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Band-aid on a five-inch gash: policy implications of the alignment between Ottawa’s construction of homelessness and lived experience
No full textDespite policy responses from multiple levels of government, homelessness in Canada continues to grow. The construction of homelessness in policy influences the nature of these responses, and incomplete problem constructions can prevent adequate solutions to the issue. This thesis considers the policy implications of how the Ottawa 10-Year Housing and Homelessness Plan's construction of homelessness compares to the lived experience of homeless individuals in Ottawa. Using data from focus groups with individuals experiencing homelessness in Ottawa and an analysis of the Ottawa 10-Year Housing and Homelessness Plan, this study argues that the construction of homelessness contained in policy fails to accurately reflect lived experience due to embedded neoliberal values. Consequently, the proposed solutions reinforce the very structures that sustain homelessness, perpetuating the social control and stigmatization of homeless populations. The findings highlight the need for meaningful inclusion of lived experience in constructing social problems to improve policy responses
Integration of Functional Mock-up Interface with Discrete Event System Specification for Co-simulation of Discrete and Continuous-Time Models
No full textThe Functional Mockup Interface (FMI) is a standardized interface designed for model exchange and co-simulation of dynamic models across various tools. It is an asset for the development of multidisciplinary systems. Integration of this standard with modeling and simulation formalism enhances interoperability and provides opportunities for collaboration. In this thesis, we present an approach for integrating FMI with the Discrete Event System Specification (DEVS). DEVS provides the modularity required for seamlessly integrating the shared model. We proposed a framework for exporting and co-simulating DEVS models as well as for importing and co-simulating continuous-time models using the FMI standard. For the co-simulation of continuous time models, we also implemented continuous time solvers capable of simulating continuous-time dynamics in the DEVS framework. The practical application of this framework is illustrated through two case studies. One case study simulates the Protein-Kinase Transduction process while the second simulates the PID-controlled Unmanned Ground Vehicle
Robust, Sparse, and Bayesian Learning of Stochastic Nonlinear Dynamical Systems with Applications in Aeroelasticity and Infectious Disease Modelling
No full textThis thesis is principally concerned with Bayesian methods for calibrating models of stochastic nonlinear dynamical systems. Of particular interest are systems where the available data represent noisy, sparse, or incomplete observations of the system states. Case studies in the fields of aeroelasticity and infectious disease modelling are considered, using established Bayesian frameworks for sparse learning of nonlinear-in-parameter models, and for the concurrent estimation of time-varying and time-invariant parameters. In nonlinear aeroelasticity, the fluid-structure interaction of wing-like structures and external flow is investigated with a specific focus on the experimental set-up of an elastically mounted single degree-of-freedom rigid airfoil undergoing pitching oscillations. The epidemiological modelling aspects consider the use of compartmental models complemented by public health data for the population-level spread of infectious diseases, such as COVID-19. Although the two applications are practically quite distinct, they both provide excellent opportunities for the deployment of the two aforementioned Bayesian computational frameworks. The modelling efforts in aeroelasticity and in infectious disease modelling both involve the description of physical systems by nonlinear coupled ordinary differential equations. Furthermore, there is precedent in both fields for the use of semi-empirical models that consist of a mechanistic component and a data-driven component. The accurate description of the system dynamics may involve a combination of time-varying and time-invariant parameters, necessitating a robust framework for handling their joint estimation in a fully Bayesian setting. The data-driven component is particularly susceptible to overfitting, which may lead to undue uncertainty in predictions. The predictive capability of such models can therefore be improved by the use of sparsity-inducing algorithms for the automatic discovery of the data-optimal nested model. The computational framework involves the use of standard methods for state estimation and sampling-based algorithms for parameter inference. The final contribution of the thesis pertains to improving the numerical implementation of the sparse learning framework by investigating the use of optimization-based sampling-free methods to bypassing the reliance on computationally expensive sampling-based methods. These investigations consider simple approximate representations of highly non-Gaussian joint probability densities in the interest of the efficient computation of the data-optimal sparse model structure
Investigating Cellular Responses to their Mechanical Environment Using Proximity Labeling
Full text linkA close relationship exists between the mechanical environment and cellular function. Properties of the mechanical environment such as substrate stiffness guide cellular functions in both physiological and pathological contexts through mechanotransduction. Cells orchestrate an appropriate cellular response to their mechanical environment through actin cytoskeleton reorganization and localization of mechanosensitive proteins. While select mechanosensitive proteins have been investigated, a study that broadly screens for changes in protein localization in response to substrate stiffness has not been performed. In this work, a new experimental workflow is developed where cells are plated onto polydimethylsiloxane substrates of varying stiffness and changes in morphology and cytoskeletal organization are investigated. Proximity labeling experiments are performed to screen for changes in protein localization in response to substrate stiffness. A nuclear enrichment analysis is applied to validate results of protein localization obtained through mass spectrometry. This work ultimately assists in improving our understanding of fundamental mechanisms of mechanotransduction
Basaltic Lava Flows and Mafic Dyke Swarms in the Neago Fluctus Area, Venus: Mapping and Developing a Geological History using Manual Methods and a Computer-Assisted Approach
