Theses and Dissertations

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Open Access
A mixed methods study on barriers and facilitators to exercise for suicidal ideation management
(Faculty of Graduate Studies and Research, University of Regina, 2024-10) Vig, Kelsey Danielle; Asmundson, Gordon; Hadjistavropoulos, Heather; Hadjistavropoulos, Thomas; Totosy, Julia; Gibb, Brandon E.
Suicide is a leading cause of premature death. Innovative and effective interventions are needed to prevent suicide deaths. Randomized controlled trials (RCTs) have demonstrated that a variety of structured exercise programs (e.g., aerobic exercise, resistance training exercise) improve mental health, including reducing anxiety and depressive symptoms. Moreover, failure to meet established guidelines for physical activity is associated with increased odds of experiencing suicidal behaviours. Exercise may, therefore, be one intervention option to reduce the suicidal behaviours (i.e., suicidal ideation [SI] and plans for suicide) that often precede suicide. In order to benefit from the effects of exercise, individuals with suicidal ideation must perceive exercise as an accessible, acceptable, and effective treatment option, otherwise they are unlikely to initiate and sustain an exercise program. This mixed-methods dissertation includes two studies that explored how individuals with SI perceive and experience exercise, with an emphasis on identifying facilitators and barriers to exercise. In Study 1, grounded theory methods were used to analyze data from semi-structured interviews with 17 adult Canadian participants with past-month SI. The overall theory derived from Study 1 suggests that exercise for individuals with SI is complex and should be tailored to each individual. This theory is made up of a core category of individualization, as well as four key concepts that relate to three major categories. The four key concepts of the theory are that facilitators and barriers to exercise (a) have individualized weights/impacts on exercise decisions, (b) are cumulative, interactive, and opposing forces, (c) are dynamic, and (d) exist on a spectrum from internal to external. The three major categories included in the study theory are (a) the cognitive-behavioural cycle, (b) priorities, values, and identity, and (c) interpersonal factors. In Study 2, 261 Canadian adult participants with past-month SI completed an online survey. The survey included measures of suicidal behaviour, facilitators and barriers to exercise (open-ended and closed-ended questions), past-week physical activity, and demographic and health questions. Due to the exploratory nature of the study, quantitative analyses were restricted to descriptive statistics. The qualitative and quantitative results of Study 2 supported and added to the theory developed in Study 1, including offering additional evidence of the core category, the four key concepts, and the three major categories. Most participants thought exercise can reduce SI. Improved health, both mental and physical, was a commonly reported motivator to exercise, and poor mental health was also a commonly reported barrier to exercise. Overall, the results of both studies demonstrated the importance of individualization when it comes to exercise for individuals with SI. Exercise may or may not be an accessible, acceptable, and/or effective intervention for any given individual with SI. The results may be used by clinicians, researchers, policy makers, and advocacy groups considering whether exercise might be an intervention option for individuals with SI. The results may assist future researchers who endeavor to develop exercise-based interventions for individuals with SI by providing a theoretical framework to guide intervention development and study planning (e.g., by highlighting the need to anticipate and address individual and fluctuating facilitators and barriers). Keywords: suicidal ideation, exercise, physical activity, facilitators and barriers to exercise, exercise adherence, grounded theory
Open Access
A novel beam management strategy using UE trajectory mapping
(Faculty of Graduate Studies and Research, University of Regina, 2024-12) Chowdhury, MD Thouhidul Islam; Paranjape, Raman; Wang, Zhanle (Gerald)
This research presents a novel beam management strategy aimed at optimizing performance in multi-user mobile communication systems, specifically within the framework of 5G mmWave networks. As the demand for reliable and high-speed data transmission increases, traditional beamforming techniques face challenges such as high computational load and inefficiencies in indoor environments. The proposed method leverages user equipment (UE) trajectory information by segmenting user trajectories into equal-length segments, focusing the beam on the centre of each segment to ensure stable and consistent signal coverage. The methodology integrates angular weighting, and dynamic power control to enhance beamforming efficiency. The angular weighting function prioritizes signals aligned closely with the beam direction, further enhancing signal strength while reducing unwanted energy dispersion. Additionally, dynamic power control is employed to adjust transmit power according to the user’s position relative to the segment center, maintaining robust Received Signal Strength (RSS) without unnecessary energy expenditure. Simulation results indicate that the proposed approach significantly reduces beam switching frequency and computational load compared to conventional methods, while maintaining stable RSS and Signal-to-Interference-plus-Noise Ratio (SINR) across multiple users. This study demonstrates the potential of combining trajectory mapping, subarray-based beamforming, and nulling techniques for effective beam management in dynamic indoor environments. Overall, the findings highlight the scalability and efficiency of the proposed strategy in enhancing wireless communication systems, paving the way for future advancements in next generation 5G networks. Key words: Beamforming; Interference Management; Dynamic Indoor Environment; Predefined Segments; Subarray; Nulling Algorithm.
Open Access
A security risk assessment framework for IoT systems
(Faculty of Graduate Studies and Research, University of Regina, 2024-12) Waqdan, Mofareh Abdullah S; Mouhoub, Malek; Louafi, Habib; Shahriar, Nashid; Hepting, Daryl; Uddin, Md. Sami; Al-Anbagi, Irfan; Amamra, Abdelfattah
The emergence and growth of the Internet of Things (IoT) have changed how we live and interact with technology. The seamless integration of connected devices, from household to industrial equipment, has brought about a new era of interconnectedness. However, this rapid expansion of the IoT also introduces new security concerns that need to be assessed. Assessing the security risks associated with deploying and using this technology is crucial. Consequently, organizations need a risk assessment framework that helps identify, evaluate, and manage the risks of IoT, including data privacy and confidentiality, system integrity, availability, and performance. The stateof- the-art has been given significant attention to security risk assessment in traditional cybersecurity with powerful computer systems, but the challenges of deploying IoT devices and their associated vulnerabilities have been overlooked. In this thesis, we first present a novel IoT security risk assessment framework for the healthcare environment, in which we have improved upon existing methodologies. The proposed framework dynamically calculates the risk score for different device profiles, considering their population and other parameters, such as network protocols, device heterogeneity, device security updates, device physical security status, device history status, layer history status, and device criticality. Second, we present a customizable framework for assessing the security risk of deploying and utilizing IoT devices in various environments. We dynamically calculate risk scores for different devices, considering their importance to the system and their vulnerabilities, among other parameters. The customizable framework considers the important parameters of the devices, their vulnerabilities, and how they impact the overall risk assessment. The importance of these devices and the severity of vulnerabilities are incorporated in the framework using the well-known Multi-Attribute Decision Making (MADM) methods, namely, Simple Additive Weighting (SAW) and Weighting Product (WP). Finally, the risk is assessed on a setup comprised of IoT devices widely deployed in healthcare systems, such as emergency rooms.
