Industry 4.0 suggests a reactive and flexible management of production lines as well as the advent of new decision support tools based on the use of data collected in real time. However, there is still a need for implementation and integration into traditional production management approaches in the broad sense. Our project seeks to answer these questions for the improvement of the articulation of tactical and operational decisions, based on new concepts of predictive and prescriptive maintenance. This will lead to new integrated approaches to planning, scheduling and maintenance, taking into account different levels of uncertainty, accompanied by new performance-oriented indicators for the robustness of production plans. In addition, this project will serve to define a set of specifications related to the data to be collected as well as associated management rules which will then form the "skeleton" of a digital shadow developed throughout the project and motivated by an industrial application in the medical device sector.

Cannabis is widely used in industrialised countries. Its consumption has particularly increased during early adolescence, a period when the developing brain is susceptible to environmental exposures. This is crucial since an association between early cannabis consumption and the risk of emerging schizophrenia has been established. A dysfunction of the glutamatergic system has recently been proposed as a pathophysiological model to explain this association. Indeed, the regulatory role of the endogenous cannabinoid system in glutamate neurotransmitter release is disrupted by chronic administration of THC. By preventing endocannabinoid-mediated control over the homeostasis of glutamate, it has been proposed that exogenous cannabinoids may dramatically affect the process of maturational refinement of cortical neuronal networks, impacting on processes based on neural plasticity such as perceptual learning. This effect could interact with a biological vulnerability on neural maturation, thus leading to schizophrenia. This model has been based on animal experiments and now needs to be confirmed in humans. In this project we target functions that are known to be sensitive to glutamatergic transmission, and we determine the impact of chronic cannabis consumption on these functions. Measuring early perceptual processing has been used to assess glutamate transmission in schizophrenia: regarding vision, a magnocellular dysfunction in schizophrenia has been well established with a large range of methodologies. Measuring low level cognitive functions has the advantage of being less sensitive to attention, thus eliminating the bias of a non specific generalized attentional deficit. We propose herein to study the impact of heavy cannabis consumption on magnocellular processing in order to evaluate the functioning of the glutamatergic system. We will also study the consequences of a potential early visual deficit on integrated visual functions refined by perceptual learning. Magnocellular processing will be appraised in several studies with a contrast sensitivity function, masking and a simultaneity threshold task. ERP will be assessed during these measurements in order to verify that behavioural results are due to early visual impairments. The impact of a magnocellular dysfunction on higher order visual processing will be evaluated in a face recognition task with spatial filtered stimuli. At last we will evaluate whether polymorphisms implied in endocannabinoid system and neuroplasticity differentially modulates magnocellular processing in chronic cannabis users

The detection of algal toxins in environmental waters, in particular in lake water, is an important challenge in public health. As concerns the phenomenon of cyanobacterial bloom, toxins such as microcystine LR can be liberated into the water by the bacteria. These toxins are dangerous for animals and humans essentially because of the risk of ingestion of contaminated water. It is thus crucial in the preliminary phases of a cyanobacterial bloom to determine if this bloom leads to a release of toxins in the water. The NEWMAT project which constitutes the scientific foundation of the present response to the MRSEI call aims at developing an autonomous aquatic rover equipped with three types of algal toxin captors coupled to a wireless network of very low-cost captors localised on the shores of the lake under study. The coastal network of captors will determine any changes in the organoleptic properties of the water (mainly the turbidity and colour) and thus guides the patrol path of the automatic rover to those areas in which a cyanobacterial bloom is suspected. Any physico-chemical analyses initiated by the rover will determine and quantify the possible presence of algal toxins, essentially of the microcystine family, the most present toxins under such circumstances. Via a wireless communications system, the rover will transmit the information to a data collection and treatment centre. The personnel within this centre will then be able to undertake, in real time, the required remediation protocols against the cyanobacteria without the need to call upon public health entities responsible for protecting the users of the lake. The rover will be equipped with three captor technologies to be developed: i) a fluorescent captor based on specific marker techniques; ii) a captor using the surface-enhanced Raman(SERS) effect comprising an organised system of noble metal nanoparticles held within a specific matrix so as to avoid any release of these particles in the water during analysis; iii) a bioelectrochemical captor calling upon a specific enzymatic reaction of microcystine LR constituted of carbon nanotubes functionalised by an antigen and deposited on an electrode. These advanced captors using nanomaterials constitute an original approach, little-explored in the literature for the quantification of toxins. The use of three captors for the simultaneous analysis of a given sample will allow extending the linear range of the toxin concentration measurements and obviously allows for crossed validation. The development of these captors will necessitate international expertise in the functionalisation of nanomaterials and their integration into robust and reliable devices. The construction of the aquatic rover will require expertise in the conception of optoelectronics systems and in optical spectroscopy. Making full use of the information contained in the data coming from the real water samples will also require the use of pertinent signal treatment algorithms. The network system of low-cost captors is based on well-known LED technology but will require optimisation in coastal site-localisation to overcome seasonal fluctuations of the level of the lake. An expertise in sensor networks is thus also a prerequisite for the success of the project. This project is thus strongly multidisciplinary and requires establishing solid contacts among the future participants. The framework established by the MRSEI tool is of great interest to finance the setup of such a European network of expertise so as to best develop a solid consortium in the aim of depositing a realisable European project.

