Search Results (3,263)

This study introduces a novel model reference adaptive control (MRAC) framework that incorporates fractional-order gradients (FGs) to regulate the displacement of an inverted pendulum-cart system. Fractional-order gradients have been shown to significantly improve convergence rates in domains such as machine learning and neural network optimization. Nevertheless, their integration with fractional-order error models within adaptive control paradigms remains unexplored and represents a promising avenue for research. The proposed control scheme extends the classical MRAC architecture by embedding Caputo fractional derivatives into the adaptive law governing parameter updates, thereby improving both convergence dynamics and control flexibility. To ensure optimal performance across multiple criteria, the controller parameters are systematically tuned using a multi-objective Particle Swarm Optimization (PSO) algorithm. Two fractional-order error models (FOEMs) incorporating fractional gradients (FOEM2-FG, FOEM3-FG) are investigated, with their stability formally analyzed via Lyapunov-based methods under conditions of sufficient excitation. Validation is conducted through both simulation and real-time experimentation on a physical pendulum-cart setup. The results demonstrate that the proposed fractional-order MRAC (FOMRAC) outperforms conventional MRAC, proportional-integral-derivative (PID), and fractional-order PID (FOPID) controllers. Specifically, FOMRAC-FG achieved superior tracking performance, attaining the lowest Integral of Squared Error (ISE) of $2.32\times {10}^{-5}$ and the lowest Integral of Squared Input (ISI) of 6.40 in simulation studies. In real-time experiments, FOMRAC-FG maintained the lowest ISE ($5.11\times {10}^{-6}$). Under real-time experiments with disturbances, it still achieved the lowest ISE ($1.06\times {10}^{-5}$), highlighting its practical effectiveness. Full article

A literature review was conducted to examine blast load characteristics of hydrogen, and the trend of hydrogen blast load and correlations between load characteristics were analyzed and compared with those of hydrocarbons. It was empirically confirmed that hydrogen explosions tend to produce higher peak overpressures and shorter durations compared with hydrocarbon explosions. In addition, blast load scenarios for hydrogen were selected considering the examined load characteristics and applied to numerical simulations. Dynamic structural responses of a corrugated blast wall were investigated through numerical simulations and analyzed from the perspective of displacement and strain energy. The results also indicated that blast walls designed for hydrocarbon explosions might not provide sufficient structural stiffness and strength to prevent excessive deflection and fracture under hydrogen blast loads. Lastly, a new type of diagram for structural response analysis was proposed, and deformation modes of corrugated blast walls were defined based on qualitative and quantitative structural responses. Full article

Seismic responses of Nuclear Island (NI) structures have great significance in the foundation adaptability analysis and the seismic design of equipment. However, with the increasing complexity of nuclear power site conditions, establishing a reasonable and effective soil–pile–structure dynamic interaction model has become the key technical problem that needs to be solved. In this study, a pseudo three-dimensional soil–pile–structure dynamic interaction model considering soil nonlinearity and heterogeneity is developed for seismic response analysis of NI structures. Specifically, the nonlinearity of the near-field soil is described via the equivalent linear method, the radiation damping effect of half space is simulated through viscous boundary, and the displacement/stress conditions at lateral boundaries of the heterogeneous site are derived from free-field response analysis. Meanwhile, an equivalent stiffness–mass principle is established to simplify NI superstructures, while pile group effects are incorporated via a node-coupling scheme within the finite-element framework. Two validation examples are presented to demonstrate the accuracy and efficiency of the proposed model. Finally, seismic response analysis of two typical NI structure of reactor types (CPR1000 and AP1000) based on the actual complex site conditions in China is also presented to study the effect of radiation damping, soil conditions, and pile foundation. Key findings demonstrate the necessity of integrating SSI effects and nonlinear characteristics of non-rock foundations. While the rock-socketed pile exhibits superior performance compared to the CFG pile alternative; this advantage is offset by higher costs and construction complexity. The research findings can serve as a valuable reference for the foundation adaptability analysis and optimizing the design of equipment under the similar complex condition of the soil site. Full article

