Search Results (175)

This study discusses the (2+1)-dimensional Kadomtsev–Petviashvili–Benjamin–Bona– Mahony equation, which emerges in weakly nonlinear dispersive plasma waves and shallow water dynamics in ocean engineering. Logarithmic dependent-variable transformations are applied to reconstruct a one-exponential tau function as a common one-soliton profile and derive its dispersion relation. This is a standard transformed solution listed as three normalized logarithmic maps, but not a new family of solutions. Only a one-exponential soliton is claimed, no two-soliton family and no arbitrary-N soliton family. The explicit rational rogue-wave families of the first, second, and third orders are derived using a modified version of a well-known center-shifted polynomial tau-function method that is applied to the KPBBM bilinear form, with both center parameters $\beta$ and $\gamma$ independent. The novelty is thus limited to the specific model and is not based on a new KPBBM equation or a fundamentally novel symbolic algorithm. The rogue-wave center translates in the longitudinal and transverse directions through $\beta$ and $\gamma$, respectively, for a fixed order N and fixed model parameters. They leave the pattern, localization width, background, and the arrangement of inner patterns unchanged. Lump solutions and lump–soliton interaction structures are also obtained and investigated. The auxiliary Hirota bilinear constraint and its reduced bilinear representation are explicitly given. The higher-degree equations found in the directional logarithmic maps are not new multilinear equations, but rather the denominator-cleared differential polynomial residuals. The validity of each solution family retained is guaranteed by means of analytical substitution or vanishing of symbolically identical-to-zero residual in the original KP–BBM equation. The two- and three-dimensional plots are used only to demonstrate the amplitude profile, localization, and propagation of the solutions, as verified by the analysis. In the weakly nonlinear, long-wave and weakly transverse regime where the KPBBM reduction is valid, these solutions give idealized mathematical representations of localization and interaction mechanisms. They are not predictive of coastal instability or offshore hydrodynamic loading, for which dimensional calibration and experimental/field validation would be necessary. Full article

Accurate AFM nanoindentation analysis requires models that account for the rounded apex of real pyramidal indenters. Although exact force-indentation equations for n-sided blunt pyramids exist, their numerical complexity limits routine use. In this work, a simple closed-form analytical approximation is developed that directly relates force to indentation depth for blunt pyramidal indenters. The method employs first-order Maclaurin series expansions of the geometric terms and the generic indentation differential equation, yielding a closed-form second-degree polynomial expression that is readily implemented in AFM data analysis. Comparison with the exact solutions showed that the approximation error decreases with indentation depth and is governed by the pyramid geometry rather than the tip radius. Simulated and experimental AFM data confirmed accurate Young’s modulus estimation above a geometry-dependent validity threshold. For a four-sided blunt pyramidal indenter, the proposed criterion predicts minimum indentation depths ranging from approximately $10.4 R_c$ for θ = 15° to 2.4 $R_c$ for θ = 45° where $R_c$ is the tip radius and θ is the pyramid’s semi-included angle. Application of the model to simulated AFM datasets yielded Young’s modulus values between 18.5 and 19.8 kPa for a true modulus of 20 kPa, corresponding to errors below 8% in all examined cases. Furthermore, the closed-form equation provided very good agreement with AFM nanoindentation data obtained from human prostate cancer cells. It is also shown that the generic derived equation includes the case of a spheroconical indenter as a limiting case. Young’s modulus is obtained directly from the quadratic coefficient, eliminating the need for tip-radius calibration. In addition, the formulation is applicable to heterogeneous materials, providing an effective local modulus through the weighted mean value theorem for integrals. The approach offers a practical and computationally efficient alternative for AFM data processing, improving the robustness of modulus estimation for soft biological materials. Full article

