Nuclear mass is an important property in both nuclear and astrophysics.In this study,we explore an improved mass model that incorporates a higher-order term of symmetry energy using algorithms.The sequential least squ...Nuclear mass is an important property in both nuclear and astrophysics.In this study,we explore an improved mass model that incorporates a higher-order term of symmetry energy using algorithms.The sequential least squares programming(SLSQP)algorithm augments the precision of this multinomial mass model by reducing the error from 1.863 MeV to 1.631 MeV.These algorithms were further examined using 200 sample mass formulae derived from theδE term of the E_(isospin) mass model.The SLSQP method exhibited superior performance compared to the other algorithms in terms of errors and convergence speed.This algorithm is advantageous for handling large-scale multiparameter optimization tasks in nuclear physics.展开更多
Challenges in stratigraphic modeling arise from underground uncertainty.While borehole exploration is reliable,it remains sparse due to economic and site constraints.Electrical resistivity tomography(ERT)as a cost-eff...Challenges in stratigraphic modeling arise from underground uncertainty.While borehole exploration is reliable,it remains sparse due to economic and site constraints.Electrical resistivity tomography(ERT)as a cost-effective geophysical technique can acquire high-density data;however,uncertainty and nonuniqueness inherent in ERT impede its usage for stratigraphy identification.This paper integrates ERT and onsite observations for the first time to propose a novel method for characterizing stratigraphic profiles.The method consists of two steps:(1)ERT for prior knowledge:ERT data are processed by soft clustering using the Gaussian mixture model,followed by probability smoothing to quantify its depthdependent uncertainty;and(2)Observations for calibration:a spatial sequential Bayesian updating(SSBU)algorithm is developed to update the prior knowledge based on likelihoods derived from onsite observations,namely topsoil and boreholes.The effectiveness of the proposed method is validated through its application to a real slope site in Foshan,China.Comparative analysis with advanced borehole-driven methods highlights the superiority of incorporating ERT data in stratigraphic modeling,in terms of prediction accuracy at borehole locations and sensitivity to borehole data.Informed by ERT,reduced sensitivity to boreholes provides a fundamental solution to the longstanding challenge of sparse measurements.The paper further discusses the impact of ERT uncertainty on the proposed model using time-lapse measurements,the impact of model resolution,and applicability in engineering projects.This study,as a breakthrough in stratigraphic modeling,bridges gaps in combining geophysical and geotechnical data to address measurement sparsity and paves the way for more economical geotechnical exploration.展开更多
Background:Recent scholarly attention has increasingly focused on filial piety beliefs'impact on youth's psychological development.However,the mechanisms by which filial piety indirectly influences adolescent ...Background:Recent scholarly attention has increasingly focused on filial piety beliefs'impact on youth's psychological development.However,the mechanisms by which filial piety indirectly influences adolescent autonomy through depression and well-being remain underexplored.This study aimed to test a sequential mediation model among filial piety beliefs,depression,well-being,and autonomy in Taiwan region of China university students.Methods:A total of 566 Taiwan region of China undergraduate and graduate students,comprising 390 females and 176 males,and including 399 undergraduates and 167 graduate students,were recruited through convenience sampling.Data were collected via an online questionnaire.Validated instruments were employed,including the Filial Piety Scale(FPS),the Center for Epidemiological Studies Depression Scale(CES-D),the Chinese Well-being Inventory(CHI),and the Adolescent Autonomy Scale-Short Form(AAS-SF).Statistical analyses included group comparisons,correlation analyses,and structural equation modeling to examine the hypothesized relationships and mediation effects.Results:The results revealed that filial piety beliefs exerted a significant positive impact on adolescent autonomy,with depression and well-being serving as key mediators in this relationship.A sequential mediation effect was confirmed through structural equation modeling(β=0.052,95%CI[0.028,0.091]),with good model fit indices(x^(2)/df=4.25,RMSEA=0.076,CFI=0.968),supporting the hypothesized pathway from filial piety to autonomy via depression and well-being.In terms of demographic differences,male students showed significantly higher autonomy than females(p<0.001);students from single-parent families reported significantly higher depression levels than those from two-parent families(p<0.05);and graduate students exhibited significantly higher autonomy and well-being than undergraduates(p<0.05).Conclusions:These findings underscore not only the importance of filial piety beliefs for developing youth autonomy but also the critical role that mental health factors,such as depression and well-being,play in this process.The study concludes with a discussion of both theoretical implications and practical recommendations.These include strategies to foster reciprocal filial piety,strengthen parent-child relationships,and promote mental health.Additionally,the study outlines its limitations and proposes directions for future research.展开更多
Objective:A risk-based sequential screening strategy,from questionnaire-based assessment to biomarker measurement and then to endoscopic examination,has the potential to enhance gastric cancer(GC)screening efficiency....Objective:A risk-based sequential screening strategy,from questionnaire-based assessment to biomarker measurement and then to endoscopic examination,has the potential to enhance gastric cancer(GC)screening efficiency.We aimed to evaluate the ability of five common stomach-specific serum biomarkers to further enrich high-risk individuals for GC in the questionnaire-identified high-risk population.Methods:This study was conducted based on a risk-based screening program in Ningxia Hui Autonomous Region,China.We first performed questionnaire assessment involving 23,381 individuals(7,042 outpatients and 16,339 individuals from the community),and those assessed as“high-risk”were then invited to participate in serological assays and endoscopic examinations.The serological biomarker model was derived based on logistic regression,with predictors selected via the Akaike information criterion.Model performance was evaluated by the area under the receiver operating characteristic curve(AUC).Results:A total of 2,011 participants were ultimately included for analysis.The final serological biomarker model had three predictors,comprising pepsinogenⅠ(PGI),pepsinogenⅠ/Ⅱratio(PGR),and anti-Helicobacter pylori immunoglobulin G(anti-H.pylori IgG)antibodies.This model generated an AUC of 0.733(95%confidence interval:0.655-0.812)and demonstrated the best discriminative ability compared with previously developed serological biomarker models.As the risk cut-off value of our model rose,the detection rate increased and the number of endoscopies needed to detect one case decreased.Conclusions:PGI,PGR,and anti-H.pylori Ig G could be jointly used to further enrich high-risk individuals for GC among those selected by questionnaire assessment,providing insight for the development of a multi-stage riskbased sequential strategy for GC screening.展开更多
