Search Results (33)

This paper proposes a new Unified Namespace (UNS)-based architecture to improve predictive and prescriptive maintenance of industrial equipment and overcome challenges such as incomplete data, poor interoperability, and disconnected IT/OT environments. The framework combines interoperable data formats in real-time sensor data, predictive modeling, prescriptive analytics, and simulations of digital twins, using UNS as a centralized, protocol-agnostic data layer that is scalable and complies with Industry 4.0 and Pharma 4.0 standards. The suggested methodology increases data accessibility, reduces integration complexity, and allows low-latency analytics and automated decision-making. Machine learning predictive models achieved more than 94% accuracy in predicting equipment failures. Prescriptive analytics provides maintenance recommendations to reduce downtime and risks. The feedback loops of digital twins can enhance the accuracy of predictions and allow decision optimization through what-if analysis. A test-bench deployment showed a higher performance compared to traditional point-to-point integration, with lower latency (approximately 18 ms vs. approximately 31 ms), decreasing packet loss (0.40% vs. 3.11%), and higher model accuracy (94.20% vs. 87.51%). The structure avoided more than 4000 simulated breakdowns in the test-bench environment, indicating dependability. The study connects the theoretical applications of the UNS with the actual maintenance processes and provides a sound approach to the industrial analytics and optimization of the equipment. Full article

Integrating third-party Machine Learning (ML) models into industrial Operational Technology (OT) creates a procurement deadlock: operators cannot verify vendor performance claims without sharing representative evaluation data with vendors, while vendors refuse to reveal proprietary model weights before purchase, rendering traditional safeguards such as Non-Disclosure Agreements technically unenforceable. This paper introduces a framework combining Zero-Knowledge Proofs (ZKPs) with smart contracts to enable trust-minimized, cryptographically verifiable competitive model procurement in Industrial Cyber-Physical Systems (ICPS). Vendors cryptographically prove that their model outperforms a legacy baseline without disclosing proprietary weights, a process we term cryptographic performance attestation, while the on-chain workflow automates escrow, proof verification, and best-vendor selection with arbiter-based dispute resolution. ZKP privacy is scoped to vendor model weights; operator-side evaluation-data confidentiality is managed separately via synthetic, de-identified, or public benchmark data. We analyze three ZKP workflow variations and evaluate them on consumer-grade hardware, achieving proving times of approximately three seconds and sub-dollar on-chain verification costs under Layer-2 fee assumptions for the recommended single-proof variation, while identifying computational trade-offs of recursive proof aggregation. The entire verification phase operates offline with no impact on real-time OT control paths, bridging the IT/OT pre-transaction trust gap while deferring artifact deployment to existing OT tooling. Full article

Critical energy infrastructures (CEIs) are fundamental pillars for economic and social development. However, their accelerated digitalization and the convergence between operational technologies (OTs) and information technologies (ITs) have increased their exposure to advanced cyber threats. This study examines the evolution of OT cybersecurity models with artificial intelligence in the energy sector between 2015 and 2024, through a systematic literature review following a four-phase method (planning, development, results, and analysis). To this end, we answer the following questions about the aspects of CEI cybersecurity models: What models exist? What energy services, technical means, and facilities do they encompass? And what algorithms do they include? From an initial set of 1195 articles, 52 studies were selected, which allowed us to identify 49 cybersecurity models classified into seven functional categories: detection, prediction and explanation; risk management; regulatory compliance; collaboration; response and recovery; architecture-based protection; and simulation. These models are related to 10 energy services, 6 technical means, 10 types of critical facilities, and 15 AI algorithms applied transversally. Furthermore, the integrated and systemic relationship of these study aspects has been identified in an IT-OT cybersecurity model for CEIs. The results show a transition from conventional approaches to solutions based on machine learning, deep learning, federated learning, and blockchain. Algorithms such as CNN, RNN, DRL, XAI, and FL are highlighted, which enhance proactive detection and operational resilience. A broader coverage is also observed, ranging from power plants to smart grids. Finally, five key challenges are identified: legacy OT environments, lack of interoperability, advanced threats, emerging IIoT and quantum computing risks, and low adoption of emerging technologies. Full article

