Search Results (1,876)

Chaotic behavior in power systems that are integrated with permanent-magnet synchronous generators (PMSGs) poses a significant threat to stability and security. Existing control methods often suffer from slow convergence, reliance on precise system models, or the inability to guarantee convergence within a predefined time. To address these issues, this paper develops a predefined-time synchronization control scheme for chaotic PMSG systems under unknown nonlinearities and external disturbances. First, an adaptive neural network with variable exponent coefficients is constructed to approximate unknown system dynamics online. Second, a predefined-time stability criterion is established, ensuring global convergence of synchronization errors within a user-specified time, independently of initial conditions. Third, the proposed controller achieves superior disturbance rejection without requiring prior knowledge of disturbance bounds. Numerical simulations demonstrate that the proposed method outperforms conventional finite-time control in convergence speed, control smoothness, and robustness to parameter variations—offering a practical and theoretically guaranteed solution for enhancing the stability of PMSG-based power systems. 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

Background and Objectives: Halitosis can impair psychosocial well-being, and orthodontic appliances may modify plaque retention and oral ecology. We compared patient-perceived halitosis burden, clinician-rated malodor, and oral health-related quality of life (OHRQoL) among clear aligner users, fixed-brace patients, and untreated controls, and explored oral and salivary correlates of worse malodor severity. Methods: This cross-sectional study (March 2024–November 2025) enrolled 184 participants aged 15–35 years (aligners n = 62; fixed braces n = 64; controls n = 58). Outcomes were HALT (0–100), organoleptic score (0–5), and OHIP-14 (0–56). Plaque index, gingival inflammation, tongue coating, and unstimulated salivary flow were recorded; low flow was defined as <0.25 mL/min. Organoleptic score ≥ 2 was used descriptively for clinically relevant malodor prevalence, whereas organoleptic score ≥3 defined a moderate-to-severe malodor phenotype for secondary exploratory internal modeling. Multivariable robust linear models (HALT) and proportional-odds ordinal logistic regression (organoleptic severity) were used. Results: Fixed braces showed higher HALT (53.7 ± 6.2) than controls (46.3 ± 6.4) and aligners (41.7 ± 7.4) (p < 0.001), higher organoleptic scores (2.9 ± 0.4 vs. 2.4 ± 0.6 vs. 2.2 ± 0.6; p < 0.001), and worse OHIP-14 (18.6 ± 4.7 vs. 15.9 ± 4.3 vs. 13.8 ± 4.8; p < 0.001). Clinically relevant malodor prevalence (organoleptic ≥ 2) was 96.9% in fixed braces, 79.3% in controls, and 66.1% in aligners (p < 0.001); because ≥2 was used as a broad descriptive threshold, these values should be interpreted as descriptive rather than diagnostic prevalence estimates. In adjusted models, greater tongue coating, higher plaque, and low salivary flow were associated with worse organoleptic severity, whereas appliance category did not remain independently associated with HALT once concurrent clinical correlates were included. Conclusions: Fixed braces showed higher unadjusted malodor burden and worse OHRQoL than aligners and untreated controls, but appliance category should be interpreted as a contextual exposure linked to plaque-retentive conditions rather than as a standalone causal determinant. Plaque accumulation, tongue coating, and lower salivary flow showed the strongest associations with worse malodor severity. These findings should be interpreted in light of the cross-sectional design, possible observer and selection bias, and residual confounding. Full article

This study examined how Twitter/X users engaged in the political discourse on the Sewol ferry accident in South Korea. We used a triangulation method by combining a social networks approach with quantitative content analysis. A comparison of the number of links across politically homogeneous clusters with the number of links across heterogeneous clusters revealed that selective exposure occurred on the Twitter topic network. Findings also showed the greater role of independent journalists armed with social media in disseminating information online. Our content analysis indicated that the tragic accident divided the public into two sides over the issue and that the public sentiment was dependent on the political orientations of the clusters within the network. The implications of these findings were discussed for scholars who aim to address the problems rooted in a polarized society. Full article

Blind and visually impaired individuals face persistent challenges when navigating unfamiliar environments, where unseen obstacles compromise their safety and independence. Although many electronic travel aids have been proposed, most remain impractical for daily use—they often rely on bulky or costly hardware, require external processing, or provide unintuitive feedback. This work presents a wearable stereo-vision-based vibrotactile system for real-time obstacle detection and navigation assistance. The device combines an off-the-shelf stereo camera integrated with a simultaneous localization and mapping framework to perceive spatial geometry and detect obstacles in the user’s path. Two stereo-matching methods were implemented to estimate depth: a block-based algorithm optimized for low-latency performance and a semi-global approach providing denser depth maps. Detected obstacles are translated into distinct vibration patterns delivered through four skin-contact body-mounted actuators encoding both direction and distance. The system was evaluated with blindfolded sighted, visually impaired, and blind participants. Both stereo approaches supported reliable real-time guidance and high obstacle-avoidance rates, demonstrating robust performance on affordable, wearable hardware. These findings confirm the feasibility of real-time tactile guidance using commercially available components, marking a concrete step toward accessible navigation support that enhances safety and autonomy for blind and visually impaired individuals. Full article

