Analysis of heat and mass transfer mechanism of vacuum freeze-drying in the primary drying

The freeze-drying process is a complex heat and mass transfer process virtually. The drying process of freeze-drying is not only the key stage which decides the success of freeze-drying, but also the most difficult stage to control. There are lots of papers about heat and mass transfer in vacuum freeze drying at home and abroad. The present status of research on heat and mass transfer during vacuum freeze drying in the primary drying is summed up and analyzed, the trend of research in this field is discussed in this paper

Mathematical model of vacuum freeze-drying in the secondary drying

The freeze-drying process is a complex heat and mass transfer process virtually. The drying process of freeze-drying is not only the key stage which decides the success of freeze-drying, but also the most difficult stage to control. There are lots of papers about heat and mass transfer in vacuum freeze drying at home and abroad. The present status of research on heat and mass transfer during vacuum freeze drying in the secondary drying is summed up and analyzed, and the trend of research in this field is discussed in this paper

Experimental analysis for the effect of dynamic capillarity on stress transformation in porous silicon

The evolution of real-time stress in porous silicon(PS) during drying is investigated using micro-Raman spectroscopy. The results show that the PS sample underwent non-negligible stress when immersed in liquid and suffered a stress impulsion during drying. Such nonlinear transformation and nonhomogeneneous distribution of stress are regarded as the coupling effects of several physical phenomena attributable to the intricate topological structure of PS. The effect of dynamic capillarity can induce microcracks and even collapse in PSstructures during manufacture and storage.This work is funded by the National Natural Science Foundation of China Contract Nos. 10732080 and 10502014

Hunting for XbX_b via hidden bottomonium decays Xb→ππχbJX_b\to \pi\pi\chi_{bJ}

In this work, we investigate the isospin breaking decay $X_b \to \pi^0\chi_{bJ}$ and the isospin conserved decay $X_b \to \pi\pi\chi_{bJ}$, where $X_b$ is taken to be the heavy quark flavor symmetry counterpart of $X(3872)$ in the bottomonium sector as a $B^*\bar B$ molecule candidate. Since the mass of this state may be far below the $B {\bar B}^*$ threshold and the mass difference between the neutral and charged bottom meson is small compared to the binding energy of the $X_b$, the isospin-violating decay channel $X_b \to \pi^0 \chi_{bJ}$ would be highly suppressed. The calculated partial width of $X_b \to \pi\pi\chi_{b1}$ is found to be about tens of $\rm{keVs}$, $1\sim 2$ order(s) of magnitude larger than those of $X_b \to \pi\pi\chi_{b2}$ and $X_b \to \pi\pi\chi_{b0}$. Taking into account the fact that the total width of $X_b$ may be smaller than a few MeV like $X(3872)$, the calculated branching ratios $X_b\to \pi\pi \chi_{b1}$ may reach to orders of $10^{-2}$, which makes it a possible channel for the experimental searching of the $X_b$.Comment: 14 pages, 5 figures, published in Phys.Rev.

Inferences in Censored Cost Regression Models with Empirical Likelihood

In many studies of health economics, we are interested in the expected total cost over a certain period for a patient with given characteristics. Problems can arise if cost estimation models do not account for distributional aspects of costs. Two such problems are 1) the skewed nature of the data and 2) censored observations. In this paper we propose an empirical likelihood (EL) method for constructing a confidence region for the vector of regression parameters and a confidence interval for the expected total cost of a patient with the given covariates. We show that this new method has good theoretical properties and compare its finite-sample properties with the existing method. Our simulation results demonstrate that the new EL-based method performs equally well with the existing method when cost data are not so skewed, and outperforms the existing method when cost data are highly skewed. Finally, we illustrate the application of our method in a real data set