Search Results (352)

This paper provides a systematic synthesis of recent developments in physics-informed neural networks (PINNs) applied to aerospace engineering, with an emphasis on their role in physically consistent surrogate modeling, forward simulation, and inverse parameter estimation. Using a PRISMA-based methodology, the study surveys peer-reviewed works published between 2017 and 2025 across aviation- and space-related domains, including aerodynamics, structural mechanics, aeroelasticity, propulsion, control, structural health monitoring, satellite-orbit prediction, space-debris collision avoidance, and spacecraft radiation-impact modeling. The analysis shows that embedding governing equations, boundary conditions, and observational data into composite loss functions enables PINNs to improve predictive consistency, reduce dependence on dense simulation or experimental datasets, and support parameter identification under sparse or noisy measurements. Attention is given to architectural variants such as XPINNs, cPINNs, gPINNs, operator-learning approaches, and hybrid PINN-CFD/FEM formulations, as well as to training strategies based on adaptive sampling, domain decomposition, transfer learning, and dynamic loss weighting. Reported benefits include reduced approximation error, improved convergence in selected high-gradient or multiphysics problems, and enhanced interpretability compared with purely data-driven models. At the same time, the review identifies persistent open challenges, including scalability to large aerospace domains, sensitivity to loss-weighting and collocation strategies, limited robustness under noise and uncertainty, high computational cost, and the lack of standardized aerospace benchmarks. Overall, the review highlights PINNs as a promising but still developing framework for fast, interpretable, and physically consistent modeling of aircraft and spacecraft systems. Full article

High-resolution, long-term gridded meteorological datasets from in situ observations are crucial for ecosystem monitoring, soil diagnostics, hydrological modelling, and Earth system model evaluation. This study presents two enhanced real-time adaptations of Thornton’s Daymet V4 interpolation method. Daymet4-r1 uses a traditional calibration strategy with exhaustive parameter search, while Daymet4-r2 applies a global optimization algorithm (find_min_global from the dlib library) to adjust parameters automatically at each time step. Both methods were tested over Tuscany using high-resolution terrain and a dense observation network. Validation with leave-one-out method was carried out for the period 1995–2011 for both versions, while Daymet4-r2 underwent extended evaluation from 1991 to 2024 to assess seasonal dynamics and long-term variability. Results show that Daymet4-r2 outperforms Daymet4-r1 and the original Daymet V4 for all variables (mean absolute error of 1.24 mm, 1.06 °C, 1.29 °C, 6.26%, 0.78 m/s, and 2.04 hPa for precipitation, maximum and minimum temperature, relative humidity, wind speed, and sea level pressure, respectively). The largest improvement was observed in minimum temperature due to an enhanced approach for detecting and modelling thermal inversions. The high performance, flexibility, and ability of Daymet4-r2 to operate without prior calibration highlight its potential for model verification, real-time environmental monitoring, and integration into climate services. Full article

