Browse
Recent Submissions
Item Open Access Application of data-driven methods in water resources engineering – case studies(Faculty of Graduate Studies and Research, University of Regina, 2024-04) Nezaratian, Hosein; Wu, Peng; Jin, Yee-ChungWater resources engineering stands at the forefront of sustainable water management, facing challenges that necessitate innovative solutions. This thesis explores the integration of data-driven methods into the field, with a dual focus on predicting Maximum Scour Depth around Piers under Ice-covered Conditions (MSDI) and enhancing Water Quality Index (WQI) predictions. In addressing the first objective, MSDI, the study utilizes three data-driven methods—M5, PSO, and GEP—to predict scour depth around piers under ice-covered conditions. A meticulous dataset preparation process precedes model development, followed by rigorous evaluation and sensitivity analysis. The proposed solution framework not only advances academic understanding but also provides practical insights for engineers and practitioners in water resources engineering, addressing a critical gap in predicting scour depth under ice-covered conditions. The second objective focuses on WQI, a key parameter for water quality assessment. The study gathers a comprehensive dataset from the North Saskatchewan River and employs the NFS-WQI method. Feature selection and algorithm evaluation, including M5, PSO, DE, GEP, and MARS, enhance predictive capabilities. The refined predictive tools highlight crucial determinants like Dissolved Oxygen and pH, contributing practical implications for environmental management. In conclusion, the thesis emphasizes the significance of data-driven methods in water resources engineering, offering a robust and efficient approach to modeling complex systems. The integration of these methods complements traditional empirical models, providing a nuanced perspective crucial for decision-makers in water resource management. The comprehensive insights derived from MSDI and WQI studies pave the way for innovative solutions and adaptive strategies in the face of evolving water-related challenges. As the field continues to evolve, the application of data-driven methods becomes increasingly imperative for sustainable water utilization and management.Item Open Access Numerical simulation of geothermal energy production in the Estevan area using water and CO2 as working fluids(Faculty of Graduate Studies and Research, University of Regina, 2024-05) Lu, Yue; Jia, Na (Jenna); Zhao, Gang (Gary); Shirif, EzeddinGeothermal energy is a promising and clean renewable resource that is widely accessible around the world (Avci, A.C., et al., 2020). The city of Estevan is proven to possess some geothermal qualities which can be utilized for heating and electrical power generation (Jacek Majorowicz et al., 2021). Water is the most common working fluid to extract geothermal energy; However, water-based geothermal systems may cause issues, such as water loss and induced seismicity. Recently, CO2 has attracted more interest as an alternative injectant to explore geothermal energy because of its high mobility, thermal properties and the additional benefits of CO2 sequestration. In this study, based on the geological and reservoir information in Estevan, a realistic base numerical model is established using Computer Modelling Group (CMG) software to simulate the geothermal energy exploitation process via injecting water and CO2. Results demonstrate that the CO2 Plume Geothermal (CPG) system is more efficient than water-based geothermal systems under specified conditions. The heat extraction rate of the CPG system is 2.5 times that of the water-based geothermal system. In addition, the co-axial closed-loop method for geothermal energy extraction is also simulated in this study. Compared with open loop geothermal energy recovery methods, closed-loop geothermal systems are not significantly affected by the flow of reservoir fluids or permeability variation because the reservoir temperature maintains relatively stable. This study simulates a co-axial closed-loop system to extract geothermal energy in the Estevan area and compares the obtained results with the base open-loop model. The results indicated that an open-loop system has better thermal recovery performance because the associated heat transfer processes includes heat conduction and convection, while for a closed-loop geothermal system, heat conduction is the major mechanism for heat transfer. However, the closed-loop method avoids the direct interaction between the working fluid and surrounding formation and minimizes the induced seismicity, which results in less environmental disturbance, and could provide more stable subsurface heat generation. To conclude, the results and conclusions obtained in this study provide a deeper understanding of optimal designs and feasibility investigations of various production methods for CPG and water-based systems in Estevan.Item Open Access Storage and utilization of CO2 in ready-mix concrete using CO2-loaded aqueous inorganic solvent(Faculty of Graduate Studies and Research, University of Regina, 2024-04) Asare, Benjamin Opuni; Idem, Raphael; Ibrahim, Hussameldin; Supap, TeeradetCement production is a significant contributor to CO2 emissions, prompting the need for innovative approaches to mitigate its environmental impact. This work is focused on developing a workable process that can capture CO2 emitted by cement production and eventually store the captured CO2 in concrete to accelerate concrete curing. Potassium glycinate (K-glycinate), an environmentally friendly aqueous inorganic solvent was employed for this work. Firstly, 6M aqueous K-glycinate solvent prepared from glycine and potassium