The clinical and genetic investigations of diseases of the Cypriot population as well as eHealth are a priority of the Smart Specialization Strategy of the Cyprus government. This strategy can best be served by creating a Centre of Excellence (CoE) with two main spear heads: A contemporary Biobank and a research facility for developing the Cyprus Human Genome Project. Biobanks are organized collections of medical records and biological material of all types, aimed to support biomedical research, serving as repositories and distribution centers. Cyprus, as a Low Performing Member State, was the last country that started a Biobank, when 4 years ago we were competitively funded by the European Regional Development Fund & the Republic of Cyprus through the Cyprus Research Promotion Foundation. That project provided €2m, 0.4m of which was for creating a seed infrastructure for Biobanking. The rest was used for research in inherited kidney diseases. The money for Biobanking was too little for supporting a contemporary operation of recruiting adequate numbers of patients with complete records and promoting translational research. Here, we propose to upgrade the existing small infrastructure and turning it into a CoE with the assistance of Advanced Partners who led a similar operation Europe-wide, Prof. K. Zatloukal, coordinator of the Biobanking & BioMolecular Resources Research Infrastructure that recently became an ERIC (European Research Infrastructure Consortium) directed by Jan-Eric Litton (BBMRI-ERIC) located in Graz, Austria. The previous activity allowed us to comprehend the problems associated with patient recruitment and record collection. This can now serve as a starting point for upgrading it into a larger scale operation of European standard, aimed at leading the Cyprus Human Genome Project, part of which will be the sequencing of 1000 Cypriot genomes. This CoE will provide the prospects for innovative research and lead Cyprus into the European Research Area.

Commercial indoor spaces such as hospitals, hotels, offices offer great potential for commercial exploitation of logistic robotics. Also, offer advantages for their deployment, since they are required by law to meet stringent building codes, and therefore the navigation space exhibits some structure. In addition, they offer reliable communications infrastructure, since this is required for normal business operation. Thus, commercial spaces are rightfully considered the next great field of logistic robotics deployment. Despite these advantages, today, few solutions exist, and these solutions do not trigger widespread acceptance by the market. This is because existing systems require costly infrastructure installation (arrays of peripheral sensors, mapping, etc.); they do not easily integrate to corporate IT solutions and as a result, they do not fully automate procedures and traceability; they are limited to a single type of service, i.e. transfer of goods. Through transfer of knowledge, multidisciplinary research and cross-fertilization between academia and industry, ENDORSE will address the aforementioned technical hurdles. Four innovation pillars will be pursued: (i) infrastructure-less multi-robot navigation, i.e. minimum (if any) installation of sensors and communications buses inside the building for the localization of robots, targets and docking stations; (ii) advanced HRI for resolving deadlocks and achieving efficient sharing of space resources in crowded spaces; (iii) deployment of the ENDORSE software as a cloud-based service facilitating its integration with corporate software solutions such as ERP, CRM, etc.; (iv) reconfigurable and modular hardware architectures so that diverse modules can be easily swapped. The latter will be demonstrated and validated by the integration of an e-diagnostic support module (equipped with non-invasive sensors/devices) and the Electronic Health Records (EHR) interfacing, which will serve as an e-diagnostic mobile station

Mental health disorders pose a massive economic and societal burden. Emerging early in development and resulting in long-term disability, neurodevelopmental dysfunctions (NDD) compromise the quality of life of millions of Europeans. The purpose of the Neo-PRISM-C ETN is (1) to train Early Stage Researchers (ESRs) in applying the Research Domain Criteria, a novel framework for understanding psychopathology, to the study of the mechanisms and treatments of NDD. It aims (2) to train ESRs from multiple disciplines (psychology, neuroscience, data science) in state-of-the-art and transferable skills for innovating the study of brain-behavior relationships in NDD, in the context of a systems-based, trans-diagnostic theoretical frame. This ETN will also (3) support training in designing evidence-based, individualized treatments of learning, behavioral, and social maladjustment, bridging across diagnostic categories. Towards these goals, we have assembled a trans-sectoral European network with expertise in cognitive, social, educational, clinical, and emotion research and in training ESRs. Six research, training and management work packages (WPs) pursue these goals. WP1 comprises innovative projects, investigating risk and protective factors that span across NDD diagnostic categories (autism, learning, emotional difficulties) and linking to healthcare industry and education. WP2 examines systems-level brain development to identify biological substrates of specific dysfunctions. WP3 applies this knowledge to develop new multi-modal interventions to address domains of impairment. The academic, industrial and clinical partners collaborate across themes, offering ESRs project-specific secondments, supervision, workshops, summer school and courses on research, transferable and entrepreneurial skills. Neo-PRISM-C is expected to further understanding of NDD and improve the competitiveness of EU health professions, providing the market with highly-skilled researchers and clinicians.

The transition to the smart grid era is associated with the creation of a meshed network of data contributors that necessitates for the transformation of the traditional top-down business model, where power system optimization relied on centralized decisions based on data silos preserved by stakeholders, to a more horizontal one in which optimization decisions are based on interconnected data assets and collective intelligence. Consequently, the need for “end-to-end” coordination between the electricity stakeholders, not only in business terms but also in exchanging information is becoming a necessity to enable the enhancement of electricity networks’ stability and resilience, while satisfying individual business process optimization targets of all stakeholders involved in the value chain. SYNERGY introduces a novel reference big data architecture and platform that leverages data, primary or secondarily related to the electricity domain, coming from diverse sources (APIs, historical data, statistics, sensors/ IoT, weather, energy markets and various other open data sources) to help electricity stakeholders to simultaneously enhance their data reach, improve their internal intelligence on electricity-related optimization functions, while getting involved in novel data (intelligence) sharing/trading models, in order to shift individual decision-making at a collective intelligence level. To this end SYNERGY will develop a highly effective a Big Energy Data Platform and AI Analytics Marketplace, accompanied by big data-enabled applications for the totality of electricity value chain stakeholders (altogether integrated in the SYNERGY Big Data-driven EaaS Framework). SYNERGY will be validated in 5 large-scale demonstrators, in Greece, Spain, Austria, Finland and Croatia involving diverse actors and data sources, heterogeneous energy assets, varied voltage levels and network conditions and spanning different climatic, demographic and cultural characteristics.

The aim of this project is to determine the functional significance of the peripheral visual field for human perception and its interaction with the motor system. While it is well established that central vision serves to resolve the fine details of the visual world and the objects in it, the reduced spatial resolution in the peripheral part of our visual field suggests that peripheral vision may have a very different role. In this project we will use an innovative combination of Virtual Reality (VR) with physiological recordings and computational modelling to examine the hypothesis that the peripheral visual field acts as a change detection mechanism. Specifically, under conditions resembling those encountered in everyday life, we will explore the presence of serial dependency (SD) effects, i.e., the integration of past with current information during visual processing. These experiments will provide the first evidence that SD strategies are employed during everyday activities in complex environments in daily life, while the nature of the SD effects (i.e. positive vs. negative) will allow us to deduce differences in the functions of the central and peripheral visual system. Importantly, the experiments will also allow us to assess whether peripheral vision is involved in a multisensory and hypersensitive "alarm system", an idea that has recently been proposed in the literature. Finally, by employing computational modelling methods we will attempt to provide a comprehensive theoretical account for experimental findings. Overall, this interdisciplinary project is expected to provide important insight into the basic mechanisms of human vision that are responsible for maintaining the equilibrium between visual stability and change detection over the whole visual field.