Search Results (786)

With the advancement of sensing, communication, and cooperative capabilities of connected automated vehicles (CAVs), the capacity and operational state of signalized intersections have become increasingly observable and suitable for prospective assessment. However, existing capacity models based on homogeneous traffic assumptions are insufficient to describe the capacity evolution of mixed traffic under varying CAV penetration levels. Motivated by this limitation, this study proposes a quantitative capacity estimation method for signalized intersections considering CAV penetration, serving as an evaluation and prediction baseline for intersection operations. The proposed method improves the CAV gain parameter and accounts for multiple typical car-following states in mixed traffic to derive equivalent headways and spacing coefficients, enabling a continuous estimation of intersection capacity with respect to CAV penetration. Using data from an actual signalized intersection, capacity and saturation trends are analyzed across different movement directions and traffic demand conditions. The results indicate a nonlinear increasing pattern of intersection capacity as CAV penetration rises, with distinct growth rates between straight-through and left-turn movements. The proposed approach provides an engineering-oriented reference for capacity estimation and traffic state prediction under mixed traffic conditions. Full article

The optimization of low-Reynolds-number airfoils for micro unmanned aerial vehicles (UAVs) is challenging due to strong geometric nonlinearities, tight endurance requirements, and the need to maintain performance across multiple operating conditions. Classical surrogate-assisted optimization (SAO) methods combined with genetic algorithms become increasingly expensive and less reliable when class–shape transformation (CST)-based geometries are coupled with several flight conditions. Although deep learning surrogates have higher expressive power, their use in this context is often limited by insufficient local feature extraction, weak adaptation to changes in operating conditions, and a lack of robustness analysis. In this study, we construct a task-specific convolutional neural network–long short-term memory (CNN–LSTM) surrogate that jointly predicts the power factor, lift, and drag coefficients at three representative operating conditions (cruise, forward flight, and maneuver) for the same CST-parameterized airfoil and integrate it into an Non-dominated Sorting Genetic Algorithm II (NSGA-II)-based three-objective optimization framework. The CNN encoder captures local geometric sensitivities, while the LSTM aggregates dependencies across operating conditions, forming a compact encoder–aggregator tailored to low-Re micro-UAV design. Trained on a computational fluid dynamics (CFD) dataset from a validated SD7032-based pipeline, the proposed surrogate achieves substantially lower prediction errors than several fully connected and single-condition baselines and maintains more favorable error distributions on CST-family parameter-range extrapolation samples (±40%, geometry-valid) under the same CFD setup, while being about three orders of magnitude faster than conventional CFD during inference. When embedded in NSGA-II under thickness and pitching-moment constraints, the surrogate enables efficient exploration of the design space and yields an optimized airfoil that simultaneously improves power factor, reduces drag, and increases lift compared with the baseline SD7032. This work therefore contributes a three-condition surrogate–optimizer workflow and physically interpretable low-Re micro-UAV design insights, rather than introducing a new generic learning or optimization algorithm. Full article

A precise characterization of molecular vibrations and thermodynamic properties is essential for applications in spectroscopy, computational modeling, and chemical process design. In this study, the q-deformed Tietz (qDT) oscillator is applied to examine vibrational energy spectra of diatomic molecules and thermodynamic properties of nonlinear symmetric triatomic molecules. Vibrational energy eigenvalues were obtained analytically using the improved Nikiforov-Uvarov method. The symmetric vibrational mode was described with the qDT oscillator, while asymmetric and bending modes were modeled using the rigid rotor harmonic oscillator (RRHO); translational and rotational contributions were incorporated from standard models. For diatomic molecules (BrF, CO⁺, CrO, ICl, KRb, NaBr), mean absolute percentage errors (MAPE) ranged from 0.53% to 1.73% for vibrational energy eigenvalues and 0.34% to 1.08% for potential fits. Extending the analysis to triatomic molecules, thermodynamic properties of AlCl₂, BF₂, Cl₂O, OF₂, O₃, and SO₂ were calculated with the qDT model, yielding low MAPE benchmarked against NIST-JANAF reference data: entropy 0.203% to 0.614%, enthalpy 1.792% to 5.861%, Gibbs free energy 0.419% to 1.270%, and constant-pressure heat capacity 1.475% to 4.978%. These results demonstrate the versatility and accuracy of the qDT oscillator as an analytical framework connecting molecular potentials, vibrational energies, and thermodynamic functions, providing a practical and tractable approach for modeling both diatomic and symmetric triatomic systems. Full article

