Electro-thermal modelling for plasmonic structures in the TLM Method

This paper presents a coupled electromagnetic-thermal model for modelling temperature evolution in nano-size plasmonic heat sources. Both electromagnetic and thermal models are based on the Transmission Line Modelling (TLM) method and are coupled through a nonlinear and dispersive plasma material model. The stability and accuracy of the coupled EM-thermal model is analysed in the context of a nano-tip plasmonic heat source example

Convalescent plasma in patients admitted to hospital with COVID-19 (RECOVERY): a randomised controlled, open-label, platform trial

SummaryBackground Azithromycin has been proposed as a treatment for COVID-19 on the basis of its immunomodulatoryactions. We aimed to evaluate the safety and efficacy of azithromycin in patients admitted to hospital with COVID-19.Methods In this randomised, controlled, open-label, adaptive platform trial (Randomised Evaluation of COVID-19Therapy [RECOVERY]), several possible treatments were compared with usual care in patients admitted to hospitalwith COVID-19 in the UK. The trial is underway at 176 hospitals in the UK. Eligible and consenting patients wererandomly allocated to either usual standard of care alone or usual standard of care plus azithromycin 500 mg once perday by mouth or intravenously for 10 days or until discharge (or allocation to one of the other RECOVERY treatmentgroups). Patients were assigned via web-based simple (unstratified) randomisation with allocation concealment andwere twice as likely to be randomly assigned to usual care than to any of the active treatment groups. Participants andlocal study staff were not masked to the allocated treatment, but all others involved in the trial were masked to theoutcome data during the trial. The primary outcome was 28-day all-cause mortality, assessed in the intention-to-treatpopulation. The trial is registered with ISRCTN, 50189673, and ClinicalTrials.gov, NCT04381936.Findings Between April 7 and Nov 27, 2020, of 16 442 patients enrolled in the RECOVERY trial, 9433 (57%) wereeligible and 7763 were included in the assessment of azithromycin. The mean age of these study participants was65·3 years (SD 15·7) and approximately a third were women (2944 [38%] of 7763). 2582 patients were randomlyallocated to receive azithromycin and 5181 patients were randomly allocated to usual care alone. Overall,561 (22%) patients allocated to azithromycin and 1162 (22%) patients allocated to usual care died within 28 days(rate ratio 0·97, 95% CI 0·87–1·07; p=0·50). No significant difference was seen in duration of hospital stay (median10 days [IQR 5 to >28] vs 11 days [5 to >28]) or the proportion of patients discharged from hospital alive within 28 days(rate ratio 1·04, 95% CI 0·98–1·10; p=0·19). Among those not on invasive mechanical ventilation at baseline, nosignificant difference was seen in the proportion meeting the composite endpoint of invasive mechanical ventilationor death (risk ratio 0·95, 95% CI 0·87–1·03; p=0·24).Interpretation In patients admitted to hospital with COVID-19, azithromycin did not improve survival or otherprespecified clinical outcomes. Azithromycin use in patients admitted to hospital with COVID-19 should be restrictedto patients in whom there is a clear antimicrobial indication

Découverte des Connaissances dans les Bases de Données: Une approche centrée objet

Knowledge Discovery in Data Bases : an object Oriented Approach This article consists in extracting automatically the implicit knowledge in a database. This work relies upon the field of Knowledge Discovery in Databases, which is at the intersection of databases, artificial intelligence, systematic learning and statistics. To express the extracted knowledge in a database in terms of high level generalized concepts, and not in terms of primitive data, we have integrated supplementary knowledge ( knowledge of applied field ) given by the expert of the field. This field knowledge, formalized in terms of hierarchy of concepts, permits to generalize the initial values of a database. The knowledge discovered may be formalized in terms of high level generalized concepts, an easily understood symbolic form by the user, without any new interpretation. They may be used to complete, for example, the construction of a knowledge base, or help the decision making process in a symbolic numerical analysis process. Key-Words: Databases/ Concepts/ knowledge discovery. Découverte des Connaissances dans les Bases de Données: Une approche centrée objet Cet article consiste à extraire automatiquement des connaissances implicites à partir d'une base de données. Ce travail relève du domaine des systèmes de découverte de connaissances dans les bases de données (Knowledge Discovery in Databases), domaine de recherche à l'intersection des bases de données, de l'intelligence artificielle, de l'apprentissage automatique et des statistiques. Pour exprimer les connaissances extraites à partir d'une base de données en termes de concepts généralisés de haut de niveau, et non en termes de données initiales, nous avons intégré des connaissances supplémentaires (connaissances du domaine d'application) qui sont données par l'expert du domaine. Cette connaissance du domaine, formalisée en terme de hiérarchie de concepts, permet de généraliser les valeurs initiales d'une base de données. Les connaissances découvertes peuvent être formalisées en termes de concepts généralisés de haut de niveau, forme symbolique facilement compréhensible par l'utilisateur, sans aucune nouvelle interprétation. Elles peuvent être utilisées pour compléter la construction d'une base de connaissances par exemple, ou aider à la prise de décision lors du processus d'une analyse symbolique numérique de données. Mots-Clés: Base de données/ Concepts/ Découverte de connaissances. Revue d'Information Scientifique & Technique Vol.11(2) 2001: 61-6

Towards the management of the databases founded on descriptions logic

This article describes an approach for enriching databases based upon the Descriptions Logic which consist in understanding the structural and semantical aspects of existing databases in order to discover new knowledge and permit the passage to richer models. The heterogeneity at different levels (semantical, structural, descriptive) leads to the translation of local databases into a richer canonical model whose the choice is fundamental because its role is very important in the comparison between local schemas and in the detection of the inter-schemas conflicts. The canonical model is defined in the concept language, developed in our research laboratory as a representation and a reasoning tool. This language uses the notion of classes to produce descriptions which are, also, used in the reasoning process. Descriptions Logic describes the structure of the objects, at a terminological level, in terms of concepts and roles. We use semantics to give sense to terms used in the description.Keys-Words: Descriptions logic/ Databases/ Semantics. Resume Cet article décrit une approche d'enrichissement des bases de données fondée sur les logiques de descriptions. Ces dernières consistent à comprendre les aspects structurels et sémantiques des bases existantes dans le but de découvrir de nouvelles connaissances permettant le passage à des modèles plus évolués. L'hétérogénéité à différents niveaux ( sémantiques, structurels, descriptifs) implique la traduction des bases de données locales dans un modèle canonique plus riche dont le choix est fondamental car il joue un rôle important dans la comparaison des schémas locaux et dans la détection des conflits inter-schémas. Le modèle canonique est défini dans le langage de concepts, qui est développé au sein de notre laboratoire comme outil de représentation et de raisonnement. Ce langage utilise la notion de classes pour produire des descriptions qui sont, en plus, utilisées dans le raisonnement. Les logiques de descriptions décrivent la structure des objets, à un niveau terminologique, en termes de concepts et de rôles. Une sémantique ensembliste est ensuite donnée pour associer un sens aux termes utilisés dans la description. Mots-Clés: Logiques de descriptions/ Base de données/ Sémantique. (Revue d'Information Scientifique et Technique: 2002 12(1): 95-104

Centrality in Networks with Overlapping Communities

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k-Truss Decomposition for Modular Centrality

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k-Truss Decomposition for Modular Centrality

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Properties of polylactide reinforced with montmorillonite/multiwalled carbon nanotube hybrid

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