Social capital as a facilitator of pro-environmental actions in the USA: a preliminary examination of mechanisms

In this article, the mechanisms through which social networks impact individuals' environmentally significant actions are examined through a pilot study. A conceptual model was developed and in-depth interviews were performed with individuals in a North Carolina town in order to understand the mechanisms through which social capital facilitates pro-environmental actions. Individuals with higher social capital were found to act pro-environmentally because they value the collective interest rather than out of self-interest. Social capital was found to play a role in the formation of this valuation mainly through micro-level socialisation processes, particularly by instilling decision-making processes based on values in individuals, which informed individuals then applied to environmentally significant decisions. The results also suggest that the prevalence of the norm of engagement with environmental issues in an individual's social network can be an especially important promoter of pro-environmental actions. The study suggests that the culture of social networks is particularly important in the formation of ecological citizens.     

Adaptive Process Control in Rubber Industry

This paper describes the problems and an adaptive solution for process control in rubber industry. We show that the human and economical benefits of an adaptive solution for the approximation of process parameters are very attractive.

The modeling of the industrial problem is done by the means of artificial neural networks. For the example of the extrusion of a rubber profile in tire production our method shows good resuits even using only a few training samples.     

浐河、灞河硝酸盐端元贡献比例——基于硝酸盐氮、氧同位素研究

近年来,河流氮污染一直是生物地球化学领域研究的热点问题。然而,识别水体硝酸盐来源、端元贡献比例及其在水体中存在生物转化(硝化、反硝化)过程,仍旧是氮循环研究的难点问题。本研究选取流经西安市的两条河流——浐河和灞河,测定其河水溶解态硝酸盐氮、氧同位素组成,并结合Bayesian同位素混合模型,有效识别了两条河流从源头到汇入渭河河口处,氮素来源的变化,同时,定量分析了其贡献比例的变化。结果显示,河流源头附近,土壤有机氮是河流硝酸盐主要来源,其贡献比例接近30%;河流中游,由于沿河农业活动的增加,同位素指示河流硝酸盐主要来源转化为化学肥料,其贡献比例接近25%;河流下游,由于城市用水的汇入,硝酸盐氮、氧同位素值偏正,主要位于污水及粪肥区间,指示硝酸盐含量较高的生活污水及工业废水的输入,其贡献比例能达到30%以上。通过本研究,研究者定性及半定量的区分和浐河、灞河氮素来源,为今后有效控制氮污染提供了理论基础。     

Beyond water data: benefits to volunteers and to local water from a citizen science program

Survey results of citizen science water data collection volunteers are presented, indicating personal benefits (e.g., being in nature, helping local water quality), and suggesting potential long-term benefits of improved watershed health (e.g., behavior change). These results can inform citizen science program development and contribute to watershed planners’ understanding of the broad benefits of such programs. We suggest that respondents’ positive feelings toward the watershed's major river and desire to learn about science and nature are place-specific elements that watershed and citizen science program managers could utilize in program development. Moreover, we explore the potential of social diffusion and behavior change and suggest the need for further research in these areas. We conclude that citizen science has potential not just as a means to collect large amounts of data (cheaply), but as a means to engage citizens to make environmentally friendly decisions.     

深水钻井隔水管紧急解脱失效动态风险分析

为了分析钻井隔水管紧急解脱失效动态风险,保证深水钻井隔水管紧急解脱安全运行,通过辨识隔水管紧急解脱相关风险因素,以及隔水管紧急解脱失效的潜在后果,采用模糊事故树和事件序列图相结合的方法,建立隔水管紧急解脱失效后果模型;基于映射准则,将模型转换成贝叶斯网络,进行深水钻井隔水管紧急解脱风险的定量分析;研究了紧急解脱动态失效概率和关键致因,并从钻井隔水管系统设计、钻井作业、紧急解脱测试和操作等方面提出预防措施,以降低紧急解脱失效概率;以南海8号钻井平台为研究对象进行案例分析。研究结果表明:1年内隔水管紧急解脱失效的概率区间为0.075 7至0.105 0;台风、不合理的解脱时刻、过提力不足、井口倾角大和内波是导致紧急解脱失效的主要原因;该模型评估结果与实际情况相符合,该方法可用于钻井隔水管紧急解脱失效风险评价。     

基于JSA-BN的水上提管维修作业风险分析

为保障海底油气管道维修作业安全,提出1种基于JSA-BN的作业风险分析方法。采用JSA方法辨识分析水上提管维修作业过程中潜在的风险因素,得出不同作业步骤的风险等级;建立水上提管维修作业贝叶斯网络模型,计算出各作业环节的成功概率;依托贝叶斯网络的逆向推理及敏感性分析能力,实现水上提管维修作业风险薄弱点的定量预测和评估。研究结果表明:提出的基于JSA-BN的作业风险分析方法可应用于近岸海底管道应急维修作业风险分析;在整个水上提管维修作业活动中,提管作业和切割破损管道作业为关键作业环节,提升过程中碰撞、掉落事件为薄弱风险因素。     

