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.

Abstract
The integration of AI into organizational settings leads to a growing need for hybrid human-AI collaborative approaches, necessary due to the increasing autonomy, impact and responsibility AI-based solutions have. Moreover, to ensure a sustainable integration into existing processes, such approaches must be context-aware, transparent, and human-centric. In line with the Industry 5.0 paradigm, this paper presents a novel Multi-Agent System architecture that enables meaningful collaboration between human and artificial agents through a socio-technical design approach. The proposed architecture is grounded in a structured, real-time context stream derived from organizational data sources, which semantically describe human actors, processes, and industrial resources. Central to this system is a set of four core LLM-based agents, each responsible for orchestrating hybrid human-AI tasks along distinct dimensions of timing, role selection, resource allocation, and execution sequencing. To assess the feasibility and effectiveness of the architecture, we report on an early-stage validation conducted within a representative industrial use case in the automotive sector, focused on information retrieval. In this use case, the architecture was tasked with answering a set of representative, domain-specific questions by dynamically interacting with distributed industrial databases. Results demonstrate the architecture's ability to coordinate relevant human and artificial agents, retrieve semantically-relevant data, and present explainable outputs, showcasing its potential for supporting decision-making processes in hybrid collaborative networks.

Abstract
Artificial Intelligence (AI) integration in supply chain systems is growing, and with it grows its potential impact on inter-organisational collaborative networks. We review existing literature on how different AI archetypes (Reflexive, Anticipatory, Supervisory, Prescriptive) could support Collaborative Supply Chain Management (CSCM) activities, and how they impact information sharing, collaborative decision-making, and trust among supply chain partners at different integration levels. Adopting a sociotechnical perspective, we synthesise existing literature and map the archetypes along four levels of AI integration, varying in scope and decision autonomy. The results are conceptual frameworks demonstrating how AI impacts collaboration dynamics as it evolves from a decision-support tool to an autonomous coordination agent. Findings show differentiated effects along archetypes and integration levels, with implications for CSCM governance, transparency, and resilience. We contribute to the discussion on human-AI collaboration in CSCM and offer a baseline for research on the human-centric values of Industry 5.0.

Abstract

Abstract
Aesthetic outcomes in plastic and oncological surgery play a fundamental role in restoring patients' self-esteem, social engagement, and overall quality of life. Yet, managing pre-operative expectations and objectively assessing post-operative results remain as difficult challenges, compounded by the subjective nature of beauty and the scarcity of standardized evaluation tools. To address these challenges, we conduct a systematic review assessing computational methods for the prediction and evaluation of the aesthetic outcomes of plastic and oncological surgery, adhering to PRISMA guidelines. We propose a goal-oriented taxonomy that partitions computational approaches into two main categories: (1) prediction methods that pre-operatively predict the results of surgery through retrieval-based systems, generative artificial intelligence and advanced 3D modeling techniques, and (2) evaluation strategies that assess the post-operative outcomes through objective measurements, traditional machine learning, and deep learning models. Our synthesis indicates a potential paradigm shift from early work that relied on manual image annotation and manipulation to recent research that predominantly employs artificial intelligence. Nevertheless, over 90% of datasets remain private, and validation processes diverge among techniques with similar goals, limiting reproducibility and fair methodological comparisons. We conclude by advocating for the creation of larger publicly accessible datasets, integration of vision-language models to capture patient-reported outcomes, and rigorous clinical validation to ensure equitable, patient-centered care. By bridging computational innovation with clinical practice, this study contributes towards a more transparent, reliable, and personalized aesthetic outcome prediction and assessment.

Abstract
The increasing adoption of the Digital Twin concept and technology for managing complex physical assets has led to the emergence of Digital Twin Ecosystems, where interconnected digital twins generate additional value. However, ensuring seamless data sharing and interoperability among diverse systems presents significant challenges. Although research on digital twin architectures has advanced, gaps remain in addressing data governance, security, and stakeholders' trust. This study performs a comprehensive literature review to investigate architectural solutions to overcome challenges in digital twin ecosystems. The findings identify key requirements such as interoperability, governance, and data management, emphasizing the role of Data Spaces as enablers of secure data sharing. By structuring the requirements for digital twin ecosystem architectures, this paper identifies gaps suggesting future research on scalable and sustainable digital twin ecosystem implementations. These insights are expected to contribute to the development of frameworks that integrate technical advances with organizational and regulatory considerations, ultimately fostering the adoption of digital twin ecosystems across industries.