Objective:To compare the adverse maternal and neonatal outcomes of multiple pregnancy and singleton pregnancy from multiple medical centers in Beijing.Methods:Data concerning maternal and neonatal adverse outcomes in ...Objective:To compare the adverse maternal and neonatal outcomes of multiple pregnancy and singleton pregnancy from multiple medical centers in Beijing.Methods:Data concerning maternal and neonatal adverse outcomes in multiple and singleton pregnancies were collected from 15 hospitals in Beijing by a systemic cluster sampling survey conducted from 20 June to 30 November 2013.The SPSS software (version 20.0) was used for data analysis.The x2 test was used tbr statistical analyses.Results:The rate of caesarean deliveries was much higher in women with multiple pregnancies (85.8％) than that in women with singleton pregnancies (42.6％,X2 =190.8,P ＜ 0.001).The incidences of anemia (X2 =40.023,P ＜ 0.001),preterm labor (X2 =1021.172,P ＜ 0.001),gestational diabetes mellitus (X2 =9.311,P ＜ 0.01),hypertensive disorders (X2 =122.708,P ＜ 0.001)and post-partum hemorrhage (X2-48.550,P ＜ 0.001) was significantly increased with multiple pregnancy.In addition,multiple pregnancy was associated with a significantly higher rate of small-for-gestational-age infants (X2 =92.602,P ＜ 0.001),low birth weight (X2 =1141.713,P ＜ 0.001),and neonatal intensive care unit (NICU) admission (X2 =340.129,P＜ 0.001).Conclusions:Multiple pregnancy is a significant risk factor for adverse maternal and neonatal outcomes in Beijing.Improving obstetric care for multiple pregnancy,particularly in reducing preterm labor,is required to reduce the risk to mothers and infants.展开更多
A novel adaptive multiple dependent state sampling plan(AMDSSP)was designed to inspect products from a continuous manufacturing process under the accelerated life test(ALT)using both double sampling plan(DSP)and multi...A novel adaptive multiple dependent state sampling plan(AMDSSP)was designed to inspect products from a continuous manufacturing process under the accelerated life test(ALT)using both double sampling plan(DSP)and multiple dependent state sampling plan(MDSSP)concepts.Under accelerated conditions,the lifetime of a product follows the Weibull distribution with a known shape parameter,while the scale parameter can be determined using the acceleration factor(AF).The Arrhenius model is used to estimate AF when the damaging process is temperature-sensitive.An economic design of the proposed sampling plan was also considered for the ALT.A genetic algorithm with nonlinear optimization was used to estimate optimal plan parameters to minimize the average sample number(ASN)and total cost of inspection(TC)under both producer’s and consumer’s risks.Numerical results are presented to support the AMDSSP for the ALT,while performance comparisons between the AMDSSP,the MDSSP and a single sampling plan(SSP)for the ALT are discussed.Results indicated that the AMDSSP was more flexible and efficient for ASN and TC than the MDSSP and SSP plans under accelerated conditions.The AMDSSP also had a higher operating characteristic(OC)curve than both the existing sampling plans.Two real datasets of electronic devices for the ALT at high temperatures demonstrated the practicality and usefulness of the proposed sampling plan.展开更多
In this paper, we propose a K-means clustering-based integral level-value estimation algorithm to solve a kind of box-constrained global optimization problem. For this purpose, we introduce the generalized variance fu...In this paper, we propose a K-means clustering-based integral level-value estimation algorithm to solve a kind of box-constrained global optimization problem. For this purpose, we introduce the generalized variance function associated with the level-value of the objective function to be minimized. The variance function has a good property when Newton’s method is used to solve a variance equation resulting by setting the variance function to zero. We prove that the largest root of the variance equation is equal to the global minimum value of the corresponding optimization problem. Based on the K-means clustering algorithm, the multiple importance sampling technique is proposed in the implementable algorithm. The main idea of the cross-entropy method is used to update the parameters of sampling density function. The asymptotic convergence of the algorithm is proved, and the validity of the algorithm is verified by numerical experiments.展开更多
In this paper,a hybrid approach which combines linear sampling method and the Bayesian method is proposed to simultaneously reconstruct multiple obsta-cles.The number of obstacles and the approximate geometric informa...In this paper,a hybrid approach which combines linear sampling method and the Bayesian method is proposed to simultaneously reconstruct multiple obsta-cles.The number of obstacles and the approximate geometric information arefirst qualitatively obtained by the linear sampling method.Based on the reconstructions of the linear sampling method,the Bayesian method is employed to obtain more refined details of the obstacles.The well-posedness of the posterior distribution is proved by using the Hellinger metric.The Markov Chain Monte Carlo algorithm is proposed to explore the posterior density with the initial guesses provided by the linear sampling method.Numerical experiments are provided to testify the effectiveness and efficiency of the proposed method.展开更多
