Search Results (2,128)

Near-field millimeter-wave synthetic aperture radar (MMW-SAR) imaging provides high-resolution sensing capability for short-range target detection and characterization, but dense spatial sampling imposes considerable storage and processing burdens. To enable reliable imaging from sparsely sampled measurements, this paper proposes a sparse adaptive imaging network, termed SASI-Net, which sequentially combines complex-domain echo completion with physics-guided image-domain sparse refinement. In the first stage, a complex-valued mask-aware completion network (CMAC-Net) jointly exploits the zero-filled echo and its binary sampling mask to recover missing measurements while promoting consistency with the available observations. The completed echo is then transformed into an initial complex image through a norm-preserving imaging operator. In the second stage, an adaptive truncated penalty sparse network (ATPS-Net) unfolds a magnitude-domain nonconvex optimization model while explicitly retaining the phase of the initial complex image. The corresponding minimax concave penalty (MCP)-based proximal update suppresses weak and diffuse responses while reducing excessive shrinkage of dominant scatterers. In addition, the regularization strength is estimated from the current magnitude features at each unfolding stage, enabling input- and stage-adaptive sparse refinement without manual parameter tuning. Experiments using real-measured near-field MMW radar data from the public 3DRIED dataset demonstrate that CMAC-Net improves complex echo and phase recovery, while ATPS-Net enhances scattering concentration and target-background separation. Under sampling rates as low as 10%, SASI-Net preserves identifiable target structures and maintains robust reconstruction performance. Additional noise experiments further verify the effectiveness of the proposed refinement strategy under measurement perturbations. The current evaluation focuses on near-field two-dimensional planar-scan MMW-SAR, and the applicability of SASI-Net to conventional strip-map SAR remains to be further investigated. These results demonstrate the potential of SASI-Net for computationally efficient sparse-aperture near-field MMW imaging. Full article

To address the issue of long ambiguity resolution (AR) time for existing medium-to-long baseline reference stations, this paper proposes a rapid ambiguity fixing method with empirical ionospheric constraints. The method directly treats the L1 and wide-lane (WL) ambiguities as unknown parameters to be estimated and performs synchronous filtering in the ambiguity domain. Furthermore, empirical ionospheric constraint factors are derived from real measurement data, thereby providing reasonable and effective variance constraints for the weighted estimation of ionospheric parameters. While accelerating ambiguity convergence, the proposed method also simplifies the solution procedure. Experimental results show that, compared with the classical ionosphere-free (IF) model, the proposed method significantly improves the accuracy of the ambiguity float solution and the baseline initialization speed. The RMS of the ambiguity float solution bias is reduced from 1.15 cycles to 0.45 cycles, representing an accuracy improvement of approximately 60.8%. The average baseline initialization time is shortened from 51.2 epochs to 21.7 epochs, corresponding to a speed enhancement of about 57.6%. In addition, the proposed method also markedly improves the ambiguity fixing speed for newly risen satellites at low elevation angles: the average time required for ambiguity fixing is reduced from 50.1 epochs to 14.8 epochs, and the average required elevation angle drops from 23.4° to 15.7°. Full article

Previous freeze–thaw studies have mainly used uniaxial tests, leaving the effects of freeze–thaw damage and confinement on basalt behavior and physically interpretable triaxial strength estimation insufficiently resolved. Here, saturated basalt specimens subjected to 0, 10, 20, and 30 freeze–thaw cycles were tested under confining pressures of 0–40 MPa using a triaxial compression system. Stress–strain responses, failure modes, deviatoric stress strength, and Mohr–Coulomb parameters were analyzed; planar, binary quadratic, and freeze–thaw-dependent Mohr–Coulomb models were developed and compared. Freeze–thaw cycling intensified initial compaction and post-peak softening, whereas confinement shifted failure from axial splitting to shear-dominated or mixed modes. After 30 cycles, deviatoric stress strength decreased by 53.6% under uniaxial loading and by 41.1% at 40 MPa confinement; cohesion decreased by 46.2%, from 39.6 to 21.3 MPa, and the internal friction angle decreased from 30.84° to 27.04°. The Mohr–Coulomb model achieved a maximum absolute error of 10.2 MPa, a mean absolute error of 4.7 MPa, and a root-mean-square error of 5.6 MPa. Within 0–30 cycles and 0–40 MPa confinement, the model provides strength estimates for parameter selection and stability assessment of basalt engineering structures in cold regions. Full article

