Abstract
Artificial Intelligence is increasingly being discussed as something essential and pressing in all aspects and areas of society. Its potential use in education is no exception. Artificial Intelligence, in particular, and technologies, in general, are unavoidable elements to be considered in the teaching-learning process at all levels of education and training. There are many initiatives, essentially exploratory in nature, for the application of Artificial Intelligence in this process. Therefore, it is imperative to understand how they can be used for this purpose and how they relate to pedagogical methods. In the present study, and within this context, we address how Artificial Intelligence can be used in software to support cognitive and motor development and stimulate reasoning. We propose a reference model for techniques for this purpose. Concrete cases of existing applications are presented to better illustrate the potential of Artificial Intelligence in education.

Abstract

Abstract
Extreme summer temperatures in the Iberia Peninsula are analyzed from ERA5-Land reanalysis data based on an extreme value mixture model combining a Normal distribution for the bulk distribution (i.e. for the non-extreme values) and a Generalized Pareto Distribution for the extremes in the upper tail. This approach allows to treat the threshold of temperature exceedances as being one of model parameters rather than fixed a priori, enabling to take into account its corresponding uncertainty. Extreme value mixture models are estimated individually for each location, and the analysis is performed separately for two distinct periods, namely from 1981 to 2000 and from 2000 to 2019, respectively. The results show significant differences in the extreme value mixture models for the two periods, and in their corresponding 20-year return levels. The mean of the bulk distribution of summer maximum temperature increases significantly, particularly in Eastern Iberia. The largest differences in the tails of the data distribution between the two periods occur in the eastern Mediterranean area, and are characterized by a significant increase in the threshold for temperature exceedances and in their corresponding return levels.

Abstract
The atmospheric electric field is a key characteristic of the Earth system. Despite its relevance, oceanic measurements of the atmospheric electric field are scarce, as typically oceanic measurements tend to be focused on ocean properties rather than on the atmosphere above. This motivated the set-up of an innovative campaign on board the sail ship NRP Sagres focused on the measurement of the atmospheric electric field in the marine boundary layer. This paper describes the monitoring system that was developed to measure the atmospheric electric field during the planned circumnavigation expedition of the sail ship NRP Sagres.

Abstract

Abstract
<p><br>The automatic classification of peaks in gamma radiation time series is relevant for both scientific and practical applications. From the practical perspective, the classification of  peaks is fundamental for  early-warning systems for radiation protection and detection of radioactive material. From the scientific point of view, peaks in gamma radiation are often driven by precipitation  and consequent  scavenging of airborne radon progeny radionuclides to the ground (mainly Pb-214 and Bi-214). Thus measurements of gamma radiation at the earth's surface have the potential to provide information on micro-physical processes occurring high above in the clouds, as the dominant source of radon progeny is thought to be associated with in-cloud processes – nucleation scavenging and interstitial aerosol collection by cloud or rain droplets. </p><p>The present study addresses the classification of peaks in high-resolution (1-minute) gamma radiation time series from the GRM (Gamma Radiation Monitoring) campaign, which is being carried out since 2015 at the Eastern North Atlantic (ENA) station of the ARM (Atmospheric Radiation Measurements) programme. In addition to the gamma time series, precipitation information from laser disdrometer measurements is considered, including rain rate, liquid water content, median drop diameter and droplet concentration. Diverse machine learning algorithms are examined with the goal to identify and classify gamma peaks driven by precipitation events, and further examine the association between precipitation characteristics and the resulting gamma radiation peak on the ground.</p><p> </p>