Search results for "nonlinear dynamic"
showing 10 items of 158 documents
Cardiorespiratory information dynamics during mental arithmetic and sustained attention
2015
An analysis of cardiorespiratory dynamics during mental arithmetic, which induces stress, and sustained attention was conducted using information theory. The information storage and internal information of heart rate variability (HRV) were determined respectively as the self-entropy of the tachogram, and the self-entropy of the tachogram conditioned to the knowledge of respiration. The information transfer and cross information from respiration to HRV were assessed as the transfer and cross-entropy, both measures of cardiorespiratory coupling. These information-theoretic measures identified significant nonlinearities in the cardiorespiratory time series. Additionally, it was shown that, alt…
Transient Dynamics of Short Josephson Junctions under the influence of non-Gaussian Noise
2009
We investigate the effects of non-Gaussian white noise source on the transient dynamics of short Josephson junctions. The noise signal is simulated generating standard stable random variables with characteristic function described by Lévy index alpha and asymmetry parameter beta. We study the lifetime of the superconductive state as a function both of the frequency of the external driving bias current and the noise intensity for different values of index alpha. We compare our results with those obtained in the presence of Gaussian white noise. We find the presence of noise induced effects such as resonant activation and noise enhanced stability.
THE ROLE OF NON-GAUSSIAN SOURCES IN THE TRANSIENT DYNAMICS OF LONG JOSEPHSON JUNCTIONS
2013
We analyze the effects of different non-Gaussian noise sources on the transient dynamics of an overdamped long Josephson junction. We find nonmonotonic behavior of the mean escape time as a function of the noise intensity and frequency of the external driving signal for all the noise sources investigated.
Co-occurrence of resonant activation and noise-enhanced stability in a model of cancer growth in the presence of immune response.
2006
We investigate a stochastic version of a simple enzymatic reaction which follows the generic Michaelis-Menten kinetics. At sufficiently high concentrations of reacting species, the molecular fluctuations can be approximated as a realization of a Brownian dynamics for which the model reaction kinetics takes on the form of a stochastic differential equation. After eliminating a fast kinetics, the model can be rephrased into a form of a one-dimensional overdamped Langevin equation. We discuss physical aspects of environmental noises acting in such a reduced system, pointing out the possibility of coexistence of dynamical regimes where noise-enhanced stability and resonant activation phenomena …
Fractional-order nonlinear hereditariness of tendons and ligaments of the human knee
2020
In this paper the authors introduce a nonlinear model of fractional-order hereditariness used to capture experimental data obtained on human tendons of the knee. Creep and relaxation data on fibrous tissues have been obtained and fitted with logarithmic relations that correspond to power-laws with nonlinear dependence of the coefficients. The use of a proper nonlinear transform allows one to use Boltzmann superposition in the transformed variables yielding a fractional-order model for the nonlinear material hereditariness. The fundamental relations among the nonlinear creep and relaxation functions have been established, and the results from the equivalence relations have been contrasted wi…
Adaptive Neural Stabilizing Controller for a Class of Mismatched Uncertain Nonlinear Systems by State and Output Feedback
2015
In this paper, first, an adaptive neural network (NN) state-feedback controller for a class of nonlinear systems with mismatched uncertainties is proposed. By using a radial basis function NN (RBFNN), a bound of unknown nonlinear functions is approximated so that no information about the upper bound of mismatched uncertainties is required. Then, an observer-based adaptive controller based on RBFNN is designed to stabilize uncertain nonlinear systems with immeasurable states. The state-feedback and observer-based controllers are based on Lyapunov and strictly positive real-Lyapunov stability theory, respectively, and it is shown that the asymptotic convergence of the closed-loop system to ze…
Comparative performances of machine learning methods for classifying Crohn Disease patients using genome-wide genotyping data
2019
Crohn Disease (CD) is a complex genetic disorder for which more than 140 genes have been identified using genome wide association studies (GWAS). However, the genetic architecture of the trait remains largely unknown. The recent development of machine learning (ML) approaches incited us to apply them to classify healthy and diseased people according to their genomic information. The Immunochip dataset containing 18,227 CD patients and 34,050 healthy controls enrolled and genotyped by the international Inflammatory Bowel Disease genetic consortium (IIBDGC) has been re-analyzed using a set of ML methods: penalized logistic regression (LR), gradient boosted trees (GBT) and artificial neural ne…
Removing Batch Effects from Longitudinal Gene Expression - Quantile Normalization Plus ComBat as Best Approach for Microarray Transcriptome Data
2016
International audience; Technical variation plays an important role in microarray-based gene expression studies, and batch effects explain a large proportion of this noise. It is therefore mandatory to eliminate technical variation while maintaining biological variability. Several strategies have been proposed for the removal of batch effects, although they have not been evaluated in large-scale longitudinal gene expression data. In this study, we aimed at identifying a suitable method for batch effect removal in a large study of microarray-based longitudinal gene expression. Monocytic gene expression was measured in 1092 participants of the Gutenberg Health Study at baseline and 5-year fol…
Spatial calibration of structured illumination fluorescence microscopy using capillary tissue phantoms.
2008
Quantitative assessment of microvascular structure is relevant to the investigations of ischemic injury, reparative angiogenesis and tumor revascularization. In light microscopy applications, thick tissue specimens are necessary to characterize microvascular networks; however, thick tissue leads to image distortions due to out-of-focus light. Structured illumination confocal microscopy is an optical sectioning technique that improves contrast and resolution by using a grid pattern to identify the plane-of-focus within the specimen. Because structured illumination can be applied to wide-field (nonscanning) microscopes, the microcirculation can be studied by sequential intravital and confocal…
Tower of London test: a comparison between conventional statistic approach and modelling based on artificial neural network in differentiating fronto…
2011
The early differentiation of Alzheimer’s disease (AD) from frontotemporal dementia (FTD) may be difficult. The Tower of London (ToL), thought to assess executive functions such as planning and visuo-spatial working memory, could help in this purpose. Twentytwo Dementia Centers consecutively recruited patients with early FTD or AD. ToL performances of these groups were analyzed using both the conventional statistical approaches and the Artificial Neural Networks (ANNs) modelling. Ninety-four non aphasic FTD and 160 AD patients were recruited. ToL Accuracy Score (AS) significantly (p < 0.05) The use of hidden information contained in the different items of ToL and the non linear processing of…