Abstract
This paper explores the intricate interplay between perceived challenge and narrative immersion within role-playing game (RPG) video games, motivated by the escalating influence of game difficulty on player choices. A quantitative methodology was employed, utilizing three specific questionnaires for data collection on player habits and experiences, perceived challenge, and narrative immersion. The study consisted of two interconnected stages: an initial research phase to identify and understand player habits, followed by an in-person intervention involving the playing of three distinct RPG video games. During this intervention, selected players engaged with the chosen RPG video games separately, and after each session, responded to two surveys assessing narrative immersion and perceived challenge. The study concludes that a meticulous adjustment of perceived challenge by video game studios moderately influences narrative immersion, reinforcing the enduring prominence of the RPG genre as a distinctive choice in narrative.

Abstract
Significant medical image visualization and annotation tools, tailored for clinical users, play a crucial role in disease diagnosis and treatment. Developing algorithms for annotation assistance, particularly machine learning (ML)-based ones, can be intricate, emphasizing the need for a user-friendly graphical interface for developers. Many software tools are available to meet these requirements, but there is still room for improvement, making the research for new tools highly compelling. The envisioned tool focuses on navigating sequences of DICOM images from diverse modalities, including Magnetic Resonance Imaging (MRI), Computed Tomography (CT) scans, Ultrasound (US), and X-rays. Specific requirements involve implementing manual annotation features such as freehand drawing, copying, pasting, and modifying annotations. A scripting plugin interface is essential for running Artificial Intelligence (AI)-based models and adjusting results. Additionally, adaptable surveys complement graphical annotations with textual notes, enhancing information provision. The user evaluation results pinpointed areas for improvement, including incorporating some useful functionalities, as well as enhancements to the user interface for a more intuitive and convenient experience. Despite these suggestions, participants praised the application's simplicity and consistency, highlighting its suitability for the proposed tasks. The ability to revisit annotations ensures flexibility and ease of use in this context.

Abstract
Smart grids with EVs have been proposed as a great contribution to sustainability. Considering environmental sustainability is of great importance to humanity, it is essential to assess whether electrical vehicles (EVs) actually contribute to improving it. The objectives of the present study are, from a macro (broad-scope) perspective, to identify the sources of emissions and to create a framework for the calculation of CO2 emissions resulting from large-scale EV use. The results show that V2G mode increases emissions and therefore reduces the benefits of using EVs. The results also show that in the best scenario (NC mode), an EV will have 32.7% less emissions, and in the worst case (V2G mode), it will have 25.6% more emissions than an internal combustion vehicle (ICV), meaning that sustainability improvement is not always ensured. The present study shows that considering a macro perspective is essential to estimate a more comprehensive value of emissions. The main contributions of this work are the creation of a framework for identifying the main contributions to CO2 emissions resulting from large-scale EV integration, and the calculation of estimated CO2 emissions from a macro perspective. These are important contributions to future studies in the area of smart grids and large-scale EV integration, for decision-makers as well as common citizens.

Abstract
User studies are paramount for advancing science. In particular, the empirical evaluation of programmer-oriented tools is important to validate research ideas and prototypes, as well as production-ready tools. Previous research has collected several tools used by the software engineering and behavioral science communities to design and run studies. In this work, we study tools used in software engineering studies and identify their features. Furthermore, we analyze three behavioral science experiment tools to identify design ideas that might be adapted to programmer user studies. With this work, we present the set of features currently offered by software engineering tools to support researchers in the design and execution of programmer user studies. We also present the characteristics of some tools used in behavioral science experiments to identify design ideas that can be adapted to programmer user studies.

Abstract
Recommendation systems can play an important role in today’s digital content platforms by supporting the suggestion of relevant content in a personalised manner for each customer. Such content customisation has not been consistent across most media domains, and particularly on radio streaming and gaming aggregators, which are the two real-world application domains focused in this work. The challenges faced in these application areas are the dynamic nature of user preferences and the difficulty of generating recommendations for less popular content, due to the overwhelming choice and polarisation of available top content. We present the design and implementation of a Reinforcement Learning-based Recommendation System (RLRS) for web applications, using a Deep Deterministic Policy Gradient (DDPG) agent and, as a reward function, a weighted sum of the user Click Distribution (CD) across the recommended items and the Dwell Time (DT), a measure of the time users spend interacting with those items. Our system has been deployed in real production scenarios with preliminary but promising results. Several metrics are used to track the effectiveness of our approach, such as content coverage, category diversity, and intra-list similarity. In both scenarios tested, the system shows consistent improvement and adaptability over time, reinforcing its applicability. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

Abstract
Several studies demonstrate that the structure of the brain increases in hierarchical complexity throughout development. We tested if the structure of artificial neural networks also increases in hierarchical complexity while learning a developing task, called the balance beam problem. Previous simulations of this developmental task do not reflect a necessary premise underlying development: a more complex structure can be built out of less complex ones, while ensuring that the more complex structure does not replace the less complex one. In order to address this necessity, we segregated the input set by subsets of increasing Orders of Hierarchical Complexity. This is a complexity measure that has been extensively shown to underlie the complexity behavior and hypothesized to underlie the complexity of the neural structure of the brain. After segregating the input set, minimal neural network models were trained separately for each input subset, and adjacent complexity models were analyzed sequentially to observe whether there was a structural progression. Results show that three different network structural progressions were found, performing with similar accuracy, pointing towards self-organization. Also, more complex structures could be built out of less complex ones without substituting them, successfully addressing catastrophic forgetting and leveraging performance of previous models in the literature. Furthermore, the model structures trained on the two highest complexity subsets performed better than simulations of the balance beam present in the literature. As a major contribution, this work was successful in addressing hierarchical complexity structural growth in neural networks, and is the first that segregates inputs by Order of Hierarchical Complexity. Since this measure can be applied to all domains of data, the present method can be applied to future simulations, systematizing the simulation of developmental and evolutionary structural growth in neural networks.