Despite the huge public budget effort (6.000M€/year) for road pavement maintenance, the European road network (5.5M km) is not in an acceptable condition. Current pavement maintenance strategies are mainly based on corrective maintenance which is an inefficient and costly approach, with negative impact on pavement service life and road safety, and also on the environment. In order to be able to implement a maintenance strategy based on preventive operations of much lower cost carried out at the optimal moment (predictive maintenance), it is necessary to have continuous and accurate information of the pavement condition, something that is not possible at present due to the high cost of current inspection services. PAV-DT is a disruptive technology that can be installed in any customer vehicle (e.g. public road administrators and concessionaires or construction companies on performance-based maintenance contracts) in order to convert these vehicles into a very low-cost real-time pavement inspection equipment through its ordinary circulation. Additionally, thanks to our advanced algorithm and a cloud-based platform, customers will be able to access the latest available information on the pavement condition at any moment and receive information on which maintenance actions are really required, and exactly where they should be applied and when is the best moment to deploy a truly cost-effective maintenance strategy. PAV-DT consortium formed by APPLUS (Spain), BECSA (Spain), M&S (Austria), MICRO-SENSOR (Germany) and IMM-UPV (Spain) agreed on creating a Joint Venture for the commercial exploitation of the results as soon as the project is completed through a Product-as-a-service business model. Revenues of more than 38.8M€, with an associated profit of 25.5M€ and the creation of 52 new highly qualified jobs are expected within the first 3 years. Thanks to this investment in PAV-DT, these customers will experience savings of more than 746M€ over that period.

Languages have always played an important role when groups of people were living side by side. Speakers might be suppressed or take up a new language. But as much as a language says a lot about individual identity, it also carries a large reference to the culture and history of the group in which it is spoken and the cultural background. It is tightly interwoven with emotions and traditions, which are worth keeping up, as they stand for personal roots. RISE UP interconnects all relevant knowledge actors including citizens, civil society and end users. It collects and analyses background information, it identifies good practices and develops new methods with the help and support of people being concerned and interested in the topic. Thus, RISE UP aims at the empowerment of these endangered language communities, fostering their self-confidence and overcoming past trauma. In the understanding of the RISE UP consortium these endangered language communities include learners, new speakers, people who have not had the chance to learn their heritage languages, supporters, and other interested parties as well as actual speakers. RISE UP explores and deals with (a) Context, reasons and policies for endangered languages within Europe (b) Collecting and creating a set of tools to support local communities (c) Interconnecting relevant groups of stakeholders and (d) Involving and attracting especially young people and other stakeholders, e.g. by using digital tools.

The MAGDA project aims at developing a toolchain for atmosphere monitoring, weather forecasting, and severe weather/irrigation/crop monitoring advisory, with GNSS (including Galileo) at its core, to provide useful information to agricultural operators. MAGDA will exploit the untapped potential of assimilating GNSS-derived, drone-derived, Copernicus EO-derived datasets, in situ weather sensors into very high-resolution, short-range (1-2 days ahead) and very short-range (less than 1 day ahead) numerical weather forecasts to provide improved prediction of severe weather events (rainfall, snow, hail, wind, heat and cold waves) as well as of weather-driven agriculture pests and diseases to the benefit of agriculture operations, also in light of ongoing effects of climate change. These targets will be achieved by setting up a database of variables of interest, and an assimilation system to feed a numerical weather prediction model, which in turn drives a hydrological model for irrigation performance and water accounting to assess water use and related productivity. In addition to already existing observational networks, new dedicated networks of sensors, including GNSS and drones, to monitor atmospheric variables at high spatial resolution will be deployed in the vicinity of large farms and cultivated areas, to provide data with high spatial and temporal resolutions for the assimilation into the weather model. The delivery of the augmented forecasts and irrigation advisories to farmers will be enabled by a dedicated dashboard and APIs to already existing Farm Management Systems. The tools developed within MAGDA will represent the technical and methodological components based on which services to support agricultural operations will be defined.

Artificial Intelligence technology is already having a great impact in many areas, especially including the manufacturing sector. The integration of AI with advanced manufacturing technologies and systems makes it possible to exploit the full potential in the manufacturing industry by achieving a higher level of adaptability, efficiency and robustness. At the same, such systems will be human centric and promote the inclusion and cooperation with humans during planning and execution which can help to improve the quality of products and processes. Both the EU and Japan have recognised these new development trends and their importance. In order to widely deploy these technologies, special attention is given to international cooperation and exchange of knowledge between EU and Japan for AI-driven innovation in manufacturing. The EU-Japan.AI project is responding to this need by implementing a platform-based approach to connect all the relevant stakeholders from EU and Japan working on AI applications for manufacturing. This platform, beside other tools, will include an open-information hub, encouraging the exchange of information on the respective research programmes and technological results. This will be supported by distribution of topic relevant materials, information on upcoming events and matchmaking opportunities and twinning activities to establish a vibrant and connected network at the heart of the platform, where a community of practice approach will facilitate the cooperation of all the participants. Convergence workshops will help to establish how research and innovation projects should address AI for manufacturing, the needs and requirements for AI from the point of manufacturers’ view as well as to address current needs and future requirements. Overall, the project aims to establish, stimulate and support a long-term cooperation between the participants, by connecting them via the project’s platform and by using modern, online-driven awareness approaches.

Our long-term vision is to develop a smart digital pathology slide scanner that can (semi)automatically generate a patient-specific tumour digital twin and simulate various drug treatments on such twin. The digital twin will be generated by combining slide-level multi-omics profiling of patients’ biopsies with other standard clinical data and will be hardware-embedded within a slide scanner. This foundational architecture involves five pillars: experimental mapping of tumor communication, reconstruction of Biomedical Knowledge Graphs (KG) based on experimental data, identification of structural and temporal properties of communication networks, development of a mathematical framework for modeling treatment effects on a tumor, and the design of a novel hardware acceleration architecture for a medical digital twin. As a model system, we will use pancreatic cancer, combining in vitro experiments with clinical data. We will analyze how tumor microenvironment composition affects cellular crosstalk and drug efficacy by developing, growing, and treating a series of tumor organoids with different TME compositions, and performing a detailed molecular analysis of samples. Clinical validation will involve matching organoid and patient biopsy structures, ensuring relevance and applicability of our findings. This project is the initial step towards a platform assisting doctors in improving the diagnosis and assessment of drug treatment efficacy for individual cancer patients so that each patient gets the best possible drug, with the best possible treatment regimen.