Search Results (984)

Motion blur degrades single-frame imaging when relative motion occurs during sensor exposure; yet, quantitative validation is difficult because ground-truth motion parameters are rarely available in real images. This paper presents an interpretable, measure-first framework for detecting, localizing, and quantifying motion blur in single-frame grayscale images under a validated operating condition of one-dimensional horizontal uniform motion. The method analyzes each image row as a one-dimensional spatial signal, where Movement Artifact denotes the scanline-level imprint of motion blur retained in the legacy algorithm names MAPE and MAQ. The pipeline combines three stages: Movement Artifact Position Estimation (MAPE) using scanline self-similarity, Reference Origin Point Estimation (ROPE) using robust structural trends, and Movement Artifact Quantification (MAQ), which summarizes blur magnitude as an average horizontal spatial displacement after adaptive filtering. The pipeline is evaluated on a controlled empirical dataset of 110 images of a high-contrast marker acquired at known tangential velocities from 0.0 to 1.0 m/s in 0.1 m/s increments (10 images per level). MAPE achieves 70–90% detection rates across velocities, and ROPE localizes reference origins with 97–99% detection. An empirical polynomial mapping from MAQ to velocity attains $R^2=0.9900$ with RMSE 0.0229 m/s and MAE 0.0221 m/s over 0.0–0.7 m/s, enabling calibrated velocity estimates from blur measurements within the validated regime. An extended additive-noise robustness analysis further shows that severe perturbation can preserve candidate self-similarity responses while progressively destabilizing reference-origin localization and MAQ pairing, thereby clarifying the empirical boundary of the current controlled single-marker regime. The approach is not claimed to generalize to uncontrolled scenes, non-uniform blur, or multi-dimensional and non-rigid motion. Full article

This study focused on elucidating the effects of chitosan (Ch), hyaluronic acid (HA), and titanium dioxide nanoparticles (nano-TiO₂) on the physicochemical characteristics of a model bacterial membrane (layer) composed of the phospholipid DPPG (1,2-dipalmitoyl-sn-glycero-3-phospho-rac-(1-glycerol) sodium salt). The membrane was prepared on mica using the Langmuir–Blodgett (LB) technique from an aqueous subphase containing Ch, HA and/or TiO₂. Its surface properties were subsequently characterized by optical profilometry and surface free energy estimation. The nanoscale topography of the DPPG layer provided a biomimetic platform that reflects the organization of bacterial membranes, enabling a precise evaluation of how external agents, such as Ch, HA, and nano-TiO₂, modify the surface’s structural and energetic properties. The results showed that the LB films exhibit mildly heterogeneous topography, which can be attributed to lipid domains with distinct molecular packing densities. Depending on the type of biopolymer employed with TiO₂, distinct topographic architectures of the DPPG monolayers were obtained. Furthermore, the presence of nano-TiO₂ was clearly manifested as a topographic irregularity, while the analysis of hydrophilic–hydrophobic properties revealed a structurally perturbed lipid film. The results provide detailed insight into how these specific molecules (Ch, HA, nano-TiO₂) interact at the molecular level with model bacterial membranes, offering a comprehensive picture of cell–microenvironment interactions. Full article

Electroweak, QCD, and QED radiative corrections to the nucleon low-energy coupling constants $g_V$ and $g_A$ are enhanced by large perturbative logarithms between the electroweak and hadronic scale, as well as between the hadronic scale and the low-energy MeV scale. Additionally, higher-order pion-mass splitting corrections to the nucleon axial-vector charge might be large. By consistently incorporating these effects, we provide an updated relation between the lattice-QCD and physical $g_A$, finding a total radiative correction of $3.5\left(2.1\right)\%$ ($5.6\left(0.7\right)\%$). This leads to an expected lattice-QCD result of $g_A^{\mathrm{QCD}}=1.265\left(26\right)$ ($g_A^{\mathrm{QCD}}=1.240\left(9\right)$) when based on a combination of lattice-QCD and data-driven (or only data-driven) inputs, respectively. Future phenomenological, chiral perturbation theory, and lattice-QCD studies can improve both the central value and the uncertainty of this estimate. Full article

