应急演练计算机三维模拟系统研究

突发事件对人们的生命财产和社会稳定有着巨大威胁.传统应急演练通过构建突发事件场景,使人们熟悉突发事件并提高应对能力,对应对突发事件有着重要的作用,但传统应急演练自身所具有的缺陷也大大限制了其使用范围与规模.分析了传统应急演练的优势和不足以及现有计算机技术的发展现状,研究开发一种基于计算机虚拟现实技术、应用广泛的应急演练系统.对该系统的技术可行性、系统结构、运营模式及系统优势进行了探讨,最终得出本练系统可与传统应急演练优势互补,从而充分挖掘应急演练在生产安全与社会安全中的巨大潜力的结论.     

基于X3D技术的化学灾害事故处置在线培训系统

从消防部门处置化学灾害事故训练需求出发,采用基于Web的虚拟现实和智能组卷技术,设计并实现了一种化学灾害事故处置在线培训系统。按照化学危险品在储存、运输、生产环节中发生泄漏、燃烧和爆炸等事故类型进行分类,提供了危险源理化性质、事故特点及危害性分析、灭火救援行动措施等知识的在线学习和考试功能。在技术实现方面,采用互联网Web3D标准X3D技术,以三维方式模拟事故场景和表现知识内容,为用户提供了一个网络化、沉浸式的三维互动学习模式和环境。系统的设计和实现为化学灾害事故处置培训工作网络化、培训内容系统化、表现形式多样化提供了一种新的思路。     

垃圾渗滤液生物处理系统BP人工神经网络模型

垃圾渗滤液成分复杂,变化范围大．对其调试及长期持续良好运行存在较大困难。本文针对武汉市二妃山垃圾渗滤液的调试运行数据,利用人工神经网络原理,建立BP人工神经网络垃圾渗滤液生物处理模型来预测处理效果．确定最优反应条件。实验结果显示网络具有良好的收敛特性,模型具有工程实用价值。     

细化HSE一体化管理 建立多层次安全监控网络体系

介绍了在胜利油田油气集输公司实施的安全监控网络体系,阐述了安全监控网络法与实施HSE管理体系的关系,从方法内涵、方法建立和运作、方法产生的安全效果几个方面进行了全面论述.     

淮安市砂土地震液化势的综合评估

建立了BP神经网络模型LM算法的砂土地震液化判别方法,采用LVQ模式分类网络对数据进行分类,选取唐山地震时76个场地、320组现场液化勘察资料为研究对象,训练和检验网络模型的数据各160组,结果表明,该方法的砂土液化预测准确度为96.8%。根据淮安市典型场地土的钻孔资料,采用《建筑抗震设计规范法》液化判别法(规范法)、美国国家地震研究中心建议的液化判别简化方法(NCEER法)和谢君斐-陈国兴液化判别方法(谢-陈法)等3种液化判别方法及本文提出的BP神经网络模型液化判别方法对22个钻孔、120标贯点进行了液化判别,结果表明:14种地震液化判别方法的计算结果基本一致;2NCEER法比谢-陈法的计算结果要保守;3谢-陈法与规范法的计算结果很吻合,预测正确率相差不大;4规范法相对BP神经网络模型法的计算结果要保守;5BP神经网络模型法比其他3种方法的预测结果更符合实际的液化规律。最后,综合4种方法的液化判别结果给出了该地区地震液化势的评估结果,并给出了部分典型地质剖面土层地震液化势分布图。     

Trophic field overlap: A new approach to quantify keystone species

It is a current challenge to better understand the relative importance of species in ecosystems, and the network perspective is able to offer quantitative tools for this. It is plausible to assume, in general, that well-linked species, being key interactors, are also more important for the community. Recently a number of methods have been suggested for quantifying the network position of species in ecological networks (like the topological importance metric, TI). Most of them are based on node centrality indices and it may happen that the two most important species in a food web have very similar interaction structure and they can essentially replace each other if one becomes extinct. For conservation considerations it is a challenge to identify species that are richly connected and, at the same time, have a relatively unique and irreplaceable interaction pattern. We present a new method and illustrate our approach by using the Kuosheng Bay trophic network in Taiwan. The new method is based on the interaction matrix, where the strength of the interaction between nodes i and j depends only on topology. By defining a threshold separating weak and strong interactors, we define the effective range of interactions for each graph node. If the overlaps between pairs of these ranges are quantified, we gain a metric expressing how unique is the interaction pattern of a focal node (TO). The combination of centrality (TI) and uniqueness (TO) is called topological functionality (TF). We compare the nodal importance rank provided by this metric to others based on a variety of centrality measures. The main conclusion is that shrimps seem to have the most unique interaction pattern despite that their structural importance has been underestimated by all conventional centrality indices. Also, our network analysis suggests that fisheries disturb the ecosystem in a more critical network position than the impingement by the local power plant.     

