ICT is embedded and pervasive into our daily lives. The notion of Cyber Physical Systems (CPS) has emerged: embedded computational collaborating devices, capable of controlling physical elements and responding to humans. The Cross-layer modEl-based fRamework for multi-oBjective dEsign of Reconfigurable systems in unceRtain hybRid envirOnments (CERBERO) project aims at developing a design environment for CPS based of two pillars: a cross-layer model based approach to describe, optimize, and analyze the system and all its different views concurrently; an advanced adaptivity support based on a multi-layer autonomous engine. To overcome the limit of current tools, CERBERO provides: libraries of generic Key Performance Indicators for reconfigurable CPSs in hybrid/uncertain environments; novel formal and simulation-based methods; a continuous design environment guaranteeing early-stage analysis and optimization of functional and non-functional requirements, including energy, reliability and security. CERBERO effectiveness will be assessed in challenging and diverse scenarios, brought by industrial leaders: an embedded CPS with self-healing capabilities for planetary explorations (TASE-S&T), an ocean monitoring CPSoS (AS), and a Smart Travelling CPSoS for Electric Vehicle (TNO-CRF-S&T). CERBERO will automate multi-objective decisions to meet requirements and correct/optimized–by–construction designs. Interoperable components (i.e. DynAA by TNO, AOW by IBM, PREESM by INSA, PAPI-ARTICo3 by UPM, MDC by UniCA-UniSS) will be enhanced with additional features (as security, USI), mostly released as open-source to foster open innovation and a real path to standardisation, and integrated (IBM- AI) into a unique framework. Design speed up (one order of magnitude), increased performance (30% less energy) and reduced costs of deployment (by rapid prototyping and system in the loop incremental design) and maintenance (by runtime verification and adaptivity) of CPSoS are expected.

EURHISFIRM designs a world-class research infrastructure (RI) to connect, collect, collate, align, and share detailed, reliable, and standardized long-term financial, governance, and geographical data on European companies. EURHISFIRM enables researchers, policymakers, and other stakeholders to develop and evaluate effective strategies to promote investment, economic growth and job creation. The RI provides the tools for long-term analysis highlighting the dynamics of the past and the way those dynamics structure our present and future. A few large stand-alone long-term databases have been built in Europe so far, while important resources have been invested into scattered and dispersed historical datasets. EURHISFIRM develops innovative models and technologies to spark a “Big data” revolution in historical social sciences and valorize Europe’s cultural heritage. These technologies match and collate historical data, and connect them to recent ones. They bring the next generation of data extraction and enrichment systems from digitized historical sources and web-based resources. The scaling up in the variety, quantity and quality of long-term data changes the way of conducting scientific enquiry in the historical social sciences. EURHISFIRM constitutes a vibrant and large users’ community around the innovative data and services provided. The 2016 ESFRI Roadmap identifies Big Data, interdisciplinarity and innovative ways to disseminate research products as the main science drivers for RIs in the Social Sciences and Humanities. It recognizes the need and the opportunity for RIs providing access to the European Cultural Heritage and innovative methods to analyze and integrate information extracted to broad communities. EURHISFIRM fulfills this mission in close cooperation with ESFRI Landmark CESSDA and other existing RIs in the field of Arts and Humanities, like DARIAH, within the Research Data Alliance.