No full textThis study (using Magellan radar data) provides detailed mapping and interpretation of lava flow fields on Venus, located in the northern hemisphere between Sedna Planitia, Lakshmi Planum, and Ishtar Terra, at a scale of 1:500,000. Expanding on previous BSc-level research, the primary focus is on basaltic lava flows and extensional structures (interpreted to overlie mafic dykes). Three areas of flows were studied: Fluctus 1 (eastern part of Neago fluctus), Fluctus-2 and an area to the north. Additionally, five major regional graben sets (dyke swarms) are recognized extending up to 1400 km in length. A total of 37 flow units are defined and organized into flow packages; 15 sources of these lava flows are identified, including both from calderas and grabens (dykes). A Volcanic Complex is identified and interpreted as a major source for the lava flows. The geological history for the area is developed incorporating the flows and dyke swarms
Assembling Bodies: the Museum Exhibition as Apparatus of Bodily Production in Canada and the United Kingdom
Full text linkMuseums have displayed archaeological human remains, alongside material culture, as a way of interpreting past human life for several centuries. Disciplines such as osteo-archaeology and physical anthropology have developed nuanced practices for analyzing and presenting knowledge about bodies alongside the project of natural and cultural history museums to tell the story of human antiquity. Beginning in the 20th century, the collection and display of human remains by museums has been met with resistance and contestation both in localized cultural and geographical contexts, and through globalized movements for repatriation and decolonization. In Canada, the strong activism of Indigenous communities to repatriate material culture and ancestral remains has made the display of any human remains an uncommon practice with a few exceptions. In the United Kingdom, archaeologists and museum curators must navigate the public’s desire to view their own prehistoric ancestors with shifting global practices. This dissertation observes how human remains came to be included within Western (Canadian and British) museum collections, how they have been displayed in museums, and how they have been contested by publics and become the centre of global movements for repatriation led by Indigenous nations around the world. Rather than take up the display of human remains as an ethical debate, it seeks to understand how bodies are otherwise materialized in museum exhibitions through ethnographic fieldwork and case studies. Drawing from new materialist and posthumanist theory, it considers how museum exhibitions “produce” bodies through a range of material and discursive practices that challenge notions of humanism and authenticity. While wider national contexts are considered, particular exhibitions are identified at the Canadian Museum of History and Canadian War Museum in Ottawa, Canada, and the National Museum of Scotland in Edinburgh, Scotland. Site visits and exhibition analysis are further considered alongside qualitative interviews with museum curators, interpretive planners, conservators and archaeologists. The dissertation finally considers the future of posthumanist approaches to studying and interpreting bodies and past human life in the museum
Resource Allocation in Virtualized Sensor Networks for Highly-Deployed IoT Services
Full text linkAs IoT technology evolves, applications become more complex, incorporating multiple sensing services and varied qualities. Users expect fast and dependable deployment without worrying about the inherent complexities and limitations in sensing and routing resources. IoT sensor nodes face challenges such as limited resources, high heterogeneity, interoperability issues, intermittent communication, and installation time and cost. Current IoT resource management techniques do not fully meet the requirements of current and future IoT applications. Virtual Sensor Networks (VSN) present a novel approach to IoT resource management, slicing the Physical Sensor Networks (PSN) and forming virtual groups that link the needed IoT sensing and routing resources. These VSN can be initiated and released on demand, allowing for the multi-use of existing sensor node resources, thereby addressing the unique requirements of IoT applications and users. The current state of IoT-enabled VSN reveals a gap in the availability of systems that can successfully integrate optimized sensing resource allocation with dynamic resource-aware routing. Specifically, systems that can adapt the allocation and routing paths in tandem, considering the sensor node’s resource utilization, are needed. This thesis addresses the practical challenge of optimizing sensor node selection for IoT applications and forming more efficient routing paths. It proposes multiple resource-aware joint techniques with both single-objective and multi-objective weighted metrics. These techniques are not just theoretical but have been refined through a weight search and empirical analysis, making them applicable in real-world scenarios. The proposed techniques end with a generalized multi-objective routing (static and dynamic) and dynamic multi-objective sensor node selection. The routing and sensor node selection algorithms are based on the resources in the sensor nodes (such as energy, memory, and processing) and the available network communication bandwidth. The proposed methods have been rigorously tested in various scenarios, including homogeneous and heterogeneous sensor networks, grid and random node placement topologies, with and without interference consideration, and heterogeneous IoT applications. The simulation results demonstrate the efficiency of the proposed methods in slicing the sensor network resources, increasing the application deployment rate, and improving the minimum node energy. These findings promise a sustainable and more efficient use of IoT sensor networks
Between (Re)Habilitation and Security: A Critical Examination of Peer Work in Ottawa’s Supervised Consumption Sites
Full text linkIn this thesis, I critically examine the roles and experiences of peer workers in Ottawa’s supervised consumption sites. Relying on qualitative interviews conducted with peer workers and nurses, this thesis argues that peer work programs within mainstream harm reduction initiatives — such as state-sanctioned supervised consumptions sites — ensnare peers in, rather than liberate them from, practices of governance and punishment that perpetuate the marginalization of drug users. Specifically, I show how peer workers are enlisted in the maintenance and enforcement of two competing and often incoherent logics: that of (re)habilitation, which endeavours to transform (ex-)drug users into particular kinds of subjects, and that of what I call the “security mindset,” which aims to produce social order through violence and punishment