PROBABILISTIC EVALUATION FOR STEEL FRAME COLUMNS OF TALL BUILDINGS

Based on properties of parameters and test data of dom-estic steel,this paper analyses the reliability and partial coefficient forresistance of steel frame columns of tall builings.The result may serveas a scientific basis.It is not only for domestic steel instead of importedsteel in building tall steel structures,but also for compiling concerendstandards.     

云南省日极端降水概率特征及时空变化研究

不同重现期的暴雨设计值是推求设计洪水的基础。选取云南省29个气象站1960―2014年日降水资料,采用极大值法和超门限峰值法2种选样方法构成研究数据序列,通过K-S检验、L-矩法和日极端降水等值线图等方法分析了云南省日极端降水的概率特征及时空变化。结果表明,Wakeby分布函数较好地拟合了云南日极端降水序列;不同重现期下,云南省日极端降水量高值区位于滇南江城、勐腊一带,低值区位于滇西北德钦、中甸一带,极端降水量从南向西北呈阶梯状递减。分别计算不同重现期下的日降水量,时段极大值法设计值均大于以日极端降水资料95th分位数作为阈值的超门限峰法设计值。多数气象站基于Wakeby分布计算出的日极端降水设计值大于PⅢ分布函数设计值。     

基于自适应概率PCA的植物叶片彩色图像修复

植物叶片图像的采集过程中，由于自然环境或成像条件的影响，特别是夜间，采集到的图像大多带有椒盐噪声，造成图像质量下降。很多植物叶片含有丰富的叶脉，被噪声污染不利于后续的表型分析、图像分割等。椒盐噪声密度较小时，中值滤波降噪效果较好，但在噪声污染严重时滤波方法也无法有效去噪。针对这一问题，提出了基于概率PCA的图像修复模型。一幅光滑的不含噪图像通常可认为服从高斯分布，概率PCA能有效地提取描述这幅图像中的主要信息，通过估计模型参数重构因噪声引起的数据缺失，从而达到图像修复的目的。但是当噪声的缺失像素点聚集在叶脉上时，直接用概率PCA修复会出现明显的边界效应，因此本文先基于树的叶脉进行追踪，再对叶脉进行概率PCA修复，然后再基于整幅图像利用概率PCA模型修复，迭代次数根据修复后图像的PSNR值自适应地选择。为了验证所提出的模型的修复性能，进行了与常用滤波方法的对比试验。试验结果表明：去噪后的图像PSNR值比使用均值滤波高出6dB左右，比使用维纳滤波高出9dB左右，比使用高斯滤波高出7dB左右，比使用中值滤波高出1dB左右，并且在结构相似性上采用本文算法去噪后的图像与原始图像的相似度最高。因此，将概率PCA模型应用于植物叶片彩色图像修复是可行的、有效的，为其后续的图像处理提供了技术支持。     

概率神经网络的水稻种植面积遥感信息提取研究

为了提高水稻种植面积遥感信息提取精度,将根据水稻生长期所选择的多时相遥感影像经过大气校正和几何校正后,实施单波段统计、主成份变换和比值变换,选出最佳组合波段,通过分析概率神经网络(probabilistic neural network,PNN)的学习算法和基本结构,对最佳组合波段影像实现PNN模型分类,并将其分类结果与反向传播(back propagation,BP)神经网络模型和最小距离法的分类结果进行比较.结果表明:PNN模型比最小距离法的分类精度高出近6个百分点;PNN模型比BP模型的分类精度高出近13个百分点;对于水稻种植面积提取精度,PNN模型比最小距离法的结果高出15个百分点.从本次试验可知,PNN模型是一种有效的遥感影像分类方法,在作物种植面积提取方面将具有独到的功效.     

