Abstract
Social networks are making part of the daily routine of millions of users. Twitter is among Facebook and Instagram one of the most used, and can be seen as a relevant source of information as users share not only daily status, but rapidly propagate news and events that occur worldwide. Considering the dynamic nature of social networks, and their potential in information spread, it is imperative to find learning strategies able to learn in these environments and cope with their dynamic nature. Time plays an important role by easily out-dating information, being crucial to understand how informative can past events be to current learning models and for how long it is relevant to store previously seen information, to avoid the computation burden associated with the amount of data produced. In this paper we study the impact of longstanding messages in micro-blogging classification by using different training time-window sizes in the learning process. Since there are few studies dealing with drift in Twitter and thus little is known about the types of drift that may occur, we simulate different types of drift in an artificial dataset to evaluate and validate our strategy. Results shed light on the relevance of previously seen examples according to different types of drift.

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This paper focuses the evaluation of susceptibility to linear erosion in the RDD, presenting the results of SIMWE (Simulated Water Erosion) model application. Furthermore, we also proceed to the results analysis by comparing the hydrological characteristics (infiltration capacity, hydraulic conductivity), soil texture and structure (evaluated by the electrical resistivity) in the high erosion areas defined through SIMWE. The study shows an association between the spatial distribution of erosive features with high values of water depth and reduced water discharge values that are consistent with the reduced values of electrical resistivity sectors. The areas with the highest percentage of erosive features related to sediment flux, the transport capacity and the sediment concentration assume medium susceptibility values. These combined to a hydraulic conductivity and soil infiltration capacity at very low values is consistent with its fine texture, allowing increased runoff and the development of linear erosion.

Abstract
Poly(sodium styrene sulfonate) (pNaSS) was grafted onto poly(epsilon-caprolatone) (PCL) surfaces via ozonation and graft polymerization. The effect of ozonation and polymerization time, as well as the Mohr's salt concentration in the grafting solution, on the degree of grafting was investigated. The degree of grafting was determined through toluidine blue staining. The surface chemical change was characterized by attenuated total reflection Fourier transform infrared spectroscopy, energy-dispersive X-ray spectroscopy and X-ray photoelectron spectroscopy. The result demonstrated that the grafting did not induce any degradation of PCL, and that pNaSS was grafted onto PCL as a thin and covalently stable layer. Furthermore, the modified PCL surface reveals a significant increase in the metabolic activity of fibroblastic cells, as well as a better cell spreading with higher adhesion strength. Consequently, bioactivity of PCL is greatly enhanced by immobilizing a thin layer of pNaSS onto its surface. The grafting of pNaSS is a promising approach to increase the bioactivity of PCL-based materials used in tissue engineering applications, such as ligament reconstruction.

Abstract
Temperature and water status profiles during the growing season are the most important factors influencing the ripening of wine grapes. To model weather influences on the quality and productivity of the vintages, it is necessary to partition the growing season into smaller growth intervals in which weather variables are evaluated. A significant part of past and ongoing research on the relationships between weather and wine quality uses calendar-defined intervals to partition the growing season. The phenology of grapevines is not determined by calendar dates but by several factors such as accumulated heat. To examine the accuracy of different approaches, this work analyzed the difference in average temperature and accumulated precipitation using growth intervals with boundaries defined by means of estimated historical phenological dates and intervals defined by means of accumulated heat or average calendar dates of the Douro Valley of Portugal. The results show that in situations where there is an absence of historical phenological dates and/or no available data that makes the estimation of those dates possible, it is more accurate to use grapevine heat requirements than calendar dates to define growth interval boundaries. Additionally, we analyzed the ability of the length of growth intervals with boundaries based on grapevine heat requirements to differentiate the best from the worst vintage years with the results showing that vintage quality is strongly related to the phenological events. Finally, we analyzed the variability of growth interval lengths in the Douro Valley during 1980-2009 with the results showing a tendency for earlier grapevine physiology.

Abstract