The epidermal growth factor receptor（EGFR）—tyrosine kinase inhibitors（TKIs） monotherapies have limited efficacy in the treatment of EGFR mutation-negative non-small cell lung cancers（NSCLCs）. In the present stu...The epidermal growth factor receptor（EGFR）—tyrosine kinase inhibitors（TKIs） monotherapies have limited efficacy in the treatment of EGFR mutation-negative non-small cell lung cancers（NSCLCs）. In the present study, we aimed to investigate the combined effect of erlotinib（ER） and cabozantinib（CAB） on NSCLC cell lines harboring wild-type EGFR and to optimize the dosage regimens using pharmacodynamic（PD） modeling and simulation. Therefore, we examined the combined effect of ER and CAB on cell viability, cloning, apoptosis induction, migration and growth dynamics in H1299 and A549 cells. PD modeling and simulation were also performed to quantitatively describe the H1299 cells growth dynamics and to optimize the dosage regimens as well. Our results showed that CAB effectively enhanced the sensitivity of both cell lines to ER. The PD models fitted the data well, and some important parameters were obtained. The exponential（λ_0） and linear（λ_1） growth rates of H1299 cells were 0.0241 h^（–1） and 360 cells？h^（–1）, respectively. The Emax of ER and CAB was 0.0091 h^（–1） and 0.0085 h^（–1）, and the EC50 was 0.812 μM and 1.16 μM, respectively. The synergistic effect observed in the experiments was further confirmed by the estimated combination index φ（1.37）,（95% confidence interval： 1.24–1.50）, obtained from PD modeling. Furthermore, the dosage regimens were optimized using simulations. In summary, both the experimental and modeling results demonstrated the synergistic interaction between ER and CAB in NSCLCs without EGFR mutations. Sequential combinations of ER and CAB provided an option for the therapy of the NSCLCs with wild-type EGFR, which would provide some references for preclinical study and translational research as well.展开更多
In order to evaluate the nonlinear performance and the possible damage to rubber-bearings （RBs） during their normal operation or under strong earthquakes, a simplified Bouc-Wen model is used to describe the nonlinea...In order to evaluate the nonlinear performance and the possible damage to rubber-bearings （RBs） during their normal operation or under strong earthquakes, a simplified Bouc-Wen model is used to describe the nonlinear hysteretic behavior of RBs in this paper, which has the advantages of being smooth-varying and physically motivated. Further, based on the results from experimental tests performed by using a particular type of RB （GZN 110） under different excitation scenarios, including white noise and several earthquakes, a new system identification method, referred to as the sequential nonlinear least- square estimation （SNLSE）, is introduced to identify the model parameters. It is shown that the proposed simplified Bouc- Wen model is capable of describing the nonlinear hysteretic behavior of RBs, and that the SNLSE approach is very effective in identifying the model parameters of RBs.展开更多
To obtain higher accurate position estimates, the stochastic model is estimated by using residual of observations, hence, the stochastic model describes the noise and bias in measurements more realistically. By using ...To obtain higher accurate position estimates, the stochastic model is estimated by using residual of observations, hence, the stochastic model describes the noise and bias in measurements more realistically. By using GPS data and broadcast ephemeris, the numerical results indicating the accurate position estimates at sub-meter level are obtainable.展开更多
Efficient experiment design is of great significance for the validation of simulation model with high nonlinearity and large input space.Excessive validation experiment raises the cost while insufficient test increase...Efficient experiment design is of great significance for the validation of simulation model with high nonlinearity and large input space.Excessive validation experiment raises the cost while insufficient test increases the risks of accepting an invalid model.In this paper,an adaptive sequential experiment design method combining global exploration criterion and local exploitation criterion is proposed.The exploration criterion utilizes discrepancy metric to improve the space-filling property of the design points while the exploitation criterion employs the leave one out error to discover informative points.To avoid the clustering of samples in the local region,an adaptive weight updating approach is provided to maintain the balance between exploration and exploitation.Besides,the credibility distribution function characterizing the relationship between the input and result credibility is introduced to support the model validation experiment design.Finally,six benchmark problems and an engineering case are applied to examine the performance of the proposed method.The experiments indicate that the proposed method achieves satisfactory performance for function approximation in accuracy and convergence.展开更多
When Kalman filter is used in the estimation of Vasicek term structure of interest rates,it is usual to assume that the measurement noise is uncorrelated.Study results are more favorable to the assumption of correlate...When Kalman filter is used in the estimation of Vasicek term structure of interest rates,it is usual to assume that the measurement noise is uncorrelated.Study results are more favorable to the assumption of correlated measurement noise.An augmented state Kalman filter form for Vasicek model is proposed to optimally estimate the unobservable state variable with the assumption of correlated measurement noise.Empirical results indicate that the model with sequentially correlated measurement noise can more accurately describe the dynamics of the term structure of interest rates.展开更多
