Search Results (1,291)

Scientific visualization is changing from passive observation to active, AI-assisted collaboration. While Extended Reality (XR) has proven valuable for comprehending dense 3D arrays, traditional VR applications are typically deployed in rigid, single-purpose, and monolithic architectures. In this paper, we present the evolution of ASCRIBE-XR: a virtual reality platform backed by remote computation that has been re-engineered into a dynamic, service-oriented ecosystem. We introduce three core innovations that make immersive data analysis easier, faster, and more flexible when using multimodal scientific imaging. First, a lightweight Python REST interface decouples XR logic from the rendering engine, enabling real-time, programmable scene customization and on-demand data generation. Second, we present a Specimen Catalog architecture that lets the platform pivot between radically different disciplines, ranging from archaeological heterogeneous concrete and fuel-cell membranes to the root system of a bioenergy grass, by describing each dataset through portable metadata rather than hard-coded application logic. Finally, we introduce a prompt-driven layer powered by the Claude Agent SDK, allowing researchers to generate, segment, and manipulate volumetric and mesh data through natural language dialogue within the virtual space. For example, applying foundation models such as the Segment Anything Model (SAM) to perform zero-shot segmentation on demand. By bridging human intent with remote computation, ASCRIBE-XR relaxes the constraints of conventional visualization tools, offering a highly adaptable, conversational platform for scientific discovery with human auditing. Full article

The growing demand for virtual reality and immersive applications has increased interest in 360° video streaming. When viewing omnidirectional content through a head-mounted display, users observe only a limited portion of the content, i.e., the viewport, at any given time. Consequently, transmitting the complete panoramic frame at uniformly high quality is bandwidth-inefficient. This review presents a system-level analysis of viewport-adaptive three-degree-of-freedom (3DoF) 360° video streaming, focusing on the coupled roles of viewport prediction, tile-based multi-rate encoding and bitrate allocation, transport mechanisms, and edge-assisted processing. The reviewed literature is examined to identify the design dependencies and trade-offs among these components. Viewport-adaptive approaches seek to reduce the bandwidth allocated to regions outside the instantaneous viewport while preserving the quality of the visible region. The analysis shows that their effectiveness cannot be attributed to prediction accuracy alone: the resulting Quality of Experience (QoE) depends jointly on tile granularity, bitrate allocation, buffer occupancy, transport delay, and whether prioritized tiles arrive before their playback deadlines. Finer tiling can improve spatial selectivity but increases coding, signaling, and request overhead. Moreover, HTTP/2, HTTP/3/QUIC, RTP/RTSP, and WebRTC present different reliability, latency, congestion-control, and scalability trade-offs across buffered video-on-demand, low-latency live streaming, and interactive immersive applications. Based on this synthesis, the review formulates a unified closed-loop cross-layer framework that coordinates prediction, tiling, bitrate allocation, request timing, transport configuration, buffering, and edge processing under bandwidth, latency, and resource constraints. Full article

Students in higher education often face challenges related to motivation, self-regulation, and engagement. This article presents TIRIA, a user-centered learning analytics dashboard designed for Moodle and adaptable to other Learning Management Systems (LMSs), and reports a formative evaluation of its high-fidelity prototype. The design followed a Design Thinking methodology informed by a targeted literature review, interviews with 10 instructors and 9 students, and iterative prototyping. TIRIA integrates visual analytics, personalized feedback, and a gamification layer (points, badges, goal-setting, and a virtual assistant), aligned with Self-Regulated Learning (SRL) theory and interpreted through Self-Determination Theory (SDT). Because the evaluation used a Figma prototype populated with mock data rather than a deployed integration, the study reports perceptions rather than learning outcomes. Six undergraduate students completed think-aloud sessions, a semi-structured interview, and a survey combining the System Usability Scale (SUS), a Technology Acceptance Model (TAM) measure, and two five-item measures adapted from learning analytics quality indicators and from gamification research. Participants rated usability highly (SUS = 93.75, PEOU = 4.75) and consistently valued organizational features, while responses to the gamification layer were markedly polarized (individual means ranging from 1.2 to 5.0). The article contributes a documented design case mapping features to SRL phases and SDT constructs, formative evidence supporting an opt-in approach to gamification, and implementation considerations covering privacy and Moodle integration. Findings are exploratory and require confirmation through deployment in an authentic learning environment. Full article

