European Master in Nuclear Energy (EMINE). When academy and industry meet

EMINE master programme is an international education initiative offered by KIC-InnoEnergy under the framework of the European Institute of Innovation and Technology (EIT). Students in the programme have the opportunity to acquire an in-depth knowledge of the nuclear industry, through unique and specialised courses covering a wide range of subjects. Students choose between UPC (Barcelona) and KTH (Stockholm) for the first year and between Grenoble-INP and Paris-Saclay University (France) for the second year. Grenoble École de Management (GEM) completes the list of academic partners: students take a 3-week summer course on energy management issues after their first year in EMINE. EMINE students also benefit from the involvement of our industrial partners (AREVA, EDF, ENDESA, INSTN-CEA, and Vattenfall) in the Programme. For the academic institutions, EMINE is the opportunity to provide a high level education aligned with the industrial needs. The international collaboration among universities helps improving the quality and the adoption of best practices. EMINE attracts good students to our centres whereas the EIT funding and the industrial involvement allows a number of activities that otherwise would have been difficult to carry out, such as the assistance of external industrial experts or field activities. MSc EMINE helps tomorrow’s nuclear engineers take up the challenges the nuclear energy industry faces in terms of safety, social acceptability and waste management. By offering outstanding technical training and addressing the economic, social and political aspects of nuclear energy, the programme broadens the scope of traditional nuclear education.Postprint (published version

Competition and norms: a self-defeating combination?

This paper investigates the effects of information feedback mechanisms on electricity and heating usage at a student hall of residence in London. In a randomised control trial, we formulate different treatments such as feedback information and norms, as well as prize competition among subjects. We show that information and norms lead to a sharp – more than 20% - reduction in overall energy consumption. Because participants do not pay for their energy consumption this response cannot be driven by cost saving incentives. Interestingly, when combining feedback and norms with a prize competition for achieving low energy consumption, the reduction effect – while present initially – disappears in the long run. This could suggest that external rewards reduce and even destroy intrinsic motivation to change behaviour

Cyberbullying Detection System with Multiple Server Configurations

Due to the proliferation of online networking, friendships and relationships - social communications have reached a whole new level. As a result of this scenario, there is an increasing evidence that social applications are frequently used for bullying. State-of-the-art studies in cyberbullying detection have mainly focused on the content of the conversations while largely ignoring the users involved in cyberbullying. To encounter this problem, we have designed a distributed cyberbullying detection system that will detect bullying messages and drop them before they are sent to the intended receiver. A prototype has been created using the principles of NLP, Machine Learning and Distributed Systems. Preliminary studies conducted with it, indicate a strong promise of our approach

Adaptive Reconstruction for Electrical Impedance Tomography with a Piecewise Constant Conductivity

In this work we propose and analyze a numerical method for electrical impedance tomography of recovering a piecewise constant conductivity from boundary voltage measurements. It is based on standard Tikhonov regularization with a Modica-Mortola penalty functional and adaptive mesh refinement using suitable a posteriori error estimators of residual type that involve the state, adjoint and variational inequality in the necessary optimality condition and a separate marking strategy. We prove the convergence of the adaptive algorithm in the following sense: the sequence of discrete solutions contains a subsequence convergent to a solution of the continuous necessary optimality system. Several numerical examples are presented to illustrate the convergence behavior of the algorithm.Comment: 26 pages, 12 figure

Impedance-optical Dual-modal Cell Culture Imaging with Learning-based Information Fusion

While Electrical Impedance Tomography (EIT) has found many biomedicine applications, a better resolution is needed to provide quantitative analysis for tissue engineering and regenerative medicine. This paper proposes an impedance-optical dual-modal imaging framework, which is mainly aimed at high-quality 3D cell culture imaging and can be extended to other tissue engineering applications. The framework comprises three components, i.e., an impedance-optical dual-modal sensor, the guidance image processing algorithm, and a deep learning model named multi-scale feature cross fusion network (MSFCF-Net) for information fusion. The MSFCF-Net has two inputs, i.e., the EIT measurement and a binary mask image generated by the guidance image processing algorithm, whose input is an RGB microscopic image. The network then effectively fuses the information from the two different imaging modalities and generates the final conductivity image. We assess the performance of the proposed dual-modal framework by numerical simulation and MCF-7 cell imaging experiments. The results show that the proposed method could significantly improve image quality, indicating that impedance-optical joint imaging has the potential to reveal the structural and functional information of tissue-level targets simultaneously