Mineral dissolution is an inherent chemo-hydro-mechanical coupled diagenetic process in sediments. This ubiquitous geological phenomenon affects all properties in sediments, however, its engineering impact remains largely unknown. This research centers on the effects of mineral dissolution on sediment behavior with emphasis on dissolution modes in nature and their engineering implications. Five different dissolution modes are identified: homogeneous, pressure-dependent, and localized dissolution, and the dissolution of shallow and deep dissolvable inclusions. The consequences of each dissolution mode are investigated through experiments and discrete element methods. While each dissolution mode triggers unique consequences, it is observed that in all cases 1) significant displacement takes places during dissolution, 2) there is a pronounced effect of internal friction and the extent of dissolution on the evolution of the sediment, 3) the sediment has higher compressibility and exhibits a more contractive tendency after dissolution, 4) a porous honeycomb-shaped internal fabric develops accompanied by contact force concentration along dissolved inclusions, and 5) horizontal stress reduction takes place during dissolution and shear localization may develop under zero lateral strain conditions. Mineral dissolution has important engineering implications, from soil characterization to slope stability and shallow foundations. Pre- and post-dissolution CPT studies show that dissolution decreases the tip resistance proportional to the extent of dissolution. Dissolution in sloping ground induces global settlement as the prevailing deformation pattern, and prominent lateral movements near the slope surface; sudden undrained shear failure may take place during otherwise quasi-static dissolution. While footings experience larger settlements during post-dissolution loading, subsequent dissolution beneath a previously loaded footing causes displacements that are greater than the sum of dissolution-induced and load-induced settlements.

The objectives of the study were to evaluate the soil disturbances and manure dispersion created by the AerWay aerator in a silt loam soil; and to generate a calibrated and validated soil-tool model using Discrete Element Methods (DEM) that simulate the draft and vertical forces of the aerator. The experimental results showed that a trend indicated that the faster tractor speeds would disturb more soil. After one hour with the manure application rate of 42 000 L/ha, manure was spread to a depth of 250 mm, 200 mm in the forward direction and 100 mm in the lateral direction. The model draft forces had a relative error of 13.4-31.2% when compared to the literature data between 100-150 mm depth while the predicted vertical force was found to linearly increase until 150 mm depth at around 700 N per rolling tine and plateaus until the full insertion of 200 mm.

The present study examined the effectiveness of a self-instructional package for teaching discrete-trials teaching (DTT) to Applied Behaviour Analysis tutors at the St.Amant Applied Behaviour Analysis Program for Children with Autism. A modified multiple- baseline design across participants was used, and replicated six times. An AB design was used for one participant. The training package include a self-instructional manual, video demonstrations, and self-practice. Participants required an average of 3 hours and 56 minutes to master the manual. Eleven of the 13 participants achieved the mastery criterion during the post-training assessment. Mean DTT accuracy increased from 46.2% to 85.5% (a statistically significant increase). One tutor participated in a generalization phase with a child with autism, and their DTT accuracy averaged 80.1%. The results suggest that the self-instructional package appears to be an effective tool for teaching newly-hired tutors to conduct DTT.

The main objective of the research was to understand hemp processing at different stages through numerical simulations. Processing of hemp materials involves breaking the hemp into different sizes of particles and separating those particles into fractions of different sizes. Numerical models were developed using the discrete element method (DEM) to simulate hemp processing using a hammermill and separations of different hemp particles using a 3D vibratory screen-type separator. The models were implemented using a commercial DE code, the Particle Flow Code in Three Dimension (PFC3D). In the models, virtual hemp, hemp fibre and core were defined using clusters of PFC3D basic spherical particles which are connected by the PFC3D parallel bonds. The microproperties (e.g. particle stiffness and friction coefficient, and bond stiffness and strength) of these particles were calibrated. For calibrations, virtual tests were performed using PFC3D for hemp stem, fibre, and core. Those virtual tests included direct shear tests of fibre and core particles, tensile tests of fibre, and compression tests of hemp stems. The microproperties of these particles were calibrated through comparing results from the virtual tests with results from laboratory tests or literature data. Those calibrated particle microproperties were used in the PFC3D models developed for simulating the hammermill for hemp processing and the 3D vibratory separator for particle separation. These two machines were constructed using various PFC3D walls and lines, and had the main features and operational conditions as the real machines. The hammermill model was able to predict the power requirement of hammermill and particle dynamic behaviours (kinetic and strain energies) within the hammermill. The separator model was capable of predicting the separation efficiency of the 3D vibratory separator for separations of different hemp particle mixtures. The behaviour of the models reflected the real behaviour observed experimentally. The model results were reasonably good as compared with literature data and the test results. The models developed have the potential to simulate many other dynamic attributes of hemp particles with the machines. This study has laid a solid foundation for future studies of biomaterial-machine interactions using the DEM.

A permutation array, represented by PA(n, d), is a subset of Sn such that any two distinct elements have a distance of at least d where d is the number of differing positions. We analyze the upper and lower bounds of permutation codes with distance equal to 4. An optimization problem on Young diagrams is used to improve the upper bound for almost all n while the lower bound is improved for small values of n by means of recursive construction methods.

