Search Results (217)

Students with attention deficit hyperactivity disorder (ADHD) face a continuous challenge with their attention span, putting them at a greater risk of academic or psychological difficulties compared to their peers. Innovative communication technologies are demonstrating potential to address these attention-span concerns. Virtual Reality (VR) is one such example, and has the potential to address attention-span difficulties among ADHD students. Accordingly, this study presents an EEG-based multimodal sensing pipeline as a methodological contribution, focusing on sensor-based data acquisition, signal processing, and neurophysiological interpretation to assess attention in VR-based environments, simulating a university supply chain educational topic. Thus, in this paper, a sequential exploratory approach investigated how 35 participants experienced an interactive VR-learning-driven supply chain game. A Brain–Computer Interaction (BCI) sensor generated insights by quantitatively analysing electroencephalogram (EEG) data that were processed through the proposed pipeline and integrated with subjective measures to validate participant’s subjective feelings. These insights originated from questions during the experiment that followed the Spatial Presence and Technology Acceptance Model to form a multimodal assessment framework. Findings demonstrated that the experimental group experienced a higher improved attention, concentration, engagement, and focus levels compared to the control group. BCI results from the experimental group showed more dominant voltage potentials in the right frontal and prefrontal cortex of the brain in areas responsible for attention, memory, and decision-making. A high acceptance of the VR technology among neurodiverse students highlights the added benefits of multimodal learning assessment methods in an educational setting. Full article

Awaken, aware, arise, perform, pause, and repeat. The actions of the everyday. Without it, we fall into dysregulation. This paper seeks to examine creative research developed as an experiment during COVID-19, an audiovisualscape in virtual reality (VR). Rhythmanalysis+ is a social, ecological, and sensorial enquiry into materiality, grounded in archipelagic thinking, through the lens of Rhythmanalysis, a form of analysis focusing on the everyday, through the lens of cyclical and linear rhythms. (Lefebvre). The research will also draw on Deleuze and Guattari’s rhizome theory, a botanical and philosophical investigation into networks. Networks form the backbone of the research. Lars Bang Larsen also argues that networks offer a distinctive view on how factual, speculative, historical, and non-human elements envelop and intertwine. Glissant’s archipelagic thought promotes transformation, multiplicity, and a sense of unpredictability. For this work, four inhabitants from Sherkin, a small island off the southwest coast of Ireland with a population of 100, became the research focus. Across four weeks, islanders gathered data from their daily sensory rhythms. Flight patterns of birds and bats were recorded, daily tasks noted, pathways cycled. Relational impacts of animal-odour on farming, weather, and tides were processed remotely, and an immersive cartographic score was created as a direct response in a three-dimensional virtual space. Rhythmanalysis+ analyses our newly altered perceptions of time and space as a material within a virtual world. VR, created as a gaming platform, is being pushed by art itself, forcing us to relook at the natural world, which is not static, but relational. Fluid but equally extractive, it is important to look at technology’s impact on all that is human and how it is perceived within the body as it is reframed digitally. Full article

Recognizing emotions accurately in virtual reality (VR) enables adaptive and personalized experiences across gaming, therapy, and other domains. However, most existing facial emotion recognition models rely on acted expressions collected under controlled settings, which differ substantially from the spontaneous and subtle emotions that arise during real VR experiences. To address this challenge, the objective of this study is to develop and evaluate generalizable emotion recognition models that jointly learn from both acted and natural facial expressions in virtual reality. We integrate two complementary datasets collected using the Meta Quest Pro headset, one capturing natural emotional reactions and another containing acted expressions. We evaluate multiple model architectures, including convolutional and domain-adversarial networks, and a mixture-of-experts model that separates natural and acted expressions. Our experiments show that models trained jointly on acted and natural data achieve stronger cross-domain generalization. In particular, the domain-adversarial and mixture-of-experts configurations yield the highest accuracy on natural and mixed-emotion evaluations. Analysis of facial action units (AUs) reveals that natural and acted emotions rely on partially distinct AU patterns, while generalizable models learn a shared representation that integrates salient AUs from both domains. These findings demonstrate that bridging acted and natural expression domains can enable more accurate and robust VR emotion recognition systems. Full article

