Search Results (2,096)

Pedestrian-induced vibration is a critical serviceability concern for flexible structures such as footbridges and long-span floors. Existing human–structure interaction models commonly rely on single-degree-of-freedom simplifications and time-domain simulations, making them less suitable for frequency-domain analysis. This paper proposes a spectral analysis model for crowd-structure interaction vibration under unrestricted pedestrian traffic. The structure was formulated as a multi-degree-of-freedom modal system, whereas each pedestrian is represented by an independent spring–mass–damper system. To address the time-varying nature of moving crowds, a piecewise stationary assumption was introduced: the continuous walking path was discretized into fixed position groups, within each of which a time-invariant coupled equation of motion was established. The response spectra obtained for different position groups were combined using residence-time weighting, thereby allowing nonuniform walking speeds to be considered. The corresponding frequency response function was derived using the state–space method, and the structural acceleration power spectral density and root mean square responses were obtained by incorporating an unrestricted crowd walking load spectral model. Comparisons with field measurements from two footbridges demonstrated reasonable agreement. The resulting framework offers an efficient frequency-domain approach for vibration serviceability assessment under unrestricted pedestrian traffic. Full article

Quantum tunneling is conventionally described by the Schrödinger wave equation with a prescribed static potential barrier, providing accurate transmission probabilities but offering limited insight into the microscopic dynamics of particle–barrier interactions. In this work, we develop a dynamical second-quantization framework in which the barrier is modeled as an ensemble of quantized internal modes rather than as an externally imposed classical potential. The tunneling particle interacts directly with these microscopic barrier excitations through a coupled particle–barrier Hamiltonian, from which the Heisenberg equations of motion are derived. Collective coherent excitations of the barrier modes give rise to an emergent effective barrier that naturally recovers the conventional rectangular barrier and the WKB transmission limit under appropriate conditions. Unlike standard treatments, the present formulation explicitly incorporates microscopic barrier dynamics and provides a unified description of particle–barrier coupling within a second-quantized formalism. The framework further suggests that repeated tunneling events may experience different microscopic interaction histories, motivating a statistical interpretation of tunneling times. Because both the particle and barrier are treated within the same operator formalism, the theory provides a natural foundation for extension to relativistic quantum transport, photonic barriers, cavity quantum electrodynamics, and other structured quantum media. Full article

Proto-AGI describes the intermediate stage of artificial intelligence between narrow task-specific tools and fully autonomous general intelligence. This paper presents the first formal operationalisation of Proto-AGI traits within a cross-domain digital workflow for structural engineering and demonstrates it through a four-domain computational ecosystem applied to seismic vulnerability assessment. Viktor.ai processes cone penetration test data to stratify a three-layer soil profile, identifying a compressible intermediate stratum at 5 to 12 m depth with amplification characteristics in the 0.3 to 0.7 second period range, based on the depth and stiffness contrast of the weak layer rather than a formal site response analysis. The Fayaz RotD script computes orientation-independent RotD50 and RotD100 response spectra for two contrasting ground motion records: the near-fault Northridge record (RSN 1086, Mw 6.69) delivers RotD50 = 2.00 g and RotD100 = 2.79 g at the structural natural period of 0.41 s, a 39.6% directional uplift; the moderate-distance Kobe record (RSN 1107, Mw 6.9) delivers RotD50 = 0.591 g and RotD100 = 0.795 g at the same period. OpenSeesPy nonlinear dynamic analysis of a five-storey reinforced concrete frame produces peak inter-storey drifts of 0.45% and 0.34% under the two records respectively, both within the FEMA 356 Immediate Occupancy threshold of 1.0%. A 3.4-fold spectral demand difference produces only a 1.32-fold drift difference, reflecting the combined effects of frequency content, pulse characteristics, duration and nonlinear structural response under the two contrasting records. The Viktor.ai RC Section Analyzer yields a curvature ductility factor of 2.3 under ACI 318-25, identifying deformation capacity as the governing constraint under more severe future demands. These four findings form a causal chain connecting site conditions, spectral demand, structural response and sectional capacity that no single domain produces independently—the emergent ecosystem intelligence that defines Proto-AGI in structural engineering practice. Full article

