Abstract
Virtual production (VP) is becoming central to film and television education, with universities offering degree programs, minors, tracks, electives, and short-term credentials. This review of 115 English-language sources, including 55 curricula from 49 higher education institutions (HEI), shows VP as a socially uneven, tool-weighted formation clustered in well-resourced Anglophone systems. Curricula overwhelmingly foreground real-time workflows, engine-driven pipelines, and stage operations over story development, audio design, and game-adjacent or interactive practices. The core tools include the Unreal Engine, motion-capture systems, and LED volumes, framed as prestige infrastructure rather than collective capacity. Programs emphasize employability, production-style blocks, and 'learning by doing real jobs', supporting industry transition but compressing experimentation, critique, and cross-cultural perspectives. Competency stacks map robust technical cores but reveal structural gaps in leadership, narrative, sound, and AI/ML literacy. The findings argue that evaluating VP education requires analyzing how programmes distribute technological and symbolic capital, organize human-machine networks, and produce learning spaces. Future research should model VP curricula as sociotechnical networks, measure AI integration maturity, test transferability, track longitudinal outcomes, map non-English ecosystems, and formalize stage pedagogy frameworks.

Abstract

Abstract
Research data centers increasingly face a tension between rapidly growing demand for microdata-driven research and strict confidentiality obligations. Output checking - reviewing tables, models, and descriptive statistics before release from safe centers-remains a key safeguard but is labor-intensive and difficult to scale. This paper presents a governed, semi-automated output-checking architecture that integrates three layers: (i) deterministic rule-of-thumb disclosure checks, (ii) a machine-learning classifier trained on historical release decisions to support triage, and (iii) a large language model (LLM) agent that performs principles-based synthesis and produces structured explanations for auditors and researchers. Using an archival corpus of output requests and released/blocked outputs from a national statistical office, we build a metadata and standardization pipeline for heterogeneous datasets and outputs, engineer disclosure-relevant features, and evaluate a prototype that routes low-risk cases quickly while escalating ambiguous cases for human control. We evaluate the architecture retrospectively on historical output-checking records and distinguish three validation targets: deterministic detection of obvious disclosure risks, ML prediction of institutional outcomes, and LLM-based evidence synthesis for audit support. The contribution is a governance-oriented architecture and empirical prototype for routing and explanation, not an autonomous release mechanism: all unsupported, ambiguous, or high-risk cases remain subject to professional output-checker review. © 2026 IEEE.

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
The growing importance of sustainability in consumer markets in recent years has attracted the attention of researchers in industry-related fields. Large-scale product networks, which comprise millions of connections between consumers and green products, facilitate the identification of consumer-driven relationships between products and enable an understanding of the dynamics of sustainable consumption. It is well-known that understanding the dynamics of these networks does not require observing the entire network. In this work, we propose AMAN-S+, a novel Adaptive Multilayer Attributed Network Sampling method that combines the Random Walk and a Neighborhood Expansion Framework incorporating sustainability categories and product attributes, enabling enhanced preservation of semantic information while maintaining statistical performance comparable to state-of-the-art network sampling techniques. The proposed hybrid framework uses a bipartite network projection, and applies adaptive sampling using weighted random walks, and neighborhood expansion in different layers, where each layer corresponds to a different type of product, based on its level of sustainability, to enable efficient and representative network exploration. Networks are first projected onto product-product similarity networks. Then, adaptive weighted random walks dynamically balance the representation of popular and niche sustainable products, while neighborhood expansion combined with multilayer approaches preserves local structural context. We apply this novel methodology to consumer-product networks of sustainability-related attributes that influence purchasing decisions, such as rankings and discounts, since the size and heterogeneity of such networks make direct analysis computationally challenging. The comparative results show that the proposed approach outperforms the benchmark approaches in terms of efficiency, structural fidelity, and retention of sustainability-related diversity. Empirical applications demonstrate its potential to identify sustainability clusters, uncover links between eco-labeled and conventional products, and support data-driven strategies for sustainable market transitions.