There is a contradiction between the evolution rate of materials and the time resolution of SR-CT characterization in the in situ synchrotron radiation computed tomography(SR-CT)characterization of ultrafast evolution...There is a contradiction between the evolution rate of materials and the time resolution of SR-CT characterization in the in situ synchrotron radiation computed tomography(SR-CT)characterization of ultrafast evolution process.The sampling strategy of the ultra-sparse angle is an effective method for improving time resolution.Accurate reconstruction under sparse sampling conditions has always been a bottleneck problem.In recent years,convolutional neural networks have shown outstanding advantages in sparse-angle CT reconstruction given the development of deep learning.However,existing ideas did not consider the expression of high-frequency details in neural networks,limiting their application in accurate SR-CT characterization.A novel high-frequency information-constrained deep learning network(HFIC-Net)is proposed in response to this problem.Additional high-frequency information constraints are added to improve the accuracy of the reconstruction results.Further,a series of numerical reconstruction experiments are conducted to verify this new method,and the results indicate that the reconstruction results of HFIC-Net method effectively improve reconstruction quality.This new method uses only eight-angle projections to achieve the reconstruction effect of the filtered backprojection method(FBP)method in 360 projections.The results of the HFIC-Net method demonstrate clear boundaries and accurate detailed structures,correcting the misinformation caused by using other methods.For quantitative evaluation,the SSIM used to evaluate image structure similarity is increased from 0.1951,0.9212,and 0.9308 for FBP,FBP-Conv,and DDC-Net,respectively,to 0.9620 for HFIC-Net.Finally,the results of actual SR-CT experimental data indicate that the new method can suppress artifacts and achieve accurate reconstruction,and it is suitable for the in situ SR-CT accurate characterization of ultxafast evolution process.展开更多
Physics-informed neural networks(PINNs)have been shown as powerful tools for solving partial differential equations(PDEs)by embedding physical laws into the network training.Despite their remarkable results,complicate...Physics-informed neural networks(PINNs)have been shown as powerful tools for solving partial differential equations(PDEs)by embedding physical laws into the network training.Despite their remarkable results,complicated problems such as irregular boundary conditions(BCs)and discontinuous or high-frequency behaviors remain persistent challenges for PINNs.For these reasons,we propose a novel two-phase framework,where a neural network is first trained to represent shape functions that can capture the irregularity of BCs in the first phase,and then these neural network-based shape functions are used to construct boundary shape functions(BSFs)that exactly satisfy both essential and natural BCs in PINNs in the second phase.This scheme is integrated into both the strong-form and energy PINN approaches,thereby improving the quality of solution prediction in the cases of irregular BCs.In addition,this study examines the benefits and limitations of these approaches in handling discontinuous and high-frequency problems.Overall,our method offers a unified and flexible solution framework that addresses key limitations of existing PINN methods with higher accuracy and stability for general PDE problems in solid mechanics.展开更多
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 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.展开更多
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 mo...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.展开更多
This study evaluates the predictive accuracy of traditional time series(TS)models versus machine learning(ML)methods in forecasting realized volatility across major cryptocurrencies—Bitcoin(BTC),Ethereum(ETH),Litecoi...This study evaluates the predictive accuracy of traditional time series(TS)models versus machine learning(ML)methods in forecasting realized volatility across major cryptocurrencies—Bitcoin(BTC),Ethereum(ETH),Litecoin(LTC),and Ripple(XRP).Employing high-frequency data,we analyze cross-cryptocurrency volatility dynamics through two complementary approaches:volatility forecasting and connectedness analysis.Our findings reveal three key insights:(i)TS models,particularly the heterogeneous autoregressive(HAR)model,exhibit superior predictive performance over their ML counterparts,with the long short-term memory(LSTM)model providing competitive yet inconsistent results due to overfitting and short-term volatility challenges;(ii)including lagged realized volatility of large-cap coins improves predictive accuracy for mid-cap coins,especially XRP,whereas forecasts for largecap coins remain stable,indicating more resilient volatility patterns;and(iii)volatility connectedness analysis reveals substantial spillover effects,particularly pronounced during market turmoil,with large-cap assets(BTC and ETH)acting as primary volatility transmitters and mid-cap assets(XRP and LTC)serving as volatility receivers.These results contribute to the understanding of volatility forecasting and risk management in cryptocurrency markets,offering implications for investors and policymakers in managing market risk and interdependencies in digital asset portfolios.展开更多
Quanto options allow the buyer to exchange the foreign currency payoff into the domestic currency at a fixed exchange rate. We investigate quanto options with multiple underlying assets valued in different foreign cur...Quanto options allow the buyer to exchange the foreign currency payoff into the domestic currency at a fixed exchange rate. We investigate quanto options with multiple underlying assets valued in different foreign currencies each with a different strike price in the payoff function. We carry out a comparative performance analysis of different stochastic volatility (SV), stochastic correlation (SC), and stochastic exchange rate (SER) models to determine the best combination of these models for Monte Carlo (MC) simulation pricing. In addition, we test the performance of all model variants with constant correlation as a benchmark. We find that a combination of GARCH-Jump SV, Weibull SC, and Ornstein Uhlenbeck (OU) SER performs best. In addition, we analyze different discretization schemes and their results. In our simulations, the Milstein scheme yields the best balance between execution times and lower standard deviations of price estimates. Furthermore, we find that incorporating mean reversion into stochastic correlation and stochastic FX rate modeling is beneficial for MC simulation pricing. We improve the accuracy of our simulations by implementing antithetic variates variance reduction. Finally, we derive the correlation risk parameters Cora and Gora in our framework so that correlation hedging of quanto options can be performed.展开更多
