Search Results (411)

This paper aims to present a non-destructive, optimized variational mode decomposition (VMD)-based ground-penetrating radar (GPR) method developed for identifying void defects in reinforced concrete (RC) structures. This study also presents an enhanced framework for defect detection in RC by integrating advanced spectrum analysis with deep learning techniques. A GPR investigation was conducted on an RC bridge deck with known structural defects to generate a representative dataset reflecting both intact and void-defective conditions. In addition to conventional spectral techniques such as fast Fourier transform (FFT), spectrogram, and scalogram, an optimized variational mode decomposition (VMD) method was implemented. The VMD approach decomposes GPR signals into intrinsic mode functions, enabling refined feature extraction beyond traditional spectral methods and allowing clear differentiation between intact and defective signals. The limited availability and quality of GPR small datasets have restricted the application of a functional 1D-CNN which generally requires at least several hundred datasets. To address this challenge, a data augmentation strategy is adopted. FFT-based features were successfully utilized to train a one-dimensional convolutional neural network (1D-CNN) for automated defect identification. The results demonstrate that both the advanced spectrum-based approach and the hybrid framework combining spectral analysis with deep learning significantly improve defect detection performance. Overall, the proposed methodology provides an effective and intelligent solution to support timely, data-driven decision-making for maintenance and safety assurance of bridge infrastructure. Full article

This study introduces C-EMDNet, a nonlinear speech denoising approach that combines the adaptive decomposition capabilities of Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) and a deep convolutional architecture operating directly in the time-intrinsic mode function (IMF) domain. Unlike conventional enhancement methods that rely on fixed time–frequency representations, such as the short-time Fourier transform (STFT), the proposed approach interprets CEEMDAN IMFs as a morphological latent space that captures the multi-scale structure of speech. A U-Net-like network was trained to estimate mode-wise masks, enabling selective noise suppression while preserving the harmonic and formant structures. Experiments on standard noisy speech datasets show that C-EMDNet outperforms classical denoising algorithms and competitive deep learning baselines. These results highlight the promise of nonlinear morphological representations for an alternative framework speech enhancement. Full article

Foaming technology can effectively reduce the viscosity of polymer-modified asphalt and significantly decrease energy consumption during pavement construction, making it an effective approach for achieving low-carbon pavement construction and maintenance. However, mechanically foamed asphalt relies on specialized equipment and requires strict parameter control. Although water-based foaming methods using zeolites or ethanol can alleviate these issues to some extent, they still present disadvantages such as significant variability in foaming performance and potential risks during transportation and construction. Therefore, this study investigates the feasibility of using crystalline hydrates with high water of crystallization for micro-foamed asphalt. Three types of micro-foamed SBS-modified asphalt (MFPA) were prepared using hydrates with different contents of water of crystallization. Physical property tests, foaming characteristic parameters, viscosity–temperature analysis, Fourier transform infrared spectroscopy (FTIR), adhesion tensile tests, scanning electron microscopy (SEM), and fluorescence microscopy were conducted to evaluate their effects on the physical and chemical properties, viscosity reduction performance, adhesion, and compatibility of SBS-modified asphalt. Furthermore, dynamic shear rheometer (DSR) tests, bending beam rheometer (BBR) tests, fatigue life modeling, and morphological analysis were employed to investigate the rheological properties, fatigue life, and bubble evolution behavior of the MFPA system. The results indicate that utilizing the thermal decomposition characteristics of crystalline hydrates with high water of crystallization (Na₂SO₄·10H₂O, Na₂HPO₄·12H₂O, and Na₂CO₃·10H₂O) to release H₂O and CO₂ in SBS-modified asphalt for micro-foaming is a short-term reversible physical viscosity reduction process. The maximum expansion ratio (ERmax) of MFPA reaches 8–10, the half-life (HL) remains stable at approximately 180 s, and the foaming index (FI) peak is about 1160. The construction temperature can be reduced by 10–15%, and the viscosity reduction effect remains stable within 60 min. Compared with unfoamed SBS-modified asphalt, the compatibility, rutting resistance, and fatigue life of MFPA increase by approximately 65%, 32%, and 30%, respectively, while the low-temperature performance decreases by 18%. Under the same short-term and long-term aging conditions, MFPA exhibits better aging resistance. Specifically, its rutting resistance increases by 37%, and fatigue resistance improves by 30% compared with aged SBS-modified asphalt, while the low-temperature performance remains essentially unchanged. Full article

