Search Results (2,140)

Time-reversed Young (TRY) interferometry reconstructs interference from a fixed detector by reading out a programmable source-label distribution. This work formulates the architecture as a source-coded Bayesian response sensor. For a perturbation parameter $\theta$, the detected source-label histogram is a posterior distribution determined by a programmed source prior, an optical likelihood for a fixed-detector click, and an evidence factor equal to the click probability. The key point is not the Bayesian identity itself, but its physical implementation: in TRY the prior is imposed before propagation and can therefore reshape the response ensemble actually sampled by the detector. The normalized posterior is shown to respond through a centered likelihood score, and the detected-event Fisher information is the posterior variance of this score. This identifies posterior-weighted score contrast, rather than local response magnitude alone, as the relevant sensing resource. The framework separates posterior-shape information from evidence information, giving a resource-aware way to judge near-null response enhancement. It also yields practical design rules: a two-label source code converts a weak perturbation into a fixed-detector label imbalance, while the multiparameter score covariance provides a route to nuisance rejection and gives a minimal-label rank condition for sensing multiple perturbations. A passive double-slit implementation with weak one-slit phase and loss perturbations is proposed, requiring only fixed-detector source scans before and after calibrated perturbations. Practical tolerances associated with source-programming error, drift, background, imperfect coherence, and polarization mismatch are analyzed, and extensions to multi-aperture and integrated photonic systems are formulated. The results position TRY as a source-programmable Bayesian sensing architecture complementary to conventional detector-plane Young interferometry. Full article

Landslide susceptibility mapping (LSM) serves as a fundamental technical support for geohazard prevention and mitigation across mountainous terrains. This research constructs a multi-scale terrain unit integrated modeling framework targeting complex mountainous geomorphic settings, taking Zhenping County as the research object. Multi-resolution digital elevation model (DEM) datasets, multi-source satellite remote sensing imagery (GF-2), geological vector datasets and hydrological survey data are jointly adopted as the basic data source. The r.slopeunits module embedded in GRASS GIS is utilized to automatically segment slope units, and a comprehensive composite index S, coupling slope partition quality indicator F and model prediction accuracy metric R, is proposed to adaptively optimize two critical slope-unit hyperparameters: circular variance (c) and minimum unit area (a). Four DEM spatial resolutions (15 m, 25 m, 50 m, 100 m) are systematically calibrated with 42 groups of c–a parameter combinations to screen out the optimal slope-unit segmentation scheme (c = 0.1, a = 200,000 m²). Twelve landslide predisposing covariates covering topography, hydrology, lithology, human engineering activities and land cover are selected after multicollinearity diagnosis via Variance Inflation Factor and mean utility factor contribution evaluation. Logistic regression tree (LMT), LMT-Adaboost and LMT-Random Subspace are compared by random cross-validation and spatial block cross-validation. Parameter sensitivity analysis is further carried out to quantify the stability of model outputs against DEM resolution and slope-unit parameter perturbations. The LMT-RSM ensemble achieved the highest spatial cross-validation AUC (0.954 ± 0.019), outperforming LMT (0.925 ± 0.023) and AdaBoost-LMT (0.934 ± 0.021). The DeLong test confirmed that LMT-RSM’s superiority over LMT is statistically significant (p < 0.0001). The proportion of landslides in the very high and high susceptibility zones under the LMT-RSM model reached 95.98%, demonstrating relatively excellent spatial discrimination. This study provides an operational framework combining optimized slope units, ensemble learning, and spatially explicit validation for robust LSM in complex terrain, and offers a reproducible technical pathway for landslide risk prevention in mountainous regions. Full article

