Search Results (571)

Industrial automation laboratories often face limitations related to restricted access to industrial equipment, safety constraints, and limited scalability for hands-on experimentation. To address these challenges, this work proposes a real-time multi-layer Digital Twin architecture integrating a physical Siemens S7-1500 PLC, an immersive Unity-based virtual environment, HMI supervision, and IoT-enabled remote monitoring within a unified communication framework. The architecture is structured into physical, digital, and integration layers, enabling modular scalability and bidirectional synchronization between the physical process and its virtual representation through Ethernet TCP/IP communication. System performance was evaluated using synchronization metrics including communication latency, jitter, deterministic timing deviation, and event synchronization accuracy. Experimental results demonstrated stable PLC–Digital Twin communication with average latencies below 15 ms and jitter below 0.5 ms, ensuring reliable real-time interaction during continuous operation. A comparative evaluation with engineering students also showed improved learning conditions, achieving high perceived usability (SUS = 86/100) and reduced cognitive workload (NASA-TLX = 34/100). These results confirm the effectiveness of the proposed architecture as a scalable platform for Industry 4.0 training environments. Full article

Industrial Control Systems (ICSs) are the backbone of modern critical infrastructure, such as electric power, water treatment, oil and gas distribution, and manufacturing operations. While the convergence of IT and OT has greatly increased efficiency and observability, it has also greatly expanded the attack surface of these once-isolated systems. High-profile cyber-physical attacks, including Stuxnet (2010), TRITON (2017), and the Colonial Pipeline ransomware attack (2021), have shown that ICS-targeted cyberattacks can cause physical damage, disrupt economic stability, and put public safety at risk. Despite the growing prevalence and intensity of such threats, ICS-based cybersecurity education remains largely under-resourced and underfunded. Traditional ICS training laboratories require highly specialized hardware, vendor-specific tools, and expensive licensing that significantly raise barriers to entry. Traditional labs typically require on-site participation and pose physical safety concerns when cyber-physical attack scenarios are performed. These barriers leave students unable to get necessary security training for ICSs. Therefore, this paper introduces ${\mathrm{AE}}^3$GIS: Agile Emulated Educational Environment for Guided Industrial Security—a fully virtual, lightweight, open-source platform designed to democratize ICS cybersecurity education. Based on the GNS3 network simulation tool, ${\mathrm{AE}}^3$GIS enables rapid deployment of comprehensive ICS environments containing IT and OT systems, industrial communication protocols, control logic, and diverse security tools. ${\mathrm{AE}}^3$GIS is designed to provide practical training for students using realistic ICS cybersecurity scenarios through a local or remote training platform without the cost, safety, or accessibility limitations of hardware-based labs. Full article

This study focuses on the challenges and opportunities of integrating industrial robots into robotic bricklaying systems (RBSs) for automation and digital transformation in the construction industry. A mobile RBS was designed, engineered, manufactured and commercially implemented for the first time in Poland. The RBS is designed to perform robotic bricklaying in situ in municipal, residential, and industrial buildings, where sustainable construction tasks are implemented. The details of the design solutions for the RBS, virtual simulation, and real robotic bricklaying processes are presented. The results of bricklaying using the RBS and the factors that influence the robotic bricklaying process are summarized. A 3D digital building information model (BIM) created using Autodesk Revit tools was used for simulated robotic bricklaying in the ABB RobotStudio 2025.5 program, from which they were transferred to the programming of the ABB IRB 4600 bricklaying robot. The laser programming method for the bricklaying robot, bricklaying procedures, and algorithms are also presented. The costs of human labor and robot construction were compared, and the return on investment (ROI) was calculated. RBS evaluations were performed in laboratory settings, on-site demonstrations, and commercial wall-laying in residential apartments. Full article

Microclimatic stress strongly influences the ecological resilience of Vasconcellea stipulata (Toronche), yet current monitoring approaches rely on sparse measurements and lack real-time predictive capability. This work introduces an AI-enhanced virtual sensing framework based on laser-induced graphene (LIG) designed to emulate the thermoresistive response of an LIG transducer and generate high-resolution environmental indicators for microclimatic analysis. Unlike conventional LIG sensors or standalone IoT systems, the proposed framework integrates experimental calibration, data-driven modeling, and embedded inference into a unified architecture suitable for lightweight deployment on edge devices. A multilayer perceptron (MLP) model trained on laboratory data reproduced the temperature- and humidity-dependent electrical behavior of the transducer with high fidelity, achieving an RMSE of 0.016 k$\Omega$ in the calibrated range (10–60 °C) and remaining below 0.09 k$\Omega$ under noisy and extrapolated conditions. Sensitivity analysis identified temperature as the dominant driver (71%), followed by solar irradiance (19%) and relative humidity (10%), consistent with the microstructural mechanisms governing LIG’s response. The virtual sensor enables continuous, low-cost environmental monitoring and provides quantitative variables that can support downstream ecological interpretation. Overall, the results highlight the potential of AI-enhanced LIG–IoT architectures for advancing real-time microclimatic assessment in resource-limited Andean ecosystems. Full article

