Abstract
In this paper, a number of possibilities are presented and discussed for the ownership of distributed storage devices (DSD) in a Smart Grid environment. The cases in which the distribution system operator (DSO) has either full control (grid owned storage) or no control whatsoever over the operation of the DSD (independently owned storage) will be differentiated. For each ownership possibility, the technical and regulatory implications are discussed, with analysis and validation of the results being performed on real MV distribution networks, both rural and urban. In order to evaluate each ownership possibility, a number of multi-period optimization models are presented, corresponding to different assumptions in regards to the operation of the DSD. The resulting daily operation strategies are subsequently used as a basis for carrying out distribution reinforcement planning.

Abstract
Time series forecasting is a challenging task, where the non-stationary characteristics of the data portrays a hard setting for predictive tasks. A common issue is the imbalanced distribution of the target variable, where some intervals are very important to the user but severely underrepresented. Standard regression tools focus on the average behaviour of the data. However, the objective is the opposite in many forecasting tasks involving time series: predicting rare values. A common solution to forecasting tasks with imbalanced data is the use of resampling strategies, which operate on the learning data by changing its distribution in favor of a given bias. The objective of this paper is to provide solutions capable of significantly improving the predictive accuracy of rare cases in forecasting tasks using imbalanced time series data. We extend the application of resampling strategies to the time series context and introduce the concept of temporal and relevance bias in the case selection process of such strategies, presenting new proposals. We evaluate the results of standard regression tools and the use of resampling strategies, with and without bias over 24 time series data sets from 6 different sources. Results show a significant increase in predictive accuracy of rare cases associated with the use of resampling strategies, and the use of biased strategies.

Abstract

Abstract
The integration of Information and Communication Technologies (ICT) in the tourism industry is an essential element for the success of any tourism enterprise. ICTs provide access to information of tourism products from anywhere and at any time. Tour companies may also reach out to target customers around the world through a series of emerging technologies. This paper aims to make a review of the main key factors of ICT in Tourism. Aspects such as the quality of the website, Digital Marketing, Social Networking, Multimedia, Mobile Technologies and Intelligent Environments are discussed.

Abstract

Abstract
Newman’s theorem states that we can take any public-coin communication protocol and convert it into one that uses only private randomness with but a little increase in communication complexity. We consider a reversed scenario in the context of information complexity: can we take a protocol that uses private randomness and convert it into one that only uses public randomness while preserving the information revealed to each player? We prove that the answer is yes, at least for protocols that use a bounded number of rounds. As an application, we prove new direct-sum theorems through the compression of interactive communication in the bounded-round setting. To obtain this application, we prove a new one-shot variant of the Slepian–Wolf coding theorem, interesting in its own right. Furthermore, we show that if a Reverse Newman’s Theorem can be proven in full generality, then full compression of interactive communication and fully-general direct-sum theorems will result. © 2016, The Author(s).