Network analysis of a stakeholder community combatting illegal wildlife trade

The illegal wildlife trade has emerged as a growing and urgent environmental issue. Stakeholders involved in the efforts to curb wildlife trafficking include nongovernmental organizations (NGOs), academia, and state government and enforcement bodies. The extent to which these stakeholders work and communicate among each other is fundamental to effectively combatting illicit trade. Using the United Kingdom as a case study, we used a social network analysis and semistructured interviews of stakeholders to assess communication relationships in the counter wildlife trafficking community. The NGOs consistently occupied 4 of the 5 most central positions in the generated networks, whereas academic institutions routinely occupied 4 of the 5 most peripheral positions. However, NGOs were the least diverse in their communication practices compared with the other stakeholder groups. Stakeholders identified personal relationships as the most important aspect of functioning communication. Participant insights also showed that stakeholder-specific variables (e.g., ethical and confidentiality concerns), competition, and fundraising can have a confounding effect on intercommunication. Evaluating communication networks and intrastakeholder communication trends is essential to creating cohesive, productive, and efficient responses to the challenges of combatting illegal wildlife trade. Article impact statement: Communication among those combatting illegal wildlife trade is confounded by stakeholder variables (ethics, confidentiality), competition, and fundraising.     

Bibliometric analysis of simulated driving research from 1997 to 2016

Objective: The objective of this study was to explore the evolution footprints of simulated driving research in the past 20 years through rigorous and systematic bibliometric analysis, to provide insights regarding when and where the research was performed and by whom and how the mainstream content evolved over the years.

Methods: The analysis began with data retrieval in Web of Science with defined search terms related to simulated driving. BibExcel and CiteSpace were employed to conduct the performance analysis and co-citation network analysis; that is, probe of the performance of institutes, journals, authors, and research hotspots.

Results: A total of 3,766 documents were filtered out and presented an exponential growth from 1997 to 2016. The United States contributed the most publications as well as international collaborations followed by Germany and China. In addition, several universities in The Netherlands and the United States dominated the list of contributing institutes. The leading journals were in transportation and ergonomics. The leading researchers were also recognized among the 8,721 contributing authors, such as J. D. Lee, D. L. Fisher, J. H. Kim, and K. A. Brookhuis. Finally, the co-citation analysis illuminated the evolution of simulated driving research that covered the following topics roughly in chronological order: task-induced stress, drivers with neurological disorders, alertness and sleepiness while driving, trust toward driving assistance systems, driver distraction, the effect of drug use, the validity of simulators, and automated driving.

Conclusions: This article employed bibliometric tools to probe the contributing countries, institutes, journals, authors, and mainstream hotspots of simulated driving research in the past 20 years. A systematic bibliometric analysis of this field will help researchers realize the panorama of global simulated driving and establish future research directions.     

疫情下医疗废物的处置现状分析

2019年新冠疫情席卷全球,在疫情环境下,医疗废物大幅增多,全国各大城市纷纷出现医疗废物处置不当的现象。本文对国内外现状分析,在认知、设施、管理和监督四个方面发现存在一定的问题,通过加强教育、加大储备、完善机制和改善监管的建议,为提升医疗废物应急处置能力提供参考。     

Ethanol mediated As(III) adsorption onto Zn-loaded pinecone biochar: Experimental investigation, modeling, and optimization using hybrid artificial neural network-genetic algorithm approach

Organic matters (OMs) and their oxidization products often influence the fate and transport of heavy metals in the subsurface aqueous systems through interaction with the mineral surfaces. This study investigates the ethanol (EtOH)-mediated As(III) adsorption onto Zn-loaded pinecone (PC) biochar through batch experiments conducted under Box–Behnken design. The effect of EtOH on As(III) adsorption mechanism was quantitatively elucidated by fitting the experimental data using artificial neural network and quadratic modeling approaches. The quadratic model could describe the limiting nature of EtOH and pH on As(III) adsorption, whereas neural network revealed the stronger influence of EtOH (64.5%) followed by pH (20.75%) and As(III) concentration (14.75%) on the adsorption phenomena. Besides, the interaction among process variables indicated that EtOH enhances As(III) adsorption over a pH range of 2 to 7, possibly due to facilitation of ligand–metal(Zn) binding complexation mechanism. Eventually, hybrid response surface model–genetic algorithm (RSM–GA) approach predicted a better optimal solution than RSM, i.e., the adsorptive removal of As(III) (10.47 μg/g) is facilitated at 30.22 mg C/L of EtOH with initial As(III) concentration of 196.77 μg/L at pH 5.8. The implication of this investigation might help in understanding the application of biochar for removal of various As(III) species in the presence of OM.     

