不同pH环境下CMS改性纳米铁在异质多孔介质中的迁移

纳米铁在污染土壤和地下水的修复中受到广泛关注。为进一步探究其在多孔介质的迁移行为，本研究采用羧甲基淀粉钠（CMS）对纳米零价铁（nZVI）进行包覆，进行了改性纳米零价铁的沉降试验，测量zeta电位与粒径分布探究其分散性；进行了不同pH条件下改性纳米零价铁在酸洗砂与水洗砂的柱实验，分析了化学异质性与pH对纳米铁在多孔介质迁移的影响。结果表明，CMS包覆纳米铁不仅使纳米颗粒本身稳定，而且还减少其在多孔介质表面沉积，大大提高了迁移性。pH=6～8时，nZVI的zeta电位由18.3mV减小到2.9mV，有效粒径由685nm增大到880nm，稳定性变差；而CMS-nZVI的zeta电位值由-19.7mV增大到-53.5mV，颗粒间静电排斥力增强稳定性变好。经能量色散X射线光谱（EDS）分析，水洗砂表面存在碳、铝、铁等氧化物杂质，这些杂质带有正电荷，会增强与带负电的CMS-nZVI的吸附作用，不利于其迁移；而经过酸洗后的石英砂，其表面杂质大大减少，在pH=8时，CMS-nZVI在酸洗砂最大迁移率为77.0%要好于水洗砂的63.0%。此外较高pH环境有助于增加石英砂介质的表面负电荷，减少颗粒与介质的吸附，促进纳米颗粒的迁移。     

基于深度学习的传感器优化布置方法

针对传感器优化布置(optimal sensor placement,简称OSP)问题,提出了一种新的使用深度神经网络的解决方案,并以简化的桥梁形状的桁架结构中的振动测试传感器优化为例进行了验证。首先,选择一种传统的传感器优化布置方法,对自动化生成的大量不同的桁架结构分别进行传感器优化布置计算,将所得优化布置结果在进行数据预处理后构建出深度学习方法所需要的训练集与验证集;其次,使用Python语言和深度学习框架TensorFlow设计实现与本研究问题适配的深度神经网络模型并训练;然后,随机生成了新的桁架结构参数;最后,将深度神经网络输出的传感器布置结果和传统方法的计算结果进行了比较,验证了本研究方法的有效性以及在速度上、可移植性与可扩展性方面的性能优势。     

公民新闻与权力变移

公民新闻的诞生源于信息网络技术的推动,同时也得力于媒介接近权的付诸实践。结合国内外学者的网络社会学主要研究成果,本文着重探索和分析了媒介权力变移所呈现出来的知识化、扁平化和分散化的趋向,并分析研究了现今阶段公民新闻在媒介权力变移过程的所面临的问题。     

A neural network based technique for vibration characterization using Gaussian laser beams

This paper presents a neural network technique combined with an optical measurement system for the characterization of mechanical vibrations in industrial machinery. In the proposed system, the Gaussian beam of a laser source illuminates on an array of photodetectors. If either the laser source or the photodetector array is coupled with a vibrating system, then the optical powers detected by the photodetectors will vary accordingly, and are expected to reflect the magnitude and frequency of the X–Y planar vibrations of the monitored system. The time-varying optical powers are input to an artificial neural network-based vibration monitoring system which maps the power distributions to the X–Y position of the laser beam center. An experimental setup of the system is built and used for training and testing purposes. The obtained experimental results demonstrate the adequacy of combining optical techniques with neural networks to estimate the vibration frequency and magnitude. Estimated frequencies were within 1% of the actual ones, and the estimated magnitudes were within 29% of the actual magnitudes when using a chirp signal in the training phase. The magnitude estimation percentage error was further reduced below 12% when the neural network was trained with a decaying chirp signal.     

中控机房的改造思路

由于我台倒班方式，造成了技术管理分散，不利于人才的培养和电台健康的发展。为进一步提高电台安全播出管理水平，推进电台的自动化建设，充分利用现代化手段，对中控机房进行了改造。新模式的成功实施，能极大地提高我台安全传输发射水平。     

Evaluation of grinding media wear-rate by a combined grinding method

It is well-known that lab tests on wear-rate of grinding media cannot precisely represent its industrial performance due to complex grinding conditions. Nevertheless the lab data provides reference to the industrial data. Therefore, a reproducible test method on wear-rate in lab is necessary.By providing detailed data, this paper challenges the traditional wear-rate testing methods. Two commonly used methods on wear-rate test, self-wear in water and grinding with mineral slurry, are respectively employed. However, obvious fluctuation of wear-rate/time curves indicates neither of the two normal methods is reliable.This paper introduces a wear-rate testing method on lab scale which combines self-wear in water and grinding with mineral slurry. By this method, some repeatable wear-rate/time curves are displayed after a few hours. The mechanism will be discussed in detail in this paper.     

