考虑保护层响应的炼化过程系统风险动态转移模型

为适时、有效地控制炼化过程系统风险,以模糊Petri网(FPN)为基础,针对炼化系统动态退化性和系统中保护层对风险转移的干预性,建立考虑保护层响应的炼化过程系统风险动态转移模型。描述基于FPN的保护层作用动态机制,分析炼化系统在保护层干预下,从非正常干扰触发开始至炼化系统退化过程的风险变化趋势。最后通过正己烷缓冲罐案例分析验证模型。结果表明:正己烷缓冲罐在开始运行的30 000 h内,系统风险等级呈阶段性变化,在工作的前16 800 h,风险为Ⅰ级;第16 800~27 600 h,风险为Ⅱ级;第27 600~30 000 h,风险为Ⅲ级。     

基于Tripod-Beta模型的船舶火灾事故风险分析

为深入分析船舶火灾事故风险因素及其后果产生的影响,通过分析1991-2017年全球船舶火灾事故调查报告,从人员、管理、船舶设备、货物、环境5个方面对船舶火灾影响因素进行识别研究;采用三脚架事故致因模型(Tripod-Beta model),构建考虑安全栅的船舶火灾事故情景演化模型,识别船舶火灾关键影响因素;并在样本量较少的情况下,采用信息扩散理论计算船舶火灾发生率;最后,利用布尔函数和风险矩阵,对船舶火灾事故风险进行评价研究。结果表明:船员不安全行为和船舶设备表面过热、设备短路是船舶火灾事故的关键风险因素;事故后果链中安全栅遭到破坏时,船舶火灾风险处于不希望发生范围内。该方法能有效评估船舶火灾风险的等级,满足海事管理部门的监管工作需求。     

The impact of coal and non-coal consumption on China's energy performance improvement

China is the world's largest energy consumer, and coal accounts for a higher proportion of the country's total energy consumption, yet during its 12th five-year plan (2011–2015), the coal share among total energy consumption significantly decreased. Previous studies exploring energy performance typically used energy consumption as an input, but this lacks the analytical capacity for the structure of energy consumption. Thus, this study splits energy input into two different inputs, coal consumption and non-coal energy consumption, and based on their differences with other variables, uses the hybrid dynamic data envelopment analysis model to assess the energy performance of China's provincial industrial sector during the period 2011 to 2015. We then compare coal consumption's and non-coal consumption's rooms for improvement and conclude that provinces in eastern and central China should reduce the amount of coal consumption, thereby improving energy performance. Conversely, provinces in the western region should target a balance between energy utilization efficiency and coal consumption.     

Monitoring,imperfect detection,and risk optimization of a Tasmanian devil insurance population

Most species are imperfectly detected during biological surveys, which creates uncertainty around their abundance or presence at a given location. Decision makers managing threatened or pest species are regularly faced with this uncertainty. Wildlife diseases can drive species to extinction; thus, managing species with disease is an important part of conservation. Devil facial tumor disease (DFTD) is one such disease that led to the listing of the Tasmanian devil (Sarcophilus harrisii) as endangered. Managers aim to maintain devils in the wild by establishing disease‐free insurance populations at isolated sites. Often a resident DFTD‐affected population must first be removed. In a successful collaboration between decision scientists and wildlife managers, we used an accessible population model to inform monitoring decisions and facilitate the establishment of an insurance population of devils on Forestier Peninsula. We used a Bayesian catch‐effort model to estimate population size of a diseased population from removal and camera trap data. We also analyzed the costs and benefits of declaring the area disease‐free prior to reintroduction and establishment of a healthy insurance population. After the monitoring session in May–June 2015, the probability that all devils had been successfully removed was close to 1, even when we accounted for a possible introduction of a devil to the site. Given this high probability and the baseline cost of declaring population absence prematurely, we found it was not cost‐effective to carry out any additional monitoring before introducing the insurance population. Considering these results within the broader context of Tasmanian devil management, managers ultimately decided to implement an additional monitoring session before the introduction. This was a conservative decision that accounted for uncertainty in model estimates and for the broader nonmonetary costs of mistakenly declaring the area disease‐free.     

Identification of policies for a sustainable legal trade in rhinoceros horn based on population projection and socioeconomic models

Between 1990 and 2007, 15 southern white (Ceratotherium simum simum) and black (Diceros bicornis) rhinoceroses on average were killed illegally every year in South Africa. Since 2007 illegal killing of southern white rhinoceros for their horn has escalated to >950 individuals/year in 2013. We conducted an ecological–economic analysis to determine whether a legal trade in southern white rhinoceros horn could facilitate rhinoceros protection. Generalized linear models were used to examine the socioeconomic drivers of poaching, based on data collected from 1990 to 2013, and to project the total number of rhinoceroses likely to be illegally killed from 2014 to 2023. Rhinoceros population dynamics were then modeled under 8 different policy scenarios that could be implemented to control poaching. We also estimated the economic costs and benefits of each scenario under enhanced enforcement only and a legal trade in rhinoceros horn and used a decision support framework to rank the scenarios with the objective of maintaining the rhinoceros population above its current size while generating profit for local stakeholders. The southern white rhinoceros population was predicted to go extinct in the wild <20 years under present management. The optimal scenario to maintain the rhinoceros population above its current size was to provide a medium increase in antipoaching effort and to increase the monetary fine on conviction. Without legalizing the trade, implementing such a scenario would require covering costs equal to approximately $147,000,000/year. With a legal trade in rhinoceros horn, the conservation enterprise could potentially make a profit of $717,000,000/year. We believe the 35‐year‐old ban on rhinoceros horn products should not be lifted unless the money generated from trade is reinvested in improved protection of the rhinoceros population. Because current protection efforts seem to be failing, it is time to evaluate, discuss, and test alternatives to the present policy.     

