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.

Sunflower seeds, one of the main oilseeds produced around the world, are widely used in the food industry. Mixtures of seed varieties can occur throughout the supply chain. Intermediaries and the food industry need to identify the varieties to produce high-quality products. Considering that high oleic oilseed varieties are similar, a computer-based system to classify varieties could be useful to the food industry. The objective of our study is to examine the capacity of deep learning (DL) algorithms to classify sunflower seeds. An image acquisition system, with controlled lighting and a Nikon camera in a fixed position, was constructed to take photos of 6000 seeds of six sunflower seed varieties. Images were used to create datasets for training, validation, and testing of the system. A CNN AlexNet model was implemented to perform variety classification, specifically classifying from two to six varieties. The classification model reached an accuracy value of 100% for two classes and 89.5% for the six classes. These values can be considered acceptable, because the varieties classified are very similar, and they can hardly be classified with the naked eye. This result proves that DL algorithms can be useful for classifying high oleic sunflower seeds.

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.