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The “New Gate of Paris.” Planning, Topography, and Queer Affect in the 18e Arrondissement.
No full textThis research inquires into contemporary urban regeneration (renouvellement urbain) in the majority immigrant and working-class 18th district of Paris. Drawing from black geographies, anthropologies of infrastructural space, queer and feminist analytics, this work propose an anthropology of western power that locates coloniality and colonial governmentality within (rather than outside) the French capital. Based on two years of ethnographic and archival fieldwork in urban expertise, urban informality, and local queer and immigrant spatiality, it examines how urban experts practice urban renewal as a project of topographical as well as sociocultural engineering. Experts develop a particular urban desire for abject space insofar as it allows them to deploy technologies of diagnostics, experimentation, and remediation. They consider queer cultural programming as crucial to this process of remediation and, namely, to transforming local Muslim social fabric and fostering social mixing. This discursive framing and the technologies of socio-spatial engineering it relies on, such as third place (tiers-lieux) start-ups and tactical urbanism (urbanisme de préfiguration), facilitate a local normalization of the question of sexual and cultural tolerance, while simultaneously reconfiguring, rather than remediating, urban stigma that surrounds both queer and Muslim communities. I track this ambivalence within departmental and municipal archives of hygiene and sanitary policy, as well as prefectural archives of sex work surveillance, to show that the co-constitutive dynamic of urban abjection and desire is a key feature of modern planning power’s relationship to urban margins.
The dissertation is structured in five chapters that serve as varying entry-points into the field. Chapter 1 focuses on urban expertise, recasting the production of infrastructures of urban social mixing (mixité) in light of the colonial and urban management of difference in France and empire. Chapter 2 looks to the social promise of community gardens (jardins partagés) and showcases the moral and political tensions that course through the vegetal regeneration of interstices. Chapter 3 focuses on third places (tiers-lieux) as sites of mixité engineering wherein abject and desirable forms are potentially hybridized. Chapter 4 focuses on queer affects and shifts the inquiry toward street space and semi-public places, putting into relief how the 18th arrondissement’s immigrant neighbourhoods already constitute resources for queer ethical poiesis. Chapter 5 turns to archives of sex work réglementarisme to engage a queer idiom of memory
Cross-Domain Recommender Systems in Cold-Start Problem
No full textIn today's digital ecosystem, recommendation systems are integral to shaping personalized user experiences across a wide range of applications, from e-commerce to entertainment to healthcare and education. Through the analysis of users' preferences and the prediction of future behavior, these systems reduce information overload and improve user satisfaction. While traditional recommendation models have become widely accepted, they still face significant challenges, such as handling sparse data, diverse user needs, and the dynamic nature of content preferences. To meet these limitations, innovative approaches are needed to ensure accurate, scalable, and personalized recommendations.
Among the potential solutions to these challenges is cross-domain recommendation, which makes use of knowledge from a source domain to improve recommendations in a target domain. This method is especially useful for overcoming cold-start situations, where few user or item interactions limit conventional models. By enabling knowledge transfer across domains, cross-domain recommendation systems provide a framework to overcome data sparsity and enhance prediction accuracy. The recent development of deep learning and transfer learning offers powerful tools for capturing complex user preferences and seamlessly transferring them from one domain to another. There is no doubt that designing effective architectures and strategies for such systems is not an easy task, as it requires careful consideration of domain-specific nuances, layer depth optimization, and the identification of transferable features.
In this thesis, we design a novel cross-domain recommendation system that utilizes personalized bridge functions and attention mechanisms to assist effective knowledge transfer between domains. By combining user embeddings with transferable characteristic encoding, the proposed framework captures user-specific preferences and dynamically adapts them to the target domain. A deep meta-network is employed to generate personalized bridges, allowing the system to adjust the unique behaviors of individual users. An in-depth evaluation of a real-world dataset demonstrates the superiority of this approach over traditional methods and outperforms the state-of-the-art model in addressing the cold-start problem while maintaining high levels of accuracy.
