几种地下水易污性评价方法在徐州张集地区的应用

选择GOD法、DRASTIC法S、INTACS法及SI法4种易污性评价方法用于徐州张集地区的地下水易污性评价,应用GIS/ARCINFO输出张集地区的地下水易污性指标图。不同易污性评价方法对张集地区裂隙岩溶地下水的易污性评价得到了不同的结论,经分析,SI法更适用于张集地区的实际情况。具体应用时,要根据评价地区的实际条件,并和评价地区污染监测情况结合,选择合适的易污性评价方法评价地下水的易污性。     

矿山岩土工程系统环境质量评价指标体系

矿山现行的环境影响评价及其提交的环境影响报告书,是将矿山开发视为单个建设项目且只注重污染物排放和简单数量分析的单要素评价.本文将矿山开发视为统一系统,充分考虑系统内矿石开采、废石场、尾矿库等岩土工程及其矿岩氧化淋滤产生的毒害物质对矿区及其区域水土环境潜在而长期的影响,建立矿山岩士工程系统环境质量评价的指标体系,以改变矿山环境评价的传统旧式,规范和监督矿山开采行为,实现矿业开发与环境保护的协调发展.     

基于随机分析和概率预测的固定式海洋平台结构风险评估研究

对极端海况进行了环境荷载的联合概率分析。引入了整体不确定性和敏感性分析理论,利用非正态过程、具有不同相关性的多变量随机模拟技术,求解结构的失效概率。提出结构失效分析的新方法,并利用DNV提供的历史数据,对结构失效的后果做了适当分析。以埕北12-C井组平台为实例,对平台甲板高程(Air Gap)失效分析进行了实例计算。结果表明,使用多维联合极值分布理论及相应的求解方法,是离岸工程结构物风险评估的重要手段。     

大坝洪水漫顶风险评估

洪水漫顶是导致大坝溃坝的主要原因之一,大坝洪水漫顶风险评估是大坝风险评估的重要组成部分。为此,简要介绍了大坝洪水漫顶的风险模型,并通过实例详细地讨论了模型中各有关参数的不确定性处理方法,探讨了入库洪水的不确定性对洪水漫顶风险率的影响以及按规范设计的大坝的防洪能力问题。     

David W. Johnson: Award for Distinguished Contributions of Applications of Psychology to Education and Training.

Provides the biography of David W. Johnson and announces that he has won the Award for Distinguished Contributions of Applications of Psychology to Education and Training for programmatic research, comprehensive research reviews, and revision of theory over four decades of persistence that have provided the knowledge foundation for cooperative learning, constructive controversy, conflict resolution, and peer mediation training. A selected bibliography is also provided. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Effect of sampling density on the measurement of stream condition indicators in two lowland Australian streams

There is widespread application of indicators to the assessment of environmental condition of streams. These indicators are intended for use by managers in making various comparative and absolute assessments and often have a role in resource allocation and performance assessment. Therefore, the problem of formally defining confidence in the results is important but difficult because the sampling strategies used are commonly based on a compromise between the requirements of statistical rigour and the pragmatic issues of access and resources. It is rare to see this compromise explicitly considered and consequently there is seldom quantification of the uncertainty that could affect the confidence a manager has in an indicator. In this paper, we present a method for quantitatively assessing the tradeoffs between sampling density and uncertainty in meeting various monitoring objectives. Assessments using judgement‐based representative reaches are shown to be unreliable; instead a sampling approach is recommended based on the random selection of measuring sites. A detailed dataset was collected along two streams in Victoria, Australia, and the effect of sampling density was assessed by subsampling from this dataset with precision related to the number of sites assessed per reach length and the intensity of the sampling at each site. The sampling scheme to achieve a given precision is shown to depend on the monitoring objective. In particular, three objectives were considered: (1) making a baseline assessment of current condition; (2) change detection; and (3) detection of a critical threshold in condition. Change detection is shown to be more demanding than assessing baseline condition with additional sampling effort required to achieve the same precision. Sampling to detect a critical threshold depends on nominating acceptable values of Type I and II error and the size of the effect to be detected. Copyright © 2006 John Wiley & Sons, Ltd.     

A framework to integrate software behavior into dynamic probabilistic risk assessment

Software plays an increasingly important role in modern safety-critical systems. Although, research has been done to integrate software into the classical probabilistic risk assessment (PRA) framework, current PRA practice overwhelmingly neglects the contribution of software to system risk. Dynamic probabilistic risk assessment (DPRA) is considered to be the next generation of PRA techniques. DPRA is a set of methods and techniques in which simulation models that represent the behavior of the elements of a system are exercised in order to identify risks and vulnerabilities of the system. The fact remains, however, that modeling software for use in the DPRA framework is also quite complex and very little has been done to address the question directly and comprehensively. This paper develops a methodology to integrate software contributions in the DPRA environment. The framework includes a software representation, and an approach to incorporate the software representation into the DPRA environment SimPRA. The software representation is based on multi-level objects and the paper also proposes a framework to simulate the multi-level objects in the simulation-based DPRA environment. This is a new methodology to address the state explosion problem in the DPRA environment. This study is the first systematic effort to integrate software risk contributions into DPRA environments.     

炼铁生产环境负荷分析和预测研究

利用寿命周期评价的思想，计算了炼铁生产过程中的环境负荷，并分析了各因素对环境负荷的影响，运用神经网络对炼铁生产过程环境负荷进行了预测。     

Douglas W. Bray (1918-2006).

Presents an obituary for Douglas W. Bray, the inventor of the modern-day assessment center, a method used by thousands of organizations around the world to identify the best people for critical roles and to guide individuals in optimizing their talents. Bray's death in Englewood, New Jersey, on May 9, 2006, ended the extraordinary career of a pioneer in industrial/organizational (I/O) psychology who optimized the interplay of research and application. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

DSS research and practice in perspective

The aim of this paper is to assess the state-of-the-art in the Decision Support Systems (DSS) field from both a research and a practice perspective. Three main dimensions of DSS research and practice are addressed: 1) supporting human decision-making processes, 2) integrating DSS into the organizational context, and 3) identifying new application domains. The related analysis and discussion provides a better understanding of past developments in the DSS field and insights into future evolution patterns.