Browse
Recent Submissions
Item Open Access Implicit and explicit approaches for efficient healthcare scheduling(Faculty of Graduate Studies and Research, University of Regina, 2024-03) Ben Said, Aymen; Mouhoub, Malek; Butz, Cory; Khan, Shakil M.; Bais, Abdul; Mohamed, Otmane AitCombinatorial optimization problems play a major role in tackling applications such as healthcare, transportation, education, etc. Solving these applications usually involves satisfying a set of hard constraints while optimizing one or more objectives. In this context, exact or approximate methods can be used. While exact methods guarantee the optimality of the solution returned, they often come with an exponential running time as opposed to approximate methods that trade the solution’s quality for a better running time cost. In this context, we tackle the Nurse Scheduling Problem (NSP). The NSP consists in assigning nurses to daily shifts in a given planning horizon such that workload constraints are satisfied while hospital’s costs and nurses’ preferences are optimized. To solve the NSP, we propose implicit and explicit approaches. In the implicit solving approach, we rely on Machine Learning (ML) methods based on Association Rules Mining (ARM) and classification algorithms using historical data (past scheduling solutions) to learn and generate new solutions through the implicitly learned constraints and objectives that may be embedded in the learned patterns (e.g., Association Rules, trained ML models, etc). To quantify the quality of using our implicit approach in capturing the embedded constraints and objectives in historical data, we rely on the Frobenius Norm (FN). The latter is a quality measure used to compute the average error between the generated solutions and historical data. To compensate for the uncertainty related to the implicit approach given that the constraints and objectives may not be concretely visible in the produced solutions, we propose an alternative explicit approach where we first model the NSP using the Constraint Satisfaction Problem (CSP) framework. Then we develop Stochastic Local Search (SLS) methods and a new Branch and Bound (B&B) algorithm enhanced with constraint propagation techniques and variables/values ordering heuristics. Considering that our proposed implicit approach may not guarantee the feasibility and/or optimality of the generated solution since the constraints and objectives are represented through the learned patterns from the data, we propose a data-driven approach to passively learn the NSP as a constraint network from historical data. The learned constraint network, formulated as a CSP, will then be solved using the methods we listed earlier.Item Open Access Application of artificial intelligence to variable rate technology in agriculture(Faculty of Graduate Studies and Research, University of Regina, 2023-12) Asad, Muhammad Hamza; Bais, Abdul; Al-Anbagi, Irfan; Paranjape, Raman; Hamilton, Howard J.; Shirtliffe, StevenVariable Rate Application (VRA) plays a pivotal role in enhancing agricultural profitability by optimizing the use of resources and promoting consistent crop growth. This also helps mitigate the negative environmental impact of farming practices. However, the implementation of VRA is heavily reliant on data. An effective VRA prescription involves an agronomist’s in-depth knowledge of the soil and crop conditions within Homogenized Management Zones (HMZs). Certain soil attributes like electrical conductivity, elevation, and soil moisture are measured using proximal sensors installed on farm machinery. However, other soil properties like soil texture and Soil Organic Matter (SOM) measurements require soil sampling and laboratorybased testing. Similarly, crop and weed information is gathered via manual scouting. The collected SOM, soil texture, and crop information based on limited sampling may not be representative of whole field conditions resulting in low spatio-temporal resolution of information. Our research seeks to bridge these gaps by proposing costeffective and scalable solutions that improve spatio-temporal resolution. We suggest installing RGB sensors on farm machinery to monitor crop and weed growth, categorize soil texture, and estimate SOM. This high spatio-temporal information gathered is subsequently processed to investigate if improved HMZs can be identified. We develop crop and weed-specific semantic segmentation methods to detect, localize and quantify crops/weeds, yielding a mean Intersection Over Union (mIOU) up to 83%. These semantic segmentation models are customized to handle agricultural image data, minimizing memory usage and computational costs during training and inference. Through this adaptation, we observe a 6% performance improvement in crop and weed semantic segmentation. The efficiency of binary semantic segmentation models is further enhanced by up to 12% using ensemble learning methods. We recognize the strong correlation between soil properties and crop/weed densities and thus use this relationship to our advantage. We train machine learning models to predict crop and weed densities based on soil properties and satellite data. To accurately predict SOM and soil texture from RGB images, we employ a hybrid approach that combines deep learning and conventional image-processing techniques to overcome the challenges posed by uncontrolled field conditions data. Lastly, we explore the potential for identifying HMZs based on resultant high-resolution crop and soil information. Parts of this research are successfully commercialized under the product name ”SWATCAM”.Item Open Access Examining the impact of gender-based microagressions and institutional betrayal on women holding multiple marginalized statuses(Faculty of Graduate Studies and Research, University of Regina, 2023-09) Anstey, Hannah Jaymes; Klest, Bridget; Gordon, Jennifer; Sangster, Sarah; Carter, Claire; Delker, Brianna C.Microaggression refers to subtle, often indirect, discriminatory behavior committed against members of marginalized groups. The subtle nature of microaggressions can often make them seem innocuous, however, they can have a determinantal impact on those who experience them. Women are a marginalized group who experience microaggressions on the basis of gender, rooted in sexism. Further, many women hold multiple marginalized statuses based on their race, sexuality, disability, age, religion, body size, or socioeconomic status. As such, they may also experience microaggressions based on these intersectional identities. While microaggressions in themselves have been shown to be negatively related to mental health, it has been suggested that how that initial microaggression is responded to by an institution can cause the mental health impact to be exacerbated through institutional betrayal (i.e., the failure of an institution to proactively prevent harm, or to be supportive following harm). Further, if institutions can respond with courage and support, the impact of a microaggression might also be changed. Despite the potentially dangerous impact of microaggressions, no research, to my knowledge, has experimentally investigated the impact of a microaggression directly after it was perpetrated. The study utilized a mixed-methods approach and investigated the impact of a single gender-based microaggression with institutional betrayal or support on state affect in women. Further, the impact of holding multiple marginalized statuses was investigated. The study took place via Zoom, and participants completed an unobtrusive measure of state mood directly before and after a male confederate perpetrated a microaggression consistent with sexual objectification. The experimenter then responded to said microaggression with institutional