This project brings together a diverse group of subject matter experts from industry and academia under one umbrella, with the main aim of enhancing and advancing future healthcare processes and systems using sensory and machine learning technologies to provide emotional (affective) and cognitive insights into patients well-being so as to provide them with more effective treatment across multiple medical domains. The objective is to develop technologies and methods that will lessen the enormous and growing health care costs of dementia and related cognitive impairments that burden European citizens, which is estimated to cost over €250 Billion by 2030 [1]. From a technical perspective, the primary objective is to “develop a cloud based affective computing [2] operating system capable of processing and fusing multiple sensory data streams to provide cognitive and emotional intelligence for AI connected healthcare systems”. In particular the consortium intends to: • Specify and engineer the architecture of the SenseCare platform and will release two versions of the platform cloud infrastructure during the life of the RISE project. • Create and evaluate two use case test pilots (relating to the dementia care and connected health medical domains) that integrate with, use and apply the services of the SenseCare platform. • Specify and engineer a number of medical informatics applications that will run on the SenseCare platform and that will also be tested and evaluated as part of the use case test pilot phases. The outputs of the project will lead to significant and lasting impact on the innovation potential of the individual researchers, their host organisations as well as impacting in a much wider sense at a European and global level.

The aim of this project is to bring together experts from the academic and non-academic sectors and to create an easy-to-use integrated hardware and software platform. This will enable the rapid analysis of large metagenomic datasets. It will provide actionable insights into probiotic supplement usage, methane production and feed conversion efficiency in cattle. In the recent years, the number of projects or studies producing very large quantities of sequencing data – analysing microbial communities make-up and their interactions with the environment – has increased. Yet, the depth of analysis done is very superficial and represents an inefficient use of available information and financial resources. This project aims to address these deficiencies and will study the change within microbial communities, under various conditions in cattle guts and impacting probiotic supplement usage, methane production and feed conversion efficiency in cattle. To succeed, we propose to develop faster and more accurate analytic platforms in order to fully utilise the datasets generated. By focusing on better hardware and software platforms, better expertise and training, this project will pave the way for a more optimal usage of metagenomic datasets, thus reducing the number of animals necessary. This will ensure better and more economic animal welfare. The Meta-Plat project objective is a mixture of innovative research, focused application and commercial awareness. The core objectives being pursued are: • Sample gut collection, from cattle, for sequencing; • Collection of publically available databases – to create a new classification of previously unclassified sequences, using machine learning algorithms; • Development of accurate classification algorithms; • Real-time or time-efficient comparison analyses; • Production of statistical and visual representations, conveying more useful information; • Platform integration; • Provide insights into probiotic supplement usage, methan

The semiconductor industry is characterised by complex supply chain structures. A common language and structure has to be developed and enrolled to enable smooth collaboration among different supply chain participants in this B2B (business to business) environment. SC³ relies on enabling a collaboration of industrial as well as academic stakeholders to ensure interoperability among semiconductor companies, and further industrial domains. SC³ implements an industrial reference platform as a de-facto standard (frequently used). This framework acts as a key enabler for realising an agile development - validation - refinement loop of a top-level ontology i.e. Digital Reference (DR). DR comprises a combination of different ontologies of semiconductor supply chains and supply chains containing semiconductors. To that end, the framework will support ontology governance e.g. development, archiving and indexing as well as the validation of high quality and interlinked ontologies and taxonomies. SC³ will incrementally add domain knowledge to the DR; the extended version of DR covers semiconductor domain vocabulary and related sub domains. The platform allows the involvement of all stakeholder groups in a customised fashion and proposes iterative engaging approaches for each community. The DR allows modularly including, developing and extending domain knowledge to provide a connected supply network structure. The project will follow a deliberate piloting methodology in order to deliver the demonstrators needed as proof-of-concept and for evaluation of project’s measurable objectives. SC³ activities will have a strong focus on sustainability and uptake of the project results, this includes on the one hand to keep the established community alive and on the other hand that the semiconductor data documentation, (i.e. the Generic Semiconductor Data Model) will be further developed and maintained even after the project duration.

GenDAI will leverage metagenomic potential to deliver powerful diagnostic results facilitating the development of Personalized Medicine, addressing assay research and development as well as productive clinical diagnostics in a comprehensive way by creating a highly innovative medical diagnostics platform that supports microbiome profiling with novel biomarkers using Artificial Intelligence (AI). This innovative platform will allow clinicians to assess and monitor patients while complying with strict regulatory requirements of laboratory diagnostics. Ultimately, GenDAI will contribute to accelerate conversion of innovative ideas and technology solutions into breakthroughs in medical analyses services. Delivery of GenDAI tangible outputs will be driven by the following Research and Innovation Objectives: a) Create new metagenomic datasets containing microbiome samples from stool of patients under informed consent suffering from inflammatory bowel disease (IBD); b) Develop and integrate the GenDAI Diagnostics Workflow, focusing on implementing a fully automated data processing pipeline; c);Provide a robust, cloud-based foundation for the development of the platform that integrates advanced data management and knowledge infrastructure focusing on security, reproducibility and long-term archiving (GenDAI Safe); d) Develop and improve AI methods to identify relevant biomarkers and classify corresponding metagenomic sequences in order to characterise microbiome profiles to provide a personalised diagnostic result of patients’ state of health (GenDAI Discovery); e) Deliver innovative visual user interfaces and interactive clinical reporting (GenDAI Interactive Reporting) and f) Deliver the marketable, regulatory compliant GenDAI technology and tool suite.

The STOP project will bring together an interdisciplinary and intersectoral group of subject matter experts from industry and academia under one umbrella, to address the health societal challenge of obesity with the specific objectives of mitigating the enormous and growing Health Care costs of obesity and related health issues (like heart disease, diabetes, arthritis, liver disease, gallstones, cancer, dementia) that burden European citizens. The STOP project will address this need through the foundation of an innovative platform to support persons with obesity with a better nutrition under supervision of healthcare professionals. Therefore, the STOP platform will capture various PwO data from different kind of smart sensor streams and chatbot technology, manage and enrich available data with existing knowledge bases and fuse these by machine learned driven data fusion approaches for sophisticated AI data analysis. Essentially, this gathered and analysed data and knowledge is accessible and usable for Health Care professionals amongst others as input for a gamification approach to teach PwO healthier nutrition. In the STOP validation an app that establishes an analogy to Dorian Gray mirror, teaching healthier nutrition.