Identifying the largest individual sample value from a two-way composite sampling design

Composite sampling techniques for identifying the largest individual sample value seem to be cost effective when the composite samples are internally homogeneous. However, since it is not always possible to form homogeneous composite samples, these methods can lead to higher costs than expected. In this paper we propose a two-way composite sampling design as a way to improve on the cost effectiveness of the methods available to identify the largest individual sample value.     

自然灾害社会脆弱性评估研究——以上海市为例

基于投影寻踪聚类模型(PPC),结合基于实数编码的加速遗传算法(RAGA),对上海市进行了自然灾害社会脆弱性评估的尝试。结果表明:①灾害社会脆弱性最高的为崇明县,其次为宝山区和金山区;②灾害社会脆弱性最低的是黄埔区,其次是徐汇区和静安区;③总体而言,灾害脆弱性较低的地区集中于上海城市中心区,而城市边缘区的社会脆弱性一般较高。     

基于层次分析法的有毒作业场所职业危害程度分级指标研究

我国目前职业危害形势依然严峻，急、慢性职业中毒案例时有发生。对可能引起急、慢性职业中毒的有毒作业场所，按照其职业危害严重程度实施分级、分重点监管，成为我国职业危害监管工作需要解决的迫切需求。实施有毒作业场所分级监管的关键，是确定合理的分级指标，从而将有毒作业场所区分开。本文应用层次分析法，对有毒作业场所的各项评价指标进行分析，并最终确定毒物浓度超标倍数作为最终的分级指标，从而为制定有毒作业场所分级标准，开展有毒作业场所分级监管提供科学的依据。     

Use of watershed characteristics to select control streams for estimating effects of metal mining wastes on extensively disturbed streams

Impacts of sediments and heavy metals on the biota of streams in the copper-mining district of southwestern Montana were examined by comparing aquatic communities of impacted streams with those of control streams. Control streams were chosen through the use of a technique that identifies similar streams based on similarities in their watershed characteristics. Significant differences between impacted and control sites existed for surface substrate, riparian vegetation, and the number of macroinvertebrate taxa. These results revealed that: (a) chemical and physical habitats at the impacted sites were disrupted, (b) the presence of trout was an inadequate measure of ecological integrity for these sites, and (c) watershed classification based on a combination of mapped terrestrial characteristics provided a reasonable method to select control sites where potential control sites upstream and downstream were unsuitable.     

Indigenous Systems of Forest Classification: Understanding Land Use Patterns and the Role of NTFPs in Shifting Cultivators’ Subsistence Economies

This article discusses the system of classification of forest types used by the Pwo Karen in Thung Yai Naresuan Wildlife Sanctuary in western Thailand and the role of nontimber forest products (NTFPs), focusing on wild food plants, in Karen livelihoods. The article argues that the Pwo Karen have two methods of forest classification, closely related to their swidden farming practices. The first is used for forest land that has been, or can be, swiddened, and classifies forest types according to growth conditions. The second system is used for land that is not suitable for cultivation and looks at soil properties and slope. The article estimates the relative importance of each forest type in what concerns the collection of wild food plants. A total of 134 wild food plant species were recorded in December 2004. They account for some 80–90% of the amount of edible plants consumed by the Pwo Karen, and have a base value of Baht 11,505 per year, comparable to the cash incomes of many households. The article argues that the Pwo Karen reliance on NTFPs has influenced their land-use and forest management practices. However, by restricting the length of the fallow period, the Thai government has caused ecological changes that are challenging the ability of the Karen to remain subsistence oriented. By ignoring shifting cultivators’ dependence on such products, the involvement of governments in forest management, especially through restrictions imposed on swidden farming practices, is likely to have a considerable impact on the livelihood strategies of these communities.     

Synergistic Techniques for Better Understanding and Classifying the Environmental Structure of Landscapes

The desire to capture natural regions in the landscape has been a goal of geographic and environmental classification and ecological land classification (ELC) for decades. Since the increased adoption of data-centric, multivariate, computational methods, the search for natural regions has become the search for the best classification that optimally trades off classification complexity for class homogeneity. In this study, three techniques are investigated for their ability to find the best classification of the physical environments of the Mt. Lofty Ranges in South Australia: AutoClass-C (a Bayesian classifier), a Kohonen Self-Organising Map neural network, and a k-means classifier with homogeneity analysis. AutoClass-C is specifically designed to find the classification that optimally trades off classification complexity for class homogeneity. However, AutoClass analysis was not found to be assumption-free because it was very sensitive to the user-specified level of relative error of input data. The AutoClass results suggest that there may be no way of finding the best classification without making critical assumptions as to the level of class heterogeneity acceptable in the classification when using continuous environmental data. Therefore, rather than relying on adjusting abstract parameters to arrive at a classification of suitable complexity, it is better to quantify and visualize the data structure and the relationship between classification complexity and class homogeneity. Individually and when integrated, the Self-Organizing Map and k-means classification with homogeneity analysis techniques also used in this study facilitate this and provide information upon which the decision of the scale of classification can be made. It is argued that instead of searching for the elusive classification of natural regions in the landscape, it is much better to understand and visualize the environmental structure of the landscape and to use this knowledge to select the best ELC at the required scale of analysis.     

A critical review of the water quality classification system in Turkey: A case study on Meric Basin

An 11-year period of water quality data, collected by the Directorate of Sate Water Works of Turkey are thoroughly analyzed for the purpose of implementing water quality classes to water resources in the Meric Basin, located on the European land mass of Turkey. Water quality parameters are divided into four groups as physical, organic, inorganic, and bacteriological. The quality class of each group is evaluated by taking into account the poorest quality of any parameter in the group, after which a quality rank is assigned to the sampling station and the waterbody in question. This method of water quality classification imposed by the Turkish Water Quality Act, is then criticized with respect to a statistical approach.     

Pipeline leakage detection and isolation: An integrated approach of statistical and wavelet feature extraction with multi-layer perceptron neural network (MLPNN)

Leakage diagnosis of hydrocarbon pipelines can prevent environmental and financial losses. This work proposes a novel method that not only detects the occurrence of a leakage fault, but also suggests its location and severity. The OLGA software is employed to provide the pipeline inlet pressure and outlet flow rates as the training data for the Fault Detection and Isolation (FDI) system. The FDI system is comprised of a Multi-Layer Perceptron Neural Network (MLPNN) classifier with various feature extraction methods including the statistical techniques, wavelet transform, and a fusion of both methods. Once different leakage scenarios are considered and the preprocessing methods are done, the proposed FDI system is applied to a 20-km pipeline in southern Iran (Goldkari-Binak pipeline) and a promising severity and location detectability (a correct classification rate of 92%) and a low False Alarm Rate (FAR) were achieved.     

纺织工业粉尘爆炸危险区域划分方法

根据国家标准,分析了爆炸性粉尘环境危险区域划分的方法和步骤,以粉尘释放源释放频率和形成爆炸混合物的可能性为指标,给出了危险区域划分方法,并以某纺织企业为例进行了说明,所得结果对纺织企业的安全生产具有借鉴意义。