Abstract
The number of electric vehicles has been increasing significantly due to various factors, such as the higher prices of fossil fuels, concerns about the increasing pollution, and the resulting incentive to use energy from renewable sources. There are currently a few charging facilities, which are still quite scattered, and several are still experimental, requiring appropriate planning of this infrastructure in order to support the growing number of electric vehicles adequately. Thus, optimising the location of charging stations becomes a critical issue, which can be achieved through the application of mathematical models and data analysis tools. An example is genetic algorithms, which have demonstrated their versatility in solving complex optimisation problems, especially those involving multiple variables. This work presents a proposal for a more comprehensive genetic algorithm model that encompasses all variables from the perspectives of all entities involved. Its experimentation was conducted using real data, with the aim of finding the best combination of locations, minimising the total number of stations and maximising the coverage of the area under study. Thus, it is essential to carefully consider user preferences, accessibility, energy demand, and existing electrical infrastructure to ensure an effective and sustainable installation. The findings highlight the crucial role of these computing tools in addressing complex problems from various viewpoints, leading to solutions that cater to the needs of all parties involved. While not necessarily perfect, these solutions represent a balanced compromise across multiple dimensions of the problem.

Abstract
Autonomous Vehicles aim to understand their surrounding environment by detecting relevant objects in the scene, which can be performed using a combination of sensors. The accurate prediction of pedestrians is a particularly challenging task, since the existing algorithms have more difficulty detecting small objects. This work studies and addresses this often overlooked problem by proposing Multimodal PointPillars (M-PP), a fast and effective novel fusion architecture for 3D object detection. Inspired by both MVX-Net and PointPillars, image features from a 2D CNN-based feature map are fused with the 3D point cloud in an early fusion architecture. By changing the heavy 3D convolutions of MVX-Net to a set of convolutional layers in 2D space, along with combining LiDAR and image information at an early stage, M-PP considerably improves inference time over the baseline, running at 28.49 Hz. It achieves inference speeds suitable for real-world applications while keeping the high performance of multimodal approaches. Extensive experiments show that our proposed architecture outperforms both MVX-Net and PointPillars for the pedestrian class in the KITTI 3D object detection dataset, with 62.78% in APBEV (moderate difficulty), while also outperforming MVX-Net in the nuScenes dataset. Moreover, experiments were conducted to measure the detection performance based on object distance. The performance of M-PP surpassed other methods in pedestrian detection at any distance, particularly for faraway objects (more than 30 meters). Qualitative analysis shows that M-PP visibly outperformed MVX-Net for pedestrians and cyclists, while simultaneously making accurate predictions of cars.

Abstract
This paper addresses the problem of maximizing the expected size of a matching in the case of unreliable vertices and/or edges. The assumption is that the solution is built in several steps. In a given step, edges with successfully matched vertices are made permanent; but upon edge or vertex failures, the remaining vertices become eligible for reassignment. This process may be repeated a given number of times, and the objective is to end with the overall maximum number of matched vertices. An application of this problem is found in kidney exchange programs, going on in several countries, where a vertex is an incompatible patient–donor pair and an edge indicates cross-compatibility between two pairs; the objective is to match these pairs so as to maximize the number of served patients. A new scheme is proposed for matching rearrangement in case of failure, along with a prototype algorithm for computing the optimal expectation for the number of matched edges (or vertices), considering a possibly limited number of rearrangements. Computational experiments reveal the relevance and limitations of the algorithm, in general terms and for the kidney exchange application. © 2025 The Authors

Abstract
This chapter examines how GenAI and predictive modelling strategies affect hyperpersonalised marketing. Through a comprehensive literature review and case studies, it examines hyper-p ersonalisation's theoretical frameworks, technical infrastructures, and ethical and governance issues. Large language models, generative adversarial networks, and diffusion models combined with advanced predictive analytics allow firms to scale real- time, highly individualised customer experiences. Effective implementation requires sophisticated data architectures, algorithmic transparency, and strong privacy protections. Integration complexity and ethical accountability are major barriers to consumer engagement and conversion, according to the research. Based on these findings, the chapter proposes an integrated framework that combines technological innovation with ethics and customer focus. This research advances marketing theory and provides practical advice for companies using AI- driven hyper-personalisation while maintaining consumer trust and regulatory compliance. © 2026, IGI Global Scientific Publishing. All rights reserved.

Abstract
The recent updates of the National Energy and Climate Plans (NECPs) for Portugal and Spain have some significant changes compared to the previous 2019 versions, especially for the Portuguese side where a greater demand and renewable generation capacity are foreseen. This work assesses the impact of these new plans on the Iberian electricity market (MIBEL) main outcomes using CEVESA market model. Simulation results allow the analysis of the expected generation mix and prices, CO2 emissions, system cost, system adequacy, interconnections capacity usage, H2 demand impact and its contribution to provide balancing flexibility, under different simulation scenarios.

Abstract
In the spring of 2023, a team of European Project Semester (EPS) students enrolled at the Instituto Superior de Engenharia do Porto (ISEP) chose to foster socialisation in urban spaces. Public spaces are ideal sites to promote social interaction and community involvement. The aim of this project is then to use such places to divert attention from smartphones by promoting physical social interaction. In recent years, the combination of interactive games and technology has emerged as a potential strategy to increase the use and allure of public areas. The proposed solution, named Shift it, is a puzzle game that combines technology with old school gaming, providing a fun and unique socialising experience. The game, to be installed in public areas, has as key features inclusiveness (invites all people to play), fun (creates a healthy competitive setup) and empathy (creates puzzles by taking and scrambling user pictures). This paper presents the proposed design, which was based on state-of-the-art, ethics, market and sustainability analyses, followed by the development and testing of a proof-of-concept prototype. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.