To address the issues of excessive current stress and the power dead zone associated with conventional phase-shift modulation in dual-active-bridge (DAB) converters, a hybrid optimized modulation strategy based on dual-side asymmetric duty modulation (ADM) is proposed. The proposed strategy aims to minimize the peak-to-peak current stress by introducing two distinct operating modes of the converter. A dynamic compensation mechanism based on mode switching is developed, enabling a coordinated dual-mode modulation to achieve minimum peak-to-peak current stress over the full power operating range. In addition, a virtual voltage control scheme is incorporated to enhance the dynamic response and stability of the system. Finally, experimental results obtained from a laboratory prototype verify that the proposed strategy effectively reduces the peak-to-peak current stress while significantly improving the dynamic performance of the DAB converter. Full article

The power-shift system employed in agricultural multi-range continuously variable transmissions (CVTs) features a complex structure and control logic, presenting significant challenges to the reliability of agricultural machinery. To enable timely detection of faults, constructing an intelligent fault diagnosis classifier to monitor the system’s health status is essential. Typically, fault samples utilized for classifier development originate from ideal bench tests, characterized by uniform patterns and limited diversity, thereby hindering the algorithm’s generalization capability. This study addresses this issue by proposing a generative adversarial network (GAN) model, integrated with a triple loss function and a novel generator architecture, to augment the fault dataset under laboratory conditions. The generator architecture comprises a variational autoencoder module and an oil pressure point attention mechanism, enabling the generation of diverse and fluctuating virtual samples. Building on this augmented dataset, a fault classifier based on one-dimensional ConvNeXt was developed. Experimental results indicate that the classifier achieves an accuracy of 99.73%. While classifier accuracy decreases with increasing noise levels, the GAN-generated dataset provides more comprehensive training, resulting in an accuracy approximately 3% higher than that achieved using the original dataset. Full article

The manufacturing sector drives industrialisation and contributes substantially to economic growth and employment creation. Despite this, it faces the challenges of diminishing size and lack of competitiveness, mainly due to operational uncertainty. The study developed an approach to managing operational uncertainty using Industry 4.0 and 5.0 technologies. It employed a multimethod quantitative design based on the post-positivist paradigm, with data collected from 22 experts and 262 responses from a manufacturing firms’ survey. The study employed an integrated fuzzy decision-making trial and evaluation laboratory (DEMATEL) with partial least squares structural equation modelling (PLS-SEM) and fuzzy set qualitative comparative analysis (fsQCA). The fuzzy DEMATEL results reveal that growing geopolitical tension, cost-of-living-driven consumer behavioural change, pandemic turbulence, lack of energy stability and security, and the entrenched power of large firms are causal dimensions of operational uncertainty. Industry 4.0 and 5.0 technologies, with capabilities for scenario planning and supply chain integration, flexible production and mass customisation, real-time system and process monitoring and response, root cause analysis, and sustainable solutions, can manage operational uncertainty. These technologies include artificial intelligence (AI), the Internet of Things (IoT), big data analytics, and, to a lesser extent, advanced robotics, blockchain, and augmented and virtual reality (AR/VR). This study advanced configuration theory and a new integrated methodology (fuzzy-DEMATEL-PLS-SEM-fsQCA) to develop solutions for sustained performance during operational uncertainty in manufacturing. This research offers valuable information to advance the subject, make meaningful changes in day-to-day manufacturing operations, and promote practical real-world problem solving. Full article

With the transition from Industry 4.0 to Industry 5.0, human–robot collaborative assembly (HRCA) has progressed from physical copresence to cognitive integration and knowledge sharing. Digital twins (DTs) serve as enabling technologies that connect physical and virtual spaces. Support is provided for dynamic, safe, and human-centered collaboration. This study presents a systematic review of the research progress and practical applications of DT-enabled HRCA. First, conceptual boundaries between HRCA and general human–robot collaboration (HRC) in manufacturing are defined. Core elements of DT-driven state perception, task planning, and constraint modeling are described. Second, four task-allocation paradigms are classified and summarized, including optimization-based, constraint satisfaction-based, data-driven intelligent, and large language model (LLM)-assisted approaches. Applicable scenarios are identified. Third, the effects of collaboration modes and interaction modalities on planning logic are analyzed. Collaboration modes are categorized as parallel, sequential, and tightly coupled. Interaction modalities are grouped into AR-based explicit interaction, implicit intention perception, and multimodal fusion. Fourth, cross-domain application characteristics and engineering bottlenecks are summarized. Target domains include precision assembly, disassembly and remanufacturing, and construction on-site operations. Finally, four core challenges are distilled, including dynamic uncertainty, multi-objective conflicts, human factor adaptation, and system integration. Four future directions are outlined: LLM-enabled adaptive planning, safety–efficiency co-optimization, personalized collaboration, and standardized integration. The proposed technology–application–challenge–outlook framework is intended to provide a theoretical reference and practical guidance for transitioning HRCA from laboratory prototypes to large-scale industrial deployment. Full article

