The convergence of artificial intelligence, cyber-physical systems, and cross-enterprise data ecosystems has propelled industrial intelligence to unprecedented scales. Yet, the absence of a unified trust foundation across data, services, and knowledge layers undermines reliability, accountability, and regulatory compliance in real-world deployments. While existing surveys address isolated aspects, such as data governance, service orchestration, and knowledge representation, none provides a holistic, cross-layer perspective on trustworthiness tailored to industrial settings. To bridge this gap, we present \textsc{Trisk} (TRusted Industrial Data-Service-Knowledge governance), a novel conceptual and taxonomic framework for trustworthy industrial intelligence. Grounded in a five-dimensional trust model (quality, security, privacy, fairness, and explainability), \textsc{Trisk} unifies 120+ representative studies along three orthogonal axes: governance scope (data, service, and knowledge), architectural paradigm (centralized, federated, or edge-embedded), and enabling technology (knowledge graphs, zero-trust policies, causal inference, etc.). We systematically analyze how trust propagates across digital layers, identify critical gaps in semantic interoperability, runtime policy enforcement, and operational/information technologies alignment, and evaluate the maturity of current industrial implementations. Finally, we articulate a forward-looking research agenda for Industry 5.0, advocating for an integrated governance fabric that embeds verifiable trust semantics into every layer of the industrial intelligence stack. This survey serves as both a foundational reference for researchers and a practical roadmap for engineers to deploy trustworthy AI in complex and multi-stakeholder environments.
Daniel Hartmann, Kristýna Hamříková, Aleš Vysocký
et al.
Industrial robotics has established itself as an integral component of large-scale manufacturing enterprises. Simultaneously, collaborative robotics is gaining prominence, introducing novel paradigms of human-machine interaction. These advancements have necessitated a comprehensive revision of safety standards, specifically incorporating requirements for cybersecurity and protection against unauthorized access in networked robotic systems. This article presents a comparative analysis of the ISO 10218:2011 and ISO 10218:2025 standards, examining the evolution of their structure, terminology, technical requirements, and annexes. The analysis reveals significant expansions in functional safety and cybersecurity, the introduction of new classifications for robots and collaborative applications, and the normative integration of the technical specification ISO/TS 15066. Consequently, the new edition synthesizes mechanical, functional, and digital safety requirements, establishing a comprehensive framework for the design and operation of modern robotic systems.
Industrial safety in high-risk sectors such as mining, construction, oil and gas, petrochemicals, and offshore fishing remains a strategic global challenge due to the high incidence of occupational accidents and their human, financial, and legal consequences. Despite international standards and advancements in safety strategies, significant barriers persist in the effective implementation of a Zero Accident culture. This scoping review, conducted under PRISMA-ScR guidelines, analyzed 11 studies selected from 232 records, focusing on documented practices in both multinational corporations from developed economies and local companies in emerging markets. The methodological synthesis validated theoretical models, practical interventions, and regulatory frameworks across diverse industrial settings. The findings led to the construction of a five-pillar model that provides the structural foundation for a comprehensive safety strategy: (1) strategic safety planning, defining long-term vision, mission, and objectives with systematic risk analysis; (2) executive leadership and commitment, expressed through decision-making, resource allocation, and on-site engagement; (3) people and competencies, emphasizing continuous training, communities of practice, and the development of safe behaviors; (4) process risk management, using validated protocols, structured methodologies, and early warning systems; and (5) performance measurement and auditing, combining reactive and proactive indicators within continuous improvement cycles. The results demonstrate that only a holistic approach, one that aligns strategy, culture, and performance, can sustain a robust safety culture. While notable reductions in incident rates were observed when these pillars were applied, the current literature is dominated by theoretical contributions and model replication from developed countries, with limited empirical evaluation in emerging contexts. This study provides a comparative, practice-oriented framework to guide the implementation and refinement of safety systems in high-risk organizations. This review was registered in Open Science Framework (OSF): 10.17605/OSF.IO/XFDPR.
People with Autism Spectrum Disorder (ASD) face significant barriers to accessing and maintaining employment, many of which stem from work environments that fail to accommodate their neurological diversity. This article aims to analyze the occupational risks faced by autistic individuals in the workplace. A total of 39 scientific studies were reviewed, and the results identified nine predominant thematic categories of occupational risks. Particularly prominent were deficient communication, lack of structured support, cognitive overload, and difficulties coping with change. The reported situations were examined in detail, with attention paid to their specific contexts. A clear predominance of psychosocial risks over ergonomic ones was observed. The review also highlights several underexplored yet equally relevant risk factors, such as discontinuity in supported employment programs, difficulties in requesting reasonable accommodations, discrimination, a lack of professional recognition, and the negative effects of digital or remote environments, such as isolation. This study underscores the importance of recognizing unsafe conditions arising from the lack of neurodiversity-informed adjustments as a necessary step toward implementing organizational and social adaptations in the workplace.