Building upon this foundation, the system is further optimized through extensive analysis of neural network architecture, with a focus on layer depth and parameter tuning. By systematically evaluating the impact of different architectural configurations, this thesis identifies the optimal layer depth that balances model complexity with performance. These optimizations enable the model to achieve state-of-the-art performance in evaluation metrics like Mean Absolute Error and Root Mean Squared Error. This thesis contributes to the advancement of cross-domain recommender systems, providing a scalable and robust framework for addressing real-world challenges in personalized recommendation
Automated Modelling and Assessment of the Impact of Damage Applied on Suspension Bridges' Main Cables Using Natural Frequency Analysis
No full textThis thesis describes the analysis of the effect of damage application to the main cables of the Lower Liard River Bridge (LLRB). A program in Python is created to generate custom suspension bridge finite element models in Abaqus CAE based on generalized parameters. While this study focuses strictly on the LLRB, the Python program is flexible to adapt to most suspension bridge designs. From there, over 2,000 test cases are defined with varying damage distributions along the main cables of the model. Damage is interpreted as corrosion based on inspection reports, and distributions are created to observe the effect of these variations on a few critical properties of the bridge. Key outputs studied are shifts in the natural frequencies of the modes of vibration of the deck and the vertical deck displacement under gravity. The Python program was enhanced to automate test execution and analysis. As part of this process, a rudimentary mode identification algorithm calibrated to the LLRB to recognize vibration modes in the deck from the outputs of Abaqus is created. Among these tested cases are a few simple wind response analyses of the bridge under dynamic wind loading. For this, an algorithm is defined to generate high-frequency wind velocity signals that are required to obtain a time history response of the bridge. The results obtained in this research confirm that, as demonstrated in previous studies, there are observable changes in the natural frequencies of the bridge when damage is applied. This research reveals how damage location and intensity affect bridge behaviour, offering insights for future developments. Key findings include the identification of vertical vibration modes of the deck as the clearest indicator of the applied damage parameters. In contrast, only a few torsional modes reveal any correlation. Specific behaviours are observed when damage is implemented at particular locations along the length of the main cables, such as near the anchor, towers, and midspan. Notably, a strong correlation is established between the shifts in natural frequencies of individual cases with single damage locations and the complementary case combining two damage locations in the same case. Findings suggest the potential to parameterize a bridge's behaviour based on single damage case results to estimate the damage applied from the measured dynamic properties. Further work should explore more damage patterns and integrate machine learning to advance this method
Technology-Mediated and In-Person Sexual Self-Concept in Sexually Minoritized Women: Conceptualization, Measurement, and (In)Congruence
No full textSexual self-concept (SSC) refers to how a person thinks, feels, and experiences themselves as a sexual being. A positive SSC has been linked to good psychosexual health. However, inconsistent conceptual and operational definitions in prior research have undermined SSC’s construct validity, leaving its applicability to lesbian women largely unexplored. Lesbian women’s unique navigation of intersecting systems of power and marginalization likely shapes how they experience and express their SSC. Thus, guided by intersectionality, social identity theories, and self-discrepancy theory, the overarching goal of this dissertation was to better understand how lesbian women’s SSC manifests across in-person and technology-mediated contexts. I produced four articles to address this goal. In Article 1, I conducted a methodological review of 67 SSC studies. I identified inconsistent conceptual definitions, diverse operationalizations, and limited representation of sexually minoritized populations in SSC research. These findings highlighted the need for inclusive SSC measures with stronger construct validity. In Article 2, I adapted Q Methodology to online platforms. Using tools like Zoom and Trello, I created a cost-effective and accessible approach to SSC research that includes hard-to-reach populations. This methodological innovation laid the groundwork for exploring SSC’s conceptual and operational definitions in Article 3. In Article 3, I conducted two studies. First, 35 sexuality researchers completed a card sort task to evaluate and categorize a comprehensive set of SSC items. This process enabled me to identify 60 relevant items representative of distinct SSC subdomains. In the second study, 20 women participated in a Q Methodology study, sorting these items into ranked categories and completing follow-up semi-structured interviews. Analyses revealed three distinct SSC definitions. These results illustrate how SSC varies by socio-demographic background and context. These findings informed the development of a measure of SSC with construct validity evidence for lesbian women, that is applicable to in-person and technology-mediated contexts. In Article 4, I used this measure to examine SSC (in)congruence among 290 lesbian women across in-person and technology-mediated contexts. Although multivariate analyses were not significant, descriptive findings highlighted potential complex relationships between SSC alignment, contextual factors, and psychosexual outcomes that challenge self-discrepancy theory. Together, these articles advance SSC research by addressing critical gaps in construct validity, innovating methodological approaches, and centering the experiences of lesbian women. This work provides a foundation for future research into lesbian women’s SSC and its implications for psychosexual health
