Simplified Aeroelastic Model for Fluid Structure Interaction between Microcantilever Sensors and Fluid Surroundings

Fluid-structural coupling occurs when microcantilever sensors vibrate in a fluid. Due to the complexity of the mechanical characteristics of microcantilevers and lack of high-precision microscopic mechanical testing instruments, effective methods for studying the fluid-structural coupling of microcantilevers are lacking, especially for non-rectangular microcantilevers. Here, we report fluid-structure interactions (FSI) of the cable-membrane structure via a macroscopic study. The simplified aeroelastic model was introduced into the microscopic field to establish a fluid-structure coupling vibration model for microcantilever sensors. We used the finite element method to solve the coupled FSI system. Based on the simplified aeroelastic model, simulation analysis of the effects of the air environment on the vibration of the commonly used rectangular microcantilever was also performed. The obtained results are consistent with the literature. The proposed model can also be applied to the auxiliary design of rectangular and non-rectangular sensors used in fluid environments.     

Three saponins from Oxytropis species.

Three flavonoids and three saponins have been isolated from Oxytropis species. Their structures were determined as isorhamnetin-3-O-beta-D-glucoside, rhamnetin-3-O-beta-D-galactoside, apigenin, 3-O-[alpha-L-rhamnopyranosyl (1----2)-beta-D-glucopyranosyl(1----4)-beta-D-glucuronopyranosyl]+ ++soyasapogenol B, 3-O-[beta-D-glucopyranosyl(1----2)-beta-D-glucuronopyranosyl] azukisapogenol and a new saponin 3-O-[beta-D-glucopyranosyl(1----2)-beta-D-glucopyranosyl]-25-O-alpha-L- rhamnopyranosyl-(20S,24S)-3 beta,16 beta, 20,24,25-pentahydroxy-9,19-cycloanostane.     

Bayesian nonparametric inference for panel count data with an informative observation process

In this paper, the panel count data analysis for recurrent events is considered. Such analysis is useful for studying tumor or infection recurrences in both clinical trial and observational studies. A bivariate Gaussian Cox process model is proposed to jointly model the observation process and the recurrent event process. Bayesian nonparametric inference is proposed for simultaneously estimating regression parameters, bivariate frailty effects, and baseline intensity functions. Inference is done through Markov chain Monte Carlo, with fully developed computational techniques. Predictive inference is also discussed under the Bayesian setting. The proposed method is shown to be efficient via simulation studies. A clinical trial dataset on skin cancer patients is analyzed to illustrate the proposed approach.     

Comparative Analysis of Endogenous Hormones and Metabolite Profiles in Early-Spring Flowering Plants and Unflowered Plants Revealing the Strategy of Blossom

Journal of Plant Growth Regulation - Early-spring plants are a special type of plant that complete their life cycle promptly in cold, early spring. Very little effort has been made into researching...     

On the computation of in vivo transmural mean stress of patient-specific aortic wall

Biomechanics and Modeling in Mechanobiology - It is well known that residual deformations/stresses alter the mechanical behavior of arteries, e.g., the pressure–diameter curves. In an effort...