Abstract
Mobile devices are now widespread and multimedia consumption on these devices has increased significantly in recent years. More and more high dynamic range (HDR) content is being produced and its imminent adoption by the broadcast community means that there will soon be a demand to visualize HDR content on mobile devices. Mobile devices, however, have certain differences compared to traditional viewing devices. In particular, they are usually used on-the-go, making the context variables such as ambient lighting levels, or reflections important variables that need to be considered. Furthermore, despite their evolution so far, mobile devices usually have additional hardware limitations such as power supply, display features, or local storage availability. This chapter provides an overview of the work that has been conducted so far in addressing HDR video for mobile devices in order to ensure an optimal experience.

Abstract
Social media networks' popularity has been growing in almost every context of daily human interaction. Particularly concerning the education field, organisations and teachers have been continuously recognizing social media as a rich environment with potential to benefit the teaching-learning process, classroom administration and social interactions. However, social media networks have been used as complementary environments to the mandatory adoption of an institutional LMS, leading to the development of fragmented teaching-learning environments, where mutual interchanges are not consolidated nor allow for an explicit academic legitimacy, computation and management. Also, social media networks' integration in education has been viewed as an ad-hoc initiative of some educators, who are prone to incorporate web trends in their pedagogic activity, which are evaluated, most of the times, under the lens of recreational initiates. Consequently, there is an urgent need to bridge between the consolidated adoption of LMSs and the integration of social media networks in education, not only in terms of technological infrastructure ( interface and usability) but also in terms of production and management of its pedagogical outcomes. With the intent of providing solutions for the above context, in this paper, we discuss the concept of Social Student Relationship Management and present the EduBridge system, its current stage of development and highlight the educational applicability of a thorough set of social network analysis.

Abstract
During forest fire fights, firefighters are exposed to several pollutants at different concentrations, which can induce critical health problems. This study main goal is to estimate firefighters' pollutants inhalation when in operational scenarios by combining environmental and physiological information. Both exposures to CO (carbon monoxide) and physiological data, such as ECG (electrocardiogram), HR (Heart Rate) and body temperature, were monitored during firefighters' activities in experimental forest fire. From the QRS complex of ECG the ECG-derived respiration (EDR) was estimated and convoluted with pollutants concentration to estimate individual smoke inhalation. The analysis of smoke inhalations allowed to detect extensive exposures and to identify critical situations namely risk of faint due to smoke intoxication. Our results support the usefulness of continuous monitoring of both physiological and environmental information to prevent and detect hazardous situations while firefighters are in operational scenario like forest fires. The results encourage the development of a decision support system to be applied in real-Time during firefighting scenarios. © 2016 ACM.

Abstract
In this paper we present a hybrid approach to integrative clustering based on the p-median problem with clients' preferences. We formulate the problem of simultaneous clustering of a set of objects, characterized by two sets of features, as a bi-level p-median model. An exact approach involving a branch-and-cut method combined with the simulated annealing algorithm is used, that allows one to find a two-source clustering. The proposed approach is compared with some well-known mathematical optimisation based clustering techniques applied to the NCI-60 tumour cell line anticancer drug screen dataset. The results obtained demonstrate the applicability of our approach to find competitive integrative clusterings. Copyright © by the paper's authors.

Abstract
The Unit Commitment (UC) problem consists on the day-ahead scheduling of thermal generation units. The scheduling process is based on a forecast for the demand, which adds uncertainty to the decision of starting or shutting down units. With the increasing penetration of renewable energy sources, namely wind power, the level of uncertainty is such that deterministic UC approaches that rely uniquely on point forecasts are no longer appropriate. The UC approach reported in this paper considers a stochastic formulation and includes constraints for the technical limits of thermal generation units, like ramp-rates and minimum and maximum power output, and also for the power flow equations by integrating the DC model in the optimization process. The objective is to assess the ability of the stochastic UC approach to decrease the expected value of load shedding and wind power loss when compared to the deterministic UC approach. A case study based on IEEE-RTS 79 system, which has 24 buses and 32 thermal generation units, for two different penetrations of wind power and a 24-hour horizon is carried out. The computational performance of the methodology proposed is also discussed to show that considerable performance gains without compromising the robustness of the stochastic UC approach can be achieved.

Abstract