Dynamic Bayesian networks for reliability evaluation of subsea wellhead connector during service life based on Monte Carlo method

The subsea wellhead connector is a critical connection component between subsea Christmas tree and subsea wellhead for preventing the leakage of oil and gas in the subsea production system. Excited by cyclical loadings due to environmental forces and the other support forces, the subsea wellhead connector is prone to the failure, which could lead to the loss of subsea tree or wellhead integrity and even catastrophic accidents. With the Monte Carlo simulation method, this paper presents a reliability analysis approach based on dynamic Bayesian Networks, aiming to assess the failure probability of the subsea wellhead connector during service life. Take the driving ring component of the subsea wellhead connector as an example to demonstrate the reasonability of the proposed model. The generation data is processed by the transform between the numerical value and the state variable. Based on the stress-strength interference theory, the structure reliability of the driving ring with 96.26% is achieved by the proposed model with the consideration the aging of the material strength and the most influential factors are figured out. Meanwhile, the corresponding control measures are proposed effectively reduce the failure risk of the subsea wellhead connector during service life.     

Using a Bayesian network to clarify areas requiring research in a host–pathogen system

Bayesian network analyses can be used to interactively change the strength of effect of variables in a model to explore complex relationships in new ways. In doing so, they allow one to identify influential nodes that are not well studied empirically so that future research can be prioritized. We identified relationships in host and pathogen biology to examine disease‐driven declines of amphibians associated with amphibian chytrid fungus (Batrachochytrium dendrobatidis). We constructed a Bayesian network consisting of behavioral, genetic, physiological, and environmental variables that influence disease and used them to predict host population trends. We varied the impacts of specific variables in the model to reveal factors with the most influence on host population trend. The behavior of the nodes (the way in which the variables probabilistically responded to changes in states of the parents, which are the nodes or variables that directly influenced them in the graphical model) was consistent with published results. The frog population had a 49% probability of decline when all states were set at their original values, and this probability increased when body temperatures were cold, the immune system was not suppressing infection, and the ambient environment was conducive to growth of B. dendrobatidis. These findings suggest the construction of our model reflected the complex relationships characteristic of host–pathogen interactions. Changes to climatic variables alone did not strongly influence the probability of population decline, which suggests that climate interacts with other factors such as the capacity of the frog immune system to suppress disease. Changes to the adaptive immune system and disease reservoirs had a large effect on the population trend, but there was little empirical information available for model construction. Our model inputs can be used as a base to examine other systems, and our results show that such analyses are useful tools for reviewing existing literature, identifying links poorly supported by evidence, and understanding complexities in emerging infectious‐disease systems.     

有序介孔碳载金/L-赖氨酸/纳米金修饰电极的制备及其对邻苯二酚、对苯二酚的检测响应研究

通过化学还原的方法合成有序介孔碳负载纳米金粒子,并构筑有序介孔碳载金/L-赖氨酸/纳米金复合膜修饰玻碳电极;利用扫描电镜观察介孔碳和介孔碳复合膜的微观结构,并用循环伏安法、电化学阻抗谱表征自组装电极的过程.在此基础上用差分脉冲伏安法研究对苯二酚和邻苯二酚混合物在该电极上的电催化氧化,研制了一种基于有序介孔碳载金/L-赖氨酸/纳米金复合膜修饰电极分别检测对苯二酚和邻苯二酚的传感器.在最优的实验条件下,该传感器在对苯二酚和邻苯二酚浓度为1×10-6～8×10-4mol·L-1的范围内具有良好的线性关系,检出限分别为3×10-7mol·L-1、7×10-7mol·L-1.     

基于人体皮肤热模型的热防护服评价方法研究

在热防护服热防护性能测试装置基础上,用自行研制的新型耐高温模拟皮肤传感器代替铜片热流计测量通过应急热防护服装面料的热流量,将热流量作为热波皮肤模型边界条件,得到人体皮肤表层下80μm处的温度值,从而得到一定条件下人体真实皮肤达到二级烧伤所需时间,用其评价热防护服用织物的热防护性能,并将热波皮肤模型(TWMBT)的测试值与Pennes模型以及铜片热流计的测试结果进行分析比较。采用热波皮肤模型分析织物层下的"皮肤"防热时间更接近实际皮肤达到二级烧伤时间值,可较为精确的量化织物热防护性能,为应急救援热防护服装的热设计提供理论依据。     

路面径流水环境下交通承载力分析

为了对路面径流水容许污染总量控制下的交通承载力问题进行探讨,利用神经网络具有的非线性映射能力和遗传算法具有的全局随机搜索能力,结合公路路面径流水质检测数据,提出了一种基于遗传神经网络进行公路交通环境承载力反计算的分析方法,应用该方法可根据路面径流水质污染数据反演出路段交通量大小,并可据此进行交通量与路而径流水质污染的关...     

