Master's Theses
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Item Open Access Women in social work leadership in Saskatchewan: The myth of shattering the glass ceiling(Faculty of Graduate Studies and Research, University of Regina, 2024-06) Masuda, Nancy Lynn; Gebhard, Amanda; Novik, Nuelle; Jeffery, BonnieThe purpose of this qualitative research was to enhance knowledge about the experiences of women in the 1% of marginalized leadership representation statistics in Saskatchewan in the field of social work. Through an intersectional feminist framework, based on the interrelatedness of our social identities, both privileged and oppressed; narrative interviews were completed with five women. The participants identified as belonging to the LGBTQ2S+, BIPOC, disability and/or trans communities. Within the social work leadership sector, the women have held upper management, director, and CEO positions. Data was analyzed through a case-by-case analysis and a thematic analysis. Several key findings emerged from the research: Social workers in upper management positions demonstrate a commitment to relational values in their leadership perspectives. Women encounter micro, mezzo and macro fractures within the profession. Women face intersecting forms of oppression, and do not relate to the metaphor of the glass ceiling in leadership. When it comes to leadership styles, humanistic and collaborative leadership models are embraced by social work leaders. Finally, mentorship is a profound legacy in the momentum of creating future leaders. This thesis offers a comprehensive exploration of leadership within the context of social work, focusing on the perspectives of marginalized women in leadership roles in Saskatchewan. The findings highlight the significance of the disruption of gendered leadership roles, and the multiple intersecting forms of oppression and privilege women in leadership contend with in their everyday work. Readers are brought to consider, through the unique and collective perspectives offered by participants, the importance of expanding and redefining the meaning of leadership in social work.Item Open Access Joint survival and longitudinal modelling for combined cohorts(Faculty of Graduate Studies and Research, University of Regina, 2024-04) Zhao, Qian; McVittie, James; Deng, DianliangA primary goal of survival analysis is modelling the time from an initial event to a failure event and the factors which affect the hazard rate. In this thesis, we consider data drawn from an incident cohort study with follow-up and a prevalent cohort study with follow-up, yielding right-censored failure time data and left-truncated right-censored failure time data, respectively. In addition to the failure/censoring times, we assume the survival data include other measured covariates, for example, longitudinal measurements collected throughout the failure/censoring time durations. In this thesis, we review the methods of estimation for the proportional hazards model, the linear mixed effects model, and the joint model, to determine the association between the survival and the longitudinal processes. We introduce novel estimation methods for the joint model when incident and prevalent cohort data are combined. Using simulated data, we compare the various individual and combined cohort estimation procedures. We also apply the combined cohort joint model to examine the relationship between the mortality of mature female baboons based on their longitudinally measured glucocorticoid levels.Item Open Access Responses to hawking attack and distress call sequences by bats in Saskatchewan(Faculty of Graduate Studies and Research, University of Regina, 2024-05) Wilson, Hannah Cummings; Brigham, Mark; Ratcliffe, John M.; Vanderwel, Mark; Somers, ChrisEavesdropping on acoustic signals can be an important decision-making tool for many animals. Despite how much research has gone into how bats use sound for orientation and foraging, surprisingly little research has focused on eavesdropping as a decision making and survival tool. I assessed how four insectivorous bats in North America, the big brown bat (Eptesicus fuscus), hoary bat (Lasiurus cinereus), silver haired bat (Lasionycteris noctivagans), and little brown bat (Myotis lucifugus) responded to conspecific and heterospecific call sequences. I played distress and aerial hawking attack sequences along Battle Creek in Southwest Saskatchewan, Canada and used acoustic detectors to determine whether there was a change in bat activity during playbacks. Call collection and playback experiments were conducted between June – August 2022 and 2023. I expected an overall increase in activity when I played back both conspecific and heterospecific call sequences for both call types, with some species-specific differences based on size and life history. However, I found that M. lucifugus did not respond to conspecific or heterospecific distress call or hawking attack sequences. This is in contrast with previous studies that have concluded M. lucifugus is attracted to conspecific vocalizations. I also found that while L. cinereus are attracted to conspecific hawking sequences, they are not attracted to conspecific distress sequences. They are, however, attracted to L. noctivagans distress sequences; but, repelled by L. noctivagans hawking attack sequences. L. noctivagans do not respond to conspecific or heterospecific distress calls, but are repelled by L. cinereus hawking sequences. My research highlights how little we know about how and why bats respond to call sequences and, by extension, how little we know about bat community relationships in general.Item Open Access Examining the gut microbiota in aging(Faculty of Graduate Studies and Research, University of Regina, 2024-07) Verma, Priya; Totosy, Julia; Cameron, Andrew; Gordon, JenniferThe human gut microbiome, comprised primarily of bacteria, plays a crucial role in overall health and physiology by regulating metabolism, immunity, and behavior through the microbiota-gut-brain axis (MGBA). Key functions include fermenting complex carbohydrates into