城市生态可持续性的内涵及其支持系统评价指标体系研究

构建城市生态可持续性支持系统评价模型并对其发展水平进行综合评价,是城市生态可持续性从理论研究阶段进入可操作的实际应用阶段的重要步骤。针对城市生态可持续性基本内涵,根据＂承载力-支持力-吸引力-延续力-发展力＂（CSAED）模型框架,构建城市生态可持续性支持系统的评价指标体系,以泛长株潭地区为例,分析了该区域各地市在生态环境有效改善、资源能源集约利用、社会民生和谐进步、经济高效公平增长、人口质量综合提升、政策和制度调整与完善等方面的基本情况与提升方向。评价结果表明：泛长株潭地区大部分城市生态环境有效改善水平、资源能源集约利用效率、社会民生进步程度以及经济高效公平增长水平都普遍偏低,这意味着当前该区域亟需解决城市生态承载力不够强、环境容量有限、产业结构仍待升级、就业结构不优及公共服务不足等区域经济社会发展不协调问题,以维持区域城市生态可持续性。     

基于基团贡献法的有机化合物好氧生物降解预测模型研究

从MITI-Ⅰ试验中筛选出587种不同类型有机化合物的可用数据,通过对这些物质的结构进行拆分,随机选择其中50种化合物作为验证集,另外537种作为训练集,利用多元线性回归(MLR)和支持向量机(SVM)2种计算方法分别建立模型。结果表明,芳香酸、醛、芳香碘和叔胺等功能基团对有机化合物的好氧生物降解性影响较大;MLR模型总体预测正确率为81.43%,验证集正确率为82%,SVM模型总体预测正确率为87.90%,验证集正确率为86%。所建立的2种定量结构与生物降解性关系(QSBR)模型有效,可用于化学品的好氧生物降解性评价。     

多类支持向量机在煤矿安全评价中的应用研究

针对矿井子系统诸多、环境复杂、影响因素多变和在现实条件下难以获得大量煤矿样本的情况,提出将对非线性、小样本问题有较高处理能力的支持向量机理论引入到机制评价中,并在归纳了支持向量分类机从一对多到一对一再到决策树模式的多渠道多层次分类原理基础上,建立了基于多分类支持向量机原理的煤矿安全多层次评价模型,同时通过提取影响煤矿安全因素的特征参数,引人类权重因子和样本权重因子,较好地解决了训练样本类别数量不平衡、数据干扰导致的错分问题,实现了对煤矿安全较高准确率和较高效率的评价.     

人群分区疏散优化算法研究

在疏散过程中,出口利用不均现象时有发生,距离最短路径并不一定是最快的路径。提出了大规模人群分区疏散优化算法,该算法以疏散时间最短为目标,综合考虑了人群分布、出口位置、出口宽度等因素,通过迭代计算,求解每个行人的最优出口选择,从而得到优化疏散分区结果。以具有多个出口的某大型步行商业街区疏散为例,利用经典的无后退有偏随机行走模型进行模拟计算,对是否采用优化分区的疏散时间进行了比较分析。结果表明:采用优化分区疏散时,部分人员放弃路径最短的疏散出口,而被导向选择宽度较宽和附近人员密度较低的出口,从而提高整体疏散效率。该算法解决了以距离最短为目标的疏散分区方式导致的出口利用不均和不充分的现象,有效提高了疏散效率,对于区域分区疏散策略的制定具有一定指导意义。     

The role of weekly high‐activated positive mood,context, and personality in innovative work behavior: A multilevel and interactional model

This article proposed and tested a multilevel and interactional model of individual innovation in which weekly moods represent a core construct between context, personality, and innovative work behavior. Adopting the circumplex model of affect, innovative work behavior is proposed as resulting from weekly positive and high‐activated mood. Furthermore, drawing on the Big Five model of personality and cognitive appraisal theory, openness to experience and support for innovation are proposed as individual and contextual variables, respectively, which interplay in this process. Openness to experience interacts with support for innovation leading to high‐activated positive mood. Furthermore, openness interacts with these feelings leading to greater levels of innovative work behavior. Overall, the model entails a moderated mediation process where weekly high‐activated positive mood represents a crucial variable for transforming contextual and individual resources into innovative outcomes. These propositions were tested and supported using a diary methodology and multilevel structural equation modeling, on the basis of 893 observations of innovative work behavior and moods nested in 10 weekly waves of data. This information was collected from 92 individuals of diverse occupations employed in 73 distinct companies. Theoretical and practical implications are discussed. Copyright © 2013 John Wiley & Sons, Ltd.     

