福建省医疗机构医疗设备质量控制检测技术能力比对结果分析与评价

目的评价福建省医疗机构医疗设备质控检测分析仪的准确可靠程度。方法参考实验室间比对能力验证的方法,对21家医疗机构的通用电气安全、高频电刀、呼吸机和多参数监护仪4种检测分析仪共12个参数进行检测,采用稳健统计Z比分数法和实验室偏移估计值法进行评价。结果在四种检测分析仪比对的全部377个参数检测结果中,符合预期的有335个(88.86%);结果可疑26个(6.90%);不符合预期15个(4.24%);四种检测分析仪完全符合预期的比例为:通用电气50.00%、高频电刀50.00%、呼吸机69.23%、多参数监护仪83.33%。结论比对数据分析结果整体情况较好,但检测分析仪整体完全符合预期的比例不高,各医疗机构应予注意,结合具体参数检测结果,自行分析查找原因并改进。

DOA ESTIMATION FOR WIDEBAND SOURCES BASED ON UCA

A new Direction Of Arrival （DOA） estimation algorithm for wideband sources based on Uniform Circular Array （UCA） is presented via analyzing widcband performance of the general ESPRIT. The algorithm effectively improves the wideband performance of ESPRIT based on the interpolation principium and UCA-ESPRIT. The simulated results by computer demonstrate its efficiency.

Numerical method for estimation of kinematical parameters for articulated rovers on loose rough terrain

Based on the study of passive articulated rover,a complete suspension kinematics model from wheel to inertial reference frame is presented,which uses D-H method of manipulator and presentation with Euler angle of pitch,roll and yaw.An improved contact model is adopted aimed at the loose and rough lunar terrain.Using this kinematics model and numerical continuous and discrete Newton's method with iterative factor,the numerical method for estimation of kinematical parameters of articulated rovers on loose and rough terrain is constructed.To demonstrate this numerical method,an example of two torsion bar rocker-bogie lunar rover with eight wheels is presented.Simulation results show that the numerical method for estimation of kinematical parameters of articulated rovers based on improved contact model can improve the precision of kinematical estimation on loose and rough terrain and decrease errors caused by contact models established based on general hypothesis.

A new source number estimation method based on the beam eigenvalue

Most source number estimation methods based on the eigenvalues are decomposed by covariance matrix in MUSIC algorithm. To develop the source number estimation method which has lower signal to noise ratio and is suitable to both correlated and uncorrelated impinging signals, a new source number estimation method called beam eigenvalue method (BEM) is proposed in this paper. Through analyzing the space power spectrum and the correlation of the line array, the covariance matrix is constructed in a new way, which is decided by the line array shape when the signal frequency is given. Both of the theory analysis and the simulation results show that the BEM method can estimate the source number for correlated signals and can be more effective at lower signal to noise ratios than the normal source number estimation methods.

Parameter identification and pressure control of dynamic system in shield tunneling using least squares method

An estimation approach using least squares method was presented for identificationof model parameters of pressure control in shield tunneling.The state equation ofthe pressure control system for shield tunneling was analytically derived based on themass equilibrium principle that the entry mass of the pressure chamber from cutting headwas equal to excluding mass from the screw conveyor.The randomly observed noise wasnumerically simulated and mixed to simulated observation values of system responses.The numerical simulation shows that the state equation of the pressure control system forshield tunneling is reasonable and the proposed estimation approach is effective even ifthe random observation noise exists.The robustness of the controlling procedure is validatedby numerical simulation results.

Motion estimation based feature selection for visual SLAM

Feature selection is always an important issue in the visual SLAM （simultaneous location and mapping） literature. Considering that the location estimation can be improved by tracking features with larger value of visible time, a new feature selection method based on motion estimation is proposed. First, a k-step iteration algorithm is presented for visible time estimation using an affme motion model; then a delayed feature detection method is introduced for efficiently detecting features with the maximum visible time. As a means of validation for the proposed method, both simulation and real data experiments are carded out. Results show that the proposed method can improve both the estimation performance and the computational performance compared with the existing random feature selection method.

Pointwise Convergence of a Nonparametric Estimator of Regression in a Measurable Space Used in Contingent Valuation Method

The Contingent Valuation Method is used to evaluate individual preferences for a change concerning a public non-market resource or property. The objective is to build a nonparametric forecasting model of an individual＇s Willingness To Pay according to geographical location. Within this framework, an estimator （of type Nadaraya-Watson） is proposed for the regression of the variable related to geolocation. The specific characteristics of the location variable lead us to a more general regression model than the traditional models. Results are established for convergence of our estimator.

