Search Results (535)

Extended reality (XR), encompassing augmented reality (AR), virtual reality (VR), and mixed reality (MR), is a key enabling technology for virtual worlds, and XR-related patents continue to grow rapidly. However, patent-based XR technology analysis faces a fundamental challenge: document–keyword matrix (DKM) built from patent titles and abstracts are typically high dimensional, sparse, and often exhibit excess zeros, which can distort inference when conventional text mining pipelines are applied without a generative count perspective. In this study, we propose a statistically grounded XR technology analysis framework that combines likelihood-based count modeling with interpretable structure mining to map XR sub-technologies from a patent DKM. Using an XR patent–keyword matrix, we fit Poisson regression (PR), negative binomial regression (NBR), and zero-inflated negative binomial regression (ZINBR) models via maximum likelihood estimation (MLE), controlling for document-length effects. Model selection by Akaike information criterion (AIC) consistently favored NBR for both target keywords, indicating substantial overdispersion in XR patent counts. We interpret exponentiated coefficients as incidence rate ratios (IRRs) and construct a technology relatedness network from significant IRR edges, revealing a dual-axis XR structure: reality is anchored in an AR or VR experience and content axis such as virtual and augment, whereas extend is embedded in a structure and integration axis for example, surface, edge, layer, and connectivity-related terms. To show how the proposed method can be applied to real domains, we searched the XR patent documents, and analyzed them for XR technology analysis. Full article

Background: Μetabolic syndrome (MetS)—comprises central adiposity, elevated blood pressure, dyslipidaemia, and dysglycaemia, increasing the risk of type 2 diabetes and cardiovascular disease. Exercise training improves cardiorespiratory fitness and several MetS components, but real-world effectiveness is limited by poor adherence, restricted supervision, and insufficient personalisation. Objective: This scoping review mapped the clinical intervention evidence on technology-enhanced exercise and structured physical activity relevant to MetS, while distinguishing direct MetS evidence from translational evidence. Methods: In accordance with PRISMA-ScR, we searched PubMed and extended the search to Scopus and Web of Science; a supplementary IEEE Xplore search and a post hoc Embase check were also conducted. Eligible studies were interventions using web-based delivery, wearables, telemonitoring/mobile health (mHealth), artificial intelligence (AI) coaching, virtual reality (VR)/exergaming, or continuous glucose monitoring (CGM) alongside exercise training or structured physical activity. Results: Nineteen studies met the eligibility criteria. The evidence base was weighted toward wearable/app-based feedback and telemonitoring/mHealth/web-based approaches, with fewer studies on VR/exergaming, CGM-enabled exercise, and AI coaching. Most studies were randomised or cluster-randomised, but interventions were usually short term. Across categories, technology most consistently supported adherence, self-monitoring, accountability, remote supervision, and, in selected cases, physiology-informed personalisation. Direct MetS evidence was strongest for wearables with structured feedback, telemonitoring, mHealth, and web-based delivery, whereas AI coaching and CGM were supported by adjacent translational evidence. Conclusions: Technology-enhanced exercise and structured physical activity show promising but heterogeneous and still preliminary potential for MetS management. Key limitations include short follow-up, uneven representation across categories, inconsistent reporting of exercise dose/intensity fidelity and adverse events, and limited equity and implementation outcomes. Full article

Background: Technology such as telerehabilitation, virtual reality, robotics, and wearable systems are reshaping pediatric physiotherapy. While evidence remains fragmented, there is little guidance on how these approaches can be integrated into coherent, family-centered care pathways. Objective: To develop the Digital Pediatric Physiotherapy Framework (DPPF) based on a systematic review of randomized evidence on digital interventions in pediatric physiotherapy. Methods: Several databases were searched for randomized trials published after 1 January 2020, including PubMed, Web of Science Core Collection, and Google Scholar. The included studies assessed the results of physiotherapist-delivered or physiotherapist-supervised digital interventions in children and adolescents aged 18 and younger. Population, intervention, outcome, implementation, and safety data were extracted. Considering the substantial heterogeneity of the findings, they were synthesized narratively. Cochrane RoB 2 was used to assess risk of bias, and GRADE was used to evaluate certainty of evidence. Results: Twenty-nine trials involving 1196 participants were included. Most studies examined virtual reality and gaming-based interventions, with fewer evaluating telerehabilitation/tele-exercise and robotic or wearable technologies. Digital interventions were most often directed at body-function and activity-level outcomes, while participation outcomes were less frequently studied. The strongest evidence supported short-term benefits in balance, gross motor function, upper-limb activity, pain, and selected fitness outcomes, particularly in children with cerebral palsy. Evidence for telerehabilitation and robotic or wearable approaches was more limited but generally promising. Implementation, equity, cost, and long-term outcomes were rarely reported. No eligible trial directly evaluated electronic patient-reported outcome measures, digital triage, or clinical decision support as stand-alone interventions. Conclusions: Digital interventions have the potential to strengthen pediatric physiotherapy, particularly for short-term motor and functional outcomes. The proposed DPPF provides an implementation-informed structure to guide future research, pathway design, and more purposeful integration of digital health into pediatric rehabilitation practice. Full article

