Jiangxi Normal University

About: Jiangxi Normal University is a education organization based out in Nanchang, China. It is known for research contribution in the topics: Catalysis & Aryl. The organization has 8029 authors who have published 8399 publications receiving 111730 citations. The organization is also known as: Jiāngxī Shīfàn Dàxué.

Recent Advances in Radical C-H Activation/Radical Cross-Coupling.

Abstract: Research and industrial interest in radical C–H activation/radical cross-coupling chemistry has continuously grown over the past few decades. These reactions offer fascinating and unconventional approaches toward connecting molecular fragments with high atom- and step-economy that are often complementary to traditional methods. Success in this area of research was made possible through the development of photocatalysis and first-row transition metal catalysis along with the use of peroxides as radical initiators. This Review provides a brief and concise overview of the current status and latest methodologies using radicals or radical cations as key intermediates produced via radical C–H activation. This Review includes radical addition, radical cascade cyclization, radical/radical cross-coupling, coupling of radicals with M–R groups, and coupling of radical cations with nucleophiles (Nu).

Global Convergence of ADMM in Nonconvex Nonsmooth Optimization

Abstract: In this paper, we analyze the convergence of the alternating direction method of multipliers (ADMM) for minimizing a nonconvex and possibly nonsmooth objective function, $$\phi (x_0,\ldots ,x_p,y)$$ , subject to coupled linear equality constraints. Our ADMM updates each of the primal variables $$x_0,\ldots ,x_p,y$$ , followed by updating the dual variable. We separate the variable y from $$x_i$$ ’s as it has a special role in our analysis. The developed convergence guarantee covers a variety of nonconvex functions such as piecewise linear functions, $$\ell _q$$ quasi-norm, Schatten-q quasi-norm ( $$0

A Risk Society

Abstract: “Risk society” is a concept that was first framed by the German sociologist Ulrich Beck in Risk Society in 1986. In Beck’s view, the modern society had deviated from (Karl Marx’s) class society or (Max Weber’s) industrial society and had developed into a social form that is highly modern, known as the “risk society.” Social theories based on unequal distribution of wealth (the functional theory, Marxism, and various kinds of postindustrial or postmodern theories that derived from it) have lost their interpretability when it comes to the crisis and inequality in the distribution of risks. Therefore, there needs to be a turn in social theories, that is to say, “risk sociology” needs to be advanced with problem awareness being “how to avoid, minimize, and direct risks or hazards systematically created as a part of modernization.”

Multi-scale Residual Network for Image Super-Resolution

Abstract: Recent studies have shown that deep neural networks can significantly improve the quality of single-image super-resolution. Current researches tend to use deeper convolutional neural networks to enhance performance. However, blindly increasing the depth of the network cannot ameliorate the network effectively. Worse still, with the depth of the network increases, more problems occurred in the training process and more training tricks are needed. In this paper, we propose a novel multi-scale residual network (MSRN) to fully exploit the image features, which outperform most of the state-of-the-art methods. Based on the residual block, we introduce convolution kernels of different sizes to adaptively detect the image features in different scales. Meanwhile, we let these features interact with each other to get the most efficacious image information, we call this structure Multi-scale Residual Block (MSRB). Furthermore, the outputs of each MSRB are used as the hierarchical features for global feature fusion. Finally, all these features are sent to the reconstruction module for recovering the high-quality image.