The presence of technology on college campuses has increased rapidly in recent years. Students come to the classroom with a variety of technological devices including smart phones, tablets, or laptops and use them during academic activity. For this reason, there are many researchers who, in recent times, have been interested in the problems derived from digital distraction in higher education. In many cases, researchers have conducted studies and surveys to obtain first-hand information from the protagonists, that is, from university professors and students. Despite the efforts, there are many questions that still remain unanswered. The authors are aware of the enormous challenge that the use of technology poses in the university classrooms and want to delve into the causes and consequences of student digital distraction and the strategies that can be used by instructors to curb student digital distraction without deteriorating student-instructor rapport in the context of higher education.

Smart grids are able to forecast customers’ consumption patterns, i.e., their energy demand, and consequently electricity can be transmitted after taking into account the expected demand. To face today’s demand forecasting challenges, where the data generated by smart grids is huge, modern data-driven techniques need to be used. In this scenario, Deep Learning models are a good alternative to learn patterns from customer data and then forecast demand for different forecasting horizons. Among the commonly used Artificial Neural Networks, Long Short-Term Memory networks—based on Recurrent Neural Networks—are playing a prominent role. This paper provides an insight into the importance of the demand forecasting issue, and other related factors, in the context of smart grids, and collects some experiences of the use of Deep Learning techniques, for demand forecasting purposes. To have an efficient power system, a balance between supply and demand is necessary. Therefore, industry stakeholders and researchers should make a special effort in load forecasting, especially in the short term, which is critical for demand response.

Technology enables a more sustainable and universally accessible educational model. However, technology has brought a paradox into students’ lives: it helps them engage in learning activities, but it is also a source of distraction. During the academic year 2021–2022, the authors conducted a study focusing on classroom distractions. One of the objectives was to identify the main digital distractions from the point of view of students. The study was carried out at an engineering school, where technology is fully integrated in the classroom and in the academic routines of teachers and students. Discussions and surveys, complemented by a statistical study based on bivariate correlations, were used with participating students (n = 105). Students considered digital distractions to have a significant impact on their performance in lab sessions. This performance was mainly self-assessed as improvable. Contrary to other contemporary research, the results were not influenced by the year of study of the subject, as the issue is important regardless of the students’ backgrounds. Professors should implement strategies to raise students’ awareness of the significant negative effects of digital distractions on their performance, as well as to develop students’ self-control skills. This is of vital importance for the use of technology to be sustainable in the long-term.

The Fourth Industrial Revolution, under the name of Industry 4.0, focuses on obtaining and using data to facilitate decision-making and thus achieve a competitive advantage. Industry 4.0 is about smart factories. For this, a series of technologies have emerged that communicate the physical and the virtual world, including Internet of Things, Big Data, and Artificial Intelligence. These technologies can be applied in many areas of the industry such as production, manufacturing, quality, logistics, maintenance, or security to improve the optimization of the production capacity or the control and monitoring of the production process. An important area of application is maintenance. Predictive maintenance is focused on monitoring the performance and condition of equipment during normal operation to reduce the likelihood of failures with the help of data-driven techniques. This chapter aims to explore the possibilities of using artificial intelligence to optimize the maintenance of the machinery and equipment components so that product costs are reduced.

Identification and monitoring of existing surface water bodies on the Earth are important in many scientific disciplines and for different industrial uses. This can be performed with the help of high-resolution satellite images that are processed afterwards using data-driven techniques to obtain the desired information. The objective of this study is to establish and validate a method to distinguish efficiently between water and land zones, i.e., an efficient method for surface water detection. In the context of this work, the method used for processing the high-resolution satellite images to detect surface water is based on image segmentation, using the Quadtree algorithm, and fractal dimension. The method was validated using high-resolution satellite images freely available at the OpenAerialMap website. The results show that, when the fractal dimensions of the tiles in which the image is divided after completing the segmentation phase are calculated, there is a clear threshold where water and land can be distinguished. The proposed scheme is particularly simple and computationally efficient compared with heavy artificial-intelligence-based methods, avoiding having any special requirements regarding the source images. Moreover, the average accuracy obtained in the case study developed for surface water detection was 96.03%, which suggests that the adopted method based on fractal dimension is able to detect surface water with a high level of accuracy.