In recent years some multi-mode pushover procedures taking into account higher mode effects, have been proposed. The responses of considered modes are combined by the quadratic combination rules, while using the elastic modal combination rules in the inelastic phases is not valid. Here, an optimum weighted mode combination method for nonlinear static analysis is presented. Genetic algorithm is ...

Two novel preanalysis sample treatment tools were evaluated in combination with four DNA extraction kits for the selective isolation of bacterial DNA from whole blood. The combination of performing a preanalysis sample treatment and using a larger sample volume increased the detection limit to 50 CFU per ml.

BACKGROUND There are limited published data on the types and appropriateness of oral and intravenous (IV) antibiotics prescribed to patients receiving haemodialysis. This information is critical to optimise antibiotic prescribing. Therefore this study aims to describe the patterns of use and the appropriateness of oral and IV antibiotics prescribed to patients receiving haemodialysis. METHODS...

To the Editor—We read with interest the article by Goicoechea et al. [1] on the role of antiretroviral medications and tenofovirassociated kidney dysfunction. The authors concludedthatregimensbasedonritonavirboosted protease inhibitor (PI/r) were associated with a greater decline in kidney function than regimens based on nonnucleoside reverse-transcriptase inhibitors (NNRTIs) among patients rec...

scanning impedance microscopy (sim) is one of the novel scanning probe microscopy (spm) techniques, which has been developed to taking image from sample surface, providing quantitative information with high lateral resolution on the interface capacitance, and investigating the local capacitance–voltage (c–v) behavior of the interface and ac transport properties. the sim is an ordinary afm equip...

In this paper we propose a supervised sparse representation method for face recognition. We assume that the test sample could be approximately represented by a sparse linear combination of all the training samples, where the term ‘‘sparse’’ means that in the linear combination most training samples have zero coefficients. We exploit a heuristic strategy to achieve this goal. First, we determine...

We explore the extension of James-Stein type estimators in a direction that enables them to preserve their superiority when the sample size goes to infinity. Instead of shrinking a base estimator towards a fixed point, we shrink it towards a data-dependent point, which makes it possible that the “prior” becomes more accurate as the sample size grows. We provide an analytic expression for the as...