Kinematic deformation and intensity assessment of the 2021 Maduo M_(S)7.4 earthquake in Qinghai revealed by high-frequency GNSS

Rapid acquisition of the kinematic deformation field and seismic intensity distribution of large earthquakes is crucial for postseismic emergency rescue,disaster assessment,and future seismic risk research.The advancement of GNSS observation and data processing makes it play an important role in this field,especially the high-frequency GNSS.We used the differential positioning method to calculate the 1 HZ GNSS data from 98 sites within 1000 km of the M_(S)7.4 Maduo earthquake epicenter.The kinematic deformation field and the distribution of the seismic intensity by using the peak ground velocity derived from displacement waveforms were obtained.The results show that:1)Horizontal coseismic response deformation levels ranging from 25 mm to 301 mm can be observed within a 1000 km radius from the epicenter.Coseismic response deformation on the east and west sides shows bilateral asymmetry,which markedly differs from the symmetry presented by surface rupture.2)The seismic intensity obtained through high-frequency GNSS and field investigations exhibits good consistency of the scope and orientation in the high seismic intensity area,although the former is generally slightly smaller than the latter.3)There may exist obstacles on the eastern side of the seismogenic fault.The Maduo earthquake induced a certain tectonic stress loading effect on the western Kunlun Pass-Jiangcuo fault(KPJF)and Maqin-Maqu segment,resulting in higher seismic risk in the future.

The relationship between the high-frequency performance of supercapacitors and the type of doped nitrogen in the carbon electrode

Nitrogen doping has been widely used to improve the performance of carbon electrodes in supercapacitors,particularly in terms of their high-frequency response.However,the charge storage and electrolyte ion response mechanisms of different nitrogen dopants at high frequencies are still unclear.In this study,melamine foam carbons with different configurations of surfacedoped N were formed by gradient carbonization,and the effects of the configurations on the high-frequency response behavior of the supercapacitors were analyzed.Using a combination of experiments and first-principle calculations,we found that pyrrolic N,characterized by a higher adsorption energy,increases the charge storage capacity of the electrode at high frequencies.On the other hand,graphitic N,with a lower adsorption energy,increases the speed of ion response.We propose the use of adsorption energy as a practical descriptor for electrode/electrolyte design in high-frequency applications,offering a more universal approach for improving the performance of N-doped carbon materials in supercapacitors.

Dynamic Response of Foundations during Startup of High-Frequency Tunnel Equipment

The specialized equipment utilized in long-line tunnel engineering is evolving towards large-scale,multifunctional,and complex orientations.The vibration caused by the high-frequency units during regular operation is supported by the foundation of the units,and the magnitude of vibration and the operating frequency fluctuate in different engineering contexts,leading to variations in the dynamic response of the foundation.The high-frequency units yield significantly diverse outcomes under different startup conditions and times,resulting in failure to meet operational requirements,influencing the normal function of the tunnel,and causing harm to the foundation structure,personnel,and property in severe cases.This article formulates a finite element numerical computation model for solid elements using three-dimensional elastic body theory and integrates field measurements to substantiate and ascertain the crucial parameter configurations of the finite element model.By proposing a comprehensive startup timing function for high-frequency dynamic machines under different startup conditions,simulating the frequency andmagnitude variations during the startup process,and suggesting functions for changes in frequency and magnitude,a simulated startup schedule function for high-frequency machines is created through coupling.Taking into account the selection of the transient dynamic analysis step length,the dynamic response results for the lower dynamic foundation during its fundamental frequency crossing process are obtained.The validation checks if the structural magnitude surpasses the safety threshold during the critical phase of unit startup traversing the structural resonance region.The design recommendations for high-frequency units’dynamic foundations are provided,taking into account the startup process of the machine and ensuring the safe operation of the tunnel.