Open Access
Absorption capacity of carbon dioxide in aqueous solution of 1,2-bis(3-aminopropylamino) ethane and Dytek EP diamine: Experimental measurements and simulation with the E-NRTL model
(Faculty of Graduate Studies and Research, University of Regina, 2024-12) Fallah, Abbas; Henni, Amr; Peng, Wei; Khan, Sharfuddin
The increasing threat of climate change has elevated the importance of carbon dioxide (CO2) capture technologies. This thesis explores the solubility of CO2 on aqueous solution of two novel amines 1,2-Bis(3-AminoPropylamino) Ethane and Dytek EP diamine at two different temperatures of 313.15 K and 333.15 K, and two different concentrations of 10 wt% and 30 wt%. These amines were selected for their potential to enhance CO2 absorption efficiency and reduce energy consumption in carbon capture and storage (CCS) processes to provide valuable data for developing more efficient CO2 capture systems. Utilizing the Electrolyte Non-Random Two-Liquid (eNRTL) model for the liquid phase and the RK equation of state for the gas phase, the research includes extensive thermodynamic modelling to simulate the experimental data and predict the behaviour of these amines in CO2 capture processes. The binary e-NRTL and molecule–ion pair parameters were obtained by regression. The overall percentage of the average absolute deviation (%AAD) between the experimental and estimated values for the temperature, pressure, and mole fractions are 0.006%, 0.052% and 0.015%, respectively, for 1,2-Bis(3-AminoPropylamino) Ethane, and similarly, 0.197%, 0.093%, 0.105% for Dytek EP diamine. 1,2-Bis(3-AminoPropylamino) Ethane showed superior solubility performance concerning other amines studied in the literature due to its high molecular weight and four amine groups in its structure, which increased its reactivity and decreased its steric hindrance. Dytek EP diamine had a moderate performance due to its lower molecular weight and the presence of only two amino groups and a methyl group in the structure, creating a steric hindrance and decreasing its capacity.
Open Access
Acceptance and commitment therapy for women experiencing infertility: A randomized controlled trial
(Faculty of Graduate Studies and Research, University of Regina, 2025-03) Balsom, Ashley Anne; Gordon, Jennifer; Hadjistavropoulos, Heather; Klest, Bridget; Lasiuk, Gerri; Corsini-Munt, Serena
Approximately one in six Canadian couples are infertile, defined as the inability to achieve pregnancy despite 12 or more months of active attempts to conceive. While the psychological burden is well-established in the literature, currently available and adapted psychological interventions have had minimal effects on infertility-related distress, anxiety, or depression. We examined the efficacy of Acceptance and Commitment Therapy (ACT) to reduce distress associated with infertility. In Study One, we created the ACT-based intervention in collaboration with a panel of women who had lived experience with infertility. We then conducted a pilot trial with 20 women and used their feedback to refine and update the intervention. Study Two involved a randomized controlled trial (RCT) of the updated ACT intervention, recruiting 90 women who were randomized to the intervention or waitlist control groups. This study aimed to rigorously evaluate the efficacy of the ACT program in alleviating infertility-related distress. Among the recruited sample, 71% completed the entire intervention. Among the eighty-five participants who provided at least partial outcome data, the intervention group showed statistically greater improvements in all three primary outcomes: anxious symptoms (B(SE) = -2.3 (1.3), p = .036), depressive symptoms (B(SE) = -3.8 (1.2), p <.001), and fertility quality of life (B(SE) = 13.6 (3.1), p <.001) relative to the waitlist control group when adjusting for baseline symptom severity. Psychological inflexibility also decreased (B(SE) = -0.6 (0.2), p = .004), while psychological flexibility and relationship satisfaction remained unchanged (p > .05) in the intervention condition compared to the waitlist control condition. Effects were maintained at a one-month follow-up. This intervention shows promise as a cost-effective and accessible intervention for individuals experiencing infertility.
Open Access
Adaptive systems for DDoS attacks detection and mitigation in IoT networks
(Faculty of Graduate Studies and Research, University of Regina, 2025-01) Saiyed, Makhdumabanu Farukali; Al-Anbagi, Irfan; Bais, Abdul; Laforge, Paul; Louafi, Habib; Karimipour, Hadis
The rapid growth of IoT devices has revolutionized industries while exposing IoT networks to cybersecurity threats, particularly DDoS attacks, which compromise network stability. Traditional detection methods struggle to address the constraints of resource-limited environments, scalability, and the need for lightweight, optimized, and reliable systems. This thesis addresses these challenges through five objectives aimed at adaptive DDoS detection and mitigation systems for IoT networks, balancing accuracy, resource efficiency, and adaptability. The first objective focuses on developing a Flow and Unified Information-based DDoS detection system (FLUID) for small-scale IoT networks, enabling DDoS detection with minimal computational overhead. The FLUID system uses flow metrics and unified information measures, to detects both high and low-volume attacks while optimizing resource use. The second objective introduces a system with novel hybrid feature selection to enhance detection accuracy in medium-scale IoT networks. By combining Genetic Algorithm and t-test for DDoS Attack Detection (GADAD), this system improves feature selection efficiency and supporting binary and multiclass classification. For large-scale networks, the third objective is the design of a Deep Ensemble Learning with Pruning (DEEPShield) system that integrates CNN and LSTM architectures, optimized through post-training pruning and a novel preprocessing method. This system achieves high detection accuracy with low resource demand, suitable for resource-constrained IoT environments. The fourth objective focuses on optimizing deep learning-based detection systems to enhance resource efficiency and explainability using the OMEGA, ADEPT, and SHIELD systems. The Optimized Ensemble Learning with Pruning (OMEGA) and Interactive and Explainable Optimized Learning (ADEPT) systems apply techniques like genetic algorithms and differential evolution for resource efficiency. The SHAP-Based Explanation and Lightweight DDoS Attack Detection (SHIELD) system uses SHapley Additive exPlanations (SHAP) for interpretability of individual predictions. The final objective addresses adaptive mitigation through a Game-Theoretic DDoS Defense Strategy Model (GT-DDSM) that dynamically adjusts defense strategies based on attack intensity. These systems are evaluated on metrics such as accuracy, precision, recall, F1-score, and scalability, while optimization efficiency is assessed by preprocessing time, inference speed, memory usage, and model size. Explainability is assessed through SHAP and priority assessment values, while mitigation effectiveness is measured by gradients, cumulative payoff, mitigation time, resource utilization, and network QoS parameters.