Using daily BTC-USD data from September 19,2014 to January 21,2024,this paper re-examines whether weekends differ from weekdays for Bitcoin along three margins:average returns,close-to-close volatility,and trading act...Using daily BTC-USD data from September 19,2014 to January 21,2024,this paper re-examines whether weekends differ from weekdays for Bitcoin along three margins:average returns,close-to-close volatility,and trading activity.We implement Welch mean comparisons and HAC-robust OLS with month fixed effects(bandwidths 5,7,and 14).In the full sample and across subsamples(2016–2019;2020–2023;early 2024),we find no detectable weekend–weekday gap in average returns,while volatility and trading activity are lower on weekends.The patterns are robust to using squared returns as a volatility proxy.The joint evidence is consistent with liquidity and attention mechanisms—quieter weekends rather than compensating return premia.Replication files reproduce all tables and figures.展开更多
The oil-based mud(OBM) borehole measurement environment presents significant limitations on the application of existing electrical logging instruments in high-resistance formations. In this paper, we propose a novel l...The oil-based mud(OBM) borehole measurement environment presents significant limitations on the application of existing electrical logging instruments in high-resistance formations. In this paper, we propose a novel logging method for detection of high-resistance formations in OBM using highfrequency electrodes. The method addresses the issue of shallow depth of investigation(DOI) in existing electrical logging instruments, while simultaneously ensuring the vertical resolution. Based on the principle of current continuity, the total impedance of the loop is obtained by equating the measurement loop to the series form of a capacitively coupled circuit. and its validity is verified in a homogeneous formation model and a radial two-layer formation model with a mud standoff. Then, the instrument operating frequency and electrode system parameters were preferentially determined by numerical simulation, and the effect of mud gap on impedance measurement was investigated. Subsequently, the DOI of the instrument was investigated utilizing the pseudo-geometric factor defined by the real part of impedance. It was determined that the detection depth of the instrument is 8.74 cm, while the effective vertical resolution was not less than 2 cm. Finally, a focused high-frequency electrode-type instrument was designed by introducing a pair of focused electrodes, which effectively enhanced the DOI of the instrument and was successfully deployed in the Oklahoma formation model. The simulation results demonstrate that the novel method can achieve a detection depth of 17.40 cm in highly-resistive formations drilling with OBM, which is approximately twice the depth of detection of the existing oil-based mud microimager instruments. Furthermore, its effective vertical resolution remains at or above 2 cm,which is comparable to the resolution of the existing OBM electrical logging instrument.展开更多
Objective:To analyze the significance of high-frequency ultrasound in differentiating benign and malignant breast micronodules.Methods:Eighty-five patients with breast micronodules admitted for diagnosis between Octob...Objective:To analyze the significance of high-frequency ultrasound in differentiating benign and malignant breast micronodules.Methods:Eighty-five patients with breast micronodules admitted for diagnosis between October 2022 and October 2024 were selected for high-frequency ultrasound diagnosis.The diagnostic efficacy of high-frequency ultrasound was evaluated by comparing it with the results of surgical pathology.Results:High-frequency ultrasound detected 50 benign nodules,primarily breast fibroadenomas,and 35 malignant nodules,mainly breast ductal carcinoma in situ.Based on surgical pathology results,the diagnostic accuracy of high-frequency ultrasound was 96.47%,specificity was 97.96%,and sensitivity was 94.44%.In high-frequency ultrasound diagnosis,the proportion of grade III and IV blood flow in malignant nodules was higher than that in benign nodules,while the proportion of regular shape and clear margins was lower.The proportion of microcalcifications and posterior echo attenuation was higher in malignant nodules,and the resistance index(RI)and peak blood flow velocity were lower than those in benign nodules(P<0.05).Conclusion:High-frequency ultrasound can effectively differentiate benign and malignant breast micronodules,determine specific nodule types,and exhibits high diagnostic accuracy and sensitivity.Additionally,benign and malignant nodules can be differentiated based on the grading of blood flow signals,sonographic features,and blood flow velocity,providing reasonable guidance for subsequent treatment plans.展开更多
Cryptocurrency,a booming decentralised asset designed based on the blockchain architecture,is particularly important to the market at the present time by studying the volatility risk of cryptocurrencies.In this paper,...Cryptocurrency,a booming decentralised asset designed based on the blockchain architecture,is particularly important to the market at the present time by studying the volatility risk of cryptocurrencies.In this paper,we empirically analyse the volatility risk of cryptocurrencies through quantitative analysis models,comprehensively using the Markov state transition GARCH model with skewed distribution(Skew-MSGARCH)and the autoregressive conditional volatility density ARJI model introducing the Poisson jump factor,and selecting the earliest developed and the most mature currency price volatility daily return series,to deeply explore the volatility risk of digital cryptocurrencies.risk.Finally,it can be seen through in-depth analyses that the expectation factor and information inducement are the main reasons leading to the exacerbation of the volatility risk of digital cryptocurrencies.It is recommended that this situation be optimised and improved in terms of the value function of digital cryptocurrencies themselves and the implementation of systematic risk management and regulatory innovation.As an important component of the digital economy,blockchain technology can effectively regulate and improve the volatility of digital cryptocurrencies under macroeconomic policies,thereby maintaining the security and stability of emerging financial markets.展开更多
