Abstract
Software ecosystems have emerged as a paradigm to structure software products, communities and business models, in a form inspired by the natural ecosystems. Mobility solutions are also evolving from individual vehicles to soft mobility services based on electric vehicles. This paper aims to address the creation of a software platform to support an ecosystem of mobility solutions-the Intelligent Mobility Ecosystem, based on connected electric vehicles. It follows the paradigm of software ecosystems, in which a technological platform provides the functionalities needed to create solutions within the ecosystem. The work being carried out is part of the A-Mover project, which aims to develop a connected electric motorcycle and electronic services to support driving and use of the vehicle in individual and business contexts. The aim is to develop a set of functionalities around the vehicle to create specific mobility solutions. The concept of a software ecosystem is reviewed below and the proposed architecture for the software platform that will support the ecosystem is described.

Abstract
This work introduces Gen-JEMA, a generative approach based on joint embedding with multimodal alignment (JEMA), to enhance feature extraction in the embedding space and improve the explainability of its predictions. Gen-JEMA addresses these challenges by leveraging multimodal data, including multi-view images and metadata such as process parameters, to learn transferable semantic representations. Gen-JEMA enables more explainable and enriched predictions by learning a decoder from the embedding. This novel co-learning framework, tailored for directed energy deposition (DED), integrates multiple data sources to learn a unified data representation and predict melt pool images from the primary sensor. The proposed approach enables real-time process monitoring using only the primary modality, simplifying hardware requirements and reducing computational overhead. The effectiveness of Gen-JEMA for DED process monitoring was evaluated, focusing on its generalization to downstream tasks such as melt pool geometry prediction and the generation of external melt pool representations using off-axis sensor data. To generate these external representations, autoencoder (AE) and variational autoencoder (VAE) architectures were optimized using Bayesian optimization. The AE outperformed other approaches achieving a 38% improvement in melt pool geometry prediction compared to the baseline and 88% in data generation compared with the VAE. The proposed framework establishes the foundation for integrating multisensor data with metadata through a generative approach, enabling various downstream tasks within the DED domain and achieving a small embedding, allowing efficient process control based on model predictions and embeddings.

Abstract
Data augmentation is a crucial tool in time series forecasting, especially for deep learning architectures that require a large training sample size to generalize effectively. However, extensive datasets are not always available in real-world scenarios. Although many data augmentation methods exist, their limitations include the use of transformations that do not adequately preserve data properties. This paper introduces Grasynda, a novel graph-based approach for synthetic time series generation that: (1) converts univariate time series into a network structure using a graph representation, where each state is a node and each transition is represented as a directed edge; and (2) encodes their temporal dynamics in a transition probability matrix. We performed an extensive evaluation of Grasynda as a data augmentation method for time series forecasting. We use three neural network variations on six benchmark datasets. The results indicate that Grasynda consistently outperforms other time series data augmentation methods, including ones used in state-of-the-art time series foundation models. The method and all experiments are publicly available. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.

Abstract
Entrepreneurial ecosystems are key drivers of regional development, yet limited attention has been paid to how startups experience these systems and how their positions within the ecosystem shape those experiences. This article examines the role of startups in the entrepreneurial ecosystem of the Porto Metropolitan Area, through an in-depth qualitative case study based on semi-structured interviews with founders, investors, intermediaries, and higher-education actors, complemented by contextual indicators analysis. The findings show that the ecosystem is experienced in differentiated ways across actors. Founders primarily experience challenges through market uncertainty, access to funding, and scale-up challenges, while academic entrepreneurs emphasize institutional pathways and knowledge translation processes. University actors focus on talent pipelines, graduates' employability, and collaboration with firms. Although the region benefits from strong early-stage support and a dense institutional infrastructure, a persistent scale-up gap and weak political embeddedness constrain startups' longer-term contributions to regional development, despite their economic significance. The article contributes to entrepreneurial ecosystem research by adopting an actor-centered, positional perspective and by highlighting how institutional diversity, within higher-education, shapes differentiated roles, while pointing to the limits of startup creation policies and the need for stronger coordination, improved scale-up finance, and more meaningful channels for startup participation in decision-making.

Abstract
As competitive and cooperative dynamics gain prominence in games, they present unique opportunities to study player behavior. This paper explores the orientations of different player types, as categorized by Bartles Taxonomy, through the lens of a Game With A Purpose (GWAP) called BartleZ. Bartle's Taxonomy identifies four distinct player types Achievers, Explorers, Socializers, and Killers. This study delves into how these different types approach competitive and cooperative gameplay, through structured dilemmas in BartleZ. Results with 45 participants, reveal that player orientations significantly influence engagement and decision-making. Achievers balanced both strategies; Explorers favored cooperation; Socializers consistently chose cooperation; and Killers preferred competition but adapted in some contexts. Overall, players leaned toward cooperation early on, with a shift toward competition as complexity increased. Our findings pinpoint the importance of tailoring GWAP mechanics with diverse player motivations, enhancing both engagement and problem-solving effectiveness.

Abstract
This study addresses a gap in literature by empirically examining the role of FLIGBY in the development of thinking skills and creativity, contributing to a more holistic understanding of the pedagogical value of serious games in higher education. Two research questions guide the study: (i) how FLIGBY assesses and contributes to the development of thinking skills; and (ii) how the pedagogical approach and challenges embedded in FLIGBY foster creativity. A mixed-methods design was adopted. Quantitative methods were first used to assess students' performance in thinking skills, which in FLIGBY are measured continuously through behavior-based analytics using the Master Analytics Profiler (MAP) system. In parallel, qualitative methods were employed to explore the development of creative competencies through thematic analysis of interview data. The results indicate that FLIGBY is an effective tool for the integrated development of thinking skills and creative competencies in higher education. Statistical analysis reveals moderate to high levels of cognitive skills such as emotional intelligence, leadership, and systemic decision-making, with strong and significant interrelationships among these dimensions. The thematic analysis further shows that FLIGBY fosters creativity by providing a safe environment for experimentation, adaptive decision-making, complex problem solving, and metacognitive reflection. Accordingly, the findings suggest that FLIGBY not only strengthens strategic cognitive skills but also stimulates creative and reflective processes transferable to real-world leadership and management contexts, offering important implications for educational practice and policy in higher education.