There have been several reports that cataract development results from unregulated Ca2+ mediated degradation of lens crystallins. The calpain isoform m-calpain, a cysteine protease, is known to be a major player in cataract formation in rodent lenses and recent evidence indicates that over-activation by Ca2+ causes cataractogenesis in other mammals. Molecular modelling studies of seventeen analogues of compound SJA6017 (our lead compound) in a calpain model are compared to measured IC50 values against ovine calpain. The studies validated the potential of the ‘model’, method and defined activity criteria that could be used as a tool to select molecules to synthesize as potential calpain inhibitors. Using this screening methodology and two virtual libraries of potential inhibitory molecules led to the synthesis of several inhibitors including macrocyclic 811. In vitro sheep eye lens culture experiments showed that macrocycle 811 possessed the characteristics to slow cataractogenesis.

While relational databases still hold the primary position in the database technology domain, and have been for the longest time of any Computer Science technology has since its inception, for the first time the relational databases now have valid and worthy opponent in the NoSQL database movement. NoSQL databases, even though not many people have heard of them, with a significant number of Computer Science people included, have spread rapidly in many shapes and forms and have done so in quite a chaotic fashion. Similarly to the way they appeared and spread, design and modeling for them have been undertaken in an unstructured manner. Currently they are subcategorized in 4 main groups as: Key-value stores, Column Family stores, Document stores and Graph databases. In this thesis, different modeling approaches for graph databases, applied to the same domain are analyzed and compared, especially from a design perspective. The database selected here as the implemented technology is Neo4J by Neo Technologies and is a directed property graph database, which means that relationships between its data entities must have a starting and ending (or source and destination) node. This research provides an overview of two competing modeling approaches and evaluates them in a context of a real world example. The work done here shows that both of these modeling approaches are valid and that it is possible to fully develop a data model based on the same domain data with both approaches and that both can be used later to support application access in a similar fashion. One of the models provides for faster access to data, but at a cost of higher maintenance and increased complexity.

A desire to advance humanity's presence in space prompts the need for improved technology to send crew to places such as the Moon, Mars, and nearby asteroids. Safely placing a crewed vehicle on and in any landing condition requires a design decision regarding the distribution of responsibilities between the crew and automation. In this thesis, a cognitive process model is used to determine the necessary automated functionality to support astronaut decision making. Current literature lacks sufficient detailed knowledge regarding astronaut decision making during this task and observations of astronauts landing on the Moon are not readily available. Therefore, a series of human-in-the-loop experiments, one of which was conducted with the NASA Astronaut Office at Johnson Space Center, have been conducted to examine the changes in performance due to differing function allocations, trajectory profiles, and scenario operations. The data collected in the human-in-the-loop study has provided empirical data that has informed the cognitive process model, the requirements analysis, and provided insight regarding cockpit display usage and information needs. The proposed system requirements include design guidance for assisting astronauts during both nominal and off-nominal landing scenarios.

Modern Molecular Dynamics force fields, such as the CHARMM36 and GLYCAM06 carbohydrate force fields, are parametrised to reproduce behaviours for specific molecules under specific conditions in order to be able to predict the behaviour of similar molecular systems, where there is often no experimental data. Coupled with the sheer number available, this makes choosing the appropriate force field a formidable task. For this reason it is important that modern force fields be regularly compared. Streptococcus pneumoniae is a cause of invasive pneumococcal disease (IPD) such as pneumonia and meningitis in children under five. While there are over 90 pneumococcal serotypes only a handful of these are responsible for disease. Immunisation with the conjugate vaccine PCV7, has markedly decreased invasive pneumoccocal disease. Following PCV7 immunisation, incidences of non-vaccine serotypes, especially serotype 19A, have increased. Serotype 19F's capsular polysaccharide differs from 19A's at a single linkage position. Where 19A possesses an a-D-Glcp-(1->3)-a-L-Rhap (G13R), 19F possesses an a-D-Glcp-(1->2)-a-L-Rhap (G12R) linkage. For this reason it was thought that a 19F conjugate would cross protect against 19A. Unfortunately PCV7 vaccination appears to have been largely ineffective against 19A disease. The lack of conformational information for the G12R and G13R disaccharides provided a good opportunity to compare the CHARMM and GLYCAM force fields. The dynamics of the G12R and G13R disaccharides were investigated under both CHARMM and GLYCAM.While we did identify some discrepancies, overall the force fields were in agreement in predicting a more flexible G12R than the more restricted G13R. While it is possible that these differences account for the lack of 19F to 19A cross protectionprotection, further research is required.

