Black Movement and Freedom: Questions of Cyclescapes, Cycling Planning, and Minstrelsy

This paper investigates the following central question: What are the outcomes of the historical and ongoing restrictions placed upon the Black diaspora's physical movement? Related to my research question, I consider what the literature and archives have to say about Black experiences with movement and I engage with cycling-related scholarship on class and race, particularly as it relates to Black communities. I explore this in this paper to sufficiently contextualize the subject-matter I am engaging with. I argue that the historical and ongoing restrictions of the movement of the Black diaspora is subjectivity-producing and provides an alternative lens to better understanding anti-Blackness, and liberatory ways of understanding and engaging with movement. Additionally, to contribute to advancing an underexplored research topic in Black Geographies and further the growing scholarship on cycling and racism. Additionally, I explore the experiences of cycling and Black communities and conduct a research analysis on late nineteenth-century minstrel and other anti-Black imagery featuring bicycles. This paper focuses on Canada and the United States, bringing cycling and transportation research into conversation with Black studies and Black geographies. I draw on archival materials from the late 1800s to early 1900s, alongside a counter-archival and discourse analysis. My sources include journalism, transportation planning data, and academic literature in social geography, anthropology, and history—all centred on cycling in North America

Transmission Dynamics And Control Of Cholera In Africa: A Mathematical Modelling Approach

Background: Cholera, caused by Vibrio cholerae, is a global health threat, with outbreaks surging since 2021, particularly in Africa. In 2024, over 13 African countries faced outbreaks worsened by climatic events, poverty, and weak healthcare systems. A shortage of vaccines further complicates control efforts. Objective: This study uses data science, machine learning, and modelling to analyze cholera dynamics, identify outbreak drivers, and propose targeted interventions. Methods: A compartmental model with Bayesian estimation analyzed cholera data from eight African countries. Sensitivity analysis identified key transmission parameters, and hierarchical clustering grouped countries by outbreak characteristics. Results: Average R0 was 2.0, ranging from 1.41 (Zimbabwe) to 2.80 (Mozambique). Factors like infection rate and human shedding increased R0, while recovery rate reduced it. Clustering identified three outbreak drivers: natural disasters, conflict, and sanitation issues. Conclusion: Tailored, data-driven interventions are critical for effective cholera management across diverse contexts

The Effect Of Ketogenic Diet On Hepatic Cholesterol Metabolism

The ketogenic diet (KD), known for its high-fat, low-carbohydrate composition, has gained popularity for weight loss and metabolic health benefits. Despite these advantages, there are concerns that the diet's high saturated fat content might elevate cholesterol levels and cardiovascular disease (CVD) risk. This study investigates the KD's impact on the molecular mechanisms of cholesterol metabolism in the liver, focusing on cholesterol synthesis markers such as 3-hydroxy-3-methylglutaryl coenzyme A reductase (HMG-CoA reductase) and sterol regulatory element-binding protein-2 (SREBP-2), as well as cholesterol uptake markers including proprotein convertase subtilisin/kexin type 9 (PCSK9) and LDL receptors (LDLr). For that, male Wistar rats (n = 6 per group) were fed for 16 weeks one of the following diets: standard chow (SC, 60% carbohydrates, 13% fat, 27% protein), high-fat sucrose-enriched (HFS, 20% carbohydrates, 60% fat, 20% protein), and ketogenic diet (KD, 0% carbohydrates, 80% fat, 20% protein). Liver tissue was extracted and analyzed for gene expression using real-time PCR and protein content using western blotting. Blood samples were collected to measure circulating cholesterol levels. We found that neither plasma cholesterol levels nor HMG-CoA reductase and SREBP-2 levels in the liver differed among the dietary interventions. However, the KD significantly reduced liver PCSK9 content and expression in comparison other diets, suggesting that the KD enhanced clearance of circulating cholesterol by the liver. To test whether there was a higher amount of LDLr on the membrane compared to the cytoplasm, the ratio of LDLr distribution between these compartments was measured. Importantly, there was an upward trend in the levels of LDLr on the membrane. In conclusion, the KD altered key steps that regulate hepatic cholesterol metabolism and prevented plasma cholesterol levels from increasing, despite its elevated saturated fat content

