液相色谱法测定辣椒中克百威农药残留量的不确定度评定

[目的]了解测定蔬菜中氨基甲酸酯类农药多残留量不确定度的主要来源,通过一些方法对不确定度贡献度大的进行改善修正,从而进一步提升检测结果的可靠性.[方法]对辣椒中克百威农药残留的不确定度进行评定和分析,找出影响不确定度的因素,对各个不确定度分量进行评估并比较各分量对总不确定度的贡献,从而给出测定结果的置信区间.[结果]分析得出影响结果的主要不确定度分量是色谱仪峰面积的测定和总重复性.[结论]在检测过程中,需在检测前做好仪器平衡和调试,确保仪器处于良好的工作状态,并在重复检测时尽可能排除其他干扰.     

Risk Perception Among Non-industrial Private Forest Owners

Based on an enquiry, risk perception among non-industrial private forest owners is described in relation to climate change and forestry hazards. Of the respondents, 11% took action to remedy the effects of climate change. Out of a given set, hazards were ranked according to each respondent's experience of recent substantial financial loss to the estate and in relation to his or her willingness to make investments aimed at risk reduction. For each hazard, the respondent assessed the risk in four classes ranging from very high to negligible risk. Six hazards were considered most problematic in all three aspects: browsing damage, falling timber prices, damage by wind, spruce bark beetle, root rot and pine weevil. A majority of the respondents claimed to take action to reduce the risk associated with at least one hazard, while 35% did not know whether they did. Excluding climate change, the need for decision support was the largest in relation to damage by wind owing to a combination of perceived high risk and a high level of ignorance in relation to whether risk-reducing measures were taken.     

HPLC法测定卷烟主流烟气中苯酚的不确定度评定

建立高效液相色谱(HPLC)法测定卷烟主流烟气中苯酚含量的不确定度评定的数学模型,通过对测量过程中的不确定度来源进行逐项分析与评定,得出了HPLC法测定卷烟主流烟气中苯酚含量的不确定度结果。     

Leaf area index uncertainty estimates for model-data fusion applications

Estimates of data uncertainties are required to integrate different observational data streams as model constraints using model-data fusion. We describe an approach with which random and systematic uncertainties in optical measurements of leaf area index [LAI] can be quantified. We use data from a measurement campaign at the spruce-dominated Howland Forest AmeriFlux site for illustrative purposes. We made measurements along two transects (one in a mature stand, one in a recently harvested shelterwood) before sunset on successive days using both the Li-Cor LAI-2000 plant canopy analyzer and digital hemispherical photography (DHP). The random measurement uncertainty (1σ) at a given point for a single measurement is about 5% for LAI-2000 and 10% for DHP. These uncertainties are small compared to potential systematic biases due to instrument calibration errors and data processing decisions, which are estimated to be 10-20% for each instrument. Sampling uncertainty (due to the spatial variability along each transect where we conducted our measurements) is an additional, but again relatively small, uncertainty. Assumptions about clumping parameters, for which standard literature values are typically used, remain large sources of uncertainty. This analysis can also be used to develop strategies to reduce measurement uncertainties.     

Sensitivity of conservation planning to different approaches to using predicted species distribution data

The main role of conservation planning is to design reserve networks to protect biodiversity in situ. Research within the field of conservation planning has focused on the development of theories and tools to design reserve networks that protect biodiversity in an efficient and representative manner. Whilst much progress has been made in this regard, there has been limited assessment of the sensitivity of conservation planning outcomes to uncertainty associated with the datasets used for conservation planning. Predicted species distribution data are commonly used for conservation planning because the alternatives (e.g. survey data) are incomplete or biased spatially. However, there may be considerable uncertainty associated with the use of predicted species distribution data, particularly given the variety of approaches available to generate a dataset from such predictions for use in conservation planning. These approaches range from using the probabilistic data directly to using a threshold identified a priori or a posteriori to convert the probabilistic data to presence/absence data. We assess the sensitivity of conservation planning outcomes to different uses of predicted species distribution data. The resulting reserve networks differed, and had different expected species representation. The choice of approach will depend on how much risk a conservation planner is willing to tolerate and how much efficiency can be sacrificed.     

