This study numerically examines the heat and mass transfer characteristics of two ternary nanofluids via converging and diverg-ing channels.Furthermore,the study aims to assess two ternary nanofluids combinations to d...This study numerically examines the heat and mass transfer characteristics of two ternary nanofluids via converging and diverg-ing channels.Furthermore,the study aims to assess two ternary nanofluids combinations to determine which configuration can provide better heat and mass transfer and lower entropy production,while ensuring cost efficiency.This work bridges the gap be-tween academic research and industrial feasibility by incorporating cost analysis,entropy generation,and thermal efficiency.To compare the velocity,temperature,and concentration profiles,we examine two ternary nanofluids,i.e.,TiO_(2)+SiO_(2)+Al_(2)O_(3)/H_(2)O and TiO_(2)+SiO_(2)+Cu/H_(2)O,while considering the shape of nanoparticles.The velocity slip and Soret/Dufour effects are taken into consideration.Furthermore,regression analysis for Nusselt and Sherwood numbers of the model is carried out.The Runge-Kutta fourth-order method with shooting technique is employed to acquire the numerical solution of the governed system of ordinary differential equations.The flow pattern attributes of ternary nanofluids are meticulously examined and simulated with the fluc-tuation of flow-dominating parameters.Additionally,the influence of these parameters is demonstrated in the flow,temperature,and concentration fields.For variation in Eckert and Dufour numbers,TiO_(2)+SiO_(2)+Al_(2)O_(3)/H_(2)O has a higher temperature than TiO_(2)+SiO_(2)+Cu/H_(2)O.The results obtained indicate that the ternary nanofluid TiO_(2)+SiO_(2)+Al_(2)O_(3)/H_(2)O has a higher heat transfer rate,lesser entropy generation,greater mass transfer rate,and lower cost than that of TiO_(2)+SiO_(2)+Cu/H_(2)O ternary nanofluid.展开更多
In this study,we examine the problem of sliced inverse regression(SIR),a widely used method for sufficient dimension reduction(SDR).It was designed to find reduced-dimensional versions of multivariate predictors by re...In this study,we examine the problem of sliced inverse regression(SIR),a widely used method for sufficient dimension reduction(SDR).It was designed to find reduced-dimensional versions of multivariate predictors by replacing them with a minimally adequate collection of their linear combinations without loss of information.Recently,regularization methods have been proposed in SIR to incorporate a sparse structure of predictors for better interpretability.However,existing methods consider convex relaxation to bypass the sparsity constraint,which may not lead to the best subset,and particularly tends to include irrelevant variables when predictors are correlated.In this study,we approach sparse SIR as a nonconvex optimization problem and directly tackle the sparsity constraint by establishing the optimal conditions and iteratively solving them by means of the splicing technique.Without employing convex relaxation on the sparsity constraint and the orthogonal constraint,our algorithm exhibits superior empirical merits,as evidenced by extensive numerical studies.Computationally,our algorithm is much faster than the relaxed approach for the natural sparse SIR estimator.Statistically,our algorithm surpasses existing methods in terms of accuracy for central subspace estimation and best subset selection and sustains high performance even with correlated predictors.展开更多
In this article,the multi-parameters Mittag-Leffler function is studied in detail.As a consequence,a series of novel results such as the integral representation,series representation and Mellin transform to the above ...In this article,the multi-parameters Mittag-Leffler function is studied in detail.As a consequence,a series of novel results such as the integral representation,series representation and Mellin transform to the above function,are obtained.Especially,we associate the multi-parameters Mittag-Leffler function with two special functions which are the generalized Wright hypergeometric and the Fox’s-H functions.Meanwhile,some interesting integral operators and derivative operators of this function,are also discussed.展开更多
Multi-parameter quantum estimation has attracted considerable attention due to its broad applications.Due to the complexity of quantum dynamics,existing research places significant emphasis on estimating parameters in...Multi-parameter quantum estimation has attracted considerable attention due to its broad applications.Due to the complexity of quantum dynamics,existing research places significant emphasis on estimating parameters in time-independent Hamiltonians.Here,our work makes an effort to explore multi-parameter estimation with time-dependent Hamiltonians.In particular,we focus on the discrimination of two close frequencies of a magnetic field by using a single qubit.We optimize the quantum controls by employing both traditional optimization methods and reinforcement learning to improve the precision for estimating the frequencies of the two magnetic fields.In addition to the estimation precision,we also evaluate the robustness of the optimization schemes against the shift of the control parameters.The results demonstrate that the hybrid reinforcement learning approach achieves the highest estimation precision,and exhibits superior robustness.Moreover,a fundamental challenge in multi-parameter quantum estimation stems from the incompatibility of the optimal control strategies for different parameters.We demonstrate that the hybrid control strategies derived through numerical optimization remain effective in enhancing the precision of multi-parameter estimation in spite of the incompatibilities,thereby mitigating incompatibilities between control strategies on the estimation precision.Finally,we investigate the trade-offs in estimation precision among different parameters for different scenarios,revealing the inherent challenges in balancing the optimization of multiple parameters simultaneously and providing insights into the fundamental distinction between quantum single-parameter estimation and multi-parameter estimation.展开更多
The impact of different global and local variables in urban development processes requires a systematic study to fully comprehend the underlying complexities in them.The interplay between such variables is crucial for...The impact of different global and local variables in urban development processes requires a systematic study to fully comprehend the underlying complexities in them.The interplay between such variables is crucial for modelling urban growth to closely reflects reality.Despite extensive research,ambiguity remains about how variations in these input variables influence urban densification.In this study,we conduct a global sensitivity analysis(SA)using a multinomial logistic regression(MNL)model to assess the model’s explanatory and predictive power.We examine the influence of global variables,including spatial resolution,neighborhood size,and density classes,under different input combinations at a provincial scale to understand their impact on densification.Additionally,we perform a stepwise regression to identify the significant explanatory variables that are important for understanding densification in the Brussels Metropolitan Area(BMA).Our results indicate that a finer spatial resolution of 50 m and 100 m,smaller neighborhood size of 5×5 and 3×3,and specific density classes—namely 3(non-built-up,low and high built-up)and 4(non-built-up,low,medium and high built-up)—optimally explain and predict urban densification.In line with the same,the stepwise regression reveals that models with a coarser resolution of 300 m lack significant variables,reflecting a lower explanatory power for densification.This approach aids in identifying optimal and significant global variables with higher explanatory power for understanding and predicting urban densification.Furthermore,these findings are reproducible in a global urban context,offering valuable insights for planners,modelers and geographers in managing future urban growth and minimizing modelling.展开更多
