During the last few decades, many industrial remote and virtual laboratories have been designed and implemented at different universities and schools with the goal of providing online experiments that are remotely available to their students from anywhere and at any time. Research into virtual and remote laboratories has shown that the vast majority of these laboratories are built using learning tools and resources that are suited to a specific institution. Thus, different universities implement the software for their particular needs and consume a significant amount of time and energy in the process. This article describes a different approach: the design and implementation of a learning management system (LMS) activity that allows sharing of electronic, control and automation, and electrical remote laboratories from different universities within industrial courses without requiring the teacher to program a single line of code.

Water quality control is essential both before use, to prevent disease, and after use, to prevent the spillage of contaminated water. Although there are sufficient water resources to meet humanity's demand, it is estimated that by 2030 there will be a crisis affecting 40% of the population, therefore it is necessary a proper management of the water resources. While water quality problems exist even in developed countries, poor countries have increasing problems of access to safe potable water, as they do not have the resources to carry out adequate water quality control. For these reasons, it is interesting to apply low-cost and easy-to-build tools to measure or control water quality. In this work it is proposed an Arduino-based cost-effective system for the measurement of several parameters involved in water quality, in which compatible sensors have been used to measure pH, conductivity, temperature and turbidity. The model is focused on portability; therefore, it has been designed with a small size. The implementation of an LCD and SD storage allows measurements to be taken on the spot and to be used as a data logger.

Nowadays there is a great number of universities and organizations working in e-learning and i-learning solutions. One of the most well-known is the learning management system or LMS that allows displaying theoretical content in an organized and controlled way. In some jobs and studies it is necessary for the student to get a practical knowledge as well as a theoretical one. To obtain this practical knowledge, the universities and organizations are developing Virtual, Remote and Web labs. At these moments the LMS and Web labs are working independently. We are studying a new architecture allowing the integration of the LMS with different Web labs. This architecture must allow the students, teachers and administrators to use the services of LMS and virtual labâ??s features as if they were working with the same software.

The present research tries to establish the most important parameters of readability when it comes to choosing reading books for students in the 2nd and 3rd stages of Primary and Compulsory Secondary Education (CSE). A computational system is postulated, through data-mining techniques, capable of automatically classify reading texts by virtue of syntactic, lexical, semantic and topological parameters regarding their overall content. The validity of this process is ensured through a careful selection by editorial experts used to accomplish the same validation task among different texts. The results in the automatic classification of the degree of readability reached in this research are promising; they allow to continue with a deeper investigation in this field of analysis with the purpose of progressing in the age rank studied, as well as in the text themes defining the corpus.

A massive integration of renewable energy sources is imperative to comply with the greenhouse emissions reduction targets fixed to achieve the limitation of global warming. Nevertheless, the present integration levels are still far from the targets. The main reason being the technical barriers arising from their non-manageable features. Photovoltaic and wind sources are the widest spread, as their maturity allows generation with a high-efficiency degree. A deep understanding of facilities’ performance and how they can match the energy demand is mandatory to reduce costs and extend the technical limits and facilitate their penetration. In this paper, we present a novel methodology to evaluate how photovoltaic–wind hybrid facilities, placed in an urban environment can give generation patterns which will be able to match the demand profiles better than facilities installed individually. This methodology has been applied to a broad number of locations spread over the whole planet. The results show that with high homogeneity in terms of site weather characteristics, the hybrid facilities improve the matching up to 15% over photovoltaic plants and up to 35% over wind.