两种释碳材料的制备及其性能研究

应用固定化技术制备了包埋淀粉的聚乙烯醇(PVA)释碳材料和海藻酸钠(SA)释碳材料,采用红外光谱和扫描电镜对其物化特性进行分析,研究了二者的释碳性能以及作为碳源对硝态氮的去除效果.结果表明,PVA释碳材料中淀粉混合较好,释碳过程满足二级动力学方程,单位质量材料释放的饱和COD达到99.60mg/(g·L).在温度为18~22℃,pH7.2~8.0,硝态氮浓度为35mg/L的条件下,PVA释碳材料的平均反硝化速率为18.5g/(m3·d),SA释碳材料的平均反硝化速率为15.5g/(m3·d),在稳定运行15d后,出水硝态氮浓度逐渐升高,脱氮效果开始下降.     

清洁生产与SA8000的相关性研究

通过对清洁生产和SA8000标准的相关性的研究发现，将二者有机结合，充分发挥各自的最大作用，能有效地促进企业资源的合理利用和可持续发展、提高企业的经济效益以及在国际竞争中的优势地位。     

航空工效中情境觉知的研究

随着航空设备自动化 /智能化水平的不断提高 ,现代飞机座舱中动态信息越来越多 ,而飞行员的主要作用已由操作控制向监视转移 ,即人的体力负荷下降、心理负荷增加的现象变得更加突出。鉴于此 ,有关飞行员情境觉知研究已成了目前航空工效研究领域中比较活跃的一个方向 ,笔者通过对情境觉知的发展历史、概念定义、影响因素、定性模型分析 ,并结合前人的研究成果建立了一个新的情境觉知定义和相应的情境觉知多级触发定性分析模型 ,初步揭示了高级飞行员和飞行学员之间的情境认知差别 ,并为进一步建立定量的情境觉知多级触发分析模型 (包括不同情境下飞行员的感觉、理解、预测等阶段的临界值 )打下了基础。     

植物体内水杨酸分析方法的探讨及其应用(Investigation and Application of Analysis Methods of Salicylic Acid in Plant Tissue)

准确测定植物中水杨酸的含量是深入研究其重要作用和抗病机理的必要前提.采用高效液相色谱(HPLC)对植物中内源水杨酸进行了分析,比较了紫外检测器和荧光检测器不同的检测结果,发现荧光监测器灵敏度更高,是紫外检测器的15倍.论文同时比较了使水杨酸与其他组分很好分离的液相色谱条件,结果表明,pH5.5的乙酸钠:甲醇(9:1,V/V)的流动相效果最好.用该方法测得的精密度的相对标准偏差为1.73%,重现性和稳定性均较好(稳定性的相对标准偏差为4.6%).论文以模式植物拟南芥(Arabidopsis thaliana)的野生型和水杨酸突变体两种类型为供试材料,对其体内水杨酸进行了提取测定,检测结果发现野生型体内的水杨酸的含量大约是水杨酸突变体的9倍,与文献报道吻合.     

融合视频监控系统设计

有线视频监控业务已经得到了广泛的应用,移动视频监控也具有突出的优势,3G出现以后,固定移动网络融合的优势带来了全新的监控业务模式.本文仅简单介绍融合监控系统的设计,以及其应用场景.     

基于GASA-BP神经网络的煤层瓦斯含量预测方法研究

为提高煤层瓦斯含量预测的精准度和效率,提出1种利用遗传算法(GA)和模拟退火算法(SA)混合初始化BP神经网络(BPNN)的瓦斯含量预测新模型(GASA-BPNN模型)。利用灰色关联分析法(GRA)筛选瓦斯含量主控因素并作为GASA-BPNN预测模型的输入。为解决BPNN收敛速度慢和易陷入局部极小陷阱的问题,将GA和具有时变概率突跳性的SA整合为GASA算法协同初始化BPNN的权值和阈值,有效地提高BPNN的参数学习能力。将该模型应用于煤炭生产现场,结果表明：BPNN模型、GA-BPNN模型和GASA-BPNN模型瓦斯含量预测总平均相对误差分别为15.79%,9.03%,5.56%。相比BPNN模型和GA-BPNN模型,GASA-BPNN模型对样本的泛化能力更强,参数训练速度最快并且预测精准度最高。     

A global Bayesian sensitivity analysis of the 1d SimSphere soil–vegetation–atmospheric transfer (SVAT) model using Gaussian model emulation

Sensitivity analysis consists of an integral and important validatory check of a computer simulation model before the code is used in performing any kind of analysis operation. The present paper demonstrates the use of a relatively new method and tool for conducting global sensitivity analysis (GSA) for environmental models, providing simultaneously the first GSA study of the widely used 1d soil–vegetation–atmospheric transfer (SVAT) model named SimSphere. A software platform called the Gaussian emulation machine for sensitivity analysis (GEM SA), which has been developed for performing a GSA via Bayesian theory, is applied to SimSphere model in order to identify the most responsive model inputs to the simulation of key model outputs, detect their interactions and derive absolute sensitivity measures concerning the model structure. This study is also very timely in that, use of this particular SVAT model is currently being considered to be used in a scheme being developed for the operational retrieval of the soil surface moisture content by National Polar-orbiting Operational Environmental Satellite System (NPOESS), in a series of satellite platforms that are due to be launched in the next 12 years starting from 2016.The employed GSA method was found capable of identifying the most responsive SimSphere inputs and also of capturing their key interactions for each of the simulated target quantities on which the GSA was conducted. The most sensitive model inputs were the topography parameters (slope, aspect) as well as the fractional vegetation cover and soil surface moisture availability. The implications of these findings for the future use of SimSphere are discussed.     