Triaxial tests,a staple in rock engineering,are labor-intensive,sample-demanding,and costly,making their optimization highly advantageous.These tests are essential for characterizing rock strength,and by adopting a fa...Triaxial tests,a staple in rock engineering,are labor-intensive,sample-demanding,and costly,making their optimization highly advantageous.These tests are essential for characterizing rock strength,and by adopting a failure criterion,they allow for the derivation of criterion parameters through regression,facilitating their integration into modeling programs.In this study,we introduce the application of an underutilized statistical technique—orthogonal regression—well-suited for analyzing triaxial test data.Additionally,we present an innovation in this technique by minimizing the Euclidean distance while incorporating orthogonality between vectors as a constraint,for the case of orthogonal linear regression.Also,we consider the Modified Least Squares method.We exemplify this approach by developing the necessary equations to apply the Mohr-Coulomb,Murrell,Hoek-Brown,andÚcar criteria,and implement these equations in both spreadsheet calculations and R scripts.Finally,we demonstrate the technique's application using five datasets of varied lithologies from specialized literature,showcasing its versatility and effectiveness.展开更多
Gastric cancer is the third leading cause of cancer-related mortality and remains a major global health issue^([1]).Annually,approximately 479,000individuals in China are diagnosed with gastric cancer,accounting for a...Gastric cancer is the third leading cause of cancer-related mortality and remains a major global health issue^([1]).Annually,approximately 479,000individuals in China are diagnosed with gastric cancer,accounting for almost 45%of all new cases worldwide^([2]).展开更多
Based on waveform fitting,full waveform inversion(FWI)is an important inversion method with the ability to reconstruct multi-parameter models in high precision.However,the strong nonlinear equation used in FWI present...Based on waveform fitting,full waveform inversion(FWI)is an important inversion method with the ability to reconstruct multi-parameter models in high precision.However,the strong nonlinear equation used in FWI presents the following challenges,such as low convergence efficiency,high dependence on the initial model,and the energy imbalance in deep region of the inverted model.To solve these inherent problems,we develop a timedomain elastic FWI method based on gradient preconditioning with the following details:(1)the limited memory Broyden Fletcher Goldfarb Shanno method with faster convergence is adopted to im-prove the inversion stability;(2)a multi-scaled inversion strategy is used to alleviate the nonlinear inversion instead of falling into the local minimum;(3)in addition,the pseudo-Hessian preconditioned illumination operator is involved for preconditioning the parameter gradients to improve the illumination equilibrium degree of deep structures.Based on the programming implementation of the new method,a deep depression model with five diffractors is used for testing.Compared with the conventional elastic FWI method,the technique proposed by this study has better effectiveness and accuracy on the inversion effect and con-vergence,respectively.展开更多
In recent years,machine learning(ML)techniques have been shown to be effective in accelerating the development process of optoelectronic devices.However,as"black box"models,they have limited theoretical inte...In recent years,machine learning(ML)techniques have been shown to be effective in accelerating the development process of optoelectronic devices.However,as"black box"models,they have limited theoretical interpretability.In this work,we leverage symbolic regression(SR)technique for discovering the explicit symbolic relationship between the structure of the optoelectronic Fabry-Perot(FP)laser and its optical field distribution,which greatly improves model transparency compared to ML.We demonstrated that the expressions explored through SR exhibit lower errors on the test set compared to ML models,which suggests that the expressions have better fitting and generalization capabilities.展开更多
Background:The COVID-1’s impact on influenza activity is of interest to inform future flu prevention and control strategies.Our study aim to examine COVID-19’s effects on influenza in Fujian Province,China,using a r...Background:The COVID-1’s impact on influenza activity is of interest to inform future flu prevention and control strategies.Our study aim to examine COVID-19’s effects on influenza in Fujian Province,China,using a regression discontinuity design.Methods:We utilized influenza-like illness(ILI)percentage as an indicator of influenza activity,with data from all sentinel hospitals between Week 4,2020,and Week 51,2023.The data is divided into two groups:the COVID-19 epidemic period and the post-epidemic period.Statistical analysis was performed with R software using robust RD design methods to account for potential confounders including seasonality,temperature,and influenza vaccination rates.Results:There was a discernible increase in the ILI percentage during the post-epidemic period.The robustness of the findings was confirmed with various RD design bandwidth selection methods and placebo tests,with certwo bandwidth providing the largest estimated effect size:a 14.6-percentage-point increase in the ILI percentage(β=0.146;95%CI:0.096–0.196).Sensitivity analyses and adjustments for confounders consistently pointed to an increased ILI percentage during the post-epidemic period compared to the epidemic period.Conclusion:The 14.6 percentage-point increase in the ILI percentage in Fujian Province,China,after the end of the COVID-19 pandemic suggests that there may be a need to re-evaluate and possibly enhance public health measures to control influenza transmission.Further research is needed to fully understand the factors contributing to this rise and to assess the ongoing impacts of post-pandemic behavioral changes.展开更多
This opinion article discusses the original research work of Yünkül et al.(the Authors)published in the Journal of Mountain Science 21(9):3108–3122.Employing non-linear regression,fuzzy logic and artificial...This opinion article discusses the original research work of Yünkül et al.(the Authors)published in the Journal of Mountain Science 21(9):3108–3122.Employing non-linear regression,fuzzy logic and artificial neural network modeling techniques,the Authors interrogated a large database assembled from the existing research literature to assess the performance of twelve equation rules in predicting the undrained shear strength(s_(u))mobilized for remolded fine-grained soils at different values of liquidity index(I_(L))and water content ratio.Based on their analyses,the Authors proposed a simple and reportedly reliable correlation(i.e.,Eq.9 in their paper)for predicting s_(u) over the I_(L) range of 0.15 to 3.00.This article describes various shortcomings in the Authors’assembled database(including potentially anomalous data and covering an excessively wide I_(L) range in relation to routine geotechnical and transportation engineering applications)and their proposed s_(u)=f(I_(L))correlation.Contrary to the Authors’assertions,their proposed correlation is not reliable for fine-grained soils with consistencies in the general firm to stiff range(i.e.,for 0.15<I_(L)<0.40),increasingly overestimating s_(u) for reducing I_(L),and eventually predicting s_(u)→+∞for I_(L)→0.15+(while producing mathematically undefined s_(u) for I_(L)<0.15),thus rendering their correlation unconservative and potentially leading to unsafe geotechnical designs.Exponential or regular-power type s_(u)=f(I_(L))models are more s_(u)itable when developing correlations that are applicable over the full plastic range(of 0<I_(L)<1),thereby providing reasonably conservative s_(u) predictions for use in the preliminary design for routine geotechnical engineering applications.展开更多
