Hassan, Heidy Hamada Saad Abdelhamed (2018)The applicability of the precaution adoption process model in understanding older adults' self-regulatory driving behaviours.PhDby Publication,Queensland University of Technology. 709
Mass-on-belt models have been developed for the study of stick-slip. For most cases, a linear spring or the Kelvin-Voigt representation form the connection between the inertial reference frame and the mass. Here, the Zener viscoelastic support was included and the linear stability indicate that adequate parameter combinations enhance the stability zone to cope with higher frictional levels. Nonlinear numerical results show that responses are represented by three-dimensional limit cycles. For several conditions, its stable branches of solution presented hysteresis according to the nondimensional belt speed, where the bistability was described by three-dimensional regions of attraction. The results also indicate that the system may present interactions between limit cycles and fixed points, generating a sequence of period increasing bifurcations.
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Variability is inherent in biology and also substantial for microbial populations. In the context of food safety risk assessment, it refers to differences in the response of different bacterial strains (between-strain variability) and different cells (within-strain variability) to the same condition (e.g. inactivation treatment). However, its quantification based on empirical observations and its incorporation in predictive models is a challenge for both experimental design and (statistical) analysis. In this article we propose the use of multilevel models to quantify (different levels of) variability and uncertainty and include them in the predictions. As proof of concept, we analyse the microbial inactivation of Listeria monocytogenes to thermal treatments including different levels of variability (between-strain and within-strain) and uncertainty. The relationship between the microbial count and time was expressed using a (non-linear) Weibullian model. Moreover, we defined stochastic hypotheses to describe the different types of variation at the level of the kinetic parameters, as well as in the observations (microbial counts). The model parameters (kinetic parameters and variances) are estimated using Bayesian statistics. The multilevel approach was compared against an analogous, single-level model. The multilevel methodology shrinks extreme parameter estimates towards the mean according to uncertainty, thus mitigating overfitting. In addition, this approach enables to easily incorporate different levels of variation (between-strain and/or within-strain variability and/or uncertainty) in the predictions. On the other hand, multilevel (Bayesian) models are more complex to define, implement, analyse and communicate than single-level models. Nevertheless, their ability to incorporate different sources of variability in predictions make them very suitable for Quantitative Microbial Risk Assessment.
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