support or institutional betrayal. Once participants recompleted the mood measure, they were asked to complete a number of questionnaires focused on past experiences of gender-based discrimination and mental health symptoms and they were asked to respond to qualitative questions regarding their experience of the microaggression. Results showed that women in the institutional betrayal condition had a decrease in negative mood after experiencing the microaggression and women in the institutional support condition had an increase in negative mood after experiencing the microaggression. Furthermore, regardless of condition all women showed an increase in positive mood. Future directions and implications are discussed.Item Open Access Saturated-unsaturated behavior of natural cohesionless soils(Faculty of Graduate Studies and Research, University of Regina, 2024-03) Akram, Ilyas; Azam, Shahid; Veawab, Amy; Zeng, Fanhua (Bill); Coulson, Ian; Naggar, Hany ElCohesionless soils exist across the globe, under various geological settings and climatic regimes, in two distinct natural states. Disintegrated sediments, where soil particles are loosely held together through particle interlocking such as freshly deposited sediments, and intact deposits, in which soil particles are primarily held together through interparticle suction bonding. The geotechnical behavior of such soils is governed by field conditions of applied stress and atmospheric conditions. This research focused on developing a clear understanding of the saturated-unsaturated behavior of cohesionless soils by mimicking the two natural soil states for laboratory investigations (flow-through, volume change, and shear strength) and by using the test results for numerical modeling (two-dimensional and transient seepage-thermal and stability analysis). The main contributions of this research are summarized below. A simple test method was developed, by utilizing a single sensor and a digital camera, to determine the unsaturated hydraulic conductivity over the entire suction range. The soil exhibited a marginal water holding capacity with air entry value of 8 kPa and a residual suction value of 21 kPa. Following a newly developed sigmoidal function, the soil exhibited a low hydraulic conductivity of 10-7 m/s (saturated value) that gradually decreased with increasing suction (desaturation). Likewise, the difference between the fitted unsaturated hydraulic conductivity values based on upper (10-5 m/s) and lower (10-7 m/s) limits of saturated hydraulic conductivity decreased with suction and converged at vapor conductivity (10-14 m/s). The conventional oedometer test was improved, by adding a controlled water inflow and a digital data recording, to determine collapse and consolidation. Results showed that with the increase in pre-collapse stress from 25 to 600 kPa, unsaturated compression increased from 0.5 to 5.3% in disintegrated soil and remained close to 0.5% in intact soil. The wetting collapse decreased from 1.1 to 0.1% in disintegrated soil and increased from 6 to 9% in intact soils whereas the total collapse increased from 2 to 5.6% (disintegrated) and from 6 to 9% (intact). The transient volume change during wetting collapse followed a curvilinear trend for both soil states. The conventional direct shear test was used to determine the shear strength parameters of disintegrated and intact soils under saturated and dried conditions. The disintegrated soil exhibited identical behavior under both saturation states with no clear peak at failure. Apparent cohesion was not observed and friction increased from 44.5° (saturated) to 48°(unsaturated). The intact soil behaved similar to the disintegrated soil in saturated state due to the absence of suction and had a clear peak and residual similar to dense soils. Apparent cohesion and friction angle respectively increased from 0 kPa and 42° (saturated) to 91 kPa and 36° (unsaturated). A transient and two-dimensional seepage-thermal model was developed to determine the stability of typical embankments with a low slope (18 m) and high slope (26 m). These slopes were analyzed under mean, extreme wet, and extreme dry climatic conditions along with four ponding conditions (none, upstream, downstream, both) with and without vehicular loading. The laboratory protocols and the numerical model are crucial for shallow and young geological deposits that are in direct contact with the atmosphere and where most civil infrastructure resides. The findings of this research are useful for the near design, construction, and rehabilitation of urban facilities exposed to climatic change impacts.Item Open Access Kiannet: An attention-based CNN-RNN model for violence detection(Faculty of Graduate Studies and Research, University of Regina, 2024-04) Ahmadi Vosta Kolaei, Soheil; Yow, Kin-Choong; Chan, Christine; Maciag, Timothy; Zilles, Sandra; Eramian, MarkViolent behaviour poses a significant risk to societal stability and public safety. As part of proactive strategies to counteract this threat, many organizations and institutions have implemented surveillance systems to monitor and identify potential violent instances. Nevertheless, manual review and analysis of vast surveillance footage can be a daunting and error-prone task for human operators, necessitating the advent of automated systems for efficient and precise violence detection. This study introduces a novel approach for violence detection composed of a CNNRNN structure based on an attention mechanism for binary and multi-class classification of abnormal behaviours. We called our proposed model KianNet because Kian is the name of an intelligent innocent murdered in a violent incident, and we chose his name as a representative of all people who suffered from violent behaviours. In this technique, a CNN-RNN structure is applied to an input video to extract features from a sequence of frames and by adding a combination of Multi-Head Self-Attention (MHSA) and ConvLSTM layers, it can detect the violent event and determine the type of the observed anomaly. The key to KianNet’s performance is implementing the MHSA layer, which allows the model to focus on specific spatiotemporal regions of relevance, improving its capacity to differentiate between normal and violent events. Consequently, the MHSA layer boosts KianNet’s discriminatory power, enabling it to discern violent incidents from regular activities better. Through empirical evaluations, KianNet has proven its superior performance in violence detection tasks. Our findings reveal that KianNet outperforms its closest competitors’ accuracy by roughly 10 percent. This substantial performance margin substantiates the robustness and reliability of KianNet, cementing its potential as an effective tool in automated surveillance systems for violence detection.Item Open Access Intelligent prediction of parameters of electric vehicles using artificial neural networks(Faculty of Graduate Studies and Research, University of Regina, 2023-10) Adedeji, Bukola Peter; Kabir, Golam; Mehrandezh, Mehran; Peng, Wei; Khan, Shakil M.; Chaabane, AminThis study is laser focused on the use of artificial neural networks for the prediction of parameters of electric vehicles. The study is divided into five segments. The first segment involves the application of an artificial neural network in the prediction of parameters of pure electric vehicles for design simulation purposes. The second segment is based on the application of supervised machine learning for the prediction of fuel consumption in plug-in hybrid electric vehicles. The third segment involves the application of an inverse neural network approach for the prediction of multiple outputs for the design simulation of battery electric vehicles with the