Abstract
In the Web realm, the adoption of Cascading Style Sheets (CSS) is unanimous, being widely used for styling web documents. Despite their intensive use, this W3C specification was written for web designers with limit programming background. Thus, it lack several programming constructs, such as variables, conditional and repetitive blocks, and functions. This absence a ects negatively code reuse, and consequently, the maintenance of the styling code. In the last decade, several languages (e.g. Sass, Less) appeared to extend CSS, defined as CSS preprocessors, with the ultimate goal to bring those missing constructs and to foster stylesheets structured programming. The paper provides an introductory survey on CSS Preprocessors. It gathers information on a specific set of preprocessors, categorizes them and compares their features regarding a set of predefined criteria such as: maturity, coverage and performance. © Ricardo Queirós

Abstract
Counterfeit detection in official documents has challenged forensic experts on trying to correlate them to improve the identification of forgery authors by criminal investigators. Past counterfeit investigation on the Portuguese Police Forensic Laboratory allowed the construction of an organized set of digital images related to counterfeited documents, helping manual identification of new counterfeiters modus operandi. However, these images are usually stored in distinct resolutions, may have different sizes and could have been captured under different types of illumination. In this paper we present a methodology to automate a counterfeit identification modus operandi, by comparing a given document image with a database of previously catalogued counterfeited documents images. The proposed method ranks the identified counterfeited documents and allows the forensic experts to drive their attention to the most similar documents. It takes advantage of scalable algorithms under the OpenCV framework that compare images, match patterns and analyse textures and colours. We present a set of tests with distinct datasets with promising results.

Abstract
In the last two decades, renewable energy forecasting progressed toward the development of advanced physical and statistical algorithms aiming at improving point and probabilistic forecast skill. This paper describes a forecasting framework to explore information from a grid of numerical weather predictions (NWP) applied to both wind and solar energy. The methodology combines the gradient boosting trees algorithm with feature engineering techniques that extract the maximum information from the NWP grid. Compared to a model that only considers one NWP point for a specific location, the results show an average point forecast improvement (in terms of mean absolute error) of 16.09% and 12.85% for solar and wind power, respectively. The probabilistic forecast improvement, in terms of continuous ranked probabilistic score, was 13.11% and 12.06%, respectively.

Abstract
The technological revolution in the electric power system sector is producing large volumes of data with pertinent impact in the business and functional processes of system operators, generation companies, and grid users. Big data techniques can be applied to state estimation, forecasting, and control problems, as well as to support the participation of market agents in the electricity market. This chapter presents a revision of the application of data mining techniques to these problems. Trends like feature extraction/reduction and distributed learning are identified and discussed. The knowledge extracted from power system and market data has a significant impact in key performance indicators, like operational efficiency (e.g., operating expenses), investment deferral, and quality of supply. Furthermore, business models related to big data processing and mining are emerging and boosting new energy services.

Abstract
The possibility of using polymer fiber as a refractive index sensor is presented. The sensor is based on a Fabry-Perot interferometer formed at the tip of the polymer fiber. The interference is granted due to reflections between a fiber Bragg grating and the fiber end-face. The sensor was characterized to refractive index changes at constant temperature using a fast Fourier transform analysis of the interference signal. A sensitivity of -1.94 RIU-1 was achieved with a resolution of 1 x 10(-3) RIU and a cross sensitivity to temperature of 1 x 10(-4) RIU/degrees C

Abstract
The present work explores the processing conditions of Bi0.9La0.1Fe0.9Mn0.1O3 (BLFM) thin films, grown by RF sputtering on platinum metalized silicon substrates, and its impact on the structural and ferroelectric properties. The optimized processing conditions were found to be a combination of deposition of an amorphous film at low substrate temperature (ae550 A degrees C), followed by a thermal treatment at 550 A degrees C during 30 min, in order to prevent bismuth volatilization. This procedure leads to the formation of high-quality monophasic crystalline films with well-defined piezoelectric response exhibiting micron size domains.