Review of the Analysis and Suppression for High-frequency Oscillations of the Grid-connected Wind Power Generation System

High-frequency oscillation(HFO)of gridconnected wind power generation systems(WPGS)is one of the most critical issues in recent years that threaten the safe access of WPGS to the grid.Ensuring the WPGS can damp HFO is becoming more and more vital for the development of wind power.The HFO phenomenon of wind turbines under different scenarios usually has different mechanisms.Hence,engineers need to acquire the working mechanisms of the different HFO damping technologies and select the appropriate one to ensure the effective implementation of oscillation damping in practical engineering.This paper introduces the general assumptions of WPGS when analyzing HFO,systematically summarizes the reasons for the occurrence of HFO in different scenarios,deeply analyses the key points and difficulties of HFO damping under different scenarios,and then compares the technical performances of various types of HFO suppression methods to provide adequate references for engineers in the application of technology.Finally,this paper discusses possible future research difficulties in the problem of HFO,as well as the possible future trends in the demand for HFO damping.

Efficient activation of the Co/SBA-15catalyst by high-frequency AC-DBD plasma thermal effects for toluene removal

Dielectric barrier discharge(DBD)plasma excited by a high-frequency alternating-current(AC)power supply is widely employed for the degradation of volatile organic compounds(VOCs).However,the thermal effect generated during the discharge process leads to energy waste and low energy utilization efficiency.In this work,an innovative DBD thermally-conducted catalysis(DBD-TCC)system,integrating high-frequency AC-DBD plasma and its generated thermal effects to activate the Co/SBA-15 catalyst,was employed for toluene removal.Specifically,Co/SBA-15 catalysts are closely positioned to the ground electrode of the plasma zone and can be heated and activated by the thermal effect when the voltage exceeds 10 k V.At12.4 k V,the temperature in the catalyst zone reached 261℃ in the DBD-TCC system,resulting in an increase in toluene degradation efficiency of 17%,CO_(2)selectivity of 21.2%,and energy efficiency of 27%,respectively,compared to the DBD system alone.In contrast,the DBD thermally-unconducted catalysis(DBD-TUC)system fails to enhance toluene degradation due to insufficient heat absorption and catalytic activation,highlighting the crucial role of AC-DBD generated heat in the activation of the catalyst.Furthermore,the degradation pathway and mechanism of toluene in the DBD-TCC system were hypothesized.This work is expected to provide an energy-efficient approach for high-frequency AC-DBD plasma removal of VOCs.

Effects of unilateral superimposed high-frequency jet ventilation on porcine hemodynamics and gas exchange during one-lung flooding

BACKGROUND Superimposed high-frequency jet ventilation(SHFJV)is suitable for respiratory motion reduction and essential for effective lung tumor ablation.Fluid filling of the target lung wing one-lung flooding(OLF)is necessary for therapeutic ultrasound applications.However,whether unilateral SHFJV allows adequate hemodynamics and gas exchange is unclear.AIM To compared SHFJV with pressure-controlled ventilation(PCV)during OLF by assessing hemodynamics and gas exchange in different animal positions.METHODS SHFJV or PCV was used alternatingly to ventilate the non-flooded lungs of the 12 anesthetized pigs during OLF.The animal positions were changed from left lateral position to supine position(SP)to right lateral position(RLP)every 30 min.In each position,ventilation was maintained for 15 min in both modalities.Hemodynamic variables and arterial blood gas levels were repeatedly measured.RESULTS Unilateral SHFJV led to lower carbon dioxide removal than PCV without abnormally elevated carbon dioxide levels.SHFJV slightly decreased oxygenation in SP and RLP compared with PCV;the lowest values of PaO_(2) and PaO_(2)/FiO_(2) ratio were found in SP[13.0;interquartile range(IQR):12.6-5.6 and 32.5(IQR:31.5-38.9)kPa].Conversely,during SHFJV,the shunt fraction was higher in all animal positions(highest in the RLP:0.30).CONCLUSION In porcine model,unilateral SHFJV may provide adequate ventilation in different animal positions during OLF.Lower oxygenation and CO_(2) removal rates compared to PCV did not lead to hypoxia or hypercapnia.SHFJV can be safely used for lung tumor ablation to minimize ventilation-induced lung motion.

