Risk factors for driving into flooded roads

Motor vehicle-related deaths account for more than half of all flood fatalities in the United States, but to date, very little is known about the risk factors associated with why people drive into flooded roads. Using data from survey questionnaires administered in Denver, CO, and Austin, TX, this paper suggests that people who do not take warnings seriously are more likely to drive through flooded roads, as are people aged 18–35, and those that do not know that motor vehicles are involved in more than half of all flood fatalities. In Denver, people who have not experienced a flood previously and those who do not know they live in flood-prone areas are also more likely to drive into flooded roads.     

关于植物中氟化物测定结果的计算

针对植物中氟化物测定时由于绘制校准曲线的标准溶液体积与分析样品的定容体积不一样而引出的结果计算时的某些问题，提出了正确的计算方法和建议。     

基于社会科学统计程序(SPSS)回归性分析的尾矿库事故预测模型

基于社会科学统计程序(SPSS)软件的分析功能,对调查研究中获取的尾矿库案例进行数据提炼和分类编码,找出相关因子并进行回归性分析。最终目的是找出尾矿库各个因素的内在联系,建立简单的尾矿库事故模型,从而可以初步预测尾矿库事故发生的可能性。该预测模型为尾矿库事故的研究提出了新方法,对于防灾减灾以及保护人民生命财产安全起到了积极作用。     

中国省际能源效率差异及其影响因素分析

我国省际能源效率存在较大的差异.要进一步促进能源综合利用,需要在区域层面对能源效率差异形成的因素作更加全面深入的研究,以寻求更加合理有效的促进宏观能源效率改进的理论与方法.本文基于2007年的截面数据,运用能很好地消除各因素间多重共线性的偏最小二乘回归建模,选取了14个变量来代表经济发展水平、产业结构、工业结构、能源消费结构、对外开放程度、投资水平、政府影响力、制度因素、能源价格和地理因素这10个因素,分析了各因素对能源效率地区差异的影响方向和影响程度.研究结果表明,各地区固定资产投资中外商投资比重、工业增加值中高耗能产业比重以及煤炭消费比重的差异是造成能源效率地区差异显著的主要原因.这说明投资水平、工业结构和能源消费结构是造成能源效率地区差异显著的主要因素.要在短期内缩小地区间的能源效率差距,必须严格限制某些地区高耗能行业的过快发展,加强高耗能行业的结构调整,加快淘汰落后产能,大力提升煤炭的使用效率.     

室内甲醛污染影响因素的多元回归分析

本文在室内甲醛质量守恒定律基本公式合理推导的基础上，对影响甲醛浓度的主要因素做了正交实验。对实验数据进行多元回归分析，建立室内甲醛浓度预测模型。对模型做检验，检验结果表明，本文的预测模型能较好地预测室内空气中的甲醛浓度。     

Estimated Injury Risk for Specific Injuries and Body Regions in Frontal Motor Vehicle Crashes

Objective: Injury risk curves estimate motor vehicle crash (MVC) occupant injury risk from vehicle, crash, and/or occupant factors. Many vehicles are equipped with event data recorders (EDRs) that collect data including the crash speed and restraint status during a MVC. This study's goal was to use regulation-required data elements for EDRs to compute occupant injury risk for (1) specific injuries and (2) specific body regions in frontal MVCs from weighted NASS-CDS data.

Methods: Logistic regression analysis of NASS-CDS single-impact frontal MVCs involving front seat occupants with frontal airbag deployment was used to produce 23 risk curves for specific injuries and 17 risk curves for Abbreviated Injury Scale (AIS) 2+ to 5+ body region injuries. Risk curves were produced for the following body regions: head and thorax (AIS 2+, 3+, 4+, 5+), face (AIS 2+), abdomen, spine, upper extremity, and lower extremity (AIS 2+, 3+). Injury risk with 95% confidence intervals was estimated for 15–105 km/h longitudinal delta-Vs and belt status was adjusted for as a covariate.

