The clinical decision support systems (CDS) are generally limited to thresholds and variance detection in the case of anesthesia monitoring, this project aims at exploiting the potentialities of control, optimization and learning methods in order to propose generic and explainable approaches allowing to merge the different measured data, and to design advanced systems for critical and complex events detection. Furthermore, this project will help to tackle highly challenging problems related to anesthesia dynamics, such as, complex dynamics modeling and estimation, in order to better characterize patients’ reactions to drugs, as well as monitoring in presence of uncertainties and critical events. The particular asset being the availability of large open-source data-bases, as well as annotated data from other projects, allowing to better characterize the widely used models, and thereby to achieve an effective and personalized monitoring.

The main objective of the HM-Science (Human-Machine Shared Control for Intelligent Safety and Energy of Smart Vehicles) is to develop new approaches and control architectures for vehicles to design shared control systems in a generic Human-centered perspective. Numerous works in the autonomous driving field have shown that, defining behaviors adapted to all users (including Person with Reduced Mobility – PRM) and all situations that may be encountered is very complex, if not impossible. HM-Science aims to provide cooperation and, especially giving the machines learning capabilities, with a goal to allow them to learn-and-adapt to users and situations. The solution developed necessitates two important fields – robust control and AI-learning – and the core will be to combine them in a framework that preserves real-time safety and performances. It does enter in the hot topics of the future of control as stated by (Recht 2019 (*)): “One final important problem, which might be the most daunting of all, is how machines should learn when humans are in the loop. What can humans who are interacting with the robots do and how can we model human actions?”. (*) Recht B. (2019). A tour of reinforcement learning: The view from continuous control. Annual Review of Control, Robotics, and Autonomous Systems 2, 253-279

The use of advanced technologies in industrial systems is expanding in view of: 1) the technical specificities that they allow to achieve, 2) the traceability of data that they offer and, 3) the relief of human operators from tedious or repetitive tasks that they make possible. The industrial systems of the Future (4.0) are characterised by an increase in their autonomy and a profusion of data produced and handled that can lead to poorly controlled decisions, which can lead to ethical dilemmas. Such decisions may concern an inappropriate use of data for the management of industrial systems or reactions, triggered by Artificial Intelligence, based on a partial vision of the system and having poorly controlled consequences on the various stakeholders. The ETHICS40 project focuses on the management of ethical issues during the operation of industrial systems of the Future. In partnership with the bearing manufacturer NTN-SNR, which is in the process of migrating to the Factory of the Future, it aims to develop a tool, a software prototype, called ETHICS4IF (Ethical Risk Assessment and Management for Industry of the Future), which will enable the identification and integration of ethical risks into the management and performance imrovement of industrial systems of the Future. As ethics is a concept derived from moral philosophy that is difficult to grasp in engineering, the ETHICS4IF tool will be the result of works led by a multidisciplinary team involving specialists in applied ethics. A definition of this notion of ethics applied to the operation of industrial systems of the Future and an identification of the associated risks will be proposed in a first step. In a second step, the taking into account of these risks in the mechanism of evaluation and continuous improvement of the performance of industrial systems will be made operational by proposing a concrete methodology to integrate the risks and their potential consequences. ETHICS4IF will be aimed at actors interacting with these systems (operators, managers, etc.). It will combine the different points of view associated with ethical risks (human, industrial system, company, society, environment). The ethical management rules and practices deduced from the risk analysis will be defined in accordance with the rules of ethics in place in the company and in compliance with the laws associated with the use of digital technology. Performance indicators related to the ethics associated with the operation of the industrial systems of the Future will be proposed and monitored in order to improve the handling of this dimension. The main deliverable of the project will consist of a software prototype encompassing the aspects mentioned. It will be accompanied by a set of rules and practices deduced from its application by the NTN-SNR partner as well as a feedback on the use of the tool and the management rules and practices, beyond the NTN-SNR case study. The tool will be fed, tested and used by the Company. A strategic committee made up of the project members, industrialists who are not competitors of NTN-SNR and a law office specialising in digital law will oversee the progress of this project. The ETHICS40 project responds to an important need of industrialists in their migration towards the Factory of the Future. It will provide the scientific community with a tool to help operationalise ethics in engineering and industrial engineering research.

