Master's Theses
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Item Open Access A multi-proxy environmental and limnologic paleo-reconstruction of two lakes in Southern Saskatchewan recording the past 6,700 cal. years BP based on ostracods(Faculty of Graduate Studies and Research, University of Regina, 2025-03) Velandia Bahamon, Maria Juliana; Velez, Maria; Robbins, Leslie; McBeth, JoycePaleoenvironmental and paleolimnological reconstructions are of great importance to understand future responses of the environment to climate change and environmental stress. Two lake archives, Deep Lake and St. Denis – Pond 1, are used to understand past environmental and limnological conditions in the northernmost part of the Great Plains of North America. Climatic conditions are inferred from the lake sediments and the ostracod assemblage for the Mid- to Late-Holocene (6,700 to 120 cal. yr BP). Mainly, dry conditions with shorter wetter conditions prevailed during the Mid-Holocene as suggested by the presence of a depositional hiatus from 6,302 to 4,366 cal. yr BP and evidences for wetter periods inferred from the bulk-sediment chemistry (Al, Si, K, Fe, K/Ti, Fe/Ti and Mn/Ti), the stable isotopes (ẟ18O, ẟ13C), and the trace element composition of the ostracod valves (Mg/Ca, U/Ca, Fe/Ca). The Late-Holocene displays overall centennial cyclicity fluctuations between wet and dry periods but mostly dry during the last 2,000 years. Both Deep Lake and St. Denis – Pond 1 records seem to respond to regional changes although the period between 2,714 and 1,691 cal. yr BP appear to be responding to local rather than regional forcings due to the moist signature that presents like some but not all the lakes in the northern Great Plains. Likewise, the Medieval Warm Period is recorded by the record of St. Denis – Pond 1, in which dry conditions were inferred.Item 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.Item 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, SharfuddinThe 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.Item 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, RaphaelAlzheimer'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.Item Open Access Alcohol and social work: Context and meanings(Faculty of Graduate Studies and Research, University of Regina, 2024-10) Redekop, Erin Violet; Fletcher, Kara; Albert, Jason; Sorensen, MicheleThere is little research available about alcohol use among social workers in Canada. This mixedmethodology study seeks to address gaps in research regarding alcohol use among social workers in Saskatchewan. Social workers in Saskatchewan were invited to participate in an online survey that collected quantitative data including demographic information, alcohol consumption, adverse childhood experiences, and professional quality of life. Qualitative data was collected using semi-structured interviews to capture the meanings social workers attribute to their experiences of alcohol use. The survey found that the overall alcohol consumption of Saskatchewan social workers may be lower than that of the general Canadian population. The survey also found find higher rates of adverse childhood experiences and mental health issues among Saskatchewan social workers than the general Canadian population. The qualitative interviews found that social workers in Saskatchewan worry about the potential negative consequences of alcohol use as a coping mechanism for stress and feel a dissonance between perceived social norms of alcohol use and what alcohol use means to them in their role as social workers. Key words: Social work; alcohol use; alcohol misuse; Saskatchewan; mental healthItem Open Access All that is carried(Faculty of Graduate Studies and Research, University of Regina, 2024-11) Tayaraniyousefabadi, Rozhin; Truszkowski, Robert; Fay, Holly; Garneau, DavidThis paper supports my Master of Fine Arts thesis exhibition, All That Is Carried, presented at The Fifth Parallel Gallery from November 25 to December 7, 2024. All That Is Carried concerns ideas of home, longing, displacement, and memory. My intention is to convey feelings of disorientation and in-betweenness caused by leaving my family home, and home country. This exhibition addresses the complexity of my transitional state between homes. The installation consists of unstretched, shaped, and cut fabrics painted with images of interiors of my former home in Iran. The paintings draw from photographs I took of my former home. A wooden suitcase filled with folded paintings accompanies the hung paintings. Migration is the most significant change of my life. Visiting my family home in Iran, while living in Rome, was like stepping back in time, but into a timeline that I no longer belonged to. I was a visitor in my parent’s house. Everything that remained behind: my family, friends, most significantly, my feelings of home, was altered. What once felt whole and meaningful was now distant. Yet, the existence of my parent’s house kept these feelings of home prevalent in my life. Despite living in Rome, I carried the unsettling feeling of being tied to