Coronary microcirculation (i.e coronary flow in vessels smaller than 300µm) plays a key role in the control of cardiac perfusion. The importance of coronary microcirculation for relevant pathological conditions including angina in patients with normal or near-normal coronary angiograms is increasingly recognized. Indeed, a large number of patients with anginal symptoms and ischemia on stress testing have a normal coronary angiogram and current evidences suggest that about two third of these patients have coronary microvascular dysfunction (CMD), also known as microvascular angina (MVA). Patients with CMD have poor prognostic with significantly higher rates of cardiovascular events, including hospitalization for heart failure, sudden cardiac death, and myocardial infarction (MI). Another important and frequent alteration of the microcirculation is associated to sustained myocardial hypoperfusion during acute myocardial infarction despite coronary revascularization. This so called no-reflow phenomenon remains largely underdiagnosed, and is associated with adverse outcome. Finally, there are also major evidences for CMD during heart failure with preserved ejection france (HFpEF). Despite the urgent clinical need, there are simply no techniques available routinely in clinic, to directly visualize the coronary microvasculature and assess the local coronary microvascular system. Up to date, only global indirect measurements through functional testing (PET, CMR and contrast echocardiography) or invasive measurements can provide hemodynamic information such as Myocardial Blood Flow (MBF) and Coronary Flow Reserve (CFR) in response to vasodilator effects. In CorUS, a novel ultrasound technology will be developed to image the anatomy and the function of coronary vessels at the microscopic scale using a non-invasive and non-ionizing technology. This approach relies on ultrafast ultrasound imaging of the heart at 5,000 images/s, a breakthrough technology pioneered about twenty years ago by researchers of the laboratory Physics for Medicine Paris and more recently on the new technology of Ultrafast Ultrasound Localization Microscopy (ULM) which was introduced to resolve blood vessels at a micrometer scale in deep organs by tracking ultrasound contrast agents (microbubbles) circulating in the blood flow. Cutting-edge technology will be developed in CorUS for local and direct imaging of the coronary blood flows at the microscopic scale to provide new anatomical and functional markers of the coronary microcirculation. Preliminary proof of concept experiments in perfused porcine hearts and in perfused beating rat hearts have demonstrated the feasibility of 2D and 3D coronary microcirculation imaging. This technology will be translated to large hearts application and the approach will be validated in vivo on preclinical large animal models with alteration of the coronary perfusion. Finally, a clinical proof of concept study will be performed on patients with coronary microcirculation alteration. The main objectives of CORUS are: 1. To develop a new ultrasound technology for coronary microcirculation imaging 2. To validate the technology on large animal preclinical models of coronary microcirculation alteration 3. To perform a proof of concept clinical study on patients with coronary microcirculation disease CorUS will have major impacts in the understanding, the management and the treatment of coronary artery diseases and the non-ionizing, non-invasive imaging technology developed in this project could become a major tool for the clinical investigation of microvascular coronary circulation at the patient’s bedside.