Spatial interpolation has been frequently encountered in earth sciences and engineering.A reasonable appraisal of subsurface heterogeneity plays a significant role in planning,risk assessment and decision making for g...Spatial interpolation has been frequently encountered in earth sciences and engineering.A reasonable appraisal of subsurface heterogeneity plays a significant role in planning,risk assessment and decision making for geotechnical practice.Geostatistics is commonly used to interpolate spatially varying properties at un-sampled locations from scatter measurements.However,successful application of classic geostatistical models requires prior characterization of spatial auto-correlation structures,which poses a great challenge for unexperienced engineers,particularly when only limited measurements are available.Data-driven machine learning methods,such as radial basis function network(RBFN),require minimal human intervention and provide effective alternatives for spatial interpolation of non-stationary and non-Gaussian data,particularly when measurements are sparse.Conventional RBFN,however,is direction independent(i.e.isotropic)and cannot quantify prediction uncertainty in spatial interpolation.In this study,an ensemble RBFN method is proposed that not only allows geotechnical anisotropy to be properly incorporated,but also quantifies uncertainty in spatial interpolation.The proposed method is illustrated using numerical examples of cone penetration test(CPT)data,which involve interpolation of a 2D CPT cross-section from limited continuous 1D CPT soundings in the vertical direction.In addition,a comparative study is performed to benchmark the proposed ensemble RBFN with two other non-parametric data-driven approaches,namely,Multiple Point Statistics(MPS)and Bayesian Compressive Sensing(BCS).The results reveal that the proposed ensemble RBFN provides a better estimation of spatial patterns and associated prediction uncertainty at un-sampled locations when a reasonable amount of data is available as input.Moreover,the prediction accuracy of all the three methods improves as the number of measurements increases,and vice versa.It is also found that BCS prediction is less sensitive to the number of measurement data and ou展开更多
In this paper, the influence of sampling intervals on the chattering in sliding mode （SM） control systems is considered. The describing function （DF） approach is employed to analyze the chattering characteristics ...In this paper, the influence of sampling intervals on the chattering in sliding mode （SM） control systems is considered. The describing function （DF） approach is employed to analyze the chattering characteristics in the sampling SM control. By the DF calculations and limit cycle existence conditions, an unstable limit cycle and two stable limit cycles are found in the SM control system. The frequencies and amplitudes of the two limit cycles can also be estimated by graphical calculations. The estimation accuracy of chattering parameters is evaluated by the simulations. The results of simulations show that the system could converge to a large and a small limit cycle from different initial conditions.展开更多
文摘Objective:To compare the adverse maternal and neonatal outcomes of multiple pregnancy and singleton pregnancy from multiple medical centers in Beijing.Methods:Data concerning maternal and neonatal adverse outcomes in multiple and singleton pregnancies were collected from 15 hospitals in Beijing by a systemic cluster sampling survey conducted from 20 June to 30 November 2013.The SPSS software (version 20.0) was used for data analysis.The x2 test was used tbr statistical analyses.Results:The rate of caesarean deliveries was much higher in women with multiple pregnancies (85.8％) than that in women with singleton pregnancies (42.6％,X2 =190.8,P ＜ 0.001).The incidences of anemia (X2 =40.023,P ＜ 0.001),preterm labor (X2 =1021.172,P ＜ 0.001),gestational diabetes mellitus (X2 =9.311,P ＜ 0.01),hypertensive disorders (X2 =122.708,P ＜ 0.001)and post-partum hemorrhage (X2-48.550,P ＜ 0.001) was significantly increased with multiple pregnancy.In addition,multiple pregnancy was associated with a significantly higher rate of small-for-gestational-age infants (X2 =92.602,P ＜ 0.001),low birth weight (X2 =1141.713,P ＜ 0.001),and neonatal intensive care unit (NICU) admission (X2 =340.129,P＜ 0.001).Conclusions:Multiple pregnancy is a significant risk factor for adverse maternal and neonatal outcomes in Beijing.Improving obstetric care for multiple pregnancy,particularly in reducing preterm labor,is required to reduce the risk to mothers and infants.
基金This research was supported by The Science,Research and Innovation Promotion Funding(TSRI)(Grant No.FRB650070/0168)This research block grants was managed under Rajamangala University of Technology Thanyaburi(FRB65E0634M.3).
