This thesis details a higher-fidelity, scalable simulation tool and model for message response time and bus utilization rate analysis for the Controller Area Network (CAN) protocol stack. This tool achieves higher fidelity than existing commercial and academic simulation tools by including details of the stack implementation that are often neglected, such as receive and transmit hardware buffer availability and usage policy (i.e., which messages are able to be copied to which buffer resources), and the buffer polling or queueing policies. Key details of these features have been identified by a thorough examination of CAN stack behaviour, taking into account the physical considerations of commercial CAN implementations. Inclusion of these details in the simulation can produce better accuracy by exposing certain priority inversion scenarios. Scalability is achieved by using a transaction-based modelling approach and modelling transmissions at the protocol level rather than the physical/bit level. The tool requires minimal user interaction, and system level model generation is automated using an AUTOSAR XML system description file (ARXML format) to specify network topology and message information (transmitter, receiver(s), period, length, etc.), and an Excel spreadsheet file (XLS or XLSX format) to specify node hardware/software implementation details (buffer resource details, polling loop rates, main control loop rates, etc.) as inputs. / Thesis / Master of Applied Science (MASc)

PhD Thesis / On congested North American urban road networks, driver concerns over increased travel time play a major role in whether or not cycling infrastructure is built. This fact is recognized by transportation planning agencies in Canada and the United States, including the Ministry of Transportation Ontario and the Federal Highway Administration. However, specific frameworks to address such driver concerns do not exist within the practice of urban planning nor the academic literature. One potentially fruitful avenue is to explore the methods and tools of critical link analysis. One such avenue is provided by the Network Robustness Index (NRI) and the Network Robustness Index Calculator, as this method and tool indexes critical links through traffic simulation from least to most critical. The specific information that can be used to address driver concerns is found in the least critical links as these roadways have additional capacity, and therefore may be considered underutilized. This thesis explores the use of the NRI as a framework for urban cycling infrastructure planning. Experiments on the utility of the NRI against common traffic and cycling planning tools are explored. The NRI Calculator’s ability to perform full network scans for potential bike lane locations, least cost corridors, and full cycling networks consisting of different designs is tested throughout the chapters of this manuscript. / Thesis / Doctor of Philosophy (PhD) / This thesis aids in the planning of urban bike lanes by addressing driver concerns through traffic simulation.

The purpose of this quantitative study was to further explore pedagogical balance using multidimensional scaling and epistemic network analysis. Teacher shortages and attrition remain a critical issue for the future, and simulated classroom environments like simSchool can provide teachers additional training to help improve teaching confidence and teaching experience. Two different data sets were analyzed at various time before and after simSchool use to see how pre-service teachers rate themselves in 8 areas of teaching as defined by the Survey of Teaching Skills. Multidimensional scaling was utilized to see how teaching confidence and teaching experience align with no simSchool use, 90 minutes of use, and 8 hours of use. Epistemic network analysis was utilized to look at the cognitive structures of different groups to determine any differences. The findings are discussed with future research directions provided.

BACKGROUND: Major depression is a pervasive condition that affects every aspect of a patient’s life, and many patients are unable to find symptom alleviation with the current available medications. Ketamine has recently shown promise as a rapid-acting antidepressant, yet its mechanisms are not yet well-understood. OBJECTIVE: We sought to understand the change in depression symptom interrelationships, with particular interest in sleep, in the context of ketamine treatment in depression by completing a network analysis. METHODS: 97 patients with treatment-resistant depression were given ketamine over six treatments, and symptoms were examined via the Quick Inventory of Depressive Symptomology (QIDS-SR-16). Two networks were constructed: one prior to the first treatment, and one prior to the sixth treatment. Each symptom of the inventory formed a node, and partial correlations were used to construct the edges of the network. Centrality indices and network structure was then evaluated and compared. RESULTS: Centrality indices measured were unstable, limiting assertions to node strength, but global network structure was revealed to be changed between the networks. CONCLUSION: The data suggests that ketamine may affect the interrelationships between depressive symptoms, by impacting some symptoms more than others.

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