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.

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.

The extensive embrace of Android has amplified malware risks, resulting in a need for better detection methods. This article investigates the area of static analysis, which analyses applications without execution by examining code and manifest files. We focus on studies from 2022–2025, regarding the feature extraction, datasets, feature selection, and approaches based on Machine Learning (ML) and Deep Learning (DL). We conclude by defining the major limitations and research gaps presented in studies regarding static analysis, and many insights for potential development of detection models that are efficient, accurate, and lightweight to improve detection patterns of Android malware.

Hydrogen gas has gained significant attention as a cleaner alternative to fossil fuels offering a sustainable energy solution. This study explores the production efficiency of oxyhydrogen (HHO) gas using a flat plate electrolyser with potassium hydroxide (KOH) as the electrolyte. Machine learning regression models were employed to estimate hydrogen generation rates and system efficiency based on key operational parameters that includes voltage, current and electrolyte concentration. A set of gradient-boosting algorithms was evaluated utilizing raw experimental data to predict (i) hydrogen output in liters per minute (LPM) and (ii) system efficiency. The results indicate that Categorical Boosting (CatBoost) excelled in forecasting system efficiency (R2 = 0.9748, RMSE = 1.6567 on testing data) and predicting HHO gas generation rate (R2 = 0.9936, RMSE = 0.0090). The experimental results show that with the increase in KOH concentration there is increase in production of Hydrogen. Maximum efficiency was noted with 0.5 N of KOH with the peak efficiency of 99.8 % because of its optimal conductivity and power consumption. It can also be absorbed that higher concentration such 0.75 N and 1 N have shown significant improvement in hydrogen production. Experimental findings further revealed that moderate operating conditions maximize hydrogen production with efficiency varying as a function of applied current and electrolyte concentration. This study highlights the advantages of integrating machine learning models with electrolysis-based hydrogen production offering a scalable and data-driven approach to optimizing energy efficiency. The results underscore the potential of KOH-based electrolysis for sustainable hydrogen generation and reinforce the role of predictive modeling in enhancing system performance.

The integration of oxyhydrogen (HHO) gas into internal combustion (IC) engines has attracted substantial interest among researchers in improving engine performance and reducing emissions. In the present work, a wet-type flat-plate electrolyser utilizing sodium hydroxide (NaOH) as electrolyte is investigated to determine the interdependent effects of voltage, current, and NaOH concentration on HHO gas generation rate and system efficiency. The results show that moderate current and voltage levels, along with higher NaOH concentrations (e.g., 5.87 V and 1 N) yield a maximum gas production rate of 0.5 L/min while conserving energy efficiency. The experimental analysis also showed that as the current increase the rate of production also increased. The maximum production of 0.5 L/min was achieved with 30 A. The study also extends to use experimental data to train machine learning algorithm to estimate the performance of the HHO gas system. Voltage, current, power consumption, resistance and electrolyte concentration were used as input parameters while efficiency and HHO gas production were the output parameters measured with a total dataset size of 112 observations. To reduce the experimental burden and establish an efficient predictive framework five gradient boosting algorithms namely, categorical boosting (CatBoost), extreme gradient boosting (XGBoost), light gradient boosting machine (LightGBM), adaptive boosting (AdaBoost) and gradient boosting (GB) are evaluated among which CatBoost achieved maximum accuracy with R2 values of 0.9903 (for hydrogen production) and 0.9583 (for efficiency) on test data. The findings highlight how crucial intermediate operating conditions are for optimizing gas output and efficiency while lowering resource usage.

The adoption of Augmented Reality (AR) is increasing to enhance Human-System Interaction (HSI) by creating immersive experiences that improve efficiency and safety in various industries. In industrial maintenance, traditional practices involve physical documentation and device interactions, which might disrupt the task, affect efficiency, and increase the cognitive load for the maintenance personnel. AR has the potential to support and enhance industrial maintenance processes in these aspects. Therefore, the purpose of this research is to study and explore how advanced technologies like Artificial Intelligence (AI), AR and speech processing can be integrated to support hands-free, real-time task logging and interaction in maintenance environments. This is done by developing a demonstrator for Microsoft HoloLens 2 using Unity, C#, Azure Cognitive Services, and Azure Functions, which enables speech-to-text transcription for hands-free maintenance support. Using Azure’s speech recognition, the demonstrator can achieve high transcription accuracy in an AR environment, facilitating natural interactions between users and the augmented environment. The study aims to explore the potential of AR to reduce cognitive load, streamline workflows, and improve safety by enhancing HSI for maintenance personnel in high-stakes environments. 

Industrial cybersecurity has become a critical concern in today's interconnected world, as critical infrastructure systems increasingly rely on digital technologies. This paper explores the unique challenges and opportunities presented by industrial cybersecurity, highlighting the need for enhanced cybersecurity measures. The paper discusses the potential consequences of cyberattacks on industrial systems, including disruptions to critical services, economic losses, and even physical harm. To address these challenges, this paper discusses cybersecurity initiatives, standards, guidelines, directives, and acts that can provide a comprehensive framework for cybersecurity and AI governance. A systematic literature review has been conducted in this paper using Scopus and Google Scholar, which provide the foundation for identifying relevant publications. These publications show key trends and themes in industrial cybersecurity research, including the growing importance of education and training, as well as cybersecurity risk assessment and mitigation.

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.