short-chain fatty acids (SCFAs) that maintain gut integrity, modulating inflammation, and influencing energy homeostasis (i.e., food intake regulation). For example, SCFAs can play a role in food intake regulation by directly or indirectly influencing episodic appetite signals (ghrelin, glucagon-like peptide 1 [GLP-1]) and tonic appetite signals (leptin, insulin). Gut dysbiosis, or an imbalance in the gut microbiome, leads to altered metabolic processes, increased epithelial permeability, and systemic inflammation that contribute to health issues like metabolic dysfunction (type 2 diabetes [T2D]), cardiovascular disease, and neuropsychiatric conditions. Aging is associated with gut dysbiosis, which can affect energy homeostasis and increase the risk for age-associated comorbidities. Regular physical activity has been shown to improve gut health and food intake regulation in young adults. This project aimed to characterize gut microbial communities in active older adults (60 years +) (GUMS) compared to a sedentary cohort with type 2 diabetes (T2D) (DMP) and investigate how gut bacteria may be influenced by physical activity/cardiorespiratory fitness (CRF), body composition, appetite hormones, cardiometabolic health, and mental health. Data was analyzed from n=22 GUMS (active older adults) and n=6 DMP (sedentary older adults with T2D). Gut bacterial diversity and composition were assessed by sequencing of 16S amplicon DNA from participant stool samples. In-person data was collected to assess CRF (via peak oxygen consumption [VO2peak] test), body composition (via air displacement plethysmography), appetite hormones (from plasma blood samples), and cardiovascular health (blood pressure, arterial stiffness). Mental health was assessed using standardized questionnaires administered through Qualtrics. Diversity measures were not significantly different between physically active older adults (GUMS) and sedentary older adults with type 2 diabetes (DMP); however compositional differences were observed. A nonsignificant increased proportion of opportunistic pathogens is apparent in DMP, suggestive of gut dysbiosis in T2D. Further, lower CRF and higher body fat [observed in DMP] were linked to decreased gut microbial diversity and increased opportunistic pathogens. Fasted appetite hormones ghrelin and GLP-1 did not differ between groups, but insulin and glucose levels were higher in DMP, reflecting their diabetic condition. No correlations were found between gut microbial diversity and appetite hormones, though positive correlations were observed between appetite hormones and SCFA-producing taxa. Dietary patterns showed higher fat intake and lower fiber consumption in DMP, potentially contributing to dysbiosis and metabolic issues. It was also observed that hypertension medication was associated with reduced gut microbial diversity and increased opportunistic pathogens, indicating a potential dysbiotic effect of hypertension treatment. Future research should recruit a larger number of participants and conduct long-term studies comparing older and younger populations to further understand the impact of aging and habitual physical activity on the gut microbiome. Additionally, exploring broader ranges of appetite hormone values could clarify their associations with gut microbial composition. Further research is also required to confirm the dysbiotic effects of hypertension medication.Item Open Access Analyzing distributions using a systematic programmable approach as persistent homology(Faculty of Graduate Studies and Research, University of Regina, 2024-06) Sokaribo, Baruch Senibo; Stanley, Donald; Volodin, Andrei; Frankland, MartinPersistent homology is a tool in mathematics used for analyzing data topologically. This analysis is made possible through one of its components called a filtered simplicial complex, which is a sequence of nested simplicial complexes. With this, we can obtain our persistent π0 (gaps) which is the number of connected components, and also apply homology on the filtration to obtain the persistent homology. This thesis focuses on the comparison of three fundamental probability distributions which are the Normal distribution, Uniform distribution, and Exponential distribution, using a Python code to derive a filtration, which we analyze to obtain persistent homology. The comparison enables us to categorize any random data into a distinct distribution after analyzing its behavior with the Python code. Firstly, the three distributions are compared statistically, after which a Python program is developed which is divided into four different sections. The first and second sections input parameters and generate the distinct probability distribution data respectively, while the third and fourth sections analyze the data generated from each distribution to output relevant information which includes the persistent π0 and the connected components. The connected components are further analyzed to form a filtration, from which we obtain our persistent gaps and persistent homology. In conclusion, this project provides worthwhile insights into the behavior and characteristics of the Normal, Uniform, and Exponential distributions through the spectacles of topology. These findings create the building blocks for analyzing and classifying random data and also for better understanding and analyzing various real-world phenomena governed by these distributions to enhance future research in diverse fields.Item Open Access A robust intrusion detection system utilizing uncertain reasoning techniques in artificial intelligence(Faculty of Graduate Studies and Research, University of Regina, 2024-05) Singh, Harpreet; Louafi, Habib; Yao, Yiyu; Shahriar, NashidNetwork Intrusion Detection Systems (NIDS) are essential components in cybersecurity, but they face several challenges, including uncertainty and a significant computational overhead. Network attacks and unauthorized access to remote computers can be detected by NIDS. Artificial Intelligence (AI) techniques have been used to automate the intrusion detection process and reduce human