Search Results (419)

Proton exchange membrane fuel cell (PEMFC) parameter identification is a nonlinear computational modeling problem involving strongly coupled electrochemical parameters and partially unobservable polarization processes. Its experimental teaching is further constrained by the cost of fuel cell stacks and the safety requirements associated with hydrogen operation. To address these challenges, this study develops a computational modeling and intelligent simulation framework for a virtual teaching experiment on PEMFC parameter identification. A semi-empirical output-voltage model is established, and the sum of squared errors (SSE) between measured and simulated voltages is formulated as the optimization objective. An enhanced Logistic–Tent reverse snow ablation optimizer (LRSAO), termed RLFDB-LRSAO, is introduced by integrating roulette-wheel-selection-enhanced fitness-distance balance and Lévy flight perturbation. Its methodological novelty lies in the stage-wise coordination of population-diversity enhancement, candidate-selection guidance, and search perturbation within the LRSAO framework, rather than in the individual component strategies themselves. The framework organizes the identification process into mechanism interpretation, model construction, algorithm implementation, parameter configuration, visualization, comparative evaluation, and reflective analysis. Case studies using the NedStack PS6 and Modular SR-12 stacks yield best SSE values of 1.2173340 and 6.13503904, respectively. A small-scale qualitative teaching evaluation involving 20 postgraduate students indicated that the framework supported programming practice, strengthened conceptual understanding of PEMFC parameter identification and intelligent optimization, and provided useful support for research-oriented skills such as engineering problem analysis, technical writing, and innovation-oriented project development. These results provide preliminary evidence of technical and educational feasibility while supporting a cautious, problem-dependent interpretation of the optimizer. Full article

Building Information Modeling (BIM) stores rich object-level metadata, yet retrieving this information typically requires navigating complex software interfaces or formal queries, and existing BIM-VR environments rarely support conversational, semantic access to this data. Large Language Models (LLMs) offer flexible natural-language interpretation but risk generating fluent, factually incorrect responses when used without grounding, which is particularly problematic for engineering decision-making. This study develops and evaluates a metadata-grounded BIM–LLM–VR framework that enables conversational BIM information retrieval inside an immersive virtual reality environment. The framework integrates a BIM-to-Unity data exchange workflow linking VR objects to BIM metadata through stable element ID, an LLM-based interpretation module that converts natural-language queries into structured metadata filters, and a deterministic retrieval module that executes these filters against verified BIM metadata rather than allowing free-form LLM generation. A case study demonstrated five successful interaction scenarios and one negative-query validation using a multi-storey BIM model, and the framework was evaluated using a 30-query benchmark validated against ground-truth BIM metadata. The system achieved a mean precision of 99.94%, mean recall of 100.00%, and mean F1-score of 99.97%, with a mean execution time of 0.347 s. These results indicate that, under a controlled metadata schema and a predefined benchmark, the framework can translate a defined set of natural-language requests into correct element-level metadata filters inside an immersive VR environment, establishing the technical soundness of the metadata-grounding architecture. Full article

This article examines whether and how work environments can foster social love, understood as a relational practice grounded in care, recognition, cooperation, and overabundance beyond instrumental exchange. Drawing on economic–managerial studies on love and agapic behaviour in organizations and sociological theories of social love, the paper analyses an experimental field study involving 200 young professionals from three Italian organizations. Participants were organized into heterogeneous and homogeneous work groups according to socio-emotional profiles assessed through standardized instruments, including BES, IRI, HSC, and BESSI. The groups collaborated on a project concerning the role of human relations in respect of artificial intelligence across three environments: face-to-face, online, and immersive virtual reality. Data were collected through focus groups, individual logbooks, peer evaluation, and lexical–thematic analysis supported by T-LAB. The findings show that relational quality emerged progressively through transitions from uncertainty to orientation, from diversity to cooperation, and from individual contributions to shared decisions. Face-to-face interaction proved particularly effective for trust, empathy, and decision-making, while digital and immersive environments supported coordination and engagement but required greater relational adaptation. The study concludes that social love is not reducible to individual soft skills but can develop within organizational interactions when work settings enable trust, solidarity, and mutual recognition. Full article