With the accelerated progress of computational aesthetics and digital interior design, the demand for real-time and precise detection of aesthetic objects on edge devices has become increasingly pressing in applications such as intelligent design assistance, domestic aesthetic assessment, and augmented reality-based interior staging. As a core task in digital home aesthetics governance, virtual interior furnishing, household cultural archive development, and automated aesthetic evaluation, multi-view aesthetic object detection plays an essential role. However, this task still faces substantial difficulties arising from pronounced viewpoint variation, scale inconsistency, reflective materials, intricate decorative patterns, and cluttered indoor scenes. To address these issues, this study presents GhostNetV2-YOLO, a lightweight yet robust detection framework designed for accurate localization of aesthetic objects under unconstrained multi-view acquisition settings. The task is formally defined as closed-set detection of 10 pre-selected home aesthetic decorative items, including both planar decorative pieces and three-dimensional ornamental objects, and all performance claims are bounded within the horizontal bounding box detection paradigm. The framework incorporates three complementary components tailored to the target task. First, a task-adapted GhostNetV2 backbone is employed to enable efficient multi-scale feature extraction and long-range dependency modeling, with optimization specifically oriented toward structured aesthetic objects with stable global contours under viewpoint variation. Second, an improved Attention-based Intra-scale Feature Interaction (AIFI) module is introduced, integrating compressed QKV projection, linear attention, depthwise spatial refinement, and channel gating so that reflection-induced noise and background disturbance can be effectively reduced. Third, an enhanced Distance-IoU regression loss is adopted, in which explicit edge alignment and dynamic sample weighting are incorporated to improve boundary regression accuracy for rectangular and regularly contoured aesthetic objects. These designs jointly enhance contextual representation, boundary localization, and computational efficiency. Extensive experiments on two newly constructed multi-view aesthetic object datasets (AestheticHome-12K and AestheticHome-2K) demonstrate that the proposed detector achieves 94.80 ± 0.32%/94.20 ± 0.37% mAP@0.5, 96.30 ± 0.28%/95.60 ± 0.31% precision, and 94.70 ± 0.35%/93.80 ± 0.39% recall across two datasets (reported as mean ± standard deviation of 5 independent training runs with distinct random seeds), with only 2.89 M parameters and 6.0 GFLOPs. Statistical significance is verified via paired two-tailed t-tests with Bonferroni correction (adjusted p < 0.05) for all performance comparisons against baseline models. Compared with the YOLOv11n baseline, the method improves mAP@0.5 by 1.87–2.09 percentage points and recall by 3.27–3.48 percentage points while reducing computational cost. Notably, it also achieves 79.2–80.5% mAP@0.5:0.95, outperforming the baseline by 4.7–4.9 percentage points, indicating significantly superior localization accuracy under stricter criteria. The proposed model achieves a remarkable balance between accuracy and efficiency, making it highly suitable for deployment on resource-constrained edge devices commonly used in digital design and home aesthetic monitoring systems. The results indicate that combining lightweight long-range feature extraction optimized for rigid aesthetic objects, compact attention-based feature interaction for interference suppression, and geometry-aware regression tailored for aesthetic targets provides an effective and efficient solution for robust aesthetic object detection in real-world computational aesthetics and digital interior design applications. Full article

To address the issues of incomplete virtual array utilization and direction of arrival (DOA) estimation performance degradation under noise interference in coprime array processing, this paper proposes the Toeplitz Assisted Subspace Projection enhanced Unitary Root Sparse Asymptotic Minimum Variance (TASP-URootSAMV) algorithm. First, trace regularized Toeplitz covariance completion is employed to fill aperture holes in the virtual domain by exploiting shift invariance structure, reconstructing the interpolated covariance matrix through convex optimization and Wiener prediction. Second, eigenspace projection is performed to suppress background noise through Toeplitz-averaged covariance estimation and signal/noise subspace separation. Third, unitary root SAMV is applied to perform grid-initialized off-grid DOA refinement through iterative polynomial rooting, thereby mitigating grid-induced modeling errors and reducing sensitivity to the initial angular grid. Algorithm performance is evaluated through two complementary experiments. Spatial spectrum and root mean square error (RMSE) analysis indicate that, at $T=200$ snapshots and $\mathrm{SNR}=-10$ dB, the proposed method reduces the RMSE by $47.86$–$56.47\%$ compared with the considered algorithms, with accuracy close to the Cramér–Rao bound (CRB) in the tested cases. Additionally, the algorithm maintains distinguishable spectral peaks for the tested source numbers. Grid-spacing analysis indicates relatively stable performance over the tested initialization-grid intervals, whereas the performance of the grid-dependent comparison method degrades as the grid spacing increases. The convergence experiments also show limited sensitivity to the tested initialization settings and comparable computational efficiency. Under the adopted simulation assumptions, these results indicate improved estimation accuracy under the tested noisy conditions. Full article

