Search Results (2,286)

Objectives: To assess the technical feasibility and interobserver reliability of transrectal biplane ultrasound with external cotton-swab marking of the urethral orifice after transobturator mid-urethral sling surgery, and secondarily to describe dynamic measurements and exploratory associations with chart-derived postoperative leakage. Methods: In this single-center retrospective cross-sectional imaging study, 112 consecutive women examined between January 2024 and February 2026 underwent a standardized protocol. The swab tip was placed lightly against, but not inserted into, the external urethral orifice. Sling width (W), distance from the sling upper edge to the internal urethral orifice (D-I), and distance from the sling lower edge to the external urethral orifice at rest (D-O) and during Valsalva (V-D-O) were measured. Forty randomly selected examinations were independently measured by two blinded readers. Agreement was evaluated using ICC(2,1), Bland–Altman bias and 95% limits of agreement (LoA), standard error of measurement (SEM), and minimal detectable change (MDC95). Missingness patterns and age-adjusted exploratory logistic models were examined. Results: W, D-I and D-O were measurable in all 112 women; V-D-O was available in 95 (84.8%). Postoperative leakage status was available in 101 women (62 without and 39 with leakage). V-D-O was shorter than D-O in paired examinations (median difference, −3.00 mm; 95% CI, −4.90 to −1.00; p < 0.001). ICCs were 0.845 for W, 0.942 for D-I, 0.936 for D-O and 0.970 for V-D-O. Bland–Altman biases (reader A minus reader B) were −0.18, −0.93, 0.70 and −0.23 mm, respectively; corresponding MDC95 values were 1.53, 2.96, 3.72 and 2.33 mm. Missing V-D-O was associated with a shorter postoperative interval after Holm correction. None of six age-adjusted ultrasound models was associated with chart-derived leakage after Holm correction. Conclusions: The protocol permitted highly available static measurements and good-to-excellent interobserver reliability. Because the study lacked an unmarked comparator, intraobserver repeats and a standardized clinical endpoint, it supports technical feasibility and measurement reliability, not superiority, diagnostic validity or prognostic utility. Full article

Soil fertility in mown-grazed grasslands on the Qinghai–Tibet Plateau reflects hydrothermal conditions, terrain, grassland type, and management disturbance. We developed a soil fertility index (SFI) from 1037 topsoil samples collected at 0–30 cm during 2023–2025. The modelling framework combined static ecological background variables, conventional climate indicators, multi-year seasonal hydrothermal statistics, soil hydraulic attributes, and self-supervised temporal embeddings. Spatial-block, year-held-out, and ecological-zone-held-out validation were used with area-of-applicability (AOA) analysis and spatial-block conformal prediction. Multi-year seasonal hydrothermal statistics supplied the strongest predictive information and increased spatial-block R² by 0.2435 relative to the static-background model. A 10-year seasonal window gave the best empirical balance among explained variance, prediction error and rank consistency. The final deployment model achieved R² = 0.5632, RMSE = 0.1105, Spearman = 0.4473 and AUC = 0.696 under spatial-block validation. These values support regional screening and sampling prioritisation, not local deterministic diagnosis or site-level management prescriptions. Grid prediction identified Zone 2 as the main concentration of relative low-SFI risk. Full-sample AOA schemes covered 82.9%, 81.9% and 85.9% of the system-evaluation grid cells, whereas deployment-grid AOA coverage was lower under the stricter deployment setting. This contrast separates feature-space support from operational grid support. Split conformal prediction achieved 90% coverage close to the nominal level (PICP = 0.899, MPIW = 0.264). Natural-background residuals separated relative low-SFI risk from local deviations below expected natural conditions. The framework provides a reproducible screening tool for regional prioritisation and follow-up field verification in alpine grasslands. Full article

Global Navigation Satellite Systems (GNSS) positioning accuracy is affected by multiple error sources, including atmospheric delays, multipath propagation, and receiver noise, which can significantly reduce positioning reliability in low-cost receivers. This study investigates the use of a feedforward neural network to compensate for positioning errors in a static GNSS receiver scenario. A synthetic dataset was generated in MATLAB/Simulink by simulating positioning perturbations around a known reference location. Consecutive coordinate differences were used as input features, and a compact feedforward neural network with 45 hidden neurons was trained using the Levenberg–Marquardt algorithm to estimate positioning error components. The proposed approach was evaluated through residual error distribution, regression, temporal dispersion, and spatial scatter analyses. The results indicate that, for the primary 10 m error scenario, neural network-based compensation reduced temporal dispersion by approximately 46% and produced a more compact spatial distribution of corrected positions around the reference location. The residual errors remained concentrated near zero, indicating improved positioning consistency under the investigated simulation conditions. Sensitivity analysis across nominal error radii of $R_{95}$ = 1, 5, 10, 15, and 20 m showed consistent reductions in both RMSE and standard deviation for radii of 10 m and above, whereas no consistent improvement was observed at lower error levels. In a preliminary comparison with random forests, XGBoost, Long Short-Term Memory (LSTM), and Gated Recurrent Unit models using the same training, validation, and test samples, the Feedforward Neural Network (FNN) achieved competitive test MSE while requiring substantially less training time and runtime memory than the LSTM. These findings support the proof-of-concept feasibility of lightweight FNN-based correction for simulated static GNSS positioning. Future work will focus on validation using real GNSS measurements and extension to dynamic positioning applications. Full article