The convergence of Operational Technology (OT) and Information Technology (IT) networks has become increasingly prevalent with the growth of Industrial Internet of Things (IIoT) applications. This shift, while enabling enhanced automation, remote monitoring, and data sharing, also introduces new challenges related to communication latency and cybersecurity. Oftentimes, legacy OT protocols were adapted to the TCP/IP stack without an extensive review of the ramifications to their robustness, performance, or safety objectives. To further accommodate the IT/OT convergence, protocol gateways were introduced to facilitate the migration from serial protocols to TCP/IP protocol stacks within modern IT/OT infrastructure. However, they often introduce additional vulnerabilities by exposing traditionally isolated protocols to external threats. This study investigates the security and reliability implications of migrating serial protocols to TCP/IP stacks and the impact of protocol gateways, utilizing two widely used OT protocols: Modbus TCP and DNP3. Our protocol analysis finds a significant safety-critical vulnerability resulting from this migration, and our subsequent tests clearly demonstrate its presence and impact. A multi-tiered testbed, consisting of both physical and emulated components, is used to evaluate protocol performance and the effects of device-specific implementation flaws. Through this analysis of specifications and behaviors during communication interruptions, we identify critical differences in fault handling and the impact on time-sensitive data delivery. The findings highlight how reliance on lower-level IT protocols can undermine OT system resilience, and they inform the development of mitigation strategies to enhance the robustness of industrial communication networks. Full article

The digital transformation of manufacturing through OT, IoT, and AI integration has created extensive networked sensor ecosystems, introducing critical cybersecurity vulnerabilities at IT-OT interfaces. This might particularly challenge the detection component of the NIST cybersecurity framework. To address this concern, the authors designed a diverse machine learning-based intrusion detection system framework for industrial control systems (DICS). DICS implements a sophisticated dual-module architecture. The screening analysis module initially categorizes network traffic as either unidentifiable or recognized packets, while the classification analysis module subsequently determines specific attack types for identifiable traffic. When unrecognized zero-day attack traffic accumulates in a buffer and reaches a predetermined threshold, the agile training module incorporates these patterns into the system, which enables continuous adaptation. During experimental validation, the authors rigorously assess dataset industrial relevance and strategically divide the datasets into four distinct groups to accurately simulate diverse network traffic patterns characteristic of real industrial environments. Moreover, the authors highlight the system’s alignment with IEC 62443 requirements for industrial control system security. In conclusion, the comprehensive analysis demonstrates that DICS delivers superior detection capabilities for malicious network traffic in industrial settings. Full article

Recently, a new concept has emerged for the development of Industrial Internet of Things (IIoT) applications, the Industrializable Industrial Internet of Things (I3oT). As a criterion for the design of industrial applications, the I3oT imposes the exclusive use of pre-installed elements in the company such as PLCs, sensors, IT/OT networks, etc., trying to minimize the impact on the factories and guaranteeing a cheap and assumable scalability for companies, something that cannot be implemented with the vast majority of IIoT applications available in the market. In our previous work, we have used I3oT applications for predictive maintenance on different components: cylinders, presses, welding clamps and also energy-saving tools, detection of bottlenecks and sub-bottlenecks, etc., all of them generalized for the entire factory. However, the main drawback comes from the flow of data through the IT/OT network. This article presents the Cross-PLC, a tool to allow massive data extraction using the company’s IT/OT network by communicating with any type of PLC or brand existing in the market. The Cross-PLC performs passive listening, and through different communication criteria, the Cross-PLC becomes a virtual PLC containing all the parameters necessary for the I3oT applications developed. This article presents the design of this tool, its implementation and use at Ford Factory in Almussafes (Valencia). Full article

