874 resultados para agent correctionnel


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Les agents des services correctionnels sont appelés à travailler et à intervenir quotidiennement auprès des individus incarcérés et se trouvent au cœur du fonctionnement des établissements de détention provinciaux du Québec. Leurs pratiques disciplinaires envers les individus incarcérés étaient auparavant considérées comme arbitraires et prenaient place dans un milieu défini comme autonome, autosuffisant, fermé sur l’extérieur et ne nécessitant pas le support de la société (Goffman, 1961). Les dernières décennies ont apporté de nombreux changements, tels la reconnaissance des droits des individus incarcérés, l’ouverture du milieu carcéral sur la société ou encore la normalisation des conditions de détention. A ceux-ci se sont ajoutés la Loi sur le système correctionnel du Québec et son Règlement d’application qui vient régir le processus disciplinaire. L’objet de cette recherche vise la compréhension des pratiques des agents correctionnels en matière de poursuites disciplinaires suite à ces changements. Cette étude vise également à comprendre les éléments venant influencer la décision, par un agent correctionnel, de dresser un rapport disciplinaire. Nos résultats se divisent en deux volets. Le premier suggère que le droit disciplinaire est une question d’équilibre carcéral. Les agents correctionnels, ayant la mission de maintenir la sécurité et l’ordre au sein du milieu carcéral, prennent également part à la mission de réinsertion sociale des individus incarcérés. Les agents doivent moduler leurs pratiques disciplinaires selon des éléments administratifs et institutionnels, tout en entretenant une relation avec les individus incarcérés basée sur la négociation et le marchandage. Le deuxième suggère que le droit disciplinaire est une question de pouvoir. En effet, malgré l’encadrement des établissements carcéraux par des textes légaux, les agents correctionnels détiennent un pouvoir discrétionnaire reconnu et accepté dans l’application de la discipline.

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The New Zealand green lipped mussel preparation Lyprinol is available without a prescription from a supermarket, pharmacy or Web. The Food and Drug Administration have recently warned Lyprinol USA about their extravagant anti-inflammatory claims for Lyprinol appearing on the web. These claims are put to thorough review. Lyprinol does have anti-inflammatory mechanisms, and has anti-inflammatory effects in some animal models of inflammation. Lyprinol may have benefits in dogs with arthritis. There are design problems with the clinical trials of Lyprinol in humans as an anti-inflammatory agent in osteoarthritis and rheumatoid arthritis, making it difficult to give a definite answer to how effective Lyprinol is in these conditions, but any benefit is small. Lyprinol also has a small benefit in atopic allergy. As anti-inflammatory agents, there is little to choose between Lyprinol and fish oil. No adverse effects have been reported with Lyprinol. Thus, although it is difficult to conclude whether Lyprinol does much good, it can be concluded that Lyprinol probably does no major harm.

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The load–frequency control (LFC) problem has been one of the major subjects in a power system. In practice, LFC systems use proportional–integral (PI) controllers. However since these controllers are designed using a linear model, the non-linearities of the system are not accounted for and they are incapable of gaining good dynamical performance for a wide range of operating conditions in a multi-area power system. A strategy for solving this problem because of the distributed nature of a multi-area power system is presented by using a multi-agent reinforcement learning (MARL) approach. It consists of two agents in each power area; the estimator agent provides the area control error (ACE) signal based on the frequency bias estimation and the controller agent uses reinforcement learning to control the power system in which genetic algorithm optimisation is used to tune its parameters. This method does not depend on any knowledge of the system and it admits considerable flexibility in defining the control objective. Also, by finding the ACE signal based on the frequency bias estimation the LFC performance is improved and by using the MARL parallel, computation is realised, leading to a high degree of scalability. Here, to illustrate the accuracy of the proposed approach, a three-area power system example is given with two scenarios.

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An adaptive agent improves its performance by learning from experience. This paper describes an approach to adaptation based on modelling dynamic elements of the environment in order to make predictions of likely future state. This approach is akin to an elite sports player being able to “read the play”, allowing for decisions to be made based on predictions of likely future outcomes. Modelling of the agent‟s likely future state is performed using Markov Chains and a technique called “Motion and Occupancy Grids”. The experiments in this paper compare the performance of the planning system with and without the use of this predictive model. The results of the study demonstrate a surprising decrease in performance when using the predictions of agent occupancy. The results are derived from statistical analysis of the agent‟s performance in a high fidelity simulation of a world leading real robot soccer team.