Complex underwater environments induce difficult-to-quantify ranging errors, constraining the localization accuracy and robustness of heterogeneous networks. To address this, a node localization method based on a Geometric Topology Perception-Enhanced Multi-Stage Reinforcement Learning Strategy is proposed. First, an uncertainty quantification model under multi-source interference is established to characterize time-varying noise and accurately quantify the ranging errors of heterogeneous links. Subsequently, using the resulting ranging variance, an adaptive weight allocation mechanism based on Minimum Variance Unbiased Estimation is constructed to dynamically adjust link weights, achieving the robust fusion of multi-modal observation data. Finally, a Weighted Least Squares objective function is formulated, and the GP-AC strategy is developed. By utilizing Gaussian Process Regression and local Geometric Dilution of Precision, a multi-stage reward mechanism is constructed to circumvent topological traps and accurately estimate the single-epoch three-dimensional coordinates of static or quasi-static underwater sensor nodes. Simulation results demonstrate that system robustness is improved by 91.9%, average accuracy is enhanced by 54.9%, and the measured average localization time is 7.45 s. Full article

Satellite video provides repeated observations of the same ground scene within short acquisition intervals, but blur, detector sampling, radiometric differences, noise, and registration errors complicate joint reconstruction. This study presents mixed sparse representation-based collaborative quality improvement (MSR-CQI), a model-based method for joint radiometric and spatial reconstruction of short optical satellite video sequences. The method combines multiframe fidelity, effective PSF modeling, an intensity prior, overlapping group sparsity, high-order nonconvex regularization, intensity bounds, and optional static observation weighting. In controlled ×2 experiments with known HR references, MSR-CQI achieved 42.8714 dB PSNR and 0.9756 SSIM. With the same seven input frames, it achieved 43.1049 dB/0.9714, compared with 42.1369 dB/0.9698 for PnP-NLM and 38.1675 dB/0.9404 for DUF-16L. Retaining measured sampling shifts in the observation operators yielded 45.7461 dB/0.98046, versus 45.0380 dB/0.97904 after LR registration and resampling. The proxy derived from the reserved real frames instead favored the common-grid reconstruction, showing that agreement with this proxy does not establish recovery beyond the native sensor resolution. Static observation weighting was also scene-dependent. These results support retaining sampling phases explicitly in controlled spatial SR, while the real-data results after common-grid resampling are interpreted as multiframe restoration and proxy agreement. Full article

Short-term traffic-flow forecasting predicts road-network states, yet spatio-temporal predictors can leave structured, context-dependent residuals. They are seldom reused at inference, while similarity-based transfer can introduce corrections that increase error. We propose Memory-Based Utility-Aligned Residual Evidence with Calibrated Activation for Selective Correction of Traffic-Flow Forecasts (MURECAST), which treats historical residuals as candidate interventions. After freezing a context-aware base forecaster, MURECAST builds a static out-of-sample residual memory from an independent period. At inference, same-node and time-valid constraints delimit records, a forecast-visible utility estimator re-ranks them, and utility-weighted top-K aggregation forms a multi-horizon proposal. A chronological calibration split provides an empirical one-sided lower score for applying the proposal or retaining the base forecast. Across PeMS03, PeMS04, PeMS07, and PeMS08, MURECAST ranked first in 11 of 12 reported dataset–metric comparisons, attaining the lowest mean absolute error (MAE) and root mean squared error (RMSE) on all four datasets and the lowest mean absolute percentage error (MAPE) on three; relative error reductions over the strongest published results were 1.69–9.76%. Non-beneficial corrections represented 17–26% of accepted proposals versus 41–48% of all valid proposals. These results show that MURECAST reuses observed errors while concentrating intervention on corrections with lower observed non-beneficial risk under the evaluated chronological protocol. Full article

