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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.

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.

Fourier spectra are powerful tools to analyze the scale behavior of turbulent flows. While such spectra are mathematically based on regular periodic data, some state‐of‐the‐art ocean and climate models use unstructured triangular meshes. Observational data is often also available only in an unstructured fashion. In this study, scale analysis specifically for the output of models with triangular meshes is discussed and the representable wavenumbers for Fourier analysis are derived. Aside from using different interpolation methods and oversampling prior to the computation of Fourier spectra, we also consider an alternative scale analysis based on the Walsh–Rademacher basis, that is, indicator functions. It does not require interpolation and can be extended to general unstructured meshes. A third approach based on smoothing filters which focus on grid scales is also discussed. We compare these methods in the context of kinetic energy and dissipation power of a turbulent channel flow simulated with the sea ice‐ocean model FESOM2. One simulation uses a classical viscous closure, another a new backscatter closure. The latter is dissipative on small scales, but anti‐dissipative on large scales leading to more realistic flow representation. All three methods clearly highlight the differences between the simulations as concerns the distribution of dissipation power and kinetic energy over scales. However, the analysis based on Fourier transformation is highly sensitive to the interpolation method in case of dissipation power, potentially leading to inaccurate representations of dissipation at different scales. This highlights the necessity to be cautious when choosing a scale analysis method on unstructured grids. Plain Language Summary To better understand the physical processes that drive and define the circulation in our oceans, it is necessary to analyze the temporal and spatial scales on which these processes act. The classical method to investigate the spatial scale behavior is Fourier analysis which splits any given data into waves of different amplitudes and wavelengths. Mathematically this requires data on an equidistantly spaced grid. However, many ocean models apply triangular or other irregular grids for their computations of oceanic flows. In this study, we describe the advantages and disadvantages of applying Fourier analysis for models that use triangular meshes, with prior interpolation of data to regularly spaced rectangular meshes. We also introduce two other methods that can analyze the distribution of kinetic energy and kinetic energy dissipation across scales without interpolation. The results show that one needs to be very careful when choosing a specific scale analysis and, potentially, an interpolation method for triangular grids, especially when it comes to analyzing the process of kinetic energy dissipation. Key Points Three different scale analysis methods for unstructured triangular grids are presented and discussed Fourier spectra after interpolation of fields should be applied with caution especially for dissipation power spectra Scale analysis via indicator functions does not rely on interpolation and can be applied to non‐smooth, unstructured data

The neuromuscular junction (NMJ) has an elaborate anatomy to ensure agile and accurate signal transmission. Based on our formerly obtained expressions of the thermal and conductance induced voltage fluctuations, in this paper, the mechanisms underlying the conductance-induced voltage fluctuation are characterized from two aspects: the scaling laws with respect to either of the two system-size factors, the number of receptors or the membrane area; and the “seesaw effect\" with respect to the intensive parameter, the concentration of acetylcholine. According to these mechanisms, several aspects of the NMJ anatomy are explained from a denoising perspective. Finally, the power spectra of the two types of voltage fluctuations are characterized by their specific scaling laws, based on which we explain why the endplate noise has the low-frequency property that is described by the term “seashell sound\".

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.

The rescaling of rainfall requires measurements of rainfall rates over many dimensions. This paper develops one approach using 10 m vertical spatial observations of the Doppler spectra of falling rain every 10 s over intervals varying from 15 up to 41 min in two different locations and in two different years using two different micro-rain radars (MRR). The transformation of the temporal domain into spatial observations uses the Taylor “frozen” turbulence hypothesis to estimate an average advection speed over an entire observation interval. Thus, when no other advection estimates are possible, this paper offers a new approach for estimating the appropriate frozen turbulence advection speed by minimizing power spectral differences between the ensemble of purely spatial radial power spectra observed at all times in the vertical and those using the ensemble of temporal spectra at all heights to yield statistically reliable scaling relations. Thus, it is likely that MRR and other vertically pointing Doppler radars may often help to obviate the need for expensive and immobile large networks of instruments in order to determine such scaling relations but not the need of those radars for surveillance.

In this paper, a water-based aerosol particle counter was used to measure aerosol number concentrations with high temporal resolution at a meteorological tower and on the ground, and the ultrasonic anemometer on the meteorological tower measured the data of the three-dimensional wind speed. The power spectrum of the aerosol particle number concentration fluctuation was obtained by using a Fourier transform, and the characteristics of the power spectrum were deeply analyzed. The results show that the aerosol concentration fluctuation power spectrum satisfies the Monin–Obukhov law in the low-frequency (0.02–0.25 Hz) part of the inertial subregion, which is consistent with the characteristics of atmospheric turbulent motion. Significant attenuation occurs in the high-frequency (0.3–5 Hz) range, which is mainly caused by the attenuation of the aerosol concentration by the intake pipe. Using the similarity of the power spectrum in the low-frequency part, using the “−5/3” line as a standard, the characteristic time of the measurement system is obtained by fitting the transfer function. The results show that in the flux measurement experiments in this paper, the characteristic time is usually less than 1 s. Finally, this paper uses the Fourier transform and wavelet transform to correct the high-frequency attenuation in the fluctuation of aerosol concentration and obtains the corrected aerosol flux. The results show that the effect of high-frequency attenuation on the flux is approximately 1–4% in this experiment.

Rubber friction on ice is studied both experimentally and theoretically. The friction tests involve three different rubber tread compounds and four ice surfaces exhibiting different roughness characteristics. Tests are carried out at four different ambient air temperatures ranging from - 5 to - 13 ∘ C , under three different nominal pressures ranging from 0.15 to 0.45 MPa , and at the sliding speed 0.65 m/s. The viscoelastic properties of all the rubber compounds are characterized using dynamic mechanical analysis. The surface topography of all ice surfaces is measured optically. This provides access to standard roughness quantities and to the surface roughness power spectra. As for modeling, we consider two important contributions to rubber friction on ice: (1) a contribution from the viscoelasticity of the rubber activated by ice asperities scratching the rubber surface and (2) an adhesive contribution from shearing the area of real contact between rubber and ice. At first, a macroscopic empirical formula for the friction coefficient is fitted to our test results, yielding a satisfactory correlation. In order to get insight into microscopic features of rubber friction on ice, we also apply the Persson rubber friction and contact mechanics theory. We discuss the role of temperature-dependent plastic smoothing of the ice surfaces and of frictional heating-induced formation of a meltwater film between rubber and ice. The elaborate model exhibits very satisfactory predictive capabilities. The study shows the importance of combining advanced testing and state-of-the-art modeling regarding rubber friction on ice.

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.