Search Results (6,915)

Stability plays essential roles for Vertical Take-Off and Landing (VTOL) vehicles. This paper investigates the stability characteristics of a novel tail-sitter VTOL vehicle employing vector thrust control, specifically focusing on nonlinear modeling and parameter optimization. Firstly, the tail-sitter VTOL which employs vector thrust controlling principles, is designed, and manufactured using 3D printing and carbon-fiber reinforced techniques, with a customized flight controller implemented on the PX4 architecture. To address the nonlinear dynamic characteristics introduced by the vector thrust mechanism, a nonlinear dynamic model for cruise flight is established based on an offline database and validated against cruise flight test data. Flight tests show that the vector-thrust-based pitch control provides rapid response and accurate tracking during cruise flight. Furthermore, based on the validated model, a hybrid optimization strategy combining pattern search and sequential quadratic programming (SQP) is used to tune the cascaded control parameters. Simulation results demonstrate that the optimized controller reduces the rise time from 6.8 s to 0.2 s and the settling time from 10.1 s to 0.9 s under the tested cruise-condition step response, indicating a marked improvement in dynamic response performance. This study provides a practical framework for cruise-flight modeling, pitch-stability analysis, and control-parameter optimization of vector-thrust tail-sitter UAVs. Full article

Flapping-wing drones (FWDs), owing to their compact size and operation in cluttered and unsteady airflow environments, encounter significant aerodynamic and stability challenges. Studies of avian flight reveal that falcons and other raptors actively deflect their covert feathers to mitigate gusts and maintain stable flight. Drawing inspiration from this mechanism, this study presents a peregrine falcon-inspired Active Flow Control Unit (AFCU) integrated with a Deep Deterministic Policy Gradient (DDPG)-based deep reinforcement learning (DRL) controller for real-time disturbance attenuation. The AFCU employs mechanical covert feathers (MCFs) that actuate to dissipate gust loads during high wind conditions. A reduced-order bond graph model that encapsulates the nonlinear interaction between the primary wing and the feather-based active flow control surfaces is created which is used as a dynamic training environment for the DDPG agent. Utilizing closed-loop interactions, the successfully obtained learned policy produces optimal actuator forces to reduce feather-displacement error and aerodynamic load variations. The designed controller stabilizes the internally unstable open-loop AFCU, attaining near-zero steady-state error and settling times under 1.6 s for gust magnitudes ranging from 12.5 to 20 m/s. Simulations further illustrate a reduction of up to 50% in gust-induced loads compared to traditional approaches. This integration of bio-inspired design with learning-based active flow control offers a viable avenue for the development of highly adaptive and gust-resilient flapping-wing aerial systems. Full article

Rapid urbanization in China has reshaped the coupling coordination between pollution and carbon emissions. However, existing studies largely rely on linear approaches and lack multidimensional and nonlinear assessments of urban growth patterns. Using panel data for 289 prefecture-level cities from 2010 to 2023, including built-up land, nighttime lights, CO₂ emissions, and PM_2.5 concentrations, this study develops three indicators: Urban Expansion Intensity (UEI), Urban Sprawl Index (USI), and Urban Compactness (UC). By integrating a coupling coordination model, K-means clustering, Geographically and Temporally Weighted Regression (GTWR), and interpretable XGBoost-SHAP analysis, four urban growth patterns are identified: High-Speed Low-Efficiency Expansion (HLE), Low-Speed Low-Efficiency Expansion (LLE), High-Speed High-Efficiency Compact (HHC), and Low-Speed High-Efficiency Compact (LHC). Results indicate that: (1) USI and UC exhibit significant nonlinear threshold effects on CCD; moderate expansion and higher compactness enhance synergy, whereas excessive dispersion or over-compactness weakens coordination. (2) UEI plays a relatively indirect and spatially heterogeneous role. (3) HHC and LHC cities achieve the highest CCD levels, while HLE cities perform the lowest. (4) Urban expansion shows an overall contraction trend, yet substantial regional disparities persist. These findings highlight nonlinear and spatially heterogeneous mechanisms linking urban growth patterns and pollution–carbon coupling coordination, providing implications for differentiated spatial governance. Full article

