapted for optimal control of the spatial power distribution in nuclear reactor cores. Two design techniques, based on the theory of automatic control, were developed: the State Variable Feedback (SVF is an application of the linear optimal control theory, and the Multivariable Frequency Response (MFR is based on a generalization of the traditional frequency response approach to control system design.

On the relation between S-Estimators and M-Estimators of multivariate location and covariance

NARCIS (Netherlands)

Lopuhaa, H.P.

1987-01-01

We discuss the relation between S-estimators and M-estimators of multivariate location and covariance. As in the case of the estimation of a multiple regression parameter, S-estimators are shown to satisfy first-order conditions of M-estimators. We show that the influence function IF (x;S F) of

Divergent series and memory of the initial condition in the long-time solution of some anomalous diffusion problems.

Science.gov (United States)

Yuste, S Bravo; Borrego, R; Abad, E

2010-02-01

We consider various anomalous d -dimensional diffusion problems in the presence of an absorbing boundary with radial symmetry. The motion of particles is described by a fractional diffusion equation. Their mean-square displacement is given by r(2) proportional, variant t(gamma)(0divergent series appear when the concentration or survival probabilities are evaluated via the method of separation of variables. While the solution for normal diffusion problems is, at most, divergent as t-->0 , the emergence of such series in the long-time domain is a specific feature of subdiffusion problems. We present a method to regularize such series, and, in some cases, validate the procedure by using alternative techniques (Laplace transform method and numerical simulations). In the normal diffusion case, we find that the signature of the initial condition on the approach to the steady state rapidly fades away and the solution approaches a single (the main) decay mode in the long-time regime. In remarkable contrast, long-time memory of the initial condition is present in the subdiffusive case as the spatial part Psi1(r) describing the long-time decay of the solution to the steady state is determined by a weighted superposition of all spatial modes characteristic of the normal diffusion problem, the weight being dependent on the initial condition. Interestingly, Psi1(r) turns out to be independent of the anomalous diffusion exponent gamma .

Hierarchical Hidden Markov Models for Multivariate Integer-Valued Time-Series

DEFF Research Database (Denmark)

Catania, Leopoldo; Di Mari, Roberto

2018-01-01

We propose a new flexible dynamic model for multivariate nonnegative integer-valued time-series. Observations are assumed to depend on the realization of two additional unobserved integer-valued stochastic variables which control for the time-and cross-dependence of the data. An Expectation......-Maximization algorithm for maximum likelihood estimation of the model's parameters is derived. We provide conditional and unconditional (cross)-moments implied by the model, as well as the limiting distribution of the series. A Monte Carlo experiment investigates the finite sample properties of our estimation...

Multivariate Marshall and Olkin Exponential Minification Process ...

African Journals Online (AJOL)

A stationary bivariate minification process with bivariate Marshall-Olkin exponential distribution that was earlier studied by Miroslav et al [15]is in this paper extended to multivariate minification process with multivariate Marshall and Olkin exponential distribution as its stationary marginal distribution. The innovation and the ...

Matrix-based introduction to multivariate data analysis

CERN Document Server

Adachi, Kohei

2016-01-01

This book enables readers who may not be familiar with matrices to understand a variety of multivariate analysis procedures in matrix forms. Another feature of the book is that it emphasizes what model underlies a procedure and what objective function is optimized for fitting the model to data. The author believes that the matrix-based learning of such models and objective functions is the fastest way to comprehend multivariate data analysis. The text is arranged so that readers can intuitively capture the purposes for which multivariate analysis procedures are utilized: plain explanations of the purposes with numerical examples precede mathematical descriptions in almost every chapter. This volume is appropriate for undergraduate students who already have studied introductory statistics. Graduate students and researchers who are not familiar with matrix-intensive formulations of multivariate data analysis will also find the book useful, as it is based on modern matrix formulations with a special emphasis on ...

Multivariate Analysis and Machine Learning in Cerebral Palsy Research

Directory of Open Access Journals (Sweden)

Jing Zhang

2017-12-01

Full Text Available Cerebral palsy (CP, a common pediatric movement disorder, causes the most severe physical disability in children. Early diagnosis in high-risk infants is critical for early intervention and possible early recovery. In recent years, multivariate analytic and machine learning (ML approaches have been increasingly used in CP research. This paper aims to identify such multivariate studies and provide an overview of this relatively young field. Studies reviewed in this paper have demonstrated that multivariate analytic methods are useful in identification of risk factors, detection of CP, movement assessment for CP prediction, and outcome assessment, and ML approaches have made it possible to automatically identify movement impairments in high-risk infants. In addition, outcome predictors for surgical treatments have been identified by multivariate outcome studies. To make the multivariate and ML approaches useful in clinical settings, further research with large samples is needed to verify and improve these multivariate methods in risk factor identification, CP detection, movement assessment, and outcome evaluation or prediction. As multivariate analysis, ML and data processing technologies advance in the era of Big Data of this century, it is expected that multivariate analysis and ML will play a bigger role in improving the diagnosis and treatment of CP to reduce mortality and morbidity rates, and enhance patient care for children with CP.

Validation of the criteria for initiating the cleaning of heating, ventilation, and air-conditioning (HVAC) ductwork under real conditions.

Science.gov (United States)

Lavoie, Jacques; Marchand, Geneviève; Cloutier, Yves; Lavoué, Jérôme

2011-08-01

Dust accumulation in the components of heating, ventilation, and air-conditioning (HVAC) systems is a potential source of contaminants. To date, very little information is available on recognized methods for assessing dust buildup in these systems. The few existing methods are either objective in nature, involving numerical values, or subjective in nature, based on experts' judgments. An earlier project aimed at assessing different methods of sampling dust in ducts was carried out in the laboratories of the Institut de recherche Robert-Sauvé en santé et en sécurité du travail (IRSST). This laboratory study showed that all the sampling methods were practicable, provided that a specific surface-dust cleaning initiation criterion was used for each method. However, these conclusions were reached on the basis of ideal conditions in a laboratory using a reference dust. The objective of this present study was to validate these laboratory results in the field. To this end, the laboratory sampling templates were replicated in real ducts and the three sampling methods (the IRSST method, the method of the U.S. organization National Air Duct Cleaner Association [NADCA] and that of the French organization Association pour la Prévention et l'Étude de la Contamination [ASPEC]) were used simultaneously in a statistically representative number of systems. The air return and supply ducts were also compared. Cleaning initiation criteria under real conditions were found to be 6.0 mg/100 cm(2) using the IRSST method, 2.0 mg/100 cm(2) using the NADCA method, and 23 mg/100 cm(2) using the ASPEC method. In the laboratory study, the criteria using the same methods were 6.0 for the IRSST method, 2.0 for the NADCA method, and 3.0 for the ASPEC method. The laboratory criteria for the IRSST and NADCA methods were therefore validated in the field. The ASPEC criterion was the only one to change. The ASPEC method therefore allows for the most accurate evaluation of dust accumulation in HVAC

Initial conditions for inflation and the energy scale of SUSY-breaking from the (nearly) gaussian sky

CERN Document Server

Álvarez-Gaumé, Luis; Jimenez, Raul

We show how general initial conditions for small field inflation can be obtained in multi-field models. This is provided by non-linear angular friction terms in the inflaton that provide a phase of non-slow-roll inflation before the slow-roll inflation phase. This in turn provides a natural mechanism to star small-field slow-roll at nearly zero velocity for arbitrary initial conditions. We also show that there is a relation between the scale of SUSY breaking sqrt (f) and the amount of non-gaussian fluctuations generated by the inflaton. In particular, we show that in the local non-gaussian shape there exists the relation sqrt (f) = 10^{13} GeV sqrt (f_NL). With current observational limits from Planck, and adopting the minimum amount of non-gaussian fluctuations allowed by single-field inflation, this provides a very tight constraint for the SUSY breaking energy scale sqrt (f) = 3-7 x 10^{13} GeV at 95% confidence. Further limits, or detection, from next year's Planck polarisation data will further tighten th...

Zn(II, Mn(II and Sr(II Behavior in a Natural Carbonate Reservoir System. Part I: Impact of Salinity, Initial pH and Initial Zn(II Concentration in Atmospheric Conditions

Directory of Open Access Journals (Sweden)

Auffray B.

2016-07-01

Full Text Available The sorption of inorganic elements on carbonate minerals is well known in strictly controlled conditions which limit the impact of other phenomena such as dissolution and/or precipitation. In this study, we evidence the behavior of Zn(II (initially in solution and two trace elements, Mn(II and Sr(II (released by carbonate dissolution in the context of a leakage from a CO2 storage site. The initial pH chosen are either equal to the pH of the water-CO2 equilibrium (~ 2.98 or equal to the pH of the water-CO2-calcite system (~ 4.8 in CO2 storage conditions. From this initial influx of liquid, saturated or not with respect to calcite, the batch experiments evolve freely to their equilibrium, as it would occur in a natural context after a perturbation. The batch experiments are carried out on two natural carbonates (from Lavoux and St-Emilion with PCO2 = 10−3.5 bar, with different initial conditions ([Zn(II]i from 10−4 to 10−6 M, either with pure water or 100 g/L NaCl brine. The equilibrium regarding calcite dissolution is confirmed in all experiments, while the zinc sorption evidenced does not always correspond to the two-step mechanism described in the literature. A preferential sorption of about 10% of the concentration is evidenced for Mn(II in aqueous experiments, while Sr(II is more sorbed in saline conditions. This study also shows that this preferential sorption, depending on the salinity, is independent of the natural carbonate considered. Then, the simulations carried out with PHREEQC show that experiments and simulations match well concerning the equilibrium of dissolution and the sole zinc sorption, with log KZn(II ~ 2 in pure water and close to 4 in high salinity conditions. When the simulations were possible, the log K values for Mn(II and Sr(II were much different from those in the literature obtained by sorption in controlled conditions. It is shown that a new conceptual model regarding multiple Trace Elements (TE sorption is

Marshall-Olkin multivariate semi-logistic distribution and minification ...

African Journals Online (AJOL)

Olkin multivariate logistic distribution (MO-ML) are introduced and studied. Various characterizations properties of Marshall-Olkin multivariate semi-logistic distribution are investigated and studied. First order autoregressive minification processes ...

Multivariate Cryptography Based on Clipped Hopfield Neural Network.

Science.gov (United States)

Wang, Jia; Cheng, Lee-Ming; Su, Tong

2018-02-01

Designing secure and efficient multivariate public key cryptosystems [multivariate cryptography (MVC)] to strengthen the security of RSA and ECC in conventional and quantum computational environment continues to be a challenging research in recent years. In this paper, we will describe multivariate public key cryptosystems based on extended Clipped Hopfield Neural Network (CHNN) and implement it using the MVC (CHNN-MVC) framework operated in space. The Diffie-Hellman key exchange algorithm is extended into the matrix field, which illustrates the feasibility of its new applications in both classic and postquantum cryptography. The efficiency and security of our proposed new public key cryptosystem CHNN-MVC are simulated and found to be NP-hard. The proposed algorithm will strengthen multivariate public key cryptosystems and allows hardware realization practicality.

A MATLAB companion for multivariable calculus

CERN Document Server

Cooper, Jeffery

2001-01-01

Offering a concise collection of MatLab programs and exercises to accompany a third semester course in multivariable calculus, A MatLab Companion for Multivariable Calculus introduces simple numerical procedures such as numerical differentiation, numerical integration and Newton''s method in several variables, thereby allowing students to tackle realistic problems. The many examples show students how to use MatLab effectively and easily in many contexts. Numerous exercises in mathematics and applications areas are presented, graded from routine to more demanding projects requiring some programming. Matlab M-files are provided on the Harcourt/Academic Press web site at http://www.harcourt-ap.com/matlab.html.* Computer-oriented material that complements the essential topics in multivariable calculus* Main ideas presented with examples of computations and graphics displays using MATLAB * Numerous examples of short code in the text, which can be modified for use with the exercises* MATLAB files are used to implem...

A multivariable model for predicting the frictional behaviour and hydration of the human skin.

Science.gov (United States)

Veijgen, N K; van der Heide, E; Masen, M A

2013-08-01

The frictional characteristics of skin-object interactions are important when handling objects, in the assessment of perception and comfort of products and materials and in the origins and prevention of skin injuries. In this study, based on statistical methods, a quantitative model is developed that describes the friction behaviour of human skin as a function of the subject characteristics, contact conditions, the properties of the counter material as well as environmental conditions. Although the frictional behaviour of human skin is a multivariable problem, in literature the variables that are associated with skin friction have been studied using univariable methods. In this work, multivariable models for the static and dynamic coefficients of friction as well as for the hydration of the skin are presented. A total of 634 skin-friction measurements were performed using a recently developed tribometer. Using a statistical analysis, previously defined potential influential variables were linked to the static and dynamic coefficient of friction and to the hydration of the skin, resulting in three predictive quantitative models that descibe the friction behaviour and the hydration of human skin respectively. Increased dynamic coefficients of friction were obtained from older subjects, on the index finger, with materials with a higher surface energy at higher room temperatures, whereas lower dynamic coefficients of friction were obtained at lower skin temperatures, on the temple with rougher contact materials. The static coefficient of friction increased with higher skin hydration, increasing age, on the index finger, with materials with a higher surface energy and at higher ambient temperatures. The hydration of the skin was associated with the skin temperature, anatomical location, presence of hair on the skin and the relative air humidity. Predictive models have been derived for the static and dynamic coefficient of friction using a multivariable approach. These

The Effect of Initial Irrigation Conditions on Heap Leaching Efficiency

Science.gov (United States)

Briseño Arellano, A. D.; Milczarek, M.; Yao, M.; Brusseau, M. L. L.

2017-12-01

Heap leaching is an unsaturated flow metal recovery process, in which mined ore is irrigated with a lixiviant to dissolve metal contained in the ore. The metal is then extracted from solution. Large scale operations involve stacking ore to depths of 6 to 18 meters on pads that may be hundreds of hectares in area. Heterogeneities within the stacked ore can lead to uneven wetting and the formation of preferential flow pathways, which reduces solution contact and lowers metal recovery. Furthermore, mineral dissolution can cause alteration of the porous media structure and loss of ore permeability. Many mine operators believe that slow initial irrigation rates help minimize permeability loss and increase metal recovery rates. However, this phenomenon has not been studied in detail. Experiments were conducted to investigate the effect of varying initial irrigation rates on leach ore stability. These were conducted with large columns (1.5 m high, 0.5 m in diameter) packed with crushed ore samples that are known to have permeability constraints. The columns were highly instrumented to assess potential changes in material properties both spatially and temporally. Water content was measured with three different methods: capacitance soil moisture sensors placed at 20-cm intervals; a neutron probe to periodically log every 30 cm from four different directions; and electrical resistivity sensors to create a 2-dimensional tomography profile of water content over time. Tensiometers were paired with the soil moisture sensors to measure matric suction and characterize moisture retention characteristics. A non-reactive tracer was used to characterize advective-dispersive transport under unsaturated conditions. A dye solution was introduced at the end of each experiment to map preferential pathways. Continuous monitoring of settling at the surface assisted in measuring consolidation and loss in permeability.

Multivariate spatial condition mapping using subtractive fuzzy cluster means.

Science.gov (United States)

Sabit, Hakilo; Al-Anbuky, Adnan

2014-10-13

Wireless sensor networks are usually deployed for monitoring given physical phenomena taking place in a specific space and over a specific duration of time. The spatio-temporal distribution of these phenomena often correlates to certain physical events. To appropriately characterise these events-phenomena relationships over a given space for a given time frame, we require continuous monitoring of the conditions. WSNs are perfectly suited for these tasks, due to their inherent robustness. This paper presents a subtractive fuzzy cluster means algorithm and its application in data stream mining for wireless sensor systems over a cloud-computing-like architecture, which we call sensor cloud data stream mining. Benchmarking on standard mining algorithms, the k-means and the FCM algorithms, we have demonstrated that the subtractive fuzzy cluster means model can perform high quality distributed data stream mining tasks comparable to centralised data stream mining.

Multivariate Time Series Decomposition into Oscillation Components.

Science.gov (United States)

Matsuda, Takeru; Komaki, Fumiyasu

2017-08-01

Many time series are considered to be a superposition of several oscillation components. We have proposed a method for decomposing univariate time series into oscillation components and estimating their phases (Matsuda & Komaki, 2017 ). In this study, we extend that method to multivariate time series. We assume that several oscillators underlie the given multivariate time series and that each variable corresponds to a superposition of the projections of the oscillators. Thus, the oscillators superpose on each variable with amplitude and phase modulation. Based on this idea, we develop gaussian linear state-space models and use them to decompose the given multivariate time series. The model parameters are estimated from data using the empirical Bayes method, and the number of oscillators is determined using the Akaike information criterion. Therefore, the proposed method extracts underlying oscillators in a data-driven manner and enables investigation of phase dynamics in a given multivariate time series. Numerical results show the effectiveness of the proposed method. From monthly mean north-south sunspot number data, the proposed method reveals an interesting phase relationship.

Effects of Heterogeneity and Uncertainties in Sources and Initial and Boundary Conditions on Spatiotemporal Variations of Groundwater Levels

Science.gov (United States)

Zhang, Y. K.; Liang, X.

2014-12-01

Effects of aquifer heterogeneity and uncertainties in source/sink, and initial and boundary conditions in a groundwater flow model on the spatiotemporal variations of groundwater level, h(x,t), were investigated. Analytical solutions for the variance and covariance of h(x, t) in an unconfined aquifer described by a linearized Boussinesq equation with a white noise source/sink and a random transmissivity field were derived. It was found that in a typical aquifer the error in h(x,t) in early time is mainly caused by the random initial condition and the error reduces as time goes to reach a constant error in later time. The duration during which the effect of the random initial condition is significant may last a few hundred days in most aquifers. The constant error in groundwater in later time is due to the combined effects of the uncertain source/sink and flux boundary: the closer to the flux boundary, the larger the error. The error caused by the uncertain head boundary is limited in a narrow zone near the boundary but it remains more or less constant over time. The effect of the heterogeneity is to increase the variation of groundwater level and the maximum effect occurs close to the constant head boundary because of the linear mean hydraulic gradient. The correlation of groundwater level decreases with temporal interval and spatial distance. In addition, the heterogeneity enhances the correlation of groundwater level, especially at larger time intervals and small spatial distances.

Multivariate Receptor Models for Spatially Correlated Multipollutant Data

KAUST Repository

Jun, Mikyoung; Park, Eun Sug

2013-01-01

The goal of multivariate receptor modeling is to estimate the profiles of major pollution sources and quantify their impacts based on ambient measurements of pollutants. Traditionally, multivariate receptor modeling has been applied to multiple air

AN APPLICATION OF FUNCTIONAL MULTIVARIATE REGRESSION MODEL TO MULTICLASS CLASSIFICATION

OpenAIRE

Krzyśko, Mirosław; Smaga, Łukasz

2017-01-01

In this paper, the scale response functional multivariate regression model is considered. By using the basis functions representation of functional predictors and regression coefficients, this model is rewritten as a multivariate regression model. This representation of the functional multivariate regression model is used for multiclass classification for multivariate functional data. Computational experiments performed on real labelled data sets demonstrate the effectiveness of the proposed ...

Hydrological modelling for flood forecasting: Calibrating the post-fire initial conditions

Science.gov (United States)

Papathanasiou, C.; Makropoulos, C.; Mimikou, M.

2015-10-01

Floods and forest fires are two of the most devastating natural hazards with severe socioeconomic, environmental as well as aesthetic impacts on the affected areas. Traditionally, these hazards are examined from different perspectives and are thus investigated through different, independent systems, overlooking the fact that they are tightly interrelated phenomena. In fact, the same flood event is more severe, i.e. associated with increased runoff discharge and peak flow and decreased time to peak, if it occurs over a burnt area than that occurring over a land not affected by fire. Mediterranean periurban areas, where forests covered with flammable vegetation coexist with agricultural land and urban zones, are typical areas particularly prone to the combined impact of floods and forest fires. Hence, the accurate assessment and effective management of post-fire flood risk becomes an issue of priority. The research presented in this paper aims to develop a robust methodological framework, using state of art tools and modern technologies to support the estimation of the change in time of five representative hydrological parameters for post-fire conditions. The proposed methodology considers both longer- and short-term initial conditions in order to assess the dynamic evolution of the selected parameters. The research focuses on typical Mediterranean periurban areas that are subjected to both hazards and concludes with a set of equations that associate post-fire and pre-fire conditions for five Fire Severity (FS) classes and three soil moisture states. The methodology has been tested for several flood events on the Rafina catchment, a periurban catchment in Eastern Attica (Greece). In order to validate the methodology, simulated hydrographs were produced and compared against available observed data. Results indicate a close convergence of observed and simulated flows. The proposed methodology is particularly flexible and thus easily adaptable to catchments with similar

Alternating multivariate trigonometric functions and corresponding Fourier transforms

International Nuclear Information System (INIS)

Klimyk, A U; Patera, J

2008-01-01

We define and study multivariate sine and cosine functions, symmetric with respect to the alternating group A n , which is a subgroup of the permutation (symmetric) group S n . These functions are eigenfunctions of the Laplace operator. They determine Fourier-type transforms. There exist three types of such transforms: expansions into corresponding sine-Fourier and cosine-Fourier series, integral sine-Fourier and cosine-Fourier transforms, and multivariate finite sine and cosine transforms. In all these transforms, alternating multivariate sine and cosine functions are used as a kernel

Fractional and multivariable calculus model building and optimization problems

CERN Document Server

Mathai, A M

2017-01-01

This textbook presents a rigorous approach to multivariable calculus in the context of model building and optimization problems. This comprehensive overview is based on lectures given at five SERC Schools from 2008 to 2012 and covers a broad range of topics that will enable readers to understand and create deterministic and nondeterministic models. Researchers, advanced undergraduate, and graduate students in mathematics, statistics, physics, engineering, and biological sciences will find this book to be a valuable resource for finding appropriate models to describe real-life situations. The first chapter begins with an introduction to fractional calculus moving on to discuss fractional integrals, fractional derivatives, fractional differential equations and their solutions. Multivariable calculus is covered in the second chapter and introduces the fundamentals of multivariable calculus (multivariable functions, limits and continuity, differentiability, directional derivatives and expansions of multivariable ...

A MULTIVARIATE WEIBULL DISTRIBUTION

Directory of Open Access Journals (Sweden)

Cheng Lee

2010-07-01

Full Text Available A multivariate survival function of Weibull Distribution is developed by expanding the theorem by Lu and Bhattacharyya. From the survival function, the probability density function, the cumulative probability function, the determinant of the Jacobian Matrix, and the general moment are derived.

