Engineering education

This study presents a multi-user virtual reality (VR) tool designed to enhance hands-on learning in engineering education through real-time sensorized digital twins. The motivation stems from the limitations of traditional laboratory settings, such as time constraints and restricted access to physical equipment, which can hinder practical learning. The developed environment allows multiple students, wearing VR headsets, to interact simultaneously with a real-time synchronized virtual model of an engine, replicating its physical counterpart at the Mechanical Engineering Laboratory of the Pontificia Universidad Católica de Valparaíso, Chile. This novel integration of VR and digital twin technology offers students a unique opportunity to observe engine behavior in operation within a safe, controlled virtual space. By bridging theoretical knowledge with practical experience, this approach deepens understanding of complex mechanical concepts while fostering the development of key technical skills. Additionally, the use of real-time data visualization and digital twins provides a safer, more interactive, and efficient alternative to traditional laboratory practices, overcoming constraints like time limitations and equipment availability. This innovative method introduces students to Industry 4.0 principles, encouraging data-driven analysis and informed decision making.

Keywords: combustion-less engine , digital twins , education , engineering education , Industry 4.0 , IoT , real-time sensing , virtual reality

Technological advancements are profoundly reshaping industrial environments, particularly by introducing paradigms such as Industry 4.0 and Industry 5.0, prioritizing increased productivity alongside worker well-being improvements. Concurrently, the European Union reports a fast-aging population, reflected in the industrial workforce. Consequently, assessing the impact of emerging technologies across different age groups is crucial. This study employed an Immersive Virtual Reality (IVR) system to simulate a high-risk industrial scenario for training workers on safety and operational procedures. Fifty participants were divided into three age groups: young ranging from 19 to 33 years old, middle-aged ranging from 34 to 48 years old, and older ranging from 49 to 63 years old. The IVR system recorded execution times and errors, while subjective measures were collected using the User Experience Questionnaire, System Usability Scale, and Virtual Reality Sickness Questionnaire (VRSQ). The results show that younger participants committed significantly more errors but completed tasks faster, although this difference was not statistically significant. Self-assessment scores revealed no significant increase in VRSQ values across groups, while younger participants rated the system's hedonic quality significantly higher than the older group. System usability was rated substantially higher than the minimum acceptable threshold, with younger participants providing significantly better evaluations compared to older ones. This study highlights the importance of a human-centered design in IVR systems, complying with age-related needs to enhance usability. This approach ensures IVR technology in industrial training achieves optimal efficiency and effectiveness across diverse workforce demographics.

Keywords: Age-related performance , Immersive virtual reality , Industrial safety training , User experience metrics , Workplace accident prevention

The advent of virtual simulation and Augmented Reality has had a profound impact on industrial processes, enhancing operational efficiency and optimising production workflows. This paper presents a comprehensive study on the development and implementation of a Mixed Reality Lab designed for the assembly and disassembly of industrial products. The primary objective is to create an advanced and intuitive Virtual Reality (VR) environment that facilitates industrial operators' learning and reduces training time while minimising errors. The project encompasses the scanning and 3D modelling of industrial components using various technologies, including photogrammetry, structured light, laser scanning, and LiDAR. These techniques are evaluated for their precision and effectiveness in generating accurate 3D models. The VR environment is developed using Unity, incorporating features that support interactive and immersive user experiences. Detailed methodologies for the creation and refinement of point clouds, mesh generation, and virtual environment setup are provided. The assembly and disassembly processes are tested using a brake assembly as a case study, thereby demonstrating the system's ability to manage complex industrial tasks. The results indicate significant improvements in training efficiency and operational precision, with potential applications across various industrial sectors.

