Abstract
We survey and analyze the relationship between 'social media' landscape and the 'over-the-top' film industry and provide a new market overview of how distinctive media platforms are leveraging each other features as part of their business model. With an elevated penetration of mass-market over the top (OTT) services and coexistence of several business models and value chains that need to be proven, our findings suggest that new entrants, to stand apart, will have to experiment new business models and with multimedia integration of content and services; and, unless they establish new niche services to communities of interest it will be difficult for them to differentiate their offerings and survive. Developing a social content network that connects people socially through films can offer media entrepreneurs and the 'world film' industry with a stable business model and a new window of opportunity in their competition for market share. By clarifying the boundaries and affordances of distinct OTT and social media platforms, the present research sustains that coupling video streaming and social networking is the future. It further bears that social multimedia computing should be used to capture and leverage the social activity and interaction of users in order to understand the drivers and trends in the film industry. Finally, it provides a direction for online world films.

Abstract
The advent of wearable technology is fundamental to the dissemination of wearable personal health monitoring devices. Recent developments of biomedical sensors have decreased the form factor and power consumption that can be worn on a permanent basis. This paper discusses a low cost reflective photoplethysmography (PPG) system using a dedicated integrated circuit (IC) solution as the core of a wearable health monitoring device. The measurement of two physiological indicators is performed, namely the pulse rate (HR) and the blood oxygen saturation (SpO(2)). The paper analyses in depth the PPG signals sensing architecture, guaranteeing high resolution measurements due to a delta-sigma analog to digital conversion unit. Post-processing digital filter operations are implemented to enhance low noise PPGs acquisition for physiological signals extraction. A complete system design is presented and a detailed evaluation is made in a real-time processing scenario. The test platform is completed with a PC based graphics application for on-line and off-line data analysis. Minimizing power dissipation is the main challenge in,a wearable design. However, it restrains PPG signal measurement sensitivity by lowering signal quality. Using the developed prototype power consumption, studies concerning the characterization of power consumption and signal quality over various working conditions are performed. Next, a performance merit figure is proposed as the main research contribution, which addresses the power consumption and signal quality trade-off subject. It aims to be used as an analysis for trade-offs between these two conflicting design criteria.

Abstract
Here we explore the possibility of controlling the inhomogeneities in quasi-1D Bose-Einstein condensates using a spatial variation of the transverse confinement potential and explore different optical strategies to realize these pinched traps. Furthermore, we also present some early stage results on the dynamics of matter-wave solitons in such systems using computational simulations of the full 3D Gross-Pitaevskii equation.

Abstract
Background: Positron Emission Tomography - Computed Tomography (PET/CT) imaging is the basis for the evaluation of response-to-treatment of several oncological diseases. In practice, such evaluation is manually performed by specialists, which is rather complex and time-consuming. Evaluation measures have been proposed, but with questionable reliability. The usage of before and after-treatment image descriptors of the lesions for treatment response evaluation is still a territory to be explored. Methods: In this project, Artificial Neural Network approaches were implemented to automatically assess treatment response of patients suffering from neuroendocrine tumors and Hodgkyn lymphoma, based on image features extracted from PET/CT. Results: The results show that the considered set of features allows for the achievement of very high classification performances, especially when data is properly balanced. Conclusions: After synthetic data generation and PCA-based dimensionality reduction to only two components, LVQNN assured classification accuracies of 100%, 100%, 96.3% and 100% regarding the 4 response- to-treatment classes.

Abstract
The concept of the water, energy, food nexus is extremely relevant to Kazakhstan as the country faces population growth, economic progress and environmental challenges such as water scarcity, desertification, and climate change. Furthermore, poor sectoral coordination and inadequate infrastructure have caused unsustainable resource use and threaten the long-term water, energy and food security in Kazakhstan. This study presents the key elements required to implement a nexus-based resource management approach in Kazakhstan, by identifying linkages between water resources, energy production and agriculture. A case study illustrates how this methodology can be applied to quantify linkages between the water and energy sectors.

Abstract
Search engines are evolving towards richer and stronger semantic approaches, focusing on entity-oriented tasks where knowledge bases have become fundamental. In order to support semantic search, search engines are increasingly reliant on robust information extraction systems. In fact, most modern search engines are already highly dependent on a well-curated knowledge base. Nevertheless, they still lack the ability to e ectively and automatically take advantage of multiple heterogeneous data sources. Central tasks include harnessing the information locked within textual content by linking mentioned entities to a knowledge base, or the integration of multiple knowledge bases to answer natural language questions. Combining text and knowledge bases is frequently used to improve search results, but it can also be used for the query-independent ranking of entities like events. In this work, we present a complete information extraction pipeline for the Portuguese language, covering all stages from data acquisition to knowledge base population. We also describe a practical application of the automatically extracted information, to support the ranking of upcoming events displayed in the landing page of an institutional search engine, where space is limited to only three relevant events. We manually annotate a dataset of news, covering event announcements from multiple faculties and organic units of the institution. We then use it to train and evaluate the named entity recognition module of the pipeline. We rank events by taking advantage of identified entities, as well as partOf relations, in order to compute an entity popularity score, as well as an entity click score based on implicit feedback from clicks from the institutional search engine. We then combine these two scores with the number of days to the event, obtaining a final ranking for the three most relevant upcoming events.