苏州河干流水质模型的开发研究

对《苏州河水质模型的开发》课题完成的大部分内容进行了介绍，包括水质模型的选择及其原理、模型研究的内容与开发方法、干流水质模型的率定与验证，并据此给出了关于苏州河水环境特性的一些参数值，计算的基准时间为1999年夏季苏州河第3次调水试验时期。需要指出的是，苏州河正处于动态的综合整治过程中，模型中有关参数也会随其相应改变。     

城市环保战略目标定量考核指标体系研究

本文在深入研究6个城市的定量考核指标体系的基础上,提出了城市环保战略目标定量考核指标体系建立的依据和原则,探讨了指标体系的结构与指标选取问题.本文认为,城市环保战略目标定量考垓指标体系应由目标层、准则层和指标层三个层次构成,整个结构是自上而下分解,自下而上递进.     

GA优化的湖泊富营养化评价的普适公式探讨

在适当设定富营养化指标的“本底值”情况下,当指标值用于对应“本底值”的相对值表示时,可采用S型曲线描述湖泊富营养化的发展程度,公式中的参数可视为与指标特性无关,采用遗传算法对公式参数优化,得到对多项指标均适用的富营养化程度的指数公式,并提出用广义模糊对比因子赋权新方法计算富营养化综合指数。该评价方法物理意义明确,计算简单,使用方便,具有普适性、可比性和实用性。     

特征编码在摩托车盘类零件CAD中的应用

采用特征编码技术对摩托车盘类零件进行特征编码，根据码位对应的形状特征进行特征造型并定制成参数化图元，煞后以零件主特征的关键尺寸为驱动参数对构成零件形状特征的图元进行拼接，实现摩托车盘类零件的参数化设计。     

模糊控制技术在污水处理中的应用

针对污水处理过程中采用传统的流量程序控制和时间程序控制的不足,提出了一种基于单片机的模糊控制方法。该方法以污水流量、溶解氧(DO)浓度和污泥回流比为主要被控对象,以DO浓度为主要控制参数,通过离散计算和在线查表的模糊推理方法可得到最佳的风机转速,使污水处理生化池内DO浓度保持在最佳状态,同时还通过控制曲线展示了当输入污水流量发生扰动时对DO浓度的控制精度,使污水处理质量保持稳定。通过实际应用表明,该控制方法不但能确保出口净化水水质达标,还可以节约15%的电能。     

基于ANFIS模型的基坑工程位移反分析

在软土地基深基坑开挖支护工程的设计与施工中,岩土体力学参数的取值问题是关键。基于MATLAB(R2007a)模糊工具箱中的自适应神经模糊推理系统(ANFIS)对基坑岩土体力学参数进行了位移反演分析,并结合汉口火车站出站厅基坑工程实例,动态反演了具体参数,检验了反演精度,从而验证了该方法用于基坑工程位移反分析的可行性及工程适用性。     

Applying Statistical Causal Analyses to Agricultural Conservation: A Case Study Examining P Loss Impacts

Estimating the effect of agricultural conservation practices on reducing nutrient loss using observational data can be confounded by factors such as differing crop types and management practices. As we may not have the full knowledge of these confounding factors, conventional statistical meta‐analysis methods can be misleading. We discuss the use of two statistical causal analysis methods for quantifying the effects of water and soil conservation practices in reducing P loss from agricultural fields. With the propensity score method, a subset of data was used to form a treatment group and a control group with similar distributions of confounding factors. With the multilevel modeling method, data were stratified based on important confounding factors, and the conservation practice effect was evaluated for each stratum. Both methods resulted in similar estimates of the conservation practice effect (total P load reduction avg. ~70%). In addition, both methods show evidence of conservation practices reducing the incremental increase in total P export per unit increase in fertilizer application. These results are presented as examples of the types of outcomes provided by statistical causal analyses, not to provide definitive estimates of P loss reduction. The enhanced meta‐analysis methods presented within are applicable for improved assessment of agricultural practices and their effects and can be used for providing realistic parameter values for watershed‐scale modeling.     

Risk assessment of sour gas inter-phase onshore pipeline using ANN and fuzzy inference system – Case study: The south pars gas field

Nowadays, pipelines have been extensively used for transporting oil and gas for long distances. Therefore, their risk assessment could help to identify the associated hazards and take necessary actions to eliminate or reduce the risk. In the present research, an artificial neural network (ANN) and a fuzzy inference system (FIS) were used to prepare a new model for pipeline risk assessment with higher accuracy. To reach this objective, the Muhlbauer method, as a common method for oil and gas pipeline risk assessment, was used for determining important and influential factors in the pipeline performance. Mamdani fuzzy model was developed in Matlab software by considering expert knowledge. The outcomes of this model were used to develop an ANN. To verify the developed model, the inter-phase shore pipe of phase 9–10 refinery in the South Pars Gas field was considered as a case study. The results showed that the proposed model gives a higher level of accuracy, precision, and reliability in terms of pipe risk assessment.     

The role of analogues in radioecology

If no data are available for a specific radionuclide its analogue could be used in radioecological models. They might be used not only in the frame of screening modelling. Relevant processes and features have to be known, such as time scales of these processes, physical, chemical and biological properties of the environment and relevant media. An analogue could only be proven to be valid by comparing its behaviour in the conditions of interest, while confidence in the validity of an analogue will increase as the quality of the justification increases, there will always be some residual uncertainty. Several main types of analogy can be used when measured or recommended values are not available: the same parameter obtained for another isotope of the same element; the same parameter obtained for another element; a different parameter obtained for the same element. There are several cases, when analogues might be applied as indicators of the determining processes in the radioecological studies.     

Use of Scale Invariance in Evaluating Judgement Indicators

Indicators are used to draw conclusions about ecological endpoints when these endpoints cannot be measured directly. In many cases, inferences about an endpoint are only possible because assumptions have been made about the relationship between indicator and endpoint; we refer to such indicators as judgement indicators. The validity of inferences made using a judgement indicator can be gauged by examining the known or assumed form of the general relationship between indicator and endpoint. The rules for this kind of inference are a consequence of scale invariance, which originates from measurement theory. For simple indicators comprised of a single indicator measurement, the inferences allowed – equivalence, rank, equality of intervals, and equality of ratios – depend on whether the data are nominal, ordinal, interval, or ratio scaled. For composite indicators containing two or more simple indicators, inferences are also affected by the mathematical form of combination; e.g., whether the terms are summed or multiplied. Standardizing simple or composite indicators can allow inferences about the relative importance of observations, based on the natural range of occurrence. Scale invariance is a particularly important consideration in landscape assessments, since these often make use of judgement indicators.