Search Results (8,275)

Lissajous figures are frequently studied and widely used objects in engineering and physics. Although these patterns are usually analysed using computer simulations or oscilloscopes, such tools may limit their educational value by covering the core physical processes that generate the curves. In order to address these problems, the design, kinematic validation, and prototyping of a dual-axis drawing robot were carried out on the LEGO Education SPIKE Prime platform. The hardware implementation centres on a LEGO-based dual Scotch yoke mechanism, which supports precise transformation of uniform circular motion into simple harmonic motion. This setup implements the superposition of two independent simple harmonic oscillations by simultaneously moving the paper tray along the x-axis and the pen along the y-axis. High-fidelity trajectories are achieved through a 40:1 worm gear reduction, which enables precise control of the parameter configuration. The phase shift can be manually set by adjustment levers. The robot’s geometry supports discrete amplitude settings of 8, 16, and 24 mm by adjusting the crankpin position. System control is managed by Python code that synchronises motor speeds and angular displacements according to frequency ratios. The research methodology used the Double Diamond design thinking framework, structuring development into four phases: identifying historical mechanical solutions, defining pedagogical and technical classroom requirements, iteratively developing the LEGO prototype, and testing the system through representative drawing experiments. Results show that the robot can reproduce a broad range of periodic Lissajous curves with high repeatability, and that its physical outputs show strong visual and mathematical correspondence to ideal trajectories simulated in the Desmos graphing calculator. The final prototype satisfies classroom constraints, providing a transparent, low-cost, modular STEAM tool that bridges the distance between abstract parametric equations and complex mechanical implementations. Full article

Imaging underpins the diagnosis and structural staging of osteonecrosis of the femoral head (ONFH) but does not directly quantify functional impairment during weight-bearing walking. We developed a subject-level ONFH classification framework using multilevel gait features acquired with wearable inertial measurement units (IMUs). Thirty healthy controls and 21 participants with imaging-confirmed ONFH completed self-paced walking trials recorded at 100 Hz. Gait cycles were segmented from bilateral foot-contact events, normalized to 120 points, and represented as 17-channel kinematic waveforms, 22-dimensional cycle-level scalar features, and 7-channel dynamic absolute asymmetry waveforms. These inputs were encoded by CNN–CBAM–BiLSTM, multilayer perceptron, and one-dimensional convolutional branches, respectively, and fused at the feature level. Evaluation used 51-fold leave-one-subject-out cross-validation, training-fold-only preprocessing, within-subject probability averaging, and five predefined random seeds. The five-seed ensemble achieved an accuracy of 0.9412, sensitivity of 0.8571, specificity of 1.0000, F1-score of 0.9231, and area under the receiver operating characteristic curve of 0.9556. Ablation analysis identified the scalar-feature vector as the principal source of incremental performance; the dynamic asymmetry branch contributed complementary information only in the complete model. These findings provide preliminary evidence for further evaluation of wearable gait-based ONFH classification in independent cohorts and objective functional assessment. Full article

Audio magnetotelluric (AMT) sounding is an essential tool for mineral exploration and subsurface electrical structure imaging. However, persistent cultural noise can severely degrade AMT data quality, particularly in mining areas with strong anthropogenic interference. In this study, we introduce an improved K-Singular Value Decomposition (K-SVD) dictionary learning method for time-domain AMT data denoising, incorporating three key innovations: (1) automatic identification of noise-contaminated segments using local kurtosis; (2) adaptive dictionary initialization combining principal component analysis with Gaussian perturbation, which accelerates convergence and avoids local minima; and (3) online atom screening and updating to maintain the noise specificity of the learned atoms. The proposed method learns noise morphology directly from raw AMT data without requiring reference stations or external training datasets. Synthetic experiments demonstrate that the method more effectively suppresses square-wave and charge-discharge noise compared with wavelet thresholding methods and conventional K-SVD, while better preserving signal morphology. Field applications further show that it significantly enhances the quality of observed data and apparent resistivity-phase curves compared with the widely used robust estimation method. The proposed method provides an efficient, reference-free solution for AMT data denoising in mining areas with strong cultural noise. Full article

