Today, the question of renewable energy should permeate all types and sizes of organizations and more specifically emerging structures that are composed of dynamic and engaged team members. At the same time, the progress in terms of developing the use of renewable energy depends on the possibility to formulate a vast scope of questions, objectives, and business answers. This only can take place in an agenda that fully integrate all the potentials of the EU entrepreneurial spirit. Therefore, this project aims at promoting a dynamic interdisciplinary perspective that articulates the corporate, scientific, and pedagogical skills and knowledge that characterize the two fields of renewable energy and entrepreneurship.EntRENEW project brings together 5 leading universities and 1 technical company from 6 EU MSs to achieve the following specific objectives:SO 1: FACILITATE THE EXCHANGE , FLOW AND CO-CREATION OF NEW KNOWLEDGE -Bringing together partners from top EU universities in the field of innovative and sustainable entrepreneurship and energy studies in order to create a trans-disciplinary innovative content tailored to deepen the technical knowledge of students with business background and strengthen the entrepreneurial mindset and skills of students from engineering majors.SO 2: ENABLING PROFESSORS TO TEACH TRANS-DISCIPLINARY STUDIES THROUGH INNOVATIVE METHODS AND IT TOOLS-Develop a new teaching course content on Entrepreneurship in renewable energy (ERE) being interdisciplinary and cross sectoral topic fostering direct link of HE to innovation and business world-Develop an Innovating Teaching Methodology through Blended Learning, Trans-Disciplinary Approach to content design and Applying the Principles of GamificationSO 3: STIMULATE SYNERGIES BETWEEN UNIVERSITIES AND ENTREPRENEURIAL SUPPORT SYSTEMS THROUGHOUT EUROPE-Improve future integration to the labour market;-Stimulate self-employment prospects of MA graduates by granting them training and access to networks for business purposes in Europe (startup hubs in climate change and entrepreneurship);-Increase of entrepreneurship initiatives in Europe in the field of Renewable energyThe project will last 36 months and will be divided in 6 WP, 5 Intellectual outpus (IOs), management and implementation activities and 5 national multiplier events with prominent international guests and short demo sessions. The EntRENEW methodology aims at i) specifying the blended-learning pedagogical approach (WP1 – IO1), ii) developing training contents for the EntRENEW course (WP2 – IO2), iii) developing the technical platform and implementing these contents on innovative LMS designed for the needs of EntRENEW project (WP3 – O3), and iv) testing the blended-learning course with students, optimisation and integration in the MA programmes of consortium HEIs (WP4 – O4). The final result from EntRENEW project is the development of a trans-disciplinary HE blended-learning course at the intersection of entrepreneurship and technology (smart, sustainable and renewable energy applied studies) addressing students enrolled in MA programmes in both business schools, engineering and design schools and joint study programmes and potentially being replicated in other educational contexts. The long-term impact will be to create a new generation of decision makers who will explore concrete entrepreneurial solutions in support of EU countries facing the important challenge of maintaining their social and economic performance and being more eco-responsible, especially in terms of energy.