Background/Objectives: Cerebral palsy is a lifelong neurodevelopmental condition associated with motor impairment and activity limitation, and adolescence is a clinically important period during which functional decline or plateau may occur. Virtual reality-assisted rehabilitation may provide task-specific practice with augmented feedback and enhanced engagement. This systematic review and meta-analysis aimed to evaluate the effects of virtual reality-assisted rehabilitation on functional outcomes in adolescents aged 10 to 19 years with cerebral palsy. Methods: Embase, MEDLINE, and the Cochrane Central Register of Controlled Trials were searched from inception to 25 February 2026 for studies enrolling adolescents with cerebral palsy or mixed-age samples with extractable adolescent data. Two reviewers independently screened studies and extracted data. Primary domains were lower-limb/balance-related function and upper-limb function. Within-group pre–post standardised mean differences were pooled using random-effects models, and certainty was assessed using the Grading of Recommendations Assessment, Development and Evaluation approach. Results: Eight studies including 83 participants were eligible; seven studies including 68 participants contributed to the meta-analysis. The pooled effect was small and uncertain for lower-limb/balance-related outcomes (Hedges’ g = 0.21, 95% confidence interval −0.17 to 0.58) and moderate for upper-limb outcomes (Hedges’ g = 0.47, 95% confidence interval 0.09 to 0.84). The overall pooled estimate was positive after virtual reality-assisted rehabilitation (Hedges’ g = 0.34, 95% confidence interval 0.07 to 0.60). Certainty of evidence for both primary domains was very low. Conclusions: Virtual reality-assisted rehabilitation may be associated with post-intervention functional gains in adolescents with cerebral palsy, with more consistent findings for upper-limb than lower-limb/balance-related outcomes. However, the certainty of evidence was very low, and the predominantly pre–post evidence cannot establish comparative treatment efficacy. VR-assisted rehabilitation may be considered an adjunct to conventional rehabilitation to increase task-specific practice and engagement, but adolescent-focused controlled studies with harmonised outcomes and clinically meaningful follow-up are needed. Full article

Background: Physical inactivity and functional decline are common among institutionalized older adults, increasing the risk of cardiovascular disease and loss of independence. Innovative strategies such as virtual reality-assisted exercise may enhance adherence and improve health outcomes in this population. Methods: A pilot intervention study with a pre-post design was conducted in five institutionalized older adults who participated in a virtual reality-assisted multicomponent exercise program. The intervention included sessions combining strength, aerobics, balance, and flexibility exercises. Anthropometric, cardiovascular, and functional variables were assessed before and after the intervention. Physiological responses were assessed at the beginning of the sessions, midway through the intervention, and at the end of the program, along with participant satisfaction. Results: Preliminary descriptive changes were identified following the intervention, including lower values for body mass, body fat percentage, and systolic blood pressure, together with higher scores in several functional capacity measures, such as lower and upper body strength, aerobic endurance, and mobility. Physiological responses during exercise remained within expected ranges across sessions. All participants reported high satisfaction with the intervention, and no adverse events were documented. Conclusions: A multicomponent exercise program integrating virtual reality appears to be a feasible and well-tolerated intervention for institutionalized older adults. The preliminary descriptive findings suggest potential favorable changes in cardiovascular health and functional capacity; however, because no control group was included, the specific contribution of the virtual reality component cannot be determined. Full article

