Search Results (2,344)

In the framework of soil erosion processes at different spatial and temporal scales, the concept of connectivity is applied to express the physical link among hillslopes and the channel network concerning flow motion and associated sediments. In this paper, basin sediment connectivity was used in the framework of the Sediment Delivery Distributed (SEDD) model together with sediment yield measurements performed at the W2 experimental basin (Calabria, Italy) in the period 1978–1994. The frequency distribution of the travel time was used to estimate the coefficient β_L of the structural component of the sediment delivery ratio of each morphological unit. Then, using the event sediment yield data, the sediment balance equation was applied to estimate the coefficient β_F of the functional component of the sediment delivery ratio. The analysis demonstrated that the increase in event intensity produces a decrease in β_F and a consequent increase in the sediment delivery ratio. At the annual scale, the sediment balance equation, coupled with a relationship to estimate β_F by the runoff coefficient, allowed for obtaining a good agreement between the measured and calculated sediment yields. The errors in the estimate of basin sediment yield were less than or equal to ±40% for 71.4% of cases. The reliability of the SEDD model is good for both the investigated event and annual scales and improves from the former to the latter, which is, therefore, particularly suited for applications. The model can also be applied to a variety of basins worldwide where it has previously been used without differentiating the structural and the functional connectivity components. Full article

The Litang fault zone, with intense late Quaternary activity, frequent strong earthquakes, and dense landslides along its trend, directly threatens the operational safety of the National Highway 318 (G318) Sichuan–Xizang transportation corridor. Predicting potential earthquake-triggered landslide (EQTL) susceptibility is limited by the spatial heterogeneity of near-fault ground motion coupled with geological/topographic conditions. This study proposes a physics-driven, data-integrated framework that couples curvilinear grid finite-difference (CG-FDM) PGA simulation with a fault-geometry-incorporated random forest model—trained on landslides from the 2008 Mw 7.9 Wenchuan earthquake and adapted to the Litang fault zone via the 71° dip sub-model—for EQTL susceptibility prediction. Validation against historical high-susceptibility landslide zones and unstable slopes confirms the results. Maximum PGA reached 0.611 g with banded distributions along the fault, reflecting significant near-fault, basin, and topographic amplification. High and very high susceptibility zones cover 7.64% of the area, mainly within 5 km of the fault and at intersections, overlapping 57% of historical landslide susceptibility zones and containing 75.0% (by number) and 53.0% (by area) of unstable slopes. The minimum angle between ground-motion vector and aspect suggests enhanced triggering along slope direction. In the G318 Litang section, 13.48% (14.21 km) lies within high–very high susceptibility zones, indicating localized hazards. This framework supports EQTL susceptibility mapping and mitigation for major projects. Full article

Existing methods for human action recognition from dynamic point clouds commonly rely on farthest point sampling and dense spatiotemporal neighborhood queries. The resulting local geometric computations are expensive, which complicates deployment in resource-constrained settings. This paper presents the Global–Local Differencing Network (GLD-Net), a lightweight framework for point-cloud sequence learning designed around feature extraction, motion representation, and temporal modeling. Feature extraction uses a two-branch architecture: the global branch encodes the complete point cloud in each frame to learn a holistic spatial representation, whereas the local branch uniformly divides the body along the vertical direction into several semantic regions, each processed by an independent network. Motion is represented directly by the distances from each point to its nearest neighbors in the preceding and subsequent frames, without requiring point correspondences. For temporal modeling, bidirectional differencing is applied to frame-level features to represent action changes explicitly. The method requires neither farthest point sampling nor complex spatiotemporal neighborhood searches. On MSR-Action3D, GLD-Net achieves 95.82% accuracy with 0.579 M parameters and 1.42 G operations. Compared with PvNeXt, a model of similar scale, GLD-Net improves accuracy by 1.05 percentage points while reducing the parameter count by 19.6%. The implementation code is publicly available. Full article