Securing Multi-Layer Federated Learning: Detecting and Mitigating Adversarial Attacks

Within the realm of federated learning (FL), adversarial entities can poison models, slowing down or destroying the FL training process. Therefore, attack prevention and mitigation are crucial for FL. Real-world scenarios may necessitate additional separation or abstraction between clients and servers. When considering multi-layer FL systems, which contain edge server layers, the structural differences warrant new strategies to handle adversaries. While existing works primarily address attack prevention and mitigation in conventional two-layer FL systems, research on attack prevention and mitigation in multi-layer federated learning systems remains limited. This thesis aims to address this gap by investigating the defense strategies in a multi-layered FL system. We propose new methods for anomaly detection and removal of attackers/adversarial entities from training in a multi-layer FL system. First, we train a variational autoencoder (VAE) using the model updates collected from the edge servers. This allows the VAE to discern between benign and adversarial model updates. Following that, we deploy the VAE to detect which edge servers at the cohort level contain malicious clients. Subsequently, we devise two malicious client exclusion strategies: the scoring-based method, which applies a score for each client based upon its appearances within cohorts labeled as benign or malicious, and the Bayesian-based method, which uses Bayesian inference to predict if a specific client is malicious based on the statistical performance of the autoencoder. Both approaches are aimed at mitigating potential harm caused by malicious clients during model training. The experimental results demonstrate the superiority of the proposed methods over previous works for traditional FL mitigation under a variety of scenarios

Multi-Method Study On Referral And Access To Heart Function Clinics

Patients with heart failure (HF) experience significant benefits from receiving comprehensive outpatient care in specialized heart failure clinics (HF clinics). These clinics have demonstrated their effectiveness in reducing frequent HF-related hospital readmissions while maintaining cost-efficiency. Unfortunately, despite established guidelines recommending the referral of HF patients to these clinics, there exists a notable discrepancy in both access and utilization of this specialized care, creating issues of low and inequitable service utilization. The underlying reasons are largely unknown and under-researched. Therefore, this doctoral dissertation aimed to advance a scholarly understanding of factors influencing the referral decisions and access to HF clinics through a multi-method study. For this purpose, three inter-linked research studies were undertaken. Firstly, qualitative interviews were conducted with key stakeholders in HF care, including policymakers, clinic providers, and patients. This initial phase established a foundational understanding of the barriers preventing optimal access to HF clinic services. Secondly, recognizing that referring providers play a pivotal role in determining patient access to HF clinics, a mixed-method design was employed, using a sequential exploratory approach to delve into their perspectives on the challenges associated with referring patients to HF clinics. Finally, a cross-sectional survey approach was adopted to compare clinic perceptions of ideal referral criteria with those of referring providers. By identifying areas of agreement between both parties, strategies for consistent application were proposed. This dissertation contributes valuable insights for HF clinics and the broader HF community. The knowledge generated has the potential, when translated into practice, to facilitate appropriate patient access to essential HF services. The findings offer guidance to policymakers, healthcare providers, and HF patients, aiming to optimize the utilization of HF clinic services, enhance the quality of care provided, and improve overall patient outcomes

Modeling of Eye contact behavior

With the rise of online platforms and avatar-based communication, understanding eye contact a key non-verbal cue is crucial for trust in conversations. This study examines eye contact behavior across face-to-face interactions, a screen-sized window interface, and online meetings. We collected twelve hours of eye contact data from 48 individuals using eye trackers and motion capture in dyadic settings. Our analysis showed consistent eye contact patterns in face-to-face and screen-sized window interactions, while online meetings caused significant shifts due to the lack of direct eye contact. To model this behavior, we trained a diffusion model (DDPM) to generate synthetic eye movements that preserved key features of real data. We evaluated our model using metrics such as eye contact frequency. This study provides insights into how communication media influence gaze behavior and explores methods for generating realistic eye movements in conversational settings

Ultrasound Reliability of Blood Flow Measurements in Neck Vasculature: A Comparison Between a Novice and Experienced Sonographer

