Abstract
Modern economy requires engineers to excel in collaborative and communication skills at an international setting. However, these competences are not usually addressed in most engineering curricula. In the Multinational Undergraduate Team Work course, MUTW, students develop their capstone project as members of an international team while working at their home institutions. Team members are geographically spread to assure heterogeneous teams and to promote international cooperation. This paradigm can be applied in any project/internship course unit. The results from the pilot editions that ran between 2009 and 2011 support our initial hypothesis that MUTW significantly promotes students' soft skills without requiring costly and time consuming changes to prior degree curricula.

Abstract
The presence in natural vineyard images of savage foliage, weed, multiple leaves with overlapping, occlusion, and obstruction by objects due to the shadows, dust, insects and other adverse climatic conditions that occur in natural environment at the moment of image capturing, turns leaf segmentation a challenging task. In this paper, we propose a segmentation algorithm based on region growing using color model and threshold techniques for classification of the pixels belonging to vine leaves from vineyard color images captured in real field environment. To assess the accuracy of the proposed vine leaf segmentation algorithm, a supervised evaluation method was employed, in which a segmented image is compared against a manually-segmented one. Concerning boundary-based measures of quality, an average accuracy of 94.8% over a 140 image dataset was achieved. It proves that the proposed method gives suitable results for an ongoing research work for automatic identification and characterization of different endogenous grape varieties of the Portuguese Douro Demarcated Region.

Abstract
Context. V923 Sco is a bright (V = 5.91), nearby (pi = 15.46 +/- 0.40 mas) southern eclipsing binary. Because both components are slow rotators, the minimum masses of the components are known with 0.2% precision from spectroscopy. The system seems ideal for very precise mass, radius, and luminosity determinations and, owing to its proximity and long orbital period (similar to 34.8 days), promises to be resolved with long-baseline interferometry. Aims. The principal aim is very accurate determinations of absolute stellar parameters for both components of the eclipsing binary and a model-independent determination of the distance. Methods. New high-precision photometry of both eclipses of V923 Sco with the MOST satellite was obtained. The system was spatially resolved with the VLTI AMBER, PIONIER, and GRAVITY instruments at nine epochs. Combining the projected size of the spectroscopic orbit (in km) and visual orbit (in mas) the distance to the system is derived. Simultaneous analysis of photometric, spectroscopic, and interferometric data was performed to obtain a robust determination of the absolute parameters. Results. Very precise absolute parameters of the components were derived in spite of the parameter correlations. The primary component is found to be overluminous for its mass. Combining spectroscopic and interferometric observations enabled us to determine the distance to V923 Sco with better than 0.2% precision, which provides a stringent test of Gaia parallaxes. Conclusions. It is shown that combining spectroscopic and interferometric observations of nearby eclipsing binaries can lead to extremely accurate parallaxes and stellar parameters.

Abstract

Abstract
Algorithm selection methods can be speeded-up substantially by incorporating multi-objective measures that give preference to algorithms that are both promising and fast to evaluate. In this paper, we introduce such a measure, A3R, and incorporate it into two algorithm selection techniques: average ranking and active testing. Average ranking combines algorithm rankings observed on prior datasets to identify the best algorithms for a new dataset. The aim of the second method is to iteratively select algorithms to be tested on the new dataset, learning from each new evaluation to intelligently select the next best candidate. We show how both methods can be upgraded to incorporate a multi-objective measure A3R that combines accuracy and runtime. It is necessary to establish the correct balance between accuracy and runtime, as otherwise time will be wasted by conducting less informative tests. The correct balance can be set by an appropriate parameter setting within function A3R that trades off accuracy and runtime. Our results demonstrate that the upgraded versions of Average Ranking and Active Testing lead to much better mean interval loss values than their accuracy-based counterparts.

Abstract
Power systems are becoming more complex and the need for increased awareness at the lower voltage levels of the distribution grid requires new tools that provide a reliable and accurate estimation of the system state. This paper describes an innovative state estimation method for low voltage (LV) grids that analyses similarities between a real-time snapshot comprising only a subset of smart meters with real-time communications and fully observed system states present in historical data. Real-time estimates of voltage magnitudes are obtained by smoothing the most similar past snapshots with a data-driven methodology that does not relies on full knowledge of the grid topology and electrical characteristics. Moreover, the output of the LV state estimator is a conditional probability distribution obtained with kernel density estimation. The results show highly accurate estimation of voltage magnitude, even in a scenario characterized by a strong integration of photovoltaic (PV) microgeneration.