Search Results (239)

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. Full article

The integration of Virtual Worlds (VW) and Intelligent Tutoring Systems (ITS) represents a transformative advancement in education, combining immersive, interactive learning with AI-driven personalization. This study explores the synergies between these technologies, analyzing their benefits, challenges, and applications in domains such as medical training, STEM education, and language learning. Findings highlight their shared characteristics of adaptability, real-time feedback, and collaborative learning. However, challenges such as computational demands, pedagogical complexity, and ethical concerns must be addressed. Future research should focus on hybrid models leveraging blockchain, IoT, and augmented reality to enhance adaptive and scalable learning experiences. Full article

This study introduces a novel Fuzzy Piecewise Fractional Derivative (FPFD) framework to enhance epidemiological modeling, specifically for the multi-phasic co-infection dynamics of Omicron and malaria. We address the limitations of traditional models by incorporating two key realities. First, we use fuzzy set theory to manage the inherent uncertainty in biological parameters. Second, we employ piecewise fractional operators to capture the dynamic, phase-dependent nature of epidemics. The framework utilizes a fuzzy classical derivative for initial memoryless spread and transitions to a fuzzy Atangana–Baleanu–Caputo (ABC) fractional derivative to capture post-intervention memory effects. We establish the mathematical rigor of the FPFD model through proofs of positivity, boundedness, and stability of equilibrium points, including the basic reproductive number (R0). A hybrid numerical scheme, combining Fuzzy Runge–Kutta and Fuzzy Fractional Adams–Bashforth–Moulton algorithms, is developed for solving the system. Simulations show that the framework successfully models dynamic shifts while propagating uncertainty. This provides forecasts that are more robust and practical, directly informing public health interventions. Full article

Conventional projection systems typically require a fixed spatial configuration relative to the projection surface, with strict control over distance and angle. In contrast, UAV-mounted projectors overcome these constraints, enabling dynamic, large-scale projections onto non-planar and complex environments. However, such flexible scenarios introduce a key challenge: severe geometric distortions caused by intricate surface geometry and continuous camera–projector motion. To address this, we propose a novel image registration method based on global dense matching, which estimates the real-time optical flow field between the input projection image and the target surface. The estimated flow is used to pre-warp the image, ensuring that the projected content appears geometrically consistent across arbitrary, deformable surfaces. The core idea of our method lies in reformulating the geometric distortion correction task as a global feature matching problem, effectively reducing 3D spatial deformation into a 2D dense correspondence learning process. To support learning and evaluation, we construct a hybrid dataset that covers a wide range of projection scenarios, including diverse lighting conditions, object geometries, and projection contents. Extensive simulation and real-world experiments show that our method achieves superior accuracy and robustness in correcting geometric distortions in dynamic UAV projection, significantly enhancing visual fidelity in complex environments. This approach provides a practical solution for real-time, high-quality projection in UAV-based augmented reality, outdoor display, and aerial information delivery systems. Full article

Occupational environments often expose workers to physical and psychological stressors that compromise well-being and productivity. While biophilic design has gained attention, there remains limited systematic integration of Nature-Based Solutions (NbS) within workplace management frameworks. This review aims to map the empirical impacts of NbSs on occupational health, productivity, and environmental quality, and to identify key barriers and facilitators for their integration into comprehensive Quality, Environmental, Health, and Safety (QEHS) management systems. A scoping literature review was conducted in accordance with the PRISMA-ScR (Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews) guidelines. A comprehensive search was performed in the Scopus and Web of Science databases for studies published between 2019 and 2024. A total of 2452 records were initially retrieved, with 39 studies retained for synthesis following screening, eligibility assessment, and critical appraisal using the Joanna Briggs Institute checklist. Findings indicate that NbSs can reduce stress, improve physical and cognitive health, and enhance workplace productivity. Reported benefits include reduced absenteeism, improved indoor air quality, and measurable financial returns. However, significant challenges persist, including high upfront costs, ongoing maintenance demands, a shortage of specialized labor, and methodological heterogeneity across studies. In particular, hybrid approaches combining physical natural elements and immersive technologies such as virtual reality emerged as promising alternatives for spatially constrained environments. Participatory co-design and stakeholder engagement were also identified as critical success factors for effective implementation. Integrating NbSs into QEHS frameworks has the potential to foster healthier, more resilient, and sustainable workplaces. Alignment with recognized certifications can further support systematic adoption and monitoring. Future research should prioritize longitudinal designs, standardized outcome metrics, and physiological markers, while addressing geographical gaps through studies in underrepresented regions. Embedding participatory processes and certification alignment can enhance stakeholder buy-in and practical scalability, advancing the integration of NbSs into holistic workplace management strategies. Full article

