Rapid progress in information and biotechnologies offers the promise of better, personalized health strategies using rich phenotypic, environmental and molecular (omics) profiles of every individual. To capitalize on this great promise, key challenge is to relate these profiles to health and disease while accounting for high diversity in individuals, populations and environments. Both Europe and Canada have long-term investments in population-based prospective cohort studies providing essential longitudinal data. These data must be analysed in unison to reach statistical power, however, presently cohort data repositories are scattered, hard to search and integrate, and data protection and governance rules discourage central pooling. EUCAN-Connect will enable large-scale integrated cohort data analysis for personalized and preventive healthcare across EU and Canada. This will be based on an open, scalable data platform for cohorts, researchers and networks, incorporating FAIR principles (Findable, Accessible, Interoperable, Reusable) for optimal reuse of existing data, and building on maturing federated technologies, with sensitive data kept locally and only results being shared and integrated, in line with key ELSI and governance guidelines. Widespread uptake will be promoted via beyond state-of-the-art research in close collaboration with leading cohort networks, focused on early-life origins of cardio-metabolic, developmental, musculoskeletal and respiratory health and disease impacting human life course. To address challenges of sustainability and curation, we will deliver innovative solutions for distributed, low-cost data harvesting and preservation, community curation/harmonization, privacy protection, open source bioinformatics toolbox development, and international governance. EUCAN-Connect platform and collaborations will be coordinated through BBMRI-ERIC (EU) and Maelstrom Research (Canada) to sustain long-term benefits to science and citizens worldwide.

STOR-HY aims to minimize CAPEX, OPEX for innovative pumped storage projects. This is achieved by enhancing the lifetime and recyclability of components and equipment, and devising operation strategies for unconventional schemes through sensor-based condition monitoring systems. These systems detect early failure mechanisms, enabling the postponement of unnecessary maintenance actions and avoiding unplanned outages. Furthermore, strategic use of digital tools for operational management is employed to improve efficiency, reliability, and availability of Pumped Storage Plants. Considering energy and market demand dynamics, variable renewable generation, and integration, STOR-HY addresses climate change effects and enhances flexibility and resilience of the EU energy grid. The project focuses on optimizing plant availability, offering increased storage potential, peak shaving, fast response regulation, and ancillary services for grid resilience. The integration of digital tools, real-time controllers, monitoring strategies, and predictive maintenance algorithms is consolidated in a Cyber-physical platform for Advanced Decision Support (CADS). This platform, along with high-tech computational models, enables the realistic estimation of critical component degradation in short- and long-term operations. This information supports informed decision-making in PSP operation and aids in the design of innovative control strategies for challenging conditions. These developments result in a broader operating range and increased flexibility in EU hydropower generation and storage potential. STOR-HY prioritizes building regional connections with local stakeholders, industry, academia, and policy institutions. Sustainability considerations encompass environmental, circularity, economic, and social aspects, drawing insights from previous projects. These insights include on-site impacts, societal acceptance, ecological concerns, and LCC.

GREEN.DAT.AI aims to channel the potential of AI towards the goals of the European Green Deal, by developing novel Energy-Efficient Large-Scale Data Analytics Services, ready-to-use in industrial AI-based systems, while reducing the environmental impact of data management processes. GREEN.DAT.AI will demonstrate the efficiencies of the new analytics services in four industries (Smart Energy, Smart Agriculture/Agri-food, Smart Mobility, Smart Banking) and six different application scenarios, leveraging the use of European Data Spaces. The ambition is to exploit mature (TRL5 or higher) solutions already developed in recent H2020 projects and deliver an efficient, massively distributed, open-source, green, AI/FL - ready platform, and a validated go-to-market TRL7/8 Toolbox for AI-ready Data Spaces. The services will cover AI-enabled data enrichment, Incentive mechanisms for Data Sharing, Synthetic Data Generation, Large-scale learning at the Edge/Fog, Federated & Auto ML at the edge/fog, Explainable AI/Feature Learning with Privacy Preservation, Federated & Automatic Transfer Learning, Adaptive FL for Digital Twin Applications, Automated IoT event-based change detection/forecasting. The GREEN.DAT.AI Consortium consists of a multidisciplinary group of 17 partners from 10 different countries (and one associated party), well balanced in terms of expertise. The vast majority of partners already have key roles in a number of projects funded under the Big Data PPP (ICT-16-2017) topic, namely BigDataStack, CLASS, Track & Know, and I-BiDaaS and are serving as active members of the BDVA/DAIRO Association, FIWARE, AIOTI, and ETSI. In addition, partners come from a variety of sectors, such as banking, mobility, energy, and agriculture, constituting a representative workforce of their respective domains, which will contribute to industry adoption and stimulate uptake in other sectors as well.

RENEE envisions flexible remanufacturing using AI and advanced robotics for circular value chains in EU industry. The objective of RENEE is to implement human-centric production systems relying on advanced robotics and AI to accommodate the remanufacturing of diverse states of used products. Additionally, RENEE will accelerate the workforce upskilling/reskilling by deploying a remanufacturing educational platform and a set of operator support technologies. RENEE follows a holistic approach providing product, process and resource-level enablers for remanufacturing applications including: - Circular value chains configuration toolbox: Including software solutions for product state diagnosis and classification, methods for product ‘design for remanufacturability’, Digital Product Passport, and for remanufacturing value chain configuration and planning. - Digital infrastructure for remanufacturing management: including a) tools for the planning and orchestration of flexible remanufacturing systems, b) deployment of Digital Twins customized for the remanufacturing process, and c) tools to enable product traceability and efficient management of the remanufacturing supplier network. - Robot skills & flexible production modules for remanufacturing: Enabling the resource level autonomy for remanufacturing through the deployment of dedicated robot skills libraries, AI methods for decision-support and process optimization as well as advanced robotics integrated in hybrid production specialized in remanufacturing, - Upskilling/reskilling the workforce for remanufacturing: Supporting operators both during the remanufacturing tasks via inclusive interfaces as well as providing the infrastructure (Educational platform) and required material (courses) for training them to meet the requirements of diversified remanufacturing operations. The RENEE modules will be validated in 4 pilots from the household appliances (refrigerators), robotics, and mobility (electrical motors and bikes) sectors