The joint effects of sulfonamides and their potentiator on Photobacterium phosphoreum: differences between the acute and chronic mixture toxicity mechanisms

Organisms are typically exposed to mixtures of chemicals over long periods of time; thus, chronic mixture toxicity analysis is the best way to perform risk assessment in regards to organisms. However, most studies focus on the acute mixture toxicity. To investigate the difference between chronic mixture toxicity and acute mixture toxicity, Photobacterium phosphoreum were exposed to chronic (24 h exposure) and acute (15 min exposure) toxicity of single sulfonamide (SA) and their potentiator (trimethoprim, TMP), both individually and mixtures (SA with TMP). A comparison of chronic vs. acute mixture toxicity revealed the presence of an interesting phenomenon, that is, that the joint effects vary with the duration of exposure; the acute mixture toxicity was antagonistic, whereas the chronic mixture toxicity was synergistic. Based on the approach of Quantitative Structure Activity Relationships (QSARs) and molecular docking, this phenomenon was proved to be caused by the presence of two points of dissimilarity between the acute and chronic mixture toxicity mechanism: (1) the receptor protein of SAs in acute toxicity was Luc, while in chronic toxicity it was Dhps, and (2) there is a difference between actual concentration of binding-Luc in acute toxicity and individual binding-Dhps in chronic toxicity. This deep insight into the difference between chronic and acute mixture toxicity will benefit environmental science, medical science, and other disciplines. The existence of these differences poses a challenge for the assessment of routine combinations in medicine, risk assessment, and mixture pollutant control, in which, previously, only a synergistic effect has been observed between SA and their potentiator.     

两种介体物质在漆酶降解磺胺类抗生素中的作用

通过比较两种介体物质丁香醛(SA)和1-羟基苯并三氮唑(HBT)对漆酶降解5种典型磺胺类抗生素的影响,考察了介体的起始浓度、pH值、反应温度、漆酶起始浓度等因子的影响,得到介体SA对漆酶催化降解磺胺类抗生素的效果更好.在漆酶+SA反应体系中,反应体系pH值为5~6,反应温度为30℃,SA起始浓度为0.5mmol/L,漆酶起始浓度为0.5mg/mL时,漆酶对5种磺胺类抗生素的降解效果最佳,降解去除率在15min时达到75%左右,1h内均达到97%以上. 本研究表明,适当的介体物质可大大提升漆酶对磺胺类抗生素的降解效果.漆酶在介体物质的作用下对抗生素类污染物的生态净化具有良好的应用潜力.     

Comparison of sensitivity analysis techniques: A case study with the rice model WARM

The considerable complexity often included in biophysical models leads to the need of specifying a large number of parameters and inputs, which are available with various levels of uncertainty. Also, models may behave counter-intuitively, particularly when there are nonlinearities in multiple input-output relationships. Quantitative knowledge of the sensitivity of models to changes in their parameters is hence a prerequisite for operational use of models. This can be achieved using sensitivity analysis (SA) via methods which differ for specific characteristics, including computational resources required to perform the analysis. Running SA on biophysical models across several contexts requires flexible and computationally efficient SA approaches, which must be able to account also for possible interactions among parameters. A number of SA experiments were performed on a crop model for the simulation of rice growth (Water Accounting Rice Model, WARM) in Northern Italy. SAs were carried out using the Morris method, three regression-based methods (Latin hypercube sampling, random and Quasi-Random, LpTau), and two methods based on variance decomposition: Extended Fourier Amplitude Sensitivity Test (E-FAST) and Sobol’, with the latter adopted as benchmark. Aboveground biomass at physiological maturity was selected as reference output to facilitate the comparison of alternative SA methods. Rankings of crop parameters (from the most to the least relevant) were generated according to sensitivity experiments using different SA methods and alternate parameterizations for each method, and calculating the top-down coefficient of concordance (TDCC) as measure of agreement between rankings. With few exceptions, significant TDCC values were obtained both for different parameterizations within each method and for the comparison of each method to the Sobol’ one. The substantial stability observed in the rankings seem to indicate that, for a crop model of average complexity such as WARM, resource intensive SA methods could not be needed to identify most relevant parameters. In fact, the simplest among the SA methods used (i.e., Morris method) produced results comparable to those obtained by methods more computationally expensive.