Keywords: Mixed reality , Simulation , Training , User-centred application

Industry 5.0 requires practical methods to translate Augmented Reality (AR) concepts into effective shop floor applications, demonstrating their value to operators. This study introduces a framework for implementing and validating Augmented Reality (AR)-based tools designed to enhance sustainability awareness and assist operators in energy management within industrial settings. The approach combines a reference software architecture for rapid AR deployment with a three-part user-experience assessment, measuring usability (System Usability Scale - SUS), technology acceptance (Technology Acceptance Model - TAM), and cognitive workload (NASA-TLX). To test this framework, an AR-based prototype tool was deployed on enterprise smartphones and evaluated in three scenarios: monitoring service-utility energy consumption, monitoring production equipment, and conducting on- and off-the-job training of the operators. Thirty shop floor professionals completed tasks and provided UX feedback. The results showed good usability (mean SUS 78.4/100), with perceived ease of use and contextual relevance driving technology acceptance. Moreover Nasa TLX analysis indicates mental demand as the predominant factor.

Keywords: Augmented Reality , Employee training , Energy monitoring , Human-centric design , Industry 5.0 , Sustainability , User experience

Background: Telemedicine is increasingly integrated into healthcare, requiring robust training for healthcare professionals. Traditional training methods often lack realism and immersion, highlighting the need for innovative approaches. Objectives: This study aims to integrate Virtual Reality with Moodle to enhance telemedicine education, focusing on chronic cardiac conditions. Methods: A VR-based training system was developed using Unity and WebXR, integrated with Moodle. The system includes a serious game with virtual patients created using Synthesia AI. Telemedicine experts configure training sessions via Moodle, while learners access preparatory materials and engage with the VR environment. Results: A usability test involving 12 participants showed high satisfaction scores (94.42% for learners, 98.29% for experts). Conclusion: This study demonstrates the potential of integrating VR with Moodle to innovate telemedicine education. The approach offers personalized and accessible training experiences for healthcare professionals to improve telemedicine skills.

Keywords: Educational Virtual Reality , Learning Health System , Medical Field Training , Telehealth

Extended reality (XR) technologies are no longer peripheral innovations but emerging cornerstones of human–technology interaction across critical sectors. This article takes the position that engineering and healthcare represent the most mature and strategically relevant domains for XR adoption, given their safety-critical nature, intensive training requirements, and strong alignment with the human-centric visions of Industry 5.0 and Healthcare 5.0. We synthesize evidence from product design, manufacturing, training, and patient care to demonstrate how XR is reshaping workflows, skills, and therapeutic practices. Beyond surveying applications, we argue that the future of XR depends on its integration with artificial intelligence, digital twins, and multisensory feedback, converging into systems capable of perceiving, reasoning, and adapting to complex physical and human environments. We contend that widespread adoption will remain limited without open standards, validated protocols, and robust evaluation frameworks addressing safety, interoperability, and data governance. By framing XR as both a technological enabler and a societal imperative, this position article calls for coordinated action among researchers, practitioners, and policymakers to realize XR's role in building sustainable, personalized, and participatory innovation ecosystems.

Keywords: digital industry , extended reality , human-centered innovation , human–computer interfaces/interactions , immersive technology , virtual and augmented reality environments , virtual prototyping , XR for training , XR in healthcare , XR in manufacturing

Background: Differentiating Parkinson's disease (PD) from essential tremor (ET) is often challenging, especially in early or atypical cases. Dopamine transporter (DAT) single-photon emission computed tomography (SPECT) with 123I-Ioflupane supports diagnosis, and semi-quantitative tools such as DaTQUANT® and BasGanV2™ provide objective measures. This study compared their diagnostic performance when integrated with supervised machine learning. Methods: We retrospectively analysed 123I-Ioflupane SPECT scans from 169 patients (133 PD, 36 ET). Semi-quantitative analysis was performed using DaTQUANT® v2.0 and BasGanV2™ v.2. Classification tree (ClT), k-nearest neighbour (k-NN), and support vector machine (SVM) models were trained and validated with stratified shuffle split (250 iterations). Diagnostic accuracy was compared between the two software packages. Results: All classifiers reliably distinguished PD from ET. DaTQUANT® consistently achieved higher accuracy than BasGanV2™: 93.8%, 93.2%, and 94.5% for ClT, k-NN, and SVM, respectively, versus 90.9%, 91.7%, and 91.9% for BasGanV2™ (p < 0.001). Sensitivity and specificity were also consistently higher for DaTQUANT® than BasGanV2. Class imbalance (PD > ET) was addressed using Synthetic Minority Over-sampling Technique (SMOTE). Conclusions: Machine learning analysis of 123I-Ioflupane SPECT enhances differentiation between PD and ET. DaTQUANT® outperformed BasGanV2™, suggesting greater suitability for AI-driven decision support. These findings support the integration of semi-quantitative and AI-based approaches into clinical workflows and highlight the need for harmonised methodologies in movement disorder imaging.

