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Nowadays a growing number of scientists relies on optical spectral measurements for their research. The market is full of new plug-and-play equipment for spectral analysis that take the fuss out of the measurements. As with other instruments (computers, lasers, etc.) the researcher doesn't need any longer to work with someone with a post-graduate formation on the technology to be able to do excellent research. But, as in every instrument, there are limitations on the instrument use that affect its precision and resolution. Currently there is in the market a large variety of equipment for spectral measurements. They range from the huge long focal length double pass monochromators to the small pocket size USB connected array spectrometers. The different configurations have different sensitivities on the light input system, light intensity, coherence, polarization, etc. In this talk we will discuss a few of the limitations in spectral measurements that can be found in experimental setups.

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This article presents an integrated vision of the context of the residential real estate market in Portugal. It analyses the evolution of several macroeconomic indicators during the last decade. The article is complemented with the analysis of the house prices, where an evaluation of average prices of apartments in Portugal according to different typologies and regions have been done. Regarding the market environment, several indicators are assessed, such as the evolution of interest rates, the evolution of households' credit, the consumption and construction confidence indexes and the evolution of foreign direct investment in housing. The conclusion is that after a booming period, the future of the real estate market is somehow worrying.

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The demand for accurate ocean sampling is continuously growing to provide a better understanding of the complex sea environment. Current economic and social activity is strongly dictated by knowledge built on data collected from thousands of sensors around the world, ranging from space-borne remote sensors to underwater devices transported by profilers. Autonomous sailboats have great potential to gather long-term data to understand multiple aspects of the ocean environment. In terms of oceanography, they can be used to study many processes occurring at the surface, like the energy exchange between the ocean and the atmosphere and how it affects the climate. They can also be a valuable tool to understand the dynamics of episodic events that evolve on a timescale of weeks or months, like harmful algae blooms or the evolution of pollution plumes. Even though these incidents can already be tracked by satellite, the ability to capture in-situ data for the full cycle can provide valuable data about the phenomena.

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Network motifs are small over represented patterns that have been used successfully to characterize complex networks. Current algorithmic approaches focus essentially on pure topology and disregard node and edge nature. However, it is often the case that nodes and edges can also be classified and separated into different classes. This kind of networks can be modeled by colored (or labeled) graphs. Here we present a definition of colored motifs and an algorithm for efficiently discovering them.We use g-tries, a specialized data-structure created for finding sets of subgraphs. G-Tries encapsulate common sub-structure, and with the aid of symmetry breaking conditions and a customized canonization methodology, we are able to efficiently search for several colored patterns at the same time. We apply our algorithm to a set of representative complex networks, showing that it can find colored motifs and outperform previous methods. © 2014 Springer International Publishing Switzerland.

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