This paper investigates cooperative tracking control for virtually coupled multi-train systems subject to nonlinear running resistance, bounded time-varying resistance parameters, state constraints, and actuator saturation. The theoretical contribution is not the separate use of barrier Lyapunov functions, adaptive control, anti-windup compensation, or distributed cooperative control. Instead, the revised analysis establishes a coupled safety-and-boundedness certificate for the actual saturated closed-loop vector field. The closing-speed-aware spacing variable and actuator-authority condition support a first-exit proof of forward invariance, after which a composite Lyapunov analysis couples the saturation residual, anti-windup state, cooperative tracking error, and time-varying parameter-estimation error to establish uniform ultimate boundedness without persistent excitation. This proof architecture distinguishes the proposed controller from recent constrained train-control methods focused separately on velocity/input bounds, distance-oriented full-state barriers, or iteration-indexed learning. Numerical studies with heterogeneous trains, stronger time-varying aerodynamic perturbations, normalized actuator limits, tracking-bound verification, constrained baselines, a near-boundary safety-allocation case, and a quantitative one-factor-at-a-time parameter-sensitivity study are provided. Full article

We study a one-dimensional heat equation with dynamic integral boundary conditions together with an inverse problem for recovering an unknown boundary source from interior temperature measurements. For the direct problem, the existence and uniqueness of a smooth solution are discussed, and a weighted finite difference scheme with weight parameter $\sigma$ is constructed and analyzed with respect to approximation, stability, and convergence. For the inverse problem, we propose a two-step reconstruction algorithm based on weighted and improved weighted finite difference discretizations. The numerical results show that the scheme with weight $\sigma =0.5$ provides higher accuracy for noise-free or weakly perturbed data, while the scheme with $\sigma =1$ exhibits better stability as the noise level increases or the measurement point moves farther from the unknown boundary. To improve stability in the presence of noisy data, a regularized reconstruction approach is introduced. Numerical experiments for different measurement locations and noise levels up to $10\%$ illustrate the convergence and stability behavior of the proposed methods and confirm the efficiency of the regularized algorithm. Full article

We use perturbation theory to study kinks in nonlinear Klein–Gordon (${\varphi }^4$ and sine-Gordon) chains and in a discrete series-connected Josephson transmission line. The expansion parameter is the ratio of the lattice period to the kink width. The next-to-leading-order approximation modifies the kink profiles obtained previously in the leading-order approximation. Full article

Accurate gas mass flow allocation is important for multi-component gas mixing systems and fuel-cell test benches, especially when a wide total-flow range is covered by several parallel mass flow controllers (MFCs). This study evaluates the coordinated allocation of three parallel MFCs using traceable third-party calibration data and a reproducible numerical simulation workflow. The calibration curves define a constrained flow-allocation problem in which several MFC setpoint combinations can satisfy the same total target flow, while producing different full-scale error levels. A modified particle swarm optimization (PSO) strategy is compared with a rule-based allocation strategy and standard PSO. The numerical evaluation was extended with 500-run reliability statistics, ablation analysis, parameter sensitivity analysis, expanded switching-region evaluation, numerical perturbation checks, and benchmark optimizer comparisons. Across the main 12 operating points, the probability of obtaining a solution within 0.10 %FS (percentage of full scale (%FS)) increased from 0.207 with standard PSO to 0.310 with the improved PSO, and the probability within 0.05 %FS increased from 0.135 to 0.193. The positive-improvement rate relative to the rule baseline increased from 0.384 to 0.547. The results support the use of calibration-curve-based optimization to reduce switching-region error peaks and to improve repeated-run low-error solution probability in parallel-MFC allocation. Full article

