Search Results (6,489)

Normal contact stiffness of textured interfaces is controlled by the load-bearing contribution of deterministic texture and by the nonlinear response of rough load-bearing regions. This study formulates a hybrid deterministic–microlevel model that couples regular relief patterns—including dimples, grooves, periodic ribs and scraped high points—with a micromechanical representation of plateau roughness. Depending on texture topology and scale hierarchy, the microlevel response is represented either by a Greenwood–Williamson (GW) statistical contact model with a smooth elastic–plastic (EP) transition or by a fractal contact model. The deterministic level accounts for open-area fraction, texture depth and load redistribution, and it includes a finite-gauge spectral correction for periodic ribs and grooves to account for the finite measurement window. In a metallic dimple benchmark, the hybrid deterministic-texture/GW–EP formulation yields a mean relative error of 16.3% across all data points in the two selected textured series. In a saturated square-wave benchmark, the finite-gauge spectral correction yields a mean relative error of 10.37% for the independent patterned points. A compliance-based topology criterion is then established to determine, from open-area fraction, element depth, applied load and the ratio between texture period and plateau-roughness spacing, whether stiffness is governed primarily by deterministic texture, by micro-roughness or by their coupled response. The resulting formulation supports early-stage design exploration without requiring a full three-dimensional contact calculation at every parameter point. Independent periodic three-dimensional checks for circular-dimple cells showed that the finest FE solution agreed with the spectral prediction of the deterministic normal approach within 1.2%; the correction estimated by direct unilateral BEM changed total stiffness by no more than approximately 5% in the tested texture-influenced case. Full article

The complex morphology and optical anisotropy of anisotropic sea-fog particles make it difficult to accurately describe the multiple scattering behavior of polarized light. To overcome the insufficient coupling between polarization evolution and scattering direction in conventional Monte Carlo models, as well as their limited representation of particle parameters, this study proposes a polarization transmission model combining an improved Discrete Dipole Approximation with a direction-adaptive Monte Carlo method. In this model, the probability density function of scattering direction is constructed as a functional of the photon Stokes vector, enabling tight coupling between polarization state updates and photon propagation. Particle structures are refined by incorporating size, aspect ratio, Euler angles, morphological perturbations, and complex refractive indices. Meanwhile, an equivalent scattering kernel containing higher-order statistical moments is introduced to balance computational efficiency and physical fidelity. An indoor sea-fog polarization transmission platform was established to measure six incident polarization states at 450, 532, 671, and 808 nm. The results show over 80% agreement with model predictions and a root mean square error below 0.1. The study further indicates that circular polarization retains polarization better than linear polarization under high optical thickness, while longer wavelengths provide more stable polarization transmission. This framework offers theoretical support for polarization imaging and optical communication in sea-fog environments. Full article

The Lower Ganjiang River is a multi-branch delta with highly uneven spatial and temporal flow distribution, and conventional static diversion or threshold-based operation fails to stabilise the water level or optimise flow allocation under varying inflows. This study develops a hydrodynamic regulation framework that couples an improved integral time-delay model and model predictive control (MPC). A nonlinear state-space equation is constructed using a quadratic storage–water level relationship and rolling optimisation is solved with CasADi-IPOPT to minimise water-level tracking error, discharge deviation and control effort. The framework is validated offline against MIKE21 simulations for three historical flow scenarios (September 2016, February 2017 and March 2018). Under these scenarios, the Waizhou water level is maintained at 15.5 ± 0.2 m, daily water level variation is limited to ≤0.5 m/d, and the diversion ratio deviation is ≤5%. Compared with the natural state, water level fluctuation is reduced by 21.3% (September 2016 storage scenario). The proposed MPC framework effectively alleviates the spatiotemporal hydrodynamic imbalance of the Lower Ganjiang River, showing satisfactory model accuracy, constraint compliance, and engineering applicability, and offers a promising approach for advanced regulation of complex multi-branch river networks. Full article