a home while also separate from it. This state of suspension had me question what home and belonging mean, especially now that my parents, too, have left that house. This double loss felt like the centre of my world had disappeared, leaving me adrift. All That Is Carried remembers and reimagines that house through painting—transforming it into a tangible memory. The paintings are on a lightweight fabric, like clothes, something I can pack and carry with me. Through the process of making, I am now at a better understanding of what my former home provided for me. I can preserve and carry the significance of home with me.Item Open Access Analyse stylistique du métissage dans Le Ventre de l’Atlantique(Faculty of Graduate Studies and Research, University of Regina, 2025-02) Akuoko, Faustina; Rheault, Sylvain; Melancon, JeromeFatou Diome, née au Sénégal, a quitté son pays natal pour aller en France. Cette période a été difficile pour elle, mais elle en a tiré l'inspiration pour Le Ventre de l'Atlantique, roman à caractère autobiographique paru en 2003. Elle y confronte les rêves d'émigration des jeunes Sénégalais, qui voient la France comme un paradis, à la dure réalité de l’exploitation des immigrants africains. Tout au long de son oeuvre, Fatou Diome navigue entre les mondes européens et africains et il en résulte de nombreux procédés de métissage culturel. Cette étude se concentre sur l’analyse du métissage culturel dans Le Ventre de l'Atlantique de Fatou Diome. Au moyen des matériaux de la stylistique, la recherche mettra en lumière les interactions parfois dramatiques et parfois humoristiques entre la culture sénégalaise et la culture française. Concernant le matériau du locuteur, l’auteure Fatou Diome, tout comme la narratrice Salie, doivent questionner leur identité métissée. Concernant le destinataire, le choix de la langue française révèle que le public est européen, tandis que la question de l’immigration s’adresse aux jeunes africains. Concernant la situation, les mélanges de lieux et de moments au Sénégal et en France permettent de combiner les cultures et de construire la nouvelle identité métissée de Diome/Salie. Concernant la visée argumentative, l’auteure fait flèche de tout bois en encensant et en critiquant en même temps les aspects positifs et négatifs des cultures africaines et européennes. Il faut, par exemple, s’inspirer du modèle européen pour s’opposer aux mariages arrangés, ou s’efforcer de développer l’Afrique au lieu de chercher à émigrer. Concernant l’immigration, c’est là le thème central du roman, qui motive les décisions et qui est, en quelque sorte, le moteur du métissage culturel. Enfin, il y a le thème du football, une activité « unificatrice » profondément ancrée dans les deux cultures qui est aussi, comme l’immigration, source de métissage. Enfin, la recherche veut mettre en lumière l’humour dans cette oeuvre. L’humour est plus qu’une tonalité narrative, c’est aussi un outil de métissage culturel et cela semble avoir été jusqu’à maintenant peu reconnu par les critiques de Fatou Diome.Item Open Access Analysis of dissolved organic matter character and dynamics using optical techniques in prairie wetland ponds(Faculty of Graduate Studies and Research, University of Regina, 2024-12) Khan, Faraz; Hall, Britt; Soares, Bruno; McBeth, JoyceCarbon plays an important role in a variety of Earth’s biogeochemical processes, through its various reservoirs and the interconnections between them. The hydrosphere is an important reservoir for carbon, and inland waters have often been regarded as unimportant in carbon biogeochemical cycling. However, important transformations and transport mechanisms occur within inland waters. Wetlands are an example of this, where carbon is in flux with the atmosphere and hydrosphere. Dissolved organic carbon (DOC), the carbon fraction and majority of dissolved organic matter (DOM), is found in the earth’s aquatic environments, is an important connection in the carbon cycle between terrestrial and aquatic systems and plays a variety of biogeochemical roles. Within aquatic environments, DOM plays an intermediary role in trace metal cycling such as that of toxic mercury. DOM is highly heterogenous and can be characterized according to its structure, origin, and molecular weight. Optical techniques have emerged as a powerful tool to analyze DOM according to its origin and chemical character, and in several techniques such as derived fluorescence indices and parallel factor analysis (PARAFAC) models based on emission-excitation matrices have become common. Within Saskatchewan, Canada and located in the prairie pothole region (PPR), the St Denis National Wildlife Area is home to a series of wetlands where a more toxic form of mercury, called methylmercury (MeHg) is found in highly differing amounts within close proximity. DOM is known to influence MeHg concentrations and the process of mercury methylation. This thesis hypothesized that the differing characteristics of DOM may explain differences in high methylmercury concentrations. Through the use of optical techniques, including