基于云概率密度分布估计的大坝监测数据分析

大坝运行监测易受自然环境和监测条件影响，存在时间和空间上的变异性，监测数据具有不确定性。以云理论的随机性和不确定性分析方法为基础，并与空间数据辐射思想相结合，建立了云滴概率密度分布估计模型，然后导出云概率密度分布函数，依据样本监测数据推求母体空间数据的分布特征，并设计了基于逆向云算法云变换的计算程序。分析陆浑水库1979~1999年测压管监测数据和位移变形数据的云概率密度分布特征和云数字特征，得出了20 a来大坝的数据分布特征和运行状态。监测数据分析结果表明，云概率密度分布估计不仅能有效合理地分析大坝的运行状态，而且能够依据云数字特征来判断监测状态和监测环境的异常变化。      

Structural health monitoring system based on multi-agent coordination and fusion for large structure

In practical applications of structural health monitoring technology, a large number of distributed sensors are usually adopted to monitor the big dimension structures and different kinds of damage. The monitored structures are usually divided into different sub-structures and monitored by different sensor sets. Under this situation, how to manage the distributed sensor set and fuse different methods to obtain a fast and accurate evaluation result is an important problem to be addressed deeply. In the paper, a multi-agent fusion and coordination system is presented to deal with the damage identification for the strain distribution and joint failure in the large structure. Firstly, the monitoring system is adopted to distributedly monitor two kinds of damages, and it self-judges whether the static load happens in the monitored sub-region, and focuses on the static load on the sub-region boundary to obtain the sensor network information with blackboard model. Then, the improved contract net protocol is used to dynamically distribute the damage evaluation module for monitoring two kinds of damage uninterruptedly. Lastly, a reliable assessment for the whole structure is given by combing various heterogeneous classifiers strengths with voting-based fusion. The proposed multi-agent system is illustrated through a large aerospace aluminum plate structure experiment. The result shows that the method can significantly improve the monitoring performance for the large-scale structure.     

Investigating software testing and maintenance reports: Case study

ContextAlthough many papers have been published on software development and defect prediction techniques, problem reports in real projects quite often differ from those described in the literature. Hence, there is still a need for deeper exploration of case studies from industry.ObjectiveThe aim of this study is to present the impact of fine-grained problem reports on improving evaluation of testing and maintenance processes. It is targeted at projects involving several releases and complex schemes of problem handling. This is based on our experience gained while monitoring several commercial projects.MethodExtracting certain features from detailed problem reports, we derive various measures and present analysis models which characterize and visualize the effectiveness of testing and problem resolution processes. The considered reports describe types of problems (e.g. defects), their locations in project versions and software modules, ways of their resolution, etc. The performed analysis is related to eleven projects developed in the same company. This study is an exploratory research with some explanatory features. Moreover, having identified some drawbacks, we present extensions of problem reports and their analysis which have been verified in another industrial case study project.ResultsFine-grained (accurate) problem handling reports provide a wider scope of possible measures to assess the relevant development processes. This is helpful in controlling single projects (local perspective) as well as in managing these processes in the whole company (global perspective).ConclusionDetailed problem handling reports extend the space and quality of statistical analysis, they provide significant enhancement in evaluation and refinement of software development processes as well as in reliability prediction.     

Adolescents’ comments in social media: Why do adolescents receive negative feedback and who is most at risk?

Receiving negative peer feedback in social media may have negative consequences for adolescents’ psychosocial development and well-being. Therefore, the first aim of this study was to investigate online behavior (i.e., online social exploration, risky online self-presentation) that predicts receiving negative online peer feedback. The second aim was to examine three types of precursors that may predict this online behavior and, indirectly, negative feedback: (a) developmental (i.e., sex, age), (b) dispositional (i.e., sensation seeking, inhibitory control), and (c) social precursors (i.e., peer problems, family conflict). We collected survey data among 785 Dutch adolescents (10–15 years old). Our results showed that adolescents who engaged in online social exploration and risky online self-presentation more often, were more likely to receive negative peer feedback. Online social exploration was more prevalent among the older adolescents and adolescents characterized by higher sensation seeking and more family conflict. In addition, risky online self-presentation was more prevalent among adolescents high in sensation seeking. Consequently, these adolescents’ online behavior, indirectly, made them more at risk of receiving negative peer feedback in social media.