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

The European Union faces several challenges caused by globalization. Both the delocalization of production plants (leading to more imported products) and the instability characterizing several industrial sectors force economies to re-think their business models and re-adapt them in a new context, where the sustainability of products and processes is more relevant. Within this overall framework, the need to think about innovative business models and industrial strategies, able to answer to these new requirements is mandatory. One chance is the exploitation of digital technologies. Another is the exploitation of secondary (and critical) resources that, currently, are wasted without any recovery. The project FENIX wants to consider both these issues and their potential at the same time, proposing something that could allow Europe to re-appropriate its pertaining position in the global market. The idea is to study innovative business models and industrial strategies (based on the circular economy paradigm) enabling the development of new product-services through the definition of novel supply chains, resulting from an unconventional mix of current ones. This could allow the easy re-use, reconfiguration and modularization of production systems, the exploitation of overcapacity and the renaissance of industrial poles all over the Europe. Furthermore, the circular economy driven business models and industrial strategies proposed by project FENIX will be demonstrated in existing pilot plants, adequately reconfigured and integrated based circular economy needs.

The IoT4Win will establish a European Industrial Doctorates (EID) training program and extend the traditional academic research training setting, equipping researchers with right combination of research-related and transferable competences, joint research training programme combines an interdisciplinary expertise of European research group, industry partners and user organisations, bring together all required knowledge, skills, and stakeholders to offer a comprehensive set of transferable skills and a training programme on ICT, data science and water engineering, including industry practice and longer-term benefit in further industrial partner collaborations and further knowledge transfer. IoT4Win will respond to well defined and interdisciplinary scientific questions and challenges on IoTs for Smart Water Network (SWN) technological area cooperating to recruit 3 ESRs to undertake research in the context of a joint research training on concepts and methodologies of IoT enabled SWN towards the PhD. IoT4Win designs 3 individual and personalised research projects covering the core SWN research areas, i.e. smart sensing and trusted communication within energy limited heterogeneous devices in IoT enabled urban water environment; dynamic sensor web and interoperable open platform with Integrated Knowledge Management for smart water networks; data security and intelligence in IoT enabled SWN. The interdisciplinary collaborations and cross industrial interactions between ICT and water sector, will expose researchers to the academic, non-academic sectors. By combining three projects, an interoperable, secure and intelligent underlying technology leading to an open platform for IoT enabled SWN applications will be developed. This platform will then be applied to and evaluated in the real water scenarios with our end user industrial partner, leading to a specialised technology platform for SWN and a best-practice smart water network demonstrator.

DATAWiSE will develop and test building and building portfolio management tools, leveraging cross-sectoral lifecycle data on the basis of an open, secure, interoperable, and scalable framework. Utilizing cutting-edge artificial intelligence and advanced analytics, the methodology will integrate data from a variety of sources to provide a holistic understanding of building operations. DATAWiSE will design and implement a scalable data architecture that ensures data quality, privacy, interoperability, and sharing, and a Data Sharing Platform that will not only ensure interoperability and scalability but will also prioritize data sovereignty to secure data handling and preserve ownership control. DATAWiSE will develop: i) a Data-driven Building Performance Management (DBPM) toolkit which will harness Building Information Modeling (BIM) data in conjunction with advanced data mining techniques through a digital twin. ii) An AI-enhanced Lifecycle Data-driven Decision Support (LD2S) toolkit. This will serve as a versatile solution for well-informed decision-making across planning, renovation, and sustainability domains. Through these tools, the project will offer a suite of added-value services aimed at optimizing building management in various aspects: Electrical and Thermal Flexibility Management; AI-Powered Energy Forecasting and Optimization; Smart Sustainability & Comfort Balancing for Building Occupants; Adaptive Building Risk & Resilience Assessment; Circular Lifecycle Assessment; Predictive Maintenance; Integrated Sustainability Performance Management; Smart Readiness Assessment. Besides technological innovation, the project also incorporates a supportive market and policy framework designed to offer evidence-based pathways for widespread adoption and commercialization.

Future wireless networks (FWNs) will need to efficiently and flexibly provide diversified services such as enhanced mobile broadband access, ultra-reliable low-latency communications (URLLC), and massive machine-type communications . RECOMBINE joins the scientific excellence and expertise of key academic and industrial players into a joint collaborative effort to build the framework for the design oif FWNs beyond 5G that are able to support multiple operational standards for exploitation of intrinsic network heterogeneity; capable of processing information generated from a huge volume of heterogeneous sources and with sufficient intrinsic resilience to counter potential security threats. RECOMBINE will pursue innovations for advancing in the areas of mm-wave technology, licensed spectrum access, antenna design, channel propagation and modeling, and network prediction and quality of experience supported by artifical intelligence. In addition, RECOMBINE will integrate scientific and business model innovations for building a framework to fulfill the economic potential of FWNs.