Analyse transitoire des modèles markoviens des systémes tolérants aux fautes avec réparation différée.

La tesis aborda el análisis transitorio de modelos Markovianos de sistemas tolerantes a fallos con reparación diferida. Se consideran dos medidas definidas sobre cadenas de Markov a tiempo continuo con tasas de recompensa asociadas a los estados: la tasa de recompensa esperada en el instante t y la tasa media de recompensa esperada en el intervalo de tiempo [0, t]. Casos particulares importantes de esas dos medidas son la no-fiabilidad, la disponibilidad en el instante t y la disponibilidad de intervalo esperada. La tesis desarrolla un método numérico para el cálculo de ambas medidas con error arbitrariamente pequeño, denominado aleatorización regenerativa partida. Una ventaja importante del método es su estabilidad numérica. También se desarrolla un método numéricamente estable, la aleatorización regenerativa partida acotante, para el cálculo de cotas para un caso particular de la la tasa de recompensa esperada en el instante t que incluye la no-fiabilidad y cotas para ella. El coste computacional de los métodos es comparado con el de otros métodos basados en la aleatorización. La aleatorización regenerativa partida puede ser mucho menos costosa que los demás métodos para t grande y, cuando la relación entre la máxima y la mínima tasa de salida desde los estados con reparación no es muy elevada, permite el análisis en tiempos de CPU razonables de cadenas de Markov con muchos estados. La aleatorización regenerativa partida acotante tiene un coste computacional relativo muy bajo y proporciona cotas ajustadas, permitiendo el análisis en tiempos de CPU razonables de cadenas de Markov con muchísimos estados. En combinación con técnicas de acotación, los métodos desarrollados permiten el análisis numérico en tiempos de CPU razonables de modelos Markovianos de sistemas tolerantes a fallos con reparación diferida con un número muy elevado de componentes

A generalized method for the transient analysis of Markov models of fault-tolerant systems with deferred repair

Randomization is an attractive alternative for the transient analysis of continuous time Markov models. The main advantages of the method are numerical stability, well-controlled computation error and ability to specify the computation error in advance. However, the fact that the method can be computationally expensive limits its applicability. Recently, a variant of the (standard) randomization method, called split regenerative randomization has been proposed for the efficient analysis of reliability-like models of fault-tolerant systems with deferred repair. In this paper, we generalize that method so that it covers more general reward measures: the expected transient reward rate and the expected averaged reward rate. The generalized method has the same good properties as the standard randomization method and, for large models and large values of the time t at which the measure has to be computed, can be significantly less expensive. The method requires the selection of a subset of states and a regenerative state satisfying some conditions. For a class of continuous time Markov models, class C'_2, including typical failure/repair reliability models with exponential failure and repair time distributions and deferred repair, natural selections for the subset of states and the regenerative state exist and results are available assessing approximately the computational cost of the method in terms of “visible” model characteristics. Using a large model class C'_2 example, we illustrate the performance of the method and show that it can be significantly faster than previously proposed randomization-based methods.Preprin

Studies on the internalization mechanisms of cationic cell-penetrating peptides.

A great deal of data has been amassed suggesting that cationic peptides are able to translocate into eucaryotic cells in a temperature-independent manner. Although such peptides are widely used to promote the intracellular delivery of bioactive molecules, the mechanism by which this cell-penetrating activity occurs still remains unclear. Here, we present an in vitro study of the cellular uptake of peptides, originally deriving from protegrin (the SynB peptide vectors), that have also been shown to enhance the transport of drugs across the blood-brain barrier. In parallel, we have examined the internalization process of two lipid-interacting peptides, SynB5 and pAntp-(43–58), the latter corresponding to the translocating segment of the Antennapedia homeodomain. We report a quantitative study of the time- and dose-dependence of internalization and demonstrate that these peptides accumulate inside vesicular structures. Furthermore, we have examined the role of endocytotic pathways in this process using a variety of metabolic and endocytosis inhibitors. We show that the internalization of these peptides is a temperatureand energy-dependent process and that endosomal transport is a key component of the mechanism. Altogether, our results suggest that SynB and pAntp-(43–58) peptides penetrate into cells by an adsorptive-mediated endocytosis process rather than temperature-independent translocation