Accurate and rapid prediction of structural responses to seismic loading is critical for ensuring structural safety. Recently, there has been active research focusing on the application of deep learning techniques, including Physics-Informed Neural Networks (PINNs) and Long Short-Term Memory (LSTM) networks, to predict the dynamic behavior of structures. While these methods have shown promise, each comes with distinct limitations. PINNs offer physical consistency but struggle with capturing long-term temporal dependencies in nonlinear systems, while LSTMs excel in learning sequential data but lack physical interpretability. To address these complementary limitations, this study proposes a hybrid LSTM-PINN model, combining the temporal learning ability of LSTMs with the physics-based constraints of PINNs. This hybrid approach allows the model to capture both nonlinear, time-dependent behaviors and maintain physical consistency. The proposed model is evaluated on both single-degree-of-freedom (SDOF) and multi-degree-of-freedom (MDOF) structural systems subjected to the El-Centro ground motion. For validation, the 1940 El-Centro NS earthquake record was used, and the ground acceleration data were normalized and discretized for numerical simulation. The proposed LSTM-PINN is trained under the same conditions as the conventional PINN models (e.g., same optimizer, learning rate, and loss structure), but with fewer training epochs, to evaluate learning efficiency. Prediction accuracy is quantitatively assessed using mean error and mean squared error (MSE) for displacement, velocity, and acceleration, and results are compared with PINN-only models (PINN-1, PINN-2). The results show that LSTM-PINN consistently achieves the most stable and precise predictions across the entire time domain. Notably, it outperforms the baseline PINNs even with fewer training epochs. Specifically, it achieved up to 50% lower MSE with only 10,000 epochs, compared to the PINN’s 50,000 epochs, demonstrating improved generalization through temporal sequence learning. This study empirically validates the potential of physics-guided time-series AI models for dynamic structural response prediction. The proposed approach is expected to contribute to future applications such as real-time response estimation, structural health monitoring, and seismic performance evaluation. Full article

Background: This study aimed to investigate how barbell type (elastic vs. inelastic) and lifting speed affect postural stability and lower limb muscle activation during the single-leg deadlift (SLDL), a common unilateral exercise in rehabilitation and performance training. Methods: Twenty-seven healthy adults performed SLDLs using both elastic and inelastic barbells under three lifting speeds (normal, fast, and power). Center of pressure (COP) displacement in the anterior–posterior (AP) and medial–lateral (ML) directions and electromyographic (EMG) activity of eight lower limb muscles were measured. Results: COP displacement was significantly lower when using elastic barbells (AP: F = 6.509, p = 0.017, η² = 0.200, ω² = 0.164; ML: F = 9.996, p = 0.004, η² = 0.278, ω² = 0.243). EMG activation was significantly higher for the gluteus medius, biceps femoris, semitendinosus, and gastrocnemius (all p < 0.01), especially under power conditions. Significant interactions between barbell type and speed were found for the gluteus medius (F = 13.737, p < 0.001, η² = 0.346, ω² = 0.176), semitendinosus (F = 6.757, p = 0.002, η² = 0.206, ω² = 0.080), and tibialis anterior (F = 3.617, p = 0.034, η² = 0.122, ω² = 0.029). Conclusions: The findings suggest that elastic barbells improve postural control and enhance neuromuscular activation during the SLDL, particularly at higher speeds. These results support the integration of elastic resistance in dynamic balance and injury prevention programs. Full article