hydroxide was loaded with CO2 at 60℃ to produce CO2-loaded Kglycinate. Prior to being allowed to cool to room temperature (24±2 ℃), the solvent was diluted (dilution factor 1.5) with distilled water to eliminate any precipitation. Secondly, the CO2-loaded K-glycinate was utilized in two different batches of concrete. The first batch of concrete which is identified as concrete batch I consisted of sand, cement, rock and water. On the other hand, the second batch of concrete which is identified as concrete batch II consisted of additional materials which were admixtures (poly 980 and micro air) and fly ash. Nine different concrete mixtures were produced for this work. Four for the concrete batch I and five for the concrete batch II. The concrete mix for the concrete batch I were Baseline concrete I, 19% carbonated K-glycinate concrete I, 27% carbonated K-glycinate concrete I and 37% carbonated K-glycinate I. The baseline concrete I composed of only cement, sand, water and rock. The carbonated K-glycinate concretes I were produced by replacing a fraction of water with CO2-loaded K-glycinate. For instance, the 19% carbonated K-glycinate concrete I was produced by replacing the 19% of the mass of water in the Baseline concrete I with CO2-loaded Kglycinate. The concrete batch II consisted of Baseline concrete II, 6% carbonated Kglycinate concrete II, 9% carbonated K-glycinate concrete II, 20% carbonated Kglycinate II, and 32% carbonated K-glycinate concrete II. The baseline concrete II was produced by adding additional materials which were fly ash and admixtures (poly 980 and micro air) to the cement, sand, rock and water. A fraction of the water in the baseline concrete II was replaced with CO2-loaded K-glycinate to form the carbonated K-glycinate concretes II. The performance of the concrete was evaluated using compressive strength, curing time, CO2 storage capacity as well as other properties such as slump, air content and density of the concrete. The CaCO3 and other compounds formed in the concrete as well as the CO2 storage capacity of the concrete was analysed using XRD and TGA/DTA respectively. Physical properties of the CO2-loaded K-glycinate – water mixture such as viscosity, surface tension, capillarity and contact angle were estimated to investigate their effect on the rate of curing and CO2 storage capacity of the concrete. The 19% carbonated K-glycinate concrete I and the 6% carbonated K-glycinate concrete II had the highest performance in concrete batch I and batch II respectively. Among the concrete batch I, the 19% carbonated K-glycinate concrete I had the shortest curing time and highest compressive strength indicating the largest amount of CO2 uptake. Similar performance was observed for the 6% carbonated K-glycinate concrete II for the concrete batch II. The XRD profiles for concrete batch I and II revealed the presence of CaCO3 and CaMg(CO3)2, which are responsible for concrete's strength. The TGA/DTA profiles for the concrete confirmed that CO2 is permanently stored in the concrete due to the magnitude of the temperatures (600 - 930℃) that was required to remove the CO2 from the concrete. The properties of the carbonated K-glycinate – water mixture used for the concrete revealed that lower viscosity, higher capillarity, higher surface tension and contact angle favour concrete performance.Item Open Access Hydrothermal liquefaction of pulp and paper mill residues for renewable biocrude production(Faculty of Graduate Studies and Research, University of Regina, 2024-05) Adetunji, Toluwanimi Oluwasegunfunmi; Ibrahim, Hussameldin; Idem, Raphael; Tontiwachwuthikul, Paitoon (P.T.);The climate change challenge has prompted global governments and international organizations to implement measures aimed at reducing greenhouse gas emissions and mitigating their impact on the ecosystem. The pulp and paper industry, a major contributor to global waste generation, often lacks efficient waste disposal methods despite the energy-rich nature of its waste products. Consequently, there is a pressing need to intensify research efforts on valorizing and utilizing pulp and paper waste residues thereby checking the inordinate disposal of effluents from this sector, hence this thesis. The objective of this research is to investigate the effects of reaction parameters such as temperature, residence time, feed concentration and catalysts on the yield of biocrude from the Hydrothermal Liquefaction (HTL) of pulp and paper mill residues. These reaction parameters were taken through non-catalytic and catalytic optimization. While Central Composite Design (CCD) was used in the design of experiments, Response Surface Methodology (RSM) was utilized in optimization. The optimum parametric conditions obtained are temperature: 340oC, residence Time: 56min and feed concentration: 5%. Zeolite (HZSM-5), gamma-alumina (γ-Al2O3), activated carbon and potassium carbonate (K2CO3) were utilized as catalysts and their performances with respect to biocrude yield improvement were evaluated. The order of catalytic effect on biocrude yield is potassium carbonate (K2CO3) > gamma-alumina (γ-Al2O3) > zeolite (HZSM-5) > activated carbon. However, due to the hygroscopic nature of potassium carbonate it cannot be recovered and re-used. As a result of this deficiency, gamma-alumina was adopted as the best (recyclable) catalyst in this study. Catalyst Characterization, such as N2 Physisorption Analysis (BET), Fourier-Transform InfraRed (FTIR) Spectroscopy, Powder X-ray Diffraction (XRD), Scanning Electron Microscopy / Energy Dispersive X-Ray Spectroscopy (SEM/EDS), and Temperature-Programmed Desorption (TPD). were performed on the fresh and spent catalysts to study their properties and make informed inferences on their