Propriedades de materiais nanoestruturados do sistema epoxídico DGEBA/TETA modificado com um éster de silsesquioxano

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Average control of Markov decision processes with Feller transition probabilities and general action spaces

This paper studies the average control problem of discrete-time Markov Decision Processes (MDPs for short) with general state space, Feller transition probabilities, and possibly non-compact control constraint sets A(x). Two hypotheses are considered: either the cost function c is strictly unbounded or the multifunctions A(r)(x) = {a is an element of A(x) : c(x, a) <= r} are upper-semicontinuous and compact-valued for each real r. For these two cases we provide new results for the existence of a solution to the average-cost optimality equality and inequality using the vanishing discount approach. We also study the convergence of the policy iteration approach under these conditions. It should be pointed out that we do not make any assumptions regarding the convergence and the continuity of the limit function generated by the sequence of relative difference of the alpha-discounted value functions and the Poisson equations as often encountered in the literature. (C) 2012 Elsevier Inc. All rights reserved.

SINGULARLY PERTURBED DISCOUNTED MARKOV CONTROL PROCESSES IN A GENERAL STATE SPACE

This paper studies the asymptotic optimality of discrete-time Markov decision processes (MDPs) with general state space and action space and having weak and strong interactions. By using a similar approach as developed by Liu, Zhang, and Yin [Appl. Math. Optim., 44 (2001), pp. 105-129], the idea in this paper is to consider an MDP with general state and action spaces and to reduce the dimension of the state space by considering an averaged model. This formulation is often described by introducing a small parameter epsilon > 0 in the definition of the transition kernel, leading to a singularly perturbed Markov model with two time scales. Our objective is twofold. First it is shown that the value function of the control problem for the perturbed system converges to the value function of a limit averaged control problem as epsilon goes to zero. In the second part of the paper, it is proved that a feedback control policy for the original control problem defined by using an optimal feedback policy for the limit problem is asymptotically optimal. Our work extends existing results of the literature in the following two directions: the underlying MDP is defined on general state and action spaces and we do not impose strong conditions on the recurrence structure of the MDP such as Doeblin's condition.

The stereochemistry of the amino acid side chain influences the inflammatory potential of muramyl dipeptide in experimental meningitis

Intrathecal injections of 50 to 100 micro g of (N-acetylmuramyl-L-alanyl-D-isoglutamine) muramyl dipeptide (MDP)/rabbit dose-dependently triggered tumor necrosis factor alpha (TNF-alpha) secretion (12 to 40,000 pg/ml) preceding the influx of leukocytes in the subarachnoid space of rabbits. Intrathecal instillation of heat-killed unencapsulated R6 pneumococci produced a comparable leukocyte influx but only a minimal level of preceding TNF-alpha secretion. The stereochemistry of the first amino acid (L-alanine) of the MDP played a crucial role with regard to its inflammatory potential. Isomers harboring D-alanine in first position did not induce TNF-alpha secretion and influx of leukocytes. This stereospecificity of MDPs was also confirmed by measuring TNF-alpha release from human peripheral mononuclear blood cells stimulated in vitro. These data show that the inflammatory potential of MDPs depends on the stereochemistry of the first amino acid of the peptide side chain and suggest that intact pneumococci and MDPs induce inflammation by different pathways.

Specification and quantitative analysis of probabilistic cloud deployment patterns

Cloud computing is a new technological paradigm offering computing infrastructure, software and platforms as a pay-as-you-go, subscription-based service. Many potential customers of cloud services require essential cost assessments to be undertaken before transitioning to the cloud. Current assessment techniques are imprecise as they rely on simplified specifications of resource requirements that fail to account for probabilistic variations in usage. In this paper, we address these problems and propose a new probabilistic pattern modelling (PPM) approach to cloud costing and resource usage verification. Our approach is based on a concise expression of probabilistic resource usage patterns translated to Markov decision processes (MDPs). Key costing and usage queries are identified and expressed in a probabilistic variant of temporal logic and calculated to a high degree of precision using quantitative verification techniques. The PPM cost assessment approach has been implemented as a Java library and validated with a case study and scalability experiments. © 2012 Springer-Verlag Berlin Heidelberg.