This paper addresses the need for compact, low-cost soil moisture sensors operating at low microwave frequencies that can provide accurate, texture-aware (i.e., sensitive to different soil particle-size distributions such as sand and loam) characterization of soil permittivity and moisture content. Conventional techniques and many existing microwave resonator sensors are constrained by limited penetration depth, relatively large or complex structures, and calibration procedures that do not robustly account for different soil textures and moisture ranges. A dual-port microstrip square split ring resonator (SRR) sensor on Rogers RO3010 (Rmit University, Melbourne, Australia) is designed for operation at 1.3 GHz and analyzed using full-wave 3D electromagnetic simulations. The structure employs a T-shaped feedline and a shunt quarter-wavelength matching section to achieve strong field confinement in the sensing region and effective impedance matching. Soil is modeled as sandy and loamy superstrates over practical agricultural moisture ranges, with their complex permittivities drawn from reference datasets. Empirical calibration models are then developed, including polynomial curve fitting between resonance frequency shift and real permittivity, machine-learning-based calibration using resonance frequency and transmission loss features, and multiple linear regression linking moisture content to both real and imaginary permittivity components. The sensor exhibits a resonance frequency shift of about 115 MHz over 0–30% moisture for sand and 0–40% for loam, with a maximum sensitivity of 3.4%. Calibration models achieve mean absolute error below 1.22%, root mean square error under 1.58%, and coefficients of determination R² > 0.98 for both soil textures. These results demonstrate that a compact 1.3 GHz square SRR sensor with data-driven calibration, i.e., empirical models learned from simulated and measured S-parameters, enables sensitive, reproducible, and texture-aware soil moisture estimation suitable for agricultural and environmental monitoring. Full article

Accurate high-temperature field characterization is important for explosion diagnostics, laser–matter interaction, combustion monitoring, and related thermal processes. This work presents an integrated thermometry framework combining fiber-optic spectrometry with monochrome imaging. Its central contribution is not a new multispectral principle or optimization algorithm, but an integration-time-dependent radiometric calibration framework coupled with representative spectral-emissivity transfer under clearly stated applicability conditions. Its central element is a three-parameter radiometric calibration model in which camera integration time is explicitly included, so that radiance conversion can be performed across the experimentally calibrated integration-time range without repeating a separate fixed-exposure calibration for each setting. Multiwavelength spectral radiance is used to jointly retrieve temperature and a continuous, second-order polynomial emissivity function with a genetic algorithm serving as the global optimizer. The emissivity function obtained from a representative spectral sampling region is then transferred to the imaging model for pixelwise temperature inversion; this step assumes that the material and surface state are sufficiently uniform over the region to which the function is applied. The method is examined using steady-state tungsten–halogen-lamp measurements with nominal color temperatures of 2200–2800 K and a transient laser-heated 316L stainless-steel case. Agreement with a Wien-based estimate is used as an internal spectral-consistency check rather than as an independent traceable accuracy validation. In the transient case, the retrieved spectral-field-of-view temperature increased from 2311.9 to 2398.5 K over 50–60 s, and the reconstructed images reproduced the corresponding increase in the central high-temperature region. The present results demonstrate the feasibility of coupling integration-time-dependent calibration with measured spectral-emissivity transfer for two-dimensional temperature reconstruction, while the achievable absolute accuracy remains subject to detector linearity, emissivity-model validity, spatial emissivity uniformity, radiometric calibration, and independent reference validation. Full article

Accurate finite element models are essential for structural condition assessment, yet nominal material properties and idealized boundary conditions can produce systematic discrepancies between numerical and measured dynamics. This study proposes a physics-constrained sparse response-surface framework that combines Elastic Net basis selection, mechanically prescribed monotonicity, adaptive sample enrichment, and identifiability-aware uncertainty assessment within a transparent finite element model-updating procedure. A scaled steel truss was tested using millimeter-wave radar, and the first three vertical natural frequencies were identified by stochastic subspace identification. The resulting sparse polynomial surrogate was independently validated before bounded inversion and ANSYS back-substitution. The mean frequency error decreased from 5.55% to 0.82%. Jacobian and bootstrap analyses further showed that several combinations of material and boundary parameters can reproduce similar modal responses, so the updated parameters are best interpreted as a coupled equivalent calibration state rather than unique direct measurements. The proposed framework therefore improves physical consistency and computational efficiency while explicitly retaining the uncertainty associated with weakly identifiable parameter directions. Full article