Traffic forecasting with high precision aids Intelligent Transport Systems(ITS)in formulating and optimizing traffic management strategies.The algorithms used for tuning the hyperparameters of the deep learning models...Traffic forecasting with high precision aids Intelligent Transport Systems(ITS)in formulating and optimizing traffic management strategies.The algorithms used for tuning the hyperparameters of the deep learning models often have accurate results at the expense of high computational complexity.To address this problem,this paper uses the Tree-structured Parzen Estimator(TPE)to tune the hyperparameters of the Long Short-term Memory(LSTM)deep learning framework.The Tree-structured Parzen Estimator(TPE)uses a probabilistic approach with an adaptive searching mechanism by classifying the objective function values into good and bad samples.This ensures fast convergence in tuning the hyperparameter values in the deep learning model for performing prediction while still maintaining a certain degree of accuracy.It also overcomes the problem of converging to local optima and avoids timeconsuming random search and,therefore,avoids high computational complexity in prediction accuracy.The proposed scheme first performs data smoothing and normalization on the input data,which is then fed to the input of the TPE for tuning the hyperparameters.The traffic data is then input to the LSTM model with tuned parameters to perform the traffic prediction.The three optimizers:Adaptive Moment Estimation(Adam),Root Mean Square Propagation(RMSProp),and Stochastic Gradient Descend with Momentum(SGDM)are also evaluated for accuracy prediction and the best optimizer is then chosen for final traffic prediction in TPE-LSTM model.Simulation results verify the effectiveness of the proposed model in terms of accuracy of prediction over the benchmark schemes.展开更多
In the applications of COX regression models, we always encounter data sets t<span>hat contain too many variables that only a few of them contribute to the</span> model. Therefore, it will waste much more ...In the applications of COX regression models, we always encounter data sets t<span>hat contain too many variables that only a few of them contribute to the</span> model. Therefore, it will waste much more samples to estimate the “noneffective” variables in the inference. In this paper, we use a sequential procedure for constructing<span><span><span style="font-family:;" "=""> </span></span></span><span><span><span style="font-family:;" "="">the fixed size confidence set for the “effective” parameters to the model based on an adaptive shrinkage estimate such that the “effective” coefficients can be efficiently identified with the minimum sample size. Fixed design is considered for numerical simulation. The strong consistency, asymptotic distributions and convergence rates of estimates under the fixed design are obtained. In addition, the sequential procedure is shown to be asymptotically optimal in the sense of Chow and Robbins (1965).</span></span></span>展开更多
Tailings produced by mining and ore smelting are a major source of soil pollution.Understanding the speciation of heavy metals(HMs)in tailings is essential for soil remediation and sustainable development.Given the co...Tailings produced by mining and ore smelting are a major source of soil pollution.Understanding the speciation of heavy metals(HMs)in tailings is essential for soil remediation and sustainable development.Given the complex and time-consuming nature of traditional sequential laboratory extraction methods for determining the forms of HMs in tailings,a rapid and precise identification approach is urgently required.To address this issue,a general empirical prediction method for HM occurrence was developed using machine learning(ML).The compositional information of the tailings,properties of the HMs,and sequential extraction steps were used as inputs to calculate the percentages of the seven forms of HMs.After the models were tuned and compared,extreme gradient boosting,gradient boosting decision tree,and categorical boosting methods were found to be the top three performing ML models,with the coefficient of determination(R^(2))values on the testing set exceeding 0.859.Feature importance analysis for these three optimal models indicated that electronegativity was the most important factor affecting the occurrence of HMs,with an average feature importance of 0.4522.The subsequent use of stacking as a model integration method enabled the ability of the ML models to predict HM occurrence forms to be further improved,and resulting in an increase of R^(2) to 0.879.Overall,this study developed a robust technique for predicting the occurrence forms in tailings and provides an important reference for the environmental assessment and recycling of tailings.展开更多
Copula functions have been widely used in stochastic simulation and prediction of streamflow.However,existing models are usually limited to single two-dimensional or three-dimensional copulas with the same bivariate b...Copula functions have been widely used in stochastic simulation and prediction of streamflow.However,existing models are usually limited to single two-dimensional or three-dimensional copulas with the same bivariate block for all months.To address this limitation,this study developed a mixed D-vine copula-based conditional quantile model that can capture temporal correlations.This model can generate streamflow by selecting different historical streamflow variables as the conditions for different months and by exploiting the conditional quantile functions of streamflows in different months with mixed D-vine copulas.The up-to-down sequential method,which couples the maximum weight approach with the Akaike information criteria and the maximum likelihood approach,was used to determine the structures of multivariate Dvine copulas.The developed model was used in a case study to synthesize the monthly streamflow at the Tangnaihai hydrological station,the inflow control station of the Longyangxia Reservoir in the Yellow River Basin.The results showed that the developed model outperformed the commonly used bivariate copula model in terms of the performance in simulating the seasonality and interannual variability of streamflow.This model provides useful information for water-related natural hazard risk assessment and integrated water resources management and utilization.展开更多