The increasing integration of artificial intelligence (AI) in educational decision-making raises a critical question: how to design AI systems that can effectively support teachers while maintaining an appropriate level of trust. Addressing this question requires not only continuous improvements in the technical capabilities of AI systems but also an examination from a human-AI interaction perspective of how different system designs influence users’ cognitive performance and affective responses, thereby providing guidance for system optimization and design. Therefore, this study conducted a randomized controlled experiment with 120 pre-service teachers to investigate how AI-assisted decision-making paradigms and AI explainability jointly influence teachers’ task performance and trust in AI, and whether these effects transfer to subsequent independent tasks. The results indicate that the effect of explanatory interface on task performance is context dependent and yields an immediate positive impact. Under the concurrent paradigm, the explanatory interface of the AI system significantly improves immediate task performance, whereas no significant effect is observed under the sequential paradigm. Moreover, this improvement is confined to the task execution stage and does not transfer to subsequent independent tasks. In contrast, the effect of explanatory interface on trust exhibits a delayed and negative pattern. The explanatory interface has no significant impact on situational trust, while it exerts a negative effect on learned trust and suppresses the natural development of both cognitive trust and emotional trust. In addition, different AI-assisted decision-making paradigms exhibit distinct patterns of influence on task performance and trust. Although the concurrent paradigm performs worse than the sequential paradigm in terms of immediate task performance, it is more effective in promoting users’ emotional trust. Overall, these findings extend the theoretical understanding of the mechanisms of explainability in human-AI interaction and provide empirical evidence for the joint design of explainable AI systems and human-AI collaboration paradigms. Full article

Pedestrians are amongst the most vulnerable road user groups. Efforts to enhance pedestrian safety have mainly focused on intersections and midblock crossings. This study investigated the effect of bus stop environments on pedestrian safety in Kumasi, an area with a high incidence of pedestrian fatalities in Ghana. Crashes within a 50 m radius of bus stops were extracted using a spatial join. The Negative Binomial regression model was applied to model pedestrian crashes around bus stops as a function of three distinct non-collinear independent variable groups: road design features, bus stop characteristics, and pedestrian exposure measures. Formal bus stops were associated with higher crash rates than informal ones. The presence of medians and crosswalks was associated with lower crash rates, whereas wider carriageways were associated with higher crash rates. Higher crashes were linked to passing pedestrians and waiting pedestrians, while crossing pedestrians were associated with reduced crashes. These findings suggest that the combined effects of infrastructure and behavioural factors influence pedestrian safety at bus stops. Prioritising low-cost safety treatments, such as guard-railed waiting areas, marked crosswalks, medians, and raised crossings, around bus stops will yield substantial safety benefits for resource-constrained contexts and advance sustainable urban mobility. Full article

Credit card fraud detection remains a critical challenge in financial security, characterized by severe class imbalance and the need to capture complex temporal patterns in transaction sequences. Traditional machine learning approaches treat transactions as independent events, failing to model the sequential nature of user behavior and suffering from inadequate handling of minority class samples. In this paper, we propose an integrated framework that combines generative modeling and time-aware sequential learning for credit card fraud detection. Our approach addresses two fundamental limitations: (1) we model transaction histories as temporal sequences using a Transformer-based architecture that captures both long-term dependencies and abrupt behavioral changes through multi-head self-attention mechanisms, and (2) we employ CTGAN to generate high-quality synthetic fraudulent samples, providing more effective oversampling than conventional techniques like SMOTE. The Time-Aware Transformer incorporates temporal encoding and position-aware attention to preserve transaction order and time intervals, while CTGAN learns the complex conditional distributions of fraudulent transactions to produce realistic synthetic samples. We evaluate our framework on the IEEE-CIS Fraud Detection dataset, demonstrating significant improvements over representative classical and sequential deep-learning baselines. Experimental results show that our method achieves superior performance with an AUC-ROC of 0.982, precision of 0.891, recall of 0.876, and F1-score of 0.883, outperforming the representative baselines considered in this study, including traditional machine learning models, standalone deep learning architectures, and supervised sequential neural models. Ablation studies confirm the individual contributions of both the sequential modeling component and the generative oversampling strategy. Our work demonstrates that combining temporal sequence modeling with generative synthesis provides a robust solution for imbalanced fraud detection, with potential applications extending to other domains requiring sequential pattern recognition under extreme class imbalance. Full article