This paper provides the complete details of current challenges and solutions in the cybersecurity of cyber-physical systems (CPS) within the context of the IIoT and its integration with edge computing (IIoT–edge computing). We systematically collected and analyzed the relevant literature from the past five years, applying a rigorous methodology to identify key sources. Our study highlights the prevalent IIoT layer attacks, common intrusion methods, and critical threats facing IIoT–edge computing environments. Additionally, we examine various types of cyberattacks targeting CPS, outlining their significant impact on industrial operations. A detailed taxonomy of primary security mechanisms for CPS within IIoT–edge computing is developed, followed by a comparative analysis of our approach against existing research. The findings underscore the widespread vulnerabilities across the IIoT architecture, particularly in relation to DoS, ransomware, malware, and MITM attacks. The review emphasizes the integration of advanced security technologies, including machine learning (ML), federated learning (FL), blockchain, blockchain–ML, deep learning (DL), encryption, cryptography, IT/OT convergence, and digital twins, as essential for enhancing the security and real-time data protection of CPS in IIoT–edge computing. Finally, the paper outlines potential future research directions aimed at advancing cybersecurity in this rapidly evolving domain. Full article

We used the Total Worker Health^® approach to develop a multi-component workplace sedentary behavior intervention and tested intervention effectiveness in a cluster randomized trial. Participants (n = 198; 75% female) were recruited from three call-centers (two intervention and one usual practice control). All worksites received pedal stand active workstations. The usual practice site received no additional support, while the intervention group completed a six-month program of activities including computer-based training, behavioral self-monitoring, health and safety discussions, and pedaling competitions. Data collection included a survey, a physical health assessment, and accelerometer measures of sedentary behavior, physical activity, and sitting/standing time. Primary analyses were generalized estimating equations comparing changes between intervention and usual practice conditions over time, along with analyses of changes in both groups combined over time. Six-month results revealed less prolonged sitting and reductions in musculoskeletal pain in both groups (all p < 0.05), while the intervention group showed additional improvements in moderate physical activity (p < 0.001) and use of pedal stands (p < 0.01). At 12-months, the additional physical activity and pedal stand use in the intervention group regressed to baseline levels, while reductions in prolonged sitting in both groups were durable (p < 0.01). This study adds to previous research showing the effectiveness of the Total Worker Health^® approach for workplace health and safety. Results also show that, while providing employees with health and safety resources is beneficial, providing ongoing support for the adoption and use of resources is more effective. Full article

While the occupational health of sign language interpreters has traditionally focused on physical health, evidence demonstrating mental health concerns is growing and supporting a shift to a more integrated approach. We embarked on a qualitative study to guide the adaptation of a previously developed Total Worker Health^® program to the context of sign language interpreting. Eight unstructured 90-min focus groups were conducted. Interpreters reported occupational safety, health, and well-being concerns and shared their solutions for management. Twenty-seven interpreters participated (aged 53.7 years; 81% female; 85% white). Predominant concerns centered on topics like workplace violence, secondary traumatic stress or vicarious trauma, lack of work–life integration or boundaries, and loss of agency or loss of self. The organizational culture of the field fostered deprioritization of self, oppression, elitism, sexism, and unhealthy relationships with interpreter peers and community members. Physical health remained a contributor, specifically the physical effects of non-physical work, aging, and differences in exposures across interpreting settings but paled in comparison to mental health and organizational culture. Solutions for management included but were not limited to prioritization of jobs, creating safe spaces/communities of supported practice, and exercise. This study supports the evolution of future research, practice, policy, and capacity building aspiring to a more integrated approach. Full article

The maritime industry’s increasing integration of IT/OT systems into vessel operations has significantly elevated its exposure to cyber–physical threats, making the development of effective cyber risk management strategies a necessity. This paper provides an outlook of the current landscape of cyber security threats and vulnerabilities for the maritime sector and vessels. An outline of the relevant governmental and industry directives, standards, and guidelines for cyber security in maritime vessels is given. Considering maritime vessels as critical elements of the maritime critical infrastructure sector, a number of relevant cyber–physical security assessment methods are presented. Bridging cyber–physical security, process safety, and security, API SRA (American Petroleum Institute Security Risk Analysis) and BTA (Bow-Tie Analysis) are presented as the most applicable cyber–physical security assessment methods for complex maritime vessels, such as an offshore oil and gas drillship. The scenario of a cyber-attack on the Dynamic Positioning (DP) system of a drillship is presented with the use of API SRA and BTA. The difficulties in the implementation of NIST CSF v2.0 and IACS UR E26 and UR E27 in the maritime sector are also discussed. The need for intensified research on and the formulation of bespoke cyber security measures to mitigate the evolving cyber threats within the maritime domain is highlighted. The need for the allocation of training and resources for the reinforcement of the capacity of a maritime vessel’s crew in the mitigation of cyber threats and safe maritime operations is emphasized. Full article