Time is often treated by analogy with space and considered to be another dimension alongside the three spatial dimensions. I refer to this conception as the “spatialization of time.” This paper aims to offer a version of presentism that avoids such spatialization. Presentism is typically characterized as the thesis that everything is present, and it has given rise to numerous objections. I primarily address the challenges concerning change and persistence raised by Leininger and Tallant, respectively. These critics seem to presuppose that change involves a thing’s having different states at various times, and persistence requires its continuous existence across those times. To counter these presuppositions, I first reconsider what the presentist thesis amounts to and then integrate it with the Aristotelian claim that time is the number of motion or change. By scrutinizing this claim, I develop a dynamic model that quantifies the duration of change and persistence but does not regard times as temporal locations. Ultimately, I argue that things can exist and possess properties without being located at a specific time, thereby providing a robust defense of presentism against its spatializing alternatives. Full article

Many existing historical narrative experiences still rely on screen-based viewing and limited multimodal interaction, which tends to keep users in a passive receiving role and limits their cognitive understanding, reflective thinking, user engagement, and emotional response. To address this, we designed Vellum of Lies, an immersive tangible user interface for anti-feudal historical narratives. The work integrates physical props, sensors, motion-based input, and projection mapping, organizing the story into continuous interaction nodes that allow users to participate in narrative progression through embodied interaction, including spatial movement, object manipulation, and final choice-making. The final choice mechanism operationalizes Tragic Agency by allowing users to make meaningful choices while constraining their ability to alter the final tragic outcome, with the aim of evoking tension between perceived choice and narrative inevitability. A mixed-methods user experience evaluation (N = 32) compared Vellum of Lies with a video-watching narrative experience. In this small-scale exploratory study, the experimental condition showed significant advantages in the HUQ Overall score, Reflection, the UES-SF Overall score, Aesthetic Appeal, Interest/Enjoyment, Pleasure/Valence, and Arousal, while also showing lower NASA-TLX Overall scores, Mental Demand, and Effort. Other measured subdimensions did not reach statistical significance. These preliminary findings suggest that multimodal tangible interaction may function not only as a presentation technique, but also as a narrative mechanism that coordinates physical input, bodily movement, projected feedback, and constrained choice-making to support selected aspects of understanding, engagement, and emotional reflection in serious historical narratives. Full article

The Boussinesq equations are widely used to model incompressible thermally driven flows, but their numerical simulation becomes more challenging in domains containing fixed or moving rigid obstacles, particularly when the prescribed obstacle motion is uncertain. In this work, we develop a Brinkman-type fictitious-domain formulation for the incompressible Boussinesq system on a fixed computational domain. Brinkman penalization is used to impose the prescribed solid velocity, while a thermal penalty term enforces the temperature inside the immersed bodies. The resulting problem is discretized by a finite element method using skew-symmetric convective forms and grad-div stabilization. The uncertainty in the obstacle motion is introduced through a random oscillation amplitude and treated by a non-intrusive stochastic collocation method based on Gauss–Legendre quadrature. Stability and convergence of the fully discrete scheme are established. Numerical experiments for moving heated and hot–cold obstacles demonstrate the influence of the uncertain oscillation amplitude on the Nusselt number, kinetic energy, mean vorticity, and the spatial distributions of the mean and standard deviation of the solution fields. The results indicate that the proposed method provides a stable numerical framework for simulating incompressible thermally coupled flows with moving immersed obstacles under uncertainty in the prescribed obstacle motion. Full article

The unified expression of the mixed, dynamic and scattered navigation situation information in complex port area environments in a computable mathematical model is the foundation for ships to achieve intelligence and unmanned capability. Existing studies remain insufficient in terms of characterizing complex navigation situations involving multiple interacting factors, such as ship attributes, dynamic encounter relationships, spatial constraints, environmental conditions and navigation rules. To address this issue, a mathematical expression method for navigation situations is proposed. The ship interest perception area is first defined and discretized to characterize the directional anisotropy of surrounding situation constraints. Then, the ship interaction field, encounter conflict field, and navigation restriction field are constructed and integrated through normalization and weighted coupling. Environmental impacts, navigation-rule compliance, direction weights, and direction consistency are further incorporated to map the comprehensive situation constraint distribution into a virtual guidance direction, and a computable relationship between situation space and short-term behavioral preference space is established. Finally, the model is calibrated and validated using real ship data. The results demonstrate that the proposed model can effectively characterize directional multi-source constraints in typical complex port scenarios and generate directional preferences that are reasonably consistent with actual short-term ship motion, providing a structured information basis for navigation situation understanding and upper-level autonomous decision-making of unmanned ships. Full article