Production-profile interpretation based on distributed temperature sensing (DTS) is an important method for the dynamic evaluation of hydraulically fractured horizontal gas wells. Because downhole temperature is jointly controlled by reservoir flow, variable-mass wellbore flow, and wellbore–formation heat transfer, the relationship between the temperature response and the production rate of an individual cluster is nonlinear, making inversion based solely on temperature fitting prone to non-uniqueness. In this study, a temperature forward model incorporating reservoir flow, wellbore flow, and heat-transfer processes was developed. Orthogonal experiments, sensitivity analysis, and error-surface analysis were combined to screen the key inversion parameters, and a dual-objective inversion function was constructed using the DTS temperature error and the relative total wellhead gas-production error. On this basis, the covariance matrix adaptation evolution strategy (CMA-ES) was used to jointly invert effective fracture half-lengths and the production profile. This framework uses the total wellhead gas production as an additional constraint during the search, allowing parameter combinations with similar temperature responses but different production responses to be distinguished. Controlled synthetic tests were further performed to assess inversion robustness under different initialization and model settings, followed by application to a field-case well. The analyses showed that the production constraint reduces the admissible low-error solution space but does not establish uniqueness. Results from the case well showed good agreement between the simulated temperature profile and the measured DTS profile, with temperature-drop errors of less than 0.05 °C near the potential producing clusters. The inverted total gas-production rate was 26,649.67 m³/d, with a relative error of 0.6492% compared with the measured wellhead value. These errors characterize the temperature fitting and whole-well production closure achieved for the present field case and are not used here as a quantitative measure of improvement over previous methods. The inversion results revealed a distinctly heterogeneous distribution of production contributions among the fracturing clusters. This method provides a model-constrained analytical approach for production-profile interpretation in hydraulically fractured horizontal gas wells without production logging tool (PLT) data. Without independent cluster-level flow measurements, the resulting production profile should be interpreted as a model-constrained estimate rather than a uniquely determined true profile. Full article

Changes in modal order alter the predictor dimension, pole-label swaps disrupt frequency continuity, and time-varying envelopes affect phasor magnitude and phase. Estimating these quantities independently can propagate errors across successive processing stages. A coordinated estimator is developed for the modal subspace, pole states, and regression parameters. An order confidence index combines the spectral gap, cumulative energy, and noise separation to select the model order and reconstruct the signal in one low-rank subspace. Variable-order recursive prediction and frequency–damping state association then form continuous pole trajectories, followed by adaptive smoothing and class-dependent unit-circle projection. The associated oscillatory and decaying direct-current (DC) poles update the Maclaurin regression atoms. Finite-window coupling is handled by either modal initialization followed by Gram iteration or a direct joint regularized solution, avoiding repeated leakage compensation. Tests with modal-order changes, frequency dynamics, modal crossings, amplitude modulation, and decaying DC show that the coordinated parameter chain preserves pole identity and improves dynamic phasor measurement. In the main dynamic test case, the mean and 95th-percentile total vector errors (TVEs) are 3.2082% and 6.0672%; the 95% paired confidence interval for the mean-TVE difference between the proposed method and estimation of signal parameters via rotational invariance techniques (ESPRIT) remains below zero. A separate RK3568 bare-metal test of the standalone three-tone Prony kernel completed 800 frames without a processing failure. Its mean processing time was 18.621 ms per frame, with observed values from 18.537 to 19.070 ms. Full article

Background: Physiologically based pharmacokinetic (PBPK) modeling is an established approach used in recent years for estimating drug disposition in challenging clinical scenarios where in vivo studies are difficult to perform. Metoclopramide is a benzamide derivative, widely indicated for its antiemetic and prokinetic actions. This study aims to develop a PBPK model for metoclopramide in infants to predict its systemic exposure in this population. Methods: To develop the model, a detailed literature review was conducted, and the required data related to the drug, human physiology, and published clinical studies were retrieved. After that, all information was integrated into the PK-Sim software, and the model was initially developed in adults to create a base; subsequently, it was extrapolated to infants. After successful development, these models were verified visually and numerically using visual predictive checks (VPCs), mean predicted-to-observed ratios (R_pre/obs), average fold error (AFE), and mean relative deviation (MRD). Results: All simulated profiles were consistent with observed data; computed AFE and R_pre/obs for key pharmacokinetic (PK) parameters, including area under the concentration–time curve from 0 to t (AUC_0–t), maximum plasma concentration (C_max), and clearance (CL), fell within an acceptable two-fold error range. Moreover, the MRD values for all profiles were <2, showing promising agreement between the reported and predicted datasets. Conclusions: The current model provides a robust framework to estimate the PK of metoclopramide in infants, which may help with dose individualization in this vulnerable population. Full article