Background/Objectives: We conducted a virtual exchange program (VEP) to facilitate discussions on the Sustainable Development Goals (SDGs) among nursing students in Belgium, Indonesia, Japan, and Thailand and to assess the improvements in international competencies. Methods: In this quasi-experimental study with a pre- and post-test design, students who voluntarily participated in the VEP were compared with a matched group of non-participants. International competencies were assessed using the STRENCO (Strengthening multi-professional competencies in mental health) Competency Assessment Tool, a self-assessment tool. Hence, this analysis is based on self-reported data, which were analyzed using a linear mixed-effects model. Results: The analysis included 29 and 34 students in the intervention and control groups, respectively. A significant interaction effect between group and time was observed for intercultural competence (p = 0.004, 95% confidence interval (CI): [0.18, 0.87]), personal growth (p = 0.013, 95%CI: [0.10, 0.79]), and digital competence (p = 0.020, 95%CI: [0.09, 1.05]). In contrast, the interaction was not significant for language skills (p = 0.168, 95%CI: [−0.10, 0.58]), global engagement (p = 0.716, 95%CI: [−0.28, 0.40]), and international discipline learning (p = 0.058, 95%CI: [−0.02, 0.87]). Conclusions: This SDG-focused VEP involving nursing students from four countries across Europe and Asia provided a structured approach to international exchange education and may have enhanced intercultural competence, personal growth, and digital competence as components of international competencies. This study is expected to explore future VEP-based practices and international exchanges. Full article

Forest planning and operational execution still often rely on separate office-based workflows, field inventories, and analogue tree-marking procedures, limiting direct communication between planners and field personnel. Mixed reality (MR) interfaces and forest digital twin concepts may help link planning decisions with field-level visualization and interaction. This study presents an initial proof-of-concept prototype for MR-based virtual tree tagging and field–office communication in forestry. The prototype integrates a Unity GE-based Forest Planner System, a Microsoft HoloLens 2-based Forest Worker System, and a locally hosted MagicOnion server for bidirectional client–server communication. Virtual trees with predefined stem diameters were created in Unity GE, and torus-shaped markers were implemented to support tree selection, color-coded designation, marker deletion, and visualization of predefined diameter-related attributes. Under controlled indoor conditions using a 5G mobile hotspot, both clients connected to the server and exchanged marker-related events. Marker creation, color assignment, deletion, and shared marker-state updates initiated from either client were reproduced in the corresponding system. The prototype demonstrates the functional feasibility of the core communication workflow but remains limited to a simulated environment. Full article

The growing complexity of cryptocurrency ecosystems has created substantial difficulties for anti-money laundering (AML) enforcement across jurisdictions. Existing AML information-sharing mechanisms are fragmented, slow, and constrained by privacy regulations. This paper presents CAHE-AML, a risk-adaptive architecture built on a context-aware hybrid encryption framework designed to enable secure and privacy-preserving cross-border AML data exchange between regulatory authorities and virtual asset service providers (VASPs). CAHE-AML integrates symmetric encryption, attribute-based encryption (ABE), a decentralized key governance model, and heuristic risk-scoring mechanisms for dynamic policy adaptation. Using an Ethereum Fraud Dataset, we compute risk scores to dynamically assign context-aware access policies across three hierarchical tiers: local, regional, and global. Experimental evaluation on an enterprise-grade multi-core system demonstrates high computational efficiency, achieving substantial throughput and low processing latency. The system identifies integrity breaches with high accuracy while maintaining a manageable data expansion ratio. These results highlight the computational feasibility of CAHE-AML’s cryptographic layer for real-time, high-frequency AML data processing in decentralized financial systems. Full article