The supervisor POS–LMX–subordinate POS chain: Moderation by reciprocation wariness and supervisor's organizational embodiment

We present three studies providing an increased understanding of the interdependence between perceived organizational support (POS) and leader–member exchange (LMX). Using employees from a social service agency and new hires from a variety of organizations, we report evidence for a relational chain leading from supervisors' perceptions of support by the organization (supervisor POS) to the formation of high‐quality LMX relationships with their subordinates (first link), who interpret high‐quality LMX as support from the organization (subordinate POS, second link) and, ultimately, repay the organization with increased dedication and effort (examined here in terms of reduced withdrawal behavior). The relationship between supervisor POS and LMX with subordinates was strongly moderated by supervisor fear of exploitation in exchange relationships (reciprocation wariness), holding only for supervisors with low reciprocation wariness. Consistent with the view that employees perceive the organization as partly responsible for treatment received from supervisors, LMX was found to be more strongly related to POS when employees highly identified their supervisors with the organization (supervisor's organizational embodiment), and this interaction extended to reduced withdrawal behavior. Copyright © 2013 John Wiley & Sons, Ltd.     

Decision‐making behaviour during urban search and rescue: a case study of Germany

The objective of assisting with tasks and decisions during incident response is to reduce the risks to victims and rescue personnel while increasing the efficiency of the rescue operation. Handling uncertain information during urban search and rescue (USAR) missions represents additional stress to the decision‐maker. The aim of this study is to identify the decision‐making behaviour of rescuers during USAR missions to pinpoint trapped or buried victims in debris in order to design assistance technologies and decision‐support systems that meet their needs. In 2010, a survey was conducted among 10–15 per cent of all German rescue personnel specialised in search tasks. One of the major results of this survey is that a subjective assessment of the reliability of information available from heterogeneous sources influences the rescuers’ actions and that there is no methodology for decision‐making involving uncertain information. In addition, the study found that compliance with procedures does not require assistance.     

基于量子遗传算法的液压支架喷雾效率的参数优化设计

为了提高综采工作面液压支架喷雾的降尘效率,通过分析影响支架喷雾降尘效率的因素,以支架喷雾效率最高为优化目标,利用量子遗传算法对喷嘴到产尘点距离,雾化角度,喷雾压力,喷嘴个数,喷嘴直径多参数进行优化。结果表明:液压支架的采煤机移动喷雾降尘效率增加了1306%,液压支架上的移架/放煤喷雾降尘效率增加了1333%。此研究对于支架喷雾降尘系统的设计,参数的合理选取有一定的参考价值。     

膨润土负载壳聚糖对Cu2+的吸附作用

将壳聚糖与膨润土结合,研制出一种复合吸附剂,并用于溶液中Cu2 的脱除,取得很好的效果,脱除率达到70%以上.通过X-射线衍射实验研究了改性膨润土的结构和改性机理.结果表明,膨润土的片状层结构未发生变化,壳聚糖仅吸附在膨润土的内外表面.该吸附剂具有投药量少、稳定性高、操作简单、无再次污染等优点.     

煤与瓦斯突出预测的QGA-LSSVM模型

为快速、有效地对煤与瓦斯突出类型作出预测,运用灰色关联和因子分析模型对所选主要的判别指标进行分析提取,利用量子遗传算法(QGA)对最小二乘支持向量机(LSSVM)的参数作寻优处理,最终建立QGA-LSSVM煤与瓦斯突出预测模型。选取从砚石台矿区历史实测的数据,以96∶20的比例对该模型进行训练与测试,并将预测结果与其他预测模型的预测效果进行了比较。研究结果表明:对判别指标进行灰色关联分析可以有效去除对煤与瓦斯突出影响作用小的指标;用因子分析进行公共因子提取,可以有效减少数据信息冗余;利用QGA优化的LSSVM模型能使结果避免陷入局部最优解,用该模型可以有效预测煤与瓦斯突出类型,误判率为0。