FELICE aspires to design the next generation assembly processes required to address urgent needs in manufacturing. It unites multidisciplinary research in robotics, AI, computer vision, data analytics, process optimization and ergonomics to deliver a modular platform capable to integrate and harmonize an array of autonomous and cognitive technologies aiming to increase the agility and productivity of an assembly production system and also ensure the safety and physical and mental well-being of human workers. To achieve these goals, technologies will be developed combining the accuracy and endurance of robots with the cognitive ability and flexibility of humans. Such flexible and configurable technologies will support future manufacturing assembly floors to become agile, and address Industry4.0 adaptation. We consider two layers of operation, control and processing, a local one introducing i) a collaborative assembly robot roaming the shop floor to assist workers in assembly tasks, and ii) adaptive workstations for optimizing worker-specific ergonomic aspects, and a global layer which will sense and operate upon the real world via an actionable digital replica of the physical assembly line. FELICE will i) implement perception and cognition capabilities allowing the system to build context-awareness, ii) advance human-robot collaboration in otherwise manual assembly lines, enabling robots to safely and ergonomically share tasks with humans, allowing the flexible reconfiguration of an assembly production process and, iii) realize a manufacturing digital twin, i.e. a virtual representation tightly coupled with production assets and the actual assembly process to enable the management of operating conditions, the simulation of the assembly process and the optimization of performance aspects and, iv) integrate developments in an industrial assembly line for agile production. FELICE has two pilot environments: one for technology validation and one related to car assembly.

Neuropsychiatric disorders are a leading cause of global disability-adjusted life years, and solutions are lacking. Can digital twins be useful? At least in some cases, we hold they will be central to progress. Recent findings suggest that non-invasive brain stimulation may be a valuable option in conditions such as epilepsy or Alzheimer's (AD). Still, a better understanding of mechanisms and patient-specific factors is needed. Personalized hybrid brain models uniting the physics of electromagnetism with physiology – neurotwins or NeTs – are poised to play a fundamental role in understanding and optimizing the effects of stimulation at the individual level. We ambition to deliver disruptive solutions through model-driven, individualized therapy. We will build a computational framework – weaved and validated across scales and levels of detail– to represent the mechanisms of interaction of electric fields with brain networks and assimilate neuroimaging data. This will allow us to characterize the dynamical landscape of the individual brain and define strategies to restore healthy dynamics. Benefitting from existing databases of healthy and AD individuals, we will deliver the first human and rodent NeTs predicting the effects of stimulation on dynamics. We will then collect detailed multimodal measurements in mice and humans to improve the predictive power of local and whole-brain models under the effects of electrical stimulation, and translate these findings into a technology pipeline for the design of new personalized neuromodulation protocols which we will test in a cohort of AD patients and healthy controls in randomized double-blinded studies. With research at the intersecting frontier of nonlinear dynamics, network theory, biophysics, engineering, neuroscience, clinical research, and ethics, Neurotwin will deliver model-driven breakthroughs in basic and clinical neuroscience, with patients ultimately benefiting from safe, individualized therapy solutions.