aid of supporting decision-making. The fourth segment is about the inverse function based on neural networks for predicting the parameters of the fuel economy label. The fifth part focuses on the application of inverse neural networks for predicting the parameters of a lithium-ion battery. In the first segment, artificial neural networks were applied to the prediction of parameters of pure electric vehicles. The objective of the study is to develop a model that can predict nine indispensable design parameters of pure electric vehicles. The developed model would assist in decision-making in terms of parameter selection. The categories of vehicles used include two-seater, full-size, compact, subcompact, mid-size, SUVs (standard), and station wagons (small). The accuracy of the model is promising for the predictions of the parameters. The second segment of the study employed supervised learning approaches to predict the fuel consumption of plug-in hybrid electric vehicles. The study also proposes adding additional parameters to the fuel economy label to make the information on it more comprehensible. The accuracy of the neural network was found to be higher than that of the multiple linear regression model. In the third segment of this study, an artificial neural network was employed to calculate and simulate the inverse functions of battery electric vehicle parameters. Nine variables were calculated and simulated as the outputs of the inverse function model at the same time. The procedure was completed for the nine cases where each of the augmented input variables of the inverse function model was the output of the direct function model. The accuracy was 142 times higher in terms of mean square error when electrical charge consumption and virtual functions were employed as input variables into the inverse function model. The proposed model will support faster decision-making in the design simulation of battery electric vehicles due to the large number of outputs simulated at once. The fourth aspect of the study focuses on the simulation of fuel consumption and fuel economy label parameters of plug-in hybrid electric vehicles using the inverse function approach of an artificial neural network. While fuel economy is a key factor in the design of plug-in hybrid electric vehicles, a fuel economy label can educate customers about the economic advantage of purchasing a particular car. The accuracy of the model was 29.1 times greater than that of the conventional inverse artificial neural network model. The fifth segment of the study introduces a feedforward deep inverse neural network for the prediction of parameters of the lithium-ion battery in electric vehicles. The accuracy of the proposed model was 44.43 times higher than in the traditional inverse deep neural network model.Item Open Access Performance evaluation of a multifractured horizonal well in an unconventional reservoir with fracture networks and flow dynamics(Faculty of Graduate Studies and Research, University of Regina, 2024-02) Zhang, Yunhao; Yang, Daoyong; Azadbakht, Saman; Shirif, Ezeddin; Xue, Jinkai; Yao, Yiyu; Wu, XingruWith the advancement of horizontal drilling and hydraulic fracturing technologies, the unconventional reservoir resources (e.g., tight oil and shale gas) have received a growing attention; however, it is a challenging task to accurately simulate the transient pressure response and single/two-phase flow behaviour due to the reservoir boundary, fracture geometry, fracture network, and flow dynamics including stress-sensitivity, slippage effect, non-Darcy flow, and gas adsorption/desorption. Therefore, it is of a fundamental and practical importance to evaluate the performance of a multifractured horizontal well (MFHW) in an unconventional reservoir conditioned to an arbitrary boundary, fracture geometry, and complex fracture networks with the consideration of pressure-dependent permeability, non-Darcy flow, slippage, and/or gas adsorption/ desorption. By taking the arbitrarily-shaped reservoir boundaries into account, the boundary element method has been proposed to accurately describe the boundary-dominated flow during the late time period for an MFHW in an unconventional oil reservoir. The stresssensitive effect of the hydraulic fracture subsystem is semi-analytically evaluated in the Laplace domain with the iteration method, while the Pedrosa's transform formulation can be incorporated into the governing equations in the matrix and fracture subsystems in order to couple the matrix-fracture flow models. As for a shale gas reservoir, the dual reciprocity boundary element method is applied to deal with the nonlinearity resulted from more complex conditions (i.e., slippage, stress-sensitivity, and gas adsorption/desorption). In addition to its flexibility, the newly proposed model can be used to simultaneously obtain solutions at multiple locations inside the matrix domain. As for the two-phase flow, a skin factor on a fracture face is defined and introduced to represent the change in relative permeability in the matrix domain at each timestep. A two-phase flow model coupled with geomechanics has been employed to capture transient flow behaviour during the flowback period in an unconventional reservoir by considering fracture geometry and capillary pressure. Different from the traditional treatment by assuming it as a constant, a function of interfacial tension (IFT) between gas and water as well as fracture aperture is employed to obtain the capillary pressure within a fracture, during which the gas/water saturation and the fracture aperture in each fracture segment with an equal length can be iteratively obtained and updated. All the proposed theoretical models have been validated and then extended to field cases. As for the single-phase flow, type curves are generated and beneficial to examine the effect of each factor on the transient pressure behaviour of an MFHW in an unconventional reservoir conditioned to different fracture networks and flow dynamics. In the two-phase flow model coupled with geomechanics, dynamic fracture properties, and capillary pressure are found to exert a considerable influence on the gas/water production rates and cumulative production. The discrepancy between the field pressure/production data and transient type curves without geometrical structure (i.e., reservoir boundaries and proppant characteristics) and flow dynamics can be adopted to evaluate the contribution of stress-sensitive effect and gas adsorption/desorption.Item Open Access Development of task specific ionic liquids incorporated porous sorbents for post-combustion CO2 capture(Faculty of Graduate Studies and Research, University of Regina, 2024-04) Philip, Firuz Alam; Henni, Amr; Ibrahim, Hussameldin; Shirif, Ezeddin; Salama, Amgad; Qing, Hairuo; Croiset, Eric B.Amino functionalized ionic liquids (AAILs), also known as task-specific ionic liquids (TSILs), have demonstrated CO2 capture ability similar to amines while maintaining ionic liquid properties such as low regeneration energy, volatility, and thermal stability. However, high synthesis costs and viscosity prevent their broad usage in CO2 capture technologies. Recently discovered porous materials like metal-organic frameworks (MOFs) and ordered mesoporous silica have stimulated scientists’ interest in CO2 capture applications. However, these materials have limited CO2 absorption and poor CO2/N2 selectivity, particularly at post-combustion CO2 capture conditions (0.15 bar). Immobilizing TSILs in solid pores to boost CO2 capture is an innovative way to address the drawbacks of both TSILs and porous materials. This study incorporated 1-ethyl-3-methylimidazolium [Emim] cations with Glycine [Gly] and Alanine [Ala] as reactive Amino Acid (AA) anion, resulting in [Emim][Gly] and [Emim] [Ala]. Three porous solid supports were used, metal-organic-framework (MOF-177), zeolitic imidazolate framework (ZIF-8), and ordered mesoporous silica (MCM-48) leading to TSILs@MOF/ZIF/MCM composites. TGA and XRD measurements were performed to determine the composites’ thermal and structural stability. The specific surface area and the pore volume distribution were determined by using N2 adsorption-desorption isotherms at 77 K. CO2 adsorption isotherms were measured using an intelligent gravimetric analyzer (IGA) at three temperatures (303, 313, and 323 K), and N2 adsorption isotherms were measured at 313 K for a pressure range of 0.1 to 10 bar, for all composites and pristine solids. The CO2/N2 selectivities were computed using the CO2 and N2 adsorption isotherms. Adsorption isotherms were modeled by the Dual-Site Langmuir (DSL) model, and the isosteric enthalpy of adsorption was computed. [Emim][Gly]@ZIF-8 composites demonstrated excellent improvements in CO2 uptake and CO2/N2 selectivity at 30 wt. % loading. CO2 uptake was 10 times higher than in pure ZIF-8 at 0.1 bar and 303 K, and selectivity improved to 28 from 5 at 0.1 bar and 313 K. At 20 wt. % loading, AAILs-encapsulated composites surpassed pure MOF-177 in CO2 uptake by a factor of 3. The ideal AAIL loading was 20 wt. % and increasing the loading to 30 wt.% did not increase CO2 uptake for the AAILs@MOF177 composite. [Emim][Gly]@MCM-48 and [Emim][Ala]@MCM-48 composites enhanced CO2 uptake 10-fold and CO2/N2 selectivity to 17 from 2 at 0.1 bar for 40 wt. % loading. The improved CO2 capacity and selectivity can be attributed to the formation of C-N bonds between CO2 and the -NH2 functional group, as suggested by the isosteric enthalpy of adsorption. In addition, blended systems of amine (PZ) with 1-ethyl-3-methylimidazolium acetate [Bmim][Ac] have the potential for high CO2 capture capabilities like TSILs without inheriting TSIL limitations such as high synthesis cost and viscosity. CO2 absorption was unaffected by 30 wt. % IL in the aqueous PZ, while 60 wt. % IL greatly increased it. Furthermore, this aqueous blended system (PZ+IL+H2O) and a second non-aqueous system of ethylene glycol (EG) mixed with MEA were examined as slurry systems in which porous solid ZIF-8 was suspended. Nonaqueous slurry systems outperformed aqueous slurry systems, which could be attributed to the collapse and/or pore filling of ZIF-8 in aqueous systems as evident from the TGA and XRD analysis of the recovered ZIF-8. These research results can be used to build sorbents with superior qualities to address environmental concerns since they shed light on the synthesis, structure, and sorption capacity of these innovative composite materials.Item Open Access Modeling of birth and death oscillations in provinces of Canada(Faculty of Graduate Studies and Research, University of Regina, 2024-04) Osmanli, Khaysa; Sardarli, Arzu; Volodin, Andrei; Floricel, Remus; Deng, Dianliang; Yao, Yiyu; Hatefi, ArminWe discuss several statistical properties of the new proposed Log-Logistic Erlang (LLogE) distribution. Additionally we analyse birth, death oscillation of Canadian provinces in depth as well as the correlation between birth and death and those with temperature that has been collected from center of population of each provinces. While the focus of this study is on Canada, temperature may have a global influence on fertility. Even if there is no consensus how seasonality of birth phenomenon modelled, yet environmental factors specifically temperature, plays a role in explaining the seasonality observed in birth in many countries over many years. There are many other factors other than temperature that plays role to disentangle the shocks presents in birth. Our research offers unique insights by exploring the effects of environmental factors on monthly and daily birth and death oscillations using spectral and correlation analysis during 2011 and 2019.Item Open Access Development of scintillator-based components for the photosensor system for the Intermediate Water Cherenkov Detector of the Hyper-K experiment and for the time of flight system of the Water Cherenkov Test Experiment(Faculty of Graduate Studies and Research, University of Regina, 2024-05) Koerich, Luan Vinicius; Barbi, Mauricio; Kolev, Nikolay; Huber, Garth; Lawler, Samantha; Zilles, Sandra; Filho, Hélio da MottaThe existence of the neutrino flavour oscillation phenomenon carries a potential CP-violation phase, 𝛿CP, that might be the answer to the matter-antimatter asymmetry question in the Universe. With a new far detector and upgraded components of the successful Tokai-to- Kamioka (T2K) experiment, the long-baseline aspect of the Hyper-Kamiokande (Hyper-K) experiment will utilize an upgraded neutrino beam from the Japan Proton Accelerator Research Complex (J-PARC). The measurement of 𝛿CP from 𝜈𝜇 and ¯ 𝜈𝜇 disappearance modes will be dominated by systematic uncertainties. To reduce these uncertainties to discovery-level precision, an Intermediate Water Cherenkov Detector (IWCD) is introduced at a distance of 1 km from the beam source to intercept the neutrino beam at a span of off-axis angles and energies. This detector will feature high granularity, directionality, and time response through the use of a multi-photomultiplier tube (mPMT) photo-detection system. In this thesis I discuss the research and development (R&D) of scintillator-based detectors for the mPMT system on two main fronts: an internal hit detector for the mPMT system and a time-of-flight detector for particle monitoring and identification in tests of an IWCD prototype, the Water Cherenkov Test Experiment. Both detectors aim to contribute to the reduction of systematic uncertainties in Hyper-K’s attempt to discover 𝛿CP in the lepton sector of the Standard Model.. Keywords: Neutrino oscillation, Hyper-Kamiokande, Intermediate Water Cherenkov Detector, multiPMT, leptonic CP-violation phase, scintillator, time-of-flight detector.Item Open Access Refugee-background students in French immersion programs: Exploring the perspectives and ideologies of educators across the Canadian Prairies(Faculty of Graduate Studies and Research, University of Regina, 2024-03) Davis, Stephen Henry; Sterzuk, Andrea; Massing, Christine; Swapp, Donna; Akinpelu, Michael; Kristmanson, PaulaFrench immersion (FI) programs are becoming more culturally and linguistically diverse because of increased global migration to Canada. Researchers have found that multilingual families and learners are often highly motivated to learn both French and English in Canada (Dagenais & Jacquet, 2000; Dagenais & Moore, 2008; Davis et al., 2019). Moreover, multilingual learners tend to develop strong language proficiency in FI programs (Bourgoin & Dicks, 2019; Knouzi & Mady, 2017; Mady, 2015). However, multilingual learners are often excluded from FI programs on the basis of ostensibly low English language abilities (Davis et al., 2021; Mady & Masson, 2018; Roy, 2015). In the present study, I explore the perspectives and ideologies of educators with respect to refugee-background students in FI programs in eight school divisions across Saskatchewan, Manitoba, and Alberta. Adopting the theoretical perspective of sociolinguistics for change, I examine the perspectives of FI teachers, principals, and central office staff through semi-structured interviews (n=40) and questionnaires (n=126). My analysis and triangulation of data generated findings pertaining to eight areas: 1) diversity in FI programs; 2) perceived suitability of FI programs for refugee-background students; 3) perspectives on the learning of refugee-background students in FI programs; 4) challenges facing refugee-background students and families; 5) beliefs