derived fluorescence indices and PARAFAC modelling, this thesis establishes that high MeHg concentration wetland ponds in SDNWA have more humic and terrestrially derived DOM, and low MeHg concentration wetland ponds have more aromatic DOM. This suggests that the highly aromatic DOM, combined with the high sulfur conditions of the SDNWA enable a greater amount of photodemethylation of MeHg in low MeHg concentration wetland ponds. Keywords: Dissolved organic matter, Methylmercury, Emission-excitation matrix, Parallel factor analysis, Prairie Pothole RegionItem 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, GolamThe 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.Item Open Access Barriers to entry: The impact of non-standard orthography on learning the Lakota language(Faculty of Graduate Studies and Research, University of Regina, 2025-03) Buffo, William; O’Reilly, E. Kathleen; Weenie, Angelina; Wolvengrey, Arok; Miller, Andrew M.The revitalization of the Lakota language is hindered by the absence of a universally accepted orthographic standard. This thesis investigates how non-standard orthographies influence Lakota language acquisition, cultural preservation, and educational practices. Guided by qualitative methodologies, this research explores the diverse orthographies used within the Lakota-speaking community and their impacts on learners, educators, and cultural authenticity. Data were collected through surveys and interviews with students, educators, and community members, focusing on their experiences and perceptions of orthographic systems. Analysis revealed key themes, including the challenges posed by inconsistent orthographies, the cultural tensions arising from orthographic choices, and the need for systems that balance phonetic accuracy with cultural sensitivity. Findings indicate that while orthographic diversity reflects the community’s linguistic heritage, it often complicates learning and teaching efforts. This study concludes that sustainable language revitalization requires a dual approach: adopting standardized orthographies to support literacy and educational consistency while respecting traditional oral practices and cultural values. Recommendations include developing flexible orthographic systems that cater to diverse learners, promoting community-led initiatives, and integrating technological resources to bridge gaps between tradition and modernity. Keywords: Lakota language, orthography, language revitalization, Indigenous education, cultural preservation, phonetic representationItem Open Access Beta-delayed proton emission of magnesium-20 and the RCMP silicon detector array(Faculty of Graduate Studies and Research, University of Regina, 2024-09) Liu, Jizhong; Grinyer, Gwen; Berg, Marcella; Mobed, NaderIn 2011, experiment e09024 was performed in National Superconducting Cyclotron Laboratory(NSCl) located at Michigan State University from Michigan state. The goal was to investigate delayed particle emission for isotopes near proton drip line. The main secondary beam was ²²Si while other exotic beam such as ²⁰Mg,²²Al,²³Si was also produced. The delayed particle emission from ²⁰Mg is of particular interest in this thesis and has been studied in details. In 2018, Dr.Gwen Grinyer's group at University of Regina started constructing a Silicon Detector Array named Regina Cube for Multiple Particle(RCMP) used for studying delayed particles at proton drop line with high resolution and high solid angle coverage. The detector is designed to be placed inside GRIFFIN chamber located at TRIUMF cyclotron facility. GRIFFIN chamber designed by GRIFFIN collaboration consists of 16 High Purity Germanium detectors with 8 fast timing LaBr3 used for studying gamma rays. It is a high effociency detector chamber with BGO shielding for Compton suppression. The combination of the two could provide more detailed study of nuclear structure for isotopes near proton drip line. The detector has been deployed in 2024 and a new experiment on ²⁰Mg S2232 is being scheduled during 2024 summer. In this thesis, I will be presenting analysis of ²⁰Mg from experiment e09024 with development and testing of RCMP detector array and experiment S2232. Key words: Proton Drip line,Beta-delayed, NSCL, TRIUMF, RCMP, GRIFFINItem 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, HussameldinThis 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.Item Open Access Carbon capture, utlization, and storage supply chain risk management framework development(Faculty of Graduate Studies and Research, University of Regina, 2023-12) Kabir, Md Ainul; Kabir, Golam; Kahn, Sharfuddin; Tontiwachwuthikul, PaitoonTechnology called Carbon Capture, Utilization, and Storage (CCUS) is important for capturing CO2 emissions before they get into the air. Because everyone wants to stop global warming by reducing CO2 emissions, CCUS is an important and emerging technology that can help slow down climate change, lower emissions in many areas, and support the move to a sustainable and carbon-neutral future. As CCUS technology and its adaptation increases, it is very