Theoretical Analysis and General Characteristics for Nonlinear Vibration Energy Harvesters

In this paper, we present a systematic theoretical and numerical study of the output performance of nonlinear energy harvesters. The general analytical expression of output power for systems with different combinations of nonlinear stiffness and nonlinear damping, as well as symmetrical and asymmetrical systems, have been derived based on harmonic balance method, observing compliance with numerical results. We theoretically prove that there is a limit power for all nonlinear systems which is determined exclusively by the vibrator mass, excitation acceleration, and mechanical damping. The results also indicate that for symmetrical stiffness systems, the asymmetrical damping components have no effect on the output performance. Additionally, we derived semi-analytical solutions of the matching loads and numerically investigated the influence of nonlinear coefficients on the output power with matched load. When the load matches device parameters and is much larger than the internal resistance, the equivalent time-average damping is equal to the mechanical damping. Although the matching load and output power vary with the nonlinear coefficients, the normalized power and matching resistance ratio follow a power function, named matching power line, which is independent of the structural parameters. With the improvement of the equivalent time-average short-circuit damping in the vibration range, the normalized power moves to the right end of the matching power line, and the output power approach to the limit power. These conclusions provide general characteristics of nonlinear energy harvesters, which can be used to guide the design and optimization of energy harvesters.

The Origin of The Soft X-Ray Excess in the Seyfert 1.5 Galaxy ESO 362-G18

We review the Seyfert 1.5 Galaxy ESO 362-G18 for exploring the origin of the soft X-ray excess. The Warm Corona and Relativistic Reflection models are two main scenarios to interpret the soft X-ray excess in AGNs at present. We use the simultaneous X-ray observation data of XMM-Newton and NuSTAR on Sep. 24th, 2016 to perform spectral analysis in two steps. First, we analyze the time-average spectra by using Warm Corona and Relativistic Reflection models. Moreover, we also explore the Hybrid model, Double Reflection model and Double Warm Corona model. We find that both of Warm Corona and Relativistic Reflection models can interpret the time-average spectra well but cannot be distinguished easily based on the time-averaged spectra fit statistics. Second, we add the RMS and covariance spectra to perform the spectral analysis with time-average spectra. The result shows that the warm corona could reproduce all of these spectra well. The the hot, optical thin corona and neutral distant reflection will increase their contribution with the temporal frequency, meaning that the corona responsible for X-ray continuum comes from the inner compact X-ray region and the neutral distant reflection is made of some moderate scale neutral clumps.

On particle separation from turbulent particle plumes in a cross-flow

We present new experiments of particle-driven turbulent plumes issuing from a constant source of dense particle-laden fluid, with buoyancy flux, $B$ , in a uniform horizontal current, $u$ . Experiments show that a turbulent, well-mixed plume develops, in which the downward vertical speed $w$ decreases with depth $z$ according to $w = 0.76 (B/uz)^{1/2}$ while the horizontal speed rapidly asymptotes to the current speed $u$ , provided that the Stokes settling speed of the particles $v<0.92 w$ . For $v > 0.92 w$ , the particles separate from the plume fluid, and their depth $z$ increases according to the simple sedimentation trajectory $\textrm {d}z/{\textrm {d}\kern0.7pt x} = v/u$ . As the particles sediment, they form clusters of particles, which lead to fluctuations in the particle load with position, but do not appear to change the time-average sedimentation speed. We explore the impact of these results for deep-sea mining, in which the fate of the plume water as well as the particles is key for assessing potential environmental impacts.

SEISMICITY of the TERRITORY of BELARUS in 2015

An annual review of the seismicity in the territory of Belarus based on the data of two analog (operated in the first half-year) and seventeen digital stations is presented. A total of 80 events with Кd=4.6–8.4 were recorded all of them being confined to the southern part of the territory, the Soligorsk mining area included. The map of all the event epicenters for 2015 is given. The table of the distribution of the seismic events by their energy classes and seismic energy in months is presented. The maximum values of the seismic energy release fell in August, and the maximum number of the events was observed in July. The level of the seismic energy released in 2015 is the same as in 2014 but 2.18 times lower than its long-time average value for 1983–2014. The number of the events in 2015 is 1.4 times more than their number in 2014 and 1.86 times more than the Nср value for the previous 32 years. The distribution of the earthquakes in the depth intervals layers showed that the earthquake foci are mostly located in the upper 20 km part of the Earth’s crust. However, the foci of 47 earthquakes are located at depths above 10 km. The distribution of all the events in 2015 is represented in real-time. The quiet seismic periods and seismic activation periods were determined. The distribution of the seismic events in the hourly intervals showed the periods of the increase of the seismic events number. The maximum and minimum values N in the seismic event distribution by the days of the week were determined.