For years, research and policy have been calling for science teaching that motivates student learning through relevant phenomena and problems, provides students with opportunities for meaningful peer collaboration, and focuses on building a need-to-know about a small set of core science ideas during a series of connected learning experiences. We refer to such instruction as “coherent”, and while coherent instruction has long been advocated within science teacher education programs, new teachers struggle to implement pedagogical approaches emphasized within science teacher education programs and abandon those in favor of more traditional methods. In a recent project called PICoSTE, partners collaborated to identify promising practices for helping new science teachers to enact coherent science instruction, and we recognized that while planning and reflection tools have the potential to function as powerful bridging elements between university-based courses and school-based field experiences, a consistent and coherent set of planning and reflection tools for science teacher education in a European context did not exist. The central objective of this project is to design and test a suite of planning and reflection tools as well as associated learning modules for supporting preservice teachers in both better understanding the principles of coherent science instruction and enacting coherent science instruction in schools. Partners in this project include a group of science teacher educators from Germany (Leibniz Institute for Science and Mathematics Education (IPN), University of Duisburg-Essen), Norway (University of Bergen), Sweden (Halmstad University), Denmark (University of Copenhagen), Finland (University of Helsinki), and Turkey (Usak University). Project leaders at each institution have extensive experience in science teacher education and the design and enactment of coherent science instruction, and project teams include university-based master teachers, school-based mentor teachers, and research specialists responsible for designing and empirically testing tools and modules developed within the project. The planning and reflection tools and science teacher education learning modules are developed and tested using an iterative design-based, outcome-driven process. To begin this process, we clarify outcomes by collaborating on the creation of a core ideas framework for coherent science instruction, which is based on existing research literature and policy documents. This framework guides the identification of observable target performances and the elaboration of benchmarks on the way to meet those target performances. These benchmarks then guide the development process and provide a roadmap for conducting ongoing formative assessment that informs iterations based on evidence and feedback from stakeholders (e.g., preservice science teachers, mentor teachers). This design process results in tools and modules that are consistent and coherent with each other and have been tested and revised through practice. The developed tools and modules will finally be discussed and shared among local, national, and EU stakeholders for science teacher education. The central objective of this project is to provide new science teachers with a coherent set of learning experiences and a consistent set of concrete planning and reflection tools that will help span the current chasm between university-based science teacher education, school science instruction, and ministry goals. Through these efforts, we hope to progress toward the ultimate goal of broadening school students' access to science instruction that is more engaging, more comprehensible, and more meaningful to their lives outside of school.

There is enough waste energy produced in the EU to heat the EU’s entire building stock; however despite of this huge potential, only a restricted number of small scale examples of urban waste heat recovery are present across the EU. The objective of REUSEHEAT is to demonstrate, at TRL8 first of their kind advanced, modular and replicable systems enabling the recovery and reuse of waste heat available at the urban level. REUSEHEAT explicitly builds on previous knowledge and EU funded projects (notably CELSIUS, Stratego and HRE4) and intends to overcome both technical and non technical barriers towards the unlocking of urban waste heat recovery investments across Europe. Four large scale demonstrators will be deployed, monitored and evaluated during the project, showing the technical feasibility and economic viability of waste heat recovery and reuse from data centres (Brunswick), sewage collectors (Nice), cooling system of a hospital (Madrid) and underground station (Berlin). The knowledge generated from the demonstrators and from other examples across the EU will be consolidated into a handbook which will provide future investors with new insight in terms of urban waste heat recovery potential across the EU. Innovative and efficient technologies and solutions, suitable business models and contractual arrangements, estimation of investment risk, bankability and impact of urban waste heat recovery investments, authorization procedures are examples of handbook content. The handbook will be promoted through a powerful dissemination and training strategy in order to encourage a rapid and widespread replication of the demonstrated solutions across the EU.

The XPM project aims to integrate explanations into Artificial Intelligence (AI) solutions within the area of Predictive Maintenance (PM). Real-world applications of PM are increasingly complex, with intricate interactions of many components. AI solutions are a very popular technique in this domain, and especially the black-box models based on deep learning approaches are showing very promising results in terms of predictive accuracy and capability of modelling complex systems. However, the decisions made by these black-box models are often difficult for human experts to understand – and therefore to act upon. The complete repair plan and maintenance actions that must be performed based on the detected symptoms of damage and wear often require complex reasoning and planning process, involving many actors and balancing different priorities. It is not realistic to expect this complete solution to be created automatically – there is too much context that needs to be taken into account. Therefore, operators, technicians and managers require insights to understand what is happening, why it is happening, and how to react. Today’s mostly black-box AI does not provide these insights, nor does it support experts in making maintenance decisions based on the deviations it detects. The effectiveness of the PM system depends much less on the accuracy of the alarms the AI raises than on the relevancy of the actions operators perform based on these alarms. In the XPM project, we will develop several different types of explanations (anything from visual analytics through prototypical examples to deductive argumentative systems) and demonstrate their usefulness in four selected case studies: electric vehicles, metro trains, steel plant and wind farms. In each of them, we will demonstrate how the right explanations of decisions made by AI systems lead to better results across several dimensions, including identifying the component or part of the process where the problem has occurred; understanding the severity and future consequences of detected deviations; choosing the optimal repair and maintenance plan from several alternatives created based on different priorities; and understanding the reasons why the problem has occurred in the first place as a way to improve system design for the future.