Keywords: 123I-Ioflupane , artificial intelligence , BasGanV2™ , DaTQUANT® , dopamine transporters , machine learning , movement disorders , Parkinson's disease , rehabilitation , SPECT

Industrial Robots (IRs) are typically employed as flexible machines to perform many types of repetitive and intensive tasks within fenced safe areas, ensuring high productivity and cost efficiency. However, their rigid programming approaches often pose challenges during cell commissioning and reset, hindering the implementation of self-reconfigurable systems. In addition, several production lines still need the presence of skilled operators to conduct assisted assembly operations and inspections. This motivates the growing interest in the development of innovative solutions for supporting safe and efficient human–robot collaborative applications. The manual guidance of the IR end-effector is a representative functionality of such collaboration, as it simplifies heavy-part manipulation and allows intuitive robot teaching and programming. The present study reports a sensor-based approach for enabling manual guidance operations with high-payload IRs and discusses its practical implementation on a production cell with an extended workspace. The setup features a KUKA robot mounted on a custom linear track actuated via Beckhoff technology to enable flexible assembly and machining operations. The developed logic and its software configuration, split into multiple control units to allow the manual guiding of both the 6-axis IR and the linear track unit, are described in detail. Finally, an experimental demonstration involving two users with different levels of expertise was conducted to evaluate the approach during target teaching on a physical cell. The results showed that the proposed manual guidance method significantly reduced task completion time by more than 55% compared with the conventional teach pendant, demonstrating the effectiveness and practical advantages of the developed framework.

Keywords: collaborative manufacturing , human–robot interaction , industrial robot , linear track , manual guiding

The future of space exploration relies not only on technological advancements but also on public awareness and policy support. Sustaining long-term investments in lunar and planetary missions requires effectively engaging both the next generation and key decision-makers. However, traditional science communication methods often fail to reach audiences beyond academic and professional circles, limiting public engagement. Virtual Reality and Serious Games offer interactive and immersive experiences that make complex scientific concepts more accessible to non-experts, fostering broader support for space exploration initiatives. This study evaluates the effectiveness of a Virtual Reality Serious Game designed to simulate the Lunar Zebro rover navigating the Moon's surface. The game aims to enhance engagement, simplify scientific concepts, and shape public perception of space missions. Through iterative development and user testing, the study examines the balance between education and entertainment. Preliminary findings indicate that while the game successfully engages users and communicates mission objectives, it is primarily perceived as an interactive experience rather than a standalone educational tool. These insights highlight the potential of VR Serious Games in science communication and their role in promoting future space exploration initiatives.

Keywords: Serious Game , User Experience , Virtual Reality

The automotive development of Electric Vehicles (EVs) is accelerating due to environmental concerns and technological advances. The complexity and performance demand of EVs require the use of Silicon-Carbide (SiC) technologies for the related efficiency, high-temperature resistance, and robust dynamic switching. To ensure the high performance of electric vehicles, the rigorous screening of the delivered SiC devices plays a pivotal role. Defects in SiC production can cause failures that directly affect the performance of electric vehicle engines, particularly in the traction inverter subsystem. To identify these defects early, advanced visual screening through optical microscopy and X-ray approaches has been proposed by leading car makers. These screening methodologies require advanced technical expertise and are still prone to significant errors, even when performed manually by experienced operators. To address these inefficiencies, the authors propose an ensemble deep learning pipeline for performing a robust automated visual inspection of SiC devices. By combining Multi-Head Attention blocks with adaptive input data distortion compensation through Jacobian regularization within convolutional architectures, the proposed model leverages global context and local feature maps, enhancing accuracy and robustness in multimodal defects detection. The proposed combined approach, tested on ACEPACK/TPACK DRIVE SiC power modules provided by STMicroelectronics, achieved an average accuracy of approximately 93% in both methodologies.

