Abstract
As organizations are entering social media, determining their 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 evaluation of social media strategies' and eventual readjustments, and a subsequent efficiency measurement. Higher Educational Institutions (HEI) are not different from other organizations in which concerns these problems. To address these challenges, we propose an automatic procedure to assess the posting behavior and strategy identification for each higher educational institution. We used a sample of the 10-top worldwide ranked educational institutions in this study and collected the posts from their official Facebook pages during an entire school year. Our study was conducted on the frequency and intensity of publications by universities, which included an analysis of the number of responses to 'posts' over time in the form of 'shares'. Finally, the content of the posts was analyzed according to the topics covered in the messages. This process allowed us to identify the editorial areas that each university uses the most and in which are more active. © 2018 The Authors. Published by Elsevier Ltd..

Abstract
Oil spill incidents in the sea or harbors occur with some regularity during exploration, production, and transport of petroleum products. In order to mitigate the impact of the oil spill in the marine life, immediate, safety, effective and ecofriendly actions must be taken. Autonomous vehicles can assume an important contribution by establishing a cooperative and coordinated intervention. This paper presents the development of a path planning control-law methods for an autonomous surface vehicle (ASV) being able to contour the oil spill while is deploying microorganisms and nutrients (bioremediation) capable of mitigating and contain the oil spill spread with the collaboration of a UAV vehicle. An oil spill simulation scenario was developed in Gazebo to support the evaluation of the cooperative actions between the ASV and UAV and to infer the ASV path planning for each one of the proposed control-law methods.

Abstract

Abstract
The smart grid paradigm has provided great opportunities to decrease energy consumption and electricity bills of end users. Among a wide variety of end users, electrical railway systems with their huge installed power capacity should be considered as a vital option in order to avoid wasted energy, provided that an energy management system is utilized. In this study, a mixed-integer linear programming model of a railway station energy management (RSEM) system is formulated by a stochastic approach, aiming to utilize the emerged regenerative braking energy (RBE) during the braking mode in order to supply station loads. Furthermore, the proposed RSEM model is composed of an energy storage system (ESS), RBE utilization, photovoltaic (PV) generation units, and an external grid in this paper. The passengers' impact on RBE as well as the stochastic behaviour of the initial state-of-energy of ESS along with uncertainty of PV generation by the RSEM model are also evaluated. The model is tested under a bunch of case studies formed considering several combinations of the cases that an ESS or PV are available or not and using RBE is possible or not.

Abstract
The large-scale integration of wind generation in power systems increases the need for reserve procurement in order to accommodate its highly uncertain nature, a fact that may overshadow its environmental and economic benefits. For this reason, the design of reserve procurement mechanisms should be reconsidered in order to embed resources that are capable of providing reserve services in an economically optimal way. In this study, a joint energy and reserve day-ahead market structure based on two-stage stochastic programming is presented. The developed model incorporates explicitly the participation of demand side resources in the provision of load following reserves. Since a load that incurs a demand reduction may need to recover this energy in other periods, different types of load recovery requirements are modeled. Furthermore, in order to evaluate the risk associated with the decisions of the system operator and to assess the effect of procuring and compensating load reductions, the Conditional Value-at-Risk metric is employed. In order to solve the resulting multi-objective optimization problem, a new approach based on an improved variant of the epsilon-constraint method is adopted. This study demonstrates that the proposed approach to risk management presents conceptual advantages over the commonly used weighted sum method.

Abstract
An optical fiber probe was developed for viscosity measurements. The sensor acts as a two-wave interferometer, sensible to the position of the fluid inside the cavity. Viscosity is measured through the fluid evacuation velocity. © OSA 2018 © 2018 The Author(s)