Un formalisme générique pour la planification des systèmes énergétiques : application à la valorisation de la chaleur fatale

Lors du fonctionnement d’un procédé de production ou de transformation, l’énergie thermique produite grâce aux combustibles n’est pas utilisée en totalité. Une partie de la chaleur est inévitablement rejetée. C’est en raison de ce caractère inéluctable qu’on parle de « chaleur fatale ». Disponible le plus souvent à des températures assez basses sa valorisation demeure délicate. Le mode de valorisation le plus courant reste aujourd’hui l’intégration du procédé dans un système plus complexe tel qu’un éco-parc ou un réseau de chaleur urbain et/ou son couplage avec le réseau électrique, donnant ainsi naissance à une véritable chaîne logistique de la chaleur fatale. Deux verrous majeurs freinent pour l’heure la généralisation de ce type de systèmes intégrés : la conception de la chaîne logistique d’une part, c’est-à-dire la définition des différents maillons depuis le fournisseur de chaleur fatale jusqu’à son utilisateur final (stockage, valorisation, transport …) et d’autre part la planification de ce système qui doit en effet tenir compte du caractère souvent intermittent de la disponibilité de la source, mais aussi de la variabilité de la demande. Dans ces travaux de thèse, une approche de planification de cette chaine logistique est introduite. Cette approche repose sur la formulation et la résolution d’un modèle de Programmation Non Linéaire Mixte (PNLM). Toutefois, pour faciliter le développement de ce modèle et son adaptation à différent types de chaîne de valorisation, une extension du formalisme ERTN (‘Extended Resource Task Network’) introduit dans des travaux précédents est proposée. L’EERTN (‘Energy Extended Resource Task Network’) permet notamment de rendre compte de manière plus fine de l’influence de la température sur les phénomènes physico-chimique complexes mis en jeu dans ce type de système et ainsi de proposer une planification du système conduisant à une réduction significative de la consommation de combustible primaire du système globale et à une exploitation efficiente de la chaleur fatale générée sur le site. L’approche est appliquée sur un cas d’étude constitué d’une unité industrielle dont les besoins en chaleur sont pourvus par une centrale de production d’utilité vapeur. Non loin de cette unité industrielle existe un réseau de chaleur urbain. La chaleur fatale est constituée par les fumées issues de la chaudière de la centrale de production d’utilité. Dans cette étude, on supposera que la conception de la chaîne logistique visant à valoriser cette chaleur fatale a déjà été réalisée et qu’elle consiste à exploiter cette énergie pour subvenir à tout ou partie des besoins du réseau de chaleur urbai

Myocardial Infarction Quantification From Late Gadolinium Enhancement MRI Using Top-hat Transforms and Neural Networks

Significance: Late gadolinium enhanced magnetic resonance imaging (LGE-MRI) is the gold standard technique for myocardial viability assessment. Although the technique accurately reflects the damaged tissue, there is no clinical standard for quantifying myocardial infarction (MI), demanding most algorithms to be expert dependent. Objectives and Methods: In this work a new automatic method for MI quantification from LGE-MRI is proposed. Our novel segmentation approach is devised for accurately detecting not only hyper-enhanced lesions, but also microvascular-obstructed areas. Moreover, it includes a myocardial disease detection step which extends the algorithm for working under healthy scans. The method is based on a cascade approach where firstly, diseased slices are identified by a convolutional neural network (CNN). Secondly, by means of morphological operations a fast coarse scar segmentation is obtained. Thirdly, the segmentation is refined by a boundary-voxel reclassification strategy using an ensemble of CNNs. For its validation, reproducibility and further comparison against other methods, we tested the method on a big multi-field expert annotated LGE-MRI database including healthy and diseased cases. Results and Conclusion: In an exhaustive comparison against nine reference algorithms, the proposal achieved state-of-the-art segmentation performances and showed to be the only method agreeing in volumetric scar quantification with the expert delineations. Moreover, the method was able to reproduce the intra- and inter-observer variability ranges. It is concluded that the method could suitably be transferred to clinical scenarios.Comment: Submitted to IEE

Generalised Mutual Information: a Framework for Discriminative Clustering

In the last decade, recent successes in deep clustering majorly involved the Mutual Information (MI) as an unsupervised objective for training neural networks with increasing regularisations. While the quality of the regularisations have been largely discussed for improvements, little attention has been dedicated to the relevance of MI as a clustering objective. In this paper, we first highlight how the maximisation of MI does not lead to satisfying clusters. We identified the Kullback-Leibler divergence as the main reason of this behaviour. Hence, we generalise the mutual information by changing its core distance, introducing the Generalised Mutual Information (GEMINI): a set of metrics for unsupervised neural network training. Unlike MI, some GEMINIs do not require regularisations when training as they are geometry-aware thanks to distances or kernels in the data space. Finally, we highlight that GEMINIs can automatically select a relevant number of clusters, a property that has been little studied in deep discriminative clustering context where the number of clusters is a priori unknown.Comment: Submitted for review at the IEEE Transactions on Pattern Analysis and Machine Intelligence. This article is an extension of an original NeurIPS 2022 article [arXiv:2210.06300

