Optimization of multiwalled carbon nanotubes reinforced hollow‐fiber solid–liquid‐phase microextraction for the determination of polycyclic aromatic hydrocarbons in environmental water samples using experimental design

A novel design of hollow‐fiber liquid‐phase microextraction containing multiwalled carbon nanotubes as a solid sorbent, which is immobilized in the pore and lumen of hollow fiber by the sol–gel technique, was developed for the pre‐concentration and determination of polycyclic aromatic hydrocarbons in environmental water samples. The proposed method utilized both solid‐ and liquid‐phase microextraction media. Parameters that affect the extraction of polycyclic aromatic hydrocarbons were optimized in two successive steps as follows. Firstly, a methodology based on a quarter factorial design was used to choose the significant variables. Then, these significant factors were optimized utilizing central composite design. Under the optimized condition (extraction time = 25 min, amount of multiwalled carbon nanotubes = 78 mg, sample volume = 8 mL, and desorption time = 5 min), the calibration curves showed high linearity (R ² = 0.99) in the range of 0.01–500 ng/mL and the limits of detection were in the range of 0.007–1.47 ng/mL. The obtained extraction recoveries for 10 ng/mL of polycyclic aromatic hydrocarbons standard solution were in the range of 85–92%. Replicating the experiment under these conditions five times gave relative standard deviations lower than 6%. Finally, the method was successfully applied for pre‐concentration and determination of polycyclic aromatic hydrocarbons in environmental water samples.     

Convergence of a non-failable mean-field particle system

The existing literature contains many examples of mean-field particle systems converging to the distribution of a Markov process conditioned to not hit a given set. In many situations, these mean-field particle systems are failable, meaning that they are not well defined after a given random time. Our first aim is to introduce an original mean-field particle system, which is always well defined and whose large number particle limit is, in all generality, the distribution of a process conditioned to not hit a given set. Under natural conditions on the underlying process, we also prove that the convergence holds uniformly in time as the number of particles goes to infinity. As an illustration, we show that our assumptions are satisfied in the case of a piece-wise deterministic Markov process.     

The mean of the running maximum of an integrated Gauss–Markov process and the connection with its first-passage time

We find explicit formulae for the mean of the running maximum of conditional and unconditional Brownian motion; they are used to obtain the mean, a(t), of the running maximum of an integrated Gauss–Markov process. Then, we deal with the connection between the moments of its first-passage-time and a(t). As explicit examples, we consider integrated Brownian motion and integrated Ornstein–Uhlenbeck process.     

Generalized immediate exchange models and their symmetries

We reconsider the discrete dual of the immediate exchange model and define a more general class of models where mass is split, exchanged and merged. We relate the splitting process to the symmetric inclusion process via thermalization and from that obtain symmetries and self-duality for it and its generalization. We show that analogous properties hold for models where the splitting is related to the symmetric exclusion process or to independent random walkers.     

阿拉尔垦区自然灾害对农业经济影响的研究

阿拉尔垦区是新疆重要的棉花、林果产业基地,然而频发的自然灾害给农业经济造成了极为严重的影响.自然灾害发生频率高、灾害地域分布不均衡以及带来的破坏效果大和损失严重等特点,引起了人们的广泛关注.采用层次分析法作为基础理论,通过专家打分法确定权重建立评价模型.阿拉尔垦区自然灾害对农业经济影响的研究是一个非常重要的理论和实践问题,它对于提高农民收入、促进农业可持续发展和社会稳定具有重要的意义.     

广义Khasminskii条件下自变量分段连续型带Poisson随机测度随机微分方程Euler方法的依概率收敛性

针对满足广义Khasminskii条件的由维纳过程和泊松随机测度驱动的自变量分段连续型随机微分方程(EPCASDEs),给出了Euler方法,广义Khasminskii条件比经典条件包容了更多的EPC.ASDEs.现有文献对该类方程的研究成果较少.针对EPCASDEs在广义Khasminskii条件下证明了全局解的存在唯一性,并研究了Euler方法的依概率收敛性.给出了数值算例支持主要结论.     

基于DEA-ANP的快递企业绩效评价研究

在建立快递企业绩效评价模型时,提出了利用DEA和ANP相结合对快递企业绩效进行评价的方法.不同于之前研究,文中提出的方法使用ANP替换AHP,充分考虑了物流行业的"效益背反"现象.其解决了AHP确定权重时,无法体现元素之间关联性的问题,单独使用DEA评价时,无法考虑决策者偏好的弊端.首先,方法基于.ANP方法,求出每个指标的权重,再分别对每个因素使用DEA方法求出各系统的相对效率值;然后,将各指标权重和相对效率值结合,求出各快递企业中营业部的整体效率值并进行排序;最后通过实例分析,验证了此方法的实用性、可操作性及优越性.     

基于几何过程和位相分布休假的两部件并联可修系统的可靠性分析

主要以两不同型部件组成的并联可修系统为研究对象.在系统对失效相位存在记忆的基础上,考虑了修理工可单重休假且休假时间服从位相(PH)分布.每个工作部件均有可能因受到两种不同类型的故障而失效,且均"修复非新".在假定部件的工作时间,修理时间分别服从PH分布的几何过程和负指数分布的条件下,利用马尔可夫过程和矩阵分析的方法,对可修系统进行了可靠性分析,并给出了相应可靠性指标的数值算例.     

模糊AHP在电网企业员工岗前培训评估中的应用

电网企业技术密集、人员众多、架构庞大,员工岗前培训与企业健康持续发展密不可分.培训工作中最突出的问题在于对培训效果缺乏合理的评估,从而无法了解企业投资的效果,以及培训对组织的贡献.以柯氏培训四级评估模型为基础,采用模糊AHP规范化整个评估过程.最后以电力企业进行的培训项目为例验证分析,证明所提算法的有效性.     

稳健残差控制图的构建及在金融时序中的应用

对于呈现自相关和波动族聚性并存的受控过程,通常采用残差控制图对其进行监控。但异常点的存在会对自相关或波动族聚性模型的拟合产生重要影响,使得基于该模型的残差并非独立同分布导致常规残差控制图监控失效。为解决这类问题,本文提出稳健残差控制图。即建立稳健的ARMA模型解决自相关问题从而得到无自相关的残差序列,用稳健的GARCH模型来构建控制图的上下限。模拟和实证研究表明,本文提出的稳健残差控制图具有很好的抗异常点能力并能更好的对金融时间序列的异常现象进行监控。