Search Results (140)

Background: Nonsteroidal anti-inflammatory drugs (NSAIDs) are widely recommended for the treatment of acute (low) back pain, despite modest effectiveness and well-known safety concerns, particularly in older patients. Pridinol is a centrally acting antispasmodic with a mechanism-oriented approach targeting muscle spasm, a key component of acute back pain. While a previous real-world analysis demonstrated a significantly better tolerability and effectiveness of pridinol compared with NSAIDs, age-dependent effects have not yet been systematically evaluated. Objective: To assess the age dependency of effectiveness, safety, and tolerability of pridinol versus NSAIDs in patients with acute (low) back pain under real-world conditions, based on already available data. Methods: This secondary analysis used propensity score-matched real-world data from the German Pain e-Registry (PROVIDENCE study; EUPAS identifier: 49718). A total of 934 patients with acute (low) back pain treated for four weeks with either pridinol (n = 467) or NSAIDs (n = 467) were stratified by age (<65 vs. ≥65 years). Outcomes included the incidence of adverse drug reactions (ADRs), ADR-related treatment discontinuations, time to ADR occurrence, and clinically meaningful improvement in pain-related disability (≥50% reduction in modified Pain Disability Index). Analyses were performed within and between age strata. Results: Overall, ADRs were reported by 9.0% of pridinol-treated patients and 20.8% of NSAID-treated patients (p < 0.001). In the pridinol cohort, ADR rates were virtually identical in patients <65 and ≥65 years (8.9% vs. 9.2%; p = 0.940). In contrast, NSAID-treated patients showed a pronounced age-related increase in ADR incidence (17.3% vs. 32.1%; p < 0.001). ADR-related treatment discontinuation rates under NSAIDs increased markedly with age (5.9% vs. 21.1%; p < 0.001), whereas rates under pridinol remained low and age independent (3.1% vs. 4.6%; p = 0.447). Gastrointestinal and cardiovascular ADRs were the main contributors to the age-related risk increase under NSAIDs, while corresponding events under pridinol were rare across age groups. Clinically meaningful improvement in pain-related disability was achieved with pridinol/NSAIDs in 91.9/48.0% (<65 years) and 88.1/47.7% (≥65 years; p < 0.001 for both). Conclusions: Age is a major modifier of NSAID-related risk but not of pridinol tolerability in acute (low) back pain. While NSAID-associated ADRs and treatment discontinuations increase substantially in patients aged 65 years or older, pridinol demonstrates a stable, age-independent safety profile combined with significantly better functional outcomes. These findings suggest that, particularly in older patients, mechanism-oriented alternatives such as pridinol may offer a more favorable benefit–risk profile than NSAIDs. Full article

Urban planning increasingly depends on methods capable of capturing citizen perspectives in forms that are both inclusive and analytically useful for decision-making. Conventional participation mechanisms, such as public meetings, paper questionnaires, and online platforms, often suffer from low reach, strong self-selection effects, and weak suitability for structured comparative analysis. This paper presents XRCity, a decision support system that combines extended reality, conversational artificial intelligence, and a planner-side backend to support participatory urban planning in public spaces. The system is centered on Olivia, a life-sized virtual assistant deployed on outdoor interactive screens, and on a backend environment that enables planners to prepare knowledge resources, configure interaction scripts, validate conversational behavior, process transcripts, and analyze elicited opinions. The contribution of the paper is not just the presentation of an XR interface, but the description and validation of a complete decision-support pipeline that connects campaign design, citizen interaction, opinion structuring, and planner-side analytics. The system was validated through real-world deployment in Torres Vedras, Portugal. Across more than 250 interactions and over 740 min of conversation, 191 usable sessions were analyzed, showing an average of 6.7 messages per user and 2.8 min per interaction. Of these sessions, 14.7% produced at least one structured response to an urban planning question, exceeding the project target of 10%. These results indicate the operational feasibility of using public-space conversational XR to elicit analyzable planning input, while a formal validation of the opinion-matching step remains future work. Full article