Skeleton-based action recognition has achieved significant progress through spatio-temporal graph convolutional networks. However, existing topology-enhanced methods treat all skeletal joints uniformly, failing to emphasize task-relevant joints for coordination-level hand-centric actions. Moreover, the graph topology is defined by fixed spatial proximity or generic channel-wise refinement, ignoring the fact that joints engaged in correlated motion patterns carry stronger discriminative signals. Therefore, we propose a Motion-Aware Spatio-Temporal Graph Convolutional Network (MA-STGCN) with a unified motion-aware framework consisting of two tightly coupled modules. First, a Motion-Aware Joint Attention (MJA) module is proposed, enabling the model to dynamically emphasize joints with salient motion for different action categories. Then, a Motion-Correlated Graph Refinement (MCR) module constructs a sample-specific inter-joint motion correlation matrix and uses it to adaptively refine the graph adjacency, strengthening connections between joints that move in coordinated patterns. Experiments are performed on the NTU RGB+D 60/120 datasets, our method achieves 94.2% and 91.3% accuracy on the bone stream. Comprehensive ablation studies validate the effectiveness of each component. Full article

Infrared small-target tracking is a fundamental yet challenging task in computer vision, owing to extremely small target sizes, severe background clutter, and low signal-to-noise ratios, which often cause tracking drift or complete target loss. As a representative method in this field, SiamDT has achieved promising performance on the challenging Anti-UAV410 benchmark—a large-scale thermal infrared dataset. However, SiamDT still suffers from three key limitations: first, its standard Feature Pyramid Network (FPN) lacks channel-wise selective focusing, causing target-related channels to be overwhelmed by numerous background clutter channels; second, its fixed receptive field cannot adapt to the drastic scale variations in infrared targets; third, its spatial-domain feature fusion lacks high-frequency component compensation, resulting in severe loss of discriminative edge and contour details in deep features. To address these issues, we propose Enhanced-SiamDT, an enhanced tracking model built upon the SiamDT framework. Our contributions are twofold. First, we design a Multi-Attention Feature Pyramid Network (MA-FPN) that sequentially integrates Efficient Channel Attention (ECA), Large Selective Kernel (LSK), and Wavelet Domain Attention (WDAM) to achieve channel-wise recalibration, adaptive multi-scale receptive field selection, and frequency-domain detail enhancement, thereby suppressing background clutter and strengthening small-target feature representations. Second, building upon the dual similarity learning architecture inherited from the SiamDT baseline, we introduce a background prototype suppression strategy that reduces false alarms by penalizing candidate boxes with high similarity to background prototypes and a conservative template update mechanism with explicit update criteria, which prevents template drift under fast motion and short-term occlusion. Extensive experiments on the Anti-UAV410 and Anti-UAV benchmarks demonstrate that Enhanced-SiamDT achieves new state-of-the-art performance, with a State Accuracy (SA) of 68.58% and 71.84%, and a Precision of 89.92% and 92.71%, respectively. These results validate that our approach effectively overcomes the limitations of existing Siamese trackers, delivering significant improvements in discriminative feature extraction and tracking robustness for infrared small targets. Full article

Satellite Interferometric Synthetic Aperture Radar (InSAR) provides spatially distributed displacement measurements that can support the monitoring of civil structures and infrastructures without permanent on-site instrumentation. However, for structures located in areas affected by subsidence or volcanic deformation, the measured Line of Sight (LOS) displacements may be dominated by ground-related motion, making the extraction of the structural response challenging. This paper proposes a methodology for compensating for large-scale ground deformation in InSAR-based structural monitoring, demonstrated on the Diego Armando Maradona Stadium in Naples, Italy, using high-resolution COSMO-SkyMed data. Persistent Scatterers (PSs) located on the roof and in the surrounding area are analysed and ascending and descending LOS measurements are combined to reconstruct longitudinal and vertical displacement components in a local structural reference system. For the selected case study, raw LOS measurements are strongly affected by ground deformation. Two correction strategies are compared: one based on the mean displacement of reference PSs and one based on polynomial fitting. While the latter does not reproduce the temporal variability of the ground motion at the site, the mean-reference correction yields displacement time series compatible with the expected thermal response of the steel roof. The study highlights the importance of accounting for large-scale ground motion when interpreting InSAR-derived structural displacements. Full article