Far-field vision-based pose measurement is a crucial technology for applications such as high-resolution Earth observation and space security early warning. However, owing to the perspective imaging model of long-range optical systems, conventional vision-based pose measurement methods are highly susceptible to image noise and pose parameter coupling, leading to significant estimation deviations. Consequently, these methods fail to meet the rigorous requirements for the accurate measurement and intelligent perception of object poses in far-field scenarios, particularly when the object distance significantly exceeds the focal length. To address these challenges, this paper presents a Perspective-n-Point (PnP) preprocessing and post-optimization method for far-field pose measurement based on weighted central normalization. First, the Robust PnP (RPnP) algorithm is employed to obtain an initial pose for the far-field object, and an objective function is formulated by minimizing the reprojection error of the image feature points. Second, central normalization is applied to the Jacobian matrix of the pose parameters, and the information matrix is weighted according to the localization uncertainty of the image feature points. Finally, a weighted nonlinear optimization is executed to obtain refined pose parameters. Under the tested conditions, this approach can reduce the sensitivity of the pose parameters to image noise, minimizes the coupling among extrinsic parameters, and reduces the tendency of noise-driven pose-update excursions. The proposed method is evaluated through simulations and scaled physical relative-comparison experiments, supporting its potential for numerically stable vision-based pose measurement of far-field objects in aerospace and related domains. Noise-and-turbulence simulations demonstrate the pose-refinement benefit of CS and improved rotation estimation with a known spatial covariance model. Full article

As renewable penetration increases, the inertia, damping, and primary frequency regulation characteristics of renewable-rich power systems become strongly time-varying, making fixed offline frequency response models increasingly difficult to maintain for emergency-control calculations. This paper proposes a physics–data fusion-driven framework for frequency response parameter identification, model validation, and emergency load shedding based on post-disturbance multi-source measurements. First, a quality-aware dual-stage LSTM-PINN fuses multi-source measurements to provide event-specific initial estimates of the disturbance magnitude and physical system frequency response (SFR) parameters. A bounded event-level local constrained refinement then aligns the dynamic parameters with the measured frequency trajectory, while differentiable SFR constraints, key response losses, and identifiability regularization improve physical consistency and parameter distinguishability. Second, local identifiability, physical parameter plausibility, and trajectory consistency are jointly evaluated to characterize the credibility of the identified SFR model. Finally, the measurement-updated controlled-SFR model determines the minimum emergency load-shedding amount satisfying the frequency nadir constraint and allocates the action to candidate buses according to electrical distance and available controllable capacity. Case studies on a modified New England 39-bus system and the renewable-rich CSEE-FS benchmark evaluate parameter identification accuracy, model credibility, robustness, generalization, control security, spatial allocation, and computational efficiency. Full article

Receptor-mediated endocytosis requires a particle to approach the cell membrane to within a few nanometers before ligand–receptor binding can occur. Existing continuum models often start from an already established contact and do not explicitly describe how crowding particles on the extracellular side influence the distribution of the particle near the membrane. We examine entropic depletion forces as one possible nonspecific contribution to this initial approach. For ideal depletants, the Asakura–Oosawa excluded-volume construction gives an exact depletion potential for the planar geometry before contact. The potential and force vanish continuously at the onset of excluded-volume overlap. This interaction provides a possible contribution to membrane proximity before specific binding, while its extension to curved wrapping geometries requires additional approximation. Within a reduced continuum model, we combine depletion attraction, ligand–receptor binding, membrane deformation, and cytoskeletal viscoelastic dissipation. The viscoelastic contact is formulated through a hereditary integral and a standard linear solid. The kinetic model gives a conditional minimum ligand density for complete engulfment, a finite particle-size window, and a stiffness-dependent upper limit. When the stationary radius lies inside the domain of finite positive wrapping times, the estimated wrapping time has a minimum at a radius that decreases with increasing binding energy density. At fixed viscosity and other independent parameters, the same time approximation predicts slower wrapping as cell stiffness increases. The two positive roots defining the size window merge at a limiting parameter value, which characterizes closure of the admissible size interval. Depletion attraction is interpreted as one possible contribution to particle-membrane association, alongside electrostatic interactions, steric effects, and membrane fluctuations. The present analysis identifies how nonspecific attraction, specific adhesion, and mechanical resistance can contribute to different stages of membrane wrapping. Full article