intervention, thereby enhancing intrusion detection systems (IDS) performance. AI techniques, such as fuzzy logic, neural networks, and evolutionary computing can also be used in IDS. One proposed application of AI is to utilize evidential reasoning to handle uncertainty in NIDS. This approach leads to more efficient abnormality detection in user behaviour, making it a powerful tool for NIDS. This research primarily focuses on NIDS based on uncertain reasoning AI. The latter is more explainable than machine learning and deep learning approaches because it relies on well-understood principles, such as probabilities. In contrast, machine learning approaches are often considered black boxes, which makes them challenging to explain. We primarily work on real-time network traffic or packet-captured files, with the main objective of looking for attack signs of various types, using Bayesian belief networks (probabilistic graphical models). Throughout this thesis, we describe the IDS and the analysis of network traffic using a BN and Markov network (MN). BN is used to formulate the problem domain, whereas the MN is used for the inference and calculation of marginal distribution. To do so, different propagation algorithms are explored, such as Variable Elimination (VE), Lauritzen-Spiegelhalter Propagation (LS), Shafer-Shenoy Propagation (SS), and Lazy Propagation(LP). The data used in the experiments originated from the CAIDA Lab. CAIDA dataset contains network traffic packets from Distributed Denial-of-Service (DDoS) attacks. Therefore, the main objective of this thesis is to develop an uncertain reasoning-based NIDS system capable of predicting DoS/DDoS attacks with higher accuracy while reducing the computation overhead. Extensive experiments are conducted using the above-listed inference algorithms, and thus three models are trained for each protocol on the CAIDA dataset. The experiments show appealing results, measured using well-known metrics, such as Precision, Recall, and F1-Score. Overall, the SS and LP are efficient, but with minor differences.Item Open Access Depicting variable elimination with Bayesian networks(Faculty of Graduate Studies and Research, University of Regina, 2024-08) Sasyniuk, Brandon Cordell; Butz, Cory; Sadaoui, Samira; Yao, YiyuThis thesis presents a novel graphical representation of variable elimination in discrete Bayesian networks (BNs) utilizing the BN’s directed acyclic graph (DAG) component. This includes methods representing both multiplication and marginalization operations. This graphical representation is achieved by introducing what are known as compound BN nodes, whose presence denotes a compound BN. The key to fitting them into the pre-existing BN DAG is to re-evaluate what a single node in the DAG represents; not a single variable, but the left-hand side of a single conditional probability table (CPT) of the BN. Using compound BNs allows for more descriptive nodes that can better represent the dependency relationship between variables as they are merged and marginalized out according to the variable elimination algorithm (VE), which is used for efficient probabilistic inference using BNs. This thesis also presents an application which implements the graphical representation of VE. This implementation follows the introduced d-separation algorithm to add edges to preserve the dependencies for the elimination target’s parents, children, and spouse nodes. Node multiplication, as it is conceptually understood, is redundant in this implementation as it does not need to generate and modify compound nodes; just delete classic BN nodes and their edges after adding compensatory edges according to d-separation. The work presented in this thesis precisely depicts the recursive elimination of arbitrary variables as classical BNs. This new graphical representation is an improvement over previous literature, which is currently limited to graphically representing the probability information as a sub-DAG prior to variable elimination starting or special cases of variable elimination. Other previous attempts in the literature would take a non-Bayesian approach to representing arbitrary variable elimination, whereas this graphical representation is done entirely by utilizing a BN DAG.Item Open Access The Bures metric: from positive linear functionals to completely positive maps(Faculty of Graduate Studies and Research, University of Regina, 2024-07) Sadikov, Avner; Floricel, Remus; Plosker, Sarah; Argerami, MartínThis thesis aims to explore the evolution of the notion of the Bures metric in Operator Algebras, from its introduction by D. Bures in 1969 for normal states of von Neumann algebras to its extension to completely positive maps of C∗-algebras by D. Kretschmann, D. Schlingemann, and R. Werner in 2008. While Bures’ work is rooted in von Neumann algebras, our primary focus will be on unital C∗-algebras. We will explore the definitions and main properties of the Bures metric for positive linear functionals, providing bounds and additional insights into fidelity — a measure closely related to the Bures metric. The thesis extends these concepts to completely bounded maps and completely positive maps, introducing the Bures metric for such mappings and characterizing them in terms of their Stinespring representations. We show that the Bures metric forms a true metric space on the set of positive linear functionals, as well as on the set of completely positive maps that take values on injective C∗-algebras. This offers both theoretical bounds and practical methods to compute distances in these sets.Item Open Access Musings on matchings, matrices, and multiplicities(Faculty of Graduate Studies and Research, University of Regina, 2024-07) Parenteau, Johnna Michele; Fallat, Shaun; Herman, Allen; Meagher, KarenThe Parter-Wiener Theorem is a celebrated contribution to the inverse eigenvalue problem for trees due to its determination of vertices whose removal affects multiplicities of eigenvalues in a non-intuitive manner. For a more general graph, G, that contains cycles, the construction of the weighted matching polynomial