China has a long history of coal mining,among which open-pit coal mines have a large number of small coal mine goafs underground.The distribution,shape,structure and other characteristics of goafs are isolated and dis...China has a long history of coal mining,among which open-pit coal mines have a large number of small coal mine goafs underground.The distribution,shape,structure and other characteristics of goafs are isolated and discontinuous,and there is no definite geological law to follow,which seriously threatens the safety of coal mine production and personnel life.Conventional ground geophysical methods have low accuracy in detecting goaf areas affected by mechanical interference from open-pit mines,especially for waterless goaf areas,which cannot be detected by existing methods.This article proposes the use of high-frequency electromagnetic waves for goaf detection.The feasibility of using drilling radar to detect goaf was theoretically analyzed,and a goaf detection model was established.The response characteristics of different fillers in the goaf under different frequencies of high-frequency electromagnetic waves were simulated and analyzed.In a certain open-pit mine in Inner Mongolia,100MHz high-frequency electromagnetic waves were used to detect the goaf through directional drilling on the ground.After detection,excavation verification was carried out,and the location of one goaf detected was verified.The results of engineering practice show that the application of high-frequency electromagnetic waves in goaf detection expands the detection radius of boreholes,has the advantages of high efficiency and accuracy,and has important theoretical and practical significance.展开更多
New electric power systems characterized by a high proportion of renewable energy and power electronics equipment face significant challenges due to high-frequency(HF)electromagnetic interference from the high-speed s...New electric power systems characterized by a high proportion of renewable energy and power electronics equipment face significant challenges due to high-frequency(HF)electromagnetic interference from the high-speed switching of power converters.To address this situation,this paper offers an in-depth review of HF interference problems and challenges originating from power electronic devices.First,the root cause of HF electromagnetic interference,i.e.,the resonant response of the parasitic parameters of the system to high-speed switching transients,is analyzed,and various scenarios of HF interference in power systems are highlighted.Next,the types of HF interference are summarized,with a focus on common-mode interference in grounding systems.This paper thoroughly reviews and compares various suppression methods for conducted HF interference.Finally,the challenges involved and suggestions for addressing emerging HF interference problems from the perspective of both power electronics equipment and power systems are discussed.This review aims to offer a structured understanding of HF interference problems and their suppression techniques for researchers and practitioners.展开更多
Single-shot ultrafast compressed imaging(UCI)is an effective tool for studying ultrafast dynamics in physics,chemistry,or material science because of its excellent high frame rate and large frame number.However,the ra...Single-shot ultrafast compressed imaging(UCI)is an effective tool for studying ultrafast dynamics in physics,chemistry,or material science because of its excellent high frame rate and large frame number.However,the random code(Rcode)used in traditional UCI will lead to low-frequency noise covering high-frequency information due to its uneven sampling interval,which is a great challenge in the fidelity of large-frame reconstruction.Here,a high-frequency enhanced compressed active photography(H-CAP)is proposed.By uniformizing the sampling interval of R-code,H-CAP capture the ultrafast process with a random uniform sampling mode.This sampling mode makes the high-frequency sampling energy dominant,which greatly suppresses the low-frequency noise blurring caused by R-code and achieves high-frequency information of image enhanced.The superior dynamic performance and large-frame reconstruction ability of H-CAP are verified by imaging optical self-focusing effect and static object,respectively.We applied H-CAP to the spatial-temporal characterization of double-pulse induced silicon surface ablation dynamics,which is performed within 220 frames in a single-shot of 300 ps.H-CAP provides a high-fidelity imaging method for observing ultrafast unrepeatable dynamic processes with large frames.展开更多
Cryptocurrency is a remarkable financial innovation that has affected the financial system in fundamental ways.Its increasingly complex interactions with the conventional financial market make precisely forecasting it...Cryptocurrency is a remarkable financial innovation that has affected the financial system in fundamental ways.Its increasingly complex interactions with the conventional financial market make precisely forecasting its volatility increasingly challenging.To this end,we propose a novel framework based on the evolving multiscale graph neural network(EMGNN).Specifically,we embed a graph that depicts the interactions between the cryptocurrency and conventional financial markets into the predictive process.Furthermore,we employ hierarchical evolving graph structure learners to model the dynamic and scale-specific interactions.We also evaluate our framework’s robustness and discuss its interpretability by extracting the learned graph structure.The empirical results show that(i)cryptocurrency volatility is not isolated from the conventional market,and the embedded graph can provide effective information for prediction;(ii)the EMGNN-based forecasting framework generally yields outstanding and robust performance in terms of multiple volatility estimators,cryptocurrency samples,forecasting horizons,and evaluation criteria;and(iii)the graph structure in the predictive process varies over time and scales and is well captured by our framework.Overall,our work provides new insights into risk management for market participants and into policy formulation for authorities.展开更多
Objective:To analyze the therapeutic effect of high-frequency electrosurgical knife surgery guided by painless digestive endoscopy(PDE)in elderly patients with gastrointestinal polyps(GP).Methods:A total of 100 elderl...Objective:To analyze the therapeutic effect of high-frequency electrosurgical knife surgery guided by painless digestive endoscopy(PDE)in elderly patients with gastrointestinal polyps(GP).Methods:A total of 100 elderly GP patients admitted between June 2021 and December 2022 were selected.Patients were randomly divided into two groups:the painless group(50 cases)underwent high-frequency electrosurgical knife surgery guided by PDE,while the conventional group(50 cases)underwent the same surgery guided by traditional digestive endoscopy(DE).The total treatment efficacy,perioperative indicators,gastrointestinal hormone levels,oxidative stress(OS)markers,and complication rates were compared between the two groups.Results:The total treatment efficacy in the painless group was higher than that in the conventional group,and perioperative indicators were superior in the painless group(P<0.05).One week after treatment,the gastrointestinal hormone levels and OS-related markers in the painless group were better than those in the conventional group(P<0.05).The complication rate in the painless group was lower than in the conventional group(P<0.05).Conclusion:High-frequency electrosurgical knife surgery guided by PDE improves the effectiveness of polyp removal in elderly GP patients and accelerates postoperative recovery.It also protects gastrointestinal function,reduces postoperative OS,and ensures higher surgical safety.展开更多