This paper develops a unified asymptotic theory for conditional single-index U-statistics and the associated conditional U-processes in the setting of locally stationary functional time series subject to missing-at-random response mechanisms. The proposed framework addresses, within a single nonparametric inferential architecture, three major sources of complexity in modern functional data analysis: infinite-dimensional covariates, smoothly time-varying stochastic dynamics, and incomplete response observations. The methodology is based on a class of kernel-type estimators combining temporal localization, functional single-index smoothing, and inverse-propensity correction. Temporal localization captures the gradual evolution of the underlying regression structure, the single-index projection provides an effective dimension-reduction mechanism for functional covariates, and the propensity adjustment restores the target conditional functional under the MAR sampling scheme. The principal contribution of the paper is the establishment of weak convergence, in a suitable space of bounded functions, for the resulting propensity-adjusted conditional U-process indexed by a general class of measurable kernels. Under absolute regularity conditions, local stationarity assumptions, small-ball probability requirements, entropy restrictions of VC type, and uniform consistency of the propensity-score estimator, the normalized process is shown to converge weakly to a tight centered Gaussian process. The limiting covariance structure explicitly reflects the interaction between temporal smoothing, functional concentration, dependence, and the random loss of responses. In parallel, uniform convergence rates are derived for the associated conditional single-index U-statistic estimators, thereby quantifying the respective contributions of smoothing bias, stochastic fluctuation, local-stationarity approximation error, and missingness-induced variance inflation. A substantial part of the analysis is devoted to the technical difficulties created by the simultaneous presence of dependence, nonstationarity, functional covariates, and incomplete observations. The proofs combine Hoeffding-type decompositions adapted to weighted incomplete data, blocking and coupling arguments for absolutely regular triangular arrays, refined entropy bounds for kernel-indexed function classes, and small-ball probability techniques for functional covariates. The MAR mechanism is incorporated via inverse-propensity weighting, and its effects on the effective sample size, asymptotic variance, and bias structure are made explicit. The theory also provides a rigorous foundation for bandwidth selection through blocked, propensity-adjusted cross-validation and clarifies its relation to the corresponding oracle risk. The proposed framework encompasses a broad class of statistical learning and inference problems involving pairwise or higher-order functionals of functional time series. In particular, it applies to conditional Kendall-type functionals, discrimination problems, metric learning with incomplete labels, and conditional independence testing under local stationarity. A simulation study illustrates the finite-sample behavior of the proposed estimators and supports the theoretical findings across varying regimes of temporal nonstationarity, serial dependence, functional concentration, and response missingness. Overall, the results provide a mathematically rigorous and methodologically flexible foundation for inference from evolving functional data when dependence, infinite dimensionality, and incomplete observation are present simultaneously. Full article

Water–bearing fractures and seepage–prone zones ahead of tunnel faces may evolve rapidly under excavation–induced disturbance, making early identification and process tracking essential for risk mitigation. Cross–hole electrical resistivity tomography (ERT) is sensitive to fluid–controlled conductivity contrasts, but time–series interpretation based on independently inverted snapshots is often unreliable due to ill–posedness, noise, and temporal inconsistency. In this study, we propose an improved time–lapse ERT inversion framework for monitoring evolving water–bearing fractures ahead of tunnels. The method is formulated as a baseline–anchored, Occam–consistent difference inversion that directly estimates resistivity changes relative to an initial state, incorporating error–aware weighting of differenced data and anisotropic regularization adapted to cross–hole sensitivity, so that temporal coherence is enforced during inversion rather than through post hoc differencing. Synthetic verification is conducted using three dynamic scenarios representing horizontal, vertical, and diagonal migration of conductive water–bearing pathways between boreholes. Quantitative comparison against independent inversion across all scenarios and time steps demonstrates that the proposed framework substantially reduces the root mean square error and mean relative error of the recovered resistivity, while significantly improving the spatial correlation coefficient between the recovered and true models, with the largest improvements observed in the diagonal–migration scenario. The reconstructed change maps exhibit more compact anomaly geometry and delineate evolution corridors aligned with the prescribed trajectories, whereas independent inversion produces diffuse and epoch–dependent change patterns. These results indicate that the proposed time–lapse inversion framework provides a more reliable basis for interpreting evolving seepage–related conductive structures in tunnel–ahead investigations. Full article

Grassland ecosystems on the Tibetan Plateau play a critical role in the global carbon cycle, however, the quantitative influence of extreme climate events on their carbon sink dynamics remains insufficiently understood. This study focused on the Sanjiangyuan (Three-River-Source) region, a representative alpine pastoral area, employed net ecosystem productivity (NEP) estimation, Theil–Sen trend analysis, the coefficient of variation, the Hurst index, and ridge regression modeling to quantify the spatiotemporal characteristics of grassland carbon source/sink dynamics and the contributions of 13 extreme climate indices during 2000–2024. The results indicate that the regional mean NEP increased at a rate of 1.49 g C m⁻² a⁻¹, where 90.78% of the area functioned as a carbon sink, reflecting relatively weak ecosystem respiration and dominant vegetation carbon absorption. However, Hurst index analysis reveals that 85.68% of regions exhibit an inverse sustainability trend, suggesting a potential shift from carbon sinks to carbon sources in the future. This implies enhanced ecosystem respiration and the possible replacement of carbon sink functions by carbon source functions. The ridge regression analysis demonstrated that the extreme temperature indices, particularly the warm days (TX90p, 38.1% relative contribution), cool nights (TN10p), and warm spell duration (WSDI) indices, were the dominant drivers of NEP variation. These findings provide adaptive management strategies were proposed: in highly variable and inversely persistent regions, regulating grazing intensity, optimizing fencing management, and restoring degraded grasslands should be implemented to mitigate excessive respiration-related carbon emissions and maintain carbon sink stability, a scientific basis for optimizing pastoral agricultural carbon management and ecosystem respiration regulation under intensifying climate extremes on the Tibetan Plateau. Full article

