We present a theoretical and numerical investigation of the role of perpendicular magnetic anisotropy (PMA) in shaping spin-wave (SW) dynamics under low magnetic fields in thin and ultrathin magnetic films. PMA introduces an in-plane torque that counteracts exchange, dipolar, and Zeeman contributions, fundamentally modifying SW dispersion and inducing a local minimum that, under specific conditions, becomes the lowest frequency across all geometric configurations. This results in a sombrero-shaped dispersion in ultrathin films and a cowboy-hat-like shape in thicker films, where dipolar interactions dominate. Using isofrequency contour (IFC) analysis, we demonstrate that these PMA-induced dispersion shapes enable nontrivial wave phenomena unprecedented in uniform media: bireflection and negative reflection in ultrathin films and trireflection in thicker films─where a single incident beam splits into three reflected components, two with negative angles. Most remarkably, we predict and demonstrate trirefraction, where one incident beam generates three refracted beams with two exhibiting negative refraction angles. We further show anti-Larmor precession of magnetization near the dispersion minimum in thicker films, arising from the interplay between PMA-induced and dipolar torques. Systematic simulations across diverse material systems─metallic films, ferrimagnetic garnets, hybrid structures, and multilayers─confirm the universal nature of these phenomena in any PMA system supporting stripe domain transitions. These results open new opportunities to explore wave phenomena beyond magnonics.
Falls have continued to pose a significant risk, particularly for the elderly. Preventing injuries and fatalities has required accurate and timely detection. However, the complexity of real-world environments and the need for precision have presented ongoing challenges to existing fall detection systems. While wear-able sensors have proven useful, they are often uncomfortable for continuous use, and traditional detection methods have demonstrated unreliability due to their sensitivity to environmental conditions. Consequently, the development of a more accurate, real-time, non-invasive, and environment-independent detection 1 approach has become essential. In this study, we have developed and evaluated two novel vision-based fall detection systems. In the first system, we have employed You Only Look Once , version 8 (YOLOv8) or YOLOv11 for real-time detection of both the person and the bed within each video frame. Subsequently, we have applied AlphaPose to extract human body keypoints, followed by action recognition using Spatial-Temporal Graph Convolutional Networks (ST-GCN). A custom fall detection logic has been integrated, which evaluates both posture and spatial position relative to the bed to confirm fall events. In the second system , we have utilized pose-based models (YOLOv8-pose or YOLOv11-pose) that simultaneously detect the person and estimate keypoints. Based on this data, we have designed an independent fall logic that classifies fall events through posture and location analysis. This system has also incorporated a real-time alert mechanism that sends WhatsApp notifications to enable immediate response in the event of a fall. Experimental results have demonstrated that both systems offer robust and reliable fall detection across various scenarios, significantly enhancing safety and supporting the well-being of individuals at risk.
We assess the influence of different Eulerian geophysical input fields on Lagrangian drift simulations using DriftNet, a learning-based method designed to simulate Lagrangian drift on the sea surface. Two experiments are conducted: a fully numerical experiment (Benchmark B1) and a real-world drifters-based experiment (Benchmark B2). Both experiments are performed in two regions with different ocean dynamics: North East Pacific and Gulf Stream regions. The performance of DrifNet is evaluated with three different metrics: separation distance between simulated and ground-truth trajectories, the normalized cumulative Lagrangian separation and the autocorrelation of Lagrangian velocities. In both regions, results from B1 show that combining assimilated sea surface currents (SSC) with fully observed sea surface height (SSH) leads to greatest improvement in trajectory simulation. This configuration reduces separation distance by over 50% and significantly decreases normalized cumulative Lagrangian separation and metrics related to velocities autocorrelation functions compared to the baseline using SSC alone. On the other hand, the inclusion of sea surface temperature (SST) either alone or in combination with SSC generally degrades performance. In B2, using satellite-derived SSH, Ekman and winds velocities improves surface drifters trajectories simulation, particularly in the North East Pacific. While the satellite-derived SST in combination with reanalysis-based SSC configuration leads to better trajectories simulation in the Gulf Stream. Overall, we highlight the added value of combining multiple geophysical fields to improve Lagrangian drift simulation on both numerical and real-world experiments.
Artificial light at night (ALAN) degrades nocturnal ecosystems and complicates astronomical observation. Although all-sky imaging and GIS-based light-pollution mapping are well established in the analysis of light pollution, identifying local contributors to ALAN still requires time-consuming cross-comparisons, done in separate views, making light halo–source attribution slow and manual. We present an interactive system that addresses this gap by co-registering Sky Quality Camera all-sky imagery and OSM-derived candidate emitters (e.g., settlements, roads, aerodromes, industrial sites) in one observer-centered scene. The viewer is placed at the locations of the captured all-sky images in 3D digital terrain model-based scenes, realistically illuminated by the sky under selected conditions for an immersive view of nighttime scenarios. OpenStreetMap features are projected onto a surrounding sphere via inverse stereographic projection, with point markers and horizontal-extent indicators to support rapid visual matching between observed halos and plausible sources. Users can switch scenes and processed sky images, adjust projection parameters, and inspect scenes in VR or in an additional cylindrical projection for a panoramic desktop view. A companion web tool configures location classes and display ranges. The presented system primarily targets exploratory analysis, with its main contribution being the novel co-visualization of light sources and light halos; expert interviews positively validated this analytical focus. As a secondary outcome, the system’s immersive first-person representation may also enrich educational communication and outreach on ALAN impacts.
This paper presents PUR, a metric designed to evaluate the computational efficiency of neural networks as a standalone measure. This metric estimates a network’s computational efficiency by calculating the average percentage of useful parameters in determining correct predictions. The experimental findings indicate that networks maintaining an optimal balance between performance and cost tend to achieve higher scores in computational efficiency. Additionally, we observed a relationship between a network’s efficiency with minimal or no training and its efficiency post full training. Consequently, this metric has been applied in Training-free Neural Architecture Search to rapidly identify models with potential efficiency. Pruning based on the parameters usefulness index also yields surprising results, achieving compression rates above 85