Industrial safety. Industrial accident prevention, Medicine (General)
Due to the complex coal mining conditions in China, safe production and environmental preservation are significantly impacted by effective mine gas extraction and orderly mine gas discharge. The surfactant cetyltrimethyl ammonium bromide (CTAB) and micro- and nano-bubbles were dissolved in water to create a liquid-gas coupling medium, which was then utilized as the mass transfer carrier to reduce drag, increase the coefficient of permeability, and improve the displacement of coal gas. Combined with instantaneous seepage flow and gas permeability measurements, the effects of the liquid-gas coupling medium in reducing drag, increasing permeability, and strengthening displacement of anthracite were quantified. The drag-reduction seepage model under the action of the liquid-gas coupling medium was established. The interface characteristics and mechanism of the liquid-gas coupling medium on coal water seepage displacement were revealed. Based on the characteristics of pore seepage and boundary slip, a drag-reduction seepage model was established by comparing the internal pore structure of coal with that of pores in the circular tube. The interface and mechanism of the liquid-gas coupling medium in the coal water seepage displacement were described using Darcian seepage as a basis. The synergistic effect of the liquid-gas coupling medium provides a new technical direction for seepage displacement and anti-outburst extraction from coal seams.
The combination of embodied intelligence and robots has great prospects and is becoming increasingly common. In order to work more efficiently, accurately, reliably, and safely in industrial scenarios, robots should have at least general knowledge, working-environment knowledge, and operating-object knowledge. These pose significant challenges to existing embodied intelligent robotics (EIR) techniques. Thus, this paper first briefly reviews the history of industrial robotics and analyzes the limitations of mainstream EIR frameworks. Then, a new knowledge-driven technical framework of embodied intelligent industrial robotics (EIIR) is proposed for various industrial environments. It has five modules: a world model, a high-level task planner, a low-level skill controller, a simulator, and a physical system. The development of techniques related to each module are also thoroughly reviewed, and recent progress regarding their adaption to industrial applications are discussed. A case study of real-world assembly system is given to demonstrate the newly proposed EIIR framework's applicability and potentiality. Finally, the key challenges that EIIR encounters in industrial scenarios are summarized and future research directions are suggested. The authors believe that EIIR technology is shaping the next generation of industrial robotics and EIIR-based industrial systems supply a new technological paradigm for intelligent manufacturing. It is expected that this review could serve as a valuable reference for scholars and engineers that are interested in industrial embodied intelligence. Together, scholars can use this research to drive their rapid advancement and application of EIIR techniques. The authors would continue to track and contribute new studies in the project page https://github.com/jackyzengl/EIIR
<b>Eye glance analysis and driving performance during texting while driving: Differential effects of varying driving speed versus text message length. Background and Objective.</b> Texting while driving continues to be a significant public health concern. Eye glances off the roadway are a measure of the visual distraction associated with texting while driving. In the present study, we examined the effects of two ‘real-world’ factors relating to the adverse effects of texting on driving performance and eye glances off the roadway: (1) text message length and (2) driving speed. <b>Methods.</b> Subjects ‘drove’ a fixed-base simulator and read, typed and sent text messages while driving. In study #1, the driving speed was 60 mph and the effects of short (1 word) versus longer (8–10 words) texts were compared. In study #2, the text messages were short only and driving speed was 60 or 80 mph. Driving performance was assessed using the Standard Deviation of Lane Position (SDLP). Video recordings of the drivers’ faces were used to assess eye glances from the road to the phone—and back—during texting. <b>Results.</b> Texting while driving impaired driving performance as measured by SDLP, and both longer text messages and faster drive speeds made driving performance even worse. Analysis of the eye glance data, however, revealed different effects of these two manipulations. Specifically, longer text messages were associated with an increase in the number of eye glances to the phone during a text message episode, an increase in the total time spent with the eyes off the road, and an increase in the single longest eye glance from the road. Moreover, with longer text messages the longest single eye glance away from the road typically occurred at or near the end of the text message episode. In contrast, increasing driving speed to 80 mph did not affect any of these eye glance measures relative to driving at 60 mph. <b>Conclusion and Application.</b> Both text message length and driving speed while texting adversely affect driving performance, but they do so via different mechanisms. These results have implications for how to tailor “don’t text and drive” messaging to better serve the public health.
Industrial safety. Industrial accident prevention, Medicine (General)
Sotiris Michaelides, Stefan Lenz, Thomas Vogt
et al.
