Search Results (377)

In traditional mechanical de-icing technologies, the time-frequency evolution and spatial propagation mechanisms of transient high-frequency impact signals in flexible transmission lines remain unclear. To address this issue, transient impact responses were experimentally investigated using a full-scale transmission line model. An active de-icing exciter, featuring controllable impact energy and the potential for sustained online operation, was independently developed. High-frequency transient acceleration signals were acquired at multiple measurement points on a 20 m single-span line. The spatial distribution and time-frequency attenuation characteristics of the impact energy were quantitatively evaluated by extracting high-order time-domain statistical features, including root mean square, kurtosis, and crest factor, together with frequency-domain analyses based on Fast Fourier Transform (FFT) and wavelet entropy. The results indicate that: (1) The exciter generated highly impulsive transient responses, with a kurtosis up to 795.3 and a crest factor approaching 40. This suggests a strong local concentration of impact energy at the excitation source, which provides a dynamic basis for analyzing potential localized stress concentration and dynamic responses of the conductor system. (2) The transmission line structure exhibited a significant low-pass filtering effect on transient high-frequency shock waves. As the shock wave propagated towards the distal end, its high-frequency components above 30 Hz were substantially attenuated, likely due to internal dry friction within the stranded conductor. Consequently, the dominant frequency decreased to a low-frequency macroscopic sway of approximately 12 Hz, indicating a reduced risk of transmitting high-frequency shock loads to distal fittings and towers. (3) Under geometric nonlinear coupling, the vertical impact energy was partially transferred to the longitudinal and lateral directions during propagation, leading to sustained out-of-plane swaying. This study reveals the signal evolution characteristics of transient impacts in overhead transmission lines and provides experimental evidence for optimizing excitation parameters and assessing the engineering safety of active impact de-icing technologies. Full article

This study addresses the challenge of separating local damage information from full-field structural responses under complex environmental and noise conditions by proposing a structural damage localization method that integrates piecewise denoising, Robust Principal Component Analysis (RPCA), and a differential damage index. First, full-field responses obtained from vision-based measurement are processed through piecewise denoising and continuous displacement extraction, and then organized into a structural spatiotemporal response matrix. RPCA is subsequently employed to separate low-rank global response components from sparse local anomalies, and a damage index is constructed by differencing sparse-component statistical features between healthy and damaged states. Moving-load tests on a simply supported beam show that the DI peak in the damaged region is approximately 28 times higher than the non-damaged background level, and the identified DI peak accurately falls within the actual damage region. Compared with RMS, kurtosis, curvature index, wavelet energy, PCA residual, and RPCA sparse-energy indicators, the proposed method is the only one that achieves zero regional localization error. Under noise levels of 40–20 dB, all 30 repeated trials achieve a 100% localization success rate, and the success rate remains 93.33% even at 10 dB. Moreover, the localization results remain stable when $\lambda /{\lambda }_0$ varies from 0.50 to 1.50. Even when the number of spatial measurement points is reduced from 3401 to 128, the method maintains zero mean localization error and a 100% localization success rate. These results demonstrate that the synergy among piecewise denoising, RPCA decomposition, and state-difference enhancement effectively highlights damage-induced local anomalies, providing a robust and physically interpretable framework for full-field-response-based structural damage localization. Full article

