Valérie Monbet

Markov-switching autoregressive models for wind time series

In this paper, non-homogeneous Markov-Switching Autoregressive (MS-AR) models are proposed to describe wind time series. In these models, several autoregressive models are used to describe the time evolution of the wind speed and the switching between these different models is controlled by a hidden Markov chain which represents the weather types. We first block the data by month in order to remove seasonal components and propose a MS-AR model with non-homogeneous autoregressive models to describe daily components. Then we discuss extensions where the hidden Markov chain is also non-stationary to handle seasonal and interannual fluctuations. The different models are fitted using the EM algorithm to a long time series of wind speed measurement on the Island of Ouessant (France). It is shown that the fitted models are interpretable and provide a good description of important properties of the data such as the marginal distributions, the second-order structure or the length of the stormy and calm periods.

Fiber evanescent wave spectroscopy using the mid-infrared provides useful fingerprints for metabolic profiling in humans

Fiber evanescent wave spectroscopy (FEWS) explores the mid-infrared domain, providing information on functional chemical groups represented in the sample. Our goal is to evaluate whether spectral fingerprints obtained by FEWS might orientate clinical diagnosis. Serum samples from normal volunteers and from four groups of patients with metabolic abnormalities are analyzed by FEWS. These groups consist of iron overloaded genetic hemochromatosis (GH), iron depleted GH, cirrhosis, and dysmetabolic hepatosiderosis (DYSH). A partial least squares (PLS) logistic method is used in a training group to create a classification algorithm, thereafter applied to a test group. Patients with cirrhosis or DYSH, two groups exhibiting important metabolic disturbances, are clearly discriminated from control groups with AUROC values of 0.94+/-0.05 and 0.90+/-0.06, and sensibility/specificity of 8684% and 8787%, respectively. When pooling all groups, the PLS method contributes to discriminate controls, cirrhotic, and dysmetabolic patients. Our data demonstrate that metabolic profiling using infrared FEWS is a possible way to investigate metabolic alterations in patients.

Mapping Bacterial Surface Population Physiology in Real-Time: Infrared Spectroscopy of Proteus Mirabilis Swarm Colonies

We mapped the space–time distribution of stationary and swarmer cells within a growing Proteus mirabilis colony by infrared (IR) microspectroscopy. Colony mapping was performed at different positions between the inoculum and the periphery with a discrete microscope-mounted IR sensor, while continuous monitoring at a fixed location over time used an optical fiber based IR–attenuated total reflection (ATR) sensor, or “optrode.” Phenotypes within a single P. mirabilis population relied on identification of functional determinants (producing unique spectral signals) that reflect differences in macromolecular composition associated with cell differentiation. Inner swarm colony domains are spectrally homogeneous, having patterns similar to those produced by the inoculum. Outer domains composed of active swarmer cells exhibit spectra distinguishable at multiple wavelengths dominated by polysaccharides. Our real-time observations agree with and extend earlier reports indicating that motile swarmer cells are restricted to a narrow (approximately 3 mm) annulus at the colony edge. This study thus validates the use of an IR optrode for real-time and noninvasive monitoring of biofilms and other bacterial surface populations.

Bivariate simulation of non stationary and non Gaussian observed processes: Application to sea state parameters

A method for artificially generating operational sea state histories has been developed. This is a distribution free method to simulate bivariate non stationary and non Gaussian random processes. This method is applied to the simulation of the bivariate process (Hs, Tp) of sea state parameters. The time series respects the physical constraints existing between the significant wave height and the peak period. Furthermore, we show that the persistence properties of the simulations match to those of the observations.

Chalcogenide optical fibers for mid-infrared sensing

Abstract. Chalcogenide glasses are a matchless material as far as mid-infrared (IR) applications are concerned. They transmit light typically from 2 to 12 μm and even as far as 20 μm depending on their composition, and numerous glass compositions can be designed for optical fibers. One of the most promising applications of these fibers consists in implementing fiber evanescent wave spectroscopy, which enables detection of the mid-IR signature of most biomolecules. The principles of fiber evanescent wave spectroscopy are recalled together with the benefit of using selenide glass to carry out this spectroscopy. Then, two large-scale studies in recent years in medicine and food safety are exposed. To conclude, the future strategy is presented. It focuses on the development of rare earth-doped fibers used as mid-IR sources on one hand and tellurium-based glasses to shift the limit of detection toward longer wavelength on the other hand.

