Objective: This study examined the risk factors of driving under the influence of alcohol (DUI) among drivers of specific vehicle categories (DSC). On the basis of this research, the variables related to DUI and involvement in traffic crashes were defined. The analysis was conducted for car drivers, bicyclists, motorcyclists, bus drivers, and truck drivers.
Method: The research sample included drivers involved in traffic crashes on the territory of Serbia in 2016 (60,666). Two types of analyses were conducted in this study. Logistic regression established the correlation between DUI and DSC and the The Technique for Order of Preference by Similarity to Ideal Solution (Multi-criteria decision making) method was applied to consider the scoring and explore the potential for the prevalence of DUI on the basis of 2 data sets (DUI and non DUI).
Results: The study results showed that driver error and male drivers were the 2 most significant risk factors for DUI, with the highest scores and potential for prevalence. The nonuse of restraint systems, driver experience, and driver age are the factors with a significant prediction of involvement in an accident and an insignificant prediction of DUI.
Conclusions: Following the development of the logistic prediction models for DUI drivers, testing of the model was conducted for 3 control driver groups: Car, motorcycle, and bicycle. The prediction model with a probability greater than 50% showed that 77% of car drivers were under the influence of alcohol. Similarly, the prediction percentage for motorcyclists and bicyclists amounted to 71 and 67%, respectively. The recommendation of the study is that drivers whose DUI probability is above 50% should be potentially suspected of DUI. The results of this study can help to understand the problem of DUI among specific driver categories and detect DUI drivers, with the aim of creating successful traffic safety policy. 相似文献
Different synthesis methods were applied to determine optimal conditions for polymerization of (3S)-cis-3,6-dimethyl-1,4-dioxane-2,5-dione (l-lactide), in order to obtain poly(l-lactide) (PLLA). Bulk polymerizations (in vacuum sealed vessel, high pressure reactor and in microwave field) were performed
with tin(II) 2-ethylhexanoate as the initiator. Synthesis in the vacuum sealed vessel was carried out at the temperature of
150 °C. To reduce the reaction time second polymerization process was carried out in the high pressure reactor at 100 °C and
at the pressure of 138 kPa. The third type of rapid synthesis was done in the microwave reactor at 100 °C, using frequency
of 2.45 GHz and power of 150 W at the temperature of 100 °C. The temperature in this method was controlled via infrared system
for in-bulk measuring. The solution polymerization (with trifluoromethanesulfonic acid as initiator) was possible even at
the temperature of 40 °C, yielding PLLA with narrow molecular weight distribution in a very short period of time (less than
6 h). The obtained polymers had the number-average molecular weights ranging from 43,000 to 178,000 g mol−1 (polydispersity index ranging from 1 to 3) according to the gel permeation chromatography measurements. The polymer structure
was characterized by Fourier transform infrared and NMR spectroscopy. Thermal properties of the obtained polymers were investigated
using thermogravimetry and differential scanning calorimetry. 相似文献
Ground level air pollution, especially fine particulate matter (PM2.5), has been associated with a number of adverse health effects. The dispersion of PM2.5 through the atmosphere depends on several mutually connected anthropogenic, geophysical and meteorological parameters, all of which are affected by climate change. This study examines how projected climate change would affect population exposure to PM2.5 air pollution in Poland. Population exposure to PM2.5 in Poland was estimated for three decades: the 1990s, 2040s and 2090s. Future climate conditions were projected by Regional Climate Model RegCM (Beta), forced by the general atmospheric circulation model ECHAM5. The dispersion of PM2.5 was simulated with chemical transport model CAMx version 4.40. Population exposure estimates of PM2.5 were 18.3, 17.2 and 17.1 μg/m3 for the 1990s, 2040s and 2090s, respectively. PM2.5 air pollution was estimated to cause approximately 39,800 premature deaths in the population of Poland in the year 2000. Our results indicate that in Poland, climate change may reduce the levels of exposure to anthropogenic particulate air pollution in future decades and that this reduction will reduce adverse health effects caused by the air pollution. 相似文献