Harmonics and interharmonics have a significant impact on the safe operation of power systems, and accurately identifying interharmonics in power systems is the basis of harmonic suppression. The accuracy with which interharmonic components in power systems are detected is easily affected by mode aliasing and noise; to address this issue, a method of detecting them based on an adjusted Fourier-based synchrosqueezing transform (AFSST) and the three-point symmetric difference energy operator (DEO3S) is proposed. First, in order to reduce the influence of endpoint effects on detection accuracy, an improved waveform feature-matching extension method is utilized to reduce endpoint effects generated during the FSST decomposition process. Then, because it is difficult to adaptively determine the number of ridges in the FSST decomposition process, the energy difference and normalized cross-correlation coefficient are utilized as the criterion for determining the number of modal decompositions in the FSST, thereby improving the accuracy of the ridge number. Finally, using AFSST, the harmonic/interharmonic signals are decomposed into a set of intrinsic mode functions (IMFs). The instantaneous frequency and amplitude of each component are extracted using DEO3S, enabling the accurate detection of harmonics and interharmonics in the power system. Experimental analysis was conducted using simulation data, arc furnace experimental system data, and hardware experimental platform data. The results showed that the proposed method can accurately detect harmonic/interharmonic parameters under different levels of noise interference. Compared with the FSST, EMD, EEMD, and CEEMDAN methods, the amplitude detection accuracy of the proposed method is improved by 0.21%, 0.78%, 0.64%, and 0.75%, respectively, and the amplitude detection accuracy is improved by 1.39%, 3.31%, 2.04%, and 3.14%, respectively. Full article

Time–frequency (TF) characterization of electromyographic (EMG) bursts is essential for accurately assessing muscle function, particularly when the signals exhibit a high degree of nonstationarity. In this exploratory study, we investigated the temporal dynamics of the spectral components associated with short-latency EMG bursts using several TF analysis techniques. Specifically, we compared the performance and interpretability of spectrograms obtained via the short-time Fourier transform (STFT), the continuous wavelet transform (CWT), and noise-assisted multivariate empirical mode decomposition (NA-MEMD), applied to EMG signals recorded from the biceps femoris muscle of freely moving rats in an animal model of Parkinson’s disease, acquired using chronically implanted bipolar electrodes during treadmill locomotion. For each method, we evaluated its effectiveness in capturing transient variations in frequency content, the stability of extracted features across bursts, and the extent to which these features reflect physiologically meaningful aspects of muscle activation. The results show that TF approaches reveal complementary information about burst structure; NA-MEMD provides greater adaptability to nonlinear and nonstationary components, whereas STFT- and CWT-based representations offer more controlled and comparable analyses. Overall, these findings highlight the value of TF analysis as a methodological tool for evaluating muscle function and provide a solid foundation for selecting analytical strategies in studies where EMG bursts exhibit complex and highly variable spectral profiles. Full article

Self-supervised contrastive learning has recently shown promise for time-series representation learning, yet most existing methods treat sequences holistically and leave trend and seasonal components entangled, limiting their effectiveness for long-horizon multivariate forecasting. We study decomposition-aware representation learning for time-series forecasting without negative pairs. We propose the Trend-Season Contrastive Learner (TSCL), a Siamese framework that decomposes each series into trend, seasonality, and residual components, encodes trend and seasonality with dedicated encoders and a learnable Fourier layer, and optimizes a positive-pair contrastive objective over component-wise representations. Experiments on five public benchmarks (ETTh1, ETTh2, ETTm1, ETTm2, and Weather) show that TSCL consistently improves downstream forecasting across prediction horizons. Averaged over all datasets and horizons, TSCL achieves 0.489 MSE and 0.488 MAE, yielding an about 20–30% lower error than representative contrastive baselines (e.g., SimTS and CoST). Paired t-tests further confirm that the improvements are statistically significant in most settings. These results indicate that decomposition-aware contrastive learning yields robust and generalizable representations for long-horizon forecasting across diverse temporal resolutions. Full article

