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The hemp plant is an attractive source of raw material for multiple products. Processing hemp requires the separation of fibre and core components of the plant. Peel tests were conducted for hemp stems to evaluate the strength required to peel fibre from the core. The average peeling force for the Alyssa variety was 0.39 N and that for the USO-14 variety was 0.87 N. The Ising model was implemented to produce a stochast ic model. The simulated peel test behaved similarly to the experimental peel test. A discrete element model (DEM) of a planetary ball mill was developed to predict the energy requirement of grinding hemp for fibre. Hemp grinding tests were performed on variety USO-31 using a planetary ball mill for model calibration purposes. Power draw measurements increased linearly increasing at greater grinding speeds. The DEM approximated power draw with relative error below 10% for grinding speeds below 400 rpm.

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This thesis experimentally investigates the statistics (mean and variance) of the fatigue-cycle-dependent evolutions of both the crack tip front penetration's distribution function and the microscopic growth rate's distribution function, as a fatigue crack propagates to final fracture. A novel technique which facilitates striation counting and striation spacing measurements, is developed and used for extracting and analyzing the relevant statistical data for characterizing the stochastic fatigue crack propagation in polycrystalline metals. Two types of pure copper materials are investigated. / The investigation confirms the existence of the mean and variance of both the crack front penetration and its growth rate. Details of the variations of the mean crack penetration with respect to the dispersion of the crack front distribution and the mean growth rate, respectively, are established. Other contributions include the evaluation of the material characteristic associated with the transition intensity of the growth process. / These results are correlated with the predictions of the "Provan-Ghonem" theory in order to ascertain the validity of the linear Markov birth stochastic process, as a viable description of the fatigue crack propagation process in polycrystalline metals. The trend of the experimental results suggest a spatially correlated Markov process which accounts for both the strong nearest-neighbour-interactions between "points" along the crack front, and the boundary effects as a more viable representation of the fatigue crack propagation process.

This thesis presents a study of stochastic arrays and learning networks. These arrays will be shown to consist of simple elements utilising probabilistic coding techniques which may interact with a random and noisy environment to produce useful results. Such networks have generated considerable interest since it is possible to design large parallel self-organising arrays of these elements which are trained by example rather than explicit instruction. Once the learning process has been completed, they then have the potential ability to form generalisations, perform global optimisation of traditionally difficult problems such as routing and incorporate an associative memory capability which can enable such tasks as image recognition and reconstruction to be performed, even when given a partial or noisy view of the target. Since the method of operation of such elements is thought to emulate the basic properties of the neurons of the brain, these arrays have been termed neural 'networks. The research demonstrates the use of stochastic elements for digital signal processing by presenting a novel systolic array, utilising a simple, replicated cell structure, which is shown to perform the operations of Cyclic Correlation and the Discrete Fourier Transform on inherently random and noisy probabilistic single bit inputs. This work is then extended into the field of stochastic learning automata and to neural networks by examining the Associative Reward-Punish (A(_R-P)) pattern recognising learning automaton. The thesis concludes that all the networks described may potentially be generalised to simple variations of one standard probabilistic element utilising stochastic coding, whose properties resemble those of biological neurons. A novel study is presented which describes how a powerful deterministic algorithm, previously considered to be biologically unviable due to its nature, may be represented in this way. It is expected that combinations of these methods may lead to a series of useful hybrid techniques for training networks. The nature of the element generalisation is particularly important as it reveals the potential for encoding successful algorithms in cheap, simple hardware with single bit interconnections. No claim is made that the particular algorithms described are those actually utilised by the brain, only to demonstrate that those properties observed of biological neurons are capable of endowing collective computational ability and that actual biological algorithms may perhaps then become apparent when viewed in this light.

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