Online assessment of remaining useful life(RUL) of a system or device has been widely studied for performance reliability, production safety, system conditional maintenance, and decision in remanufacturing engineering...Online assessment of remaining useful life(RUL) of a system or device has been widely studied for performance reliability, production safety, system conditional maintenance, and decision in remanufacturing engineering. However,there is no consistency framework to solve the RUL recursive estimation for the complex degenerate systems/device.In this paper, state space model(SSM) with Bayesian online estimation expounded from Markov chain Monte Carlo(MCMC) to Sequential Monte Carlo(SMC) algorithm is presented in order to derive the optimal Bayesian estimation.In the context of nonlinear & non-Gaussian dynamic systems, SMC(also named particle filter, PF) is quite capable of performing filtering and RUL assessment recursively. The underlying deterioration of a system/device is seen as a stochastic process with continuous, nonreversible degrading. The state of the deterioration tendency is filtered and predicted with updating observations through the SMC procedure. The corresponding remaining useful life of the system/device is estimated based on the state degradation and a predefined threshold of the failure with two-sided criterion. The paper presents an application on a milling machine for cutter tool RUL assessment by applying the above proposed methodology. The example shows the promising results and the effectiveness of SSM and SMC online assessment of RUL.展开更多
The HASM(high accuracy surface modeling) technique is based on the fundamental theory of surfaces,which has been proved to improve the interpolation accuracy in surface fitting.However,the integral iterative solution ...The HASM(high accuracy surface modeling) technique is based on the fundamental theory of surfaces,which has been proved to improve the interpolation accuracy in surface fitting.However,the integral iterative solution in previous studies resulted in high temporal complexity in computation and huge memory usage so that it became difficult to put the technique into application,especially for large-scale datasets.In the study,an innovative model(HASM-AD) is developed according to the sequential least squares on the basis of data adjustment theory.Sequential division is adopted in the technique,so that linear equations can be divided into groups to be processed in sequence with the temporal complexity reduced greatly in computation.The experiment indicates that the HASM-AD technique surpasses the traditional spatial interpolation methods in accuracy.Also,the cross-validation result proves the same conclusion for the spatial interpolation of soil PH property with the data sampled in Jiangxi province.Moreover,it is demonstrated in the study that the HASM-AD technique significantly reduces the computational complexity and lessens memory usage in computation.展开更多
In view of the forwarding microblogging,secondhand smoke,happiness,and many other phenomena in real life,the spread characteristic of the secondary neighbor nodes in this kind of phenomenon and network scheduling is e...In view of the forwarding microblogging,secondhand smoke,happiness,and many other phenomena in real life,the spread characteristic of the secondary neighbor nodes in this kind of phenomenon and network scheduling is extracted,and sequence influence maximization problem based on the influence of neighbor nodes is proposed in this paper.That is,in the time sequential social network,the propagation characteristics of the second-level neighbor nodes are considered emphatically,and k nodes are found to maximize the information propagation.Firstly,the propagation probability between nodes is calculated by the improved degree estimation algorithm.Secondly,the weighted cascade model(WCM) based on static social network is not suitable for temporal social network.Therefore,an improved weighted cascade model(IWCM) is proposed,and a second-level neighbors time sequential maximizing influence algorithm(STIM) is put forward based on node degree.It combines the consideration of neighbor nodes and the problem of overlap of influence scope between nodes,and makes it chronological.Finally,the experiment verifies that STIM algorithm has stronger practicability,superiority in influence range and running time compared with similar algorithms,and is able to solve the problem of maximizing the timing influence based on the influence of neighbor nodes.展开更多
This paper examines city growth patterns and the corresponding city size distribution evolution over long periods of time using a simple New Economic Geography(NEG) model and urban population data from Canada. The mai...This paper examines city growth patterns and the corresponding city size distribution evolution over long periods of time using a simple New Economic Geography(NEG) model and urban population data from Canada. The main findings are twofold. First, there is a transition from sequential to parallel growth of cities over long periods of time: city growth shows a sequential mode in the stage of rapid urbanization, i.e., the cities with the best development conditions will take the lead in growth, after which the cities with higher ranks will become the fastest-growing cities; in the late stage of urbanization, city growth converges according to Gibrat′s law, and exhibits a parallel growth pattern. Second, city size distribution is found to have persistent structural characteristics: the city system is self-organized into multiple discrete size groups; city growth shows club convergence characteristics, and the cities with similar development conditions eventually converge to a similar size. The results will not only enhance our understanding of urbanization process, but will also provide a timely and clear policy reference for promoting the healthy urbanization of developing countries.展开更多
Sequential indicator simulation is a commonly used method for discrete variable simulation in 3D geological modeling and a widely used stochastic simulation method, which can be used not only for continuous variable s...Sequential indicator simulation is a commonly used method for discrete variable simulation in 3D geological modeling and a widely used stochastic simulation method, which can be used not only for continuous variable simulation but also for discrete variable simulation. In this paper, the X Oilfield in the western South China Sea is taken as an example to compare the sequential indicator simulation method and the Indicator Kriging interpolation method. The results of the final comparison show that the results of the lithofacies model established by the Indicator Kriging deterministic interpolation method are overly smooth, and its coincidence rate with the geological statistical results is not high, thus cannot well reflect the heterogeneity of the underground reservoir, while the simulation results of the lithofacies model established by the sequential indicator stochastic simulation method can fit well with the statistical law of the well, which has eliminated the smoothing effect of Kriging interpolation, thus can better reflect the heterogeneity of the underground reservoir. Therefore, the sequential indicator simulation is more suitable for the characterization of sand bodies and the study of reservoir heterogeneity.展开更多
基金supported by the National Natural Science Foundation of China(Nos.U2267205 and 12475124)a ZSTU intramural grant(22062267-Y)Excellent Graduate Thesis Cultivation Fund(LW-YP2024011).
