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.

Abstract
This study presents and applies generalized angular phase space analysis to lower limb joint angles of specific subject during normal and modified gait for discrimination of gait and joint angular movements. Case study of an adult healthy male in-vivo and noninvasive kinematic assessment of skin surface adhesive markers at lower limb was performed at human movement lab during normal gait, stiff knee gait and slow running. Musculoskeletal modeling was performed using AnyGait v.0.92 morphing Twente Lower Extremity Model (TLEM) to match the size and joint morphology of the stick-figure model. Inverse kinematics was performed obtaining hip, knee and ankle joint flexion-extension angular displacements, velocities and accelerations. Generalized phase space analysis was applied to lower limb joint angular displacements, velocities and accelerations. Directional statistics was applied to generalized phase planes with mean direction, resultant length and circular standard deviation assessment. Rayleigh test was employed for directional concentration and coordination assessment, and Watson's U² goodness of fit test applied to the von Mises distribution. Results point for the importance of subject specific study, generalized joint angular phase space analysis, comparing results with other normalization methods and validation of applied methods with qualitative clinical analysis.

Abstract
Organizations are rushing into social media networks following a worldwide trend to create a social presence in multiple media channels. However, a social media strategy needs to be aligned with and framed in the overall organizational strategic management goals. Higher Educational Institutions (HEI) are not different from other organizations in which concerns these problems. Determining the organizational positioning of an organization current strategy will allow to combine monitoring and benchmarking methods to foster the identification of opportunities and threats, which can serve as inputs for the internal evaluation of social media strategies', for the necessary strategic readjustments and a subsequent efficiency measurement. In order to address these challenges, we propose a three-step automatic data-mining procedure to assess the posting behavior and strategy of HEI, understand the editorial policy behind it, and predict the future HEI engagement. We used a sample of the 5-top ranked educational institutions in 2017. We collected the posts from each HEI official Facebook page during an entire school year. Our method showed high degree of accuracy and is also capable of describing which topics are most common in each university's social media content strategy and relate them to the corresponding response from their publics.