impacts on biocrude yield. Finally, an intrinsic kinetic model was developed to derive a rate equation that could be easily integrated with generalized equations for rates of physical transport processes. This integration would enable the development of a reactor model that is capable of extrapolating across a spectrum of reactor operating circumstances. Limitations to mass transport were initially eliminated to establish the kinetic limited region. From the kinetic study, a first-order equation was proposed. The activation energy and pre-exponential factor of the reaction are 15.981 KJ/mol and 0.254 s-1 respectively. The average absolute deviation (3.40%) of the kinetic model showed that the model is an excellent fit for the hydrothermal liquefaction process. Based on the findings from this research, it was concluded that γ-alumina is an appropriate catalyst precursor to efficiently convert pulp and paper mill residues into biocrude, producing the highest biocrude yield of around 26%, which may be attributed to its selectivity, high level of crystallinity, and acidity. Further investigations into hydrothermal liquefaction are recommended, with an emphasis on varying factors such as pressure, feed mass to catalyst ratio, and reactor stirrer speed.Item Open Access An exploration of school-based sexual abuse prevention programming: No Is a Full Sentence program evaluation(Faculty of Graduate Studies and Research, University of Regina, 2023-06) Chetty, Taylor Allison; Milne, Lise; Chalmers, Darlene; Collin-Vézina, Delphine; Daignault, IsabelleChild sexual abuse (CSA) is a global issue with potentially long-lasting and severe consequences. In response, school-based prevention programs have been developed and proven to be effective at enhancing child knowledge of CSA concepts. The Saskatoon Sexual Assault and Information Centre’s (SSAIC) new program, “No Is a Full Sentence” (NIAFS), provides grade eight students in both Greater Catholic and Public-School Divisions in Saskatoon, Saskatchewan with foundational and complementary education about CSA concepts, healthy relationships, boundaries, bodily autonomy, understanding sexual violence, and more. This thesis research used a program evaluation framework and program fidelity principles to conduct an evaluation of NIAFS implementation. Semi-structed interviews were conducted with seven participants considered key figures in program development and/or delivery: five SSAIC staff members responsible for program development, implementation, and facilitation; and two teachers who assisted in program facilitation. Thematic analysis led to the construction of four main themes: (1) compromise is required in a number of ways for successful program delivery; (2) facilitators must be attuned to the knowledge, needs, and energy of the classroom organism; (3) facilitators must resonate with the content being shared for its effective delivery; and (4) sexual education isn’t 'just about sex' – it is about planting the seeds required to make change through education. This research looked deeper into the nuances of program delivery in the youth sexual education arena, triangulating findings from SSAIC's post-program satisfaction survey findings, which revealed very positive feedback. Recommendations for future NIAFS delivery, social work practice, and research are provided.Item Open Access Beyond bubble baths and wine: Broadening perspectives on self-care in female social workers in Saskatchewan(Faculty of Graduate Studies and Research, University of Regina, 2023-06) Armstrong, Ivy Allyson; Fletcher, Kara; Gebhard, Amanda; Brand, Denise; Delay, DavidA basic interpretive qualitative research design was used to explore the definition and application of self-care with five female social workers in Saskatchewan. These social workers volunteered to provide information about their experience as a social worker and the potential impact of self-care in their life. Data was analyzed using thematic analysis and included analysis with a feminist lens. There were seven main themes: shifting perspectives about the meaning of self-care, communication, relationships, proactivity, workplaces, holistic self-care, and therapy/counselling. Within these themes were sub-themes used to clarify the self-care practices. Findings are discussed in relation to current research on self-care in social work with recommendations for further research, and implications for practice are included.Item Open Access Perceptions and experiences of leisure-time physical activity among older adults following a heart attack(Faculty of Graduate Studies and Research, University of Regina, 2023-07) Sultana, Sabiha; Genoe, Rebecca; Kelsey, Roz; Kulczycki, Cory; Wickson-Griffiths, AbigailLeisure has been found to improve later-life well-being and to help people in coping with life changes (Dupuis & Alzheimer, 2008; Michèle et al., 2019). Leisure activities, including leisuretime physical activity, may significantly affect healthy aging and improve health-related quality of life among older persons. However, there is a lack of literature revealing the determining factors of participation in leisure-time physical activity among older adults following a heart attack. The aim of this research was to explore the perceptions and experiences of leisure-time physical activity among older people who have had a heart attack. To obtain participants’ perspectives, a parallel mixed-methods design was used. Data were collected from 10 participants using a survey (Rapid Assessment of Physical Activity questionnaire) to measure leisure-time physical activity, followed by a face-to-face interview. A qualitative descriptive technique was used to guide the qualitative data collection and analysis. SPSS version 