We present the first application of the Multifractal Model of Asset Returns (MMAR) to an implied volatility index, using 36 years of daily CBOE VIX observations spanning four economic cycles. Three general conclusions emerge. First, implied volatility is multifractal: its scaling function is strictly concave, and this curvature survives explicit comparison against monofractal, ARMA, and ARFIMA nulls fitted to the same data, so it cannot be reproduced by anti-persistence or short-range linear dependence alone. Second, unlike equity price indices which are persistent, the VIX is strongly subdiffusive ($\widehat{H}\approx 0.18$, far below $\frac{1}{2}$), which is the multifractal signature of its mean-reverting character; the lognormal cascade is nonetheless admissible, so the construction is internally consistent. Third, admissibility notwithstanding, the lognormal cascade is insufficient in the extreme tails. Across Monte Carlo validation, higher-moment and tail-risk (VaR/ES) comparisons, and a GARCH/EGARCH/FIGARCH benchmark, it captures the bulk of the distribution but systematically underestimates the most violent volatility spikes and does not reproduce VIX’s pronounced positive skewness. We quantify this: the admissible cascade recovers about 84% of the excess kurtosis and reproduces 95–99% Value-at-Risk and 95% Expected Shortfall almost exactly, but it understates the deepest Expected Shortfall, and, being symmetric, it cannot reproduce the positive skew, underpricing far-out-of-the-money option premia by up to 100%. The indicated direction is asymmetric, heavier-tailed cascade extensions. Beyond VIX, the analysis offers a reproducible template for distinguishing genuine multifractality from its linear imitators in any volatility series. Full article

This paper proposes a dynamic model incorporating centrifugal forces and elastohydrodynamic lubrication (EHL) to analyze the vibration characteristics of high-speed rolling bearings with localized outer raceway defects. A four-degree-of-freedom (4-DOF) motion equation is established using Hertzian contact theory and isothermal EHL equations. Numerical solutions incorporating defect-induced time-varying displacement excitations are experimentally and theoretically validated. Results confirm the oil film’s vibration-damping effect, reducing peak acceleration by 11.8% and the root-mean-square (RMS) value by 3.7% compared to dry contact conditions. Higher rotational speeds thin the oil film and reduce comprehensive stiffness, amplifying vibration and impact intensity without altering fault characteristic frequencies, which remain stable with a relative error within 0.5%. As the defect size increases, RMS and peak values rise monotonically, with the RMS acceleration increasing by 69.5% (from 0.6102 m/s² to 1.0344 m/s²) as the outer race defect expands from 0.2 mm to 0.8 mm, while kurtosis peaks and subsequently declines. The dual-impact phenomenon is most prominent under low rotational speed and large defect conditions, providing a basis for a a quantitative fault diagnosis strategy to invert defect size from dual-impact time intervals is proposed and experimentally validated, yielding an inversion error of less than 2% under such favorable conditions. While this inversion method is condition-dependent—with its precision degrading under increased speeds and micro-defect scenarios—it provides an accurate and reliable quantitative tool within its applicable boundaries. The developed dynamic model and multi-index diagnostic approach provide a theoretical basis and practical reference for fault diagnosis, condition monitoring and quantitative defect identification of rotating machinery. Full article

Electric bicycles are popular due to their environmental benefits and convenience. However, electric bicycle fires caused by series arc faults remain a serious safety concern. This study focuses on series arc fault identification for electric bicycles under complex operating conditions, covering state of charge (SoC), torque, and speed variations, and simultaneously considers normal state, DC-side series arc fault, and AC-side series arc fault conditions. Five time-domain features, namely root mean square (RMS), standard deviation (STD), skewness (SK), kurtosis (KUR), and current amplitude (CA), and three frequency-domain features, namely amplitude–frequency energy (AFE), amplitude–frequency mean (AFM), and amplitude–frequency kurtosis (AFK), are extracted. An improved principal component analysis (PCA)-based feature fusion method transforms the eight original time–frequency features into a five-dimensional PCA-fused feature representation consisting of PC1, PC2, PC3, fused PC4–PC7, and PC8. The fused features are classified using a radial basis function (RBF)-support vector machine (SVM) model. The proposed method achieves 98.68% test accuracy, 0.9869 Macro-F1, and 0.9931 Macro-AUC. A classifier comparison and feature-level latency analysis are also provided to clarify the accuracy–cost tradeoff and deployment feasibility. The results indicate that the proposed method can provide an interpretable and lightweight solution for electric bicycle controllers, battery management systems (BMSs), and onboard safety-monitoring applications. Full article

