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.

With the rise in population and drop in the economy, Jordan needs new ideas and schemes involving the younger population for the country to strive in the future. JOB-JO is an initiative brought together by universities in the South of Jordan lead by Mutah University, the Ministry of Public Works and Housing, as well as European partners. The aim of the project is to promote youth employment and reduce poverty in Jordan’s remote areas which will empower economic and social aspects of these communities as well as provide women more opportunities for success. Although the literate rate in Jordan is high, and people are getting more educated than ever, the employment rate is low due to many social and economic factors. As most universities provide the best education possible, students still lack many skills that would be easy to gain. The career centers would provide services in order to teach and train unemployed graduates to be requalified for more job opportunities. Services such as training courses, skills and communication workshops, as well as support entrepreneurships and initiatives brought by the community would be offered. These services would have European influence through exchange programs as well as the great relationship to happen between universities in Jordan and the experienced universities in Europe. The short term impact would be to allow graduates to gain skills and training needed for their professions where in the long term, these career centers would foster the economic growth and reduce the unemployment and poverty in the remote areas of Jordan.

The world population living in urban settlements is expected to increase to 70% of 9.7 billion by 2050. Historically, as cities grew, new water infrastructures followed as needed. However, these developments had less to do with real planning than with reacting to crisis situations and urgent needs, due to the inability of urban water planners to consider long-term, deeply uncertain and ambiguous factors affecting urban development and water demand. These, coupled with increasingly uncertain climate conditions, indicate the need for a more holistic and intelligent decision-making framework for managing water infrastructures in the cities of the future. This project aims to develop a new theoretical framework for the allocation and development decisions on drinking water infrastructure systems, so that they are socially equitable, economically efficient and environmentally resilient, as advocated by the UN Agenda 2030, Sustainable Development Goals. The framework will integrate real-time monitoring and control with long-term robustness and flexibility-based pathway methods, and incorporate economic, social, ethical and environmental considerations for sustainable transitioning of urban water systems under deep uncertainty with multiple possible futures. The Water-Futures team will build on synergies from the four research groups, transcending methodologies from water science, systems and control theory, economics and decision science, and machine learning, into an integrated decision and control framework, to be implemented as an open-source research toolbox. The new science outcomes will be applied to three case studies exemplifying different types of urban water systems: a mature, relatively stable system; a mature and rapidly expanding system; and a relatively recent supply system in a developing country with high growth and special challenges, including limited resources, intermittent supply and high water losses.