25.0 was used to analyze the demographics and the RAPA questionnaire. Thematic analysis was used to analyze the qualitative data. Four main themes, making lifestyle changes after a heart attack, engagement in leisure-time physical activity, perceptions about leisure-time physical activity after a heart attack, and constraints were generated to describe participants’ perceptions and experiences of leisure-time physical activity. Leisure-time physical activity participation after a heart attack was influenced by several motivators which led to engagement in leisure activities. Participants experienced several constraints in engaging in leisure-time physical activity, however, they described different ways of negotiating those constraints.Item Open Access Using unmanned aerial vehicles to examine how aboveground forest biomass and bat activity are related to three-dimensional canopy structure(Faculty of Graduate Studies and Research, University of Regina, 2023-07) Sprott, Adam Harold; Vanderwel, Mark; Brigham, Mark; McDermid, Gregory J.Canopies are emergent properties of a large number of individual trees and provide the underlying structure which affects many other components of forest ecosystems. Unmanned aerial vehicle (UAV) data fills an important gap in aerial imaging by offering an affordable and repeatable source of data to assess variation between forest stands across a given landscape. Individual tree crowns can be segmented from canopy height models to provide detailed information about forest and canopy structure, and photogrammetric point cloud data can be used to identify key habitat variables which affect bat activity. A greater density of biomass may reflect higher productivity and greater energetic and material exchange between the atmosphere and biosphere, whereas increased animal activity indicates a healthier ecosystem with greater resiliency and stability. In this thesis I use data on forest canopy structure 1) to estimate aboveground biomass (AGB) and 2) to model bat activity. I developed allometric scaling models to relate field observations of diameter-at-breast-height (DBH) and tree height to UAV derived tree height and crown area to estimate AGB of individual crown segments. Using these estimates, I constructed two Bayesian regression models to examine variation of AGB of large stands throughout a forest in response to either a set of stand structural predictors or a set of topographic predictors. With these models, I found that at the stand level, AGB was closely related to several structural variables including canopy-area weighted height and tree density and that AGB showed modest relationships with topographic variables such as topographic position, elevation and soil moisture. These results suggest that UAV-derived data calibrated with field observations can be effective for estimating forest AGB and studying its variation among stands, and that stand structural characteristics are more strongly related to AGB that topographic variables. I also captured acoustic recordings of bat passes from some of these forest stands and assigned automatically classified bat calls into three echolocation guilds (short-, mid-, and long-range echolocators; SRE, MRE, and LRE respectively). Using a Bayesian generalized linear model, I was able to model the activity of these guilds in relation to canopy structure. SRE activity was more frequent in taller stands, in stands with less canopy cover, and in stands that were further away from the canopy edge; MRE activity was slightly greater in stands with shorter canopies; and LRE activity was more likely to be observed in stands with more canopy cover and in stands closer to the forest edge. Greater activity of all three bat guilds was observed in plots closer to open water sources. My results illustrate how photogrammetric point clouds can identify fine-scale features useful for AGB modelling and important to bat habitat use, demonstrating how useful UAV captured data can be for forest researchers and managers.Item Open Access Studying the background response of Minihalo for design optimizations(Faculty of Graduate Studies and Research, University of Regina, 2023-08) Sajid, Shayaan; Barbi, Mauricio; Kolev, Nikolay; Ouimet, Philippe; VIrtue, Clarence; Floricel, RemusThe Neutron Detection Characterization Facility also called Minihalo Neutrino Detector is a planned research and development project that will enhance the detection capabilities of lead based neutrino detectors for supernova physics. It will will be used to construct low background He-3 counters for HALO-1kT supernova neutrino detector and will also provide experimental data on cross-sections of ν-Pb interactions at supernova energy scale. The detector will be placed at the SNS Facility in Oak Ridge National Laboratory on Oak Ridge, Tennesse, USA. The SNS Facility produces three ν species from impinging protons at liquid-mecury target. The νμ, ¯νμ, and νe are produced with a flux of approximately 4.3 ×107 cm−2 s−1 at supernova energy scale. Studying these neutrinos at supernova energy scales at SNS will provide necessary data for HALO-1KT supernova neutrino detector. Detecting neutrinos from core-collapse supernovae through detectors like HALO-1kT accurately can provide invaluable information on the explosion mechanism of massive stars which is not fully understood. This is because neutrinos are emitted from the core of a dying star a couple of hours before the star explodes. Therefore, by detecting these neutrinos, we can not only probe into the heart of exploding stars but also develop full 3D models on their complete explosion mechanism. In order to determine an optimal design for Minihalo, GEANT4 simulations of the proposed design are carried out to study