Abstract
The optical Vernier effect consists of overlapping responses of a sensing and a reference interferometer with slightly shifted interferometric frequencies. The beating modulation thus generated presents high magnified sensitivity and resolution compared to the sensing interferometer, if the two interferometers are slightly out of tune with each other. However, the outcome of such a condition is a large beating modulation, immeasurable by conventional detection systems due to practical limitations of the usable spectral range. We propose a method to surpass this limitation by using a few-mode sensing interferometer instead of a single-mode one. The overlap response of the different modes produces a measurable envelope, whilst preserving an extremely high magnification factor, an order of magnification higher than current state-of-the-art performances. Furthermore, we demonstrate the application of that method in the development of a giant sensitivity fibre refractometer with a sensitivity of around 500 mu m/RIU (refractive index unit) and with a magnification factor over 850.

Abstract
This chapter presents an in-depth critical overview of the theoretical and methodological approaches that have been used to assess the impact of Universities on regional competitiveness and development, including short-term/demand-side (economic) perspective and long-term/supply side (endogenous growth, technological transfer and commercialization, and institutional) perspective. It gives special attention to the potential impacts of universities' technology transfer and entrepreneurship activities on regional competitiveness, considering the ongoing transformation process of universities towards a ‘regional engaged entrepreneurial university' model.

Abstract

Abstract
One of the great challenges in programming education is to keep students motivated while working on their programming assignments. Of the techniques proposed in the literature to engage students, gamification is arguably the most widely spread and effective method. Nevertheless, gamification is not a panacea and can be harmful to students. Challenges comprising intrinsic motivators of games, such as graphical feedback and game-thinking, are more prone to have longterm positive effects on students, but those are typically complex to create or adapt to slightly distinct contexts. This paper presents Asura, a game-based programming assessment environment providing means to minimize the hurdle of building game challenges. These challenges invite the student to code a Software Agent to solve a certain problem, in a way that can defeat every opponent. Moreover, the experiment conducted to assess the difficulty of authoring Asura challenges is described.

Abstract
Lung cancer late diagnosis has a large impact on the mortality rate numbers, leading to a very low five-year survival rate of 5%. This issue emphasises the importance of developing systems to support a diagnostic at earlier stages. Clinicians use Computed Tomography (CT) scans to assess the nodules and the likelihood of malignancy. Automatic solutions can help to make a faster and more accurate diagnosis, which is crucial for the early detection of lung cancer. Convolutional neural networks (CNN) based approaches have shown to provide a reliable feature extraction ability to detect the malignancy risk associated with pulmonary nodules. This type of approach requires a massive amount of data to model training, which usually represents a limitation in the biomedical field due to medical data privacy and security issues. Transfer learning (TL) methods have been widely explored in medical imaging applications, offering a solution to overcome problems related to the lack of training data publicly available. For the clinical annotations experts with a deep understanding of the complex physiological phenomena represented in the data are required, which represents a huge investment. In this direction, this work explored a TL method based on unsupervised learning achieved when training a Convolutional Autoencoder (CAE) using images in the same domain. For this, lung nodules from the Lung Image Database Consortium and Image Database Resource Initiative (LIDC-IDRI) were extracted and used to train a CAE. Then, the encoder part was transferred, and the malignancy risk was assessed in a binary classification-benign and malignant lung nodules, achieving an Area Under the Curve (AUC) value of 0.936. To evaluate the reliability of this TL approach, the same architecture was trained from scratch and achieved an AUC value of 0.928. The results reported in this comparison suggested that the feature learning achieved when reconstructing the input with an encoder-decoder based architecture can be considered an useful knowledge that might allow overcoming labelling constraints.

Abstract