SC4EU, a collaborative Innovation Action aims at strengthening European digital sovereignty by mitigation of the chip shortage through reduction of bullwhip effect in the semiconductor industry and supply chains containing semiconductors. This will be reached via a “truer”-demand signal gained from an anonymous MPC (Multi-Party Computation) survey on coarse granularity which will be broken down via AI methods to fine granularities following the semantic web based digital reference structure. The bullwhip effect has led to a range of negative outcomes, including excessive inventory, inventory write-offs, decreased revenue, workforce reduction, and ultimately, significant shortages, as observed in the last COVID years, during the financial crisis in 08/09 and during the .com crisis in the Zero Years. The ambition of SC4EU consortium is to overcome these obstacles and to obtain high-quality, reliable data for semiconductor demand forecasting. In the solution proposed by SC4EU, data should be gathered via an anonymous survey based on Multi-Party Computing technology. Anonymity and security of data flow will encourage business partners to share their true demand data. Then, the gathered data will be mapped onto ontologies (semantic representations of the semiconductor industry) and processed with AI tools for demand breakdown of fine granularity.

The COVID pandemic has triggered drastic changes in the delivery of education and knowledge transfer at global scale. Two dramatic changes have been observed so far. Firstly, the education sector was transformed with the deployment of e-learning and blended learning approaches. Secondly, businesses shifted their work practices using remote work practices. Both phenomena have been widely observed and research in the literature for decades, while there are numerous case studies discussing how universities and organisations use technology to overcome barriers associated with the lack of face-to-face interaction. Therefore, once the pandemic arrived, there were several perspectives arguing that both Universities and Organisations should demonstrate a relatively high level of readiness for a seamless transition towards full deployment of e-learning and e-work. After more than six months of the new ways of studying and working, it is observed that there are significant problems with regards to the effectiveness of the adopted practices. The primary source of these problems is that the study and work cannot be supported by a blended mode, where some (even minimal) face to face contact is possible. In blended learning and working modes, the face-to-face contact serves as a support mechanism for (i) enhanced communication, (ii) effective collaboration and (iii) efficient coordination. As a result of the pandemic Universities already report a number of issues associated with the transition to a fully online delivery including:Significantly reduced engagement with synchronous sessions.Increased miscommunication and confusion regarding learning tasks.Inconsistent achievement between individuals depending on their ability to seek and find help.Low progression rates due to inability to effectively use available resources.Lack of motivation as learners find it difficult to self-regulate their study. The impact for knowledge transfer projects is also evident in organisations, as remote work is also affected by the lack of face-to-face meetings. Some of the most common issues include:Reduced success in knowledge transfer activities due to insufficient support such as mentoring and coaching.Inconsistent performance affected by the ability individuals have to work together remotely.Lack of clarity with regards to knowledge transferred, as key performance indicators are difficult to apply online.Increased time waste, as teams find it more difficult to reach consensus with members demonstrating different levels of understanding.Constraints with regards to available resources that can be accessed without the need of face-to-face contact. The consortium proposes the creation of a Sharing my Learning (Platform-Network-Toolkit) to support the transition towards e-study and e-work. The focus of the project is on University study, while the project ideas will be also tested at a small-scale pilot for organisational knowledge transfer scenarios. The proposal is to introduce the Sharing-my-Learning (SmL) concept across the participating Universities. This will enable students to be learning providers but also learning requesters. This will be achieved through Provision of Learning (PoL) and Request for Learning (RfL) transactions with the system. Learners who have a sufficiently significant volume of learning to share can create a PoL record that can be accessed by other learners who need support in the same area, expressed in the form of a RfL. The project will be delivered as a Platform-Network-Toolkit (PNT) combination. With regards to Platform, the University of Siegen will adapt its current outputs to provide a supporting medium for the provision of the training. Middlesex University will assist the match-making process with its MUSKET tools that use the XCRI-CAP information model to align course data, as used in the PAWER CBHE project. Middlesex University will also contribute in terms of the creation of the Toolkit using the SCATE e-learning platform as developed for the FORC CBHE project. Each learning unit will be in the form (i) content, (ii) presentation and (iii) video, following a consistent format. The learning units will be in the form of micro-learning outputs. Neapolis University will support the creation of the learning content in collaboration with Oracle TES. Finally, a network of learning will be established involving all individuals producing or requesting learning units. Harokopio University will provide the necessary data analytics for the use of this network, supported by Middlesex University for the creation of the necessary learning analytics visualisation dashboards.