Background: Pediatric head trauma is a common reason for emergency department presentation. Although most children have minor injuries, a small proportion harbor clinically important traumatic brain injuries requiring urgent intervention. Artificial intelligence (AI) may offer structured support in computed tomography (CT) decision making, but evidence regarding the performance of general-purpose large language models in pediatric head trauma remains limited. Objective: To evaluate the association between AI-based cranial CT recommendations and clinically meaningful outcomes in pediatric patients with blunt head trauma and to assess the diagnostic performance and clinical utility of the model. Methods: This retrospective single-center observational study included pediatric patients younger than 18 years with blunt head trauma who underwent cranial CT imaging and had complete outcome data. A general-purpose large language model generated binary CT recommendations (“CT recommended” or “CT not recommended”) using structured clinical information available at the time of emergency department presentation. The primary outcome was a composite adverse clinical outcome defined as the occurrence of at least one of the following: emergency surgical intervention, intensive care unit admission, intubation, neurological sequelae or mortality. Diagnostic performance metrics, calibration analysis and decision curve analysis were performed. Results: A total of 819 pediatric patients were included, and the AI model recommended CT in 530 patients (64.7%). The primary outcome occurred in 143 patients (17.5%) and was significantly more frequent in the CT-recommended group than in the CT-not recommended group (24.5% vs. 4.5%; OR 6.90, 95% CI 3.82–12.45; p < 0.001). Abnormal CT findings, emergency surgery, intubation and neurological sequelae were also significantly more common in patients for whom CT was recommended by the AI system. For the primary outcome, the AI recommendation demonstrated a sensitivity of 90.9%, specificity of 40.8%, positive predictive value of 24.5% and negative predictive value of 95.5%. Calibration analysis showed acceptable agreement between predicted probabilities and observed event rates. Decision curve analysis demonstrated greater net benefit than both the “treat-all” and “treat-none” strategies across a range of threshold probabilities. Conclusions: In this clinically selected cohort of pediatric patients with blunt head trauma who underwent cranial CT imaging, AI-based CT recommendations were strongly associated with adverse clinical outcomes and demonstrated high sensitivity and negative predictive value for identifying children at risk of clinically important events. These findings suggest that, within a clinically selected cohort of children who underwent cranial CT imaging, AI-generated CT recommendations were associated with clinically meaningful outcomes. However, these results should not be interpreted as validation of CT decision making in the broader pediatric head trauma population and require prospective validation in unselected cohorts. 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

A Nomex aramid paper honeycomb sandwich structure is the core material of the main load-bearing structures in aviation. Macroscopic full-scale refined modeling faces the problems of large mesh quantity and high calculation cost. Homogenization equivalence is the core path to achieve its efficient simulation design. Most of the existing mature equivalent models are based on the ideal assumption that the honeycomb always remains macroscopically straight, without considering the equivalent performance changes caused by the morphological distortion of micro-cells under bending conditions. Therefore, it is difficult to support a high-precision simulation of large-curvature special-shaped honeycomb sandwich structures. This paper takes the over-stretched rectangular lattice aramid honeycomb as the research object. The mechanical parameters of the matrix are calibrated through experiments, and the reliability of the fine shell finite element model is verified (the maximum error of the end-face strain characteristics between simulation and the DIC test is less than 10%). A customized finite element sample matrix for compression bending is designed, and the angle distribution laws of honeycomb cells under different thicknesses and different bending curvatures are extracted. It is found that the cell angle shows a linear change trend along the wall-thickness direction, which is only strongly correlated with the initial geometric parameters and the bending radius. Finally, a semi-empirical model that can quickly predict the morphology of bent honeycomb cells is obtained through fitting. Verified by the glass compression-molding visualization experiment, the maximum relative error of the predicted cell angle is only 5.05%. This research establishes a rapid characterization method for the deformation of honeycomb cells under bending deformation, providing theoretical support for the microscopic homogenization equivalent modeling of curved honeycomb sandwich structures. Full article

