In the past twelve years, digital image colorimetry (DIC) on smartphones has acquired great importance as an alternative to the most common analytical techniques. This analysis method is based on fast, low-cost, and easily-accessible technology, which can provide quantitative information about an analyte through the color changes of a digital image. Despite the fact that DIC is very widespread, it is not exempt from a series of problems that are not fully resolved yet, such as variability of the measurements between smartphones, image format in which color information is stored, power distribution of the illuminant used for the measurements, among others. This article proposes a methodology for the standardization and correction of these problems using self-developed software, together with the use of a 3D printed light box. This methodology is applied to three different colorimetric analyses using different types and brands of smartphones, proving that comparable measurements between devices can be achieved. As color can be related to many target analytes, establishing this measurement methodology can lead to new control analysis applicable to diverse sectors such as alimentary, industrial, agrarian, or sanitary.

Anticipating and understanding fluctuations in the agri-food market is very important in order to implement policies that can assure fair prices and food availability. In this paper, we contribute to the understanding of this market by exploring its efficiency and whether the local Hurst exponent can help to anticipate its trend or not. We have analyzed the time series of the price for different agri-commodities and classified each day into persistent, anti-persistent, or white-noise. Next, we have studied the probability and speed to mean reversion for several rolling windows. We found that in general mean reversion is more probable and occurs faster during anti-persistent periods. In contrast, for most of the rolling windows we could not find a significant effect of persistence in mean reversion. Hence, we conclude that the Hurst exponent can help to anticipate the future trend and range of the expected prices in this market.

In this work, we study the impact that the withdrawal of institutions from the United Kingdom caused by BREXIT has on the European research collaboration networks. To this aim, we consider BREXIT as a targeted attack to those graphs composed by the European institutions that have collaborated in research projects belonging to the three main H2020 programs (Excellent Science, Industrial Leadership, and Societal Challenges). The consequences of this attack are analyzed at the global, mesoscopic, and local scales and compared with the changes suffered by the same collaboration networks when a similar quantity of nodes is randomly removed from the network. Our results suggest that changes depend on the specific program, with Excellent Science being the most affected by BREXIT perturbation. However, the structure of the integrated collaboration network is not significantly affected by BREXIT compared to the variations observed after the random removal of institutions.