Therapeutic efficacy of methylprednisolone sodium succinate via diverse administration routes for mid-to high-frequency sudden sensorineural hearing loss

BACKGROUND Sudden sensorineural hearing loss(SSNHL),characterized by a rapid and unexplained loss of hearing,particularly at moderate to high frequencies,presents a significant clinical challenge.The therapeutic use of methylprednisolone sodium succinate(MPSS)via different administration routes,in combination with conventional medications,remains a topic of interest.AIM To compare the therapeutic efficacy of MPSS administered via different routes in combination with conventional drugs for the treatment of mid-to high-frequency SSNHL.METHODS The medical records of 109 patients with mid-to high-frequency SSNHL were analyzed.The patients were divided into three groups based on the route of administration:Group A[intratympanic(IT)injection of MPSS combined with mecobalamin and Ginkgo biloba leaf extract injection],Group B(intravenous injection of MPSS combined with mecobalamin and Ginkgo biloba leaf extract injection),and Group C(single IT injection of MPSS).The intervention effects were compared and analyzed.RESULTS The posttreatment auditory thresholds in Group A(21.23±3.34)were significantly lower than those in Groups B(28.52±3.36)and C(30.23±4.21;P<0.05).Group A also exhibited a significantly greater speech recognition rate(92.23±5.34)than Groups B and C.The disappearance time of tinnitus,time to hearing recovery,and disappearance time of vertigo in Group A were significantly shorter than those in Groups B and C(P<0.05).The total effective rate in Group A(97.56%)was significantly greater than that in Groups B and C(77.14%and 78.79%,χ^(2)=7.898,P=0.019).Moreover,the incidence of adverse reactions in Groups A and C was significantly lower than that in Group B(4.88%,3.03%vs 2.57%,χ^(2)=11.443,P=0.003),and the recurrence rate in Group A was significantly lower than that in Groups B and C(2.44%vs 20.00%vs 21.21%,χ^(2)=7.120,P=0.028).CONCLUSION IT injection of MPSS combined with conventional treatment demonstrates superior efficacy and safety compared to systemic administration via intravenous infusion and a single IT injection of MPSS.This approach effectively improves patients'hearing and reduces the risk of disease recurrence.

Research on the Dynamic Volatility Relationship between Chinese and U.S. Stock Markets Based on the DCC-GARCH Model under the Background of the COVID-19 Pandemic

This study utilizes the Dynamic Conditional Correlation-Generalized Autoregressive Conditional Heteroskedasticity (DCC-GARCH) model to investigate the dynamic relationship between Chinese and U.S. stock markets amid the COVID-19 pandemic. Initially, a univariate GARCH model is developed to derive residual sequences, which are then used to estimate the DCC model parameters. The research reveals a significant rise in the interconnection between the Chinese and U.S. stock markets during the pandemic. The S&P 500 index displayed higher sensitivity and greater volatility in response to the pandemic, whereas the CSI 300 index showed superior resilience and stability. Analysis and model estimation suggest that the market’s dependence on historical data has intensified and its sensitivity to recent shocks has heightened. Predictions from the model indicate increased market volatility during the pandemic. While the model is proficient in capturing market trends, there remains potential for enhancing the accuracy of specific volatility predictions. The study proposes recommendations for policymakers and investors, highlighting the importance of improved cooperation in international financial market regulation and investor education.

Application of Elzaki Transform Method to Market Volatility Using the Black-Scholes Model

Black-Scholes Model (B-SM) simulates the dynamics of financial market and contains instruments such as options and puts which are major indices requiring solution. B-SM is known to estimate the correct prices of European Stock options and establish the theoretical foundation for Option pricing. Therefore, this paper evaluates the Black-Schole model in simulating the European call in a cash flow in the dependent drift and focuses on obtaining analytic and then approximate solution for the model. The work also examines Fokker Planck Equation (FPE) and extracts the link between FPE and B-SM for non equilibrium systems. The B-SM is then solved via the Elzaki transform method (ETM). The computational procedures were obtained using MAPLE 18 with the solution provided in the form of convergent series.

