Railway transportation traffic is rapidly growing, which demands a more effective and efficient generation analysis for more reliable predictive maintenance planning. This is achievable if prognostic indicators are known, e.g. stress, deformation and displacement fields. These parameter values unlock the underlying physics knowledge about fault modes and mechanisms to solve the wheel-rail deterioration. Commercial multi-physics software lack source code accessibility, flexibility and interoperability between computing platforms. On the other hand, physics-informed neural networks (PINNs), which belong to the second AI revolution and scientific ML (SciML) that combines physical and machine learning models, show promise in computational fluid dynamics and electrodynamics; however, their application to railwayremains largely unexplored.  This study addresses these research gaps through a comprehensive open-source and reproducible PINN PhysicsNeMo framework for 3D wheel-rail contact analysis as proof of concept. Current railway maintenance often relies on reactive approaches; this PhysicsNeMo framework supports integration by providing predictive stress analyses. The aim is to explore the PhysicsNeMo simulations for railway, establishing a foundation for an interpretable, explainable and trustworthy AI. Results demonstrate detailed and intuitive 3D wheel visualisations of stress distributions and displacement fields, with insights into damage mechanisms for railway designers and maintainers, facilitating more efficient maintenance workflows.

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.

This paper presents the use of proportional hazards regression models for predicting the Mean Time Between Failures (MTBF) of centrifugal pumps in the oil and gas industry. To that end, a dataset collected over 8 years including both design and operational variables from 675 pumps in an oil refinery was used to fit statistical models. Parametric and non-parametric transformations and restricted cubic splines were used to fit the covariates, thereby relaxing linearity assumptions and potentiating predictors with strong nonlinear effects on the outcome. Standard Principal Component Analysis (PCA) and sparse robust PCA methods were used for data reduction to simplify the fitted models and minimize overfitting. Models fitted with sparse robust PCA on non-parametrically transformed variables using an additive variance stabilizing (AVAS) method are suggested for further investigation. The complexity of the fitted models was reduced by 85% while at the same time providing for a more robust model as indicated by an improvement of the calibration slope from 0.830 to 0.936 with an essentially stable Akaike information criterion (AIC) (0.34% increase).

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.

Railway transportation demand is rapidly increasing, which requires enhanced prognostics and health management (PHM) approaches for effective predictive maintenance planning. There has been a significant evolution in prognostics and prediction techniques, e.g. reliability-, damage accumulation-, data analytics- and condition-based prediction. However, the ability to predict damage progression requires an understanding of initiation criteria or root causes through accurate stress, displacement, and deformation field analysis. However, existing commercial multiphysics simulation tools lack the source code accessibility, customisation flexibility, and computing platform portability essential for railway prognostic applications.This study addresses these limitations by implementing a physics-informed neural network (PINN) framework, i.e. PhysicsNeMo, as a computational instrument for 3D wheel-rail contact analysis. The PhysicsNeMo-based platform integrates classical Hertzian contact theory with neural network capabilities, enabling accurate prediction of stress and displacement fields critical for damage accumulation assessment. The open-source framework provides complete customisation, collaborative development, and cost-effective deployment across diverse computing environments from desktop to high-performance computing clusters.Validation demonstrates appreciable accuracy in predicting contact mechanics parameters essential for prognostic health management. The framework provides railway maintenance practitioners with detailed visualisation of stress distributions and displacement fields, enabling identification of fault root causes and damage propagation mechanisms. Results establish a foundation for intelligent predictive maintenance strategies, supporting the railway industry's transition toward physics-informed prognostics for improved asset health management and operational reliability.

This paper proposes a methodological approach that can be applied in practice for evaluating stakeholder dynamics and assessing projects against appropriate value propositions within an industrial maintenance project context. A conceptual framework is proposed and is demonstrated through a case analysis. It is expected that the proposed methodology, the Stakeholder Interdependent Performance Opportunities and Threats, (Stakeholder iPOT), can advance project management practice by offering a mechanism for analysing stakeholder expectations and responses to the opportunities and threats that different project events present. This study highlights the need for continued investigation not only within the context of industrial maintenance projects but also in other sectors to improve our understanding and ability to effectively manage stakeholder dynamics.

The digital technologies accompanying Industry 4.0 have ushered in a new era in the management of industrial economic systems. The concept of the digital twin is at the heart of this transformation. Stemming from the convergence of advanced data analytics, Internet of Things (IoT) technologies, and virtual modelling and domain knowledge, digital twins were conceptualized to create virtual replicas of physical assets and systems.