Space-based single-photon ranging of space debris is affected by sparse signal photons, strong background noise, motion-induced echo broadening, and the amplification of local ranging errors during velocity estimation. To address these problems, this paper proposes a reliability-aware joint distance–velocity extraction framework that couples coded-echo processing with inter-window motion continuity. In each observation window, a phase-space trajectory matrix is constructed from the photon-counting echo, and fixed-rank truncated SVD is used as a front-end structural enhancement step to preserve the dominant temporal structure of the broadened coded echoes while suppressing part of the diffuse photon-count fluctuation. Coded cross-correlation is then used for coarse delay localization, followed by peak-neighborhood sub-bin estimation. A composite range-observation reliability measure is constructed from the retained singular-value-energy ratio, correlation-peak significance, and peak-to-sidelobe ratio. Reliability-weighted local polynomial fitting and distance–velocity joint optimization subsequently provide continuous range and radial-velocity estimates. In an orbital-reference simulation, the proposed method reduced the range RMSE, MAE, and MaxAE by 84.74%, 83.22%, and 83.83%, respectively, relative to VBSPC, and reduced the corresponding velocity errors by 89.36%, 89.04%, and 94.30% relative to IMMK. A 30-trial ablation study indicated the complementary contributions of SVD enhancement, sub-bin estimation, and joint optimization, while repeated tests under different coding conditions and 20–100% signal-photon levels demonstrated robustness to photon-count reduction. A scaled moving-target experiment further achieved range and velocity RMSEs of 0.066 m and 0.033 m/s, respectively. The experiment validates the measured-echo processing chain rather than the complete orbital optical link. The results demonstrate that jointly exploiting coded-echo structure, observation reliability, and motion continuity provides robust range-trajectory and radial-velocity extraction for photon-starved moving targets. Full article

Background: Training has been shown to be associated with competence improvement among healthcare providers. This study aimed to evaluate the relationship between external training and self-reported competence improvement among county hospital healthcare providers in the context of China’s “Thousand-County Project” policy. Methods: A multi-center cross-sectional survey was conducted from June to September 2025, involving 605 healthcare providers from 30 county hospitals across six provinces in China. Propensity score matching was used to control for observed confounding, and multivariable ordinal logistic regression was applied to estimate the odds ratio (OR) and 95% confidence interval (CI) for the association between post-policy external training and self-reported competence improvement, using 5-point Likert scale as the ordered outcome. Interaction tests and a series of sensitivity analyses were conducted to assess robustness and explore potential effect modification. Results: Matching effectively balanced the distribution of covariates between the two groups. Ordinal logistic regression showed that participants who received post-policy external training reported significantly higher self-reported competence improvement than those who did not (OR = 3.36, 95%CI: 1.74–6.48, p < 0.001). Policy awareness was also strongly associated with self-reported competence improvement (OR = 4.50, 95%CI: 2.53–7.99, p < 0.001), and a significant interaction indicated that this association was substantially stronger among those who were aware of the policy (p for interaction = 0.043). Sensitivity analyses, including cluster-robust standard errors and alternative propensity score specifications, yielded consistent results, supporting the robustness of the findings. Conclusions: Post-policy external training was significantly associated with higher self-reported competence improvement among county hospital healthcare providers, and this association appeared stronger among those with greater policy awareness. To better support such initiatives, policymakers could consider strengthening financial support for external training, while hospitals might optimize staffing arrangements to facilitate training participation. Additionally, improving policy dissemination may help raise healthcare providers’ awareness of relevant policies. Full article