Microgrid integration introduces fast, stochastic disturbances that challenge frequency stability. This paper presents a two-degree-of-freedom fractional-order proportional tilt integral derivative plus one controller (2DOF-FOPTID+1) tuned with a Modified Walrus Optimization Algorithm (MWA) to mitigate frequency deviations while preserving tracking performance. The novelty lies in jointly deploying a 2DOF-FOPTID+1 structure for decoupled tracking and regulation, an MWA-based tuning strategy tailored for resilient frequency control, and the explicit use of aggregated electric vehicles as fast distributed storage to damp frequency and tie-line power excursions; hardware-in-the-loop validation using an OPAL-RT platform is included to demonstrate practical feasibility. The controller is evaluated under step and random load variations, and robustness is examined for $\pm 25\%$ parameter perturbations and stochastic renewable inputs. Compared with the strong baselines PID, FOPID, 2DOF-PID, and FOPTID, the proposed approach reduces settling time by up to 39.27% and lowers peak-to-peak frequency deviation by about 20.88% under these operating scenarios, indicating a practical and effective solution for enhancing frequency resilience in microgrid-integrated power systems. Full article

To examine the mechanisms through which supply-side policy instruments influence the early diffusion of high-quality technologies in technology markets, this study constructs a bilateral evolutionary model incorporating finitely rational technology suppliers and demanders based on evolutionary game theory and a bipartite small-world network. Through multi-agent simulations, the study analyzes the effects of government R&D funding, tax relief and technology transaction subsidies on the diffusion of high-quality technologies and the process of supply-demand strategy adaptation, with the aim of characterizing the role of public fiscal funds in screening for effective technology supply. The study reached the following main conclusions: First, under the baseline scenario, high-quality technologies exhibit a strong tendency toward endogenous diffusion, and supply-side policies primarily serve to accelerate marginal growth in the early stages rather than fundamentally altering the long-term convergence trend. Among these policies, R&D funding, as an ex ante incentive tool, has the most direct impact on early-stage diffusion; however, high-intensity funding leads to a decline in fiscal efficiency; Second, the independent effects of ex post incentive tools like tax relief and technology transaction subsidies are relatively moderate, with the former exhibiting high fiscal efficiency at low to medium intensities, and the latter exerting a moderate incentive effect by increasing the returns on successful transactions, though both suffer from diminishing marginal returns; Third, the supply-demand strategy fit index can be used to help characterize the supply-demand coordination process, but it cannot be directly interpreted as an indicator of technology quality or diffusion quality; Finally, robustness tests indicate that the main conclusions remain stable under perturbations to network topology, initial conditions, and Fermi noise parameters. Overall, the design of supply-side policies should simultaneously consider the timing of policy implementation, market-matching mechanisms, and fiscal cost-effectiveness. Full article

Permanent magnet synchronous motors (PMSMs) used in hybrid shunting locomotive traction systems operate under complex conditions, and their electrical parameters may vary with temperature rise, load disturbance and magnetic saturation. To improve online parameter tracking under such conditions, this paper investigates a forgetting-factor recursive least squares (FFRLS)-based identification method for stator resistance, stator inductance and permanent magnet flux linkage. The main contribution lies in the traction-oriented formulation of the identification model, DSP28335-based real-time implementation, and simulation/experimental validation of three-parameter online tracking. Simulation results show that the proposed method can track the three key parameters under selected perturbation conditions. The experimental results provide algorithm-level evidence for the real-time implementation and three-parameter tracking capability of the proposed method on a scaled-down PMSM platform, thereby establishing a basis for subsequent full-scale validation and studies on traction-control robustness and energy-efficiency optimization. Full article