A dynamic model is developed and presented that predicts the voltage response for a Severinghaus electrode-based point-of-care pCO2 sensor. Eight partial differential equations are derived to describe the diffusion and reaction phenomena in the sensor. The model is able to predict the potential response versus time behaviour from different CO2 concentrations in the calibration fluid and control fluids. The two most influential and uncertain parameters in the model are determined to be the forward rate constant for benzoquinone consumption at the gold surface ( k_(f_Au ) ), and the partition coefficient for CO2 between the membrane and the electrolyte (κ_(〖CO〗_(2_m ) )). These parameters were adjusted heuristically to obtain a good fit (within 2 mV) between the dynamic voltage response data and the model predictions during a critical 4 second period. The model predictions are sufficient for design sensitivity studies, however an improved fit might be possible using a formal least-squares parameter estimation approach, or if additional parameters were estimated. Several design parameters are varied to study the influence of the electrolyte concentration and the sensor geometry on the voltage response. The most influential design parameter studied is the amount of water present in the electrolyte during sensor operation. This can be affected by the amount of water evaporated during manufacturing and storage, and by the amount of water present when the sensor “wets up” again during operation. The amount of water picked up by the sensor in turn is affected by design parameters such as component/membrane dimensions and thicknesses. The initial buffer concentration in the electrolyte is the second most influential parameter. The resulting model can be used to perform “what if” analyses in order to understand the impact of design decisions on the sensor performance, and to potentially improve the sensor from performance and manufacturing cost perspectives. / Thesis (Master, Chemical Engineering) -- Queen's University, 2014-02-06 15:00:47.555

An understanding of network traffic behavior is essential in the evolution of today's wireless networks, and thus leads to a more efficient planning and management of the network's scarce bandwidth resources. Prior reservation of radio resources at the future locations of a user's mobile travel path can assist with optimizing the allocation of the network's limited resources. Such actions are intended to support the network with sustaining a desirable Quality-of-Service (QoS) level. To help ensure the availability of the network services to its users at anywhere and anytime, there is the need to predict when and where a user will demand any network usage. In this thesis, the mobility behavior of the wireless users are modeled as a Markov renewal process for predicting the likelihoods of the next-cell transition. The model also includes anticipating the duration between the transitions for an arbitrary user in a wireless network. The proposed prediction technique is further extended to compute the likelihoods of a user being in a particular state after $N$ transitions. This technique can also be applied for estimating the future spatial-temporal traffic load and activity at each location in a network's coverage area. The proposed prediction method is evaluated using some real traffic data to illustrate how it can lead to a significant improvement over some of the conventional methods. The work considers both the cases of mobile users with homogeneous applications (e.g. voice calls) and data connectivity with varying data loads being transferred between the different locations.

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.

Over the past decade, the constraint-based approach to metabolic modeling has become an important tool for understanding and controlling biology. Unfortunately, the application of this novel approach to systems biology in biotechnology has been limited by three significant technical issues: existing metabolic modeling methods cannot completely model the overflow metabolism, cannot model the metabolism of microbial communities, and cannot design strains optimized for productivity and titer. Three computational methods – the Flux Balance Analysis with Membrane Economics (FBAME) method, the Dynamic Multi-species Metabolic Modeling (DyMMM) framework, and the Dynamic Strain Scanning Optimization (DySScO) strategy – have been developed to resolve these issues respectively. First, the FBAME method, which adopts the membrane occupancy limitation hypothesis, was used to explain and predict the phenomenon of overflow metabolism, an important metabolic phenomenon in industrial fermentation, in Escherichia coli. Then, the DyMMM framework was used to investigate the community metabolism during uranium bioremediation, and demonstrated that the simultaneous addition of acetate and Fe(III) may be a theoretically viable uranium bioremediation strategy. Lastly, the DySScO strategy, which combines the DyMMM framework with existing strain design algorithms, was used to design commodity-chemical producing E. coli optimized for a balanced product yield, titer, and volumetric productivity. These novel computational methods allow for broader applications of constraint-based metabolic models in biotechnology settings.