Assessment of the Design Displacement Values at Seismic Fault Crossings and of Their Excess Probability

Line structures such as pipelines that cross active faults should be designed to retain leak-tightness if the design displacement (Ddesign) occurs. Principal approaches to the Ddesign and rupture kinematics assessment are described. They are based on relationships between earthquake magnitude, rupture length and displacement, and on the detailed field data on a specific fault that crosses the pipeline route. Since the future offset at the crossing may exceed the design value, the probability of a displacement occurrence where the safety of the structure can not be ensured should be estimated. Assessment method on such event probability is described and exemplified through active fault studies carried out at several pipeline projects in Russia.

Anisotropic Interpolation Error Estimates of a Nonconforming Element

The main aim of this paper is to study the local anisotropic interpolation error estimates. We show that the interpolation of a nonconforming element satisfy the anisotropic property for both the second and fourth order problems.

Analysis of Salaries and Some Non-traditional Measures of Location

The paper deals with an analysis of how to use certain measures of location in analysis of salaries. One of the traditional measures of location, the mean should offer typical value of variable, representing all its values by the best way. Sometimes, the mean is located in the tail of the distribution and gives a very biased idea about the location of the distribution. In these cases, using different measures of location could be useful. Trimmed mean is described. The trimmed mean refers to a situation where a certain proportion of the largest and smallest observations are removed and the remaining observations are averaged. The construction of some measures of location is based on the analysis of outliers. Outliers are characterized. Then the possibilities of the detection of outliers are analyzed. Computing of one-step M-estimator and modified one-step M-estimator of location is described. A comparison of the trimmed means and M-estimators of location is presented. Finally, the paper focuses on the application of the trimmed mean and M-estimators of location in analysis of salaries. The analysis of salaries of employers of the big Slovak companies in second half of the year 2009 is realized. The data from the census are used in the analysis. The median, 20% trimmed mean and the characteristics, based on the one-step M-estimator of location and modified one step M-estimator, are calculated.

Financial Statement Valuations in Italian Accounting Thought Between the 19th and the 20th Century： From ＂Exchange Value＂ to ＂Historical Cost＂

The main subject of this paper is the theory of financial statement valuations observed in its historical development. More notably, regarding the subject, the research is concerned with some theoretical concepts developed by the Italian doctrine in a very specific age, namely, between the 19th and the 20th century, which in fact, devoid of any accounting regulation. This paper analyzes in particular the shift from the exchange value rule to the historical cost method and tries to explain the reasons of such a development. In the second half of the 19th century, some of the best Italian scholars, who were faced with the need to properly develop the problem of accounting valuations, thought that it was appropriate to rely on concepts that belonged to similar sciences, such as economics and real estate appraisal, by blindly borrowing the theory of value from the former and the theory of valuations from the latter. During that age, everything hinged around the concept of exchange value. At the dawn of the 20th century, the Italian accounting doctrine began to wonder about a subject that was crucial to the financial statement theory： the informative purposes underlying the financial statements. At the same time, the first principle took shape, which might be called as the ＂finalistic principle of value＂. It is still the basis of the theory of financial accounting measurements, for which different evaluative criteria must be applied to different informative purposes. Thus, an alternative criterion to that of the exchange value makes its appearance on the scene of the accounting valuations, notably the historical cost. The introduction of the historical cost criteria and above all the relinquishment of the combination of the ＂economic cost＂ in favor of that of the ＂manufacturing cost＂ allow the Italian accounting to get rid of the theories of economics and real estate appraisal, thus, becoming independent regarding the financial statement valuations.

Harnack Estimates for Weak Solutions to a Singular Parabolic Equation

By an interpolation method,an intrinsic Harnack estimate and some supestimates are established for nonnegative solutions to a general singular parabolic equation.

Error estimates of numerical methods for the nonlinear Dirac equation in the nonrelativistic limit regime

We present several numerical methods and establish their error estimates for the discretization of the nonlinear Dirac equation （NLDE） in the nonrelativistic limit regime, involving a small dimensionless parameter 0 〈 ε〈〈1 which is inversely proportional to the speed of light. In this limit regime, the solution is highly oscillatory in time, i.e., there are propagating waves with wavelength O（ ε^2） and O（1） in time and space, respectively. We begin with the conservative Crank-Nicolson finite difference （CNFD） method and establish rigorously its error estimate which depends explicitly on the mesh size h and time step τ- as well as the small parameter 0 〈 ε≤1 Based on the error bound, in order to obtain ＇correct＇ numerical solutions in the nonrelativistic limit regime, i.e., 0 〈 ε≤1 , the CNFD method requests the ε-scalability： τ- = O（ε3） and h = O（√ε）. Then we propose and analyze two numerical methods for the discretization of NLDE by using the Fourier spectral discretization for spatial derivatives combined with the exponential wave integrator and time- splitting technique for temporal derivatives, respectively. Rigorous error bounds for the two numerical methods show that their ε-scalability is improved to τ = O（ε2） and h = O（1） when 0 〈 ε 〈〈 1. Extensive numerical results are reported to confirm our error estimates.