and its many properties are derived. These properties are shown to determine a relationship between the multiplicities of the roots of the weighted matching polynomial and the graph operation of vertex deletion in G, which is the operation at the core of the Parter-Wiener Theorem. Solutions for locating vertices whose removal increases the multiplicity of a root are presented, which gives rise to a new classification of graphs, called SRSI graphs. These graphs, along with graphs that have Hamilton paths, are determined to have a trivial variation of the Parter-Wiener Theorem. In an effort to determine the location of Parter vertices, vertices are categorized into classes based on their effects of root multiplicities, and, in the case of zero roots, the location of Parter vertices are explicitly noted. Moreover, computational results regarding the process of categorizing vertices into these classes are outlined, and the Vandermonde eigenvector test is established with the assistance of companion matrices. A myriad of results throughout the thesis are then used to determine a partially-generalized Parter-Wiener Theorem for this weighted matching polynomial.Item Open Access Participation in 12 weeks of the Saskatchewan Health Authority’s Diabetes Wellness Series: Effects on appetite hormone response to a mixed meal tolerance test(Faculty of Graduate Studies and Research, University of Regina, 2024-07) Nafisi, Raha; Totosy, Julia; Bomhof, Marc; Mang, CameronFood intake (FI) dysregulation in type 2 diabetes (T2D) may be due to impaired tonic satiety signals (e.g., insulin, leptin) and episodic FI signals (e.g., glucose, acyl-ghrelin [a-ghrelin; appetite-stimulating], active glucagon-like peptide-1 [aGLP-1; appetite-inhibiting]), promoting caloric overconsumption. Research has shown that regular physical activity is associated with more sensitive appetite control. The present thesis aimed to: (1) investigate the acute responsiveness of FI regulatory signals (glucose, insulin, a-ghrelin, aGLP-1) and perception of appetite to a 120-minute mixed meal tolerance test (MMTT) in individuals with T2D, and (2) assess the impact of participation in 12 weeks of the Saskatchewan Health Authority’s (SHA) Diabetes Wellness Series (DWS) program, incorporating exercise (aerobic and resistance) and education (e.g., nutrition, mental health, stress, etc.), on FI regulatory signal responsiveness and perception of appetite to an MMTT. Data from 21 participants (9M, 12F; age = 68.0±9.8 y; duration of T2D diagnosis = 11.0±10.7 y) was collected for Objective 1; values for blood glucose (6.4±1.7, 7.5±2.2, 8.8±2.2, 9.5±2.6, 9.6±3.1, 8.4±3.0mmol/L), a-ghrelin (251.9±198.5, 189.3±147.5, 151.1±124.7, 159.1±147.7, 129.3±113.2, 213.4±193.5pg/ml), aGLP-1(1.1±1.1, 3.0±2.6, 3.4±4.0, 1.8±1.6, 1.7±2.5, 1.5±1.3 pM), insulin (19.3±9.1, 28.5±19.4, 40.7±31.9, 56.6±40.9, 47.9±33.2, 36.9±28.6 ulU/mL), and average appetite (AA; via visual analog scale) (47.7±22.2, 38.6±23.2, 38.7±19.2, 36.1±17.1, 46.4±20.0, 51.5±16.7 mm) were observed before, and at 15, 30, 60, 90, and 120-mintute after consuming a 237g Boost® (240kcal [56.4% carbohydrate, 24.9% protein, and 18.7% fat]). Repeated measures General Linear Model (GLM) analyses revealed a significant effect of time for all FI regulatory signals and perception of appetite (p<0.05). Compared to the literature on metabolically healthy adults, fasted values of a-ghrelin were low while fasted values of glucose, insulin, leptin, and aGLP-1 were high, and there was an impaired responsiveness to the MMTT. No relationship between FI regulatory signals and AA suggests a disrupted association between FI signals and perception of appetite in T2D. Sixteen participants (7M, 9F; age = 69.0±11.0 y; duration of T2D diagnosis = 11.0±10.3 y) returned after the 12-weeks of SHA DWS programming. Factorial repeated GLM analyses assessed main effects of training state (pre vs. post) and time (0, 15, 30, 60, 90, 120 minutes) and their interactions; no interactions or main effects of training-state were found; however, a main effect of time was observed for all FI regulatory signals and perception of appetite (p<0.05). Incremental area under the curve (iAUC) analyses showed increased aGLP-1 (p=0.03) and a trend towards decreased a-ghrelin (p=0.07). The hormone ‘sensitivity’ did not change at post-intervention; however, no significant change in the relationships between FI regulatory signals and AA were observed. While 12 weeks of adherence to the SHA DWS had a positive influence on some aspects of FI regulation, it may not have been enough to elicit clinically significant changes. The DWS should continue to be promoted to expose individuals with T2D to exercise programming and education; however, extending the program duration beyond 12 weeks to allow more time for habit formation (i.e., positive lifestyle change), introducing more intensive and frequent exercise sessions, and adopting a more focused approach to nutrition and dietary changes may be necessary to enhance the effectiveness of the DWS. Further, future directions to address the high incidence and prevalence of T2D in Saskatchewan should include prevention in addition to management.Item Open Access Machine Learning on simulated forward calorimeter upgrade for JEF(Faculty of Graduate Studies and Research, University of Regina, 2024-06) Mahmood, Azizah Tahir; Papandreou, Zisis; Fanelli, Cristiano; Teymurazyan, Aram; Berg, MarcellaThis thesis centers around using Machine Learning to distinguish true photons from hadronic split-offs detected by the JEF and GlueX experiments. The FCAL is an electromagnetic detector undergoing an upgrade to its inner region for improving neutral particle identification for the JEF project. Search for exotic η and η′ decays are the focal point JEF experiment as they test the limits of the current Standard Model and provide unique opportunities to search for Dark Matter candidates. The aim of this project is to develop of a shower classification scheme that can confidently separate between true photons and split-offs showers in the FCAL2 detector, as an extension