Current literature on the reliability of ultrasound in measuring blood flow volume of the neck vasculature remains sparse, especially when comparing the reliability of these measurements taken by a novice sonographer to an experienced sonographer. This study sought to examine the reliability of blood flow measurements taken by a novice and experienced sonographer in the left common carotid artery (CCA), internal carotid artery (ICA), and vertebral artery (VA) using duplex Doppler ultrasound (DDU). Intraclass correlation coefficients (ICCs) revealed poor inter-rater reliability within the CCA (-.127 to .445) and ICA (.066 to .321), and moderate reliability in the VA (.694 to .725). ICCs also revealed moderate intra-rater reliability of novice blood flow measurements in the CCA (.701) and ICA (.729), and good reliability in the VA (.818). Results demonstrated an overall lack of inter-rater reliability, suggesting a single sonographer be used for research involving repeated evaluations to increase consistency in measures

Spillover Modelling And Dynamics In Multi-Host Pathogens Transmission

Many pathogens of concern to both human and animal populations exhibit a generalist nature of infecting multiple host species. The behavior and transmission dynamics within reservoir hosts not only influence outbreaks within their own population but also contribute to the spillover of pathogens to new target hosts. Although existing works have incorporated spillover transmission into zoonotic models, significant gaps remain in understanding the epidemic or endemic spread of disease in target hosts due to spillover, particularly in epizootic contexts. One typical example is the monkeypox. In this research, by delineating host roles and examining transmission dynamics of monkeypox, we can effectively assess the risk of spillover events and inform mitigation and control strategies. We start with a foundational framework that models monkeypox transmission in a single host species. Two kinds of stochasticity, namely demographic and environmental stochasticity, are incorporated. We find population-size-dependent shift in the relative influence of demographic and environmental stochasticity on disease dynamics. By developing a basic reservoir-target epidemic systems, we observe that the basic reproduction number of the system fails to capture interspecific transmissibility. Our novel threshold derived from the final size relation reflects the influence of spillover processes and intraspecific transmission within target hosts, providing an appropriate measure for quantifying the spillover phenomena. Subsequently, incorporating population demographics allows us to determine the population extinction threshold and the maximum persistence threshold. We further verify that stochasticity in the spillover rate induces Phenomenological bifurcation (P-bifurcation) within the model. These analyses reveal that the spillover rate is the most critical factor influencing the epidemic and endemic prevalence in target hosts. Finally, we evaluate the effectiveness of reservoir control strategies such as quarantine and culling. Our findings indicate that the interactions between spillover events and the implementation of reservoir control strategies lead to complex dynamics due to the higher codimension bifurcations. A novel observation from our analysis and numerical simulations is the existence and collision of two limit cycles generated by distinct endemic equilibria within the system. Our study underscores the importance of controlling spillover events and managing reservoir prevalence as key interventions to mitigate spillover effects on target hosts

Characterizing Boreal Forest Fire Disturbance Boundaries Through Space And Time In Ontario

The Ontario boreal forest contains vast natural resources but is increasingly threatened by wildland fires, which are becoming more frequent and affecting larger areas due to climate change. In response, this thesis compares wildland fire boundaries derived from vegetation index slopes with those provided by BorealDB a newly developed database that compiles consistent disturbance maps from 1972 to the present. BorealDB includes various attribute combinations and an ensemble confidence measure that shows how often different data sources agree. By examining which attribute combinations produce fire boundaries that most closely match remote sensing data, this research offers practical guidance for BorealDB users on selecting the most reliable disturbance points for their analyses

Fighting Bias In Hiring

Implicit bias, unconscious attitudes that influence decision-making, remains a critical barrier to achieving Diversity, Equity, and Inclusion (DEI) goals (Stephens et al., 2020). Despite evidence linking diversity to organizational performance (Hunt et al., 2015), racial and ethnic minorities continue facing higher unemployment (Wanberg et al., 2020). This disconnect suggests that DEI strategies may lack integration or fail to address embedded biases in hiring (Dobbin & Kalev, 2016). Using a multidisciplinary lens and an inductive qualitative approach, this study investigates current strategies for mitigating hiring bias by examining evidence-based employers’ post-COVID practices and the biases job seekers reported to have encountered. Key findings reveal misalignment and inconsistencies such as limited bias recognition despite strong DEI awareness, subjectivity in recruitment practices, and bias manifestations like accent bias and undervaluation of international experience affecting immigrants and newcomers. The research offers actionable insights to address these vulnerabilities, promoting equity and inclusivity in recruitment