Open Access
Advanced CNN architecture integrating machine learning algorithms for precise Alzheimer's disease classification
(Faculty of Graduate Studies and Research, University of Regina, 2024-08) Mollazadeh, Shima; Torabi, Farshid; Tontiwachwuthikul, Paitoon (P.T.); Idem, Raphael
Alzheimer's disease (AD) is a degenerative neurological disorder that affects millions of individuals worldwide and is very difficult to detect and treat in its early stages. This thesis presents a novel architecture for a convolutional neural network (CNN) designed exclusively to classify Alzheimer's disease using functional magnetic resonance imaging (fMRI) data. This work improves the accuracy and reliability of early Alzheimer's identification by using state-of-the-art deep learning techniques to the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset. The basis of this research is the ADNI dataset, a vast collection of brain imaging and associated data from people with different degrees of cognitive impairment. The primary objectives are to classify Alzheimer's disease into distinct categories using cognitively normal (CN), early mild cognitive impairment (EMCI), late mild cognitive impairment (LMCI), and Alzheimer's disease (AD) using the recently developed CNN architecture. This study also uses transfer learning techniques to compare the performance of the new CNN with well-known deep learning models like ResNet50 and VGG16, as well as with more conventional machine learning algorithms like XG Boost, k-nearest neighbor (KNN), and Random Forest. The innovative CNN architecture is meticulously designed to maximize classification accuracy. The preprocessing steps involve resizing fMRI images to 109x91 pixels and labeling them accordingly. The network incorporates convolution layers with 3x3 kernels, ReLU activation functions, and 2x2 pooling layers, transforming the images into feature vectors that are subsequently classified. Compared to previous tested models, the innovative CNN architecture performed better, achieving an impressive 99.51% classification accuracy. In terms of comparison analysis, the accuracy of the VGG16 model was 98.24%, whereas the accuracy of the ResNet50 model was 96.05%. The XG Boost classifier, combined with VGG16 for feature extraction, reached an accuracy of 96.93%. The KNN algorithm, also paired with VGG16, exhibited outstanding performance with an accuracy of 98.68%, making it the most effective among the traditional machine learning methods tested. With VGG16 included, the Random Forest classifier produced an accuracy of 94.70%. The outcomes demonstrate how well the suggested CNN architecture performs in comparison to current deep learning and machine learning models in precisely classifying Alzheimer's disease stages. This study demonstrates how sophisticated CNN designs and transfer learning can be used to enhance Alzheimer's disease early detection and diagnosis. The findings suggest that further exploration of alternative deep learning networks, such as convolutional auto encoders, Alex Net, and Google Net, as well as ensemble methods, could enhance model generalization and minimize overfitting. In conclusion, this thesis presents a significant advancement in Alzheimer’s disease classification using fMRI data, providing a robust framework for future research and development in neuroimaging and deep learning applications. The superior performance of the novel CNN architecture demonstrates its potential as a valuable tool for early diagnosis, which is crucial for managing and potentially mitigating the way Alzheimer's disease advances.
Open Access
Alliances, assemblages, and affect: Teacher activism within and beyond the union in Saskatchewan
(Faculty of Graduate Studies and Research, University of Regina, 2025-03) Keil, Trudy Lynn; Osmond-Johnson, Pamela; Sterzuk, Andrea; Massing, Christine; Sorensen, Michele; Winton, Sue
In recent decades, Canadian governments have increasingly drawn upon global neoliberal policies to regulate the work of teachers and their professional organizations (Bascia, 2015; Smaller, 1998). Neoliberalism has radically altered educational policies with respect to curriculum, teaching pedagogy, and teachers’ professionalism (Apple, 2006; Ball, 2003), impeding teachers’ ability to deliver democratic education (Darder, 2019). Viewing teacher activism broadly, this research explored Saskatchewan teacher activists’ resistance to marketdriven educational reforms through their engagement with both the power of grassroots social movements and the institutional authority of their union. Guided by Harding et al.’s (2018) poststructural framework of alliances, assemblages, and affects, this dissertation utilized Bartlett and Vavrus's (2017) comparative case study approach to explore teachers’ activism across place, space, and time. Sources included semi-structured interviews with ten known teacher activists and two focus groups with the same participants. Informed by Saldaña (2021), data was analyzed using eclectic coding then interweaved to generate poetry-like narratives representing teachers’ individual and collective voices. Findings showed that teachers strategically navigated activism amidst conflicting personal, professional, and political demands. Both cognitive and affective motivations served as the impetus for participation and activists’ involvements shifted dependent upon factors such as work-life balance and perceived risks. Though teachers viewed grassroots activism as more responsive and hopeful, they also valued the collective strength of the union and recognized its role in their political development. This dissertation distinguishes itself by exploring multifaceted teacher activism through poststructuralism thereby moving beyond mere critique and emphasizing how teachers resist neoliberalism through intentional acts. Keywords: teacher activism, teacher unions, social movements, poststructuralism
Open Access
Automated aerial detection of spruce tree crowns through YOLOv5 and watershed segmentation
(Faculty of Graduate Studies and Research, University of Regina, 2025-01) Mohemi Moshkenani, Mahdi; Peng, Wei; Henni, Amr; Kabir, Golam
