Engagement is described as a state in which an individual involved in an activity can ignore other influences. The engagement level is important to obtaining good performance especially under study conditions. Numerous methods using electroencephalograph (EEG), electrocardiograph (ECG), and near-infrared spectroscopy (NIRS) for the recognition of engagement have been proposed. However, the results were either unsatisfactory or required many channels. In this study, we introduce the implementation of a low-density hybrid system for engagement recognition. We used a two-electrode wireless EEG, a wireless ECG, and two wireless channels NIRS to measure engagement recognition during cognitive tasks. We used electrooculograms (EOG) and eye tracking to record eye movements for data labeling. We calculated the recognition accuracy using the combination of correlation-based feature selection and k-nearest neighbor algorithm. Following that, we did a comparative study against a stand-alone system. The results show that the hybrid system had an acceptable accuracy for practical use (71.65 ± 0.16%). In comparison, the accuracy of a pure EEG system was (65.73 ± 0.17%), pure ECG (67.44 ± 0.19%), and pure NIRS (66.83 ± 0.17%). Overall, our results demonstrate that the proposed method can be used to improve performance in engagement recognition.

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CaO-based slag used in hot metal pretreatment and converters in steelmaking processes typically contains dispersed gas phases. This is called foaming slag, which is known to degrade the quality of slag. The rheological behavior of this slag is dependent on the dispersed part of the gas phase. This gas is generated by the chemical reaction between the hot metal and the slag. In this study, simulated foaming slag was prepared by reacting sodium hydrogen carbonate and oxalic acid in glycerol, which disperses carbon dioxide. Next, we systematically investigated the effects of the volume fraction of the dispersed gas phase and the proportion of glycerol on the viscosity and bubble diameter. According to the model used in this study, the bubbles were smaller than those in the model in which the gas was directly dispersed. The bubble size increased as the gas phase ratio and liquid viscosity increased, likely because the bubble growth is promoted by increase in the gas phase ratio and liquid phase viscosity, and the frequency with which the bubbles contact one other. The increase of the gas phase ratio at low liquid-phase viscosity and low shear rate caused an increase in both apparent viscosity and relative viscosity, which was obtained by dividing the apparent viscosity by liquid-phase viscosity. However, these increases in viscosity were not observed at a high shear rate. This is likely because the mechanism of bubble diffusion and flow is affected by the liquid-phase viscosity and shear rate. We found that the model in this study exemplified a Herschel-Bulkley fluid. In addition, we proposed an equation for measuring viscosity from the gas phase ratio.