Risk factors associated with truck-involved fatal crash severity: Analyzing their impact for different groups of truck drivers

Introduction: Fatal crashes that include at least one fatality of an occupant within 30 days of the crash cause large numbers of injured persons and property losses, especially when a truck is involved. Method: To better understand the underlying effects of truck-driver-related characteristics in fatal crashes, a five-year (from 2012 to 2016) dataset from the Fatality Analysis Reporting System (FARS) was used for analysis. Based on demographic attributes, driving violation behavior, crash histories, and conviction records of truck drivers, a latent class clustering analysis was applied to classify truck drivers into three groups, namely, ‘‘middle-aged and elderly drivers with low risk of driving violations and high historical crash records,” ‘‘drivers with high risk of driving violations and high historical crash records,” and ‘‘middle-aged drivers with no driving violations and conviction records.” Next, equivalent fatalities were used to scale fatal crash severities into three levels. Subsequently, a partial proportional odds (PPO) model for each driver group was developed to identify the risk factors associated with the crash severity. Results' Conclusions: The model estimation results showed that the risk factors, as well as their impacts on different driver groups, were different. Adverse weather conditions, rural areas, curved alignments, tractor-trailer units, heavier weights and various collision manners were significantly associated with the crash severities in all driver groups, whereas driving violation behaviors such as driving under the influence of alcohol or drugs, fatigue, or carelessness were significantly associated with the high-risk group only, and fewer risk factors and minor marginal effects were identified for the low-risk groups. Practical Applications: Corresponding countermeasures for specific truck driver groups are proposed. And drivers with high risk of driving violations and high historical crash records should be more concerned.     

Microtargeting for conservation

Widespread human action and behavior change is needed to achieve many conservation goals. Doing so at the requisite scale and pace will require the efficient delivery of outreach campaigns. Conservation gains will be greatest when efforts are directed toward places of high conservation value (or need) and tailored to critical actors. Recent strategic conservation planning has relied primarily on spatial assessments of biophysical attributes, largely ignoring the human dimensions. Elsewhere, marketers, political campaigns, and others use microtargeting—predictive analytics of big data—to identify people most likely to respond positively to particular messages or interventions. Conservationists have not yet widely capitalized on these techniques. To investigate the effectiveness of microtargeting to improve conservation, we developed a propensity model to predict restoration behavior among 203,645 private landowners in a 5,200,000 ha study area in the Chesapeake Bay Watershed (U.S.A.). To isolate the additional value microtargeting may offer beyond geospatial prioritization, we analyzed a new high-resolution land-cover data set and cadastral data to identify private owners of riparian areas needing restoration. Subsequently, we developed and evaluated a restoration propensity model based on a database of landowners who had conducted restoration in the past and those who had not (n = 4978). Model validation in a parallel database (n = 4989) showed owners with the highest scorers for propensity to conduct restoration (i.e., top decile) were over twice as likely as average landowners to have conducted restoration (135%). These results demonstrate that microtargeting techniques can dramatically increase the efficiency and efficacy of conservation programs, above and beyond the advances offered by biophysical prioritizations alone, as well as facilitate more robust research of many social–ecological systems.     

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。     

基于模糊评价的VDT系统搜索策略绩效形成机理研究

为优化视觉显示终端（VDT）结构性搜索界面的布局,提高作业绩效和安全,设计了VDT系统搜索实验,构建了搜索目标位置在4个主观偏好指标下的Pythagorean模糊集决策矩阵,运用交互式多准则决策（TODIM）法计算各目标位置偏好的总体优势度并排序;同时对VDT系统搜索实验中各目标位置的绩效进行方差分析并排序,分析系统搜索策略下作业者对目标位置的主观偏好与绩效之间的关系。研究结果表明:基于系统搜索策略的VDT作业绩效在不同目标位置差异显著;导致绩效差异的根本原因是作业者对搜索目标位置的主观偏好度,而不是搜索目标的位置属性;除中心区域外,偏好度高的区域搜索绩效要优于偏好度低的区域。     

