Probing the properties of Be star discs with spectroastrometry and NLTE radiative transfer modelling: beta CMi

While the presence of discs around classical Be stars is well established, their origin is still uncertain. To understand what processes result in the creation of these discs and how angular momentum is transported within them, their physical properties must be constrained. This requires comparing high spatial and spectral resolution data with detailed radiative transfer modelling. We present a high spectral resolution, R similar to 80 000, sub-milliarcsecond precision, spectroastrometric study of the circumstellar disc around the Be star beta CMi. The data are confronted with 3D, non-local thermodynamic equilibrium radiative transfer calculations to directly constrain the properties of the disc. Furthermore, we compare the data to disc models featuring two velocity laws: Keplerian, the prediction of the viscous disc model, and angular momentum conserving rotation. It is shown that the observations of beta CMi can only be reproduced using Keplerian rotation. The agreement between the model and the observed spectral energy distribution, polarization and spectroastrometric signature of beta CMi confirms that the discs around Be stars are well modelled as viscous decretion discs.

Phenol biodegradation by a microbial consortium: application of artificial neural network (ANN) modelling

In this study, an effective microbial consortium for the biodegradation of phenol was grown under different operational conditions, and the effects of phosphate concentration (1.4 g L-1, 2.8 g L-1, 4.2 g L-1), temperature (25 degrees C, 30 degrees C, 35 degrees C), agitation (150 rpm, 200 rpm, 250 rpm) and pH (6, 7, 8) on phenol degradation were investigated, whereupon an artificial neural network (ANN) model was developed in order to predict degradation. The learning, recall and generalization characteristics of neural networks were studied using data from the phenol degradation system. The efficiency of the model generated by the ANN was then tested and compared with the experimental results obtained. In both cases, the results corroborate the idea that aeration and temperature are crucial to increasing the efficiency of biodegradation.

Modelling Mathematical Reasoning in Physics Education

Many findings from research as well as reports from teachers describe students' problem solving strategies as manipulation of formulas by rote. The resulting dissatisfaction with quantitative physical textbook problems seems to influence the attitude towards the role of mathematics in physics education in general. Mathematics is often seen as a tool for calculation which hinders a conceptual understanding of physical principles. However, the role of mathematics cannot be reduced to this technical aspect. Hence, instead of putting mathematics away we delve into the nature of physical science to reveal the strong conceptual relationship between mathematics and physics. Moreover, we suggest that, for both prospective teaching and further research, a focus on deeply exploring such interdependency can significantly improve the understanding of physics. To provide a suitable basis, we develop a new model which can be used for analysing different levels of mathematical reasoning within physics. It is also a guideline for shifting the attention from technical to structural mathematical skills while teaching physics. We demonstrate its applicability for analysing physical-mathematical reasoning processes with an example.