Abstract
The chapter presents an entrepreneurial perspective to rural tourism. It is based on the utilization of endogenous resources that exist within a rural region, and leads to a group of business opportunities related to tourism, craftwork, and agriculture, which are taken into account to define the strategic objectives for the ADRIMAG region. The chapter follows a qualitative approach to business opportunities. Through our analysis, it was possible to create, in a simple manner, a group of business opportunities based on the endogenous resources of the region. With this study, we expect to bring forth an entrepreneurial perspective that will sustainably foster tourism development within rural regions, but with high potential for tourism attraction.

Abstract
Long period fiber gratings produced by the electric arc technique have found an increasing interest by the scientific community due to their ease to fabricate, virtually enabling the inscription in any kind of fiber, low cost, and flexibility. In 2005 we have presented the first review on this subject. Since then, important achievements have been reached such as the identification of the mechanisms responsible for gratings formation, the type of symmetry, the conditions to increase fabrication reproducibility, and their inscription in the turning points with grating periods below 200 mu m. Several interesting applications in the sensing area, including those sensors working in reflection, have been demonstrated and others are expected, namely, related to the monitoring of extreme temperatures, cryogenic and high temperatures, and high sensitivity refractometric sensors resulting from combining arc-induced gratings in the turning points and the deposition of thin films in the transition region. Therefore, due to its pertinence, in this paper we review the main achievements obtained concerning arc-induced long period fiber gratings, with special focus on the past ten years.

Abstract
The blooming of different cloud data management infrastructures, specialized for different kinds of data and tasks, has led to a wide diversification of DBMS interfaces and the loss of a common programming paradigm. In this paper, we present the design of a cloud multidatastore query language (CloudMdsQL), and its query engine. CloudMdsQL is a functional SQL-like language, capable of querying multiple heterogeneous data stores (relational and NoSQL) within a single query that may contain embedded invocations to each data store's native query interface. The query engine has a fully distributed architecture, which provides important opportunities for optimization. The major innovation is that a CloudMdsQL query can exploit the full power of local data stores, by simply allowing some local data store native queries (e.g. a breadth-first search query against a graph database) to be called as functions, and at the same time be optimized, e.g. by pushing down select predicates, using bind join, performing join ordering, or planning intermediate data shipping. Our experimental validation, with three data stores (graph, document and relational) and representative queries, shows that CloudMdsQL satisfies the five important requirements for a cloud multidatastore query language.

Abstract
Energy consumption is a major problem in Wireless Sensor Networks (WSNs) since in most scenarios nodes operate on batteries with reduced size and low capacity. Battery lifetime estimation is therefore crucial to evaluate the network behavior over time. In addition, battery lifetime estimation is a major challenge since it depends on many factors, such as the duty cycle of supported applications and the intrinsic chemical reactions within the batteries. Several battery models have been proposed to deal with battery lifetime estimation. However, most of the available models address only high-capacity batteries. This paper discusses a method for extracting the parameters for the Kinetic Battery Model (KiBaM) used within WSN context. The target is to define a battery model that helps to estimate the lifetime of nodes using low-capacity batteries and which operate in a duty cycle scheme. Results show that KiBaM is an adequate model for WSNs since it presents low error when compared with experimental results using real batteries.

Abstract
Many real-world data-mining applications involve obtaining predictive models using datasets with strongly imbalanced distributions of the target variable. Frequently, the least-common values of this target variable are associated with events that are highly relevant for end users (e.g., fraud detection, unusual returns on stock markets, anticipation of catastrophes, etc.). Moreover, the events may have different costs and benefits, which, when associated with the rarity of some of them on the available training data, creates serious problems to predictive modeling techniques. This article presents a survey of existing techniques for handling these important applications of predictive analytics. Although most of the existing work addresses classification tasks (nominal target variables), we also describe methods designed to handle similar problems within regression tasks (numeric target variables). In this survey, we discuss the main challenges raised by imbalanced domains, propose a definition of the problem, describe the main approaches to these tasks, propose a taxonomy of the methods, summarize the conclusions of existing comparative studies as well as some theoretical analyses of some methods, and refer to some related problems within predictive modeling.

Abstract
The electrification of the transportation sector is likely to contribute reducing the global dependency on oil and is expected to drive investments to renewable and intermittent energy sources, by taking advantage of it energy storage capacity. In order to facilitate the EV integration to the grid, and to take advantage of the battery storage and the Vehicle-to-Grid (V2G) scheme, smart charging strategies will be required. However, these strategies rarely consider all relevant costs, such as battery degradation. This work analyses the profitability of bidirectional energy transfer, i.e. the possibility of using aggregated EV batteries as storage for energy which can be injected back to the grid, by considering battery degradation as a cost included in the proposed strategy. A mixed integer linear problem (MILP) for minimizing energy costs and battery ageing costs for EV owners is formulated. The battery degradation due to charging and discharging in the V2G scheme is accounted for in the model used. Two case studies of overnight charging of EVs in Sweden and in Spain are proposed. Results show that given current energy prices and battery costs, V2G is not profitable for EV owners, but if battery prices decrease as expected, the V2G will be present in the medium term. © 2016 IEEE.