Understanding the thermal decomposition behavior and kinetic characteristics of blended biomass is crucial for optimizing thermochemical conversion processes. This study systematically investigates the synergistic pyrolysis (thermal decomposition) behavior of Salix psammophila (SP) and corn stover (CS) under a nitrogen atmosphere, with particular emphasis on process behavior and reaction kinetics (and thermodynamic feasibility). Based on elemental and proximate analyses, SP provides high calorific value and lignin content, while CS contributes high volatile matter and cellulose, enabling complementary interaction during thermal conversion. Three blending ratios (CS:SP = 2:1, 3:1, and 5:2) were analyzed using scanning electron microscopy (SEM), Fourier-transform infrared spectroscopy (FTIR), thermogravimetric analysis (TGA), and kinetic evaluation via the Coats–Redfern, Flynn–Wall–Ozawa (FWO), and Kissinger–Akahira–Sunose (KAS) methods, together with thermodynamic parameter estimation (ΔH, ΔS, and ΔG). The results indicate that the 3:1 blend forms an optimized “continuous phase–dispersed phase” structure with an interfacial transition layer of 11–15 μm and uniformly distributed fine pores, promoting effective heat and mass transfer and facilitating volatile-release pathways across the blend interface. At a heating rate of 15 °C·min⁻¹, this blend exhibits the lowest onset temperature of rapid mass loss (T_onset, 209 °C), the highest comprehensive pyrolysis performance index (S_N, 3.01), and stable DTG profiles. Kinetic analysis confirmed that the 3:1 blend exhibits the lowest activation energy during the devolatilization stage, indicating enhanced reaction feasibility under inert conditions. The results provide mechanistic insight into biomass blending effects and offer guidance for process optimization in inert-atmosphere thermochemical conversion systems. Full article

Aero engine surge diagnosis is a key technology in engine health management, and its diagnostic accuracy is of great significance for ensuring operational safety. Traditional threshold-based diagnostic methods are significantly affected by working conditions, which makes it difficult to achieve full working condition coverage. Moreover, due to issues such as varying feature thresholds across conditions, weak signal characteristics, and low identifiability, the diagnostic accuracy remains limited. To address these challenges, this paper proposes an STFF-CANet (Spatio-Temporal Feature Fusion Cross-Attentional Network) diagnosis model of aero engine surge based on spatio-temporal feature fusion. The model first employs a Convolutional Neural Network (CNN) to extract spatial features from the frequency domain of dynamic signals via Fast Fourier Transform (FFT). Simultaneously, a Bidirectional Long Short-Term Memory (BiLSTM) network is used to capture temporal features from signals optimized by Variational Mode Decomposition (VMD). A cross-attention mechanism is further introduced to achieve deep fusion of spatiotemporal features, thereby enhancing the capability to identify weak fault characteristics. In addition, the sliding window slice method is used to expand the sample size for the small sample fault data of the engine surge of an aero engine. This ensures both informational continuity between slices and statistical stability of features, effectively mitigating the difficulty of diagnosing early and weak surge characteristics under small-sample conditions. Experimental results demonstrate that the model achieves an F1-score, Recall, Precision, and Accuracy of 97.96%, 97.52%, 98.43%, and 99.01%, respectively, in surge fault classification. These outcomes meet the practical requirements for aero engine surge diagnosis and provide an effective solution for early fault warning in complex industrial equipment. Full article

Background/Objectives: Methicillin-resistant Staphylococcus aureus (MRSA) remains a major cause of both community-onset and hospital-acquired infections, yet longitudinal data from Latin American hospitals spanning the COVID-19 pandemic are scarce. We characterized temporal trends, seasonality, and the impact of the COVID-19 pandemic on MRSA prevalence and incidence density among clinical S. aureus isolates at a tertiary-care hospital in western Mexico over 9.5 years. Methods: We analyzed 6625 non-duplicate clinical S. aureus isolates (6609 with valid resistance data) from June 2016 to December 2025. Temporal trends were assessed using Mann–Kendall tests, Theil–Sen estimation, and binomial generalized linear models. Seasonality was evaluated through STL decomposition, generalized additive models, and Fourier analysis. An interrupted time series (ITS) model with GLS-AR(1) and Newey–West corrections compared three COVID-19 phases: pre-pandemic (2016–2020), high viral circulation (2020–2022), and post-peak stabilization (2022–2025). Exposure-adjusted incidence densities (per 1000 patient-days) were analyzed in parallel. Results: MRSA prevalence declined from 28.1% pre-pandemic to 14.0% post-peak (Mann–Kendall z = −9.03, p < 0.001; OR = 0.85 per year, 95% CI: 0.829–0.871). MRSA incidence density decreased by 50%, from 1.27 to 0.63 per 1000 patient-days, while aggregate S. aureus incidence density remained stable (z = −0.17, p = 0.868). The ITS joint Wald test confirmed a significant cumulative shift in MRSA trajectory post-pandemic (p = 0.019 counts; p = 0.012 incidence density), with a significant post-peak level drop (p = 0.008). S. aureus exhibited moderate seasonality peaking in May–July (GAM edf = 7.26, p < 0.001), whereas MRSA showed only marginal seasonal variation. Conclusions: MRSA declined markedly across the study period, with the steepest reduction following the Omicron peak. The decline persisted after adjustment for pandemic-related fluctuations in hospital volume, supporting periodic reassessment of empiric anti-MRSA prescribing policies in similar settings. Full article

