Les gaietés de l'escadron ; Le commissaire est bon enfant ; Monsieur Badin ; Théodore cherche des allumettes / Georges Courteline ; ill. d'après les croquis de Barrère

Collection : Modern-théâtre ; 30

From being ill in time to love of eternity

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Relationship between monitored elements and prescribed ventilator setting modifications in critically ill children

Les pédiatres intensivistes ont plusieurs éléments disponibles pour guider leurs décisions par rapport à la ventilation mécanique. Par contre, aucune étude prospective ne décrit les éléments auxquels les intensivistes se réfèrent pour modifier les paramètres du respirateur. Objectifs : Décrire la pratique actuelle de la modification des paramètres du respirateur aux soins intensifs du CHU Sainte-Justine, un hôpital pédiatrique tertiaire. Hypothèse : 80% des modifications des paramètres du respirateur influant sur l’épuration du CO2 sont liées à l’analyse de la PCO2 ou du pH et 80% des modifications des paramètres d’oxygénation sont liés à l’analyse de l’oxymétrie de pouls. Méthodes : En se servant d’un logiciel de recueil de données, les soignants ont enregistré un critère de décision primaire et tous les critères de décision secondaires menant à chaque modification de paramètre du respirateur au moment même de la modification. Résultats : Parmi les 194 modifications des paramètres du respirateur influant sur l’épuration du CO2, faites chez vingts patients, 42.3% ±7.0% avaient pour critère primaire la PCO2 ou le pH sanguin. Parmi les 41 modifications de la pression expiratoire positive et les 813 modifications de la fraction d’oxygène inspirée, 34.1% ±14.5% et 84.5% ±2.5% avaient pour critère primaire l’oxymétrie de pouls, respectivement. Conclusion : Les médecins surestiment le rôle de la PCO2 et du pH sanguins et sousestiment le rôle d’autres critères de décision dans la gestion de la ventilation mécanique. L’amélioration de notre compréhension de la pratique courante devrait aider à l’éboration des systèmes d’aide à la décision clinique en assistance respiratoire.

University of Southampton Inter-Library Loan (ILL) Form

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Curriculum theory, curriculum policy and the problem of ill-disciplined thinking

This paper examines the implications of policy fracture and arms length governance within the decision making processes currently shaping curriculum design within the English education system. In particular it argues that an unresolved ‘ideological fracture’ at government level has been passed down to school leaders whose response to the dilemma is distorted by the target-driven agenda of arms length agencies. Drawing upon the findings of a large scale on-line survey of history teaching in English secondary schools, this paper illustrates the problems that occur when policy making is divorced from curriculum theory, and in particular from any consideration of the nature of knowledge. Drawing on the social realist theory of knowledge elaborated by Young (2008), we argue that the rapid spread of alternative curricular arrangements, implemented in the absence of an understanding of curriculum theory, undermines the value of disciplined thinking to the detriment of many young people, particularly those in areas of social and economic deprivation.

Regularization techniques for ill-posed inverse problems in data assimilation

Optimal state estimation from given observations of a dynamical system by data assimilation is generally an ill-posed inverse problem. In order to solve the problem, a standard Tikhonov, or L2, regularization is used, based on certain statistical assumptions on the errors in the data. The regularization term constrains the estimate of the state to remain close to a prior estimate. In the presence of model error, this approach does not capture the initial state of the system accurately, as the initial state estimate is derived by minimizing the average error between the model predictions and the observations over a time window. Here we examine an alternative L1 regularization technique that has proved valuable in image processing. We show that for examples of flow with sharp fronts and shocks, the L1 regularization technique performs more accurately than standard L2 regularization.

L1-regularisation for ill-posed problems in variational data assimilation

We consider four-dimensional variational data assimilation (4DVar) and show that it can be interpreted as Tikhonov or L2-regularisation, a widely used method for solving ill-posed inverse problems. It is known from image restoration and geophysical problems that an alternative regularisation, namely L1-norm regularisation, recovers sharp edges better than L2-norm regularisation. We apply this idea to 4DVar for problems where shocks and model error are present and give two examples which show that L1-norm regularisation performs much better than the standard L2-norm regularisation in 4DVar.

On discrimination algorithms for ill-posed problems with an application to magnetic tomography

In this paper we explore classification techniques for ill-posed problems. Two classes are linearly separable in some Hilbert space X if they can be separated by a hyperplane. We investigate stable separability, i.e. the case where we have a positive distance between two separating hyperplanes. When the data in the space Y is generated by a compact operator A applied to the system states ∈ X, we will show that in general we do not obtain stable separability in Y even if the problem in X is stably separable. In particular, we show this for the case where a nonlinear classification is generated from a non-convergent family of linear classes in X. We apply our results to the problem of quality control of fuel cells where we classify fuel cells according to their efficiency. We can potentially classify a fuel cell using either some external measured magnetic field or some internal current. However we cannot measure the current directly since we cannot access the fuel cell in operation. The first possibility is to apply discrimination techniques directly to the measured magnetic fields. The second approach first reconstructs currents and then carries out the classification on the current distributions. We show that both approaches need regularization and that the regularized classifications are not equivalent in general. Finally, we investigate a widely used linear classification algorithm Fisher's linear discriminant with respect to its ill-posedness when applied to data generated via a compact integral operator. We show that the method cannot stay stable when the number of measurement points becomes large.