This article addresses the state of the art of methodologies for bridge inspection with potential for inclusion in Bridge Management Systems (BMS) and within the scope of the IABSE Task Group 5.9 on Remote Inspection of Bridges. The document covers computer vision approaches, including 3D geometric reconstitution (photogrammetry, LiDAR, and hybrid fusion strategies), damage and component identification (based on heuristics and Artificial Intelligence), and non-contact measurement of key structural parameters (displacements, strains, and modal parameters). Additionally, it addresses techniques for handling the large volumes of data generated by bridge inspections (Big Data), the use of Digital Twins for asset maintenance, and dedicated applications of Augmented Reality based on immersive environments for bridge inspection. These methodologies will contribute to safe, automated, and intelligent assessment and maintenance of bridges, enhancing resilience and lifespan of transportation infrastructure under changing climate. Full article

Simulated room impulse responses (RIRs) are important tools for studying architectural acoustics. Many methods exist to generate RIRs, each with unique properties that need to be considered when choosing an RIR synthesis technique. Despite the variation in synthesis techniques, there is a dearth of comparisons between these techniques. To address this, a comprehensive comparison of four major categories of RIR synthesis techniques was conducted: wave-based methods (hybrid FEM and modal analysis), geometrical acoustics methods (the image source method and ray tracing), delay-network reverberators (SDNs), and statistical methods (Sabine-NED). To compare these techniques, RIRs were recorded in a simple shoebox-shaped racquetball court, and we compared the synthesized RIRs against these recordings. We conducted both objective analyses, such as energy decay curves, normalized echo density, and frequency-dependent decay times, and a perceptual assessment of synthesized RIRs, which consisted of a listening assessment with 29 participants that utilized a MUSHRA comparison methodology. Our results reveal distinct advantages and limitations across synthesis categories. For example, the Sabine-NED technique was indistinguishable from the recorded IR, but it does not scale well with increasing geometric complexity. These findings provide valuable insights for selecting appropriate synthesis techniques for applications in architectural acoustics, immersive audio rendering, and virtual reality environments. Full article

The purpose of this narrative review is to critically appraise recent advances in sports injury rehabilitation—primarily focusing on biopsychosocial (BPS) approaches alongside emerging technological innovations—and identify current gaps and future directions. A literature search was conducted in PubMed, Scopus, and Web of Science for the years 2018–2024. Eligible records were English-language, human studies comprising systematic reviews, clinical trials, and translational investigations on wearable sensors, artificial intelligence (AI), virtual reality (VR), regenerative therapies (platelet-rich plasma [PRP], bone marrow aspirate concentrate [BMAC], stem cells, and prolotherapy), and BPS rehabilitation models; single-patient case reports, editorials, and non-scholarly sources were excluded. The synthesis yielded four themes: (1) BPS implementation remains underutilised owing to a lack of validated tools, variable provider readiness, and system-level barriers; (2) wearables and AI can enhance real-time monitoring and risk stratification but are limited by data heterogeneity, non-standardised pipelines, and sparse external validation; (3) VR/gamification improves engagement and task-specific practice, but evidence is dominated by pilot or laboratory studies with scarce longitudinal follow-up data; and (4) regenerative interventions show mechanistic promise, but conclusions are constrained by methodological variability and regulatory hurdles. Conclusions: BPS perspectives and emerging technologies have genuine potential to improve outcomes, but translation to practice hinges on (1) pragmatic or hybrid effectiveness–implementation trials, (2) standardisation of data and intervention protocols (including core outcome sets and effect-size reporting), and (3) integration of psychological and social assessment into routine pathways supported by provider training and interoperable digital capture. Full article

