浅析分布式防火墙技术

随着计算机网络的不断发展,现有的防火墙体系已不能满足要求,发展新的网络防火墙技术已非常重要.分布式防火墙具有明显的防护优势,了解分布式防火墙技术,并在实际应Ζ用中用好它,已成为保障我们网络安全的必要.     

Information Network Topologies for Enhanced Local Adaptive Management

We examined the principal effects of different information network topologies for local adaptive management of natural resources. We used computerized agents with adaptive decision algorithms with the following three fundamental constraints: (1) Complete understanding of the processes maintaining the natural resource can never be achieved, (2) agents can only learn by experimentation and information sharing, and (3) memory is limited. The agents were given the task to manage a system that had two states: one that provided high utility returns (desired) and one that provided low returns (undesired). In addition, the threshold between the states was close to the optimal return of the desired state. We found that networks of low to moderate link densities significantly increased the resilience of the utility returns. Networks of high link densities contributed to highly synchronized behavior among the agents, which caused occasional large-scale ecological crises between periods of stable and high utility returns. A constructed network involving a small set of experimenting agents was capable of combining high utility returns with high resilience, conforming to theories underlying the concept of adaptive comanagement. We conclude that (1) the ability to manage for resilience (i.e., to stay clear of the threshold leading to the undesired state as well as the ability to re-enter the desired state following a collapse) resides in the network structure and (2) in a coupled social–ecological system, the systemwide state transition occurs not because the ecological system flips into the undesired state, but because managers lose their capacity to reorganize back to the desired state. An erratum to this article can be found at .     

A multi-objective optimisation approach to water management

The management of river basins is complex especially when decisions about environmental flows are considered in addition to those concerning urban and agricultural water demand. The solution to these complex decision problems requires the use of mathematical techniques that are formulated to take into account conflicting objectives. Many optimization models exist for water management systems but there is a knowledge gap in linking bio-economic objectives with the optimum use of all water resources under conflicting demands. The efficient operation and management of a network of nodes comprising storages, canals, river reaches and irrigation districts under environmental flow constraints is challenging. Minimization of risks associated with agricultural production requires accounting for uncertainty involved with climate, environmental policy and markets. Markets and economic criteria determine what crops farmers would like to grow with subsequent effect on water resources and the environment. Due to conflicts between multiple goal requirements and the competing water demands of different sectors, a multi-criteria decision-making (MCDM) framework was developed to analyze production targets under physical, biological, economic and environmental constraints. This approach is described by analyzing the conflicts that may arise between profitability, variable costs of production and pumping of groundwater for a hypothetical irrigation area.     

BASIN SCALE WATER MANAGEMENT AND FORECASTING USING ARTIFICIAL NEURAL NETWORKS1

ABSTRACT: Water scarcity in the Sevier River Basin in south‐central Utah has led water managers to seek advanced techniques for identifying optimal forecasting and management measures. To more efficiently use the limited quantity of water in the basin, better methods for control and forecasting are imperative. Basin scale management requires advanced forecasts of the availability of water. Information about long term water availability is important for decision making in terms of how much land to plant and what crops to grow; advanced daily predictions of streamflows and hydraulic characteristics of irrigation canals are of importance for managing water delivery and reservoir releases; and hourly forecasts of flows in tributary streams to account for diurnal fluctuations are vital to more precisely meet the day‐to‐day expectations of downstream farmers. A priori streamflow information and exogenous climate data have been used to predict future streamflows and required reservoir releases at different timescales. Data on snow water equivalent, sea surface temperatures, temperature, total solar radiation, and precipitation are fused by applying artificial neural networks to enhance long term and real time basin scale water management information. This approach has not previously been used in water resources management at the basin‐scale and could be valuable to water users in semi‐arid areas to more efficiently utilize and manage scarce water resources.     

城市燃气管网的地震紧急处置系统研究

燃气管网的地震破坏有时会导致火灾、爆炸等严重的次生灾害。城市中的燃气管网系统若能具备地震紧急处置功能,在地震来临时及时采取自动关闭燃气供应阀门等紧急处置措施,可以避免或减少此类次生灾害的发生。介绍了普遍用于燃气管网日常调度管理的SCADA系统的组成与工作原理,探讨了在SCADA系统中加入地震紧急处置功能的可行性,并基于ArcGIS地理软件,通过VBA二次开发编制了燃气管网的地震紧急处置模拟程序,为燃气管网地震紧急处置系统的实现提供了一定依据。     

人工神经网络在预测深基坑周边地表沉降变形中的应用研究

深基坑开挖引起的周边地表变形预测是一个复杂非线性问题,引起地表沉降的影响因素很多,各因素之间呈高度的非线性关系。传统的基坑用边地表沉降变形预测方法存在着一定的局限性,其预测精度有待提高,而人工神经网络是一种多元非线性动力学系统,可以灵活方便地对多成因的复杂未知系统进行高度建模,实现全面考虑各种主要影响因素的深基坑周边地表沉降变形预测。本文介绍了误差反向传播(BP)网络模型的结构、学习过程及其算法的改进,径向基函数(RBF)网络模型的结构及其学习过程;分析了影响深基坑开挖周边土体沉降变形的主要影响因素;以25个基坑工程的地表沉降实测资料为训练样本,建立了11个输入影响因素的BP神经网络模型和RBF神经网络模型,通过对样本的学习训练过程及对5个检验样本的预测精度,说明了人工神经网络用于预测基坑周边地表沉降的可行性和准确性。     

加油站地下储油罐压力管理系统的相关研究

在当前普遍采用的加油站真空辅助式第二阶段油气回收系统中,为了避免回收到地下储油罐中的油气再次通过P/V阀排空,必须对其中气相空间的压力实施有效控制。从国外的发展情况来看,地下储油罐的压力管理系统主要分为基于膜分离技术、基于弹性气囊和基于活性炭吸附三大类,其中膜法油气回收装置在国内已经得到了较多关注。首次系统介绍了气囊式压力管理系统的工作原理及其主要结构设计,具体包括管线布局、钢制压力容器壳体、位于容器顶部封头上的接口组件和全部位于容器内的气囊等;同时对另外一种可行方案进行了分析。     

土壤重金属的植物污染化学研究进展

针对中国土壤重金属污染加剧的趋势,为改善土壤环境质量和保障农产品安全,提出了土壤重金属的植物污染化学研究领域.结合多年的研究工作,从土壤重金属的植物根际化学行为、土壤重金属的植物吸收与解毒机制和重金属污染土壤的植物-微生物交互作用等方面简要阐述了土壤-植物系统中重金属的分布、存在形态、迁移转化、累积及生物学效应和控制规律的研究进展,并对将来的植物污染化学理论研究提出了展望.     

Information technologies and the sharing of disaster knowledge: the critical role of professional culture

A comparative survey of a diverse sample of 96 US and Italian emergency management agencies shows that the diffusion of new information technologies (IT) has transformed disaster communications. Although these technologies permit access to and the dissemination of massive amounts of disaster information with unprecedented speed and efficiency, barriers rooted in the various professional cultures still hinder the sharing of disaster knowledge. To be effective the available IT must be attuned to the unique settings and professional cultures of the local emergency management communities. Findings show that available technology, context, professional culture and interaction are key factors that affect the knowledge transfer process. Cultural filters appear to influence emergency managers' perceptions of their own professional roles, their vision of the applicability of technology to social issues, and their perspective on the transferability of disaster knowledge. Four cultural approaches to the application of IT to disaster communications are defined: technocentric; geographic,; anthropocentric; and ecocentric.