Vortex-induced vibration (VIV) is one of the main causes of fatigue failure in subsea pipelines and has recently attracted significant attention from researchers. Previous studies have mainly focused on idealized straight pipes, with limited consideration of gravity-induced slight curvature in free-spanning fluid-conveying pipes. In reality, the deformation configuration of a free-spanning fluid-conveying pipe is not fixed but varies with parameters such as internal flow velocity and tension, which in turn affect its dynamic behavior. A theoretical model, taking into account the axial stretching effect and the gravity-induced initial slight curvature, is developed to predict the VIV responses of free-spanning fluid-conveying pipes. The governing equations are derived based on Hamilton’s principle. The interaction between the external flow and the pipe structure is simulated using the van der Pol equation. By combining the Galerkin method and the Runge–Kutta method, the vibration responses of the pipe are obtained. The accuracy of the proposed model is validated by comparing the predicted VIV response curves and bifurcation diagrams with those reported in previous studies. The initial static deformation of the structure under different tensions and internal velocities is obtained through numerical calculations. It is found that the gravity-induced slight curvature leads to a reduction in the VIV response mode. The static deformation of the pipe decreases with increasing axial tension, while it increases with increasing internal flow velocity. Under the same external flow velocity, the gravity-induced initial deformation reduces the dominant vibration frequency and causes the vibration response to transition from quasi-periodic to periodic motion. Full article

Background: Adolescent idiopathic scoliosis (AIS) affects approximately 0.5–5.2% of adolescents. Only a minority of curves progress, and surgery is generally reserved for curves exceeding 45–50°. Selection of the lowest instrumented vertebra (LIV) remains contested and directly influences postoperative coronal balance, distal adding-on (DA) and revision risk. Aim: This narrative review synthesizes the biomechanical principles governing LIV selection, compares traditional anatomical landmarks with contemporary prediction formulas and machine learning (ML) tools, and defines what these tools can and cannot currently deliver at the bedside. Methods: A structured but deliberately non-systematic search of PubMed/MEDLINE, EMBASE and the Cochrane Library (January 2000–December 2024) was supplemented by purposive inclusion of seminal pre-2000 work. No PRISMA-compliant screening, risk-of-bias appraisal or quantitative synthesis was undertaken, and the article is therefore reported as a narrative review. Results: Traditional landmarks—end, neutral, stable, last touched (LTV) and last substantially touched vertebra (LSTV)—are compared; fusion at or distal to the LTV/LSTV lowers DA incidence, yet 8–28% of patients still develop DA. Prediction formulas estimate the residual lumbar curve to within approximately 6°, but each was derived within a single surgical team and none nominates a vertebral level. A composite index combining (LIV− Neutral vertebra) and (LIV− Stable vertebra) reported sensitivity 100% and specificity 92–94% in its derivation cohort, without external validation. Hypercorrection of the main thoracic curve (>53%) combined with postoperative LIV tilt < 10° is associated with DA. Machine learning models predict three-dimensional alignment within 5–7°, yet every published model predicts an outcome; none has shown that acting on a model-derived recommendation prevents DA. Conclusions: LIV selection is best framed as a multidimensional judgment integrating coronal balance, vertebral body rotation, sagittal alignment, skeletal maturity, lumbopelvic morphology and non-radiographic factors such as paraspinal muscle quality, bracing and rehabilitation. Prediction models and artificial intelligence remain hypothesis-generating: external validation and prospective outcome trials are prerequisites before they can be considered practice-changing. Full article

