A text retrieval system for language learning returns reading materials at the appropriate difficulty level for the user. The system typically maintains a learner model on the user's vocabulary knowledge, and identifies texts that best fit the model.
As the user's language proficiency increases, model updates are necessary to retrieve texts with the corresponding lexical complexity. We investigate an open learner model that allows user modification of its content, and evaluate its effectiveness with respect to the amount of user update effort. We compare this model with the graded approach, in which the system returns texts at the optimal grade. When the user makes at least half of the expected updates to the open learner model, simulation results show that it outperforms the graded approach in retrieving texts that fit user preference for new-word density.
In this research a performance of TurboExpander and Joule-
Thomson valve will be compared in a proposed system developed
in order to recover flare gas in oil fields outstations which not
connected to any gas plant and burns continually the entire
associated gases in the flare, and reuses the mentioned gases in
central process facilities as a fuel in gas turbines which use diesel
(as they have dual system gas-diesel), while the associated gases in
related outstations are burned.