To elucidate the diffusion behavior between virgin and aged asphalt binders and its relationship with fatigue performance, a concentric-circle blending system with a well-defined initial interface was constructed. By varying the blending temperature and time, the interfacial blending process was characterized from the perspectives of macroscopic performance, component migration, and molecular motion using dynamic shear rheometry, linear amplitude sweep, thin-layer chromatography with flame ionization detection, fluorescence microscopy, and molecular dynamics simulations. The results showed that thermal exposure produced a continuous interfacial transition region between the virgin and aged asphalt binders. Fatigue life generally increased as the sampling position shifted from the aged-binder side toward the virgin-binder side. At 5% strain, increasing the blending temperature from 135 to 165 °C increased the interfacial fatigue life by 173.8%, while extending the blending time from 15 to 60 min increased the fatigue life by 96.6%. The SARA fractions and fluorescence morphology results indicated that thermal exposure promoted component migration and redistribution on both sides of the interface, causing the initially distinct interface to gradually evolve into a continuous transition region. Molecular simulations further demonstrated that increasing temperature significantly enhanced the molecular mobility of all components and expanded the spatial overlap between the virgin and aged asphalt binders in the interfacial region. These results indicate that an increased extent of diffusion corresponds to an increase in fatigue life within the interfacial region and that the blending state between virgin and aged asphalt binders is an important factor affecting the local fatigue performance of recycled asphalt binder. Full article

This paper presents a stage-complete programmable-logic architecture for pairwise monocular structure from motion using calibrated, pre-undistorted 1920 × 1080 video. It integrates streaming feature extraction, Block-RAM-backed Top-K selection, spatial-bucket matching, two-pass essential-matrix estimation and pruning, fixed-point pose recovery, and triangulation with point-coordinate output. Bounded feature storage, local correspondence search, and mixed floating- and fixed-point arithmetic support the complete pairwise chain without processor-side geometry computation. The complete VCU118/XCVU9P design was synthesized, placed, routed, and compiled to a bitstream in Vivado 2026.1, meeting setup and hold timing with $+0.031$ ns and $+0.010$ ns slack, respectively. It uses 12.09% of the device’s logic lookup tables and 32.94% of its Block RAM tiles. Controlled numerical tests characterize the operating domain of the geometry stages. The cycle-based model estimates a 13.13 ms geometry-back-end subtotal at 1000 matches; 60 fps is the architectural input target. To the authors’ knowledge, within the directly comparable literature surveyed, this is the first reported stage-complete mapping of the listed pairwise chain entirely to programmable logic. Full article

Discrete symmetries govern the motion and real axis crossings of interference zeros in the meromorphic continuation of a wave field, thereby determining the singular structure of its logarithmic derivatives. We considered two counter-propagating second-order rational pulses that satisfy the one-dimensional massless wave equation exactly. With $q=r\mathrm{e}\mathrm{x}\mathrm{p}\left(i\phi \right)$ denoting the relative complex amplitude, the field admits two antilinear reflection symmetries: real $q$ preserves collision-centered spacetime inversion followed by complex conjugation, whereas $\left|q\right|=1$ preserves fixed time spatial reflection followed by conjugation up to an overall phase. The balanced in-phase state, $q=1$, is the non-degenerate intersection of these symmetry manifolds; $q=-1$ produces global cancellation on the collision slice. For $q=1$, the two interference zero branches lie on the imaginary axis and cross the real axis at $ct=x_0\pm \mathcal{l}$, on opposite sides of the pulse center collision. Away from the symmetry manifolds, the zero trajectories deform continuously, and the associated pairing constraints are lost, while the crossing conditions remain available in closed form. A logarithmic complex action representation yields local momentum, energy, transport ratio, and a derived second-order complex action descriptor without altering the underlying wave dynamics. Near an isolated non-characteristic moving zero, the leading simple pole factors cancel in the transport ratio, whereas the second-order term develops a double pole. The leading real axis response therefore scales as $d_{min}^{-2}$. A reference finite-window fit yields an exponent of $-1.885$ ($\rho =-0.995$), and the fitted exponent approaches $-1.998$ as the fitting interval is narrowed toward the isolated-zero regime. These results provide an exact benchmark linking antilinear symmetry, complex zero-pole geometry, and real axis differential amplification. The second-order quantity $Qc$ is used only as a descriptor generated by the logarithmic representation; it is neither an externally imposed potential nor an additional dynamical term. The loss of reflection symmetry away from the two symmetry manifolds is explicit and parameter-induced, not spontaneous. Full article

