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。     

基于地理加权回归模型评估土地利用对地表水质的影响

针对传统线性回归模型大多忽视空间数据局部变化特征这一缺陷,引入地理加权回归模型(GWR)用于评估土地利用对地表水质的影响,分析了不同子流域内两者关系出现空间变化的规律并阐释了原因.同时,对比了GWR模型与普通最小二乘模型(OLS)的校正R2、Akaike信息准则(AICc)及残差的空间自相关指数(Moran's I),验证了GWR模型在预测精度和处理空间自相关过程中是否优于OLS模型.结果表明,同一土地利用类型对水质的影响随空间位置的改变而发生方向或大小的变化.以温瑞塘河流域总氮(TN)与农用地的关系为例,从GWR模型局部回归系数的方向分析,两者关系表现为农村正、城区负的现象,从大小分析,旧城区TN与农用地回归系数的绝对值高于其它区域;在溶解氧(DO)与人口密度所构建的GWR模型中,两者关系在整个研究区域内均表现为负值,与OLS结果吻合,从回归系数的大小分析,人口密度对DO的作用在郊区及农村更为显著.针对此类关系出现空间变化的原因分析表明,相邻子流域土地利用百分比的改变及水体主要污染源的不同,是导致土地利用对水质作用发生变化的根本因素.最后,对比所构建的80个GWR与OLS模型校正R2、AICc指标,验证了GWR作为一种局部统计模型,其预测精度优于OLS等传统全局模型且更能反映实际空间特征.     

Stepwise multiple regression method of greenhouse gas emission modeling in the energy sector in Poland

The energy sector in Poland is the source of 81% of greenhouse gas (GHG) emissions. Poland, among other European Union countries, occupies a leading position with regard to coal consumption. Polish energy sector actively participates in efforts to reduce GHG emissions to the atmosphere, through a gradual decrease of the share of coal in the fuel mix and development of renewable energy sources. All evidence which completes the knowledge about issues related to GHG emissions is a valuable source of information. The article presents the results of modeling of GHG emissions which are generated by the energy sector in Poland. For a better understanding of the quantitative relationship between total consumption of primary energy and greenhouse gas emission, multiple stepwise regression model was applied. The modeling results of CO₂ emissions demonstrate a high relationship (0.97) with the hard coal consumption variable. Adjustment coefficient of the model to actual data is high and equal to 95%. The backward step regression model, in the case of CH₄ emission, indicated the presence of hard coal (0.66), peat and fuel wood (0.34), solid waste fuels, as well as other sources (− 0.64) as the most important variables. The adjusted coefficient is suitable and equals R² = 0.90. For N₂O emission modeling the obtained coefficient of determination is low and equal to 43%. A significant variable influencing the amount of N₂O emission is the peat and wood fuel consumption.     

Stepwise multiple regression method of greenhouse gas emission modeling in the energy sector in Poland

The energy sector in Poland is the source of 81% of greenhouse gas (GHG) emissions. Poland, among other European Union countries, occupies a leading position with regard to coal consumption. Polish energy sector actively participates in efforts to reduce GHG emissions to the atmosphere, through a gradual decrease of the share of coal in the fuel mix and development of renewable energy sources. All evidence which completes the knowledge about issues related to GHG emissions is a valuable source of information. The article presents the results of modeling of GHG emissions which are generated by the energy sector in Poland. For a better understanding of the quantitative relationship between total consumption of primary energy and greenhouse gas emission, multiple stepwise regression model was applied. The modeling results of CO₂ emissions demonstrate a high relationship (0.97) with the hard coal consumption variable. Adjustment coefficient of the model to actual data is high and equal to 95%. The backward step regression model, in the case of CH₄ emission, indicated the presence of hard coal (0.66), peat and fuel wood (0.34), solid waste fuels, as well as other sources (-0.64) as the most important variables. The adjusted coefficient is suitable and equals R² = 0.90. For N₂O emission modeling the obtained coefficient of determination is low and equal to 43%. A significant variable influencing the amount of N₂O emission is the peat and wood fuel consumption.     

乌梁素海Chl-a时空分布及相关水质因子研究

根据2010年1月-2015年11月乌梁素海水质因子监测数据,分析其Chl-a的时空分布及其与主要水质因子的相互关系.结果表明:河套灌区农田退水对乌梁素海Chl-a浓度变化产生较大影响,入口区高于湖心区与出口区.从空间分布上来看,Chl-a浓度分布呈现出入口区>湖心区>出口区的趋势;从时间分布上来看,呈现5月份>7月份>3月份>11月份>9月份>1月份,枯水期>丰水期>平水期.在采样时间段内,Chl-a与NO3-N与NO3-含量比成正比.     

在用轻型汽油车排放水平及趋势变化拟合研究

基于佛山市2.7万条稳态加载模拟工况法(ASM)的尾气排放检测数据,在分析了总体排放劣化特征随行驶里程呈规律性变化的基础上,通过分类统计和回归分析方法研究了在用轻型汽油车的排放劣化增长模型及不同排放标准机动车的排放特征.分析结果表明,线性增长模型能很好地表现CO,HC,NO三种污染物随行驶里程的劣化规律;不同排放标准的轻型汽油车排放特征差异很大,国零、Ⅰ、Ⅱ排放水平很高,对总体排放影响较大.研究结论对于预测机动车污染变化趋势,完善在用车检查/维护制度等方面可以提供理论支持.     

有机化学品不同温度下(过冷)液体蒸气压预测模型的建立与评价

(过冷)液体蒸气压(PL)是评价化学品在环境中分配、迁移和归趋行为的重要参数。PL具有较强的温度依附性。发展一种能够精确预测不同环境温度下化学品PL的方法,有助于填补化学品生态风险评估的大量数据缺失。本研究收集整理了661种有机化合物在不同温度下(200~830 K)共计10 478个log PL值。在此基础上,采用偏最小二乘(PLS)回归和支持向量机(SVM)方法,构建了PL的线性和非线性预测模型。结果表明:2种模型均具有良好的拟合度、稳健性及预测能力,SVM模型的预测性能略高于PLS模型(PLS:R2adj.tra=0.912,RMSEtra=0.477,Q2ext=0.910;SVM:R2adj.tra=0.997,RMSEtra=0.092,Q2ext=0.967)。机理分析表明,温度是影响PL的主要因素,温度越高,蒸气压越大;其次,X1sol也影响PL大小,X1sol用来描述分子间的色散作用,分子间色散力越小,蒸气压越大;此外,化合物的氢键个数、极性和分子构型等因素也影响PL大小。采用Wiliams plot方法表征了PLS模型应用域。所建立的模型可用来预测烷烃、烯烃、醇、酮、羧酸、苯、酚、联苯、卤代芳香烃、含N化合物及含S化合物在不同温度下的PL数据。     

Multi-objective transportation route optimization for hazardous materials based on GIS

Safety and security are of paramount importance, it is important to optimize and improve the routes of trucks that carry hazardous materials. In this study, we not only ensure the risk in the network, but also consider the transportation cost and the factors such as buildings and emergency facilities around the routes. The Geographic Information System (GIS) is used to quantify the factors on each section in the network. We present an epsilon constrained multi-objective mixed-integer linear programming optimization model to find the robust and stable transportation optimization solutions. At the end, we complete a case analysis of the proposed methodology to determine the motorway segments in Jiangsu province, China and test the above algorithm on the network, which has 144 nodes and 388 sections. The results we get show that the factors of buildings play a very important role in the model, and the multi-objective mixed-integer linear optimization model is reasonable and performs good quality.