Virtual reality can support the preparation and rehearsal of assembly tasks by providing a safe and repeatable digital representation of workstations. This study presents the development and laboratory feasibility validation of a geometry- and procedure-oriented VR simulation model for the assembly and disassembly of end-effectors on an industrial robot. The workflow was implemented using the Almega AX-V6 robotic workstation as a case study and included geometric acquisition of the real robot, CAD modelling in SolidWorks, redesign of the original end-effector connection using a quick-change flange concept, creation of two alternative end-effector models, modelling of the laboratory workspace in SketchUp, and scene enhancement in Twinmotion. The resulting robot and environment models were integrated in Pixyz Review and deployed through an Oculus Rift-based VR setup. Compared with the original flange concept, which required twelve screws, the redesigned training concept used two screws and two nuts, reducing the number of fastening elements by 66.7% and the number of screw positions by 83.3%. The VR implementation supported visual inspection, controller-based placement and alignment, and symbolic confirmation of fastening steps; it did not include force feedback, threaded fastening physics, automatic error scoring, or quantified transfer-of-training evaluation. Laboratory feasibility validation confirmed correct asset integration, spatial correspondence with the physical workplace, and functional executability of the target exchange sequence. The results show that the workflow is useful as a case-study pipeline for CAD-to-VR modelling and assembly rehearsal, while controlled user studies are still required before claims about training effectiveness can be made. Full article

The integration of online Digital Twin (DT) technologies with industrial control systems represents an important step toward real-time monitoring and synchronization of manufacturing processes within Industry 4.0 environments. However, reproducible approaches for connecting simulation environments with real industrial control hardware using standardized communication protocols remain insufficiently described in the existing literature. This study presents the development of an online Digital Twin for real-time monitoring of manufacturing processes using OPC UA communication and programmable logic controller (PLC) data exchange. The proposed approach combines discrete-event simulation with real-time industrial data acquisition to enable synchronization between a physical manufacturing system and its virtual representation. The implementation was experimentally validated in a laboratory-scale cyber–physical production system using Tecnomatix Plant Simulation, Siemens S7-1200 PLC, and KEPServerEX middleware. The developed architecture enables real-time process state monitoring, event-driven synchronization, and verification of selected control and safety functions within the simulation environment. The results demonstrate stable synchronization between the physical and digital systems with response times ranging from 50 to 200 ms, confirming the feasibility of near-real-time integration. The implemented light barrier scenario further demonstrated the capability of the online DT to reflect safety-related events occurring in the physical system. The main contribution of this study lies in the implementation and experimental verification of an OPC UA-based online Digital Twin architecture for manufacturing process monitoring in a laboratory environment. The presented approach provides a foundation for future extensions toward predictive analytics, scenario-based simulation, and advanced manufacturing optimization applications. Full article

Slow convergence remains a critical limitation hindering the practical deployment of consensus-based time synchronization protocols (CTSPs). Increasing algebraic connectivity is a key mechanism for improving the convergence speed of distributed algorithms. However, existing strategies inevitably introduce redundant packet communication, while forwarding stale timing information may degrade synchronization accuracy. To address this challenge, this paper proposes a high-order consensus time synchronization protocol (HCTSP). Unlike traditional CTSPs, HCTSP incorporates previous clock states from two-hop neighboring nodes to establish a virtual topology and further employs this information to enhance the estimation of logical clock parameters, thereby achieving fast estimation of clock parameters while effectively suppressing fluctuations in parameter estimation caused by a large initial synchronization error. Although the proposed method utilizes single-hop communication to relay information from non-adjacent nodes—an indirect transmission mechanism that inherently introduces additional communication overhead—we further develop an accumulator-based redundant information optimization scheme. Furthermore, the consensus algorithm integrates both an accumulator-based update mechanism and multi-hop historical memory, partially alleviating the impact of Gaussian communication delay jitter on clock correction. Theoretical proofs have verified the fast convergence of the proposed protocol. Extensive simulation experiments also demonstrate the superior efficiency of HCTSP in terms of convergence speed, communication overhead, and synchronization accuracy. Specifically, in random networks with 25 nodes, there is an approximately 50% reduction in single-round synchronization message length and a 66.67% decrease in total packet exchange volume compared to the virtual topology-based time synchronization protocol (VTSP). In the typical ring topology, where the convergence speed of the consensus algorithm is slow, HCTSP has a 59.33% increase in convergence speed compared to VTSP and a 75.29% increase compared to the gradient time synchronization protocol (GTSP). Full article