The revolution of robotics and artificial intelligence now makes it possible to dream of ambitious applications. In particular, the implementation of intelligently managed fleets of robots would provide innovative solutions to many concrete problems. Among these, the SOS project (Self-Organizing, Smart and safe heterogeneous robots fleet by collective emergence for a mission) focuses on forest fire detection using a fleet of aerial and ground robots. It brings together three research teams from two laboratories (CRIStAL and CRAN) and one SME (Lynxdrone) with the aim of proposing, designing and developing a mechanism for intelligent management of heterogeneous robot fleets by collective emergence: i) taking into account the specific characteristics of the robots ii) adaptive to the dynamic and evolving environment iii) as well as to the estimated and predicted health state of the robots (in terms of control actuators, localization sensors, battery charge, residual life, ...) iv) and resilient to the occurrence of an incident, in order to fulfill a defined mission. We put ourselves under the following assumptions: i) the system is composed of several mobile robots, ii) the robots are heterogeneous: terrestrial or aerial, with varying load, energy, computation, perception, localization, control, decision, communication, ... capacities, iii) environmental conditions could be difficult, iv) the robots are subject to sensor and/or actuator failures, v) the chosen architecture of the multi-robot aero-terrestrial system is a decentralized architecture: there is no master. The research conducted will bring contributions, under a safe framework, on three axes in particular: i) Intelligent and decentralized self-organization of fleets This involves designing the individual behaviors of robots, whether air or ground, which have different capacities of perception and action depending on their equipment. These robots must determine by themselves the role they should take on in order to best contribute to the collective realization of the mission. For example, these robots must be able to adapt their communication vector according to the characteristics of the environment, or to become "information relays" instead of patrolling an area. The goal is to obtain a system capable of dynamically reorganizing itself to face unexpected events, while each one has only incomplete information. ii) Robot control The aim is to explore within the learning paradigm the development of reconfiguration schemes for robot control that take into account the health of the system as well as predictions of future failures, thus guaranteeing the completion of the mission with a certain level of performance in terms of stability and safety. iii) Robot localization On the one hand, we will efficiently and innovatively exploit the cooperation between robots to improve the accuracy, availability and safety of the estimation of the robots' position. UAVs can, for example, be used as GNSS receivers remote and redundant to those of AGVs. Indeed, while the accuracy or even the availability of the AGV GNSS measurements can be affected by poor reception of satellite signals due to nearby obstacles, the quality of the UAV GNSS measurements can be better due to their higher altitude, above the obstacles. Thanks to perception, AGVs can then position themselves relatively to UAVs. Conversely, their positions in situ, can allow the AGVs to have a better positioning relative to the environment (possibly a marker) and benefit the UAVs. On the other hand, it will be necessary to make the localization fault-tolerant by adding a diagnostic layer in charge of detecting and isolating the faulty measurements and/or sensors in order to exclude them from the multi-sensor fusion procedure. The combination of model-based and data-based (machine learning) approaches will be investigated. The proposed solutions will be tested and evaluated in simulation and on real data.

In sight of current low building renewal rates, the development of energy retrofitting activities for existing buildings emerges as a major challenge to reduce energy waste. External thermal insulation appears as an interesting solution. Trades such as wood and concrete construction must now turn to digital and collaborative planning tools to improve their efficiency and respond to this challenge. The ISOBIM project aims to contribute to the transformation of this sector by developing decision support tools integrated into a solution that fosters the digital engineering of retrofitting processes utilizing modular framing panels. The originality of the project lies in proposing an environment overarching optimizer models for nesting, along with a collaborative scheduling framework. The latter will work under constrained models of Lean logics and 4D simulation that will help validating overall results.