This project will develop a decision-support system (DSS) for disaster risk management by considering multiple interacting natural hazards and cascading impacts using a novel resilient-informed and service-oriented approach that accounts for forecasted modifications in the hazard (e.g., climate change), vulnerability/resilience (e.g., aging structures and populations) and exposure (e.g., population decrease/increase). The primary deliverable from MEDiate will be a decision support framework in the form of service-orientated web tool and accompanying disaster risk management framework providing end users (local authorities, businesses etc) with the ability to build accurate scenarios to model the potential impact of their mitigation and adaptation risk management actions. The scenarios, which can be customised to reflect local conditions and needs (e.g., demographics, deprivation, natural resources etc), will be based on a combination of the historical record and future climate change projections to forecast the location and intensity of climate related disaster events and to predict their impacts, including cascading impacts, on the vulnerability of the local physical, economic and social systems. The scenarios will allow end users to evaluate the potential impact of different risk management strategies to reduce vulnerability and enhance community resilience. The project will consist of analysis of relevant data and co-development with testbed decision-makers of a DSS to enable more reliable resilience assessments, accounting for risk mitigation and adaptive capabilities, to be made, therefore reducing losses (human, financial, environmental etc) from future climate-related and geophysical disasters. The project will involve a multi-disciplinary team of geophysical and meteorological scientists, risk engineers, social scientists, information technologists and end-users, working together to ensure that the system is user-led and supported by appropriate technology.

Supercomputers have been extensively used to solve complex scientific and engineering problems, boosting the capability to design more efficient systems. The pace at which data are generated by scientific experiments and large simulations (e.g., multiphysics, climate, weather forecast, etc.) poses new challenges in terms of capability of efficiently and effectively analysing massive data sets. Artificial Intelligence, and more specifically Machine Learning (ML) and Deep Learning (DL) recently gained momentum for boosting simulations’ speed. ML/DL techniques are part of simulation processes, used to early detect patterns of interests from less accurate simulation results. To address these challenges, the ACROSS project will co-design and develop an HPC, BD, and Artificial Intelligence (AI) convergent platform, supporting applications in the Aeronautics, Climate and Weather, and Energy domains. To this end, ACROSS will leverage on next generation of pre-exascale infrastructures, still being ready for exascale systems, and on effective mechanisms to easily describe and manage complex workflows in these three domains. Energy efficiency will be achieved by massive use of specialized hardware accelerators, monitoring running systems and applying smart mechanisms of scheduling jobs. ACROSS will combine traditional HPC techniques with AI (specifically ML/DL) and BD analytic techniques to enhance the application test case outcomes (e.g., improve the existing operational system for global numerical weather prediction, climate simulations, develop an environment for user-defined in-situ data processing, improve and innovate the existing turbine aero design system, speed up the design process, etc.). The performance of ML/DL will be accelerated by using dedicated hardware devices. ACROSS will promote cooperation with other EU initiatives (e.g., BDVA, EPI) and future EuroHPC projects to foster the adoption of exascale-level computing among test case domain stakeholders.

The "A Soil Deal for Europe" Mission aims to accelerate the transition to healthy soils by 2030 through the establishment of 100 Soil Health Living Labs and Lighthouses (SHLL/LHs) that will drive the development and adoption of solutions. The Framework Partnership SOILL, led by ENoLL (the international association of certified Living Labs), has been created to coordinate, support, expand, and promote the network of 100 SHLL/LHs. During the initial two years of SOILL, the SOILL-Startup project will collaborate with the first waves of SHLL/LHs and key stakeholders to launch the SOILL one-stop structure. This involves: 1) Establishing a high-quality structure for SHLL/LHs with clear processes and procedures. A web-based Hub will be created to facilitate outreach, collaboration, support, and monitoring within and beyond the 100 SHLL/LHs network. 2) Providing comprehensive support to SHLL/LHs for a harmonized and sustainable approach within the network. This includes capacity building through training and supporting tools on methodologies, technical aspects, and cross-cutting areas. Regular monitoring and evaluation of SHLL/LHs will be conducted based on a consolidated assessment framework. 3) Supporting network expansion by promoting, engaging, matchmaking, and advising applicants to Mission calls. Events will be organized to showcase funding opportunities, provide examples, and disseminate information on the SHLL/LH concept and its implementation. Webinars, coaching, and matchmaking will assist applicants in conceptualization and consortia creation for their applications. 4) Facilitating knowledge exchange, collaboration, and synergy creation within the SHLL/LHs network and the wider community. SOILL-Startup will enhance the visibility and accessibility of the SHLL/LHs, their work, and achievements. Mutual-learning events, collaborative platforms, mutual visits, and matchmaking will facilitate collaboration opportunities, knowledge sharing, and solution uptake.