Abstract
In addition to evaluating tumor morphology using H&E staining, immunohistochemistry is used to assess the presence of specific proteins within the tissue. However, this is a costly and labor-intensive technique, for which virtual staining, as an image-to-image translation task, offers a promising alternative. Although recent, this is an emerging field of research with 64% of published studies just in 2024. Most studies use publicly available datasets of H&E-IHC pairs from consecutive tissue sections. Recognizing the training challenges, many authors develop complex virtual staining models based on conditional Generative Adversarial Networks but ignore the impact of adversarial loss on the quality of virtual staining. Furthermore, overlooking the issues of model evaluation, they claim improved performance based on metrics such as SSIM and PSNR, which are not sufficiently robust to evaluate the quality of virtually stained images. In this paper, we developed CSSP2P GAN, which we demonstrate to achieve heightened pathological fidelity through a blind pathological expert evaluation. Furthermore, while iteratively developing our model, we study the impact of the adversarial loss and demonstrate its crucial role in the quality of virtually stained images. Finally, while comparing our model with reference works in the field, we underscore the limitations of the currently used evaluation metrics and demonstrate the superior performance of CSSP2P GAN.

Abstract
This work reflects upon what Immersion can mean from the perspective of an Artificial Intelligence (AI). Applying the lens of immersive learning theory, it seeks to understand whether this new perspective supports ways for AI participation in cognitive ecologies. By treating AI as a participant rather than a tool, it explores what other participants (humans and other AIs) need to consider in environments where AI can meaningfully engage and contribute to the cognitive ecology, and what the implications are for designing such learning environments. Drawing from the three conceptual dimensions of immersion-System, Narrative, and Agency-this work reinterprets AIs in immersive learning contexts. It outlines practical implications for designing learning environments where AIs are surrounded by external digital services, can interpret a narrative of origins, changes, and structural developments in data, and dynamically respond, making operational and tactical decisions that shape human-AI collaboration. Finally, this work suggests how these insights might influence the future of AI training, proposing that immersive learning theory can inform the development of AIs capable of evolving beyond static models. This paper paves the way for understanding AI as an immersive learner and participant in evolving human-AI cognitive ecosystems.

Abstract
3D models are crucial in predicting aesthetic outcomes in breast reconstruction, supporting personalized surgical planning, and improving patient communication. In response to this necessity, this is the first application of Radiance Fields to 3D breast reconstruction. Building on this, the work compares six SoTA 3D reconstruction models. It introduces a novel variant tailored to medical contexts: Depth-Splatfacto, designed to improve denoising and geometric consistency through pseudo-depth supervision. Additionally, we extended model training to grayscale, which enhances robustness under grayscale-only input constraints. Experiments on a breast cancer patient dataset demonstrate that Splatfacto consistently outperforms others, delivering the highest reconstruction quality (PSNR 27.11, SSIM 0.942) and the fastest training times (x1.3 faster at 200k iterations). At the same time, the depth-enhanced variant offers an efficient and stable alternative with minimal fidelity loss. The grayscale train improves speed by x1.6 with a PSNR drop of 0.70. Depth-Splatfacto further improves robustness, reducing PSNR variance by 10% and making images less blurry across test cases. These results establish a foundation for future clinical applications, supporting personalized surgical planning and improved patient-doctor communication.

Abstract
The integration of AI in supply chains offers opportunities to enhance efficiency, sustainability, and decision-making. However, effective implementation requires attention to both technical and socio-technical aspects. This study examines AI maturity in the pulp and paper sector using the SC-STAI profiling tool, assessing AI integration across technical, social, human, and organizational domains. Based on nine case studies from Brazil and Portugal, the research identifies key areas for improvement and highlights uneven AI adoption. Findings show that performance and resilience are most impacted, while job role adoption remains the lowest. The study emphasizes the importance of Socio-Technical AI Maturity Models in guiding responsible AI adoption and improving socio-technical alignment in supply chains, contributing to a better understanding of AI readiness in traditional industries and demonstrating the SC-STAI tool's applicability for strategic AI planning.

Abstract
Interprofessional collaboration is an essential competency for healthcare professionals, and escape rooms have emerged as an innovative strategy to enhance teamwork and communication. The purpose of this scoping review was to identify and summarize how escape rooms are used in the teaching and enhancement of interprofessional collaboration skills. We conducted a scoping review following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses for Scoping Review (PRISMA-ScR) guidelines. A search of five databases, Scopus (R), Web of Science (R), CINAHL Complete (R), MEDLINE (R) and PsychINFO (R) was conducted for all articles until 1 January 2024. The review included 15 studies, mostly from the USA, involving a total of 2,434 participants across various healthcare professions. Key findings indicated significant improvements in group cohesion, communication, understanding of team roles, and interprofessional skills. Escape rooms can be an effective pedagogical tool in enhancing interprofessional competencies among healthcare students and professionals. Further research is needed to explore the sustainability of skills gained over time through escape rooms and to refine assessment methods.

Abstract
As organizations navigate through complex and collaborative digital environments, Generative AI (GenAI) emerges as a transformative force for Knowledge Management (KM) processes. This paper highlights how GenAI technologies impact collaborative KM processes across individual, intraorganizational, and inter-organizational levels within the evolving paradigm of Industry 5.0 (i5.0). Through a literature review, the study explores how GenAI augments human cognition, enhances knowledge creation and sharing, and fosters organizational adaptability and innovation. The findings highlight GenAI's potential as cognitive partner, streamlining information flows, and improving decision-making across collaborative networks. However, challenges such as over-reliance, ethical risks, and the decline of critical human skills are also discussed. Furthermore, the paper identifies the evolution and gaps in current literature on Collaborative Networks (CNs) regarding the integration of AI technologies. It contributes to the ongoing discussion towards a socio-technical transformation while also providing an overview for rethinking collaboration and social strategies in the GenAI era.