The spruce tree, a key species in Canada, is crucial to industries like timber and pulp. Effective management of these resources relies on monitoring forest parameters to ensure long-term forest health and productivity. Tree crown dimensions contribute valuable insights into these parameters. This study investigates an automated approach for detecting and measuring spruce tree crowns using the YOLOv5 machine learning model combined with watershed segmentation. The method enhances the accuracy of crown measurements from aerial drone images. Over 2,000 spruce trees in a limited forested area of Saskatchewan, Canada, are analysed using top-view images captured by a DJI Mavic 3 Classic drone, which is sufficient for this project as the model trained well. The YOLOv5 model is initially employed to detect trees, following by watershed segmentation to refine the tree crown boundaries. In the forest measuring area, some regions contain closely spaced trees with overlapping crowns that cause challenges for accurate recognition of tree boundaries. Although feeding watershed segmentation with YOLO-detected individual trees addresses this issue, tuning the IoU threshold in the NMS stage, applying data augmentation, and utilizing high-resolution images further enhance detection accuracy. One issue in predicting tree crowns is the underestimation of diameters, often resulting from systematic errors in image capture, measurement methods, environmental conditions, and limitations in image resolution. To address this issue, a linear regression model is applied to adjust the predicted crown diameters, aligning them more closely with the actual field measurements. This approach demonstrates an acceptable accuracy of 89.1% compared to prior research and existing methodologies, which reported accuracies ranging from 67.72% to 95.4%, particularly in complex forest environments. Although the primary focus of this research is on measuring tree crowns, the findings have broader implications for forestry management activities, such as biomass estimation and forest health monitoring. Future research could explore further improvements to the model for real-time forest management applications, as well as expand its use to detect and measure other tree species in mixed forests and agriculture plants.
Open Access
Beam asymmetry in the reaction channel γp→ηΔ + at Glue X
(Faculty of Graduate Studies and Research, University of Regina, 2024-08) Neelamana, Varun; Papandreou, Zisis; Lolos, George; Huber, Garth; Stevens, Justin R.; McBeth, Joyce; Watts, Dan
Photoproduction mechanisms studied in the GlueX experiment allows the mapping of light mesons in unprecedented detail with particular interest in exotic meson candidates. This is achieved by impinging an 8.2-8.8 GeV linearly polarized photon beam on a liquid hydrogen target. The measurement of beam asymmetry Σ will help constrain quasi-particle t-channel exchange processes using Regge theory. Understanding the photoproduction exchange mechanisms is a crucial ingredient in establishing hybrid and exotic photoproduced light meson states. Σ is extracted from the azimuthal angular distribution between the meson production plane and the polarized photon beam. In particular, we will report results on the beam asymmetry measurements for η in the reaction p →η Δ+. This reaction with a recoiling Δ+ will allow for comparison and validation of theoretical calculations and provide additional validation of the η asymmetry with a recoiling proton. The different isospin of the Δ+ imposes additional restrictions that further constrain allowed Regge exchanges. The results were similar to η-proton i.e Σ ≈ 1 but showed a deviation from theoretical models of the η - Δ+ especially towards higher t values. This may help guide modifications to these models for production and exchange processes involving η meson.
Open Access
Beyond net-zero carbon emissions in industrial processes through catalyst-aided amine solvents for the indirect co-combustion of natural gas and biomass
(Faculty of Graduate Studies and Research, University of Regina, 2025-01) Adjetey, Samuel; Idem, Raphael; Supap, Teeradet; Ibrahim, Hussameldin
This thesis investigates the application of absorber catalysts developed and optimized for effective CO₂ capture in a power production process involving the indirect co-combustion of biomass and natural gas, addressing a crucial case study challenge of carbon emissions from large. By employing a novel bi-blend amine solvent system, improved by heterogeneous solid base catalysts, the study explores the synthesis of various super basic catalysts in a bid to optimize CO₂ absorption rates, solvent loading, and overall process efficiency A series of heterogeneous catalysts which include PEI modified catalysts, K/MgO, K/MgO-CaO, and activated carbon blends, were synthesized and tested using a semi-batch apparatus. The initial CO₂ absorption rates of these catalysts were thoroughly analyzed against a non-catalytic baseline (control experiment). The results obtained revealed that catalysts such as AC Hydrothermal and K/MgO-CaO (5-35-60) significantly increased CO₂ absorption rates by up to 46% and 21%, respectively, over the baseline. Contrarily, despite characterized by high basic strength, some PEI-modified catalysts, exhibited lower performance due to reduced surface area and electron transfer limitations. However, further analysis was conducted on the K/MgO-CaO (5-35-60) catalyst over the activated carbon catalyst considering its superior chemical, thermal and mechanical stability, as well as the ease of preparation and reduced waste. The screening of the catalysts was carried out at a gas composition of 4.5% CO2 (balance N2, an absorption temperature of 40℃ ± 2, and a gas flow rate of 200± 5 ml/min). Additionally, extensive catalyst characterization test, including Powder X-ray Diffraction (XRD), CO₂ Temperature Programmed Desorption (TPD), and BET surface analysis, were conducted to understand how catalyst properties such as basic site strength, surface area, and pore structure influence CO₂ capture rates observed. The environmental impact and potential cost savings of catalyst-aided carbon capture were then evaluated in a simulated power generation process, where an LCA, life cycle assessment, model was applied based on the ReCiPe methodology. From comparing traditional MEA benchmark solvent, the novel AMP-PRLD amine bi-blend, and the AMP-PRLD solvent enhanced with a K/MgO-CaO catalyst, the results demonstrated that the catalyst-enhanced system achieved superior carbon dioxide reductions across various gas compositions, underscoring its potential for net-zero emissions. Conclusively, this catalyst-solvent system provides a promising pathway for the power and energy sectors to significantly reduce emissions while enhancing cost-effectiveness and sustainability.