Reliable calibration that converts transduced signals into physical displacement is essential for quantitative studies and applications of nanoelectromechanical resonators, including nonlinear dynamics, precision sensing, and optomechanical and electromechanical coupling. Existing harmonic calibration based on nonlinear optical transduction is generally restricted to systems with optically thin suspended layers and highly reflective substrates. Their weak higher-order harmonic signals are also susceptible to noise, drift, and inconsistencies between separately acquired frequency sweeps. Here, we generalize this approach to hexagonal boron nitride/graphene (h-BN/Gra) heterostructure resonators without local metallic reflectors. Multilayer thin-film interference calculations show that a branch-local phase correction enables the effective two-beam inversion to recover vibration amplitude and local static gap within acceptable error bounds. Experimentally, we use multi-demodulator lock-in detection to acquire the ω, 2ω, and 3ω optical responses simultaneously at each frequency point. Ratios among these harmonics then yield frequency-resolved vibration amplitude and local static gap. Repeated frequency sweeps at a constant gate bias simultaneously track the resonance characteristics and local static gap, revealing a time-dependent relaxation of approximately 24 nm in the local device configuration. This work provides a practical in situ route for simultaneously resolving resonance characteristics and configurational evolution across a broader range of nanomechanical resonator architectures. Full article

The increasing penetration of photovoltaic (PV) generation in industrial parks creates challenges associated with renewable-energy accommodation due to temporal mismatches between PV output and electricity demand. Electric vehicle (EV) fleets with coordinated charging and discharging capabilities provide potential flexibility for mitigating PV curtailment and improving local energy utilization. This paper proposes a hierarchical multiobjective rolling-horizon vehicle-to-grid (V2G) control framework for EV fleets in industrial parks. First, an individual EV model is developed by considering charging/discharging modes, travel requirements, V2G willingness, and acceptable departure state-of-charge (SOC) shortfall. Based on the uncontrolled-charging baseline, pointwise fleet flexibility limits are evaluated by considering SOC constraints and departure-energy recoverability. Second, a lexicographic three-level mixed-integer linear programming (MILP) model is established to prioritize PV accommodation, followed by purchased-energy cost and user-related operating costs. The proposed optimization is implemented through a causal rolling-horizon strategy, where updated measurements and forecast information are used to revise future schedules without accessing future realized PV outputs. Case studies on a representative industrial-park system show that the causal rolling-horizon strategy increases PV utilization from 90.50% to 93.89% and reduces total operating cost by approximately 13.0% compared with the static day-ahead schedule under the tested PV deviation scenario. Furthermore, 20 paired trials with a fixed fleet composition and regenerated EV-state and PV-error realizations show consistent system-level gains, accompanied by higher user compensation and battery use. The multi-scenario comparison further shows that the proposed hierarchical rolling-horizon V2G configuration achieves 99.26% PV utilization and lower total operating cost than uncontrolled charging and unidirectional smart-charging (V1G) operation. The results indicate that the proposed framework can effectively coordinate EV-fleet flexibility and hierarchical objectives for PV accommodation in industrial parks. Full article

Lanthanum oxide (La₂O₃) powder is widely used in optical, catalytic, and ceramic applications, but its flowability is highly sensitive to ambient humidity, and reliable discrete element method (DEM) contact parameters under varying moisture conditions remain unavailable. To address this, the present study introduces coarse-graining theory to reduce computational cost while preserving macroscopic mechanical equivalence, and systematically calibrates the DEM contact parameters of La₂O₃ powder under three controlled humidity levels (5%, 50%, and 95% RH) using the Hertz–Mindlin with JKR contact model. The angle of repose was measured as the macroscopic response. A Plackett–Burman design was employed to screen three significant factors from seven candidate parameters, followed by a steepest ascent test to determine optimal parameter ranges, and a Box–Behnken design to construct response surface models. Quantitative relationships were established between the angle of repose and the particle–particle static friction coefficient, particle–particle rolling friction coefficient, and particle–stainless steel static friction coefficient under each humidity condition. The calibrated parameters were verified against experimental anglef of repose measurements, showing good agreement, with errors of 0.48%, 2.15%, and 1.67% for the three humidity levels, respectively. This work provides reliable DEM parameters specifically for the three tested relative humidity levels (5%, 50%, and 95% RH) under the specific conditioning procedures used, and offers a calibration framework extendable to other moisture-sensitive cohesive powders. Full article

