航空维修差错模式及差错成因的灰色关联分析

提出一种基于HFACS-ME框架和灰色关联分析法的航空维修差错模式和差错成因之间关系的分析方法。利用该方法分别从"模式—成因"和"成因—模式"2个方向,以"技能差错"和"身体状态差"为例,对差错模式和差错成因之间的关系进行分析,得到影响"技能差错"的主要差错成因依次为空间狭窄、培训不足、工具损坏、身体状态差、精神状态差、空间阻碍,以及"身体状态差"主要影响的差错模式为记忆差错、决策差错、技能差错。结果表明,灰色关联分析法对航空维修差错模式和差错成因之间关系的分析是有效的、可行的。     

欧日中实施GHS政策的对比和思考

通过研究欧盟、日本和我国实施全球化学品统一分类和标签制度(GHS)的基本情况,并从实施GHS的框架体系、实施GHS的时间节点、GHS的危险类别和GHS危险类别的判定,4个方面对欧盟、日本和我国实施GHS的现状进行重点研究。通过对比分析得出我国当前实施GHS主要存在4个方面的问题,即缺乏实施GHS的综合法律、实施GHS缓冲期的设置存在问题、缺乏开展GHS分类的配套指南、没有合理的运用"积木原则"。结合我国的实际情况提出3个方面的建议:适时推动我国GHS法律法规体系的建设、在各部门的协作下编制我国的GHS分类指南、合理的运用"积木原则"。通过分析表明:欧盟实施的GHS政策最为完善且力度最强,日本次之;由于充分利用"积木原则",欧盟的GHS类别与日中存在一定的差异;欧日中在数据运用和GHS危险判定原则上存在差异。     

模糊聚类分析在中国火灾危害分类中的应用

火灾现象具有随机性、模糊性,是个复杂的模糊系统行为。运用模糊聚类分析方法,借助于M ATLAB计算软件,对中国火灾的危害程度进行了分类。根据2000年中国火灾统计资料,把31个省市分为重灾区、较重灾区、一般灾区、轻灾区等4类,与传统的概率统计方法相比,本方法更符合客观实际。城市火灾分类是动态的,重灾区吸取教训,增强消防意识,增大消防投入,可减少火灾隐患;而轻灾区如不注意防范,也会增加火灾隐患。因此不论轻重灾区,均应时时增强防范意识,把火灾危害降至最低。     

国家级名镇(村)的旅游价值与开发模式探析

阐述了历史文化名镇（村）的概念和意义，并对80个国家级名镇（村）进行了划分，在此基础上分析了历史文化名镇（村）的开发现状与存在的问题。指出在80个历史文化名镇（村）中，有的已经创建了等级旅游区点，或被评为优秀旅游城市等，已将资源优势转化为产品优势；有的目前则少有开发成效。基于区域经济学的增长极、点轴、网络开发理论和可持续发展理念，提出了历史文化名镇（村）可持续开发的模式与建议。     

基于风险管理方法的危险源评价分级研究

以化工企业内涉及危险物质的装置、设施而构成的危险源为研究对象,在简要分析重大危险源评价分级的作用与方法的基础上,针对中小型化工企业,提出基于风险管理方法的危险源评价分级概念。通过计算危险源引发事故的概率和事故后果来确定危险源的风险值,并将其分为4个等级,指导企业制定合适的安全管理制度、使用恰当的安全技术措施,以最小化的代价确保危险源安全运行,从而提高中小型化工企业的危险源管理水平。     

煤矿危险源辨识与多层评价分级研究

根据煤炭系统生产的复杂特点,提出了采用系统科学的方法,对煤炭生产进行系统的单元划分,采用安全系统科学辨识的方法对危险源进行辨识.在此基础上,提出利用层次分析法和模糊动态评判的方法对危险源进行初次分级,根据危险源的耦合特性,提出二次动态分级的方法,对初次分级进行调整,确定危险源科学、合理的级别,并提出了进一步现实性评价的建议.     

基于事故理论的城市轨道交通风险评价模型研究

笔者分析了城市轨道交通事故,在分析我国其他行业事故分类的基础上,确定城市轨道交通事故分类标准,即重大事故、大事故、险性事故和一般事故,并将不同事故分类情况及专家判断评分,按事故的大小不同换算成可以计算的计算尺度,根据事故种类不同计算出事故折算因子,根据风险理论的评价方法,建立了地铁风险评价模型,对地铁的危险性进行量化定级,并通过具体实例进行综合分析评价,该风险评价模型具有一定的工程意义。     

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.     

Soil erosion and non-point source pollution impacts assessment with the aid of multi-temporal remote sensing images

Soil erosion associated with non-point source pollution is viewed as a process of land degradation in many terrestrial environments. Careful monitoring and assessment of land use variations with different temporal and spatial scales would reveal a fluctuating interface, punctuated by changes in rainfall and runoff, movement of people, perturbation from environmental disasters, and shifts in agricultural activities and cropping patterns. The use of multi-temporal remote sensing images in support of environmental modeling analysis in a geographic information system (GIS) environment leading to identification of a variety of long-term interactions between land, resources, and the built environment has been a highly promising approach in recent years. This paper started with a series of supervised land use classifications, using SPOT satellite imagery as a means, in the Kao-Ping River Basin, South Taiwan. Then, it was designed to differentiate the variations of eight land use patterns in the past decade, including orchard, farmland, sugarcane field, forest, grassland, barren, community, and water body. Final accuracy was confirmed based on interpretation of available aerial photographs and global positioning system (GPS) measurements. Finally, a numerical simulation model (General Watershed Loading Function, GWLF) was used to relate soil erosion to non-point source pollution impacts in the coupled land and river water systems. Research findings indicate that while the decadal increase in orchards poses a significant threat to water quality, the continual decrease in forested land exhibits a potential impact on water quality management. Non-point source pollution, contributing to part of the downstream water quality deterioration of the Kao-Ping River system in the last decade, has resulted in an irreversible impact on land integrity from a long-term perspective.     

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.