A dataset and bibliometric approach to estimating annual APCs for six scholarly publishers: Presentation at SPARC’s OpenCon Community Calls
No full textThe author-pays model, in which publishers charge authors an article processing charge (APC) to publish their article open access, is now a well-established and popular revenue source for publishers. Given the prevalence of APCs and emerging models like read-and-publish agreements, reliable data is crucial for informed decision-making by funders, libraries, consortia, and institutions. However, estimating these fees is challenging due to the decentralized nature of scholarly publishing and limited transparency around these fees. This presentation introduces an open dataset of APCs from six scholarly publishers over five years and outlines the methodology and findings of a study estimating APC expenditures using this dataset
Transitions for youth and young adults with eating disorders and/or other mental health conditions: a Canadian guideline
No full textAbstract Background Eating disorders (EDs) are severe mental illnesses with high rates of mortality, morbidity, and reduced quality of life. Their onset occurs during adolescence and early adulthood, coinciding with the critical transition from pediatric to adult care. To address the lack of guidelines to support ED transitions in Canada, this study developed evidence-based guideline recommendations. Methods Scoping review methodology was employed using comprehensive searches across seven databases, supplemented by forward and backward citation chaining to identify records on youth and young adults (YYAs) (16–25 years) with EDs and/or mental health conditions transitioning from pediatric to adult care. The Grading of Recommendations Assessment, Development, and Evaluation approach was used to evaluate the quality of the evidence for applicable studies. Using a modified Delphi method, the evidence was reviewed by a guideline development panel and consensus was achieved in a single round of voting. Results After de-duplication, 14,350 records from all sources were screened, with 1817 studies undergoing full-text review. A total of 419 studies were included. Of these, 199 were primary research studies which fell under one or more of the following categories: descriptive (n = 86), qualitative (n = 75), predictors (n = 48), transition interventions (n = 21), measurement tools (n = 8), and key outcomes (n = 3). The certainty of the evidence for specific interventions and tools was generally low. Recommendations The panel issued strong recommendations for integrated, collaborative transition approaches involving YYAs, families, and providers, and for the use of the Transition Readiness Assessment Questionnaire (TRAQ) to support transition planning. Additional recommendations are included, with an emphasis on future research focused on long-term outcomes such as care continuity and treatment retention. Conclusion This research addressed the absence of a cohesive approach to transitions for YYAs experiencing EDs and/or mental health conditions, highlighting evidence variability and the need for a unified approach. These guidelines propose actionable steps for improving care transitions for YYAs with EDs and/or mental health conditions by promoting collaborative care models, prioritizing outcome-focused research, and using measurement tools.Plain English summary Young people with eating disorders (EDs) and/or other mental health conditions often face challenges when moving from pediatric to adult care, both in mental health and physical health realms. This transition typically occurs during adolescence or early adulthood which is a critical developmental period when continuation of care and support is essential. In Canada, there are no clear guidelines to help navigate this process. To address this gap, our team developed six recommendations based on a comprehensive scoping review of existing research. In these recommendations, we emphasize the importance of a collaborative approach that involves young people, families, and healthcare providers to ensure continuity and coordination of their care during the transition. In addition, research should prioritize the study of long-term outcomes, such as whether young people stay in treatment, leave early, or achieve successful transitions. Tools like the Transition Readiness Assessment Questionnaire (TRAQ) can help assess how prepared someone is for this change, but these tools need more testing to ensure they work well for people with EDs. These guidelines aim to improve care during this transition and to ensure that young people with EDs and/or other mental health conditions are prepared and supported as they move into adult healthcare services
Handling Missing Data in the Multivariate Growth Curve Model: Comparative Evaluations Using Extensive Simulations
No full textThe multivariate growth curve models (GCMs) are generalized multivariate analysis of variance (GMANOVA) models useful in analyzing of longitudinal data, growth curves or other datasets involving response curves. Unlike the MANOVA model and traditional linear models (eg. generalized linear mixed models), the GCM allows a structured mean. That is the mean trajectory for each group is expressed as a function of and is assumed to be represented by a polynomials with appropriate degree. Therefore, the GCM involves two design matrices: the within-individual design matrix capturing the mean structure as a function of time and the between-individual design matrix representing group membership coded as dummy variables. Several extensions are made to accommodate clustered longitudinal data, high-dimensional data as well as data that do not follow the multivariate normal distribution. Models that allow structured covariances (eg. compound symmetry, autoregressive, etc) are also available.