In space target surveillance missions, effective detection of dim space targets remains a challenge due to dense star interference and the random nature of target motion. To quickly and accurately extract dim space targets from complex star backgrounds, this paper proposes a dim space target detection and track association method for dense star backgrounds. This paper analyzes various features of the target and background from a combined spatiotemporal perspective, including three main stages. First, inter-frame registration is used to filter out bright stars, followed by connected domain post-processing, which simplifies the star map background while enhancing the signal-to-noise ratio of dim targets. Secondly, an image difference fusion coarse processing module is proposed. The reconstructed multi-impulse function is derived from the spectral phase difference to estimate the displacement parameters of different components, after which the image difference fusion is designed to obtain candidate targets. Third, a directional track association algorithm is designed, with the candidate targets as the center and the motion parameters as thresholds, narrowing the association range to a fan-shaped region. This enables fast detection of target tracks while removing excess false alarms. The experimental results on four datasets demonstrate that this method outperforms traditional baseline methods in terms of target detection and localization accuracy. Full article

Granular collapse-induced wave generation is a critical process in coastal engineering and natural hazards, yet its rapid and complex fluid–solid coupling mechanism poses significant challenges for numerical modeling. This paper presents a comprehensive validations and performance benchmarking study of non-spherical granular collapse-induced wave generation using a GPU-accelerated resolved SPH-DEM coupling framework. Through three benchmark cases with increasing complexity, the numerical accuracy and robustness of the model are thoroughly verified with respect to free-surface flows, multi-body collisions, and intense fluid–solid interactions. Subsequently, the influence of SPH resolution and particle shape on computational efficiency is quantitatively assessed. It is found that the total runtime is dominated by the number of SPH particles, while the GPU acceleration advantage becomes more pronounced as the number of DEM faces increases. Furthermore, in the granular collapse-induced wave case, the temporal evolution of the leading wave amplitude and the difference in granular runout distance under dry and wet conditions are analyzed, revealing from the particle scale how fluid resistance modulates the coupling between wave generation and granular motion. This study not only validates the capability of the model to capture complex particle–wave interactions, but also provides quantifiable performance benchmarks and physical insights for its engineering applications. Full article

The Greater Cape Floristic Region (GCFR) in South Africa is a fire-prone biodiversity hotspot where high species richness, structural complexity, and small plant sizes (0.0001–4 m²) pose substantial challenges for remote sensing-based biodiversity assessment. Spectral similarity among species and the mismatch between plant size and sensor pixel dimensions limit the capacity of current and forthcoming spaceborne systems to resolve individual species and accurately detect plot-level diversity changes. We therefore developed a physics-based simulation framework that couples fynbos trait measurements with radiative transfer modeling in the DIRSIG (Digital Imaging and Remote Sensing Image Generation) environment towards quantifying information loss across spectral and spatial scales and to define theoretical limits for biodiversity monitoring. We constructed a three-dimensional virtual scene of post-fire fynbos communities in Grootbos Private Nature Reserve, integrating high-resolution imagery, terrestrial laser scanning (TLS), and structure-from-motion (SfM)-derived point clouds. Field measurements of mean diameter and percent cover were used to scale vegetation models and constrain species abundance. We distributed plant instances using a blue noise sampling algorithm, guided by density maps derived from unmanned aerial system (UAS) imagery. Species-specific optical properties were parameterized using field-measured reflectance data and the PROSPECT radiative transfer model, while terrain structure was derived from SfM-based digital terrain models. The integrated scene was used to simulate multispectral (DJI Mavic 3 MSI), hyperspectral (AVIRIS-NG), and light detection and ranging (LiDAR) observations. Agreement between simulated outputs were evaluated against corresponding field-acquired datasets using spectral signatures and vegetation indices. This framework enables systematic assessment of sensor specification effects on spectral biodiversity metrics and provides a pathway for evaluating theoretical limits of species discrimination across airborne and satellite platforms. Full article

