Search results for "SIMULATION"
showing 10 items of 5095 documents
Understanding and Controlling Food Protein Structure and Function in Foods: Perspectives from Experiments and Computer Simulations
2020
The structure and interactions of proteins play a critical role in determining the quality attributes of many foods, beverages, and pharmaceutical products. Incorporating a multiscale understanding of the structure–function relationships of proteins can provide greater insight into, and control of, the relevant processes at play. Combining data from experimental measurements, human sensory panels, and computer simulations through machine learning allows the construction of statistical models relating nanoscale properties of proteins to the physicochemical properties, physiological outcomes, and tastes of foods. This review highlights several examples of advanced computer simulations at mol…
Poisson convergence on continuous time branching random walks and multistage carcinogenesis.
1982
A theorem for Poisson convergence on realizations of two-dimensional Branching Random Walks with an underlying continuous time Markov Branching Process is proved. This result can be used to gain an approximation for the number of cells having sustained a certain deficiency after a long time in multistage carcinogenesis.
Uncertainty evaluation of design rainfall for urban flood risk analysis
2011
A reliable and long dataset describing urban flood locations, volumes and depths would be an ideal prerequisite for assessing flood frequency distributions. However, data are often piecemeal and long-term hydraulic modelling is often adopted to estimate floods from historical rainfall series. Long-term modelling approaches are time- and resource-consuming, and synthetically designed rainfalls are often used to estimate flood frequencies. The present paper aimsto assess the uncertainty of such an approach and for suggesting improvements in thedefinition of synthetic rainfall data for flooding frequency analysis. According to this aim, a multivariate statistical analysis based on a copulameth…
New cut-off criterion for uninformative variable elimination in multivariate calibration of near-infrared spectra for the determination of heroin in …
2008
A new cut-off criterion has been proposed for the selection of uninformative variables prior to chemometric partial least squares (PLS) modelling. After variable elimination, PLS regressions were made and assessed comparing the results with those obtained by PLS models based on the full spectral range. To assess the prediction capabilities, uninformative variable elimination (UVE)-PLS and PLS were applied to diffuse reflectance near-infrared spectra of heroin samples. The application of the proposed new cut-off criterion, based on the t-Students distribution, provided similar predictive capabilities of the PLS models than those obtained using the original criteria based on quantile value. H…
MuTE: a MATLAB toolbox to compare established and novel estimators of the multivariate transfer entropy.
2014
A challenge for physiologists and neuroscientists is to map information transfer between components of the systems that they study at different scales, in order to derive important knowledge on structure and function from the analysis of the recorded dynamics. The components of physiological networks often interact in a nonlinear way and through mechanisms which are in general not completely known. It is then safer that the method of choice for analyzing these interactions does not rely on any model or assumption on the nature of the data and their interactions. Transfer entropy has emerged as a powerful tool to quantify directed dynamical interactions. In this paper we compare different ap…
Measuring Connectivity in Linear Multivariate Processes: Definitions, Interpretation, and Practical Analysis
2011
This tutorial paper introduces a common framework for the evaluation of widely used frequency-domain measures of coupling (coherence, partial coherence) and causality (directed coherence, partial directed coherence) from the parametric representation of linear multivariate (MV) processes. After providing a comprehensive time-domain definition of the various forms of connectivity observed in MV processes, we particularize them to MV autoregressive (MVAR) processes and derive the corresponding frequency-domain measures. Then, we discuss the theoretical interpretation of these MVAR-based connectivity measures, showing that each of them reflects a specific time-domain connectivity definition an…
Digital simulation of multivariate earthquake ground motions
2000
In this paper a new generation procedure of multivariate earthquake ground motion is presented. The technique takes full advantage of the decomposition of the power spectral density matrix by means of its eigenvectors. The application of the method to multivariate ground accelerations shows some very interesting physical properties which allows one to obtain significant reduction of the computational effort in the generation of sample functions relative to multivariate earthquake ground motion processes. Copyright © 2000 John Wiley & Sons, Ltd.
Decomposing the transfer entropy to quantify lag-specific Granger causality in cardiovascular variability.
2013
We present a modification of the well known transfer entropy (TE) which makes it able to detect, besides the direction and strength of the information transfer between coupled processes, its exact timing. The approach follows a decomposition strategy which identifies--according to a lag-specific formulation of the concept of Granger causality--the set of time delays carrying significant information, and then assigns to each of these delays an amount of information transfer such that the total contribution yields the overall TE. We propose also a procedure for the practical estimation from time series data of the relevant delays and lag-specific TE in both bivariate and multivariate settings…
Stochastic Nonlinear Time Series Forecasting Using Time-Delay Reservoir Computers: Performance and Universality
2014
International audience; Reservoir computing is a recently introduced machine learning paradigm that has already shown excellent performances in the processing of empirical data. We study a particular kind of reservoir computers called time-delay reservoirs that are constructed out of the sampling of the solution of a time-delay diFFerential equation and show their good performance in the forecasting of the conditional covariances associated to multivariate discrete-time nonlinear stochastic processes of VEC-GARCH type as well as in the prediction of factual daily market realized volatilities computed with intraday quotes, using as training input daily log-return series of moderate size. We …
A discrete mathematical model for addictive buying: Predicting the affected population evolution
2011
This paper deals with the construction of a discrete mathematical model for addictive buying. Firstly, identifications of consumers buying behavior are performed by using multivariate statistical techniques based on real data bases and sociological approaches. Then the population is divided into appropriate groups according to the level of overbuying and a discrete compartmental model is constructed. The future short term addicted population is computed assuming several future economic scenarios. © 2010 Elsevier Ltd.