Search results

Driving needs high levels of alertness. Increased somnolence is the most important negative influence to maintain proper watchfulness and vigilance. Drowsiness, working conditions and their affective impact must be taken into account to determine driving safety. The purpose of this paper is to assess excessive daytime sleepiness, sleep habits, quality of sleep, stress-related symptoms, and working conditions in a large sample of short-distance bus drivers in the city of Buenos Aires.

This was a cross-sectional study performed to evaluate sleep habits and obstructive apnea risk in short-distance bus drivers of the Metropolitan Area of Buenos Aires, Argentina. Questionnaires regarding anthropometric data, sleep habits, snoring, daytime sleepiness (Epworth Sleepiness Scale), quality of sleep (Pittsburgh Quality of Sleep Index, PQSI), working conditions and fatigue and anxiety related to work were administered to professional short-distance bus drivers (n=1023).

A prevalence of 34.6 percent of obesity and 80 percent of snoring was observed. Mean sleep time during workdays was 6.5±0.1 h and bad sleep quality was reported by 54.6 percent of the subjects. Excessive daytime sleepiness had a prevalence of 48.8 percent and was independently associated with reduced sleep time, increased sleep debt, long time to wake up, snoring, and short resting time along the working day (p<0.05).

Short-distance drivers in Buenos Aires, the largest urban area of Argentina, are a partially sleep-deprived, overweighted population, showing a high daytime somnolence, poor work-rest conditions and high levels of anxiety and fatigue. This association can be very harmful in view of the demanding working conditions considered.

The purpose of this study is to evaluate the efficiency of a Brazilian steelmaking company’s reheating process of the hot rolling mill.

The research method is a quantitative modeling. The main research techniques are data envelopment analysis, TOBIT regression and simulation supported by artificial neural networks. The model’s input and output variables consist of the average billet weight, number of billets processed in a batch, gas consumption, thermal efficiency, backlog and production yield within a specific period. The analysis spans 20 months.

The key findings include an average current efficiency of 81%, identification of influential variables (average billet weight, billet count and gas consumption) and simulated analysis. Among the simulated scenarios, the most promising achieved an average efficiency of 95% through increased equipment availability and billet size.

Additional favorable simulated scenarios entail the utilization of higher pre-reheating temperatures for cold billets, representing a large amount of savings in gas consumption and a reduction in CO2 emissions.

This study’s primary innovation lies in providing steelmaking practitioners with a systematic approach to evaluating and enhancing the efficiency of reheating processes.