This research experimentally and numerically evaluates the effectiveness of viscoelastic (VE) and rotational friction (RF) dampers in enhancing the seismic performance of coupled shear wall (CSW) systems. This study consists of two phases: (1) element testing to characterize the hysteretic behavior and energy dissipation capacity of VE and RF dampers, and (2) shake table testing of a large-scale CSW structure equipped with these dampers under the white noise, sinusoidal and Kokuji waves. The experimental results are validated through numerical analysis using STERA 3D (version 11.5), a nonlinear finite element software, to simulate the dynamic response of the damped CSW system. Key performance indicators, including inter-story drift, base shear, and energy dissipation, are compared between experimental and numerical results, demonstrating strong correlation. The findings reveal that VE dampers effectively control high-frequency vibrations, while RF dampers provide stable energy dissipation across varying displacement amplitudes. The validated numerical model facilitates the optimization of damper configurations for performance-based seismic design. This study provides valuable insights into the selection and implementation of supplemental damping systems for CSW structures, contributing to improved seismic resilience in buildings. Full article

A concentric double-flange butterfly valve (DN-500, PN-10) was analyzed to examine its dynamic behavior and internal fluid flow across multiple opening angles. Finite Element Analysis (FEA) was employed to determine natural frequencies, mode shapes, and effective mass participation factors (EMPFs) for valve positions at 30°, 60°, and 90°. The valve geometry was discretized using a curvature-based mesh with linear elastic isotropic properties for 1023 carbon steel. Lower-order vibration modes produced global deformations primarily along the valve disk, while higher-order modes showed localized displacement near the shaft–bearing interface, indicating coupled torsional and translational dynamics. The highest EMPF in the X-direction occurred at 1153.1 Hz with 0.2631 kg, while the Y-direction showed moderate contributions peaking at 0.1239 kg at 392.06 Hz. The Z-direction demonstrated lower influence, with a maximum EMPF of 0.1218 kg. Modes 3 and 4 were critical for potential resonance zones due to significant mass contributions and directional sensitivity. Computational Fluid Dynamics (CFD) simulation analyzed flow behavior, pressure drops, and turbulence under varying valve openings. At a lower opening angle, significant flow separation, recirculation zones, and high turbulence were observed. At 90°, the flow became more streamlined, resulting in a reduction in pressure losses and stabilizing velocity profiles. Full article

Early warning systems depend heavily on the accuracy of landslide displacement forecasts. This study focuses on the Bazimen landslide located in the Three Gorges Reservoir region and proposes a hybrid prediction approach combining support vector regression (SVR) and long short-term memory (LSTM) networks. These models are optimized via the K-Nearest Neighbor (KNN) algorithm. Initially, cumulative displacement data were separated into trend and cyclic elements using a smoothing approach. SVR and LSTM were then used to predict the components, and KNN was introduced to optimize input factors and classify the results, improving accuracy. The final KNN-optimized SVR-LSTM model effectively integrates static and dynamic features, addressing limitations of traditional models. The results show that LSTM performs better than SVR, with an RMSE and MAPE of 24.73 mm and 1.87% at monitoring point ZG111, compared to 30.71 mm and 2.15% for SVR. The sequential hybrid model based on KNN-optimized SVR and LSTM achieved the best performance, with an RMSE and MAPE of 23.11 mm and 1.68%, respectively. This integrated model, which combines multiple algorithms, offers improved prediction of landslide displacement and practical value for disaster forecasting in the Three Gorges area. Full article

RC piers in mountainous coastal or saline areas face the dual threats of rockfall impacts and chloride-induced steel corrosion, but their combined effects on dynamic response and residual bearing capacity remain unquantified. This study aims to investigate these combined effects over a 90-year service time and propose a damage assessment formula. A validated numerical model (relative error ≤14.7%) of corroded RC columns under impact is developed using ABAQUS, based on which the dynamic response and residual bearing capacity of an actual RC pier subjected to rockfall impacts during the service time of 90 years incorporating corrosion initiation (via Life-365 software 2.2) and propagation are analyzed, with the consideration of various impact energies (1–5 t mass, 5–15 m/s velocity). Results show that (1) increasing impact mass/velocity expands damage and increases displacement (e.g., the velocity of increases peak displacement by 33.41 mm in comparison to 5 m/s); (2) a 90-year service time leads to >50% severe surface damage and 47.1% residual capacity loss; and (3) the proposed and validated damage formula assessment formula for the residual bearing capacity enables lifecycle maintenance guidance. This work provides a validated framework for assessing combined corrosion-rockfall effects, aiding design and maintenance of structures. Full article