Nano- and micro-sized vesicular and colloidal structures mediate cell–cell communication. They are important players in the physiology of plants, animals, and humans, and are a subject of increasing interest. We investigated the effect of three surfactants, N-cetylpyridinium chloride (CPC), sodium dodecyl sulfate (SDS), and Triton X-100 (TX100), and two anionic polyelectrolytes, sodium polystyrene sulfonate (NaPSS) and sodium polymethacrylate (NaPMA), on nanoliposomes. In addition, the effect of SDS and TX100 on selected biological membranes (erythrocytes and microalgae) was investigated. The liposomes were produced by extrusion and evaluated by microcalorimetry and light scattering, based on the total intensity of the scattered light (I_tot), hydrodynamic radius (R_h), radius of gyration (R_g), shape parameter p (=R_h/R_g,0), and polydispersity index. The EPs shed from erythrocytes and microalgae Dunaliella tertiolecta and Phaeodactylum tricornutum were visualized by scanning electron microscopy (SEM) and analyzed by flow cytometry (FCM). The R_h and I_tot values in POPC liposome suspensions with added CPC, SDS, and TX100 were roughly constant up to the respective critical micelle concentrations (CMCs) of the surfactants. At higher compound concentrations, I_tot dropped towards zero, whereas R_h increased to values higher than in pure POPC suspensions (R_h ≈ 60–70 nm), indicating the disintegration of liposomes and formation of larger particles, i.e., various POPC–S aggregates. Nanoliposomes were stable upon the addition of NaPSS and NaPMA, as indicated by the constant R_h and I_tot values. The interaction of CPC, SDS, or TX100 with liposomes was exothermic, while there were no measurable heat effects with NaPSS or NaPMA. The SDS and TX100 increased the number density of EPs several-fold in erythrocyte suspensions and up to 30-fold in the conditioned media of Dunaliella tertiolecta at the expense of the number density of cells, which decreased to less than 5% in erythrocytes and several-fold in Dunaliella tertiolecta. The SDS and TX100 did not affect the number density of the microalgae Phaeodactylum tricornutum, while the number density of EPs was lower in the conditioned media than in the control, but increased several-fold in a concentration-dependent manner. Our results indicate that amphiphilic molecules need to be organized in nanosized particles to match the local curvature of the membrane for facilitated uptake. To pursue this hypothesis, other surfactants and biological membranes should be studied in the future for more general conclusions. Full article

Cyber-physical systems (CPS) are vital in automating complex tasks across various sectors, yet they face significant vulnerabilities due to the rising threats of cybersecurity attacks. The recent surge in cyber-attacks on critical infrastructure (CI) and industrial control systems (ICSs), with a 150% increase in 2022 affecting over 150 industrial operations, underscores the urgent need for advanced cybersecurity strategies and education. To meet this requirement, we develop a specialised cyber-physical testbed (CPT) tailored for transportation CI, featuring a simplified yet effective automated level-crossing system. This hybrid CPT serves as a cost-effective, high-fidelity, and safe platform to facilitate cybersecurity education and research. High-fidelity networking and low-cost development are achieved by emulating the essential ICS components using single-board computers (SBC) and open-source solutions. The physical implementation of an automated level-crossing visualised the tangible consequences on real-world systems while emphasising their potential impact. The meticulous selection of sensors enhances the CPT, allowing for the demonstration of analogue transduction attacks on this physical implementation. Incorporating wireless access points into the CPT facilitates multi-user engagement and an infrared remote control streamlines the reinitialization effort and time after an attack. The SBCs overwhelm as traffic surges to 12 Mbps, demonstrating the consequences of denial-of-service attacks. Overall, the design offers a cost-effective, open-source, and modular solution that is simple to maintain, provides ample challenges for users, and supports future expansion. Full article