important to pay attention to the CCUS risks from the supply chain (SC) point of view. The ultimate goal of this study is to is to identify CCUS supply chain risks and develop a framework that provides a structured approach to ensure safe and reliable CCUS supply chain. This study develops a complete CCUS SC risk management framework including CCUS SC functions, risks and risk management strategies. Therefore, this study analyzes the literature related to SC of different sectors and identifies the SC risks which is the foundation for CCUS SC risks identification. This study depicts that there is no research article that provides a comprehensive CCUS SC risks as well as risk management framework that connects with risk management strategies. Moreover, the framework provides a guideline for future research which enrich the CCUS supply chain risk management system as well as fight against the climate change. This study has deployed Delphi technique to validate the risks that have been identified from the literature. In addition, principal component analysis (PCA) has been used to identify the communalities of the risks related to the CCUS SC functions. At the same time, 44 risk management strategies have been developed from the supply chain risk management literature in relation with the CCUS SC risks. Finally, the risk management framework has been developed in CCUS SC context which is the main objective of this study. This is the first risk management framework in CCUS SC context which connects the CCUS SC functions, risks, and strategies. This framework provides a comprehensive guideline for the management to develop sustainable roadmaps in CCUS SC management systems. Moreover, this framework helps to prevent the environment and promotes further research in CCUS risk management systems. Keywords: Carbon Capture, Utilization, and Storage (CCUS); Supply Chain (SC); Supply Chain Risk; Risk Management (RM); Principal Component Analysis (PCA), Risk Management Strategies.Item Open Access Classifying ovarian cancer using machine learning methods(Faculty of Graduate Studies and Research, University of Regina, 2023-11) Rahman, Rushda; Peng, Wei; Henni, Amr; Muthu, S. D. JacobOvarian cancer is one of the most fatal cancers for women nowadays. It is ranked as fifth most common cancer deaths among women resulting more deaths than any other cancers in female reproductive system. According to Canadian Cancer society that about 3000 ovarian cancer patients were detected, and among them 1950 patients died in 2022 which indicating more than 50% of mortality rate. Ovarian cancer is mainly generated from cancerous ovarian tumour. So, it is very important to classify cancerous tumour from noncancerous tumour to prevent false positive for ovarian cancer. Moreover, if cancerous tumour is diagnosed in early stage, it can be prevented from spreading and thus survival rate for ovarian cancer can be increased. Also, by separating cancer patients from benign tumour patients, it will be easier for doctors to know the stages of the cancer and know patient’s prognosis and life expectancy. The principal and initial objective of this thesis is building a feasible system using Artificial Intelligence which is easy to use and compatible to classify ovarian cancer. Proposed study will give a new non-conventional way to classify ovarian cancer from ovarian tumour which will be affordable for the patients. Moreover, one of the primary benefits of this study is that doctors/physicians can detect ovarian cancer with only blood test/ serum test. There is no need to do any expensive tests such as: ultrasound, MRI or CT-Scan. The main concept of this research is the application of several machine learning techniques to correctly classify ovarian cancer and finding best technique among those in terms of Accuracy, Precision, Sensitivity, and Specificity. Original dataset is taken from website named Kaggle (https://www.kaggle.com/). This dataset is screened, cleaned and normalized first and then expert’s advice has been taken to extract the most important features to do the correct classification. Later, a correlation test has been done for better understanding of the relations and independency among the input features. 10 input features have been selected including age, menopause, CA-125, AFP, NEU etc. From correlation test result 7 inputs were taken again and a comparison had been made between 10 inputs and 7 inputs. And the output is TYPE which denotes 1 for benign ovarian tumour and 0 for ovarian cancer. Four machine learning models have been used for classification and they are, ANN, SVM, Naïve Bayes, and k-NN. Training of each model is performed and after training, each algorithm is tested and hence performance is calculated and compared. After analysing results, it is found that for the problem considered, the Artificial Neural Network (ANN) is the best classifier in terms of accuracy giving 85.91% accurate results on test data whereas SVM, NB and k-NN gave accuracy of 76.05%, 83.09% and 76.06% respectively. In terms of sensitivity and precision calculation, Naïve