Pratiques d’infirmière clinicienne et obésité

Tese apresentada à Universidade Fernando Pessoa como parte dos requisitos para obtenção do grau de Doutor em Ciências Sociais, especialidade em Psicologia

Fasciathérapie et identité professionnelle

Tese apresentada à Universidade Fernando Pessoa como parte dos requisitos para obtenção do grau de Doutor em Ciências Sociais, especialidade em Psicologia

Contributo para o desenvolvimento de um Instrumento de Investigação Neuropsicológica

Tese apresentada à Universidade Fernando Pessoa como parte dos requisitos para obtenção do grau de Doutor em Ciências Sociais, especialidade em Psicologia

Contributo para o desenvolvimento de um Instrumento de Investigação Neuropsicológica

Tese apresentada à Universidade Fernando Pessoa como parte dos requisitos para obtenção do grau de Doutor em Ciências Sociais, especialidade em Psicologia

Analysis of OD Flows (Raw Data)

In a recent paper, Structural Analysis of Network Traffic Flows, we analyzed the set of Origin Destination traffic flows from the Sprint-Europe and Abilene backbone networks. This report presents the complete set of results from analyzing data from both networks. The results in this report are specific to the Sprint-1 and Abilene datasets studied in the above paper. The following results are presented here: 1 Rows of Principal Matrix (V) 2 1.1 Sprint-1 Dataset ................................ 2 1.2 Abilene Dataset.................................. 9 2 Set of Eigenflows 14 2.1 Sprint-1 Dataset.................................. 14 2.2 Abilene Dataset................................... 21 3 Classifying Eigenflows 26 3.1 Sprint-1 Dataset.................................. 26 3.2 Abilene Datase.................................... 44

Diagnosing Network-Wide Traffic Anomalies

Anomalies are unusual and significant changes in a network's traffic levels, which can often involve multiple links. Diagnosing anomalies is critical for both network operators and end users. It is a difficult problem because one must extract and interpret anomalous patterns from large amounts of high-dimensional, noisy data. In this paper we propose a general method to diagnose anomalies. This method is based on a separation of the high-dimensional space occupied by a set of network traffic measurements into disjoint subspaces corresponding to normal and anomalous network conditions. We show that this separation can be performed effectively using Principal Component Analysis. Using only simple traffic measurements from links, we study volume anomalies and show that the method can: (1) accurately detect when a volume anomaly is occurring; (2) correctly identify the underlying origin-destination (OD) flow which is the source of the anomaly; and (3) accurately estimate the amount of traffic involved in the anomalous OD flow. We evaluate the method's ability to diagnose (i.e., detect, identify, and quantify) both existing and synthetically injected volume anomalies in real traffic from two backbone networks. Our method consistently diagnoses the largest volume anomalies, and does so with a very low false alarm rate.

Characterization of Network-Wide Anomalies in Traffic Flows

Detecting and understanding anomalies in IP networks is an open and ill-defined problem. Toward this end, we have recently proposed the subspace method for anomaly diagnosis. In this paper we present the first large-scale exploration of the power of the subspace method when applied to flow traffic. An important aspect of this approach is that it fuses information from flow measurements taken throughout a network. We apply the subspace method to three different types of sampled flow traffic in a large academic network: multivariate timeseries of byte counts, packet counts, and IP-flow counts. We show that each traffic type brings into focus a different set of anomalies via the subspace method. We illustrate and classify the set of anomalies detected. We find that almost all of the anomalies detected represent events of interest to network operators. Furthermore, the anomalies span a remarkably wide spectrum of event types, including denial of service attacks (single-source and distributed), flash crowds, port scanning, downstream traffic engineering, high-rate flows, worm propagation, and network outage.

Bayesian Packet Loss Detection for TCP

One of TCP's critical tasks is to determine which packets are lost in the network, as a basis for control actions (flow control and packet retransmission). Modern TCP implementations use two mechanisms: timeout, and fast retransmit. Detection via timeout is necessarily a time-consuming operation; fast retransmit, while much quicker, is only effective for a small fraction of packet losses. In this paper we consider the problem of packet loss detection in TCP more generally. We concentrate on the fact that TCP's control actions are necessarily triggered by inference of packet loss, rather than conclusive knowledge. This suggests that one might analyze TCP's packet loss detection in a standard inferencing framework based on probability of detection and probability of false alarm. This paper makes two contributions to that end: First, we study an example of more general packet loss inference, namely optimal Bayesian packet loss detection based on round trip time. We show that for long-lived flows, it is frequently possible to achieve high detection probability and low false alarm probability based on measured round trip time. Second, we construct an analytic performance model that incorporates general packet loss inference into TCP. We show that for realistic detection and false alarm probabilities (as are achievable via our Bayesian detector) and for moderate packet loss rates, the use of more general packet loss inference in TCP can improve throughput by as much as 25%.

Learning Temporal Contexts and Priming-Preparation Modes for Pattern Recognition

The system presented here is based on neurophysiological and electrophysiological data. It computes three types of increasingly integrated temporal and probability contexts, in a bottom-up mode. To each of these contexts corresponds an increasingly specific top-down priming effect on lower processing stages, mostly pattern recognition and discrimination. Contextual learning of time intervals, events' temporal order or sequential dependencies and events' prior probability results from the delivery of large stimuli sequences. This learning gives rise to emergent properties which closely match the experimental data.