Terrestrial ecosystems are facing increasingly severe threats driven by the dual pressures of climate change and anthropogenic activities. However, current remote sensing-based ecological research still exhibits notable deficiencies in the integration of multi-source data. This study develops a Critical Transition Index (CTI) for Northeast China. The CTI integrates four remotely sensed vegetation variables (LAI, NDVI, SIF, and VOD) with time series decomposition (STL), multiple early-warning signals (ar1, variance, skewness, and kurtosis), consistency scoring, and Mahalanobis distance. The framework systematically assesses vegetation resilience and its spatiotemporal responses to climatic stressors. Results reveal pronounced differences among variables: the structural indicator LAI identified the highest proportion of high-risk areas (60.8%, CTI ≥ 0.8), whereas the functional indicator SIF showed relatively high stability, with a mean CTI of 0.619 and a high-risk proportion of only 16.0%. High-risk areas are primarily concentrated in cropland–grassland mosaics, while forested regions maintain lower risk. Temporal analysis of land cover composition within high-risk areas shows a clear “structural diffusion” trend: the proportion of deciduous broadleaf forests in the high-risk category increased from being negligible in early periods (2003–2007) to approximately 20% in later periods (2013–2017) for both SIF and VOD indicators. This study underscores the necessity of multi-indicator frameworks for detecting critical transitions and provides quantitative, spatially explicit scientific insights for ecosystem early-warning and regional management strategies. Full article

Reliable bearing fault diagnosis in induction motors is essential for predictive maintenance and Industrial Internet of Things (IIoT) applications. However, diagnostic models that perform well under random or measurement-wise data splits may fail when deployed under unseen operating conditions. This study presents a robustness-oriented comparative evaluation of induction motor bearing fault diagnosis models using vibration and phase-current signals from a controlled medium subset of the Paderborn bearing dataset. Raw temporal 1D-CNN models, STFT-based 2D-CNN representations, and vibration–current fusion strategies were evaluated under measurement-wise and operating-condition holdout protocols. Under measurement-wise validation, the 1D-CNN Early Fusion model achieved a Macro-F1 score of 0.9251. Under the stricter operating-condition holdout setting, the same model achieved the highest robustness among the evaluated CNN models. Multi-seed validation confirmed its stability, with a mean Macro-F1 of 0.8626, a worst-case Macro-F1 of 0.7159, and a robustness score of 0.7850. The selected model remained lightweight, requiring 73,891 trainable parameters and an estimated model size of 0.282 MB. Additional revision experiments were conducted to address bearing-identity sharing and classical baseline comparisons. In the bearing-code-disjoint validation test, both raw temporal models showed reduced performance, and early fusion did not significantly outperform vibration-only learning. The 1D-CNN Vibration model achieved a mean Macro-F1 of 0.5616, while the 1D-CNN Early Fusion model achieved 0.5485; the paired Wilcoxon test was not significant (p = 0.2016). Classical baselines using handcrafted time-domain, frequency-domain, envelope-inspired, and spectral-kurtosis features were also evaluated. The strongest classical baseline, vibration-feature XGBoost, achieved a mean Macro-F1 of 0.8582 under condition-holdout validation. Overall, the findings show that lightweight vibration–current early fusion provides a favorable robustness–complexity trade-off under operating-condition shift. However, the bearing-code-disjoint results indicate that complete generalization to unseen bearing identities remains challenging. Therefore, the deployment claims are limited to computational feasibility indicators, and further validation on embedded hardware, additional datasets, and stricter cross-domain protocols is required. Full article

We employ an empirical framework for real estate securities that incorporates portfolio optimization, return distribution tail diagnostics, risk metrics, modeling of long-range dependence in return volatility, regression against benchmark indices, and option pricing, treating these as necessary layers of a risk-management structure that concentrates on downside risk. Optimization compared mean–variance against downside-sensitive conditional value at risk. Tail behavior was assessed via skewness, kurtosis, and extreme value theory; volatility persistence was examined using ARMA–FIGARCH models. Benchmark dependence was examined via the capital asset pricing model (CAPM), employing endogenous and exogenous market proxies. Insurance instruments via European options were priced using a doubly subordinated normal inverse Gaussian pricing model capable of modeling skewed, heavy-tailed return distributions. Significant findings for the optimized portfolios include return distributions with losses that are heavier-tailed than gains; a transition in time from moderate-to-high long-range dependence in conditional volatility; smaller values of CAPM “alpha” and “beta” for minimum-risk portfolios compared to tangent portfolios; and significant implied volatility values. Full article

