Application of the deflection criterion to classification of radar SAR images

International audienceAn original application of the weighted deflection is proposed for radar target classification by quadratic filters. An explicit formulation of optimal filters is derived. We analyse the impact of the weighting parameter on real data recognition, and show that performances are better when the deflection coincides with the Fisher ratio

Big Data et Santé publique : plus que jamais, les enjeux de la connaissance

International audienc

Microbial fuel cell stack power to lithium battery stack ::pilot concept forscale up

A stack to stack microbial fuel cell power to batteries storage was investigated on the pilot scale with the aim to scale up in future. A 12 unit MFC-stack, equipped with maximum power point tracking (MPPT) and lithium polymer batteries (3.7 V), was set up. The MFC-stack architecture was simplified by sharing partially electrolytes. The serial 12 unit MFC-stack was first used as a linear assembly of all MFC units and then subdivided into three MFC-sub-stacks which enhanced power extraction by 8.5 times. To balance the stack power generation, the external circuits were alternated into zigzag, braid and random figurations as well in rational directed configurations. Finally, batteries permutation along with MPPT enabled faster and balanced lithium battery stack charging. Balanced conditions resulted in time shift oscillations, the absence of unwanted power pooling and voltage reversals. All in all, the work showed how to generate and store power from an 12 L microbial fuel cell stack with partly common electrolytes

Phased-Array Antenna Pattern Optimization with Evolution Strategies

International audienc

Simulation and resolution of voltage reversal in microbial fuel cell stack

To understand the biotic and non-biotic contributions of voltage reversals in microbial fuel cell stacks (MFC) they were simulated with an electronic MFC-Stack mimic. The simulation was then compared with results from a real 3 L triple MFC-Stack with shared anolyte. It showed that voltage reversals originate from the variability of biofilms, but also the external load plays a role. When similar biofilm properties were created on all anodes the likelihood of voltage reversals was largely reduced. Homogenous biofilms on all anodes were created by electrical circuit alternation and electrostimulation. Conversely, anolyte recirculation, or increased nutriment supply, postponed reversals and unfavourable voltage asymmetries on anodes persisted. In conclusion, voltage reversals are often a negative event but occur also in close to best MFC-Stack performance. They were manageable and this with a simplified MFC architecture in which multiple anodes share the same anolyte

Mixed Hidden Markov Model for Heterogeneous Longitudinal Data with Missingness and Errors in the Outcome Variable

International audienceAnalysing longitudinal declarative data raises many difficulties, such as the processing of errors and missingness in the outcome variable. Moreover, long-term monitored cohorts (commonly encountered in life-course epidemiology) may reveal a problem of time heterogeneity, especially regarding the way subjects respond to the investigator. We propose a Mixed Hidden Markov Model which considers several causes of randomness in response and also enables the effect of a past health outcome to act on present responses through a memory state. Hence, we take into account both errors and missing responses, time heterogeneity, and retrospective questions. We thus propose a Stochastic Expectation Maximization algorithm (SEM), which is less time-consuming than usual EM algorithms to perform the estimation of the parameters of our MHMM. We carry out a simulation study to assess the performances of this algorithm in the context of cancer epidemiology with the British NCDS 1958 cohort. Simulations show that the effect of covariates on the transitions probabilities is estimated with moderate bias. At last, we investigate a brief real data application on the effect of early social class on cancer through a smoking behaviour. It appears that in the female sample we used, the early social class does not mainly act on smoking behaviours. Moreover, more information is needed to compensate for data missingness and declarative errors in the view to improve our statistical analysis. Résumé : L'analyse de données déclaratives longitudinales fait apparaître de nombreuses difficultés, comme le traitement des erreurs et des données manquantes de la variable de sortie. En outre, les cohortes suivies sur le long terme, telles que celles utilisées en épidémiologie "life-course" peuvent soulever un problème d'hétérogénéité du temps, surtout en ce qui concerne la façon de répondre aux questions de l'enquêteur. Nous proposons dans cet article l'introduction d'un modèle de Markov caché mixte qui comprend les possibilités d'erreur et de non-réponse, et permet également de considérer que l'effet d'un résultat de santé passé peut agir sur les réponses actuelles à travers une mémoire d' état. En ce qui concerne les estimations, nous avons proposé d'utiliser un algorithme EM Stochastique (SEM), qui est moins gourmand en temps de calcul que l'algorithme EM usuel utilisant une intégration sur les effets aléatoires. Nous avons effectué une étude par simulation afin d'évaluer les performances de cet algorithme dans le contexte de l'épidémiologie du cancer avec les données de la cohorte britanniques "NCDS 1958". Les simulations ont montré que l'effet des covariables sur les probabilités de transitions a été estimée avec un biais modéré. Enfin, nous avons réalisé une application à des données réelles en étudiant l'effet de la classe sociale précoce sur le cancer à travers un comportement tabagique. Il est apparu que, dans l'échantillon de femmes utilisé pour cette enquête, la classe sociale précoce n'agit pas principalement sur l'usage du tabac. Cependant, plus d'information est nécessaire pour compenser les données manquantes et les erreurs de déclaration et obtenir de meilleurs résultats statistiques