Pronounced spatio-temporal variability of rainfall in semi-arid basins remains a central challenge for rainfall–infiltration–runoff modeling, particularly in ungauged or newly instrumented catchments where continuous soil-moisture data are unavailable. This study evaluates the comparative predictive performance of the SCS-CN and Green-Ampt (GA) infiltration methods, implemented within HEC-HMS at the sub-basin scale, using eight rainfall–runoff analysis time windows recorded during the 2020–2025 rainy seasons within a monitoring network operational since October 2019 in an instrumented semi-arid basin in Mexico. A blind-validation framework was adopted: parameters for both methods were derived a priori from tabulated sources indexed by land use, hydrologic soil group, soil textural class, and locally supported by textural analysis at three depths per sub-basin, in situ testing of saturated hydraulic conductivity, and gravimetric determination of field capacity; the initial moisture content required by GA was set equal to the measured field capacity (θ_i = θ_fc) to equate initial conditions between the two methods. Spatially distributed rainfall was captured by four monitoring stations under a one-to-one gauge–sub-basin assignment scheme, with monthly rainfall depth varying from 22.8 to 204.9 mm across the four sub-basins. Both methods reproduced observed discharge with varying levels of agreement: SCS-CN yielded very good performance (Pearson R = 0.95; Nash–Sutcliffe efficiency NSE = 0.76), whereas Green-Ampt yielded moderate correlation but unsatisfactory NSE (R = 0.70; NSE = 0.45) against the Levelogger records. Contrary to the initial expectation that the physically based GA would outperform SCS-CN, SCS-CN yielded substantially higher performance across windows, with the two simulated discharge series differing by a mean absolute deviation of 36.9 m³/s. A systematic sensitivity analysis (±6%, ±10%, ±20% perturbations) revealed an asymmetric response: SCS-CN was highly sensitive to Curve Number perturbations (mean-deviation amplitude 65.9 m³/s), whereas Green-Ampt was nearly insensitive to its compound soil-hydraulic parameterization (amplitude 3.2 m³/s), indicating a structural limitation of the physically based method under blind validation. Full article

This study investigates the dynamic response of non-circular gear planetary trains in transplanting mechanisms, focusing on variable transmission effects. A time-varying mesh stiffness model was developed for non-circular gears using pitch curve parameters, incorporating pressure angle, contract ratio, and equivalent teeth number as dynamic variables. A dynamic model of the planetary gear train was established to analyze component vibration characteristics. Comparative analysis reveals that non-circular gears’ variable-speed transmission significantly amplifies gear train vibrations compared to that of circular gears. Structural angle effects were examined, demonstrating the structural angle’s critical role in modulating vibration energy distribution between sun and planet gears. Frequency-domain analysis identified optimal structural angle ranges that minimize resonance risks by controlling component center vibrations. This work clarifies the coupling mechanisms between geometric parameters and transmission characteristics in non-circular gear systems. A design criterion based on frequency–energy distribution is proposed to optimize high-speed transplanting mechanisms. These findings advance the understanding of vibration modulation in variable-ratio gear trains and provide theoretical guidance for enhancing operational stability in agricultural machinery. Full article

To explore the high-value utilization of liquid recovery products from waste wind turbine blades in road binders, a liquid-rich fraction obtained from subcritical acetic acid degradation and subsequent vacuum distillation of waste wind turbine blade epoxy composites was introduced into styrene–butadiene–styrene (SBS)-modified asphalt to prepare composite-modified asphalt. Conventional property tests, dynamic shear rheological tests, bending beam rheological tests, steady shear tests, master curve analysis, Fourier transform infrared spectroscopy (FTIR), and gel permeation chromatography (GPC) were conducted to systematically evaluate the influence of the liquid-rich fraction on the properties and structural characteristics of the composite-modified asphalt. The results showed that, with increasing liquid-rich fraction content, the softening point increased, while penetration and ductility decreased, and the rotational viscosity at 135 °C increased, indicating enhanced overall stiffness and high-temperature flow resistance. High-temperature rheological results showed that the liquid-rich fraction increased the storage modulus, loss modulus, and rutting factor, while decreasing the phase angle improved the high-temperature deformation resistance of the composite-modified asphalt. Low-temperature rheological results indicated that the creep stiffness S increased, the m-value decreased, and the S/m ratio increased, suggesting weakened stress relaxation capacity and reduced cracking resistance at low temperature. Fatigue factor and steady shear results revealed that the liquid-rich fraction enhanced structural stability and flow resistance but also increased fatigue damage sensitivity at intermediate temperature. Master curves, Black diagram, and Cole–Cole plots further demonstrated that the liquid-rich fraction increased the modulus level over a wide frequency domain and strengthened the structural stability of the asphalt system. FTIR and GPC results indicated that the introduction of the liquid-rich fraction increased the relative contents of aromatic structures and polar oxygen-containing groups and promoted molecular association and increased the apparent molecular weight level of the system. Overall, the liquid-rich fraction acted as a structure-enhancing modifier in SBS-modified asphalt, improving its high-temperature performance while causing a certain trade-off in low-temperature and fatigue properties. Therefore, the dosage should be selected by balancing high-temperature stability, low-temperature cracking resistance, and fatigue durability, and the practical sustainability of this recycling route still requires dedicated economic and environmental evaluation. Full article