Moving towards Smart Manufacturing is a key challenge for the 4.0 Industry. This involves using digitized processes and technologies to enable significant adaptability and optimize the performance of processes and products. In this context, the control of the functional properties of surfaces, and particularly of the surface appearance, constitutes an important lever of added value. Many scientific and technological challenges are associated with this issue. The objective is, by quantifying the visual properties of manufactured surfaces at a roughness scale, to objectify a subjective, unconscious and complex process of sensory perception that integrates a wide range of previous representations. The approach proposed in the RTI4.0 project consists in implementing a measurement of the angular and spectral component of the re?ectance of surfaces according to the principle of the RTI technique (Reflectance transformation Imaging). The information obtained is multidimensional, allowing to estimate both apperance descriptors associated with the distribution of measured local luminances, but also geometric descriptors (altitudes, slopes and directional curvatures) through the estimation of stereo-photometric models. The challenges associated with this approach are numerous and multifactorial. The research actions envisaged mainly concern, on the instrumental level, the development of new RTI acquisition approaches (multimodal, adaptive, multiscale), and on the methodological level, the development of methods for the analysis of the properties of surface states allowing the implementation of a functional control of the appearance of manufactured surfaces.

Historically the research developed for understanding how cells and tissue respond to surface topography has been done with the aim to optimize implant surface performance. For several years, we and others have developed research to understand the mechanisms underlying the human bone cell response to materials used in orthopaedics and dental surgery for bone replacement. In several studies, we have demonstrated that the long-term adhesion of primary human bone cells was statistically better correlated with parameters describing organization of topography than with other parameters. This project is a basic research project aiming to elucidate the mechanisms underlying the cell deformation induced by isotropic topography, and notably the intracellular mechano-transduction mechanisms, thanks to the association of experimental and modelling approaches. More specifically, we will focus our attention on peak-and valleys topographies that are the surface morphologies mostly met by cells in vivo contrary to surfaces with geometrical and anisotropic morphologies that have been studied until now in the field. With this objective we will notably develop sinusoidal surfaces presenting peak-and-valleys at the cellular scale. On these sinusoidal and on control surfaces, we will perform original live imaging to visualize and quantify deformation of cytoskeleton and cell membrane, focal adhesion formation and RhoGTPase signalling . Moreover, an original mechanical model based on tensegrity and divided medium mechanics will be adapted to identify the intracellular tensions as well as nucleus deformation on these sinusoidal surface morphologies. Our objective is also to develop new sinusoidal standards with different amplitude and frequencies usable in roughness measurements in biomaterials field but also in other fields not directly related to the project. We propose to fabricate replicas of these surfaces in order to obtain an unlimited number of samples. Then we will develop dynamics studies of intracellular organelles deformation by live imaging under a confocal microscope specifically adapted for cell imaging on materials. This project will bring new input on mechanical intracellular mechanisms underlying the cell response to topography thanks to the association of live imaging approaches and 3D reconstruction of cells and quantification of activities of molecules involved in signal mechanotransduction. Thus, at the end of this project, we will possess a library of sinusoidal model surfaces suitable for replication. This perfectly new library could be reused for future cell-surface interaction studies but also in other fields (ex. fluid mechanics, wear, adhesion, etc.). On a basic point of view, we will have developed a mechanical model of cytoskeleton based on tensegrity and divided medium mechanics. The integration in this model of actin fiber network reconstructed in 3D from images of stained CSK in cells cultured on sinusoidal surfaces will make it a perfectly original model able to manage the cellular deformation on textured surfaces. Such model should have a lot of application for predicting cell deformation on a large variety of surfaces from model surfaces with anisotropic distribution of geometrical motifs to real implant surface presenting isotropic random topography.