公路网对湿地生态功能的累积效应研究——以云南纵向岭谷区为例

采用遥感面积反演生态功能法对公路网建设对湿地生态功能的生态累积效应进行研究,试图在多数湿地缺乏多年序列生态资料的现实情况下,为生态累积效应的定量化研究找出一种简捷而又有效的计算方法.首先,通过GIS手段分析得出公路网影响域范围内湿地面积的演变:然后,通过对生态功能与湿地面积和水深的相关关系式进行分析,得出1980~2005年湿地生态功能在公路网作用下的变化情况和规律,并预测出2010.2020年公路网规划年湿地生态功能的演变状态.研究结果显示,纵向岭谷区公路网建设对湿地生态功能有一定的负面驱动效应,随着公路网的逐渐加密湿地生态功能破坏程度加大,2005年区域内湿地生态功能指数为1980年的86%;根据该速率预测规划公路网将进一步对湿地生态功能造成破坏,到2010年研究区域内湿地生态功能指数为1980年的80%,到2020年仅为59%,因此,必须采取相应湿地的保护和恢复措施.     

城镇街尘污染与平原河网水体的源-汇效应研究

太湖流域是我国经济发展最为快速的区域之一,迅速的城市化进程使该地区面源污染问题日益突出.通过对嘉兴市新塍镇的地表街尘、地表径流、受纳河网水体和沉积物样品的分析,揭示了面源污染在平原河网景观格局中和水文条件下的发生及迁移规律.结果表明,街尘作为城镇各种污染物的载体和降雨径流污染的主要来源,其5种重金属(Cr、Cu、Ni、Pb、Zn)的含量分别是122.5、152.6、90.2、172.1和319.0mg·kg-1,高于城镇土壤和农业土壤.在城镇地表径流过程中,污染物的形态主要为颗粒态.城镇地表径流使街尘发生运移并造成受纳水体的污染物浓度较降雨前有大幅提升,其中,重金属和TSS总浓度分别增加了43%～166%和270%～510%.平原河网的水文条件和景观空间格局特征使地表径流颗粒物在进入水体后发生原位沉积,城镇段河道表层沉积物重金属含量比其他河段高.在平原河网地区地表径流的运移过程中,城镇地表是面源污染物的"源",地表街尘是面源污染物的"载体",河道是面源污染物的"汇".     

废胶粉与聚氨酯聚苯乙烯互穿聚合物网络

对废胶粉进行了裂解气相色谱、红外光谱、热重分析,并且制备了共轭三组分聚氨酯/聚苯乙烯/废胶粉半互穿聚合物网络(PU/PS/SRP-IPN),并进行了力学性能测试。结果表明:在废胶粉中含有天然橡胶、顺丁橡胶、丁苯橡胶,其中天然橡胶含量较高,顺丁橡胶和丁苯橡胶含量较低,三种胶的含量比为天然橡胶:顺丁橡胶:丁苯橡胶为12:6.2:1;胶粉含胶率为49.61%,炭黑含量为18.89%,无机填料含量为21.01%。当聚苯乙烯含量为40%时,体系的扯断伸长率与拉伸强度都出现最大值,随着聚苯乙烯在体系中所占比例增大,硬度与永久变形逐渐增大。差示扫描量热仪测定的玻璃化转变温度和扫描电镜分析结果表明作为公共组分的聚苯乙烯与废胶粉及聚氨酯发生了互穿缠结,整个材料性能得以提高。     

Bayesian networks based laboratory retrofitting towards inherent safety: A risk-based implementation framework

Over the years, a number of high-profile laboratory accidents involving severe injuries, fatalities, and economic losses have been reported, prompting a significant increase in efforts towards laboratory safety. However, the dominant safety measures rely excessively on add-on safeguards such as sprinklers and respirators and pay little attention to reducing the hazardous factors at their sources. This study introduced the inherent safety concept to minimize laboratory hazards and developed a dedicated implementation tool called Generic Laboratory Safety Metric (GLSM). The Traditional Laboratory Safety Checklist (TLSC) was first used to represent the safety indicators, and then the Precedence Chart (PC) and Bayesian Networks (BN) methods were used to reconcile the safety indicators to develop the GLSM. The developed GLSM was subsequently demonstrated through a case study of a university laboratory. The results revealed that the safety level increased from 2.44 to 3.52 after the risk-based inherently safer retrofitting, thus creating laboratory conditions with a relatively satisfactory safety level. This work presented a set of generic solutions to laboratory retrofitting towards inherent safety with a novel GLSM as the implementation tool. The proposed GLSM would contribute to risk quantification and identification of key risk factors for assigning targeted and fundamental safety measures to achieve inherently safer laboratories.     