Keywords: Deep Learning , Defects analysis , Silicon-Carbide

Accurate monitoring of the gate–oxide health of Silicon-Carbide (SiC) MOSFET devices is pivotal for condition-based maintenance of electrified vehicles, high-frequency converters and consumer electronic applications. A MOSFET parameter closely related to the device oxide interface is the threshold voltage. The SiC MOSFETs threshold voltage (Vth) drifts with interface oxide–trap accumulation and it is therefore a direct early-marker of the device degradation. The Vth drift monitoring is then significantly relevant in the mentioned application fields. This work proposes a non-intrusive, single-shot intelligent pipeline that infers Vth solely from an input data-snapshot of the steady-state MOSFET static behavior based on current-to-voltage I – V family curves sampled during routine gate driving. We introduce HyperDeep, a novel Hyperbolic Möbius Deep architecture that embeds each I – V input snapshot in the hyperbolic Poincaré ball, processes it through ad-hoc designed residual Möbius layers followed by hyperbolic self-attention blocks. An adaptive exponential Lipschitz regularization block compensates process-spread-induced statistical drift in SiC devices, preserving mapping stability across SiC MOSFET's wide threshold-voltage window. The proposed HyperDeep system was trained on large dataset that includes both synthetic and real data obtained through Dynamic Gate Stress (DGS) methodology applied to SiC MOSFET GEN3 devices delivered by STMicroelectronics. The proposed deep pipeline attains in average of 0.015 MSE (Mean Squared Error) in retrieving actual Vth, outperforming compared state-of-the-art architectures.

Keywords: Deep learning , gate driving , hyperbolic geometry , MOSFET , residual lifetime

Silicon Carbide (SiC) power devices are highly sought in modern power electronics for their superior performance; however, yield is severely impacted by crystal-growth defects often termed killer defects. Fast and accurate post-growth inspection of SiC wafers is therefore critical to identify such defects and improve manufacturing outcomes. We propose an improved YOLO-based one-stage detector that integrates a Non-Local Block + Gradient Reversal Layer (GRL) module into the YOLO backbone between the Spatial Pyramid Pooling (SPP) and BottleneckCSP layers. The Non-Local blocks enable the CNN to leverage global context in the microscopic images, improving its ability to recognize subtle defect patterns across the wafer. Meanwhile, the GRL-based domain adaptation allows the model to learn domain-invariant features, addressing distribution shifts between different datasets or imaging conditions. The proposed architecture, comprising these components, is evaluated on a dataset of SiC wafer optical micrographs containing various defect types to demonstrate its effectiveness (e.g., dislocations and stacking faults). The proposed method achieves mAP@0.5=95% and mAP@0.5:0.95=94.5%. These improvements demonstrate the efficacy of combining attention and adversarial domain adaptation in the YOLO architecture. The enhanced model detects SiC defects with higher accuracy and robustness to varying imaging conditions, marking a significant step forward in automated wafer defect inspection.

Keywords: Deep Learning , Defects analysis , Non-local block , Silicon-Carbide

Deploying deep neural networks on embedded systems is challenging due to stringent constraints on memory, compute, and power. DISTILLO (aDvanced Interactive ST knowledge distILLation) is a knowledge distillation framework tailored for classification tasks in resource-limited environments. It features two strategies: DISTILLO-Classical, using dynamic loss weighting with Kullback-Leibler or Jensen-Shannon divergence, and DISTILLO-Ensemble, which combines multiple student models distilled at different temperatures. Jensen-Shannon divergence is adopted to provide symmetric, bounded, and stable gradients, improving the robustness of the training. Experiments using Vision Transformers as teachers and lightweight CNNs as students show that DISTILLO-Classical achieves up to +5.37% accuracy gain on CIFAR-100 and +4.42% on Imagenette. DISTILLO-Ensemble further improves performance, reaching +13.97% and +8.32% gains, respectively, with minimal memory and latency overhead. DISTILLO enables the effective deployment of deep models on embedded platforms such as STM32 microcontrollers.

