Aligned with the guidelines of the Cybersecurity Act and the existing guidance on cybersecurity for medical devices, ENTRUST envisions a Trust Management Architecture intended to dynamically and holistically manage the lifecycle of connected medical devices, strengthening trust and privacy in the entire medical ecosystem. Even from the proposal stage, ENTRUST has identified gaps and necessary revisions of the current guidance (e.g., absence of post-market conformity and certification, real-time surveillance and corrective mechanisms – see 1.2.2). Towards that ENTRUST will leverage a series of breakthrough solutions to enhance assurance without limiting the applicability of connected medical devices by enclosing to them cybersecurity features. The project will introduce a novel remote attestation mechanism to ensure the device’s correct operation at runtime regardless of its computational power; will be efficient enough to run in also resource-constrained real-time systems such as the medical devices. This will be accompanied by dynamic trust assessment models capable of identifying the Required Level of Trustworthiness (RTL) per device and function (service) that will then be verified through a new breed of efficient, attestation mechanisms (to be deployed and executed during runtime). This will also enable us to be aligned with the existing standards on defining appropriate Protection profiles per device (especially considering the heterogeneous types of medical devices provided by different vendors with different requirements) including Targets of Validation Properties to be attested during runtime. The motivation behind ENTRUST is to ensure end-to-end trust management of medical devices including formally verified trust models, risk assessment process, secure lifecycle procedures, security policies, technical recommendations, and the first-ever real-time Conformity Certificates to safeguard connected medical devices.

Circular TwAIn will lower the barriers for all the stakeholders in manufacturing and process industry circular value chains to adopt and fully leverage of trusted AI technologies, in ways that will enable end-to-end sustainability, i.e. from eco-friendly product design to the maximum exploitation of production waste across the circular chain. To this end, the project will research, develop, validate and exploit a novel AI platform for circular manufacturing value chains, which will support the development of interoperable circular twins for end-to-end sustainability. Circular TwAIn will unlock the innovation potential of a collaborative AI-based intelligence in production based on the use of cognitive digital twins. Moreover, based on the use of trustworthy AI techniques, Circular TwAIn will enable human centric sustainable manufacturing, fostering the transition towards Industry 5.0. Furthermore, Circular TwAIn will enable the integration and combination of different data from various sources over entire product life cycle considering sustainability aspects. The goal is to create and deliver innovative services among the members of the data ecosystem; these services will be embedded in AI-based Digital Twins, supporting an unambiguous communication when realizing complex services for sustainable manufacturing. The ambition of Circular TwAIn is to unleash the sustainability potential of AI technologies in circular manufacturing chains through: (i) Introducing AI optimizations in stages where AI is still not used (e.g., AI-based product design); (ii) Using AI for multi-stage and multi-objective circular optimizations that could improve sustainability performance. In this direction, the project will leverage information from a circular manufacturing dataspace that will provide access to the datasets needed for multi-stage and multi-objective optimizations.

AI-DAPT brings forward a data-centric mentality in AI, that is effectively fused with a model-centric, science-guided approach, across the complete lifecycle of AI-Ops, by introducing end-to-end automation and AI-based systematic methods to support the design, the execution, the observability and the lifecycle management of robust, intelligent and scalable data-AI pipelines that continuously learn and adapt based on their context. AI-DAPT will design a novel AI-Ops / intelligent pipeline lifecycle framework cross-cutting the different business, legal/ethics, data, AI logic/models, and system requirements while always ensuring a human-in-the-loop (HITL) approach across five axis: “Data Design for AI”, “Data Nurturning for AI”, “Data Generation for AI”, “Model Delivery for AI”, “Data-Model Optimization for AI”. AI-DAPT will contribute to the current research and advance the state-of-the-art techniques and technologies across a number of research paths, including sophisticated Explainable AI (XAI)-driven data operations from purposing, harvesting/mining, exploration, documentation and valuation to interoperability, annotation, cleaning, augmentation and bias detection; collaborative feature engineering minimizing the data where appropriate; adaptive AI for model retraining purposes. Overall, AI-DAPT aims at reinstating the pure data-related work in its rightful place in AI and at reinforcing the generalizability, reliability, trustworthiness and fairness of Al solutions. In order to demonstrate the actual innovation and added value that can be derived through the AI-DAPT scientific advancements, the AI-DAPT results will be validated in two, interlinked axes: I. Through their actual application to address real-life problems in four (4) representative industries: Health, Robotics, Energy, and Manufacturing; II. Through their integration in different AI solutions, either open source or commercial, that are currently available in the market.

Even if significant progress has been made towards the Twin Transition, the recent energy crisis revealed the EU energy systems vulnerability and dependence on external energy sources and highlighted the need for intensifying the integration of RES in electricity, transport and building (heating) sectors. To achieve on this, the energy system shall transform from a centralised/fossil-fuel-based to an energy efficient, RES-based and interdependent system, operating with a high degree of flexibility offered by distributed assets. ODEON is conceived under the principle that this can only be realized through the creation of an inclusive ecosystem of stakeholders characterized a mesh of Data, Intelligence, Service and Market flows, jointly enabling the resilient operation of the energy system under increased RES integration and distributed flexibility. ODEON introduces a sound, reliable, scalable and openly accessible federated technological framework (i.e. ODEON Cloud-Edge Data and Intelligence Service Platform and corresponding Federated Energy Data Spaces. AI Containers, Smart Data/AIOps orchestrators) for the delivery of a wealth of services addressing the complete life-cycle of Data/AIOps and their smart spawn in federated environments and infrastructures across the continuum. It will integrate highly reliable and secure federated data management, processing, sharing and intelligence services, enabling the energy value chain actors and 3rd parties to engage in data/intelligence sharing, towards the delivery of innovative data-driven and intelligence-powered energy services in accordance to the objectives set by the DoEAP. ODEON results will be extensively validated in 5 large-scale demonstration sites in Greece, Spain, France, Denmark and Ireland involving all required value chain actors, diverse assets, heterogeneous grid and market contexts, and multi-variate climatic and socio-economic characteristics to support its successful replication and market uptake.

TwinERGY will introduce a first-of-a-kind Digital Twin framework that will incorporate the required intelligence for optimizing demand response at the local level without compromising the well-being of consumers and their daily schedules and operations. The main idea behind the conception of the TwinERGY project lies on the interest of the project partners to exploit the new business opportunities that project implementation delivers and increase the relevance of the DR optimization tools and strategies in the new generation of energy management systems. By coupling mature practice for citizen engagement with service innovation through the lenses of public value, TwinERGY will ensure that a wide range of interests and especially of consumers/prosumers will be represented and supported in the energy marketplace. In this context, TwinERGY will develop, configure and integrate an innovative suite of tools, services and applications for consumers, enabling increase of awareness and knowledge about consumption patterns, energy behaviours, generation/ demand forecasts and increase of local intelligence via properly established Digital Twin-based Consumer-Centric Energy Management and Control Decision Support mechanisms that locally optimize demand response. Key use cases will be trialed across 4 pilot regions making use of cutting-edge methods and tools. Special focus is given on standardization and policy & market reform as key enablers for the successful commercialization of the TwinERGY results. Additional attention is given in establishing knowledge transfer and exchange synergies with similar projects listed under the BRIDGE Initiative, while explicit focus will be given on the establishment of close collaboration with the projects funded under the LC-SC3-ES-5-2018 topic, to further reinforce and catalyze collaborative advancements in research, innovation, regulatory and market issues around Demand Response, RES Integration and Consumer Engagement.