about inclusion in FI programs; 6) gatekeeping practices in FI programs; 7) perspectives on policy in FI programs; and 8) supports in FI programs. The findings of this research are presented in a manuscript-style dissertation, including an introduction, three peer-reviewed journal manuscripts, and a conclusion. In the final chapter, I discuss the contributions of this research, propose ideas for future inquiry, and advance recommendations for school divisions to create more equitable and inclusive FI programs across Canada. Keywords: French immersion; multilingualism; immigration; language ideology; policyItem Open Access Elucidating mechanisms of acid tolerance and antibiotic resistance in Salmonella and Klebsiella using transposon insertion sequencing (INSeq) and whole genome sequencing(Faculty of Graduate Studies and Research, University of Regina, 2024-04) Amin, Mohammad Ruhul; Cameron, Andrew; Yost, Christopher; Hansmeier, Nicole; Dahms, Tanya; Tahlan, KapilSalmonella enterica and Klebsiella pneumoniae are two major public health concerns that are responsible for millions of illnesses every year throughout the world. Much remains to explore the genes and mechanisms that are critical for their pathogenesis and antibiotic resistance. I used transposon Insertion Sequencing (INSeq), a powerful high-throughput screening technique that can link bacterial genes to phenotypes and identify genes that are essential for bacterial survival, to determine genes that are associated with survivability of Salmonella Typhimurium in Luria Bertani (LB), E-minimal medium (EMM) and under acid stress, and of K. pneumoniae under exposure to five classes of antibiotics. Growing a pool of 450,000 mutants on LB identified a total of 362 essential genes, the majority of which (90.6%) are within the S. Typhimurium core genome. The pCol1B9 replication initiation and its regulator, repZ and repY, are among the essential genes found in the strain's accessory genome. Comparing essential genes identified in LB to two earlier studies of S. Typhimurium strains and identifying genes that are conditionally essential in EMM suggest that a single growth environment and strain cannot provide a comprehensive understanding of essential genes at the species level. S. Typhimurium is hypothesized to have a special acid tolerance system in which the cytoplasm becomes more acidic. I applied INSeq to find genes that contribute to acid tolerance at pH 4.0 and pH 5.0. For which I developed a modified INSeq approach capable of identifying genes required for persistence in non-growth conditions. In addition to several known genes, this project identified novel acid tolerance genes including trxB (thioredoxin reductase), pykF (pyruvate kinase), sspA (starvation protein), and revealed that as the stress increases through time and decreasing pH, additional tolerance mechanisms are required to protect cells. Next, I used INSeq to find intrinsic resistance genes in a clinical isolate of multidrug resistant K. pneumoniae. We found pstB (ABC transporter ATP binding protein), gltA (Citrate synthase), tgt (tRNA guanosine transglycosylase), fabF (fatty acid synthase), and glycosyltransferase encoding genes: pgaptmp_000142, pgaptmp_000147, and pgaptmp_000148, each contribute to resistance across multiple classes of antibiotics. Considering all the INSeq data, healthy cell envelopes were found to be crucial for optimum growth and cell protection, regardless of the growth environment, which included laboratory conditions under acid and antibiotic stresses. In addition, numerous known genes were identified for corresponding features, such as phoP-phoQ system for acid tolerance and acrAB-tolC for multidrug resistance, confirming the effectiveness of INSeq. I next used whole genome sequencing to find genetic changes in S. Typhimurium isolates to characterize a sugar metabolism and an antibiotic resistance phenotype. First, I helped characterize how a C→T transition in the dctA promoter allows for growth at lower orotate concentrations by creating an improved binding site for the transcriptional activator CRP. Secondly, by progressively challenging cells with higher concentrations of antibiotics, I discovered an A→T transition in codon 466 of gyrB reduces ciprofloxacin sensitivity in a S. Typhimurium mutant that cannot synthesize the intracellular signaling molecule cAMP. Both Salmonella and Klebsiella are considered top priority pathogens for research and development of new antibiotics; this PhD thesis provides an improved understanding of the biology of both organisms and simultaneously identifies high-quality candidate genes that can be targeted for the development of improved antibiotics and other therapeutics.Item Open Access Predictive visual servoing; uncertainty analysis and probabilistic robust frameworks(Faculty of Graduate Studies and Research, University of Regina, 2023-06) Sajjadi, Sina; Mehrandezh, Mehran; Janabi-Sharifi, Farrokh; Dai, Liming; Stilling, Denise; Paranjape, Raman; Mouhoub, Malek; Xie, Wen-FangMotion control of robots in unstructured environments is a challenging task. The utilization of cameras as an information-rich sensor shows promise. In this context, image-based visual predictive controllers have gained attention due to their optimal-ity and constraint-handling capabilities. However, their performance deteriorates in presence of uncertainties in the robotic platforms, system models, and measurements. This work proposes a set of robust image-based visual predictive control methods that overcome the shortcomings of the previous visual servoing methods in the presence of uncertainties. In this dissertation, we have proposed a set of adaptive, stochastic, risk-averse, and learning-based visual servoing schemes that improve the performance and constraint compliance of visual servoing systems compared to their classical coun-terparts. The validity of the proposed control frameworks has been evaluated on a 6-DOF serial industrial manipulator and a model unmanned aerial vehicles via various experiments and simulations.Item Open Access Systems Biology of Host-Pathogen Protein-Protein Interactions(Faculty of Graduate Studies and Research, University of Regina, 2023-06) Rahmatbakhsh, Matineh; Babu, Mohan; Dahms, Tanya; Hansmeier, Nicole; Hu, PingzhaoDespite undeniable therapeutic developments in infectiology, emerging infectious diseases continue to be a growing threat to public health, as seen by the current COVID- 19 pandemic caused by the novel virus severe acute respiratory syndrome coronavirus (SARS-CoV-2). This virus is classified as an obligate intracellular parasite that co-opts host cellular proteins, often through protein-protein interactions (PPIs), to ensure its replication. Therefore, this thesis aims to integrate high-throughput proteomic approaches with computational modelling to systematically characterize SARS-CoV-2-human networks for a detailed understanding of SARS-CoV-2 pathogenesis. The angiotensin-converting enzyme (ACE2) receptor of SARS-CoV-2 is displayed on many human cells, including the lungs and other organs. However, despite considerable knowledge explaining the SARS-CoV-2 infection mechanism, organ-specific SARS-CoV- 2-host protein interactions remain understudied. In Chapter 2, we carried out an organ/tissue-unbiased proteomic profiling approach of mapping SARS-CoV-2-human protein interactions using high-throughput mass spectrometry (MS)-based proteomic approaches. First, automated machine learning (ML)-based computational workflows with different algorithmic strategies were devised to generate high-quality tissue-specific and tissue-common SARS-CoV-2-human PPIs. Subsequent clustering of highly conserved networks using an optimized complex-based analysis framework