The jump height of overhead transmission lines after ice shedding is closely related to electrical clearance, and structural safety. For different conductor parameters, span lengths, horizontal stress, and icing conditions, transient finite-element analysis can provide detailed dynamic responses, but repeated simulations are not convenient for fast engineering assessment. In addition, a deterministic prediction model only gives a single jump-height value, and the reliability of this value is difficult to judge when the input condition is close to the boundary of the sampled range. In this study, a Dimensionless-Group-Guided Bayesian Neural Network (DG-BNN) is developed to predict the ice-shedding jump height and estimate the associated uncertainty. The original physical variables are first transformed into seven dimensionless Pi-groups according to Buckingham Pi dimensional analysis. These variables describe the main effects of geometry, mass distribution, stress state, and ice shedding in a compact form. The network is trained through a two-stage procedure. A heteroscedastic regression model is first obtained with a trend-regularization term based on a fixed second-order polynomial, and then the deterministic layers are converted into Bayesian layers for probabilistic inference. Monte Carlo sampling is used to calculate the predictive mean, epistemic uncertainty, and aleatoric uncertainty. Temperature scaling is further introduced to adjust the prediction intervals on the validation set. The comparison with several regression models shows that DG-BNN can maintain accurate jump-height prediction while giving calibrated uncertainty information. On the FEM-generated test set, DG-BNN achieved an R² of 0.986, an RMSE of 0.958 m, and an MAE of 0.649 m. After temperature calibration, the coverage probability of the 90% prediction interval reached 89.96%. The model can therefore serve as a fast surrogate tool for ice-shedding response assessment, especially when the reliability of the predicted result needs to be considered. Full article

This study evaluates a fixed Kronecker-sequence interleaver under controlled synthetic three-dimensional correlated-fault models. Spatial-cluster, column-correlated, and bit-plane-dependent probability fields are used as coding-level abstractions and are not calibrated device measurements. The K-IPA mapping is compared with random, structured 3D block, modular-stride, and length-adapted quadratic-permutation-polynomial (QPP-style) mappings using BCH(63,45) and RS(63,45) backends. At p = 0.015 and ρ = 0.85, K-IPA BCH has lower FER than random, 3D block, and QPP-style BCH, but its difference from stride BCH is small, and the paired confidence interval includes zero. Within the RS backend, the paired comparisons among K-IPA, stride, and QPP-style mappings do not resolve a difference at this operating point. Because the BCH and RS tensor partitions contain different numbers and types of decoder units, their FER values are not used to rank the two code families. The topology study further shows that no fixed mapping is uniformly best. A separate address-remapping implementation check verifies the fixed lookup table only; device-calibrated fault validation and complete codec hardware evaluation are outside the evidence provided here. Full article