Recently, cold-formed steel (CFS) structural systems have been increasingly used in building applications due to their lightweight characteristics, ease of fabrication, and efficient construction processes. Among these systems, built-up CFS columns are widely adopted to enhance load-carrying capacity; however, their axial compression behavior and failure mechanisms have not yet been fully clarified. This study aims to investigate the axial compression performance of built-up cold-formed steel columns through a combined experimental and numerical approach. This study investigates the axial compression performance of built-up cold-formed steel columns using a combined experimental and numerical approach. Following the full-scale testing of five different configurations, finite element models were developed in ABAQUS using the obtained material properties. The experimental results were used to validate and calibrate the finite element models, which provided a detailed simulation of the nonlinear structural behavior of the columns. The experimental load–displacement responses were compared with the numerical results to evaluate the accuracy of the finite element models and to identify the axial load-carrying capacity and dominant failure modes of the built-up columns. Furthermore, the tensile pull-out behavior of 3.9 mm diameter self-drilling screws utilized in the built-up column connections was examined through expedient fastener tests to facilitate a more profound understanding of the load transfer mechanism. The results highlight the influence of built-up configuration and connection behavior on the axial compression performance of CFS columns, providing practical insights for improving the design and numerical modeling of screw-connected built-up cold-formed steel column systems. Full article

Due to their low noise and high efficiency, ducted fans are extensively used in unmanned aerial vehicles (UAVs). As the core lift and propulsion units, the aerodynamic performance of dual-ducted fans critically determines propulsion efficiency and flight stability. However, when operating near the ground, variations in ground clearance and the gap between ducts disrupt the isolated flow fields, introducing ground effect and aerodynamic coupling that pose significant stability risks. To address this, we developed a high-fidelity numerical model using the Unsteady Reynolds-Averaged Navier–Stokes approach with sliding mesh technology and the Shear-Stress Transport k-$\omega$ turbulence model. This study reveals the macroscopic aerodynamic characteristics of dual-ducted fans as functions of ground clearance and duct gap, and clarifies the underlying flow mechanisms. The research results indicate that the performance of a signle-ducted fan is highly sensitive to ground clearance: a critical threshold of thrust occurs when the ground clearance (h) at the duct outlet is 0.75 times the rotor disk diameter (D) . Under ground-effect-free conditions, the dual duct gap dominates the aerodynamic interference pattern: the total thrust of the system reaches its maximum value when the minimum spacing between the outer edges of the two ducts is 6 times the rotor disk radius. The coupling effect of ground clearance and duct gap exhibits significant nonlinear characteristics: thrust first decreases and then increases with increasing ground clearance, and the sensitive range of gap variation is $h/D=0.5$–$1.0$. These findings are crucial for optimizing the layout of ducted UAVs and enhancing UAV flight control to ensure safe and efficient operation under near-ground conditions. Full article

Surface subsidence induced by underground coal mining is a primary driver of ecological degradation. The traditional Remote Sensing Ecological Index (RSEI), however, struggles to capture surface deformation constraints and vegetation response lags. To address this, we developed a Mining Deformation–Ecology Coupling Index (MDECI). This index integrates Interferometric Synthetic Aperture Radar (InSAR)-monitored surface stability with multi-spectral indicators via Principal Component Analysis (PCA). We applied this method to the Datong Coalfield, China, using 231 Sentinel-1A SAR scenes and 8 Landsat images (2017–2024) to validate the effectiveness of the index. Meanwhile, we systematically analyzed non-linear response mechanisms, the Ecological Turning Point (ETP), and spatial clustering characteristics. The results demonstrate the following: (1) InSAR and MDECI effectively identified patterns of surface subsidence and ecological decline. Subsidence centers expanded to a maximum of −2085 mm, causing the mean MDECI in these areas to drop to 0.185 (<−1800 mm). This represents a 57.4% decrease relative to the regional average (0.434). (2) MDECI outperformed traditional models with a stable Average Correlation Coefficient (ACC) (0.63–0.75) and high cross-correlation coefficients with RSEI (0.906) and the Mine-specific Eco-environment Index (MSEEI) (0.931). During the 2018 drought, MDECI maintained a robust ACC of 0.628 while RSEI dropped to 0.482. (3) Multi-scale analysis revealed a unimodal MDECI response with an ETP at −100 mm. Initial ‘micro-disturbance gain’ (0.371 to 0.471) is followed by a progressive decline to a minimum of 0.185 under severe deformation. (4) Local Indicators of Spatial Association (LISA) spatial clustering characterized the distribution patterns of ecological damage and localised high-maintenance areas. High–Low damaged areas accounted for 5.09%, while High–High high-maintenance areas reached 9.00%. The scale of High–High areas was approximately 1.77 times that of the damaged areas. The MDECI addresses the deficiencies of traditional indices in high-disturbance areas and isolates the impact of mining on the ecology, providing a quantitative basis for risk identification and differentiated restoration. Full article

