We prepared a series of graphene-like materials by thermal exfoliation/reduction of a graphite oxide (GO) at temperatures between 127ºC and 2400ºC. The extent of the exfoliation and reduction of the GO at different temperatures, as well as the impact on the resultant graphene-like materials (TRGs), were studied through their chemical/structural characterization. The main oxygen loss was observed at 127ºC during the blasting of the GO, which produced its exfoliation into monolayer functionalized TRG with hydroxyl groups and minor amounts of epoxy and carboxyl groups. Above 600ºC, the reduction continued smoothly, with oxygen and hydrogen loss and the conversion of hybridized carbon atoms from sp 3 into sp 2 . 1000ºC appears to be a critical temperature for the efficiency of the reduction process, as the resulting TRG contained less than 2% oxygen and 81.5% sp 2 -carbon atoms. The materials obtained at 2000ºC and 2400ºC were almost oxygen-free and the layers exhibited a dramatic restoration of the pristine graphite structure, as confirmed by the increase in the average size of the sp 2 -domains. The typical * Corresponding author: Fax. + 34 985 29 76 62; E-mail: rosmenen@incar.csic.es (Prof. R. Menéndez) 2 disordered stacking of TRGs increases with temperature, although they can be dispersed yielding monolayers at 127 and 300°C and stacks of up to 4-6 layers above 1000°C, as determined by AFM.

With the proliferation of social networks and blogs, the Internet is increasingly being used to disseminate personal health information rather than just as a source of information. In this paper we exploit the wealth of user-generated data, available through the micro-blogging service Twitter, to estimate and track the incidence of health conditions in society. The method is based on two stages: we start by extracting possibly relevant tweets using a set of specially crafted regular expressions, and then classify these initial messages using machine learning methods. Furthermore, we selected relevant features to improve the results and the execution times. To test the method, we considered four health states or conditions, namely flu, depression, pregnancy and eating disorders, and two locations, Portugal and Spain. We present the results obtained and demonstrate that the detection results and the performance of the method are improved after feature selection. The results are promising, with areas under the receiver operating characteristic curve between 0.7 and 0.9, and f-measure values around 0.8 and 0.9. This fact indicates that such approach provides a feasible solution for measuring and tracking the evolution of health states within the society.

Abstract:Semi-automatic wrapper generation tools aim to ease the task of building structured views over semi-structured web sources. But the wrapper generation techniques presented up to date are unable to properly deal with sources requiring complex navigational sequences for accessing data. In this paper, we present WARGO, a semiautomatic wrapper generation tool, which has been used by nonprogrammer staff to successfully wrap more than 700 commercial web sources in several industrial applications. We describe our approach for wrapper generation and show the difficulties found with other systems for wrapping this kind of sources.

This paper presents the results of a modeling study of the hypothetical dam break of Chipembe dam in Mozambique. The modeling approach is based on the software Iber, a freely available dam break and two-dimensional finite volume shallow water model. The shuttle radar topography mission (SRTM) online digital elevation model (DEM) is used as main source of topographic data. Two different DEMs are considered as input for the hydraulic model: a DEM based on the original SRTM data and a hydrologically-conditioned DEM. A sensitivity analysis on the Manning roughness coefficient is performed. The results demonstrate the relevant impact of the DEM used on the predicted flood wave propagation, and a lower influence of the roughness value. The low cost modeling approach proposed in this paper can be an attractive option for modeling exceptional flood caused by dam break, when limited data and resources are available, as in the presented case. The resulting flood-inundation and hazard maps will enable the Regional Water Management Administration of Mozambique (ARA) to develop early warning systems.

Abstract. The crawler engines of today cannot reach most of the information contained in the Web. A great amount of valuable information is "hidden" behind the query forms of online databases, and/or is dynamically generated by technologies such as JavaScript. This portion of the web is usually known as the Deep Web or the Hidden Web. We have built DeepBot, a prototype hiddenweb crawler able to access such content. DeepBot receives as input a set of domain definitions, each one describing a specific data-collecting task and automatically identifies and learns to execute queries on the forms relevant to them. In this paper we describe the techniques employed for building DeepBot and report the experimental results obtained when testing it with several real world data collection tasks.