Abstract
Virtual staining is a promising technique that uses deep generative models to recreate histological stains, providing a faster and more cost-effective alternative to traditional tissue chemical staining. Specifically for H&E-HER2 staining transfer, despite a rising trend in publications, the lack of sufficient public datasets has hindered progress in the topic. Additionally, it is currently unclear which model frameworks perform best for this particular task. In this paper, we introduce the HER2match dataset, the first publicly available dataset with the same breast cancer tissue sections stained with both H&E and HER2. Furthermore, we compare the performance of several Generative Adversarial Networks (GANs) and Diffusion Models (DMs), and implement a novel Brownian Bridge Diffusion Model for H&E-HER2 translation. Our findings indicate that, overall, GANs perform better than DMs, with only the BBDM achieving comparable results. Moreover, we emphasize the importance of data alignment, as all models trained on HER2match produced vastly improved visuals compared to the widely used consecutive-slide BCI dataset. This research provides a new high-quality dataset, improving both model training and evaluation. In addition, our comparison of frameworks offers valuable guidance for researchers working on the topic.

Abstract
Additive Manufacturing (AM) has emerged as a transformative production technology which enables complex geometries, part consolidation, and lightweight structures. Across multiple industries, AM is recognized as a strategic enabler of digital manufacturing and design optimisation. In the space sector, where mass reduction, structural performance, and functional integration are critical, AM presents significant potential. Yet its adoption remains limited. This study analyses the factors influencing AM adoption by European space organizations using an integrated Technology-Organization-Environment (TOE) framework and Diffusion of Innovation (DOI) theory. A qualitative multi-case design was adopted, combining 24 interviews with industry suppliers, research organizations, and the European Space Agency, complemented by documentary analysis. Findings indicate that adoption is primarily driven by perceived relative advantage (design freedom and associated performance gains), organisational innovativeness and agency support mechanisms, while limited organisational readiness (skills and experience), agency-driven certification pressure and low visibility of flight-qualified demonstrators remain major barriers. Adoption cost plays a dual role: potential savings through mass reduction and part consolidation are offset by substantial qualification, testing and compliance efforts. Overall, the results highlight persistent misalignments between technological potential, organisational capabilities and institutional requirements that constrain the transition from prototypes to flight-qualified parts, pointing to the central role of institutional actors in qualification/standardisation and the need for firms to strengthen design-for-AM capabilities.

Abstract
Swarm robotics enables groups of autonomous agents to coordinate and perform tasks beyond the capabilities of individual robots. This state-of-the-art review focuses on the defining features, principles, and algorithms of swarm robotic systems, with an emphasis on recent advances. It examined classical and modern bio-inspired coordination strategies, decentralized control algorithms and hybrid approaches, highlighting their strengths, limitations and applicability to real-world deployments. Key challenges, such as scalability, robustness, adaptability and the gap between simulation and hardware implementation are analyzed.

Abstract
This study provides a comprehensive review of how game theory can be applied to model and optimize dynamic coalitions in contemporary energy markets. With the increasing decentralization of energy systems driven by technologies such as solar photovoltaics, home energy storage, and electric vehicles, consumers have begun to play a more active and influential role in the market. In this new context, where cooperative and collective decision-making is gaining importance, game theory emerges as a valuable tool for analyzing and structuring these interactions. The primary objective of this work is to systematically review existing models, assess their methodological strengths and limitations, and identify open research gaps that hinder their applicability to real-world settings. By synthesizing the current state-of-the-art, this study aims to highlight pathways toward the development of more realistic and effective models that capture the dynamic and interdependent behaviors of energy consumers and the coalitions they form. Ultimately, this review seeks to provide an updated overview of this growing field, serving both as a basis for future research and as a foundation for the design of solutions that promote fairer, more efficient, and more participatory energy markets, especially for small-scale consumers, who now have greater voice and power of choice. © 2026 IEEE.

Abstract
Achieving sustainable urban mobility requires shifting travelers toward public transport. However, policies often assume uniform preferences, leaving a critical gap in understanding how different travelers prioritize mobility factors. To address this, the study examines behavioral heterogeneity among urban travelers using a data-driven clustering approach based on the relative importance assigned to cost, comfort, environmental sustainability, and flexibility. Using data from 698 respondents in the Asprela area of Porto, Portugal, a mixed-use district combining universities, hospitals, and commercial facilities, the study applies principal component analysis (PCA) and K-means clustering to derive distinct traveler profiles. Unlike segmentation based solely on socio-demographics or observed mode choice, this approach groups individuals according to their underlying value structures. Six clusters were identified, ranging from car-dependent, comfort-oriented users to environmentally conscious and low-engagement groups. The findings show that one-size-fits-all policies are unlikely to address behavioral diversity effectively. Building on these insights, the study proposes tailored and cross-cutting policies to enhance the attractiveness of public transport and promote sustainability. By uncovering latent preference structures, the study contributes to more inclusive and value-informed mobility planning.

Abstract
Quality performance in manufacturing has a direct influence on efficiency, generated waste, and costs. In collaboration with a textile manufacturer as a case study, this paper develops an automated defect detection system for a weaving process and evaluates its impact on operational performance. The system identifies defects immediately at their onset and prevents their propagation to subsequent fabric and production stages. A deep learning image classification model is developed, with six well-established network architectures being compared, leveraging a non-invasive image acquisition method that averts machinery disturbances for data collection. Based on the best-performing model, key indicators of operational performance are estimated using Markov Chain modelling, addressing a gap in linking model performance to operational impacts. Notable operational gains are demonstrated, namely a cost reduction of 1.3% and over 90% of waste reduction. A sensitivity analysis guides the definition of the image acquisition frame rate to minimise false alarms and shows that different operational indicators are impacted differently by different predictive performance metrics, affecting model selection. This research not only underscores the potential of integrating deep learning into textile production but also guarantees the effective communication of its impact to industry stakeholders, thus offering valuable practical insights to enhance operational performance.