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In present work, the heart rate variability (HRV) characteristics, calculated by sample entropy (SampEn), were used to analyze the driving fatigue state at successive driving stages. Combined with the relative power spectrum ratio β/(θ + α), subjective questionnaire, and brain network parameters of electroencephalogram (EEG) signals, the relationships between the different characteristics for driving fatigue were discussed. Thus, it can conclude that the HRV characteristics (RR SampEn and R peaks SampEn), as well as the relative power spectrum ratio β/(θ + α) of the channels (C3, C4, P3, P4), the subjective questionnaire, and the brain network parameters, can effectively detect driving fatigue at various driving stages. In addition, the method for collecting ECG signals from the palm part does not need patch electrodes, is convenient, and will be practical to use in actual driving situations in the future.

Hail is one of the typical manifestations of severe convective weather, characterized by its sudden onset and strong localization. In this study, a compact all-fiber coherent Doppler lidar (CDL) working at the 1.5 μm wavelength is employed to detect a hail event. Combined with ERA5 reanalysis data, Parsivel2, and cloud-type products from the Fengyun satellite, the synoptic background of the hail event was analyzed. Owing to its high-precision spectrum measurement capability, the CDL can effectively separate the multi-component power spectra of precipitation particles. By comparing particle velocity, spectrum width and skewness as characteristic parameters from signal separation across light rain, hail and heavy rain, the distinctive power spectrum characteristics of hail were identified. This study verifies that CDL can provide high-spatiotemporal-resolution data support for the short-term forecasting of hail events.

Depressive states in both healthy individuals and those with major depressive disorder exhibit differences primarily in symptom severity rather than symptom type, suggesting that there is a spectrum of depressive symptoms. The increasing prevalence of mild depression carries lifelong implications, emphasizing its clinical and social significance, which parallels that of moderate depression. Early intervention and psychotherapy have shown effective outcomes in subthreshold depression. Electroencephalography serves as a non-invasive, powerful tool in depression research, with many studies employing it to discover biomarkers and explore underlying mechanisms for the identification and diagnosis of depression. However, the efficacy of these biomarkers in distinguishing various depressive states in healthy individuals and in understanding the associated mechanisms remains uncertain. In our study, we examined the power spectrum density and the region-based phase-locking value in healthy individuals with various depressive states during their resting state. We found significant differences in neural activity, even among healthy individuals. Participants were categorized into high, middle, and low depressive state groups based on their response to a questionnaire, and eyes-open resting-state electroencephalography was conducted. We observed significant differences among the different depressive state groups in theta- and beta-band power, as well as correlations in the theta–beta ratio in the frontal lobe and phase-locking connections in the frontal, parietal, and temporal lobes. Standardized low-resolution electromagnetic tomography analysis for source localization comparing the differences in resting-state networks among the three depressive state groups showed significant differences in the frontal and temporal lobes. We anticipate that our study will contribute to the development of effective biomarkers for the early detection and prevention of depression.

This paper delineates the procedure for crack detection in helical spring installed on a vibratory feeder using operational modal analysis technique. The variations in modal parameters were identified using vibration data acquired in the form of auto-power spectrum (APS) and cross-power spectrum (CPS) during normal operation of the vibratory feeder. In the second step, the crack was induced in one of the springs of the vibratory feeder, and APS and CPS were acquired during its operating conditions. Modal parameters were recorded through a dynamic signal analyzer in the form of auto-power spectra (APS) and cross-power spectra (CPS) in healthy as well as cracked helical springs installed in the vibratory feeder during operating conditions. The variations in modal parameters, which include vibrating frequencies, operating deflecting shapes and values damping ratios (%), MAC values, CoMAC values, contours and Bode plots were compared. Observations of these parameters were used to predict the failures in the spring of vibratory feeder.

The second reprocessing of all GPS data gathered by the Analysis Centers of IGS was conducted in late 2013 using the latest models and methodologies. Improved models of antenna phase center variations and solar radiation pressure in JPL’s reanalysis are expected to significantly reduce errors. In an earlier work, JPL estimates of position time series, termed first reprocessing campaign, were examined in terms of their spatial and temporal correlation, power spectra, and draconitic signal. Similar analyses are applied to GPS time series at 89 and 66 sites of the second reanalysis with the time span of 7 and 21 years, respectively, to study possible improvements. Our results indicate that the spatial correlations are reduced on average by a factor of 1.25. While the white and flicker noise amplitudes for all components are reduced by 29–56 %, the random walk amplitude is enlarged. The white, flicker, and random walk noise amount to rate errors of, respectively, 0.01, 0.12, and 0.09 mm/yr in the horizontal and 0.04, 0.41 and 0.3 mm/yr in the vertical. Signals reported previously, such as those with periods of 13.63, 14.76, 5.5, and 351.4 /  n for n = 1 , 2 , … , 8  days, are identified in multivariate spectra of both data sets. The oscillation of the draconitic signal is reduced by factors of 1.87, 1.87, and 1.68 in the east, north and up components, respectively. Two other signals with Chandlerian period and a period of 380 days can also be detected.

