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.

Our common heritage is a central element of our communities and economies, and a principal but vulnerable dimension of our common identity as Europeans. It has been proven that cultural heritage contributes to well-being, social cohesion, identity, local economy, territorial attractiveness, and environmental sustainability, but the climate crisis and natural hazards endanger this heritage. We propose RescueME to take immediate action for demonstrating how an innovative data-driven, community-based, heritage-centric actionable landscape approach to resilience enhancement can protect our cultural heritage and landscapes while supporting the transition toward a green society and economy that sustains resilient, cohesive, nature-connected communities. RescueME proposes a call for action, broadening the scope, triggering action, untapping and mobilizing resources, engaging actors, and facilitating the decision making and the implantation of co-created just resilience solutions to protect our common heritage. RescueME will develop, test and demonstrate the effectiveness of an Actionable Framework based on the Resilient Historical Landscape approach (RHL) complemented by data, models, methods, and tools able to assess risks and opportunities, co-develop inclusive and just resilience strategies and innovative solutions to protect European cultural heritage and cultural landscapes from climate change, disaster risk, as well as other stressors (such as pollution and over-tourism) with special focus on European coastal landscapes since a large share of this endangered heritage there. The five case studies (Psiloritis in Creta, Neuwerk in Hamburg, Portovenere, Cinque Terre & the Islands, València and the city of Zadar) have been selected carefully as complementary representatives of European coastal landscapes. They will act as resilience landscape laboratories (R- labscapes), validate the results and ensure their replicability.

The term first responders usually refers to law enforcement, fire, and emergency medical personnel. These responders, however, are not the only assets that may be required in the aftermath of a strike on the homeland. In contrast, the more appropriate term, emergency responders, comprises all personnel within a community that might be needed in the event of a natural or technological (man-made) disaster or terrorist incident. These responders might include hazardous materials response teams, urban search and rescue assets, community emergency response teams, anti-terrorism units, special weapons and tactics teams, bomb squads, emergency management officials, municipal agencies, and private organizations responsible for transportation, communications, medical services, public health, disaster assistance, public works, and construction. In addition, professional responders and volunteers, private nonprofit, nongovernmental groups (NGOs), such as the Red Cross, can also play an important role in emergency response. As a result, the tasks that a national emergency response system would be required to perform are more complex than simply aiding victims at the scene of a disaster, carried out by several kinds of professional users with different roles and expertise. Moreover, emergency preparedness and response lifecycle is a complex process that consists of the preparation, response, and recovery from a disaster, including planning, logistical support, maintenance and diagnostics, training, and management as well as supporting the actual activities at a disaster site and post-recovery after the incident.

The SoundCity Project MONICA aims to provide a very large scale demonstration of multiple existing and new Internet of Things technologies for Smarter Living. The solution will be deployed in 6 major cities in Europe. MONICA demonstrates a large scale IoT ecosystem that uses innovative wearable and portable IoT sensors and actuators with closed-loop back-end services integrated into an interoperable, cloud-based platform capable of offering a multitude of simultaneous, targeted applications. All ecosystems will be demonstrated in the scope of large scale city events, but have general applicability for dynamically deploying Smart City applications in many fixed locations such as airports, main traffic arterials, and construction sites. Moreover, it is inherent in the MONICA approach to identify the official standardisation potential areas in all stages of the project. MONICA will demonstrate an IoT platform in massive scale operating conditions; capable of handling at least 10.000 simultaneous real end-users with wearable and portable sensors using existing and emerging technologies (TRL 5-6) and based upon open standards and architectures. It will design, develop and deploy a platform capable of integrating large amounts of heterogeneous, interoperable IoT enabled sensors with different data capabilities (video, audio, data), resource constraints (wearables, Smartphones, Smartwatches), bandwidth (UWB, M2M), costs (professional, consumer), and deployment (wearable, mobile, fixed, airborne) as well as actuators (lights, LED, cameras, alarms, drones, loudspeakers). It will demo end-to-end, closed loop solutions covering everything from devices and middleware with semantic annotations through a multitude of wireless communication channels to cloud based applications and back to actuation networks. Humans-in-the-Loop is demonstrated through integrating Situational Awareness and Decision Support tools for organisers, security staff and sound engineers situation rooms.

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.