A critical challenge we encounter in data analysis is presence of missing data, which is relatively more common in studies involving longitudinal data. Approaches for handling missing longitudinal data do exist and are used in practical applications. However, applications involving the GCM are limited to data with no missing values or complete data analysis, where individuals with missing data are deleted. This leads to either the GCM not being used in practice (because of its demonstrated optimality, in particular in when sample size is small) or loss of information, hence suboptimal inference.
We performed a comprehensive literature review and identified three multivariate methods for handling missing data, developed under the framework of GCMs, taking advantage of the bilinear nature of the model. However, our literature review also revealed that implementations and practical applications of these methods are limited. This is because of a combination of many factors including perhaps the complexity of the proposed approaches and lack of software readily available to implement the methods, despite the methods being available for several decades. Moreover, performances of the methods have not yet been investigated and compared. In this study, we considered the three multivariate methods: Kleinbaum’s (1973) method based on the generalized growth curve model (GGCM), Liski’s (1984) Bayesian method and Liski’s (1985) EM algorithm.
We implemented these methods in R, allowing flexible adaptations to different settings. We also made some extensions to allow generalization of the methods to any number of time points, groups and sample size. We conducted extensive simulations to evaluate and compare performance of these methods in estimation of the mean and variance-covariance parameters of the GCM, we also identified limitations and methodological gaps, some of which we addressed in this thesis. The methods are compared by considering element-wise and aggregated bias and mean squared error (MSE) as well as by comparing them to the gold-standard (assuming no missing data) and complete case analysis (list-wise deletion of missing data, which is not current practice in applications involving the GCM).
Our simulation results show that complete case analysis (ignoring missing data) is not desirable in most of the scenarios considered, but in particular when the sample size is small and the missing percentage is high as well as when the missingness mechanism is non-ignorable. Even when the missingness percentage is small and the mechanism is missing completely at random (MCAR) (which is considered ignorable), the methods developed under the GCM framework provided uniformly smaller MSE values than the complete case analysis. Overall, the simulation results also show that the Liski’s EM algorithm and Liski's Bayesian method overall showed comparable performance as gold-standard in estimating both the mean and variance-covariance parameters. However, these methods become unstable in estimating the variance-covariance matrix in scenarios where the missing percentage is high. This is the case even when the missing mechanism is MCAR or missing at random (MAR). Kleinbaum's GGCM method is unreliable for estimating the mean parameter estimators of the GCM, while it remains stable for estimating the variance-covariance matrix. In fact, in most scenarios, Kleinbaum's method is the most optimal approach for estimating the variance-covariance matrix
Revisiting the Effect of Cultural Similarities on Mergers & Acquisitions
No full textOur research study investigates the relationship between cultural similarities in CSR policies and M&As results. This research builds upon Bereskin et al. (2018) by examining how CSR Strength and CSR Concern dimensions affect M&A occurrence rates, as well as both Cumulative Abnormal Returns (CARs) and post-merger operating performance.
The research demonstrates that both CSR similarity dimensions enhance M&A deal success through better negotiation processes and stakeholder trust development. Our results for the CARs tests do not show a significant influence of either firm's CSR Strength or Concern similarity. Post-merger performance shows evidence of positive effects from CSR Concern, yet CSR Strength fails to produce significant effects, which demonstrates complex interactions that affect long-term results. The research demonstrates to practitioners and policymakers that CSR-based cultural alignment creates successful M&A strategies through its presentation of this need. Future research should analyze different CSR aspects with their market-wide effects across various market environments.