This study investigated the effects of construction design and seasonal climatic conditions on bedding dynamics in bedded-pack dairy systems with contrasting composting functionality. The study intentionally included systems representing both composting bedded-pack barns (CBP), characterized by active management (regular turning and ventilation), and non-composting bedded-pack barns (BPB), which lacked aeration and did not promote active composting, resulting in limited or absent composting activity. Nine farms were divided into three groups: CONV (large, full-time CBP), ADAP (adapted, full-time CBP), and PART (partially used BPB). Evaluations were conducted during both cold and hot seasons. Composting dynamics were assessed over 24 h by measuring bedding temperature and moisture at eight points. During daytime, additional measurements at twenty points allowed for spatial distribution analysis using the inverse distance weighting method. Bedding attributes—including pH, density, depth, and particle size—were also measured in eight points. A 2 × 3 factorial design (two seasons, three barn types) was applied, and data were analyzed using Tukey’s test and Pearson correlation. Microclimate conditions were monitored through air temperature and humidity. Bedding temperature was significantly higher in the hot season (36.55 °C) compared to the cold season (32.12 °C), and was highest in the ADAP group (40.01 °C), followed by CONV (37.39 °C) and PART (26.18 °C) (p < 0.05). The 24 h temperature curve indicated favorable composting conditions only in the CONV and ADAP groups. Spatial temperature distribution varied significantly across locations in most barns (p < 0.05). Moisture content was lower in the hot season (46.91% and 41.41%) than in the cold season (57.03% and 51.97%) for CONV and ADAP, respectively. Moisture and temperature were significantly correlated with key bedding characteristics (p ≤ 0.05). Overall, a greater combination of characteristics associated with more favorable composting conditions was observed in ADAP barns, particularly during the hot season, whereas PART systems showed conditions incompatible with active composting. Full article

Conventional settlement monitoring techniques are inadequate for seawall construction environments due to severe physical impacts, the absence of terrestrial communication networks, and highly dynamic disturbances. This research proposes a multi-source fusion settlement monitoring system designed specifically for the construction phase to overcome these constraints. An integrated inclinometer–magnetoresistive sensing unit is the central component of this system. The unit achieves physical isolation from the severe impact loads of rock backfilling, guarantees protection in high-salinity and high-humidity environments, and accommodates the large deformations typical of soft foundations by utilizing a structural design that includes a rigid channel steel sheath, anti-corrosion sealing, and flexible joints. In terms of computation, a cascaded attitude fusion framework is developed that combines a Multiplicative Extended Kalman Filter (MEKF) with Quaternion Estimator (QUEST) initialization. High-precision displacement inversion via quaternion rotation is made possible by the introduction of an adaptive mechanism based on the Mahalanobis distance that precisely detects and suppresses transient acceleration disturbances induced by construction machinery and waves. Additionally, data transmission issues in remote offshore areas are resolved by combining solar power and BeiDou short-message communication technologies. This adaptive technique minimizes attitude estimate errors in dynamic situations by approximately 84.56%, as demonstrated by experimental and field validation. The system was deployed as a 165 m array comprising 49 sensing units and monitored continuously for 458 days, achieving a normalized RMSE of 9.44–11.02% compared to reference settlement tubes and capturing a maximum settlement of 1.7 m in the core high-fill section. These results confirm the system’s high monitoring accuracy and resilience in harsh construction conditions. Full article