Scattering amplitudes from multivariate polynomial division

Energy Technology Data Exchange (ETDEWEB)

Mastrolia, Pierpaolo, E-mail: pierpaolo.mastrolia@cern.ch [Max-Planck-Institut fuer Physik, Foehringer Ring 6, 80805 Muenchen (Germany); Dipartimento di Fisica e Astronomia, Universita di Padova, Padova (Italy); INFN Sezione di Padova, via Marzolo 8, 35131 Padova (Italy); Mirabella, Edoardo, E-mail: mirabell@mppmu.mpg.de [Max-Planck-Institut fuer Physik, Foehringer Ring 6, 80805 Muenchen (Germany); Ossola, Giovanni, E-mail: GOssola@citytech.cuny.edu [New York City College of Technology, City University of New York, 300 Jay Street, Brooklyn, NY 11201 (United States); Graduate School and University Center, City University of New York, 365 Fifth Avenue, New York, NY 10016 (United States); Peraro, Tiziano, E-mail: peraro@mppmu.mpg.de [Max-Planck-Institut fuer Physik, Foehringer Ring 6, 80805 Muenchen (Germany)

2012-11-15

We show that the evaluation of scattering amplitudes can be formulated as a problem of multivariate polynomial division, with the components of the integration-momenta as indeterminates. We present a recurrence relation which, independently of the number of loops, leads to the multi-particle pole decomposition of the integrands of the scattering amplitudes. The recursive algorithm is based on the weak Nullstellensatz theorem and on the division modulo the Groebner basis associated to all possible multi-particle cuts. We apply it to dimensionally regulated one-loop amplitudes, recovering the well-known integrand-decomposition formula. Finally, we focus on the maximum-cut, defined as a system of on-shell conditions constraining the components of all the integration-momenta. By means of the Finiteness Theorem and of the Shape Lemma, we prove that the residue at the maximum-cut is parametrized by a number of coefficients equal to the number of solutions of the cut itself.

Features of the development of round jets for different initial conditions and in the presence of obstacles

Science.gov (United States)

Kozlov, V. V.; Litvinenko, M. V.; Litvinenko, Yu. A.; Kozlov, G. V.

2016-10-01

The goal of this work is an experimental study of the influence of the initial conditions (nozzle configuration, mean velocity profile at the nozzle exit, surface roughness, and jet diameter) on the flow structure in a round jet by various methods: hot-wire anemometry, smoke visualization, and particle image velocimetry (PIV).

Multivariate max-stable spatial processes

KAUST Repository

Genton, Marc G.; Padoan, S. A.; Sang, H.

2015-01-01

Max-stable processes allow the spatial dependence of extremes to be modelled and quantified, so they are widely adopted in applications. For a better understanding of extremes, it may be useful to study several variables simultaneously. To this end, we study the maxima of independent replicates of multivariate processes, both in the Gaussian and Student-t cases. We define a Poisson process construction and introduce multivariate versions of the Smith Gaussian extreme-value, the Schlather extremal-Gaussian and extremal-t, and the Brown–Resnick models. We develop inference for the models based on composite likelihoods. We present results of Monte Carlo simulations and an application to daily maximum wind speed and wind gust.

Multivariate max-stable spatial processes

KAUST Repository

Genton, Marc G.

2015-02-11

Max-stable processes allow the spatial dependence of extremes to be modelled and quantified, so they are widely adopted in applications. For a better understanding of extremes, it may be useful to study several variables simultaneously. To this end, we study the maxima of independent replicates of multivariate processes, both in the Gaussian and Student-t cases. We define a Poisson process construction and introduce multivariate versions of the Smith Gaussian extreme-value, the Schlather extremal-Gaussian and extremal-t, and the Brown–Resnick models. We develop inference for the models based on composite likelihoods. We present results of Monte Carlo simulations and an application to daily maximum wind speed and wind gust.

Initial reaction between CaO and SO₂ under carbonating and non-carbonating conditions

DEFF Research Database (Denmark)

Rasmussen, Martin Hagsted; Wedel, Stig; Pedersen, Kim H.

2015-01-01

The initial kinetics of the CaO/SO2 reaction have been investigated for reaction times shorter than 1s and in the temperature interval between 450 and 600°C under both carbonating and non-carbonating conditions (0-20 vol% CO2) to clarify how recirculating CaO influences the emission of SO2 from...... showed that the CaO conversion with respect to SO2 declined when the CO2 concentration was increased. Under all conditions, larger specific surface areas of CaO gave higher reaction rates with SO2. Higher temperatures had a positive effect on the reaction between SO2 and CaO under non......-carbonating conditions, but no or even a negative effect under carbonating conditions. The results led to the conclusion that SO2 released from raw meal in the upper stages of the preheater does not to any significant extent react with CaO recirculating in the preheater tower....

Multivariate analysis in the frequency mastery applied to the Laguna Verde Central; Analisis multivariable en el dominio de la frecuencia aplicado a la Central Laguna Verde

Energy Technology Data Exchange (ETDEWEB)

Castillo D, R.; Ortiz V, J. [ININ, 52045 Ocoyoacac, Estado de Mexico (Mexico); Calleros M, G. [CFE, Central Nucleoelectrica de Laguna Verde, carretera Nautla-Cardel Km. 42.5, Alto Lucero, Veracruz (Mexico)]. e-mail: rcd@nuclear.inin.mx

2006-07-01

The noise analysis is an auxiliary tool in the detection of abnormal operation conditions of equipment, instruments or systems that affect to the dynamic behavior of the reactor. The spectral density of normalized power has usually been used (NPSD, by its initials in English), to watch over the behavior of some components of the reactor, for example, the jet pumps, the recirculation pumps, valves of flow control in the recirculation knots, etc. The behavior change is determined by individual analysis of the NPSD of the signals of the components in study. An alternative analysis that can allow to obtain major information on the component under surveillance is the multivariate autoregressive analysis (MAR, by its initials in English), which allows to know the relationship that exists among diverse signals of the reactor systems, in the time domain. In the space of the frequency, the relative contribution of power (RPC for their initials in English) it quantifies the influence of the variables of the systems on a variable of interest. The RPC allows, therefore that for a peak shown in the NPSD of a variable, it can be determine the influence from other variables to that frequency of interest. This facilitates, in principle, the pursuit of the important physical parameters during an event, and to study their interrelation. In this work, by way of example of the application of the RPC, two events happened in the Laguna Verde Central are analyzed: the rods blockade alarms by high scale in the monitors of average power, in which it was presents a power peak of 12% of width peak to peak, and the power oscillations event. The main obtained result of the analysis of the control rods blockade alarm event was that it was detected that the power peak observed in the signals of the average power monitors was caused by the movement of the valve of flow control of recirculation of the knot B. In the other oscillation event the results its show the mechanism of the oscillation of

A comparison of multivariate genome-wide association methods

DEFF Research Database (Denmark)

Galesloot, Tessel E; Van Steen, Kristel; Kiemeney, Lambertus A L M

2014-01-01

Joint association analysis of multiple traits in a genome-wide association study (GWAS), i.e. a multivariate GWAS, offers several advantages over analyzing each trait in a separate GWAS. In this study we directly compared a number of multivariate GWAS methods using simulated data. We focused on six...... methods that are implemented in the software packages PLINK, SNPTEST, MultiPhen, BIMBAM, PCHAT and TATES, and also compared them to standard univariate GWAS, analysis of the first principal component of the traits, and meta-analysis of univariate results. We simulated data (N = 1000) for three...... for scenarios with an opposite sign of genetic and residual correlation. All multivariate analyses resulted in a higher power than univariate analyses, even when only one of the traits was associated with the QTL. Hence, use of multivariate GWAS methods can be recommended, even when genetic correlations between...

Multivariate return periods in hydrology: a critical and practical review focusing on synthetic design hydrograph estimation

Directory of Open Access Journals (Sweden)

B. Gräler

2013-04-01

Full Text Available Most of the hydrological and hydraulic studies refer to the notion of a return period to quantify design variables. When dealing with multiple design variables, the well-known univariate statistical analysis is no longer satisfactory, and several issues challenge the practitioner. How should one incorporate the dependence between variables? How should a multivariate return period be defined and applied in order to yield a proper design event? In this study an overview of the state of the art for estimating multivariate design events is given and the different approaches are compared. The construction of multivariate distribution functions is done through the use of copulas, given their practicality in multivariate frequency analyses and their ability to model numerous types of dependence structures in a flexible way. A synthetic case study is used to generate a large data set of simulated discharges that is used for illustrating the effect of different modelling choices on the design events. Based on different uni- and multivariate approaches, the design hydrograph characteristics of a 3-D phenomenon composed of annual maximum peak discharge, its volume, and duration are derived. These approaches are based on regression analysis, bivariate conditional distributions, bivariate joint distributions and Kendall distribution functions, highlighting theoretical and practical issues of multivariate frequency analysis. Also an ensemble-based approach is presented. For a given design return period, the approach chosen clearly affects the calculated design event, and much attention should be given to the choice of the approach used as this depends on the real-world problem at hand.

Relating N2O emissions during biological nitrogen removal with operating conditions using multivariate statistical techniques.

Science.gov (United States)

Vasilaki, V; Volcke, E I P; Nandi, A K; van Loosdrecht, M C M; Katsou, E

2018-04-26

Multivariate statistical analysis was applied to investigate the dependencies and underlying patterns between N 2 O emissions and online operational variables (dissolved oxygen and nitrogen component concentrations, temperature and influent flow-rate) during biological nitrogen removal from wastewater. The system under study was a full-scale reactor, for which hourly sensor data were available. The 15-month long monitoring campaign was divided into 10 sub-periods based on the profile of N 2 O emissions, using Binary Segmentation. The dependencies between operating variables and N 2 O emissions fluctuated according to Spearman's rank correlation. The correlation between N 2 O emissions and nitrite concentrations ranged between 0.51 and 0.78. Correlation >0.7 between N 2 O emissions and nitrate concentrations was observed at sub-periods with average temperature lower than 12 °C. Hierarchical k-means clustering and principal component analysis linked N 2 O emission peaks with precipitation events and ammonium concentrations higher than 2 mg/L, especially in sub-periods characterized by low N 2 O fluxes. Additionally, the highest ranges of measured N 2 O fluxes belonged to clusters corresponding with NO 3 -N concentration less than 1 mg/L in the upstream plug-flow reactor (middle of oxic zone), indicating slow nitrification rates. The results showed that the range of N 2 O emissions partially depends on the prior behavior of the system. The principal component analysis validated the findings from the clustering analysis and showed that ammonium, nitrate, nitrite and temperature explained a considerable percentage of the variance in the system for the majority of the sub-periods. The applied statistical methods, linked the different ranges of emissions with the system variables, provided insights on the effect of operating conditions on N 2 O emissions in each sub-period and can be integrated into N 2 O emissions data processing at wastewater treatment plants

Broccoli (Brassica oleracea var. italica head initiation under field conditions

Directory of Open Access Journals (Sweden)

Alina Kałużewicz

2012-12-01

Full Text Available A two–year study on the influence of temperature on broccoli head initiation was carried out at the ''Marcelin'' experimental station of the Poznań University of Life Sciences. In each year of the study, plants were planted in the field at four dates. The evaluation of the developmental phase of the broccoli shoot apex was based on the analysis of microscope slides. The date of head initiation was assumed as the day on which the first of the examined apices were found to be at the early generative phase. The plant characteristics (number of leaves, leaf area and stem diameter on the date of initiation were also determined. Variation in length of the period from planting to head initiation was found both between dates of planting and between experimental years. The shortest period from planting to initiation was when the plants were planted in April and June (17-18 days in the first year and the longest one for planting in April in the first year of the study (29 days. The length of the period from planting to head initiation depended on mean daily air temperature. The higher the temperature was, the shorter was the period.

Nonparametric Bayes Modeling of Multivariate Categorical Data.

Science.gov (United States)

Dunson, David B; Xing, Chuanhua

2012-01-01

Modeling of multivariate unordered categorical (nominal) data is a challenging problem, particularly in high dimensions and cases in which one wishes to avoid strong assumptions about the dependence structure. Commonly used approaches rely on the incorporation of latent Gaussian random variables or parametric latent class models. The goal of this article is to develop a nonparametric Bayes approach, which defines a prior with full support on the space of distributions for multiple unordered categorical variables. This support condition ensures that we are not restricting the dependence structure a priori. We show this can be accomplished through a Dirichlet process mixture of product multinomial distributions, which is also a convenient form for posterior computation. Methods for nonparametric testing of violations of independence are proposed, and the methods are applied to model positional dependence within transcription factor binding motifs.

Multivariate methods in nuclear waste remediation: Needs and applications

International Nuclear Information System (INIS)

Pulsipher, B.A.

1992-05-01

The United States Department of Energy (DOE) has developed a strategy for nuclear waste remediation and environmental restoration at several major sites across the country. Nuclear and hazardous wastes are found in underground storage tanks, containment drums, soils, and facilities. Due to the many possible contaminants and complexities of sampling and analysis, multivariate methods are directly applicable. However, effective application of multivariate methods will require greater ability to communicate methods and results to a non-statistician community. Moreover, more flexible multivariate methods may be required to accommodate inherent sampling and analysis limitations. This paper outlines multivariate applications in the context of select DOE environmental restoration activities and identifies several perceived needs

Oil and stock market volatility: A multivariate stochastic volatility perspective

International Nuclear Information System (INIS)

Vo, Minh

2011-01-01

This paper models the volatility of stock and oil futures markets using the multivariate stochastic volatility structure in an attempt to extract information intertwined in both markets for risk prediction. It offers four major findings. First, the stock and oil futures prices are inter-related. Their correlation follows a time-varying dynamic process and tends to increase when the markets are more volatile. Second, conditioned on the past information, the volatility in each market is very persistent, i.e., it varies in a predictable manner. Third, there is inter-market dependence in volatility. Innovations that hit either market can affect the volatility in the other market. In other words, conditioned on the persistence and the past volatility in their respective markets, the past volatility of the stock (oil futures) market also has predictive power over the future volatility of the oil futures (stock) market. Finally, the model produces more accurate Value-at-Risk estimates than other benchmarks commonly used in the financial industry. - Research Highlights: → This paper models the volatility of stock and oil futures markets using the multivariate stochastic volatility model. → The correlation between the two markets follows a time-varying dynamic process which tends to increase when the markets are more volatile. → The volatility in each market is very persistent. → Innovations that hit either market can affect the volatility in the other market. → The model produces more accurate Value-at-Risk estimates than other benchmarks commonly used in the financial industry.

Bayesian inference for multivariate point processes observed at sparsely distributed times

DEFF Research Database (Denmark)

Rasmussen, Jakob Gulddahl; Møller, Jesper; Aukema, B.H.

We consider statistical and computational aspects of simulation-based Bayesian inference for a multivariate point process which is only observed at sparsely distributed times. For specicity we consider a particular data set which has earlier been analyzed by a discrete time model involving unknown...... normalizing constants. We discuss the advantages and disadvantages of using continuous time processes compared to discrete time processes in the setting of the present paper as well as other spatial-temporal situations. Keywords: Bark beetle, conditional intensity, forest entomology, Markov chain Monte Carlo...

A Tourism Conditions Index

NARCIS (Netherlands)

C-L. Chang (Chia-Lin); H-K. Hsu (Hui-Kuang); M.J. McAleer (Michael)

2014-01-01

markdownabstract__Abstract__ This paper uses monthly data from April 2005 to August 2013 for Taiwan to propose a novel tourism indicator, namely the Tourism Conditions Index (TCI). TCI accounts for the spillover weights based on the Granger causality test and estimates of the multivariate BEKK

A Spatially Constrained Multi-autoencoder Approach for Multivariate Geochemical Anomaly Recognition

Science.gov (United States)

Lirong, C.; Qingfeng, G.; Renguang, Z.; Yihui, X.

2017-12-01

Separating and recognizing geochemical anomalies from the geochemical background is one of the key tasks in geochemical exploration. Many methods have been developed, such as calculating the mean ±2 standard deviation, and fractal/multifractal models. In recent years, deep autoencoder, a deep learning approach, have been used for multivariate geochemical anomaly recognition. While being able to deal with the non-normal distributions of geochemical concentrations and the non-linear relationships among them, this self-supervised learning method does not take into account the spatial heterogeneity of geochemical background and the uncertainty induced by the randomly initialized weights of neurons, leading to ineffective recognition of weak anomalies. In this paper, we introduce a spatially constrained multi-autoencoder (SCMA) approach for multivariate geochemical anomaly recognition, which includes two steps: spatial partitioning and anomaly score computation. The first step divides the study area into multiple sub-regions to segregate the geochemical background, by grouping the geochemical samples through K-means clustering, spatial filtering, and spatial constraining rules. In the second step, for each sub-region, a group of autoencoder neural networks are constructed with an identical structure but different initial weights on neurons. Each autoencoder is trained using the geochemical samples within the corresponding sub-region to learn the sub-regional geochemical background. The best autoencoder of a group is chosen as the final model for the corresponding sub-region. The anomaly score at each location can then be calculated as the euclidean distance between the observed concentrations and reconstructed concentrations of geochemical elements.The experiments using the geochemical data and Fe deposits in the southwestern Fujian province of China showed that our SCMA approach greatly improved the recognition of weak anomalies, achieving the AUC of 0.89, compared

Initial growth of Costus longebracteolatus and Costus spiralis ‘French Kiss’ under different light conditions

Directory of Open Access Journals (Sweden)

Renata Bachin Mazzini-Guedes

2016-12-01

Full Text Available The Brazilian native Costus longebracteolatus and Costus spiralis ‘French Kiss’, in the family Costaceae, have been used as both cut flowers and cut foliage. It is known that Costus species grow better under partial shade, but studies on the influence of shading or light on plant growth, development, and flower production are still incipient. As this kind of information is essential on planning of planting, production, and agribusiness activities, the objective of this research was to evaluate the influence of different colored shade nets and light conditions on the initial growth of both C. longebracteolatus and C. spiralis ‘French Kiss’. Plants, obtained from cuttings of pseudostems, were cultivated under six light conditions, which comprised six treatments, along 270 days: red net with 50% shading, blue net with 50% shading, black net with 70% shading, black net with 50% shading, black net with 30% shading, and full sun. The initial growth of C. longebracteolatus (up to 270 days is more successful under the blue net with 50% shading, which promoted highest values of pseudostem length and dry matter of aerial part. For C. spiralis ‘French Kiss’ plants, both the red and blue nets with 50% shading implied best results and differed for the other treatments with greater pseudostem length. Plant exposure to full sun inhibited growth and development, and favored early leaf necrosis.

A new multivariate zero-adjusted Poisson model with applications to biomedicine.

Science.gov (United States)

Liu, Yin; Tian, Guo-Liang; Tang, Man-Lai; Yuen, Kam Chuen

2018-05-25

Recently, although advances were made on modeling multivariate count data, existing models really has several limitations: (i) The multivariate Poisson log-normal model (Aitchison and Ho, ) cannot be used to fit multivariate count data with excess zero-vectors; (ii) The multivariate zero-inflated Poisson (ZIP) distribution (Li et al., 1999) cannot be used to model zero-truncated/deflated count data and it is difficult to apply to high-dimensional cases; (iii) The Type I multivariate zero-adjusted Poisson (ZAP) distribution (Tian et al., 2017) could only model multivariate count data with a special correlation structure for random components that are all positive or negative. In this paper, we first introduce a new multivariate ZAP distribution, based on a multivariate Poisson distribution, which allows the correlations between components with a more flexible dependency structure, that is some of the correlation coefficients could be positive while others could be negative. We then develop its important distributional properties, and provide efficient statistical inference methods for multivariate ZAP model with or without covariates. Two real data examples in biomedicine are used to illustrate the proposed methods. © 2018 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Multivariate analysis between air pollutants and meteorological variables in Seoul

International Nuclear Information System (INIS)

Kim, J.; Lim, J.

2005-01-01

Multivariate analysis was conducted to analyze the relationship between air pollutants and meteorological variables measured in Seoul from January 1 to December 31, 1999. The first principal component showed the contrast effect between O 3 and the other pollutants. The second principal component showed the contrast effect between CO, SO 2 , NO 2 , and O 3 , PM 10 , TSP. Based on the cluster analysis, three clusters represented different air pollution levels, seasonal characteristics of air pollutants, and meteorological conditions. Discriminant analysis with air environment index (AEI) was carried out to develop an air pollution index function. (orig.)

Hierarchical multivariate covariance analysis of metabolic connectivity.

Science.gov (United States)

Carbonell, Felix; Charil, Arnaud; Zijdenbos, Alex P; Evans, Alan C; Bedell, Barry J

2014-12-01

Conventional brain connectivity analysis is typically based on the assessment of interregional correlations. Given that correlation coefficients are derived from both covariance and variance, group differences in covariance may be obscured by differences in the variance terms. To facilitate a comprehensive assessment of connectivity, we propose a unified statistical framework that interrogates the individual terms of the correlation coefficient. We have evaluated the utility of this method for metabolic connectivity analysis using [18F]2-fluoro-2-deoxyglucose (FDG) positron emission tomography (PET) data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) study. As an illustrative example of the utility of this approach, we examined metabolic connectivity in angular gyrus and precuneus seed regions of mild cognitive impairment (MCI) subjects with low and high β-amyloid burdens. This new multivariate method allowed us to identify alterations in the metabolic connectome, which would not have been detected using classic seed-based correlation analysis. Ultimately, this novel approach should be extensible to brain network analysis and broadly applicable to other imaging modalities, such as functional magnetic resonance imaging (MRI).

Geometric constraints in semiclassical initial value representation calculations in Cartesian coordinates: accurate reduction in zero-point energy.

Science.gov (United States)

Issack, Bilkiss B; Roy, Pierre-Nicholas

2005-08-22

An approach for the inclusion of geometric constraints in semiclassical initial value representation calculations is introduced. An important aspect of the approach is that Cartesian coordinates are used throughout. We devised an algorithm for the constrained sampling of initial conditions through the use of multivariate Gaussian distribution based on a projected Hessian. We also propose an approach for the constrained evaluation of the so-called Herman-Kluk prefactor in its exact log-derivative form. Sample calculations are performed for free and constrained rare-gas trimers. The results show that the proposed approach provides an accurate evaluation of the reduction in zero-point energy. Exact basis set calculations are used to assess the accuracy of the semiclassical results. Since Cartesian coordinates are used, the approach is general and applicable to a variety of molecular and atomic systems.

Simplicial band depth for multivariate functional data

KAUST Repository

López-Pintado, Sara

2014-03-05

We propose notions of simplicial band depth for multivariate functional data that extend the univariate functional band depth. The proposed simplicial band depths provide simple and natural criteria to measure the centrality of a trajectory within a sample of curves. Based on these depths, a sample of multivariate curves can be ordered from the center outward and order statistics can be defined. Properties of the proposed depths, such as invariance and consistency, can be established. A simulation study shows the robustness of this new definition of depth and the advantages of using a multivariate depth versus the marginal depths for detecting outliers. Real data examples from growth curves and signature data are used to illustrate the performance and usefulness of the proposed depths. © 2014 Springer-Verlag Berlin Heidelberg.

An architecture for implementation of multivariable controllers

DEFF Research Database (Denmark)

Niemann, Hans Henrik; Stoustrup, Jakob

1999-01-01

Browse > Conferences> American Control Conference, Prev | Back to Results | Next » An architecture for implementation of multivariable controllers 786292 searchabstract Niemann, H. ; Stoustrup, J. ; Dept. of Autom., Tech. Univ., Lyngby This paper appears in: American Control Conference, 1999....... Proceedings of the 1999 Issue Date : 1999 Volume : 6 On page(s): 4029 - 4033 vol.6 Location: San Diego, CA Meeting Date : 02 Jun 1999-04 Jun 1999 Print ISBN: 0-7803-4990-3 References Cited: 7 INSPEC Accession Number: 6403075 Digital Object Identifier : 10.1109/ACC.1999.786292 Date of Current Version : 06...... august 2002 Abstract An architecture for implementation of multivariable controllers is presented in this paper. The architecture is based on the Youla-Jabr-Bongiorno-Kucera parameterization of all stabilizing controllers. By using this architecture for implementation of multivariable controllers...

