The project objective of the project HiPERFORM is based on the investigation of industrial applicability of high-performance semiconductors with wide-band gap materials in the field of Smart Mobility. For this purpose, a holistic approach is selected that includes the entire supply chain - from the manufacturer of semiconductors as well as power modules through suppliers of development methods and tools to the system manufacturer and ultimately the vehicle manufacturer. The integration of academic partners with a high level of competence in these domains completes this approach. On the other hand, specific requirements for power electronics are addressed in specific application areas, which include both power inverters in the vehicle, electrical charging modules inside and outside the vehicle, as well as the associated development and test systems. The high performance spectrum of wide-band gap semiconductors and the resulting potential for improvement and savings within the concrete applications of the electrified power train contribute to a substantial saving of CO2 in transport and thus support the achievement of the set climate targets in Europe. The jointly planned objectives and research activities will further strengthen European research and industry partners in the field of electronic components and systems. Besides Semiconductor manufacturing capabilities, the project requires also high capabilities in Cyber Physical Systems and Design Technologies and supports the domain Smart Mobility and Smart Energy as well.

COGNIPLANT project will develop and demonstrate an innovative approach for the advanced digitization and intelligent management of the process industries. This approach will be based on a novel vision to data monitoring and analysis, that will make the most of the latest developments on advanced analytics and cognitive reasoning, coupled with a disruptive use of the Digital Twin concept to improve Production plants’ operation performance by up to 68% in real time control of the productive environment, 65% in quality control of the final products and 70 % in response time to uncontrolled incidents. The concept will be implemented by four end-users from four different SPIRE industries, one chemical industry in Austria, one aluminum refinery in Ireland, one concrete manufacturing industry in Italy and one metal industry in Spain. The COGNIPLANT solution will provide a hierarchical monitoring and supervisory control that will give a comprehensive vision of the plants’ production performance as well as the energy and resource consumption. Advanced data analytics will be applied to extract valuable information from the data collected about the processes and their effect on the production plant’s overall performance enabling to design and simulate operation plans in digital twin models based on the conclusions. As a result, optimal operation plans will be obtained that will improve the performance of those cognitive production plants. In addition, the project will demonstrate the positive impact derived from the implementation of COGNIPLANT solution that will allow industries reducing their CO2 emissions up to 20%. A training strategy will be designed to provide a comprehensive framework for the dissemination of the project outcomes and a clear understanding of the new solution for the employees of the SPIRE sectors.

AISym4Med aims at developing a platform that will provide healthcare data engineers, practitioners, and researchers access to a trustworthy dataset system augmented with controlled data synthesis for experimentation and modeling purposes. This platform will address data privacy and security by combining new anonymization techniques, attribute-based privacy measures, and trustworthy tracking systems. Moreover, data quality controlling measures, such as unbiased data and respect to ethical norms, context-aware search, and human-centered design for validation purposes will also be implemented to guarantee the representativeness of the synthetic data generated. Indeed, an augmentation module will be responsible for exploring and developing further the techniques of creating synthetic data, also dynamically on demand for specific use cases. Furthermore, this platform will exploit federated technologies for reproducing un-indentifiable data from closed borders, promoting the indirect assessment of a broader number of databases, while respecting the privacy, security, and GDPR-compliant guidelines. The proposed framework will support the development of innovative unbiased AI-based and distributed tools, technologies, and digital solutions for the benefit of researchers, patients, and providers of health services, while maintaining a high level of data privacy and ethical usage. AISym4Med will help in the creation of more robust machine learning (ML) algorithms for real-world readiness, while considering the most effective computation configuration. Furthermore, a machine-learning meta-engine will provide information on the quality of the generalized model by analyzing its limits and breaking points, contributing to the creation of a more robust system by supplying on-demand real and/or synthetic data. This platform will be validated against local, national, and cross-border use-cases for both data engineers, ML developers, and aid for clinicians’ operations.