Open Access
Classifying men who perpetrate intimate partner violence: A 50-year systematic review and a new typology applicable to case management
(Faculty of Graduate Studies and Research, University of Regina, 2024-11) Giesbrecht, Crystal Joy; Bruer, Kaila; Keown, Leslie Anne; Jones, Nick; Vaughan, Adam; Hilton, Zoe; Scott, Katreena
This dissertation includes two studies: a systematic review of typologies of perpetrators of intimate partner violence (IPV) and a new typology of men who perpetrated IPV created using assessment data collected with the Service Planning Instrument (SPIn™). The systematic review included 177 typologies contained in 201 articles published between 1974 and 2024. Typologies in the review comprised: 1) family-only and generally violent; 2) family-only, generally violent, and borderline/dysphoric; 3) family-only, generally violent, low-level antisocial, and borderline/dysphoric; 4) severity and frequency of violence; 5) reactive and instrumental, 6) situational couple violence and coercive control; 7) personality types; 8) other typologies (e.g., treatment responsivity, physiological reactivity); and 9) perpetrators of intimate partner femicide. These typologies are summarized and compared, and findings from studies that examined recidivism and treatment outcomes by typology are reported. The new typology was created using data from 7,781 men in Alberta, Canada, who had been identified as having perpetrated IPV using the SPIn. Men in the sample were classified using seven indicator variables linked to general and IPV recidivism in empirical research and available in the SPIn: criminal history, failure while on conditions, violations of protection or no-contact orders, procriminal attitudes, antisocial peers, social/cognitive skills, and aggression/violence. The resulting typology included three classes: High Criminal History—High Antisocial Attitudes (18.5%; n = 1,439), High Criminal History—Low Antisocial Attitudes (51.6%; n = 4,015), and Low Criminal History—Low Antisocial Attitudes (29.9%; n = 2,327). Both classes with high criminal history report a greater prevalence of static variables relating to criminal history; the most notable difference between these two types is that the High Criminal History—High Antisocial Attitudes class scores high on variables relating to antisocial attitudes, whereas the High Criminal History—Low Antisocial Attitudes class does not. Individuals in the Low Criminal History—Low Antisocial Attitudes class have a low probability of all seven indicator variables. The three classes were compared on external variables linked to general and IPV recidivism (including history of violence, substance misuse, childhood trauma, mental health conditions, homicidal ideation, and employment problems). The High Criminal History—High Antisocial Attitudes class displayed the highest prevalence of all external variables (i.e., additional risk factors), the Low Criminal History—Low Antisocial Attitudes class had the lowest rates, and the High Criminal History—Low Antisocial Attitudes class scored intermediate to the other two classes. The three classes were also compared on four dichotomous measures of reoffending (any recidivism, technical violations, new non-violent offence, and new violent offence) at both one and three years. The High Criminal History—High Antisocial Attitudes class displayed the highest rate of recidivism on all four measures. The High Criminal History—Low Antisocial Attitudes class had a slightly lower prevalence than the High Criminal History—High Antisocial Attitudes class on all recidivism measures. The Low Criminal History—Low Antisocial Attitudes class had low rates of all forms of recidivism. Given the distinct differences between the three classes in terms of static and dynamic risk factors (i.e., criminogenic needs) and risk for reoffending, this typology is expected to have clinical utility for case management with men who have perpetrated IPV. Recommendations for risk management (e.g., supervision) and risk reduction (e.g., treatment/intervention programs) are discussed. Keywords: intimate partner violence, domestic violence, typology, perpetrators, systematic review, latent class analysis
Open Access
Determinants of food choice: The role of nudging, affective forecasting, habitual behaviour, and values
(Faculty of Graduate Studies and Research, University of Regina, 2025-01) Wallace, Jamie Charles Terrence; Arbuthnott, Katherine; Carleton, Nicholas; Pennycook, Gordon; Mahani, Akram; Forestell, Catherine A.
Modern eating habits have created a tremendous burden of disease in Canada and other western countries. There is a large research literature that has investigated interventions for improving dietary health in the population, including work that focuses on rational or conscious factors (e.g., the provision of health information) and heuristic factors (e.g., social norms and priming healthier food choices). More broadly, researchers have worked to identify determinants of food choice to better understand why people select particular foods, resulting in an array of known factors that in some way predict food choice. The current research was designed to examine and compare empirically supported predictors to inform future interventions and to test a possible priming intervention in an online meal selection context that presents both healthy and unhealthy meal options to participants. Participants were exposed to one of two primes, or a control condition, and then made either a whole or processed food meal selection. Participants also rated their affect towards the foods and completed questionnaires that measured food choice values and meanings, habitual behaviour, typical meal sources, and demographics. Results indicated that affect ratings and familiarity were the strongest predictors of food choice, although other meanings and values, such as health values and habitual behaviours were also important. Priming did not influence meal selection in the current study. The current results suggest that interventions focusing on developing positive feelings, familiarity, and habitual behaviour towards healthy whole foods are likely to be more successful than interventions focusing on rational factors; however, values and meanings were also statistically significant predictors of whole food meal selections and may also be useful for improving food choice.