In clinical research,subgroup analysis can help identify patient groups that respond better or worse to specific treatments,improve therapeutic effect and safety,and is of great significance in precision medicine.This...In clinical research,subgroup analysis can help identify patient groups that respond better or worse to specific treatments,improve therapeutic effect and safety,and is of great significance in precision medicine.This article considers subgroup analysis methods for longitudinal data containing multiple covariates and biomarkers.We divide subgroups based on whether a linear combination of these biomarkers exceeds a predetermined threshold,and assess the heterogeneity of treatment effects across subgroups using the interaction between subgroups and exposure variables.Quantile regression is used to better characterize the global distribution of the response variable and sparsity penalties are imposed to achieve variable selection of covariates and biomarkers.The effectiveness of our proposed methodology for both variable selection and parameter estimation is verified through random simulations.Finally,we demonstrate the application of this method by analyzing data from the PA.3 trial,further illustrating the practicality of the method proposed in this paper.展开更多
High-dimensional heterogeneous data have acquired increasing attention and discussion in the past decade.In the context of heterogeneity,semiparametric regression emerges as a popular method to model this type of data...High-dimensional heterogeneous data have acquired increasing attention and discussion in the past decade.In the context of heterogeneity,semiparametric regression emerges as a popular method to model this type of data in statistics.In this paper,we leverage the benefits of expectile regression for computational efficiency and analytical robustness in heterogeneity,and propose a regularized partially linear additive expectile regression model with a nonconvex penalty,such as SCAD or MCP,for high-dimensional heterogeneous data.We focus on a more realistic scenario where the regression error exhibits a heavy-tailed distribution with only finite moments.This scenario challenges the classical sub-gaussian distribution assumption and is more prevalent in practical applications.Under certain regular conditions,we demonstrate that with probability tending to one,the oracle estimator is one of the local minima of the induced optimization problem.Our theoretical analysis suggests that the dimensionality of linear covariates that our estimation procedure can handle is fundamentally limited by the moment condition of the regression error.Computationally,given the nonconvex and nonsmooth nature of the induced optimization problem,we have developed a two-step algorithm.Finally,our method’s effectiveness is demonstrated through its high estimation accuracy and effective model selection,as evidenced by Monte Carlo simulation studies and a real-data application.Furthermore,by taking various expectile weights,our method effectively detects heterogeneity and explores the complete conditional distribution of the response variable,underscoring its utility in analyzing high-dimensional heterogeneous data.展开更多
Branch size is a crucial characteristic,closely linked to both tree growth and wood quality.A review of existing branch size models reveals various approaches,but the ability to estimate branch diameter and length wit...Branch size is a crucial characteristic,closely linked to both tree growth and wood quality.A review of existing branch size models reveals various approaches,but the ability to estimate branch diameter and length within the same whorl remains underexplored.In this study,a total of 77 trees were sampled from Northeast China to model the vertical distribution of branch diameter and length within each whorl along the crown.Several commonly used functions were taken as the alternative model forms,and the quantile regression method was employed and compared with the classical two-step modeling approach.The analysis incorporated stand,tree,and competition factors,with a particular focus on how these factors influence branches of varying sizes.The modified Weibull function was chosen as the optimal model,due to its excellent performance across all quantiles.Eight quantile regression curves(ranging from 0.20 to 0.85)were combined to predict branch diameter,while seven curves(ranging from 0.20 to 0.80)were used for branch length.The results showed that the quantile regression method outperformed the classical approach at model fitting and validation,likely due to its ability to estimate different rates of change across the entire branch size distribution.Lager branches in each whorl were more sensitive to changes in DBH,crown length(CL),crown ratio(CR)and dominant tree height(H_(dom)),while slenderness(HDR)more effectively influenced small and medium-sized branches.The effect of stand basal area(BAS)was relatively consistent across different branch sizes.The findings indicate that quantile regression is a good way not only a more accurate method for predicting branch size but also a valuable tool for understanding how branch growth responds to stand and tree factors.The models developed in this study are prepared to be further integrated into tree growth and yield simulation system,contributing to the assessment and promotion of wood quality.展开更多
Global Navigation Satellite System(GNSS)imaging method(GIM)has been successfully applied to global regions to investigate vertical land motion(VLM)of the Earth's surface.GNSS images derived from conventional GIM m...Global Navigation Satellite System(GNSS)imaging method(GIM)has been successfully applied to global regions to investigate vertical land motion(VLM)of the Earth's surface.GNSS images derived from conventional GIM method may present fragmented patches and encounter problems caused by excessive smoothing of velocity peaks,leading to difficulty in short-wavelength deformation detection and improper geophysical interpretation.Therefore,we propose a novel GNSS imaging method based on Gaussian process regression with velocity uncertainty considered(GPR-VU).Gaussian processing regression is introduced to describe the spatial relationship between neighboring site pairs as a priori weights and then reweight velocities by known station uncertainties,converting the discrete velocity field to a continuous one.The GPR-VU method is applied to reconstruct VLM images in the southwestern United States and the eastern Qinghai-Xizang Plateau,China,using the GNSS position time series in vertical direction.Compared to the traditional GIM method,the root-mean-square(RMS)and overall accuracy of the confusion matrix of the GPR-VU method increase by 5.0%and 14.0%from the 1°×1°checkerboard test in the southwestern United States.Similarly,the RMS and overall accuracy increase by 33.7%and 15.8%from the 6°×6°checkerboard test in the eastern Qinghai-Xizang Plateau.These checkerboard tests validate the capability to effectively capture the spatiotemporal variations characteristics of VLM and show that this algorithm outperforms the sparsely distributed network in the Qinghai-Xizang Plateau.The images from the GPR-VU method using real data in both regions show significant subsidence around Lassen Volcanic in northern California within a 30 km radius,slight uplift in the northern Sichuan Basin,and subsidence in its central and southern sections.These results further qualitatively illustrate consistency with previous findings.The GPR-VU method outperforms in diminishing the effect by fragmented patches,excessive smoothing of velocity peaks,and detecting potential short-wavelength deformations.展开更多
