Water model experiments were performed in a full-scale, delta-shaped water model tundish, in order to study the removal of inclusions by micro-bubbles. Micro-bubbles were generated using a specially designed ladle shroud with twelve laser-drilled orifices. Gas flow rates, injection positions and multi-port injection were all taken into consideration to create different bubble conditions. Bubbles were recorded using a high speed camera and post-processed with commercial software, Image J. Hollow glass borosilicate microspheres, smaller than 100 μm, were used to simulate inclusions, and detected, in-situ, using a new generation of the Aqueous Particle Sensor, APS III. The results revealed that the effect of microbubbles on inclusion removal depends greatly on the gas injection protocols used. The optimum gas flow rate was an intermediate value, which indicates a minimum particle number density, n p , of about 7.85/ml. This results from the counter-balancing effects of bubble sizes against the total number of bubbles. The highest inclusion removal rate was 80%, when gas was injected through the four ports located closest to the slide gate, at a gas flow rate of 0.2 L/min.

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IntroductionSpeech comprehension involves context-based lexical predictions for efficient semantic integration. This study investigated how noise affects the predictability effect on event-related potentials (ERPs) such as the N400 and late positive component (LPC) in speech comprehension.MethodsTwenty-seven listeners were asked to comprehend sentences in clear and noisy conditions (hereinafter referred to as “clear speech” and “noisy speech,” respectively) that ended with a high-or low-predictability word during electroencephalogram (EEG) recordings.ResultsThe study results regarding clear speech showed the predictability effect on the N400, wherein low-predictability words elicited a larger N400 amplitude than did high-predictability words in the centroparietal and frontocentral regions. Noisy speech showed a reduced and delayed predictability effect on the N400 in the centroparietal regions. Additionally, noisy speech showed a predictability effect on the LPC in the centroparietal regions.DiscussionThese findings suggest that listeners achieve comprehension outcomes through different neural mechanisms according to listening conditions. Noisy speech may be comprehended with a second-pass process that possibly functions to recover the phonological form of degraded speech through phonetic reanalysis or repair, thus compensating for decreased predictive efficiency.