Several products embed different types of electronic components, and they are even more fundamental in some of the European strategic markets (e.g. automotive). However, reference producers come from extra-EU countries in the far-east side of the world (e.g. China and Taiwan). Trying to cope with all these challenges and the current semiconductors crisis, the European Commission (EC) published (and in some cases is still working on) specific EU strategies/directives for automotive, e-waste (e.g. Digital Product Passport) and, specifically, semiconductors (e.g. European Chips Act). However, trying to make these sectors more sustainable, circular and resilient, it is mandatory to boost both EoL strategies (e.g. sorting, reuse, remanufacturing and recycling) and intra-EU production through innovations and investments. The current international scenario represents a good chance to decouple the European economy from both natural resource depletion (e.g. Critical Raw Materials - CRMs) and dependency from extra-EU supplies of strategic products. In order to better prove what the benefits are of a joined circular/resilient use of secondary resources, the automotive and mass electronics sectors have been identified as the reference contexts for establishing a set of innovative solutions. To this aim, the CIRC-UITS project will focus on demonstrate the improvement to the circularity of automotive and mass electronics sectors by reuse of semiconductors from different sources, as well as support the reuse & remanufacturing of semiconductors into new (high added-value) components and products in these sectors.

The modern manufacturing landscape is undergoing a transformative twin transition, integrating green and digital technologies to bolster value chain resilience and explore re-shoring options. Amidst this paradigm shift, concerns about job displacement by machines, particularly through the rise of Artificial Intelligence (AI) and Machine Learning (ML), have become more pronounced. This proposal delves into the challenges posed by the pursuit of excellence in the Industry 5.0 framework, focusing on the potential impact of digital technologies on job nature and the ensuing need for human-technology complementarity. The potential for AI to exacerbate social disparities and inequalities, especially for vulnerable groups, is also a significant concern. Additionally, the manufacturing sector faces labor shortages, impacting innovation capacity and economic competitiveness. The mission of SKillAIbility is to address these challenges, emphasizing the need for a human-centric approach to assess digital technologies and enhance workers' employability. The proposal also outlines the short, medium, and long-term contributions of SKillAIbility towards a resilient, inclusive, digital transition in the manufacturing industry. This initiative provides tools and methodologies to understand and respond to the impacts of emerging digital technology advancements on human tasks, skills, training, and policymaking. SKillAIbility's holistic impact spans various societal, industrial, academic, and regulatory dimensions, affecting citizens, people with disabilities, workers, trade unions, industry players, research institutions, and governmental bodies. Through upskilling and reskilling initiatives, SKillAIbility aims to empower citizens and workers, mitigate the risk of task automatization, and increase employability within the advanced manufacturing sector. Furthermore, the proposal contributes to the manufacturing industry's growth by filling skills gaps, increasing performance and pro

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.

The project aerOS aims at transparently utilising the resources on the edge-to-cloud computing continuum for enabling applications in an effective manner, incorporating multiple services deployed on such a path. Therefore, aerOS will establish the missing piece: a common meta operating system that follows a collaborative IoT-edge–cloud architecture supporting flexible deployments (e.g., federated or hierarchical), bringing tremendous benefits as it enables the distribution of intelligence and computation – including Artificial Intelligence (AI), Machine Learning (ML), and big data analytics – to achieve an optimal solution while satisfying the given constraints. The overarching goal of aerOS is to design and build a virtualized, platform-agnostic meta operating system for the IoT edge-cloud continuum. As a solution, to be executed on any Infrastructure Element within the IoT edge-cloud continuum – hence, independent from underlying hardware and operating system(s) – aerOS will: (i) deliver common virtualized services to enable orchestration, virtual communication (network-related programmable functions), and efficient support for frugal, explainable AI and creation of distributed data-driven applications; (ii) expose an API to be available anywhere and anytime (location-time independent), flexible, resilient and platform-agnostic; and (iii) include a set of infrastructural services and features addressing cybersecurity, trustworthiness and manageability. aerOS will: (a) use context-awareness to distribute software task (application) execution requests; (b) support intelligence as close to the events as possible; (c) support execution of services using “abstract resources” (e.g., virtual machines, containers) connected through a smart network infrastructure; (d) allocate and orchestrate abstract resources, responsible for executing service chain(s) and (e) support for scalable data autonomy.

The European economy's Digital Transformation (DT) is crucial for preserving and boosting international competitive advantages (Digital European Programme). A green and sustainable transformation is a vital step for protecting the health and wellbeing of citizens from environment-related risks and impacts where digital techs are considered a critical enabler for attaining the European Sustainable Development Goals (SDGs). It is urgent to define innovative R&D models to systematically integrate artistic collaboration in Digital Innovation Hubs to provide companies with new forms of transdisciplinary collaboration aiming to explore future scenarios for technologies application using artistic practice to anticipate innovative products and services to meet the SDGs for innovation towards a sustainable and healthy planet. The project aims to set up the MUSAE Factory Model based on the Design Future Art-driven (DFA) method to be included in the (E)DIHs to strategically guide digital technology innovation and address future challenges in the food domain to improve people and planet wellbeing. MUSAE will build on a DFA method that merges the Design Futures method by POLIMI with Art Thinking approach by STARTS partner Gluon and UB - School of Art. The DFA will help artists envision future scenarios (5-10 years), critically reflect on them, and collaborate with technology providers to develop new technological solutions that meet the future humanity needs with a human-centred approach, opening up new markets and activities. Four technological partners will empower the approach by bringing relevant expertise in Artificial Intelligence-UB, wearables-ABACUS, robotics-PAL, human-machine-interaction-UoM. To validate replicability, the MUSAE project will set up and activate one factory within the DIH partner (MADE) and create the Factory Model Pack that will allow other DIHs to adopt it. The project focusing on Food as Medicine includes an expert in food and wellbeing themes (UCD)