基于人工神经网络的火灾图象探测方法

提出了一种新型的火灾探测方法,基于ＢＰ神经网络的火灾图象探测方法。该方法利用了早期火灾火焰的形体变化特性,用图象处理的方法提取特征信息,最后利用人工神经网络来进行火灾判,别。     

氯代脂肪烃构效关系的遗传神经网络模式

通过检索美国国立职业与卫生研究所化学物质毒性效应登录（RTECS）1998年版光盘系统,搜集了65种氯代脂肪烃化合物对大鼠急性毒性口服LD₅₀数据,并利用遗传神经网络建立了氯代脂肪烃分子结构与对大鼠急性毒性关系的BP神经网络模式对预报集8种氯代脂肪烃测试结果表明,所建立的网络具有较好的实用性遗传神经网络是由遗传算法和神经网络耦合而成讨论了遗传神经网络的构造以及怎样利用遗传神经网络建立构效关系（QSAR）模式     

Software sensor design based on empirical data

Software sensor design consists of building an estimate of some quantity of interest. This estimate can be used either to replace a physical measurement, or to validate an existing one. This paper provides some general guidelines for the design of software sensors based on empirical data. When the model is a priori unknown, the problem can be stated in terms of non-parametric regression or black-box modelling. Complexity control is the main difficulty in this setting. A trade-off must be achieved between two antagonist goals: the model should not be too simple, and model identification should not be too variable. We propose to address this issue by a penalization algorithm, which also estimates the relevance of input features in the identification process. A data-driven software sensor should also provide accuracy and validity indexes of its prediction. We show how these indexes can be estimated for complex non-parametric methods, such as neural networks. An application in environmental monitoring, the design of an ammonia software sensor, illustrates each step of the approach.     

Grasslands, People, and Conservation: Over-the-Horizon Learning Exchanges between African and American Pastoralists

Abstract:  The world's grasslands and large migratory populations of wildlife have been disproportionately lost or disrupted by human activities, yet are poorly represented in protected areas. The major threats they face are land subdivision and the loss of large-scale dynamic processes such as wildlife migrations and fire. The large-scale dynamical processes and ubiquity of livestock economies and cultures across the grasslands calls for an integrated ecosystem approach to conservation to make up the shortfall in protected-area coverage. Ranchers and pastoralists will be more inclined to adopt an integrated landscape approach to conservation if they also see the threats to wildlife and grassland ecosystems as affecting their livelihoods and way of life. We arranged a series of learning exchanges between African and American pastoralists, ranchers, scientists, and conservationists aimed at building the collaboration and consensus needed to conserve grasslands at a landscape level. There was broad agreement on the threat of land fragmentation to livelihoods, wildlife, and grasslands. The exchanges also identified weaknesses in prevailing public, private, and community modes of ownership in halting fragmentation. New collaborative approaches were explored to attain the benefits of privatization while keeping the landscape open. The African–U.S. exchanges showed that learning exchanges can anticipate over-the-horizon problems and speed up the feedback loops that underlie adaptive management and build social and ecological resilience.     

Least-inference methods for constructing networks of trophic flows

The construction of material and energy budgets within ecosystems has long been accomplished via manual calculation. Recently, optimization techniques have been adapted to automate the procedure, but these methods require assumptions that may not square with biological reality. Two algorithms are developed to construct ecosystem budgets under minimal inference. Although the methods do not recapitulate the model used to generate the input data, analysis reveals that the results do not differ statistically from networks that were constructed manually.     

Path selection and foraging efficiency in Argentine ant transport networks

We experimentally investigated both individual and collective behavior of the Argentine ant Linepithema humile as they crossed symmetrical and asymmetrical bifurcations in gallery networks. Ants preferentially followed the branch that deviated the least from their current direction and their probability to perform a U-turn after a bifurcation increased with the turning angle at the bifurcation. At the collective level, colonies were better able to find the shortest path that linked the nest to a food source in a polarized network where bifurcations were symmetrical from one direction and asymmetrical from the other than in a network where all bifurcations were symmetrical. We constructed a model of individual behavior and showed that an individual’s preference for the least deviating path will be amplified via the ants’ mass recruitment mechanism thus explaining the difference found between polarized and non-polarized networks. The foraging efficiency measured in the simulations was three times higher in polarized than in non-polarized networks after only 15 min. We conclude that measures of transport network efficiency must incorporate both the structural properties of the network and the behavior of the network users.