Hyperspectral imaging (HSI) captures the same scene across multiple spectral bands, providing richer spectral characteristics of materials than conventional RGB images. The spectral reconstruction task seeks to map RGB images into hyperspectral images, enabling high-quality HSI data acquisition without additional hardware investment. Traditional methods based on linear models or sparse representations struggle to effectively model the nonlinear characteristics of hyperspectral data. Although deep learning approaches have made significant progress, issues such as detail loss and insufficient modeling of spatial–spectral relationships persist. To address these challenges, this paper proposes the Cascaded Multi-Attention Feature Recurrent Enhancement Network (CMFREN). This method achieves targeted breakthroughs over existing approaches through a cascaded architecture of feature purification, spectral balancing and progressive enhancement. This network comprises two core modules: (1) the Hierarchical Residual Attention (HRA) module, which suppresses artifacts in illumination transition regions through residual connections and multi-scale contextual feature fusion, and (2) the Cascaded Multi-Attention (CMA) module, which incorporates a Spatial–Spectral Balanced Feature Extraction (SSBFE) module and a Spectral Enhancement Module (SEM). The SSBFE combines Multi-Scale Residual Feature Enhancement (MSRFE) with Spectral-wise Multi-head Self-Attention (S-MSA) to achieve dynamic optimization of spatial–spectral features, while the SEM synergistically utilizes attention and convolution to progressively enhance spectral details and mitigate spectral aliasing in low-resolution scenes. Experiments across multiple public datasets demonstrate that CMFREN achieves state-of-the-art (SOTA) performance on metrics including RMSE, PSNR, SAM, and MRAE, validating its superiority under complex illumination conditions and detail-degraded scenarios. Full article

Circuit implementation is a widely accepted method for validating theoretical insights observed in chaotic systems. It also serves as a basis for numerous chaos-based engineering applications, including data encryption, random number generation, secure communication, neuromorphic computing, and so forth. To get feasible, compact, and cost-effective circuit implementations of chaotic systems, the underlying mathematical model may be simplified while preserving all rich nonlinear behaviors. In this framework, this manuscript presents a simplified Hopfield Neural Network (HNN) capable of generating a broad spectrum of complex behaviors using a minimal number of electronic elements. Based on Shil’nikov’s theorem for heteroclinic orbits, the number of non-zero synaptic connections in the matrix weights is reduced, while simultaneously using only one nonlinear activation function. As a result of these simplifications, we obtain the most compact electronic implementation of a tri-neuron HNN with the lowest component count but retaining complex dynamics. Comprehensive theoretical and numerical analyses by equilibrium points, density-colored continuation diagrams, basin of attraction, and Lyapunov exponents, confirm the presence of periodic oscillations, spiking, bursting, and chaos. Such chaotic dynamics range from single-scroll chaotic attractors to double-scroll chaotic attractors, as well as coexisting attractors to transient chaos. A brief security application of an S-Box utilizing the presented HNN is also given. Finally, a physical implementation of the HNN is given to confirm the proposed approach. Experimental observations are in good agreement with numerical results, demonstrating the usefulness of the proposed approach. Full article

Efficient and high-precision prediction of underwater vehicle radiated noise is crucial for warship stealth assessment. To overcome the high modeling complexity and limited prediction capability of traditional methods, this paper proposes ADE-PNN-ResNet, a fast underwater radiated noise (URN) prediction model integrating Adaptive Differential Evolution (ADE) with a Parallel Residual Neural Network (PNN-ResNet). This data-driven framework replaces conventional physics-based modeling, significantly reducing complexity while preserving high prediction accuracy. This study includes three core points: Firstly, for each 1/3-octave target noise band, a joint feature selection strategy of measurement points and frequency bands based on the ADE is proposed to provide high-quality inputs for the subsequent model. Secondly, a Parallel Neural Network (PNN) is constructed by integrating Radial Basis Function Neural Network (RBFNN) that excels at handling local features and Multi-Layer Perceptron (MLP) that focuses on global features. PNN is then cascaded via residual connections to form PNN-ResNet, deepening the network layers and efficiently capturing the complex nonlinear relationships between vibration and noise. Thirdly, the proposed ADE-PNN-ResNet is validated using vibration and noise data collected from lake experiments of a scaled underwater vehicle model. Under the validation conditions, the absolute prediction error is below 3 dB for 96% of the 1/3-octave bands within the frequency range of 100–2000 Hz, with the inference time for prediction taking merely a few seconds. The research demonstrates that ADE-PNN-ResNet balances prediction accuracy and efficiency, providing a feasible intelligent solution for the rapid prediction of underwater vehicle radiated noise in engineering applications. Full article

