TechCare is a multi-actor approach project aiming to develop appropriate business models using innovative technologies to improve welfare management for all EU small ruminant (SR) systems, to enable stakeholders to choose animal welfare-friendly products. SRs play a key socio-economic role in Europe, especially in harsh environments where innovative technology is not much implanted. SRs are often managed as a flock/herd, allowing only average welfare states to be considered. Innovative technologies are a unique opportunity to monitor and improve SR welfare management at the individual or flock/herd level, along the value chain. Based on SR welfare state-of-the-art, TechCare will undertake a multi-actor approach to encapsulate stakeholders’ expectations in terms of welfare and innovative technologies, and provide them with adapted solutions, in a co-design approach. TechCare will cover all stages of SR production and build novel welfare approaches to develop and validate tools, early warning systems, algorithms and indicators for efficient identification of welfare issues, including positive welfare, to include them in welfare management models. The innovative technologies identified in TechCare will pass different stages of validation, from prototyping to large scale studies. TechCare identified solutions will therefore be replicable and adapted to different SR systems, production purposes, and value chains, for improvement of SR welfare management EU-wide. Business models will be constructed and validated with stakeholders. Dissemination, communication and exploitation materials will be produced and widely shared for easy use of TechCare solutions, ensuring their uptake and relevance to all sectors and stakeholders. TechCare will offer a durable improvement of SR welfare management using innovative technologies along the whole value chain and across the EU, and will act as an example in terms of approach and solutions for other species and production systems.

The objective of FitOptiVis is to develop an integral approach for smart integration of image- and video-processing pipelines for CPS covering a reference architecture, supported by low-power, high-performance, smart devices, and by methods and tools for combined design-time and run-time multi-objective optimisation within system and environment constraints. Low latency Image processing is often crucial for autonomy, and performing the right interaction of the CPS with its environment. The most important CPS in the project have sensors and processing at distributed places. For many reasons (parts of) CPS has to operate on low energy, whereas the complete system needs results with low latency. The focus of the project is on multi-objective optimisation for performance and energy use. However, other qualities, like reliability, security etc. also play a role in the optimisation.

The Shift2SDV project aims to revolutionize the European automotive domain by creation of an SDV ecosystem around middleware & API framework enabling collaboration across the automotive value chain. This ambitious endeavour envisions a comprehensive shift towards a modular framework that transcends the limitations of current monolithic systems, fostering agility and innovation through the development of complementary middleware services and software development solutions. Central to Shift2SDV is the development of a cutting-edge middleware framework that provides micro-services to build automotive applications upon, abstracting from underlying hardware components – supporting stepwise migration, open source and proprietary components, in-vehicle safety critical and off-vehicle cloud functionality. It is specifically designed to streamline software development and integration while ensuring compatibility and flexibility with existing and emerging technologies. Key technical objectives include the development of a modern, flexible micro-services-based architecture, middleware framework that simplifies the brand-specific application development, and establishment of a safe and secure system architecture compliant with functional safety standards. Additionally, the project aims to develop an orchestration for efficient resource management, integrate edge and cloud computing, and demonstrate the practical viability of the developed middleware through concrete use cases. To maximize impact, Shift2SDV prioritizes active communication, dissemination, and exploitation of project outcomes, fostering collaboration among stakeholders and existing projects and initiatives aligning technological advancements with market demands. Through these concerted efforts, Shift2SDV seeks to propel European leadership in Software Defined Vehicles, driving innovation and economic growth in the automotive industry.

The project idea is focusing on AI-augmented automation supporting modeling, coding, testing, and monitoring as part of a continuous development in Cyber-Physical Systems (CPSs). The growing complexity of CPS poses several challenges throughout all software development and analysis phases, but also during their usage and maintenance. Many leading companies have started envisaging the automation of tomorrow to be brought about by Artificial Intelligence (AI) tech. While the number of companies that invest significant resources in software development is constantly increasing, the use of AI in the development and design techniques is still immature. The project targets the development of a model-based framework to support teams during the automated continuous development of CPSs by means of integrated AI-augmented solutions. The overall AIDOaRT infrastructure will work with existing data sources, including traditional IT monitoring, log events, along with software models and measurements. The infrastructure is intended to operate within the DevOps process combining software development and information technology (IT) operations. Moreover, AI technological innovations have to ensure that systems are designed responsibly and contribute to our trust in their behaviour (i.e., requiring both accountability and explainability). AIDOaRT aims to impact organizations where continuous deployment and operations management are standard operating procedures. DevOps teams may use the AIDOaRT framework to analyze event streams in real-time and historical data, extract meaningful insights from events for continuous improvement, drive faster deployments and better collaboration, and reduce downtime with proactive detection.

ICT is embedded and pervasive into our daily lives. The notion of Cyber Physical Systems (CPS) has emerged: embedded computational collaborating devices, capable of controlling physical elements and responding to humans. The Cross-layer modEl-based fRamework for multi-oBjective dEsign of Reconfigurable systems in unceRtain hybRid envirOnments (CERBERO) project aims at developing a design environment for CPS based of two pillars: a cross-layer model based approach to describe, optimize, and analyze the system and all its different views concurrently; an advanced adaptivity support based on a multi-layer autonomous engine. To overcome the limit of current tools, CERBERO provides: libraries of generic Key Performance Indicators for reconfigurable CPSs in hybrid/uncertain environments; novel formal and simulation-based methods; a continuous design environment guaranteeing early-stage analysis and optimization of functional and non-functional requirements, including energy, reliability and security. CERBERO effectiveness will be assessed in challenging and diverse scenarios, brought by industrial leaders: an embedded CPS with self-healing capabilities for planetary explorations (TASE-S&T), an ocean monitoring CPSoS (AS), and a Smart Travelling CPSoS for Electric Vehicle (TNO-CRF-S&T). CERBERO will automate multi-objective decisions to meet requirements and correct/optimized–by–construction designs. Interoperable components (i.e. DynAA by TNO, AOW by IBM, PREESM by INSA, PAPI-ARTICo3 by UPM, MDC by UniCA-UniSS) will be enhanced with additional features (as security, USI), mostly released as open-source to foster open innovation and a real path to standardisation, and integrated (IBM- AI) into a unique framework. Design speed up (one order of magnitude), increased performance (30% less energy) and reduced costs of deployment (by rapid prototyping and system in the loop incremental design) and maintenance (by runtime verification and adaptivity) of CPSoS are expected.