DROP-IT proposes combining optoelectronics and photonics in a single flexible drop-on demand inkjet technology platform by means of exploiting the enormous potential of lead-free perovskite (LFP) materials. Specifically, novel crystalline structures beyond conventional ABX3 LFP (double-perovskites and rudorffites) will be computationally screened and chemically synthesized with superior properties as LFPs proposed in the literature. A(Sn-Ge)X3 (A=organic,Cs; X=Cl,Br,I) materials will be considered for initial benchmark devices. The future of DROP-IT technology is envisioned at long-term in the fields of photovoltaics, lighting and printed integrated photonics. This will be possible by developing highly innovative fabrication routes (inkjet printing towards Roll-to-Roll) of LFP pioneering materials (in bulk and nanoscale) by low-cost, high throughput, sustainable, large-scale fabrication techniques on flexible substrates (PET, f.e.) to revolutionize future power, lighting and communication systems. DROP-IT major novelty relies on the innovative use of newly synthesized LFPs in combination with the use of affordable, mask-less, drop on demand inkjet printing onto flexible substrates. The targeted breakthroughs towards the long-term vision of our technology will be based on the following challenges: (1) Theoretical screening of different LFP compound families and chemical synthesis of most suitable ones in the form of nanocrystals and polycrystalline thin films, (2) Formulation of specific and suitable inks of these materials for (3) Inkjet printing of thin films on flexible substrates and (4) Development of stable optoelectronic and photonic devices (solar cells with 12-15% and LEDs with 14-18% efficiencies, amplifiers-lasers with low threshold) as proofs-of-concept for a future technology based on new inorganic LFPs and charge transport layers. DROP-IT is supported by a strong and interdisciplinary consortium with complementary expertise to achieve these objectives.

The 4th Industrial Revolution (4IR) builds on the Internet-of-Things (IoT) paradigm, as it relies upon the scenario of having billions of interconnected autonomous mobile devices, with unprecedented processing power, storage capacity and access to knowledge. While enabling such massive deployment, the 4IR should be increasingly eco-friendly. The 4IR is a disrupting approach that will force companies in almost every domain to re-organize themselves in a more efficient way, by exploiting technological breakthroughs such us artificial intelligence , wireless communication and quantum computing. The integration of these emerging technologies into every day life requires efficient power supply solutions in computing, sensing, memory enlargement and human-machine interaction. One perceived bottleneck for 4IR is that in most situations, IoT devices/networks will be remotely deployed, so that maintenance may be either incovenient or impossible. In particular, this implies that IoT devices either have to embed energy sources consistent with their operative lifespan or that clean and renewable energy convertors, if working off-grid, must sit on board. The significant broadening of the wireless communication spectrum in Europe makes the Radio frequency (RF) energy scavenging a highly desirable way forward for clean powering of the next-generation IoT.NANO-EH has the ambitious vision of creating a pathway for translating forefront knowledge of unique high frequency properties of emerging classes of nanomaterials into advanced device engineering for scalable miniaturized energy harvesting/storage submodules that are tailored for the specific needs of stand-alone, mobile or portable uses. It surpasses the current paradigm of energy harvesting materials by developing non-toxic and rare earth/lead-free materials exhibiting CMOS-compatibility and scalability for low cost and large-scale manufacturing.

Today's robots are good at executing programmed motions, but they do not understand their actions in the sense that they could automatically generalize them to novel situations or recover from failures. IMAGINE seeks to enable robots to understand the structure of their environment and how it is affected by its actions. "Understanding" here means the ability of the robot (a) to determine the applicability of an action along with parameters to achieve the desired effect, and (b) to discern to what extent an action succeeded, and to infer possible causes of failure and generate recovery actions. The core functional element is a generative model based on an association engine and a physics simulator. "Understanding" is given by the robot's ability to predict the effects of its actions, before and during their execution. This allows the robot to choose actions and parameters based on their simulated performance, and to monitor their progress by comparing observed to simulated behavior. This scientific objective is pursued in the context of recycling of electromechanical appliances. Current recycling practices do not automate disassembly, which exposes humans to hazardous materials, encourages illegal disposal, and creates significant threats to environment and health, often in third countries. IMAGINE will develop a TRL-5 prototype that can autonomously disassemble prototypical classes of devices, generate and execute disassembly actions for unseen instances of similar devices, and recover from certain failures. For robotic disassembly, IMAGINE will develop a multi-functional gripper capable of multiple types of manipulation without tool changes. IMAGINE raises the ability level of robotic systems in core areas of the work programme, including adaptability, manipulation, perception, decisional autonomy, and cognitive ability. Since only one-third of EU e-waste is currently recovered, IMAGINE addresses an area of high economical and ecological impact.