GRIP on MASH will address the unmet public health need of reducing disease burden and comorbidities associated with Metabolic dysfunction-Associated Steatotic Liver Disease (MASLD). Together with seven medical technology, pharmaceutical and biotechnology companies, we will devise a sustainable and scalable GRIP on MASH Platform that will enable access to at-risk patients developing or having MASLD through the early detection of this condition at the primary care level. This Platform will allow A) the early detection of patients with MASLD: distributed in 12 European Centers of Excellence (CoEs), 10,000 patients at high risk of MASLD - defined as patients with type-2 diabetes mellitus, metabolic syndrome, obesity or arterial hypertension - will be screened and characterized; B) better patients’ stratification: the Platform will comprise artificial intelligence-based decision support tools that will make use of existing and novel biomarkers/biomarker combinations. Their predictive accuracy will be tested at the primary care level; there we will perform multi-OMICs analysis (proteomics, lipidomics, metabolomics, genomics, metagenomics and fluxomics) in fasted blood samples and we will explore imaging biomarkers/organ-on-a-chip to find future non-invasive diagnostic alternatives for the current standard (liver biopsies); and C) personalized lifestyle advice, by exploring evidence-based lifestyle features and the effect of nutritional recommendations: among the cohorts at the CoEs, we will use validated questionnaires to assess physical activity, diet, sleep, smoking, alcohol consumption, and perception of stress. Integrating patients’ perspectives with the participation of two patient organizations, the trustworthiness and sustainability of our GRIP on MASH Platform will be assessed by investigating potential economic, ethical and regulatory barriers to its future adoption. GRIP on MASH will change healthcare practice in MASLD and reduce the disease burden for patients.

FNS-Cloud will overcome fragmentation problems by integrating existing FNS data, which is essential for high-end, pan-European FNS research, addressing FNS, diet, health, and consumer behaviours as well as on sustainable agriculture and the bio-economy. Current fragmented FNS resources not only result in knowledge gaps that inhibit public health and agricultural policy, and the food industry from developing effective solutions, making production sustainable and consumption healthier, but also do not enable exploitation of FNS knowledge for the benefit of European citizens. FNS-Cloud will, through three Demonstrators; Agri-Food, Nutrition & Lifestyle and NCDs & the Microbiome to facilitate: (1) Analyses of regional and country-specific differences in diet including nutrition, (epi)genetics, microbiota, consumer behaviours, culture and lifestyle and their effects on health (obesity, NCDs, ethnic and traditional foods), which are essential for public health and agri-food and health policies; (2) Improved understanding agricultural differences within Europe and what these means in terms of creating a sustainable, resilient food systems for healthy diets; and (3) Clear definitions of boundaries and how these affect the compositions of foods and consumer choices and, ultimately, personal and public health in the future. Long-term sustainability of the FNS-Cloud will be based on Services that have the capacity to link with new resources and enable cross-talk amongst them; access to FNS-Cloud data will be open access, underpinned by FAIR principles (findable, accessible, interoperable and re-useable). FNS-Cloud will work closely with the proposed Food, Nutrition and Health Research Infrastructure (FNHRI) as well as METROFOOD-RI and other existing ESFRI RIs (e.g. ELIXIR, ECRIN) in which several FNS-Cloud Beneficiaries are involved directly.

The SHIELD project seeks to revolutionise early detection of pancreatic cancer, focusing on individuals with high heritable genetic risk. Pancreatic ductal adenocarcinoma (PDAC) has a 5-year survival rate of less than 10%, primarily due to late-stage diagnosis. Consequently, 85% of PDAC cases are identified too late for curative treatment. However, early detection can significantly improve outcomes, increasing the survival rate to 42% with surgical intervention. There is a pressing need for better early detection methods, especially for those with familial or genetic predispositions. The only FDA-approved biomarker, CA19-9, is limited to monitoring treatment response due to its lack of sensitivity and specificity, while imaging methods ofter fail to detect early-stage cancers and cause a strain to the healthcare system due to their cost and limited availability. SHIELD aims to validate a new blood-based diagnostic test designed for early PDAC detection in high-risk individuals and pilot an early detection programme in Greece, Slovenia and Lithuania. Developed by partner Reccan, this test uses a 5-plex multiple immunoassay to analyze protein readouts and provides a probability score for pancreatic cancer. Initial studies with over 450 samples showed excellent performance with >91% sensitivity and >96% specificity. The project will validate the test's clinical performance in a prospective multi-center study across seven EU countries, targeting individuals with familial or genetic predispositions. It will also identify new protein biomarkers for other high-risk indications, such as new-onset diabetes (NOD). Collaboration with national screening authorities will help integrate this test into existing programs, and partnerships with patient organizations will enhance recruitment. SHIELD envisions transforming pancreatic cancer diagnostics by increasing the 5-year survival rate to 30% by 2035 in Europe. This action is part of the Cancer Mission cluster of projects on “Prevention & early detection (early detection heritable cancers)