Alzheimer’s disease is a progressive neurodegenerative disorder marked by declining cognitive function. While early-stage treatment focuses on acetylcholinesterase (AChE) inhibition, butyrylcholinesterase (BChE) activity increases as the disease progresses, contributing to cholinergic deficits and neuroinflammation. This shift in enzyme dominance presents a compelling rationale for developing BChE-specific inhibitors as a potential therapeutic avenue. This study explores small, three-membered rings, scaffolds offering potential for interaction with the enzyme’s active site, as building blocks for novel BChE inhibitors. Employing a computational approach based on quantum–chemical multiligand simultaneous molecular docking, we virtually fitted these compounds into the BChE active site to predict binding affinity and key interactions. Our calculations extend beyond simple shape matching by incorporating accurate electronic properties, leading to more reliable predictions of binding strength and stability. The goal was not immediate identification of potent inhibitors, but a systematic assessment of how these rings interact with BChE. This foundational knowledge will inform the design and synthesis of larger, more complex molecules with enhanced binding affinity and selectivity, ultimately aiming to develop compounds to inhibit BChE activity and potentially slow Alzheimer’s progression. Full article

High-fidelity haptic interfaces are widely assumed to enhance virtual reality (VR) training; however, they can trigger a “fidelity paradox” where hardware complexity paradoxically degrades usability. Grounded in Task-Technology Fit (TTF) theory and Hassenzahl’s pragmatic-hedonic quality framework, this study investigates the mechanisms underlying this paradox through a within-subject experiment ($N=70$) in a VR cooking simulation comparing three interface paradigms: VR controllers (VRC), hand tracking (HT), and haptic gloves (HG). Results confirmed that HG’s low task-technology fit—manifested as tracking errors, physical resistance, and increased operational overhead—generated significantly higher extraneous cognitive load (H1) and degraded interaction satisfaction (H2) despite its superior intended sensory resolution. Critically, in the HG condition, pragmatic quality (technical reliability) was identified as the dominant driver of satisfaction, while hedonic quality additions (thermal feedback) did not show a significant independent contribution to satisfaction in the HG condition. Perceived training effectiveness remained above the neutral threshold across all conditions (H3), indicating that content-level TTF is preserved independently of interface-level TTF mismatch. These findings suggest that VR interface design should prioritize “functional sufficiency”—ensuring tools serve as transparent, seamless extensions of the user—over the blind pursuit of sensory maximization. Full article

This study addresses virtual metrology (VM) for the taping process of chip electronic components, in which partial observability, unmeasured disturbances, and severe label imbalance make direct batch-wise yield prediction unstable. Rather than proposing a new standalone learning algorithm, we develop a data-centric VM framework that reformulates the task as the prediction of operating-condition-level typical yield. First, physically relevant features are retained based on process knowledge and analyzed using Pearson correlation, Spearman correlation, and mutual information. We then perform multidimensional equal-frequency binning to partition the observable feature space into locally homogeneous operating condition groups, and define the within-bin median yield as the typical yield, thereby constructing an operating condition dictionary. Based on this dictionary-based representation, low-yield-oriented sample weighting is combined with nested cross-validation and Bayesian optimization for model comparison and hyperparameter tuning. Using desensitized production data from an electronic component taping process, the results under this representation show more stable prediction than direct modeling on unbinned batch samples while also improving tail-oriented fitting relative to unweighted baselines. These findings suggest that, for partially observable manufacturing data, operating condition stratification provides a practical basis for stabilizing VM prediction, while low-yield-oriented sample weighting further improves sensitivity to the low-yield tail, supporting picture yield early warning and process-level decision making. Full article