Current 360-degree video streaming systems consider viewport prediction, adaptive bitrate allocation, tile selection, and quality-of-experience (QoE) estimation as independent activities, yielding fragmented pipelines that do not scale well across content type and network conditions and do not scale well to individual users. We propose ImmerseFM-3D, a foundation model that jointly solves all four sub-tasks through a single shared representation. Seven input modalities, namely video frames, network traces, head-motion trajectories, ambisonics audio, depth maps, eye-tracking signals, and CLIP scene semantics, are fused by four-layer cross-modal attention and compressed into a 256-dimensional bottleneck latent via a variational information bottleneck. Four task-specific decoders operate on this shared latent simultaneously. A model-agnostic meta-learning adapter augmented with episodic memory and a hypernetwork personalizes the model from as little as 1 s of user interaction data. An extended branch supports six-degrees-of-freedom volumetric content through spherical harmonic viewport decoding and depth-aware tile importance weighting. Trained and evaluated on the IMMERSE-1M combined dataset (1000 h of 360° and volumetric video, 524 users, and over 50,000 mean opinion scores), ImmerseFM-3D reduces the mean angular viewport error by 34%, lowers the bandwidth violation rate from 8.3% to 3.1%, and achieves a QoE Pearson correlation of 0.891. The personalization adapter reaches 90% of peak performance in 22 s, while zero-shot cross-format transfer attains 72% of full in-domain accuracy. Full article

Depression is notable for high rates of disability. The medical model typically characterizes depression as a physiological dysfunction or psychological disorder. However, both views fail to appreciate the phenomenology of depressed experience. Drawing on the existential phenomenology of Merleau-Ponty, this article contends that the lived experience of chronic depression is marked by a disturbance between the body-subject and the world. More specifically, the experience of depression is characterized by alienation from the world, self and others. While anti-depressants have long been the first line of treatment of depression, many governments subsidize cognitive behavioral therapy (CBT) as an adjunct treatment. CBT is said to be the gold standard psychotherapeutic treatment given that it is evidence-based, cost-effective, and short in duration. However, not only are these justifications questionable, but the theoretical underpinnings of CBT have ideological significance. Rather than approaching depressed persons as body-subjects, CBT casts service users as neo-liberal subjects, insofar as depression is characterized as disordered thinking that is independent of a person’s situated life. The emphasis on quickly returning people to work to reduce strain on welfare systems, while a valid economic concern, is not a valid therapeutic concern. The limited choice of subsidized psychotherapeutic options fails to recognize that depression is a heterogenous phenomenon, meaning that the CBT model of disordered thinking is not necessarily representative of the way in which depression manifests. Full article

In this paper, we propose a downlink (DL) channel estimation scheme for frequency-division duplex (FDD) multi-antenna orthogonal frequency-division multiplexing (OFDM) systems, leveraging atomic norm minimization (ANM) and deep neural networks (DNN). Unlike time-division duplex (TDD) systems, where uplink (UL) and DL channels are reciprocal, FDD systems do not share this reciprocity, leading to increased channel training overhead. However, both theoretical analyses and empirical evidence reveal that key channel characteristics—such as angles of arrival and departure, path delays, and the number of propagation paths—exhibit partial reciprocity between UL and DL. Building on this insight, we design a DL channel estimation scheme that exploits frequency-independent UL parameters along with estimated DL channel gains. Our method integrates ANM with DNN to enhance estimation accuracy and efficiency. Specifically, ANM formulates the estimation problem while avoiding the off-grid errors inherent in traditional grid-based methods. To further mitigate performance degradation in clustered-path channels and reduce computational complexity, we introduce a DNN-based architecture that predicts channel parameters. The DNN captures hidden relationships between received pilot signals and frequency-independent channel parameters, enabling accurate estimation with linear time complexity. During training, ANM assists in serving users, ensuring reliable performance. Once the DNN is fully trained, it takes over to balance quality of service (QoS) and latency, providing an efficient and accurate solution for DL channel estimation in FDD-OFDM systems. Full article

This study introduces a novel virtual robotic approach for automated characterization of pedestrian network accessibility from semantically segmented 3D LiDAR point clouds. With approximately 8 million Canadians living with disabilities, scalable accessibility assessment methods are critical. The proposed methodology integrates a Tangent Bug navigation algorithm—extended from 2D to 3D point cloud environments—with a triangular virtual robot grounded in ADA and IBC accessibility standards. The robot navigates classified point cloud data to simultaneously extract related parameters per step including those related to the accessibility assessment, including running slope, cross-slope, path width, surface type, and step height, aligned with the Measure of Environmental Accessibility (MEA) framework. Unlike existing approaches, the method characterizes not only formal sidewalk segments but also the critical transitional linkages between building entrances and the pedestrian network. Rather than evaluating features against fixed binary thresholds, it records continuous raw measurements enabling personalized accessibility assessment tailored to individual user profiles. Quantitative validation demonstrates high accuracy for path width (NRMSE = 2.71%) and reliable slope tracking. The proposed approach is faster, more cost-effective, and more comprehensive than traditional manual methods, and its segment-independent architecture makes it well-suited for future city-scale deployment. Full article

