Additive manufacturing (or 3D printing) processes has achieved a certain level of industrial maturity. It makes it possible to manufacture parts with complex geometry, personalized in small series, within a reasonable time frame and at a reasonable cost without using specific tooling. However, the additive manufacturing of continuous fiber composite parts remains still limited by the orientation of the fibers in the printing planes. The assembly of 3D printed composite components by laser welding makes it possible, on the other hand, to create functional final 3D parts of large sizes with high mechanical properties (reinforcement fibers in all directions of the space) comparable to those of composite parts which are unfortunately usually limited in shape and geometry, and which are produced by conventional processes requiring expensive tools. Coupling these two processes to produce functionalized and personalized 3D composite parts with very high mechanical performance is unprecedented, and allows the production flexibility and agility with rapid change of product ranges expected by the Industry of the Future. The optimization of this innovative production process implementing a hybridization of technologies will also be based on the development of a simulation tool integrating multi-physics couplings, contributing to the deployment of Industry 4.0. Thus, two types of achievements are expected at the end of the project: a numerical simulation tool of the process, and an optimized functionalized structural part (open source) demonstrator.

The design of learning games for learning is a complex task. It involves a large number of challenges for the different stakeholders (e.g. institutions, teachers, technical designers, players, video game experts). Among these challenges, we can note the acculturation to the game, the difficulty to align pedagogical concepts with the game mechanics and diegesis, or the specific needs of communities of practice. Consequently, we observe in the TEL community a strong ad hoc aspect of the design of serious games, especially regarding the game elements used to address specific pedagogical intentions. However, this ad hoc character does not allow to capitalize efficiently on both the serious games created, nor the choices between pedagogical intentions and game elements to implement them. The expertise of the whole community is then difficult to share and to reuse, and it is difficult to efficiently assist the actors in this design stage. The goal of the TALE4GDA project is to bring new assistance to the stakeholders in the design of learning games and to allow the capitalization of these experiences. To do so, we will propose a first formalization of the concept of alignment between a game entity and a pedagogical intention - a pedago-ludic alignment. This will allow us to propose the first topology of shareable alignments: each alignment will be characterized by its relations with the others (e.g. proximity, overlap). We will take a pioneering approach by allowing the annotation of these alignments in a controlled way, exploring even the possibility of exemplifying them with real situations. Thanks to this, we will be able to set up innovative mechanisms for decision support, design and capitalization based on automatic semantic and topological reasoning.

SAF-MED aims to develop a better understanding of the origins of the high secondary organic aerosol (SOA) concentrations observed in the western Mediterranean in summer with a focus on the role of atmospheric chemical processing and particle properties in SOA formation, in the framework of ChArMEx (The Chemistry-Aerosol Mediterranean Experiment, http://charmex.lsce.ipsl.fr). SOA are a significant fraction of atmospheric Particulate Matter (PM) and, therefore, are believed to contribute to adverse health effects and climate change. High SOA concentrations have been observed over the Mediterranean basin, where high natural emissions (biogenic and oceanic) are common and where aged anthropogenic plumes are transported. Understanding the formation of SOA is complicated because of difficulties to correctly characterize the gas-phase oxidant chemistry and the multi-step oxidation of volatile organic compounds (VOC) that leads to SOA formation. Most state-of-the-art air quality models (AQM) may actually not be valid far from source regions because SOA formation in AQM is based only on the first and, in some cases, second VOC oxidation steps. Consequently, a better description of PM concentrations and characteristics (particle size distribution, chemical composition, volatility, hygroscopicity and mixing state) from both measurements and modelling is desirable to improve our understanding of the origins, evolution, and properties of SOA. Our strategy is based on a combination of ground-based and airborne measurements during a summer field campaign and 3D modeling. A better characterization of SOA and PM will allow us to evaluate existing AQM. Not only PM concentrations, but also PM properties will be compared to measurements, leading to stronger constraints on AQM. This will allow us to improve our insights into the processes that need to be improved for air quality modelling. As a large part of SOA may be formed from the interactions between biogenic and anthropogenic precursors, improvements in the modeling of natural and anthropogenic aerosols will allow us to better quantify the part of biogenic SOA that can be controlled.

The APPRENTIS project concerns the safety of industrial or port areas presenting risks (e.g., fire, explosion or toxic leakage). The operational objective is to provide a decision support software tool to plan monitoring and rescue patrols. This tool will minimize the cost of the patrols carried out by mobile agents (as drones or automated vehicles) by optimizing physical and financial resources based on the analysis of data flows. The questions we would like to answer are as follows: • During monitoring, how many mobile agents are required to perform a given set of measurements at given positions? What sensors should each of these agents equip? How to define the patrols of each of the agents in order to meet the overall monitoring requirements? • In the event of an incident, how to use these same monitoring agents to quickly obtain relevant information on the incident, the damage and any victims? How to transport and distribute rescue supplies with the help of intervention agents? Finally, how can we jointly and effectively use monitoring and intervention agents? The originality of the method proposed to solve these problems is based on modeling aspects and a resolution methodology that are derived from discrete event systems (DESs) and artificial intelligence (AI). This dual approach is motivated by the exponential complexity of the problem which appears when the problems of configuration and planning of the patrols of each of the agents are combined, the latter depending on the evaluated configuration. The expected result of the APPRENTIS project is a demonstrator that can serve as a basis for the development of a software devoted to the configuration of the monitoring and intervention patrols from a catalog of equipment, the description of the infrastructure, and the patrol specifications. We target, in particular, 3 types of audiences: 1) Companies with SEVESO classified sites (156 sites in Hauts de France region, 86 sites in Normandy region, 99 sites in the PACA region and 94 sites in the Ile de France region) which are called upon to strengthen the monitoring of their installations; 2) Organizations and local authorities in charge of crisis intervention (SDIS, urban communities, associations as ORMES); 3) Economic interest groups which are concerned with the production, transport or storage of products at risk (Ports of Le Havre, Rouen and Paris - HAROPA, Grand Port Maritime de Marseille - GPMM). The consortium of partners (ULHN - GREAH - EA 3220, AMU - LIS - UMR 7020, IMTLD, USPN - LURPA - EA 1385) was formed on the basis of the partners’ experience in the risk management and in the implementation and use of DES and AI tools. ULHN in Normandy region and IMTLD in Hauts de France region are located in the two regions targeted by the call RA-SIOMRI. AMU and USPN are located in two large and densely populated cities for which the potential impacts of industrial incidents are particularly serious. Finally, the city of Marseille offers similarities with the city of Le Havre through its port activity, an additional vector of risks due to the storage of hazardous materials, and through its concentration of SEVESO industrial sites near residential areas (Fos-sur-Mer near Marseille and Tancarville near Le Havre). The longer-term challenge we initiate here is to coordinate the means of monitoring and intervention in an automated way by combining predictive and decision-making models, and using model-based methods as well as database-based methods.