Abstract
Artificial intelligence (AI) has gained significant evolution in recent years that, if properly harnessed, may meet or exceed expectations in a wide range of application fields. However, because Machine Learning (ML) models have a black-box structure, end users frequently seek explanations for the predictions made by these learning models. Through tools, approaches, and algorithms, Explainable Artificial Intelligence (XAI) gives descriptions of black-box models to better understand the models' behaviour and underlying decision-making mechanisms. The AI development in companies enables them to participate in Industry 4.0. The need to inform users of transparent algorithms has given rise to the research field of XAI. This paper provides a brief overview and introduction to the subject of XAI while highlighting why this topic is generating more and more attention in many sectors, such as industry.

Abstract
Within the scope of the Mobilizing Agenda for the Development of Intelligent Green Mobility Products and Systems (A-MoVeR), specifically in the second PPS2 defined the presentation of a “new electric motorcycle, with high autonomy, aimed at promoting comfortable, efficient and green urban mobility". In this context, we intend to develop user interfaces (UI) for an electric motorcycle that meet the end-user’s expectations by promoting optimal user experience and security. To achieve this goal, this paper provides a preliminary literature analysis, with a compilation of literature related to major aspects for developing an optimized User Interface (UI) and consequently increasing User eXperience (UX), specifying accessibility, adaptability, appeal, and conciseness of motorcycle interfaces in an attempt to determine its constructive qualities. Therefore, it was analyzed studies regarding filtering of displayed information; the controlling of a user’s focus and emotions through means of efficient visual representations; the differences in various types of input methods regarding user attention; and, the relevance of dynamic UI as a solution to a variety of problems related to UI/UX design. Therefore, a systematic literature review was performed, which resulted in the finding of various advantageous practices and ideas that are relevant to the design of a motorcycle's UI/UX. © 2024 World Scientific and Engineering Academy and Society. All rights reserved.

Abstract
With the progressive increase in electric vehicles and the carbon neutrality goals set for 2050, it is important to commit to optimizing batteries and their lifespan. Studies have been conducted to improve and understand storage systems and to determine the best ones to use in specific situations. Combining battery lifespan, the number of charging cycles, specific energy, and power can sometimes be challenging for the optimal functioning of an electric vehicle. This article aims to facilitate the understanding of how batteries truly influence the choice of an electric vehicle, and how some of them have more capacity than is commonly known. Additionally, advancements in battery technology, such as fast-charging capabilities, are being explored to address these challenges and enhance overall performance. Moreover, this paper highlights the importance of sustainable battery production, which are crucial for minimizing the environmental impact of increased electric vehicle adoption. By understanding these aspects, consumers and manufacturers can make more informed decisions that support both technological progress and smart grid sustainability.

Abstract
With the increasing adoption of electric motorcycles in urban environments, efficient energy management becomes essential to maximize the autonomy and sustainability of these vehicles. This study proposes the development of forecasting models to predict energy consumption and generation as means to optimize the charging of electric motorcycle batteries. Three models are explored in this work, namely multiple linear regression, LSTM (Long Short-Term Memory) neural networks, and XGBoost (Extreme Gradient Boosting). The performance of each model is assessed through various metrics. The results indicate that the LSTM model exhibited the best performance, particularly in identifying complex temporal patterns in solar radiation data. However, XGBoost also proved to be reasonable, while multiple linear regression was less satisfactory. The study discusses its limitations, such as the lack of deep refinement of model parameters, and future perspectives, including the exploration of other models and the implementation of strategies for predictive battery charging management.

Abstract
Virtual reality (VR) offers an immersive experience by simulating realistic environments, reducing the need for physical spaces and equipment. This technology addresses the challenges of traditional testing and learning, which often involve high costs, hardware limitations, and health concerns. In this paper, we use Blender, an open-source 3D creation software, to model a virtual vineyard and a wine sensory analysis laboratory. The vineyard is populated with various plants across a large virtual space, and the sensory lab is designed to simulate wine-tasting processes. The virtual lab enables users to explore and understand the sensory characteristics of wine in a controlled, immersive environment, providing an effective alternative to physical labs. Our simulations demonstrate that VR can replicate real-world environments with high fidelity and improve user engagement. The results suggest that using VR for wine sensory analysis can enhance educational outcomes, reduce costs, and offer an innovative platform for research and learning.

Abstract
We present a novel solution for automatic task allocation in multidevice environments, where configured robots compete for task assignment when announcing tasks, minimizing manual intervention. To this end, we propose the specification of a task assignment system and a task-oriented programming method aimed at automating processes and optimizing resource utilization in multiple controller environments. The proposed solution with its market-based algorithm and developed architecture improves the adaptability, scalability and overall efficiency of the system. The research discussion extends to broader implications that are consistent with the overall goal of improving robot capabilities in various deployment scenarios.