The industrial landscape is undergoing a significant transformation, moving away from traditional wired fieldbus networks to cutting-edge 5G mobile networks. This transition, extending from local applications to company-wide use and spanning multiple factories, is driven by the promise of low-latency communication and seamless connectivity for various devices in industrial settings. However, besides these tremendous benefits, the integration of 5G as the communication infrastructure in industrial networks introduces a new set of risks and threats to the security of industrial systems. The inherent complexity of 5G systems poses unique challenges for ensuring a secure integration, surpassing those encountered with any technology previously utilized in industrial networks. Most importantly, the distinct characteristics of industrial networks, such as real-time operation, required safety guarantees, and high availability requirements, further complicate this task. As the industrial transition from wired to wireless networks is a relatively new concept, a lack of guidance and recommendations on securely integrating 5G renders many industrial systems vulnerable and exposed to threats associated with 5G. To address this situation, in this paper, we summarize the state-of-the-art and derive a set of recommendations for the secure integration of 5G into industrial networks based on a thorough analysis of the research landscape. Furthermore, we identify opportunities to utilize 5G to enhance security and indicate remaining challenges, identifying future academic directions.
As an emerging technology that enables seamless integration between the physical and virtual worlds, the Metaverse has great potential to be deployed in the industrial production field with the development of extended reality (XR) and next-generation communication networks. This deployment, called the Industrial Metaverse, is used for product design, production operations, industrial quality inspection, and product testing. However, there lacks of in-depth understanding of the enabling technologies associated with the Industrial Metaverse. This encompasses both the precise industrial scenarios targeted by each technology and the potential migration of technologies developed in other domains to the industrial sector. Driven by this issue, in this article, we conduct a comprehensive survey of the state-of-the-art literature on the Industrial Metaverse. Specifically, we first analyze the advantages of the Metaverse for industrial production. Then, we review a collection of key enabling technologies of the Industrial Metaverse, including blockchain (BC), digital twin (DT), 6G, XR, and artificial intelligence (AI), and analyze how these technologies can support different aspects of industrial production. Subsequently, we present numerous formidable challenges encountered within the Industrial Metaverse, including confidentiality and security concerns, resource limitations, and interoperability constraints. Furthermore, we investigate the extant solutions devised to address them. Finally, we briefly outline several open issues and future research directions of the Industrial Metaverse.
Level 3 automated driving systems could introduce challenges to traffic systems as they require a specific lead time in their procedures to ensure the safe return of vehicle control to the driver. These processes, called ’transitions of control’, may particularly pose complications in accelerating traffic flows when regulations mandate control transitions due to an operational speed limitation of 60 km/h as established in recent certification processes based on UNECE regulations from 2021. To investigate these concerns, we conducted a comprehensive simulation study to examine potential safety implications arising from control transitions within mixed-autonomy traffic. The simulation results indicate adverse safety impacts due to increased safety-relevant interactions between vehicles caused by transitions of control in dynamic traffic flow conditions. Our findings also reveal that those effects could become stronger once string unstable ACC controllers are deployed as well.
Industrial safety. Industrial accident prevention, Medicine (General)
The recent emergence of large language models (LLMs) demonstrates the potential for artificial general intelligence, revealing new opportunities in Industry 4.0 and smart manufacturing. However, a notable gap exists in applying these LLMs in industry, primarily due to their training on general knowledge rather than domain-specific knowledge. Such specialized domain knowledge is vital for effectively addressing the complex needs of industrial applications. To bridge this gap, this paper proposes a unified industrial large knowledge model (ILKM) framework, emphasizing its potential to revolutionize future industries. In addition, ILKMs and LLMs are compared from eight perspectives. Finally, the "6S Principle" is proposed as the guideline for ILKM development, and several potential opportunities are highlighted for ILKM deployment in Industry 4.0 and smart manufacturing.
Thanks to rapid technological developments, new innovative solutions and practical applications of the Industrial Internet of Things (IIoT) are being created, upgrading the structures of many industrial enterprises. IIoT brings the physical and digital environment together with minimal human intervention and profoundly transforms the economy and modern business. Data flowing through IIoT feed artificial intelligence tools, which perform intelligent functions such as performance tuning of interconnected machines, error correction, and preventive maintenance. However, IIoT deployments are vulnerable to sophisticated security threats at various levels of the connectivity and communications infrastructure they incorporate. The complex and often heterogeneous nature of chaotic IIoT infrastructures means that availability, confidentiality and integrity are difficult to guarantee. This can lead to potential mistrust of network operations, concerns about privacy breaches or loss of vital personal data and sensitive information of network end-users. This paper examines the privacy requirements of an IIoT ecosystem in industry standards. Specifically, it describes the industry privacy dimensions of the protection of natural persons through the processing of personal data by competent authorities for the prevention, investigation, detection or prosecution of criminal offences or the execution of criminal penalties. In addition, it presents an overview of the state-of-the-art methodologies and solutions for industrial privacy threats. Finally, it analyses the privacy requirements and suggestions for an ideal secure and private IIoT environment.