Precise short-term load forecasting is critical for smart grid reliability, yet the resilience of predictive models against ambient noise and sensor degradation remains insufficiently explored. This study systematically evaluates the noise sensitivity and operational stability of three forecasting paradigms (baseline LSTM, EEMD-LSTM, and CEEMD-LSTM) across varying signal-to-noise ratios (0–25 dB SNR) and sampling densities. Empirical analyses reveal a critical operational paradox: despite its theoretical sophistication, the CEEMD-LSTM architecture exhibits severe algorithmic instability and vulnerability to residual noise artifacts, yielding an RMSE standard deviation of ±1918 MW under high-fidelity conditions. Conversely, the EEMD-LSTM framework demonstrates superior robustness. Acting as an effective noise-mitigating filter, it sustains a Mean Absolute Percentage Error (MAPE) below 3.5% across all noise regimes, notably achieving sub-3.0% errors under severe interference (5 dB SNR). Furthermore, evaluating memory-optimized sparse sampling exposes a predictive trade-off, whereby reduced sampling density substantially exacerbates forecasting errors. These findings indicate that the EEMD-LSTM model, underpinned by dense sampling, provides a highly stable, noise-resilient predictive mechanism for smart grids subjected to uncertain measurements. Full article

Existing NH₃/CH₄ combustion reaction mechanisms still exhibit a pronounced trade-off between predictive accuracy and computational efficiency. It is difficult to guarantee global optimality, which limits their application in gas turbine combustion simulations. The objective of this study was to introduce machine learning methods into the parameter optimization process of combustion reaction mechanisms and to construct an optimized mechanism (Bys-BP Mech) with both high accuracy and high computational efficiency. First, a reduced mechanism was obtained through mechanism coupling and the DRGEP method. Subsequently, the pre-exponential factors and activation energies of three key reactions were optimized based on sensitivity analysis. An artificial neural network was used to construct the model, and cross-validation combined with Bayesian optimization was employed to achieve automatic hyperparameter optimization. Validation results showed that the optimized Bys-BP Mech achieved an average relative error of 4.56% for LBV, and the average relative error of IDT decreased to 17.16%. CFD simulations indicated that the minimum prediction error of NO emissions was 3.74% when the CH₄ co-firing ratio ranged from 40% to 70%. This mechanism addressed the limitations of existing mechanisms in combustor-scale validation under variable operating conditions and reduced the computational time of combustion simulations by half. Full article

Accurate modeling and partitioning of forest evapotranspiration (ET) are essential for understanding water cycle processes in forest ecosystems. This study develops an improved three-source ET model by integrating a physically based canopy interception evaporation (Ei) scheme into the Shuttleworth–Wallace–Hu (SWH) model. A Monte Carlo stochastic parameterization scheme was applied to optimize model parameters. The proposed framework disaggregates the total ET flux into three distinct components: vegetation transpiration, soil evaporation, and Ei, thereby reducing uncertainties associated with the original SWH model in humid forest regions. The new model’s performance was assessed using flux observations from two subtropical forest sites and compared to the SWH model. The verification results indicate that the three-source model provided reliable estimates of daily ET. At the QYZ station (2004–2007) and the DHS station (2005–2007), the fitting slopes for simulating daily ET were 0.97 and 1.01, respectively, with corresponding coefficients of determination of 0.92 and 0.81. The root mean square errors (RMSE) for the three-source model were 0.38 mm day⁻¹ and 0.52 mm day⁻¹, respectively, with a reduction of 4.33% and 3.10% in RMSE compared to the SWH model. Additionally, the new model simulated the annual T/ET ratio more accurately, with values closer to site-measured data than the SWH model’s estimates. At both sites, the T/ET ratios simulated by the new model were closer to the observed values than those simulated by the SWH model, indicating an improved representation of ecohydrological processes. Furthermore, environmental analysis revealed that vapor pressure deficit and precipitation primarily govern the T/ET ratio, exerting the strongest positive and negative effects, respectively. Importantly, it requires only one additional precipitation parameter compared to the SWH model, yet achieves higher simulation accuracy and a more realistic representation of hydrological processes. Overall, the three-source model provides an improved framework for estimating ET in humid forest ecosystems. Ultimately, these results offer deeper insights into the coupled water and energy fluxes within forest ecosystems, thereby facilitating more effective water management and guiding sustainable forestry under a shifting climate. Full article