Results: Overall, belted occupants had lower estimated risks compared to unbelted occupants and the risk of injury increased as longitudinal delta-V increased. Belt status was a significant predictor for 13 specific injuries and all body region injuries with the exception of AIS 2+ and 3+ spine injuries. Specific injuries and body region injuries that occurred more frequently in NASS-CDS also tended to carry higher risks when evaluated at a 56 km/h longitudinal delta-V. In the belted population, injury risks that ranked in the top 33% included 4 upper extremity fractures (ulna, radius, clavicle, carpus/metacarpus), 2 lower extremity fractures (fibula, metatarsal/tarsal), and a knee sprain (2.4–4.6% risk). Unbelted injury risks ranked in the top 33% included 4 lower extremity fractures (femur, fibula, metatarsal/tarsal, patella), 2 head injuries with less than one hour or unspecified prior unconsciousness, and a lung contusion (4.6–9.9% risk). The 6 body region curves with the highest risks were for AIS 2+ lower extremity, upper extremity, thorax, and head injury and AIS 3+ lower extremity and thorax injury (15.9–43.8% risk).

Conclusions: These injury risk curves can be implemented into advanced automatic crash notification (AACN) algorithms that utilize vehicle EDR measurements to predict occupant injury immediately following a MVC. Through integration with AACN, these injury risk curves can provide emergency medical services (EMS) and other patient care providers with information on suspected occupant injuries to improve injury detection and patient triage.     

On-Scene Injury Severity Prediction (OSISP) Algorithm for Truck Occupants

Objective: The aim of this study is to develop an on-scene injury severity prediction (OSISP) algorithm for truck occupants using only accident characteristics that are feasible to assess at the scene of the accident. The purpose of developing this algorithm is to use it as a basis for a field triage tool used in traffic accidents involving trucks. In addition, the model can be valuable for recognizing important factors for improving triage protocols used in Sweden and possibly in other countries with similar traffic environments and prehospital procedures.

Methods: The scope is adult truck occupants involved in traffic accidents on Swedish public roads registered in the Swedish Traffic Accident Data Acquisition (STRADA) database for calendar years 2003 to 2013. STRADA contains information reported by the police and medical data on injured road users treated at emergency hospitals. Using data from STRADA, 2 OSISP multivariate logistic regression models for deriving the probability of severe injury (defined here as having an Injury Severity Score [ISS] > 15) were implemented for light and heavy trucks; that is, trucks with weight up to 3,500 kg and ??16,500 kg, respectively. A 10-fold cross-validation procedure was used to estimate the performance of the OSISP algorithm in terms of the area under the receiver operating characteristic curve (AUC).

Results: The rate of belt use was low, especially for heavy truck occupants. The OSISP models developed for light and heavy trucks achieved cross-validation AUC of 0.81 and 0.74, respectively. The AUC values obtained when the models were evaluated on all data without cross-validation were 0.87 for both light and heavy trucks. The difference in the AUC values with and without use of cross-validation indicates overfitting of the model, which may be a consequence of relatively small data sets. Belt use stands out as the most valuable predictor in both types of trucks; accident type and age are important predictors for light trucks.

Conclusions: The OSISP models achieve good discriminating capability for light truck occupants and a reasonable performance for heavy truck occupants. The prediction accuracy may be increased by acquiring more data. Belt use was the strongest predictor of severe injury for both light and heavy truck occupants. There is a need for behavior-based safety programs and/or other means to encourage truck occupants to always wear a seat belt.     

基于二值逻辑的复合绝缘子状态判别策略研究

复合绝缘子是架空输电线路上广泛使用的设备,它的老化会给电力系统的正常运行带来威胁。电力相关工作人员需要一种能够对复合绝缘子的运行状态进行判别的方法,帮助他们确定复合绝缘子是否满足运行要求,而对复合绝缘子的定性表征就成为一种优选的表征策略。将运行后的复合绝缘子分成"满足运行要求"和"不满足运行要求",使用二值逻辑回归模型,选取合适的特征量,建立了复合绝缘子状态表征体系。     

回采工作面瓦斯涌出量遗传投影寻踪回归预测

为快速、准确预测回采工作面瓦斯涌出量,基于投影降维思想,建立一种遗传算法(GA)投影寻踪回归预测方法。选取煤层瓦斯原始含量、埋藏深度、煤层厚度、煤层倾角、工作面长度、推进速度、采出率、临近层瓦斯含量、临近层厚度、临近层层间距、岩层岩性、开采深度作为评价因子,对某矿15个学习样本进行训练,建立GA投影寻踪回归预测模型。利用该矿3个实测样本对模型进行检验,并与主成分分析和BP神经网络方法结果进行对比。研究表明:利用GA投影寻踪回归预测回采工作面瓦斯涌出量,平均误差为3.43%,最大误差为5.7%,精度优于其他2种方法。