The packaging quality of coaxial laser diodes(CLDs)plays a pivotal role in determining their optical performance and long-term reliability.As the core packaging process,high-precision laser welding requires precise co...The packaging quality of coaxial laser diodes(CLDs)plays a pivotal role in determining their optical performance and long-term reliability.As the core packaging process,high-precision laser welding requires precise control of process parameters to suppress optical power loss.However,the complex nonlinear relationship between welding parameters and optical power loss renders traditional trial-and-error methods inefficient and imprecise.To address this challenge,a physics-informed(PI)and data-driven collaboration approach for welding parameter optimization is proposed.First,thermal-fluid-solid coupling finite element method(FEM)was employed to quantify the sensitivity of welding parameters to physical characteristics,including residual stress.This analysis facilitated the identification of critical factors contributing to optical power loss.Subsequently,a Gaussian process regression(GPR)model incorporating finite element simulation prior knowledge was constructed based on the selected features.By introducing physics-informed kernel(PIK)functions,stress distribution patterns were embedded into the prediction model,achieving high-precision optical power loss prediction.Finally,a Bayesian optimization(BO)algorithm with an adaptive sampling strategy was implemented for efficient parameter space exploration.Experimental results demonstrate that the proposedmethod effectively establishes explicit physical correlations between welding parameters and optical power loss.The optimized welding parameters reduced optical power loss by 34.1%,providing theoretical guidance and technical support for reliable CLD packaging.展开更多
Pinus densiflora is a pine species native to the Korean peninsula,and seed orchards have supplied mate-rial needed for afforestation in South Korea.Climate vari-ables affecting seed production have not been identified...Pinus densiflora is a pine species native to the Korean peninsula,and seed orchards have supplied mate-rial needed for afforestation in South Korea.Climate vari-ables affecting seed production have not been identified.The purpose of this study was to determine climate variables that influence annual seed production of two seed orchards using multiple linear regression(MLR),elastic net regres-sion(ENR)and partial least square regression(PLSR)mod-els.The PLSR model included 12 climatic variables from 2003 to 2020 and explained 74.3%of the total variation in seed production.It showed better predictive performance(R2=0.662)than the EN(0.516)and the MLR(0.366)mod-els.Among the 12 climatic variables,July temperature two years prior to seed production and July precipitation after one year had the strongest influence on seed production.The time periods indicated by the two variables corresponded to pollen cone initiation and female gametophyte development.The results will be helpful for developing seed collection plans,selecting new orchard sites with favorable climatic conditions,and investigating the relationships between seed production and climatic factors in related pine species.展开更多
Seismic fault rupture can extend to the surface,and the resulting surface deformation can cause severe damage to civil engineering structures crossing the fault zones.Coseismic Surface Rupture Prediction Models(CSRPMs...Seismic fault rupture can extend to the surface,and the resulting surface deformation can cause severe damage to civil engineering structures crossing the fault zones.Coseismic Surface Rupture Prediction Models(CSRPMs)play a crucial role in the structural design of fault-crossing engineering and in the hazard analysis of fault-intensive areas.In this study,a new global coseismic surface rupture database was constructed by compiling 171 earthquake events(Mw:5.5-7.9)that caused surface rupture.In contrast to the fault classification in traditional empirical relationships,this study categorizes earthquake events as strike-slip,dip-slip,and oblique-slip.CSRPMs utilizing Bayesian ridge regression(BRR)were developed to estimate parameters such as surface rupture length,average displacement,and maximum displacement.Based on Bayesian theory,BRR combines the benefits of both ridge regression and Bayesian linear regression.This approach effectively addresses the issue of overfitting while ensuring the strong model robustness.The reliability of the CSRPMs was validated by residual analysis and comparison with post-earthquake observations from the 2023 Türkiye earthquake doublet.The BRR-CSRPMs with new fault classification criteria are more suitable for the probabilistic hazard analysis of complex fault systems and dislocation design of fault-crossing engineering.展开更多
Knowing the influence of the size of datasets for regression models can help in improving the accuracy of a solar power forecast and make the most out of renewable energy systems.This research explores the influence o...Knowing the influence of the size of datasets for regression models can help in improving the accuracy of a solar power forecast and make the most out of renewable energy systems.This research explores the influence of dataset size on the accuracy and reliability of regression models for solar power prediction,contributing to better forecasting methods.The study analyzes data from two solar panels,aSiMicro03036 and aSiTandem72-46,over 7,14,17,21,28,and 38 days,with each dataset comprising five independent and one dependent parameter,and split 80–20 for training and testing.Results indicate that Random Forest consistently outperforms other models,achieving the highest correlation coefficient of 0.9822 and the lowest Mean Absolute Error(MAE)of 2.0544 on the aSiTandem72-46 panel with 21 days of data.For the aSiMicro03036 panel,the best MAE of 4.2978 was reached using the k-Nearest Neighbor(k-NN)algorithm,which was set up as instance-based k-Nearest neighbors(IBk)in Weka after being trained on 17 days of data.Regression performance for most models(excluding IBk)stabilizes at 14 days or more.Compared to the 7-day dataset,increasing to 21 days reduced the MAE by around 20%and improved correlation coefficients by around 2.1%,highlighting the value of moderate dataset expansion.These findings suggest that datasets spanning 17 to 21 days,with 80%used for training,can significantly enhance the predictive accuracy of solar power generation models.展开更多
Damage to electrical equipment in an earthquake can lead to power outage of power systems.Seismic fragility analysis is a common method to assess the seismic reliability of electrical equipment.To further guarantee th...Damage to electrical equipment in an earthquake can lead to power outage of power systems.Seismic fragility analysis is a common method to assess the seismic reliability of electrical equipment.To further guarantee the efficiency of analysis,multi-source uncertainties including the structure itself and seismic excitation need to be considered.A method for seismic fragility analysis that reflects structural and seismic parameter uncertainty was developed in this study.The proposed method used a random sampling method based on Latin hypercube sampling(LHS)to account for the structure parameter uncertainty and the group structure characteristics of electrical equipment.Then,logistic Lasso regression(LLR)was used to find the seismic fragility surface based on double ground motion intensity measures(IM).The seismic fragility based on the finite element model of an±1000 kV main transformer(UHVMT)was analyzed using the proposed method.The results show that the seismic fragility function obtained by this method can be used to construct the relationship between the uncertainty parameters and the failure probability.The seismic fragility surface did not only provide the probabilities of seismic damage states under different IMs,but also had better stability than the fragility curve.Furthermore,the sensitivity analysis of the structural parameters revealed that the elastic module of the bushing and the height of the high-voltage bushing may have a greater influence.展开更多
基金supported by DST-FIST(Government of India)(Grant No.SR/FIST/MS-1/2017/13)and Seed Money Project(Grant No.DoRDC/733).
