Abstract Objectives Prior research has established a correlation between heightened stress and a variety of health consequences. In addition, various measures of stress have been shown to be affected by mind–body practices. This secondary data analysis aimed to evaluate respiratory responses to acute cognitive stress in individuals of various meditation experience levels. Method Breath-to-breath interval (BBI) and respiration amplitude were recorded during four conditions using a piezoelectric chest belt. The effects of condition and group were evaluated using repeated measures ANOVA. The group x condition interaction of the non-meditator ( n = 15) and meditator ( n = 30) groups was the primary effect of interest. Results The group x condition interaction was not significant for BBI ( F [1.26,42.70] = 0.34, p = 0.61) or amplitude ( F [2.09,71.02] = 0.60, p = 0.56). Regarding BBI, the main effect of condition ( F [1.26,42.70] = 21.94, p < 0.01) was significant, while the main effect of group ( F [1,34] = 0.05, p = 0.83) was not significant. Regarding breath amplitude, the main effect of condition ( F [2.09,71.02] = 34.39, p < 0.01) was significant, while the main effect of group was not significant ( F [1,34] = 2.81, p = 0.10). Conclusions Meditators exhibited greater breath amplitude than non-meditators. Changes in BBI and breath amplitude across conditions did not differ significantly by group; however, the absence of a significant group × condition interaction should be interpreted cautiously given the secondary nature of the analysis and limited power to detect interaction effects. Preregistration This study is not preregistered.

Abstract As critical component of heavy-haul railway (HHR) bridge, the interlayer is designed to transfer vehicle load and dissipate residual oscillation. The increasing transport volumes and axle loads can degrade interlayer condition, thereby threatening the operation safety of HHR bridge. This paper proposes an innovative drive-by inspection methodology combining vertical axle box acceleration with a hybrid filtering approach for rapid interlayer damage detection in multi-span HHR bridges. The developed framework introduces a Hilbert-transform-based indicator, instantaneous amplitude quartic index (IAQI), to enhance damage localization accuracy. The hybrid filtering methodology integrates two components: (1) bandpass filtering targeting sleeper-passing frequency components to suppress track irregularity effects and enhance the interlayer damage detection efficiency; (2) a statistical diagnostic tool to diagnose whether the abnormal signal from the sleeper-related driving component of axle box acceleration is interlayer damage in multi-span HHR bridges. The feasibility of the proposed method is validated by numerical analyses and a field test of an 18-span HHR bridge. The analyses results indicate that the proposed method has good effectiveness and efficiency in detecting interlayer damage, even combined with beam damage, irregularity and noise. This research offers a new strategy to enhance the inspection efficiency and ensure the operation safety of HHR bridges.

Heart rate variability (HRV) comprises several components driven by various internal processes, the least understood of which is the ultra-low frequency (ULF) one. Recently published research has shown that the HRV frequency distribution in this range is bimodal. The main aims of this work were to verify this finding, to determine the basic characteristics of these two components and to analyze their potential physiological couplings. For this purpose, two components within the conventional ULF band (below 4 mHz) were extracted from HRVs of 25 patients with apnea using adaptive variational mode decomposition (AVMD) and continuous wavelet transform (CWT), and then analyzed with the Hilbert transform (HT), Savitzky–Golay filter, and empirical distributions of instantaneous amplitudes and frequencies. These studies have demonstrated the existence of both components in HRVs of all subjects and apnea groups: extremely low frequencies (ELFs) in the range of 0.01–0.4 mHz and narrowed ultra-low frequencies (nULFs) in the range of 0.1–4 mHz. The independence of both components is also shown. Concluding, heart rate variability is separately regulated by circadian rhythms (ELF bound) and ultradian fluctuations (nULF bound), which can be assessed by decomposing HRV, and the obtained components may be helpful to better understand the underlying homeostatic mechanisms, as well as in the long-term monitoring of patients.

