Dispatcher3 will develop a software prototype for the acquisition and preparation of historical flight data in order to give support to the optimisation of future flights providing predictive capabilities and advice to dispatchers and pilots. This will be done considering airline preferences and the impact of flight missions on overall airline objectives. Dispatcher3 focuses on activities prior to departure: dispatching and pilot advice on how to operate the flight. Dispatcher3 is composed of three layers: data infrastructure, predictive capabilities and advice capabilities. The data infrastructure will be powered by DataBeacon, a multi-sided and open-source data storage and processing platform. DataBeacon provides private environments, secure data frames, a full-stack artificial intelligence environment and a scalable highly available on-demand cluster. DataBeacon has been developed and successfully been used in other initiatives by members of the consortium. The infrastructure will allow further developments, based on data science techniques, to be built on the pre-processed datasets. The predictive capabilities will be provided by the development of two modules: data acquisition and preparation, encompassing data wrangling and descriptive analytics, and a predictive model, which will perform target variable labelling and feature engineering, plus the training, testing and validation of machine learning predictive models for targeted airlines' KPIs. With the same predictions, different advice could be generated considering user policies. The advice capabilities of Dispatcher3 will be provided by a dedicated advice generator module, which will collect all the information from the predictive analytics and build a decision framework, which could be used by dispatchers and pilots. Dispatcher3 fits within the activities of CS2 Systems ITD WP1.3 "FMS and functions" and addresses some of the high-level objectives and challenges for this ITD defined by CS2.

The Language MagicianLanguage learning in schools in the EU has been showing ever more dramatic differences between member states. This has been particularly true for the United Kingdom where in 2014 languages were made a statutory requirement in primary schools in England without stipulating the targets for assessing the young language learners' progress. Whilst language teaching in primary schools is the foundation for a successful language strategy at all levels of education, research has shown a strong decline in motivation for pupils once they enter secondary education. Two of the main factors for this are the lack of assessment of learning progress in the UK and traditional, de-motivating testing methods in other member states. Mindful of the need for urgent action regarding this issue, educational organisations with a stake in the improvement of language learning provision decided to enter a strategic partnership under the Erasmus + programme. Their main aim was to combine primary language learning with a method of assessment which motivates young learners to continue learning languages. The participating organisations agreed to improve language teaching and the assessment of linguistic competencies in primary schools by producing an assessment tool in the format of a computer game. For the development of this game the partners used a common core of curricula from Germany, Italy, Spain and the UK as its foundation and – with the respective language versions - created converging standards on a European level. The ten partner organisations were chosen for their expertise in promoting languages, research, teaching primary languages, assessment, translation, e-learning and language games. The Goethe-Institut London, a supporter of language teachers in the UK and all EU member states, was project lead. Together with its British and other European partners, it identified and developed the project idea. Both the Spanish Embassy in the UK and the British language teachers network ALL were extremely helpful and influential in discussing the need to address the imbalance of language learning in the UK and other member states. The Universities of Reading and Leipzig were identified as leading primary language research experts while the University of Perugia was chosen as assessment expert. In order to ensure the necessary practical focus, the Education Ministry of Tenerife supported the development of the tool for Spanish and together with the Education Ministry in Rioja conducted the piloting in its regions. The University of Siena with its e-learning centre joined the partnership and provided the basis for the development of the language game for Italian. The University of Westminster added its translation and localisation expertise for French in the UK. As the project progressed, this tool was tested in 40 schools with overall 6000 pupils in four countries and the outcomes were evaluated using standard quality criteria. In order to support the anchorage of the assessment tool in state schools. a specific training manual for teachers was developed at the Education Ministry in Rioja. Teachers can now use this methodology to be implemented in their schools. Project partners using this manual will draw on this experience for their own teacher training courses.The design of the game was based on intensive research to provide insight into assessing progress in language learning. To enable teachers to analyse the students’ attainment swiftly and with high validity, the so-called “Teacher Dashboard” was used to develop the new tool. This was part of the programming of an existing computer game developed by the Goethe-Institut’s head office in Munich in cooperation with the software company OVOS. The game was then piloted to calibrate the e-learning content to ensure validity of content and construct. The feedback of pupils, teachers, parents and external educators was recorded, analysed and integrated in the implementation of the game. Thus it was possible to test the game in various classroom situations and the obtained data clearly showed the pupils’ progress. The results were compared with traditional pen and paper tests and showed highly significant validity. The researchers’ papers clearly state that THE LANGUAGE MAGICIAN is a fully fledged assessment tool for young language learners.The project has shown benefits for all participants. The feedback from the multiplier events shows what an innovative tool THE LANGUAGE MAGICIAN has proved to be. Students clearly found language learning more enjoyable. This means their learning outcomes will be better and their acquisition of transversal skills will be enhanced. Teachers can now develop their professional skills further using this innovative method and digital advantages. The participating universities have benefited from research and disseminated them to a European and worldwide educational audience.

The DIGITbrain project is deeply rooted in the innovation ecosystem of the I4MS project CloudiFacturing and the industrial platforms FIWARE and IDS, and it will build on these results, by means of extending the CloudiFacturing solution with an augmented digital-twin concept called “Digital Product Brain” (DPB) and a smart business model called “Manufacturing as a Service” (MaaS). By having access to on-demand data, models, algorithms, and resources for industrial products (i.e. mechatronic systems supporting the production of other products), the DBP will enable their customisation and adaptation according to individual conditions. The availability of industrial-product capacity will facilitate the implementation of MaaS, which will allow manufacturing SMEs to access advanced manufacturing facilities within their regions or to distribute their orders across different ones. The DIGITbrain project will address four principles that will foster the uptake of advanced digital and manufacturing technologies. A) Technology: leverage edge-, cloud- and HPC-based modelling, simulation, optimisation, analytics, and machine learning tools and augment the concept of digital twin with a memorizing capacity that records the provenance of the industrial product over its full lifecycle. B) Feasibility: support more than 20 highly innovative cross-border experiments, bringing together technology providers and manufacturing end users, and facilitating cost-effective distributed and localised production, based on on-demand manufacturing machine capacity. C) Sustainability: coach and empower DIHs to implement the smart business model MaaS and contribute to their long-term sustainability, by increasing their portfolio with services tailored to the industrial needs of their regions. D) Network of DIHs: engage DIHs across Europe that implement MaaS, enable manufacturing SMEs to co-create and experiment with digital innovations before investing, and attract national and regional funding.