The industrial evolution has emerged the concept of Industry 5.0 paradigm. The concept of Industry 5.0 emphasizes three main aspects of industrial development, i.e. sustainability, resilience, and human centric. The emerging tech-nologies related to digitalisation and artificial intelligence are expected to augment human-system-interaction in various industrial processes. Integrating AI and digital technologies to enable human-centric solutions in industry can manifest in a concept called “Industrial Metaverse”. The Industrial Metaverse is considered a concept that emerged from integrating the Metaverse and Digital Twin, but adapted to specific characteristics of industrial contexts. Industrial Metaverse can enhance Asset Management (AM) within the Industry 5.0 framework. By integrating AI and advanced digital technologies, the Metaverse offers a promising roadmap for optimising human-system interaction in industrial contexts. Metaverse is expected to enhance human centricity of Industry 5.0 and developing metaverse in industrial contexts is challenging and requires a framework. Hence, this paper provides a framework that helps to develop Industrial Metaverse in the context of AM specifically in maintenance. This paper employs literature review in popular databases and discussions with industrial stakeholders to present the current landscape of Metaverse application in Industry 5.0 with a specific focus on maintenance. A case study from the railway sector demonstrates the practical benefits of the Metaverse in improving operational efficiency and maintenance processes. The findings contribute to understanding the Metaverse’s role in shaping the future of industrial asset management and maintenance within Industry 5.0.

Humans play a vital role in designing, developing, implementing, and using technical systems. For this reason, it is crucial to keep humans in the loop at each phase of these systems to make them more secure and user-friendly. There needs to be a balance between using these systems securely and making them easy to use. Today, under pressure to secure our systems from cyberattacks, we primarily focus on making them secure but often overlook making them easy to use. Thus, the objective of this paper is to provide a human-centric perspective on cybersecurity and to introduce a human-centric framework that enables Industry 5.0, where humans have direct interaction with systems and solutions that are more customer-oriented. To carry out this research, the authors have applied the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines to investigate human-centric research over a 10-year period, from 2015 to 2025. The literature shows that most human-centric research contributions are well-balanced, with conceptual, experimental, and survey approaches each accounting for approximately 64% of the total, indicating a mature blend of theoretical and applied research. These studies are focused on developing structured, strategic approaches that integrate human factors into cybersecurity practices across sectors such as education, government, health, software, smart home networks, and others. To conduct this research, the authors have prepared an anonymous questionnaire with fundamental questions about secure system’s design, which can be easily used. The evaluation results show that frequent password resets (33.3%) and frequent authentication (26.7%) are the most “annoying” cybersecurity measures. Additionally, most respondents consider biometric login the most user-friendly security feature, followed by single sign-on and automatic security patch updates. What is missing in existing literature and studies is a holistic perspective on human-centrism, beyond mere ease of use. We aim to cover that blind spot by introducing our independently developed framework in this paper.

The construction industry is embracing transformation through the integration of digitization, artificial intelligence (AI), and immersive technologies. On a construction site, continuous assessment is vital for ensuring both the reliability of assets and safety of workers. Scaffolding is a key structural support asset that requires regular inspections for detection and identification of alterations from the design rules that could compromise integrity and stability. At present, such inspections to identify deviations are primarily visual and conducted by the site managers or accredited personnel. However, visual inspection is time-intensive and susceptible to human errors, which can lead to unsafe conditions. This study explores the use of AI and digital technologies to automate and enhance scaffolding inspections process to contribute toward safety improvement. A cloud-based AI platform is developed to process and analyze 3D point-cloud data of scaffolding structures to detect modifications through comparisons as well as evaluate the certified reference scan with a recent scan. The proposed workflow incorporates prognostics and health management concepts with continuous monitoring to identify structural modifications and further assist with decision-making. The results indicate that the proposed approach can limit reliance on manual visual inspections. By enabling automated monitoring of scaffolding, the proposed approach reduces the time and effort required for inspection process, while enhancing the safety on a construction site.

Purpose: Operator 5.0 represents a paradigm shift in how humans and machines interact and work together in industrial contexts. It leverages advanced digital technologies and automation to optimise processes, enhance productivity and create new possibilities. However, these advanced possibilities also introduce significant cybersecurity challenges. This paper explores the cybersecurity challenges and threats faced by Operator 5.0, emphasising the vulnerabilities inherent in human-machine collaboration, industrial IoT and supply chain integration.

Design/methodology/approach: The paper uses a qualitative approach, using a literature review, to analyse the cybersecurity risks associated with Operator 5.0 and proposes a comprehensive cybersecurity framework to address these challenges.

Findings: The paper identifies key vulnerabilities arising from human–machine collaboration, IIoT and supply chain integration within the Operator 5.0 context. It argues that these vulnerabilities pose significant risks to industrial operations and sensitive data. The proposed framework offers a multi-layered approach to mitigating these risks.

Originality/value: This paper offers a novel perspective on the cybersecurity implications of Operator 5.0. It proposes a holistic framework integrating hardware, software and liveware (human element) to establish human-centric security measures, advanced threat detection and response mechanisms and resilient infrastructure for Operator 5.0 deployments.