The Magdalena River Estuary hosts the access channel to the Port of Barranquilla, where recurrent dredging is required to maintain navigable depths of up to approximately 12 m. Chronic siltation in this channel is closely linked to the dynamics of the Turbidity Maximum Zone (TMZ), which remain poorly understood in tropical, microtidal systems with extreme sediment loads. This study investigates the spatiotemporal variability of the TMZ in the Magdalena River Estuary (MRE), Colombia, using a previously calibrated and validated MOHID 3D numerical model coupled with sediment transport. Sixteen scenarios covering river discharges from 2000 to 5500 m³ s⁻¹ under neap and spring tidal conditions were analyzed. Results show that the TMZ core position follows a nonlinear inverse relationship with discharge (R² = 0.976), migrating from km 13–15 under extreme low-flow conditions (Q = 2000 m³ s⁻¹) to the estuary mouth for discharges above 5000 m³ s⁻¹. Within the simulated discharge range of 2000–5500 m³ s⁻¹ and under the modeled neap and spring tidal conditions, the position where ε = 0.005 tracks the TMZ core location (R² = 0.96, RMSE ≈ 1 km), suggesting that this threshold can be used as a first-order spatial indicator of maximum sedimentation under the conditions evaluated in this study. Contrary to macrotidal estuaries, the MRE exhibits higher suspended-sediment concentrations during neap tides than during spring tides, with SSC up to 77 percent greater for Q = 2000 m³ s⁻¹. This reversal is driven by the suppression of turbulent mixing (R_i > 20) during neap conditions, which preserves the salt-wedge structure and enhances stratification-controlled sediment trapping. These results provide two process-based criteria for predicting turbidity-maximum behavior in the MRE: the ε = 0.005 stratification isoline and the discharge–TMZ polynomial. More broadly, the methodological framework may support the development of site-specific predictors for other highly stratified, microtidal estuaries subject to strong discharge variability. Full article

The security of a signature scheme given in the standard model (SM) will be more stable and reliable than that given in the random oracle model (ROM). Fuzzy identity-based signature (FIBS) enables a user to generate a signature for a set of descriptive attributes, defined as $\omega ={\left\{{\omega }_j\right\}}_{j=1}^n$. Any attributes set ${\omega }^{\prime }={\left\{{\omega }_j^{\prime }\right\}}_{j=1}^n$ can validate the signature provided that the distance between $\omega$ and ${\omega }^{\prime }$ is below a predefined threshold. Most of the existing FIBS schemes are based on the ROM. It is of great significance to design a FIBS scheme based on the SM. In this work, we adopt fingerprint minutiae as the biometric modality and present a feature extraction algorithm $\mathcal{E}$ that transforms raw minutiae into quantized, privacy-preserving attribute sets, and we present a False Rejection Rate (FRR)–False Acceptance Rate (FAR) trade-off framework to calibrate matching threshold t, with adjustable n for qualified error performance. Subsequently, we present a novel and efficient FIBS scheme, which is proven to be unforgeable in SM for any polynomially bounded adversary under selective identity attack model. Compared to the existing FIBS schemes based on the ROM, our new FIBS scheme has a strong security model. Compared to the existing FIBS scheme based on the SM, our new FIBS scheme reduces total computation consumption by approximately $33.55\%$ and achieves a significant reduction in communication consumption, saving approximately $68.07\%$ of the message and signature size, which is suitable for biometric authentication. Full article

Protective reinforced-concrete (RC) elements designed to resist contact blast loading must reconcile high energy dissipation with material and weight efficiency. This study examines HollowHex, an RC slab architecture in which periodic hexagonal cellular voids redistribute blast-induced stresses along inclined web-walls through a Vierendeel-type framing action. A full-factorial design of experiments across web thickness, charge mass, and hexagonal cell radius was carried out with Abaqus/Explicit using a concrete-damaged-plasticity model and mass-dependent Friedlander overpressure histories calibrated to UFC 3-340-02 scaled-distance relations. A six-level mesh-convergence study with three independent fine-mesh verification runs established the residual mesh effect as regime-dependent, bounded within approximately 13% in the elastic and severe-damage regimes and approximately 18% in the transition regime. Ten surrogate-model families—linear, polynomial, kernel, ensemble, and multilayer-perceptron—were benchmarked under leave-one-out, 5-fold, and 7-fold cross-validation. The best models achieved out-of-sample R² = 0.96 for peak displacement and R² = 0.93 for a continuous damage volume ratio (DVR), with train-to-validation gaps of only 0.03 and 0.06, indicating genuine generalization on the small dataset. A direct identical-condition comparison against circular-hollow slabs of matched void area shows blast-equivalent performance across the elastic, transition, and severe damage regimes (peak displacements within 2%, damage volume ratios within 7%), positioning the hexagonal architecture as a blast penalty-free alternative whose selection can be driven by non-blast criteria. A cross-validated parametric design heatmap is provided as a screening tool within the verified envelope. The uniform loading idealization is cross-checked against the spatially resolved CONWEP model, conservative on peak displacement by a factor of approximately 3.5, while approximately damage-equivalent and the constitutive model is validated at the damage level against documented contact-explosion tests through coupled FEM–SPH simulation. The findings position HollowHex not as a universally superior geometry but as a quantitatively beneficial alternative within the service/transition design range of greatest practical interest for blast protection. Full article