Keywords: embedded systems , knowledge distillation , low-resources

Background: This study investigated the biomechanical and physiological demands of the hiking position in Laser sailing, a posture requiring sailors to extend their upper bodies outside the boat to counter wind-induced heeling. This study utilized a mixed-methods approach. Methods: Twenty-two experienced Laser sailors participated in both on-land and offshore assessments. The study combined subjective discomfort ratings, biomechanical measurements, digital human modeling, and muscle activation analysis to evaluate the effects of hiking during and after exertion. Results: A two-way ANOVA showed significant effects by body region and time. The quadriceps, abdominals, and lower back reported the highest discomfort. Key postural angles were identified, including knee and hip flexion, trunk inclination, and ankle dorsiflexion. Muscle activation analysis revealed the highest engagement in the rectus abdominis (46.1% MVC), brachialis (~45%), and psoas major (~41%), with notable bilateral asymmetries. The trunk region had the highest overall activation (28.7% MVC), followed by the upper limbs (~18.7%), while the lower limbs were minimally engaged during static hiking. Conclusions: On-water conditions resulted in greater variability in joint angles, likely reflecting wind fluctuations and wave-induced boat motion. Findings highlight the quadriceps, abdominals, and lower back as primary contributors to sustained hiking, while also emphasizing the importance of targeted endurance training and ergonomic equipment design. These insights can guide training, recovery, and ergonomic strategies to optimize performance and reduce injury risk in Laser sailors.

Keywords: biomechanics , digital human modeling , hiking , laser sailing , muscle fatigue , physical strain , postural discomfort

Spinal surgery demands exceptional theoretical knowledge and practical skills, with pedicle screw procedures posing significant risks due to proximity to critical anatomical structures. This study validates a virtual reality (VR) simulation platform for training in pedicle screw arthrodesis using patient-specific vertebral drilling templates. The practical simulation of a specific case study was evaluated with both neurosurgical residents and medical students. Results demonstrate that while experienced residents completed simulated procedures significantly faster than students (p < 0.01), these latter ones showed marked improvement across consecutive training sessions (p < 0.001). The most substantial performance gains occurred between the first and second trials, highlighting the rapid learning curve facilitated by the VR environment. System Usability Scale assessments revealed high satisfaction with the simulation platform, with participants emphasizing the value of risk-free repetitive practice. Qualitative feedback from experienced participants confirmed the precision and realism of the training environment as critical factors contributing to their efficiency. This validation confirms that VR simulation platforms offer an effective training environment for complex spinal procedures, providing a safe space for skill development before clinical application, particularly when incorporating innovative surgical aids such as patient-specific drilling templates.

Keywords: Pedicle screw insertion , Surgical template , Training , Vertebra , Virtual reality

This paper presents the design, development, and preliminary validation of a haptic virtual reality simulator for Shielded Metal Arc Welding (SMAW) at the Pontificia Universidad Católica de Valparaíso, Chile, aimed at enhancing psychomotor training for mechanical engineering students in line with Industry 4.0 demands. The system integrates Unity 3D, a commercial haptic device, and a custom 3D-printed electrode holder replicating the welding booth, enabling interaction through visual, auditory, and tactile feedback. Thirty students with minimal welding experience and seven experts participated in usability and realism assessments. The results showed that 80% of students perceived motor skill improvement, 60% rated realism as adequate, and 90% preferred hybrid training (simulator + workshop). The prototype was practically implemented at the mechanical engineering school, requiring only a mid-range workstation, the Touch haptic device, and the developed software, demonstrating feasibility in real academic settings. The findings indicate potential to build confidence, support motor coordination, and provide a safe, resource-efficient training environment, while experts emphasized the need for automated feedback and improved haptic fidelity. The modular architecture allows scalability, extension to other welding processes, and adaptation for inclusive education. This prototype demonstrates how locally developed immersive technologies can modernize technical education while promoting sustainability, accessibility, and skill readiness.