基于电子鼻的果园荔枝成熟阶段监测

为了无损快速监测荔枝成熟阶段,该文提出了一种基于电子鼻技术的果园荔枝成熟阶段监测方法,采用PEN3电子鼻获取挂果约25 d到果实成熟过程中6个成熟阶段荔枝样本的仿生嗅觉信息并同步获取了各成熟阶段荔枝的3项物理特征(果实直径、果实质量与果实可溶性固形物含量)。根据不同成熟阶段荔枝物理特征变化可知,荔枝果实直径与果实质量2项物理指标在挂果约32 d~39 d,以及53 d~60 d增长较快,可溶性固形物含量在挂果约32 d前无法测量,53 d~60 d阶段增长速度较慢。提取各样本电子鼻采样数据75 s时刻的各传感器响应值作为特征值后,采用载荷分析(loadings)进行传感器阵列优化,优选了传感器R2、R4、R6、R7、R8、R9和R10的响应数据进行后续分析。将优化后的传感器响应数据进行归一化处理。采用线性判别分析(linear discriminant analysis,LDA)进一步提取特征信息,降低数据中包含的冗余信息。LDA对荔枝成熟阶段的分类识别效果不佳。为进一步探究电子鼻监测果园荔枝成熟阶段的可行性,采用模糊C均值聚类分析(fuzzy C means clustering,FCM)、k最近邻函数分析(k nearest neighbor,KNN)和概率神经网络(probabilistic neural network,PNN)进行模式识别。研究结果表明,FCM对果园荔枝成熟阶段识别的正确率为89.17%。采用KNN与PNN建立识别模型后,KNN与PNN识别模型对训练集的回判正确率均为100%,对测试集的识别率均为96.67%,具有较好的分类识别效果。试验证明了采用电子鼻进行果园荔枝成熟度监测的可行性,为果园水果品质的实时监测提供参考。     

Comparing Deterministic and Probabilistic Risk Assessments. A case study at a closed steel mill in southern Sweden (7 pp)

Background, Aims and Scope   Contaminated land is a high priority environmental problem in most of Europe and North-America. Sweden is no exception and generic guideline values have been developed for the initial assessment, but site-specific assessments are also needed. The generic guideline values are not applicable when the exposure conditions are different from the typical Swedish conditions or when the site contains a particularly sensitive ecosystem. The Swedish guideline values have, like in many other countries, been set by using deterministic point estimates for all variables and constants in the used multimedia model. The same approach is common also for site-specific assessments, and a limitation is that it fails to quantify variability and uncertainty. Probabilistic risk assessment provided a method to deal with this problem. Variability and uncertainty in the input parameters (variables or constants) are described by probability distributions, and likewise the output (risk or exposure) is presented as a probability distribution. A substantial number of probabilistic risk assessments for contaminated land at sites in North America, Europe and Asia have been published. However, an extensive review of the literature did not identify any study where probabilistic risk assessment was applied to a site contaminated by an iron or steel industry. Here we will describe such a case, where we have compared a deterministic point estimate with a probabilistic risk assessment for six elements and benzo[a]pyrene. Methods   The site had different metallurgical plants in operation for more than 100 years. Most parts of the steel mill were closed by the mid 1980s, and today the site is used by small-sized enterprises. The soil is contaminated with metals from the previous industrial operations. The present owner plans to develop the site and has therefore initiated extensive investigations of soil contamination. Sixty-two soil samples collected between 1997 and 2000 provided a good coverage of the whole site, and were analyzed for the content of different elements and polycyclic aromatic hydrocarbons (PAH). The exposure assessments were focused on six elements with high concentrations compared to the generic guideline values; arsenic (As), lead (Pb), cadmium (Cd), chromium (Cr), copper (Cu) and zinc (Zn). In addition, benzo[a]pyrene was included due to the high toxicity and comparatively high concentrations. Variability and uncertainty were characterized in a Monte Carlo simulation of exposures (10 000 iterations), and the exposures were evaluated with two land use scenarios; less sensitive use and sensitive use. Results and Discussion   The deterministic point estimates and the probabilistic estimates of the 95th percentile are in approximately the same ranges in the scenario of less sensitive land use. It is only the exposure for arsenic that is slightly above the toxicological reference value (TRV) in the deterministic assessment. In the probabilistic assessment, the exposure for all elements is below the TRV. The results for sensitive land use are applicable to a scenario where the site is developed for general housing. The deterministic point estimates and the probabilistic estimates of the 95th percentile are also here in approximately the same ranges, but the exposure exceeds the TRV for arsenic, cadmium and lead. Drinking water, vegetables grown on site and soil ingestion are the major exposure pathways for this scenario. In this assessment, the estimated intake distributions are applicable to a randomly selected individual. The probability distributions used here to characterize the different soil parameters are typically representing both variability and uncertainty, and the same is true the majority of the exposure variables. We therefore decided not to attempt to separate variability and uncertainty at this stage, but with additional data from a more in-depth site investigation it might be possible to achieve this. Conclusions and Outlook   To the best of our knowledge, this study is the first report on a probabilistic risk assessment on a former iron and steel works site. The materials handled by this industry were less toxic than for many other metallurgical operations, but contaminants may still severely limit the options for future land use. This case study shows that probabilistic exposure estimates for a set of soil contaminants can be quite similar to deterministic point estimates. The main difference is instead to be found in the additional information obtained with the probabilistic assessment. The sensitivity analyses show pathways and input variables that contribute most to variations in the total intake of each contaminant, e.g. dermal contact and ingestion of soil, vegetables and drinking water. This information can be used both in the planning of future land use and for active measures to reduce current exposure. The probabilistic assessment also provides information on the magnitude of exposure and the margin of safety. This information may facilitate risk communication between decision-makers and stakeholders. The presentation of results from probabilistic risk assessments is only briefly discussed in the literature and here we see a need for research and opportunities for enhancement. The choice of data analytical tools may then be of importance, since more complex multimedia models are rather difficult to decipher when implemented within traditional spreadsheet software. Some of the research needs are identified here and in a previous review article in this journal.     