文摘Nuclear mass is an important property in both nuclear and astrophysics.In this study,we explore an improved mass model that incorporates a higher-order term of symmetry energy using algorithms.The sequential least squares programming(SLSQP)algorithm augments the precision of this multinomial mass model by reducing the error from 1.863 MeV to 1.631 MeV.These algorithms were further examined using 200 sample mass formulae derived from theδE term of the E_(isospin) mass model.The SLSQP method exhibited superior performance compared to the other algorithms in terms of errors and convergence speed.This algorithm is advantageous for handling large-scale multiparameter optimization tasks in nuclear physics.
基金the financial support from the National Key R&D Program of China(Grant No.2021YFC3001003)Science and Technology Development Fund,Macao SAR(File No.0056/2023/RIB2)Guangdong Provincial Department of Science and Technology(Grant No.2022A0505030019).
文摘Challenges in stratigraphic modeling arise from underground uncertainty.While borehole exploration is reliable,it remains sparse due to economic and site constraints.Electrical resistivity tomography(ERT)as a cost-effective geophysical technique can acquire high-density data;however,uncertainty and nonuniqueness inherent in ERT impede its usage for stratigraphy identification.This paper integrates ERT and onsite observations for the first time to propose a novel method for characterizing stratigraphic profiles.The method consists of two steps:(1)ERT for prior knowledge:ERT data are processed by soft clustering using the Gaussian mixture model,followed by probability smoothing to quantify its depthdependent uncertainty;and(2)Observations for calibration:a spatial sequential Bayesian updating(SSBU)algorithm is developed to update the prior knowledge based on likelihoods derived from onsite observations,namely topsoil and boreholes.The effectiveness of the proposed method is validated through its application to a real slope site in Foshan,China.Comparative analysis with advanced borehole-driven methods highlights the superiority of incorporating ERT data in stratigraphic modeling,in terms of prediction accuracy at borehole locations and sensitivity to borehole data.Informed by ERT,reduced sensitivity to boreholes provides a fundamental solution to the longstanding challenge of sparse measurements.The paper further discusses the impact of ERT uncertainty on the proposed model using time-lapse measurements,the impact of model resolution,and applicability in engineering projects.This study,as a breakthrough in stratigraphic modeling,bridges gaps in combining geophysical and geotechnical data to address measurement sparsity and paves the way for more economical geotechnical exploration.
文摘Background:Recent scholarly attention has increasingly focused on filial piety beliefs'impact on youth's psychological development.However,the mechanisms by which filial piety indirectly influences adolescent autonomy through depression and well-being remain underexplored.This study aimed to test a sequential mediation model among filial piety beliefs,depression,well-being,and autonomy in Taiwan region of China university students.Methods:A total of 566 Taiwan region of China undergraduate and graduate students,comprising 390 females and 176 males,and including 399 undergraduates and 167 graduate students,were recruited through convenience sampling.Data were collected via an online questionnaire.Validated instruments were employed,including the Filial Piety Scale(FPS),the Center for Epidemiological Studies Depression Scale(CES-D),the Chinese Well-being Inventory(CHI),and the Adolescent Autonomy Scale-Short Form(AAS-SF).Statistical analyses included group comparisons,correlation analyses,and structural equation modeling to examine the hypothesized relationships and mediation effects.Results:The results revealed that filial piety beliefs exerted a significant positive impact on adolescent autonomy,with depression and well-being serving as key mediators in this relationship.A sequential mediation effect was confirmed through structural equation modeling(β=0.052,95%CI[0.028,0.091]),with good model fit indices(x^(2)/df=4.25,RMSEA=0.076,CFI=0.968),supporting the hypothesized pathway from filial piety to autonomy via depression and well-being.In terms of demographic differences,male students showed significantly higher autonomy than females(p<0.001);students from single-parent families reported significantly higher depression levels than those from two-parent families(p<0.05);and graduate students exhibited significantly higher autonomy and well-being than undergraduates(p<0.05).Conclusions:These findings underscore not only the importance of filial piety beliefs for developing youth autonomy but also the critical role that mental health factors,such as depression and well-being,play in this process.The study concludes with a discussion of both theoretical implications and practical recommendations.These include strategies to foster reciprocal filial piety,strengthen parent-child relationships,and promote mental health.Additionally,the study outlines its limitations and proposes directions for future research.
