Pharmaceuticals have undoubtably made our world a better place, ensuring longer and healthier lives. However, pharmaceuticals and their active metabolites are rapidly emerging environmental toxicants. It is thus critical that we fully understand, and mitigate where nec-essary, the environmental impact resulting from their production, use and disposal. In this direction, ENVIROMED addresses two aspects of the environmental impact of pharmaceuticals, a) impact of the processes in manufacturing the compound, and b) impact of the compound itself, during its lifecycle. The project narrows the knowledge gap when it comes to the effect of pharmaceutical compounds, and their derivatives, in the environment as it enables the better understanding the environmental impact of such compounds, throughout their lifecycle. It aims to offer (via extensive monitoring campaigns & scientific studies) information regarding occurrence of pharmaceuticals in the environment, their persistence, environmental fate, and toxicity (via in-vitro & in-vivo models) as well as application of in-silico methods to provide information about the basic risk management and fate prediction in the environment. Brief ideas about toxicity endpoints, available ecotoxicity databases, and expert systems employed for rapid toxicity predictions of ecotoxicity of pharmaceuticals will also be taken into account, in order to have a comprehensive approach to pharmaceuticals' Lifecycle Assessment (LCA). Moreover, the project aims at developing a set of technologies that enable greener and overall, more efficient pharmaceuticals production, which include: a) Green-by-design in-silico drug development; b) Novel sensing to allow reduction of rinsing chemicals and cycles; c) a robust Continuous Biomanufacturing line (CBM), which makes use of AI-enabled process optimisation and prediction, using data assimilation based on chemical sensing and energy disaggregation/monitoring. Training activities and a robust exploitation

YADES aims to efficiently train a network of fellows on the field of the resilience of Cultural Heritage (CH) areas and historic cities against Climate Change (CC) and other types of hazards. Towards this direction, YADES aims to introduce a research framework for downscaling the created climate and atmospheric composition as well as associated risk maps down to the 1x1 km (historic area) scale, and specific damage functions for CH materials. Applying atmospheric modelling for specific CC scenarios at such refined spatial and time scales allows for an accurate quantitative and qualitative impact assessment of the estimated micro-climatic and atmospheric stressors. YADES will perform combined structural/geotechnical analysis of the CH sites and damage assessment under normal and changed conditions, based on the climatic zone, the micro-climate conditions, the petrographic and textural features of building materials, historic data for the structures, the effect of previous restoration processes and the environmental/physical characteristics of the surrounding environment. The data coming from installed monitoring system will be coupled with simulated data (under our cultural heritage resilience assessment platform-CHRAP) and will be further analysed through our data management system, while supporting communities’ participation and public awareness. The data from the monitoring system will feed the DSS so as to provide proper adaptation and mitigation strategies. The produced vulnerability map will be used by the local authorities to assess the threats of CC (and other natural hazards), visualize the built heritage and cultural landscape under future climate scenarios, model the effects of different adaptation strategies, and ultimately prioritize any rehabilitation actions to best allocate funds in both pre- and post-event environments. To train the fellows, the project will make use of extensive workshop and training sessions, as well organise summer schools.

The ambition of PRYSTINE is to strengthen and to extend traditional core competencies of the European industry, research and universities in smart mobility and in particular the electronic component and systems and cyber-physical systems domains. PRYSTINE's target is to realize Fail-operational Urban Surround perceptION (FUSION) which is based on robust Radar and LiDAR sensor fusion and control functions in order to enable safe automated driving in urban and rural environments. Therefore, PRYSTINE's high-level goals are: 1. Enhanced reliability and performance, reduced cost and power of FUSION components 2. Dependable embedded control by co-integration of signal processing and AI approaches for FUSION 3. Optimized E/E architecture enabling FUSION-based automated vehicles 4. Fail-operational systems for urban and rural environments based on FUSION PRYSTINE will deliver (a) fail-operational sensor-fusion framework on component level, (b) dependable embedded E/E architectures, and (c) safety compliant integration of Artificial Intelligence (AI) approaches for object recognition, scene understanding, and decision making within automotive applications. The resulting reference FUSION hardware/software architectures and reliable components for autonomous systems will be validated in in 22 industrial demonstrators, such as: 1. Fail-operational autonomous driving platform 2. An electrical and highly automated commercial truck equipped with new FUSION components (such as LiDAR, Radar, camera systems, safety controllers) for advanced perception 3. Highly connected passenger car anticipating traffic situations 4. Sensor fusion in human-machine interfaces for fail-operational control transition in highly automated vehicles PRYSTINE’s well-balanced, value chain oriented consortium, is composed of 60 project partners from 14 different European and non-European countries, including leading automotive OEMs, semiconductor companies, technology partners, and research institutes.