High-frequency pulsed(HFP)gas tungsten arc welding(GTAW)has shown excellent performance in welding of aluminum alloys in recent years,which makes itself a promisingly potential technique for part manufacturing in avia...High-frequency pulsed(HFP)gas tungsten arc welding(GTAW)has shown excellent performance in welding of aluminum alloys in recent years,which makes itself a promisingly potential technique for part manufacturing in aviation industry.However,existing researches generally focuses on the effect of a single parameter while lacks multivariable researches.Considering of the fact that gap and misalignment are inevitable in real part clamping,adaptive intelligent welding is usually used during automatic manufacturing,which means under the control of filler wire amount per length of a weld,other parameters including current,welding speed and wire feed speed during one single weld are changing according to the specific clamping situation.Therefore,the influence of specific energy input led by different welding parameters within one adaptive welding program on microstructure and mechanical property of the weld needs to be clarified.This study investigates the effect of welding heat input(ranging from 1048.3 J/mm to 825.6 J/mm within one adaptive welding program control)on the formation quality of 3.25 mm thick 6061 aluminum alloy joints fabricated by HFP-GTAW with 4043 filler wire.According to the obtained results,non-monotonic relationship between heat input and porosity,with an optimal minimum of 4.92%achieved at an intermediate heat input of 856.8 J/mm.The 21.2%decrease of energy input during welding process would reduce the average grain size in the weld center and adjacent to fusion line by 18.6%and 19.4%,respectively.The ratios between fluctuation range to minimum value in average yield and the relative ranges of yield strength and ultimate tensile strength across the tested heat inputs were 14.7%and 12.7%,respectively.The findings provide a general overview on how the microstructure and mechanical properties would fluctuate in an adaptively controlled HFP-GTAW fabricated aluminum alloy weld.展开更多
Soft magnetic composites made from metallic magnetic particles with an easy magnetization plane(referred to as easy-plane metallic soft magnetic composites(SMC))are considered ideal materials for the next generation o...Soft magnetic composites made from metallic magnetic particles with an easy magnetization plane(referred to as easy-plane metallic soft magnetic composites(SMC))are considered ideal materials for the next generation of power electronic devices.This advantage is attributed to their ability to maintain high permeability at elevated frequencies.Despite these advantages,a definitive mathematical model that connects the high-frequency magnetic properties(e.g.,effective permeability)of easy-plane metallic SMCs to the intrinsic properties of the particles is still lacking.In this work,a theoretical calculation model for the effective permeability of easy-plane metallic SMCs was formulated.This model was derived from a skin effect-corrected Landau-Lifshitz-Gilbert(LLG)equation and integrated with effective medium theory incorporating inter-particle interaction.To validate the model,we prepared samples of easy-plane Y_(2)Co_(17)particle/PU SMCs with varying particle sizes and volume fractions.The experimental results showed a strong agreement with the calculated values.This research offers critical theoretical backing for the design and optimization of soft magnetic materials intended for high-frequency applications.展开更多
With the evolution of DC distribution networks from traditional radial topologies to more complex multi-branch structures,the number of measurement points supporting synchronous communication remains relatively limite...With the evolution of DC distribution networks from traditional radial topologies to more complex multi-branch structures,the number of measurement points supporting synchronous communication remains relatively limited.This poses challenges for conventional fault distance estimation methods,which are often tailored to simple topologies and are thus difficult to apply to large-scale,multi-node DC networks.To address this,a fault distance estimation method based on sparse measurement of high-frequency electrical quantities is proposed in this paper.First,a preliminary fault line identification model based on compressed sensing is constructed to effectively narrow the fault search range and improve localization efficiency.Then,leveraging the high-frequency impedance characteristics and the voltage-current relationship of electrical quantities,a fault distance estimation approach based on high-frequency measurements from both ends of a line is designed.This enables accurate distance estimation even when the measurement devices are not directly placed at both ends of the faulted line,overcoming the dependence on specific sensor placement inherent in traditional methods.Finally,to further enhance accuracy,an optimization model based on minimizing the high-frequency voltage error at the fault point is introduced to reduce estimation error.Simulation results demonstrate that the proposed method achieves a fault distance estimation error of less than 1%under normal conditions,and maintains good performance even under adverse scenarios.展开更多
The valuation of financial derivatives often assumes risk neutrality with respect to the risk-neutral martingale measure,which prevents arbitrage opportunities.However,casual traders may still incur substantial losses...The valuation of financial derivatives often assumes risk neutrality with respect to the risk-neutral martingale measure,which prevents arbitrage opportunities.However,casual traders may still incur substantial losses when trading at this risk-neutral price,especially when the price has to be paid now and the payoff is only realized in the future.This study proposes a new valuation framework that provides risksensitive investors with an additional safeguard.The proposed framework embraces a worst-case perspective while exploiting the underlier’s stochastic process,representing a combination of robust optimization and stochastic programming.Notably,it aims to mitigate losses in the likelier scenarios of the underlying asset’s prices.When the underlier’s returns are independent and lognormally but not necessarily identically distributed,our approach for pricing variance and volatility swaps could be greatly simplified,benefit from parallel computing,and be solved by a two-dimensional grid search.We further derive a closed-form solution in some special stationary cases and provide experimental results to highlight the effect of risk aversion on fending off sizable trading losses.展开更多