Open Access
Develop innovative methodology to optimally fill in missing values and predict progression on multiple sclerosis
(Faculty of Graduate Studies and Research, University of Regina, 2024-11) Pilehvari, Shima; Peng, Wei; Shirif, Ezeddin; Khan, Sharfuddin; Fan, Lisa; Bui, Francis
Applying Machine Learning (ML) to predict and track Multiple Sclerosis (MS) progression is a significant advancement in medical research, with the potential to enhance patient outcomes. Accurate MS prediction enables personalized treatment, timely interventions, and improved quality of life by slowing disease progression and preventing complications. This research aims to deepen our understanding of MS by developing ML models and comprehensive risk assessments to support early prognosis, guide treatment strategies, and reduce disease impact. A major challenge in medical research, especially in predicting MS progression, is effectively managing missing data in MS datasets. This study introduces an innovative sequential Multi-Imputation (MI) bootstrapping method to address the challenge of missing data in MS datasets. Initially, several ML algorithms, including k-Nearest Neighbors (kNN), Random Forest (RF), and Multilayer Perceptron (MLP), are evaluated for imputation efficiency. RF and MLP perform best, achieving overall accuracies of 92% and 91.5%, respectively, in handling missing data more accurately than other models. Given the effectiveness of RF and MLP in capturing complex patterns in data, these models are selected for further development. The next step applies Multi-Imputation (MI) bootstrapping in a sequential manner, prioritizing features based on the strength of their relationships, as determined by Pearson correlation analysis. This statistical technique identifies features with the highest correlations, ensuring that attributes with stronger relationships with other attributes, are imputed first. These imputed features then inform the next imputation in the sequence, cooperating with the subsequent ranked feature in the order. Bootstrapping, a resampling technique that involves replacement, creates multiple training datasets by repeatedly sampling from the original data, enhancing the robustness of the imputation process. The proposed sequential imputation method integrates bootstrapping with RF, achieving an accuracy up to 97 % for MS datasets. This iterative approach effectively imputes missing data attributes while accounting for feature significance and relationships. The results also show that prioritizing normalization improves scaling impact, and that the significant features in the original dataset are crucial to the accuracy of MS missing data estimations. These findings provide valuable insights into effective imputation techniques for MS prediction, offering a foundation for future improvements in handling missing data in specific datasets. In addition, this study solves the common overfitting problem caused by data imbalance through a comprehensive method combining feature extraction, undersampling, Synthetic Minority Oversampling Technique (SMOTE) and optimal threshold method. Support Vector Machine (SVM), Logistic Regression (LogR), Decision Tree (DT), RF, KNN, MLP and Naive Bayes (NB) are used for prognostic modeling while examining risk factor associations. The results showed that the proposed method prevented overfitting during model training and developed a robust MS progression prognosis model, achieving a prediction accuracy of 98%, particularly for SVM and MLP The methods proposed in this dissertation can help develop more concise guidelines for the medical research communities and improve their evaluation processes. These innovations not only advance prognostic analysis in MS, but also pave the way for future research focused on optimizing patient outcomes and treatment strategies.
Open Access
Development of a pellet extruder with co-axial nozzle for 3D printing using inflatable extrudates
(Faculty of Graduate Studies and Research, University of Regina, 2024-08) Habib, Md Ahsanul; Khondoker, Mohammad; Muthu, SD Jacob; Peng, Wei
Additive manufacturing (AM) has emerged as one of the core components of the fourth industrial revolution, Industry 4.0. Among others, the extrusion AM (EAM) of thermoplastic materials has been named as the most widely adopted technology. Fused filament fabrication (FFF) relies on the commercial availability of expensive filaments; hence pellet extruder-based EAM techniques are desired. Large-format EAM systems would benefit from the ability to print lightweight objects with less materials and lower power consumption which can be possible by using hollow extrudates rather than solid extrudates to print objects. In this work, we designed a custom extruder head and developed an EAM system that allows the extrusion of inflatable hollow extrudates of a relatively wide material choice. By incorporating a co-axial nozzle-needle system, a thermoplastic shell was extruded while the hollow core was generated by using pressurized Nitrogen gas. The ability to print using hollow extrudates with controllable inflation allows printing objects with gradient part density with different degrees of mechanical properties. In this article, the effect of different process parameters namely, extrusion temperature, extrusion speed, and gas pressure were studied using poly-lactic acid (PLA) pellets. Initially, a set of preliminary tests was conducted to identify the maximum and minimum ranges of these parameters that result in consistent hollow extrudates. Later, the parameters were varied to understand how they affect the core diameter and shell thickness of the hollow extrudates. These findings were supported by analyses of microscopic images taken under an optical microscope. In the next phase of our experiment, we printed an inflated cylindrical part using the process parameters derived from the initial set of experiments. We carefully compared the results with the data obtained earlier to ensure accuracy and consistency. Finally, we successfully printed an object with varying densities in different sections. Keywords: Additive Manufacturing; Extrusion Additive Manufacturing; Hollow Extrudates; Pellet Extrusion; Fused Filament Fabrication.
Open Access
Dielectric characterization of materials and lossy filter design using reflected group delay
(Faculty of Graduate Studies and Research, University of Regina, 2024-12) Walia, Gaurav; Laforge, Paul; Wang, Zhanle (Gerald); Azam, Shahid; Teymurazyan, Aram; Zarifi, Mohammad Hossein
Filters play a significant role in different types of communication systems such as radars, cellular mobile and satellite communication. They are helpful in improving the performance of a communication system by restricting the transmission to the intended frequency band and rejecting the interfering signals from outside. Filters are expected to provide distortion free transmission to the signals in the passband and thus require flat in-band response and an adequate amount of out-of-band rejection. Filter technologies employing coaxial, waveguide and dielectric resonators can meet these requirements but at the cost of large size. Low loss in these filter technologies can be identified by their high quality factor (Q-factor). Higher insertion loss (lower Q-factor) in the filter response is acceptable based on the communication system using that filter and its position in that system. Filter design must deal with the electrical and physical specifications based on the required filter response. The concept of lossy filter is based on the fact that loss in a filter is distributed among the various resonators in a way that helps to achieve flat in-band response. This is achieved at the cost of degraded insertion loss. In this way, lossy filters provide an effective solution to the applications that can afford low Q-factor and higher insertion loss. Resonator is a building block in a filter network and a good understanding of resonators is quite useful in developing an insight of a filter network. Resonators also have a good deal of applications in methods used to perform dielectric characterization of materials. Keeping this connection of resonators and filter in view, the initial research presented in this thesis is focused on lossy resonators. This was helpful in developing some methods for dielectric characterization of materials. In addition to that, the research carried out using lossy resonators has also been helpful in understanding the behavior of resonators in a filter network with the change in amount of loss. The thesis provides a detailed discussion on methods developed to determine the dielectric properties of materials using the reflected group delay of lossy resonators. Methods of dielectric characterization proposed in the thesis can be categorized as methods using an overcoupled coaxial resonator for testing the materials filled inside it and methods using a capacitively coupled microstrip circuit for testing the microstrip substrates. Mathematical models for these methods are based on the reflected group delay of an overcoupled lossy resonator and can be applied in a procedural manner to extract the dielectric constant and loss tangent of the material under test. The errors in extracting dielectric constant and loss tangent are 2%-5% and 30%-100% respectively and depend on the resonator type, material under test, and test setup parameters. The methods are validated through the characterization of various materials using both the coaxial and microstrip resonators. A method of lossy filter design using reflected group delay is also proposed in this thesis. The research presented in this part of thesis describes the effect of decrease in Q-factor on the in-band response in terms of insertion loss and return loss. It also explains the effect of loss on the group delay of a filter network and introduces the use of negative group delay in lossy filter design. The proposed method tends to recover the loss of in-band flatness due to decrease in Q-factor through resistive cross coupling. A circuit model for microstrip coupled line bandpass filter with different specifications are derived using the proposed method to study the improvement in the in-band flatness. The method of group delay is then integrated with Implicit Space Mapping (ISM) technique to derive EM models for the corresponding circuit models. The quantitative analysis of the simulated scenarios using the proposed method shows a significant improvement of 0.5-2.0 dB in the in-band flatness of the filter response. Finally, a microstrip filter is fabricated and tested to validate the method proposed for lossy filter design.