how the detector responds to background at the Facility. Accurate fluxes of cosmic muon and gamma-ray backgrounds at the SNS Facility are simui lated and fired onto the detector. The data from the simulations is analyzed using th ROOT package. This work focuses on the energy deposition of cosmic muons and gamma-rays in the scintillators, the optical photon spectra of scintillators and the background neutrons produced inside the lead from muon interactions. The background neutron spectrum is investigated in detail in order to determine the efficiency of the detector and the necessary change to the proposed design are also investigated to increase the efficiency.Item Open Access Credit card fraud detection using incremental feature learning(Faculty of Graduate Studies and Research, University of Regina, 2023-01) Sadreddin, Armin; Sadaoui, Samira; Khan, Shakil; Bais, AbdulDetecting credit card fraud is essential and it is one of the most popular payment methods. Credit card fraud can cause huge losses for cardholders. Therefore, so many studies have focused on proposing different standard machine learning methods and limited use of incremental learning to create a robust detective system. None of these studies can solve all the credit card fraud challenges together. The reason is the complicated real-world scenario and data we have in our hands. Some of these challenges are rapid data arrival rate, concept drift which causes model performance to decline over time and data sensitivity which causes a limited amount of instances in hand for training a model. We have proposed a chunk-based credit card fraud detection model which is based on incremental feature learning and transfer learning. Our proposed approach gives our model the capability to adjust its topology to find the near-optimal solution for the problem at hand. Our approach creates submodels per chunk and for the predictive model creation. We use the most relevant sub-models to the current data distribution we have. By doing so, we do not need to store all the transactions and we can avoid the model infinite growth by setting a limit on the number of used sub-models. There are a limited number of datasets for credit card fraud detection available due to the data sensitivity issue. So, we have evaluated our approach using two of the existing datasets: A mid-scale dataset consisting of two days of European cardholders’ transactions in September 2013 and A large-scale dataset consisting of 6 months of transactions in 2019. We have separated each dataset into a different number of chunks to be able to test and train our approach incrementally. We have compared our approach with a static model based on the initial chunk and re-trained on each chunk. Moreover, we have changed the number of sub-models to evaluate its impact on the performance.Item Open Access Precision-based boosting for regression(Faculty of Graduate Studies and Research, University of Regina, 2023-06) Razavi, Mahsasadat; Zilles, Sandra; Mouhoub, Malek; Weger, HaroldRegression is a type of predictive modeling problem that involves estimating a continuous numerical value based on input variables. The goal of this research is to investigate whether incorporating the precision of regression models on specific target values can improve the performance of ensemble-based regression models. We begin by reviewing two existing ensemble methods for classification, namely AdaBoost and PrAdaBoost, which will form the basis of our proposed ensemble method for regression. We also provide a formal analysis of the training error upper bounds for PrAdaBoost and AdaBoost. The mathematical proof shows that PrAdaBoost’s upper bound is always less than or equal to AdaBoost’s. This result is important because it implies that PrAdaBoost’s training error upper bound decreases exponentially as the number of iterations increases, assuming that each individual predictor in the ensemble is better than random guessing. We modify the PrAdaBoost algorithm and implement it in the context of regression, thus introducing a new regression algorithm called PrSAMME-R. To evaluate the performance of PrSAMME-R, several experiments are conducted on various regression datasets, and the results are compared to those obtained from other ensemble-based regression models. The results show that incorporating the precision of regression models on specific target values into their weights can improve the performance of ensemble-based regression models significantly. PrSAMME-R outperforms other ensemble-based rei gression models such as Random Forest, Gradient-based Boosting, AdaBoost.R2 and AdaBoost.RT, in terms of mean absolute error.Item Open Access Sword fighting in virtual reality: Where are we and how do we make it real(Faculty of Graduate Studies and Research, University of Regina, 2023-03) Pitura, Philip Remo Stanley; Gerhard, David; Hamilton, Howard; Dorsch, KimVirtual Reality has historically been a research space concerned with recreating the natural world around us. It is currently best known for its role in gaming and escapism. A number of different mediums have used virtual reality for its strengths in training. It is particularly useful for its ability to recreate real world locations and situations while still having full control over the environment. One area where it has failed in this realism is in its portrayal of fencing. Fencing, also known as sword fighting, is a common interaction in virtual reality gaming. Virtual reality fencing is plagued by a lack of features that are necessary to achieve realistic fencing. In this thesis I present eleven features gathered through observations of seven games. Weapon weight, parries, edge detection, edge alignment, point detection, weapon flex, blade