In this paper,we incorporate Markov regime-switching into a two-factor stochastic volatility jump-diffusion model to enhance the pricing of power options.Furthermore,we assume that the interest rates and the jump inte...In this paper,we incorporate Markov regime-switching into a two-factor stochastic volatility jump-diffusion model to enhance the pricing of power options.Furthermore,we assume that the interest rates and the jump intensities of the assets are stochastic.Under the proposed framework,first,we derive the analytical pricing formula for power options by using Fourier transform technique,Esscher transform and characteristic function.Then we provide the efficient approximation to calculate the analytical pricing formula of power options by using the FFT approach and examine the accuracy of the approximation by Monte Carlo simulation.Finally,we provide some sensitivity analysis of the model parameters to power options.Numerical examples show this model is suitable for empirical work in practice.展开更多
基金supported by the National Nature Science Foundation of China(Nos.12027901 and 12041202)Synchrotron Radiation Joint Fund of University of Science and Technology of China(Nos.KY2090000059 and KY2090000054)。
文摘There is a contradiction between the evolution rate of materials and the time resolution of SR-CT characterization in the in situ synchrotron radiation computed tomography(SR-CT)characterization of ultrafast evolution process.The sampling strategy of the ultra-sparse angle is an effective method for improving time resolution.Accurate reconstruction under sparse sampling conditions has always been a bottleneck problem.In recent years,convolutional neural networks have shown outstanding advantages in sparse-angle CT reconstruction given the development of deep learning.However,existing ideas did not consider the expression of high-frequency details in neural networks,limiting their application in accurate SR-CT characterization.A novel high-frequency information-constrained deep learning network(HFIC-Net)is proposed in response to this problem.Additional high-frequency information constraints are added to improve the accuracy of the reconstruction results.Further,a series of numerical reconstruction experiments are conducted to verify this new method,and the results indicate that the reconstruction results of HFIC-Net method effectively improve reconstruction quality.This new method uses only eight-angle projections to achieve the reconstruction effect of the filtered backprojection method(FBP)method in 360 projections.The results of the HFIC-Net method demonstrate clear boundaries and accurate detailed structures,correcting the misinformation caused by using other methods.For quantitative evaluation,the SSIM used to evaluate image structure similarity is increased from 0.1951,0.9212,and 0.9308 for FBP,FBP-Conv,and DDC-Net,respectively,to 0.9620 for HFIC-Net.Finally,the results of actual SR-CT experimental data indicate that the new method can suppress artifacts and achieve accurate reconstruction,and it is suitable for the in situ SR-CT accurate characterization of ultxafast evolution process.
基金Project supported by the Basic Science Research Program through the National Research Foundation(NRF)of Korea funded by the Ministry of Science and ICT(No.RS-2024-00337001)。
文摘Physics-informed neural networks(PINNs)have been shown as powerful tools for solving partial differential equations(PDEs)by embedding physical laws into the network training.Despite their remarkable results,complicated problems such as irregular boundary conditions(BCs)and discontinuous or high-frequency behaviors remain persistent challenges for PINNs.For these reasons,we propose a novel two-phase framework,where a neural network is first trained to represent shape functions that can capture the irregularity of BCs in the first phase,and then these neural network-based shape functions are used to construct boundary shape functions(BSFs)that exactly satisfy both essential and natural BCs in PINNs in the second phase.This scheme is integrated into both the strong-form and energy PINN approaches,thereby improving the quality of solution prediction in the cases of irregular BCs.In addition,this study examines the benefits and limitations of these approaches in handling discontinuous and high-frequency problems.Overall,our method offers a unified and flexible solution framework that addresses key limitations of existing PINN methods with higher accuracy and stability for general PDE problems in solid mechanics.
基金Projects(71633006,7184207,7184210)supported by the National Natural Science Foundation of ChinaProject(2019CX016)supported by the Annual Innovation-driven Project in Central South University,China。
文摘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.
文摘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.
文摘This study evaluates the predictive accuracy of traditional time series(TS)models versus machine learning(ML)methods in forecasting realized volatility across major cryptocurrencies—Bitcoin(BTC),Ethereum(ETH),Litecoin(LTC),and Ripple(XRP).Employing high-frequency data,we analyze cross-cryptocurrency volatility dynamics through two complementary approaches:volatility forecasting and connectedness analysis.Our findings reveal three key insights:(i)TS models,particularly the heterogeneous autoregressive(HAR)model,exhibit superior predictive performance over their ML counterparts,with the long short-term memory(LSTM)model providing competitive yet inconsistent results due to overfitting and short-term volatility challenges;(ii)including lagged realized volatility of large-cap coins improves predictive accuracy for mid-cap coins,especially XRP,whereas forecasts for largecap coins remain stable,indicating more resilient volatility patterns;and(iii)volatility connectedness analysis reveals substantial spillover effects,particularly pronounced during market turmoil,with large-cap assets(BTC and ETH)acting as primary volatility transmitters and mid-cap assets(XRP and LTC)serving as volatility receivers.These results contribute to the understanding of volatility forecasting and risk management in cryptocurrency markets,offering implications for investors and policymakers in managing market risk and interdependencies in digital asset portfolios.
文摘Quanto options allow the buyer to exchange the foreign currency payoff into the domestic currency at a fixed exchange rate. We investigate quanto options with multiple underlying assets valued in different foreign currencies each with a different strike price in the payoff function. We carry out a comparative performance analysis of different stochastic volatility (SV), stochastic correlation (SC), and stochastic exchange rate (SER) models to determine the best combination of these models for Monte Carlo (MC) simulation pricing. In addition, we test the performance of all model variants with constant correlation as a benchmark. We find that a combination of GARCH-Jump SV, Weibull SC, and Ornstein Uhlenbeck (OU) SER performs best. In addition, we analyze different discretization schemes and their results. In our simulations, the Milstein scheme yields the best balance between execution times and lower standard deviations of price estimates. Furthermore, we find that incorporating mean reversion into stochastic correlation and stochastic FX rate modeling is beneficial for MC simulation pricing. We improve the accuracy of our simulations by implementing antithetic variates variance reduction. Finally, we derive the correlation risk parameters Cora and Gora in our framework so that correlation hedging of quanto options can be performed.