We propose a lightweight cone-based Region of Interest (ROI) filtering method for camera–LiDAR fusion on distributed edge systems. Multiple Neural Processing Unit (NPU) nodes perform camera inference, and a central edge board combines their detections with LiDAR point clouds. The relative rotation is obtained from IMU quaternions under a common attitude reference and aligned sensor axes, while camera Field of View (FOV) parameters define the viewing rays. The method avoids a separate offline extrinsic-rotation estimation procedure, but it requires initial alignment, a measured translation vector, and timestamp-based synchronization. Because the rotation follows from the attitude streams rather than from a per-pair calibration session, a camera node can be added or re-aimed without a new calibration session, which lowers the setup cost of extending the system to further viewpoints. A cone membership test replaces four plane-normal dot products with a forward sign test and a squared angular cosine comparison that reuse the same axis–point dot product; on the same hardware, the mean per-camera ROI-filtering and clustering latency decreases from 6.02 to 4.46 ms, a 25.9% reduction. An adaptive threshold tightens the ROI boundary using the angular separation between neighboring detections. Across six overlap events in a parking scenario, pair-level separation succeeds in 2/6 cases (33.3%) with Pyramid and 5/6 cases (83.3%) with Cone+Adp. These preliminary results indicate improved ROI point selection for the tested configurations, rather than a general increase in intrinsic spatial separation capability. Full article

The Industrial Internet of Things (IIoT) is being used a lot in important areas like advanced manufacturing, smart energy systems, and intelligent cities. Watching for intrusion detection is very important for the safety of the IIoT. Nevertheless, multivariate sensor sequences frequently demonstrate pronounced physical coupling, non-stationarity, and periodicity concurrently, rendering it prone for detection models grounded in statistical correlation to erroneously classify normal collaborative variations as anomalies. Moreover, prevailing methods predominantly concentrate on single-domain representations within either the time or frequency domain, posing challenges in addressing both burst and periodic attacks concurrently. To address these challenges, this article introduces the Time–Frequency Dual-Branch Fusion and PCMCI-Based Association-Guided Network (TFPAG-Net) for IIoT Intrusion Detection. This model initially constructs a lightweight temporal convolutional network backbone employing depthwise separable convolutions. Subsequently, parallel branches in the time and frequency domains are established to respectively model local abrupt changes, long-range dependencies, and periodic spectral structures, with time–frequency feature fusion facilitated through a sample-dependent gating mechanism. Building on this, multi-scale temporal pyramids are employed to amalgamate fine, intermediate, and coarse-scale information. Furthermore, as an auxiliary refinement, a lagged conditional-dependence prior estimated from the training data via PCMCI is projected into a bounded attention bias to provide supplementary guidance for channel feature reweighting. Evaluations on Edge-IIoTset, X-IIoTID, and SWaT yield mean Macro-F1 scores of 0.9886, 0.9466, and 0.9607, respectively, over five predefined random seeds. Under the unified training protocol, TFPAG-Net ranks second on Edge-IIoTset and achieves the highest mean Macro-F1 on X-IIoTID and SWaT. Ablation experiments show dataset-dependent effects of the proposed components. On Edge-IIoTset and X-IIoTID, the final attention-stage improvement reflects the joint effect of SE-based modulation and the PCMCI-derived association prior. Accordingly, PCMCI is treated as an auxiliary association refinement rather than a principal contribution of TFPAG-Net. Additionally, TFPAG-Net maintains a moderate computational footprint, with approximately 0.29 M parameters, providing a favorable balance between model complexity and intrusion detection performance. Full article

The beta autoregressive moving average ($\beta$ARMA) models are a dynamic model based on beta regression, used to model time series that take values in the interval (0, 1) and exhibit serial dependence. However, the presence of outliers can affect parameter estimation and the quality of predictions. Effective detection of these observations is therefore essential to ensure the model’s reliability. In this paper, we propose four methods for detecting outliers in the $\beta$ARMA model. Three of them are adaptations of methods initially developed for beta regression models to the $\beta$ARMA model, which integrate Tukey’s boxplot and Pearson residuals. The fourth procedure combines the Pearson residuals with the modified Z-score. All four procedures are adapted to the time series context and account for the serial dependence between observations. The performance of the proposed methods is evaluated through a Monte Carlo simulation study. We illustrate their practical relevance by applying them to real proportional data. Then the four methods exhibit high robustness in identifying true outliers, while limiting errors in identifying inliers as outliers, across different rates, amplitudes of contamination and sample sizes. Full article

