Comparative analysis of data reduction techniques for questionnaire validation using self-reported driver behaviors

Introduction: Exploratory data reduction techniques, such as Factor Analysis (FA) and Principal Component Analysis (PCA), are widely used in questionnaire validation with ordinal data, such as Likert Scale data, even though both techniques are indicated to metric measures. In this context, this study presents an e-survey, conducted to obtain self-reported behaviors between Brazilian drivers (N = 1,354, 55.2% of males) and Portuguese drivers (N = 348, 46.6% of males) based on 20 items from the Driver Behavior Questionnaire (DBQ) on a five-point Likert Scale. This paper aimed to examine DBQ validation using FA and PCA compared to Categorical Principal Component Analysis (CATPCA) which is more indicative to use with Likert Scale data. Results: The results from all techniques confirmed the most replicated factor structure of DBQ, distinguishing behaviors as errors, ordinary violations, and aggressive violation. However, after Varimax rotation, CATPCA explained 11% more variance compared to FA and 2% more than PCA. We identified cross-loadings among the component of the techniques. An item changed its dimension in the CATPCA results but did not change the structural interpretability. Individual scores from dimension 1 of CATPCA were significantly different from FA and PCA. Individual scores from factor 1 of CATPCA were significantly different from FA and PCA. Practical applications: The CATPCA seems to be more advantageous in order to represent the original data and considering data constrains. In addition to finding an interpretable factorial structure, the representation of the original data is regarded as relevant since the factor scores could be used for crash prediction in future analyses.     

Development of machine learning based prediction models for hazardous properties of chemical mixtures

Lower flammability limit (LFL), upper flammability limit (UFL), auto-ignition temperature (AIT) and flash point (FP) are crucial hazardous properties for fire and explosion hazards assessment and consequence analysis. In this study, a comprehensive prediction model set was constructed by using expanded chemical mixture databases of chemical mixture hazardous properties. Machine learning based gradient boosting quantitative structure-property relationship (GB-QSPR) method is implemented for the first time to improve the model performance and prediction accuracy. The result shows that all developed models have significantly higher accuracy than other regular QSPR models, with the 5-fold cross-validation RMSE of LFL, UFL, AIT, and FP models being 1.06, 1.14, 1.08, and 1.17, respectively. All developed QSPR models can be used to estimate reliable chemical mixture hazardous properties and provide useful guidance in chemical mixture hazard assessment and consequence analysis.     

基于TiO2载体的锰铈系低温SCR脱硝催化剂研究进展

开发低温、高活性、高抗硫抗水性能的选择性催化还原(SCR)脱硝催化剂已成为国内外的研究热点。综述了基于TiO2载体的锰铈系低温脱硝催化剂的脱硝性能,探讨了助剂掺杂改性、制备方法、反应条件等对催化剂脱硝性能的影响,总结了现有低温脱硝催化剂的技术难点,指出SCR催化剂的研究需要着重考虑以下几方面:深入研究催化剂的反应机理和中毒机理;研究催化剂的循环再生;探究拓宽催化剂温度窗口,使其适应不同的温度条件。     

Assessing Spatial, Temporal, and Analytical Variation of Groundwater Chemistry in a Large Nuclear Complex, USA

Statistical analyses were applied at the Hanford Site, USA, to assess groundwater contamination problems that included (1) determining local backgrounds to ascertain whether a facility is affecting the groundwater quality and (2) determining a ‘pre-Hanford' groundwater background to allow formulation of background-based cleanup standards. The primary purpose of this paper is to extend the random effects models for (1) assessing the spatial, temporal, and analytical variability of groundwater background measurements; (2) demonstrating that the usual variance estimate s ², which ignores the variance components, is a biased estimator; (3) providing formulas for calculating the amount of bias; and (4) recommending monitoring strategies to reduce the uncertainty in estimating the average background concentrations. A case study is provided. Results indicate that (1) without considering spatial and temporal variability, there is a high probability of false positives, resulting in unnecessary remediation and/or monitoring expenses; (2) the most effective way to reduce the uncertainty in estimating the average background, and enhance the power of the statistical tests in general, is to increase the number of background wells; and (3) background for a specific constituent should be considered as a statistical distribution, not as a single value or threshold. The methods and the related analysis of variance tables discussed in this paper can be used as diagnostic tools in documenting the extent of inherent spatial and/or temporal variation and to help select an appropriate statistical method for testing purposes.     