Image restoration lies between camera acquisition and geometric estimation, but pixel improvements may not transfer to motion estimates. We evaluated directional-kernel Richardson–Lucy (RL) deblurring under kernel-length and direction controls. The restoration analysis covered 3071 paired GoPro, RealBlur-J, and RealBlur-R images. An exploratory feature analysis used a fixed 155-image subset with Oriented FAST and Rotated BRIEF (ORB), scale-invariant feature transform (SIFT), two geometry models, ten random directions, NAFNet, and Restormer. A separate task analysis used ten red–green–blue plus depth (RGB-D) sequences from the Technical University of Munich (TUM) benchmark, synthetic 20 ms exposures, and fixed RGB-D perspective-n-point odometry. Estimated directions contained information relative to random angles, yet the tested global RL branches remained below Blur Input on average. Changes in sequence-mean absolute trajectory error (ATE) RMSE ranged from +0.004 to +0.051 m for ORB and from −0.008 to +0.036 m for SIFT. Seeds were averaged within each sequence before inference. No tested branch achieved a robust ATE improvement across both detectors. Pixel, raw-feature, normalized-feature, geometry-state, and trajectory endpoints produced different method rankings. These findings motivate endpoint-specific evaluation. The task experiment does not validate naturally blurred long-exposure video, a deployed simultaneous localization and mapping system, or sensor hardware. Full article

The stable and reliable operation of precision motion stages constitutes a core prerequisite for accurate machining and operation in high-end equipment. Aiming at solving the problems of insufficient dynamic modeling accuracy, difficulty in achieving the precise prediction of guide rail parameters, and inadequate system performance optimization, this paper takes a four-axis precision motion stage as the research object and conducts a systematic study on dynamic modeling, guide rail parameter prediction, and system performance optimization. Verification results show that the maximum errors of natural frequencies in the x, y, z, and β directions are 1.3%, 4%, 5%, and 1%, respectively. The predicted results of the model are in good agreement with the experimental data, based on the dynamic model of the precision motion stage and the prediction model of guide rail dynamic parameters. On this basis, the guide rail-related parameters were optimized. After optimization, compared with the original state, the performance in the z and β directions was significantly improved: the natural frequency in the z direction was increased from 57 Hz to 70 Hz, and the natural frequency in the β direction was increased from 110 Hz to 154 Hz. This improvement enhances the overall anti-vibration capability at low frequencies and effectively ensures the stability and reliability of the precision motion stage during operation. The method used in this study has generality and transferability, which can provide a theoretical basis and technical support for the dynamic design and parameter optimization of various precision motion systems. Full article

Although the role of the mammalian hippocampus in representing locomotor speed has been widely investigated, how the avian hippocampus represents continuous flight speed under free-flight conditions in the outdoor environment remains unclear. In this study, we used homing pigeons as a model system and synchronously recorded hippocampal formation (HF) local field potentials (LFPs), global positioning system (GPS) trajectories, and inertial measurement unit (IMU) data during natural homing flights. We aimed to determine whether and how the avian HF encodes flight speed. Flight-speed-related neural features were extracted from both frequency-domain and time-domain signals, including the 50–70 Hz power spectral density (PSD) ratio and theta-demodulated amplitude (DAmp). We then constructed models for discrete flight-speed state decoding and continuous flight-speed prediction. The results showed that the 50–70 Hz PSD ratio in the HF was significantly negatively correlated with flight speed, whereas DAmp was significantly positively correlated with flight speed. Both features exhibited consistent speed-related trends across different spatial release sites. Support vector machine (SVM)-based classification showed that PSD, DAmp, and their combined features could effectively decode four flight-speed states, including non-flight, low-speed, medium-speed, and high-speed states, with the combined features achieving the best performance. Further Gaussian process regression (GPR) analysis demonstrated that the combined features predicted continuous flight speed more accurately than either single feature. These findings provide evidence that the avian hippocampal formation encodes continuous flight speed during natural navigation through the complementary integration of frequency-domain and time-domain features, extending the known role of the avian hippocampal formation from static spatial mapping to dynamic self-motion representation. Full article