Analytical contact mechanics theories depend on surface roughness through the surface roughness power spectrum. In the present study, we evaluated the usability of various experimental methods for studying surface roughness. Our findings indicated that height data obtained from optical methods often lack accuracy and should not be utilized for calculating surface roughness power spectra. Conversely, engineering stylus instruments and atomic force microscopy (AFM) typically yield reliable results that are consistent across the overlapping roughness length scale region. For surfaces with isotropic roughness, the two-dimensional (2D) power spectrum can be derived from the one-dimensional (1D) power spectrum using several approaches, which we explored in this paper. Graphical Abstract

As the evaluation accuracy of operational transfer path analysis (OTPA) is seriously affected by crosstalk effect, OTPA with crosstalk cancellation based on least variance spectrum estimation with stronger anti-noise performance is proposed in this paper. Firstly, to overcome the insufficient variance performance of conventional methods, the least variance spectrum estimation method is proposed to obtain more stable power spectrum. Secondly, after Burg algorithm is used to determine the optimal iteration times, the prior transmissibility can be calculated from acquired power spectra. Thirdly, reference signals without crosstalk components can be obtained by solving the equations, which eliminates crosstalk effect of OTPA. Finally, according to numerical case studies on a cylindrical shell simulation system and experimental case studies on a shell testbed, the performance of the proposed method is comparatively studied with conventional methods. Generally, the proposed method can increase the reliability and accuracy of transfer path identification and contribution evaluation of OTPA, which can benefit vibration monitoring, reduction and control of mechanical systems.

Decimetric continua are commonly observed during long-lasting solar flares. Their frequency boundaries vary with time. We studied frequency boundary variations using the power spectrum analysis. Analyzing five decimetric continua, we found that their power spectra have a power-law form with the power-law index close to the Kolmogorov turbulence index −5/3. The same power index was also found in the power spectra of radio flux variations at frequencies in the range of the frequency boundary variations. Moreover, these frequency boundary variations were highly correlated with the radio flux ones. We interpret these results to be due to turbulent density variations in the reconnection plasma outflow to the termination shock formed above flare loops. In three cases of decimetric continua, we estimated the level of the plasma density turbulence to be 7.6 – 11.2% of the mean plasma density. We think that the analysis of variations of decimetric continua can be used in studies of the plasma turbulence in solar flares.

The extreme value theory has been object of engineering studies for more than a century. The analysis of extreme winds plays a key role for complex civil structures and a driving role in different stages of wind turbines lifetime. Most of extremes probability models depend on the annual rate of independent events (ARIE) which has been traditionally considered a constant value. The authors have embraced a recent belief considering the ARIE as a function of the wind velocity. Even though a certain agreement has been achieved across the researches, some issues are still pending. In this regard, the paper shows that the annual, seasonal and daily fluctuations embedded in time series of the mean wind speeds, constrain its probability distribution and time correlation to be physically consistent. Besides, a new physical interpretation of the ARIE is presented, expressing how the independence across wind observations increases with the wind speed, up to the point that all yearly observations are independent if larger than a suitable speed value. Such a tendency is not revealed if the annual, seasonal and daily fluctuations are excluded by the analysis, leading to a deceitful shape of the ARIE. Finally, the paper shows how the velocity‐dependent ARIE model is consistent with the conventional asymptotic extreme value theory, if a sufficiently large left‐censorship applies to the dataset. The study of the ARIE presented in this paper is based on long‐term Monte Carlo simulation of the mean wind speed.

Supercooled water in mixed-phase clouds plays a significant role in precipitation formation, atmospheric radiation, weather modification, and aircraft flight safety. Identifying supercooled water in mixed-phase clouds is a crucial-frontier scientific issue in atmospheric detection research. In this study, we propose a new algorithm for identifying supercooled water based on the multi-spectral peak characteristics in cloud radar power spectra, combined with radar reflectivity factor and mean Doppler velocity. Using microwave radiometer data, we conducted retrieval analyses on two stratocumulus cases in the spring over the northeastern Daxing’anling region, China. The retrieval results show that the supercooled water in the spring stratocumulus clouds over the region is widespread, with liquid water content (LWC) ranging around 0.1 ± 0.05 g/m3, and particle sizes not exceeding 10 μm. The influence of updrafts on supercooled water is evident, with both showing good consistency in spatiotemporal variation trends. Comparing the liquid water path (LWP) variations retrieved from cloud radar and microwave radiometer, both showed good consistency in variation trends and high LWC areas, indicating the reliability of the identification algorithm developed in this study.