Hot corrosion Type II of FeCr‐based model alloys for boiler and heat exchanger applications

The hot corrosion Type II of the alloys FeCr20, FeCr20Ni10, FeCr20Ni20, and FeCr20Co10 is investigated at 700°C in air + 0.5% SO₂ with deposits consisting of Na₂SO₄ and a eutectic mixture of Na₂SO₄ and MgSO₄ for 24, 100, and 300 h. The alloying elements nickel and cobalt have a positive influence when tests are conducted using a MgSO₄‐Na₂SO₄ deposit. In this case, they reduce the metal loss and increase the time to the propagation stage. In contrast, when the alloys are exposed with a Na₂SO₄ deposit, these alloying elements increase the metal loss and allow for the transition to the propagation stage because they can form molten phases with the Na₂SO₄. During the incubation stage an oxide scale forms on the FeCr20 alloy, which is thicker than the one formed during exposure without a deposit, and iron oxides are observed, which precipitate in the deposit. The propagation stage occurs by a dissolution and precipitation mechanism forming localized pitting attack. Iron is the main species that dissolves and precipitates, while chromium remains mainly as an oxide beneath the initial surface. The additional elements are found in the pit and in the salt deposit.     

Thermal-lens and photo-acoustic methods for the determination of SiC thermal properties

In this paper we report the use of photothermal techniques such as Thermal lens (TL) spectrometry, Photoacoustic and heat capacity, ρc_p, to determine the thermo-optical parameters, such as thermal conductivity (K), thermal diffusivity (D), specific heat (c_p) and the optical path dependence with temperature (ds/dT), of an undoped polycrystalline 3C-SiC. To our knowledge, this is the first time that Thermal lens technique is used for wide band-gap systems. Results obtained for the polycrystalline sample with TL technique indicates that ds/dT is negative at room temperature. Moreover, the obtained values of thermal diffusivity and thermal conductivity are in good agreement with that found in the literature, indicating that the phototermal techniques can be used to obtain the referred parameters in circumstances where other techniques cannot be used, for example, in harsh environments.     

Learning user preferences of route choice behaviour for adaptive route guidance

As the use of navigation systems becomes more widespread, the demand for advanced functions of navigation systems also increases. In the light of user satisfaction, personalisation of route guidance by incorporating user preferences is one of the most desired features. A user model applied to personalised route guidance is presented. The user model adaptively updates route selection rules when it discovers the predicted choice differs from the actual choice of the driver. This study employs a decision tree learning algorithm, the C4.5 algorithm, which has advantages over other data mining methods in terms of its comprehensible model structure. Simulation experiments with a real-world network were conducted to analyse the applicability of the model to adaptive route guidance and the accuracy of its prediction     

An experiment for showing some kind of artificial understanding

In this paper we design an experiment which can be depicted as a simple scenario, a very limited 'world'. In this world, there are an actor that can pursue a project and an observer that is keeping its eyes on the actor. We try to show in the experiment that the observer can to some degree understand the actor based on its knowledge and some metaphors, i.e. understand what the actor is doing and why. As the conclusion of this experiment, we try to show some features of 'understanding'. These are (1) that 'understanding' has to be based on some preliminary knowledge; (2) that 'understanding' is a process of incremental learning; (3) that, as for symbolic systems, some metaphors are necessary for mapping real entities into concepts in mind.