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The behaviour of biological materials and their subsequent disposition are important aspects of geochemistry, generally termed organic geochemistry and biogeochemistry.
Biomechanical analysis is an interdisciplinary branch which combines biology and mechanical engineering to understand the behaviour of biological materials.
Spiking Neural Network (SNN) is the most recent computational model that can emulate the behaviour of biological neuron system.
Here we present a general strategy for mimicking the mechanical behaviour of biological materials by precisely encoding their stress strain curves in solvent-free brush- and comb-like polymer networks (elastomers).
This paper makes clear that a univariable model, as is mostly used in the literature, is not always satisfactory to describe the vibration behaviour of biological products.
Chromatographic modelling can be used to describe and further understand the behaviour of biological species during their chromatography separation on adsorption resins.
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Using machine-learning and data-mining techniques, which sort through mountains of data looking for patterns, it is possible both to confirm known behaviours of biological systems, and to predict other, unknown behaviours.
We call a computational model of a biological system a bio-model if it allows for an explanation of the mechanism behind the observed behaviour of the biological system.
Our study suggested significant changes in the behaviour of the biological system when the concentration of MLSS was increased.
Generalised Maxwell and Kelvin Voigt rheological models are proposed for analysing the mechanical behaviour of each biological tissue.
Determining and quantifying the mechanical behaviour of soft biological tissues is, however, highly challenging due to their intrinsic labile nature.
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