Predictors of mortality in patients initiating antiretroviral therapy in ...

African Journals Online (AJOL)

a history of oral candidiasis (HR 2.58, 95% CI 1.37 - 4.88) remained significant in multivariate analysis. A history of tuberculosis was not a significant predictor of mortality. Conclusions. Simple clinical and laboratory data independently predict mortality and allow for risk stratification in patients initiating ART in South Africa.

Multivariate Local Polynomial Regression with Application to Shenzhen Component Index

Directory of Open Access Journals (Sweden)

Liyun Su

2011-01-01

Full Text Available This study attempts to characterize and predict stock index series in Shenzhen stock market using the concepts of multivariate local polynomial regression. Based on nonlinearity and chaos of the stock index time series, multivariate local polynomial prediction methods and univariate local polynomial prediction method, all of which use the concept of phase space reconstruction according to Takens' Theorem, are considered. To fit the stock index series, the single series changes into bivariate series. To evaluate the results, the multivariate predictor for bivariate time series based on multivariate local polynomial model is compared with univariate predictor with the same Shenzhen stock index data. The numerical results obtained by Shenzhen component index show that the prediction mean squared error of the multivariate predictor is much smaller than the univariate one and is much better than the existed three methods. Even if the last half of the training data are used in the multivariate predictor, the prediction mean squared error is smaller than the univariate predictor. Multivariate local polynomial prediction model for nonsingle time series is a useful tool for stock market price prediction.

Fractional differential equation with the fuzzy initial condition

Directory of Open Access Journals (Sweden)

Sadia Arshad

2011-02-01

Full Text Available In this paper we study the existence and uniqueness of the solution for a class of fractional differential equation with fuzzy initial value. The fractional derivatives are considered in the Riemann-Liouville sense.

Importance of initial buoyancy field on evolution of mantle thermal structure: Implications of surface boundary conditions

Directory of Open Access Journals (Sweden)

Petar Glišović

2015-01-01

Full Text Available Although there has been significant progress in the seismic imaging of mantle heterogeneity, the outstanding issue that remains to be resolved is the unknown distribution of mantle temperature anomalies in the distant geological past that give rise to the present-day anomalies inferred by global tomography models. To address this question, we present 3-D convection models in compressible and self-gravitating mantle initialised by different hypothetical temperature patterns. A notable feature of our forward convection modelling is the use of self-consistent coupling of the motion of surface tectonic plates to the underlying mantle flow, without imposing prescribed surface velocities (i.e., plate-like boundary condition. As an approximation for the surface mechanical conditions before plate tectonics began to operate we employ the no-slip (rigid boundary condition. A rigid boundary condition demonstrates that the initial thermally-dominated structure is preserved, and its geographical location is fixed during the evolution of mantle flow. Considering the impact of different assumed surface boundary conditions (rigid and plate-like on the evolution of thermal heterogeneity in the mantle we suggest that the intrinsic buoyancy of seven superplumes is most-likely resolved in the tomographic images of present-day mantle thermal structure. Our convection simulations with a plate-like boundary condition reveal that the evolution of an initial cold anomaly beneath the Java-Indonesian trench system yields a long-term, stable pattern of thermal heterogeneity in the lowermost mantle that resembles the present-day Large Low Shear Velocity Provinces (LLSVPs, especially below the Pacific. The evolution of subduction zones may be, however, influenced by the mantle-wide flow driven by deeply-rooted and long-lived superplumes since Archean times. These convection models also detect the intrinsic buoyancy of the Perm Anomaly that has been identified as a unique

I - Multivariate Classification and Machine Learning in HEP

CERN Multimedia

CERN. Geneva

2016-01-01

Traditional multivariate methods for classification (Stochastic Gradient Boosted Decision Trees and Multi-Layer Perceptrons) are explained in theory and practise using examples from HEP. General aspects of multivariate classification are discussed, in particular different regularisation techniques. Afterwards, data-driven techniques are introduced and compared to MC-based methods.

Citizen participation and citizen initiatives

International Nuclear Information System (INIS)

Matthoefer, H.

1977-01-01

Contents: Social conditions for citizen initiatives - technical change and employment - crisis behaviour - socio-psychological analysis of political planning; legitimation - presentation and criticism - conditions for citizen initiatives coming into being within the field of tension citizen : administration - legal problems of citizen initiatives - environmental protection in the energy discussion; participation; models. (HP) [de

A Newton Algorithm for Multivariate Total Least Squares Problems

Directory of Open Access Journals (Sweden)

WANG Leyang

2016-04-01

Full Text Available In order to improve calculation efficiency of parameter estimation, an algorithm for multivariate weighted total least squares adjustment based on Newton method is derived. The relationship between the solution of this algorithm and that of multivariate weighted total least squares adjustment based on Lagrange multipliers method is analyzed. According to propagation of cofactor, 16 computational formulae of cofactor matrices of multivariate total least squares adjustment are also listed. The new algorithm could solve adjustment problems containing correlation between observation matrix and coefficient matrix. And it can also deal with their stochastic elements and deterministic elements with only one cofactor matrix. The results illustrate that the Newton algorithm for multivariate total least squares problems could be practiced and have higher convergence rate.

Admissible Estimators in the General Multivariate Linear Model with Respect to Inequality Restricted Parameter Set

Directory of Open Access Journals (Sweden)

Shangli Zhang

2009-01-01

Full Text Available By using the methods of linear algebra and matrix inequality theory, we obtain the characterization of admissible estimators in the general multivariate linear model with respect to inequality restricted parameter set. In the classes of homogeneous and general linear estimators, the necessary and suffcient conditions that the estimators of regression coeffcient function are admissible are established.

Application of multivariate splines to discrete mathematics

OpenAIRE

Xu, Zhiqiang

2005-01-01

Using methods developed in multivariate splines, we present an explicit formula for discrete truncated powers, which are defined as the number of non-negative integer solutions of linear Diophantine equations. We further use the formula to study some classical problems in discrete mathematics as follows. First, we extend the partition function of integers in number theory. Second, we exploit the relation between the relative volume of convex polytopes and multivariate truncated powers and giv...

Reagent-free bacterial identification using multivariate analysis of transmission spectra

Science.gov (United States)

Smith, Jennifer M.; Huffman, Debra E.; Acosta, Dayanis; Serebrennikova, Yulia; García-Rubio, Luis; Leparc, German F.

2012-10-01

The identification of bacterial pathogens from culture is critical to the proper administration of antibiotics and patient treatment. Many of the tests currently used in the clinical microbiology laboratory for bacterial identification today can be highly sensitive and specific; however, they have the additional burdens of complexity, cost, and the need for specialized reagents. We present an innovative, reagent-free method for the identification of pathogens from culture. A clinical study has been initiated to evaluate the sensitivity and specificity of this approach. Multiwavelength transmission spectra were generated from a set of clinical isolates including Escherichia coli, Klebsiella pneumoniae, Pseudomonas aeruginosa, and Staphylococcus aureus. Spectra of an initial training set of these target organisms were used to create identification models representing the spectral variability of each species using multivariate statistical techniques. Next, the spectra of the blinded isolates of targeted species were identified using the model achieving >94% sensitivity and >98% specificity, with 100% accuracy for P. aeruginosa and S. aureus. The results from this on-going clinical study indicate this approach is a powerful and exciting technique for identification of pathogens. The menu of models is being expanded to include other bacterial genera and species of clinical significance.

The multivariate egg: quantifying within- and among-clutch correlations between maternally derived yolk immunoglobulins and yolk androgens using multivariate mixed models.

Science.gov (United States)

Postma, Erik; Siitari, Heli; Schwabl, Hubert; Richner, Heinz; Tschirren, Barbara

2014-03-01

Egg components are important mediators of prenatal maternal effects in birds and other oviparous species. Because different egg components can have opposite effects on offspring phenotype, selection is expected to favour their mutual adjustment, resulting in a significant covariation between egg components within and/or among clutches. Here we tested for such correlations between maternally derived yolk immunoglobulins and yolk androgens in great tit (Parus major) eggs using a multivariate mixed-model approach. We found no association between yolk immunoglobulins and yolk androgens within clutches, indicating that within clutches the two egg components are deposited independently. Across clutches, however, there was a significant negative relationship between yolk immunoglobulins and yolk androgens, suggesting that selection has co-adjusted their deposition. Furthermore, an experimental manipulation of ectoparasite load affected patterns of covariance among egg components. Yolk immunoglobulins are known to play an important role in nestling immune defence shortly after hatching, whereas yolk androgens, although having growth-enhancing effects under many environmental conditions, can be immunosuppressive. We therefore speculate that variation in the risk of parasitism may play an important role in shaping optimal egg composition and may lead to the observed pattern of yolk immunoglobulin and yolk androgen deposition across clutches. More generally, our case study exemplifies how multivariate mixed-model methodology presents a flexible tool to not only quantify, but also test patterns of (co)variation across different organisational levels and environments, allowing for powerful hypothesis testing in ecophysiology.

Multivariate Methods for Meta-Analysis of Genetic Association Studies.

Science.gov (United States)

Dimou, Niki L; Pantavou, Katerina G; Braliou, Georgia G; Bagos, Pantelis G

2018-01-01

Multivariate meta-analysis of genetic association studies and genome-wide association studies has received a remarkable attention as it improves the precision of the analysis. Here, we review, summarize and present in a unified framework methods for multivariate meta-analysis of genetic association studies and genome-wide association studies. Starting with the statistical methods used for robust analysis and genetic model selection, we present in brief univariate methods for meta-analysis and we then scrutinize multivariate methodologies. Multivariate models of meta-analysis for a single gene-disease association studies, including models for haplotype association studies, multiple linked polymorphisms and multiple outcomes are discussed. The popular Mendelian randomization approach and special cases of meta-analysis addressing issues such as the assumption of the mode of inheritance, deviation from Hardy-Weinberg Equilibrium and gene-environment interactions are also presented. All available methods are enriched with practical applications and methodologies that could be developed in the future are discussed. Links for all available software implementing multivariate meta-analysis methods are also provided.

Southeast Atlantic Cloud Properties in a Multivariate Statistical Model - How Relevant is Air Mass History for Local Cloud Properties?

Science.gov (United States)

Fuchs, Julia; Cermak, Jan; Andersen, Hendrik

2017-04-01

This study aims at untangling the impacts of external dynamics and local conditions on cloud properties in the Southeast Atlantic (SEA) by combining satellite and reanalysis data using multivariate statistics. The understanding of clouds and their determinants at different scales is important for constraining the Earth's radiative budget, and thus prominent in climate-system research. In this study, SEA stratocumulus cloud properties are observed not only as the result of local environmental conditions but also as affected by external dynamics and spatial origins of air masses entering the study area. In order to assess to what extent cloud properties are impacted by aerosol concentration, air mass history, and meteorology, a multivariate approach is conducted using satellite observations of aerosol and cloud properties (MODIS, SEVIRI), information on aerosol species composition (MACC) and meteorological context (ERA-Interim reanalysis). To account for the often-neglected but important role of air mass origin, information on air mass history based on HYSPLIT modeling is included in the statistical model. This multivariate approach is intended to lead to a better understanding of the physical processes behind observed stratocumulus cloud properties in the SEA.

Evaluating the influence of initial magnetization conditions on extracted exchange parameters in NMR relaxation experiments: applications to CPMG and CEST

Energy Technology Data Exchange (ETDEWEB)

Yuwen, Tairan; Sekhar, Ashok; Kay, Lewis E., E-mail: kay@pound.med.utoronto.ca [The University of Toronto, Departments of Molecular Genetics, Biochemistry and Chemistry (Canada)

2016-08-15

Transient excursions of native protein states to functionally relevant higher energy conformations often occur on the μs–ms timescale. NMR spectroscopy has emerged as an important tool to probe such processes using techniques such as Carr–Purcell–Meiboom–Gill (CPMG) relaxation dispersion and Chemical Exchange Saturation Transfer (CEST). The extraction of kinetic and structural parameters from these measurements is predicated upon mathematical modeling of the resulting relaxation profiles, which in turn relies on knowledge of the initial magnetization conditions at the start of the CPMG/CEST relaxation elements in these experiments. Most fitting programs simply assume initial magnetization conditions that are given by equilibrium populations, which may be incorrect in certain implementations of experiments. In this study we have quantified the systematic errors in extracted parameters that are generated from analyses of CPMG and CEST experiments using incorrect initial boundary conditions. We find that the errors in exchange rates (k{sub ex}) and populations (p{sub E}) are typically small (<10 %) and thus can be safely ignored in most cases. However, errors become larger and cannot be fully neglected (20–40 %) as k{sub ex} falls near the lower limit of each method or when short CPMG/CEST relaxation elements are used in these experiments. The source of the errors can be rationalized and their magnitude given by a simple functional form. Despite the fact that errors tend to be small, it is recommended that the correct boundary conditions be implemented in fitting programs so as to obtain as robust estimates of exchange parameters as possible.

Enhancing e-waste estimates: Improving data quality by multivariate Input–Output Analysis

Energy Technology Data Exchange (ETDEWEB)

Wang, Feng, E-mail: fwang@unu.edu [Institute for Sustainability and Peace, United Nations University, Hermann-Ehler-Str. 10, 53113 Bonn (Germany); Design for Sustainability Lab, Faculty of Industrial Design Engineering, Delft University of Technology, Landbergstraat 15, 2628CE Delft (Netherlands); Huisman, Jaco [Institute for Sustainability and Peace, United Nations University, Hermann-Ehler-Str. 10, 53113 Bonn (Germany); Design for Sustainability Lab, Faculty of Industrial Design Engineering, Delft University of Technology, Landbergstraat 15, 2628CE Delft (Netherlands); Stevels, Ab [Design for Sustainability Lab, Faculty of Industrial Design Engineering, Delft University of Technology, Landbergstraat 15, 2628CE Delft (Netherlands); Baldé, Cornelis Peter [Institute for Sustainability and Peace, United Nations University, Hermann-Ehler-Str. 10, 53113 Bonn (Germany); Statistics Netherlands, Henri Faasdreef 312, 2492 JP Den Haag (Netherlands)

2013-11-15

Highlights: • A multivariate Input–Output Analysis method for e-waste estimates is proposed. • Applying multivariate analysis to consolidate data can enhance e-waste estimates. • We examine the influence of model selection and data quality on e-waste estimates. • Datasets of all e-waste related variables in a Dutch case study have been provided. • Accurate modeling of time-variant lifespan distributions is critical for estimate. - Abstract: Waste electrical and electronic equipment (or e-waste) is one of the fastest growing waste streams, which encompasses a wide and increasing spectrum of products. Accurate estimation of e-waste generation is difficult, mainly due to lack of high quality data referred to market and socio-economic dynamics. This paper addresses how to enhance e-waste estimates by providing techniques to increase data quality. An advanced, flexible and multivariate Input–Output Analysis (IOA) method is proposed. It links all three pillars in IOA (product sales, stock and lifespan profiles) to construct mathematical relationships between various data points. By applying this method, the data consolidation steps can generate more accurate time-series datasets from available data pool. This can consequently increase the reliability of e-waste estimates compared to the approach without data processing. A case study in the Netherlands is used to apply the advanced IOA model. As a result, for the first time ever, complete datasets of all three variables for estimating all types of e-waste have been obtained. The result of this study also demonstrates significant disparity between various estimation models, arising from the use of data under different conditions. It shows the importance of applying multivariate approach and multiple sources to improve data quality for modelling, specifically using appropriate time-varying lifespan parameters. Following the case study, a roadmap with a procedural guideline is provided to enhance e

Enhancing e-waste estimates: Improving data quality by multivariate Input–Output Analysis

International Nuclear Information System (INIS)

Wang, Feng; Huisman, Jaco; Stevels, Ab; Baldé, Cornelis Peter

2013-01-01

Highlights: • A multivariate Input–Output Analysis method for e-waste estimates is proposed. • Applying multivariate analysis to consolidate data can enhance e-waste estimates. • We examine the influence of model selection and data quality on e-waste estimates. • Datasets of all e-waste related variables in a Dutch case study have been provided. • Accurate modeling of time-variant lifespan distributions is critical for estimate. - Abstract: Waste electrical and electronic equipment (or e-waste) is one of the fastest growing waste streams, which encompasses a wide and increasing spectrum of products. Accurate estimation of e-waste generation is difficult, mainly due to lack of high quality data referred to market and socio-economic dynamics. This paper addresses how to enhance e-waste estimates by providing techniques to increase data quality. An advanced, flexible and multivariate Input–Output Analysis (IOA) method is proposed. It links all three pillars in IOA (product sales, stock and lifespan profiles) to construct mathematical relationships between various data points. By applying this method, the data consolidation steps can generate more accurate time-series datasets from available data pool. This can consequently increase the reliability of e-waste estimates compared to the approach without data processing. A case study in the Netherlands is used to apply the advanced IOA model. As a result, for the first time ever, complete datasets of all three variables for estimating all types of e-waste have been obtained. The result of this study also demonstrates significant disparity between various estimation models, arising from the use of data under different conditions. It shows the importance of applying multivariate approach and multiple sources to improve data quality for modelling, specifically using appropriate time-varying lifespan parameters. Following the case study, a roadmap with a procedural guideline is provided to enhance e

Regularized multivariate regression models with skew-t error distributions

KAUST Repository

Chen, Lianfu; Pourahmadi, Mohsen; Maadooliat, Mehdi

2014-01-01

We consider regularization of the parameters in multivariate linear regression models with the errors having a multivariate skew-t distribution. An iterative penalized likelihood procedure is proposed for constructing sparse estimators of both

A climate-based multivariate extreme emulator of met-ocean-hydrological events for coastal flooding

Science.gov (United States)

Camus, Paula; Rueda, Ana; Mendez, Fernando J.; Tomas, Antonio; Del Jesus, Manuel; Losada, Iñigo J.

2015-04-01

Atmosphere-ocean general circulation models (AOGCMs) are useful to analyze large-scale climate variability (long-term historical periods, future climate projections). However, applications such as coastal flood modeling require climate information at finer scale. Besides, flooding events depend on multiple climate conditions: waves, surge levels from the open-ocean and river discharge caused by precipitation. Therefore, a multivariate statistical downscaling approach is adopted to reproduce relationships between variables and due to its low computational cost. The proposed method can be considered as a hybrid approach which combines a probabilistic weather type downscaling model with a stochastic weather generator component. Predictand distributions are reproduced modeling the relationship with AOGCM predictors based on a physical division in weather types (Camus et al., 2012). The multivariate dependence structure of the predictand (extreme events) is introduced linking the independent marginal distributions of the variables by a probabilistic copula regression (Ben Ayala et al., 2014). This hybrid approach is applied for the downscaling of AOGCM data to daily precipitation and maximum significant wave height and storm-surge in different locations along the Spanish coast. Reanalysis data is used to assess the proposed method. A commonly predictor for the three variables involved is classified using a regression-guided clustering algorithm. The most appropriate statistical model (general extreme value distribution, pareto distribution) for daily conditions is fitted. Stochastic simulation of the present climate is performed obtaining the set of hydraulic boundary conditions needed for high resolution coastal flood modeling. References: Camus, P., Menéndez, M., Méndez, F.J., Izaguirre, C., Espejo, A., Cánovas, V., Pérez, J., Rueda, A., Losada, I.J., Medina, R. (2014b). A weather-type statistical downscaling framework for ocean wave climate. Journal of

On the initial condition problem of the time domain PMCHWT surface integral equation

KAUST Repository

Uysal, Ismail Enes

2017-05-13

Non-physical, linearly increasing and constant current components are induced in marching on-in-time solution of time domain surface integral equations when initial conditions on time derivatives of (unknown) equivalent currents are not enforced properly. This problem can be remedied by solving the time integral of the surface integral for auxiliary currents that are defined to be the time derivatives of the equivalent currents. Then the equivalent currents are obtained by numerically differentiating the auxiliary ones. In this work, this approach is applied to the marching on-in-time solution of the time domain Poggio-Miller-Chan-Harrington-Wu-Tsai surface integral equation enforced on dispersive/plasmonic scatterers. Accuracy of the proposed method is demonstrated by a numerical example.

Behaviour of rock-like oxide fuels under reactivity-initiated accident conditions

International Nuclear Information System (INIS)

Kazuyuki, Kusagaya; Takehiko, Nakamura; Makio, Yoshinaga; Hiroshi, Akie; Toshiyuki, Yamashita; Hiroshi, Uetsuka

2002-01-01

Pulse irradiation tests of three types of un-irradiated rock-like oxide (ROX) fuel - yttria-stabilised zirconia (YSZ) single phase, YSZ and spinel (MgAl 2 O 4 ) homogeneous mixture and particle-dispersed YSZ/spinel - were conducted in the Nuclear Safety Research Reactor to investigate the fuel behaviour under reactivity-initiated accident conditions. The ROX fuels failed at fuel volumetric enthalpies above 10 GJ/m 3 , which was comparable to that of un-irradiated UO 2 fuel. The failure mode of the ROX fuels, however, was quite different from that of the UO 2 fuel. The ROX fuels failed with fuel pellet melting and a part of the molten fuel was released out to the surrounding coolant water. In spite of the release, no significant mechanical energy generation due to fuel/coolant thermal interaction was observed in the tested enthalpy range below∼12 GJ/m 3 . The YSZ type and homogenous YSZ/spinel type ROX fuels failed by cladding burst when their temperatures peaked, while the particle-dispersed YSZ/spinel type ROX fuel seemed to have failed by cladding local melting. (author)

The evolution of multivariate maternal effects.

Directory of Open Access Journals (Sweden)

Bram Kuijper

2014-04-01

Full Text Available There is a growing interest in predicting the social and ecological contexts that favor the evolution of maternal effects. Most predictions focus, however, on maternal effects that affect only a single character, whereas the evolution of maternal effects is poorly understood in the presence of suites of interacting traits. To overcome this, we simulate the evolution of multivariate maternal effects (captured by the matrix M in a fluctuating environment. We find that the rate of environmental fluctuations has a substantial effect on the properties of M: in slowly changing environments, offspring are selected to have a multivariate phenotype roughly similar to the maternal phenotype, so that M is characterized by positive dominant eigenvalues; by contrast, rapidly changing environments favor Ms with dominant eigenvalues that are negative, as offspring favor a phenotype which substantially differs from the maternal phenotype. Moreover, when fluctuating selection on one maternal character is temporally delayed relative to selection on other traits, we find a striking pattern of cross-trait maternal effects in which maternal characters influence not only the same character in offspring, but also other offspring characters. Additionally, when selection on one character contains more stochastic noise relative to selection on other traits, large cross-trait maternal effects evolve from those maternal traits that experience the smallest amounts of noise. The presence of these cross-trait maternal effects shows that individual maternal effects cannot be studied in isolation, and that their study in a multivariate context may provide important insights about the nature of past selection. Our results call for more studies that measure multivariate maternal effects in wild populations.

The evolution of multivariate maternal effects.