Near-field direct illumination from extended luminaires presents a fundamental modeling challenge in automated visual inspection: standard far-field IES (Illuminating Engineering Society) photometric data treat luminaires as point sources, an assumption that breaks down when source-to-target distances are comparable to luminaire dimensions. This paper addresses this breakdown by establishing, both analytically and experimentally, the conditions under which IES-based point-source discretization remains valid and the minimum discretization required when it does not. A solid-angle-based illuminance formulation on triangular meshes is coupled with a controlled virtual-emitter discretization of elongated luminaires, and a reproducible selection workflow is derived that relates emitter count to the source–target distance ratio and a specified error criterion. The dependence of discretization requirements on source–target distance is characterized by an angular-subtense argument, yielding the scaling relation $N\sim (L/H)·C$, where L is the luminaire length, H the working distance, and C a constant determined by the IES angular gradient and the required accuracy. Validation experiments with one and two industrial linear luminaires (1.2 m, Banner Engineering WLS15xDW1200Dx) include single-luminaire heights of $H=20\mathrm{cm}$, $25\mathrm{cm}$, $35\mathrm{cm}$, $50\mathrm{cm}$, and $60\mathrm{cm}$, as well as effective-length reduction cases produced by opaque end masking at $H=35\mathrm{cm}$. The single-luminaire validation set compares $N=1$–7 virtual emitters for all cases, with the $H=20\mathrm{cm}$ case extended to $N=9$. The selected emitter counts are chosen as the best-performing candidate simulations after considering scalar error, smoothness, and profile consistency. The selected uncovered cases are $N=9$ at $H=20\mathrm{cm}$, $N=7$ at $H=25\mathrm{cm}$, $N=5$ at $H=35\mathrm{cm}$, and $N=3$ at $H=50$–$60\mathrm{cm}$. The masked-source experiments confirm that reducing the effective source length at fixed height reduces the required emitter count. The fitted selection relation developed from these cases indicates that the working distance H has the stronger influence within the tested range. The method provides a computationally efficient, practitioner-ready tool for inspection-lighting design when near-field goniophotometry or optical ray tracing is unavailable. Full article

Reliable assessment of shorelines extracted from medium-resolution satellite imagery requires independent high-resolution reference data and statistical methods that account for spatial dependence. This study compared three conventional analyst-assisted shoreline-extraction workflows—histogram thresholding, band ratio, and the Normalised Difference Water Index (NDWI)—at Coronation Park, Lake Ontario, Canada, using Sentinel-2 Level-2A imagery. A manually digitised shoreline derived from a UAV-based orthomosaic acquired approximately 27 h before the Sentinel-2 scene served as the independent reference. The UAV-based reference and each Sentinel-2-derived shoreline were divided into 31 ordered segments. For each Sentinel-2-derived segment midpoint, the shortest planar Euclidean distance to the nearest UAV-based reference midpoint was calculated and used to derive mean absolute error (MAE) and root mean square error (RMSE). Residual spatial autocorrelation was assessed using Moran’s I with 9999 permutations. Because the paired differences departed from normality, the Friedman test was treated as the primary overall comparison, while contiguous spatial-block permutation tests across block sizes of two to eight shoreline locations assessed robustness to local spatial dependence. NDWI achieved the highest positional agreement (MAE = 5.645 m; RMSE = 6.429 m), followed by band ratio (MAE = 14.303 m; RMSE = 14.797 m) and histogram thresholding (MAE = 26.167 m; RMSE = 26.910 m). Significant positive residual spatial autocorrelation was identified for all three methods (Moran’s I = 0.587–0.832, all p < 0.001). The Friedman test confirmed a significant extraction-method effect, χ²(2) = 49.226, p < 0.001, Kendall’s W = 0.794, and the effect remained significant across all tested spatial-block sizes, with empirical p-values ranging from 0.000007 to 0.004630. Among the three conventional methods tested at this large-lake site, NDWI provided the highest positional agreement and therefore offers a defensible baseline for evaluating future Sentinel-2 image-enhancement approaches. Full article

