Safety in underground mining operations relies on understanding the geological and geotechnical properties of the site. The creation of an underground void for mining induces instability in the rock structure, resulting in deformation. The compressive strength of the rocks is maintained by the tensioning of ground support, such as rockbolts. Monitoring and predicting the condition of the mining ground support system is crucial for ensuring the safety of operations. Inspecting the mining tunnels poses challenges due to their large span and ongoing production activities. Light Detection and Ranging (LiDAR) technology can scan physical structures and generate point cloud data, which is valuable for creating applications like topographic mapping and spatial models. Extracting rockbolt information from point cloud data from underground mines can offer comprehensive mine coverage. This information can be utilised to monitor the condition of the rockbolts over time. Extracted rockbolt data can assist in the health monitoring of ground support, indicating deformation due to geostatic pressure. This paper proposes a method for extracting rockbolt spatial information from point cloud datasets collected via LiDAR technology to facilitate Prognostics and Health Management for ground support in underground mining.

This paper explores the potential of Metaverse technology in industrial Asset Management (AM). By integrating AI and digital technologies, the Metaverse can enhance Human-System-Interaction (HSI) and optimise AM processes. However, implementing a Metaverse in industrial contexts faces challenges, particularly in visualising physical and virtual assets. This paper conducts a systematic review to address these challenges and identify potential solutions. The findings reveal that while the necessary technologies are available, their widespread adoption in industrial AM is limited. The paper presents a comprehensive overview of research themes related to Metaverse applications in industrial contexts, highlighting the evolving landscape and potential benefits. Ultimately, this research aims to contribute to the advancement of Metaverse technology in industrial AM by providing insights into its development, implementation, and challenges along with an Industrial Metaverse Framework. An example of applying the Metaverse concept in the railway sector has been presented and validated using railway digital assets available within the eMaintenance LAB. The practical implications of this work are expected to result in increased efficiency and effectiveness in the operation and maintenance procedures across various industrial sectors.

Digitalisation within industries has many posi- tive opportunities but poses many cybersecurity-related threats. Cybersecurity is a critical concern for many indus- tries, such as railway, aviation, mining, construction, health- care, and finance, where vital information and operational security are at risk of being compromised. Today, research- ers are looking into various solutions to tackle cybersecurity risks while still retaining the desired functionality of the system. However, it is believed that these challenges can be approached by integrating game theory and the cyber kill chain (CKC), which describes the different stages of a cyberattack, to understand the complexities of cybersecu- rity. Thus, the primary objective of this paper is to demon- strate the application of a strategic game model within the context of cybersecurity, specifically using the cyber kill chain (CKC) model. The focus is on validating the proposed strategic game model through a case study. The case study involves a scenario where defenders select strategies such as “monitor system,” “detecting system,” and “respond to attack,” while attackers choose various attack strategies like “monitoring attack,” “scan the organization’s website,” and “develop malicious payloads” within the stages of the CKC. This approach aims to enhance understanding of the complex challenges and facilitate the development of effective cyber- security solutions. This approach will help in evaluating the effectiveness of different security strategies. The proposed strategic approach uses a non-cooperative game based on mixed strategies. The authors have defined a scenario for simultaneous-move games by estimating values for various elements of the game. By analysing the behaviour of both attacker and defender, the proposed game-based model can help industries develop more effective and efficient security strategies. Further, the proposed model will provide a better understanding of the complex challenges of cybersecurity in industrial contexts. It can also be used to develop appropri- ate strategies to mitigate cybersecurity risks.

The railway system is a complex technical system-of-systems (SoS). To address the complexity of the railway system, a holistic approach is needed that facilitates the development of an appropriate asset management regime. A systems-of-systems (SoS) approach considers the complex nature of the railway system, comprising interconnected subsystems like rolling stock and infrastructure. Neglecting these interdependencies risks sub-optimization of the overall system performance. Asset management, of the railway system utilising a SoS approach ensures the focus of asset management on overall system requirements. The efficiency and effectiveness of the railway system is based on aspects such as availability, reliability, and safety performance. To enhance these aspects, monitoring, and improvement of key performance indicators (KPIs) emphasizing increased capacity and reduced operational costs is essential. The KPIs offer quantifiable parameters for performance optimization. Augmenting asset management through data-driven technologies can improve the efficiency and effectiveness of asset management. However, challenges persist in the implementation of data-driven solutions due to the railway system's complexity and lack of a holistic perspective. A systematic performance-driven framework with a system-of-systems approach for augmented asset management of railway system provides handrail for the utilisation of data-driven technologies with railway system requirements at the centre while developing an asset management regime. The proposed framework aims to establish a clear relationship between system KPIs, and the performance of sub-systems and components aiding railway organizations in asset management design and implementation. This paper explains the important components of the proposed framework and demonstrates the application the framework for asset management and maintenance planning of high value components in the fleet of railway rolling stock. Adoption of the proposed framework is expected to enhance asset management through development and implementation of data-driven solutions that are aligned with system KPIs, to support asset management decision making.

Industry 5.0 promises to revolutionize the industry by focusing on human-centric, sustainability, and resilience empowered by emerging technologies such as Artificial Intelligence (AI) and digitalization. This paradigm shift is expected to bring significant advancements in sustainability, resilience, productivity, effectiveness, efficiency, customization, reliability, safety, security, maintainability etc. However, this shift of the industrial paradigm introduces substantial cybersecurity challenges due to the increased attack surface and data sensitivity. Therefore, the objective of this paper is to conduct a thorough literature review of the recent research on cybersecurity in Industry 5.0, highlighting emerging trends, gaps, and potential solutions. To conduct this research, the authors have applied the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology to investigate cybersecurity solutions in Industry 5.0. The findings reveal that conceptual research dominates, with AI, Blockchain, and Internet of Things (IoT) most prevalent but highlights a gap in linking cybersecurity to resilience and sustainability. Furthermore, the paper aims to present trends in cybersecurity research with more relevant results from 2022 to 2024. It conducts a thorough review of the literature, highlighting the evolving landscape of cybersecurity applications in Industry 5.0.