The research contributes to corporate strategy and CSR knowledge by demonstrating how cultural alignment leads to successful M&As in the current globalized business environment
Improving Efficiency in the Discontinuous Galerkin Spectral Element Method for Complex Geometry Physics
No full textThe discontinuous Galerkin spectral element method (DGSEM) is a numerical scheme that has risen in popularity, largely due to its high-order accuracy and geometrical flexibility. It is well suited for convection dominated problems such as high-speed flows and wave propagation. Despite these benefits, it still suffers from the drawback of a high computational cost. In this thesis, we aim to model physics in complex geometries using DGSEM with a focus on efficiency. We report on four different approaches to improve efficiency.
First, we use a transfinite mapping to extend a Cartesian grid DGSEM solver for the acoustic wave equation. This code possesses adaptive mesh refinement (AMR) capabilities through both h-refinement where the elements are split, and p-refinement where the polynomial order of the element is increased. hp-Adaptivity increases the capabilities of simulating complex physics as resolution can be provided in select regions of the domain where necessary. To address the load imbalance caused by AMR in parallel computing, the Cartesian code is dynamically load balanced and was shown to scale well in a high performance computing (HPC) environment. We were able to successfully model the acoustic wave equation in complex geometries using transfinite mapping while retaining the high accuracy of the method. We tested the scaling performance of the code after applying the transfinite mapping and compared to the Cartesian grid performance. The transfinite mapped code scales relatively well but does incur some computational overhead that affects the scaling. Based on the results, we pursued the immersed boundary method as an alternative approach to model complex geometries.
The immersed boundary method (IBM) is a technique where an object is modeled, without conforming to the computational grid. We specifically use the volume penalty method to model objects as porous obstacles for acoustic wave propagation problems. The IBM promises time savings for the end user by simplifying the mesh generation process, as only Cartesian grids are required. However, it does come with the tradeoff of a loss in accuracy and oscillations. We address these issues with hp-adaptivity and a low porosity parameter, where we observed an increase in accuracy and a reduction in oscillatory behavior. In addition to the time savings during the mesh generation process, computational savings can be obtained as the metrics associated with the transfinite mapping are not required.
Next, we explore the half-closed discontinuous Galerkin (HCDG) method to model incompressible flows. In the HCDG method, "half-closed" nodes are used which allow for more efficient operator construction and good sparsity patterns. Complex geometries are represented using unstructured grids. We successfully simulate the incompressible Navier-Stokes equations for a variety of problems at low Reynolds numbers.
Finally, we improve efficiency by developing a new "eliminated" linear solver. We use static condensation to eliminate degrees of freedom based on the local discontinuous Galerkin (LDG) switch function, and apply p-multigrid on the eliminated linear system. We apply this eliminated p-multigrid solver on elliptic problems and the incompressible Navier-Stokes equations for both structured and unstructured grids. We observed significant reductions in the number of degrees of freedom and number of non-zero entries for such problems using our elimination procedure. In addition to the benefits of operating on the smaller linear systems, we also observed reductions in the number of V-multigrid-cycles required for convergence, up to a factor of two for elliptic problems and a 20% reduction for the incompressible Navier-Stokes equations
Why Am I Still Tired? Adaptation, Implementation, and Evaluation of an Intervention for Cancer-Related Fatigue.
