Abstract
Deaf students, who use sign language as their mother language, continuously experience difficulties to communicate with non-deaf in their daily lives. This is a severe handicap in education settings seriously jeopardizing deaf people chances to progress in their professional career. Deaf people's comprehension of texts is limited due to grammar differences between sign and oral languages. There is a need to improve the communication between deaf and non-deaf and to support deaf students in environments where they are unable to be accompanied by sign interpreters. This article details the improvements and current structure of the VirtualSign platform, a bidirectional sign language to text translation tool in development since 2015. The platform has two main components, sign to text and text to sign, that are both described. Translation from text to sign relies on a 3D avatar. Translation from sign to text relies on a set of data gloves and Kinect. In this paper we discuss the relevance of different types of data gloves. VirtualSign is being developed in cooperation with the deaf communities from six different European countries and Brazil. This solution to support deaf students in educational settings has received positive feedback on several tests and pilot experiments. Some planned improvements and future functionalities for the tool are also mentioned and detailed.

Abstract
Optimizing the participation of a large number of prosumers in the electricity markets is a challenging problem, especially for portfolios with thousands or millions of flexible resources. To address this problem, this paper proposes a cluster-based optimization approach to support an aggregator in the definition of demand and supply bids for the day-ahead energy market. This approach consists of two steps. In the first step, the aggregated flexibility of the entire portfolio is computed by a centroid-based clustering algorithm. In the second step, the supply and demand bids are defined by an optimization model that can assume the form of a deterministic or a two-stage stochastic problem. A case study of 10,000 prosumers from the Iberian market is used to evaluate and compare the performance of the bidding optimization models with and without pre-clustering. The numerical results show that the optimized bidding strategies outperform an inflexible strategy by more than 20% of cost savings. The centroid-based clustering algorithm reduces effectively the execution times of the bidding optimization problems, without affecting the quality of the energy bids.

Abstract
The main objective of this paper is to present a new and cost-effective Information and Communication Technology (ICT) tool that can lead to efficient energy management in buildings and optimal operation of electricity networks with increased share of Renewable Energy Sources (RES) and Electric Vehicles (EVs). The new ICT infrastructure is based on the Digital Audio Broadcasting (DAB) standard and its interoperability with smart metering technology, Intelligent Transportation Systems (ITS) and Building Automation Systems (BAS). The main idea involves the attachment of a DAB receiver to electric devices (from small household appliances up to EVs and solar systems and other RES). In this paper, the DAB protocol is described, enabling high cyber-physical security. Moreover, the results of addressing a thermostatically-controlled load using DAB-signaling in Switzerland are also presented. The next steps envisioned are i) the validation of the final protocol version and of the DAB receivers for various electric appliances and DR schemes and, ii) demonstration of the new technology in real-life cases through the National DAB broadcaster in Greece. (C) 2019 The Authors. Published by Elsevier Ltd.

Abstract
Demand management is becoming an indispensable part of grid operation with its potential to aid supply/demand balancing, reduce peaks, mitigate congestions and improve voltage profiles in the grid. Effective deployments require a huge number of reliable participators who are aware of the flexibilities of their devices and who continuously seek to achieve savings and earnings. In such applications, smart meters can ease consumption behavior visibility, while building automation systems can enable the remote and automated control of flexible loads. Moreover, gamification techniques can be used to motivate and direct customers, evaluate their performance, and improve their awareness and knowledge in the long term. This study focuses on the design and field demonstration of a flexible device-oriented, smart meter and building automation system (BAS) compatible with a gamified load management (LM) platform for residential customers. The system is designed, based on exploratory surveys and systematic gamification approaches, to motivate the customers to reduce their peak period consumption and overall energy consumption through competing or collaborating with others, and improving upon their past performance. This paper presents the design, development and implementation stages, together with the result analysis of an eight month field demonstration in four houses with different user types in Istanbul, Turkey.

Abstract
The use of fossil fuel vehicles is one of the factors responsible for the degradation of air quality in urban areas. In order to reduce levels of air pollution in metropolitan areas, several countries have encouraged the use of electric vehicles in the cities. However, due to the high investment costs in this class of vehicles, it is expected that the spatial distribution of electric vehicles' adopters will be heterogeneous. The additional charging power required by electric vehicles' batteries can change operation and expansion planning of power distribution utilities. In addition, urban planning agencies should analyze the most suitable locations for the construction of electric vehicle recharging stations. Thus, in order to provide information for the planning of electric mobility services in the city, this paper presents a spatiotemporal model for estimating the rate of electric vehicles' adopters per subareas. Results are spatial databases that can be viewed in geographic information systems to observe regions with greater expectancy of residential electric vehicle adopters. These outcomes can help utilities to develop new services that ground on the rising availability of electric mobility in urban zones.

Abstract
The foreseen participation of aggregators of prosumers in the electricity markets will require the development of computational tools to support them in the definition and delivery of market products. This paper proposes a new hierarchical model predictive control (MPC) to support an aggregator in the delivery of multiple market products through the real-time control of heterogeneous flexible resources. The hierarchical MPC covers the participation of an aggregator in both energy and secondary reserve markets. The results show that the aggregator is capable of delivering several combinations of energy and secondary reserve without compromising the comfort and preferences of its clients.