Science.gov (United States)

Kuijper, Bram; Johnstone, Rufus A; Townley, Stuart

2014-04-01

There is a growing interest in predicting the social and ecological contexts that favor the evolution of maternal effects. Most predictions focus, however, on maternal effects that affect only a single character, whereas the evolution of maternal effects is poorly understood in the presence of suites of interacting traits. To overcome this, we simulate the evolution of multivariate maternal effects (captured by the matrix M) in a fluctuating environment. We find that the rate of environmental fluctuations has a substantial effect on the properties of M: in slowly changing environments, offspring are selected to have a multivariate phenotype roughly similar to the maternal phenotype, so that M is characterized by positive dominant eigenvalues; by contrast, rapidly changing environments favor Ms with dominant eigenvalues that are negative, as offspring favor a phenotype which substantially differs from the maternal phenotype. Moreover, when fluctuating selection on one maternal character is temporally delayed relative to selection on other traits, we find a striking pattern of cross-trait maternal effects in which maternal characters influence not only the same character in offspring, but also other offspring characters. Additionally, when selection on one character contains more stochastic noise relative to selection on other traits, large cross-trait maternal effects evolve from those maternal traits that experience the smallest amounts of noise. The presence of these cross-trait maternal effects shows that individual maternal effects cannot be studied in isolation, and that their study in a multivariate context may provide important insights about the nature of past selection. Our results call for more studies that measure multivariate maternal effects in wild populations.

Multivariate generalized linear mixed models using R

CERN Document Server

Berridge, Damon Mark

2011-01-01

Multivariate Generalized Linear Mixed Models Using R presents robust and methodologically sound models for analyzing large and complex data sets, enabling readers to answer increasingly complex research questions. The book applies the principles of modeling to longitudinal data from panel and related studies via the Sabre software package in R. A Unified Framework for a Broad Class of Models The authors first discuss members of the family of generalized linear models, gradually adding complexity to the modeling framework by incorporating random effects. After reviewing the generalized linear model notation, they illustrate a range of random effects models, including three-level, multivariate, endpoint, event history, and state dependence models. They estimate the multivariate generalized linear mixed models (MGLMMs) using either standard or adaptive Gaussian quadrature. The authors also compare two-level fixed and random effects linear models. The appendices contain additional information on quadrature, model...

Multivariate Regression Analysis and Slaughter Livestock,

Science.gov (United States)

AGRICULTURE, *ECONOMICS), (*MEAT, PRODUCTION), MULTIVARIATE ANALYSIS, REGRESSION ANALYSIS , ANIMALS, WEIGHT, COSTS, PREDICTIONS, STABILITY, MATHEMATICAL MODELS, STORAGE, BEEF, PORK, FOOD, STATISTICAL DATA, ACCURACY

Heterogeneous nucleation helps the search for initial crystallization conditions of γ-glutamyl transpeptidase from Bacillus licheniformis

International Nuclear Information System (INIS)

Lin, Long-Liu; Merlino, Antonello

2013-01-01

An additional example in which heterogeneous nucleation has helped in the search for crystallization conditions of a protein is reported. Optimization of the crystallization conditions led to the formation of single crystals of γ-glutamyl transpeptidase from B. licheniformis that diffracted to about 3.0 Å resolution. Here, the crystallization and preliminary X-ray diffraction studies of Bacillus licheniformis γ-glutamyl transpeptidase (BlGT) are reported. The serendipitous finding of heterogeneous nucleants in the initial experiments provided the first crystallization conditions for the protein. Crystals were grown by hanging-drop vapour diffusion using a precipitant solution consisting of 20%(w/v) PEG 3350, 0.2 M magnesium chloride hexahydrate, 0.1 M Tris–HCl pH 8.2. The protein crystallized in the orthorhombic space group P2 1 2 1 2 1 , with one heterodimer per asymmetric unit and unit-cell parameters a = 60.90, b = 61.97, c = 148.24 Å. The BlGT crystals diffracted to 2.95 Å resolution

Initial orthostatic hypotension: review of a forgotten condition

NARCIS (Netherlands)

Wieling, Wouter; Krediet, C. T. Paul; van Dijk, Nynke; Linzer, Mark; Tschakovsky, Michael E.

2007-01-01

Several studies have shown that standing up is a frequent (3-10 %) trigger of loss of consciousness both in young and old subjects. An exaggerated transient BP (blood pressure) fall upon standing is the underlying cause. IOH (initial orthostatic hypotension) is defined as a transient BP decrease

Scale and shape mixtures of multivariate skew-normal distributions

KAUST Repository

Arellano-Valle, Reinaldo B.; Ferreira, Clé cio S.; Genton, Marc G.

2018-01-01

We introduce a broad and flexible class of multivariate distributions obtained by both scale and shape mixtures of multivariate skew-normal distributions. We present the probabilistic properties of this family of distributions in detail and lay down

Banach frames for multivariate alpha-modulation spaces

DEFF Research Database (Denmark)

Borup, Lasse; Nielsen, Morten

2006-01-01

The α-modulation spaces [$Mathematical Term$], form a family of spaces that include the Besov and modulation spaces as special cases. This paper is concerned with construction of Banach frames for α-modulation spaces in the multivariate setting. The frames constructed are unions of independent Ri...... Riesz sequences based on tensor products of univariate brushlet functions, which simplifies the analysis of the full frame. We show that the multivariate α-modulation spaces can be completely characterized by the Banach frames constructed....

An uncertain journey around the tails of multivariate hydrological distributions

Science.gov (United States)

Serinaldi, Francesco

2013-10-01

Moving from univariate to multivariate frequency analysis, this study extends the Klemeš' critique of the widespread belief that the increasingly refined mathematical structures of probability functions increase the accuracy and credibility of the extrapolated upper tails of the fitted distribution models. In particular, we discuss key aspects of multivariate frequency analysis applied to hydrological data such as the selection of multivariate design events (i.e., appropriate subsets or scenarios of multiplets that exhibit the same joint probability to be used in design applications) and the assessment of the corresponding uncertainty. Since these problems are often overlooked or treated separately, and sometimes confused, we attempt to clarify properties, advantages, shortcomings, and reliability of results of frequency analysis. We suggest a selection method of multivariate design events with prescribed joint probability based on simple Monte Carlo simulations that accounts for the uncertainty affecting the inference results and the multivariate extreme quantiles. It is also shown that the exploration of the p-level probability regions of a joint distribution returns a set of events that is a subset of the p-level scenarios resulting from an appropriate assessment of the sampling uncertainty, thus tending to overlook more extreme and potentially dangerous events with the same (uncertain) joint probability. Moreover, a quantitative assessment of the uncertainty of multivariate quantiles is provided by introducing the concept of joint confidence intervals. From an operational point of view, the simulated event sets describing the distribution of the multivariate p-level quantiles can be used to perform multivariate risk analysis under sampling uncertainty. As an example of the practical implications of this study, we analyze two case studies already presented in the literature.

Determination and demarcation of fatigue crack initiation phase in rotating bending condition

International Nuclear Information System (INIS)

Pasha, R.A.; Rehman, K.; Shah, M.

2012-01-01

In engineering applications, components often experience cyclic loading and therefore, have crack initiation propagation phase. In this research work experimental demarcation of fatigue crack initiation has been investigated. Initiation phase of fatigue life of Aluminium was determined by using single and two step fatigue loading test on four point rotating bending fatigue testing machine. Experimental data is used to determine the distinction between the initiation and propagation phase. Initiation phase is determined at different stress levels. The obtained results demonstrate the effect of stress level on initiation phase and propagation phase. (author)

Multivariate statistical analysis a high-dimensional approach

CERN Document Server

Serdobolskii, V

2000-01-01

In the last few decades the accumulation of large amounts of in­ formation in numerous applications. has stimtllated an increased in­ terest in multivariate analysis. Computer technologies allow one to use multi-dimensional and multi-parametric models successfully. At the same time, an interest arose in statistical analysis with a de­ ficiency of sample data. Nevertheless, it is difficult to describe the recent state of affairs in applied multivariate methods as satisfactory. Unimprovable (dominating) statistical procedures are still unknown except for a few specific cases. The simplest problem of estimat­ ing the mean vector with minimum quadratic risk is unsolved, even for normal distributions. Commonly used standard linear multivari­ ate procedures based on the inversion of sample covariance matrices can lead to unstable results or provide no solution in dependence of data. Programs included in standard statistical packages cannot process 'multi-collinear data' and there are no theoretical recommen­ ...

A MULTIVARIATE ANALYSIS OF CROATIAN COUNTIES ENTREPRENEURSHIP

Directory of Open Access Journals (Sweden)

Elza Jurun

2012-12-01

Full Text Available In the focus of this paper is a multivariate analysis of Croatian Counties entrepreneurship. Complete data base available by official statistic institutions at national and regional level is used. Modern econometric methodology starting from a comparative analysis via multiple regression to multivariate cluster analysis is carried out as well as the analysis of successful or inefficacious entrepreneurship measured by indicators of efficiency, profitability and productivity. Time horizons of the comparative analysis are in 2004 and 2010. Accelerators of socio-economic development - number of entrepreneur investors, investment in fixed assets and current assets ratio in multiple regression model are analytically filtered between twenty-six independent variables as variables of the dominant influence on GDP per capita in 2010 as dependent variable. Results of multivariate cluster analysis of twentyone Croatian Counties are interpreted also in the sense of three Croatian NUTS 2 regions according to European nomenclature of regional territorial division of Croatia.

Shannon Entropy and Mutual Information for Multivariate Skew-Elliptical Distributions

KAUST Repository

Arellano-Valle, Reinaldo B.

2012-02-27

The entropy and mutual information index are important concepts developed by Shannon in the context of information theory. They have been widely studied in the case of the multivariate normal distribution. We first extend these tools to the full symmetric class of multivariate elliptical distributions and then to the more flexible families of multivariate skew-elliptical distributions. We study in detail the cases of the multivariate skew-normal and skew-t distributions. We implement our findings to the application of the optimal design of an ozone monitoring station network in Santiago de Chile. © 2012 Board of the Foundation of the Scandinavian Journal of Statistics.

Shannon Entropy and Mutual Information for Multivariate Skew-Elliptical Distributions

KAUST Repository

Arellano-Valle, Reinaldo B.; Contreras-Reyes, Javier E.; Genton, Marc G.

2012-01-01

The entropy and mutual information index are important concepts developed by Shannon in the context of information theory. They have been widely studied in the case of the multivariate normal distribution. We first extend these tools to the full symmetric class of multivariate elliptical distributions and then to the more flexible families of multivariate skew-elliptical distributions. We study in detail the cases of the multivariate skew-normal and skew-t distributions. We implement our findings to the application of the optimal design of an ozone monitoring station network in Santiago de Chile. © 2012 Board of the Foundation of the Scandinavian Journal of Statistics.

Beer fermentation: monitoring of process parameters by FT-NIR and multivariate data analysis.

Science.gov (United States)

Grassi, Silvia; Amigo, José Manuel; Lyndgaard, Christian Bøge; Foschino, Roberto; Casiraghi, Ernestina

2014-07-15

This work investigates the capability of Fourier-Transform near infrared (FT-NIR) spectroscopy to monitor and assess process parameters in beer fermentation at different operative conditions. For this purpose, the fermentation of wort with two different yeast strains and at different temperatures was monitored for nine days by FT-NIR. To correlate the collected spectra with °Brix, pH and biomass, different multivariate data methodologies were applied. Principal component analysis (PCA), partial least squares (PLS) and locally weighted regression (LWR) were used to assess the relationship between FT-NIR spectra and the abovementioned process parameters that define the beer fermentation. The accuracy and robustness of the obtained results clearly show the suitability of FT-NIR spectroscopy, combined with multivariate data analysis, to be used as a quality control tool in the beer fermentation process. FT-NIR spectroscopy, when combined with LWR, demonstrates to be a perfectly suitable quantitative method to be implemented in the production of beer. Copyright © 2014 Elsevier Ltd. All rights reserved.

Multivariate analysis in the frequency mastery applied to the Laguna Verde Central

International Nuclear Information System (INIS)

Castillo D, R.; Ortiz V, J.; Calleros M, G.

2006-01-01

The noise analysis is an auxiliary tool in the detection of abnormal operation conditions of equipment, instruments or systems that affect to the dynamic behavior of the reactor. The spectral density of normalized power has usually been used (NPSD, by its initials in English), to watch over the behavior of some components of the reactor, for example, the jet pumps, the recirculation pumps, valves of flow control in the recirculation knots, etc. The behavior change is determined by individual analysis of the NPSD of the signals of the components in study. An alternative analysis that can allow to obtain major information on the component under surveillance is the multivariate autoregressive analysis (MAR, by its initials in English), which allows to know the relationship that exists among diverse signals of the reactor systems, in the time domain. In the space of the frequency, the relative contribution of power (RPC for their initials in English) it quantifies the influence of the variables of the systems on a variable of interest. The RPC allows, therefore that for a peak shown in the NPSD of a variable, it can be determine the influence from other variables to that frequency of interest. This facilitates, in principle, the pursuit of the important physical parameters during an event, and to study their interrelation. In this work, by way of example of the application of the RPC, two events happened in the Laguna Verde Central are analyzed: the rods blockade alarms by high scale in the monitors of average power, in which it was presents a power peak of 12% of width peak to peak, and the power oscillations event. The main obtained result of the analysis of the control rods blockade alarm event was that it was detected that the power peak observed in the signals of the average power monitors was caused by the movement of the valve of flow control of recirculation of the knot B. In the other oscillation event the results its show the mechanism of the oscillation of

A simple method to calculate first-passage time densities with arbitrary initial conditions

Science.gov (United States)

Nyberg, Markus; Ambjörnsson, Tobias; Lizana, Ludvig

2016-06-01

Numerous applications all the way from biology and physics to economics depend on the density of first crossings over a boundary. Motivated by the lack of general purpose analytical tools for computing first-passage time densities (FPTDs) for complex problems, we propose a new simple method based on the independent interval approximation (IIA). We generalise previous formulations of the IIA to include arbitrary initial conditions as well as to deal with discrete time and non-smooth continuous time processes. We derive a closed form expression for the FPTD in z and Laplace-transform space to a boundary in one dimension. Two classes of problems are analysed in detail: discrete time symmetric random walks (Markovian) and continuous time Gaussian stationary processes (Markovian and non-Markovian). Our results are in good agreement with Langevin dynamics simulations.

On The Structure of The Inverse of a Linear Constant Multivariable ...

African Journals Online (AJOL)

On The Structure of The Inverse of a Linear Constant Multivariable System. ... It is shown that the use of this representation has certain advantages in the design of multivariable feedback systems. typical examples were considered to indicate the corresponding application. Keywords: Stability Functions, multivariable ...

Multivariate stability of force-reflecting teleoperation: Structures of finite and infinite zeros

International Nuclear Information System (INIS)

Daniel, R.W.; McAree, P.R.

2000-01-01

This paper presents a stability analysis of force-position teleoperation under general end-effector contact. The analysis is based on the finite and infinite zero structure of the multivariable root-locus resulting from modulation of the environment stiffness. The starting point is an analysis of the stability of robot force control, motivated by the observation that the human-operator in a force reflection loop acts as a force servo, generating position commands in response to reflected force. Asymptotic root loci properties are used to establish passivity conditions on force feedback to give root locus interpretations of the well-known results that (1) feedback via the inverse joint Jacobian can lead to (kinematic) instability and that (2) passivity is preserved by kinematically proper force feedback through the transpose of the joint angle Jacobian. It is demonstrated that a fully constrained force-position teleoperation loop has an identical infinite zero structure to that of a slave manipulation under kinematically proper force control and that the dominant vibration modes of a force-position loop are fully described by a multivariable analogue of the single-input single-output pseudo-system investigated in a study by Daniel and McAree. Extension of the analysis to cover partial end-effector constraint provides a design tool for teleoperation control and serves to aid selection of teleoperation slate-arms. The paper concludes by giving a passivity condition for multiple-input multiple-output force-position teleoperation for stable contact against all environments

Directional outlyingness for multivariate functional data

KAUST Repository

Dai, Wenlin

2018-04-07

The direction of outlyingness is crucial to describing the centrality of multivariate functional data. Motivated by this idea, classical depth is generalized to directional outlyingness for functional data. Theoretical properties of functional directional outlyingness are investigated and the total outlyingness can be naturally decomposed into two parts: magnitude outlyingness and shape outlyingness which represent the centrality of a curve for magnitude and shape, respectively. This decomposition serves as a visualization tool for the centrality of curves. Furthermore, an outlier detection procedure is proposed based on functional directional outlyingness. This criterion applies to both univariate and multivariate curves and simulation studies show that it outperforms competing methods. Weather and electrocardiogram data demonstrate the practical application of our proposed framework.

Methods for Analyzing Multivariate Phenotypes in Genetic Association Studies

Directory of Open Access Journals (Sweden)

Qiong Yang

2012-01-01

Full Text Available Multivariate phenotypes are frequently encountered in genetic association studies. The purpose of analyzing multivariate phenotypes usually includes discovery of novel genetic variants of pleiotropy effects, that is, affecting multiple phenotypes, and the ultimate goal of uncovering the underlying genetic mechanism. In recent years, there have been new method development and application of existing statistical methods to such phenotypes. In this paper, we provide a review of the available methods for analyzing association between a single marker and a multivariate phenotype consisting of the same type of components (e.g., all continuous or all categorical or different types of components (e.g., some are continuous and others are categorical. We also reviewed causal inference methods designed to test whether the detected association with the multivariate phenotype is truly pleiotropy or the genetic marker exerts its effects on some phenotypes through affecting the others.

Drunk driving detection based on classification of multivariate time series.

Science.gov (United States)

Li, Zhenlong; Jin, Xue; Zhao, Xiaohua

2015-09-01

This paper addresses the problem of detecting drunk driving based on classification of multivariate time series. First, driving performance measures were collected from a test in a driving simulator located in the Traffic Research Center, Beijing University of Technology. Lateral position and steering angle were used to detect drunk driving. Second, multivariate time series analysis was performed to extract the features. A piecewise linear representation was used to represent multivariate time series. A bottom-up algorithm was then employed to separate multivariate time series. The slope and time interval of each segment were extracted as the features for classification. Third, a support vector machine classifier was used to classify driver's state into two classes (normal or drunk) according to the extracted features. The proposed approach achieved an accuracy of 80.0%. Drunk driving detection based on the analysis of multivariate time series is feasible and effective. The approach has implications for drunk driving detection. Copyright © 2015 Elsevier Ltd and National Safety Council. All rights reserved.

Height distribution tails in the Kardar-Parisi-Zhang equation with Brownian initial conditions

Science.gov (United States)

Meerson, Baruch; Schmidt, Johannes

2017-10-01

For stationary interface growth, governed by the Kardar-Parisi-Zhang (KPZ) equation in 1 + 1 dimensions, typical fluctuations of the interface height at long times are described by the Baik-Rains distribution. Recently Chhita et al (2016 arXiv:1611.06690) used the totally asymmetric simple exclusion process (TASEP) to study the height fluctuations in systems of the KPZ universality class for Brownian interfaces with arbitrary diffusion constant. They showed that there is a one-parameter family of long-time distributions, parameterized by the diffusion constant of the initial random height profile. They also computed these distributions numerically by using Monte Carlo (MC) simulations. Here we address this problem analytically and focus on the distribution tails at short times. We determine the (stretched exponential) tails of the height distribution by applying the optimal fluctuation method (OFM) to the KPZ equation. We argue that, by analogy with other initial conditions, the ‘slow’ tail holds at arbitrary times and therefore provides a proper asymptotic to the family of long-time distributions studied in Chhita et al (2016 arXiv:1611.06690). We verify this hypothesis by performing large-scale MC simulations of a TASEP with a parallel-update rule. The ‘fast’ tail, predicted by the OFM, is also expected to hold at arbitrary times, at sufficiently large heights.

Topology in two dimensions. IV - CDM models with non-Gaussian initial conditions

Science.gov (United States)

Coles, Peter; Moscardini, Lauro; Plionis, Manolis; Lucchin, Francesco; Matarrese, Sabino; Messina, Antonio

1993-02-01

The results of N-body simulations with both Gaussian and non-Gaussian initial conditions are used here to generate projected galaxy catalogs with the same selection criteria as the Shane-Wirtanen counts of galaxies. The Euler-Poincare characteristic is used to compare the statistical nature of the projected galaxy clustering in these simulated data sets with that of the observed galaxy catalog. All the models produce a topology dominated by a meatball shift when normalized to the known small-scale clustering properties of galaxies. Models characterized by a positive skewness of the distribution of primordial density perturbations are inconsistent with the Lick data, suggesting problems in reconciling models based on cosmic textures with observations. Gaussian CDM models fit the distribution of cell counts only if they have a rather high normalization but possess too low a coherence length compared with the Lick counts. This suggests that a CDM model with extra large scale power would probably fit the available data.

Determination of sulfamethoxazole and trimethoprim mixtures by multivariate electronic spectroscopy

OpenAIRE

Cordeiro, Gilcélia A.; Peralta-Zamora, Patricio; Nagata, Noemi; Pontarollo, Roberto

2008-01-01

In this work a multivariate spectroscopic methodology is proposed for quantitative determination of sulfamethoxazole and trimethoprim in pharmaceutical associations. The multivariate model was developed by partial least-squares regression, using twenty synthetic mixtures and the spectral region between 190 and 350 nm. In the validation stage, which involved the analysis of five synthetic mixtures, prediction errors lower that 3% were observed. The predictive capacity of the multivariate model...

A Multivariate Time Series Method for Monte Carlo Reactor Analysis

International Nuclear Information System (INIS)

Taro Ueki

2008-01-01

A robust multivariate time series method has been established for the Monte Carlo calculation of neutron multiplication problems. The method is termed Coarse Mesh Projection Method (CMPM) and can be implemented using the coarse statistical bins for acquisition of nuclear fission source data. A novel aspect of CMPM is the combination of the general technical principle of projection pursuit in the signal processing discipline and the neutron multiplication eigenvalue problem in the nuclear engineering discipline. CMPM enables reactor physicists to accurately evaluate major eigenvalue separations of nuclear reactors with continuous energy Monte Carlo calculation. CMPM was incorporated in the MCNP Monte Carlo particle transport code of Los Alamos National Laboratory. The great advantage of CMPM over the traditional Fission Matrix method is demonstrated for the three space-dimensional modeling of the initial core of a pressurized water reactor

Evaluation of optimization strategies and the effect of initial conditions on IMAT optimization using a leaf position optimization algorithm

International Nuclear Information System (INIS)

Oliver, Mike; Jensen, Michael; Chen, Jeff; Wong, Eugene

2009-01-01

Intensity-modulated arc therapy (IMAT) is a rotational variant of intensity-modulated radiation therapy (IMRT) that can be implemented with or without angular dose rate variation. The purpose of this study is to assess optimization strategies and initial conditions using a leaf position optimization (LPO) algorithm altered for variable dose rate IMAT. A concave planning target volume (PTV) with a central cylindrical organ at risk (OAR) was used in this study. The initial IMAT arcs were approximated by multiple static beams at 5 deg. angular increments where multi-leaf collimator (MLC) leaf positions were determined from the beam's eye view to irradiate the PTV but avoid the OAR. For the optimization strategy, two arcs with arc ranges of 280 deg. and 150 deg. were employed and plans were created using LPO alone, variable dose rate optimization (VDRO) alone, simultaneous LPO and VDRO and sequential combinations of these strategies. To assess the MLC initialization effect, three single 360 deg. arc plans with different initial MLC configurations were generated using the simultaneous LPO and VDRO. The effect of changing optimization degrees of freedom was investigated by employing 3 deg., 5 deg. and 10 deg. angular sampling intervals for the two 280 deg., two 150 deg. and single arc plans using LPO and VDRO. The objective function value, a conformity index, a dose homogeneity index, mean dose to OAR and normal tissues were computed and used to evaluate the treatment plans. This study shows that the best optimization strategy for a concave target is to use simultaneous MLC LPO and VDRO. We found that the optimization result is sensitive to the choice of initial MLC aperture shapes suggesting that an LPO-based IMAT plan may not be able to overcome local minima for this geometry. In conclusion, simultaneous MLC leaf position and VDRO are needed with the most appropriate initial conditions (MLC positions, arc ranges and number of arcs) for IMAT.

