From ancient times dice have been used to denote randomness. A dice throw experiment is set up in order to examine the predictability of the die orientation through time using visualization techniques. We apply and compare a deterministic-chaotic model and a stochastic model and we show that both suggest predictability in die motion that deteriorates with time, just as in hydro-meteorological processes. Namely, a die's trajectory can be predictable for short horizons and unpredictable for long ones. Furthermore, we show that the same models can be applied, with satisfactory results, to high temporal resolution time series of rainfall intensity and wind speed magnitude, occurring during mild and strong weather conditions. The difference among the experimental and two natural processes is in the time length of the high-predictability window, which is of the order of 0.1 s, 10 min and 1 h for dice, rainfall and wind processes, respectively.
The increase of available data on household water consumption is expected to bring a new awareness of household water practices. Such awareness needs to be further supported by customized advice to the household. This paper presents the development of a Moodle-based eLearning platform that aims to support end-users to understand, manage and hopefully change their water and energy consumption profile. The paper presents the structure and components of the platform, including, inter alia, FAQ's, quizzes and tips whereas more focus is given on the two most advanced web application for the exploration of domestic water demand profile. The course structure is further informed by a qualitative survey on customers' cognition on the use of web services.
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