Project Ô intends to demonstrate approaches and technologies to drive an integrated and symbiotic use of water within a specific area, putting together the needs of different users and waste water producers, involving regulators, service providers, civil society, industry and agriculture. The project seeks to apply the pillars of integrated water management (IWM) as a model for “water planning” (akin to spatial planning) and to demonstrate low cost, modular technologies that can be easily retrofitted into any water management infrastructure at district/plant level, hence enabling even small communities and SMEs to implement virtuous practices. Technologies and planning instruments complement each other as the first make possible the second and the latter can provide as example or even prescribe the former (and similar technologies allowing virtuous water use practices). Indeed the technologies support the regulators in implementing policy instruments, as foreseen by IWM, for convincing stakeholders (like developers and industry) to implement water efficiency strategies and could include instruments for e.g. rewarding virtuous behaviours (for example: advantageous water tariffs), planning regulations that award planning consent more swiftly or even prescribe the use of water from alternative sources (including recycling). Project Ô has in summary the overall objective of providing stakeholders (everybody using or regulating the use of water in an area) with a toolkit that enables them to plan the use of and utilise the resource water whatever its history and provenance, obtaining significant energy savings in terms of avoided treatment of water and waste water and release of pressure (quantity abstracted and pollution released) over green water sources. This overall objective will be demonstrated in up to four sites each in different Countries of Europe and in Israel, involving industries, aquaculture and agriculture as well as local authorities of different sizes.

AI4PublicPolicy is a joint effort of policymakers and Cloud/AI experts to unveil AI’s potential for automated, transparent and citizen-centric development of public policies. To this end, the project will deliver, validate, demonstrate and promote a novel Open Cloud platform (i.e. AI4PublicPolicy platform) for automated, scalable, transparent and citizen-centric policy management based on unique AI technologies. The AI4PublicPolicy platform will be an Open Virtualized Policy Management Environment (VPME) that will provide fully-fledged policy development/management functionalities based on AI technologies such as Machine Learning (ML), Deep Learning (DL), NLP and chatbots, while leveraging citizens’ participation and feedback. It will support the entire policy development lifecycle, based on technologies for the extraction, simulation, evaluation and optimization of interoperable and reusable public policies, with emphasis on citizen-centric policies development and optimization through the realization of citizen-oriented feedback loops. AI4PublicPolicy will complement public policy development functionalities with the ever-important process reengineering and organization transformation activities towards ensuring the effective transition from legacy policy development models to emerging AI-based policymaking. The AI4PublicPolicy VPME will be integrated with EOSC with a dual objective. First to facilitate access to the Cloud and HPC resources of EOSC/EGI that are required to enable the project’s AI tools, and second to boost the sustainability and wider use of the project’s developments. AI4PublicPolicy’s business plan for sustaining, expanding and commercializing the AI tools and the VPME is based on the development of a community of interested and engaged stakeholders (i.e. public authorities and other policymakers) around the project’s platform.

XR5.0 will build, demonstrate, and validate a novel Person-Centric and AI-based XR paradigm that will be tailored to the requirements and nature of I5.0 applications. In this direction, the project will specify structuring principles and blueprints for using XR in I5.0 applications with emphasis on the development of innovative “XR-made-in-Europe” technology that blends with human-centric manufacturing technologies and adheres to European values. The XR5.0 applications will consider the characteristics and context of the worker based on the integration of human-centred digital twins (DTs) that comprise the “digital image” of the worker. At the same time, XR5.0 will design and implement a unique blending of XR technology and advanced AI paradigms, including AI technologies that foster the interplay between humans and AI such as explainable AI (XAI), Active Learning (AL), Generative AI (GenAI), and neurosymbolic learning. The XR5.0 technologies will be coupled with a cloud-based XR training platform for Operator 5.0 applications, which will enable ergonomic and personalized training of industrial workers on popular processes. The XR5.0 paradigm will empower the development of six (6) novel high-TRL pilot applications spanning the areas of AI-based product design, remote and intelligent maintenance of assets, workers’ training, support in product assembly, as well as guidance and instructions for troubleshooting. These applications will be demonstrated in realistic manufacturing environments. Moreover, they will be integrated to the EU XR platform to be developed as part of the call. Most importantly, XR5.0 will build a vibrant community of interested stakeholders around the project’s outcomes. This community will provide a basis for the sustainability and wider uptake of the project’s results towards maximising the impact of the project’s use cases. In this direction, all XR5.0 technologies will be high TRL>=7-8 and ready for immediate commercialisation.

A growing number of applications rely on AI-based solutions to carry-out mission-critical tasks, many of which are of temporal nature, dealing with ever-evolving flows of information. Crucial for mitigating threats and taking advantage of opportunities in such domains, is the ability to forecast imminent situations and critical complex events ahead of time. EVENFLOW will develop hybrid learning techniques for complex event forecasting, which combine deep learning with logic-based learning and reasoning into neuro-symbolic forecasting models. The envisioned methods will combine (i) neural representation learning techniques, capable of constructing event-based features from streams of perception-level data with (ii) powerful symbolic learning and reasoning tools, that utilize such features to synthesize high-level, interpretable patterns of critical situations to be forecast. Crucial in the EVENFLOW approach is the online nature of the learning methods, which makes them applicable to evolving data flows and allows to utilize rich domain knowledge that is becoming available progressively, over time. To deal with the brittleness of neural predictors and the high volume/velocity of temporal data flows, the EVENFLOW techniques will rely on novel, formal verification techniques for machine learning, in addition to a suite of scalability algorithms for federated training and incremental model construction. The learnt forecasters will be interpretable and scalable, allowing for fully explainable insights, delivered in a timely fashion and enabling proactive decision making. EVENFLOW will be evaluated on three challenging use cases related to oncological forecasting in precision medicine, safe and efficient behavior of autonomous transportation robots in smart factories and reliable life cycle assessment of critical infrastructure.