The effects of drug and alcohol consumption on driver injury severities in single-vehicle crashes

Objective: It is well known that alcohol and drugs influence driving behavior by affecting the central nervous system, awareness, vision, and perception/reaction times, but the resulting effect on driver injuries in car crashes is not fully understood. The purpose of this study was to identify factors affecting the injury severities of unimpaired, alcohol-impaired, and drug-impaired drivers.

Method: The current article applies a random parameters logit model to study the differences in injury severities among unimpaired, alcohol-impaired, and drug-impaired drivers. Using data from single-vehicle crashes in Cook County, Illinois, over a 9-year period from January 1, 2004, to December 31, 2012, separate models for unimpaired, alcohol-impaired, and drug-impaired drivers were estimated. A wide range of variables potentially affecting driver injury severity was considered, including roadway and environmental conditions, driver attributes, time and location of the crash, and crash-specific factors.

Results: The estimation results show significant differences in the determinants of driver injury severities across groups of unimpaired, alcohol-impaired, and drug-impaired drivers. The findings also show that unimpaired drivers are understandably more responsive to variations in lighting, adverse weather, and road conditions, but these drivers also tend to have much more heterogeneity in their behavioral responses to these conditions, relative to impaired drivers. In addition, age and gender were found to be important determinants of injury severity, but the effects varied significantly across all drivers, particularly among alcohol-impaired drivers.

Conclusions: The model estimation results show that statistically significant differences exist in driver injury severities among the unimpaired, alcohol-impaired, and drug-impaired driver groups considered. Specifically, we find that unimpaired drivers tend to have more heterogeneity in their injury outcomes in the presence potentially adverse weather and road surface conditions. This makes sense because one would expect unimpaired drivers to apply their full knowledge/judgment range to deal with these conditions, and the variability of this range across the driver population (with different driving experiences, etc.) should be great. In contrast, we find, for the most part, that alcohol-impaired and drug-impaired drivers have far less heterogeneity in the factors that affect injury severity, suggesting an equalizing effect resulting from the decision-impairing substance.     

鸡粪高效降解菌群对堆肥的影响

为明确自行筛选开发的高效降解菌对鸡粪发酵的影响,分析研究了其堆肥的主要腐熟指标。结果表明,加入高效降解菌剂的堆肥处理方法温度相对较高,脱水快,pH值适宜,C／N调整较好,种子发芽指数高,堆肥后营养养分含量高,且大肠菌群数量少,堆肥效果较好。     

同时硝化/反硝化除磷工艺的脱氮除磷效能

为实现同时硝化/反硝化除磷(SNDPR),在序批式活性污泥反应器(SBR)中,采用厌氧/好氧和厌氧/缺氧/好氧2种运行模式驯化污泥,并考察了厌氧/低氧模式下SNDPR过程中COD、PHB、TP、TN、DO和电化学参数的变化规律。结果表明,经2阶段驯化,反硝化聚磷菌比例提升至85.9%,硝化速率达5.97 mg(/L.h),实现了反硝化除磷菌和硝化菌的良好共存;在厌氧/低氧模式下,SNDPR对低碳城市污水具有良好脱氮除磷效果,TP、TN和COD去除率达到93.7%、79%和87.7%;PHB与COD降解、TN降解和TP吸收有良好的相关性,也是SNDPR过程的碳源驱动力;pH和ORP曲线上"谷点"预示厌氧释磷结束,pH曲线"折点"指示SNDPR结束。     

SBR处理制药废水影响因素的研究

结合工程实际采用水解酸化-SBR工艺处理制药废水,处理水量为2 000 m3/d。SBR对CODcr的处理率稳定在92.2%～95.8%间,平均为94.23%,对氨氮的去除率在82.7%～97.6%,平均去除率达到90.73%。水解酸化-SBR稳定运行后,系统出水各项指标均达到国家《污水综合排放标准》(8978-1996)二级排放标准。运行结果表明,SBR运行最佳参数为:曝气时间8小时,污泥负荷控制0.23～0.28(kg CODcr/kg MLSS.d),温度26℃～30℃。该工艺用于处理高浓度制药废水效果稳定,耐冲击负荷高。     