Vegetation phenology refers to the cyclical growth patterns of vegetation in nature, which are influenced by climatic conditions, human activities, and genetic factors. It plays an irreplaceable role in regulating carbon cycling and energy flow within natural ecosystems. However, the combination of a cloudy and rainy climate with a landscape characterized by the interplay of land and water and fragmented patches has long posed challenges for remote sensing phenological monitoring data, including a scarcity of valid observations, frequent temporal gaps, and spectral distortion in mixed pixels. These issues make it difficult to reliably support the needs of wetland phenological inversion and mapping. To address this issue, this study uses vegetation inversion in the Poyang Lake wetlands as a case study and reconstructs high-spatiotemporal-resolution time-series kNDVI data based on multi-source remote sensing data. Methodologically, we propose an improved and enhanced spatiotemporal adaptive reflectance fusion model, IESTARFM. This model enhances the homogeneity of similar pixel selection through adaptive matching windows and land cover constraints. Additionally, it explicitly incorporates cloud probability and time-lag factors into the weighting structure to systematically downweight unreliable observations, and further employs quadratic term corrections to account for the nonlinear growth response of kNDVI. Using the reconstructed dataset, key phenological information is extracted by combining third-order harmonic analysis with a dynamic thresholding method, thereby enhancing the robust characterization of seasonal trajectories under conditions of missing data and noise. Accuracy evaluation results show that the 10m/8d high-frequency kNDVI dataset reconstructed by IESTARFM achieves at least a 12.61% improvement in fusion accuracy compared to classical methods such as ESTARFM, STARFM, and FSDAF, with a maximum reduction in RMSE of 0.026, and effectively restores details in areas with thin cloud cover. The reconstructed kNDVI series achieved a coefficient of determination $R^2$ = 0.875 and RMSE = 0.066 relative to Sentinel-2 observations, indicating that the reconstructed series closely reproduces the reference imagery in both amplitude and spatial structure. The phenological parameters derived from kNDVI exhibit an RMSE of 4.81 days compared to field observations, demonstrating that the reconstructed time series reliably captures the timing of key phenological events. It should be noted that the proposed approach is designed for post-event time-series reconstruction and is not intended for real-time forecasting. In summary, this study collaboratively enhanced the reliability of high-resolution index time-series reconstruction and phenological identification in cloudy and rainy wetlands through three key aspects: cloud noise suppression, heterogeneous boundary preservation, and nonlinear growth characterization. It provides a generalizable technical foundation for dynamic monitoring of wetland vegetation, ecological restoration assessment, and refined management in regions with frequent cloud and rainfall. Full article

This paper proposes finite-time (FT) and fixed-time (FXT) neurodynamic models with time-varying coefficients for solving inverse quasi-variational inequality problems (IQVIPs). Two projected models with time-dependent gains are developed to enhance convergence speed and transient performance. A nominal model establishes the equivalence between equilibrium points and IQVIP solutions. Under Lipschitz continuity and strong monotonicity assumptions, the existence, uniqueness, and global convergence of the proposed models are ensured. By employing Lyapunov stability theory, finite-time and fixed-time convergence of the continuous-time models are rigorously established, where explicit settling-time bounds independent of initial conditions are derived for the FXT case. Furthermore, the robustness of the proposed models under bounded disturbances is analyzed. To validate the theoretical findings, a discrete-time implementation based on the forward Euler method is developed. Numerical experiments demonstrate that all trajectories converge within a uniform upper bound, showing convergence behavior consistent with the fixed-time characteristics of the continuous-time model. Although the convergence time varies with initial conditions, it remains uniformly bounded, which is consistent with the fixed-time stability characteristics of the continuous-time model. The proposed framework provides a computationally efficient and scalable approach for solving IQVIPs, with potential applications in traffic equilibrium, communication networks, distributed control systems, and multi-agent coordination. Its adaptive structure and fixed-time convergence properties make it particularly suitable for real-time optimization in dynamic and uncertain environments. Full article

