船舶溢油应急反应人的可靠性分析研究

为减小船舶溢油事故应急反应人员失误概率,提升应急处置效果,在对应急人员可靠性主要影响因素识别的基础上,利用模糊贝叶斯网络(BN)建立船舶溢油应急人的可靠性分析(HRA)模型,将应急反应流程抽象为可进行概率推算的BN,实现单个应急任务和全过程人的可靠性的量化评估,并依据该模型对一起船舶溢油事故进行实例研究。结果表明,首先模糊集值专家评估较好地解决应急人员可靠性情境依赖性强,难以量化评估的问题;其次,BN卓越的概率推理性能实现由单个应急任务向全过程人的可靠性的推算;最后得到应急反应人的可靠性主要取决于关键应急任务的完成情况和相关情境因素的影响。     

基于在线检测信息的储罐底板腐蚀状态智能评价方法

为了解决利用声发射在线检测技术对储罐底板腐蚀状态进行评价时,主要依赖检测人员经验的问题,使该项技术能更好地推广和应用,利用储罐底板在线检测的声发射信息和外观检查信息,并根据相关标准及专家经验,确定与储罐底板腐蚀状态相关的表征因素。采用遗传算法(GA)改进贝叶斯网络(BN)搜索方法,建立基于GA的BN智能评价方法。针对声发射在线检测信息和外观检查信息,分别建立基于标准用声发射因素、基于声发射因素和综合考虑声发射因素和外观检查因素的基于在线检测信息的储罐底板腐蚀状态评价模型。通过对测试样本的评价,对比声发射检测专家评价结果,其中基于在线检测信息的储罐底板腐蚀状态评价模型的准确率为96%,该模型能够对储罐底板腐蚀状态进行可靠的智能评价。     

物联网技术在地震救灾中的应用

研究了物联网技术在地震救灾中的应用。在体系结构设计中主要针对无线传感器网络节点、自组网技术、数据采集与灾情分析软件、多源信息融合等几个方面开展研究,构造出基于物联网技术的信息处理平台,在此基础上组建一个地震监测、预警及救灾部署控制中心,并实现信息处理平台与城市基础设施的互连接口。最终,在地震救灾中能够利用多源信息融合技术为控制中心实现决策和指挥提供科学、高效的服务。     

基于成本核算的废旧动力电池回收模式分析与趋势研究

概述了动力电池流向、回收管理体系和回收模式等管理现状,总结提出了以新能源汽车生产企业、动力电池生产企业、第三方综合利用企业和产业联盟为回收主体的四种动力电池回收模式;考虑建设成本、运行维护成本、收集成本、贮存成本、运输成本、人工成本、税收成本、管理成本等8个构成要素,构建了废旧动力电池回收成本模型;核算了四种回收模式的废旧动力电池回收成本和利润情况.核算数据显示,回收1万t/a的废旧动力电池项目,在年总收益均为8500万元的条件下,四种回收模式的利润区间为-461~401万元.结果与趋势分析表明,我国可优先推广新能源汽车生产企业为回收主体的回收模式,便于迅速布局;市场成熟后以动力电池生产企业和综合利用企业为回收主体的模式将进入市场,专业性和技术性将大幅提升;当市场更加成熟,以产业联盟为主体的回收模式将更具优势,回收成本降到最低.综上,建议从避免重复建设,缩短资金周转周期,探索创新模式,构建绿色供应链以及完善回收法律体系等方面入手,进一步完善我国废旧动力电池回收体系.     

3D打印技术在固体废弃物资源循环中的应用

打印材料是限制3D打印技术发展和推广应用的瓶颈问题,目前已经发现部分固体废弃物与3D打印的契合度很高,可以用于制备3D打印材料.本文综述了4类可以用于3D打印的固体废弃物,包括硅铝基废弃物、农林废弃物、废旧塑料和废旧金属,着重讨论了这4类废弃物制备3D打印材料的方法以及废弃物的添加对原打印材料造成的影响,同时分析了当前废弃物制备3D打印材料需要解决的问题,并对废弃物基3D打印材料的发展趋势进行了展望.     

遥感技术在湖泊水质监测中的应用

环境污染遥感监测技术具有监测范围广、速度快、成本低,且便于进行长期的动态监测等优点,是实现宏观、快速、连续、动态地监测环境污染的有效方法,已成为湖泊环境动态变化监测的重要技术手段.湖泊水质遥感监测是基于经验、统计分析或水质参数的光谱特征,选择遥感波段数据与地面实测水质参数数据进行数学分析,建立水质参数反演模型实现的.在运用遥感技术对湖泊进行水质监测的方法中有传统方法和神经网络模型.与传统方法相比,神经网络模型有较强的水质识别的容错性,水质状况识别的可信度.今后,神经网络模型、高光谱遥感技术以及RS与GIS、GPS的结合运用等将是遥感技术在此领域中的发展方向.     