In this relatively informal discussion-paper we summarise issues in the domains of safety and security in machine learning that will affect industry sectors in the next five to ten years. Various products using neural network classification, most often in vision related applications but also in predictive maintenance, have been researched and applied in real-world applications in recent years. Nevertheless, reports of underlying problems in both safety and security related domains, for instance adversarial attacks have unsettled early adopters and are threatening to hinder wider scale adoption of this technology. The problem for real-world applicability lies in being able to assess the risk of applying these technologies. In this discussion-paper we describe the process of arriving at a machine-learnt neural network classifier pointing out safety and security vulnerabilities in that workflow, citing relevant research where appropriate.
Industrial arms need to evolve beyond their standard shape to embrace new and emerging technologies. In this paper, we shall first perform an analysis of four popular but different modern industrial robot arms. By seeing the common trends we will try to extrapolate and expand these trends for the future. Here, particular focus will be on interaction based on augmented reality (AR) through head-mounted displays (HMD), but also through smartphones. Long-term human-robot interaction and personalization of said interaction will also be considered. The use of AR in human-robot interaction has proven to enhance communication and information exchange. A basic addition to industrial arm design would be the integration of QR markers on the robot, both for accessing information and adding tracking capabilities to more easily display AR overlays. In a recent example of information access, Mercedes Benz added QR markers on their cars to help rescue workers estimate the best places to cut and evacuate people after car crashes. One has also to deal with safety in an environment that will be more and more about collaboration. The QR markers can therefore be combined with RF-based ranging modules, developed in the EU-project SafeLog, that can be used both for safety as well as for tracking of human positions while in close proximity interactions with the industrial arms. The industrial arms of the future should also be intuitive to program and interact with. This would be achieved through AR and head mounted displays as well as the already mentioned RF-based person tracking. Finally, a more personalized interaction between the robots and humans can be achieved through life-long learning AI and disembodied, personalized agents. We propose a design that not only exists in the physical world, but also partly in the digital world of mixed reality.
In Japan, over 6,000 workers commit suicide every year, and the Japanese government has taken several countermeasures to prevent Karoshi (death due to overwork) and mental health disorders among workers. Risk factors for suicide among workers include long working hours, adverse psychosocial job characteristics, economic recession or financial crisis, job insecurity, and workplace harassment. Depressive symptoms are supposed to play a vital role in mediating mechanisms. Owing to the coronavirus disease (COVID-19) pandemic, economic crises continue and seemingly deepen, and the risk of unemployment increases. Workers with low socioeconomic status and who do not enjoy occupational health services are considered vulnerable, and essential workers (including health care workers) require special attention. Little evidence prevails with respect to workplace suicide prevention measures in a population approach, and hence, suicide prevention should be integrated into the existing workplace mental health activities. Although evidence of secondary prevention, such as screening for depression, is scarce for workplace mental health, such measures, including regular psychological counseling, should be applicable during this crisis. Research is thus crucial for preventing suicide in the workplace using surrogate outcomes, such as suicidality, help-seeking, stigma, access to means, and improving workplace support. Prevention of suicide among temporary workers, freelancers, foreign workers, and self-employed individuals who lack support from regional and occupational healthcare domains remains an untackled issue.
Industrial safety. Industrial accident prevention, Medicine (General)
The next revolution of industry will turn the industries as well as the entire society into a human-centric shape. The human presence in industrial environment and the human participation in industrial processes will be magnified more than ever before. To cope with the emerging challenges raised by this revolution, 6G ambitions to bridge the three domains of digital information, physical assets and humans into one merged cyber-physical-human world. This proposes not only an unprecedented demand for digital twin solutions, but also new technical requirements. Especially, aiming at a human-centric industrial DT system, novel multi-sensory human-machine interfaces will play a key role in this paradigm shift.
Felicien Ihirwe, Davide Di Ruscio, Silvia Mazzini
et al.
The development of Industrial Internet of Things systems (IIoT) requires tools robust enough to cope with the complexity and heterogeneity of such systems, which are supposed to work in safety-critical conditions. The availability of methodologies to support early analysis, verification, and validation is still an open issue in the research community. The early real-time schedulability analysis can help quantify to what extent the desired system's timing performance can eventually be achieved. In this paper, we present CHESSIoT, a model-driven environment to support the design and analysis of industrial IoT systems. CHESSIoT follows a multi-view, component-based modelling approach with a comprehensive way to perform event-based modelling on system components for code generation purposes employing an intermediate ThingML model. To showcase the capability of the extension, we have designed and analysed an Industrial real-time safety use case.