The Extended Digital Narrative (XDN) research project explores how experimental creative practice with emerging technologies generates critical insights into algorithmic narrativity—the intersection of human narrative understanding and computational data processing. This article presents five case studies demonstrating that direct engagement with AI and Extended Reality platforms is essential for humanities research on new genres of digital storytelling. Lina Harder’s Hedy Lamar Chatbot examines how generative AI chatbots construct historical personas, revealing biases in training data and platform constraints. Scott Rettberg’s Republicans in Love investigates text-to-image generation as a writing environment for political satire, documenting rapid changes in AI aesthetics and content moderation. David Jhave Johnston’s Messages to Humanity demonstrates how Runway’s Act-One enables solo filmmaking, collapsing traditional production hierarchies. Haoyuan Tang’s video game project reframes LLM integration by prioritizing player actions over dialogue, challenging assumptions about AI’s role in interactive narratives. Sérgio Galvão Roxo’s Her Name Was Gisberta employs Virtual Reality for social education against transphobia, utilizing perspective-taking techniques for empathy development. These projects demonstrate that practice-based research is not merely artistic production but a vital methodology for understanding how AI and XR platforms shape—and are shaped by—human narrative capacities. Full article

After nearly two decades of developments in Historic/Heritage Building Information Modeling (HBIM), the field has reached a stage of maturity that calls for a critical reassessment of its evolution, achievements, and remaining challenges. Digital representation has become a central component of contemporary heritage conservation, enabling advanced methods for analysis, management, and communication. This review examines the maturation of HBIM as a comprehensive framework that integrates extended reality (XR), artificial intelligence (AI), machine learning (ML), semantic segmentation and Digital Twin (DT). Six major research domains that have shaped recent progress are outlined: (1) the application of HBIM to restoration and conservation workflows; (2) the expansion of public engagement through XR, virtual museums, and serious games; (3) the stratigraphic documentation of building archaeology, historical phases, and material decay; (4) data-exchange mechanisms and interoperability with open formats and Common Data Environments (CDEs); (5) strategies for modeling geometric and semantic complexity using traditional, applied, and AI-driven approaches; and (6) the emergence of heritage DT as dynamic, semantically enriched systems integrating real-time and lifecycle data. A comparative assessment of international case studies and bibliometric trends (2015–2025) illustrates how HBIM is transforming proactive and data-informed conservation practice. The review concludes by identifying persistent gaps and outlining strategic directions for the next phase of research and implementation. Full article

Background: Pancreatic cancer is the least survivable malignancy, with five-year survival below 10%. Its vague, non-specific symptoms contribute to late diagnosis and poor outcomes. Targeted education for healthcare professionals, students, patients, carers, and the public may improve awareness, confidence, and early help-seeking. This scoping review aimed to map and synthesize peer-reviewed evidence on pancreatic cancer education, identifying intervention types, outcomes, and gaps in knowledge. Methods: A scoping review was undertaken using the Joanna Briggs Institute (JBI) framework and the Arksey and O’Malley framework and reported in accordance with PRISMA-ScR guidelines. The protocol was registered on the Open Science Framework. Four databases (MEDLINE, Embase, CINAHL, PsycINFO) were searched for English-language, peer-reviewed studies evaluating educational interventions on pancreatic cancer for healthcare students, professionals, patients, carers, or the public. Grey literature was excluded to maintain a consistent methodological standard. Data were charted and synthesised narratively. Results: Nine studies (2018–2024) met inclusion criteria, predominantly from high-income countries. Interventions targeted students and professionals (n = 3), patients (n = 2), the public (n = 2), or mixed groups (n = 2), using modalities such as team-based learning, workshops, virtual reality, serious games, and digital animations. Four interrelated themes were identified, encompassing (1) Self-efficacy; (2) Knowledge; (3) Behavior; and (4) Acceptability. Digital and interactive approaches demonstrated particularly strong engagement and learning gains. Conclusions: Pancreatic cancer education shows clear potential to enhance knowledge, confidence, and engagement across diverse audiences. Digital platforms offer scalable opportunities but require quality assurance and long-term evaluation to sustain impact. The evidence base remains limited and fragmented, highlighting the need for validated outcome measures, longitudinal research, and greater international representation to support the integration of education into a global pancreatic cancer control strategy. Future studies should also evaluate how educational interventions influence clinical practice and real-world help-seeking behaviour. Full article

