The association between low alcohol use and traffic risk behaviors among Brazilian college students

Although there are a large number of studies focused on binge drinking and traffic risk behaviors (TRB), little is known regarding low levels of alcohol consumption and its association to TRB. The aim of this cross-sectional study is to examine the association of low to moderate alcohol intake pattern and TRB in college students in Brazil. 7037 students from a National representative sample were selected under rigorous inclusion criteria. All study participants voluntarily fulfilled a structured, anonymous, and self-questionnaire regarding alcohol and drug use, social-demographic data, and TRB. Alcohol was assessed according to the average number of alcoholic units consumed on standard occasions over the past 12 months. The associations between alcohol intake and TRB were summarized with odds ratio and their confidence interval obtained from logistic regression. Compared with abstainers students who consumed only one alcohol unit had the risk of being a passenger in a car driven by a drunk driver increased by almost four times, students who reported using five or more units were increased by almost five times the risk of being involved in a car crash. Compared with students who consumed one alcohol unit, the risk of driving under the influence of alcohol increased four times in students using three alcohol units. Age group, use of illicit drugs, employment status, gender, and marital status significantly influenced occurrence of TRB among college students. Our study highlights the potential detrimental effects of low and moderate pattern of alcohol consumption and its relation to riding with an intoxicated driver and other TRB. These data suggest that targeted interventions should be implemented in order to prevent negative consequences due to alcohol use in this population. (C) 2012 Elsevier Inc. All rights reserved,

A New Control Architecture for Robust Controllers in Rear Electric Traction Passenger HEVs

It is well known that control systems are the core of electronic differential systems (EDSs) in electric vehicles (EVs)/hybrid HEVs (HEVs). However, conventional closed-loop control architectures do not completely match the needed ability to reject noises/disturbances, especially regarding the input acceleration signal incoming from the driver's commands, which makes the EDS (in this case) ineffective. Due to this, in this paper, a novel EDS control architecture is proposed to offer a new approach for the traction system that can be used with a great variety of controllers (e. g., classic, artificial intelligence (AI)-based, and modern/robust theory). In addition to this, a modified proportional-integral derivative (PID) controller, an AI-based neuro-fuzzy controller, and a robust optimal H-infinity controller were designed and evaluated to observe and evaluate the versatility of the novel architecture. Kinematic and dynamic models of the vehicle are briefly introduced. Then, simulated and experimental results were presented and discussed. A Hybrid Electric Vehicle in Low Scale (HELVIS)-Sim simulation environment was employed to the preliminary analysis of the proposed EDS architecture. Later, the EDS itself was embedded in a dSpace 1103 high-performance interface board so that real-time control of the rear wheels of the HELVIS platform was successfully achieved.

RISK FOR INJURIES AMONG MOTORCYCLISTS INVOLVED IN TRAFFIC INCIDENTS

The objective of this study was to identify, among motorcyclists involved in traffic incidents, the factors associated with risk of injuries. In 2004, in the city of Maringa-PR, it was determined that there were a total of 2,362 motorcyclists involved in traffic incidents, according to records from the local Military Police. Multivariate analysis was applied to identify the factors associated with the presence of injury. A significantly higher probability of injury was observed among motorcyclists involved in collisions (odds Ratio = 11.19) and falls (odds Ratio = 3.81); the estimated odds ratio for females was close to four, and those involved in incidents including up to two vehicles were 2.63 times more likely to have injuries. Women involved in motorcycle falls and collisions with up to two vehicles stood out as a high-risk group for injuries.