of the study done on the original FCAL. Using simulated ω → π+π−π0 data, multiple Machine Learning models are trained and tested in their separation capabilities. The MLP and BDT models are found to provide robust classification. Their performance is tested in separate areas of the FCAL2, and validated on η → π+π−π0 to ensure application of these models to the JEF. They were both found to have consistently strong separation capabilities in all areas tested. This thesis outlines the motivation for JEF, details the ongoing detector upgrade, and explains the machine learning efforts on improve particle classification for the FCAL2.Item Open Access Exploration of the antimicrobial capabilities of Pantoea: Insights into a metabolically diverse genus(Faculty of Graduate Studies and Research, University of Regina, 2024-06) Kirk, Ashlyn Michelle; Stavrinides, John; Russell, JenniferThe rise of antimicrobial resistance has once again renewed interest in the development of novel antimicrobials for the treatment of resistant pathogens. The genus of bacteria Pantoea, a member of the Erwiniaceae, has been shown to produce several unique antimicrobials with diverse targets. Chapter one gives a review of the known antimicrobials produced by Pantoea including their discovery, genetic origins, biosynthetic pathways, structures, mechanisms of action and resistance, distributions, and insights into their evolutionary histories. Chapter two describes a replica-plating technique that was developed to quickly and easily screen transposon mutant libraries to discover the biosynthetic gene clusters responsible for antimicrobial biosynthesis in bacteria, using Pantoea as a model. This method uses filter paper to transfer up to 200 mutants simultaneously onto soft agar overlays or spread plates containing a target microbe to screen for loss of antibiotic production. The efficacy of this method was demonstrated by re-screening for antibiotic biosynthetic gene clusters for three previously identified Pantoea antibiotics: Pantoea natural product (PNP) 1, 2, and 3. This method was then used to identify the biosynthetic gene cluster that was producing an unknown antibiotic in the clinical isolate P. agglomerans 20KB447973, which we named PNP-5. The recovered biosynthetic gene cluster for PNP-5 shows similarity to the broadspectrum dithiolopyrrolone antibiotic, holomycin. PNP-5 shows broad-spectrum activity against members of the Enterobacteriaceae, Erwiniaceae, and Streptococcaceae, including clinically relevant pathogens such as Klebsiella spp. and Escherichia coli. We also identified a second antibiotic produced by P. agglomerans 20KB447973: pantocin A. Chapter three aims to uncover the distribution and provide insight into the potential evolutionary history of known Pantoea antimicrobial biosynthetic gene clusters. A survey of 12 known clusters against public databases and our genome collection show that some clusters are found strictly in Pantoea, while others are more broadly distributed in distantly related genera within Vibrionaceae, Pectobacteriaceae, Yersiniaceae, Morganellaceae, and Hafniaceae. We compared the evolutionary history of these gene clusters to a cpn60-based species tree, considering the flanking regions of each cluster to provide some context for potential horizontal transfer events. Our analyses identified potential occurrences of horizontal gene transfer in most clusters, supporting relatively frequent exchanges of these metabolite biosynthetic genes. Chapter four provides conclusions and future directions for this work. Overall, this work provides insight into the antimicrobial versatility of Pantoea and its potential for providing novel antimicrobial scaffolds that may be useful for therapeutic development. Keywords: Pantoea, antimicrobial resistance, bacterial natural products, antimicrobial biosynthetic gene clustersItem Open Access Search Timelines: Contextualized search history in support of exploratory search(Faculty of Graduate Studies and Research, University of Regina, 2024-06) Islam, MD Nazmul; Hoeber, Orland; Uddin, Md. SamiThe timespan over which exploratory searching can occur, as well as the scope and volume of the search activities undertaken, can make it difficult for searchers to remember key details about their complex search tasks. These difficulties are present both during a single search session and when resuming a search task that spans multiple sessions. Existing public digital library search interfaces do not provide adequate support to allow patrons to pursue a complex search task for a prolonged period across multiple sessions. In this thesis, I present a novel search interface designed to support cross-session exploratory search in a public digital library context through the introduction of Search Timelines. The interface provides context for past and current search activities via a dynamically updating timeline of the search (queries and saved documents). This timeline is presented alongside the search results in a typical search engine results page design. A more detailed timeline is provided in a workspace, where searchers can review the history of their search activities and their saved resources. In order to assess the value of Search Timelines, a controlled laboratory study was conducted to compare it with a baseline interface modelled after the Regina Public Library search interface. The results of this study show that searchers were more engaged while working with Search Timelines both during the initial search session and when resuming the search after a gap of 7-8 days. The results also show the value of the novel interface in terms of usability measures, perceived knowledge gain, and search performance for both initial and resumed sessions. Overall, the results illustrate that Search Timelines can effectively support public digital library searchers while performing exploratory search that span multiple sessions.Item Open Access Biases in adults’ truth and lie judgments of Black children’s statements: An examination of anti-Black attitudes and unawareness of racial inequalities(Faculty of