A propos d'une activite ribonucleasique des Sm snRNPS et de lasnRNP U5, facteur d'epissage

SIGLECNRS T Bordereau / INIST-CNRS - Institut de l'Information Scientifique et TechniqueFRFranc

A generalized method for the transient analysis of Markov models of fault-tolerant systems with deferred repair

Randomization is an attractive alternative for the transient analysis of continuous time Markov models. The main advantages of the method are numerical stability, well-controlled computation error and ability to specify the computation error in advance. However, the fact that the method can be computationally expensive limits its applicability. Recently, a variant of the (standard) randomization method, called split regenerative randomization has been proposed for the efficient analysis of reliability-like models of fault-tolerant systems with deferred repair. In this paper, we generalize that method so that it covers more general reward measures: the expected transient reward rate and the expected averaged reward rate. The generalized method has the same good properties as the standard randomization method and, for large models and large values of the time t at which the measure has to be computed, can be significantly less expensive. The method requires the selection of a subset of states and a regenerative state satisfying some conditions. For a class of continuous time Markov models, class C'_2, including typical failure/repair reliability models with exponential failure and repair time distributions and deferred repair, natural selections for the subset of states and the regenerative state exist and results are available assessing approximately the computational cost of the method in terms of “visible” model characteristics. Using a large model class C'_2 example, we illustrate the performance of the method and show that it can be significantly faster than previously proposed randomization-based methods

Transient analysis of Markov models of fault-tolerant systems with deferred repair using split regenerative randomization

The (standard) randomization method is an attractive alternative for the transient analysis of continuous time Markov models. The main advantages of the method are numerical stability, well-controlled computation error, and ability to specify the computation error in advance. However, the fact that the method can be computationally very expensive limits its applicability. In this paper, we develop a new method called split regenerative randomization, which, having the same good properties as standard randomization, can be significantly more efficient. The method covers reliability-like models with a particular but quite general structure and requires the selection of a subset of states and a regenerative state satisfying some conditions. For a class of continuous time Markov models, model class C_2, including typical failure/repair reliability-like models with exponential failure and repair time distributions and deferred repair, natural selections are available for both the subset of states and the regenerative state and, for those natural selections, theoretical results are available assessing the efficiency of the method in terms of “visible” model characteristics. Those results can be used to anticipate when the method can be expected to be competitive. We illustrate the application of the method using a large class C_2 model and show that for models in that class the method can indeed be significantly more efficient than previously available randomization-based method

Reliability bounds for fault-tolerant systems with deferred repair using bounding split regenerative randomization

A numerically stable method is developed which computes seemingly tight bounds at a small computational cost relative to the model size, when that model size is large, for the unreliability and bounds for the unreliability using, respectively, exact and bounding failure/repair continuous-time Markov chain models of fault-tolerant systems with exponential failure and repair time distributions, in which repair is deferred until some condition on the collection of failed components is satisfied, and, then, proceeds until reaching the state without failed components, with failure rates much smaller than repair rates and not too different output rates from states with deferred repair

A generalized method for the transient analysis of Markov models of fault-tolerant systems with deferred repair

Randomization is an attractive alternative for the transient analysis of continuous time Markov models. The main advantages of the method are numerical stability, well-controlled computation error and ability to specify the computation error in advance. However, the fact that the method can be computationally expensive limits its applicability. Recently, a variant of the (standard) randomization method, called split regenerative randomization has been proposed for the efficient analysis of reliability-like models of fault-tolerant systems with deferred repair. In this paper, we generalize that method so that it covers more general reward measures: the expected transient reward rate and the expected averaged reward rate. The generalized method has the same good properties as the standard randomization method and, for large models and large values of the time t at which the measure has to be computed, can be significantly less expensive. The method requires the selection of a subset of states and a regenerative state satisfying some conditions. For a class of continuous time Markov models, class C'_2, including typical failure/repair reliability models with exponential failure and repair time distributions and deferred repair, natural selections for the subset of states and the regenerative state exist and results are available assessing approximately the computational cost of the method in terms of “visible” model characteristics. Using a large model class C'_2 example, we illustrate the performance of the method and show that it can be significantly faster than previously proposed randomization-based methods

The 3β-hydroxysteroid dehydrogenase inhibitor trilostane shows antidepressant properties in mice

International audienc