Keywords: engineering education , haptic feedback , Industry 4.0 , SMAW , virtual reality , welding simulator

Virtual Reality (VR) is currently one of the most influential technological trends and is projected to grow significantly in the coming years. A defining feature of VR is its ability to fully immerse users in a digital environment, eliminating real-world distractions and enabling focused engagement with virtual processes. This study presents the design and implementation of a VR-based training application for the Charly4U CNC milling machine. Developed using the Unity3D engine, the tool offers an interactive, step-by-step simulation aimed at enhancing technical education and improving accessibility for users with limited machining experience. The application was tested with two user groups: engineering students and adolescents with cognitive disabilities from the ASPOC Onlus Lab. Usability was evaluated through the System Usability Scale (SUS), with results showing high user satisfaction and strong usability scores across both groups (SUS scores of 81 and 87, respectively). The findings support the effectiveness of immersive VR environments for skill development and inclusive education, demonstrating the potential of this approach to complement traditional CNC training methods in a safe and engaging way.

Keywords: Education , People with disabilities , Social equity , Special Education , Virtual Reality

Extended Reality (XR) and the Metaverse play a transformative role in the future of work, making it essential to introduce them within educational contexts. However, widespread adoption is often hindered by teachers' reliance on traditional methodologies and limited exposure to emerging digital tools. To address this challenge, a theoretical literacy course was delivered at a local high school, involving 92 teachers, to foster a foundational understanding and confidence in the use of XR and the Metaverse for teaching purposes. Data collected through demographic surveys and self-assessment items revealed a statistically significant increase in familiarity and trust toward these technologies following the course. While no significant changes were noted in technological competence or general interest, positive trends were observed in reduced insecurity and a more positive perception toward their adoption. Teacher feedback emphasized the need for practical sessions to complement theoretical training. These results underscore the importance of ongoing teacher development not only to improve digital skills but also to ensure that students are meaningfully exposed to the technologies that will shape their future professional environments. The approach proposed in this study offers a scalable model for educational institutions seeking to integrate XR and Metaverse technologies as complementary enhancements to traditional teaching methods.

Keywords: Digital transformation , Education , Extended Reality , Improving classroom teaching , Metaverse

In response to the global concern over road traffic accidents, particularly among young drivers, there is increasing interest in innovative educational interventions to promote safer driving behaviors. Traditional media campaigns often fail to engage audiences meaningfully. This study, part of a broader research initiative, investigates the effects of Cinematic Virtual Reality (CVR) on participants' physiological responses, specifically focusing on heart rate variability, to assess the effectiveness of fear-based and positive reinforcement scenarios influencing road safety behaviors. A custom-developed CVR application delivered immersive 360° driving scenarios, one based on fear and the other on positive reinforcement, via a Meta Quest 2 headset. A total of 95 participants, aged 18 to 24 and all holding valid driver's licenses, were randomly assigned to one of the two experimental conditions. The study assessed the impact of these approaches on participants' physiological responses by recording electrocardiogram (ECG) data using the BioSignalPlux system. HRV parameters, indicators of stress and arousal, were analyzed to distinguish the physiological effects of the two scenarios. Time-domain, frequency-domain, and nonlinear HRV features were extracted and used for classification. Machine learning algorithms were employed to evaluate the system's performance in differentiating between the two experimental groups. The classification accuracy, sensitivity, and specificity were assessed using 10-fold cross-validation. Results showed that Linear Discriminant Analysis (LDA) achieved the highest classification accuracy.

Keywords: Cinematic Virtual Reality , Machine Learning Approach , Physiological Parameters

This paper presents the educational implementation of a Model-based Systems Engineering (MBSE) approach within the course Modeling and Simulation of Mechatronic Systems (MSMS) at the University of Naples Federico II. The course combines lectures and laboratory sessions based on a problem-based learning setting, guiding students through the complete RFLP (Requirements-Functional-Logical-Physical) framework aligned with the V-model lifecycle. Students develop both virtual and physical prototypes of mechatronic systems. Over five academic years (2020-2024), feedback from 35 students was collected through a survey based on the Technology Acceptance Model (TAM). Results show that conceptual modeling activities are perceived as easier, while implementation phases are more demanding yet highly valuable. The findings highlight MBSE's effectiveness in fostering system-level thinking but also point to the need for enhanced tool integration, automation, and early-stage modeling support.

Keywords: Educational Engineering , Mechatronic Systems Design , Model-based Systems Engineering , project-based learning , Technology Acceptance Model