Prediction of metal futures price volatility and empirical analysis based on symbolic time series of high-frequency

The metal futures price fluctuation prediction model was constructed based on symbolic high-frequency time series using high-frequency data on the Shanghai Copper Futures Exchange from July 2014 to September 2018,and the sample was divided into 194 histogram time series employing symbolic time series.The next cycle was then predicted using the K-NN algorithm and exponential smoothing,respectively.The results show that the trend of the histogram of the copper futures earnings prediction is gentler than that of the actual histogram,the overall situation of the prediction results is better,and the overall fluctuation of the one-week earnings of the copper futures predicted and the actual volatility are largely the same.This shows that the results predicted by the K-NN algorithm are more accurate than those predicted by the exponential smoothing method.Based on the predicted one-week price fluctuations of copper futures,regulators and investors in China’s copper futures market can timely adjust their regulatory policies and investment strategies to control risks.

Volatility in High-Frequency Intensive Care Mortality Time Series: Application of Univariate and Multivariate GARCH Models

Mortality time series display time-varying volatility. The utility of statistical estimators from the financial time-series paradigm, which account for this characteristic, has not been addressed for high-frequency mortality series. Using daily mean-mortality series of an exemplar intensive care unit (ICU) from the Australian and New Zealand Intensive Care Society adult patient database, joint estimation of a mean and conditional variance (volatility) model for a stationary series was undertaken via univariate autoregressive moving average (ARMA, lags (p, q)), GARCH (Generalised Autoregressive Conditional Heteroscedasticity, lags (p, q)). The temporal dynamics of the conditional variance and correlations of multiple provider series, from rural/ regional, metropolitan, tertiary and private ICUs, were estimated utilising multivariate GARCH models. For the stationary first differenced series, an asymmetric power GARCH model (lags (1, 1)) with t distribution (degrees-of- freedom, 11.6) and ARMA (7,0) for the mean-model, was the best-fitting. The four multivariate component series demonstrated varying trend mortality decline and persistent autocorrelation. Within each MGARCH series no model specification dominated. The conditional correlations were surprisingly low (<0.1) between tertiary series and substantial (0.4 - 0.6) between rural-regional and private series. The conditional-variances of both the univariate and multivariate series demonstrated a slow rate of time decline from periods of early volatility and volatility spikes.

Optimal Quota-Share and Excess-of-Loss Reinsurance and Investment with Heston’s Stochastic Volatility Model

An optimal quota-share and excess-of-loss reinsurance and investment problem is studied for an insurer who is allowed to invest in a risk-free asset and a risky asset.Especially the price process of the risky asset is governed by Heston's stochastic volatility(SV)model.With the objective of maximizing the expected index utility of the terminal wealth of the insurance company,by using the classical tools of stochastic optimal control,the explicit expressions for optimal strategies and optimal value functions are derived.An interesting conclusion is found that it is better to buy one reinsurance than two under the assumption of this paper.Moreover,some numerical simulations and sensitivity analysis are provided.

Radio-frequency measurements of energetic-electron-driven emissions using high-frequency magnetic probe on XuanLong-50 spherical torus

A high-frequency magnetic probe is designed and developed on the XuanL ong-50(EXL-50)spherical torus to measure high-frequency magnetic field fluctuation.The magnetic loop,radio filters,radio-frequency limiter,and data acquisition system of the probe are comprehensively examined.The fluctuation data from the EXL-50 plasma are analyzed in the time–frequency domain using fast Fourier transforms.Moreover,distinct high-frequency instabilities are detected using this diagnostic system.In particular,significant frequency chirping is observed,which is consistent with the bumpon-tail drive instability predicted using the Berk–Breizman model.