This paper presents a formulation for the dynamic analysis of dam–reservoir–foundation systems, employing a coupled finite element model that integrates displacements and reservoir pressures. An innovative coupled approach, without separating the solid and fluid equations, is proposed to directly solve the single non-symmetrical governing equation for the whole system with non-proportional damping. For the modal analysis, a state–space method is adopted to solve the coupled eigenproblem, and complex eigenvalues and eigenvectors are computed, corresponding to non-stationary vibration modes. For the seismic analysis, a time-stepping method is applied to the coupled dynamic equation, and the stress–transfer method is introduced to simulate the nonlinear behavior, innovatively combining a constitutive joint model and a concrete damage model with softening and two independent scalar damage variables (tension and compression). This formulation is implemented in the computer program DamDySSA5.0, developed by the authors. To validate the formulation, this paper provides the experimental and numerical results in the case of the Cahora Bassa dam, instrumented in 2010 with a continuous vibration monitoring system designed by the authors. The good comparison achieved between the monitoring data and the dam–reservoir–foundation model shows that the formulation is suitable for simulating the modal response (natural frequencies and mode shapes) for different reservoir water levels and the seismic response under low-intensity earthquakes, using accelerograms measured at the dam base as input. Additionally, the dam’s nonlinear seismic response is simulated under an artificial accelerogram of increasing intensity, showing the structural effects due to vertical joint movements (release of arch tensions near the crest) and the concrete damage evolution. Full article

This study focuses on the numerical solution and dynamics analysis of fractional governing equations related to double-layer nanoplates based on the shifted Legendre polynomials algorithm. Firstly, the fractional governing equations of the complicated mechanical behavior for bilayer nanoplates are constructed by combining the Fractional Kelvin–Voigt (FKV) model with the Caputo fractional derivative and the theory of nonlocal elasticity. Then, the shifted Legendre polynomial is used to approximate the displacement function, and the governing equations are transformed into algebraic equations to facilitate the numerical solution in the time domain. Moreover, the systematic convergence analysis is carried out to verify the convergence of the ternary displacement function and its fractional derivatives in the equation, ensuring the rigor of the mathematical model. Finally, a dimensionless numerical example is given to verify the feasibility of the proposed algorithm, and the effects of material parameters on plate displacement are analyzed for double-layer plates with different materials. Full article

Traditional collision risk warning methods primarily focus on vehicle-to-vehicle collisions, neglecting conflicts between vehicles and vulnerable road users (VRUs) such as pedestrians, while the difficulty in predicting pedestrian trajectories further limits the accuracy of collision warnings. To address this problem, this study proposes a vehicle-to-everything-based (V2X) multi-fusion vehicle–pedestrian collision warning method, aiming to enhance the traffic safety protection for VRUs. First, Unmanned Aerial Vehicle aerial imagery combined with the YOLOv7 and DeepSort algorithms is utilized to achieve target detection and tracking at unsignalized intersections, thereby constructing a vehicle–pedestrian interaction trajectory dataset. Subsequently, key foundational modules for collision warning are developed, including the vehicle trajectory module, the pedestrian trajectory module, and the risk detection module. The vehicle trajectory module is based on a kinematic model, while the pedestrian trajectory module adopts an Attention-based Social GAN (AS-GAN) model that integrates a generative adversarial network with a soft attention mechanism, enhancing prediction accuracy through a dual-discriminator strategy involving adversarial loss and displacement loss. The risk detection module applies an elliptical buffer zone algorithm to perform dynamic spatial collision determination. Finally, a collision warning framework based on the Monte Carlo (MC) method is developed. Multiple sampled pedestrian trajectories are generated by applying Gaussian perturbations to the predicted mean trajectory and combined with vehicle trajectories and collision determination results to identify potential collision targets. Furthermore, the driver perception–braking time (TTM) is incorporated to estimate the joint collision probability and assist in warning decision-making. Simulation results show that the proposed warning method achieves an accuracy of 94.5% at unsignalized intersections, outperforming traditional Time-to-Collision (TTC) and braking distance models, and effectively reducing missed and false warnings, thereby improving pedestrian traffic safety at unsignalized intersections. Full article