The free vibration behaviour of functionally graded (FG) doubly curved nanoshells is explored by adopting a refined first-order shear deformation theory (FSDT) formulated with only four displacement variables. The presented kinematic model, which decomposes the transverse displacement to bending and shear components, provides an efficient and accurate framework for capturing structural response while requiring substantially lower computational effort than traditional higher-order theories. By utilising a power-law pattern, the nanoshell’s material properties are changing continuously within the thickness. Eringen’s nonlocal elasticity theory is implemented, which considers the size-dependent impact that occurs at the nanoscale. The governing equations of motion are constructed via the application of Hamilton’s principle and solved analytically by Navier’s method for simply supported boundary conditions. The current model’s accuracy and dependability are validated by comparisons with published results for various limiting cases such as spherical, cylindrical, and hyperbolic paraboloidal shells. A thorough parametric study is then carried out to examine the effects of the nonlocal parameter, power-law index, side-to-thickness ratio, curvature ratio, and aspect ratio on natural frequencies. Full article

We reexamined the critical importance of water motion in measuring algal growth and photosynthetic rates, addressing a significant limitation in traditional static incubation approaches. It presents a comprehensive framework for dynamic measurement methods that better simulate natural hydrodynamic conditions experienced by both macroalgae and other benthic autotrophs. We considered the barrier effects of the diffusion boundary layer surrounding the algae on the fluxes of gases and nutrients across the cellular membrane, and integrates findings demonstrating that flow-enhanced mass transfer of nutrients, inorganic carbon and O₂ across diffusion boundary layers can increase photosynthetic performance in macroalgae including the tested Sargassum, Porphyra and Macrocystis species. We provide quantitative comparisons demonstrating that increased velocities of water current can enhance photosynthetic and/or nutrient uptake rates compared to stagnant conditions. The comparative analysis highlights the advantages of flowing water systems in reducing diffusion limitations and better simulating natural environments against the technical simplicity of static methods. We detailed practical methodologies including flow-through system setup and sealed chamber with magnetic stirring techniques for macroalgae, and brush substrate for benthic diatoms. These approaches enable diurnal physiological tracking and reveal saturation responses to increasing water velocities. The methodological framework provides researchers with robust protocols for more accurate assessment of algal photosynthesis and primary productivity in both experimental and applied contexts such as aquaculture, carbon sequestration, and ecological monitoring. Full article

To address the unstable detection of weak orbital maneuvers from publicly available Two-Line Element (TLE) data affected by natural perturbations, orbit-determination errors, outliers, and irregular update intervals, an adaptive multi-feature maneuver-detection method is proposed. The method incorporates a dominant $J_2$ perturbation correction into the computation of the mean semimajor axis and combines semimajor-axis variation, eccentricity variation rate, and local dispersion of mean motion. A two-stage outlier-processing scheme, iterative background-noise estimation, and an adaptive switching mechanism based on the sampling interval are further introduced. Multi-object simulations for low- and high-altitude satellites show that the average detection accuracies for along-track maneuvers are 94.47% and 93.95%, respectively, while those for radial maneuvers are 92.83% and 93.26%, respectively. The proposed method demonstrates stable detection performance and good applicability across different orbital altitudes and maneuver directions. Full article