Comparative study of estimation methods for genomic breeding values

Estimation of genomic breeding values is important in genomic selection. Bayesian and BLUP methods are the main techniques employed. In this study,we conducted a comparative study of Bayes A, Bayes B,Bayes Cp and GBLUP methods in simulated data and real data of Chinese Holstein cattle. Results showed that, in simulated data, the accuracies of all methods were all similarly elevated with the increase of reference population size, but they made different responses to the changes of marker number or QTL number. In real data of Chinese Holstein cattle, Bayes A generated the highest accuracy almost for all six traits, and GBLUP performed as well as Bayes A for the traits of milk yield, fat yield and protein yield, while for the trait of fat percentage, protein percentage and somatic cell score, three Bayesian methods showed superior to GBLUP. Comprehensively analyzing above results, it can be speculated that accuracies of the three Bayesian methods are not only influenced by the absolute value of QTL number or marker number, but may also be influenced by the ratio of QTL number to marker number. And there is at least one kind of Bayesian methods performing better than GBLUP, when the ratio of QTL number versus marker number is very small or involving large-effect QTL.

On expansion of estimated stability region:Theory,methodology,and application to power systems

The expansion of the estimated stability region plays an important role in the stability analysis of nonlinear systems.However,current literatures have not provided a complete mathematical description for this problem.This paper reveals that essentially the enlargement or the compression of the estimated stability region results directly from the diffeomorphism map,which is induced by the flow contained in the stability region.By proving that any integration algorithm with an order higher than one can approximately trace the flow of the system,a generalized methodology is proposed to construct various algorithms to realize the enlargement or the compression of the estimated stability region.With this methodology,two new algorithms based on symbolic calculation are suggested to reduce the computational burden.Furthermore,this methodology is applied to construct a scalable numerical algorithm to calculate the critical clearing time(CCT) of the power system for given faults.Tests on the IEEE 10-machine 39-bus system show that the computational results coincide well with the step-by-step simulation with high accuracy.

ADAPTIVE TRACKING OF A CLASS OF FIRST-ORDER SYSTEMS WITH BINARY-VALUED OBSERVATIONS AND FIXED THRESHOLDS

This paper considers the adaptive tracking problem for a class of first-order systems with binary-valued observations generated via fixed thresholds. A recursive projection algorithm is proposed for parameter estimation based on the statistical properties of the system noise. Then, an adaptive control law is designed via the certainty equivalence principle. By use of the conditional expectations of the innovation and output prediction with respect to the estimates, the closed-loop system is shown to be stable and asymptotically optimal. Meanwhile, the parameter estimate is proved to be both almost surely and mean square convergent, and the convergence rate of the estimation error is also obtained. A numerical example is given to demonstrate the efficiency of the adaptive control law.

An adaptive time stepping method with efcient error control for second-order evolution problems

This work is concerned with time stepping finite element methods for abstract second order evolution problems. We derive optimal order a posteriori error estimates and a posteriori nodal superconvergence error estimates using the energy approach and the duality argument. With the help of the a posteriori error estimator developed in this work, we will further propose an adaptive time stepping strategy. A number of numerical experiments are performed to illustrate the reliability and efficiency of the a posteriori error estimates and to assess the effectiveness of the proposed adaptive time stepping method.

Joint semiparametric mean-covariance model in longitudinal study

Semiparametric regression models and estimating covariance functions are very useful for longitudinal study. To heed the positive-definiteness constraint, we adopt the modified Cholesky decomposition approach to decompose the covariance structure. Then the covariance structure is fitted by a semiparametric model by imposing parametric within-subject correlation while allowing the nonparametric variation function. We estimate regression functions by using the local linear technique and propose generalized estimating equations for the mean and correlation parameter. Kernel estimators are developed for the estimation of the nonparametric variation function. Asymptotic normality of the the resulting estimators is established. Finally, the simulation study and the real data analysis are used to illustrate the proposed approach.