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
This article proposes methods for maximising the detection rates of thermal fiducial markers using thermography. By exploring the combination of image processing techniques with the use of an affordable thermographic camera, the aim is to mitigate the negative effects of thermography and improve accurate marker identification in a variety of mounting and distance conditions. The research identified a diversity of processing techniques capable of improving thermal marker recognition, offering the potential to surpass previous results. The results highlight the possibility of using low-cost thermographic cameras for this purpose, which could democratise and reduce the costs of recognition processes. This methodology validates the proposed approach, providing a robust basis for future improvements in thermal marker detection and promoting the feasibility of practical, low-cost applications in an assortment of fields.

Abstract
Lung Cancer (LC) is still among the top main causes of death worldwide, and it is the leading death number among other cancers. Several AI-based methods have been developed for the early detection of LC, trying to use Computed Tomography (CT) images to identify the initial signs of the disease. The survival prediction could help the clinicians to adequate the treatment plan and all the proceedings, by the identification of the most severe cases that need more attention. In this study, several deep learning models were compared to predict the survival of LC patients using CT images. The best performing model, a CNN with 3 layers, achieved an AUC value of 0.80, a Precision value of 0.56 and a Recall of 0.64. The obtained results showed that CT images carry information that can be used to assess the survival of LC.

Abstract
Portugal made a climate commitment when it ratified the Paris Climate Agreement in 2015. As a result, Portugal, along with other EU members, has created a national roadmap for the deployment of hydrogen as a crucial component of Portugal ' s energy transition towards carbon neutrality, creating synergies between the electric and gas systems. The increased variability of generation from variable renewable power sources will create challenges regarding the security of supply, requiring investment in storage solutions to minimize renewable energy curtailment and to provide dispatchability to the electric power system. Hydrogen can be a renewable energy carrier capable of ensuring not only the desired transformation of the infrastructures of the gas system but also an integrator of the Electric System, such as in Power -to -Power (P2P) systems. Hydrogen can be produced with a surplus of renewable electricity from wind and solar, allowing a long-term energy seasonal storage strategy, namely by using underground salt caverns, to be subsequently transformed into electricity when demand cannot be supplied due to a shortage of renewable generation from solar or wind. P2P investments are capital intensive and require the development of transitional regulation mechanisms to both create opportunities to market agents while fostering the energy surplus valuation and decreasing the energy dependency. In order to maintain the electric system ' s security of supply, the suggested methodology innovatively manages the importance of seasonal storage of renewable energy surplus using hydrogen in power systems. It suggests a novel set of regulatory strategies to foster the creation of a P2P solution that maintains generation adequacy while assisting in decarbonising the electric power industry. Such methodology combines long-term adequacy assessment with regulatory framework evaluation to evaluate the cost of the proposed solutions to the energy system. A case study based on the Portuguese power system outlook between 2030 and 2040 demonstrates that the considerable renewable energy surplus can be stored as hydrogen and converted back into electricity to assure adequate security of supply levels throughout the year with economic feasibility under distinct public policy models.

Abstract
Robotics competitions are highly strategic tools to engage and motivate students, cultivating their curiosity and enthusiasm for technology and robotics. These competitions encompass various disciplines, such as programming, electronics, control systems, and prototyping, often beginning with developing a mobile platform. This paper focuses on designing and implementing an omnidirectional mecanum platform, encompassing aspects of mechatronics, mechanics, electronics, kinematics models, and control. Additionally, a simulation model is introduced and compared with the physical robot, providing a means to validate the proposed platform.

Abstract
The Ambient Assisted Living (AAL) systems are human-centered and designed to prioritize the needs of elderly individuals, providing them with assistance in case of emergencies or unexpected situations. These systems involve caregivers or selected individuals who can be alerted and provide the necessary help when needed. To ensure effective assistance, it is crucial for caregivers to understand the reasons behind alarm triggers and the nature of the danger. This is where an explainability module comes into play. In this paper, we introduce an explainability module that offers visual explanations for the fall detection module. Our framework involves generating anchor boxes using the K-means algorithm to optimize object detection and using YOLOv8 for image inference. Additionally, we employ two well-known XAI (Explainable Artificial Intelligence) algorithms, LIME (Local Interpretable Model) and Grad-CAM (Gradient-weighted Class Activation Mapping), to provide visual explanations.