Windows are key elements of the building’s system; they connect workers with the outdoor environment, influence daylight penetration, sound insulation, and thermal exchanges of façades, but they also moderate the workers’ well-being and productivity. This research investigates how the window-to-wall ratio, as well as the position and orientation of mullions, in movable offices affect the combination of workers’ perceptual and emotional responses. A smart co-working prefabricated movable office was modelled in virtual reality to include dynamic visual elements and acoustic stimuli. Experiments were performed in a laboratory under controlled thermal conditions involving 32 volunteers. The Igroup Presence and Emotional Salience Questionnaires were used to collect subjective responses. ANOVA analysis and post hoc test with the Bonferroni correction were used for data elaboration. Results revealed that window design affects emotional salience. High window-to-wall ratio and no mullions achieved the highest scores. Increasing the number of mullions, particularly when they obstruct key visual elements, reduced the positive emotional salience rating. Horizontal mullions diminish the outdoors’ spatial perception, interrupting visual continuity and restricting users’ capacity to recognise variations in the views. Finally, the results suggest some valuable insights and suggestions that can help designers improve window design and people’s well-being and satisfaction. Full article

The cement industry is central to sustainable manufacturing due to its high energy demand and associated CO₂ emissions. In cement production, a substantial share of electrical energy is consumed in the clinker grinding circuit, where Blaine fineness (specific surface area, cm²/g), a key quality output, affects both cement performance and specific energy consumption. However, laboratory Blaine measurements are typically available with a 30–60 min delay, which limits timely process interventions and may promote conservative operating practices (e.g., precautionary over-grinding) to secure quality. This study develops machine-learning models to predict the finished-product Blaine fineness (Blaine-F) from routinely recorded industrial quality-control inputs, including XRF-based oxide composition, derived chemical moduli (lime saturation factor, LSF; silica modulus, SM; alumina modulus, AM), laser-diffraction particle-size distribution descriptors (Q10/Q50/Q90 corresponding to D10/D50/D90 percentile diameters; and R3 residual fractions at selected cut sizes), and intermediate in-process fineness (Blaine-P). The models were trained on over 200 finished-product samples obtained from the quality-control laboratory information management system (LIMS) of Seza Cement Factory (SYCS Group, Turkey). Ridge regression, Random Forest, XGBoost, LightGBM, and CatBoost were tuned using RandomizedSearchCV with five-fold cross-validation and evaluated on a held-out test set using MAE, RMSE, and R². The results show that the linear baseline provides limited explanatory power (Ridge: R² ≈ 0.50), consistent with the strongly non-linear behavior of the grinding–separation system, whereas tree-based ensemble methods achieve higher predictive accuracy. XGBoost yields the best overall performance (R² = 0.754; RMSE = 76.9 cm²/g), while Random Forest attains R² = 0.744 with the lowest MAE (61.7 cm²/g). Explainability analyses indicate that Blaine-F is primarily influenced by the fine-tail PSD descriptor Q10 (D10 particle size) and the intermediate fineness Blaine-P, whereas chemistry-related variables (e.g., LSF and SiO₂, and particularly SM) provide secondary yet meaningful contributions. These findings support the use of the proposed model as a virtual sensor to reduce decision latency associated with delayed laboratory Blaine measurements and to enable tighter fineness targeting. Potential energy and CO₂ implications should be quantified using site-specific, plant-calibrated relationships between kWh/t and Blaine fineness, rather than inferred as measured outcomes within the present study. Full article

Traditional hydraulic transmission education is often hindered by the subject’s theoretical complexity and abstract nature. To address these challenges, this study introduces the Immersive Hydraulic Transmission Laboratory (IHTL), a virtual teaching system designed to enhance practical learning and theoretical comprehension. The IHTL comprises three key modules: hydraulic components, disassembly experiments, and hydraulic circuits. The system’s effectiveness was evaluated through a comparative study of 80 mechanical engineering students. Results showed that the experimental group exhibited a 20% higher rate of inquiry and achieved average test scores 20.475 points higher than the control group. Statistical analysis confirms that the IHTL significantly outperforms traditional teaching methods in both stimulating student interest and improving learning outcomes. Full article

