Uncertainty in context-aware systems: A case study for intelligent environments

Data used be context-aware systems is naturally incomplete and not always reflect real situations. The dynamic nature of intelligent environments leads to the need of analysing and handling uncertain information. Users can change their acting patterns within a short space of time. This paper presents a case study for a better understanding of concepts related to context awareness and the problem of dealing with inaccurate data. Through the analysis of identification of elements that results in the construction of unreliable contexts, it is aimed to identify patterns to minimize incompleteness. Thus, it will be possible to deal with flaws caused by undesired execution of applications.Programa Operacional Temático Factores de Competitividade (POCI-01-0145-

On the role of high spin multiplicity electronic molecular states

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Theoretical study of the HNS+/HSN+ radical cations

International audienceThe three dimensional CASSCF-MRCI potential energy functions have been calculated for the X(2)A' and A(2)A" electronic states of HNS+ and for the X(2)A' state of the HSN+ isomer. In HN+, the two lowest states form a linear-bent Renner-Teller pair, the MRCI barrier to linearity for the X-state has been calculated to be 1876 cm(-1). Both isomers possess a conical intersection resulting from the crossing between the (2)Sigma(+) and (2)Pi states along the colinear H(S-2) + NS+(X(1)Sigma(+)) dissociation path. The minima of both isomers are found to be separated by a large barrier of 18,042 cm(-1) (CASSCF), the HNS+ isomer is calculated to be more stable than HSN+ by 12,202 cm(-1) (34.9 kca/mol) (RCCSD(T)). The proton affinity of the NS radical (NS (X(2)Pi + H+) has been calculated to be 7.59 eV (RCCSD(T)), the dissociation energy of HNS+ --> H(S-2) + NS+ (X(1)Sigma(+)) to be 2.86 eV (RCCSD(T)). Due to several low-lying electronic states of NS+, the protonation and the exothermic charge transfer reactions H+ --> NS --> + NS+ are found to proceed via very complex reaction paths including vibronic and spin-orbit couplings. The results of Renner-Teller variational calculations of the rovibronic levels for the electronic ground state of HNS+ are presented. The onset of this coupling is calculated already in the first bending overtone of the X-state. Also the vibrational levels obtained variationally for the X(2)A' state of the HSN+ isomer are given. (C) 2002 Elsevier Science B.V. All rights reserved

Characterization of a plasmid coding for resistance to broad-spectrum cephalosporins in Salmonella typhimurium.

International audienceFive strains of Salmonella typhimurium isolated from faeces of infants hospitalized at Trousseau Hospital in Paris have been found to be resistant to third-generation cephalosporins. This resistance is by a transferable plasmid-mediated mechanism. The beta-lactamase gene which encodes for an enzyme similar to SHV-2 has been cloned into plasmid pBR322 and expressed in Escherichia coli RR1

Belief Graphical Models for Uncertainty representation and reasoning

International audienceMany real world problems and applications require to exploit incomplete, complex and uncertain information. Belief graphical models encompass a wide range of graphical formalisms for representing and reasoning with uncertain and complex information. They generally involve a graphical component which can be directed or undirected and a numerical one depending on the considered uncertainty setting. The graphical component encodes a set of independence statements while the numerical one quantifies the uncertainty regarding variables. The main use of belief graphical models is knowledge representation, reasoning and decision making for multivariate problems. Belief graphical models can be built either by eliciting the uncertain knowledge of an expert or automatically learnt from data using machine learning techniques. Many types of inference algorithms exist and many platforms are now available allowing modeling and reasoning with belief graphical models in many application areas such as diagnosis, forecasting, decision making and classification. This chapter provides an overview of the most common belief graphical models. In particular, it gives an overview on various aspects related to graphical models for uncertainty: representation, inference, learning and finally applications