AIMS5.0, a collaborative Innovation Action aims at strengthening European digital sovereignty in comprehensively sustainable production, by adopting, extending and implementing AI-enabled hardware and software components and systems across the whole industrial value chain to further increase the overall efficiency. Vulnerability of existing supply chains in crisis shows the need for shorter supply chains and for keeping production in Europe. AI enabled fabs will be given more output and higher sustainability, which makes them more competitive on a global scale. New technologies from IoT and based on semantic web ontologies, ML and AI will help to enable the transformation from Industry4.0 to Industry5.0, to create human-centric workplace conditions and to enable the transformation of European industry to climate-friendly production. Above all, sustainability and resilience will be improved. In essence, AIMS5.0 will deliver: - AI-enabled electronic hardware components & systems for sustainable production - AI tools, methods & algorithms for sustainable industrial processes - SoS-based architectures & micro-services for AI-supported sustainable production - Semantic modelling & data integration for an open access productive sustainability platform - Acceptance, trust & ethics for explainable industrial AI leading to human-centered sustainable manufacturing 20 use cases in 9 industrial domains resulting in high TRLs will validate the project’s findings in an interdisciplinary manner. A professional dissemination, communication, exploitation and standardisation will ensure the highest impact possible. For the first time a joint approach for implementing AI and AI-enabled hardware will be developed that overarches different industrial domains. AIMS5.0 will result in lower manufacturing costs, increased product quality through AI-enabled innovation, decreased time-to-market and increased user acceptance of versatile technology offerings. They will foster a sustainable development, in an economical, ecological and societal sense and act as enablers for the Green Deal and push the industry towards Industry5.0. The innovations will leverage the experience of the 53 partners, such as renowned OEMs, Tier-1 and Tier-2 suppliers, technology and application large enterprises and SMEs, supported by academic research specialists in fields like AI, industrial hard-ware and software, decision making and management algorithms. Specific outcomes of the project are - 20% faster time to market, - Participation of disabled people in the factory environment > 5% (in relation to the total number of employees employed in production), - AI based MES capability > 10 %, - Increased user awareness and trust by 10%, - Subsequent reduction of environmental footprint for wafer transport, handling and storage > 20 %, - 50% reduction of time for monitoring industrial equipment. AIMS5.0 is a pan-European initiative to boost industrial competitiveness through interdisciplinary innovations, establishing sustainable ECS value chains and therefore contribute to European Digital Sovereignty addressing urgent issues like Security of Supply, Monitoring and Crisis Response, and Chip Shortage.

DigiChecks proposes to build a digital framework that implements the following steps to overcome the challenges mentioned and pave the way to a more streamlined approach to manage and process permits: Step 1: Standardized Permit Ontology. The first step is to create a shared language for permitting. This language, formalized in a permit ontology, enables the framework to map data from various sources into a common structure and make it processable by a computer in a repeatable manner. Step 2: Digitizing Permit Processes. To deal with the many different actors and their respective processes for permitting, DigiChecks proposes to develop a tool, based on OMG standards, where these actors can model their processes into DigiChecks. These process models can be updated and or removed when the processess change. Step 3: Building Permit Rules. DigiChecks' proposed solution contains the ability for permitting authorities to build their own rules. These rules are used as a base for an automated compliancy checker. Step 4: Integration of the previous steps into a Permit Service (API). To transform the solution into a service, DigiChecks combines steps one (Permit Ontology), two (Permit Process) and three (Permit Rules) into a service offered through an (Open) API. The DigiChecks Permit Service API implements the concepts from the ontology to defined rules and these rules are mapped to a process, thus digitizing the permit workflow. By having an accessible Permit Service, third party developers will be enabled to create new, innovative and reliable permitting applications. The ultimate objective of the solution is to provide flexibility, ease-of-use and efficiency to the permit validation and approval system in the construction project environments. A solution framework is thus required that allows - regardless of the country, region or municipality -, an easy interoperability with the tools commonly used in construction.