This paper makes a brief review on 30 years of history of the wind power short-term prediction, since the first ideas and sketches on the theme to the actual state of the art on models and tools, giving emphasis to the most significant proposals and developments. The two principal lines of thought on short-term prediction (mathematical and physical) are indistinctly treated here and comparisons between models and tools are avoided, mainly because, on the one hand, a standard for a measure of performance is still not adopted and, on the other hand, it is very important that the data are exactly the same in order to compare two models (this fact makes it almost impossible to carry out a quantitative comparison between a huge number of models and methods). In place of a quantitative description, a qualitative approach is preferred for this review, remarking the contribution (and innovative aspect) of each model. On the basis of the review, some topics for future research are pointed out.

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Radiofrequency (RF) treatment appears to be involved in production of new collagen fibrils and the improvement of existing collagen structures; however, the molecular bases of the effect of non-invasive RF on the skin tissue have not been fully elucidated. This study reports the effects of RF associated or not with hydrolyzed collagen (HC) in the skin tissue. Wistar rats were randomly divided into four groups, according to the treatment received: control group (G1, n = 5), no treatment; subjects in group G2 (n = 5) were treated with HC; and capacitive RF was applied to the back of each subject in G3 (n = 5) and RF associated with HC in G4 (n = 5). Biopsies were taken 30 days after treatment and then were histologically processed and studied for inflammatory cell counting, collagen content, and morphometry. In addition, FGF2, CD105, and COX-2 expression was assessed by immunohistochemical staining. The most relevant changes were the increase in cellularity and accumulation of intercellular substance in RF-treated animals (G3 and G4). The greatest dermis thickness rate was observed in G4, followed by G3 and G2 (p < 0.05). RF-treated skins (G3 and G4) exhibited a significant overexpression of FGF2 (p < 0.0001) and increased microvessel density (p < 0.0001) in comparison with G1 and G2. Moreover, the amount of COX-2 was significantly higher (p < 0.0001) in dermis of RF-treated areas compared to G1 and G2, and demonstrated differences in G3 (RF) compared to G4 (RF + HC) (p < 0.0001). Our results suggests that RF treatment associated or not with HC induces FGF2 overexpression, promotes neoangiogenesis and modulates the COX-2 expression, subsequently promotes neocollagenesis, and increased thickness rate of dermis.

OBJECTIVE To estimate the prevalence of illicit drug use and its association with binge drinking and sociodemographic factors among adolescent students.METHODS This is a cross-sectional study with probabilistic conglomerate sampling, involving 1,154 students, aged 13 to 19 years old, from the public school system, in the city of Olinda, State of Pernambuco, Brazil, carried out in 2014. We used the Youth Risk Behavior Survey questionnaire, validated for use with Brazilian adolescents. The Chi-square test (≤ 0.05) and Poisson regression analysis were used to estimate the prevalence ratios, with 95% confidence intervals.RESULTS Use in life of illicit drugs was four times more prevalent among students who reported binge drinking (95%CI 3.19–5.45). Being in the age group of 16 to 19 years, being male, and having no religion were also significantly associated with illicit drug use.CONCLUSIONS The prevalence of use in life of illicit drugs was higher in this study than in other studies carried out in Brazil and it was strongly associated with binge drinking. This factor was associated with gender, age, and religion.

Wind power time series usually show complex dynamics mainly due to non-linearities related to the wind physics and the power transformation process in wind farms. This article provides an approach to the incorporation of observed local variables (wind speed and direction) to model some of these effects by means of statistical models. To this end, a benchmarking between two different families of varyingcoefficient models (regime-switching and conditional parametric models) is carried out. The case of the offshore wind farm of Horns Rev in Denmark has been considered. The analysis is focused on one-step ahead forecasting and a time series resolution of 10 min. It has been found that the local wind direction contributes to model some features of the prevailing winds, such as the impact of the wind direction on the wind variability, whereas the non-linearities related to the power transformation process can be introduced by considering the local wind speed. In both cases, conditional parametric models showed a better performance than the one achieved by the regime-switching strategy. The results attained reinforce the idea that each explanatory variable allows the modelling of different underlying effects in the dynamics of wind power time series.

Abstract. Ramp events are large rapid variations within wind power time series. Ramp forecasting can benefit from specific strategies so as to particularly take into account these shifts in the wind power output dynamic. In the short-term context (characterized by prediction horizons from minutes to a few days), a Regime-Switching (RS) model based on Artificial Neural Nets (ANN) is proposed. The objective is to identify three regimes in the wind power time series: Ramp-up, Ramp-down and No-ramp regime. An on-line regime assessment methodology is also proposed, based on a local gradient criterion. The RS-ANN model is compared to a single-ANN model (without regime discrimination), concluding that the regime-switching strategy leads to significant improvements for one-hour ahead forecasts, mainly due to the improvements obtained during ramp-up events. Including other explanatory variables (NWP outputs, local measurements) during the regime assessment could eventually improve forecasts for further horizons.

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