Magnetic particle imaging (MPI), as an emerging radiation-free, high-sensitivity molecular imaging technique, holds broad application prospects in fields such as medical diagnosis, angiography, and targeted drug tracking. However, traditional three-dimensional MPI reconstruction algorithms face a problem in balancing reconstruction speed and image resolution. A hybrid reconstruction algorithm (Full Hybrid) based on spatial density constraints and residual iterative optimization is proposed in this work. This paper simulates Lissajous trajectory scanning and the non-linear response of magnetic particles based on the three-dimensional MPI simulation framework. The proposed hybrid method first utilizes the X-space method to obtain a basic spatial prior, then introduces field-free point (FFP) trajectory density to impose spatial weighting constraints on the reconstructed image. Experimental results demonstrated that this hybrid algorithm performs better in the reconstruction of complex three-dimensional topological structures (an H-shaped phantom). Comprehensive evaluation demonstrated that the reconstructed outputs reach a peak signal-to-noise ratio (PSNR) of 12.85 dB, a structural similarity index measure (SSIM) of 0.7321, and a root mean square error (RMSE) of 0.2278. Ablation experiments and comparison experiments further reinforced the advantages of the proposed method. These results demonstrate the numerical feasibility of the proposed reconstruction method for a three-dimensional phantom and provide a basis for further evaluation under multiple simulation conditions and real-scanner measurements. Full article

Unauthorized unmanned aerial vehicle (UAV) intrusions in sensitive areas such as airports have made accurate UAV detection and localization a pressing need. Acoustic sensing is passive and weather-independent, but conventional microphone arrays require many elements and a large aperture. This paper proposes an acoustic vector sensor (AVS)-based method, termed Covariance Enhancement and Confidence-guided Tracking for 3D Acoustic Localization (CECT-3DAL). A single AVS measures the sound pressure and three-axis particle velocity at one point. Adaptive diagonal loading improves the robustness of the covariance matrix at a low signal-to-noise ratio (SNR). An exponential spectral enhancement strategy sharpens the spatial spectrum peaks for direction estimation, and an eigenvalue-ratio-based confidence drives confidence-weighted smoothing of the angle sequences. Meanwhile, a dual-sensor geometric model provides a closed-form three-dimensional solution. In simulations, the azimuth and elevation root-mean-square errors (RMSEs) were below 1.5° for SNR above 4 dB. In an anechoic chamber, confidence-weighted smoothing reduced the azimuth and elevation standard deviations from 4.34° and 2.63° to 1.46° and 0.86°. In field experiments, the hovering azimuth stayed within a 90% span of 2–3.5°, with an average horizontal RMSE of 0.209 m against a GPS reference, and trajectories under various flight modes remained continuous and smooth. The proposed method offers a compact, passive, and low-cost solution for counter-UAV acoustic surveillance. Full article

Flow patterns in vertical wellbores are highly complex and variable. Classical theoretical models for pressure drop prediction frequently yield significant errors when applied to different geological blocks. However, existing correction models suffer from incomplete coverage of flow patterns. Therefore, it is necessary to develop new pressure drop prediction models specifically for gas–liquid two-phase flow in vertical wellbores under various flow patterns. This study conducted physical experiments on gas–liquid two-phase flow in vertical wellbores, successfully reproducing four typical flow patterns—bubbly flow, slug flow, churn flow, and annular flow—and determining their transition boundaries. Based on the experimental data, a systematic comparison was performed among four classic flow pattern discrimination models: Aziz, Beggs–Brill, Mukherjee–Brill, and Ansari. The results indicated that the Aziz model demonstrates superior applicability for identifying vertical flow patterns. To address the substantial prediction errors of the Aziz model in pressure drop calculations, the liquid holdup ratio and friction factor under different flow patterns were manually corrected using the experimental data, yielding a new pressure drop prediction model (Model 1). Furthermore, the particle swarm optimization (PSO) algorithm was introduced to further automatically optimize and fit the model parameters, thereby establishing another new pressure drop prediction model (Model 2) specifically tailored for different flow patterns. Validated against the experimental measured data, the new Model 2 achieved a Mean Absolute Percentage Error (MAPE) of 11.9% and a Root Mean Square Error (RMSE) of 1.9 kPa. Further verified by independent validation experiments outside the calibration dataset, Model 2 achieves an average prediction error of 12.0%, maintaining stable and high prediction accuracy. In comparison, the Aziz model and the new Model 1 yielded MAPE/RMSE values of 50.6%/5.3 kPa and 18.2%/4.9 kPa, respectively. The errors of the new Model 2 are markedly lower than those of the aforementioned models, demonstrating its superior accuracy in calculating pressure drops under various flow patterns. These findings provide a more accurate and reliable theoretical model for pressure drop calculation in gas–liquid two-phase flow within vertical wellbores, thereby laying a solid foundation for numerical simulation history matching and subsequent production forecasting. Full article