Mixed-scene holographic 3D display for film and television visual content presentation remains challenging because recorded digital holograms and computer-generated holograms (CGHs) are produced under different numerical and hardware constraints. Direct hologram superposition typically causes strong zero-order interference, diffraction efficiency degradation, and sampling pitch mismatch between the recording sensor and the replay panel, while conventional resizing reduces the effective replay aperture and narrows the available parallax. To address these issues, this paper proposes a zero-order-suppressed single-hologram fusion framework with parallax-preserving digital resizing. A recorded digital hologram is first processed by Gaussian high-pass filtering to suppress the dominant zero-order component, then resampled to match the LCOS replay pitch, and finally normalized and fused with a CGH generated through bipolar intensity encoding. On this basis, two resizing routes are developed: a spatial-domain method for aperture-preserving whole-scene scaling and a frequency-domain method for object-selective scaling and translation. Optical validation on a three-channel LCOS prototype shows that the quantitative diffraction efficiency analysis predicts an increase from approximately 10.1% to 20.05% per reconstructed object for the two-hologram fusion case, and the revised experimental results are consistent with this improvement trend. The experiments further verify replay scaling at multiple factors, the selective manipulation of physical and virtual objects, mixed-scene color replay, and occlusion-consistent depth ordering. Together with the distortion analysis, these results demonstrate improved replay visibility after fusion while maintaining geometric controllability and effective replay aperture. By relying on hologram-domain preprocessing and resizing rather than full mixed-scene recomputation, the proposed method also reduces computational burden. The study therefore provides an efficient and controllable mixed-scene holographic replay framework for visually enriched film and television content presentation, although its depth applicability remains bounded and dedicated real-time timing benchmarks are left for future work. Full article

Long-term time series forecasting (LTSF) is critical for modern power systems, energy management, and grid planning. Yet virtually all existing forecasting models employ stationary activation functions that apply identical nonlinear mappings regardless of temporal context—a fundamental mismatch with real-world load data, which exhibits strongly regime-dependent dynamics such as summer demand peaks, winter heating patterns, and overnight low-load periods. We address this gap by proposing TC-KAN (Time-Conditioned Kolmogorov–Arnold Network), the first forecasting architecture to augment KAN activation functions with position-aware coefficient parameterisation. The core innovation replaces the static polynomial coefficients in standard KAN activations with position-conditioned coefficients produced by a lightweight positional-embedding MLP, providing additional learnable capacity beyond standard KAN while adding negligible parameter overhead. TC-KAN further integrates a dual-pathway processing block—combining depthwise convolution for local temporal pattern extraction with the time-conditioned KAN layer for enhanced nonlinear transformation—within a channel-independent framework with Reversible Instance Normalisation. Experiments were conducted on four standard ETT benchmark datasets and the high-dimensional Weather dataset. TC-KAN achieves superior or competitive accuracy in most configurations while requiring merely 51K parameters—approximately 40% of DLinear and ∼100× fewer than iTransformer. On ETTh2, TC-KAN reduces the mean squared error by up to 61.4% over DLinear, and matches the current state-of-the-art iTransformer on ETTm2 at a fraction of the computational cost. This extreme parameter reduction circumvents the steep memory bottlenecks endemic to massive Transformer models, positioning TC-KAN as a highly practical architecture tailored precisely for resource-constrained edge deployments—such as on-device load forecasting inside smart grid sensors and industrial IoT controllers. Full article

Accurate alignment of real-world object poses with their virtual counterparts using sensors, e.g. cameras, is essential for consistent interaction in mixed-reality systems. However, objects can undergo abrupt, untracked movements during periods when a tracking system is inactive, e.g., overnight, causing stored pose records to become inconsistent with the real scene and breaking user interaction in the virtual environment. Off-the-shelf 3D reconstruction networks such as MASt3R (Matching and Stereo 3D Reconstruction) method provide metrically scaled 3D point maps and pixel correspondences, but they are trained on static scenes and therefore fail to produce reliable object correspondences when the object has moved. We propose a robust pipeline that combines MASt3R’s metrically scaled 3D outputs with a background-based alignment strategy to recover and apply the true pose change of moved objects. Our method first segments foreground and background and extracts 3D background point sets for a reference day and a current day. An affine transformation between these background point sets is estimated via a standard registration technique and used to express the current-day object 3D coordinates in the reference coordinate frame. Within that unified frame we compute the object pose change and apply the resulting transform to the virtual object, restoring real–virtual consistency. Experiments on real scenes demonstrate that the proposed approach reliably corrects pose misalignments introduced during inactive periods and substantially improves over applying MASt3R alone, thereby enabling restored and consistent user interaction in the virtual environment. Full article

