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.     

Pipeline leakage detection and isolation: An integrated approach of statistical and wavelet feature extraction with multi-layer perceptron neural network (MLPNN)

Leakage diagnosis of hydrocarbon pipelines can prevent environmental and financial losses. This work proposes a novel method that not only detects the occurrence of a leakage fault, but also suggests its location and severity. The OLGA software is employed to provide the pipeline inlet pressure and outlet flow rates as the training data for the Fault Detection and Isolation (FDI) system. The FDI system is comprised of a Multi-Layer Perceptron Neural Network (MLPNN) classifier with various feature extraction methods including the statistical techniques, wavelet transform, and a fusion of both methods. Once different leakage scenarios are considered and the preprocessing methods are done, the proposed FDI system is applied to a 20-km pipeline in southern Iran (Goldkari-Binak pipeline) and a promising severity and location detectability (a correct classification rate of 92%) and a low False Alarm Rate (FAR) were achieved.     

基于演练决策文本分析的决策者分类及应急决策差异研究

为研究特大地震初期的高层决策行为，通过演练模拟决策者在可得灾情信息下进行决策部署的情形，采集参演决策者独立确认的最终决策文本为样本，对比分析样本对草案修改的方式和程度，将决策者分为独立型、合作型和依赖型。结构化处理提取样本的决策任务，分析与灾情信息及决策建议的相关性。结果表明:在高层决策者中合作型人数比例最高，群组内决策差异较小，质量相对稳定；独立型决策者偏好主观判断，对灾情的关注以及草案漏洞的审查等指标明显低于其他类型；依赖型决策者在使用合理化建议的前提下易做出高质量决策，但在决策支持失误而非漏洞的条件下，能否保证决策质量有待进一步验证。     

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。     

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.     

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。     

Clustering of aerosols in atmospheric turbulent flow

A mechanism of formation of small-scale inhomogeneities in spatial distributions of aerosols and droplets associated with clustering instability in the atmospheric turbulent flow is discussed. The particle clustering is a consequence of a spontaneous breakdown of their homogeneous space distribution due to the clustering instability, and is caused by a combined effect of the particle inertia and a finite correlation time of the turbulent velocity field. In this paper a theoretical approach proposed in Elperin et al. (2002) Phys Rev E 66:036302 is further developed and applied to investigate the mechanisms of formation of small-scale aerosol inhomogeneities in the atmospheric turbulent flow. The theory of the particle clustering instability is extended to the case when the particle Stokes time is larger than the Kolmogorov time scale, but is much smaller than the correlation time at the integral scale of turbulence. We determined the criterion of the clustering instability for the Stokes number larger than 1. We discussed applications of the analyzed effects to the dynamics of aerosols and droplets in the atmospheric turbulent flow.     

煤矿区水环境质量现状评价研究

运用灰色 聚类方 法对韩城 矿区桑树 坪煤矿 水环境质 量现 状进 行了 评价， 其评 价结 果与 模糊综合评 判大体 吻合。该 方法不仅 注意到水 质分级 界限的模 糊性， 而且 信息 利用 率和 精度 均有 较大幅度的 提高。因 此，利用 灰色聚类 进行水 环境质量 评价不 仅方法简 便，而且 结论更接 近客观实 际     

基于空间聚类分析的中国旅游业碳排放效率

旅游业碳排放效率是考量旅游经济增长与生态环境关系的重要指标,对旅游业碳排放效率的有效测度和分析是实现旅游业节能减排与可持续发展的基础支撑.采用“自下而上”法核算2001—2015年我国旅游业能源消耗量与碳排放量（不含港澳台及西藏自治区数据,下同）;继而运用非期望产出SBM模型对旅游业碳排放效率进行测度,并通过空间自相关分析揭示其空间特征;最后采用Malmquist指数（MI）评估旅游业碳排放效率的动态趋势.结果表明:①研究期内我国旅游业总体碳排放效率较低,平均水平为60%;各年度达到最佳生产前沿面（旅游业碳排放效率值为1）的省(市、自治区)(简称“省区”)数量较少,绝大多数省区的碳排放效率具有较大改善空间;旅游业碳排放效率水平存在明显的省际差异;东、中、西部地区的效率存在梯度差,形成“东高西低”的空间格局.②Moran's I指数和LISA聚类图显示,各省区旅游业碳排放效率存在明显的正向空间相关性,在空间分布上呈现出显著的地理聚集特征,形成“高—高”型与“低—低”型聚集区,空间联动格局尚未形成.③2001—2015年MI均在1以上（2004年除外）,且总体平均值高达1.195,体现出持续改善的态势;各省区碳排放效率的提升来源于技术进步与技术效率双重贡献,其中,技术进步是促进旅游业碳排放效率提升的主要贡献因素.研究显示,我国旅游业碳排放效率的空间分布不均衡,但整体呈现持续上升的态势,各省区在依赖技术进步提高碳排放效率的同时,要注重全局空间联动格局的形成,最终实现低碳旅游业的协调发展.      

一种HSI空间基于模糊聚类的火灾彩色图像分割算法

针对通用模糊聚类算法进行彩色图像分割存在对初值敏感,迭代过程耗时等问题,在HSI空间结合火焰图像分布特征,采用平均值法进行初值优选,构造抑制算子和抑制因数对火焰无关区域S和I分量进行有效抑制,采用直方图聚类后进行数据融合等方式,最终实现彩色火灾图像分割。实验表明,该算法提高了彩色火灾图像分割的准确性和收敛速度。