The Flexible Job Shop Scheduling Problem (FJSP) is central to smart manufacturing, yet standard algorithms often prioritize productivity (makespan) at the expense of cost and reliability. This paper introduces t-MOHHO, a collaborative optimization framework designed to equilibrate machine load, processing costs, and delivery timeliness alongside throughput. By incorporating an adaptive Student’s t-distribution mutation operator and a non-linear energy escape mechanism, t-MOHHO effectively navigates high-dimensional search spaces. Extensive validation on 10 MK benchmark instances reveals that t-MOHHO demonstrates significant advantages over classic HHO, MOPSO, and MOEA/D across most metrics. Notably, in comparison to the state-of-the-art NSGA-III, t-MOHHO executes a clear trade-off: it trades marginal makespan efficiency for substantial reductions in cost and tardiness. Specifically, on the large-scale MK10 instance, t-MOHHO reduces total tardiness by 56.2% and lowers processing costs by 3.4% compared to NSGA-III. These results demonstrate that t-MOHHO can strategically sacrifice maximum speed to secure superior punctuality and cost-efficiency, making it a robust decision-support tool for Just-in-Time (JIT) production environments. Full article

To address the actuator fault problem faced by underactuated surface vessels (USVs), this study develops an active fault-tolerant control scheme based on finite-time output feedback. First, a finite-time neural terminal homogeneous state observer with a portional-integral structure is established. High-precision pose reconstruction enables finite-time synchronous reconstruction of unmeasured states. This allows unknown nonlinearities to be explicitly expressed online and incorporated into the compensation channel, significantly reducing the sensitivity of modeling errors to control performance. A neural damping mechanism is used to structurally reconstruct uncertain dynamics and loss-of-effectiveness (LOE) fault factors within the system, thereby constructing an online approximator to achieve real-time identification and compensation of composite uncertainties. This integrates the unknown nonlinearities and fault effects of the original system into an online-updatable estimation channel. Adopting a backstepping-based design methodology, a finite-time hybrid event-triggered control (ETC) architecture is further constructed. By introducing an event-triggered update mechanism at the control layer, the real-time continuous control signal is transformed into a discrete update. Based on Lyapunov stability theory, a comprehensive analysis is carried out to verify the stability of the proposed control scheme. Numerical simulations are finally carried out to validate the effectiveness of the scheme. Simulation results show that the tracking error is reduced by about $93\%$ and $60\%$ compared to the comparison scheme. Full article

Extracting causal relations from complex dynamic systems has become an appealing topic for decades, especially for machine design engineering, industrial manufacturing, and equipment maintenance, which usually suffer from a large number of tangled relationships. Although many causality detection methods have been utilized, evaluating and choosing appropriate methods, and developing proper workflow remain challenges. In this paper, a causal analysis workflow designed to detect hidden patterns involved with mechanical mechanisms is presented. In particular, various causality measures are ensembled, enabling the search for refined causal mechanisms, the impact of constitutive law, and spatial distribution of causality from the entangled raw network. Based on numerical experiments, several beneficial conclusions can be drawn: Separating typical stages is necessary for a complex process; The constitutive property has a great impact on causal inference; The discrepancy of causality among different locations of monitor points mainly depends on whether it is near the fixed boundary, near to the load, or in contact with friction; Granger Causality is suitable for discovering linear dependencies among material, load, and geometry, while constraint-based and score-based algorithms excel in identifying nonlinear causality in metal plasticity, severe discontinuity in contact, impulsive dynamic load, or damping phenomenon. Full article

The TWC Sigma model, part of the Topological Weighted Centroid (TWC) family, is introduced as a spatial framework for source localization in systems where network information is incomplete or unavailable. Its architecture relies on two alternative approaches: one based on nonlinear correlation, capable of capturing complex spatial dependencies among observed signals, and another based on supervised neural networks, which use adaptive learning on a discretized spatial grid to estimate the probability of hidden source localization. In both cases, TWC Sigma provides a robust and consistent mechanism to estimate the probable positions of hidden sources using only spatial coordinates and signal intensity. Applications on both synthetic and real-world datasets—such as those collected by Minna-no Data Site on post-Fukushima radiocesium contamination—confirm the model’s ability to identify both primary and secondary emission zones with strong spatial coherence. These results highlight TWC Sigma as an efficient and interpretable model that can be used both independently and as a complementary tool to more complex network-based frameworks, offering rapid and reliable localization even in the presence of sparse, noisy, or heterogeneous data. Full article

Aiming at the problems of low control efficiency and great regulation difficulty of traveling wave ultrasonic motors (TWUSMs) caused by nonlinearity, strong coupling characteristics, and parameter uncertainty, this paper proposes a speed regulation scheme based on discrete contact modeling and monotonically increasing weighted nonlinear model predictive control (MIW-NMPC). On the basis of analyzing the existing TWUSM models, a discrete contact model of the motor is constructed, which reduces the modeling complexity while completely retaining the core dynamic characteristics of the motor’s operating mechanism. Based on this model, the MIW-NMPC algorithm for the precision speed control of TWUSM is designed, and its feasibility and stability are deduced and verified. Comparative simulations and experimental results demonstrate that the proposed discrete-model-based MIW-NMPC scheme can effectively enhance the precision and dynamic response of TWUSM speed control, offering a novel technical approach for its high-precision speed regulation applications. Full article