The increasing risk of air pollution in closed areas like passenger vehicles requires smart and real-time air quality reading solutions. Gases such as carbon monoxide (CO)—which is colorless and odorless and is produced by exhaust systems—air conditioners, and combustion sources are very dangerous to health because they can cause respiratory distress and poisoning at high levels. Traditional in-vehicle CO monitoring systems use a single-point sensor and a fixed threshold, which are insufficient in a dynamic cabin environment subject to factors such as vehicle size, ventilation rate, number of occupants, and incoming traffic. To address these drawbacks, this paper proposes a new E-Nose system with Virtual Sensor-Augmented Embedded Intelligence to estimate the CO concentration in vehicle cabins in real time. The system combines data from cheap gas sensors and improves it using virtual sensor machine learning models trained to predict or enhance sensor responses in real time. Embedded intelligence, deployed locally on edge hardware, supports low-latency processing, dynamic calibration, and noise filtering to respond to fluctuating environmental conditions adaptively. This architecture enables more accurate, robust, and context-aware estimation of CO levels compared to traditional threshold-based methods. Experimental validation across varied vehicular scenarios demonstrates superior precision and responsiveness, providing timely warnings even under complex dispersion patterns. Classifier Gradient Boosting, which builds an ensemble of weak learners sequentially, matched the Random Forest with 99.94% training and 98.59% model accuracy, confirming its strong predictive capability. The system is designed to be cost-effective, scalable, and easily integrable into modern automotive platforms. This study also contributes to the field of smart ecological recording and demonstrates the effectiveness of the virtual sensor-enhanced embedded system as an effective way to improve passenger safety by providing pre-emptive on-board air quality monitoring. Full article

This paper presents a novel data fusion framework that integrates Automatic Identification System (AIS) data with radar surveillance for real-time vessel monitoring in inland restricted waterways. The approach exploits the complementarity between heterogeneous sensors: AIS provides semantic information with temporal sparsity, while radar offers high-frequency observations without vessel identity. The proposed solution combines Kalman filtering and Newton interpolation (K-N) for high-resolution AIS resampling, followed by optimal data association using the Kuhn-Munkres (KM) algorithm. By formulating data association as a global optimization problem, the framework achieves globally optimal sensor fusion while effectively handling data imbalance through virtual point augmentation. Experimental validation using real-world data demonstrates a matching accuracy of 94.2% in low-density scenarios and 80.1% in high-traffic conditions, with computational efficiency suitable for real-time deployment. The system performs consistently across different waterway geometries, although performance varies slightly between curved and straight channels. By fusing the high temporal resolution of radar data with the rich identity information from AIS, this framework enables more accurate and reliable vessel tracking, providing waterway authorities with enhanced situational awareness for improved traffic management and scheduling in restricted waterways. Full article

Cross-view geo-localization tries to find the matching place in large satellite or aerial pictures from photos taken at ground level, which is useful for applications like self-driving cars, flying drones, and adding virtual objects to real city scenes. However, the traditional deep learning hybrid CNN-Transformer architecture and complex geometric submodules result in a large computational overhead, making it difficult to apply in real-time on resource-constrained devices. To make it light, fast, and accurate, this paper suggests an effective way to make a state-space model for cross-view geo-localization tasks. The model replaces the traditional self-attention structure with a state-space vision backbone, lowering the sequence modeling complexity from quadratic to linear and greatly accelerating the inference process; it devises a channel-group aggregation strategy without any learnable parameters, producing a comprehensive yet lightweight representation, and introduces a dynamic difficulty-aware loss function that assigns varying weights to all negative samples within a batch according to their similarities, greatly improving the efficiency of hard-negative sample mining and the quality of convergence. The results on the authoritative public datasets CVUSA and CVACT indicate that our method has high accuracy and low computational complexity, providing a feasible approach for the lightweight design of more powerful cross-view geolocation models in the future. Full article