This study proposes a metaheuristic-based route optimization framework that balances earthworks in highway design by referencing a constant-slope “zero polygon.” The aim is to distribute excavation and embankment volumes evenly between fixed endpoints. The positions of Point of Intersection nodes and curve radii are optimized using the Social Spider Optimization (SSO) algorithm, guided by an objective function that minimizes the weighted signed distances between the route and the zero polygon while enforcing geometric constraints such as minimum curve radius, tangent length, and alignment continuity through penalty terms. Applications on two terrains with varying slopes demonstrate that the calibrated model substantially improves the cut–fill balance compared with both manual and pre-calibration solutions: the absolute difference between excavation and embankment volumes decreased from thousands to hundreds of cubic meters, and the cut–fill ratio fell below 0.1 in representative cases. Coupling a swarm-intelligence search with an interpretable geometric reference line, the framework offers a reproducible decision-support tool for preliminary corridor design. It enhances transparency and reduces reliance on trial-and-error practice. It targets preliminary, open-terrain corridor selection under a constant longitudinal slope without vertical curves, and does not yet incorporate land-use, environmental, or geotechnical constraints. Full article

Mobile platforms such as high-speed trains and unmanned aerial vehicles (UAVs) experience quasi-periodic variation in link quality as they move through a cellular base-station lattice, so the service rate of their on-board uplink buffer is itself time-periodic. We model this buffer as a periodic $M/M\left(t\right)/1$ queue whose service rate follows from a signal-to-noise-ratio (SNR)-to-rate map and construct an edge-resident controller that exploits this periodic structure for real-time quality-of-service (QoS) control. From a harmonic-balance (Fourier–Galerkin) solution of the periodic regime, the controller derives backlog and tail-probability indicators and uses them to drive admission, redundancy and handover decisions on the device. The method rests on a stability criterion and a quantitative error bound for the spectral truncation, under stated regularity and stability conditions, and is validated against Monte Carlo simulation along a ∼650 km geo-anchored corridor: on the periodic backbone, the solver matches simulation to within about $1.6\%$, and a coefficient-driven admission rule lowers the 99th-percentile delay by about $28\%$ relative to a reactive baseline at high load. On the full map-derived profile with aperiodic coverage gaps, the proposed proactive controller—spectral backbone admission combined with a radio-map look-ahead—attains the lowest mean and tail delay, about $27\%$ and $21\%$ below the reactive baseline and $54\%$ and $42\%$ below uncontrolled DropTail, with buffer overflow cut from $2.2\%$ to $0.1\%$, at a deliberate admitted-load cost (goodput ≈0.84 vs. $0.94$). An operation-count analysis indicates compatibility with sub-$100$ms control deadlines on a Cortex-A55-class system-on-chip. The controller runs on the device itself, without cloud or GPU, and the architecture is realised in a granted patent; end-to-end hardware benchmarking and an extension to non-Poisson traffic are left for future work. Full article