Collaborative work is pivotal in the development work that the consortium proposes through the PANTERA CSA. Through this coordinated activity the consortium is confident that it will deliver a multi-dimensional platform of pan-European status and influence capable of leveraging coherence and trust as a pull towards enhanced R&I in energy systems centered around an integrated grid active and responsive. This proposed platform, can work for Local Energy Systems in an integrated PAN European Smart Grid with specific emphasis on the less privileged / low spending countries. It will bring together the attractiveness of successful partnerships being national, regional or European building through them the will for enhanced adaption to areas and partnerships that can broaden active participation for mutual benefit. Emphasis will be given to develop an innovative top-down and bottom-up approach for effectively identifying the key challenges in accelerating R&I activities in low spending countries. Formalize a governance structure capable of delivering targeted objectives that will bring under the same umbrella all active entities / stakeholders in the field of smart grids / storage and local energy systems to leverage synergies and maximize benefits. Develop enhanced knowledge-sharing mechanisms that help identify, discuss and structure the key R&I challenges. Deliver through the platform ready-made tools that will facilitate the collection of real data / results from on-going projects, build a useful shareable data repository, capable of supporting case studies of exploitable results, scenario building and local energy system analysis accessible by all interested stakeholders. Organize dedicated workshops which facilitate exchanges of experience and capacities between members of R&I community in collaboration with already on-going activities aiming to wider participation, strengthen objectives and extent impact of achieved results.

In recent years, the digital environment and digital transformation of enterprises of all sizes have made AI-based solutions vital to mission-critical. AI-based systems are used in every technical field, including smart cities, self-driving cars, autonomous ships, 5G/6G, and next-generation intrusion detection systems. The industry's significant exploitation of AI systems exposes early adopters to undiscovered vulnerabilities such as data corruption, model theft, and adversarial samples because of their lack of tactical and strategic capabilities to defend, identify, and respond to attacks on their AI-based systems. Adversaries have created a new attack surface to exploit AI-system vulnerabilities, targeting Machine Learning (ML) and Deep Learning (DL) systems to impair their functionality and performance. Adversarial AI is a new threat that might have serious effects in crucial areas like finance and healthcare, where AI is widely used. AIAS project aims to perform in-depth research on adversarial AI to design and develop an innovative AI-based security platform for the protection of AI systems and AI-based operations of organisations, relying on Adversarial AI defence methods (e.g., adversarial training, adversarial AI attack detection), deception mechanisms (e.g., high-interaction honeypots, digital twins, virtual personas) as well as on explainable AI solutions (XAI) that empower security teams to materialise the concept of “AI for Cybersecurity” (i.e., AI/ML-based tools to enhance the detection performance, defence and respond to attacks) and “Cybersecurity for AI” (i.e., protection of AI systems against adversarial AI attacks).

ELIXIRION will create the first fully-integrated, inter- and multi-disciplinary, highly-innovative training and research network that will set the foundations of the emerging Healthcare 4.0 paradigm by leveraging 6G technologies targeting to: i) provide all citizens/patients with a wide range of services of different requirements, such as ultra-low latency for latency-critical applications, high speed for data hungry services and ubiquitous secure access to healthcare resources, anytime, anywhere, respecting all privacy aspects, and ii) ensure a secure, efficient, and profitable healthcare ecosystem to all involved stakeholders, while creating a sustainable open market easing access to new players. To achieve the aforementioned objective, ELIXIRION will: 1) leverage a gamut of 6G technologies towards a powerful interconnected network for ultra-high performance access to the healthcare ecosystem targeting up to 99,99999% reliability, 100% coverage, down to sub-ms E2E latency, up to 1 Tbps capacity, high energy- and cost-efficiency, while supporting a massive number of connections, 2) design edge-aware algorithms leveraging diverse computing capabilities offering ultra-fast task execution through parallelization, serverless and distributed computing by intra- and inter- edge node orchestration techniques for real-time mission critical healthcare applications, 3) provide an E2E slicing and zero-touch orchestration framework for optimized 6G network performance across the healthcare ecosystem, targeting at full network automation and secure information handling especially when a massive number of medical devices is considered, by leveraging AI/ML, while considering all different network parts and data analytics to derive useful information helping in the decision-making process, and 4) create a sustainable healthcare ecosystem and new business models leveraging blockchain-based incentive engineering for secure incentivized collaboration among the involved stakeholders.

The main aim of FAITH is to apply the latest Artificial Intelligence (AI) and Big Data analytics techniques to better model and predict disease/treatment trajectories of cancer patients, with the goal of improving their quality of life and aftercare. To protect privacy of the individual, but still gain insights that are beneficial to the broader population, FAITH will be applying the concept of federated machine learning, which makes it possible to build machine learning systems without direct access to personal treatment data that will be used for training in machine learning. Devices private to the patient will run their own personalised AI models, via the project’s ‘AI Angel’ application, while a global AI model aggregates the individual model learnings (rather than the traditional approach of a central repository of holding all private patient data). FAITH’s ‘AI Angel’ will remotely analyse depression markers, predicting negative trends in their disease trajectory, giving their healthcare providers advanced warnings to allow for timely intervention. These markers are treated under several distinct categories: Activity, Outlook, Sleep, and Appetite, in accordance with the 3M strategy for population health: Monitor–Measure–Manage. Central to the vision of the FAITH project is to measure population health deeply, it is necessary to monitor individuals on a continuous basis to cast a wide enough net over a user’s health data. A key strength of FAITH is the involvement of eminent cancer hospitals and specialists in the consortium to provide relevant applicable cancer care related use cases that can effectively leverage a big data framework using computational intelligence approaches and methodologies that can be used for long term cancer care health risk and symptom minimisation for patients. FAITH has trial sites in Madrid, Waterford, and Lisbon, with real end users to assess and validate the adoption and usage of the FAITH technologies and platform.

The iAMP-Hydro project will improve the digital operation of existing plants through the development of 6 expected results (R) which will collectively form iAMP - a novel intelligent Asset Management Platform (see result R5 in Fig. 1) encompassing secure open and transparent data sharing protocols (R4) and three novel digital solutions: R1. Condition monitoring and predictive maintenance modelling; R2. Ecological status monitoring and water management; R3. Improved weather and flow forecasting. The full package of digital solutions will be validated at a diverse set of five real-world existing hydropower plants producing evidence for policy making to support the green and digital transition of hydropower (R6). The existing plants include differing power capacities, electro-mechanical equipment type, water end-use, flow and head regimes, climatic conditions, and environmental sensitivities (biodiversity). The project will increase the technology competitiveness of existing hydro by reducing O&M costs by 5-10%, improving generation and revenues, increasing flexibility and data-driven decision making in hydropower operations. It will also increase the market penetration of renewables in the grid by 8.4 TWh, and getting closer to the EU 2030 Climate and Energy targets, and EU green deal. iAMP-Hydro will improve environmental and socio-economic sustainability of the existing hydropower fleet by reducing operating costs by €1 billion per annum, reducing CO2 emissions by 1260 tonnes, creating 10,000 future-proof jobs, and enabling environmentally sustainable flow regulation using digital solutions. The project will advance the scientific basis for hydropower digitalization by developing, validating and providing a roadmap for the further development of 5 new digital technologies. We will produce 10 peer reviewed journal publications and 20 conference publications.