Diagnostic imaging is central to clinical decision-making across many care pathways, yet the expertise needed to use these images well is unevenly distributed across health systems, with workforce limitations identified as a major barrier to equitable access, particularly in low- and middle-income countries. Digital education has emerged as one response to this gap, offering scalability, asynchronous and just-in-time access, and the cost-efficiency required for global deployment. This paper examines the digital education portfolio of the International Atomic Energy Agency’s Nuclear Medicine and Diagnostic Imaging Section, hosted mainly on the open-access Human Health Campus, which in 2025 recorded approximately 45,800 active users and 150,000 views across 159 countries. The portfolio combines structured e-learning courses, interactive webinars, virtual conference access through the Livestream programme, and a broader repository of publications, teaching cases, and reference resources, supported by an internal e-learning framework and learning management system infrastructure. Partnerships with international scientific societies further extend the reach of expert knowledge and professional exchange. The paper argues that these initiatives are best understood not as content delivery alone but as a coordinated strategy to support diagnostic quality at the level of the practising physician, extending access to expertise and strengthening the conditions for better practice, while remaining a complement to, rather than a substitute for, supervised clinical training. Full article

This paper examines the transformation of traditional aircraft maintenance into an AI-driven, digitized process through the evolution of the Onboard Maintenance System (OMS). It conceptualizes the OMS as an “airborne e-hangar,” where embedded artificial intelligence functions operate as virtual engineering teams performing continuous monitoring, diagnostics, and predictive maintenance during flight. Using the literature review synthesis of aviation regulations, technical manuals, and industry practices, the study outlines how OMS integrates subsystems, such as condition monitoring, central maintenance, and electronic logbooks, to enable real-time data processing and fault isolation. Findings highlight that AI-enhanced OMS improves maintenance efficiency, reduces human error, and supports proactive decision-making by converting operational data into actionable insights. The system facilitates seamless data exchange between aircraft and ground operations, enhancing troubleshooting, maintenance planning, and airworthiness compliance. Furthermore, the continuous feedback loop among manufacturers, maintenance organizations, and regulatory authorities contributes to improved aircraft reliability and design optimization. The study underscores the role of AI in minimizing downtime, optimizing maintenance schedules, and enhancing flight safety while maintaining human oversight through advanced interfaces. The originality lies in framing OMS as a fully digitized, intelligent maintenance ecosystem that redefines aircraft maintenance practices and supports safer, more efficient aviation operations. Full article

Background: Glucosinolates, secondary metabolites present in Brassicales, and their breakdown products have demonstrated various biological effects, including anti-carcinogenic activities in some animal models. The active compounds include indole-3-carbinol (I3C) and N-methoxyindole-3-carbinol (NI3C). Building on these findings, several synthetic N-substituted I3Cs strongly inhibited the growth of human cancer cell lines. This effect was mediated by reactive intermediates formed by sulphotransferase (SULT) 1A1. Objective: We present genotoxicity findings on I3C and N-substituted derivatives, with special consideration given to SULTs. Methods: We review genotoxicity findings with I3C, NI3C and their parental glucosinolates. Then, we present some findings hitherto unpublished. Results: Neoglucobrassicin and its breakdown product NI3C demonstrated high genotoxic activity in vitro and formed high levels of DNA adducts in animal studies. These effects were strongly enhanced in the presence of SULT1A1. By contrast, glucobrassicin and I3C were weakly mutagenic. New observations include: enhanced activation of NI3C to a mutagen by human SULT1C4 compared to SULT1A1; SULT1A1-dependent genotoxicity of I3C (induction of sister chromatid exchange, SCE); cellular co-localisation of SULT1A1 and DNA adducts formed in the kidneys of NI3C-treated mice. Conclusions: I3C and NI3C are genotoxic in the presence of an appropriate SULT, but with large quantitative and qualitative differences (I3C required higher concentrations and induced only SCE, virtually no gene mutations). No information is available regarding the genotoxicity of other N-substituted I3C derivatives being developed as antineoplastic drugs. We suspect that they may greatly vary in this activity, which in turn might impact clinical effectiveness as well as adverse side-effects. Full article