Mixed Hidden Markov Model for Heterogeneous Longitudinal Data with Missingness and Errors in the Outcome Variable

International audienceAnalysing longitudinal declarative data raises many difficulties, such as the processing of errors and missingness in the outcome variable. Moreover, long-term monitored cohorts (commonly encountered in life-course epidemiology) may reveal a problem of time heterogeneity, especially regarding the way subjects respond to the investigator. We propose a Mixed Hidden Markov Model which considers several causes of randomness in response and also enables the effect of a past health outcome to act on present responses through a memory state. Hence, we take into account both errors and missing responses, time heterogeneity, and retrospective questions. We thus propose a Stochastic Expectation Maximization algorithm (SEM), which is less time-consuming than usual EM algorithms to perform the estimation of the parameters of our MHMM. We carry out a simulation study to assess the performances of this algorithm in the context of cancer epidemiology with the British NCDS 1958 cohort. Simulations show that the effect of covariates on the transitions probabilities is estimated with moderate bias. At last, we investigate a brief real data application on the effect of early social class on cancer through a smoking behaviour. It appears that in the female sample we used, the early social class does not mainly act on smoking behaviours. Moreover, more information is needed to compensate for data missingness and declarative errors in the view to improve our statistical analysis. Résumé : L'analyse de données déclaratives longitudinales fait apparaître de nombreuses difficultés, comme le traitement des erreurs et des données manquantes de la variable de sortie. En outre, les cohortes suivies sur le long terme, telles que celles utilisées en épidémiologie "life-course" peuvent soulever un problème d'hétérogénéité du temps, surtout en ce qui concerne la façon de répondre aux questions de l'enquêteur. Nous proposons dans cet article l'introduction d'un modèle de Markov caché mixte qui comprend les possibilités d'erreur et de non-réponse, et permet également de considérer que l'effet d'un résultat de santé passé peut agir sur les réponses actuelles à travers une mémoire d' état. En ce qui concerne les estimations, nous avons proposé d'utiliser un algorithme EM Stochastique (SEM), qui est moins gourmand en temps de calcul que l'algorithme EM usuel utilisant une intégration sur les effets aléatoires. Nous avons effectué une étude par simulation afin d'évaluer les performances de cet algorithme dans le contexte de l'épidémiologie du cancer avec les données de la cohorte britanniques "NCDS 1958". Les simulations ont montré que l'effet des covariables sur les probabilités de transitions a été estimée avec un biais modéré. Enfin, nous avons réalisé une application à des données réelles en étudiant l'effet de la classe sociale précoce sur le cancer à travers un comportement tabagique. Il est apparu que, dans l'échantillon de femmes utilisé pour cette enquête, la classe sociale précoce n'agit pas principalement sur l'usage du tabac. Cependant, plus d'information est nécessaire pour compenser les données manquantes et les erreurs de déclaration et obtenir de meilleurs résultats statistiques

Évaluation du coût de la prise en charge des patients atteints de Leucémie Myéloïde Chronique (LMC) en Midi-Pyrénées : impact économique des différents Inhibiteurs de Protéines Kinases

International audienc

Economic impact of generic antiretrovirals in France for HIV patients’ care: a simulation between 2019 and 2023

International audienceBackground: In a context where the economic burden of HIV is increasing as HIV patients now have a close to normal lifespan, the availability of generic antiretrovirals commonly prescribed in 2017 and the imminence of patent expiration are expected to provide substantial savings in the coming years. This article aims to assess the economic impact of these generic antiretrovirals in France and specifically over a five-year period. Methods: An agent-based model was developed to simulate patient trajectories and treatment use over a five-year period. By comparing the results of costs for trajectories simulated under different predefined scenarios, a budget impact model can be created and sensitivity analyses performed on several parameters of importance. Results: The potential economic savings from 2019 to 2023 generated by generic antiretrovirals range from €309 million when the penetration rate of generics is set at 10% to €1.5 billion at 70%. These savings range from €984 million to €993 million as the delay between patent and generic marketing authorisation varies from 10 to 15 years, and from €965 million to €993 million as the Negotiated Price per Unit (NPU) of generics at market-entry varies from 40 to 50% of the NPU for patents. Discussion: This economic savings simulation could help decision makers to anticipate resource allocations for further innovation in antiretrovirals therapies as well as prevention, especially by funding the Pre-Exposure Prophylaxis (PrEP) or HIV screening