Proteomic approach to oxidative stress in Daphnia magna

The keystone aquatic organism Daphnia magna is extensively used to assess the toxicity of chemicals. This has recently lead to an increase in the omics literature focusing on daphnids, an increase fuelled by the sequencing of the Daphnia pulex genome. Yet, no omics study has looked directly at oxidative stress (OS) in daphnids, even though OS is of primary importance in the response of aquatic organisms to their changing environment and is often induced by anthropogenic xenobiotics. This thesis thus focuses on the application of redox-proteomics, the study of the oxidative modification of proteins, to D. magna Specifically, daphnids were exposed to copper or paraquat, two well studied prooxidants, and protein carbonyls were labelled with fluorescein-5-thiosemicarbazide prior to twodimensional electrophoresis (2DE). This showed clearly that both compounds affect a different portion of the proteome. The identified proteins indicated that energy metabolism was affected by paraquat, while copper induced a reduction of the heat shock response (heat shock proteins, proteases and chaperones) a counterintuitive result which may be adaptative to metal toxicity in arthropods. The same approach was then applied to the study of the toxicity mechanism of silver nanoparticles (AgNP), an increasingly utilised form of silver with expected environmental toxicity, and its comparison to silver nitrate. The results demonstrate that, although less toxic than silver ions, AgNP toxicity functions through a different mechanism. AgNP toxicity is thus not a product of silver dissolution and increased protein carbonylation indicates that AgNP cause OS. Interestingly three of the four tested compounds altered vitellogenin levels and oxidation. Vitellogenins could thus represent an interesting subproteome for the detection of stress in daphnids. Finally, an experiment with oxidised BSA demonstrates the applicability of solid phase hydrazide in the enrichment of undigested carbonylated proteins.

Development of a new blood test for the diagnosis of the allergies to dental metals

Congrès du GIRSO, Lille, avril 2011

Usages étudiants des outils informatiques liés à Smiley EE

Ce projet de recherche s’inscrit dans un contexte institutionnel particulier. Depuis 2011, l’ULB rénove ses différentes plateformes informatiques afin d’améliorer les outils et services rendus à la communauté étudiante. Portant le nom de « SMILEY », ce grand projet gère entre autres les outils MonULB, GeHol, Web mail, U.V. etc. et a pour objectif une gestion plus simple, plus efficace et plus flexible de l’Université. Étant donné la complexité initiale du système de gestion de l’ULB, la mise en place du projet Smiley ne s’est pas faite sans difficultés. Divers bugs et imprévus ont été et sont encore rencontrés. Dans ce cadre, les autorités de l’ULB ont décidé de créer un observatoire pour mener des recherches sur les usages étudiants des outils informatiques liés à SMILEY EE (pour Études et Étudiants) afin d’améliorer l’acceptation des outils concernés. Au cours de ces recherches, différentes variables en lien avec l’utilisabilité et l’expérience utilisateur seront envisagées :connaissance du web, connaissance des TIC, sentiment d’efficacité personnel, perception de l’utilité et de la facilité d’utilisation des interfaces, stratégies de recherche d’information utilisées, sentiment d’autonomie t de contrôle, satisfaction cognitive et affective, identification à l’institution… Dès lors, les objectifs sont ;outre l’étude des déterminants de l’acceptation des Tic étudiants, de collecter l’avis des utilisateurs concernés, de fournir des recommandations, d’accompagner la mise en œuvre des améliorations et d’anticiper les besoins/attentes futures en matière de TIC à l’université.Pour cela, les méthodologies employées seront qualitatives et quantitatives. Des groupes de discussions exploratoire seront organisés afin d’identifier plus précisément les facteurs de satisfaction et d’insatisfaction, et des tests d’utilisabilités seront élaborés pour déterminer in situ les difficultés rencontrées. Des questionnaires en ligne seront aussi diffusés afin de déterminer le rôle des variables susmentionnées auprès d’un échantillon représentatif de la communauté estudiantine. L’ensemble des résultats permettra alors de guider des actions concrètes sur les outils étudiés.

Réponses immunitaires de l'homme à l'égard de la HBHA :détection de l'infection latente et aide au diagnostic de l'infection active par "Mycobacterium tuberculosis"

info:eu-repo/semantics/nonPublished

A note on stopping rules for iterative regularisation methods and filtered SVD

"This volume contains the proceedings of a meeting held at Montpellier from December 1st to December 5th 1986 .sponsored by the Centre national de la recherche scientifique ."--Preface.

Traitement de données issues d'un corpus écrit multilingue. Approche agile pour l'analyse du discours eurorégional

L'article présente quelques éléments de la procédure mise en place pour traiter un corpus écrit comportant 617 textes (près de 500 000 mots) relatifs aux eurorégions. Complexe et hétérogène à plusieurs titres (technique, linguistique, éditorial, générique, énonciatif), le corpus pose la difficulté majeure de l’appréhension de données multilingues (français, italien, espagnol, anglais, allemand, néerlandais). Sa manipulation a nécessité une réflexion adaptée et une démarche de modélisation que nous qualifions d’« agile » en raison de son caractère souple et itératif. La plateforme d’analyse élaborée permet de disposer de résultats utiles à l’analyse qualitative ultérieure du discours eurorégional. Elle articule un logiciel d'analyse morphosyntaxique éprouvé (TreeTagger) à des programmes (Perl) et à une base de données (SQLite) développés pour optimiser les requêtes multilingues simultanées et l’exportation automatique des résultats. Les fonctionnalités liées à la localisation contextualisée de mots- pivots, au recueil de dénominations et à la détection de segments répétés nous servent ici de guides pour exprimer les besoins de la recherche, les problèmes rencontrés et les solutions proposées. L'analyse d'observables récurrents, à savoir les notions de décision et de responsabilité, illustre le propos.