Induction logging is a well-developed geophysical method with multiple applications. It has been used extensively in academic research as well as in industry. Induction logging is a controlled-source electromagnetic (CSEM) exploration method. It characterizes geologic formations through the measurements of induced magnetics fields. The purpose of this research project is to better understand induction logs and the effects fractured geologic formations have on them. Computer modeling is used to generate synthetic logs for analysis in this research project. The original program required certain modifications to fit this research project’s goals. The computer program, Seatem is based on the finite element method. It is able to use a layered Earth model that is the basis for the synthetic log analysis. The geologic layers in this model are assigned various conductivities and also have the option of being assigned a geologic roughness value. The geologic roughness parameter is used to simulate fractured rocks in the subsurface. The synthetic logs generated by the modified Seatem program produce some encouraging results. In a thinning bed analysis, it is shown that as a conductive bed is thinned in a step-size procedure, the resulting induction log underestimates the actual conductivity of the layer. It also shows that the boundary layers around the thinned layer are better characterized in the log. The next synthetic log was calculated for a fractured resistive layer. This log shows that as the layer becomes more fractured, there is an increase in the underestimation of the actual conductivity. This layer is then thinned down and another synthetic log is calculated. The resulting log shows similar traits to the thinning bed analysis and shows an underestimation of the apparent conductivity. The same procedure is performed for a fractured conductive layer. The analysis produce similar results; however, that are much more drastic changes in the induction logs. As the unit becomes more fractured, the apparent conductivity is lower then the actual conductivity, as in the resistive case. However, smaller increases in the roughness parameter produced more severe underestimations than larger increases in the roughness parameter did for the resistive layer.

The search for heat regenerators is currently very important due to the amount of wasted heat produced in different human activities. Thermoelectric materials have emerged as a possible solution to the world’s demand and reuse of energy. Recent advances have included the development of materials with tailored phonon properties, including localized "rattling" oscillator modes. In addition a number of interesting physical properties have emerged in rattling systems. This dissertation reports a study of several such systems, experimentally and computationally. Experiments performed include XRD, electron micro-probe, electrical and thermal conductivity, Seebeck coefficient measurements, dc magnetization, dc susceptibility and NMR. In the computational side several ab-initio models have been considered to understand the structural, vibrational and magnetic properties observed in these compounds. Among the studied compounds, the Fe-Al-Zn materials showed interesting magnetic properties combined with anomalous vibrational behavior in a chain geometry. Computational results indicated that the moment is affected by Fe antisites, but also the neighbor configuration contributes to it. Al-V-La is an example of a classical Einstein oscillator material. These properties are related to the existence of loose atoms inside the material. A purely computational study on these materials denoted the existence of two weakly bonded sites. The clathrate structural results from first-principles considerations elucidated the preferred structural configurations in several clathrates. This included Ba-Cu-Ge clathrates, where it was confirmed that the compound follows the Zintl electron counting balance. Also the bonding inside these materials was studied to address the binding of the local-oscillator atoms within the material. For Ba-Ga-Sn clathrates an unusual dimorphism was studied, with both of the two different types of structures investigated. For type-I Ba8Ga16Sn30 the preferred configuration was obtained from NMR lineshape simulations and energy considerations. For the type-VIII Ba8Ga16Sn30 the experimental thermoelectric properties were analyzed in conjunction with computational modeling. Finally in Ba-Al-Ge clathrates the local environments, preferred configuration and vacancy formation were clarified. This included an extensive experimental and computational study on Ba8AlxGe46-x-y2(box)y systems. The different local Al environments were elucidated, with the location of vacancies influencing the surroundings. Also the correlation between the Al substitution and number of vacancies was studied.