Abstract
This chapter aims to combine the contributions scattered in the literature by analyzing the different types of social media marketing actions and their expected outcomes. A systematic literature review was conducted and complemented with interviews with practitioners (n=8) in order to validate the findings. The analysis confirmed that the literature is particularly fragmented, although it approaches a very diversified list of social media marketing actions (n=29). Four types of actions were identified: actions that evoke emotions, actions that foster interaction and involvement, actions of information sharing, and commercial actions. Practitioners involved in the validation process confirmed the adequacy and usefulness of the classification. Social media marketing actions are organized into four blocks according to their objectives and impacts on consumer behavior, hence providing a tool that was recognized by a sample of practitioners as useful to guide their efforts and budgets. © 2023, IGI Global. All rights reserved.

Abstract
Extensive literature is available for modeling and simulating local electricity markets, often called P2P electricity markets, and for pricing local energy transactions in energy communities. Market models and pricing mechanisms provide simulation tools to better understand how these new markets behave, helping to design their main rules for real applications, and assessing the financial compensations of the internal energy transactions. As such, pricing mechanisms are often needed in energy management systems when centralized management approaches are preferred to market-based ones. First, this paper highlights the links between local electricity markets, pricing mechanisms for local electricity transactions, and other approaches to sharing the collective benefits of participating in transactive energy communities. Then, a standard nomenclature is defined to review some of the main pricing mechanisms for local energy transactions, an innovative pricing mechanism based on the economic principles of a post-delivery pool market is proposed, and other relevant approaches for local electricity market simulation such as Nash equilibrium or agent-based simulation are also revisited. The revision was based on systematic searches in common research databases and on the authors' experience in European and national projects, including local industrial applications for the past five years. A qualitative assessment of the reviewed methods is also provided, and the research challenges are highlighted. This review is intended to serve as a practical guide to pricing mechanisms and market simulation procedures for practical designs of internal financial compensation to share the collective benefits of energy communities.

Abstract
In recent years, the use of remote sensing data obtained from satellite or unmanned aerial vehicle (UAV) imagery has grown in popularity for crop classification tasks such as yield prediction, soil classification or crop mapping. The ready availability of information, with improved temporal, radiometric, and spatial resolution, has resulted in the accumulation of vast amounts of data. Meeting the demands of analysing this data requires innovative solutions, and artificial intelligence techniques offer the necessary support. This systematic review aims to evaluate the effectiveness of deep learning techniques for crop classification using remote sensing data from aerial imagery. The reviewed papers focus on a variety of deep learning architectures, including convolutional neural networks (CNNs), long short-term memory networks, transformers, and hybrid CNN-recurrent neural network models, and incorporate techniques such as data augmentation, transfer learning, and multimodal fusion to improve model performance. The review analyses the use of these techniques to boost crop classification accuracy by developing new deep learning architectures or by combining various types of remote sensing data. Additionally, it assesses the impact of factors like spatial and spectral resolution, image annotation, and sample quality on crop classification. Ensembling models or integrating multiple data sources tends to enhance the classification accuracy of deep learning models. Satellite imagery is the most commonly used data source due to its accessibility and typically free availability. The study highlights the requirement for large amounts of training data and the incorporation of non-crop classes to enhance accuracy and provide valuable insights into the current state of deep learning models and datasets for crop classification tasks.

Abstract
The complex and interconnected infrastructure of smart cities offers several opportunities for attackers to exploit vulnerabilities and carry out cyberattacks that can have serious consequences for the functioning of cities' critical infrastructures. This study aims to address this phenomenon and characterize the dimensions of security risks in smart cities and present mitigation proposals to address these risks. The study adopts a qualitative methodology through the identification of 62 European research projects in the field of cybersecurity in smart cities, which are underway during the period from 2022 to 2027. Compared to previous studies, this work provides a comprehensive view of security risks from the perspective of multiple universities, research centers, and companies participating in European projects. The findings of this study offer relevant scientific contributions by identifying 7 dimensions and 31 sub-dimensions of cybersecurity risks in smart cities and proposing 24 mitigation strategies to face these security challenges. Furthermore, this study explores emerging cybersecurity issues to which smart cities are exposed by the increasing proliferation of new technologies and standards.

Abstract
In recent years, sustainable power supply has become a necessary asset for the daily survival and development of populations. The incentive to the use of renewable energies has been increasing worldwide. Solar energy, in particular, is widespreading fast in countries whose location allows to obtain excellent radiation conditions. In this work, autonomous photovoltaic (PV) systems are studied, having as main aim its application in the supply of urban loads. For this purpose, a PV system is designed to supply the decorative lighting of a monument. Particular emphasis is given to studying the behavior of the energy storage system. The achieved results demonstrate that the use of this type of systems is a very efficient solution for the municipalities to supply several urban loads such as fountains, traffic lights, decorative lighting, among others. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Abstract
ObjectivesThere is a growing interest in mindfulness-based programs. Yet, research in the area is limited, and little is known about the factors that moderate the effects of these programs. The two-fold aim of this study was (1) to examine the effects of a mindfulness-based program on dispositional mindfulness, inattention and emotional lability, handwriting fluency, spelling accuracy, and composing quality, as well as school achievement; and (2) to evaluate the moderating role of lesson absences, intervention-related knowledge, and social validity.MethodUsing a quasi-experimental design, 257 fourth graders were assigned to an experimental group receiving a mindfulness-based program (n = 130) or an active control group receiving a health-based program (n = 127). Both programs were implemented in the classroom for 8 weekly units, which included two 30-min sessions delivered by psychologists, followed by three 5-min sessions delivered by teachers. All children were evaluated before and after the programs.ResultsCompared to the control condition, the mindfulness-based program resulted in higher levels of internal and external awareness, and decentering and nonreactivity, as well as better composing quality and mathematics grades. Lesson absences, intervention-related knowledge, and social validity did not moderate the effects of the mindfulness-based program.ConclusionsThese findings support the integration of mindfulness practices in primary school as a means to improve children's academic-related skills and ability to be mindful.