Zearalenone (ZEA) is a potent estrogenic mycotoxin known to disrupt reproductive functions, but its precise central neuroendocrine mechanisms remain unclear. This study investigated the effects of ZEA on the hypothalamic-pituitary Kiss1/GPR54 signaling pathway in weaned gilts. A total of 32 gilts were randomly assigned to four dietary treatments contained with 0, 0.15, 1.5, or 3.0 mg/kg ZEA for a 32-day feeding trial. Histopathology, immunohistochemistry, and mRNA/protein expression analyses of GPR30, Kiss1, GPR54, GnRH, and GnRHR in the hypothalamus and pituitary gland were conducted. ZEA exposure induced significant histological damage in both tissues. In the hypothalamus, Kiss1, GPR54, GnRH, and GnRHR exhibited a non-linear response, increasing at moderate doses and decreasing at 3.0 mg/kg ZEA, whereas GPR30 expression was continuously upregulated. In the pituitary gland, GnRHR showed a similar non-linear pattern. Furthermore, high-dose ZEA down-regulated pituitary Kiss1 and GPR54 while up-regulating GnRH and GPR30 expressions. In conclusion, ZEA induces reproductive neuroendocrine toxicity through a complex, dose-dependent modulation of the Kiss1/GPR54 signaling axis. The persistent upregulation of GPR30 suggests it acts as a crucial mediator in disrupting this endocrine feedback loop within the hypothalamus and pituitary gland. Full article

The mechanical performance of joints in prefabricated subway stations is a key factor governing the overall structural stability. This study investigates the grouted mortise–tenon joint (GMTJ), which is widely used in prefabricated subway station structures. A refined finite element model was established by incorporating material nonlinearity and a cohesive–friction hybrid constitutive model for the grout–concrete interface, and the accuracy of the model was validated against experimental results. Using the prototype GMTJ from an engineering project as the baseline, parametric analyses were conducted considering three concrete strength grades (CSGs) and three longitudinal reinforcement ratios (LRRs). The results show that increasing the CSG improves the joint’s flexural capacity and delays crack propagation. Although a higher LRR enhances the overall deformation resistance, an excessively high LRR intensifies stress concentration in the tenon region due to the absence of reinforcement in this area. Therefore, merely increasing the LRR cannot effectively improve joint durability, and local reinforcement of critical components such as the tenon is recommended in practical engineering. These findings provide meaningful references and insights for the structural design of prefabricated subway station joints. Full article

The squat is a fundamental multi-joint movement widely studied in strength training and biomechanics. While numerous experimental and computational studies have examined squat kinematics and joint loading, the mechanisms governing how squat technique adapts to increasing external load remain insufficiently understood. In particular, inverse-dynamics-based approaches often overlook explicit constraints imposed by limited joint moment capacity. This study presents a computational framework for predicting load-dependent adaptations of squat posture. The human body was represented as a multi-segment rigid-body system, with joints modeled as nonlinear rotational elements with bounded moment capacity. A reference squat trajectory was first generated kinematically, and a constrained optimization procedure was then applied at each motion frame to determine a mechanically admissible posture under increasing barbell load. The results show that higher loads lead to systematic posture adaptations, including increased torso inclination and redistribution of rotational demand from the knee toward the hip joint. For the highest load, peak torso pitch increased from 30° to over 40°, while joint utilization exceeded unity, indicating the onset of yielding. These findings identify joint moment capacity as a key constraint governing squat technique and demonstrate the potential of the proposed framework for predictive biomechanical analysis. Full article

To address the core challenges in air-to-ground target re-identification (ReID), including network focus on invalid background information, poor adaptability to nonlinear feature distribution, and insufficient cross-domain generalization, this paper proposes a novel air-to-ground ReID framework based on Neighborhood Feature Centralization Attention (NFCA). On the basis of Coordinate Attention, the framework introduces a parameter-free Neighborhood Feature Centralization mechanism to build a lightweight attention module, which enhances cross-feature semantic interaction and suppresses background noise while retaining precise position encoding. It achieves end-to-end direct optimization of sample pair similarity through binary cross-entropy loss, eliminating the proxy task bias of traditional classification loss and adapting to the nonlinear structure of feature space. A multi-source data-driven training strategy is constructed by fusing ReID datasets and general classification datasets, which expands the coverage of feature space and narrows the distribution gap between training data and real air-to-ground scenarios without additional manual annotation. Experiments show that the proposed method achieves leading mAP values on the self-developed UAV air-to-ground dataset JC-1, the public person ReID dataset Market-1501, and the public vehicle ReID dataset VehicleID. Sufficient statistical validation, ablation experiments and cross-domain tests verify the advancement, reliability and generalization of the proposed method in complex air-to-ground scenarios. Full article