Overview of pharmacological activities of Andrographis paniculata and its major compound andrographolide

Abstract

Andrographis paniculata (A. paniculata) is a medicinal plant traditionally used as anti-inflammation and anti-bacteria herb. Andrographolide, the major active component of A. paniculata, exhibits diverse pharmacological activities, including anti-inflammation, anti-cancer, anti-obesity, anti-diabetes, and other activities. In this article, we comprehensively review the therapeutic potential of A. paniculata and andrographolide focusing on the mechanisms of action and clinical application. We systemically discuss the structure-activity relationship of andrographolide and derivatives. Despite the various pharmacological activities and formula of A. paniculata and andrographolide, we propose further development of more structural derivatives of andrographolide with reduced toxicity and increased therapeutic efficacy is still needed for the clinical application of this ancient mighty herb and its major component.     

Simulation of the effects of different substrates,temperature,and substrate roughness on the mechanical properties of Al_2O_3 coating as tritium penetration barrier

Residual thermal stress in the system is a serious problem that affects the application of tritium permeation barrier coatings in fusion reactors. The stress not only determines the adhesion between coating and substrate, but also changes the properties of the material. In this study,finite element analysis was used to investigate the relationship between the residual thermal stress and the mechanical properties of Al_2O_3 tritium penetration barrier systems. Moreover, the residual thermal stress influenced by factors such as different substrates, temperature, and substrate roughness was also analyzed. The calculation showed that the hardness and elastic modulus increased with increasing compressive stress. However, the hardness and elastic modulus decreased with increasing tensile stress. The systems composed of Al_2O_3 coatings and different substrates exhibited different trends in mechanical properties. As the temperature increased, the hardness and the elastic modulus increased in an Al_2O_3/316 L stainless steel system; the trend was opposite in an Al_2O_3/Si system.Apart from this, the roughness of the substrate surface in the system could magnify the change in hardness and elastic modulus of the coating. Results showed that all these factors led to variation in the mechanical properties of Al_2O_3 tritium permeation barrier systems. Thus, thedetailed reasons for the changes in mechanical properties of these materials need to be analyzed.     

Distant Supervised Relation Extraction with Cost-Sensitive Loss

Recently, many researchers have concentrated on distant supervision relation extraction (DSRE). DSRE has solved the problem of the lack of data for supervised learning, however, the data automatically labeled by DSRE has a serious problem, which is class imbalance. The data from the majority class obviously dominates the dataset, in this case, most neural network classifiers will have a strong bias towards the majority class, so they cannot correctly classify the minority class. Studies have shown that the degree of separability between classes greatly determines the performance of imbalanced data. Therefore, in this paper we propose a novel model, which combines class-to-class separability and cost-sensitive learning to adjust the maximum reachable cost of misclassification, thus improving the performance of imbalanced data sets under distant supervision. Experiments have shown that our method is more effective for DSRE than baseline methods.     

Facile synthesis of thin coating C/ZnO composites with strong electromagnetic wave absorption

C/ZnO composites with increased electromagnetic (EM) wave absorbing features have been synthesized through a simple one-pot hydrothermal process and subsequent high temperature carbonization under the protection of argon. The results depict that the maximum absorption of C/ZnO composites synthesized with the optimal molar ratio of zinc acetate to glucose is ?50.43?dB at 15.77?GHz. The 1.16-mm-thick coating shows a wide effective absorption bandwidth (3.52?GHz) of EM wave (RL≤?10?dB). The thin coating thickness of the C/ZnO composites is desirable for decreasing the absorber weight in EM wave absorption. And there are no other reagents used throughout the synthesis process except for the green glucose and zinc acetate. Thus, C/ZnO composites would be highly promising lightweight EM wave absorbing materials.     

磁铁尾矿与石灰石双掺生产优质水泥

受环保压力影响，西乡公司生产水泥用河道淤沙供应紧张，难以满足生产需求且价格上涨幅度较大，严重影响到水泥生产及成本，我们开展了磁铁尾矿在水泥中的应用研究工作，通过连续几个月的生产实践，对物料配比优化，磨机技术改造，实现利用磁铁尾矿与石灰石双掺解决混合材紧缺问题，降低了水泥生产成本，水泥质量及性能稳定。     

Alkali metal ion substitution induced luminescence enhancement of NaLaMgWO6:Eu3+ red phosphor for white light-emitting diodes

Alkali metal ion substitution is an effective strategy to improve the luminescence properties of phosphors. In this work, a series of red-emitting phosphors Na_1-xLi_x/2K_x/2La_0.6Eu_0.4MgWO₆ were prepared by a traditional high-temperature solid-state reaction. Their phase structure, microstructure, luminescence properties and potential application in phosphor-converted white light-emitting diodes (pc-WLEDs) were investigated in detail. X-ray diffraction (XRD) result revealed the formation of a solid solution when x?≤?0.3, which kept monoclinic structure of NaLaMgWO₆. Photoluminescence investigation indicated that the partial substitution of Li⁺/K⁺ ions for Na⁺ ions improved largely the red emission of Eu³⁺. Based on the optimized Na_0.7Li_0.15K_0.15La_0.6Eu_0.4MgWO₆ sample with relatively good thermal stability, a WLED device was fabricated by combining a near-ultraviolet (NUV) chip (~400?nm) with the phosphor mixture of commercial green/blue phosphors and the optimized red phosphor. The results indicated that the optimized red phosphor in this work could be a potential candidate for WLEDs pumped by NUV chips.     

Architecture design and implementation of image based autonomous car: THUNDER-1

Autonomous driving with high velocity is a research hotspot which challenges the scientists and engineers all over the world. This paper proposes a scheme of indoor autonomous car based on ROS which combines the method of Deep Learning using Convolutional Neural Network (CNN) with statistical approach using liDAR images and achieves a robust obstacle avoidance rate in cruise mode. In addition, the design and implementation of autonomous car are also presented in detail which involves the design of Software Framework, Hector Simultaneously Localization and Mapping (Hector SLAM) by Teleoperation, Autonomous Exploration, Path Plan, Pose Estimation, Command Processing, and Data Recording (Co- collection). what’s more, the schemes of outdoor autonomous car, communication, and security are also discussed. Finally, all functional modules are integrated in nVidia Jetson TX1.

     

Optimization of Process Conditions for Styrene Epoxidation Based on the Artificial Intelligence Method

A novel prediction and optimization method based on improved generalized regression neural network (GRNN) and particle swarm optimization (PSO) algorithm is proposed to optimize the process conditions for styrene epoxidation to achieve higher yields. This model was designed to optimize the five input parameters reaction temperature and time as well as catalyst, solvent, and oxidant dosage. The output of the improved GRNN was given to the PSO algorithm to optimize the process conditions. The optimal smoothing parameter σ of GRNN was chosen from the training sample with a minimum cross validation error. Under the five optimized process conditions the maximum yield reached 95.76 %. This innovative model of improved GRNN hybrid PSO algorithm proved to be a useful tool for optimization of process conditions for styrene epoxidation.