Satellite clock bias exhibits complex, time-varying periodic characteristics due to environmental disturbances. Accurate modeling and prediction of periodic terms play a crucial role in improving the precision and stability of short-term predictions. Traditional models such as spectral analysis model (SAM) estimate the frequency, amplitude, and phase of periodic terms through global fitting, which limits their ability to adapt to abrupt changes at the prediction boundary. To address this limitation, this paper proposes an improved spectral analysis model (IM-SAM) based on the inaction method (IM). The model employs IM to extract the instantaneous frequency, amplitude, and phase parameters of periodic terms precisely at the data endpoint, and utilizes the parameters of periodic terms at the data endpoint for prediction, effectively suppressing periodic fluctuations in prediction errors. Experimental results based on real GPS clock bias data demonstrate that the root mean square (RMS) of IM-SAM prediction errors is reduced by 19.14%, 14.39%, and 10.48% for 3 h, 6 h, and 12 h prediction tasks, respectively, compared with SAM. Furthermore, a kinematic precise point positioning experiment was performed using IM-SAM-predicted clock products and compared with the predicted half of IGS ultra-rapid clock products. The RMS of position error was reduced by 14.3%, 12.6%, and 7.9% in the east, north, and up directions, respectively. These results demonstrate the practical effectiveness and accuracy of IM-SAM in real-time clock prediction and GPS positioning applications.

Over recent times, interconnectivity has increased in modern power transmission networks, requiring intelligent systems for fault detection, which require accurate operational stability and reliable power delivery. Classifying and identifying different fault types under various system conditions is challenging due to the complex, nonlinear, and dynamic nature of fault signals. This publication suggests an intelligent fault analysis and layout in power transmission networks to offer efficiency in fault detection and categorization at the line level in various operating conditions. With the help of the electrical fault detection and classification dataset, the framework provides information about preprocessing techniques, addressing the problem of the missing values with the assistance of multivariate imputation by chained equations (MICE) as well as feature normalization with Rank Gauss Normalization that improves the stability of the model. Hilbert–Huang Transform (HHT) is used to obtain instantaneous frequency and amplitude characteristics of electrical fault signals that characterize nonstationary and nonlinear properties of waveforms. Lastly, SqueezeNet1D with Informer (Squeeze-Informer) model is trained on the features to conduct predictions on the faulty transmission lines — which also involved the prediction of the severity of the types of faults and changing conditions of the network — before and after the fault. The findings derived through the experiment support the claim that the framework offers an accuracy of 99.22%, recall of 99.14%, and precision and F1-score of 99.03, which is why it is an effective tool to use for intelligent monitoring and protection of power transmission infrastructure.

Abstract As the demand for flexible operation in pumped storage power stations increases, understanding the pressure pulsation behaviour of pump-turbines under complex conditions becomes crucial for ensuring operational stability. This study focuses on the temporal and spatial evolution characteristics of pressure pulsation in a reversible pump-turbine under design working condition in pump mode. A time-space analysis method combining computational fluid dynamics (CFD) and variational mode decomposition was proposed to extract and visualize key pulsation features. The results show that high-frequency pulsations induced by rotor-stator interaction are dominant in the vaneless region and propagate both upstream and downstream. By decomposing broadband signals into mode components and mapping their instantaneous amplitude and phase, the spatial structure and propagation paths of pressure waves were clearly identified. The study reveals that different operating modes exhibit distinct pulsation sources and energy distributions, and that coupling between time and spatial phase plays a critical role in flow instability. These findings provide new insights into the mechanism of pressure pulsation in pump-turbines and offer guidance for improving flow control strategies and structural reliability in large-scale pumped storage systems.

Abstract The start-up of the pump-storage unit is widely recognized as one of the most critical operating conditions due to the high instantaneous amplitudes of pressure pulsations in the vaneless zone of the pump-turbine. These transient pressure pulsations generate significant dynamic forces on the runner, leading to substantial damage. Previous studies have primarily focused on the unsteady mechanisms of pressure pulsations during start-up cycles, often overlooking the importance of predicting pressure pulsations using existing measurements. This study predicts the amplitude-frequency characteristics of the pressure pulsations in the vaneless zone of the pump-turbine based on experimental data. The main components of the pressure pulsation including the blade-passing frequencies and the low frequencies are considered. The transient pressure pulsations in short durations assumed to represent steady-state pressure pulsations on the characteristic curves, achieving the final prediction. In the case study, only a large RMSE (0.7 m) was found between the prediction and the measurement over a short duration in the frequency domain, the prediction successfully captures the variation tendencies of main components in both time and frequency domains, demonstrating the effectiveness of the approach.

Multi-domain feature extraction and Sand Cat Swarm Optimized Broad Learning System for EEG-based Motor Imagery decoding in stroke patients.

