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,

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.

Analysis of NDVI time series using cross-correlation and forecasting methods for monitoring sugarcane fields in Brazil

Brazil is the largest sugarcane producer in the world and has a privileged position to attend to national and international market places. To maintain the high production of sugarcane, it is fundamental to improve the forecasting models of crop seasons through the use of alternative technologies, such as remote sensing. Thus, the main purpose of this article is to assess the results of two different statistical forecasting methods applied to an agroclimatic index (the water requirement satisfaction index; WRSI) and the sugarcane spectral response (normalized difference vegetation index; NDVI) registered on National Oceanic and Atmospheric Administration Advanced Very High Resolution Radiometer (NOAA-AVHRR) satellite images. We also evaluated the cross-correlation between these two indexes. According to the results obtained, there are meaningful correlations between NDVI and WRSI with time lags. Additionally, the adjusted model for NDVI presented more accurate results than the forecasting models for WRSI. Finally, the analyses indicate that NDVI is more predictable due to its seasonality and the WRSI values are more variable making it difficult to forecast.

Hybrid Water Demand Forecasting Model Associating Artificial Neural Network with Fourier Series

This paper addressed the problem of water-demand forecasting for real-time operation of water supply systems. The present study was conducted to identify the best fit model using hourly consumption data from the water supply system of Araraquara, Sa approximate to o Paulo, Brazil. Artificial neural networks (ANNs) were used in view of their enhanced capability to match or even improve on the regression model forecasts. The ANNs used were the multilayer perceptron with the back-propagation algorithm (MLP-BP), the dynamic neural network (DAN2), and two hybrid ANNs. The hybrid models used the error produced by the Fourier series forecasting as input to the MLP-BP and DAN2, called ANN-H and DAN2-H, respectively. The tested inputs for the neural network were selected literature and correlation analysis. The results from the hybrid models were promising, DAN2 performing better than the tested MLP-BP models. DAN2-H, identified as the best model, produced a mean absolute error (MAE) of 3.3 L/s and 2.8 L/s for training and test set, respectively, for the prediction of the next hour, which represented about 12% of the average consumption. The best forecasting model for the next 24 hours was again DAN2-H, which outperformed other compared models, and produced a MAE of 3.1 L/s and 3.0 L/s for training and test set respectively, which represented about 12% of average consumption. DOI: 10.1061/(ASCE)WR.1943-5452.0000177. (C) 2012 American Society of Civil Engineers.