Background: Immersive virtual reality (VR) has emerged as a promising tool to enhance neuroplasticity, motivation, and engagement during post-stroke motor rehabilitation. However, evidence on its feasibility and data-driven integration into clinical practice remains limited. Objective: This pilot study aimed to evaluate the feasibility, usability, and short-term motor outcomes of an immersive VR-assisted rehabilitation program using the Travee-VR system. Methods: Fourteen adults with post-stroke upper-limb paresis completed a 10-day hybrid rehabilitation program combining conventional therapy with immersive VR sessions. Feasibility and tolerability were assessed through adherence, adverse events, the System Usability Scale (SUS), and the Simulator Sickness Questionnaire (SSQ). Motor outcomes included active and passive range of motion (AROM, PROM) and a derived GAP index (PROM–AROM). Correlations between clinical changes and in-game performance metrics were explored to identify potential digital performance metrics of recovery. Results: All participants completed the program without adverse events. Usability was rated as high (mean SUS = 79 ± 11.3), and cybersickness remained mild (SSQ < 40). Significant improvements were observed in shoulder abduction (+7.3°, p < 0.01) and elbow flexion (+5.8°, p < 0.05), with moderate-to-large effect sizes. Performance gains in the Fire and Fruits games correlated with clinical improvement in shoulder AROM (ρ = 0.45, p = 0.041). Cluster analysis identified distinct responder profiles, reflecting individual variability in neuroplastic adaptation. Conclusions: The Travee-VR system proved feasible, well tolerated, and associated with measurable short-term improvements in upper-limb function. By linking clinical outcomes with real-time kinematic data, this study supports the role of immersive, feedback-driven VR as a catalyst for data-informed neuroplastic recovery. These results lay the groundwork for adaptive, clinic-to-home rehabilitation models integrating clinical and exploratory digital performance metrics. Full article

Brain–Computer Interface (BCI) systems are rapidly evolving and increasingly integrated into interactive environments such as gaming and Virtual/Augmented Reality. In such applications, user adaptability and engagement are critical. This study applies deep learning to predict user performance in a 3D BCI-controlled game using pre-game Motor Imagery (MI) electroencephalographic (EEG) recordings. A total of 72 EEG recordings were collected from 36 participants, 17 using the Muse 2 headset and 19 using the Emotiv Insight device, during left and right hand MI tasks. The signals were preprocessed and transformed into time–frequency spectrograms, which served as inputs to a custom convolutional neural network (CNN) designed to classify users into three performance levels: low, medium, and high. The model achieved classification accuracies of 83% and 95% on Muse 2 and Emotiv Insight data, respectively, at the epoch level, and 75% and 84% at the subject level, using LOSO-CV. These findings demonstrate the feasibility of using deep learning on MI EEG data to forecast user performance in BCI gaming, enabling adaptive systems that enhance both usability and user experience. Full article

As video games increasingly replicate real-world locations, they have become powerful tools influencing players’ perceptions and behaviors toward travel destinations. Based on the principles of Transfer Trust Theory (TTT), this research investigates how the trust established in a destination within a virtual game context, referred to as perceived destination trust, translates into real-world travel willingness. Using data from a survey of 262 Tunisian gamers who played games set in real-world environments, we employed a structural equation modeling approach incorporating SPSS and SmartPLS analyses. The results indicate that immersion and enjoyment of the game significantly strengthen emotional attachment and the image of the destination, thereby reinforcing perceived trust. This trust positively predicts the willingness to visit real-world destinations. Furthermore, moderation analysis reveals that this effect is more pronounced among individuals classified as Explorers and Achievers, highlighting the influence of motivational typologies on the translation of virtual behaviors into real-world actions. These results extend the scope of TTT to video game-induced tourism (VGIT), empirically validating the psychological mechanisms that link virtual trust to real-world travel behaviors. From a practical standpoint, tourism organizations and game developers are advised to collaborate on creating immersive and authentic environments that enhance destination credibility while aligning with brand objectives. Full article