Wear prediction for agricultural soil-engaging components is computationally demanding when discrete element method (DEM) simulations are repeatedly used for design evaluation and operating-parameter screening. In this study, an Archard-inspired Gaussian process regression (GPR) residual-learning surrogate was developed for rapid prediction of the total wear volume calculated by EDEM for a ploughshare. The physical prior was a monotonic operational-parameter proxy motivated by the load and sliding trends in Archard theory, which did not directly use DEM-derived normal force, sliding distance, or frictional work. A soil–ploughshare interaction model was used to generate 100 full-factorial samples with tillage depth, tillage speed, and penetration angle as inputs. The Archard-inspired prior, cubic polynomial Ridge regression, standard GPR, and prior-guided residual GPR were evaluated by cross-validation, repeated random splits, and boundary-level extrapolation tests. Across 30 repeated 90%/10% splits, standard and Archard-inspired GPR achieved mean R² values of 0.9927 ± 0.0014 and 0.9908 ± 0.0021, respectively. In the 200 mm tillage-depth extrapolation test, the latter performed best, with R² = 0.9752, RMSE = 0.000252 mm³, and MAPE = 2.68%; however, the former was more accurate in the tillage-speed and penetration-angle extrapolation tests, and the 48% interval coverage of the prior-guided model in the penetration-angle test indicated overconfidence when the prior was biassed. These results show a conditional, rather than universal, benefit of the Archard-inspired prior: it improved extrapolation plausibility for the load-dominated tillage-depth case but did not improve all boundary predictions. The surrogate predicts EDEM-simulated wear, and its engineering validity depends on DEM calibration, the selected wear coefficient, and future soil-bin or field validation. Full article

Scrap-based electric arc furnace (EAF) steelmaking powered by on-site variable renewable energy is a key decarbonisation route, but the heteroscedastic, non-Gaussian nature of joint wind–photovoltaic forecast errors makes the EAF—a large, metallurgically constrained load—hard to coordinate with on-site generation under feeder limits. We develop a unified stochastic dispatch and adaptive setpoint-control framework. A chance-constrained dispatch over a zone-wise Beta uncertainty model is propagated through a degree-two polynomial chaos expansion (PCE) and reformulated as a second-order cone programme via the Cantelli inequality, with EAF-specific metallurgical constraints (electrode slew, short-circuit-ratio-tied flicker, stage-dependent melt-power floor, multi-stage tap-to-tap profile) embedded by the same procedure. The EAF setpoint gain is then extracted in closed form—without Jacobian inversion—as a ratio of first-order PCE coefficients, so it inherits the dispatch’s 95% feeder-security guarantee. Calibrated on 24 months of real wind/PV data for a Qingdao site (ERA5 reanalysis vs. archived ECMWF-IFS forecast), which confirms the heteroscedastic premise and a measured wind–PV error correlation of 0.015, the extracted gain scales across the Low–Mid–High zones (medians 6.07, 11.91, 17.22 p.u.) following the operating regime rather than the disturbance magnitude. The scheme bounds worst-case tracking below 1.18 MW per zone (vs. up to 3.34 MW for no droop), satisfies the feeder limit in 100% of realisations, matches model-predictive control without online optimisation, and lowers within-EAF specific CO₂ emissions by 4.4% versus no droop. An out-of-sample test on real records confirms a decisive advantage in the data-rich zones and, candidly, a shortfall in the data-limited high-wind zone. Full article