Optimal non-periodic inspection for a multivariate degradation model

NARCIS (Netherlands)

Barker, C.T.; Newby, M.J.

2009-01-01

We address the problem of determining inspection and maintenance strategy for a system whose state is described by a multivariate stochastic process. We relax and extend the usual approaches. The system state is a multivariate stochastic process, decisions are based on a performance measure defined

A Range-Based Multivariate Model for Exchange Rate Volatility

NARCIS (Netherlands)

B. Tims (Ben); R.J. Mahieu (Ronald)

2003-01-01

textabstractIn this paper we present a parsimonious multivariate model for exchange rate volatilities based on logarithmic high-low ranges of daily exchange rates. The multivariate stochastic volatility model divides the log range of each exchange rate into two independent latent factors, which are

Experimental characterization of initial conditions and spatio-temporal evolution of a small Atwood number Rayleigh-Taylor mixing layer

Energy Technology Data Exchange (ETDEWEB)

Mueschke, N J; Andrews, M J; Schilling, O

2005-09-26

The initial multi-mode interfacial velocity and density perturbations present at the onset of a small Atwood number, incompressible, miscible, Rayleigh-Taylor instability-driven mixing layer have been quantified using a combination of experimental techniques. The streamwise interfacial and spanwise interfacial perturbations were measured using high-resolution thermocouples and planar laser-induced fluorescence (PLIF), respectively. The initial multi-mode streamwise velocity perturbations at the two-fluid density interface were measured using particle-image velocimetry (PIV). It was found that the measured initial conditions describe an initially anisotropic state, in which the perturbations in the streamwise and spanwise directions are independent of one another. The evolution of various fluctuating velocity and density statistics, together with velocity and density variance spectra, were measured using PIV and high-resolution thermocouple data. The evolution of the velocity and density statistics is used to investigate the early-time evolution and the onset of strongly-nonlinear, transitional dynamics within the mixing layer. The early-time evolution of the density and vertical velocity variance spectra indicate that velocity fluctuations are the dominant mechanism driving the instability development. The implications of the present experimental measurements on the initialization of Reynolds-averaged turbulent transport and mixing models and of direct and large-eddy simulations of Rayleigh-Taylor instability-induced turbulence are discussed.

Multivariate analysis of 2-DE protein patterns - Practical approaches

DEFF Research Database (Denmark)

Jacobsen, Charlotte; Jacobsen, Susanne; Grove, H.

2007-01-01

Practical approaches to the use of multivariate data analysis of 2-DE protein patterns are demonstrated by three independent strategies for the image analysis and the multivariate analysis on the same set of 2-DE data. Four wheat varieties were selected on the basis of their baking quality. Two...... of the varieties were of strong baking quality and hard wheat kernel and two were of weak baking quality and soft kernel. Gliadins at different stages of grain development were analyzed by the application of multivariate data analysis on images of 2-DEs. Patterns related to the wheat varieties, harvest times...

Search for Heavy Stable Charged Particles at $\\sqrt{s}$ = 13 TeV Utilizing a Multivariate Approach

CERN Document Server

AUTHOR|(INSPIRE)INSPIRE-00375809

Heavy stable charged particles (HSCPs) have been searched for at the Large Hadron Collider since its initial data taking in 2010. The search for heavy stable charged particles provide a means of directly probing the new physics realm, as they produce a detector signature unlike any particle discovered to date. The goal of this research is to investigate an idea that was introduced in the later stages of 2010-2012 data taking period. Rather than utilizing the current tight selection on the calculated particle mass the hypothesis is that by incorporating a multivariate approach, specif- ically an artificial neural network, the remaining selection criteria could be loosened allowing for a greater signal acceptance while maintaining acceptable background rejection via the multivariate discriminator from the artificial neural network. The increase in signal acceptance and retention or increase in background rejection increases the discovery potential for HSCPs and as a secondary objective calculates improved limit...

Introduction to multivariate discrimination

Science.gov (United States)

Kégl, Balázs

2013-07-01

Multivariate discrimination or classification is one of the best-studied problem in machine learning, with a plethora of well-tested and well-performing algorithms. There are also several good general textbooks [1-9] on the subject written to an average engineering, computer science, or statistics graduate student; most of them are also accessible for an average physics student with some background on computer science and statistics. Hence, instead of writing a generic introduction, we concentrate here on relating the subject to a practitioner experimental physicist. After a short introduction on the basic setup (Section 1) we delve into the practical issues of complexity regularization, model selection, and hyperparameter optimization (Section 2), since it is this step that makes high-complexity non-parametric fitting so different from low-dimensional parametric fitting. To emphasize that this issue is not restricted to classification, we illustrate the concept on a low-dimensional but non-parametric regression example (Section 2.1). Section 3 describes the common algorithmic-statistical formal framework that unifies the main families of multivariate classification algorithms. We explain here the large-margin principle that partly explains why these algorithms work. Section 4 is devoted to the description of the three main (families of) classification algorithms, neural networks, the support vector machine, and AdaBoost. We do not go into the algorithmic details; the goal is to give an overview on the form of the functions these methods learn and on the objective functions they optimize. Besides their technical description, we also make an attempt to put these algorithm into a socio-historical context. We then briefly describe some rather heterogeneous applications to illustrate the pattern recognition pipeline and to show how widespread the use of these methods is (Section 5). We conclude the chapter with three essentially open research problems that are either

Introduction to multivariate discrimination

International Nuclear Information System (INIS)

Kegl, B.

2013-01-01

Multivariate discrimination or classification is one of the best-studied problem in machine learning, with a plethora of well-tested and well-performing algorithms. There are also several good general textbooks [1-9] on the subject written to an average engineering, computer science, or statistics graduate student; most of them are also accessible for an average physics student with some background on computer science and statistics. Hence, instead of writing a generic introduction, we concentrate here on relating the subject to a practitioner experimental physicist. After a short introduction on the basic setup (Section 1) we delve into the practical issues of complexity regularization, model selection, and hyper-parameter optimization (Section 2), since it is this step that makes high-complexity non-parametric fitting so different from low-dimensional parametric fitting. To emphasize that this issue is not restricted to classification, we illustrate the concept on a low-dimensional but non-parametric regression example (Section 2.1). Section 3 describes the common algorithmic-statistical formal framework that unifies the main families of multivariate classification algorithms. We explain here the large-margin principle that partly explains why these algorithms work. Section 4 is devoted to the description of the three main (families of) classification algorithms, neural networks, the support vector machine, and AdaBoost. We do not go into the algorithmic details; the goal is to give an overview on the form of the functions these methods learn and on the objective functions they optimize. Besides their technical description, we also make an attempt to put these algorithm into a socio-historical context. We then briefly describe some rather heterogeneous applications to illustrate the pattern recognition pipeline and to show how widespread the use of these methods is (Section 5). We conclude the chapter with three essentially open research problems that are either

Global Sensitivity Analysis for multivariate output using Polynomial Chaos Expansion

International Nuclear Information System (INIS)

Garcia-Cabrejo, Oscar; Valocchi, Albert

2014-01-01

Many mathematical and computational models used in engineering produce multivariate output that shows some degree of correlation. However, conventional approaches to Global Sensitivity Analysis (GSA) assume that the output variable is scalar. These approaches are applied on each output variable leading to a large number of sensitivity indices that shows a high degree of redundancy making the interpretation of the results difficult. Two approaches have been proposed for GSA in the case of multivariate output: output decomposition approach [9] and covariance decomposition approach [14] but they are computationally intensive for most practical problems. In this paper, Polynomial Chaos Expansion (PCE) is used for an efficient GSA with multivariate output. The results indicate that PCE allows efficient estimation of the covariance matrix and GSA on the coefficients in the approach defined by Campbell et al. [9], and the development of analytical expressions for the multivariate sensitivity indices defined by Gamboa et al. [14]. - Highlights: • PCE increases computational efficiency in 2 approaches of GSA of multivariate output. • Efficient estimation of covariance matrix of output from coefficients of PCE. • Efficient GSA on coefficients of orthogonal decomposition of the output using PCE. • Analytical expressions of multivariate sensitivity indices from coefficients of PCE

Multivariable Real-Time Control of Viscosity Curve for a Continuous Production Process of a Non-Newtonian Fluid

Directory of Open Access Journals (Sweden)

Roberto Mei

2018-01-01

Full Text Available The application of a multivariable predictive controller to the mixing process for the production of a non-Newtonian fluid is discussed in this work. A data-driven model has been developed to describe the dynamic behaviour of the rheological properties of the fluid as a function of the operating conditions using experimental data collected in a pilot plant. The developed model provides a realistic process representation and it is used to test and verify the multivariable controller, which has been designed to maintain viscosity curves of the non-Newtonian fluid within a given region of the viscosity-vs-shear rate plane in presence of process disturbances occurring in the mixing process.

Artificial boundary conditions for certain evolution PDEs with cubic nonlinearity for non-compactly supported initial data

Science.gov (United States)

Vaibhav, V.

2011-04-01

The paper addresses the problem of constructing non-reflecting boundary conditions for two types of one dimensional evolution equations, namely, the cubic nonlinear Schrödinger (NLS) equation, ∂tu+Lu-iχ|u|2u=0 with L≡-i∂x2, and the equation obtained by letting L≡∂x3. The usual restriction of compact support of the initial data is relaxed by allowing it to have a constant amplitude along with a linear phase variation outside a compact domain. We adapt the pseudo-differential approach developed by Antoine et al. (2006) [5] for the NLS equation to the second type of evolution equation, and further, extend the scheme to the aforementioned class of initial data for both of the equations. In addition, we discuss efficient numerical implementation of our scheme and produce the results of several numerical experiments demonstrating its effectiveness.

Multivariate phase type distributions - Applications and parameter estimation

DEFF Research Database (Denmark)

Meisch, David

The best known univariate probability distribution is the normal distribution. It is used throughout the literature in a broad field of applications. In cases where it is not sensible to use the normal distribution alternative distributions are at hand and well understood, many of these belonging...... and statistical inference, is the multivariate normal distribution. Unfortunately only little is known about the general class of multivariate phase type distribution. Considering the results concerning parameter estimation and inference theory of univariate phase type distributions, the class of multivariate...... projects and depend on reliable cost estimates. The Successive Principle is a group analysis method primarily used for analyzing medium to large projects in relation to cost or duration. We believe that the mathematical modeling used in the Successive Principle can be improved. We suggested a novel...

Modelling conditional correlations of asset returns: A smooth transition approach

DEFF Research Database (Denmark)

Silvennoinen, Annastiina; Teräsvirta, Timo

In this paper we propose a new multivariate GARCH model with time-varying conditional correlation structure. The time-varying conditional correlations change smoothly between two extreme states of constant correlations according to a predetermined or exogenous transition variable. An LM-test is d......In this paper we propose a new multivariate GARCH model with time-varying conditional correlation structure. The time-varying conditional correlations change smoothly between two extreme states of constant correlations according to a predetermined or exogenous transition variable. An LM......-test is derived to test the constancy of correlations and LM- and Wald tests to test the hypothesis of partially constant correlations. Analytical expressions for the test statistics and the required derivatives are provided to make computations feasible. An empirical example based on daily return series of ve...

A study on gap heat transfer of LWR fuel rods under reactivity initiated accident conditions

International Nuclear Information System (INIS)

Fujishiro, Toshio

1984-03-01

Gap heat transfer between fuel pellet and cladding have a large influence on the LWR fuel behaviors under reactivity initiated accident (RIA) conditions. The objective of the present study is to investigate the effects of gap heat transfer on RIA fuel behaviors based on the results of the gap-gas parameter tests in NSRR and on their analysis with NSR-77 code. Through this study, transient variations of gap heat transfer, the effects of the gap heat transfer on fuel thermal behaviors and on fuel failure, effects of pellet-cladding sticking by eutectic formation, and the effects of cladding collapse under high external pressure have been clearified. The studies have also been performed on the applicability and its limit of modified Ross and Stoute equation which is extensively utilized to evaluate the gap heat transfer coefficient in the present fuel behavior codes. The method to evaluate the gap conductance to the conditions beyond the applicability limit of the Ross and Stoute equation has also been proposed. (author)

The influence of initial conditions of water-entry on ricochet phenomenon

Energy Technology Data Exchange (ETDEWEB)

Guoming, Chen; Jinfu, Feng; Junhua, Hu; An, Liu [Aeronautics and Astronautics Engineering College, Air Force Engineering University, Xi’an, 710038 (China); Yongli, Li, E-mail: 18192081790@163.com [Engineering University of CAPF, Xi’an 710086 (China)

2017-08-15

The ricochet phenomenon of the water-entry of the water–air crossing vehicle is investigated by both experiments and numerical simulations. Experiments and numerical simulations of the water-entry process with different inclination angles, velocities, and attack angles are performed. The whole ricochet progress and the changing rules of angular acceleration, angular velocity, and displacement are obtained and analyzed by numerical simulation for a deeper understanding of ricochet phenomenon. The experiment is carried out to study the underwater trajectory by changing the initial condition only. The experimental results are in good agreement with the simulations. The results show that: (1) A small inclination angle causes the trajectory to bend upward, favoring the ricochet phenomenon. (2) A large velocity value also favors the ricochet phenomenon, making it occur more easily and quickly, but lower velocities are insufficient to provide the necessary kinetic energy. (3) The ricochet phenomenon is more likely to occur under a negative attack angle that causes the trajectory to bend upward, but a positive angle balances the underwater trajectory. These results can provide guidance to design a new water–air cross vehicle. (paper)

The Multivariate Gaussian Probability Distribution

DEFF Research Database (Denmark)

Ahrendt, Peter

2005-01-01

This technical report intends to gather information about the multivariate gaussian distribution, that was previously not (at least to my knowledge) to be found in one place and written as a reference manual. Additionally, some useful tips and tricks are collected that may be useful in practical ...

A multiresolution approach for the convergence acceleration of multivariate curve resolution methods.

Science.gov (United States)

Sawall, Mathias; Kubis, Christoph; Börner, Armin; Selent, Detlef; Neymeyr, Klaus

2015-09-03

Modern computerized spectroscopic instrumentation can result in high volumes of spectroscopic data. Such accurate measurements rise special computational challenges for multivariate curve resolution techniques since pure component factorizations are often solved via constrained minimization problems. The computational costs for these calculations rapidly grow with an increased time or frequency resolution of the spectral measurements. The key idea of this paper is to define for the given high-dimensional spectroscopic data a sequence of coarsened subproblems with reduced resolutions. The multiresolution algorithm first computes a pure component factorization for the coarsest problem with the lowest resolution. Then the factorization results are used as initial values for the next problem with a higher resolution. Good initial values result in a fast solution on the next refined level. This procedure is repeated and finally a factorization is determined for the highest level of resolution. The described multiresolution approach allows a considerable convergence acceleration. The computational procedure is analyzed and is tested for experimental spectroscopic data from the rhodium-catalyzed hydroformylation together with various soft and hard models. Copyright © 2015 Elsevier B.V. All rights reserved.

Complementary biomarker-based methods for characterising Arctic sea ice conditions: A case study comparison between multivariate analysis and the PIP25 index

Science.gov (United States)

Köseoğlu, Denizcan; Belt, Simon T.; Smik, Lukas; Yao, Haoyi; Panieri, Giuliana; Knies, Jochen

2018-02-01

The discovery of IP25 as a qualitative biomarker proxy for Arctic sea ice and subsequent introduction of the so-called PIP25 index for semi-quantitative descriptions of sea ice conditions has significantly advanced our understanding of long-term paleo Arctic sea ice conditions over the past decade. We investigated the potential for classification tree (CT) models to provide a further approach to paleo Arctic sea ice reconstruction through analysis of a suite of highly branched isoprenoid (HBI) biomarkers in ca. 200 surface sediments from the Barents Sea. Four CT models constructed using different HBI assemblages revealed IP25 and an HBI triene as the most appropriate classifiers of sea ice conditions, achieving a >90% cross-validated classification rate. Additionally, lower model performance for locations in the Marginal Ice Zone (MIZ) highlighted difficulties in characterisation of this climatically-sensitive region. CT model classification and semi-quantitative PIP25-derived estimates of spring sea ice concentration (SpSIC) for four downcore records from the region were consistent, although agreement between proxy and satellite/observational records was weaker for a core from the west Svalbard margin, likely due to the highly variable sea ice conditions. The automatic selection of appropriate biomarkers for description of sea ice conditions, quantitative model assessment, and insensitivity to the c-factor used in the calculation of the PIP25 index are key attributes of the CT approach, and we provide an initial comparative assessment between these potentially complementary methods. The CT model should be capable of generating longer-term temporal shifts in sea ice conditions for the climatically sensitive Barents Sea.

Shared decision making among parents of children with mental health conditions compared to children with chronic physical conditions.

Science.gov (United States)

Butler, Ashley M; Elkins, Sara; Kowalkowski, Marc; Raphael, Jean L

2015-02-01

High quality care in pediatrics involves shared decision making (SDM) between families and providers. The extent to which children with common mental health disorders experience SDM is not well known. The objectives of this study were to examine how parent-reported SDM varies by child health (physical illness, mental health condition, and comorbid mental and physical conditions) and to examine whether medical home care attenuates any differences. We analyzed data on children (2-17 years) collected through the 2009/2010 National Survey of Children with Special Health Care Needs. The sample consisted of parents of children in one of three child health categories: (1) children with a chronic physical illness but no mental health condition; (2) children with a common mental health condition but no chronic physical condition; and (3) children with comorbid mental and chronic physical conditions. The primary dependent variable was parent-report of provider SDM. The primary independent variable was health condition category. Multivariate linear regression analyses were conducted. Multivariate analyses controlling for sociodemographic variables and parent-reported health condition impact indicated lower SDM among children with a common mental health condition-only (B = -0.40; p mental health condition-only were no longer significant in the model adjusting for medical home care. However, differences in SDM for children with comorbid conditions persisted after adjusting for medical home care. Increasing medical home care may help mitigate differences in SDM for children with mental health conditions-only. Other interventions may be needed to improve SDM among children with comorbid mental and physical conditions.

Multivariate Process Control with Autocorrelated Data

DEFF Research Database (Denmark)

Kulahci, Murat

2011-01-01

As sensor and computer technology continues to improve, it becomes a normal occurrence that we confront with high dimensional data sets. As in many areas of industrial statistics, this brings forth various challenges in statistical process control and monitoring. This new high dimensional data...... often exhibit not only cross-­‐correlation among the quality characteristics of interest but also serial dependence as a consequence of high sampling frequency and system dynamics. In practice, the most common method of monitoring multivariate data is through what is called the Hotelling’s T2 statistic....... In this paper, we discuss the effect of autocorrelation (when it is ignored) on multivariate control charts based on these methods and provide some practical suggestions and remedies to overcome this problem....

Transient multivariable sensor evaluation

Energy Technology Data Exchange (ETDEWEB)

Vilim, Richard B.; Heifetz, Alexander

2017-02-21

A method and system for performing transient multivariable sensor evaluation. The method and system includes a computer system for identifying a model form, providing training measurement data, generating a basis vector, monitoring system data from sensor, loading the system data in a non-transient memory, performing an estimation to provide desired data and comparing the system data to the desired data and outputting an alarm for a defective sensor.

Gas-water two-phase flow characterization with Electrical Resistance Tomography and Multivariate Multiscale Entropy analysis.

Science.gov (United States)

Tan, Chao; Zhao, Jia; Dong, Feng

2015-03-01

Flow behavior characterization is important to understand gas-liquid two-phase flow mechanics and further establish its description model. An Electrical Resistance Tomography (ERT) provides information regarding flow conditions at different directions where the sensing electrodes implemented. We extracted the multivariate sample entropy (MSampEn) by treating ERT data as a multivariate time series. The dynamic experimental results indicate that the MSampEn is sensitive to complexity change of flow patterns including bubbly flow, stratified flow, plug flow and slug flow. MSampEn can characterize the flow behavior at different direction of two-phase flow, and reveal the transition between flow patterns when flow velocity changes. The proposed method is effective to analyze two-phase flow pattern transition by incorporating information of different scales and different spatial directions. Copyright © 2014 ISA. Published by Elsevier Ltd. All rights reserved.

Scale and shape mixtures of multivariate skew-normal distributions

KAUST Repository

Arellano-Valle, Reinaldo B.

2018-02-26

We introduce a broad and flexible class of multivariate distributions obtained by both scale and shape mixtures of multivariate skew-normal distributions. We present the probabilistic properties of this family of distributions in detail and lay down the theoretical foundations for subsequent inference with this model. In particular, we study linear transformations, marginal distributions, selection representations, stochastic representations and hierarchical representations. We also describe an EM-type algorithm for maximum likelihood estimation of the parameters of the model and demonstrate its implementation on a wind dataset. Our family of multivariate distributions unifies and extends many existing models of the literature that can be seen as submodels of our proposal.

Dynamics of leaf and spikelet primordia initiation in wheat as affected by Ppd-1a alleles under field conditions.

Science.gov (United States)

Ochagavía, Helga; Prieto, Paula; Savin, Roxana; Griffiths, Simon; Slafer, GustavoA

2018-04-27

Wheat adaptation is affected by Ppd genes, but the role of these alleles in the rates of leaf and spikelet initiation has not been properly analysed. Twelve near isogenic lines (NILs) combining Ppd-1a alleles from different donors introgressed in A, B, and/or D genomes were tested under field conditions during two growing seasons together with the wild type, Paragon. Leaf initiation rate was unaffected by Ppd-1a alleles so the final leaf number (FLN) was reduced in parallel with reductions in the duration of the vegetative phase. Spikelet primordia initiation was accelerated and consequently the effect on spikelets per spike was less than proportional to the effect on the duration of spikelet initiation. The magnitude of these effects on spikelet plastochron depended on the doses of Ppd-1 homoeoalleles and the specific insensitivity alleles carried. Double ridge was consistently later than floral initiation, but the difference between them was not affected by Ppd-1a alleles. These findings have potential for selecting the best combinations from the Ppd-1 homoeoallelic series for manipulating adaptation taking into consideration particular effects on spikelet number.

Robust Initial Wetness Condition Framework of an Event-Based Rainfall–Runoff Model Using Remotely Sensed Soil Moisture

OpenAIRE

Wooyeon Sunwoo; Minha Choi

2017-01-01

Runoff prediction in limited-data areas is vital for hydrological applications, such as the design of infrastructure and flood defenses, runoff forecasting, and water management. Rainfall–runoff models may be useful for simulation of runoff generation, particularly event-based models, which offer a practical modeling scheme because of their simplicity. However, there is a need to reduce the uncertainties related to the estimation of the initial wetness condition (IWC) prior to a rainfall even...