模糊逻辑在土地利用遥感分类中的应用

由于影像信息提取过程蕴涵的诸多不确定性以及土地类别描述语境信息的含糊性影响,遥感数据的常规土地利用分类面临诸多困难与挑战。而模糊分类系统作为一种最为强大的软分类器,能处理、分析和表征遥感信息中传感器测量数据的不精确性、土地类别描述中的含糊性以及模型模拟中的不严密性,从而输出更能表达人类知识缺陷、更符合真实世界客观事实的分类结果,因此被认为是一种较好的土地利用遥感分类手段。本文以南京城市边缘带一样区为例,在采用地物导向分割技术对遥感影像分割的基础上,充分利用影像地物自身的光谱组合特征值以及其他空间形状、拓扑特征以及语境关系信息,按照模糊监督分类的过程来对研究区土地利用信息进行提取。研究结果表明基于遥感数据源的土地利用模糊分类系统可以获得比常规硬分类手段更为合理、信息含量更为丰富的输出结果。     

不确定条件下的生产外包时机研究

不确定条件下的生产外包具有期权特征。考虑价格与成本都具有不确定性,借助实物期权方法建立了生产外包决策模型,对外包时机进行了研究,得到了期权价值与外包阈值公式。通过数值模拟,分析了相关系数、波动率对阈值及波动率对期权价值的影响。     

Uncertainty analysis favours selection of spatially aggregated reserve networks

It has been widely argued that habitat fragmentation is bad for (meta)population persistence and that a high level of fragmentation is a similarly undesirable characteristic for a reserve network. However, modelling the effects of fragmentation for many species is very difficult due to high data demands and uncertainty concerning its effect on particular species. Hence, several reserve selection methods employ qualitative heuristics such as boundary length penalties that aggregate reserve network structures. This aggregation usually comes at a cost because low quality habitats will be included for the sake of increased connectivity. Here a biologically justified method for designing aggregated reserve networks based on a technique called distribution smoothing is investigated. As with the boundary length penalty, its use incurs an apparent biological cost. However, taking a step further, potential negative effects of fragmentation on individual species are evaluated using a decision-theoretic uncertainty analysis approach. This analysis shows that the aggregated reserve network (based on smoothed distributions) is likely to be biologically more valuable than a more fragmented one (based on habitat model predictions). The method is illustrated with a reserve design case study in the Hunter Valley of south-eastern Australia. The uncertainty analysis method, based on information-gap decision theory, provides a systematic framework for making robust decisions under severe uncertainty, making it particularly well adapted to reserve design problems.     

Optimal reserve selection in a dynamic world

In this paper we present a novel expansion of the problem of optimal reserve site selection over time. We explore a case where areas with valuable biodiversity cannot all be protected immediately due to budget restrictions and there is a probability of species extinction on reserved as well as non-reserved sites. Add to this the risk of land-use conversion facing all non-reserved areas. We furthermore introduce a new type of control by making the planning authorities have the option to sell reserved land on which biodiversity value has decreased. We formulate and solve this problem through stochastic dynamic integer-programming. The current study shows that, due to the dynamic and stochastic nature of biodiversity evolution, the inclusion of a swapping option may increase overall efficiency. Finally, we test a number of decision criteria (heuristics) to investigate alternatives to the cumbersome task of determining the true optimum.     

Probabilistic approaches to scheduling reserve selection

Most existing reserve selection algorithms are static in that they assume that a reserve network is designed and patches are selected by decision-makers at a single point in time. In reality, however, selection processes are often dynamic and patches are selected one by one or in several groups because for example there are insufficient funds at the beginning of the process to put all the patches under protection. Finding an optimal dynamic selection strategy is tricky since due to the complementarity principle the value of a particular patch depends on the presence of other patches in the network - including those that have not yet been selected. As unprotected patches may be lost, e.g., through development, the long-term value of selecting a particular patch is uncertain. Existing dynamic selection algorithms are either ‘myopic’ and consider only those patches that have already been protected, totally ignoring future uncertainty, or they are based on stochastic dynamic programming, which delivers the optimal strategy taking uncertainty into account but is numerically too complex to be employed in actual selection problems. In this paper, a ‘foresighted’ selection strategy as well as a number of variants are developed using probability theory. The different strategies are compared for a large number of selection problems. All variants outperform the myopic strategy and perform close to the optimal strategy. However, the performances of all strategies, including the optimal and the myopic one, are not dramatic.