Despite recent advances in artificial intelligence for agriculture, reliable crop recommendation remains constrained by limited access to soil diagnostics, insufficient integration of environmental context, and the absence of transparent, quantitative evaluation frameworks. This study addresses the research question: How can we integrate multiple indicators to generate accurate, explainable, and context-sensitive crop recommendations? To this end, we propose a multimodal decision-support framework that combines image-based soil texture classification with geospatial, and climatic information. A convolutional neural network was trained on a curated dataset of 3250 soil images aggregated from four publicly available sources, covering four primary soil texture classes, alongside tabular soil and nutrient data. The model was evaluated using 5-fold stratified cross-validation, achieving an average classification accuracy of 99.30% (standard deviation ≈ 0.66), and was further validated on an independent hold-out test set to assess generalization performance. To enhance practical applicability, the framework incorporates elevation, rainfall, temperature, and major soil nutrients, and employs a large language model to generate user-oriented, interpretable justifications for each recommendation. Crop recommendations were quantitatively evaluated using a novel Agronomic Suitability Score (ASS), which measures alignment across soil compatibility, climatic suitability, seasonal alignment, and elevation tolerance. Across six geographically diverse case studies, the framework achieved mean ASS values ranging from 3.76 to 4.96, with five regions exceeding 4.45, demonstrating strong agronomic validity, robustness, and scalability. A Streamlit-based application further illustrates the system’s ability to deliver accessible, location-aware, and explainable agronomic guidance. The results indicate that the proposed approach constitutes a scalable decision-support tool with significant potential for sustainable agriculture and food security initiatives. Full article

Older adults often need guidance when visiting new buildings for the first time. However, indoor navigation remains challenging due to the lack of Global Positioning System (GPS) availability, visually repetitive corridors, and frequent location failures. This article presents a multimodal indoor navigation assistant that combines graph-based route planning with visual landmark verification to provide step-by-step guidance. The environment is modelled as a directed graph whose nodes are annotated with semantic landmarks, and the graph is constructed primarily from a video of the building, reducing the need for 3D scanners, beacons, or other specialised instruments. Routes are calculated using Dijkstra’s shortest-path algorithm over the semantic graph. During navigation, camera frames are analysed using a restricted vision-language recognition strategy that only considers candidate landmarks from the current and next nodes, reducing false detections and improving interpretability. To increase robustness, a temporary voting mechanism was introduced to confirm node transitions, as well as a hierarchical redirection strategy with local and global recovery. The system is implemented in two modes: handheld mode with visual cues using augmented reality arrows, mini map and voice instructions, and hands-free mode with front camera using voice instructions and keywords. Evaluation involved preliminary technical testing in the United Kingdom followed by formal user validation in Spain. During these trials, participants reported high usability, strong confidence and safety, and increased perceived independence. Full article

AI travel assistants are increasingly designating hotels as “eco”, yet when the evidence is not independently verifiable, these recommendations may serve as persuasive cues or credible decision support. We present a preregistered 2 × 2 between-subject laboratory experiment (n = 63) that manipulates autonomy framing (Recommend vs. Plan) and evidence verifiability (verifiable vs. non-verifiable) in a realistic hotel-booking workflow with a standardized “Verify eco-claim” drawer. Phasic arousal was recorded at recommendation onset (E1) and verification initiation (E3), employing eye-tracking indexed verification behavior (verify clicks, time-to-verify, verification depth) and event-locked galvanic skin response (GSR). Verifiability did not directly speed up or deepen verification (H1 not supported), but verification was common (74.6% clicked Verify). Rather, autonomy influenced checking: Plan slowed verification and altered verification depth. E1 SCR revealed an Evidence × Autonomy interaction, which is consistent with an autonomy-boundary account (H4), rather than credibility stress emerging as a simple evidence main effect at E1 (H2 not supported as stated). Verification served as a repair moment: depending on the availability of diagnostic cues, arousal dynamics from E1 to E3 supported differential “repair” (H3). SCR dynamics explained incremental variance in perceived manipulation/greenwashing concern beyond condition and eye-tracking indices (H5b supported), but verification depth did not mediate effects on trust or delegation (H5a not supported). Overall, users’ interpretation of AI sustainability advice is influenced by autonomy, and multimodal process measures offer useful signals for auditing eco-recommendation designs in travel platforms. Full article