Early prediction and management of Diabetic Foot Ulcers (DFUs) is an important health factor of Europe. Recent clinical trials have concluded that NIR sensing captures oxy(deoxy)haemoglobin (HbO2, Hb) and peripheral/ tissue oxygen saturations (StO2, SpO2), thermal Infrared-IR detects hyperthermia, among Regions of Interest (ROIs) and Mid-IR contains rich information about the proteomics, lipidomics and metabolomics (e.g., glucose). All these medical indices are important factors for early prediction of DFU. Current medical approaches are i) invasive (e.g., skin lesion biopsy), ii) requires consumables, and iii) being operated by certified physicians (e.g., ultrasound and/or biopsy). PHOOTONICS aims at developing a non-invasive, reliable and cost-effective photonics-driven device for DFU monitoring and management which can be applied for wide use. The project supports two versions: (i) the PHOOTONICS In-Home, used for DFU monitoring by patients and (ii) the PHOOTONICS PRO operated by physicians. Reliability is achieved by optimizing i) passive Hyperspectral (HIS) NIR photo-detector, with an active tuneable diode illuminator for detecting SpO2/StO2, HbO2 and Hb, ii) a thermal-IR sensor of detecting hyperthermia/hypothermia distributions in ROIs and iii) a passive Mid-IR sensing with a Quantum Cascade Laser (QCL) optimized to capture additional tissue attributes such as proteomics (elastin, collagen) and metabolomics (glucose). Cost-effectiveness is achieved by introducing i) targeted photonics technologies for DFU, ii) implementing advanced signal processing/learning algorithms to increase the discrimination accuracy while maintaining hardware cost-benefit, (iii) developing a user-friendly framework operated by non-certified physicians, and even by patients (for the In-Home version), and (iv) minimising operational cost with our non-invasive device. Clinical studies are performed to validate the reliability of the new cost-effective device in real-life settings.

Automated driving is a disruptive technology which opens the door to future multi-billion markets providing business opportunities to value chains in automotive and semiconductor industry.The European industry has leading competitive strength in the development and manufacturing of highly reliable electro-mechanical systems. In order to preserve this capability Europe needs to setup European standards for high level control such as real-time computing or big data processing. In order to respond on the global challenge AutoDrive has gathered Europe’s leading semiconductor companies, suppliers, OEMs, and research institutes committed to create a pan-European eco-system, which has the critical mass to initiate standards and provides the components and subsystems for automated driving. Currently, even the most sophisticated vehicle automation technology on the road is not able to surpass human driving capabilities – especially considering context awareness in any situation. Moreover, there is no common agreement on quantifiable dependability measures which hardware and embedded software have to achieve to allow safe automated driving for SAE Levels 3-5. AutoDrive aims for the design of (i) fail-aware (self-diagnostics), (ii) fail-safe, (iii) fail-operational (HW and SW redundancy) electronic components and systems architecture that enable the introduction of automated driving in all car categories. AutoDrive results will significantly contribute to safer and more efficient mobility. It will raise end-user acceptance and comfort by supporting drivers in highly challenging situations (active safety) as well as in regular driving situations. Combining both will reduce the number of road fatalities especially in rural scenarios and under adverse weather conditions. AutoDrive will contribute to Europe’s Vision Zero and to improved efficiency. This will sustain Leadership and even grow the market position of all AutoDrive partners.