重金属阳离子荧光传感器合成及光谱性研究

通过亚胺缩合的反应制备出阳离子荧光传感器（Cation Fluorescent Sensors,CFSs）,并对其结构、光谱性能检测、荧光量子产率等进行了详细的研究.通过荧光化学传感器产生的荧光光谱一直是高效的分析方法,因此合成了新的基于席夫碱的荧光化学传感器,并研究了对各种金属离子的结合特性.在CH3CN溶液中测试了阳离子荧光传感器时不同阳离子的选择性响应,发现对Cu2＋有较好的选择性,可用于Cu2＋的检测.该类阳离子荧光传感器是基于2,2-联噻吩-5-乙醛的荧光发射基团和邻氨基苯甲醚为识别基团的一类阳离子荧光传感器.     

太湖流域上游平原河网污染物综合衰减系数的测定

改善太湖水质需要削减上游河流进入太湖的污染物总量.为了探求太湖流域上游平原河网的自净能力,开展原位实验测定了枯水期高锰酸盐指数、氨氮(NH_4~+-N)、总氮(TN)和总磷(TP)的综合衰减系数,根据河道的水力特征对综合衰减系数进行了修正,并利用一维稳态水质模型对修正前后综合衰减系数的可靠性进行了验证.结果表明,高锰酸盐指数、NH_4~+-N、TN和TP的综合衰减系数分别为:0.0296~0.4106、0.0224~0.3564、0.0137~0.3046和0.0555~0.5725 d~(-1).可靠性验证表明高锰酸盐指数、NH_4~+-N、TN和TP综合衰减系数修正前的平均相对误差分别为8.39%、14.40%、11.43%和19.22%,修正后的平均相对误差分别为10.65%、14.34%、11.37%和19.24%.修正前后高锰酸盐指数、NH_4~+-N、TN和TP的平均相对误差均小于20%且变化不显著,表明综合衰减系数的测定结果能够为太湖流域上游平原河网的污染物总量控制管理提供科学参数;也表明枯水期的水力条件对综合衰减系数的影响较小.     

Simultaneous determination of three herbicides by differential pulse voltammetry and chemometrics

A novel differential pulse voltammetry method (DPV) was researched and developed for the simultaneous determination of Pendimethalin, Dinoseb and sodium 5-nitroguaiacolate (5NG) with the aid of chemometrics. The voltammograms of these three compounds overlapped significantly, and to facilitate the simultaneous determination of the three analytes, chemometrics methods were applied. These included classical least squares (CLS), principal component regression (PCR), partial least squares (PLS) and radial basis function-artificial neural networks (RBF-ANN). A separately prepared verification data set was used to confirm the calibrations, which were built from the original and first derivative data matrices of the voltammograms. On the basis relative prediction errors and recoveries of the analytes, the RBF-ANN and the DPLS (D – first derivative spectra) models performed best and are particularly recommended for application. The DPLS calibration model was applied satisfactorily for the prediction of the three analytes from market vegetables and lake water samples.     

PREDICTING MEASUREMENT ERROR OF AREAL MEAN PRECIPITATION DURING EXTREME EVENTS1

ABSTRACT: The areal mean precipitation (AMP) over a catchment is normally calculated using point measurements at rainfall gages. Error in AMP estimates occurs when an insufficient number of gages are used to sample precipitation which is highly variable in space. AMP error is investigated using historic, severe rainfalls with a set of hypothetical catchments and raingage networks. The potential magnitude of error is estimated for typical gage network densities and arrangements. Possible sources of error are evaluated, and a method is proposed for predicting the magnitude of error using data that are commonly available for severe, historic rainfall.