L-Rhamnose (6-deoxy-L-mannose) is a rare monosaccharide exiting in nature but not easily accessible. It is described as a high value molecule for the food industry, pharmaceuticals, or nutraceuticals due to its unique physiological effects. Regarding a complex and expending market and the existing issues for its actual production (old and non green chemistry, sourcing, purity, etc.) , the methodology (processes) for the next decades must be challenged through (i) an intelligent sourcing, (ii) more up-to-date processing technologies (especially for purification) and (iii) new enzymatic proposals (cocktails, designed enzymes, etc.) for boosting L-Rha production (yield, performance, etc.). RAh is an interdisplinary public partnership that combines approaches in microbiology, biochemistry, physico-chemistry, and process engineering. Its overarching to significantly increase the level of knowledge and proficiency of new sustainable processes for large-scale production of L-Rhamnose. RAh project is directly relevant to the axis H.7 “Bioeconomy, from biomass to uses: chemistry, materials, processes and systemic approaches” of the transversal domain “Ecological and environmental transition” as well as the themes LS_07 “Environmental technology and bioengineering” and LS_11 “Biomass production and utilization, biofuels”. This project will therefore directly support the development and competitiveness of French industry in a strategic domain, integrating important constraints such as carbon, water footprints and the ethical use of natural resources, according to the axis H.7. Adopting an approach based on circular economy and sustainability will also help to support the cross-sectorial approaches and monitor future developments regarding societal and environmental challenges on a 2030 horizon.

The quantitative knowledge of the trajectory of discrete elements immersed in fluids is an important data that can be used in many scientific and industrial fields. In the field of Building science and technology, the challenge is both environmental and energetic. On the environmental front, the issue is the control of indoor air quality, a public health issue. This involves predicting the motion of airborne contaminants or of dangerous gases in a confined space, in order to optimize ventilation or evacuation strategies. The current health crisis is a reminder of the importance of such a prediction. In terms of energy, knowing the trajectory of the air makes possible to understand the nature and dynamics of airflows and therefore to optimize heating or cooling systems dedicated to indoor thermal comfort. The numerical simulation tools (Computational Fluid Dynamics models) currently widely used to predict the flows motion, struggle to give satisfactory results except in very well-defined typical cases. Therefore, high quality experimental data is still a key step in the indoor airflow studies as well as in the bioreactor design, and plays a fundamental role in the validation and development of numerical models. However, the current measurement techniques are insufficient for the complete characterization of the flows involved. For example in buildings, airflows are mostly turbulent, low-velocity, strongly three-dimensional and large-scale, whereas the current velocity measurement techniques only yield point-wise data or data in a thin laser sheet measuring volume, in the best case. The most common current trajectory visualization technique, based on the use of tracer gases, only gives qualitative information. The objective of the TRAQ project is both the operational development of a new diagnosis tool that can be used outside laboratories to measure, in real-time, the extended three-dimensional trajectory and the velocity of particles immersed in fluids; and the application of the designed tool to scale 1 case studies. This tool, called 3DPTV (3D Particle Tracking Velocimetry), will initially be applied for Building science and technology and to bioreactor engineering, before being adapted to other industrial fields. 3DPTV is a quantitative measurement technique composed of particles of neutrally buoyant particles, also called tracers, a tracer illumination system, at least three time- synchronous cameras, and finally an algorithm which uses the recorded images to calculate the 3D trajectory of each individual tracer. Real-time access to 3D statistics involves developing and assembling, within the Institut Pascal, 3DPTV-specific smart cameras and the parallelization of existing algorithms. The project also aims to extend the lifetime of the tracers currently used in buildings by an interfacial chemistry study and to test the possibility of obtaining a thermochromic tracer. Measuring volumes will be expanded by devising a procedure allowing combining several different 3DPTV systems. Self-calibration of the camera network will also be investigated. Within TRAQ, 3DPTV will be applied to real cases such as studying the propagation of particles emitted during a coughing fit or the dispersion of pollutants in a dwelling. The data obtained will be made freely available to the CFD community. Those different technological barriers can be overcome thanks to the interdisciplinary research within the Pascal Institute, which harbors high-level researchers in electrical engineering, mechanical engineering, computer science and chemistry. The project is planned to last 48 months and should result in at least two patents, one on the new tracer and the other one on the real–time 3DPTV system. By addressing air pollutant monitoring, energy efficiency in buildings and the optimization of bioenergy production processes, the project must help to meet society's high expectations for indoor air quality and clean, efficient energy.

Urban infrastructures are essential for maintaining the vital functions of a society. The contraction of local authority budgets, the issues of risk management and aging are all major challenges. Each infrastructure is currently managed independently with little consideration of physical or functional interactions. The implementation of collaborative management strategies largely depends on the ability to deal with these interconnections, in physical and informational terms. In this context, the DIBIM project proposes a collaborative approach for the management of dykes interconnected with urban infrastructures (roads, water and sewer networks) and vegetation with respect to technical and economic risks via the structuring, the centralization and the sharing of data in BIM (Building Information Modelling) between managers. The proposed approach consists of formalizing the technical and economic indicators for monitoring and diagnosing dykes according to a systemic approach (WP1); to propose an efficient collaborative exchange process by collecting the needs of managers and analyzing current processes (WP2) and to structure the data, resulting from WP1&2, for their graphic modeling, their centralization and their sharing in order to plan and manage maintenance work costs (WP3). The collaborative approach, the purpose of this project, will also be a transversal work axis for the WPs. The project team brings together skills in technical and economic infrastructure expertise, vegetation expertise, systemic analysis, performance evaluation, governance and analysis of practices, collection of needs, cost management and structuring, modeling and data management. It relies on four local authorities and a professional association for the collection of needs and the experimentation of the proposed approach.