No full textCancer related fatigue (CrF) is one of the most prevalent and debilitating post treatment side effects and has been reported as an long-standing unmet need for individuals living beyond cancer (Roseleur et al., 2023). CrF has been defined as a “distressing, persistent, subjective sense of tiredness or exhaustion related to cancer or cancer treatment that is not proportional to recent activity and interferes with usual functioning” (Berger, et al., 2015). Approximately a third of cancer survivors will continue to experience moderate to severe fatigue for several years post treatment (Al Maqbali et al., 2021; Kang et al., 2023). Persistent fatigue has been found to have significant adverse impacts on quality of life and contribute to high levels of disability (Jones et al., 2016). Given the prominence of CrF, guidelines for the assessment and management of CrF have been developed. In Canada, evidence-based guidelines by the Canadian Association for Psychosocial Oncology (CAPO) are available (Howell et al., 2015). Internationally, evidence-based guidelines are available from several international organizations, including European Society for Medical Oncology (ESMO) (Fabi et al., 2020), National Comprehensive Cancer Network (NCCN), (Berger et al., 2015) and most recently the American Society of Clinical Oncology (ASCO) (Bower et al., 2024). Guidelines suggest a variety of effective nonpharmacological interventions for the management of CrF including cognitive behavioural therapy, psychoeducation, mind-body interventions, and physical activity (Howell et al., 2015). Despite the availability of guidelines for CrF and well researched interventions that have demonstrated effectiveness implementation has been lacking (Pearson et al., 2017). Several barriers have been identified including lack of healthcare provider training and knowledge, a lack of leadership, attitudes of patients and HCP towards CrF including perceptions of treatment futility, complexity of guidelines, and inefficient referral pathways (Jones et al., 2021; Pearson et al., 2023; Thong, van Noorden, et al., 2020). This thesis’ objectives were to adapt, implement, and evaluate an intervention for CrF in a community context using the Knowledge-to-Action framework (KTA) (Field et al., 2014), RE-AIM framework (Holtrop et al., 2021), and community and patient partnership.
Study 1 captures KTA stages of adaptation of knowledge to the local context and identification of barriers by exploring what an ideal intervention for CrF would look like from the perspectives of different interest holders and the barriers to its implementation. A total of 62 participants—16 patients, 32 healthcare providers (HCPs), and 15 community support providers (CSPs) were recruited. This needs assessment described what patients, CSPs, and HCPs wanted in an ideal intervention for CrF and the potential barriers to implementing such intervention through qualitative methodology. Data was collected via nine focus groups and four semi-structured interviews. Data was coded into themes using content analysis. Two main themes emerged around addressing CrF: “It takes a village” and “This will not be easy”. Participants discussed an intervention for CrF could be anywhere, offered by anyone and everyone, and provided early and frequently throughout the cancer experience and could include peer support, psychoeducation, physical activity, mind–body interventions, and interdisciplinary care. Patients, HCPs, and CSPs described several potential barriers to implementation, including patient barriers (i.e., patient variability, accessibility, online literacy, and overload of information) and systems barriers (i.e., costs, lack of HCP knowledge, system insufficiency, and time). This study laid the groundwork for study 2’s implementation of a patient-centered intervention for CrF in a community context by identifying a significant need for CrF programming in the local context, guiding the selection of an intervention, and preparing for anticipated barriers.
Study 2 addresses KTA stages of selection/tailoring of an intervention, monitoring knowledge use, evaluating outcomes, and sustaining knowledge use. Based on our needs assessment, an evident knowledge to practice gap exists for CrF management in Ottawa, Canada. This pilot study employed a type-2 hybrid effectiveness-implementation design. Participants (N=50) were randomized to either an intervention group or a wait-list control, with both groups completing questionnaires at baseline, at 5 weeks, and at 2-month follow-up. Participants assigned to the intervention group received four virtual 90-minute sessions facilitated by a registered nurse/cancer coach. The CrF intervention showed promise of improvements in quality-of-life improvements on the Functional Assessment of Chronic Illness Therapy-Fatigue (FACIT-F-13) (F2,84=4.73, p= .015, ηp2 = .101) and reduced fatigue on the Multidimensional Fatigue Inventory-20 (MFI-20) (F2,84=6.18, p= .007, ηp2 = .128), compared to the wait-list control. Improvements in sleep and depression were also found. Evaluation of implementation outcomes were guided by the RE-AIM framework (Holtrop et al., 2021). The intervention was delivered with high levels of fidelity and low attrition. The intervention was found to be feasible with high satisfaction rates suggesting acceptability. Through partnership with a community organization and patient advisory board, this project was able to ensure a) end users and providers’ needs were met and b) maintenance of the CrF program for individuals living with and beyond cancer who are experiencing CrF in the Ottawa region