In both experimental and theoretical analyses of data, we often look to select a set of simple components that can be combined to create an appropriate model for the data. A convenient way to do this is to use quantile functions that can be added or transformed to obtain new distributions. In this work, we connect quantile statistics and mean absolute deviations (MADs) by deriving a general class of MAD-based shape metrics expressed as integrals of the quantile function, with a direct geometric interpretation. Our approach is applicable to distributions with finite mean that include many of the commonly used distributions, including those without a variance, such as the Pareto. When simple midpoint quadrature is used, the proposed metrics recover widely used quantile-shape metrics, including the interquartile range, Galton skewness, and Moore’s octile kurtosis as special cases. We further propose a C-Trapezoid quadrature approximation that combines cubic polynomial endpoint extrapolation with trapezoidal integration, achieving approximation errors that are significantly lower than those of the midpoint approximation for many common distributions. The proposed approximation provides simple-to-compute formulas for shape analysis and yields closed-form, non-iterative parameter-estimation formulas. These formulas are easy to compute and interpret, and they are applicable to a wide class of distributions, including those without an explicit cumulative distribution function or some with heavy tails. Unlike maximum likelihood estimation, the proposed method is more robust and has simple geometric interpretation. We illustrate the methodology with two detailed case studies. The proposed approach gives a simple way to quickly assess distributional shape without any specialized tools. Full article

Silicon carbide particle-reinforced aluminum matrix (SiCp/Al) composites are prone to surface defects during grinding owing to the heterogeneous deformation of the aluminum matrix and SiC particles, rendering conventional two-dimensional roughness parameters inadequate for precise surface characterization. In this study, three-dimensional surface roughness parameters were adopted to assess the ground surface quality of SiCp/Al composites. Orthogonal grinding experiments were carried out with four key process parameters (grinding wheel grit size, spindle speed, feed speed, and grinding depth), and the quantitative relationships between processing parameters and 3D roughness parameters, including arithmetical mean height (Sa), root mean square height (Sq), skewness (Ssk), kurtosis (Sku), surface bearing index (Sbi), core fluid retention index (Sci), and valley fluid retention index (Svi), were analyzed. The results reveal that the machined surface presents typical features including grooves from abrasive–matrix interaction, pits induced by SiC particle pull-out, scratches caused by dragged SiC particles, and tailing phenomena due to aluminum matrix melting under grinding heat. Grinding parameters exert distinct effects on surface topography: grinding wheel grit size shows the most significant influence on the Sa index, with its weight decreasing from 34% to 13% as grit becomes finer, while the combined influence weight of spindle speed, feed speed and grinding depth increases from 22% to 29%. Based on the comprehensive 3D roughness evaluation index, the optimal grinding parameter combination is determined as 320# grinding wheel, 4000 r/min spindle speed, 20 mm/min feed speed and 20 μm grinding depth. Additionally, the PSO-BP neural network achieves higher accuracy and better stability in predicting Sa and Sci than the conventional BP neural network. Full article