The TOPACS project aims at large scale analysis of 3D medical images stored in hospitals. The primary goal is large scale study of the Human Anatomy (Computational anatomy). One major challenge is the size of the data to analyse, while respecting anonymity of the individuals. Keypoint extraction comes as a solution to this problem. Indeed keypoints offer a compact summary of an image, storing only important features present in the image. Each keypoint is associated with a feature vector describing the local neighborhood around the keypoint, and an efficient comparison can be computed between keypoints by measuring the distance between their respective feature vectors. During this project, we plan to be able to analyse more than 10000 individuals. The TOPACS project falls into four parts: The first part will address keypoint extraction from 3D medical images. For 2D images, many keypoint approaches have been proposed, such as SIFT, SURF, KAZE, and recent advances in machine learning have resulted in better kepyoint algorithms, such as LIFT. But few works have proposed keypoint techniques in the field of 3D medical image processing. In this first task, we will propose new keypoint algorithms tailored to medical images, by studying both hand-crafted approaches and machine learning approaches. The proposed methods should exhibit specific characteristics : robustness to large inter-patient variability, ability to compare data extracted from different imaging modalities. We plan to extract keypoints from three hospitals, in Lyon, Saint-Etienne and Geneva. The second part consists in devising new approaches for registration and segmentation using keypoints. A major difficulty is large scale groupwise registration. In this context, groupwise registration appears as a better means to register a large set of images, as choosing or building a single reference model would introduce a severe bias. Current approaches can register about hundreds of images together. Our goal is then to propose approaches with much larger capacities. This task will also address keypoint-based segmentation, for which few works have been proposed. The third part will deal with statistical representations of large population as well as inference at the single-subject level. Manifold learning techniques will be considered to capture both geometric and textural normal/pathological variabilities. Classification / regression methods on manifold will then be developed to infer prediction for a given individual. The fourth part will link the theoretical works of the first three parts to medical applications. A first task will consist in extracting data from the three hospitals, where a computer will be installed in each hospital and extract large databases of keypoints. We plan to mainly extract data from 3D CT and MRI images. A second task is the application of population analysis for anthropology, mainly for forensic science : estimating the profile of an unknown individual (gender, age, ...) or estimating the date of death. A third task will be the proposal of an online tool to provide access to the new general purpose algorithms : segmentation, registration. More generally, the TOPACS project aims at contributing to open science, by publishing algorithms and databases, while keeping the anonymity of data present in the databases.

In a context where additive manufacturing is still too slow and too expensive, the objective of this project is to conduct research work to formalize the process of additive manufacturing to optimize the industrialization . It is part of the desire to remove the locks on industrialization strategy, cost reduction and human / process interaction to streamline the process and make it more efficient. The objective of this project is to participate in the industrial development of additive manufacturing processes, whether at the level of subcontracting in the framework of the competition of processes, or at the level of the principals in the context of the development. new products, in connection with the establishment of a relevant industrialization economic model, based on the proposal of specific performance indicators and the skills base needed for its industrialization. It relates to metal additive manufacturing processes. The point of view chosen is a process point of view, considering the industrial production of mechanical parts and not a process point of view. Because of its complexity and competition between processes, the development of additive manufacturing necessarily involves an overall optimization of the process in all its dimensions. Thus, additive manufacturing offers a field of investigation that invites us to implement new research approaches, based on mechanical models, but no longer necessarily focused on processes. A broader base of disciplinary skills is needed. Today the development phases of the process and its industrialization must be addressed jointly. The project will take care to lift the following locks: - propose a decision support method, by comparing different industrialization process based on approximate models and taking into account the risks linked to technological changes and innovations; - propose a cost estimation model of the complete value chain of the process making it possible to decide on the use of the process according to the different configurations of competition and product development; - propose the relevant set of performance indicators necessary for decision-making and risk modeling; - propose a specific multi-physics model for calculating the technical performance indicators of the additive manufacturing process; - develop and formalize the skills base needed to implement this process in an industrial situation, in a context of technological change for a process requiring a high level of security, so as to estimate the costs related to the transformations of the company and to estimate the resistance to the changes induced by these evolutions. The chosen approach will lead us to the following end products: - the expression of indicators to evaluate the performance of the processes studied in terms of implementation costs, pre-treatment of post-processing, cycle times, material health, mechanical strength of the part, geometric quality .... ; - a base of skills necessary for the industrialization of the process, with an accompanying methodology taking into account the resistance to change; - a decision-making methodology, based on a complex technical-economic model, taking into account the entire process, including the indicators expressed and taking into account the expected corpus of competence. The consortium is formed by two laboratories: Estia Research and Institut Pascal. Both partners rely on rich industrial collaborations to give industrial meaning to their work. the duration of the project is 48 months. The funding concerns 3 theses and experimental trials.