tracking, response to physical locomotion, quality of expected movements, and weapon interaction with the environment are identified as necessary features in a realistic fencing experience. These features are explored with respect to their appearance in games as well as with regard to the current issues surrounding their implementation. It is found that all eleven features are necessary for the creation of a realistic fencing experience and they will require a physics based approach to their implementation and interactions.Item Open Access Two-parameter super-product systems of compact Hausdorff spaces(Faculty of Graduate Studies and Research, University of Regina, 2023-06) Melanson, Patrick; Floricel, Remus; Argerami, Martín; Zilles, SandraThe theory of C∗−algebras [1, 7,17] is incredibly rich and provides a great starting point for exploring various types of operator algebras within the realm of Functional Analysis. Previous papers [8, 12] have used these algebras to analyze what are called C∗-product systems and C∗-subproduct systems, as a natural generalization of two parameter product systems of Hilbert spaces, introduced by B. Tsirelson in [18]. The Gelfand duality shows that commutative unital C∗-product and subproduct systems are directly related to certain two-parameter families of compact Hausdorff spaces, referred to in this paper as compact super-product systems. Building on this, we define the concept of flattening and show that each compact super-product system can be flattened through a projective limit construction. Furthermore, we are able to define a one-parameter “multiplication” induced by this flattening, which behaves well within the framework of a C∗-product system. Finally, we show that these results hold when considering the appropriate measures for these spaces, as well as the various constructions defined within them.Item Open Access Role of physiologically relevant hypoxia in neural stem and progenitor cell proliferation, migration and differentiation into oligodendrocytes(Faculty of Graduate Studies and Research, University of Regina, 2023-04) Masood, Mahrukh; Buttigieg, Josef; Hart, Mel; Candow, DarrenStem cells are undifferentiated cells, defined by their capability to self-renew and differentiate to give rise to different cells of the body. Neural stem and progenitor cells are a type of multipotent stem cell capable of giving rise to the cells of the mature central nervous system (CNS): neurons, astrocytes and oligodendrocytes. The mechanism by which various factors influence stem fate is of wide interest, as these cells play a key role in development, and have a potential role in repair of CNS injury. I investigated the role of physiologically relevant hypoxic levels as a driving force for the proliferation and differentiation of Neural Stem and Progenitor Cells (NSPCs) into Oligodendrocytes (OLs) as well as increased migration. In most research, cells are cultured at 21% O2, which is significantly higher than what these cells, and other cells of the CNS, are typically exposed to. Physioxia is what could be considered low concentrations of O2 in the external environment, but normal in the body. The O2 level in the human body is tightly regulated; particularly low levels of O2 may positively aid in NSPC differentiation through the regulation of certain genes. One mechanism that may aid in the differentiation process is the involvement of transcription factors that are sensitive to changes in O2 levels. Hypoxic Inducible Factor (HIF) is a transcription factor that plays an integral role in the detection of hypoxia and can induce changes in genes responsible for vascular growth (vascular endothelial growth factor (VEGF)), cell migration (matrix metalloproteinase 2 (MMP2)) or A disintegrin and metalloproteinase with thrombosponfin motifs 1 (ADAMTS-1)) and cell differentiation (platelet-derived growth factor (PDGF)). This study demonstrates that a low O2 environment can be confirmed through the upregulation of HIF-1a at low levels of physiologically relevant oxygen levels. Furthermore, the upregulation of VEGF at different O2 concentrations alludes to NSPC proliferation, especially at 5% O2. MMP2 upregulation showed that migration of the OL lineage cells is highest at 5% O2. Lastly, differentiation of NSPCs to OPCs seemed to increase when exposed to low levels of O2 and was the highest at 5% O2. Moderate levels of physiologically relevant oxygen levels such as 5% seem to have the optimal effect on NSPC proliferation, differentiation, and migration as gene expression for several of the gene is highest at that O2 concentration.Item Open Access Training agents to play cooperative games: A reinforcement learning approach(Faculty of Graduate Studies and Research, University of Regina, 2023-07) Marahemi, Sara; Zilles, Sandra; Hamilton, Howard; Smith, AustenDue to recent advances in research on reinforcement learning, self-learning agents are capable of accomplishing numerous kinds of tasks in complex environments without any prior knowledge. Recently, deep reinforcement learning algorithms have shown promising results in game-playing tasks that were previously impractical, including playing video games directly from raw screen pixels. In this project, we created a game engine for a card game called “The Mind”, and used reinforcement learning techniques in order to train agents to master this game. The Mind is a multi-player cooperative card game with the challenge of synchronizing the agents’ actions. We used Q-learning and deep Q-learning to estimate a Q-function which describes an agent’s best action to take at any state of the game. In this research, we implemented three types of agents based on two different reinforcement learning algorithms. The