Filled joints significantly influence the dynamic response of rock masses, exhibiting coupled nonlinear compression-hardening and viscous deformation. However, the combined effects of these mechanisms on wave propagation remain unclear. This study develops a theoretical model based on a nonlinear viscoelastic formulation, in which a compression-hardening spring (governed by the Bandis–Barton model, with its initial compressive stiffness and maximum allowable closure) is connected in series with a viscous dashpot. Using the displacement discontinuity method and the method of characteristics, we analyze the transmission of compressive stress waves across a filled joint. The results show that the transmission coefficient increases with incident wave amplitude but decreases with frequency, whereas reflection exhibits the opposite trends. The initial compressive stiffness has a minimal impact on transmission but induces a nonlinear decrease in reflection. Increasing the maximum allowable closure slightly reduces transmission but sharply increases reflection, whereas higher viscous stiffness enhances transmission and slightly suppresses reflection. Energy attenuation grows rapidly with amplitude before stabilizing. The initial compressive stiffness is most influential at low amplitudes, the maximum allowable closure is most significant at moderate amplitudes, and viscous effects remain consistent across all amplitudes. Increases in frequency lead to a nonlinear decrease in attenuation, with the initial compressive stiffness and maximum allowable closure dominating at high frequencies, and viscous effects prevailing at low frequencies. This work systematically reveals the coupled roles of nonlinear compression-hardening and viscosity in wave propagation across filled joints, providing theoretical support for dynamic hazard mitigation and geophysical exploration. Full article

To ensure reliable and accurate short-term photovoltaic power generation prediction, this study introduces an integrated forecasting framework that combines the gradient boosting paradigm with a dendritic neural structure, termed Gradient Boosting Multi-Bias Dendritic Units Integrated in a Fully Connected Neural Network (GBMDF). The proposed GBMDF algorithm minimizes prediction deviations by progressively capturing the nonlinear mappings between residual predictions and environmental variables through an iterative error-correction process. Compared with traditional data-driven learning algorithms, GBMDF can comprehensively utilize multiple meteorological inputs while maintaining strong interpretability and analytical transparency. Furthermore, leveraging the flexibility of the GBMDF, the prediction accuracy of existing models is improved through a proposed compensation enhancement technique. Under this mechanism, GBMDF is trained to offset the residual differences in alternative predictors by examining the correlations between the error patterns of alternative predictors and weather attributes. This enhancement method features a simple concept and effective practical performance. Validation experiments confirm that GBMDF not only achieves higher accuracy in photovoltaic output prediction but also improves the overall efficiency of other forecasting methods. Full article

All grid faults can cause significant problems within the power grid, including disconnection or malfunctions of wind energy conversion systems (WECSs) connected to the power grid. This study proposes a comparative analysis of the fault ride-through capability of a WECS-based permanent magnet synchronous generator (PMSG) system. To overcome these issues, active crowbar and capacitive bridge fault current limiter-based machine learning algorithm protection methods are implemented within the WECS system, both separately and in a hybrid. The regression approach is applied for the machine-side converter (MSC) and the grid side converter (GSC) controllers, which involve numerical data. The classification method is employed for protection system controllers, which work with data in distinct classes. These approaches are trained on historical data to predict the optimal control characteristics of the wind turbine system in real time, taking into account both fault and normal operating conditions. The neural network trilayered model has the lowest root mean squared error and mean squared error values, and it has the highest R-squared values. Therefore, the neural network trilayered model can accurately model the nonlinear relationships between its variables and demonstrates the best performance. The neural network trilayered model is selected for the MSC control system in this study. On the other hand, support vector machine regression is selected for the GSC controller due to its superior results. The simulation results demonstrate that the proposed machine learning algorithm performance for WECS based on a PMSG is robustly utilized under different operating conditions during all grid faults. Full article