The emergence of mass personalized production has increased the adaptability and intelligence requirements of welding robots. To address the challenges associated with mass personalized production, this paper proposes a novel knowledge-driven framework for intelligent welding process planning in cloud robotics systems. This framework integrates cloud-edge-end collaborative computing with ontology-based knowledge representation to enable efficient welding process optimization. A hierarchical knowledge-based architecture was developed using the SQLite 3.38.0, Redis 5.0.4, and HBase 2.1.0 tools. The ontology models formally define the welding tasks, resources, processes, and results, thereby enabling semantic interoperability across heterogeneous systems. A hybrid knowledge evolution method that combines cloud-based welding simulation and transfer learning is presented as a means of achieving inexpensive, efficient, and intelligent evolution of welding process knowledge. Experiments demonstrated that, with respect to pure cloud-based solutions, edge-based knowledge bases can reduce the average response time by 86%. The WeldNet-152 model achieved a welding parameter prediction accuracy of 95.1%, while the knowledge evolution method exhibited a simulation-to-reality transfer accuracy of 78%. The proposed method serves as a foundation for significant enhancements in the adaptability of welding robots to Industry 5.0 manufacturing environments. Full article

Immersive technologies are increasingly acknowledged as powerful tools in sustainability education, capable of fostering deeper engagement and emotional resonance. This study investigates the potential of 360° VR storytelling to enhance learning through embodied knowledge, attitudinal change, and emotional awareness. Conducted within the EMOTIONAL project, the research explores a first-person narrative told from the perspective of a ceramic object rooted in Italian cultural heritage, designed to facilitate meaningful, affective learning. The present study addresses the following research questions: RQ1 Can 360° VR story-living narrations effectively promote embodied learning and semantic and attitudinal shifts in the context of sustainability education? RQ2 What added insights can be gained from integrating subjective assessments with physiological measures? To this end, a hybrid assessment framework was developed and validated, combining subjective self-report tools (including attitudinal scales, semantic differential analysis, and engagement metrics) with objective physiological measures, specifically Electrodermal Activity (EDA). Sixty participants, including students and entrepreneurs, experienced the immersive narrative, and a subset underwent physiological tracking to evaluate the effectiveness of the experience. The findings show that immersive storytelling can enhance emotional and cognitive engagement, producing shifts in semantic interpretation, self-perceived knowledge, and attitudes toward material culture. A convergence of high emotional engagement, embodied learning, and technology acceptance was observed, although individual differences emerged based on prior experience and disciplinary background. EDA data offered complementary insights, identifying specific moments of heightened arousal during the narrative. The study demonstrates that emotionally driven immersive narratives (supported by integrated assessment methods) can make abstract sustainability values more tangible and personally resonant, thereby fostering more reflective and relational approaches to sustainable consumption and production. Full article

Background/Objectives: Mental health and emotional well-being are critical yet often overlooked in older adults. Immersive virtual reality (VR) may offer novel strategies for promoting relaxation in this population, but the diversity of intervention designs and underlying mechanisms remains insufficiently mapped. This scoping review aimed to systematically examine head-mounted display (HMD) VR interventions for relaxation in older adults, focusing on core scenarios, mechanisms, and conceptual paradigms. Methods: Following the JBI Manual for Evidence Synthesis and PRISMA-ScR guidelines, comprehensive searches of MEDLINE, Cochrane Library, Web of Science, and Scopus were conducted for empirical studies of HMD-based VR relaxation interventions in adults aged 60 and older. Data on study characteristics, hardware, intervention scenarios, and reported physiological and psychological mechanisms were extracted and categorized into conceptual paradigms. Results: Twenty-four studies were included, covering diverse clinical and community samples. Four main VR relaxation paradigms emerged: environmental relaxation, guided meditation, psychotherapy integration, and interactive gamified relaxation. Reported mechanisms included parasympathetic activation, multisensory immersion, attentional distraction, positive affect induction, mindfulness, cognitive engagement, and reminiscence activation. Considerable heterogeneity was found in intervention design and outcomes, with mechanisms often inferred rather than directly described. Conclusions: Similar relaxation mechanisms were engaged across paradigms, including hybrid interventions combining multiple approaches, highlighting the adaptability of VR to meet diverse needs. Identifying these four paradigms provides a foundation for future research and development of targeted VR-based relaxation for older adults. Further studies should clarify mechanisms, use standardized physiological outcome measures, and assess long-term benefits of VR relaxation interventions in geriatric populations. Full article

