基于P2P技术的CSRe冷却装置控制系统设计；Design of Control System for CSRe Cooling Equipment Based on P2P Technology

HIRFL-CSR工程对CSRe冷却装置电子冷却部分的控制系统在实时性和可靠性方面提出了非常高的要求。电子冷却工作环境复杂,各种干扰难以预测。从电子冷却的控制系统改进出发,以实现电子冷却的自动调束为目标,以高端FPGA和ARM嵌入式系统为基础,采用P2P通讯技术和神经元网络算法来实现对电子冷却的自动控制。该控制系统对电子冷却控制的完善提供了先进的硬件平台和软件实现方案。

基于DMOS管的电荷灵敏前置放大器设计；Design of A Charge Sensitive Preamplifier Based on DMOS FET

提出了一种由DMOS场效应管构成的电荷灵敏前置放大器,可用于硅,Si(Li),CdZnTe及CsI探测器。该前置放大器采用不同于传统的阻容反馈式的电路结构,完全使用MOS管搭建,该前放的设计完成为设计实现ASIC电路准备了技术基础。由Multisi m仿真结果看出该电荷灵敏前置放大器输出信号上升时间小于15ns,并且具有很好的稳定性。

基于CPLD和ATMEGAL128的数字电源系统的设计；Design of Digital Power Control System Based on CPLD and ATMEGAL128

介绍了一种基于CPLD设计的电源控制模块,并且利用Atmegal128单片机和RTL8019S实现逻辑功能和远程控制功能。该电源系统主要用于重离子加速器注入器(SFC)中,具有很好的灵活性、可远程控制、性能稳定等特点。

Enhanced resonance light scattering based on biocatalytic growth of gold nanoparticles for biosensors design

The biocatalytic growth of gold nanoparticles (Au-NPs) has been employed in the design of new optical biosensors based on the enhanced resonance light scattering (RLS) signals. Both absorption spectroscopy and transmission electron microscopy (TEM) analysis revealed Au-NP seeds could be effectively enlarged upon the reaction with H2O2, an important metabolite that could be generated by many biocatalytic reactions.

Design of Fluorescent Assays for Cyanide and Hydrogen Peroxide Based on the Inner Filter Effect of Metal Nanoparticles

We have demonstrated the design of a new type fluorescent assay based on the inner filter effect (IFE) of metal nanoparticles (NPs), which is conceptually different from the previously reported metal NPs-based fluorescent assays. With a high extinction coefficient and tunable plasmon absorption feature, metal NPs are expected to be capable of functioning as a powerful absorber to tune the emission of the fluorophore in the IFE-based fluorescent assays. In this work, we presented two proof-of-concept examples based on the IFE of Au NPs by choosing MDMO-PPV as a model fluorophore, whose fluorescence could be tuned by the absorbance of Au NPs with a much higher sensitivity than the corresponding absorbance approach.

Design of silicone rubber according to requirements based on the multi-objective optimization of chemical reactions

The explicit expression between composition and mechanical properties of silicone rubber was derived from the physics of polymer elasticity, the implicit expression among material composition, reaction conditions and reaction efficiency was obtained from chemical thermodynamics and kinetics, and then an implicit multi-objective optimization model was constructed. Genetic algorithm was applied to optimize material composition and reaction conditions, and the finite element method of cross-linking reaction processes was used to solve multi-objective functions, on the basis of which a new optimization methodology of crosslinking reaction processes was established. Using this methodology, rubber materials can be designed according to pre-specified requirements.

An in silico approach for screening flavonoids as P-glycoprotein inhibitors based on a Bayesian-regularized neural network

P-glycoprotein (P-gp), an ATP-binding cassette (ABC) transporter, functions as a biological barrier by extruding cytotoxic agents out of cells, resulting in an obstacle in chemotherapeutic treatment of cancer. In order to aid in the development of potential P-gp inhibitors, we constructed a quantitative structure-activity relationship (QSAR) model of flavonoids as P-gp inhibitors based on Bayesian-regularized neural network (BRNN). A dataset of 57 flavonoids collected from a literature binding to the C-terminal nucleotide-binding domain of mouse P-gp was compiled. The predictive ability of the model was assessed using a test set that was independent of the training set, which showed a standard error of prediction of 0.146 +/- 0.006 (data scaled from 0 to 1). Meanwhile, two other mathematical tools, back-propagation neural network (BPNN) and partial least squares (PLS) were also attempted to build QSAR models. The BRNN provided slightly better results for the test set compared to BPNN, but the difference was not significant according to F-statistic at p = 0.05. The PLS failed to build a reliable model in the present study. Our study indicates that the BRNN-based in silico model has good potential in facilitating the prediction of P-gp flavonoid inhibitors and might be applied in further drug design.