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)

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

The reduction of manufacturing waste is a key challenge to achieve a EuropThe reduction of manufacturing waste is a key challenge to achieve a European transition towards climate neutrality and the goals of the Green Deal. In this context, to achieve the zero-defect manufacturing paradigm has becomes a must for those European productive companies that wants to take the step towards sustainable production. Composites play an important role in strategic European manufacturing sectors like energy, aerospace, naval or automotive, with a growing forecast. So, FLASH-COMP project will develop a human-oriented solution, capable of identifying in an early-manner feasible defectiveness in composite parts manufacturing and to determine if corrective actions should be adopted. All with the ultimate goal of optimizing the composite manufacturing process, reaching zero-defects and consequently, reducing the generation of waste ?process and product associated. FLASH-COMP will develop non-destructive inspection and monitoring instruments to retrieve key process parameters. Will develop an on-line Defect Severity Estimation Tool that will predict the appearance of defects and will estimate their severity to suggest the optimum Feedback and Feedforward control strategies. And will develop a decision support system. In addition, an interoperable software platform for data-sharing will be created. The full FLASH-COMP solution will be demonstrated in a test bed, enabling the experimental validation of the FLASH-COMP concept, and 2 industrial use cases, assuring its future replicability, and paving the way for a future exploitation of results. FLASH-COMP will include a complete skills development training program since the FLASH-COMP solution has been conceived to assist the operator in controlling and making decisions about the manufacturing process. Finally, the project will deal with the development of a standardization roadmap, to ensure the widest possible impact of project results

P2CODE envisions the design and development of an open platform for the deployment and dynamic management of end user applications, over distributed, heterogeneous and trusted IoT-Edge node infrastructures, with enhanced programmability features and tools at both the network infrastructure level and the service design and operational level. The platform is implemented following three innovative design approaches: i) The deployment and management of the applications is conducted by an orchestration framework that follows a vertical layered approach from the end user interface to the infrastructure management while spanning horizontally across the device-edge-core-cloud continuum. The deployment follows the user-defined networking and operational features of the application in its northbound interface and a tight integration with state-of-the-art IoT, edge/cloud computing, and networking platforms in its southbound interface through a well-define driver API framework. With this approach the full programmability and reconfigurability of resources across the continuum is enabled. ii) An open and extensible, programming toolset facilitates application development and deployment for large swarms of devices at the edge through a multi-role Internal Developer Platform (IDP) and new feature development and testing, iii) A secure and trusted framework for registering and authenticating IoT device and edge nodes entering the system as well as the data sharing and application deployment. The concept is tested and validated over a mature testing environment that integrates diverse IoT application areas in smart logistics, manufacturing, utility inspection, and community PPDR over a programable infrastructure extended to O-RAN, 5G, SDN enable core Cloud. The consortium addresses all the required development sectors from the platform technology innovations, to supported IoT infrastructure and applications, including the end user interfacing and resource management intelligence.

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.