results showed that our trained agents performed better than random agent models. The highest testing win rate using the Q-learning algorithm was 86%. We also designed a reinforcement learning strategy, called observer learning, in which an agent updates its knowledge not only based on the feedback to its own actions, but also based on the feedback other agents receive for their actions. We reached the best testing win rates of nearly 99% for two Q-learning agents using our observer learning strategy in four levels of The Mind.Item Open Access Detection of DoS and DDoS attacks on 5G network slices using deep learning approach(Faculty of Graduate Studies and Research, University of Regina, 2023-09) Khan, Md. Sajid; Shahriar, Nashid; Yao, Yiyu; Louafi, Habib; Morgan, YasserA new degree of connectedness and interaction has been introduced by the development of 5G networks. By dividing a physical network into several logical networks, 5G network slicing is a special feature that gives network operators the ability to allocate specific resources and services to various applications and customers. However, 5G network slicing is susceptible to cyberattacks, particularly Denial-of-Service (DoS) or Distributed Denial-of-Service (DDoS) attacks, just like any other network. Such attacks can have a significant negative effect on network performance, degrading services and reducing the availability of slices. The primary objective of this thesis is to examine the impact of DoS/DDoS attacks on 5G network slicing and their potential to disrupt the performance of legitimate users and slice availability. Additionally, a novel dataset specifically tailored to DoS/DDoS attacks in 5G network slicing is generated, as there is no available dataset based on a 5G network slice. Through extensive research, key features relevant to DoS/DDoS attacks are identified and prioritized. To categorize and detect different types of DoS/DDoS attacks, two deep learning techniques, namely the convolutional neural network (CNN) and the Bidirectional Long Short-Term Memory (BLSTM) models, are employed. These models not only utilize the newly created dataset but also enable comparison with existing datasets to assess their effectiveness. This thesis emphasizes how crucial it is to create strong security measures to guard against DoS/DDoS attacks on 5G network slicing. A step in the right direction toward reaching this goal is the construction of a deep learning model for the classification, detection, and production of a new dataset specifically for 5G network slicing. To keep enhancing the security and stability of 5G network slicing, more study in this area will be required. The results indicate that the proposed models have a high accuracy rate of 99.96% in distinguishing different types of DoS/DDoS attacks within the networking slice environment. This achievement is noteworthy as it pertains to a novel context. Additionally, the newly developed models exhibit comparable performance in terms of other confusion metrics. To verify the research outcome, some well-known data sets are used to show the results.Item Open Access Analyzing the effectiveness of Covid-19 vaccines among different age groups using multinomial logistic regression model(Faculty of Graduate Studies and Research, University of Regina, 2023-05) Khalid, Arfa; Deng, Dianliang; Volodin, Andrei; Peng, WeiThis study is conducted to evaluate the effectiveness of Covid-19 vaccines in different age groups in Saskatchewan, Canada. Data was collected between September 2021 and December 2021, and a statistical method called multinomial logistic regression was used to analyze the relationships between multiple categorical variables. In this study, the categorical variables were the age groups and the vaccination status (fully vaccinated cases, partially vaccinated cases, and unvaccinated cases) of the individuals with the interaction effect of rate of cases. The mathematical proof for the multinomial logistic regression model with interaction effect was derived in this study. The study demonstrated the effectiveness of Covid-19 vaccines among vaccinated age groups and provided theory and practical application of the multinomial logistic regression model. Results show that there is a statistically significant impact of age group and vaccination status on the effectiveness of Covid-19 cases in Saskatchewan. Specifically, there is a difference in vaccine effectiveness based on age groups and vaccination status. The findings of this study provide crucial insights for policymakers and public health officials to optimize vaccination rollout strategies and control the spread of Covid-19. Overall, this study represents an important step in the ongoing efforts to understand the effectiveness of Covid-19 vaccines and to develop policies and interventions that can help mitigate the pandemic impact.Item Open Access Multiple independent lineups: A procedure for corroborating eyewitness identification evidence in children(Faculty of Graduate Studies and Research, University of Regina, 2023-06) Carr, Shaelyn; Kaila, Bruer; Jeff, Loucks; Chris, Oriet; Emily, PicaChild eyewitnesses exhibit problematic choosing on police lineups at a higher rate than adults (Fitzgerald & Price, 2015), which is an issue as mistaken eyewitness testimony is a leading cause of wrongful convictions (National Registry of Exonerations, 2019). This study examined a novel eyewitness technique to use with children, the multiple independent lineup (MIL) technique, to assess facial identification accuracy. A total of 486 children (60% male, 39% female, and 1% other; Mage = 8.59) witnessed a live event and, the following day, engaged in a lineup identification task (i.e., single simultaneous face lineup