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 European health services have well responded to the COVID-19 emerging crisis, especially if and where the intensive care unit (ICU) capacities were sufficient, were prepared and collectively cooperating, sharing knowledge and were able to protect from further spreading of the disease among the healthcare workforce and the patients. Today, only 47% of hospitals have the recommended coverage of intensive care specialists and they are unevenly distributed between centres and periphery. The Cyber-Physical System for Telemedicine and Intensive Care (CPS4TIC) enables existing or new ICU structures to transform and operate as one ICU Hub with one central ICU and connected ICUs in peripheral hospitals. CPS4TIC was used successfully in the first wave of COVID-19 to ensure efficient and effective diagnosis and treatment of COVID-19 patients, while reducing the risk of infection drastically. The CPS4TIC consists of a telemedicine cockpit, telemedicine consoles at each peripheral hospital, a connector platform and smart bedside hubs including robotic arm at the bedsides of both, the central telemonitoring clinics and the peripheral telemonitored hospitals. The ICU Hub operates telemedicine, continuous real-time telemonitoring and bedside smart care environment. The bedside smart care environment reduces the risk of infection for the health workforce significantly both for the central and the peripheral hospitals. ICU4Covid will deploy and test the CPS4TIC at large-scale, in 10 ICU Hubs in Europe, involving more than 30000 patients/year with a coverage of approximately 60 Million citizens.

Despite the indisputable benefits of AI, humans typically have little visibility and knowledge on how AI systems make any decisions or predictions due to the so-called “black-box effect” in which many of the machine learning/deep learning algorithms are not able to be examined after their execution to understand specifically how and why a decision has been made. The inner workings of machine learning and deep learning are not exactly transparent, and as algorithms become more complicated, fears of undetected bias, mistakes, and miscomprehensions creeping into decision making, naturally grow among manufacturers and practically any stakeholder In this context, Explainable AI (XAI) is today an emerging field that aims to address how black box decisions of AI systems are made, inspecting and attempting to understand the steps and models involved in decision making to increase human trust. XMANAI aims at placing the indisputable power of Explainable AI at the service of manufacturing and human progress, carving out a “human-centric”, trustful approach that is respectful of European values and principles, and adopting the mentality that “our AI is only as good as we are”. XMANAI, demonstrated in 4 real-life manufacturing cases, will help the manufacturing value chain to shift towards the amplifying AI era by coupling (hybrid and graph) AI "glass box" models that are explainable to a "human-in-the-loop" and produce value-based explanations, with complex AI assets (data and models) management-sharing-security technologies to multiply the latent data value in a trusted manner, and targeted manufacturing apps to solve concrete manufacturing problems with high impact.

i4Q Project aims to provide an IoT-based Reliable Industrial Data Services (RIDS), a complete suite consisting of 22 i4Q Solutions, able to manage the huge amount of industrial data coming from cheap cost-effective, smart, and small size interconnected factory devices for supporting manufacturing online monitoring and control. The i4Q Framework will guarantee data reliability with functions grouped into five basic capabilities around the data cycle: sensing, communication, computing infrastructure, storage, and analysis and optimization. i4Q RIDS will include simulation and optimization tools for manufacturing line continuous process qualification, quality diagnosis, reconfiguration and certification for ensuring high manufacturing efficiency, leading to an integrated approach to zero-defect manufacturing. The i4Q RIDS will be demonstrated in 6 Uses Cases from relevant industrial sectors and representing two different levels of the manufacturing process: machine tool providers and production companies. i4Q pan-European consortium entails Industrial partners: WHIRLPOOL (White goods manufacturer), BIESSE (Wood industrial equipment), FACTOR (Metal machining), RIASTONE (Ceramic pressing), FARPLAS (Plastic injection) and FIDIA (Metal industrial equipment); Implementers: TIAG (Industrial Communication Protocols and Standards), CESI (Machine tools, Advanced Materials, Micro-technology) and AIMPLAS (Thermoplastic and thermosetting plastic materials); Technology Providers: IBM (Information Technologies Company), ENGINEERING (Software and Services Company), ITI (Information Technologies Institute), KNOWLEDGEBIZ (Information Systems Company), EXOS (Operations Consulting Company); R&D partners: CERTH (Research Institute), IKERLAN (Technological Centre), BIBA (Research Institute), UPV (University), TUBERLIN (University), UNINOVA (Research Institute); Specialist partners: FUNDINGBOX (Exploitation), INTEROP-VLAB (Dissemination), DIN (Standardisation), LIF (Legal).

The Conveyor System market and, in particular, robot segment for the automated material handling are experiencing double-digit growth at a compound annual growth rate (CAGR). EU manufacturers in this sector plays an important role, covering 23% of the market but this technological edge is being challenged due to the astonishing growth of China, Japan and Korea. In real world, many objects to be handled, including food, clothes, bottles, or plastic items, are soft or deformable and robots are not yet efficient and effective in handling these objects. In this context, AGILEHAND aims at developing advanced technologies for grading, handling and packaging autonomously soft and deformable products, as a strategic instrument to improve flexibility, agility and reconfigurability of production and logistic systems of the European manufacturing companies. AGILEHAND will deploy 3 integrated Suites: 1) SMART SENSING SUITE, self-calibrating sensing solutions to grade the quality (both interior and exterior) of delicate objects and to produce a mesh of integrated and overlapping sensors that will improve production-line traceability, agility and reconfigurability; 2) SELF-ADAPTIVE HANDLING, SORTING AND PACKAGING SUITE, robotic manipulation systems that reacts to product quality and that can Pick-Up and Re-Orientated Different Soft and Deformable Products without causing product damage considering collaborative (human-in-the-middle) approaches; 3) AGILE, FLEXIBLE AND RAPID RECONFIGURABLE SUITE, a set of AI based solutions that will allow for monitoring, adaptive control and synchronisation of production and logistics flows in a factory, even when faced with a variability of products, production mix or fresh market, guaranteeing high performance in customer response time, and an efficient use of resources. The AGILEHAND Solutions will be demonstrated in 4 industrial pilots that differ in characteristics of the surface, deformability, and consistency of the products to be handle.