To address the issue that distributed renewable energy grid-connected Virtual Synchronous Generator (VSG) systems are prone to significant power and frequency fluctuations under changing operating conditions, this paper proposes a multi-parameter coordinated control strategy for VSGs based on a fusion framework of fuzzy logic and the Soft Actor–Critic (SAC) algorithm, termed Improved SAC-based Virtual Synchronous Generator control (ISAC-VSG). First, the method uses fuzzy logic to map the frequency deviation and its rate of change into a five-dimensional membership vector, which characterizes the uncertainty and nonlinear features during the transient process, enabling segmented policy optimization for different transient regions. Second, a stage-based guidance mechanism is introduced into the reward function to balance the agent’s exploration and stability, thereby improving the reliability of the policy. Finally, the action space is expanded from inertia–damping to the coordinated regulation of inertia, damping, and active power droop coefficient, achieving multi-parameter dynamic optimization. MATLAB/Simulink R2022b simulation results indicate that, compared with the traditional SAC-VSG and DDPG-VSG method, the proposed strategy can reduce the maximum frequency overshoot by up to 29.6% and shorten the settling time by approximately 15.6% under typical operating conditions such as load step changes and grid phase disturbances. It demonstrates superior frequency oscillation suppression capability and system robustness, verifying the effectiveness and application potential of the proposed method in high-penetration renewable energy power systems. Full article

Laminated bamboo (LB) has shown enough exceptional performance to be used in constructions, but the performance of the bolted connections remains to be explored. To meet the criteria of low-carbon construction and fill the research gap in LB dowel embedment performance, this study examined the longitudinal dowel embedment behavior of LB. Failure modes, load–displacement curves, embedment strength, and elastic foundation parameters were examined after four sets of half-hole specimens with dowel diameters (6, 8, 10, and 12 mm) were tested in accordance with ISO 10984-2. The majority of the data was confirmed to follow a normal distribution by the Kolmogorov–Smirnov test. Interlaminar shear failure (dominant in 10 and 12 mm groups) and local crushing (dominant in 6 and 8 mm groups) were the primary failure modes. There were clear linear and nonlinear phases in the load–displacement curves (excellent ductility). The average elastic foundation modulus was 3565.55 MPa (0.39 times the compressive modulus); meanwhile, the average proportional limit, yield, and ultimate strengths were 35.48, 63.08, and 74.44 MPa (0.59, 1.06, and 1.25 times the parallel-to-grain compressive strength). The ultimate strength varied from 72.64 MPa to 76.71 MPa as the diameter rose; however, the elastic foundation beam coefficient dropped significantly. A novel calculation based on LB’s parallel-to-grain compressive strength accorded well with test results, while the existing code formulae (GB 50005, NDS, and CSA O86) overestimated LB embedment strength. The design of LB bolted connections is guided by this study, which also explains LB embedment criteria and offers design parameters and a prediction method. Full article

Lake eutrophication exhibits pronounced spatial heterogeneity at the watershed scale, yet a systematic and quantitative understanding of how landscape characteristics drive these variations remains limited. In this study, a long-term and internally consistent trophic state dataset for the Liangzi Lake Basin was constructed by integrating Landsat imagery from 1990 to 2022 with a semi-analytical water color inversion method. A multi-scale landscape feature system incorporating both land use composition and landscape pattern metrics was developed at the sub-basin level to elucidate the mechanisms by which landscape characteristics influence eutrophication dynamics. The XGBoost model was employed to characterize the nonlinear relationships between landscape attributes and trophic conditions, while the SHAP interpretability approach was applied to quantify the relative contribution of individual landscape components and their interaction pathways. The analytical framework demonstrates that landscape pattern attributes—such as fragmentation, diversity, and connectivity—play essential roles in shaping the spatial variability of eutrophication by modulating hydrological processes, nutrient transport, and ecological buffering capacity. By integrating remote sensing observations with interpretable machine learning, the study reveals the complexity and scale dependence of landscape–water interactions, providing a methodological foundation for advancing the understanding of eutrophication drivers. The findings offer theoretical guidance and practical references for optimizing watershed landscape planning, controlling non-point source pollution, and supporting ecological restoration efforts in lake basins. Full article

