Abstract

Abstract
The objective of this work is to identify the relevant factors that have influenced the changes in the level of CO2 emissions among four groups (eastern, western, northern and southern) of European countries. Our results show that CO2 emissions are correlated with the energy consumption of the economy for the group of countries under analysis, which is determined by the change of population among the various countries. Similarly, renewable energy consumption is also determined by the size and structure of the countries, as reflected by the value added to the economy. When comparing the results of the post-Kyoto period to the previous period for the four groups of European countries, one concludes that there are clear improvements in the reductions of emissions. This resulted primarily from changes to the energy mix, switching to cleaner fuels for end-user energy production (a volte-face in the behaviour of the energy mix effect), while the changes in the factors driving emissions resulted from a reduction in the usage of fossil fuels for producing energy. In general, the relative contributions of these two factors show the importance of the impact of changing the structure of the mix for producing energy, with a view to complying with the targets for reductions in CO2 emissions, reflecting the value placed on the significant impact assigned to emissions levels.

Abstract
A new procedure is proposed for estimating actual basal crop coefficients from vegetation indices (K-cb VI) considering a density coefficient (K-d) and a crop coefficient for bare soil. K-d is computed using the fraction of ground cover by vegetation (f(c VI)), which is also estimated from vegetation indices derived from remote sensing. A combined approach for estimating actual crop coefficients from vegetation indices (K-c VI) is also proposed by integrating the K-cb VI with the soil evaporation coefficient (K-e) derived from the soil water balance model SIMDualKc. Results for maize, barley and an olive orchard have shown that the approaches for estimating both f(c VI) and K-cb VI compared well with results obtained using the SIMDualKc model after calibration with ground observation data. For the crops studied, the correlation coefficients relative to comparing the actual K-cb VI and K-c VI with actual K-cb and K-c obtained with SIMDualKc were larger than 0.73 and 0.71, respectively. The corresponding regression coefficients were close to 1.0. The methodology herein presented and discussed allowed for obtaining information for the whole crop season, including periods when vegetation cover is incomplete, as the initial and development stages. Results show that the proposed methods are adequate for supporting irrigation management.

Abstract

Abstract
Deep Transfer Learning (DTL) emerged as a new paradigm in machine learning in which a deep model is trained on a source task and the knowledge acquired is then totally or partially transferred to help in solving a target task. Even though DTL offers a greater flexibility in extracting high-level features and enabling feature transference from a source to a target task, the DTL solution might get stuck at local minima leading to performance degradation-negative transference-, similar to what happens in the classical machine learning approach. In this paper, we propose the Source-Target-Source (STS) methodology to reduce the impact of negative transference, by iteratively switching between source and target tasks in the training process. The results show the effectiveness of such approach.

Abstract
Robots created for education have different purposes, from learning algorithms to learning robotics. It has been shown that robots can increase the student's interest. Our proposed robot, REDi, uses wire programming to introduce students to the basics of robotics and algorithms. With this robot, attractive, interactive demonstrations can be achieved even with students that have no background in the area.