Modelling Development in Radical (Co)Polymerization of Multivinyl Monomers

Radical polymerization (RP) of multivinyl monomers (MVMs) provides a facile solution for manipulating polymer topology and has received increasing attention due to their industrial and academic significance. Continuous efforts have been made to understand their mechanism, which is the key to regulating materials structure. Modelling techniques have become a powerful tool that can provide detailed information on polymerization kinetics which is inaccessible by experiments. Many publications have reported the combination of experiments and modelling for free radical polymerization (FRP) and reversible-deactivation radical polymerizations (RDRP) of MVMs. Herein, a minireview is presented for the most important modelling techniques and their applications in FRP/RDRP of MVMs. This review hopes to illustrate that the combination of modelling and wet experiments can be a great asset to polymer researchers and inspire new thinking for the future MVMs experiment optimization and product design.     

Sigma Delta Quantization for Images

In signal quantization, it is well-known that introducing adaptivity to quantization schemes can improve their stability and accuracy in quantizing bandlimited signals. However, adaptive quantization has only been designed for one-dimensional signals. The contribution of this paper is two-fold: (i) we propose the first family of two-dimensional adaptive quantization schemes that maintain the same mathematical and practical merits as their one-dimensional counterparts, and (ii) we show that both the traditional 1-dimensional and the new 2-dimensional quantization schemes can effectively quantize signals with jump discontinuities, which immediately enable the usage of adaptive quantization on images. Under mild conditions, we show that by using adaptivity, the proposed method is able to reduce the quantization error of images from the presently best $O\left(\sqrt{P}\right)$ to the much smaller $O\left(\sqrt{s}\right)$, where s is the number of jump discontinuities in the image and P ($P\gg s$) is the total number of samples. This $\sqrt{P/s}$-fold error reduction is achieved via applying a total variation norm regularized decoder, whose formulation is inspired by the mathematical super-resolution theory in the field of compressed sensing. Compared to the super-resolution setting, our error reduction is achieved without requiring adjacent spikes/discontinuities to be well-separated, which ensures its broad scope of application. We numerically demonstrate the efficacy of the new scheme on medical and natural images. We observe that for images with small pixel intensity values, the new method can significantly increase image quality over the state-of-the-art method. © 2022 Wiley Periodicals, Inc.     

Property of intrinsic drift coefficients in globally-evolving-based generalized density evolution equation for the first-passage reliability assessment

Acta Mechanica Sinica - The turbulent Poiseuille flow between two parallel plates is one of the simplest possible physical situations, and it has been studied intensively. In this paper, we propose...