文摘This study numerically examines the heat and mass transfer characteristics of two ternary nanofluids via converging and diverg-ing channels.Furthermore,the study aims to assess two ternary nanofluids combinations to determine which configuration can provide better heat and mass transfer and lower entropy production,while ensuring cost efficiency.This work bridges the gap be-tween academic research and industrial feasibility by incorporating cost analysis,entropy generation,and thermal efficiency.To compare the velocity,temperature,and concentration profiles,we examine two ternary nanofluids,i.e.,TiO_(2)+SiO_(2)+Al_(2)O_(3)/H_(2)O and TiO_(2)+SiO_(2)+Cu/H_(2)O,while considering the shape of nanoparticles.The velocity slip and Soret/Dufour effects are taken into consideration.Furthermore,regression analysis for Nusselt and Sherwood numbers of the model is carried out.The Runge-Kutta fourth-order method with shooting technique is employed to acquire the numerical solution of the governed system of ordinary differential equations.The flow pattern attributes of ternary nanofluids are meticulously examined and simulated with the fluc-tuation of flow-dominating parameters.Additionally,the influence of these parameters is demonstrated in the flow,temperature,and concentration fields.For variation in Eckert and Dufour numbers,TiO_(2)+SiO_(2)+Al_(2)O_(3)/H_(2)O has a higher temperature than TiO_(2)+SiO_(2)+Cu/H_(2)O.The results obtained indicate that the ternary nanofluid TiO_(2)+SiO_(2)+Al_(2)O_(3)/H_(2)O has a higher heat transfer rate,lesser entropy generation,greater mass transfer rate,and lower cost than that of TiO_(2)+SiO_(2)+Cu/H_(2)O ternary nanofluid.
文摘In this study,we examine the problem of sliced inverse regression(SIR),a widely used method for sufficient dimension reduction(SDR).It was designed to find reduced-dimensional versions of multivariate predictors by replacing them with a minimally adequate collection of their linear combinations without loss of information.Recently,regularization methods have been proposed in SIR to incorporate a sparse structure of predictors for better interpretability.However,existing methods consider convex relaxation to bypass the sparsity constraint,which may not lead to the best subset,and particularly tends to include irrelevant variables when predictors are correlated.In this study,we approach sparse SIR as a nonconvex optimization problem and directly tackle the sparsity constraint by establishing the optimal conditions and iteratively solving them by means of the splicing technique.Without employing convex relaxation on the sparsity constraint and the orthogonal constraint,our algorithm exhibits superior empirical merits,as evidenced by extensive numerical studies.Computationally,our algorithm is much faster than the relaxed approach for the natural sparse SIR estimator.Statistically,our algorithm surpasses existing methods in terms of accuracy for central subspace estimation and best subset selection and sustains high performance even with correlated predictors.
基金Supported by The National Undergraduate Innovation Training Program(Grant No.202310290069Z).
文摘In this article,the multi-parameters Mittag-Leffler function is studied in detail.As a consequence,a series of novel results such as the integral representation,series representation and Mellin transform to the above function,are obtained.Especially,we associate the multi-parameters Mittag-Leffler function with two special functions which are the generalized Wright hypergeometric and the Fox’s-H functions.Meanwhile,some interesting integral operators and derivative operators of this function,are also discussed.
基金supported by the National Natural Science Foundation of China(Grant No.12075323)the Innovation Program for Quantum Science and Technology(Grant No.2021ZD0300702).
文摘Multi-parameter quantum estimation has attracted considerable attention due to its broad applications.Due to the complexity of quantum dynamics,existing research places significant emphasis on estimating parameters in time-independent Hamiltonians.Here,our work makes an effort to explore multi-parameter estimation with time-dependent Hamiltonians.In particular,we focus on the discrimination of two close frequencies of a magnetic field by using a single qubit.We optimize the quantum controls by employing both traditional optimization methods and reinforcement learning to improve the precision for estimating the frequencies of the two magnetic fields.In addition to the estimation precision,we also evaluate the robustness of the optimization schemes against the shift of the control parameters.The results demonstrate that the hybrid reinforcement learning approach achieves the highest estimation precision,and exhibits superior robustness.Moreover,a fundamental challenge in multi-parameter quantum estimation stems from the incompatibility of the optimal control strategies for different parameters.We demonstrate that the hybrid control strategies derived through numerical optimization remain effective in enhancing the precision of multi-parameter estimation in spite of the incompatibilities,thereby mitigating incompatibilities between control strategies on the estimation precision.Finally,we investigate the trade-offs in estimation precision among different parameters for different scenarios,revealing the inherent challenges in balancing the optimization of multiple parameters simultaneously and providing insights into the fundamental distinction between quantum single-parameter estimation and multi-parameter estimation.
基金funded by the INTER program and cofunded by the Fond National de la Recherche,Luxembourg(FNR)and the Fund for Scientific Research-FNRS,Belgium(F.R.S-FNRS),T.0233.20-‘Sustainable Residential Densification’project(SusDens,2020–2024).
文摘The impact of different global and local variables in urban development processes requires a systematic study to fully comprehend the underlying complexities in them.The interplay between such variables is crucial for modelling urban growth to closely reflects reality.Despite extensive research,ambiguity remains about how variations in these input variables influence urban densification.In this study,we conduct a global sensitivity analysis(SA)using a multinomial logistic regression(MNL)model to assess the model’s explanatory and predictive power.We examine the influence of global variables,including spatial resolution,neighborhood size,and density classes,under different input combinations at a provincial scale to understand their impact on densification.Additionally,we perform a stepwise regression to identify the significant explanatory variables that are important for understanding densification in the Brussels Metropolitan Area(BMA).Our results indicate that a finer spatial resolution of 50 m and 100 m,smaller neighborhood size of 5×5 and 3×3,and specific density classes—namely 3(non-built-up,low and high built-up)and 4(non-built-up,low,medium and high built-up)—optimally explain and predict urban densification.In line with the same,the stepwise regression reveals that models with a coarser resolution of 300 m lack significant variables,reflecting a lower explanatory power for densification.This approach aids in identifying optimal and significant global variables with higher explanatory power for understanding and predicting urban densification.Furthermore,these findings are reproducible in a global urban context,offering valuable insights for planners,modelers and geographers in managing future urban growth and minimizing modelling.
