Abstract
Recent studies have focused on forecasting power demand in buildings to enhance energy management. However, the literature still lacks comparative analyses of power demand forecasting algorithms. In addition, more case studies involving different building typologies are needed, as each building exhibits distinct behavior and load profiles. This paper aims to develop machine learning models to forecast the power demand of a large shopping center in the northern region of Portugal. The main objective is to compare the performance of several machine learning models. The results are promising, demonstrating adequate performance even during most holidays.

Abstract
The evolution of transport technologies, marked by integrating connectivity and automation, has led to innovative approaches such as truck platooning. This concept involves linking multiple trucks through automated driving and vehicle-to-vehicle communication, promising to revolutionize the freight industry by enhancing efficiency and reducing operational costs. This systematic review explores the current state of truck platooning testing literature, focusing on simulator and on-road tests. The objective is to identify key scenarios and requirements for successfully developing and implementing the truck platooning concept. Following the Preferred Reporting Items for Systematic Review and Meta-Analysis Protocols (PRISMA) guidelines, we searched the Web of Science and Scopus databases, leading to the inclusion of thirty pertinent articles encompassing simulation-based, on-road, and mixed-environment experiments. In addition to the type of testing environment, these articles were assorted into three groups corresponding to their main thematic scope, human-centered, technology-centered, and energy efficiency studies, each providing unique insights into core themes for the development of truck platooning. The results reveal a commonly preferred platoon formation consisting of three trucks maintaining a constant speed of 80 km/h and a stable distance of 10 m between them. Simulator-based studies have predominantly concentrated on human factors, examining driver behavior and interaction within the platooning framework. In contrast, on-road trials have yielded tangible data, offering a more technology-driven perspective and contributing practical insights to the field. While the literature on truck platooning has grown considerably, this review recognizes some limitations in the existing literature and suggests paths for future research. Overall, this systematic review provides valuable insights to the ongoing development of robust and effective truck platooning systems.

Abstract
This chapter aims to understand how Esports organizations can improve digital marketing strategies, considering the unique characteristics of this sector and the importance of maintaining solid relationships with the target audience. The research was carried out using a mixed methodology, which included the application of quantitative research to evaluate the behaviors of Esports fans and a qualitative literature review to explore the trends and challenges of digital marketing in this context. The results show that the esports audience consists predominantly of young males, with a strong interest in video games, technology and pop culture. The personalization of digital strategies, focusing on platforms such as YouTube and Twitch, as well as the use of promotions and sweepstakes, proved essential for audience engagement. Although the use of influencers has a neutral perception, campaigns that offer direct benefits, such as promotions, are more attractive.

Abstract
Project NuClim (Nuclear observations to improve Climate research and GHG emission estimates) aims to use high-quality measurements of atmospheric radon activity concentration and ambient radioactivity to advance climate science and improve radiation protection and nuclear surveillance capabilities. It is supported by new metrological capabilities developed in the EMPIR project 19ENV01 traceRadon. This work reviews the scientific objectives of project NuClim in terms of both climate science and radiological protection, and provides an overview of the NuClim field campaign and the various nuclear measurements being implemented within the scope of the project.

Abstract

Abstract
The learning from continuous data streams is a relevant area within machine learning, focusing on the creation and updating of predictive models in real time as new data becomes available for training and prediction. Among the most widely used methods for this type of task, Hoeffding Trees are highly valued for their simplicity and robustness across a variety of applications and are considered the primary choice for generating decision trees in data stream contexts. However, Hoeffding Trees tend to continuously expand as new data is incorporated, resulting in increased processing time and memory consumption, often without providing significant gains in accuracy. In this study, we propose an instance selection scheme that combines different strategies to regularize Hoeffding Trees and their variants, mitigating excessive growth without compromising model accuracy. The method selects misclassified instances and a fraction of correctly classified instances during the training phase. After extensive experimental evaluation, the instance selection scheme demonstrates superior predictive performance compared to the original models (without selection), for both real and synthetic datasets for data streams, using a reduced subset of examples. Additionally, the method achieves relevant improvements in processing time, model complexity, and memory consumption, highlighting the effectiveness of the proposed instance selection scheme.