The stochastic fluctuations of AI computational loads inject harmonic currents into the DC bus, amplifying bus voltage ripples and weakening the power quality. Existing strategies typically rely on high-gain control strategies to minimize harmonic output impedance, aiming at full absorption of harmonic currents. However, such designs rarely consider engineering constraints such as capacity and current boundaries, which impose inherent limits on harmonic absorption. To address these issues, this paper proposes an adaptive harmonic impedance control and flexible compensation method for AI data centers. By integrating DC bus voltage feedforward with output current feedback, a virtual harmonic impedance control channel is constructed to enable real-time impedance shaping. Then, an adaptive gain regulation mechanism is developed to adjust harmonic impedance according to the available capacity and current margin. Compared with traditional strategies relying on fixed high gains or resonant links, the proposed method allows for the continuous regulation of harmonic impedance over a wide range. This enables the dynamic matching of harmonic absorption capability with the available capacity, effectively suppressing the risks of overcurrent, saturation, and stability degradation. Simulation and 8 kW experimental results verify the correctness and effectiveness of the proposed analysis and control strategy. Full article

Droop control is an effective power regulation method for islanded microgrids to cope with fluctuations in renewable energy and loads. However, its power coordination performance is easily affected by the line impedance. When virtual impedance is introduced to enhance impedance matching, fixed values struggle to adapt flexibly to varying grid conditions. To address this specific limitation, this paper proposes a novel power coordination control strategy based on real-time line impedance identification. The method first analyzes the power distribution principle and equilibrium conditions under droop control. Crucially, it then establishes a dynamic virtual impedance regulation mechanism. By continuously identifying the actual line impedance, the proposed strategy dynamically adjusts the virtual impedance, thereby reshaping the inverter’s output impedance in real-time to match the grid conditions. This approach directly enhances the inverter’s adaptability to impedance variations, which is the core challenge in robust power coordination. Simulation results demonstrate that, compared to methods using fixed virtual impedance, the proposed strategy significantly improves power-sharing accuracy and system robustness under uncertainties such as fluctuating line impedance and load changes. Full article

Autonomous driving systems rely on vast and diverse datasets for robust object recognition. However, acquiring real-world data, especially for rare and hazardous scenarios, is prohibitively expensive and risky. While purely synthetic data offers flexibility, it often suffers from a significant reality gap due to discrepancies in visual fidelity and physics. To address these challenges, this paper proposes a novel real–virtual fusion framework for efficiently generating highly realistic augmented image datasets for autonomous driving. Our methodology leverages real-world driving data from South Korea’s K-City, synchronizing it with a digital twin environment in Morai Sim (v24.R2) through a robust look-up table and fine-tuned localization approach. We then seamlessly inject diverse virtual objects (e.g., pedestrians, vehicles, traffic lights) into real image backgrounds. A critical contribution is our focus on inconsistency mitigation, employing advanced techniques such as illumination matching during virtual object injection to minimize visual discrepancies. We evaluate the proposed approach through experiments. Our results show that this real–virtual fusion strategy significantly bridges the reality gap, providing a cost-effective and safe solution for enriching autonomous driving datasets and improving the generalization capabilities of perception models. Full article

The integration of eye-tracking into consumer-grade virtual reality (VR) headsets presents a transformative opportunity for assessing user mental states within simulated, immersive environments. However, the validity of this built-in technology must be established against gold-standard real-world eye-tracking systems. This study employs a novel paradigm using a physically moving object to evaluate the accuracy of dynamic smooth pursuit, a key oculomotor function in mental state assessment. We rigorously validated the performance of the HTC Vive Pro Eye’s integrated eye-tracker against the Tobii Pro Glasses 3 using a high-precision OptiTrack motion capture system as ground-truth for object position. Eight participants completed both 2D and 3D gaze-tracking tasks. In the 2D condition, they tracked a dot on a screen, while in the 3D condition, they tracked a physically moving object. The real-world object trajectories captured by OptiTrack were replicated within a VR environment. Gaze data from both the VR headset and the Tobii glasses were recorded simultaneously and compared to the OptiTrack baseline using Dynamic Time Warping (DTW) to quantify accuracy. Results revealed a task-dependent performance. In the 2D task, the Tobii glasses demonstrated significantly lower DTW distances, indicating superior accuracy. Conversely, in the 3D task, the VR headset significantly outperformed the glasses, showing a closer match to the real object trajectory. This suggests that while traditional eye-trackers excel in constrained 2D contexts, integrated VR eye-tracking is more accurate for naturalistic 3D gaze pursuit. We conclude that VR headset eye-tracking is not only a reliable but also a cost-effective tool for research, particularly offering enhanced performance for studies conducted within immersive 3D simulations. Full article