High-speed Fused Filament Fabrication (FFF) printers are prone to nozzle vibration caused by moving-part inertia, frame flexibility, and modal coupling during high-acceleration motion, which can reduce deposition-trajectory stability. This study evaluates the dynamic adaptability of electronics layout and X-axis beam configurations for a CoreXY FFF printer under complete-machine boundary conditions. A finite element model including the frame, XY motion mechanism, print head, heated bed, and electronics was established. Modal and Y-direction harmonic response analyses were performed by first comparing rear-mounted and bottom-mounted electronics layouts and then by comparing three beam designs. With the baseline beam, both layouts had a first natural frequency of 83 Hz, whereas the rear-mounted layout increased the second- to sixth-order frequencies by 6.8%, 24.2%, 32.0%, 29.2%, and 15.3%. Under the rear-mounted layout, the three beams showed similar first six modal frequencies, but the perforated beam produced the lowest nozzle peak, with a full-band Y-direction response of 0.402 mm, which was 16.1% and 20.4% lower than those of the baseline and hollow square beams, respectively. This beam also had a mass of 41.98 g, which was 45.1% lower than that of the baseline beam. Therefore, rear-mounted electronics combined with a perforated beam was preferred within the current simulation. Full article

Functional near-infrared spectroscopy (fNIRS) has attracted increasing attention in affective brain–computer interface research due to its non-invasive nature, portability, and robustness to motion artifacts. However, substantial inter-subject variability in neural responses remains a major challenge for subject-independent emotion recognition. To address this issue, this work presents an effective integration of multi-scale temporal convolution and dual-attention mechanisms for subject-independent fNIRS emotion recognition evaluated under the leave-one-subject-out protocol within a single dataset. The proposed framework employs multi-scale temporal convolutions to capture hemodynamic characteristics at different temporal resolutions and incorporates channel and temporal attention mechanisms to adaptively emphasize informative brain regions and critical temporal segments. Experiments were conducted on both a self-collected fNIRS emotion dataset and the publicly available ENTER dataset using the Leave-One-Subject-Out (LOSO) evaluation protocol. On the self-collected dataset, MSA-CNN achieved an accuracy of 65.06 ± 7.10% with an F1-score of 0.605. On the ENTER dataset, the proposed model obtained an accuracy of 68.91% and an F1-score of 0.621, outperforming conventional machine learning approaches and several representative deep learning baselines. Ablation studies further demonstrated the positive contributions of both the multi-scale convolutional structure and the dual-attention mechanism. Experimental results on both the self-collected and ENTER datasets demonstrate that the proposed MSA-CNN achieves competitive emotion recognition performance under the LOSO protocol. Class-wise evaluation using precision, recall, and the F1-score further provides a comprehensive assessment of the model’s classification behavior. These results indicate the effectiveness of the proposed framework for cross-subject fNIRS-based emotion recognition under the current experimental settings. The results indicate that multi-scale temporal feature learning combined with attention mechanisms can effectively enhance fNIRS-based emotion recognition performance and provides a promising framework for within-dataset cross-subject evaluation in fNIRS-based emotion recognition. Future work will focus on expanding the subject population, conducting cross-dataset train–test evaluations, and incorporating multimodal neural signals to further improve robustness and generalization. Full article

The airborne corner reflector (ACR), a novel radar passive jamming device, has attracted increasing attention from researchers worldwide due to its enhanced interference coverage when suspended by a parachute. However, the directional nature of ACRs renders their effectiveness highly sensitive to in-flight attitude dynamics. By analyzing the parachute body and the corner reflector separately, we propose an improved dynamic model to describe the parachute–payload system. Key innovations include the following: (i) by introducing an 11-degree-of-freedom model for motion analysis of the parachute-mounted double-pyramid octahedron structure, the issue of imprecise analysis in previous methods has been overcome; (ii) explicit modeling of tether tension and geometric constraints is undertaken to capture the parachute–payload coupling mechanism. Numerical simulations of the steady-descent phase demonstrate convergence of the payload’s angular rates and Euler angles, and the results show good agreement with full-scale flight test data. The model strikes a favorable balance between computational efficiency and physical fidelity, and is particularly suited for dynamic analysis of non-axisymmetric payloads in parachute descent systems. Full article