Abstract
After a comprehensive review on mobile tourism experience, the authors have identified the benefits and impacts of mobile use to tourists and their experiences. Besides locating similarities and differences in using mobile for tourism, the authors have confirmed that mobile empowers tourists to get more from their vacations and to have more flexible planning, resulting in satisfaction and accomplishment. This chapter enlightens tourism operators, among other stakeholders, on the opportunities for contextualized mobile advertising, which would attract and convert tourists into potential customers.

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Abstract
The necessity of end-user engagement in power systems have become a reality in recent times. One of the solutions to this engagement is the creation of local energy markets. The distribution systems operators are compelled to investigate and optimize their asset investment cost in reinforcement of grids by introducing smart grid functionalities in order to avoid investments. The congestion management is the one of the most promising strategies to deal with the network issues. This paper presents a local electricity market or flexibility negotiation as a strategy in order to help the distribution system operator in congestion management. The local market is performed considering an asymmetric action model and is coordinated by an aggregator. A case study is presented considering a simulation that uses a low voltage network with 17 buses, which includes 9 consumers and 3 prosumers, all domestic users. Results show that using the proposed market model, the network congestion is avoided by taking advantage from the trading of consumers flexibility. © 2019 IEEE.

Abstract
Online citizen public participation in consultation and decision-oriented processes supported by local governments is a key ingredient for successful digital democracy. As the participatory process is a voluntary activity, social capital, and individual motivation can help to understand citizen engagement in the usage of electronic participatory platforms (e-participation). This study presents and discusses the results of a research model evaluated with 200 respondents who experienced e-participation. The research model integrates a well-known theory of information systems, UTAUT, with the social capital theory, and the individual motivators. We found that, besides the positive effects of UTAUT constructs, such as perceived usefulness, effort expectancy, and facilitating conditions on the intention to use e-participation; altruism also plays a role as a driver of the intention to use. Social capital partially impacts on the actual usage of e-participation. (C) 2018 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license.

Abstract
Different activities are performed by people during the day and many aspects of life are associated with places of human mobility patterns. Among those activities, there are some that are recurrent and demand displacement of the individual between regular places like going to work, going to school, going back home from wherever the individual is located. To accomplish these recurrent daily activities, people tend to follow regular paths with similar temporal and spatial characteristics. In this paper, we propose a method for discovering common pathways across users’ habits. By using density-based clustering algorithms, we detect the users’ most preferable locations and apply a Gaussian Mixture Model (GMM) over these locations to automatically separate the trajectories that follow patterns of days and hours, in order to discover the representations of individual’s habits. Over the set of users’ habits, we search for the trajectories that are more common among them by using the Longest Common Sub-sequence (LCSS) algorithm considering the distance that pairs of users travel on the same path. To evaluate the proposed method we use a real-world GPS dataset. The results show that the method is able to find common routes between users that have similar habits paving the way for future recommendation, prediction and carpooling research techniques.

Abstract
The Overhead Power Distribution Lines present a wide range of insulator components, which have different shapes and types of building materials. These components are usually exposed to weather and operational conditions that may cause deviations in their shapes, colors or textures. These changes might hinder the development of automatic systems for visual inspection. In this perspective, this work presents a robust methodology for image classification, which aims at the efficient distribution insulator class identification, regardless of its degradation level. This work can be characterized by the following steps: implementation of Convolutional Neural Network (CNN); transfer learning; attribute vector acquisition and design of hybrid classifier architectures to improve the discrimination efficiency. In summary, a previously trained CNN goes through a fine tuning stage for later use as a feature extractor for training a new set of classifiers. A comparative study was conducted to identify which classifier architecture obtained the best discrimination performance for non-conforming components. The proposed methodology showed a significant improvement in classification performance, obtaining 95% overall accuracy in the identification of non-conforming component classes.