To address the influence of multi-source probabilistic uncertain parameters on the dynamic transmission error (DTE) of harmonic drives, this paper proposes a robust DTE modeling and multi-objective optimization method. First, a Chebyshev surrogate model is constructed by integrating the measured static transmission error (STE) probability model, system dynamic equations, and identified nominal parameters. Prototype validations show a prediction mean absolute percentage error (MAPE) of 8.14% and a mean absolute error (MAE) of 7.761″. Meanwhile, compared to the original dynamic equations, the surrogate model reduces the single-evaluation time from 0.147 s to 0.000003 s (a 49,000-fold acceleration), effectively overcoming the efficiency bottleneck of numerical integration in dynamic response evaluation. Secondly, to achieve the collaborative optimization of system transmission accuracy and anti-disturbance robustness, a Chebyshev–AMP–MOPSO algorithm integrating a diversity entropy state-driven weight and a pyramid-hierarchical dual-track search strategy is proposed, which improves upon the issues of local convergence and uneven solution set distribution in the classical MOPSO and NSGA-II algorithms. On this basis, parameter optimization under three decision preferences was completed. The accuracy-first scheme reduces the DTE mean by 3.67%, the robustness-first scheme reduces the standard deviation by 9.36%, and the balanced scheme improves both. Finally, comparative tests on five prototypes show the actual dynamic parameters’ deviation (D_i) relative to the theoretical optimal configuration exhibits a consistent corresponding trend with measured DTE means. Prototypes with the minimum (D_i = 0.365) and maximum (D_i = 0.474) deviations yield the lowest and highest measured means, respectively, matching theoretical optimization expectations. Full article

Static rotor eccentricity distorts the air-gap magnetic field of permanent-magnet (PM) traction motors and may aggravate electromagnetic vibration and noise in electric vehicles. The equivalent remanence method (ERM) is an established eccentric-field modeling strategy; this study does not claim ERM itself as new. Instead, it extends the framework to link a prescribed static-eccentricity ratio to equivalent-remanence harmonics and low-order radial electromagnetic-force waves. Fourier decomposition is used to obtain the radial air-gap flux density, and the Maxwell stress tensor is used to calculate and decompose the radial electromagnetic-force density. The analytical magnetic field is evaluated against finite element method (FEM) results over relative eccentricity ratios from 0.16 to 0.80, and the first-order resultant force is further compared with measurements from a 4-pole, 24-slot prototype operating at 2000 r/min under open-circuit no-load conditions. Static eccentricity introduces first-, second-, and third-order force waves in addition to the fourth-order component under a uniform air gap; the first-order component is the dominant additional low-order force. The measured first-order force increases from approximately 300 to 675 N as eccentricity increases, while FEM and analytical predictions reproduce the same nonlinear trend. The FEM and analytical mean absolute percentage errors relative to experiment are 3.78% and 8.55%, respectively. The analytical overprediction is attributed mainly to idealized assumptions such as neglected local saturation, end leakage, and manufacturing deviations. The formulation is therefore suitable for harmonic interpretation and preliminary force prediction under no-load or weakly saturated conditions. Full article

Unmanned Underwater Vehicle (UUV) swarms operating in complex marine environments must accurately assess threats from surrounding targets to ensure mission success and navigational safety. However, existing threat assessment methods face three fundamental bottlenecks when applied to underwater swarms: the inability to track temporally evolving target intentions, reliance on subjective indicator weighting for multi-target ranking, and vulnerability to spatially heterogeneous sonar noise. This paper proposes a hierarchical threat assessment framework that addresses these bottlenecks through three integrated modules. First, a Dynamic Bayesian Network with a specially designed heading factor tracks target intention over time, propagating threat probabilities across sequential observations and enabling early warning before the closest point of approach. Second, a Vieta’s theorem-based algebraic ranking algorithm constructs comprehensive threat vectors via elementary symmetric polynomials of six indicator utilities, avoiding explicit expert-defined weighting coefficients in the multi-attribute ranking stage while capturing both independent and synergistic indicator interactions. Third, a distance-weighted swarm aggregation strategy suppresses individual sonar noise by assigning higher fusion weights to geographically closer nodes, exploiting the spatial diversity inherent in swarm configurations. Simulation experiments under representative target-motion scenarios validate the framework across four complementary experimental studies. Results demonstrate that the DBN reduces output variance by over 56% compared to static Bayesian networks and responds to abrupt intention changes within 15 s. The algebraic ranking algorithm achieves identical prioritization to TOPSIS without requiring any manual or data-dependent weights. The distance-weighted aggregation reduces root mean square error by 63.2% and improves signal-to-noise ratio by 8.7 dB over equal-weight averaging. The proposed framework provides a principled and interpretable solution for simulation-based autonomous threat perception in representative underwater swarm scenarios. Full article