uncovered several virally targeted protein complexes (VTCs), reflecting conserved mechanisms of replication. Finally, organ/tissue-specific interaction revealed that NSP3 protein evades host antiviral innate immune signaling by targeting IFIT5 for de-isgylation. Although host interactome is indirectly affected during viral infection, earlier studies have only focused on characterizing the properties of the viral proteins within the host-viral interactions. However, systematically exploring the host-viral interactions from the perspective of the host interactome is essential and should be included in PPI network for a better understanding of viral pathogenesis. In Chapter 3, we combined cofractionation mass spectrometry (CF-MS) with a novel deep learning-based framework, DeepiCE, to map physiologically relevant viral-host and host interactome. First, through comprehensive statistical validations, we demonstrated the remarkable performance of DeepiCE over the state-of-the-art method for network construction. DeepiCE was then applied to co-elution data from salivary samples of individuals infected with SARS-CoV- 2, which led to the generation of high-quality viral-host and host interactome maps highly relevant to SARS-CoV-2 infection. Subsequent clustering of resulting networks using a sophisticated two-stage clustering framework generated high-quality SARS-CoV-2 affected protein complexes, many of which were enriched for diverse cellular processes related to viral pathogenesis and provided new insights into SARS-CoV-2 infection from both the host and pathogen perspective. Despite arduous and time-consuming experimental efforts, PPIs for many pathogenic microbes with their human host are still unknown, limiting our understanding of the intricate interactions during infection and the identification of therapeutic targets. Since computational tools offer a promising alternative, in Chapter 4, we developed a R/Bioconductor package, HPiP software with a series of amino acid sequence property descriptors and an ensemble machine learning classifiers to predict the yet unmapped interactions between pathogen and host proteins. Using SARS-CoV-1 or the novel SARSCoV- 2 coronavirus-human PPI training sets as a case study, we show that HPiP achieves good performance with PPI predictions between SARS-CoV-2 and human proteins, which we confirmed experimentally using several quality control metrics. HPiP also exhibited strong performance in accurately predicting the previously reported PPIs when tested against the sequences of pathogenic bacteria, Mycobacterium tuberculosis and human proteins. Collectively, our fully documented HPiP software will hasten the exploration of PPIs for a systems-level understanding of many understudied pathogens and uncover molecular targets for repurposing existing drugs.Item Open Access Examining Motivational Interviewing and Booster Sessions in Internet-Delivered Cognitive Behaviour Therapy for Post- Secondary Students: An Implementation Trial(Faculty of Graduate Studies and Research, University of Regina, 2022-01) Peyenburg, Vanessa Angelica; Hadjistavropoulos, Heather; Beshai, Shadi; Wright, Kristi; Hebert, Cristyne; Alavi, NazaninApproximately one in three post-secondary students experience clinical levels of anxiety or depression during their academic career, with many students not receiving treatment. Internet-delivered cognitive behaviour therapy (ICBT) is an alternative to face-to-face services that is effective in general adult populations, but has been associated with high attrition rates and smaller effect sizes in student populations. In this implementation trial, the efficacy and uptake of an ICBT course for anxiety and depression (i.e. the UniWellbeing Course) was examined in Saskatchewan. Given the evidence from the face-to-face literature, the role of motivational interviewing (MI) and booster lessons was also examined. Using a two-factor design (factor 1: online MI); factor 2: booster lesson), a total of 308 clients were randomized to one of four groups: standard care (n = 78), MI (n = 76), booster (n = 77), and MI + booster (n = 77). Overall, 89.9% (n = 277) of clients started treatment. The aims of the study were to assess (1) the efficacy of the UniWellbeing Course in reducing symptoms of anxiety and depression and increasing perceived academic functioning; (2) the impact of a pre-treatment MI component on attrition and engagement; (3) the impact of a booster lesson on depression, anxiety, and perceived academic functioning at 3-month follow-up; and (4) the combined effect of MI and booster. Overall, students reported significant, large decreases in symptoms of depression (Cohen’s d: 1.25 – 1.67) and anxiety (Cohen’s d: 1.42 – 2.01) from pretreatment to post-treatment, with 47.5% and 56.6% of clients experiencing reliable recovery on measures of depression and anxiety, respectively. Small, but significant, effects were seen for improvements in perceived academic functioning across the four conditions (Cohen’s d: 0.20 – 0.48). Changes were maintained at 1-month and 3-month ii follow-up on all primary measures across conditions. Overall, 54.0% (n = 150) of clients accessed all four lessons of the UniWellbeing Course. The addition of pre-treatment MI did not confer improvements to treatment completion rates or engagement (e.g., mean logins or messages sent to therapists). Small between-group effects were seen in favour of MI for depression (Cohen’s d: 0.23), anxiety (Cohen’s d: 0.25), and mental healthrelated disability (between-group Cohen’s d: 0.35) at post-treatment. In terms of the booster lesson, only 30.9% (n = 43) of clients accessed the booster lesson, although clients who accessed it were satisfied with the content and timing of the booster overall. Between-group effects were not significant for the booster at 3-month follow-up. Subanalyses comparing clients who utilized the booster to those who did not were underpowered, but revealed a larger decrease in depressive symptoms (between-group Cohen’s d: 0.31) at 3-month follow-up. No advantage was found for the combination of MI and booster on treatment completion, engagement, or outcomes. Overall, there is some evidence to suggest that including MI at pre-treatment results in greater symptom reduction although these benefits do not persist to 1-month and 3-month follow-up. The inclusion of a self-guided booster lesson may also help with continued symptom management up to 3-month follow-up, but low uptake is a barrier to clients experiencing these benefits. Uptake of the course was highest among White female participants and at large universities, suggesting a need for alternative recruitment strategies to increase uptake among other student populations. Findings from this trial contribute to the literature on improving ICBT outcomes for post-secondary students.Item Open Access Examining native Saskatchewan bumble bees health using species occurrence data, pathogen incidence and gut microbial associations(Faculty of Graduate Studies and Research, University of Regina, 2023-07) Palmier, Kirsten Michelle; Cameron, Andrew; Sheffield, Cory; Davis, Maria; Somers, Chris; Siemer, Julia; Currie, RobBees are important pollinators, though, recent evidence suggests some species of bumble bees (Hymenoptera, Apidae, Bombini, Bombus Latreille) are declining in parts of their ranges due to a combination of drivers such as climate change, pesticide use, habitat loss, competition for resources and pathogens acting upon the bees at once. A relatively new and important area of gut microbial research, the fungal and bacterial gut community members, could offer insight on why some species of bumble bees are declining while others remain stable. In this thesis, I use a combination of field and