The integration of AI vision-based sensing and human motion analysis has grown to be a key element of intelligent perception systems, which now allow for automated interpretation of human movement, activity patterns, and complex visual behaviors. However, accurate functional movement assessment from monocular aerial and ground-view videos remains challenging due to low spatial resolution, background clutter, occlusions, and large variations in body posture, limiting the reliability of AI-assisted future healthcare applications. This study presents a multi-level framework that integrates asymmetric deep feature representation with transformer-based architecture and graph-driven optimization for robust vision-based human movement analysis. First, a Heavy Attention Transformer is employed to enhance image quality and emphasize clinically relevant anatomical and motion patterns by suppressing background interference. Panoptic segmentation and Real-Time Detection Transformer V2 are then used for subject localization, followed by skeletal keypoint extraction using YOLOv8. The proposed framework adopts an asymmetric dual-stream feature extraction strategy, where global contextual information is captured through Bag of Visual Words, Video Swin Transformer, Video Masked Autoencoder, and TimeSformer, while local biomechanical motion dynamics are modeled using DiffPose, PoseFormer, and Spatial–Temporal Graph Convolutional Networks. The key contribution lies in the asymmetric feature design that preserves the distinct information structures of visual context and skeletal dynamics. To reduce feature redundancy and select discriminative clinical representations, the Slime Mould Algorithm is utilized as a metaheuristic optimizer. The optimized features are subsequently classified using a Graph Attention Network for automated functional movement assessment. Experimental evaluation on the UAV-Human and UCF-ARG benchmark datasets achieved an accuracy of 82.50% and 78.20%, respectively. The proposed framework illustrates the potential of asymmetry-aware AI-enabled vision sensing to perform strong human movement analysis in complex viewpoints and lays the groundwork for future healthcare-related applications such as remote human movement evaluation and rehabilitation monitoring. Full article

Traffic sign recognition (TSR) sits on the critical path of advanced driver assistance and autonomous driving, yet deployed classifiers fail in two qualitatively distinct regimes that prior work has largely defended against in isolation worst case adversarial manipulation from imperceptible digital perturbations to physically realizable stickers, patches, and outline-conforming edge attacks and average case environmental degradation such as fog, glare, motion blur, fading, and occlusion. We observe that, despite their differing origins, both regimes leave the same observable signature on the over specified structure of a sign whose class is redundantly encoded by the silhouette, color scheme, and central pictogram: a spatially localized disagreement among otherwise independent cues, scored against a small, enumerable grammar of physically valid attribute tuples. Recasting robustness as detection of this signature rather than defense against any single threat, we propose SAFER-Sign, which integrates four components that each repair a documented failure mode of prior approaches: (i) class conditionally decorrelated shape, color, and glyph encoders that render over the specification genuine rather than nominal; (ii) evidential per channel uncertainty that lets a degraded cue abstain instead of voting confidently wrong; (iii) a soft, factorized, confidence gated sign grammar prior that rewards jointly consistent tuples without becoming a single attribute attack surface; and (iv) a jointly trained spatial reliability gate anchored to a parameter-free cross-channel disagreement signal, so it cannot be suppressed like a decoupled front end. Taken together, these components mean that a successful adaptive attack in our evaluated settings had to jointly address class evidence, cross channel consistency, grammar compatibility, reliability gating, and abstention. This raises the number of coupled attack objectives, but we emphasize that it does not guarantee that all three channels must be corrupted. Full article