考虑滚动阻抗的线性接触模型离散元宏细观参数敏感性研究*

为克服无黏性土的离散元研究中未考虑颗粒间抗转动作用的缺陷,基于抗转动线性接触模型开展一系列排水固结三轴压缩模拟试验,结合随机森林算法详细探讨抗转动线性接触模型中各细观参数（有效模量、刚度比、摩擦系数、抗转动系数）与试样的宏观参数（初始弹性模量、泊松比、峰值强度）间的关系。结果表明:颗粒有效模量的提高会使试样的初始弹性模量提高而泊松比提高,权重值分别为0.695 14和0.201 57;刚度比的增加会使试样的初始弹性模量降低而泊松比提高,权重值分别为0.751和0.193 64;抗转动系数与摩擦系数提高会显著提高试样的抗剪强度,权重值分别为0.564 72和0.427 5;抗转动系数对试样的剪胀性具有显著影响。     

Exploring relationships between microscopic kinetic parameters of tires under normal driving conditions,road characteristics and accident types

IntroductionFreeway accidents are a leading cause of death in China, which also triggers substantial economic loss and an emotional burden to society. However, the internal mechanism of how microscopic kinetic parameters of vehicles influenced by road characteristics determine the occurrence of different types of accidents has not been explicitly studied. This research aimed to explore the “link role” of tire microscopic kinetic parameters in road characteristic variables and traffic accidents to aid in facilitating the traffic design and management, and thus to prevent traffic accident. Method: A mountain freeway in Zhejiang Province, China was used as the research object and the data used in this paper were obtained through a real-time vehicle experiment. Multiple estimation models, including the standard ordered logit (SOL) model, fixed parameters logit (FPL) model, and random parameters logit (RPL) model were established. Results: The findings show that road characteristics will affect the longitudinal kinetic characteristics of the vehicle and, consequently, map the level of risk of rear-end accidents. Driving compensation effects were also identified in this paper (i.e., the drivers tend to be more cautious in complicated driving circumstances). Another finding relating to the mountain freeway is that different tunnel characteristics (e.g., tunnel entrance and tunnel exit) have different effects on different types of traffic accidents. Practical Applications: The framework proposed in this article can provide new insight for researchers to enlarge the research subjects of both explanatory and outcome variables in accident analysis. Future research could be implemented to consider more driving conditions.     

正压空气呼吸器定期检验及存在问题分析

正压空气呼吸器是抢险救援的重要装备。本文对正压空气呼吸器的基本组成、工作原理进行了阐述，对长庆油田在用正压空气呼吸器使用现状和存在的问题进行了汇总和分析，提出了定期检验的内容、依据和程序．明确了开展定期检验是确保正压空气呼吸器正常使用、减少隐患的重要手段。     

应用BP神经网络进行隧道围岩快速分级

在隧道施工过程中,为准确、及时地进行围岩快速分级,引入BP神经网络方法,通过制定快速分级参数标准,对已经开挖的隧道工作面按照隧道围岩分级规范进行分级,并测量其快速分级参数,将围岩分级的结果与其对应的快速分级参数建立BP神经网络的训练集合,从而得到围岩分级模型。最后测量正在开挖隧道工作面的快速分级参数,并提供给模型进行判别,从而达到快速、精确分级目的。通过某隧道围岩样品实例验证,该模型判断结果与实际施工情况吻合,可用于指导施工阶段的隧道围岩快速分级。     

还原雷击参数的“黑匣子”——雷击在线实时监测记录仪研究

本文所研究的＂黑匣子＂是一种在线监测、记录、还原雷击参数的智能仪表,对特定环境设施（如：避雷针、低压供、配电线路与信息传输线路）入侵的雷击具备在线实时监测、记录并还原雷击参数的雷电流幅值、持续时间、极性（雷击电流走向）与波形的功能。     

Traffic Parameters Estimation to Predict Road Side Pollutant Concentrations using Neural Networks

The analysis aims to evaluate which is the most important among traffic parameters (flows, queues length, occupancy degree, and travel time) to forecast CO and C₆H₆ concentrations. The study area was identified by Notarbartolo Road and bounded by Libertà Street and Sciuti Street in the urban area of Palermo in Southern Italy. In this area, various loop detectors and one pollution-monitoring site were located. Traffic data related to the pollution-monitoring site immediately near the road link were estimated by Simulation of Urban MObility (SUMO) traffic microsimulator software using as input the flows measured by loop detectors on other links of road network. Traffic and weather data were used as input variables to predict pollutant concentrations by using neural networks. Finally, after a sensitivity analysis, it was showed that queues length were the mostly correlated traffic parameters to pollutant concentrations. An erratum to this article can be found at