新型重金属絮凝剂巯基乙酰化羟甲基聚丙烯酰胺的优化制备

为提高含铜废水的处理效果及简化处理流程,以聚丙烯酰胺(PAM)、甲醛、氢氧化钠、巯基乙酸(TGA)为原料,先经羟甲基化反应制备中间产物羟甲基聚丙烯酰胺(MPAM),再通过酰胺化反应将巯基接枝到MPAM分子链上,制备出新型重金属絮凝剂巯基乙酰化羟甲基聚丙烯酰胺(MAMPAM).以水样中Cu(Ⅱ)的去除率为考察目标,采用Plackett-Burman实验、最陡爬坡实验和响应面法中CCD实验优化MAMPAM的制备条件.结果表明,MAMPAM最优制备条件为:MPAM浓度0.31%、MPAM与TGA物质的量比为1∶3.2、反应介质p H值为4.76、反应温度为25℃、反应时间为2 h,在此条件下制备的MAMPAM对Cu(Ⅱ)的去除率为95.30%,与模型的理论预测值94.47%接近,相对偏差仅为0.83%,模型合理可靠.红外分析表明MPAM分子链上成功接上了巯基.MAMPAM对不同初始浓度的含Cu(Ⅱ)水样具有很好的去除效果,Cu(Ⅱ)去除率均能达到90%以上.MAMPAM有望成为一种有效的含铜废水处理剂,具有一定的应用前景.     

基于遗传神经网络的除湿机故障诊断与寿命预测

目的识别除湿机的性能状态和预测吸附剂的剩余寿命。方法针对除湿机故障过程缓变的特点,提出一种基于数据驱动的遗传神经网络模型。首先,为解决设备失效程度划分模糊的问题,由5个热力参数组成反映吸附剂劣化程度的特征向量,关联分析得到除湿机的5类故障模式。其次,利用遗传神经网络建立状态参数和故障模式的映射关系。最后,对表征设备吸附能力的主参数进行外推预测。结果训练好的诊断网络可准确地识别出设备的劣化程度及其演变过程,预测网络的预测精度非常高。结论该方法可有效地实现对除湿机的故障诊断与预测。。     

电晕放电辐射信号的特征提取和模式识别方法研究

目的研究电晕放电辐射信号的特征提取和模式识别方法。方法在分析信号特征提取方法的基础上,对实测的电晕放电辐射信号特征提取,利用概率神经网络开展电晕放电辐射信号目标识别,检验特征提取的有效性。结果以奇异值作为输入特征量的PNN在整体上效果更优,稳定性好,对两类不同放电辐射信号的正确识别率均可达到80%以上,并且当输入特征量个数达到10个时,对实测样本的正确识别率均达到了最高值。电晕放电的正确识别率为96.7%,火花放电的正确识别率为93.3%。结论该方法能基本满足实际放电信号的识别应用。     

基于WebGIS的水环境模拟关键技术研究

近年来随着水环境模拟领域的发展出现了较多水环境数学模型,目前日益成熟的WebGIS技术是使模型使用变得简单化和直观化的一大途径.是在通过Flex和ArcGIS Server搭建WebGIS系统平台的基础上,利用PHP进行WebGIS和水环境模拟模型的数据交互,实现系统平台和数值模型的松散耦合.用户在浏览器端进行模型输入条件的设置,模型在远程服务器端进行运行计算,反馈的模拟结果通过RIA在WebGIS中进行多元化动态展示.该方法对一般模型具有适用性,可降低模型使用门槛,提高模拟结果展示和分析能力.     

基于T-S模糊神经网络的大沽河水质变化特征分析研究

基于T-S模糊神经网络,利用大沽河2010年-2015年水质监测数据,选取溶解氧、化学需氧量、高锰酸盐指数、氨氮、总氮、总磷对水质具有重要影响的6项指标,建立适用的水质评价模型,对大沽河水质变化特征进行分析.结果显示:上游水质评价结果明显优于中游、下游水质评价结果,网络评价水质等级变化趋势同真实指标数据变化趋势一致.验证结果充分表明了T-S模糊神经网络用于水质变化特征分析是可行、有效的.     

含疏松缺陷的ZL205A合金力学性能研究

目的获取含疏松缺陷ZL205A材料主要力学性能参数的变化规律及力学行为。方法通过单轴拉伸实验测试,采用割线法估算含不同等级疏松ZL205A试样的力学性能参数;基于试样断面及载荷-位移曲线分析,归纳ZL205A材料的力学行为,并通过有限元模拟对假设的力学模型进行验证。结果发现ZL205A材料的抗拉强度、伸长率、弹性模量及屈服强度指标呈现随疏松等级增加而降低的趋势,材料变形塑性屈服阶段不明显且具有脆性断裂特征。结论 Ramberg-Osgood方程适合于描述ZL205A材料的弹塑性本构关系。