The prioritization of user requirements is neglected in most existing interface designs for driverless vehicle systems, which may incur safety risks, fragmented user experiences, development resource wastage, and weakened market competitiveness. Accordingly, this paper proposes a hybrid interface design method for a virtual reality (VR) driverless vehicle system by combining a A-KANO model and system usability scale (SUS). Firstly, we obtain key words, and a total of 23 demand points are collected through word frequency analysis via combining with user interview and observation method; secondly, 21 demand points are derived from A-KANO model analysis and prioritized for function development; and finally, design practice is carried out according to the ranking results, and virtual reality technology is used to build a VR unmanned vehicle system in order to simulate the interface interaction of a driverless vehicle system. Then, the VR driverless vehicle system is used as a test experimental environment for user evaluation, and combined with the SUS scale to evaluate the user-prioritized experience requirements for practical verification. Empirical results demonstrate that this method effectively categorizes multifaceted user needs, providing actionable solutions to enhance passenger experience and optimize service system design in future autonomous driving scenarios. Full article

Due to the existence of domain shift, the synthetic aperture radar (SAR) automatic target recognition (ATR) model trained on simulated data will have significant performance degradation when applied to real-world measured data. To bridge the domain gap, this paper proposes a single-domain generalization (SDG) method based on multilevel data augmentation (MLDA), enabling SAR-ATR models that have been fully trained on simulated data to be generalized to unseen real SAR data. The proposed method aims to enhance the model’s generalizable capability through three key components: (1) pixel-level augmentation, which enriches data distribution via random Gaussian noise injection in the spatial domain and high-frequency perturbation in the frequency domain to enhance pixel-level diversity; (2) feature-level style augmentation, which probabilistically mixes instance-wise feature statistics, generating hybrid-styled feature maps to enhance feature-level diversity; (3) domain-adversarial training, which constructs an adversarial framework between a feature extractor and discriminator to enforce the learning of domain-invariant representations. Experiments on two simulation-to-reality SAR datasets demonstrate that the proposed method outperforms existing baselines and other SDG algorithms, achieving state-of-the-art (SOTA) performance on both datasets (96.76% accuracy on the public SAMPLE dataset and 93.70% accuracy on the self-built S2M-5 dataset). Full article

Mobile augmented reality (AR) applications require high-performance, energy-efficient deep learning solutions to deliver immersive experiences on resource-constrained devices. We propose SAHA-WS, a Sparsity-Aware Hybrid Architecture with Weight-Stationary Dataflow, combining Convolutional Neural Networks (CNNs) and Graph Convolutional Networks (GCNs) to efficiently process grid-like (e.g., images) and graph-structured (e.g., human skeletons) data. SAHA-WS leverages channel-wise sparsity in CNNs and adjacency matrix sparsity in GCNs, paired with weight-stationary dataflow, to minimize computations and memory access. Evaluations on ImageNet, COCO, and NTU RGB+D datasets demonstrate SAHA-WS achieves 87.5% top-1 accuracy, 75.8% mAP, and 92.5% action recognition accuracy at 0% sparsity, with 40 ms latency and 42 mJ energy consumption at 60% sparsity, outperforming a baseline by 1020% in efficiency. Ablation studies confirm the contributions of sparsity and dataflow optimizations. SAHA-WS enables complex AR applications to run smoothly on mobile devices, enhancing immersive and engaging experiences. Full article

Addressing key challenges in unstructured environments, including local optimum traps, limited real-time interaction, and convergence difficulties, this research pioneers a hybrid reinforcement learning approach that combines simulated annealing (SA) with proximal policy optimization (PPO) for robotic arm trajectory planning. The framework enables the accurate, collision-free grasping of randomly appearing objects in dynamic obstacles through three key innovations: a probabilistically enhanced simulation environment with a 20% obstacle generation rate; an optimized state-action space featuring 12-dimensional environment coding and 6-DoF joint control; and an SA-PPO algorithm that dynamically adjusts the learning rate to balance exploration and convergence. Experimental results show a 6.52% increase in success rate (98% vs. 92%) and a 7.14% reduction in steps per set compared to the baseline PPO. A real deployment on the AUBO-i5 robotic arm enables real machine grasping, validating a robust transfer from simulation to reality. This work establishes a new paradigm for adaptive robot manipulation in industrial scenarios requiring a real-time response to environmental uncertainty. Full article