Open Access
Dynamic user motion prediction using advanced Kalman filtering in 5G mmWave systems
(Faculty of Graduate Studies and Research, University of Regina, 2025-04) Jalali, Erfan; Paranjape, Raman; Wang, Zhanle (Gerald)
Achieving reliable communication in high-data-rate applications and densely populated environments remains a significant challenge for next-generation wireless networks. Millimeterwave (mmWave) technology offers substantial bandwidth and data rate advantages but necessitates precise beam steering to maintain connectivity. The dynamic nature of mobile environments, coupled with diverse user behaviors and trajectories, complicates this task. Traditional beamforming approaches struggle to adapt to such scenarios, often leading to signal degradation, connectivity drops, inefficient resource utilization and more power consumption. This research presents an advanced framework that integrates historical user trajectories through beam sweeping, user tracking via sensor-based information combined with beam sweeping, and Kalman Filter-based prediction to address these challenges effectively. By leveraging key measurements such as Angle of Arrival (AoA), Received Signal Strength (RSS) and Signal-to-Noise-and-Interference Ratio (SINR), the system dynamically tracks and predicts user locations based on data gathered by beam sweeping. This research introduces a sophisticated framework that leverages historical user movement patterns, collected via IMEI-based tracking with beam sweeping over time, to define prototype trajectories. These trajectories are subsequently refined using advanced trajectory identification algorithms. Additionally, multiple sensors installed along the pathway provide approximate user positions as they move along their trajectories. The Kalman Filter, particularly its nonlinear models (Advanced Kalman Filters), significantly enhances prediction accuracy, enabling real-time tracking and prediction of user movements. This capability facilitates future beam steering and adaptive beamforming to optimize signal transmission. The proposed framework addresses several critical challenges. It effectively manages dynamic user behaviors and nonlinear trajectories through Kalman Filter-based prediction. By dynamically allocating channels to users, even in overlapping paths, it ensures efficient resource management, reducing interference and enhancing connectivity. Moreover, the integration of trajectorybased tracking preemptively predicts user positions, allowing for seamless beam adjustments, particularly at curves and cell edges, thereby preventing connectivity drops and maintaining robust communication links. The simulation results demonstrate the robustness of the proposed approach in complex indoor environments, showcasing significant improvements in user tracking, enhanced system awareness of user motion, accurate estimations within an acceptable range, and overall system reliability. The results of this study serve as a valuable asset for the adaptive beamforming framework, not only enhancing RSS and SINR but also reducing power consumption by activating idle-mode antennas. These improvements collectively enhance user experience and ensure seamless connectivity in high-density environments. This study offers a scalable and efficient solution for next-generation wireless networks in diverse indoor scenarios such as smart cities, shopping malls, large public venues and mining tunnels. The findings provide a strong foundation for the development of robust and reliable communication systems in the era of mmWave technology.