基金supported by the Tencent Charity Foundationthe Ningxia Hui Autonomous Region Key Research and Development Program(No.2021BEG 02025)+1 种基金the Flexible Introduction of Technological Innovation Teams of Ningxia Hui Autonomous Region(No.2021RXTDLX15)the Natural Science Foundation of China(No.82160644)。
文摘Objective:A risk-based sequential screening strategy,from questionnaire-based assessment to biomarker measurement and then to endoscopic examination,has the potential to enhance gastric cancer(GC)screening efficiency.We aimed to evaluate the ability of five common stomach-specific serum biomarkers to further enrich high-risk individuals for GC in the questionnaire-identified high-risk population.Methods:This study was conducted based on a risk-based screening program in Ningxia Hui Autonomous Region,China.We first performed questionnaire assessment involving 23,381 individuals(7,042 outpatients and 16,339 individuals from the community),and those assessed as“high-risk”were then invited to participate in serological assays and endoscopic examinations.The serological biomarker model was derived based on logistic regression,with predictors selected via the Akaike information criterion.Model performance was evaluated by the area under the receiver operating characteristic curve(AUC).Results:A total of 2,011 participants were ultimately included for analysis.The final serological biomarker model had three predictors,comprising pepsinogenⅠ(PGI),pepsinogenⅠ/Ⅱratio(PGR),and anti-Helicobacter pylori immunoglobulin G(anti-H.pylori IgG)antibodies.This model generated an AUC of 0.733(95%confidence interval:0.655-0.812)and demonstrated the best discriminative ability compared with previously developed serological biomarker models.As the risk cut-off value of our model rose,the detection rate increased and the number of endoscopies needed to detect one case decreased.Conclusions:PGI,PGR,and anti-H.pylori Ig G could be jointly used to further enrich high-risk individuals for GC among those selected by questionnaire assessment,providing insight for the development of a multi-stage riskbased sequential strategy for GC screening.
基金National Natural Science Foundation of China(NSFC,Grant No.81273583)
文摘The epidermal growth factor receptor（EGFR）—tyrosine kinase inhibitors（TKIs） monotherapies have limited efficacy in the treatment of EGFR mutation-negative non-small cell lung cancers（NSCLCs）. In the present study, we aimed to investigate the combined effect of erlotinib（ER） and cabozantinib（CAB） on NSCLC cell lines harboring wild-type EGFR and to optimize the dosage regimens using pharmacodynamic（PD） modeling and simulation. Therefore, we examined the combined effect of ER and CAB on cell viability, cloning, apoptosis induction, migration and growth dynamics in H1299 and A549 cells. PD modeling and simulation were also performed to quantitatively describe the H1299 cells growth dynamics and to optimize the dosage regimens as well. Our results showed that CAB effectively enhanced the sensitivity of both cell lines to ER. The PD models fitted the data well, and some important parameters were obtained. The exponential（λ_0） and linear（λ_1） growth rates of H1299 cells were 0.0241 h^（–1） and 360 cells？h^（–1）, respectively. The Emax of ER and CAB was 0.0091 h^（–1） and 0.0085 h^（–1）, and the EC50 was 0.812 μM and 1.16 μM, respectively. The synergistic effect observed in the experiments was further confirmed by the estimated combination index φ（1.37）,（95% confidence interval： 1.24–1.50）, obtained from PD modeling. Furthermore, the dosage regimens were optimized using simulations. In summary, both the experimental and modeling results demonstrated the synergistic interaction between ER and CAB in NSCLCs without EGFR mutations. Sequential combinations of ER and CAB provided an option for the therapy of the NSCLCs with wild-type EGFR, which would provide some references for preclinical study and translational research as well.
基金National Natural Science Foundation of China Under Grant No.10572058the Science Foundation of Aeronautics of China Under Grant No.2008ZA52012
文摘In order to evaluate the nonlinear performance and the possible damage to rubber-bearings （RBs） during their normal operation or under strong earthquakes, a simplified Bouc-Wen model is used to describe the nonlinear hysteretic behavior of RBs in this paper, which has the advantages of being smooth-varying and physically motivated. Further, based on the results from experimental tests performed by using a particular type of RB （GZN 110） under different excitation scenarios, including white noise and several earthquakes, a new system identification method, referred to as the sequential nonlinear least- square estimation （SNLSE）, is introduced to identify the model parameters. It is shown that the proposed simplified Bouc- Wen model is capable of describing the nonlinear hysteretic behavior of RBs, and that the SNLSE approach is very effective in identifying the model parameters of RBs.
基金Supported by the National 863 Program of China （No.2006AA12Z325） and the National Natural Science Foundation of China （No.40274005）.
文摘To obtain higher accurate position estimates, the stochastic model is estimated by using residual of observations, hence, the stochastic model describes the noise and bias in measurements more realistically. By using GPS data and broadcast ephemeris, the numerical results indicating the accurate position estimates at sub-meter level are obtainable.
基金supported by the National Natural Science Foundation of China(No.61627810)。
文摘Efficient experiment design is of great significance for the validation of simulation model with high nonlinearity and large input space.Excessive validation experiment raises the cost while insufficient test increases the risks of accepting an invalid model.In this paper,an adaptive sequential experiment design method combining global exploration criterion and local exploitation criterion is proposed.The exploration criterion utilizes discrepancy metric to improve the space-filling property of the design points while the exploitation criterion employs the leave one out error to discover informative points.To avoid the clustering of samples in the local region,an adaptive weight updating approach is provided to maintain the balance between exploration and exploitation.Besides,the credibility distribution function characterizing the relationship between the input and result credibility is introduced to support the model validation experiment design.Finally,six benchmark problems and an engineering case are applied to examine the performance of the proposed method.The experiments indicate that the proposed method achieves satisfactory performance for function approximation in accuracy and convergence.
