Abstract
The large-scale integration of photovoltaic generation (PVG) on distribution systems (DSs) preserving their technical constraints related to voltage fluctuations and active power (AP) flow is a challenging problem. Solar resources are accompanied by uncertainty regarding their estimation and intrinsically variable nature. This study presents a new probabilistic methodology based on quasi-static time-series analysis combined with the golden section search algorithm to integrate low and high levels of PVG into DSs to prevent AP flow in reverse direction. Based on the analysis of two illustrative case studies, it was concluded that the successful integration of PVG is not only related to the photovoltaic-cell manufacturing prices and conversion efficiency but also with the manufacturing prices of power electronic devices required for reactive power control.

Abstract
Augmented Reality (AR) is emerging as a technology that is reshaping the current society, especially the fields of Business and Economics (B&E). Therefore, the scientific studies produced on AR call for an interdisciplinary systematic review of the knowledge generated to structure an organized framework. Three main questions are addressed: How has the production of AR scientific knowledge evolved? What user-related aspects does AR affect? Also, which set of subtopics is associated with each motivation to develop an AR solution? The content of 328 papers produced between 1997 and 2016 in the field of AR is analyzed, unveiling 58 coding categories. There are 13 digital media characteristics that assume instrumental roles in addressing four major motivations to develop AR solutions. Technological topics dominate the research focus over behavioral ones. The investigations on AR in mobile displays show the highest increase. This research identifies the main scientific topics that have led researchers' agenda. Consequently, they contributed to develop and to adopt AR solutions and to forecast its future application in the organizations' strategies.

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Developing robust and universal methods for unsupervised segmentation of moving objects in video sequences has proved to be a hard and challenging task that has attracted the attention of many researchers over the last decades. State-of-the-art methods are, in general, computationally heavy preventing their use in real-time applications. This research addresses this problem by proposing a robust and computationally efficient method, coined BMOG, that significantly boosts the performance of a widely used method based on a Mixture of Gaussians. The proposed solution explores a novel classification mechanism that combines color space discrimination capabilities with hysteresis and a dynamic learning rate for background model update. The complexity of BMOG is kept low, proving its suitability for real-time applications. BMOG was objectively evaluated using the ChangeDetection.net 2014 benchmark. An exhaustive set of experiments was conducted, and a detailed analysis of the results, using two complementary types of metrics, revealed that BMOG achieves an excellent compromise in performance versus complexity.