Comparative evaluation of commercial Douchi by different molds:biogenic amines,non-volatile and volatile compounds

To provide new insights into the development and utilization of Douchi artificial starters,three common strains(Aspergillus oryzae,Mucor racemosus,and Rhizopus oligosporus)were used to study their influence on the fermentation of Douchi.The results showed that the biogenic amine contents of the three types of Douchi were all within the safe range and far lower than those of traditional fermented Douchi.Aspergillus-type Douchi produced more free amino acids than the other two types of Douchi,and its umami taste was more prominent in sensory evaluation(P<0.01),while Mucor-type and Rhizopus-type Douchi produced more esters and pyrazines,making the aroma,sauce,and Douchi flavor more abundant.According to the Pearson and PLS analyses results,sweetness was significantly negatively correlated with phenylalanine,cysteine,and acetic acid(P<0.05),bitterness was significantly negatively correlated with malic acid(P<0.05),the sour taste was significantly positively correlated with citric acid and most free amino acids(P<0.05),while astringency was significantly negatively correlated with glucose(P<0.001).Thirteen volatile compounds such as furfuryl alcohol,phenethyl alcohol,and benzaldehyde caused the flavor difference of three types of Douchi.This study provides theoretical basis for the selection of starting strains for commercial Douchi production.

Effect of different drying methods on the amino acids,α-dicarbonyls and volatile compounds of rape bee pollen

The significant demand for high quality food has motivated us to adopt appropriate processing methods to improve the food nutritional quality and flavors.In this study,the effects of five drying methods,namely,pulsed vacuum drying(PVD),freeze drying(FD),infrared drying(IRD),hot-air drying(HAD)and sun drying(SD)on free amino acids(FAAs),α-dicarbonyl compounds(α-DCs)and volatile compounds(VOCs)in rape bee pollen(RBP)were determined.The results showed that FD significantly released the essential amino acids(EAAs)compared with fresh samples while SD caused the highest loss.Glucosone was the dominantα-DCs in RBP and the highest loss was observed after PVD.Aldehydes were the dominant volatiles of RBP and SD samples contained more new volatile substances(especially aldehydes)than the other four drying methods.Comprehensively,FD and PVD would be potential methods to effectively reduce the quality deterioration of RBP in the drying process.

Tuning magneto-dielectric properties of Co_(2)Z ferrites via Gd doping for high-frequency applications

Magneto-dielectric properties of Co_(2)Z ferrite materials are tuned via Gd doping for applications in high-frequency antennas and filters in the present work.Ba_(3)Co_(2)Fe_(24-x)Gd_(x)O_(41)(x=0.00,0.05,0.10,0.15,and 0.20)materials are successfully prepared by using solid-state method at 925℃for 4 h with 2.5-wt%Bi_(2)O_(3)sintering aids.The content of Gd^(3+)ion can affect micromorphology,grain size,bulk density,and magneto-dielectric properties of the ferrite.With Gd^(3+)ion content increasing,saturation magnetization(Ms)first increases and then decreases.The maximum value of Ms is 44.86 emu/g at x=0.15.Additionally,sites occupied by Gd^(3+)ions can change magnetic anisotropy constant of the ferrite.Magnetocrystalline anisotropy constant(K_1)is derived from initial magnetization curve,and found to be related to spin-orbit coupling and intersublattice interactions between metal ions.The real part of magnetic permeability(μ′)and real part of dielectric permittivity(ε′)are measured in a frequency range of 10 MHz-1 GHz.When x=0.15,material has excellent magneto-dielectric properties(μ′≈12.2 andε′≈17.61),low magnetic loss(tanδμ≈0.03 at 500 MHz),and dielectric loss(tanδε≈0.04 at 500 MHz).The results show that Gd-doped Co_(2)Z ferrite has broad application prospects in multilayer filters and high-frequency antennas.

