Abstract
The preferences adopted by individuals are constantly modified as these are driven by new experiences, natural life evolution and, mainly, influence from friends. Studying these temporal dynamics of user preferences has become increasingly important for personalization tasks in information retrieval and recommendation systems domains. However, existing models are too constrained for capturing the complexity of the underlying phenomenon. Online social networks contain rich information about social interactions and relations. Thus, these become an essential source of knowledge for the understanding of user preferences evolution. In this work, we investigate the interplay between user preferences and social networks over time. First, we propose a temporal preference model able to detect preference change events of a given user. Following this, we use temporal networks concepts to analyze the evolution of social relationships and propose strategies to detect changes in the network structure based on node centrality. Finally, we look for a correlation between preference change events and node centrality change events over Twitter and Jam social music datasets. Our findings show that there is a strong correlation between both change events, specially when modeling social interactions by means of a temporal network.

Abstract
Following the reflection around the emergence of “research university” in the context of the slow but progressive increase in value of science and technology and research and development in Portugal, a study applied to the knowledge transfer and the process of innovation in the university, in the context of a master dissertation in information science (IS), study area of information management, is presented. The university is one of the most important institutions in the context of the national innovation system (SNI), being part of its mission the creation and transfer of knowledge. At the University of Porto (U.Porto), projects, such as the University of Porto Innovation unit (U.Porto Inovação) and the Science and Technology Park of the University of Porto (UPTEC) seek to support the university's innovation value chain, promoting the reinforcement and solidification of knowledge transfer and of the relations between the university and companies, as well as the promotion and support to the creation of companies with a technological, scientific, and creative base, and the attraction of numerous innovation centers of national and international companies. This chapter points out an informational perspective on I&D+i (research and development and innovation) and entrepreneurship, based on the systemic theory and the quadripolar method, as theoretical and methodological guidance tools, and an information management/knowledge management approach of innovation models for the knowledge economy, the national and international referents, and corresponding set of indicators. An exploratory study, which allowed the identification of internal and external agents, the resources, the relations between actors and institutions, the processes and flows, and the main inputs and outputs, is presented. The most relevant result is embodied in a model of innovation indicators in an academic context and applied to the University of Porto.

Abstract
Each year thousands of people suffer from lower limb amputation, mainly due to three causes: wars, accidents and vascular diseases. The development of lower limb prosthesis is crucial to restore people’s mobility, improving the quality of life of millions of people. This contribution presents a gait events detector algorithm capable of detecting all the events of the human walking. Results show the correct transition between phases during several gait cycles for a human model walking in flat terrain. © 2018 by World Scientific Publishing Co. Pte. Ltd.

Abstract
SLODAR (SLOpe Detection And Ranging) methods recover the atmospheric turbulence profile from cross-correlations of wavefront sensor (WFS) measurements, based on known turbulence models. Our work grows out of several experiments showing that turbulence statistics can deviate significantly from the classical Kolmogorov/ von Kármán models, especially close to the ground. We present a novel SLODAR-type method which simultaneously recovers both the turbulence profile in the atmosphere and the turbulence statistics at the ground layer - namely the slope of the spatial frequency power law. We consider its application to outer scale (L0)- reconstruction and investigate the limits of the joint estimation of such parameters.

Abstract

Abstract
Smart grids technologies are enablers of new business models for domestic consumers with local flexibility (generation, loads, storage) and where access to data is a key requirement in the value stream. However, legislation on personal data privacy and protection imposes the need to develop local models for flexibility modeling and forecasting and exchange models instead of personal data. This paper describes the functional architecture of an home energy management system (HEMS) and its optimization functions. A set of data-driven models, embedded in the HEMS, are discussed for improving renewable energy forecasting skill and modeling multi-period flexibility of distributed energy resources.