Temperature Effect on Migration of Zn and Cd Through Natural Clay

To investigate the effect of temperature on effective diffusion coefficients and retardation factors for Zn and Cd, combined diffusion and sequential extraction analyses were conducted at 15 ^˚C and 55 ^˚C. The effective diffusion coefficients of the metals increased up to ten times according to the increased temperature. On the other hand, the effect of temperature on the retardation factor depended on the retention mechanisms of the metals. The distribution coefficient for Zn, which was mainly partitioned in the carbonate phase, increased up to two times with the increase in temperature. On the other hand, the distribution coefficient for Cd, which was mainly partitioned in the exchangeable phase, was hardly affected by the temperature change. Results of combined diffusion and sequential extraction analysis showed that the effect of temperature on the heavy metals’ (Zn and Cd) migration through the compacted natural clay was influenced by the combined effects of the diffusion coefficient and the retardation factor. Additionally, we could also observe the change in retention mechanism for the metals with the change in pore water concentration.     

天津地区集装箱内部低温环境条件研究

以天津地区冬季采用集装箱储存货物为背景,在天津对集装箱内部温度和大气温度等数据进行测试,对数据进行回归分析后,建立集装箱内部温度模型,预测出该地区集装箱内部最恶劣的低温极值范围,为危险货物集装箱安全储运提供理论依据,并提出了相应的对策及建议。     

冬季动态槽式堆肥温度的空间变异

以鲜鸡粪、蘑菇渣和污泥按照体积比3∶1∶1混合进行动态堆肥模拟试验.堆肥槽沿物料前进方向分为7个部分,对每个部分按等间距分别做5个水平方向上切分和5个垂直方向上切分,在形成的125个交叉点上进行温度监测.研究结果表明,第1天的混合物料温度在同一层中变异很小,不同层之间略有差异.随着动态过程的进行,同一层温度变异逐渐增大,从第一天相差1～3℃,增加到相差30～40℃,靠近墙体的堆料温度较低,远离墙体的温度较高.随着堆肥时间延长,差异增大.机械翻堆能起到通风的作用,同时使每一个堆方的堆料在纵向方向上上下混合,但达不到横向混匀,因此,靠近墙体两侧的堆料始终处于较低的温度,只有中部能达到较高的温度,以堆肥温度50℃作为无害化指标,自墙体向中心方向的1m为没有达到无害化厚度,无害化体积占堆肥总体积50%.整个动态堆肥过程符合二级动力学方程.     

城市污泥-磷石膏陶粒的制备条件及性能分析

以城市污水处理厂剩余污泥和磷酸生产废渣磷石膏为原料制备多孔陶粒,考察不同配比、烧结温度及烧结时间对多孔陶粒堆积密度、吸水率及盐酸可溶率的影响。结果表明:在污泥与磷石膏混合比1∶4、烧结温度1 050℃、烧结时间15 min的条件下,可制得堆积密度为685.78 kg/m~3、吸水率27.34%、盐酸可溶率11.38%的陶粒,该陶粒内部含有丰富的膨胀气孔,可用作水处理滤料或建材骨料。     

火源位置对L型管廊电缆火灾影响规律的数值模拟研究

选取某城市L型综合管廊电缆舱为研究对象,采用FDS数值模拟软件研究了不同火源位置对L型管廊电缆火灾温度纵向衰减规律、烟气浓度分布规律及烟气危害性的影响。研究结果表明,L型廊道构型影响了不同火源位置的管廊电缆火灾最高温度纵向衰减的连续性,基于热边界层理论提出了适用于L型管廊的二维平面最高温度纵向衰减模型。基于峰宽时间计算了L型管廊火灾的烟气总危害性参数,不同火源位置的烟气危害性总在靠近管廊节点位置处最低。这些结果可对综合管廊的消防设计与火灾防控提供参考。