淮河沉积物总磷和重金属沿程变化及污染评价

为了探究淮河沉积物中污染物的分布规律、来源及污染程度,于2014年7月沿淮河中下游设置16个采样断面,现场采样并检测沉积物中总磷(TP)质量比和重金属(As、Cu、Pb、Cd、Zn)的质量比,分析沉积物中污染物的沿程分布及来源并对污染状况进行评价。研究结果表明,淮河中下游沿程各城市下游断面沉积物中污染物平均含量比上游断面高;污染物来源分析表明,所有污染物均受到点源污染的影响,其中TP和Cu受到点源污染和面源污染的共同影响;单因子标准指数法对沉积物磷污染评价结果表明,淮河干流沉积物环境质量受磷的污染相对较轻;对重金属潜在生态危害程度分析表明,无论是单个重金属的生态危害程度还是综合生态危害程度都为轻微。     

地震作用下重力坝非线性振型响应的耦合性分析

以结构初始弹性振型为基向量,将地震荷载向量展开为振型荷载的组合。以Koyna重力坝为例,采用混凝土损伤塑性模型和非线性时程分析法,计算各振型荷载引起的大坝非线性响应。取地震加速度峰值为0.5g,给出了Koyna、El Centro实测地震波和1种人工地震波作用下大坝前5阶振型荷载引起的坝顶位移响应。结果表明,非线性情况下,坝顶位移响应依然由与荷载分布相同的振型分量主导,重力坝振型响应具有弱耦合性。     

外秦淮河武定门闸上游偏流淤积成因及流态改善措施

针对南京市外秦淮河武定门闸上游偏流易淤问题,建立物理模型试验,结合实测资料和理论分析,对闸上游河道淤积成因进行了深入分析,并提出增建导流墙对闸前河道流态改善的措施。研究结果表明:受闸上游弯道影响形成偏流,闸前河道两侧产生左大右小两个明显回流区,导致左侧河床较右侧淤积明显;水闸常年运行,大部分时间闸前河道水流动力较弱,易形成缓流淤积。通过闸前设置导流墙,可以改变闸前河道流态,使左右两侧回流区范围显著减小。     

秦淮河水质改善现场调水试验

为了探究不同水闸调度方案对秦淮河水质改善的效果,进行了洪水期、丰水期调度、枯水期调度和降雨初期雨水污染调度等典型时段调水试验。结果表明:通过合理调控闸泵可对秦淮河水质产生积极效果,上游来水、引水的质量和调度时段对于调度效果具有明显影响。     

基于改进模糊C均值聚类的弥散张量成像图像分割

为了避免随机选取初始聚类中心点的缺陷,利用最大最小距离的方法确定初始聚类中心点.实验结果表明,和传统的模糊C均值聚类(FCM)算法相比,所提聚类算法具有较高的稳定性和准确性,所分割的胼胝体图像边缘信息更加清晰.     

基于磁流变阻尼器的双跨轴系PID振动控制研究

为了解决双轴串联的旋转机械在临界转速处产生共振问题,搭建了双跨转子实验台,在不改变原有支撑形式的条件下将磁流变阻尼器分别安装在串联的轴1、轴2上,并建立双跨轴系振动的半主动控制系统,采用比例-积分-微分（PID）控制方法,以振幅为反馈参数实时调节阻尼器电流,在线抑制双跨轴系的振动,并在此基础上通过整定PID控制的比例系数K研究其值对振动控制效果的影响。实验结果表明：基于磁流变阻尼器的PID控制系统可以有效控制两个串联轴在临界转速附近的振动;对临界振幅过大的转子宜采用较大K值,而对临界振幅较小的转子可适当减小K值。     

五常市傍河地下水遗传毒性及健康风险评价

应用SOS/Umu遗传毒性检测方法,检测五常市河水及傍河地下水遗传毒性物质并开展健康风险评价;表征不同季节傍河地下水遗传毒性变化特征.结果表明:五常市河水和傍河地下水中均存在遗传毒性物质,河水遗传毒性水平较高,傍河地下水遗传毒性水平显著低于河水,说明傍河取水(riverbankfiltration,RBF)对河水中的遗传毒性物质有一定去除作用,去除率最高可达99.6%.健康风险评价结果表明:河水及傍河地下水存在致癌风险,风险水平最高可达5.53×10-6;河水及傍河地下水遗传毒性季节变化特征显著,融冰期致癌风险值高于平枯水期和丰水期.表明温度和水量对河水和傍河地下水中遗传毒性物质含量有一定的影响.研究结果可为健全水源地安全管理,优化饮用水处理技术提供基础数据.      

Systems Thinking and Scenario Planning: Application in the Clothing Sector

Industrial sectors that operate in uncertain environments - with demand variability, product seasonality and different industrialisation structures - need studies that enable identification and forecast trends. Therefore, the development of competitiveness extends beyond a company’s individual performance. Collective action, whether toward consumer markets, supplier markets, competitors and substitutes, can reinforce or help reformulate the current practices of an organisation, besides providing better results in the development of strategies and competitive positioning. Thus, clothing, the sector addressed in this work, is characterised by a long, fragmented, heterogeneous production chain, the competitiveness of which is linked to product differentiation. Therefore, the use of systemic approaches to study this sector is effective. In this sense, this research aims at adapting Systems Thinking and Scenario Planning (STSP) so that it supports the development and planning process in a given sector. Thus, this research applies STSP adapted to an analysis of the clothing sector in the northern region of Rio Grande do Sul State, Brazil. As a result, in academic terms, this research proposed and validated a method for analysing industrial sectors of the clothing industry. In the sectoral context, this research identified elements that leverage the sector’s competitiveness, besides generating knowledge and learning aimed at strengthening the sectoral structure identified, and fostering the formation of a new clothing cluster.     

Systemic Indicators for Rural Communities in Developing Economies: Bringing the Shared Vision into Being

Community indicators have been a frequent focus of the scholarly literature. There has been little exploration, however, in relation to rural communities, especially in developing countries. This reflects the special challenges associated with the complexity of rural systems, and the difficulties involved in developing appropriate and systemic indicators for rural communities. Identifying indicators that help the community to monitor progress towards sustainable outcomes requires a framework that is both practical and holistic. This paper introduces a participatory systemic framework for identifying community indicators, which respects the principles of complexity and honours the sense of ownership present in the communities. This framework is an iterative, sharing, co-learning engagement process that extends from creating a shared vision and extracting its core messages, to identifying indicators of progress and determining what actions to try. Importantly, this framework enables us to rank the indicators identified by communities with reference to ‘leverage points’, the best places to intervene in the social-environmental system for transformational change. This framework provides a potential pathway for sustainable rural development and perhaps also for organisations and urban communities.     

Adaptive Event-Based Consensus of Multi-Agent Systems with General Linear Dynamics

In this paper, adaptive event-based consensus of multi-agent systems with general linear dynamics is considered. A novel adaptive event-based controller and a state-dependent triggering function are proposed for each agent. The consensus can be achieved without the assumption that (A,B) is stabilizable. Furthermore, the Zeno-behavior of the concerned closed-loop system is also excluded under certain conditions. Finally, a numerical simulation example is presented to show the effectiveness of the theoretical results.