The Juncal Norte Glacier (33°00′ S, 70°06′ W) is in the Dry Andes of central Chile within the Juncal Basin, a headwater watershed of the Aconcagua River, a semi-arid region experiencing an ongoing megadrought since 2010 and a 37% reduction in streamflow relative to pre-1990 baselines. This study provides the first glacier-specific annual melt and runoff estimate for Juncal Norte during mature megadrought conditions. Mass balance was estimated using a temperature index (positive degree day, PDD) model calibrated with automatic weather station (AWS) air temperature data and glacier hypsometry, assuming limited snow accumulation given that 2018–2019 precipitation and snow water equivalent (SWE) were extremely low relative to the long-term mean. Basin runoff was evaluated using a closure method comparing proglacial sub-basin-integrated discharge with modeled glacier melt volumes. Modeled glacier melt for 2018–2019 was equivalent to approximately 30% of observed annual discharge at the proglacial sub-basin, a disproportionate contribution given the glacier covers only 2.7% of the total basin area. The lower ablation zone (2900–4000 m), comprising 30% of glacier area, produced 90% of total melt volume. A + 1 °C temperature perturbation increased glacier-wide melt by 21.4%, confirming high climatic sensitivity. These results underscore the glacier’s critical but increasingly vulnerable buffering role for downstream water availability in the Dry Andes. Full article

Emergency maneuvers can drive vehicles into severe instability regimes within sub-second time scales, motivating last-moment warning interfaces with auditable false-alarm budgets. We study a proxy-triggered imminent-recognition setting: given a 0.1 s past-only slice of onboard signals, decide whether a conservative physics-defined instability proxy will trigger within the next $\tau =0.2$ s. The contribution is, therefore, a calibrated warning for a safety-relevant surrogate event, not a claim of predicting crashes or true instability outcomes directly. Because the corpus is terminal-phase aligned, the default causal monitor ($w=d=0.1$ s, $k=2$) is warnable on only $18.3\%$ of event runs; we, therefore, report run-level effectiveness both overall and conditional on warnability. We learn a lightweight hazard scorer and convert its scores into an operator-facing alarm rule via split-conformal calibration on held-out negative slices, exposing a slice-level false-alarm budget $\alpha$ with finite-sample, one-sided control of the marginal slice-level false positive rate (FPR) on exchangeable negatives. To address fleet heterogeneity, we additionally calibrate vehicle-conditioned (Mondrian) thresholds, enabling per-vehicle risk budgeting without retraining separate models. On the held-out test split at $\tau =0.2$ s, the scorer achieves AUPRC $\approx 0.251$ against a base rate of $0.638\%$, AUROC $\approx 0.986$, and ECE $\approx 0.034$. After calibration at $\alpha =5\%$, realized slice-level FPR concentrates near the prescribed budget while slice-level TPR on imminent positives remains high (≈0.982). We explicitly separate this slice-level guarantee from empirical run-level metrics such as FAR_run, EWR on warnable runs, and lead time, and we report dependence and shift diagnostics to delineate where the guarantee may degrade. The reported $\mu$-sensitivity analyses concern run-level descriptor perturbation and omission rather than validation of a within-run friction estimator with temporal lag. The result is a transparent, risk-budgeted monitoring primitive for last-moment vehicle-stability warning under clearly stated exchangeability assumptions. Full article

State-of-charge (SOC) estimation for lithium-ion batteries in smartphones is complicated by nonlinear load variation, electro-thermal coupling, aging effects, and heterogeneous user behaviors. This study proposes a multi-physics coupled SOC estimation framework, termed the Multi-Physics Thermo-Electrochemical Coupled SOC Model (MTEC-SOC), to characterize battery behavior under representative user-load conditions within controlled ambient thermal boundaries. The model combines system-level power profiling, thermal evolution, voltage dynamics, and aging-related capacity correction within a unified framework. To support model development and validation, a dual-source dataset is established using laboratory battery characterization data and real-world smartphone behavioral data, from which users are classified into light, heavy, and mixed usage patterns. Comparative results against four benchmark models (M1–M4) show that MTEC-SOC achieves the highest overall accuracy, with average MAE, RMSE, and TTE error values of 0.0091, 0.0118, and 0.08 h, respectively. The results suggest distinct degradation tendencies across user types: calendar aging dominates under prolonged high-voltage dwell in light-use scenarios, whereas, within the tested thermal range, heavy-use scenarios exhibit stronger voltage sag, relative temperature rise, and polarization-related stress; mixed-use scenarios are characterized by transient responses induced by abrupt load switching. Sensitivity analysis further indicates that the predictive behavior of the model is strongly scenario-dependent, with higher-load operation within the calibrated range amplifying parameter perturbations. Overall, the proposed MTEC-SOC framework provides accurate SOC estimation and physically interpretable insight within the evaluated dataset and operating conditions, offering potential guidance for battery management and energy optimization in intelligent mobile terminals. Full article