3D reconstruction of root system architecture in urban forest parks based on ground penetrating radar instantaneous amplitude analysis

Abstract The objective of this study was to explore the electromagnetic wave reflection characteristics of soil-rock mixtures. Using the FDTD-based gprMax software, we developed numerical models incorporating multiple survey lines to analyzes the impacts of three factors—dielectric constant, conductivity, and water content—on electromagnetic echo characteristics through forward modeling with a focus on wave forms and the electromagnetic wave propagation process were examined to gain deeper insights. Results showed that the propagation time of an electromagnetic wave increased with increasing dielectric constant; meanwhile, when the dielectric constant remained unchanged, changes in conductivity did not impact the reflected waveform, radar wave propagation time, or directly coupled wave, though increased conductivity decreased the amplitude of the reflected wave. The echo characteristics were pronounced in water-rich areas, observing high values of instantaneous amplitude. These findings provide a reference for advancing ground-penetrating radar applications in nondestructive testing and enhancing forward modeling accuracy.

Psychotic syndromes, such as schizophrenia (SCZ) and bipolar disorder (BD), significantly disrupt brain electrical activity, with functional connectivity (FC) being particularly affected. However, FC is often estimated as a static measure, overlooking the brain dynamic fluctuations that naturally occur, even at rest. In this study, we investigated alterations in dynamic FC (dFC) using resting-state electroencephalographic (EEG) data from SCZ patients, BD patients, and age-matched healthy control (HC) subjects. To achieve this, the instantaneous amplitude correlation (IAC) was computed for each EEG recording within the canonical frequency bands. We then analyzed the first- to fourth-order cumulants of the average strength (aS) time series derived from the IAC matrices. Statistically significant differences were obtained between the SCZ and HC groups in aS mean (first-order cumulant) and aS skewness (third-order cumulant) at the gamma band, while the BD group reported differences against the HC group in aS mean at the delta band. Additionally, both disorders exhibited altered aS skewness in the beta band; these findings suggest disruptions in interneuronal communication, manifesting as "pathologically Gaussian" aS distributions over time. Our results highlight the potential of dFC analysis to uncover brain function anomalies that remain undetected with conventional approaches.

ObjectivesSeizure unpredictability remains a major clinical challenge for people with epilepsy. Previous works have shown that seizure risk is associated with circadian and multi-day cycles in both brain and physiological signals. However, it remains unclear whether neural activity from deep brain structures such as the anterior nucleus of the thalamus (ANT), the only FDA-approved deep-brain stimulation (DBS) target for treating medication-resistant epilepsy, exhibits similar cyclic modulation related to seizures. This study aimed to assess whether long-term local field potential (LFP) recordings from the ANT exhibit circadian and multi-day cycles that are associated with seizures that could be used to forecast seizure risk in a retrospective approach.MethodsSeven participants implanted with the Medtronic Percept PC system for ANT-DBS underwent continuous at-home LFP recording of the theta/alpha (4-12 Hz) and self-reported seizure logs. Wavelet and Hilbert transforms were used to identify rhythmic cycles in LFPs. Circular statistics quantified seizure phase-locking to LFP cycles and patterns estimated from seizure diaries. Gaussian process regression (GPR) models were trained using the instantaneous phase and amplitude of these cycles to forecast short-term seizure risk.ResultsAll participants exhibited circadian and multi-day cycles in their ANT LFPs, with seizures significantly phase-locked to some of these cycles. Seizure risk forecasting using LFP cycles achieved performance above chance (mean AUROC: 0.63 [0.57–0.69]). Incorporating the instantaneous cycle amplitude modestly improved prediction in some cases. Moreover, a substantial, though non-significant, positive correlation between circadian cycle power and seizure frequency was found in most participants, suggesting an elevated seizure risk when circadian cycles are stronger.SignificanceThis study demonstrates that long-term LFP recordings from the ANT reflect rhythmic brain activity linked to seizure risk and may support seizure forecasting. Future studies should explore multi-modal approaches that incorporate both the phase and amplitude of cycles to improve prediction accuracy.