This review article provides a systematic analysis of synthesis methods, structural characteristics, and functional properties of spinel-structured ferrite nanoparticles (MFe₂O₄). The physicochemical principles, advantages, and limitations of various synthesis techniques—including co-precipitation, combustion, sol–gel, thermal decomposition, hydrothermal, solvothermal, microwave-assisted, sonochemical, electrochemical, and solid-state reaction methods—are comparatively discussed. The influence of synthesis parameters on crystal structure, morphology, and cation distribution between tetrahedral and octahedral sites, as well as on magnetic, dielectric, and optical properties, is critically analyzed. Furthermore, the capabilities of characterization techniques such as X-ray diffraction (XRD), scanning electron microscopy with energy-dispersive spectroscopy (SEM/EDS), Fourier-transform infrared spectroscopy (FTIR), FT-Raman spectroscopy, dielectric measurements, and magnetic measurements for investigating spinel ferrites are comprehensively summarized. Finally, the high potential of spinel ferrite nanoparticles for applications in electronics, microwave devices, water treatment, catalysis, sensors, and biomedical fields is highlighted. Full article

Electromagnetic interference (EMI) rectification of grid-tied inverters is crucial for their practical application, and the variable frequency modulation (VFM) technique is a low-cost and simple way for EMI reduction. However, changes in loss and harmonic behaviors make it hard for parameter determination of VFM. In this paper, the parameters required for switching frequency (SF) function are determined for loss optimization of MOSFETs and inductors, while total harmonic distortion (THD) and temperature rise in MOSFETs and inductor core are constrained to guarantee the feasibility of the calculated parameters. Current transient is derived through multidimensional Fourier decomposition (MFD) and characteristics of Bessel function for loss estimation of MOSFET and inductor. Modified Steinmetz equation (MSE) is applied for core loss estimation and AC resistance is considered for copper loss estimation. With the constraints of THD and temperature, the loss optimization problem is solved by the augmented Lagrangian (AL) method. With the assistance of the proposed method, total loss optimization can be realized in feasible regions while the temperature rise in essential components can be restricted to the preset values. Full article

(1) Background: Electroencephalography (EEG) is widely used to monitor the depth of anesthesia; however, conventional Fourier-based analyses are limited in their ability to characterize non-stationary anesthetic-induced EEG dynamics. In this study, we investigated the utility of singular spectrum analysis (SSA) combined with the Hilbert transform for extracting physiologically meaningful EEG features under sevoflurane general anesthesia. (2) Methods: Frontal EEG data from ten patients undergoing sevoflurane anesthesia were analyzed from the maintenance phase through emergence. Using SSA, short EEG segments were decomposed into six intrinsic mode functions (IMFs) without pre-specified basis functions or frequency bands. Hilbert spectral analysis was applied to each IMF to obtain instantaneous frequency and amplitude characteristics. (3) Results: The SSA-based decomposition clearly captured phase-dependent EEG changes, including α spindle activity during maintenance and increasing high-frequency components preceding emergence. Multiple linear regression models incorporating IMF center frequencies and total power demonstrated strong correlations with the bispectral index (BIS), achieving high predictive accuracy (R² = 0.88, MAE < 4). Compared with conventional spectral approaches, SSA provided superior temporal resolution and stable feature extraction for non-stationary EEG signals. (4) Conclusions: These findings indicate that SSA combined with Hilbert analysis is a robust framework for quantitative EEG analysis during general anesthesia and may enhance real-time, individualized assessments of anesthetic depth. Full article

