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
This paper proposes a Building Energy Management System (BEMS) as part of an organization-based Multi-Agent system that models the Smart Home Electricity System (MASHES). The proposed BEMS consists of an Energy Management System (EMS) and a Prediction Engine (PE). The considered Smart Home Electricity System (SHES) consists of different agents, each with different tasks in the system. In this context, smart homes are able to connect to the power grid to sell/buy electrical energy to/from the Local Electricity Market (LEM), and manage electrical energy inside of the smart home. Moreover, a Modified Stochastic Predicted Bands (MSPB) interval optimization method is used to model the uncertainty in the Building Energy Management (BEM) problem. A demand response program (DRP) based on time of use (TOU) rate is also used. The performance of the proposed BEMS is evaluated using a JADE implementation of the proposed organization-based MASHES.

Abstract
Cancer genome projects are characterizing the genome, epigenome and transcriptome of a large number of samples using the latest high-throughput sequencing assays. The generated data sets pose several challenges for traditional statistical and machine learning methods. In this work we are interested in the task of deriving the most informative genes from a cancer gene expression data set. For that goal we built denoising autoencoders (DAE) and stacked denoising autoencoders and we studied the influence of the input nodes on the final representation of the DAE. We have also compared these deep learning approaches with other existing approaches. Our study is divided into two main tasks. First, we built and compared the performance of several feature extraction methods as well as data sampling methods using classifiers that were able to distinguish the samples of thyroid cancer patients from samples of healthy persons. In the second task, we have investigated the possibility of building comprehensible descriptions of gene expression data by using Denoising Autoencoders and Stacked Denoising Autoencoders as feature extraction methods. After extracting information related to the description built by the network, namely the connection weights, we devised post-processing techniques to extract comprehensible and biologically meaningful descriptions out of the constructed models. We have been able to build high accuracy models to discriminate thyroid cancer from healthy patients but the extraction of comprehensible models is still very limited.

Abstract
While renewable energy generation from time variable sources keeps increasing, end-user interactions through smart grid development and the adoption of smart appliances lead to significant changes in consumer behavior. Hence, renewable energy generation must be curtailed more frequently when the expected demand is surpassed. Likewise, demand side measures should be considered more thoroughly so that appropriate capacity limits for new generation units can be defined. An analysis of load shifting is performed for Sao Miguel Island, Azores, and indicates that through defined rules of load shifts the base load limit can be elevated and new limits for the maximum installed capacity can be set. The effects of load shifts are crucial for decision makers since investments in additional renewable energy capacities can be limited and back-up capacities can be reduced. (C) 2016 The Authors. Published by Elsevier Ltd.

Abstract
In this paper, operation management of microgrids is performed. To do so, some contingencies including outage of distributed generators (DG), energy storage (ES) and the upstream network are considered. Since the microgrids have suitable capabilities in terms of control and communication, demand response reserve can be applied to improve the operation management. Using Monte Carlo simulation method and Markov chain, several scenarios are generated to show the possible contingencies in various hours. Then, a scenario reduction method is used for reducing the number of scenarios. Finally, a two-stage stochastic model is applied to solve a day-ahead scheduling problem in mixed-integer linear programming by GAMS. Consequently, the effect of demand response in the reduction of operation cost is demonstrated.

Abstract