This study investigates the dynamic behavior of an articulated boom offshore crane under various load application laws. The following steps were taken to perform numerical simulations using the finite-element method (FEM): Definition of the model’s geometry, materials, and boundary conditions. The modal analyses reveal significant resonance frequencies in the direction of load application (payload). The crane’s displacement, velocity, and acceleration responses are closely related to load application laws, specifically the time required to reach the structure’s full payload (epsilon). It is highly correlated with the dynamic factor (maximum acceleration multiplied by payload), which has a wide range of effects on the structure, including the effects of overstress, overturning, buckling, and so on. The main findings reveal a very strong exponential correlation, allowing the dynamic effect to be estimated as a function of epsilon time. This is a useful tool for increasing the safety and reliability of offshore lifting operations. Full article

Nucleolin (NCL), an RNA-binding protein which regulates critical cellular processes, is frequently dysregulated in human cancers, including breast cancer, making it an attractive therapeutic target. However, molecular details of the RNA-NCL interaction have not been investigated yet. A tRNA fragment named tRF3E, displaying tumor suppressor roles in breast cancer, was found to bind NCL with high affinity displacing NCL-controlled transcripts. Here, we investigated the determinants and cooperativity of tRF3E-NCL interaction by Electrophoretic Mobility Shift Assays and in silico docking analysis, using wild-type or mutated tRF3E. We found that NCL, through its RNA-binding domains (RBD1–2 and RBD3–4), binds simultaneously two tRF3E molecules, giving rise to an energetically favored complex. Instead, a mutant form of tRF3E (M19–24), in which the NCL recognition element in position 19–24 has been disrupted, contacts NCL exclusively at RBD3–4, causing the loss of cooperativity among RBDs. Importantly, when expressed in MCF7 breast cancer cells, tRF3E significantly reduced cell proliferation and colony formation, confirming its role as tumor suppressor, but tRF3E functional properties were lost when the 19–24 motif was mutated, suggesting that cooperativity among multiple domains is required for the NCL-mediated tRF3E antitumor function. This study sheds light on the dynamic of RNA-NCL interaction and lays the foundations for using tRF3E as a promising NCL-targeted biodrug candidate. Full article

Trajectory prediction under multimodal information is critical for autonomous driving, necessitating the integration of dynamic vehicle states and static high-definition (HD) maps to model complex agent–scene interactions effectively. However, existing methods often employ static scene encodings and unstructured latent spaces, limiting their ability to capture evolving spatial contexts and produce diverse yet contextually coherent predictions. To tackle these challenges, we propose MS-SLV, a novel generative framework that introduces (1) a time-aware scene encoder that aligns HD map features with vehicle motion to capture evolving scene semantics and (2) a structured latent model that explicitly disentangles agent-specific intent and scene-level constraints. Additionally, we introduce an auxiliary lane prediction task to provide targeted supervision for scene understanding and improve latent variable learning. Our approach jointly predicts future trajectories and lane sequences, enabling more interpretable and scene-consistent forecasts. Extensive evaluations on the nuScenes dataset demonstrate the effectiveness of MS-SLV, achieving a 12.37% reduction in average displacement error and a 7.67% reduction in final displacement error over state-of-the-art methods. Moreover, MS-SLV significantly improves multi-modal prediction, reducing the top-5 Miss Rate (${\mathrm{MR}}_5$) and top-10 Miss Rate (${\mathrm{MR}}_{10}$) by 26% and 33%, respectively, and lowering the Off-Road Rate (ORR) by 3%, as compared with the strongest baseline in our evaluation. Full article