文摘Triaxial tests,a staple in rock engineering,are labor-intensive,sample-demanding,and costly,making their optimization highly advantageous.These tests are essential for characterizing rock strength,and by adopting a failure criterion,they allow for the derivation of criterion parameters through regression,facilitating their integration into modeling programs.In this study,we introduce the application of an underutilized statistical technique—orthogonal regression—well-suited for analyzing triaxial test data.Additionally,we present an innovation in this technique by minimizing the Euclidean distance while incorporating orthogonality between vectors as a constraint,for the case of orthogonal linear regression.Also,we consider the Modified Least Squares method.We exemplify this approach by developing the necessary equations to apply the Mohr-Coulomb,Murrell,Hoek-Brown,andÚcar criteria,and implement these equations in both spreadsheet calculations and R scripts.Finally,we demonstrate the technique's application using five datasets of varied lithologies from specialized literature,showcasing its versatility and effectiveness.
基金supported by the Natural Science Foundation of Shanghai(23ZR1463600)Shanghai Pudong New Area Health Commission Research Project(PW2021A-69)Research Project of Clinical Research Center of Shanghai Health Medical University(22MC2022002)。
文摘Gastric cancer is the third leading cause of cancer-related mortality and remains a major global health issue^([1]).Annually,approximately 479,000individuals in China are diagnosed with gastric cancer,accounting for almost 45%of all new cases worldwide^([2]).
基金supported by the Marine S&T Fund of Shandong Province for Pilot National Laboratory for Marine Science and Technology(Qingdao)(Grant No.2021QNLM020001)the National Key R&D Program of China(Grant No.2019YFC0605503C)+2 种基金the Major Scientific and Technological Projects of China National Petroleum Corporation(CNPC)(Grant No.ZD2019-183-003)the National Outstanding Youth Science Foundation(Grant No.41922028)the National Innovation Group Project(Grant No.41821002).
文摘Based on waveform fitting,full waveform inversion(FWI)is an important inversion method with the ability to reconstruct multi-parameter models in high precision.However,the strong nonlinear equation used in FWI presents the following challenges,such as low convergence efficiency,high dependence on the initial model,and the energy imbalance in deep region of the inverted model.To solve these inherent problems,we develop a timedomain elastic FWI method based on gradient preconditioning with the following details:(1)the limited memory Broyden Fletcher Goldfarb Shanno method with faster convergence is adopted to im-prove the inversion stability;(2)a multi-scaled inversion strategy is used to alleviate the nonlinear inversion instead of falling into the local minimum;(3)in addition,the pseudo-Hessian preconditioned illumination operator is involved for preconditioning the parameter gradients to improve the illumination equilibrium degree of deep structures.Based on the programming implementation of the new method,a deep depression model with five diffractors is used for testing.Compared with the conventional elastic FWI method,the technique proposed by this study has better effectiveness and accuracy on the inversion effect and con-vergence,respectively.
基金supported by the National Natural Science Foundation of China(No.92370117)the CAS Project for Young Scientists in Basic Research(No.YSBR-090)。
文摘In recent years,machine learning(ML)techniques have been shown to be effective in accelerating the development process of optoelectronic devices.However,as"black box"models,they have limited theoretical interpretability.In this work,we leverage symbolic regression(SR)technique for discovering the explicit symbolic relationship between the structure of the optoelectronic Fabry-Perot(FP)laser and its optical field distribution,which greatly improves model transparency compared to ML.We demonstrated that the expressions explored through SR exhibit lower errors on the test set compared to ML models,which suggests that the expressions have better fitting and generalization capabilities.
基金supported by the Youth Scientific Research Project of Fujian Provincial Center for Disease Control and Prevention(2022QN02)the Fujian Provincial Health Youth Scientific Research Project(2023QNA040).
文摘Background:The COVID-1’s impact on influenza activity is of interest to inform future flu prevention and control strategies.Our study aim to examine COVID-19’s effects on influenza in Fujian Province,China,using a regression discontinuity design.Methods:We utilized influenza-like illness(ILI)percentage as an indicator of influenza activity,with data from all sentinel hospitals between Week 4,2020,and Week 51,2023.The data is divided into two groups:the COVID-19 epidemic period and the post-epidemic period.Statistical analysis was performed with R software using robust RD design methods to account for potential confounders including seasonality,temperature,and influenza vaccination rates.Results:There was a discernible increase in the ILI percentage during the post-epidemic period.The robustness of the findings was confirmed with various RD design bandwidth selection methods and placebo tests,with certwo bandwidth providing the largest estimated effect size:a 14.6-percentage-point increase in the ILI percentage(β=0.146;95%CI:0.096–0.196).Sensitivity analyses and adjustments for confounders consistently pointed to an increased ILI percentage during the post-epidemic period compared to the epidemic period.Conclusion:The 14.6 percentage-point increase in the ILI percentage in Fujian Province,China,after the end of the COVID-19 pandemic suggests that there may be a need to re-evaluate and possibly enhance public health measures to control influenza transmission.Further research is needed to fully understand the factors contributing to this rise and to assess the ongoing impacts of post-pandemic behavioral changes.
文摘This opinion article discusses the original research work of Yünkül et al.(the Authors)published in the Journal of Mountain Science 21(9):3108–3122.Employing non-linear regression,fuzzy logic and artificial neural network modeling techniques,the Authors interrogated a large database assembled from the existing research literature to assess the performance of twelve equation rules in predicting the undrained shear strength(s_(u))mobilized for remolded fine-grained soils at different values of liquidity index(I_(L))and water content ratio.Based on their analyses,the Authors proposed a simple and reportedly reliable correlation(i.e.,Eq.9 in their paper)for predicting s_(u) over the I_(L) range of 0.15 to 3.00.This article describes various shortcomings in the Authors’assembled database(including potentially anomalous data and covering an excessively wide I_(L) range in relation to routine geotechnical and transportation engineering applications)and their proposed s_(u)=f(I_(L))correlation.Contrary to the Authors’assertions,their proposed correlation is not reliable for fine-grained soils with consistencies in the general firm to stiff range(i.e.,for 0.15<I_(L)<0.40),increasingly overestimating s_(u) for reducing I_(L),and eventually predicting s_(u)→+∞for I_(L)→0.15+(while producing mathematically undefined s_(u) for I_(L)<0.15),thus rendering their correlation unconservative and potentially leading to unsafe geotechnical designs.Exponential or regular-power type s_(u)=f(I_(L))models are more s_(u)itable when developing correlations that are applicable over the full plastic range(of 0<I_(L)<1),thereby providing reasonably conservative s_(u) predictions for use in the preliminary design for routine geotechnical engineering applications.