This study focuses on the longitudinal dynamic stability of swifts in cruising forward flight, which is critical for their high maneuverability but remains insufficiently investigated. Understanding longitudinal dynamic stability is the essential prerequisite for revealing the physical mechanism underlying their maneuverability: it is the dynamic stability characteristics that determine how the flight state responds to disturbances and control inputs, thereby laying a foundation for subsequent flight control during agile maneuvers. Conventional studies mostly adopt steady or quasi-steady assumptions, which cannot accurately reflect the influence of periodic body vibration. This study combines CFD numerical simulation and dynamic modeling to systematically analyze the unsteady dynamic stability of swifts. A bio-inspired dynamic model is established using the BE3357B airfoil with a 5° sweep angle, and the flapping-wing motion is decomposed into three degrees of freedom: sweeping, pitching, and flapping. Numerical reliability is assessed through grid independence and time-step independence verification. Aerodynamic force and moment trimming are performed on fixed-DOF and free-DOF models, where the latter considers coupled heaving–pitching motion and adjusted trim parameters. Stability analysis is conducted using three aerodynamic derivative methods: fixed velocity, forced oscillation, and Floquet. By solving small perturbation equations, eigenvalues and eigenmodes are obtained. All three methods identify two stable modes: a short-period mode with damping coefficient 0.1236–0.1870 and oscillation period 0.1121 s–0.1380 s, and a long-period mode with damping coefficient 0.2456–0.6203 and damping half-life 3.5803 s–4.8890 s, verifying stability under periodic vibration and unsteady aerodynamic coupling. Flow field results show clear distinct dynamic pressure and drag fluctuation characteristics between the downstroke and the upstroke. The unsteady stability framework provides a theoretical reference for analyzing the longitudinal stability of biomimetic flapping-wing aircraft and offers useful insight for future bird-inspired flight dynamics studies. Full article

Cross-domain collaborative filtering effectively alleviates the data sparsity issue but raises serious privacy concerns. Federated learning has been integrated into cross-domain collaborative filtering to reduce these risks by securely exchanging embeddings or model parameters. However, the current federated paradigm often relies on the simple alignment of coarse-grained representations, while propagating information on a rigid local graph. Without fine-grained preference modeling, models easily suffer from representation collapse. The inherent sparsity of local graphs further limits their robustness. To address these issues, we propose P2P-IAGR, a privacy-preserving peer-to-peer cross-domain collaborative filtering framework based on intent-adaptive graph reconstruction. Specifically, our framework first disentangles user and item representations into fine-grained latent intent prototypes. We perturb these prototypes using Local Differential Privacy (LDP) and securely exchange them across domains. A contrastive learning strategy is then used for cross-domain alignment. Next, guided by the combined cross-domain intent prior, P2P-IAGR differentiably reconstructs an augmented graph view. We apply a dual-view structural contrastive learning objective to dynamically inject external collaborative signals into the sparse local topology. Extensive experiments on real-world datasets show that P2P-IAGR significantly outperforms state-of-the-art methods, achieving an average improvement of 5.85% in NDCG and 6.89% in HR. Full article

In China’s expressway work zones, it is a common phenomenon for drivers to have a low compliance rate with speed-limit instructions. Existing microscopic traffic simulation calibrations mainly focus on car-following and lane-changing behaviors, lacking research on speed-limit compliance behavior. Therefore, this paper proposes a method for collaborative calibration of the key parameters of a “car-following, lane-changing, speed-limit compliance” three-layer model based on the SUMO simulation platform. The research selects the key parameters in the IDM car-following model, LC2013 lane-changing model, and speed-limit compliance model to form a calibration parameter set, taking the time-mean speed and space-mean speed as optimization indicators, using the simultaneous perturbation stochastic approximation (SPSA) algorithm combined with a restart strategy, and aiming to minimize the root-mean-square error (RMSE) of the speed between the simulated and observed data for global optimization. The model is verified by the measured traffic flow and speed data in the expressway work zone. The verification results show that the three-layer calibration framework incorporating the speed-limit compliance model not only improves speed fitting but also better reproduces the distributional characteristics of real traffic flow in the work zone, especially the dispersion and heterogeneity of operating speeds. This research fills a gap in research involving SUMO calibration of speed-limit compliance in China and provides a theoretical basis and method-based support for microscopic simulation considering driver differences in speed-limit compliance. Full article

