特种车辆柴油发动机故障诊断专家系统推理机设计

针对特种车辆柴油发动机故障诊断专家系统知识表示的模糊特性，建立了故障类型的模糊诊断模型，提出了不精确推理方法和模糊匹配策略，采用柴油发动机故障类型诊断的反向推理和故障原因分析及故障消除措施正向推理相结合的混合推理机制，设计了总体目标推理和级目标推理相结合的推理机．系统现场运行的快速推理结果表明了该推理机设计是成功的．     

Locating and protecting critical reserve sites to minimize expected and worst-case losses

There has been much recent interest in the development of systematic reserve selection methods that are capable of incorporating uncertainty associated with site destruction. This paper makes a contribution to this line of research by presenting two different optimization models for minimizing species losses within a planning region. Given limited acquisition budgets, the first minimizes expected species losses over all possible site loss patterns outside the reserve network while the second minimizes maximum species losses following the worst-case loss of a restricted subset of nonreserve sites. By incorporating the uncertainty of site destruction directly into the decision planning process, these models allow a conservation planner to take a less defensive and more strategic view of reserve selection that seeks to minimize species losses through the targeted acquisition of high-value/high-risk sites. We compare both of these methods to a more standard approach, which simply maximizes within reserve representation without regard for the varied level of threat faced by different sites and species. Results on a realistic dataset show that significant reductions in species losses can be achieved using either of these more intelligent modeling frameworks.     

凯氏定氮法测定大米中蛋白质的不确定度分析

采用GB/T5009.5-2003第一法,凯氏定氮法测定大米中蛋白质的含量,其测量不确定度来源于样品称量、消化、定容、移取消化液、蒸馏及滴定等过程。通过对各个不确定度分量的计算合成,找出影响测量不确定度的因素,并对各个不确定度分量进行评估,最终分析出样品大米中蛋白质的标准不确定度及扩展不确定度并描述了影响大米蛋白质测定结果各分量对其不确定度的相对贡献。     

利用广义不确定关系计算Vaidya-Bonner黑洞的熵

利用广义不确定关系计算量子态数目，进而计算Vaidya-Bonner黑洞的熵,此方法与brick-wall模型相比，优点为不必引入截断因子，就避免了发散问题。     

Incorporating parametric uncertainty into population viability analysis models

Uncertainty in parameter estimates from sampling variation or expert judgment can introduce substantial uncertainty into ecological predictions based on those estimates. However, in standard population viability analyses, one of the most widely used tools for managing plant, fish and wildlife populations, parametric uncertainty is often ignored in or discarded from model projections. We present a method for explicitly incorporating this source of uncertainty into population models to fully account for risk in management and decision contexts. Our method involves a two-step simulation process where parametric uncertainty is incorporated into the replication loop of the model and temporal variance is incorporated into the loop for time steps in the model. Using the piping plover, a federally threatened shorebird in the USA and Canada, as an example, we compare abundance projections and extinction probabilities from simulations that exclude and include parametric uncertainty. Although final abundance was very low for all sets of simulations, estimated extinction risk was much greater for the simulation that incorporated parametric uncertainty in the replication loop. Decisions about species conservation (e.g., listing, delisting, and jeopardy) might differ greatly depending on the treatment of parametric uncertainty in population models.     

Effect of pesticide fate parameters and their uncertainty on the selection of 'worst-case' scenarios of pesticide leaching to groundwater

BACKGROUND: For the registration of pesticides in the European Union, model simulations for worst‐case scenarios are used to demonstrate that leaching concentrations to groundwater do not exceed a critical threshold. A worst‐case scenario is a combination of soil and climate properties for which predicted leaching concentrations are higher than a certain percentile of the spatial concentration distribution within a region. The derivation of scenarios is complicated by uncertainty about soil and pesticide fate parameters. As the ranking of climate and soil property combinations according to predicted leaching concentrations is different for different pesticides, the worst‐case scenario for one pesticide may misrepresent the worst case for another pesticide, which leads to ‘scenario uncertainty’. RESULTS: Pesticide fate parameter uncertainty led to higher concentrations in the higher percentiles of spatial concentration distributions, especially for distributions in smaller and more homogeneous regions. The effect of pesticide fate parameter uncertainty on the spatial concentration distribution was small when compared with the uncertainty of local concentration predictions and with the scenario uncertainty. CONCLUSION: Uncertainty in pesticide fate parameters and scenario uncertainty can be accounted for using higher percentiles of spatial concentration distributions and considering a range of pesticides for the scenario selection. Copyright © 2010 Society of Chemical Industry     

Uncertainties in static closed chamber measurements of the carbon isotopic ratio of soil-respired CO2

The δ¹³C of soil-respired CO₂ (δ_r) is frequently determined using static closed chamber methods. δ_r is obtained as the intercept of the least squares linear regression of δ vs 1/C*, where measured δ¹³C-CO₂ (δ) and volume fraction of CO₂ (C*) values of chamber headspace samples are used. Theoretically, we show that the variance of the estimate of δ_r can be reduced by extending the 1/C* interval of the regression towards (i) higher or (ii) lower values, or (iii) distributing the 1/C* values optimally within the pre-selected headspace CO₂ sampling time period. Experimental applications of these approaches indicated that: (1) lowering the initial CO₂ level, thereby increasing 1/C*, yielded a positive bias to the δ_r result. (2) It was feasible to obtain lower variance in the δ_r estimate by lowering 1/C* values through extended CO₂ sampling time. We also recommend that each chamber is sampled only once, mainly because this allows freedom to select the sampling times, in order to optimize the distribution of 1/C* values.     

近10年黄河三角洲地区粮食产量及灰色预测

通过收集粮食产量的历史数据,分析了黄河三角洲地区近10年来粮食产量的变化趋势、19个县(市、区)的粮食贡献以及其空间分布格局。基于灰色理论和Matlab软件编程建立了现代黄河三角洲地区的粮食产量定量预测模型,并进行不确定性分析。结果表明,黄河三角洲地区未来几年内粮食产量呈上升趋势,GM(1,1)模型模拟预测值序列与实际值序列残差的关联度R、均方差比值C及小误差概率均判定该模型的精度为一级,误差较小,预测结果可靠。本研究所得结果可为粮农管理部门做相关决策提供依据。     

自然保护区适应性管理研究

文章旨在尝试采用适应性管理模式改进自然保护区传统管理中存在的问题。通过分析自然保护区存在的不确定性问题,构建自然保护区适应性管理的体系结构,针对自然保护区可能产生的管理不确定性的问题,提出一种新的可行的管理理念,实现生态相容和可持续管理的目标。     

全自动定氮仪测定植物油料粗蛋白质含量的不确定度评估

通过对全自动定氮仪测定植物油料中粗蛋白质含量的检验过程进行分析,找出影响测定不确定度的因素,对各个不确定度分类进行评估和计算合成,并最终给出样品中粗蛋白质的合成标准不确定度及扩展不确定度。