Simulation of Mobility and Retention of Selected Engineered Nanoparticles Beneath Landfills

Engineered Nanoparticles (ENPs) have generated significant public and scientific excitement due to their unique physical, chemical, and electrical properties which has led to their application in a wide variety of industries. Landfills are a likely disposal site for ENPs at the end of their useful life, either encapsulated in a product as discrete nanoparticles or in nanoparticle agglomerates. Most countries and jurisdictions have landfill design regulations to provide an effective impermeable barrier between a landfill and soil/groundwater, however, landfills are still of concern due to the potential threat to groundwater resources. This study assesses the fate of selected ENPs (multi-walled carbon nanotubes, single-walled carbon nanotubes, nC60, and Quantum dots) beneath a representative landfill using a two-dimensional finite element model that solves modified colloid filtration theory. Simulation conditions were representative of conditions present in landfill systems (e.g., porous media as fine as silt to clay and a natural groundwater flow). Findings suggest that site blocking function is an important factor governing ENP mobility. These findings suggest that properly designed and constructed landfills will be able to significantly limit ENP transport to the environment for extended periods of time (i.e., 100 years)

Investigation of the brain magnetisation transfer ratio, cognitive and neurological measures in prion disease

The work described in this thesis examines the application of magnetisation transfer ratio (MTR) measurement, a quantitative magnetic resonance imaging (MRI) technique, in evaluating patients with different forms of human prion disease. In particular whether MTR changes can be shown: 1. to correlate with clinical disease severity and disease type 2. to evolve on serial MRIs in clinically progressive disease 26 patients were assessed over 3 years. Global and regional cerebral MTRs were calculated using validated software and regions of interest manually defined on MTR maps. Whole brain, grey matter and white matter MTR histograms were computed and mean, peak height, peak location, and 25th, 50th and 75th percentile MTR histogram values were calculated to demonstrate localised and subtle diffuse pathological changes. A blinded assessment of DWI/FLAIR images was performed to determine MTR changes in areas with or without signal change on conventional MRI. Patients were assessed using clinical video scores and neurological scales: Clinician's Global Impression of Disease Severity, Clinician's Dementia Rating, Alzheimer's disease Assessment Scale, Activities of Daily Living, Brief Psychiatric Rating Scale, Mini Mental Score Examination, Glasgow Coma Score and Rankin scores. Temporal changes in these tests of cognition, functional abilities, psychiatric symptoms and conscious state are described. Spearman rank correlation and linear regression analyses were performed. At baseline, lower whole brain and grey matter MTR histogram parameters correlated significantly with lower cognitive, extrapyramidal and cerebellar impairment, as well as with MMSE, CDR, ADAS-COG, CGIS and Rankin scores. Longitudinal decline in multiple whole brain, white matter and grey matter MTR histogram parameters was associated with progressive extrapyramidal and CDR impairment. Four patients at baseline and 2 patients longitudinally had conventional MRI abnormalities. Decreased MTR may be used as a biomarker of disease severity and is a potential outcome measure in future therapeutic trials in prion disease

Calcium carbonate precipitation by different bacterial strains

Bacteria are capable of performing metabolic activities which thereby promote precipitation of calcium carbonate in the form of calcite. In this study, it is shown that microbial mineral precipitation was a result of metabolic activities of some specific microorganisms. Concrete microorganisms were used to improve the overall behavior of concrete. It was predicted that bacterial calcium carbonate (CaCO3) precipitation occurs as a byproduct of common metabolic processes such as urea hydrolysis. In this study, ureolytic bacteria that were capable of precipitating calcium carbonate were isolated and further their urease activity was tested based on the production of urease. Scanning electron microscopy (SED) analysis revealed the direct involvement of these isolates in calcium carbonate precipitation. The production of calcite was further confirmed by x-ray diffraction (XRD) and energy-dispersive x-ray (EDX) analysis.Key words: Bacteria, urease activity, microbial mineral precipitation, scanning electron microscope-energydispersive x-ray, x-ray diffraction

2-Dimensional finger gesture based mobile robot control using touch screen

The purpose of this study was to present a reliable mean for human-computer interfacing based on finger gestures made in two dimensions, which could be interpreted and adequately used in controlling a remote robot's movement. The gestures were captured and interpreted using an algorithm based on trigonometric functions, in calculating the angular displacement from one point of touch to another as the user’s finger moved within a time interval; thereby allowing for pattern spotting of the captured gesture. In this paper the design and implementation of such a gesture based user interface was presented, utilizing the aforementioned algorithm. These techniques were then used to control a remote mobile robot's movement. A resistive touch screen was selected as the gesture sensor, then utilizing a programmed microcontroller to interpret them respectively

Intrinsic Rewards for Maintenance, Approach, Avoidance and Achievement Goal Types

In reinforcement learning, reward is used to guide the learning process. The reward is often designed to be task-dependent, and it may require significant domain knowledge to design a good reward function. This paper proposes general reward functions for maintenance, approach, avoidance, and achievement goal types. These reward functions exploit the inherent property of each type of goal and are thus task-independent. We also propose metrics to measure an agent's performance for learning each type of goal. We evaluate the intrinsic reward functions in a framework that can autonomously generate goals and learn solutions to those goals using a standard reinforcement learning algorithm. We show empirically how the proposed reward functions lead to learning in a mobile robot application. Finally, using the proposed reward functions as building blocks, we demonstrate how compound reward functions, reward functions to generate sequences of tasks, can be created that allow the mobile robot to learn more complex behaviors