Abstract
In this paper, we present the tools of the MediaViz project, a work-in progress platform that aims to provide researchers, academics and professionals from the media field with a set of analytical and exploratory resources to answer high level and complex questions about the online media panorama, in an eficient, visual and interactive way. Our approach consists of aggregating and processing news data from multiple online sources, and provide programatic access to it through an Application Programming Interface (API). The visualization tools leverage the data provided by the API, allowing users to interact, explore and interrogate that information. Through the use of data visualization techniques, we aim to characterize the publication patterns of multiple online news sources by analyzing and comparing distinct dimensions. Dimensions of interest include the frequency and flow of publications and social shares throughout time, and the geographic coverage of online news outlets. We present some of the developed visualization tools and describe how they can offer meaningful insights by providing a bird's-eye view of distinct characteristics of the online mediascape.

Abstract

Abstract
Contemporary AO systems, such as the Multi-Object Adaptive Optics system (MOAO) RAVEN currently associated with the Subaru Telescope, can suffer from significant Non-Common Path Aberrations (NCPA). These errors ultimately affect image quality and arise from optical path differences between the wavefront sensor (WFS) path and the science path. A typical correction of NCPA involves estimating the aberration phase and correcting the system with an offset on the deformable mirror (DM). We summarize two methods used to correct for NCPA on an experimental bench. We also successfully calibrate the NCPA on RAVEN using one of these methods. Finally, we report on some first science results with RAVEN, obtained after NCPA correction.

Abstract
The smart grid concept defines a new way to architect the power energy system. This evolution is dictated, at large extent, by the need to integrate distributed generation systems,. mainly based on renewable energy sources, consider electrical vehicles and enhance the power system infrastructure in degraded conditions. This change of paradigm implies a modification of the way the networked nodes operate and interconnect, including new control algorithms by introducing decision and action capabilities to controllable devices. This paper introduces an innovative way of modelling each node of the grid based on the holonic paradigm, with the objective of decreasing the number of different control algorithms set up on the grid. A single holon, able to model the behavior of any node (producing, consuming and storing energy), is defined and applied on a low voltage case study.

Abstract
The theoretical potential for renewable energy resources (RES) to meet the global demands of energy is generally high and the ambitions for introducing RES into energy systems are growing worldwide, which also can contribute to global climate change mitigation if it is produced in a sustainable manner. To address these issues, more and more governments are implementing various programs and energy policies to accelerate the deployment of RES. The aforementioned two reasons lead to an urgent need to add new generating capacity or reduce consumption during peak periods, or both. The first option for power generation is to use RES which can inject electric energy to the grid while avoiding greenhouse gas emissions. However, the capacities of RES are not enough to supply all the required power from the side of the load. Facts that are leading to the proposal of original ways to reduce the use of energy in many sectors, namely in commercial, residential, and industrial sectors, in order to reduce the total energy costs of the consumer, to reduce the energy demand specially during on-peak hours and the greenhouse gas emissions while safeguarding end-user preferences. The aim of this paper is to determine the impact of model predictive control (MPC) on energy savings of residential households. Furthermore, the value and impact of generated power by local power sources, such as roof-top-solar, will be determined during off-peak, mid-peak, and on-peak, providing simulations during 24 hours in a house.

Abstract
In this article we present a smart textile system for the continuous monitoring of cardiorespiratory signals, produced and integrated with an industrial embroidery unit. The design of a T-shirt system, having embedded textile sensors and interconnects and custom designed circuit for data collection and Bluetooth transmission is presented. The performance of skin-contact textile electrodes, having distinctive electrical characteristics and surface morphologies, was characterized by measurements of signal to noise ratio, under dry and moisture conditions. The influence of the electrodes size and the wear resistance were addressed. Results of an electrocardiogram acquisition with a subject wearing the T-shirt and display on a smartphone are also shown. The presented smart textile systems exhibit good performance and versatility for custom demand production.