Fourier Decomposition (FD) and Koopman Mode Decomposition (KMD) are important tools for time series data analysis, applied across a broad spectrum of applications. Both aim to decompose time series functions into superpositions of countably many wave functions, with strikingly similar mathematical foundations. These methodologies derive from the linear decomposition of functions within specific function spaces: FD uses a fixed basis of sine and cosine functions, while KMD employs eigenfunctions of the Koopman linear operator. A notable distinction lies in their scope: FD is confined to periodic functions, while KMD can decompose functions into exponentially amplifying or damping waveforms, making it potentially better suited for describing phenomena beyond FD’s capabilities. However, practical applications of KMD often show that despite an accurate approximation of training data, its prediction accuracy is limited. This paper clarifies that this issue is closely related to the number of wave components used in decomposition, referred to as the degree of a KMD. Existing methods use predetermined, arbitrary, or ad hoc values for this degree. We demonstrate that using a degree different from a uniquely determined value for the data allows infinite KMDs to accurately approximate training data, explaining why current methods, which select a single KMD from these candidates, struggle with prediction accuracy. Furthermore, we introduce mathematically supported algorithms to determine the correct degree. Simulations verify that our algorithms can identify the right degrees and generate KMDs that can make accurate predictions, even with noisy data. Full article

The contents of biochemical components, such as theanine, tea polyphenols, water extract, and soluble sugar in Hakka stir-fried green tea (HSGT), serve as important indicators reflecting the intrinsic quality of tea leaves. In this study, 171 HSGT samples are collected, and their near-infrared spectroscopy (NIRS), together with the contents of the four indicators, are determined. The aim is to establish prediction models for these four indicators by extracting multiple features from the NIRS data. First, the NIRS data is preprocessed. Then, multiple features are extracted using competitive adaptive reweighted sampling (CARS), adaptive Fourier decomposition (AFD), fast Fourier transform (FFT), continuous wavelet transform (CWT), and band combination (BC). Finally, ridge regression (RR) and partial least squares regression (PLSR) models are constructed based on the NIRS features to predict the four indicators. Experimental results show that the model combining multiple features, namely CARS + AFD + BC, delivers the best overall performance. Specifically, the RR model based on multiple features provides the most accurate predictions for theanine, tea polyphenols, and soluble sugar, while the PLSR model performs better for water extract. This study provides a rapid and accurate method for detecting the substance content in HSGT. Full article

To enhance the anti-ultraviolet aging capacity of high-density polyethylene (HDPE) monofilaments for fishery applications, this study prepared pure HDPE and a blend of HDPE/UHMWPE (80/20 wt%) monofilaments via a melt spinning process. Systematic ultraviolet accelerated-aging experiments were conducted on these monofilaments for durations ranging from 0 to 600 h. The evolution of material properties was assessed using various quantitative characterization methods, including scanning electron microscopy (SEM), Fourier transform infrared spectroscopy (FTIR), thermogravimetric analysis (TGA), differential scanning calorimetry (DSC), and mechanical tensile testing. The results indicate that after 600 h of aging, the density and size of surface cracks in the blended monofilament are significantly lower than those observed in pure HDPE. The carbonyl index (CI) and unsaturated index (UI) of the blend are approximately 55% and 40% of those of pure HDPE, respectively. Additionally, the initial thermal decomposition temperature (T_5%), as determined by TGA, decreases by only 13 °C, which is a considerably lower reduction than the 28 °C observed for pure HDPE. Furthermore, the attenuation rates of breaking strength and elongation at break for the blended monofilament are 43.7% and 54.0%, respectively, which are markedly lower than the corresponding rates of 54.5% and 66.0% for pure HDPE. Research indicates that the observed performance improvement is closely linked to the synergistic mechanism of the “physical hindration–structural skeleton” formed by the UHMWPE phase. Furthermore, this mechanism may interact synergistically with the antioxidants present in the system, thereby altering the material’s failure mode from “rapid brittle failure” to “progressive slow deterioration.” This study offers novel modification strategies and experimental references for developing high-performance, UV-resistant polyolefin materials suitable for fishery applications. Full article