Competition of bloom-forming marine phytoplankton at low nutrient concentrations

Competition of three bloom-forming marine phytoplankton (diatom Skeletonema costatum, and dinoflagellates Prorocentrum minimum and Alexandrium tamarense) was studied through a series of multispecies cultures with di erent nitrate (NaNO3) and phosphate (NaH2PO4) levels and excess silicate to interpret red tide algae succession. S. costatum outgrew the other two dinoflagellates in nitrate and phosphate replete cultures with 10 mol/L Na2SiO3. Under nitrate limited (8.82 mol/L NaNO3) conditions, the growth of S. costatum was also dominant when phosphate concentrations were from 3.6 to 108 mol/L. Cell density of the two dinoflagellates only increased slightly, to less than 400 and 600 cells/mL, respectively. Cell density of S. costatum decreased with time before day 12, and then increased to 4000 cells/mL (1.5 mg/L dry biomass) at NaNO3 concentrations between 88.2 and 882 mol/L with limited phosphate (0.36 mol/L NaH2PO4) levels. In addition, P. minimum grew well with a maximal cell density of 1690–2100 cells/mL (0.5–0.6 mg/L dry biomass). Although S. costatum initially grew fast, its cell density decreased quickly with time later in the growth phase and the two dinoflagellates were dominant under the nitrate-limited and high nitrate conditions with limited phosphate. These results indicated that the diatom was a poor competitor compared to the two dinoflagellates under limited phosphate; however, it grew well under limited nitrate when growth of the dinoflagellates was near detection limits.     

基于模糊层次分析法的应急能力评估方法探讨

对危险行业的安全生产事故应急能力进行安全评估，可以持续改进应急管理工作，提高事故应急能力的总体水平。作为综合性的安全评估方法，层次分析法在安全研究的多个领域中得到了应用。通过介绍安全生产事故应急管理能力及其评估要求，分析国内外应急能力评估的现状，提出了应急能力评估体系建立的总体思路。论述了事故应急能力评估指标集的建立方法和基于模糊层次综合评价法的评估模型构建方案，初步建立起事故应急管理能力评估方法。     

基于粗糙集-神经网络的矿山地质环境影响评价模型及应用

采用衡山白果地区石膏矿山的11个评价指标,综合运用粗糙集和神经网络理论,构建了基于粗糙集-神经网络(RS-ANN)的矿山地质环境影响评价模型,对RSES软件约简的数据和无约简的数据采用EasyNN-plus软件进行预测评价。神经网络模型的输入属性为8个,而粗糙集-神经网络模型的输入属性为6个,训练样本均为13个,预测样本均为4个,前者的平均预测精度为1.85%～24.86%,后者为1.23%～15.28%。研究发现,粗糙集在保留关键信息的前提下可有效地对数据表进行约简,约简后的神经网络预测结果与实际情况吻合,并比无约简时总体精度有较大幅度提高。     

泥石流危险度的改进集对分析模型

泥石流危险度的正确评估是泥石流灾害治理和工程措施的基础,但泥石流危险度评价是一个极其复杂的不确定性问题.基于集对分析理论,探讨了泥石流危险度的集对分析改进新方法,即基于样本评价指标实测值与讨论泥石流标准危险度类别间的接近程度,判别集对同异反,进而应用模糊联系度的概念挖掘样本与讨论类别相邻类别的集对同异反有效信息,以综合定量分析样本的危险度,从而提高评价结果的町靠性和准确性.实例应用及与其他方法对比结果表明,基于改进的集对分析方法来评价泥石流危险度是有效可行的,町以取得理想的结果,且操作简单.     

Economic sustainability and the preservation of environmental assets

The core requirement of sustainability is that current economic activities should not result in an excessive burden on future generations. This criterion is general enough to imply different decision rules for the preservation of environmental assets. Neoclassical economics does not have a sustainability criterion for environmental assets independent of the intertemporal efficiency criterion, which allocates environmental and man-made capital based on projected monetary benefits and costs. This criterion is examined in terms of the feasibility of valuing the benefits of environmental assets, the substitution possibilities between natural and man-made capital, and the ethical grounds for using efficiency as the sole determinant of the allocation of environmental assets. An alternative ecological sustainability criterion is the preservation of safe minimum levels of environmental assets in physical terms rather than the dollar value of a composite of natural and man-made capital. Safe minimum standards for environmental assets constrain the efficiency criterion in order to ensure the sustainability of economic systems. It is argued that the ecological approach to sustainability should limit the economic approach for decisions involving the allocation of environmental assets.     