Focusing on the self-powering demand of aircraft engine bladed disks (blisks), this paper investigates piezoelectric vibration energy-harvesting modeling and non-linear circuit performance. A multi-sector electromechanical coupled model is established to analyze the frequency splitting and vibration localization induced by minor structural mistuning. By breaking the cyclic symmetry, mistuning severely concentrates vibration energy into a specific sector, providing a localized high-energy concentration region for optimal energy extraction. To enhance recovery efficiency and load adaptability, three interface circuit topologies—Standard Energy-Harvesting (SEH), Parallel Synchronized Switch Harvesting on Inductor (P-SSHI), and Double Synchronized Switch Harvesting (D-SSHI)—are comparatively analyzed. Through wideband spatial–spectral dynamic response and steady-state impedance matching analyses, the non-linear energy conversion and transfer mechanisms are systematically characterized. Results demonstrate that synchronized switching circuits significantly improve energy transmission via forced voltage inversion, accompanied by a notable equivalent stiffness enhancement effect induced by electromechanical coupling. Furthermore, the D-SSHI topology not only exhibits substantial advantages in peak power extraction, but also, owing to its internal LC energy decoupling mechanism, forms a broad load-independent power plateau across an extremely wide impedance range. This research provides robust theoretical foundations for designing highly resilient self-powered intelligent blades under extreme operating conditions. Full article

Magnetorheological (MR) dampers enable semi-active control in prosthetic knees by providing rapidly adjustable resistance with low mechanical complexity. This paper evaluates three torque level control methodologies for a transfemoral prosthetic leg incorporating an MR damper: a model-based feedforward strategy, an adaptive inverse-dynamics controller, and a robust inverse-dynamics controller. A Lagrange-based planar leg model with explicit force-to-torque mapping is formulated, and a reference knee trajectory is estimated from measurable gait variables using a cubic polynomial model whose order is selected through least-squares RMSE analysis. Each controller is assessed using knee-angle tracking accuracy and control effort to capture the practical trade-off between motion quality and energy demand. Results demonstrated that the adaptive inverse-dynamics controller has the smallest tracking error but requires the highest effort, whereas the robust inverse-dynamics approach realizes approximately the same tracking performance with reduced effort, thereby suggesting the best accuracy–effort compromise in the present work. Results, likewise, examined actuator feasibility by considering the MR damper as the primary dissipative element and the DC motor as a supplemental active actuator required when damping alone cannot satisfy the commanded knee torque. Full article

Fish disease and species identification is critical for intelligent aquaculture, directly influencing productivity, sustainability, and economic viability. However, existing approaches largely treat species identification and pathological classification as independent tasks, limiting their ability to capture interdependent features under complex real-world conditions such as occlusion, low contrast, dynamic backgrounds, and high inter-class similarity. Moreover, challenges including class imbalance, cross-species variability, and fine-grained feature discrimination remain insufficiently addressed. To overcome these limitations, this paper proposes a hybrid ConvNeXt–BiLSTM–multi-head self-attention (MHSA) framework for joint fish species and disease classification, where a ConvNeXt-Small backbone extracts hierarchical spatial features that are transformed into a structured sequence and processed by a bidirectional LSTM to capture contextual dependencies, followed by an MHSA module for adaptive feature refinement. An auxiliary species classification branch is incorporated to provide multi-task regularization without additional inference costs. The training pipeline integrates CLAHE-based image enhancement, square-root inverse-frequency focal loss, targeted minority oversampling, and a two-stage progressive learning strategy with differential-rate cosine annealing, complemented by five-view test-time augmentation. For practical deployment, a YOLOv8s detector is employed for fish localization prior to classification. The experimental results demonstrate that the proposed model achieves superior performance, attaining overall top-1 classification accuracy of 94.33%, precision of 97.1%, recall of 90.9%, 96.1% mAP50, and an F1-score of 0.9264, while achieving a macro AUC of 0.994 and maintaining high computational efficiency (213.3 FPS), demonstrating a robust and efficient solution for real-time fish disease screening. Full article