Frequent fluctuations in virtual item transactions make data access in virtual games highly dynamic. These heat changes denote temporal variations in data popularity driven by trading activity, which in turn cause traditional storage systems to struggle with timely heat adaptation, increased latency, and energy waste. This study proposes an AI-driven modeling framework for virtual game data centers. The heat feature vector composed of transaction frequency, price fluctuation, and scarcity forms the state space of a Markov decision process, while data migration between multi-layer storage structures constitutes the action space. The model captures temporal locality and spatial clustering in transaction behaviors, applies a sliding-window prediction mechanism to estimate access intensity, and enhances load perception. A scheduling mechanism combining an R2D3 (Recurrent Replay Distributed DQN from Demonstrations) policy network with temporal attention and mixed integer programming jointly optimizes latency, energy consumption, and resource constraints to achieve global data allocation tuning. Experiments on a simulated high-frequency trading dataset show that the system reduces access delay to 420 ms at a transaction intensity of 1000 per second and controls the total migration energy consumption to 85.7 Wh. The Edge layer achieves a peak hit rate of 63%, demonstrating that the proposed method enables accurate heat identification and energy-efficient multi-layer scheduling under highly dynamic environments. Full article

Brain–computer interfaces (BCIs) can be used to monitor and provide real-time feedback on brain signals, directly influencing external systems, such as virtual environments (VE), to support self-regulation. We piloted a novel immersive, first-person shooting BCI-VE during which the avatars’ movement speed was directly influenced by neural activity in the supplementary motor area (SMA). Previous analyses revealed behavioral and localized neural effects for active versus reduced contingency neurofeedback in a randomized controlled trial design. However, the modeling of neural dynamics during such complex tasks challenges traditional event-related approaches. To overcome this limitation, we employed a data-driven framework utilizing group-level independent networks derived from BOLD-specific components of the multi-echo fMRI data obtained during the BCI regulation. Individual responses were estimated through dual regression. The spatial independent components corresponded to established cognitive networks and task-specific networks related to gaming actions. Compared to reduced contingency neurofeedback, active regulation induced significantly elevated fractional amplitude of low-frequency fluctuations (fALFF) in a frontoparietal control network, and spatial reweighting of a salience/ventral attention network, with stronger expression in SMA, prefrontal cortex, inferior parietal lobule, and occipital regions. These findings underscore the distributed network engagement of BCI regulation during a behavioral task in an immersive virtual environment. Full article

Introduction: Cerebral palsy (CP) affects motor function development, requiring intensive rehabilitation. Virtual reality (VR) interventions show promise for improving motor learning through immersive, engaging experiences. This systematic review and meta-analysis evaluated VR effectiveness for motor function improvement in children with CP. Methods: Following PRISMA 2020 guidelines, we searched six electronic databases from inception to 15 June 2025. Included studies compared VR interventions versus control conditions in children with CP (ages 4–18 years), measuring motor function outcomes. Sixteen studies (n = 397 participants) met the inclusion criteria for qualitative synthesis. Random-effects models, subgroup analyses, and meta-regression were performed. Evidence certainty was evaluated using GRADE methodology. Results: Five randomized controlled trials with complete extractable data (N = 190 participants, 40 effect sizes) were included in the primary quantitative meta-analysis. The primary meta-analysis demonstrated moderate overall effects favoring VR interventions (standardized mean difference [SMD] = 0.41, 95% CI [0.16, 0.66], p = 0.001; I² = 74%); however, GRADE quality was rated LOW due to risk of bias and imprecision. Technology type critically moderated outcomes: robotic exoskeleton systems showed large effects (SMD = 1.00, p = 0.002), commercial gaming platforms showed small-to-moderate effects (SMD = 0.38, p = 0.013), while custom VR systems showed no significant benefit (SMD = 0.01, p = 0.905; Q = 29.00, p < 0.001). Age emerged as the strongest moderator: children (<6 years) demonstrated significant benefits (SMD = 0.98, p < 0.001), whereas school-age children (6–12 years) showed no effect (SMD = −0.01, p = 0.903; meta-regression slope = −0.236 per year, p < 0.001). Dose–response was non-linear, with optimal benefits at 30–40 intervention hours and diminishing returns beyond 50 h. VR proved superior to standard care (SMD = 0.83) but not to active intensive therapies (SMD = 0.09). The safety profile was favorable (1.3% adverse event rate, no serious events). No publication bias was detected. Conclusions: VR interventions demonstrated moderate, technology-dependent motor function improvements in children with CP, with benefits concentrated in young children using robotic systems. Evidence certainty is low, requiring further high-quality trials. Implementation should prioritize robotic VR for children with 30–40 h protocols. Full article

