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Background: In technical subjects, the "hands-on" aspect is important because the students cannot assimilate some concepts until they practice with real industrial systems. They need to have the demonstration that what they are performing is real and works in order to create a true mental image of the subject concepts, which translates as an increase in professional skills. Application Design: In this work, it is described a step forward in integrating mobile devices and real physical laboratories in a ubiquitous learning approach. Contribution: The latest experiences with mobile devices showed that it is possible to get near real hands-on by combining specially designed equipment and flexible virtual-scale models running on the mobile device, without requiring physical laboratories or classic remote labs. This approach can be applied to many technical subjects that face the same issues. Findings: Based on the evaluation results, the proposed u-lab architecture provided strong evidence of the benefits achieved. The proposal presents interesting advantages from the economic and professor productivity point of view, obtaining better academic achievements. The resulting platform should facilitate educational activities which use mobile devices. This approach can be applied to many technical subjects that suffer economic restrictions. This opens up new scenarios for mobile-device-based learning without excessive additional costs.

Simple Summary: Models of species distribution allow us to learn how different environmental factors determine where species are found. Our study focused on the Chilean dolphin in Seno Skyring, Chilean Patagonia. We used three different methods to predict where these dolphins are likely to be found, based on environmental data like water temperature, salinity, and proximity to the coast, as well as human activities like fish farming. We found that dolphins are most commonly found within six kilometers of the coast, in areas with many fish farms. This suggests that the dolphins may be attracted to areas near fish farms. Understanding these patterns is crucial for developing strategies to protect the endangered Chilean dolphin, especially given the large fish-farming industry in Chile. Our research highlights the need for more knowledge and comprehensive conservation efforts to ensure the dolphins' long-term survival. Species distribution modeling helps understand how environmental factors influence species distribution, creating profiles to predict presence in unexplored areas and assess ecological impacts. This study examined the habitat use and population ecology of the Chilean dolphin in Seno Skyring, Chilean Patagonia. We used three models—random forest (RF), generalized linear model (GLM), and artificial neural network (ANN)—to predict dolphin distribution based on environmental and biotic data like water temperature, salinity, and fish farm density. Our research has determined that the RF model is the most precise tool for predicting the habitat preferences of Chilean dolphins. The results indicate that these dolphins are primarily located within six kilometers of the coast, strongly correlating with areas featuring numerous fish farms, sheltered waters close to the shore with river inputs, and shallow productive zones. This suggests a potential association between dolphin presence and fish-farming activities. These findings can guide targeted conservation measures, such as regulating fish-farming practices and protecting vital coastal areas to improve the survival prospects of the Chilean dolphin. Given the extensive fish-farming industry in Chile, this research highlights the need for greater knowledge and comprehensive conservation efforts to ensure the species' long-term survival. By understanding and mitigating the impacts of fish farming and other human activities, we can better protect the habitat and well-being of Chilean dolphins. [ABSTRACT FROM AUTHOR]

The in-line determination of chemical parameters in water is of capital importance for environmental reasons. It must be carried out frequently and at a multitude of points; thus, the ideal method is to utilize automated monitoring systems, which use sensors based on many transducers, such as Ion Selective Electrodes (ISE). These devices have multiple advantages, but their management via traditional methods (i.e., manual sampling and measurements) is rather complex. Wireless Sensor Networks have been used in these environments, but there is no standard way to take advantage of the benefits of new Internet of Things (IoT) environments. To deal with this, an IoT-based generic architecture for chemical parameter monitoring systems is proposed and applied to the development of an intelligent potassium sensing system, and this is described in detail in this paper. This sensing system provides fast and simple deployment, interference rejection, increased reliability, and easy application development. Therefore, in this paper, we propose a method that takes advantage of Cloud services by applying them to the development of a potassium smart sensing system, which is integrated into an IoT environment for use in water monitoring applications. The results obtained are in good agreement (correlation coefficient = 0.9942) with those of reference methods. [ABSTRACT FROM AUTHOR]