Accurate and non-invasive monitoring of glucose levels remains a critical challenge in diabetes management, motivating the development of highly sensitive optical biosensors. In this work, a surface plasmon resonance-based biosensor operating in the Kretschmann configuration is proposed and numerically investigated for glucose detection. The sensor architecture consists of a BK7 prism/TiO₂/Ag/graphene multilayer, and the effect of incorporating a hexagonal boron nitride (h-BN) interlayer with varying thicknesses is systematically analyzed to enhance sensing performance. Electromagnetic simulations were performed using the finite-difference time-domain method in Lumerical FDTD Solutions at a wavelength of 633 nm. Key performance parameters, including angular sensitivity, full width at half maximum, detection accuracy, figure of merit, signal-to-noise ratio, and limit of detection, were evaluated for glucose concentrations corresponding to refractive indices ranging from 1.3282 to 1.3767 RIU. The conventional BK7/TiO₂/Ag/TiO₂/Graphene/Sensing Medium (SM) configuration achieved a sensitivity of 167.48 deg/RIU. By introducing an h-BN layer, significant performance enhancement was observed. The optimized structure with an 8 nm h-BN layer exhibited a maximum angular sensitivity of 205.35 deg/RIU, representing an improvement of approximately 22.6% over the reference design, while maintaining a low detection limit of 2.43 × 10⁻⁴ RIU. The results further reveal that h-BN thickness plays a crucial role in balancing sensitivity and resonance quality, where excessive thickness broadens the resonance curve and degrades detection accuracy. The proposed graphene-h-BN-assisted SPR platform demonstrates high potential for high-performance, non-invasive glucose monitoring and provides practical design guidelines for next-generation plasmonic biosensors. Full article

Unmanned surface vehicle (USV) formations operating under GNSS denial require accurate relative localization using proprioceptive sensors and inter-vehicle ranging. This paper presents an anomaly-aware adaptive extended Kalman filter for four-USV formations using inertial measurements, compass, and ultra-wideband ranging, and systematically characterizes its operating envelope. Observability analysis establishes that S-curve maneuvering achieves structural rank 24, with only global translation unobservable, while straight-line motion leads to a rank deficiency of exactly seven dimensions All four gyroscope biases remain observable under both trajectories. The proposed filter integrates chi-square testing, cumulative sum (CUSUM) detection, and bias drift rate monitoring to trigger coordinated R adaptation and Q-boost mechanisms. Controlled experiments spanning outlier magnitudes and drift rates reveal three performance regimes, clean conditions with equivalent performance across all variants, moderate outliers $[3{\sigma }_d,10{\sigma }_d]$ where the proposed method achieves 4.8–13.4% improvement, and extreme outliers where all robust methods converge. Critically, pure bias drift experiments expose a structural limitation of single-hypothesis, residual domain robustification within the tested drift range—all variants exhibit equivalent performance across the tested drift rates, analytically attributable to Kalman gain partitioning that distributes innovations between position and bias subspaces. The characterized operating envelope establishes that robust mechanisms provide measurable benefits for transient anomalies but encounter hard boundaries under persistent drift conditions, with all variants converging to equivalent performance across the tested range, necessitating multi-hypothesis or constraint-based approaches. Full article