molecular methods to investigate aspects of bumble bee health in Saskatchewan, Canada, including species occurrence and pathogen incidence and explore microbial associations with known and potential pathogens. The second chapter explores the need for a bumble bee monitoring program in Saskatchewan and how standardization compares to non-standardized survey methods. I compared bumble bee occurrence data from four datasets in terms of sampling effort over time and how properties of each dataset influenced species conservation assessments. The Palmier dataset was a single collection event in 2018 using a standardized survey methods. The Royal Saskatchewan Museum (RSM) and Global Biodiversity Information Facility (GBIF) datasets represented specimens collected with unstandardized collection events over decades. The iNaturalist dataset contained citizen science observations. The Palmier dataset was the largest of the four datasets and despite the single collection event, species richness in the Palmier dataset was comparable to the RSM and GBIF datasets. The iNaturalist dataset was biased to locations with higher population density and overrepresented species at-risk compared to the Palmier, RSM and GBIF datasets. The third chapter (previously published) documents the first occurrences of the Common Eastern Bumble Bee (B. impatiens), a managed species that is not native to the Canadian prairies, recorded from southeastern Alberta. The fourth chapter (also previously published) documents the first Canadian occurrence of a recently characterized trypanosomatid bumble bee pathogen, Crithidia expoeki, in native Saskatchewan bumble bees. The fifth chapter explores the fungal and bacterial gut communities of bumble bees and their associations with common bee pathogens. The results indicate that pathogens cause dysbiosis, or imbalance of microbial communities in bumble bees. A differential abundance analysis revealed significantly enriched and depleted taxa in bees testing positive for specific pathogens. The results from this study can be used to compare microbial strain level differences across geographic landscapes over time. The sixth chapter investigates a novel yeast association and swollen proventriculus in the digestive tract of at-risk bumble bee species across Canada. It was discovered that the swollen proventriculus morphology occurred only in males in the subgenus Bombus, a taxon in which the majority of North American species are at-risk. Classic culturing methods and Sanger sequencing revealed that bumble bees with a swollen proventriculus harboured distinct yeast communities in high numbers. Using 16S rRNA sequencing, I also found higher abundances of lactic acid bacteria and Gillimella bacteria in male bumble bees with a swollen proventriculus compared to bumble bees of both sexes without.Item Open Access Heavy oil recovery by combined solvent and hot water (CS-HW) injection: Experimental, numerical and data mining-based analysis(Faculty of Graduate Studies and Research, University of Regina, 2023-06) Masoomi, Reza; Torabi, Farshid; Muthu, Jacob; Tontiwach, Paitoon; Zeng, Fanhua; Mobed, Nader; Hassanzadeh, HassanIn this study, a hybrid EOR process is developed and optimized in a two-well configuration for heavy-oil recovery which combines solvent injection using different solvents such as carbon dioxide (CO2), methane (CH4) and propane (C3H8) with a moderate reservoir heating by hot-water flooding (HWF) as a solution to enhanced heavy oil recovery (EHOR), reduce energy consumption, and improved solvent retrieval efficiency. The combined injection of solvent and hot water offers several advantages including reduction of energy consumption compared to steam-based thermal EOR methods, and reduction of solvent volume required to reduce the viscosity of heavy oil. The proposed hybrid EOR method of combined solvent and hot water (CS-HW) injection outperformed the sole injection of solvent, conventional water flooding (WF) and hot-water flooding (HWF) by sustaining the foamy oil flow and effectively delaying water and gas breakthrough times. A total of 21 laboratory tests including water flooding (WF), hot-water flooding (HWF), solvent injection, combined solvent and hot water (CS-HW) injection in a two-well configuration were designed and conducted. More specifically, the design parameters such as injection rate (qinj) and temperature (Tinj), solvent composition and slug size were optimized with the objective of maximizing heavy oil recovery. In order to study the numerical simulation of CS-HW injection and other laboratory tests presented in this research, CMG-STARS module was used. Sensitivity analysis was performed on the effective parameters of CS-HW injection process to obtain the best history-match between the experimental data and the simulation model. Furthermore, a new computational approach for predicting the performance of hot-water flooding (HWF) in unconsolidated heavy oil reservoirs was presented. The proposed model predicts the changes in the oil–water viscosity ratio (μo/μw) by estimating the reservoir temperature distribution through porous media. Then, the dimensionless and normalized variables were redefined to forecast water fractional flow as a function of temperature and water saturation. Moreover, the proposed approach predicts the cumulative heavy oil production and recovery factor more accurately and with less required input data and runtime compared to CMG-STARS (computer modeling group), etc. Estimated results were validated using laboratory experimental data and numerical simulation outputs. The relative errors between oil recovery factors obtained from computational approach and experimental data were measured to be about 5.3%, 1.7% and 1.2% and 2.5% for injection temperatures of 40, 60, 80 and 100 °C, respectively. Finally, this thesis provides a novel data mining-based analysis by using artificial neural network (ANN) methodology to develop a high-performance neural simulation tool for predicting the efficiency of CS-HW injection process. In order to train, test and validate the model the experimental and simulation data obtained in this study together with available data in the literature were fed to the machine learning technique to develop a CSHW recovery performance predictive model. The proposed intelligent predictive model is expected to help petroleum engineers as an alternative model to predict the efficiency of CS-HW injection process, where other models have limitations and their input parameters are often not easily accessible.Item Open Access A narrative exploration of the right to health in the lives of Indigenous women(Faculty of Graduate Studies and Research, University of Regina, 2023-06) Latta, Lori Patricia; Hoeber, Larena; Cooper , Elizabeth; Green , Brenda; Abonyi, Sylvia; McIntosh, Thomas; Forman, LisaThis study explores, through critical narrative analysis, the understanding of Indigenous women about conditions that they need to be healthy, and how their stories and reflections provide a critique that can inform thinking around the right to health. Literature from varied disciplinary perspectives describes the right to health, and a body of health human rights, as conceptual tools that identify the conditions all people require to be healthy, encompassing not just health care and access to material goods, but equality, culture, power and participation. Literature also provides some critique of human rights, and indicates that their alignment with dominant discourses and powers may be a barrier to their effectiveness for Indigenous people. With reference to Habermas’ theories of communicative action, including the colonization thesis, the lifeworlds of 14 Indigenous women were explored in relation to the institutional