Despite the considerable attention given to virtual streamers, their effectiveness in live streaming warrants further investigation. Drawing on the stimulus-organism-response (SOR) framework, social role theory and social cognitive theory, this study examines the influence mechanism of virtual streamer roles on consumers’ virtual place attachment. An online scenario experiment employing a 2 (assistant-role virtual streamers vs. friend-role virtual streamers) and 2 (search products vs. experience products) between-subjects design (N = 328) was conducted to verify research hypotheses. The results demonstrate that, compared with the assistant-role virtual streamer, the friend-role virtual streamer elicits a higher level of virtual place attachment among consumers. This effect is mediated through three distinct pathways: the positive mediation of social self-efficacy, the negative mediation of task self-efficacy, and the positive serial mediation involving both. Moreover, product type exerts a significant moderation effect on the association between social self-efficacy and task self-efficacy. Specifically, this relationship is stronger under search (vs. experience) product conditions. Such a moderation effect, however, is conditional upon consumer expertise and is only significant for consumers with low expertise. Additionally, consumer expertise moderates the influence of task self-efficacy on virtual place attachment, and this influence is stronger for high-expertise consumers than for low-expertise consumers. These findings shed empirical light on the functioning and influence of virtual streamers in e-commerce live streaming and extend the theoretical literature on virtual place attachment. They also offer practical insights for companies to optimize virtual streamer design, implement differentiated interaction strategies, and configure personalized features. Full article

Background: Three-dimensional cephalometric landmarking provides the spatial reference framework for computer-aided surgical simulation in craniomaxillofacial (CMF) surgery. Conventional workflows rely on two-dimensional computer interfaces (CIs), yet imaging data are inherently volumetric, and repeated CBCT imaging creates pressure to minimise patient radiation exposure. Immersive virtual reality (VR) offers a more intuitive environment for spatial tasks, yet the feasibility of ultra-low-dose (uLD) protocols for cephalometric landmarking in CI and VR remains unevaluated. This pilot study evaluated accuracy, reproducibility and workload across low-dose (LD) and uLD CBCT protocols in both environments. Methods: Four CMF radiologists placed ten 3D cephalometric landmarks on 20 CBCT datasets across three rounds. Round 1 used deep learning-predicted coordinates as ground truth. Rounds 2–3 assessed blind reproducibility. Workload was evaluated using the NASA Task Load Index. Results: Median Round 1 accuracy was 1.00 mm in both environments; mean CI accuracy was 1.05 mm and mean VR accuracy was 2.03 mm after outlier exclusion. Log analysis identified controller slips and software logging errors as a primary VR outlier source (22/793, 2.77%). Reproducibility was higher in CIs; VR medians remained clinically relevant. Dose level had no meaningful effect. The NASA-TLX showed decreasing VR workload across sessions, with AI-guided landmarking associated with the lowest mental demand. Conclusions: VR-based landmarking is feasible: its median accuracy was comparable to that of CIs and radiologists responded positively. Reproducibility was lower in VR, attributable to software constraints rather than the visualisation modality. The uLD protocol performed comparably to LD, supporting dose optimisation. Integration of AI assistance within VR represents the most promising direction for further development. Full article

This paper formulates a self-organized dynamic fencing problem for multiple AUVs under finite target-sensing range and bounded nonuniform acoustic communication delays. Here, self-organization means that the fence is generated by local interactions without assigning fixed angular slots, virtual leaders, or persistent vehicle roles. A minimal observer-assisted attraction-repulsion fencing controller is proposed for AUV implementation, comprising target-AUV radial attraction–repulsion, AUV–AUV distance attraction–repulsion computed from timestamp-aligned delayed neighbor states, a state-only target-motion observer, and a label-free bearing-coverage repulsion that acts only on oversized target-centered angular gaps. To address acoustic delay without delaying physical execution, each AUV stores its own state history and evaluates pairwise relative geometry at the timestamp carried by the received neighbor packet. The resulting command is applied at the current time. The analysis shows that timestamp alignment converts acoustic delay into a bounded geometric perturbation and establishes collision avoidance, radial confinement, angular-gap contraction, target tracking, and packet-range preservation on a locally order-consistent regular fencing interval. The theorem does not claim global entry from arbitrary non-enclosing configurations. Numerical simulations, including a five-degree-of-freedom ocean-current robustness test without current feedforward compensation and an evasive-target stress test with four rapid finite-acceleration turns, demonstrate self-organized entry and maintenance of compact convex-hull fencing in the tested cases. Full article

