Electrochemical Immunoanalysis for Carcinoembryonic Antigen Based on Multilayer Architectures of Gold Nanoparticles and Polycation Biomimetic Interface on Glassy Carbon Electrode

This paper describes a layer‐by‐layer (LBL) self‐assembly process of chitosan (CTS) and gold nanoparticles (Au) on the pretreated negatively charged glassy carbon (GC) electrode to fabricate electrochemistry immunosensor with a nontoxic biomimetic interface, which provided an environment similar to a native system and allowed more freedom in orientation for immobilization of carcinoembryonic antibody (anti‐CEA) to monitor carcinoembryonic antigen (CEA). UV‐vis spectroscope, atomic force microscopy (AFM), and cyclic voltammetric (CV) measurements were used to follow the multilayer film formation. The performance of the biominetic interface and factors influencing the assay system were investigated in detail. The differential pulse voltammetry (DPV) current response is used for the CEA concentration assay. The dynamic range was from 0.50 to 80.00 ng mL^?1 with a detection limit of 0.27 ng mL^?1 at 3σ. In addition, the experiment results indicate that immobilization described in this proposed method exhibits a good sensitivity, selectivity, and stability.     

Automatic detection and identification of shocks in Gaussian state‐space models: a Bayesian approach

An automatic monitoring and intervention algorithm that permits the supervision of very general aspects in an univariate linear Gaussian state–space model is proposed. The algorithm makes use of a model comparison and selection approach within a Bayesian framework. In addition, this algorithm incorporates the possibility of eliminating earlier interventions when subsequent evidence against them comes to light. Finally, the procedure is illustrated with two empirical examples taken from the literature. Copyright © 2005 John Wiley & Sons, Ltd.     

On Numerical Solution of Shape Inverse Problems

The new method is proposed for the numerical solution of a class of shape inverse problems. The size and the location of a small opening in the domain of integration of an elliptic equation is identified on the basis of an observation. The observation includes the finite number of shape functionals. The approximation of the shape functionals by using the so-called topological derivatives is used to perform the learning process of an artificial neural network. The results of computations for 2D examples show, that the method allows to determine an approximation of the global solution to the inverse problem, sufficiently closed to the exact solution. The proposed method can be extended to the problems with an opening of general shape and to the identification problems of small inclusions. However, the mathematical theory of the proposed approach still requires futher research. In particular, the proof of global convergence of the method is an open problem.     

超声波波型转换与表面波的检测

分析了超声波纵波、横波、表面波3种波型的产生条件和声速关系,检测了超声波在固体表面的波型转换,并测量了声表面波的速度,讨论了声表面波在超声延迟线方面的应用.     

利用莫尔条纹的准正弦特性的三维轮廓术

分析了两个矩形光栅迭合产生的莫尔条纹的光强分布特性，通过选择适当的光栅参数，可得到一个近似的正弦分划板，并把它用于三维面形测量中，实验结果表明，这种方法简单，易于自动处理，有广泛的实用价值。     

Bayesian Object Identification: Variants

We present a Bayesian theory of object identification. Here, identifying an object means selecting a particular observation from a group of observations (variants), this observation (the regular variant) being characterized by a distributional model. In this sense, object identification means assigning a given model to one of several observations. Often, it is the statistical model of the regular variant, only, that is known. We study an estimator which relies essentially on this model and not on the characteristics of the “irregular” variants. In particular, we investigate under what conditions this variant selector is optimal. It turns out that there is a close relationship with exchangeability and Markovian reversibility. We finally apply our theory to the case of irregular variants generated from the regular variant by a Gaussian linear model.     

Adaptive Interpolation by Scaled Multiquadrics

We present an adaptive method to extract shape-preserving information from a univariate data sample. The behavior of the signal is obtained by interpolating at adaptively selected few data points by a linear combination of multiquadrics with variable scaling parameters. On the theoretical side, we give a sufficient condition for existence of the scaled multiquadric interpolant. On the practical side, we give various examples to show the applicability of the method.     

On accurate boundary conditions for a shape sensitivity equation method

This paper studies the application of the continuous sensitivity equation method (CSEM) for the Navier–Stokes equations in the particular case of shape parameters. Boundary conditions for shape parameters involve flow derivatives at the boundary. Thus, accurate flow gradients are critical to the success of the CSEM. A new approach is presented to extract accurate flow derivatives at the boundary. High order Taylor series expansions are used on layered patches in conjunction with a constrained least‐squares procedure to evaluate accurate first and second derivatives of the flow variables at the boundary, required for Dirichlet and Neumann sensitivity boundary conditions. The flow and sensitivity fields are solved using an adaptive finite‐element method. The proposed methodology is first verified on a problem with a closed form solution obtained by the Method of Manufactured Solutions. The ability of the proposed method to provide accurate sensitivity fields for realistic problems is then demonstrated. The flow and sensitivity fields for a NACA 0012 airfoil are used for fast evaluation of the nearby flow over an airfoil of different thickness (NACA 0015). Copyright © 2005 John Wiley & Sons, Ltd.     

A new algorithm for fixed design regression and denoising

In this paper, we present a new algorithm to estimate a regression function in a fixed design regression model, by piecewise (standard and trigonometric) polynomials computed with an automatic choice of the knots of the subdivision and of the degrees of the polynomials on each sub-interval. First we give the theoretical background underlying the method: the theoretical performances of our penalized least-squares estimator are based on non-asymptotic evaluations of a mean-square type risk. Then we explain how the algorithm is built and possibly accelerated (to face the case when the number of observations is great), how the penalty term is chosen and why it contains some constants requiring an empirical calibration. Lastly, a comparison with some well-known or recent wavelet methods is made: this brings out that our algorithm behaves in a very competitive way in term of denoising and of compression.