Floor Ordovician karst confined water inrush severely restricts safe exploitation of lower coal seams across North China-type coal basins. Surface directional drilling regional grouting serves as the dominant aquiclude reconstruction technology for water hazard mitigation, yet existing research lacks quantitative decoupling and hierarchical sensitivity quantification of medium intrinsic attributes and controllable grouting parameters. To resolve this knowledge gap, this work delineates five core governing variables: porous medium permeability, matrix porosity, injection pressure, slurry dynamic viscosity and slurry bulk density. A coupled Darcy–Bingham two-phase flow numerical framework based on the COMSOL Multiphysics fluid–solid interaction module is constructed—combined with L₂₅(5⁶) orthogonal experimental design to quantitatively characterize the gradient response law of slurry effective diffusion volume against multi-factor perturbation. Variance analysis (ANOVA) demonstrates a hierarchical control sequence: porous medium permeability > matrix porosity > grouting pressure > slurry dynamic viscosity > slurry bulk density. Medium permeability, porosity and injection pressure dominate slurry migration behavior with diffusion volume perturbation amplitudes ranging 2–191%; whereas, rheological and density parameters exert secondary marginal effects limited within 1–8%. Fracture hydraulic theoretical interpretation reveals permeability acts as the primary groutability discriminant index, and injection pressure exhibits prominent marginal diminishing effect with an efficiency threshold of 8 MPa. This study establishes a quantitative parameter optimization framework for Ordovician top aquiclude reconstruction engineering, providing theoretical support for targeted grouting parameter regulation and risk reduction in blind high-pressure injection. Full article

Electroporation outcomes are governed by the local electric field distribution and transmembrane voltage, both of which may be altered by nanoscale elements positioned near the cell membrane. In this study, we developed a two-dimensional finite-element electromagnetic model to investigate the effect of a membrane-proximal silica-coated superparamagnetic iron oxide nanoparticle cluster during a trapezoidal electroporation pulse. The model couples electric and magnetic field components with a membrane electroporation formulation based on Smoluchowski-type pore-density dynamics. Simulations were performed with and without a nanoparticle positioned 5 nm from the membrane, considering different cytosol and extracellular medium conductivities. The results show that the nanoparticle induces a highly localized perturbation of the electric field, whose magnitude depends on the sampling region and conductivity contrast. Transmembrane voltage is modestly and transiently modulated during pulse rise time, whereas the effect is limited during the pulse plateau. Pore-density analysis further indicates that the nanoparticle does not induce a generalized increase in electroporation-related parameters and may locally reduce pore density near the nanoparticle–membrane interface. Overall, the model identifies transient and conductivity-dependent nanoscale field redistribution caused by membrane-proximal silica-coated magnetic nanoparticles, while highlighting the need for three-dimensional modeling and experimental validation before inferring electroporation enhancement. Full article

To address load reconstruction misidentification caused by low-damping free-decay oscillations in thin-walled engineering structures under transient impact, a dynamics-embedded sparse Bayesian inference (DE-SBI) framework is developed for impact load reconstruction. The proposed method embeds a representative Duhamel temporal kernel into Gaussian temporal atoms and combines it with spatial Gaussian load atoms, finite-element modal strain mapping, and automatic relevance determination (ARD)-based sparse Bayesian inference. This formulation yields a dynamics-embedded spatiotemporal dictionary, which reduces the risk that conventional quasi-static dictionaries misinterpret structural ringing responses as sustained external loads. Controlled numerical validation on a representative stiffened plate structure shows that DE-SBI can effectively reconstruct the impact load histories and spatial distributions under single-impact, off-grid impact, and spatiotemporally overlapping dual-impact cases. Compared with Tikhonov regularization, Lasso regularization, and standard SBI, DE-SBI exhibits more stable identification performance in terms of correlation coefficient, peak error, and spatial relative error. Further analyses of parameter sensitivity, sensor layout, noise perturbation, and Duhamel temporal-kernel mismatch indicate that the method maintains good robustness under the considered controlled numerical conditions. These results provide an interpretable dynamics-embedded Bayesian inversion strategy for transient impact load identification under sparse-observation conditions. Full article