Eco-Bot aims to utilize recent advances in chatbot tools and advanced signal processing (i.e. energy disaggregation) using low-resolution smart meter-type data with the goal of changing their behaviour towards energy efficiency. Eco-Bot targets to a personalized virtual energy assistant to deliver information on itemized (appliance-level) energy usage through a chat-bot tool. The "chat-bot" functionality will be use an attractive frontend interface, permitting seamless communication in a more natural and interactive way than a traditional mobile application. This way, Eco-Bot aims to achieve a higher level of engagement with consumers than previous efforts (i.e. serious games, gamification, competitions or other interactive ICT), by adding a more engaging form of interaction with existing platforms that has been proven in different market settings. The proposed system considers knowledge of the delivered multi-factorial models, including rebound-effects, as a result of the baseline research on both European and International activities. Then, based on advanced ICT, such as knowledge engineering, machine learning, expert systems, the project transforms the multi-factorial models for energy reduction to interactive, personalized and targeted recommendations to consumers on how to save energy. Eco-Bot uses also existing NILM, e.g. energy disaggregation methods, and data analytics to break down consumption to the appliance level, where this is possible (smart meters at reasonable granularity, adequate number of information collected) so as to make consumers aware of their most energy-consuming devices. The project will demonstrate the system in three different use cases, each one representing a different business model (B2B / B2B2C /B2C). We aim to validate our system across real and diverse conditions such as socio-cultural, environmental, demographic, climate and consumption, so as to draw concrete conclusions regarding performance, effectiveness, affordability, etc.

YADES aims to efficiently train a network of fellows on the field of the resilience of Cultural Heritage (CH) areas and historic cities against Climate Change (CC) and other types of hazards. Towards this direction, YADES aims to introduce a research framework for downscaling the created climate and atmospheric composition as well as associated risk maps down to the 1x1 km (historic area) scale, and specific damage functions for CH materials. Applying atmospheric modelling for specific CC scenarios at such refined spatial and time scales allows for an accurate quantitative and qualitative impact assessment of the estimated micro-climatic and atmospheric stressors. YADES will perform combined structural/geotechnical analysis of the CH sites and damage assessment under normal and changed conditions, based on the climatic zone, the micro-climate conditions, the petrographic and textural features of building materials, historic data for the structures, the effect of previous restoration processes and the environmental/physical characteristics of the surrounding environment. The data coming from installed monitoring system will be coupled with simulated data (under our cultural heritage resilience assessment platform-CHRAP) and will be further analysed through our data management system, while supporting communities’ participation and public awareness. The data from the monitoring system will feed the DSS so as to provide proper adaptation and mitigation strategies. The produced vulnerability map will be used by the local authorities to assess the threats of CC (and other natural hazards), visualize the built heritage and cultural landscape under future climate scenarios, model the effects of different adaptation strategies, and ultimately prioritize any rehabilitation actions to best allocate funds in both pre- and post-event environments. To train the fellows, the project will make use of extensive workshop and training sessions, as well organise summer schools.

Intangible Cultural Heritage (ICH) content means "the practices, representations, expressions, knowledge, skills – as well as the instruments, objects, artefacts and cultural spaces associated therewith". Although, ICH content, especially traditional folklore performing arts, is commonly deemed worthy of preservation by UNESCO (Convention for the Safeguarding of ICH) and the EU Treaty, most of the current research efforts are on the focus is on tangible cultural assets, while the ICH content has been overlooked. The primary difficulty stems by the complex structure of ICH, its dynamic nature, the interaction among the objects and the environment, as well as emotional elements (e.g., the way of expression and dancers' style). TERPSICHORE aims to study, analyse, design, research, train, implement and validate an innovative framework for affordable digitization, modelling, archiving, e-preservation and presentation of ICH content related to folk dances, in a wide range of users (dance professionals, dance teachers, creative industries and general public) . The project targets at integrating the latest innovative results of photogrammetry, computer vision, semantic technologies, time evolved modeling, combined with the story telling and folklore choreography. An important output of the project will be a Web based cultural server/viewer with the purpose to allow user’s interaction, visualization, interface with existing cultural libraries (EUROPEANA) and enrichment functionalities to result in virtual surrogates and media application scenarios that release the potential economic impact of the ICH. The final product will support a set of services such as virtual/augmented reality, social media, interactive maps, presentation and learning of European Folk dances with tremendous impact on the European society, culture and tourism

RISKADAPT will provide, in close cooperation with the end-users/other stakeholders, a novel, integrated, modular, interoperable, public and free, customizable user-friendly platform (PRISKADAPT), to support systemic, risk-informed decisions regarding adaptation to CC induced compound events at the asset level, focusing on the structural system. PRISKADAPT will explicitly model dependencies between infrastructures, which, inter alia, will provide a better understanding of the nexus between climate hazards and social vulnerabilities and resilience. Moreover, this project will identify gaps in data and propose ways to overcome them and advance the state of the art of asset level modelling through advanced climate science to predict CC forcing on the structure of interest, structural analyses, customized to the specific structure of interest, that consider all major CC induced load effects in tandem with material deterioration, novel probabilistic environmental life cycle assessment (LCA) and life cycle cost (LCC) of structural adaptation measures and a new model to assess climate risk that will combine technical risk assessment with assessment of social risks. PRISKADAPT will provide values to a set of indicators for each asset of interest, quantifying primary parameters and impacts, in the form of a Model Information System (MIS) that will provide all required information for adaptation decisions. PRISKADAPT will be implemented in the case studies in the pilots that involve specific assets, however, it will permit customization with local values of parameters and data, so it can be applicable throughout Europe for CC adaptation decisions involving assets of similar function, exposed to multiple climate hazards.

Structural Health Monitoring (SHM) is expected to play a predominant role in the management of the transport infrastructure. Yet, SHM techniques continue to rely on point-based, as opposed to spatial, sensing requiring a dense network of these point-sensors increasing considerably the monitoring cost. Additionally, commercially available, strain sensors cannot measure strains beyond 1% to 2% and, thus, are not able to provide an alarm for an imminent catastrophe. SENSKIN aims to: (a) develop a dielectric-elastomer and micro-electronics-based skin-like sensing solution for the structural monitoring of the transport infrastructure that will offer spatial sensing of reversible (repeated) strains in the range of 0.012% to more than 10%, that requires little power to operate, is easy to install on an irregular surface, is low cost compared to existing sensors, allows simple signal processing and includes the ability of self-monitoring and self-reporting. (b) use the new and emerging technology of Delay Tolerant Network to secure that strain measurements acquired through the 'sensing skin' will reach the base station even under extreme environmental conditions and natural disaster events such as, high winds or an earthquake, where some communication networks could become inoperable. (c) develop a Decision-Support-System for proactive condition-based structural intervention under operating loads and intervention after extreme events. It will be based on an accurate structural assessment based on input from the strain sensors in (a) above and will examine the life-cycle economic, social and environmental implications of the feasible rehabilitation options and the resilience of the infrastructure to future changes in traffic demand that these options offer. (d) implement the above in the case of bridges and test, refine, evaluate and benchmark the monitoring system (integrated a and b) and package (integrated a, b and c) on actual bridges.