Osmotic dehydration in plant tissues

The primary aim of the thesis is to provide a comprehensive investigation of the osmotic dehydration processes in plant tissue. Effort has been concentrated on the modelling for simulating the processes. Two mathematical models for simulating the mass transfer during osmotic dehydration processes in plant tissues are developed and verified using existing experimental data. Both models are based on the mechanism of diffusion and convection of any mobile material that can transport in plant tissues. The mass balance equation for the transport of each constituent is established separately for intracellular and extra-cellular volumes with taking into account the mass transfer across the cell membrane the intracellular and extra-cellular volumes and the shrinkage of the whole tissue. The contribution from turgor pressure is considered in both models. Model two uses Darcy’s law to build the relation between shrinkage velocity and hydrostatic pressure in each volume because the plant tissue can be considered as the porous medium. Moreover, it has been extended to solve the multi-dimensional problems. A lot of efforts have been made to the parameter study and the sensitivity analyses. The parameters investigated including the concentration of the osmotic solution, diffusion coefficient, permeability of the cell membrane, elastic modulus of the cell wall, critical cell volume etc. The models allow us to quantitatively simulate the time evolution of intracellular and extra-cellular volumes as well as the time evolution of concentrations in each cross-section.

The evolution of learning systems:to Bayes or not to be

Bayesian algorithms pose a limit to the performance learning algorithms can achieve. Natural selection should guide the evolution of information processing systems towards those limits. What can we learn from this evolution and what properties do the intermediate stages have? While this question is too general to permit any answer, progress can be made by restricting the class of information processing systems under study. We present analytical and numerical results for the evolution of on-line algorithms for learning from examples for neural network classifiers, which might include or not a hidden layer. The analytical results are obtained by solving a variational problem to determine the learning algorithm that leads to maximum generalization ability. Simulations using evolutionary programming, for programs that implement learning algorithms, confirm and expand the results. The principal result is not just that the evolution is towards a Bayesian limit. Indeed it is essentially reached. In addition we find that evolution is driven by the discovery of useful structures or combinations of variables and operators. In different runs the temporal order of the discovery of such combinations is unique. The main result is that combinations that signal the surprise brought by an example arise always before combinations that serve to gauge the performance of the learning algorithm. This latter structures can be used to implement annealing schedules. The temporal ordering can be understood analytically as well by doing the functional optimization in restricted functional spaces. We also show that there is data suggesting that the appearance of these traits also follows the same temporal ordering in biological systems. © 2006 American Institute of Physics.