Probabilistic model for estimating field mortality of target and non-target bird populations when simultaneously exposed to avicide bait

A probabilistic model was developed to estimate target and non-target avian mortality associated with the application of the avicide CPTH (3-chloro-p-toluidine hydrochloride) to minimize sprouting rice damage in the southern USA. CPTH exposures for individual birds were predicted by random sampling from species-specific non-parametric distributions of bait seed consumption and CPTH residues detected on individual bait seeds. Mortality was predicted from the species-specific exposure versus mortality relationship. Individual variations in this response were captured in the model by Monte Carlo sampling from species-specific distributions of slopes and median toxicity values (LD50) for each bird. The model was used to simultaneously predict mortality (percentage of exposed population and number of birds killed/weight of consumed bait) for a target (blackbird) and non-target (mourning dove) species feeding on bait sites for up to five consecutive days.     

Risk assessment of dietary exposure to pesticides using a Bayesian method

Risk assessment of pesticides can be a statistically difficult problem because pesticides occur only occasionally, but they may occur on multiple components in the diet. A Bayesian statistical model is presented which incorporates multivariate modelling of food consumption and modelling of pesticide measurements which are for a large part below a measurement threshold. It is shown that Bayesian modelling is feasible for a limited number of food components, and that in a data-rich situation the model compares well with an empirical Monte Carlo modelling.     

Exposure to sulfosulfuron in agricultural drainage ditches: field monitoring and scenario-based modelling

Field monitoring and scenario-based modelling were used to assess exposure of small ditches in the UK to the herbicide sulfosulfuron following transport via field drains. A site in central England on a high pH, clay soil was treated with sulfosulfuron, and concentrations were monitored in the single drain outfall and in the receiving ditch 1 km downstream. Drainflow in the nine months following application totalled 283 mm. Pesticide lost in the first 12.5 mm of flow was 99% of the total loading to drains (0.5% of applied). Significant dilution was observed in the receiving ditch and quantifiable residues were only detected in one sample (0.06 microg litre(-1)). The MACRO model was evaluated against the field data with minimal calibration. The parameterisation over-estimated the importance of macropore flow at the site. As a consequence, the maximum concentration in drainflow (2.3 microg litre(-1)) and the total loading to drains (0.76 g) were over-estimated by factors of 2.4 and 5, respectively. MACRO was then used to simulate long-term fate of the herbicide for each of 20 environmental scenarios. Resulting estimates for concentrations of sulfosulfuron in a receiving ditch were weighted according to the prevalence of each scenario to produce a probability distribution of daily exposure.     

概率配点法在流域非点源污染不确定性分析中的应用

农业非点源污染的形势十分严峻,流域水环境模型虽能为非点源污染管理提供决策支撑,但显著的不确定性制约着模型的应用效果。系统的不确定性分析十分重要,而传统的基于Monte Carlo模拟的不确定性分析方法存在着计算成本高昂的缺点。因此,尝试将概率配点法（Probabilistic Collocation Method）应用于流域非点源污染的不确定性分析。研究以美国Newport Bay流域的二嗪磷农药非点源污染为案例,利用WARMF模型进行污染模拟。结果表明,在许多情况下,概率配点法可以通过很小的计算成本获得与传统Monte Carlo模拟方法相近的不确定性分析结果,体现出该方法在非点源污染研究中应用的优势。此外,还揭示了管理因素对于非点源污染模拟及其不确定性分析的影响作用。