基金Supported by the Natural Science Foundation of Fujian Province(2022J011177,2024J01903)the Key Project of Fujian Provincial Education Department(JZ230054)。
文摘In clinical research,subgroup analysis can help identify patient groups that respond better or worse to specific treatments,improve therapeutic effect and safety,and is of great significance in precision medicine.This article considers subgroup analysis methods for longitudinal data containing multiple covariates and biomarkers.We divide subgroups based on whether a linear combination of these biomarkers exceeds a predetermined threshold,and assess the heterogeneity of treatment effects across subgroups using the interaction between subgroups and exposure variables.Quantile regression is used to better characterize the global distribution of the response variable and sparsity penalties are imposed to achieve variable selection of covariates and biomarkers.The effectiveness of our proposed methodology for both variable selection and parameter estimation is verified through random simulations.Finally,we demonstrate the application of this method by analyzing data from the PA.3 trial,further illustrating the practicality of the method proposed in this paper.
基金Supported by the Hangzhou Joint Fund of the Zhejiang Provincial Natural Science Foundation of Chi-na(LHZY24A010002)the MOE Project of Humanities and Social Sciences(21YJCZH235).
文摘High-dimensional heterogeneous data have acquired increasing attention and discussion in the past decade.In the context of heterogeneity,semiparametric regression emerges as a popular method to model this type of data in statistics.In this paper,we leverage the benefits of expectile regression for computational efficiency and analytical robustness in heterogeneity,and propose a regularized partially linear additive expectile regression model with a nonconvex penalty,such as SCAD or MCP,for high-dimensional heterogeneous data.We focus on a more realistic scenario where the regression error exhibits a heavy-tailed distribution with only finite moments.This scenario challenges the classical sub-gaussian distribution assumption and is more prevalent in practical applications.Under certain regular conditions,we demonstrate that with probability tending to one,the oracle estimator is one of the local minima of the induced optimization problem.Our theoretical analysis suggests that the dimensionality of linear covariates that our estimation procedure can handle is fundamentally limited by the moment condition of the regression error.Computationally,given the nonconvex and nonsmooth nature of the induced optimization problem,we have developed a two-step algorithm.Finally,our method’s effectiveness is demonstrated through its high estimation accuracy and effective model selection,as evidenced by Monte Carlo simulation studies and a real-data application.Furthermore,by taking various expectile weights,our method effectively detects heterogeneity and explores the complete conditional distribution of the response variable,underscoring its utility in analyzing high-dimensional heterogeneous data.
基金supported by the Young Scientists Fund of the National Key R&D Program of China(No.2022YFD2201800)the Youth Science Fund Program of National Natural Science Foundation of China(No.32301581)+2 种基金the Joint Funds for Regional Innovation and Development of the National Natural Science Foundation of China(No.U21A20244)the China Postdoctoral Science Foundation(No.2024M750383)the Heilongjiang Touyan Innovation Team Program(Technology Development Team for High-Efficiency Silviculture of Forest Resources).
文摘Branch size is a crucial characteristic,closely linked to both tree growth and wood quality.A review of existing branch size models reveals various approaches,but the ability to estimate branch diameter and length within the same whorl remains underexplored.In this study,a total of 77 trees were sampled from Northeast China to model the vertical distribution of branch diameter and length within each whorl along the crown.Several commonly used functions were taken as the alternative model forms,and the quantile regression method was employed and compared with the classical two-step modeling approach.The analysis incorporated stand,tree,and competition factors,with a particular focus on how these factors influence branches of varying sizes.The modified Weibull function was chosen as the optimal model,due to its excellent performance across all quantiles.Eight quantile regression curves(ranging from 0.20 to 0.85)were combined to predict branch diameter,while seven curves(ranging from 0.20 to 0.80)were used for branch length.The results showed that the quantile regression method outperformed the classical approach at model fitting and validation,likely due to its ability to estimate different rates of change across the entire branch size distribution.Lager branches in each whorl were more sensitive to changes in DBH,crown length(CL),crown ratio(CR)and dominant tree height(H_(dom)),while slenderness(HDR)more effectively influenced small and medium-sized branches.The effect of stand basal area(BAS)was relatively consistent across different branch sizes.The findings indicate that quantile regression is a good way not only a more accurate method for predicting branch size but also a valuable tool for understanding how branch growth responds to stand and tree factors.The models developed in this study are prepared to be further integrated into tree growth and yield simulation system,contributing to the assessment and promotion of wood quality.
基金supported by the National Natural Science Foundation of China(Grant No.42274035)the Major Science and Technology Program for Hubei Province(No.2022AAA002)the Hunan Provincial Land Surveying and Mapping Project(HNGTCH-2023-05)。
文摘Global Navigation Satellite System(GNSS)imaging method(GIM)has been successfully applied to global regions to investigate vertical land motion(VLM)of the Earth's surface.GNSS images derived from conventional GIM method may present fragmented patches and encounter problems caused by excessive smoothing of velocity peaks,leading to difficulty in short-wavelength deformation detection and improper geophysical interpretation.Therefore,we propose a novel GNSS imaging method based on Gaussian process regression with velocity uncertainty considered(GPR-VU).Gaussian processing regression is introduced to describe the spatial relationship between neighboring site pairs as a priori weights and then reweight velocities by known station uncertainties,converting the discrete velocity field to a continuous one.The GPR-VU method is applied to reconstruct VLM images in the southwestern United States and the eastern Qinghai-Xizang Plateau,China,using the GNSS position time series in vertical direction.Compared to the traditional GIM method,the root-mean-square(RMS)and overall accuracy of the confusion matrix of the GPR-VU method increase by 5.0%and 14.0%from the 1°×1°checkerboard test in the southwestern United States.Similarly,the RMS and overall accuracy increase by 33.7%and 15.8%from the 6°×6°checkerboard test in the eastern Qinghai-Xizang Plateau.These checkerboard tests validate the capability to effectively capture the spatiotemporal variations characteristics of VLM and show that this algorithm outperforms the sparsely distributed network in the Qinghai-Xizang Plateau.The images from the GPR-VU method using real data in both regions show significant subsidence around Lassen Volcanic in northern California within a 30 km radius,slight uplift in the northern Sichuan Basin,and subsidence in its central and southern sections.These results further qualitatively illustrate consistency with previous findings.The GPR-VU method outperforms in diminishing the effect by fragmented patches,excessive smoothing of velocity peaks,and detecting potential short-wavelength deformations.