This study presents a machine learning-based framework for detecting critical events in railway infrastructure by analyzing vibration signals from trackside accelerometers. Traditional maintenance is often reactive and labor-intensive, but this approach uses continuous sensing and data analytics to enable proactive, real-time monitoring. The research leverages a comprehensive pipeline that includes data preprocessing, segmentation of time-series data into one-second intervals labeled as "event" or "no-event", and the extraction of statistical, temporal, and spectral features like crest factor and kurtosis. Key contribution of this work is the systematic evaluation of 72 algorithm-feature selection configurations. Twelve diverse classification algorithms were compared, including tree-based, linear, and neural network models. Extensive hyperparameter optimization was performed to benchmark performance using metrics such as accuracy, precision, recall, and F1-score. The Multi-Layer Perceptron (MLPClassifier) achieved a peak cross-validation accuracy of 98.89% with the full feature set. The study also found that comparable accuracy (98.67%) could be achieved with a 47% dimensionality reduction using Recursive Feature Elimination (RFE) with only eight features, demonstrating a balance between efficiency and performance. The findings provide actionable insights for developing scalable, high-performance event detection systems.

In underground mining, geostatic pressure results in drift surface deformation. To ensure the the stability and safety of the mining environment, a so-called ground support systems are commonly used. Today, there are various types of ground support systems, such as rockbolts, steel sets, mesh and shotcrete, cable bolts, ground anchors etc. Ground support systems based on rockbolts are used to provide the tensioning support to the compressive strength of the rocks. Surface deformation occurring in the drift brings changes in the rockbolt protruding the surface. Monitoring and predicting the health of the ground support would assist the decision process for rehabilitation of the ground support in the drift to maintain safe working conditions. In this paper, a methodology has been proposed aimed at the quantification of deformation of underground mining drifts through point cloud processing and other data sources to support health monitoring, development of decision support tools and integration with the mine Trigger, Action, Response Plans (TARPs). The proposed methodology processes point cloud data, collected in underground mining drifts in a campaign-based manner. PCD processing focuses on i) comparison of PCD quality, ii) computation of deformation while compensating for imperfect registration, iii) comparison of rockbolt regions using Wasserstein distance. The results were used to augment the PCD to create interactive visualizations on Virtual Reality (VR) and Augmented Reality (AR) systems for off-site or on-site inspections.

The metaverse is an emerging technological shift that enhances collaboration, telepresence, and decision-making, and can revolutionise industrial maintenance practices. While immersive technologies, such as Extended Reality (XR) encompassing Virtual Reality (VR), Augmented Reality (AR), and Mixed Reality (MR), are widely applied in domains like gaming, healthcare, and education, their adoption in industrial workflows remains limited. Its development and implementation carry challenges, especially from a Human-System Interaction (HSI) perspective. The purpose of this research is to understand the key technological issues and challenges associated with the implementation and use of the metaverse in industrial maintenance from an HSI perspective. This study employs a structured, systematic literature review focusing on the metaverse, as enabled by immersive technologies, in the context of industrial maintenance. The reviewed literature was analysed using thematic qualitative analysis to identify recurring HSI-related challenges and to develop a taxonomy categorising these challenges. The analysis resulted in a taxonomy comprising seven key challenge categories: usability, data management, accessibility, user experience (UX), technological performance, environmental and contextual awareness, and trust and transparency. The findings highlight UX as the core factor influencing adoption, as most challenges directly or indirectly impact user experience. The findings indicate that addressing these challenges can enable intuitive, transparent, and reliable metaverse systems tailored to industrial needs. However, advancing the industrial metaverse will require an interdisciplinary approach that combines engineering, human factors, data science, and design to deliver systems that are both technologically advanced and human-centred.

Cloud computing has become an essential paradigm for modern enterprises by providing scalable, on-demand access to computational resources. Despite its widespread adoption, significant concerns persist regarding data security, integrity, and the trustworthiness of service transactions—particularly due to the centralized nature of conventional cloud infrastructures. This paper presents a novel cloud framework that integrates Ethereum blockchain technology to enhance trust, enforce restricted access control, and secure service interactions. The proposed approach employs a two-factor authentication mechanism combined with blockchain-based smart contracts to authenticate legitimate service requests, record immutable transaction logs, and mitigate prevalent threats such as Distributed Denial-of-Service (DDoS) and Sybil attacks. By embedding access control policies directly into the blockchain, the framework ensures transparency, immutability, and resilience against unauthorized data manipulation. Experimental validation demonstrates the framework's effectiveness in improving the security, reliability, and scalability of cloud environments. The findings highlight the potential of blockchain as a foundational technology for developing trustworthy and robust cloud computing systems.

Purpose: This paper proposes an approach for the development of a dynamic maintenance policy for the railway rolling stock system considering the dynamic characteristics of the system, the operation environment, and the maintenance process. The proposed approach can also be applied to other similar complex technical systems.

Design/methodology/approach: This study explores the state-of-the-art in dynamic maintenance policy development for railway rolling stock to identify the research gap. The proposed approach is demonstrated through a case study on dynamic maintenance policy development for railway rolling stock maintenance support planning, emphasising constant assessment and improvement in the maintenance policy.

Findings: This study emphasises on the need for an effective, efficient, and dynamic maintenance policy that adapts to evolving system requirements such as organisational contexts, technological advancements, and regulatory requirements. This study finds that there are specific dynamic characteristics in each step of the maintenance policy development process. These dynamic characteristics are dependent on the changes in system requirements, the environment and in the maintenance process. The proposed approach for dynamic maintenance policy development provides a framework for the identification of these dynamic characteristics for different complex technical systems operating in a specific context.

Originality/value: Despite the significant impact of railway rolling stock maintenance on railway system availability, research in this area is limited. The proposed approach for maintenance policy development for rolling stock integrates adaptability and responsiveness to dynamic factors in the maintenance process and the system requirements. Thus, the proposed approach provides a guide for industry professionals and directions for future research in dynamic maintenance policy development for other similar complex technical systems.Keywords