Open Access
Energy literacy in the Canadian elementary classroom
(Faculty of Graduate Studies and Research, University of Regina, 2025-02) Mosscrop, Larkin Elizabeth; Hurlbert, Margot; Coates, Ken; Longo, Justin; Bazzul, Jesse; Novog, Dave
This dissertation presents a body of research that addresses the policy issue of the effectiveness and role of curriculum in building energy literacy. Energy literacy, which encompasses broad content knowledge as well as affective and behavioural characteristics, will empower students to make responsible and appropriate energy-related choices, and embrace changes in the way they use and produce energy. Students who are energy literate will be more capable of engaging in thoughtful energy-related decisions as they become adults, informing policies and energy projects moving forward. A framework to assess if the elementary science curricula across Canada would meet the required elements to establish energy literacy in elementary school students was developed. This framework suggests that many of the curricula do not support energy literacy but rather focus on content knowledge. The science curriculum assessment identified all aspects of energy expected to be understood by the end of grade 8. This assessment was used to formulate the student survey, and the in-class focus group. Students showed general energy literacy during the focus group discussions in addition to significant learning and interest in energy after the discussion. Grade 6 students showed surprisingly high degrees of understanding and application when compared to the middle-school students. Teachers were interviewed to evaluate their use of curriculum in the classroom, teaching practices, and understanding of science literacy. Teaching science was raised as a way to increase engagement and accessibility for those who struggle in other areas in school. Science through self-directed project-based learning does not require the same level of literacy or numeracy for engagement. However, this focus on inquiry-based learning was bound by the teacher having enough engagement to answer questions and involve students emphasizing the need for professional development for new or complex topics. The ever-increasing demands on teachers to integrate social issues (e.g., environmental and climate justice) to science education is particularly relevant when considering that energy literacy has three core pillars including affect or the values and beliefs one has. This is particularly important with an increasingly polarized world, where students are bombarded with polarized media and face teachers who have their own implicit and explicit biases. Education must focus on facts while still providing a strong foundation of scientific skills that enable students to develop their own beliefs about science. The curriculum is not structured in such a way as it too has many implicit biases, such as defining energy as either renewable or nonrenewable. These findings support the complex relationship between knowledge, affect, and behaviour, underscoring the importance of using educational strategies that focus not only on cognitive development but engage the whole student in the learning process while still maintaining balance and focus on scientific outcomes. The results also provide evidence for using educational pedagogies that incorporate projects and inquiry-based models to connect the content to student lives outside of school. The results of this study do provide some insight as to the utility of curriculum as a policy tool, that is that curriculum is only one part of a complex system and curriculum reform alone is unlikely to equate to changes in the classroom. Overall, the results of this study support the need for wider implementation of science professional development, including project-based energy education and the creation of resources that can be easily and freely accessed by teachers. Key words: education, energy literacy, science literacy, pedagogy
Open Access
Enriched model categories and the Dold-Kan correspondence
(Faculty of Graduate Studies and Research, University of Regina, 2024-10) Ngopnang Ngompe, Arnaud; Frankland, Martin; Stanley, Donald; Fallat, Shaun; Herman, Allen; Zilles, Sandra; Ponto, Kate
The work we present in this thesis is an application of the monoidal properties of the Dold–Kan correspondence and is constituted of two main parts. In the first one, we observe that by a theorem of Christensen and Hovey, the category of nonnegatively graded chain complexes of left R-modules has a model structure, called the Hurewicz model structure, where the weak equivalences are the chain homotopy equivalences. Hence, the Dold–Kan correspondence induces a model structure on the category of simplicial left R-modules and some properties, notably it is monoidal. In the second part, we observe that changing the enrichment of an enriched, tensored and cotensored category along the Dold–Kan correspondence does not preserve the tensoring nor the cotensoring. Thus, we generalize this observation to any weak monoidal Quillen adjunction and we give an insight of which properties are preserved and which are weakened after changing the enrichment of an enriched model category along a right weak monoidal Quillen adjoint.
Open Access
Experimental characterization and machine learning optimization of polymer nanocomposite membranes for carbon capture systems
(Faculty of Graduate Studies and Research, University of Regina, 2025-04) Aletan, Dirar; Muthu, SD Jacob; Shirif, Ezeddin; Jia, Na (Jenna); Henni, Amr; Mouhoub, Malek; Benamor, Abdelbaki
The study aimed to characterize the CO2 capture capabilities of Polyacrylonitrile (PAN) nanocomposite membranes by reinforcing them with multi-walled carbon nanotubes (MWCNT) and silica (SiO2). These membranes were made using the electrospinning manufacturing method. The nanoparticles were functionalized using Gum Arabic (GA) to improve nanoparticle distribution, which further improved the capture efficiency. The morphological techniques were used to examine the nanoparticle structures after functionalization to optimize the functionalization parameters. Experimental results showed that increasing nanoparticle concentrations enhanced CO2 permeability while maintaining stable N2 permeability, resulting in favourable CO2/N2 selectivity ratios. The 4 wt. % MWCNTs nanocomposite membrane exhibited the best CO2/N2 separation with a CO2 permeability of 289.4 Barrer and a selectivity of 6.3, while the 7 wt.% SiO2 nanocomposite membrane achieved a CO2 permeability of 325 Barrer and a selectivity of 7. These results indicated significant CO2 permeability and selectivity improvements compared to pure PAN membrane. The Maxwell mathematical model was used for validation, and the experimental results exceeded the predicted values, possibly due to well-dispersed nanoparticles and functional groups. Based on the CO2 capturing results from the previous experiments, a second experiment study focused on enhancing the CO2 capture capabilities of PAN membranes by modifying them with polyethyleneimine (PEI), a polymer with high CO2 absorption capacity. PAN was modified with three weight fractions of PEI (25%, 40%, and 50%) and then reinforced with various weight fractions of MWCNT, SiO2, and alumina (Al2O3) nanoparticles. The reinforced PAN-PEI nanocomposite membranes were produced using an electrospinning technique. The morphological characterization techniques confirmed that the PEI has effectively modified PAN polymer, which has improved the distribution of nanoparticles within the nanocomposite membranes. Gas permeation tests revealed that the 40 wt.% PEI-modified membrane achieved the best CO2/N2 separation, with a CO2 permeability of 509.4 Barrer and selectivity of 7.4. The PAN with 40% PEI was then reinforced with 1, 4, 7, and 10 wt. % nanoparticles and the highest performance was observed with 7 wt.% Al2O3, resulting in a CO2 permeability of 849 Barrer and selectivity of 9.6. The results were validated using mathematical models (Resistant Model Approach and Effective Medium Approach), confirming the effectiveness of nanoparticle infusion for CO2 separation. Finally, this research developed and applied three machine learning (ML) techniques, Deep Neural Networks (DNN), Random Forest (RF), and XGBoost models, to analyze the CO2 permeability and CO2/N2 selectivity of nanocomposite membranes. The datasets for CO2/N2 separation were sourced from our experimental results and the published experimental data available in the literature. Key performance metrics such as polymer type, nanofiller type, size, loading amount, membrane surface area and thickness, temperature, and feed pressure were analyzed. Feature importance plots provided insights into the most influential parameters for the material design. The study involved hyperparameter tuning of the DNN, RF, and XGBoost models to achieve optimal performance. Each model was tested using literature data and combined experimental and literature data to validate the models and assess the impact of incorporating experimental data. Performance metrics were evaluated to establish the research's credibility and generalizability. The XGBoost optimized ML model achieved the best prediction performance, with R2 values of 0.93 for CO2 permeability and 0.83 for CO2/N2 selectivity, highlighting the effectiveness of using ML for optimizing nanocomposite membranes.