Vignetting introduces spatial radiometric nonuniformity into remote sensing images and degrades subsequent radiometric analysis, image interpretation, and calibration-related applications. To address this problem, this paper proposes a vignetting correction method based on low-rank modeling and polynomial fitting. The method constructs a multi-frame data matrix in the logarithmic domain, extracts the shared vignette component through rank-1 low-rank modeling, and further recovers a smooth vignette field through polynomial fitting. Experiments were conducted using real remote sensing images, simulated vignetted images, and star images. Among the three ablation variants, the proposed full method achieved the best performance, with MAE, MAD, Center-MAE, and Edge-MAE values of 0.48%, 3.65%, 0.14%, and 0.52%, respectively. Compared with the low-rank-only method, these metrics were reduced by 23.8%, 32.8%, 71.4%, and 20.0%, respectively. An additional all-frame comparison across 28 dataset settings showed that the proposed rank-1 model achieved mean accuracy comparable to nuclear-norm-based standard RPCA, while exhibiting lower cross-dataset variability in MAE, MAD, and Edge-MAE. For star images, the method reduced image-plane nonuniformity from 1.39–1.92% to 0.59–0.80% while preserving background-subtracted stellar DN values. These results demonstrate that the proposed method provides physically interpretable and stable vignetting correction while maintaining radiometric consistency. Full article

Accurate lens distortion correction is important for calibration, registration, image stitching, and 3D reconstruction, especially in low-data device-specific settings where disposable or specialised cameras cannot provide large calibration datasets. We address distortion correction for cameras with highly irregular or non-stationary distortion fields, where fixed polynomial models and generic learning-based rectification methods can struggle. We propose a framework based on Deep Gaussian Processes (DGPs) to model the non-linear mapping required for undistortion. The key motivation is that conventional single-layer GPs with stationary kernels must use one global notion of smoothness, whereas DGPs can represent spatially varying behaviour through composed latent mappings while preserving per-pixel predictive uncertainty. This uncertainty can be used to identify or downweight unreliable corrected regions in downstream tasks. We evaluate the method on three real camera datasets with increasing distortion complexity. The full structured acquisitions contain 512 horizontal and 512 vertical line images per camera. These are not thousands of natural calibration images, but they yield up to 29,532, 11,311, and 31,686 detected intersection correspondences for the RPI, Theta, and Pillcam datasets, respectively. This distinction is important for cameras where acquiring many independent images is impractical. The results are assessed using qualitative rectification, uncertainty maps, normalised collinearity errors, and total training time. Polynomial calibration remains strongest for the regular radial RPI distortion, while DGP and DGP2 models show lower normalised collinearity-error distributions than the standard GP and lightweight MLP baselines on the more distorted Theta and Pillcam datasets. For the full datasets, total DGP/DGP2 training times ranged from 2383.50 s to 10092.50 s, reflecting the additional computational cost of probabilistic non-stationary modelling. Full article

This paper presents a novel decomposition method for split-film probes to improve pitch angle determination over a wide range of flow velocities. Conventional approaches often suffer from the velocity dependence of the directional response function, resulting in large angular errors. The proposed method introduces a new functional formulation based on effective cooling velocities and velocity-dependent reference parameters. These parameters are explicitly derived from calibration data and modeled using fourth-order polynomial regressions to suppress velocity-induced variance. Experimental verification conducted for velocities between 2.2 and 14.6 m/s demonstrates that the proposed method collapses the calibration data more effectively than previous models. The total angular estimation error does not exceed ±2° within the pitch angle range from −60° to 60°. The proposed approach is therefore suitable for reliable measurements in low-velocity regions of complex flows, such as wakes and recirculation zones. Full article