Forecasting Stock Volatility Using Wavelet-based Exponential Generalized Autoregressive Conditional Heteroscedasticity Methods

In this study,we proposed a new model to improve the accuracy of fore-casting the stock market volatility pattern.The hypothesized model was validated empirically using a data set collected from the Saudi Arabia stock Exchange(Tada-wul).The data is the daily closed price index data from August 2011 to December 2019 with 2027 observations.The proposed forecasting model combines the best maximum overlapping discrete wavelet transform(MODWT)function(Bl14)and exponential generalized autoregressive conditional heteroscedasticity(EGARCH)model.The results show the model's ability to analyze stock market data,highlight important events that contain the most volatile data,and improve forecast accuracy.The results were compared from a number of mathematical mod-els,which are the non-linear spectral model,autoregressive integrated moving aver-age(ARIMA)model and EGARCH model.The performance of the forecasting model will be evaluated based on some of error functions such as Mean absolute percentage error(MAPE),Mean absolute scaled error(MASE)and Root means squared error(RMSE).

Do U.S.economic conditions at the state level predict the realized volatility of oil‑price returns?A quantile machine‑learning approach

Because the U.S.is a major player in the international oil market,it is interesting to study whether aggregate and state-level economic conditions can predict the subse-quent realized volatility of oil price returns.To address this research question,we frame our analysis in terms of variants of the popular heterogeneous autoregressive realized volatility(HAR-RV)model.To estimate the models,we use quantile-regression and quantile machine learning(Lasso)estimators.Our estimation results highlights the dif-ferential effects of economic conditions on the quantiles of the conditional distribution of realized volatility.Using weekly data for the period April 1987 to December 2021,we document evidence of predictability at a biweekly and monthly horizon.

Modern approaches for detection of volatile organic compounds in metabolic studies focusing on pathogenic bacteria:Current state of the art

Pathogenic microorganisms produce numerous metabolites,including volatile organic compounds(VOCs).Monitoring these metabolites in biological matrices(e.g.,urine,blood,or breath)can reveal the presence of specific microorganisms,enabling the early diagnosis of infections and the timely implementation of tar-geted therapy.However,complex matrices only contain trace levels of VOCs,and their constituent com-ponents can hinder determination of these compounds.Therefore,modern analytical techniques enabling the non-invasive identification and precise quantification of microbial VOCs are needed.In this paper,we discuss bacterial VOC analysis under in vitro conditions,in animal models and disease diagnosis in humans,including techniques for offline and online analysis in clinical settings.We also consider the advantages and limitations of novel microextraction techniques used to prepare biological samples for VOC analysis,in addition to reviewing current clinical studies on bacterial volatilomes that address inter-species in-teractions,the kinetics of VOC metabolism,and species-and drug-resistance specificity.

An Improved Whale Optimization Algorithm for Global Optimization and Realized Volatility Prediction

The original whale optimization algorithm(WOA)has a low initial population quality and tends to converge to local optimal solutions.To address these challenges,this paper introduces an improved whale optimization algorithm called OLCHWOA,incorporating a chaos mechanism and an opposition-based learning strategy.This algorithm introduces chaotic initialization and opposition-based initialization operators during the population initialization phase,thereby enhancing the quality of the initial whale population.Additionally,including an elite opposition-based learning operator significantly improves the algorithm’s global search capabilities during iterations.The work and contributions of this paper are primarily reflected in two aspects.Firstly,an improved whale algorithm with enhanced development capabilities and a wide range of application scenarios is proposed.Secondly,the proposed OLCHWOA is used to optimize the hyperparameters of the Long Short-Term Memory(LSTM)networks.Subsequently,a prediction model for Realized Volatility(RV)based on OLCHWOA-LSTM is proposed to optimize hyperparameters automatically.To evaluate the performance of OLCHWOA,a series of comparative experiments were conducted using a variety of advanced algorithms.These experiments included 38 standard test functions from CEC2013 and CEC2019 and three constrained engineering design problems.The experimental results show that OLCHWOA ranks first in accuracy and stability under the same maximum fitness function calls budget.Additionally,the China Securities Index 300(CSI 300)dataset is used to evaluate the effectiveness of the proposed OLCHWOA-LSTM model in predicting RV.The comparison results with the other eight models show that the proposed model has the highest accuracy and goodness of fit in predicting RV.This further confirms that OLCHWOA effectively addresses real-world optimization problems.