Abstract
The nutritional properties and safety of olive oil are strongly dependent on the content of mineral nutrients and trace elements. Hence, this work assesses the content for quality and toxic metals of olive oils from three major Portuguese cultivars ('Galega', 'Cobran double dagger osa' and 'Picual') obtained from olives in different ripening stages. This evaluation was achieved by flame atomic spectrometry and atomic absorption in a graphite furnace, depending on the metal to be analysed. The results showed mean concentrations of the mineral nutrients Ca and Mg of 20.00 A mu g g(-1), and K and Na of 40.00 and 170.00 A mu g g(-1), respectively, while the trace elements Fe, Co, Cu, Mn, Zn and Ni had mean values of 0.53, a parts per thousand currency sign 0.01, 0.33, 0.10, 1.09 and 0.64 A mu g g(-1), respectively. The mean content of the toxic metals Al, Cd, Pb and As were 27.46, 0.03, 0.09 and 0.36 A mu g g(-1), respectively, which were according to the values proposed by European regulations. The evaluation of the data obtained by partial least square regression allowed the identification of the minerals with the strongest potential to discriminate between the season (Na, Cu, Zn and Ni) and the cultivar employed in the olive oil performance (Fe and Mn).

Abstract
Impaired angiogenesis in systemic sclerosis has a major role in tissue injury pathogenesis. Our objective was to determine whether angiogenic biomarkers (vascular endothelial growth factor (VEGF), endoglin, and endostatin) are related to microvascular damage and to determine their predictive value for new digital ulcers (DU). The main outcome of the study was the occurrence of a new digital ulcer during 3-year follow-up. This prospective longitudinal study was performed between October 2011 and December 2014. Seventy-seven patients definitely diagnosed with systemic sclerosis where divided into two groups: those with active DU at baseline and those with no DU until enrollment. Patients were matched by sex and age with healthy controls. Serum levels of VEGF, endoglin, and endostatin were measured at enrollment, and several nailfold videocapillaroscopies were performed during the 3-year follow-up. Serum levels of VEGF were lower (245.06, 158.68-347.33; p < 0.001) and those of endoglin were higher (3.013, 1.463-7.023; p < 0.001) in patients with active DU than those with no DU history (339.49, 202.00-730.93/1.879, 0.840-3.280), and they were higher than those found in controls (178.030, 101.267-222.102)/0.277, 0.154-0.713), respectively. No differences in endostatin levels were found between groups (p = 0.450). Endoglin was the only biomarker significantly different (p = 0.031) between patients with diffuse versus limited systemic sclerosis and between early, active, and late patterns (p = 0.020). VEGF was identified as an independent predictor for the development of new DU. Our study confirmed the relationship between angiogenic vascular biomarkers and the occurrence of DU. Endoglin and VEGF serum levels are potential risk factors, and VEGF has a predictive value for the occurrence of new DU.

Abstract
Develop ground robots for crop monitoring and harvesting in steep slope vineyards is a complex challenge due to two main reasons: harsh condition of the terrain and unstable localization accuracy got from Global Positioning Systems (GPS). For this context, a reliable localization system requires a high density of natural/artificial features and an accurate detector. This paper presents a novel visual detector for Vineyards Trunks and Masts (ViTruDe). The ViTruDe detector was developed considering the constrains of a cost-effective robot to carry-out crop monitoring tasks in steep slope vineyard environment. The obtained results with real data shows an accuracy higher than 95% for all tested configurations. The training and test data are made public for future research work. This approach is a contribution for an accurate and reliable localization system that is GPS-free.

Abstract
A number of traditional liqueurs are obtained by maceration of red fruits in aqueous ethanol liquor, namely sloe berries or sour cherry. On the other hand, the exploration of residual plant material derived from the winery industry (grape (Vitis vinifera L.) stems), which has been regarded as an interesting source of colored and uncolored (poly)phenols, could lead to an industrial alternative to the traditional distilled spirits produced, with valuable physicochemical and phytochemical properties. In the present work, vinification residues (grape stems) were used to produce a new beverage. The evaluation of the physic-chemical characteristics and phytochemical composition as well as the evolution of the determined parameters during maceration (90 and 180 days) allowed a number of interesting bioactive compounds to be identified. This new beverage is a liqueur with a high retention of phenolic compounds (ortho-diphenols, flavanols, flavonols, and anthocyanins), with interesting physic-chemical characteristics, that revealed significant antioxidant activity.

Abstract
We consider a toral Anosov automorphism G(gamma) : T-gamma --> T-gamma given by G(gamma) (x, y) = (ax + y, x) in the < v, w > base, where , a is an element of N\{1}, gamma = 1/(a + 1/(a + 1/...)), v = (gamma, 1) and w = (-1, gamma) in the canonical base of R-2 and T-gamma = R-2 / (vZ x wZ). We introduce the notion of gamma-tilings to prove the existence of a one-to-one correspondence between (i) marked smooth conjugacy classes of Anosov diffeomorphisms, with invariant measures absolutely continuous with respect to the Lebesgue measure, that are in the isotopy class of G(gamma); (ii) affine classes of gamma-tilings; and (iii) gamma-solenoid functions. Solenoid functions provide a parametrization of the infinite dimensional space of the mathematical objects described in these equivalences.

Abstract
Network traffic prediction is a fundamental tool to harness several management tasks, such as monitoring and managing network traffic. Online traffic prediction is usually performed based on large sets of historical data used in training algorithms, for example, to determine the size of static windows to bound the amount of traffic under consideration. However, using large sets of historical data may not be suitable in highly volatile environments, such as cloud computing, where the coupling between time series observations decreases rapidly with time. To fill this gap, this work presents a dynamic window size algorithm for traffic prediction that contains a methodology to optimize a threshold parameter alpha that affects both the prediction and computational cost of our scheme. The alpha parameter defines the minimum data traffic variability needed to justify dynamic window size changes. Thus, with the optimization of this parameter, the number of operations of the dynamic window size algorithm decreases significantly. We evaluate the alpha estimation methodology against several prediction models by assessing the normalized mean square error and mean absolute percent error of predicted values over observed values from two real cloud computing datasets, collected by monitoring the utilization of Dropbox, and a data center dataset including traffic from several common cloud computing services. Copyright (C) 2016 John Wiley & Sons, Ltd.