With the development of distributed energy sources such as photovoltaic and wind power, power grids have imposed increasingly higher requirements on power quality. As common nonlinear loads in power grids, multi-pulse rectifiers (MPRs) inject significant harmonics into the grid side. To reduce harmonic pollution at the source, this paper proposes a novel triple-passive harmonic suppression method to reduce the input current harmonics of MPRs. The proposed 48-pulse rectifier comprises a main circuit based on delta-connected auto-transformer (DCT) and a triple-passive harmonic suppression circuit (TPHSC). The TPHSC consists of two interphase reactors (IPRs) and eight diodes. Based on Kirchhoff’s Current Law (KCL), the output currents of the main circuit are calculated, and the operating modes of the TPHSC are analyzed. From the main circuit’s output currents and the DCT topology, the rectifier’s input currents are derived, and the optimal turns ratio of the IPRs for minimizing the input current total harmonic distortion (THD) is determined. The total capacity of the IPRs accounts for only 2.3% of the output load power. Experimental results show that the measured input current THD is close to the theoretical value of 3.8%. Overall, the proposed rectifier offers a cost-effective solution with stronger harmonic suppression capability, making it suitable for applications requiring low grid harmonic pollution. Full article

Landscape fragmentation as a process of landscape transformation affects the structure and composition of plant communities; however, relationships between fragmentation metrics and vegetation characteristics often remain weakly expressed and difficult to interpret, especially under conditions of multiple natural (wildfires, windstorms, pest outbreaks) and anthropogenic stressors (construction, forest management, agriculture). The aim of this study was to identify the sensitivity of forest community characteristics to landscape fragmentation metrics using methods that are effective at low correlation coefficients. The study analyzed 1694 vegetation relevés of forest communities in the center of the Russian Plain in the territory of the Moscow region. Seven uncorrelated metrics were calculated using the moving window method (2000 m) in Fragstats 4.3. The relationships between selected metrics and 20 community characteristics were evaluated using Spearman’s rank correlation method, assessment of statistically significant differences between classes, and testing for non-linear interactions. The species richness and Shannon index showed no correlation with fragmentation for tree and herb layers; however, the composition of ecological–coenotic groups demonstrated high sensitivity. The proportion of boreal and oligotrophic species, as well as the moss layer abundance, increased with increasing patch size, while nemoral and adventive species dominated in small-contrast patches. Results showed that fragmentation leads to asynchronous responses from ecosystem components, reducing correlations between structure and functioning. The conservation of large connected forest patches is critical for preserving the boreal–oligotrophic complex and moss layer, and is a priority task for climate adaptation. The robustness of the findings is supported by the extensive number of analyzed vegetation relevés. The multi-method approach demonstrated effectiveness in identifying significant ecological patterns under conditions of high multifactorial impact, emphasizing the need for a functionally oriented approach to managing fragmented temperate forests. Full article

Citrus orchards are especially vulnerable owing to low inter-row vegetation cover, and frequent tillage. Here, we combine controlled field experiments with proximal remote sensing–derived geomorphometric variables and machine learning (ML) to identify key factors of erosion in a Mediterranean climate citrus plantation located close to Seville and the National Park of Doñana (Southern Spain) on Gleyic Regosols (clayic, arenic). We conducted rainfall simulations with 30 s sampling, measured infiltration (mini-disc infiltrometer), saturated hydraulic conductivity (Kfs; Guelph permeameter), compaction (penetrologger), and soil respiration (gas analyzer) at multiple points, and derived high resolution morphometric indices from proximal sensing (UAV-LiDAR). Linear models and Random Forests were trained to explain three responses: soil loss, sediment concentration (SC), and runoff. Results show that soil loss is most strongly associated with maximum compaction and Kfs (multiple regression: R² = 0.68; adjusted R² = 0.52; p = 0.063), while SC increases with surface compaction and exhibits weak relationships with topographic metrics. Runoff decreases with average infiltration, which is related to compaction (β = −4.83 ± 2.38; R² = 0.34; p = 0.077). Diagnostic checks indicate centered residuals with mild heteroscedasticity and a few high leverage observations. Random Forests captured part of the variance for soil loss (≈29%) but performed poorly for runoff, consistent with limited sample size and modest nonlinear signal. Morphometric analysis revealed gentle relief but pronounced convergent–divergent patterns that modulate hydrological connectivity. There were strong differences in the experiments conducted close to the trees and in the tractor trails. We conclude that compaction and near surface hydraulic properties are the most influential and measurable controls of erosion at plot scale and the UAV-LiDAR could not give us extra-insights. We highlight that integrating standardized field protocols with proximal morphometrics and ML can be the best method to prioritize a small set of explanatory variables, helping to reduce experimental effort while maintaining explanatory power. Full article