文摘Using daily BTC-USD data from September 19,2014 to January 21,2024,this paper re-examines whether weekends differ from weekdays for Bitcoin along three margins:average returns,close-to-close volatility,and trading activity.We implement Welch mean comparisons and HAC-robust OLS with month fixed effects(bandwidths 5,7,and 14).In the full sample and across subsamples(2016–2019;2020–2023;early 2024),we find no detectable weekend–weekday gap in average returns,while volatility and trading activity are lower on weekends.The patterns are robust to using squared returns as a volatility proxy.The joint evidence is consistent with liquidity and attention mechanisms—quieter weekends rather than compensating return premia.Replication files reproduce all tables and figures.
基金the National Natural Science Foundation of China(42074134,42474152,42374150)CNPC Innovation Found(2024DQ02-0152).
文摘The oil-based mud(OBM) borehole measurement environment presents significant limitations on the application of existing electrical logging instruments in high-resistance formations. In this paper, we propose a novel logging method for detection of high-resistance formations in OBM using highfrequency electrodes. The method addresses the issue of shallow depth of investigation(DOI) in existing electrical logging instruments, while simultaneously ensuring the vertical resolution. Based on the principle of current continuity, the total impedance of the loop is obtained by equating the measurement loop to the series form of a capacitively coupled circuit. and its validity is verified in a homogeneous formation model and a radial two-layer formation model with a mud standoff. Then, the instrument operating frequency and electrode system parameters were preferentially determined by numerical simulation, and the effect of mud gap on impedance measurement was investigated. Subsequently, the DOI of the instrument was investigated utilizing the pseudo-geometric factor defined by the real part of impedance. It was determined that the detection depth of the instrument is 8.74 cm, while the effective vertical resolution was not less than 2 cm. Finally, a focused high-frequency electrode-type instrument was designed by introducing a pair of focused electrodes, which effectively enhanced the DOI of the instrument and was successfully deployed in the Oklahoma formation model. The simulation results demonstrate that the novel method can achieve a detection depth of 17.40 cm in highly-resistive formations drilling with OBM, which is approximately twice the depth of detection of the existing oil-based mud microimager instruments. Furthermore, its effective vertical resolution remains at or above 2 cm,which is comparable to the resolution of the existing OBM electrical logging instrument.
文摘Objective:To analyze the significance of high-frequency ultrasound in differentiating benign and malignant breast micronodules.Methods:Eighty-five patients with breast micronodules admitted for diagnosis between October 2022 and October 2024 were selected for high-frequency ultrasound diagnosis.The diagnostic efficacy of high-frequency ultrasound was evaluated by comparing it with the results of surgical pathology.Results:High-frequency ultrasound detected 50 benign nodules,primarily breast fibroadenomas,and 35 malignant nodules,mainly breast ductal carcinoma in situ.Based on surgical pathology results,the diagnostic accuracy of high-frequency ultrasound was 96.47%,specificity was 97.96%,and sensitivity was 94.44%.In high-frequency ultrasound diagnosis,the proportion of grade III and IV blood flow in malignant nodules was higher than that in benign nodules,while the proportion of regular shape and clear margins was lower.The proportion of microcalcifications and posterior echo attenuation was higher in malignant nodules,and the resistance index(RI)and peak blood flow velocity were lower than those in benign nodules(P<0.05).Conclusion:High-frequency ultrasound can effectively differentiate benign and malignant breast micronodules,determine specific nodule types,and exhibits high diagnostic accuracy and sensitivity.Additionally,benign and malignant nodules can be differentiated based on the grading of blood flow signals,sonographic features,and blood flow velocity,providing reasonable guidance for subsequent treatment plans.
文摘Cryptocurrency,a booming decentralised asset designed based on the blockchain architecture,is particularly important to the market at the present time by studying the volatility risk of cryptocurrencies.In this paper,we empirically analyse the volatility risk of cryptocurrencies through quantitative analysis models,comprehensively using the Markov state transition GARCH model with skewed distribution(Skew-MSGARCH)and the autoregressive conditional volatility density ARJI model introducing the Poisson jump factor,and selecting the earliest developed and the most mature currency price volatility daily return series,to deeply explore the volatility risk of digital cryptocurrencies.risk.Finally,it can be seen through in-depth analyses that the expectation factor and information inducement are the main reasons leading to the exacerbation of the volatility risk of digital cryptocurrencies.It is recommended that this situation be optimised and improved in terms of the value function of digital cryptocurrencies themselves and the implementation of systematic risk management and regulatory innovation.As an important component of the digital economy,blockchain technology can effectively regulate and improve the volatility of digital cryptocurrencies under macroeconomic policies,thereby maintaining the security and stability of emerging financial markets.
文摘China has a long history of coal mining,among which open-pit coal mines have a large number of small coal mine goafs underground.The distribution,shape,structure and other characteristics of goafs are isolated and discontinuous,and there is no definite geological law to follow,which seriously threatens the safety of coal mine production and personnel life.Conventional ground geophysical methods have low accuracy in detecting goaf areas affected by mechanical interference from open-pit mines,especially for waterless goaf areas,which cannot be detected by existing methods.This article proposes the use of high-frequency electromagnetic waves for goaf detection.The feasibility of using drilling radar to detect goaf was theoretically analyzed,and a goaf detection model was established.The response characteristics of different fillers in the goaf under different frequencies of high-frequency electromagnetic waves were simulated and analyzed.In a certain open-pit mine in Inner Mongolia,100MHz high-frequency electromagnetic waves were used to detect the goaf through directional drilling on the ground.After detection,excavation verification was carried out,and the location of one goaf detected was verified.The results of engineering practice show that the application of high-frequency electromagnetic waves in goaf detection expands the detection radius of boreholes,has the advantages of high efficiency and accuracy,and has important theoretical and practical significance.
