Abstract

Abstract
Nowadays, Mobility, in all its dimensions (transport mobility, sustainable mobility, active mobility, and Mobility as a Service (MaaS)), is an essential dimension in sustainable development goals, allowing to increase in the quality of life, the health, the social inclusion and to reduce climate action in any society. To increase the citizens' awareness and promote a true behavioral change, the citizens need to feel part of the process. Gamification has proved to be effective in raising citizens' awareness, encouraging their participation, and promoting a gradual but profound behavior change in various areas such as participatory governance, tourism, culture, education, etc. Gamification can also propel a Smart Living Society 5.0 among the younger groups of the society, especially in the context of academic communities that are more knowledgeable and eager to foster a healthier, more sustainable, and more inclusive society. Smart Living Society 5.0 is an activity in the scope of the TECH - Tecnologia, Ambiente, Criatividade e Saude - a project of NORTE 2020, focusing on creating an Academic MaaS (AMaaS). At this stage, it is essential to know about gamification use cases related to new mobility solutions and practices. The paper presents successful cases of mobility systems and services that consider gamification to promote and incentivize their use concerning active mobility and sustainable mobility; it discusses the potential of gamified systems to achieve a gamification proposal approach to implement in the AMaaS under development.

Abstract
The field of Explainable Predictive Maintenance (PM) is concerned with developing methods that can clarify how AI systems operate in the PM domain. One of the challenges of creating maintenance plans is integrating AI output with human decision-making processes and expertise. For AI to be helpful and trustworthy, fault predictions must be contextualized and easily comprehensible to humans. This involves providing tailored explanations to different actors depending on their roles and needs. For example, engineers can be connected to technical installation blueprints, while managers can evaluate system downtime costs, and lawyers can assess safety-threatening failures' potential liability. In many industries, black-box AI systems analyze sensor data to predict failures by detecting anomalies and deviations from typical behavior with impressive accuracy. However, PM is just one part of a broader context that aims to identify the most probable causes, develop a recovery plan, and estimate remaining useful life while providing alternative solutions. Achieving this requires complex interactions among various actors in industrial and decision-making processes. Our tutorial explores current trends, promising research directions in Explainable AI (XAI) relevant to Explainable Predictive Maintenance (XPM), and future challenges and open issues on this topic. We will also present three case studies that highlight XPM's challenges in bus and train operations and steel factories.

Abstract
Renewable energy sources have transformed the electricity market, allowing virtual power players or aggregators to participate and benefit from selling surplus energy. However, meeting demand and ensuring energy production stability can be challenging due to the intermittent nature of renewable sources. Accurate forecasting of energy consumption, generation, and electricity prices is critical to address these issues. Moreover, the selection of the best algorithm for forecasting is usually based on the predictions’ accuracy, neglecting other factors such as the impact of errors on the real context. This paper presents a study on the economic risk of price forecasting errors on the electricity market’s trading. For this, a simulation model is proposed to analyze the deviations between actual and predicted prices and how these deviations can affect trading in the electricity market, where the main purpose is to maximize profit, depending on whether the player is buying or selling electricity. The economic risk analysis and the predictions scores are used to improve the forecasts, using an approach based on reinforcement learning to evaluating and selecting which models demonstrated better performance in past transactions. The study involved simulating an aggregator’s transactions in the Iberian electricity market for two consecutive days in October 2021. Real data from the market operator between 2020 and 2021 and seven forecasting models were used. The findings showed that errors have a significant impact on profit. Including the economic impact analysis and score evaluation of forecasting methods to determine which method can offer better results has proven to be a viable approach. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Abstract
In a clinical context, physicians usually take into account information from more than one data modality when making decisions regarding cancer diagnosis and treatment planning. Artificial intelligence-based methods should mimic the clinical method and take into consideration different sources of data that allow a more comprehensive analysis of the patient and, as a consequence, a more accurate diagnosis. Lung cancer evaluation, in particular, can benefit from this approach since this pathology presents high mortality rates due to its late diagnosis. However, many related works make use of a single data source, namely imaging data. Therefore, this work aims to study the prediction of lung cancer when using more than one data modality. The National Lung Screening Trial dataset that contains data from different sources, specifically, computed tomography (CT) scans and clinical data, was used for the study, the development and comparison of single-modality and multimodality models, that may explore the predictive capability of these two types of data to their full potential. A ResNet18 network was trained to classify 3D CT nodule regions of interest (ROI), whereas a random forest algorithm was used to classify the clinical data, with the former achieving an area under the ROC curve (AUC) of 0.7897 and the latter 0.5241. Regarding the multimodality approaches, three strategies, based on intermediate and late fusion, were implemented to combine the information from the 3D CT nodule ROIs and the clinical data. From those, the best model-a fully connected layer that receives as input a combination of clinical data and deep imaging features, given by a ResNet18 inference model-presented an AUC of 0.8021. Lung cancer is a complex disease, characterized by a multitude of biological and physiological phenomena and influenced by multiple factors. It is thus imperative that the models are capable of responding to that need. The results obtained showed that the combination of different types may have the potential to produce more comprehensive analyses of the disease by the models.

Abstract
Video games have become an increasingly popular and influential form of entertainment in recent years, with a growing market and diverse range of players. As a result, there is a need to better understand and improve various aspects of the video game industry, including storytelling, localisation, and immersion. Storytelling in video games refers to the use of narrative elements, such as character development, plot, and dialogue, to create a compelling and engaging experience. Localisation involves adapting a game for different cultural and linguistic audiences, which can be a complex process that requires careful consideration of the original content and the target audience. This can include translation, voice acting, and other modifications to ensure a seamless and enjoyable experience for players. Immersion refers to the extent to which the player feels fully absorbed and engaged in the game world and its gameplay. By understanding and addressing these three areas, game developers can create more engaging and successful games for players around the world. This study focuses on the interrelated areas of storytelling, localisation, and immersion within the context of role-playing games, using The Witcher III: Wild Hunt as a case study. The study used 41 participants who played the game in both English and Brazilian-Portuguese localised versions and completed questionnaires and interviews about their perceptions of these three areas. The results of the study offer recommendations for improving graphic design in video games and suggest the need to explore whether the impact of localisation on story and immersion is dependent only on language or influenced by other factors. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.