Intelligent fault diagnosis of rotating machinery is essential for manufacturing reliability and predictive maintenance, yet deployment of deep learning models is limited by data scarcity: fault samples are rare, costly, and hazardous to obtain. Conventional synthetic data methods such as Generative Adversarial Networks and Variational Autoencoders often exhibit mode collapse, spectral distortion, and limited physical interpretability. This work presents MechaForge, a multi-strategy framework that employs Large Language Models (LLMs) as physics-guided generators for bearing fault time-series data. The approach is grounded in bearing kinematics, Motor Current Signature Analysis (MCSA), and the interpretation of in-context learning as implicit Bayesian inference. Within MechaForge, four progressively constrained tracks are defined: a real-data baseline, few-shot LLM mimicry, multi-stage semantic reasoning, and physics-guided generation with constraints on root mean square, kurtosis, and fault-band spectral energy. For direct benchmarking, conventional VAE- and GAN-based augmentation baselines are additionally evaluated under the same dataset split, synthetic-data budget, downstream CNN architecture, and evaluation metrics. Experiments on the Paderborn bearing dataset show that the Basic LLM track achieves the strongest performance under the present protocol (0.7862 accuracy, 0.7648 macro-F1), exceeding the added VAE and GAN baselines (both 0.7428 accuracy; 0.7202 and 0.7257 macro-F1, respectively), while a control experiment confirms that synthetic data provides discriminative structure rather than labeled noise. These results indicate the promise of LLM-based diagnostic augmentation under data scarcity in the present Paderborn setting, rather than a definitive demonstration of broad transferability across fault-diagnosis scenarios. Full article

The response of temperature extremes to recent warming at the local scale remains uncertain because changes in mean temperature may be accompanied by changes in the shape of the temperature distribution. While higher mean temperatures generally lead to more frequent heat waves and fewer cold events, variations in higher-order statistical moments can either amplify or moderate these effects. This study examines how the probability distribution of 2 m temperature has evolved during the last 80 years in Greece using the ERA-5 reanalysis dataset. The evolution of the first four statistical moments (mean, standard deviation, skewness and kurtosis) and of the 5th and 95th percentiles of daily mean temperature is calculated by splitting the time series into eight decades, with each decade representing a separate climatology. A clear increase in mean temperature is observed across Greece. However, trends in the higher-order moments are more complex: the standard deviation and skewness exhibit positive and negative trends that depend on the region and the season, while kurtosis trends are weaker with a few regional exceptions. These changes alter the response of temperature extremes to warming, resulting in non-uniform shifts of the 5th and 95th percentiles. In mountainous regions, extreme cold events during winter and autumn have decreased more strongly than expected from mean warming alone, while in marine regions extreme warm events during summer and autumn have increased beyond what would be expected by a shift in the mean. In other areas, changes in the distribution shape lead to weaker extremes than those predicted by mean warming alone. These results highlight the role that changes in temperature variability have in modulating the evolution of temperature extremes under climate warming. Full article

To explore the regulatory mechanisms of different vegetation types on soil crust grain-size characteristics in sandy lands, this study focused on five typical plant species (Haloxylon ammodendron, Artemisia ordosica, Nitraria tangutorum, Agriophyllum squarrosum, and Phragmites australis) in an artificial vegetation restoration area on the northeastern edge of the Ulan Buh Desert. Using laser granulometry and graphical methods, we systematically determined the soil particle size composition and parameters of the crust (Layer A) and sub-crust (Layer B) layers, and analyzed their correlations with plant morphological parameters (crown width, plant height, basal diameter). The results showed that (1) different vegetation types significantly increased the content of soil fine particulate matter (silt and clay), with fine sand accounting for 42.85% and silt accounting for 23.64%; (2) there are significant differences in the impact of different vegetation types on particle size parameters. The average particle size of soil crust under Phragmites australis is the smallest (1.91), and the sorting is the worst (standard deviation 2.01). Under the vegetation type of Nitraria tangutorum, the average particle size of the soil crust layer is the largest (5.25), and the fractal dimension is the highest (2.46). (3) The crown width, plant height, and basal diameter of vegetation are negatively correlated with mean particle size, kurtosis, and fractal dimension (r= −0.62 to −0.45), and positively correlated with standard deviation and skewness (r = 0.51 to 0.68). (4) The frequency curve indicates that vegetation types broaden the distribution range of soil particles, and Phragmites australis and Artemisia ordosica exhibit bimodal characteristics. This study reveals the impact of vegetation restoration on soil grain size parameters in arid regions. These findings provide actionable strategies for optimizing vegetation configuration in actual desert restoration projects, notably proposing a “herbs first, shrubs follow” approach that can be directly applied to enhance restoration efficiency. Full article