The Impact of Social Isolation on Cognition and Quality of Life Among Stroke Patients in China

This study examined the impactof social isolation on cognitive function and Quality of Life (QoL) among acute ischemic stroke (AIS) patients in China. We conducted in-person interviews among 206 AIS patients during the acute stage and at 3-month after onset in three cities between May 2020 and February 2021. The data was collected during and post-COVID-19 period in China. We conducted bivariate and multipleregression analyses.Results show that over time, average level of social isolation decreased, and cognitive function and QoL increased.After controllingfor covariates, social isolation was negatively associated with cognitive function (β=-0.438, p&lt;0.01) and QoL (β=-2.521, p&lt;0.01). These findings suggest that addressing the issue of social isolation could potentially impact patients’ cognitive function and QoL.Future studies are needed to further examine the linkages between long-term social isolation and changes in cognitive function and QoL among AIS patients.

A Variable Step-Size Leaky LMS Algorithm

The leaky LMS algorithm has been extensively studied because of its control of parameter drift. This unexpected parameter drift is linked to the inadequacy of excitation in the input sequence. And generally leaky LMS algorithms use fixed step size to force the performance of compromise between the fast convergence rate and small steady-state misalignment. In this paper, variable step-size (VSS) leaky LMS algorithm is proposed. And the variable step-size method combines the time average estimation of the error and the time average estimation of the normalized quantity. Variable step-size method proposed incorporating with leaky LMS algorithm can effectively eliminate noise interference and make the early convergence, and final small misalignments are obtained together. Simulation results demonstrate that the proposed algorithm has better performance than the existing variable step-size algorithms in the unexcited environment. Furthermore, the proposed algorithm is comparable in performance to other variable step-size algorithms under the adequacy of excitation.

An Attempt to Pick Teleseismic P Wave Arrival Using Envelope and Artificial Neural Network Algorithm

Seismic events detection and phase picking play an essential role in earthquake studies. Typical event detection is done visually or manually on recorded seismogram by choosing a series of higher amplitude signals recorded on at least 4 stations. More sophisticated methods have been used in event detection and picking with additional attributes such as Short Time Average over Long Time Average (STA/LTA). This method is based on average number sampled at multiple predefined windows. However, STA/LTA is dependent on the window size which becomes its drawback. In this study, we explore one derivative attribute, popularly known as envelope or instantaneous amplitude. It has been extensively used in seismic reflection and refraction method. In principle, this method uses the Hilbert Transform to calculate complex seismic trace and take the magnitude of complex seismic trace as envelope amplitude that can be used to analyze P wave arrival time. We employed one of the machine learning methods, Artificial Neural Network (ANN). The ANN method works by analyzing various inputs and training them to recognize patterns in P wave arrival signals. We started our study by applying envelope attribute to synthetic data with noise addition. We found that with noisy data the envelope attribute still gives a clear signal for first-time arrival. Next, we trained 300 seismograms of teleseismic events recorded on IRIS-US networks and tested our trained program on 20 seismograms as a blind test. To compare performance between the two methods, we calculated the difference between the results of automatic picking and manual picking. The final calculation shows an average deviation of 0.355 seconds. Twenty-five percent of testing data (5 samples) has a deviation above 0.5 seconds, and 75% of the remainder (15 samples) already had a deviation under 0.5 seconds. The more significant deviations of the P wave picks are likely due to noisy signals in the data set and complex arrival signals. This study shows that the combination of envelope attribute and machine learning method is promising to distinguish teleseismic P wave arrival and automatically pick them.