or the multiple independent lineup technique). Largely, the results found support for the multiple independent lineup technique to help infer the accuracy of child eyewitnesses. Interestingly, children of all ages performed similarly on the multiple independent lineup technique. The results also revealed that facial identification responses are similar between the two lineup conditions (i.e., single simultaneous lineup and multiple independent lineup technique). Implications and future directions are discussed.Item Open Access Efficacy of a brief online mindfulness and self-compassion intervention (Mind-OP+) to increase connectedness: Randomized controlled trial(Faculty of Graduate Studies and Research, University of Regina, 2023-07) Bueno, Christine Frances Bast; Beshai, Shadi; Hadjistavropoulos, Heather; Sharpe, Donald; O'Rourke, HannahConnectedness is defined as a connection with others that promotes well-being. Although studies examining connectedness are few to date, the extant literature on closely related concepts suggests connectedness is associated with reduced symptoms of psychological and physical disorders and higher overall well-being. Cultivating feelings of connectedness also appears to encourage prosocial behaviour, such as volunteering or donating to charity. Mindfulness and compassion interventions (MBIs) may be adapted to cultivate feelings of connectedness, thereby unlocking a protective mechanism in mental health and beyond. Further, brief self-guided MBIs are particularly promising, given demonstrations of their efficacy combined with their potential for wide scalability and dissemination. Accordingly, this author examined the effectiveness of an augmented, self-guided, brief, online mindfulness and self-compassion-based intervention (Mind-OP+) to facilitate perceptions of connectedness in undergraduate students. Total of 117 undergraduate students were randomized into a waitlist (n = 55) or Mind-OP+ (n = 62) condition. Participants in the Mind-OP+ condition completed five modules at a pace of one module per week. Correlation analyses with participants that passed baseline attention-checks (n = 101 ) revealed that social connectedness at baseline was correlated positively with mindfulness and self-compassion, and correlated negatively with fears of compassion, depression, anxiety, and stress. Relatedness at baseline was correlated positively with mindfulness and negatively with fears of compassion and depression, stress, and anxiety. Intent-to-treat mixed-model analyses on all randomized participants indicated that, compared to participants in the waitlist condition, participants in Mind-OP+ reported increased feelings of social connectedness (d = 0.81) and relatedness (d = 0.64) at post-treatment, and increased feelings of social connectedness (d = 0.80) and relatedness (d = .38) at one-month follow-up. Mediation analyses completed with protocol adherent participants at post-treatment (n = 47) demonstrated no statistically significant mediation of self-compassion or mindfulness scores on the relationship between group membership and connectedness nor relatedness scores at post-treatment. These findings provide support for the use of brief, accessible, self-guided interventions to cultivate connectedness. Larger, more definitive trials should compare the effects of Mind-OP+ for connectedness against an active control, and examine whether the effects on connectedness are independent of effects of reducing psychological disorder symptoms. This intervention holds promise as an option for those seeking protective factors for their mental health and general resiliency.Item Open Access Self-training for cyberbully detection: Achieving high accuracy with a balanced multi-class dataset(Faculty of Graduate Studies and Research, University of Regina, 2023-08) Ahmadinejad, Mohamad Hosein; Nashid, Shahriar; Lisa, Fan; Samira, Sadaoui; Andrei, VolodinCyberbullying has become an alarming issue in the digital era, causing significant harm to its victims. The development of automated methods for detecting cyberbullying in social media is of paramount importance to safeguard vulnerable individuals. In this thesis, we propose a robust approach based on Machine Learning (ML) and Deep Learning (DL) techniques for cyberbully detection in social media platforms. Our approach involves the meticulous curation of a balanced dataset specifically designed for training the ML/ DL models. To overcome the challenge of limited labeled data, we employ a semi-supervised self-training algorithm, which effectively expands the size of the labeled dataset. By leveraging real-world social media data, we train and test the model, evaluating its performance using key metrics such as precision, recall, and F1-score. In addition, we present our meticulously annotated dataset comprising 99,991 tweets, which we have made publicly available for future scientific investigations. This dataset serves as a valuable resource for further research in this field, facilitating the development and evaluation of novel techniques for cyberbully detection. Our results underscore the near-perfect performance of the proposed approach in the context of cyberbully detection, reaffirming the efficacy of ML and DL techniques for addressing this pervasive problem. These findings offer crucial insights for future research endeavors in this domain and hold practical implications for the development of automated systems capable of detecting and combating cyberbullying in social media platforms. By continuously advancing our understanding of cyberbullying detection and developing sophisticated ML and DL models, we can foster safer digital environments and protect individuals from the detrimental effects of cyberbullying.