Purpose: This study aims to evaluate the ability of radiomic features obtained from technetium-99m dimercaptosuccinic acid (Tc-99m DMSA) planar images to distinguish renal cortical uptake patterns among patients with chronic kidney disease (CKD). We also assessed the association between selected radiomic features and progressive renal function loss during follow-up. Methods: The study included a total of 185 patients: patients with Diabetes mellitus (DM) + hypertension (HTN) diagnosis (Group 1, n = 30), patients with HTN diagnosis alone (Group 2, n = 86), and patients with no history of DM or HTN who were followed for CKD (Group 3, n = 69). Intergroup comparisons were performed using the Kruskal–Wallis test with Bonferroni-corrected post hoc pairwise testing; the proportion of significantly different features was assessed using FDR correction. As a secondary exploratory analysis, the relationship between selected radiomic features and time to first observed ≥20% eGFR decline at follow-up was evaluated using univariate L2-penalised Cox proportional hazards regression with feature selection guided by the events-per-variable principle and model discrimination quantified using Harrell’s C-index. Results: Intensity Kurtosis values showed a statistically significant difference among the groups: −0.11 (−0.31 to 0.12) for Group 1, −0.24 (−0.41 to −0.04) for Group 2, and −0.33 (−0.45 to −0.16) for Group 3 (p = 0.001). Mean Intensity values were found to be 60.66 (31.01–89.39) in Group 1 and 90.46 (72.87–106.34) in Group 3 (p < 0.001). Age, gender, and baseline eGFR did not differ between groups. Radiomic analysis revealed significant intergroup differences predominantly in intensity- and texture-based features, while morphological features showed more limited differentiation. In the secondary exploratory longitudinal analysis, Centre of Mass Shift was the only morphological feature significantly associated with time to first observed ≥20% eGFR decline at follow-up (HR per SD: 0.74; 95% CI: 0.58–0.94; p = 0.015; C-index: 0.57). Conclusions: Radiomic features from Tc-99m DMSA planar images reveal quantitative differences between clinically defined CKD subgroups even when cortical uptake appears visually indistinguishable. The threshold-specific association of Centre of Mass Shift with subsequent eGFR decline, beyond baseline renal function, suggests that DMSA radiomics may provide exploratory prognostic information that warrants prospective validation. Full article

Skewness (S) and kurtosis (K) are statistical measures that provide insights into the characteristics of turbulence. This paper investigates the effects of adverse pressure gradients (APG) on S and K for mean and fluctuating velocities in the turbulent boundary layer (TBL), using the probability distribution function (PDF) and cumulative distribution function (CDF). The velocity distributions in the TBL are obtained experimentally. The experiments are conducted at Re ~ 1.12 × 10⁵. According to the Clauser criterion, the APG parameter is β = 0.62. Two test sections are examined: a straight duct (zero pressure gradient) and a straight diffuser with a divergence angle of 6° and a cross-sectional area ratio of 1:4. Measurements are performed at five streamwise stations (x/c = 1, 1.5, 2, 3, and 4, where c = 100 mm). The results show that the APG does not influence the maximum or minimum values of the PDFs for mean and fluctuating velocities. Compared to the third and fourth moments, variations in the first and second moments are minimal. It is found that S values for the straight duct are lower than those for the straight diffuser. The largest difference is observed in the fourth moment of the PDF, i.e., K. Additionally, four PDF curve-fitting equations are presented for the mean velocity and velocity fluctuations in the TBL for both the straight duct and the straight diffuser. Differential entropy analysis indicates that the decrease in entropy resulting from wall shear and the turbulent boundary layer in the straight channel is more pronounced than the reduction in mean velocity entropy caused by the APG in the diffuser channel. Full article