Bayes is best, and the ANN Classifier is second best algorithm. Taking specificity into Consideration, the ANN is best with 87.50%. Keywords: Machine Learning Classifier, Ovarian Cancer, Benign Ovarian Tumour, Artificial Intelligence, Artificial Neural Network (ANN), Support Vector Machine (SVM), Naïve Bayes (NB), k-nearest Neighbour (k-NN), Confusion Matrix.Item Open Access Community-based programming for families and children with disabilities(Faculty of Graduate Studies and Research, University of Regina, 2025-02) Silljer, Rachelle Kathleen; Hopper, Tristan; Genoe, Rebecca; Mang, CameronLeisure and recreation are known to positively impact families and children with disabilities. Leisure is defined as an activity that participants take part in when they are free from the demands of other responsibilities. However, opportunities for individuals with disabilities to take part in leisure activities in the community can be limited. The purpose of this research is to explore the experiences of families and children with disabilities in community recreation programming. To obtain participants’ perspectives the study was done using a constructivist viewpoint, and a qualitative descriptive design. Data were collected from four families using both one-on-one interviewing and larger connection groups. Thematic analysis was used to analyze the qualitative data. Three main themes were identified as the largest barriers to inclusion: attitudes, opportunities, and acceptance. Participants experienced barriers based on the views of other attendees, their caregivers, their self-doubts, and their coaches. The participants expressed the need for intervention and expressed how assistance in accessing community-based recreation could reduce these barriers. Keywords: disability, inclusion, family, acceptance, opportunity, attitudes.Item Open Access Crushed: Climate anxiety and grief support through ceramic art(Faculty of Graduate Studies and Research, University of Regina, 2024-07) Snider, Amy Ruth; Garneau, David; Chambers, Ruth; Fay, Holly“Crushed: Climate Anxiety and Grief Support through Ceramic Art” is the support paper that accompanies my Master of Fine Arts graduating thesis exhibition, Crushed, an installation held at the Fifth Parallel Gallery from August 19-28, 2024. This paper delineates the exhibition its background, development, context, methodology, and artistic influence. It describes components of this project that extend beyond the gallery, and anticipants audience participation and reception. Crushed is an expression of my climate change anxiety and grief. With a floor scattered with super fragile ceramic vessels and a text about my fear and sadness on the walls, Crushed shows participants that such feelings are justified and shared and offers resources and support. At the end of the wall text is information about a support group I founded, and a discussion circle led by a psychologist that will take place in the gallery at the end of the show.Item Open Access Decolonizing mathematics education by disrupting power through the 4 Rs of Indigenous research(Faculty of Graduate Studies and Research, University of Regina, 2025-01) Giroux, Megan Ann; Nolan, Kathleen; Graham, ShanaMany mathematics teachers in Canada are searching for ways to decolonize their teaching practice and include Indigenous epistemologies and pedagogies in their classroom. Unfortunately, these same mathematics teachers struggle to find practical ways to do this decolonizing work that do not reduce complex Indigenous cultural phenomena to simplistic, and often tokenistic, textbook questions. In this thesis, inspired by the 4 Rs of Indigenous Research (respect, responsibility, relevance, and reciprocity), I present and enact a 4 Rs pedagogical framework composed of theoretical approaches (outcomes) and practical teacher actions (indicators). Instead of focusing on lesson topics/standards, my framework focuses on shifts in pedagogical actions, designed to support my ongoing journey to decolonize my daily teaching practice. As I aimed to decolonize my practice by disrupting power dynamics and strengthen classroom relationships, my primary aim in this research is to understand the value of my 4 Rs framework for my own professional growth. By thematically analyzing my personal journaling, anonymous student questionnaires, and critical friend observations, several semantic and latent themes emerged. The semantic themes are noticeable student voice and choice, comfortability between the teacher and students, and the powerful use of stories to engage in mathematics. The latent themes presented and discussed here are teacher anxiety related to time and the positive effects of blurring the lines between producers and consumers. Based on these thematic findings, I conclude that the 4 Rs pedagogical framework can be a valuable professional growth tool for mathematics teachers who want to work towards decolonizing their teaching by disrupting power and strengthening classroom relationships.Item Open Access Design, development, and control of an off-road hybrid electric vehicle