Clothing pressure influences the comfort, mobility, and welfare of dogs; however, quantitative evidence on how obesity affects localized garment pressure is limited. Using CLO 3D virtual fitting, we evaluated clothing pressure according to body condition (normal vs. obese), posture, and fabric type. We constructed normal and obese avatars for three breeds and simulated a short-sleeved T-shirt across six postures and three fabrics, yielding n = 108 simulation conditions (two body conditions × three breeds × six postures × three fabrics). Clothing pressure was quantified as ROI-averaged pressure (kPa) at four body regions (P1–P4). The overall mean pressure (averaged across P1–P4) increased from 16.69 ± 3.69 kPa (normal) to 19.56 ± 5.03 kPa (obese), with the highest pressures consistently observed at the chest (P2) and abdomen (P4). Region-specific ANOVA/GLM analyses (breed treated as a fixed factor) showed significant main effects of body condition, posture, fabric type, and breed on clothing pressure (all p < 0.001), while the three-way interaction (body condition × posture × fabric) was not significant (p > 0.05). These findings show that CLO 3D virtual fitting enables controlled, simulation-based comparisons of clothing pressure across body conditions; however, because no in vivo wear trials were conducted, the results should be interpreted as preliminary, and they require future experimental validation before practical application. Full article

Virtual anchor livestreams have rapidly become an efficient e-commerce format, attracting large audiences through distinctive avatars and real-time interactivity. However, technical constraints and the physical limitations of virtual anchors limit the ability to evoke the vicarious proximal sensory perceptions of products. Therefore, it is essential to investigate strategies that compensate for this deficiency and enhance the effectiveness of live streaming to meet consumer demand. This study explores the effect of a virtual anchor’s proximal sensory fit in product descriptions on purchase intention through two experiments. The results show that a higher sensory fit positively affects purchase intention. The chain mediating effect of perceived authenticity and attractiveness is significant. Product display positively moderates the relationship between sensory fit and purchase intention. This study provides new theoretical perspectives and practical guidance for virtual anchor marketing, emphasizing that marketing effectiveness can be enhanced through reasonable product display and sensory descriptions. Full article

This study investigates the development of workwear designed to withstand harsh conditions and support physically demanding tasks. Its central aim is to create garments that enhance workers’ comfort and mobility by optimizing ergonomic and anthropometric factors. First of all, expert surveys were collected, and the importance of posture adaptability and material comfort was highlighted. To investigate realistic body–garment interactions, the 3D body scans of the upper body from 34 participants in common working poses were captured. These scans revealed the zones of high deformation, guiding the placement of elastic inserts to improve flexibility in targeted areas. The redesigned garments underwent a two-stage evaluation process. First, Clo3D virtual fittings provided qualitative insights into overall jacket fit and movement behavior. Next, stress and strain mapping offered quantitative validation, showing that fabric stress levels remained below 120 kPa, providing evidence that the added elasticity effectively reduced mechanical load and improved wearability. Expert reviewers confirmed the enhanced fit and functional performance. Overall, the study demonstrates an integrated design strategy that unites textile behavior, body dimensions and biomechanics. This approach not only improves workwear but also offers a transferable framework for developing specialized clothing across other physically intensive professions. Full article

As environmental challenges intensify globally, there is an urgent need for more effective environmental communication practices. In response, Virtual Influencers (VIs) have just recently started to emerge as influential voices in environmental messaging, aiming to foster environmental citizenship through more sustainable consumption patterns. However, despite growing interest, VIs represent a relatively new research phenomenon within the field of environmental sustainability. Aiming to consolidate the available empirical research, this study provides the first systematic scoping review in the emerging field of VIs for environmental sustainability. Using the Theory–Context–Characteristics–Methodology framework, this review synthesizes 19 studies. The analysis reveals that research in this field is largely driven by China and the United States and is characterized by a predominance of quantitative, experimental approaches based on social media-like stimuli. Sustainable consumption, especially eco-product purchasing, emerges as the most common environmental focus. This review proposes a conceptual framework that integrates antecedents, outcomes, and underlying mechanisms of environmental VI campaigns; individual characteristics; contextual and campaign-level moderators; and strategic anthropomorphism fit. While the emerging empirical base limits meta-analytical synthesis, this review consolidates current knowledge and outlines a forward-looking research agenda with theory-driven pathways to advance VI-led sustainability communication. Full article

