AI-EFFECT will establish a European Testing Experimentation Facility (TEF) for developing, testing, and validating AI applications in the energy sector. It will be distributed across nodes, virtually connecting existing European facilities. The solution includes a digital platform leveraging European building blocks for interoperability, flexibility, and scalability. AI-EFFECT aims to be a central hub for testing energy sector AI algorithms, fostering collaboration across utilities, industry, academia, and regulatory authorities. Resilience is ensured through a decentralized design, aligning with the EU Energy Data Spaces framework. The project involves developing 4 use cases/nodes addressing key energy challenges, focusing on district heating, transmission congestion management, DERs integration, and energy communities. The framework involves utilities proposing challenges, vendors developing algorithms, and researchers contributing solutions. Each use case has evaluation criteria, baselines, and benchmarks. AI certification procedures, including interpretability and verification, will be implemented, and the evaluation process will be automated. Benchmarks and certifications are publicly available, encouraging open-source contributions. The project breaks sector barriers, leveraging existing infrastructures and technologies for cross-sectoral collaboration. The platform enforces policies for data quality, integrity, and privacy, promoting controlled data sharing and collaboration. Secure APIs ensure controlled interactions, including risk and security assessments. The consortium explores certification, standardization, and quality requirements in line with the EU AI Act. Governance and business models for the enduring AI-EFFECT will be examined, considering the EU AI Act. The consortium aims to make AI-EFFECT a sustained business beyond initial funding, seeking input from members, other TEFs, and regulatory authorities for the preferred model.

SEAMLESS aims at developing and adapting missing building blocks and enablers into a fully automated, economically viable, cost-effective, and resilient waterborne freight feeder loop service for Short Sea Shipping (SSS) and/or Inland Waterways Transport (IWT). Autonomous systems will be integrated to ensure safe, resilient, efficient, and environmentally friendly operation to shift road freight movements to hinterland waterways, while enhancing the performance of the TEN-T network. The service will be delivered 24/7 by a fleet of autonomous cargo shuttles, with humans-in-the-loop located in Remote Operation Centres (ROCs), which efficiently cooperate with automated and autonomous shore-side infrastructure and safely interact with conventional systems. The services will rely on a redesigned logistics system enabling seamless freight flows by minimising delays at intermodal nodes. A digital bird’s eye view of the supply chain allows the exploitation of real-time information for planning optimisation and reconfiguration to support resilient logistics, incl. digitalised administrative procedures. The SEAMLESS building blocks will be verified and validated by conducting full-scale demonstrations in selected real-world scenarios. Transferability will be fully demonstrated in selected use cases that cover a wide range of transport applications and geographical regions throughout Europe. Based on a structured methodological framework evaluating sustainability criteria, they will act as guidance for the replication of the project results beyond the project scope and timespan. Novel business models will be thus developed and provide a framework for implementing the SEAMLESS service to minimise investment risk for first movers. Regulatory gaps and challenges related to autonomous vessel operation (e.g. social aspects) will be identified, and recommendations for policy makers to allow the smooth and safe deployment of fully automated services will be provided.

The scope of DYNAMO is to combine the two fields of business continuity management (BCM) and cyber threat intelligence (CTI) to generate a situational awareness picture for decision support across all stages of the resilience cycle (prepare, prevent, protect, response, recover). Professionals of different backgrounds will work together with end-users to develop, refine and combine selected tools into a single platform. In alignment to end-user needs, human factors, high ethical standards and societal impacts, DYNAMO includes the following goals: Resilience assessment as basis for BCM - An assessment with different levels of detail offers with varying existent data a fast or detailed evaluation of the investigated sector and helps to identify critical processes. - End-user data will be integrated to measure determined performance targets. With respect to the functional description, AI-based approaches will be used for a deeper understanding and potential self-learning of the interconnected process. - The results generate knowledge concerning susceptibility and vulnerability of the investigated sector. - The solutions support the BCM with respect to the five resilience phases. Leveraging CTI - CTI will be improved with respect to existing solutions (H2020 ECHO, PANACEA). - The H2020 Early Warning System (EWS) will be extended and integrated. A Malware Information Sharing Platform (MISP) will be used to raise the situational awareness between different security actors. - The CTI approach deliver data that will be integrated into the resilience and BCM approach. The use of AI will support the development. Solutions will be integrated with the Cyber Knowledge Graph to visualize the analysis of threat intelligence. The DYNAMO platform will be able to collect organization’s skills data, elaborate and create custom tailored organisational training to improve organisational resilience which will be demonstrated within three different (cross-)sectoral use-cases.

The scope of AI4REALNET covers the perspective of AI-based solutions addressing critical systems (electricity, railway, and air traffic management) modelled by networks that can be simulated, and are traditionally operated by humans, and where AI systems complement and augment human abilities. It has two main strategic goals: 1) to develop the next generation of decision-making methods powered by supervised and reinforcement learning, which aim at trustworthiness in AI-assisted human control with augmented cognition, hybrid human-AI co-learning and autonomous AI, with the resilience, safety, and security of critical infrastructures as core requirements, and 2) to boost the development and validation of novel AI algorithms, by the consortium and AI community, through existing open-source digital environments capable of emulating realistic scenarios of physical systems operation and human decision-making. The core elements are: a) AI algorithms mainly composed by supervised and reinforcement learning, unifying the benefits of existing heuristics, physical modelling of these complex systems and learning methods, as well as, a set of complementary techniques to enhance transparency, safety, explainability and human acceptance; b) human-in-the-loop decision making for co-learning between AI and humans, considering integration of model uncertainty, human cognitive load and trust; c) autonomous AI systems relying on human supervision, embedded with human domain knowledge and safety rules. The AI4REALNET framework will be validated in 6 uses cases driven by industry requirements, across 3 network infrastructures with common properties. The use cases are focused on critical challenges and tasks of network operators, considering strategic long-term goals, such as decarbonisation, digitalisation, and resilience to disturbances, and are formulated in a unified sequential decision problem where many AI and non-AI algorithms can be applied and benchmarked.

Most maritime products are typically associated with large investments and are seldom built in large series. Where other modes of transport benefit from the economy of series production, this is not the case for maritime products which are typically designed to refined customer requirements increasingly determined by the need for high efficiency, flexibility and low environmental impact at a competitive price. Product design is thus subject to global trade-offs among traditional constraints (customer needs, technical requirements, cost) and new requirements (life-cycle, environmental impact, rules). One of the most important design objectives is to minimise total cost over the economic life cycle of the product, taking into account maintenance, refitting, renewal, manning, recycling, environmental footprint, etc. The trade-off among all these requirements must be assessed and evaluated in the first steps of the design process on the basis of customer / owner specifications. Advanced product design needs to a