Alzheimer's Disease (AD) is a chronic neurological disorder that impairs the cognitive and behavioral abilities of older people. Early detection and treatment are crucial for minimizing the progression of the disease. Electroencephalogram (EEG) makes it possible to investigate the brain activities linked to various forms of disabilities experienced by individuals with AD. Nevertheless, the EEG signals are non-linear and non-stationary in nature making it difficult to retrieve the concealed information from the EEG signals. Therefore, a Fourier Decomposition Method (FDM) and Hilbert Transform (HT) based EEG signals analysis (FHESA) method is developed in this paper for the automated detection of AD. The FHESA method aims to efficiently analyze the EEG data to identify the important brain regions vulnerable to AD, and to assess the impact of various EEG channels for the timely and early detection of AD. The proposed FHESA method is divided into three primary stages. The first stage deals with the decomposition of the EEG signals into a finite number of Fourier Intrinsic Band Functions (FIBFs). In the second stage, HT is applied to all FIBFs to obtain instantaneous amplitude, frequency, and phase, that are then used to construct feature vectors. In the last stage, various Machine Learning (ML) algorithms are used to classify these feature vectors for efficient AD detection. Two distinct data sets are employed to assess the effectiveness of the proposed FHESA method. The outcome demonstrates that with dataset-I and dataset-II, the proposed methodology can detect AD with 98% and 99% accuracy, respectively. The performance of the proposed FHESA method is compared to other state-of-the-art methods used for AD detection. The promising results show that the proposed FHESA method can assist neurological experts in identifying and utilizing EEG signals for AD detection.

Abstract China's Chang'E‐4 probe successfully soft‐landed on the lunar far side at Von Kármán crater in January 2019. Onboard, the Lunar Penetrating Radar (LPR) detected subsurface structures and properties such as dielectric permittivity, aiding in our understanding of regolith composition and origin. This study introduces an automatic method for estimating dielectric permittivity using radar diffraction focusing analysis. While developed from traditional seismic analysis, the method is tailored for LPR data by incorporating random noise removal, entropy‐based focusing, and lunar‐specific parameter optimization to address the challenges of noise interference, complex diffraction overlapping and applicability to LPR data. Applied to Chang'E‐4's first 42 lunar‐day data, this method revealed subsurface structure and permittivity distributions. Combined with instantaneous amplitudes, centroid frequencies, and geological features, we present a preferred geological interpretation. Our findings suggest that, after experiencing the latest basaltic magma intrusion and subsequent weathering, the region underwent multiple episodes of high ilmenite content ejecta deposition at a depth of 20–33 m. This was followed by several impacts, leaving craters visible today. Subsequently, the area was overlain by at least two ejecta layers with low ilmenite content at a depth of 13–25 m. Later, two meteoroids struck the paleo‐surface; the larger one created the most prominent irregular crater on the current surface. Thereafter, ejecta from nearby craters such as Finsen and Von Kármán L covered the area, weathering into the current lunar regolith. Our inversion results demonstrate high reliability, align with previous studies and geological context, and can offer methodological and empirical insights for future planetary missions.

Abstract We develop a physico-mathematical model of the “eddy current probe – test object” (ECP–TO) system that analytically describes current dynamics in coupled probe circuits while accounting for key physical and electrical parameters. The model, derived from the characteristic equation of transformer-type configurations for nonmagnetic and magnetic targets, explains when the measured response is harmonic or damped harmonic as a function of excitation mode and system parameters, thereby revealing additional informative features for material evaluation. We validate the model numerically using finite element (FEM) simulations (COMSOL, Magnetic Fields, frequency domain) and introduce a digital signal-processing workflow that extracts instantaneous amplitude- and phase-time characteristics during scanning. Experiments on aluminum alloy specimens demonstrate sensitivity to small conductivity variations and identify optimal excitation frequencies for reliable subsurface defect detection; in the tested configuration, an “infinitely deep crack” was detectable to 15.3 mm at 50 Hz. The combined analytical–numerical–experimental approach supports sensitivity-driven ECP design, accelerates inspection parameter selection, and facilitates integration with structural health monitoring (SHM) systems for aerospace structures.

Electrocardiogram (ECG) signals contain cardiopulmonary information that can facilitate sleep apnea detection. Traditional methods rely on extracting numerous ECG features, which is labor-intensive and computationally cumbersome. To reduce feature complexity and enhance detection accuracy, we propose a spectral feature-based approach using single-lead ECG signals. First, the ECG signal is preprocessed via ensemble empirical mode decomposition combined with independent component analysis (EEMD-ICA) to identify the most representative intrinsic mode function (IMF) based on the maximum instantaneous frequency in the frequency domain. Next, Hilbert transform-based time-frequency analysis is applied to derive the component's 2D time-frequency spectrum. Finally, three spectral features-maximum instantaneous frequency (femax), instantaneous frequency amplitude (V), and marginal spectrum energy (S)-are quantitatively compared between normal and sleep apnea populations using an independent-sample t-test. These features are classified via a random forest machine learning model. The femax and IMF7 components of the reconstructed signal exhibited statistically significant differences (p < 0.001) between normal and sleep apnea subjects. The random forest classifier achieved optimal performance, with 92.9% accuracy, 86.6% specificity, and 100% sensitivity. This study demonstrates that spectral features derived from single-lead ECG signals, combined with EEMD-ICA and time-frequency analysis, offer an efficient and accurate method for sleep apnea detection.