Load-redistribution false-data-injection (LR-FDI) attacks can degrade power-system operation by reshaping the perceived nodal demand pattern, thereby inducing congestion-aware redispatch and economic inefficiency while preserving the net system load. Prior LR-FDI studies commonly adopt bilevel/Stackelberg formulations with a continuous attack vector and an embedded operator response; however, these formulations often (i) do not represent explicit compromised-load selection, (ii) become computationally restrictive when combinatorial target sets are considered, and (iii) offer limited transparency for structured, stage-wise attack planning. This paper proposes a sequential two-stage attacker–operator framework for LR-FDI vulnerability assessment that integrates sparse load compromise decisions with screening-regularized attack synthesis and post-attack operational evaluation. In Stage-1, a mixed-integer nonlinear program identifies economically influential load buses via binary selection and determines admissible perturbation magnitudes under total-load conservation and proportional shift bounds. To confine the attacker-side search region and avoid economically exaggerated solutions, a screening-derived conservative operating-cost ceiling is first estimated through a parametric load-sensitivity analysis and then used to regularize the attack-synthesis step. In Stage-2, the system operator’s corrective redispatch is evaluated by solving an active-power-oriented economic dispatch model with nonlinear network-consistent assessment of operational outcomes. Using the IEEE 24-bus RTS, results show that the hourly operating-cost deviation reaches ≈0.2% in the most adverse feasible cases, and the cumulative daily impact approaches ≈5% only under selectively realizable compromised-load patterns, accompanied by a nearly 80% increase in total active-power transmission losses relative to the base case. Overall, the framework yields a practically grounded quantification of conditionally severe economic and network stress under coordinated LR-FDI scenarios and provides actionable insight for prioritizing vulnerable load locations for protection and monitoring. Full article

Vision-based pose estimation has been widely applied in unmanned aerial vehicle (UAV) navigation. However, existing visual pose estimation algorithms are highly sensitive to camera imaging distortion, which degrades estimation accuracy, and often suffer from noticeable jitter between frames in dynamic scenarios. To address these issues, this paper proposes an improved visual pose estimation algorithm built upon the Perspective Similar Triangle (PST) geometric model. Using a planar fiducial marker as the observation target, the single-frame pose estimation problem is reformulated as a hierarchical geometric inference framework, including image point distortion correction, depth recovery based on planar similar triangle constraint, and rigid transformation estimation between the camera and world coordinate systems. This formulation improves pose estimation accuracy under distorted imaging conditions. To accommodate distortion variations in practical scenarios, a radial distortion coefficient update method is further designed to adaptively adjust the radial distortion parameters under single-frame observations, ensuring that the distortion model remains consistent with the actual imaging distortion and providing reliable model inputs for distortion correction in pose estimation. In addition, to enhance pose stability in dynamic scenarios, a multi-frame optical center consistency constraint (MOCCC) method is introduced to optimize the pose estimation for more stability. By constraining pose estimation across adjacent frames using the mean optical center over multiple frames as the optimization objective, the proposed method effectively suppresses pose jitter caused by single-frame observation noise. Finally, a three-degree-of-freedom (3-DOF) attitude motion platform is established, and both static and dynamic experimental scenarios are designed to validate the accuracy and stability of the proposed algorithm. Experimental results demonstrate that the proposed algorithm achieves high accuracy and high stability pose estimation under imaging distortion and small perturbations, exhibiting good robustness and suitability for practical UAV visual navigation applications. Full article