Health Improvement Through Employee Control (HITEC) is a 16-year program directed toward the health of corrections personnel and developed through the application of the principles of Participatory Action Research (PAR) and participatory ergonomics. Its impetus has always been the adverse health status of the corrections workforce: early mortality, depression, obesity, and hypertension. The HITEC program trained small “Design Teams” (DTs) of front-line personnel in participatory methods for intervention design for health improvement and organizational change in line with the Total Worker Health^® principles. Periodic surveys and physical testing were introduced for longitudinal assessments. Comparative interventions at comparable sites included DTs without a priori assignation, problem-focused kaizen effectiveness teams (KETs), and bargaining unit-centered DTs. DT resilience and the replacement of members who transferred facilities or retired was aided by novel cooperative administrative structures. DT-generated interventions included stress lounges, changes in critical event report writing, a joint program with trained inmates to improve air quality, and training in staff mental health and sleep behavior. A specialized peer-to-peer Health Mentoring Program (HMP) paired new officers with trained peers. Many interventions and program features were institutionalized, thus improving prospects for self-supporting program longevity. Participatory interventions designed and supported by the corrections workforce were found to be both feasible and exceptionally effective. Full article

In the current digital transformation to Industry 4.0, the demands on the ability of countries to react responsibly and effectively to threats in the field of cyber security (CS) are increasing. Cyber safety is one of the pillars and concepts of Industry 4.0, as digitization brings convergence and integration of information technologies (IT) and operational technologies (OT), IT/OT systems, and data. Collecting and connecting a large amount of data in smart factories and cities poses risks, in a broader context for the entire state. The authors focus attention on the issue of CS, where, despite all digitization, the human factor plays a key role—an actor of risk as well as strengthening the sustainability and resilience of CS. It is obvious that in accordance with how the individuals (decision-makers) perceive the risk, thus they subsequently evaluate the situation and countermeasures. Perceiving cyber threats/risks in their complexity as a part of hybrid threats (HT) helps decision-makers prevent and manage them. Due to the growing trend of HT, the need for research focused on the perception of threats by individuals and companies is increasing. Moreover, the literature review points out a lack of methodology and evaluation strategy. This study presents the results of the research aimed at the mathematical modelling of risk perception of threats to the state and industry through the disruption of CS. The authors provide the developed factor model of cyber security (FMCS), i.e., the model of CS threat risk perception. When creating the FMCS, the researchers applied SEM (structural equation modelling) and confirmatory factor analysis to the data obtained by the implementation of the research tool (a questionnaire designed by the authors). The pillars and sub-pillars of CS defined within the questionnaire enable quantification in the perception of the level of risk of CS as well as differentiation and comparison between the analyzed groups of respondents (students of considered universities in SK and CZ). The convergent and discriminant validity of the research instrument is verified, and its reliability is confirmed (Cronbach’s alpha = 0.95047). The influence of the individual pillars is demonstrated as significant at the significance level of α = 5%. For the entire research set N = 964, the highest share of risk perception of CS threats is achieved by the DISRIT pillar (disruption or reduction of the resistance of IT infrastructure). Full article

In this paper, in order to help improve the information capacity of modern factories, a full connection digital factory for industrial Internet identity resolution is built, and digital technology is used to perform the digital interconnection of the production equipment. The full connection digital factory is an advanced factory with extensive connection of production units, deep integration of IT–OT, full release of data value, and improvement of the total factor productivity. This is enabled by the full integration and use of the new generation technologies represented by 5G, edge computing, and identity resolution to reach new factory level, workshop level, and production line level production scenarios. Based on the integration of infrastructure, information technology, and platform service resources, a full connection digital factory architecture is constructed. The fusion application of 5G, edge computing, and mark resolution technology in transformer discrete manufacturing digital plant is then discussed. The challenges and development trends are summarized. Finally, it is demonstrated that the full connection digital factory of identity resolution is a possible future development direction. Full article