FOURIER INFERENCE: SOME METHODS FOR THE ANALYSIS OF ARRAY AND NONGAUSSIAN SERIES DATA1

Fourier inference is a collection of analytic techniques and philosophic attitudes, for the analysis of data, wherein essential use is made of empirical Fourier transforms. This paper sets down some basic results concerning the finite Fourier transforms of stationary process data and then, to illustrate the approach, uses those results to develop procedures for: 1) estimating cloud and storm motion, 2) passive sonar and 3) fitting finite parameter models to nonGaussian time series via bispectral fitting. This last procedure is illustrated by an analysis of a stretch of Mississippi River runoff data. Examples 1), 2) refer to data having the form Y(x_j, y_j, t) for j = 1, …, J and t = 0, …, T-l say, and view that data as part of a realization of a spatial-temporal process. Such data has become common in geophysics generally and in hydrology particularly. The goal of this paper is to present some new statistical procedures pertinent to problems in the water sciences, equally it is to illustrate the genesis of those procedures and how their properties may be approximated.     

For What Applications Can Probability and Non-Probability Sampling Be Used?

Almost any type of sample has some utilitywhen estimating population quantities. The focus inthis paper is to indicate what type or combination oftypes of sampling can be used in various situationsranging from a sample designed to establishcause-effect or legal challenge to one involving asimple subjective judgment. Several of these methodshave little or no utility in the scientific area buteven in the best of circumstances, particularlycomplex ones, both probabilistic and non-probabilisticprocedures have to be used because of lack ofknowledge and cost. We illustrate this with a marbledmurrelet example.     

胺萃取法处理含氰废水工艺的研究

对黄金选冶过程中排放的含氰废水用N₂₃₅作萃取剂从含氰废水中萃取铜、锌进行了研究,探讨了预处理有机相、温度条件、相比、平衡pH、相接触时间和萃取级数等因素对萃取的影响。试验结果表明,在选定的条件下,铜的萃取率达99%以上,从负载有机相中反萃铜,反萃率可达99%。同时还可以回收氰化物,根据试验结果,提出了处理含氰废水的工艺流程      

利用遗传算法进行软件可靠性增长模型参数估计

为解决利用传统数值方法估计软件可靠性增长模型参数值时存在的问题 ,笔者讨论了利用遗传算法进行求解的相关策略 ;提出了一种新的编码方式及变异算子 ;通过涉及多个模型的实验 ;采用实际数据 ,对所提出算法的效果与传统数值方法进行了对比。实验与对比分析结果显示 :该算法针对这些模型表现出了良好的普适性。     

环境空气质量评价的普适指数公式

在适当设定空气污染物的基准值和用相对于基准值的空气污染物的“相对”值情况下,采用S型曲线表示空气污染物对环境空气质量的危害程度,并应用遗传算法优化公式中的参数,得出了一个对多种空气污染物通用的环境空气质量评价的普适指数公式。借助于折衷型激励功效函数将单基污染物分指数加权为综合指数。该公式形式简单,容量计算、评价结果精度较高,且具有可比性。     

苯胺和硝基苯胺对大型蚤(Daphnia magna)的联合毒性

测得了苯胺与硝基苯胺对大型蚤(Daphnia magna)的单一毒性和二元混合物的联合毒性.用相加指数法(AI)和相似性参数(λ)进行评价,两种方法得出一致的结论,即3种二元混合物的联合作用均为协同作用;比较混合物在等剂量,毒性单位比分别为1∶1,1∶4,2∶3,3∶2,4∶1下的AI值和λ值,发现均在等毒配比时的协同作用最强.      

市政脱水污泥的间接干化动力学研究

以食用油为热介质,在不同油浴温度下,对市政脱水污泥进行间接干化试验,推导出间接干化动力学模型,并对收集的冷凝液进行水质分析。结果表明:冷凝液为高浓度有机废水,TOC占TC的比例为73.2010%~75.5983%;VFAs的主要组分是乙酸、丙酸、丁酸、异丁酸、异戊酸和正戊酸,其中异丁酸所占比例最高(45.597%),其次为丙酸(24.554%);通过动力学模型的参数估值,Z模型(n≠1)因高R~2值和低RSS值而能很好的预测间接干化过程。     

应用ANFIS预测蠕虫反应器的污泥减量速率并优化其运行条件

利用水生蠕虫的捕食作用可以有效地实现污泥减量.为了研究环境条件波动对蠕虫捕食污泥减量效率的影响,应用自适应神经模糊推理系统(ANFIS)和人工神经网络(ANN)模型分别预测蠕虫反应器的污泥减量速率.结果表明,溶解氧浓度(D0)、温度(T)、蠕虫密度和污泥负荷是蠕虫捕食过程的主要影响因素,通过性能比较得出ANFIS模型预测值与实验测定值间具有更好的一致性,其相关系数(r)为0.82,绝对平均误差百分比(MAPE)为71.5％,均方根误差(RMSE)为16.7.根据ANFIS模型的预测结果,得出蠕虫反应器的最适运行条件为:DO 1.8 ～3.1mg·L-1,温度18.4～21.7℃,蠕虫密度低于1.7 g·cm-2(以湿重计),污泥负荷563～734 mg·g-1(以TSS计),在此操作条件下获得的污泥减量速率均高于100 mg· g-1·d-1.     

Value of information analysis for adequate monitoring of carbon dioxide storage in geological reservoirs under uncertainty

As monitoring is essential for the proper management of geological storage of carbon dioxide (CO₂), the ability to value information from monitoring is indispensable to adequately design a monitoring program. It is necessary to judge whether the expected improvement in management is worth the cost of monitoring. The value of information (VOI) is closely related to the possible increase in expected utility gained by gathering the information, the concept of which can be applied to such judgement. Although VOI analysis has been extensively studied in the context of decision analysis, its application to the management of carbon dioxide capture and storage (CCS) operations is rare. This paper introduces and discusses the methodology of VOI analyses in the context of monitoring CO₂ storage. A motivating problem with discrete probabilities is used to illustrate the concept of VOI. It is demonstrated that information is not always of value; for information to be worthwhile, monitoring under uncertainty must satisfy certain conditions. This concept is then extended to continuous probability distributions. The effects of prior uncertainty and information reliability on the VOI are examined. It is shown that an excessive improvement in information accuracy yields little value and that the optimal level of reliability can be inferred. VOI analyses provide quantitative insights into the value of information-gathering activities and therefore can be an objective means to adequately design and impartially justify a monitoring program.