Worldwide Distribution of Major Clones of Listeria monocytogenes

Listeria monocytogenes is worldwide a pathogen, but the geographic distribution of clones remains largely unknown. Genotyping of 300 isolates from the 5 continents and diverse sources showed the existence of few prevalent and globally distributed clones, some of which include previously described epidemic clones. Cosmopolitan distribution indicates the need for genotyping standardization

The energy Extended Resource Task Network, a general formalism for the modeling of production systems:Application to waste heat valorization

While real-time control of process plays an important role, it is now increasingly necessary to forecast and plan production systems in order to be energy efficient and to ensure a balance between energy demand and production. In this context, a short-term planning approach of energy supply chain is presented in this paper. Because of the presence of enthalpy balance in the optimization model, the core of this system is based on the formulation and the resolution of a Mixed-Integer Non Linear Programming (MINLP) model. To facilitate the instantiation of this optimization model and its adaptation to different kinds of value chain, a specific graphical formalism named Energy Extended Resource Task Network (EERTN) is exploited. This generic framework makes it possible to model in an unambiguous way the material and energy flows passing through any type of production system. In addition, it takes into account the influence of temperature on the physicochemical phenomena involved in the process. To illustrate the potentiality of this modeling framework, it is applied to a case study aimed at carrying out the operational planning and performance evaluation of a waste heat recovery chain. This system consists, on the one hand of an industrial unit whose heat requirements are provided by a steam utility plant, and on the other hand, of a district heating network (DHN). In this study, the problem consists to optimally plan the energy use of the district heating network by recovering the flus gas (the waste heat) from the industrial site’s power plant. The planning system leads to a significant reduction in the primary fuel consumption of the overall system and an efficient exploitation of the waste heat generated by the industrial site

Comment objectiver les moyens et outils à utiliser pour réduire l’artificialisation ?

Afin d’atteindre les objectifs de réduction de l’artificialisation, l’objectivation des tendances en cours, des moyens et outils à utiliser apparait comme une aide à la décision indispensable pour les autorités. Cet atelier fait un focus sur différents éléments mis en avant par la recherche CPDT « Intensification et requalification des centralités pour lutter contre l’étalement urbain et la dépendance à la voiture » - Volet 2. L’atelier proposé s’organise autour de trois grands axes : (i) contextualisation de la lutte contre l’artificialisation des sols, (ii) tendances et perspectives relatives à l’artificialisation et au recyclage urbain et (iii) objectivation de quelques mesures/actions/outils envisageables pour réduire l’artificialisation.recherche CPDT « Intensification et requalification des centralités pour lutter contre l’étalement urbain et la dépendance à la voiture »11. Sustainable cities and communities13. Climate action15. Life on lan

Risk factors for sporadic listeriosis: a systematic review and meta-analysis

Listeriosis is a major public health concern associated with high hospitalization and mortality rates. The objective of this work was to summarize evidence on the associations between risk factors and sporadic cases by meta-analysing outcomes from currently published case-control studies. Suitable scientific articles were identified through systematic literature search, and subjected to a methodological quality assessment. From each study, odds-ratio (OR) measures as well as study characteristics such as population type, design, type of model and risk factor hierarchy were extracted. Mixed-effects meta-analysis models were adjusted by population type to appropriate data partitions. Twelve primary studies investigating sporadic listeriosis conducted between 1985 and 2013 passed through a quality assessment stage. These studies provided 226 OR considered for meta-analysis. According to the meta-analysis, the main risk factor for acquiring listeriosis is suffering from an immunocompromising disease. In relation to the food exposures, this meta-analysis confirmed known risk factors such as consumption of RTE dairy, seafood and processed meat and underlined new food vehicles as fruits and vegetables, recently involved in outbreaks. There were not enough data to appraise travel, animal-contact and person-to-person as transmission pathways for listeriosis. These results will allow refining the case-control studies in the aim of improving risk factors characterisation for listeriosis in the susceptible population.U. Gonzales-Barron and V. Cadavez are grateful to the Foundation for Science and Technology (FCT, Portugal) for financial support through national funds FCT/MCTES to CIMO (UIDB/00690/2020). U. Gonzales- Barron also thanks FCT, P.I., for the institutional scientific employment program.info:eu-repo/semantics/publishedVersio

Sudden Onset of Pseudotuberculosis in Humans, France, 2004–05

Cases of Yersinia pseudotuberculosis infection increased in France during the winter of 2004–05 in the absence of epidemiologic links between patients or strains. This increase represents transient amplification of a pathogen endemic to the area and may be related to increased prevalence of the pathogen in rodent reservoirs