Canales, sifones y almenaras: el impacto ambiental de las infraestructuras del Canal de Isabel II (Madrid)

El presente artículo tiene como finalidad, valorar el impacto ambiental de las conducciones del Canal de Isabel II en el contexto del paisaje. Partimos de la idea según la cual, las infraestructuras del Canal de Isabel II, y más que formar parte del paisaje por el que se extienden, son el propio paisaje. Nuestra zona de estudio es el noroeste de la Comunidad de Madrid (síntesis de la interacción de los propios agentes naturales, de la ocupación humana y de los usos del suelo), área a la que nos aproximarnos a través de la investigación de la integración paisajística, entendida ésta como una estrategia de intervención en el territorio, que tiene como objetivo principal orientar las transformaciones del paisaje o corregir las ya realizadas, para conseguir su adaptación al propio paisaje. En definitiva, nos encontramos ante la necesidad de ajustar un objeto o actuación territorial a las características fisonómicas de un paisaje dado, o de algunos de sus componentes, así como a su carácter y a sus contenidos semánticos.

Dynamic Social and Media Content Syndication for Second Screen

Social networking apps, sites and technologies offer a wide range of opportunities for businesses and developers to exploit the vast amount of information and user-generated content produced through social networking. In addition, the notion of second screen TV usage appears more influential than ever, with viewers continuously seeking further information and deeper engagement while watching their favourite movies or TV shows. In this work, the authors present SAM, an innovative platform that combines social media, content syndication and targets second screen usage to enhance media content provisioning, renovate the interaction with end-users and enrich their experience. SAM incorporates modern technologies and novel features in the areas of content management, dynamic social media, social mining, semantic annotation and multi-device representation to facilitate an advanced business environment for broadcasters, content and metadata providers, and editors to better exploit their assets and increase their revenues.

A semantic framework for textual data enrichment

In this work we present a semantic framework suitable of being used as support tool for recommender systems. Our purpose is to use the semantic information provided by a set of integrated resources to enrich texts by conducting different NLP tasks: WSD, domain classification, semantic similarities and sentiment analysis. After obtaining the textual semantic enrichment we would be able to recommend similar content or even to rate texts according to different dimensions. First of all, we describe the main characteristics of the semantic integrated resources with an exhaustive evaluation. Next, we demonstrate the usefulness of our resource in different NLP tasks and campaigns. Moreover, we present a combination of different NLP approaches that provide enough knowledge for being used as support tool for recommender systems. Finally, we illustrate a case of study with information related to movies and TV series to demonstrate that our framework works properly.