Derivatives of Multivariate Bernstein Operators and Smoothness with Jacobi Weights

Directory of Open Access Journals (Sweden)

Jianjun Wang

2012-01-01

Full Text Available Using the modulus of smoothness, directional derivatives of multivariate Bernstein operators with weights are characterized. The obtained results partly generalize the corresponding ones for multivariate Bernstein operators without weights.

Multivariate sparse group lasso for the multivariate multiple linear regression with an arbitrary group structure.

Science.gov (United States)

Li, Yanming; Nan, Bin; Zhu, Ji

2015-06-01

We propose a multivariate sparse group lasso variable selection and estimation method for data with high-dimensional predictors as well as high-dimensional response variables. The method is carried out through a penalized multivariate multiple linear regression model with an arbitrary group structure for the regression coefficient matrix. It suits many biology studies well in detecting associations between multiple traits and multiple predictors, with each trait and each predictor embedded in some biological functional groups such as genes, pathways or brain regions. The method is able to effectively remove unimportant groups as well as unimportant individual coefficients within important groups, particularly for large p small n problems, and is flexible in handling various complex group structures such as overlapping or nested or multilevel hierarchical structures. The method is evaluated through extensive simulations with comparisons to the conventional lasso and group lasso methods, and is applied to an eQTL association study. © 2015, The International Biometric Society.

Lasso and probabilistic inequalities for multivariate point processes

DEFF Research Database (Denmark)

Hansen, Niels Richard; Reynaud-Bouret, Patricia; Rivoirard, Vincent

2015-01-01

Due to its low computational cost, Lasso is an attractive regularization method for high-dimensional statistical settings. In this paper, we consider multivariate counting processes depending on an unknown function parameter to be estimated by linear combinations of a fixed dictionary. To select...... for multivariate Hawkes processes are proven, which allows us to check these assumptions by considering general dictionaries based on histograms, Fourier or wavelet bases. Motivated by problems of neuronal activity inference, we finally carry out a simulation study for multivariate Hawkes processes and compare our...... methodology with the adaptive Lasso procedure proposed by Zou in (J. Amer. Statist. Assoc. 101 (2006) 1418–1429). We observe an excellent behavior of our procedure. We rely on theoretical aspects for the essential question of tuning our methodology. Unlike adaptive Lasso of (J. Amer. Statist. Assoc. 101 (2006...

Directional outlyingness for multivariate functional data

KAUST Repository

Dai, Wenlin; Genton, Marc G.

2018-01-01

The direction of outlyingness is crucial to describing the centrality of multivariate functional data. Motivated by this idea, classical depth is generalized to directional outlyingness for functional data. Theoretical properties of functional

Multivariate realised kernels

DEFF Research Database (Denmark)

Barndorff-Nielsen, Ole Eiler; Hansen, Peter Reinhard; Lunde, Asger

2011-01-01

We propose a multivariate realised kernel to estimate the ex-post covariation of log-prices. We show this new consistent estimator is guaranteed to be positive semi-definite and is robust to measurement error of certain types and can also handle non-synchronous trading. It is the first estimator...... which has these three properties which are all essential for empirical work in this area. We derive the large sample asymptotics of this estimator and assess its accuracy using a Monte Carlo study. We implement the estimator on some US equity data, comparing our results to previous work which has used...

DTW-APPROACH FOR UNCORRELATED MULTIVARIATE TIME SERIES IMPUTATION

OpenAIRE

Phan , Thi-Thu-Hong; Poisson Caillault , Emilie; Bigand , André; Lefebvre , Alain

2017-01-01

International audience; Missing data are inevitable in almost domains of applied sciences. Data analysis with missing values can lead to a loss of efficiency and unreliable results, especially for large missing sub-sequence(s). Some well-known methods for multivariate time series imputation require high correlations between series or their features. In this paper , we propose an approach based on the shape-behaviour relation in low/un-correlated multivariate time series under an assumption of...

Multivariable nonlinear analysis of foreign exchange rates

Science.gov (United States)

Suzuki, Tomoya; Ikeguchi, Tohru; Suzuki, Masuo

2003-05-01

We analyze the multivariable time series of foreign exchange rates. These are price movements that have often been analyzed, and dealing time intervals and spreads between bid and ask prices. Considering dealing time intervals as event timing such as neurons’ firings, we use raster plots (RPs) and peri-stimulus time histograms (PSTHs) which are popular methods in the field of neurophysiology. Introducing special processings to obtaining RPs and PSTHs time histograms for analyzing exchange rates time series, we discover that there exists dynamical interaction among three variables. We also find that adopting multivariables leads to improvements of prediction accuracy.

Multi-variable systems in nuclear power plant

International Nuclear Information System (INIS)

Collins, G.B.; Howell, J.

1982-01-01

Nuclear power plant are complex multi-variable dynamically interactive systems which employ many facets of systems and control theory in their analysis and design. Whole plant mathematical models must be developed and validated and in addition to their obvious role in control system synthesis and design, they are also widely used for operational constraint and plant malfunction analysis. The need for and scope of an integrated power plant control system is discussed and, as a specific example, the design of an integrated feedwater regulator is reviewed. The multi-variable frequency response analysis employed in the design is described in detail. (author)

A "Model" Multivariable Calculus Course.

Science.gov (United States)

Beckmann, Charlene E.; Schlicker, Steven J.

1999-01-01

Describes a rich, investigative approach to multivariable calculus. Introduces a project in which students construct physical models of surfaces that represent real-life applications of their choice. The models, along with student-selected datasets, serve as vehicles to study most of the concepts of the course from both continuous and discrete…

An Introduction to Applied Multivariate Analysis

CERN Document Server

Raykov, Tenko

2008-01-01

Focuses on the core multivariate statistics topics which are of fundamental relevance for its understanding. This book emphasis on the topics that are critical to those in the behavioral, social, and educational sciences.

Multivariate Formation Pressure Prediction with Seismic-derived Petrophysical Properties from Prestack AVO inversion and Poststack Seismic Motion Inversion

Science.gov (United States)

Yu, H.; Gu, H.

2017-12-01

A novel multivariate seismic formation pressure prediction methodology is presented, which incorporates high-resolution seismic velocity data from prestack AVO inversion, and petrophysical data (porosity and shale volume) derived from poststack seismic motion inversion. In contrast to traditional seismic formation prediction methods, the proposed methodology is based on a multivariate pressure prediction model and utilizes a trace-by-trace multivariate regression analysis on seismic-derived petrophysical properties to calibrate model parameters in order to make accurate predictions with higher resolution in both vertical and lateral directions. With prestack time migration velocity as initial velocity model, an AVO inversion was first applied to prestack dataset to obtain high-resolution seismic velocity with higher frequency that is to be used as the velocity input for seismic pressure prediction, and the density dataset to calculate accurate Overburden Pressure (OBP). Seismic Motion Inversion (SMI) is an inversion technique based on Markov Chain Monte Carlo simulation. Both structural variability and similarity of seismic waveform are used to incorporate well log data to characterize the variability of the property to be obtained. In this research, porosity and shale volume are first interpreted on well logs, and then combined with poststack seismic data using SMI to build porosity and shale volume datasets for seismic pressure prediction. A multivariate effective stress model is used to convert velocity, porosity and shale volume datasets to effective stress. After a thorough study of the regional stratigraphic and sedimentary characteristics, a regional normally compacted interval model is built, and then the coefficients in the multivariate prediction model are determined in a trace-by-trace multivariate regression analysis on the petrophysical data. The coefficients are used to convert velocity, porosity and shale volume datasets to effective stress and then

Study on initial stage of diesel spray formation. Effects of the condition inside the nozzle sac; Diesel funmu no shoki keisei katei ni kansuru kenkyu. Sac nai nenryo no eikyo

Energy Technology Data Exchange (ETDEWEB)

Ishikawa, N.; Tsujimura, K. [Nissan Diesel Motor Co. Ltd., Saitama (Japan); Komori, M.

1996-06-25

To realize clean diesel exhaust, it is very important to clarify the atomization phenomena of the fuel spray. In this study, the initial stage of the atomization process of a diesel injection fuel spray was analyzed with a high-speed image converter camera under the conditions of atmospheric gas pressure and room temperature. As a result, it was found that the initial spray formation was greatly affected lay the condition inside the nozzle sac. In the case in which fuel existed in the sac, pin-like structure spray formation was observed at the initial injection stage. This phenomenon was not observed in the case in which no fuel was present in the sac, and a widely spread fuel spray formation was observed at the initial injection stage. The relatively low-speed fuel spray injected in the initial low-sac-pressure condition was pushed away by the subsequent fuel spray injected in the high-sac-pressure condition. 7 refs., 12 figs., 1 tab.

Regression Models For Multivariate Count Data.

Science.gov (United States)

Zhang, Yiwen; Zhou, Hua; Zhou, Jin; Sun, Wei

2017-01-01

Data with multivariate count responses frequently occur in modern applications. The commonly used multinomial-logit model is limiting due to its restrictive mean-variance structure. For instance, analyzing count data from the recent RNA-seq technology by the multinomial-logit model leads to serious errors in hypothesis testing. The ubiquity of over-dispersion and complicated correlation structures among multivariate counts calls for more flexible regression models. In this article, we study some generalized linear models that incorporate various correlation structures among the counts. Current literature lacks a treatment of these models, partly due to the fact that they do not belong to the natural exponential family. We study the estimation, testing, and variable selection for these models in a unifying framework. The regression models are compared on both synthetic and real RNA-seq data.

Multivariate prediction of spontaneous repetitive responses in ventricular myocardium exposed in vitro to simulated ischemic conditions.

Science.gov (United States)

Schiariti, M; Puddu, P E; Rouet, R

1994-06-01

Guinea-pig ventricular myocardium was partly exposed to normal Tyrode's superfusion and partly to altered conditions (using modified Tyrode's solution) set to simulate acute myocardial ischemia (PO2 80 +/- 10 mmHg; no glucose; pH 7.00 +/- 0.05; K+ 12 mM). Using a double-chamber tissue bath and standard microelectrode technique, the occurrence of spontaneous repetitive responses was investigated during simulated ischemia (occlusion) and after reperfusing the previously ischemic superfused tissue with normal Tyrode's solution (reperfusion). In 62 experiments (42 animals) the effects of: (1) duration of simulated ischemia (1321 +/- 435 s), (2) stimulation rate (1002 +/- 549 ms) and (3) number of successive simulated ischemic periods (occlusions) (1.58 +/- 0.92) on: (1) resting membrane potential, (2) action potential amplitude, (3) duration of 50 and 90% action potentials and (4) maximal upstroke velocity of action potential were studied. All variables were considered as gradients (delta) between normal and ischemic tissue. Both during occlusion and upon reperfusion, spontaneous repetitive responses were coded as single, couplets, salvos (three to nine and > 10) or total spontaneous repetitive responses (coded present when at least one of the above-mentioned types was seen). The incidence of total spontaneous repetitive responses was 31% (19/62) on occlusion and 85% (53/62) upon reperfusion. Cox's models (forced and stepwise) were used to predict multivariately the occurrence of arrhythmic events considered as both total spontaneous repetitive responses and as separate entities. These models were applicable since continuous monitoring of the experiments enabled exact timing of spontaneous repetitive response onset during both occlusion and reperfusion. In predicting reperfusion spontaneous repetitive responses, total spontaneous repetitive responses and blocks observed during the occlusion period were also considered. Total occlusion spontaneous repetitive responses

Reconstruction of boundary conditions from internal conditions using viability theory

KAUST Repository

Hofleitner, Aude; Claudel, Christian G.; Bayen, Alexandre M.

2012-01-01

This article presents a method for reconstructing downstream boundary conditions to a HamiltonJacobi partial differential equation for which initial and upstream boundary conditions are prescribed as piecewise affine functions and an internal condition is prescribed as an affine function. Based on viability theory, we reconstruct the downstream boundary condition such that the solution of the Hamilton-Jacobi equation with the prescribed initial and upstream conditions and reconstructed downstream boundary condition satisfies the internal value condition. This work has important applications for estimation in flow networks with unknown capacity reductions. It is applied to urban traffic, to reconstruct signal timings and temporary capacity reductions at intersections, using Lagrangian sensing such as GPS devices onboard vehicles.

Reconstruction of boundary conditions from internal conditions using viability theory

KAUST Repository

Hofleitner, Aude

2012-06-01

This article presents a method for reconstructing downstream boundary conditions to a HamiltonJacobi partial differential equation for which initial and upstream boundary conditions are prescribed as piecewise affine functions and an internal condition is prescribed as an affine function. Based on viability theory, we reconstruct the downstream boundary condition such that the solution of the Hamilton-Jacobi equation with the prescribed initial and upstream conditions and reconstructed downstream boundary condition satisfies the internal value condition. This work has important applications for estimation in flow networks with unknown capacity reductions. It is applied to urban traffic, to reconstruct signal timings and temporary capacity reductions at intersections, using Lagrangian sensing such as GPS devices onboard vehicles.

EXPLORATORY DATA ANALYSIS AND MULTIVARIATE STRATEGIES FOR REVEALING MULTIVARIATE STRUCTURES IN CLIMATE DATA

Directory of Open Access Journals (Sweden)

2016-12-01

Full Text Available This paper is on data analysis strategy in a complex, multidimensional, and dynamic domain. The focus is on the use of data mining techniques to explore the importance of multivariate structures; using climate variables which influences climate change. Techniques involved in data mining exercise vary according to the data structures. The multivariate analysis strategy considered here involved choosing an appropriate tool to analyze a process. Factor analysis is introduced into data mining technique in order to reveal the influencing impacts of factors involved as well as solving for multicolinearity effect among the variables. The temporal nature and multidimensionality of the target variables is revealed in the model using multidimensional regression estimates. The strategy of integrating the method of several statistical techniques, using climate variables in Nigeria was employed. R2 of 0.518 was obtained from the ordinary least square regression analysis carried out and the test was not significant at 5% level of significance. However, factor analysis regression strategy gave a good fit with R2 of 0.811 and the test was significant at 5% level of significance. Based on this study, model building should go beyond the usual confirmatory data analysis (CDA, rather it should be complemented with exploratory data analysis (EDA in order to achieve a desired result.

MULTIVARIATERESIDUES : A Mathematica package for computing multivariate residues

Science.gov (United States)

Larsen, Kasper J.; Rietkerk, Robbert

2018-01-01

Multivariate residues appear in many different contexts in theoretical physics and algebraic geometry. In theoretical physics, they for example give the proper definition of generalized-unitarity cuts, and they play a central role in the Grassmannian formulation of the S-matrix by Arkani-Hamed et al. In realistic cases their evaluation can be non-trivial. In this paper we provide a Mathematica package for efficient evaluation of multivariate residues based on methods from computational algebraic geometry.

Rap Music Use, Perceived Peer Behavior, and Sexual Initiation Among Ethnic Minority Youth.

Science.gov (United States)

Johnson-Baker, Kimberly A; Markham, Christine; Baumler, Elizabeth; Swain, Honora; Emery, Susan

2016-03-01

Research shows that rap music use is associated with risky sexual behavior in ethnic minority youth; however, it is unknown whether rap music use impacts sexual initiation specifically and, if so, which factors mediate this impact. Thus, we investigated the longitudinal relationship between hours spent listening to rap music in seventh grade and sexual initiation in ninth grade. We also examined the role of perceived peer sexual behavior as a potential mediator of this relationship. We analyzed data from students (n = 443) enrolled in a school-based randomized controlled trial of a sexual health education curriculum collected at baseline and at 18-month follow-up. Rap music use and perceived peer sexual behavior were assessed in seventh grade, whereas sexual initiation was assessed in ninth grade. Univariate, multivariate, and mediation analyses were conducted. At baseline, rap music use was significantly associated with race/ethnicity, parental music rules, and sexual behavior, but not with gender or parental education. Rap music use was a significant predictor of sexual initiation on univariate analysis but not multivariate analysis. Mediation analysis showed that the association between hours spent listening to rap music and sexual initiation was significantly mediated by perceived peer sexual behavior. Rap music use in early adolescence significantly impacts sexual initiation in late adolescence, partially mediated by perceived peer sexual behavior. More research is needed to understand how rap music influences perceptions of peer sexual behavior, which, in turn, influence early sexual initiation. Copyright © 2016 Society for Adolescent Health and Medicine. Published by Elsevier Inc. All rights reserved.

Multivariate methods and forecasting with IBM SPSS statistics

CERN Document Server

Aljandali, Abdulkader

2017-01-01

This is the second of a two-part guide to quantitative analysis using the IBM SPSS Statistics software package; this volume focuses on multivariate statistical methods and advanced forecasting techniques. More often than not, regression models involve more than one independent variable. For example, forecasting methods are commonly applied to aggregates such as inflation rates, unemployment, exchange rates, etc., that have complex relationships with determining variables. This book introduces multivariate regression models and provides examples to help understand theory underpinning the model. The book presents the fundamentals of multivariate regression and then moves on to examine several related techniques that have application in business-orientated fields such as logistic and multinomial regression. Forecasting tools such as the Box-Jenkins approach to time series modeling are introduced, as well as exponential smoothing and naïve techniques. This part also covers hot topics such as Factor Analysis, Dis...

An optimal multivariable controller for transcritical CO2 refrigeration cycle with an adjustable ejector

International Nuclear Information System (INIS)

He, Yang; Deng, Jianqiang; Yang, Fusheng; Zhang, Zaoxiao

2017-01-01

Highlights: • Dynamic model for transcritical CO 2 ejector refrigeration system is developed. • A model-driven optimal multivariable controller is proposed. • Gas cooler pressure and cooling capacity are tracked independently. • Maximal performance for a given load is achieved by the optimal controller. - Abstract: The fixed ejector has to work under a restricted operating condition to keep its positive effectiveness on the transcritical CO 2 refrigeration cycle, and a controllable ejector will be helpful. In this paper, an optimal multivariable controller based on the dynamic model is proposed to improve transcritical CO 2 refrigeration cycle with an adjustable ejector (TCRAE). A nonlinear dynamic model is first developed to model the dynamic characteristic of TCRAE. The corresponding model linearization is carried out and the simulation results reproduce transient behavior of the nonlinear model very well. Based on the developed model, an optimal multivariable controller with a tracker based linear quadratic state feedback algorithm and a predictor using steepest descent method is designed. The controller is finally applied on the experimental apparatus and the performance is verified. Using the tracker only, the gas cooler pressure and chilled water outlet temperature (cooling capacity) are well tracked rejecting the disturbances from each other. Furthermore, by the predictor, the optimal gas cooler pressure for a constant cooling capacity is actually approached on the experimental apparatus with a settling time about 700 s.

Early Caffeine Prophylaxis and Risk of Failure of Initial Continuous Positive Airway Pressure in Very Low Birth Weight Infants.

Science.gov (United States)

Patel, Ravi M; Zimmerman, Kanecia; Carlton, David P; Clark, Reese; Benjamin, Daniel K; Smith, P Brian

2017-11-01

To test the hypothesis that early caffeine treatment on the day of birth, compared with later treatment in very low birth weight (VLBW, caffeine in the first week of life. We used multivariable conditional logistic regression to compare the risk of CPAP failure, defined as invasive mechanical ventilation or surfactant therapy on DOL 1-6, by timing of caffeine treatment as either early (initiation on DOL 0) or routine (initiation on DOL 1-6). We identified 11 133 infants; 4528 (41%) received early caffeine and 6605 (59%) received routine caffeine. Median gestational age was lower in the early caffeine group, 29 weeks (25th, 75th percentiles; 28, 30) vs the routine caffeine group, 30 weeks (29, 31); P caffeine groups: 22% vs 21%; adjusted OR = 1.05 (95% CI: 0.93, 1.18). Early caffeine treatment on the day of birth was not associated with a decreased risk of CPAP failure in the first week of life for VLBW infants initially treated with CPAP. Copyright © 2017 Elsevier Inc. All rights reserved.

A general, multivariate definition of causal effects in epidemiology.

Science.gov (United States)

Flanders, W Dana; Klein, Mitchel

2015-07-01

Population causal effects are often defined as contrasts of average individual-level counterfactual outcomes, comparing different exposure levels. Common examples include causal risk difference and risk ratios. These and most other examples emphasize effects on disease onset, a reflection of the usual epidemiological interest in disease occurrence. Exposure effects on other health characteristics, such as prevalence or conditional risk of a particular disability, can be important as well, but contrasts involving these other measures may often be dismissed as non-causal. For example, an observed prevalence ratio might often viewed as an estimator of a causal incidence ratio and hence subject to bias. In this manuscript, we provide and evaluate a definition of causal effects that generalizes those previously available. A key part of the generalization is that contrasts used in the definition can involve multivariate, counterfactual outcomes, rather than only univariate outcomes. An important consequence of our generalization is that, using it, one can properly define causal effects based on a wide variety of additional measures. Examples include causal prevalence ratios and differences and causal conditional risk ratios and differences. We illustrate how these additional measures can be useful, natural, easily estimated, and of public health importance. Furthermore, we discuss conditions for valid estimation of each type of causal effect, and how improper interpretation or inferences for the wrong target population can be sources of bias.

Bayesian Inference of a Multivariate Regression Model

Directory of Open Access Journals (Sweden)

Marick S. Sinay

2014-01-01

Full Text Available We explore Bayesian inference of a multivariate linear regression model with use of a flexible prior for the covariance structure. The commonly adopted Bayesian setup involves the conjugate prior, multivariate normal distribution for the regression coefficients and inverse Wishart specification for the covariance matrix. Here we depart from this approach and propose a novel Bayesian estimator for the covariance. A multivariate normal prior for the unique elements of the matrix logarithm of the covariance matrix is considered. Such structure allows for a richer class of prior distributions for the covariance, with respect to strength of beliefs in prior location hyperparameters, as well as the added ability, to model potential correlation amongst the covariance structure. The posterior moments of all relevant parameters of interest are calculated based upon numerical results via a Markov chain Monte Carlo procedure. The Metropolis-Hastings-within-Gibbs algorithm is invoked to account for the construction of a proposal density that closely matches the shape of the target posterior distribution. As an application of the proposed technique, we investigate a multiple regression based upon the 1980 High School and Beyond Survey.

Mulch materials in processing tomato: a multivariate approach

Directory of Open Access Journals (Sweden)

Marta María Moreno

2013-08-01

Full Text Available Mulch materials of different origins have been introduced into the agricultural sector in recent years alternatively to the standard polyethylene due to its environmental impact. This study aimed to evaluate the multivariate response of mulch materials over three consecutive years in a processing tomato (Solanum lycopersicon L. crop in Central Spain. Two biodegradable plastic mulches (BD1, BD2, one oxo-biodegradable material (OB, two types of paper (PP1, PP2, and one barley straw cover (BS were compared using two control treatments (standard black polyethylene [PE] and manual weed control [MW]. A total of 17 variables relating to yield, fruit quality, and weed control were investigated. Several multivariate statistical techniques were applied, including principal component analysis, cluster analysis, and discriminant analysis. A group of mulch materials comprised of OB and BD2 was found to be comparable to black polyethylene regarding all the variables considered. The weed control variables were found to be an important source of discrimination. The two paper mulches tested did not share the same treatment group membership in any case: PP2 presented a multivariate response more similar to the biodegradable plastics, while PP1 was more similar to BS and MW. Based on our multivariate approach, the materials OB and BD2 can be used as an effective, more environmentally friendly alternative to polyethylene mulches.