This study examines the differences in sports learning among Generation Z based on digital literacy, using the Unified Theory of Acceptance and Use of Technology (UTAUT) and Media Richness Theory (MRT). As non-face-to-face sports learning—including online lectures, remote coaching, and virtual reality—rapidly expands, digital literacy has become a key factor influencing learning outcomes and equity. Data were collected from Generation Z adults engaged in sports learning through platforms including YouTube, social networking services, online lecture platforms, and mobile applications. Participants were classified into low (n = 87)-, medium (n = 80)-, and high (n = 70)-digital-literacy groups. A 32-item questionnaire adapted from prior studies assessed digital literacy (4 items), four UTAUT constructs (performance expectancy, effort expectancy, social influence, and facilitating conditions; 16 items), and three media richness dimensions (multiple channels, immediacy of feedback, and personalness; 12 items). Confirmatory factor analysis demonstrated acceptable model fit (χ² = 779.013, df = 436, p < 0.001, NFI = 0.914, IFI = 0.960, TLI = 0.954, CFI = 0.960, SRMR = 0.037, RMSEA = 0.058), reliability (all ω and α > 0.70), and convergent/discriminant validity (all AVE > 0.50; C.R. > 0.70). Group comparisons indicated that higher digital literacy was linked to higher scores in technology acceptance and media richness perceptions (F = 40.364–64.150, p < 0.001, ηp² = 0.257–0.354) These findings indicate that intra-generational differences in digital literacy shape technology use and media experience in sports learning, highlighting the need to enhance media richness and systematically develop learners’ digital literacy to improve digital sports education’s effectiveness and equity. But causal inferences are limited by the cross-sectional design. Full article

Background and Objectives: Fibromyalgia (FM) is a chronic and complex condition characterized by widespread pain, fatigue, psychological burden, and cognitive impairment, posing significant challenges for treatment. Immersive virtual reality exercise (iVRE) has been proposed as an innovative therapeutic approach to increase adherence, motivation, and multidimensional benefits, but evidence in FM remains limited. This study aimed to evaluate the effects of a six-week iVRE program on cortisol levels, quality of life, cognitive function, and psychological symptoms in women with FM. Materials and Methods: A quasi-experimental pre–post design was conducted with 21 women (mean age 48.1 ± 10.7 years) diagnosed with FM, who completed twelve 30 min sessions of iVRE using Oculus Quest 2™ and the FitXR platform. Outcomes assessed pre- and post-intervention included salivary cortisol (ELISA), quality of life (FIQR), emotional status (DASS-21), and cognitive function (MoCA). Adherence and safety were monitored throughout. Results: The intervention was well tolerated, with no adverse events and 100% adherence. Statistically significant improvements were observed in FIQR scores (p < 0.001, d = 3.54), depression (p < 0.001, d = 1.19), anxiety (p < 0.001, d = 1.39), and stress (p < 0.001, d = 2.28). Cognitive performance improved significantly, with higher MoCA total scores (p < 0.001, d = 1.52) and better outcomes in visuospatial ability, language, and delayed recall domains. No significant changes were detected in salivary cortisol levels (p = 1.000). Conclusions: A six-week iVRE program is safe and feasible, promoting clinically relevant improvements in quality of life, emotional well-being, and cognitive function in women with FM, despite the absence of changes in cortisol. These findings highlight iVRE as a promising complementary therapeutic strategy within multidisciplinary FM management, warranting further controlled studies with larger samples and long-term follow-up to confirm its efficacy and explore underlying mechanisms. Full article