To address the vibration reduction problem of the six-degrees of freedom(6-DOF) half-vehicle model and to improve ride comfort and handling stability, a piezoelectric stack actuator based on the inverse piezoelectric effect was introduced. A 6-DOF half-vehicle dynamic model coupling the cab, body, and wheels was established based on the Lagrange equation. Based on this model, a vertical-pitch dual sliding surface RBF neural network sliding mode control strategy was proposed, with two independent RBF neural networks designed to separately approximate, online, the comprehensive uncertainties in the vertical and pitch channels associated with unmodeled dynamics, external disturbances, and modeling simplifications. The variable-speed reaching law (dsat) function was used to design the sliding mode reaching law, balancing sliding surface convergence speed and vibration suppression. Six indicators, including vertical acceleration of the cab and vertical acceleration of the vehicle body, were selected as performance evaluation metrics to establish the fitness function. Combined with a genetic algorithm, the dual sliding surface coefficients, RBF network parameters, adaptive update rates, and variable-speed reaching law parameters were globally optimized. The vibration reduction effects of four schemes—passive control, traditional sliding mode control, RBF sliding mode control, and genetic algorithm optimized RBF dual-sliding-mode control—were compared and analyzed. Simulation results show that the genetic algorithm optimized RBF dual-sliding-mode control achieves improved vibration suppression in several key ride-comfort-related indices and provides better overall coordination among ride comfort, suspension working space, and tire dynamic deflection. The research results validate the effectiveness of this method and provide a new solution for addressing vehicle vibration reduction problems. Full article

This paper presents a generalized epidemic modeling framework based on g-tempered Caputo fractional derivatives with discrete time delays. The proposed approach incorporates nonlocal memory effects, nonlinear temporal scaling, and delayed epidemiological responses within a unified mathematical structure. The introduction of the nonlinear time transformation $g\left(t\right)$ and the tempering parameter $\lambda$ eliminates the unrealistic infinite-memory behavior associated with classical power-law kernels while simultaneously introducing new challenges related to parameter identifiability and inverse problems. We investigate the structural properties of the resulting dynamical systems and show that the associated inverse problem is inherently ill-posed. To illustrate the practical implications of these results, the framework is applied to a delayed SIQR epidemiological model. Numerical simulations are performed using a generalized L1-type scheme adapted to delayed fractional histories, and a multi-phase parameter estimation procedure is proposed to address the ill-posedness of the reconstruction problem. The results demonstrate the ability of the model to capture both short- and long-term memory effects in epidemic evolution while highlighting the challenges of statistical identifiability in generalized fractional systems. Full article

Land subsidence poses a significant threat to infrastructure stability and urban sustainability, particularly in rapidly developing coastal regions. In this study, land subsidence over Penang Island, Malaysia, was investigated by integrating Persistent Scatterer Interferometric Synthetic Aperture Radar (PS-InSAR) and Small Baseline Subset InSAR (SBAS-InSAR) techniques using both ascending and descending Sentinel-1 datasets. The combined PS-SBAS framework enables high-resolution and reliable deformation monitoring by exploiting the complementary advantages of the two approaches. To further improve deformation retrieval accuracy, an adaptive machine learning-based weighting strategy was incorporated into the InSAR time-series inversion process. Specifically, a data-driven model was employed to evaluate the reliability of interferometric observations using multiple quality indicators, enabling adaptive weighting of interferometric pairs and suppressing the influence of low-quality or noisy measurements. This strategy enhances the robustness and stability of deformation estimation without requiring additional external datasets. The results reveal spatially heterogeneous subsidence patterns across Penang Island, with pronounced deformation mainly concentrated in coastal and urbanized regions. Compared with conventional approaches, the proposed framework demonstrates improved temporal consistency and reduced sensitivity to noise, resulting in more reliable deformation time series. The findings provide valuable insights into regional subsidence dynamics and demonstrate the potential of the proposed framework for InSAR-based deformation monitoring in complex environments. Full article