The analysis of multivariate group differences using common principal components

NARCIS (Netherlands)

Bechger, T.M.; Blanca, M.J.; Maris, G.

2014-01-01

Although it is simple to determine whether multivariate group differences are statistically significant or not, such differences are often difficult to interpret. This article is about common principal components analysis as a tool for the exploratory investigation of multivariate group differences

Initial data sets for the Schwarzschild spacetime

International Nuclear Information System (INIS)

Gomez-Lobo, Alfonso Garcia-Parrado; Kroon, Juan A. Valiente

2007-01-01

A characterization of initial data sets for the Schwarzschild spacetime is provided. This characterization is obtained by performing a 3+1 decomposition of a certain invariant characterization of the Schwarzschild spacetime given in terms of concomitants of the Weyl tensor. This procedure renders a set of necessary conditions--which can be written in terms of the electric and magnetic parts of the Weyl tensor and their concomitants--for an initial data set to be a Schwarzschild initial data set. Our approach also provides a formula for a static Killing initial data set candidate--a KID candidate. Sufficient conditions for an initial data set to be a Schwarzschild initial data set are obtained by supplementing the necessary conditions with the requirement that the initial data set possesses a stationary Killing initial data set of the form given by our KID candidate. Thus, we obtain an algorithmic procedure of checking whether a given initial data set is Schwarzschildean or not

Simulations of full multivariate Tweedie with flexible dependence structure

DEFF Research Database (Denmark)

Cuenin, Johann; Jørgensen, Bent; Kokonendji, Célestin C.

2016-01-01

The paper introduces a variables-in-common method for constructing and simulating multivariate Tweedie distribution, based on linear combinations of independent univariate Tweedie variables. The method is facilitated by the convolution and scaling properties of the Tweedie distributions, using....... The method allows simulation of multivariate distributions from many known, including the Gaussian, Poisson, non-central gamma, gamma and inverse Gaussian distributions....

Efficient Procedure to Compute the Microcanonical Volume of Initial Conditions that Lead to Escape Trajectories from a Multidimensional Potential Well

NARCIS (Netherlands)

Waalkens, Holger; Burbanks, Andrew; Wiggins, Stephen

2005-01-01

A procedure is presented for computing the phase space volume of initial conditions for trajectories that escape or ‘‘react’’ from a multidimensional potential well. The procedure combines a phase space transition state theory, which allows one to construct dividing surfaces that are free of local

Ultrawide Bandwidth Receiver Based on a Multivariate Generalized Gaussian Distribution

KAUST Repository

Ahmed, Qasim Zeeshan

2015-04-01

Multivariate generalized Gaussian density (MGGD) is used to approximate the multiple access interference (MAI) and additive white Gaussian noise in pulse-based ultrawide bandwidth (UWB) system. The MGGD probability density function (pdf) is shown to be a better approximation of a UWB system as compared to multivariate Gaussian, multivariate Laplacian and multivariate Gaussian-Laplacian mixture (GLM). The similarity between the simulated and the approximated pdf is measured with the help of modified Kullback-Leibler distance (KLD). It is also shown that MGGD has the smallest KLD as compared to Gaussian, Laplacian and GLM densities. A receiver based on the principles of minimum bit error rate is designed for the MGGD pdf. As the requirement is stringent, the adaptive implementation of the receiver is also carried out in this paper. Training sequence of the desired user is the only requirement when implementing the detector adaptively. © 2002-2012 IEEE.

Input saturation in nonlinear multivariable processes resolved by nonlinear decoupling

Directory of Open Access Journals (Sweden)

Jens G. Balchen

1995-04-01

Full Text Available A new method is presented for the resolution of the problem of input saturation in nonlinear multivariable process control by means of elementary nonlinear decoupling (END. Input saturation can have serious consequences particularly in multivariable control because it may lead to very undesirable system behaviour and quite often system instability. Many authors have searched for systematic techniques for designing multivariable control systems in which saturation may occur in any of the control variables (inputs, manipulated variables. No generally accepted method seems to have been presented so far which gives a solution in closed form. The method of elementary nonlinear decoupling (END can be applied directly to the case of saturation control variables by deriving as many control strategies as there are combinations of saturating control variables. The method is demonstrated by the multivariable control of a simulated Fluidized Catalytic Cracker (FCC with very convincing results.

Support for voluntary and nonvoluntary euthanasia: what roles do conditions of suffering and the identity of the terminally ill play?

Science.gov (United States)

Ho, Robert; Chantagul, Natalie

2015-01-01

This study investigated the level of support for voluntary and nonvoluntary euthanasia under three conditions of suffering (pain; debilitated nature of the body; burden on the family) experienced by oneself, a significant other, and a person in general. The sample consisted of 1,897 Thai adults (719 males, 1,178 females) who voluntarily filled in the study's questionnaire. Initial multivariate analysis of variance indicated significant group (oneself, significant other, person in general) differences in level of support for voluntary and nonvoluntary euthanasia and under the three conditions of suffering. Multigroup path analysis conducted on the posited euthanasia model showed that the three conditions of suffering exerted differential direct and indirect influences on the support of voluntary and nonvoluntary euthanasia as a function of the identity of the person for whom euthanasia was being considered. The implications of these findings are discussed.

Initial 12-h operative fluid volume is an independent risk factor for pleural effusion after hepatectomy.

Science.gov (United States)

Cheng, Xiang; Wu, Jia-Wei; Sun, Ping; Song, Zi-Fang; Zheng, Qi-Chang

2016-12-01

Pleural effusion after hepatectomy is associated with significant morbidity and prolonged hospital stays. Several studies have addressed the risk factors for postoperative pleural effusion. However, there are no researches concerning the role of the initial 12-h operative fluid volume. The aim of this study was to evaluate whether the initial 12-h operative fluid volume during liver resection is an independent risk factor for pleural effusion after hepatectomy. In this study, we retrospectively analyzed clinical data of 470 patients consecutively undergoing elective hepatectomy between January 2011 and December 2012. We prospectively collected and retrospectively analyzed baseline and clinical data, including preoperative, intraoperative, and postoperative variables. Univariate and multivariate analyses were carried out to identify whether the initial 12-h operative fluid volume was an independent risk factor for pleural effusion after hepatectomy. The multivariate analysis identified 2 independent risk factors for pleural effusion: operative time [odds ratio (OR)=10.2] and initial 12-h operative fluid volume (OR=1.0003). Threshold effect analyses revealed that the initial 12 h operative fluid volume was positively correlated with the incidence of pleural effusion when the initial 12-h operative fluid volume exceeded 4636 mL. We conclude that the initial 12-h operative fluid volume during liver resection and operative time are independent risk factors for pleural effusion after hepatectomy. Perioperative intravenous fluids should be restricted properly.

Multivariate calibration applied to the quantitative analysis of infrared spectra

Energy Technology Data Exchange (ETDEWEB)

Haaland, D.M.

1991-01-01

Multivariate calibration methods are very useful for improving the precision, accuracy, and reliability of quantitative spectral analyses. Spectroscopists can more effectively use these sophisticated statistical tools if they have a qualitative understanding of the techniques involved. A qualitative picture of the factor analysis multivariate calibration methods of partial least squares (PLS) and principal component regression (PCR) is presented using infrared calibrations based upon spectra of phosphosilicate glass thin films on silicon wafers. Comparisons of the relative prediction abilities of four different multivariate calibration methods are given based on Monte Carlo simulations of spectral calibration and prediction data. The success of multivariate spectral calibrations is demonstrated for several quantitative infrared studies. The infrared absorption and emission spectra of thin-film dielectrics used in the manufacture of microelectronic devices demonstrate rapid, nondestructive at-line and in-situ analyses using PLS calibrations. Finally, the application of multivariate spectral calibrations to reagentless analysis of blood is presented. We have found that the determination of glucose in whole blood taken from diabetics can be precisely monitored from the PLS calibration of either mind- or near-infrared spectra of the blood. Progress toward the non-invasive determination of glucose levels in diabetics is an ultimate goal of this research. 13 refs., 4 figs.

Comprehensive drought characteristics analysis based on a nonlinear multivariate drought index

Science.gov (United States)

Yang, Jie; Chang, Jianxia; Wang, Yimin; Li, Yunyun; Hu, Hui; Chen, Yutong; Huang, Qiang; Yao, Jun

2018-02-01

It is vital to identify drought events and to evaluate multivariate drought characteristics based on a composite drought index for better drought risk assessment and sustainable development of water resources. However, most composite drought indices are constructed by the linear combination, principal component analysis and entropy weight method assuming a linear relationship among different drought indices. In this study, the multidimensional copulas function was applied to construct a nonlinear multivariate drought index (NMDI) to solve the complicated and nonlinear relationship due to its dependence structure and flexibility. The NMDI was constructed by combining meteorological, hydrological, and agricultural variables (precipitation, runoff, and soil moisture) to better reflect the multivariate variables simultaneously. Based on the constructed NMDI and runs theory, drought events for a particular area regarding three drought characteristics: duration, peak, and severity were identified. Finally, multivariate drought risk was analyzed as a tool for providing reliable support in drought decision-making. The results indicate that: (1) multidimensional copulas can effectively solve the complicated and nonlinear relationship among multivariate variables; (2) compared with single and other composite drought indices, the NMDI is slightly more sensitive in capturing recorded drought events; and (3) drought risk shows a spatial variation; out of the five partitions studied, the Jing River Basin as well as the upstream and midstream of the Wei River Basin are characterized by a higher multivariate drought risk. In general, multidimensional copulas provides a reliable way to solve the nonlinear relationship when constructing a comprehensive drought index and evaluating multivariate drought characteristics.

The impact of covariance misspecification in multivariate Gaussian mixtures on estimation and inference: an application to longitudinal modeling.

Science.gov (United States)

Heggeseth, Brianna C; Jewell, Nicholas P

2013-07-20

Multivariate Gaussian mixtures are a class of models that provide a flexible parametric approach for the representation of heterogeneous multivariate outcomes. When the outcome is a vector of repeated measurements taken on the same subject, there is often inherent dependence between observations. However, a common covariance assumption is conditional independence-that is, given the mixture component label, the outcomes for subjects are independent. In this paper, we study, through asymptotic bias calculations and simulation, the impact of covariance misspecification in multivariate Gaussian mixtures. Although maximum likelihood estimators of regression and mixing probability parameters are not consistent under misspecification, they have little asymptotic bias when mixture components are well separated or if the assumed correlation is close to the truth even when the covariance is misspecified. We also present a robust standard error estimator and show that it outperforms conventional estimators in simulations and can indicate that the model is misspecified. Body mass index data from a national longitudinal study are used to demonstrate the effects of misspecification on potential inferences made in practice. Copyright © 2013 John Wiley & Sons, Ltd.

Forecasting multivariate volatility in larger dimensions: some practical issues

OpenAIRE

Adam E Clements; Ayesha Scott; Annastiina Silvennoinen

2012-01-01

The importance of covariance modelling has long been recognised in the field of portfolio management and large dimensional multivariate problems are increasingly becoming the focus of research. This paper provides a straightforward and commonsense approach toward investigating whether simpler moving average based correlation forecasting methods have equal predictive accuracy as their more complex multivariate GARCH counterparts for large dimensional problems. We find simpler forecasting techn...

Time-varying correlations in global real estate markets: A multivariate GARCH with spatial effects approach

Science.gov (United States)

Gu, Huaying; Liu, Zhixue; Weng, Yingliang

2017-04-01

The present study applies the multivariate generalized autoregressive conditional heteroscedasticity (MGARCH) with spatial effects approach for the analysis of the time-varying conditional correlations and contagion effects among global real estate markets. A distinguishing feature of the proposed model is that it can simultaneously capture the spatial interactions and the dynamic conditional correlations compared with the traditional MGARCH models. Results reveal that the estimated dynamic conditional correlations have exhibited significant increases during the global financial crisis from 2007 to 2009, thereby suggesting contagion effects among global real estate markets. The analysis further indicates that the returns of the regional real estate markets that are in close geographic and economic proximities exhibit strong co-movement. In addition, evidence of significantly positive leverage effects in global real estate markets is also determined. The findings have significant implications on global portfolio diversification opportunities and risk management practices.

Synthetic environmental indicators: A conceptual approach from the multivariate statistics

International Nuclear Information System (INIS)

Escobar J, Luis A

2008-01-01

This paper presents a general description of multivariate statistical analysis and shows two methodologies: analysis of principal components and analysis of distance, DP2. Both methods use techniques of multivariate analysis to define the true dimension of data, which is useful to estimate indicators of environmental quality.

Robust methods for multivariate data analysis A1

DEFF Research Database (Denmark)

Frosch, Stina; Von Frese, J.; Bro, Rasmus

2005-01-01

Outliers may hamper proper classical multivariate analysis, and lead to incorrect conclusions. To remedy the problem of outliers, robust methods are developed in statistics and chemometrics. Robust methods reduce or remove the effect of outlying data points and allow the ?good? data to primarily...... determine the result. This article reviews the most commonly used robust multivariate regression and exploratory methods that have appeared since 1996 in the field of chemometrics. Special emphasis is put on the robust versions of chemometric standard tools like PCA and PLS and the corresponding robust...

Analysis of multi-species point patterns using multivariate log Gaussian Cox processes

DEFF Research Database (Denmark)

Waagepetersen, Rasmus; Guan, Yongtao; Jalilian, Abdollah

Multivariate log Gaussian Cox processes are flexible models for multivariate point patterns. However, they have so far only been applied in bivariate cases. In this paper we move beyond the bivariate case in order to model multi-species point patterns of tree locations. In particular we address t...... of the data. The selected number of common latent fields provides an index of complexity of the multivariate covariance structure. Hierarchical clustering is used to identify groups of species with similar patterns of dependence on the common latent fields.......Multivariate log Gaussian Cox processes are flexible models for multivariate point patterns. However, they have so far only been applied in bivariate cases. In this paper we move beyond the bivariate case in order to model multi-species point patterns of tree locations. In particular we address...... the problems of identifying parsimonious models and of extracting biologically relevant information from the fitted models. The latent multivariate Gaussian field is decomposed into components given in terms of random fields common to all species and components which are species specific. This allows...

Angioarchitectural characteristics associated with initial hemorrhagic presentation in supratentorial brain arteriovenous malformations

Energy Technology Data Exchange (ETDEWEB)

Pan, Jianwei, E-mail: swordman_pan@yahoo.com.cn [Department of Neurosurgery, The First Affiliated Hospital, School of Medicine, Zhejiang University, 79 Qingchun Road, Hangzhou 310006 (China); Feng, Lei, E-mail: lei_feng66@yahoo.com [Department of Radiology, Kaiser Permanente Medical Center, Los Angeles, CA 90027 (United States); Vinuela, Fernando, E-mail: fvinuela@mednet.ucla.edu [Interventional Neuroradiology Division, Department of Radiological Sciences, Ronald Reagan UCLA Medical Center, Los Angeles, CA 90095 (United States); He, Hongwei, E-mail: ttyyhhw@126.com [Department of Interventional Neuroradiology, Beijing Tiantan Hospital, Beijing, Capital Medical University, 6 Tiantan Xili, Beijing 100050 (China); Wu, Zhongxue, E-mail: 252694812@qq.com [Department of Interventional Neuroradiology, Beijing Tiantan Hospital, Beijing, Capital Medical University, 6 Tiantan Xili, Beijing 100050 (China); Zhan, Renya, E-mail: neurovasword@gmail.com [Department of Neurosurgery, The First Affiliated Hospital, School of Medicine, Zhejiang University, 79 Qingchun Road, Hangzhou 310006 (China)

2013-11-01

Objective: The difference in arterial supply, venous drainage, functional localization in supratentorial and infratentorial compartments may contribute to the conflicting results about risk factors for hemorrhage in published case series of brain arteriovenous malformation (bAVM). Further investigation focused on an individual brain compartment is thus necessary. This retrospective study aims to identify angioarchitectural characteristics associated with the initial hemorrhagic event of supratentorial bAVMs. Materials and methods: The clinical and angiographic features of 152 consecutive patients with supratentorial bAVMs who presented to our hospital from 2005 to 2008 were retrospectively reviewed. All these patients had new diagnosis of bAVM. Univariate (χ{sup 2} test) and multivariate analyses were conducted to assess the angiographic features in patients with and without initial hemorrhagic presentations. A probability value of less than 0.05 was considered statistically significant in each analysis. Results: In 152 patients with supratentorial AVMs, 70.6% of deep and 52.5% of superficial sbAVMs presented with hemorrhage. The deep location was correlated with initial hemorrhagic presentation in univariate analysis (χ{sup 2} = 3.499, p = 0.046) but not in the multivariate model (p = 0.144). There were 44 sbAVMs with perforating feeders, 39 (88.6%) of which bled at a significantly higher rate than those with terminal feeders (χ{sup 2} = 25.904, p = 0.000). 87.5% (21/24) of exclusive deep venous drainage presented with hemorrhage, a significantly higher rate than those of the other type of venous drainage (χ{sup 2} = 11.099, p = 0.004). All 10 patients with both perforating feeders and exclusive deep draining vein presented with initial hemorrhage. Hemorrhagic presentation was correlated with perforating feeders (p = 0.000) and exclusive deep draining vein (p = 0.007) in multivariate analysis as well. Conclusions: Supratentorial bAVMs with perforating feeders

Angioarchitectural characteristics associated with initial hemorrhagic presentation in supratentorial brain arteriovenous malformations

International Nuclear Information System (INIS)

Pan, Jianwei; Feng, Lei; Vinuela, Fernando; He, Hongwei; Wu, Zhongxue; Zhan, Renya

2013-01-01

Objective: The difference in arterial supply, venous drainage, functional localization in supratentorial and infratentorial compartments may contribute to the conflicting results about risk factors for hemorrhage in published case series of brain arteriovenous malformation (bAVM). Further investigation focused on an individual brain compartment is thus necessary. This retrospective study aims to identify angioarchitectural characteristics associated with the initial hemorrhagic event of supratentorial bAVMs. Materials and methods: The clinical and angiographic features of 152 consecutive patients with supratentorial bAVMs who presented to our hospital from 2005 to 2008 were retrospectively reviewed. All these patients had new diagnosis of bAVM. Univariate (χ 2 test) and multivariate analyses were conducted to assess the angiographic features in patients with and without initial hemorrhagic presentations. A probability value of less than 0.05 was considered statistically significant in each analysis. Results: In 152 patients with supratentorial AVMs, 70.6% of deep and 52.5% of superficial sbAVMs presented with hemorrhage. The deep location was correlated with initial hemorrhagic presentation in univariate analysis (χ 2 = 3.499, p = 0.046) but not in the multivariate model (p = 0.144). There were 44 sbAVMs with perforating feeders, 39 (88.6%) of which bled at a significantly higher rate than those with terminal feeders (χ 2 = 25.904, p = 0.000). 87.5% (21/24) of exclusive deep venous drainage presented with hemorrhage, a significantly higher rate than those of the other type of venous drainage (χ 2 = 11.099, p = 0.004). All 10 patients with both perforating feeders and exclusive deep draining vein presented with initial hemorrhage. Hemorrhagic presentation was correlated with perforating feeders (p = 0.000) and exclusive deep draining vein (p = 0.007) in multivariate analysis as well. Conclusions: Supratentorial bAVMs with perforating feeders and deep venous

A Range-Based Multivariate Model for Exchange Rate Volatility

OpenAIRE

Tims, Ben; Mahieu, Ronald

2003-01-01

textabstractIn this paper we present a parsimonious multivariate model for exchange rate volatilities based on logarithmic high-low ranges of daily exchange rates. The multivariate stochastic volatility model divides the log range of each exchange rate into two independent latent factors, which are interpreted as the underlying currency specific components. Due to the normality of logarithmic volatilities the model can be estimated conveniently with standard Kalman filter techniques. Our resu...

Modeling multivariate time series on manifolds with skew radial basis functions.

Science.gov (United States)

Jamshidi, Arta A; Kirby, Michael J

2011-01-01

We present an approach for constructing nonlinear empirical mappings from high-dimensional domains to multivariate ranges. We employ radial basis functions and skew radial basis functions for constructing a model using data that are potentially scattered or sparse. The algorithm progresses iteratively, adding a new function at each step to refine the model. The placement of the functions is driven by a statistical hypothesis test that accounts for correlation in the multivariate range variables. The test is applied on training and validation data and reveals nonstatistical or geometric structure when it fails. At each step, the added function is fit to data contained in a spatiotemporally defined local region to determine the parameters--in particular, the scale of the local model. The scale of the function is determined by the zero crossings of the autocorrelation function of the residuals. The model parameters and the number of basis functions are determined automatically from the given data, and there is no need to initialize any ad hoc parameters save for the selection of the skew radial basis functions. Compactly supported skew radial basis functions are employed to improve model accuracy, order, and convergence properties. The extension of the algorithm to higher-dimensional ranges produces reduced-order models by exploiting the existence of correlation in the range variable data. Structure is tested not just in a single time series but between all pairs of time series. We illustrate the new methodologies using several illustrative problems, including modeling data on manifolds and the prediction of chaotic time series.

Multivariate Variables Recognition using Hotelling’s T2 and MEWMA via ANN’s

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Chiñas-Sánchez Pamela

2014-01-01

Full Text Available In this article, a method for multivariate pattern recognition using artificial neural networks (ANN is proposed. The method is useful for monitoring multiple variables during the statistical process control. It employs descriptive statistics and multivariate control techniques. Three different ANN’s are evaluated to identify the network with higher efficiency during pattern recognition of multivariate variables tasks from data bases. Two data bases are analyzed; the first one is generated by simulation using the Montecarlo method, and the second data base was obtained from a public data base repository. The method consists of three stages: multivariate variables generation, multivariate analysis and pattern recognition using ANN’s. Several multivariate scenarios were generated using a combination of 2, 3 and 4 patterns in multivariate variables for the Hotelling’s T2 and MEWMA statistics that were analyzed to know its behavior and to determine their statistical characteristics. The pattern recognition task was evaluated using the ANN. In both study cases, experimental results showed an improved efficiency when using the Perceptron and the Backpropagation networks compared to the RBF network.

Implementation Challenges for Multivariable Control: What You Did Not Learn in School

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Garg, Sanjay

2008-01-01

Multivariable control allows controller designs that can provide decoupled command tracking and robust performance in the presence of modeling uncertainties. Although the last two decades have seen extensive development of multivariable control theory and example applications to complex systems in software/hardware simulations, there are no production flying systems aircraft or spacecraft, that use multivariable control. This is because of the tremendous challenges associated with implementation of such multivariable control designs. Unfortunately, the curriculum in schools does not provide sufficient time to be able to provide an exposure to the students in such implementation challenges. The objective of this paper is to share the lessons learned by a practitioner of multivariable control in the process of applying some of the modern control theory to the Integrated Flight Propulsion Control (IFPC) design for an advanced Short Take-Off Vertical Landing (STOVL) aircraft simulation.