We study equilibrium and stability properties of self-gravitating bosonic configurations in the nonrelativistic regime by numerically solving the nonlinear Gross–Pitaevskii–Poisson (GPP) equations system. Using an appropriate coordinate transformation, the equations are written in a dimensionless form independent of the physical model parameters, so that each configuration is determined only by the central value of the wave function. We compute sequences of stationary solutions including ground and radially excited states and identify bifurcation points between them. The virial relation is used as a diagnostic condition for equilibrium, leading to the determination of a critical central density and a maximum particle number above which no stationary solutions are found. Excited configurations satisfying the virial relation are expected to be metastable since they violate stability conditions resulting from radial perturbation analyses. From the critical particle number, we estimate the maximum stable mass and radius. For axion-like bosons with mass ${10}^{-5}$ eV, the resulting configurations have masses of the order of tens of Earth masses and meter-scale radii. Full article

Snowpack dynamics in continental climates are important for water-resource monitoring and snow water equivalent (SWE) estimation, yet the response of the snow depth–snow water equivalent (SD-SWE) relationship to changing thermodynamic and accumulation forcing remains insufficiently understood. This study develops a process-based framework to evaluate how moderate perturbations in air temperature and precipitation influence snowpack evolution and depth–mass coupling in representative snow regimes of northeastern Kazakhstan. SNTHERM (the Snow Thermal Model) simulations were combined with regression analysis, ANCOVA diagnostics, and bulk-density evaluation under controlled delta-change perturbations of air temperature (±1–2 °C) and precipitation (±5–10%). The results show that the SD-SWE relationship remains approximately linear within the tested perturbation range (R² ≈ 0.78–0.84), although its parameters are partially sensitive to precipitation-driven accumulation. Temperature perturbations mainly affect melt timing, seasonal persistence, and snow-density redistribution, whereas precipitation modifies snowpack mass and overburden, enhancing mechanical compaction and increasing the regression slope. These findings indicate that snow density is a key integrative state variable linking energy balance, phase change, and compaction processes. Under the tested conditions, snow depth remains a physically consistent proxy for SWE, although the conclusions are limited by the one-dimensional model structure, reanalysis-based forcing, and restricted observational coverage. Full article

The widespread development of medium-voltage and high-voltage direct current transmission systems has highlighted the modular multilevel converter (MMC) as a crucial enabling technology. However, the overall performance and lifetime of the MMC strongly depend on the integrity of its submodules (SMs), making online capacitance condition monitoring a critical requirement. Unlike recent related studies that rely on computationally heavy matrix-based algorithms or “black-box” artificial neural networks requiring massive offline training datasets, this paper proposes a parametric, adaptive linear neuron network. Mapped directly to the physical equations of the MMC, the method simultaneously exploits the arm current, SM switching state, and capacitor voltage to identify online parametric variations caused by aging or harsh conditions. The proposed scheme is fully non-intrusive, requiring no additional hardware sensors or signal injections, thereby reducing implementation complexity. The simulation results obtained in MATLAB/Simulink (vR2024b) demonstrate the method’s fast convergence and a quantified steady-state estimation error within ±1%. Furthermore, the estimator exhibits strong robustness under severe operating conditions, successfully maintaining accuracy during a 20% capacitance reduction, a 100% active power step variation, dc-link voltage fluctuations, measurement noise, grid unbalances, and harmonic perturbations. Full article

Background: The phase of electroencephalogram (EEG) signals critically influences cortical reactivity to external inputs. Phase-dependent effects and their sensitivity to stimulation intensity have been observed at suprathreshold levels, while subthreshold transcranial magnetic stimulation (TMS) cannot induce motor evoked potentials (MEPs), resulting in limited research on phase-dependent responses under subthreshold stimulation. In this study, we used a combined transcranial magnetic stimulation and electroencephalography (TMS–EEG) approach to examine how the ongoing EEG phase influences cortical responses at subthreshold intensity and to characterize these responses in terms of temporal, spatial, and spectral features. Methods: Thirty-four healthy adults received subthreshold single-pulse TMS at the motor hotspot during 64-channel EEG recording. The mu-phase at the time of TMS delivery was estimated using autoregression-based forward prediction and categorized into four bins (0°, 90°, 180°, and 270°). The cortical responses were assessed using inter-trial phase coherence (ITPC), TMS-evoked potentials (TEPs), global mean field power (GMFP), and event-related spectral perturbation (ERSP). Results: Phase estimation reliably distinguished four mu-phase bins. Subthreshold TMS–EEG responses showed clear phase dependence: early ITPC and several TEP components (N15, P30, N45, P60, and N100) differed significantly across phases, with 180° and 270° often eliciting stronger responses. GMFP revealed robust phase effects at mid-latency components, and TMS-induced mu-rhythms were the greatest at 180°. Conclusions: Our results showed that the EEG phase significantly modulates cortical reactivity at subthreshold stimulation levels, supporting mu-phase-based closed-loop TMS as a promising strategy for precise neuromodulation. Full article