Water Supply Planning Considering Uncertainties in Future Water Demand and Climate: A Case Study in an Illinois Watershed

Ensuring an adequate, reliable, clean, and affordable water supply for citizens and industries requires informed, long-range water supply planning, which is critically important for water security. A balance between water supply and demand must be considered for a long-term plan. However, water demand projections are often highly uncertain. Climate change could impact the hydrologic processes, and consequently, threaten water supply. Thus, understanding the uncertainties in future water demand and climate is critical for developing a sound water supply plan. In Illinois, regional water supply planning attempts to explore the impacts of future water demand and climate on water supply using scenario analyses and hydrologic modeling. This study is aimed at developing a water supply planning framework that considers both future water demand and climate change impacts. This framework is based on the Soil and Water Assessment Tool to simulate the watershed hydrology and conduct scenario analyses that consider the uncertainties in both future water demand and climate as well as their impacts on water supply. The framework was applied to water supply planning efforts in the Kankakee River watershed. The Kankakee River watershed model was calibrated and validated to observed streamflow records at four long-term United States Geological Survey streamflow gages. Because of the many model parameters involved, the calibration process was automated and was followed by a manual refinement, resulting in good model performance. Long-range water demand projections were prepared by the Illinois State Water Survey. Six future water demand scenarios were established based on a suite of assumptions. Climate scenarios were obtained from the Coupled Model Intercomparison Projection Phase 5 datasets. Three representative concentration pathways (RCPs), RCP2.6, RCP4.5, and RCP8.5, are used in the study. The scenario simulation results demonstrated that climate change appears to have a greater impact on water availability in the study area than water demand. The framework developed in this study can also be used to explore the impacts of uncertainties of water demand and climate on water supply and can be extended to other regions and watersheds.     

Cracking the Achilles’ heel of energy performance contracting projects: the credit risk identification method for clients

ABSTRACT

A credit risk identification model is established to examine the credit status of Energy performance contracting (EPC) project clients (i.e., energy-using companies) in China based on rough set theory. The model is verified with data from 120 listed companies at different times. Study shows that lack of credit is one of the main obstacles to the implementation of EPC projects, and information asymmetry is the main reason for this lack of credit among potential clients in China. The credit risk identification method based on rough set theory can make up for the shortcomings of existing EPC projects in terms of credit risk identification, including redundant information and indicators, and unclear decision rules. Credit risk identification indicators of clients are dynamic. The research results can help energy service companies (ESCOs) determine the credit status of clients, facilitate cooperation between ESCOs and clients, and help explain the various dynamics of clients’ credit risk identification indicators over time.     

Rough Set Rule Induction for Suitability Assessment

The data that characterize an environmental system are a fundamental part of an environmental decision-support system. However, obtaining complete and consistent data sets for regional studies can be difficult. Data sets are often available only for small study areas within the region, whereas the data themselves contain uncertainty because of system complexity, differences in methodology, or data collection errors. This paper presents rough-set rule induction as one way to deal with data uncertainty while creating predictive if–then rules that generalize data values to the entire region. The approach is illustrated by determining the crop suitability of 14 crops for the agricultural soils of the Willamette River Basin, Oregon, USA. To implement this method, environmental and crop yield data were spatially related to individual soil units, forming the examples needed for the rule induction process. Next, four learning algorithms were defined by using different subsets of environmental attributes. ROSETTA, a software system for rough set analysis, was then used to generate rules using each algorithm. Cross-validation analysis showed that all crops had at least one algorithm with an accuracy rate greater than 68%. After selecting a preferred algorithm, the induced classifier was used to predict the crop suitability of each crop for the unclassified soils. The results suggest that rough set rule induction is a useful method for data generalization and suitability analysis.     

可靠性分配理论在系统安全目标实现中的应用

在系统安全理论中,确定各基本事件的可靠度以实现所给定的系统安全目标值问题一直没有得到很好解决。笔者将可靠性分配理论与故障树分析方法相结合,提出了2个层次可靠度分配法:最小割集之间采取可靠度再分配法,对构成最小割集的各个基本事件之间采取等分配法。从而建立系统中各单元可靠度再分配量化模型。为便于应用,通过实例,详述系统各单元可靠性分配的计算过程。研究表明,运用该模型可准确、简便地计算出各单元的可靠度,使系统在给定的目标值条件下达到系统的安全性优化。     

Application of fuzzy sets and cognitive maps to incorporate social science scenarios in integrated assessment modelsA case study of urbanization in Ujung Pandang, Indonesia

Decision–support systems in the field of integrated water management could benefit considerably from social science knowledge, as many environmental changes are human-induced. Unfortunately the adequate incorporation of qualitative social science concepts in a quantitative modeling framework is not straightforward. The applicability of fuzzy set theory and fuzzy cognitive maps for the integration of qualitative scenarios in a decision–support system was examined for the urbanization of the coastal city of Ujung Pandang, Indonesia. The results indicate that both techniques are useful tools for the design of integrated models based on a combination of concepts from the natural and social sciences. This revised version was published online in July 2006 with corrections to the Cover Date.