Visual–inertial odometry (VIO) on industrial stereo camera–IMU platforms suffers from frame-dependent timing errors and degraded feature tracking under auto-exposure, auto-gain adjustment, and rapid motion. This paper presents Hardware-Synchronized Exposure-State-Aware VINS-Fusion (HS-ES-VINS), a hardware-synchronized, exposure-aware extension of VINS-Fusion. The effective sampling time of each global-shutter image is reconstructed at the exposure midpoint using synchronized trigger timestamps, the camera debounce delay, and the frame-wise exposure duration. An exposure–gain–motion risk model adaptively adjusts feature detection criteria, Kanade–Lucas–Tomasi (KLT) tracking parameters, and correspondence filtering, while IMU-integrated rotation supplies initial predictions for feature tracking. On hardware-synchronized indoor and outdoor datasets with independent ground truth, controlled experiments show that the exposure-midpoint timestamp removes the frame-dependent exposure component of the camera–IMU offset that neither fixed nor online scalar compensation can remove, reducing the ATE RMSE by 28.6% (fixed offset) and 22.7% (online estimation) relative to the exposure-start timestamp under 20 ms low-light exposure. Compared with baseline VINS-Fusion, the complete adaptive front end reduces the ATE RMSE by 16.8% and 12.9% on indoor multi-floor and outdoor cycling sequences, and the risk score explains frame-wise tracking quality (correlation of 0.587 with the inlier ratio). The front end runs faster than the baseline (17.1 ms per frame), confirming real-time operation and improved robustness against variable illumination and rapid motion. Full article

Background: The Royal Marsden Hospital (RMH) score is a simple prognostic score based on serum albumin, lactate dehydrogenase, and the number of metastatic organ sites. Although its prognostic value has been demonstrated across several advanced cancer populations, evidence specifically in patients with de novo metastatic colorectal cancer (mCRC) initiating first-line treatment remains limited. Methods: This single-center retrospective cohort study included 189 patients with de novo mCRC who initiated first-line systemic treatment between January 2015 and January 2026. Patients were classified as low risk (RMH score 0–1) or high risk (RMH score 2–3). Progression-free survival (PFS) and overall survival (OS) were estimated using the Kaplan–Meier method, and factors independently associated with survival outcomes were evaluated using multivariable Cox regression. Results: Of the 189 patients, 142 (75.1%) were classified as low risk and 47 (24.9%) as high risk. Median PFS and OS for the entire cohort were 11.3 and 27.4 months, respectively. Median PFS was 11.8 months in the low-risk group and 7.6 months in the high-risk group (p < 0.001). Median OS was 33.8 and 13.7 months, respectively (p < 0.001). After adjustment for relevant clinicopathological factors, high-risk RMH status remained independently associated with shorter PFS (HR, 2.16; 95% CI, 1.51–3.09; p < 0.001) and OS (HR, 2.95; 95% CI, 2.03–4.29; p < 0.001). Conclusions: The RMH score was independently associated with both PFS and OS in patients with de novo mCRC initiating first-line systemic treatment. Based on simple and routinely available baseline parameters, the RMH score may serve as a complementary prognostic marker alongside established clinical and molecular factors. Full article

Reliable state-of-charge (SOC) estimation is essential for lithium-ion battery management, yet parameter drift, operating-profile variation, and sensor faults can compromise observer consistency. This study presents a reproducible constrained FFRLS-EKF framework in which online second-order RC parameter updates are subjected to resistance, capacitance, and time-constant feasibility constraints before being scheduled in the EKF. Estimator residuals and parameter variations are then reused for exploratory concurrent fault analysis. Because the dynamic driving-cycle datasets do not provide independently measured continuous reference SOC, SOC RMSE/MAE is not reported for DST, FUDS, UDDS, US06, or BJDST; Coulomb counting is treated only as a non-independent trajectory reference because it also contributes to the FFRLS regression target. A separate 21-checkpoint HPPC validation, with reference labels withheld from the estimator, yields SOC RMSE/MAE values of 2.24/1.76 percentage points for the constrained adaptive method, compared with 2.50/2.04 percentage points for the fixed EKF. A 270-run robustness study varies fault magnitude, onset time, voltage-noise level, and initial SOC. The results identify physical projection as the dominant stabilizing mechanism, with adaptive forgetting providing secondary transient-memory adjustment. An additional 243-run two-fault stress test shows that residual-sensitivity decoupling is not universally identifiable: exact-pair recovery degrades as noise increases and remains strongly dependent on the operating profile and fault pair. Accordingly, the concurrent fault module is presented as a transparent diagnostic baseline rather than a universally validated fault-isolation method. Full article