Sea-surface reflections and wind–wave motion render maritime channels strongly time-varying and statistically non-stationary, while nearshore deployments face sparse infrastructure and co-channel multiuser interference. This study integrates reconfigurable intelligent surfaces (RISs) with rate-splitting multiple access (RSMA) for joint online resource allocation. A physics-inspired time-varying channel model is established by embedding fractional Brownian motion-driven slow statistical drift and reflection-phase perturbations. With imperfect, delayed channel state information (CSI) and discrete RIS phase quantization, a proportional-fairness utility maximization problem is formulated to jointly optimize shore base-station precoding, RIS phase shifts, and RSMA common-rate allocation. To cope with strong non-convexity, high dimensionality, mixed continuous–discrete coupling, and partial observability, a fractal-aware recurrent robust Actor–Critic (FRRAC) algorithm is developed. FRRAC encodes short observation histories using a gated recurrent unit and incorporates a lightweight Hurst-proxy estimator to capture slow channel statistics for robust value evaluation and policy learning. Truncated quantile critics and mixed prioritized–uniform replay further improve value robustness, training stability, and sample efficiency. Simulation results show that FRRAC converges faster and more stably under both conventional and fractal non-stationary channel modeling, and outperforms representative baselines across the objective and multiple statistical metrics, validating its effectiveness for joint resource optimization in maritime RIS–RSMA systems. Full article

This work develops a zero-trust, physics-constrained mathematical framework for detecting stealthy drift perturbations in power system dynamical models. Such perturbations constitute adversarial, statistical deviations that preserve first-order operating trends, making them difficult to identify using classical residual-based estimators or unconstrained data-driven models. We introduce ZETWIN, a spatio-temporal learning architecture formulated as a constrained optimization problem in which the nodal admittance matrix $Y_{bus}$ acts as a graph-structured linear operator embedded directly into the loss functional. This construction enforces Kirchhoff-consistent latent representations and yields a mathematically grounded zero-trust decision rule that flags any trajectory violating physical feasibility, independent of prior attack signatures. The proposed framework is evaluated using a PyPSA-based AC–DC meshed network, demonstrating an AUROC = 0.994, and F1 = 0.969. The formulation highlights how physics-informed constraints, graph operators, and spatio-temporal approximation theory can be combined to construct mathematically interpretable zero-trust detectors for complex dynamical systems. Full article

Lago Argentino and Lago Viedma are large lakes fed by glaciers in Southern Patagonia, characterized by extraordinarily strong, persistent westerly winds and sharp gradients in regional relief, climate, and gravity field. We present operational models of spatio-temporal lake-level variations that represent instantaneous ellipsoidal lake-surface height as the superposition of three components: (i) a time-averaged lake-level topography derived from geoid modeling and ICESat-2 residuals, (ii) temporally varying water-volume changes in the lake estimated from tide gauge time series corrected for atmospherically driven perturbations, and (iii) a static hydrodynamic response to wind stress and air-pressure forcing. The atmospheric response is parametrized through empirically derived transfer functions obtained by regressing instantaneous lake-level anomalies against ERA5 wind and pressure fields, capturing wind-driven tilting. Standard deviations of ICESat-2 ATL13 elevations amount to 106 cm and 70 cm over Lago Argentino and Lago Viedma, respectively. The subtraction of our models reduces these standard deviations to 8 cm (Argentino) and 14 cm (Viedma). Surface waves incompletely averaged out within ICESat-2’s narrow footprint are identified as a principal source for the residual variability. A standard deviation of ATL13 elevations below 2 cm on calm days demonstrates ICESat-2’s unprecedented capability of monitoring water resources from space in a region of sparse hydrological infrastructure. Full article