High penetration of wind farms into the power grid lowers system inertia and compromises stability. This paper proposes a grid-forming control strategy for Permanent Magnet Synchronous Generator (PMSG) wind turbines based on DC-link voltage matching and virtual inertia. First, a relationship between grid frequency and DC-link voltage is established, replacing the need for a phase-locked loop. Then, DC voltage dynamics are utilized to trigger a real-time switching of the power tracking curve, releasing the rotor’s kinetic energy for inertia response. This is further coordinated with a de-loading control that maintains active power reserves through over-speeding or pitch control. Finally, the MATLAB/Simulink simulation results and RT-LAB hardware-in-the-loop experiments demonstrate the capability of the proposed control strategy to provide rapid active power support during grid disturbances. Full article

Understanding surface textures through visual cues is crucial for applications in haptic rendering and virtual reality. However, accurately translating visual information into tactile feedback remains a challenging problem. To address this challenge, this paper presents a class-conditional Generative Adversarial Network (cGAN) for cross-modal translation from texture images to vibrotactile spectrograms, using samples from the LMT-108 dataset. The generator is adapted from pix2pix and enhanced with Conditional Batch Normalization (CBN) at the bottleneck to incorporate texture class semantics. A dedicated label predictor, based on a DenseNet-201 and trained separately prior to cGAN training, provides the conditioning label. The discriminator is derived from pix2pixHD and uses a multi-scale architecture with three discriminators, each comprising three downsampling layers. A grid search over multi-scale discriminator configurations shows that this setup yields optimal perceptual similarity measured by Learned Perceptual Image Patch Similarity (LPIPS). The generator is trained using a hybrid loss that combines adversarial, $L_1$, and feature matching losses derived from intermediate discriminator features, while the discriminators are trained using standard adversarial loss. Quantitative evaluation with LPIPS and Fréchet Inception Distance (FID) confirms superior similarity to real spectrograms. GradCAM visualizations highlight the benefit of class conditioning. The proposed model outperforms pix2pix, pix2pixHD, Residue-Fusion GAN, and several ablated versions. The generated spectrograms can be converted into vibrotactile signals using the Griffin–Lim algorithm, enabling applications in haptic feedback and virtual material simulation. Full article

Background: The high prevalence of overweight and obesity in Singapore necessitates scalable primary prevention strategies. This study evaluated the short-term effectiveness of FitterLife, a 12-week, digitally delivered, group-based behavioural weight management programme targeting at-risk adults without diabetes or hypertension in the community. Methods: In a retrospective matched cohort study, we compared 306 FitterLife participants (enrolled from October 2021 to January 2025) with 5087 controls identified from a population health data mart, matched on age, sex, ethnicity, and baseline body mass index (BMI). The primary outcome was achieving ≥5% weight loss or a ≥1 kg/m² BMI reduction at 12 weeks. Programme effectiveness was analysed using propensity score matching (1:1) and inverse probability weighted regression. Mixed-effects models assessed weight/BMI trajectories and modified Poisson regression identified behavioural factors associated with success. Results: After matching, FitterLife participants were more likely to achieve the weight loss target than controls (45.7% vs. 13.7%, coefficient = 0.32, 95% confidence interval [CI]: 0.26–0.38) and were over three times as likely to succeed (Adjusted incidence rate ratio [aIRR] = 3.37, 95% CI: 2.87–3.93). The programme group showed significant reductions in weight (−2.23 kg, 95% CI: −2.57 to −1.90) and BMI (−0.86 kg/m², 95% CI: −0.95 to −0.73) at the end of programme. Higher session attendance and improved behavioural factors were associated with success. Conclusions: FitterLife was effective in achieving clinically significant short-term weight loss in a real-world setting. The findings demonstrate the potential of a scalable, behavioural theory-informed, virtual group model as a viable primary prevention strategy within national chronic disease management efforts. Full article