文摘When Kalman filter is used in the estimation of Vasicek term structure of interest rates,it is usual to assume that the measurement noise is uncorrelated.Study results are more favorable to the assumption of correlated measurement noise.An augmented state Kalman filter form for Vasicek model is proposed to optimally estimate the unobservable state variable with the assumption of correlated measurement noise.Empirical results indicate that the model with sequentially correlated measurement noise can more accurately describe the dynamics of the term structure of interest rates.
文摘Traffic forecasting with high precision aids Intelligent Transport Systems(ITS)in formulating and optimizing traffic management strategies.The algorithms used for tuning the hyperparameters of the deep learning models often have accurate results at the expense of high computational complexity.To address this problem,this paper uses the Tree-structured Parzen Estimator(TPE)to tune the hyperparameters of the Long Short-term Memory(LSTM)deep learning framework.The Tree-structured Parzen Estimator(TPE)uses a probabilistic approach with an adaptive searching mechanism by classifying the objective function values into good and bad samples.This ensures fast convergence in tuning the hyperparameter values in the deep learning model for performing prediction while still maintaining a certain degree of accuracy.It also overcomes the problem of converging to local optima and avoids timeconsuming random search and,therefore,avoids high computational complexity in prediction accuracy.The proposed scheme first performs data smoothing and normalization on the input data,which is then fed to the input of the TPE for tuning the hyperparameters.The traffic data is then input to the LSTM model with tuned parameters to perform the traffic prediction.The three optimizers:Adaptive Moment Estimation(Adam),Root Mean Square Propagation(RMSProp),and Stochastic Gradient Descend with Momentum(SGDM)are also evaluated for accuracy prediction and the best optimizer is then chosen for final traffic prediction in TPE-LSTM model.Simulation results verify the effectiveness of the proposed model in terms of accuracy of prediction over the benchmark schemes.
文摘In the applications of COX regression models, we always encounter data sets t<span>hat contain too many variables that only a few of them contribute to the</span> model. Therefore, it will waste much more samples to estimate the “noneffective” variables in the inference. In this paper, we use a sequential procedure for constructing<span><span><span style="font-family:;" "=""> </span></span></span><span><span><span style="font-family:;" "="">the fixed size confidence set for the “effective” parameters to the model based on an adaptive shrinkage estimate such that the “effective” coefficients can be efficiently identified with the minimum sample size. Fixed design is considered for numerical simulation. The strong consistency, asymptotic distributions and convergence rates of estimates under the fixed design are obtained. In addition, the sequential procedure is shown to be asymptotically optimal in the sense of Chow and Robbins (1965).</span></span></span>
基金financially supported by the Natural Science Foundation of Hunan Province,China(No.2024JJ2074)the National Natural Science Foundation of China(No.22376221)the Young Elite Scientists Sponsorship Program by CAST,China(No.2023QNRC001).
文摘Tailings produced by mining and ore smelting are a major source of soil pollution.Understanding the speciation of heavy metals(HMs)in tailings is essential for soil remediation and sustainable development.Given the complex and time-consuming nature of traditional sequential laboratory extraction methods for determining the forms of HMs in tailings,a rapid and precise identification approach is urgently required.To address this issue,a general empirical prediction method for HM occurrence was developed using machine learning(ML).The compositional information of the tailings,properties of the HMs,and sequential extraction steps were used as inputs to calculate the percentages of the seven forms of HMs.After the models were tuned and compared,extreme gradient boosting,gradient boosting decision tree,and categorical boosting methods were found to be the top three performing ML models,with the coefficient of determination(R^(2))values on the testing set exceeding 0.859.Feature importance analysis for these three optimal models indicated that electronegativity was the most important factor affecting the occurrence of HMs,with an average feature importance of 0.4522.The subsequent use of stacking as a model integration method enabled the ability of the ML models to predict HM occurrence forms to be further improved,and resulting in an increase of R^(2) to 0.879.Overall,this study developed a robust technique for predicting the occurrence forms in tailings and provides an important reference for the environmental assessment and recycling of tailings.
基金supported by the National Natural Science Foundation of China(Grant No.52109010)the Postdoctoral Science Foundation of China(Grant No.2021M701047)the China National Postdoctoral Program for Innovative Talents(Grant No.BX20200113).
文摘Copula functions have been widely used in stochastic simulation and prediction of streamflow.However,existing models are usually limited to single two-dimensional or three-dimensional copulas with the same bivariate block for all months.To address this limitation,this study developed a mixed D-vine copula-based conditional quantile model that can capture temporal correlations.This model can generate streamflow by selecting different historical streamflow variables as the conditions for different months and by exploiting the conditional quantile functions of streamflows in different months with mixed D-vine copulas.The up-to-down sequential method,which couples the maximum weight approach with the Akaike information criteria and the maximum likelihood approach,was used to determine the structures of multivariate Dvine copulas.The developed model was used in a case study to synthesize the monthly streamflow at the Tangnaihai hydrological station,the inflow control station of the Longyangxia Reservoir in the Yellow River Basin.The results showed that the developed model outperformed the commonly used bivariate copula model in terms of the performance in simulating the seasonality and interannual variability of streamflow.This model provides useful information for water-related natural hazard risk assessment and integrated water resources management and utilization.