基金funded by the National Key R&D Program of China,Grant No.2024YFF0504904.
文摘The packaging quality of coaxial laser diodes(CLDs)plays a pivotal role in determining their optical performance and long-term reliability.As the core packaging process,high-precision laser welding requires precise control of process parameters to suppress optical power loss.However,the complex nonlinear relationship between welding parameters and optical power loss renders traditional trial-and-error methods inefficient and imprecise.To address this challenge,a physics-informed(PI)and data-driven collaboration approach for welding parameter optimization is proposed.First,thermal-fluid-solid coupling finite element method(FEM)was employed to quantify the sensitivity of welding parameters to physical characteristics,including residual stress.This analysis facilitated the identification of critical factors contributing to optical power loss.Subsequently,a Gaussian process regression(GPR)model incorporating finite element simulation prior knowledge was constructed based on the selected features.By introducing physics-informed kernel(PIK)functions,stress distribution patterns were embedded into the prediction model,achieving high-precision optical power loss prediction.Finally,a Bayesian optimization(BO)algorithm with an adaptive sampling strategy was implemented for efficient parameter space exploration.Experimental results demonstrate that the proposedmethod effectively establishes explicit physical correlations between welding parameters and optical power loss.The optimized welding parameters reduced optical power loss by 34.1%,providing theoretical guidance and technical support for reliable CLD packaging.
基金supported by the National Institute of Forest Sciencethe R&D Program for Forest Science Technology (No.2022458B10-2224-0201) of the Korea Forest Service
文摘Pinus densiflora is a pine species native to the Korean peninsula,and seed orchards have supplied mate-rial needed for afforestation in South Korea.Climate vari-ables affecting seed production have not been identified.The purpose of this study was to determine climate variables that influence annual seed production of two seed orchards using multiple linear regression(MLR),elastic net regres-sion(ENR)and partial least square regression(PLSR)mod-els.The PLSR model included 12 climatic variables from 2003 to 2020 and explained 74.3%of the total variation in seed production.It showed better predictive performance(R2=0.662)than the EN(0.516)and the MLR(0.366)mod-els.Among the 12 climatic variables,July temperature two years prior to seed production and July precipitation after one year had the strongest influence on seed production.The time periods indicated by the two variables corresponded to pollen cone initiation and female gametophyte development.The results will be helpful for developing seed collection plans,selecting new orchard sites with favorable climatic conditions,and investigating the relationships between seed production and climatic factors in related pine species.
基金Foundation of China under Grant Nos. U2139207 and 52378517the Natural Science Foundation of Hubei Province under Grant No. 2023AFB934
文摘Seismic fault rupture can extend to the surface,and the resulting surface deformation can cause severe damage to civil engineering structures crossing the fault zones.Coseismic Surface Rupture Prediction Models(CSRPMs)play a crucial role in the structural design of fault-crossing engineering and in the hazard analysis of fault-intensive areas.In this study,a new global coseismic surface rupture database was constructed by compiling 171 earthquake events(Mw:5.5-7.9)that caused surface rupture.In contrast to the fault classification in traditional empirical relationships,this study categorizes earthquake events as strike-slip,dip-slip,and oblique-slip.CSRPMs utilizing Bayesian ridge regression(BRR)were developed to estimate parameters such as surface rupture length,average displacement,and maximum displacement.Based on Bayesian theory,BRR combines the benefits of both ridge regression and Bayesian linear regression.This approach effectively addresses the issue of overfitting while ensuring the strong model robustness.The reliability of the CSRPMs was validated by residual analysis and comparison with post-earthquake observations from the 2023 Türkiye earthquake doublet.The BRR-CSRPMs with new fault classification criteria are more suitable for the probabilistic hazard analysis of complex fault systems and dislocation design of fault-crossing engineering.
文摘Knowing the influence of the size of datasets for regression models can help in improving the accuracy of a solar power forecast and make the most out of renewable energy systems.This research explores the influence of dataset size on the accuracy and reliability of regression models for solar power prediction,contributing to better forecasting methods.The study analyzes data from two solar panels,aSiMicro03036 and aSiTandem72-46,over 7,14,17,21,28,and 38 days,with each dataset comprising five independent and one dependent parameter,and split 80–20 for training and testing.Results indicate that Random Forest consistently outperforms other models,achieving the highest correlation coefficient of 0.9822 and the lowest Mean Absolute Error(MAE)of 2.0544 on the aSiTandem72-46 panel with 21 days of data.For the aSiMicro03036 panel,the best MAE of 4.2978 was reached using the k-Nearest Neighbor(k-NN)algorithm,which was set up as instance-based k-Nearest neighbors(IBk)in Weka after being trained on 17 days of data.Regression performance for most models(excluding IBk)stabilizes at 14 days or more.Compared to the 7-day dataset,increasing to 21 days reduced the MAE by around 20%and improved correlation coefficients by around 2.1%,highlighting the value of moderate dataset expansion.These findings suggest that datasets spanning 17 to 21 days,with 80%used for training,can significantly enhance the predictive accuracy of solar power generation models.
基金National Key R&D Program of China under Grant Nos.2018YFC1504504 and 2018YFC0809404。
文摘Damage to electrical equipment in an earthquake can lead to power outage of power systems.Seismic fragility analysis is a common method to assess the seismic reliability of electrical equipment.To further guarantee the efficiency of analysis,multi-source uncertainties including the structure itself and seismic excitation need to be considered.A method for seismic fragility analysis that reflects structural and seismic parameter uncertainty was developed in this study.The proposed method used a random sampling method based on Latin hypercube sampling(LHS)to account for the structure parameter uncertainty and the group structure characteristics of electrical equipment.Then,logistic Lasso regression(LLR)was used to find the seismic fragility surface based on double ground motion intensity measures(IM).The seismic fragility based on the finite element model of an±1000 kV main transformer(UHVMT)was analyzed using the proposed method.The results show that the seismic fragility function obtained by this method can be used to construct the relationship between the uncertainty parameters and the failure probability.The seismic fragility surface did not only provide the probabilities of seismic damage states under different IMs,but also had better stability than the fragility curve.Furthermore,the sensitivity analysis of the structural parameters revealed that the elastic module of the bushing and the height of the high-voltage bushing may have a greater influence.