Structural health monitoring (SHM) has evolved into an indispensable component for ensuring the safety, durability, and life-cycle efficiency of civil infrastructure. Over the past five years, significant technological advancements have been made in innovative sensing systems, facilitating real-time assessment of structural performance and the early detection of deterioration. This comprehensive review presents recent developments in smart sensor-based SHM, with particular emphasis on the convergence of the Internet of Things (IoT), artificial intelligence (AI), and digital twin (DT) frameworks. Our review critically examines advances in fiber-optic, piezoelectric, MEMS-based, vision-based, acoustic, and environmental sensors, as well as emerging multi-sensor fusion architectures. In addition, bibliometric insights highlight the significant rise in global research activity and influential thematic clusters in SHM between 2020 and 2025. The discussion underscores how AI-integrated data analytics, IoT-enabled wireless networks, and DT-driven virtual replicas enable intelligent, autonomous, and predictive monitoring of bridges, buildings, tunnels, and other large-scale civil infrastructure. Field deployments and case studies are analyzed to bridge the gap between laboratory-scale demonstrations and real-world implementation. Finally, key scientific and practical challenges—including the durability of embedded sensors, the interoperability of heterogeneous data, cybersecurity in connected systems, and the explainability of AI models—are outlined to guide future research. Overall, this review positions contemporary SHM as a transition from traditional damage detection to comprehensive life-cycle management of infrastructure through self-diagnosing, data-centric, and sustainability-driven monitoring ecosystems. Full article

The development of an immersive virtual reality laboratory in the facilities of a private Mexican university is presented. This laboratory contemplates the use of different disciplines and different student profiles, for which it was developed considering technological, ergonomic, educational, and disciplinary requirements. A primary assessment of a selected group of students was developed to find out the initial level of satisfaction with the user experience in the laboratory and the improvements to be proposed for future adaptations. Full article

This study introduces a mesoscale modeling methodology for polyurethane-solidified ballast beds (PSBBs) that eliminates reliance on X-ray computed tomography (XCT) and addresses constraints in specimen size, capital cost, and post-processing complexity. The approach couples the Discrete Element Method (DEM) with the Finite Element Method (FEM). A high-fidelity discrete-element geometry is reconstructed from three-dimensional laser scans of ballast particles. The virtual-ray casting algorithm is then employed to identify the spatial distribution of ballast and polyurethane and map this information onto the finite-element mesh, enabling heterogeneous material reconstruction at the mesoscale. The accuracy of the model and mesh convergence are validated through comparisons with laboratory uniaxial compression tests, determining the optimal mesh size to be 0.4 times the minimum particle size (0.4 D_min). Based on this, a parametric study on the effect of sleeper width on ballast bed mechanical responses is conducted, revealing that when the sleeper width is no less than 0.73 times the ballast bed width (0.73 W_b) an optimal balance between stress diffusion and displacement control is achieved. This method demonstrates excellent cross-material applicability and can be extended to mesoscale modeling and performance evaluation of other multiphase particle–binder composite systems. Full article

Corporate Social Responsibility (CSR) has evolved into a strategic governance framework through which organisations address environmental sustainability, stakeholder expectations, and long-term institutional viability. In knowledge-intensive organisations such as universities, Green Artificial Intelligence (GAI) is increasingly recognised as an internal CSR agenda. GAI can reduce digital and energy-related environmental impacts while enhancing educational and operational performance. This study examines how higher education leaders, as organisational decision-makers, form intentions to adopt GAI within institutional CSR and digital sustainability strategies. It focuses specifically on leadership intentions to implement key GAI practices, including Smart Energy Management Systems, Energy-Efficient Machine Learning models, Virtual and Remote Laboratories, and AI-powered sustainability dashboards. Grounded in the Unified Theory of Acceptance and Use of Technology (UTAUT), the study investigates how performance expectancy, effort expectancy, social influence, and facilitating conditions shape behavioural intentions to adopt GAI. Survey data were collected from higher education leaders across Saudi universities, representing diverse national and cultural backgrounds within a shared institutional context. The findings indicate that facilitating conditions, performance expectancy, and social influence significantly influence adoption intentions, whereas effort expectancy does not. Gender and cultural context also moderate several adoption pathways. Generally, the results demonstrate that adopting GAI in universities constitutes a governance-level CSR decision rather than a purely technical choice. This study advances CSR and digital sustainability research by positioning GAI as a strategic tool for responsible digital transformation and by offering actionable insights for higher education leaders and policymakers. Full article