Large deviation analysis of the single server queue

We establish the large deviation principle (LDP) for the virtual waiting time and queue length processes in the GI/GI/1 queue. The rate functions are found explicitly. As an application, we obtain the logarithmic asymptotics of the probabilities that the virtual waiting time and queue length exceed high levels at large times. Additional new results deal with the LDP for renewal processes and with the derivation of unconditional LDPs for conditional ones. Our approach applies in large deviations ideas and methods of weak convergence theory.This work was supported in part by AT&T Bell Labs.     

空间听觉与虚拟听觉空间的计算机仿真

一些科学工作者对人类以及动物听觉的自由声场至耳膜的传递函数进行了测量,他们发现在宽带信号激励的情况下,不同频率分量之间的幅度差异是与声源的方向有关的,这一发现已被用于展现“真实”的三维听觉,称作虚拟听觉空间仿真。     

三维场景的交互仿真技术

目的坦克和高炮仿真软件中关键技术的研究与实现。方法 采用ＯｐｅｎＧＬ和Ｖｉｓｕａｌ Ｃ＾＋＋进行实时交互的三维图形开发。结果采用键盘进行交互,实现了三维场景的编辑和实时漫游,实现了光照,纹理,反走样,雾化等一系列提高真实感的效果。     

虚拟技术及其应用

简要介绍了虚拟技术及其基本原理、虚拟现帝技术的基本要素和关键技术,着重就虚拟技术在先进制造技术中的应用及现状进行分析,包括虚拟设计技术、虚拟制造及系统技术、虚拟企业、虚拟仓储、虚拟产品开发和虚拟仪器等,最后,还分别说明了虚拟技术在教学、医疗和建筑等方面的综合应用,拟帝制造技术研究重点以及发展趋势。     

当量齿轮

介绍了当量齿轮的概念和当量齿轮的应用．     

利用虚盘技术改进 NCP 生成过程

ＮＣＰ（ＮｅｔｗｏｒｋＣｏｎｔｒｏｌＰｒｏｇｒａｍ）是通讯控制器上运行的网络管理软件,当网络资源发生变化或网络参数修改时,ＮＣＰ都要重新生成,其生成时间直接影响到网络使用效率．本文陈述了ＮＣＰ的生成过程并通过引入虚盘技术,改进了ＮＣＰ生成过程,有效地缩短了其生成时间．     

The matched queueing network PH/M/c→oPH/PH/1

We consider a matched queueing network PH/M/coPH/PH/1, where the output of the first subsystem PH/M/c is regarded as one of the inputs of the second subsystem and matched with another input of PH-renewal process, and they are served by a single server with PH-service-distribution. The stationary state probabilities, the distribution of the virtual waiting times in the two subsystems, and the distribution of the total virtual waiting time are derived.The work was supported by the Max-Planck-Gesellschaft, Germany, and the Chinese Academy ofSciences.     

In vitro and in silico study of Aloe vera leaf extract against human breast cancer

Abstract

Aloe vera leaf contains some bioactive compounds that have a strong binding affinity toward estrogen receptor as compared to standard drug tamoxifen. In this study, we have found that the IC₅₀ of Aloe vera leaf extract against breast cancer cell line (MCF-7) is 23?µg/mL which is much lower than the IC₅₀ (332?µg/mL) of Aloe vera leaf extract against non-cancerous cell line (NIH-3T3). We have also calculated the total concentration of phenolic acid (385.662?µg/mL), flavonoids (160.402?µg/mL) and alkaloids (276.754?µg/mL) in Aloe vera leaf extract. The free radical scavenging activity of Aloe vera leaf extract is 67% to 89% (at 50 to 300?µg/ml). Our virtual molecular docking study suggests that bioactive compounds like Aloe-emodin (?8.8?Kcal/mol), 7-hydroxy-2,5 dimethylchromone (?7.5?Kcal/mol), Beta-sitosterol (?7.3?Kcal/mol) etc. have a greater binding affinity toward estrogen alpha receptor as compared to standard drug Tamoxifen (?6.4?Kcal/mol).

     

Improving (Q)SAR predictions by examining bias in the selection of compounds for experimental testing

ABSTRACT

Existing data on structures and biological activities are limited and distributed unevenly across distinct molecular targets and chemical compounds. The question arises if these data represent an unbiased sample of the general population of chemical-biological interactions. To answer this question, we analyzed ChEMBL data for 87,583 molecules tested against 919 protein targets using supervised and unsupervised approaches. Hierarchical clustering of the Murcko frameworks generated using Chemistry Development Toolkit showed that the available data form a big diffuse cloud without apparent structure. In contrast hereto, PASS-based classifiers allowed prediction whether the compound had been tested against the particular molecular target, despite whether it was active or not. Thus, one may conclude that the selection of chemical compounds for testing against specific targets is biased, probably due to the influence of prior knowledge. We assessed the possibility to improve (Q)SAR predictions using this fact: PASS prediction of the interaction with the particular target for compounds predicted as tested against the target has significantly higher accuracy than for those predicted as untested (average ROC AUC are about 0.87 and 0.75, respectively). Thus, considering the existing bias in the data of the training set may increase the performance of virtual screening.     

SARS-CoV protease inhibitors design using virtual screening method from natural products libraries

Two natural products databases, the marine natural products database (MNPD) and the traditional Chinese medicines database (TCMD), were used to find novel structures of potent SARS-CoV protease inhibitors through virtual screening. Before the procedure, the databases were filtered by Lipinski's ROF and Xu's extension rules. The results were analyzed by statistic methods to eliminate the bias in target-based database screening toward higher molecular weight compounds for enhancing the hit rate. Eighteen lead compounds were recommended by the screening procedure. They were useful for experimental scientists in prioritizing drug candidates and studying the interaction mechanism. The binding mechanism was also analyzed between the best screening compound and the SARS protein.