To improve the static and dynamic stiffness of the LV500 horizontal CNC lathe and reduce machining errors, this study focuses on integrated structural simulation, bed–saddle collaborative optimization, and standardized precision evaluation. A whole-machine structural model is established in SolidWorks, and static, modal, and harmonic response co-simulations are performed in ANSYS, followed by multi-objective optimization of the two key weak components. MATLAB is used to process the dynamic simulation data. Based on a Renishaw XL-80 laser interferometer and the accompanying CARTO software, axis accuracy detection and measurement uncertainty evaluation are performed, forming a reproducible full-process engineering analysis system applicable to similar machine tools. The simulation results show that the maximum structural deformation after optimization is 0.016 mm, and the first-order natural frequency increases from 86.99 Hz to 92.55 Hz. Experimental tests demonstrate positioning accuracies of 3.0 μm (U = 0.38 μm, k = 2) for the X-axis and 3.3 μm (U = 0.45 μm, k = 2) for the Z-axis. Owing to the enhanced static–dynamic stiffness after structural optimization, the workpiece machining error can be stably controlled within 0.01 mm. In this study, a unified whole-machine model enables continuous static and dynamic analysis. The coupling stiffness of assembled components is considered in the modeling process, and the static and dynamic performance of the whole machine is improved through dual-component collaborative optimization. The inclusion of metrological-level uncertainty evaluation enhances the reliability of the experimental data. The proposed method provides a standardized engineering scheme for the static and dynamic performance optimization of similar horizontal CNC lathes. Full article

Static images and vehicle-mounted video are the two primary data sources for road inspection. Since cracks, potholes, and patched areas can all be considered anomalies on the road surface, Contrastive Language–Image Pre-training (CLIP)-based anomaly segmentation provides a promising approach under limited labeled data. However, two challenges remain in practical applications: whole-image resizing may weaken fine anomalous structures, while weak and irregular damage regions can exhibit spatially varying prediction difficulty; for video input, frame-wise prediction often produces inter-frame flickering. This paper proposes ST-PaveCLIP for road anomaly segmentation in images and videos. For single images, we introduce a training-time residual-scale auxiliary supervision based on heteroscedastic negative log-likelihood and adopt a local–global dual-scale inference scheme to preserve global road context and fine anomaly structures. For video input, a temporal alignment module based on RoMa v2 dense matching and homography estimation warps the previous fused probability map to the current frame before temporal fusion, without additional video-level training. Multi-seed experiments on public and self-collected datasets show that ST-PaveCLIP improves the principal segmentation metrics over the AA-CLIP baseline and remains competitive with supervised baselines under the same limited annotation budget. Video experiments further show reduced inter-frame inconsistency in Aligned Temporal Consistency Error (TCE) and Aligned Threshold-Crossing Rate (ATCR), with a modest additional improvement in key-frame segmentation. Full article

When a lithium-ion cell’s series resistance is underestimated, the pre-update terminal-voltage innovation contains the first-order term $e_k\approx -I_k{R}_{0,k}\delta b$. This term breaks conditional sign symmetry and creates an odd response under current reversal. We test that mechanism before using it as an activation rule. The operational null is a near-zero conditional innovation centre with weak innovation–current coupling; declared falsifiers are comparable coupling under the nominal model, the wrong correlation sign under positive resistance error, failure of charge/discharge polarity reversal, or negative-control activation approaching ohmic-mismatch activation. A persistence-confirmed gate combines normalised-innovation-squared exceedances, innovation–current compatibility, a positive local resistance correction, and five consecutive qualifying windows. Sixty settings were ranked on 10 calibration seeds and frozen before disjoint holdouts. From 1.0× to 2.0× R₀, the sign-imbalance index increased from 0.0040 to 0.0786, and |$corr\left(e,I\right)$| increased from 0.0658 to 0.7777. The 30-seed static holdout produced 0/30 nominal activations, 27/30 detections at 1.5×, and 30/30 detections at 2.0–3.0×. A disjoint linear-drift audit yielded 0/30 pre-ramp activations and 30/30 detections at a median 1.71× multiplier, reducing late-drift SOC RMSE from 3.129% to 0.895%. A signed-current audit confirmed the predicted polarity reversal. An estimator-unseen audit gave 30/30 ohmic detections but retained 2/30 current-linked non-ohmic and 1/30 current-bias triggers, so the rule is not a unique fault classifier. NASA and LG records remain diagnostics of fixed versus always-on adaptation; they do not validate the gate or independent absolute SOC. The contribution is a falsifiable residual-symmetry mechanism with an explicit evidence boundary, rather than a post hoc symmetry label or hardware claim. Full article