Long Term Object Drift Forecast in the Ocean with Tide and Wind

In this paper, we propose a new method to forecast the drift of objects in the near coastal ocean over a period of several weeks. The proposed approach consists in estimating the probability of events linked to the drift using Monte Carlo simulations. It couples an averaging method which permits us to decrease the computational cost with a statistical method in order to take into account the variability of meteorological loading factors.

Combining Analog Method and Ensemble Data Assimilation: Application to the Lorenz-63 Chaotic System

Nowadays, ocean and atmosphere sciences face a deluge of data from space, in situ monitoring as well as numerical simulations. The availability of these different data sources offers new opportunities, still largely underexploited, to improve the understanding, modeling, and reconstruction of geophysical dynamics. The classical way to reconstruct the space-time variations of a geophysical system from observations relies on data assimilation methods using multiple runs of the known dynamical model. This classical framework may have severe limitations including its computational cost, the lack of adequacy of the model with observed data, and modeling uncertainties. In this paper, we explore an alternative approach and develop a fully data-driven framework, which combines machine learning and statistical sampling to simulate the dynamics of complex system. As a proof concept, we address the assimilation of the chaotic Lorenz-63 model. We demonstrate that a nonparametric sampler from a catalog of historical datasets, namely, a nearest neighbor or analog sampler, combined with a classical stochastic data assimilation scheme, the ensemble Kalman filter and smoother, reaches state-of-the-art performances, without online evaluations of the physical model.

Estimation of Wave Height Return Periods Using a Nonstationary Time Series Modelling

A new method for calculating return periods of various level values from nonstationary time series data is presented. The key-idea of the method is a new definition of the return period, based on the Mean Number of Upcrossings of the level x* (MENU method). The whole procedure is numerically implemented and applied to long-term measured time series of significant wave height. The method is compared with other more classical approaches that take into acount the time dependance for time series of significant wave height. Estimates of the extremal index are given and for each method bootstrap confidence intervals are computed. The predictions obtained by means of MENU method are lower than the traditional predictions. This is in accordance with the results of other methods that take also into account the dependence structure of the examined time series.Copyright

Sparse vector Markov switching autoregressive models Application to multivariate time series of temperature

Multivariate time series are of interest in many fields including economics and environment. The dynamical processes occurring in these domains often exhibit a mixture of different dynamics so that it is common to describe them using Markov Switching vector autoregressive processes. However the estimation of such models is difficult even when the dimension is not so high because of the number of parameters involved. A Smoothly Clipped Absolute Deviation penalization of the likelihood is proposed to shrink the parameters towards zeros and regularize the inference problem which is generally ill-posed. The Expectation Maximization algorithm built for maximizing the penalized likelihood is described in detail and tested on simulated data and real data consisting of daily mean temperature.

Mid-infrared fibre evanescent wave spectroscopy of serum allows fingerprinting of the hepatic metabolic status in mice

Non-alcoholic fatty liver disease is associated with obesity, diabetes, and metabolic syndrome. The detection of systemic metabolic changes associated with alterations in the liver status during non-alcoholic fatty liver disease could improve patient follow-up. The aim of the present study was to evaluate the potential of mid-infrared fibre evanescent wave spectroscopy as a minimum-invasive method for evaluating the liver status during non-alcoholic fatty liver disease. Seventy-five mice were subjected to a control, high-fat or high-fat-high carbohydrate diets. We analysed the serum biochemical parameters and mRNA levels of hepatic genes by quantitative RT-PCR. Steatosis was quantified by image analysis. The mid-infrared spectra were acquired from serum, and then analysed to develop a predictive model of the steatosis level. Animals subjected to enriched diets were obese. Hepatic steatosis was found in all animals. The relationship between the spectroscopy-predicted and observed levels of steatosis, expressed as percentages of the liver biopsy area, was not linear. A transition around 10% steatosis was observed, leading us to consider two distinct predictive models (<10% and >10%) based on two different sets of discriminative spectral variables. The model performance was evaluated using random cross-validation (10%). The hypothesis that additional metabolic changes occur beyond this transition was supported by the fact that it was associated with increased serum ALT levels, and Col1α1 chain mRNA levels. Our data suggest that mid-infrared spectroscopy combined with statistical analysis allows identifying serum mid-infrared signatures that reflect the liver status during non-alcoholic fatty liver disease.