Abstract

Abstract

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.

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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.