Graduate Studies and Research, University of Regina, 2024-07) Hagi Hussein, Siham Ali; Zanette, Sarah; Sharpe, Donald; Sangster, SarahA growing body of literature has sought to examine factors that impede adults’ abilities to detect children’s lies. The current study extends previous research that found that adults demonstrate a racial bias when evaluating statements made by children and that these judgements are related to their motivations to respond without prejudice. The goal of the current study was to explore whether adults’ veracity judgments of Black and White children’s statements differ as a function of adults’ anti-Black attitudes and their unawareness of racial privilege, institutional discrimination, and racial issues within society. Participants reviewed fictitious transcripts of a child who denied committing a misbehaviour, judged whether they believed the child to be lying or telling the truth, and completed questionnaires assessing their prejudicial attitudes. Consistent with previous research, adults displayed a general truth bias when judging the veracity of children’s statements. The strength of this truth bias was dependent on the race of the child, such that Black children were more likely to be labelled as telling the truth compared to White children. After controlling for participants’ race and overall willingness to label a child as truthful, participants’ prejudicial attitudes predicted their deception scores for Black children, such that participants with lower prejudicial attitude scores rated Black children to be more honest compared to participants with higher prejudicial attitudes. The results of this study carry the potential to enhance our understanding of cross-race deception detection and may help identify factors that interfere with our ability to accurately detect children’s lies.Item Open Access Identifying the support needs of the informal caregivers of long-term care residents: The potential benefits of a mobile application(Faculty of Graduate Studies and Research, University of Regina, 2024-07) Haczkewicz, Kelsey Marie; Gallant, Natasha; Hadjistavropoulos, Thomas; Wright, KristiInformal caregivers of older adults who reside in long-term care facilities play a crucial role in ensuring the needs of the resident are being met. Such institutional informal caregivers (IICs), however, are at a greater risk of experiencing subjective burden and loneliness. They also tend to report lower levels of perceived social support than their non-caregiver counterparts. Despite web-based messaging services as well as virtual and in-person informal caregiver support groups showing promise in reducing subjective burden and loneliness, barriers such as a lack of transportation or required web services, caregiver busyness, and competing responsibilities of the caregiver often prevent caregivers from engaging in such groups. In order to make social support more accessible, mobile applications (apps) have been developed, but often focus on training the informal caregiver to provide care rather than providing the informal caregiver with support for managing their own stressors. Therefore, the purpose of the current study was to investigate the support needs of informal caregivers in addition to the potential benefits of a mobile app intended to provide them with support to improve their experiences of subjective burden, loneliness, and perceived social support. One hundred and twenty-four of participants (72.6% female, 25.8% male, 0.8% intersex) completed online self-report measures of subjective burden, loneliness, perceived social support with participants' inclinations to use a hypothetical IIC support app. One-way Analyses of Variance (ANOVAs) and independent samples t-tests were conducted to explore the effect of demographic characteristics on psychosocial variables and technology acceptance and readiness. Multiple linear regression analysis demonstrated that perceived social support from significant others was a significant predictor of technology acceptance. One-way ANOVAs and independent samples t-test determined that institutional informal caregivers (IICs), experienced moderate to high subjective burden, particularly among older, male, substitute decision-makers (SDMs), and those with other dependents. Older, retired, or female IICs experienced higher levels of loneliness, while SDMs and those with additional dependents were less lonely. Older IICs reported greater levels of perceived social support from friends and married IICs reported greater levels of perceived social support overall. IICs who identified as female tended to be more accepting of technology. Qualitative analysis found that IICs tend to experience their caregiving role as being all-consuming, impacting nearly every aspect of their life and that IICs would likely consider performance expectancy, effort expectancy, and facilitating conditions when deciding to engage with a new mobile app. Unique contributions of this study consist of the focus on IICs and the examination of psychosocial outcomes and technology acceptance. Further research into the coping mechanisms utilized by IICs as well as the impacts of role strain onto psychosocial outcomes such as subjective burden, loneliness, and perceived social support may be warranted to inform the creation of interventions geared towards providing psychosocial support to IICs. Overall, these results suggest that IICs’ experiences of subjective burden and loneliness differ from that of the home-based informal caregiving population and that IICs may benefit from a mobile application to provide psychosocial support.Item Open Access Deep transfer learning-based DDoS attack detection in 5G and beyond networks(Faculty of Graduate Studies and Research, University of Regina, 2024-09) Farzaneh, Behnam; Shahriar, Nashid; Louafi, Habib; Yao, YiyuNetwork slicing is a crucial technology for enabling 5G and beyond mobile networks which support a wide range of new services such as Enhanced Mobile Broadband (eMBB), Ultra-Reliable and Low Latency Communications (URLLC), and Massive Machine-Type Communications (mMTC) on the same physical infrastructure. However, this technology also makes networks more vulnerable to cyber threats, especially Distributed Denial-of-Service (DDoS) attacks. These kinds of attacks can degrade service quality by overwhelming essential network functions necessary for the seamless operation of network slices. To address this issue, an Intrusion Detection System (IDS) is needed to protect against various DDoS attacks. A promising solution is the use of Deep Learning (DL) models to detect potential DDoS attacks, a method already proving effective in the field. However, DL models require large amounts of labeled data for effective training, which are often scarce in operational networks. To address this, Transfer Learning (TL) techniques can be used by transferring knowledge from previously trained models to a target domain with limited labeled data. In this thesis, Bidirectional Long Short-Term Memory (BiLSTM), Convolutional Neural Network (CNN), Residual Network (ResNet), and Inception are used as base models for Deep Transfer Learning (DTL) methods that look into how they can improve DDoS attack detection in 5G networks. A comprehensive dataset generated in our 5G network slicing testbed, which contains both benign and various DDoS attack traffic, serves as the source dataset for DTL. After learning features, patterns, and representations from the source dataset, the base models are fine-tuned using different TL processes on a target DDoS attack dataset. The 5G-NIDD (5G Network Intrusion Detection Dataset), which has limited annotated traffic from several DDoS attacks generated in a real 5G network, is chosen as the target dataset. The results indicate that the proposed DTL models improve the detection of various DDoS attacks in the 5G-NIDD dataset compared to models without TL. Specifically, the BiLSTM and Inception models are identified as the top performers. BiLSTM shows an improvement of 13.90%, 21.48%, and 12.22% in terms of accuracy, recall, and F1-score, respectively, while Inception demonstrates a 10.09% increase in precision compared to models not using TL.Item Open Access On the class of graphs ZP(Faculty of Graduate Studies and Research, University of Regina, 2024-05) Andrusiak, Brendan Kyle; Fallut, Shaun; Mojallal, Seyed AhmadThe following research was primarily focused on the class of graphs denoted by ZP. Let G = (V,E) be a graph made up of vertices V and edges E. The path cover number and zero forcing number are two graph parameters that have been of recent research interests and are closely related. Zero forcing at it’s most rudimentary, is a graph colouring game. There is a significant preexisting body of work on zero forcing, which includes relations between zero forcing and path cover numbers (denoted by Z(G) and P(G), respectively), as well as a relation between zero forcing and a notion of maximum nullity of a graph. One natural question along these lines that emerged was to impose equality conditions between Z(G) and P(G), and assuming these equality constraints hold for both G and all induced subgraphs of G, what class of graphs might arise and what is special about said graphs? Thus, we study the class ZP in which the zero forcing number and the path cover number are equal over all induced subgraphs. As many graphs are known to belong to ZP, such a trees, cycles, and cacti, these graphs are an excellent starting point for study. Hence, the cycle graph, denoted by Cn, provide the primary point of study early on in the research process. As Cn is a graph known to belong to ZP, we add interior chords to the cycle graph in many different orientations and in many numbers, then examine the resulting changes in both Z(G) and P(G). We then consider analyzing graphs that belong to ZP by conditioning on possible values of the path cover number, namely assuming P(G) = 2 and P(G) = 3. Finally, graph operations and their effect on graphs in ZP are considered. Of particular importance are the vertex and edge-sum operations. Ultimately, we are able to prove that the vertex or edge-sums of graphs in ZP do indeed remain in the class ZP.Item Open Access Exploring factors that influence trust in non-standard stem cell therapies among patients with musculoskeletal conditions(Faculty of Graduate Studies and Research, University of Regina, 2024-06) Shaker, Marina; Zarzeczny, Amy; Longo, Justin; Yang, YangAlthough stem cell interventions (SCIs) may offer some therapeutic potential, the development of regulatory frameworks for their safe clinical application remains a significant challenge. As the regulation of these innovative therapies is still being developed, it is crucial to examine the factors that shape patients’ trust in these interventions that lack clear oversight. The purpose of this study is to explore the factors that influence the trust in nonstandard SCIs among patients with musculoskeletal disorders as well as their understanding of the role regulatory bodies play in ensuring safe and effective treatments. This understanding will be relevant to policy development and regulatory reform for innovative regenerative medicine therapies, potentially addressing the role that professional regulation plays in providing oversight of this developing field. This study employed a qualitative approach, using constructivist grounded theory. The data were obtained through in-depth, semi-structured one-on-one interviews with eight participants lasting from 45 to 75 minutes. The interview transcripts were analyzed initially with line-by-line coding, then focused coding. The codes were later collapsed and organized into categories, which guided theory construction. The findings unveiled a range of factors involving the patients, their knowledge of the intervention, and their practitioners that influence their trust in non-standard SCIs. The results also suggest that health practitioners play a central role in guiding participants' consideration of non-standard SCIs. This role also extends beyond medical doctors and includes allied healthcare professionals, as patients with musculoskeletal conditions often