Abstract
The problem of information overload motivated the appearance of Recommender Systems. From the several open problems in this area, the decision of which is the best recommendation algorithm for a specific problem is one of the most important and less studied. The current trend to solve this problem is the experimental evaluation of several recommendation algorithms in a handful of datasets. However, these studies require an extensive amount of computational resources, particularly processing time. To avoid these drawbacks, researchers have investigated the use of Metalearning to select the best recommendation algorithms in different scopes. Such studies allow to understand the relationships between data characteristics and the relative performance of recommendation algorithms, which can be used to select the best algorithm(s) for a new problem. The contributions of this study are two-fold: 1) to identify and discuss the key concepts of algorithm selection for recommendation algorithms via a systematic literature review and 2) to perform an experimental study on the Metalearning approaches reviewed in order to identify the most promising concepts for automatic selection of recommendation algorithms.

Abstract
In Ecuador, as in the world the most commonly used prostheses are only aesthetic, and the problem with the people that uses them is that they don’t feel fully comfortable and independent with their activities, so in looking for solving this problem, researchers have designed different active prostheses but as the technology advances, these equipment gets more complex, heavy and expensive, so the people who need them doesn’t feel acceptance. The goal of this and the further investigations is development of a new design that can properly integrate the top technologies in a skeletal design which makes natural movements and will improve the quality of life of the people who uses it. This paper analyses the different designs on the available prosthesis and extract from them the best characteristics of the upper limb prostheses design.

Abstract
Atmospheric turbulence and precise measurement of the astrometric baseline vector between any two telescopes are two major challenges in implementing phase-referenced interferometric astrometry and imaging. They limit the performance of a fibre-fed interferometer by degrading the instrument sensitivity and the precision of astrometric measurements and by introducing image reconstruction errors due to inaccurate phases. A multiple-beam acquisition and guiding camera was built to meet these challenges for a recently commissioned four-beam combiner instrument, GRAVITY, at the European Southern Observatory Very Large Telescope Interferometer. For each telescope beam, it measures (a) field tip-tilts by imaging stars in the sky, (b) telescope pupil shifts by imaging pupil reference laser beacons installed on each telescope using a 2x2 lenslet and (c) higher-order aberrations using a 9x9 Shack-Hartmann. The telescope pupils are imaged to provide visual monitoring while observing. These measurements enable active field and pupil guiding by actuating a train of tip-tilt mirrors placed in the pupil and field planes, respectively. The Shack-Hartmann measured quasi-static aberrations are used to focus the auxiliary telescopes and allow the possibility of correcting the non-common path errors between the adaptive optics systems of the unit telescopes and GRAVITY. The guiding stabilizes the light injection into single-mode fibres, increasing sensitivity and reducing the astrometric and image reconstruction errors. The beam guiding enables us to achieve an astrometric error of less than 50 mu as. Here, we report on the data reduction methods and laboratory tests of the multiple-beam acquisition and guiding camera and its performance on-sky.

Abstract
This paper proposes two unidirectional single-phase ac-dc-ac converters with a reduced number of controlled switches composed of two three-leg converters connected in series. The proposed converters allow us to feed the load voltage with sinusoidal voltages with constant amplitude and frequency and to operate with sinusoidal grid current with a high power factor. The converters can then be used as uninterruptible power supplies and unified power quality conditioners for nonregenerative applications. The system model, space-vector pulse width modulation technique, and a complete control system are given. A method to regulate the dc-link voltages using the voltage vector redundancies is presented. The proposed topologies are compared with the conventional one in terms of operation range, capability to operate with a unity grid power factor, voltage harmonic distortions, semiconductor losses, and others characteristics. Simulation and experimental results in many operating conditions are provided to validate the feasibility of the system.

Abstract
A reflective fiber optic sensor based on multimode interference for the measurement of refractive index variations in glucose aqueous solutions is proposed. The sensor is fabricated by splicing a short section of coreless silica fiber to standard single mode fiber. The influence of the coreless fiber dimensions on the sensor performance is analyzed. By changing the sensor length, no significant impact is observed. However, the reduction of the sensing head diameter leads to a large improvement of the sensitivity. The smaller sensor, with a length of 5 mm and a diameter of 24 mu m, presents a maximum sensitivity of 1467.59 nm/RIU, for the refractive index range between 1.364 and 1.397 RIU. Taking into account the acquisition system, a maximum theoretical resolution of 6.8 x 10(-5) RIU is achieved.

Abstract
Multi-stage manufacturing, typical in important industrial sectors, is inherently a complex process. The application of the zero defect manufacturing (ZDM) philosophy, together with recent technological advances in cyber-physical systems (CPS), presents significant challenges and opportunities for the implementation of new methodologies towards the continuous system improvement. This paper introduces the main principles of a multi-agent CPS aiming the application of ZDM in multi-stage production systems, which is being developed under the EU H2020 GOOD MAN project. In particular, this paper describes the MAS architecture that allows the distributed data collection and the balancing of the data analysis for monitoring and adaptation among cloud and edge layers, to enable the earlier detection of process and product variability, and the generation of new optimized knowledge by correlating the aggregated data.