In this research, a biometric speech coding method is developed where empirical wavelet transform is used to extract biometric features of speech signals for voice identification of the speaker. This method differs from existing methods because it uses a set of adaptive bandpass Meyer wavelet filters and Hilbert spectral analysis to determine the instantaneous amplitudes and frequencies of internal empirical modes. This makes it possible to use multiscale wavelet analysis for biometric coding of speech signals based on an adaptive empirical wavelet transform, which increases the efficiency of spectral analysis by 1.2 times or 14 % by separating high-frequency speech oscillations into their low-frequency components, namely internal empirical modes. Also, a biometric method for encoding speech signals based on mel-frequency cepstral coefficients has been improved, which uses the basic principles of adaptive spectral analysis using an empirical wavelet transform, which also significantly improves the separation of the Fourier spectrum into adaptive bands of the corresponding formant frequencies of the speech signal.

The rapid development of direct current (DC) grids poses significant challenges to the speed, reliability, and selectivity of fault protection systems. These systems are required to identify and distinguish between internal and external faults despite the constraints of limited information and time. This study introduces a non-unit protection scheme based on the classification of two-dimensional feature parameters utilizing the Hilbert–Huang transform (HHT) and a support vector machine (SVM). Through time–frequency analysis of the voltage waveform following DC faults, critical information within the high-frequency component of the fault voltage, specifically, the instantaneous frequency and amplitude of the wavefront, is extracted to distinguish internal from external faults. Two-dimensional feature parameters are associated with signal attenuation and distortion during fault propagation via the transmission path, thereby providing a foundation for precise fault identification. The employment of an SVM ensures the selectivity of this scheme without relying on protection settings. The efficacy of the scheme is validated through simulations conducted using PSCAD/EMTDC across various fault scenarios.

In this article, a novel framework is proposed for damage detection and localisation using vehicle scanning. Unlike traditional vibration-based approaches that rely on modal parameters, the proposed method focuses on the vehicle’s response to the static displacements of a bridge. The article demonstrates that structural damage has a more significant effect on the bridge’s static response than on its dynamic behaviour. An analytical solution is derived for the vehicle’s response, revealing a dominant harmonic component at the vehicle’s natural frequency. Leveraging this insight, a novel damage index is introduced: the absolute difference between the instantaneous amplitudes of vehicle responses at the vehicle’s natural frequency when crossing damaged and undamaged bridges. The proposed damage index is shown to be robust against road roughness effects, provided the roughness profile remains constant. Parametric analyses confirm the framework’s ability to accurately detect and locate damage across varying damage levels, positions, noise conditions, and bridge stiffness. By overcoming the limitations of modal parameter-based methods, this work offers a scalable and efficient solution for bridge health monitoring, advancing the real-world applicability of vehicle scanning methods.

A stochastic model for a superposition of uncorrelated pulses with a random distribution of and correlations between amplitudes and velocities is analyzed. The pulses are assumed to move radially with fixed shape and amplitudes decreasing exponentially in time due to linear damping. The pulse velocities are taken to be time-dependent with a power law dependence on the instantaneous amplitudes, as suggested by blob velocity scaling theories. In accordance with experimental measurements, the pulse function is assumed to be exponential, and the amplitudes are taken to be exponentially distributed. As a consequence of linear damping and time-dependent velocities, it is demonstrated that the pulses stagnate during their radial motion. This makes the average pulse waiting time increase radially outwards in the scrape-off layer of magnetically confined plasmas. In the case that pulse velocities are proportional to their amplitudes, the mean value of the process decreases exponentially with radial coordinate, similar to the case when all pulses have the same, time-independent velocity. The profile e-folding length is then given by the product of the average pulse velocity and the parallel transit time. Moreover, both the average pulse amplitude and the average velocity are the same at all radial positions due to stagnation of slow and small-amplitude pulses. In general, an increasing average pulse velocity results in a flattened radial profile of the mean value of the process as well as a higher relative fluctuation level, strongly enhancing plasma–surface interactions.