Rheumatoid arthritis (RA) is an autoimmune disease involving joints, with clinical manifestations of joint inflammation, bone damage and cartilage destruction, joint dysfunction and deformity, and extra-articular organ damage. As an important source of new drug molecules, natural medicines have many advantages, such as a wide range of biological effects and small toxic and side effects. They have become a hot spot for the vast number of researchers to study various diseases and develop therapeutic drugs. In recent years, the research of natural medicines in the treatment of RA has made remarkable achievements. These natural medicines mainly include flavonoids, polyphenols, alkaloids, glycosides and terpenes. Among them, resveratrol, icariin, epigallocatechin-3-gallate, ginsenoside, sinomenine, paeoniflorin, triptolide and paeoniflorin are star natural medicines for the treatment of RA. Its mechanism of treating RA mainly involves these aspects: anti-inflammation, anti-oxidation, immune regulation, pro-apoptosis, inhibition of angiogenesis, inhibition of osteoclastogenesis, inhibition of fibroblast-like synovial cell proliferation, migration and invasion. This review summarizes natural medicines with potential therapeutic effects on RA and briefly discusses their mechanisms of action against RA.

This paper presents a radial basis function prediction model improved by differential evolution algorithm for coking energy consumption process, which is very difficult to get online and real time because of the complex process. In the energy consumption prediction model, target flue temperature, flue suction, water content, volatile coal and coking time are considered as input variables, and coking energy consumption as output variables. To overcome the shortcomings of radial basis function network, such as poor learning ability and slow convergence speed, the energy consumption prediction model optimized by differential evolution algorithm is improved. Using the strong global search ability of differential evolution algorithm, the center value, width and output weight of the basis function in radial basis function network is obtained by differential evolution algorithm. Then the optimal values are taken as the center value, width and output weight of the of radial basis function neural network. The results show that the improved radial basis function prediction has higher accuracy, stability and training speed of the network. The radial basis function prediction model has great significance in reducing coking energy consumption, saving enterprise costs, increasing coke production and improving enterprise economic benefits.

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Nonlinearity may cause a model deviation problem, and hence, it is a challenging problem for process monitoring. To handle this issue, local kernel principal component analysis was proposed, and it achieved a satisfactory performance in static process monitoring. For a dynamic process, the expectation value of each variable changes over time, and hence, it cannot be replaced with a constant value. As such, the local data structure in the local kernel principal component analysis is wrong, which causes the model deviation problem. In this paper, we propose a new two-step dynamic local kernel principal component analysis, which extracts the static components in the process data and then analyzes them by local kernel principal component analysis. As such, the two-step dynamic local kernel principal component analysis can handle the nonlinearity and the dynamic features simultaneously.