SUMMAR Y The aim of the present study was to evaluate time-on-task effects on subjective fatigue in two different tasks of varying monotony during night-time testing (20:00 to 4:00 hours) in a sleep deprivation intervention. The experiment included eight test runs separated by breaks of approximately 20 min. Twenty healthy volunteers performed a driving simulator and the Mackworth clock vigilance task in four of the test runs each. Sequence of tasks was varied across subjects. Before and after each task, subjective sleepiness was assessed by means of the Karolinska sleepiness scale and subjective fatigue was rated on the Samn-Perelli checklist. Fatigue and sleepiness significantly increased over the course of the night. Both tasks led to an increase in fatigue and sleepiness across test runs. However, this time-on-task effect was larger in the vigilance than in the driving simulator task. It is important to note that fatigue and sleepiness in one test run were not influenced by the task performed in the preceding test run, that is there were no cross-over effects. The results suggest that time-on-task effects superimpose circadian and sleep-related factors affecting fatigue. They depend on the monotony of the task and can be quantified by means of a design including separate test runs divided by breaks.k e y w o r d s driving simulator, fatigue, model, time-on-task, vigilance

Time series data such as monthly stream flows can be modelled using time series methods and then used to simulate or forecast flows for short term planning. Two methods of time series modelling were reviewed and compared: the well-known auto regressive moving average (ARMA) method and the state-space time-series (SSTS) method. ARMA has been used in hydrology to model and simulate flows with good results and is widely accepted for this purpose. SSTS modelling is a more recently developed method that is relatively unused for hydrologic modelling. This paper focuses on modelling the stream flows from basins of different sizes using these two time series modelling methods and comparing the results. Three rivers in Labrador and South-East Quebec were modelled: the Romaine, Ugjoktok and Alexis Rivers. Both models were compared for accuracy of prediction, ease of software use and simplicity of model to determine the preferred time series methodology approach for modelling these rivers. The SSTS was considered very easy to use but model diagnostics were found to require a high level of statistical understanding. Ultimately, the ARMA method was determined to be the better method for the typical engineer to use, considering the diagnostics were simple and the monthly flows could be easily simulated to verify results.

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