Comparing network analysis methodologies for consumer–resource relations at species and ecosystems scales

This research compares two existing methodologies, mixed trophic impact analysis and utility analysis, which use network analysis to evaluate the direct, pair-wise, and indirect, holistic, ecological relations between ecosystem compartments. The two approaches have many similarities, but differ in some key assumptions which affect both the final results and interpretations. Here, we briefly introduce both methodologies through a series of two simple examples; a 3-compartment competition model and a 3-compartment food chain model, and then apply the methodologies to a 15-compartment ecosystem model of the Chesapeake Bay. This example demonstrates how implementing the various conceptual and methodological assumptions lead to differing results. Notably, the overall number of positive relations is greatly affected by the treatment of the self-interactions and the handling of detritus compartments lead to a distinction between ecological or trophic relations. We recommend slight changes to both methodologies, not necessarily in order to bring them completely together, but because each has some points which are stronger and better defensible.     

Comparative network analysis toward characterization of systemic organization for human–environmental sustainability

A preliminary study in comparative ecological network analysis was conducted to identify key assumptions and methodological challenges, test initial hypotheses and explore systemic and network structural characteristics for environmentally sustainable ecosystems. A nitrogen network for the U.S. beef supply chain – a small sub-network of the industrial food system analyzed as a pilot study – was constructed and compared to four non-human carbon and nitrogen trophic networks for the Chesapeake Bay and the Florida Everglades. These non-human food webs served as sustainable reference systems. Contrary to the main original hypothesis, the “window of vitality” and the number of network roles did not clearly differentiate between a human sub-network and the more complete non-human networks. The effective trophic level of humans (a partial estimate of trophic level based on the single food source of beef) was much higher (8.1) than any non-human species (maximum of 4.88). Network connectance, entropy, total dependency coefficients, trophic efficiencies and the ascendency to capacity ratio also indicated differences that serve as hypotheses for future tests on more comprehensive human food webs. The study elucidated important issues related to (1) the steady state assumption, which is more problematic for industrial human systems, (2) the absence or dearth of data on contributions of dead humans and human wastes to feed other species in an integrated food web, (3) the ambiguity of defining some industrial compartments as living versus non-living, and (4) challenges with constructing compartments and trophic transfers in industrial versus non-human food webs. The two main novel results are (1) the progress made toward adapting ecological network analysis (ENA) methodology for analysis of human food networks in industrial cultures and (2) characterizing the critical aspects of comparative ENA for understanding potential causes of the problems, and providing avenues for solutions, for environmental sustainability. Based on this work, construction and comparative network analysis of a more comprehensive industrial human food network seems warranted and likely to provide valuable insights for modifying structures of industrial food networks to be more like natural networks and more sustainable.     

Artificial neural network modeling of the river water quality—A case study

The paper describes the training, validation and application of artificial neural network (ANN) models for computing the dissolved oxygen (DO) and biochemical oxygen demand (BOD) levels in the Gomti river (India). Two ANN models were identified, validated and tested for the computation of DO and BOD concentrations in the Gomti river water. Both the models employed eleven input water quality variables measured in river water over a period of 10 years each month at eight different sites. The performance of the ANN models was assessed through the coefficient of determination (R²) (square of the correlation coefficient), root mean square error (RMSE) and bias computed from the measured and model computed values of the dependent variables. Goodness of the model fit to the data was also evaluated through the relationship between the residuals and model computed values of DO and BOD. The model computed values of DO and BOD by both the ANN models were in close agreement with their respective measured values in the river water. Relative importance and contribution of the input variables to the model output was evaluated through the partitioning approach. The identified ANN models can be used as tools for the computation of water quality parameters.     

Optimization of tourism impacts within protected areas by means of genetic algorithms

The search for a balance between nature conservation and tourism development within protected areas is becoming an increasingly multifaceted problem worldwide, as outlined by an increasing number of authors and highlighted at several international events. Since it is unlikely that all management objectives will reach their optimum values simultaneously, an optimization approach is required to meet multiple, conflicting goals and to obtain an overall trade-off in terms of the conceived objectives.