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This paper addresses the problem of decision making in the context of financial markets, more specifically, the problem of forecasting the correct trading action for a certain future horizon. We study and compare two alternative ways of addressing these forecasting tasks: (a) using standard numeric prediction models to forecast the variation on the prices of the target asset and, on a second stage, transform these numeric predictions into a decision according to some predefined decision rules; and (b) use models that directly forecast the right decision thus ignoring the intermediate numeric forecasting task. The objective of our study is to determine if both strategies provide identical results or if there is any particular advantage worth being considered that may distinguish each alternative in the context of financial markets.

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Deaf people face serious communication problems. The use of smartphones has been explored as a solution for breaking communication barriers and enhancing their communication, by providing access to basic services. This paper explores the usage of iconographic interfaces in smartphones as a means for contributing to further autonomy for deaf people. We applied the model for asynchronous and non-verbal communication through iconographic and interactive flows to develop the MyCarMobile application, a travel assistance android mobile application for deaf people. Our research explores a solution which enables travel assistance services without involving audio, using an iconographic interface to report road accidents. A user centered design approach was applied in the development of the prototype and usability tests were performed with eleven deaf users, in order to validate the mobile application. The results revealed a good performance and user satisfaction when interacting with the application.

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We consider the problem of robust model predictive control for linear sampled data dynamical systems subject to state and control constraints and additive and bounded disturbances. We propose a rigid tube model predictive control algorithm utilizing recent and topologically compatible notions for the sampled data forward reach sets as well as robust positively invariant sets. The proposed method inherits almost all desirable features associated with rigid tube model predictive control of discrete-time systems, and, in addition, it ensures robust constraint satisfaction and safety in a continuous-time sense.

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Introduction In this chapter, we present a number of applications of the Arrowhead Framework with special attention to services related to awareness and optimisation of energy consumption. First, we present the notion of FlexOffers as a general mechanism for describing energy flexibility. FlexOffers can be aggregated into larger flexibility units to be used as an Arrowhead service in the virtual market of energy [1]. This is followed by two examples on how to exploit such a flexibility service in the energy management of heat pumps and a campus building. Then we present two examples on how to exploit renewable energy to provide elevator services. Next, two examples of context aware services are described - smart lighting and smart car heating, and finally it is described how the Arrowhead Framework can play a role in the optimisation of municipal service systems. In the final section, we indicate future work. © 2017 by Taylor & Francis Group, LLC.

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