基金supported by the science and technology project of State Grid Shanghai Municipal Electric Power Company(No.52094023003L).
文摘New electric power systems characterized by a high proportion of renewable energy and power electronics equipment face significant challenges due to high-frequency(HF)electromagnetic interference from the high-speed switching of power converters.To address this situation,this paper offers an in-depth review of HF interference problems and challenges originating from power electronic devices.First,the root cause of HF electromagnetic interference,i.e.,the resonant response of the parasitic parameters of the system to high-speed switching transients,is analyzed,and various scenarios of HF interference in power systems are highlighted.Next,the types of HF interference are summarized,with a focus on common-mode interference in grounding systems.This paper thoroughly reviews and compares various suppression methods for conducted HF interference.Finally,the challenges involved and suggestions for addressing emerging HF interference problems from the perspective of both power electronics equipment and power systems are discussed.This review aims to offer a structured understanding of HF interference problems and their suppression techniques for researchers and practitioners.
基金supported by the National Science Foundation of China(No.12127806,No.62175195 and No.12304382)the International Joint Research Laboratory for Micro/Nano Manufacturing and Measurement Technologies.
文摘Single-shot ultrafast compressed imaging(UCI)is an effective tool for studying ultrafast dynamics in physics,chemistry,or material science because of its excellent high frame rate and large frame number.However,the random code(Rcode)used in traditional UCI will lead to low-frequency noise covering high-frequency information due to its uneven sampling interval,which is a great challenge in the fidelity of large-frame reconstruction.Here,a high-frequency enhanced compressed active photography(H-CAP)is proposed.By uniformizing the sampling interval of R-code,H-CAP capture the ultrafast process with a random uniform sampling mode.This sampling mode makes the high-frequency sampling energy dominant,which greatly suppresses the low-frequency noise blurring caused by R-code and achieves high-frequency information of image enhanced.The superior dynamic performance and large-frame reconstruction ability of H-CAP are verified by imaging optical self-focusing effect and static object,respectively.We applied H-CAP to the spatial-temporal characterization of double-pulse induced silicon surface ablation dynamics,which is performed within 220 frames in a single-shot of 300 ps.H-CAP provides a high-fidelity imaging method for observing ultrafast unrepeatable dynamic processes with large frames.
基金financial support from the National Natural Science Foundation of China(Grant Nos.71971079,72271087,and 71871088)the Major Projects of the National Social Science Foundation of China(Grant No.21ZDA114)+1 种基金the National Social Science Foundation of China(Grant No.19BTJ018)the Hunan Provincial Natural Science Foundation of China(Grant No.21JJ20019).
文摘Cryptocurrency is a remarkable financial innovation that has affected the financial system in fundamental ways.Its increasingly complex interactions with the conventional financial market make precisely forecasting its volatility increasingly challenging.To this end,we propose a novel framework based on the evolving multiscale graph neural network(EMGNN).Specifically,we embed a graph that depicts the interactions between the cryptocurrency and conventional financial markets into the predictive process.Furthermore,we employ hierarchical evolving graph structure learners to model the dynamic and scale-specific interactions.We also evaluate our framework’s robustness and discuss its interpretability by extracting the learned graph structure.The empirical results show that(i)cryptocurrency volatility is not isolated from the conventional market,and the embedded graph can provide effective information for prediction;(ii)the EMGNN-based forecasting framework generally yields outstanding and robust performance in terms of multiple volatility estimators,cryptocurrency samples,forecasting horizons,and evaluation criteria;and(iii)the graph structure in the predictive process varies over time and scales and is well captured by our framework.Overall,our work provides new insights into risk management for market participants and into policy formulation for authorities.
文摘Objective:To analyze the therapeutic effect of high-frequency electrosurgical knife surgery guided by painless digestive endoscopy(PDE)in elderly patients with gastrointestinal polyps(GP).Methods:A total of 100 elderly GP patients admitted between June 2021 and December 2022 were selected.Patients were randomly divided into two groups:the painless group(50 cases)underwent high-frequency electrosurgical knife surgery guided by PDE,while the conventional group(50 cases)underwent the same surgery guided by traditional digestive endoscopy(DE).The total treatment efficacy,perioperative indicators,gastrointestinal hormone levels,oxidative stress(OS)markers,and complication rates were compared between the two groups.Results:The total treatment efficacy in the painless group was higher than that in the conventional group,and perioperative indicators were superior in the painless group(P<0.05).One week after treatment,the gastrointestinal hormone levels and OS-related markers in the painless group were better than those in the conventional group(P<0.05).The complication rate in the painless group was lower than in the conventional group(P<0.05).Conclusion:High-frequency electrosurgical knife surgery guided by PDE improves the effectiveness of polyp removal in elderly GP patients and accelerates postoperative recovery.It also protects gastrointestinal function,reduces postoperative OS,and ensures higher surgical safety.