江苏省农户秸秆还田的影响因素分析

农户秸秆还田不仅有利于土壤有机质的增加,实现耕地的可持续利用,而且在发挥我国农田土壤固碳潜力,积极应对气候变化方面具有重要意义.文章以江苏省农户调查数据为基础,建立Logistic模型,对影响农户秸秆还田的因素进行分析,以期为制定促进农户秸秆还田的政策提供科学依据.结果表明,不同地区农户对于秸秆还田的决策存在差异:在较为发达地区,秸秆还田与否,农户更多的是从秸秆处理的边际成本出发;而略欠发达地区,则主要是基于秸秆的利用效益.此外,技术因素也影响农户秸秆还田的行为决策.在制定促进秸秆还田技术应用的激励政策时,要充分考虑地区农业生产以及农户生产经营的基本特征及差异,因地制宜,有效地促进秸秆还田在各地区最大限度地被应用和推广.     

Predictability of marine nematode biodiversity

In this paper, we investigated: (1) the predictability of different aspects of biodiversity, (2) the effect of spatial autocorrelation on the predictability and (3) the environmental variables affecting the biodiversity of free-living marine nematodes on the Belgian Continental Shelf. An extensive historical database of free-living marine nematodes was employed to model different aspects of biodiversity: species richness, evenness, and taxonomic diversity. Artificial neural networks (ANNs), often considered as “black boxes”, were applied as a modeling tool. Three methods were used to reveal these “black boxes” and to identify the contributions of each environmental variable to the diversity indices. Since spatial autocorrelation is known to introduce bias in spatial analyses, Moran's I was used to test the spatial dependency of the diversity indices and the residuals of the model. The best predictions were made for evenness. Although species richness was quite accurately predicted as well, the residuals indicated a lack of performance of the model. Pure taxonomic diversity shows high spatial variability and is difficult to model. The biodiversity indices show a strong spatial dependency, opposed to the residuals of the models, indicating that the environmental variables explain the spatial variability of the diversity indices adequately. The most important environmental variables structuring evenness are clay and sand fraction, and the minimum annual total suspended matter. Species richness is also affected by the intensity of sand extraction and the amount of gravel of the sea bed.     

Network structure and parasite transmission in a group living lizard,the gidgee skink, <Emphasis Type="Italic">Egernia stokesii</Emphasis>

Gidgee skinks (Egernia stokesii) form large social aggregations in rocky outcrops across the Flinders Ranges in South Australia. Group members share refuges (rock crevices), which may promote parasite transmission. We measured connectivity of individuals in networks constructed from patterns of common crevice use and observed patterns of parasitism by three blood parasites (Hemolivia, Schellackia and Plasmodium) and an ectoparasitic tick (Amblyomma vikirri). Data came from a 1-year mark-recapture study of four populations. Transmission networks were constructed to represent possible transmission pathways among lizards. Two lizards that used the same refuge within an estimated transmission period were considered connected in the transmission network. An edge was placed between them, directed towards the individual that occupied the crevice last. Social networks, a sub-set of same-day only associations, were small and highly fragmented compared with transmission networks, suggesting that non-synchronous crevice use leads to more transmission opportunities than direct social association. In transmission networks, lizards infested by ticks were connected to more other tick-infested lizards than uninfected lizards. Lizards infected by ticks and carrying multiple blood parasite infections were in more connected positions in the network than lizards without ticks or with one or no blood parasites. Our findings suggest higher levels of network connectivity may increase the risk of becoming infected or that parasites influence lizard behaviour and consequently their position in the network. Electronic supplementary material  The online version of this article (doi:) contains supplementary material, which is available to authorized users. This contribution is part of the special issue “Social Networks: new perspectives” (Guest Editors: J. Krause, D. Lusseau and R. James).     

基于小波神经网络的芦苇潜流人工湿地水质预测

人工湿地系统对污水的处理效果好,工艺简单,投资运行费用低,但影响其出水水质的因素很多,并且往往是非线性的,因此目前很难将这些影响因素模型化并用于水质预测. 已有的预测方法不是过于复杂就是预测精度不高. 神经网络是一种具有较强预测能力的新方法,适用于各种非线性模型的预测. 在小试研究的基础上,使用3种不同的、经过训练的小波神经网络,对芦苇潜流人工湿地沿程各采样口的水温,ρ(DO),pH,Eh和ρ(COD_Cr)等水质指标进行了预测. 结果显示,各指标的平均相对误差分别为:水温≤4.21%,pH≤1.36%,ρ(DO)≤9.77%,Eh≤6.50%,ρ(COD_Cr)≤17.76%,表明小波神经网络模型适用于人工湿地模型的预测.