Background: Skeletal muscle (SM) plays a critical role in movement, metabolism, and organ protection, with its maintenance and regeneration relying on muscle satellite (stem) cells (MSCs). Prostaglandin E2 (PGE2) regulates MSCs, but PGE2 levels decline with aging due to increased catabolism by 15-hydroxyprostaglandin dehydrogenase (15PGDH), a negative regulator of muscle repair. Methods: This study aimed to employ artificial intelligence and machine learning (ML)-assisted, structure-based screening approaches to identify novel 15PGDH inhibitors. Supervised models (support vector machine, random forest, and XGBoost were trained on curated bioactivity data (IC₅₀ values) from the ChEMBL database and used to virtually screen the Maybridge compound library (~51,000 compounds). Results: The area under the curve (AUC) values of the developed models SVM, RF, and XGBoost were 0.96, 0.99, and 1.00, respectively. Promising inhibitors were further validated using structure-based virtual screening (docking), molecular dynamics simulations (200 ns), and MM-PBSA/GBSA analyses. The top five inhibitors (PD00616, HTS11491, HTS02629, AW00889, and HTS11190) were identified as active (ML analysis) and potential 15PGDH inhibitors based on their subsequent binding affinities, involvement of catalytic residues (Ser138, Tyr151, and Lys155), and complex stability. Additionally, these inhibitors were found to follow the drug-likeness criteria. Conclusions: These findings offer valuable insights for the development of novel therapeutics targeting 15PGDH to combat muscle degeneration and related pathologies, including aging and sarcopenia. Full article

Background: As population aging accelerates, pharmacological treatments provide limited benefits for cognitive decline. Exercise and technology-assisted exercise have thus emerged as important non-pharmacological approaches for supporting cognitive health in older adults. However, comparative evidence on the relative effectiveness of different intervention modalities across cognitive outcomes remains limited. Objective: This study used a network meta-analysis to systematically compare and rank the effects of traditional and non-traditional exercise interventions on cognitive function in older adults. Methods: This systematic review and network meta-analysis was registered on PROSPERO (CRD420261278685). PubMed, Embase, the Cochrane Library, Web of Science, and Scopus were searched from inception to May 2026. Randomized controlled trials enrolling participants aged 60 years or older were included. Interventions comprised aerobic, resistance, mind–body, finger, and multicomponent exercise, as well as virtual reality-based interventions, artificial intelligence-assisted exercise, and wearable exoskeleton training. Primary outcomes included global cognition, executive function, memory, attention, and activities of daily living. A random effects network meta-analysis was applied to estimate standardized mean differences with 95 percent confidence intervals, and intervention rankings were derived using SUCRA values. Results: A total of 70 randomized controlled trials involving 5573 older adults were included. All active interventions demonstrated overall cognitive benefits compared with usual care or non-active control conditions. Mind–body exercise showed the highest probability of improving global cognitive performance. Virtual reality-based interventions were particularly effective for executive function, memory, and activities of daily living, while artificial intelligence-assisted exercise showed favorable rankings for attention outcomes. Mind–body exercise showed favorable effects on global cognition, whereas several technology-assisted interventions ranked highly for selected cognitive and functional outcomes. Conclusions: Both traditional and non non-traditional exercise interventions improve cognitive function in older adults, although their effects differ across cognitive domains. Technology-assisted approaches appear more effective for enhancing specific cognitive functions, while traditional interventions such as mind–body exercise are more advantageous for preserving overall cognitive performance. These findings highlight the potential value of stage specific and combined intervention strategies for promoting cognitive health in aging populations. Full article