power system(Faculty of Graduate Studies and Research, University of Regina, 2024-01) Hedayat Pour, Mehdi; Mehranddezh, Mehran; Stilling, Denise; Kabir, GolamWith global warming and climate change threatening the planet, the auto industry is transitioning from conventional internal combustion engines to electrical power trains. Globally, electric vehicles and hybrid electric vehicles are becoming popular with 10 million cars sold in 2022. Electrification is not limited to the personal cars and public transport, but includes applications in transportation, mining, and military. The power system and battery pack are the main components of electric and hybrid electric vehicles. Battery pack capacity and performance affect the overall mileage. This thesis models and sizes the battery pack based on the mechanical and electrical specifications of the vehicle. The control algorithm is the heart of an unmanned, remote controlled, electric vehicle. This work presents a combined control strategy for an unmanned hybrid electric vehicle using a Joystick. The control algorithm consists of a dynamic controller designed using the mechanical modeling of the vehicle combined with an electrical control strategy known as Field Oriented Control. The proposed technique generates the torque and speed signals using dynamic model of the vehicle and trajectories applied to the model. These signals are then utilized by the inverter controller in the vehicle's power system. The proposed algorithm generates excellent results based on trajectory following and can guarantee the safe operation of the battery pack without over discharging the battery higher than its nominal discharge rate to provide longer lifespan during harsh operations. The simulation results show that the vehicle can operate in different road conditions without violating the discharge limit of the battery pack, while following the input trajectories. The highest discharge currents are associated with the toughest road conditions and shorter terminal time during which the vehicle should reach the desired speed applied through the trajectories. The proposed control algorithm can be applied to other vehicular application by taking their specific mechanical behaviors and characteristics into account. Keywords: electric vehicle, hybrid electric vehicle, power system, battery pack, field oriented control, dynamic modelingItem Open Access Developing photoresponsive lipid nanoparticles for the triggered release of small molecules(Faculty of Graduate Studies and Research, University of Regina, 2025-01) Korbo, Abygail Rose; Murphy, R. Scott; MacKinnon, MarcThis thesis will examine numerous photo responsive drug delivery nanoparticles containing two different first-generation (1G) donor-acceptor Stenhouse adduct (DASA) molecules, DOPC, DLPC, cholesterol and lauric acid. The DASA isomerizes upon visible light irradiation, thereby increasing membrane permeability through disruption of the bilayer and releasing cargo. A 1G dioctyl DASA and a new 1G cholesterol-DASA conjugate were synthesized, and their photophysical properties were studied and compared. The dioctyl DASA was studied in DLPC with and without lauric acid, as it was observed that the lauric acid promotes thermal isomerization of the photochrome, eliminating photocontrol of the system. Thus, the incorporation of acid was removed for the remaining studies. DLPC was found to be a “leaky” system, so DOPC was studied afterwards. The dioctyl DASA in DOPC was found to be a highly stable system capable of responding to a visible light trigger, but additional disruption is required for therapeutical applications. Cholesterol was incorporated into this system and found to further stabilize the nanoparticle, releasing even less cargo. Again, this system responded to a visible light trigger but released insufficient cargo. The cholesterol-DASA conjugate was hypothesized to create further disruption within the DOPC liposome, triggering a greater amount of release upon its photoswitch, but unfortunately the opposite was observed. However, useful insights were gained regarding the effects of the donor group on the switching capacity of 1G DASAs, the impact of the incorporation of cholesterol on a DOPC liposome, as well as important aspects of the calcein release assay were discovered, providing valuable information to researchers continuing with these methods. Finally, it was discovered that the highly lipophilic cholesterol-DASA conjugate was able to incorporate into the lipid bilayer to a higher extent than was seen with the dioctyl DASA. This has potential in creating a photo responsive nanoparticle system, as photochrome incorporation tends to be a challenge in developing such systems. Overall, advancements were made in tailoring the design of the components within these nanoparticles as well as the methods used to study release. Keywords: DASA, Photoswitching, Liposome Nanoparticle, Cholesterol-DASA conjugate, Triggered releaseItem 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, WeiAdditive 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.