Unmanned aerial vehicle (UAV) swarms can form sparse virtual antenna arrays (VAAs) for airborne sensing and communications, but their beamforming performance is highly vulnerable to quasi-static formation drift and the limited number of snapshots available within each coherent processing interval. This paper proposes a beacon-aided self-calibration and robust beamforming framework for narrowband UAV-swarm uplinks in strong-interference, low-snapshot regimes. We consider one signal of interest (SOI) and multiple co-channel interferers characterized by their coarse direction-of-arrival (DOA) information. The key idea is to exploit a single dominant non-SOI emitter as a strong calibration source (beacon) to learn the quasi-static geometry drift from data. First, the beacon spatial signature is extracted from the sample covariance matrix via eigenvector–steering-vector alignment, and a correlation-based gate is used to decide whether geometry calibration is reliable. When the gate is passed, the inter-UAV position drift is estimated from element-wise steering ratios to build a calibrated array manifold. Second, using the calibrated steering vectors and coarse DOA information, the interference-plus-noise covariance matrix (INCM) is reconstructed through a low-dimensional non-negative power fitting with mild diagonal loading. Finally, a geometry-aware minimum-variance distortionless response (MVDR) beamformer is designed based on the reconstructed INCM. Simulations on coprime-inspired UAV formations with a single dominant interferer show that the proposed scheme recovers most of the SINR loss caused by geometry mismatch and consistently outperforms baseline MVDR, worst-case MVDR, a recent covariance-reconstruction baseline, and URGLQ in the low-snapshot regime. For example, in a representative setting with $N_{\mathrm{uav}}=7$, ${\sigma }_p=0.10$, ${\mathrm{INR}}_c=30$ dB, and $L=10$, the proposed method achieves approximately 14 dB output SINR at ${\mathrm{SNR}}_{\mathrm{in}}=10$ dB, outperforming nominal SCM-MVDR by about 13 dB and approaching a genie-aided MVDR bound within a few dB, while retaining a computational complexity comparable to standard MVDR. Full article

Background/Objectives: One key objective in temporomandibular joint replacement is to precisely position the implant according to the virtual surgical plan, utilizing drilling and osteotomy guides for accuracy. However, implementing this process can be challenging, as—even though the drilling and osteotomy guides should only fit in one position—there often are still multiple potential positions for both guides and implants on smooth bony surfaces. Even minor deviations in the implant’s placement can affect wear, influence biomechanical behavior, and lead to adverse outcomes. Intraoperative navigation has emerged to verify the alignment of implants with the preoperatively planned ideal position. While the use of navigation systems in TMJ surgery is well documented for certain procedures, its application in TMJ replacement cases has been limited. Methods: In this study, two methods to improve the accuracy of TMJ replacement are introduced: a new marker-based navigation workflow and the use of orientation screws in two patients. Results: Unlike conventional navigation methods, the marker-based system provides a more intuitive method for assessing the 3D orientation of the TMJ implant concerning the planned position, enhancing surgical accuracy. The addition of a guiding screw provides a reference point to enhance the accuracy of guide placement. Conclusions: The accurate placement of the prosthesis largely relies on the precise positioning of the guides. Even slight inaccuracies in the position of the TMJ prosthesis, resulting from suboptimal guide placement, can lead to significant negative clinical outcomes. Marker-based navigation and the use of guiding screws may potentially improve the precision of TMJ replacement procedures. Full article

For the problem of multi-UAV cooperative trajectory planning, this study proposes an integrated static–dynamic trajectory optimization method based on a Dubins-CPSO algorithm. An improved Dubins static path planning method utilizing virtual “Intermediate Points” is introduced, and the reference trajectory generated by this method is employed to design the fitness function for the CPSO algorithm. Within the CPSO-based dynamic optimization framework, real-time local trajectory adjustments are performed by incorporating the UAV’s current state and multi-dimensional physical constraints. This approach combines the high reliability and low command variation rate of conventional algorithms with the flexibility and strong disturbance robustness of intelligent algorithms, achieving complementary advantages. The result is a flight trajectory planning method that is more compatible with the physical mechanisms of the aircraft while possessing a degree of autonomy and intelligence. The simulation results demonstrate that the proposed algorithm can adapt to uncertain initial conditions in the studied scenarios. Furthermore, under interference, it exhibits superior real-time regulation capability compared with traditional algorithms alone and greater robustness and practicality than standalone intelligent algorithms. This provides a more implementable trajectory planning solution for UAVs with strict physical constraints in engineering applications. Full article