文摘A novel adaptive multiple dependent state sampling plan(AMDSSP)was designed to inspect products from a continuous manufacturing process under the accelerated life test(ALT)using both double sampling plan(DSP)and multiple dependent state sampling plan(MDSSP)concepts.Under accelerated conditions,the lifetime of a product follows the Weibull distribution with a known shape parameter,while the scale parameter can be determined using the acceleration factor(AF).The Arrhenius model is used to estimate AF when the damaging process is temperature-sensitive.An economic design of the proposed sampling plan was also considered for the ALT.A genetic algorithm with nonlinear optimization was used to estimate optimal plan parameters to minimize the average sample number(ASN)and total cost of inspection(TC)under both producer’s and consumer’s risks.Numerical results are presented to support the AMDSSP for the ALT,while performance comparisons between the AMDSSP,the MDSSP and a single sampling plan(SSP)for the ALT are discussed.Results indicated that the AMDSSP was more flexible and efficient for ASN and TC than the MDSSP and SSP plans under accelerated conditions.The AMDSSP also had a higher operating characteristic(OC)curve than both the existing sampling plans.Two real datasets of electronic devices for the ALT at high temperatures demonstrated the practicality and usefulness of the proposed sampling plan.
文摘In this paper, we propose a K-means clustering-based integral level-value estimation algorithm to solve a kind of box-constrained global optimization problem. For this purpose, we introduce the generalized variance function associated with the level-value of the objective function to be minimized. The variance function has a good property when Newton’s method is used to solve a variance equation resulting by setting the variance function to zero. We prove that the largest root of the variance equation is equal to the global minimum value of the corresponding optimization problem. Based on the K-means clustering algorithm, the multiple importance sampling technique is proposed in the implementable algorithm. The main idea of the cross-entropy method is used to update the parameters of sampling density function. The asymptotic convergence of the algorithm is proved, and the validity of the algorithm is verified by numerical experiments.
基金supported by the Jilin Sci-Tech fund under JJKH20210797KJsupported by a startup grant from City University of Hong Kong and Hong Kong RGC General Research Funds(projects 12301218,12302919 and 12301420).
文摘In this paper,a hybrid approach which combines linear sampling method and the Bayesian method is proposed to simultaneously reconstruct multiple obsta-cles.The number of obstacles and the approximate geometric information arefirst qualitatively obtained by the linear sampling method.Based on the reconstructions of the linear sampling method,the Bayesian method is employed to obtain more refined details of the obstacles.The well-posedness of the posterior distribution is proved by using the Hellinger metric.The Markov Chain Monte Carlo algorithm is proposed to explore the posterior density with the initial guesses provided by the linear sampling method.Numerical experiments are provided to testify the effectiveness and efficiency of the proposed method.
基金supported by grants from the Research Grants Council of Hong Kong Special Administrative Region,China(Project No.City U 11213119 and T22-603/15N)The financial support is gratefully acknowledgedfinancial support from the Hong Kong Ph.D.Fellowship Scheme funded by the Research Grants Council of Hong Kong,China。
文摘Spatial interpolation has been frequently encountered in earth sciences and engineering.A reasonable appraisal of subsurface heterogeneity plays a significant role in planning,risk assessment and decision making for geotechnical practice.Geostatistics is commonly used to interpolate spatially varying properties at un-sampled locations from scatter measurements.However,successful application of classic geostatistical models requires prior characterization of spatial auto-correlation structures,which poses a great challenge for unexperienced engineers,particularly when only limited measurements are available.Data-driven machine learning methods,such as radial basis function network(RBFN),require minimal human intervention and provide effective alternatives for spatial interpolation of non-stationary and non-Gaussian data,particularly when measurements are sparse.Conventional RBFN,however,is direction independent(i.e.isotropic)and cannot quantify prediction uncertainty in spatial interpolation.In this study,an ensemble RBFN method is proposed that not only allows geotechnical anisotropy to be properly incorporated,but also quantifies uncertainty in spatial interpolation.The proposed method is illustrated using numerical examples of cone penetration test(CPT)data,which involve interpolation of a 2D CPT cross-section from limited continuous 1D CPT soundings in the vertical direction.In addition,a comparative study is performed to benchmark the proposed ensemble RBFN with two other non-parametric data-driven approaches,namely,Multiple Point Statistics(MPS)and Bayesian Compressive Sensing(BCS).The results reveal that the proposed ensemble RBFN provides a better estimation of spatial patterns and associated prediction uncertainty at un-sampled locations when a reasonable amount of data is available as input.Moreover,the prediction accuracy of all the three methods improves as the number of measurements increases,and vice versa.It is also found that BCS prediction is less sensitive to the number of measurement data and ou
基金supported by Industrial Research Projects in department of education of Shaanxi province(2014K05-29)Science Research Projects in department of education of Shaanxi province(14JK1669,14JF028)
文摘In this paper, the influence of sampling intervals on the chattering in sliding mode （SM） control systems is considered. The describing function （DF） approach is employed to analyze the chattering characteristics in the sampling SM control. By the DF calculations and limit cycle existence conditions, an unstable limit cycle and two stable limit cycles are found in the SM control system. The frequencies and amplitudes of the two limit cycles can also be estimated by graphical calculations. The estimation accuracy of chattering parameters is evaluated by the simulations. The results of simulations show that the system could converge to a large and a small limit cycle from different initial conditions.