基于模糊评判和RCM的CTCS-3级车地通信子系统维修决策

CTCS-3级车地通信子系统是列控系统的重要组成部分之一,且列控系统需求的列车位置、行车许可、临时限速等安全信息都由其提供,因此,对其进行可靠性分析具有十分重要的意义。采用贝叶斯网络对系统进行可靠性分析,并对维修方式进行了讨论。首先,根据车地通信子系统的功能与结构构建其贝叶斯网络模型。然后,综合考虑维修性、共因失效等因素,对车地通信子系统进行可靠性分析。最后,在可靠性分析的基础上运用模糊综合评判法对其进行维修决策。结果表明:利用贝叶斯网络的双向推理,不仅可以计算出车地通信子系统的可靠度,还可以有效识别系统的薄弱环节;若不考虑车地通信系统冗余结构中的共因失效,则得到的可靠性指标会偏于乐观。地面GSM-R单元失效是引起车地通信子系统失效的关键事件,因此针对此薄弱环节进行状态维修检查能够有效降低事故的发生概率。     

结构解析型神经Petri网模型及其在雾霾危害性评价中的应用

为了评价不同PM_(2.5)质量浓度的雾霾对人群健康的危害性,采用函数Petri网与人工神经网络的学习机制相结合的方法,构建了结构解析型神经Petri网模型,简称SP_NPN模型。该模型可以继承ANN的自学习功能,具有ANN的联想存储功能,并能够快速寻找最优解;该模型的网络结构可以是任意的,从而具有良好的适应性;该模型对输入信息没有依赖,它将网络内部的动态作为关注焦点,因而特别适用于复杂系统结构的解析与因果关联分析;该模型的内部节点具有明确的物理含义,因而方便对复杂系统建模。应用结果表明:利用SP_NPN模型解析雾霾对人体健康的危害性,一组观测输入和输出会获得多种解析结果,据此可以发现人体系统内部存在的所有致病类型,从而为相关精确诊断方案的制定指明方向;通过固定某些弧的权系数,可以使解析结果达到唯一,从而发现导致解析结果唯一时的关键因果关联关系,依据这些关联关系设置观测方案,能够为精确诊断方案的制定提供依据;此外,利用SP_NPN模型的因果关联关系解析功能,能够揭示雾霾中PM_(2.5)质量浓度变化与人体致病机制之间存在的因果关联关系,并计算出不同PM_(2.5)质量浓度的雾霾对人群健康的危害性程度,从而达到对雾霾危害性进行评价的目的。     

Tri-generation investment analysis using Bayesian network: A case study

The increasing energy demand, increasing energy dependency, energy supply security, and environmental concerns have become a part of business policies since COP21 agreements in Paris, 2015. Combined cooling, heating, and power (CCHP or tri-generation) systems play an important role in paying the necessary attention to these policies. Tri-generation investment is a complex decision with hybrid use of energy resources. This article aims to reduce the complexity of this decision by the use of Bayesian belief networks in pre-investment stage of tri-generation investment project cycle. The proposed model gives an insight into decision analysis and helps the decision-makers either generate or purchase from it in order to meet the energy demand with different scenarios. The model is studied for a university case. The investment decision for a CCHP (tri-generation) system will be discussed as an alternative for purchasing the electricity and natural gas from the national grids.     

Modeling crash severity by considering risk indicators of driver and roadway: A Bayesian network approach

Introduction: Traffic crashes could result in severe outcomes such as injuries and deaths. Thus, understanding factors associated with crash severity is of practical importance. Few studies have deeply examined how prior violation and crash experience of drivers and roadways are associated with crash severity. Method: In this study, a set of risk indicators of road users and roadways were developed based on their prior violation and crash records (e.g., cumulative crash frequency of a roadway), in order to reflect certain aspect or degree of their driving risk. To explore the impacts of those indicators on crash severity and complex interactions among all contributing factors, a Bayesian network approach was developed, based on citywide crash data collected in Kunshan, China from 2016 to 2018. A variable selection procedure based on Information Value (IV) was developed to identify significant variables, and the Bayesian network was employed to explicitly explore statistical associations between crash severity and significant variables. Results: In terms of balanced accuracy and AUCs, the proposed approach performed reasonably well. Bayesian modeling results indicated that the prior crash/violation experiences of road users and roadways were very important risk indicators. For example, migrant workers tend to have high injury risk due to their dangerous violation behaviors, such as retrograding, red-light running, and right-of-way violation. Furthermore, results showed that certain variable combinations had enhanced impacts on severity outcome than single variables. For example, when a migrant worker and a non-motorized vehicle are involved in a crash happening on a local road with high cumulative violation frequency in the previous year, the probability for drivers suffering serious injury or fatality is much higher than that caused by any single factor. Practical applications: The proposed methodology and modeling results provide insights for developing effective countermeasures to reduce crash severity and improve traffic system safety performance.