discourse of health human rights. The study finds that in the stories that women shared there was some validation of human rights instruments relating to health, which identify as rights violations health harms such as violence, disruption of families, experiences of racism, and lack of support for mental health. However, women’s interpretation of these events often differed from institutional discourse in that they located responsibility for violations less in the people or organizations that harmed them, and more in processes of colonization carried out by successive Canadian governments, that effectively undermined their rights and their health. As they reflected on their stories, women identified a right to knowledge about history and the impact of colonization on Indigenous people as being important to their physical and mental health. Other findings are that a rights-based assessment of women’s health that focuses on experiences of violations and harms may be perceived as deficit-based. To be more meaningful to Indigenous women, a discourse of human rights in health could speak to their strengths and resources, and support broadly defined goals in physical, spiritual and mental health by removing barriers to agency. This study joins a body of other research in finding that explicit rights-based participation in service delivery and health policy development and evaluation may help to avoid abuses in the future, but may require more autonomous forms of governance and service delivery to address longstanding power imbalance and distrust. The study concludes that a discourse of health human rights can better meet the needs of Indigenous women when colonialism is named as a human rights abuse and the primary cause of health inequity that affects their families and communities, reinforcing their life world knowledge with rights-based accountability, and creating common understanding in the public sphere.Item Open Access Policy issue networks: Social network analysis case studies(Faculty of Graduate Studies and Research, University of Regina, 2023-07) Katchuck, Michelle Lisa; McNutt, Kathleen; Longo, Justin; Rayner, Jeremy; Childs, Jason; Stoddart, Mark CJThis research demonstrates that Social Network Analysis (SNA) can be a powerful, proactive tool for policy makers to understand the online policy networks in which they operate. It does so by undertaking SNA at two points in time to quantify the actor nodes of three Canadian public policy networks, comparing the network evolution over time, and visualizing their structure and relationships with related policy issues. The three Canadian policy case study subjects are cannabis legalization, nuclear energy development, and the Trans Mountain Pipeline expansion project (TMX). The cases were selected for their current social importance and national concern, and complexity as socio-technical systems. Cannabis legalization represents a social policy shift, while the other two policy issues involve highly technical infrastructure projects to provide the energy that drives modern society at a time when energy solutions and needs are shifting. The research was undertaken to answer three main questions: Does a network structure consist of multiple clusters of subnetworks primarily concerned with tangential issues but bridged together to form a network for this policy issue? Is there any evidence of network effects that affect the network’s evolution over time? Finally, is there evidence that regional or international networks are present? The study’s findings provide significant evidence that addresses these questions. For example, for question one, the cannabis legalization network shows an isolated online community primarily interested in the research and use of cannabis as a medical treatment, an issue tangential to the primary policy focus but connected to the policy issues. For question two, Canada’s stated nuclear policy shift toward small modular reactors reveals an online issue network dominated by industry rather than government actors. Finally, regarding question three, the study found that regional clusters were especially apparent in the cannabis legalization and TMX networks. This research provides insight into the policy networks of the specific cases, which contributes to the literature on these policy topics and network analysis in terms of network structure and evolution. It also validates the use of SNA in a policy analysis toolkit. Where existing literature has examined Internet-age government, it has found that governments often replicate routine procedures and processes in new, virtual forms rather than innovate or reimagine their capabilities. Government actors have improved their responsiveness, but they also need to fundamentally change their behaviour, particularly in engaging stakeholders in meaningful public policy analysis. SNA is a novel use afforded by technology that has gone unexplored to innovate government performance. This dissertation adds to the lengthy body of research in SNA by experimenting with a practical application of its theories and methods. The critical conceptual approach underpinning this thesis is complexity theory, which provides the framework to situate the dynamic environment of policy making and stakeholder engagement. It is hoped that this research will help policy makers by providing a toolkit that enables visualizing how issue patterns emerge in real-time, patterns that can represent the “unknown unknowns” — the voices not yet heard, the unanticipated concerns, and the opportunities not yet discovered to reach out to broader or underrepresented communities in the policy arena.Item Open Access Exploring the work-related experiences of retail workers in Saskatchewan: A critical narrative study(Faculty of Graduate Studies and Research, University of Regina, 2023-08) Gyimah, Issah; Abu, Bockarie; Xia, Ji; Twyla, Salm; Gabriela, Novotna; Ken, MontgomeryThe retail industry is predominant in providing goods and services to customers worldwide. For example, studies have found that more than 10% of employees work in the retail sector in Canada. However, frontline retail employees experience considerable challenges, such as mistreatment and hostility from managers. Yet, research has generally failed to explore the nature of those challenges or offer strategies to address them. Using a narrative inquiry/approach methodology, the study explored the work-related experiences and conditions of four frontline retail workers at a Regina, Saskatchewan, Canada store. The study drew on critical theory and social justice theory as theoretical lenses to challenge the prevalence of neoliberal ideology at the studied workplace and its influence on the work-related experiences of frontline workers at that workplace. The narrative inquiry building blocks of temporality, sociality, spatiality, and other narrative approaches, as well as the theoretical framework, guided the presentation and discussion of the study findings. The participants’ narratives revealed they experienced neoliberal policies and practices that they thought constituted voicelessness, sexism, individualism, racism, nepotism, underemployment, and cronyism at their retail store. The integration of the participants’ data created informative narratives of their work-related experiences and offered a way to improve their fragmented, scattered, and sometimes contradictory narratives into coherent narratives. The narratives also revealed that some participants perceived their workplace conditions as overwhelming, harsh, and alien. The implications of the findings of the study for policy, practice, and theory development, as well as suggestions for further research and recommendations arising from the study, are discussed.