seek their services to manage their symptoms. Lastly, the results indicate a strong and implicit trust that patients place in regulatory bodies; suggesting that patients hold expectations of these bodies without a full understanding of how they meet them.Item Open Access Evaluating the effectiveness of analogies in an infographic on low-dose radiation(Faculty of Graduate Studies and Research, University of Regina, 2024-05) Sahagun, Francisco Javier; Hurlbert, Margot; Longo, Justin; Dalzell, MatthewThe aim of this research is to address misconceptions about Low Dose Radiation (LDR) by using analogies. The nuclear industry has shown that trust is asymmetrical, meaning it is easily lost and hard to regain. This was evident in Saskatchewan, where a public inquiry in 2008 revealed a significant lack of trust. Conversely, Saskatchewan possesses one of the largest uranium reserves globally, offering a carbon-neutral energy source. Additionally, it is increasingly cost-effective in the context of carbon pricing, making it an appealing option to fulfill the province’s energy and carbon objectives while ensuring energy security. The choice of analogies is based on their proven effectiveness as a teaching tool over time. This study aims to evaluate the impact of a brief training program, delivered through infographics, on the public’s perception of LDR. The effectiveness of this infographic was assessed by comparing it with another that presented LDR information differently, focusing on protocols and safety regulations in hospitals and nuclear power plants. Additionally, the training included an infographic that outlined basic benefits of LDR, such as its use in diagnosis and therapy, and highlighted scientific sources. The methodology employed involved focus groups, which were conducted in three provinces: Alberta, Saskatchewan, and Ontario. Respondents were divided into three focus groups, with approximately eight individuals per infographic. With two infographics in total, this amounted to about 48 participants per province, culminating in a total sample size of 150 people across the three provinces. A Small Modular Reactor, known as an SMR, represents a compact version of traditional nuclear reactors and is engineered for simplified and adaptable installation due to its reduced size and power output. For the data analysis 4 outcome variables were created to make an SPSS binary logistic regression to assess the possibilities to generate positive LDR perceptions; the possibilities to generate positive LDR perceptions and/or concluding that LDR outweigh the risk; the possibilities to generate positive LDR perceptions and/or having positive SMR support; and the 4th variable was the generation of positive SMR support after accessing the infographic. For the qualitative analysis the responses of the open-ended questions pre-infographic, and the learning and concerns post-infographic were reviewed. This research indicates that females may have a more positive attitude towards LDR when presented with Infographic 2, which details protocols and safety rules in hospitals and nuclear power plants. Conversely, males may feel more secure when exposed to Infographic 1, which utilized analogies. Notably, in Saskatchewan, Infographic 1 effectively garnered positive support for SMRs among females. Age consistently emerged as an independent variable that negatively affected the outcome variables most of the time. Meanwhile, income and education levels had a minimal impact on these outcomes. Keywords: LDR positive perceptions, LDR misperceptions, positive SMR support, climate change solution, trust in scientists, benefits from LDR, teaching protocols and rules, teaching analogies.Item Open Access The role of municipal decision-making in community wellbeing in times of drought: a case study of Canada’s Sunshine Coast Regional District(Faculty of Graduate Studies and Research, University of Regina, 2024-07) Beltran Camacho, Anna Gabriela; Hurlbert, Margot; Khovrenkov, Iryna; Boucher, MartinAccess to clean water is essential for meeting personal, societal, and environmental needs. Climate change is causing more frequent and extreme weather events, leading to less dependable water access and intensified droughts (Intergovernmental Panel on Climate Change, 2023; UNICEF, 2023). This poses significant threats to human health, environmental sustainability, and economic prosperity (Brandes & O’Riordan, 2014; United Nations, 2024). This research explores the role of municipal decision-making during the 2022 drought in the Sunshine Coast Regional District (SCRD), focusing on the impact of water policies on community wellbeing. The study reviews secondary sources, including provincial documents and public reports, and analyzes online questionnaires completed by SCRD’s municipal representatives. Qualitative and quantitative data were analyzed using a convergent parallel design. Findings indicate low levels of awareness about water problems, usage practices and general water literacy in the region, highlighting the need for behavioural changes in water consumption, conservation, and management. During the 2022 drought, municipalities primarily employed information-based policies and Water Conservation Regulations. Efforts also focused on expanding and diversifying water supply sources. Significant progress in adaptive governance could be achieved and supported by adjusting policies and practices proactively. Participants emphasized the need to consider human, natural, economic, and social aspects of community wellbeing, advocating for a holistic approach to enhance municipal decision-making. The value a community places on water is intricately connected to how the resource is managed. Municipal decision-making plays a crucial role in delimiting the perspectives and ideas that can lead to a more sustainable and collaborative community. This research contributes to the discourse on sustainable communities and the effectiveness of governmental efforts in addressing droughts. Keywords: Droughts, community wellbeing, decision-making, water policies, adaptive governance