Preparation of molecularly imprinted fluorescence sensor based on carbon quantum dots via precipitation polymerization for fluorescence detection of tetracycline

A facile and effective method was proposed to prepare the molecularly imprinted fluorescence sensor with carbon quantum dots, which were modified vinyl groups by acrylic acid on the surface. The obtained fluorescence composite material was investigated by transmission electron microscope and Fourier transform infrared spectra. After the experimental conditions were optimized, a linear range of 1.0–60 μmol L⁻¹ was obtained and the detection limit was 0.17 μmol L⁻¹. The novel fluorescence sensor can be successfully used to detect tetracycline in real samples. This study provides a convenient strategy for selective recognition and rapid detection of tetracycline in the complex environment.     

Proton-Detected Solid-State NMR of the Cell-Free Synthesized α-Helical Transmembrane Protein NS4B from Hepatitis C Virus

Proton-detected 100 kHz magic-angle-spinning (MAS) solid-state NMR is an emerging analysis method for proteins with only hundreds of microgram quantities, and thus allows structural investigation of eukaryotic membrane proteins. This is the case for the cell-free synthesized hepatitis C virus (HCV) nonstructural membrane protein 4B (NS4B). We demonstrate NS4B sample optimization using fast reconstitution schemes that enable lipid-environment screening directly by NMR. 2D spectra and relaxation properties guide the choice of the best sample preparation to record 2D ¹H-detected ¹H,¹⁵N and 3D ¹H,¹³C,¹⁵N correlation experiments with linewidths and sensitivity suitable to initiate sequential assignments. Amino-acid-selectively labeled NS4B can be readily obtained using cell-free synthesis, opening the door to combinatorial labeling approaches which should enable structural studies.     

Process monitoring method based on correlation variable classification and vine copula

Chemical processes are becoming increasingly complicated, leading to an increase in process variables and more complex relationships among them. The vine copula has a significant advantage in portraying the dependence of high-dimensional variables. However, as the dimensions increase, the vine copula model incurs a high computational load; such pressure greatly reduces model efficiency. Relationships among variables in the industrial process are complex. Different variables may be strongly or weakly associated or even independent. This paper proposes a process monitoring method based on correlation variable classification and vine copula. The weighted correlation measure is first used to divide variables into a correlated subspace and weakly correlated subspace. Then, two vine structures, C-vine and D-vine, are applied to the correlated and weakly correlated subspaces, respectively. This method takes advantage of C-vine for correlated variables and the flexibility of D-vine for weakly correlated variables. Finally, comprehensive statistics are established based on different subspaces. Monitoring results of the numerical system and the Tennessee Eastman process demonstrate the effectiveness and validity of the proposed method.     

环境中污染源废气检测方法及影响因素分析

对污染源废气监测原理进行了简单分析,阐述了当前我国常用的环境中污染源废气检测方法,并对废气检测影响因素及质量控制进行了深入探究。     

Model Selection for Unsupervised Learning of Visual Context

This study addresses the problem of choosing the most suitable probabilistic model selection criterion for unsupervised learning of visual context of a dynamic scene using mixture models. A rectified Bayesian Information Criterion (BICr) and a Completed Likelihood Akaike’s Information Criterion (CL-AIC) are formulated to estimate the optimal model order (complexity) for a given visual scene. Both criteria are designed to overcome poor model selection by existing popular criteria when the data sample size varies from small to large and the true mixture distribution kernel functions differ from the assumed ones. Extensive experiments on learning visual context for dynamic scene modelling are carried out to demonstrate the effectiveness of BICr and CL-AIC, compared to that of existing popular model selection criteria including BIC, AIC and Integrated Completed Likelihood (ICL). Our study suggests that for learning visual context using a mixture model, BICr is the most appropriate criterion given sparse data, while CL-AIC should be chosen given moderate or large data sample sizes.     

AT89C2051芯片在车载产品中的应用

本文论述了采用单片机技术研制成功的泊车用超声波测距仪的基本原理,用AT89C2051作为控制器的实现方案,以及采用软件校正的方法,提高了测量精度和整机的可靠性.     

IDS告警融合与关联模型的研究

入侵检测是保障网络安全的重要手段,针对现有入侵检测系统中告警数量多、协调性差等问题,论文提出了一种具有告警融合与关联功能的告警处理系统模型,该模型冗余告警量少、整体检测能力强,并能进行攻击企图的预测,能有效提高入侵检测的效率,有助于进一步增强网络的健壮性。     

基于压电陶瓷动态信息的结构裂纹识别方法

利用压电陶瓷的动态特性和压电系统的动态信息,对铝梁的裂纹损伤进行了分析研究。随着梁裂纹尺寸的增加,压电陶瓷片的导钠幅值下降,且系统固有频率减小。利用导钠幅值的变化和系统的动态信息,可以对裂纹的位置和尺寸大小进行识别。实验证明该方法的有效性。     

差分跳频通信系统的特点及性能

文章介绍了一种新型的短波跳频通信技术——差分跳频,分析了差分跳频技术区别于常规跳频技术的主要特点。针对按序列检测的信号接收方法,对差分跳频通信系统在AWGN信道下的性能进行了理论分析,同时做出相应的计算机仿真,证实了差分跳频通信技术和按序列检测方法的结合,使通信系统在AWGN信道下的性能得到了比较显著的提升。     

基于积分原理的低功耗超声波流量检测方法

文章通过对时差式超声波流量检测方法的分析，提出了基于过零检测技术及积分法的信号处理方法，避免了繁琐的信号处理，降低了整机功耗。结合工作时序的设计，提出了实现低功耗超声波液体流量检测的措施。