206,094 research outputs found
On the self-similarity of line segments in decaying homogeneous isotropic turbulence
The self-similarity of a passive scalar in homogeneous isotropic decaying
turbulence is investigated by the method of line segments (M. Gauding et al.,
Physics of Fluids 27.9 (2015): 095102). The analysis is based on a highly
resolved direct numerical simulation of decaying turbulence. The method of line
segments is used to perform a decomposition of the scalar field into smaller
sub-units based on the extremal points of the scalar along a straight line.
These sub-units (the so-called line segments) are parameterized by their length
and the difference of the scalar field between the ending
points. Line segments can be understood as thin local convective-diffusive
structures in which diffusive processes are enhanced by compressive strain.
From DNS, it is shown that the marginal distribution function of the
length~ assumes complete self-similarity when re-scaled by the mean
length . The joint statistics of and , from which
the local gradient can be defined, play an important role
in understanding the turbulence mixing and flow structure. Large values of
occur at a small but finite length scale. Statistics of are characterized
by rare but strong deviations that exceed the standard deviation by more than
one order of magnitude. It is shown that these events break complete
self-similarity of line segments, which confirms the standard paradigm of
turbulence that intense events (which are known as internal intermittency) are
not self-similar
On a random number of disorders
We register a random sequence which has the following properties: it has three segments being the homogeneous Markov processes. Each segment has his own one step transition probability law and the length of the segment is unknown and random. It means that at two random successive moments (they can be equal also and equal zero too) the source of observations is changed and the first observation in new segment is chosen according to new transition probability starting from the last state of the previous segment. In effect the number of homogeneous segments is random. The transition probabilities of each process are known and a priori distribution of the disorder moments is given. The former research on such problem has been devoted to various questions concerning the distribution changes. The random number of distributional segments creates new problems in solutions with relation to analysis of the model with deterministic number of segments. Two cases are presented in details. In the first one the objectives is to stop on or between the disorder moments while in the second one our objective is to find the strategy which immediately detects the distribution changes. Both problems are reformulated to optimal stopping of the observed sequences. The detailed analysis of the problem is presented to show the form of optimal decision function.disorder problem, sequential detection, optimal stopping, Markov process, change point, double optimal stopping
Structural Equation Modeling and simultaneous clustering through the Partial Least Squares algorithm
The identification of different homogeneous groups of observations and their
appropriate analysis in PLS-SEM has become a critical issue in many appli-
cation fields. Usually, both SEM and PLS-SEM assume the homogeneity of all
units on which the model is estimated, and approaches of segmentation present
in literature, consist in estimating separate models for each segments of
statistical units, which have been obtained either by assigning the units to
segments a priori defined. However, these approaches are not fully accept- able
because no causal structure among the variables is postulated. In other words,
a modeling approach should be used, where the obtained clusters are homogeneous
with respect to the structural causal relationships. In this paper, a new
methodology for simultaneous non-hierarchical clus- tering and PLS-SEM is
proposed. This methodology is motivated by the fact that the sequential
approach of applying first SEM or PLS-SEM and second the clustering algorithm
such as K-means on the latent scores of the SEM/PLS-SEM may fail to find the
correct clustering structure existing in the data. A simulation study and an
application on real data are included to evaluate the performance of the
proposed methodology
Bayesian Detection of Changepoints in Finite-State Markov Chains for Multiple Sequences
We consider the analysis of sets of categorical sequences consisting of
piecewise homogeneous Markov segments. The sequences are assumed to be governed
by a common underlying process with segments occurring in the same order for
each sequence. Segments are defined by a set of unobserved changepoints where
the positions and number of changepoints can vary from sequence to sequence. We
propose a Bayesian framework for analyzing such data, placing priors on the
locations of the changepoints and on the transition matrices and using Markov
chain Monte Carlo (MCMC) techniques to obtain posterior samples given the data.
Experimental results using simulated data illustrates how the methodology can
be used for inference of posterior distributions for parameters and
changepoints, as well as the ability to handle considerable variability in the
locations of the changepoints across different sequences. We also investigate
the application of the approach to sequential data from two applications
involving monsoonal rainfall patterns and branching patterns in trees
A statistical approach for array CGH data analysis
BACKGROUND: Microarray-CGH experiments are used to detect and map chromosomal imbalances, by hybridizing targets of genomic DNA from a test and a reference sample to sequences immobilized on a slide. These probes are genomic DNA sequences (BACs) that are mapped on the genome. The signal has a spatial coherence that can be handled by specific statistical tools. Segmentation methods seem to be a natural framework for this purpose. A CGH profile can be viewed as a succession of segments that represent homogeneous regions in the genome whose BACs share the same relative copy number on average. We model a CGH profile by a random Gaussian process whose distribution parameters are affected by abrupt changes at unknown coordinates. Two major problems arise : to determine which parameters are affected by the abrupt changes (the mean and the variance, or the mean only), and the selection of the number of segments in the profile. RESULTS: We demonstrate that existing methods for estimating the number of segments are not well adapted in the case of array CGH data, and we propose an adaptive criterion that detects previously mapped chromosomal aberrations. The performances of this method are discussed based on simulations and publicly available data sets. Then we discuss the choice of modeling for array CGH data and show that the model with a homogeneous variance is adapted to this context. CONCLUSIONS: Array CGH data analysis is an emerging field that needs appropriate statistical tools. Process segmentation and model selection provide a theoretical framework that allows precise biological interpretations. Adaptive methods for model selection give promising results concerning the estimation of the number of altered regions on the genome
MEASURING FOOD SAFETY PREFERENCES: IDENTIFYING CONSUMER SEGMENTS
Conjoint analysis was used to estimate individual preference functions for food safety attributes. Consumer segments were constructed by using cluster analysis to form groups which were homogeneous with respect to preferences regarding food safety. Although substantial differences existed among the three distinct groups, consumers in all segments were willing to pay a moderate amount to ensure that apples met established safety standards. However, a policy which restricts pesticide use would likely result in substantial consumer dissatisfaction, unless it could be achieved with little impact on price or quality.Food Consumption/Nutrition/Food Safety,
Modelling cytoskeletal traffic: an interplay between passive diffusion and active transport
We introduce the totally asymmetric exclusion process with Langmuir kinetics
(TASEP-LK) on a network as a microscopic model for active motor protein
transport on the cytoskeleton, immersed in the diffusive cytoplasm. We discuss
how the interplay between active transport along a network and infinite
diffusion in a bulk reservoir leads to a heterogeneous matter distribution on
various scales. We find three regimes for steady state transport, corresponding
to the scale of the network, of individual segments or local to sites. At low
exchange rates strong density heterogeneities develop between different
segments in the network. In this regime one has to consider the topological
complexity of the whole network to describe transport. In contrast, at moderate
exchange rates the transport through the network decouples, and the physics is
determined by single segments and the local topology. At last, for very high
exchange rates the homogeneous Langmuir process dominates the stationary state.
We introduce effective rate diagrams for the network to identify these
different regimes. Based on this method we develop an intuitive but generic
picture of how the stationary state of excluded volume processes on complex
networks can be understood in terms of the single-segment phase diagram.Comment: 5 pages, 7 figure
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