基金Supported by Basic Research and Development Plan of China (973 Program,Grant Nos.2011CB013401,2011CB013402)Special Fundamental Research Funds for Central Universities of China(Grant No.DUT14QY21)
文摘Online assessment of remaining useful life(RUL) of a system or device has been widely studied for performance reliability, production safety, system conditional maintenance, and decision in remanufacturing engineering. However,there is no consistency framework to solve the RUL recursive estimation for the complex degenerate systems/device.In this paper, state space model(SSM) with Bayesian online estimation expounded from Markov chain Monte Carlo(MCMC) to Sequential Monte Carlo(SMC) algorithm is presented in order to derive the optimal Bayesian estimation.In the context of nonlinear & non-Gaussian dynamic systems, SMC(also named particle filter, PF) is quite capable of performing filtering and RUL assessment recursively. The underlying deterioration of a system/device is seen as a stochastic process with continuous, nonreversible degrading. The state of the deterioration tendency is filtered and predicted with updating observations through the SMC procedure. The corresponding remaining useful life of the system/device is estimated based on the state degradation and a predefined threshold of the failure with two-sided criterion. The paper presents an application on a milling machine for cutter tool RUL assessment by applying the above proposed methodology. The example shows the promising results and the effectiveness of SSM and SMC online assessment of RUL.
基金Supported by the National Science Fund for Distinguished Young Scholars (No. 40825003)the Major Directivity Projects of Chinese Academy of Science (No. kzcx2-yw-429)the National High-tech R&D Program of China (No. 2006AA12Z219)
文摘The HASM(high accuracy surface modeling) technique is based on the fundamental theory of surfaces,which has been proved to improve the interpolation accuracy in surface fitting.However,the integral iterative solution in previous studies resulted in high temporal complexity in computation and huge memory usage so that it became difficult to put the technique into application,especially for large-scale datasets.In the study,an innovative model(HASM-AD) is developed according to the sequential least squares on the basis of data adjustment theory.Sequential division is adopted in the technique,so that linear equations can be divided into groups to be processed in sequence with the temporal complexity reduced greatly in computation.The experiment indicates that the HASM-AD technique surpasses the traditional spatial interpolation methods in accuracy.Also,the cross-validation result proves the same conclusion for the spatial interpolation of soil PH property with the data sampled in Jiangxi province.Moreover,it is demonstrated in the study that the HASM-AD technique significantly reduces the computational complexity and lessens memory usage in computation.
基金Supported by the National Natural Science Foundation of China(No.62172352,61871465,42002138)the Natural Science Foundation of Hebei Province(No.F2019203157)the Science and Technology Research Project of Hebei(No.ZD2019004)。
文摘In view of the forwarding microblogging,secondhand smoke,happiness,and many other phenomena in real life,the spread characteristic of the secondary neighbor nodes in this kind of phenomenon and network scheduling is extracted,and sequence influence maximization problem based on the influence of neighbor nodes is proposed in this paper.That is,in the time sequential social network,the propagation characteristics of the second-level neighbor nodes are considered emphatically,and k nodes are found to maximize the information propagation.Firstly,the propagation probability between nodes is calculated by the improved degree estimation algorithm.Secondly,the weighted cascade model(WCM) based on static social network is not suitable for temporal social network.Therefore,an improved weighted cascade model(IWCM) is proposed,and a second-level neighbors time sequential maximizing influence algorithm(STIM) is put forward based on node degree.It combines the consideration of neighbor nodes and the problem of overlap of influence scope between nodes,and makes it chronological.Finally,the experiment verifies that STIM algorithm has stronger practicability,superiority in influence range and running time compared with similar algorithms,and is able to solve the problem of maximizing the timing influence based on the influence of neighbor nodes.
基金Under the auspices of Key Program of Chinese Academy of Sciences(No.KZZD-EW-06-01)
文摘This paper examines city growth patterns and the corresponding city size distribution evolution over long periods of time using a simple New Economic Geography(NEG) model and urban population data from Canada. The main findings are twofold. First, there is a transition from sequential to parallel growth of cities over long periods of time: city growth shows a sequential mode in the stage of rapid urbanization, i.e., the cities with the best development conditions will take the lead in growth, after which the cities with higher ranks will become the fastest-growing cities; in the late stage of urbanization, city growth converges according to Gibrat′s law, and exhibits a parallel growth pattern. Second, city size distribution is found to have persistent structural characteristics: the city system is self-organized into multiple discrete size groups; city growth shows club convergence characteristics, and the cities with similar development conditions eventually converge to a similar size. The results will not only enhance our understanding of urbanization process, but will also provide a timely and clear policy reference for promoting the healthy urbanization of developing countries.
文摘Sequential indicator simulation is a commonly used method for discrete variable simulation in 3D geological modeling and a widely used stochastic simulation method, which can be used not only for continuous variable simulation but also for discrete variable simulation. In this paper, the X Oilfield in the western South China Sea is taken as an example to compare the sequential indicator simulation method and the Indicator Kriging interpolation method. The results of the final comparison show that the results of the lithofacies model established by the Indicator Kriging deterministic interpolation method are overly smooth, and its coincidence rate with the geological statistical results is not high, thus cannot well reflect the heterogeneity of the underground reservoir, while the simulation results of the lithofacies model established by the sequential indicator stochastic simulation method can fit well with the statistical law of the well, which has eliminated the smoothing effect of Kriging interpolation, thus can better reflect the heterogeneity of the underground reservoir. Therefore, the sequential indicator simulation is more suitable for the characterization of sand bodies and the study of reservoir heterogeneity.