This proposal is concerned with the development of novel methodologies (including identification and validation strategies), stochastic representations and numerical methods in stochastic micromechanical modeling of nonlinear microstructures and imperfect interfaces. For the sake of feasibility, the applications will specifically focus on the modeling of hyperelastic microstructures and materials exhibiting surface effects and containing nano-inhomogeneities (such as nanoreinforcements and nanopores). For the case of nonlinear microstructures, the project aims at developing relevant probabilistic models for quantities of interests at both the microscale and mesoscale. The consideration of the latter turns out to be especially suitable for random nonlinear microstructures (such as living tissues) for which the scale separation, which is usually assumed in nonlinear homogenization, cannot be stated. Random variable and random field models for strain-energy functions will be constructed by invoking the maximum entropy principle and propagated through stochastic nonlinear homogenization techniques. A complete methodology for identifying the proposed representations will be further introduced and validated on a simulated database. Concerning the imperfect interface modeling, one may note that surface effects are usually taken into account by retaining an interface model (such as the widely used membrane-type model) involving several assumptions such as those related to the mechanical description of the membrane. Such arbitrary choices certainly generate model uncertainties which may be critical while propagated to coarsest scales and which may therefore penalize the predictive capabilities of the associated multiscale approaches. In this project, we propose to tackle the issue of model uncertainties in multiscale analysis of random microstructures with nano-heterogeneities by constructing nonparametric probabilistic representations for the homogenized properties. A complementary aspect is the construction of robust random generators, able to simulate random variables taking their values in given subspaces defined by inequality constraints and non-Gaussian random fields. Whereas such random fields can typically be generated making use of point-wise polynomial chaos expansions, the preservation of the statistical dependence is hardly achievable with the currently available techniques. In this proposal, we will subsequently address the construction of new random generators relying on the definition of families of Itô stochastic differential equations. Such generators are intended to depend on a limited number of parameters (independent of the probabilistic dimension), for which tuning guidelines will be provided. The proposed models will clearly go a step beyond what is currently done in deterministic mechanics for such materials and the expected results are in the forefront of the ongoing developments within the scopes of uncertainty quantification and material science. In addition, it worth pointing out that such theoretical derivations are absolutely required in order to support the current new developments of 3D-fields measurements and image processing at the microscale of complex materials.

The DIA-SOLAIRE project aims to develop integrated diagnostic and prognostic approaches for existing and future photovoltaic (PV) power plants. These approaches will be hybrid, integrating both physical models and models based on experimental data, while exploring the potential of statistical and machine learning methods. The aim is to extend the lifetime of PV power plants and to optimize the management of their energy performance, while taking into account technological, human and environmental factors of influence, over the medium and long term, in a context of climate change. Energy production drifts and their causes can be identified, and performance and profitability projections can be proposed by introducing degradation distributions and economic models. Finally, changes in maintenance practices brought about by the implementation of artificial intelligence tools will be analyzed with PV plant operators. The tools developed will incorporate a man-machine interface enabling users and operators to support them in making decisions in real time, based on adapted performance criteria.

Nitrogen protoxide (N2O) is a powerful greenhouse gas (GHG), with an impact 300 times higher than carbon dioxide, contributing significantly to global warming. Microbial processes (nitrification or denitrification) in soils or water contribute significantly to the production of N2O. To date, the contribution of wastewater management is still controversial as N2O emissions were poorly measured in wastewater treatment plants. Recent campaigns demonstrated however that the values assumed by the IPPC are much lower than reality. Moreover intensification of nitrogen removal in wastewater treatment and innovation for minimizing energy consumption can potentially increase the N2O emissions if nitrification and denitrification are insufficiently controlled with appropriate tools. This project aims to quantify, model and reduce N2O emissions from wastewater treatment facilities. The ambition of the project is to evaluate solutions in intensive processes receiving domestic wastewater which are used for nutrient removal. The project is divided in different tasks: (1) monitoring of full scale systems during long term campaigns, (2) tracking the main microbial pathways by innovative techniques (isotopes signature and NO:N2O ratio), (3) validation of a multiple pathway model for simulation and evaluation of mitigation strategies, (4) demonstration of innovative sensors and control tools for energy reduction and N2O mitigation. N2OTRACK will provide representative and objective information on direct greenhouse gas emissions from depollution systems. The contribution of these systems to the national anthropogenic N2O emissions will be estimated. Special effort will be deployed on biofilters at full scale, systems poorly characterized so far. The aim is also to provide an N2O modelling framework validated by lab-scale data with isotopic signature measurements and calibrated by full scale campaigns. Finally innovative control tools based on well-known and new sensors will be developed for both activated sludge processes and biofilters. The project involves six partners: three academic laboratories (LISBP-INSA, IEES-UPMC, RBPE-ECOBIO), one applied research institute (IRSTEA), a large WWTP facility (SIAAP-Paris) and a private company SME (BIOTRADE).