基金supported by the Commercial Aircraft Corporation of China Ltd.(Grant No.COMAC-SFGS-2024–569)Fundamental Research Funds for the Central Universities and Institute of Marine Equipment,Shanghai Rising-Star Program of Science and Technology Commission of Shanghai Municipality(Grant No.23QA1404700)+1 种基金National Natural Science Foundation of China(Grant No.52475384,52505409)China Postdoctoral Science Foundation(Grant No.2024M761963)。
文摘High-frequency pulsed(HFP)gas tungsten arc welding(GTAW)has shown excellent performance in welding of aluminum alloys in recent years,which makes itself a promisingly potential technique for part manufacturing in aviation industry.However,existing researches generally focuses on the effect of a single parameter while lacks multivariable researches.Considering of the fact that gap and misalignment are inevitable in real part clamping,adaptive intelligent welding is usually used during automatic manufacturing,which means under the control of filler wire amount per length of a weld,other parameters including current,welding speed and wire feed speed during one single weld are changing according to the specific clamping situation.Therefore,the influence of specific energy input led by different welding parameters within one adaptive welding program on microstructure and mechanical property of the weld needs to be clarified.This study investigates the effect of welding heat input(ranging from 1048.3 J/mm to 825.6 J/mm within one adaptive welding program control)on the formation quality of 3.25 mm thick 6061 aluminum alloy joints fabricated by HFP-GTAW with 4043 filler wire.According to the obtained results,non-monotonic relationship between heat input and porosity,with an optimal minimum of 4.92%achieved at an intermediate heat input of 856.8 J/mm.The 21.2%decrease of energy input during welding process would reduce the average grain size in the weld center and adjacent to fusion line by 18.6%and 19.4%,respectively.The ratios between fluctuation range to minimum value in average yield and the relative ranges of yield strength and ultimate tensile strength across the tested heat inputs were 14.7%and 12.7%,respectively.The findings provide a general overview on how the microstructure and mechanical properties would fluctuate in an adaptively controlled HFP-GTAW fabricated aluminum alloy weld.
基金supported by the National Key R&D Program of China(Grant No.2021YFB3501300)the 9th Research Institute of China Electronics Technology Group Corporation’s open projects(Grant No.2024SK-002-01)the Science and Technology Project of Gansu Province(Grant No.22YF7GA001).
文摘Soft magnetic composites made from metallic magnetic particles with an easy magnetization plane(referred to as easy-plane metallic soft magnetic composites(SMC))are considered ideal materials for the next generation of power electronic devices.This advantage is attributed to their ability to maintain high permeability at elevated frequencies.Despite these advantages,a definitive mathematical model that connects the high-frequency magnetic properties(e.g.,effective permeability)of easy-plane metallic SMCs to the intrinsic properties of the particles is still lacking.In this work,a theoretical calculation model for the effective permeability of easy-plane metallic SMCs was formulated.This model was derived from a skin effect-corrected Landau-Lifshitz-Gilbert(LLG)equation and integrated with effective medium theory incorporating inter-particle interaction.To validate the model,we prepared samples of easy-plane Y_(2)Co_(17)particle/PU SMCs with varying particle sizes and volume fractions.The experimental results showed a strong agreement with the calculated values.This research offers critical theoretical backing for the design and optimization of soft magnetic materials intended for high-frequency applications.
基金National Natural Science Foundation of China, grant number 52177074.
文摘With the evolution of DC distribution networks from traditional radial topologies to more complex multi-branch structures,the number of measurement points supporting synchronous communication remains relatively limited.This poses challenges for conventional fault distance estimation methods,which are often tailored to simple topologies and are thus difficult to apply to large-scale,multi-node DC networks.To address this,a fault distance estimation method based on sparse measurement of high-frequency electrical quantities is proposed in this paper.First,a preliminary fault line identification model based on compressed sensing is constructed to effectively narrow the fault search range and improve localization efficiency.Then,leveraging the high-frequency impedance characteristics and the voltage-current relationship of electrical quantities,a fault distance estimation approach based on high-frequency measurements from both ends of a line is designed.This enables accurate distance estimation even when the measurement devices are not directly placed at both ends of the faulted line,overcoming the dependence on specific sensor placement inherent in traditional methods.Finally,to further enhance accuracy,an optimization model based on minimizing the high-frequency voltage error at the fault point is introduced to reduce estimation error.Simulation results demonstrate that the proposed method achieves a fault distance estimation error of less than 1%under normal conditions,and maintains good performance even under adverse scenarios.
基金supported by the Ministry of Education,Singapore,under its Academic Research Fund Tier 2 Grant MOE-T2EP20222-0003.
文摘The valuation of financial derivatives often assumes risk neutrality with respect to the risk-neutral martingale measure,which prevents arbitrage opportunities.However,casual traders may still incur substantial losses when trading at this risk-neutral price,especially when the price has to be paid now and the payoff is only realized in the future.This study proposes a new valuation framework that provides risksensitive investors with an additional safeguard.The proposed framework embraces a worst-case perspective while exploiting the underlier’s stochastic process,representing a combination of robust optimization and stochastic programming.Notably,it aims to mitigate losses in the likelier scenarios of the underlying asset’s prices.When the underlier’s returns are independent and lognormally but not necessarily identically distributed,our approach for pricing variance and volatility swaps could be greatly simplified,benefit from parallel computing,and be solved by a two-dimensional grid search.We further derive a closed-form solution in some special stationary cases and provide experimental results to highlight the effect of risk aversion on fending off sizable trading losses.
文摘In this paper,we incorporate Markov regime-switching into a two-factor stochastic volatility jump-diffusion model to enhance the pricing of power options.Furthermore,we assume that the interest rates and the jump intensities of the assets are stochastic.Under the proposed framework,first,we derive the analytical pricing formula for power options by using Fourier transform technique,Esscher transform and characteristic function.Then we provide the efficient approximation to calculate the analytical pricing formula of power options by using the FFT approach and examine the accuracy of the approximation by Monte Carlo simulation.Finally,we provide some sensitivity analysis of the model parameters to power options.Numerical examples show this model is suitable for empirical work in practice.