MAGI: many-component galaxy initializer

Science.gov (United States)

Miki, Yohei; Umemura, Masayuki

2018-04-01

Providing initial conditions is an essential procedure for numerical simulations of galaxies. The initial conditions for idealized individual galaxies in N-body simulations should resemble observed galaxies and be dynamically stable for time-scales much longer than their characteristic dynamical times. However, generating a galaxy model ab initio as a system in dynamical equilibrium is a difficult task, since a galaxy contains several components, including a bulge, disc, and halo. Moreover, it is desirable that the initial-condition generator be fast and easy to use. We have now developed an initial-condition generator for galactic N-body simulations that satisfies these requirements. The developed generator adopts a distribution-function-based method, and it supports various kinds of density models, including custom-tabulated inputs and the presence of more than one disc. We tested the dynamical stability of systems generated by our code, representing early- and late-type galaxies, with N = 2097 152 and 8388 608 particles, respectively, and we found that the model galaxies maintain their initial distributions for at least 1 Gyr. The execution times required to generate the two models were 8.5 and 221.7 seconds, respectively, which is negligible compared to typical execution times for N-body simulations. The code is provided as open-source software and is publicly and freely available at https://bitbucket.org/ymiki/magi.

Multivariate Location Estimation Using Extension of $R$-Estimates Through $U$-Statistics Type Approach

OpenAIRE

Chaudhuri, Probal

1992-01-01

We consider a class of $U$-statistics type estimates for multivariate location. The estimates extend some $R$-estimates to multivariate data. In particular, the class of estimates includes the multivariate median considered by Gini and Galvani (1929) and Haldane (1948) and a multivariate extension of the well-known Hodges-Lehmann (1963) estimate. We explore large sample behavior of these estimates by deriving a Bahadur type representation for them. In the process of developing these asymptoti...

Application of multivariate statistical techniques in microbial ecology.

Science.gov (United States)

Paliy, O; Shankar, V

2016-03-01

Recent advances in high-throughput methods of molecular analyses have led to an explosion of studies generating large-scale ecological data sets. In particular, noticeable effect has been attained in the field of microbial ecology, where new experimental approaches provided in-depth assessments of the composition, functions and dynamic changes of complex microbial communities. Because even a single high-throughput experiment produces large amount of data, powerful statistical techniques of multivariate analysis are well suited to analyse and interpret these data sets. Many different multivariate techniques are available, and often it is not clear which method should be applied to a particular data set. In this review, we describe and compare the most widely used multivariate statistical techniques including exploratory, interpretive and discriminatory procedures. We consider several important limitations and assumptions of these methods, and we present examples of how these approaches have been utilized in recent studies to provide insight into the ecology of the microbial world. Finally, we offer suggestions for the selection of appropriate methods based on the research question and data set structure. © 2016 John Wiley & Sons Ltd.

Constructing ordinal partition transition networks from multivariate time series.

Science.gov (United States)

Zhang, Jiayang; Zhou, Jie; Tang, Ming; Guo, Heng; Small, Michael; Zou, Yong

2017-08-10

A growing number of algorithms have been proposed to map a scalar time series into ordinal partition transition networks. However, most observable phenomena in the empirical sciences are of a multivariate nature. We construct ordinal partition transition networks for multivariate time series. This approach yields weighted directed networks representing the pattern transition properties of time series in velocity space, which hence provides dynamic insights of the underling system. Furthermore, we propose a measure of entropy to characterize ordinal partition transition dynamics, which is sensitive to capturing the possible local geometric changes of phase space trajectories. We demonstrate the applicability of pattern transition networks to capture phase coherence to non-coherence transitions, and to characterize paths to phase synchronizations. Therefore, we conclude that the ordinal partition transition network approach provides complementary insight to the traditional symbolic analysis of nonlinear multivariate time series.

Small Sample Properties of Bayesian Multivariate Autoregressive Time Series Models

Science.gov (United States)

Price, Larry R.

2012-01-01

The aim of this study was to compare the small sample (N = 1, 3, 5, 10, 15) performance of a Bayesian multivariate vector autoregressive (BVAR-SEM) time series model relative to frequentist power and parameter estimation bias. A multivariate autoregressive model was developed based on correlated autoregressive time series vectors of varying…

Under Pressure: Financial Effect of the Hospital-Acquired Conditions Initiative-A Statewide Analysis of Pressure Ulcer Development and Payment.

Science.gov (United States)

Meddings, Jennifer; Reichert, Heidi; Rogers, Mary A M; Hofer, Timothy P; McMahon, Laurence F; Grazier, Kyle L

2015-07-01

To assess the financial effect of the 2008 Hospital-Acquired Conditions Initiative (HACI) pressure ulcer payment changes on Medicare, other payers, and hospitals. Retrospective before-and-after study of all-payer statewide administrative data for more than 2.4 million annual adult discharges in 2007 and 2009 using the Healthcare Cost and Utilization Project State Inpatient Datasets for California. How often and by how much the 2008 payment changes for pressure ulcers affected hospital payment was assessed. Nonfederal acute care California hospitals (N = 311). Adults discharged from acute-care hospitals. Pressure ulcer rates and hospital payment changes. Hospital-acquired pressure ulcer rates were low in 2007 (0.28%) and 2009 (0.27%); present-on-admission pressure ulcer rates increased from 2.3% in 2007 to 3.0% in 2009. According to clinical stage of pressure ulcer (available in 2009), hospital-acquired Stage III and IV ulcers occurred in 603 discharges (0.02%); 60,244 discharges (2.42%) contained other pressure ulcer diagnoses. Payment removal for Stage III and IV hospital-acquired ulcers reduced payment in 75 (0.003%) discharges, for a statewide payment decrease of $310,444 (0.001%) for all payers and $199,238 (0.001%) for Medicare. For all other pressure ulcers, the Hospital-Acquired Conditions Initiative reduced hospital payment in 20,246 (0.81%) cases (including 18,953 cases with present-on-admission ulcers), reducing statewide payment by $62,538,586 (0.21%) for all payers and $47,237,984 (0.32%) for Medicare. The total financial effect of the 2008 payment changes for pressure ulcers was negligible. Most payment decreases occurred by removal of comorbidity payments for present-on-admission pressure ulcers other than Stages III and IV. The removal of payment for hospital-acquired Stage III and IV ulcers by implementation of the HACI policy was 1/200th that of the removal of payment for other types of pressure ulcers that occurred in implementation of the

On the appropriate definition of soil profile configuration and initial conditions for land surface-hydrology models in cold regions

Science.gov (United States)

Sapriza-Azuri, Gonzalo; Gamazo, Pablo; Razavi, Saman; Wheater, Howard S.

2018-06-01

Arctic and subarctic regions are amongst the most susceptible regions on Earth to global warming and climate change. Understanding and predicting the impact of climate change in these regions require a proper process representation of the interactions between climate, carbon cycle, and hydrology in Earth system models. This study focuses on land surface models (LSMs) that represent the lower boundary condition of general circulation models (GCMs) and regional climate models (RCMs), which simulate climate change evolution at the global and regional scales, respectively. LSMs typically utilize a standard soil configuration with a depth of no more than 4 m, whereas for cold, permafrost regions, field experiments show that attention to deep soil profiles is needed to understand and close the water and energy balances, which are tightly coupled through the phase change. To address this gap, we design and run a series of model experiments with a one-dimensional LSM, called CLASS (Canadian Land Surface Scheme), as embedded in the MESH (Modélisation Environmentale Communautaire - Surface and Hydrology) modelling system, to (1) characterize the effect of soil profile depth under different climate conditions and in the presence of parameter uncertainty; (2) assess the effect of including or excluding the geothermal flux in the LSM at the bottom of the soil column; and (3) develop a methodology for temperature profile initialization in permafrost regions, where the system has an extended memory, by the use of paleo-records and bootstrapping. Our study area is in Norman Wells, Northwest Territories of Canada, where measurements of soil temperature profiles and historical reconstructed climate data are available. Our results demonstrate a dominant role for parameter uncertainty, that is often neglected in LSMs. Considering such high sensitivity to parameter values and dependency on the climate condition, we show that a minimum depth of 20 m is essential to adequately represent

Vasculogenesis and angiogenesis initiation under normoxic conditions through Wnt/β-catenin pathway in gliomas.

Science.gov (United States)

Vallée, Alexandre; Guillevin, Rémy; Vallée, Jean-Noël

2018-01-26

The canonical Wnt/β-catenin pathway is up-regulated in gliomas and involved in proliferation, invasion, apoptosis, vasculogenesis and angiogenesis. Nuclear β-catenin accumulation correlates with malignancy. Hypoxia activates hypoxia-inducible factor (HIF)-1α by inhibiting HIF-1α prolyl hydroxylation, which promotes glycolytic energy metabolism, vasculogenesis and angiogenesis, whereas HIF-1α is degraded by the HIF prolyl hydroxylase under normoxic conditions. We focus this review on the links between the activated Wnt/β-catenin pathway and the mechanisms underlying vasculogenesis and angiogenesis through HIF-1α under normoxic conditions in gliomas. Wnt-induced epidermal growth factor receptor/phosphatidylinositol 3-kinase (PI3K)/Akt signaling, Wnt-induced signal transducers and activators of transcription 3 (STAT3) signaling, and Wnt/β-catenin target gene transduction (c-Myc) can activate HIF-1α in a hypoxia-independent manner. The PI3K/Akt/mammalian target of rapamycin pathway activates HIF-1α through eukaryotic translation initiation factor 4E-binding protein 1 and STAT3. The β-catenin/T-cell factor 4 complex directly binds to STAT3 and activates HIF-1α, which up-regulates the Wnt/β-catenin target genes cyclin D1 and c-Myc in a positive feedback loop. Phosphorylated STAT3 by interleukin-6 or leukemia inhibitory factor activates HIF-1α even under normoxic conditions. The activation of the Wnt/β-catenin pathway induces, via the Wnt target genes c-Myc and cyclin D1 or via HIF-1α, gene transactivation encoding aerobic glycolysis enzymes, such as glucose transporter, hexokinase 2, pyruvate kinase M2, pyruvate dehydrogenase kinase 1 and lactate dehydrogenase-A, leading to lactate production, as the primary alternative of ATP, at all oxygen levels, even in normoxic conditions. Lactate released by glioma cells via the monocarboxylate lactate transporter-1 up-regulated by HIF-1α and lactate anion activates HIF-1α in normoxic endothelial cells by

Power Estimation in Multivariate Analysis of Variance

Directory of Open Access Journals (Sweden)

Jean François Allaire

2007-09-01

Full Text Available Power is often overlooked in designing multivariate studies for the simple reason that it is believed to be too complicated. In this paper, it is shown that power estimation in multivariate analysis of variance (MANOVA can be approximated using a F distribution for the three popular statistics (Hotelling-Lawley trace, Pillai-Bartlett trace, Wilk`s likelihood ratio. Consequently, the same procedure, as in any statistical test, can be used: computation of the critical F value, computation of the noncentral parameter (as a function of the effect size and finally estimation of power using a noncentral F distribution. Various numerical examples are provided which help to understand and to apply the method. Problems related to post hoc power estimation are discussed.

A note on inconsistent families of discrete multivariate distributions

KAUST Repository

Ghosh, Sugata; Dutta, Subhajit; Genton, Marc G.

2017-01-01

We construct a d-dimensional discrete multivariate distribution for which any proper subset of its components belongs to a specific family of distributions. However, the joint d-dimensional distribution fails to belong to that family and in other words, it is ‘inconsistent’ with the distribution of these subsets. We also address preservation of this ‘inconsistency’ property for the symmetric Binomial distribution, and some discrete distributions arising from the multivariate discrete normal distribution.

A note on inconsistent families of discrete multivariate distributions

KAUST Repository

Ghosh, Sugata

2017-07-05

We construct a d-dimensional discrete multivariate distribution for which any proper subset of its components belongs to a specific family of distributions. However, the joint d-dimensional distribution fails to belong to that family and in other words, it is ‘inconsistent’ with the distribution of these subsets. We also address preservation of this ‘inconsistency’ property for the symmetric Binomial distribution, and some discrete distributions arising from the multivariate discrete normal distribution.

On set-valued functionals: Multivariate risk measures and Aumann integrals

Science.gov (United States)

Ararat, Cagin

In this dissertation, multivariate risk measures for random vectors and Aumann integrals of set-valued functions are studied. Both are set-valued functionals with values in a complete lattice of subsets of Rm. Multivariate risk measures are considered in a general d-asset financial market with trading opportunities in discrete time. Specifically, the following features of the market are incorporated in the evaluation of multivariate risk: convex transaction costs modeled by solvency regions, intermediate trading constraints modeled by convex random sets, and the requirement of liquidation into the first m ≤ d of the assets. It is assumed that the investor has a "pure" multivariate risk measure R on the space of m-dimensional random vectors which represents her risk attitude towards the assets but does not take into account the frictions of the market. Then, the investor with a d-dimensional position minimizes the set-valued functional R over all m-dimensional positions that she can reach by trading in the market subject to the frictions described above. The resulting functional Rmar on the space of d-dimensional random vectors is another multivariate risk measure, called the market-extension of R. A dual representation for R mar that decomposes the effects of R and the frictions of the market is proved. Next, multivariate risk measures are studied in a utility-based framework. It is assumed that the investor has a complete risk preference towards each individual asset, which can be represented by a von Neumann-Morgenstern utility function. Then, an incomplete preference is considered for multivariate positions which is represented by the vector of the individual utility functions. Under this structure, multivariate shortfall and divergence risk measures are defined as the optimal values of set minimization problems. The dual relationship between the two classes of multivariate risk measures is constructed via a recent Lagrange duality for set optimization. In

Sexual selection on multivariate phenotypes in Anastrepha Fraterculus (Diptera: Tephritidae) from Argentina

International Nuclear Information System (INIS)

Sciurano, R.; Rodriguero, M.; Gomez Cendra, P.; Vilardi, J.; Segura, D.; Cladera, J.L.; Allinghi, Armando

2007-01-01

Despite the interest in applying environmentally friendly control methods such as sterile insect technique (SIT) against Anastrepha fraterculus (Wiedemann) (Diptera: Tephritidae), information about its biology, taxonomy, and behavior is still insufficient. To increase this information, the present study aims to evaluate the performance of wild flies under field cage conditions through the study of sexual competitiveness among males (sexual selection). A wild population from Horco Molle, Tucuman, Argentina was sampled. Mature virgin males and females were released into outdoor field cages to compete for mating. Morphometric analyses were applied to determine the relationship between the multivariate phenotype and copulatory success. Successful and unsuccessful males were measured for 8 traits: head width (HW), face width (FW), eye length (EL), thorax length (THL), wing length (WL), wing width (WW), femur length (FL), and tibia length (TIL). Combinations of different multivariate statistical methods and graphical analyses were used to evaluate sexual selection on male phenotype. The results indicated that wing width and thorax length would be the most probable targets of sexual selection. They describe a non-linear association between expected fitness and each of these 2 traits. This non-linear relation suggests that observed selection could maintain the diversity related to body size. (author) [es

Multivariate Meta-Analysis of Genetic Association Studies: A Simulation Study.

Directory of Open Access Journals (Sweden)

Binod Neupane

Full Text Available In a meta-analysis with multiple end points of interests that are correlated between or within studies, multivariate approach to meta-analysis has a potential to produce more precise estimates of effects by exploiting the correlation structure between end points. However, under random-effects assumption the multivariate estimation is more complex (as it involves estimation of more parameters simultaneously than univariate estimation, and sometimes can produce unrealistic parameter estimates. Usefulness of multivariate approach to meta-analysis of the effects of a genetic variant on two or more correlated traits is not well understood in the area of genetic association studies. In such studies, genetic variants are expected to roughly maintain Hardy-Weinberg equilibrium within studies, and also their effects on complex traits are generally very small to modest and could be heterogeneous across studies for genuine reasons. We carried out extensive simulation to explore the comparative performance of multivariate approach with most commonly used univariate inverse-variance weighted approach under random-effects assumption in various realistic meta-analytic scenarios of genetic association studies of correlated end points. We evaluated the performance with respect to relative mean bias percentage, and root mean square error (RMSE of the estimate and coverage probability of corresponding 95% confidence interval of the effect for each end point. Our simulation results suggest that multivariate approach performs similarly or better than univariate method when correlations between end points within or between studies are at least moderate and between-study variation is similar or larger than average within-study variation for meta-analyses of 10 or more genetic studies. Multivariate approach produces estimates with smaller bias and RMSE especially for the end point that has randomly or informatively missing summary data in some individual studies, when

Depth-weighted robust multivariate regression with application to sparse data

KAUST Repository

Dutta, Subhajit; Genton, Marc G.

2017-01-01

A robust method for multivariate regression is developed based on robust estimators of the joint location and scatter matrix of the explanatory and response variables using the notion of data depth. The multivariate regression estimator possesses desirable affine equivariance properties, achieves the best breakdown point of any affine equivariant estimator, and has an influence function which is bounded in both the response as well as the predictor variable. To increase the efficiency of this estimator, a re-weighted estimator based on robust Mahalanobis distances of the residual vectors is proposed. In practice, the method is more stable than existing methods that are constructed using subsamples of the data. The resulting multivariate regression technique is computationally feasible, and turns out to perform better than several popular robust multivariate regression methods when applied to various simulated data as well as a real benchmark data set. When the data dimension is quite high compared to the sample size it is still possible to use meaningful notions of data depth along with the corresponding depth values to construct a robust estimator in a sparse setting.

Depth-weighted robust multivariate regression with application to sparse data

KAUST Repository

Dutta, Subhajit

2017-04-05

A robust method for multivariate regression is developed based on robust estimators of the joint location and scatter matrix of the explanatory and response variables using the notion of data depth. The multivariate regression estimator possesses desirable affine equivariance properties, achieves the best breakdown point of any affine equivariant estimator, and has an influence function which is bounded in both the response as well as the predictor variable. To increase the efficiency of this estimator, a re-weighted estimator based on robust Mahalanobis distances of the residual vectors is proposed. In practice, the method is more stable than existing methods that are constructed using subsamples of the data. The resulting multivariate regression technique is computationally feasible, and turns out to perform better than several popular robust multivariate regression methods when applied to various simulated data as well as a real benchmark data set. When the data dimension is quite high compared to the sample size it is still possible to use meaningful notions of data depth along with the corresponding depth values to construct a robust estimator in a sparse setting.

Multivariate Discrete First Order Stochastic Dominance

DEFF Research Database (Denmark)

Tarp, Finn; Østerdal, Lars Peter

This paper characterizes the principle of first order stochastic dominance in a multivariate discrete setting. We show that a distribution  f first order stochastic dominates distribution g if and only if  f can be obtained from g by iteratively shifting density from one outcome to another...

A Valid Matérn Class of Cross-Covariance Functions for Multivariate Random Fields With Any Number of Components

KAUST Repository

Apanasovich, Tatiyana V.

2012-03-01

We introduce a valid parametric family of cross-covariance functions for multivariate spatial random fields where each component has a covariance function from a well-celebrated Matérn class. Unlike previous attempts, our model indeed allows for various smoothnesses and rates of correlation decay for any number of vector components.We present the conditions on the parameter space that result in valid models with varying degrees of complexity. We discuss practical implementations, including reparameterizations to reflect the conditions on the parameter space and an iterative algorithm to increase the computational efficiency. We perform various Monte Carlo simulation experiments to explore the performances of our approach in terms of estimation and cokriging. The application of the proposed multivariate Matérnmodel is illustrated on two meteorological datasets: temperature/pressure over the Pacific Northwest (bivariate) and wind/temperature/pressure in Oklahoma (trivariate). In the latter case, our flexible trivariate Matérn model is valid and yields better predictive scores compared with a parsimonious model with common scale parameters. © 2012 American Statistical Association.

Treatment and conditioning of low-level radioactive waste in Belgium: initial operating results of the Cilva facility

International Nuclear Information System (INIS)

Monsch, O.; Renard, C.; Deckers, J.; Luycx, P.

1995-01-01

The Belgian National Radioactive Waste and Enriched Fissile Material Agency (ONDRAF), which is responsible for the management of all radioactive waste in Belgium, recently decided to commission the CILVA facility. Operation of this facility, which comprises a number of units for the treatment of low-level radwaste, has been contracted to ONDRAF's Belgoprocess subsidiary based at the Dessel site. A consortium comprising SGN and Fabricom was in charge of building the CILVA facility's waste preparation and conditioning (concrete solidification) units. The concrete solidification processes, which were devised and developed by SGN, have been qualified to secure ONDRAF certification of the process and the facility. This enabled active commissioning of the waste conditioning unit in mid-August 1994. Active commissioning of the waste preparation unit was carried out in several stages up to the beginning of 1995 in accordance with operating requirements. Initial operating results of the two units are presented. (author)

Fourier expansions and multivariable Bessel functions concerning radiation programmes

International Nuclear Information System (INIS)

Dattoli, G.; Richetta, M.; Torre, A.; Chiccoli, C.; Lorenzutta, S.; Maino, G.

1996-01-01

The link between generalized Bessel functions and other special functions is investigated using the Fourier series and the generalized Jacobi-Anger expansion. A new class of multivariable Hermite polynomials is then introduced and their relevance to physical problems discussed. As an example of the power of the method, applied to radiation physics, we analyse the role played by multi-variable Bessel functions in the description of radiation emitted by a charge constrained to a nonlinear oscillation. (author)

Noise source analysis of nuclear ship Mutsu plant using multivariate autoregressive model

International Nuclear Information System (INIS)

Hayashi, K.; Shimazaki, J.; Shinohara, Y.

1996-01-01

The present study is concerned with the noise sources in N.S. Mutsu reactor plant. The noise experiments on the Mutsu plant were performed in order to investigate the plant dynamics and the effect of sea condition and and ship motion on the plant. The reactor noise signals as well as the ship motion signals were analyzed by a multivariable autoregressive (MAR) modeling method to clarify the noise sources in the reactor plant. It was confirmed from the analysis results that most of the plant variables were affected mainly by a horizontal component of the ship motion, that is the sway, through vibrations of the plant structures. Furthermore, the effect of ship motion on the reactor power was evaluated through the analysis of wave components extracted by a geometrical transform method. It was concluded that the amplitude of the reactor power oscillation was about 0.15% in normal sea condition, which was small enough for safe operation of the reactor plant. (authors)

Chaotic digital communication by encoding initial conditions.

Science.gov (United States)

Xiaofeng, Gong; Xingang, Wang; Meng, Zhan; Lai, C H

2004-06-01

We investigate the possibility to improve the noise performance of a chaotic digital communication scheme by utilizing further dynamical information. We show that by encoding the initial information of the chaotic carrier according to the transmitting bits, extra redundance can be introduced into the segments of chaotic signals corresponding to the consecutive bits. Such redundant information can be exploited effectively at the receiver end to improve the noise performance of the system. Compared to other methods (e.g., differential chaos shift keying), straightforward application of the proposed modulation/demodulation scheme already provides significant performance gain in the low signal-to-noise ratio (SNR) region. Furthermore, maximum likelihood precleaning procedure based on the Viterbi algorithm can be applied before the demodulation step to overcome the performance degradation in the high SNR region. The study indicates that it is possible to improve the noise performance of the chaotic digital communication scheme if further dynamics information is added to the system. (c) 2004 American Institute of Physics