Abstract
Much effort is put into Information Technology (IT) to achieve better efficiency and quality of expressing communication between autistic children with the surrounding. This paper presents an application that aims to help the autistic child to interact and express their feeling with their loved ones in easy manner. The major objective of the project is to connect autistic children with their family and friends by providing tools that enable an easy way to express their feeling and emotions. To accomplish this goal an Android app has been developed through which, autistic child can express their emotion based on emoji. Child’s emotions are share by sending the emoji to their relatives. The project aims a high impact within the autistic child community by providing a mechanism to share emotions in an “emotionless world”. The project was developed under the Sustainable Development Goal (SDG) 3: good health and well-being in the society by making the meaningful impact in the life of autistic child. © 2019, Springer Nature Switzerland AG.

Abstract
This paper presents a hybrid optimization model based on the metaheuristic Evolutionary Particle Swarm Optimization (EPSO) and Linear Programming for solving the problems of sizing, location and network interface technology selection of battery energy storage system (BESS). The batteries are integrated in a distribution network that has dispersed photovoltaic (PV) generation. Thus, a stochastic scenario generation model is also proposed for creating a database for the PV generation, load and energy prices curves considering historical data. The proposed approach is applied with success to the CIGRE MV benchmark network in the European configuration. Several tests were carried out in order to evaluate the EPSO approach for planning and operate BESS into the modern distribution networks. Experimental results indicate that dispersed solutions to locate the batteries throughout of the network were privileged over concentrated solutions.

Abstract
Cluster-tree topologies are commonly employed in large-scale Wireless Sensor Networks applications where nodes are randomly deployed. This topology presents several challenges related to connectivity and message schedulability. As a consequence, effective methods for network formation and operation are required. This work proposes a combination of methods of neighborhood discovery, network formation and message scheduling to deal with this problem. This proposal aims to build a flexible network, with better performance and is also able to adapt for different scenarios. Simulation results highlight significant improvements of the network performance, when compared to the use of LEACH protocol.

Abstract
Open Design (OD) is characterized as fast-growing community-based generative process, that is supported in a digital repository or platform, in order to develop a specific topic area or field. Many examples of OD are available nowadays, from software (probably the most common) to hardware, and from science and technology to business. One of the most known platforms of OD is an open editable content encyclopedia, the Wikipedia. Trough the involvement of volunteers (community that do not receive any payment for the work) in writing articles in a collaborative way was possible to develop, most probably, the extensive multi-language encyclopedia in the World started from scratch within 18 years. Based on analysis made from available Wikipedia statistical data, it is possible to conclude that from a stochastic behaviour the variables reached a transition phase in which a presence of a hysteresis between all variables were reached in between 60 to 80 months, and the editors (agents of the community) are an important asset for the system "transformation". (C) 2019 The Authors. Published by Elsevier B.V.

Abstract
Futures contracts are a valuable market option for electricity negotiating players, as they enable reducing the risk associated to the day-ahead market volatility. The price defined in these contracts is, however, itself subject to a degree of uncertainty; thereby turning price forecasting models into attractive assets for the involved players. This paper proposes a model for futures contracts price forecasting, using artificial neural networks. The proposed model is based on the results of a data analysis using the spearman rank correlation coefficient. From this analysis, the most relevant variables to be considered in the training process are identified. Results show that the proposed model for monthly average electricity price forecast is able to achieve very low forecasting errors.

Abstract
The internet of things (IoT) is attracting increased attention from researchers, practitioners, consumers, and the media, and it is expected to change dramatically the production and consumption of goods and services, as well as the interaction between organizations and their customers. This chapter explores the challenges of IoT for marketing management. The authors present the main concepts associated to the theme based on the extant literature, considering information management, technological and ethical aspects of its adoption by corporations and consumers, and they discuss the expected impacts on different marketing application domains such as product placement, purchasing behavior, storytelling and communication, customer experience and consumer' brand perception, real-time persona development, and product and content development, among others.