The Advanced Solutions for Asphalt Pavements (ASAP) project involves the development of a unique road paving technology which will use a bio-bitumen rejuvenator to rejuvenate aged asphalt bitumen. This technology will help to extend the lifespan of asphalt pavements (roads) and will reduce the environmental and economic impact of roads and road maintenance processes. Recycling and self-healing processes will replace fossil fuel dependent technology. Self-healing will involve rejuvenating aged asphalt bitumen using a bio-rejuvenator developed using microalgae oils (rejuvenating bio-oil). Microalgae has been selected because of its fast growth, versatility and ability to survive within hostile environments, such as wastewater. ASAP will utilise microalgae, cultivated within the wastewater treatment process, as a source of the rejuvenating bio-oil. The solvent (Soxhlet) processes will be used to extract the oil from the microalgae. To ensure the efficiency of the oil extraction process, an ultrasonication process will be used to pre-treat the microalgae. The suitability of rejuvenating bio-oil as a replacement for the bitumen rejuvenator (fossil fuel based) will be ascertained via a series of standard bituminous and accelerated tests. A rejuvenator-binder diffusion numerical model will be developed, based on the Delft Lattice concrete diffusion model, to determine the conditions required for rejuvenation to occur and to ascertain the healing rate of the asphalt binder. These parameters will facilitate the selection and optimisation of the asphalt self-healing systems (specifically the amount of bio-oil rejuvenator and time required) to achieve full rejuvenation. This novel approach will benchmark the effectiveness of this intervention against existing asphalt design and maintenance processes and assess feasibility. The ASAP project presents an opportunity to revolutionise road design and maintenance processes and reduce its environmental and financial costs.

Data on our cultural heritage hold enormous potential for Europe’s economic growth, for the construction of a more inclusive narrative of its past, and for the future collective identity of its citizens. The biggest obstacle to unlocking that potential is the current lack of integration and interoperability among the countless datasets describing the holdings of European heritage institutions. ManuscriptAI will help remove that obstacle for the data on Europe’s medieval written heritage, manuscripts. Premodern handwritten books are a pivotal category of our heritage, yet they are currently underrepresented in large research infrastructures and their catalog data locked in digital silos. ManuscriptAI will employ machine learning algorithms to construct a model capable of facilitating the automatic integration of distinct data sources describing medieval manuscripts, under a predefined set of machine-understandable vocabulary terms. The model will be made accessible through a human engagement interface and tested during a pilot in a real-world setting. The project will fill two important desiderata: (1) a user-friendly AI-tool to allow heritage professionals to convert their metadata on manuscripts to Linked Open Data, and (2) a dedicated ontology for the description of medieval manuscripts to complete CIDOC-CRM extensions for the cultural heritage domain. The project, building on the achievements of the ERC-2018-stg PASSIM, is supported by a strong consortium of domain experts, heritage professionals and institutes, and (inter)national research infrastructures. ManuscriptAI will advance the EU’s agenda for digital heritage. The tool will help democratise datafication, making Linked Open Data accessible to small heritage institutions and actively involving them in its development. This integration tool for data on medieval manuscripts will be a huge step forward for the digital preservation and usability of Europe’s unique handwritten heritage.

The ambition of AMIGDALA-HOP-ON is to enhance the integration of global trade data and models into the ongoing integrated modelling in AMIGDALA, making the work even more relevant for policymakers and industry leaders. The objectives are (1) to further develop predictive international trade models using additional features derived from AMIGDALA research and modelling, and to align and incorporate these results with and into AMIGDALA's integrated modelling; (2) to enhance international trade and trade relations in scenario development; and (3) to advice on technology governance and policy development, from the perspective of international trade. The inclusion of granular, accurate, and global trade data as a result of AMIGDALA-HOP-ON enhances the AMIGDALA’s ability to model and predict the complex dynamics of the green transition. Using insights from detailed trade data modelling, the model framework can more precisely assess the economic and environmental impacts of international trade on the EU's transition to a low-carbon economy. This enables the identification of industrial upgrading opportunities and strengthens the accuracy of scenarios and pathways, ultimately leading to more informed policymaking and increased competitiveness. The enriched data-driven approach also fosters greater societal awareness and supports resilience and inclusivity within Europe’s research and innovation landscape. The widening applicant Policy Lab is a private research centre specialising in big data science solutions for global trade strategies and economic development policies. Its expertise lies in predictive market analytics, industrial competitiveness, and scenario studies. The team, a mix of economists and data scientists, brings key experience in economic resilience, export promotion, and strategic positioning of companies within global supply chains and related policies.