Bio-LUSH addresses a growing need and demand to utilize underexploited biomass feedstocks for sustainable and high-quality fibre extraction. Within the project, we will establish innovative processes for optimal biomass refining and fibrillation to nanoscale to convert obtained sustainable fibres into functional biobased materials. Along with hemp hurd and forest residues, selection of other green underexplored biomass feedstocks (nettle and seagrasses) was based on availability in Europe as well as ecological benefits such as utilization of marginal lands, biodiversity protection and without competing with food production. Also, traditional breeding through crosses will be used to improve the agronomical and fiber yield properties of the identified “novel crops”. Certification procedures and a standardization roadmap will be established in a form of nanocellulose quality index to assure effective benchmarking within the obtained (nano)fibres. Moreover, the results of advanced characterization of feedstock, fibers and biobased products will be collected in dataset to employ machine learning tools to design biobased materials based on fibres properties. We will demonstrate the biomass processing and manufacturing (both at TRL5) of biobased products such as edible packaging, antibacterial textiles and 3D printable bio(nano)composite filaments for impact resistant car interior products. The Bio-LUSH consortium brings together actors from the entire value chain, comprised of 12 partners from 8 countries including 7 industry partners (3 SMEs, 4 LEs), who will ensure that the developed solutions will be industrially viable (environmentally, economically, socially). Bio-LUSH includes activities on enhancement of social acceptance of the materials to facilitate the route to market, which will be supported by a dedicated business plan. Integration of Agro sector/farmer communities and access to EU feedstock is ensured through advisory board member, Bast Fibre Technology.

The ROSSINI project aims to develop a disruptive, inherently safe hardware-software platform for the design and deployment of human-robot collaboration (HRC) applications in manufacturing. By combining innovative sensing, actuation and control technologies (developed by world market leaders in their field), and integrating them in an open development environment, the ROSSINI platform will deliver a set of tools which will enable the spread of HRC applications where robots and human operators will become members of the same team, increasing job quality, production flexibility and productivity. Thanks to enhanced robot sensing capabilities, the deployment of artificial intelligence to optimise productivity and safety, and natively collaborative manipulation technologies, ROSSINI will deliver high performance HRC workcells, combining the safety of traditional cobots with the working speed and payloads of industrial robots. The ROSSINI research lines will this be: SENSING: A high performance Smart and Safe Sensor System for human and robot detection & tracking, capable of quickening sensors response time by 70% CONTROL: A safety aware control architecture for robot dynamic reconfiguration, capable of reducing robot task execution time by 45% ACTUATION: An innovative, collaborative by birth robot manipulator, to be marketed through the ROSSINI platform, capable of increasing the working speed when collaborating with humans by 45% HUMAN FACTORS: Human factors analysis for mutual predictability of robot and human intentions, capable of increasing job quality index by 15% RISK ASSESSMENT: A risk assessment procedure based on new interpretation of collision values, capable of reducing (combined with other results) by 30% the time and cost of reconfiguration, and increasing the allowed robot working speed by 20% The research lines will be combined into 3 demonstrators (white goods, electronic equipment, and food packaging).

AI4Work will investigate practical methods and tools for optimal sharing of work between humans and AI/robots. AI and robotics are likely to be most powerful means for radical improvement of working conditions in diverse domains, as they can support human operators in diverse tasks starting from difficult and tedious manual labor tasks up to complex decision-making tasks. The vision of the AI4Work project is to improve communication and collaboration between humans, AI and robots, allowing for an improvement of the working conditions within different processes in organisations in several domains in terms of increased efficiency of work, reduction in stress upon employees, increased confidence in decision-making process etc. Due to the high level of uncertainty in modern organisations an appropriate balance between human and machine activities must be found. The key assumption is that to cope with the required flexibility and dynamics, Sliding Work Sharing (SWS), where this balance varies during the operation depending on the situational context, machine-based confidence levels and human interactions, is likely to be the most appropriate for modern organisations. The key challenge of the project is to develop a set of common methods and tools (methodology framework, digital twin service platform, SW building blocks for SWS) that can be applied in diverse sectors and with different AI/robotics services, allowing for an effective experience exchange. The project will make use of living digital twins of working systems as a mean to increase efficiency and trustworthiness of AI/robotics solutions. By this, the project, aiming at improved quality of jobs and creating more decent work for human operators, will contribute to the acceptance of the AI/robots support of work in diverse domains. The project will be driven by six pilots in different sectors: logistics, manufacturing industry, construction, healthcare, education and agriculture.

Heart failure (HF) is a chronic clinical condition involving up to 6.5 million people in Europe, the most frequent cause of hospitalization in adults with a 5-year mortality rate up to 70%. Several drugs positively modify the course of disease in the patients with HF with reduced ejection fraction (HFrEF), with a high level of evidence. Besides, the use of diuretics, the basic cornerstone of symptoms relief in HFrEF by targeting congestion, is supported by poor and outdated evidence. Congestion drives symptoms worsening leading to hospital admission. Clinical evaluation of congestion is often inaccurate and insensitive to detect interstitial or intravascular fluid overload, and thus insufficient to guide use of diuretics. Indeed, their use is inefficient, with studies showing that up to 70% of patients with chronic HFrEF show congestion despite diuretic therapy, the use of diuretics does prevent clinical events in patients discharged after an acute heart failure episode, and diuretics may represent a barrier to adherence to disease modifier therapies. An appropriate management of diuretic therapy is therefore crucial to overcome the risk of re-hospitalisation, manage patients symptoms, and achieve target guideline-directed medical therapy. To fill these gaps, BIOTOOL-CHF will 1) validate a set of qualified biomarkers estimating congestion, 2) define a multiparametric artificial intelligence-based score predicting congestion and prognosis, 3) develop a decision-making tool to manage congestion by diuretics, 4) develop a Point of Care companion diagnostic (CD) to assess biomarkers concentrations 5) set up a Strategy plan for industrial development and market access of the CD. This approach will support the definition of a framework to regulatory agencies and scientific societies to disseminate recommendations for a more efficient use of existing pharmaceuticals and allow a personalised strategy for the management of HFrEF, by using new tools and digital solutions.

The I-MECH target is to provide augmented intelligence for wide range of cyber-physical systems having actively controlled moving elements, hence support development of smarter mechatronic systems. They face increasing demands on size, motion speed, precision, adaptability, self-diagnostic, connectivity, new cognitive features, etc. Fulfillment of these requirements is essential for building smart, safe and reliable production complexes. This implies completely new demands also on bottom layers of employed motion control system which cannot be routinely handled by available commercial products. On the ground of this, the main mission of this project is to bring novel intelligence into Instrumentation and Control Layers mainly by bridging the gap between latest research results and industrial practice in related model based engineering fields. Next, I-MECH will deliver new interfaces and diagnostic data quality for System Behavior Layer. It strives to provide a cutting edge reference motion control platform for non-standard applications where the control speed, precision, optimal performance, easy reconfigurability and traceability are crucial. The high added value of I-MECH reference platform will be directly verified in high-speed/big CNC machining, additive manufacturing, semicon, high-speed packaging and healthcare robotics. In these sectors, the main project pilots will be validated. However, the platform will be applicable in many other generic motion control fields. The project outputs will impact on the entire value chain of the production automation market and, through envisioned I-MECH center, create sustainable proposition for future smart industry.