Background: Parkinson’s disease (PD) is characterized by a complex interplay of dopaminergic degeneration, glial activation, and lipid metabolic dysregulation. However, accurately describing how natural product interventions remodel these pathologies across distinct brain regions and cellular microenvironments remains a critical challenge. Methods: We established an integrated multi-omics framework to decode the neuroprotective mechanisms of solanesol (Sol) in an MPTP-induced PD mouse model. We combined single-cell eQTL-based Mendelian randomization (scMR), transcriptomic localization, and virtual knockout analyses to prioritize cell-type-specific regulatory nodes across neuronal, glial, and vascular populations, avoiding the limitations of traditional bulk targeting. In vivo behavioral assays were conducted, alongside orthogonal validation via airflow-assisted desorption electrospray ionization mass spectrometry imaging (AFADESI-MSI) and gene–metabolite co-enrichment analysis, to map regional metabolic networks and structural spatial reprogramming. Results: Computational prioritization highlighted cell-type-specific regulatory nodes including PRKCB, PRKCE, PDGFRB, and FABP3/5. In vivo, Sol attenuated motor and cognitive deficits and largely restored the highly compartmentalized spatial distributions of striatal dopamine, L-DOPA, and acetylcholine. Crucially, AFADESI-MSI and co-enrichment analysis revealed that Sol specifically reversed MPTP-induced spatial disruptions by rescuing key neuromodulatory metabolites—including cervonoyl ethanolamide, phosphatidylcholine species, taurine, and NADHX—which were tightly coupled to sphingolipid signaling, fatty-acid transport, mitochondrial translation, and cell-adhesion pathways. Conclusions: Sol ameliorates PD-like pathology not through a singular target, but by choreographing a spatially and cellularly compartmentalized restoration of lipid–inflammatory homeostasis. Furthermore, our integrated single-cell and spatial metabolomic blueprint sets a new methodological paradigm for elucidating the precise execution programs of natural neurotherapeutics. Full article

Unmanned aerial vehicles (UAVs) are increasingly used in applications such as inspection and search and rescue, yet safe and intuitive teleoperation remains challenging in cluttered and GPS-denied environments. This paper presents a modular haptic teleoperation framework that integrates Unity, ROS2, optical motion capture, a physical UAV platform, and a stylus-based haptic device for bidirectional human–robot interaction. Operator inputs are mapped to UAV velocity commands, while obstacle proximity is rendered as continuous haptic feedback to enhance spatial awareness. A Control Barrier Function (CBF)-based command filtering layer is incorporated to modify unsafe velocity commands in real time. The system is evaluated through both Unity-based simulation and real-world experiments using a DJI Tello UAV and OptiTrack motion capture. In the virtual experiments, four conditions were compared: baseline, CBF only, haptic only, and CBF + haptic. The combined CBF + haptic condition reduced the average task completion time from 62.9 s to 42.8 s and resulted in no observed collisions under the evaluated virtual scenarios. The real-world experiments further confirmed stable force–distance behavior, bounded latency, and feasible haptic-assisted UAV navigation in a constrained indoor environment. These results indicate that combining haptic feedback with CBF-based safety control can improve teleoperation efficiency, safety, and usability under the tested conditions while providing a practical step toward simulation-to-real haptic UAV teleoperation. Full article

Background: Extended reality (XR) technologies, including augmented reality (AR), mixed reality (MR), and virtual reality (VR), are increasingly used in craniomaxillofacial (CMF) surgery; yet, no study has systematically mapped the global development of this research. This study provides the first comprehensive evidence mapping of XR and related computational technologies in CMF surgery, identifying research trends, collaborative networks, and future directions. Methods: A systematic search of the Web of Science (WoS) Core Collection (data extracted April 2025) combined XR terms (AR, MR, VR, image-guided surgery, and computer-assisted surgery) with CMF surgery terminology. After applying the inclusion criteria, 778 English-language articles published between 1988 and 2024 were analyzed using performance analysis and science mapping tools, including HistCite and VOSviewer. Results: Publication output grew exponentially, accelerating markedly after 2010. The Journal of Cranio-Maxillofacial Surgery led in both volume and internal citation impact. The USA, Germany, and China accounted for most of the output, with Germany showing a disproportionately high internal citation impact relative to its publication volume. Three developmental phases were identified: foundational computer-assisted surgery (1990s–2000s), technological development and validation (2000s–2010s), and clinical application optimization centered on AR and VR (2010s–present). Co-authorship networks revealed strong regional clusters with limited cross-cluster connectivity. No publications were identified from African countries other than Egypt. Conclusions: XR research in CMF surgery has grown substantially over 36 years, with a clear shift toward AR and VR applications. Geographic concentration limited international collaboration, and a focus on technical rather than patient-centered outcomes remains a key challenge. Future work should prioritize global participation, comparative clinical evidence, and patient-reported outcomes to support wider surgical adoption. Full article