This dissertation presents a methodology to simulate the dispersion of water droplets in the air flow typical of an Icing Tunnel. It is based on the understanding the physical parameters that influence the uniformity and the distribution of cloud of droplets in the airflow and to connect them with analytical parameters which may be used to describe the dispersion process. Specifically it investigates the main geometrical and physical parameters contributing to the droplets dispersion at different tunnel operative conditions, finding a consistent numerical approach to reproduce the local droplets dynamic, quantifying the possible limits of commercial CFD methods, pulling out the empirical parameters/constant needing to simulate properly the local conditions and validating the results with calibrated experiment. An overview of the turbulence and multiphase flow theories, considered relevant to the Icing Tunnel environment, is presented as well as basic concepts and terminology of particle dispersion. Taylor’s theory of particle dispersion has been taken as starting point to explain further historical development of discrete phase dispersion. Common methods incorporated in commercial CFD software are explained and relative shortcomings underlined. The local aerodynamic condition within tunnel, which are required to perform the calculation with the Lagrangian particle equation of motions, are generated numerically using different turbulent models and are compared to the historical K-ε model. Verification of the calculation is performed with grid independency studies. Stochastic Separated Flow methods are applied to compute the particle trajectories. The Discrete Random Walk, as described in the literature, has been used to perform particle dispersion analysis. Numerical settings in the code are related to the characteristics of the local turbulent condition such as turbulence intensity and length scales. Cont/d.

In wireless channels and many other channels, interference is a central phenomenon. Mitigating interference is a key to improving system performance. To find the limit of the achievable rates for these channels in the presence of interference, the two-user Gaussian interference channel has been the subject of intensive study in network information theory. However, most current results have been obtained by assuming Gaussian input distributions. While optimal in single-user Gaussian channels, the issue with this assumption is that the Gaussian noise becomes the worst noise when the input distribution is also Gaussian. In this thesis, we propose a class of discrete constellations. We show that this class of constellations can automatically achieve the same sum rates as schemes that treat interference as noise or perform time sharing.

Innovation diffusion models have been studied extensively to forecast and explain the adoption process for new products or services. These models are often formulated using one of two approaches: The first, and most common is a macro-level approach that aggregates much of the market behaviour. An advantage of this method is that forecasts and other analyses may be performed with the necessity of estimating few parameters. The second is a micro-level approach that aims to utilize microeconomic information pertaining to the potential market and the innovation. The advantage of this methodology is that analyses allow for a direct understanding of how potential customers view the innovation. Nevertheless, when individuals are making adoption decisions, the reality of the situation is that the process consists of at least two stages: First, a potential adopter must become aware of the innovation; and second the aware individual must decide to adopt. Researchers, have studied multi-stage diffusion processes in the past, however a majority of these works employ a macro-level approach to model market flows. As a result, a direct understanding of how individuals value the innovation is lacking, making it impossible to utilize this information to model realistic word-of-mouth behaviour and other network dynamics. Thus, we propose a two-stage integrated model that utilizes the benefits of both the macro- and micro-level approaches. In the first stage, potential customers become aware of the innovation, which requires no decision making by the individual. As a result, we employ a macro-level diffusion process to describe the first stage. However, in the second stage potential customers decide whether to adopt the innovation or not, and we utilize a micro-level methodology to model this. We further extend the application to include forward looking behaviour, heterogeneous adopters and segmented Bayesian learning, and utilize the adopter's satisfaction levels to describe biasing and word-of-mouth behaviour. We apply the proposed model to Canadian colour-TV data, and cross-validation results suggest that the new model has excellent predictive capabilities. We also apply the two-stage model to early U.S. hybrid-electric vehicle data and results provide insightful managerial observations.

In wireless channels and many other channels, interference is a central phenomenon. Mitigating interference is a key to improving system performance. To find the limit of the achievable rates for these channels in the presence of interference, the two-user Gaussian interference channel has been the subject of intensive study in network information theory. However, most current results have been obtained by assuming Gaussian input distributions. While optimal in single-user Gaussian channels, the issue with this assumption is that the Gaussian noise becomes the worst noise when the input distribution is also Gaussian. In this thesis, we propose a class of discrete constellations. We show that this class of constellations can automatically achieve the same sum rates as schemes that treat interference as noise or perform time sharing.

Recent interest in identifying and measuring health outcomes represents an advance in our understanding of how health care for individuals should be evaluated. However, the concept of health outcomes has mainly focussed on improvements in health status. Non-health outcomes of health care may also be important to patients. In this thesis, four tasks were undertaken with the aim of identifying non-health outcomes and establishing the extent of their relevance and importance to patients. First, the illness experience literature was reviewed to identify potential non-health outcomes. Seven categories of non-health outcomes were identified: information, being treated with dignity, being able to trust the health care provider, having distress recognised and supported, participating in decision making, legitimation and reassurance. Second, to gain an in-depth understanding of these concepts, topic-specific literature was reviewed and synthesised. Third, in order to confirm how relevant and important the concepts were to patients, a qualitative study was conducted with each of two different groups of health service users. Broadly, patients considered that all the non-health concepts were relevant, although the extent to which they were important varied. Fourth, to test the relative importance of the seven concepts, a Stated Preference Discrete Choice experiment in the context of general practice was conducted. This study showed that most people thought their GP demonstrated behaviour likely to result in the production of non-health outcomes. The results showed that although all the non-health outcomes were, to some extent, preferred by respondents, trust was most important, followed by legitimation and recognition of and support for emotional distress. Once again, these results point to the importance of context in the evaluation of health care from the patient's perspective. While still being perceived as positive aspects of health care, the provision of information and acting autonomously or participating in decisions about their health care were the non-health outcomes considered least important by patients