In smart home projects, building data management at the design stage increasingly relies on digital-twin assets delivered via game engines. Without a clear governance workflow, however, these practices tend to generate non-standard building data on the consumption side, causing broken data chains and increasing construction and management risks. To address this problem, this study proposes a traceability-oriented governance workflow that strengthens IfcGUID compliance and automatically detects and converts inconsistent digital-twin assets into IFC-compliant, auditable data, thereby reducing data chain breakage and improving cross-system traceability in building data management. The workflow uses IfcGUID as a cross-system primary key and is evaluated in a virtual smart home project through a pre-test–repair–post-test experiment at the design stage. We examine four indicators of IfcGUID quality—completeness, validity, uniqueness, and stability—together with a bridge recognition rate that reflects game engine interoperability on the consumption side. The results show that all four IfcGUID indicators converge towards 1 after applying the workflow, and the bridge recognition rate approaches 100%, indicating that the risk of data chain breakage, measured on an IFC basis, is substantially reduced. Within existing toolchains, this workflow provides design teams, visualization teams, clients, and auditors with a low-cost and reproducible path for standardizing design-stage digital-twin assets and establishing a traceable, auditable baseline for cross-system interoperability and lifecycle building data management and data reuse. Full article

This research presents a novel, real-time Pakistani Sign Language (PSL) recognition system utilizing a custom-designed sensory glove integrated with advanced machine learning techniques. The system aims to bridge communication gaps for individuals with hearing and speech impairments by translating hand gestures into readable text. At the core of this work is a smart glove engineered with five resistive flex sensors for precise finger flexion detection and a 9-DOF Inertial Measurement Unit (IMU) for capturing hand orientation and movement. The glove is powered by a compact microcontroller, which processes the analog and digital sensor inputs and transmits the data wirelessly to a host computer. A rechargeable 3.7 V Li-Po battery ensures portability, while a dynamic dataset comprising both static alphabet gestures and dynamic PSL phrases was recorded using this setup. The collected data was used to train two models: a Support Vector Machine with feature extraction (SVM-FE) and a Long Short-Term Memory (LSTM) deep learning network. The LSTM model outperformed traditional methods, achieving an accuracy of 98.6% in real-time gesture recognition. The proposed system demonstrates robust performance and offers practical applications in smart home interfaces, virtual and augmented reality, gaming, and assistive technologies. By combining ergonomic hardware with intelligent algorithms, this research takes a significant step toward inclusive communication and more natural human–machine interaction. Full article

The global energy transition, characterized by the proliferation of intermittent renewables and the evolution of electricity markets, has positioned virtual power plants (VPPs) as crucial aggregators of distributed energy resources. However, their participation in competitive markets is fraught with multifaceted uncertainties stemming from price volatility, renewable generation intermittency, and unpredictable prosumer behavior, which necessitate sophisticated, risk-averse bidding strategies to ensure financial viability. This review provides a comprehensive analysis of the state-of-the-art in risk-averse bidding for VPPs. It first establishes a resource-centric taxonomy, categorizing VPPs into four primary archetypes: DER-driven, demand response-oriented, electric vehicle-integrated, and multi-energy systems. The paper then delivers a comparative assessment of different optimization techniques—from stochastic programming with conditional value-at-risk and robust optimization to emerging paradigms such as distributionally robust optimization, game theory, and artificial intelligence. It critically evaluates their application contexts and effectiveness in mitigating specific risks across diverse market types. Finally, the review synthesizes these insights to identify persistent challenges—including computational bottlenecks, data privacy, and a lack of standardization—and outlines a forward-looking research agenda. This agenda emphasizes the development of hybrid AI–physical models, interoperability standards, multi-domain risk modeling, and collaborative VPP ecosystems to advance the field towards a resilient and decarbonized energy future. Full article