Atrial fibrillation (AF) is a complex cardiac arrhythmia with multifactorial causes and poses significant global healthcare challenges due to increased morbidity, mortality, and healthcare costs. The TrackAF project will train 15 researchers to address gaps in our understanding of AF, atrial cardiomyopathy (ACM) their management and consequences by integrating advanced data-driven and mechanistic computational modelling, clinical AI and wet-lab research with a focus on extended time scales of AF aetiology and progression. Systematic identification of their longitudinal effects of age and sex on AF progression and treatment outcomes remains challenging. The 2024 European AF guidelines highlight evidence gaps, including the variability in AF and ACM evolution and the need for personalised risk prediction. TrackAF will bridge these gaps through a comprehensive, interdisciplinary and intersectoral research and training program to develop innovative approaches to AF prevention, diagnosis and treatment, supporting key health policies and contributing to the UN's Sustainable Development Goals. Our objectives include enhancing AF prediction and risk stratification using novel biomarkers, refining diagnostic techniques by multimodal stratification and improving acute and long-term management and treatment outcomes through integration of mechanistic and data-driven decision-support models. TrackAF's doctoral training will produce medical scientists with expertise in AF through a balanced structure of core research, formal training and practical secondments. This interdisciplinary approach gives doctoral candidates the ideal preparation for diverse career paths in medical science by integrating technical, ethical and legal aspects of medical research to foster innovation and collaboration across academia, industry and healthcare. The TrackAF outcomes will contribute to reducing the burden of AF and thereby improve patient quality of life across Europe and beyond.

Armenia, Georgia, Belarus and Kazakhstan are currently facing economical difficulties as consequences of economic policy, recent international financial crisis, collapse of world oil/gas prices and economcal slow down in China and other emerging countries. The situation causes an urgent need to implement structural economic reform to transform traditional economy to diversified, knowledge-based and innovation-driven economy. However, present higher education in these countries does not have a strong link to innovation and entrepreneurship. It has failed to become the drivning force to create new innovative enterprises which lead to new jobs and economic growth.This project aims to enhance innovation competences and entrepreneurial skills in engineering education through university-business cooperation in order to support creation of new enterprises, new jobs and economic growth in the partner countries.The specific objectives of the project are to:- foster an innovation culture among partner university staff and students- establish 8 innovation centers at partner countries- provide training to 104 partner country staff on innovation and entrepreneurship - introduce innovation pedagogy in teaching and learning- define transversal innovation competences and develop tools to assess such competences- introduce new courses on innovation and entrepreneurship to engineering students- promote university-business cooperation- provide business incubator and start-up accelerator services to staff and students to develop successful businesses- organize an innovation competition to promote entrepreneurship- disseminate project results to other universities in the partner countries- help partner countriey to develop a national innovation strategyThe project is coordinated by Royal Institute of Technology (KTH) in Sweden, with 2 more EU universities, 8 partner country universities, 4 education ministries as well as 1 NGO and 3 small private enterprises.

Software is everywhere and the productivity of Software Engineers has increased radically with the advent of new specification, design and programming paradigms and languages. The main objective of the project DECODER is to introduce radical solutions to increase productivity and by means of new languages that improve the situation by abstractions of the formalisms used today for requirements analysis and specification. We will develop a methodology and tools to improve the productivity of the software development process for medium-criticality applications in the domains of IoT, Cloud Computing, and Operating Systems by combining Natural Language Processing techniques, Modelling techniques and Formal Methods. The combination is a novel approach that permits a smooth transition from informal requirements engineering to deployment and maintenance phases. A radical improvement is expected from the management and transformation of informal data into material (herein called ‘knowledge’) that can be assimilated by any party involved in a development process. Thus, the DECODER project will 1) introduce new languages to represent knowledge in a more abstract manner, 2) develop transformations leading from informal material into specifications and code and vice-versa, 3) define and prototype a Persistent Knowledge Monitor for managing all relevant knowledge, and 4) develop a prototype IDE. The project will automate the transformation steps using existing techniques from the Big Data (knowledge extraction), Model-Driven Engineering (knowledge representation and refinement), and Formal Methods (specifications and proofs). The project will produce a novel Framework combining these techniques and demonstrate its efficiency on several uses cases belonging to the beforehand mentioned domains. The project expects an average benefit of 20% in terms of efforts on these use-cases and will provide recommendations on how to generalise the approach to other medium-criticality domains.