Hollow fiber loose nanofiltration (HF-LNF) has attracted increasing attention as a pressure-driven membrane platform that combines loose nanofiltration (LNF) selectivity with the high packing density and self-supporting geometry of hollow fibers. This review critically evaluates recent advances in HF-LNF membranes, including controllable fabrication strategies, surface functionalization techniques, and practical engineering applications, with a discussion of life cycle assessment (LCA) for evaluating the environmental and economic sustainability of HF membrane systems. Phase inversion, interfacial polymerization (IP), coating, and grafting are compared in terms of structural controllability, process complexity, selective-layer stability, modification uniformity, reproducibility, and scale-up feasibility. Phase inversion is relatively compatible with continuous hollow-fiber spinning, but independent regulation of the support and selective layer remains difficult. IP provides greater control over selective-layer chemistry and effective pore size, whereas coating and grafting offer flexible surface functionalization but may be limited by additional transport resistance, layer durability, and non-uniform modification of curved surfaces. Direct HF-LNF application remains concentrated on dye/salt separation. Based on the evidence from HF-NF or flat LNF systems, the potential of HF-LNF in water softening, heavy metal removal and emerging pollutant control is analyzed. Critical challenges restricting industrial translation are discussed, including poor long-term antifouling capacity and difficulties in large-scale, low-cost manufacturing. On this basis, LCA is further introduced as a decision-support framework for identifying potential environmental hotspots in membrane manufacturing and operation, while the limited availability and comparability of HF-LNF-specific life-cycle data are explicitly recognized. Ultimately, it is proposed to focus on novel functional materials, eco-friendly preparation processes, and scaled membrane engineering, aiming to offer theoretical support for the rational design and real-world industrial deployment of next-generation HF-LNF membranes. Full article

In this study, the Boscovich curve of 1763 is reinterpreted as a mean-field potential for interacting particles in condensed matter. In a dense many-body system, each particle experiences an effective potential arising from the average distribution of all the others. This mean-field potential, which exhibits alternating maxima (energy barriers) and minima (coordination shells), thereby reducing the complexity of the N-body problem to an effective two-body radial problem, with the correlation distance r as the key variable. The relationship between the PMF and the radial distribution function g(r) is given by the Kirkwood equation U_B(r) = −kT ln g(r), which provides a multi-well potential in condensed matter. Furthermore, the system is described by the Fisher density functional equation for the correlation amplitudes, −2kT ∇²ψ(r) + U_B(r)ψ(r) = μψ(r) whose eigenvalues μ_i correspond to potential levels and whose eigenfunctions ψ_i are the correlation amplitudes of the coordination shell structure. Based on the multi-well potential picture, the oscillatory behavior of U_B(r) is modeled analytically by a weighted sum of Lennard-Jones potentials, modulated by sigmoid functions. The parameters—well depths, widths, and coordination distances—are assigned on the basis of known structural properties of the system, derived either from experimental data or from geometric models such as FCC or HCP lattices. The radial distribution function is then reconstructed as a linear combination of the squared eigenfunctions obtained from the Fisher equation. The resulting discrete eigenvalue spectrum provides a spectral interpretation of the shell structure of condensed matter, wherein the complexity of many-body interactions is encoded in a hierarchy of correlation modes, each associated with a specific coordination shell. Unlike classical DFT—which relies on approximate excess free-energy functionals—and Ornstein–Zernike theory—which requires closure approximations—our approach provides a direct spectral interpretation of the coordination shell structure through the eigenvalue spectrum of the Fisher equation, where the PMF acts as the effective potential and the radial distribution function is reconstructed as a combination of squared eigenfunctions. The method is validated for liquid argon and FCC lattices and establishes a historical connection with Boscovich’s curve as a statistical potential. Full article