Incorporating evolutionary processes into population viability models

We examined how ecological and evolutionary (eco‐evo) processes in population dynamics could be better integrated into population viability analysis (PVA). Complementary advances in computation and population genomics can be combined into an eco‐evo PVA to offer powerful new approaches to understand the influence of evolutionary processes on population persistence. We developed the mechanistic basis of an eco‐evo PVA using individual‐based models with individual‐level genotype tracking and dynamic genotype–phenotype mapping to model emergent population‐level effects, such as local adaptation and genetic rescue. We then outline how genomics can allow or improve parameter estimation for PVA models by providing genotypic information at large numbers of loci for neutral and functional genome regions. As climate change and other threatening processes increase in rate and scale, eco‐evo PVAs will become essential research tools to evaluate the effects of adaptive potential, evolutionary rescue, and locally adapted traits on persistence.     

Socio‐economic benefits from protected areas in southeastern Australia

International case studies of protected area performance increasingly report that conservation and socio‐economic outcomes are interdependent. Effective conservation requires support and cooperation from local governments and communities, which in turn requires that protected areas contribute to the economic well‐being of the communities in which they are sited. Despite increasing recognition of their importance, robust studies that document the socio‐economic impacts of protected areas are rare, especially in the developed world context. We proposed 3 potential pathways through which protected areas might benefit local communities in the developed world: the improved local housing value, local business stimulus, and increased local funding pathways. We examined these pathways by undertaking a statistical longitudinal analysis of 110 regional and rural communities covering an area of approximately 600,000 km² in southeastern Australia. We compared trends in 10 socio‐economic indicators describing employment, income, housing, business development and local government revenue from 2000 to 2010. New protected areas acquisitions led to an increased number of new dwelling approvals and associated developer contributions, increased local business numbers, and increased local government revenue from user‐pays services and grants. Longer‐term effects of established protected areas included increased local council revenue from a variety of sources. Our findings provide support for each of our 3 proposed benefit pathways and contribute new insights into the cycling of benefits from protected areas through the economy over time. The business and legislative models in our study are typical of those operating in many other developed countries; thus, the benefit pathways reported in our study are likely to be generalizable. By identifying and communicating socio‐economic benefits from terrestrial protected areas in a developed world context, our findings represent an important step in securing local support and ongoing high‐level protection for key components of the world's biodiversity.     

危险化学品火灾爆炸事故不安全动作分布规律研究

以我国210起典型危险化学品火灾爆炸事故为样本,对造成事故的不安全动作进行归类统计,应用云模型云发生器算法计算大类不安全动作的云模型参数值并绘制隶属云图,得出了危险化学品火灾爆炸事故中不安全动作类型和发出者分布规律及特点。结果表明:造成火源型火灾爆炸事故的违规型不安全动作平均发生次数最多,分布不均匀且不稳定,其中现场工作缺乏指导或监护发生次数最多,发出对象为监理;蓄热型火灾爆炸事故中技术型不安全动作分布表现出较大的随机性,其中物料添加不当和隐患排查不彻底发生次数最多,发出对象分别为基层人员和监理。     

基于博弈论-云模型的露天矿岩质边坡稳定性分析

针对露天矿岩质边坡稳定性具有随机性和模糊性的特点,建立了基于博弈论-云模型的边坡稳定性分级评价方法。首先,通过分析边坡稳定性影响因素,构建了包括地质、环境和工程条件等多方面的综合指标评价体系;其次,采用改进层次分析和熵权理论分别确定指标的主观和客观权重,基于博弈论计算指标综合权重;最后,通过计算综合确定度,基于最大隶属度原则确定边坡的稳定性等级。将所建立的评价模型应用到某露天矿边坡稳定性评价中,结果显示该模型的评价结果与实际情况完全符合,与未确知测度和可拓学理论的评价结果一致,表明该模型具有一定可靠性和实用性。     

Driver’s mental workload prediction model based on physiological indices

Developing an early warning model to predict the driver’s mental workload (MWL) is critical and helpful, especially for new or less experienced drivers. The present study aims to investigate the correlation between new drivers’ MWL and their work performance, regarding the number of errors. Additionally, the group method of data handling is used to establish the driver’s MWL predictive model based on subjective rating (NASA task load index [NASA-TLX]) and six physiological indices. The results indicate that the NASA-TLX and the number of errors are positively correlated, and the predictive model shows the validity of the proposed model with an R² value of 0.745. The proposed model is expected to provide a reference value for the new drivers of their MWL by providing the physiological indices, and the driving lesson plans can be proposed to sustain an appropriate MWL as well as improve the driver’s work performance.