Abstract
Model-based matrix-free wavefront reconstruction algorithms have proven to provide highly accurate results for both Shack-Hartmann and pyramid wavefront sensors in various simulation environments (OCTOPUS, YAO, COMPASS, OOMAO). Previously, test bench as well as on-sky tests were performed with the CuReD for the Shack-Hartmann sensor providing a convincing performance level together with highly reduced computational efforts. The P-CuReD is a method with linear complexity for wavefront reconstruction from pyramid sensor data which employs the CuReD algorithm and a data preprocessing step converting pyramid signals into Shack-Hartmann-like data. Here we present experimental results for the pyramid sensor being controlled with the P-CuReD on the LOOPS test bench of the Laboratoire d’Astrophysique de Marseille. Through the example of the P-CuReD a comparison of control using matrix-free Fourier domain based methods to standard interaction-matrix-based approaches is provided.

Abstract
This paper deals with a control technique based on inherent characteristics of synchronous generators (SG) for control of interfaced converters with high penetration of renewable energy resources (RERs) into the power grid, as a new contribution to earlier studies. To present an appropriate assessment of the proposed control technique, under dynamic operating condition, a P-Q curve is extracted and analysed based on the different components and characteristics of the interfaced converter as well as the conventional relationship between the active and reactive power. By combining the swing equation of SG and the power-based dynamic model, a P-m-Q curve is achieved and the effects of the variations of embedded virtual inertia on virtual mechanical power are assessed. Moreover, by using small-signal linearization, the grid frequency stability is investigated based on both virtual inertia and mechanical power variations. In order to assess the power sharing ability of the proposed control technique, two transfer functions are obtained and then, the impacts of variations of virtual mechanical power on the active and reactive power of interfaced converter are evaluated through Nyquist and Root Locus diagrams. Simulation results confirm that the proposed control technique can guarantee the operation of interfaced converters, based on inherent characteristics of SG, to deal with the power grid stability with high penetration of RERs.

Abstract
Current market demands require an increasingly agile production environment throughout many manufacturing branches. Traditional automation systems and industrial robots, on the other hand, are often too inflexible to provide an economically viable business case for companies with rapidly changing products. The introduction of cognitive abilities into robotic and automation systems is, therefore, a necessary step toward lean changeover and seamless human-robot collaboration.

Abstract
Portfolio optimization in stock markets has been investigated by many researchers. It looks for a subset of assets able to maintain a good trade-off control between risk and return. Several algorithms have been proposed to portfolio management. These algorithms use known return and correlation data to build subset of recommended assets. Dynamic stock correlation networks, whose vertices represent stocks and edges represent the correlation between them, can also be used as input by these algorithms. This study proposes the definition of constants of the classical mean-variance analysis using machine learning and weighted link prediction in stock networks (method named as MLink). To assess the performance of MLink, experiments were performed using real data from the Brazilian Stock Exchange. In these experiments, MLink was compared with mean-variance analysis (MVA), a popular method to portfolio optimization. According to the experimental results, using weighted link prediction in stock networks as input considerably increases the performance in portfolio optimization task, resulting in a gross capital increase of 41% in 84 days.

Abstract
Physician emigration can either function as an escape valve to help the health labour market clear from a supply surplus, or aggravate the problem further in case of a shortage. Either way, policy-makers should be particularly aware and devise policies to minimize the occurrence of an imbalance in the physician workforce, which may require physician retention policies if barriers to entry and other market rigidities can not be removed. To this purpose we have developed an agent-based computational economics model to analyse physician emigration, and have used it to study the impact of potential short- and long-term retention policies. As a real case study we have calibrated it with data from Portugal, which features a very particular health system with many rigidities. Results show that all policies are capable of increasing the workforce size, but not all reduce emigration. Also, the effect of return migration is non-negligible, and may substantially offset the impact on the workforce size. Furthermore, the welfare impact of the policies varies considerably. Whether policies to retain physicians should be enacted or whether policy makers should let physicians go will depend on the type of imbalance present in the health system.

Abstract
Capsule endoscopy is becoming the major medical technique for the examination of the gastrointestinal tract, and the detection of small bowel lesions. With the growth of endoscopic capsules and the lack of an appropriate tracking system to allow the localization of lesions, the need to develop software-based techniques for the localisation of the capsule at any given frame is also increasing. With this in mind, and knowing the lack of availability of labelled endoscopic datasets, this work aims to develop a unsupervised method for homography estimation in video capsule endoscopy frames, to later be applied in capsule localisation systems. The pipeline is based on an unsupervised convolutional neural network, with a VGG Net architecture, that estimates the homography between two images. The overall error, using a synthetic dataset, was evaluated through the mean average corner error, which was 34 pixels, showing great promise for the real-life application of this technique, although there is still room for the improvement of its performance. (C) 2019 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/) Peer-review under responsibility of the scientific committee of the CENTERIS -International Conference on ENTERprise Information Systems / ProjMAN - International Conference on Project MANagement / HCist - International Conference on Health and Social Care Information Systems and Technologies.