As robots and humans start to share common spaces and perform collaborative tasks, it has become critical to facilitate information exchange between them for communicating and interpreting each other’s intentions. By overlaying virtual objects on a view of the physical world, mixed reality (MR) technology offers a compelling approach for designing innovative models of human–robot interaction (HRI). For robot manipulators, mobile MR frameworks that allow a user to communicate a goal position for the robot’s end effector have been widely studied. However, HRI applications that may require other relevant information for the manipulator to complete more complex tasks remain unexplored. Thus, we propose an MR-enhanced HRI framework, deployed on a touchscreen tablet, that utilizes a virtual arrow object to communicate force intent (i.e., location, direction, and magnitude) to the manipulator and provide visual force feedback to the user. To evaluate the system performance and user experience, we conducted a user study with 25 participants who used a manipulator robot to complete four insertion subtasks, reporting a task success score of 96%, a usability overall mean score of 4.35 out of 5, and a low task load index of 21.49 out of 100. The results show that the MR-HRI framework is intuitive to operate, allowing users to successfully perform assigned tasks by effectively communicating their intentions through the virtual arrow. Full article

Traditional sliding mode control (SMC) lacks an active mechanism for redistributing energy among state channels during transient convergence, resulting in a rigid trade-off between response speed, overshoot suppression, and energy efficiency. This paper proposes a virtual state coupled SMC method that introduces a dynamic virtual state with bilinear product coupling $x_1{x}_2$ into the sliding surface. Unlike conventional virtual states that serve as static linear combinations or observer-based estimates, the proposed virtual state evolves dynamically and establishes an active energy exchange channel between the real and virtual state dynamics. Linearization and Lyapunov-based analyses prove local asymptotic stability of the closed-loop system. The coupling strength $\gamma$ is shown to be decoupled from the linearized local eigenvalues and thus governs the energy–performance trade-off independently, while the condition $c>\gamma /4$ guarantees a non-vanishing domain of attraction. Simulations demonstrate that the proposed method achieves up to 53.2% control energy reduction under disturbance-free conditions compared with conventional SMC. Under persistent high-frequency disturbances, increasing $\gamma$ reduces oscillations by 54.2% at a controllable energy cost of 45.7%. Systematic parameter selection guidelines are provided, and Monte Carlo simulations (500 trials, $\pm 30\%$ parameter perturbations) confirm 100% convergence. The proposed method offers an independently adjustable energy–performance trade-off mechanism suitable for sensor-based motion systems with stringent transient and energy requirements. Full article

This paper introduces a topology optimization framework for steady incompressible Stokes flow based on the non-conforming Virtual Element Method, VEM. The proposed framework combines the geometric flexibility of VEM with an optimality criteria update scheme to minimize viscous and Darcy dissipation under a prescribed volume constraint. The method is applied to the Stokes-flow pipe bend benchmark with parabolic inlet velocity, no-slip wall, and prescribed outlet velocity boundary conditions. By allowing general polygonal elements, including concave and semi-structured polygonal meshes, the method alleviates mesh-related restrictions commonly encountered in conventional finite element discretizations. The methodology is demonstrated through Stokes-flow benchmark problems on different polygonal meshes. The numerical results show that the proposed VEM-based formulation can obtain stable and mesh-insensitive optimized flow channels for Stokes-flow topology optimization. This work offers a systematic approach to obtaining accurate, efficient, and mesh-independent optimal designs for complex fluid systems, providing a stable numerical tool for low-energy-consumption flow channel design in microfluidics, heat exchangers, and biomedical engineering. Extensions to Navier–Stokes and non-Newtonian flow models are left for future work. It should be clarified that the proposed method is only validated for steady Stokes flow and has not been validated for complex fluid models including unsteady Navier–Stokes and non-Newtonian flow models; extensions to these complex models are left for future work. Full article