The SkincAIr project aims to develop an innovative AI-driven mobile application to support the early detection and of skin Neglected Tropical Diseases (skin NTDs) in Sub-Saharan Africa (SSA). NTDs significantly affect marginalized communities due to several factors, such as lack of trained healthcare staff and diagnostic tools. The project's objectives are: to improve the accuracy of Front-line Health Workers (FHW) of skin NTD identification, to create the largest public dataset of skin NTDs in the world (first in SSA), to reduce disease transmission through early diagnosis, to enhance real-time epidemiological surveillance, to enhance the knowledge of FHW, to develop novel AI models for skin disease monitoring, to ensure the digital solution is culturally tailored, to ensure compliance with clinical practices, ethical, legal aspects and participant rights, to ensure scalability and broad outreach of SkincAIr’s results, to advocate for greater awareness and policy support for NTDs SkincAIr will equip FHW with an app capable of detecting skin NTDs using advanced machine learning techniques while preserving privacy through geolocation features. The app will facilitate real-time epidemiological surveillance, contributing to improved disease mapping and hotspot identification. The project will be implemented in five SSA countries: Kenya, Senegal, Ethiopia, Nigeria, and the Democratic Republic of the Congo. Aligned with the Work Program, SkincAIr is anchored in the scope of Global Health EDCTP3 and regional strategies, targeting the highest demonstrated medical needs in SSA and addressing context-specific needs. It develops a solution with early-stage involvement of end users and implicated health services. The solution ensures seamless integration and interoperability, is sustainable, accessible, open-source, evidence-based, and compliant with data protection standards and global digital health public goods.

6G is envisioned to accelerate the path started in 5G for catering to the needs of a wide variety of vertical use cases, both current and emerging. This will require major enhancements of the current 5G capabilities especially in terms of bandwidth, latency, reliability, security, and energy. PREDICT-6G’s mission is therefore set towards the development of an end-to-end 6G (E2E) solution including architecture and protocols that can guarantee seamless provisioning of services for vertical use cases requiring extremely tight timing and reliability constraints. To succeed, the solution will target determinism network infrastructures at large, including wired and wireless segments and their interconnections. PREDICT-6G will develop a novel Multi-technology Multi-domain Data-Plane (MDP) overhauling the reliability and time sensitiveness design features existing in current wired and wireless standards. The ambition is for the MDP design to be inherently deterministic. To achieve this, PREDICT-6G will develop an AI-driven Multi-stakeholder Inter-domain Control-Plane (AICP) for the provisioning of deterministic network paths to support time sensitive services as requested by end-customers and with different scaling ambitions, e.g., from the network in a single vehicle to a large, geographically dispersed network. This requires timely monitoring and prediction of the behavior of the complete network, including identifying potential sources of quality violations and analyzing various routes of the traffic flows. These capabilities will be delivered through the PREDICT-6G AI-powered Digital Twin (DT) framework, allowing the prediction of the behavior of the end-to-end network infrastructure, and enabling anticipative control and validation of the network provisions to meet the real-world time-sensitive and reliability requirements of the running services.

In support of the development and adoption of ECCCH by Cultural Heritage Professionals and Researchers (CHPR), ECHOLOT will make the creation, provision, and reuse of high-quality, semantically rich, and interoperable Cultural Heritage (CH) data accessible to scholars and institutions, significantly lowering the threshold for joining the collaborative cloud. ECHOLOT will address the fundamental issue that, while ever larger amounts of diverse CH data are available through CH institutions (CHIs), the active reuse of this data especially in research and the creative sectors, is hampered by poor quality and lack of interoperability. It will achieve this by seamlessly integrating as a core service in the ECCCH that enables the quality curation and enrichment of CH data, including multimedia, through AI-enhanced workflows combining automated processing and human input. Moreover, it will natively support embedding rights metadata and chain-of-production provenance, thereby preserving the value and integrity of CH datasets. ECHOLOT will serve as an interoperability hub facilitating the exchange of CH data between systems. It will enable CHPRs to publish simultaneously to aggregators, such as Europeana, and open knowledge platforms from the Wikimedia ecosystem, as well as contribute to ECCCH and Common European Data Space for Cultural Heritage (DS4CH). Together, these technical innovations will revolutionise CHPR practices and increase the availability of CH data for reuse and collaboration across institutional and sectoral boundaries. ECHOLOT’s solutions will be validated and tested in five case studies presenting a wide spectrum of CH actors. Maximising the adoption of ECHOLOT and ECCCH will be enabled by social and organisational change measures, including innovative business models co-created with relevant stakeholders. Training resources, interactive workshops and open source software best practices will further support capacity building and long-term sustainability.

AI-PRISM is an industrial-end-user driven project that will provide a human-centred AI-based solutions ecosystem targeted to manufacturing scenarios with tasks difficult to automate and where speed and versatility are essential. The result will be an integrated and scalable ecosystem with installation-specific solutions for semi-automated and collaborative manufacturing in flexible production processes and for which specific robotic programming skills will not be required, thanks to its programming-by-demonstration modules. The ecosystem will be composed by four main pillars including 1) Human Centred Collaborative Robotic Platform, 2) Human Robot Cooperation Ambient, 3) Social Human-Agent-Robots Teams Collaboration and 4) Open Access Network Portal. In order to facilitate the assessment of the performance, transferability, scalability and large-scale deployment of these solutions, the demonstrations will be conducted under real operational environments in four pilot involving key manufacturing sectors - Furniture (ES), Food/Beverage (GR), Built-in Appliances (TR) and Electronics (PL) -, plus one generic demonstration facility (AT). The project is not just aiming at quantitative improvements in a specific sector, but to use technology innovation to support a change of paradigm where AI, robotics and Social Sciences and Humanities (SSH) integrated in the manufacturing domain for the improvement of flexible production processes, become a feasible and widespread alternative for European factories, especially SMEs. To achieve this, the project relies on a strong consortium of 25 partners from 12 countries including international cooperation with Korea. The consortium brings together all the actors of the Human Robot Collaboration (HRC) value chain including relevant competence centres, technology providers, equipment providers, integrators, and manufacturers/end users; and involves key expert partners in SSH, standardisation, exploitation, and dissemination.

The primary goal of SMAUG is to improve the underwater detection of threats in ports and their entrance routes, by means of a integrated system capable of providing data concerning threat analysis between 3 main elements: ports security infrastructure, advanced underwater detection systems and surveillance vessels. Underwater detection and location will be performed by four primary methods: i) acoustic detection, where a series of hydrophones will listen for sounds emitted by small underwater vehicles and will be processed by artificial intelligence methods, ii) rapid sonar hull scan, used to scan ships hulls and perform harbour floor scanning, iii) high resolution sonar inspection, to inspect objects in water with poor visibility and iv) collective autonomous location, where a swarm of autonomous underwater vehicles will act cooperatively. This will provide information to Artificial Intelligence modules which will improve the way detecting illicit and dangerous goods and/or of threats hidden below the water surface is currently done, taking into account sources such as Unmanned Surface Vehicle Systems, (USV), underswater remote operation vehicle (ROV), UAV (Aerial autonomous vehicle) and Port current information sources. The combination of these tools will allow SMAUG to prompt solutions capable of detecting possible threats to infrastructure or vessels, as well as identify vessels with concealed goods.