跨区域应急协作视角下化工园区空间布局的失衡与应对策略分析

随着工业化进程加快,我国化工园区呈现出数量逐年递增且集中分布的态势,增加了跨区域重大突发事件的风险,如何实现跨区域的应急协作成为化工园区安全规划所面临的重点与难点问题之一。基于此,搜集近15 a全国化工园区的安全事故,采用自组织特征映射神经网络分类方法与多元回归模型研究全国范围内化工园区安全事故的空间分布规律及影响因素,进而从风险分布与应急协作需求的匹配性角度讨论我国现有应急协作机制存在的潜在问题。研究结果表明:我国化工园区整体空间布局呈现出以区域优势为导向的趋势,其选址大多忽略了化工园区集中区域的高风险与应急能力不足的问题,这种布局的失衡使得政府无法形成高效的跨区域应急协作体制;化工园区数量上的增加并非是导致危机事故的主要原因,强调了化工园区自身特征与危机事故严重程度的相关性,区域间应急联动协作在化工园区危机应对过程中的重要作用;最后,从空间布局、跨区域协作角度提出化工园区区域应急协作的相应政策建议。     

Variance estimation in the EMAP strategy for sampling discrete ecological resources

The United States Environmental Protection Agency's Environmental Monitoring and Assessment Program (EMAP) is designed to describe status, trends and spatial pattern of indicators of condition of the nation's ecological resources. The proposed sampling design for EMAP is based on a triangular systematic grid and employs both variable probability and double sampling. The Horvitz-Thompson estimator provides the foundation of the design-based estimation strategy used in EMAP. However, special features of EMAP designed to accommodate the complexity of sampling environmental resources on a national scale require modifications of standard variance estimation procedures as well as development of new techniques. An overview of variance estimation methods proposed for application to EMAP's sampling strategy for discrete resources is presented.     

文本聚类算法的分析与比较

聚类是一种重要的文本信息处理方法,文章介绍了常用的文本聚类算法,从这些算法的适用范围、初始参数的影响、终止条件以及对噪声的敏感性等方面对它们进行了分析与比较.对文本聚类算法的应用有一定的指导意义.     

Comparison of distribution function estimators for forestry applications following variable probability sampling

Practical considerations often motivate employing variable probability sampling designs when estimating characteristics of forest populations. Three distribution function estimators, the Horvitz-Thompson estimator, a difference estimator, and a ratio estimator, are compared following variable probability sampling in which the inclusion probabilities are proportional to an auxiliary variable, X. Relative performance of the estimators is affected by several factors, including the distribution of the inclusion probabilities, the correlation () between X and the response Y, and the position along the distribution function being estimated. Both the ratio and difference estimators are superior to the Horvitz-Thompson estimator. The difference estimator gains better precision than the ratio estimator toward the upper portion of the distribution function, but the ratio estimator is superior toward the lower end of the distribution function. The point along the distribution function at which the difference estimator becomes more precise than the ratio estimator depends on the sampling design, as well as the coefficient of variation of X and . A simple confidence interval procedure provides close to nominal coverage for intervals constructed from both the difference and ratio estimators, with the exception that coverage may be poor for the lower tail of the distribution function when using the ratio estimator.     

山西高原油松种群遗传多样性

用酸性聚丙烯酰胺凝胶电泳（A-PAGE）技术，分析了山西高原9个油松种群在醇溶蛋白水平上的遗传多样性。135份材料共分离出23条带，其中3条为共有带，多态性高达86．95％。全部材料共出现53种带型，9个不同油松种群的带型有差异，同一种群不同个体的带型也有所不同，说明山西高原的油松在遗传上已产生一定程度分化，在醇溶蛋白水平上呈现出遗传多态性。从供试材料的带型计算出油松遗传分化系数为0．1547。即在种群间的变异占总变异的15．47％，种群内变异为84．53％，大部分的遗传变异存在于种群内，但种群间的分化程度在松属树种中也属于较高水平。根据23个多态位点计算遗传相似系数和遗传距离，进行聚类分析，将山西高原9个油松种群聚为3个类群。